@prefix vivo: . @prefix edm: . @prefix ns0: . @prefix dcterms: . @prefix skos: . vivo:departmentOrSchool "Medicine, Faculty of"@en, "Medicine, Department of"@en, "Experimental Medicine, Division of"@en ; edm:dataProvider "DSpace"@en ; ns0:degreeCampus "UBCV"@en ; dcterms:creator "Anthony, Joseph Stephan"@en ; dcterms:issued "2010-04-26T18:28:54Z"@en, "2010"@en ; vivo:relatedDegree "Doctor of Philosophy - PhD"@en ; ns0:degreeGrantor "University of British Columbia"@en ; dcterms:description """Elucidating the events and mechanism of regulation of apoptosis is of wide interest to the scientific community, and to humanity, since apoptosis, so important for proper development and maintenance of an organism, is also responsible for disease when the process goes awry. In this thesis, a proteomics investigation into changes in protein concentrations and half-lives in early apoptosis is presented, enhancing our understanding of this process. Using Stable Isotope Labeling with Amino Acids in Cell Culture (SILAC), cytokine withdrawal-induced apoptosis of a human hematopoietic cell line, TF-1, was studied. This is a useful model in which signaling pathways regulating apoptosis have been extensively studied previously. A study such as this can be considered “hypothesis-generating”, but at the outset the hypothesis is that proteins whose functions are closely tied to regulation of apoptosis will show detectable changes in quantity in cells undergoing apoptosis. Initially three biological replicates were performed, comprising 200 samples in all, analyzed using an FT-ICR mass spectrometer. Relative abundance of 1451 proteins identified in common between three biological replicates was determined, and 124 proteins showing the largest concentration changes in response to cytokine withdrawal are discussed in more detail. A subsequent effort investigated protein half-life changes in response to cytokine withdrawal and identified 255 proteins for which half-lives were calculated. The apparent changes in protein half-life in response to cytokine withdrawal are discussed. A high level of coverage of the proteome was achieved, giving a large number of protein identifications and relative quantitations. Further I have been able to identify several apparently synchronous changes in concentration between proteins with related functions, suggesting possible interactions not previously described, or identified as playing a role in cell survival, proliferation or death. Further, I observed cytokine withdrawal-induced alterations in concentration in some proteins for which little is known. The proteomic analysis of apoptosis using SILAC to determine protein half-life data is also a novel approach. Together, the work in this thesis suggests numerous avenues of investigation potentially leading to novel findings regarding cells undergoing apoptosis; and also suggests a potentially fruitful avenue of investigation for clinical management of patients undergoing chemotherapy."""@en ; edm:aggregatedCHO "https://circle.library.ubc.ca/rest/handle/2429/24167?expand=metadata"@en ; skos:note " A QUANTITATIVE PROTEOMICS ANALYSIS OF HUMAN CELLS UNDERGOING APOPTOSIS. by JOSEPH STEPHAN ANTHONY A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY in THE FACULTY OF GRADUATE STUDIES (Experimental Medicine) THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver) April, 2010 © Joseph Stephan Anthony, 2009 ii ABSTRACT Elucidating the events and mechanism of regulation of apoptosis is of wide interest to the scientific community, and to humanity, since apoptosis, so important for proper development and maintenance of an organism, is also responsible for disease when the process goes awry. In this thesis, a proteomics investigation into changes in protein concentrations and half-lives in early apoptosis is presented, enhancing our understanding of this process. Using Stable Isotope Labeling with Amino Acids in Cell Culture (SILAC), cytokine withdrawal-induced apoptosis of a human hematopoietic cell line, TF-1, was studied. This is a useful model in which signaling pathways regulating apoptosis have been extensively studied previously. A study such as this can be considered “hypothesis-generating”, but at the outset the hypothesis is that proteins whose functions are closely tied to regulation of apoptosis will show detectable changes in quantity in cells undergoing apoptosis. Initially three biological replicates were performed, comprising 200 samples in all, analyzed using an FT-ICR mass spectrometer. Relative abundance of 1451 proteins identified in common between three biological replicates was determined, and 124 proteins showing the largest concentration changes in response to cytokine withdrawal are discussed in more detail. A subsequent effort investigated protein half-life changes in response to cytokine withdrawal and identified 255 proteins for which half-lives were calculated. The apparent changes in protein half-life in response to cytokine withdrawal are discussed. iii A high level of coverage of the proteome was achieved, giving a large number of protein identifications and relative quantitations. Further I have been able to identify several apparently synchronous changes in concentration between proteins with related functions, suggesting possible interactions not previously described, or identified as playing a role in cell survival, proliferation or death. Further, I observed cytokine withdrawal-induced alterations in concentration in some proteins for which little is known. The proteomic analysis of apoptosis using SILAC to determine protein half-life data is also a novel approach. Together, the work in this thesis suggests numerous avenues of investigation potentially leading to novel findings regarding cells undergoing apoptosis; and also suggests a potentially fruitful avenue of investigation for clinical management of patients undergoing chemotherapy. iv TABLE OF CONTENTS ABSTRACT ............................................................................................................................ ii! TABLE OF CONTENTS ...................................................................................................... iv! LIST OF TABLES.................................................................................................................. x! LIST OF FIGURES.............................................................................................................. xii! LIST OF ABBREVIATIONS .............................................................................................. xv! ACKNOWLEDGEMENTS ............................................................................................... xvii! DEDICATION ..................................................................................................................... xix! 1! 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Proteomic Analysis of A Hemopoietic Cell Line Undergoing Apoptosis................... 79! .#\"! -F'6&JA=')&F################################################################################################################################# UX! .#+! 5T%96)<9F'17!]^96^)93!1FJ!M1=_G6&AFJ!I1'96)17 ######################################################## V;! vi :#2#\"! L%,8!$F+,M.+4F!<-&%F'('!,)!<;,;+,'('!(-!XLJ\"!$.%%'#############################################################I1! :#2#2! $&';&'.!<*+(7(+F!<-&%F'('!(-!XLJ\"!$.%%' #######################################################################################I9! :#2#:! /.+.4M(-&+(,-!,)!$,-*.-+4&+(,-'!,)!<4H(-(-.!&-A!ZF'(-.!G.`D(4.A!),4!$.%%!T4,8+0! 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LD-*+(,-! #############################################################################################################################################################################2P>! >#E#2#2! X,+&%!@4,+.(-!$,M;&4.A!8(+0!UDQ'.+!+0&+!U0,8'!\\/.*4.&'.A!$,-*.-+4&+(,-^!Y! O,%.*D%&4!LD-*+(,- ########################################################################################################################################################2P9! 4.7! Pathway Analysis Using Ingenuity ® Pathway Analysis (IPA) ########################################+\"L! ;#V! B&F=7AJ)FG!`9<16_( ###############################################################################################################++\"! >#I#\"! UDHH.'+(,-'!),4!LD+D4.!/(4.*+(,-'############################################################################################## 22>! 5! Validation of the Mass Spectrometry Results ............................................................ 226! *#\"! -F'6&JA=')&F###############################################################################################################################++>! *#+! Z17)J1')&F!&N!,6&'9)F!B21FG9(!SA6)FG!$%&%'&()( ##########################################################++X! 5.3! Immunoblotting N&6!'29 Validation &N Proteins###################################################################+.L! 1#:#\"! \\];JG.HD%&+.A^!@4,+.(-'!Y!@4,+.(-'!U0,8(-H!<-!?-*4.&'.!(-!$,-*.-+4&+(,- ########## 2:\"! 1#:#2! \\/,8-JG.HD%&+.A^!@4,+.(-'!Y!@4,+.(-'!+0&+!U0,8!\"! *#;! $F1<&6()F ##################################################################################################################################+;*! 1#>#\"! <-&M,4'(-!(-!$F+,V(-.Ja(+0A4&8&%!Y!O&''!U;.*+4,M.+4F!G.'D%+'# ########################### 2>E! 1#>#2! <-&M,4'(-!/.*4.&'.'!(-!G.';,-'.!+,!$F+,V(-.!a(+0A4&8&%# ######################################## 21>! 1#>#:! B7(A.-*.!),4!@0,';0,4F%&+(,-###################################################################################################### 21>! 1#>#>! <-&M,4'(-!(-!G.';,-'.!+,!$F+,V(-.!a(+0A4&8&%!8(+0!&-A!a(+0,D+!Zf2=>PP2!&-A! ]6\"2E!X4.&+M.-+# ############################################################################################################################################ 21I! 1#>#1! <-&M,4'(-!5&%)J%().########################################################################################################################### 21I! 1#>#E! <-&M,4'(-!Y!$,-*%D'(,-'!&-A!LD+D4.!/(4.*+(,-' ################################################################ 21=! *#*! ,6&G61<<9J!B977!S91'2!;!O,SBS;Q ###################################################################################+>+! 1#1#\"! <''(H-M.-+!,)!@/$/>!@.;+(A.'!X,!!@4,+.(-!/.H4&A&+(,- ####################################################################################### 2I9! 1#1#:! 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B&F=7A()&F( ################################################################################################################################+X;! 6! Determination of Rates of Protein Synthesis – Exploratory Data ........................... 295! >#\"! -F'6&JA=')&F###############################################################################################################################+X*! >#+! 5T%96)<9F'17!]^96^)93 #########################################################################################################+XV! >#.! `9(A7'(##########################################################################################################################################.L.! E#:#\"! @4,+.(-'!?A.-+()(.A!(-!\\$,-+4,%^!U&M;%.################################################################################### :P:! E#:#2! @4,+.(-'!?A.-+()(.A!(-!\\U+&47(-H^!U&M;%. ################################################################################# :\"E! 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B&F=7A()&F( ################################################################################################################################.>*! 7! Conclusions and Future Directions............................................................................. 368! U#\"! ?29!`971')&F(2)%!M9'399F!'29!,69^)&A(!B21%'96(!C!-F'9G61')&F!&N!'29!I1'96)17#! #### ! ########################################################################################################################################################.UL! U#+! ?29!BA669F'!W&6_!)F!'29!]^96177!B&F'9T'!&N!'29!Y)97J# ################################################.U\"! U#.! @'69FG'2(!1FJ!E)<)'1')&F(!&N!'29!BA669F'!@'AJ4 ############################################################.U;! U#;! @%9=)N)=!\\4%&'29(9(!1FJ!YA'A69!S)69=')&F( ####################################################################.U>! REFERENCES ................................................................................................................... 385! Appendix 1. Proteins Observed to Increase in Concentration. ...................................... 412! ix Appendix 2. Proteins Observed to Decrease in Concentration. ..................................... 432! Appendix 3 - Derivation of the Protein Half-Life Equations.......................................... 462! x LIST OF TABLES TABLE 2.1 DETAILS OF THE CHROMATOGRAPHY RUNS ON THE 1100 HPLC ............................ 66 TABLE 2.2 PRIMARY ANTIBODIES USED IN THE EXPERIMENTS REPORTED IN THIS THESIS. ........ 69 TABLE 3.1 THE NUMBER OF PROTEINS IDENTIFIED IN EACH EXPERIMENT, AND THE NUMBER OF PEPTIDES USED TO IDENTIFY THESE PROTEINS................................................................ 100 TABLE 3.2 RELATIVE 13 C/ 12 C RATIOS AT VARIOUS LIKELIHOODS OF CHANCE........................ 104 TABLE 3.3 THE LIST OF PROTEINS OBSERVED TO INCREASE IN RESPONSE TO CYTOKINE WITHDRAWAL. ............................................................................................................... 106 TABLE 3.4 THE LIST OF PROTEINS OBSERVED TO DECREASE IN RESPONSE TO CYTOKINE WITHDRAWAL. ............................................................................................................... 108 TABLE 3.5. DIRECTION OF CHANGE OF PROTEINS SHOWING GREATER THAN 30% CHANGE IN ANTHONY, COMPARED WITH OTHER PUBLISHED RESULTS ............................................. 113 TABLE 3.6 RECENT PROTEOMICS PUBLICATIONS REFERRED TO IN THE TEXT, WITH DETAILS OF EACH STUDY. ................................................................................................................. 114 TABLE 3.7 COMPARISON OF PROTEINS AND QUANTITATION RATIOS FOUND IN THE CURRENT STUDY WITH PREVIOUSLY PUBLISHED WORK. ................................................................ 115 TABLE 4.1 SUMMARY OF DETAILS FOR CALCULATION OF FALSE DISCOVERY RATE (FDR). .. 136 TABLE 4.2 DIFFERENCES BETWEEN MEASURED RATIOS AND EXPECTED RATIOS FOR HSP-! AND \" AT DIFFERENT MIXTURE RATIOS OF 12 C WITH 13 C........................................................ 138 TABLE 4.3 NUMBER OF STANDARD DEVIATIONS FOR DIFFERENT LEVELS OF SIGNIFICANCE – NORMALLY DISTRIBUTED DATA. .................................................................................... 140 TABLE 4.4 CUT-OFFS FOR I/W RATIOS AT DIFFERENT LEVELS OF SIGNIFICANCE. .................... 141 TABLE 4.5 RAW DATA FROM WHICH FIGURE 4.4 HAS BEEN DRAWN. ..................................... 152 TABLE 4.6 RAW DATA FROM WHICH FIGURE 4.5 HAS BEEN DRAWN....................................... 153 TABLE 4.7 RAW DATA FROM WHICH FIGURE 4.6 HAS BEEN DRAWN....................................... 177 TABLE 4.8. RAW DATA FROM WHICH FIGURE 4.7 HAS BEEN DRAWN...................................... 178 TABLE 4.9 RAW DATA FROM WHICH FIGURE 4.8 HAS BEEN DRAWN....................................... 203 TABLE 4.10 THE TOP TEN FOLD CHANGES OBSERVED AMONGST PROTEINS SHOWN TO INCREASE IN CONCENTRATION IN RESPONSE TO CYTOKINE WITHDRAWAL...................................... 211 TABLE 4.11 THE TOP TEN FOLD CHANGES OBSERVED AMONGST PROTEINS SHOWN TO DECREASE IN CONCENTRATION IN RESPONSE TO CYTOKINE WITHDRAWAL...................................... 212 xi TABLE 5.1. DETAILS OF THE PEPTIDES USED TO IDENTIFY ANAMORSIN IN THREE SILAC EXPERIMENTS ................................................................................................................ 253 TABLE 5.2 TWENTY PEPTIDES USED TO IDENTIFY PDCD4 IN THREE MASS SPECTROMETRY EXPERIMENTS. ............................................................................................................... 265 TABLE 5.3. RESULTS OF ANALYSIS OF RAW DATA USING THE GPM....................................... 278 TABLE 6.1 LIST OF PROTEINS FROM CONTROL SAMPLE AT 15 HOURS, IDENTIFIED AND QUANTITATED AS HAVING A RATIO OF LESS THAN 0.67.................................................. 304 TABLE 6.2 LIST OF PROTEINS FROM CONTROL SAMPLE AT 15 HOURS, IDENTIFIED AND QUANTITATED A RATIO OF GREATER THAN 1.85............................................................. 310 TABLE 6.3 LIST OF PROTEINS FROM “STARVE” SAMPLE AT 15 HOURS, IDENTIFIED AND QUANTITATED AS HAVING A RATIO OF LESS THAN 0.67.................................................. 317 TABLE 6.4 LIST OF PROTEINS FROM “STARVE” SAMPLE AT 15 HOURS, IDENTIFIED AND QUANTITATED AS HAVING A RATIO OF GREATER THAN 1.84 .......................................... 321 TABLE 6.5. PROTEINS FOUND IN SILAC PROTEIN CONCENTRATION EXPERIMENTS AND IN HALF- LIFE EXPERIMENTS SHOWING A CHANGE IN BOTH CONCENTRATION AND IN HALF-LIFE .. 329 TABLE 6.6. PROTEINS FOUND IN SILAC (PROTEIN CONCENTRATION) EXPERIMENTS AND IN HALF-LIFE EXPERIMENTS SHOWING NO CHANGE IN CONCENTRATION AND A CHANGE IN HALF-LIFE ...................................................................................................................... 332 TABLE 6.7. PROTEINS FOUND IN SILAC (PROTEIN CONCENTRATION) EXPERIMENTS AND IN HALF-LIFE EXPERIMENTS SHOWING A CHANGE IN CONCENTRATION AND NO CHANGE IN HALF-LIFE ...................................................................................................................... 335 TABLE 6.8. PROTEINS FOUND IN SILAC (PROTEIN CONCENTRATION) EXPERIMENTS AND IN HALF-LIFE EXPERIMENTS SHOWING NO CHANGE IN CONCENTRATION OR IN HALF-LIFE. . 336 TABLE 6.9. NINE PROTEINS FOUND IN COMMON BETWEEN PUBLISHED PROTEIN DEGRADATION DATA AND HALF-LIFE DATA COMPUTED FROM OUR DATA SET........................................ 344 xii LIST OF FIGURES FIGURE 1.1 APOPTOSIS SIGNALING PATHWAYS........................................................................ 17 FIGURE 1.2 THE BCL-2 FAMILY OF PRO-SURVIVAL AND PRO-DEATH PROTEINS........................ 21 FIGURE 1.3 FT-ICR MASS SPECTROMETRY. ............................................................................. 47 FIGURE 2.1 EXPERIMENTAL OVERVIEW FOR THREE CYTOKINE-WITHDRAWAL EXPERIMENTS. . 59 FIGURE 3.1 FLOW CYTOMETRY OF PROPIDIUM IODIDE-STAINED TF-1 CELLS. .......................... 86 FIGURE 3.2 DEVD-PNA CASPASE ACTIVITY ASSAY. ............................................................. 88 FIGURE 3.3 PROTEIN LOAD (#G) OF WHOLE CELL LYSATE AND BOVINE SERUM (BSA)............ 93 FIGURE 3.4 A 12% MINI-GEL WAS USED AS THE 40-SLICE GEL IN THE FIRST EXPERIMENT. ...... 95 FIGURE 3.5 AN 8% - 16.5% LARGE FORMAT GEL WAS USED AS THE 90-SLICE GEL FOR THE SECOND EXPERIMENT....................................................................................................... 98 FIGURE 3.6 AN 8%-16.5% LARGE FORMAT GRADIENT GEL WAS USED FOR THE 70-SLICE GEL FOR THE THIRD EXPERIMENT............................................................................................ 99 FIGURE 4.1 HISTOGRAM AND PROBIT CURVE OF DATA DISTRIBUTION. .................................. 129 FIGURE 4.2 LEVEL 4 BIOLOGICAL PROCESS GENE ONTOLOGIES OF SUBSET OF PROTEINS SHOWING AN INCREASE IN CONCENTRATION IN RESPONSE TO CYTOKINE WITHDRAWAL.146 FIGURE 4.3 LEVEL 4 BIOLOGICAL PROCESS GENE ONTOLOGIES OF SUBSET OF PROTEINS SHOWING A DECREASE IN CONCENTRATION IN RESPONSE TO CYTOKINE WITHDRAWAL.. 147 FIGURE 4.4 PROTEIN CATEGORIZATION BY GENE ONTOLOGY (I) – CATEGORIZATION BY LEVEL 4 TERMS OF A BIOLOGICAL PROCESS DIRECTED ACYCLIC GRAPH. ..................................... 150 FIGURE 4.5 PROTEIN CATEGORIZATION BY GENE ONTOLOGY (II) – CATEGORIZATION BY LEVEL 4 TERMS OF A BIOLOGICAL PROCESS DIRECTED ACYCLIC GRAPH. ..................................... 151 FIGURE 4.6 PROTEIN CATEGORIZATION BY GENE ONTOLOGY (III) – CATEGORIZATION BY LEVEL 4 TERMS OF A BIOLOGICAL PROCESS DIRECTED ACYCLIC GRAPH.................................... 175 FIGURE 4.7 PROTEIN CATEGORIZATION BY GENE ONTOLOGY (IV) – CATEGORIZATION BY LEVEL 4 TERMS OF A BIOLOGICAL PROCESS DIRECTED ACYCLIC GRAPH.................................... 176 FIGURE 4.8 PROTEIN CATEGORIZATION BY GENE ONTOLOGY (V) – CATEGORIZATION BY LEVEL 4 TERMS OF A MOLECULAR FUNCTION DIRECTED ACYCLIC GRAPH.................................... 202 FIGURE 4.9. FIRST EXAMPLE OF A FUNCTIONAL NETWORK GENERATED USING INGENUITY PATHWAY ANALYSIS ®. ................................................................................................ 214 xiii FIGURE 4.10. SECOND EXAMPLE OF A FUNCTIONAL NETWORK GENERATED USING INGENUITY PATHWAY ANALYSIS ®. ................................................................................................ 215 FIGURE 4.11. PROPOSED MODEL OUTLINING OBSERVED CHANGES IN PROTEIN CONCENTRATIONS AS THEY RELATE TO THE INDUCTION OF APOPTOSIS BY ALTERATIONS IN MEVALONATE PATHWAY AND CDC42 SIGNALING. ................................................................................ 221 FIGURE 5.1 IMMUNOBLOT OF WHOLE CELL LYSATES - CONTROL WITH HOURS OF CYTOKINE WITHDRAWAL AS SHOWN............................................................................................... 232 FIGURE 5.2 REPRESENTATIVE MASS SPECTRUM OF CDC42 SHOWING RATIO OF HEAVY TO LIGHT ISOFORMS OF PEPTIDE YVECSALTQK......................................................................... 233 FIGURE 5.3 IMMUNOBLOT OF WHOLE CELL LYSATES - CONTROL (CONT.) WITH 15- AND 18- HOURS OF CYTOKINE WITHDRAWAL............................................................................... 235 FIGURE 5.4 REPRESENTATIVE MASS SPECTRUM OF HMGB2 ................................................. 236 FIGURE 5.5 WESTERN BLOT SHOWING LEVELS OF SHIP IN RESPONSE TO 15-HOURS OF CYTOINE WITHDRAWAL. ............................................................................................................... 237 FIGURE 5.6 REPESENTATIVE MASS SPECTRUM OF SHIP......................................................... 238 FIGURE 5.7 THYMIDYLATE SYNTHASE (TS) IMMUNOBLOT SHOWING CONTROL AND 15-HOURS OF CYTOKINE WITHDRAWAL. ......................................................................................... 239 FIGURE 5.8 REPRESENTATIVE MASS SPECTRUM OF THYMIDYLATE SYNTHASE ...................... 239 FIGURE 5.9 WESTERN BLOT SHOWING LEVELS OF CLEAVED CASPASE 3 IN RESPONSE TO CYTOKINE WITHDRAWAL FOR THE TIMES SHOWN........................................................... 240 FIGURE 5.10 REPRESENTATIVE MASS SPECTRUM OF VINCULIN. ............................................ 242 FIGURE 5.11 REPRESENTATIVE MASS SPECTRUM OF HSP90-ALPHA (!) ................................. 243 FIGURE 5.12 REPRESENTATIVE MASS SPECTRUM OF HSP90-BETA (\") ................................... 244 FIGURE 5.13 EIGHT PEPTIDES WERE USED IN THE IDENTIFICATION OF ANAMORSIN................ 248 FIGURE 5.14 ANAMORSIN IMMUNOBLOT IN FDCP-1 CELLS................................................... 254 FIGURE 5.15 (A) & (B). ANTI-ANAMORSIN IPS....................................................................... 255 FIGURE 5.16. ANTI-ANAMORSIN IMMUNOBLOT, 12% LOW-BIS GEL. ...................................... 255 FIGURE 5.15A ANTI-ANAMORSIN IP WITH 4G10 PROBE......................................................... 254 FIGURE 5.15B ANTI-ANAMORSIN IP WITH ANAMORSIN PROBE............................................... 254 FIGURE 5.17. ANTI-ANAMORSIN IP, ANTI-PHOSPHOSERINE PROBE......................................... 256 FIGURE 5.18. ANTI-ANAMORSIN IP TREATED WITH CALF INTESTINAL PHOSPHATASE. ........... 257 xiv FIGURE 5.19. ANAMORSIN RESPONSE TO CYTOKINE WITHDRAWAL FOR 6 AND 12 HOURS, AND TO LY294002 AND UO126............................................................................................ 258 FIGURE 5.20. ANAMORSIN EXPRESSION IN FDCP-1 CELLS FOLLOWING CYCLOHEXIMIDE TREATMENT. .................................................................................................................. 259 FIGURE 5.21. REPRESENTATIVE SPECTRA IDENTIFYING TWELVE OF THE PEPTIDES FROM TABLE 5.2 ................................................................................................................................. 269 FIGURE 5.22. IMMUNOBLOT SHOWING PDCD4 IN TF-1 CELLS - CONTROL WITH 15- AND 18- HOURS OF CYTOKINE WITHDRAWAL............................................................................... 273 FIGURE 5.23. WB SHOWING PDCD4 IN CONTROL AND 15 HOURS CYTOKINE WITHDRAWAL (ST). .............................................................................................................................. 274 FIGURE 5.24. WB SHOWING PDCD4 IN CONTROL WITH 15-HOURS CYTOKINE WITHDRAWAL. CELLS LYSED DIRECTLY INTO HOT LOADING BUFFER. .................................................... 275 FIGURE 6.1 LEVEL 4 DAG FOR BIOLOGICAL PROCESS GENE ONTOLOGIES. PROTEINS IN CONTROL AND STARVING CONDITIONS WITH HALF-LIFE LESS THAN 15 HOURS. ............. 339 FIGURE 6.2. LEVEL 4 DAG FOR MOLECULAR FUNCTION GENE ONTOLOGIES. PROTEIN IN CONTROL AND STARVING CONDITIONS WITH HALF-LIFE LESS THAN 15 HOURS. ............. 340 341 FIGURE 6.3. LEVEL 4 DAG FOR BIOLOGICAL PROCESS GENE ONTOLOGIES. PROTEINS IN CONTROL AND STARVING CONDITIONS WITH HALF-LIFE GREATER THAN 24 HOURS. ...... 341 FIGURE 6.4. LEVEL 4 DAG FOR MOLECULAR FUNCTION GENE ONTOLOGIES. PROTEIN IN CONTROL AND STARVING CONDITIONS WITH HALF-LIFE GREATER THAN 24 HOURS. ...... 342 xv LIST OF ABBREVIATIONS AIF apoptosis inducing factor APAF-1 apoptotic protease activating factor 1 Bcl-2 B-cell lymphoma-2 BH Bcl-2 homology CARD caspase recruitment domain CGMI BHK cell line stably transfected with human GM-CSF gene Da Dalton (unit of mass) DAG directed acyclic graph DD death domain DISC death-inducing signaling complex Erk extracellular signal-related kinase ESI electrospray ionization FADD Fas associated death domain Fas-L Tumor necrosis factor ligand superfamily, member 6 FDR false discovery rate FT-ICR Fourier transform ion cyclotron resonance mass spectrometry GM-CSF granulocyte-macrophage colony stimulating factor GO gene ontology ICAT isotope-coded affinity tags IL-3 interleukin-3 IL-5 interleukin-5 IPA Ingenuity® Pathway Analysis JAK Janus kinase LC-MS/MS liquid chromatography-tandem mass spectrometry LTQ-FT Thermo Electron IonTrap / FT-ICR mass spectrometer MALDI matrix-assisted laser desorption and ionization MAPK Mitogen-activated protein kinase MRM multiple reaction monitoring MS mass spectrometry xvi NF-$B nuclear factor kappa b OD optical density PBS phosphate buffered saline PDK-1 3-phosphoinositide-dependent protein kinase 1 PH pleckstrin homology PI-3,4-P2 phosphatidylinositol-3,4-bisphosphate PI-3,4,5-P3 phosphatidylinositol-3,4,5-trisphosphate PI3-K phosphatidylinositol-3 kinase PKB protein kinase B PTEN Phosphatase and tensin homologue PTM post-translational modification PTP (mitochondrial) permeability transition pore SDS-PAGE sodium dodecylsulfate polyacrylamide gel electrophoresis SH-2 Src-homology-2 SHIP SH2 domain-containing inositol-5'-phosphatase SILAC stable isotope labeling with amino acids in cell culture STAT Signal transducer and transcription activator TBS Tris buffered saline TBST Tris buffered saline with Tween® TF-1 an erythroleukemia human cell line Th Thomson (unit of mass-to-charge ratio) TNF tumour necrosis factor TOF time of flight TRADD TNF receptor activated death domain TRAIL TNF-related apoptosis-inducing ligand xvii ACKNOWLEDGEMENTS Dr Vincent Duronio – for the opportunity he has given me; who, through the trust he has placed in me (as in all his students), has allowed me to pursue this long-held dream, and who has imbued in me some of his own integrity, and scholarship, as well as his rigor and critical thinking – enhancing not only my knowledge and skill, but my understanding of what it means to be a scientist. Dr Juergen Kast, a member of the Supervisory Committee, without whose willingness to collaborate, this project could not have been completed; who has always been ready to give of his time, to advise and assist. Dr Youssef Av-Gay and Dr Aly Karsan, members of the Supervisory Committee, for the scholarship and enthusiasm they have consistently brought to this endeavour. Thank you. Dr Leonard Foster, who has always been gracious and so generous with the gifts of his time and knowledge. Jason Rogalski, for his technical skill and support, as well as his engaging discussions. Shujun Lim, for her technical expertise, her patience, and her willingness to help. Payman Hojabrpour, for his technical assistance, and his skill in managing the Duronio laboratory. Penny Brasher, PhD, for her statistical insights, and her willingness to be available to discuss this project. David Creese, for his skill in translating my Latin dedication. xviii Andrei Godoroja, for his friendship and support, and for the Perl ® scripts which made some of the data handling possible. Matthew Sniatynski, for his Python ® script which saved me so much time. My colleagues and fellow lab members: Dr Sarwat Jamil, Sherry Wang, Ivan Waissbluth and Stefanie Cheah for their encouragement and support during the difficult times, and their laughter during the good times. David Shih, for his help with some of the Western blot images in Chapter 5. Greg Martin, PhD, for his friendship, and mathematical insights, especially the equations for the protein half-life calculations. Elizabeth Dean, P.T., PhD, Ginny Mulhall, Sheila Mannell for their friendship and support, without which, this undertaking would have been much more difficult. xix DEDICATION “Dicebat Bernardus Carnotensis nos esse quasi nanos, gigantium humeris insidentes, ut possimus plura eis et remotiora videre, non utique proprii visus acumine, aut eminentia corporis, sed quia in altum subvenimur et extollimur magnitudine gigantea.” John of Salisbury [d.1180], Metalogicon [III, lv, 900a] Eis in quorum vestigiis semitisque ambulavi gratias ago et haec ego scripta dedico.* *I thank those in whose footsteps and paths I have trod, and dedicate these writings to them. 1 1 Introduction Apoptosis, one form of programmed cell death, is essential for life. This natural process of cell loss is critically important to the function of the organism, in such diverse ways as (i) embryonic development, e.g. for neural development (Putcha & Johnson, 2004) and for the functional development of the hands and feet (Jacobson, Weil, & Raff, 1997), (ii) cellular (tissue) homeostasis (Green & Evan, 2002), or (iii) tissue remodeling during healing or functional regression, e.g. cessation of lactation after weaning (Baxter, Neoh, & Tevendale, 2007), (iv) for defense, e.g. for the control of lymphocyte proliferation (Newton & Strasser, 2000), (v) for preventing the development of auto-immunity by the deletion of self-reactive T-cells (McCaughtry & Hogquist, 2008), (vi) as a defense against pathogens (Lilley, Schwartz, & Weitzman, 2007), and (vii) in aging (Y. Zhang & Herman, 2002)). The process of apoptosis is highly conserved throughout evolution, with evidence for apoptosis found in all metazoan organisms from marine sponges to man (Earnshaw, Martins, & Kaufmann, 1999; Twomey & McCarthy, 2005; Wiens, Krasko, Muller, & Muller, 2000). Indeed, for several years evidence has been accumulating to suggest that much simpler organisms, such as the unicellular eukaryotes Leishmania sp. and yeast, undergo a form of apoptosis (Arnoult et al., 2002; Buttner et al., 2006; Eisenberg, Buttner, Kroemer, & Madeo, 2007; Frohlich, Fussi, & Ruckenstuhl, 2007). Apoptosis, the process that is so critically important for the development and homeostasis of an organism, can cause morbidity when it goes awry. For example, in the case of cells that suffer DNA damage that is so severe that the cellular DNA repair machinery cannot repair the damage, the cells should be directed towards apoptosis. If, for some reason, cells escape 2 the signal to undergo apoptosis or have defective apoptosis machinery, allowing survival and subsequent division, then the damaged DNA may be copied and transmitted to daughter cells following mitosis. Further DNA damage can result from this reproduction of abnormal genetic material. Evading apoptosis is the classic pathway to oncogenesis (Bellamy, Malcomson, Harrison, & Wyllie, 1995; Letai, 2008). However, DNA damage is not the only cause of apoptosis - viral infection can induce apoptosis (Lilley et al., 2007), as can stress, for example, starvation (Schamberger, Gerner, & Cerni, 2005), oxidative stress (Buttke & Sandstrom, 1995) or nitrosative stress (Thomas et al., 2008). In other cases, cells that are healthy may improperly be directed towards apoptosis, which results in the loss of functional tissue. This occurrence has been implicated in conditions such as Alzheimer’s disease (Lafay-Chebassier et al., 2005), Parkinson’s disease (Burke, 2008), AIDS (Alimonti, Ball, & Fowke, 2003) and osteoarthritis (Del Carlo & Loeser, 2008). Although the process we understand as apoptosis has been described since the mid- nineteenth century (Clarke & Clarke, 1995), a deeper understanding of this process has only been possible with the development of the more sophisticated histological studies in the 1970s, and biochemical studies beginning in the early 1980s. Despite decades of work, there remain many unanswered questions concerning the details of the signaling pathways and molecular interactions involved in apoptosis. An enhanced understanding of the complex process of apoptosis may allow better treatments of the diseases caused by “inappropriate” apoptosis, or those caused by evasion of apoptosis. “Many human diseases can be attributed directly or indirectly to a derangement of apoptosis, resulting in either cell accumulation, in which cell eradication or cell turnover is impaired, or cell loss, in which the apoptotic 3 programme is inadvertently triggered” (Fadeel & Orrenius, 2005). However, there remains always more work to be done to understand the many cellular responses that occur in cells undergoing apoptosis. The control of any complex process such as apoptosis is achieved via hundreds of signalling proteins. These multiple signalling components make up a cellular machinery that is not composed simply of linear signaling elements or pathways, but rather, one which can be seen as a complex network of interconnections (Pawson & Saxton, 1999). Perturbation of one element of the network disturbs the whole “structure” – much like plucking one strand of a spider-web may distort the whole web. A number of techniques have been developed for the investigation of signaling networks, which involve perturbation of one element of the network. For example, by selective inhibition of one “node” in the network by using a pharmacological small molecule inhibitor; or by enhancing the translation of one gene, and over-expressing its protein product; or by inhibiting translation of one protein, and determining the effects of these changes on the whole system. Antibodies to a protein of interest are frequently used to visualize the results of any perturbation of the system. These techniques have worked extremely well – the thousands of publications generated by scientists using these well- established techniques are a tribute not only to the ingenuity of these investigators, but to the effectiveness of this approach in dissecting the elements of the apoptosis machinery. Indeed, such is the state of knowledge generated by this “single element” approach to studying apoptosis that the scientific community is now able to begin to investigate cellular function from a more “global” perspective; we understand many of the individual elements well enough now to start to see the picture as a whole. 4 One more recently devised approach is to use techniques that take “snapshots” of the whole cellular system at one point. In this regard, recent advances in experimental techniques have allowed for the development of the field of proteomics - analysis of the whole protein content of a group of cells (or as much of the whole as may be visualized with current technology) as an approach to answer questions of cellular function. The work described in this thesis has made use of a proteomics approach to further our understanding of the signaling pathways involved in apoptosis, specifically, the changes encountered in early or “pre-apoptosis”, using cytokine-dependent hematopoietic cells as an experimental model. The PI3-K pathway is an important element in this approach because of the pivotal role that PI3-K and its key downstream kinase, PKB/Akt, plays in cellular survival (reviewed in (Cantley, 2002; Duronio, 2008). While the ultimate goal of apoptosis research may be considered the development of new therapeutic approaches for the treatment of cancer and other diseases of inappropriate apoptosis, new therapeutics need to be built upon a molecular understanding of apoptosis (Antignani & Youle, 2005; Labi, Grespi, Baumgartner, & Villunger, 2008) and it is in this area, the molecular understanding of apoptosis, that this current work has been able to make a contribution. The results shown are unexpected in the wide range of proteins affected by withdrawal of pro-survival cytokine signaling. In over 1450 proteins found in common in three biological replicates of our experiment, totalling 200 individual LC-MS/MS runs, we observe both increases and decreases in the concentrations of a large percentage of proteins, and we have classified these proteins with altered concentrations into groups based on gene ontologies, to allow further 5 analysis of the complex response to cytokine withdrawal. Further, we have been able to demonstrate that the techniques utilized in this work are sufficiently robust to be applied to answer questions such as the ones we posed. As well, we show some preliminary data obtained using stable isotope labelling, concerning apparent changes in protein half-life in response to cytokine withdrawal. Although the TF-1 cell line does not perfectly mimic human primary cells, the study is valuable in as much as the TF-1 cell line is a human cell line, hence similarities in proteome are to be expected. As well, the cytokine dependence of the TF-1 cell line remains a relevant model in which to assess apoptosis. However, there are inherent limitations in studying cell lines, and eventually these data would need to be compared with those obtained by the study of primary cells. This notwithstanding, the proteomics approach has been useful in helping to elucidate the complex changes occurring in protein concentrations, resulting either from changes in translation or in degradation, that occur when cells undergo apoptosis, and further, has provided valuable data which will help to direct future studies. 1.1 Apoptosis 1.1.1 Classification of Cell Death Cell death may be broadly classified as occurring in one of two modes; either through programmed cell death, or through necrosis, although the distinction is becoming increasingly blurred as our understanding of these processes deepens. Variations of programmed cell death have been described over the years, using different means of classification. The current classification for cell death modes, based on the morphological 6 changes observed, consists of – apoptosis, autophagy, necrosis and mitotic catastrophe (Galluzzi et al., 2007). Other forms of cell death, such as anoikis, and caspase-independent programmed cell death have been described (Bursch, Ellinger, Gerner, Frohwein, & Schulte- Hermann, 2000; Frisch & Screaton, 2001; Kroemer & Martin, 2005). These latter modes of cell death are based upon observations other than morphological changes, and so are not included in the classification above. A further form of programmed cell death, called pyroptosis, has also been recently described. Pyroptosis is a novel form of cell death induced by infections with Salmonella and Shigella species (Fink & Cookson, 2005). These various types of cell death differ substantially in process, although it is not always easy to distinguish one type from another (Galluzzi et al., 2007). 7 1.1.2 Brief Historical Overview Karl Vogt (1817-1895), a German physician and scientist who published in the fields of zoology, physiology and geology, first reported apoptosis while studying the development of the tadpole of the midwife toad (Alytes obstetricians). Vogt’s description is found in his work entitled “Untersuchungen über die Entwicklungsgeschichte der Geburtshelferkröte“ (Alytes obstetricians) (Jent & Gassmann, Solothurn, 1842) (Vogt, 1842). In this work, Vogt describes the “resorption” (or absorption) of the cells of the notochord in response to pressure exerted by the “rampant growth” of the surrounding cartilage (p 86 – my loose translation). Walther Flemming, the German physician and scientist who first stained and coined the term “chromatin” and also coined the term “mitosis”, gave a more detailed description of the process of apoptosis in his observations on the degeneration of antral follicles in the rabbit ovary ((Flemming, 1885), quoted in (Clarke & Clarke, 1996)). Flemming named the process he observed “chromatolysis” because of the changes observed in nuclear chromatin. These are but two of the early pioneers in the study of apoptosis. There are very many others who have contributed to the field over the past hundred and fifty years. An extensive review of the nineteenth century literature concerning naturally occurring cell death was undertaken by Clarke and Clarke (Clarke & Clarke, 1996). This review makes fascinating reading for those interested in the early history of the field of apoptosis. Another informative document is the 1997 historical review by Häcker and Vaux (Hacker & Vaux, 1997). The work described in these early accounts was based on observation, mostly using tissue staining and 8 light microscopy, and so it was to remain until the early 1970s, when the concept of apoptosis as we understand it began to form in the minds of John Kerr, Andrew Wylie, and Alastair Currie. In 1962, as part of his Ph.D. work, John Kerr began investigating the cellular processes involved in the shrinkage of liver tissue following the induction of ischemia. Kerr observed the induction of classical necrosis in the ischemic tissue, but also observed a different type of cell death – some dying cells were converted to small, round masses, with condensed nuclear chromatin. These cell remnants were engulfed by other hepatocytes, and by phagocytes. Histopathology of the shrunken cells showed that the lysosomes were intact, as well as the mitochondria and ribosomes, a finding that suggested that these shrunken cells were not undergoing a form of necrotic cell death. This “newly” observed cell death was initially named “shrinkage necrosis” (Kerr, 1965). Electron microscopy of shrinkage necrosis In 1971, Kerr collaborated with Andrew Wyllie and Alastair Currie in work which showed that shrinkage necrosis was observed in the adrenal cortex of rats treated with prednisolone, as well as in neonatal rats where the levels of adrenocorticotrophic hormone (ACTH) taper physiologically. Shrinkage necrosis was also observed in breast carcinomas of female rats that had been treated by oophrectomy. Discussion with Allison Crawford at that time brought to light the concept of cell death during normal embryonic development – a concept previously unknown to most who were not developmental biologists. From this “serendipitous confluence of ideas” (Kerr, 2002), the concept of apoptosis was born (Kerr, 9 Wyllie, & Currie, 1972). The word “apoptosis” was coined by Professor James Cormack, of the Department of Greek in the University of Aberdeen (from the Greek !\"#\"$%&'(, which is derived from !\")- in Greek, apo-, in transliteration = “from”, and \"$*&'( in Greek, pt!sis, in transliteration = “falling” in the sense of leaves falling from trees in the autumn) (Kerr et al., 1972). 1.1.3 Description of Apoptosis Apoptosis has been extensively characterized in many sources. Two (of many) helpful reviews of apoptosis have been published by Majno and Joris (Majno & Joris, 1995) and by Hengartner (Hengartner, 2000). Briefly, apoptosis involves the tidy elimination of cells by an ordered process which is characterized histologically by cell shrinkage, cell membrane blebbing, nuclear chromatin condensation (pyknosis) and margination (becoming packed against the inner nuclear membrane), and by nuclear DNA cleavage into segments that are approximately 185 bp long, or multiples of this. The cell generates membrane bound “processes”, by budding, which can detach from the cell body as vesicles, to become apoptotic bodies. The vesicles often contain pyknotic nuclear fragments. Apoptotic bodies undergo phagocytosis by macrophages, or other specialized cell types. Biochemically, these changes are brought about principally by the activation of eight proteolytic enzymes from the family of caspases (see 1.1.4 below). Caspases selectively cleave a subset of proteins causing inactivation (in most cases). For example, the caspase-activated DNase (CAD) is responsible for cutting genomic DNA between nucleosomes, leading to DNA fragments of approximately 185 bp or integer multiples of this. Caspase cleavage of nuclear lamins causes nuclear shrinking and budding, while cleavage of cytoskeletal proteins such as fodrin and gelsolin is thought to be responsible for loss of the cell shape. Cleavage of PAK2 (a member 10 of the p21-actvated protein kinase family), which causes constitutive activation, seems to be responsible for active blebbing. Apoptosis is tightly regulated and morphologically uniform across cell types. Despite this, events that occur downstream of caspase activation have not yet been fully decribed. Apoptosis can occur very quickly – it has been observed to progress from the onset of budding to complete cell breakup within thirty-four minutes. The process takes place in the absence of inflammation - apoptosis is a non-inflammatory cell death process. In contrast, necrosis is an inflammatory process, morphologically distinct from apoptosis and autophagy. Necrosis is a descriptive term used when dead cells or tissues are present (Fink & Cookson, 2005) – the remnants of “untidy” cell death. Necrotic cell death is marked by cytoplasmic vacuolation, plasma membrane breakdown and the presence of inflammation around the dying cell, due to release of pro-inflammatory cytokines from the cell (Edinger & Thompson, 2004). Necrosis differs biochemically from apoptosis and autophagy by the absence of cytochrome c release from mitochondria, and the absence of caspase activation and DNA fragmentation (Krysko, Vanden Berghe, D'Herde, & Vandenabeele, 2008). Biochemical studies of the molecular process of apoptosis first started to appear in the early 1980s with publication of the discovery that one early feature of apoptosis is the activation of a Ca ++ - and Mg ++ -dependent DNA-endonuclease (Duke, Chervenak, & Cohen, 1983). It was not long before other enzymes were discovered which become activated as part of the apoptosis process: the lysosomal protease cathepsin D (Levy-Strumpf & Kimchi, 1998; Tanabe, Lee, & Grayhack, 1982); tissue-type plasminogen activator (Rennie, Bouffard, Bruchovsky, & Cheng, 1984); collagenases and metalloproteinases. RNase has been shown 11 to play a role in apoptosis by degrading mRNA and ribosomal RNA. Another important biochemical change in early apoptosis is the switch in orientation of phosphatidylserine (PS) from the inner leaflet of the lipid bilayer cell membrane to the outer leaflet (Homburg et al., 1995; S. J. Martin et al., 1995). It was thought that this process served to “mark” the apoptotic cells for phagocytosis by macrophages (Fadok et al., 1992). However, recent evidence suggests that the presence of PS on the outer leaflet of the bilayer cell membrane is due to a failure of the unidirectional aminophospholipid translocase, which normally excludes PS from the outer membrane and which requires ATP to function (reviewed in (Vance, 2008). Indeed the identity of a PS receptor on macrophages remains controversial. The aforementioned discoveries were of enormous importance in defining apoptosis, and in delineating the process. However, it was the discovery of the caspases that was critical to understanding the “mechanics” of the process of apoptosis. 1.1.4 Caspases The product of the gene ced-3, was known to be essential for the developmental apoptosis that takes place in the nematode Caenorhabditis elegans (Ellis & Horvitz, 1986). In 1992, it was recognized by Horvitz and colleagues that the mammalian equivalent of CED-3 is interleukin-1!-converting enzyme (ICE, or caspase-1) (Thornberry et al., 1992; Yuan, Shaham, Ledoux, Ellis, & Horvitz, 1993), an enzyme involved in inflammation (although not directly in cell death, unless over-expressed). Soon after this discovery, it was learned that over-expression of murine ICE in rat cells caused the induction of apoptosis (Miura, Zhu, Rotello, Hartwieg, & Yuan, 1993). ICE (as caspase-1) thus became the founding member of a family of cysteine-aspartic acid proteases, the caspases, of which there are now fourteen 12 members (Degterev, Boyce, & Yuan, 2003). Caspases function in both inflammation and cell death, and may be classified accordingly (see, e.g. (Algeciras-Schimnich, Barnhart, & Peter, 2002; Newton & Strasser, 2003; Siegel, 2006)). Most of the morphological changes observed in cells undergoing apoptosis can be explained by the action of the caspases (Hengartner, 2000), although cells can survive low levels of caspase activation and caspases do not provide the only pathway to programmed cell death – see caspase-independent cell death (below). A large number of caspase substrates have been identified (Earnshaw et al., 1999; Stroh & Schulze-Osthoff, 1998; Utz & Anderson, 2000), and the list continues to grow (but see also the caveat in (Timmer & Salvesen, 2007)). Recent evidence suggests that the caspases also play roles in development, activation and differentiation of cells of the immune system (Newton & Strasser, 2003; Siegel, 2006), and perhaps other tissues (Algeciras- Schimnich et al., 2002). 1.1.5 Initiation Phase and Execution Phase Apoptosis is described as having two distinct phases, an initiation phase and an execution phase. (Leber, Lin, & Andrews, 2007; Zimmermann & Green, 2001). During the initiation phase, caspase-8 or caspase-9 become activated by protein-protein interactions. Two pathways have been described; the internal pathway (for activation of caspase-9), and the external pathway (for activation of caspase-8) (see discussion below). During the execution phase, downstream caspases become activated by up-stream caspase(s), leading to a cascade of signals, causing the induction of apoptosis. 13 1.1.6 Internal Pathway Vs. External Pathway Figure 1.1 shows an overview of the internal and external pathways to the activation of the caspase cascade. i. Internal or intrinsic pathway (mitochondrial mediated) The internal or intrinsic pathway to apoptosis is mediated by the mitochondria, beginning with permeabilization of the outer mitochondrial membrane. Mitochondria undergo two changes early in apoptosis – permeabilization of the outer mitochondrial membrane, regulated by protein-protein and protein-membrane interactions of Bcl-2 family members (Chipuk & Green, 2008; Scorrano & Korsmeyer, 2003); and a decrease in mitochondrial transmembrane potential (%&m), critically important for oxidative phosphorylation (Green & Reed, 1998). A decrease in %&m has been observed in many different cell types early in apoptosis. (Deckwerth & Johnson, 1993; Zamzami, Marchetti, Castedo, Decaudin et al., 1995; Zamzami, Marchetti, Castedo, Zanin et al., 1995) The reduction in mitochondrial transmembrane potential causes a decrease in mitochondrial protein translation and gene transcription (Vayssiere, Petit, Risler, & Mignotte, 1994). While it has been argued that a reduction in %&m allowed the escape of cytochrome c from the mitochondria via the formation of a permeability transition pore (PTP), Green and colleagues have convincingly shown that cytochrome c release is independent of the change in %&m(Bossy-Wetzel, Newmeyer, & Green, 1998). Thus, the role of mitochondrial membrane depolarization in apoptosis remains unclear. However, the permeabilization of the outer mitochondrial membrane is still considered to be the “point of no return” in many models of apoptosis (reviewed in (Leber et al., 2007)). 14 Cytochrome c, released from the space between the inner and outer mitochondrial membranes, binds in the cytoplasm with ”apoptotic protease activating factor 1” (APAF-1), in the presence of deoxyadenosine triphosphate (X. Liu, Kim, Yang, Jemmerson, & Wang, 1996), and these two molecules oligomerize, causing a conformational change in APAF-1, which exposes the CARD (caspase recruitment domain), allowing pro-caspase-9 to bind and activate its proteolytic function (P. Li et al., 1997). Activated caspase-9 cleaves pro-caspase- 3, which becomes active caspase-3. Active caspase-3 cleaves the CAD/I CAD complex, freeing CAD (caspase-activated deoxyribonuclease), which causes nuclear DNA fragmentation (X. Liu, Zou, Slaughter, & Wang, 1997). Caspase-3 also activates caspase-7 and caspase-6, each of which has multiple protein targets, causing the cell to proceed to apoptosis (Nicholson & Thornberry, 1997). A second activator of the caspase cascade, called SMAC/Diablo, is released along with cytochrome c from the mitochondria during apoptosis (Du, Fang, Li, Li, & Wang, 2000; Verhagen et al., 2000). SMAC/Diablo binds to members of the “inhibitors of apoptosis” (IAPs) family, which are endogenous protein inhibitors of active caspases, normally associated with caspase-9, caspase-3 and caspase-7 (reviewed in (Richter & Duckett, 2000; Vaux & Silke, 2005)). SMAC/Diablo disrupts the association of XIAP with cleaved caspase- 9, allowing caspase-9 to activate caspase-3 (Ekert, Silke, Hawkins, Verhagen, & Vaux, 2001). ii. External pathway (receptor mediated) External signals, mediated by molecules such as TNF, TRAIL/Apo-2L and Fas-L, can induce apoptosis in target cells expressing the corresponding cell surface receptor to which these ligands can bind. 15 TNF – the prototypic member of a family of pro-apoptosis cytokines - was discovered as the TNF-' and TNF-( forms in 1968 and 1975 respectively (Carswell et al., 1975; Kolb & Granger, 1968). TNFs are mainly produced by activated macrophages, but also by other cell types, including mast cells, endothelial cells and fibroblasts. TNF causes apoptosis of certain tumour cells, modulates immune function and mediates the inflammatory response (Locksley, Killeen, & Lenardo, 2001). TNF has been recognized as a major cytokine in the pathogenesis of chronic inflammatory disease (Clark, 2007). The TNFs can have opposing signalling effects in target cells, depending on the intracellular signalling molecules engaged by the receptor, but this discussion will focus on the pro-apoptotic effects. A large family of receptors binds TNF family members – the TNF Receptor Superfamily. The binding of ligand to receptor exposes a ‘death domain’ (DD), which engages the cytoplasmic TNF receptor activated death domain-containing (TRADD) protein and Fas-associated death domain protein (FADD). The complex of these proteins leads directly to the activation of caspase 8, and to indirect activation of other caspases (Locksley et al., 2001). Specifically, procaspase-8 is activated by association with the co-factor FADD, binding via the receptor’s DD (Boldin, Goncharov, Goltsev, & Wallach, 1996; Muzio et al., 1996). Procaspase-8 molecules, when in proximity, can activate each other, generating caspase-8. Active caspase- 8 cleaves Bid, creating active tBid (X. Luo, Budihardjo, Zou, Slaughter, & Wang, 1998), which induces Bax oligomerization (Roucou, Montessuit, Antonsson, & Martinou, 2002), allowing Bax to permeabilize the outer mitochondrial membrane, causing the release of cytochrome c (Roucou et al., 2002). In a similar way, FasL, the ligand that binds the Fas receptor, induces the formation of a death-inducing signaling complex (DISC), by recruiting FADD to the Fas receptor’s DD. 16 The complex, consisting of Fas receptor, FADD and recruited procaspase-8, similarly causes the activation of caspase-8 and leads to apoptosis. 17 Figure 1.1 Apoptosis signaling pathways. Cellular stress induces proapoptotic Bcl-2 family members to translocate from the cytosol to the mitochondria, where they induce the release of cytochrome c. Cytochrome c catalyzes the oligomerization of Apaf-1, which recruits and promotes the activation of procaspase-9. This, in turn, activates procaspase-3, leading to apoptosis. Alternatively, the activation of caspase-8 by ligation of the death receptor Fas is illustrated. Ligated Fas recruits FADD to the intracellular region, which in turn recruits procaspase-8. The procaspase-8 transactivates, and the mature caspase now can cleave and activate procaspase-3, leading to apoptosis. Signaling from the Fas receptor to mitochondria involves cleavage of the BH3-only protein, Bid, by caspase-8. Bid subsequently induces cytochrome c release and downstream apoptotic events. (Reprinted from Journal of Allergy and Clinical Immunology, Vol 108/Issue 4, Katja C. Zimmerman and Douglas R. Green, How Cells Die: Apoptosis pathways, Pages S99-S103, Copyright (2001), with permission from Elsevier). 18 1.1.7 Regulation of Caspases Caspases are tightly regulated, since the improper timing of caspase activation would have disastrous consequences for a cell. Caspases are produced as inactive zymogens, constitutively expressed, and become active by proteolytic cleavage. The initiator caspases are activated as a result of their associations with each other (which brings them into proximity so that they undergo self-proteolysis), and the effector caspases are activated by the action of the initiator caspases, releasing the inhibitory portion of the molecule (Duan, Chinnaiyan et al., 1996; Duan, Orth et al., 1996). Inhibition of caspase activity is achieved by (i) regulating the interaction between caspases and their cofactors, (ii) by compartmentalizing the various elements of a caspase activation complex, or (iii) by inhibiting the effector caspases (or procaspases) directly. Caspase-8 may be inhibited by FADD-like ICE inhibitory proteins (FLIPs) (Irmler et al., 1997) – probably by competitive inhibition between caspase-8 and its cofactor FADD. Caspase-9 appears to be inhibited by ARC (apoptosis repressor with caspase recruitment domain), a CARD-domain-containing competitive inhibitor (Koseki, Inohara, Chen, & Nunez, 1998). Early work by Liu and colleagues suggests that another means of control of caspase activation is the compartmentalization of the elements required to activate caspases - specifically the presence of cytochrome c being sufficient to activate caspase-9 in the presence of dATP and APAF-1 in vivo, suggesting that the presence of the elements that make up the caspase activation complex is sufficient to cause activation of the caspase (X. Liu et al., 1996). Consequently by keeping the elements apart, caspase activation may be inhibited. 19 Lastly, the discovery of selective inhibition of caspase-3 and caspase-7 has led to an understanding of negative regulation of these initiator caspases by specific IAPs (inhibitors of apoptosis) (Deveraux et al., 1998), which function to inhibit a broad spectrum of apoptotic stimuli (reviewed in (Deveraux & Reed, 1999; LaCasse, Baird, Korneluk, & MacKenzie, 1998). 1.1.8 Caspase-independent Pathway (AIF mediation) Other pathways to programmed cell death exist, which are not mediated by the caspases. Two families of proteases, the calpains, and the cathepsins (lysosomal proteases), as well as AIF (apoptosis inducing factor) have been shown to cause programmed cell death without activation of the caspases. For example, cathepsin D has been shown to trigger Bax activation and mitochondrial translocation, causing the selective release of AIF from the mitochondrial inner membrane space, inducing apoptosis in a caspase-independent manner (Bidere et al., 2003). There is some evidence to suggest that the calpains can induce cell death without the activation of the caspases, at least under some circumstances (Mathiasen, Lademann, & Jaattela, 1999; Mathiasen et al., 2002; Narvaez & Welsh, 2001) implicating the endoplasmic reticulum in some forms of programmed cell death. Lastly, AIF appears to be capable of causing “large scale DNA fragmentation and peripheral chromatin condensation” in a caspase-independent manner (Susin et al., 2000). However, it should be borne in mind that in some cases, caspase-independent apoptosis might have appeared to be “caspase- independent” because of the inability to detect low levels of caspase activity that could have been present, and still have played a part in inducing cell death. 20 1.1.9 From Pathology to Molecular Biology 1.1.9.1 Bcl-2 Family David Vaux and others in 1988 identified B-cell lymphoma-2 (BCL-2) as a pro- survival gene (Vaux, Cory, & Adams, 1988). The BCL-2 gene product was found not to cause cell proliferation, as most of the oncogenes discovered to that time had done, rather, overexpression of BCL-2 caused an inhibition of programmed cell death (Vaux et al., 1988). In the intervening years it has been demonstrated that members of the Bcl-2 family can play both positive and negative roles in regulation of apoptosis (Adams & Cory, 1998). 1.1.9.2 Pro- and Anti-Apoptotic Members The Bcl-2 family is currently comprised of twenty members. All contain '-helical sequences homologous to that of the Bcl-2 Homology (BH) domains in the founding member, Bcl-2. Domains are labeled BH1, BH2, BH3 and BH4. The Bcl-2 family members are categorized as pro-survival, all of which contain at least 3 BH domains (e.g. Bcl-2, Bcl-XL, Mcl-1 (mcl-1 doesn’t have a BH4)), or pro-death. Pro-death Bcl- 2 family members are subdivided into those containing several BH domains (e.g. Bax and Bak) and those containing only the BH3 domain (e.g. Bid, Bim, Bad). The Bcl-2 family members are shown in figure 1.2, reproduced with permission from a recent review article by Youle and Strasser (Youle & Strasser, 2008). 21 Figure 1.2 The Bcl-2 family of pro-survival and pro-death proteins. (Reprinted by permission from Macmillan Publishers Ltd: Nature Reviews Molecular Cell Biology, Youle RJ and Strasser A, vol. 9 (1) pp. 47-59, © 2008.) 22 1.1.9.3 Function of Bcl-2 Family Members The function of the Bcl-2 family members remains a source of some controversy. The consensus opinion seems to be that interactions between pro- and anti-apoptotic members of the Bcl-2 family largely determine cell fate. The balance between the function of the pro- survival and the pro-death members of the Bcl-2 family is an important determinant of cell survival or death. The pro-death members are thought to function by binding via their BH3 domain to pro-survival members of the family (Zha, Aime-Sempe, Sato, & Reed, 1996), and thus inhibiting the function of the pro-survival proteins, especially as they guard Bax and Bak (Willis et al., 2007). The activation of Bax and Bak is generally held to allow the formation of the mitochondrial outer membrane permeabilization (MOMP) (Kluck, Bossy- Wetzel, Green, & Newmeyer, 1997; Wei et al., 2001). The MOMP forms in response to pro- apoptotic signals, and allows proteins such as cytochrome c and SMAC/Diablo to leak from the mitochondrial intramembrane space. Once this occurs, the mitochondrial pathway to apoptosis proceeds (Martinou & Green, 2001; Waterhouse, Ricci, & Green, 2002). The precise mechanism by which this occurs remains unclear. However, it is known that over- expression of Bcl-2 prevents the release of cytochrome c, as well as SMAC/DIBALO from the mitochondria, and inhibits apoptosis (Adrain, Creagh, & Martin, 2001; J. Yang et al., 1997), at least under some circumstances. Three models have been proposed to describe the interaction between pro-survival and pro- apoptotic members of the Bcl-2 family (Chipuk & Green, 2008)– firstly the “rheostat model”, in which the cell exists in a steady state where pro-apoptotic and anti-apoptotic Bcl- 2 proteins are actively functioning, but are held in balance, with the result being cell survival. 23 However, disruption of the balance, such as by DNA damage, or cytokine withdrawal (in cytokine dependent cells) causes the balance to shift in favour of the pro-apoptotic proteins, and cell death follows. The second model is the “anti-apoptotic protein neutralization model”. Here the activation of Bax and Bak is continually inhibited by the action of the pro- survival Bcl-2 family members. When the function of the pro-survival Bcl-2 proteins is inhibited, for example by DNA damage or other stress, the inhibition ceases, and Bax and Bak become activated, with the formation of the MOMP following. In this case, Bcl-2 pro- survival members block apoptosis by sequestering BH3 domain-only molecules in stable complexes, located within the mitochondria, thus preventing the BH3-only molecules from interacting with, and activating Bax and Bak (Cheng et al., 2001). Lastly, the “direct activation of Bax and Bak” model proposes that Bax and Bak undergo conformational changes and become active, by the action of the BH-3 only proteins Bim and Bak, In addition to activation by Bim and Bid, there is some evidence to suggest that Bax/Bak may be activated by PUMA (Kim et al., 2006). 1.2 PI3-K Pathway The phosphatidylinositol-3 kinases are a group of phosphoinositide kinases - heterodimeric proteins, each containing a catalytic subunit of 110-120 kDa, and a regulatory subunit of molecular mass varying between 50 and 101 kDa (reviewed in (Fruman, Meyers, & Cantley, 1998)). The catalytic subunit functions as a lipid kinase, while the regulatory subunit functions as an adaptor protein that allows appropriate associations that lead to activation of the kinase subunit. Three classes of PI3-Ks have been described, however, when considering 24 cytokine or mitogen signaling, the Class I PI3-Ks are the main group responsible for transducing these signals (Vanhaesebroeck & Waterfield, 1999), and this brief discussion will focus on this sub-class of the PI3-K family of enzymes. Phosphatidylinositol (PI) is a cellular acidic phospholipid. This diacylglycerophospholipid has an inositol ring on the third position of the glycerol backbone, linked through a phosphodiester bond (Lehninger, Nelson, & Cox, 1993). PI3-K phosphorylates the free 3- position on the inositol ring of PI-4-phosphate, or PI-4,5-bisphosphate to generate PI-3,4- bisphosphate (PI-3,4-P2) or PI-3,4,5-trisphosphate (PI-3,4,5-P3) respectively. Another important, and perhaps the most physiological, means of generating PI-3,4-P2 is by dephosphorylation of PI-3,4,5-P3 (the main in vivo product of the class I PI 3-kinases), by 5- phosphatases, which will be discussed below. PI-3,4-P2 and PI-3,4,5-P3 are important signaling molecules, being critically involved in cell cycle progression, protein synthesis, cell survival and proliferation, actin polymerization and more (reviewed in (Vanhaesebroeck et al., 2001)). As one of the major signaling pathways involved in survival signaling, and as one of the major pathways through which GM-CSF is known to signal, the PI3-K pathway warrants discussion in more detail. 1.2.1 Activation of PI3-K The binding of cytokines to their respective transmembrane receptors in many cases causes the receptor '-chain to heterodimerize with the common (-chain. The cytokines interleukin-3 (IL-3), interleukin-5 (IL-5) and granulocyte-macrophage colony stimulating factor (GM- CSF) provide classic examples of this signaling process (reviewed in (Martinez- 25 Moczygemba & Huston, 2003b)). Activation of PI3-K causes a strong survival signal. Activting mutations of PI3-K are responsible for disease. For example, constitutive activation of PI3-K is detectable in 50% of acute myeloid leukemias (S. Park et al., 2009). The PI3-K pathway is one of three major pathways stimulated by cytokine receptors (the other two are the JAK/STAT pathway and the MAPK pathway). The p110 subunit of Class IA PI3-Ks is found associated in a complex with an adaptor protein (Vanhaesebroeck & Waterfield, 1999) that contains two Src-homology-2 (SH-2) domains (Sadowski, Stone, & Pawson, 1986). The SH-2 domains bind to phosphorylated tyrosine residues in a specific amino acid milieu, recognizing between three and six specific amino acid residues C- terminal to the phosphorylated tyrosine (Songyang et al., 1993). Where tyrosine phosphorylation has occurred by the activation of tyrosine kinase receptors, the interaction of the SH-2-containing adaptor protein brings the p110 subunit of PI3K into approximation with the inner leaflet of the cell bilayer lipid membrane. The phosphatidylinositol lipid substrates of PI3-K are then in close physical proximity for the enzyme to modify. 1.2.2 Deactivation of PI3-K While phosphorylation is the “on” switch to activate a signaling pathway, there also exists a corresponding “off” switch, to deactivate the pathway and turn off the signal. In the case of PI3-K, the only major phosphatase that exists to directly reverse and turn the signal off is PTEN (phosphatase and tensin homologue) (J. Li et al., 1997; Steck et al., 1997). However, there are other important 5-phosphataes (Astle, Horan, Ooms, & Mitchell, 2007; Astle et al., 2006). 26 PTEN, a phosphoinositide 3-phosphatase, is a tumour suppressor protein that hydrolyzes PI- 3,4,5-P3 to PI-4,5-P2, and so acts in direct opposition to the function of PI3-K. The ubiquitously expressed PTEN can promote cell cycle arrest, induce apoptosis, inhibit cell motility, and inhibit angiogenesis when its activity is elevated (Chow & Baker, 2006; Engelman, Luo, & Cantley, 2006; Sansal & Sellers, 2004). On the other hand, loss of PTEN activity allows PI3-K activation to continue unchecked. As a result, almost 50% of human cancers have been found to contain inactivated PTEN (Cantley & Neel, 1999), contributing to overactive downstream pathways controlled by PI3-K. SHIP, a phosphoinositide 5-phosphatase, is found only in hematopoietic cells, and functions to hydrolyze PI-3,4,5-P3 to PI-3,4-P2 (Damen et al., 1996). SHIP is included in the discussion here because of its role in decreasing the levels of the PI3-K product, PI-3,4,5-P3. SHIP functions in mast cells, controlling the level of degranulation, cytokine production, and adhesion (Sly, Rauh, Kalesnikoff, Buchse, & Krystal, 2003). There is some evidence that SHIP may function as a tumour suppressor in hematopoietic progenitor cells (J. M. Luo et al., 2003; Wisniewski et al., 1999). However, it is worth remembering that since SHIP generates PI-3,4-P2, which may act as a second messenger in some cells, by its ability to attract PH-containing proteins (A. J. Marshall, Krahn, Ma, Duronio, & Hou, 2002; Scheid et al., 2002), the evidence for SHIP functioning as a tumor suppressor is controversial. 1.2.3 Downstream of PI3-K The lipid products of PI3-K, phosphatidylinositol-3,4-bisphosphate and phosphatidylinositol- 3,4,5-trisphosphate, contain binding sites for molecules containing one of two distinct lipid binding domains – the Pleckstrin homology (PH) domain (Gray, Van Der Kaay, & Downes, 27 1999; Haslam, Koide, & Hemmings, 1993; Ingley & Hemmings, 1994), or the FYVE zinc finger domain (Stenmark, Aasland, Toh, & D'Arrigo, 1996). These binding domains allow specific interactions between the phosphatidylinositol lipids and downstream signaling molecules. PI3-K signaling is held to be one of the most important pathways for cell survival and proliferation (reviewed in (B. D. Manning & Cantley, 2007; Toker & Cantley, 1997)). The importance of phosphatidylinositol-3 kinase signaling in cell survival was discovered in 1995, and published in two important works. Yao et al (R. Yao & Cooper, 1995) determined that nerve growth factor (NGF) could prevent apoptosis in a rat cell-line, but not if small molecule inhibitors of PI3-K, specifically wortmannin (Ui, Okada, Hazeki, & Hazeki, 1995) and LY294002 (Vlahos, Matter, Hui, & Brown, 1994), were included. At the same time, Duronio’s group discovered that PI3-K signaling is important for the inhibition of apoptosis in hematopoietic cells (Scheid, Lauener, & Duronio, 1995). Specifically, the actions of cytokines that are critical for cell survival were shown to require the activity of PI3-K. Duronio’s group also discovered that because of cross-talk between signaling pathways, downstream signaling through PI3-K differs depending on the cytokine used to activate the kinase (Scheid et al., 1995). PI3-K signaling for survival has subsequently been extensively studied and is known to be mediated via several signaling pathways (Downward, 2004; Duronio, 2008; McCubrey et al., 2008; Song, Ouyang, & Bao, 2005; Toker & Cantley, 1997). One of the key events following the generation of PI-3,4-P2 and PI-3,4,5-P3 is the activation of PDK1 (3-phosphoinositide dependent kinase 1) (Alessi et al., 1997), which contains a PH 28 domain, and is therefore bound to the plasma membrane in areas where PI3-K activation has occurred. The putative autophosphorylation site on PDK1 is phosphorylated by PDK1 itself, probably through both cis- and trans- mechanisms (Wick et al., 2003), and PDK1 also phosphorylates most of the kinases of the AGC family – including PKB, several protein kinase C (PKC) isoforms p70 S6 kinase, RSK, SGK as well as itself (reviewed in (Bayascas, 2008)). Critically important to PI3-K survival signaling is the activation of PKB/Akt - the mammalian homologue of v-akt (Bellacosa, Testa, Staal, & Tsichlis, 1991; Coffer & Woodgett, 1991; Jones, Jakubowicz, Pitossi, Maurer, & Hemmings, 1991), the N-terminus of which contains a PH domain (Ferguson et al., 2000; Lietzke et al., 2000). PKB is recruited via its PH domain to the plasma membrane along with PDK1, where activation of PKB takes place (Franke et al., 1995). PKB is a key mediator of PI3-K-dependent cell survival (Downward, 2004; Lawlor & Alessi, 2001; Marte & Downward, 1997; Song et al., 2005) and is activated by phosphorylation by PDK-1 at Thr308. A second site of phosphorylation in the hydrophobic domain (also common to all AGC family members) is Ser473. Phosphorylation at that site is mediated by one or more kinases, which are still not completely identified in terms of their upstream regulation (for further discussion, see (Duronio, 2008)). PKB also functions in other important cellular roles, such as growth (i.e. protein synthesis), proliferation, angiogenesis, metabolism and migration (reviewed in (Engelman et al., 2006; B. D. Manning & Cantley, 2007)). 29 As well as activating PDK-1 and PKB/Akt signaling, the lipid products of PI3-K activity are important for activating the following kinases - PLC-) (Falasca et al., 1998), the Tec kinases Btk, Tec and Itk (see, e.g. (Lindvall et al., 2005)), the Rac GEFs, e.g. Vav-1 (Reynolds et al., 2002), and cdc42 (reviewed in (Ward, 2004). Activation of PI3-K/PKB also causes the inhibition of BAD, BAX, FOXO-3a, TSC1/2 and GSK-3, resulting in inhibition of pro-apoptotic activity and increased protein translation, and repression of transcriptional events that can contribute to apoptosis (reviewed in (Duronio, 2008)). The regulation of protein synthesis by PI3-K/PKB via TOR has been extensively reviewed (see, e.g. (Duronio, 2008; Franke, 2008; Ruggero & Sonenberg, 2005; Wymann & Marone, 2005)). Being such an important element in the control of protein synthesis, the PI3-K pathway is centrally positioned to form a critical regulator of the cellular proteome. The outcome of the activating and inhibiting effects of the lipid products of PI3-K is to determine the balance between the pro-survival and pro-apoptotic forces within the cell. 1.2.4 Dysregulation of PI3-K/PKB Dysregulation of PI3-K/PKB signaling has been implicated in diverse human diseases ranging from cancer and cardiovascular disease to diabetes, acute leukemia, non-Hodgkin’s lymphoma and more (Chang et al., 2003; Fresno Vara et al., 2004; Oudit et al., 2004). It is well recognized that in most diseases in which PI3-K/PKB is disrupted, it is the alteration in the survival/apoptosis signaling by PI3-K/PKB that is most important. Such is the abundance of evidence linking dysregulation of the PI3-K/PKB pathway “by various genetic and 30 epigenetic mechanisms in a wide range of tumor types [that] there is now extensive evidence validating various components of this pathway as molecular targets in cancer.” (Yap et al., 2008) With evidence of the far-reaching effects of PI3-K signaling, effects on cell survival, proliferation, metabolism, growth, angiogenesis and cytoskeletal structure and function, as well as the importance of the PI3-K signaling pathway as a regulator of protein synthesis, it is apparent that there is much potential for fruitful investigation of this pathway. 1.2.5 GM-CSF Signalling Granulocyte macrophage colony stimulating factor (GM-CSF) was originally discovered for its ability to generate granulocytes and macrophages from progenitor cells in mouse bone marrow (Burgess, Camakaris, & Metcalf, 1977). However, GM-CSF was soon found to function in mature cells, such as granulocytes, macrophages and eosinophils. GM-CSF is understood to be a major regulator of cell number for cells of the granulocyte and macrophage lineage and to affect the level of immune cell activation (Hamilton & Anderson, 2004). That is, GM-CSF functions in developing the tissue inflammatory reaction and in host protection, hence GM-CSF appears to be involved in inducible hematopoiesis in response to infection, and also in allergic and inflammatory reactions. It has been determined that, in the presence of GM-CSF, circulating neutrophil half-life is approximately 10 hours in both the normal and activated states (Dale, Liles, Llewellyn, & Price, 1998). GM-CSF is secreted by macrophages, activated T-cells, mast cells, endothelial cells, bone marrow stromal cells and basophils (Martinez-Moczygemba & Huston, 2003a), and GM- 31 CSF signals through a unique '-receptor, which heterodimerizes with a common beta chain (shared with IL-3 and IL-5) (Martinez-Moczygemba & Huston, 2003a). Apart from signaling via the PI3-K pathway (described above), the binding of GM-CSF to its cognate receptor causes signalling via the extracellular signal-regulated kinase (ERK), and the Janus Kinase (Jak)/signal transducers and activators of transcription (STAT) pathways. These pathways will be briefly reviewed. 1.2.5.1 GM-CSF Signaling via Erk The mitogen-activated protein kinase (MAP Kinase) family consists of six sub-groups – ERK1/2, JNKs, p38 isoforms, ERK5, ERK3/4 and ERK 7/8. The subgroup containing the kinases ERK1 and ERK2 has been shown to be activated by IL-3, IL-5 and GM-CSF signaling, and to result in survival signaling (i.e. inhibition of apoptosis) and cell cycle progression (Crews, Alessandrini, & Erikson, 1992; Egan & Weinberg, 1993; McCubrey, May, Duronio, & Mufson, 2000; Weinstein-Oppenheimer, Blalock, Steelman, Chang, & McCubrey, 2000). Binding of GM-CSF to the GM-CSF receptor causes the inactive ras-GDP complex to become active ras-GTP. Ras-GTP activates raf, which then signals via MEK to ERK. Erk targets transcription factors such as NF-$B, AP-1, c-Myc and Ets-1. However this is by no means an exhaustive list – a recent review lists 160 well-documented substrates of Erk1/2 (Yoon & Seger, 2006). 32 As well as causing the activation of ERK1/2, there is evidence that p38 may be activated by GM-CSF in human neutrophils (Nahas, Molski, Fernandez, & Sha'afi, 1996). As well, there are reports of activation of JNK/SAPK in some cells by IL-3/IL-5/GM-CSF (de Groot et al., 1997; Foltz & Schrader, 1997; Terada, Kaziro, & Satoh, 1997). 1.2.5.2 GM-CSF Signaling via JAK/STAT The Janus kinases (JAKs) are a family of four cytosolic tyrosine kinases (JAK1, JAK2, JAK3, and Tyk2) that bind to cytokine receptors and play an important role in cytokine signaling (Darnell, 1997; Schindler & Darnell, 1995; Watanabe, Itoh, & Arai, 1996). Inactive JAKs bind to the cytoplasmic tail of receptors for cytokines such as IL-3, IL-5 and GM-CSF. Upon activation of the receptor, two JAKs trans-phosphorylate each other to become active. Activated JAKs phosphorylate and activate several substrates, one of which is the cytokine receptor itself (Darnell, 1997; Schindler & Darnell, 1995; Watanabe et al., 1996). Phosphorylated tyrosine residues on the receptor form a docking site for molecules containing an SH-2 domain. One class of such molecules is the family of transcription factors known as STATs (signal transducers and activators of transcription)(Darnell, 1997; Schindler & Darnell, 1995). The binding of ligand to receptors for IL-3, IL-5 and GM-CSF activates JAK2. GM-CSF activated JAK2 forms a binding site for (predominantly) STAT5. Binding of a STAT5 monomer to JAK2 results in phosphorylation of the STAT5 monomers, which dimerize, and translocate to the nucleus. Once within the nucleus, dimerized STAT5 plays a role in cell proliferation, via such gene targets as c-fos, pim-1, osm and cis (Basham et al., 2008), which is important in the context of the work described here. 33 The TF-1 cells used in many of the studies reported here are dependent on a continuous supply of IL-3, GM-CSF, or EPO for survival (see discussion below)(Kitamura et al., 1989). Withdrawal of the cytokine results in the cell colonies undergoing apoptosis within a period of approximately 18-30 hours. Of importance for these studies, the addition of GM-CSF to hemopoietic cells such as the TF-1 cell line, results in the activation of the PI3-K pathway, some JAK/STAT pathways, and some MAPK pathways, which promote cell survival and growth. 1.3 Early Apoptosis or “Pre-Apoptosis” The primary goal of this study was to examine changes in protein concentrations in early apoptosis, so-called “pre-apoptosis”; that is, after the cells have committed to apoptosis, but before the cells have begun to be dismantled by the process. Analysis of the observed concentration changes could then lead to a new understanding of early apoptosis. The induction of apoptosis was brought about by the withdrawal of mitogens, in this case GM- CSF, required by the cells for survival and growth. Under these conditions, the cells are in the presence of the full complement of nutrients and growth factors derived from serum, and yet we know that these cells will undergo apoptosis if specific cytokines are not present. Perhaps this loss of cytokine stimulation induces a “stress” response in the cell? If so, this may be because the cellular “wiring” is set to require a specific complement of signals to be triggered, and if these signals are not present, the threshold of the Bcl-2 family proteins is set close to the point where apoptosis will then be initiated. Another possibility needs to be considered. Under conditions of cytokine withdrawal, it is possible that the cells might 34 initially develop an autophagic response, in an attempt to survive. Autophagy, also called type II programmed cell death, is a highly conserved cellular mechanism which involves the “recycling” of long-lived proteins and cellular organelles, so that the components may be used for basal synthesis of proteins and other molecules, and the generation of energy under conditions of stress (Ferraro & Cecconi, 2007). Autophagy is, under most circumstances, a survival process; activated in response to stress condtions. However, autophagy may also play a role in cell death; where cellular damage is too extensive for survival, or if apoptosis is compromised, autophagy may function to kill the cell (Klionsky, 2007; Thorburn, 2008). While there is evidence that nutritional deprivation brings about autophagy, at least in some situations, the question of whether or not cytokine withdrawal will cause the same response as nutrient deprivation has not been addressed. A study by Li, et. al. was able to provide some evidence that cell death caused by growth-factor withdrawal (in this case, withdrawal of both fetal calf serum and IL-2 from the murine T-cell line, D10) may be autophagic (C. Li et al., 2006). Lum and co-workers describe a study of Bax/Bak double knock-out mice in which immortalized IL-3 dependent cell lines were generated from the bone marrow. IL-3 was withdrawn and the cells were able to survive for several weeks using autophagy of organelles to provide a source of ATP and nutrients (Lum et al., 2005). In this case, autophagy allowed cell survival. Transfection of either Bax or Bak into the cells restored the apoptosis response to IL-3 withdrawal. (Lum et al., 2005). Bearing this in mind, we must allow for the possibility that some of the concentration changes observed in response to cytokine withdrawal might be due to an autophagic response to cytokine withdrawal. Determining the presence of autophagy in response to cytokine withdrawal may be attempted in two ways – morphologically and molecularly. Morphological evidence is usually obtained 35 by using electron microscopy. The presence of autophagic vesicles within cells, as well as the absence of cell engulfment through phagocytes, are considered hallmarks of autophagy (at least during the early stages of cell death) (Conradt, 2009). Molecularly, the use of an LC3-based assay (mammalian Atg8 homologue) to observe flux in the levels of LC3 following cytokine withdrawal is considered standard (Klionsky, 2007). 1.3.1 Protein Concentration Changes versus PTMs The major means of transmitting information via cell signaling networks is through post- translational modifications (PTMs) of proteins (see, e.g. (Hunter, 1995; Wold, 1981)). It has been estimated that as many as three hundred post-translational modifications of proteins occur physiologically (Witze, Old, Resing, & Ahn, 2007). The most common PTM is protein phosphorylation, which affects about one-third of all proteins, and is the most widely studied PTM (Cohen, 2001). Apart from phosphorylation, there are at least twenty other forms of post-translational modification commonly seen in proteins, and many more possible modifications, as noted above. Modifications such as methylation, acetylation, glycosylation and lipid modifications such as farnesylation and gernaylgeranylation are frequently encountered, and function to regulate the biological activity of the protein. From our understanding of the importance of PTM in cell signaling, it is reasonable to assume that many of the changes involved in signaling for apoptosis will occur via modifications of proteins already present in the cell. This is not to understate the importance of changes in gene expression levels that will occur as a result of activation and repression of various signaling pathways during early apoptosis., leading to changes in the concentrations of proteins. With this in mind the present study provides interesting results, since I have clearly 36 shown changes in the concentrations of many proteins in response to cytokine withdrawal – that is, that the process of inducing apoptosis causes both increases and decreases in the concentrations of proteins. These changes have occurred in addition to the post-translational modifications expected, and analysis of these changes in protein concentrations adds to our understanding of apoptosis. 1.4 Proteomics Proteomics is perhaps the most important approach to the study of cellular function in post- genomic biology. The term was first coined in 1994 by Marc Wilkins (Wilkins, 1994) and the first study utilizing the term “proteomics” was published in 1995 (Wasinger et al., 1995). Some of the techniques have existed since much earlier – for example 2-D gel electrophoresis (Margolis & Kenrick, 1969; O'Farrell, 1975); and mass spectrometry was first developed by J.J. Thomson in the early Twentieth century (Thomson, 1913). However, it was not until the 1990s that “biological” mass spectrometry began to emerge as a powerful tool for the analysis of complex biological samples (Pandey & Mann, 2000). The proteome was defined by Wilkins et. al. as the proteins expressed by the genome of an organism, or, in multi-cellular organisms, as the protein complement expressed by a tissue or differentiated cell (quoted in (Haynes, Gygi, Figeys, & Aebersold, 1998). Proteomics involves a study of the totality of proteins present within an organism or a tissue or differentiated cells, under experimental and control conditions, and has been defined as “the use of quantitative protein-level measurements of gene expression to characterize biological processes (e.g., disease processes and drug effects) and decipher the mechanisms 37 of gene expression control” (Anderson & Anderson, 1998). While the early definitions included the term “quantitative”, early proteomics was a field of investigation which sought to define the proteome, and as such, was originally mostly descriptive – presenting a catalogue of the proteins identified in cells under different conditions. Indeed the term “descriptive proteomics” is frequently seen in the literature. With the advent of new technologies, and the advancement of existing technologies, the field has progressed, and now seeks quantitative data, despite the enormous technical challenges in such a goal (Bantscheff, Schirle, Sweetman, Rick, & Kuster, 2007). Ong and Mann summarize the potential of proteomics by observing that “proteomics directly addresses the level of gene products present in a given cell state and can further characterize protein activities, interactions and subcellular distributions.” (Ong & Mann, 2005). Two-dimensional gel electrophoresis and mass spectrometry are both valuable tools for proteome profiling. Each has strengths and weaknesses, and there are some in the field who argue that the techniques are complimentary, and should be used together to seek the answer to certain questions. However, the work described in this thesis was carried out using mass spectrometry, and so this will be the focus of the ensuing discussion. 1.4.1 Mass Spectrometry – Brief Historical Overview J.J. Thomson designed and built what is considered the first mass spectrometer - an instrument capable of separating the ionized components of a mixture of gases, according to the mass of the ions, thereby allowing determination of the mass-charge ratios of the ions (Thomson, 1913). Thomson’s instrument was refined by A.J. Dempster, who built the first modern mass spectrometer in 1913 (Dempster, 1918). The British chemist and physicist F.W. Aston, beginning in 1919, further developed mass spectrometry by refining the magnetic 38 field, and the exit grate, and so was able to examine the isotopic composition of over fifty elements (Aston, 1919). The field of mass spectrometry progressed with the development of the quadrupole mass spectrometer (Wolfgang Paul and Helmut Steinwedel, 1953) and the ion trap (Wolfgang Paul and Hans G. Dehmelt). However it was not until the development of soft ionization techniques in the late 1980s – in particular, electrospray ionization (ESI) and matrix-assisted laser desorption and ionization (MALDI) (Fenn, Mann, Meng, Wong, & Whitehouse, 1989; Karas & Hillenkamp, 1988), that it became possible to analyze very complex protein mixtures using mass spectrometry (Siuzdak, 1994). Even with these technical advances, it wasn’t until the completion of the genome sequencing projects (McPherson et al., 2001; Venter et al., 2001) that it became possible to adopt a high- throughput “shotgun” approach to the field, and thereby to gather information on the products of large portions of the genome from a single experiment. This shotgun approach allows for the visualization of a much greater portion of the proteome at one time, and hence for the large scale examination of the perturbations of the proteome induced by various experimental conditions, allowing for the function of genes and cells to be determined directly at the protein level (Aebersold et al. 2003). The field of proteomics continues to develop, with the emphasis now being on improving techniques to encompass more of the proteome, and to improve protein quantitation. 1.4.2 SILAC Being able to determine quantitative changes in the concentrations of proteins in cells under various conditions is a major goal in systems biology. In our work, having a clear identification of proteins that exhibit concentration changes under conditions of cytokine 39 withdrawal provides information on cellular function at the level of gene products, rather than post-translational modification. Such information will provide a clearer understanding of the changes at the level of gene expression induced by the signaling pathways involved in apoptosis and, at the same time, provide information on potential targets for drug development, or potential targets for different uses of already existing drugs. The ultimate goal is to provide better means of dealing with the diseases of inappropriate apoptosis. For several reasons, it is not possible to quantitate peptides based on the intensity of MS signals - different peptides ionize with different efficiencies (Gygi & Aebersold, 2000), some peptides are too small, or too large to be analyzed by mass spectrometry, and ion suppression can cause apparent differences in the strength of peptide signals, unrelated to the amount of peptide present (Jessome & Volmer, 2006). The size, charge and hydrophobicity of peptides lead to different behaviour in the mass spectrometer, and so to different signal strengths (Bantscheff et al., 2007). Furthermore, peptides may behave differently at different stages of sample preparation, leading to an accumulation of nonsystematic errors (Ong & Mann, 2005). Hence, quantitative proteomics approaches are based on techniques other than the comparison of peptide signal intensities. The use of known quantities of synthetic internal standards mixed with the sample of interest to achieve absolute quantitation using mass spectrometry has been used since the early 1980s. The technique is becoming more broadly applied, and in proteomics is known as AQUA (absolute quantitation of proteins) (Gerber, Rush, Stemman, Kirschner, & Gygi, 40 2003). One disadvantage concerning the use of AQUA is the need for a priori knowledge of the protein to be quantitated. Relative quantitation is currently used in all cases where quantitation involves unknown proteins. Current techniques for the relative quantitation of proteins may be broadly divided into two classes – labelling and label-free. Labelling may be subdivided into either metabolic labelling (i.e. in vivo) or chemical labelling (in vitro) (reviewed in (Ong & Mann, 2005)). Examples of metabolic labelling include incorporation of 15 N (Wu, MacCoss, Howell, Matthews, & Yates, 2004), and SILAC (see discussion below). Post-harvest (chemical) labelling techniques include ICAT (Gygi et al., 1999), iTRAQ TM (Ross et al., 2004) and 18 O labelling (Mirgorodskaya et al., 2000; X. Yao, Freas, Ramirez, Demirev, & Fenselau, 2001). Metabolic labelling techniques have the advantage of being able to label almost all proteins in a cell, however the techniques cannot be applied to tissue samples. Post-harvest labelling can be applied, in theory, to any sample. The disadvantage of post-harvest techniques is that not all peptides will be labelled. Label-free techniques include peptide spectral counting (H. Liu, Sadygov, & Yates, 2004; Old et al., 2005; Zybailov, Coleman, Florens, & Washburn, 2005) in which the number of spectra identifying a protein are counted. For simple mixtures, there is a near linear relationship over two orders of magnitude between the number of copies of a spectrum and 41 the relative abundance of the protein identified by the spectrum (H. Liu et al., 2004). A second label-free technique, utilizes a calculated “protein abundance index” (PAI). PAIs are based on the observed fact that as the amount of protein increases so does the number of peptides detected. Since a large protein will generate more peptides than a small protein, the scores need to be normalized (Rappsilber, Ryder, Lamond, & Mann, 2002). Stable Isotope Labelling by Amino Acids in Cell Culture (SILAC) was developed by Mann’s group as a means of characterizing changes in the concentrations of proteins present in cells under different conditions, so called “expression proteomics” (Ong et al., 2002). The technique involves growing cells in culture containing amino acids synthesized using stable isotopes of biologically common atoms – usually carbon-13 or nitrogen-15. After six doublings of the cell population, greater than 98% of the protein content of the cells will contain labelled amino acid residues – that is less than two percent of the proteins will be original (unlabelled) material. Concurrently with stable isotope labelling, cells are grown in media formulated with “wild-type”, i.e. 12 C- or 14 N-containing amino acids. Amino acids chosen for labelling are usually arginine and lysine – selected because trypsin, which is commonly used for the generation of peptides from proteins for mass spectrometry analysis, cleaves at the C-terminus side of an arginine or lysine reside (Olsen, Ong, & Mann, 2004). After labelling has occurred, “experimental” cells are perturbed in some way, and compared with “control” cells. After the intervention, cells are lysed, the protein concentration determined and lysates mixed in a 1:1 ratio based on protein concentration. The lysates can then be fractionated either by chromatography or by SDS-PAGE and the fractions 42 trypsinized, prior to mass spectrometry analysis. It is the analysis of SILAC data that will form a major part of this study. 1.4.2.1 Determining the Accuracy of Protein Quantitation The protein ratio (“control” versus “experimental”) is determined by averaging the individual peptide ratios – matching the intensities of pairs of heavy- and light-labelled occurrences of the same peptides. Under optimal conditions, very high quantitation accuracies may be achieved – in fact, it is possible to achieve accuracies which are greater than the variability introduced by biological or sample preparation factors. Under ideal conditions the accuracy of quantitation involves an error of less than five percent (Ong, Kratchmarova, & Mann, 2003). Accuracy is affected by the following factors: a) Low abundance peptides may appear in only one or two consecutive spectra, therefore the average peptide ratios will be less precise b) If a protein is identified with only a few peptides, the protein quantitation may be less accurate if one peptide quantitation is of poor quality c) The co-elution of two or more peptides in a complex mixture may lead to overlap of the isotope clusters identified by the mass spectrometer. This overlap leads to errors in quantitation d) The signal-to-noise ratio for each of the two peaks used in the comparison is important. If both peaks have a high signal-to-noise ratio, there would not usually be a problem with the quantitation calculated from data, however, if 43 one peak is small, the contribution of background noise to peak area (or height) can be significant. This may create a ratio of heavy to light peaks that was lower than the correct value. At the other extreme, an abundant peak might cause overload of the detector, and so the true value of the intensity might be under-reported, leading to a reduction in the value of the reported ratio compared with the true value. e) In some cell lines, arginine is converted to proline, and so 13 C-Arg becomes 13 C-Pro. In this case, the 13 C-Pro must be counted with the 13 C-Arg to accurately quantitate the two cell states in the experiment. In summary, the accuracy of protein quantitation depends on: (i) The number of peptides used (ii) The accuracy of the data for each peptide (especially if there are a low number of peptides) (iii) Isotopic peak clusters standing alone, that is, no peak overlaps from co- eluting peptides (iv) Good signal-to-noise ratio (in practice, better than 3:1) 1.4.3 Mass Spectrometry – Fourier Transform – Ion Cyclotron Resonance Fourier-transform ion cyclotron resonance mass spectrometry (FT-ICR MS) is the most sensitive mass spectrometry method currently used (A.G. Marshall, 2000). FT-ICR developed from ion cyclotron resonance (ICR) mass spectrometry, which was first described in the late 1940s and early 1950s (Sommer, Thomas, & Hipple, 1951). ICR mass spectrometry technique was regarded with mainly academic interest until Allan G. Marshall 44 and M. Comisarow at the University of British Columbia, Vancouver, applied the Fourier transform (Cooley & Tukey, 1965) methods that had previously been used successfully with nuclear magnetic resonance spectroscopy (Comisarow & Marshall, 1974). The ion detection capabilities of FT-ICR allow mass resolution of greater than 0.003 Da (A G Marshall & Hendrickson, 2002), with sensitivity down to attomole quantities (Quenzer, Emmett, Hendrickson, Kelly, & Marshall, 2001). Coupled with on-line chromatography, this technique allows the analysis of complex protein samples and as such, is particularly suited to experiments involving analysis of whole cell lysates. The analysis problem caused by differences in protein abundance in a sample can be at least partially addressed by using an instrument with the resolving power and sensitivity of the FT-ICR. Indeed “FT-ICR MS offers 10 –100 times higher mass resolution, mass resolving power, and mass accuracy than any other mass analysis technique.” (A.G. Marshall, 2000) In an “on-line” experiment, such as is necessary for the analysis of a whole cell lysate, current mass spectrometers are limited to the analysis of the peptides with the strongest signals present in the instrument at one time. This constraint is the result of the continuous in-flow of sample into the instrument, and the time taken for a mass spectrum to be generated. Because there are more of them, peptides of an abundant protein will frequently give a much stronger signal than peptides from a low-abundance protein. It has been determined that protein expression covers greater than ten orders of magnitude in human serum (Patterson & Aebersold, 2003b). While this figure is reported to be lower in a whole cell lysate (perhaps “only” 7-8 orders of magnitude in human cells (Anderson & Anderson, 1998)), many of the signaling proteins in cells are present in lower copy numbers, and it is 45 the signaling proteins, rather than the structural or other ”housekeeping” proteins, which will be of interest in an experiment in which the proteins that play key regulatory roles in apoptosis are the focus. 1.4.3.1 Fourier Transform Ion Cyclotron Resonance Mass Spectrometry – A Brief Overview An excellent primer on technological aspects of FT-ICR, was published by Marshall and Hendricksen in 1998 (A. G. Marshall, Hendrickson, & Jackson, 1998a). The FT-ICR consists of an analyzer (cyclotron) cell (also called a Penning trap), surrounded by a superconducting magnet that generates a spatially uniform static magnetic field. The Penning trap is maintained under vacuum to allow ions introduced into the cell to orbit (typically more than 100,000 times) without collision with gas molecules. The Penning trap has two sets of opposing plates (electrodes) arranged perpendicularly at the perimeter – one set is used to excite the ions introduced into the trap, and the other set detects the tiny current generated by the charged ions moving in a static magnetic field. The magnetic field strength determines instrument sensitivity, resolution and mass accuracy; and determining efficient ways to increase the magnetic field strength drives development in this field. The largest FT- ICR magnet currently in existence is at the National High Magnetic Field Laboratory in Florida, and has a field strength of 14.5 Tesla. Ions enter the spatially uniform, static magnetic field generated by the superconducting magnet. As noted above, the charged particles moving in a static magnetic field generate an electric field, of frequency dependent on the mass and charge of the charged particle. This so-called cyclotron frequency is also dependent on the strength of the magntic field – as the 46 magnetic field strength increases, so does the cyclotron frequency for any charged particle. Further, as the cyclotron frequency increases, so does the difference between any two ICR frequencies, making detection of mass differences easier. As the ions enter the Penning trap, they are guided into a circular orbit, and the ion orbital motion is compressed or restrained to become spatially coherent (that is, to orbit in phase) by applying a spatially uniform radio frequency (RF) electric field at the same frequency as the ion orbital frequency. This RF field is applied via the excitation electrodes. Orbiting ions pass the two opposing parallel electrodes (detector plates) and induce an oscillating signal - an electric field in sine wave form - with frequency dependent on the mass and charge of the circulating ions. The generated electric field can be detected and analyzed. The technique is non-destructive, as the ions are trapped and can be re-analyzed, allowing for increased sensitivity and resolution. The frequency of the ion signal (the generated electric field sine wave) is a function of the ion mass and charge state, and the magnetic field strength. Where all the ions are of a single mass and all carry the same charge, a single frequency will be generated. In this case, it is trivial to convert the single frequency to mass units. However, where the circulating ions are of different masses, or carry different charges, a much more complex electric field is generated. Each ion produces its own sine wave electric field, with characteristic frequency and amplitude, and each individual sine wave becomes superimposed (linear superposition) to produce a complex waveform. In order to reduce this complex waveform back into its component simple sine waves, a Fourier transform is applied to the oscillating signal. The Fourier transform generates a frequency-domain spectrum, which can then be converted to a mass-domain spectrum. (See Figure 1.3) 47 It is because of the use of frequency to determine mass, rather than the use of time (in a TOF mass analyzer, for example) that FT-ICR is capable of such high resolution – it is possible to measure frequency much more accurately than to measure time. Figure 1.3 FT-ICR mass spectrometry. (a) Schematic representation of excited ion cyclotron rotation, (b) time- domain image-current signal from opposed detection electrodes, (c) frequency-domain spectrum obtained by fast Fourier transform of the digitized time-domain signal, and (d) Fourier transform-ion cyclotron resonance m/z spectrum obtained by calibrated frequency-to-m/z conversion. A full-range mass spectrum (including computation) is typically generated in ~1 s. Reprinted, with permission, from the Annual Review of Analytical Chemistry, Volume 1 © 2008 by Annual Reviews www.annualreviews.org. 48 1.4.3.2 Key Mass Spectrometer Parameters for Proteomics The following list outlines the key mass spectrometry parameters of relevance to proteomics. i. Mass accuracy – the absolute deviation of measured mass from true mass (Balogh, 2004), can be reported in four ways – as Daltons (Da), as absolute milli-mass units (mmu), or in ppm or as a fraction expressed as a percent. Mass accuracy is determined by performing an internal calibration using a calibrant of known mass. Generally a mass accuracy of less than 10 ppm is desirable. FT-ICR MS is capable of mass accuracy at sub-parts-per-million (Alan G. Marshall & Hendrickson, 2008). ii. Mass resolution – Marshall defines mass resolution as “the minimum mass difference at which two equal-height peaks may be base-line resolved” (A.G. Marshall, 2000). FT-ICR MS is capable of mass resolution to 0.000 45 Da (F. He, Hendrickson, & Marshall, 2001). This number is smaller than the mass of an electron (0.000 55 Da)! Mass-resolving power, a related value, is defined as R = M/*M, where *M is determined by full peak width at half maximum height (FWHM), as above (A. G. Marshall et al., 1998a). Since it is the usual practice to define resolution as the full width of a peak at half the peak height, in this case *m is written as *m50% (A. G. Marshall, Hendrickson, & Jackson, 1998b). For an ion with m/z of 1000, and using a 3 s observation, FT-ICR is capable of mass-resolving power (m/*m50%) of \"400,000 (Alan G. Marshall & Hendrickson, 2008). 49 iii. Sensitivity – a measured value. Progressively smaller amounts of material are loaded into the mass spectrometer, until a point is reached where the material can no longer be detected. The FT-ICR is capable of achieving attomole sensitivity (S. E. Martin, Shabanowitz, Hunt, & Marto, 2000). 1.4.3.3 “Bottom-Up” versus “Top-Down” Approach Proteomics experiments may be designated as either “bottom-up” or “top-down”. In a “bottom-up” approach, intact proteins are digested with trypsin, or another cleavage-specific protease, and the peptides are introduced into the mass spectrometer. Proteins are identified based on the peptides present using either “peptide mass fingerprinting”, or “tandem mass spectrometry”. The work described in this thesis utilizes the latter approach – MS/MS or tandem mass spectrometry – to infer the presence of various protein species based on the peptides identified in the mass spectrometer. The “bottom-up” approach contrasts with the “top-down” approach, in which intact proteins are ionized and introduced into the mass spectrometer for analysis. The bottom-up approach has several advantages over the top-down approach for the analysis of complex mixtures of proteins: the identification of intact proteins from cell lysates is difficult (Mann, Hendrickson, & Pandey, 2001); sensitivity of electrospray ionization mass spectrometry is poorer for large molecules than it is for peptides, because the signal is “diluted” by being split over several charge states. Also, the heterogeneity of the protein (with various post- translational modifications) spreads the signal (Mann et al., 2001). The size limit for both electrospray and MALDI ionizations is about 100 kDa (Bakhtiar & Nelson, 2000). The 50 molecular weight of a protein cannot be predicted from its database entry (sequence), because of various post-translational modifications, chemical modifications (oxidation of methionine, for example), and N- and C-terminal processing (Mann et al., 2001). Hence for complex mixtures of proteins, the bottom-up approach is more practical and gives more reliable results. 1.4.3.4 Database Searching and de novo Sequencing (MS/MS) With the enormous increase in the amount of data that it is possible to generate from a modern proteomics experiment, it has been essential to develop computational tools capable of handling large data sets. The generation of such large numbers of mass spectra requires an automated approach to analysis of the spectra. The automated analysis of uninterpreted fragment spectra may be achieved by utilizing one of two approaches – the spectra may be compared with spectra in an in silico digest of a protein sequence database, or alternatively, the peptide identity may be determined without reference to a database – so called “de novo” sequencing. These approaches will be discussed briefly below. a) Peptide identity may be determined without reference to a database – so called de novo sequencing. With the human genome project nearing completion, most, if not all protein sequences are available in sequence databases. However, for proteins whose sequence is not known, the only way to reach an identification is by using de novo sequencing. In the de novo sequencing approach, the experimental peptides masses are matched against a “universal” database that contains all linear amino acid combinations. Many software programs are available for performing de novo sequencing. For review see (Palagi, Hernandez, Walther, & 51 Appel, 2006; Xu & Ma, 2006). This approach was not necessary for the work presented in this thesis, since the human genome has been sequenced. b) Peptides may be searched against a sequence database: Three approaches have been developed for the identification of proteins based on spectra searches against an in silico digest of a sequence database. (i) Peptide sequence tags were developed by Mann’s group in 1994 (Mann & Wilm, 1994). The peptide sequence tag consists of a “short, easily identifiable series of sequence ions” from the fragmentation spectrum, plus the masses of the fragment on either side of the identifiable series. It was discovered that this information could be used as a “highly specific identifier of the peptide” (Mann & Wilm, 1994). Implementation of the algorithm was achieved using the software PeptideSearch (Mann, Hojrup, & Roepstorff, 1993) which correlates mass spectrometric and sequence (MS/MS) data with a sequence database, and calculates the likelihood that a match between the experimental and predicted peptides is a random event. (ii) A second early approach is the cross-correlation method, developed in the Yates lab, and offered to the community as the SEQUEST program (Eng, McCormack, & JR Yates, 1994). Using this approach, the experimentally derived spectra are cross-correlated against predicted spectra. The quality of the correlation is determined, and the results ranked on the basis of this determination. (iii) A third approach is to use probability-based matching. The most commonly used probability-based software is MASCOT (Perkins, Pappin, Creasy, & Cottrell, 1999). In this approach, a probability score is derived, indicating the likelihood that a match between 52 the experimentally derived peptides and the sequence database-derived peptides is correct. In MASCOT, the probability scores are derived using a modified derivation of the “molecular weight search” MOWSE probability model (Pappin, Hojrup, & Bleasby, 1993), in which experimentally derived peptide masses are compared with the sequence database and the probability that a match between the two is a random event is computed. The match with the lowest probability of being random is reported as the best match (Perkins et al., 1999). The MASCOT implementation of the MOWSE probability scoring has been described for peptide mass fingerprinting, and it is generally held that the aforementioned is a correct description of the functioning of MASCOT for the interpretation of MS/MS spectra, but the details of exactly how MASCOT works for tandem mass spectrometry data have never been published. Given that the above algorithms all utilize some form of probability scoring in determining the likelihood that the match between experimental and predicted peptides is a random event, there will occur, in the spread of scores, some overlap between positive (correct) identifications and false positive results. Under these circumstances, it is necessary to set a score threshold so that as many as possible of the correct identifications are included in the results, and a few as possible of the incorrect assignments are included. One way in which the score thresholds may be set is by using a reversed database concatenated with the forward database, and searching both simultaneously (R. E. Moore, Young, & Lee, 2002). Extensions of this concept have arisen, by using a randomized database instead of a reversed one, or a randomized database with arginine and lysine in the 53 same relative positions. By using one of these approaches, the number of false positive matches may be minimized, by selecting threshold scores that keep the ratio of false to correct identifications at less than 5% (MacCoss, 2005). 1.5 The Approach My approach to the questions posed in this thesis was to analyze proteins obtained from stable isotope labelling of cells, which were caused to undergo cytokine withdrawal, and the induction of apoptosis. This enabled us to study the effects of withdrawal of cytokine binding to cell surface receptors. The normally induced tyrosine-kinase phosphorylation, causing a cascade of further signaling events that regulate many biological processes, was interrupted. The effects of this interruption were to induce apoptosis, and as part of that process, to induce changes (both up and down) in the concentrations of many proteins in the cell. I have been able to visualize some of the effects of changes in the activation of various signaling pathways, especially the PI3-K, JAK/STAT and MAPK pathways. One advantage of this approach is that it requires minimal a priori knowledge of the changes induced by cytokine binding to receptor. I have been able to uncover a set of proteins associated with the inhibition of the pathways activated by GM-CSF. Future work will be required to test the function of this set of proteins by “targeted” follow-up experiments, using alternative approaches. In this work, I pose the following questions: 54 - what changes occur in cellular protein concentrations in response to cytokine withdrawal-induced apoptosis? - can a global view of these protein concentration changes be interpreted in any way that suggests a new understanding of the complex interactions occurring between proteins as cells commit to undergoing apoptosis? - do any specific proteins appear in these data that have previously been unrecognized as being involved in the PI3-K signaling pathway? - and finally, does the current technology allow adequate analysis of complex protein mixtures to permit the above questions to be addressed in this way? The answers to these questions appear in the following pages, along with the supporting material used to discover the answers. 1.6 Summary Apoptosis is an important field of study, because of the impact of abnormal apoptosis on human health (the diseases of “inappropriate apoptosis”). Apoptosis is a complex, well- regulated, highly conserved process, mediated by multiple interactions between elements of the pathways for cell survival and proliferation, as well as specific signaling related directly to apoptosis. Because of this complexity, a proteomics approach is a useful means of addressing questions of global changes during apoptosis. Cytokine withdrawal-induced apoptosis is a useful model for the investigation of apoptosis. 55 We chose to investigate early apoptosis (pre-apoptosis), in the hope of identifying important changes in protein concentrations which might point the way to further work, leading ultimately to an advancement in the therapeutic approach to some of the diseases of inappropriate apoptosis. 56 2 Materials and Methods 2.1 Cell Culture TF-1 cells (American Type Culture Collection, Manassas, VA) were grown in RPMI 1640 medium supplemented with 500 mg/L L-Glutamine (Sigma-Aldrich Canada, Oakville, ON), 50 µM (-mercaptoethanol (Mallinckrodt Baker Inc. Phillipsburg, NJ), 10% (v/v) Fetal Bovine Serum (Invitrogen Canada, Burlington, ON) and 1% (v/v) CGMI cell conditioned medium (a source of human GM-CSF (see below)). Cells were sub-cultured every 2-3 days to ensure that cell density was never greater than 8 x 10 5 cells/mL. Cells were grown in 10cm- or 15cm-diameter sterile Petri dishes in 5% CO2 at 37° C in a humidified incubator. 2.2 Cell Culture – Stable Isotope Labelling For the SILAC experiments, TF-1 cells (American Type Culture Collection, Manassas, VA) were grown as described above in either “ 12 C medium” or “ 13 C medium” for labelling. These media were formulated from specially manufactured RPMI 1640 medium that had been prepared without L-Arginine, L-Lysine or L-Glutamine (JR Scientific, Woodland, CA). The 12 C medium was supplemented with 100 mg/L of 12 C-L-Arginine and 20 mg/L of 12 C-L- Lysine (both Sigma-Aldrich Canada, Oakville, ON). These concentrations of supplemental amino acids are half the normal concentrations of L-arginine and L-lysine found in RPMI 1640, since I was able to confirm in early trials that TF-1 cells would grow normally in media containing reduced amounts of L-arginine and L-lysine. 57 For the 13 C medium, the amino acid deficient RPMI was supplemented with 100 mg/L of 13 C-L-Arginine and 20 mg/L of 13 C-L-Lysine (Cambridge Isotope Laboratories, Andover, MA). Both isotopes are uniformly labeled, i.e. each of the six carbon atoms per molecule in both isotopes is substituted with 13 C. Stock solutions at 1000X concentrations of both 12 C- and 13 C-L-arginine and 12 C- and 13 C-L-Lysine were made up in phosphate buffered saline (PBS). Both the 12 C and the 13 C media formulations were supplemented with 500 mg/L L-Glutamine (Sigma-Aldrich Canada, Oakville, ON), 50 µM (-mercaptoethanol (Mallinckrodt Baker Inc. Phillipsburg, NJ), 10% (v/v) dialyzed, heat inactivated fetal bovine serum (Invitrogen Canada, Burlington, ON) (fetal bovine serum was heat-inactivated by placing in a water bath at 55 ° C for 30 minutes), and 1% (v/v) CGMI cell conditioned medium as a source of human GM-CSF (see below). The constituents were mixed together and then filtered through a 0.2 µM filter with vacuum. Prepared media were stored at 4° C and warmed to 37° C immediately prior to use. Cells were cultured for six-doublings, by which time greater than 98% of all proteins would have incorporated labels. I was able to confirm very high incorporation rates by examining 13 C-labelled cell lysate using mass spectrometry. After six doublings, cells were washed three times in sterile PBS to deplete growth factors (especially GM-CSF). Cells labeled with 13 C were returned to the 13 C medium from which they had been removed for washing. Cells labeled with 12 C were re-suspended in fresh 12 C 58 medium and “starved” for fifteen hours by omitting GM-CSF from the medium. At fifteen hours, both groups of cells were spun down and washed once with PBS, then lysed in solubilization buffer (recipe below). Cells were disrupted with an ultrasound sonicator for three x 10 seconds on ice. Lysates were cleared by centrifugation at 13,200 rpm, 4ºC for 5 minutes, and the supernatant collected. Protein concentration in the supernatant was determined using the Bradford Assay (BioRad Laboratories (Canada), Mississauga, ON) (see protocol below). 13 C-labelled and 12 C-labelled lysates were mixed in a ratio of 1:1 based on protein concentration. Three experiments were carried out using the above procedure. The experimental procedure is outlined in Figure 2.1 below. 59 Figure 2.1 Experimental overview for three cytokine-withdrawal experiments. Forty, 90 and 70 fractions were subjected to in-gel trypsin digestion and peptide extraction, giving 200 samples in total, which were examined using an FT-ICR mass spectrometer. 13 C - Control 12 C - Starve Lyse cells Determine Protein Conc’n Mix 1:1 SDS-PAGE 40, 90 or 70 Fractions Lyse cells Determine Protein Conc’n 15 hr Starvation 60 2.3 CGMI – Preparation and Usage CGMI is the name given to baby hamster kidney (BHK) cell line that has been stably transfected with the human GM-CSF gene, enabling these cells to secrete hGM-CSF. CGMI cells were a kind gift of Dr C. Brown, University of Calgary. Cells were grown to confluence in 15cm-diameter tissue culture dishes in Dulbecco’s Modified Eagle’s Medium (DMEM, Invitrogen Canada, Burlington ON), supplemented with 500 mg/L L-Glutamine (Sigma- Aldrich Canada, Oakville, ON), 50 µM (-mercaptoethanol (Mallinckrodt Baker Inc. Phillipsburg, NJ), 10% (v/v) fetal bovine serum (Invitrogen Canada, Burlington, ON) in 5% CO2 at 37° C. After the cells reached confluence, they were left for another two days. The medium was decanted, spun down at 6000 rpm at room temperature, and filtered through a 0.2 µM filter with vacuum. This conditioned medium contained hGM-CSF, and was used at a final concentration of 1% (v/v), after confirming its activity in a bioassay of TF-1 cell proliferation. 2.4 Cell Lysis Buffer (Solubilization buffer) & Sample Loading Buffer Cell lysis buffer consisted of 50mM Tris-HCl, pH 7.7, 1% Triton X-100, 10% Gycerol, 100 mM NaCl, 2.5 mM EDTA, 10 mM NaF, ddH2O. Immediately prior to use, this buffer was supplemented with 0.2 mM sodium orthovanadate, 1 mM sodium molybdate and a 1:1000 dilution of Protease Inhibitor Cocktail (Sigma-Aldrich Canada, Oakville, ON). This buffer was used throughout this series of experiments at 50-+L lysis buffer for each 1 x 10 6 cells as determined by automated cell counting. Sample loading buffer (4X) was prepared according to the following recipe. 61 For 40 mLs of sample buffer: 100% glycerol, 20 mLs, 1M Tris-HCl pH 6.8, 5 mLs, 20% SDS, 8 mLs, 1% Bromophenol blue, 3.2 mLs, Distilled water, 1.8 mLs Immediately prior to use, (-mercaptoethanol at 5% (v/v) was added. 2.5 SDS-PAGE – Gel Preparation and Staining Gels were cast in a BioRad Mini-Protean II apparatus (BioRad Laboratories (Canada), Mississauga, ON) unless otherwise stated. Solutions used were as follows: 30% acrylamide/Bis-acrylamide, 37.5:1 (2.6%C) (BioRad Laboratories (Canada), Mississauga, ON) TEMED (N,N,N’,N’-tetra-methyl-ethylenediamine) (BioRad Laboratories (Canada) Mississauga, ON) Ammonium persulfate (BioRad Laboratories (Canada), Mississauga, ON). Mini-gels were prepared using a 9% or 12% (as noted) resolving gel with a 4% stacking gel according to the method described by Laemmli (Laemmli, 1970). All gel casting components were cleaned with detergent and water, thoroughly rinsed in distilled water, and dried prior to assembling the casting chamber. All reagents were combined, leaving the initiators (APS and TEMED) until just before pouring. When ready to pour the gel, the APS and TEMED were added to the other components in solution, and gently mixed (to avoid oxygenating the solution) before being poured into the gel cast(s). 62 Large format gradient gels were prepared according to the methods of the manufacturer (BioRad Laboratories (Canada), Mississauga, ON) with a linear gradient from 8%-16.5%, using separate acrylamide-bis-acrylamide solutions of 8% acrylamide and 16.5% acrylamide. As with the preparation of the mini-gels, all gel casting components were cleaned with detergent and water, thoroughly rinsed in distilled water, and dried prior to assembling the casting chamber. All reagents were combined, leaving the initiators (APS and TEMED) until just before pouring. When ready to pour the gel, the APS and TEMED were added to both solutions, which were gently mixed (to avoid oxygenating the solutions) and poured into the appropriate chambers of the gradient pourer (BioRad Laboratories (Canada), Mississauga, ON). A stir bar, which had been previously placed in the mixing chamber of the gradient pourer, mixed the acrylamide solutions, which were allowed to feed by gravity into the casting chamber. Coomassie R-250 gel staining Gels were washed for 5 minutes in purified water (Milli-Q water, Millipore Corporation, Billerica, MA), three times, prior to being fixed in an aqueous solution of 40% methanol, 10% acetic acid for one hour. Gels were rinsed for 5 minutes in water three times, prior to being covered in Coomassie ® Blue R-250 solution (2 gm Coomassie R-250 (Sigma-Aldrich Canada, Oakville, ON), 100 mL glacial acetic acid, 250 mL methanol, 150 mL water), and left to rotate gently for 45 minutes. Gels were rinsed for 5 minutes in water three times prior to de-staining overnight in 40% methanol, 10% acetic acid with gentle rotation. Gels were rinsed in water prior to cutting. 63 2.6 In-Gel Trypsin Digestion and Peptide Extraction The procedure used for in-gel trypsin digestion and peptide extraction was developed by the European Molecular Biology Laboratory – Protein and Peptide Group in 1997, and published in modified form by Pandey et. al. in 2000 (Pandey, Andersen, & Mann, 2000). I received training in this procedure, but it was felt that the results might be more reproducible if this procedure were to be left to the hands of Ms Shujun Lin, technician in the laboratory of Dr Juergen Kast. I was responsible for the gel slicing. In brief, the gel was rinsed with water, and the bands excised with a clean scalpel. A hair cover, face-mask and gloves were worn in addition to a clean laboratory coat, to prevent or diminish potential keratin contamination. The bands were chopped into approximately 1 x 1 mm cubes and transferred to 0.5 mL Eppendorf tubes. Gel pieces were washed with 100-150 µL distilled-deionized (dd) H20 for 5 minutes. Tubes were centrifuged and the supernatant removed. Acetonitrile (at 3-4 times the volume of the gel pieces) was added. Gel fragments were incubated for approximately 15 minutes until gel pieces had shrunk, becoming white and sticking together. Tubes were centrifuged and the liquid removed and discarded. Gel fragments were dried in a vacuum centrifuge at 40°C for approximately 10 minutes. Gel fragments were swollen by the addition of 10 mM dithiothrietol/0.1M NH4HCO3 solution (just enough volume to cover the gel pieces), and incubated for 30 minutes at 56° C to reduce the proteins. Tubes were spun down, the liquid removed, and 100 µL acetonitrile added to shrink the gel pieces. After incubation for 15 minutes at room temperature, the supernatant was removed and replaced with 100 µL 55 mM iodoacetamide/0.1M NH4HCO3 to alkylate the proteins. Tubes were incubated for 20 minutes at room temperature, in the dark. Tubes were centrifuged, supernatant was removed and gel fragments washed using 150-200 µL 0.1M NH4HCO3 for 64 15 minutes. Tubes were once again centrifuged, liquid removed and gel fragments shrunk using acetonitrile (100 µL for 15 minutes). After removal of the liquid, since the fragments were still blue from Coomassie staining, the gel fragments were rehydrated in 150 µL of 0.1M NH4HCO3 for 15 minutes, then an equal volume of acetonitrile was added (for a 1:1 mixture). Tubes were placed into a thermal mixer and mixed for 15 minutes, spun, and the liquid removed. Gel fragments were shrunk by adding 100 µL acetonitrile. Tubes were spun down, liquid removed and fragments dried in a vacuum centrifuge at 40°C for approximately 10 minutes. Gel fragments were rehydrated in digestion buffer containing 50 mM NH4HCO3, 5 mM CaCl2 and 12.5 ng/µL of trypsin at 4°C (on ice) for 40 minutes (after 15-20 minutes, tubes were checked, and more digestion buffer was added if all the liquid had been absorbed by the gel fragments). After 40 minutes total, any remaining supernatant was removed and 5- 25µL of the same digestion buffer, but without trypsin, was added (enough to cover the gel pieces). Tubes were incubated at 37°C overnight. Tryptic peptides were extracted from the gel fragments as follows. Ten -15 µL of 25 mM NH4HCO3 was added to the buffer in each tube and the tubes incubated at 37°C for 15 minutes. Acetonitrile was added (1-2 times the volume of the gel fragments). Tubes were incubated at 37°C for a further 15 minutes with shaking. Supernatants were transferred to separate tubes and 40 µL of 5% formic acid added. Tubes were placed into a thermal mixer and mixed for 15 minutes at 37°C. Acetonitrile was added (1-2 times the gel fragments volume). Tubes were incubated at 37°C for a further 15 minutes with shaking, spun down and both supernatants (i.e. pre-extraction and post- extraction from the same gel fragments) were pooled together. Supernatants were dried down in a vacuum centrifuge. Prior to use, desiccated peptides were reconstituted with 5% formic acid. 65 Peptides extracted from each gel slice were loaded onto an in-house-manufactured reverse phase C-18 column, 15 cm length, 75uM OD, 3µM ID for nano-flow liquid chromatography using a 1100 HPLC (Agilent Technologies, Canada, Mississauga, ON) as a front end interface to mass spectrometry analysis on a Thermo LTQ-FT instrument (Thermo Fisher Scientific, Waltham, MA). Sample runs were 105 minutes in length. The mobile phase A consisted of 0.5% acetic acid in H20 and the phase B was 0.5% acetic acid in 80% acetonitrile + 19.5% H20. Detailed information on the chromatography is shown in Table 2.1. 66 Time Module Setting Value t(min) %A %B Flow µL/min µL A µL B 0.00 Nano Pump Column Flow 0.6 µL 0.00 Nano Pump %B 6.0% 0.00 Micro WPS External Contact Closed 0.50 Micro WPS External Contact 1 Open new 20.00 Nano Pump Column Flow 0.6 µL/min 0 94.00 6.00 0.60 11.28 0.72 20.00 Nano Pump %B 6.0% 20 94.00 6.00 0.60 0.19 0.21 21.00 Nano Pump Column Flow 0.2 µL/min 21 94.00 6.00 0.20 3.78 7.02 75.00 Nano Pump %B 30.0% 75 70.00 30.00 0.20 0.90 1.10 85.00 Nano Pump Column Flow 0.2 µL/min new 85 20.00 80.00 0.20 0.40 1.60 85.00 Nano Pump %B 80.0% 90 20.00 80.00 0.60 0.60 2.40 90.00 Nano Pump Column Flow 0.6 µL/min new 95 20.00 80.00 0.60 0.34 0.26 95.00 Nano Pump %B 80.0% modified 96 94.00 6.00 0.60 5.08 0.32 96.00 Nano Pump %B 6.0% modified 105 94.00 6.00 0.60 Table 2.1 Details of the chromatography runs on the 1100 HPLC (Agilent Technologies, Canada, Mississauga, ON) used in each of the three FT-ICR experiments. 2.7 SDS-PAGE – Immunoblotting In preparation for immunoblotting (Western blotting (Burnette, 1981)), cells were counted, then spun down (6000 rpm, 4°C, 5 minutes) and the supernatant removed without disturbing the pellet. Cells were washed once in ice cold PBS (Sigma-Aldrich Canada, Oakville, ON). Cells were spun again (6000 rpm, 4° C, 5 minutes) and the supernatant removed, without disturbing the pellet, before being lysed on ice in cell lysis buffer (recipe 2.4 above) using 50 67 µL lysis buffer for each 1 x 106 cells as determined by an automated cell counter (Beckman- Coulter Z-1 particle counter). Cells were vortexed three times for ten seconds over three minutes, then sonicated for 3 x 10 seconds on ice. Lysates were spun at 13,200 rpm for 5 minutes at 4°C to remove debris. Supernatants were carefully removed. A Bradford protein assay (see below) was performed at this point, if required. Lysates were mixed in ratio of 3:1 (v/v) with 4X loading buffer (recipe below) prior to “boiling” for five minutes in a heat- block set to 95°C. Samples were loaded onto a gel (mini-gel) using a wet-loading technique, and the gel was transferred to nitrocellulose using a semi-dry transfer on a Pharmacia-LKB MultiPhor unit as recommended by manufacturer. 2.8 Immunoblotting (Western blotting) Following transfer the nitrocellulose membranes were stained using a Ponceau-S solution (Sigma-Aldrich Canada, Oakville, ON) to verify transfer of proteins. Membranes were rinsed prior to being blocked in 5% (w/v) skim milk in TBST for one hour at room temperature (TBS is 50 mM Tris, 150 mM NaCl, pH 7.6. TBST has Tween-20 ® added to TBS at 0.0005% v/v). Membranes were rinsed for five minutes in TBST, three times. Primary antibody (usually at 1:1000, but actual dilutions are given for each antibody used – see Table 2.2 below) were diluted in TBS containing 5% BSA or skim milk powder, depending on antibody, plus 0.02% sodium azide and sufficient liquid added to cover the membranes. Membranes were incubated either overnight at 4°C or for two hours at room temperature, depending on the antibody, with gentle rocking. Membranes were washed three times five minutes in TBST. Secondary antibody (dilutions varied from 1:2000 to 1:5000 in TBST with 5% skim milk powder) was added for one hour at room temperature. Membranes were washed three times five minutes in TBST prior to the application of Enhanced 68 Chemiluminescence solution (0.5 mL each solution A & B) (Amersham ECL, GE Healthcare Bio-Sciences Corp, Piscataway, NJ), and exposure to X-ray film, or image was captured using a high sensitivity camera. 69 2.9 Antibodies The following primary antibodies were used in the work reported in this thesis: Table 2.2 Primary antibodies used in the experiments reported in this thesis. Antibody Supplier Species Type Conditions; size of protein detected Anti- phosphotyrosine, clone 4G10 Upstate Cell Signaling Solutions, Lake Placid, NY Mouse Monoclonal 1:1000; Various mW Overnight, 4°C Anti-PDCD4 Abcam Inc., Cambridge, MA Rabbit Polyclonal 1:1000; 52 kDa 2 hours, RT Anti-HMGB2 Abcam Inc., Cambridge, MA Mouse Monoclonal 1:200; 24 kDa Overnight, 4°C Anti-Thymidylate synthase Abcam Inc., Cambridge, MA Mouse Monoclonal 1:200; 35 kDa 2 hours, RT Anti-SHIP A kind gift from Dr Gerald Krystal Rabbit Polyclonal 1:1000; 145 kDa 2 hours, RT Anti-actin (I-19) Santa Cruz Biotechnology, Santa Cruz, CA Goat polyclonal 1:1000; 43 kDa 2 hours, RT Anti-vinculin Sigma-Aldrich (Canada), Oakville, ON Mouse Monoclonal 1 µg/mL; 130 kDa 2 hours, RT Anti-cdc42 Santa Cruz Biotechnology, Santa Cruz, CA Rabbit Polyclonal 1:500; 25 kDa Overnight, 4°C Anti-p85 (PI3-K) Cell Signaling Technology, Beverly, MA Rabbit Polyclonal 1:1000; 85 kDa Overnight, 4°C 70 Secondary antibodies were obtained from Dako Cytomation (Dako Denmark, Glostrup, Denmark) and were peroxidase-conjugated goat anti-rabbit, goat anti-mouse or rabbit anti- goat immunoglobulins – determined according to the primary antibody used. Dilutions were 1:3000 to 1:5000 depending on results obtained. Antibodies were diluted in TBST with 3% skim milk powder for the anti-rabbit and anti-mouse antibodies, and 1% BSA for the anti- goat secondary. 2.10 Flow Cytometry Cells were stained with propidium iodide for analysis of apoptosis using flow cytometry using an adaptation of the protocol given in Current Protocols (Darzynkiewicz & Huang, 2004). One-milliliter aliquots of cells at approximately 0.5 x 10 6 per mL were removed for each condition to be tested. Cells were centrifuged in 15 mL conical tubes at 400 x g for 5 minutes at room temperature. The cell pellets were washed once in 1 X PBS (Ca 2+ -, Mg 2+ - free, + 0.1% glucose). Cells were centrifuged at 400 x g for 5 minutes at room temperature. Supernatant was removed (except for approximately 100 µL liquid, in order to avoid disturbing the pellet). The pellet was vortexed briefly to resuspend the pellet and to break up confluent cells. One milliliter of ice-cold 70% ethanol was added drop-wise while vortexing. Cells were fixed for a minimum of one hour at 4°C. For propidium iodide staining, cells were vortexed briefly. Tubes were centrifuged at 3000 rpm for 5 minutes at room temperature (because cells are more buoyant in ethanol). All but 100 µL of supernatant was removed. The pellets were resuspended in one milliliter of 71 propidium iodide solution containing 1 mL PBS, Ca 2+ -, Mg 2+ - free, + 0.1% glucose, 10 µL 10 mg/mL RNase A, 5 µL 10 mg/mL propidium iodide (Sigma-Aldrich Canada, Oakville, ON). Cells were allowed to stain in the dark for at least 30 minutes prior to analysis with the flow cytometer. 2.11 Bradford Protein Concentration Assay The Bradford protein assay (Bradford, 1976; Stoscheck, 1990) (Bio-Rad, Hercules, CA) measures the change in the colour of Coomassie ® Brilliant Blue G-250 dye as it binds with protein. The dye absorption shifts from 465nm to 595 nm with protein binding. The procedure followed was as outlined in the Bio-Rad Protein Assay instruction manual (catalogue number 500-0006). Briefly, a series of albumin protein standards were prepared at 2000, 1000, 500, 250, 125 +g/mL. A blank was also prepared. Cell lysates were prepared as described elsewhere. Lysates were diluted 1:20 in water. Ten +L of each protein standard, the blank, and each unknown sample were pipetted into a microtitre plate, in triplicate. Two hundred +L of the diluted Bio-Rad Bradford solution (5X stock solution, diluted in water) was added to each well containing sample or standard. The plate was shaken, and allowed to rest as colour developed over ten minutes. The plate was read at 595 nm using a microtitre plate reader. 72 2.12 Ac-DEVD-pNA Caspase Assay Ac-DEVD-pNA is a colorimetric substrate for caspase-3. The sequence is based on the poly- ADP-ribose polymerase (PARP) cleavage site Asp-216. Cleavage of the artificial substrate by active caspase-3 results in development of a colorimetric reaction in a quantitative manner. Release of pNA is monitored by absorbance at 405 nm. The DEVD-pNA stock solution is 10 mM in DMSO (Calbiochem, San Diego, CA). Buffer for DEVD-pNA activity is 50 mM HEPES, pH 7.6, 5 mM EDTA, 0.5% Triton X-100 and 2 mM DTT. TF-1 cells were seeded at a density of 2 x 10 6 /mL in 4 mL RPMI 1640 in 5-cm Petri dishes. Cells were incubated in the presence and absence of 1% CGMI. At the time-points indicated, cells were harvested, washed once in PBS and the pellets resuspended in 30 µL DEVD-pNA activity buffer. Cells were rotated at room temperature for 10 minutes, centrifuged for 10 minutes at 19,000 x g and the supernatants collected. Two +L of supernatant were used for the Bradford protein assay. The DEVD-pNA substrate was diluted 1:100 in buffer and 100 µL added for each 50 µg protein. Samples were incubated in the dark at 37°C for 30 minutes, prior to reading absorbance at 405 nm. 73 2.13 Methanol-Chloroform Protein Precipitation Sample volume was determined prior to protein precipitation. Three volumes of methanol and one volume of chloroform were added to the samples and vortexed. Three volumes of purified water were added, and the samples vortexed for one minute. Samples were centrifuged for five minutes at 10,000 x g. The upper phase was discarded. Three volumes of methanol were added. Samples were vortexed briefly. Samples were centrifuged for 5 minutes at 10,000 x g. The supernatant was removed and discarded. The pellet was allowed to air dry, prior to storage at -80 °C. 2.14 Data Handling Mass spectrometry raw data files from the LTQ-FT were obtained in a proprietary .raw format. The raw data were converted to “.dta” format using extract_MSN.exe (Thermo Fisher Scientific, Waltham, MA). Using the software program DTA-Supercharge (available from http://msquant.sourceforge.net), the .dta files were converted to “.mgf” files for the next step in the analysis. Files in the .mgf format were submitted for analysis with MASCOT (Matrix Science, www.matrixscience.com) for protein identification. MASCOT parameters included: use of the MSDB database, taxonomy homo sapiens, allow up to 1 missed cleavage, fixed modification: carbamidomethyl, 74 variable modifications: oxidized methionine, lysine-6-13C, arginine-6-13C, peptide tolerance ± 5ppm, MS/MS tolerance ± 0.6Da, peptide charge 2+ and 3+. Analysis with MASCOT returned an “.html” file which was then analyzed using MSQuant (Schulze & Mann, 2004) which returns the list of identified proteins with information on the relative quantitation of the 12 C-labelled proteins and the 13 C-labelled proteins. The peptide information was obtained from the MSQuant program by loading the original MSQuant-generated “.m3b” file and exporting the protein and peptide information. This yielded a redundant list of peptides identified by the mass spectrometer. To derive the final definitive protein list, a non-redundant peptide list was generated by sorting the raw data peptides according to descending Mascot MOWSE scores, and then removing redundant peptides. This procedure generated a list of the highest-scoring peptides, with each peptide occurring only once. The list of peptides was then searched against the list of all human proteins in “fasta” format using the program “finalList.pl”. The fasta file used was dated 13th March, 2008, the list of human proteins used was the IPI human database (Kersey et al., 2004), version 3.40, containing 69965 entries. The perl program, finalList.pl, was used to find the smallest number of protein sequences that can be used to explain all the peptide data (Dobreva, Fielding, Foster, & Dedhar, 2008; Rogers & Foster, 2007). The resultant list of proteins identified was based on each peptide being used once only, to identify a single protein. (finalList-mod.pl was a kind gift from Dr Leonard Foster, University of British Columbia). 75 Next, the quantitation data for each peptide was extracted from the MSQuant .m3b file, using the peptide sequence information and the i/w average of ratios data. Entries without quantitation data were removed, and redundant entries were again removed. The above two steps generated a final list of proteins identified from a non-redundant list of the highest scoring peptides, and with associated quantitation ratios for each protein. 2.14.1 Working with the Final List of Proteins The final list of proteins required some manual reviewing to ensure that the data were satisfactory. The following steps were undertaken: 1. Proteins with outlying ratios were checked manually – outlying ratios are often not accurate. For example, keratin contamination will sometimes appear with a very low isotope/wild-type ratio. In these cases, the keratin is a contaminant. Note that the labeled cells can have intrinsic keratin identified, but in these cases of genuine labelling, the keratin ratio does not change much, and so is found closer to a ratio of 1:1 (and not 20:1 as was sometimes reported for the contaminants). For each protein with the highest or lowest ratios the spectra were manually reviewed. Spectra from the list of proteins with outlying ratios were reviewed sequentially, starting with the proteins showing the highest and lowest ratios, and continuing until the protein spectra showed a clear indication that the data were genuine, and not the result of noise or contamination. The procedure was as follows: a. copy peptide used for ID 76 b. search against the raw data from the .mb3 file to locate the protein identified by MSQ/Mascot c. open MSQ and search for the spectra for that protein d. determine if the spectra represent a real occurrence, or artifact e. remove protein entry if artifact 2. remove proteins identified by only one peptide 3. remove proteins identified by two peptides where one (or both) of the two are less than seven (7) residues long 4. calculate the average i/w for the remaining proteins 5. using the average above, correct the i/w ratios for pipetting error and any other error in determining protein concentration. 77 2.14.2 Blast2GO Lists of IPI accession numbers were obtained from each experiment as described above, and used to determine the gene ontologies (Ashburner et al., 2000) of the proteins detected. Lists were converted to fasta format using the EMBL-EBI website “dbfetch” function (http://www.ebi.ac.uk/cgi-bin/dbfetch). The fasta lists were imported into Blast2GO (Ver. 2.2.3) (Conesa et al., 2005). The non-redundant blast database at the NCBI was used as the reference for a protein blast (blastp). Settings for blastp were: expectation value minimum: 1 x e-3 high scoring segment pair cut-off: 33. Default parameters were used to make the Annotations: pre-eValue-Hit-Filter: 1 x e-6, Annotation cut-off: 55, GO Weight: 5. Directed acyclic graphs were then prepared using a cut-off (sequence filter) of 5, a score # of 0.6 and a node score filter of 0. This allowed us to generate directed acyclic graphs at level 4 gene ontologies. 2.14.3 Ingenuity ® Pathway Analysis Functional and pathway analysis of proteins identified as being cytokine withdrawal- regulated was carried out using Ingenuity Pathway Analysis, version 7.5 (IPA, Ingenuity ® Systems, www.ingenuity.com). IPA is a tool for the description of molecular networks and signaling pathways. The IPA proprietary database has been extracted from the experimental 78 literature by manual evaluation of the full texts of papers (less than 10% of the database has been automatically extracted and modeled). The database contains only results that have been confirmed by multiple laboratories, thus controversial results are not included in an IPA analysis. Analysis was carried out using the 41 proteins identified as increasing in concentration in response to cytokine withdrawal, and the 82 proteins identified as decreasing in concentration in response to cytokine withdrawal. Proteins were assigned to various functions, and networks were generated. The Functional Analysis identified the biological functions that were most significant to the data set. Proteins used for the analysis were those that had previously met the determined cut-off for inclusion, and were associated with biological functions and/or diseases in the Ingenuity Pathways Knowledge Base. Fischer’s exact test was used to calculate a p-value determining the probability that each biological function and/or disease assigned to that data set is due to chance alone. 79 3 Proteomic Analysis of a Hemopoietic Cell Line Undergoing Apoptosis. 3.1 Introduction Cell lines derived from hematopoietic lineages have served as useful models for studying the onset of apoptosis. In hematopoietic cells that are dependent on specific cytokines for growth and survival, the removal of the cytokine, or cytokine starvation, can result in apoptosis, even though the cells are in the presence of all other nutrient and serum-derived factors that are normally present in growth medium (see, e.g. (Brach, deVos, Gruss, & Herrmann, 1992; Lotem & Sachs, 1996; Minshall, Arkins, Freund, & Kelley, 1996; J. R. Park, 1996)). By virtue of this behaviour, hematopoietic cell lines are good models of inflammatory cells in the bloodstream that normally have a very short half-life, and are dependent for survival upon the presence of cytokines, which they may encounter at a site of infection or inflammation. Hence this experimental model mimics the natural process of lymphocyte blood cell homeostasis through cytokine-withdrawal apoptosis that occurs in the body in the aftermath of infection; for example, when increased numbers of neutrophils previously generated to fight infection are no longer required and undergo apoptosis as a result of decreased cytokine levels generated to support the (temporarily enlarged) cell population (Blank et al., 1997; Pellegrini, Belz, Bouillet, & Strasser, 2003). We now know that a major reason for cells undergoing apoptosis is the loss of survival signals, many of which stem from activation of the PI3-K/PKB signaling pathway (Alessi & Cohen, 1998; Klein et al., 2000; Marte & Downward, 1997; Rameh & Cantley, 1999). In order to have a more complete understanding of the specific events that are critical for 80 survival of a particular cell type, it was felt that a global analysis of the proteome of cells undergoing apoptosis would provide many leads for further investigation. The studies reported here were carried out using a human erythroleukemia cell line - TF-1 – established in 1989 in the Takaku lab using mononuclear cells from a 35-year-old male with erythroleukemia (Kitamura et al., 1989). TF-1 cells are cytokine-dependent, that is, they require the continued presence of cytokine for survival and proliferation. In the case of TF-1 cells, the required cytokines are (one of) interleukin-3 (IL-3), interleukin-5 (IL-5), granulocyte-macrophage colony stimulating factor (GM-CSF), or erythropoietin (EPO – although EPO only sustains the short-term growth of these cells)(Kitamura et al., 1989). A study by Rosas et. al. in 2005 investigating cytokine-withdrawal apoptosis in TF-1 cells showed that IL-5 withdrawal led to dephosphorylation and activation of the Forkhead transcription factor FOXO3a, leading to increased expression of Bim, and that increased levels of Bim were sufficient to cause apoptosis in these cells (Rosas, Birkenkamp, Lammers, Koenderman, & Coffer, 2005). This study demonstrated the importance of activation of the PI3-K pathway in causing inhibition of apoptosis in TF-1 cells. In this work, normal growth media was supplemented with recombinant hGM-CSF, produced by cells of the CGMI cell line (Schubert & Duronio, 2001). GM-CSF signaling in hematopoietic cells is mediated by several pathways, including; PI3-K, MAPK, JAK/STAT and PKC (de Groot, Coffer, & Koenderman, 1998; Wheadon, Roberts, Watts, & Linch, 1999). GM-CSF has been shown to inhibit apoptosis in neutrophils (Klein et al., 2000; Yasui et al., 2002) and macrophages. Apoptosis begins in TF-1 cells following several hours of cytokine 81 withdrawal. Cells show visually detectable signs of apoptosis by about 18 hours and the colonies proceed to die completely within the period from 24 to about 30 hours. The use of TF-1 cells in these experiments offered several advantages: (i) TF-1 cells undergo cytokine-withdrawal apoptosis more slowly than most murine cytokine-dependent hematopoietic cell lines, giving a larger “window” in which to observe protein changes under conditions of cytokine withdrawal, (ii) TF-1 cells have been fairly well characterized in terms of signaling events, and (iii) because TF-1 are human cells, it is hoped that our data might be of use to the Human Proteome Organization (HuPO – http://www.hupo.org). Several previous studies have characterized some aspects of changes to the proteome during apoptosis (see, e.g.(An & Seong, 2006; Bruneel et al., 2005; Gerner et al., 2000; Herzog, Kuntz, Daniel, & Wenzel, 2004; Hwang et al., 2006b; Kozielski et al., 2008; Lau, He, & Chiu, 2004; S. C. Lee, Chan, Clement, & Pervaiz, 2006; Machuy et al., 2005; Qiu, Gao, Li, & Shen, 2008; Short et al., 2007; Thiede, Dimmler, Siejak, & Rudel, 2001; Thiede, Siejak, Dimmler, & Rudel, 2002; Winkelmann, Nassl, Daniel, & Wenzel, 2008)). This thesis describes work performed using cytokine-withdrawal for the induction of apoptosis. By adopting this approach, we have been able to observe the effects on the proteome of the withdrawal of survival signaling, rather than the imposition of a death-inducing signal by such means as the use of Fas-L, staurosporine, camptothecin or oligomycin for the induction of apoptosis. As well, many of the published studies have used two-dimensional gel electrophoresis, which has been an enormously valuable tool in proteomics. However, we believe that mass spectrometry offers significant advantages over two-dimensional gel 82 electrophoresis for the study of whole cell lysates – greater depth of discovery of the proteome, along with increased opportunities to observe membrane-associated proteins, and proteins with a high pKa. Further, this study was performed using three biological replicates – giving increased confidence in the identification of the proteins and in the quantitations reported. It was decided at the outset of these experiments to use whole cell lysates in the analysis. This was decided for two reasons. Firstly, sub-cellular fractionation at this point would have introduced more experimental variability, and would have complicated quantitation by producing a wider margin of error (however, fractionation could be introduced as an additional step in later experiments, once the basic protocol had been established). Secondly, while recognizing that whole cell lysates are more complex than lysates from sub-cellular fractions, and that this complexity would necessarily limit the total number of proteins identified, and hence the coverage of the proteome, sub-cellular fractionation as part of the sample preparation would have led to a very large number of samples for the analysis of the complete cellular proteome. Such a large number of samples would make it impossible to get sufficient mass spectrometry instrument time for analysis. Hence for experimental design, and for pragmatic reasons, whole cell lysates were used. In this chapter, the background work done in order to optimize the conditions for the mass spectrometry analysis is presented, followed by the most important results obtained from the mass spectrometry analysis – the lists of proteins which have shown an important increase or decrease in concentration (i.e. apparent up-regulation or down-regulation) in response to 83 fifteen hours of cytokine withdrawal. Results from three biological replicates of this experiment are presented. The protein abundance figures give an indication of the technically demanding nature of experiments of this type, with protein concentrations varying by up to nine orders of magnitude in serum (Adkins et al., 2002), and at least six orders of magnitude within cells (Schirle, Heurtier, & Kuster, 2003). Initial experiments to determine changes in protein concentration in response to IL-3 stimulation were disappointing. My first approach was to use two-dimensional gel electrophoresis, comparing separate gels used for control and starving lysates, and using Surface Enhanced Laser Desorption/Ionization (SELDI) mass spectrometry and peptide mass fingerprinting for protein identification. No attempt at quantitation could be made with the technology at the time. A typical experiment highlights the low yield from this approach – thirty-eight proteins shown to increase in response to cytokine stimulation, and eight shown to decrease. Apart from the difficulty in visualizing a large enough number of proteins (due to the dynamic range required of the technique, as stated above), one of the problems with this approach was that spots frequently contained more than one protein. A second difficulty was that smaller spots did not always contain sufficient material to allow analysis with the SELDI instrument. The most interesting change detected in this series of experiments was an increase in the concentration of the highly abundant protein hnRNP-A1 in response to cytokine withdrawal. When it became apparent that this method would be inadequate for my needs, I undertook to investigate changes in the proteome in response to cytokine withdrawal using a Q-Star instrument (quadrupole time-of- flight). This also was unsatisfactory. I was unable to see sufficient detail of changes in the 84 proteome in response to cytokine withdrawal. By using FT-ICR mass spectrometry for analysis of the stable isotope-labeled samples it became possible to visualize the proteome down to the level of some of the less abundant signaling molecules; and by doing so, to view changes in relative quantitation of these lower abundance proteins in response to various experimental conditions. 3.2 Experimental Overview and Background Material The details of the experimental procedure are given in chapter 2. Briefly, using the SILAC procedure, TF-1 cells were labelled, incorporating a stable isotope of carbon ( 13 C), to enable comparison with TF-1 cells which had been labeled with wild-type carbon ( 12 C), and starved of cytokine for fifteen hours. Cells were lysed, and whole cell lysates were prepared and fractionated on SDS-PAGE gels prior to separation on a reverse phase chromatography column for analysis using an FT-ICR mass spectrometer. This procedure was carried out three times, giving a series of biological replicates. In this series of experiments, the time-point chosen for investigation, i.e. fifteen hours of cytokine withdrawal, was chosen because of the opportunities it presented to study early apoptosis, or what might be referred to as “pre-apoptosis”. – Pre-apoptosis can be defined as that period in the process of apoptosis after which the cells have committed to apoptosis, but (prior to) or (much earlier than) the point at which to fragmentation of the cells can be observed. In TF-1 cells, fifteen hours of cytokine withdrawal is sufficient to cause activation of caspases, hence it is likely after the “point of no return”, but is not so far along in the process of apoptosis that the cells are visibly fragmenting. TF-1 cells undergoing factor 85 withdrawal for 18-24 hours begin to show microscopically apparent cell death – cell shrinkage and obvious fragmentation. 3.2.1 Flow Cytometry Analysis of Apoptosis in TF-1 Cells Figure 3.1 shows the results of analysis of TF-1 cells stained with propidium iodide, and analyzed using flow cytometry. Control cells show about 4% sub-diploid DNA, while cells undergoing cytokine withdrawal for 18 and 24 hours show 28% and 36% sub-diploid DNA respectively. This work was done in conjunction with the caspase activity assay (below), to determine an appropriate time-point for investigation. 86 F ig u r e 3 .1 F lo w c y to m e tr y o f p r o p id iu m i o d id e -s ta in e d T F -1 c e ll s. C o n tr o l v e r s u s 1 8 - an d 2 4 -h o u rs c y to k in e w it h d ra w al . R ep re se n ta ti v e o f 3 e x p er im en ts . C y to k in e w it h d ra w al c au se d c el l d ea th , in d ic at ed b y t h e p re se n ce o f su b -G 1 D N A , as w el l as l iv e ce ll a cc u m u la ti o n i n G 1 . Cell Count Cell Count Cell Count 87 3.2.2 Caspase Activity Analysis in TF-1 Cells Caspase activity was visualized by using a colorimetric assay that is dependent on the activation of caspase-3 – the Ac-DEVD-pNA caspase-3 activity assay. Caspase-3 is the most abundant caspase in the cell, and is the one ultimately responsible for most of the effects of apoptosis (although caspase-6 and caspase-7 also play important roles) (Zimmermann, Bonzon, & Green, 2001). Activation of caspase-3 is considered by many to be a hallmark of early apoptosis. Relative levels of caspase-3 activation were determined after 9, 12, 15 and 18 hours of cytokine withdrawal, and compared with control cells – in the presence of normal amounts of cytokine added, as a negative control; Staurosporine (1 !M for 3 hours) was added to induce apoptosis as a positive control (1 µM staurosporine for three hours). Results (shown in Figure 3.2) indicate that by 12 hours, levels of activated caspase-3 were twice baseline levels (52.41 +/- 0.22 versus 25.77 +/- 0.06), and further increased by 15 hours (61.4 +/- 9.28). The apparent decrease at 18 hours (55.14 +/- 10.74) may be attributable to the fact that almost 30% of these cells have begun to fragment (based on flow cytometry data). At 15 hours, almost all the cells still looked relatively normal microscopically, although the cells did appear to be somewhat smaller in size than control cells. It was thus confirmed that caspase-3 was activated at 15 hours, and so this was the time point chosen for investigation. 88 Figure 3.2 DEVD-pNA Caspase Activity Assay. Upper panel shows Caspase 3 activity in response to cytokine withdrawal. Lower panel shows Caspase 3 activity in respones to staurosporine. Note difference in scales used. TF-1 cells were washed 3 times in PBS, then incubated for the time indicated in medium in the absence of GM- CSF, or in the presence of 1 !M staurosporine (Sts) for 3 hours. Control cells were kept in the presence of complete medium. Caspase activity was determined as described in Chapter 2. 89 3.2.3 Determination of Concentrations of Arginine and Lysine Required for Cell Growth in RPMI-1640 In preparation for cell metabolic labelling with stable isotopes, cells were tested at different concentrations of arginine and lysine – 0%, 10%, 25%, 50%, 75% and 100%. This was done to determine a minimal level of amino acids that still allow normal proliferation of cells, in order that the labelling media might be prepared at a lower concentration of 13 C-labelled arginine and lysine, which would reduce the expense involved, yet not interfere with cell growth. Cells survived, and appeared to grow normally at concentrations of arginine and lysine at 25% of normal levels found in the growth medium (RMPI 1640), hence it was decided that arginine and lysine at 50% of normal concentration would allow an adequate supply of these essential amino acids for cell metabolism, and yet still afford substantial savings in the quantities of labeled amino acids used. A further advantage of using reduced concentrations of arginine and lysine is that at reduced concentrations, any conversion of arginine to proline is reduced as well. 3.2.4 Incorporation of 13 C-Labelled Amino Acids A test was performed to determine levels of incorporation of 13 C-labelled amino acids in cellular proteins. A sample of whole cell lysate from 13 C-labelled TF-1 cells was analyzed using the FT-ICR mass spectrometer. Results indicated that over 95% of the peptides detected had been labelled with 13 C amino acids 90 3.2.5 Optimization of Sample Fractionation Conditions The fractionation of whole cell lysates was an important step in our experimental design. Since we had decided to use whole cell lysates, careful attention would be required for “pre- fractionation” of the lysates prior to analysis using the mass spectrometer. Some preliminary work was carried out using a quadrupole time-of-flight mass spectrometer (Q-Star, Applied Biosystems) to analyze whole cell lysates in control cells compared with cells undergoing cytokine withdrawal for six hours and fifteen hours. Two different types of sample fractionation were tested – strong anion exchange (SAX = Mono-Q), and SDS-PAGE. 3.2.5.1 Strong Anion Exchange Chromatography Separation Whole cell lysate from cells that had undergone six-hours of cytokine withdrawal was separated by strong anion exchange chromatography (SAX). Control and labelled cells were counted, using approximately 42 x 10 6 of each for approximately 84 x 10 6 cells total. Cells were lysed in 500 µL of solubilization buffer, yielding about 8.0 mg of protein. The sample was loaded onto a 5 mL Mono-Q column, and separated over 30 minutes using a step-wise gradient (150mM, 200 mM, 300 mM, 400 mM and 600 mM sodium chloride). Thirty fractions of one millilitre were collected. Proteins were precipitated using a methanol- chloroform precipitation protocol and the samples dried. Some samples were combined, based on estimates of the amount of material in each of the thirty samples, to give a total of eighteen samples with approximately equal quantities of material in each. Samples were subjected to in-solution trypsin digestion and the peptides run (in eighteen separate runs) via a reverse-phase column into the Q-Star for mass analysis. A total of 997 proteins were identified, of which 436 or 44% were identified based on a single peptide. The number of 91 proteins with a relative quantitation ratio of < 0.67 (i.e. decreased 30% or more in response to starvation), and with identification based on greater than one peptide was 9. The number of proteins with a relative quantitation ratio of > 1.3 (i.e. increased 30% or more in response to starvation), and with identification based on greater than one peptide was 3. 92 3.2.5.2 SDS-PAGE Separation While the approximate load capacity of the strong anion exchange column I proposed to use was known, the question of how much protein could be loaded in a gel lane had to be addressed. The need to maximize the amount of material separated on the gel – to give the best possible chance for detection of as many proteins as possible - needed to be weighed against the loss of protein separation that occurs when an SDS-PAGE gel is overloaded with material. Initially this question was posed using the mini-gel format (gel dimensions approximately 8.5cm x 6 .5 cm, BioRad Mini-Protean II). Whole cell lysate from TF-1 cells was prepared as described in Chapter 2. Samples were loaded on a 1.5 mm thickness 9% SDS-PAGE mini-gel in the amounts described under each lane in Figure 3.3 (!g protein). As will be noted, it appeared that band resolution started to become less distinct at an 80-!g total protein load. From this it was determined that a load of 70-!g protein per well would be used. 93 Figure 3.3 Protein load (µg) of whole cell lysate and bovine serum (BSA) Comparison was made between a simple protein mix and a complex protein mix to determine maximum protein load per lane. Protean II mini-gels shown. Images show increasing protein loads, and the comparison between a complex protein mixture (whole cell lysate) and a simple protein mix (BSA). Different protein loads were tested to determine how much material could be loaded before protein separation became less distinct – determined by the visual appearance (i.e. blurring) of the Coomassie! R250-stained protein bands. Once the maximum protein load had been determined, the second approach to sample fractionation was tested using whole cell lysate from cells that had undergone fifteen-hours of cytokine withdrawal. In this instance, the sample was separated by using polyacrylamide gel electrophoresis. Two hundred micrograms of protein (100 µg from each of the labeled conditions) was split into three aliquots and each aliquot loaded into one of three lanes of a 1.5mm thick, 9% polyacrylamide gel. The gel was cut (across all three lanes) into 18 vertical slices, giving 18 samples for in-gel trypsin digestion and peptide extraction. A total of 616 proteins were identified, of which 222 or 36% were identified with only a single peptide. The number of proteins with a relative quantitation ratio of < 0.67 (i.e. decreased 30% or more in response to starvation), and with identification based on greater than one peptide was 4. The number of proteins with a relative quantitation ratio of > 1.3 (i.e. increased 30% or more in response to starvation), and with identification based on greater than one peptide was 6. 94 Based on the results of these two experiments, it was apparent that with the current experimental design, and using the technology available at the time, the results detected were not significant enough to warrant further investigation. The total number of proteins identified and quantitated by more than one peptide was disappointingly small. In all cases, it appeared that the proteins identified were highly abundant proteins, and so less likely to be involved in the survival signaling I wished to investigate. 3.2.6 Fourier-Transform Ion Cyclotron Resonance Mass Spectrometry Of TF-1 Proteins The acquisition of a Fourier-transform ion-cyclotron resonance mass spectrometer (FT-ICR) gave hope that the question of what proteins change in concentration in early apoptosis in response to cytokine withdrawal might be answered in more detail. For the initial FT-ICR experiment a 12% mini-gel was used, with the same protein load (70 µg per lane, in three lanes). However, for this initial FT-ICR experiment, the gel was cut into forty slices – in an attempt to gain further pre-fractionation of the whole cell lysate. This experiment was designed in such a way that samples would be run on both the Q-Star and the FT-ICR in parallel, for a comparison of both instruments under these experimental conditions. In brief, whole cell lysate was fractionated using one-dimensional polyacrylamide gel electrophoresis in a 12% polyacrylamide mini-gel. Two sets of three lanes were loaded with 70-µg total protein per lane. After separation, the gel was cut into forty horizontal slices across the six lanes. Each horizontal slice (six lanes) was considered as one sample, and was 95 divided into half, giving two sets of three lane’s worth of material – three lane’s worth for each mass spectrometer, and a total of forty samples for each mass spectrometer. An image of the gel, showing the approximate position of the slices, is shown as Figure 3.4. Figure 3.4 A 12% mini-gel was used as the 40-slice gel in the first experiment. Scan of stained gel used for the first experiment. Values for the molecular weight markers are shown on the right side. Approximate cut points for the 40 fractions (slices) are shown on the left. The six lanes are identical – each loaded with 70-µg (total) protein. All six lanes were used for analysis. See text for details. Early results showed promise using the FT-ICR. Analysis of nine of the forty fractions showed that by using the Q-Star I was able to identify 321 proteins, however, by using the FT-ICR I was able to identify over 1,000 proteins with approximately 75% being identified 96 using more than one peptide. This difference was such that it was decided not to continue with the Q-Star analysis, and to concentrate efforts on analysis of data the FT-ICR. On the basis of the results from the earlier mass spectrometry experiments, and in discussion with other researchers who had had more experience with SILAC, it was decided to try a large format gradient gel, in an attempt to “drill deeper” into the proteome by using greater protein separation and further increasing the number of slices into which the lysate was fractionated. The large format gradient gel was used (see Materials & Methods – Chapter 2). For this large format gel, the well size was calculated to be approximately 20mm x 22 mm x 1.5 mm for a total volume of 660 mm 3 . Up to 500 !L could be loaded comfortably into a well of this volume. Using TF-1 whole cell lysate, a series of protein loads was tested - in this case 400-, 450-, 500-, 550-, 600-, 650-, 750- and 850-!g protein was loaded into wells. The results of the protein load tests indicated that acceptable resolution was maintained up to at least 600-!g protein load per well, but that beyond 600-!g protein load, the resolution appeared to diminish, with the visible bands becoming more blurred. It was determined that for the proposed series of experiments, the maximum protein load under these gel conditions would be 600-!g per well. The question arose as to whether there would be sufficient sodium dodecylsulfate (SDS) contained in the loading buffer to bind adequately to all the protein contained in the sample (inadequate SDS could be one explanation for the blurring observed at higher protein loads). Sample protein was determined to be 5.2 µg/µL, and so it was calculated that the ratio of 97 SDS to protein in sample prepared above was 2.6:1 – well above the 1.4:1 ratio required for complete binding of SDS to polypeptide. The blurring observed at the higher protein loads was interpreted as indicating that the gel was overloaded. For the second biological replicate using the FT-ICR, whole cell lysate was fractionated using a large-format polyacrylamide gradient gel, with gradient from 8%-16.5% acrylamide, prepared in-house. Two lanes of the large format gel were loaded with 600-µg total protein in each lane. Two lanes were loaded so that one could kept as a “back-up”, for use if necessary. After separation, the gel lane was cut into ninety horizontal slices, giving ninety samples. The gel was cut into ninety slices in order that no individual slice would contain too large a volume of gel, which might cause technical difficulties with the in-gel trypsin digestion and peptide extraction later in the sample handling. An image of the second gel and the approximate position of the slices is shown as Figure 3.5. 98 Figure 3.5 An 8% - 16.5% large format gel was used as the 90-slice gel for the second experiment. Approximate cut points for the 90 slices are shown on the image. Two lanes of a large format gel were loaded with 600-µg (total) protein in each lane. Two lanes were loaded so that one could kept as a “back-up”, for use if necessary. Only the contents of the right-hand lane were used for mass spectrometry analysis. The third biological replicate of the FT-ICR experiment also used a large format gradient gel. In this case the gel was cut into seventy horizontal slices. Cutting the gel into seventy slices was chosen as a compromise between the enhanced numbers of proteins identified by cutting the gel into more slices, and the instrument time required to run the number of samples on the mass spectrometer. An image of the third gel and the approximate position of the slices is shown as Figure 3.6. 99 Figure 3.6 An 8%-16.5% large format gradient gel was used for the 70-slice gel for the third experiment. Approximate cut points for the 70 slices are shown on the image. Two lanes of a large format gel were loaded with 600-µg (total) protein in each lane. Two lanes were loaded so that one could kept as a “back-up”, for use if necessary. Only the contents of the right-hand lane were used for mass spectrometry analysis. Gel slices from each of the three experiments were treated in the same way: slices were subjected to in-gel trypsin digestion and peptide handling as described in Chapter 2. Samples were separated using HPLC, on a reverse-phase chromatography column as a front end to the FT-ICR mass spectrometer. The final number of proteins identified and quantitated is shown in Table 3.1. These numbers represent the final totals, after data handling and processing as outlined below, and described in detail in the data handling section of Chapter 2. In table 3.1, protein identifications are based on two or more high confidence unique peptides, that is, the 100 number of peptides shown in the table refers to the actual number of peptides used in the identification of the proteins shown. Experiment #1 #2 #3 Number of slices 40 90 70 Number of proteins identified 1987 2186 2149 Number of peptides used in protein identification 24425 27067 27784 Table 3.1 The number of proteins identified in each experiment, and the number of peptides used to identify these proteins. See text in “Materials & Methods” for details of data handling used to identify these numbers. As can be observed from the table, the number of proteins identified follows the same pattern as the number of slices used in each experiment, however the differences in number identified are not as great as might be expected – only about a 10% increase in the number of proteins identified, from an approximately 125% increase in the number of slices used between experiments #1 and #2. It should be noted that the protein and peptide counts have been made after the following data handling procedures, which are covered in detail in Chapter 2: 101 i. removal of redundant peptides – only unique peptides are used in the analysis ii. Mascot score set to 25 iii. assignment of unique peptides to proteins in the most efficient manner by the Perl® script “finalList.pl”, which functions to find the smallest number of protein sequences that can be used to explain all the peptide data (see Chapter 2) iv. removal of protein entries that refer to keratin contamination v. removal of protein entries identified using only single peptides vi. removal of protein entries identified by only two peptides, where each peptides was not greater than seven amino acid residues in length vii. removal of protein entries where quantitation was based on artifact (noise), as determined by manual inspection of the spectra. These proteins were usually associated with ratios that were quite different from the normal range of ratios seen in this experimental design (for example, 20:1, or 1:16). For all proteins with “outlying” ratios, manual inspection of the spectra of all peptides used to identify the “outlying” proteins was undertaken. Spectra that contained obviously overlapping peaks were excluded from the ratio determination, and spectra 102 in which the signal-to-noise ratio was less than 3:1 were excluded from the ratio determination. viii. For any protein that was to be investigated in more detail (for example by immunoblotting) the spectra were reviewed manually to ensure that the quality of the mass spectrometry data was sufficient to warrant more detailed investigation. 3.3 Proteins Showing Consistent Changes in Concentration in Response to Cytokine Withdrawal The total number of proteins identified and quantitated in common in all three experiments, using two or more high confidence unique peptides, was 1451. Proteins showing important changes in concentration in response to cytokine withdrawal are presented in detail in appendices 1 and 2, and discussed below and in Chapters 4 and 5. In order to determine the level of change in protein concentration that might be important, I used the average change in protein concentration, and the standard deviation of the changes in concentration across all three experiments. From these I calculated concentration changes at different levels of “significance” - from a change that might occur by chance in 5% of instances to a change that might occur by chance in only 0.5% of instances. These calculations are shown below. The average of the mean i/w ratios from the three experiments was 0.99, with the standard deviation of 0.18. Using these figures, the following table (Table 3.2) shows the i/w ratios both greater than and less than 1.0 (no change in concentration) that are determined by the calculations above. Hence the i/w ratios shown represent a decrease and an increase in protein concentration for each of several levels of “confidence” from 95% 103 improbable by chance to 99.5% improbable by chance. The table also shows the number of proteins identified and quantitated at each of the levels. 104 i/w ratio cutoffs 13 C/ 12 C ratio Number of proteins identified and quantitated* Increased concentration 0.5% 0.54 1 1.0% 0.58 3 2.5% 0.65 13 5.0% 0.70 42 Decreased concentration 0.5% 1.46 3 1.0% 1.42 27 2.5% 1.35 50 5.0% 1.30 82 Table 3.2 Relative 13 C/ 12 C ratios at various likelihoods of chance. Probabilities that the change reported occurred at random; along with the number of proteins identified at each level. * numbers are cumulative – that is, the numbers shown at the extremes include the proteins at less extreme ratios. 3.3.1 Results Two tables of results are presented as appendices. These tables show details of the proteins of interest – proteins identified with a change in relative quantitation of up to 30% (increase or decrease) in starving cells compared with control cells. This represents all results at the level of 5% chance of occurring at random. Ratios are expressed as isotype/wild-type (i/w), or 13 C/ 12 C, that is, control/starving. The proteins identified in the tables have been found in all three experiments. In the tables, the column marked “avg (3) corrected i/w” shows the 105 average 13 C/ 12 C (i.e. control/starving) ratio for each protein in the three experiments. The tables were felt to be too large to be included with the text of this chapter, and so they are included as appendices, however, a list containing only the protein names and relative quantitation ratios is presented here. The list of 42 proteins shown to increase in response to cytokine withdrawal is given as Table 3.3 and the list of 82 proteins shown to decrease is given as Table 3.4. Appendix 1 contains the details of the proteins that showed an increase in concentration of 30% or more in response to cytokine withdrawal for fifteen hours, while Appendix 2 contains the details of the proteins determined to decrease in concentration by 30% or more in response to cytokine withdrawal. 106 Table 3.3 The list of proteins observed to increase in response to cytokine withdrawal. The i/w ratio is the inverse of the change in concentration. 107 ID Avg (3) corrected i/w CBX3; LOC653972 Chromobox protein homolog 3 0.52 PSMD10 26S proteasome non-ATPase regulatory subunit 10 0.55 HLTF Isoform 1 of Helicase-like transcription factor 0.56 HIST1H1B Histone H1.5 0.61 ELOVL1 Elongation of very long chain fatty acids protein 1 0.61 CDC42 Isoform 2 of Cell division control protein 42 homolog precursor 0.61 PDCD4 Programmed cell death protein 4 0.62 FECH ferrochelatase isoform a precursor 0.62 FUS Isoform Short of RNA-binding protein FUS 0.62 MGC4172 Isoform 1 of Dehydrogenase/reductase SDR family member 11 precursor 0.63 H3F3A;H3F3B Histone H3.3 0.63 ACSM3 Isoform 1 of Acyl-coenzyme A synthetase ACSM3 mitochondrial precursor 0.63 METTL7A Methyltransferase-like protein 7A precursor 0.65 ATP6V1H Isoform 2 of Vacuolar proton pump subunit H 0.65 52 kDa protein 0.66 ARHGDIB Rho GDP-dissociation inhibitor 2 0.66 HIST1H1D Histone H1.3 0.66 HIST1H1C Histone H1.2 0.66 NNT NAD(P) transhydrogenase mitochondrial precursor 0.67 C2orf47 Uncharacterized protein C2orf47 mitochondrial precursor 0.67 HMGB2 High mobility group protein B2 0.67 TXNRD1 Isoform 4 of Thioredoxin reductase 1 cytoplasmic 0.68 HDGF Hepatoma-derived growth factor 0.68 PSMF1 Proteasome inhibitor PI31 subunit 0.69 NME6 nucleoside diphosphate kinase type 6 0.69 GRPEL1 GrpE protein homolog 1 mitochondrial precursor 0.69 MRPL22 Isoform 1 of 39S ribosomal protein L22 mitochondrial precursor 0.69 ADH5 Alcohol dehydrogenase class-3 0.69 EXOSC8 Exosome complex exonuclease RRP43 0.69 FKBP3 FK506-binding protein 3 0.69 MT-CO2 Cytochrome c oxidase subunit 2 0.69 HCFC1 Isoform 1 of Host cell factor 0.69 RSL1D1 RSL1D1 protein 0.69 30 kDa protein 0.69 ALAD delta-aminolevulinic acid dehydratase isoform b 0.70 PDHB Isoform 1 of Pyruvate dehydrogenase E1 component subunit beta mitochondrial precursor 0.70 HMGB1 High mobility group protein B1 0.70 ANXA7 Isoform 1 of Annexin A7 0.70 CDC37 Hsp90 co-chaperone Cdc37 0.70 ATPAF2 ATP synthase mitochondrial F1 complex assembly factor 2 mitochondrial precursor 0.70 TMPO Lamina-associated polypeptide 2 isoform alpha 0.70 TAF15 Isoform Short of TATA-binding protein-associated factor 2N 0.70 108 Table 3.4 The list of proteins observed to decrease in response to cytokine withdrawal. The i/w ratio is the inverse of the change in concentration. 109 ID Avg (3) corrected i/w YTHDF3 YTH domain family protein 3 1.30 RBM26 Isoform 3 of RNA-binding protein 26 1.30 ZC3HAV1 Isoform 1 of Zinc finger CCCH type antiviral protein 1 1.30 PROSC Proline synthetase co-transcribed bacterial homolog protein 1.31 LRRC40 Leucine-rich repeat-containing protein 40 1.31 CLNS1A Methylosome subunit pICln 1.31 ARL1 ADP-ribosylation factor-like protein 1 1.31 PSME3 Isoform 1 of Proteasome activator complex subunit 3 1.31 SLC1A5 Neutral amino acid transporter B 1.31 DDX3X ATP-dependent RNA helicase DDX3X 1.31 VAMP7 Isoform 1 of Vesicle-associated membrane protein 7 1.31 CTNNBL1 Isoform 2 of Beta-catenin-like protein 1 1.31 STIM1 Stromal interaction molecule 1 precursor 1.32 SFRS2 Splicing factor arginine/serine-rich 2 1.32 HSPH1 Isoform Beta of Heat shock protein 105 kDa 1.32 PSMD4 Proteasome 1.32 GALE UDP-glucose 4-epimerase 1.32 LCP2 Lymphocyte cytosolic protein 2 1.33 CIAPIN1 Isoform 3 of Anamorsin 1.33 RAB21 Ras-related protein Rab-21 1.33 RDH11 Isoform 1 of Retinol dehydrogenase 11 1.34 ARHGEF1 Isoform 1 of Rho guanine nucleotide exchange factor 1 1.34 RNH1 Ribonuclease inhibitor 1.34 KIF2C Kinesin family member 2C 1.34 PMVK Phosphomevalonate kinase 1.34 DNAJC7 DnaJ homolog subfamily C member 7 1.34 CASP3 Caspase-3 precursor 1.35 NCAPG Condensin complex subunit 3 1.36 VPS26B Vacuolar protein sorting-associated protein 26B 1.36 SPCS3 Signal peptidase complex subunit 3 1.36 FHOD1 FH1/FH2 domain-containing protein 1 1.37 HSPBP1 Isoform 1 of Hsp70-binding protein 1 1.37 COPE Coatomer subunit epsilon 1.38 RAB27A Isoform Long of Ras-related protein Rab-27A 1.38 FADS2 Isoform 1 of Fatty acid desaturase 2 1.39 EIF3H Eukaryotic translation initiation factor 3 subunit 3 1.39 PSMG2 Proteasome assembly chaperone 2 1.39 TSR1 Pre-rRNA-processing protein TSR1 homolog 1.39 CSDE1 Isoform Short of Cold shock domain-containing protein E1 1.39 EIF2B4 eukaryotic translation initiation factor 2B subunit 4 delta isoform 3 1.39 CRKRS Isoform 1 of Cell division cycle 2-related protein kinase 7 1.40 RAB5B Ras-related protein Rab-5B 1.40 CDK6 Cell division protein kinase 6 1.40 SEC23A Protein transport protein Sec23A 1.41 GRB2 Isoform 1 of Growth factor receptor-bound protein 2 1.41 GLG1 Golgi apparatus protein 1 precursor 1.41 110 ID Avg (3) corrected i/w KPNA2 Importin subunit alpha-2 1.42 RTN3 Isoform 4 of Reticulon-3 1.42 LDHAL6A L-lactate dehydrogenase A-like 6A 1.42 GCLM Glutamate--cysteine ligase regulatory subunit 1.42 PDCL3 Phosducin-like protein 3 1.42 DKC1 Uncharacterized protein DKC1 1.42 HPCAL1 Hippocalcin-like protein 1 1.43 HECTD1 HECT domain containing 1 1.43 BTF3L4 Transcription factor BTF3 homolog 4 1.44 CPSF3 Cleavage and polyadenylation specificity factor subunit 3 1.44 HSPA1A;HSPA1B heat shock 70kDa protein 1A 1.45 HSPA1A;HSPA1B Heat shock 70 kDa protein 1 1.45 TRABD Isoform 1 of TraB domain-containing protein 1.45 HCLS1 Hematopoietic lineage cell-specific protein 1.45 PPIE Isoform A of Peptidyl-prolyl cis-trans isomerase E 1.45 PCTK1 Serine/threonine-protein kinase PCTAIRE-1 1.46 KIAA0664 Putative eukaryotic translation initiation factor 3 subunit 1.46 PPP1R14A Isoform 1 of Protein phosphatase 1 regulatory subunit 14A 1.47 EP300 Histone acetyltransferase p300 1.48 CREBBP CREB-binding protein 1.48 CDK4 Cell division protein kinase 4 1.50 PYGB Glycogen phosphorylase brain form 1.50 C6orf66 UPF0240 protein C6orf66 1.52 TRRAP Isoform 1 of Transformation/transcription domain-associated protein 1.54 ATAD1 ATPase family AAA domain-containing protein 1 1.54 SAPS1 Isoform 2 of SAPS domain family member 1 1.55 HSPA6 Heat shock 70 kDa protein 6 1.55 RANGAP1 Ran GTPase-activating protein 1 1.57 GBF1 Golgi-specific brefeldin A-resistance guanine nucleotide exchange factor 1 1.59 ACSL1 Isoform 1 of Long-chain-fatty-acid--CoA ligase 1 1.60 KIF2A Isoform 1 of Kinesin-like protein KIF2A 1.63 KIF11 Kinesin-like protein KIF11 1.67 HMGCS1 Hydroxymethylglutaryl-CoA synthase cytoplasmic 1.68 TAF9 Adenylate kinase isoenzyme 6 1.89 TYMS Thymidylate synthase 2.29 UCK2 Isoform 1 of Uridine-cytidine kinase 2 2.40 111 3.3.2 The Current Work in the Context of Previous Studies Several previous studies have reported proteomics analyses of cells undergoing apoptosis. While the discussion below is not meant to include every published study, enough have been chosen to highlight the difficulty of comparing studies with disparate methodologies and different cell types. Table 3.6 lists recent proteomics analyses, summarizing the details of each study. Table 3.7 lists proteins that were identified and quantitated in the current study, where these have been found in the published studies, and shows the quantitation ratio for each protein, where available. Differences will be readily apparent, both in the proteins identified in each case, and the relative quantitation ratios observed. Several reasons might be given to explain these differences, including the use of different cell types, different means of inducing apoptosis, different time along the path to apoptosis (see especially Gerner 2000 and Gerner 2002), different sample preparation and separation, and mass spectrometry analyses. Because of these differences, making a direct comparison between studies is difficult, however, some comments can be made, especially with reference to the work of Gerner et. al., who have published two analyses, using Jurkat cells in both studies, and using the same mechanism to induce apoptosis, but analyzed at different time points after the induction of apoptosis. From these two studies we find in a few cases, the same protein behaving in an opposite manner, for example, T-complex protein 1 (alpha) which goes down at five hours, but up eight hours after the induction of apoptosis; and heat shock protein 90kDa (alpha), which shows the same pattern. In the case of several other proteins, we observe no change at one time point, and an observable change at the other time point. These differences, observed using the same cells, with the same means of inducing apoptosis, and the same sample preparation and analysis, but at different time points after the induction 112 of apoptosis, are instructional, since they highlight the dynamic nature of apoptosis. Gerner claims that no viable cells were found three hours after treatment with CD-95 antibody, and the two papers published by his group used cells at 5 hours and at 8 hours after antibody treatment, so the differences observed must be attributed to something other than differences in cellular metabolism at different times along the path to apoptosis. In contrast, my study examined the proteome fifteen hours after the withdrawal of cytokine, where we found less than 28% of cells to have undergone apoptosis, when analyzed using propidium iodide flow cytometry. It is apparent that much richer information, and a clearer picture of the dynamic response of protein concentrations in response to cytokine withdrawal might be gleaned from a series of studies that analyze the proteome of cells at several time-points after the withdrawal of cytokine. Most of the proteins listed in Table 3.7 show little change in my study, and more apparent change in the published studies. However, there are fourteen proteins that do show a change of 30% or more up or down in my study, and have also been found in the published studies. These are marked in bold in Table 3.7. Of the fourteen, we observe twelve proteins showing an increase in concentration in my study and two showing a decrease. Comparing my results with others, nine of the fourteen proteins show an increase in my study and a decrease in the other studies, one decreases in my study and increases in another study, four are increased in my study and in other studies, and one is decreased in my study and another study. Note that the total is fifteen, since one protein, Rho GDP-dissociation inhibitor 2, changed both up and down in the previously published work. This material is summarized in Table 3.5. 113 Up/Dn * Dn/Up Up/Up Dn/Dn 9 1 4 1 Table 3.5. Direction of change of proteins showing greater than 30% change in Anthony, compared with other published results. *Anthony/Others What can be made of these differences? Most of the proteins found in common between my study and others, and showing a change of 30% or more in concentration, show an increase in my study, and a decrease in the other studies. Perhaps some of this might be attributed to the relatively early time-point I chose to study. In each of the papers quoted (with the exception of the papers by Hwang and Thiede, which do not indicate percentages of apoptotic cells) the authors have used samples containing at least 50% apoptotic cells. In my study, less than 28% of the cells used were apoptotic at 15 hours. With more viable cells, we expect more protein metabolism, as cells prepare to undergo apoptosis, and this is reflected in the larger number of proteins found to have increased concentrations in my study compared with the same proteins in the other studies. 114 F ir st A u th o r , Y e a r C e ll t y p e In d u c ti o n o f a p o p to si s C e ll u la r C o m p o n e n ts u se d L a b e ll in g S e p a r a ti o n M a ss S p e c tr o m e tr y T h ie d e, 2 0 0 6 ( 1 9 /3 1 ) Ju rk at T c el ls , li n e E 6 an ti -C D 9 5 x 6 h rs , 2 5 0 n g /m L w h o le c el l L eu -D 3 , L eu - D 0 2 D g el s M A L D I- T O F /T O F S ch m id t, 2 0 0 7 ( 1 5 /2 6 ) Ju rk at T c el ls , li n e E 6 ci sp la ti n x 1 6 h rs , 6 0 µ M w h o le c el l A rg -1 3 C 6 , A rg - 1 2 C 6 2 D g el s L C -M A L D I- T O F /T O F W in k el m an n , 2 0 0 8 (1 2 /2 0 ) H T -2 9 h u m an co lo re ct al c an ce r fl av o n e x 2 4 h rs , 1 5 0 µ M ; ca m p to th ec in x 2 4 h rs , 5 0 µ M w h o le c el l - 2 D g el s M A L D I- T O F ( P M F ) H w an g , 2 0 0 6 ( 2 4 /5 9 ) Ju rk at T c el ls , li n e A 3 an ti -F as I g M x 3 .5 h rs , 2 5 0 n g /m L n u cl ei L eu -1 2 C 6 ,L eu - 1 3 C 6 ;L y s- 1 2 C 6 1 4 N 2 ,L y s- 1 3 C 6 1 5 N 2 S D S -P A G E L T Q l in ea r io n t ra p G er n er , 2 0 0 2 ( 1 3 /2 6 ) Ju rk at T c el ls ; U 9 3 7 ; H eL a an ti -F as I g M 5 0 n g /m L ; 1 .2 5 µ M s ta u ro sp o ri n e; 5 µ M c am p to th ec in ; al l x 8 h rs n u cl ea r m at ri x - 2 D g el s M A L D I- T O F ( P M F ) G er n er , 2 0 0 0 ( 1 9 /3 2 ) Ju rk at T c el ls an ti -F as I g M 5 0 n g /m L ; 1 .2 5 µ M s ta u ro sp o ri n e; 5 µ M c am p to th ec in ; al l x 5 h rs w h o le c el ls ; cy to so l; n u cl ea r m at ri x - 2 D g el s M A L D I- T O F ( P M F ) R ee s- U n w in , 2 0 0 7 (1 2 /1 8 ) M M .1 S , M M .1 R h u m an m y el o m a 1 µ M /L d ex am et h as o n e + se ru m s ta rv at io n x 2 4 h rs w h o le c el l - 2 D g el s M A L D I- T O F /T O F C ec co n i, 2 0 0 8 ( 7 /3 2 ) Je k o -1 h u m an m y el o m a re sv er at ro l at co n ce n tr at io n = L D 7 0 x 8 o r 2 4 h rs w h o le c el l - 2 D g el s h ig h c ap ac ty i o n t ra p T a b le 3 .6 R e c e n t p r o te o m ic s p u b li c a ti o n s r e fe r r e d t o i n t h e t e x t, w it h d e ta il s o f e a c h s tu d y . T h e n u m b er s in p ar en th es es b eh in d t h e au th o r/ d at e in fo rm at io n r ef er t o t h e n u m b er o f p ro te in s fo u n d i n m y c u rr en t st u d y , co m p ar ed w it h t h e n u m b er r ep o rt ed a s d if fe re n t in t h e q u o te d s tu d y . 115 Table 3.7 Comparison of proteins and quantitation ratios found in the current study with previously published work. Proteins found in the current study and in two or more previously published studies are highlighted in grey. Quantitation ratios for the papers published by Gerner et.al in 2000 and 2002 are unavailable, as the authors note only an increase (up) or decrease (dn) in spot intensity in experimental versus control gels. Proteins with ratios in red in the Hwang study were found using a second or third lysis protocol (see Hwang for details). Proteins in bold show a change of 30% or more up or down in my study. T ab le 3 .7 C o m p ar is o n o f p ro te in s an d q u an ti ta ti o n r at io s fo u n d i n t h e cu rr en t st u d y w it h p re v io u sl y p u b li sh ed w o rk 116 I P I I D A n th o n y C ec co n i R ee s- U n w in G er n er 2 0 0 0 T h ie d e G er n er 2 0 0 2 S ch m id t W in k el m an n H w an g IP I: IP I0 0 0 0 0 0 1 5 .2 S F R S 4 S p li ci n g f ac to r ar g in in e/ se ri n e- ri ch 4 1 .0 6 1 .4 4 IP I: IP I0 0 0 0 0 8 7 4 .1 P R D X 1 P er o x ir ed o x in -1 1 .1 6 1 .5 6 IP I: IP I0 0 0 0 1 6 3 9 .2 K P N B 1 I m p o rt in s u b u n it b et a -1 0 .9 3 d n IP I: IP I0 0 0 0 3 4 3 8 .2 D N A J C 8 D n a J h o m o lo g s u b fa m il y C m e m b e r 8 1 .3 1 0 .9 9 IP I: IP I0 0 0 0 3 5 8 8 .1 E E F 1 E 1 E u k ar y o ti c tr an sl at io n el o n g at io n f ac to r 1 e p si lo n -1 1 .0 2 0 .2 1 IP I: IP I0 0 0 0 3 7 0 4 .4 R B M 4 I so fo rm 1 o f R N A -b in d in g p ro te in 4 0 .9 6 1 .6 7 IP I: IP I0 0 0 0 3 8 1 5 .3 A R H G D IA R h o G D P -d is so ci at io n in h ib it o r 1 1 .0 4 u p IP I: IP I0 0 0 0 3 8 1 7 .3 A R H G D IB R h o G D P -d is so c ia ti o n in h ib it o r 2 1 .5 2 d n ? 0 .3 6 2 .3 5 IP I: IP I0 0 0 0 3 8 8 1 .5 H N R P F H et er o g en eo u s n u cl ea r ri b o n u cl eo p ro te in F 1 .2 1 0 .6 5 IP I: IP I0 0 0 0 7 0 7 4 .5 Y A R S T y ro sy l- tR N A s y n th et as e cy to p la sm ic 1 .1 7 IP I: IP I0 0 0 0 7 6 1 1 .1 A T P 5 O A T P s y n th as e su b u n it O m it o ch o n d ri al p re cu rs o r 0 .9 7 2 .3 IP I: IP I0 0 0 0 7 7 5 0 .1 T U B A 4 A T u b u li n a lp h a- 4 A c h ai n 1 .0 6 0 .4 5 u p u p IP I: IP I0 0 0 0 7 7 5 2 .1 T U B B 2 C T u b u li n b et a- 2 C c h ai n 1 .0 3 0 .1 9 IP I: IP I0 0 0 0 7 7 6 5 .5 H S P A 9 S tr es s- 7 0 p ro te in m it o ch o n d ri al p re cu rs o r 0 .9 2 u p 2 .3 2 IP I: IP I0 0 0 0 8 5 2 4 .1 P A B P C 1 I so fo rm 1 o f P o ly ad en y la te - b in d in g p ro te in 1 0 .8 0 1 .9 1 IP I: IP I0 0 0 0 8 5 3 0 .1 R P L P 0 6 0 S a ci d ic r ib o so m al p ro te in P 0 1 .0 6 u p IP I: IP I0 0 0 0 9 3 1 6 .3 P P IE I so fo rm A o f P ep ti d y l- p ro ly l ci s- tr an s is o m er as e E 0 .6 9 1 .1 5 T ab le 3 .7 C o m p ar is o n o f p ro te in s an d q u an ti ta ti o n r at io s fo u n d i n t h e cu rr en t st u d y w it h p re v io u sl y p u b li sh ed w o rk 117 IP I I D A n th o n y C ec co n i R ee s- U n w in G er n er 2 0 0 0 T h ie d e G er n er 2 0 0 2 S ch m id t W in k el m an n H w an g IP I: IP I0 0 0 0 9 9 2 3 .1 P 4 H A 1 I so fo rm 1 o f P ro ly l 4 - h y d ro x y la se s u b u n it a lp h a -1 p re cu rs o r 0 .9 6 2 .9 IP I: IP I0 0 0 1 0 1 3 3 .3 C O R O 1 A C o ro n in -1 A 1 .1 8 2 .9 IP I: IP I0 0 0 1 0 4 7 1 .5 L C P 1 P la st in -2 1 .0 9 2 .3 4 IP I: IP I0 0 0 1 0 7 2 0 .1 C C T 5 T -c o m p le x p ro te in 1 s u b u n it ep si lo n 1 .0 8 u p 2 .4 9 IP I: IP I0 0 0 1 0 7 7 9 .4 T P M 4 I so fo rm 1 o f T ro p o m y o si n al p h a- 4 c h ai n 0 .8 4 0 .5 6 IP I: IP I0 0 0 1 1 4 1 6 .2 E C H 1 D el ta (3 5 )- D el ta (2 4 )- d ie n o y l- C o A i so m er as e m it o ch o n d ri al p re cu rs o r 1 .1 0 2 .6 8 IP I: IP I0 0 0 1 1 6 5 4 .2 T U B B T u b u li n b et a ch ai n 1 .0 6 0 .1 9 u p 2 .0 4 IP I: IP I0 0 0 1 1 9 3 7 .1 P R D X 4 P er o x ir ed o x in -4 1 .0 8 2 .6 1 IP I: IP I0 0 0 1 2 0 7 4 .3 H N R N P R H et er o g en eo u s n u cl ea r ri b o n u cl eo p ro te in R 1 .1 7 0 .1 8 IP I: IP I0 0 0 1 2 4 7 9 .1 N A C A P 1 N a sc e n t p o ly p e p ti d e - a ss o c ia te d c o m p le x s u b u n it a lp h a - li k e p r o te in 1 .3 4 2 .3 IP I: IP I0 0 0 1 3 2 1 4 .1 M C M 3 D N A r ep li ca ti o n l ic en si n g fa ct o r M C M 3 1 .0 3 d n IP I: IP I0 0 0 1 3 4 7 5 .1 T U B B 2 A T u b u li n b et a- 2 A c h ai n 1 .0 6 0 .1 9 IP I: IP I0 0 0 1 3 5 0 8 .5 A C T N 1 A lp h a- ac ti n in -1 0 .9 5 d n /u p u p IP I: IP I0 0 0 1 3 6 7 9 .1 D U T I so fo rm D U T -M o f D eo x y u ri d in e 5 '- tr ip h o sp h at e n u cl eo ti d o h y d ro la se m it o ch o n d ri al p re cu rs o r 0 .9 4 0 .4 5 IP I: IP I0 0 0 1 3 6 8 3 .2 T U B B 3 T u b u li n b et a- 3 c h ai n 1 .0 2 0 .1 9 IP I: IP I0 0 0 1 3 8 8 1 .6 H N R P H 1 H et er o g en eo u s n u cl ea r ri b o n u cl eo p ro te in H 1 .1 1 0 .4 4 0 .6 2 IP I: IP I0 0 0 1 4 2 6 3 .1 E IF 4 H ;L O C 6 5 3 9 9 4 I so fo rm L o n g o f E u k ar y o ti c tr an sl at io n i n it ia ti o n fa ct o r 4 H 0 .8 3 0 .3 8 T ab le 3 .7 C o m p ar is o n o f p ro te in s an d q u an ti ta ti o n r at io s fo u n d i n t h e cu rr en t st u d y w it h p re v io u sl y p u b li sh ed w o rk 118 IP I I D A n th o n y C ec co n i R ee s- U n w in G er n er 2 0 0 0 T h ie d e G er n er 2 0 0 2 S ch m id t W in k el m an n H w an g IP I: IP I0 0 0 1 5 9 5 2 .1 E IF 4 G 2 E u k ar y o ti c tr an sl at io n in it ia ti o n f ac to r 4 g am m a 2 0 .8 1 d n IP I: IP I0 0 0 1 6 6 1 0 .2 P C B P 1 P o ly (r C )- b in d in g p ro te in 1 0 .9 0 d n IP I: IP I0 0 0 1 7 3 3 4 .1 P H B P ro h ib it in 1 .1 6 d n IP I: IP I0 0 0 1 7 3 8 1 .1 R F C 4 R ep li ca ti o n f ac to r C s u b u n it 4 0 .8 9 0 .5 3 IP I: IP I0 0 0 1 8 2 0 6 .3 G O T 2 A sp ar ta te a m in o tr an sf er as e m it o ch o n d ri al p re cu rs o r 1 .0 3 u p IP I: IP I0 0 0 1 8 4 5 2 .1 C P N E 1 C o p in e- 1 1 .0 3 0 .4 8 IP I: IP I0 0 0 1 9 2 6 9 .3 W D R 6 1 W D r ep ea t- co n ta in in g p ro te in 6 1 0 .8 4 u p IP I: IP I0 0 0 1 9 3 7 6 .6 S E P T 1 1 S ep ti n -1 1 0 .9 4 2 .7 IP I: IP I0 0 0 1 9 5 0 2 .3 M Y H 9 M y o si n -9 0 .9 1 0 .2 5 IP I: IP I0 0 0 2 0 0 7 5 .4 A B H D 1 0 A b h y d r o la se d o m a in - c o n ta in in g p r o te in 1 0 m it o c h o n d r ia l p r e c u r so r 1 .3 5 2 .3 IP I: IP I0 0 0 2 0 9 0 6 .1 IM P A 1 I n o si to l m o n o p h o sp h at as e 0 .8 2 0 .2 2 IP I: IP I0 0 0 2 1 1 8 7 .4 R U V B L 1 I so fo rm 1 o f R u v B -l ik e 1 1 .1 4 3 .1 8 IP I: IP I0 0 0 2 1 4 0 5 .3 L M N A I so fo rm A o f L am in -A /C 1 .0 9 2 .8 8 IP I: IP I0 0 0 2 1 4 3 9 .1 A C T B A ct in c y to p la sm ic 1 1 .1 7 u p u p 4 .9 2 .4 5 IP I: IP I0 0 0 2 1 7 0 0 .3 P C N A P ro li fe ra ti n g c el l n u cl ea r an ti g en 1 .1 0 0 .3 1 u p IP I: IP I0 0 0 2 3 5 9 8 .2 T U B B 4 T u b u li n b et a- 4 c h ai n 0 .9 8 0 .1 9 IP I: IP I0 0 0 2 4 3 1 7 .1 G C D H I so fo rm L o n g o f G lu ta ry l- C o A d eh y d ro g en as e m it o ch o n d ri al p re cu rs o r 1 .1 5 2 .5 IP I: IP I0 0 0 2 4 8 7 1 .1 C B F B c o r e -b in d in g f a c to r b e ta su b u n it i so fo r m 1 1 .3 2 0 .4 7 IP I: IP I0 0 0 2 4 9 1 1 .1 E R P 2 9 E n d o p la sm ic r e ti c u lu m p r o te in E R p 2 9 p r e c u r so r 1 .3 0 0 .5 5 IP I: IP I0 0 0 2 5 2 5 2 .1 P D IA 3 P ro te in d is u lf id e -i so m er as e A 3 p re cu rs o r 1 .0 6 u p IP I: IP I0 0 0 2 5 8 1 5 .2 T A R D B P T D P 4 3 1 .1 2 IP I: IP I0 0 0 2 6 9 7 0 .4 F A C T c o m p le x s u b u n it S P T 1 6 1 .3 9 1 .3 6 T ab le 3 .7 C o m p ar is o n o f p ro te in s an d q u an ti ta ti o n r at io s fo u n d i n t h e cu rr en t st u d y w it h p re v io u sl y p u b li sh ed w o rk 119 IP I I D A n th o n y C ec co n i R ee s- U n w in G er n er 2 0 0 0 T h ie d e G er n er 2 0 0 2 S ch m id t W in k el m an n H w an g IP I: IP I0 0 0 2 7 1 0 7 .5 T U F M T u t ra n sl at io n e lo n g at io n fa ct o r m it o ch o n d ri al 1 .1 6 6 .7 2 IP I: IP I0 0 0 2 7 6 2 6 .3 C C T 6 A T -c o m p le x p ro te in 1 s u b u n it ze ta 0 .9 8 u p IP I: IP I0 0 0 3 0 1 3 1 .3 T M P O I so fo r m B e ta o f L a m in a - a ss o c ia te d p o ly p e p ti d e 2 is o fo r m s b e ta /g a m m a 1 .3 4 0 .6 4 IP I: IP I0 0 0 3 1 3 7 0 .3 T U B B 2 B T u b u li n b et a- 2 B c h ai n 1 .0 4 0 .1 9 IP I: IP I0 0 0 3 1 8 0 1 .4 C S D A I so fo rm 1 o f D N A -b in d in g p ro te in A 1 .0 7 2 .7 6 IP I: IP I0 0 0 3 3 1 3 0 .3 S A E 1 S U M O -a ct iv at in g e n zy m e su b u n it 1 1 .0 3 0 .4 8 IP I: IP I0 0 0 3 3 4 9 4 .3 M R L C 2 M y o si n r eg u la to ry l ig h t ch ai n 0 .8 4 1 .2 4 IP I: IP I0 0 0 9 3 0 5 7 .6 C P O X C o p ro p o rp h y ri n o g en I II o x id as e m it o ch o n d ri al p re cu rs o r 1 .1 3 u p IP I: IP I0 0 1 6 5 3 9 3 .1 A N P 3 2 E A ci d ic l eu ci n e- ri ch n u cl ea r p h o sp h o p ro te in 3 2 f am il y m em b er E 1 .0 5 IP I: IP I0 0 1 6 9 3 8 3 .3 P G K 1 P h o sp h o g ly ce ra te k in as e 1 1 .2 4 0 .4 2 IP I: IP I0 0 1 7 1 9 0 3 .2 H N R P M I so fo rm 1 o f H et er o g en eo u s n u cl ea r ri b o n u cl eo p ro te in M 1 .0 7 0 .5 4 IP I: IP I0 0 1 7 7 7 2 8 .3 C N D P 2 C y to so li c n o n -s p e c if ic d ip e p ti d a se 1 .3 4 0 .5 3 IP I: IP I0 0 1 7 7 8 1 7 .4 A T P 2 A 2 I so fo rm S E R C A 2 A o f S ar co p la sm ic /e n d o p la sm ic r et ic u lu m ca lc iu m A T P as e 2 1 .1 1 u p IP I: IP I0 0 1 7 8 4 4 0 .3 E E F 1 B 2 E lo n g at io n f ac to r 1 -b et a 1 .2 1 d n IP I: IP I0 0 1 8 0 9 5 4 .4 C IR B P C o ld -i n d u ci b le R N A -b in d in g p ro te in 0 .9 9 2 .4 7 IP I: IP I0 0 1 8 4 3 3 0 .5 M C M 2 D N A r ep li ca ti o n l ic en si n g fa ct o r M C M 2 1 .1 5 d n IP I: IP I0 0 1 8 6 2 9 0 .6 E E F 2 E lo n g at io n f ac to r 2 0 .9 0 u p 1 .5 3 1 .6 7 T ab le 3 .7 C o m p ar is o n o f p ro te in s an d q u an ti ta ti o n r at io s fo u n d i n t h e cu rr en t st u d y w it h p re v io u sl y p u b li sh ed w o rk 120 IP I I D A n th o n y C ec co n i R ee s- U n w in G er n er 2 0 0 0 T h ie d e G er n er 2 0 0 2 S ch m id t W in k el m an n H w an g IP I: IP I0 0 2 1 6 2 3 0 .3 T M P O L a m in a -a ss o c ia te d p o ly p e p ti d e 2 i so fo r m a lp h a 1 .4 2 d n IP I: IP I0 0 2 1 6 5 9 2 .2 H N R N P C I so fo r m C 1 o f H e te r o g e n e o u s n u c le a r r ib o n u c le o p r o te in s C 1 /C 2 1 .3 0 0 .1 8 d n IP I: IP I0 0 2 1 6 7 4 6 .1 H N R P K I so fo rm 2 o f H et er o g en eo u s n u cl ea r ri b o n u cl eo p ro te in K 1 .0 6 ? 0 .4 0 .4 9 IP I: IP I0 0 2 1 8 3 1 9 .3 T P M 3 I so fo rm 2 o f T ro p o m y o si n al p h a- 3 c h ai n 0 .9 0 0 .6 4 IP I: IP I0 0 2 1 8 3 4 3 .4 T U B A 1 C T u b u li n a lp h a- 1 C c h ai n 1 .0 4 0 .4 5 IP I: IP I0 0 2 1 8 7 7 5 .2 F K B P 5 F K 5 0 6 -b in d in g p ro te in 5 1 .1 9 3 IP I: IP I0 0 2 1 8 9 9 3 .1 H S P H 1 I so fo rm B et a o f H ea t sh o ck p ro te in 1 0 5 k D a 0 .7 6 d n IP I: IP I0 0 2 1 9 0 1 8 .7 G A P D H G ly ce ra ld eh y d e- 3 -p h o sp h at e d eh y d ro g en as e 0 .9 3 2 .1 6 IP I: IP I0 0 2 1 9 2 1 7 .3 L D H B L -l ac ta te d eh y d ro g en as e B ch ai n 1 .0 7 5 .1 IP I: IP I0 0 2 1 9 4 5 1 .1 A C O T 7 I so fo rm 5 o f C y to so li c ac y l co en zy m e A t h io es te r h y d ro la se 1 .1 0 1 .6 3 IP I: IP I0 0 2 1 9 7 5 7 .1 3 G S T P 1 G lu ta th io n e S -t ra n sf er as e P 1 .2 2 u p 5 .5 7 IP I: IP I0 0 2 1 9 9 9 4 .2 C S E 1 L I so fo rm 3 o f E x p o rt in -2 1 .0 3 2 IP I: IP I0 0 2 2 0 6 4 8 .5 P M V K P h o sp h o m ev al o n at e k in as e 0 .7 5 3 .5 IP I: IP I0 0 2 2 1 0 3 5 .4 B T F 3 I so fo rm 1 o f T ra n sc ri p ti o n fa ct o r B T F 3 0 .8 0 0 IP I: IP I0 0 2 4 6 0 5 8 .6 P D C D 6 IP P ro g ra m m ed c el l d ea th 6 - in te ra ct in g p ro te in 1 .0 7 u p IP I: IP I0 0 2 8 9 8 0 0 .7 G 6 P D I so fo rm S h o rt o f G lu co se -6 - p h o sp h at e 1 -d eh y d ro g en as e 1 .1 4 u p IP I: IP I0 0 2 9 0 5 6 6 .1 T C P 1 T -c o m p le x p ro te in 1 s u b u n it al p h a 0 .9 6 d n u p IP I: IP I0 0 2 9 1 5 1 0 .3 IM P D H 2 I n o si n e- 5 '- m o n o p h o sp h at e d eh y d ro g en as e 2 0 .9 7 0 0 .4 9 T ab le 3 .7 C o m p ar is o n o f p ro te in s an d q u an ti ta ti o n r at io s fo u n d i n t h e cu rr en t st u d y w it h p re v io u sl y p u b li sh ed w o rk 121 IP I I D A n th o n y C ec co n i R ee s- U n w in G er n er 2 0 0 0 T h ie d e G er n er 2 0 0 2 S ch m id t W in k el m an n H w an g IP I: IP I0 0 2 9 7 5 7 9 .4 C B X 3 ;L O C 6 5 3 9 7 2 C h r o m o b o x p r o te in h o m o lo g 3 1 .9 3 d n IP I: IP I0 0 2 9 7 7 7 9 .7 C C T 2 T -c o m p le x p ro te in 1 s u b u n it b et a 0 .9 9 2 .1 9 IP I: IP I0 0 2 9 9 0 0 0 .5 P A 2 G 4 P ro li fe ra ti o n -a ss o ci at ed p ro te in 2 G 4 1 .0 4 2 .2 4 IP I: IP I0 0 2 9 9 0 8 4 .1 T M E M 3 3 T ra n sm em b ra n e p ro te in 3 3 0 .8 4 1 .8 4 IP I: IP I0 0 3 0 2 9 2 7 .6 C C T 4 T -c o m p le x p ro te in 1 s u b u n it d el ta 0 .9 3 u p IP I: IP I0 0 3 7 6 7 9 8 .3 R P L 1 1 I so fo rm 1 o f 6 0 S r ib o so m al p ro te in L 1 1 0 .9 7 2 IP I: IP I0 0 3 8 2 4 7 0 .3 H S P 9 0 A A 1 h ea t sh o ck p ro te in 9 0 k D a al p h a (c y to so li c) cl as s A m em b er 1 is o fo rm 1 0 .9 0 d n u p IP I: IP I0 0 3 8 4 9 9 2 .7 M Y L 4 M y o si n l ig h t ch ai n 4 1 .1 7 d n IP I: IP I0 0 3 8 5 0 4 2 .4 G T P B P 4 N u cl eo la r G T P -b in d in g p ro te in 1 0 .9 3 2 .3 3 IP I: IP I0 0 3 9 5 6 2 7 .3 C A C Y B P I so fo rm 1 o f C al cy cl in - b in d in g p ro te in 1 .2 0 0 .6 5 IP I: IP I0 0 3 9 6 3 7 8 .3 H N R N P A 2 B 1 I so fo rm B 1 o f H et er o g en eo u s n u cl ea r ri b o n u cl eo p ro te in s A 2 /B 1 1 .1 8 0 .6 1 0 .6 5 2 .1 8 IP I: IP I0 0 3 9 6 4 8 5 .3 E E F 1 A 1 E lo n g at io n f ac to r 1 -a lp h a 1 0 .9 4 2 .3 5 IP I: IP I0 0 3 9 7 5 2 6 .2 M Y H 1 0 I so fo rm 1 o f M y o si n -1 0 1 .0 3 3 .8 6 IP I: IP I0 0 4 0 2 1 8 3 .2 S Y N C R IP I so fo rm 3 o f H et er o g en eo u s n u cl ea r ri b o n u cl eo p ro te in Q 0 .9 7 0 .6 IP I: IP I0 0 4 1 2 7 9 2 .2 B T F 3 L 4 T r a n sc r ip ti o n f a c to r B T F 3 h o m o lo g 4 0 .7 0 0 IP I: IP I0 0 4 1 4 6 7 6 .6 H S P 9 0 A B 1 H ea t sh o ck p ro te in H S P 9 0 -b et a 0 .9 3 1 9 .3 2 .4 IP I: IP I0 0 4 1 8 4 7 1 .6 V IM V im en ti n 1 .0 2 u p u p IP I: IP I0 0 4 4 9 0 4 9 .5 P A R P 1 P o ly [ A D P -r ib o se ] 1 .1 7 7 5 T ab le 3 .7 C o m p ar is o n o f p ro te in s an d q u an ti ta ti o n r at io s fo u n d i n t h e cu rr en t st u d y w it h p re v io u sl y p u b li sh ed w o rk 122 IP I I D A n th o n y C ec co n i R ee s- U n w in G er n er 2 0 0 0 T h ie d e G er n er 2 0 0 2 S ch m id t W in k el m an n H w an g p o ly m er as e 1 IP I: IP I0 0 4 5 2 7 4 7 .6 L O C 6 5 3 5 6 6 s im il ar t o S ig n al p ep ti d as e co m p le x s u b u n it 2 (M ic ro so m al s ig n al p ep ti d as e 2 5 k D a su b u n it ) (S P as e 2 5 k D a su b u n it ) is o fo rm 4 0 .8 3 1 .5 IP I: IP I0 0 4 5 5 1 3 4 .1 H N R P A 3 I so fo rm 2 o f H et er o g en eo u s n u cl ea r ri b o n u cl eo p ro te in A 3 1 .1 7 0 .4 6 IP I: IP I0 0 4 6 5 3 6 5 .4 H N R N P A 1 I so fo rm A 1 -A o f H et er o g en eo u s n u cl ea r ri b o n u cl eo p ro te in A 1 1 .0 7 0 .6 IP I: IP I0 0 4 7 9 2 1 7 .1 H N R N P U I so fo rm S h o rt o f H et er o g en eo u s n u cl ea r ri b o n u cl eo p ro te in U 1 .1 0 d n IP I: IP I0 0 5 5 4 7 3 7 .3 P P P 2 R 1 A S er in e/ th re o n in e -p ro te in p h o sp h at as e 2 A 6 5 k D a re g u la to ry su b u n it A a lp h a is o fo rm 1 .0 5 d n IP I: IP I0 0 6 0 4 6 2 0 .3 N C L I so fo rm 1 o f N u cl eo li n 1 .1 7 0 .5 5 IP I: IP I0 0 7 8 4 0 9 0 .2 C C T 8 T -c o m p le x p ro te in 1 s u b u n it th et a 0 .9 3 u p IP I: IP I0 0 7 8 4 1 5 4 .1 H S P D 1 6 0 k D a h ea t sh o ck p ro te in m it o ch o n d ri al p re cu rs o r 1 .0 9 u p IP I: IP I0 0 7 9 3 1 9 9 .1 A N X A 4 a n n ex in I V 0 .8 6 u p d n IP I: IP I0 0 7 9 6 3 3 7 .1 P C B P 2 p o ly (r C )- b in d in g p ro te in 2 is o fo rm a 0 .9 2 d n IP I: IP I0 0 7 9 7 1 2 6 .1 N A C A n as ce n t p o ly p ep ti d e- as so ci at ed c o m p le x a lp h a su b u n it is o fo rm a 0 .9 8 2 .0 2 IP I: IP I0 0 8 0 7 5 4 5 .1 H N R P K I so fo rm 3 o f H et er o g en eo u s n u cl ea r ri b o n u cl eo p ro te in K 1 .0 6 d n 0 .4 0 .4 9 IP I: IP I0 0 8 7 2 1 0 7 .1 IL F 2 I n te rl eu k in e n h an ce r b in d in g fa ct o r 2 v ar ia n t (F ra g m en t) 1 .2 5 0 .5 9 123 3.4 Concluding Remarks Cytokine-dependent hematopoietic cells require the continued presence of specific cytokines for survival and proliferation. I have used a hematopoietic cell line as a model system for studying cytokine-withdrawal apoptosis to further explore the system-wide changes that occur in these cells as a result of the withdrawal of growth factors. The goal was to identify as many changes as possible in the proteome in order to understand this process further. The methodology for performing the proteomic analysis was developed based on a series of earlier experiments that culminated in the final set of data used for further analysis, obtained from experiments done using an FT-ICR mass spectrometer. From the data obtained using the FT-ICR and presented in this thesis, it may be seen that it is possible to obtain a large degree of coverage of the proteome – in two separate experiments, I have been able to identify and quantitate over 2100 proteins (see Table 3.2). In three biological replicates of the experiment, and including only those proteins observed in all three experiments, I have been able to identify and quantitate 1451 proteins. Furthermore, criteria for inclusion of proteins in the final data set are stringent, as described above and in Chapter 2. In this way, I present this approach as a useful and valid means of identifying important changes in the cell undergoing apoptosis. Bioinformatic analysis and further validation of these data are discussed in the following chapters. This approach is a useful starting point, but has limitations in the coverage of the proteome achievable. Future studies in which cells are fractionated, for example into soluble cytosolic 124 fractions, membranes, and nuclear fractions, would require even more instrument time, but could potentially yield even more information. In summary, I have shown that by using one-dimensional gel electrophoresis and FT-ICR mass spectrometry in the analysis of whole cell lysates, that I can identify and quantitate a large fraction of the proteome. Many of the proteins identified are expected to be involved in apoptosis based on previously published work, however, many others have not previously been described as playing a role in the mechanism of apoptosis, and are discussed in more detail below. 125 4 Bioinformatics 4.1 Introduction Modern proteomics techniques allow for the generation of large amounts of data from a single experiment. Analysis of such large data sets would be challenging indeed, were it not for tools and techniques that facilitate the organization, handling and analysis of this material. Such tools and techniques fall under the purview of bioinformatics. The term “bioinformatics” was first coined in 1978 by Paulien Hogeweg and Ben Hesper as a means of labelling “the study of informatic processes in biotic systems” (Hogeweg & Hesper, 1978). However, the term did not fall into popular usage until the early 1990s (J. H. Moore, 2007). Since the late 1970s, the field has developed rapidly, and with advances in computational systems and molecular biology techniques, this multidisciplinary field now forges links between computer science, statistical techniques, and modeling and cell and molecular biology and physiology to facilitate the management and analysis of biological and physiological data. At its most basic level, bioinformatics in proteomics provides interrogation tools for protein databases to allow for the identification of large numbers of proteins based on mass spectrometry data; and allows “conversion” of the protein identities from one form into another, e.g. protein names from accession numbers; or protein function, sub-cellular location etc. from protein names. Bioinformatics also includes the tools and conceptual framework to address questions concerning data quality –identifying distortions or skewing of the data that could influence the interpretation of results. Perhaps more importantly, 126 bioinformatics tools allow the visualization of patterns within large data sets, allowing associations to be seen within the confusion of a set of perhaps thousands of proteins. Thus, use of bioinformatics approaches allows one to make discoveries or predictions about the biological system under study. One important aspect of the bioinformatics analysis of proteomics data is to associate proteins and protein expression information with cellular function. By categorizing proteins according to function, the biological implications of an alteration in protein expression may more easily be determined. As well, it becomes easier to assess the functional differences caused by changes in sub-sets of proteins – in this case the most up-regulated and down- regulated proteins. One way in which this categorization may be performed is by using Gene Ontologies – developed by the Gene Ontology consortium (Ashburner et al., 2000). Using this scheme, genes are classified according to three independent categories or ontologies; biological process, molecular function and cellular component. Genes, gene products or gene-product groups may be assigned to each of these three top-level ontologies, which are seen as representing independent attributes. Beneath these three top-level ontologies are multiple sub-levels of ontologies, grouped according to functional classification. One protein may be assigned to several different ontologies, an occurrence which recognizes that a particular protein may have functions in more than one biological process, or have domains which carry out differing molecular functions, or be located in various locations within the cell. As the levels go deeper, the number of ontologies increases. In this way a hierarchical structure is built up, in the shape of an inverted tree. Because of the hierarchical structure of 127 the gene ontology, known as a directed acyclic graph (DAG) structure, it is possible to view ontologies in the context of their “biological dependencies” (Conesa et al., 2005). Before any analysis of data from an “elevated” perspective – looking for patterns that help in describing system-wide changes observed in a high-throughput proteomics experiment - can be attempted, an important question to be addressed is the matter of false positive identifications. This question is addressed in the present chapter. However, I begin this chapter by examining the quality of the data generated in the biological replicates analyzed using the FT-ICR mass spectrometer. 4.2 Statistical Distribution of the Data As a first step in the bioinformatics analysis of the proteomics data presented in Chapter 3, it is useful to determine the statistical distribution of the quantitation ratios. The distribution of the quantitation ratios may be analyzed in two ways: a histogram, showing the frequency distribution of the natural log of the 13 C/ 12 C ratios; and a Probit curve showing the distribution of the natural log of the 13 C/ 12 C ratios (Bliss, 1934a, 1934b). Each of these visualizations allows for the determination of any deviation from normal distribution. Since the perturbation studied in these experiments (cytokine withdrawal) took place against a normal background, the changes induced are expected to affect only a portion of the proteome, hence the average value for the group of all quantitation ratios is expected to be “1”, with some subset of the proteome showing values that deviate from the average value. It 128 is these “deviations” that are of interest, since a large deviation from the average ratio indicates an important change, and one that is worth further investigation. A histogram showing the frequency distribution of the natural log of the quantitation ratios gives a visual representation of the distribution of the quantitation ratios. The Probit curve is another visual representation of the data, again showing the normal distribution of the quantitation ratios. These two graphs are presented in Figure 4.1, which shows the combined data from three experiments (since the proteins presented as being “of interest” were found in all three experiments, and the quantitation ratio for each protein presented is the average of three experiments). From these graphs, it may be seen that the data cluster around an isotype/wild-type (i.e. 13 C/ 12 C) ratio of 1 (natural log = 0), with a symmetrical shape to the distribution of values. This gives confidence that no important bias has been introduced during labelling or sample preparation. The sigmoid shape of the Probit curve is again consistent with data in a normal distribution, showing most ratios clustering around the 1:1 point, with a few outliers. The magnitude of the outlying ratios is not greater than about 3.5:1 (or the inverse). Once again, these graphs have been drawn from data that have been pre-processed in the way described in Chapter 2, hence the data shown in these graphs are referring only to those proteins that are considered to be “real” identifications and quantitations. 129 (a) Histogram showing the frequency distribution of the natural log of the 13 C/ 12 C ratios from the combined results of three experiments (b) Probit curve showing the distribution of the natural log of the 13 C/ 12 C ratios – three experiments, data combined. Figure 4.1 Histogram and Probit curve of data distribution. 4.3 Determination of False Positive Protein Identifications In reporting proteomics data, the question of false positive protein identifications needs to be addressed. A good starting point in addressing this question was published by the 130 “Working Group on Publication Guidelines for Protein Identification Data” in Molecular and Cellular Proteomics in 2004 (Carr et al., 2004). This document proposes that in order to allow some assessment of the rate of false positive protein identifications in reported data the following information must be provided: - a) The method or program used in the creation of the peak list. - b) The database search program and search parameters used. - c) Scores used to interpret MS/MS data, along with thresholds and values specific to judging the certainty of the identification. A description of any statistical analysis used to validate the results. - d) The name and version of the sequence database used. - e) Information regarding sequence coverage for each protein (ideally the sequence of each peptide used in the identification). - f) Sample mass spectra and associated information should be provided when biological conclusions are made based on observation of a single peptide matching to a protein. - g) Description of the measures taken to eliminate redundancy where the same protein appears in several places in the database under different names and accession numbers. - h) An explanation for why a single protein member of a multi-protein family is singled out. Other guidelines for the publication of data based on protein identification from observation of a single peptide are also presented, but are not relevant to the work 131 described here, because no proteins identified by a single peptide are included in the present analysis. Each of the suggested items will be addressed in turn. a) Method of peak list creation and database search parameters Raw data from the FT-ICR were first processed using DTASuperCharge (available from msquant.sourceforge.net). DTA is a Thermo Finnigan file format for a peak list file in the form of mass/intensity pairs that represent the raw data from the MS/MS spectra. DTASuperCharge converts the XCalibur binary (.RAW) files into peak list files, which can then be searched using Mascot (Matrix Science, UK) and providing output in a format suitable for later use with MSQuant. DTASuperCharge uses its own peak detection algorithm and charge state determination in processing the binary file. DTASuperCharge Fragment spectra reduction parameters included: maximum search level 5; Segment size (Th.) 200. DTASuperCharge processing parameters included: precursor tolerance (Th.) 0.15; bad precursor cut-off (Da) 15.0; use charge state from DTA file. 132 b) The database search program and parameters used In the analyses presented, the database search program used was Mascot (http://www.matrixscience.com), version 2.1 initially, and then version 2.2. Details of the search parameters used are presented in Chapter 2. c) Scores used to interpret MS/MS data. Statistical analysis used to validate the results – calculation of false discovery rate (FDR). In the results presented here, the Mascot threshold score was set at 25. According to the Mascot web-site, and in discussion with Richard Jacob, of Matrix Science, the Mascot threshold score is obtained in the following way. The threshold score is generated by a probability-based implementation of the MOWSE algorithm (Perkins et al., 1999). The score is -10 x log10(P) where (P) is the absolute probability that the result of a match between the peptide discovered and the database is a random match. For example, let the level of significance be set at < 0.05. In a theoretical digest of the database being queried, let, for example, 1.5 x 10 5 peptides fall within the tolerances set for the precursor mass of the peptide we are searching on. So the peptide discovered, is matched against 1.5 x 10 5 peptides generated in a theoretical digest from the database being queried. Here the absolute probability of a random match may be calculated as follows: P = 1/20 x 1/150,000 P = 1/(20 x 150,000) P = 1/(3 x 10 6 ) Hence P = 3.333 x 10 -7 133 From the absolute probability, the threshold score is calculated: Threshold score = -10 x log10 (P) Threshold score = -10 x -6.477 Threshold (probability) score = 65 In the experiments described here, the threshold score was set to 25. A score of 25 will imply a greater possibility that the match is random, than a score of say, 50, but this (lower) score was chosen so that I would not lose any potentially interesting identifications at this early stage in the data handling. The Matrix Sciences website provides more information about threshold scores. Importantly, “if the quality of an MS/MS spectrum is poor, particularly if the signal to noise ratio is low, a match to the \"correct\" sequence might not exceed [an] absolute threshold. Even so, the match to the correct sequence could have a relatively high score, which is well differentiated from the quasi-normal distribution of [e.g.] 1.5 x 10 5 random scores. In other words, the score is an outlier. This would indicate that the match is not a random event and, on inspection, such matches are often found to be either the correct match or a match to a close homologue.” With this in mind, I accepted 25 as the threshold score, as is commonly seen in the literature, with the understanding that the spectra of any proteins of interest would be manually inspected, and any doubtful protein assignment would be rejected. 134 4.3.1 Calculation of the False Discovery Rate (FDR) The concept of searching mass spectra against a decoy database in order to determine the likelihood of a random assignment of peptides to proteins was first proposed by Perkins et. al. in 1999 (Perkins et al., 1999). Decoy or randomized databases may be generated in one of three ways – (i) using the normal database in reverse sequence (Peng, Elias, Thoreen, Licklider, & Gygi, 2003), (ii) using the normal database with the sequence of amino acid residues randomized, but with arginine and lysine in the same relative positions, or (iii) a completely randomized database of amino acid residues (Elias & Gygi, 2007). FDR results from each are very similar, at least as reported by Matthiesen, using high quality Q-Star data (Matthiesen, 2007). Calculation of the FDR was carried out in the following manner, with help from Dr Leonard Foster, University of British Columbia. Peptides were searched using Mascot, against both the normal and decoy databases concurrently. In the analysis done here, the IPI_Human and the IPI_Human(decoy) databases were used. The Mascot search results were saved as an html file, and this file was then opened using MSQuant (http://msquant.sourceforge.net). Using MSQuant, the peptide information was exported as a spreadsheet from the Mascot html file. These spreadsheets were large, so columns of data not needed for the determination of the FDR were removed, leaving only the following information in the spreadsheet: Peptide Sequence Accession number MCR (Mass charge ratio) [Th] 135 Charge Score Left flanking AA Right flanking AA Calibrated mass relative error [ppm] Absolute mass accuracy The peptide sequence was sorted (alphabetically) and any redundant entries removed. The peptide entries were linked to accession numbers for the proteins with which the peptides were associated. The list was sorted according to the accession numbers. The peptides identified in the decoy database fall below the peptides from the forward or normal database in the sorted list. In this case, peptides identified in the decoy database were marked by the software with a series of hashes as a prefix to the accession number. A column was added in which the length of each peptide was counted. Peptides with a length of seven or fewer amino acid residues were discarded (i.e. were not included in the determination of FDR). The percentage of false positives was calculated by dividing the number of identifications obtained in the decoy database against the number of identifications obtained in the normal database. This process was carried out on a representative subset (just over 10 %) of the total sample set used in the three experiments. Results are presented in Table 4.1. The FDR is only 0.26% - very low in this data set, and comparing favourably with other published FDRs. This gives further confidence in the final list of protein identifications. 136 Table 4.1 Summary of details for calculation of False Discovery Rate (FDR). See text for description of data handling procedure. d) The name and version of the sequence database used. For these analyses, the database used was the IPI_human database, version 3.40 e) Information regarding sequence coverage for each protein (ideally the sequence of each peptide used in the identification). This information is presented in Appendices 1 and 2 for each of the proteins determined to have deviations greater than 30% from a quantitation ratio of “1”. f) Sample mass spectra and associated information should be provided when biological conclusions are made based on observation of a single peptide matching to a protein. Some sample mass spectra are included, showing the quality of the spectra, as well as providing a visualization of spectra from which the quantitation ratios were determined. Total peptides identified (22 samples) 20,823 Total non-redundant peptides identified 8740 Number of peptides identified in decoy database 23 False Discovery Rate (23/8740)x100 0.26% 137 However, it is worth reiterating that none of the protein identifications used in this work were based on only a single peptide. g) Description of the measures taken to eliminate redundancy where the same protein appears in several places in the database under different names and accession numbers. A full explanation of this process is given in Chapter 2 under “2.18. Data Handling”. h) An explanation for why a single protein member of a multi-protein family is singled out. Once again, a full explanation of this process is given in Chapter 2 under “2.18. Data Handling”. 4.4 Protein Quantitation – Ratio Testing In a usual quantitation experiment, the 12 C- and 13 C-labelled lysates are mixed in a 1:1 ratio and deviations from this 1:1 ratio imply a change in the relative amounts of the 12 C-labelled protein as compared with the 13 C-labelled protein under the experimental conditions. If a set of samples is prepared in which the lysates are mixed in ratios that are different from 1:1, then measured ratios may be compared with expected ratios, giving an indication of the robustness of the experimental technique. For this validation, 12 C- and 13 C-labeled lysates were mixed in ratios of 1:1, 2:1, 4:1 and 8:1. Using 12 C and 13 C-labelled samples previously prepared for a SILAC experiment and stored at -80° C, protein concentrations were determined by Bradford assay, and used to normalize 138 equal amounts of protein per sample. Approximately 70 µg total protein was loaded per lane (i.e. 35 µgm 12 C-labelled and 35 µgm 13 C-labelled). Relative protein quantitation was determined by using a protein that had earlier been shown not to change in response to our experimental conditions. In this case Heat-shock protein 90 (both ! and \" isoforms) was used (! isoform quantitation ratio 1.12 and \" isoform ratio 1.07). Results are presented in Table 4.2. Expected 12 C: 13 C ratio HSP-90 ! & \" Measured 12 C: 13 C ratio Difference Between Expected Ratio and Measured (%) 1:1! 0.97 3.4 1:1\" 0.97 2.7 2:1! 2.27 13.5 2:1\" 2.47 23.5 4:1! 3.91 2.25 4:1\" 3.83 4.25 8:1! 6.67 16.7 8:1\" 6.41 19.8 Table 4.2 Differences between measured ratios and expected ratios for HSP-! and \" at different mixture ratios of 12 C with 13 C. 139 As can be seen in Table 4.2, agreement between the measured and expected values for the ratio of 12 C: 13 C at the 1:1 and 4:1 ratios is within 5%. This is equivalent to the best values reported in the literature (Hwang et al., 2006a; Ong et al., 2003). The ratio at 2:1 is a little disappointing, in that the beta isoform shows results that vary between measured and expected by almost 25%. However, the alpha isoform results from the same gel show less than 15% variability. Perhaps it is worth noting that in the original experiment, in which the HSP ! and \" values were determined to be 1.12 and 1.07 respectively, that is, a difference of approximately 10% between measured and expected results occurred in what should have been a 1:1 mixing of lysates. In this light, the 25% variability does not look so discouraging. The small variability of the differences between the 1:1 and 4:1 gives some suggestion that the disappointing results at 2:1 are likely to be due to error. In the current series of experiments, no protein ratio was greater than 3.5:1, and so the 8:1 values are less relevant to the current data. Of note is that the signal-to-noise ratio of the MS data used in these analyses is such that a difference in ratio of greater than about 10:1 is not expected to be seen (Dr. Ron Beavis, University of British Columbia, personal communication). This analysis was performed only once; multiple replicates of the experiment could have been done to provide some sense of reproducibility, however, this was not deemed necessary, since the concentration experiments were performed in three biological replicates, and the averages of the changes observed were used. 140 4.5 Determining Levels of Significance Reference to the histogram and Probit curve in Figure 4.1 will demonstrate that the data generated by these experiments follow a normal distribution. Since the data appear to follow a normal distribution, it becomes possible to determine “cut-offs” at different points of deviation of the ratios from “1”, where the probabilities that the likelihood of the ratio deviation from the average (1) is a random event. These “cut-offs” are determined by the average (1) and the standard deviation of the quantitation ratios. The number of standard deviations for each level of probability is shown in Table 4.3. Likelihood of ratio being a random event # SDs 5% each tail 1.64 2.5% each tail 1.96 1% each tail 2.3 0.5% each tail 2.6 Table 4.3 Number of standard deviations for different levels of significance – normally distributed data. In our data, the standard deviation for the average i/w ratio of the three experiments combined was 0.18, and the mean was 1.0. Based on these figures, the quantitation ratios representing the different levels of probability are shown in Table 4.4. The data presented in 141 Chapter 3 show proteins with quantitation ratios that differ from average by 30% or more – that is, ratios that are 5% or less likely to have occurred by random. Left Tail i/w ratio 0.54 0.58 0.64 0.70 Likelihood of random event 0.5% 1.0% 2.5% 5% Right Tail i/w ratio 1.3 1.35 1.42 1.6 Likelihood of random event 5% 2.5% 1% 0.5% Table 4.4 Cut-offs for i/w ratios at different levels of significance. 4.6 Protein Categorization by Gene Ontology (GO). The proteins identified and quantitated in these experiments were analyzed according to gene ontology, using Blast2GO (B2G) software (Conesa et al., 2005). This software functions in the following way. Lists of proteins are converted to fasta format, as described in Chapter 2. B2G performs a BLAST query (Altschul, Gish, Miller, Myers, & Lipman, 1990) to find homologs to the fasta formatted input sequences. B2G then maps GO terms to each obtained result. An annotation rule finally assigns GO terms to each protein query. Results may then be filtered in several ways, and a variety of graphical representations of the results produced. 142 In our case, differences in biological process and molecular function may be visualized – comparing normal conditions with conditions of cytokine withdrawal. The structure of the gene ontologies is that of an inverted tree. With three ontologies at the highest level, the tree branches out as we descend from the top towards the bottom of the tree. The three top-level ontologies are Biological Process, Molecular Function and Cellular Component. (B2G in its current incarnation will allow visualization of only the first two of these ontologies.) Under each of these three top-level ontologies are further levels of ontologies, and under each further term (one level below) are still more ontologies. The tree continues to divide and become more tightly descriptive for eight or more levels. For illustrative purposes, the top level Biological Process ontology, and the ontologies in the next level under the Biological Process ontology are shown below. For the first of these second level ontologies, “anatomical structure formation”, the third level ontologies are shown on this page and the page following. The tree quickly becomes unwieldy, and so only these selected levels are shown, to illustrate the structure of the ontology hierarchy. GO:0008150 : biological_process [167944 gene products] o GO:0010926 : anatomical structure formation [5510 gene products] ! GO:0048646 : anatomical structure formation involved in morphogenesis [1639 gene products] ! GO:0022607 : cellular component assembly [4450 gene products] ! GO:0001325 : formation of extrachromosomal circular DNA [0 gene products] o GO:0022610 : biological adhesion [1594 gene products] o GO:0065007 : biological regulation [32500 gene products] 143 o GO:0001906 : cell killing [236 gene products] o GO:0009987 : cellular process [80961 gene products] o GO:0032502 : developmental process [21353 gene products] o GO:0051234 : establishment of localization [15690 gene products] o GO:0040007 : growth [4724 gene products] o GO:0002376 : immune system process [2887 gene products] o GO:0051179 : localization [18621 gene products] o GO:0040011 : locomotion [4010 gene products] o GO:0008152 : metabolic process [61760 gene products] o GO:0051704 : multi-organism process [5388 gene products] o GO:0032501 : multicellular organismal process [22224 gene products] o GO:0048519 : negative regulation of biological process [6050 gene products] o GO:0043473 : pigmentation [270 gene products] o GO:0048518 : positive regulation of biological process [7730 gene products] o GO:0050789 : regulation of biological process [30152 gene products] o GO:0000003 : reproduction [7451 gene products] o GO:0022414 : reproductive process [5530 gene products] o GO:0050896 : response to stimulus [19897 gene products] o GO:0048511 : rhythmic process [443 gene products] o GO:0016032 : viral reproduction [743 gene products] Using B2G, it is possible to assign proteins found in an experiment to their various ontologies, and to view these assigned ontologies at whatever level of the hierarchical tree is desired. If, for example, one chose to view the ontologies into which proteins were assigned, and to do so using the top-most level, one would find that (virtually) all proteins were assigned to each of the top three ontologies, since (almost) all proteins will have been assigned a biological process, a molecular function and a cellular location (component). Experience has shown that by describing the assignment of proteins to ontologies using the 144 ontologies found at the fourth level down from the top of the hierarchy (the so-called Level 4 gene ontologies) that patterns may be seen to emerge, and which may be used to describe the changes identified in an experiment. Figures 4.2 and 4.3 show graphical representations of the Level 4 gene ontologies into which proteins identified in three biological replicates of a cytokine withdrawal experiment have been assigned. Figure 4.2 shows ontologies into which proteins observed to increase in concentration have been assigned, while Figure 4.3 shows ontologies into which proteins observed to decrease in concentration have been assigned. Of the eleven ontologies observed in the group “increased protein concentration”, eight are concerned with biosynthesis or cellular metabolism, and the remaining three are involved in cellular organizational processes. One possible explanation for an increase in the concentration of proteins subsequently assigned to ontologies related to biosynthesis or cellular metabolism as the cell prepares to undergo apoptosis is that these processes might be suppressed in an active way; that is, that biosynthesis or cellular metabolism might be suppressed by proteins functioning in an inhibitory manner. Another possible explanation for the observed increase in some proteins related to biosynthesis or cellular metabolism could encompass a “rescue” mechanism, as a response to a perceived lack of nutrients or resources under conditions of cytokine withdrawal. The thirty-two ontologies into which proteins decreasing in concentration have been assigned may be grouped as follows: metabolic or catabolic processes (8), positive or negative 145 regulation of cellular processes (5), cellular “machinery” – component assembly, transport etc (9), cell cycle specific (2), cell death specific (2), and other (5). The difference between the numbers of ontologies in the two categories (increasing and decreasing) may be at least partly attributed to the idea that as the cell undergoes preparation for apoptosis, many more proteins will be switched “off” than will be switched “on”, and so this imbalance is not unexpected. 146 F ig u r e 4 .2 L e v e l 4 B io lo g ic a l P r o c e ss g e n e o n to lo g ie s o f su b se t o f p r o te in s sh o w in g a n i n c r e a se i n c o n c e n tr a ti o n i n r e sp o n se t o c y to k in e w it h d r a w a l. L ev el 4 G O s ar e o n to lo g ie s fo u n d a t th e fo u rt h l ev el d o w n f ro m t h e to p l ev el o f th e h ie ra rc h y – i n t h is c as e th e B io lo g ic al P ro ce ss o n to lo g y P er ce n ta g es re la te t o a t o ta l n u m b er o f p ro te in s in t h e su b se t eq u al t o 1 0 5 . T h e to ta l n u m b er i n cl u d es p ro te in s as si g n ed t o m o re t h an o n e o n to lo g y . 147 F ig u r e 4 .3 L e v e l 4 B io lo g ic a l P r o c e ss g e n e o n to lo g ie s o f su b se t o f p r o te in s sh o w in g a d e c r e a se i n c o n c e n tr a ti o n i n r e sp o n se t o c y to k in e w it h d r a w a l. L ev el 4 G O s ar e o n to lo g ie s fo u n d a t th e fo u rt h l ev el d o w n f ro m t h e to p l ev el o f th e h ie ra rc h y – i n t h is c as e th e B io lo g ic al P ro ce ss o n to lo g y P er ce n ta g es a re b as ed o n a t o ta l n u m b er o f p ro te in s in t h e su b se t eq u al t o 3 4 9 . T h e to ta l n u m b er i n cl u d es p ro te in s as si g n ed t o m o re t h an o n e o n to lo g y . 148 4.6.1 Biological Process Proteins from the total set (n=1440) were classified into 458 ontologies using level 4 GO terms, for biological process. The sub-set of proteins with the greatest apparent increases in concentration (most “up-regulated”) (i/w ratio ! 0.70, n=43) fell into 11 ontologies using level 4 GO terms, biological process. The subset of proteins with the greatest apparent decreases in concentration (most “down-regulated”) (i/w ratio \" 1.30, n=82) fell into 32 ontologies using level 4 terms, biological process. Patterns become visible when observing the distribution of proteins between level 4 GO terms under biological processes. The complete set of all proteins identified and quantitated was distributed across 458 ontologies, with a fraction of the total being classified under each ontology. This pattern of classification across ontologies becomes different when observing only the proteins with the greatest apparent changes in concentration (up or down). Firstly, fewer than 458 ontologies as seen in the complete protein set were observed when considering only the protein subset represented by the proteins with the greatest changes in concentration in response to cytokine withdrawal. Secondly, and perhaps more importantly, the emphasis shifts between ontologies – the fraction of the complete protein set found in each ontology is different from the fraction of the protein sub-set of greatest concentration changes. Using gene ontology analysis, it is possible to visualize a number of differences in biological process trends between the complete protein set and the sub-set consisting of those proteins most increased and decreased in concentration in response to cytokine-withdrawal. These differences are shown graphically in Figure 4.4, and give an insight into alterations of cellular function in response to cytokine withdrawal. The raw data from which Figure 4.4 is 149 drawn are shown in Table 4.5. Discussion of the details of these observed changes follows the figure. 150 F ig u r e 4 .4 P r o te in c a te g o r iz a ti o n b y g e n e o n to lo g y ( i) – c a te g o r iz a ti o n b y l e v e l 4 t e r m s o f a b io lo g ic a l p r o c e ss d ir e c te d a c y c li c g r a p h . T h e g ra p h s h o w s p ro p o rt io n o f p ro te in s fo u n d u n d er e ac h t er m c o m p ar in g t h e to ta l p ro te in s et ( 1 4 5 1 p ro te in s, b lu e) , w it h t h e m o st u p -r eg u la te d p ro te in s (4 2 p ro te in s, r ed ). O n to lo g ie s sh o w n i n w h ic h t h e fr ac ti o n a ss ig n ed t o u p -r eg u la te d p ro te in s is g re at er t h an t h e fr ac ti o n f ro m t o ta l p ro te in s. 151 F ig u r e 4 .5 P r o te in c a te g o r iz a ti o n b y g e n e o n to lo g y ( ii ) – c a te g o r iz a ti o n b y l e v e l 4 t e r m s o f a b io lo g ic a l p r o c e ss d ir e c te d a c y c li c g r a p h . T h e g ra p h s h o w s p ro p o rt io n o f p ro te in s fo u n d u n d er e ac h t er m c o m p ar in g t h e to ta l p ro te in s et ( 1 4 5 1 p ro te in s, b lu e) , w it h t h e m o st u p -r eg u la te d p ro te in s (4 2 p ro te in s, r ed ). O n to lo g ie s sh o w n i n w h ic h t h e fr ac ti o n f ro m u p -r eg u la te d p ro te in s is l es s th an t h e fr ac ti o n f ro m t o ta l p ro te in s. 152 T e r m N u m b e r s o f p r o te in s F r a c ti o n o f p r o te in s p e r t e r m i n e a c h s e t U p c f. A ll % d if fe r e n c e A ll U p A ll U p n u cl eo b as e, n u cl eo si d e, n u cl eo ti d e an d n u cl ei c ac id m et ab o li c p ro ce ss 3 6 7 1 3 0 .2 5 0 .3 0 1 1 8 .6 2 o rg an el le o rg an iz at io n a n d b io g en es is 2 3 8 1 5 0 .1 7 0 .3 5 2 1 1 .0 6 m ac ro m o le cu la r co m p le x a ss em b ly 1 2 0 9 0 .0 8 0 .2 1 2 5 1 .1 6 n eg at iv e re g u la ti o n o f b io lo g ic al p ro ce ss 1 4 7 6 0 .1 0 0 .1 4 1 3 6 .6 9 ce ll u la r co m p o n en t as se m b ly 1 2 5 9 0 .0 9 0 .2 1 2 4 1 .1 2 re g u la ti o n o f ce ll u la r m et ab o li c p ro ce ss 2 3 0 1 1 0 .1 6 0 .2 6 1 6 0 .1 6 n eg at iv e re g u la ti o n o f ce ll u la r p ro ce ss 1 4 3 6 0 .1 0 0 .1 4 1 4 0 .5 1 ce ll u la r b io sy n th et ic p ro ce ss 2 2 9 9 0 .1 6 0 .2 1 1 3 1 .6 1 T a b le 4 .5 R a w d a ta f r o m w h ic h F ig u r e 4 .4 h a s b e e n d r a w n . N u m b er s o f p ro te in s as si g n ed t o e ac h o n to lo g y f ro m t h e to ta l p ro te in l is t ( to ta l = 1 4 4 0 ) (A ll ), a n d f ro m t h e su b se t o f p ro te in s th at s h o w i n cr ea se d co n ce n tr at io n (t o ta l = 4 3 )( U p ). 153 T e r m N u m b e r s o f p r o te in s F r a c ti o n o f p r o te in s p e r t e r m i n e a c h s e t U p c f. A ll % d if fe r e n c e A ll U p A ll U p b io p o ly m er m et ab o li c p ro ce ss 4 5 3 1 1 0 .3 1 0 .2 6 8 1 .3 2 ce ll u la r m ac ro m o le cu le m et ab o li c p ro ce ss 4 5 2 7 0 .3 1 0 .1 6 5 1 .8 6 p ro te in m et ab o li c p ro ce ss 4 7 7 9 0 .3 3 0 .2 1 6 3 .1 9 T a b le 4 .6 R a w d a ta f r o m w h ic h F ig u r e 4 .5 h a s b e e n d r a w n . N u m b er s o f p ro te in s as si g n ed t o e ac h o n to lo g y f ro m t h e to ta l p ro te in l is t (t o ta l = 1 4 4 0 ) (A ll ), a n d f ro m t h e su b se t o f p ro te in s th at s h o w i n cr ea se d co n ce n tr at io n ( to ta l = 4 2 )( U p ). 154 4.6.1.1 Total Protein Compared with Subset that Shows “Increased Concentration” – Biological Process (see figure 4.4) When the complete protein set was compared with the subset of proteins that showed increased concentration, the subset of “increased” proteins showed a greater than 100% relative increase in the fraction of proteins assigned to the ontologies, “organelle organization and biogenesis”, “macromolecular complex assembly,” and “cellular component assembly”. The list of proteins assigned to each of these ontologies follows, and individual proteins are discussed in more detail at the end of each section (the information is presented in this way in all the following comparisons to avoid repeating information on proteins which appear in more than one ontology). The information for proteins assigned to the ontologies for Biological Process as well as those for Molecular Function are presented together. The assignment of several proteins to more than one ontology bears commenting on. If many proteins appear in greater than one ontology in the same analysis, does this indicate that these proteins are really involved in each of the assigned ontologies – at the same time? Or is this an effect resulting from the categorization of cellular biological processes and molecular functions into gene ontologies? I don’t believe we have an answer to this question, and yet the question goes to the heart of the process of assigning proteins to gene ontologies. 155 (i) Organelle Organization and Biogenesis The following proteins were assigned to this ontology from the subset of proteins showing increased concentration in response to cytokine withdrawal. RHO GDP-DISSOCIATION INHIBITOR 2 ISOFORM 2 OF CELL DIVISION CONTROL PROTEIN 42 HOMOLOG GRPE PROTEIN HOMOLOG 1, MITOCHONDRIAL LAMINA-ASSOCIATED POLYPEPTIDE 2 ISOFORM ALPHA HISTONE H1.2 HISTONE H1.3 HISTONE H1.5 HISTONE H3.3 HIGH MOBILITY GROUP PROTEIN B2 CHROMOBOX PROTEIN HOMOLOG 3 ISOFORM 1 OF HELICASE-LIKE TRANSCRIPTION FACTOR ISOFORM 2 OF V-TYPE PROTON ATPASE SUBUNIT H ISOFORM 1 OF 39S RIBOSOMAL PROTEIN L22, MITOCHONDRIAL HIGH MOBILITY GROUP PROTEIN B1 EXOSOME COMPLEX EXONUCLEASE RRP43 (ii) Macromolecular Complex Assembly The following proteins were assigned to this ontology from the subset of proteins showing increased concentration in response to cytokine withdrawal. ISOFORM 2 OF CELL DIVISION CONTROL PROTEIN 42 HOMOLOG ISOFORM 1 OF HOST CELL FACTOR HISTONE H1.2 HISTONE H1.3 HISTONE H1.5 HISTONE H3.3 HIGH MOBILITY GROUP PROTEIN B2 ATP SYNTHASE MITOCHONDRIAL F1 COMPLEX ASSEMBLY FACTOR 2 156 HIGH MOBILITY GROUP PROTEIN B1 (iii) Cellular Component Assembly The following proteins were assigned to this ontology from the subset of proteins showing increased concentration in response to cytokine withdrawal. ISOFORM 2 OF CELL DIVISION CONTROL PROTEIN 42 HOMOLOG ISOFORM 1 OF HOST CELL FACTOR HISTONE H1.2 HISTONE H1.3 HISTONE H1.5 HISTONE H3.3 HIGH MOBILITY GROUP PROTEIN B2 ATP SYNTHASE MITOCHONDRIAL F1 COMPLEX ASSEMBLY FACTOR 2 HIGH MOBILITY GROUP PROTEIN B1 Following cytokine withdrawal, the subset of “increased” proteins showed a 60% relative increase in the fraction of proteins that are involved in “regulation of cellular metabolic process”, and a 30-40% relative increase in the number of proteins in “negative regulation of biological process”, “negative regulation of cellular process”, and “cellular biosynthetic process” when compared with total proteins. The list of proteins assigned to each of these ontologies follows, and individual proteins are discussed in more detail at the end of this section. 157 (i) Regulation of Cellular Metabolic Process The following proteins were assigned to this ontology from the subset of proteins showing increased concentration in response to cytokine withdrawal. 26S PROTEASOME NON-ATPASE REGULATORY SUBUNIT 10 ISOFORM 1 OF HOST CELL FACTOR LAMINA-ASSOCIATED POLYPEPTIDE 2 ISOFORM ALPHA HISTONE H1.3 HISTONE H1.5 HIGH MOBILITY GROUP PROTEIN B2 ISOFORM SHORT OF RNA-BINDING PROTEIN FUS PROGRAMMED CELL DEATH PROTEIN 4 CHROMOBOX PROTEIN HOMOLOG 3 ISOFORM 1 OF HELICASE-LIKE TRANSCRIPTION FACTOR HIGH MOBILITY GROUP PROTEIN B1 RHO GDP-DISSOCIATION INHIBITOR 2 ISOFORM 2 OF CELL DIVISION CONTROL PROTEIN 42 HOMOLOG NUCLEOSIDE DIPHOSPHATE KINASE TYPE 6 PROGRAMMED CELL DEATH PROTEIN 4 CHROMOBOX PROTEIN HOMOLOG 3 HIGH MOBILITY GROUP PROTEIN B1 (ii) Negative Regulation of Biological Process RHO GDP-DISSOCIATION INHIBITOR 2 ISOFORM 2 OF CELL DIVISION CONTROL PROTEIN 42 HOMOLOG NUCLEOSIDE DIPHOSPHATE KINASE TYPE 6 PROGRAMMED CELL DEATH PROTEIN 4 CHROMOBOX PROTEIN HOMOLOG3 HIGH MOBILITY GROUP PROTEIN B1 158 (iii) Negative Regulation of Cellular Process The following proteins were assigned to this ontology from the subset of proteins showing increased concentration in response to cytokine withdrawal. RHO GDP-DISSOCIATION INHIBITOR 2 ISOFORM 2 OF CELL DIVISION CONTROL PROTEIN 42 HOMOLOG NUCLEOSIDE DIPHOSPHATE KINASE TYPE 6 PROGRAMMED CELL DEATH PROTEIN 4 CHROMOBOX PROTEIN HOMOLOG 3 HIGH MOBILITY GROUP PROTEIN B1 (iv) Cellular Biosynthetic Process The following proteins were assigned to this ontology from the subset of proteins showing increased concentration in response to cytokine withdrawal. ISOFORM 1 OF PYRUVATE DEHYDROGENASE E1 COMPONENT SUBUNIT BETA, MITOCHONDRIAL ELONGATION OF VERY LONG CHAIN FATTY ACIDS PROTEIN 1 NUCLEOSIDE DIPHOSPHATE KINASE TYPE 6 ISOFORM 1 OF ACYL-COENZYME A SYNTHETASE ACSM3, MITOCHONDRIAL ISOFORM 1 OF 39S RIBOSOMAL PROTEIN L22, MITOCHONDRIAL HIGH MOBILITY GROUP PROTEIN B1 FERROCHELATASE ISOFORM A PRECURSOR RSL1D1 PROTEIN DELTA-AMINOLEVULINIC ACID DEHYDRATASE Proteins that increase in response to cytokine withdrawal – subset showing decreased representation in level 4 gene ontologies. Following cytokine withdrawal, the subset of “increased” proteins showed a 50-60% relative decrease in the fraction of proteins that are involved in “cellular macromolecule metabolic 159 process” and “protein metabolic process” when compared with the complete protein set. (see Figure 4.5. The raw data from which Figure 4.5 has been drawn are shown in Table 4.4). The list of proteins assigned to each of these ontologies follows, and individual proteins are discussed in more detail at the end of this section. (i) Cellular Macromolecule Metabolic Process The following proteins were assigned to this ontology from the subset of proteins showing increased concentration in response to cytokine withdrawal. PROTEASOME INHIBITOR PI31 SUBUNIT FK506-BINDING PROTEIN 3 GRPE PROTEIN HOMOLOG 1, MITOCHONDRIAL ATP SYNTHASE MITOCHONDRIAL F1 COMPLEX ASSEMBLY FACTOR 2 ISOFORM 1 OF 39S RIBOSOMAL PROTEIN L22, MITOCHONDRIAL RSL1D1 PROTEIN 52 KDA PROTEIN (ii) Protein Metabolic Process The following proteins were assigned to this ontology from the subset of proteins showing increased concentration in response to cytokine withdrawal. PROTEASOME INHIBITOR PI31 SUBUNIT ISOFORM 2 OF CELL DIVISION CONTROL PROTEIN 42 HOMOLOG ISOFORM 1 OF HOST CELL FACTOR FK506-BINDING PROTEIN 3 GRPE PROTEIN HOMOLOG 1, MITOCHONDRIAL ATP SYNTHASE MITOCHONDRIAL F1 COMPLEX ASSEMBLY FACTOR 2 ISOFORM 1 OF 39S RIBOSOMAL PROTEIN L22, MITOCHONDRIAL RSL1D1 PROTEIN 160 52 KDA PROTEIN 161 4.6.1.2 Proteins Observed To Increase In Concentration, And Previously Described As Having A Role In Cell Cycle, Cell Proliferation Or Cell Death. An increase in protein concentration in response to the induction of apoptosis has been previously described in the literature for some proteins, and so it is expected that this change might be observed in our work, where these proteins have been identified. Three examples are found amongst those proteins described as having a role in cell cycle, proliferation or cell death. The tumor suppressor PDCD4 down-regulates the expression of MAP4K1, inhibiting activation of MAPK85 (H. S. Yang et al., 2006), and inhibits translation by binding to eIF4A (Suzuki et al., 2008). It is known to be up-regulated upon induction of apoptosis (Shibahara et al., 1995). PDCD4 was determined to show a 1.6-fold increase in response to cytokine withdrawal. PDCD4 is discussed in some detail in Chapter 5. HMGB1 and HMGB2 have been shown to activate DNA cleavage by the caspase-activated DNAse (X. Liu et al., 1998; Toh, Wang, & Li, 1998). HMGB1 increased 1.4-fold and HMGB2 increased 1.5-fold in response to cytokine withdrawal. It has been shown that cells undergoing apoptosis induced by X-ray irradiation, or exposure to doxorubicin secrete HMGB1 after caspase activation (Apetoh et al., 2007). The authors draw the conclusion that HMGB1 is an important “molecular pattern” that dictates the TLR4-dependent immune response to apoptotic tumor cells. The secretion of HMGB1 in cells undergoing apoptosis induced by cytokine withdrawal has not been studied, and so our observation of an increase in the concentration of HMGB1 in response to cytokine withdrawal may be the first evidence 162 of this response. Another interesting aspect to this observation is that the HMGB1 signaling pathway uses Cdc42 as a node. Cdc42 was also observed to increase in reponse to cytokine withdrawal (see below). Other proteins have been described as playing a role in cell cycle, cell proliferation or cell death, but have not previously been described as altering in concentration upon induction of apoptosis. The observation of these changes under conditions of cytokine withdrawal is therefore novel. One protein known to play a role in cell proliferation is chromobox protein homolog 3 (heterochromatin protein 1 homolog gamma), which was found to be 1.9-fold higher in response to cytokine withdrawal. This protein is involved in mitosis, in the formation of a functional kinetochore through interaction with MIS12 complex proteins (Obuse et al., 2004). Interestingly, chromobox protein homolog 3 was observed to decrease in the nucleus upon induction of apoptotic chromatin condensation by anti-Fas antibody in HeLa, Jurkat and U937 cells (Gerner et al., 2002). However, this study involved the analysis of proteins extracted from nuclear matrix, and so the apparent decrease in chromobox protein homolog 3 in the Gerner study may represent a translocation event. Thus, the possibility of an alteration of nuclear localization having a role in apparent overall protein expression levels will have to be further investigated. Another protein involved in cell cycle control is Cdc42 (Isoform 2 of cell division control protein 42 homolog), which was observed to have a 1.6-fold increase in response to cytokine withdrawal. Cdc42, a rho GTPase, functions in the cell cycle, and in cell 163 proliferation (Cerione, 2004; Narumiya, Oceguera-Yanez, & Yasuda, 2004), hence it might be unexpected to observe an increase in concentration in response to the induction of apoptosis. Cdc42 stabilizes the kinetochore-microtubule attachment; perhaps its up-- regulation is the result of signals as a response to the dying cell attempting to survive. The observed change in the concentration of Cdc42 is interesting in light of the observed increase in chromobox protein homolog 3, which acts to form a functional kinetochore (see above), as well as the observed increase in concentration of the cdc42 inhibitor Rho GDP-dissociation inhibitor 2 (see below), which functions to inactivate cdc42 (and other GTPases). Might this result be explained as a change in the concentration of one protein leading to changes in other (related) proteins in an attempt to stabilize a system that has started to drift away from homeostasis? A possible role for Cdc42 in cytokine withdrawal-induced apoptosis is described at the end of this chapter in the model I propose linking the mevalonate pathway with Cdc42 and Jnk activation. We observed a 1.5-fold increase in ARHGDIB – the Rho GDP-dissociation inhibitor 2, which functions by inhibiting the dissociation of GDP from the Rho proteins cdc42, Rac1, Rac2 and RhoA, keeping these GTPases in an inactive form (DerMardirossian & Bokoch, 2005; Olofsson, 1999). ARHGDIB functions to keep the rho GTPases named above as soluble cytosolic proteins, bound to GDP, and therefore inactive. The inhibition of the rho GTPases above has implications for actin polymerization (reported to be PI3-K dependent (Nobes, Hawkins, Stephens, & Hall, 1995)) and cell polarity (Heasman & Ridley, 2008). However, because of its role in inhibiting cdc42 (which in turn plays a role in cell cycle control), ARHGDIB may now be considered further in light of a role in cell cycle control, 164 and perhaps in regulating some aspects of apoptosis, as explained in the model discussed at the end of this chapter. The observed increase in concentration in ARHGDIB was particularly interesting in view of the observed increase in concentration in the rhoGTPase cdc42 (see above). Hepatoma-derived growth factor (HDGF) has been shown to have mitogenic activity (Everett, Stoops, & McNamara, 2001; Kishima et al., 2002) HDGF is a nuclear-targeted protein, highly expressed in the smooth muscle and endothelial cells of developing vasculature. Further, HDGF plays a role in tumorigenesis, and over-expression has been shown to significantly activate the Erk1/2 pathway in a human gastric cancer epithelial cell- line, leading to proliferation, and causes proliferation in fibroblasts, hepatoma cells, vascular smooth muscle cells and endothelial cells (Kishima et al., 2002; Mao et al., 2008). There have been no published reports concerning the activity of HDGF in hemopoietic cells. HDGF was observed here to increase by 1.5-fold in response to cytokine withdrawal – a puzzling response, and one which could merit further investigation. NME6 (nucleoside diphosphate kinase, type 6), one of eight isoforms of NDP kinase, was determined to increase by 1.4-fold, and is known to play a major role in the synthesis of nucleoside triphosphates other than ATP, as well as to act as an inhibitor of p53-induced apoptosis (Mehus, Deloukas, & Lambeth, 1999). Over-expression of NME6 has been shown to suppress cell growth (Lacombe, Milon, Munier, Mehus, & Lambeth, 2000). The potential for inhibition of p53 raises the possibility that the up-regulation of NME6 is functioning (or 165 attempting) to stabilize TF-1 cells during cytokine withdrawal-induced apoptosis. FKBP3 (FK506-binding protein 3) is a peptidyl-prolyl cis/trans isomerase, and the receptor for the immunosuppressants FK506 and rapamycin, which function to inhibit T-cell proliferation by inhibiting the peptidylprolyl cis/trans isomerase (PPIase) activity of the FKBP family members (Kang, Hong, Dhe-Paganon, & Yoon, 2008). The FKBP/FK506 complex inhibits the phosphatase activity of calcineurin, and thereby prevents the dephosphorylation of NF-AT, required for IL-2 gene expression (Kang et al., 2008). FKBP3 also binds histone deacetylases, the transcription factor YY1, casein kinase II and nucleolin. FKBP3 is involved in protein folding and trafficking. A recent study demonstrated that FKBP3 (known as FKBP25 in the study) stimulated the auto-ubiquitylation and proteasomal degradation of MDM2, leading to the induction of p53 (Ochocka et al., 2009) and hence to cell cycle arrest or to apoptosis. With this information available, the observed increase of FKBP3 by 1.4-fold in response to cytokine withdrawal was not surprising. Isoform 1 of Host Cell Factor (HCF-1) is an abundant chromatin-associated protein required for progression through G1 of the cell cycle (Scarpulla, 2008; Tyagi, Chabes, Wysocka, & Herr, 2007). HCF-1 is observed to increase in concentration in response to cytokine withdrawal by 1.4-fold. 166 4.6.1.3 Proteins Observed to Increase in Concentration, and not Previously Described as Being Involved in Cell Survival, Cell Cycle or Cell Death. PSMD10 26S (Proteasome non-ATPase regulatory subunit 10 (also PA28)), which was determined to be 1.8-fold higher upon 15 hours of cytokine withdrawal, acts as a regulatory subunit of the 26S proteasome (involved in the ATP-dependent degradation of ubiquitinated proteins)(Tanaka, 1998). An increase in proteasomal activity might be expected under conditions leading to apoptosis. Interestingly, we also observe an increase of 1.4-fold in the levels of PSMF1 Proteasome Inhibitor PI31, which inhibits the activity of PSMD10 (PA28) (McCutchen-Maloney et al., 2000). This pair of changes might represent another attempt by the cell to maintain homeostasis. In this case, one of the pairs might be driven higher in concentration by the effects of cytokine withdrawal, and the other change in direct reponse. One possible explanation is that PSMD10 acts as a negative regulator for the proteasome, and the negative regulation might initially be increased as the cell tries to avoid widespread destruction of proteins. However, after time, under the continued adverse conditions, the negative regulation needs to be inhibited, hence the increase in PSMF1? GrpE protein homolog 1, mitochondrial is an essential component of the presequence translocase-associated motor (PAM) complex, a complex required for the translocation of transit peptide-containing proteins from the mitochondrial inner membrane into the mitochondrial matrix in an ATP-dependent manner. Another important PAM component is mtHsp70 (mitochondrial Hsp70). GrpE protein homolog 1 seems to control the nucleotide- dependent binding of mitochondrial Hsp70 to substrate proteins. We observe a 1.4-fold increase in the concentration of GrpE protein homolog 1 in response to cytokine withdrawal. 167 Lamina-associated polypeptide 2, isoform alpha is observed to increase in concentration by 1.4-fold in response to cytokine withdrawal. This protein may be involved in the structural organization of the nucleus and in post-mitotic nuclear assembly. The protein is thought to play an important role, together with lamin A, in the nuclear anchorage of RB1. Isoform 1 of Helicase-like Transcription Factor possesses intrinsic ATP-dependent nucleosome-remodeling activity, which may be required for transcriptional activation or repression of specific target promoters. A 1.8-fold increase in concentration was observed in response to cytokine withdrawal. Isoform 2 of V-Type Proton ATPase, subunit H is an essential component of the endosomal pH-sensing machinery. This ATPase may play a role in maintaining the Golgi functions, such as glycosylation maturation, by controlling the Golgi pH. Isoform 2 is targeted to the early endosome compartment, and interacts with ARNO (cytohesin-2) in an intra-endosomal acidification-dependent manner. Disruption of this interaction results in reversible inhibition of endocytosis (Hurtado-Lorenzo et al., 2006). We observe a 1.5-fold increase in concentration of this proton pump under our experimental conditions. Isoform 1 of 39S Ribosomal Protein L22, mitochondrial, is a component of mitochondrial ribosomes, involved in mitochondrial ribosomal protein translation. A 1.4-fold increase in concentration is observed in response to cytokine withdrawal. 168 Exosome Complex Exonuclease RRP43 is a component of the exosome 3'->5' exoribonuclease complex. This complex degrades inherently unstable mRNAs containing AU-rich elements (AREs) within their 3'-untranslated regions, and is observed to undergo a 1.4-fold increase in concentration in response to cytokine withdrawal. ATP Synthase Mitochondrial F1 Complex Assembly Factor (ATPAF2) may play a role in the assembly of the mitochondrial ATP synthase F1 complex. This mitochondrial respiratory chain-related protein, ATPAF2, was observed to increase by 1.4-fold in response to cytokine withdrawal. Isoform short of RNA-binding Protein FUS binds both single-stranded and double- stranded DNA and promotes ATP-independent annealing of complementary single-stranded DNAs and D-loop formation in superhelical double-stranded DNA. We observe a 1.6-fold increase in concentration in RNA-binding protein FUS. Histones The histones H1.2, H1.3, H1.5 and H3.3 were observed to increase in concentration in response to cytokine withdrawal, with concentration increases ranging from 1.4-fold to 1.6- fold. The H1 histones are necessary for the condensation of nucleosome chains into higher order structures. Histone H3.3 is a variant of histone H3, and tends to be found in nucleosomes clustered around transcriptionally active genes and over regulatory elements (Jin & Felsenfeld, 2007). Histones have not prevsiouly been described as being associated with cell survival or cell death, and so it was initially surprising to see apparent increases in 169 the concentration of these proteins in response to cytokine withdrawal. One possible explanation of this might be that in this model, cells undergoing cytokine withdrawal have committed to apoptosis and have commenced the process of disassembly. Under these conditions, it might transpire that the histones, normally bound with DNA, might be loosened and more easily enter into the sample supernatant during the process of cell lysis and sample preparation, leading to an apparent increase in concentration. It has been shown that nucleosome particles containing histone H3.3 are more unstable than those containing histone H3 (Jin & Felsenfeld, 2007), and while this might lend support for the argument of increased solubility of histones in cells that are in early apoptosis, if this were the case, we would expect to see the similarly sized histones H2A and H2A.Z showing an apparent increase in concentration as well. This latter change is not observed, leaving us to ponder further the meaning of an apparent increase in concentration of a subset of histones in response to cytokine withdrawal. Isoform 1 of Pyruvate Dehydrogenase E1 Component Subunit Beta, mitochondrial (PDHB) forms part of the pyruvate dehydrogenase complex, which catalyzes the overall conversion of pyruvate to acetyl-CoA and CO2. PDHB was observed to increase by 1.4-fold. Elongation of Very Long Chain Fatty Acids Protein 1 is, by similarity, involved in tissue- specific synthesis of very long chain fatty acids and sphingolipids. The protein may catalyze one or both of the reduction reactions for fatty acid elongation, i.e., conversion of beta- ketoacyl CoA to beta-hydroxyacyl CoA, or reduction of trans-2-enoyl CoA to the saturated acyl CoA derivative. A 1.6-fold increase in concentration was observed. 170 Isoform 1 of Acyl-coenzyme A synthetase ACSM3, mitochondrial has, by similarity, medium-chain fatty acid:CoA ligase activity with broad substrate specificity (in vitro). In vitro, this enzyme acts on acids from C(4) to C(11) and on the corresponding 3-hydroxy- and 2,3- or 3,4-unsaturated acids. A 1.6-fold increase in concentration was observed in this enzyme in response to cytokine withdrawal. This is another mitochondrial respiratory chain related enzyme, which may indicate an importance of maintaining energy levels when the cell is undergoing apoptosis. Ferrochelatase Isoform A Precursor (FECH) is involved in porphyrin metabolism, and is observed to increase in concentration by 1.6-fold in response to cytokine withdrawal. Defects in FECH are the cause of erythropoietic protoporphyria (EPP). Little is known about the RSL1D1 protein, the Ribosomal L1 domain-containing protein 1. However, a recent paper suggests that RSL1D1 negatively regulates PTEN and p27(Kip1) expressions, thereby promoting cell proliferation (Ma et al., 2008). We observe a 1.4-fold increase in concentration in response to cytokine withdrawal. Delta-aminolevulinic acid dehydratase (ALADH) is involved in porphyrin metabolism. Defects in ALADH cause acute hepatic porphyria. A 1.4-fold increase in concentration is observed in response to cytokine withdrawal. Expression of this protein may not be surprising in an erythroleukemia cell line. 171 Cytochrome c oxidase (subunit 2) was determined to be 1.4-fold higher in response to cytokine withdrawal – an observation that could warrant further investigation. For example, it might be that a number of mitochondrial enzymes cause “danger” signals if released into the cytosol. Curiously, the 52 kDa Protein was observed to increase by 1.5-fold in response to cytokine withdrawal. The 52 kDa protein is a ribonucleoprotein particle composed of a single polypeptide and one of four small cytoplasmic RNA components known as the “hY RNAs”. The protein is present in all mammalian cells studied but has no known function. An apparent increase in concentration observed in response to cytokine withdrawal might provide an important clue leading towards discovery of a possible role for this protein. Isoform 1 of Annexin A7 (Anxa7) was observed to increase in concentration by 1.4-fold in response to cytokine withdrawal. Anxa7 is a calcium/phospholipid-binding protein, which promotes membrane fusion and is involved in exocytosis. In brain tissue at least, ANXA7 mobilizes Ca ++ from an endoplasmic reticulum-like pool, which can be recruited to enhance IP3-mediated Ca ++ release (Watson et al., 2004). Alcohol Dehydrogenase Class-3 is not effective in oxidizing ethanol, but effectively catalyzes the oxidation of long-chain primary alcohols and the oxidation of S- (hydroxymethyl) glutathione. A 1.4-fold increase in concentration is observed in response to cytokine withdrawal. 172 HSP90 Co-chaperone CDC37 binds to several kinases and promotes interaction with the HSP90 complex, resulting in stabilization and promotion of their kinase activity (Stepanova, Leng, Parker, & Harper, 1996). Cdc37/HSP90 appear to stabilize cdk4, playing a role in progression of the cell cycle through G1 (Stepanova et al., 1996). An increase in concentration of 1.4-fold is observed in response to cytokine withdrawal. Isoform Short of TATA-binding protein-associated Factor 2N is an RNA and ssDNA- binding protein that may play a role in transcription initiation at certain promoters. The protein can enter the pre-initiation complex together with the RNA polymerase II (Pol II) . A 1.4-fold increase in concentration was observed in response to cytokine withdrawal. 4.6.1.4 Potential Relevance of These Observations Amongst the proteins that were observed to increase in concentration in response to cytokine withdrawal, and which have not previously been described as having an association with cell survival and growth, or cell death, there are five proteins present that appear to be involved in the cellular energy system; ATP Synthase Mitochondrial F1 Complex Assembly Factor, Nucleoside Diphosphate Kinase Type 6, Isoform 1 of Pyruvate Dehydrogenase E1 Component Subunit Beta, mitochondrial, Isoform 1 of Acyl-coenzyme A synthetase ACSM3, mitochondrial, and Cytochrome c oxidase (subunit 2). A link can easily be postulated between cellular energetics and cell survival, and so it is perhaps not surprising that under conditions of cytokine withdrawal, leading to pre-apoptosis, and ultimately apoptosis, that a perturbation in the status quo of cellular energy systems might be observed. The increase in mitochondrial proteins involved in cellular energetics might be viewed as 173 cellular “survival tactics” - an attempt to maintain cellular function for as long as possible, until after a certain point (the point of no return?), the cellular machinery switches from attempted survival to orderly disintegration. Two other mitochondrial proteins are observed to increase in concentration in pre-apoptosis. GrpE protein homolog 1, mitochondrial and Isoform 1 of 39S Ribosomal Protein L22, mitochondrial are involved in mitochondrial protein transport and mitochondrial ribosomal protein translation respectively. Increased concentration of these proteins might be expected under conditions of increased mitochondrial function, as implied by the increases in concentration of the five mitochondrial proteins mentioned above. Two proteins involved in porphyrin metabolism were observed to increase in pre-apoptosis - Ferrochelatase Isoform A Precursor and Delta-aminolevulinic acid dehydratase. These two enzymes are involved in the generation of heme. TF-1 cells are an erythroleukemia cell line, with the erythroid lineage arising from myeloid precursors. Despite this, TF-1 cells will produce hemoglobin under certain conditions, and so the observed increase in the concentrations of two enzymes in the pathway to porphyrin synthesis may be cell-line specific. Section 4.7 of this chapter outlines a proposed model, which incorporates elements from amongst the proteins that increase in concentration as well as the ones that decrease. 174 4.6.1.5 Total Protein Compared with the Subset that Shows “Decreased Concentration” – Biological Process Figures 4.6 and 4.7 show the level 4 gene ontologies into which proteins showing a decrease in concentration in response to cytokine withdrawal have been classified. The two figures show the subsets of proteins (with decreased concentrations) that are present in greater proportion (“increased”, Fig. 4.6) when compared with the control set, and present in lower proportion (“decreased”, Fig. 4.7) when compared with control. The raw data from which Figures 4.6 and 4.7 are drawn are shown in Tables 4.7 and 4.8. 175 F ig u r e 4 .6 P r o te in c a te g o r iz a ti o n b y g e n e o n to lo g y ( ii i) – c a te g o r iz a ti o n b y l e v e l 4 t e r m s o f a b io lo g ic a l p r o c e ss d ir e c te d a c y c li c g r a p h . T h e g ra p h s h o w s p ro p o rt io n o f p ro te in s fo u n d u n d er e ac h t er m c o m p ar in g t h e to ta l p ro te in s et ( 1 4 5 1 p ro te in s, b lu e) , w it h t h e m o st d o w n -r eg u la te d p ro te in s (8 2 p ro te in s, r ed ). O n to lo g ie s sh o w n i n w h ic h t h e fr ac ti o n f ro m d o w n -r eg u la te d p ro te in s is g re at er t h an t h e fr ac ti o n f ro m t o ta l p ro te in s. 176 F ig u r e 4 .7 P r o te in c a te g o r iz a ti o n b y g e n e o n to lo g y ( iv ) – c a te g o r iz a ti o n b y l e v e l 4 t e r m s o f a b io lo g ic a l p r o c e ss d ir e c te d a c y c li c g r a p h . T h e g ra p h s h o w s p ro p o rt io n o f p ro te in s fo u n d u n d er e ac h t er m c o m p ar in g t h e to ta l p ro te in s et ( 1 4 5 1 p ro te in s, b lu e) , w it h t h e m o st d o w n -r eg u la te d p ro te in s (8 2 p ro te in s, r ed ). O n to lo g ie s sh o w n i n w h ic h t h e fr ac ti o n f ro m d o w n -r eg u la te d p ro te in s is l es s th an t h e fr ac ti o n f ro m t o ta l p ro te in s. 177 T a b le 4 .7 R a w d a ta f r o m w h ic h F ig u r e 4 .6 h a s b e e n d r a w n . N u m b er s o f p ro te in s as si g n ed t o e ac h o n to lo g y f ro m t h e to ta l p ro te in l is t ( to ta l = 1 4 4 0 ) (A ll ), a n d f ro m t h e su b se t o f p ro te in s th at s h o w d ec re as ed co n ce n tr at io n ( to ta l = 8 2 )( D n ). T er m N u m b er s o f p ro te in s fr ac ti o n o f p ro te in s p er t er m i n e ac h se t D n c f. A ll % d if fe re n ce A ll D n A ll D n n eg at iv e re g u la ti o n o f d ev el o p m en ta l p ro ce ss 6 3 8 0 .0 4 0 .1 0 2 2 3 .0 0 ce ll d ea th 1 3 1 1 2 0 .0 9 0 .1 5 1 6 0 .8 6 p o si ti v e re g u la ti o n o f ce ll u la r p ro ce ss 1 3 9 1 1 0 .1 0 0 .1 3 1 3 8 .9 7 o rg an d ev el o p m en t 1 2 4 8 0 .0 9 0 .1 0 1 1 3 .3 0 p o si ti v e re g u la ti o n o f b io lo g ic al p ro ce ss 1 4 9 1 1 0 .1 0 0 .1 3 1 2 9 .6 4 o rg an el le o rg an iz at io n a n d b io g en es is 2 3 8 1 7 0 .1 7 0 .2 1 1 2 5 .4 4 p h o sp h o ru s m et ab o li c p ro ce ss 1 0 0 6 0 .0 7 0 .0 7 1 0 5 .3 7 m ac ro m o le cu la r co m p le x a ss em b ly 1 2 0 9 0 .0 8 0 .1 1 1 3 1 .7 1 p ro te in t ra n sp o rt 1 5 3 9 0 .1 1 0 .1 1 1 0 3 .3 0 ce ll c y cl e p h as e 6 6 7 0 .0 5 0 .0 9 1 8 6 .2 5 ce ll u la r co m p o n en t as se m b ly 1 2 5 9 0 .0 9 0 .1 1 1 2 6 .4 4 re g u la ti o n o f ca ta ly ti c ac ti v it y 5 5 6 0 .0 4 0 .0 7 1 9 1 .5 7 m it o ti c ce ll c y cl e 5 8 8 0 .0 4 0 .1 0 2 4 2 .2 2 h o m eo st at ic p ro ce ss 7 1 8 0 .0 5 0 .1 0 1 9 7 .8 7 re g u la ti o n o f p ro g ra m m ed c el l d ea th 9 9 9 0 .0 7 0 .1 1 1 5 9 .6 5 sy st em d ev el o p m en t 1 6 1 1 0 0 .1 1 0 .1 2 1 0 9 .0 7 p ro te in l o ca li za ti o n 1 6 6 1 0 0 .1 2 0 .1 2 1 0 5 .7 9 in tr ac el lu la r tr an sp o rt 1 8 2 1 2 0 .1 3 0 .1 5 1 1 5 .7 9 v es ic le -m ed ia te d t ra n sp o rt 9 5 8 0 .0 7 0 .1 0 1 4 7 .8 8 si g n al t ra n sd u ct io n 2 5 1 1 6 0 .1 7 0 .2 0 1 1 1 .9 4 ce ll d if fe re n ti at io n 2 1 1 1 8 0 .1 5 0 .2 2 1 4 9 .8 1 se cr et io n b y c el l 5 8 6 0 .0 4 0 .0 7 1 8 1 .6 7 m ac ro m o le cu le c at ab o li c p ro ce ss 1 0 4 6 0 .0 7 0 .0 7 1 0 1 .3 1 178 T e r m N u m b e r s o f p r o te in s F r a c ti o n o f p r o te in s p e r t e r m i n e a c h s e t D n c f. A ll % d if fe r e n c e A ll D n A ll D n n u cl eo b as e, n u cl eo si d e, n u cl eo ti d e an d n u cl ei c ac id m et ab o li c p ro ce ss 6 3 8 0 .0 4 0 .1 0 2 2 3 .0 0 b io p o ly m er m et ab o li c p ro ce ss 4 5 3 2 5 0 .3 1 0 .3 0 9 6 .9 1 ce ll u la r ca ta b o li c p ro ce ss 1 5 0 6 0 .1 0 0 .0 7 7 0 .2 4 n eg at iv e re g u la ti o n o f b io lo g ic al p ro ce ss 1 4 7 8 0 .1 0 0 .1 0 9 5 .5 7 ce ll u la r m ac ro m o le cu le m et ab o li c p ro ce ss 4 5 2 2 0 0 .3 1 0 .2 4 7 7 .7 0 p ro te in m et ab o li c p ro ce ss 4 7 7 2 1 0 .3 3 0 .2 6 7 7 .3 1 re g u la ti o n o f ce ll u la r m et ab o li c p ro ce ss 2 3 0 1 2 0 .1 6 0 .1 5 9 1 .6 2 n eg at iv e re g u la ti o n o f ce ll u la r p ro ce ss 1 4 3 8 0 .1 0 0 .1 0 9 8 .2 4 ce ll u la r b io sy n th et ic p ro ce ss 2 2 9 7 0 .1 6 0 .0 9 5 3 .6 8 T a b le 4 .8 . R a w d a ta f r o m w h ic h F ig u r e 4 .7 h a s b e e n d r a w n . N u m b er s o f p ro te in s as si g n ed t o e ac h o n to lo g y f ro m t h e to ta l p ro te in l is t ( to ta l = 1 4 4 0 ) (A ll ), a n d f ro m t h e su b se t o f p ro te in s th at s h o w d ec re as ed co n ce n tr at io n ( to ta l = 8 2 )( D n ). 179 Following cytokine withdrawal, the subset of “decreased” proteins showed a greater than 100% relative increase in the fraction of proteins that are involved in “negative regulation of developmental process” and “mitotic cell cycle” when compared with the complete protein set. (see Figure 4.6) The list of proteins assigned to each of these ontologies follows, and individual proteins are discussed in more detail at the end of this section. (i) Negative Regulation of Development Process The following proteins were assigned to this ontology from the subset of proteins showing decreased concentration in response to cytokine withdrawal. GLUTAMATE--CYSTEINE LIGASE REGULATORY SUBUNIT CREB-BINDING PROTEIN CELL DIVISION PROTEIN KINASE 6 ISOFORM 3 OF ANAMORSIN CASPASE-3 HEAT SHOCK 70 KDA PROTEIN 1 HEAT SHOCK 70 KDA PROTEIN 6 CDNA FLJ54392, HIGHLY SIMILAR TO HEAT SHOCK 70 KDA PROTEIN 1 180 (ii) Mitotic Cell Cycle The following proteins were assigned to this ontology from the subset of proteins showing decreased concentration in response to cytokine withdrawal IMPORTIN SUBUNIT ALPHA-2 CELL DIVISION PROTEIN KINASE 4 CELL DIVISION PROTEIN KINASE 6 ISOFORM 1 OF TRANSFORMATION/TRANSCRIPTION DOMAIN-ASSOCIATED PROTEIN CONDENSIN COMPLEX SUBUNIT 3 KINESIN-LIKE PROTEIN KIF11 PROTEASOME ASSEMBLY CHAPERONE 2 KINESIN FAMILY MEMBER 2C Following cytokine withdrawal, the subset of “decreased” proteins showed a greater than 60% and up to 99% relative increase in the fraction of proteins that are involved in “cell death”, “cell cycle phase”, “regulation of catalytic activity”, “homeostatic process”, “regulation of programmed cell death”, and “secretion by cell” when compared with the complete protein set. The list of proteins assigned to each of these ontologies follows, and individual proteins are discussed in more detail at the end of this section. 181 (i) Cell Death The following proteins were assigned to this ontology from the subset of proteins showing decreased concentration in response to cytokine withdrawal GLUTAMATE--CYSTEINE LIGASE REGULATORY SUBUNIT ISOFORM LONG OF RAS-RELATED PROTEIN RAB-27A HISTONE ACETYLTRANSFERASE P300 ISOFORM 3 OF ANAMORSIN PHOSDUCIN-LIKE PROTEIN 3 CASPASE-3 HEAT SHOCK 70 KDA PROTEIN 1 HEAT SHOCK 70 KDA PROTEIN 6 ISOFORM 4 OF RETICULON-3 ISOFORM 2 OF BETA-CATENIN-LIKE PROTEIN 1 PROTEASOME ASSEMBLY CHAPERONE 2 CDNA FLJ54392, HIGHLY SIMILAR TO HEAT SHOCK 70 KDA PROTEIN 1. (ii) Cell Cycle Phase The following proteins were assigned to this ontology from the subset of proteins showing decreased concentration in response to cytokine withdrawal IMPORTIN SUBUNIT ALPHA-2 CELL DIVISION PROTEIN KINASE 4 ISOFORM 1 OF GROWTH FACTOR RECEPTOR-BOUND PROTEIN 2 CELL DIVISION PROTEIN KINASE 6 CONDENSIN COMPLEX SUBUNIT 3 KINESIN-LIKE PROTEIN KIF11 KINESIN FAMILY MEMBER 2C (iii) Regulation of Catalytic Activity The following proteins were assigned to this ontology from the subset of proteins showing decreased concentration in response to cytokine withdrawal GLUTAMATE--CYSTEINE LIGASE REGULATORY SUBUNIT CASPASE-3 182 HEAT SHOCK 70 KDA PROTEIN 1 HEAT SHOCK 70 KDA PROTEIN 6 CDNA FLJ54392, HIGHLY SIMILAR TO HEAT SHOCK 70 KDA PROTEIN 1 ISOFORM 2 OF SAPS DOMAIN FAMILY MEMBER 1 METHYLOSOME SUBUNIT PICLN GLUTAMATE--CYSTEINE LIGASE REGULATORY SUBUNIT HISTONE ACETYLTRANSFERASE P300 CREB-BINDING PROTEIN CELL DIVISION PROTEIN KINASE 6 HEMATOPOIETIC LINEAGE CELL-SPECIFIC PROTEIN CASPASE-3 EUKARYOTIC TRANSLATION INITIATION FACTOR 2B, SUBUNIT 4 DELTA ISOFORM 3 (iv) Regulation of Programmed Cell Death The following proteins were assigned to this ontology from the subset of proteins showing decreased concentration in response to cytokine withdrawal GLUTAMATE--CYSTEINE LIGASE REGULATORY SUBUNIT ISOFORM LONG OF RAS-RELATED PROTEIN RAB-27A ISOFORM 3 OF ANAMORSIN CASPASE-3 HEAT SHOCK 70 KDA PROTEIN 1 HEAT SHOCK 70 KDA PROTEIN 6 ISOFORM 2 OF BETA-CATENIN-LIKE PROTEIN 1 PROTEASOME ASSEMBLY CHAPERONE 2 CDNA FLJ54392, HIGHLY SIMILAR TO HEAT SHOCK 70 KDA PROTEIN 1 (v) Secretion by Cell The following proteins were assigned to this ontology from the subset of proteins showing decreased concentration in response to cytokine withdrawal ISOFORM LONG OF RAS-RELATED PROTEIN RAB-27A PROTEIN TRANSPORT PROTEIN SEC23A ISOFORM 1 OF VESICLE-ASSOCIATED MEMBRANE PROTEIN 7 GOLGI-SPECIFIC BREFELDIN A-RESISTANCE GUANINE NUCLEOTIDE EXCHANGE FACTOR 1 LYMPHOCYTE CYTOSOLIC PROTEIN 2 183 COATOMER SUBUNIT EPSILON Following cytokine withdrawal, the subset of “decreased” proteins showed a greater than 40% relative decrease in the fraction of proteins that are involved in “cellular biosynthetic process” and a 30% relative decrease in “cellular catabolic process” when compared with the complete protein set. (see Figure 4.7) (i) Cellular Biosynthetic Process CELL DIVISION PROTEIN KINASE 4 GLUTAMATE--CYSTEINE LIGASE REGULATORY SUBUNIT PROTEIN KIAA0664 ISOFORM 1 OF FATTY ACID DESATURASE 2 THYMIDYLATE SYNTHASE EUKARYOTIC TRANSLATION INITIATION FACTOR 3, SUBUNIT 3 GAMMA, 40KDA, ISOFORM CRA_B EUKARYOTIC TRANSLATION INITIATION FACTOR 2B, SUBUNIT 4 DELTA ISOFORM 3 (ii) Cellular Catabolic Process GLYCOGEN PHOSPHORYLASE, BRAIN FORM L-LACTATE DEHYDROGENASE A-LIKE 6A CASPASE-3 HEAT SHOCK 70 KDA PROTEIN 1 RIBONUCLEASE INHIBITOR CDNA FLJ54392, HIGHLY SIMILAR TO HEAT SHOCK 70 KDA PROTEIN 1 An inspection of the level 4 GO terms under biological process reveals that no members of the subset of proteins showing a decrease in concentration were assigned to the following ontologies – 184 “negative regulation of developmental process”, “cell death”, “cellular catabolic process”, “positive regulation of cellular process”, “organ development”, “positive regulation of biological process”, “phosphorus metabolic process”, “protein transport”, “cell cycle phase”, “regulation of catalytic activity”, “mitotic cell cycle”, “homeostatic process”, “regulation of programmed cell death”, “system development”, “protein localization”, “intracellular transport”, “vesicle-mediated transport”, “signal transduction”, “cell differentiation” and “secretion by cell”. Of further interest is the observation that there were no ontologies in which all three sets of proteins were similarly represented (the smallest difference between sets was about 20%). 185 4.6.1.6 Proteins Observed to Decrease in Concentration, and Previously Described as Having a Role in Cell Cycle, Cell Proliferation or Cell Death. The list of proteins observed to decrease in concentration in response to cytokine withdrawal includes many which have putative roles in cell survival, cell death or cell proliferation, as well as others not previously described in these roles. Some of these might be expected to decrease – these will be discussed first, followed by those that seem to group together in some way. In discussing proteins which might decrease in concentration in response to cytokine withdrawal, it is worthwhile making the point that as cells prepare to undergo apoptosis, it is to be expected that production of much of the cell’s normal machinery will diminish, and so it is to be expected that more proteins appear to decrease in concentration than to increase in concentration under these circumstances. The difficulty comes in trying to determine which proteins are decreased as a result of the generalized ‘slow-down’ and which proteins are decreased as an active response to cytokine withdrawal, that is, that this latter decrease forms part of the active process of apoptosis, rather than the passive process of shutting down cellular function. Perhaps such a distinction is artificial, and in any event, it is impossible to make such a distinction under the current experimental conditions. While it might be possible to make an a posteriori argument for a change in a particular protein concentration being part of the active process of apoptosis based on the function of the protein in question, such arguments would be conjecture until supported by further studies. With these thoughts in mind, I begin with a discussion of the proteins that have been observed to decrease in concentration in response to cytokine withdrawal, and follow by a discussion of those which have not been described in these roles. 186 Thymidylate synthase (TS) was determined to be 2.3-fold lower (57% reduction) under conditions of cytokine withdrawal. TS activity forms the sole intracellular de novo source of dTMP, and is essential for cell growth and proliferation (reviewed in (Voeller, Rahman, & Zajac-Kaye, 2004)). As such, it is not unexpected that a decreased cellular concentration of TS might occur under conditions in which cell growth and proliferation are not suppressed. Ciapin1 (anamorsin) is an anti-apoptotic protein found in cells of hematopoietic lineage (Shibayama et al., 2004). Expression of anamorsin is cytokine-dependent, and so it was encouraging to observe a 1.3-fold (24%) reduction in levels of anamorsin in response to cytokine withdrawal. Anamorsin is discussed in greater detail in Chapter 5. In cells in which caspase-3 has become activated, it might be expected that the caspase-3 precursor would decrease in concentration, and so it was not unexpected to observe the caspase-3 precursor (Casp3) to decrease by 1.3-fold (24%) in response to cytokine- withdrawal-induced apoptosis. The cyclinD-cdk4 complex regulates cell cycle progression through the G1 phase. We observe a 1.5-fold (34%) reduction in the levels of Cell Division Protein Kinase 4 (cdk4) in our data. The cyclinD-cdk4 complex regulates cell cycle progression through the G1 phase (Sherr & Roberts, 1999). Under conditions of cytokine withdrawal, it is not unexpected to observe a reduction in the concentration of a protein that regulates cell cycle progression. While cdk4 was found in each experiment, cyclin D was not observed in any of the three experiments reported here. 187 Cdk6 (cell division protein kinase 6) partners with the D-type cyclins, in a mitogen- dependent manner, to control progression of the cell cycle. Cdk6 may also play a role in cell differentiation (Grossel & Hinds, 2006). We have observed cdk6 to be reduced by 1.4-fold (29%) in response to cytokine withdrawal. RANGAP1, the Ran GTPase activating protein 1, converts Ran, a small Ras-like GTP- binding nuclear protein, to its inactive GDP-bound state. Ran plays a key role in the modulation of various cellular signaling events including the cell cycle (reviewed in (Heo, 2008)). RANGAP 1 was determined to have a 1.6-fold (38%) reduction in response to cytokine withdrawal. KIF11 (kinesin-like protein KIF11) is involved in the cell cycle – blocking the function of KIF11 (by microinjectin of antibodies to the protein) prevents centrosome migration and causes cells to arrest in mitosis (Blangy et al., 1995). We observed a 1.7-fold (42%) reduction in KIF11 in response to cytokine withdrawal. Similarly, KIF2A has been implicated in the formation of bipolar mitotic spindles (A. L. Manning et al., 2007). KIF2A is observed to have a 1.6-fold (38%) reduction in our work. Kinesin Family Member 2C (KIF2C) is the mitotic centromere associated kinesin, and as such, is involved in cell cycle progression. KIF2C was observed to decrease in concentration by 1.3-fold (24%). The above three proteins are all involved in cell cycle progression, and a reduction in the 188 concentration of proteins involved in progression through the cell cycle is not surprising under conditions leading to apoptosis. PDCL3 (Phosducin-like protein 3) modulates the activation of caspases during apoptosis. PDCL3 (also known as VIAF) interacts with IAP to co-regulate the promotion of apoptosis (Wilkinson et al., 2004). A 1.4-fold (29%) decrease in concentration was observed in response to cytokine withdrawal. One possible explanation for this change apparently opposite to that expected is that PDCL3 is degraded in the process of promoting apoptosis. This observation will be important to follow up in future studies. Endogenous RTN3 (Isoform 4 of Reticulon-3) can bind with Bcl-2 and promote the anti- apoptotic effects of Bcl-2, while over-expression of RTN3 seems to be pro-apoptotic (Zhu, Xiang, Dong, Liu, & Qi, 2007). These apparently contradictory activities might be attributed to a requirement for RTN3 to be held at the proper level. We observed RTN3 to be down- regulated by 1.4-fold (29%) in our experiments, which would be consistent with a loss of its pro-survival effects via Bcl-2. Isoform 1 of Growth Factor Receptor-bound Protein 2 (Grb2), is an adapter protein that provides a critical link between cell surface growth factor receptors and the Ras signaling pathway (Lowenstein et al., 1992; Tari & Lopez-Berestein, 2001). Grb2 is observed to be down-regulated by 1.4-fold (29%)in response to cytokine withdrawal. This response is not unexpected, considering the reduction in cellular growth factors with cytokine withdrawal. 189 Uridine-cytidine kinase 2 has been shown to be up-regulated in keratinocytes by the viral oncogene E7 (K. A. Lee et al., 2005). We observe a 2.4-fold (58%) reduction in concentration in response to cytokine withdrawal. When considered with the up-regulation occurring in response to E7, suggests a possible role for UCK2 in cell survival or proliferation. This could warrant further investigation. Hydroxymethylglutaryl-CoA synthase (HMGCS)was determined to be 1.7-fold (42%) lower in response to cytokine starvation. This enzyme generates HMG-CoA (the substrate for HMG-CoA reductase) as part of the mevalonic acid-cholesterol biosynthesis pathway. Interestingly, inhibition of HMG-CoA reductase by statins has been shown to cause apoptosis in several different types of cancer (see, e.g. (Demierre, Higgins, Gruber, Hawk, & Lippman, 2005; Dudakovic et al., 2008)). The observed decrease in concentration of HMGCS forms an integral part of the model proposed in section 4.7. TRRAP (Isoform 1 of Transformation/transcription domain-associated protein) has a role in the regulation of gene transcription and plays a critical, non-redundant role in cell cycle progression and mitotic progression (reviewed in (Herceg & Wang, 2005)). TRRAP showed 1.5-fold (34%) reduction in response to cytokine withdrawal. C6orf66 (HRPAP20) has a putative (positive?) role in cell growth and the regulation (negative?) of apoptosis (Karp, Shukla, Buckley, & Buckley, 2007). C6orf66 shows a 1.5- fold (34%) reduction in our data, which is consistent with its reported regulatory roles. 190 Overexpression of CTNNBL1 in CHO cells results in nuclear localization and the induction of apoptosis (Jabbour, Welter, Kollar, & Hering, 2003). We observed CTNNBL1 to be down-regulated by 1.3-fold (24%). It is possible that decreasing levels of this protein may also have pro-apoptotic effects. Condensin Complex Subunit 3 is the regulatory subunit of the condensin complex, a complex required for the conversion of interphase chromatin into mitotic-like condensed chromosomes. A decrease in concentration of 1.4-fold (29%) was observed in response to cytokine withdrawal. Proteasome assembly chaperone 2 is also known as “tumor necrosis factor superfamily member 5-induced protein 1”. This protein is a chaperone which promotes assembly of the 20S proteasome as part of a heterodimer with proteasome assembly chaperone 1 (PSMG1). A 1.4-fold (29%) decrease in concentration was observed. Eukaryotic translation initiation factor 2B, subunit 4 delta, isoform 3 (eIF2B) is part of the eIF family of proteins that are essential for protein synthesis. We observed eIF2B to decrease in concentration by 1.4-fold (29%) in response to cytokine withdrawal. Eukaryotic translation initiation factor 3, subunit 3 gamma, 40kDa, isoform CRA_b (EIF3H) is believed to promote translation initiation activity (inferred from sequence). EIF3H was observed to decrease in concentration by 1.4-fold (29%) in response to cytokine withdrawal. 191 The decrease in concentration of the above two proteins, both forming part of eukaryotic translation iniation factors, is to be expected under conditions of cytokine withdrawal, when protein synthesis is expected to reduce in reponse to decreased mitogenic signals. Transient expression of Isoform 2 of Beta-Catenin-like Protein 1 has been shown to induce apoptosis in CHO cells (Jabbour et al., 2003). CTNNBL1 was observed to decrease by 1.3- fold (24%) in response to cytokine withdrawal. 4.6.1.7 Proteins Observed to Decrease in Concentration, and not Previously Described as Having a Role in Cell Cycle, Cell Proliferation or Cell Death. Glutamate-Cysteine Ligase Regulatory Subunit (GCLM) is the regulatory light chain, which forms a heterodimer with the catalytic heavy chain subunit involved in glutathione biosynthesis, regulating the synthesis of glutathione from L-glutamate and L-cysteine. GCLM was observed to decrease in concentration by 1.4-fold (29%). Isoform Long of Ras-related Protein Rab27 is a member of the small GTPase superfamily, Rab family. The protein is membrane-bound and may be involved in protein transport and small GTPase mediated signal transduction. Little is known of the protein except that mutations cause Griscelli syndrome – an error in pigmentation, plus immunodeficiency and organomegaly (Rajadhyax, Neti, Crow, & Tyagi, 2007). A 1.3-fold decrease in concentration was observed in response to cytokine withdrawal (24%). 192 Isoform 2 of SAPS Domain Family Member 1 forms a regulatory subunit of protein phosphatase 6 (PP6). SAPS Domain Family Member 1 helps regulate the PP6-mediated dephosphorylation of NF-!B1, preventing its degradation in response to TNF-alpha stimulation. A 1.6-fold (38%) decrease in concentration was observed in response to cytokine withdrawal. Methylosome Subunit pICln is a chloride conductance regulatory protein that is thought to play a role in cellular volume control by the activation of a volume-regulated anion current (VRAC) associated with hypotonic cell swelling (Hubert et al., 2000). The protein also inhibits snRNP biogenesis by binding core spliceosomal proteins (Pu, Krapivinsky, Krapivinsky, & Clapham, 1999). A 1.3-fold (24%) decrease in concentration was observed in pICln. Hematopoietic Lineage Cell-specific Protein (HS1) is the substrate of the antigen receptor- coupled tyrosine kinase. HS1 is expressed in the early stage of myeloid and erythroid differentiation. The protein plays a role in antigen receptor signaling for both clonal expansion and deletion in lymphoid cells, and may also be involved in the regulation of gene expression. In B cells, at least, tyrosine phosphorylation of HS1 is required for BCR-induced apoptosis and nuclear translocation of HS1 may be a prerequisite for B cell apoptosis (Yamanashi et al., 1997). HS1 was observed to decrease in concentration by 1.5-fold (34%) in response to cytokine withdrawal. 193 Adenylate Kinase Isoenzyme 6 (AK6) is a nuclear NMP kinase. AMP and dAMP are the preferred substrates, but CMP and dCMP are also good substrates (Ren et al., 2005). As well as playing a role in the homeostasis of adenine nucleotide metabolism, AK6 is also involved in cellular energetics. A 1.9-fold (47%) decrease in concentration was observed in response to cytokine withdrawal. Splicing Factor, Arginine/Serine-Rich 2 (SFRS2) is necessary for the splicing of pre- mRNA. A 1.3-fold (24%) reduction in concentration was observed in SFRS2. Putative Uncharacterized Protein DKC1 has not been described in the literature. The sequence is derived from an Ensembl automatic analysis pipeline. A 1.4-fold (29%) decrease in concentration was observed in this protein. Phosphomevalonate Kinase forms diphosphomevalonate from phosphomevalonate + ATP. A 1.4-fold (29%) increase in concentration was observed in response to cytokine withdrawal. Isoform 1 of Cell Division Cycle 2-related Protein Kinase 7 is involved in RNA splicing. The protein, also known as Cdc2-related kinase with an arginine/serine-rich (RS) domain (CrkRS), is thought to form a link between RNA transcription and splicing (Ko, Kelly, & Pines, 2001). A 1.4-fold (29%) decrease in concentration was observed in this protein. Also among the proteins that were reduced in response to cytokine withdrawal, we observed a 1.5-fold (34%) reduction in both CREB-binding protein, and in EP300 histone 194 acetyltransferase p300, which binds specifically to phosphorylated CREB protein and so mediates cAMP-gene regulation. KPNA2 (Importin subunit alpha-2) functions as an adaptor protein for the nuclear receptor KPNB1. Passage through the nuclear pore complex occurs via a Ran-dependent mechanism. Hence the 1.4-fold (29%) reduction in KPNA2 is of similar size to the 1.6-fold (38%) reduction observed in RANGAP1 (above). This may be an example of cellular ”parsimony” – two components important for regulating the passage of molecules through a nuclear pore complex are both reduced when such transport is no longer required (in early apoptosis). Protein Transport Protein SEC23A forms a component of the COPII coat (coat protein II), which covers ER-derived vesicles involved in transport of proteins from the rough endoplasmic reticulum to the Golgi apparatus. SEC23A was observed to decrease by 1.4-fold (29%) in response to cytokine withdrawal. Golgi-specific Brefeldin A-Resistance Guanine Nucleotide Exchange Factor 1 promotes guanine-nucleotide exchange on ARF5 (ADP-ribosylation factor 5). A 1.6-fold (38%) reduction in concentration was observed in response to cytokine withdrawal. Lymphocyte Cytosolic Protein 2 (LCP2) functions in T-cell antigen receptor mediated signaling. LCP2 was observed to decrease in concentration by 1.3-fold (24%). 195 Coatomer Subunit Epsilon (COPE) functions in transporting proteins from the endoplasmic reticulum, via the Golgi up to the trans Golgi network. A decrease in concentration of 1.4-fold (29%) was observed. Isoform 1 of Zinc Finger CCCH-type Antiviral Protein 1 induces an innate immunity to viral infections by preventing the accumulation of viral RNAs in the cytoplasm, apparently by the recruitment of the RNA processing exosome, which degrades the target RNAs. Concentration decreased by 1.3-fold (24%) in response to cytokine withdrawal. Nothing has been published on the function of Isoform 3 of RNA-binding Protein 26, which was observed to decrease by 1.3-fold (24%) in our work. Hippocalcin-like Protein 1 may be involved in the calcium-dependent regulation of rhodopsin phosphorylation. A 1.4-fold (29%) reduction in concentration was observed in response to cytokine withdrawal. Isoform 1 of Fatty Acid Desaturase 2 (FADS2) is a delta6-fatty acid desaturase – a component of the lipid metabolic pathway that catalyzes biosynthesis of highly unsaturated fatty acids (HUFA) from precursor essential polyunsaturated fatty acids (PUFA) linoleic acid (LA) (18:2n-6) and alpha-linolenic acid (ALA) (18:3n-3). FADS2 shows a reduction of 1.4- fold (29%) in response to cytokine withdrawal. 196 Isoform 1 of Proteasome Activator Complex Subunit 3 is implicated in the assembly of the immunoproteasome and is required for the efficient processing of antigens. A 1.3-fold reduction was observed in response to cytokine withdrawal. Isoform 1 of Long-Chain-Fatty-Acid--CoA Ligase 1 is involved in the activation of long- chain fatty acids in preparation both for synthesis of cellular lipids, and degradation via beta- oxidation. A 1.6-fold reduction in concentration was observed in response to cytokine withdrawal. This might reflect a decrease in synthesis of cellular lipids (in response to a decreased requirement, since growth is reduced and the need for membrane lipids would also be decreased). Cleavage and Polyadenylation Specificity Factor Subunit 3 forms a component of the cleavage and polyadenylation specificity factor (CPSF) complex that play a key role in pre- mRNA 3'-end formation. The protein acts as an endonuclease, and functions in mRNA 3'- end-processing, and was observed to decrease by 1.4-fold in response to cytokine withdrawal. Isoform Short of Cold Shock Domain-containing Protein E1 (CSDE1) is an RNA- binding protein, which is required for the internal initiation of translation of human rhinovirus RNA, and which may be involved in translationally coupled mRNA turnover. A 1.4-fold reduction was observed in CSDE1. 197 ATP-Dependent RNA Helicase DDX3X was observed to decrease in concentration by 1.3- fold in response to cytokine withdrawal. The literature discusses the role of this protein in HIV infection and Hepatitis C infection, but there are no reports of function in non-infected cells. Isoform A of Peptidyl-Prolyl Cis-Trans Isomerase E is also named FK506-binding protein 1A, and is related to FKBP3, discussed above. However, FKBP3 was observed to increase in concentration in response to cytokine withdrawal, whereas FKBP1A is observed to decrease. FKBP1A catalyzes the cis-trans isomerization of proline imidic peptide bonds in oligopeptides, which is a different function from that described for FKBP3. A 1.5-fold decrease in concentration was observed in response to cytokine withdrawal. Protein KIAA0664 is a hypothetical protein which has translation initiation factor activity inferred from the sequence. KIAA0664 is also known as putative eukaryotic translation initiation factor 3 and was observed to decrease by an average of 1.5-fold in three experiments. Glycogen phosphorylase, brain form, found in adult brain and in embryonic tissue, was found to decrease by an average of 1.5-fold in response to cytokine withdrawal. This enzyme is involved in glycogen metabolism, and a reduction in this might be explained as part of a cellular “shutdown“ during early apoptosis. 198 L-Lactate Dehydrogenase A-Like 6a is involved in glycolysis, and was observed to decrease in concentration by 1.4-fold in response to cytokine withdrawal. This decrease in protein concentration might be explained in the same way as the decrease observed for glycogen phosphrylase (above), that is, as part of an energy “shutdown” during apoptosis. Ribonuclease inhibitor (RNH1) was observed to decrease by 1.3-fold in response to cytokine withdrawal. RNH1 functions as an inhibitor of pancreatic RNase and angiogenin, and is proposed to function in the modulation of cellular activities (SwissPROT entry), however, no further details of this proposed modulation of cellular activities could be found. 4.6.1.8 Potential Relevance of these Observations While the increase in protein concentration in response to cytokine withdrawal may be more easily attributed to the “stimulus” of cytokine withdrawal, a decrease in protein concentrations under similar conditions might well be because the cell is shutting down, in preparation for programmed cell death. Hence, interpreting the changes observed in proteins that appear to decrease in concentration in pre-apoptosis must be conducted with more care. A further consideration is the possible impact of caspase cleavage of proteins on the apparent changes in concentration of the proteins in question. A protein cleaved into two (or more) peptides that run on a gel (or column) at different molecular weights etc. could still be identified and quantitated. Amounts of each peptide would be similar, even if the peptides were detected in different fractions, provided there was enough material in each fraction to be detected by the mass spectrometer. However, in the case of a protein cleaved into very 199 small peptides, where such peptides might run off the gel, for example, such cleavage would cause the protein to appear at a lower concentration than an intact protein. Detection of protein cleavage may be determined on a case-by-case basis, by identifying the fraction/s in which the protein was detected. Such an exercise has been completed for the protein PDCD4 (see Chapter 5). This notwithstanding, amongst the proteins that were observed to decrease in concentration, are several Golgi and ER-related proteins. These changes may be a response to decreasing protein synthesis – for the conservation of energy, as well as in preparation for programmed cell death. Further, several RNA-binding proteins were observed to decrease, which might correlate well with the expected reduction in protein synthesis under conditions of cytokine withdrawal. As well, two proteins involved in fatty acid metabolism were seen to decrease in concentration. This change (in energetics) might correlate with the changes observed in the proteins that increase in concentration – a shift away from fatty acid metabolism and towards the respiratory chain energy supply. Isoform 1 of Long-Chain-Fatty-Acid--CoA Ligase 1, a protein involved in beta-oxidation of fatty acids, was observed to decrease in concentration following cytokine withdrawal. However, the protein Elongation of Very Long Chain Fatty Acids Protein 1 which is involved in fatty acid synthesis, was observed to increase, while Isoform 1 of Fatty Acid Desaturase 2, also involved in the biosynthesis of highly unsaturated fatty acids, appeared to decrease. These changes might be explained by a cellular move away from fatty acid oxidation, and towards fatty acid synthesis. 200 Another interesting observation is the apparently balanced increase in the concentrations of seeming antagonists - PSMD10 26S (Proteasome non-ATPase regulatory subunit 10 (also PA28)), which was determined to be 1.8-fold higher upon 15 hours of cytokine withdrawal, and PSMF1 Proteasome Inhibitor PI31, which inhibits the activity of PSMD10 (PA28) and was observed to increase by 1.4-fold. Both are involved in regulating proteasome function, but it seems strange to observe an increase in both an agonist and its antagonist, since the level of proteasome function might be presumed to remain constant under these conditions. It would be interesting to learn the half-lives of these two proteins, to determine if one might have increased early in pre-apoptosis, and (having a longer half-life) were still present in increased concentration when the second protein increased in concentration later in pre- apoptosis. 4.6.2 Molecular Function Proteins from the total set (n=1440) were classified into 81 ontologies using level 4 GO terms, for molecular function. Proteins with the greatest apparent increase in concentration (most “up-regulated”) (i/w ratio ! 0.70, n=43) fell into 5 ontologies using level 4 terms, molecular function. Proteins with the greatest apparent decrease in concentration (most “down-regulated”) (i/w ratio \" 1.30, n=82) fell into 8 ontologies using level 4 terms, under the classification “molecular function”. 201 These differences are shown graphically in Figure 4.8, and give an insight into alterations of cellular function in response to cytokine withdrawal, using the perspective gained by classification according to molecular function gene ontologies. 202 F ig u r e 4 .8 P r o te in c a te g o r iz a ti o n b y g e n e o n to lo g y ( v ) – c a te g o r iz a ti o n b y l e v e l 4 t e r m s o f a m o le c u la r f u n c ti o n d ir e c te d a c y c li c g r a p h . T h e g ra p h s h o w s p ro p o rt io n o f p ro te in s fo u n d u n d er e ac h t er m c o m p ar in g t h e to ta l p ro te in s et ( b lu e) , th e m o st u p -r eg u la te d p ro te in s (r ed ), a n d t h e m o st d o w n -r eg u la te d p ro te in s (g re en ). 203 T e r m N u m b e r s o f p r o te in s F r a c ti o n o f p r o te in s p e r t e r m i n e a c h s e t a ll u p d o w n a ll u p d o w n U p v s. A ll % d if fe r e n c e D n v s. A ll % d if fe r e n c e tr an sf er as e ac ti v it y , tr an sf er ri n g p h o sp h o ru s- co n ta in in g g ro u p s 9 6 0 8 0 .0 7 0 .0 0 0 .1 0 0 .0 0 1 4 6 .3 4 m et al i o n b in d in g 2 5 0 1 0 1 0 0 .1 7 0 .2 3 0 .1 2 1 3 3 .9 5 7 0 .2 4 ca ti o n b in d in g 1 9 9 8 9 0 .1 4 0 .1 9 0 .1 1 1 3 4 .6 3 7 9 .4 2 D N A b in d in g 1 4 5 1 2 7 0 .1 0 0 .2 8 0 .0 9 2 7 7 .1 5 8 4 .7 8 R N A b in d in g 1 8 8 0 8 0 .1 3 0 .0 0 0 .1 0 0 .0 0 7 4 .7 3 ri b o n u cl eo ti d e b in d in g 2 9 9 6 1 8 0 .2 1 0 .1 4 0 .2 2 6 7 .2 0 1 0 5 .7 2 tr an sc ri p ti o n f ac to r b in d in g 6 3 0 7 0 .0 4 0 .0 0 0 .0 9 0 .0 0 1 9 5 .1 2 p u ri n e n u cl eo ti d e b in d in g 3 1 6 7 1 8 0 .2 2 0 .1 6 0 .2 2 7 4 .1 8 1 0 0 .0 3 T a b le 4 .9 R a w d a ta f r o m w h ic h F ig u r e 4 .8 h a s b e e n d r a w n . N u m b er s o f p ro te in s as si g n ed t o e ac h o n to lo g y f ro m t h e to ta l p ro te in l is t (A ll ), f ro m t h e su b se t o f p ro te in s th at s h o w i n cr ea se d c o n ce n tr at io n ( U p ), a n d f ro m th e su b se t o f p ro te in s th at s h o w d ec re as ed c o n ce n tr at io n ( D n ). 204 4.6.2.1 Total Protein Compared with Subset that Shows “Increased Concentration” – Molecular Function When the complete protein set was compared with the subset of proteins that showed increased concentration, the subset of “increased” proteins showed a greater than 170% relative increase in the fraction of proteins in “DNA binding”. The list of proteins assigned to this ontology follows. Individual proteins have been discussed in more detail above. (i) DNA Binding The following proteins were assigned to this ontology from the subset of proteins showing increased concentration in response to cytokine withdrawal. 26S PROTEASOME NON-ATPASE REGULATORY SUBUNIT 10 ISOFORM SHORT OF TATA-BINDING PROTEIN-ASSOCIATED FACTOR 2N LAMINA-ASSOCIATED POLYPEPTIDE 2, ISOFORM ALPHA HISTONE H1.2 HISTONE H1.3 HISTONE H1.5 HISTONE H3.3 ISOFORM SHORT OF RNA-BINDING PROTEIN FUS ISOFORM 1 OF HELICASE-LIKE TRANSCRIPTION FACTOR HIGH MOBILITY GROUP PROTEIN B1 HIGH MOBILITY GROUP PROTEIN B2 When the complete protein set was compared with the subset of proteins that showed increased concentration, the subset of “increased” proteins showed a greater than 30% 205 relative increase in the fraction of proteins involved in “metal ion binding” and “cation binding”. The list of proteins assigned to this ontology follows. Individual proteins have been discussed in more detail above. (i) Metal Ion Binding The following proteins were assigned to this ontology from the subset of proteins showing increased concentration in response to cytokine withdrawal. CDNA, FLJ92798, HIGHLY SIMILAR TO HOMO SAPIENS ANNEXIN A7 (ANXA7), TRANSCRIPT VARIANT 2, MRNA ISOFORM SHORT OF TATA-BINDING PROTEIN-ASSOCIATED FACTOR 2N ISOFORM SHORT OF RNA-BINDING PROTEIN FUS ISOFORM 1 OF ACYL-COENZYME A SYNTHETASE ACSM3, MITOCHONDRIAL ISOFORM 1 OF HELICASE-LIKE TRANSCRIPTION FACTOR FERROCHELATASE ISOFORM A PRECURSOR ALCOHOL DEHYDROGENASE CLASS-3 DELTA-AMINOLEVULINIC ACID DEHYDRATASE CYTOCHROME C OXIDASE SUBUNIT 2 NUCLEOSIDE DIPHOSPHATE KINASE TYPE 6 (ii) Cation Binding The following proteins were assigned to this ontology from the subset of proteins showing increased concentration in response to cytokine withdrawal. CDNA, FLJ92798, HIGHLY SIMILAR TO HOMO SAPIENS ANNEXIN A7 (ANXA7) TRANSCRIPT VARIANT 2, MRNA CYTOCHROME C OXIDASE SUBUNIT 2 ISOFORM SHORT OF TATA-BINDING PROTEIN-ASSOCIATED FACTOR 2N ISOFORM SHORT OF RNA-BINDING PROTEIN FUS ISOFORM 1 OF HELICASE-LIKE TRANSCRIPTION FACTOR FERROCHELATASE ISOFORM A PRECURSOR ALCOHOL DEHYDROGENASE CLASS-3 206 DELTA-AMINOLEVULINIC ACID DEHYDRATASE Following cytokine withdrawal, the subset of “increased” proteins showed a greater than 30% relative decrease in the fraction of proteins that are involved in “ribonucleotide binding” and “purine nucleotide binding”. The list of proteins assigned to this ontology follows. Individual proteins have been discussed in more detail above. (i) Ribonucleotide Binding The following proteins were assigned to this ontology from the subset of proteins showing increased concentration in response to cytokine withdrawal. HSP90 CO-CHAPERONE CDC37 ISOFORM 2 OF CELL DIVISION CONTROL PROTEIN 42 HOMOLOG NUCLEOSIDE DIPHOSPHATE KINASE TYPE 6 ISOFORM 1 OF ACYL-COENZYME A SYNTHETASE ACSM3, MITOCHONDRIAL ISOFORM 1 OF HELICASE-LIKE TRANSCRIPTION FACTOR ISOFORM 2 OF V-TYPE PROTON ATPASE SUBUNIT H (ii) Purine Nucleotide Binding The following proteins were assigned to this ontology from the subset of proteins showing increased concentration in response to cytokine withdrawal. HSP90 CO-CHAPERONE CDC37 ISOFORM 2 OF CELL DIVISION CONTROL PROTEIN 42 HOMOLOG GRPE PROTEIN HOMOLOG 1, MITOCHONDRIAL ISOFORM 1 OF ACYL-COENZYME A SYNTHETASE ACSM3, MITOCHONDRIAL ISOFORM 1 OF HELICASE-LIKE TRANSCRIPTION FACTOR ISOFORM 2 OF V-TYPE PROTON ATPASE SUBUNIT H NUCLEOSIDE DIPHOSPHATE KINASE TYPE 6 207 4.6.2.2 Total Protein Compared with Subset that Shows “Decreased Concentration” – Molecular Function Following cytokine withdrawal, the subset of “decreased” proteins showed a greater than 95% relative increase in the fraction of proteins that are involved in the level 4 molecular function ontology “transcription factor binding” when compared with total proteins. The list of proteins assigned to this ontology follows. Individual proteins have been discussed in more detail above. (i) Transcription Factor Binding The following proteins were assigned to this ontology from the subset of proteins showing decreased concentration in response to cytokine withdrawal. CDNA FLJ54392, HIGHLY SIMILAR TO HEAT SHOCK 70 KDA PROTEIN 1 HEAT SHOCK 70 KDA PROTEIN 1 ISOFORM 1 OF TRANSFORMATION/TRANSCRIPTION DOMAIN-ASSOCIATED PROTEIN ADENYLATE KINASE ISOENZYME 6 CREB-BINDING PROTEIN HISTONE ACETYLTRANSFERASE P300 SPLICING FACTOR, ARGININE/SERINE-RICH 2 Following cytokine withdrawal, the subset of “decreased” proteins showed a greater than 46% relative increase in the fraction of proteins that are involved in “transferase activity, transferring phosphorus-containing groups” when compared with the complete protein set. 208 The list of proteins assigned to this ontology follows. Individual proteins have been discussed in more detail above. 1. Transferase Activity, Transferring Phosphorus-containing Groups The following proteins were assigned to this ontology from the subset of proteins showing decreased concentration in response to cytokine withdrawal. PUTATIVE UNCHARACTERIZED PROTEIN DKC1 PHOSPHOMEVALONATE KINASE ISOFORM 1 OF TRANSFORMATION/TRANSCRIPTION DOMAIN-ASSOCIATED PROTEIN ISOFORM 1 OF URIDINE-CYTIDINE KINASE 2 ADENYLATE KINASE ISOENZYME 6 CELL DIVISION PROTEIN KINASE 6 ISOFORM 1 OF CELL DIVISION CYCLE 2-RELATED PROTEIN KINASE 7 CELL DIVISION PROTEIN KINASE 4 The subset of “decreased” proteins showed a greater than 20% - 30% relative decrease in the fraction of proteins that are involved in “metal ion binding”, “cation binding” and “RNA binding” when compared with the complete protein set. The list of proteins assigned to this ontology follows. Individual proteins have been discussed in more detail above. 209 (i) Metal Ion Binding The following proteins were assigned to this ontology from the subset of proteins showing decreased concentration in response to cytokine withdrawal. ISOFORM 1 OF ZINC FINGER CCCH-TYPE ANTIVIRAL PROTEIN 1 ISOFORM 3 OF RNA-BINDING PROTEIN 26 HIPPOCALCIN-LIKE PROTEIN 1 ISOFORM 1 OF FATTY ACID DESATURASE 2 ISOFORM 1 OF PROTEASOME ACTIVATOR COMPLEX SUBUNIT 3 CREB-BINDING PROTEIN HISTONE ACETYLTRANSFERASE P300 PROTEIN TRANSPORT PROTEIN SEC23A ISOFORM 1 OF LONG-CHAIN-FATTY-ACID--COA LIGASE 1 CLEAVAGE AND POLYADENYLATION SPECIFICITY FACTOR SUBUNIT 3 (ii) Cation Binding The following proteins were assigned to this ontology from the subset of proteins showing decreased concentration in response to cytokine withdrawal. ISOFORM 1 OF ZINC FINGER CCCH-TYPE ANTIVIRAL PROTEIN 1 ISOFORM 3 OF RNA-BINDING PROTEIN 26 HIPPOCALCIN-LIKE PROTEIN 1 ISOFORM 1 OF FATTY ACID DESATURASE 2 ISOFORM 1 OF PROTEASOME ACTIVATOR COMPLEX SUBUNIT 3 CREB-BINDING PROTEIN HISTONE ACETYLTRANSFERASE P300 PROTEIN TRANSPORT PROTEIN SEC23A CLEAVAGE AND POLYADENYLATION SPECIFICITY FACTOR SUBUNIT 3 (iii) RNA Binding The following proteins were assigned to this ontology from the subset of proteins showing decreased concentration in response to cytokine withdrawal. PUTATIVE UNCHARACTERIZED PROTEIN DKC1 ISOFORM SHORT OF COLD SHOCK DOMAIN-CONTAINING PROTEIN E1 ISOFORM 1 OF ZINC FINGER CCCH-TYPE ANTIVIRAL PROTEIN 1 210 ISOFORM 3 OF RNA-BINDING PROTEIN 26 ATP-DEPENDENT RNA HELICASE DDX3X ISOFORM A OF PEPTIDYL-PROLYL CIS-TRANS ISOMERASE E CLEAVAGE AND POLYADENYLATION SPECIFICITY FACTOR SUBUNIT 3 SPLICING FACTOR, ARGININE/SERINE-RICH 2 There are no Level 4 Molecular Function ontologies where the three protein sets (total protein, increased concentration and decreased concentration) are closer than 25% relative difference apart. There are three ontologies in which there were no proteins observed from the subset “increased” – “transferase activity”, “transferring phosphorus-containing groups”, and “RNA binding and transcription factor binding”. 4.7 Pathway Analysis Using Ingenuity ® Pathway Analysis (IPA) Data analysis using Ingenuity ® Pathway Analysis (IPA) was carried out using 41 proteins identified as increasing in concentration in response to cytokine withdrawal, and 82 proteins identified as decreasing in concentration in response to cytokine withdrawal. Details of the analysis are described in Chapter 2. IPA analysis identifies and lists the top molecular networks, canonical pathways and biological processes most perturbed under the experimental conditions. This is achieved by analyzing the experimental data against millions of results in the curated Ingenuity Knowledge Base. 211 The top five canonical pathways identified as most likely to be associated with the proteins showing increased concentration were Glucocorticoid Receptor Signaling, Glycolysis/Gluconeogenesis, Valine, Leucine and Isoleucine Biosynthesis, HMGB1 Signaling and Oxidative Phosphorylation. The top ten molecules identified in our data, along with their fold changes, are given in Table 4.10. Fold Change Increased Concentration Molecules Exp. Value Exp. Chart Fold Change PSMD10 1.8 HLTF 1.8 CDC42 1.6 FECH 1.6 FUS 1.6 PDCD4 1.6 ATP6V1H 1.5 ARHGDIB 1.5 HIST1H1C 1.5 HIST1H1D (includes EG:3007) 1.5 Table 4.10 The top ten fold changes observed amongst proteins shown to increase in concentration in response to cytokine withdrawal. The top five canonical pathways identified as most likely to be associated with the proteins showing decreased concentration were Estrogen Receptor Signaling, Molecular Mechanisms of Cancer, Lymphotoxin ! Receptor Signaling, RAN Signaling and Huntington’s Disease Signaling. The top ten molecules identified in our data, along with their fold changes, are given in Table 4.11. 212 Fold Change Decreased Concentration Molecules Exp. Value Exp. Chart Fold Change UCK2 0.42 TYMS 0.44 TAF9 0.53 HMGCS1 0.59 KIF11 0.60 KIF2A 0.61 ACSL1 0.63 GBF1 0.63 RANGAP1 0.64 HSPA6 0.65 Table 4.11 The top ten fold changes observed amongst proteins shown to decrease in concentration in response to cytokine withdrawal. Using the Ingenuity® software, I was able to visualize networks incorporating the proteins observed to change in my data. Network Graphical Representation The networks generated by analysis with IPA are graphical representations of the molecular relationships between gene products. Gene products are represented as nodes, and the biological relationship between two nodes is represented as an edge (line). All edges are supported by at least one reference from the literature, from a textbook, or from canonical information stored in the Ingenuity Pathways Knowledge Base. The intensity of the node color indicates the degree of up- (red) or down- (green) regulation. Nodes are displayed using various shapes that represent the functional class of the gene product. Edges are displayed with various labels that describe the nature of the relationship between the nodes (e.g., P for phosphorylation, T for transcription). 213 Figures 4.9 and 4.10 show an example of the type of output generated using IPA. In Figure 4.9 ten nodes (gene products) are shown in red within the network representation. These ten nodes represent the subset of proteins found to increase in response to cytokine withdrawal, and which are also found in the IPA database to form part of this network. From this representation, we can more easily observe how the proteins identified as increasing in response to cytokine withdrawal interact with networks and functions as determined by IPA analysis. For example, two proteins observed in my experiments form part of the HMGB1 canonical signaling pathway (outlined in blue in Figure 4.9) – HMGB1 and CDC42, both of which are shown in red in figure 4.9 and which increase in response to cytokine withdrawal. Figure 4.10 shows twelve nodes in green, representing the subset of proteins found to decrease in response to cytokine withdrawal, and which are also found in the IPA database to form part of this network. In this case three proteins observed in my work form part of the estrogen receptor signaling pathway. These two graphical network representations are shown as examples of the analysis that was performed using IPA. The model presented below came not from any one network representation, but rather from considering the changes observed as a whole. 214 Figure 4.9. First example of a functional network generated using Ingenuity Pathway Analysis ®. One of the top five canonical pathways identified as most likely to be associated with the 41 proteins previously shown using SILAC to increase in concentration in response to cytokine withdrawal. Gene products are represented as nodes, and the biological relationship between two nodes is represented as an edge (line). Nodes in red represent proteins identified in the current experiment as increasing in concentration in response to cytokine withdrawal. Proteins are assigned to functional networks based on evidence published in the literature, and relationships shown between nodes are supported by at least one reference from the literature, from a textbook, or from canonical information stored in the Ingenuity Pathways Knowledge Base. 215 Figure 4.10. Second example of a functional network generated using Ingenuity Pathway Analysis ®. One of the top five canonical pathways identified as most likely to be associated with the 82 proteins previously shown using SILAC to decrease in concentration in response to cytokine withdrawal Gene products are represented as nodes, and the biological relationship between two nodes is represented as an edge (line). Nodes in green represent proteins identified in the current experiment as decreasing in concentration in response to cytokine withdrawal. Proteins are assigned to functional networks based on evidence published in the literature, and relationships shown between nodes are supported by at least one reference from the literature, from a textbook, or from canonical information stored in the Ingenuity Pathways Knowledge Base. TYMS 216 Analysis of those proteins observed to increase and to decrease in concentration in response to cytokine withdrawal has generated a more cohesive model from the intriguing observations around Hydroxymethylglutaryl-CoA synthase (HMGCS) and Cdc42. I have shown that cytokine withdrawal caused a consistent reduction in the levels of HMGCS (i/w ratios - 1.74/1.62/1.74; avg 1.68, equals reduction of 41%), and also that cytokine withdrawal resulted in a consistent increase in the levels of Cdc42 (i/w ratios - 0.82/0.56/0.56; avg. 0.61, equals increase of 64%). In essence, the proposed model hypothesizes that cytokine withdrawal causes a reduction in the levels of HMGCS, resulting in increased expression of Cdc42, causing activation of the SAPK/JNK pathway, contributing to apoptosis. The details are as follows, and the model is presented graphically in Figure 4.11. I propose that this new model be considered as an addition to the current understanding of the mechanisms of cytokine withdrawal-induced apoptosis. Cytokine withdrawal causes well-known effects on signaling via the PI3-K, JAK/STAT and Erk pathways, and the effects of these alterations in signaling on the regulation of protein synthesis, and the elements of survival signaling, have been well described. It has been shown in fibroblasts that the PI3-K inhibitor LY294002 causes a reduction in the expression of HMGCS mRNA (Demoulin et al., 2004). Further, it has been shown that PDGF causes up-regulation of HMGCS (by the action of SREB, regulated by PI3-K) in fibroblasts (Demoulin et al., 2004). These two changes suggest that cytokine signaling – specifically PI3K signaling – plays a role in the regulation of expression of HMGCS. Consistent with this, I have shown a reduction in the concentration of HMGCS in response to cytokine withdrawal in the TF-1 erythroleukemia cell-line. 217 One step further up the HMGCS signaling pathway, we find peroxisome proliferator- activated receptors (PPARs), which are ligand-activated transcription factors that control lipid and glucose metabolism. HMGCS is a PPAR!-targeted gene (Glosli et al., 2005). Activation of PPAR! by ciprofibrate (a selective and potent PPAR! ligand-activator) causes an increase in HMGCS mRNA in rat liver and brain (Cullingford, Dolphin, & Sato, 2002). Hence it is not unreasonable to propose that a decrease in PPAR! signaling leads to a decrease in the expression of HMGCS. PPAR! signaling is complex, integrating signals from multiple inputs. Inputs include signaling via the IL-1 receptor, insulin, G protein coupled receptors, growth hormone receptor and TGF-! receptor. I have shown a reduction in three elements of the PPAR! signaling pathway, namely CREBBP/EP300 transcriptional co-activator (i/w ratios - 1.79/1.41/1.40; avg. 1.48, equals reduction of 33%, and i/w ratios - 1.79/1.41/1.40; avg. 1.48, equals reduction of 33% respectively), and GRB2 (i/w ratios - 1.49/1.49/1.29; avg 1.41 data, equals reduction of 29%). The PPAR! pathway is complex, however a reduction in concentration of the three members (above) of the PPAR! signaling pathway might be expected to lead (amongst many effects) to a decrease in PPAR! signaling, leading to the observed reduction in concentration of HMGCS. Lending support to this idea is a further observation from my data, supported in the literature – a reduction in PPAR\" has been reported to cause a decrease in FADS2 (also observed in my data) (Y. Wang et al., 2006). HMGCS functions to condense acetyl-CoA with acetoacetyl-CoA to form HMG-CoA. Decreased levels of HMGCS result in reduced levels of HMG-CoA, inhibiting the synthesis of L-mevalonic acid, causing a reduction in the synthesis of important isoprenoid 218 intermediates in the mevalonate pathway, especially geranylgeranylpyroposphate (GGPP) and farnesylpyrophosphate (FPP). Isoprenylation is important for subcellular localization and intracellular trafficking of post-translationally modified proteins. The post-translational modification of small GTPases such as RhoA, Rac and Cdc42 with isoprenoids is critical in allowing these GTPases to translocate to the cellular membrane, which is required for their activity (Liao & Laufs, 2005). Treatment with statins causes disruption of the mevalonate pathway, by the inhibition of HMG-CoA reductase, the rate-limiting enzyme in the mevalonate pathway necessary for the conversion of HMG-CoA into mevalonic acid (Goldstein & Brown, 1990). Treatment with statins has been observed to cause an increase in expression of Cdc42. Lovastatin significantly induced the expression of Rac1and Cdc42, but not RhoA, in a dose- and time- dependent manner in macrophages (Liang, Liu, & Zhou, 2006). In this same series of experiments, supplementation with garanylgeranylpyrophosphate (GGPP) and farnesylpyrophosphate (FPP) prevented apoptosis induced by lovastatin (Liang et al., 2006). Further, inhibition of the JNK pathway using SP600125 resulted in 60% less cell death. The reduction in cell death caused by supplementation with GGPP and FPP, or with inhibition of JNK using SP600125 was reported as being achieved through suppressing c-jun phosphorylation. Lovastatin has also been shown to induce apoptosis in smooth muscle cells, endothelial cells and plasma cells (Liang et al., 2006). In addition, statins have been reported as limiting NF!B nuclear accumulation, and DNA binding; also to reduce expression levels of c-jun (Paumelle & Staels, 2008). Activation of Cdc42 can cause the activation of SAPK/JNK (Coso et al., 1995; Minden, Lin, 219 Claret, Abo, & Karin, 1995), probably via PAK1 (Brown et al., 1996), resulting in the induction of apoptosis (Nishina, Wada, & Katada, 2004). Coso and coworkers found that over-expression of wild-type Cdc42 in COS-7 cells was able to activate JNK only if expressed at very high levels. These authors suggest that a certain proportion of wild-type Cdc42 is in an activated state, and can trigger downstream effectors if expressed at high enough levels. This proposal offers one possible explanation for the activation of JNK under conditions of cytokine withdrawal, where Cdc42 is at levels almost 65% greater than normal. Further evidence supporting Cdc42 activation of JNK comes from Minden et. al. who found that constitutively activated Rac1/Rac2 and Cdc42, but not RhoA, are efficient and specific activators of the JNK cascade in HeLa and NIH 3T3 cells and in COS-1 cells, stimulating the transcriptional activity of c-Jun (Minden et al., 1995). More recently, Wang et. al, using Cdc42GAP -/- mouse embryonic fibroblasts, were able to show that the resultant elevated activity of Cdc42 in these cells resulted in significantly increased JNK phosphorylation and phosphorylation of c-Jun (L. Wang, Yang, Burns, Kuan, & Zheng, 2005). Lending support for the activation of JNK in response to cytokine withdrawal, we also observe a decrease in the concentration of ARHGEF1 (i/w ratios 0.94/1.64/1.23; avg 1.34, equals 25% reduction), which normally functions to inhibit JNK activity (Nishida et al., 2005). In some way that has not yet been described, inhibition of the mevalonate pathway is associated with an increase in expression of Cdc42, as shown by Liang (Liang et al., 2006), with the action of lovastatin, and as observed in our work in response to cytokine withdrawal with a reduction in levels of HMGCS. Of possible relevance to this is the observation of an increase in ARHGDIB (Rho GDP dissociation inhibitor) which functions to bind Cdc42 (and 220 other Rho family GTPases) in a GDP-bound (inactive) form in the cytoplasm. On one hand, the 1.5-fold (50%) increase in concentration of an inhibitor of Cdc42 may be thought of as negating the effects of the 60% increase in the concentration of Cdc42. On the other hand, since we have evidence that increased Cdc42 is associated with apoptosis, it may be that maintaining Cdc42 in a soluble, cytosolic state is an important part of the cause of Cdc42- induced apoptosis. Obviously, further work is required to clarify this situation. In summary, this new model proposes that cytokine withdrawal results in apoptosis, at least in part, by reduction in the levels of HMGCS, causing reduced activity in the mevalonate pathway, leading to increased expression of Cdc42, resulting in activation of SAPK/JNK, leading to apoptosis. Evidence in support of this proposed new model comes from observed changes in the concentrations of proteins forming the model, as well as support from the literature in the form of “ancillary” changes expected (such as those described for FADS2), as well as literature support for some individual steps in the model. 221 Figure 4.11. Proposed model outlining observed changes in protein concentrations as they relate to the induction of apoptosis by alterations in mevalonate pathway and Cdc42 signaling. 4.8 Concluding Remarks Bioinformatic analysis of the data set generated by proteomic analysis of cytokine- withdrawal-induced apoptosis in TF-1 cells has resulted in a wealth of information – pattern 222 shifts in cellular function, as well as identifying individual proteins, many of which are known to be involved in cell survival and proliferation, or cell death, and others which had not previously been associated with these phenomena. The lists of proteins presented as part of Chapter 3 are those proteins which have been shown to have consistent changes in concentration (based on our criteria) in response to cytokine withdrawal – both increases and decreases in protein concentration were observed amongst different proteins. These lists contain many proteins with putative roles in cell survival, cell death or cell proliferation. These proteins are of interest in the context of this work, given that the experimental conditions were based on cytokine withdrawal (thereby altering survival signaling) as the means of inducing apoptosis. As well as identifying proteins that have already been described as having a role in cell survival and proliferation, or cell death, several proteins have been identified in this work that have not previously been associated with these functions. These proteins are also of interest, because of the absence of information previously linking them with cell survival and proliferation, or cell death. In response to cytokine withdrawal, I observed alterations in the concentrations of several proteins involved in protein translation and several enzymes involved in cellular energy systems. Changes were also observed in some proteins that have no described function – these in particular might be fruitful avenues of further investigation. Curiously, multiple isoforms of Hsp70 were observed to decrease in concentration in response to cytokine withdrawal. This is unexpected, since these proteins are stress response proteins, and cytokine withdrawal might be considered a ‘stress’. Indeed, two groups have reported a 223 direct interaction between Hsp70 and Apaf-1 – preventing recruitment of procaspase-9 to the apoptosome, resulting in the inhibition of apoptosis (Beere et al., 2000; Saleh, Srinivasula, Balkir, Robbins, & Alnemri, 2000). However, both of these studies used heat shock as the stimulus, and neither followed Hsp70 as a time course, and so possible changes in concentration were not part of these study designs. It has been noted that Hsp70 concentration in Jurkat cells is approximately 0.40 µM before stress, rising to 10 µM after heat shock, but in an undefined time-frame (Beere et al., 2000). One possible explanation for the decrease in concentration observed in our study, may be that in binding to Apaf-1 to prevent apoptosis, Hsp70 is subsequently degraded – used up in the attempt to prevent apoptosis. This would need further investigation to confirm or refute. From these observations, and others, I believe it to be possible to observe changes in the concentrations of cellular signaling proteins, and other proteins important to cellular function using the proteomics approach adopted in these experiments. Analysis of the results points the way to several potentially fruitful avenues of investigation. Verification of the results using immunoblotting and other techniques is discussed in Chapter 5. 224 4.8.1 Suggestions for Future Directions The 1.5-fold increase in concentration observed in Rho GDP-Dissociation Inhibitor 2 should mean that the GTPases cdc42, Rac1, Rac2 and RhoA are (at least partially) inactivated. The role of cdc42 in the cell cycle (stabilizes the kinetochore-microtuble attachment) was mentioned above. Exploration of a possible role in cell cycle regulation for this protein might prove to be fruitful, especially with an emphasis on the PI3-K pathway, given the PI3-K involvement in actin polymerization (with which RhoGDI is involved, in its capacity to inactivate the GTPases above). Further, RhoGDI2 functions as a metastasis suppressor (Harding & Theodorescu, 2007), inhibiting endothelin-1, for which there are already specific small molecule inhibitors (Titus et al., 2005). Further investigation of the function of RhoGDI2 in this light might enhance the available therapeutic options. One curiosity perhaps worthy of further investigation is the 52 kDa Protein, found in all mammalian cells studied, and with no known function, and yet observed to increase in concentration by 1.5-fold in response to cytokine withdrawal. The observed changes, coupled with the unknown function of this protein make it an interesting target for further work. A time-course experiment examining the levels of Hsp70 in response to cytokine withdrawal might generate interesting and useful data to complement the story of Hsp70 and the inhibition of apoptosis. 225 Further investigation of the apparently balanced increase in the concentrations of seeming antagonists - PSMD10 26S (Proteasome non-ATPase regulatory subunit 10 (also PA28)), and PSMF1 Proteasome Inhibitor PI31 might prove interesting. Since both are involved in the regulation of proteasome function, it would be interesting to learn the half-lives of these two proteins, and to view a time-course response of both following cytokine withdrawal. This would determine if one protein might have increased early in pre-apoptosis, and (having a longer half-life) were still present in increased concentration when the second protein increased in concentration later in pre-apoptosis. 226 5 Validation of the Mass Spectrometry Results 5.1 Introduction Early proteomics experiments resulted mainly in a cataloguing of proteins, producing a “parts list” for various cells and tissues. In order to move beyond this, the development of quantitative mass spectrometry has been essential (Krijgsveld, 2008). Early attempts to quantitate proteins under different conditions were performed using 2D-gel electrophoresis (reviewed in (Gorg, Weiss, & Dunn, 2004)). However, it was soon determined that this technique, the “work-horse for proteomics” (Gorg et al., 2004), while useful under many circumstances, has limitations. Some of these limitations might be addressed by using mass spectrometry directly, as had been achieved by the so-called “shotgun” proteomics approach to discovery. This led to the development of gel-free techniques to address the question of protein quantitation. Gel-free quantitative mass spectrometry has been achieved using several approaches, for example stable-isotope dilution, in which pairs of chemically identical peptides are generated - the pairs differing only by weight, with one member of the pair bearing a light isotope, and the other member of the pair labelled with the heavy isotope. Other approaches to gel-free quantitative mass spectrometry include metabolic labelling (the incorporation of in vivo stable isotopes), chemical labelling (stable isotope chemical tags are added to samples during sample preparation), and absolute quantitation of proteins. The earliest description of metabolic labelling for protein quantitation is 15 N-labelling (Oda, Huang, Cross, Cowburn, & Chait, 1999), while the earliest chemical tagging technique described is isotope-coded affinity tags (ICAT) (Gygi et al., 1999). Despite a decade of gel- 227 free quantitative proteomics, most investigators continue to supplement the publication of results of a quantitative proteomics experiment with some form of external validation of the results, usually immunoblots of the proteins of interest. This situation does appear to be changing, with recent publications discussing a move away from immunoblotting for validation, exploring the use of multiple reaction monitoring mass spectrometry (MRM-MS), for example, as a possible alternative to antibodies, both for protein detection and for absolute quantitation, by using the addition of known amounts of an isotopically labelled protein to the sample (Issaq & Veenstra, 2008). One method commonly used to validate quantitative changes observed using mass spectrometry is quantitative immunoblotting, which uses software to perform densitometry on the protein bands being visualized. The intensity of the bands visualized is dependent upon the amount of material present in the bands. The advantage of quantitative immunoblotting, apart from the familiarity and ease of application, as well as the relatively low cost, is the sensitivity of the technique (visualization is possible down to picogram quantities). The disadvantages are the lower dynamic range of the visualization technique (0- 1.8 O.D. for film and 0-2.5 O.D. for CCD camera, perhaps as much a 4.0 for infra-red imaging systems such as the Licor Odyssey), and the lack of linearity of response to concentration at lower protein concentrations. Nonetheless, quantitative immunoblotting (densitometry) data are frequently seen published alongside quantitative mass spectrometry data. 228 Densitometry is based on Beer’s law, which states that a substance’s concentration is proportional to its optical density (related to radiation loss in the medium). I have analyzed the optical density (OD) of images captured using a camera, or a scanner. This approach is described as a “non-flat field” imaging technique, that is, the technique does not require the use of two images – one of the background light, the second of the film over the background light. In the non-flat field approach, the OD of the band of interest is compared with a band for a “standard”, or “control” sample on the same image. In this case, images of bands from starving cells were compared with bands from non-starving cells. (In some cases I have used infrared fluorescence densitometry with the Licor ® Odyssey ®). In order to achieve accurate densitometry, the original film exposure needs to be below saturation. Given the limited dynamic range of film, saturation of the image can easily occur, and attempts to determine the optical density (OD) of any saturated (over-exposed) band on film skew the results relative to other bands, since it becomes impossible to normalize the OD values. Accurate densitometry is dependent on maintaining the full dynamic range of all OD measurements. Furthermore, not only should the variations in density be within the range of saturation of the film or other means of visualization, the detector in any scanner used to convert the original into a digital image will behave in a linear fashion for a range of values that is only a subset of the scanner’s full dynamic range. This means that an image that is under-exposed, or one that is over-exposed will be outside the linear range of the scanner’s capabilities, and variations in light transmission (T) will not be accurately converted into variations in pixel intensity. Further complicating the matter, the relationship between pixel intensity and optical density (OD) is complex - there is a linear relationship between pixel 229 value and T, and a logarithmic relationship between OD and 1/T i.e. OD is (-) log10 T (Vivino, 2008). Since T is not linearly related to OD, but pixel value is, as numbers for T get larger, changes in OD become smaller – i.e. densitometry is much more accurate when T is low, so OD changes in much larger “steps” when the image is dark, compared with when it is light. To achieve the most accurate densitometry, the film needs to be exposed near to, but not reaching, the point of saturation, and within the linear dynamic range of the scanner. Differences in OD measurements have more range near the point of image saturation, but once the image becomes saturated, the entire measurement becomes invalid. I begin this chapter by presenting some immunoblotting data to support the mass spectrometry results. This is followed by a more detailed analysis of the response of two proteins, anamorsin and PDCD4, to cytokine withdrawal. 5.2 Validation of Protein Changes During Apoptosis The data presented here are the result of three biological replicates of a SILAC experiment to determine changes in protein concentration in response to cytokine withdrawal. The fact that each of the proteins presented in this thesis has been identified and quantitated in three separate experiments lends weight to the robustness of the results. However, to further support the findings presented, several proteins were chosen for analysis using quantitative immunoblotting – the idea being that obtaining similar results for protein quantitation changes by using a separate technique would further enhance the acceptance of the mass spectrometry results. To this end, six proteins were chosen for analysis using quantitative 230 immunoblotting. Proteins were not chosen at random; rather, they were chosen based on the amount of quantitative change observed in three mass spectrometry experiments (from amongst the largest changes observed), and on the availability of antibodies to the protein. Our approach was to use quantitative immunoblotting to determine the level of protein expression in control and “starving” cells in response to cytokine withdrawal, and to compare this with known ‘housekeeping’ proteins whose expression levels were found not to change, to be used as loading control. 5.3 Immunoblotting for the Validation of Proteins Several proteins were chosen, as described previously, in order that immunoblots might be performed to determine if the protein response identified in three mass spectrometry experiments might be reproduced using a different analytical technique. In each case, separate biological replicates were run, immunoblots obtained and images of the blots generated for densitometry analysis. Images presented here were analyzed using the densitometry software “TopSpot”, available for free download from the Max Planck Institute in Berlin (http://web.mpiib-berlin.mpg.de/pdbs/2d-page/downloads.html). 231 5.3.1 “Up-Regulated” Proteins – Proteins Showing an Increase in Concentration Proteins that showed an apparent increase in concentration in cells undergoing cytokine withdrawal, and chosen for immunoblotting include cdc42, HMGB2 and SHIP. Cdc42 Cdc42 is a small rhoGTPase associated with the plasma membrane. The protein cycles between an active GTP-bound form and an inactive GDP-bound state. When in the active state, cdc42 binds to a variety of effector proteins to regulate cellular responses such as actin reorganization for epithelial cell polarization and the formation of filopodia. As well, cdc42 plays a role in the regulation of the cell cycle, and in cell proliferation (Cerione, 2004; Narumiya et al., 2004), and so was of interest in the context of this study. In Figure 5.1 I show a representative immunoblot using whole cell lysates from TF-1 cells undergoing a cytokine withdrawal time course. Cells were “starved” of cytokine for 3, 6, 9, 12, 15 and 18 hours, and compared with control cells. Cdc42 showed a 1.6-fold increase in response to cytokine withdrawal when quantitated using mass spectrometry. In three SILAC experiments, cdc42 was identified by 6 unique peptides. Densitometry analysis of the membrane shown yields the following readings for control and starving bands, normalized to “control” = 1.0; 0.98; 1.08; 0.95; 1.5; 1.39; 1.31. These immunoblot densitometry results are of a similar magnitude, and in the same direction as the results obtained in three quantitative mass spectrometry analyses. 232 Figure 5.1 Immunoblot of whole cell lysates - Control with hours of cytokine withdrawal as shown. Anti-cdc42 antibody probe. Anti-p85 used as loading control. A representative mass spectrum for cdc42 is shown below in Figure 5.2. This is a mass spectrum of the peptide YVECSALTQK, corresponding to residues 154-163 of cdc42. The sequence has a predicted mass (monoisotopic) of 1198.5772 for the 12 C form. A note concerning the mass spectra shown below Several examples of mass spectra are given below, to illustrate the basis on which protein quantitation has been achieved. I show only representative spectra for each protein, however, in all cases, protein quantitation has been based on an intensity-weighted average of within- spectra ratios from all spectra across the chromatographic peak of the eluting peptide. 233 Figure 5.2 Representative mass spectrum of cdc42 showing ratio of heavy to light isoforms of peptide YVECSALTQK (Mass-to-charge values (Th) and charge (in brackets)) as shown. i/w ratio equals 0.61 +/- 0.15. Monoisotopic peaks for quantitation: 599.79 and 602.80. HMGB2, with a molecular weight of approximately 24 kDa, was shown by mass spectrometry to have an average quantitated increase in response to cytokine withdrawal of 1.5-fold. HMGB2, and its close relative HMGB1 are chromatin-associated proteins that bind reversibly to single-stranded DNA, and unwind double stranded DNA to form DNA circles. The HMGB family are thought to play an important role in remodeling the assembly of chromatin and in regulating gene transcription in higher eukaryotic cells by distorting, bending or modifying the structure of DNA, which is bound with histones and transcriptional factors (Q. Zhang & Wang, 2008). HMGB2 was identified in three SILAC experiments by 14 unique peptides. A representative Western blot image is shown as figure 5.3. While the image appears to show an increase in the levels of HMGB2 at 18 hours of cytokine withdrawal, when loading control is taken into 234 account, the levels appear unchanged. The average difference between control and starving states in two separate blots being 1.06 +/- 0.03. In this case, I was not able to support the mass spectrometry results with densitometry analysis of immunoblots. The most obvious possible explanation is that HMGB2 does not change in concentration under the conditions reviewed. This explanation is unlikely, given the strength of the mass spectrometry data – identification based on fourteen unique peptides, and with protein rations determined to be 0.73/0.61/0.69 for an average change of 0.68 with SD +/- 0.07. We are left to ponder some of the technical aspects of densitometry for a possible explanation of the discrepancy observed between the mass spectrometry results and the immunoblotting results. The question of exposure of the HMGB2 images on the original films might be called into question. In the example shown below, the band for HMGB2 at eighteen hours is slightly darker than the other two bands, but the (non-specific) band below HMGB2 is apparently darker still, which indicates that the HMGB2 band is not at the point of saturation. However, it may be that the combination of HMGB2 band density close to, but not at saturation (theoretically the ideal place to be for densitometry) in combination with vinculin bands close to but not at saturation, might be sufficient to distort the densitometry and give results at odds with those obtained by mass spectrometry. While there is no clear explanation, the complexity of the visualization and densitometry techniques is such that it casts doubt on the immunoblot results in this instance. It could be argued that the bands in this gel are over- exposed. 235 Figure 5.3 Immunoblot of whole cell lysates - control (Cont.) with 15- and 18-hours of cytokine withdrawal. HMGB2 probe. Vinculin used as loading control. Time points were chosen to determine if the change at 15 hours became more apparent as time progressed. A representative mass spectrum for HMGB2 is shown in Figure 5.4 below. This is a mass spectrum of the peptide IKSEHPGLSIGDTAK, corresponding to residues 113-127 of HMGB2. The sequence has a predicted mass (monoisotopic) of 1551.83 for the 12 C form. 236 Figure 5.4 Representative mass spectrum of HMGB2 Ratio of heavy to light isoforms of peptide IKSEHPGLSIGDTAK (Mass-to-charge values (Th) and charge (in brackets) as shown). i/w ratio equals 0.54 +/- 0.18. Monoisotopic peaks for quantitation: 776.92 and 782.94. Note – this pair of peaks is 6 Th apart (despite being doubly charged) because of the missed cleavage, leaving two heavy lysines in the peptide. Phosphatidylinositol-3,4,5-trisphosphate 5-phosphatase 1 (SHIP) was chosen for investigation with Western blots, even though it falls just outside the cut-off for significance (average i/w = 0.77 +/- 0.03; 1.3-fold change), because the protein is of interest in a hematopoietic model of induced apoptosis. SHIP functions in hematopoietic cells to dephosphorylate PI-3,4,5-P3 to generate PI-3,4,-P2, leading to alterations in several signaling pathways – in general functioning to negatively regulate cell survival and proliferation. Changes of the magnitude observed in three mass spectrometry analyses (a 1.3-fold increase) proved difficult to confirm using immunoblotting, however I was able to demonstrate a change in the same direction. SHIP runs at about 145 kDa on a Western blot. No apparent increase in the amount of SHIP protein was observed with immunoblotting in response to 15 hours of cytokine withdrawal. Densitometry performed on three blots showed an increase of 1.37 +/- 0.52, with the average change being similar to that seen in the SILAC experiments. 237 The large standard deviation reflects the difficulty in getting a consistent response with immunoblotting. A representative immunoblot is shown in Figure 5.5. Figure 5.5 Western blot showing levels of SHIP in response to 15-hours of cytoine withdrawal. A representative mass spectrum for SHIP is shown below in Figure 5.6. This is a mass spectrum of the peptide EVPFSNENPR, corresponding to residues 142-151 of SHIP. The sequence has a predicted mass (monoisotopic) of 1187.56 for the 12 C form. 238 Figure 5.6 Repesentative mass spectrum of SHIP Ratio of heavy to light isoforms of peptide EVPFSNENPR (Mass-to-charge values (Th) and charge (in brackets)) as shown). i/w ratio equals 0.59 +/- 0.15. Monoisotopic peaks for quantitation: 594.79 and 597.80. 5.3.2 “Down-Regulated” Proteins – Proteins that Show a Decrease in Concentration. Mass spectrometry results show thymidylate synthase to be down-regulated by about 0.6- fold in response to cytokine withdrawal. Thymidylate synthase (TS) is involved in nucleotide biosynthesis, functioning to generate thymidine monophosphate, which is used in DNA synthesis and repair. TS was identified in three SILAC experiments by 8 unique peptides. The concentration change identified by mass spectrometry was confirmed by Western blotting, and a representative image is shown in Figure 5.7. Densitometry analysis of three images shows an average relative quantity of 0.45 +/- 0.10 in the cytokine starved samples when normalized to “control”. 239 Figure 5.7 Thymidylate synthase (TS) immunoblot showing control and 15-hours of cytokine withdrawal. A representative mass spectrum for thymidylate synthase is shown in Figure 5.8. This is a mass spectrum of the peptide DFLDSLGFSTR, corresponding to residues 116-126 of TS. The sequence has a predicted mass (monoisotopic) of 1256.60 for the 12 C form. Figure 5.8 Representative mass spectrum of Thymidylate synthase . Ratio of heavy to light isoforms of peptide DFLDSLGFSTR (Mass-to-charge values (Th) and charge (in brackets) as shown). i/w ratio equals 3.3 +/- 0.66. Monoisotopic peaks for quantitation: 629.31 and 632.32. The caspase-3 precursor is down-regulated in response to 15 hours of cytokine withdrawal by about 0.4-fold. In three SILAC experiments, the caspase-3 precursor was identified by 6 240 unique peptides. By determining from which gel slices the peptides used to identify the caspase-3 precursor were recovered, and determining the molecular weights of other proteins obtained from the same gel slices, I was able to confirm that the protein identified (caspase-3 precursor) is indeed the precursor, with a molecular weight of 31.6 kDa, and not the 17 kDa or 12 kDa subunits formed after cleavage. Using an antibody to cleaved caspase-3, we were able to demonstrate an increase in the cleaved form of caspase-3. Figure 5.9 shows this change in the activation of caspase-3 during a time-course experiment. Figure 5.9 Western blot showing levels of cleaved caspase 3 in response to cytokine withdrawal for the times shown. 241 5.3.3 Unchanged Proteins The vast majorty (almost 85%) of proteins identified and quantitated in these experiments did not change in concentration in reponse to cytokine withdrawal. Two are shown here – vinculin and HSP90. Vinculin is shown since it was used as the loading control in many of the Wesern blots shown in this work, and it is valuable to have the lack of change in concentration confirmed by SILAC. HSP90 is shown as an example of a protein showing no change in concentration, where a change might have been expected to be observed. Vinculin was chosen as a loading control in many of these immunoblotting studies for two reasons – it is a protein frequently used as a loading control in immunoblotting, with excellent antibodies, readily available; and it was shown to be unchanged in response to cytokine withdrawal in three SILAC experiments (ratio 1.05 ± 0.05). Vinculin is involved in cell adhesion, and may be involved in the attachment of actin-based microfilaments to the plasma membrane. Vinculin may also play important roles in cell morphology and locomotion. Vinculin is one representative of the large group of proteins that remain unchanged in response to fifteen hours of cytokine withdrawal, and was identified in three SILAC experiments by 56 unique peptides. A representative mass spectrum for vinculin is shown in Figure 5.10 below. This is a mass spectrum of the peptide SLGEISALTSK, corresponding to residues 434-442 of vinculin. The sequence has a predicted mass (monoisotopic) of 1104.60 for the 12 C form. 242 Figure 5.10 Representative mass spectrum of Vinculin. Ratio of heavy to light isoforms of peptide SLGEISALTSK (Mass-to-charge values (Th) and charge (in brackets) as shown). Uncorrected i/w ratio equals 1.16 +/- 0.36. Monoisotopic peaks for quantitation: 553.31 and 556.32. Heat-shock protein 90 (alpha and beta) (HSP90) HSP90 -alpha and -beta isoforms are involved in the cellular stress response as molecular chaperones with ATPase activity. HSP90 is a member of a large class of molecular chaperones that act in concert with co-chaperones and adaptor molecules to form large protein complexes that function to provide cellular protection from potentially lethal stress (Mahalingam et al., 2009). Many tumors, both solid and hematoligic, show increased levels of HSPs. The HSPs function to promote cell survival, growth and tumor metastasis, even under conditions of growth factor withdrawal (Mahalingam et al., 2009), hence it might have been expected to see a change in concentration of HSP90 in response to cytokine withdrawal in these experiments; no change was observed. It may be that HSP90 has already been up- regulated in the TF-1 cell line, but this would not be detected with SILAC, since it gives a relative quantitation only. 243 HSP90-! was identified in three SILAC experiments as having an average i/w ratio of 1.12 +/- 0.11. HSP90-\" was identified in three SILAC experiments as having an average i/w ratio of 1.07 +/- 0.07. In three SILAC experiments, HSP90-! was identified by 62 unique peptides, and HSP90-\" was identified by 65 unique peptides. Representative mass spectra for HSP90-! and -\" are shown in Figures 5.11 and 5.12, respectively. Figure 5.11 shows a mass spectrum of the peptide RAPFDLFENR, corresponding to residues 346-355 of HSP90!. The sequence has a predicted mass (monoisotopic) of 1263.74 for the 12 C form. Figure 5.11 representative mass spectrum of HSP90-alpha (!) Ratio of heavy to light isoforms of peptide RAPFDLFENR (Mass-to-charge values (Th) and charge (in brackets) as shown). Uncorrected i/w ratio equals 0.86 +/- 0.5. Monoisotopic peaks for quantitation: 632.83 and 638.84. Note – this pair of peaks is 6 Th apart (despite the double charge) because of the missed cleavage, leaving two heavy arginines. 244 Figure 5.12 shows a mass spectrum of the peptide IDIIPNPQER, corresponding to residues 73-82 of HSP90!. The sequence has a predicted mass (monoisotopic) of 1193.64 for the 12 C form. Figure 5.12 Representative mass spectrum of HSP90-beta (!) Ratio of heavy to light isoforms of peptide IDIIPNPQER (Mass-to-charge values (Th) and charge (in brackets) as shown). Uncorrected i/w ratio equals 1.11 +/- 0.43. Monoisotopic peaks for quantitation: 597.83 and 600.84. 245 5.4 Anamorsin As one of the proteins showing a decrease in concentration in reponse to cytokine withdrawal, anamorsin is a candidate for inclusion amongst the proteins validated by immunoblotting. Of further benefit to choosing anamorsin for the validation is the fact that I had done some work attempting to elucidate the function of anamorsin – in collaboration with the Kanakura group. In particular, I had examined the role of anamorsin in the cell in relation to the PI-3K signaling pathway, using cytokine withdrawal as the model. Because of its direct relevance to the cytokine withdrawal experiments presented above, this work is presented here, along with the quantitative immunoblotting results. Anamorsin (‘ana’ = against, ‘mors’ = death (in Latin)) is a pro-survival protein discovered by Shibayama and colleagues in 2004 (Shibayama et al., 2004). The authors were searching for genes that conferred resistance to cytokine-withdrawal induced apoptosis in an IL-3- independent sub-line (named Ba/F3-Ad) that they had generated from IL-3-dependent Ba/F3 cells. Using expression cloning by constructing a retroviral cDNA library from IL-3–starved Ba/F3-Ad cells, the authors were able to isolate and clone anamorsin. The human homologue of anamorsin had previously been discovered by Loftus et. al. as a protein of unknown function on chromosome 16 (Loftus et al., 1999). Anamorsin is a novel anti-apoptotic molecule of approximately 37 kDa relative mass. Over- expression of anamorsin confers resistance to cytokine withdrawal-induced apoptosis in the factor-dependent murine hematopoietic cell lines Ba/F3 and 32D. Anamorsin shows no 246 homology to members of the Bcl-2 family or the caspase family. Expression of anamorsin is induced (at least in part) by ras activation, but anamorsin is not an immediate early response (3 hr) gene. Anamorsin is expressed in murine hematopoietic cells, liver, heart and skeletal muscle. Anamorsin knock-out mice were generated, but the knockout was lethal at late gestation (E12.5 – E14.5) (Shibayama et al., 2004). Gene expression analysis of E14.5 embryos from anamorsin -/- mice showed 184 genes were significantly down-regulated and 40 were up-regulated in comparison with anamorsin +/+ embryos. The genes for Bcl-xL and Jak2 were most significantly down-regulated amongst the apoptosis-related genes. (Shibayama et al., 2004). Since no information was available about the roles of PI3-K/PKB or STATs in anamorsin signaling, we entered into a collaboration with Dr Shibayama of the Kanakura group, who kindly provided antibodies for use in these experiments. Working within the constraints of the collaboration agreement, we set about to determine what could be learned about the role of PI3-K in anamorsin signaling. 5.4.1 Anamorsin in Cytokine-Withdrawal – Mass Spectrometry Results. In three SILAC experiments using cytokine withdrawal for 15 hours, I show an average reduction in the relative level of anamorsin of 0.3-fold (25%) (average i/w ratio 1.33 +/- 0.28). Anamorsin, which appears in the data tables as “IPI00025333.4, CIAPIN1 Isoform 3 of Anamorsin”, was identified using eight unique peptides. Isoform 3 differs from the canonical sequence as follows: 53-66: SAHKESSFDIILSG ! C that is, a G – C substitution has occurred in isoform 3. 247 Anamorsin was identified in three SILAC experiments by 8 unique peptides. Spectra for the peptides are shown in Figure 5.13 and peptide details are given in Table 5.1. Immunoblotting results, shown following the mass spectra and peptide details, are in agreement with the mass spectrometry results. 248 Figure 5.13 Eight peptides were used in the identification of anamorsin. Peptide sequence, measured m/z and charge state are shown for each peptide. Modifications detected are shown for each peptide. Ratios for each peptide are shown in Table 5.1. Monoisotopic peaks on which quantitation has been based are highlighted using boxes. 249 Figure 5.13 (Part i) Eight peptides were used in the identification of anamorsin. Cont’d over. 250 Figure 5.13 (Part ii) Eight peptides were used in the identification of anamorsin. Cont’d over. 251 Figure 5.13 (Part iii) Eight peptides were used in the identification of anamorsin. Cont’d over. 252 Figure 5.13 (Part iv) Eight peptides were used in the identification of anamorsin. Cont’d over. 253 A n a m o rs in p ep ti d e d et a il s P ep ti d e E x p er im en t N u m b er R at io ( C o rr ec te d R at io ) M as s/ ch ar g e M o d if ic at io n s E P V E T A V D N N S K # 3 X # 2 2 5 -3 5 0 .8 4 ( 0 .7 8 ) 6 5 1 .8 1 1 8 /2 n o n e # 1 X K P N F E V G S S R # 3 2 5 -4 8 1 .3 9 ( 1 .5 6 ) 5 6 6 .8 1 2 4 /2 1 A rg 1 3 C 6 , 1 L y s1 3 C 6 # 2 2 5 -3 5 2 .0 7 ( 1 .9 2 ) 5 6 0 .7 9 0 3 /2 n o n e # 1 X E P L T P E E V Q S V R # 3 2 5 -4 8 0 .6 1 ( 0 .6 8 ) 6 9 2 .3 6 0 2 /2 n o n e # 2 X # 1 2 1 -4 0 E L Q R E P L T P E E V Q S V R # 3 X # 2 X # 1 2 1 -4 0 0 .7 3 ( 0 .9 3 ) 6 3 7 .3 3 9 2 /3 n o n e G L V D K L Q A L T G N E G R # 3 X # 2 X # 1 2 1 -4 0 0 .8 4 ( 1 .0 8 ) 5 2 4 .2 9 0 1 /3 n o n e E H L G H E S D N L L F V Q IT G K # 3 2 5 -4 8 2 ( 2 .2 5 ) 6 8 1 .6 9 4 2 /3 1 L y s1 3 C 6 # 2 X # 1 X V S V E N IK # 3 2 5 -4 8 1 .4 4 ( 1 .6 2 ) 3 9 7 .7 4 1 /2 1 L y s1 3 C 6 # 2 X # 1 X T a b le 5 .1 . D e ta il s o f th e p e p ti d e s u se d t o i d e n ti fy a n a m o r si n i n t h r e e S IL A C e x p e r im e n ts . “ E x p er im en t n u m b er ” s h o w s 1 st , 2 n d o r 3 rd e x p er im en t an d th e sa m p le g ro u p i n w h ic h t h e p ep ti d e w as i d en ti fi ed . “X ” in d ic at es p ep ti d e n o t fo u n d . O th er d et ai ls o f th e p ep ti d es a s in d ic at ed . 254 5.4.2 Anamorsin Decreases in Response to Cytokine Withdrawal. Figure 5.14 Anamorsin immunoblot in FDCP-1 cells. Cells were grown in the absence of cytokine for the times indicated. Numbers below the image represent hours. Anamorsin was shown by mass spectrometry to decrease by 0.3-fold (30%). By immunoblotting, we observe the levels of anamorsin to decrease in response to cytokine withdrawal. Figure 5.14 shows a time-course with samples taken at the times indicated after cytokine withdrawal. 5.4.3 Evidence for Phosphorylation Performing an anamorsin IP and then probing with the generic anti-phosphorylated tyrosine antibody, 4G10, revealed a band at the correct molecular weight for anamorsin. This is shown in figure 5.15a as evidence of possible tyrosine phosphorylation. An anamorsin IP probed with anti-anamorsin antibody is shown in figure 5.15b for comparison. 255 Figure 5.15 (a) & (b). Anti-anamorsin IPs. Further evidence for possible phosphorylation is that anamorsin appears to run as a (faint) doublet - on regular gels as well as on a 12% low-bis gel, as shown in Figure 5.16. Figure 5.16. Anti-anamorsin immunoblot, 12% low-bis gel. A low-bis gel was used to detect the presence of a “doublet”. Anamorsin immunoprecipitations were probed with a generic !-phospho-serine antibody and an !-phospho-threonine antibody. I found some evidence to suggest that anamorsin may be phosphorylated on serine residues, however, I found no evidence to suggest threonine residue phosphorylation (data not shown). I note that four putative phosphoserine sites have been described based on evidence from a global proteomic profiling experiment described above (Molina, Horn, Tang, Mathivanan, & Pandey, 2007). Figure 5.17 shows an anamorsin IP, probed with a generic anti-phosphoserine antibody. Visible bands at the correct molecular Figure 5.15a Anti-anamorsin IP with 4G10 probe. Figure 5.15b Anti-anamorsin IP with anamorsin probe. 256 weight for anamorsin in both control and starving conditions provide evidence for the serine phosphorylation of anamorsin (under both conditions). While this confirms the data of Molina et. al., it does not suggest that the phosphorylation of anamorsin is affected by growth factor withdrawal. Figure 5.17. Anti-anamorsin IP, anti-phosphoserine probe. Control (Cont) and Starving (ST). A further investigation into possible anamorsin phosphorylation was undertaken by observing the effect of a !-phosphatase, calf intestinal phosphatase (CIP). Control and starving samples were treated with CIP in a 50-µL reaction using 1-µL of enzyme for two hours at 37 °C. No difference in the doublet was observed, as shown in figure 5.18. 257 Figure 5.18. Anti-anamorsin IP treated with calf intestinal phosphatase. Control (Cont ) vs. growth factor withdrawal (ST) for 8 hours. Comparison of the doublet in both cases shows no visible differences in phosphorylation under control or starving conditions. Lastly, cells that had undergone cytokine withdrawal were labeled using !- 32 P-ATP for 1.5 hours, prior to the addition of synthetic IL-3 at 10 µgm/mL for 30 minutes to the “ST + IL-3” sample. After two hours (total) an immunoprecipitation was performed using an anti- anamorsin antibody. Despite making two attempts, I was unable to obtain conclusive results to suggest that there was incorporation of 32 P into the anamorsin band (data not shown). 258 5.4.4 Anamorsin In Response to Cytokine Withdrawal With And Without LY294002 And UO126 Treatment. Changes in anamorsin expression were evaluated in another cell model system of cytokine- dependent hemopoietic cell survival. Levels of anamorsin were tested in murine FDCP-1 cells in response to cytokine withdrawal for 6 and 12 hours, and in response to the PI3-K inhibitor LY294002 at 10 µM and 50 µM concentrations and to the MAP kinase inhibitor UO126 at 10 µM concentration. Results are shown in Figure 5.19. Anamorsin appears to decrease in response to cytokine withdrawal, as well as to PI3-K inhibition with LY294002. Anamorsin also appears to be reduced in response to MAP kinase inhibition using UO126. Figure 5.19. Anamorsin response to cytokine withdrawal for 6 and 12 hours, and to LY294002 and UO126. Control vs. cytokine withdrawal for 6 or 12 hours, with and without inhibitors as indicated. Samples with inhibitors were incubated in the presesnce of cytokine. See text for further details. 5.4.5 Anamorsin Half-life An attempt was made to determine the half-life of anamorsin, by observing changes in the levels of anamorsin present in cells incubated in the presence of IL-3 with cycloheximide at 10 µg/mL and 50 µg/mL for up to eight hours. No apparent difference was observed in the 259 levels of anamorsin, leading to the conclusion that the half-life of anamorsin appears to be greater than 8 hours (Figures 5.20 a and b show up to 6 hours. Figure 5.20c shows up to 8 hours)). Figure 5.20. Anamorsin expression in FDCP-1 cells following cycloheximide treatment. FDCP-1 cells were incubated in the presence of IL-3 with cycloheximide at 10 µg/mL and 50 µg/mL for the times indicated. A. Anamorsin immunoblot after 6 hours of IL-3 with cycloheximide at 10 ug/mL. B. Anamorsin immunoblot after 6 hours of IL-3 with cycloheximide at 50 ug/mL. C. Anamorsin immunoblot after 8 hours of IL-3 with cycloheximide at 10 ug/mL. 5.4.6 Anamorsin – Conclusions and Future Directions Anamorsin is a recently discovered protein which functions as a pro-survival molecule. As part of a collaboration with Dr Shibayama, Tokyo, I undertook to identify further the role of anamorsin in PI3-K signalling. I found evidence that anamorsin decreases in reponse to Figure 5.20a. Figure 5.20b. Figure 5.20c. 260 cytokine withdrawal, and to treatment of cells with the PI3-K inhibitor LY 294002 as well as with the MAP kinase inhibitor UO126. I have been able to find evidence that suggests that anamorsin may be tyrosine phosphorylated and may be serine phosphorylated. The half-life of anamorsin appears to be greater than eight hours. Since the publication of the original anamorsin paper in 2004, there have been eighteen papers published concerning anamorsin or Ciapin1. Under the name “anamorsin”, there appear two review papers, a further paper concerning anamorsin levels in B-cell lymphoma, and one concerning the anamorsin homologue in yeast. From the English-language reports on Ciapin1 the following information is obtained: ciapin1 is involved in the development of multidrug resistance in gastric cancer cells, by up-regulating MDR-1 and MRP-1, and that Ciapin1 expression appears to be involved in cell differentiation, and may cause down- regulation of cyclin D1 and up-regulation of p27 (Hao, Li, Qiao, Du et al., 2006; Hao, Li, Qiao, Zhang et al., 2006; X. Li, Hao et al., 2007). There is evidence to support Ciapin1 up- regulation of expression of Bcl-2 and down-regulation of Bax in leukemia cells (X. Li, Hong et al., 2007). Ciapin1 was clearly absent or significantly decreased in clear cell renal cell carcinoma (CCRCC), and adenoviral vector expression of Ciapin1 caused significant inhibition of CCRCC cells (L. He et al., 2009). In yeast cells, the Ciapin1 homologue is Dre2, which functions as a mitochondrial iron carrier, the absence of which is lethal. Yeast cells could be rescued by expression of human Ciapin1 (Y. Zhang et al., 2008). Hence anamorsin, or ciapin1 is a mediator of ras signaling, plays a role in hematopoiesis, is embryonically lethal when knocked out, is associated with several cancers, causing increased growth when absent or at low levels, and is involved in iron metabolism in yeast. 261 In conclusion, I have found evidence for a role for PI3-K in anamorsin signalling, since the PI3-K pathway appears to be involved in the regulation of the level of anamorsin, although the exact details of this relationship have not been elucidated so far. Further work would involve attempting to differentiate the action of the two inhibitors – LY294002 and UO126, in order to identify the pathway involved in regulation of anamorsin levels more clearly. The solidity of the mass spectrometry data (strong identification in three biological replicates, based on eight unique peptides, and with quantitation data showing reasonable agreement between replicates (1.33-fold average change with SD +/- 0.28)) gives confidence that the change observed in anamorsin levels is a real change. Further, I have been able to confirm by immunoblotting the changes in the levels of anamorsin in response to cytokine withdrawal. Anamorsin apparently decreases in response to cytokine withdrawal. However, beyond this fact, limited conclusions can be drawn from our work. . 262 5.5 Programmed Cell Death 4 (PDCD4) PDCD4 was discovered by Colburn’s group in 2001 (Cmarik et al., 1999; H. S. Yang et al., 2001). The murine pdcd4 gene cDNA is identical to murine MA-3 (Shibahara et al., 1995), and TIS (Onishi & Kizaki, 1996) genes, and highly homologous to human H731 (Matsuhashi et al., 1997) and 197/15 (Azzoni et al., 1998) genes (H. S. Yang et al., 2001). PDCD4 is located on human chromosome band 10q24. PDCD4 is a predominantly nuclear protein under normal growth conditions and is exported from the nucleus by a leptomycin B- sensitive mechanism upon serum withdrawal. The protein contains two nuclear export signals, one of which is very potent (Bohm et al., 2003). PDCD4 been shown to increase in the context of apoptosis (Goke et al., 2004; H. S. Yang et al., 2001). The mass spectrometry results presented in our study show PDCD4 to increase 1.6-fold in response to 15 hours of cytokine withdrawal (average in three SILAC experiments: i/w ratio = 0.62 ± 0.06). PDCD4 was chosen as a protein to validate by immunoblotting because of its clear identification and change in concentration determined in the SILAC experiments, because of the availability of antibodies, and because of its already established role in cell death. PDCD4 functions as a “tumour suppressor”, inhibiting cell proliferation and preventing neoplastic transformation, perhaps by inhibiting protein translation (H. S. Yang et al., 2001) (Hilliard et al., 2006). PDCD4 inhibits AP-1 transactivation, while apparently having no 263 effect on NF-kB. This occurs via inhibition of activation of c-Jun and c-Fos, caused by decreasing expression of MAPKKKK1 (MAP4K1) (H. S. Yang, Knies, Stark, & Colburn, 2003)(Lankat-Buttgereit, 2003)(H. S. Yang et al., 2006). PDCD4 is involved in regulating oncogenesis and inflammation. Rates of synthesis of IL-4, IL-10 and IFN-! were increased greater than two-fold in PDCD4 -/- mice, via post-transcriptional regulation, since the mRNA levels were not significantly affected by the knock-out. PDCD4 inhibits cap-dependent translation by blocking the activity of eIF4A (Cmarik et al., 1999; H. S. Yang, Jansen et al., 2003; H. S. Yang, Knies et al., 2003). PDCD4 expression is down-regulated in several tumour types (lung, glial-derived and renal-derived, tongue, and hepatocellular carcinoma), while being up-regulated in some bladder and breast carcinomas (Lankat-Buttgereit, 2008). PDCD4 is interesting in the context of our work on cytokine-mediated cell survival and cytokine-withdrawal induced apoptosis, for several reasons. It has been shown by Palarmachuk et. al. that PKB/Akt specifically phosphorylates Ser(67) and Ser(457) residues of Pdcd4 in vitro and in vivo, and that phosphorylation of PDCD4 by Akt causes nuclear translocation of PDCD4. Using a luciferase assay, the authors were able to show that phosphorylation of PDCD4 by Akt also causes a significant decrease in the ability of PDCD4 to interfere with the transactivation of the AP-1-responsive promoter by c-Jun. (Palarmachuk, 2005). Further, it has more recently been shown that PDCD4 is phosphorylated by p70 S6K and that phosphorylation ultimately results in the degradation of PDCD4 by the ubiquitin ligase TRCP (Dorrello et al., 2006). Hence there are several interactions between PDCD4 and members of the signaling pathways typically activated by cytokine stimulation. In summary, the role of PDCD4 appears to be centred on the inhibition 264 of cell proliferation. There is clear interaction of PDCD4 with the PI3-K pathway, as well as apparent involvement in the regulation of apoptosis, hence, similar to the decrease in anamorsin discussed above, it was encouraging to find PDCD4 among the proteins identified to increase in concentration in response to cytokine withdrawal in our SILAC experiments. Mass spectrometry results consistently show that PDCD4 quantitatively increases in response to cytokine withdrawal for 15 hours – three experiments produced the following i/w ratios: 0.59, 0.68, 0.57, with the average ratio being 0.62 with a SD of 0.06. This change is equivalent to an approximately 1.6-fold increase in the relative amount of PDCD4 under conditions of cytokine withdrawal. PDCD4 was identified on the basis of twenty unique peptides. The twenty peptides, along with details from the mass spectrometry analysis are presented in Table 5.2. Examples of the spectra are shown in Figure 5.21. 265 Table 5.2 Twenty peptides used to identify PDCD4 in three mass spectrometry experiments. Details of the specific experiment (#1, #2, or #3) and sample in which each peptide was found are noted, as are the relevant peptide details. See text for details. “X” means not found in that sample set. The underlined peptide is the immunogenic peptide used by Colburn’s group to raise the PDCD4 antibodies. 266 P D C D 4 – D et a il s o f P ep ti d es I d en ti fi ed P ep ti d es i d en ti fi ed R es u lt s fi le P ro te in I D m /z ( ch ar g e) R at io R aw F il e S p ec tr u m # M o d if ic at io n s D S V W G S G G G Q Q S V N H L V K # 1 - 1 -2 0 P D C D 4 9 2 7 .9 5 7 6 ( 2 ) 0 .5 8 1 7 4 8 0 0 n o n e # 2 - 3 5 -3 5 P D C D 4 9 2 7 .9 5 9 1 ( 2 ) 1 .1 2 2 5 4 1 7 5 n o n e # 3 - X M IL D L L K # 1 - 1 -2 0 P D C D 4 4 2 3 .2 6 1 2 ( 2 ) 0 .6 3 1 7 6 8 1 2 n o n e # 2 - 1 -2 4 P D C D 4 4 2 3 .2 6 1 9 ( 2 ) 0 .5 6 2 3 6 1 2 4 n o n e # 2 - 2 5 -3 5 P D C D 4 4 2 3 .2 6 2 1 ( 2 ) 0 .6 2 2 5 6 0 2 2 n o n e # 3 - 2 5 -4 8 P D C D 4 4 3 1 .2 5 9 4 ( 2 ) 0 .3 7 2 8 6 4 2 4 o x M et G T V D C V Q A R # 1 - 1 -2 0 P D C D 4 5 0 3 .2 4 2 1 ( 2 ) 0 .3 3 1 7 1 1 4 7 n o n e # 2 - X # 3 - X IY N E IP D IN L D V P H S Y S V L E R # 1 - 1 -2 0 P D C D 4 1 2 4 3 .6 2 7 4 ( 2 ) 0 .2 5 1 7 7 3 2 5 n o n e # 2 - 2 5 -3 5 P D C D 4 8 2 9 .4 2 2 ( 3 ) 0 .8 6 2 5 6 4 7 9 n o n e # 3 - X S S T IT V D Q M K # 1 - 1 -2 0 P D C D 4 5 5 5 .2 7 8 6 ( 2 ) 0 .2 9 1 7 3 3 6 4 n o n e # 2 - 1 -2 4 P D C D 4 5 5 5 .2 7 8 6 ( 2 ) .4 7 & . 4 6 2 4 & 2 3 3 1 7 4 & 3 1 0 5 n o n e # 3 - 2 5 -4 8 P D C D 4 5 5 5 .2 7 8 7 ( 2 ) 0 .3 9 2 8 4 0 2 0 n o n e T L T P II Q E Y F E H G D T N E V A E M L R # 1 - 1 -2 0 P D C D 4 9 0 2 .7 7 6 1 ( 3 ) 0 .4 2 1 7 7 9 0 4 n o n e # 2 - 1 -2 4 P D C D 4 9 0 2 .7 7 3 1 ( 3 ) .7 4 & . 7 7 2 4 & 2 3 7 2 6 7 & 7 2 3 8 n o n e # 3 - X K D S V W G S G G G Q Q S V N H L V K # 1 - 1 -2 0 P D C D 4 6 6 1 .6 7 2 3 ( 3 ) 0 .2 2 1 7 4 2 1 7 n o n e # 2 - X # 3 - X G D S V S D S G S D A L R # 1 - 1 -2 0 P D C D 4 6 3 3 .2 8 2 9 ( 2 ) 0 .2 7 1 7 2 1 7 2 n o n e # 2 - 1 -2 4 P D C D 4 6 3 3 .2 8 2 4 ( 2 ) 0 .5 8 2 4 2 3 9 4 # 3 - 2 5 -4 8 P D C D 4 6 3 3 .2 8 6 7 ( 2 ) 0 .3 0 2 9 3 2 5 1 n o n e 267 P e p ti d e s id e n ti fi e d R e su lt s fi le P r o te in I D m /z ( c h a r g e ) R a ti o R a w F il e S p e c tr u m # M o d if ic a ti o n s P e p ti d e s id e n ti fi e d L K P E S Y A T V L L S M S K # 1 - 1 -2 0 P D C D 4 4 7 5 .2 7 2 6 ( 2 ) 0 .3 3 1 7 4 5 3 2 n o n e # 2 - X # 3 P D C D 4 4 7 5 .2 7 3 6 ( 2 ) 0 .8 3 2 8 4 9 9 8 n o n e D S G R G D S V S D S G S D A L R # 1 - 1 -2 0 P D C D 4 8 4 0 .8 7 6 8 ( 2 ) 0 .4 4 1 7 2 2 2 6 n o n e # 2 - X # 3 - 2 5 -4 8 P D C D 4 8 4 0 .8 7 4 ( 2 ) 0 .8 9 2 8 3 3 7 1 n o n e E ID M L L K # 1 - 1 -2 0 P D C D 4 4 3 9 .2 3 8 5 ( 2 ) 0 .3 7 1 7 5 1 9 2 o x M et # 2 - X # 3 - 2 5 -4 8 P D C D 4 4 3 1 .2 4 1 5 ( 2 ) 0 .3 3 2 8 5 5 5 2 n o n e E Y L L S G D IS E A E H C L K # 1 - 1 -2 0 P D C D 4 9 3 2 .4 4 2 ( 2 ) 1 .2 1 1 7 6 4 1 5 n o n e # 2 - X # 3 - X F V S E G D G G R # 1 - X # 2 - 1 -2 4 P D C D 4 4 6 2 .2 1 5 1 ( 2 ) 0 .4 8 2 4 1 0 4 9 n o n e # 2 - 1 -2 4 P D C D 4 4 6 2 .2 1 4 1 ( 2 ) 0 .3 0 2 2 9 9 2 n o n e # 3 - 2 5 -4 8 P D C D 4 4 6 2 .2 1 5 ( 2 ) 0 .4 4 2 8 1 6 6 9 n o n e A P Q L V G Q F IA R # 1 - 1 -2 0 P D C D 4 6 0 0 .3 4 7 1 ( 2 ) 0 .3 9 1 7 5 7 7 6 n o n e # 2 - 1 -2 4 P D C D 4 6 0 3 .3 5 8 4 ( 2 ) 0 .6 0 2 4 5 3 1 0 1 A rg C 6 P D C D 4 6 0 3 .3 5 6 9 ( 2 ) 0 .5 8 2 3 5 2 5 3 1 A rg C 6 P D C D 4 6 0 3 .3 5 7 ( 2 ) 0 .5 9 2 2 5 3 2 8 1 A rg C 6 P D C D 4 6 0 3 .3 5 8 3 ( 2 ) 0 .6 8 2 1 5 3 3 4 1 A rg C 6 # 3 - 2 5 -4 8 P D C D 4 6 0 0 .3 4 8 7 ( 2 ) 0 .6 0 2 6 6 1 2 1 n o n e S G V P V L A V S L A L E G K # 1 - 1 -2 0 P D C D 4 7 2 0 .4 2 7 ( 2 ) 0 .5 1 1 7 7 6 3 7 n o n e # 2 - 1 -2 4 P D C D 4 7 2 0 .4 2 7 ( 2 ) 0 .7 4 2 3 6 9 8 8 n o n e 268 P e p ti d e s id e n ti fi e d R e su lt s fi le P r o te in I D m /z ( c h a r g e ) R a ti o R a w F il e S p e c tr u m # M o d if ic a ti o n s P e p ti d e s id e n ti fi e d # 3 - X A V G D G IL C N T Y ID S Y K # 1 - 1 -2 0 P D C D 4 8 9 4 .9 2 7 5 ( 2 ) 0 .6 2 1 7 6 4 3 4 n o n e # 2 - 2 5 -3 5 P D C D 4 8 9 4 .9 2 7 7 ( 2 ) 0 .6 3 2 5 5 6 8 6 n o n e # 3 - 2 5 -4 8 P D C D 4 8 9 4 .9 2 9 7 ( 2 ) 0 .3 5 2 8 6 6 6 7 n o n e S G L T V P T S P K # 1 - X # 2 - 1 -2 4 P D C D 4 4 9 3 .7 7 9 7 ( 2 ) 0 .4 7 2 3 3 3 3 2 n o n e # 3 - 2 5 -4 8 P D C D 4 4 9 3 .7 7 9 7 ( 2 ) 0 .5 1 2 8 4 2 0 7 n o n e D L P E L A L D T P R # 1 - 1 -2 0 P D C D 4 6 2 0 .3 3 3 1 ( 2 ) 0 .8 0 1 7 6 2 2 9 n o n e # 2 - 1 -2 4 P D C D 4 6 2 0 .3 3 1 8 ( 2 ) 0 .7 3 2 3 5 5 3 7 n o n e # 3 - 2 5 -4 8 P D C D 4 6 2 0 .3 3 3 3 ( 2 ) 0 .2 1 2 7 6 3 7 0 n o n e L L S D L C G T V M S T T D V E K # 1 - 1 -2 0 P D C D 4 9 3 4 .9 5 1 1 ( 2 ) 0 .6 3 1 7 6 4 2 2 n o n e # 2 - X # 3 - X 269 F ig u r e 5 .2 1 . R e p r e se n ta ti v e s p e c tr a i d e n ti fy in g t w e lv e o f th e p e p ti d e s fr o m T a b le 5 .2 270 271 272 273 In three SILAC experiments, the mass spectrometry results clearly show that PDCD4 increases in response to cytokine withdrawal. However attempts to validate this quantitative change using immunoblotting consistently showed the opposite change – i.e. a decrease in amount of PDCD4 in response to 15 hours of cytokine withdrawal. This curious finding is examined in detail below. Figure 5.22 shows levels of PDCD4 in TF-1 cells control and 15- and 18-hours of cytokine withdrawal. Levels of the protein detected by the anti-PDCD4 antibody are obviously reduced under conditions of cytokine withdrawal. Figure 5.22. Immunoblot showing PDCD4 in TF-1 cells - control with 15- and 18-hours of cytokine withdrawal. In response to this unexpected result, the experiment was repeated multiple times. Each repeat produced similar results – an apparent decrease in the levels of PDCD4 in response to cytokine withdrawal. 274 In order to elucidate a possible difference in sample preparation between the original SILAC samples, and the later TF-1 cell lysates generated for immunoblotting, Western blots were performed using a portion of the original SILAC lysate that had been analyzed using mass spectrometry (and stored at -80°C) – with results consistently opposite those observed with the mass spectrometer. A representative immunoblot is shown in Figure 5.23. Figure 5.23. WB showing PDCD4 in control and 15 hours cytokine withdrawal (ST). Original SILAC sample (used for mass spectrometry) shown. In order to account for a possible bias in sample preparation, in which the PDCD4 was localized to the nucleus, and then accidentally discarded with the cellular debris, samples were prepared by lysing cells directly into hot sample buffer and sonicated (no lysis buffer, centrifugation, et cetera). Once again, the results were consistent with previous Western blot analyses in showing an apparent reduction in the levels of PDCD4 in response to cytokine withdrawal (see Figure 5.24). 275 Figure 5.24. WB showing PDCD4 in control with 15-hours cytokine withdrawal. Cells lysed directly into hot loading buffer. In summary, immunoblotting using several different cell preparations, and conditions of sample preparation, consistently showed a decrease in levels of PDCD4 in response to cytokine withdrawal. By contrast, three SILAC experiments showed the levels of PDCD4 to increase in response to cytokine withdrawal, with highly reproducible changes in levels, and with multiple peptides being identified in each experiment. There are several possible sources of the discrepancy between the mass spectrometry analysis results and the immunoblotting analysis results. These will be addressed individually below. 276 5.5.1 Assignment of PDCD4 Peptides to a Gene Product - Sample Analysis Using the Global Protein Machine (GPM) Because of the apparently opposite results found in the analysis by immunoblotting, when compared with analysis by mass spectrometry, one concern was that peptides may have been assigned in error to PDCD4, when they in fact should have been assigned to a different PDCD family member. While unlikely, the possiblity that peptides had been wrongly assigned was considered. As a way of confirming the assigment of peptides to the protein PDCD4, we used the GPM (www.thegpm.org) to check that the assignment of peptides to gene products had been unambiguous. The results are shown in Table 5.3. The table shows the results of analysis of each mgf file containing one or more of the twenty peptides used in PDCD4 identification. Results are grouped according to experiment and sample numbers. Peptide assignment is shown, along with confidence data as explained in the notes on the column headings to be found at the end of the table. Following each group of peptides, the GPM information on homologues is reproduced. The tables show sixteen of the twenty peptides found in Mascot to be associated with PDCD4 (the other four peptides did not appear in the GPM analysis). The results of the analysis using the GPM show that the assignment of the peptides in Mascot was correct – only a single gene product (PDCD4) shows up in the GPM analysis. All “homologues” are assigned as PDCD4 as well (data shown below). From this, we can be more confident that the assignment of peptides to the proteins PDCD4 is unambiguous. 277 Parameters used in the GPM search are as follows: Taxonomy: Homo sapiens (IPI) Include reversed sequences: mixed Find proteins with peptide log(e) < -1 and protein log(e) < -1 Fragment mass error: 0.6 Da Complete modifications: carbamidomethyl Potential modificatons: Methionine (Ox), Arginine 13C(6), Lysine 13C(6) Protein cleavage: trypsin; Semi-cleavage: yes spectrum conditioning 1. Remove redundant: no, angle: 40 2. Spectrum synthesis: yes Predefined method: FTICR (10ppm) 278 Table 5.3. Results of analysis of raw data using the GPM. Assignment of peptides to gene product is shown along with peptide-specific information. 279 S a m p le # 1 7 E x p er im en t # 1 H o m o lo g u es ( S a m p le # 1 7 , E x p er im en t # 1 O ct ) r a n k to ta l lo g (e ) lo g (I ) A c c e ss io n # 1 . 2 9 -1 6 7 .6 6 .8 6 IP I0 0 2 4 0 6 7 5 g p m D B p ro te in [0 /2 8 ] P ro g ra m m ed c el l d ea th p ro te in 4 ( N u cl ea r an ti g en H 7 3 1 -l ik e) ( N eo p la st ic tr an sf o rm at io n i n h ib it o r p ro te in ) (P ro te in 1 9 7 /1 5 a) . S o u rc e: U n ip ro t/ S W IS S P R O T Q 5 3 E L 6 A n n o ta te d d o m ai n s: sp ec tr u m lo g (e ) lo g (I ) m + h d el ta z p re st ar t se q u en ce en d p o st m o d if ic at io n s 2 3 9 1 .1 -5 .1 5 .1 2 1 8 7 5 .8 8 3 9 -0 .0 0 0 2 2 ee ik 2 7 N E IN G N W IS A S S IN E A R 4 3 in ak N [ 3 0 ] 0 .9 8 4 8 5 5 8 .1 -6 .1 4 .4 9 1 6 8 0 .7 4 2 0 .0 0 4 3 2 n ss r 5 9 D S G R G D S V S D S G S D A L R 7 5 sg lt 5 7 0 .1 -6 .9 4 .5 2 1 2 7 1 .5 8 0 5 -0 .0 0 1 9 2 d sg r 6 3 G D S V S D S G S D A L R 7 5 sg lt R [ 7 5 ] 6 .0 2 0 1 3 1 2 1 3 .1 -1 .7 5 .5 3 9 8 6 .5 5 1 7 0 .0 0 1 2 2 d al r 7 6 S G L T V P T S P K 8 5 g rl l 3 2 2 2 .1 -5 .6 5 .5 3 1 4 3 9 .8 4 6 8 0 .0 0 0 3 2 g em k 1 8 7 S G V P V L A V S L A L E G K 2 0 1 as h r 2 5 8 0 .1 -1 .8 5 .0 6 1 8 6 8 .8 9 8 -0 .0 0 3 2 m ts k 2 1 1 L L S D L C G T V M S T T D V E K 2 2 7 sf d k C [ 2 1 6 ] 5 7 .0 2 1 5 2 4 7 7 .1 -4 .3 6 .3 1 1 2 3 9 .6 5 7 9 0 .0 0 0 9 2 k ll k 2 3 5 D L P E L A L D T P R 2 4 5 ap q l 2 2 2 7 .1 -3 .7 5 .5 1 1 2 0 5 .7 0 9 6 -0 .0 0 0 3 2 d tp r 2 4 6 A P Q L V G Q F IA R 2 5 6 av g d R [ 2 5 6 ] 6 .0 2 0 1 3 2 5 8 5 .1 -7 .2 5 .7 1 7 8 8 .8 4 7 3 0 .0 0 0 5 2 fi ar 2 5 7 A V G D G IL C N T Y ID S Y K 2 7 2 g tv d C [ 2 6 4 ] 5 7 .0 2 1 5 4 1 .1 -3 .5 5 .1 1 1 0 1 1 .4 9 8 3 -0 .0 0 1 2 2 d sy k 2 7 3 G T V D C V Q A R 2 8 1 aa ld C [ 2 7 7 ] 5 7 .0 2 1 5 , R [2 8 1 ] 6 .0 2 0 1 3 9 1 3 .1 -2 .4 4 .9 1 9 6 5 .5 3 3 6 0 .0 0 1 4 2 al d k 2 8 7 A T V L L S M S K 2 9 5 g g k r M [ 2 9 3 ] 1 5 .9 9 4 9 1 5 1 6 .1 -2 .1 5 .2 6 1 9 8 3 .0 0 4 3 -0 .0 0 2 1 3 g g k r 3 0 0 K D S V W G S G G G Q Q S V N H L V K 3 1 8 ei d m 1 7 8 3 .1 -4 .5 5 .3 3 1 8 5 4 .9 0 9 3 -0 .0 0 1 4 2 g k rk 3 0 1 D S V W G S G G G Q Q S V N H L V K 3 1 8 ei d m 2 5 7 7 .1 -5 .9 5 .0 2 1 8 6 3 .8 7 9 3 -0 .0 0 2 6 2 m ll k 3 2 6 E Y L L S G D IS E A E H C L K 3 4 1 el ev C [ 3 3 9 ] 5 7 .0 2 1 5 1 1 0 3 .1 -2 5 .1 4 1 1 0 9 .5 5 0 7 -0 .0 0 0 8 2 sl w k 3 8 2 S S T IT V D Q M K 3 9 1 rg y e 3 0 3 4 .1 -7 .7 5 2 4 8 6 .2 5 6 2 -0 .0 0 8 6 2 g y er 3 9 7 IY N E IP D IN L D V P H S Y S V L E R 4 1 7 fv ee 280 r a n k to ta l lo g (e ) lo g (I ) A c c e ss io n I P R 0 0 3 8 9 1 I n it ia ti o n f ac to r eI F -4 g am m a, M A 3 # 2 . 0 -1 6 7 .6 6 .8 6 IP I0 0 2 9 0 1 1 0 g p m D B p ro te in [0 /9 4 ] P ro g ra m m ed c el l d ea th p ro te in 4 ( N u cl ea r an ti g en H 7 3 1 -l ik e) ( N eo p la st ic t ra n sf o rm at io n i n h ib it o r p ro te in ) (P ro te in 1 9 7 /1 5 a) . S o u rc e: U n ip ro t/ S W IS S P R O T Q 5 3 E L 6 A n n o ta te d d o m ai n s: I P R 0 0 3 8 9 1 I n it ia ti o n f ac to r eI F -4 g am m a, M A 3 # 3 . 1 -6 5 .1 6 .3 1 IP I0 0 6 4 7 0 8 4 .1 g p m D B p ro te in [0 /6 ] P R O G R A M M E D C E L L D E A T H 4 . 281 S a m p le # 2 8 , E x p er im en t # 3 sp ec tr u m lo g (e ) lo g (I ) m + h d el ta z P re st ar t se q u en ce en d p o st m o d if ic at io n s 6 6 5 .1 -6 .2 4 .7 1 1 6 8 0 .7 4 2 -0 .0 0 1 2 2 N ss r 5 9 D S G R G D S V S D S G S D A L R 7 5 sg lt 1 0 1 2 .1 -2 4 .6 8 9 8 6 .5 5 1 7 0 .0 0 0 4 2 D al r 7 6 S G L T V P T S P K 8 5 g rl l 1 9 6 7 .1 -3 .3 5 .4 1 1 2 3 9 .6 5 7 9 -0 .0 0 1 9 2 K ll k 2 3 5 D L P E L A L D T P R 2 4 5 ap q l 1 8 3 9 .1 -4 .5 5 .4 1 2 0 5 .7 0 9 6 0 .0 0 0 5 2 D tp r 2 4 6 A P Q L V G Q F IA R 2 5 6 av g d R [ 2 5 6 ] 6 .0 2 0 1 3 2 1 2 2 .1 -5 .5 5 .2 6 1 7 8 8 .8 4 7 3 0 .0 0 4 9 2 F ia r 2 5 7 A V G D G IL C N T Y ID S Y K 2 7 2 g tv d C [ 2 6 4 ] 5 7 .0 2 1 5 1 3 6 8 .1 -2 .2 5 .0 4 9 4 9 .5 3 8 7 0 .0 0 1 2 2 A ld k 2 8 7 A T V L L S M S K 2 9 5 g g k r 4 6 0 .1 -3 .5 4 .8 5 1 1 2 5 .5 4 5 6 0 .0 0 5 2 S lw k 3 8 2 S S T IT V D Q M K 3 9 1 rg y e M [ 3 9 0 ] 1 5 .9 9 4 9 H o m o lo g u e s (S a m p le # 2 8 , E x p e r im e n t # 3 ) r a n k to ta l lo g (e ) lo g (I ) a c c e ss io n # 1 . 1 0 -6 0 .9 6 .3 E N S P 0 0 0 0 0 3 7 6 8 1 6 g p m D B p ro te in [2 /2 8 ] P ro g ra m m ed c el l d ea th p ro te in 4 ( N u cl ea r an ti g en H 7 3 1 -l ik e) ( N eo p la st ic t ra n sf o rm at io n i n h ib it o r p ro te in ) (P ro te in 1 9 7 /1 5 a) . S o u rc e: U n ip ro t/ S W IS S P R O T Q 5 3 E L 6 A n n o ta te d d o m ai n s: I P R 0 0 3 8 9 1 I n it ia ti o n f ac to r eI F -4 g am m a, M A 3 # 2 . 0 -6 0 .9 6 .3 E N S P 0 0 0 0 0 2 8 0 1 5 4 g p m D B p ro te in [2 /9 4 ] P ro g ra m m ed c el l d ea th p ro te in 4 ( N u cl ea r an ti g en H 7 3 1 -l ik e) ( N eo p la st ic t ra n sf o rm at io n i n h ib it o r p ro te in ) (P ro te in 1 9 7 /1 5 a) . S o u rc e: U n ip ro t/ S W IS S P R O T Q 5 3 E L 6 282 ra n k to ta l lo g (e ) lo g (I ) a c c e ss io n A n n o ta te d d o m ai n s: I P R 0 0 3 8 9 1 I n it ia ti o n f ac to r eI F -4 g am m a, M A 3 # 3 . 1 -1 9 .9 5 .1 4 IP I0 0 6 4 7 0 8 4 .1 g p m D B p ro te in [1 /6 ] P R O G R A M M E D C E L L D E A T H 4 . 283 S a m p le # 2 4 , E x p er im en t # 2 sp ec tr u m lo g (e ) lo g (I ) m + h d el ta z P re st ar t se q u en ce en d p o st m o d if ic at io n s 6 1 4 .1 -6 .6 4 .4 1 1 2 6 5 .5 6 0 4 -0 .0 0 2 9 2 D sg r 6 3 G D S V S D S G S D A L R 7 5 sg lt 2 8 6 1 .1 -5 .8 4 .6 2 1 4 3 9 .8 4 6 8 0 2 g em k 1 8 7 S G V P V L A V S L A L E G K 2 0 1 as h r 2 1 9 0 .1 -4 5 .1 2 1 2 4 5 .6 7 8 1 -0 .0 0 1 8 2 K ll k 2 3 5 D L P E L A L D T P R 2 4 5 ap q l R [ 2 4 5 ] 6 .0 2 0 1 3 2 0 3 5 .1 -4 .9 5 .2 1 1 2 0 5 .7 0 9 6 -0 .0 0 0 2 2 D tp r 2 4 6 A P Q L V G Q F IA R 2 5 6 av g d R [ 2 5 6 ] 6 .0 2 0 1 3 9 9 0 .1 -2 .9 5 .0 9 1 1 0 9 .5 5 0 7 -0 .0 0 0 8 2 S lw k 3 8 2 S S T IT V D Q M K 3 9 1 rg y e H o m o lo g u es ( S a m p le # 2 4 , E x p er im en t # 2 ) r a n k to ta l lo g (e ) lo g (I ) a c c e ss io n # 1 . 9 -4 6 .2 5 .8 9 E N S P 0 0 0 0 0 3 7 6 8 1 6 g p m D B p ro te in [3 /2 8 ] P ro g ra m m ed c el l d ea th p ro te in 4 ( N u cl ea r an ti g en H 7 3 1 -l ik e) ( N eo p la st ic tr an sf o rm at io n i n h ib it o r p ro te in ) (P ro te in 1 9 7 /1 5 a) . S o u rc e: U n ip ro t/ S W IS S P R O T Q 5 3 E L 6 A n n o ta te d d o m ai n s: IP R 0 0 3 8 9 1 I n it ia ti o n f ac to r eI F -4 g am m a, M A 3 # 2 . 0 -4 6 .2 5 .8 9 E N S P 0 0 0 0 0 2 8 0 1 5 4 g p m D B p ro te in [4 /9 4 ] P ro g ra m m ed c el l d ea th p ro te in 4 ( N u cl ea r an ti g en H 7 3 1 -l ik e) ( N eo p la st ic tr an sf o rm at io n i n h ib it o r p ro te in ) (P ro te in 1 9 7 /1 5 a) . S o u rc e: U n ip ro t/ S W IS S P R O T Q 5 3 E L 6 284 r a n k to ta l lo g (e ) lo g (I ) a c c e ss io n A n n o ta te d d o m ai n s: IP R 0 0 3 8 9 1 I n it ia ti o n f ac to r eI F -4 g am m a, M A 3 # 3 . 0 -1 6 .9 5 IP I0 0 6 4 7 0 8 4 .1 g p m D B p ro te in [3 /6 ] P R O G R A M M E D C E L L D E A T H 4 . S a m p le # 2 2 , E x p er im en t # 2 sp ec tr u m lo g (e ) lo g (I ) m + h d el ta z p re st ar t se q u en ce en d p o st m o d if ic at io n s 5 8 7 .1 -5 .4 4 .7 7 1 2 6 5 .5 6 0 4 0 .0 0 0 6 2 d sg r 6 3 G D S V S D S G S D A L R 7 5 sg lt 2 2 5 0 .1 -2 .7 4 .8 1 1 2 4 5 .6 7 8 1 -0 .0 0 1 7 2 k ll k 2 3 5 D L P E L A L D T P R 2 4 5 ap q l R [ 2 4 5 ] 6 .0 2 0 1 3 2 0 6 8 .1 -3 .6 5 .1 7 1 2 0 5 .7 0 9 6 -0 .0 0 3 2 d tp r 2 4 6 A P Q L V G Q F IA R 2 5 6 av g d R [ 2 5 6 ] 6 .0 2 0 1 3 8 7 2 .1 -2 .2 4 .7 4 9 6 5 .5 3 3 6 0 .0 0 0 9 2 al d k 2 8 7 A T V L L S M S K 2 9 5 g g k r M [ 2 9 3 ] 1 5 .9 9 4 9 H o m o lo g u es ( S a m p le # 2 2 , E x p er im en t # 2 ) r a n k to ta l lo g (e ) lo g (I ) A c c e ss io n # 1 . 7 -3 0 .9 5 .8 3 E N S P 0 0 0 0 0 3 7 6 8 1 6 g p m D B p ro te in [1 6 /2 8 ] P ro g ra m m ed c el l d ea th p ro te in 4 ( N u cl ea r an ti g en H 7 3 1 -l ik e) ( N eo p la st ic t ra n sf o rm at io n i n h ib it o r p ro te in ) (P ro te in 1 9 7 /1 5 a) . S o u rc e: U n ip ro t/ S W IS S P R O T Q 5 3 E L 6 A n n o ta te d d o m ai n s: I P R 0 0 3 8 9 1 I n it ia ti o n f ac to r eI F -4 g am m a, M A 3 285 r a n k to ta l lo g (e ) lo g (I ) A c c e ss io n # 2 . 0 -3 0 .9 5 .8 3 E N S P 0 0 0 0 0 2 8 0 1 5 4 g p m D B p ro te in [4 9 /9 4 ] P ro g ra m m ed c el l d ea th p ro te in 4 ( N u cl ea r an ti g en H 7 3 1 -l ik e) ( N eo p la st ic t ra n sf o rm at io n i n h ib it o r p ro te in ) (P ro te in 1 9 7 /1 5 a) . S o u rc e: U n ip ro t/ S W IS S P R O T Q 5 3 E L 6 A n n o ta te d d o m ai n s: I P R 0 0 3 8 9 1 I n it ia ti o n f ac to r eI F -4 g am m a, M A 3 # 3 . 0 -5 .4 5 .0 2 IP I0 0 6 4 7 0 8 4 .1 g p m D B p ro te in [5 /6 ] P R O G R A M M E D C E L L D E A T H 4 . S p e c tr u m c o lu m n n o te s (f r o m w w w .t h e g p m .o r g ) 1 . sp ec tr u m : w ri tt en i n t h e fo rm \" X .Y \", w h er e X i s a u n iq u e id en ti fi er f o r a p ar ti cu la r ta n d em m as s sp ec tr u m i n t h is d at a se t an d Y i s an i d en ti fi er f o r th is p ar ti cu la r se q u en ce s o lu ti o n . 2 . lo g (e ): t h e b as e- 1 0 l o g o f th e ex p ec ta ti o n t h at a n y p ar ti cu la r p ep ti d e as si g n m en t w as m ad e at r an d o m ( E -v al u e) . 3 . lo g (I ): t h e b as e- 1 0 l o g o f th e su m o f th e fr ag m en t io n i n te n si ti es i n t h e ta n d em m as s sp ec tr u m u se d t o m ak e th is a ss ig n m en t. 4 . m + h : th e ca lc u la te d m as s o f th e p ro to n at ed p ar en t io n f o r th is s eq u en ce a ss ig n m en t. 5 . d el ta : th e d if fe re n ce b et w ee n t h e m ea su re d a n d c al cu la te d p ro to n at ed p ar en t io n m as se s. 6 . z: t h e ch ar g e o f th e p ar en t io n m ea su re d . 7 . se q u en ce : th e se q u en ce o f th e as si g n ed p ep ti d e se q u en ce . T h e se q u en ce s im m ed ia te ly N -t er m in al a n d C -t er m in al to t h e as si g n ed p ep ti d e in t h e p ro te in s eq u en ce a re a ls o s h o w n . 8 . n : th e n u m b er o f o b se rv at io n s o f th is p ep ti d e se q u en ce i n G P M D B . 9 . ! : th e fr eq u en cy o f o b se rv at io n f o r th is p ep ti d e in t h is p ro te in ( o n ly a v ai la b le f o r so m e sp ec ie s) . H o m o lo g u e c o lu m n n o te s (f r o m w w w .t h e g p m .o r g ) 1 . ra n k : th e re la ti v e p o si ti o n o f a p ro te in i n t h e li st , o n t h e b as is o f E -v al u es . If m u lt ip le p ro te in s h av e th e sa m e E -v al u e, th e re la ti v e o rd er o f th o se p ro te in s is n o t m ea n in g fu l. 286 th e re la ti v e o rd er o f th o se p ro te in s is n o t m ea n in g fu l. 2 . to ta l: t h e to ta l n u m b er o f ta n d em m as s sp ec tr a th at c an b e u n iq u el y a ss ig n ed t o t h is p ro te in . 3 . lo g (e ): t h e b as e- 1 0 l o g o f th e ex p ec ta ti o n t h at a n y p ar ti cu la r p ro te in a ss ig n m en t w as m ad e at r an d o m ( E -v al u e) . 4 . lo g (I ): t h e b as e- 1 0 l o g o f th e su m o f th e fr ag m en t io n i n te n si ti es i n t h e ta n d em m as s sp ec tr a u se d t o m ak e th is a ss ig n m en t. 5 . ac ce ss io n : th e ac ce ss io n n u m b er a n d d es cr ip ti o n f o r th is p ro te in . 1 . [x /y ]: y i s th e n u m b er o f ti m es t h is a cc es si o n n u m b er h as b ee n f o u n d i n G P M D B , x i s th e re la ti v e ra n k o f th is p ar ti cu la r o b se rv at io n c o m p ar ed t o a ll o th er s, b as ed o n i t E -v al u e. 287 5.5.2 Possible PDCD4 Protein Degradation Protein degradation could distort the results in either the Western blots or the mass spectrometry analysis in the following ways. Degraded peptides would appear in more than a single sample run for the mass spectrometry analysis. By reference to Table 5.2, we can see that of twenty peptides used to identify PDCD4, one peptide (APQLVGQFIAR) appears in four consecutive samples. A further two peptides appear in two consecutive samples. Lastly, one peptide appears in two non-consecutive samples. In all cases, these peptides appearing in greater than one slice were observed in samples from the second experiment – where the gel was cut into 90 slices. In the case of the peptide being found in two consecutive samples, the peptide band might well have been transected in the gel cutting. Since each sample represents a single slice of polyacrylamide gel, it may be inferred from the limited location in the gel of each peptide that the majority of the peptides have not been degraded. It may be relevant to note that the immunogenic peptide is found in two non-consecutive samples – number 22 and 24 in the second experiment (90 slices). This indicates that the peptide was located in two different areas on the gel, and might point to degradation of the protein in this case. However, this single instance does not strongly support the possibility of degradation of the protein – the majority of peptides were found within a single gel slice, or in two consecutive gel slices. Protein degradation might account for the apparent decrease in the amount of PDCD4 under conditions of cytokine withdrawal observed by Western blot, but only if the antibody epitopes were spread along the length of the protein, and degradation products could in this way be visualized. However, the antibody was raised using fifteen amino acids at the C-terminus of PDCD4, and so degradation would not appear on the 288 membrane unless the C-terminus end of the protein were degraded and ran at different molecular weights. Reference to the Western blots shown as figures 5.16, 5.17 and 5.18 shows that the protein appears as a single band, not as a smear, hence there is no indication of the protein being picked up at several (different) molecular weights on the gel. Analysis of the sequence of PDCD4 using the ExPASy Peptidecutter shows that there are no putative caspase cleavage sites in PDCD4 – there are, however, many other putative enzyme cleavage sites, allowing for the possibility of non-caspase cleavage of PDCD4 under conditions of cytokine withdrawal. In summary, there is limited evidence of protein degradation in the mass spectrometry data. Four peptides out of twenty are observed in more than one sample (i.e. gel slice). In one case the peptide appears in four consecutive samples, in another (the immunogenic peptide) the peptide appears in two non-consecutive samples (i.e. in two samples separated by separated by a third sample). In the other two cases the peptides appear in two consecutive samples. I have found no evidence of apparent protein fragmentation in the Western blot data, but would not expect to find this, given the limited size of the epitope used to raise the antibodies. On the basis of the evidence, protein degradation could not acount for the apparent reduction in amount of PDCD4 observed on Western blot. 289 5.5.3 Might the Apparent Protein Ratio be Distorted by Inconsistent Quantitation Ratios? The overall quantitation ratio for the protein is composed of the individual ratios of the peptides used to identify the protein. It is possible that the overall protein quantitation ratio is distorted by one or two large peptide ratios showing a change in the wrong direction. However, examination of the individual peptide ratios shows this to be not the case – the ratios are very similar (with two exceptions, see Table 5.2), and so the average ratio is not distorted by a few large ratios in the incorrect direction of change. Examination of the peptide spectra, shown in Figure 5.15 shows high quality spectra on which the peptide ratios have been based. 5.5.4 Might PDCD4 Levels be Low in TF-1, Leading to Inaccurate Quantitation? PDCD4 might be present in low concentration in TF-1 cells, which could be a source for quantitation error. However, identification of PDCD4 is based on mass spectrometry identification and quantitation of twenty peptides – indicating that the protein is not very low in abundance, or low in concentration in TF-1 cells. Further, the antibody response appears to be robust (if indeed the antibody is correctly identifying PDCD4 – see below) 290 5.5.5 Consistency of Sample Preparation Between Experiments It is conceivable that if samples were prepared in a different manner for each method of analysis, e.g. causing under-representation of nuclear proteins in one form of preparation, that the changes in expression may not be correctly attributed. However, the same procedure was used in the preparation of all samples. And for one series of immunoblots, the original SILAC sample (which had been stored at -80°C) was used, and yielded results consistent with the other immunoblots, and opposite to those obtained by mass spectrometry. 5.5.6 Possible Role for Subcellular Distribution of PDCD4 Again, it is reasonable to suggest that the sub-cellular localization of PDCD4 could determine how it is quantified in different experiments. However, this question was addressed by lysing cells directly into hot lysis buffer – again with results consistent with the other immunoblots. 5.5.7 Differing Temporal Dynamics The question of the temporal dynamics of the change in PDCD4 concentration is another consideration. If the literature were to suggest an increase at time “x”, and I showed a decrease at time “y”, the difference in time of analysis could provide an explanation for the discrepancy, however, in all cases referred to here - both for mass spectrometry analysis, and for immunoblotting analysis - the cells were examined after 15 hours of cytokine withdrawal, hence a difference in the temporal dynamics of the protein concentration change cannot 291 explain the apparent discrepancy between the mass spectrometry results and the immunoblotting results. 5.5.8 Is the Antibody Detecting the Correct Protein? The anti-PDCD4 antibody was obtained from AbCam (www.abcam.com). The antibody has been confirmed in discussion with the company to be sourced from Rockland Immunochemicals, which had obtained the original material from the Colburn group, who carried out the initial work on PDCD4 (H. S. Yang et al., 2001). The antibody is a rabbit polyclonal, generated using a 15 amino acid peptide corresponding to residues 455 - 469 of PDCD4. This polypeptide is the C-terminus of PDCD4, and has the sequence FVSEGDGGRLKPESY. The underlined portion of the epitope was observed three times by mass spectrometry (in three different samples). That is, the immunogenic peptide is present in the mass spectrometry analysis. Further, the peptide (or portions of it) is not found in any other protein in the data set. Concerning the image shown, the quality of the spectra for this peptide is not uniformly excellent - the best of the three spectra is shown in Figure 5.15. In discussion with Abcam, after explaining our unanticipated results, it was decided that a different source of the antibody should be trialled. The original antibody was provided from a US supplier. The second lot was supplied from the UK. I observed no difference in the apparent behaviour of the protein when detected with the second antibody, and so it was decided to investigate the gel position of the protein as detected by mass spectrometry. 292 Reference to Table 5.3 will show the gel slice numbers from which the peptides identified by mass spectrometry have been detected. All peptides in Experiment #1 were detected in gel slice #17. By referring to Figure 3.4 (Chapter 3) it is possible to approximate the molecular weight on the gel at which slice #17 was cut. By graphing out the Rf values (relative migration) of the molecular weight ladder and plotting these against the log of the molecular weight standards, a standard curve of protein migration for each gel can be derived. This gives an almost linear graph, to which a trendline can be fitted, and the equation for the trendline determined. Using the equation for the trendline of the standard curve, it is possible to approximate the molecular weight of an “unknown” by measuring the distance the unknown has run on the gel. This gives the following information - slice #17 was cut from a position at approximately 53 kDa (PDCD4 has a molecular weight of 51.7 kDa), whereas the anti-PDCD4 antibody has detected a band at approximately 68 kDa. Hence, after several attempts with various batches of anti-PDCD4 antibodies, the antibodies consistently detected a protein with an apparent molecular weight of approximately 68 kDa. While it is possible only to estimate the molecular weights from which the gel slices were cut, and being mindful of the caveat that pre-stained molecular weight markers do not always run exactly true to molecular weight, there is strong evidence to support the idea that in this case, the antibody is not detecting PDCD4. In conclusion, when examining PDCD4, a tumour suppressor that appeared in each of three mass spectrometry experiments and was shown to increase in response to 15 hours of cytokine withdrawal, an attempt was made to verify this result using immunoblotting. 293 Immunoblots against PDCD4 were performed with a variety of lysates, including the original SILAC lysates, and analyzed with different systems – Licor Odyssey, ECL with film, and ECL using a CDC camera. Two different antibodies were tested. In all cases, the behaviour of the protein when viewed with immunoblotting was opposite the behaviour seen with mass spectrometry analysis. One can conclude, based on the migration of the protein identified in the MS analysis, compared to the protein identified by immunoblotting, that the antibody was detecting a protein other than PDCD4. 294 5.6 Conclusions In conclusion, attempts to validate our mass spectrometry quantitation results using a different approach – that of immunoblotting and densitometry, were for the most part successful. In one case, the difficulty in obtaining a robust antibody visualization gave a result that was weaker than expected. One way to improve this situation might be to try different antibodies, or different visualization techniques – e.g. infrared fluorescence -tagged antibodies. In the case of PDCD4, where a result opposite to that expected by mass spectrometry was consistently obtained, the divergent results appear to have been caused by the antibody picking up a protein that is different from PDCD4. In general, this approach (using Western blotting) has been valuable in confirming the results obtained by SILAC and mass spectrometry quantitation. 295 6 Determination of Rates of Protein Synthesis – Exploratory Data 6.1 Introduction To observe differences in the rates of protein synthesis under control and starving conditions in TF-1 cells, a “pulse-chase” experiment was designed, using a stable isotope of carbon as the metabolic label. Information on the rates of protein synthesis, used in association with the identification of proteins which show an increase or decrease in concentration in response to cytokine withdrawal, combine to contribute to our understanding of the dynamics of the changes observed in protein concentrations in response to cytokine withdrawal. For example, cellular changes that cause a protein to increase in concentration (apparent up-regulation) in response to cytokine withdrawal, might occur in several ways: an increase in the rate of protein synthesis, a decrease in the rate of protein degradation, or an increase in the longevity of the protein mRNA, could each result in an increase in the concentration of the protein. In a similar way, a decrease in protein concentration might be caused by any of several mechanisms. By determining the half-life of a protein under normal conditions, and comparing this with the half-life of the same protein observed under conditions of cytokine withdrawal, the goal was to be able to identify more clearly the possible explanation of an observed increase or decrease in protein concentration. 296 The intention of this experiment was to use Multiple Reaction Monitoring (MRM), a technique which is an extension of Selected Reaction Monitoring (Wolf-Yadlin, Hautaniemi, Lauffenburger, & White, 2007), in order to more easily detect low abundance proteins, and to quantitate differences observed in the incorporation of the isotopic labels, allowing determination of protein half-lives. The usual mode in which mass spectrometers are operated is “information-dependent acquisition” (IDA). Using IDA, the mass spectrometer cycles through a full-spectrum mass scan, and then selects (for fragmentation) the top few most abundant peptides found in the just-completed mass scan, for MS/MS analysis. In MRM mode, the mass spectrometer is tuned to monitor specifically selected precursor ion- to-fragment ion “transitions” that may be used to detect and thereby uniquely identify a particular protein (or other molecule) of interest. The use of MRM offers not only improved reproducibility of mass analysis experiments, but also improved sensitivity. Because the instrument is set to monitor only selected m/z events, this results in an increase in the proportion of the duty cycle spent analyzing each m/z being scanned, and hence an increase in detection time, allowing an improved signal-to-noise ratio. The improved signal-to-noise ratio allows for a significant increase in sensitivity. Further, there is a substantial increase in dynamic range: about 5 orders of magnitude on the Q- TRAP® (Wolf-Yadlin et al., 2007). However, the improved sensitivity and dynamic range do not come without cost – the disadvantages of an MRM approach to protein identification and quantitation include the requirement for “a priori” knowledge of the proteins to be detected, and a limitation in the number of transitions that can be monitored with current instrumentation and software. 297 The first step in designing an MRM-based experiment is to determine which proteins would be considered to be of interest. Using data obtained from three previous SILAC experiments (described in earlier chapters), twenty-four proteins were chosen based on the concentration changes observed, both increased and decreased (I chose those with the greatest concentration changes). For each of the twenty-four, a search of the gpm’s protein database (www.thegpm.org) was conducted, which allowed for the determination of MRM transitions based on historical data from experiments stored in the (public) gpm database. MRM transitions are determined based on peptides from the protein of interest that are most commonly detected in mass spectrometers, so called “proteotypic” peptides, which are unique to the protein of interest, thus allowing a positive identification to be determined on the basis of detection of this single peptide. One potential source of difficulty with using this approach to determine MRM transitions is that proteins of interest need to be present in the gpm database, and since the database is comprised of data freely submitted by members of the community, the data is not a complete representation of the proteome of any organism. In this case, most of the proteins of interest were present in the gpm database, and it was possible to determine theoretical transitions for these. Once the MRM transitions had been determined, they could be entered into the software controlling the Q-Trap® to cause the mass spectrometer to scan only those areas of the m/z spectrum in which each specific transition species might be found. Detection of a transition would be considered a positive identification for the presence of the protein of interest. Protein quantitation could then be undertaken as previously described. The results of this initial experiment would be used to confirm the presence of the proteins of interest, and to allow quantitation of these. The usual reason for adopting this approach is to allow the detection of low abundance proteins that 298 might otherwise not be detected due to the presence of multiple peptides from higher abundance proteins in the mass spectrometer at the same time. In this case, because the work was preliminary, and several aspects were unknown, proteins that had previously been detected using the FT-ICR mass spectrometer were chosen. Another element to be determined was the protein separation requirements (sensitivity) of the approach, and so it was that samples would be run on the FT-ICR mass spectrometer, to determine if the twenty- four proteins of interest, previously detected from large format gels, might be detected in samples separated using a mini-gel format, given the (much) higher sensitivity expected when the MRM approach was utilized. In this way, it was possible to confirm that the proteins would be found, and the expected peptides identified - the “proteotypic” peptides for the proteins of interest (with the caveat that peptides might not be generated by, or “fly” in the Q-Trap with the same abundance as in the FT-ICR; but that was one of the unknowns, and exploring factors such as this was part of the reason for these experiments). 6.2 Experimental Overview TF-1 cells were grown in specially formulated RPMI 1640 containing 13 C-arginine and 13 C- lysine to allow uniform incorporation, as described in previous chapters. Cells were divided into two sets of nine to allow for evaluation under control and starving conditions at eight time-points – 30 minutes, 1, 2, 3, 6, 9, 12, and 15 hours. At t=0, cells were washed three times in sterile PBS, and re-suspended in RPMI 1640 containing 12 C-arginine and 12 C-lysine (wild-type amino acid-containing medium). GM-CSF was added to the set labeled “control” 299 (in the form of CGM1 cell conditioned medium, as described previously). The GM-CSF was omitted from the set labeled “starving”. Cells were allowed to grow in wild-type amino acid- containing medium for the specified times. At each time point, cells were collected, washed once in PBS, counted and lysed in solubilization buffer, as described in Chapter 2. A Bradford protein concentration assay was performed on each sample, and the samples quickly frozen at -80°C. One sample, t=15 hours, was chosen for the initial analysis described here. Control and starving samples were loaded in equal quantities and separated on a 9% SDS-PAGE mini- gel, prior to cutting into 27 slices for sample pre-fractionation. Samples from the “control” and “starving” lanes were run on the FT-ICR using the same conditions as outlined in Chapter 2. Initially, sixteen slices were analyzed – two sets of eight slices numbered 6, 7, 11, 12, 18, 19, 20 and 21 cut from both the “control” lane and “starving” lane of the gel. These slices were chosen based on the molecular weights of four proteins of interest from the previous set of experiments. These four proteins consisted of two proteins previously observed to increase in concentration in response to cytokine withdrawal - Chromobox Protein Homologue 3 (20.8 kDa) and Cdc42 (21.3 kDa), and two proteins observed to decrease in concentration in response to cytokine withdrawal - Thymidylate synthase (35.7 kDa) and HMG CoA Synthase (57.3 kDa). These four proteins were chosen based on the magnitude of the previously observed quantitative change in response to cytokine withdrawal, the number of 300 peptides found and used to identify each protein, and the availability of antibodies for further study of the proteins. Samples run on the FT-ICR were analyzed as previously described (Chapter 2). The raw data were pre-processed before analysis using Mascot and then MSQuant, as described earlier. The raw results obtained by the Mascot search and subsequent MSQuant analysis were further processed as outlined above. In brief, after protein identification and quantitation, proteins identified by only one peptide were removed; proteins identified by two peptides where both peptides were not greater than 7 AA residues in length were removed, entries labeled as keratin were removed where the ratio was very low, for example 0.02, which was determined to be artifact. Note that keratin does appear naturally, but naturally occurring keratin was observed to show a ratio of about 1.3 (i.e. the protein was 13 C-labelled). Other proteins with very low or very high ratios were removed after manual examination of the spectra, where the spectra appeared to be artifact. Proteins were further analyzed using the Gene Ontologies, as previously described (Chapters 2 and 4). In brief, generation of the Level 4 Directed Acyclic Graphs (DAGs) proceeded as earlier described - the protein accession numbers allowed conversion to fasta format, which could then be analyzed using Blast2GO (B2G), followed by the generation of DAGs using Level 4 of the Gene Ontology. In the previously described SILAC experiments, most of the proteins identified were unchanged in response to cytokine withdrawal and since the vast majority remained unchanged between the control and starving populations, the average i/w ratio was 1:1. In the current experiment, the average i/w ratio would not be expected to cluster around 1. In order 301 to identify interesting changes, the cut-off ratios had to be based on something other than a multiple of the standard deviation of the i/w ratios. In this case we are attempting to measure protein turnover, based on a measure of the loss of 13 C starting material. Hence we are not measuring the decay of a defined quantity of starting material; rather, we calculate the (instantaneous) rate of loss of the 13 C starting material, measured at a time point of interest, and use this instantaneous rate as the average value for the rate of loss over the time period. That is, we assume a linear change in concentration over the time between 0 and 15 hours. This is a valid assumption, since any non-linear changes would become apparent by differences in the instantaneous rates of loss of 13 C-labelled proteins when observing different time points. In this way we have made a simplification to the calculation, which enables us to use the data available. For the purpose of this discussion, we define half-life as being the predicted or expected time to obtain a 1:1 isotope ratio based on protein synthesis and degradation. Using the formula below, it is possible to calculate a half-life value for any protein identified, based on the mass spectrometry data. The derivation for this formula is given in Appendix 3. We have previously established that changes occur in the concentration for some proteins under conditions of cytokine withdrawal. In this situation, the calculation for protein half-life must take into account the change in concentration. Here we use two different instantaneous rates – the rate of change of the total concentration of the protein, and the rate of change of the concentration of 13 C-labelled protein. 302 The formula for calculating half-lives is: Half-life = (ln 2) / { [ (ln 2)/J ] - [ (ln b1) / t1 ] }…………………………………….§1 Where J is a value calculated as (ln 2)/V, and V is the instantaneous rate of change of the total ( 12 C + 13 C) concentration of the protein of interest (as % per hour). The term “b1” is given by [ i/w ratio / (1 + i/w ratio) ] as shown in Appendix 3. Using this formula, it is possible to calculate the half-life for each protein identified in this subset of the samples for “control” and “starving” cells at the 15-hour time-point. The use of the 15-hour time point makes the data more relevant to the work described in the previous chapters. Data for those proteins with a half-life of less than fifteen hours is presented, (based on the time-point chosen for evaluation) along with data for proteins with a half-life of greater than twenty-four hours, thus describing those proteins showing the longest half-lives, and those with the shortest half-lives. 303 6.3 Results In the sub-set of gel slices processed from the fifteen-hour time point analyzed, it was possible to identify and quantitate 359 unique proteins in the “control” sample, and 343 proteins in the “starving” sample. Results are presented below. Interestingly, none of the four proteins for which identification was attempted (Chromobox Protein Homologue 3, Cdc42, Thymidylate synthase and HMG CoA Synthase) were identified in this analysis. One possible explanation for this is that the samples were fractionated using a mini-gel format with fewer slices cut, rather than the large format used in the previous SILAC experiments (or the single mini-gel format used earlier, and cut into 40 slices). 6.3.1 Proteins Identified in “Control” Sample Shown from the “control” sample are proteins with i/w ratios of less than 0.67 (corresponding to a half life of less than 11.4 hours) and i/w ratios greater than 1.85 (corresponding to a half–life of greater than 24 hours. In this way we present those proteins with the greatest rate of change, and the smallest rate of change. Table 6.1 lists 68 proteins with an i/w ratio of less than 0.67, showing rapid incorporation of 12 C in fifteen hours. Table 6.2 lists proteins with a half-life of greater than twenty-four hours (i/w ratio greater than 1.84; slower incorporation of 12 C in 15 hours; 89 proteins). Together, these two groups include approximately 43% of all the proteins identified and quantitated in the eight samples analyzed from the control set. 304 Table 6.1 List of proteins from control sample at 15 hours, identified and quantitated as having an isotope/wild-type ( 13 C/ 12 C) ratio of less than 0.67, representing a half-life of less than 11.4 hours, and indicating rapid incorporation of 12 C over 15 hours, and thus having a more rapid rate of protein turn over. Table shows IPI Accession Number, Peptide Mass (Da), the number of peptides identified, and the Mascot score, as well as the protein name, i/w ratio and the calculated half-life. 305 A c c e ss io n n u m b e r M a ss [D a ] P e p ti d e s Id e n ti fi e d T o ta l S c o r e D e sc r ip ti o n i/ w r a ti o C a lc u la te d h a lf -l if e (h r s) IP I0 0 0 0 0 0 7 5 4 4 9 9 7 3 1 3 5 T G F B 1 T ra n sf o rm in g g ro w th f ac to r b et a- 1 0 .1 3 5 4 .9 IP I0 0 0 1 2 0 0 7 4 8 2 5 5 2 1 5 2 A H C Y A d en o sy lh o m o cy st ei n as e 0 .1 3 7 4 .9 IP I0 0 0 2 0 9 4 4 4 8 5 9 7 5 2 9 5 F D F T 1 S q u al en e sy n th et as e 0 .1 4 3 5 .0 IP I0 0 0 2 4 9 1 1 2 9 0 3 2 2 1 0 1 E R P 2 9 E n d o p la sm ic r et ic u lu m p ro te in E R p 2 9 0 .1 4 9 5 .1 IP I0 0 0 1 2 0 7 4 7 1 1 8 4 2 1 3 9 H N R N P R H et er o g en eo u s n u cl ea r ri b o n u cl eo p ro te in R 0 .1 5 4 5 .2 IP I0 0 0 0 6 0 7 0 4 1 0 9 1 2 9 1 C D 3 4 I so fo rm C D 3 4 -F o f H em at o p o ie ti c p ro g en it o r ce ll an ti g en C D 3 4 0 .1 7 8 5 .5 IP I0 0 0 1 0 1 3 0 4 2 6 6 5 2 6 8 G L U L G lu ta m in e sy n th et as e 0 .1 8 4 5 .6 IP I0 0 0 1 7 5 9 2 8 3 9 8 6 7 3 6 8 L E T M 1 L E T M 1 a n d E F -h an d d o m ai n -c o n ta in in g p ro te in 1 , m it o ch o n d ri al 0 .2 0 3 5 .8 IP I0 0 2 9 3 3 0 7 4 8 2 7 4 3 1 7 9 A D F P A d ip o p h il in 0 .2 2 9 6 .2 IP I0 0 2 9 6 1 9 0 2 5 8 6 1 2 1 2 7 C 1 0 o rf 5 8 U n ch ar ac te ri ze d p ro te in C 1 0 o rf 5 8 0 .2 3 4 6 .3 IP I0 0 0 3 3 1 4 3 2 5 3 2 9 3 2 0 4 E IF 3 K E u k ar y o ti c tr an sl at io n i n it ia ti o n f ac to r 3 s u b u n it K 0 .2 4 6 .3 IP I0 0 4 7 2 0 1 3 4 1 1 5 1 8 5 0 6 H L A c la ss I h is to co m p at ib il it y a n ti g en , A -3 3 a lp h a ch ai n 0 .2 4 6 .3 IP I0 0 2 1 6 1 1 3 7 6 3 6 9 1 1 5 2 7 K IF 2 C I so fo rm 2 o f K in es in -l ik e p ro te in K IF 2 C 0 .2 4 7 6 .4 IP I0 0 4 1 0 0 6 7 1 0 3 1 4 8 2 1 2 1 Z C 3 H A V 1 I so fo rm 1 o f Z in c fi n g er C C C H -t y p e an ti v ir al p ro te in 1 0 .2 5 2 6 .5 IP I0 0 7 6 0 5 5 4 4 1 2 3 6 3 1 8 8 H L A c la ss I h is to co m p at ib il it y a n ti g en , A -6 9 a lp h a ch ai n 0 .2 5 2 6 .5 IP I0 0 4 7 0 8 9 1 8 9 6 8 4 3 1 7 2 C S D E 1 I so fo rm L o n g o f C o ld s h o ck d o m ai n -c o n ta in in g 0 .2 5 3 6 .5 306 A c c e ss io n n u m b e r M a ss [D a ] P e p ti d e s Id e n ti fi e d T o ta l S c o r e D e sc r ip ti o n i/ w r a ti o C a lc u la te d h a lf -l if e (h r s) p ro te in E 1 IP I0 0 0 1 7 3 0 3 1 0 5 4 1 8 2 1 4 2 M S H 2 D N A m is m at ch r ep ai r p ro te in M sh 2 0 .2 5 6 6 .5 IP I0 0 0 0 9 9 4 3 2 1 6 2 6 5 3 1 9 T P T 1 T u m o r p ro te in , tr an sl at io n al ly -c o n tr o ll ed 1 0 .2 7 4 6 .8 IP I0 0 0 0 9 5 4 2 6 5 0 8 5 2 8 8 M A G E D 2 I so fo rm 1 o f M el an o m a- as so ci at ed a n ti g en D 2 0 .2 7 6 6 .8 IP I0 0 0 7 2 3 7 7 3 3 4 6 9 2 1 1 8 S E T I so fo rm 1 o f P ro te in S E T 0 .3 0 4 7 .1 IP I0 0 0 1 0 1 5 7 4 3 9 7 5 8 4 2 2 M A T 2 A S -a d en o sy lm et h io n in e sy n th et as e is o fo rm t y p e - 2 0 .3 1 7 .2 IP I0 0 0 2 6 8 2 9 4 1 1 7 3 2 9 8 D H P S I so fo rm L o n g o f D eo x y h y p u si n e sy n th as e 0 .3 1 4 7 .3 IP I0 0 0 2 5 5 1 2 2 2 8 2 6 4 2 7 5 H S P B 1 H ea t sh o ck p ro te in b et a- 1 0 .3 1 7 7 .3 IP I0 0 5 5 4 5 2 1 2 1 3 8 3 2 8 1 F T H 1 F er ri ti n h ea v y c h ai n 0 .3 2 1 7 .3 IP I0 0 1 4 9 6 8 0 2 4 8 4 8 3 1 0 8 T E S C T es ca lc in 0 .3 4 6 7 .7 IP I0 0 0 1 2 5 3 5 4 5 5 8 1 1 2 6 5 5 D N A JA 1 D n aJ h o m o lo g s u b fa m il y A m em b er 1 0 .3 5 2 7 .7 IP I0 0 2 1 5 6 1 0 5 2 4 9 2 2 8 0 M P P 1 5 5 k D a er y th ro cy te m em b ra n e p ro te in 0 .3 5 2 7 .7 IP I0 0 0 1 4 3 1 2 8 9 4 4 4 2 1 4 1 C U L 3 I so fo rm 1 o f C u ll in -3 0 .4 0 8 8 .4 IP I0 0 1 7 9 3 3 0 1 8 2 9 6 2 1 0 1 U B C ;U B B ;R P S 2 7 A u b iq u it in a n d r ib o so m al p ro te in S 2 7 a p re cu rs o r 0 .4 2 1 8 .5 IP I0 0 0 1 6 6 1 3 4 6 0 5 0 5 2 2 8 C S N K 2 A 1 C S N K 2 A 1 p ro te in 0 .4 2 4 8 .6 IP I0 0 3 0 0 0 9 4 7 5 8 6 3 2 8 8 L S G 1 L ar g e su b u n it G T P as e 1 h o m o lo g 0 .4 2 5 8 .6 IP I0 0 2 9 0 4 6 0 3 5 8 7 4 5 2 2 6 E IF 3 G E u k ar y o ti c tr an sl at io n i n it ia ti o n f ac to r 3 s u b u n it G 0 .4 2 7 8 .6 IP I0 0 5 5 4 7 2 3 2 5 0 4 4 8 3 7 5 R P L 1 0 6 0 S r ib o so m al p ro te in L 1 0 0 .4 3 2 8 .7 IP I0 0 0 0 3 5 2 7 3 9 1 3 0 5 2 5 5 S L C 9 A 3 R 1 E zr in -r ad ix in -m o es in -b in d in g 0 .4 4 6 8 .8 307 A c c e ss io n n u m b e r M a ss [D a ] P e p ti d e s Id e n ti fi e d T o ta l S c o r e D e sc r ip ti o n i/ w r a ti o C a lc u la te d h a lf -l if e (h r s) p h o sp h o p ro te in 5 0 IP I0 0 0 3 3 0 3 0 4 2 4 1 2 2 8 9 A D R M 1 P ro te in A D R M 1 0 .4 5 8 .9 IP I0 0 0 2 0 5 1 3 6 8 6 1 1 3 1 5 3 Z Y X Z y x in 0 .4 5 3 8 .9 IP I0 0 3 0 1 5 1 8 2 5 2 3 5 2 9 8 M O B K L 1 B I so fo rm 1 o f M p s o n e b in d er k in as e ac ti v at o r- li k e 1 B 0 .4 7 3 9 .2 IP I0 0 0 2 9 1 5 9 8 0 8 8 5 2 1 1 4 M R E 1 1 A I so fo rm 1 o f D o u b le -s tr an d b re ak r ep ai r p ro te in M R E 1 1 A 0 .4 7 6 9 .2 IP I0 0 0 0 5 1 7 9 3 9 4 5 3 2 1 1 0 P O L R 1 C I so fo rm 1 o f D N A -d ir ec te d R N A p o ly m er as es I an d I II s u b u n it R P A C 1 0 .4 7 9 9 .2 IP I0 0 0 2 8 0 0 4 2 3 2 1 9 3 2 1 0 P S M B 3 P ro te as o m e su b u n it b et a ty p e -3 0 .4 8 4 9 .3 IP I0 0 0 2 1 8 3 1 4 3 1 8 3 5 2 5 7 P R K A R 1 A c A M P -d ep en d en t p ro te in k in as e ty p e I- al p h a re g u la to ry s u b u n it 0 .4 8 5 9 .3 IP I0 0 0 0 5 0 4 5 7 1 8 1 5 3 1 2 2 A B C F 2 A T P -b in d in g c as se tt e su b -f am il y F m em b er 2 0 .4 9 8 9 .4 IP I0 0 0 3 2 1 5 8 1 0 2 3 9 1 3 1 1 4 N A R G 1 I so fo rm 2 o f N M D A r ec ep to r- re g u la te d p ro te in 1 0 .4 9 9 9 .5 IP I0 0 0 0 2 4 0 8 7 5 9 5 7 2 1 6 9 R P A P 3 I so fo rm 1 o f R N A p o ly m er as e II -a ss o ci at ed p ro te in 3 0 .5 2 5 9 .8 IP I0 0 8 2 7 6 3 4 8 0 9 0 2 4 2 1 4 L A R P 5 L a- re la te d p ro te in 5 0 .5 2 7 9 .8 IP I0 0 1 8 2 8 3 9 4 6 3 4 3 2 8 1 E X O C 3 L 2 E x o cy st c o m p le x c o m p o n en t 3 -l ik e p ro te in 2 0 .5 3 1 9 .8 IP I0 0 7 8 5 0 9 6 4 8 1 8 4 8 3 6 3 B Z W 1 I so fo rm 1 o f B as ic l eu ci n e zi p p er a n d W 2 d o m ai n -c o n ta in in g p ro te in 1 0 .5 3 5 9 .9 IP I0 0 2 9 7 1 6 0 3 9 9 0 4 2 8 6 C D 4 4 I so fo rm 1 2 o f C D 4 4 a n ti g en 0 .5 3 9 9 .9 308 A c c e ss io n n u m b e r M a ss [D a ] P e p ti d e s Id e n ti fi e d T o ta l S c o r e D e sc r ip ti o n i/ w r a ti o C a lc u la te d h a lf -l if e (h r s) IP I0 0 0 1 2 2 6 8 1 0 0 8 7 7 1 4 7 8 1 P S M D 2 2 6 S p ro te as o m e n o n -A T P as e re g u la to ry s u b u n it 2 0 .5 4 5 1 0 .0 IP I0 0 0 1 4 4 2 4 5 0 7 8 0 3 1 8 5 E E F 1 A 2 E lo n g at io n f ac to r 1 -a lp h a 2 0 .5 6 1 1 0 .2 IP I0 0 0 1 5 9 5 4 2 2 4 6 7 3 2 0 1 S A R 1 A G T P -b in d in g p ro te in S A R 1 a 0 .5 6 7 1 0 .2 IP I0 0 0 2 2 3 3 4 4 8 8 4 6 1 2 7 2 4 O A T O rn it h in e am in o tr an sf er as e, m it o ch o n d ri al 0 .5 7 4 1 0 .3 IP I0 0 0 0 1 6 3 6 5 4 1 9 6 5 3 5 8 A T X N 1 0 A ta x in -1 0 0 .5 8 6 1 0 .4 IP I0 0 3 9 6 4 8 5 5 0 4 5 1 7 3 5 3 E E F 1 A 1 E lo n g at io n f ac to r 1 -a lp h a 1 0 .5 8 6 1 0 .4 IP I0 0 0 0 3 8 8 1 4 5 9 8 5 4 2 6 3 H N R N P F H et er o g en eo u s n u cl ea r ri b o n u cl eo p ro te in F 0 .5 9 4 1 0 .5 IP I0 0 2 1 8 9 9 3 9 2 9 7 0 4 2 6 1 H S P H 1 I so fo rm B et a o f H ea t sh o ck p ro te in 1 0 5 k D a 0 .5 9 5 1 0 .5 IP I0 0 0 1 6 3 3 9 2 3 6 9 6 3 1 7 9 R A B 5 C R as -r el at ed p ro te in R ab -5 C 0 .5 9 9 1 0 .6 IP I0 0 0 3 1 5 1 7 9 3 8 0 1 4 2 0 0 M C M 6 D N A r ep li ca ti o n l ic en si n g f ac to r M C M 6 0 .6 0 8 1 0 .7 IP I0 0 0 2 1 3 2 7 2 5 3 0 4 6 2 9 2 G R B 2 I so fo rm 1 o f G ro w th f ac to r re ce p to r- b o u n d p ro te in 2 0 .6 0 9 1 0 .7 IP I0 0 0 3 1 8 1 2 3 5 9 0 3 8 6 8 0 Y B X 1 N u cl ea se -s en si ti v e el em en t- b in d in g p ro te in 1 0 .6 2 5 1 0 .9 IP I0 0 4 1 4 6 7 6 8 3 5 5 4 4 0 2 6 4 2 H S P 9 0 A B 1 H ea t sh o ck p ro te in H S P 9 0 -b et a 0 .6 2 6 1 0 .9 IP I0 0 0 6 4 7 6 5 2 4 9 5 9 2 8 6 R P L 1 0 L 6 0 S r ib o so m al p ro te in L 1 0 -l ik e 0 .6 2 7 1 0 .9 IP I0 0 4 1 4 8 3 6 2 3 9 4 3 2 1 4 0 O S T F 1 O st eo cl as t- st im u la ti n g f ac to r 1 0 .6 2 8 1 0 .9 IP I0 0 0 1 4 2 6 3 2 7 4 2 5 2 9 4 E IF 4 H I so fo rm L o n g o f E u k ar y o ti c tr an sl at io n i n it ia ti o n fa ct o r 4 H 0 .6 3 1 1 0 .9 IP I0 0 0 1 0 8 6 5 2 5 2 6 8 2 7 5 C S N K 2 B C as ei n k in as e II s u b u n it b et a 0 .6 3 7 1 1 .0 IP I0 0 0 0 9 8 4 1 6 9 2 0 8 2 1 1 0 E W S R 1 c D N A F L J3 1 7 4 7 f is , cl o n e N T 2 R I2 0 0 7 3 7 7 , h ig h ly s im il ar t o R N A -B IN D IN G P R O T E IN E W S 0 .6 4 2 1 1 .1 309 A c c e ss io n n u m b e r M a ss [D a ] P e p ti d e s Id e n ti fi e d T o ta l S c o r e D e sc r ip ti o n i/ w r a ti o C a lc u la te d h a lf -l if e (h r s) IP I0 0 0 0 7 4 0 2 1 2 0 7 5 1 3 1 9 9 IP O 7 I m p o rt in -7 0 .6 6 7 1 1 .4 IP I0 0 0 2 7 4 9 3 5 8 0 2 3 2 7 7 S L C 3 A 2 4 F 2 c el l- su rf ac e an ti g en h ea v y c h ai n 0 .6 6 7 1 1 .4 310 T a b le 6 .2 L is t o f p r o te in s fr o m c o n tr o l sa m p le a t 1 5 h o u r s, i d e n ti fi e d a n d q u a n ti ta te d a s h a v in g a n i so to p e /w il d -t y p e ( 1 3 C /1 2 C ) r a ti o o f g r e a te r t h a n 1 .8 5 , r e p r e se n ti n g a h a lf -l if e o f g r e a te r t h a n 2 4 h o u r s, a n d i n d ic a ti n g s lo w e r i n c o r p o r a ti o n o f 1 2 C o v e r 1 5 h o u r s, i m p ly in g a s lo w e r r a te o f p r o te in tu r n -o v e r . T a b le s h o w s IP I A c c e ss io n N u m b e r , P e p ti d e M a ss ( D a ), t h e n u m b e r o f p e p ti d e s id e n ti fi e d , a n d t h e M a sc o t sc o r e , a s w e ll a s th e p r o te in n a m e , i/ w r a ti o a n d t h e c a lc u la te d h a lf -l if e . 311 A c c e ss io n n u m b e r M a ss [ D a ] P e p ti d e s Id e n ti fi e d T o ta l S c o r e D e sc r ip ti o n i/ w r a ti o H a lf - li fe (h r s) IP I0 0 0 1 7 5 1 0 2 5 7 1 9 2 1 0 2 M T -C O 2 C y to ch ro m e c o x id as e su b u n it 2 8 .4 6 7 9 3 .1 IP I0 0 0 2 9 1 3 3 2 8 9 4 7 4 2 1 3 A T P 5 F 1 A T P s y n th as e su b u n it b , m it o ch o n d ri al 7 .7 2 5 8 5 .4 IP I0 0 0 1 4 8 0 8 2 5 8 3 2 2 1 4 0 P A F A H 1 B 3 P la te le t- ac ti v at in g f ac to r ac et y lh y d ro la se I B s u b u n it g am m a 7 .3 7 3 8 1 .7 IP I0 0 0 2 4 9 1 9 2 8 0 1 7 3 1 3 6 P R D X 3 T h io re d o x in -d ep en d en t p er o x id e re d u ct as e, m it o ch o n d ri al 5 .0 4 3 5 7 .5 IP I0 0 3 0 6 3 0 1 4 8 2 9 0 2 8 5 P D H A 1 M it o ch o n d ri al P D H A 1 4 .3 5 5 5 0 .3 IP I0 0 0 1 3 8 4 7 5 3 2 9 7 4 2 6 0 U Q C R C 1 C y to ch ro m e b -c 1 c o m p le x s u b u n it 1 , m it o ch o n d ri al 4 .0 8 4 4 7 .5 IP I0 0 0 1 2 8 3 5 4 7 9 6 2 2 1 0 2 C T B P 1 C -t er m in al -b in d in g p ro te in 1 3 .8 9 4 5 .4 IP I0 0 2 1 8 6 9 3 1 9 7 6 6 4 2 3 4 A P R T A d en in e p h o sp h o ri b o sy lt ra n sf er as e 3 .8 3 2 4 4 .8 IP I0 0 2 2 0 4 8 7 1 8 5 3 7 2 1 2 4 A T P 5 H I so fo rm 1 o f A T P s y n th as e su b u n it d , m it o ch o n d ri al 3 .6 3 1 4 2 .7 IP I0 0 0 2 4 6 6 4 9 6 6 3 8 4 2 0 0 U S P 5 I so fo rm L o n g o f U b iq u it in c ar b o x y l- te rm in al h y d ro la se 5 3 .5 7 6 4 2 .2 IP I0 0 0 1 1 9 3 7 3 0 7 4 9 6 3 1 9 P R D X 4 P er o x ir ed o x in -4 3 .5 5 5 4 1 .9 IP I0 0 0 2 9 0 4 8 7 5 1 5 4 5 2 4 2 T T L L 1 2 T u b u li n -- ty ro si n e li g as e- li k e p ro te in 1 2 3 .5 5 4 1 .9 IP I0 0 3 0 3 4 7 6 5 6 5 2 5 1 5 9 3 3 A T P 5 B A T P s y n th as e su b u n it b et a, m it o ch o n d ri al 3 .4 3 4 4 0 .7 IP I0 0 0 0 1 6 6 1 4 8 6 8 5 2 1 4 4 R C C 1 r eg u la to r o f ch ro m o so m e co n d en sa ti o n 1 i so fo rm a 3 .4 1 5 4 0 .5 IP I0 0 2 8 9 8 0 7 5 0 3 4 0 4 2 1 0 T R N T 1 I so fo rm 1 o f tR N A -n u cl eo ti d y lt ra n sf er as e 1 , m it o ch o n d ri al 3 .3 5 7 3 9 .9 IP I0 0 0 2 7 1 0 7 5 0 1 8 5 1 3 8 0 7 T U F M T u t ra n sl at io n e lo n g at io n f ac to r, m it o ch o n d ri al p re cu rs o r 3 .2 5 1 3 8 .8 IP I0 0 4 6 5 2 5 6 2 5 5 5 0 2 8 6 A K 3 G T P :A M P p h o sp h o tr an sf er as e m it o ch o n d ri al 3 .2 2 3 8 .4 IP I0 0 0 0 2 4 6 0 5 3 0 7 8 6 3 4 6 A N X A 7 I so fo rm 1 o f A n n ex in A 7 3 .2 1 8 3 8 .4 312 A c c e ss io n n u m b e r M a ss [ D a ] P e p ti d e s Id e n ti fi e d T o ta l S c o r e D e sc r ip ti o n i/ w r a ti o H a lf - li fe (h r s) IP I0 0 0 1 5 8 3 3 2 6 4 2 1 3 2 5 2 C H C H D 3 C o il ed -c o il -h el ix -c o il ed -c o il -h el ix d o m ai n -c o n ta in in g p ro te in 3 , m it o ch o n d ri al 3 .1 9 4 3 8 .2 IP I0 0 0 2 5 3 2 9 2 3 5 6 5 4 2 0 6 R P L 1 9 6 0 S r ib o so m al p ro te in L 1 9 3 .1 8 3 3 8 .1 IP I0 0 3 0 5 3 8 3 4 8 5 8 4 9 5 8 2 U Q C R C 2 C y to ch ro m e b -c 1 c o m p le x s u b u n it 2 , m it o ch o n d ri al 3 .1 6 4 3 7 .9 IP I0 0 2 1 9 9 5 3 2 6 1 8 0 4 2 3 9 C M P K 1 c D N A , F L J9 3 0 9 1 , H o m o s ap ie n s U M P -C M P k in as e (U M P -C M P K ), m R N A 3 .0 0 1 3 6 .2 IP I0 0 0 2 8 9 4 6 2 5 7 6 4 2 1 0 0 R T N 3 I so fo rm 3 o f R et ic u lo n -3 2 .9 9 7 3 6 .1 IP I0 0 3 0 6 5 1 6 5 1 6 6 6 3 2 1 9 T IM M 4 4 M it o ch o n d ri al i m p o rt i n n er m em b ra n e tr an sl o ca se su b u n it T IM 4 4 2 .8 2 6 3 4 .3 IP I0 0 2 5 6 6 8 4 1 0 6 3 8 7 6 2 8 4 A P 2 A 1 I so fo rm B o f A P -2 c o m p le x s u b u n it a lp h a- 1 2 .7 9 9 3 4 .0 IP I0 0 2 4 6 9 7 5 2 6 9 9 8 3 1 4 9 G S T M 3 G lu ta th io n e S -t ra n sf er as e M u 3 2 .7 7 4 3 3 .8 IP I0 0 0 3 1 1 3 1 4 6 6 2 2 3 1 6 7 C 2 0 o rf 3 A d ip o cy te p la sm a m em b ra n e- as so ci at ed p ro te in 3 2 .7 3 2 3 3 .3 IP I0 0 0 2 7 2 3 0 9 2 6 9 6 3 1 1 8 1 2 H S P 9 0 B 1 E n d o p la sm in 2 .6 8 2 3 2 .8 IP I0 0 0 2 2 9 7 7 4 2 9 0 2 1 4 1 0 1 8 C K B C re at in e k in as e B -t y p e 2 .6 3 7 3 2 .3 IP I0 0 0 3 1 7 0 8 4 6 7 4 3 4 2 1 8 F A H F u m ar y la ce to ac et as e 2 .6 1 8 3 2 .1 IP I0 0 0 4 5 8 3 9 9 1 6 9 9 2 1 1 3 L E P R E 1 I so fo rm 3 o f P ro ly l 3 -h y d ro x y la se 1 2 .5 9 5 3 1 .9 IP I0 0 0 1 3 8 6 2 2 3 9 7 6 2 8 5 T h y m id y la te k in as e 2 .5 7 9 3 1 .7 IP I0 0 0 0 2 8 2 1 2 3 3 8 9 3 1 6 1 R P L 1 4 6 0 S r ib o so m al p ro te in L 1 4 2 .5 6 3 3 1 .6 IP I0 0 0 2 1 4 3 9 4 2 0 5 2 2 7 0 A C T B A ct in , cy to p la sm ic 1 2 .5 3 4 3 1 .3 IP I0 0 0 0 7 7 2 2 1 0 2 5 8 7 2 7 1 A M P D 2 A d en o si n e m o n o p h o sp h at e d ea m in as e 2 2 .5 1 4 3 1 .0 IP I0 0 0 2 2 0 0 2 4 7 9 8 2 2 7 8 M R P S 2 7 2 8 S r ib o so m al p ro te in S 2 7 , m it o ch o n d ri al 2 .5 1 3 3 1 .0 313 A c c e ss io n n u m b e r M a ss [ D a ] P e p ti d e s Id e n ti fi e d T o ta l S c o r e D e sc r ip ti o n i/ w r a ti o H a lf - li fe (h r s) IP I0 0 0 2 5 3 6 6 5 1 9 0 8 7 3 4 8 C S C it ra te s y n th as e, m it o ch o n d ri al 2 .4 9 8 3 0 .9 IP I0 0 4 6 5 3 6 1 2 4 3 0 4 5 2 3 0 R P L 1 3 6 0 S r ib o so m al p ro te in L 1 3 2 .4 9 2 3 0 .8 IP I0 0 0 0 5 0 4 0 4 7 0 1 5 7 3 2 5 A C A D M M ed iu m -c h ai n s p ec if ic a cy l- C o A d eh y d ro g en as e, m it o ch o n d ri al 2 .4 8 2 3 0 .7 IP I0 0 2 1 9 4 4 6 2 1 1 5 8 5 3 3 0 P E B P 1 P h o sp h at id y le th an o la m in e -b in d in g p ro te in 1 2 .4 8 1 3 0 .7 IP I0 0 0 1 1 4 5 4 1 0 9 8 2 5 1 5 6 7 1 G A N A B I so fo rm 2 o f N eu tr al a lp h a- g lu co si d as e A B 2 .4 7 8 3 0 .7 IP I0 0 0 2 7 3 5 0 2 2 0 4 9 7 3 5 8 P R D X 2 P er o x ir ed o x in -2 2 .4 4 1 3 0 .3 IP I0 0 0 2 0 4 3 6 2 4 5 8 8 9 5 5 2 R A B 1 1 B R as -r el at ed p ro te in R ab -1 1 B 2 .4 3 5 3 0 .2 IP I0 0 0 2 2 8 3 0 3 7 3 0 3 2 9 1 N S F L 1 C I so fo rm 2 o f N S F L 1 c o fa ct o r p 4 7 2 .4 3 2 3 0 .2 IP I0 0 0 2 5 0 1 9 2 6 7 0 0 2 1 0 7 P S M B 1 P ro te as o m e su b u n it b et a ty p e -1 2 .4 2 2 3 0 .1 IP I0 0 0 2 4 3 6 4 1 0 3 7 7 1 7 4 1 6 T N P O 1 t ra n sp o rt in 1 i so fo rm 1 2 .4 0 9 2 9 .9 IP I0 0 2 4 7 5 8 3 1 8 6 1 0 3 1 6 2 R P L 2 1 ;L O C 7 2 9 4 0 2 6 0 S r ib o so m al p ro te in L 2 1 2 .3 9 9 2 9 .8 IP I0 0 0 2 2 3 1 4 2 4 8 7 8 4 2 3 8 S O D 2 S u p er o x id e d is m u ta se [ M n ], m it o ch o n d ri al 2 .3 7 4 2 9 .6 IP I0 0 0 3 0 3 6 3 4 5 4 5 6 1 1 7 0 6 A C A T 1 A ce ty l- C o A a ce ty lt ra n sf er as e, m it o ch o n d ri al 2 .3 7 4 2 9 .6 IP I0 0 4 1 1 6 8 0 2 4 8 0 6 4 2 4 8 P C M T 1 I so fo rm 1 o f P ro te in -L -i so as p ar ta te (D -a sp ar ta te ) O - m et h y lt ra n sf er as e 2 .3 2 4 2 9 .1 IP I0 0 3 0 3 8 8 2 4 7 1 8 9 7 5 2 4 M 6 P R B P 1 I so fo rm B o f M an n o se -6 -p h o sp h at e re ce p to r- b in d in g p ro te in 1 2 .3 2 1 2 9 .0 IP I0 0 2 9 6 4 4 1 4 1 0 2 4 2 6 3 A D A A d en o si n e d ea m in as e 2 .2 6 5 2 8 .4 IP I0 0 0 2 4 9 9 3 3 1 8 2 3 6 3 5 8 E C H S 1 E n o y l- C o A h y d ra ta se , m it o ch o n d ri al 2 .2 6 3 2 8 .4 IP I0 0 0 0 5 1 9 8 4 3 2 6 3 1 0 6 2 9 I L F 2 I n te rl eu k in e n h an ce r- b in d in g f ac to r 2 2 .2 5 5 2 8 .3 314 A c c e ss io n n u m b e r M a ss [ D a ] P e p ti d e s Id e n ti fi e d T o ta l S c o r e D e sc r ip ti o n i/ w r a ti o H a lf - li fe (h r s) IP I0 0 0 0 6 8 6 5 2 4 8 9 6 3 2 2 6 S E C 2 2 B V es ic le -t ra ff ic k in g p ro te in S E C 2 2 b 2 .2 4 7 2 8 .2 IP I0 0 2 9 7 0 8 4 5 0 9 4 0 8 4 4 3 D D O S T d o li ch y l- d ip h o sp h o o li g o sa cc h ar id e -p ro te in g ly co sy lt ra n sf er as e p re cu rs o r 2 .2 1 8 2 7 .9 IP I0 0 0 0 3 8 1 7 2 3 0 3 1 4 1 3 7 A R H G D IB R h o G D P -d is so ci at io n i n h ib it o r 2 2 .2 1 4 2 7 .9 IP I0 0 0 1 0 7 0 6 5 2 5 2 3 5 1 9 6 G S S G lu ta th io n e sy n th et as e 2 .1 9 9 2 7 .7 IP I0 0 0 0 0 8 7 4 2 2 3 2 4 1 4 8 0 5 P R D X 1 P er o x ir ed o x in -1 2 .1 8 5 2 7 .6 IP I0 0 2 1 9 6 2 2 2 5 9 9 6 3 1 7 5 P S M A 2 P ro te as o m e su b u n it a lp h a ty p e- 2 2 .1 8 2 2 7 .6 IP I0 0 0 0 5 7 8 0 1 1 8 1 0 4 2 1 1 3 O G T I so fo rm 3 o f U D P -N -a ce ty lg lu co sa m in e- -p ep ti d e N - ac et y lg lu co sa m in y lt ra n sf er as e 1 1 0 k D a su b u n it 2 .1 5 8 2 7 .3 IP I0 0 2 1 9 9 1 0 2 2 2 9 1 3 1 9 6 2 2 k D a p ro te in 2 .1 3 5 2 7 .1 IP I0 0 4 5 3 4 7 6 2 9 0 0 3 5 3 2 3 U n ch ar ac te ri ze d p ro te in E N S P 0 0 0 0 0 3 4 8 2 3 7 2 .0 9 2 2 6 .6 IP I0 0 4 1 9 2 5 8 2 5 0 4 9 7 3 9 5 H M G B 1 H ig h m o b il it y g ro u p p ro te in B 1 2 .0 9 2 6 .6 IP I0 0 0 1 8 9 3 1 9 2 4 4 7 5 3 1 7 V P S 3 5 V ac u o la r p ro te in s o rt in g -a ss o ci at ed p ro te in 3 5 2 .0 8 7 2 6 .6 IP I0 0 2 1 9 7 5 7 2 3 5 6 9 9 7 4 9 G S T P 1 G lu ta th io n e S -t ra n sf er as e P 2 .0 7 8 2 6 .5 IP I0 0 2 2 1 0 8 8 2 2 6 3 5 1 2 5 9 0 R P S 9 4 0 S r ib o so m al p ro te in S 9 2 .0 7 5 2 6 .4 IP I0 0 0 0 4 9 0 2 2 8 0 5 4 6 3 1 4 E T F B I so fo rm 1 o f E le ct ro n t ra n sf er f la v o p ro te in s u b u n it b et a 2 .0 7 3 2 6 .4 IP I0 0 0 2 6 2 0 2 2 1 0 3 4 4 1 5 2 R P L 1 8 A 6 0 S r ib o so m al p ro te in L 1 8 a 2 .0 6 5 2 6 .3 IP I0 0 0 0 7 6 1 1 2 3 3 7 7 7 4 2 4 A T P 5 O A T P s y n th as e su b u n it O , m it o ch o n d ri al 2 .0 1 4 2 5 .8 IP I0 0 0 2 5 7 9 6 3 0 3 3 7 2 1 5 7 N D U F S 3 N A D H d eh y d ro g en as e [u b iq u in o n e] i ro n -s u lf u r p ro te in 3 , m it o ch o n d ri al 2 .0 1 4 2 5 .8 IP I0 0 3 0 4 6 1 2 2 3 6 1 9 7 2 9 2 R P L 1 3 A 6 0 S r ib o so m al p ro te in L 1 3 a 2 .0 0 7 2 5 .7 315 A c c e ss io n n u m b e r M a ss [ D a ] P e p ti d e s Id e n ti fi e d T o ta l S c o r e D e sc r ip ti o n i/ w r a ti o H a lf - li fe (h r s) IP I0 0 2 9 8 5 4 7 2 0 0 5 0 4 2 0 7 P A R K 7 P ro te in D J- 1 2 .0 0 3 2 5 .7 IP I0 0 5 5 5 9 5 6 2 9 2 4 2 2 8 8 P S M B 4 P ro te as o m e su b u n it b et a ty p e -4 1 .9 9 8 2 5 .6 IP I0 0 3 8 3 0 7 1 2 7 2 1 1 5 3 6 9 L O C 7 2 9 7 0 8 ;L O C 3 8 8 6 4 2 T ri o se p h o sp h at e is o m er as e (F ra g m en t) 1 .9 9 2 2 5 .6 IP I0 0 3 2 9 6 0 0 4 7 4 6 4 3 1 4 2 S C C P D H P ro b ab le s ac ch ar o p in e d eh y d ro g en as e 1 .9 8 7 2 5 .5 IP I0 0 0 2 4 3 1 7 4 8 6 1 0 4 1 8 3 G C D H I so fo rm L o n g o f G lu ta ry l- C o A d eh y d ro g en as e, m it o ch o n d ri al 1 .9 8 2 2 5 .5 IP I0 0 2 2 0 7 6 6 2 0 9 9 2 7 3 4 6 G L O 1 L ac to y lg lu ta th io n e ly as e 1 .9 7 9 2 5 .4 IP I0 0 2 9 4 5 7 8 7 8 4 2 0 1 4 8 8 3 T G M 2 I so fo rm 1 o f P ro te in -g lu ta m in e g am m a- g lu ta m y lt ra n sf er as e 2 1 .9 7 4 2 5 .4 IP I0 0 1 8 1 3 5 2 9 7 7 7 8 1 2 5 9 4 D N M 2 d y n am in 2 i so fo rm 4 1 .9 5 8 2 5 .2 IP I0 0 0 3 1 6 9 1 2 1 9 6 4 4 2 1 9 R P L 9 6 0 S r ib o so m al p ro te in L 9 1 .9 5 7 2 5 .2 IP I0 0 0 1 1 2 8 5 8 2 4 6 5 1 4 7 5 8 C A P N 1 C al p ai n -1 c at al y ti c su b u n it 1 .9 4 8 2 5 .1 IP I0 0 0 2 5 2 7 7 2 1 9 1 2 3 1 3 8 P D C D 6 P ro g ra m m ed c el l d ea th p ro te in 6 1 .9 4 2 2 5 .0 IP I0 0 2 9 1 9 3 0 6 8 2 7 3 2 1 1 9 C L IN T 1 I so fo rm 1 o f C la th ri n i n te ra ct o r 1 1 .9 3 8 2 5 .0 IP I0 0 0 2 2 7 9 3 5 1 5 4 7 1 4 7 7 0 H A D H B T ri fu n ct io n al e n zy m e su b u n it b et a, m it o ch o n d ri al 1 .9 1 8 2 4 .8 IP I0 0 4 7 9 7 8 6 7 3 4 4 3 9 5 0 6 K H S R P I so fo rm 1 o f F ar u p st re am e le m en t- b in d in g p ro te in 2 1 .9 2 4 .6 IP I0 0 2 9 6 0 5 3 5 4 7 7 3 1 1 6 3 4 F H I so fo rm M it o ch o n d ri al o f F u m ar at e h y d ra ta se , m it o ch o n d ri al 1 .8 8 6 2 4 .4 IP I0 0 0 0 8 2 4 0 1 0 2 2 4 9 4 2 2 6 M A R S M et h io n y l- tR N A s y n th et as e, c y to p la sm ic 1 .8 7 5 2 4 .3 IP I0 0 2 1 7 9 2 0 8 6 0 8 6 4 2 0 5 A L D H 1 6 A 1 I so fo rm 1 o f A ld eh y d e d eh y d ro g en as e fa m il y 1 6 m em b er A 1 1 .8 5 2 2 4 .1 316 6.3.2 Proteins Identified in “Starving” Sample From the “starving” sample, it was possible to identify and quantitate 343 unique proteins. Using the same cut-offs for ratios set above, there are 39 unique proteins with a ratio of lower than 0.67 (corresponding to a half-life of 11.4 -hours or less; showing more rapid incorporation of 12 C in 15 hours, shown in Table 6.3; 39 proteins), and 101 proteins with a ratio of greater than 1.84 (i.e. 24-hour or greater half-life; slow incorporation of 12 C, shown in Table 6.4; 101 proteins). Together, these groups account for approximately 40% of the total number of proteins identified and quantitated. These proteins and the ratios observed for each are shown in Tables 6.3 and 6.4 below. The half-life values shown for the proteins identified in the “starving” sample are given as a close approximation to the actual situation. As explained above, changes in protein concentration in response to cytokine withdrawal must be taken into consideration in the calculation of the protein half-lifes. As we have observed in the earlier set of experiments (detailed in previous chapters) protein concentrations change in a measureable way for approximately 10% of the proteins detected and quantitated. Without using the change in concentration data, half-life calculations will be close, but not completely accurate. Accurate calculations can be made only where protein concentration data are known. Not all of the proteins shown in Tables 6.3 and 6.4 have concentration data available. Accurate half-life figures are presented in Tables 6.5 – 6.10, where protein concentration changes are shown. 317 Table 6.3 List of proteins from “starve” sample at 15 hours, identified and quantitated as having an isotope/wild-type ( 13 C/ 12 C) ratio of less than 0.67, representing a half-life of less than 11.4 hours, and indicating rapid incorporation of 12 C over 15 hours, implying a fast rate of protein turn over. Table shows IPI Accession Number, Peptide Mass (Da), the number of peptides identified, and the Mascot score, as well as the protein name, i/w ratio and the calculated half-life. 318 A c c e ss io n n u m b e r M a ss [D a ] # P e p ti d e s Id e n ti fi e d T o ta l S c o r e D e sc r ip ti o n i/ w r a ti o C a lc u la te d h a lf -l if e (h r s) IP I0 0 0 1 0 1 3 0 4 2 6 6 5 2 6 8 G L U L G lu ta m in e sy n th et as e 0 .1 5 4 5 .2 IP I0 0 0 0 5 7 8 0 1 1 8 1 0 4 2 1 1 5 O G T I so fo rm 3 o f U D P -N -a ce ty lg lu co sa m in e- -p ep ti d e N -a ce ty lg lu co sa m in y lt ra n sf er as e 1 1 0 k D a su b u n it 0 .1 8 7 5 .6 IP I0 0 0 3 2 1 4 0 4 6 5 2 5 2 1 4 3 S E R P IN H 1 S er p in H 1 0 .2 0 2 5 .8 IP I0 0 2 9 0 4 3 5 8 2 1 7 4 1 0 5 2 1 K IF 2 C I so fo rm 1 o f K in es in -l ik e p ro te in K IF 2 C 0 .2 1 4 6 .0 IP I0 0 0 0 0 0 7 5 4 4 9 9 7 2 1 0 9 T G F B 1 T ra n sf o rm in g g ro w th f ac to r b et a- 1 0 .2 1 6 6 .0 IP I0 0 4 7 0 8 9 1 8 9 6 8 4 7 3 5 6 C S D E 1 I so fo rm L o n g o f C o ld s h o ck d o m ai n -c o n ta in in g p ro te in E 1 0 .2 3 6 6 .3 IP I0 0 0 2 0 9 4 4 4 8 5 9 7 4 2 1 5 F D F T 1 S q u al en e sy n th et as e 0 .2 4 7 6 .4 IP I0 0 2 1 5 6 1 0 5 2 4 9 2 9 4 3 3 M P P 1 5 5 k D a er y th ro cy te m em b ra n e p ro te in 0 .2 4 7 6 .4 IP I0 0 0 1 7 5 6 7 7 1 5 5 9 2 1 1 8 E N G I so fo rm L o n g o f E n d o g li n 0 .2 5 6 .5 IP I0 0 4 7 2 0 1 3 4 1 1 5 1 7 4 7 3 H L A c la ss I h is to co m p at ib il it y a n ti g en , A -3 3 a lp h a ch ai n 0 .2 8 5 6 .9 IP I0 0 0 1 1 1 1 8 5 1 4 5 9 3 1 6 7 R R M 2 R ib o n u cl eo si d e- d ip h o sp h at e re d u ct as e M 2 su b u n it 0 .3 0 3 7 .1 IP I0 0 3 3 7 3 2 5 8 4 4 4 8 2 1 0 9 H M M R I so fo rm A o f H y al u ro n an m ed ia te d m o ti li ty re ce p to r 0 .3 1 8 7 .3 IP I0 0 0 0 9 1 2 3 5 0 3 0 5 2 8 2 N U C B 2 N u cl eo b in d in -2 0 .3 3 2 7 .5 IP I0 0 0 2 2 3 3 4 4 8 8 4 6 8 4 4 7 O A T O rn it h in e am in o tr an sf er as e, m it o ch o n d ri al 0 .3 5 8 7 .8 IP I0 0 0 1 2 5 3 5 4 5 5 8 1 7 3 1 1 D N A JA 1 D n aJ h o m o lo g s u b fa m il y A m em b er 1 0 .3 7 1 8 .0 319 A c c e ss io n n u m b e r M a ss [D a ] # P e p ti d e s Id e n ti fi e d T o ta l S c o r e D e sc r ip ti o n i/ w r a ti o C a lc u la te d h a lf -l if e (h r s) IP I0 0 3 0 6 4 3 6 8 8 7 2 3 2 1 2 1 S T A T 3 I so fo rm D el -7 0 1 o f S ig n al t ra n sd u ce r an d ac ti v at o r o f tr an sc ri p ti o n 3 0 .3 7 2 8 .0 IP I0 0 3 3 2 9 3 6 7 9 3 3 6 2 1 0 8 Z C 3 H A V 1 I so fo rm 2 o f Z in c fi n g er C C C H -t y p e an ti v ir al p ro te in 1 0 .3 7 4 8 .0 IP I0 0 2 9 3 3 0 7 4 8 2 7 4 4 2 6 8 A D F P A d ip o p h il in 0 .3 9 7 8 .3 IP I0 0 2 1 7 5 6 3 9 1 7 1 4 5 2 1 1 IT G B 1 I so fo rm B et a- 1 A o f In te g ri n b et a- 1 0 .3 9 9 8 .3 IP I0 0 0 2 5 5 1 2 2 2 8 2 6 5 2 8 3 H S P B 1 H ea t sh o ck p ro te in b et a- 1 0 .4 1 2 8 .4 IP I0 0 1 7 9 3 3 0 1 8 2 9 6 2 1 1 3 U B C ;U B B ;R P S 2 7 A u b iq u it in a n d r ib o so m al p ro te in S 2 7 a p re cu rs o r 0 .4 1 7 8 .5 IP I0 0 0 1 0 1 5 7 4 3 9 7 5 9 5 0 5 M A T 2 A S -a d en o sy lm et h io n in e sy n th et as e is o fo rm ty p e- 2 0 .4 3 8 .7 IP I0 0 0 2 1 3 2 7 2 5 3 0 4 6 3 0 5 G R B 2 I so fo rm 1 o f G ro w th f ac to r re ce p to r- b o u n d p ro te in 2 0 .4 5 5 8 .9 IP I0 0 0 3 1 8 3 6 4 0 8 0 2 2 1 0 7 D R G 1 D ev el o p m en ta ll y -r eg u la te d G T P -b in d in g p ro te in 1 0 .4 9 9 9 .5 IP I0 0 0 0 2 2 0 3 3 6 2 9 9 2 1 1 4 B C C IP I so fo rm 1 o f B R C A 2 a n d C D K N 1 A -i n te ra ct in g p ro te in 0 .5 2 7 9 .8 IP I0 0 0 2 0 0 2 1 4 2 9 3 3 2 1 3 7 D E K P ro te in D E K 0 .5 4 9 .9 IP I0 0 3 0 0 0 9 4 7 5 8 6 3 2 9 4 L S G 1 L ar g e su b u n it G T P as e 1 h o m o lo g 0 .5 4 5 1 0 .0 IP I0 0 0 0 3 5 2 7 3 9 1 3 0 6 3 2 5 S L C 9 A 3 R 1 E zr in -r ad ix in -m o es in -b in d in g p h o sp h o p ro te in 5 0 0 .5 5 8 1 0 .1 IP I0 0 0 0 9 0 3 2 4 6 9 7 9 6 2 9 4 S S B L u p u s L a p ro te in 0 .5 7 2 1 0 .3 320 A c c e ss io n n u m b e r M a ss [D a ] # P e p ti d e s Id e n ti fi e d T o ta l S c o r e D e sc r ip ti o n i/ w r a ti o C a lc u la te d h a lf -l if e (h r s) IP I0 0 0 2 6 2 1 5 4 2 9 0 8 2 1 0 9 F E N 1 F la p e n d o n u cl ea se 1 0 .5 8 1 1 0 .4 IP I0 0 2 9 0 4 6 0 3 5 8 7 4 5 2 7 7 E IF 3 G E u k ar y o ti c tr an sl at io n i n it ia ti o n f ac to r 3 s u b u n it G 0 .5 8 2 1 0 .4 IP I0 0 0 2 0 1 9 6 5 5 1 9 1 3 1 3 0 A R M C 6 A rm ad il lo r ep ea t- co n ta in in g p ro te in 6 0 .5 8 5 1 0 .4 IP I0 0 0 2 7 1 9 2 8 4 0 6 8 6 3 8 7 P L O D 1 P ro co ll ag en -l y si n e, 2 -o x o g lu ta ra te 5 - d io x y g en as e 1 0 .6 1 0 .6 IP I0 0 0 0 1 6 3 6 5 4 1 9 6 3 1 6 4 A T X N 1 0 A ta x in -1 0 0 .6 0 5 1 0 .7 IP I0 0 3 0 5 0 6 8 1 0 7 6 5 6 9 5 6 4 P R P F 6 P re -m R N A -p ro ce ss in g f ac to r 6 0 .6 1 3 1 0 .7 IP I0 0 5 5 0 0 6 9 5 1 7 6 6 2 1 5 3 R N H 1 R ib o n u cl ea se i n h ib it o r 0 .6 4 5 1 1 .1 IP I0 0 0 1 4 3 1 2 8 9 4 4 4 2 1 2 6 C U L 3 I so fo rm 1 o f C u ll in -3 0 .6 5 1 1 .2 IP I0 0 0 3 1 8 1 2 3 5 9 0 3 6 4 6 7 Y B X 1 N u cl ea se -s en si ti v e el em en t- b in d in g p ro te in 1 0 .6 5 5 1 1 .2 IP I0 0 3 9 6 4 8 5 5 0 4 5 1 1 0 5 1 7 E E F 1 A 1 E lo n g at io n f ac to r 1 -a lp h a 1 0 .6 6 1 1 .3 321 T a b le 6 .4 L is t o f p r o te in s fr o m “ st a r v e ” s a m p le a t 1 5 h o u r s, i d e n ti fi e d a n d q u a n ti ta te d a s h a v in g a n i so to p e /w il d -t y p e ( 1 3 C /1 2 C ) r a ti o o f g r e a te r t h a n 1 .8 4 , r e p r e se n ti n g a h a lf -l if e o f g r e a te r t h a n 2 4 h o u r s, a n d i n d ic a ti n g s lo w e r i n c o r p o r a ti o n o f 1 2 C o v e r 1 5 h o u r s, i m p ly in g a l o w e r r a te o f p r o te in tu r n -o v e r . T a b le s h o w s IP I A c c e ss io n N u m b e r , P e p ti d e M a ss ( D a ), t h e n u m b e r o f p e p ti d e s id e n ti fi e d , a n d t h e M a sc o t sc o r e , a s w e ll a s th e p r o te in n a m e , i/ w r a ti o a n d t h e c a lc u la te d h a lf -l if e . 322 A c c e ss io n n u m b e r M a ss [D a ] # P e p ti d e s Id e n ti fi e d T o ta l S c o r e D e sc r ip ti o n i/ w r a ti o R e c a lc u la te d h a lf -l if e (h r s) IP I0 0 0 1 7 3 3 4 2 9 8 4 3 3 1 7 2 P H B P ro h ib it in 4 .8 2 5 5 .1 IP I0 0 0 2 5 0 1 9 2 6 7 0 0 4 2 4 6 P S M B 1 P ro te as o m e su b u n it b et a ty p e -1 4 .7 9 7 5 4 .9 IP I0 0 0 1 4 8 0 8 2 5 8 3 2 3 2 3 9 P A F A H 1 B 3 P la te le t- ac ti v at in g f ac to r ac et y lh y d ro la se I B s u b u n it g am m a 4 .7 5 7 5 4 .5 IP I0 0 0 2 4 9 1 9 2 8 0 1 7 3 1 4 7 P R D X 3 T h io re d o x in -d ep en d en t p er o x id e re d u ct as e, m it o ch o n d ri al 4 .3 9 2 5 0 .7 IP I0 0 2 2 0 4 8 7 1 8 5 3 7 2 1 0 0 A T P 5 H I so fo rm 1 o f A T P s y n th as e su b u n it d , m it o ch o n d ri al 4 .0 3 9 4 7 .0 IP I0 0 0 0 5 1 5 4 8 1 3 6 7 2 1 0 8 S S R P 1 F A C T c o m p le x s u b u n it S S R P 1 3 .7 4 2 4 3 .9 IP I0 0 0 1 3 8 4 7 5 3 2 9 7 6 2 8 9 U Q C R C 1 C y to ch ro m e b -c 1 c o m p le x s u b u n it 1 , m it o ch o n d ri al 3 .6 1 2 4 2 .5 IP I0 0 0 0 5 7 1 9 2 2 8 9 1 3 1 6 7 R A B 1 A I so fo rm 1 o f R as -r el at ed p ro te in R ab -1 A 3 .4 8 4 1 .2 IP I0 0 0 2 5 3 6 6 5 1 9 0 8 8 4 7 4 C S C it ra te s y n th as e, m it o ch o n d ri al 3 .4 2 4 4 0 .6 IP I0 0 0 0 4 9 0 2 2 8 0 5 4 8 4 2 4 E T F B I so fo rm 1 o f E le ct ro n t ra n sf er f la v o p ro te in s u b u n it b et a 3 .3 8 2 4 0 .1 IP I0 0 0 0 1 6 6 1 4 8 6 8 5 4 2 4 2 R C C 1 r eg u la to r o f ch ro m o so m e co n d en sa ti o n 1 i so fo rm a 3 .3 4 8 3 9 .8 IP I0 0 0 1 5 8 3 3 2 6 4 2 1 2 1 8 0 C H C H D 3 C o il ed -c o il -h el ix -c o il ed -c o il -h el ix d o m ai n -c o n ta in in g p ro te in 3 , m it o ch o n d ri al 3 .1 8 7 3 8 .1 IP I0 0 0 1 8 1 4 6 2 8 0 3 2 2 1 5 1 Y W H A Q 1 4 -3 -3 p ro te in t h et a 3 .1 8 3 3 8 .1 IP I0 0 0 2 4 6 6 4 9 6 6 3 8 3 1 4 9 U S P 5 I so fo rm L o n g o f U b iq u it in c ar b o x y l- te rm in al h y d ro la se 5 3 .1 7 7 3 8 .0 IP I0 0 0 2 6 1 5 4 6 0 3 5 7 3 1 6 8 P R K C S H G lu co si d as e 2 s u b u n it b et a 3 .1 7 4 3 8 .0 IP I0 0 0 0 2 4 6 0 5 3 0 7 8 6 3 4 2 A N X A 7 I so fo rm 1 o f A n n ex in A 7 3 .1 6 9 3 7 .9 IP I0 0 0 2 7 3 5 0 2 2 0 4 9 4 2 3 2 P R D X 2 P er o x ir ed o x in -2 3 .1 5 5 3 7 .8 IP I0 0 0 2 7 1 0 7 5 0 1 8 5 1 5 8 5 4 T U F M T u t ra n sl at io n e lo n g at io n f ac to r, m it o ch o n d ri al p re cu rs o r 3 .1 5 1 3 7 .7 323 A c c e ss io n n u m b e r M a ss [D a ] # P e p ti d e s Id e n ti fi e d T o ta l S c o r e D e sc r ip ti o n i/ w r a ti o R e c a lc u la te d h a lf -l if e (h r s) IP I0 0 0 1 0 7 0 6 5 2 5 2 3 4 2 3 0 G S S G lu ta th io n e sy n th et as e 3 .0 7 8 3 7 .0 IP I0 0 2 1 8 6 9 3 1 9 7 6 6 2 1 1 9 A P R T A d en in e p h o sp h o ri b o sy lt ra n sf er as e 3 .0 6 9 3 6 .9 IP I0 0 3 8 3 0 4 6 2 8 3 7 2 4 2 0 5 C M B L C ar b o x y m et h y le n eb u te n o li d as e h o m o lo g 3 .0 2 1 3 6 .4 IP I0 0 0 2 2 7 9 3 5 1 5 4 7 1 1 6 2 4 H A D H B T ri fu n ct io n al e n zy m e su b u n it b et a, m it o ch o n d ri al 3 .0 0 8 3 6 .2 IP I0 0 3 0 0 5 6 7 3 3 0 8 0 3 2 4 0 D C I Is o fo rm 1 o f 3 ,2 -t ra n s- en o y l- C o A i so m er as e, m it o ch o n d ri al 2 .9 7 2 3 5 .8 IP I0 0 0 1 8 2 4 6 1 0 3 5 6 1 3 1 6 1 H K 1 I so fo rm 1 o f H ex o k in as e- 1 2 .8 5 9 3 4 .7 IP I0 0 4 4 0 4 9 3 5 9 8 2 8 1 1 7 6 6 A T P 5 A 1 A T P s y n th as e su b u n it a lp h a, m it o ch o n d ri al 2 .7 9 1 3 4 .0 IP I0 0 3 0 3 4 7 6 5 6 5 2 5 1 8 1 2 9 6 A T P 5 B A T P s y n th as e su b u n it b et a, m it o ch o n d ri al 2 .7 7 3 3 3 .8 IP I0 0 3 0 5 3 8 3 4 8 5 8 4 9 6 2 0 U Q C R C 2 C y to ch ro m e b -c 1 c o m p le x s u b u n it 2 , m it o ch o n d ri al 2 .7 3 8 3 3 .4 IP I0 0 0 2 9 1 3 3 2 8 9 4 7 5 3 4 7 A T P 5 F 1 A T P s y n th as e su b u n it b , m it o ch o n d ri al 2 .6 1 4 3 2 .1 IP I0 0 0 0 7 6 1 1 2 3 3 7 7 7 4 0 1 A T P 5 O A T P s y n th as e su b u n it O , m it o ch o n d ri al 2 .5 9 7 3 1 .9 IP I0 0 2 9 4 9 1 1 3 2 4 0 7 2 1 0 7 S D H B S u cc in at e d eh y d ro g en as e [u b iq u in o n e] i ro n -s u lf u r su b u n it , m it o ch o n d ri al 2 .5 7 8 3 1 .7 IP I0 0 0 2 1 7 2 8 3 8 7 0 6 2 1 0 3 E IF 2 S 2 E u k ar y o ti c tr an sl at io n i n it ia ti o n f ac to r 2 s u b u n it 2 2 .5 4 4 3 1 .4 IP I0 0 3 2 8 7 1 5 6 3 8 5 6 3 2 1 1 M T D H P ro te in L Y R IC 2 .5 3 5 3 1 .3 IP I0 0 3 0 3 8 8 2 4 7 1 8 9 3 2 3 4 M 6 P R B P 1 I so fo rm B o f M an n o se -6 -p h o sp h at e re ce p to r- b in d in g p ro te in 1 2 .4 8 4 3 0 .7 IP I0 0 0 2 7 2 3 0 9 2 6 9 6 2 6 1 4 3 2 H S P 9 0 B 1 E n d o p la sm in 2 .4 6 7 3 0 .6 IP I0 0 0 1 3 8 6 2 2 3 9 7 6 2 1 2 3 T h y m id y la te k in as e 2 .4 4 1 3 0 .3 IP I0 0 0 0 5 0 4 0 4 7 0 1 5 6 3 5 1 A C A D M M ed iu m -c h ai n s p ec if ic a cy l- C o A d eh y d ro g en as e, m it o ch o n d ri al 2 .4 3 6 3 0 .2 IP I0 0 6 4 1 8 2 9 5 1 1 0 3 7 3 4 7 B A T 1 I so fo rm 2 o f S p li ce o so m e R N A h el ic as e B A T 1 2 .4 1 4 3 0 .0 324 A c c e ss io n n u m b e r M a ss [D a ] # P e p ti d e s Id e n ti fi e d T o ta l S c o r e D e sc r ip ti o n i/ w r a ti o R e c a lc u la te d h a lf -l if e (h r s) IP I0 0 0 1 1 9 3 7 3 0 7 4 9 5 2 4 7 P R D X 4 P er o x ir ed o x in -4 2 .4 0 2 2 9 .9 IP I0 0 0 0 8 2 4 0 1 0 2 2 4 9 4 2 3 3 M A R S M et h io n y l- tR N A s y n th et as e, c y to p la sm ic 2 .3 8 7 2 9 .7 IP I0 0 0 1 1 4 5 4 1 0 9 8 2 5 1 3 6 7 1 G A N A B I so fo rm 2 o f N eu tr al a lp h a- g lu co si d as e A B 2 .3 8 3 2 9 .7 IP I0 0 4 6 5 0 2 8 3 1 0 5 7 1 0 7 0 3 T P I1 I so fo rm 1 o f T ri o se p h o sp h at e is o m er as e 2 .3 7 5 2 9 .6 IP I0 0 0 0 6 8 6 5 2 4 8 9 6 2 1 1 9 S E C 2 2 B V es ic le -t ra ff ic k in g p ro te in S E C 2 2 b 2 .3 7 3 2 9 .6 IP I0 0 2 1 8 3 4 2 1 0 2 1 8 0 1 3 7 6 5 M T H F D 1 C -1 -t et ra h y d ro fo la te s y n th as e, c y to p la sm ic 2 .3 7 2 2 9 .6 IP I0 0 0 2 2 5 9 7 2 1 1 7 2 2 1 1 1 U B E 2 M N E D D 8 -c o n ju g at in g e n zy m e U b c1 2 2 .3 4 2 9 .2 IP I0 0 0 2 4 3 6 4 1 0 3 7 7 1 7 4 4 2 T N P O 1 t ra n sp o rt in 1 i so fo rm 1 2 .3 3 5 2 9 .2 IP I0 0 0 2 2 9 7 7 4 2 9 0 2 1 6 1 0 0 3 C K B C re at in e k in as e B -t y p e 2 .3 2 4 2 9 .1 IP I0 0 4 1 9 2 5 8 2 5 0 4 9 8 4 7 0 H M G B 1 H ig h m o b il it y g ro u p p ro te in B 1 2 .3 2 3 2 9 .0 IP I0 0 0 2 1 1 8 7 5 0 5 3 8 6 3 9 5 R U V B L 1 I so fo rm 1 o f R u v B -l ik e 1 2 .3 1 9 2 9 .0 IP I0 0 0 1 5 9 5 4 2 2 4 6 7 3 2 0 6 S A R 1 A G T P -b in d in g p ro te in S A R 1 a 2 .3 0 7 2 8 .9 IP I0 0 0 0 0 8 7 4 2 2 3 2 4 1 0 5 3 9 P R D X 1 P er o x ir ed o x in -1 2 .3 0 1 2 8 .8 IP I0 0 5 5 0 0 2 1 4 6 3 6 5 3 1 4 4 R P L 3 6 0 S r ib o so m al p ro te in L 3 2 .2 9 2 2 8 .7 IP I0 0 2 1 9 6 2 2 2 5 9 9 6 2 1 2 6 P S M A 2 P ro te as o m e su b u n it a lp h a ty p e- 2 2 .2 7 7 2 8 .6 IP I0 0 2 1 6 3 1 8 2 8 1 7 9 9 5 9 5 Y W H A B I so fo rm L o n g o f 1 4 -3 -3 p ro te in b et a/ al p h a 2 .2 7 2 2 8 .5 IP I0 0 2 1 7 0 3 0 2 9 8 0 7 4 1 7 5 R P S 4 X 4 0 S r ib o so m al p ro te in S 4 , X i so fo rm 2 .2 4 2 2 8 .2 IP I0 0 2 1 9 9 1 0 2 2 2 9 1 3 1 7 4 2 2 k D a p ro te in 2 .2 2 3 2 8 .0 IP I0 0 2 2 0 7 6 6 2 0 9 9 2 6 2 9 5 G L O 1 L ac to y lg lu ta th io n e ly as e 2 .2 2 2 2 8 .0 IP I0 0 2 9 8 9 6 1 1 2 4 4 4 7 7 4 8 3 X P O 1 E x p o rt in -1 2 .2 1 1 2 7 .9 IP I0 0 0 0 5 1 9 8 4 3 2 6 3 1 0 7 0 1 IL F 2 I n te rl eu k in e n h an ce r- b in d in g f ac to r 2 2 .2 0 8 2 7 .8 IP I0 0 2 9 5 4 0 0 5 3 4 7 4 5 3 4 8 W A R S T ry p to p h an y l- tR N A s y n th et as e, c y to p la sm ic 2 .1 8 4 2 7 .6 325 A c c e ss io n n u m b e r M a ss [D a ] # P e p ti d e s Id e n ti fi e d T o ta l S c o r e D e sc r ip ti o n i/ w r a ti o R e c a lc u la te d h a lf -l if e (h r s) IP I0 0 2 1 5 7 1 9 2 1 7 3 5 4 2 8 1 R P L 1 8 6 0 S r ib o so m al p ro te in L 1 8 2 .1 5 7 2 7 .3 IP I0 0 0 2 0 4 3 6 2 4 5 8 8 7 3 9 0 R A B 1 1 B R as -r el at ed p ro te in R ab -1 1 B 2 .1 2 9 2 7 .0 IP I0 0 0 2 7 4 8 5 2 5 3 1 0 3 1 4 4 E IF 4 E E u k ar y o ti c tr an sl at io n i n it ia ti o n f ac to r 4 E 2 .1 2 2 2 6 .9 IP I0 0 4 7 0 5 2 8 2 4 2 4 5 3 1 5 9 R P L 1 5 6 0 S r ib o so m al p ro te in L 1 5 2 .1 1 2 6 .8 IP I0 0 0 2 8 0 9 1 4 7 7 9 7 7 3 9 5 A C T R 3 A ct in -r el at ed p ro te in 3 2 .0 8 7 2 6 .6 IP I0 0 2 2 0 3 0 1 2 5 1 3 3 8 3 9 9 P R D X 6 P er o x ir ed o x in -6 2 .0 7 8 2 6 .5 IP I0 0 2 5 6 6 8 4 1 0 6 3 8 7 5 2 1 8 A P 2 A 1 I so fo rm B o f A P -2 c o m p le x s u b u n it a lp h a- 1 2 .0 6 2 2 6 .3 IP I0 0 0 2 5 2 3 9 5 2 9 1 1 2 1 2 0 N D U F S 2 N A D H d eh y d ro g en as e [u b iq u in o n e] i ro n -s u lf u r p ro te in 2 , m it o ch o n d ri al 2 .0 5 7 2 6 .2 IP I0 0 4 1 3 1 0 8 3 3 4 6 4 7 5 3 1 R P S A 3 3 k D a p ro te in 2 .0 5 2 6 .2 IP I0 0 0 2 1 4 3 9 4 2 0 5 2 2 2 1 4 7 7 A C T B A ct in , cy to p la sm ic 1 2 .0 2 6 2 5 .9 IP I0 0 0 0 4 8 4 5 2 8 5 6 3 3 1 5 8 N IP S N A P 3 A P ro te in N ip S n ap h o m o lo g 3 A 2 .0 2 3 2 5 .9 IP I0 0 0 1 1 1 0 7 5 1 3 3 3 6 3 3 7 ID H 2 I so ci tr at e d eh y d ro g en as e [N A D P ], m it o ch o n d ri al 2 .0 1 2 2 5 .8 IP I0 0 0 0 0 8 1 1 2 5 5 7 0 2 1 3 2 P S M B 6 P ro te as o m e su b u n it b et a ty p e- 6 2 .0 1 2 5 .7 IP I0 0 0 0 6 7 2 1 1 1 2 1 5 8 1 2 6 9 7 O P A 1 I so fo rm 1 o f D y n am in -l ik e 1 2 0 k D a p ro te in , m it o ch o n d ri al 2 .0 0 4 2 5 .7 IP I0 0 2 1 9 9 5 3 2 6 1 8 0 2 9 0 C M P K 1 U M P -C M P k in as e (U M P -C M P K ), m R N A 1 .9 9 5 2 5 .6 IP I0 0 0 2 8 8 8 8 3 8 5 8 1 5 3 9 4 H N R N P D I so fo rm 1 o f H et er o g en eo u s n u cl ea r ri b o n u cl eo p ro te in D 0 1 .9 8 8 2 5 .5 IP I0 0 0 1 8 7 6 8 2 6 2 8 1 2 1 2 6 T S N T ra n sl in 1 .9 8 2 5 .4 IP I0 0 4 6 5 2 4 8 4 7 4 8 1 2 6 1 7 7 8 E N O 1 I so fo rm a lp h a- en o la se o f A lp h a- en o la se 1 .9 7 9 2 5 .4 IP I0 0 0 2 5 2 7 3 1 0 8 9 5 3 4 2 2 6 G A R T I so fo rm L o n g o f T ri fu n ct io n al p u ri n e b io sy n th et ic p ro te in ad en o si n e- 3 1 .9 6 7 2 5 .3 326 A c c e ss io n n u m b e r M a ss [D a ] # P e p ti d e s Id e n ti fi e d T o ta l S c o r e D e sc r ip ti o n i/ w r a ti o R e c a lc u la te d h a lf -l if e (h r s) IP I0 0 0 2 6 9 6 4 2 9 9 3 4 4 1 6 4 U Q C R F S 1 C y to ch ro m e b -c 1 c o m p le x s u b u n it R ie sk e, m it o ch o n d ri al 1 .9 5 8 2 5 .2 IP I0 0 1 7 1 1 9 9 2 7 8 5 8 4 2 4 5 P S M A 3 I so fo rm 2 o f P ro te as o m e su b u n it a lp h a ty p e- 3 1 .9 5 7 2 5 .2 IP I0 0 0 0 1 5 3 9 4 2 3 5 4 5 2 9 6 A C A A 2 3 -k et o ac y l- C o A t h io la se , m it o ch o n d ri al 1 .9 5 2 5 .1 IP I0 0 0 2 4 9 9 3 3 1 8 2 3 5 2 8 7 E C H S 1 E n o y l- C o A h y d ra ta se , m it o ch o n d ri al 1 .9 4 8 2 5 .1 IP I0 0 2 9 4 5 7 8 7 8 4 2 0 1 5 9 7 2 T G M 2 I so fo rm 1 o f P ro te in -g lu ta m in e g am m a- g lu ta m y lt ra n sf er as e 2 1 .9 4 1 2 5 .0 IP I0 0 0 2 3 2 3 4 7 1 7 4 9 9 4 0 6 U B A 2 S U M O -a ct iv at in g e n zy m e su b u n it 2 1 .9 3 1 2 4 .9 IP I0 0 0 0 6 9 8 0 2 8 1 6 5 2 1 0 3 C 1 4 o rf 1 6 6 U P F 0 5 6 8 p ro te in C 1 4 o rf 1 6 6 1 .9 2 9 2 4 .9 IP I0 0 2 1 9 5 2 5 5 3 6 1 9 1 5 1 0 8 8 P G D 6 -p h o sp h o g lu co n at e d eh y d ro g en as e, d ec ar b o x y la ti n g 1 .9 2 4 2 4 .8 IP I0 0 2 9 9 1 5 5 2 9 7 5 0 2 1 0 5 P S M A 4 P ro te as o m e su b u n it a lp h a ty p e- 4 1 .9 1 3 2 4 .7 IP I0 0 3 3 7 7 4 1 8 2 1 4 2 5 2 2 7 A P E H A cy la m in o -a ci d -r el ea si n g e n zy m e 1 .9 1 2 2 4 .7 IP I0 0 0 0 1 6 3 9 9 8 4 2 0 1 4 9 2 1 K P N B 1 I m p o rt in s u b u n it b et a- 1 1 .9 2 4 .6 IP I0 0 0 1 4 1 7 7 4 1 6 8 9 6 2 7 6 S E P T 2 S ep ti n -2 1 .8 7 8 2 4 .4 IP I0 0 4 1 4 6 7 6 8 3 5 5 4 4 2 1 0 H S P 9 0 A B 1 H ea t sh o ck p ro te in H S P 9 0 -b et a 1 .8 7 8 2 4 .4 IP I0 0 0 3 1 7 0 8 4 6 7 4 3 4 1 8 5 F A H F u m ar y la ce to ac et as e 1 .8 7 5 2 4 .3 IP I0 0 2 9 1 6 4 6 1 0 6 6 3 6 2 1 5 6 M T H F D 1 L I so fo rm 1 o f M o n o fu n ct io n al C 1 -t et ra h y d ro fo la te sy n th as e, m it o ch o n d ri al 1 .8 7 3 2 4 .3 IP I0 0 6 4 3 0 4 1 2 4 5 7 9 4 2 7 8 R A N G T P -b in d in g n u cl ea r p ro te in R an 1 .8 7 3 2 4 .3 IP I0 0 0 0 6 9 3 2 4 6 9 4 2 2 1 0 0 L U C 7 L 2 I so fo rm 1 o f P u ta ti v e R N A -b in d in g p ro te in L u c7 -l ik e 2 1 .8 7 1 2 4 .3 IP I0 0 0 1 2 3 4 5 3 9 6 7 7 2 1 0 2 S F R S 6 I so fo rm S R P 5 5 -1 o f S p li ci n g f ac to r, a rg in in e/ se ri n e- ri ch 6 1 .8 7 1 2 4 .3 IP I0 0 2 9 9 0 0 0 4 4 1 0 1 1 2 8 4 7 P A 2 G 4 P ro li fe ra ti o n -a ss o ci at ed p ro te in 2 G 4 1 .8 6 2 2 4 .2 327 A c c e ss io n n u m b e r M a ss [D a ] # P e p ti d e s Id e n ti fi e d T o ta l S c o r e D e sc r ip ti o n i/ w r a ti o R e c a lc u la te d h a lf -l if e (h r s) IP I0 0 4 4 9 0 4 9 1 1 3 8 1 1 4 2 6 7 P A R P 1 P o ly [ A D P -r ib o se ] p o ly m er as e 1 1 .8 5 4 2 4 .1 IP I0 0 2 2 0 3 2 7 6 6 1 4 9 9 6 6 2 K R T 1 K er at in , ty p e II c y to sk el et al 1 1 .8 4 9 2 4 .0 IP I0 0 6 4 5 0 7 8 1 1 8 8 5 8 1 6 1 1 0 9 U B A 1 U b iq u it in -l ik e m o d if ie r- ac ti v at in g e n zy m e 1 1 .8 4 7 2 4 .0 IP I0 0 0 1 8 9 3 1 9 2 4 4 7 6 3 9 4 V P S 3 5 V ac u o la r p ro te in s o rt in g -a ss o ci at ed p ro te in 3 5 1 .8 4 3 2 4 .0 328 6.3.3 Protein Half-Life Data vs. Protein Concentration Data When considering the proteins identified in the half-life experiment, many of the proteins were also found in the concentration experiments (not all were found, but the missing ones may have been found in one or in two experiments, and so would not show up in the table of the proteins found in all three experiments). Tables 6.5 to 6.8 show 36 proteins found in common between the two arms of the half-life experiment (i.e. found in both the control and the cytokine withrawal samples) and in the (earlier) concentration experiments. Proteins found in common are grouped according to the following relationships: • proteins with a change in both concentration and in half-life • proteins with no change in concentration and a change in half-life • proteins with a change in concentration and no change in half-life • proteins showing no changes in concentration or in half-life. Table legends describe the relationships between the observed changes in protein half-lives and concentrations. The changes observed in individual proteins are described in more detail in the discussion. 329 Table 6.5. Proteins found in SILAC protein concentration experiments and in half-life experiments showing a change in both concentration and in half-life. Concentration changes are shown in the “SILAC (avg i/w)” column; changes in half-life (Starving / Control) are shown in the “Difference Observed” column. 330 C o n tr o l C y to k in e w it h d r a w a l D if fe r e n c e o b se r v e d S IL A C A c c e ss io n n u m b e r M a ss [D a ] D e sc r ip ti o n i/ w r a ti o h a lf -l if e (h r s) i/ w r a ti o h a lf -l if e (h r s) i/ w ( a v g .) IP I0 0 0 2 2 3 3 4 4 8 8 4 6 O A T O rn it h in e am in o tr an sf er as e, m it o ch o n d ri al 0 .5 7 4 1 0 .3 0 .3 5 8 8 .7 0 .8 4 0 .8 7 IP I0 0 0 3 1 7 0 8 4 6 7 4 3 F A H F u m ar y la ce to ac et as e 2 .6 1 8 3 2 .1 1 .8 7 5 5 4 .2 1 .6 9 0 .7 9 IP I0 0 2 1 8 6 9 3 1 9 7 6 6 A P R T A d en in e p h o sp h o ri b o sy lt ra n sf er as e 3 .8 3 2 4 4 .8 3 .0 6 9 7 9 .2 1 .7 7 0 .8 6 IP I0 0 3 0 3 4 7 6 5 6 5 2 5 A T P 5 B A T P s y n th as e su b u n it b et a, m it o ch o n d ri al 3 .4 3 4 4 0 .7 2 .7 7 3 6 1 .6 1 .5 2 0 .8 7 IP I0 0 0 2 1 4 3 9 4 2 0 5 2 A C T B A ct in , cy to p la sm ic 1 2 .5 3 4 3 1 .3 2 .0 2 6 4 1 .5 1 .3 3 0 .8 6 IP I0 0 0 2 0 9 4 4 4 8 5 9 7 F D F T 1 S q u al en e sy n th et as e 0 .1 4 3 5 .0 0 .2 4 7 5 .8 1 .1 5 1 .2 0 IP I0 0 0 2 4 9 9 3 3 1 8 2 3 E C H S 1 E n o y l- C o A h y d ra ta se , m it o ch o n d ri al 2 .2 6 3 2 8 .4 1 .9 4 8 3 9 .5 1 .3 9 0 .8 6 IP I0 0 4 7 0 8 9 1 8 9 6 8 4 C S D E 1 I so fo rm L o n g o f C o ld s h o ck d o m ai n -c o n ta in in g p ro te in E 1 0 .2 5 3 6 .5 0 .2 3 6 5 .2 0 .8 1 1 .3 9 IP I0 0 0 2 4 6 6 4 9 6 6 3 8 U S P 5 I so fo rm L o n g o f U b iq u it in ca rb o x y l- te rm in al h y d ro la se 5 3 .5 7 6 4 2 .2 3 .1 7 7 1 0 4 .7 2 .4 8 0 .8 4 IP I0 0 0 2 2 9 7 7 4 2 9 0 2 C K B C re at in e k in as e B -t y p e 2 .6 3 7 3 2 .3 2 .3 2 4 6 0 .6 1 .8 7 0 .8 3 IP I0 0 0 2 7 1 0 7 5 0 1 8 5 T U F M T u t ra n sl at io n e lo n g at io n fa ct o r, m it o ch o n d ri al p re cu rs o r 3 .2 5 1 3 8 .8 3 .1 5 1 8 3 .3 2 .1 5 0 .8 6 IP I0 0 0 0 2 4 6 0 5 3 0 7 8 A N X A 7 I so fo rm 1 o f A n n ex in A 7 3 .2 1 8 3 8 .4 3 .1 6 9 -1 2 6 .2 -3 .2 8 0 .7 0 IP I0 0 3 0 5 3 8 3 4 8 5 8 4 U Q C R C 2 C y to ch ro m e b -c 1 c o m p le x su b u n it 2 , m it o ch o n d ri al 3 .1 6 4 3 7 .9 2 .7 3 8 5 3 .4 1 .4 1 0 .8 9 331 C o n tr o l C y to k in e w it h d r a w a l D if fe r e n c e o b se r v e d S IL A C A c c e ss io n n u m b e r M a ss [D a ] D e sc r ip ti o n i/ w r a ti o h a lf -l if e (h r s) i/ w r a ti o h a lf -l if e (h r s) i/ w ( a v g .) IP I0 0 0 2 5 3 6 6 5 1 9 0 8 C S C it ra te s y n th as e, m it o ch o n d ri al 2 .4 9 8 3 0 .9 3 .4 2 4 9 8 .6 3 .1 9 0 .8 6 IP I0 0 2 2 0 4 8 7 1 8 5 3 7 A T P 5 H I so fo rm 1 o f A T P s y n th as e su b u n it d , m it o ch o n d ri al 3 .6 3 1 4 2 .7 4 .0 3 9 9 9 1 .2 2 3 .1 9 0 .8 1 IP I0 0 0 2 1 3 2 7 2 5 3 0 4 G R B 2 I so fo rm 1 o f G ro w th f ac to r re ce p to r- b o u n d p ro te in 2 0 .6 0 9 1 0 .7 0 .4 5 5 6 .9 0 .6 5 1 .4 1 IP I0 0 0 0 3 5 2 7 3 9 1 3 0 S L C 9 A 3 R 1 E zr in -r ad ix in -m o es in - b in d in g p h o sp h o p ro te in 5 0 0 .4 4 6 8 .8 0 .5 5 8 1 2 .2 1 .3 8 0 .8 4 IP I0 0 0 2 7 3 5 0 2 2 0 4 9 P R D X 2 P er o x ir ed o x in -2 2 .4 4 1 3 0 .3 3 .1 5 5 1 3 5 .3 4 .4 7 0 .8 2 332 T a b le 6 .6 . P r o te in s fo u n d i n S IL A C ( p r o te in c o n c e n tr a ti o n ) e x p e r im e n ts a n d i n h a lf -l if e e x p e r im e n ts s h o w in g n o c h a n g e i n c o n c e n tr a ti o n a n d a c h a n g e i n h a lf -l if e . C o n c e n tr a ti o n c h a n g e s a r e s h o w n i n t h e “ S IL A C ( a v g i /w )” c o lu m n ; c h a n g e s in h a lf -l if e ( S ta r v in g / C o n tr o l) a r e s h o w n i n t h e “ D if fe r e n c e O b se r v e d ” c o lu m n . 333 C o n tr o l C y to k in e w it h d r a w a l D if fe r e n c e o b se r v e d S IL A C A c c e ss io n n u m b e r M a ss [D a ] D e sc r ip ti o n i/ w r a ti o h a lf -l if e (h r s) i/ w r a ti o h a lf -l if e (h r s) i/ w (a v g .) IP I0 0 2 1 5 6 1 0 5 2 4 9 2 M P P 1 5 5 k D a er y th ro cy te m em b ra n e p ro te in 0 .3 5 2 7 .7 0 .2 4 7 6 .5 0 .8 4 0 .9 8 IP I0 0 2 1 9 4 4 6 2 1 1 5 8 P E B P 1 P h o sp h at id y le th an o la m in e - b in d in g p ro te in 1 2 .4 8 1 3 0 .7 1 .8 2 9 2 6 .3 0 .8 6 0 .9 6 IP I0 0 0 2 9 1 3 3 2 8 9 4 7 A T P 5 F 1 A T P s y n th as e su b u n it b , m it o ch o n d ri al 7 .7 2 5 8 5 .4 2 .6 1 4 4 1 .4 0 .4 8 0 .9 3 IP I0 0 0 1 4 8 0 8 2 5 8 3 2 P A F A H 1 B 3 P la te le t- ac ti v at in g f ac to r ac et y lh y d ro la se I B s u b u n it g am m a 7 .3 7 3 8 1 .7 4 .7 5 7 4 3 .4 0 .5 3 1 .0 5 IP I0 0 0 1 1 9 3 7 3 0 7 4 9 P R D X 4 P er o x ir ed o x in -4 3 .5 5 5 4 1 .9 2 .4 0 2 3 7 .7 0 .9 0 0 .9 3 IP I0 0 0 2 5 0 1 9 2 6 7 0 0 P S M B 1 P ro te as o m e su b u n it b et a ty p e- 1 2 .4 2 2 3 0 .1 4 .7 9 7 3 5 .4 1 .1 8 1 .1 1 IP I0 0 0 1 1 4 5 4 1 0 9 8 2 5 G A N A B I so fo rm 2 o f N eu tr al a lp h a- g lu co si d as e A B 2 .4 7 8 3 0 .7 2 .3 8 3 4 2 .4 1 .3 8 0 .9 0 IP I0 0 0 2 4 9 1 9 2 8 0 1 7 P R D X 3 T h io re d o x in -d ep en d en t p er o x id e re d u ct as e, m it o ch o n d ri al 5 .0 4 3 5 7 .5 4 .3 9 2 7 8 .4 1 .3 6 0 .9 3 IP I0 0 0 1 5 8 3 3 2 6 4 2 1 C H C H D 3 C o il ed -c o il -h el ix -c o il ed - co il -h el ix d o m ai n -c o n ta in in g p ro te in 3 , m it o ch o n d ri al 3 .1 9 4 3 8 .2 3 .1 8 7 6 2 .1 1 .6 3 0 .9 0 IP I0 0 0 2 7 2 3 0 9 2 6 9 6 H S P 9 0 B 1 E n d o p la sm in 2 .6 8 2 3 2 .8 2 .4 6 7 2 8 .1 0 .8 6 1 .0 3 334 C o n tr o l C y to k in e w it h d r a w a l D if fe r e n c e o b se r v e d S IL A C A c c e ss io n n u m b e r M a ss [D a ] D e sc r ip ti o n i/ w r a ti o h a lf -l if e (h r s) i/ w r a ti o h a lf -l if e (h r s) i/ w (a v g .) IP I0 0 0 1 3 8 4 7 5 3 2 9 7 U Q C R C 1 C y to ch ro m e b -c 1 c o m p le x su b u n it 1 , m it o ch o n d ri al 4 .0 8 4 4 7 .5 3 .6 1 2 4 0 .9 0 .8 6 1 .0 1 IP I0 0 4 1 4 6 7 6 8 3 5 5 4 H S P 9 0 A B 1 H ea t sh o ck p ro te in H S P 9 0 -b et a 0 .6 2 6 1 0 .9 1 .8 7 8 2 1 .0 1 .9 3 1 .0 7 335 C o n tr o l C y to k in e w it h d r a w a l D if fe r e n c e o b se r v e d S IL A C A c c e ss io n n u m b e r M a ss [D a ] D e sc r ip ti o n i/ w r a ti o h a lf -l if e (h r s) i/ w r a ti o h a lf -l if e (h r s) i/ w ( a v g .) IP I0 0 0 1 2 5 3 5 4 5 5 8 1 D N A JA 1 D n aJ h o m o lo g , su b fa m il y A m em b er 1 0 .3 5 2 7 .7 0 .3 7 1 7 .1 0 .9 1 1 .1 8 T a b le 6 .7 . P r o te in s fo u n d i n S IL A C ( p r o te in c o n c e n tr a ti o n ) e x p e r im e n ts a n d i n h a lf -l if e e x p e r im e n ts s h o w in g a c h a n g e i n c o n c e n tr a ti o n a n d n o c h a n g e i n h a lf -l if e . C o n c e n tr a ti o n c h a n g e s a r e s h o w n i n t h e “ S IL A C ( a v g i /w )” c o lu m n ; c h a n g e s in h a lf -l if e ( S ta r v in g / C o n tr o l) a r e s h o w n i n t h e “ D if fe r e n c e O b se r v e d ” c o lu m n . 336 C o n tr o l C y to k in e w it h d r a w a l D if fe r e n c e o b se r v e d S IL A C A c c e ss io n n u m b e r M a ss [D a ] D e sc r ip ti o n i/ w r a ti o h a lf -l if e (h r s) i/ w r a ti o h a lf -l if e (h r s) i/ w ( a v g .) IP I0 0 0 1 3 8 6 2 2 3 9 7 6 T h y m id y la te k in as e 2 .5 7 9 3 1 .7 2 .4 4 1 3 2 .2 1 .0 1 0 .9 8 IP I0 0 0 2 4 3 6 4 1 0 3 7 7 1 T N P O 1 t ra n sp o rt in 1 i so fo rm 1 2 .4 0 9 2 9 .9 2 .3 3 5 2 8 .4 0 .9 5 1 .0 1 IP I0 0 3 0 3 8 8 2 4 7 1 8 9 M 6 P R B P 1 I so fo rm B o f M an n o se -6 -p h o sp h at e re ce p to r- b in d in g p ro te in 1 2 .3 2 1 2 9 .0 2 .4 8 4 2 5 .6 0 .8 8 1 .0 7 IP I0 0 3 9 6 4 8 5 5 0 4 5 1 E E F 1 A 1 E lo n g at io n f ac to r 1 - al p h a 1 0 .5 8 6 1 0 .4 0 .6 6 1 0 .6 1 .0 2 1 .0 6 IP I0 0 4 7 2 0 1 3 4 1 1 5 1 H L A c la ss I h is to co m p at ib il it y an ti g en , A -3 3 a lp h a ch ai n 0 .2 4 6 .3 0 .2 8 5 6 .6 1 .0 4 1 .0 7 IP I0 0 0 2 5 5 1 2 2 2 8 2 6 H S P B 1 H ea t sh o ck p ro te in b et a- 1 0 .3 1 7 7 .3 0 .4 1 2 7 .8 1 .0 7 1 .1 1 IP I0 0 0 2 0 4 3 6 2 4 5 8 8 R A B 1 1 B R as -r el at ed p ro te in R ab -1 1 B 2 .4 3 5 3 0 .2 2 .1 2 9 2 8 .5 0 .9 4 0 .9 8 IP I0 0 0 1 1 9 3 7 3 0 7 4 9 P R D X 4 P er o x ir ed o x in -4 3 .5 5 5 4 1 .9 2 .4 0 2 3 7 .7 0 .9 0 0 .9 3 IP I0 0 2 1 9 9 5 3 2 6 1 8 0 U M P -C M P k in as e (U M P - C M P K ), m R N A 3 .0 0 1 3 6 .2 1 .9 9 5 3 5 .9 0 .9 9 0 .8 9 T a b le 6 .8 . P r o te in s fo u n d i n S IL A C ( p r o te in c o n c e n tr a ti o n ) e x p e r im e n ts a n d i n h a lf -l if e e x p e r im e n ts s h o w in g n o c h a n g e i n c o n c e n tr a ti o n o r i n h a lf - li fe . 337 6.4 Changes in the Pattern of Distribution of Proteins to Gene Ontologies When considering the subgroup of proteins with a half-life of less than fifteen hours, and the sub group with a half-life of greater than twenty-four hours, it is possible to observe changes in the distribution of proteins within level 4 gene ontologies in cells undergoing cytokine withdrawal, when compared with control cells. These changes are illustrated below in the directed acyclic graphs (DAGs) for biological process and molecular function (Figures 6.1, 6.2, 6.3 and 6.4). These graphs were prepared in the following manner. Lists of IPI accession numbers were obtained from each experiment as described above, and used to determine the gene ontologies (Ashburner et al., 2000) of the proteins detected. Lists were converted to fasta format using the EMBL-EBI website “dbfetch” function (http://www.ebi.ac.uk/cgi- bin/dbfetch). The fasta lists were imported into Blast2GO (Ver. 2.2.3) (Conesa et al., 2005). The non-redundant blast database at the NCBI was used as the reference for a protein blast (blastp). Settings for blastp were: expectation value minimum of 1 x e-3 and high scoring segment pair cut-off of 33. Default parameters were used to make the Annotations - the pre- eValue-Hit-Filter was 1 x e-6, the Annotation cut-off was 55, and the GO Weight was 5. Directed acyclic graphs were then prepared using a cut-off (sequence filter) of 5, a score ! of 0.6 and a node score filter of 0. This allowed the generation of directed acyclic graphs at level 4 gene ontologies. These data must be interpreted with circumspection, since they represent only a sub-set of the proteome, however, we believe that some value may be found in examining these data - even if all gel slices had been analyzed, this would not constitute 338 the whole proteome; and we propose that some indication of the way forward might be gleaned from examining the partial data set presented here. 339 F ig u r e 6 .1 L e v e l 4 D A G f o r B io lo g ic a l P r o c e ss G e n e O n to lo g ie s. P r o te in s in c o n tr o l a n d s ta r v in g c o n d it io n s w it h h a lf -l if e l e ss t h a n 1 5 h o u r s. 340 F ig u r e 6 .2 . L e v e l 4 D A G f o r M o le c u la r F u n c ti o n G e n e O n to lo g ie s. P r o te in i n c o n tr o l a n d s ta r v in g c o n d it io n s w it h h a lf -l if e l e ss t h a n 1 5 h o u r s. 341 F ig u r e 6 .3 . L e v e l 4 D A G f o r B io lo g ic a l P r o c e ss G e n e O n to lo g ie s. P r o te in s in c o n tr o l a n d s ta r v in g c o n d it io n s w it h h a lf -l if e g r e a te r t h a n 2 4 h o u r s. 342 F ig u r e 6 .4 . L e v e l 4 D A G f o r M o le c u la r F u n c ti o n G e n e O n to lo g ie s. P r o te in i n c o n tr o l a n d s ta r v in g c o n d it io n s w it h h a lf -l if e g r e a te r t h a n 2 4 h o u r s. 343 6.5 Discussion Selective degradation of proteins is important for the control of cellular functions such as cell cycle, cell differentiation and signal transduction (Hershko & Ciechanover, 1998; Jesenberger & Jentsch, 2002; King, Deshaies, Peters, & Kirschner, 1996). Changes in protein half-life can be induced by altering the rate of protein synthesis, or the rate of protein degradation. In the work discussed here, one may observe apparent alterations in the calculated half-lives of proteins under conditions of starvation (cytokine withdrawal), versus normal conditions, implying a change in the rates of protein synthesis or degradation. These changes are not unexpected; in cells that depend on cytokine stimulation for survival, the rate of protein synthesis will be expected to change upon cytokine withdrawal. However, the situation is not necessarily expected to be “steady-state” – rather, we might expect to see dynamic changes – with the rate of change varying as the duration of cytokine withdrawal extends. With this in mind, I am not proposing that the calculated half-lives shown here represent a steady state. Rather, that these data represent a “snap-shot” – a momentary overview of complex cellular machinery, an “instantaneous rate”; and the half-lives calculated here represent the amount of unaltered (original) protein present at 15 hours of cytokine withdrawal. A recently published paper by Doherty et. al. provides details of the degradation of 576 proteins in human lung adenocarcinoma A549 cells (Doherty, Hammond, Clague, Gaskell, & Beynon, 2008). The authors used SILAC to label cells, and conducted a “pulse-chase” 344 experiment, washing out the heavier carbon isotope with 12 C and determining the rates of incorporation of the lighter isotope. Degradation rates are published for the 576 non- redundant proteins. Interestingly, the authors label this data as coming from HeLa cells, but HeLa cells are not mentioned elsewhere in the published paper. Nine of the published proteins in the degradation study of Doherty et. al. were found in our data. Table 6.9 shows the nine proteins, the kdeg (degradation rate constant) from the Doherty data, a calculation converting the kdeg into half-life (H = ln(2)/kdeg), and the half-life determined in my work (using a different model system). Data from Doherty, et.al. (2008) Data from the current work Protein kdeg Half-life (hrs) calculated as kdeg = ln(2)/H Half-life determined in current work Ornithine aminotransferase, mitochondrial 0.07793 8.9 10.3 S-adenosylmethionine synthetase isoform type-2 0.25299 2.7 8.7 Eukaryotic translation initiation factor 3 subunit 7 0.01069 64.8 10.4 Elongation factor 1-alpha 0.03408 20.3 10.2 ATP synthase subunit beta, mitochondrial 0.2399 2.9 40.7 Platelet-activating factor acetylhydrolase, isoform Ib, gamma subunit 0.0206 33.6 81.7 Peroxiredoxin-4 0.00863 80.3 41.9 Citrate synthase, mitochondrial 0.06809 10.2 30.9 Electron transfer flavoprotein subunit beta 0.01344 51.6 26.4 Table 6.9. Nine proteins found in common between published protein degradation data and half-life data computed from our data set. Only one of the proteins found in common between the Doherty data and our data shows a half-life that might be thought to be comparable. The other eight proteins show quite 345 different half-life data, although most of these data fall within two-fold of each other. It may be that these differences observed are due to differences in the model systems. Our data show several proteins that were identified and quantitated in both the control and the starving samples, allowing comparisons to be made between these two conditions using the calculated half-lives. Of the proteins found in both control and starving samples, many were also found in the (earlier) protein concentration experiments, allowing more accurate estimation of the protein half-lives in the starving group, where several proteins were shown earlier to have altered concentrations in response to cytokine withdrawal. The details of the proteins found both in the SILAC experiments examining protein concentration, and the current experiment, examining half-lives, appear below. Shown with the protein information are numbers which show the calculated half-life changes, and the individual quantitation ratios from the three earlier experiments, followed by the average quantitation changes (the ratio is expressed as isotope/wild-type, and since the isotope was used to label the control cells; the ratio as shown is the inverse of the change in concentration in response to cytokine “starvation”). An “x” here indicates that no result was observed in that particular biological replicate of the earlier experiment. Below are presented data from Tables 6.5 – 6.8, that is, the lists of proteins with a half-life of less than fifteen hours or greater than twenty-four hours, and the relationships between the half-life and concentration data. Proteins are grouped according to the table in which they appear. These data are presented with the caveat that the quoted changes in half-life or concentration must be 346 interpreted with caution when these figures approach the bounds of experimental error for the techniques used. 6.5.1 Proteins with a Change in both Concentration and in Half-Life OAT Ornithine aminotransferase, mitochondrial is involved in amino acid biosynthesis. OAT is observed to have a 16% reduction in half-life and to show a 15% increase in concentration in response to cytokine withdrawal (1.26/0.80/0.76 – avg. 0.87). A reduction in half-life of 16%, with an apparent increase in concentration might be attributed to an increased protein synthesis accompanied by an decreased rate of degradation. Fumarylacetoacetase is observed to have an increase in half-life of 69%, and an increase in concentration of 26% (average i/w ratio equals 0.86). This might be explained by increased protein synthesis and a decreased rate of degradation. Adenine phosphoribosyltransferase is shown to have an increase in half-life of 77%, and an increase in concentration of 16%. This also might be explained by increased protein synthesis and a decreased rate of degradation. ATP5B, ATP synthase, subunit beta, mitochondrial forms part of Complex V, along with ATP5F1 (below), and produces ATP from ADP in the mitochondrial electron transport chain. ATP5B shows an increase in half-life of 52%, and an increase in concentration of 15% (0.9/0.9/0.84 – avg. 0.87), suggesting a decrease in the turn-over of the enzyme. 347 Actin, cytoplasmic 1 (also known as beta actin) is involved in cell motility, and structure. The 33% increase in half-life and small (16%) increase in concentration (0.94/0.88/0.80 – avg. 0.86) might be explained by decreased turn-over of the protein. Squalene synthetase This protein is located in the membrane of the endoplasmic reticulum, and is involved in cholesterol synthesis. Squalene synthetase is observed to have a 15% increase in half-life following fifteen hours of cytokine withdrawal, and to show a 17% decrease in concentration ((0.67/1.3/2.1 – avg. 1.20). The increased half-life, accompanied by the reduction in concentration might be an attempt to maintain a steady-state for this enzyme. Enoyl-CoA hydratase, mitochondrial shows a 39% increase in half-life and a 16% increase in concentration (0.89/0.83/0.88 - avg 0.86), possibly caused by an decrease in the rate of protein degradation. Isoform long of cold shock domain-containing protein E1 (CSDE1) shows a 19% decrease in half-life, and we observe a decrease in concentration of almost 30% (1.57/1.34/1.38 – avg. 1.39). CSDE1 may be involved in translationally coupled mRNA turnover. The decreasing half-life might be due to more rapid protein turn-over, leading to the decreased concentration. Isoform Long of Ubiquitin carboxy-terminal hydrolase 5 (USP5) shows a 148% increase in half-life, and an approximately 20% increase in concentration (0.98/0.69/0.87 – avg. 0.84). USP5 cleaves linear and branched multiubiquitin polymers, hence the increases in half-life 348 and concentration might be expected, given the pre-apoptotic state of the cells. This might be explained by a reduction in protein degradation. Creatine kinase B-type (CKB) shows an 87% increase in half-life, and an approximately 20% increase in concentration (0.89/0.85/0.69 – avg. 0.81). CKB catalyzes the transfer of phosphate from ATP to various molecules involved in energy transduction. Under conditions of cytokine withdrawal, an argument might be made for an increase in the energy requirements as the cell tries to survive; hence an increase in the half-life and concentration of CKB might be explained by this mechanism. This might be explained by a reduction in protein degradation. TUFM Tu translation elongation factor, mitochondrial shows a 115% increase in half- life, and an increase in concentration of 16% (0.78/0.93/0.85 – avg. 0.86). This may be explained by the need for mitochondrial protein synthesis during early apoptosis, in an effort to maintain homeostasis, or to attempt survival, achieved by decreasing the rate of degradation of TUFM. Isoform 1 of Annexin 7 is unique in this data set, in that it shows a negative calculated half- life. This protein shows a -228% difference in half-life, (from +38.4 hours to -126.2 hours) and an increase in concentration of 42% (0.62/0.58/0.88 – avg. 0.70). The negative value is generated because of an apparent increase (8%) in the amount 13 C-labelled annexin 7 under cytokine withdrawal conditions. Since this should not happen (there is no more 13 C available for incorporation into new protein) this negative half-life value might be generated by 349 “recycling” 13 C material from other proteins what have been degraded. But if this were to be the case, we should surely expect to see other examples – although in a data set of only some 300 proteins, such an occurrence might be rare. Cytochrome b-c1 complex, subunit 2, mitochondrial is a component of the ubiquinol- cytochrome c reductase complex (complex III or cytochrome b-c1 complex), which is part of the mitochondrial respiratory chain. The core protein 2 is required for the assembly of the complex. The protein shows a 41% increase in half-life, and an approximately 14% (0.86/0.94/0.87 – avg. 0.89) increase in concentration in response to cytokine withdrawal. This might be explained by a decrease in protein degradation, required to ensure continued energy production in the mitochondrial respiratory chain. Citrate synthase, mitochondrial This mitochondrial protein is involved in oxidative phosphorylation. Citrate synthase shows a large (219%) increase in half-life, and an increase in concentration of 16% (0.93/0.71/1.0 – avg. 0.88). These changes might be explained by a reduction in protein degradation, perhaps because of the change in energy dynamics in a cell deprived of mitogens. ATP5H Isoform 1 of ATP synthase subunit d, mitochondrial, along with 5B and 5F1, is part of Complex V, which produces ATP from ADP in the inner mitochondrial membrane, as part of the electron transport chain. As such it is involved in cellular energy generation. This protein shows an enormous (23-fold) increase in half-life and a 23% increase in concentration in response to cytokine withdrawal. These changes might be explained by a 350 decrease in protein degradation, required for maintaining energy production in early apoptosis. GRB2 Isoform 1 of Growth factor receptor-bound protein 2 This cytosolic protein contains both SH2 and SH3 domains, and acts as an adaptor between proteins with a phosphorylated tyrosine and other, proline rich proteins. GRB2 associates with tyrosine- phosphorylated epidermal growth factor receptors (EGFRs) and platelet-derived growth factor receptors (PDGFRs) via its SH2 domain, and thereby activates the ras signaling pathway (Lowenstein et al., 1992). Grb2 shows a 35% reduction in half-life and a 30% increase in concentration ((1.5/1.3/1.5 – avg. 1.4) in response to cytokine withdrawal, one possible explanation for which might be an increased turn-over of Grb2 – making more Grb2, but shorter-lived. Perhaps Grb2 is being “used up” by some process, resulting in the decreased half-life, and the cell tries to compensate for this by increasing the concentration of Grb2. Perhaps by removing CGM1 signaling, the cell might try to compensate by relying more on signaling through other cytokines – turning up the volume on the listening device (cytokine receptors), hence the need for more Grb2? Ezrin-radixin-moesin-binding phosphoprotein 50 is a membrane-bound protein which acts as a scaffold protein, connecting plasma membrane proteins with members of the ezrin/ radixin/moesin family and thereby helping to link them to the actin cytoskeleton and to regulate their surface expression. Ezrin-radixin-moesin-binding phosphoprotein 50 shows a 38% increase in half-life, and a 20% increase in concentration (average i/w ratio equals 0.84). These changes might be explained by a reduction in protein degradation. 351 Peroxiredoxin-2 (Prx2) shows a large (347%) increase in half-life, and a 22% increase in concentration (0.89/0.79/0.74 - avg 0.81) in response to cytokine withdrawal. Another cytosolic member of the multifunctional anti-oxidant thioredoxin-dependent peroxidases (six in total), Prx2 modulates signaling through the PDGF receptor to link reactive oxygen species metabolism to redox-dependent signaling events, for example, by playing a role in maintaining cell cycle arrest under oxidizing conditions (Phalen et al., 2006). The changes observed here might be explained by a decrease in protein degradation – perhaps to be expected in early apoptosis. 6.5.2 Proteins with no Change in Concentration and a Change in Half-Life 55 kDa erythrocyte membrane protein (MPP1). This membrane-bound protein interacts with DLG5 (Disks large homolog 5, which may transmit extracellular signals to the cytoskeleton and membrane). MPP1 shows a decrease in half-life of 16%, and no change in concentration (1.1/0.98/0.75 – avg. 0.94). An increase in the rate of protein turn-over could explain this observation; such as by increasing the rate of degradation, while at the same time increasing the rate of protein production. Phosphatidylethanolamine-binding protein 1 (1.0/0.94/0.95 – avg. 0.96) appears to inhibit Raf-1. We observe a decrease in half-life by about 14%, with no apparent change in concentration. These changes might be explained by an increase in the rate of degradation, accompanied by an increase in the rate of synthesis. 352 ATP5F1 synthase subunit b, mitochondrial is part of Complex V, which produces ATP from ADP in the inner mitochondrial membrane, as part of the electron transport chain. As such it is involved in cellular energy generation. The apparent 52% reduction in the half-life of this protein, located in the mitochondrial inner membrane, may be a reflection of mitochondrial flux in early apoptosis. It is curious to note that ATP5B, another component of Complex V, was also identified, and shows a 52% increase in half-life, with a slightly increased concentration observed. Also see ATP5H (above) with a 23-fold increase in half- life. The ATP5B and ATP5F1 end up with roughly similar half-lives (61 hours and 41 hours respectively). The observed half-life for ATP5H is over 990 hours. It may be that an i/w ratio of greater than 3.5 (see tables 6.5 and 6.6) in this type of experiment does not allow accurate enough quantitation of 12 C compared with 13 C, leading to such a large apparent half-life. More work would need to be done to explain these apparent differences in results. Platelet-activating factor acetylhydrolase IB subunit gamma. This cytosolic protein inactivates platelet-activating factor. We observe a 47% decrease in half-life. The apparent decrease in half-life, under conditions of unaltered protein concentration, as determined by three earlier experiments (1.1/0.96/1.1 – avg. 1.05), may be explained by an increase in protein degradation and a simultaneous increase in protein synthesis. Proteasome subunit beta, type-1. forms part of the proteasome, involved in ATP-dependent proteolytic activity. The 18% increase in half-life may be a cellular response to the increased requirements for proteolysis during apoptosis; leading to reduced degradation of this protein. 353 There is a suggestion of a small reduction in concentration (1.16/1.18/1.03 – avg. 1.11). The less than 10% change observed must be interpreted with caution. GANAB Isoform 2 of Neutral alpha-glucoside AB cleaves glucose residues from an oligosaccharide precursor of immature glycoporoteins. GANAB shows a 38% increase in half-life, while maintaining an almost unaltered protein concentration. Such a change might be explained by a reduction in protein degradation, and a reduction in protein synthesis, required to free glucose molecules for cellular energy production in early apoptosis. PRDX3 Thioredoxin-dependent peroxide reductse, mitochondrial functions to protect radical-sensitive enzymes from oxidative damage. PRDX3 acts synergistically with MAP3K13 to regulate the activation of NF!B in the cytosol. PRDX3 shows a 36% increase in half-life and almost no change in protein concentration in response to cytokine withdrawal. Such an observation might be explained by a reduction in protein degradation accompanied by a reduction in protein synthesis. CHCHD3 Coiled-coil-helix-coiled-coil-helix domain-containing protein 3, mitochondrial is found in a complex with the mitochondrial inner membrane protein mitofilin, and metaxins 1 and 2, SAM50, CHCHD6 and DNAJC11. CHCHD3 has also been described as a novel substrate of cAMP-dependent protein kinase (PKA) – isoform unreported; the construct was genetically enineered. This protein shows a 63% increase in half-life, while maintaining protein concentration. This observation might be explained by a reduction in protein degradation, along with a reduction in protein synthesis. 354 HSP90B1 Endoplasmin is a molecular chaperone, functioning in the processing and transport of secreted proteins. HSP90B1 shows a 14% decrease in half-life, while maintaining concentration (1.03/1.04/1.03 – avg. 1.03). This might be explained by an increase in protein turn-over; perhaps a response to an increased requirement for HSP90B1, which is then degraded as it chaperones proteins in early apoptosis. Cytochrome b-b1 complex subunit 1, mitochondrial is also part of the ubiquinol- cytochrome c reductase complex (complex III or cytochrome b-c1 complex), however this protein may mediate formation of the complex between cytochromes c and c1. The subunit 1 protein shows a 14% decrease in half-life and no change in concentration. This is in contrast with c-b1 subunit 2 (above), which shows an increase in concentration and half-life. This finding is initially perplexing, since these proteins, members of the same complex, might be expected to alter in the same way in response to cytokine withdrawal. However, the two subunits play quite different roles in mitochondria. Subunit 1 assists in the formation of cytochromes c and c1 complex, whereas subunit 2 is required for the formation of the cytochrome b-c1 complex. If the differences observed here are borne out by further experimentation this could lead to a deeper understanding of the mitochondrial respiratory chain. HSP90AB1 HSP 90, beta functions as a molecular chaperone, with ATPase activity. This protein shows a 93% increase in half-life in response to cytokine withdrawal, while showing 355 no change in concentration (1.26/1.01/1.06 – avg. 1.07). These changes might be explained by a reduction in protein degradation, and a slowing of protein synthesis. 6.5.3 Proteins with a Change in Concentration and no Change in Half-Life DnaJ homolog subfamily A, member 1 (also known as Heat shock 40 kDa protein 4) shows a small (9%) decrease in half-life, and a small (15%) decrease in concentration (1.50/0.90/1.09 – avg. 1.16). This protein is thought to play a role in the importation of proteins into the mitochondria. The small changes in half-life and concentration observed here would need to be confirmed by other means before making any comments about a possible effect of cytokine withdrawal. The observations might be explained by increased degradation of the protein. 356 6.5.4 Proteins Found in the Half-Life Experiment and not Found in Three Concentration Experiments Transforming growth factor beta-1 (half-life 1.3-fold change) (x/x/x). – a multifunctional protein involved with cell proliferation and differentiation, as well as other functions in many cell types. Almost all cells have specific receptors for TGFB-1. The protein regulates the actions of many other growth factors. This protein was not found in the earlier mass spec experiments. ADFP Adipophilin (half-life 1.6-fold change) (1.8/x/x – 1.8) – may be involved in the development and maintenance of adipose tissue. S-adenosylmethionine synthetase isoform type-2 (half-life 1.4-fold change) (1.0/1.1/x – avg. 1.05) catalyzes the formation of S-adenosylmethionine from methionine and ATP. LSG1 Large subunit GTPase 1 homolog (half-life 1.4-fold change) (x/1.6/x – 1.6) This protein shuttles between the nucleus and the endoplasmic reticulum and is the GTPase required for the XPO1/CRM1-mediated nuclear export of the 60S ribosomal subunit. Eukaryotic translation initiation factor 3, subunit G (half-life 1.6-fold change) (1.5/1.2/x – avg. 1.35). The eukaryotic translation initiation factor 3, subunit G (eIF3G) increased in half-life by 1.6. The protein was found in two of three large mass spec experiments, and was determined to decrease in response to cytokine withdrawal by an average of 0.25-fold. A decrease in relative concentration, coincident with an increase in calculated half-life might be 357 explained by a decrease in the rate of eIF3G gene transcription, coincident with a slowing in the rate of degradation of eIF3G. EIF3G binds to the 40S ribosome and promotes the binding of methionyl-tRNAi and mRNA. The eIF3G subunit binds to the 18S rRNA. EIF3G functions in translating mRNAs that have a 5’ cap, or an IRES. When inactive, for example, under conditions of serum starvation, eIF3 is bound to S6K1. Activation of the mTOR/Raptor complex, for example by PI3-K stimulation, causes phosphorylation of S6K1 on T389 creating a conformational change that causes the kinase S6K1 to dissociate from eIF3. The S6K1 can then be fully activated by phosphorylation of Thr 229 by PDK1, allowing protein translation to occur. Hence, the eIF3G found in this series of experiments is closely linked to cell growth. The findings of a decrease in concentration of eIF3G, and an apparent increase in half-life of eIF3G under conditions of cytokine withdrawal raises the possibility that this is a survival response – an attempt by the cell to maintain homeostasis under adverse conditions. Or it may be that we are witnessing the effects of the cellular response to an “unbalancing” of the normal cytokine milieu – a response to the absence of GM-CSF, but the continued presence of other mitogenic factors found in dialyzed serum. CUL3 Isoform 1 of Cullin-3 (half-life 2.1-fold change) (Isoform 2 found in earlier SILAC experiments) (0.88/1.8/1.1 – avg. 1.26). CUL3 forms part of the core of the E3 ubiquitin- protein ligase complex which mediates the ubiquitination and subsequent proteasomal degradation of target proteins. The doubling in the half-life of CUL3 observed in response to cytokine withdrawal, when cells are beginning to undergo apoptosis, may well be significant, since it might be expected that the process of ubiquitination would proceed at an increased pace under this circumstance. Of note is the decrease in concentration of isoform 2 found in 358 the protein concentration experiments (concentration decreases by about 20%). Data concerning one isoform may not necessarily be attributable to a different isoform, but it is possible to speculate that in this instance, the two isoforms might behave similarly – isoform 2 is missing residues 1-24 when compared with isoform 1. In this case, a decrease in concentration might occur because CUL3 is being degraded as part of the ubiquitination process. Glutathione synthetase (half-life 1.3-fold change) (1.1/0.82/x – avg. 0.96). This enzyme generates glutathione from ATP, !-L-glutamyl-L-cysteine and glycine. Isoform 1 of Electron transfer flavoprotein subunit beta (half-life 1.5-fold change) (0.83/0.64/0.85 – avg. 0.77). This mitochondrial protein functions as a specific electron acceptor for several dehydrogenases, including five acyl-CoA dehydrogenases, glutaryl-CoA and sarcosine dehydrogenase. It transfers the electrons to the main mitochondrial respiratory chain via ETF-ubiquinone oxidoreductase (ETF dehydrogenase). Trifunctional enzyme subunit beta, mitochondria (half-life 1.5-fold change) (0.89/0.83/x – avg. 0.86). This mitochondrial thiolase acts in the generation of coenzyme A, involved in fatty acid metabolism. 359 6.5.5 General Comments It is interesting to note that of the eight proteins shown here, from the list of proteins with a half-life of greater than twenty-four hours, four are mitochondrial proteins. It is also interesting to note that the total number of unique proteins identified in the control sample (359) is similar to the number identified in the starved sample (343), and the number of proteins with a half life of greater than 24 hours is reasonably similar under both conditions (89 versus 101). However, the number of rapidly turned over proteins under control conditions is almost double the number of rapidly turned-over proteins found under conditions of cytokine withdrawal (43 versus 24). This is not a controversial result, since under conditions of cytokine withdrawal, it might be expected that protein synthesis will slow, or even stop in some cases. Despite this expectation of generally slowing protein synthesis, we have evidence from the previous SILAC experiments determining changes in protein concentration in response to cytokine withdrawal, that some proteins increase in concentration in response to cytokine withdrawal. These data are consistent with the concept of apoptosis as an active process in cells. 6.5.6 Distribution Among Level 4 Gene Ontologies in Control and Starving Cells Differences are apparent in the distribution among ontologies when comparing the control and starving cells with shorter half-lives, and with longer half-lives. One way to highlight these differences is to list the ontologies in which there is a difference of 30% or greater in the fraction of proteins assigned to the ontology. These data are presented, with the caveat 360 that they represent only a fraction of the proteome, and hence must be interpreted with caution. 361 Proteins with half-life less than 15 hours – biological process ontology. The following ontologies show differences of 30% or greater in the fraction of proteins assigned to level 4 of the biological process ontologies - control group compared with the starving group (proteins with a half-life of less than 15 hours). This includes those ontologies in which there are no proteins assigned from one of the groups: A. More represented in Control biopolymer metabolic process protein metabolic process macromolecular complex assembly negative regulation of biological process cellular macromolecule metabolic process cellular component assembly macromolecule biosynthetic process negative regulation of cellular process cell differentiation B. More represented in Starving signal transduction Ten of thirteen ontologies show changes of greater than 30% in the fraction of proteins assigned. 362 Proteins with half-life less than 15 hours – molecular function ontology The following ontologies show differences of 30% or greater in the fraction of proteins assigned to level 4 of the molecular function ontologies - control group compared with the starving group (proteins with a half-life of less than 15 hours). This includes those ontologies in which there are no proteins assigned from one of the groups: A. More represented in Control DNA binding ribonucleotide binding Two of four ontologies show changes of greater than 30% in the fraction of proteins assigned in control compared with starving cells. Proteins with half-life greater than 24 hours – biological process ontology The following ontologies show differences of 30% or greater in the fraction of proteins assigned to level 4 of the biological process ontologies - control group compared with the starving group (proteins with a half-life of greater than 24 hours). This includes those ontologies in which there are no proteins assigned from one of the groups: A. More represented in Control cell death regulation of programmed cell death vesicle-mediated transport B. More represented in Starving 363 nucleobase, nucleoside, nucleotide and nucleic acid metabolic process biopolymer metabolic process membrane organization and biogenesis cellular catabolic process positive regulation of cellular process protein transport regulation of cellular metabolic process homeostatic process protein localization cofactor metabolic process intracellular transport macromolecule catabolic process organ development macromolecular complex assembly cellular component assembly cellular lipid metabolic process lipid catabolic process lipid metabolic process regulation of protein metabolic process Twenty-two of forty-one ontologies show changes of greater than 30% in the fraction of proteins assigned in control compared with starving cells. 364 Proteins with half-life greater than 24 hours – metabolic function ontology The following ontologies show differences of 30% or greater in the fraction of proteins assigned to level 4 of the metabolic function ontologies - control group compared with the starving group (proteins with a half-life of greater than 24 hours). This includes those ontologies in which there are no proteins assigned from one of the groups: A. More represented in Control transferase activity, transferring acyl groups B. More represented in Starving peptidase activity ribonucleotide binding purine nucleotide binding hydrolase activity, acting on acid anhydrides transcription factor binding coenzyme binding identical protein binding DNA binding Nine of thirteen ontologies show changes of greater than 30% in the fraction of proteins assigned in control compared with starving cells. An interesting pattern emerges when the ontologies are classified in this way – for the proteins with the shorter half-lives (less than 15 hours) there are more ontologies 365 representing the proteins from the control cells, whereas for the proteins with longer half- lives (greater than 24 hours) there are more ontologies representing the proteins from the starving cells. This is the case for both biological process and molecular function ontologies. One possible explanation for this is that protein synthesis is generally reduced under conditions of cytokine withdrawal – with the exception of the group of proteins that includes the proteins shown to increase in concentration under conditions of cytokine withdrawal. Under such conditions of general inhibition of protein synthesis, it might be expected that proteins with short half-lives would be the first to be degraded, and hence to be under- represented in a study assigning proteins to gene ontologies. Further to this, it might be worth considering that under conditions of cytokine withdrawal, the proteins that are actively synthesized might necessarily have long half-lives – given the limited opportunity for protein synthesis to occur. 6.6 Conclusions The data presented here seem to indicate that the induction of apoptosis is a very dynamic process – we see evidence that a subset of proteins change both concentration and half-life in response to cytokine withdrawal. Some proteins maintain the same relative concentration, while showing a decrease in half-life. This might be explained by an increase in the rate of protein translation to compensate for the reduction in half-life (caused by an increased rate of protein degradation). Other proteins show a change in concentration, along with a change in calculated half-life. These changes might be explained again by alterations in the balance between protein transcription and degradation. These preliminary data point the way to 366 further work. Analysis of the remaining samples would yield a wealth of mass spectrometry data that could be used to calculate half-lives for many more proteins. Because the samples have been prepared at several time-points, it would be possible to calculate half-lives for the same protein following different durations of cytokine withdrawal. In this way, it would be possible to determine if the half-life changes were “static” or “dynamic” – do the half-lives of individual proteins which have been observed to change, become progressively longer or shorter with increasing duration of cytokine withdrawal? Or does cytokine withdrawal induce a change in half-life that is constant, regardless of the duration of the cytokine withdrawal? Another outcome of this work would be to attempt to identify further protein “groupings” – by function or other means – of proteins shown to have altered half-lives in response to cytokine withdrawal, and in so doing to view the process of apoptosis from a different perspective – one based more on a view of the system as a whole. Where some protein members of a group show alterations in half-life, other members of the same group, perhaps not observed by mass spectrometry under these conditions, might be expected to change in a similar manner. By using SILAC as an approach (or: ‘using stable isotope containing amino acids to label cells’) to label cells, and then to observe the rates of incorporation into proteins of the normal isotope, and subsequently to calculate the half-life of the protein based on the rate of incorporation of the normal isotope; it has been possible to show changes in the calculated half-lives of proteins under conditions of cytokine withdrawal when compared with the same proteins under normal conditions. These alterations, when considered with the previously determined changes in protein concentrations in response to cytokine withdrawal, point to 367 the very dynamic nature of early apoptosis. Analysis of the proteins according to ontologies points to subsets of cellular function that seem to be more represented under conditions of cytokine withdrawal. This information may be used to add to our understanding of early apoptosis. 368 7 Conclusions and Future Directions During the past decade and more, mass spectrometry has emerged as the preferred tool for proteomic studies of biological systems (Aebersold, 2003; Cravatt, Simon, & Yates, 2007; Ong & Mann, 2005; Patterson & Aebersold, 2003a). Using mass spectrometry, insights into the composition of cells, the dynamic changes (in protein localization or concentration), and the composition of various molecular complexes have more easily been determined under various experimental conditions. Mass-spectrometry-based proteomics has shown promise as a powerful ‘hypothesis-generating engine’ (Cravatt et al., 2007), which, when combined with other tools, such as molecular and pharmacological techniques, provides the underpinning and structure for the development of a real understanding of complex biological processes. The work described in this thesis is hypothesis-generating, and has resulted in several findings that indicate potentially fruitful avenues of investigation concerning the complex biology of cell survival and cell death. Initially, the following questions were posed: - what changes occur in cellular protein concentrations in response to cytokine withdrawal-induced apoptosis? - can a global view of these protein concentration changes be interpreted in any way that suggests a new understanding of the complex interactions occurring between proteins as cells commit to undergoing apoptosis? - do any specific proteins appear in these data that have previously been unrecognized as being involved in the PI3-K signaling pathway? 369 - and overall, does the current technology allow adequate analysis of complex protein mixtures to permit the above questions to be addressed in this way? One advantage of taking a hypothesis-generating approach to answering these questions is that it requires minimal a priori knowledge. It has been possible to determine answers to the posed questions, as well as to show that this approach to answering the questions is legitimate and provides valuable data. Evidence for this latter takes the form of visualization of the proteome to a “deep” enough level (that is, in enough detail to observe low-abundance proteins) to allow signaling proteins to be detected/identified/quantitated, and to visualize complex interactions of the members of pathways involved in apoptosis or mitogenesis. “Interaction” in the sense that it is used here is not intended to convey physical association of proteins. Rather, the word is used more generically to convey the sense of multiple proteins being affected in a coherent manner, so that the concentrations of the “group” members increases, or decreases, coherently, or perhaps that an increase in some members of an identified group of proteins might cause a decrease in the concentration of other members. As such, these interactions may be visualized as the complex changes in concentrations of proteins involved in various biological processes or molecular functions, grouped by assignment of the proteins to gene ontologies. 370 7.1 The Relationship Between the Previous Chapters – Integration of the Material. Chapters two, three, four and five describe the generation of the protein concentration data, and present these data on the changes in protein concentrations observed in response to fifteen hours of cytokine withdrawal. Interpretation of these data is achieved in part by using the identities of the proteins that changed most significantly, taking specific proteins and discussing a role or putative role in cell survival, cell cycle or cell death. Further, patterns of change are discovered using gene ontologies to identify biological processes or molecular functions which report altered patterns of proteins representing the process or function under experimental conditions. Chapter six describes the use of the same experimental technique – stable isotope labelling – to calculate protein half-lives. Calculated protein half-lives were shown to be different in some proteins under experimental conditions when compared with control. Some of these changes in calculated half-lives have been combined with data on alterations in protein concentration from the earlier SILAC experiments, where the same protein was found in all experiments. Changes in calculated half-life associated with changes in protein concentration lead to interesting suggestions of a coordinated response to the induction of apoptosis. 371 7.2 The Current Work in the Overall Context of the Field. Some other work has been published examining proteome-level changes in cells undergoing apoptosis. The present work is different from other published work in the following ways:- 1. Cell type. No published reports of the proteomic analysis of apoptosis in TF-1 cells have been found. 2. Induction of apoptosis by cytokine withdrawal. Various methods of inducing apoptosis have been used in previously published proteomics studies of apoptosis, for example, resveratrol (Cecconi et al., 2008), dexamethasone (Rees-Unwin et al., 2007), anti- Fas antibody (Gerner et al., 2000; Hwang et al., 2006b; Thiede, Kretschmer, & Rudel, 2006), camptothecin (Winkelmann et al., 2008), cisplatin (Schmidt et al., 2007), or staurosporine (Gerner et al., 2002). However, we could find no published reports of proteomic analyses of apoptosis induced by cytokine withdrawal. Induction of apoptosis by cytokine withdrawal, while leading eventually to the same outcome as induction of apoptosis by the other methods described above, arrives at this end-point via a different route – removal of a stimulus required for growth, rather than imposition of a stimulus leading to death. 3. Use of whole cell lysates. Hwang and others characterized the nuclear proteome during apoptosis (Hwang et al., 2006a). Gerner and others used two-dimensional gel electrophoresis and MALDI-TOF peptide mass fingerprinting to analyze the proteome of 372 nuclear matrix proteins in cells undergoing apoptosis (Gerner et al., 2002). Thiede and colleagues published a report using whole cells lysates from Fas-induced apoptosis in Jurkat T cells, using two-dimensional gel electrophoresis and MALDI-TOF/TOF (Thiede et al., 2006). 4. Three biological replicates. The use of three biological replicates appears to be unique, in that no published studies in which the use of three biological replicates has been reported were found. 5. Pre-apoptosis. Other published studies appear to use cells which are far advanced on the path to apoptosis – for example, after six hours of Fas ligand (Thiede et al., 2006), or sixteen hours of cisplatin (Schmidt et al., 2007) treatment. In our study, apoptosis was induced by fifteen hours of cytokine withdrawal – a time point at which the cells have committed to apoptosis (shown by the activation of caspase-3), and yet have not begun to fragment – the cells remain predominantly intact. Because this model allows apoptosis to develop over a period longer than 15 hours following cytokine withdrawal, and because the cells appeared predominantly intact when viewed microscopically (the physical hallmarks of apoptosis were not readily apparent), this study has examined changes that occur early in the process of apoptosis. 6. The use of FT-ICR. Schmidt and others examined SILAC labeled Jurkat T cells after cisplatin-induced apoptosis, using MALDI-TOF/TOF (Schmidt et al., 2007). Thiede and colleagues published several papers examining proteome changes during apoptosis using 373 analysis by MALDI-TOF/TOF (Kozielski et al., 2008; Schmidt et al., 2007; Thiede et al., 2006). 7. The SILAC labeled half-life determination in cells undergoing apoptosis has not been published before. 374 7.3 Strengths And Limitations Of The Current Study The data gathered through the use of SILAC, examining whole cell lysates, pre-fractionated using SDS-PAGE, and analyzed using an FT-ICR mass spectrometer, are considered reliable data for the following reasons: (i) the experiment was conducted in three biological replicates, (ii) a non-redundant list of only the highest-scoring peptides was prepared for each replicate, and this list was used to assign peptides to proteins, (iii) peptides were assigned in such a way that the smallest number of protein sequences that could explain the peptide data was generated, with each peptide being used only once in the final list, (iv) proteins were identified on the basis of two or more peptides, and where only two peptides were found, both were required to be greater than seven amino acid residues in length, and (v) proteins included in the final data set are those which were found in all three experiments, hence each final i/w ratio is the average of three identifications, and so each final i/w ratio has a standard deviation. The average standard deviation for the quantitation ratios across all 1451 proteins is 0.16, with the median standard deviation being 0.12 and the standard deviation of the standard deviations being 0.13. However, even with such data, there are limitations to the technique, which result in constraints on the uses of the data generated. Limitations of the technique/ measurement design. The current work examines only changes in protein concentrations, rather than post- translational modification (PTM) of proteins. PTMs are known to be enormously important in cellular signaling processes, and analysis of protein phosphorylation, or acetylation, or 375 methylation, etc, would yield a vast storehouse of information concerning the signaling events around various cellular processes and responses. Further detail of the proteome could be obtained were the samples to be further pre- fractionated. However, further pre-fractionation would result in many more samples to be analyzed, and would require much more starting material. Perhaps the experimental design would eventually get to a point of diminishing returns – where a further increase in the number of samples would result in only a tiny increase in the number of proteins identified and quantitated. Indeed we have seen evidence for this in the data from our first and second experimental replicates; the first of which was fractionated into 40 samples, and second into 90. An increase in the number of proteins identified and quantitated using the higher fractionation was observed, but the increase was less than a doubling, in fact, we observed only an approximately 10% increase in the number of proteins identified, with a more than doubling of the fractionation. This study was conducted using a human cell line (TF-1). This necessarily narrows the interpretations that might be drawn from the data. Our data analysis could be extended, for example by including protein information where proteins appeared in two of the three experiments; or even in one of the three experiments, where the criteria for identification were even more stringent. This study examined a single time-point (fifteen hours after cytokine withdrawal), and hence provides only a snap-shot of what is undoubtedly a very dynamic process. More time points would be required for a truly deep understanding of pre-apoptosis. 376 7.4 Specific Hypotheses and Future Directions From the analysis of the data generated by this work, the following hypotheses are proposed, and following future directions for study suggested: 1. Thymidylate synthase Thymidylate synthase (TS) is a key enzyme in the nucleotide biosynthesis of thymidine monophosphate, used in DNA synthesis and repair. Inhibition of TS causes induction of apoptosis via a caspase-mediated pathway (Backus et al., 2003), without the involvement of p53 and Bax (Giudice et al., 2007; Munoz-Pinedo, Robledo, & Lopez-Rivas, 2004). Chemotherapeutic agents such as 5-fluorouracil and Raltitrexed function as TS inhibitors to induce apoptosis (Rose, Farrell, & Schmitz, 2002). By extension, a decrease in the concentration of TS, such as we have observed using both mass spectrometry and immunoblotting, is expected to be associated with a reduction in activity – effectively an “inhibition” of TS – and as such, our results are consistent with previous observations of a reduction in TS activity being associated with apoptosis. Furthermore, we may now hypothesize that a decrease in the concentration of TS in response to cytokine withdrawal can be a contributing factor to the onset of apoptosis. This is interesting and worthy of further investigation, since an increased understanding of the inhibition of TS may result in enhanced effectiveness of the chemotherapeutic agents used to inhibit TS in the treatment of cancer. 377 Further work based on this observation would include the following: - Confirm the observation in other cell lines, and in primary cells - Over-express TS and withdraw cytokine – does this alter the onset or amount of apoptosis observed? - Is the reduction in TS caused by a reduction in levels of mRNA? Perform quantitative polymerase chain reaction to determine levels of TS mRNA with and without cytokine. - Is the reduction in TS caused by an increase in degradation? Check levels of TS with immunoblotting under conditions of cytokine withdrawal, both with and without proteasome inhibitors. - The transcription factor for TS is LSF, which has recently been shown to be inhibited by Erk and cyclin C/CDK during early G1 (Hansen, Owens, & Saxena, 2009; Saxena et al., 2009). It is the release of this inhibition that allows the transcription of TS during G1/S. Check the effect of the MAP kinase inhibitor UO126 on levels of TS with and without cytokine (using immunoblotting). The goal is to explore possible ways to make TS more susceptible to inhibition by chemotherapeutic agents. If the above line of investigation were to be fruitful, it may be, for example, that by enhancing the activity of Erk by preventing the gradual dephosphorylation of LSF observed in late G1, that levels of TS might be kept low, allowing a more effective inhibition of available TS by chemotherapeutic agents. 378 2. Cdc42 activating JNK leading to apoptosis In relation to the model proposed in Chapter 4 concerning HMGCS and Cdc42, we note the following observations – that HMGB1 and Cdc42 were both observed to increase, and that HMGCS, CREB/BP and EP300 were observed to decrease. - These observations should be confirmed using a different measure, for example antibody studies (note that the Cdc42 results have already been confirmed with antibody studies). - The mRNA levels of these proteins should be checked using qPCR. This will answer the question, “does the message change?” If the message level were to remain constant, then an increase in protein concentration might be attributed to a reduction in proteolysis, while a decrease in protein concentration (with mRNA levels unchanged) could be attributed to an increase in proteolysis. - The observations should then be confirmed by functional studies, specifically by confirming that PPAR! signaling decreases. \" good marker for this might be to observe changes in the levels of I#B!, since PPAR! upregulates the expression of the NF#B repressor I#B!, so a change in I#B! levels would be a functional change observed in response to cytokine withdrawal. We are looking at PPAR! signaling, not necessarily at levels of PPAR! protein, so we should use a functional assay for PPAR! activity. There are several other functional effects of PPAR! that could be measured; for example, observing alterations in the expression of genes regulated by NF#B, such as VCAM1. A reduction here in response to cytokine withdrawal would support the notion of a reduction in PPAR! signaling in response to cytokine withdrawal. - Need to confirm activation of SAPK/JNK by the process outlined in the model. 379 Note that a recent paper describes the importance of the temporal aspect of JNK activation – early, short activation is pro-survival, but longer activation is pro-apoptotic (Ventura et al., 2006). So we need to confirm the duration of the Cdc42 increase is sustained (suspect the increase is at least 15 hours duration, unless it starts later in preapoptosis for some reason). This could be done with a time-course and immunoblotting; note that I have already shown some evidence of a steady increase in levels of Cdc42 in response to cytokine withdrawal - this would need to be repeated, and for a longer duration. As well, the duration of JNK activation needs to be checked, perhaps by using an anti-activated JNK antibody. - Over-express HMGCo-A synthase – does this affect cytokine withdrawal-induced apoptosis? It is hypothesized that over-expression of HMGCo-A synthase will reduce the amount of apoptosis caused by cytokine withdrawal. But the key question is, “What is the relationship between reduced activity in the mevalonate synthesis pathway, and an increase in expression of Cdc42?” A useful tool might be the construction of GFP-linked Cdc42. This would allow determination of the destination of the newly synthesized protein – is it isoprenylated (and therefore destined for the inner plasma membrane) or is it not isoprenylated and therefore left to remain in the cytoplasm? Is cytoplasmic Cdc42 bound to GDP and inactive, or is it possible to have cytoplasmic Cdc42 bound to GTP? If so, could the cytoplasmic bound cdc42/GTP complex be active? Coso and coworkers found that over-expression of wild-type Cdc42 in COS-7 cells was able to activate JNK only if expressed at very high levels (Coso et al., 1995). These authors suggest that a certain proportion of wild-type Cdc42 is in an activated state, and can trigger downstream 380 effectors if expressed at high enough levels. Further evidence supporting Cdc42 activation of JNK comes from Minden et. al. who found that constitutively activated Rac1/Rac2 and Cdc42, but not RhoA, are efficient and specific activators of the JNK cascade in HeLa and NIH 3T3 cells and in COS-1 cells, stimulating the transcriptional activity of c-Jun (Minden et al., 1995). This evidence offers one possible explanation for the activation of JNK under conditions of cytokine withdrawal, where Cdc42 is at levels almost 65% greater than normal. Lending support for the activation of JNK in response to cytokine withdrawal, we also observe a decrease in the concentration of ARHGEF1 (i/w ratios 0.94/1.64/1.23; avg 1.34, equals 25% reduction), which normally functions to inhibit JNK activity (Nishida et al., 2005). If so, how does it stimulate SAPK/JNK? Is SAPK/JNK stimulation determined by the quantity of Cdc42 in the cell? That is, would over-expression of Cdc42 activate SAPK/JNK? This could be answered by over-expressing Cdc42 - as a transient transfection, or as a stable transfection (perhaps using a Cre/LOX contruct to permit control of the protein expression). There are many questions to be answered before the proposed model can be accepted. I have presented a sampling of these questions. But I believe that an attempt to validate the model should be made, since the clinical implications are quite important. If, as the model proposes, a reduction in activity in the mevalonate pathway leads to increased apoptosis, and if such a reduction can be achieved by the use of statins, then perhaps patients receiving chemotherapy for the treatment of malignancy, and who have normal cholesterol levels, should be receiving statins to increase apoptosis (in the tumour cells). Perhaps this mechanism would only work on those with “normal” cholesterol levels, since treatment with a statin might make such a 381 patient hypocholesterolemic, which is the effect that would mimic the conditions in the model. There have been some studies attempting to correlate response to chemotherapy with statins, and the results have shown some promise (Budman, Tai, & Calabro, 2007; Calabro, Tai, Allen, & Budman, 2008; J. Lee et al., 2009; Tsai et al., 2006). Perhaps the studies might be reanalyzed, taking cholesterol levels into account. If the model should prove to be correct, then those patients with lower cholesterol levels should have a better response to chemotherapy. Indeed, a recent paper (Calabro et al., 2008) found that a prenylation inhibitor enhanced the effects of classical cytotoxic agents in vivo. And so the proposed model, if it should be validated, could have some very immediate benefits in helping to direct current medical management of patients receiving chemotherapy, using drugs already approved for use in other conditions. 3. ATP Synthase Mitochondrial F1 Complex Assembly Factor I have observed an increase in the concentrations of several mitochondrial “energy system” proteins in response to cytokine withdrawal – ATP Synthase Mitochondrial F1 Complex Assembly Factor, Nucleoside Diphosphate Kinase Type 6, Isoform 1 of Pyruvate Dehydrogenase E1 Component Subunit Beta, mitochondrial, Isoform 1 of Acyl- coenzyme A synthetase, mitochondrial, and Cytochrome c oxidase (subunit 2). We also note a 30% increase in half-life for citrate synthase. It is expected that the level of ATP synthase in cells would be regulated in such a way that it does not become an unintentional limit to the supply of ATP. However, ATP synthase is a large protein complex, and its amount should not be increased beyond that which is required, to avoid becoming an energetic burden. Practically nothing is known about the tissue- and time-specific control of 382 ATP synthase concentration in mammals (Kramarova et al., 2008). However, I have shown an increase in the concentration of ATP synthase mitochondrial F1 complex assembly factor of 40% in response to cytokine withdrawal, which adds to the knowledge of this protein’s behaviour. Hence I can propose the hypothesis that the mitochondrial-specific proteins observed to increase in response to cytokine withdrawal play an active role in the pathway to apoptosis. That is, mitochondrial energy-related proteins play a greater role in apoptosis than was previously understood. The action of the proteins observed to increase might be to stave off apoptosis for as long as possible, by ensuring sufficient energy for cellular function. This hypothesis might be tested by over-expressing some of the above proteins, and observing any alteration in the duration of the “pre-apoptosis” period – from activation of caspase 3 to obvious cell dismantling. An increased duration of the pre-apoptosis period in the presence of the over-expressed protein/s might lead to the conclusion that these proteins play a role in prolonging cell survival under adverse conditions. 4. Interesting by association Using gene ontology analysis, proteins observed to change in concentration were grouped according to various biological processes or molecular functions. In some cases, most of the proteins assigned to a particular ontology are known to have a role in cell cycle, cell growth, or cell death, whereas one or more proteins assigned to the same ontology have not previously been ascribed these functions. In these cases, it is interesting to follow up the proteins not previously recognized as playing a role in cell cycle, cell growth or cell death, to determine if the observed response to cytokine withdrawl might be important. For example, Ferrochelatase Isoform A Precursor (FECH) is involved in porphyrin metabolism, and is 383 observed to increase in concentration by 1.6-fold in response to cytokine withdrawal a response which might be unexpected, and therefore worth confirming at least by immunoblotting. A second protein, Ribosomal L1 domain-containing protein 1 (RSL1D1 protein), showed a 1.4-fold increase in concentration. A recent paper suggests that RSL1D1 negatively regulates expression of PTEN and p27(Kip1), thereby promoting cell proliferation (Ma et al., 2008), hence an increase in concentration in response to cytokine withdrawal is unexpected and might also warrant further investigation. 5. Interesting because “unknown” Finally, the 52 kDa Protein was observed to increase by 1.5-fold in response to cytokine withdrawal. The 52 kDa protein is a ribonucleoprotein particle composed of a single polypeptide and one of four small cytoplasmic RNA components known as the “hY RNAs”. The protein is present in all mammalian cells studied but has no known function. The increase in concentration observed in response to cytokine withdrawal might provide an important clue leading towards discovery of a possible role for this protein. 6. Investigate other cell lines and primary cells Overall, one important future direction for study would be to repeat these experiments using a different cell line, or perhaps primary cells. The current work was undertaken with a single, human cell-line, which limits the interpretation of the results. In summary, I present data from three biological replicates of an experiment examining the changes to the proteome in TF-1 cells in response to cytokine withdrawal. I have been able to 384 identify and quantitate almost 2,000 proteins, and focus attention on the approximately 200 proteins which showed important increases or decreases in concentration in response to cytokine withdrawal (importance was determined as those changes which were calculated to have a lower than 5% chance of having occurred by chance). Further, in a separate experiment, I was able to observe changes in protein half-life in response to cytokine withdrawal. 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P ro te in s li st ed i n t h is t ab le a re t h o se i d en ti fi ed a s in cr ea si n g i n c o n ce n tr at io n i n t h re e b io lo g ic al r ep li ca te s o f a S IL A C e x p er im en t in v es ti g at in g c y to k in e w it h d ra w al -i n d u ce d a p o p to si s. P ro te in s h er e sh o w a n i n cr ea se i n r el at iv e co n ce n tr at io n o f 3 0 % o r m o re a ft er fi ft ee n h o u rs o f cy to k in e w it h d ra w al . P ro te in s n am e, a v er ag e i/ w r at io f ro m t h re e S IL A C e x p er im en ts , p ep ti d es i d en ti fi ed f o r ea ch p ro te in , an d t h e ch ar g e, r an k in g s co re , le ft a n d r ig h t fl an k in g a m in o a ci d s an d c al ib ra te d m as s er ro r ar e sh o w n a s in d ic at ed . IP I A cc es si o n N u m b er ID A v g ( 3 ) co rr ec te d i/ w S tD ev A v g (3 ) co rr ec te d i/ w P ep ti d es I d en ti fi ed M C R (M as s ch ar g e ra ti o ) [T h ] C h ar g e S co re L ef t fl an k in g A A R ig h t fl an k in g A A C al ib ra te d m as s re la ti v e er ro r [p p m ] IP I0 0 2 9 7 5 7 9 .4 C B X 3 ;L O C 6 5 3 9 7 2 C h ro m o b o x p ro te in h o m o lo g 3 0 .5 2 0 .0 3 II G A T D S S G E L M F L M K 8 6 4 .9 3 1 9 2 9 8 R W 0 .3 7 5 W K D S D E A D L V L A K 7 4 5 .3 8 1 2 2 6 7 K E 0 .6 8 0 K V E E A E P E E F V V E K 8 3 1 .4 1 8 8 2 8 8 K V 1 .5 7 3 L T W H S C P E D E A Q 7 3 6 .8 0 8 9 2 5 4 R - -0 .9 2 6 D S D E A D L V L A K 5 9 1 .3 0 2 8 2 7 9 K E -1 .2 7 7 V E E A E P E E F V V E K 7 6 7 .3 7 0 6 2 7 7 K V 0 .7 4 7 IP I0 0 0 0 3 5 6 5 .1 P S M D 1 0 2 6 S p ro te as o m e n o n - A T P as e re g u la to ry su b u n it 1 0 0 .5 5 0 .1 7 L E E L K E S IL A D K 6 9 4 .3 8 7 6 2 6 1 K S -0 .4 5 6 D D A G W S P L H IA A S A G R 5 4 1 .9 3 3 7 3 3 9 K D -0 .7 1 0 T P L Q V A K 7 1 9 .3 5 2 7 8 K L -1 .6 9 2 D H Y E A T A M H R 4 1 0 .8 4 9 5 3 3 1 K A 1 .8 2 7 G G L G L IL K 8 2 3 .9 1 6 6 2 9 3 R I 0 .3 3 5 A S T N IQ D T E G N T P L H L A C 8 2 4 .3 7 9 2 3 2 6 K V 4 .5 1 7 A p p en d ix 1 . P ro te in s sh o w in g i n cr ea se d c o n ce n tr at io n . 413 IP I A cc es si o n N u m b er ID A v g ( 3 ) co rr ec te d i/ w S tD ev A v g (3 ) co rr ec te d i/ w P ep ti d es I d en ti fi ed M C R (M as s ch ar g e ra ti o ) [T h ] C h ar g e S co re L ef t fl an k in g A A R ig h t fl an k in g A A C al ib ra te d m as s re la ti v e er ro r [p p m ] D E E R IP I0 0 3 3 9 3 8 1 .3 H L T F I so fo rm 1 o f H el ic as e- li k e tr an sc ri p ti o n f ac to r 0 .5 6 0 .1 8 T P E E L R 5 8 5 .7 8 9 2 5 1 R A -0 .3 3 0 L L S F L K 8 2 9 .7 5 9 4 3 4 5 R D -3 .1 3 1 D L W S L L S F L K 6 1 1 .3 4 7 5 2 4 8 K L -0 .2 9 2 IP I0 0 2 1 7 4 6 8 .3 H IS T 1 H 1 B H is to n e H 1 .5 0 .6 1 0 .0 3 N G L S L A A L K K 7 5 8 .1 5 1 8 4 6 3 K Q 1 .0 1 6 A L A A G G Y D V E K N N S R 7 8 2 .8 8 8 3 2 1 0 4 K I -1 .8 7 4 A T G P P V S E L IT K 5 5 9 .3 5 5 2 5 8 K S 0 .1 1 0 G T G A S G S F K 6 5 4 .3 8 4 8 2 7 8 K L 0 .8 5 7 K A L A A G G Y D V E K 9 8 4 .0 3 5 6 2 7 8 K Q 0 .6 2 6 G T L V Q T K 7 2 2 .8 5 6 3 2 7 7 R E 0 .0 2 4 N G L S L A A L K 4 4 3 .7 7 1 5 2 7 6 R K -0 .3 7 8 K A T G P P V S E L IT K 6 7 0 .8 9 2 2 2 8 5 R A -1 .3 8 9 A L A A G G Y D V E K 5 4 7 .2 7 9 5 2 6 3 K N -0 .8 1 0 IP I0 0 0 1 0 1 8 7 .1 E L O V L 1 E lo n g at io n o f v er y l o n g c h ai n f at ty ac id s p ro te in 1 0 .6 1 0 .2 2 A L Q Q N G A P G IA K 5 6 4 .8 2 8 9 2 6 7 K Q 0 .3 9 0 K P F Q L R 9 5 9 .0 1 9 2 2 8 3 R D -0 .3 6 8 C D P V D Y S N S P E A L R 8 1 1 .8 5 9 8 2 7 6 R M -0 .1 1 8 V A W L F L F S K 5 5 5 .8 2 0 6 2 5 0 R F -0 .9 1 1 IP I0 0 0 1 6 7 8 6 .1 C D C 4 2 I so fo rm 2 o f C el l d iv is io n c o n tr o l p ro te in 4 2 h o m o lo g 0 .6 1 0 .1 5 N V F D E A IL A A L E P P E P K K 7 1 3 .3 6 9 1 2 6 7 K V -1 .3 2 3 A p p en d ix 1 . P ro te in s sh o w in g i n cr ea se d c o n ce n tr at io n . 414 IP I A cc es si o n N u m b er ID A v g ( 3 ) co rr ec te d i/ w S tD ev A v g (3 ) co rr ec te d i/ w P ep ti d es I d en ti fi ed M C R (M as s ch ar g e ra ti o ) [T h ] C h ar g e S co re L ef t fl an k in g A A R ig h t fl an k in g A A C al ib ra te d m as s re la ti v e er ro r [p p m ] p re cu rs o r T P F L L V G T Q ID L R D D P S T I E K 6 2 8 .8 5 6 2 2 7 4 R E -3 .3 9 9 C V V V G D G A V G K 5 3 3 .7 8 6 9 2 4 1 K T 0 .2 4 4 N V F D E A IL A A L E P P E P K 7 7 1 .9 2 8 1 2 8 0 K S 0 .4 4 1 Y V E C S A L T Q K 7 6 5 .4 0 4 2 9 0 K D 0 .7 8 0 Q K P IT P E T A E K 3 9 6 .7 5 2 8 2 3 8 R G 1 .3 9 6 IP I0 0 2 9 0 1 1 0 .5 P D C D 4 P ro g ra m m ed ce ll d ea th p ro te in 4 0 .6 2 0 .0 6 D S V W G S G G G Q Q S V N H L V K 9 2 7 .9 5 7 6 2 5 5 K E -1 .2 3 7 M IL D L L K 7 2 2 .8 9 1 2 7 6 R G -0 .2 7 1 G T V D C V Q A R 5 0 3 .2 4 2 1 2 7 3 K A -1 .3 6 8 IY N E IP D IN L D V P H S Y S V L E R 1 2 4 3 .6 2 7 4 2 6 9 R F -4 .0 3 4 S S T IT V D Q M K 5 5 5 .2 7 8 7 2 5 4 K R -0 .5 4 9 T L T P II Q E Y F E H G D T N E V A E M L R 5 8 5 .2 8 6 8 2 5 8 K A -1 .6 1 5 K D S V W G S G G G Q Q S V N H L V K 6 6 1 .6 7 2 3 3 3 8 R E -1 .2 6 8 G D S V S D S G S D A L R 6 3 3 .2 8 2 9 2 7 2 R S -1 .7 3 5 L K P E S Y 3 6 8 .6 9 7 9 2 2 6 R - 1 .4 3 3 A T V L L S M S K 4 7 5 .2 7 3 6 2 7 1 K G 1 .2 4 7 D S G R G D S V S D S G S D A L R 8 4 0 .8 7 4 2 7 4 R S -0 .7 5 9 E ID M L L K 4 3 9 .2 3 8 5 2 3 6 K E -0 .1 8 9 E Y L L S G D IS E A E H C L K 9 3 2 .4 4 2 2 5 4 K E -1 .8 2 5 F V S E G D G G R 6 4 5 .3 1 3 1 2 4 5 R G 6 .2 8 0 A P Q L V G Q F IA R 9 1 3 .4 6 1 3 2 9 8 K V 2 .8 6 7 S G V P V L A V S L A L E G K 6 9 6 .3 4 3 6 2 5 9 R Y 0 .1 6 2 A p p en d ix 1 . P ro te in s sh o w in g i n cr ea se d c o n ce n tr at io n . 415 IP I A cc es si o n N u m b er ID A v g ( 3 ) co rr ec te d i/ w S tD ev A v g (3 ) co rr ec te d i/ w P ep ti d es I d en ti fi ed M C R (M as s ch ar g e ra ti o ) [T h ] C h ar g e S co re L ef t fl an k in g A A R ig h t fl an k in g A A C al ib ra te d m as s re la ti v e er ro r [p p m ] A V G D G IL C N T Y ID S Y K 8 9 4 .9 2 7 5 2 7 6 R G -0 .1 8 8 S G L T V P T S P K 5 3 2 .2 9 3 6 2 5 9 K S 1 .9 9 9 D L P E L A L D T P R 6 2 0 .3 3 3 1 2 5 0 K A 0 .5 0 7 L L S D L C G T V M S T T D V E K 9 3 4 .9 5 1 1 2 5 6 K S -2 .0 9 0 IP I0 0 5 5 4 5 8 9 .3 F E C H f er ro ch el at as e is o fo rm a p re cu rs o r 0 .6 2 0 .1 0 L L D E L S P N T A P H K 7 1 7 .8 8 0 8 2 3 0 K Y -3 .0 6 1 Y Y N Q V G R 4 5 0 .2 2 2 6 2 3 3 R K 0 .9 7 9 S F F T S Q Q L 5 5 8 .7 9 3 7 2 4 8 K K 0 .9 0 3 G D P Y P Q E V S A T V Q K 7 5 9 .8 7 6 2 6 4 R V 0 .8 0 1 IG G G S P IK 3 6 4 .7 1 8 7 2 4 2 R I -0 .0 4 2 A L A D L V H S H IQ S N E L C S K 6 8 5 .3 7 3 9 2 5 3 K E -1 .1 5 6 Y V H P L T E E A IE E M E R 6 2 1 .2 9 2 7 3 4 3 R D -0 .3 8 4 IP I0 0 2 2 1 3 5 4 .1 F U S I so fo rm S h o rt o f R N A -b in d in g p ro te in F U S 0 .6 2 0 .0 9 T G Q P M IN L Y T D R 8 2 1 .3 7 6 5 2 7 7 R T 2 .0 1 8 A P K P D G P G G G P G G S H M G G N Y G D D R 6 9 6 .7 3 5 9 R L 0 .1 1 0 L K G E A T V S F D D P P S A K 8 3 1 .4 2 4 2 2 8 4 K A 1 .3 7 9 G E A T V S F D D P P S A K 7 1 0 .8 3 3 1 2 7 6 K A -0 .1 3 8 E F S G N P IK 4 4 6 .2 3 2 8 2 3 1 K V 1 .3 7 3 A A ID W F D G K E F S G N P IK 9 4 7 .9 7 3 2 2 6 7 K V 2 .2 7 0 A A ID W F D G K 8 4 5 .9 6 4 5 2 7 4 R C -0 .9 3 5 IP I0 0 0 3 4 2 8 0 .2 M G C 4 1 7 2 I so fo rm 1 o f D eh y d ro g en as e/ re d u ct a se S D R f am il y m em b er 1 1 p re cu rs o r 0 .6 3 0 .1 9 Y A V T A L T E G L R 5 9 9 .3 3 1 1 2 6 4 R I -1 .4 3 4 A p p en d ix 1 . P ro te in s sh o w in g i n cr ea se d c o n ce n tr at io n . 416 IP I A cc es si o n N u m b er ID A v g ( 3 ) co rr ec te d i/ w S tD ev A v g (3 ) co rr ec te d i/ w P ep ti d es I d en ti fi ed M C R (M as s ch ar g e ra ti o ) [T h ] C h ar g e S co re L ef t fl an k in g A A R ig h t fl an k in g A A C al ib ra te d m as s re la ti v e er ro r [p p m ] A L V Q Q G L K 4 2 8 .7 6 5 4 2 4 2 R V -1 .6 6 4 V L P L S V T H F Y S A T K 7 8 1 .9 3 4 5 2 6 1 R Y 2 .0 3 0 IP I0 0 2 1 9 0 3 8 .9 H 3 F 3 A ;H 3 F 3 B H is to n e H 3 .3 0 .6 3 0 .1 2 D IQ L A R 9 5 2 .5 2 9 4 2 7 2 K E 2 .6 0 3 K L P F Q R 8 0 8 .4 1 1 2 2 8 8 K S -2 .8 0 0 E IA Q D F K 5 1 3 .8 2 7 2 7 0 R D 1 .7 3 6 R V T IM P K 7 5 0 .9 0 3 8 2 7 5 R K -0 .6 0 5 E IA Q D F K T D L R 1 0 2 4 .0 8 5 1 2 8 7 K K -0 .2 5 5 Y R P G T V A L R 8 7 9 .4 6 0 6 2 7 7 R S 2 .3 7 2 S T E L L IR K 4 8 6 .3 1 7 1 2 5 4 K L -2 .3 5 4 Y Q K S T E L L IR 6 2 5 .8 6 0 2 2 4 4 R K 1 .7 2 2 S T E L L IR 1 0 4 9 .0 2 5 2 2 9 2 R T 0 .5 7 9 V T IM P K 5 9 5 .8 4 4 4 2 3 9 K K 3 .2 6 3 IP I0 0 2 9 7 6 3 5 .5 A C S M 3 I so fo rm 1 o f A cy l- co en zy m e A sy n th et as e A C S M 3 m it o ch o n d ri al p re cu rs o r 0 .6 3 0 .0 8 A F V V L N P D Y K 5 8 3 .3 1 6 3 2 4 3 K S -0 .0 9 4 G N F Y IT G D R 5 1 8 .8 0 5 6 2 4 7 K N 0 .9 6 2 V IL IL P R 4 1 2 .2 9 2 3 2 4 3 R V 1 .1 9 1 W S F E E L G S L S R 6 5 5 .8 2 2 2 2 8 4 R K -0 .5 5 9 S H D Q E Q L IK 4 1 8 .7 6 8 6 2 5 2 R V 1 .1 8 7 T G T V L IP G T T Q L T Q K 7 2 9 .3 6 6 9 2 8 3 K T -0 .3 6 1 IG P F E V E N A L N E H P S V A E S A V V S S P D P IR 1 0 2 0 .8 4 7 1 3 6 9 R G -0 .6 6 2 F E P T S IL Q T L S K 5 4 3 .2 9 4 8 2 6 5 R V -0 .4 8 6 F A N IL S E A C S L Q R 7 5 4 .8 7 8 8 2 4 8 K G -1 .8 5 7 A p p en d ix 1 . P ro te in s sh o w in g i n cr ea se d c o n ce n tr at io n . 417 IP I A cc es si o n N u m b er ID A v g ( 3 ) co rr ec te d i/ w S tD ev A v g (3 ) co rr ec te d i/ w P ep ti d es I d en ti fi ed M C R (M as s ch ar g e ra ti o ) [T h ] C h ar g e S co re L ef t fl an k in g A A R ig h t fl an k in g A A C al ib ra te d m as s re la ti v e er ro r [p p m ] D V L D Q W T D K E K 6 8 8 .8 3 9 3 2 5 8 K A 1 .1 7 2 K V E F IQ E L P K 7 7 1 .4 5 8 4 2 4 9 R E 0 .7 9 6 L G IP E Y F N F A K 6 4 9 .8 4 1 2 2 3 4 K D -2 .4 8 3 L IV S E N S R 9 1 1 .9 8 4 2 2 5 2 R H 0 .6 1 9 V P E W W L A N V A C L R 8 0 7 .4 1 6 8 2 4 8 R T 0 .0 0 7 M L V Q N D IT S Y K 6 5 6 .3 3 2 2 2 5 3 R F -3 .4 5 9 A D D V IL S S G Y R 5 9 8 .3 0 0 6 2 5 1 R I -1 .4 5 2 IP I0 0 0 2 2 3 0 0 .5 M E T T L 7 A M et h y lt ra n sf er as e- li k e p ro te in 7 A p re cu rs o r 0 .6 5 0 .1 7 L S L L E V G C G T G A N F K 7 8 3 .4 0 4 2 2 8 1 K F 0 .6 3 6 F T V IY N E Q M A S K 5 4 7 .7 8 2 5 2 7 6 K T -0 .1 9 5 H L Q F E R 4 4 0 .7 6 4 2 4 3 R L 2 .2 1 9 V T C ID P N P N F E K 7 1 7 .3 4 2 4 1 R F -0 .7 0 0 E L F S N L Q E F A G P S G K 8 1 2 .4 0 4 2 2 7 6 R L -0 .7 3 5 IP I0 0 4 1 4 0 7 9 .1 A T P 6 V 1 H I so fo rm 2 o f V ac u o la r p ro to n p u m p su b u n it H 0 .6 5 0 .4 2 G A V D A A V P T N II A A K 7 0 5 .9 0 0 3 2 7 0 R A -1 .2 7 0 Q L Q S E Q P Q T A A A R 7 1 4 .3 6 3 2 2 5 2 K S -3 .2 1 7 L L E V S D D P Q V L A V A A H D V G E Y V R 8 3 2 .4 2 9 6 3 3 2 K H -1 .7 8 3 Y N II P V L S D IL Q E S V K 5 3 6 .8 1 3 7 2 4 6 K G -0 .1 4 6 IP I0 0 7 9 5 7 6 9 .1 5 2 k D a p ro te in 0 .6 6 0 .1 9 F G L N V S S IS R 5 4 0 .2 9 5 3 2 6 5 R K -1 .0 2 9 T V D V A A E K K 4 8 0 .7 7 0 9 2 3 1 K V -1 .5 8 1 IT S E IP Q T E R 5 9 0 .3 2 0 5 2 5 0 K M 2 .2 0 3 L Q D D L K 7 0 8 .3 6 6 1 3 3 7 R Y -3 .1 7 9 A p p en d ix 1 . P ro te in s sh o w in g i n cr ea se d c o n ce n tr at io n . 418 IP I A cc es si o n N u m b er ID A v g ( 3 ) co rr ec te d i/ w S tD ev A v g (3 ) co rr ec te d i/ w P ep ti d es I d en ti fi ed M C R (M as s ch ar g e ra ti o ) [T h ] C h ar g e S co re L ef t fl an k in g A A R ig h t fl an k in g A A C al ib ra te d m as s re la ti v e er ro r [p p m ] F G IS S V P T K 4 6 8 .2 6 3 6 2 4 6 R G 0 .2 7 4 F G IV T S S A G T G T T E D T E A K 9 3 6 .4 4 7 4 2 9 3 R K -0 .4 1 2 F N V P V S L E S K 7 2 8 .6 9 5 3 3 4 4 K Y 0 .6 7 8 IP I0 0 0 0 3 8 1 7 .3 A R H G D IB R h o G D P - d is so ci at io n i n h ib it o r 2 0 .6 6 0 .0 5 V N R D IV S G L K 5 5 0 .8 2 3 7 2 5 1 K Y -2 .4 1 9 K T L L G D G P V V T D P K 7 2 0 .4 0 8 9 2 7 7 K A -0 .5 2 7 A T F M V G S Y G P R P E E Y E F L T P V E E A P K 7 7 8 .9 3 7 9 2 9 2 R Y 1 .2 9 2 A P E P H V E E D D D D E L D S K 9 7 0 .4 0 6 7 2 8 8 K L 0 .3 2 3 E T IV L K E G S E Y R 7 1 8 .3 9 7 3 2 6 2 K V 2 .4 7 0 A P N V V V T R 9 5 8 .5 1 9 9 2 7 3 R N 0 .8 2 1 T G V K V D K 3 7 9 .7 4 4 1 2 3 6 R A -0 .6 0 8 L T L V C E S A P G P IT M D L T G D L E A L K K 6 3 9 .3 3 5 5 2 5 3 K E 0 .4 2 1 T L L G D G P V V T D P K A P N V V V T R 1 0 7 4 .6 0 3 5 2 5 4 K L -1 .8 0 3 A P E P H V E E D D D D E L D S K L N Y K P P P Q K 1 0 3 0 .5 2 4 1 2 7 7 R Y -0 .6 8 1 T L L G D G P V V T D P K 6 5 6 .3 6 1 8 2 6 4 K A 0 .2 1 6 D IV S G L K 7 5 2 .3 9 9 8 2 6 4 R D 1 .6 1 5 L N Y K P P P Q K 9 4 6 .1 4 6 7 3 5 2 K L -1 .8 8 2 E L Q E M D K D D E S L IK 8 4 6 .9 0 2 7 2 6 6 K Y -1 .4 2 2 E T IV L K 3 5 1 .7 2 3 2 2 2 9 K E -0 .1 2 7 S F F T D D D K Q D H L S W E W N L S IK 8 7 1 .0 7 3 8 3 3 8 K K -1 .8 9 9 Y V Q H T Y R 4 8 9 .2 4 9 5 2 3 7 K K -1 .9 3 3 L T L V C E S A P G P IT M D L T G 8 4 8 .7 7 0 6 3 4 6 R K 0 .7 6 5 A p p en d ix 1 . P ro te in s sh o w in g i n cr ea se d c o n ce n tr at io n . 419 IP I A cc es si o n N u m b er ID A v g ( 3 ) co rr ec te d i/ w S tD ev A v g (3 ) co rr ec te d i/ w P ep ti d es I d en ti fi ed M C R (M as s ch ar g e ra ti o ) [T h ] C h ar g e S co re L ef t fl an k in g A A R ig h t fl an k in g A A C al ib ra te d m as s re la ti v e er ro r [p p m ] D L E A L K IP I0 0 2 1 7 4 6 6 .3 H IS T 1 H 1 D H is to n e H 1 .3 0 .6 6 0 .0 8 S G V S L A A L K 4 2 3 .2 5 7 8 2 6 6 R K -0 .7 1 9 S G V S L A A L K K 4 8 7 .3 0 5 5 2 5 7 R A -0 .4 2 8 A L A A A G Y D V E K 9 2 9 .9 8 9 5 2 5 8 K E 0 .3 6 8 K A S G P P V S E L IT K 6 6 3 .8 8 5 9 2 7 5 R A 1 .3 6 1 K V A G A A T P K 4 2 1 .7 5 8 9 2 3 1 K K 0 .8 2 1 A S G P P V S E L IT K 5 9 9 .8 3 7 7 2 7 3 K A -0 .0 3 7 G T G A S G S F K 6 5 4 .3 8 4 8 2 7 8 K L 0 .8 5 7 G T L V Q T K 7 2 2 .8 5 6 3 2 7 7 R E 0 .0 2 4 K A L A A A G Y D V E K 6 1 8 .3 3 4 4 2 7 4 K N -1 .5 4 0 A L A A A G Y D V E K N N S R 7 8 9 .8 9 8 9 2 9 2 K I 1 .6 8 6 IP I0 0 2 1 7 4 6 5 .5 H IS T 1 H 1 C H is to n e H 1 .2 0 .6 6 0 .0 8 S G V S L A A L K 4 2 3 .2 5 7 8 2 6 6 R K -0 .7 1 9 S G V S L A A L K K 4 8 7 .3 0 5 5 2 5 7 R A -0 .4 2 8 A L A A A G Y D V E K 9 2 9 .9 8 9 5 2 5 8 K E 0 .3 6 8 K A S G P P V S E L IT K 6 6 3 .8 8 5 9 2 7 5 R A 1 .3 6 1 K P A A A T V T K 4 2 1 .7 5 8 9 2 3 1 K K 0 .8 2 1 A S G P P V S E L IT K 5 9 9 .8 3 7 7 2 7 3 K A -0 .0 3 7 G T G A S G S F K 6 5 4 .3 8 4 8 2 7 8 K L 0 .8 5 7 G T L V Q T K 7 2 2 .8 5 6 3 2 7 7 R E 0 .0 2 4 K A L A A A G Y D V E K 6 1 8 .3 3 4 4 2 7 4 K N -1 .5 4 0 A L A A A G Y D V E K N N S R 7 8 9 .8 9 8 9 2 9 2 K I 1 .6 8 6 IP I0 0 3 3 7 5 4 1 .3 N N T N A D (P ) tr an sh y d ro g en as e 0 .6 7 0 .1 1 A P M V N P T L G V H E A D L L K 6 0 7 .6 5 5 7 3 3 2 R T -2 .9 5 4 A p p en d ix 1 . P ro te in s sh o w in g i n cr ea se d c o n ce n tr at io n . 420 IP I A cc es si o n N u m b er ID A v g ( 3 ) co rr ec te d i/ w S tD ev A v g (3 ) co rr ec te d i/ w P ep ti d es I d en ti fi ed M C R (M as s ch ar g e ra ti o ) [T h ] C h ar g e S co re L ef t fl an k in g A A R ig h t fl an k in g A A C al ib ra te d m as s re la ti v e er ro r [p p m ] m it o ch o n d ri al p re cu rs o r A V V L A A N H F G R 5 7 7 .8 2 5 2 5 8 K F 0 .5 1 4 E V L A S D L V V K 5 3 6 .8 1 5 4 2 3 2 K V -0 .3 3 9 G IT H IG Y T D L P S R 4 7 7 .2 5 2 7 3 3 0 K M 0 .0 3 8 S L G A E P L E V D L K 7 2 1 .3 6 1 3 2 5 4 R Y 3 .6 8 6 S L G V G Y A A V D N P IF Y K P N T A M L L G D A K 9 4 7 .8 2 1 9 3 3 8 R K 0 .2 6 8 T V A E L E A E K 4 9 5 .2 6 1 3 2 2 6 K A 0 .2 4 2 V A L S P A G V Q N L V K 6 4 8 .3 8 6 7 2 6 9 R Q -1 .8 6 9 V T IA Q G Y D A L S S M A N IA G Y K 3 5 2 .7 0 4 5 2 4 2 R V 1 .0 2 8 F F T G Q IT A A G K 5 7 0 .8 0 5 4 2 6 0 R V -1 .3 0 6 IL IV G G G V A G L A S A G A A K 7 6 2 .9 5 8 5 2 8 7 K S -1 .0 5 9 F G IH P V A G R 5 3 6 .3 2 4 3 2 6 0 R E 1 .2 6 2 V A G A Q IQ G A K 4 7 1 .7 7 2 1 2 3 8 R E 0 .1 9 0 T T V L A M D Q V P R 6 1 5 .8 2 9 3 2 4 0 K V -0 .0 5 2 IP I0 0 2 9 1 7 5 1 .6 C 2 o rf 4 7 U n ch ar ac te ri ze d p ro te in C 2 o rf 4 7 m it o ch o n d ri al p re cu rs o r 0 .6 7 0 .1 3 L G N Q N V E T K 5 0 1 .7 6 5 4 2 4 5 K Q 1 .9 5 1 G A S V F Q V K 4 1 8 .2 3 7 9 2 3 4 R L 1 .6 8 8 F D L L E E L V A K 5 8 8 .8 2 8 2 2 5 6 K E -1 .9 6 6 V T S L P D N H K 5 0 5 .7 6 7 1 2 3 1 K N 0 .2 3 9 Q L L S A S Y E F Q R 6 7 4 .3 5 4 4 2 6 8 K E 0 .6 6 5 M IV L G F S N P IN W V R 8 3 1 .4 4 4 3 2 6 6 K T -1 .7 7 6 A p p en d ix 1 . P ro te in s sh o w in g i n cr ea se d c o n ce n tr at io n . 421 IP I A cc es si o n N u m b er ID A v g ( 3 ) co rr ec te d i/ w S tD ev A v g (3 ) co rr ec te d i/ w P ep ti d es I d en ti fi ed M C R (M as s ch ar g e ra ti o ) [T h ] C h ar g e S co re L ef t fl an k in g A A R ig h t fl an k in g A A C al ib ra te d m as s re la ti v e er ro r [p p m ] IP I0 0 2 1 9 0 9 7 .4 H M G B 2 H ig h m o b il it y g ro u p p ro te in B 2 0 .6 7 0 .0 6 M S S Y A F F V Q T C R E E H K 6 7 9 .3 0 5 1 3 4 3 K K -0 .2 2 9 D IA A Y R 5 1 3 .2 2 1 2 3 8 R I -0 .2 1 6 H P D S S V N F A E F S K 7 3 2 .8 4 2 9 2 7 4 K K 1 .5 9 0 Y E K D IA A Y R 5 6 4 .7 8 6 8 2 6 5 K A -2 .0 7 4 K H P D S S V N F A E F S K 7 9 6 .8 8 8 6 2 7 6 K K -0 .8 4 6 R P P S A F F L F C S E H R P K 6 5 9 .3 3 8 6 3 3 5 K I 2 .2 3 3 K L G E M W S E Q S A K 6 9 7 .3 4 3 9 2 5 9 K D 1 .3 3 4 S K F E D M A K 6 4 7 .3 4 2 8 2 5 8 K V -0 .8 0 9 M S S Y A F F V Q T C R 7 5 6 .8 3 5 4 2 9 0 K E 1 .1 0 9 S E H P G L S IG D T A K 6 5 6 .3 3 1 4 2 5 7 K K 0 .6 9 9 L G E M W S E Q S A K 6 3 3 .2 9 5 9 2 6 2 K D 0 .7 5 4 S E H P G L S IG D T A K K 8 9 9 .4 2 8 8 2 8 1 R K -3 .6 7 1 IK S E H P G L S IG D T A K 9 9 9 .0 4 5 8 2 8 8 R N -2 .1 7 9 H P D S S V N F A E F S K K 7 9 6 .8 9 1 2 7 0 K C 2 .1 6 9 IP I0 0 8 4 7 4 8 2 .1 T X N R D 1 I so fo rm 4 o f T h io re d o x in r ed u ct as e 1 cy to p la sm ic 0 .6 8 0 .1 5 V V G F H V L G P N A G E V T Q G F A A A L K 7 6 1 .4 1 5 5 3 6 1 R C 0 .8 4 8 Q F V P IK V E Q IE A G T P G R 1 0 4 8 .5 0 6 5 2 9 0 R M 2 .6 8 0 G F D Q D M A N K 4 5 6 .2 9 4 6 2 4 7 K V 0 .2 2 0 IG L E T V G V K 8 4 5 .9 7 2 3 2 8 8 K Y 1 .0 1 8 K IG L E T V G V K 5 2 8 .3 4 7 8 2 5 3 R I 1 .9 8 0 F L IA T G E R P R 4 4 4 .7 5 1 8 2 3 4 R R -0 .2 6 6 IG E H M E E H G IK 6 4 8 .3 0 5 9 2 3 4 K F -0 .5 5 6 V E Q IE A G T P G R 5 9 8 .8 1 8 1 2 6 3 R H -1 .7 4 1 L Y K P E V Q L R 3 8 6 .5 7 2 4 3 3 2 K R -2 .1 6 9 L H D E K E E T A G S Y D S R 5 7 9 .5 9 3 7 3 3 2 R N -2 .6 0 7 A p p en d ix 1 . P ro te in s sh o w in g i n cr ea se d c o n ce n tr at io n . 422 IP I A cc es si o n N u m b er ID A v g ( 3 ) co rr ec te d i/ w S tD ev A v g (3 ) co rr ec te d i/ w P ep ti d es I d en ti fi ed M C R (M as s ch ar g e ra ti o ) [T h ] C h ar g e S co re L ef t fl an k in g A A R ig h t fl an k in g A A C al ib ra te d m as s re la ti v e er ro r [p p m ] E L G E Y F L F D P K 6 7 9 .3 4 0 9 2 5 0 K K 4 .8 8 6 N D Y E A R P Q Y Y K 7 2 3 .8 3 5 8 2 4 2 R L -0 .6 5 5 S G T T IA L K 3 9 5 .7 3 6 1 2 4 3 K V -0 .1 6 1 L G L H Q V L Q D L R 6 7 0 .7 0 2 4 3 7 0 R R 1 .4 3 0 E Q L ID M N A E G D E T G V M D S L L E A L Q S G A A F R 1 0 7 0 .8 3 4 4 3 8 5 R R -2 .3 3 5 T A Q N L S IF L G S F R 1 0 1 0 .5 1 1 2 8 9 K N -0 .9 4 6 Q Q IA T E K Q D L E A E V S Q L T G E V A K 8 4 3 .1 1 2 5 3 2 8 K L -2 .7 9 8 L T L T F S A Q T K 5 5 5 .3 1 2 8 2 2 7 K T -0 .7 8 8 F E N N E L F A K 5 5 9 .2 8 3 8 2 4 3 R L -0 .7 1 4 F P D E L A H V E K 5 9 2 .8 0 0 8 2 6 0 K A -0 .4 4 4 F Q P L L D G L K 5 1 5 .7 9 9 5 2 3 8 R S -0 .3 2 9 K L Y K P E V Q L R 4 2 5 .2 5 9 1 3 3 6 K R 0 .0 6 8 V S L N N N P V S W V Q T F G A E G L A S L L D IL K R 1 0 1 4 .5 5 6 7 3 6 3 R L 4 .0 1 0 L S V E E F F M D L H N F R 5 9 5 .2 8 7 5 3 5 6 K N 0 .1 9 9 Q M P E P E Q L K 5 5 0 .2 7 5 6 2 3 5 K M -1 .3 2 3 V S A E N L Q K 9 2 1 .4 5 7 8 2 7 6 K L 0 .8 4 4 L N A IL F K 7 4 2 .9 3 9 6 2 6 9 R L 0 .3 2 7 L V A E D L S Q D C F W T K 8 5 6 .4 0 5 3 2 6 0 K V 1 .6 6 1 L IE E C IS Q IV L H K 5 2 7 .9 6 0 9 3 3 9 K N 1 .7 9 2 M E M D D F N E V F Q IL L N T V K D S K 8 3 9 .4 0 9 1 3 3 9 R A 2 .7 2 9 E M V S Q Y L Y T S K 6 7 4 .8 2 6 4 2 5 8 R A -0 .1 2 2 H E L Q V E M K 7 7 9 .0 3 7 9 3 4 1 K N -2 .0 5 0 A E P H F L S IL Q H L L L V R 4 7 2 .2 8 1 7 4 3 3 K N 1 .9 9 3 L Q D L Q G E K D A L H S E K 5 7 0 .9 5 9 5 3 4 0 K Q -1 .2 1 8 A p p en d ix 1 . P ro te in s sh o w in g i n cr ea se d c o n ce n tr at io n . 423 IP I A cc es si o n N u m b er ID A v g ( 3 ) co rr ec te d i/ w S tD ev A v g (3 ) co rr ec te d i/ w P ep ti d es I d en ti fi ed M C R (M as s ch ar g e ra ti o ) [T h ] C h ar g e S co re L ef t fl an k in g A A R ig h t fl an k in g A A C al ib ra te d m as s re la ti v e er ro r [p p m ] M E M D D F N E V F Q IL L N T V K 1 0 9 3 .5 2 9 9 2 1 0 3 R D 0 .2 8 7 S P D E L P S A G G D G G K 6 4 3 .7 9 6 9 2 6 3 R S 0 .2 2 4 Q D L E A E V S Q L T G E V A K 8 5 8 .9 3 6 9 2 1 1 0 K L 0 .4 8 0 IP I0 0 0 2 0 9 5 6 .1 H D G F H ep at o m a- d er iv ed g ro w th f ac to r 0 .6 8 0 .0 9 E A E N P E G E E K E A A T L E V E R P L P M E V E K 1 0 1 8 .4 9 0 8 3 6 8 K N -1 .3 2 1 K G F S E G L W E IE N N P T V K 8 7 7 .8 9 6 2 2 9 9 R W -0 .6 4 0 ID E M P E A A V K 5 5 1 .7 7 5 7 2 5 0 R S -0 .9 0 6 A G D L L E D S P K 5 2 5 .7 7 3 5 2 7 1 R R -1 .1 5 4 D L F P Y E E S K E K 6 9 2 .8 3 2 4 2 4 7 K F -4 .0 4 2 G P P Q E E E E E E D E E E E A T K E D A E A P G IR 1 0 1 4 .7 6 8 6 3 6 4 R D 2 .3 3 0 C G D L V F A K 4 5 5 .2 2 9 2 5 0 K M 0 .8 5 9 G F S E G L W E IE N N P T V K 6 7 6 .8 7 1 3 2 7 4 K L 0 .2 1 5 Y Q V F F F G T H E T A F L G P K 1 0 0 4 .8 3 0 6 3 6 9 K L -2 .1 1 8 E A A T L E V E R P L P M E V E K 5 7 8 .3 1 2 9 2 6 0 K F -0 .3 6 2 IP I0 0 0 0 9 9 4 9 .2 P S M F 1 P ro te as o m e in h ib it o r P I3 1 s u b u n it 0 .6 9 0 .2 4 F D P F G P IG T S P P G P N P D H L P P P G Y D D M Y L 1 0 4 2 .8 1 4 6 3 5 7 R - 2 .4 5 0 A L ID P S S G L P N R 1 0 6 0 .9 9 9 1 2 1 0 8 K Q -0 .4 7 5 S E L L P A G W N N N K 6 7 1 .8 4 1 3 2 5 3 K D -0 .1 9 7 Q P P W C D P L G P F V V G G E D L D P F G P R 8 8 4 .7 5 6 1 3 8 3 R R -1 .2 5 2 IV S G II T P IH E Q W E K 8 4 0 .8 8 3 9 2 6 9 K E -1 .9 4 9 A L ID P S S G L P N R L P P G A V P P G A R 4 8 6 .7 5 8 8 2 4 5 R N 0 .0 9 9 L P P G A V P P G A R 5 1 6 .3 0 3 6 2 4 1 R F 0 .1 0 6 A p p en d ix 1 . P ro te in s sh o w in g i n cr ea se d c o n ce n tr at io n . 424 IP I A cc es si o n N u m b er ID A v g ( 3 ) co rr ec te d i/ w S tD ev A v g (3 ) co rr ec te d i/ w P ep ti d es I d en ti fi ed M C R (M as s ch ar g e ra ti o ) [T h ] C h ar g e S co re L ef t fl an k in g A A R ig h t fl an k in g A A C al ib ra te d m as s re la ti v e er ro r [p p m ] IP I0 0 2 1 8 2 1 4 .4 N M E 6 n u cl eo si d e d ip h o sp h at e k in as e ty p e 6 0 .6 9 0 .1 6 L V E F M A S G P IR 1 2 3 2 .1 6 4 9 2 1 0 8 K C -1 .7 5 3 E IA A F F P D F S E Q R 7 0 2 .8 7 8 1 2 7 3 R V 1 .5 4 7 G S F G L T D T R 4 7 7 .2 3 7 7 2 4 9 R N -1 .1 2 8 W Y E E E E P Q L R 3 9 3 .2 6 4 9 2 4 7 R K -2 .2 3 1 IP I0 0 0 2 9 5 5 7 .3 G R P E L 1 G rp E p ro te in h o m o lo g 1 m it o ch o n d ri al p re cu rs o r 0 .6 9 0 .2 6 T L L E E K 6 5 3 .8 7 9 2 5 3 R I -0 .8 1 0 E E IK D D N P H L K 6 6 9 .3 3 7 8 2 5 6 K N -1 .3 8 9 A L A D T E N L R 5 0 1 .7 6 4 2 6 9 R Q -0 .7 4 5 L Y G IQ A F C K 5 5 0 .2 8 3 7 2 4 4 K D -0 .5 4 1 N S G Q N L E E D M G Q S E Q K 9 0 5 .3 8 1 6 2 8 3 K A -0 .5 3 9 F D P Y E H E A L F H T P V E G K 6 7 2 .6 5 4 5 3 3 3 K E -0 .9 6 9 T L R P A L V G V V K 5 5 7 .7 8 9 2 2 5 7 K V -0 .2 6 8 D L L E V A D V L E K 9 5 0 .5 2 0 2 2 6 9 K E 1 .3 4 1 IP I0 0 4 1 4 4 1 0 .2 M R P L 2 2 I so fo rm 1 o f 3 9 S r ib o so m al p ro te in L 2 2 m it o ch o n d ri al p re cu rs o r 0 .6 9 0 .2 1 N Y IQ G IN L V Q A K 6 8 0 .8 8 2 3 2 5 7 K K -1 .2 5 9 A A L E A V G G T V V L E 1 0 7 6 .5 6 9 4 2 5 5 K F 2 .6 2 3 T H F T V R 6 6 2 .6 8 3 4 3 3 4 R T 1 .5 9 6 L T E A K P V D K V K 6 0 9 .8 0 8 7 2 4 1 K K -0 .5 7 9 K L V E S L P Q E IK 6 4 2 .3 8 3 8 2 7 2 K A 2 .1 3 1 IQ D V G L V P M G G V M S G A V P A A A A Q E A V E E D IP IA K 8 3 2 .4 3 9 3 2 4 6 K L -0 .6 5 3 L V E S L P Q E IK 1 0 0 7 .5 0 5 2 3 4 8 R R -0 .2 6 7 A p p en d ix 1 . P ro te in s sh o w in g i n cr ea se d c o n ce n tr at io n . 425 IP I A cc es si o n N u m b er ID A v g ( 3 ) co rr ec te d i/ w S tD ev A v g (3 ) co rr ec te d i/ w P ep ti d es I d en ti fi ed M C R (M as s ch ar g e ra ti o ) [T h ] C h ar g e S co re L ef t fl an k in g A A R ig h t fl an k in g A A C al ib ra te d m as s re la ti v e er ro r [p p m ] IP I0 0 7 4 6 7 7 7 .3 A D H 5 A lc o h o l d eh y d ro g en as e cl as s- 3 0 .6 9 0 .0 7 II G V D IN K 3 6 7 .7 1 3 6 2 5 4 K T 0 .6 1 3 A F E L M H S G K 6 2 0 .3 3 6 8 2 4 4 R L -1 .4 4 2 V D E F V T H N L S F D E IN K 9 5 3 .9 6 4 2 6 1 K A 1 .3 3 2 A G D T V IP L Y IP Q C G E C K 9 6 0 .9 6 3 9 2 4 7 K F -0 .1 8 0 A A V A W E A G K P L S IE E IE V A P P K 7 6 9 .0 8 6 9 3 3 6 K A 0 .4 3 8 ID P L A P L D K 5 8 6 .3 1 9 1 2 6 9 R G 0 .1 7 7 II G V D IN K D K 1 0 6 4 .0 0 9 6 2 1 0 8 K S 1 .9 9 6 IK V D E F V T H N L S F D E IN K 7 1 6 .7 0 4 3 4 7 K A -0 .3 9 6 E IY T H F T C A T D T K 7 9 3 .8 6 0 8 2 5 2 K N -1 .4 1 0 L W A D H G V Q A C F G R 5 0 6 .2 4 2 6 3 4 1 R S 1 .1 2 8 L F D S IC N N K 5 5 5 .7 6 6 7 2 6 1 K W 0 .9 8 2 IP I0 0 5 5 2 9 2 0 .2 E X O S C 8 E x o so m e co m p le x e x o n u cl ea se R R P 4 3 0 .6 9 0 .3 1 S G P P G E E A Q V A S Q F IA D V IE N S Q II Q K 4 2 3 .2 5 7 9 2 7 2 R K -0 .0 1 3 A E F A A P S T D A P D K 6 6 0 .3 0 9 9 2 3 8 K G 0 .8 3 7 IP I0 0 0 2 4 1 5 7 .1 F K B P 3 F K 5 0 6 -b in d in g p ro te in 3 0 .6 9 0 .1 0 A R L E IE P E W A Y G K 6 6 9 .3 5 5 7 2 3 9 K L 0 .8 7 3 L N E D K P K E T K 6 0 1 .3 2 2 5 2 3 9 K S -4 .3 4 7 E T K S E E T L D E G P P K 8 5 6 .4 6 1 5 2 8 1 K R 1 .3 7 5 F L Q E H G S D S F L A E H K 8 7 2 .9 1 9 4 2 7 2 K L 0 .9 5 4 A W T V E Q L R 6 2 4 .8 1 0 5 2 4 3 R N -0 .3 7 8 L E IE P E W A Y G K 1 1 3 6 .2 0 9 6 3 7 8 K L -1 .0 3 6 D H L V T A Y N H L F E T K 5 6 3 .2 8 6 9 3 4 0 K R 0 .7 9 3 S E E T L D E G P P K 6 0 1 .2 8 2 9 2 8 0 K Y -0 .1 2 4 A p p en d ix 1 . P ro te in s sh o w in g i n cr ea se d c o n ce n tr at io n . 426 IP I A cc es si o n N u m b er ID A v g ( 3 ) co rr ec te d i/ w S tD ev A v g (3 ) co rr ec te d i/ w P ep ti d es I d en ti fi ed M C R (M as s ch ar g e ra ti o ) [T h ] C h ar g e S co re L ef t fl an k in g A A R ig h t fl an k in g A A C al ib ra te d m as s re la ti v e er ro r [p p m ] N A K P L S F K 7 1 1 .6 6 3 3 3 4 3 K C -0 .2 7 0 G W D E A L L T M S K 6 9 2 .3 8 8 2 2 9 2 R I 1 .3 3 0 F K G T E S IS K 8 9 6 .9 2 9 7 2 6 7 R S -1 .8 5 3 L T F E V E L V D ID 6 4 6 .8 3 4 8 2 3 5 K - -0 .2 2 3 T A N K D H L V T A Y N H L F E T K 7 1 1 .8 7 1 1 2 7 4 R L -0 .6 5 7 IP I0 0 0 1 7 5 1 0 .3 M T -C O 2 C y to ch ro m e c o x id as e su b u n it 2 0 .6 9 0 .0 8 L L D V D N R V V L P IE A P IR 4 7 7 .2 4 7 9 2 4 4 R W -0 .7 2 9 V V L P IE A P IR 5 5 6 .8 6 0 9 2 4 8 R M 0 .5 6 9 IF E M G P V F T L 9 6 3 .4 7 5 1 2 1 0 0 R F -2 .0 8 4 L L D V D N R 5 3 6 .2 9 3 3 2 3 9 K L 0 .3 6 4 T D A IP G R 6 5 6 .3 6 0 9 2 5 2 K V -0 .4 2 5 M M IT S Q D V L H S W A V P T L G L K 7 5 3 .7 2 1 7 3 3 7 R T -0 .8 5 0 IP I0 0 0 1 9 8 4 8 .2 H C F C 1 I so fo rm 1 o f H o st c el l fa ct o r 0 .6 9 0 .2 6 E N Q W F D V G V IK 6 6 7 .8 4 1 6 2 4 8 K G 1 .1 6 0 S P A F V Q L A P L S S K 6 7 2 .8 7 9 4 2 7 8 R V -0 .3 5 0 S L H S A T T IG N K 1 0 1 8 .2 1 5 6 3 8 4 K L 1 .7 2 0 IS V A T G A L E A A Q G S K 7 0 1 .8 8 1 2 7 6 R S 0 .4 9 2 IA T G H G Q Q G V T Q V V L K 9 0 4 .9 5 3 2 2 8 5 K T -0 .7 1 6 S P IS V P G G S A L IS N L G K 7 9 8 .9 5 0 8 2 4 6 K V -1 .1 7 4 G P L P A G T IL K 4 8 3 .8 0 3 2 3 2 K L 1 .8 1 3 IP I0 0 6 4 2 0 4 6 .1 R S L 1 D 1 R S L 1 D 1 p ro te in 0 .6 9 0 .2 5 IG H V G M Q IE H II E N IV A V T K 7 3 4 .4 0 5 1 3 5 0 R G -4 .7 1 8 L L P S L IG R 4 3 4 .7 8 4 9 2 3 8 R H 1 .1 2 7 V P V S V N L L S K 5 2 8 .3 2 7 7 2 4 7 K N 2 .0 7 5 A p p en d ix 1 . P ro te in s sh o w in g i n cr ea se d c o n ce n tr at io n . 427 IP I A cc es si o n N u m b er ID A v g ( 3 ) co rr ec te d i/ w S tD ev A v g (3 ) co rr ec te d i/ w P ep ti d es I d en ti fi ed M C R (M as s ch ar g e ra ti o ) [T h ] C h ar g e S co re L ef t fl an k in g A A R ig h t fl an k in g A A C al ib ra te d m as s re la ti v e er ro r [p p m ] D D V A P E S G D T T V K 6 0 4 .3 1 9 6 2 5 0 R Q 1 .0 4 7 T V S Q II S L Q T L K 6 8 8 .3 3 4 2 8 1 R - 1 .4 9 6 L L S S F D F F L T D A R 7 6 6 .3 9 4 4 2 5 4 R I 1 .1 6 3 IP I0 0 8 7 3 8 9 0 .1 3 0 k D a p ro te in 0 .6 9 0 .1 3 K F H Y L P F L P S T E D V Y D C R 7 0 6 .3 6 6 4 3 3 6 K S 0 .3 5 4 E P N V L IC F ID K 6 7 6 .8 8 4 9 2 8 5 R A 0 .7 3 3 K E T V W R 1 0 4 7 .0 4 9 8 2 2 7 R L -1 .5 2 0 F A S F E A Q G A L A N IA V D K 8 7 6 .4 5 2 1 2 9 8 R A 0 .3 5 9 N G K P V T T G V S E T V F L P R 6 5 7 .3 5 7 3 2 4 3 K S 1 .3 3 2 F H Y L P F L P S T E D V Y D C R 7 0 9 .3 8 1 2 6 4 R L 1 .9 7 1 V E H W G L D E P L L K 4 7 9 .2 5 7 1 3 5 5 R H -0 .9 2 4 A N L E IM T K 7 4 7 .3 9 5 4 2 6 6 K S 0 .2 7 6 IP I0 0 7 9 0 3 7 3 .1 A L A D d el ta - am in o le v u li n ic a ci d d eh y d ra ta se i so fo rm b 0 .7 0 0 .2 0 A A V L E A M T A F R 5 6 1 .2 7 6 5 2 4 1 K T -0 .5 9 9 F A S C F Y G P F R 6 2 6 .2 8 1 6 2 3 5 K D -4 .5 5 8 G S A A D S E E S P A IE A IH L L R 6 5 6 .0 0 1 5 3 3 8 R K 0 .7 7 7 L A E V A L A Y A K 4 3 1 .7 4 2 4 2 7 0 R W -0 .4 1 7 IP I0 0 0 0 3 9 2 5 .6 P D H B I so fo rm 1 o f P y ru v at e d eh y d ro g en as e E 1 co m p o n en t su b u n it b et a m it o ch o n d ri al p re cu rs o r 0 .7 0 0 .0 4 V V S P W N S E D A K 6 1 6 .3 0 0 8 2 4 0 K G -0 .8 9 5 D II F A IK 4 1 0 .2 5 2 7 2 3 3 K K 0 .7 0 2 T Y Y M S G G L Q P V P IV F R 9 2 2 .4 7 2 8 2 7 4 K G -1 .5 5 5 IL E D N S IP Q V K 4 0 7 .7 5 5 3 2 5 0 K G 0 .2 5 4 A p p en d ix 1 . P ro te in s sh o w in g i n cr ea se d c o n ce n tr at io n . 428 IP I A cc es si o n N u m b er ID A v g ( 3 ) co rr ec te d i/ w S tD ev A v g (3 ) co rr ec te d i/ w P ep ti d es I d en ti fi ed M C R (M as s ch ar g e ra ti o ) [T h ] C h ar g e S co re L ef t fl an k in g A A R ig h t fl an k in g A A C al ib ra te d m as s re la ti v e er ro r [p p m ] D F L IP IG K 7 5 4 .9 3 0 6 2 4 4 R R -0 .8 3 9 V F L L G E E V A Q Y D G A Y K 9 0 1 .4 5 5 2 2 6 5 K V 0 .6 3 1 V T G A D V P M P Y A K 8 0 6 .3 8 4 5 2 9 4 M K -1 .7 2 7 D N N P V V V L E N E L M Y G V P F E F P P E A Q S K 1 0 2 1 .5 0 0 1 3 5 1 R D 0 .5 4 1 IM E G P A F N F L D A P A V R 8 5 8 .4 6 3 2 2 9 6 R N -0 .9 0 4 IP I0 0 4 1 9 2 5 8 .4 H M G B 1 H ig h m o b il it y g ro u p p ro te in B 1 0 .7 0 0 .0 5 M S S Y A F F V Q T C R E E H K 6 7 9 .3 0 5 1 3 4 3 K K -0 .2 2 9 D IA A Y R 5 1 3 .2 2 1 2 3 8 R I -0 .2 1 6 Y E K D IA A Y R 5 6 4 .7 8 6 8 2 6 5 K A -2 .0 7 4 T Y IP P K 3 5 9 .7 1 0 3 2 3 0 K G 0 .3 9 9 G K F E D M A K 8 4 8 .0 7 5 6 3 4 3 R V 2 .1 4 1 R P P S A F F L F C S E Y R 5 9 2 .9 5 6 4 3 5 0 K - 0 .7 9 6 K H P D A S V N F S E F S K 7 9 6 .8 8 9 9 2 7 3 K K 0 .7 2 6 G E H P G L S IG D V A K K 7 0 4 .3 8 2 4 2 6 0 K L -1 .6 6 3 L G E M W N N T A A D D K Q P Y E K 1 0 6 3 .4 8 2 2 5 5 K K 2 .5 6 1 L G E M W N N T A A D D K 7 4 0 .8 2 0 4 2 6 3 K Q -3 .0 9 2 F K D P N A P K 5 1 8 .2 7 0 5 2 6 3 R F -0 .1 9 9 M S S Y A F F V Q T C R 7 5 6 .8 3 5 4 2 9 0 K E 1 .1 0 9 R P P S A F F L F C S E Y R P K 6 6 8 .0 0 4 4 3 6 7 K I -1 .3 1 4 IK G E H P G L S IG D V A K 7 6 0 .9 2 3 6 2 6 4 K K -2 .6 5 5 H P D A S V N F S E F S K 7 3 2 .8 4 0 5 2 6 8 K K -1 .6 8 9 H P D A S V N F S E F S K K 7 9 6 .8 8 8 6 2 5 1 K C -0 .8 4 6 T Y IP P K G E T K 6 2 4 .3 1 9 2 6 8 K - 0 .5 8 5 A p p en d ix 1 . P ro te in s sh o w in g i n cr ea se d c o n ce n tr at io n . 429 IP I A cc es si o n N u m b er ID A v g ( 3 ) co rr ec te d i/ w S tD ev A v g (3 ) co rr ec te d i/ w P ep ti d es I d en ti fi ed M C R (M as s ch ar g e ra ti o ) [T h ] C h ar g e S co re L ef t fl an k in g A A R ig h t fl an k in g A A C al ib ra te d m as s re la ti v e er ro r [p p m ] G E H P G L S IG D V A K 8 8 4 .1 1 0 5 3 7 1 K W -0 .2 9 8 IP I0 0 0 0 2 4 6 0 .3 A N X A 7 I so fo rm 1 o f A n n ex in A 7 0 .7 0 0 .1 6 E F S G Y V E S G L K 5 9 9 .2 9 3 7 2 6 0 K C -0 .7 1 2 T IL Q C A L N R P A F F A E R 6 3 6 .3 3 6 9 3 3 0 K L -0 .0 9 7 D E N Q S IN H Q M A Q E D A Q R 6 7 1 .9 6 1 1 3 4 6 R L -0 .0 2 3 D L L S S V S R 4 6 6 .2 4 0 1 2 5 6 K A 0 .4 6 4 G A G T D D S T L V R 5 4 9 .2 7 9 2 6 9 K I -2 .0 1 4 L L V S M C Q G N R D E N Q S IN H Q M A Q E D A Q R 7 9 3 .8 6 3 7 4 2 6 R L -0 .2 1 2 G F G T D E Q A IV D V V A N R 8 4 8 .9 3 3 1 2 8 2 K S -0 .4 5 6 S D T S G H F E R 5 1 8 .2 2 8 2 2 4 8 R L 0 .0 8 5 L Y Q A G E G R 4 4 7 .2 2 8 7 2 3 4 R L 2 .7 7 8 L G T D E S C F N M IL A T R 8 6 4 .4 0 9 1 2 8 6 R S 0 .3 7 6 T L G T M IA G D T S G D Y R 7 7 9 .3 6 5 2 2 7 7 K R 0 .5 1 9 V L IE IL C T R 6 4 0 .8 5 1 8 2 6 5 K I -0 .9 0 9 L Y Y A M K 3 9 4 .7 0 4 3 2 3 0 R G 0 .6 1 0 S E ID L V Q IK 8 2 3 .4 4 2 2 2 8 4 R D -1 .3 0 8 L L V S M C Q G N R 5 8 9 .2 9 4 4 2 5 9 R D -0 .3 5 1 Q M F A Q M Y Q K 4 7 2 .7 5 6 4 2 6 5 K L 1 .3 3 5 IP I0 0 0 1 3 1 2 2 .1 C D C 3 7 H sp 9 0 c o - ch ap er o n e C d c3 7 0 .7 0 0 .1 3 Q Y M E G F N D E L E A F K E R 6 6 9 .3 0 2 3 4 2 R V -2 .2 9 1 L K E L E V A E G G K 8 3 0 .9 3 0 7 2 1 0 7 K I 0 .4 8 9 T G D E K D V S V 4 7 8 .2 3 6 6 2 5 5 K - -1 .5 9 0 L Q A E A Q Q L R 5 3 1 .8 0 3 2 2 5 8 R K -0 .6 7 0 S M V N T K P E K 5 2 3 .2 9 1 2 2 3 1 K T 0 .1 8 0 S M P W N V D T L S K 6 2 7 .3 2 1 2 6 0 K A -1 .6 2 3 A p p en d ix 1 . P ro te in s sh o w in g i n cr ea se d c o n ce n tr at io n . 430 IP I A cc es si o n N u m b er ID A v g ( 3 ) co rr ec te d i/ w S tD ev A v g (3 ) co rr ec te d i/ w P ep ti d es I d en ti fi ed M C R (M as s ch ar g e ra ti o ) [T h ] C h ar g e S co re L ef t fl an k in g A A R ig h t fl an k in g A A C al ib ra te d m as s re la ti v e er ro r [p p m ] L G P G G L D P V E V Y E S L P E E L Q K 1 1 3 5 .0 8 4 5 2 6 6 R C -0 .0 3 3 E G E E A G P G D P L L E A V P K 8 5 4 .4 2 6 4 2 9 7 K T 1 .1 2 9 D V Q M L Q D A IS K 6 2 4 .3 1 9 2 8 5 K M 0 .2 2 5 C ID S G L W V P N S K 4 5 7 .2 4 6 2 2 3 9 R K 0 .5 5 2 E L E V A E G G K 6 2 5 .3 2 9 3 2 3 7 R E -0 .6 1 7 A M K E Y E E E E R 6 5 7 .2 8 7 1 2 4 7 K K -0 .6 8 3 E K E E L D R 4 6 5 .7 5 1 2 3 0 K G 1 .9 1 7 Q Y M E G F N D E L E A F K 8 6 0 .8 8 1 2 2 8 5 R E 0 .9 6 5 A S E A K E G E E A G P G D P L L E A V P K 7 3 2 .0 3 3 7 3 5 9 K T -0 .4 0 2 IP I0 0 2 9 6 9 9 9 .9 A T P A F 2 A T P s y n th as e m it o ch o n d ri al F 1 co m p le x a ss em b ly fa ct o r 2 m it o ch o n d ri al p re cu rs o r 0 .7 0 0 .3 3 L T V E Q A V L L S R 8 3 3 .4 3 4 3 2 8 8 R A -0 .9 5 2 V E E P E T L V E L Q R 7 2 1 .3 8 0 4 2 5 5 R N -0 .3 5 8 L E E E Y Q IQ K 1 0 5 8 .5 1 6 9 3 5 8 R D -1 .5 1 2 L F T V P S E A L A IA V A T E W D S Q Q D T IK 9 1 1 .8 0 3 1 3 5 7 K Y -1 .9 9 2 W G N IE W A H D Y E L Q E L R 6 8 6 .9 9 4 6 3 3 5 K A 0 .7 2 1 IP I0 0 2 1 6 2 3 0 .3 T M P O L am in a- as so ci at ed p o ly p ep ti d e 2 i so fo rm a lp h a 0 .7 0 0 .1 2 P E F L E D P S V L T K 7 2 9 .8 6 5 5 2 6 1 R V 1 .1 7 0 N R P P L P A G T N S K 1 0 8 5 .0 4 3 6 2 8 1 K E -0 .9 7 2 ID A S E L S F P F H E S IL K 6 1 1 .6 5 2 3 3 4 K V -1 .0 3 0 Y G V N P G P IV G T T R 6 6 5 .3 6 5 7 2 8 1 R A -1 .1 8 4 A p p en d ix 1 . P ro te in s sh o w in g i n cr ea se d c o n ce n tr at io n . 431 IP I A cc es si o n N u m b er ID A v g ( 3 ) co rr ec te d i/ w S tD ev A v g (3 ) co rr ec te d i/ w P ep ti d es I d en ti fi ed M C R (M as s ch ar g e ra ti o ) [T h ] C h ar g e S co re L ef t fl an k in g A A R ig h t fl an k in g A A C al ib ra te d m as s re la ti v e er ro r [p p m ] Q E D K D D L D V T E L T N E D L L D Q L V K 8 9 6 .7 7 3 7 3 5 7 R Y -1 .6 4 1 E P L V A T N L P G R 5 8 3 .8 3 0 1 2 5 7 R G -0 .0 9 5 S S T P L P T IS S S A E N T R 8 2 4 .4 1 2 2 2 8 1 R Q -1 .1 6 4 S E L V A N N V T L P A G E Q R 8 4 9 .4 4 7 2 2 8 2 K K 3 .1 9 8 E A T Q IL S V P K 5 4 3 .3 1 4 1 2 4 0 R V 0 .6 0 7 G P P D F S S D E E R E P T P V L G S G A A A A G R 5 9 6 .3 7 3 3 2 1 1 0 K I 0 .1 1 7 IP I0 0 0 2 0 1 9 4 .1 T A F 1 5 I so fo rm S h o rt o f T A T A -b in d in g p ro te in -a ss o ci at ed fa ct o r 2 N 0 .7 0 0 .1 7 G E A T V S F D D P P S A K 7 1 0 .8 3 3 1 2 7 6 K A -0 .1 3 8 A A ID W F D G K 8 4 5 .9 6 4 5 2 7 4 R C -0 .9 3 5 A A ID W F D G K E F H G N II K 6 5 4 .3 3 4 7 3 2 8 K V -0 .8 1 1 A p p en d ix 2 . P ro te in s sh o w in g d ec re as ed c o n ce n tr at io n . 432 A p p e n d ix 2 . P r o te in s O b se r v e d T o D e c r e a se I n C o n c e n tr a ti o n . P ro te in s li st ed i n t h is t ab le a re t h o se i d en ti fi ed a s d ec re as in g i n c o n ce n tr at io n i n t h re e b io lo g ic al r ep li ca te s o f a S IL A C e x p er im en t in v es ti g at in g c y to k in e w it h d ra w al -i n d u ce d a p o p to si s. P ro te in s h er e sh o w a d ec re as e in r el at iv e co n ce n tr at io n o f 3 0 % o r m o re a ft er fi ft ee n h o u rs o f cy to k in e w it h d ra w al . P ro te in s n am e, a v er ag e i/ w r at io f ro m t h re e S IL A C e x p er im en ts , p ep ti d es i d en ti fi ed f o r ea ch p ro te in , an d t h e ch ar g e, r an k in g s co re , le ft a n d r ig h t fl an k in g a m in o a ci d s an d c al ib ra te d m as s er ro r ar e sh o w n a s in d ic at ed . IP I A cc es si o n N u m b er ID A v g ( 3 ) co rr ec te d i/ w S tD ev A v g (3 ) co rr ec te d i/ w P ep ti d es I d en ti fi ed M C R (M as s ch ar g e ra ti o ) [T h ] C h ar g e S co re L ef t fl an k in g A A R ig h t fl an k in g A A C al ib ra te d m as s re la ti v e er ro r [p p m ] IP I0 0 3 9 6 1 3 1 .3 Y T H D F 3 Y T H d o m ai n fa m il y p ro te in 3 1 .3 0 0 .3 3 S Y S E D D IH R 1 1 3 4 .9 7 6 1 2 1 0 4 R L -3 .7 6 5 H T T S IF D D F A H Y E K 5 7 2 .9 3 6 6 3 3 0 K R -0 .4 7 2 A IT D G Q A G F G N D T L S K 7 9 7 .8 9 2 9 2 8 0 R V 4 .8 4 4 D T Q E V P L E K 6 9 5 .0 1 3 3 3 6 9 K I 1 .4 5 1 G N V G IG G S A V P P P P IK 7 3 0 .4 1 7 2 2 5 4 K H 0 .2 1 4 R L D A A Y R 4 2 9 .2 7 6 6 2 4 4 R K 1 .1 5 1 IP I0 0 3 2 9 5 4 5 .7 R B M 2 6 I so fo rm 3 o f R N A -b in d in g p ro te in 2 6 1 .3 0 0 .2 2 M Q A G E E V T E L R 6 3 1 .8 0 6 7 2 4 0 K R 1 .0 3 7 IV V D S E S R 4 5 5 .7 4 9 2 2 8 R K -1 .5 2 1 A IS S T E A V L N N R 6 4 0 .8 4 9 7 2 6 3 K F 1 .9 6 9 T L L V S T S A V D N N E A Q K 8 4 5 .4 3 6 6 2 8 6 K K -0 .0 9 0 Y V L A L V K 4 0 3 .2 6 2 8 2 2 9 K K 0 .2 4 2 E G S T Q Q L Q T T S P K 7 0 2 .8 5 3 2 4 3 R V 1 .6 0 4 IP I0 0 4 1 0 0 6 7 .1 Z C 3 H A V 1 I so fo rm 1 o f Z in c fi n g er C C C H t y p e an ti v ir al p ro te in 1 1 .3 0 0 .0 6 S S L G S L Q T P E A V T T R 7 7 3 .9 0 6 9 2 6 8 R K -0 .7 9 7 K F T Y L G S Q D R 6 0 7 .8 1 1 3 2 4 0 R A -1 .1 1 3 A p p en d ix 2 . P ro te in s sh o w in g d ec re as ed c o n ce n tr at io n . 433 IP I A cc es si o n N u m b er ID A v g ( 3 ) co rr ec te d i/ w S tD ev A v g (3 ) co rr ec te d i/ w P ep ti d es I d en ti fi ed M C R (M as s ch ar g e ra ti o ) [T h ] C h ar g e S co re L ef t fl an k in g A A R ig h t fl an k in g A A C al ib ra te d m as s re la ti v e er ro r [p p m ] E H G L N P D V V Q N IQ D IC N S K 7 2 7 .3 5 1 6 3 4 4 R H -0 .2 6 6 S G T Q D IQ P G P L F N N N A D G V A T D IT S T R 9 3 0 .4 4 8 3 7 1 K S -1 .9 7 1 V A L V N D S L S D V T S T T S S R 9 2 6 .4 7 1 2 2 1 1 0 R V 2 .5 9 9 S N P S V F V IF Q K 6 3 3 .3 4 8 7 2 2 6 R D 0 .7 1 6 IE N S E L L D K 5 3 0 .7 7 8 7 2 4 2 K F -2 .0 0 1 N E S G T W IQ Y G E E K 7 7 0 .8 4 8 1 2 6 5 K D -1 .9 6 6 D Q V Y P Q Y V IE Y T E D K 9 4 5 .4 4 2 9 2 5 7 K A -1 .4 4 1 T V F S P T L P A A R 5 8 0 .3 2 7 6 2 3 2 K S 0 .6 0 0 A S L E D A P V D D L T R 8 3 7 .3 8 1 1 3 6 9 K I -0 .1 2 4 Y S H E V L S E E N F K 7 4 1 .3 4 9 4 2 4 6 K V 0 .4 2 1 A T D L G G T S Q A G T S Q R 7 2 5 .3 5 2 9 1 K F -0 .4 4 8 IP I0 0 0 1 6 3 4 6 .1 P R O S C P ro li n e sy n th et as e co -t ra n sc ri b ed b ac te ri al h o m o lo g p ro te in 1 .3 1 0 .1 1 A P L E V A Q E H 7 1 3 .8 8 1 3 2 9 6 R A 2 .2 4 9 IG S T IF G E R 4 9 0 .2 6 3 9 2 7 8 R D -0 .2 1 6 V N S S W Q R 4 3 8 .7 2 2 4 0 K K 0 .6 2 4 T F G E N Y V Q E L L E K 7 8 5 .3 9 2 2 2 8 2 R A -2 .0 6 1 H G L P P S E T IA IV E H IN A K 7 8 6 .8 8 9 7 2 6 5 R F 2 .8 8 5 T K P A D M V IE A Y G H G Q R 1 0 4 7 .0 3 6 7 2 9 2 K - 0 .0 5 1 D L P A IQ P R 4 5 5 .2 6 1 5 2 4 0 R L -0 .0 5 3 L M A V P N L F M L E T V D S V K 9 6 2 .0 0 2 2 5 7 K L -0 .7 0 1 IP I0 0 1 5 2 9 9 8 .3 L R R C 4 0 L eu ci n e- ri ch re p ea t- co n ta in in g p ro te in 4 0 1 .3 1 0 .2 1 F L P E F P S C S L L K 6 6 2 .8 7 7 2 6 9 K T -0 .6 4 4 E L E N L Q K 1 0 8 8 .0 6 2 2 2 1 1 4 R C 0 .8 1 8 S G Q L N L S G R 4 6 6 .2 5 0 9 2 3 5 K N -1 .1 9 5 L Q T IN L S F N R 5 8 2 .8 2 0 2 2 7 6 K G 1 .7 6 7 S V P D E II L L R 5 7 7 .8 4 3 2 4 1 K S 0 .6 0 2 L Q S L T D D L R 5 3 3 .7 9 5 5 2 5 5 K L 0 .1 7 8 A p p en d ix 2 . P ro te in s sh o w in g d ec re as ed c o n ce n tr at io n . 434 IP I A cc es si o n N u m b er ID A v g ( 3 ) co rr ec te d i/ w S tD ev A v g (3 ) co rr ec te d i/ w P ep ti d es I d en ti fi ed M C R (M as s ch ar g e ra ti o ) [T h ] C h ar g e S co re L ef t fl an k in g A A R ig h t fl an k in g A A C al ib ra te d m as s re la ti v e er ro r [p p m ] T L L L D G N P F R 5 7 6 .3 2 9 9 2 5 1 R V 0 .8 6 4 S N IV T S IN F S K 6 0 5 .3 2 6 8 2 4 0 K N -0 .9 1 2 Q A T L IP D E V F D A V K 7 7 3 .4 0 9 8 2 2 7 K S -2 .6 7 4 IL P E E IT N L R 7 5 9 .3 9 3 7 2 5 4 K V -2 .4 9 9 L T F L D L R 6 3 3 .8 7 4 7 2 5 7 R L 0 .8 1 3 G T A A IL E Y L R 5 5 3 .8 1 4 4 2 3 9 K D 0 .9 8 7 L N L S S N E L K 6 3 6 .3 1 6 8 2 7 5 R E 0 .1 0 5 IP I0 0 0 0 4 7 9 5 .1 C L N S 1 A M et h y lo so m e su b u n it p IC ln 1 .3 1 0 .2 5 G L G T G T L Y IA E S R 6 7 2 .3 6 8 2 8 6 K L 1 .9 4 1 Q Q P D T E A V L N G K 6 5 0 .3 2 5 6 2 4 7 R G -8 .0 8 4 IP I0 0 2 1 9 5 1 8 .7 A R L 1 A D P -r ib o sy la ti o n fa ct o r- li k e p ro te in 1 1 .3 1 0 .2 9 S E L V A M L E E E E L R K 8 3 8 .4 3 4 5 2 7 1 K A 2 .1 0 7 S E L V A M L E E E E L R 7 8 2 .3 8 1 3 2 7 1 K K -1 .6 6 4 A IL V V F A N K 4 8 7 .8 0 5 4 2 5 2 K Q 0 .2 0 2 T T IL Y R 3 8 3 .7 2 6 7 2 2 8 K L 0 .8 0 1 IL IL G L D G A G K 5 5 3 .9 7 0 2 3 7 5 R N 0 .8 5 8 IP I0 0 0 3 0 2 4 3 .1 P S M E 3 I so fo rm 1 o f P ro te as o m e ac ti v at o r co m p le x s u b u n it 3 1 .3 1 0 .2 0 M W V Q L L IP R 8 9 5 .4 6 5 3 2 6 5 R V 4 .6 4 3 IT S E A E D L V A N F F P K 1 1 9 7 .5 7 8 8 3 1 1 8 K S -0 .2 2 2 K L L E L D S F L K 8 1 9 .4 5 1 4 3 7 4 R I -1 .6 7 3 L II S E L R 1 1 7 7 .1 1 2 2 1 0 8 R A 0 .7 2 5 S N Q Q L V D II E K 6 4 3 .8 5 1 8 2 6 3 K V 0 .4 1 3 T V E S E A A S Y L D Q IS R 7 1 5 .8 8 7 2 2 7 8 R G -0 .0 2 3 N Q Y V T L H D M IL K 6 1 1 .3 4 3 2 6 3 K T -0 .1 8 3 L L E L D S F L K 5 6 1 .2 9 5 5 2 6 2 R N 0 .3 8 0 IP I0 0 0 1 9 4 7 2 .4 S L C 1 A 5 N eu tr al a m in o ac id t ra n sp o rt er B 1 .3 1 0 .2 7 G P A G D A T V A S E K 5 8 4 .8 3 7 7 2 6 0 R Q 0 .6 0 2 A p p en d ix 2 . P ro te in s sh o w in g d ec re as ed c o n ce n tr at io n . 435 IP I A cc es si o n N u m b er ID A v g ( 3 ) co rr ec te d i/ w S tD ev A v g (3 ) co rr ec te d i/ w P ep ti d es I d en ti fi ed M C R (M as s ch ar g e ra ti o ) [T h ] C h ar g e S co re L ef t fl an k in g A A R ig h t fl an k in g A A C al ib ra te d m as s re la ti v e er ro r [p p m ] L S A F V F P G E L L L R 6 0 7 .8 1 5 5 2 6 3 R S -0 .4 1 8 S E L P L D P L P V P T E E G N P L L K 6 9 5 .8 4 5 4 2 8 7 K Y 2 .1 7 7 M II L P L V V C S L IG G A A S L D P G A L G R 8 3 1 .8 0 2 3 3 7 6 R L -0 .7 4 0 S T E P E L IQ V K 5 7 5 .3 2 8 2 3 5 R S 3 .3 1 5 N IF P S N L V S A A F R 7 1 8 .3 8 9 7 2 2 9 R S 1 .7 1 6 E V L D S F L D L A R 6 3 9 .3 4 0 8 2 7 8 K N 0 .3 1 0 L G P E G E L L IR 5 4 8 .8 2 0 8 2 7 3 K F -1 .7 8 1 IP I0 0 2 1 5 6 3 7 .5 D D X 3 X A T P -d ep en d en t R N A h el ic as e D D X 3 X 1 .3 1 0 .1 5 Q S S G A S S S S F S S S R 6 8 4 .3 0 7 1 2 9 0 R A -4 .2 7 2 D K D A Y S S F G S R 6 1 6 .7 8 0 9 2 6 1 K S 0 .7 0 0 H T M M F S A T F P K 6 4 9 .3 0 6 1 2 3 3 R E -1 .7 1 3 S S F F S D R 4 2 6 .2 0 3 1 2 4 0 K G 1 .8 7 2 H A IP II K 3 9 9 .2 6 9 6 2 4 8 K E -0 .1 8 6 F S G G F G A R 9 1 7 .3 9 2 2 7 9 R S -1 .3 9 5 G L D IS N V K 7 9 8 .9 5 1 2 2 4 4 K V 0 .7 9 8 Q Y P IS L V L A P T R 6 7 9 .3 9 5 4 2 4 5 K E -0 .0 8 0 Y T R P T P V Q K 3 6 3 .8 7 3 3 2 9 R H -0 .4 2 0 L E Q E L F S G G N T G IN F E K 9 4 1 .9 6 1 2 9 6 R Y -2 .2 4 0 V G N L G L A T S F F N E R 7 6 5 .9 0 1 6 2 8 9 R N -3 .3 4 8 V G S T S E N IT Q K 5 8 2 .2 9 8 4 2 5 8 R V -0 .8 6 7 D L L D L L V E A K 5 5 1 .7 7 1 4 2 5 4 R E -0 .9 9 9 V R P C V V Y G G A D IG Q Q IR 6 2 9 .9 9 8 7 3 7 1 R D -2 .5 0 8 D R E E A L H Q F R 4 3 8 .2 3 0 9 3 4 5 R S 0 .7 8 6 S D Y D G IG S R 4 5 0 .2 4 8 8 2 5 3 R T -2 .1 4 5 H V IN F D L P S D IE E Y V H R 6 9 5 .0 1 2 3 7 1 K I -1 .3 2 2 T A A F L L P IL S Q IY S D G P G E A L R 1 1 6 6 .6 3 3 2 8 1 K A 1 .2 0 3 S P IL V A T A V A A R 5 8 4 .8 5 5 2 2 1 0 6 K G -0 .8 1 2 IG L D F C K 4 2 6 .7 1 7 9 2 3 8 K Y 0 .1 8 3 L V D M M E R 4 4 7 .2 1 4 2 2 3 6 R G 0 .8 6 0 A p p en d ix 2 . P ro te in s sh o w in g d ec re as ed c o n ce n tr at io n . 436 IP I A cc es si o n N u m b er ID A v g ( 3 ) co rr ec te d i/ w S tD ev A v g (3 ) co rr ec te d i/ w P ep ti d es I d en ti fi ed M C R (M as s ch ar g e ra ti o ) [T h ] C h ar g e S co re L ef t fl an k in g A A R ig h t fl an k in g A A C al ib ra te d m as s re la ti v e er ro r [p p m ] S F L L D L L N A T G K 6 4 6 .3 6 7 2 2 9 1 R D 0 .9 1 7 M L D M G F E P Q IR 7 1 0 .8 8 9 2 2 7 3 K G 2 .3 3 6 E IQ M L A R 6 4 1 .8 5 9 6 2 3 4 R E 1 .3 9 0 Y L V L D E A D R 4 2 0 .7 3 3 7 2 4 9 R L 3 .4 1 6 IP I0 0 0 2 0 8 8 7 .3 V A M P 7 I so fo rm 1 o f V es ic le -a ss o ci at ed m em b ra n e p ro te in 7 1 .3 1 0 .3 6 L E L L ID K 4 2 2 .2 6 2 6 2 2 7 R T -0 .5 9 8 T E N L V D S S V T F K 6 7 0 .3 4 1 6 2 5 6 K T 1 .0 0 1 H A W C G G N F L E V T E Q IL A K 6 9 1 .6 8 3 4 2 K I 1 .0 9 2 V M E T Q A Q V D E L K 7 0 3 .8 4 3 7 2 8 2 K G -2 .6 7 6 IV Y L C IT D D D F E R 8 2 9 .8 9 0 7 2 5 8 R S -0 .0 2 8 N ID L V A Q R 4 6 4 .7 6 4 7 2 4 5 R G 1 .1 5 0 A Q T A L P Y A M N S E F S S V L A A Q L K 1 1 7 8 .5 9 9 1 2 9 0 R H 1 .4 8 3 F Q T T Y G S R 6 7 7 .4 2 7 6 2 8 2 R A 2 .0 6 4 L T Y S H G N Y L F H Y IC Q D R 5 4 7 .5 0 7 3 4 2 6 K I -0 .3 5 3 L E L L ID K T E N L V D S S V T F K 7 2 2 .0 6 4 6 3 5 2 R T 1 .5 8 3 A F N F L N E IK 5 4 8 .2 9 5 9 2 5 0 R K 0 .9 2 5 A IL F A V V A R 4 8 0 .3 0 5 1 2 5 5 - G -0 .8 1 4 IP I0 0 4 7 2 9 8 1 .3 C T N N B L 1 I so fo rm 2 o f B et a- ca te n in -l ik e p ro te in 1 1 .3 1 0 .1 9 IL G L L E N F 4 5 9 .7 6 8 2 4 1 R - -0 .8 7 2 E Y A E N IG D G R 5 6 2 .2 5 3 6 2 3 6 K S -1 .4 9 1 E L L G E L D G ID V L L Q Q L S V F K 7 4 3 .7 5 1 3 3 4 8 R R 0 .3 9 4 Y L G A M Q V A D K 5 4 8 .2 7 9 2 2 8 K K 0 .2 2 8 F V D IL G L R 4 6 6 .7 8 1 4 2 6 2 K T -0 .9 7 9 F T E N D S E K V D R 6 7 0 .3 0 9 3 2 4 8 K L -0 .7 1 3 L M E L H F K 4 1 6 .7 4 8 1 2 4 7 K G 0 .5 0 4 G E G L Q L M N L M L R 1 0 1 3 .4 8 7 7 2 8 6 K A -0 .0 2 7 A p p en d ix 2 . P ro te in s sh o w in g d ec re as ed c o n ce n tr at io n . 437 IP I A cc es si o n N u m b er ID A v g ( 3 ) co rr ec te d i/ w S tD ev A v g (3 ) co rr ec te d i/ w P ep ti d es I d en ti fi ed M C R (M as s ch ar g e ra ti o ) [T h ] C h ar g e S co re L ef t fl an k in g A A R ig h t fl an k in g A A C al ib ra te d m as s re la ti v e er ro r [p p m ] IP I0 0 2 9 9 0 6 3 .1 S T IM 1 S tr o m al in te ra ct io n m o le cu le 1 p re cu rs o r 1 .3 2 0 .2 4 E L E S H S S W Y A P E A L Q K 6 2 5 .6 3 5 7 3 3 2 K W 0 .0 9 5 L T E P Q H G L G S Q R 1 0 4 0 .5 0 0 5 3 8 4 R R -0 .0 4 3 A L D T V L F G P P L L T R 7 5 6 .9 4 3 1 2 3 6 K H -0 .0 6 8 Q A L S E V T A A L R 6 7 7 .8 7 6 5 2 6 3 R T -0 .1 9 5 L S F E A V R 4 1 1 .2 2 8 9 2 4 3 K N -1 .2 4 1 A L L A L N H G L D K 5 8 2 .8 3 9 3 2 4 7 K A -1 .2 3 4 S H S P S S P D P D T P S P V G D S R 6 4 1 .2 8 8 6 3 4 4 R A -0 .8 1 3 IP I0 0 0 0 5 9 7 8 .8 S F R S 2 S p li ci n g f ac to r ar g in in e/ se ri n e- ri ch 2 1 .3 2 0 .2 5 V G D V Y IP R 6 7 0 .3 3 8 7 2 5 9 R - -1 .0 0 9 D A E D A M D A M D G A V L D G R 8 7 6 .3 6 4 3 2 1 0 2 R E 0 .1 3 1 Y G G G G Y G R 3 9 6 .6 9 0 1 2 3 7 R R -0 .2 8 6 V D N L T Y R 6 7 7 .8 7 4 5 2 7 7 R D -0 .2 5 7 T S P D T L R R 4 7 3 .2 6 0 8 2 2 8 R V 3 .2 6 3 IP I0 0 2 1 8 9 9 3 .1 H S P H 1 I so fo rm B et a o f H ea t sh o ck p ro te in 1 0 5 k D a 1 .3 2 0 .3 4 N A V E E Y V Y E F R 7 0 9 .8 3 1 7 2 6 1 K D -1 .8 2 6 S L D Q D P V V R 4 3 1 .7 2 9 4 2 4 2 K M 0 .4 4 6 K P V T D C V IS V P S F F T D A E R 7 2 3 .3 6 0 9 3 3 4 K R -0 .8 6 3 N Q Q IT H A N N T V S N F K 8 5 8 .4 2 7 2 8 2 K R 0 .0 1 5 M F E E L G Q R 5 0 5 .2 4 2 2 2 5 1 K L -0 .0 7 7 E K E N L S Y D L V P L K 7 7 4 .4 2 0 2 2 5 6 K N 0 .6 9 0 S V N E V M E W M N N V M N A Q A K 6 9 8 .9 8 5 4 3 5 7 K K 0 .2 3 5 L M N D M T A V A L N Y G IY K 9 0 8 .9 5 1 7 2 9 7 R Q -1 .0 2 4 Q D L P S L D E K P R 6 4 9 .3 4 1 1 2 3 3 K I -0 .0 9 3 S Q F E E L C A E L L Q K 7 9 7 .8 9 2 5 2 8 2 R I -0 .6 1 0 K S L D Q D P V V R 7 5 0 .4 1 2 6 5 R D 2 .6 6 6 C T P S V IS F G S K 5 9 1 .7 9 4 5 2 3 5 R N -0 .7 8 2 A p p en d ix 2 . P ro te in s sh o w in g d ec re as ed c o n ce n tr at io n . 438 IP I A cc es si o n N u m b er ID A v g ( 3 ) co rr ec te d i/ w S tD ev A v g (3 ) co rr ec te d i/ w P ep ti d es I d en ti fi ed M C R (M as s ch ar g e ra ti o ) [T h ] C h ar g e S co re L ef t fl an k in g A A R ig h t fl an k in g A A C al ib ra te d m as s re la ti v e er ro r [p p m ] L L T E T E D W L Y E E G E D Q A K 1 0 8 4 .9 9 6 5 2 8 8 R Q -1 .0 8 8 G C A L Q C A IL S P A F K 7 6 8 .3 8 8 4 2 6 6 R V -1 .2 5 3 E N L S Y D L V P L K 6 1 5 .3 5 1 1 2 5 4 K R 1 .3 0 1 F V V Q N V S A Q K D G E K 7 7 4 .9 0 7 6 2 5 3 R S 3 .7 6 3 IE V P L Y S L L E Q T H L K 5 9 5 .0 0 4 8 3 4 2 K V -0 .3 0 5 F Q E A E E R P K 6 6 5 .3 6 7 3 2 7 6 R A 1 .9 2 3 N H A A P F S K 4 3 9 .2 3 3 6 2 3 2 K V -0 .7 8 2 D L L N M Y IE T E G K 7 1 3 .3 4 9 9 2 6 3 K M -0 .6 5 2 V V N V E L P IE A N L V W Q L G K 1 0 1 1 .0 7 4 7 2 7 1 K D -0 .9 0 7 R G P F E L E A F Y S D P Q G V P Y P E A K 8 3 3 .0 7 2 9 3 3 7 R I -0 .0 9 9 S V L D A A Q IV G L N C L R 8 1 4 .9 4 2 1 2 8 8 R L -1 .5 9 8 V L T F L R 7 6 0 .9 2 4 2 2 9 6 K I 2 .4 2 6 D K L C G P Y E K 5 5 5 .2 6 9 6 2 3 0 R F 1 .9 2 9 M IM Q D K L E K 5 6 8 .2 9 7 7 2 2 6 K E 3 .4 8 1 A F N D P F IQ K 5 6 4 .2 9 2 3 2 5 2 R L -0 .3 2 1 D IS T T L N A D E A V A R 7 3 8 .3 7 0 1 2 6 9 K G -0 .7 9 5 A G G IE T IA N E F S D R 7 4 0 .3 5 5 5 2 7 2 R C -2 .6 1 7 V L G T A F D P F L G G K 8 9 5 .9 0 6 3 2 7 1 K I 1 .3 0 9 L K E T A E N S L K 6 5 1 .3 2 7 3 2 6 6 R A -1 .8 0 3 F V V Q N V S A Q K 5 6 0 .3 1 0 8 2 6 0 R D -1 .4 0 4 Y N H ID E S E M K 6 3 3 .2 7 9 2 2 4 0 K K 3 .2 4 7 Q A Y V D K L E E L M K 4 8 0 .7 6 8 2 2 6 0 R K 0 .1 6 1 V E D V S A V E IV G G A T R 7 5 1 .3 9 6 9 2 1 0 5 K I 0 .2 0 4 IP I0 0 2 1 6 2 4 7 .2 P S M D 4 P ro te as o m e 1 .3 2 0 .4 6 N A M G S L A S Q A T K 5 8 9 .7 9 6 2 7 3 R D 1 .1 0 0 II A F V G S P V E D N E K D L V K 8 0 7 .9 5 7 2 2 6 7 K S -0 .8 0 5 V A H L A L K 3 7 6 .2 4 5 1 2 2 8 R H 0 .7 2 1 L Q A Q Q D A V N IV C H S K 5 7 0 .9 5 8 2 3 3 3 R T 0 .3 0 2 M T IS Q Q E F G R 9 5 0 .0 1 9 3 2 8 2 K G -0 .2 7 8 A p p en d ix 2 . P ro te in s sh o w in g d ec re as ed c o n ce n tr at io n . 439 IP I A cc es si o n N u m b er ID A v g ( 3 ) co rr ec te d i/ w S tD ev A v g (3 ) co rr ec te d i/ w P ep ti d es I d en ti fi ed M C R (M as s ch ar g e ra ti o ) [T h ] C h ar g e S co re L ef t fl an k in g A A R ig h t fl an k in g A A C al ib ra te d m as s re la ti v e er ro r [p p m ] IP I0 0 5 5 3 1 3 1 .2 G A L E U D P -g lu co se 4 - ep im er as e 1 .3 2 0 .4 9 E A L N V F G N D Y D T E D G T G V R 1 0 3 6 .4 6 4 2 1 2 8 R D -0 .6 6 4 G H IA A L R 4 0 8 .7 1 3 2 2 4 6 K L 0 .3 0 8 F F IE E M IR 5 5 0 .7 7 5 6 2 3 5 K D -0 .5 5 4 V Q E L T G R 4 0 4 .7 3 3 4 2 3 5 R S -3 .0 0 4 D Y IH V V D L A K 5 8 6 .8 2 2 5 7 R G 1 .1 9 0 A V G E S V Q K P L D Y Y R 8 1 2 .9 1 9 9 2 7 1 K V -0 .2 9 4 T W N A V L L R 4 8 6 .7 8 5 1 2 4 2 K Y 0 .4 1 1 IP I0 0 2 9 7 1 6 9 .1 L C P 2 L y m p h o cy te cy to so li c p ro te in 2 1 .3 3 0 .3 0 L S Q E IN K N E E R 6 8 0 .3 4 6 3 2 2 6 K R -1 .0 1 1 F L N L T E N D IQ K 6 6 7 .8 4 6 7 2 3 2 R F -7 .0 1 2 G K E D F L S V S D II D Y F R 7 7 1 .3 9 7 5 2 6 0 K K -0 .5 2 8 E S Q V Y L L G T G L R 6 6 8 .3 6 7 4 2 7 0 K G 0 .3 3 5 V Y N IQ IR 4 5 3 .2 6 3 4 2 4 3 K Y -0 .8 7 3 IN Q D G T F L V R 5 4 4 .3 0 3 4 2 4 8 R T 0 .4 6 2 K M P L L L ID G K 5 6 4 .3 4 5 9 2 5 1 R N -0 .2 9 0 L P A P S ID R 4 3 4 .7 4 7 3 2 3 1 K S -1 .5 6 6 IP I0 0 0 2 5 3 3 3 .4 C IA P IN 1 I so fo rm 3 o f A n am o rs in 1 .3 3 0 .2 7 V L L S D S N L H D A 1 0 1 2 .0 7 7 9 2 9 3 K E 0 .6 3 9 E P V E T A V D N N S K 8 9 5 .9 7 8 2 2 6 8 K A 2 .3 3 3 K P N F E V G S S R 7 3 1 .3 7 1 4 2 6 8 K T -0 .2 9 5 E P L T P E E V Q S V R 6 9 2 .3 6 0 2 2 4 6 R E 1 .2 3 5 E L Q R E P L T P E E V Q S V R 6 3 7 .3 3 9 2 3 2 7 K E 2 .3 6 3 G L V D K L Q A L T G N E G R 5 2 4 .2 9 0 1 3 3 4 K V 0 .4 6 4 E H L G H E S D N L L F V Q IT G K 6 8 1 .6 9 4 2 3 3 7 R K 3 .8 7 4 V S V E N IK 3 9 7 .7 4 1 2 2 8 R Q 4 .1 2 6 IP I0 0 0 0 7 7 5 5 .3 R A B 2 1 R as -r el at ed p ro te in R ab -2 1 1 .3 3 0 .4 4 D S N G A IL V Y D IT D E D S F Q K 6 3 7 .3 0 6 9 2 4 4 K I 1 .3 8 9 M IE T A Q V D E R 6 0 4 .2 8 3 9 2 6 5 R A -1 .7 8 9 A p p en d ix 2 . P ro te in s sh o w in g d ec re as ed c o n ce n tr at io n . 440 IP I A cc es si o n N u m b er ID A v g ( 3 ) co rr ec te d i/ w S tD ev A v g (3 ) co rr ec te d i/ w P ep ti d es I d en ti fi ed M C R (M as s ch ar g e ra ti o ) [T h ] C h ar g e S co re L ef t fl an k in g A A R ig h t fl an k in g A A C al ib ra te d m as s re la ti v e er ro r [p p m ] H V S IQ E A E S Y A E S V G A K 9 0 2 .9 4 1 1 2 1 0 4 R H 1 .5 1 3 G IE E L F L D L C K 6 6 8 .8 4 4 9 2 6 2 K R 0 .1 8 3 L A IW D T A G Q E R 6 3 0 .3 2 3 7 2 6 6 K F 1 .4 0 0 IP I0 0 3 3 9 3 8 4 .5 R D H 1 1 I so fo rm 1 o f R et in o l d eh y d ro g en as e 1 1 1 .3 4 0 .5 0 IV N V S S L A H H L G R 5 8 4 .8 3 1 5 2 2 6 R A -0 .8 9 1 E IQ T T T G N Q Q V L V R 9 4 3 .4 7 3 8 2 7 3 R - 1 .8 7 0 R L W D V S C D L L G L P ID 8 8 6 .4 5 8 2 4 4 R - 1 .5 1 5 V V V V T G A N T G IG K 8 2 4 .3 8 3 7 2 6 9 R D 3 .7 2 3 L A N IL F T Q E L A R 9 1 7 .4 6 3 2 9 7 K V -0 .0 3 6 D V E K G E L V A K 5 4 4 .3 0 3 3 2 3 5 R E -0 .2 5 4 IH F H N L Q G E K 4 0 8 .2 1 6 3 3 2 8 R F 1 .6 0 3 K L D L S D T K 4 6 0 .2 5 7 3 2 3 2 R S -2 .4 1 6 F Y N A G L A Y C H S K 4 7 7 .5 5 5 5 3 2 6 K L -0 .1 8 1 G S G V T T Y S V H P G T V Q S E L V R 6 9 2 .0 2 4 7 3 2 8 K H 0 .9 7 1 IP I0 0 6 4 7 7 8 6 .1 A R H G E F 1 I so fo rm 1 o f R h o g u an in e n u cl eo ti d e ex ch an g e fa ct o r 1 1 .3 4 0 .3 5 Q L L F P A E E D N G A G P P R 8 5 5 .9 2 5 4 2 7 6 K D -1 .0 3 6 R Q E V IS E L L V T E A A H V R 6 5 0 .6 9 7 2 3 5 2 K M -0 .0 9 5 F D G A E G S W F Q K 6 3 6 .2 8 7 4 2 5 1 R I -1 .5 7 3 E IL H H V N Q A V R 6 1 2 .8 6 8 6 2 5 6 K F -0 .0 0 8 A D L IS E D V Q R 1 0 5 8 .0 1 2 1 2 7 8 R S 0 .8 9 1 Y P L L L Q S IG Q N T E E P T E R 1 0 4 4 .5 3 6 4 2 9 2 K E 1 .7 7 8 L M G M T P W E Q E L A Q L E A W V G R 7 9 3 .0 4 7 8 3 6 6 R D -1 .4 9 2 T Q E IQ E N L L S L E E T M K 9 5 3 .4 7 7 7 2 7 4 R Q -0 .0 7 1 IP I0 0 5 5 0 0 6 9 .3 R N H 1 R ib o n u cl ea se in h ib it o r 1 .3 4 0 .0 3 L E D A G V R 3 8 3 .2 1 4 3 2 3 8 R E 2 .0 5 6 V N P A L A E L N L R 6 0 5 .3 5 1 6 2 7 7 R S 0 .4 4 5 D L C G IV A S K 4 8 1 .7 5 2 4 2 4 9 R A -0 .0 8 9 A p p en d ix 2 . P ro te in s sh o w in g d ec re as ed c o n ce n tr at io n . 441 IP I A cc es si o n N u m b er ID A v g ( 3 ) co rr ec te d i/ w S tD ev A v g (3 ) co rr ec te d i/ w P ep ti d es I d en ti fi ed M C R (M as s ch ar g e ra ti o ) [T h ] C h ar g e S co re L ef t fl an k in g A A R ig h t fl an k in g A A C al ib ra te d m as s re la ti v e er ro r [p p m ] T L W IW E C G IT A K 7 3 9 .3 7 9 8 2 6 2 R G 0 .4 9 4 L L C E T L L E P G C Q L E S L W V K 1 1 4 4 .5 8 8 3 2 4 8 R S 0 .3 4 8 V L W L A D C D V S D S S C S S L A A T L L A N H S L R 1 0 2 1 .1 6 4 2 3 6 4 R E 1 .2 2 8 S N E L G D V G V H C V L Q G L Q T P S C K 8 0 0 .0 5 4 7 3 5 7 R I -0 .3 8 9 E L C Q G L G Q P G S V L R 7 5 7 .3 9 4 2 6 5 R V 0 .4 4 6 IP I0 0 6 4 5 7 5 8 .4 K IF 2 C K in es in f am il y m em b er 2 C 1 .3 4 0 .0 9 F S L V D L A G N E R 6 1 0 .8 1 6 2 7 9 K G -1 .3 9 6 G S S S A N P V N S V R 5 0 1 .2 7 1 8 2 4 9 R A -0 .3 5 9 L T Q V L R 3 6 5 .2 3 2 7 2 3 2 K D -2 .2 6 0 L A M Q L E E Q A S R 6 3 8 .3 2 2 2 4 5 R Q 0 .2 3 3 D S F IG E N S R 5 1 2 .7 3 6 7 2 4 9 R T -2 .0 7 2 D N L P L Q E N V T IQ K 7 5 6 .4 0 7 3 2 5 8 K Q 0 .2 6 3 A T C F A Y G Q T G S G K 6 7 4 .3 0 4 4 2 6 1 K T 0 .9 9 3 A E S A L A Q Q A K 8 1 0 .9 2 2 2 3 8 R A 0 .2 5 3 IP I0 0 2 2 0 6 4 8 .5 P M V K P h o sp h o m ev al o n at e k in as e 1 .3 4 0 .4 3 E A Y G A V T Q T V R 5 9 7 .8 0 9 2 6 3 R V -1 .0 1 8 V V A L E Q S R 4 5 4 .2 6 9 5 2 5 0 R Q 1 .0 5 1 L E E Q L E N L IE F IR 7 2 5 .4 0 0 4 2 8 2 K G -0 .4 4 1 A P L G G A P R 3 6 9 .7 1 7 3 2 2 7 - L 2 .6 5 3 L V L L F S G K 4 3 8 .7 8 1 2 3 5 R R -1 .1 7 6 V S D IQ W F R 5 2 5 .7 7 2 2 2 2 7 R E 0 .2 6 8 D F V T E A L Q S R 5 8 3 .2 9 6 4 2 6 3 K L 0 .3 4 8 IV E G IS Q P IW L V S D T R 9 0 6 .9 9 7 2 8 5 K R 0 .0 1 1 K IV E G IS Q P IW L V S D T R 6 4 7 .6 9 9 4 3 2 8 R R 1 .2 7 0 IP I0 0 3 2 9 6 2 9 .6 D N A JC 7 D n aJ h o m o lo g su b fa m il y C m em b er 7 1 .3 4 0 .3 3 A V Q F F V Q A L R 7 0 2 .8 6 4 2 5 2 K R 0 .2 6 5 A p p en d ix 2 . P ro te in s sh o w in g d ec re as ed c o n ce n tr at io n . 442 IP I A cc es si o n N u m b er ID A v g ( 3 ) co rr ec te d i/ w S tD ev A v g (3 ) co rr ec te d i/ w P ep ti d es I d en ti fi ed M C R (M as s ch ar g e ra ti o ) [T h ] C h ar g e S co re L ef t fl an k in g A A R ig h t fl an k in g A A C al ib ra te d m as s re la ti v e er ro r [p p m ] E A L G D A Q Q S V R 5 8 7 .2 9 6 3 2 5 6 R L -0 .6 9 6 M D S T N A D A L Y V R 6 7 8 .3 1 6 9 2 8 1 R G 0 .0 9 6 N A Q A Q Q E F K 5 0 9 .7 6 9 5 2 5 1 K L 0 .5 2 8 E V G E A F T IL S D P K 7 0 3 .3 6 2 6 2 5 9 K K -2 .2 0 0 IP I0 0 2 9 2 1 4 0 .4 C A S P 3 C as p as e- 3 p re cu rs o r 1 .3 5 0 .4 0 S G T D V D A A N L R 5 5 9 .7 7 4 9 2 6 2 R E -1 .0 8 1 IP V E A D F L Y A Y S T A P G Y Y S W R 8 2 3 .7 3 1 3 4 0 K N 0 .3 0 8 L F II Q A C R 4 7 7 .2 8 7 4 2 5 2 R A 1 .2 6 6 Q Y A D K L E F M H IL T R 6 7 4 .9 1 2 1 2 3 8 K R -1 .9 0 1 D G S W F IQ S L C A M L K 8 2 8 .4 0 0 4 2 8 3 K Q 0 .2 7 7 E E IV E L M R 5 1 7 .7 6 3 2 2 5 4 R D 0 .2 9 5 IP I0 0 1 0 6 4 9 5 .1 N C A P G C o n d en si n co m p le x s u b u n it 3 1 .3 6 0 .2 3 L M F S G L L V S S R 6 0 5 .3 3 7 2 6 1 K I 0 .3 7 5 L L S D F L D S E V S E L R 8 1 1 .9 1 5 6 2 8 8 K T -2 .4 0 2 A S E L K E E IK 5 2 3 .7 8 9 7 2 3 3 R A -0 .7 7 4 V M L L Q Q G L N D R 5 5 5 .8 2 1 3 2 3 4 R F 0 .5 4 2 L N L A Q F L N E D L S 6 8 8 .8 5 6 8 2 5 4 K - 0 .2 2 7 E T E Q L E IK 4 9 5 .2 6 1 4 2 3 5 K E 0 .4 4 5 A IF D Q L M T F G IE P F K 8 7 8 .9 5 1 2 6 2 K T -2 .2 2 7 A P IV T V G V N N D P A D V R 8 1 8 .9 3 6 3 2 7 0 R K -0 .3 7 2 L A Y Q V L A E K 5 1 7 .7 9 7 5 2 3 8 K V -0 .4 8 2 L V V A L S R 3 7 9 .2 5 2 4 0 K T -0 .3 4 9 IQ A V L A L S R 4 8 5 .8 0 5 7 2 5 3 R L 1 .0 7 1 F S E G N IL E L L H R 4 7 6 .5 9 2 3 4 9 R L -1 .9 2 3 T Q IV T E II S E IR 7 0 1 .4 0 0 3 2 8 1 R A -1 .3 6 3 IP I0 0 0 5 9 2 6 4 .3 V P S 2 6 B V ac u o la r p ro te in so rt in g -a ss o ci at ed p ro te in 2 6 B 1 .3 6 0 .1 4 F E G T T S L G E V R 5 9 8 .3 0 1 1 2 7 2 R T -0 .7 0 3 A p p en d ix 2 . P ro te in s sh o w in g d ec re as ed c o n ce n tr at io n . 443 IP I A cc es si o n N u m b er ID A v g ( 3 ) co rr ec te d i/ w S tD ev A v g (3 ) co rr ec te d i/ w P ep ti d es I d en ti fi ed M C R (M as s ch ar g e ra ti o ) [T h ] C h ar g e S co re L ef t fl an k in g A A R ig h t fl an k in g A A C al ib ra te d m as s re la ti v e er ro r [p p m ] IY F L L V R 1 2 0 5 .1 0 8 6 2 1 2 3 R I 1 .1 4 0 G N H H E F V S L V K 6 3 6 .8 4 2 6 2 4 8 R D -0 .9 4 3 IP I0 0 3 0 0 2 9 9 .6 S P C S 3 S ig n al p ep ti d as e co m p le x s u b u n it 3 1 .3 6 0 .6 9 N V E D F T G P R 5 1 7 .7 4 8 3 2 5 6 K E -0 .9 6 3 Y F F F D D G N G L K 5 3 9 .7 7 1 6 2 5 7 K D 1 .0 6 3 N N A L N Q V V L W D K 7 0 7 .3 7 8 9 2 6 7 K I 0 .9 4 0 M N T V L S R 4 1 8 .7 1 8 6 2 5 3 - A -0 .0 0 7 IP I0 0 0 0 1 7 3 0 .3 F H O D 1 F H 1 /F H 2 d o m ai n -c o n ta in in g p ro te in 1 1 .3 7 0 .3 8 S G L G D D L V Q A L G L S K 7 3 6 .9 0 4 5 2 7 6 K G 3 .8 9 4 A V N S V A S T T G A P P W A N L V S I L E E K 8 1 8 .7 6 6 8 3 4 1 R N -1 .0 6 5 A E T L A G A M P N E A G G H P D A R 6 2 2 .2 9 1 3 3 4 1 R Q -0 .9 1 9 Q L W D S P E T A P A A R 7 2 1 .3 5 8 2 2 4 2 R T 1 .1 5 0 A A L L N F D E F A V S K 4 9 0 .2 6 9 1 2 3 1 K A 1 .4 3 8 L L T M M P T E E E R 6 7 5 .3 2 6 4 2 5 5 K Q 0 .9 1 6 L E D G D IE E A P G A G G R 7 4 3 .3 4 4 9 2 6 9 K R 0 .2 8 3 L Q L W A F K 4 5 3 .2 6 6 2 2 4 2 R L 0 .7 8 5 V G M E Q L V Q N A T F R 7 4 6 .8 8 3 2 6 2 K C 0 .3 4 4 E M L E G F Y E E IS K 7 3 7 .8 4 3 6 2 6 3 R G 1 .6 4 8 V G A A A D H N Y Q S Y IL R 8 4 2 .4 3 2 4 2 6 1 R A 1 .3 2 2 H L G T A G T D V D L R 6 2 7 .8 2 6 2 5 8 R T 0 .5 7 1 L Y S S S G P E L R 5 5 4 .7 8 5 1 2 3 3 K R -0 .5 0 7 F L E N V A A A E T E K 6 6 1 .3 3 4 9 2 5 0 R Q -0 .9 4 0 V D F E Q L T E N L G Q L E R 8 9 5 .9 4 9 2 2 7 6 K R -0 .8 5 9 A E P IW E L P T R 6 0 6 .3 2 5 5 2 3 1 K A 1 .2 8 2 N G A D P E L L V Y T V T L IN K 9 3 0 .5 1 3 9 2 6 9 K T 4 .5 2 0 L A G G H G V S A S R 5 0 9 .2 7 8 3 2 5 0 K F -2 .6 5 6 T Q L V L Y E N A L K 6 4 6 .3 6 6 2 3 1 R L -0 .9 4 2 A p p en d ix 2 . P ro te in s sh o w in g d ec re as ed c o n ce n tr at io n . 444 IP I A cc es si o n N u m b er ID A v g ( 3 ) co rr ec te d i/ w S tD ev A v g (3 ) co rr ec te d i/ w P ep ti d es I d en ti fi ed M C R (M as s ch ar g e ra ti o ) [T h ] C h ar g e S co re L ef t fl an k in g A A R ig h t fl an k in g A A C al ib ra te d m as s re la ti v e er ro r [p p m ] IP I0 0 1 0 0 7 4 8 .3 H S P B P 1 I so fo rm 1 o f H sp 7 0 -b in d in g p ro te in 1 1 .3 7 0 .1 7 E Q E A G L L Q F L R 8 0 1 .4 4 2 7 1 R E 0 .1 7 5 Y L E A G A A G L R 3 6 4 .5 5 0 8 3 6 7 R Q -4 .7 7 2 L D G F S V L M R 5 9 9 .2 9 4 2 2 6 0 K C -0 .3 5 1 S A F L L Q N L L V G H P E H K 5 1 8 .3 0 0 1 2 3 2 R Y 0 .5 1 4 IP I0 0 4 6 5 1 3 2 .4 C O P E C o at o m er s u b u n it ep si lo n 1 .3 8 0 .4 8 W E A A E G L L Q E A L D K 6 5 7 .8 3 7 5 2 7 8 - T 2 .3 7 7 A P P A P G P A S G G S G E V D E L F D V K 1 0 4 9 .0 0 9 7 2 9 6 - N -1 .4 8 5 N A F Y IG S Y Q Q C IN E A Q R 6 8 7 .9 8 7 3 3 1 K V 0 .3 7 8 L D R L D L A R 4 8 6 .2 8 4 5 2 2 8 K K -1 .6 6 5 S V D V T N T T F L L M A A S IY L H D Q N P D A A L R 7 6 9 .8 9 1 2 4 6 2 R A 0 .1 2 2 D V E R D V F L Y R 6 6 2 .3 6 2 4 7 R A 2 .4 9 8 F G V V L D E IK P S S A P E L Q A V R 6 7 7 .8 8 1 9 2 8 0 R Q 0 .1 4 3 K F G V V L D E IK P S S A P E L Q A V R 7 6 1 .7 6 2 3 4 7 R M -0 .2 4 4 S T E S L Q A N V Q R 6 1 9 .8 2 4 2 2 7 7 K L -2 .1 3 4 G F S L E E L R 7 1 2 .3 6 0 4 2 6 6 K H -0 .6 3 1 T IG IS V D P R 4 8 2 .2 8 1 2 2 5 7 R R -1 .9 5 3 IP I0 0 0 1 6 3 8 1 .2 R A B 2 7 A I so fo rm L o n g o f R as -r el at ed p ro te in R ab -2 7 A 1 .3 8 0 .2 7 S L T T A F F R 1 2 9 5 .6 8 7 2 2 8 9 K G 0 .2 1 8 F L A L G D S G V G K 6 4 9 .2 9 3 1 2 6 7 K Y 1 .3 1 5 F IT T V G ID F R 4 2 4 .2 5 4 7 2 3 1 R L -1 .1 8 1 V V K E E E A IA L A E K 4 0 6 .2 3 7 7 2 3 0 R I 1 .3 0 5 T S V L Y Q Y T D G K 7 5 2 .8 5 9 2 2 8 9 R N -0 .8 7 6 IP I0 0 1 8 3 7 8 6 .1 F A D S 2 I so fo rm 1 o f F at ty a ci d d es at u ra se 2 1 .3 9 0 .2 3 F L K P L L IG E L A P E E P S Q D H G K 7 7 3 .4 1 8 6 3 4 0 K N 0 .3 4 1 V IG H Y A G E D A T D A F R 7 4 1 .8 9 8 6 2 1 0 3 R M -1 .9 0 4 A p p en d ix 2 . P ro te in s sh o w in g d ec re as ed c o n ce n tr at io n . 445 IP I A cc es si o n N u m b er ID A v g ( 3 ) co rr ec te d i/ w S tD ev A v g (3 ) co rr ec te d i/ w P ep ti d es I d en ti fi ed M C R (M as s ch ar g e ra ti o ) [T h ] C h ar g e S co re L ef t fl an k in g A A R ig h t fl an k in g A A C al ib ra te d m as s re la ti v e er ro r [p p m ] H G IE Y Q E K P L L R 7 4 1 .9 0 7 6 2 5 4 K A 1 .0 8 9 W N H L V H K 6 8 8 .8 6 9 5 2 5 9 R H -1 .5 6 8 IT E D F R 5 3 8 .3 0 8 1 2 3 3 K I 0 .3 6 6 E V S V P T F S W E E IQ K 8 3 9 .9 2 1 1 2 5 8 R H 1 .1 6 6 A F H P D L E F V G K 6 3 0 .3 2 6 1 2 4 8 R F 2 .3 8 4 V Y N IT K 6 4 0 .6 5 4 3 4 8 M V -1 .0 0 8 A L L D II R 4 0 7 .2 6 2 9 2 5 1 R S -1 .0 1 6 W S IQ H P G G Q R 8 6 8 .9 9 7 1 2 6 7 K V -0 .8 3 0 F V IG H L K 8 7 2 .4 3 6 2 8 4 R D 0 .3 4 6 K V Y N IT K 4 3 9 .2 8 1 1 2 2 8 R W 0 .5 4 6 IP I0 0 6 4 7 6 5 0 .3 E IF 3 H E u k ar y o ti c tr an sl at io n i n it ia ti o n fa ct o r 3 s u b u n it 3 1 .3 9 0 .1 9 H V N ID H L H V G W Y Q S T Y Y G S F V T R 6 9 5 .5 8 9 4 4 3 0 R A -1 .1 4 2 V D E M S Q D IV K 5 8 2 .2 8 5 3 2 6 0 R Y 1 .4 7 0 L F K P P Q P P A R 6 8 3 .8 4 8 7 2 7 6 K K -0 .5 1 2 N S H L IN V L M W E L E K 8 6 3 .4 5 6 2 3 9 K K 2 .5 2 3 M D S L L IA G Q IN T Y C Q N IK 1 0 4 9 .5 1 9 1 2 9 5 R E -0 .7 2 2 G E P P L P E E D L S K 8 3 3 .0 7 3 5 3 4 0 R I 2 .1 1 1 Y N T Y M R 1 0 1 8 .2 0 9 6 3 7 0 K L -4 .0 1 9 Q V Q ID G L V V L K 9 7 1 .4 9 5 3 2 8 2 K L -2 .0 8 1 IP I0 0 6 4 4 4 8 2 .1 P S M G 2 P ro te as o m e as se m b ly c h ap er o n e 2 1 .3 9 0 .2 6 V IV L S S S H S Y Q R 5 9 4 .8 2 4 8 2 6 1 K N -1 .8 3 8 C IP E ID D S E F C IR 6 8 2 .3 7 6 3 2 5 5 R Y -0 .0 6 7 N D L Q L R 6 4 5 .2 7 8 4 2 6 0 K - 0 .5 8 7 L L F G S G L P P A L F 1 0 5 3 .0 3 6 6 2 7 8 K G 3 .1 5 0 L V A L Q L R 7 1 3 .3 3 5 6 2 5 8 R V 0 .0 3 0 IP I0 0 2 9 2 8 9 4 .5 T S R 1 P re -r R N A - p ro ce ss in g p ro te in T S R 1 h o m o lo g 1 .3 9 0 .2 8 S Q D T V L M N L Y K 6 5 9 .3 4 3 1 2 4 3 K R -1 .6 6 3 A p p en d ix 2 . P ro te in s sh o w in g d ec re as ed c o n ce n tr at io n . 446 IP I A cc es si o n N u m b er ID A v g ( 3 ) co rr ec te d i/ w S tD ev A v g (3 ) co rr ec te d i/ w P ep ti d es I d en ti fi ed M C R (M as s ch ar g e ra ti o ) [T h ] C h ar g e S co re L ef t fl an k in g A A R ig h t fl an k in g A A C al ib ra te d m as s re la ti v e er ro r [p p m ] E N L P Q D Y A R 5 5 3 .2 6 5 7 2 2 9 K I -3 .0 4 1 E D V L W F K P V E L R 5 1 0 .9 4 8 5 3 2 6 R T -0 .4 7 8 IF Q F Q N F T N T R 7 1 1 .3 6 7 1 2 8 4 R K 0 .0 7 2 Q G T P L IA F S L L P H E Q K 5 9 3 .6 6 4 4 3 4 3 R M -0 .9 7 9 G Q T L N V N R 8 7 5 .9 4 1 9 2 4 0 R V -0 .8 4 9 M S V L N M V V R 5 2 4 .7 8 4 7 2 6 3 K R -2 .1 1 0 Q ID A P G D P F P L N P R 7 6 8 .8 9 4 2 2 4 7 K G 0 .1 7 0 W T Y D P Y V P E P V P W L K 9 4 5 .4 7 9 3 2 4 5 K S 1 .6 8 2 E A V L A E K 3 8 0 .2 1 6 4 2 3 2 K R 1 .2 3 2 L E E M F P D E V D T P R 7 8 9 .3 6 0 6 2 5 1 R D -1 .1 7 8 A Y L F A H A V D F V P S E E N N L V G T L K 8 4 5 .4 3 6 3 3 2 9 R I 0 .2 8 5 IP I0 0 8 7 3 2 4 4 .1 C S D E 1 I so fo rm S h o rt o f C o ld s h o ck d o m ai n - co n ta in in g p ro te in E 1 1 .3 9 0 .1 2 V T L L E G D H V R 5 7 2 .8 2 6 3 2 4 8 K F 3 .8 6 7 G P D N S M G F G A E R 5 1 5 .2 6 9 2 2 4 6 K T 1 .2 4 9 N Q N D P L P G R 6 1 4 .8 1 6 9 2 7 6 K I 0 .4 6 8 IN F V ID N N K 5 3 8 .7 8 9 4 2 7 1 K H -1 .9 8 4 L L G Y V A T L K 4 8 9 .3 0 4 5 2 4 8 R D -1 .3 8 3 D Q F G F IN Y E V G D S K 8 0 9 .8 7 4 2 8 3 K K 1 .0 5 5 V G D D V E F E V S S D R R 5 3 7 .2 5 3 3 3 3 9 K T -0 .1 2 3 T H S V N G IT E E A D P T IY S G K 6 7 3 .6 6 1 6 3 3 9 K V 1 .9 5 1 L E E F G R 3 7 8 .7 0 3 2 2 3 1 R F 1 .1 5 3 IP I0 0 6 5 4 8 4 3 .1 E IF 2 B 4 e u k ar y o ti c tr an sl at io n i n it ia ti o n fa ct o r 2 B su b u n it 4 d el ta is o fo rm 3 1 .3 9 0 .3 1 IV L A A Q A IS R 5 2 1 .3 2 4 1 2 6 6 K F -0 .6 7 7 V G T A Q L A L V A R 5 4 9 .8 3 6 2 8 7 R A 1 .5 4 1 A S P S T A G E T P S G V K 6 4 4 .8 2 1 5 2 4 9 K R -1 .7 8 4 L G L Q Y S Q G L V S G S N A R 8 2 5 .4 3 5 3 2 6 0 R C 1 .6 5 2 A p p en d ix 2 . P ro te in s sh o w in g d ec re as ed c o n ce n tr at io n . 447 IP I A cc es si o n N u m b er ID A v g ( 3 ) co rr ec te d i/ w S tD ev A v g (3 ) co rr ec te d i/ w P ep ti d es I d en ti fi ed M C R (M as s ch ar g e ra ti o ) [T h ] C h ar g e S co re L ef t fl an k in g A A R ig h t fl an k in g A A C al ib ra te d m as s re la ti v e er ro r [p p m ] S L V H A G V P A S Y L L IP A A S Y V L P E V S K 8 9 4 .4 9 8 9 3 3 2 R V -3 .8 4 4 L P E Y P Q V D D L L L R 7 8 5 .9 2 7 8 2 4 1 R R 0 .1 5 8 IP I0 0 0 2 1 1 7 5 .1 C R K R S I so fo rm 1 o f C el l d iv is io n c y cl e 2 -r el at ed p ro te in k in as e 7 1 .4 0 0 .1 5 L A D F G L A R 8 2 6 .8 9 6 9 2 6 7 K S 0 .1 8 5 N S G P A L T E S L V Q T L V K 8 2 8 .9 6 2 9 2 4 5 R N 0 .7 1 0 IP I0 0 0 1 7 3 4 4 .3 R A B 5 B R as -r el at ed p ro te in R ab -5 B 1 .4 0 0 .8 4 G V D L H E Q S Q Q N K 6 9 1 .8 3 9 8 2 4 9 R S 4 .1 0 8 G A Q A A IV V Y D IT N Q E T F A R 1 0 5 7 .4 8 5 5 2 9 6 R H -0 .4 8 2 Y H S L A P M Y Y R 6 5 0 .7 6 7 1 2 6 7 K N -1 .9 1 0 F E IW D T A G Q E R 6 7 6 .3 1 8 2 2 6 8 K Y 0 .6 0 9 S E P Q N L G G A A G R 5 7 8 .7 8 8 6 2 6 5 K S -0 .7 6 2 T A M N V N D L F L A IA K 6 3 4 .3 7 1 1 2 8 6 K K -0 .2 1 0 Q A S P S IV IA L A G N K 6 8 4 .8 9 4 7 2 7 6 R A -2 .1 9 6 L V L L G E S A V G K 7 1 3 .3 6 5 9 2 7 6 R S -0 .6 0 1 IP I0 0 0 2 3 5 2 9 .1 C D K 6 C el l d iv is io n p ro te in k in as e 6 1 .4 0 0 .1 4 L A D F G L A R 8 2 6 .8 9 6 9 2 6 7 K S 0 .1 8 5 IS A Y S A L S H P Y F Q D L E R 5 5 0 .2 9 3 2 4 4 R F 0 .7 3 8 H L E T F E H P N V V R 4 9 3 .2 5 5 1 3 3 6 R L -2 .7 2 4 G S S D V D Q L G K 5 0 3 .2 4 5 9 2 5 1 R I -0 .3 1 3 L F D V C T V S R 5 4 8 .7 7 4 9 2 4 5 R T -2 .8 3 8 IP I0 0 0 1 7 3 7 5 .2 S E C 2 3 A P ro te in t ra n sp o rt p ro te in S ec 2 3 A 1 .4 1 0 .5 3 A V L N P L C Q V D Y R 7 2 7 .3 8 2 1 2 4 1 K A 0 .2 8 0 A E T E E G P D V L R 6 0 8 .2 9 7 2 4 4 R W 1 .0 2 8 M V V P V A A L F T P L K 6 6 7 .3 1 0 3 2 6 9 R M -0 .8 6 8 H F E A L A N R 4 8 2 .2 5 8 3 2 3 2 K A 0 .4 7 8 F S W N V W P S S R 6 3 3 .3 0 6 2 2 2 6 R L -1 .1 0 2 A p p en d ix 2 . P ro te in s sh o w in g d ec re as ed c o n ce n tr at io n . 448 IP I A cc es si o n N u m b er ID A v g ( 3 ) co rr ec te d i/ w S tD ev A v g (3 ) co rr ec te d i/ w P ep ti d es I d en ti fi ed M C R (M as s ch ar g e ra ti o ) [T h ] C h ar g e S co re L ef t fl an k in g A A R ig h t fl an k in g A A C al ib ra te d m as s re la ti v e er ro r [p p m ] ID M N L T D L L G E L Q R 5 4 4 .2 8 6 8 3 5 8 K D -0 .8 0 7 H L L Q A P V D D A Q E IL H S R 7 2 6 .9 1 1 7 2 7 9 R T 0 .5 1 5 IP I0 0 0 2 1 3 2 7 .3 G R B 2 I so fo rm 1 o f G ro w th f ac to r re ce p to r- b o u n d p ro te in 2 1 .4 1 0 .1 2 A E E M L S K 4 0 4 .1 9 9 9 2 3 6 K Q 1 .3 0 3 A T A D D E L S F K 5 4 8 .7 6 1 8 2 5 3 K R 0 .0 3 9 H D G A F L IR 4 2 2 .2 3 9 6 2 4 0 R Q 0 .1 1 1 E S E S A P G D F S L S V K 7 2 6 .8 4 7 2 5 4 R F 0 .4 6 0 F N S L N E L V D Y H R 8 2 4 .4 2 5 3 2 9 5 R R 0 .0 9 3 F G N D V Q H F K 5 4 6 .2 6 7 6 2 3 8 K V 0 .4 0 8 V L N E E C D Q N W Y K 7 9 9 .3 5 1 9 2 6 1 K A 0 .2 8 0 Q R H D G A F L IR 4 0 8 .9 0 4 5 3 4 4 K E -1 .3 0 1 Y F L W V V K 8 7 5 .9 7 9 1 2 6 3 K V -0 .3 6 3 N Q Q IF L R 8 4 9 .1 1 6 6 3 6 8 R A 2 .3 5 8 D IE Q V P Q Q P T Y V Q A L F D F D P Q E D G E L G F R 1 1 2 7 .8 7 0 1 3 7 6 R R 0 .5 2 4 IP I0 0 6 4 1 1 5 3 .2 G L G 1 G o lg i ap p ar at u s p ro te in 1 p re cu rs o r 1 .4 1 0 .3 2 L L E L Q Y F IS R 6 4 1 .3 6 5 8 2 7 1 R D 3 .0 3 9 V A E L S S D D F H L D R 5 0 1 .9 0 7 3 3 4 8 R H -0 .1 3 8 Q IT Q N T D Y R 5 6 9 .7 7 9 2 2 3 9 R L 2 .8 4 9 II IQ E S A L D Y R 6 6 0 .8 6 2 2 2 8 0 R L 0 .7 2 7 L S S D C E D Q IR 5 5 9 .8 4 4 9 2 4 7 R T 0 .8 2 7 M T A II F S D Y R 6 0 8 .8 0 5 5 2 5 8 K L 0 .9 9 5 IP I0 0 0 0 2 2 1 4 .1 K P N A 2 I m p o rt in s u b u n it al p h a- 2 1 .4 2 0 .1 9 Q D Q IQ Q V V N H G L V P F L V S V L S K 8 1 6 .7 9 0 6 3 6 4 R A -1 .5 3 9 L L G A S E L P IV T P A L R 8 8 5 .5 5 1 2 2 5 7 K V 0 .9 5 5 E K Q P P ID N II R 4 7 8 .7 9 7 3 2 3 1 K D -0 .7 6 3 Y G A V D P L L A L L A V P D M S S L A C G Y L R 8 8 9 .1 3 0 9 3 5 5 K N 1 .5 8 0 A p p en d ix 2 . P ro te in s sh o w in g d ec re as ed c o n ce n tr at io n . 449 IP I A cc es si o n N u m b er ID A v g ( 3 ) co rr ec te d i/ w S tD ev A v g (3 ) co rr ec te d i/ w P ep ti d es I d en ti fi ed M C R (M as s ch ar g e ra ti o ) [T h ] C h ar g e S co re L ef t fl an k in g A A R ig h t fl an k in g A A C al ib ra te d m as s re la ti v e er ro r [p p m ] A S L S L IE K 1 0 3 1 .0 5 5 4 2 3 0 K A 0 .1 4 5 G IN S S N V E N Q L Q A T Q A A R 9 5 0 .9 7 5 2 8 4 K K -2 .2 9 6 T G V V P Q L V K 5 3 6 .8 1 2 1 2 2 8 K R -1 .7 7 9 F V S F L G R 4 1 6 .2 4 4 2 2 4 5 K T -0 .9 9 3 IP I0 0 4 1 2 1 5 4 .4 R T N 3 I so fo rm 4 o f R et ic u lo n -3 1 .4 2 0 .3 5 S E E G H P F K 4 6 5 .7 1 9 1 2 4 2 K A -0 .6 8 5 T Q ID H Y V G IA R 6 8 6 .3 4 3 1 2 8 0 K E 1 .1 3 5 S V IQ A V Q K 4 3 6 .7 6 3 3 2 5 1 K S -1 .2 9 3 Y K T Q ID H Y V G IA R 6 9 5 .8 4 5 5 2 1 0 6 K Y 2 .2 6 4 L F L V E D L V D S L K 6 9 5 .8 9 5 1 2 7 7 R L -0 .3 8 7 IP I0 0 1 4 8 0 6 1 .3 L D H A L 6 A L -l ac ta te d eh y d ro g en as e A -l ik e 6 A 1 .4 2 0 .1 2 L D L V Q R 3 7 2 .2 2 4 3 2 3 5 R N 0 .8 7 3 V IG S G C N L D S A R 1 0 0 7 .9 6 9 9 2 8 2 K K -1 .2 4 7 L V II T A G A R 4 5 7 .2 9 5 4 2 6 3 K Q 0 .6 3 6 IP I0 0 0 1 0 0 9 0 .1 G C L M G lu ta m at e- - cy st ei n e li g as e re g u la to ry su b u n it 1 .4 2 0 .7 3 K IV A IG T S D L D K 6 3 0 .3 6 3 2 2 5 0 K T -1 .7 1 0 IV A IG T S D L D K 6 3 0 .3 6 3 2 2 5 0 K T -1 .7 1 0 Q F D IQ L L T H N D P K 7 8 4 .9 0 4 9 2 3 3 K E -3 .2 7 4 T L N E W S S Q IN P D L V R 8 8 6 .4 5 1 2 8 9 K E -2 .1 5 6 L F IV E S N S S S S T R 7 1 3 .8 6 3 7 2 9 6 K S 1 .4 3 8 IP I0 0 0 3 1 6 2 9 .1 P D C L 3 P h o sd u ci n -l ik e p ro te in 3 1 .4 2 0 .2 4 A Q F IG P L V F G G M N L T R 6 6 6 .3 9 7 5 2 5 3 R E 1 .0 2 1 E S L K E L E E E A E E E Q R 6 1 6 .6 2 1 8 3 4 9 K I -0 .1 5 9 L S E S G A IM T D L E E N P K 5 0 4 .2 9 1 1 3 4 2 K D 0 .2 7 6 K P IE D V L L S S V R 7 1 7 .9 2 7 9 2 4 8 R T -1 .2 9 0 F G E V L E IS G K 7 4 6 .9 3 0 5 2 7 6 K K -0 .7 9 8 IL Q Q S V V K 4 5 7 .7 8 7 1 2 3 8 R T -0 .1 3 1 A p p en d ix 2 . P ro te in s sh o w in g d ec re as ed c o n ce n tr at io n . 450 IP I A cc es si o n N u m b er ID A v g ( 3 ) co rr ec te d i/ w S tD ev A v g (3 ) co rr ec te d i/ w P ep ti d es I d en ti fi ed M C R (M as s ch ar g e ra ti o ) [T h ] C h ar g e S co re L ef t fl an k in g A A R ig h t fl an k in g A A C al ib ra te d m as s re la ti v e er ro r [p p m ] IP I0 0 8 7 3 9 3 5 .1 D K C 1 U n ch ar ac te ri ze d p ro te in D K C 1 1 .4 2 0 .2 5 H G K P T D S T P A T W K 4 7 5 .9 0 7 7 3 3 0 K Q -2 .5 5 6 L D T S Q W P L L L K 6 4 5 .8 7 4 8 2 6 6 R Q 0 .9 6 7 L H N A IE G G T Q L S R 6 9 8 .3 7 0 3 2 7 0 R A -0 .7 2 0 IM L P G V L R 4 4 9 .7 8 1 2 2 2 6 K Y 0 .8 6 9 Q E Y V D Y S E S A K 7 3 2 .8 8 0 9 2 6 2 K T 0 .2 0 7 A G L E S G A E P G D G D S D T T K 8 5 3 .8 7 1 2 2 5 7 K K -0 .4 5 0 K S L P E E D V A E IQ H A E E F L IK P E S K 6 9 2 .3 5 9 8 4 2 9 R V -1 .1 1 7 E V V A E V V K 4 3 6 .7 5 8 5 2 2 6 K A 1 .0 7 7 IP I0 0 2 1 9 3 4 4 .4 H P C A L 1 H ip p o ca lc in - li k e p ro te in 1 1 .4 3 0 .5 7 L S L E E F IR 1 0 3 4 .4 9 8 7 3 7 6 R T 0 .6 9 4 T F D T N G D G T ID F R 4 7 6 .7 9 4 7 2 6 0 R R -0 .5 5 9 S E M L E IV Q A IY K 7 2 0 .3 7 6 9 2 5 3 R M 0 .5 4 8 Q M D T N N D G K L S L E E F IR 4 7 7 .7 6 7 9 2 3 8 R Q -0 .4 6 5 L R P E V L Q D L R 4 1 3 .5 7 9 1 3 3 9 K E 1 .2 3 9 IY A N F F P Y G D A S K 6 6 6 .8 7 8 9 2 6 8 K D -0 .8 4 3 IP I0 0 8 7 1 3 7 2 .1 H E C T D 1 H E C T d o m ai n co n ta in in g 1 1 .4 3 0 .1 7 S S F V F V R 4 2 4 .2 4 1 4 2 3 5 R K 0 .4 7 7 S G L N Q G A IS T L Q S S D IL N L T K 1 0 8 0 .5 8 0 6 2 1 1 1 K E 0 .9 9 6 S T IF Y Y V Q K 5 7 7 .8 1 1 7 2 2 9 R L -1 .6 5 3 V L Q L L R 9 2 6 .4 6 4 3 2 5 1 K N -2 .9 4 1 T N A T N N M N L S R 6 2 1 .3 0 4 1 2 5 7 R S 1 .3 1 8 H G L T E E L L S R 5 8 0 .8 2 1 2 2 3 5 K M -0 .5 3 9 A IV W L Q N R 5 0 0 .2 8 9 7 2 3 0 R R -0 .0 4 7 D L V D K G G D IF L D Q L A R 5 9 2 .3 1 7 7 3 5 9 R L 3 .4 7 3 L F A A Y N E G II N L L E K 8 5 4 .4 7 2 2 9 9 R Y 2 .5 9 6 S G L Q G Y D M S T F IR 7 3 7 .8 5 2 2 2 5 5 K R -1 .6 2 0 S T N V IV D S G G F D E L G G L L K P 1 1 2 3 .5 9 5 9 3 7 2 K L 0 .5 0 9 A p p en d ix 2 . P ro te in s sh o w in g d ec re as ed c o n ce n tr at io n . 451 IP I A cc es si o n N u m b er ID A v g ( 3 ) co rr ec te d i/ w S tD ev A v g (3 ) co rr ec te d i/ w P ep ti d es I d en ti fi ed M C R (M as s ch ar g e ra ti o ) [T h ] C h ar g e S co re L ef t fl an k in g A A R ig h t fl an k in g A A C al ib ra te d m as s re la ti v e er ro r [p p m ] T V A S Q N Q N L P V A K Q V A F D F T K 4 7 8 .2 4 7 5 2 3 0 R V -0 .7 5 1 V A E Q V G ID R 4 9 3 .7 6 6 8 2 6 9 K G -0 .3 6 3 IS E F L K 5 7 0 .2 7 3 4 2 5 0 R L -0 .1 4 2 IP I0 0 4 1 2 7 9 2 .2 B T F 3 L 4 T ra n sc ri p ti o n fa ct o r B T F 3 h o m o lo g 4 1 .4 4 0 .5 2 L A V N N IA G IE E V N M IK D D G T V IH F N N P K 7 6 7 .1 4 9 8 4 4 0 K V 1 .0 4 1 D D G T V IH F N N P K 6 7 8 .8 3 2 5 3 K V -1 .6 1 2 A P K P E D ID E E D D D V P D L V E N F D E A S K N E A N 1 1 2 0 .4 8 9 3 3 5 8 K - -1 .5 7 7 A P K P E D ID E E D D D V P D L V E N F D E A S K 9 7 7 .7 6 9 4 3 6 0 K N 0 .3 9 4 L A V N N IA G IE E V N M IK 8 7 2 .4 6 8 5 2 6 6 K D -1 .3 6 3 L Q A Q V R 6 6 3 .8 5 3 4 2 4 2 K S 0 .4 3 2 K L A E Q F P R 6 9 6 .3 6 4 3 7 5 K E 0 .7 3 3 L A E Q F P R 4 3 0 .7 3 5 1 2 5 3 K Q -0 .0 2 3 IP I0 0 0 0 7 8 1 8 .3 C P S F 3 C le av ag e an d p o ly ad en y la ti o n sp ec if ic it y f ac to r su b u n it 3 1 .4 4 0 .1 3 G L IP V F A L G R 9 9 7 .8 0 5 5 3 5 3 R Q -2 .1 5 9 T A N L N L E T R 5 1 6 .2 7 6 8 2 3 6 K T -1 .7 4 3 L L Y T G D F S R 5 3 6 .2 7 7 6 2 4 3 K Q 0 .7 1 9 L Y E A L T P V H 6 6 0 .8 5 1 9 2 6 3 K K 1 .7 0 2 Q IN IN N P F V F K 6 6 7 .3 6 8 1 2 3 6 K H 1 .6 8 7 IP I0 0 8 4 5 3 3 9 .1 H S P A 1 A ;H S P A 1 B h ea t sh o ck 7 0 k D a p ro te in 1 A 1 .4 5 0 .4 5 L D K A Q IH D L V L V G G S T R 9 1 7 .5 3 0 9 2 9 2 K I -2 .7 9 3 F G D P V V Q S D M K 6 1 9 .7 9 0 4 2 6 2 K H 0 .8 3 1 T L S S S T Q A S L E ID S L F E G ID F Y T S IT R 9 9 4 .4 9 6 9 3 4 6 R A 4 .0 7 4 N A V IT V P A Y F N D S Q R 8 4 7 .9 3 1 6 2 6 3 K Q 3 .1 8 3 V Q V S Y K 6 8 8 .8 6 1 1 2 5 4 R D -0 .1 7 7 A p p en d ix 2 . P ro te in s sh o w in g d ec re as ed c o n ce n tr at io n . 452 IP I A cc es si o n N u m b er ID A v g ( 3 ) co rr ec te d i/ w S tD ev A v g (3 ) co rr ec te d i/ w P ep ti d es I d en ti fi ed M C R (M as s ch ar g e ra ti o ) [T h ] C h ar g e S co re L ef t fl an k in g A A R ig h t fl an k in g A A C al ib ra te d m as s re la ti v e er ro r [p p m ] N Q V A L N P Q N T V F D A K 8 2 9 .9 2 8 8 2 8 0 K R 0 .3 7 6 IT IT N D K G R 5 0 9 .2 8 7 8 2 5 4 K L 0 .7 8 7 S IN P D E A V A Y G A A V Q A A IL M G D K 7 6 8 .7 2 3 1 3 6 4 K S -0 .0 3 7 IP I0 0 3 0 4 9 2 5 .5 H S P A 1 A ;H S P A 1 B H ea t sh o ck 7 0 k D a p ro te in 1 1 .4 5 0 .4 5 L D K A Q IH D L V L V G G S T R 9 1 7 .5 3 0 9 2 9 2 K I -2 .7 9 3 F G D P V V Q S D M K 6 1 9 .7 9 0 4 2 6 2 K H 0 .8 3 1 T L S S S T Q A S L E ID S L F E G ID F Y T S IT R 9 9 4 .4 9 6 9 3 4 6 R A 4 .0 7 4 N A V IT V P A Y F N D S Q R 8 4 7 .9 3 1 6 2 6 3 K Q 3 .1 8 3 V Q V S Y K 6 8 8 .8 6 1 1 2 5 4 R D -0 .1 7 7 N Q V A L N P Q N T V F D A K 8 2 9 .9 2 8 8 2 8 0 K R 0 .3 7 6 IT IT N D K G R 5 0 9 .2 8 7 8 2 5 4 K L 0 .7 8 7 S IN P D E A V A Y G A A V Q A A IL M G D K 7 6 8 .7 2 3 1 3 6 4 K S -0 .0 3 7 E IA E A Y L G Y P V T N A V IT V P A Y F N D S Q R 1 0 0 1 .1 7 1 5 3 6 5 K Q 1 .4 4 2 E L E Q V C N P II S G L Y Q G A G G P G P G G F G A Q G P K 1 0 1 9 .1 7 0 4 3 9 5 K G 0 .3 2 1 V E II A N D Q G N R 9 8 7 .0 0 7 5 2 9 0 K I 3 .2 3 6 S T L E P V E K 5 3 3 .3 1 1 2 7 3 R - 0 .4 9 1 G G S G S G P T IE E V D 6 3 1 .2 8 4 7 2 7 4 K R -1 .7 0 7 II N E P T A A A IA Y G L D R 8 4 4 .4 5 4 3 2 8 9 R T -0 .3 8 6 A T A G D T H L G G E D F D N R 8 3 8 .3 7 1 8 2 6 3 K L 2 .9 6 1 A F Y P E E IS S M V L T K 8 0 7 .9 0 8 1 2 7 5 K M -0 .0 5 5 D A G V IA G L N V L R 5 9 9 .3 5 0 5 2 8 5 K I -0 .5 3 0 A Q IH D L V L V G G S T R 7 3 6 .4 1 9 6 2 8 4 K I -0 .3 7 2 V Q V S Y K G E T K 5 7 5 .8 2 7 9 2 3 3 K A -1 .0 3 9 L S K E E IE R 5 0 8 .2 9 4 2 5 7 R M -0 .1 9 2 L V N H F V E E F K R 4 7 3 .2 5 7 2 3 3 6 R K 0 .2 9 6 D N N L L G R 7 3 2 .4 0 5 1 2 5 0 K L 5 .3 7 7 IT IT N D K 6 8 7 .8 5 9 4 2 4 7 K E 0 .7 3 3 A p p en d ix 2 . P ro te in s sh o w in g d ec re as ed c o n ce n tr at io n . 453 IP I A cc es si o n N u m b er ID A v g ( 3 ) co rr ec te d i/ w S tD ev A v g (3 ) co rr ec te d i/ w P ep ti d es I d en ti fi ed M C R (M as s ch ar g e ra ti o ) [T h ] C h ar g e S co re L ef t fl an k in g A A R ig h t fl an k in g A A C al ib ra te d m as s re la ti v e er ro r [p p m ] L V N H F V E E F K 6 3 1 .3 3 2 1 2 5 4 R R -0 .7 8 6 M K E IA E A Y L G Y P V T N A V IT V P A Y F N D S Q R 1 0 1 9 .4 9 2 3 2 6 7 R E 2 .5 6 8 S A V E D E G L K 4 7 4 .2 3 7 5 2 6 7 K G -0 .3 4 3 N Q V A L N P Q N T V F D A K R 9 0 7 .9 8 2 9 2 6 6 K L 3 .6 0 3 K F G D P V V Q S D M K 3 7 2 .2 2 3 3 2 4 0 K F 0 .0 7 5 A R F E E L C S D L F R 7 7 7 .8 9 5 3 2 5 0 R S 3 .6 7 3 H W P F Q V IN D G D K P K 5 6 0 .9 5 5 8 3 4 0 K V 2 .8 8 1 N A L E S Y A F N M K 6 5 2 .3 0 1 7 2 7 8 K S -2 .2 4 6 S E N V Q D L L L L D V A P L S L G L E T A G G V M T A L IK 1 0 6 0 .9 2 1 1 3 5 8 K R 1 .1 2 4 T T P S Y V A F T D T E R 7 4 4 .3 5 3 4 2 9 5 R L -1 .5 3 5 Y K A E D E V Q R 5 6 9 .2 8 2 2 2 4 4 K E 3 .9 1 0 L L Q D F F N G R 5 5 5 .2 9 0 2 2 5 5 K D -0 .7 7 8 F E E L C S D L F R 6 5 8 .3 0 2 6 2 4 1 R S -0 .8 6 4 Q T Q IF T T Y S D N Q P G V L IQ V Y E G E R 9 2 9 .4 6 0 3 3 5 5 K A 0 .6 8 5 M V Q E A E K Y K 5 6 9 .3 0 5 6 2 2 7 R A 3 .2 0 6 IP I0 0 0 0 8 7 3 2 .1 T R A B D I so fo rm 1 o f T ra B d o m ai n -c o n ta in in g p ro te in 1 .4 5 0 .6 8 T V T Q L V A E D G S R 6 3 8 .3 3 3 1 2 6 6 R V 3 .8 4 5 A IA A L S F W Q K 5 6 7 .8 1 9 2 2 4 5 R V 0 .2 2 3 IP I0 0 0 2 6 1 5 6 .1 H C L S 1 H em at o p o ie ti c li n ea g e ce ll -s p ec if ic p ro te in 1 .4 5 0 .7 9 S A V G F D Y K 4 4 3 .7 1 9 1 2 3 0 K G 0 .4 8 2 R S P E A P Q P V IA M E E P A V P A P L P K 8 0 8 .7 7 1 5 3 5 1 K K 1 .4 5 9 S A V G F N E M E A P T T A Y K 8 6 6 .3 9 9 8 2 8 6 K K 1 .2 7 8 T T P IE A A S S G A R 5 8 0 .7 9 9 3 2 5 0 K G 0 .4 2 7 N K V S E E H D V L R 6 6 3 .3 4 6 9 2 4 3 R K 4 .5 1 9 S A V G H E Y V A E V E K 7 0 9 .3 5 0 5 2 6 0 K H -1 .7 8 1 A p p en d ix 2 . P ro te in s sh o w in g d ec re as ed c o n ce n tr at io n . 454 IP I A cc es si o n N u m b er ID A v g ( 3 ) co rr ec te d i/ w S tD ev A v g (3 ) co rr ec te d i/ w P ep ti d es I d en ti fi ed M C R (M as s ch ar g e ra ti o ) [T h ] C h ar g e S co re L ef t fl an k in g A A R ig h t fl an k in g A A C al ib ra te d m as s re la ti v e er ro r [p p m ] S P E A P Q P V IA M E E P A V P A P L P K 7 5 6 .7 3 5 8 3 4 0 R K -1 .0 3 8 T E H IN IH Q L R 6 3 0 .8 4 5 2 3 5 R N 1 .9 6 8 V S E E H D V L R 5 6 8 .3 2 1 2 5 4 K S -0 .9 6 4 G F G G Q Y G IQ K 7 5 5 .9 1 1 1 2 6 8 R L 9 .2 9 8 IP I0 0 0 0 9 3 1 6 .3 P P IE I so fo rm A o f P ep ti d y l- p ro ly l ci s- tr an s is o m er as e E 1 .4 5 0 .5 6 T A E N F R 5 3 5 .3 1 0 4 2 4 3 R R -1 .6 9 1 T D W L D G K 4 1 7 .7 0 3 3 2 3 6 K H 0 .3 7 2 V L H A A F IP F G D IT D IQ IP L D Y E T E K 9 4 9 .1 6 1 5 3 3 6 K H 0 .6 6 3 IP I0 0 5 4 9 8 5 8 .5 P C T K 1 S er in e/ th re o n in e- p ro te in k in as e P C T A IR E - 1 1 .4 6 0 .1 1 L A D F G L A R 8 2 6 .8 9 6 9 2 6 7 K S 0 .1 8 5 L H F IF R 8 7 8 .9 3 7 9 2 6 0 R D 3 .6 3 3 L G E G T Y A T V Y K 6 0 1 .3 0 8 8 2 3 4 K G 0 .0 4 2 A L K E IR 3 6 5 .2 3 5 6 2 3 5 R K 2 .5 4 9 IP I0 0 0 2 4 4 2 5 .5 K IA A 0 6 6 4 P u ta ti v e eu k ar y o ti c tr an sl at io n in it ia ti o n f ac to r 3 s u b u n it 1 .4 6 0 .1 4 H Y IL D L L R 5 6 0 .2 9 3 1 2 2 8 R L 0 .5 6 0 V H S D F T A A A T R 5 9 1 .3 0 1 2 2 3 8 K G -3 .9 7 0 V A L S H H L V A R 6 9 5 .8 9 5 8 2 7 5 R L 1 .1 2 2 A V G S IS S T A F D IR 6 6 5 .3 5 8 4 2 7 6 K F -0 .5 4 6 V T A Q S II P G IL E R 6 9 8 .9 1 3 3 2 4 9 R D 2 .5 6 2 Y L L F M K 4 0 7 .7 3 0 4 2 2 8 R L 0 .4 7 6 IG IG E L IT R 4 8 6 .2 9 8 2 6 9 K S 0 .2 6 2 V V E E P Y T V R 5 4 6 .2 8 9 5 2 4 5 R E -0 .4 7 7 C L T Q Q A V A L Q R 6 4 4 .3 4 6 2 4 7 K T 1 .0 1 0 V L E L V L R 4 2 1 .2 7 7 8 2 5 4 K S -0 .6 8 8 Q V S IT A S T R 6 6 8 .3 4 0 1 2 4 7 K L 0 .5 5 6 A p p en d ix 2 . P ro te in s sh o w in g d ec re as ed c o n ce n tr at io n . 455 IP I A cc es si o n N u m b er ID A v g ( 3 ) co rr ec te d i/ w S tD ev A v g (3 ) co rr ec te d i/ w P ep ti d es I d en ti fi ed M C R (M as s ch ar g e ra ti o ) [T h ] C h ar g e S co re L ef t fl an k in g A A R ig h t fl an k in g A A C al ib ra te d m as s re la ti v e er ro r [p p m ] A S D A F H F F Q S G Q A K 5 1 4 .2 4 3 3 5 6 K V -1 .1 3 3 T G IQ V L L K 4 3 6 .2 8 2 6 2 4 0 K E -1 .8 0 1 F T A P S M A S V L E Q L N V IN G IL F IP L S Q K 9 7 7 .5 3 7 3 3 7 2 K D -3 .7 7 7 F L E N A L A V S T K 5 9 6 .8 3 1 7 2 7 0 R Y -1 .1 1 8 S V E G L Q E G S V L R 6 4 0 .3 5 2 1 2 8 2 R V 1 .9 7 3 Q E L V D A F V E H R 6 7 4 .8 5 0 5 2 6 2 R Y -0 .8 7 7 L H Y IM G D Y A E A L S N Q Q K 6 6 0 .9 8 7 1 3 3 4 R A -0 .6 3 1 D F G G D V A A Y V A P T N D L N G V R 1 0 2 5 .9 9 2 3 2 6 5 K T -3 .6 4 1 F L S H S L V E L L N Q IS P T F K K 5 5 1 .0 6 3 7 4 4 0 R N -0 .1 4 1 D W N E E L Q T T R 6 4 6 .3 0 0 1 2 5 5 R E 0 .9 9 5 Y L E L L E R 4 7 1 .2 7 3 2 2 4 2 R T 0 .7 7 1 A V L M S E R 4 0 6 .2 2 4 5 2 3 5 K V -0 .3 5 7 N F A V L Q K 4 1 0 .2 3 9 2 2 6 K K 0 .2 2 8 N R P P G A A D N T A W A V M T P Q E L W K 8 1 8 .4 0 6 5 3 3 7 K N 0 .4 0 3 IP I0 0 0 5 9 1 3 5 .1 P P P 1 R 1 4 A I so fo rm 1 o f P ro te in p h o sp h at as e 1 re g u la to ry s u b u n it 1 4 A 1 .4 7 0 .0 8 W ID G R L E E L Y R 5 7 8 .2 5 4 2 2 6 K N 3 .4 3 9 L E E L Y R 8 4 5 .4 2 5 6 2 6 9 K E 0 .2 5 6 K IQ G L L K 9 3 3 .0 2 5 8 2 7 7 R T -2 .0 6 2 L Q G L H R 3 6 2 .2 1 6 6 2 3 6 K Q -0 .6 0 9 IP I0 0 0 2 0 9 8 5 .3 E P 3 0 0 H is to n e ac et y lt ra n sf er as e p 3 0 0 1 .4 8 0 .2 2 D A F L T L A R 4 5 3 .7 5 3 8 2 3 2 R D -2 .8 3 9 L V Q A IF P T P D P A A L K 7 9 0 .9 5 6 8 2 2 5 K D 0 .6 5 8 L S E L L R 7 0 2 .0 2 4 2 3 4 8 K S -0 .0 7 6 Q A L M P T L E A L Y R 7 0 3 .3 7 9 8 2 3 8 R Q 0 .7 7 4 D A T Y Y S Y Q N R 6 4 0 .7 8 1 1 2 3 6 R Y 0 .3 0 7 A p p en d ix 2 . P ro te in s sh o w in g d ec re as ed c o n ce n tr at io n . 456 IP I A cc es si o n N u m b er ID A v g ( 3 ) co rr ec te d i/ w S tD ev A v g (3 ) co rr ec te d i/ w P ep ti d es I d en ti fi ed M C R (M as s ch ar g e ra ti o ) [T h ] C h ar g e S co re L ef t fl an k in g A A R ig h t fl an k in g A A C al ib ra te d m as s re la ti v e er ro r [p p m ] IP I0 0 0 2 3 3 3 9 .2 C R E B B P C R E B -b in d in g p ro te in 1 .4 8 0 .2 2 S A L S S E L S L V G D T T G D T L E K 1 0 1 2 .0 0 8 4 2 6 8 R F 0 .3 7 9 D A F L T L A R 4 5 3 .7 5 3 8 2 3 2 R D -2 .8 3 9 L V Q A IF P T P D P A A L K 7 9 0 .9 5 6 8 2 2 5 K D 0 .6 5 8 L S E L L R 7 0 2 .0 2 4 2 3 4 8 K S -0 .0 7 6 Q A L M P T L E A L Y R 7 0 3 .3 7 9 8 2 3 8 R Q 0 .7 7 4 IP I0 0 0 0 7 8 1 1 .1 C D K 4 C el l d iv is io n p ro te in k in as e 4 1 .5 0 0 .3 6 L M D V C A T S R 5 2 6 .7 4 6 5 2 4 6 R T -0 .6 8 7 L A D F G L A R 8 2 6 .8 9 6 9 2 6 7 K S 0 .1 8 5 R L E A F E H P N V V R 1 0 2 4 .0 7 2 7 2 9 7 R K -2 .4 7 9 IF D L IG L P P E D D W P R 8 5 7 .3 9 7 5 2 9 4 K D -0 .3 1 8 V T L V F E H V D Q D L R 6 3 8 .8 3 8 2 2 5 5 K D 0 .7 1 3 IP I0 0 0 0 4 3 5 8 .4 P Y G B G ly co g en p h o sp h o ry la se b ra in f o rm 1 .5 0 0 .0 7 G L A G L G D V A E V R 5 7 8 .8 1 9 7 2 6 7 R K -0 .1 7 5 L L P L V S D E V F IR 7 0 0 .9 1 2 5 2 4 1 K D 1 .7 8 0 V S L A E K 6 5 2 .3 9 7 4 1 2 9 R V 0 .7 3 0 L Q D F N V G D Y IE A V L D R 9 3 3 .9 6 5 8 2 8 7 K N 0 .1 5 9 L V T S IG D V V N H D P V V G D R 6 3 1 .3 3 4 6 3 4 3 K L 0 .7 7 4 N L A E N IS R 5 6 4 .2 9 2 3 2 6 9 K V -0 .7 9 5 V IP A A D L S Q Q IS T A G T E A S G T G N M K 8 1 6 .4 0 9 4 3 4 0 K F 1 .7 9 6 V L Y P N D N F F E G K 7 2 1 .8 5 1 4 2 3 6 R E -0 .0 3 0 V IF L E N Y R 5 6 7 .8 1 5 2 4 0 K D 1 .5 9 3 IG E E F L T D L S Q L K 7 4 6 .8 9 9 3 2 9 0 K K 0 .9 4 8 IP I0 0 0 2 3 0 6 4 .1 C 6 o rf 6 6 U P F 0 2 4 0 p ro te in C 6 o rf 6 6 1 .5 2 0 .9 1 D V Y V D S K D P V S S L Q V K 4 8 9 .2 7 5 2 2 3 2 K L 2 .4 6 1 D V N S L L K 4 8 2 .7 6 1 3 2 3 7 K C 0 .7 0 9 L L S F L K 8 2 9 .7 5 9 4 3 4 5 R D -3 .1 3 1 L F P E T W T A E K 6 1 1 .3 1 0 9 2 2 7 K I -0 .7 2 1 A p p en d ix 2 . P ro te in s sh o w in g d ec re as ed c o n ce n tr at io n . 457 IP I A cc es si o n N u m b er ID A v g ( 3 ) co rr ec te d i/ w S tD ev A v g (3 ) co rr ec te d i/ w P ep ti d es I d en ti fi ed M C R (M as s ch ar g e ra ti o ) [T h ] C h ar g e S co re L ef t fl an k in g A A R ig h t fl an k in g A A C al ib ra te d m as s re la ti v e er ro r [p p m ] D H H F D M IN IK 4 2 3 .8 7 3 1 3 3 8 K S 0 .3 8 4 E Q IS L Y P E V K 6 0 3 .3 2 3 5 2 3 7 R G -1 .7 6 1 IM Q E Y Q L E Q K 6 3 0 .3 7 4 4 2 1 0 4 R H 0 .4 9 6 IS IV E A L T L L N N H K 5 2 2 .3 0 6 4 3 4 3 K L -0 .8 0 8 H P S T N S L L R 7 0 5 .4 2 1 8 2 9 5 K G 2 .2 5 7 N F N L E N R 5 1 8 .8 0 2 7 2 3 7 K E 0 .6 1 0 IP I0 0 0 6 9 0 8 4 .2 T R R A P I so fo rm 1 o f T ra n sf o rm at io n /t ra n sc ri p ti o n d o m ai n -a ss o ci at ed p ro te in 1 .5 4 0 .3 4 L F D E S IL IG S G Y T A R 8 2 1 .4 2 8 9 2 3 0 K E 0 .9 6 8 L L L N L IS Q V G R 6 1 3 .3 8 5 6 2 5 9 K V 0 .7 6 7 F V D F N D P N F G D E L K 8 2 8 .8 8 1 7 2 6 5 R A 0 .8 3 1 V L Q L L R 9 2 6 .4 6 4 3 2 5 1 K N -2 .9 4 1 N F IQ A IL T S L IE K 7 4 5 .4 3 6 8 2 8 7 K S 2 .2 9 5 IP I0 0 1 7 1 4 4 5 .1 A T A D 1 A T P as e fa m il y A A A d o m ai n -c o n ta in in g p ro te in 1 1 .5 4 0 .5 3 F H IN Q P A L K 5 3 4 .3 0 3 9 2 2 7 R Q 0 .7 0 8 L Q P S II F ID E ID S F L R 9 5 3 .5 2 2 7 2 7 8 K N 3 .1 7 0 F IN L Q P S T L T D K 6 8 8 .8 7 7 2 2 3 7 R W 3 .7 1 3 IP I0 0 8 7 3 5 8 6 .2 S A P S 1 I so fo rm 2 o f S A P S d o m ai n f am il y m em b er 1 1 .5 5 0 .9 8 L Y G F L Q S T G S L N P L L A S F F S K 7 6 4 .0 7 4 1 3 4 5 R V -2 .1 2 9 V S G E E E L H T G P P A P Q G P L S V P Q G L P T Q S L A S P P A R 8 7 6 .2 0 4 3 4 3 2 R D -0 .9 7 4 Q D V V N W L N E E K 6 8 7 .3 3 7 9 2 3 3 R I -1 .0 8 2 S Q D P T P P S A P Q E A T E G S K 9 1 3 .9 2 5 2 2 7 9 R V 1 .1 1 3 G P N A E Q L R 4 4 2 .7 3 3 2 4 0 K Q 0 .2 5 8 IL T S W E E N D R 6 3 1 .8 0 3 8 2 3 1 R V -0 .9 4 4 V A G A L V Q N T E K 5 6 5 .3 1 4 6 2 5 4 R G 0 .4 6 2 E L P S E Q Q E Q W E A F V S G P L A E 9 1 5 .7 8 3 3 3 4 2 K N -0 .8 9 6 A p p en d ix 2 . P ro te in s sh o w in g d ec re as ed c o n ce n tr at io n . 458 IP I A cc es si o n N u m b er ID A v g ( 3 ) co rr ec te d i/ w S tD ev A v g (3 ) co rr ec te d i/ w P ep ti d es I d en ti fi ed M C R (M as s ch ar g e ra ti o ) [T h ] C h ar g e S co re L ef t fl an k in g A A R ig h t fl an k in g A A C al ib ra te d m as s re la ti v e er ro r [p p m ] T N K K IP I0 0 3 3 9 2 6 9 .1 H S P A 6 H ea t sh o ck 7 0 k D a p ro te in 6 1 .5 5 0 .4 7 IT IT N D K G R 5 0 9 .2 8 7 8 2 5 4 K L 0 .7 8 7 S T L E P V E K 5 3 3 .3 1 1 2 7 3 R - 0 .4 9 1 G V P Q IE V T F D ID A N G IL S V T A T D R 8 4 4 .4 3 3 8 3 3 2 R S -5 .1 2 9 II N E P T A A A IA Y G L D R 8 4 4 .4 5 4 3 2 8 9 R T -0 .3 8 6 A T A G D T H L G G E D F D N R 8 3 8 .3 7 1 8 2 6 3 K L 2 .9 6 1 L L Q D F F N G K E L N K 7 8 3 .4 1 8 8 2 5 3 K S -1 .4 8 6 D N N L L G R 7 3 2 .4 0 5 1 2 5 0 K L 5 .3 7 7 IT IT N D K 6 8 7 .8 5 9 4 2 4 7 K E 0 .7 3 3 A R F E E L C S D L F R 7 7 7 .8 9 5 3 2 5 0 R S 3 .6 7 3 T T P S Y V A F T D T E R 7 4 4 .3 5 3 4 2 9 5 R L -1 .5 3 5 F E E L C S D L F R 6 5 8 .3 0 2 6 2 4 1 R S -0 .8 6 4 L L Q D F F N G K 5 4 4 .2 9 6 5 2 6 3 K E -1 .0 2 0 IP I0 0 2 9 4 8 7 9 .1 R A N G A P 1 R an G T P as e- ac ti v at in g p ro te in 1 1 .5 7 0 .1 5 E IE D F D S L E A L R 5 2 2 .8 0 7 6 2 5 0 K H 0 .1 0 4 S S V L IA Q Q T D T S D P E K 8 5 9 .9 2 6 8 2 9 0 K V 0 .7 7 0 G A V A IA D A IR 8 7 1 .9 7 3 3 2 7 2 R T -1 .3 1 9 T Q V A G G Q L S F K 5 6 8 .3 0 9 4 2 4 5 K G 0 .7 8 7 A F N S S S F N S N T F L T R 8 4 9 .9 1 4 5 2 5 3 K L 1 .8 1 4 L E G N T V G V E A A R 4 5 3 .2 6 4 2 2 7 2 R L 1 .1 8 5 H S L L Q T L Y K 5 5 1 .8 1 7 2 5 5 R V 1 .0 8 2 V IN L N D N T F T E K 7 0 7 .3 6 8 2 5 6 R G -2 .0 4 6 S S A C F T L Q E L K 6 4 2 .3 1 9 3 2 5 0 K L 1 .0 1 5 IP I0 0 0 2 1 9 5 4 .1 G B F 1 G o lg i- sp ec if ic b re fe ld in A -r es is ta n ce g u an in e n u cl eo ti d e ex ch an g e fa ct o r 1 1 .5 9 0 .3 2 S E D T T G P IT G L A L T S V N K 9 0 2 .4 7 1 6 2 7 4 R F 0 .9 1 6 A p p en d ix 2 . P ro te in s sh o w in g d ec re as ed c o n ce n tr at io n . 459 IP I A cc es si o n N u m b er ID A v g ( 3 ) co rr ec te d i/ w S tD ev A v g (3 ) co rr ec te d i/ w P ep ti d es I d en ti fi ed M C R (M as s ch ar g e ra ti o ) [T h ] C h ar g e S co re L ef t fl an k in g A A R ig h t fl an k in g A A C al ib ra te d m as s re la ti v e er ro r [p p m ] N IM E A M L Q L F R 6 8 6 .3 6 5 2 2 5 0 K A 0 .9 8 5 L L E A F T E R 4 8 9 .7 6 5 7 2 3 9 R W -0 .0 8 7 L L E N IS P A D V G G M E E T R 9 1 8 .9 5 5 4 2 9 4 K M -4 .3 7 3 A L L V H D L Q K 5 1 8 .8 1 1 2 2 7 R L 0 .8 1 4 L S E L L R 7 0 2 .0 2 4 2 3 4 8 K S -0 .0 7 6 R L S E L L R 7 3 6 .3 6 5 5 2 4 2 K D -0 .6 1 1 M Q A L T Y L Q R 5 6 2 .2 9 9 3 2 5 1 R A -0 .5 4 8 IP I0 0 0 1 2 7 2 8 .1 A C S L 1 I so fo rm 1 o f L o n g -c h ai n -f at ty -a ci d -- C o A l ig as e 1 1 .6 0 0 .4 3 G IT L H P E L F S ID N G L L T P T M K 7 6 6 .4 1 2 3 3 3 1 K A 0 .2 2 7 L L L E G V E N K 5 0 7 .7 9 4 3 2 4 0 K L -1 .7 5 8 V L T F L R 7 6 0 .9 2 4 2 2 9 6 K I 2 .4 2 6 S A L L D S D E P L V Y F Y D D V T T L Y E G F Q R 1 0 1 9 .4 8 4 4 3 5 4 R G -1 .3 1 6 IP I0 0 0 1 0 3 6 8 .6 K IF 2 A I so fo rm 1 o f K in es in -l ik e p ro te in K IF 2 A 1 .6 3 0 .7 7 L T Q V L R 3 6 5 .2 3 2 7 2 3 2 K D -2 .2 6 0 L Q Q Q E L R 4 5 7 .7 5 5 8 2 2 7 R E -1 .2 0 1 D S F IG E N S R 5 1 2 .7 3 6 7 2 4 9 R T -2 .0 7 2 A T C F A Y G Q T G S G K 6 7 4 .3 0 4 4 2 6 1 K T 0 .9 9 3 F S L ID L A G N E R 6 1 7 .8 2 6 2 6 4 K G 1 .4 8 5 IP I0 0 3 0 5 2 8 9 .2 K IF 1 1 K in es in -l ik e p ro te in K IF 1 1 1 .6 7 0 .8 4 G L E E IT V H N K D E V Y Q IL E K 5 9 9 .8 2 4 8 2 5 0 K K -0 .5 3 2 L M N L W T E R 5 3 1 .7 7 3 1 2 3 4 K F -1 .2 4 6 T V L Q E L IN V L K 6 3 5 .3 9 3 3 2 7 1 K T 1 .0 2 7 IG A V E E E L N R 5 6 5 .2 9 5 4 2 5 6 K V -1 .3 1 0 E E Y IT S A L E S T E E K 8 1 4 .8 8 0 9 2 9 1 K L 0 .1 2 2 V IT A L V E R 4 5 0 .7 7 9 2 4 6 R T -0 .6 5 1 A p p en d ix 2 . P ro te in s sh o w in g d ec re as ed c o n ce n tr at io n . 460 IP I A cc es si o n N u m b er ID A v g ( 3 ) co rr ec te d i/ w S tD ev A v g (3 ) co rr ec te d i/ w P ep ti d es I d en ti fi ed M C R (M as s ch ar g e ra ti o ) [T h ] C h ar g e S co re L ef t fl an k in g A A R ig h t fl an k in g A A C al ib ra te d m as s re la ti v e er ro r [p p m ] G IN T L E R 4 0 1 .7 2 4 4 2 2 6 R S -0 .1 2 8 S K V E E T T E H L V T K 5 0 0 .9 3 5 2 3 4 8 R S 0 .3 3 0 IL Q D S L G G R 7 6 6 .0 8 8 2 3 5 0 R R 0 .1 6 5 A V D Q H N A E A Q D IF G K 5 4 8 .2 6 5 3 3 7 K N -1 .1 1 2 L T D N G T E F S V K 6 0 5 .8 0 1 7 2 4 5 K V 0 .7 0 5 N L N S L F N N M E E L IK 8 3 9 .9 2 6 4 2 4 1 K D -1 .2 1 8 L N L V D L A G S E N IG R 7 3 5 .8 9 7 8 2 6 3 K S -2 .0 0 3 T D L L S S L E M IL S P T V V S IL K 7 2 5 .7 4 6 1 3 5 1 K I 1 .7 1 3 E A G N IN Q S L L T L G R 7 4 3 .4 0 4 7 2 4 4 R V -0 .2 5 6 E T T ID G E E L V K 6 1 7 .3 1 3 6 2 3 1 K I -1 .0 2 8 T S II A T IS P A S L N L E E T L S T L E Y A H R 9 4 4 .1 6 6 6 3 2 6 R A 0 .2 3 7 IP I0 0 0 0 8 4 7 5 .1 H M G C S 1 H y d ro x y m et h y lg lu ta ry l- C o A s y n th as e cy to p la sm ic 1 .6 8 0 .0 7 Y T IG L G Q A K 4 7 9 .2 5 3 1 2 5 3 R Q -0 .9 3 5 M L L N D F L N D Q N R 8 3 0 .4 4 8 5 3 5 5 R Y 1 .1 3 2 A S S E L F S Q K 4 9 8 .7 5 2 9 2 4 6 K T -1 .2 7 2 G T H M Q H A Y D F Y K P D M L S E Y P IV D G K 7 3 6 .3 4 2 4 4 4 1 R L -0 .6 9 2 N N L S Y D C IG R 6 0 9 .2 8 8 1 2 4 3 R L 1 .1 4 9 T G V A P D V F A E N M K 6 8 9 .8 3 8 5 2 6 0 R L 1 .8 2 0 L S IQ C Y L S A L D R 7 1 9 .8 7 0 8 2 7 5 K C -1 .4 1 9 L E V G T E T II D K 6 0 6 .8 3 7 8 2 6 0 K R 0 .7 3 5 R P T P N D D T L D E G V G L V H S N I A T E H IP S P A K 7 9 5 .9 0 3 8 4 4 3 R K 0 .0 5 1 V T Q D A T P G S A L D K 6 5 1 .8 3 0 1 2 7 2 K I -0 .7 8 8 IP I0 0 0 3 2 8 7 9 .1 T A F 9 A d en y la te k in as e is o en zy m e 6 1 .8 9 0 .1 1 M L L P N IL L T G T P G V G K 5 0 1 .7 7 1 8 2 3 7 R S 0 .1 4 2 T D T N V L Y E R 8 8 3 .4 2 2 2 1 0 6 K I 1 .4 3 8 Y IN V G D L A R 5 1 0 .7 7 8 1 2 5 7 K E 1 .0 6 6 A p p en d ix 2 . P ro te in s sh o w in g d ec re as ed c o n ce n tr at io n . 461 IP I A cc es si o n N u m b er ID A v g ( 3 ) co rr ec te d i/ w S tD ev A v g (3 ) co rr ec te d i/ w P ep ti d es I d en ti fi ed M C R (M as s ch ar g e ra ti o ) [T h ] C h ar g e S co re L ef t fl an k in g A A R ig h t fl an k in g A A C al ib ra te d m as s re la ti v e er ro r [p p m ] V V D E L D N Q M R 6 0 9 .7 9 2 5 2 4 3 R E -0 .8 0 1 IP I0 0 2 2 1 1 0 8 .5 T Y M S T h y m id y la te sy n th as e 2 .2 9 0 .8 8 R P L P P A A Q E R 1 0 4 0 .4 6 9 6 2 8 9 K G -1 .9 6 3 D F L D S L G F S T R 6 2 9 .3 0 8 8 2 5 5 R E -0 .9 0 3 P V A G S E L P R 9 0 5 .5 2 5 9 2 4 8 R K -1 .3 8 2 ID D F K A E D F Q IE G Y N P H P T IK 6 2 0 .0 5 5 1 4 2 5 K M -0 .2 4 8 Y S L R D E F P L L T T K 6 8 8 .8 2 8 9 2 7 1 R R 1 .9 9 3 E E G D L G P V Y G F Q W R 8 2 6 .8 8 8 5 2 7 3 R H -1 .0 5 1 T G T G T L S V F G M Q A R 7 1 6 .3 7 2 2 5 9 R Y 0 .7 7 6 D A E P R P P H G E L Q Y L G Q IQ H I L R 4 1 3 .2 4 9 7 2 4 0 K K 0 .2 0 1 IP I0 0 0 6 5 6 7 1 .1 U C K 2 I so fo rm 1 o f U ri d in e- cy ti d in e k in as e 2 2 .4 0 0 .8 3 T V Q IP V Y D F V S H S R 5 0 8 .2 9 8 2 2 6 5 K Y -0 .4 1 3 Q V V IL S Q D S F Y R 7 3 0 .8 9 4 6 2 6 9 K V -1 .9 6 6 G A D N L V A IN L IV Q H IQ D IL N G G P S K 8 6 7 .1 4 4 9 3 4 2 R R -0 .7 0 8 G Q F N F D H P D A F D N E L IL K 7 0 7 .3 4 0 1 3 2 9 K T -1 .7 5 0 IV Q L L G Q N E V D Y R 7 7 3 .9 1 4 3 2 6 4 K Q -1 .2 7 8 462 Appendix 3 - Derivation Of The Protein Half-Life Equations. The following shows the derivation of the two formulae used to calculate the protein half- lives – with my thanks to Greg Martin, PhD. Let b = 13 C proportion at time t - that is { 13 C / 12 C + 13 C}. And b0 = 13 C proportion at time t=0 (therefore b0 = 1, assuming all protein is labeled with 13 C; this is a close approximation to reality). H = half-life The ratio of 13 C to 12 C = b/(b0-b) If we're assuming that b obeys a half-life equation... then b = b0 * (1/2)^{t/H} where (H = half life), and t = the time we are interested in. (reality check: at t=H, the formula gives b = b0 * (1/2)^{H/H} = b0 * (1/2), as it should) Another way of phrasing \"a substance has a half life\" is \"the logarithm of the substance amount/concentration decreases linearly\" By taking logs of both sides of the b= equation above we get: ln b = ln b0 + {t/H} * ln (1/2) ……………………………………………………...§1 463 Eq. §1 is an equation for a line. To determine this line exactly, we need two data points; but the first data point (the fact that b=b0 at t=0) is built into the equation - so we just need one other data point. Say we know that b=b1 at time t=t1, Then solving equation §1 for H gives: H = [t1 * ln(1/2)] / [ln b1 - ln b0] ………………………………………………….§2 Remember that b1 = 13 C / ( 12 C + 13 C). To give b1 in terms of the observed ratio, we divide all by 12 C, giving: b1 = [ 13 C / 12 C] / ( [ 12 C / 12 C] + [ 13 C / 12 C] ) In other words, b1 = i/w / ( 1 + i/w) Hence, for t1=15 (hours): Half-life = [ln 0.5 ÷ ln (observed ratio)] x 15 This situation is strictly speaking only true in cases where the total protein concentration in the SILAC experiment does not change. Such a situation is a reasonable assumption in the case of control cells, growing under steady-state conditions. However, under the experimental conditions decsribed, where protein concentration has been shown to change in response to cytokine withdrawal (in earlier work), we must compensate for the 464 change in total protein amount, as determined by the isotope ratios observed in the initial SILAC experiment. We begin by recapping the constant-total-protein-amount equations, presenting a new way of describing the rate of change. As before, b = 13 C proportion, that is the ratio ( 13 C-labelled protein to total protein) at time t, hence: b = 13 C-fraction ( 13 C / 13 C + 12 C) at time t, b0 = 13 C concentration at t=0 = 1 (that is, all 13 C, no 12 C) Let D (for degradation) be a rate of change, in % change per hour How do we then relate D to the half-life H? D = (ln 2)/H The equation for b in terms of degradation is therefore: ln b = -t * D This gives the same result as equation §2. That is, ln b = -t * (ln 2)/H, or ln b = t * (ln 1/2) / H ………………………………………...………..§3 This concludes the recap! 465 Now let's write two equations - one for the total amount of the protein of interest (i.e. 12 C + 13 C), and one for the amount of 13 C-labelled protein of interest. (The ratio b is the quotient of these two amounts, as above.) Definitions: Let b = 13 C / ( 12 C + 13 C) at time t; that is 12 C + 13 C Let M = amount of 13 C-labeled protein of interest at time t. Let D (for degradation) be the instantaneous rate of change, that is, rate of loss of 13 C- labelled protein (expressed in % per hour). This is a value calculated at a particular instant, however for this study we use this as the average value over the time period being examined. Let A = total amount of protein of interest at time t. Let V be the instantaneous rate of change in total protein-of-interest concentration (% per hour). V can be calculated from the concentration changes obtained in the earlier SILAC experiments as follows… First calculate the doubling time (DblT)… DblT = t * (ln 2) / (ln observed i/w ratio) Hence using data from the 15 hours time point… DblT = 15 * (ln 2)/(ln i/w) 466 Then using the doubling time, we calculate V - the instantaneous rate of change using the formula, V = (ln 2)/DblT Hence, V = (ln 2) / t * [( ln 2 ) / ( ln i/w )] (We'll assume that total protein concentration, A, is decreasing ... if A is increasing, we reflect that by letting V have a negative value.) The half-life (H) for 13 C-labeled protein is still (ln 2)/D ... and the half-life of the total protein amount is (ln 2)/V. If V is negative (in the case where A is increasing), then this quantity is negative; in which case its absolute value represents the doubling time of A. Let A0 be the initial amount (so that A = A0 at time t=0 and also M = A0 at time t=0). (Please also note: b = M/A.) This gives the equation for A as: ln A = ln A0 - t * V …………………………………………………………………..§4 and the equation for M is: ln M = ln A0 - t * D ………………………………………………………………….§5 What we really want is ln b. Since b = M/A ... we have ln b = ln M - ln A. 467 And so ln b = (ln A0 - t * D) - (ln A0 - t * V), Hence ln b = t * (V - D). In the first equation (§1), we assumed that the total protein concentration was constant, that is, that A didn't change at all – hence, V = 0. Suppose we have a measurement that b = b1 at time t = t1. B1 is then given by the following ratio: b1 = i/w / (1 + i/w) for i/w at t1. Then we solve ln b1 = t1 * (V - D) for D, obtaining: D = V - [ (ln b1) / t1 ]………………………………………………………….……..§6 This gives the rates of loss of 13 C-labeled protein. But if we want everything in terms of half-lives, we define two new variables – H and J: Let H = (ln 2)/D be the half-life of the 13 C-labeled protein (degradation of protein of interest). Let J = (ln 2)/V be the half-life of the overall protein amount. Then D = (ln 2)/H and V = (ln 2)/J ... And so: (ln 2)/H = [ (ln 2)/J ] - [ (ln b1) / t1 ]. Solving for H, we get: H = (ln 2) / { [ (ln 2)/J ] - [ (ln b1) / t1 ] }…………………………………………….§7 Note: in the case where A doesn't change at all, that is under control conditions, then (J = infinity and) H = (ln 2) / { 0 - [ (ln b1) / t1 ] }."@en ; edm:hasType "Thesis/Dissertation"@en ; vivo:dateIssued "2010-11"@en ; edm:isShownAt "10.14288/1.0069929"@en ; dcterms:language "eng"@en ; ns0:degreeDiscipline "Experimental Medicine"@en ; edm:provider "Vancouver : University of British Columbia Library"@en ; dcterms:publisher "University of British Columbia"@en ; dcterms:rights "Attribution-NonCommercial-NoDerivs 3.0 Unported"@en ; ns0:rightsURI "http://creativecommons.org/licenses/by-nc-nd/3.0/"@en ; ns0:scholarLevel "Graduate"@en ; dcterms:title "A quantitative proteomics analysis of human cells undergoing apoptosis"@en ; dcterms:type "Text"@en ; ns0:identifierURI "http://hdl.handle.net/2429/24167"@en .