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Proteomic profiling of the pathogenic fungus Cryptococcus neoformans upon regulation of the cyclic-AMP/protein… Geddes, Jennifer 2015

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Proteomic Profiling of the Pathogenic Fungus Cryptococcus neoformans upon Regulation of the cyclic-AMP/Protein Kinase A Signaling Pathway  by  Jennifer Geddes  B.Sc., University of Lethbridge, 2005 M.Sc., University of Lethbridge, 2007  A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF  DOCTOR OF PHILOSOPHY in THE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES (Microbiology and Immunology)  THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver)  April, 2015  © Jennifer Geddes, 2015 ii  Abstract The pathogenic yeast Cryptococcus neoformans causes life-threatening meningoencephalitis in immunocompromised individuals.  The ability of C. neoformans to cause disease depends on the elaboration of virulence factors including a polysaccharide capsule, melanin deposition in the cell wall, the ability to grow at 37°C, and the secretion of extracellular enzymes.  The cyclic-AMP/Protein Kinase A (PKA) signal transduction pathway is a key regulator of virulence in C. neoformans and may also regulate the trafficking of virulence factors.  The influence of PKA1 expression on the intracellular and extracellular proteomes and identification of Pka1 phosphorylation targets using phosphoproteomics have not been investigated for C. neoformans.  In our current study, I performed quantitative proteomics using a galactose-inducible/glucose-repressible expression strain of the PKA1 gene to identify regulated proteins in the secretome and proteome.  During investigation of the secretome, five proteins showed changes in extracellular abundance upon Pka1 induction.  These included the Cig1 and Aph1 proteins with known roles in virulence, as well as an α-amylase, a glyoxal oxidase, and a novel protein.  Targeted proteomics of these Pka1-regulated proteins allowed us to identify the secreted proteins in biological samples suggesting their potential as biomarkers of infection.  During investigation of the intracellular proteome, I identified a broad and conserved influence by PKA1.  Furthermore, an analysis of protein-ptotein interactions emphasized the impact of PKA activity on several clusters of proteins involving translation and the ribosome, the proteasome, and diverse metabolic processes.  Lastly, a phosphoproteomic study identified six potential targets of Pka1 phosphorylation including the master iron regulator, Cir1.  Construction of site-directed mutants showed that Pka1 phosphorylation of Cir1 impacted the production of capsule and melanin, cell size, and the ability to grow under low iron conditions.  Overall, the iii  data presented in this thesis have contributed a better understanding of the broad and conserved influence of Pka1 on cellular regulation and secretion in C. neoformans, and the discovery of potential biomarkers may facilitate the monitoring of disease progression.  Additionally, the identification of new Pka1 phosphorylation targets present opportunities for the development of a molecular understanding of the regulation of virulence as well as novel therapeutic strategies for treatment of cryptococcosis.    iv  Preface  The design and implementation of the research program was achieved through collaborative efforts among Jennifer Geddes, Dr. James W. Kronstad, and Dr. Leonard J. Foster.  The research presented in this thesis was solely conducted by J. Geddes unless otherwise stated as collaborative efforts.  The analysis and interpretation of the research data was performed by J. Geddes under the guidance of Dr. J. W. Kronstad and Dr. L. J. Foster.  Relative contributions of all collaborators  Some of the work presented in this thesis resulted from collaborative efforts.  The details of these contributions are as follows: The work presented in Chapter 2 has been submitted for publication in the manuscript entitled “Secretome profiling of Cryptococcus neoformans Reveals Regulation of a Subset of Virulence-Associated Proteins and Potential Biomarkers by Protein Kinase A”.  All experimental procedures, including fungal strain growth, protein sample preparation, bioinformatic analyses, transcriptional profiling, validation, and multiple reaction monitoring experimentation was performed by J. Geddes, unless otherwise stated.  Dr. Jaehyuk Choi contributed to this study by preparing the galactose-inducible and glucose-repressible PKA1 expression strain.  Dr. Daniel Croll performed gene ontology enrichment analyses, Dr. Melissa Caza performed the macrophage assays and macrophage sample collection, and Dr. Debora Oliveira collected bronchoalveolar lavage and mouse blood samples.  Dr. Nikolay Stoynov loaded prepared supernatant samples on the LTQ-Orbitrap Velos and performed protein identification searches.  All mass spectrometry was performed on instrumentation supplied and maintained by Dr. L. J. Foster.  In addition, Dr. Matthias Kretschmer, Dr. Joust Gouw, Jason v  Rogalski, and Dr. Nichollas Scott contributed discussions and technical assistance to this study.  Data analysis and interpretation was performed by J. Geddes.  The manuscript was written by J. Geddes and Dr. J. W. Kronstad, except for some details about the collaborative work included in the Experimental procedures, which were provided by Dr. D. Croll, Dr. M. Caza, and Dr. N. Stoynov. The majority of work presented in Chapter 3 has been submitted for publication in the manuscript entitled “Analysis of the Protein Kinase A-regulated proteome of Cryptococcus neoformans identifies a role for the ubiquitin-proteasome pathway in capsule formation”.  All experimental procedures, including fungal strain growth, protein sample preparation, bioinformatic analyses, transcriptional profiling, and immunoblot and enzyme assay validation was performed by J. Geddes, unless otherwise stated.  Dr. J. Choi contributed to this study by preparing the galactose-inducible and glucose-repressible PKA1 expression strain.  Dr. D. Croll performed gene ontology enrichment analyses, Dr. N. Stoynov loaded prepared samples on the LTQ-Orbitrap Velos, and Dr. N. Scott assisted with protein identification searches.  All mass spectrometry was performed on instrumentation supplied and maintained by Dr. L. J. Foster.  In addition, Dr. Guanggan Hu, Dr. M. Kretschmer, Sarah Michaud, and Jenny Moon contributed discussions and technical assistance to this study.  Data analysis and interpretation was performed by J. Geddes.  The manuscript was written by J. Geddes and Dr. J. W. Kronstad, except for some details about the collaborative work included in the Experimental procedures, which were provided by Dr. D. Croll and Dr. N. Stoynov. The work presented in Chapter 4 is in preparation for publication entitled “Protein Kinase A Phosphorylates Cir1, Impacting its Role in Iron Homeostasis and Virulence Factor Expression in Cryptococcus neoformans”.  All experimental procedures, including vi  fungal strain growth, phosphoprotein sample preparation, bioinformatic analyses, assessment of phosphorylation states, in vitro phosphorylation assays, phenotypic assays, and transcriptional profiling was performed by J. Geddes, unless otherwise stated.  Dr. J. Choi contributed to this study by preparing the galactose-inducible and glucose-repressible PKA1 expression strain.  Dr. G. Hu constructed the site-directed mutants and CIR1::GFP fusion allele and performed the cellular localization and yeast two-hybrid assays.  Dr. N. Stoynov loaded prepared phosphopeptide samples on the LTQ-Orbitrap Velos and performed protein identification searches.  All mass spectrometry was performed on instrumentation supplied and maintained by Dr. L. J. Foster.  Data analysis and interpretation was performed by J. Geddes.  In addition, Sarah Michaud and Jenny Moon contributed technical assistance to this study.  The manuscript was written by J. Geddes and Dr. J. W. Kronstad, except for some details about the collaborative work included in the Experimental procedures, which were provided by Dr. G. Hu and Dr. N. Stoynov.  Publications arising from graduate work Geddes, J.M.H., Croll, D., Stoynov, N., Foster, L.J., Kronstad, J.W.  Analysis of the Protein Kinase A-regulated proteome of Cryptococcus neoformans identifies a role of the ubiquitin-proteasome pathway in capsule formation.  In Review (Apr. 2015).  Geddes, J.M.H., Croll, D., Caza, M., Stoynov, N., Foster, L.J., Kronstad, J.W. Secretome Profiling of Cryptococcus neoformans Reveals Regulation of a Subset of Virulence-Associated Proteins and Potential Biomarkers by Protein Kinase A.  In Review (Apr. 2015).  vii  Geddes, J.M.H., Croll, D., Caza, M., Stoynov, N., Foster, L.J., Kronstad, J.W. Secretome Profiling of Cryptococcus neoformans Reveals Regulation of a Subset of Virulence-Associated Proteins and Potential Biomarkers by Protein Kinase A.  In Review (Dec. 2014).  University of British Columbia Ethics Board approval The protocol for the virulence assays was approved by the University of British Columbia’s Committee on Animal Care (protocol A13-0093).  viii  Table of Contents Abstract .......................................................................................................................................... ii Preface ........................................................................................................................................... iv Table of Contents ....................................................................................................................... viii List of Tables .............................................................................................................................. xvi List of Figures ...............................................................................................................................xx List of Abbreviations ............................................................................................................... xxiii Acknowledgements ................................................................................................................ xxviii Dedication ...................................................................................................................................xxx Chapter 1: Introduction ................................................................................................................1 1.1 Cryptococcus neoformans .................................................................................................. 1 1.1.1 Cryptococcal disease ................................................................................................... 1 1.1.2 Infection ...................................................................................................................... 2 1.1.3 Disease treatment ........................................................................................................ 3 1.1.4 Serotypes and cryptococcal genomes ......................................................................... 4 1.1.5 Life cycle .................................................................................................................... 5 1.1.6 Virulence factors ......................................................................................................... 6 1.1.6.1 Polysaccharide capsule ........................................................................................ 6 1.1.6.2 Melanin ................................................................................................................ 8 1.1.6.3 Extracellular enzymes .......................................................................................... 9 1.1.6.4 Virulence-related functions ................................................................................ 10 1.1.6.4.1 Growth at 37°C ........................................................................................... 10 1.1.6.4.2 Mating ......................................................................................................... 10 ix  1.1.6.4.3 Signal transduction pathways ..................................................................... 11 1.1.6.4.4 Intracellular trafficking ............................................................................... 11 1.1.6.4.5 Iron regulation and uptake .......................................................................... 12 1.1.6.4.6 Titan cell formation ..................................................................................... 13 1.1.6.4.7 Additional virulence-related factors ........................................................... 13 1.1.7 Secretion ................................................................................................................... 14 1.1.7.1 Vesicular transport ............................................................................................. 15 1.2 Signal transduction pathways .......................................................................................... 16 1.2.1 The cAMP/PKA pathway in mammalian systems.................................................... 17 1.2.2 The cAMP pathway in bacteria ................................................................................. 18 1.2.3 The cAMP/PKA pathway in fungi ............................................................................ 20 1.2.3.1 The cAMP/PKA pathway in C. neoformans...................................................... 22 1.2.3.1.1 Protein Kinase A in C. neoformans ............................................................ 25 1.2.3.1.2 The use of a galactose-inducible, glucose-repressible promoter to modulate PKA1 expression. .......................................................................................................... 26 1.2.3.1.3 Phosphorylation targets of Pka1 ................................................................. 27 1.3 Research purpose and significance .................................................................................. 28 1.3.1 Hypotheses ................................................................................................................ 28 1.3.2 Objective 1 ................................................................................................................ 29 1.3.3 Objective 2 ................................................................................................................ 29 1.3.4 Objective 3 ................................................................................................................ 29 Chapter 2: Secretome Profiling of Cryptococcus neoformans Reveals Regulation of a Subset of Virulence-Associated Proteins and Potential Biomarkers by Protein Kinase A ...............30 x  2.1 Synopsis ........................................................................................................................... 30 2.2 Introduction ...................................................................................................................... 31 2.3 Experimental procedures ................................................................................................. 34 2.3.1 Fungal strains and culture conditions ....................................................................... 34 2.3.2 Protein quantification, precipitation and in-solution digestion ................................. 35 2.3.3 Peptide chemical labeling and purification ............................................................... 36 2.3.4 Protein identification by liquid chromatography-tandem mass spectrometry (LC-MS/MS) and mass spectrometry data analysis ..................................................................... 37 2.3.5 Gene ontology analyses ............................................................................................ 39 2.3.6 Prediction of the extracellular location of identified proteins .................................. 40 2.3.7 RNA isolation and quantitative Real-Time PCR analysis ........................................ 40 2.3.8 RNA blot analysis ..................................................................................................... 41 2.3.9 Multiple Reaction Monitoring (MRM) sample collection from macrophage, mouse bronchoalveolar lavage, and mouse blood ............................................................................ 42 2.3.10 MRM sample preparation ....................................................................................... 43 2.3.11 Peptide selection, internal standardization, and MRM development ..................... 44 2.3.12 Mass spectrometry and data analysis for MRM ..................................................... 45 2.3.13 Validation of secretome data .................................................................................. 46 2.4 Results .............................................................................................................................. 46 2.4.1 Control of PKA1 expression results in a change of the protein secretion profile ..... 46 2.4.2 Identification of secreted proteins regulated by Pka1 ............................................... 50 2.4.3 Gene Ontology analyses of the secretome revealed enrichment of proteins associated with metabolic and catabolic processes ................................................................................ 56 xi  2.4.4 A bioinformatic analysis of the secretome predicts modes of secretion ................... 60 2.4.5 Examination of transcription and protein abundance in the context of Pka1 regulation .............................................................................................................................. 63 2.4.6 Detection of secreted Pka1-regulated proteins using Multiple Reaction Monitoring (MRM) .................................................................................................................................. 65 2.5 Discussion ........................................................................................................................ 73 2.5.1 Modulation of PKA1 expression leads to a change in the secretome ....................... 73 2.5.2 Pka1 regulation of mannoproteins and cell wall functions: connections with Rim101................................................................................................................................... 75 2.5.3 Pka1 influences the secretion of α-amylase and glyoxal oxidase enzymes .............. 76 2.5.4 Pka1 influences the secretion of the virulence-associated acid phosphatase, Aph1 . 77 2.5.5 PKA regulation and the intersection of secretome studies in C. neoformans ........... 78 2.5.6 Detection of potential biomarkers during cryptococcal infection ............................. 79 2.6 Conclusion ....................................................................................................................... 81 Chapter 3: Quantitative Proteomic Profiling Reveals a Conserved Influence of Protein Kinase A on the Abundance of Proteins for Translation, the Proteasome, and Metabolism in Cryptococcus neoformans ........................................................................................................82 3.1 Synopsis ........................................................................................................................... 82 3.2 Introduction ...................................................................................................................... 83 3.3 Experimental procedures ................................................................................................. 86 3.3.1 Fungal strains and culture conditions ....................................................................... 86 3.3.2 Preparation of protein extracts, quantification, and trypsin digestion ...................... 86 xii  3.3.3 Peptide chemical labeling, purification, and fractionation using strong-cation exchange (SCX) .................................................................................................................... 87 3.3.4 Protein identification by liquid chromatography-tandem mass spectrometry (LC-MS/MS) and mass spectrometry data analysis ..................................................................... 88 3.3.5 Gene ontology analysis ............................................................................................. 90 3.3.6 Bioinformatic analyses.............................................................................................. 91 3.3.7 RNA isolation and qRT-PCR of Pka1-regulated genes ............................................ 92 3.3.8 RNA blot analysis ..................................................................................................... 92 3.3.9 Enzyme assays and immunoblot analysis ................................................................. 93 3.4 Results .............................................................................................................................. 94 3.4.1 Quantitative profiling of the proteome upon modulation of PKA1 expression ........ 94 3.4.2 Identification of Pka1-regulated proteins .................................................................. 97 3.4.3 Pka1 influences a broad spectrum of functions in C. neoformans .......................... 100 3.4.4 Several clusters of interactions are predicted by network mapping of Pka1-regulated proteins ................................................................................................................................ 111 3.4.5 Characterization of Pka1-regulated novel proteins ................................................. 119 3.4.6 Comparisons of transcript and protein abundance for Pka1-regulated functions ... 126 3.5 Discussion ...................................................................................................................... 128 3.5.1 The impact of Pka1 regulation on translation ......................................................... 129 3.5.2 PKA and human diseases: a conserved connection between translation and the ubiquitin-proteasome pathway ............................................................................................ 130 3.5.3 Pka1 influences cellular metabolism and amino acid biosynthesis ........................ 132 3.5.4 Pka1 influences the abundance of mannoproteins and novel proteins ................... 134 xiii  3.6 Conclusions .................................................................................................................... 136 Chapter 4: Protein Kinase A Phosphorylates Cir1, Impacting its Role in Iron Homeostasis and Virulence Factor Expression in Cryptococcus neoformans .............................................137 4.1 Synopsis ......................................................................................................................... 137 4.2 Introduction .................................................................................................................... 138 4.3 Experimental procedures ............................................................................................... 141 4.3.1 Fungal strains and culture conditions ..................................................................... 141 4.3.2 Preparation of protein extracts, quantification, and trypsin digestion .................... 142 4.3.3 Peptide chemical labeling and purification ............................................................. 143 4.3.4 Enrichment of phosphopeptides using TiO2 or Strong Cation Exchange (SCX) chromatography .................................................................................................................. 144 4.3.5 Protein identification by liquid chromatography-tandem mass spectrometry (LC-MS/MS) and mass spectrometry data analysis ................................................................... 145 4.3.6 In vitro phosphorylation assay ................................................................................ 147 4.3.7 Construction of Cir1 site-directed mutants (SDM) and confirmation .................... 148 4.3.8 Capsule formation, laccase production, and growth at 37°C .................................. 149 4.3.9 Cell wall-related phenotypic assays ........................................................................ 149 4.3.10 Analysis of growth upon iron deprivation ............................................................ 150 4.3.11 Construction of a CIR1::GFP fusion allele .......................................................... 150 4.3.12 Cellular localization of Cir1.................................................................................. 151 4.3.13 Yeast two-hybrid assay ......................................................................................... 152 4.3.14 RNA isolation and qRT-PCR of Cir1-regulated genes in substitution mutants ... 152 4.4 Results ............................................................................................................................ 153 xiv  4.4.1 Profiling of the phosphoproteome of C. neoformans upon modulation of PKA1 expression ........................................................................................................................... 153 4.4.2 Identification of potential Pka1 phosphorylation targets ........................................ 159 4.4.3 Pka1 phosphorylates Cir1 in vitro........................................................................... 163 4.4.4 Site-directed mutagenesis of the Pka1 phosphorylation site in Cir1 influences virulence factor expression ................................................................................................. 167 4.4.5 Pka1 phosphorylation of Cir1 may have an influence on cell wall integrity .......... 170 4.4.6 Growth on low-iron medium is influenced by mutation of the Pka1 phosphorylation site on Cir1 .......................................................................................................................... 174 4.4.7 Effects of mutations at the PKA phosphorylation site on the transcription of Cir1-regulated genes.................................................................................................................... 176 4.5 Discussion ...................................................................................................................... 179 4.5.1 Modulation of Pka1 expression leads to a change in the phosphoproteome .......... 181 4.5.2 Diversity of novel targets of Pka1 phosphorylation ............................................... 182 4.5.3 Pka1 phosphorylation of Cir1 impacts virulence factor expression and possibly cell wall integrity: a link to Rim101 .......................................................................................... 184 4.5.4 Cir1 phosphorylation by Pka1 influences adaptation to low iron medium ............. 187 4.6 Conclusion ..................................................................................................................... 188 Chapter 5: Thesis Summary and Future Directions ..............................................................189 References ...................................................................................................................................194 Appendices ..................................................................................................................................225 Appendix A ............................................................................................................................. 225 Appendix B ............................................................................................................................. 252 xv  Appendix C ............................................................................................................................. 336  xvi  List of Tables Table 2.1: Proteins identified in the secretome of C. neoformans collected at 16, 48, 72, and 120 hpi grown in Pka1-repressed (glucose-containing medium) and Pka1-induced (galactose-containing medium) conditions..................................................................................................... 48 Table 2.2: Proteins identified in the secretome of C. neoformans collected at 96 hpi from cells grown in Pka1-repressed (glucose-containing medium) conditions. ............................................ 52 Table 2.3: Proteins identified in the secretome of C. neoformans collected at 96 hpi from cells grown in Pka1-induced (galactose-containing medium) conditions. ............................................ 54 Table 2.4: Bioinformatic analysis of identified and quantified proteins in the secretome of C. neoformans under Pka1-repressed (glucose-containing medium) and Pka1-induced (galactose-containing medium) conditions..................................................................................................... 61 Table 3.1: Enrichment of Pka1-regulated genes represented in the proteome analysis upon modulation of PKA1 expression compared to all genes present in the WT strain. ..................... 104 Table 3.2: KEGG pathways impacted by Pka1-regulated proteins upon modulation of PKA1 expression. .................................................................................................................................. 105 Table 3.3: Proteins associated with metabolic and biosynthetic processes identified in the proteome of C. neoformans collected at 16 hpi grown under Pka1 repression (glucose-containing medium) and Pka1 induction (galactose-containing medium) conditions. ................................. 108 Table 3.4: Proteins associated with translational regulation and RNA processing identified in the proteome of C. neoformans collected at 16 hpi grown under Pka1 repression (glucose-containing medium) and Pka1 induction (galactose-containing medium) conditions. ................................. 114 xvii  Table 3.5: Proteins associated with the proteasome and ubiquitin pathways in the proteome of C. neoformans collected at 16 hpi grown under Pka1 repression (glucose-containing medium) and Pka1 induction (galactose-containing medium) conditions. ....................................................... 117 Table 3.6: Proteins associated with response to stress, signaling, and virulence identified in the proteome of C. neoformans collected at 16 hpi grown under Pka1 repression (glucose-containing medium) and Pka1 induction (galactose-containing medium) conditions. ................................. 118 Table 3.7: Characterization and classification of hypothetical and unclassified proteins in the proteome C. neoformans under regulation of Pka1. ................................................................... 121 Table 4.1: Phosphoproteins identified upon repression of PKA1 (glucose-containing medium) in C. neoformans. ............................................................................................................................ 156 Table 4.2: Phosphoproteins identified upon induction of PKA1 (galactose-containing medium) in C. neoformans. ............................................................................................................................ 158 Table 4.3: Phosphoproteins of C. neoformans present upon Pka1 induction (galactose-containing medium). ..................................................................................................................................... 161 Table 4.4: Potential Pka1 phosphorylation sites. ........................................................................ 162  Table A.1: Primer sequences. ..................................................................................................... 225 Table A.2: Proteins selected for Multiple Reaction Monitoring assays and their respective isotopically-labeled synthetic peptides. ...................................................................................... 227 Table A.3: Isotopically-labeled peptides monitored during Multiple Reaction Monitoring multiplex assay............................................................................................................................ 228 Table A.4: Quantitative proteomic analysis of the secretome of C. neoformans at 16, 48, 72, and 120 hpi under Pka1-repressed (glucose-containing medium) conditions. .................................. 234 xviii  Table A.5: Quantitative proteomic analysis of the secretome of C. neoformans at 16, 48, 72, and 120 hpi under Pka1-induced (galactose-containing medium) conditions. .................................. 238 Table A.6: Proteins identified in the secretome of C. neoformans from both the end-point (96 hpi) samples and the time course (16, 48, 72, 120 hpi) samples prepared in Pka1-repressed (glucose-containing medium) and Pka1-induced (galactose-containing medium) conditions. .. 243 Table A.7: Quantitative proteomic analysis of the secretome of C. neoformans at 96 hpi under Pka1-repressed (glucose-containing medium) conditions. ......................................................... 244 Table A.8: Quantitative proteomic analysis of the secretome of C. neoformans at 96 hpi under Pka1-induced (galactose-containing medium) conditions. ......................................................... 247 Table B.1: Primer sequences. ...................................................................................................... 252 Table B.2: Quantitative proteomic analysis of C. neoformans at 96 hpi upon Pka1-repression (glucose-containing medium) and Pka1-induction (galactose-containing medium) conditions. 256 Table B.3: Cellular component enrichment of Pka1-regulated genes represented in the proteome analysis upon modulation of PKA1 expression compared to all genes present in the WT strain...................................................................................................................................................... 330 Table B.4: Molecular function enrichment of Pka1-regulated genes represented in the proteome analysis upon modulation of PKA1 expression compared to all genes present in the WT strain...................................................................................................................................................... 331 Table B.5: Pka1-regulated proteins from C. neoformans present in the ATCC gene deletion set...................................................................................................................................................... 332 Table C.1: Identified phosphoproteins in C. neoformans WT strain upon Pka1-repression (glucose-containing medium). .................................................................................................... 336 xix  Table C.2: Identified phosphoproteins in C. neoformans WT strain upon Pka1-induction (galactose-containing medium). .................................................................................................. 341 Table C.3: Primer sequences. ...................................................................................................... 344 Table C.4: Construction of Cir1 site-directed mutants in C. neoformans WT strain. ................ 346  xx  List of Figures Figure 1.1: Elaboration of the polysaccharide capsule as a virulence factor in C. neoformans H99 strain. ............................................................................................................................................... 8 Figure 1.2: Production of melanin as a virulence factor in C. neoformans H99 strain. ................. 9 Figure 2.1: Quantitative proteomic analysis of the C. neoformans secretome over the course of all time-points (16, 48, 72, and 120 hpi). ...................................................................................... 50 Figure 2.2: Quantitative proteomic analysis of the C. neoformans secretome under Pka1-repressed (glucose-containing medium) and Pka1-induced (galactose-containing medium) conditions. ..................................................................................................................................... 55 Figure 2.3: Enrichment of genes represented in the secretome analysis of cells grown under Pka1-repressed (glucose-containing medium) and Pka1-induced (galactose-containing medium) conditions compared to all genes present in the WT strain. ......................................................... 58 Figure 2.4: Comparison of GO terms classification of biological processes from the identified secreted proteins from cells grown under Pka1-repressed (glucose-containing medium) and Pka1-induced (galactose-containing medium) conditions compared to proteins represented in the Fungal Secretome Knowledgebase. .............................................................................................. 59 Figure 2.5: Comparison of RNA expression levels using qRT-PCR versus secreted protein abundance using quantitative proteomics. .................................................................................... 65 Figure 2.6: Interactions of WT and Pka1-regulated strains with J774A.1 murine macrophages. 68 Figure 2.8: Detection of Pka1-regulated proteins in mouse bronchoalveolar lavage samples. .... 71 Figure 2.9:  Detection of Pka1-regulated proteins in mouse blood samples. ............................... 72 Figure 3.1:  Venn diagram illustrating the dispersion of total C. neoformans proteins identified by quantitative mass spectrometry. ............................................................................................... 96 xxi  Figure 3.2: Quantitative proteomic analysis of the C. neoformans proteome. ............................. 97 Figure 3.3: Density scatterplot of identified proteins in C. neoformans upon modulation of Pka1 activity........................................................................................................................................... 99 Figure 3.4: Pka1-regulated protein abundance under induction of PKA1 expression in C. neoformans. ................................................................................................................................. 100 Figure 3.5: Enrichment of genes represented in the proteome analysis under Pka1-repression (glucose-containing medium) and Pka1-induction (galactose-containing medium) compared to all genes present in the WT strain. .............................................................................................. 103 Figure 3.6:  Interaction network mapping using STRING of Pka1-regulated proteins identified in the proteome of C. neoformans. .................................................................................................. 113 Figure 3.7: Comparison of RNA expression levels using qRT-PCR at 16 hpi and protein abundance using quantitative proteomics at 16 hpi. ................................................................... 127 Figure 4.1: Phosphoproteomic analysis of C. neoformans. ........................................................ 159 Figure 4.2: Fragmentation spectrum following in vitro Pka1 phosphorylation of a Cir1 synthetic peptide. ........................................................................................................................................ 166 Figure 4.3: Virulence factor expression in Cir1 site-directed mutants. ...................................... 170 Figure 4.4: Cell wall-related phenotypic assasy for Cir1 site-directed mutants. ........................ 173 Figure 4.5: Capsule shedding immunoblot for Cir1 site-directed mutants. ................................ 173 Figure 4.6:  Iron-related phenotypic assasy for Cir1 site-directed mutants. ............................... 175 Figure 4.7:  Transcript analysis using qRT-PCR of Cir1-regulated genes. ................................ 179 Figure 4.8: A model depicting the impact of PKA phosphorylation on the phenotypes known to be influenced by Cir1 in C. neoformans. .................................................................................... 180 Figure A.1: RNA expression levels of PKA1. ............................................................................ 226 xxii  Figure A.2: Secreted enzyme activity. ........................................................................................ 232 Figure A.3: PCR to confirm absence of cellular factors due to cell lysis in secretome of C. neoformans. ................................................................................................................................. 233 Figure A.4: Enrichment of genes represented in our secretome analysis grown under Pka1-repressed (glucose-containing medium, D) and Pka1-induced (galactose-containing medium, G) conditions compared to all genes present in the WT strain. ....................................................... 249 Figure A.5: Comparison of GO term classifications to proteins represented in the Fungal Secretome Knowledgebase. ........................................................................................................ 251 Figure B.1: RNA expression levels. ........................................................................................... 253 Figure B.2: Validation of proteomic profiling. ........................................................................... 255 Figure B.3: Enrichment of genes represented in the proteome analysis grown upon modulation of PKA1 expression compared to all genes present in the WT strain. ............................................ 329 Figure C.1: Localization assay for Cir1 upon modulation of PKA1 expression. ........................ 347 Figure C.2: Identification of the Cir1 phosphopeptide by mass spectrometry. .......................... 349  xxiii  List of Abbreviations AC   adenylyl cyclase ACS   American Chemical Society AD   Alzheimer disease AIDS  acquired immune deficiency syndrome ALS   amyotropic lateral sclerosis AQ   aqueous ATCC  American Type Culture Collection ATP   adenosine triphosphate BAL   bronchoalveolar lavage BBB   blood brain barrier BCA   bicinchoninic acid assay cAMP  cyclic adenosine monophosphate cDNA  complementary deoxyribonucleic acid CE   collision energy CFTR  cystic fibrosis transmembrane conductance regulator CFUs  colony forming units CID   collision induced dissociation CNS   central nervous system CREB  cAMP response element binding protein D   glucose Da   daltons DAPI  4-,6-diamidino-2-phenylindole xxiv  DEPC  diethylpyrocarbonate DIC   differential interference microscopy DMEM  Dulbecco’s modified eagle’s medium DNA   deoxyribonucleic acid DTT   dithiothreitol EDTA  ethylenediaminetetraacetic acid ER   endoplasmic reticulum ESCRT  endosomal sorting complex required for transport ESI   electrospray ionisation EtOH  ethanol FA   formic acid FDR   false discovery rate FunSecKB  Fungal Secretome KnowledgeBase FV   fragmentor voltage G   galactose GalXM  galactoxylomannan gDNA  genomic deoxyribonucleic acid GFP   green fluorescent protein GO   gene ontology GPCR  G-protein coupled receptor GPI   glycophosphatidylinositol GXM  glucuronoxylomannan h   hour xxv  HAART  highly active antiretroviral therapy HCD   higher-energy collisional dissociation HD   Huntington disease HIV   human immunodeficiency virus hpi   hours post inoculation HPLC  high performance liquid chromatography HYG   hygromycin IAA   iodoacetamide IPR1   iron regulatory protein 1 IPTG   isopropyl β-D-1-thiogalactopyranoside KEGG  Kyoto encyclopedia of genes and genomes L-DOPA  L-3,4-dihydrophenylalanine LC-MS/MS liquid chromatography-tandem mass spectrometry LIM   low iron medium LTQ   linear-trapping quadrupole MAPK  mitogen activated protein kinase max   maximum min   minute MIT   Massachusetts Institute of Technology MM   minimal medium MOI   multiplicity of infection MP   mannoprotein MRM  multiple reaction monitoring xxvi  mRNA  messenger ribonucleic acid MS   mass spectrometry NCBI  National Centre for Biotechnology Information NEM   N-ethylmaleimide nr   non-redundant NTA   nitrilotriacetic acid PBS   phosphate-buffered saline PCR   polymerase chain reaction PD   Parkinson disease PKA   protein kinase A PLB   phospholipase B PMA   phorbol myristate acetate ppm   parts per million qRT-PCR  quantitative real time polymerase chain reaction RNA   ribonucleic acid rpm   revolutions per minute s   second SAGE  serial analysis of gene expression SCX   strong cation exchange SDM   site-directed mutants SDS   sodium dodecyl sulfate SDS-PAGE sodium dodecyl sulfate polyacrylamide gel electrophoresis STAGE  STop and Go Extraction xxvii  STRE  stress response element STRING  search tool for retrieval of interacting genes/proteins T3SS   type III secretion system TCA   trichloroacetic acid TOR   target of rapamycin complex UV   ultraviolet WT   wild-type YNB   yeast nitrogen base  YPD   yeast extract peptone dextrose xxviii  Acknowledgements I would like to extend my sincerest appreciation to my supervisor, Dr. James W. Kronstad, for providing me with the opportunity to study under his mentorship and for always supporting independence in my research.  Your continuous support and guidance have been instrumental in the development of my scientific career.   Next, I would like to thank my supervisory committee, Dr. Leonard J. Foster, Dr. Michael E. P. Murphy, and Dr. Erin C. Gaynor, for your support and valuable input into the design of my research objectives.  The opportunity to apply an interdisciplinary approach to my research has been vital for the success of my graduate work and for the development of my critical thinking skills.  In particular, thank you to Dr. L. J. Foster for your outstanding mentorship and the opportunity to work with state-of-the-art instrumentation and supportive colleagues throughout my graduate program. I would like to extend a special thanks to the current and past members of the Kronstad lab for your suggestions, support, and friendship over the years.  Specifically, Dr. Brigitte Cadieux for your challenging questions and technical expertise.  Dr. Matthias Kretschmer for your vast scientific knowledge and amazing ability to teach.  Thank you to Dr. Guanggan Hu for providing advice and encouragement over the years, and for sharing your technical expertise.  I would also like to thank Dr. Daniel Croll for our fruitful scientific discussions and Dr. Melissa Caza and Dr. Emma Griffiths for collaborative research opportunities. I would also like to acknowledge and thank current and past members of the Foster lab for their technical assistance and support during my graduate program.  A special thank you to Jenny Moon for sharing her knowledge of techniques and troubleshooting suggestions, Dr. xxix  Nichollas Scott for assistance with data analysis tools, and Dr. Nikolay Stoynov for your continuous technical assistance and helpful suggestions for sample preparation.  I would like to thank the University of British Columbia, specifically, the Department of Microbiology and Immunology for financial support during my graduate program and for providing countless opportunities for graduate student involvement.  The education, experience, and research opportunities I have had during my doctoral studies have been invaluable and have provided me with the foundation I need to continue my scientific career and achieve my goals.  I would also like to acknowledge the financial support awarded by NSERC and the BC Proteomics Network.  Lastly, I would like to thank my family.  Thank you to my mom, Marilyn Lamb, who has always supported my dreams and independence.  Thank you to my amazing husband, Jason McAlister, for your positive outlook and words of encouragement.  You challenge me to be a better person and a better scientist.  Finally, I would like to thank my daughter, Hazel.  Your giggles make everyday brighter.  xxx  Dedication       To my family, whose love and encouragement make anything possible.    1 Chapter 1: Introduction 1.1 Cryptococcus neoformans 1.1.1 Cryptococcal disease Cryptococcus neoformans is a yeast-like, opportunistic fungal pathogen capable of infecting immunocompromised individuals such as those patients suffering from Acquired Immune Deficiency Syndrome (AIDS), individuals with lymphoproliferative disorders, or patients receiving immunosuppressive drugs or undergoing chemotherapy (Wright et al., 1997; Korfel et al., 1998; Urbini et al., 2000).  Globally, C. neoformans causes approximately one million cases of life-threatening cryptococcal meningitis and meningoencephalitis per year in AIDS patients, resulting in an estimated 625,000 deaths (Park et al., 2009).  Highly active antiretroviral therapy (HAART) has decreased infection rates in developed countries, but C. neoformans infections continue to be a leading mycological cause of morbidity and mortality among patients infected with human immunodeficiency virus (HIV) and subsequently suffering from AIDS in sub-Saharan Africa (Kovacs et al., 1985; Chuck and Sande, 1989; Stansell, 1993; Rozenbaum and Goncalves, 1994; Mitchell and Perfect, 1995; Bicanic and Harrison, 2005).  Cryptococcal infections occur in 6 to 10% of immunocompromised individuals in the United States, Western Europe, and Australia, and up to 30% mortality occurs in AIDS patients in Southeast Asia and South and East Africa (Powderly, 1993; Casadevall and Perfect, 1998; Hakim et al., 2000; Holmes et al., 2003; Kumarasamy et al., 2005; Helbok et al., 2006).   A closely related species, Cryptococcus gattii, is capable of causing disease in immunocompromised as well as immunocompetent individuals.  C. gattii is known to be endemic to tropical parts of Africa and Australia.  However, since 1999, C. gattii infections have resulted in approximately 230 cases of disease and twenty-six deaths in immunocompetent   2 individuals as part of an ongoing outbreak on the east coast of Vancouver Island, British Columbia (Bartlett et al., 2008).  Taken together, infection by C. neoformans and C. gattii poses a high and lethal risk to both healthy and immunocompromised individuals around the globe. 1.1.2 Infection  Cryptococcal infection is initiated by inhalation of airborne desiccated yeast cells or spores from the environment into the lungs, followed by colonization of alveolar spaces.  Environmental sources of these infectious propagules include soil contaminated with pigeon guano in the case of C. neoformans, or eucalyptus trees and decaying wood for C. gattii (Pfeiffer and Ellis, 1992; Chakrabarti et al., 1997; Casadevall and Perfect, 1998; Idnurm et al., 2005; Lin and Heitman, 2006).  Following inhalation of the infectious propagules (cells that are 1-2 μm in diameter), C. neoformans fungal cells may colonize an immunocompetent host without causing disease.  The absence of disease is achieved through fungal cell clearance by the immune system, or the fungal cells may exist in a dormant, latent form that appears asymptomatic.  In the immunocompromised host, dormant fungal cells can reactivate to replicate within phagocytes and tissues, disseminate to any organ within the body, and cause localized damage at the brain microvasculature to eventually infect the central nervous system (CNS) (Barber et al., 1995; Ghigliotti and Demarchi, 1995; Seaton et al., 1997; Christianson et al., 2003).  To gain access to the CNS, fungal cells must cross the blood-brain barrier (BBB).  In C. neoformans, crossing the BBB is achieved via transcytosis or by a ‘Trojan Horse’ mechanism.  Transcytosis involves the direct uptake of fungal cells by endothelial cells, or the potential breakdown of tight junctions between endothelial cells, to result in subsequent brain invasion  (Chen et al., 2003; Olszewski et al., 2004; Chang et al., 2004; Shea et al., 2006; Kechichian et al., 2007; Shi et al., 2010; Kronstad et al., 2011b).  Alternatively, a ‘Trojan Horse’ mechanism has been proposed in which   3 circulating mononuclear cells containing internalized fungal cells are capable of disguising the fungal cells, and carrying them across the BBB to gain entry to the CNS (Luberto et al., 2003; Santangelo et al., 2004; Charlier et al., 2009).  The preference displayed by C. neoformans to infect the CNS has been attributed to several factors including the presence of neuronal substrates assisting fungal growth, a place of refuge from the host immune response, the ability of the fungus to survive and proliferate in a hypoxic environment, and the possibility of neuronal cell receptors to attract the fungal cells (Chang et al., 2004; Bahn et al., 2005; Lin and Heitman, 2006).  Following a successful breach into the CNS, C. neoformans is capable of causing disease, specifically, an infection and inflammation of the meninges and the brain.   1.1.3 Disease treatment  In developed countries, the incidence and mortality of cryptococcal infections have declined as a result of antifungal drugs, as well as HAART for HIV infection.  Antifungal drugs effective against C. neoformans infections include amphotericin B, a polyene antifungal drug, which causes monovalent ion leakage and cell death by binding to ergosterol and forming transmembrane channels.  Additionally, fluconazole is used to treat cryptococcosis and this triazole antifungal drug inhibits a fungal cytochrome P450 enzyme, preventing the conversion of lanosterol to ergosterol, an essential component of the fungal plasma membrane.  Amphotericin B and fluconazole, as well as other drugs, can be effective in treating cryptococcosis.  However, a number of factors confound treatment.  These include the inability of patients to completely clear infections, the high cost of prolonged treatment, ineffective treatment due to drug toxicity, and the appearance of resistant organisms.  These factors necessitate a better understanding of the fungus and cryptococcosis (Chayakulkeeree and Perfect, 2006).    4 Vaccine development is an alternative and key area of interest and research.  Vaccine candidates in mouse trials have shown promise, including longer survival following infection, and immunization resulting in subsequent protection to cryptococcal infections (Datta et al., 2008; Wozniak et al., 2009).  Although vaccines to the polysaccharide capsule are not feasible due to its immunosuppressive properties and limited antibody response, conjugated vaccines using polysaccharide attached to an immunogenic carrier protein to induce T cell-dependent immune responses and immunogenic memory, appear promising (Goldblatt, 2000).  Despite the availability of antifungal drugs and recent vaccine developments, initial and subsequent infections by C. neoformans still remain a global issue.  Therefore, there is an urgent need for new diagnostic screening tools and therapeutic developments to detect and eradicate infections at the earliest stage. 1.1.4 Serotypes and cryptococcal genomes Five cryptococcal serotypes have been identified based on capsular agglutination reactions and further classified into nine molecular types based on DNA sequence polymorphisms.  C. neoformans var. neoformans consists of serotype D (molecular type: VNIV) and the hybrid serotype AD (molecular type: VNIII), C. neoformans var. grubii consists of serotype A (molecular types: VNI, VNII, VNB), and C. gattii consists of serotypes B (molecular types: VGI, VGII, VGIII) and C (molecular types: VGIII, VGIV) (Belay et al., 1996; Franzot et al., 1999; Meyer et al., 1999; Meyer et al., 2003; Litvintseva et al., 2003; Litvintseva et al., 2005).  Overall, molecular and phylogenetic studies attribute reproductive isolation to the formation of two distinct monophyletic lineages for C. neoformans and C. gattii (Kwon-Chung et al., 2002; Diaz and Fell, 2005; Fraser et al., 2005; Kohn, 2005).  Genetic and molecular   5 studies, now based on available genome sequences, provide the tools for investigating the role of specific genes in the virulence of C. neoformans. Genome sequencing has been completed for C. neoformans serotype D strains JEC21 and B-3501A, and the serotype A strain H99 (the most common serotype accounting for more than 95% of cryptococcal infections in AIDS patients), as well as for the C. gattii serotype B strain WM276 and the clinical strain R265 (Loftus et al., 2005).  As a representative example, the genome of strain JEC21 consists of 14 chromosomes, a total of 20 Mb of DNA, and a predicted 6,574 genes (Loftus et al., 2005).   1.1.5 Life cycle  Commonly found as budding yeast, the heterothallic, single-celled basidiomycete C. neoformans, undergoes dimorphic transition to a filamentous form by mating and monokaryotic fruiting (Casadevall and Perfect, 1998).  Mating involves the fusion of opposite mating type haploid cells, a and α, for the production of dikaryotic filaments, resulting in basidium formation, meiosis, and the production of four chains of basidiospores, which are capable of penetrating the alveoli of the lung and causing infection as described above (Kwon-Chung, 1975; Kwon-Chung, 1976).  Conversely, monokaryotic fruiting involves haploid and asexual forms and is primarily observed in α strains (Wickes et al., 1996; Lin et al., 2005).  Environmental stimulation of mating and monokaryotic fruiting requires several conditions including nitrogen starvation, desiccation and darkness, in addition to the presence of mating pheromones.  These conditions result in the development of hyphae and clamp connections, and the production of terminal fruiting structures (Wickes et al., 1996).  The ability of Cryptococcus spp. to undergo mating or monokaryotic fruiting as an adaptation to a changing environment is a key component of its   6 ability to survive in multiple conditions and hosts, as well as its ability to infect both immunocompromised and immunocompetent individuals.  1.1.6 Virulence factors The ability of C. neoformans to cause disease depends on the production of virulence factors including the polysaccharide capsule, melanin deposition in the cell wall, the ability to grow at 37°C, and the secretion of extracellular enzymes and proteins capable of assisting with iron acquisition, fungal survival within the host, and virulence (Bulmer et al., 1967; Kwon-Chung et al., 1982; Rhodes et al., 1982; Kwon-Chung and Rhodes, 1986; Polacheck and Kwon-Chung, 1988; Chang and Kwon-Chung, 1994; Cadieux et al., 2013; Coelho et al., 2014).  Under environmental conditions, virulence factor expression is thought to be maintained for protection from other soil microorganisms, and as a defense mechanism against predation by amoeba, for example (Casadevall et al., 2003).  Understanding virulence factor production and regulation is vital to better understanding the fungus, and for the development of novel treatment therapies. 1.1.6.1 Polysaccharide capsule  The polysaccharide capsule is the main virulence factor of C. neoformans.  It is synthesized intracellularly and exported to the cell surface where it attaches to the fungal cell wall (Yoneda and Doering, 2006).  It is comprised of 88% glucuronoxylomannan (GXM), 10% galactoxylomannan (GalXM), and 1-2% mannoproteins (MP).  GXM is a long, unbranched polymer (1,700-7,000 kDa) comprised of O-acetylated α-1,3-linked mannose residues decorated by xylose and glucuronic acid (McFadden et al., 2006; Zaragoza et al., 2009).  The smaller GalXM (275 kDa) consists of an α-1,6-galactan backbone with xylosylated side chains of mannose and galactose (Cherniak et al., 1980; Zaragoza et al., 2009).  Capsule biosynthesis is induced and regulated by the presence of serum, iron limitation, and physiological CO2 levels, as   7 well as the cyclic adenosine monophosphate (cAMP)/Protein Kinase A (PKA) signaling pathway (Granger et al., 1985; Vartivarian et al., 1993; Jung and Kronstad, 2008).  Figure 1.1 shows the capsular phenotype induced under low-iron conditions in the C. neoformans H99 strain.  Importantly, the polysaccharide capsule has several immunomodulatory influences.  Such influences include activation of the alternative complement pathway, depletion of complement, inhibition of phagocytosis by macrophages, induction of immune unresponsiveness, and inhibition of neutrophil migration (Kozel, 1993; Coenjaerts et al., 2001; McFadden and Casadevall, 2001; Bose et al., 2003; Walenkamp et al., 2003; Del Poeta, 2004; Ellerbroek et al., 2004; Janbon, 2004; Shoham and Levitz, 2005).  In addition, the capsule provides a protective role from dehydration for the fungal cells (McFadden and Casadevall, 2001; Bose et al., 2003).  The cell-associated capsule is continually shed from the fungal cells during infection and is capable of promoting intracellular survival, which is crucial for long-term latency.  This is compared to acapsular mutants, which have been shown to be avirulent in mouse infection models (Levitz, 2006; Doering, 2009; Zaragoza et al., 2009; Voelz and May, 2010).     8   Figure 1.1: Elaboration of the polysaccharide capsule as a virulence factor in C. neoformans H99 strain.  Polysaccharide capsule from overnight cultures grown under low iron conditions at 30°C was revealed by india ink staining for visualization of capsular material around the cells (indicated by arrow).  1.1.6.2 Melanin  Melanin production is another key virulence factor of C. neoformans.  Melanin provides protection from environmental ultraviolet (UV) radiation, provides structural support for the cell wall, and protection against phagocytosis and oxidative killing by macrophages, as well as contributing to extra pulmonary dissemination (Kwon-Chung and Rhodes, 1986; Casadevall et al., 2000; Nosanchuk and Casadevall, 2006; Panepinto and Williamson, 2006).  Melanin is synthesized from catecholamine substrates via activity of the copper-dependent laccases, Lac1 and Lac2, resulting in deposition of dark pigments in the fungal cell wall (Kwon-Chung and Rhodes, 1986; Zhu and Williamson, 2004; Missall et al., 2005).  Glucose, iron and copper levels, as well as temperature, the available nitrogen source, and the cAMP/PKA pathway regulate melanin formation in C. neoformans (Nurudeen and Ahearn, 1979; McFadden and Casadevall, 2001; Pukkila-Worley et al., 2005; Jung et al., 2008; Lee et al., 2011).  Figure 1.2 shows melanin   9 production in C. neoformans H99 strain on agar plates containing L-3,4-dihydrophenylalanine (L-DOPA).  Figure 1.2: Production of melanin as a virulence factor in C. neoformans H99 strain.  Melanin production after 2 days of growth of cells plated on medium containing the laccase substrate L-3,4-dihydroxyphenylalanine (L-DOPA).    1.1.6.3 Extracellular enzymes  The production of extracellular enzymes by C. neoformans, such as phospholipases, laccase, urease, and proteinases, protect the fungus and allow its survival and proliferation within the host.  Phospholipase B (PLB) is located in the cell wall and contributes to cell wall integrity (Siafakas et al., 2007).  PLB is involved in hydrolyzing ester bonds and aiding in the degradation and destabilization of host cell membranes to promote cell lysis (Ghannoum, 2000; Cox et al., 2001).  PLB also contributes to invasion of host lung tissue and fungal cell dissemination (Santangelo et al., 2004).  In addition, inositol phosphosphingolipid phospholipase C1 has been shown to regulate stability of the plasma membrane ATPase in C. neoformans (Farnoud et al., 2014).  Furthermore, the production of urease, which is responsible for the hydrolysis of urea to ammonia and carbamate resulting in the induction of a localized increase in pH, enhances the ability of C. neoformans to invade the CNS (Casadevall and Perfect, 1998; Cox et al., 2000; Olszewski et al., 2004; Singh et al., 2013).  Lastly, proteinases may provide nutrients and protection for the pathogen through host protein degradation (Chen et al., 1996).  Overall, the production and elaboration of these virulence factors by C. neoformans, along with its ability to grow at an elevated host temperature, contribute to its virulence.     106    105    104     103    102     101   10 1.1.6.4 Virulence-related functions  In addition to the classical virulence-related factors previously discussed, several other functions are critical to the pathogenicity and virulence of C. neoformans.  These are described in the following sections. 1.1.6.4.1 Growth at 37°C  The ability of C. neoformans to grow and survive at 37°C contributes significantly to its role as a human pathogen.  Upon entry to the human host, one of the first challenges faced by the fungus is the increase in temperature.  Signaling pathway components have been attributed to the demonstrated thermotolerance of C. neoformans (Odom et al., 1997; Perfect, 2006; Brown et al., 2007).  Additionally, C. neoformans has developed two main resistance mechanisms to combat an increase in temperature.  The first involves prevention of protein denaturation and the capability of protein renaturation using disaccharide trehalose and heat shock protein chaperones.  The second mechanism involves antioxidant protection using superoxide dismutase (Giles et al., 2005; Petzold et al., 2006; Crowe, 2007).  In particular, the mitochondrial superoxide dismutase (Sod2), a major componenet of the antioxidant defense system in C. neoformans, is also associated with adaptation to growth at elevated temperatues (Giles et al., 2005).  1.1.6.4.2 Mating  Mating type has also been associated with virulence in C. neoformans.  Early studies using a mouse model of infection demonstrated that MATα strains were more virulent than MATa strains (Kwon-Chung et al., 1992).  Additionally, the majority of clinical and environmental isolates belong to the α-mating type.  A limited number of studies have explored the connection between mating type and virulence.  For example, transcriptional profiling following macrophage infection showed an increase in expression of the MATα locus after   11 internalization of the fungal cells (Fan et al., 2005).  Additionally, MATα strains vs. MATa strains are more likely to penetrate the CNS (Kwon-Chung and Bennett, 1978; Kwon-Chung et al., 1992; Nielsen et al., 2005).  In general, the underlying mechanisms that account for the connection between mating and virulence have yet to be defined.  1.1.6.4.3 Signal transduction pathways  Signal transduction pathways contribute to fungal virulence by sensing host conditions, controlling fungal stress responses due to challenges associated with host temperature and defense mechanisms, and regulating the expression of virulence factors (Hu et al., 2007; Hu et al., 2008; Kozubowski et al., 2009).  Examples include the cAMP/PKA pathway which is important for the elaboration of virulence factors including capsule and melanin production, the mitogen activated protein kinase (MAPK) pathways involved in regulating cell wall integrity and controlling stress responses, the Ca2+-Calcineurin pathway associated with regulation of mating, growth at host temperature, and virulence.  Additionally, a phosphatidylinositol 3-kinase (PI3K) signaling pathway regulates melanin formation (Kozubowski et al., 2009).  Signal transduction pathways are discussed in greater detail below (Section 1.2).  1.1.6.4.4 Intracellular trafficking  Intracellular trafficking, exocytosis, and secretion are important for the delivery of polysaccharide capsule material and laccase (for melanin production) to the cell surface, and are associated with virulence in C. neoformans (Hu et al., 2007; Panepinto et al., 2009; O'Meara et al., 2010).  Several studies indicate that regulation of secretory pathway components and trafficking are critical for the virulence of C. neoformans.  For example, defects in secretory pathway components or inhibitors of cellular trafficking machinery result in reduced capsule size (Walton et al., 2006; Yoneda and Doering, 2006; Rodrigues et al., 2007; Hu et al., 2007;   12 Rodrigues et al., 2008).  These recent reports also verified that exocytosis and the release of specialized extracellular vesicles mediate the secretion of capsule polysaccharide to influence virulence.  Additionally, protein transport, vesicle exocytosis, and glycosylation processes in the secretory pathway are important for fungal temperature sensitivity and proliferation within the host (Kim et al., 2002; Walton et al., 2006; Goulart et al., 2010).   1.1.6.4.5 Iron regulation and uptake  For C. neoformans, along with most pathogens, iron availability within the host can be a limiting factor for proliferation and survival (Hentze et al., 2004; Doherty, 2007; Kronstad, 2013).  To combat limited iron sources and increase intracellular iron availability for fungal cells, C. neoformans activates several uptake systems including siderophore iron transporters (e.g., SIT1) and a high-affinity iron transport complex (including, for example, an iron permease) (Lian et al., 2005; Tangen et al., 2007; Jung and Kronstad, 2008).  The high affinity uptake system has been studied in some detail and is composed of the iron permease Cft1 and the ferroxidase Cfo1 (Jung et al., 2008; Jung et al., 2009).  Additionally, the transcription factor Cir1 regulates expression of the genes for iron acquisition, capsule formation, laccase, phospholipase, cell wall and membrane biosynthesis, in addition to signaling pathways (Jung et al., 2006).  Cir1 is often designated as a master iron regulator in C. neoformans.  Its activity as a transcription factor influences the expression of genes associated with iron uptake from heme, CIG1, an iron transporter, SIT1, and the production of melanin, LAC1, in addition to other iron-related genes (Jung et al., 2006).  Cir1 deletion mutants have a defect in capsule and melanin production, as well as reduced growth at 37°C, increased sensitivity to drug treatment, and avirulence in a mouse model (Jung et al., 2006; Jung et al., 2008).  In other fungi such as Schizosaccharomyces pombe, Candida albicans, and Ustilago maydis, the transcriptional repressors Fep1, Sfu1, and   13 Urbs1, respectively, regulate the expression of iron-responsive genes (Haas, 2003).  These proteins possess conserved cysteine-rich regions and two zinc funger motifs characteristic of GATA-type transcription factors, showing sequence similarity to Cir1, but possess an additional zinc finger  (An et al., 1997; Pelletier et al., 2002; Pelletier et al., 2003).  Other transcription factors involved in iron regulation include HapX and Rim101 (Jung et al., 2010; O'Meara et al., 2010). 1.1.6.4.6 Titan cell formation  Unusually large cells (called giant or titan cells and ranging in diameter from 40-50 μm) have been identified from clinical samples and lung tissue of C. neoformans-infected mice (Cruickshank et al., 1973; Love et al., 1985; Okagaki et al., 2010; Zaragoza et al., 2010).  The formation of giant cells is regulated in part by the cAMP/PKA pathway and by pheromone regulation via the MAPK pathway (Okagaki et al., 2010; Zaragoza et al., 2010; Okagaki et al., 2011; Choi et al., 2012).  Reasons behind titan cell formation in C. neoformans are not known, but possible roles include a defense strategy to evade the initial host immune response, as well as a morphological response to the host pulmonary environment (Zaragoza et al., 2010; Okagaki et al., 2010; Okagaki and Nielsen, 2012).  Recent studies have shown that the production of titan cells also enhances the virulence of C. neoformans (Crabtree et al., 2012). 1.1.6.4.7 Additional virulence-related factors  In addition to factors previously discussed, metabolic processes, lipid signaling, and resistance to oxidative and nitrosative stress also have virulence-related functions in C. neoformans (Cox et al., 2003; de Jesus-Berrios et al., 2003; Jung et al., 2006; Perfect, 2006; Brown et al., 2007; Gerik et al., 2008; Hu et al., 2008; Rhome and Del Poeta, 2009; Bien and Espenshade, 2010).  These virulence-related factors are affiliated with fungal proliferation in   14 host tissues, elaboration of capsule and production of melanin, in addition to roles associated with fungal survival against host defenses.  Taken together, C. neoformans possesses an array of diverse and effective virulence-related factors that it utilizes to initially infect the host and subsequently cause disease. 1.1.7 Secretion Secretion in C. neoformans can follow conventional or non-conventional routes.  Conventional secretion involves proteins with an N-terminal signal peptide, whereas non-conventional secretion may involve protein dissociation from the plasma membrane via a glycophosphatidylinositol (GPI)-anchor, exocytosis, membrane blebbing or the export of vesicles (Kronstad et al., 2011b).  The ability of C. neoformans to cause disease requires the elaboration of virulence factors via transport to the plasma membrane, cell wall, or by secretion to the extracellular environment.  For example, protein trafficking is important for the localization of laccase to the cell wall for survival of the fungus within alveolar macrophages, resistance to oxidative stress, extra pulmonary dissemination, and environmental protection (Huffnagle et al., 1995; Williamson, 1997; Liu et al., 1999; Steenbergen et al., 2001; Noverr et al., 2004; Garcia-Rivera et al., 2005).  However, little is known about the mechanisms and components associated with the export of capsule polysaccharide and other virulence factors in C. neoformans.  Researchers have investigated synthesis of the capsule polysaccharide to determine whether it occurs at the cell membrane or at an intracellular location.  Staining with anti-capsule antibodies revealed that intracellular vesicles contain the polysaccharide and these vesicles may be responsible for secretion of capsule material by exocytosis following intracellular synthesis (Yoneda and Doering, 2006).  Furthermore, secretion inhibitors such as brefeldin A, nocodazole, monensin and NEM, have been shown to block vesicle trafficking and   15 reduce capsule size (Hu et al., 2007).  A recent study investigating the role of Snf7, a key protein of the endosomal sorting complex required for transport (ESCRT), found the protein to be essential for the export of capsule and melanin, and virulence (Godinho et al., 2014).  In addition to the secretion of melanin and capsule, other studies have examined exocytosis functions (Sec6, Sec14), the secretion of phospholipases, and the involvement of extracellular vesicles (Chayakulkeeree and Perfect, 2006; Rodrigues et al., 2007; Rodrigues et al., 2008; Panepinto et al., 2009; Lev et al., 2013; Lev et al., 2014).  Investigations into the secretome of C. neoformans also identified proteins associated with fungal survival and virulence (Biondo et al., 2006; Eigenheer et al., 2007; Lev et al., 2014).  Specifically, the discovery of proteins involved in iron acquisition and homeostasis, virulence, cell wall biosynthesis, and cell signaling highlight the diversity of the secretome maintained by the fungus and provide a foundation for better understanding fungal modulation of the host environment (Lev et al., 2014).  The effects of modulating Pka1 activity on the secretome of C. neoformans are discussed further in Chapter 2. 1.1.7.1 Vesicular transport Protein secretion in C. neoformans involves vesicular migration from the ER to the Golgi apparatus followed by loading into a complex network of vesicles including exocytic vesicles and multivesicular bodies (Rodrigues et al., 2008).  Of particular interest is the release of extracellular vesicles or so-called virulence-factor delivery bags, which occurs via fusion of multivesicular bodies with the plasma membrane (Rodrigues et al., 2007; Rodrigues et al., 2008; Casadevall et al., 2009).  For example, vesicles containing capsular material and phospholipases are targeted to the plasma membrane for exocytosis, or via multivesicular bodies and are thought to participate in GXM assembly at the cell surface (Rodrigues et al., 2007; Doering, 2009).  In yeast, characterization of extracellular vesicles provides evidence for the participation of   16 different pathways of cellular trafficking in vesicle biogenesis (Oliveira et al., 2010).  Rodrigues et al. (2008) identified four types of extracellular vesicles in C. neoformans based on their morphology, size, contents, and origin.  The vesicles range from 30 to 400 nm in size and are surrounded by a lipid bilayer membrane (Eisenman et al., 2009).  A proteomic study identified 76 different vesicular proteins including those associated with virulence (e.g. laccase, acid phosphatase, urease, and signal transduction regulators) and the response to stress including protection from oxidative stress (e.g. chaperones, heat shock proteins, and superoxide dismutase) (Rodrigues et al., 2008).  The release of vesicular contents into the extracellular environment and the diversity of protein components identified in the extracellular vesicles present an interesting interface to study secretion and the delivery of virulence-related factors into the extracellular environment of C. neoformans. 1.2 Signal transduction pathways Signal transduction pathways mediate the sensing and processing of external or internal stimuli to generate specific responses.  Many signal transduction pathways are highly conserved across organisms, with specific adaptations of upstream receptors, sensor systems, or downstream effectors to customize output and subsequent influence on the organism or host.  Signaling pathways are capable of impacting a broad and diverse collection of downstream effectors and are important for the expression and regulation of different processes in fungi including growth, survival, mating, and virulence.  Crosstalk between pathways can also be important for regulating diverse cellular processes and for investigating interconnections between cellular components.  Of particular interest is the cAMP/PKA pathway, which will be discussed in greater detail in the following sections.     17 1.2.1 The cAMP/PKA pathway in mammalian systems  The cAMP/PKA pathway is a well-characterized and highly conserved signal transduction pathway.  In mammalian systems, cAMP is synthesized from adenylyl cyclases (ACs), following ligand binding to a G-protein coupled receptor (GPCR), resulting in activation of heterotrimeric G-proteins (Kleppe et al., 2011).  Early studies found the stimulating ligands to consist primarily of growth factors or hormones (Federman et al., 1992).  Following production of cAMP, the downstream regulatory effects are mediated through allosteric interactions with cAMP-binding proteins.  These interactions induce conformational changes resulting in alterations in the activation states of the binding partners (Taylor et al., 1990).  Targets of cAMP include PKA types I and II, which are released from the regulatory subunits RI and RII upon cAMP binding.  This dissociative activation makes PKA unique within the mammalian set of protein kinases.  Control of cAMP production is critically important for proper pathway operation and is under the control of PKA activity, AC, and phosphodiesterases (Kleppe et al., 2011).   The first described regulatory roles of PKA in mammalian cells were associated with energy metabolism and blood glucose levels (Delange et al., 1968).  Since this initial discovery, the regulatory roles of PKA have expanded to include regulation of fatty acid usage and energy store usage in tissues such as skeletal muscle, heart, liver, and adipose tissues (Petersen et al., 2008).  In particular, in the cardiovascular system and blood cells, cAMP plays important roles in regulating cardiac performance (Metrich et al., 2008).  Deregulation of the cAMP/PKA pathway, with established roles in cardiac excitation-contraction coupling, can lead to cardiac disease (Lee et al., 2013).  PKA also plays a role in regulation of the CNS, memory, neurodegeneration, and mood disorders (Schafe and LeDoux, 2000; Millan et al., 2008; Bourgault et al., 2009; Zhang et   18 al., 2009; Malleret et al., 2010).  In addition, cAMP/PKA pathway plays a significant role in tumor formation.  Abnormalities in the signaling pathway have been linked to the formation of various tumor types including benign adrenal tumors and bone-associated tumors, and may predispose patients to cancer (Rauschecker and Stratakis, 2012; Stratakis, 2013).  Specifically, defects in GPCR, PKA, and in cAMP regulators such as phosphodiesterase, influence tumor formation and disease progression.  Understanding the role of the cAMP/PKA pathway in disease progression is also of interest for the development of novel therapeutic agents and drugs that target the signaling pathway.  Strategies to target PKA signaling include focusing on cAMP levels, cAMP binding sites, the catalytic subunit, and localization.  The ubiquitous nature of the cAMP/PKA pathway presents challenges when designing pathway inhibitors, because general inhibitors may have non-specific and universal side effects.  Recent advances in the discovery of peptides and small molecules that disrupt the protein-protein interactions associated with cAMP-signaling proteins show promise, in particular for the treatment of cardiovascular disease (Lev et al., 2013).  1.2.2 The cAMP pathway in bacteria  Similar to mammalian systems, the cAMP pathway in bacteria is highly conserved and implicated in a broad spectrum of cellular processes.  It plays a critical role in sensing and responding to the external environment for many microbes.  Stimulation of the cAMP pathway in bacteria is similar to that described above involving a vast array of small, soluble signaling molecules within and outside the cell resulting in the production of the second messenger, cAMP, and subsequent activation of its downstream targets (Camilli and Bassler, 2006; Rumbaugh, 2007; McDonough and Rodriguez, 2012).  However, cAMP signaling is unique in bacteria because the mechanism involved in controlling gene expression does not engage a   19 protein kinase (such as PKA in eukaryotes).  In bacteria, direct binding of the second messenger to the cAMP-receptor protein family of transcription factors results in their activation (Kolb et al., 1993; Altarejos and Montminy, 2011).  Specifically in Escherichia coli, the role of cAMP  in mediating glucose response or catabolite repression, the production of cAMP, and the activation of the bound transcription factors has been extensively studied (Botsford, 1981; Kolb et al., 1993).  More recently, a role for cAMP in microbial virulence has been described and is discussed in the following section. The influence of cAMP on virulence has been confirmed through investigating the secretion of toxins into host cells, biofilm formation, type III secretion systems (T3SS), carbon metabolism, and virulence gene regulation (Masure et al., 1987; Botsford and Harman, 1992; Smith et al., 2004; Rickman et al., 2005; Zhan et al., 2008).  In Pseudomonas aeruginosa, cAMP provides transcriptional regulation of the T3SS genes and the function of the T3SS to deliver AC toxin to the host (Yahr et al., 1998; Engel and Balachandran, 2009; Fuchs et al., 2010).  cAMP signaling in Vibrio cholera integrates carbon source availability with population density, biofilm formation, resistance to bacteriophages, and virulence gene expression (Hammer and Bassler, 2003; Zhu and Mekalanos, 2003; Beyhan et al., 2007; Liang et al., 2007).  For Mycobacterium tuberculosis, stimulation of the cAMP pathway does not rely on glucose sensing, but rather on host conditions including pH, fatty acid and CO2 levels, and the expression of AC-encoding genes controlled by hypoxia and starvation (Bai et al., 2011).  In addition to the significance of the cAMP pathway for microbial virulence, the ability to disrupt important host cell signal transduction pathways by an invading pathogen is another effective virulence strategy.  Research shows that elevated levels of cAMP can suppress innate immune functions and cause excessive fluid secretion associated with infections (Serezani et al., 2008).  Elevation of cAMP can be   20 efficiently and effectively achieved through the use of toxins that are capable of up-regulating the activity of host AC and secretion of cAMP into host cells (Krueger and Barbieri, 1995; Ahuja et al., 2004; Lory et al., 2004; Agarwal et al., 2009; Bai et al., 2009).  Understanding the role of cAMP in modulating microbial virulence and characterizing the connections between pathogen and host provide a unique opportunity to better understand the diverse capabilities of this pathway. 1.2.3 The cAMP/PKA pathway in fungi  In fungi, stimulation of the cAMP/PKA signal transduction pathway can be achieved by sensing various nutrients and molecules, which results in the activation of PKA to broadly impact numerous cellular processes.  Many fungi utilize the cAMP pathway to control mating and to regulate virulence (Fuller and Rhodes, 2012).  Classic studies were performed in Saccharomyces cerevisiae to understand and characterize the pathway.  The results show that the membrane-bound adenylyl cyclase (Cdc35) is responsible for the conversion of ATP to cAMP, which serves as a second messenger and goes on to stimulate downstream receptors (Toda et al., 1987a).  Furthermore, during the inactive state, PKA is a heteromeric tetramer with coupled regulatory (Bcy1) and catalytic subunits (Tpk1, Tpk2, and Tpk3).  In the absence of cAMP, the regulatory subunits are bound to the catalytic subunits and kinase activity is inhibited, whereas upon binding of cAMP to the regulatory subunits, the catalytic subunits dissociate and PKA is activated (Toda et al., 1987b).  To stimulate the pathway in S. cerevisiae, sensing of glucose and amino acids is done through a GPCR, Gpr1, which in turn activates the G proteins, Gpa2 and Ras2 to stimulate AC, resulting in the production of cAMP and activation of PKA (Lengeler et al., 2000).  Stimulation of the cAMP/PKA pathway in S. cerevisiae impacts many cellular processes including stress responses, in particular the response to nutrient starvation, heat shock,   21 DNA damage, osmotic stress, and oxidative stress (Colombo et al., 1998; Gasch et al., 2000; Gorner et al., 2002; Harashima and Heitman, 2002).  Such a response is achieved through the influence of PKA on the stress responsive transcription factors, Msn2 and Msn4, resulting in regulation of the cellular reaction to oxidative stress and heat shock (Gorner et al., 1998; Smith et al., 1998; Hasan et al., 2002).  In addition, the pathway controls cell cycle progression, cell adhesion, pseudohyphal differential and invasive growth (Liu et al., 1996; Pan and Heitman, 1999; Lorenz et al., 2000).  For example, entry into stationary phase and high temperature resistance is controlled through the phosphorylation of the protein kinase, Rim15, by PKA (Reinders et al., 1998).  In addition to pathway stimulation, regulation of PKA activity is important for cellular control.  For example, catabolism of storage carbohydrates occurs upon pathway activation.  The associated glycolytic activity is capable of restoring ATP levels, resulting in a rise of intracellular pH and ultimately, down-regulation of the cAMP/PKA pathway (Thevelein and de Winde, 1999; Colombo et al., 2004).  Additional examples describing the stimulation and activation of the cAMP/PKA pathway in other fungi are summarized as follows (Bahn et al., 2007).  In C albicans, a human fungal pathogen capable of causing mucosal infections and systemic disease, the cAMP/PKA pathway plays a role in filamentation and virulence (Odds, 1996; Weig et al., 1998; Cao et al., 2006).  Pathway stimulation occurs from the sensing of glucose and amino acids, along with the sensing of high CO2 levels (Rocha et al., 2001; Klengel et al., 2005; Maidan et al., 2005).  Sensing of gases involves the diffusion of CO2 across the plasma membrane followed by hydration by a carbonic anhydrase to convert CO2 into HCO3- and the subsequent direct binding of HCO3- to AC, resulting in pathway activation (Bahn et al., 2005).  Pathway activation directly influences the transcription factors, Efg1 and Flo8, associated with the switch from a budding   22 yeast form to a polarized form involved in virulence (Bockmuhl and Ernst, 2001; Cao et al., 2006).  In S. pombe, the sensing of nutrients such as glucose is responsible for pathway stimulation and upon activation the pathway regulates mating and gene repression, and is associated with pheromone sensing (Hoffman and Winston, 1991; Yamamoto et al., 1997; Welton and Hoffman, 2000; Otsubo and Yamamoto, 2012).  Additionally, nutrient sensing and changes in O2 and CO2 levels are responsible for activating the cAMP/PKA pathway in U. maydis (Gold et al., 1994; Mayorga and Gold, 1998).  Upon stimulation, the cAMP/PKA pathway plays a role in sexual differentiation, filamentous growth and mating, as well as progression through the cell cycle (Barrett et al., 1993; Gold et al., 1994; Kruger et al., 1998).  Although numerous stimulating molecules and conditions result in the activation of the cAMP/PKA pathway across several fungal species, its primary role as a nutrient-sensing pathway to influence global regulation is of interest for further studies to identify downstream effectors and their targets. 1.2.3.1 The cAMP/PKA pathway in C. neoformans In C. neoformans, the cAMP/PKA signal transduction pathway regulates several important cellular processes including capsule production, melanin formation, mating, and virulence (Figure 1.3).  Components of the cAMP/PKA pathway include a Gα protein (Gpa1), adenylyl cyclase (Cac1), adenylyl cyclase-associated protein (Aca1), a candidate receptor (Gpr4), a phosphodiesterase (Pde1), and the catalytic (Pka1, Pka2) and regulatory (Pkr1) subunits of PKA (Kozubowski et al., 2009; Kronstad et al., 2011a).  In response to environmental signals, including exogenous methionine, nutrients such as glucose, and high CO2 levels, the GPCR, Gpr4, undergoes a conformational change to activate Cacl and subsequently stimulate the production of cAMP (Granger et al., 1985; Yang et al., 2002).  Conversely, the   23 low-affinity phosphodiesterase, Pde1, negatively regulates the cAMP/PKA pathway in C. neoformans through catalyzing cAMP hydrolysis (Hicks et al., 2005).  Following production of cAMP, as described above, PKA is activated when cAMP binds to the regulatory subunit, inducing a conformational change and releasing the active catalytic subunit (Taylor et al., 1990).  The broad and diverse impact of modulating PKA1 activity on the proteome is discussed in detail in Chapter 3 of this thesis. Many studies investigating the impact of gene deletions for components of the cAMP/PKA pathway have illustrated the connection between cAMP and the elaboration of virulence factors.  Mutation of gpr4 resulted in reduced endogenous cAMP levels and impaired capsule production implicating its function at an early step in the pathway (Xue et al., 2006).  Mutations in the genes encoding the Gpa1, Cac1, and Aca1 proteins result in reduced capsule and melanin formation, sterility, and attenuated virulence in a mouse model of cryptococcosis (Alspaugh et al., 1997; D'Souza et al., 2001; Alspaugh et al., 2002; Bahn et al., 2004).  Further investigation into the role of Gpa1 involved a transcriptome analysis of the gpa1 mutant revealing that the laccase enzymes, Lac1 and Lac2, are regulated by cAMP (Pukkila-Worley et al., 2005).  Additional evidence for the role of the cAMP cascade in mating and production of melanin and capsule was determined following the addition of exogenous cAMP in vitro, which suppressed gpa1 mutant phenotypes (Alspaugh et al., 1997).  Recently, an investigation into the interaction between Gpa1 and a homolog of Ric8 (resistance to inhibitors of cholinesterase 8) found that deletion of RIC8 results in reduced capsule size and melanin formation, and an attenuation of virulence.  The researchers concluded that Ric8 promotes Gpa1 activation to regulate production of cAMP in C. neoformans (Gong et al., 2014).  Protein-protein interactions can also impact cAMP signaling.  For example, two Ras proteins have been identified in C.   24 neoformans, Ras1 and Ras2 (Alspaugh et al., 2000).  It was found that Ras1 supports high temperature and invasive growth essential for survival, sexual differentiation, and proliferation in the host.  Ras-dependent regulation of invasive growth and mating is mediated through the cAMP signaling pathway, although the interaction of Ras1 with Cac1 or the Aca1-Cac1 complex has not been well characterized (Waugh et al., 2003). In addition to the impact of components of the cAMP/PKA pathway, its downstream targets can also have a significant influence on cellular processes and virulence.  Nrg1, a transcription factor and downstream target of the pathway has been shown to regulate a glucose dehydrogenase (Ugd1) involved in capsule production and growth at 37°C (Moyrand and Janbon, 2004; Cramer et al., 2006).  Transcriptional profiling of the nrg1 deletion mutant demonstrated a role for the transcription factor in the regulation of genes encoding enzymes related to carbohydrate metabolism, cell wall, and substrate oxidation, in addition to transporters, cell cycle proteins, and signaling components.  Along with capsule production, the cAMP/PKA pathway also mediates iron acquisition and pH sensing in C. neoformans (Jung et al., 2006; Jung and Kronstad, 2008; Jung et al., 2009; O'Meara et al., 2010).  Previous work by Jung et al. (2006; 2008) has characterized the connection between cAMP and iron.  Cir1 positively regulates expression of Gpr4 under iron-deplete and replete conditions, which in turn regulates capsule size via activation of the cAMP/PKA pathway.  In addition, O’Meara et al. (2010) demonstrated that the role of pH sensing by the cAMP/PKA pathway was associated with the phosphorylation of Rim101 by Pka1.  Taken together, characterization of the cAMP/PKA pathway has uncovered a diverse array of functions by the signal transduction pathway critical for survival and regulation of virulence in C. neoformans.    25  Figure 1.3: The cAMP/Protein Kinase A (PKA) pathway of C. neoformans.  The cAMP/PKA pathway is activated by sensing external stimuli including nutrient starvation and the presence of amino acids, followed by the production of cAMP and its binding to the regulatory subunit (Pkr1) of PKA.  This binding results in the release and activation of the catalytic subunits (Pka1, Pka2), which influence downstream targets and regulate the production of capsule and melanin.  1.2.3.1.1 Protein Kinase A in C. neoformans Pka1 is a key regulator of virulence in the serotype A strain H99 that is used by the research community to study virulence.  PKA signaling depends on the interactions of the catalytic and regulatory subunits.  Specifically, the regulatory subunit, Pkr1, binds to and inhibits the catalytic activity of PKA in addition to functioning as a receptor for cAMP.  As stated above for S. cerevisiae, upon binding of cAMP to the regulatory subunit of PKA, the catalytic subunit is released and capable of phosphorylating downstream targets.  Mutations in the gene encoding the Pka1 protein result in reduced capsule and melanin formation, sterility, and attenuated Capsule Cell wall Plasma membrane Nutrients Gpr4 Gpa1 Cac1 ATP cAMP R R Pkr1 C C Pka1 Capsule Melanin Mating ? Aca1 Pde1   26 virulence in a mouse model of cryptococcosis (Alspaugh et al., 1997).  In contrast to Pka1, disruption of the gene encoding Pkr1 causes cells to display an enlarged capsule phenotype, and to be hypervirulent in mice (D'Souza et al., 2001).   PKA has been shown to have a role in remodeling components of the secretory pathway and in controlling capsule formation by regulating the expression of secretory pathway components that control the export of virulence factors to the cell surface (Hu et al., 2007).  Serial analysis of gene expression (SAGE) for pka1 and pkr1 mutant strains compared to wild type revealed that PKA influences transcript levels for genes involved in cell wall synthesis, transport functions such as iron uptake, the tricarboxylic acid cycle, and glycolysis (Hu et al., 2007).  Additionally, the authors observed differential expression of ribosomal protein genes, genes encoding stress and chaperone functions, and genes for secretory pathway components, and phospholipid synthesis.  Functional analyses to follow up the SAGE study demonstrate that the pka1 mutant has a differential response to temperature stress, challenges to cell wall integrity, and secretion inhibitors that block capsule production.  Despite the significant role of PKA1 in C. neoformans serotype A, the second catalytic gene, PKA2, has no apparent role in virulence (Hicks et al., 2004; Jung and Kronstad, 2008).  The observed influence of the cAMP/PKA pathway on the elaboration of virulence factors, and more specifically, the role of Pka1, is of great interest in C. neoformans as a potential target for the development of novel therapeutic drugs against cryptococcal infection. 1.2.3.1.2 The use of a galactose-inducible, glucose-repressible promoter to modulate PKA1 expression. Recently, the role of PKA in C. neoformans was examined with strains carrying galactose-inducible and glucose-repressible versions of PKA1 and PKR1 constructed by inserting   27 the GAL7 promoter upstream of the genes (Choi et al., 2012).  By elevating Pka1 activity through growth of the PGal7::PKA1 strain in galactose-containing media, capsule thickness, cell size, ploidy, and vacuole enlargement were found to be influenced.  In addition, wild-type levels of melanization and laccase activity, and the correct localization of laccase required Pka1 activity.  The ability to modulate Pka1 activity provides an opportunity to investigate the influences that the cAMP/PKA pathway has on the secretion of virulence factors and secretory pathway components.   1.2.3.1.3 Phosphorylation targets of Pka1 PKA phosphorylation targets in C. neoformans can be predicted using the consensus recognition sequence (R/K-R/K-X-S/T-B) (Gibson et al., 1997; O'Meara et al., 2010).  Based on previous investigations in S. cerevisiae and C. neoformans, expected Pka1 targets include transcription factors and metabolic enzymes (Thevelein and de Winde, 1999; Lengeler et al., 2000; Cramer et al., 2006; Hu et al., 2007; Tangen et al., 2007).  For C. neoformans, these studies have identified the transcription factors Ste12α and Ngr1, as potential PKA targets, along with Sit1, Sec15 and Pep12 previously shown by SAGE analysis to be associated with secretion and capsule formation.  Additional PKA targets in S. cerevisiae are missing from C. neoformans, including the transcription factors Flo8, required for pseudohyphal growth, and Msn2/Msn4 and Yap1, responsible for modulation of general and oxidative stress, respectively (Estruch and Carlson, 1993; Marchler et al., 1993; Kuge and Jones, 1994; Liu et al., 1996; Gorner et al., 1998; Cao et al., 2006).  As mentioned previously, recent work also identified the pH-responsive, transcription factor, Rim101, as a phosphorylation target of Pka1 (O'Meara et al., 2010).  The authors showed that mutation of the potential Pka1 phosphorylation site, serine 773, to alanine resulted in both nuclear and cytoplasmic localization of a GFP-Rim101 protein, although direct   28 phosphorylation by Pka1 was not reported.  Rim101 regulates cell wall synthesis and integrity, capsule attachment at the cell surface, iron and copper homeostasis, and titan cell formation (O'Meara et al., 2010; Okagaki et al., 2011).  Although the components of the cAMP/PKA pathway have been well characterized, a comprehensive study identifying and characterizing the downstream phosphorylation targets of PKA has not been completed for C. neoformans.  As described in detail in Chapter 4, a phosphoproteome analysis upon modulation of Pka1 activity identified Cir1 as a target of phosphorylation.  1.3 Research purpose and significance  The purpose of my research was to better understand the influence of the cAMP/PKA pathway on the virulence of the pathogenic fungus C. neoformans.  To accomplish this goal, I performed quantitative proteomic studies on the secretome (Chapter 2) and intracellular proteome (Chapter 3), as well as a phosphoproteomic study (Chapter 4) under conditions where modulation of Pka1 activity was achieved.  My results revealed an impact of Pka1 on proteins associated with the secretory pathway, cell wall synthesis and integrity, translation, metabolism, iron regulation and homeostasis, and ultimately, virulence.  I identified novel secreted proteins, direct targets of Pka1 regulation and phosphorylation, and new connections between the cAMP/PKA pathway and key regulators of virulence in C. neoformans. 1.3.1 Hypotheses  I hypothesize that PKA regulates the expression of proteins associated with the secretory pathway in C. neoformans to control the elaboration of virulence-related factors, the extracellular proteome and virulence.  In addition, I propose that targets of PKA phosphorylation play roles in virulence factor production, regulation or trafficking to the cell surface.       29 1.3.2 Objective 1  To characterize the impact of PKA1 modulation on the secretome of C. neoformans by using quantitative proteomics to identify secreted proteins regulated by Pka1 and to investigate their potential as biomarkers using targeted proteomics experiments. 1.3.3 Objective 2  To characterize the impact of PKA1 modulation on the proteome of C. neoformans using quantitative proteomics to measure the global impact of Pka1 regulation on cellular proteins and to investigate the role of these proteins in fungal survival. 1.3.4 Objective 3  To characterize the impact of PKA1 modulation on the phosphoproteome of C. neoformans using phosphoproteomics to identify phosphorylation targets of Pka1 followed by site-directed mutagenesis of a target transcription factor to investigate the connection between Pka1, iron regulation, and virulence.       30 Chapter 2: Secretome Profiling of Cryptococcus neoformans Reveals Regulation of a Subset of Virulence-Associated Proteins and Potential Biomarkers by Protein Kinase A  2.1 Synopsis The pathogenic yeast Cryptococcus neoformans causes life-threatening meningoencephalitis in individuals suffering from HIV/AIDS.  The cyclic-AMP/protein kinase A (PKA) signal transduction pathway regulates the production of virulence factors in C. neoformans, but the influence of the pathway on the secretome has not been investigated.  In this study, I performed quantitative proteomics using galactose-inducible and glucose-repressible expression of the PKA1 gene encoding the catalytic subunit of PKA to identify regulated proteins in the secretome.  We identified 61 secreted proteins and found that changes in PKA1 expression influenced the extracellular abundance of five proteins, including the Cig1 and Aph1 proteins with known roles in virulence.  We also observed a change in the secretome profile upon induction of Pka1 from proteins primarily involved in catabolic and metabolic processes to an expanded set that included proteins for translational regulation and the response to stress.  We further characterized the secretome data using enrichment analyses and by predicting conventional versus non-conventional secretion.  Targeted proteomics of the Pka1-regulated proteins allowed us to identify secreted proteins in biological samples suggesting their potential as biomarkers of infection.  Overall, I found that the cAMP/PKA pathway regulates specific components of the secretome including proteins that affect the virulence of C. neoformans.      31 2.2 Introduction Cryptococcus neoformans is an opportunistic, yeast-like fungus that is a significant threat to immunocompromised individuals such as patients with HIV/AIDS (Mitchell and Perfect, 1995).  Globally, C. neoformans causes approximately one million cases of life-threatening cryptococcal meningoencephalitis per year in AIDS patients, resulting in an estimated 625,000 deaths (Park et al., 2009).  The ability of C. neoformans to cause disease depends on the production of virulence factors including a polysaccharide capsule, melanin deposition in the cell wall, the ability to grow at 37qC, and the secretion of extracellular enzymes (Bulmer et al., 1967; Kwon-Chung et al., 1982; Rhodes et al., 1982; Kwon-Chung and Rhodes, 1986; Polacheck and Kwon-Chung, 1988; Chang and Kwon-Chung, 1994).  Extracellular enzymes with roles in virulence include phospholipases, which hydrolyze ester bonds and aid in the degradation and destabilization of host cell membranes and cell lysis, and urease, which hydrolyzes urea to ammonia and carbamate, inducing a localized increase in pH (Casadevall and Perfect, 1998; Ghannoum, 2000; Cox et al., 2001; Maruvada et al., 2012).  Proteinases may also cause tissue damage, provide nutrients to the pathogen and facilitate migration to the central nervous system (Chen et al., 1996; Rodrigues et al., 2003; Vu et al., 2014).  In general, the secretion of extracellular enzymes is important for fungal survival within the host but a comprehensive investigation of the secretome and its regulation by the cyclic-AMP/Protein Kinase A (PKA) signal transduction pathway has not been performed for C. neoformans.  The cAMP/PKA pathway regulates capsule production, melanin formation, mating, and virulence in C. neoformans (Alspaugh et al., 1997; D'Souza et al., 2001; Kozubowski et al., 2009; Kronstad et al., 2011b; McDonough and Rodriguez, 2012).  Components of the pathway include a G-α protein (Gpa1), adenylyl cyclase (Cac1), adenylyl cyclase-associated protein   32 (Aca1), a candidate receptor (Gpr4), phosphodiesterases (Pde1 and Pde2), and the PKA catalytic (Pka1, Pka2) and regulatory (Pkr1) subunits.  In response to environmental signals, including exogenous methionine and nutrient starvation, the G-protein coupled receptor (GPCR), Gpr4, undergoes a conformational change to activate Cacl and subsequently stimulate the production of cAMP.  Mutations in genes encoding the Gpa1, Cac1, Aca1, and Pka1 proteins result in reduced formation of capsule and melanin, as well as sterility and attenuated virulence in a mouse model of cryptococcosis (Alspaugh et al., 1997; Hicks et al., 2004).  In particular, Pka1 is a key regulator of virulence in C. neoformans serotype A.  In contrast, disruption of the gene encoding Pkr1 results in enlargement of the capsule and a hypervirulence phenotype (D'Souza et al., 2001). Previous transcriptional profiling experiments compared a wild-type strain with pka1Δ and pkr1Δ mutant strains of C. neoformans H99, and identified differences in transcript levels for genes related to cell wall synthesis, transport (e.g., iron uptake), the tricarboxylic acid cycle and glycolysis (Hu et al., 2007).  Differential expression patterns were also observed for genes encoding ribosomal proteins, stress and chaperone functions, secretory pathway components and phospholipid biosynthetic enzymes.  Specifically, loss of PKA1 influenced the expression of genes involved in secretion, and Pka1 was hypothesized to influence capsule formation by regulating expression of secretory pathway components that control the export of capsular polysaccharide to the cell surface.  Additionally, the secretion inhibitors brefeldin A, nocodazole, monensin, and NEM, reduced capsule size, a phenotype similar to that observed in a pka1 mutant (Hu et al., 2007).  In general, the mechanisms and components required for the export of capsule polysaccharide and other virulence factors in C. neoformans are poorly understood.  Beyond the role of PKA, other studies have examined exocytosis functions (Sec6, Sec14), the secretion of   33 phospholipases, and the involvement of extracellular vesicles (Rodrigues et al., 2007; Rodrigues et al., 2008; Panepinto et al., 2009; Chayakulkeeree et al., 2011; Lev et al., 2013; Lev et al., 2014).  Additionally, O’Meara et al. (2010) recently demonstrated that PKA influences capsule attachment via phosphorylation of the pH-responsive transcription factor Rim101, a key regulator of cell wall function.   The role of PKA in secretion in C. neoformans has also been examined with strains carrying galactose-inducible and glucose-repressible versions of PKA1 and PKR1 constructed by inserting the GAL7 promoter upstream of the genes (Choi et al., 2012).  Elevated Pka1 activity, stimulated by growth of the PGal7::PKA1 strain in galactose-containing media, was found to influence capsule thickness, cell size, ploidy, and vacuole enlargement (Choi et al., 2012).  The authors also showed that Pka1 activity was required for wild-type levels of melanization and laccase activity, and influenced the correct localization of laccase.  The ability to regulate expression of PKA1 and, subsequently, the activity of Pka1, is a powerful tool for investigating the mechanisms of its influence on the secretion of virulence factors and secretory pathway components.   In this study, I used the strain with galactose-inducible and glucose-repressible expression of PKA1 to investigate the influence of Pka1 on the secretome using quantitative proteomics.  We identified 61 different secreted proteins and found that Pka1 regulated the extracellular abundance of five.  These proteins included three enzymes (α-amylase, acid phosphatase, and glyoxal oxidase), the Cig1 protein (cytokine-inducing glycoprotein) associated with virulence and heme uptake, and a conserved hypothetical protein containing a carbohydrate-binding domain (CNAG_05312).  We also observed a change in the secretome profile under Pka1-inducing conditions from proteins involved primarily in catabolic and metabolic processes   34 to an expanded set that included proteins for translational regulation and the response to stress.  Enrichment analyses of our Pka1-influenced secretome data compared to the whole genome showed over-representation of genes associated with a broad spectrum of processes including metabolic and catabolic processing.  Although no enrichment was observed between our secretome data and the Fungal Secretome KnowledgeBase (FunSecKB), a comparison of GO terms between the data sets showed the majority of our identified proteins to be represented in the FunSecKB.  Next, I exploited our secretome data using a targeted proteomics approach to identify potential biomarkers of cryptococcal infection.  Multiple Reaction Monitoring (MRM) in the presence of stable isotope dilutions (SID) allows for identification and quantification of specifc peptides in a sample.  Specifically, I was able to identify Pka1-regulated proteins of C. neoformans in host samples including blood, bronchoalveolar lavage fluid, and infected macrophage lysates.  Overall, our study reveals that the cAMP/PKA pathway regulates specific components of the secretome including the Cig1 and Aph1 proteins that contribute to virulence in C. neoformans.  2.3 Experimental procedures  2.3.1 Fungal strains and culture conditions The C. neoformans var. grubii wild-type strain H99 (WT) and the PGAL7::PKA1 strain with galactose-inducible/glucose repressible expression of PKA1 were used for this study (Alspaugh et al., 1997; Choi et al., 2012).  The strains were maintained on yeast extract peptone dextrose (YPD) medium (1% yeast extract, 2% peptone, 2% dextrose, and 2% agar).  For studies involving regulation of PKA1, cells of the WT and regulated strains were pre-grown overnight with agitation at 30°C in YPD broth, transferred to yeast nitrogen base medium with amino acids   35 (YNB, Sigma-Aldrich) and incubated overnight with agitation at 30°C.  Cell counts were performed and 5 x 107 cells/ml were transferred to Minimal Medium (MM) (29.4 mM KH2PO4, 10 mM MgSO4y7H2O, 13 mM glycine, 3 μM thiamine, 0.27% carbon source) containing either glucose (MM+D) or galactose (MM+G).  For end-point studies, cells were incubated with agitation at 30°C in MM+D or MM+G for 96 h; for time-course studies, cells were incubated with agitation at 30°C in MM+D or MM+G for 16, 48, 72, and 120 h.  Time points were selected based on previous studies on the timing of protein secretion as well as the analysis of proteins in extracellular vesicles of C. neoformans, which used samples collected at 48 and 72 h of growth (Chen et al., 1997; Rodrigues et al., 2007; Rodrigues et al., 2008).  Samples were collected in triplicate for analysis. 2.3.2 Protein quantification, precipitation and in-solution digestion  To collect supernatant samples, cells were removed by centrifugation at 3,500 rpm for 15 min at 4°C and the culture medium was transferred to new tubes; this step was repeated four times until all cell debris had been removed.  Supernatant samples were kept on ice and total protein concentration was measured by a BCA-Protein-assay (Pierce).  Ultrapure bovine serum albumin was used as a calibration standard.  We used two approaches for protein precipitation to maximize protein detection and obtain a comprehensive view of the secretome.  For time-course studies, trichloroacetic acid (TCA)/acetone precipitation was performed (Damerval et al., 1986).  In brief, an aliquot of culture supernatant (50 μg total protein) was mixed with five volumes of ice-cold TCA/acetone (20%/80% w/v) and incubated overnight at -20°C.  Precipitated proteins were collected by centrifugation at 10,000 rpm for 20 min at 4°C.  The pellet was washed four times with ice-cold acetone, air-dried and stored at -20°C.  For end-point studies, ethanol (EtOH)/acetate precipitation was performed (Foster et al., 2003).  In brief, an aliquot of culture   36 supernatant (50 μg total protein) was diluted with 4 volumes of absolute EtOH, 2.5 M NaCH3COO was used to bring the solution to 50 mM NaCH3COO, pH 5.0 and 20 μg of glycogen was added to the sample.  Samples were vortexed and incubated at room temperature for 2 h with periodic agitation.  Precipitated proteins were collected by centrifugation at 15,000 rpm for 10 min at 4°C.  The pellet was washed twice with EtOH, then air-dried and stored at  -20°C.  All supernatant samples were subjected to in-solution digestion using ACS grade chemicals or HPLC grade solvents (Thermo Scientific and Sigma-Aldrich) (Fang et al., 2010).   In brief, the precipitated protein pellet was solubilized in digestion buffer (1% sodium deoxycholate, 50 mM NH4HCO3), incubated at 99°C for 5 min with agitation, followed by reduction (2 mM of dithiothreitol (DTT) for 25 min at 56°C), alkylation (4 mM of iodoacetamide (IAA) for 30 min at room temperature in the dark), and trypsinization (0.5 μg/μl of sequencing grade modified trypsin (Promega)) overnight at 37°C.  Based on our results, the TCA/acetone precipitation method appeared to be more stringent, perhaps due to more extensive washing in the protocol.  2.3.3 Peptide chemical labeling and purification  Digested peptides from supernatants were desalted, concentrated, and filtered on C18 STop And Go Extraction (STAGE) tips (Rappsilber et al., 2003).  Reductive dimethylation using formaldehyde isotopologues was performed to differentially label peptides from the different experimental conditions.  Light formaldehyde (CH2O) and medium formaldehyde (CD2O) (Cambridge Isotope Laboratories, Andover, MA) were combined with cyanoborohydride (NaBH3CN, Sigma-Aldrich) to give a 4 Da difference for labeled peptides (Boersema et al., 2008).  Samples from the WT strain were routinely labeled with light formaldehyde, and PGAL7::PKA1 samples were labeled with medium formaldehyde.  Briefly, eluted and dried   37 STAGE-tip peptides were resuspended in 100 mM triethylammonium bicarbonate, and incubated in 200 mM formaldehyde and 20 mM sodium cyanoborohydride for 90 min in the dark.  After labeling, 125 mM NH4Cl was added and incubated for 10 min to react with excess formaldehyde, followed by the addition of acetic acid to a pH < 2.5 to degrade sodium cyanoborohydride.  For each comparison, equal amounts of labeled peptides were mixed and desalted on C18 STAGE tips.  2.3.4 Protein identification by liquid chromatography-tandem mass spectrometry (LC-MS/MS) and mass spectrometry data analysis  Purified peptides were analyzed using a linear-trapping quadrupole - Orbitrap mass spectrometer (LTQ-Orbitrap Velos; Thermo Fisher Scientific) on-line coupled to an Agilent 1290 Series HPLC using a nanospray ionization source (Thermo Fisher Scientific).  This includes a 2-cm-long, 100-μm-inner diameter fused silica trap column, 50-μm-inner diameter fused silica fritted analytical column, and a 20-μm-inner diameter fused silica gold coated spray tip (6-μm-diameter opening, pulled on a P-2000 laser puller from Sutter Instruments, coated on Leica EM SCD005 Super Cool Sputtering Device).  The trap column was packed with 5 μm-diameter Aqua C-18 beads (Phenomenex, www.phenomenex.com) while the analytical column was packed with 3.0 μm-diameter Reprosil-Pur C-18-AQ beads (Dr. Maisch, www.Dr-Maisch.com).  Buffer A consisted of 0.5% aqueous acetic acid, and buffer B consisted of 0.5% acetic acid and 80% acetonitrile in water.  Samples were resuspended in buffer A and loaded with the same buffer.  Standard 90 min gradients were run from 10% B to 32% B over 51 min, then from 32% B to 40% B in the next 5 min, then increased to 100% B over a 2 min period, held at 100% B for 2.5 min, and then dropped to 0% B for another 20 min to recondition the column.  The HPLC system included Agilent 1290 series Pump and Autosampler with   38 Thermostat; temperature was set at 6°C.  The sample was loaded on the trap column at 5 μl/min and the analysis was performed at 0.1 μl/min.  The LTQ-Orbitrap was set to acquire a full-range scan at 60,000 resolution from 350 to 1600 Th in the Orbitrap to simultaneously fragment the top ten peptide ions by CID and top 5 by HCD (resolution 7500) in each cycle in the LTQ (minimum intensity 1000 counts).  Parent ions were then excluded from MS/MS for the next 30 s.  Singly charged ions were excluded since in ESI mode peptides usually carry multiple charges.  The Orbitrap was continuously recalibrated using lock-mass function (Olsen et al., 2005).  Mass accuracy included an error of mass measurement within 5 ppm and did not exceed 10 ppm.    For analysis of mass spectrometry data, centroid fragment peak lists were processed with Proteome Discoverer v. 1.2 (Thermo Fisher Scientific).  The search was performed with the Mascot algorithm (v. 2.4) against a database comprised of 6,692 protein sequences from the source organism C. neoformans H99 database (C. neoformans var. grubii H99 Sequencing Project, Broad Institute of Harvard and MIT, http://www.broadinstitute.org/) using the following parameters: peptide mass accuracy 10 ppm; fragment mass accuracy 0.6 Da; trypsin enzyme specificity, 1 max missed cleavage, fixed modifications - carbamidomethyl, variable modifications - methionine oxidation, deamidated N, Q and N-acetyl peptides, dimethyl (K), dimethyl (N-term), dimethyl 2H(4) (K), and dimethyl 2H(4) (N-term),  ESI-Trap fragment characteristics.  Only those peptides with Ion Scores exceeding the individually calculated 99% confidence limit (as opposed to the average limit for the whole experiment) were considered as accurately identified.  The acceptance criteria for protein identification were as follows: only proteins containing at least one unique peptide with a Mascot score > 25 were considered in the dataset.  Quantitative ratios were extracted from the raw data using Proteome Discoverer.  Proteome Discoverer parameters – Event Detector: mass precision 4 ppm (corresponds to   39 extracted ion chromatograms at  ±12 ppm max error), S/N threshold 1; Quantitation Method – Ratio Calculation – Replace Missing Quantitation Values with Minimum Intensity – yes, Use Single Peak Quantitation Channels – yes, - Protein Quantification – Use All Peptides – yes.  Experimentally determined fold changes for WT and PGAL7::PKA1 strains grown under Pka1-repressed (glucose-containing medium) and Pka1-induced (galactose-containing medium) conditions were converted to a log2 scale and the average fold change and standard deviation were used for analysis.  A fold change of >10 was used as a cut-off limit for the time-point and end-point analyses.   For the comparative analysis of the time-point samples, the statistical significance of the fold changes of the identified secreted proteins present under both Pka1-repressed and Pka1-induced conditions and at equivalent time points (i.e. 16, 48, 72, and 120 hpi) was assessed for an influence of PKA regulation using a Student’s t-test (p-value < 0.05).  For the comparative analysis of the end-point samples, the statistical significance of the fold changes of the identified secreted proteins present under both Pka1-repressed and Pka1-induced conditions was evaluated using a Student’s t-test (p-value < 0.05).  To confirm the statistically significant Pka1-regulated proteins identified from the end-point analysis, a multiple-hypothesis testing correction was performed on the secretome data using the Benjamini and Hochberg method with a false discovery rate of 0.05 (Benjamini and Yekutieli, 2001). 2.3.5 Gene ontology analyses Proteins were characterized with Gene Ontology (GO) terms using a local installation of Blast2GO (Conesa et al., 2005).  Gene annotation data of the C. neoformans H99 reference genome were retrieved from the Broad Institute (May 2014) and a copy of the non-redundant (nr) protein database was downloaded from NCBI (May 2014) (Janbon, 2014).  The most current associations between the nr protein database and GO terms were retrieved in May 2014 from   40 Blast2GO.  GO terms were assigned to WT proteins and filtered using default settings of the Blast2GO pipeline (Conesa et al., 2005).  We performed GO term enrichment analyses for sets of proteins using hypergeometric tests and the Benjamini and Hochberg false discovery rate multiple testing correction (p-value < 0.05) implemented in the R packages GSEABase and GOstats.  GO term categories containing singleton entries were excluded.  GO categories and enrichment datasets were visualized using the R package ggplot2 (Wickham, 2010). 2.3.6 Prediction of the extracellular location of identified proteins SignalP 4.1 (http://www.cbs.dtu.dk/services/SignalP/) was used to predict whether identified proteins were secreted based on the presence of a signal peptide (Petersen et al., 2011).  Identified protein sequences were also analyzed using Signal-3L (http://www.csbio.sjtu.edu.cn/bioinf/Signal-3L/) and Phobius (http://phobius.sbc.su.se) to confirm results (Kall et al., 2004; Shen and Chou, 2007).  Additionally, secreted proteins were analyzed for the presence of a glycophosphatidylinositol (GPI) anchor using (http://gpi.unibe.ch) (Fankhauser and Maser, 2005).  2.3.7 RNA isolation and quantitative Real-Time PCR analysis  Cells from WT and PGAL7::PKA1 strains were prepared for the examination of gene expression by overnight growth in YNB medium followed by dilution to 5.0 x 107 cells/ml in 5 ml of MM+D or MM+G and incubation at 30°C with agitation for 16 and 96 h.   Samples were collected in triplicate for analysis.  Cells were collected at the designated time points, flash frozen in liquid N2, and stored at -80°C.  Total RNA was extracted using an EZ-10 DNAaway RNA Miniprep kit (Bio Basic) according to the manufacturer’s protocol.  Complementary DNA was synthesized using a Verso cDNA kit (Thermo Scientific) and used for quantitative real-time PCR (qRT-PCR).  Primers were designed using Primer3 v.4.0 (http://bioinfo.ut.ee/primer3-  41 0.4.0/) and targeted to the 3’ regions of transcripts (Table A.1) (Untergasser et al., 2012).  Relative gene expression was quantified using the Applied Biosystems 7500 Fast Real-time PCR system.  Control genes CNAG_00483 (Actin) and CNAG_06699 (GAPDH) were used for normalization, and tested for statistical significance using the Student’s t-test.  As a control, PKA1 RNA expression levels under Pka1-repressed and Pka1-induced conditions in the WT and PGAL7::PKA1 strains were also analyzed at various time points to confirm the regulated PKA expression (Figure A.1).  2.3.8 RNA blot analysis  To confirm qRT-PCR results, total RNA was isolated for the PGAL7::PKA1 strain grown in 50 ml of MM+D or MM+G for 16 h.  Briefly, cell pellets were collected and flash frozen in liquid N2, followed by overnight lyophilization.  One milliliter of buffer 1 (2% SDS, 68 mM Na3C6H5O7, 132 mM C6H8O7, 10 mM EDTA) was added to the samples, along with 600 μl of glass beads; samples were subjected to bead beating for two, 3 min intervals at power 3 (BioSpec, Mini-Beadbeater) and subsequently stored on ice.  Next, 340 μl of buffer 2 (4 M NaCl, 17 mM Na3C6H5O7, 33 mM C6H8O7) was added and samples were inverted several times and incubated on ice for 5 min.  Samples were then centrifuged at 15,000 rpm for 10 min, the supernatant fraction was collected and transferred to a new tube, one volume of isopropanol was added, and samples were mixed and incubated at room temperature for 15 min.  The pellet was collected following centrifugation at 15,000 rpm for 5 min, and washing of the pellet with 70% diethylpyrocarbonate (DEPC)-EtOH was performed.  The pellet was collected, air dried, and dissolved in 20 μl of DEPC-H2O.  The hybridization probes were prepared with a PCR-amplified DNA fragment of CNAG_00483 (Actin) or CNAG_00396 (PKA1) using the specific primers   42 outlined in Table A.1 and labeled with 32P using an Oligolabeling kit (Amersham Biosciences).  Scanned images were analyzed using a Bio-Rad ChemiDoc MP Imaging System (Figure A.1). 2.3.9 Multiple Reaction Monitoring (MRM) sample collection from macrophage, mouse bronchoalveolar lavage, and mouse blood   The survival rates of the WT, pka1Δ, and PGAL7::PKA1 strains during incubation with macrophages were determined and lysates were prepared for protein analysis (Griffiths et al., 2012).  Briefly, cells of the J774A.1 macrophage-like cell line were grown to 80% confluence in Dulbecco’s Modified Eagle’s Medium (DMEM; Sigma) supplemented with 10% fetal bovine serum and 2 mM L-glutamine at 37°C and 5% CO2.  The macrophages were stimulated 1 h prior to infection with 150 ng/ml phorbol myristate acetate (PMA).  Fungal cells were grown in YNB overnight at 30°C, followed by inoculation in MM+D or MM+G at 5.0 x 107 cells/ml.  Following overnight growth, the fungal cells were washed with phosphate-buffered saline (PBS, Invitrogen) and opsonized with 0.5 μg/ml of the anti-capsule monoclonal antibody 18B7 in DMEM or DMEM supplemented with 0.20% glucose or galactose (30 min at 37°C).  Stimulated macrophages were infected with 2 x 105 opsonized fungal cells at a multiplicity of infection (MOI) of 1:1 for 2 h and 24 h at 37°C and 5% CO2.  To measure fungal survival, macrophages containing internalized cryptococcal cells were washed thoroughly four times with PBS and then lysed in 1 ml of sterile dH2O for 30 min at room temperature.  Lysate dilutions were plated on YPD agar and incubated at 30°C for 48 h, at which time the resulting colony forming units (CFUs) were counted and intracellular rates of infection (%) were calculated as the ratio of the CFUs at 2 h and 24 h over the initial number of macrophages.  The statistical significance of differences between WT, pka1Δ mutant, and PGAL7::PKA1 strains were determined by unpaired t-  43 tests.  For proteomic analysis, lysates from infected macrophage at 24 h of incubation were collected, flash frozen in liquid N2 and stored at -80°C. Female BALB/c mice (10-12 weeks old) obtained from Charles River Laboratories (Senneville, Ontario, Canada) were used to collect bronchoalveolar lavage (BAL) and blood samples following cryptococcal infection.  C. neoformans WT cells were grown overnight in YPD at 30°C with agitation, washed in PBS and re-suspended at 1.0 x 108 cells/ml in PBS.  For collection of BAL, intranasal inoculation of three mice with 100 μl of the WT cell suspensions (1.0 x 107 cells) was performed.  For collection of blood samples, intravenous inoculation of three mice with 100 μl of the WT cell suspensions (1.0 x 107 cells) was performed.  At 48 hpi, the infected mice were euthanized by CO2 inhalation and 1 ml of BAL fluid and 500 μl of blood samples were collected from each mouse (Hu et al., 2008).  Mouse lavage and blood samples were flash frozen with liquid N2 and stored at -80°C.  Mouse assays were conducted in accordance with University of British Columbia’s Committee on Animal Care (protocol A13-0093).  2.3.10 MRM sample preparation  Macrophage lysate samples were prepared as described above followed by trypsin in-solution digestion.  Samples were collected for WT and PGAL7::PKA1 strains at 24 hpi in triplicate.  Mouse BAL samples were prepared as described above, followed by trypsin in-solution digestion.  Samples were collected in triplicate at 48 hpi.  For mouse blood samples, highly abundant proteins were removed as previously described (Ahmed et al., 2003).  Briefly, proteins were precipitated by the addition of two volumes of acetonitrile and 1.0% acetic acid, followed by centrifugation at 10,000 rpm for 5 min at 4°C.  The supernatant was collected and evaporated and the residual proteins were then subjected to trypsin in-solution digestion as   44 described above. Following trypsin digestion, all samples were desalted, concentrated, and filtered on high-capacity C18 STAGE tips.  2.3.11 Peptide selection, internal standardization, and MRM development  Skyline (v2.1) was used to build and optimize the MRM method for the relative quantification of peptides (MacLean et al., 2010a).  Synthesized peptides for MRM analysis were designed in-house using the following parameters: tryptic peptides, 0 max missed cleavages, minimum of 7 and maximum of 25 amino acids, excluding peptides containing Met or Cys residues (if possible) and N-terminal glutamine, hydrophobicity between 10-40 (Sequence Specific Retention Calculator, http://hs2.proteome.ca/SSRCalc/SSRCalcX.html), desirable spectral intensities (GenePattern ESPPredictor, http://www.broadinstitute.org/cancer/software/genepattern/modules/ESPPredictor.html), and transition settings selecting for precursor charges of 2 and 3, ion charge of 1, monitoring both b and y ions.  SpikeTides labeled with stable isotopes (C-term Arg U-13C6;U-15N4 or Lys U-13C6;U-15N2) were purchased from JPT Peptide Technologies GmbH (Berlin, Germany).  N-terminal Arginine (R) and Lysine (K) were labeled with a stable isotope mass of 10.008269 and 8.014199, respectively.  Collision energy (CE) and fragmentor voltage (FV) for each peptide was predicted utilizing Skyline software and then confirmed experimentally (MacLean et al., 2010b).  Doubly and triply charged precursor ions were optimized and three to five transitions were measured per peptide.  The final MRM method included the monitoring of a total of 23 peptides, representing 5 proteins (Table A.2) was developed.  Stable isotope-labeled peptides were resuspended in 100 μl of 0.5% acetic acid with agitation at room temperature.  The peptides were further diluted and combined to result in final concentrations of 100 fmol/μl to 1 pmol/μl of each   45 peptide.  Five μl of the peptide mixture was injected into an Agilent 6460 Triple Quadrupole (Agilent) for data acquisition and peptide optimization.  2.3.12 Mass spectrometry and data analysis for MRM MRM assays were performed on an Agilent 6460 Triple Quadrupole coupled with Agilent 1200 Series HPLC.  The instrument was operated in positive electrospray ionization mode using MassHunter Workstation Data Acquisition (v.B.04.04, Agilent).  Chromatography was performed on a Large Capacity Chip with 160 nl Trap, analytical column was 150 mm x 75 μm, stationary phase for both trapping and analytical columns were Zorbax-SB C-18, 300 A and 5 μm particles (Agilent).  Peptides were separated using gradient elution with a stable flow of 0.30 μl/min, beginning with 97% buffer A (97% dH2O, 3% acetonitrile, 0.1% formic acid (FA)) and 3% buffer B (10% dH2O, 90% ACN, 0.1% FA) followed by a step gradient of buffer B from 3% to 80%, which was achieved at 10.5 min.  Subsequent equilibration was performed for 4.5 min at 3% buffer B.  The column was maintained at room temperature during analysis, and the samples were kept at 4-7°C.  The MS was operating in selective reaction mode using electrospray ionization in positive ion mode, with a capillary voltage of 1850 V and a source temperature of 325°C.  Cone voltage was static, collision energies and fragmentor voltages were optimized for each compound individually (Table A.3).  Peak identification was performed using MassHunter Qualitative Analysis (Agilent v.B.04.04).   Quantification of natural proteins was performed using peak areas relative to the known amounts of added isotopically-labeled synthetic peptides during a multiplexed MRM run.  Natural protein levels were identified from the following matrices: WT and PGAL7::PKA1 macrophage lysate MM+D and MM+G collected at 24 hpi, mouse BAL collected at 48 hpi from WT, and mouse blood collected at 48 hpi from WT.  Experimentally determined peak areas and   46 the subsequent quantification values were converted to a log2 scale.  Samples were collected and run in triplicate.  Positive association of natural peptides to their respective isotopically-labeled peptides was determined based on co-elution patterns.  For positive identification of a natural protein in a collected sample, at least one peptide with a minimum of two transitions per protein must be identified or more than one peptide with at least one transition per protein must be present.   2.3.13 Validation of secretome data  Enzymatic activity was assayed for α-amylase and acid phosphatase.  The assays were performed with kits for both enzymes according to the manufacturer’s protocol (BioVision Incorporated) (Figure A.2).  To confirm that proteins identified in the secretome were a result of secretion and not a product of cell lysis, a PCR was performed on secretome samples from Pka1-repressed conditions for WT strain at 96 hpi.  Actin (CNAG_00483) and PKA1 (CNAG_00396) were used as control genes for amplification (Figure A.3) (Holbrook et al., 2011).   2.4 Results 2.4.1 Control of PKA1 expression results in a change of the protein secretion profile Given the virulence defect of a pka1Δ mutant, I hypothesized that Pka1 influences the secretion of proteins associated with the virulence and survival of C. neoformans in the host.  To test this idea, I quantitatively identified proteins secreted by C. neoformans in the context of regulated expression of PKA1.  We collected supernatant cultures of WT and PGAL7::PKA1 strains grown under Pka1-repressed (glucose) and Pka1-induced (galactose) conditions at 16, 48, 72, and 120 hours post-inoculation (hpi), and analyzed the samples using quantitative mass spectrometry.  The analysis of these supernatant samples resulted in the identification of 164 (54   47 quantifiable) and 207 (83 quantifiable) proteins under Pka1-repressed and Pka1-induced conditions, respectively (Table A.4; Table A.5).  As shown in Table 2.1, 23 proteins were identified and quantified under Pka1-repressed and Pka1-induced conditions at the specified time-points, but I did not observe proteins whose abundance was influenced by Pka1.  A comparison of Gene Ontology (GO) term biological classifications between secreted proteins identified under Pka1-repressed and Pka1-induced conditions at all time points revealed changes in the secretome profiles under the influence of Pka1 (Figure 2.1).  Under Pka1-repressed conditions, the majority of secreted proteins were associated with catabolic and metabolic (33%), unknown (20%), and hypothetical (20%) processes (totaling 73%), with additional proteins associated with transport (8%), oxidation-reduction processes (4%), dephosphorylation (4%), proteolysis (4%), glycolysis (4%), and regulation of transcription (3%).  Conversely, a change in the secretome profile was observed under the Pka1-induction condition.  Here, I again observed the majority of proteins to be associated with catabolic and metabolic (26%), unknown (19%), and hypothetical (17%) processes (totaling 62%).  A slight decline was found for proteins associated with transport (from 8% to 6%), oxidation-reduction processes (from 4% to 3%), dephosphorylation (from 4% to 2%), proteolysis (from 4% to 3%), and regulation of transcription (from 3% to 0%); however, a greater emphasis was found for proteins associated with glycolysis (from 4% to 6%), response to stress (from 0% to 8%), translation (from 0% to 7%), and nucleosome assembly (from 0% to 3%).  Although, our secretome analysis at specific time points did not identify Pka1-regulated proteins, a change toward the secretion of proteins for glycolysis, translational regulation, nucleosome assembly, and the response to stress was observed upon induction of PKA1 expression.    48 Table 2.1: Proteins identified in the secretome of C. neoformans collected at 16, 48, 72, and 120 hpi grown in Pka1-repressed (glucose-containing medium) and Pka1-induced (galactose-containing medium) conditions.      Fold changeb Std. Dev.  GO categoriesa Accession number Protein Name Time point Pka1-repression Pka1-induction Pka1-repression Pka1-induction  p-valuec Carbohydrate catabolic process         CNAG_02189  α-Amylase 16 hpi 0.298 1.096 0.314 1.270 0.479 GTP catabolic process          CNAG_06125  Translation elongation factor 1 α 16 hpi 0.135 0.379 0.145 0.517 0.587 Carbohydrate metabolic process         CNAG_01239  Chitin deacetylase  16 hpi 0.906 1.350 0.310 0.515 0.301   CNAG_04245  Chitinase  16 hpi 0.448 0.286 0.007 0.296 0.522    48 hpi 0.826 0.603 1.019 0.017 0.954   CNAG_06501  1,3-β-glucanosyltransferase  16 hpi 0.573 0.896 0.383 0.169 0.360 Transmembrane transport          CNAG_02974  Voltage-dependent ion-selective channel  16 hpi 0.223 0.466 0.089 0.645 0.534 Oxidation-reduction process          CNAG_03465  Laccase 16 hpi 0.637 0.870 0.651 1.038 0.771 Unknown/Unclassified          CNAG_02030  Glyoxal oxidase  16 hpi 0.278 0.600 0.324 0.672 0.508    48 hpi 0.242 0.350 0.063 0.144 0.407   CNAG_06267  Rds1 protein  16 hpi 0.336 0.756 0.303 0.222 0.125    120 hpi 0.860 5.106 0.444 4.588 0.323   CNAG_00776  Immunoreactive mannoprotein MP88  16 hpi 0.627 0.994 0.214 1.193 0.611   CNAG_02864  Predicted protein  16 hpi 0.346 0.259 0.056 0.183 0.583   CNAG_04753  Lactonohydrolase 16 hpi 1.031 0.751 0.631 0.413 0.652    48 hpi 1.250 1.055 0.996 0.546 0.831 Hypothetical          CNAG_00587  Hypothetical protein 16 hpi 1.275 1.620 0.018 0.371 0.320   CNAG_01047  Hypothetical protein  16 hpi 0.279 0.471 0.070 0.272 0.303   49     Fold changeb Std. Dev.  GO categoriesa Accession number Protein Name Time point Pka1-repression Pka1-induction Pka1-repression Pka1-induction  p-valuec    72 hpi 0.928 0.719 1.034 0.432 0.763   CNAG_03492  Hypothetical protein 16 hpi 0.506 0.799 0.400 0.030 0.399    72 hpi 0.640 1.180 0.199 0.431 0.208   CNAG_05893  Hypothetical protein  48 hpi 1.609 1.676 1.380 1.639 0.965       120 hpi 1.641 1.413 0.953 0.348 0.781 aProteins were categorized based on GO terms associated with their biological classification. bFold change is reported as the average quantification for PGAL7::PKA1 vs. WT, ± standard deviation. cStatistical analysis was performed using a Student’s t-test (p-value < 0.05), between conditions.     50  Figure 2.1: Quantitative proteomic analysis of the C. neoformans secretome over the course of all time-points (16, 48, 72, and 120 hpi).  A) Pka1-repressed (glucose-containing medium) and B) Pka1-induced (galactose-containing medium) conditions.  Identified proteins were grouped according to GO terms associated with their biological classifications.   2.4.2 Identification of secreted proteins regulated by Pka1 Given that I identified secreted proteins from strains with modulated Pka1 activity, but did not observe any proteins directly regulated by Pka1, I extended our analysis to examine protein secretion at an intermediate time point of 96 hpi, and I used an alternative, less stringent method for protein precipitation (EtOH/acetate).  We collected supernatant cultures of WT and !A) B) Catabolic and Metabolic processes Transport Oxidation-reduction process Dephosphorylation Proteolysis Glycolysis   Response to stress Regulation of transcription Translation  Nucleosome assembly Unknown Hypothetical   51 PGAL7::PKA1 strains grown under Pka1-repressed (glucose) and Pka1-induced (galactose) conditions at 96 hpi and analyzed the samples using quantitative mass spectrometry.  Similar trends in protein abundance were observed for the majority of proteins in both experimental approaches (EtOH and TCA/acetone precipitation) (Table A.6) (Zellner et al., 2005).  We identified 61 proteins under Pka1-repressed conditions of which 34 were successfully dimethyl-labeled and quantified (Table 2.2; Table A.7).  These 34 proteins covered a broad spectrum of biological classifications (17 categories) for GO terms, including proteins associated with catabolic and metabolic processes, ubiquitination, transport, dephosphorylation, glycolysis, oxidation-reduction, translation, proteolysis, and the response to stress.  Under Pka1-induced conditions, I identified 38 proteins, of which 21 were successfully dimethyl-labeled and quantified (Table 2.3; Table A.8).  These 21 proteins covered 11 biological classifications for GO terms and included proteins associated with catabolic and metabolic processes, along with ubiquitination, transport, dephosphorylation, oxidation-reduction, proteolysis, and the response to stress.  A comparison of the proteins identified under the Pka1-repressed and Pka1-induced conditions identified 17 proteins that were present and quantifiable under both conditions.  A Student’s t-test revealed that five of these proteins showed statistically significant differences (p-value < 0.05) in abundance in response to regulation of Pka1 (Figure 2.2).  Under Pka1-induced conditions, a cytokine-inducing glycoprotein (Cig1), an α-amylase, a glyoxal oxidase, and a novel protein each showed an increase in abundance, whereas an acid phosphatase (Aph1) showed a decrease in abundance.  Taken together, these findings suggest that Pka1 regulates the extracellular abundance of specific proteins secreted by C. neoformans.     52 Table 2.2: Proteins identified in the secretome of C. neoformans collected at 96 hpi from cells grown in Pka1-repressed (glucose-containing medium) conditions.  GO categoriesa Accession number Protein Name Fold changeb Std. Dev. Carbohydrate catabolic process     CNAG_02189  α-Amylase 0.777 0.019 GTP catabolic process     CNAG_06125  Translation elongation factor 1 α  2.062 2.181 Carbohydrate metabolic process     CNAG_02860  Endo-1,3(4)-β-glucanase  0.947 0.583   CNAG_06501  1,3-β-glucanosyltransferase  0.968 0.100   CNAG_05799  Chitin deacetylase 1.439 0.775   CNAG_06291  Deacetylase 1.464 0.778   CNAG_01239  Chitin deacetylase 2.014 0.584 Cellular carbohydrate metabolic process     CNAG_03225  Malate dehydrogenase 0.472 0.546 Pentose-phosphate pathway     CNAG_07561  Phosphogluconate dehydrogenase >10 >10 Protein ubiquitination     CNAG_01920  Polyubiquitin 1.261 0.396 ATP hydrolysis coupled proton transport     CNAG_05918  F0F1 ATP synthase subunit β 0.451 0.249   CNAG_05750  ATPase α subunit 0.604 0.387 Transmembrane transport     CNAG_06101  Eukaryotic ADP/ATP carrier 7.524 9.753 Methionine biosynthetic process     CNAG_01890  5-methyltetrahydropteroyltriglutamate-homocysteine S-methyltransferase 0.778 0.327 Dephosphorylation     CNAG_02944  Acid phosphatase 1.862 0.083 Glycolysis     CNAG_03072  Phosphopyruvate hydratase 3.093 3.377 Oxidation-reduction process     CNAG_01019  Cu/Zn superoxide dismutase 0.302 0.161   CNAG_03465  Laccase 0.718 0.668 Proteolysis     CNAG_00919  Carboxypeptidase D  4.664 5.261 Response to stress     CNAG_01727  Hsc70-4 1.883 2.094 Translation     CNAG_06095  Ribosomal protein L13  0.147 0.057 Unknown/Unclassified     53 GO categoriesa Accession number Protein Name Fold changeb Std. Dev.   CNAG_01653  Cytokine-inducing glycoprotein  0.134 0.158   CNAG_00407  Glyoxal oxidase  0.719 0.230   CNAG_04291  Glycosyl-hydrolase 0.583 0.287   CNAG_02030 Glyoxal oxidase 0.513 0.109   CNAG_06267  Rds1 protein 3.409 0.106 Hypothetical     CNAG_05312  Conserved hypothetical protein 0.341 0.100   CNAG_03007  Conserved hypothetical protein 0.518 0.649   CNAG_01562  Conserved hypothetical protein 0.942 0.194   CNAG_05893  Conserved hypothetical protein 0.991 0.487   CNAG_01047  Conserved hypothetical protein 6.714 6.494   CNAG_00588  Conserved hypothetical protein >10 >10   CNAG_03223  Conserved hypothetical protein >10 >10    CNAG_00586  Conserved hypothetical protein >10 >10 aProteins were categorized based on GO terms associated with their biological classification. bFold change is reported as the average quantification for PGAL7::PKA1 vs. WT, ± standard deviation.      54 Table 2.3: Proteins identified in the secretome of C. neoformans collected at 96 hpi from cells grown in Pka1-induced (galactose-containing medium) conditions.  GO categoriesa Accession number Protein Name Fold changeb Std. Dev. Carbohydrate catabolic process     CNAG_02189  α-Amylase  1.061 0.080 GTP catabolic process     CNAG_06125  Translation elongation factor 1 α  0.462 0.555 Carbohydrate metabolic process     CNAG_04245  Chitinase  0.621 0.562   CNAG_06501  1,3-β-glucanosyltransferase  1.101 0.140   CNAG_01239  Chitin deacetylase  2.659 1.996   CNAG_02860  Endo-1,3(4)-β-glucanase  3.210 1.665 Protein ubiquitination     CNAG_01920  Polyubiquitin 0.509 0.501 ATP hydrolysis coupled proton transport     CNAG_05750  ATPase α subunit 1.785 1.765 Dephosphorylation     CNAG_02944  Acid phosphatase  0.233 0.061 Oxidation-reduction process     CNAG_01019  Cu/Zn superoxide dismutase 0.350 0.142   CNAG_03465  Laccase  1.000 0.107 Proteolysis     CNAG_00919  Carboxypeptidase D  5.770 3.708 Response to stress     CNAG_01750  Chaperone  >10 >10 Unknown/Unclassified     CNAG_04291  Glycosyl-hydrolase 0.989 0.959   CNAG_00407  Glyoxal oxidase 1.209 0.648   CNAG_06267  Rds1 protein  2.834 0.518   CNAG_01653  Cytokine-inducing glycoprotein 2.951 2.753   CNAG_04753  Lactonohydrolase >10 >10 Hypothetical     CNAG_06109  Conserved hypothetical protein 0.463 0.431   CNAG_05893  Conserved hypothetical protein 1.062 0.307    CNAG_05312  Conserved hypothetical protein 3.737 2.342 aProteins were categorized based on GO terms associated with their biological classification. bFold change is reported as the average quantification for PGAL7::PKA1 vs. WT, ± standard deviation.     55  Figure 2.2: Quantitative proteomic analysis of the C. neoformans secretome under Pka1-repressed (glucose-containing medium) and Pka1-induced (galactose-containing medium) conditions.  The secreted proteins were identified and quantified by LC-MS/MS in the PGAL7::PKA1 strain compared to WT, and the log2 of relative fold changes are indicated.  Fold change is reported as the average log2 quantification for PGAL7::PKA1 vs. WT, ± standard deviation.  Statistical analysis was performed using a Student’s t-test (p-value < 0.05), between conditions.  !!-8.00 -6.00 -4.00 -2.00 0.00 2.00 4.00 6.00 Log2 fold change Pka1-repressed Pka1-induced * * * * * 1,3-β-glucanosyltransferase (CNAG_06501) Endo-1,3(4)-β-glucanase (CNAG_02860) Cytokine-inducing glycoprotein (CNAG_01653) Laccase (CNAG_03465) Acid phosphatase (CNAG_02944) α-Amylase (CNAG_02189) Glyoxal oxidase (CNAG_00407) Glycosyl hydrolase (CNAG_04291) Chitin deacetylase (CNAG_01239) Carboxypeptidase D (CNAG_00919) Cu/Zn Superoxide dismutase (CNAG_01019) Polyubiquitin (CNAG_01920) Translation elongation factor 1α (CNAG_06125) ATPase α subunit (CNAG_05750) Rds1 protein (CNAG_06267) Hypothetical protein (CNAG_05893) Hypothetical protein (CNAG_05312)   56 2.4.3 Gene Ontology analyses of the secretome revealed enrichment of proteins associated with metabolic and catabolic processes  Based on our identification and quantification of 192 proteins in the secretome of C. neoformans, I next sought to classify the corresponding genes according to their GO terms of biological process, cellular component, and molecular function.  Our goal was to assess whether subsets of genes showed signficant over-representation relative to all genes in C. neoformans.  To perform the enrichment analyses, all unique proteins identified under Pka1-repressed conditions were combined into a single data set as were proteins identified under Pka1-induced conditions.  As shown in Figure 2.3, the identified secreted proteins under Pka1-repressed conditions were enriched in 15 biological categories, with the most significant enrichment associated with carbohydrate metabolic process, catabolic process, generation of precursor metabolites and energy, organic substance metabolic process, and primary metabolic process.  Under Pka1-induced conditions, enrichment was only associated with the five most signficantly enriched categories under Pka1-repressed conditions.  Classification by cellular components showed the most significant enrichment associated with the cytoplasm under both conditions, which may be an artifact of the classification process or indicative of the location of protein synthesis (Figure A.4), whereas classification by molecular function showed no enrichment. Our gene sets were also compared to all reported secreted proteins in the Fungal Secretome KnowledgeBase (FunSecKB) for C. neoformans strain JEC21 (Loftus et al., 2005; Lum and Min, 2011).  The analysis showed no significant enrichment; however, similarities among the identified GO terms were observed (Figure 2.4).  Forty-seven GO term categories were shared between the FunSecKB and our identified proteins under Pka1-repressed and Pka1-induced conditions; the greatest number of proteins being associated with metabolic processes.    57 Twenty-five categories were represented only in our secretome data, and one category (GO:0009607; response to biotic stimulus) was represented only in the FunSecKB.  Upon comparison of GO term categories for cellular components, 16 categories were shared between the FunSecKB and our identified proteins under Pka1-repressed and Pka1-induced conditions; the greatest number of proteins being associated with the cell, cytoplasm, and intracellular (Figure A.5).  Upon comparison of GO term categories for molecular function, 17 categories were shared between the FunSecKB and our identified proteins under Pka1-repressed and Pka1-induced conditions; the greatest number of proteins associated with binding as well as enzyme activity (Figure A.5).  Taken together, the enrichment analyses of the secretome data under modulation of Pka1 activity compared to the whole genome showed over-representation of genes associated with a broad spectrum of processes including metabolic and catabolic processing.  Although no enrichment was observed between our secretome data and the FunSecKB, a comparison of GO terms between the data sets showed all but one of our identified proteins to be represented in the FunSecKB.   58  Figure 2.3: Enrichment of genes represented in the secretome analysis of cells grown under Pka1-repressed (glucose-containing medium) and Pka1-induced (galactose-containing medium) conditions compared to all genes present in the WT strain.  The enrichment is based on GO terms associated with biological processes.    59  Figure 2.4: Comparison of GO terms classification of biological processes from the identified secreted proteins from cells grown under Pka1-repressed (glucose-containing medium) and Pka1-induced (galactose-containing medium) conditions compared to proteins represented in the Fungal Secretome Knowledgebase.    60 2.4.4 A bioinformatic analysis of the secretome predicts modes of secretion  We next examined the secreted proteins, under modulation of Pka1 activity, for the presence of predicted signal peptides and GPI anchors.  Specifically, I used SignalP 4.1, Signal-3L, and Phobius for the prediction of protein extracellular location based on the presence or absence of N-terminal signal peptides.  The presence of a signal peptide suggests conventional secretion versus potential non-conventional export if a signal peptide is absent.  Additionally, I used GPI-SOM to predict the presence or absence of a GPI-anchor on proteins, indicative of plasma membrane association, which may or may not be capable of dissociation and subsequent protein secretion.  Of the 61 proteins used for this analysis, 14 had both an N-terminal signal peptide and a GPI-anchor, 17 had only an N-terminal signal peptide, one had a GPI-anchor but no N-terminal signal peptide, and 29 proteins did not have an N-terminal signal peptide or a GPI-anchor (Table 2.4).  Taken together, these results suggest that C. neoformans may employ a non-conventional secretory pathway for regulation of part of its secretome, including potential protein secretion via vesicle export (Rodrigues et al., 2008).     61 Table 2.4: Bioinformatic analysis of identified and quantified proteins in the secretome of C. neoformans under Pka1-repressed (glucose-containing medium) and Pka1-induced (galactose-containing medium) conditions.  GO categoriesa Accession number Protein Name Signal peptideb (position) GPI anchorc (position) Sample preparation Carbohydrate catabolic process      CNAG_02189  α-Amylase Yes (21/22) Yes (C-26) EtOH, TCA/acetone GTP catabolic process      CNAG_06125  Translation elongation factor 1 α  No No EtOH, TCA/acetone   CNAG_06840  Translation elongation factor 2  No No TCA/acetone Carbohydrate metabolic process      CNAG_00799  Cellulase  Yes (22/23) No TCA/acetone   CNAG_01239  Chitin deacetylase Yes (18/19) Yes (C-28) EtOH, TCA/acetone   CNAG_02860  Endo-1,3(4)-β-glucanase  Yes (22/23) No EtOH   CNAG_04245  Chitinase  Yes (21/22) No EtOH, TCA/acetone   CNAG_05799  Chitin deacetylase Yes (18/19) Yes (C-28) EtOH   CNAG_06291  Deacetylase Yes (19/20) No EtOH, TCA/acetone   CNAG_06313  Phosphoglucomutase No No TCA/acetone   CNAG_06501  1,3-β-glucanosyltransferase  Yes (20/21) Yes (C-6) EtOH, TCA/acetone Cellular carbohydrate metabolic process      CNAG_03225  Malate dehydrogenase No No EtOH, TCA/acetone Trehalose metabolic process      CNAG_03525  Trehalase Yes (18/19) No TCA/acetone Glyoxylate metabolic process      CNAG_01137  Aconitase No No TCA/acetone Pentose-phosphate pathway      CNAG_01984  Transaldolase  No No TCA/acetone   CNAG_07561  Phosphogluconate dehydrogenase No No EtOH Protein ubiquitination      CNAG_01920  Polyubiquitin No No EtOH ATP hydrolysis coupled proton transport      CNAG_05750  ATPase α subunit No No EtOH, TCA/acetone   CNAG_05918  F0F1 ATP synthase subunit β No No EtOH Transmembrane transport      CNAG_02974  Voltage-dependent ion-selective channel No No TCA/acetone   CNAG_06101  Eukaryotic ADP/ATP carrier No Yes (C-25) EtOH, TCA/acetone Methionine biosynthetic process      CNAG_01890  5-methyltetrahydropteroyltriglutamate-homocysteine S-methyltransferase No No EtOH   62 GO categoriesa Accession number Protein Name Signal peptideb (position) GPI anchorc (position) Sample preparation Dephosphorylation      CNAG_02944  Acid phosphatase Yes (16/17) No EtOH, TCA/acetone Glycolysis      CNAG_03072  Phosphopyruvate hydratase No No EtOH, TCA/acetone   CNAG_06699  Glyceraldehyde-3-phosphate dehydrogenase No No TCA/acetone Oxidation-reduction process      CNAG_01019  Cu/Zn superoxide dismutase No No EtOH   CNAG_03465  Laccase Yes (20/21) No EtOH, TCA/acetone Proteolysis      CNAG_00581  Endopeptidase Yes (19/20) No TCA/acetone   CNAG_00919  Carboxypeptidase D  Yes (21/22) No EtOH, TCA/acetone Response to stress      CNAG_00334  Heat shock protein  No No TCA/acetone   CNAG_01727  Hsc70-4 No No EtOH   CNAG_01750  Chaperone  No No EtOH, TCA/acetone   CNAG_06150  Heat-shock protein 90 No No TCA/acetone Translation      CNAG_04021  60S ribosomal protein L26 No No TCA/acetone   CNAG_04114  40S ribosomal protein S0  No No TCA/acetone   CNAG_06095  Ribosomal protein L13  No No EtOH   CNAG_06605  Ribosomal protein S2  No No TCA/acetone Regulation of transcription      CNAG_00483  Actin  No No TCA/acetone Nucleosome assembly      CNAG_06746  Histone h2b  No No TCA/acetone Unknown/Unclassified      CNAG_00407  Glyoxal oxidase  Yes (16/17) Yes (C-27) EtOH, TCA/acetone   CNAG_00776  Immunoreactive mannoprotein MP88 Yes (22/23) Yes (C-30) TCA/acetone   CNAG_01653  Cytokine-inducing glycoprotein  Yes (19/20) No EtOH   CNAG_02030  Glyoxal oxidase Yes (21/22) Yes (C-32) EtOH, TCA/acetone   CNAG_02850  Glucan endo-1,3-α-glucosidase agn1  Yes (21/22) Yes (C-26) TCA/acetone   CNAG_02864  Predicted protein Yes (16/17) No TCA/acetone   CNAG_02943  Cytoplasmic protein  No No TCA/acetone   CNAG_04291  Glycosyl-hydrolase Yes (17/18) Yes (C-31) EtOH   CNAG_04753  Lactonohydrolase No No EtOH, TCA/acetone   CNAG_06267  Rds1 protein Yes (20/21) Yes  EtOH, TCA/acetone Hypothetical      CNAG_00586  Conserved hypothetical protein Yes (23/24) No EtOH,   63 GO categoriesa Accession number Protein Name Signal peptideb (position) GPI anchorc (position) Sample preparation TCA/acetone   CNAG_00587  Conserved hypothetical protein Yes (19/20) No TCA/acetone   CNAG_00588  Conserved hypothetical protein Yes (16/17) No EtOH   CNAG_01047  Conserved hypothetical protein Yes (19/20) No EtOH, TCA/acetone   CNAG_01562  Conserved hypothetical protein Yes (18/19) No EtOH   CNAG_03007  Conserved hypothetical protein No No EtOH   CNAG_03223  Conserved hypothetical protein Yes (19/20) Yes (C-18) EtOH   CNAG_03492  Conserved hypothetical protein Yes (19/20) No TCA/acetone   CNAG_05312  Conserved hypothetical protein Yes (19/20) Yes (C-19) EtOH, TCA/acetone   CNAG_05595  Conserved hypothetical protein Yes (18/19) Yes (C-29) TCA/acetone   CNAG_05893  Conserved hypothetical protein Yes (16/17) Yes (C-13) EtOH, TCA/acetone    CNAG_06109  Conserved hypothetical protein No No EtOH aProteins were categorized based on GO terms associated with their biological classification. bThe presence of a signal peptide was determined using SignalP, Phobius, and Signal-3L. cThe presence of a signal peptide was determined using GPI-SOM.  2.4.5 Examination of transcription and protein abundance in the context of Pka1 regulation   Based on our identification and quantification of five secreted proteins regulated by Pka1 in C. neoformans, I evaluated whether transcript levels correlated with the observed protein abundance.  Specifically, I performed qRT-PCR on RNA collected at 16 and 96 hpi from cells grown in Pka1-repressed and Pka1-induced conditions for the WT and PGal7::PKA1 strains, and compared the observed values to our quantitative proteomic results at 96 hpi.  Figure 2.5 summarizes the RNA expression levels at 16 hpi and 96 hpi and protein abundance at 96 hpi for Cig1, the acid phosphatase Aph1, an α-amylase, a glyoxal oxidase, and a novel protein (CNAG_05312).  Cig1 and the novel protein both showed down-regulation of their transcripts under Pka1-repressed conditions at 16 and 96 hpi, followed by minimal or slight up-regulation with induced Pka1 activity.  α-Amylase and glyoxal oxidase showed an initial peak in transcript levels at 16 hpi, followed by minimal change or a decrease in RNA levels at 96 hpi under Pka1-  64 repressed conditions, and the transcript levels decreased in response to Pka1 induction.  Acid phosphatase showed elevated transcript levels upon PKA1 repression at both time points, compared to a drop in RNA levels at 16 hpi or no change at 96 hpi upon induction of PKA1.  In general, Pka1 appears to positively regulate the transcript levels of Cig1 and the novel protein (CNAG_05312), and to negatively regulate the transcript levels of the other three proteins.  Taken together, our results suggest that although Pka1 activity influences the extracellular abundance of the five proteins, a correlation between transcript and protein levels was not always observed, and this was particularly notable for glyoxal oxidase.  These differences indicate additional levels of potential influence of Pka1 beyond transcriptional regulation, including differences in mRNA versus protein stability, the timing of expression and the regulation of protein export (Arava et al., 2003; Raj et al., 2006; Taniguchi et al., 2010).     65  Figure 2.5: Comparison of RNA expression levels using qRT-PCR versus secreted protein abundance using quantitative proteomics.  RNA samples collected from cells at 16 and 96 hpi; protein samples collected at 96 hpi.  The samples were evaluated in triplicate, grown under Pka1-repressed (glucose) and Pka1-induced (galactose) conditions, and values are reported as average log2 quantification ± standard deviation.   2.4.6 Detection of secreted Pka1-regulated proteins using Multiple Reaction Monitoring (MRM) Based on our identification of five Pka1-regulated proteins, including two with roles in virulence, I hypothesized that these proteins would be secreted during infection and that they might be potentially useful biomarkers of cryptococcosis.  To test this idea, I used a targeted proteomics approach to detect Cig1, Aph1, glyoxal oxidase, α-amylase, and the novel protein (CNAG_05312) in samples from a macrophage-like cell line and from infected mice.  The use of MRM for targeted proteomics, is a powerful method for the relative quantitative measurement of target proteins.  In the presence of an internal standard, a stable isotope labeled peptide, the -10.00 -8.00 -6.00 -4.00 -2.00 0.00 2.00 4.00 Log2 fold change RNA @ 16 hpi Pka1-repressed RNA @ 96 hpi Pka1-repressed RNA @ 16 hpi Pka1-induced RNA @ 96 hpi Pka1-induced Secretome @ 96 hpi Pka1-repressed Secretome @ 96 hpi Pka1-induced Cytokine-inducing glycoprotein (CNAG_01653) Acid phosphatase (CNAG_02944) α-Amylase (CNAG_02189) Glyoxal oxidase (CNAG_00407) Hypothetical protein (CNAG_05312)   66 amount of natural protide can be measured by comparing the signals to the labeled species.  The isotopically labeled, proteotypic peptides terminate with C-terminal heavy arginine or lysine (C-term Arg U-13C6;U-15N4 or Lys U-13C6;U-15N2).  In principle, the stable isotopes have the same physiochemical properties as the natural peptides and only differ by mass resulting in co-elution of the peptides.  However, studies have suggested that in the presence of complex biological samples, such as blood or serum, the retention times between the peptides can shift, impacting the co-elution patterns (Abbatiello et al., 2010).  The samples from the J774A.1 macrophage-like cell line came from cells inoculated with WT and PGAL7::PKA1 strains under Pka1-repressed (DMEM medium supplemented with glucose) and Pka1-induced (DMEM medium supplemented with galactose) conditions.  Intracellular uptake at 2 hpi showed a significant difference in the number of colony forming units (CFUs) per macrophage between the WT and PGAL7::PKA1 strains under Pka1-repressed conditions, but not under induced conditions (Figure 2.6A).  This difference is most likely due to the absence of the capsule for the Pka1-repressed cells, a phenotype that enhances phagocytosis.  By 24 hpi, rates of intracellular fungal cells per macrophage were significantly different for WT and PGAL7::PKA1 strains under both conditions (Figure 2.6C).  Specifically, intracellular rates of infection at 24 hpi in repressed conditions were 11.49 ± 2.11% for the WT and 55.67 ± 12.76% for PGAL7::PKA1 strains.  However, intracellular rates under induced conditions were 9.06 ± 2.91% for WT and 1.97 ± 0.82% for PGAL7::PKA1 strains.  Importantly, intracellular uptake rates showed no differences between WT, PGAL7::PKA1, and the pka1Δ strains under controlled growth conditions (DMEM – high glucose (0.45%)) at 2 and 24 hpi (Figure 2.6B, Figure 2.6D).  These results indicate that modulation of PKA1 expression influences the intracellular survival of cryptococcal cells.     67 MRM on macrophage lysates infected with fungal cells at 24 hpi identified the Pka1-regulated secreted proteins α-amylase and glyoxal oxidase in both induced and repressed conditions.  Figure 2.7 shows representative chromatographic co-elution patterns of the isotopically-labeled and natural peptides, which allowed for relative quantification of peptides in the samples.  For both enzymes, the highest amount of protein was detected in the WT strain in DMEM medium under Pka1-repressed conditions, whereas the PGAL7::PKA1 strain under Pka1-induction showed the lowest amount of secreted protein.  This observation may be associated with reduced intracellular rates of the PGAL7::PKA1 strain due to the presence of an enlarged capsule.  Overall, I was able to detect 29.8 ± 37.0  fmol of α-amylase and 149.1 ± 130.0 fmol of glyoxal oxidase in 5 μg of total protein from the macrophage lysate following the uptake of PGAL7::PKA1 under Pka1-induced conditions at 24 hpi. The samples from infected mice included BAL and blood from animals inoculated with the WT strain.  Representative chromatograms of isotopically-labeled and natural peptides detected in mouse BAL are presented in Figure 2.8.  The MRM analysis identified Cig1, α-amylase, glyoxal oxidase, and the novel protein (CNAG_05312) in BAL following infection with WT cells.  In 5 μg of total protein, glyoxal oxidase was the most abundant protein with detection at 779.5 ± 436.1 fmol, followed by the novel protein (CNAG_05312) at 451.0 ± 90.5 fmol, Cig1 at 291.3 ± 54.5 fmol, and α-amylase with the lowest abundance at 40.1 ± 9.4 fmol.  Lastly, I was able to detect Cig1, glyoxal oxidase, and the novel protein (CNAG_05312) in blood.  Representative chromatograms of the isotopically-labeled and natural peptides detected in mouse blood are presented in Figure 2.9.  Again, glyoxal oxidase was the most abundant protein detected at 319.4 ± 272.7 fmol, followed by Cig1 at 62.0 ± 17.4 fmol, and the novel protein (CNAG_05312) at 3.1 ± 3.8 fmol in 5 μg of total protein.  Aph1 levels were below the  68 limit of detection in all samples.  Taken together, our targeted proteomics approach identified and quantified the Pka1-regulated secreted proteins as potential biomarkers following host challenge with cryptococcal cells.   Figure 2.6: Interactions of WT and Pka1-regulated strains with J774A.1 murine macrophages.  A) Intracellular rate at 2 hpi of WT and PGAL7::PKA1 strains grown under Pka1-repression (glucose) and Pka1-induction (galactose).  B) As a control, the colony forming units (CFUs) per macrophage grown in standard DMEM medium (containing 0.45% glucose) are presented.  C) Rate of intracellular fungal cell per macrophage at 24 hpi of WT and PGAL7::PKA1 strains grown under Pka1-repression (glucose) and Pka1-induction (galactose).  D) The CFUs per macrophage grown in standard DMEM medium (containing 0.45% glucose) are presented as a control.  The experiments were performed in triplicate; the average percent of survival was reported ± standard error of the mean.  For statistical analysis, an unpaired t-test with Welch’s correction (p-value < 0.05) was performed between conditions (* denotes significant difference).  The samples at 24 hpi were employed for the analysis of protein abundance shown in Figure 2.7.  A) B) C) D) WT PGAL7::PKA1 WT PGAL7::PKA1 WT PGAL7::PKA1 WT PGAL7::PKA1 WT PGAL7::PKA1 pka1Δ WT PGAL7::PKA1 pka1Δ   69  5.35 5.85 6.35 6.85 7.35 27.7 28.9 30.1 31.3 32.4 Intensity values (log 2) Retention time (min) 5.35 5.85 6.35 6.85 7.35 29.3 30.5 31.7 32.8 34.0 Intensity values (log 2) Retention time (min) 5.35 5.85 6.35 6.85 7.35 30.1 31.3 32.4 33.6 34.8 Intensity values (log 2) Retention time (min) 5.35 5.85 6.35 6.85 7.35 31.7 32.8 34.0 35.2 36.4 Intensity values (log 2) Retention time (min) A) B) C) D) 5.35 5.65 5.95 6.25 6.55 6.85 13.1 14.3 15.5 16.6 Intensity values (log 2) Retention time (min) 5.35 5.65 5.95 6.25 6.55 6.85 13.5 14.7 15.9 17.0 18.2 Intensity values (log 2) Retention time (min) 5.35 5.65 5.95 6.25 14.3 15.5 16.6 17.8 19.0 Intensity values (log 2) Retention time (min) 5.35 5.65 5.95 6.25 6.55 6.85 14.7 15.9 17.0 18.2 Intensity values (log 2) Retention time (min) E) F) G) H)   70  Figure 2.7: Detection of Pka1-regulated proteins in lysates of macrophage-like cells containing C. neoformans.  Chromatographic representation of the most abundant peptide and its transition for α-amylase (CNAG_02189) identified from isotopically-labeled peptide or natural peptide for each of the following samples: A) macrophage lysate challenged with WT cells grown in 0.2% glucose; B) macrophage lysate challenged with WT cells grown in 0.2% galactose; C) macrophage lysate challenged with PGAL7::PKA1 cells grown in 0.2% glucose; D) macrophage lysate challenged with PGAL7::PKA1 cells grown in 0.2% galactose.  Chromatographic representation of the most abundant peptide and its transition for glyoxal oxidase (CNAG_00407) identified from isotopically-labeled peptide or natural peptide for each of the following samples: E) macrophage lysate challenged with WT cells grown in 0.2% glucose; F) macrophage lysate challenged with WT cells grown in 0.2% galactose; G) macrophage lysate challenged with PGAL7::PKA1 cells grown in 0.2% glucose; H) macrophage lysate challenged with PGAL7::PKA1 cells grown in 0.2% galactose. Black indicates isotopically-labeled peptide; red indicates natural peptide.  I) Quantification of α-amylase identified in the macrophage lysates was based on the area under the curve for the isotopically-labeled peptide versus the natural peptide in WT and PGal7::PKA1 strains under Pka1-repressed (D) and Pka1-induced (G) conditions.  J) Quantification of glyoxal oxidase identified in the macrophage lysates was based on the area under the curve for the isotopically-labeled peptide versus the natural peptide in WT and PGal7::PKA1 strains under Pka1-repressed (D) and Pka1-induced (G) conditions.  Five μg of total protein was used for the assays.    5.35 5.55 5.75 5.95 6.15 6.35 6.55 6.75 6.95 7.15 7.35 27.7 28.3 28.9 29.5 30.1 30.7 31.3 31.9 32.4 33.0 Intensity values (log 2) Retention time (min) 5.35 5.55 5.75 5.95 6.15 6.35 6.55 6.75 6.95 7.15 7.35 29.3 29.9 30.5 31.1 31.7 32.2 32.8 33.4 34.0 34.6 Intensity values (log 2) Retention time (min) 5.35 5.55 5.75 5.95 6.15 6.35 6.55 6.75 6.95 7.15 7.35 30.1 30.7 31.3 31.9 32.4 33.0 33.6 34.2 34.8 35.4 Intensity values (log 2) Retention time (min) 5.35 5.55 5.75 5.95 6.15 6.35 6.55 6.75 6.95 7.15 7.35 31.7 32.2 32.8 33.4 34.0 34.6 35.2 35.8 36.4 37.0 Intensity values (log 2) Retention time (min) A) B) C) D) 5.35 5.55 5.75 5.95 6.15 6.35 6.55 6.75 13.1 13.7 14.3 14.9 15.5 16.0 16.6 17.2 Intensity values (log 2) Retention time (min) 5.35 5.55 5.75 5.95 6.15 6.35 6.55 6.75 13.5 14.1 14.7 15.3 15.9 16.4 17.0 17.6 18.2 Intensity values (log 2) Retention time (min) 5.35 5.55 5.75 5.95 6.15 6.35 6.55 6.75 14.3 14.9 15.5 16.0 16.6 17.2 17.8 18.4 19.0 Intensity values (log 2) Retention time (min) 5.35 5.55 5.75 5.95 6.15 6.35 6.55 6.75 14.7 15.3 15.9 16.4 17.0 17.6 18.2 18.8 Intensity values (log 2) Retention time (min) E) F) G) H) I) J) -1 1 3 5 7 9 Amount log 2 (fmol) WT (D) WT (G) P GAL7::PKA1 (D) P GAL7::PKA1 (G) 0 2 4 6 8 10 Amount log 2 (fmol) WT (D) WT (G) P GAL7::PKA1 (D) P GAL7::PKA1 (G)   71  Figure 2.8: Detection of Pka1-regulated proteins in mouse bronchoalveolar lavage samples.  Chromatographic representation of the most abundant peptide and its transition identified from isotopically-labaled peptide or natural peptide for each of the following proteins: A) Cig1 (CNAG_01653), B) α-Amylase (CNAG_02189), C) Glyoxal oxidase (CNAG_00407), D) Novel protein (CNAG_05312).  Black indicates isotopically-labeled peptide; red indicates natural peptide.  E) Quantification of proteins identified in the mouse BAL samples based the area under the curve for the isotopically-labeled peptide versus the natural peptide, for Cig1, α-amylase, glyoxal oxidase, and novel (CNAG_05312) proteins. 5.35 5.45 5.55 5.65 63.4 64.2 64.9 65.6  Intensity values (log 2) Retention time (min) 5.35 6.35 7.35 8.35 9.35 10.35 42.0 43.2 44.4 45.5 46.7  Intensity values (log 2) Retention time (min) 5.35 5.55 5.75 5.95 64.0 65.0 66.0 67.0 68.1 Intensity values (log 2) Retention time (min) 5.35 5.45 5.55 5.65 54.4 55.1 55.7 56.3 57.0  Intensity values (log2) Retention time (min) A) B) C) D) E) 0 2 4 6 8 10 12 Amount log2 (fmol) Cig1 α-Amylase Glyoxal oxidase Novel   72  Figure 2.9:  Detection of Pka1-regulated proteins in mouse blood samples.  Chromatographic representation of the most abundant peptide and its transition identified from isotopically-labaled peptide or natural peptide for each of the following proteins: A) Cig1 (CNAG_01653), B) Glyoxal oxidase (CNAG_004070), C) Novel protein (CNAG_05312).  Black indicates isotopically-labeled peptide; red indicates natural peptide.  D) Quantification of proteins identified in the mouse blood samples based the area under the curve for the isotopically-labeled peptide versus the natural peptide, for Cig1, glyoxal oxidase, and novel (CNAG_05312) proteins.     5.35 5.65 5.95 6.25 6.55 64.4 65.1 65.9 66.6  Intensity values (log 2) Retention time (min) 5.35 5.85 6.35 6.85 7.35 7.85 65.0 65.7 66.4 67.1 Intensity values (log 2) Retention time (min) 5.35 5.45 5.55 5.65 45.5 45.8 46.2 46.5 46.8 Intensity values (log2) Retention time (min) A) B) C) D) -4 -2 0 2 4 6 8 10 Amount log2 (fmol) Cig1 Glyoxal oxidase Novel   73 2.5 Discussion The secretion of extracellular enzymes and virulence-associated factors is important for the proliferation and survival of pathogens in the host environment.  For the pathogenic yeast C. neoformans, virulence depends to a large extent on the export of polysaccharide to form a capsule, as well as targeted delivery of laccase to the cell wall for deposition of melanin, and secretion of extracellular enzymes (Yoneda and Doering, 2006; Rodrigues et al., 2007; Rodrigues et al., 2008; Lev et al., 2014).  The cyclic-AMP/Protein Kinase A signal transduction pathway plays a key role in regulating these processes but the underlying mechanisms remain to be understood in detail (Alspaugh et al., 1997; D'Souza et al., 2001).  We therefore used PGAL7::PKA1 strains under Pka1-repressed and Pka1-induced conditions in this study to investigate the influence of Pka1 on the secretome of C. neoformans.  Quantitative proteomics allowed us to identify 61 different proteins in the secretome including a subset of five whose abundance was regulated by Pka1.  These five proteins include a cytokine-inducing glycoprotein (Cig1), an α-amylase, a glyoxal oxidase, an acid phosphatase (Aph1), and a novel protein (CNAG_05312).  We also observed a change in the secretome profile upon induction of PKA1 expression thus establishing a view of the impact of PKA activity on the extracellular protein composition.  In general, this analysis highlighted the enrichment of Pka1-regulated biological processes in the secretome, revealed potential targets for conventional and non-conventional modes of secretion, and provided candidate biomarkers for investigating cryptococcosis.   2.5.1 Modulation of PKA1 expression leads to a change in the secretome  Our analysis revealed a change in the abundance of secreted C. neoformans proteins associated with glycolysis, translational regulation, nucleosome assembly, and stress response over a time course from 16 to 120 h.  We speculate that some of these proteins may result from   74 packaging in vesicles known to transit through the cell wall and accumulate in the extracellular environment (Rodrigues et al., 2008; Wolf et al., 2014).  In this case, modulation of PKA activity may indirectly influence the proteome of vesicles as a reflection of an impact on the intracellular proteome.  This idea is supported by our observed influence of PKA1 modulation on the abundance of translation machinery because ribosomal proteins, in particular, are abundant in extracellular vesicles (Wolf et al., 2014).  It is also well known that PKA influences the transcription of ribosomal protein genes in other organisms and this influence is conserved in C. neoformans (Klein and Struhl, 1994; Hu et al., 2007).  Our analysis of the intracellular proteome also revealed suppression of ribosomal cellular protein abundance upon induction of Pka1 (Chapter 3).  We also observed a connection between Pka1 activation and the abundance of glycolytic proteins.  This is interesting in light of previous reports demonstrating the importance of glycolysis for virulence and the persistence of C. neoformans in the cerebral spinal fluid (Price et al., 2011).  These findings are consistent with a previous analysis of the transcriptome, which showed that Pka1 influences the levels of transcript for genes involved in glycolysis (Hu et al., 2007).  Furthermore, the observed influence of Pka1 induction on the secretion of proteins associated with stress response is consistent with observed Pka1 regulation at the transcriptional level.  In this context, I identified a heat shock protein 70 (Hsc70-4), which is associated with the response to stress and which was previously localized to the cell surface of C. neoformans (Silveira et al., 2013).  The observed connection between the stress response and Pka1 induction may indicate coordination for facilitation of fungal survival and proliferation during colonization of vertebrate hosts.      75 2.5.2 Pka1 regulation of mannoproteins and cell wall functions: connections with Rim101 The influence of PKA on the abundance of the mannoprotein Cig1 is of particular interest because we previously showed that its transcript is one of the most abundant in cells grown in low iron medium (Lian et al., 2005).  In addition, the protein is important for iron acquisition from heme and virulence in C. neoformans (Cadieux et al., 2013).  We found that the extracellular abundance of Cig1 increased upon induction of Pka1 and that transcript levels and protein abundance were well correlated.  CIG1 is positively regulated by the pH-responsive transcription factor Rim101, which in turn is activated by the cAMP/PKA pathway (O'Meara et al., 2010).  Therefore, the regulation of CIG1 mRNA and Cig1 protein levels observed upon induction of Pka1 likely reflect regulation by Rim101.  This finding is consistent with recent discoveries that Rim101 controls cell wall composition and capsule attachment via an influence on the expression of cell wall biosynthetic genes (O'Meara et al., 2013; O'Meara et al., 2014). In general, a number of proteins associated with cell wall synthesis and integrity, pathogenesis and the immune response were prominent in the secretome of C. neoformans upon modulation of PKA1 expression.  These proteins included an endo-1,3(4)-β glucanase and a 1,3-β-glucanosyltransferase, both of which have been previously identified in studies of the extracellular proteomes of C. neoformans and other fungal pathogens such as Histoplasma capsulatum  (Biondo et al., 2006; Macekova et al., 2006; Eigenheer et al., 2007; Holbrook et al., 2011; Lev et al., 2014).  Endo-1,3(4)-β glucanase is located in the surface layers of the cell wall or in the capsule and has roles in metabolism, autolysis, and cell separation (Meyer and Phaff, 1977; Macekova et al., 2006).  The 1,3-β-glucanosyltransferase is described as a glycolipid protein anchored to the cell membrane in yeasts and may have a role in virulence (Sarthy et al., 1997).  Our proteomic analysis also identified chitin deacetylases associated with the formation   76 of chitin and cell wall integrity, and the enzyme laccase, which is responsible for melanin deposition in the cell wall and influences cryptococcal virulence  (Liu et al., 1999; Casadevall et al., 2000; Baker et al., 2007; Eigenheer et al., 2007; Gilbert et al., 2012; Lev et al., 2014).  These findings are consistent with our previous transcriptomic analysis, which revealed an influence of PKA on the expression of cell wall associated genes (Hu et al., 2007). We also identified a novel protein (CNAG_05312) with a pattern of mRNA and protein regulation by Pka1 activity that was quite similar to that of Cig1.  This novel protein contains a predicted carbohydrate-binding domain and was annotated as a macrophage-activating glycoprotein (reminiscent of the cytokine-inducing glycoprotein designation of Cig1). These observations suggest that further investigation is warranted for this protein in the context of iron acquisition and virulence.  This idea is reinforced by the finding that Rim101 also positively regulates expression of the CNAG_05312 gene (O'Meara et al., 2010).  Interestingly, the CNAG_05312 gene is also regulated at the transcript level by Gat201, a transcription factor that, like Pka1, influences capsule size, virulence, and uptake by macrophages (Liu et al., 2008; Chun et al., 2011).  Considering these similar phenotypes, it is possible that Gat201 and Pka1/Rim101 both regulate the expression of the CNAG_05312 protein and subsequently influence the activation of macrophages during infection.  Overall, our investigation of the secretome reinforced connections between modulation of Pka1 activity, Rim101 and cell wall integrity, and it revealed an impact of PKA on the extracellular abundance of proteins with known (Cig1) and potential (the novel CNAG_05312 protein) influences on virulence. 2.5.3 Pka1 influences the secretion of α-amylase and glyoxal oxidase enzymes Pka1 also positively regulated the abundance in the secretome of an α-amylase and a glyoxal oxidase which were previously identified in the extracellular proteome of C. neoformans    77 (Eigenheer et al., 2007; Lev et al., 2014).  Amylases are associated with carbohydrate metabolism, particularly starch degradation for energy production (Iefuji et al., 1996).  In C. neoformans, the secretion of amylases in the PKA-regulated strains was reported previously and we were able to measure and confirm α-amylase activity in the extracellular medium (Choi et al., 2012).  Glyoxal oxidases are extracellular H2O2-producing enzymes associated with cellulose metabolism (Iefuji et al., 1996).  There is evidence that glyoxal oxidase activity is involved in filamentous growth and pathogenicity of Ustilago maydis, as well as fertility in Cryptococcus gattii (Leuthner et al., 2005; Ngamskulrungroj et al., 2011).  A similar pattern in response to PKA1 expression was observed upon comparison of the transcript and protein levels for both the α-amylase and the glyoxal oxidase.  A direct correlation between transcript levels and protein abundance was not as evident as for Cig1.  This could potentially be due to post-transcriptional regulation, differences in mRNA and protein half-lives and issues with timing (Greenbaum et al., 2002).  It is also possible that PKA may regulate additional processes to influence extracellular protein abundance, such as the activity of the secretory pathway.  Overall, the secretome data revealed a new connection between PKA regulation and the α-amylase and glyoxal oxidase enzymes, and this discovery indicates that further analysis of their potential roles in virulence is warranted. 2.5.4 Pka1 influences the secretion of the virulence-associated acid phosphatase, Aph1 The extracellular abundance of the acid phosphatase Aph1 and its transcript levels were negatively regulated by induction of PKA1 expression thus revealing an opposite pattern of regulation compared with the other four genes.  Phosphatases have been predicted to have roles in cell wall biosynthesis, cell signaling, phosphate scavenging, and in adhesion of C. neoformans to epithelial cells (Chen et al., 1997; Dickman and Yarden, 1999; Zhan et al., 2000; Collopy-  78 Junior et al., 2006; Rodrigues et al., 2008; Lev et al., 2014).  The APH1 gene was recently characterized and its expression was found to be induced by phosphate limitation; the Aph1 protein was also the major conventionally secreted acid phosphatase in C. neoformans (Lev et al., 2014).  Aph1 was also shown to hydrolyze a variety of substrates to potentially scavenge phosphate from the environment, and an aph1 deletion mutant had a slight virulence defect in both Galleria mellonella and mouse models of cryptococcosis.  The latter phenotype is consistent with our recent study showing that a high affinity phosphate uptake system is required for growth on low-phosphate medium, for formation of the virulence factors melanin and capsule, for survival in macrophages, and for virulence in mice (Kretschmer et al., 2014).  This study also revealed that defects in PKA influence the growth of C. neoformans on phosphate-limited medium.  Our discovery of PKA regulation of Aph1 abundance in the secretome therefore further reinforces a connection between phosphate acquisition and PKA regulation associated with virulence. 2.5.5 PKA regulation and the intersection of secretome studies in C. neoformans  Our profiling of the secretome upon modulation of Pka1 activity confirmed the presence of previously identified extracellular and vesicular proteins, including those associated with virulence and fungal survival within the host, as well as novel secreted proteins.  We identified the classically secreted C. neoformans protein, laccase, associated with fungal virulence, but other proteins such as urease and phospholipase B were not identified in our study.  Their absence could be attributed to growth conditions, precipitation methods, supernatant collection times, and relative abundance in the secretome.  A recent proteome study that removed free capsular polysaccharide from the extracellular environment identified 105 secreted proteins and a direct comparison with our study showed an overlap of 52% (Lev et al., 2014).  Previous   79 investigation of the proteins in extracellular vesicles of C. neoformans also showed an overlap of nearly 56% with proteins identified in our study (Rodrigues et al., 2008; Wolf et al., 2014).  This overlap is primarily associated with proteins not typically expected in the secretome.  For example, ATP subunits/carriers, translation elongation factor, actin, and multiple ribosomal proteins were identified and their presence was attributed to packaging in extracellular vesicles, and not necessarily due to direct secretion.  In the absence of an N-terminal signal peptide, proteins may be exported via non-conventional secretion.  This may include the use of membrane-bound, extracellular vesicles capable of traversing the cell wall, the possible fusion of multi-vesicular bodies with the plasma membrane, or the capture of cytosolic material to form vesicles (blebbing), as discussed above (Rodrigues et al., 2007; Rodrigues et al., 2008; Casadevall et al., 2009; Doering, 2009; Eisenman et al., 2009; Oliveira et al., 2010).  Taken together, our profile of secreted proteins in C. neoformans is in agreement with previous secretome studies.  However, our ability to modulate Pka1 activity provides an opportunity to identify novel proteins in the extracellular environment as well as identify proteins specifically regulated by Pka1.  This approach led to the unique identification of the novel secreted protein (CNAG_05312) that was specifically associated with modulation of Pka1 activity and not found in other proteomic studies.   2.5.6 Detection of potential biomarkers during cryptococcal infection Biomarkers are indicators of normal or pathogenic processes as well as the efficacy of therapy (Tucci et al., 2014).  In this regard, targeted detection of secreted cryptococcal proteins provides an opportunity to identify potential biomarkers for early diagnosis of infection and to monitor antifungal therapy.  Early and rapid diagnosis remains limited for systemic fungal infections, such as those caused by Candida and Aspergillus species, as well as C. neoformans   80 and C. gattii (Schuetz, 2013).  Biomarkers of infection by specific fungal species would therefore be valuable for identification and for accurate measurements of fungal burden.  A recent study using the presence of the cell wall component galactomannan in BAL as a diagnostic tool for invasive fungal disease highlights an opportunity for biomarker discovery in fungal pathogens (Affolter et al., 2014).  Additionally, the use of targeted proteomics (and MRM in particular) is a novel approach to study the secretion of virulence factors in C. neoformans, particularly in the context of signaling functions like PKA that sense conditions relevant to the host environment.   The secreted proteins that I identified to be regulated in abundance by Pka1 provide an opportunity to develop diagnostic biomarkers that are also informative about signaling via the cAMP/PKA pathway in vitro and during infection.  For example, Cig1 is an important candidate biomarker given its abundance in iron-starved cells and its role in virulence through iron acquisition and uptake.  Our ability to detect Cig1 in the blood and BAL fluid of infected animals confirms its expression and establishes the protein as a potential biomarker.  These findings may also indicate a role for Cig1 in iron uptake in these environments although, interestingly, I did not detect Cig1 in macrophage lysates.  Based on our observed differences in intracellular replication, Pka1 seems to impact the intracellular environment of macrophages.  In this regard, I did detect the glyoxal oxidase and α-amylase proteins by MRM in macrophages containing cryptococcal cells.  Expression of these proteins has not previously been reported during interactions with macrophages, although the production of H2O2 and induction of oxidative stress via glyoxal oxidase could potentially influence intracellular survival.  It is known that oxidative stress induces autophagy in macrophages and can impair phagocytic activity (Kirkham, 2007; Perrotta et al., 2011).  Additionally, loss of an α-amylase in H. capsulatum attenuated the ability   81 of the fungus to kill macrophages and to colonize murine lungs (Marion et al., 2006).  This influence appeared to be related to the ability to produce D-(1,3)-glucan.  The regulation of glyoxal oxidase and α-amylase by Pka1 activity and their detection in macrophage lysates suggests that it would be interesting to examine the roles of these enzymes in intracellular survival and virulence.  Our approach with MRM is also informative about tissue specific expression of fungal proteins during disease.  In addition to the examples described above, I found that colonization of murine lungs resulted in secretion of α-amylase, glyoxal oxidase and the novel protein from gene CNAG_05312.  The novel protein was also found in blood and, given its similar regulation with Cig1 these results suggest future studies on the role of this protein in iron acquisition and virulence.   2.6 Conclusion In this study I characterized the overall impact of PKA1 modulation on the secretome and discovered five proteins regulated by Pka1.  The identified proteins had known roles associated with cell wall functions, fungal survival within the host, and virulence.  Our identification of a novel protein with potential roles in iron uptake and virulence also suggested a previously undescribed interaction between Pka1 and Gat201.  We were also able to detect Pka1-regulated secreted proteins in biological samples as potential biomarkers, providing a new opportunity for diagnosing fungal infection and monitoring disease progression.    82 Chapter 3: Quantitative Proteomic Profiling Reveals a Conserved Influence of Protein Kinase A on the Abundance of Proteins for Translation, the Proteasome, and Metabolism in Cryptococcus neoformans   3.1 Synopsis The opportunistic fungal pathogen Cryptococcus neoformans causes life-threatening meningitis in immunocompromised individuals.  The expression of virulence factors including capsule and melanin is regulated in part by the cyclic-AMP/Protein Kinase A (PKA) signal transduction pathway.  It was therefore of interest to investigate the influence of PKA on the composition of the intracellular proteome to obtain a comprehensive understanding of the regulation that underpins virulence.  In this study, I discovered a conserved pattern of regulation by Pka1 for proteins associated with translation, the proteasome, metabolism, amino acid biosynthesis and additional functions.  Pka1 regulation of proteins for the ubiquitin-proteasome pathway in C. neoformans showed a striking parallel with connections between PKA and protein degradation in chronic neurodegenerative disorders and other human diseases.  Enrichment analyses of the proteome data confirmed the over representation of proteins associated with metabolic and biosynthetic processes, and translation.  Additionally, an interactome analysis reinforced the impact of PKA activity on several clusters of proteins involving translation and the ribosome, the proteasome, and diverse metabolic processes.  Finally, expression studies on up-regulated corresponding genes upon Pka1-induction revealed correlation differences between transcript levels and the proteome.  Overall, this study revealed a remarkably broad influence of the cAMP/PKA pathway on the proteome that underlies nutritional adaptation and the elaboration of virulence factors in C. neoformans.   83 3.2 Introduction The opportunistic fungal pathogen Cryptococcus neoformans causes cryptococcosis or cryptococcal meningitis in immunocompromised individuals, particularly patients with AIDS, resulting in an estimated 625,000 deaths annually (Mitchell and Perfect, 1995; Park et al., 2009).  The virulence of the fungus is attributed to its ability to grow at 37°C, to produce a polysaccharide capsule and melanin, and to secrete extracellular enzymes and proteins that facilitate proliferation within the host (Bulmer et al., 1967; Kwon-Chung et al., 1982; Rhodes et al., 1982; Kwon-Chung and Rhodes, 1986; Polacheck and Kwon-Chung, 1988; Chang and Kwon-Chung, 1994).  The capsule is a major virulence factor and the constituent polysaccharides are synthesized intracellularly, exported to the cell surface, and attached to the cell wall to contribute immunomodulatory and antiphagocytic properties during infection (Coenjaerts et al., 2001; Bose et al., 2003; Walenkamp et al., 2003; Del Poeta, 2004; Janbon, 2004; Shoham and Levitz, 2005; Yoneda and Doering, 2006).  Defects in secretory pathway components or cellular trafficking machinery result in reduced capsule size, and recent reports verify that exocytosis and the cellular release of specialized vesicles mediate the secretion of capsule polysaccharide, influencing virulence (Walton et al., 2006; Yoneda and Doering, 2006; Rodrigues et al., 2007).  Trafficking is also important for melanin production since the laccase enzyme that catalyzes formation of the pigment is targeted to the cell wall.  Additionally, protein transport, vesicle exocytosis, and glycosylation processes in the secretory pathway are important for fungal temperature sensitivity and proliferation within the host (Kim et al., 2002; Walton et al., 2006; Goulart et al., 2010).    The cyclic-AMP/Protein Kinase A (PKA) signal transduction pathway is a key regulator of virulence in C. neoformans and may also regulate the trafficking of virulence factors (Hu et   84 al., 2007).  In particular, PKA activity is known to regulate capsule production, melanin formation, and mating (Alspaugh et al., 1997; D'Souza et al., 2001; Kozubowski et al., 2009; Kronstad et al., 2011b; McDonough and Rodriguez, 2012).  Pathway components include a Gα protein (Gpa1), adenylyl cyclase (Cac1), adenylyl cyclase-associated protein (Aca1), a candidate receptor (Gpr4), phosphodiesterase (Pde1), and the catalytic (Pka1, Pka2) and regulatory (Pkr1) subunits of PKA.  Environmental signals, such as exogenous methionine and nutrient starvation, are capable of activating the pathway via a G-protein coupled receptor Gpr4.  Sensing of amino acids from the environment is an important activator of fungal signaling cascades and is associated with regulation of the cell cycle, gene expression, cellular differentiation, mating, and virulence (Iraqui et al., 1999; Rohde and Cardenas, 2003; Xue et al., 2006).  Mutants lacking Gpa1, Cac1, Aca1 or Pka1 show a reduction in capsule and melanin formation, along with sterility, and virulence attenuation in a mouse model of cryptococcosis (Alspaugh et al., 1997; Hicks et al., 2004).  In contrast, loss of Pkr1 results in cells with an enlarged capsule phenotype and hypervirulence (D'Souza et al., 2001).    The influence of PKA on the transcriptome has been studied in a comparison of a wild-type strain with pka1Δ and pkr1Δ strains of C. neoformans (Hu et al., 2007).  This study revealed an influence of PKA on transcript levels for genes related to cell wall synthesis, transport, metabolism, and glycolysis, as well as genes encoding ribosomal proteins, stress and chaperone functions, and components of the secretory pathway.  PKA may therefore influence capsule formation by regulating the expression of genes involved in cell wall synthesis, protein trafficking and the secretory pathway, which control the elaboration of virulence factors to the cell surface (Hu et al., 2007).  Importantly, PKA has been shown to influence capsule attachment   85 by phosphorylating the pH-responsive transcription factor, Rim101, which is a key regulator of cell wall functions (O'Meara et al., 2010).   The recent construction and characterization of galactose-inducible and glucose-repressible versions of PKA1 by inserting the GAL7 promoter upstream of the gene in C. neoformans provides an opportunity to investigate the influence of PKA activity on the proteome (Choi et al., 2012).  The authors showed that galactose induction of PKA1 influences capsule thickness, cell size, ploidy, vacuole enlargement, and melaninization and laccase activity, as well as the secretion of proteases and urease.  Our recent study, presented in Chapter 2, employed the PGAL7::PKA1 strain to characterize the secretome of C. neoformans.  This work identified five proteins whose extracellular abundance was regulated by Pka1, along with a change in the secretome profile under Pka1-inducing conditions from primarily catabolic and metabolic processes to include proteins for translational regulation and stress response.  In the present study, I used a galactose-inducible and glucose-repressible strain to investigate the influence of Pka1 modulation on the intracellular proteome.  We used quantitative proteomics to analyze protein abundance, and I identified 302 proteins that were regulated by Pka1 and that covered a broad spectrum of biological processes.  The observed changes in protein abundance revealed an impact of Pka1 regulation on cellular processes including metabolism, translation, and protein degradation.  Enrichment analyses of the Pka1-influenced proteome, compared to the whole genome, also revealed over-representation of genes associated with metabolic and biosynthetic processes, and translation.  Overall, this study provides a deeper understanding of the influence of the cAMP/PKA pathway on the proteome of C. neoformans and identifies a wealth of PKA-regulated proteins with potential connections to virulence.     86 3.3 Experimental procedures  3.3.1 Fungal strains and culture conditions The Cryptococcus neoformans var. grubii wild-type strain H99 (WT) and the galactose-inducible PKA1 strain, PGAL7::PKA1 (Alspaugh et al., 1997; Choi et al., 2012), were used for proteome analyses.  The strains were maintained on yeast extract peptone dextrose (YPD) medium (1% yeast extract, 2% peptone, 2% dextrose, and 2% agar).  For studies involving the regulation of PKA1, cells of the WT and regulated strains were pre-grown overnight with agitation at 30°C in YPD broth, transferred to yeast nitrogen base medium with amino acids (YNB, Sigma-Aldrich) and incubated overnight with agitation at 30°C.  Cell counts were performed and 5 x 107 cells/ml were transferred to Minimal Medium (MM) (29.4 mM KH2PO4, 10 mM MgSO4y7H2O, 13 mM glycine, 3 μM thiamine, 0.27% carbon source) containing either 0.27% glucose (MM+D) or 0.27% galactose (MM+G).  Cells were incubated with agitation at 30°C in MM+D or MM+G for 16 h.  Samples were collected in triplicate for analysis. 3.3.2 Preparation of protein extracts, quantification, and trypsin digestion   Cellular fractions were processed for total protein extraction (Crestani et al., 2012).  In brief, cells were collected following removal of the supernatant by centrifugation at 3,500 rpm for 15 min at 4°C; the cells were then washed three times with either cold MM+D or MM+G and collected by centrifugation at 3,500 rpm for 15 min at 4°C.  Cells were flash frozen in liquid N2 and lyophilized overnight.  Following lyophilization, the cells were disrupted with a mortar and pestle and suspended in cold lysis buffer (50 mM Tris-HCl pH 7.5, 5 mM EDTA, 5 mM iodoacetamide (IAA), protease inhibitor cocktail (Roche)).  Solubilization of proteins was performed by vortexing for 5 min, followed by incubation on ice for 5 min, and centrifugation at 13,000 rpm for 20 min.  Supernatant fractions were collected and stored at -20°C.  The   87 remaining cellular debris was re-suspended in a second aliquot of cold lysis buffer followed by vortexing for 5 min and sonication at power 3 in an ice bath for three 30 s cycles with 1 min intervals (Thermo Fisher Scientific).  The supernatant was collected, pooled with the supernatant fraction from the first extraction, and stored at -80°C.  Protein concentration was determined using the BCA Protein assay (Pierce).  Cellular proteins in the collected fractions were subjected to in-solution trypsin digestion using ACS grade chemicals or HPLC grade solvents (Thermo Fisher Scientific and Sigma-Aldrich) (Fang et al., 2010).  In brief, digestion buffer (1% sodium deoxycholate, 50 mM NH4HCO3) was added to the supernatant and samples were incubated at 99°C for 5 min with agitation, followed by reduction (2 mM of dithiothreitol (DTT) for 25 min at 56°C), alkylation (4 mM of IAA for 30 min at room temperature in the dark), and trypsinization (0.5 μg/μl of trypsin overnight at 37°C).  3.3.3 Peptide chemical labeling, purification, and fractionation using strong-cation exchange (SCX)  Digested peptides from cellular fractions were desalted, concentrated, and filtered on C18 STop And Go Extraction (STAGE) tips (Rappsilber et al., 2003).  Reductive dimethylation using formaldehyde isotopologues was performed to differentially label peptides from the different experimental conditions.  Light formaldehyde (CH2O) and medium formaldehyde (CD2O) (Cambridge Isotope Laboratories, Andover, MA) were combined with light cyanoborohydride (NaBH3CN) (Sigma-Aldrich) to give a 4 Da difference for labeled peptides (Boersema et al., 2008).  Samples from the WT strain were routinely labeled with light formaldehyde, and PGAL7::PKA1 samples were labeled with medium formaldehyde.  Briefly, eluted and dried STAGE-tip peptides were resuspended in 100 mM triethylammonium bicarbonate, and incubated in 200 mM formaldehyde and 20 mM sodium cyanoborohydride for 90 min in the dark.  After   88 labeling, 125 mM NH4Cl was added to react with excess formaldehyde.  Samples were incubated for 10 min, followed by the addition of acetic acid to a pH < 2.5 to degrade the sodium cyanoborohydride.  For each comparison, equal amounts of labeled peptides were mixed and desalted on C18 STAGE tips.   Digested and purified peptides were further fractionated by strong-cation exchange chromatography (SCX) into approximately 20 fractions based on measurable UV output.  Fractions with low peptide concentrations were pooled.  Briefly, 600 μg of pooled light and medium formaldehyde-labeled peptides were fractionated using an Agilent 1100 HPLC system coupled with a PolySULFOETHYL A SCX column 50 mm x 1.0 mm with 3 μM particles and a flow rate of 50 μl/min.  Buffer A consisted of 20% acetonitrile and 0.05% formic acid in water, and buffer B consisted of 20% acetonitrile, 0.05% formic acid, and 0.5 M KCl in water.  Samples were re-suspended in buffer A and loaded with the same buffer.  Standard 70 min gradients were run from 0% B to 13% B over 15 min, then from 13% B to 21% B in the next 10 min, then increased to 100% B over a 2 min period, held at 100% B for 5 min, and then dropped to 0% B for another 38 min to recondition the column.  All fractions were desalted on C18 STAGE tips prior to mass spectrometry. 3.3.4 Protein identification by liquid chromatography-tandem mass spectrometry (LC-MS/MS) and mass spectrometry data analysis  Purified and SCX-fractionated peptides were analyzed using a linear-trapping quadrupole - Orbitrap mass spectrometer (LTQ-Orbitrap Velos; Thermo Fisher Scientific) on-line coupled to an Agilent 1290 Series HPLC using a nanospray ionization source (Thermo Fisher Scientific) including a 2-cm-long, 100-μm-inner diameter fused silica trap column, 50-μm-inner diameter fused silica fritted analytical column and a 20-μm-inner diameter fused silica gold coated spray   89 tip (6-μm-diameter opening, pulled on a P-2000 laser puller from Sutter Instruments, coated on Leica EM SCD005 Super Cool Sputtering Device).  The trap column was packed with 5 μm-diameter Aqua C-18 beads (Phenomenex, www.phenomenex.com) while the analytical column was packed with 3.0 μm-diameter Reprosil-Pur C-18-AQ beads (Dr. Maisch, www.Dr-Maisch.com).  Buffer A consisted of 0.5% aqueous acetic acid, and buffer B consisted of 0.5% acetic acid and 80% acetonitrile in water.  Samples were re-suspended in buffer A and loaded with the same buffer.  Standard 90 min gradients were run from 10% B to 32% B over 51 min, then from 32% B to 40% B in the next 5 min, then increased to 100% B over a 2 min period, held at 100% B for 2.5 min, and then dropped to 0% B for another 20 min to recondition the column.  The HPLC system included an Agilent 1290 series Pump and Autosampler with the Thermostat temperature set at 6°C.  The sample was loaded on the trap column at 5 μl/min and the analysis was performed at 0.1 μl/min.  The LTQ-Orbitrap was set to acquire a full-range scan at 60,000 resolution from 350 to 1600 Th in the Orbitrap to simultaneously fragment the top ten peptide ions by CID and top 5 by HCD (resolution 7500) in each cycle in the LTQ (minimum intensity 1000 counts).  Parent ions were then excluded from MS/MS for the next 30 s.  Singly charged ions were excluded since in ESI mode peptides usually carry multiple charges.  The Orbitrap was continuously recalibrated using lock-mass function (Olsen et al., 2005).  Mass accuracy: error of mass measurement was typically within 5 ppm and was not allowed to exceed 10 ppm.    MaxQuant 1.3.0.25 was used for the analysis and quantification of mass spectrometry data, with statistical analysis and data visualization performed using Perseus 1.3.09 (Cox and Mann, 2008; Cox et al., 2009; Cox et al., 2011).  The search was performed against a database comprised of 6,692 protein sequences from the source organism C. neoformans WT   90 (Cryptococcus neoformans var. grubii H99; http://www.uniprot.org) using the following parameters: peptide mass accuracy, 10 ppm; fragment mass accuracy, 0.6 Da; trypsin enzyme specificity, fixed modifications - carbamidomethyl, variable modifications - methionine oxidation, deamidated N, Q and N-acetyl peptides, dimethyl (K), dimethyl (N-term), dimethyl 2H(4) (K), and dimethyl 2H(4) (N-term), ESI-TRAP fragment characteristics.  Only those peptides with Ion Scores exceeding the individually calculated 99% confidence limit (as opposed to the average limit for the whole experiment) were considered as accurately identified.  The acceptance criteria for protein identification were as follows: only proteins containing at least two unique peptides were considered in the dataset.  Quantitative ratios were extracted from the raw data using MaxQuant.  Experimentally determined fold changes for WT and PGAL7::PKA1 strains grown under Pka1-repression (glucose-containing medium) and Pka1-induction (galactose-containing medium) conditions were normalized, converted to a log2 scale and the average fold change and standard deviation were used for analysis.  A Student’s t-test (p-value < 0.05) was performed for cellular proteins identified under both Pka1-repressed and Pka1-induced conditions to evaluate the statistical significance of the data.  Multiple hypothesis testing correction was performed on the proteome data using the Benjamini and Hochberg method (FDR < 0.05) (Benjamini and Yekutieli, 2001).  3.3.5 Gene ontology analysis Proteins were characterized with Gene Ontology (GO) terms using a local installation of Blast2GO (Conesa et al., 2005).  Gene annotation data of the C. neoformans H99 reference genome were retrieved from the Broad Institute (May 2014) and a copy of the non-redundant (nr) protein database was downloaded from NCBI (May 2014) (Janbon, 2014).  The most current associations between the nr protein database and GO terms were retrieved in May 2014 from   91 Blast2GO (Conesa et al., 2005).  GO terms were assigned to WT proteins and filtered using default settings of the Blast2GO pipeline (Conesa et al., 2005).  We performed GO term enrichment analyses for sets of proteins using hypergeometric tests and the Benjamini and Hochberg false discovery rate multiple testing correction (p-value < 0.05) implemented in the R packages GSEABase and GOstats.  GO term categories containing singleton entries were excluded.  GO categories and enrichment datasets were visualized using the R package ggplot2 (Wickham, 2010). 3.3.6 Bioinformatic analyses  Of the 302 Pka1-regulated proteins identified, 300 proteins could be mapped to the C. neoformans JEC21 database (Loftus et al., 2005).  The unmapped proteins, CNAG_03668 and CNAG_05100, do not have homologues in the C. neoformans JEC21 strain, but homologues exist in C. gattii serotype B as hypothetical proteins.  Kyoto Encyclopedia of Genes and Genome (KEGG) mapping was performed on the 300 Pka1-regulated proteins using the pathway mapping ‘search and colour’ tool to allow for visualization of the protein’s participation in their respective metabolic pathways and cellular processes (Okuda et al., 2008).  STRING (Search Tool for Retrieval of Interacting Genes/Proteins) was used to visualize predicted protein-protein interactions for the identified 300 Pka1-regulated proteins (http://string-db.org) (Franceschini et al., 2013).  KEGG and STRING data search tools utilize the C. neoformans var. neoformans JEC21 database and so our C. neoformans var. grubii H99 proteins were mapped to the available database for visualization.  Hypothetical proteins were characterized beyond the level of the database annotation using TargetP 1.1 (cutoff > 0.9) (http://www.cbs.dtu.dk/services/TargetP/) and TMHMM 2.0 (http://www.cbs.dtu.dk/services/TMHMM/) for prediction of subcellular location; SignalP 4.1 (http://www.cbs.dtu.dk/services/SignalP/) and GPI-SOM   92 (http://gpi.unibe.ch) were used for prediction of secretion via an N-terminal signal peptide and the presence glycophosphatidylinositol (GPI) anchor, respectively (Emanuelsson et al., 2000; Krogh et al., 2001; Fankhauser and Maser, 2005; Petersen et al., 2011).  ProtFun 2.2 (http://www.cbs.dtu.dk/services/ProtFun/) was used to predict protein function and enzymatic classification, if applicable, and HMMER v3.1b1 (http://hmmer.janelia.org) was used to search for homologues (Jensen et al., 2003; Finn et al., 2011). 3.3.7 RNA isolation and qRT-PCR of Pka1-regulated genes  Cells from the WT and PGAL7::PKA1 strains were prepared for RNA extraction by overnight growth in YNB medium followed by dilution to 5.0 x 107 cells/ml in 5 ml of MM+D or MM+G and incubation at 30°C with agitation for 16 h.   Samples were collected in triplicate for analysis.  Cells were collected at 16 hpi, flash frozen in liquid N2, and stored at -80°C.  Total RNA was extracted using an EZ-10 DNAaway RNA Miniprep kit (Bio Basic) according to the manufacturer’s protocol.  Complementary DNA was synthesized using a Verso cDNA kit (Thermo Scientific) and used for qRT-PCR.  Primers were designed using Primer3 v.4.0 (http://bioinfo.ut.ee/primer3-0.4.0/) and targeted to the 3’ regions of transcripts (Table B.1) (Untergasser et al., 2012).  Relative gene expression was quantified using the Applied Biosystems 7500 Fast Real-time PCR system.  Control genes CNAG_00483 (Actin) and CNAG_06699 (GAPDH) were used for normalization.  Additionally, PKA1 RNA expression levels under Pka1-repressed and Pka1-induced conditions in the WT and PGAL7::PKA1 strains were also analyzed at 16 hpi to confirm regulated expression of PKA1 (Figure B.1).  3.3.8 RNA blot analysis  Total RNA was isolated for the PGAL7::PKA1 strain grown in 50 ml of MM+D or MM+G for 16 h.  Briefly, cell pellets were collected and flash frozen in liquid N2, followed by overnight   93 lyophilization.  One milliliter of buffer 1 (2% SDS, 68 mM Na3C6H5O7, 132 mM C6H8O7, 10 mM EDTA) was added to the samples, along with 600 μl of glass beads; samples were subjected to bead beating for two, 3 min intervals at power 3 (BioSpec, Mini-Beadbeater) and subsequently stored on ice.  Next, 340 μl of buffer 2 (4 M NaCl, 17 mM Na3C6H5O7, 33 mM C6H8O7) was added and samples were inverted several times and incubated on ice for 5 min.  Samples were then centrifuged at 15,000 rpm for 10 min, the supernatant fraction was collected and transferred to a new tube, 1 volume of isopropanol was added, and samples were mixed and incubated at room temperature for 15 min.  The pellet was collected following centrifugation at 15,000 rpm for 5 min, and washing of the pellet with 70% DEPC-EtOH was performed.  The pellet was collected, air dried, and dissolved in 20 μl of diethylpyrocarbonate (DEPC)-H2O.  The hybridization probe was prepared with a PCR-amplified DNA fragment of CNAG_00483 (Actin) and CNAG_00396 (PKA1) using specific primers (Table B.1) and labeled with 32P using an Oligolabeling kit (Amersham Biosciences).  Scanned images were analyzed using a Bio-Rad ChemiDoc MP Imaging System (Figure B.1). 3.3.9 Enzyme assays and immunoblot analysis  Enzymatic assays were performed for phosphatase and superoxide dismutase (SOD) activity with kits for each enzyme according to the manufacturer’s protocol (BioVision Incorporated).  Immunoblot analysis was also used to validate the proteomic result for the heat shock protein Hsp70.  Briefly, 20 μg of C. neoformans cellular protein from PGAL7::PKA1 strain grown in MM+D or MM+G and collected at 16 hpi was separated by 5-10% SDS-PAGE and transferred to a nitrocellulose membrane.  Hsp70 was detected using a monoclonal anti-mouse antibody (Thermo Scientific Pierce; 1:3000 dilution) followed by enhanced chemiluminescence (ECL, Amersham) for visualization.  The HSP70 antibody detects the mitochondrial HSP70 kDa   94 protein and its family members including HSP72 and HSC70.  β-tubulin was used as a loading control.  All assay results are presented in Figure B.2.  3.4 Results 3.4.1 Quantitative profiling of the proteome upon modulation of PKA1 expression  Given the virulence defect of a pka1 mutant and Pka1 regulation of secreted virulence factors and cell size in C. neoformans, I hypothesized that Pka1 may influence the abundance of proteins associated with these processes as well as general functions such as translation and metabolism (D'Souza et al., 2001; Hu et al., 2007; Choi et al., 2012) (Chapter 2).  We therefore evaluated the effect of Pka1 regulation on the proteome by collecting cells of the WT and PGAL7::PKA1 strains grown under Pka1-repressed (glucose) and Pka1-induced (galactose) conditions at 16 hours post inoculation (hpi).  We analyzed the samples using quantitative mass spectrometry and identified 3222 proteins representing 48.1% of the 6,692 predicted proteins from the genome sequence (Janbon, 2014).  Under Pka1-repressed conditions, 1453 proteins were identified in two or more replicates and quantified using dimethyl labeling.  We also identified 1435 dimethyl-labeled proteins in two or more replicates under Pka1-induced conditions.  In total, 1176 proteins were identified in common between the two conditions (Figure 3.1).  A comparison of Gene Ontology (GO) term biological classifications for the unique cellular proteins identified under either Pka1-repressed (277 proteins) or Pka1-induced (259 proteins) conditions revealed changes in the proteome profiles in the strains responding to modulation of Pka1 activity (Figure 3.2).  Specifically, I observed that the majority of proteins (61%) were associated with metabolic, catabolic, and biosynthetic processes (22%), or were unknown or unclassified (including hypothetical proteins) (39%) under the Pka1-repressed   95 condition.  Additional proteins were associated with cellular processes including response to stress (3%), transcription (6%), translation and RNA processing (6%), oxidation-reduction (9%), transport (6%), signal transduction and signaling (4%), proteolysis and protein folding (2%), and phosphorylation/dephosphorylation (3%).   A similar proteome profile was observed in the strain responding to induction of Pka1, although changes in the proportions of proteins in specific categories were observed.  In this case, the majority of proteins (63%) were again associated with metabolic, catabolic, and biosynthetic processes (15%), although to a lesser extent, but a greater proportion of unknown or unclassified proteins (including hypothetical proteins) (48%) were observed.  We also observed an increase in the proportion of proteins associated with transcription (from 6% to 8%), translation and RNA processing (from 6% to 12%), and phosphorylation/dephosphorylation (from 3% to 5%).  A decrease was observed for proteins associated with cellular processes and response to stress (from 3% to 1%), oxidation-reduction (from 9% to 2%), transport (from 6% to 5%), signal transduction and signaling (from 4% to 3%), and proteolysis and protein folding (from 2% to 1%).  Overall, the analysis of protein abundance in the PKA1-modulated strains revealed proteins for a diverse array of cellular processes.     96  Figure 3.1:  Venn diagram illustrating the dispersion of total C. neoformans proteins identified by quantitative mass spectrometry.  Cells grown upon Pka1-repression (glucose-containing medium, D) and Pka1-induction (galactose-containing medium, G) conditions.   277 (D) 1176 (D&G) 259 (G)   97  Figure 3.2: Quantitative proteomic analysis of the C. neoformans proteome.  Cells grown upon A) Pka1-repression (glucose-containing medium) and B) Pka1-induction (galactose-containing medium) conditions.  Proteins identified in each condition (Pka1-repressed or Pka1-induced) are categorized according to their GO term biological classifications.  3.4.2 Identification of Pka1-regulated proteins  We next identified the proteins regulated by Pka1 within the set of 1176 quantified and shared proteins between Pka1-repressed and Pka1-induced conditions.  We performed a Student’s t-test for explorative data analysis on the shared protein list and found 302 proteins with statistically significant differences (p-value < 0.05) in protein abundance in response to regulation by Pka1 (Table B.2).  Adjusting significance levels to account for multiple-testing hypothesis, this more stringent analysis revealed 40 Pka1-regulated proteins with the highest A) B) Metabolic, catabolic, and biosynthetic processes Cellular processes and response to stress Transcription Translation and RNA processing Oxidation-reduction Transport Signal transduction and signaling Proteolysis and protein folding  Phosphorylation/dephosphorylation Hypothetical Unknown/Unclassified   98 statistical significance; these proteins are indicated with asterisks in the data tables presented below.  The broader set of 302 Pka1-regulated proteins identified in our analysis covered a diverse spectrum of GO term biological classifications (14 categories) including metabolic, catabolic, biosynthetic, and cellular processes, transcription and translation, proteolysis and protein folding, oxidation-reduction, response to stress, transport, phosphorylation/dephosphorylation, signaling and signal transduction, hypothetical proteins, and unknown/unclassified proteins.  A density plot comparing the proteins identified under Pka1-repressed and Pka1-induced conditions highlighted the influence of modulation of Pka1 activity and revealed that the majority of regulated proteins decreased in abundance upon induction of PKA1 (Figure 3.3).  A closer inspection confirmed that the majority of the proteins in all categories showed a decrease in abundance upon PKA1 induction (Figure 3.4).  In contrast, induction of PKA1 expression caused an increase in protein abundance for only a small number of proteins for metabolic and biosynthetic processes, proteolysis and protein folding, and phosphorylation/dephosphorylation, as well as hypothetical, unknown or unclassified proteins (Figure 3.4).  As described below, these observations prompted a more comprehensive examination of the functions influenced by modulation of PKA1 expression using GO term classifications and enrichment, KEGG pathway analysis and predictions of protein interaction networks.   99  Figure 3.3: Density scatterplot of identified proteins in C. neoformans upon modulation of Pka1 activity.  Group 1 refers to proteins identified in 2 or more replicates under Pka1-repressed conditions; group 2 refers to proteins identified in 2 or more replicates under Pka1-induced conditions.  Normalized log2 values are presented and statistical analysis using a Student’s t-test identified 302 significantly different proteins (p < 0.05) between the two conditions (green labels).  A subset of 40 of these proteins showed a significant difference following correction for multiple-hypothesis testing using the Benjamini-Hochberg method (FDR = 0.05) (red labels).   -6-4-20246Group2Loading...-5 -4 -3 -2 -1 0 1 2 3 4 5 6Group1-60 -50 -40 -30 -20 -10 0 10 20 Number of Proteins Increased abundance Decreased abundance Metabolic processes Catabolic processes Biosynthetic processes Cellular processes Transcription Translation Proteolysis and  Protein folding Oxidation-reduction Response to stress Transport Phosphorylation and  Dephosphorylation Signaling and  Signal transduction Unclassified/Unknown Hypothetical A B Pka1-induced proteins  (fold change) Pka1-repressed proteins  (fold change)   100  Figure 3.4: Pka1-regulated protein abundance under induction of PKA1 expression in C. neoformans.  Classification based on GO term biological processes.    3.4.3 Pka1 influences a broad spectrum of functions in C. neoformans  To gain an appreciation for the overall impact of modulating PKA1 expression on the proteome, I categorized corresponding genes according to their GO terms associated with biological processes, cellular components, and molecular function for the 1453 and 1435 identified and quantified proteins under Pka1-repression and Pka1-induction, respectively.  We then assessed whether genes in the modulated PKA1 subsets showed significant over-representation.  To perform the enrichment analyses, all proteins identified under both conditions were compared to all genes in the genome of C. neoformans.  As shown in Figure 3.5, the proteome under Pka1-repressed (glucose, D) conditions was enriched for 23 biological processes, with the most significant enrichment associated with metabolic and biosynthetic -6-4-20246Group2Loading...-5 -4 -3 -2 -1 0 1 2 3 4 5 6Group1-60 -50 -40 -30 -20 -10 0 10 20 Number of Proteins Increased abundance Decreased abundance Metabolic processes Catabolic processes Biosynthetic processes Cellular processes Transcription Translation Proteolysis and  Protein folding Oxidation-reduction Response to stress Transport Phosphorylation and  Dephosphorylation Signaling and  Signal transduction Unclassified/Unknown Hypothetical A B Pka1-induced proteins  (fold change) Pka1-repressed proteins  (fold change)   101 processes, along with generation of precursor metabolites and energy.  Under Pka1-induced conditions (galactose, G), enrichment of the proteome was associated with 29 biological processes.  Most significantly, these included metabolic and biosynthetic processes, along with translation, gene expression, and additional biosynthetic processes of organic substance, macromolecule, cellular, and cellular macromolecule.  Under both Pka1-repressed and Pka1-induced conditions, classification by cellular components showed the greatest enrichment of proteins associated with the cytoplasm (Figure B.3), and classification by molecular function showed the greatest enrichment of proteins associated with structural molecule activity (Figure B.3). Enrichment analyses were also performed to reveal the overall influence of Pka1 activity on the proteome.  As shown in Table 3.1, 16 GO term biological process categories were enriched under Pka1-repressed and Pka1-induced conditions, with the most significant enrichment associated with translation, biosynthetic processes, followed by gene expression.  Enrichment according to cellular components identified 14 GO term categories of which enrichment was most significant for genes associated with the cytoplasm, macromolecular complex, and ribosome (Table B.3).  Lastly, enrichment according to molecular functions identified one GO term category associated with structural molecule activity (Table B.4).  Taken together, these results showed that within the characterized proteome as a whole, enrichment patterns for translation, metabolic and biosynthetic processes, and a broad spectrum of genes associated with biological proccess, cellular components, and molecular functions, were over-represented in the data. To examine the metabolic pathways and cellular processes impacted by Pka1 in more depth, I analyzed our list of Pka1-regulated proteins using Kyoto Encyclopedia of Genes and   102 Genomes (KEGG) pathways.  Pka1-regulated proteins showing either an increase or decrease in abundance impacted 70 pathways (Table 3.2).  General metabolic pathways displayed the greatest impact by Pka1 and involved 55 genes across 22 pathways.  The majority of these genes were associated with the 2-oxocarboxylic acid, methane, propanoate, pyruvate, glycerophospholipid, and amino sugar and nucleotide sugar metabolism pathways.  The identified proteins that were associated with metabolic and biosynthetic processes are listed in Table 3.3.  The second greatest impact was associated with the biosynthesis of amino acids, which involved 43 genes across 13 processes including amino acid biosynthesis, metabolism, and degradation.  Metabolic processes associated with the biosynthesis of secondary metabolites and the ribosome, were also greatly impacted involving 30 and 36 genes, respectively.  Additionally, general biosynthesis pathways, trafficking processes including endocytosis and vesicular transport, oxidative phosphorylation, fatty acid biosynthesis, degradation, elongation, and metabolism, DNA- and RNA-associated processes, and unclassified processes including the peroxisome, phagosome, and proteasome were impacted under modulation of Pka1 activity.  Taken together, these results revealed the direct or indirect impact of Pka1 on a remarkably diverse set of metabolic pathways in C. neoformans.   103  Figure 3.5: Enrichment of genes represented in the proteome analysis under Pka1-repression (glucose-containing medium) and Pka1-induction (galactose-containing medium) compared to all genes present in the WT strain.  Enrichment based on GO terms associated with biological processes.  Statistical analysis of the dataset was performed using the Benjamini and Hochberg false discovery rate multiple testing correction (p-value < 0.05).    metabolic process (GO:0008152)biosynthetic process (GO:0009058)generation of precursor metabolites and energy (GO:0006091) translation (GO:0006412)organic substance biosynthetic process (GO:1901576) macromolecule biosynthetic process (GO:0009059)cellular macromolecule biosynthetic process (GO:0034645) cellular biosynthetic process (GO:0044249)gene expression (GO:0010467)catabolic process (GO:0009056)carbohydrate metabolic process (GO:0005975)protein metabolic process (GO:0019538)primary metabolic process (GO:0044238)organic substance metabolic process (GO:0071704)cellular metabolic process (GO:0044237)cellular protein metabolic process (GO:0044267)secondary metabolic process (GO:0019748)cellular process (GO:0009987)macromolecule metabolic process (GO:0043170)single−organism metabolic process (GO:0044710)regulation of biological quality (GO:0065008)homeostatic process (GO:0042592)cellular homeostasis (GO:0019725)cellular macromolecule metabolic process (GO:0044260)organic cyclic compound metabolic process (GO:1901360) nucleobase−containing compound metabolic process (GO:0006139) nitrogen compound metabolic process (GO:0006807)heterocycle metabolic process (GO:0046483)cellular nitrogen compound metabolic process (GO:0034641) cellular aromatic compound metabolic process (GO:0006725)Enriched GO terms−30−20−10Pka1-induced enrichment (−log10 p value)Pka1-repressed   104 Table 3.1: Enrichment of Pka1-regulated genes represented in the proteome analysis upon modulation of PKA1 expression compared to all genes present in the WT strain.    aBiological processes.  bStatistical analysis of the dataset was performed using the Benjamini and Hochberg false discovery rate multiple testing correction (p-value < 0.05).   GO categorya p-valueb Translation 3.49e-13 Macromolecule biosynthetic process 3.49e-13 Cellular macromolecule biosynthetic process 3.49e-13 Cellular biosynthetic process 3.49e-13 Organic substance biosynthetic process 3.49e-13 Gene expression 6.85e-13 Biosynthetic process 3.56e-11 Metabolic process 3.87e-9 Generation of precursor metabolites and energy 5.50e-7 Protein metabolic process 1.06e-5 Cellular protein metabolic process 6.35e-5 Cellular metabolic process 3.60e-4 Primary metabolic process 9.32e-3 Macromolecule metabolic process 9.63e-3 Organic substance metabolic process 1.24e-2 Cellular macromolecule metabolic process 4.32e-2 !m etabolic proces s  (G O :000815 2 )bio s yn thetic proces s  (G O :000905 8)gen eratio n  of precurs or m etabolites  an d en ergy (G O :0006 091) tran s latio n  (G O :0006 412 )organ ic s ubs tan ce bio s yn thetic proces s  (G O :19015 76 ) m acrom olecule bio s yn thetic proces s  (G O :000905 9)cellular m acrom olecule bio s yn thetic proces s  (G O :003 4 6 4 5 ) cellular bio s yn thetic proces s  (G O :00442 49)gen e expres s io n  (G O :00104 6 7)catabolic proces s  (G O :000905 6 )carbohydrate m etabolic proces s  (G O :0005 975 )protein  m etabolic proces s  (G O :00195 3 8)prim ary m etabolic proces s  (G O :00442 3 8)organ ic s ubs tan ce m etabolic proces s  (G O :0071704)cellular m etabolic proces s  (G O :00442 3 7)cellular protein  m etabolic proces s  (G O :00442 6 7)s econ dary m etabolic proces s  (G O :0019748)cellular proces s  (G O :0009987)m acrom olecule m etabolic proces s  (G O :0043 170)s in gle− organ is m  m etabolic proces s  (G O :0044710)regulatio n  of bio logical quality (G O :006 5 008)hom eo s tatic proces s  (G O :0042 5 92 )cellular hom eo s tas is  (G O :001972 5 )cellular m acrom olecule m etabolic proces s  (G O :00442 6 0)organ ic cyclic com poun d m etabolic proces s  (G O :19013 6 0) n ucleobas e− co n tain in g com poun d m etabolic proces s  (G O :0006 13 9) n itrogen  com poun d m etabolic proces s  (G O :0006 807)heterocycle m etabolic proces s  (G O :004 6 483 )cellular n itrogen  com poun d m etabolic proces s  (G O :003 4 6 41) cellular arom atic com poun d m etabolic proces s  (G O :0006 72 5 )Enriched GO terms− 3 0− 2 0− 10Pka1-induced en richm en t (− log10 p value)Pka1-repressed A B Enriched GO terms   105 Table 3.2: KEGG pathways impacted by Pka1-regulated proteins upon modulation of PKA1 expression.  General ID KEGG pathway Number of genes General metabolism   Metabolic pathways* 55  2-Oxocarboxylic acid metabolism* 14  Amino sugar and nucleotide sugar metabolism 5  Arachidonic acid metabolism 1  Butanoate metabolism* 1  C5-branched dibasic acid metabolism 4  Carbon metabolism* 1  Citrate cycle* 3  Fructose and mannose metabolism 1  Glutathione metabolism 1  Glycerophospholipid metabolism 5  Glycolysis/Gluconeogenesis 1  Glyoxylate and dicarboxylate metabolism* 3  Inositol phosphate metabolism 4  Methane metabolism 6  Propanoate metabolism* 6  Purine metabolism* 1  Pyruvate metabolism* 6  Riboflavin metabolism* 2  Selenocompound metabolism 1  Starch and sucrose metabolism 1  Taurine and hypotaurine metabolism* 3  Vitamin B6 metabolism 2 Ribosome   Ribosome* 36  Ribosome biogenesis in eukaryotes 1 Secondary metabolites   Biosynthesis of secondary metabolites* 30 Amino acids   Biosynthesis of amino acids* 12  Alanine, aspartate and glutamate metabolism* 3  Arginine and proline metabolism* 2  Beta-alanine metabolism* 2  Cysteine and methionine metabolism 3  Glycine, serine, and threonine metabolism 2  Histidine metabolism 1   106 General ID KEGG pathway Number of genes  Lysine degradation* 5  Lysine metabolism 3  Phenylalanine, tyrosine, and tryptophan biosynthesis 1  Tryptophan metabolism* 4  Tyrosine metabolism 1  Valine, leucine, isoleucine biosynthesis 1  Valine, leucine, isoleucine degradation* 3 General biosynthesis   Aminoacyl-tRNA biosynthesis* 4  Pantothenate and CoA biosynthesis 1  Terpenoid backbone biosynthesis* 1  Ubiquinone and other terpenoid-quinone biosynthesis 2 Trafficking   Endocytosis 1  Protein export* 1  Protein processing in Endoplasmic reticulum 3  SNARE interactions in vesicular transport 1 Phosphorylation   Oxidative phosphorylation* 10 Fatty Acids   Fatty acid biosynthesis 2  Fatty acid degradation* 4  Fatty acid elongation 1  Fatty acid metabolism* 4 DNA-associated   DNA replication* 1  Homologous recombination* 1  Meiosis* 1  Mismatch repair* 1  Nucleotide excision repair* 1 RNA-associated   mRNA surveillance pathway* 2  RNA degradation 1  RNA transport* 2  Splicesome 3 Other   One carbon pool by folate 2  Pentose and glucuronate interconversions 3  Pentose phosphate pathway* 4  Peroxisome 2   107 General ID KEGG pathway Number of genes  Phagosome 1  Proteasome 8  Synthesis and degradation of ketone bodies* 1   Ubiquitin mediated proteolysis 1  *Pathways containing significant proteins after multiple hypothesis testing (FDR < 0.05).     108 Table 3.3: Proteins associated with metabolic and biosynthetic processes identified in the proteome of C. neoformans collected at 16 hpi grown under Pka1 repression (glucose-containing medium) and Pka1 induction (galactose-containing medium) conditions.    Fold Change  Accession number Protein Identification Pka1-repression Pka1-induction p-valueb CNAG_00057 Fructose-1,6-bisphosphatase I  0.347 -1.840 0.041 CNAG_00084* Glutamine-tRNA ligase -0.333 -1.883 0.001 CNAG_00094 NAD-dependent epimerase/dehydratase  -0.148 -1.796 0.026 CNAG_00275* Uncharacterized protein 0.279 -2.126 0.000 CNAG_00311 3-hydroxyisobutyryl-CoA hydrolase  0.821 -0.768 0.004 CNAG_00393 1,4-alpha-glucan-branching enzyme  1.076 -2.472 0.031 CNAG_00441 Inosine-5-monophosphate dehydrogenase -0.209 1.088 0.023 CNAG_00520 Uncharacterized protein 0.933 -1.839 0.031 CNAG_00524 Acetyl-CoA acyltransferase 2 1.327 -1.302 0.016 CNAG_00573 NADH dehydrogenase (Ubiquinone) 1 alpha subcomplex 6  1.367 -1.558 0.017 CNAG_00797 Acetyl-coenzyme A synthetase  0.126 -0.621 0.018 CNAG_00827 Ribose 5-phosphate isomerase 1.173 -2.182 0.027 CNAG_00834 Phosphatidylserine decarboxylase  1.256 -1.407 0.004 CNAG_00866 Transketolase  1.367 -0.862 0.046 CNAG_00992 Homocitrate synthase, mitochondrial 1.115 -0.991 0.043 CNAG_01137* Aconitate hydratase, mitochondrial  0.956 -0.164 0.001 CNAG_01238* Arginine biosynthesis bifunctional protein ArgJ, mitochondrial  0.966 -0.913 0.000 CNAG_01539 Myo-inositol-1-phosphate synthase 0.244 -0.306 0.043 CNAG_01623 tRNA pseudouridine(55) synthase  -0.559 -0.031 0.016 CNAG_01657 Fumarate hydratase, mitochondrial  0.838 -2.059 0.019 CNAG_01890 5-methyltetrahydropteroyltriglutamate-homocysteine S-methyltransferase  0.411 -1.374 0.022 CNAG_02035 Triosephosphate isomerase 0.537 -0.712 0.033 CNAG_02181 Dihydrokaempferol 4-reductase  -0.038 -1.429 0.031   109   Fold Change  Accession number Protein Identification Pka1-repression Pka1-induction p-valueb CNAG_02230 Phosphoketolase  1.841 -1.086 0.013 CNAG_02326 V-type proton ATPase catalytic subunit A  0.973 -1.145 0.012 CNAG_02445 Phosphoacetylglucosamine mutase  0.864 -0.926 0.043 CNAG_02460 Coproporphyrinogen III oxidase 0.759 -3.133 0.010 CNAG_02763 Phenylalanine-tRNA ligase, beta subunit  0.681 -0.789 0.034 CNAG_02825 Argininosuccinate lyase  0.549 -1.635 0.041 CNAG_02918* Acetyl-CoA C-acetyltransferase  1.251 -1.135 0.000 CNAG_02976* Riboflavin kinase  0.711 -0.337 0.002 CNAG_03019 Long-chain acyl-CoA synthetase 1.069 -1.414 0.025 CNAG_03040* Transketolase  1.370 -1.378 0.000 CNAG_03128* Gamma-glutamyltransferase 0.832 -1.830 0.000 CNAG_03225 Malate dehydrogenase  0.868 -0.995 0.027 CNAG_03270 Adenylosuccinate lyase 0.693 -1.802 0.007 CNAG_03345* DIS3-like exonuclease 2  0.305 -1.084 0.003 CNAG_03596 2-oxoglutarate dehydrogenase E2 component  0.920 -0.789 0.049 CNAG_03738 Pantetheine-phosphate adenylyltransferase  0.530 -1.436 0.016 CNAG_04025* Transaldolase  1.972 -2.181 0.000 CNAG_04195 O-methyltransferase 0.702 -1.943 0.005 CNAG_04264 Ubiquinone biosynthesis monooxygenase COQ6  -0.869 -2.614 0.013 CNAG_04346 Dihydrodipicolinate synthase  0.728 -1.197 0.039 CNAG_04388 Superoxide dismutase  0.905 -2.272 0.023 CNAG_04485 Long-chain acyl-CoA synthetase 0.551 -1.577 0.024 CNAG_04531 Enoyl-CoA hydratase 0.485 -2.695 0.013 CNAG_04621 Glycogen(Starch) synthase  0.139 -0.570 0.023 CNAG_04822 Deoxyhypusine synthase 0.921 -1.755 0.014 CNAG_04835 Dihydrodipicolinate synthase  0.352 -1.799 0.034 CNAG_04879 Glycogen debranching enzyme  0.453 -1.164 0.006 CNAG_05077 Glycosyl hydrolase 0.771 -1.358 0.014   110   Fold Change  Accession number Protein Identification Pka1-repression Pka1-induction p-valueb CNAG_05097 ATP-dependent (S)-NAD(P)H-hydrate dehydratase  0.846 -1.730 0.046 CNAG_05122 Homoserine O-acetyltransferase  1.311 -1.553 0.026 CNAG_05144 Carbonic anhydrase 0.663 -1.706 0.020 CNAG_05148 Beta-1,2-xylosyltransferase 1  -0.176 -2.938 0.021 CNAG_05179 Ubiquinol-cytochrome c reductase core subunit 2  0.551 -0.642 0.042 CNAG_05260* Glutamate decarboxylase 0.441 -0.895 0.003 CNAG_05828  UDP-N-acetylglucosamine pyrophosphorylase  0.920 -1.253 0.042 CNAG_05900 Glycine-tRNA ligase  1.036 -2.112 0.026 CNAG_05907 Pyruvate carboxylase  0.211 -0.840 0.020 CNAG_06316 Glycine cleavage system H protein  -0.931 0.182 0.008 CNAG_06400 Plasma-membrane proton-efflux P-type ATPase 1.113 -1.253 0.010 CNAG_06421 Acetolactate synthase, small subunit  0.344 -1.272 0.042 CNAG_06432 Acetate kinase 0.622 -2.153 0.016 CNAG_06489 Adenosine kinase  0.723 -1.512 0.014 CNAG_06679 Anthranilate synthase component I  0.636 -1.048 0.016 CNAG_06830 Histidinol dehydrogenase  0.424 -0.591 0.047 CNAG_06849 Saccharopine dehydrogenase [NAD(+), L-lysine-forming]  0.090 -1.783 0.048 CNAG_06908 Pyridoxal biosynthesis lyase pdxS  0.886 -0.818 0.014 CNAG_07347* Heat shock protein  0.252 -1.331 0.001 CNAG_07363 Isocitrate dehydrogenase, NAD-dependent  0.186 -0.923 0.021 CNAG_07372 6-phosphofructo-2-kinase/fructose-2, 6-bisphosphatase 1.435 -2.549 0.006 CNAG_07400 Aspartate-tRNA(Asn) ligase 0.771 -2.251 0.017 CNAG_07547 Uncharacterized protein  0.954 -1.752 0.004 CNAG_07780 Geranylgeranyl diphosphate synthase, type III  0.495 -0.803 0.017 aPGAL7::PKA1/WT normalized log2 average fold change for three replicates under glucose (repression) or galactose (induction) conditions. bStatistical analysis performed between Pka1-repression and Pka1-induction values using Student’s t-test (p-value < 0.05). *Significant proteins after multiple hypothesis testing (FDR < 0.05).      111 3.4.4 Several clusters of interactions are predicted by network mapping of Pka1-regulated proteins Given the diverse impact of Pka1 on the C. neoformans proteome including components of several metabolic pathways, I next evaluated the predicted interaction network of Pka1-regulated proteins using STRING software.  We mapped the identified Pka1-regulated proteins and found that 1509 protein-protein interactions were predicted (Figure 3.6).  Several clusters of protein-protein interactions were prominent in the interaction network map with the greatest number of interactions occurring among proteins associated with the ribosome and translation. The proteins in this category are listed in Table 3.4.  A second interaction cluster included proteins associated with the ubiquitin-proteasome pathway.  Although this category was noted in the KEGG analysis, the network mapping highlighted the prominence of these proteins (Table 3.5) and revealed their association in the network with the proteins for translation (Figure 3.6).  Additional clusters in the network mapping were more diverse and included interactions between proteins associated with a variety of biological processes including metabolic and biosynthetic processes (Table 3.3), as well as phosphorylation/dephosphorylation, oxidation-reduction, and other functions.  Interactions were also predicted for some of the proteins in categories associated with stress, signaling, secretion and virulence (Table 3.6).  Our analysis revealed PKA regulation of catalase, urease, an acid phosphatase, and a superoxide dismutase; assays of the latter two enzymes supported the observed differences in the proteome data (Figure B.2) (Cox et al., 2003; Olszewski et al., 2004; Giles et al., 2005; Choi et al., 2012; Lev et al., 2014).  Similarly, our previous finding that Pka1 induction reduced secreted urease activity was consistent with the lower protein level for the enzyme observed in the current proteome analysis (Table 3.6) (Choi et al., 2012).  Additionally, I used immunoblotting to confirm a decrease in the   112 abundance of an HSP70 protein upon PKA induction (Figure B.2).  Protein-protein interactions were not predicted for ~85 proteins within the dataset.  Taken together, the protein-protein interaction network for Pka1-regulated proteins emphasized the significant impact of Pka1 on the regulation of ribosomal proteins and translation, the ubiquitin-proteasome pathway and metabolism.     113  Figure 3.6:  Interaction network mapping using STRING of Pka1-regulated proteins identified in the proteome of C. neoformans.  STRING was used to visualize predicted protein-protein interactions for the identified 300 (JEC21 mapped) Pka1-regulated proteins (http://string-db.org).  Several clusters can be identified from the network mapping and some examples include proteins: 1) associated with ribosomes and translation, including small subunit ribosomal protein S13e (CNAG_01153) and eukaryotic translation initiation factor 3 subunit F (CNAG_06563); 2) associated with the proteasome including 26S proteasome regulatory subunit N2 (CNAG_06175) and nascent polypeptide-associated complex subunit α (CNAG_04985) and; 3) diverse proteins associated with metabolism, phosphorylation and virulence including urease (CNAG_05540) and acetyl-CoA C-acetyltransferase (CNAG_02918).  Nodes directly linked to the input are coloured or nodes of a higher iteration/depth are white.  Edges or predicted functional links consist of up to eight lines: one colour representing each type of evidence (e.g.: neighborhood, gene fusion, co-occurrence, co-expression, experiments, databases, text mining, homology).   114 Table 3.4: Proteins associated with translational regulation and RNA processing identified in the proteome of C. neoformans collected at 16 hpi grown under Pka1 repression (glucose-containing medium) and Pka1 induction (galactose-containing medium) conditions.     Fold changea  Accession number Protein Identification Pka1-repression Pka1-induction p-valueb CNAG_00034 Large subunit ribosomal protein L9e  0.363 -1.034 0.021 CNAG_00116 Small subunit ribosomal protein S3 0.694 -1.646 0.010 CNAG_00640 40S ribosomal protein S4  1.011 -2.183 0.013 CNAG_00656 Large subunit ribosomal protein L7e  0.728 -2.201 0.049 CNAG_00779 Large subunit ribosomal protein L27e  2.050 -0.797 0.048 CNAG_00819 Small subunit ribosomal protein S30  1.667 -3.881 0.034 CNAG_01152 40S ribosomal protein S6  0.869 -1.200 0.008 CNAG_01153* Small subunit ribosomal protein S13e  0.950 -1.257 0.002 CNAG_01181 Small subunit ribosomal protein S27Ae 2.252 -0.510 0.025 CNAG_01300 40S ribosomal protein S21  0.966 -2.924 0.045 CNAG_01332 Small subunit ribosomal protein S24e  1.318 -2.812 0.040 CNAG_01455 Large subunit ribosomal protein L39  1.359 -2.756 0.027 CNAG_01480 Large subunit ribosomal protein L12  0.259 -1.889 0.048 CNAG_01843 Elongation factor Ts, mitochondrial 1.066 -1.523 0.027 CNAG_01884 Large subunit ribosomal protein L3  1.305 -1.408 0.019 CNAG_01897 Bromodomain-containing factor 1  0.102 -0.604 0.012 CNAG_01951 Small subunit ribosomal protein S22-A  1.879 -1.756 0.027 CNAG_01990 Small subunit ribosomal protein S5  0.584 -1.195 0.032 CNAG_02145 Uncharacterized protein  0.848 -2.701 0.008 CNAG_02209 Nucleolar protein 56  0.291 -1.459 0.049 CNAG_02234 60S ribosomal protein L6  0.984 -2.161 0.041 CNAG_02330 Large subunit ribosomal protein L21e  1.433 -1.945 0.017 CNAG_02331 Small subunit ribosomal protein S9  1.492 -1.643 0.047 CNAG_02671 Pre-mRNA-splicing factor CEF1  1.137 -2.223 0.012   115   Fold changea  Accession number Protein Identification Pka1-repression Pka1-induction p-valueb CNAG_03198 40S ribosomal protein S8  0.949 -1.396 0.016 CNAG_03283 Large subunit ribosomal protein L24e  1.577 -4.493 0.032 CNAG_03577 Large subunit ribosomal protein LP0 0.332 -1.031 0.019 CNAG_03739 Large subunit ribosomal protein L10-like  2.030 -3.596 0.027 CNAG_03747 Large subunit ribosomal protein L27Ae  1.055 -2.462 0.046 CNAG_04004 40S ribosomal protein S1  1.442 -2.205 0.010 CNAG_04011 Large subunit ribosomal protein L37a  1.441 -1.462 0.024 CNAG_04068 Large subunit ribosomal protein L28e  1.210 -2.524 0.029 CNAG_04082 Proline-tRNA ligase  0.391 -0.988 0.026 CNAG_04445 Small subunit ribosomal protein S7e  0.544 -2.111 0.048 CNAG_04448 Ribosomal protein L19  1.106 -2.674 0.014 CNAG_04609 Argonaute  0.864 -0.811 0.050 CNAG_04628 Eukaryotic translation initiation factor 6  1.085 -0.212 0.045 CNAG_04726 60S ribosomal protein L20  2.692 -2.196 0.022 CNAG_04762 Large subunit ribosomal protein L4e  1.531 -2.273 0.034 CNAG_04883 Small subunit ribosomal protein S18  0.760 -2.768 0.042 CNAG_05232 Large subunit ribosomal protein L8 1.362 -2.096 0.031 CNAG_05416 Pre-mRNA-processing protein 45  1.998 -1.781 0.020 CNAG_05525 Small subunit ribosomal protein S26  2.900 -3.011 0.049 CNAG_05689 Pre-mRNA-splicing factor SPF27  1.115 -1.774 0.034 CNAG_05762 Large subunit acidic ribosomal protein P2  1.026 -0.994 0.021 CNAG_05904 Small subunit ribosomal protein S14 2.288 -1.705 0.019 CNAG_06123 Leucine-tRNA ligase  0.352 -0.757 0.026 CNAG_06231 Large subunit ribosomal protein L13 1.762 -2.044 0.045 CNAG_06563* Eukaryotic translation initiation factor 3 subunit F  0.978 -1.397 0.001 CNAG_07839 Large subunit ribosomal protein L11  2.105 -1.925 0.044    116 aPGAL7::PKA1/WT normalized log2 average fold change for three replicates under the respective glucose (repression) or galactose (induction) conditions. bStatistical analysis performed between Pka1-repression and Pka1-induction values using Student’s t-test (p-value < 0.05). *Significant proteins after multiple hypothesis testing (FDR < 0.05).     117 Table 3.5: Proteins associated with the proteasome and ubiquitin pathways in the proteome of C. neoformans collected at 16 hpi grown under Pka1 repression (glucose-containing medium) and Pka1 induction (galactose-containing medium) conditions.   Fold changea  Accession number Protein Identification Pka1-repression Pka1-induction p-valueb CNAG_00136 Ubiquitin-activating enzyme E1  0.177 -1.216 0.006 CNAG_00180 Ubiquitin carboxyl-terminal hydrolase  1.090 -2.260 0.004 CNAG_00482 26S proteasome regulatory subunit N10 0.715 -1.531 0.028 CNAG_01861 26S proteasome non-ATPase regulatory subunit 10 0.569 -1.739 0.026 CNAG_01881 Molecular chaperone GrpE  1.557 -2.879 0.005 CNAG_01899 Prefoldin alpha subunit  1.574 -2.132 0.031 CNAG_02239 26S protease regulatory subunit 4 0.714 -2.185 0.035 CNAG_02725 20S proteasome subunit beta 2  0.594 -1.365 0.045 CNAG_02827 Ubiquitin-like protein Nedd8  0.764 -0.761 0.028 CNAG_03627 Peptidyl-prolyl cis-trans isomerase  0.648 -1.725 0.049 CNAG_03721 26S proteasome regulatory subunit N12  1.433 -2.933 0.049 CNAG_04014 26S proteasome regulatory subunit N9 0.379 -1.537 0.021 CNAG_04071 Proteasome subunit alpha type  1.093 -1.295 0.029 CNAG_04906 26S protease regulatory subunit 10B  0.519 -0.929 0.033 CNAG_06106 Chaperone regulator -0.603 0.152 0.006 CNAG_06175 26S proteasome regulatory subunit N2 0.228 -2.166 0.031 CNAG_06602 Cysteine-type peptidase  1.580 -2.278 0.001 CNAG_07717 Ubiquitin carboxyl-terminal hydrolase 0.606 -0.705 0.003 CNAG_07719 26S protease regulatory subunit 7  0.168 -1.619 0.021 aPGAL7::PKA1/WT normalized log2 average fold change for three replicates under the respective glucose (repression) or galactose (induction) conditions. bStatistical analysis performed between Pka1-repression and Pka1-induction values using Student’s t-test (p-value < 0.05).     118 Table 3.6: Proteins associated with response to stress, signaling, and virulence identified in the proteome of C. neoformans collected at 16 hpi grown under Pka1 repression (glucose-containing medium) and Pka1 induction (galactose-containing medium) conditions.   Fold changea  Accession number Protein Identification Pka1-repression Pka1-induction p-valueb CNAG_01404 Hsp71-like protein  0.680 -1.014 0.007 CNAG_01446 Uncharacterized protein  1.639 -3.295 0.014 CNAG_01653* Cytokine inducing-glycoprotein  -4.515 3.130 0.002 CNAG_01744 Phosphatase  0.934 -1.785 0.011 CNAG_01817* Signal recognition particle receptor subunit alpha  0.967 -0.574 0.003 CNAG_02817 GTP-binding protein ypt2  0.872 -0.783 0.015 CNAG_03143* Uncharacterized protein  1.239 -1.371 0.000 CNAG_03891 Hsp60-like protein  0.697 -0.770 0.021 CNAG_03985 Glutaredoxin 1.215 -2.359 0.009 CNAG_05218 Adenylyl cyclase-associated protein 1.294 -0.075 0.045 CNAG_05540 Urease 0.476   -1.777 0.033 CNAG_06208 Heat shock 70kDa protein 4  0.844 -0.931 0.021 CNAG_06287 Glutathione peroxidase  0.746 -0.330 0.044 aPGal7::PKA1/WT normalized log2 average fold change for three replicates under the respective glucose (repression) or galactose (induction) conditions. bStatistical analysis performed between Pka1-repression and Pka1-induction values using Student’s t-test (p-value < 0.05). *Significant proteins after multiple hypothesis testing (FDR < 0.05).   119 3.4.5 Characterization of Pka1-regulated novel proteins As described above, our quantitative proteomic analysis identified 302 candidates for Pka1-regulated proteins, of which, 64 were conserved hypothetical proteins.  To better understand the potential functions of this latter group of proteins in the context of Pka1-regulation, I used bioinformatic analyses to predict subcellular location, secretion, protein function and possible enzymatic classification, and to identify homologues (Table 3.7).  The subcellular location prediction tools TargetP and TMHMM identified 14 proteins associated with the mitochondrion, along with one protein containing a transmembrane helix.  We noted that mitochondrial functions also appeared prominently among the metabolic and biosynthetic proteins listed in Table 3.3.  None of the hypothetical proteins contained a predicted GPI anchor and only one contained an N-terminal signal peptide for conventional secretion.  ProtFun identified proteins with cellular roles related to central intermediary metabolism, energy metabolism, amino acid biosynthesis, biosynthesis of co-factors, translation, regulation and transcription, regulatory functions, transport and binding, purines and pyrimidines, and the cell envelope.  Forty candidate enzymes were identified and eight were classifiable as lyases, three as ligases, and three as isomerases.  Additional GO term categories were associated with growth factors, hormones, transcription and transcriptional regulation, immune response, voltage-gated ion channel, and structural proteins.  Lastly, HMMER identified protein homologues including NAD-, RNA-, and carbohydrate-binding proteins, enzymes (methyltransferase, glutathione-S-transferase, phosphatases, and a protein tyrosine kinase), a heat shock protein, translation/ribosomal proteins, and proteins associated with transcription and transcriptional repression.  Taken together, these results reveal a diverse classification of hypothetical proteins, along with the potential for novel protein discovery in the context of PKA regulation of the   120 proteome.  In addition, some of these proteins may contribute to virulence and proliferation in vertebrate hosts.  For example, we previously found that the transcript for the CipC protein was highly abundant in cryptococcal cells from the central nervous system of infected rabbits (Steen et al., 2003).     121 Table 3.7: Characterization and classification of hypothetical and unclassified proteins in the proteome C. neoformans under regulation of Pka1.   Accession number TargetPa TMHMMb SignalPc GPI-SOMd ProtFune Homologuesf CNAG_00012 O 0 No No Central intermediary metabolism/Enzyme/-/- NAD-binding protein CNAG_00275* M 0 No No Amino acid biosynthesis/Enzyme/Lyase/Growth factor NAD-binding protein CNAG_00286 O 0 No No Translation/Non-enzyme/-/Transcription regulation NAD-binding protein CNAG_00409 M 0 No No Amino acid biosynthesis/Enzyme/Lyase/Immune response Methyltransferase type 11 CNAG_00465 O 0 No No Regulation and transcription/Non-enzyme/-/Transcription regulation RNA methylase CNAG_00520 O 0 No No Regulation and transcription/Enzyme/-/Transcription regulation Translation initiation factor IF-2 CNAG_00577 O 0 No No Regulatory functions/Non-enzyme/-/Transcription  Mago-bind domain-containing protein CNAG_00626 M 0 No No Translation/Non-enzyme/-/Growth factor Uncharacterized protein CNAG_00858 O 0 No No Central intermediary metabolism/Enzyme/Isomerase/- RNA-binding protein CNAG_00995 S 0 Yes (20/21) No Transport and binding/Enzyme/-/Growth factor Meiotic recombination-related protein CNAG_01052 M 0 No No Translation/Enzyme/-/Voltage-gated ion channel Uncharacterized protein CNAG_01089 M 0 No No Central intermediary metabolism/Non-enzyme/-/Structural protein Mitochondrial zinc maintenance protein 1 CNAG_01222 M 0 No No Purines and pyrimidines/Enzyme/Ligase/Growth factor Replication factor C subunit 2   122 Accession number TargetPa TMHMMb SignalPc GPI-SOMd ProtFune Homologuesf CNAG_01317 O 0 No No Translation/Non-enzyme/-/Growth factor Centromere-binding protein 1 CNAG_01375 O 0 No No Regulatory functions/Enzyme/-/Transcription Related to Drebrin F CNAG_01446 O 0 No No Translation/Non-enzyme/-/Transcription HSP12 CNAG_01644 O 0 No No Regulation and transcription/Non-enzyme/-/Transcription regulation HAT family dimerization protein CNAG_01743 O 0 No No Translation/Non-enzyme/-/Transcription regulation Proteophosphoglycan ppg4 CNAG_01811 O 0 No No Translation/Non-enzyme/-/Growth factor DUF1014-domain-containing protein CNAG_01892 O 0 No No Energy metabolism/Enzyme/-/Growth factor Uncharacterized protein CNAG_02118 O 0 No No Replication and transcription/Enzyme/-/Transcription regulation Uncharacterized protein CNAG_02129 O 0 No No Translation/Enzyme/Ligase/- DUF4449 family protein CNAG_02145 M 0 No No Amino acid biosynthesis/Enzyme/-/Growth factor 50S ribosomal protein L28 CNAG_02263* O 0 No No Energy metabolism/Enzyme/Isomerase/- Glutathione S-transferase CNAG_02335* O 0 No No Translation/Enzyme/-/- Genomic scaffold, msy-sf-12 protein CNAG_02400 O 0 No No Replication and transcription/Enzyme/-/- Proteasomal ubiquitin receptor adrm1-like CNAG_02842 M 0 No No Translation/Enzyme/Lyase/Growth factor 50S ribosomal subunit L30 CNAG_02843* O 0 No No Central intermediary metabolism/Non-enzyme/-/Growth factor Protein vip1 CNAG_02994 U 0 No No Purines and pyrimidines/Enzyme/-/Transcription regulation Carbohydrate-binding module family 48 protein CNAG_03007 O 0 No No Translation/Enzyme/-/- CipC protein CNAG_03038 O 0 No No Translation/Enzyme/Lyase/Growth factor and domain-containing   123 Accession number TargetPa TMHMMb SignalPc GPI-SOMd ProtFune Homologuesf protein CNAG_03058 O 0 No No Regulation and transcription/Non-enzyme/-/Transcription regulation Hmp1 protein CNAG_03143* O 0 No No Translation/Non-enzyme/-/Hormone 12kDa heat shock protein (Glucose and lipid-regulation protein) CNAG_03566* O 1 No No Translation/Non-enzyme/-/Transcription regulation Phosphatidylserine decarboxylase proenzyme CNAG_03677 O 0 No No Amino acid biosynthesis/Enzyme/-/- TRAPP domain protein CNAG_03688 O 0 No No Translation/Non-enzyme/-/Transcription regulation Uncharacterized protein CNAG_03705 O 0 No No Regulatory functions/Non-enzyme/-/Growth factor Uncharacterized protein CNAG_03841 O 0 No No Replication and transcription/Non-enzyme/-/- Dsp1-1-like protein CNAG_03864 O 0 No No Regulatory functions/Enzyme/-/Growth factor Protein tyrosine kinase CNAG_03873 O 0 No No Fatty acid metabolism/Enzyme/-/- Similar to MKL/myocardin-like protein 1 CNAG_03961 M 0 No No Energy metabolism/Enzyme/-/Growth factor Bcs1p-like protein CNAG_04163* O 0 No No Transport and binding/Non-enzyme/-/- CsbD domain-containing protein CNAG_04203 O 0 No No Purines and pyrimidines/Enzyme/Lyase/Growth factor Uncharacterized protein CNAG_04212 O 0 No No Cell envelope/Non-enzyme/-/Transcription DUG1690 domain protein CNAG_04284 O 0 No No Regulation and transcription/Non-enzyme/-/Growth factor Tudor/PWWP/MBT CNAG_04475* O 0 No No Translation/Non-enzyme/-/Growth factor Phosphoglycerate mutase-  124 Accession number TargetPa TMHMMb SignalPc GPI-SOMd ProtFune Homologuesf like protein CNAG_04680 M 0 No No Biosynthesis of cofactors/Enzyme/lyase/- Uncharacterized protein CNAG_04954 O 0 No No Purines and pyrimidines/Enzyme/Ligase/Transcription WD40 repeat-containing nuclear protein CNAG_04962 O 0 No No Cell envelope/Non-enzyme/-/Structural protein Chromosome segregation ATPase-like protein CNAG_05001 O 0 No No Energy metabolism/Enzyme/-/Growth factor Thioredoxin-like protein CNAG_05131 M 0 No No Energy metabolism/Enzyme/-/Growth factor Ixr1p CNAG_05312 O 0 No No Transport and binding/Enzyme/-/Immune response Carbohydrate-binding module family 13 protein CNAG_05570 O 0 No No Central intermediary metabolism/Enzyme/-/- Ubiquitin C variant CNAG_06109 O 0 No No Energy metabolism/Enzyme/-/Growth factor Uncharacterized protein CNAG_06113 O 0 No No Translation/Non-enzyme/-/Growth factor Hyaluronan/mRNA-binding protein CNAG_06328 O 0 No No Translation/Enzyme/Isomerase/Growth factor Ycii-related domain protein CNAG_06475* O 0 No No Replication and transcription/Enzyme/-/Growth factor Rnapii degradation factor def1 CNAG_06577 M 0 No No Translation/Enzyme/-/- Allergen CNAG_06765 O 0 No No Replication and transcription/Enzyme/-/Transcription regulation Transcriptional repressor LEUNIG (or GG17798) CNAG_07322 O 0 No No Regulatory functions/Non-enzyme/-/Transcription regulation RanBP1 domain containing protein CNAG_07382* M 0 No No Central intermediary metabolism/Enzyme/Lyase/- Mitochondrial ribosomal small subunit protein Mrp51 CNAG_07547 M 0 No No Regulatory functions/Enzyme/-/Growth factor Histidine phosphatase family containing protein   125 Accession number TargetPa TMHMMb SignalPc GPI-SOMd ProtFune Homologuesf CNAG_07665 O 0 No No Replication and transcription/Enzyme/-/Transcription regulation Uncharacterized protein CNAG_07740 O 0 No No Central intermediary metabolism/Enzyme/Lyase/- 2,3-diketo-5-methylthio-1-phosphopentane phosphatase aTargetP (cut off > 0.9) predicts subcellular location, M: mitochondrion, O: other, U: unknown (value below cut-off).  bTMHMM predicts transmembrane helices.  cSignalP predicts N-terminal signal peptide. dGPI-SOM predicts GPI-anchor proteins.  eProtFun predicts protein function and enzymology.  fHMMER predicts homologues. *Significant proteins after multiple hypothesis testing (FDR < 0.05).     126 3.4.6 Comparisons of transcript and protein abundance for Pka1-regulated functions  In the context of our identification and quantification of 302 proteins regulated by Pka1 in C. neoformans, I next evaluated whether transcript levels correlated with the observed protein abundance.  Specifically, I performed qRT-PCR on RNA collected at 16 hpi from cells grown in Pka1-repressed and Pka1-induced conditions for the WT and PGAL7::PKA1 strains and compared the observed RNA expression values to our quantitative proteomic results (Figure 3.7).  For this analysis, I focused on the unusual set of proteins, which displayed an increase in abundance upon induction of PKA1 expression.  For inosine-5-monophosphate dehydrogenase, an endoplasmic reticulum protein, and tRNA pseudouridine (55) synthase, RNA levels were elevated under Pka1-repressed conditions and down-regulated upon induction of Pka1.  That is, the transcript level and protein abundance of these proteins were contradictory under modulation of Pka1 activity.  Conversely, in the condition of decreased Pka1 activity, reduced transcript levels correlated with decreased protein abundance for the mannoprotein Cig1, a serine/threonine-protein phosphatase, and a novel protein (CNAG_05312).  In the presence of PKA1 induction, a positive correlation was observed between RNA expression and protein abundance for these proteins.  Additionally, for a chaperone regulator and a glycine cleavage system H protein, transcript levels and protein abundance were both lower upon a decrease in PKA1 expression; however, transcript levels remained low upon PKA1-induction while protein abundance increased.  Taken together, the results indicate that regulation of the transcriptome and proteome do not always correlate upon modulation of PKA1 expression, thus indicating additional layers of regulation beyond transcriptional control.  In particular, a negative correlation between the transcriptome and proteome may be due to Pka1 control of the translation of newly synthesized and pre-existing mRNA, the timing of mRNA and expression, as well as the lifespan and   127 turnover of transcripts and proteins (Arava et al., 2003; Raj et al., 2006; Taniguchi et al., 2010).  Additionally, our data suggest that Pka1-repression of transcriptional machinery, such as a DNA-directed RNA polymerase II (CNAG_02022), pre-mRNA splicing factor CEF1 (CNAG_02671), pre-mRNA processing protein 45 (CNAG_05416), and a pre-mRNA splicing factor SPF27 (CNAG_05689), may influence the correlation between transcription and protein abundance.   Figure 3.7: Comparison of RNA expression levels using qRT-PCR at 16 hpi and protein abundance using quantitative proteomics at 16 hpi.  Samples evaluated in triplicate, values reported as average log2 quantification ± standard deviation.    -8.0 -6.0 -4.0 -2.0 0.0 2.0 4.0 Average log 2 fold change RNA Pka1-repressed RNA Pka1-induced Proteome Pka1-repressed Proteome Pka1-induced Inosine-5-monophosphate  dehydrogenase (CNAG_00441) Endoplasmic reticulum protein (CNAG_00469) tRNA pseudouridine synthase (CNAG_01623) Cig1 (CNAG_01653) Serine/threonine-protein  phosphatase (CNAG_03706) Hypothetical protein (CNAG_05312) Chaperone regulator (CNAG_06106) Glycine cleavage system H protein (CNAG_06316)   128 3.5 Discussion The cyclic-AMP/Protein Kinase A (PKA) signal transduction pathway plays a critical role in the pathogenesis of C. neoformans because it regulates key virulence traits such as capsule and melanin formation (Alspaugh et al., 1997; D'Souza et al., 2001; Hu et al., 2007).  We previously constructed and employed strains carrying galactose-inducible and glucose-repressible versions of PKA1 to demonstrate an influence of Pka1 on the regulation of virulence factors and an impact on cell size (Choi et al., 2012) (Chapter 2).  In our current study, the regulated PGAL7::PKA1 strain was employed to investigate the influence of PKA1 induction and repression on the proteome.  Using a quantitative approach, I identified 48.1% of the 6,692 predicted proteins from the genome sequence including 1453 and 1435 under Pka1-repressed and Pka1-induced conditions, respectively.  This level of protein identification is similar to that in a recent proteome study of biofilm formation in C. neoformans (Santi et al., 2014).  Overall, I identified 302 Pka1-regulated proteins and discovered that Pka1 influences the abundance of proteins with functions in a broad spectrum of biological processes including translation, metabolism, and virulence.  Additionally, an interactome analysis of proteins whose abundance was influenced by Pka1 highlighted several clusters of interactions including: 1) ribosomal and translational proteins; 2) proteins associated with the ubiquitin-proteasome pathway and; 3) proteins associated with metabolism and biosynthesis.  Remarkably, the major pattern of regulation that I observed was a decrease in protein abundance upon induction of PKA1 expression.  The regulatory mechanism(s) underlying this pattern requires further investigation, but may in part, reflect a conserved influence of PKA on specific transcription factors that control the expression of the translation machinery or processes such as autophagy, as discussed below.     129 3.5.1 The impact of Pka1 regulation on translation A key observation from our analysis was that induction of Pka1 suppressed the abundance of ribosomal proteins and additional components of the translational machinery.  Enrichment analysis, mapping of proteins to KEGG pathways, and STRING prediction of protein interactions further highlighted the impact of Pka1 on translation.  These results are consistent with our previous transcript profiling of pka1 and pkr1 deletion mutants in C. neoformans that also revealed a connection between Pka1 and expression of the translational machinery (Hu et al., 2007).  More broadly, an influence of PKA on the expression of proteins involved in translation has been reported in a number of fungi including Candida albicans, Ustilago maydis, and Saccharomyces cerevisiae (Klein and Struhl, 1994; Jones et al., 2003; Jung and Stateva, 2003; Harcus et al., 2004; Larraya et al., 2005).  This regulation is best understood in S. cerevisiae where nutritional signals (carbon and nitrogen) influence the transcription of ribosomal RNA, and genes for ribosome biogenesis and ribosomal proteins, via transcription factors controlled by the cAMP/PKA and TOR pathways (Broach, 2012).  Although S. cerevisiae may not be the best model for other fungi because of its ability, unlike C. neoformans, to carry out aerobic fermentation, extensive studies in yeast document PKA regulation of the translation machinery during the transient response to glucose as well as regulation of metabolic functions such as carbohydrate storage, phospholipid biosynthesis, glycolysis and gluconeogenesis.  Additionally, PKA regulates the environmental stress response, autophagy and the transition between yeast and pseudohyphal/filamentous growth, a process that may reflect a role for PKA on a longer time scale compared with its participation in the transient response to glucose (Broach, 2012).  These other influences of PKA may therefore be more relevant to the sustained activation expected from induction of PKA1 expression in our experiments with C. neoformans.    130 In particular, autophagy and the response to stress warrant further study to determine whether they play a major role in the observed reduction in abundance for proteins involved in translation (Jorgensen et al., 2004; Marion et al., 2004; Kraft et al., 2008; Broach, 2012). The link between cAMP/PKA signaling and the stress response in fungi has been characterized in some detail and is relevant to fungal pathogenesis (Bonnet et al., 2000; Robertson et al., 2000; Jones et al., 2003; Harcus et al., 2004; Larraya et al., 2005; Hu et al., 2007; Broach, 2012).  In particular, the connection between the regulation of translation may be important during the interaction of fungal pathogens with phagocytic cells in vertebrate hosts.  It is known, for example, that the phagocytosis of C. neoformans and C. albicans by mammalian macrophages results in transcriptional down-regulation of translational machinery (Lorenz et al., 2004; Fan et al., 2005).  Therefore, future studies should investigate whether the sustained activation of PKA in the regulated Pka1 strain influences the intracellular survival of C. neoformans in phagocytic cells 3.5.2 PKA and human diseases: a conserved connection between translation and the ubiquitin-proteasome pathway In addition to Pka1 regulation of proteins associated with translation, a second cluster of interacting and regulated proteins contained components of the ubiquitin-proteasome pathway.  This observation indicates an interesting conservation of the connection between Pka1 and the proteasome because a similar association is found in chronic neurodegenerative disorders and other diseases in humans.  In particular, the ubiquitin-proteasome pathway is a potential pharmacological target for the prevention and treatment of Alzheimer disease (AD), Parkinson disease, (PD) Huntington disease (HD), and amyotropic lateral sclerosis (ALS), as well as cardiovascular conditions such as hypertrophic and dilated cardiomyopathies, and ischemic heart   131 disease (Huang and Figueiredo-Pereira, 2010; Nijholt et al., 2011; Wang et al., 2011).  All of these conditions are associated with impaired protein turnover and the accumulation of intracellular ubiquitin-protein aggregates (Huang and Figueiredo-Pereira, 2010; Wang et al., 2011).  For example, proteasome impairment is associated with lower PKA activity leading to progression of HD (Lin et al., 2013).  Research indicates that positive feedback regulation between PKA and the proteasome is critical for HD pathogenesis.  PKA has also been shown to control neurite outgrowth and morphogenesis and plays an essential role in synaptic plasticity and memory (Sepe et al., 2014).  Specifically, cAMP stimulated ubiquitination and degradation of NOGO-A positively impacts neurite outgrowth in mammalian brain.  Moreover, manipulation of the ubiquitin-proteasome pathway via cAMP signaling in neurons and heart of mice showed that activation of endogenous PKA prevented 20S proteasome inhibition, which is associated with isoproterenol-induced cardiac hypertrophy (Drews et al., 2010).  In addition to control of protein degradation via the ubiquitin-proteasome pathway, the ribosome plays an important role in cotranslational ubiquitination and quality control since newly synthesized polypeptides must be properly folded to avoid aggregation (Duttler et al., 2013; Pechmann et al., 2013).  Specifically, a tiered system of quality control at the ribosome is responsible for protein homeostasis during protein synthesis by sensing the nature of nascent protein chains, recruiting protein folding and translocation components, and integrating mRNA and nascent chain quality control (Pechmann et al., 2013).  Taken together, our observation that Pka1 modulation influences translation and the proteasome in C. neoformans highlights a conserved role of Pka1 in controlling these processes, and particularly highlights a potential role for protein degradation in the phenotypic influences of the cAMP/PKA pathway.     132 3.5.3 Pka1 influences cellular metabolism and amino acid biosynthesis  As mentioned, gene enrichment analysis and KEGG mapping of the proteome upon Pka1 modulation identified an overrepresentation of genes associated with metabolic and biosynthetic processes. An examination of the corresponding proteins, as well as bioinformatic characterization of novel Pka1-regulated proteins, highlighted an influence of Pka1 on amino acid biosynthesis and central carbon metabolism.  In the context of carbon metabolism, I noted that Pka1 induction regulated the abundance of acetyl-CoA acyltransferase 2, acetyl-CoA synthetase, C-acetyltransferase, long-chain acyl-CoA synthetase, and enoyl-CoA hydratase.  These enzymes are interesting because we previously demonstrated the importance of acetyl-CoA formation during cryptococcal infection (Hu et al., 2008).  Additionally, our transcriptional profiling of cryptococcal cells harvested from mouse macrophages revealed elevated transcript levels for the ACL1 gene encoding ATP-citrate lyase, a key enzyme for producing acetyl-CoA (Griffiths et al., 2012).  PKA phosphorylation regulates Acly activity in mammalian cells although this connection has not yet been established in C. neoformans (Pierce et al., 1981).  Acly is important in C. neoformans because mutants lacking ACL1 showed delayed growth on glucose-containing medium, reduced cellular levels of acetyl-CoA, defects in the production of the virulence factors capsule and melanin, increased susceptibility to the antifungal drug fluconazole, and an inability to cause disease in a murine inhalation model of cryptococcosis (Griffiths et al., 2012).  Together, our observation that Pka1 regulates abundance of acetyl-CoA-associated enzymes and the observed virulence impact in C. neoformans by deletion of ACL1, suggests a link between acetyl-CoA production and the cAMP/PKA pathway.   The link between amino acid biosynthesis and the cAMP/PKA pathway is also important in C. neoformans because amino acids such as methionine may serve as ligands for the G-protein   133 coupled receptor, Gpr4, and thereby contribute to pathway activation (Xue et al., 2006).  However, the regulation of amino acid biosynthesis by Pka1 has not yet been reported.  Our observation that Pka1 impacts metabolic pathways associated with arginine, proline, tryptophan, alanine, aspartate, and glutamate metabolism, in addition to lysine, valine, leucine, and isoleucine degradation is therefore of particular interest.  Our observed reduction in protein abundance of cysteine-type peptidase upon Pka1-induction may reflect changes in the demand for amino acids as a result of Pka1 regulation (e.g., of translation), and/or the potential for the cAMP/PKA pathway to be a component of common control mechanisms for amino acid biosynthesis.  Amino acid biosynthesis also has important connections with virulence in C. neoformans.  For example, threonine synthase is essential for fungal growth, particularly at host temperature, as well as virulence, and could be a potential antifungal drug target (Kingsbury and McCusker, 2008).  Similarly, deletion of genes associated with the first step of the methionine/cysteine pathway in C. neoformans resulted in a defect in melanin formation, reduced growth and an increase in thermotolerance, along with avirulence and an inability of the fungus to survive in mice (Yang et al., 2002).  Furthermore, transportation of amino acids is important for the normal growth of C. neoformans, which is regulated through the transcription factor, Gat1 (Kmetzsch et al., 2011).  Interestingly, Gat1 is a known interaction protein between Pka1 and Tor1 (target of rapamycin signal transduction pathway important for cell growth) in S. cerevisiae (Nandy et al., 2010).  Overall, our findings suggest that Pka1 influences the biosynthesis of amino acids, which may directly impact its role in the transport of amino acids acting through a transcriptional regulator.     134 3.5.4 Pka1 influences the abundance of mannoproteins and novel proteins  Our previous analysis of the secretome in C. neoformans revealed that Pka1 regulates the abundance of the mannoprotein Cig1 (Chapter 2).  Cig1 is important for iron acquisition from heme and for virulence in C. neoformans (Cadieux et al., 2013).  As in the extracellular proteome, I also found that the proteome abundance of Cig1 increased upon induction of Pka1 and that transcript levels and protein abundance were well correlated.  CIG1 is positively regulated by the pH-responsive transcription factor Rim101, which is itself activated by the cAMP/PKA pathway (O'Meara et al., 2010).  I propose that the regulation of CIG1 mRNA and its protein levels under Pka1 induction are likely associated with regulation by Rim101.  In addition to Cig1, I also identified a novel protein (CNAG_05312) with a pattern of mRNA and protein regulation by Pka1 activity that was quite similar to that of Cig1.  The CNAG_05312 product is one of a large proportion of the identified Pka1-regulated proteins that were categorized as hypothetical (21%).  These proteins present an interesting opportunity to identify and characterize novel proteins regulated by Pka1 and potentially involved in additional processes.  Specifically, the annotated macrophage-activating glycoprotein (novel protein CNAG_05312) contains a predicted carbohydrate-binding domain and it is positively regulated by Rim101 at the transcriptional level (O'Meara et al., 2010).  These observations suggest that further investigation is warranted for this protein in the context of iron acquisition and virulence.  Our identification of both Cig1 and the CNAG_05312-encoded protein in the intracellular proteome and the secretome may indicate their association with the cell wall or capsular material at the time of analysis.  Additionally, it highlights our ability to follow their progress through the cell from intracellular synthesis to secretion, providing a unique opportunity to track protein   135 release from the cell and to gain further insight into the impact of Pka1 on secretion in C. neoformans.  Finally, I specifically mined the proteome data for proteins with known or predicted roles in cryptococcal virulence or that are represented in the collection of deletion mutants (Liu et al., 2008).  A comparison of the C. neoformans gene deletion set with our list of Pka1-regulated proteins allowed for a more in-depth analysis of the impact of PKA1 modulation on the proteome (Liu et al., 2008; Brown et al., 2014).  In total, I linked our Pka1-regulated proteins with 59 deletion mutants that had previously been assessed for the production of virulence factors and growth at 37°C, as well as recent chemical genetic mapping showing small molecule sensitivity and resistance phenotypes (Table B.5).  Of these 59 genes, three displayed a defect in melanin production including an ubiquitin-like protein (CNAG_02827), a class E vacuolar protein-sorting machinery protein HSE1 (CNAG_05882), and an uncharacterized protein (CNAG_01644) (Liu et al., 2008).  These results establish a link between these three proteins and the known regulation of melanin production by PKA (D'Souza et al., 2001).   In addition, CNAG_02827 showed a growth defect compared to the WT strain.  Twenty-nine deletion mutants showed phenotypic sensitivity to challenge by small molecules including sensitivity to H2O2, FeCl2, rapamycin, and amphotericin B.  Conversely, 27 deletion mutants showed phenotypic resistance to challenge by small molecules.  Notably, a dihydrodipicolinate synthase and known virulence-related factor, urease, displayed sensitivity to treatment with the antifungal agent, amphotericin B, along with a small subunit ribosomal protein S27Ae and argonaute which displayed sensitivity to fluconazole.  Several deletion strains displayed sensitivity to cell wall stressors including SDS, Congo red, and caffeine suggesting a role of the gene in cell wall integrity and potential capsule attachment, as well as sensitivity to iron suggesting a potential role in   136 virulence.  Observed resistance to treatment with H2O2 and fluconazole was reported for several genes suggesting a mode of protection towards oxidative stress and possible hypervirulence.  Interestingly, Cig1 did not display phenotypic sensitivity or resistance to the presence of small molecules; however, the similarly regulated novel protein (CNAG_05312) showed sensitivity to several small molecules including CuSO4 and resistance to fungicidal treatment by benomyl.  Overall, the interpretation of the proteome data in the context of genetic and chemical genetic data revealed novel connections with PKA regulation that warrant further study.  3.6 Conclusions In this study I characterized the overall impact of PKA modulation on the proteome of C. neoformans and discovered several categories of proteins regulated by Pka1.  The identified proteins had known roles associated with translational regulation, protein folding and degradation via the proteasome, metabolism, amino acid biosynthesis, and virulence.  Our observed connection between Pka1 and regulation of translational machinery, the ribosome, and the proteasome highlighted a broad and conserved role of Pka1.  Additionally, the proteome data provide a wealth of novel proteins and new connections between Pka1 and known proteins that will be valuable for further defining the role of PKA in cryptococcal virulence.     137 Chapter 4: Protein Kinase A Phosphorylates Cir1, Impacting its Role in Iron Homeostasis and Virulence Factor Expression in Cryptococcus neoformans  4.1 Synopsis Cryptococcus neoformans is the leading cause of fungal meningitis in immunocompromised individuals.  The severity of disease is influenced by the expression of virulence factors including a polysaccharide capsule, melanin, and the secretion of extracellular enzymes.  In C. neoformans, the cAMP/Protein Kinase A (PKA) signal transduction pathway regulates the expression of these factors, likely via PKA phosphorylation of downstream targets.  A comprehensive investigation of the impact of modulating the expression of PKA1 (encoding the catalytic subunit of PKA) on the phosphoproteome has not been performed for C. neoformans.  In the present study, I identified 98 and 67 phosphoproteins under Pka1 repression and Pka1 induction, respectively.  Six novel targets of Pka1 phosphorylation were identified, of which three contained the Pka1 recognition sequence, R/K-R/K-X-S/T-B, including Cir1, a master iron regulator associated with iron acquisition and virulence, that was of particular interest.  We also observed a change in the phosphoproteome profile under Pka1 induction to include proteins for glycolysis and cellular processes, transcription, and transport.  Finally, construction and characterization of site-directed mutants revealed that mutations at the Pka1 phosphorylation site of Cir1 impact the production of capsule and melanin, cell size, and iron homeostasis.  Additionally, I showed similar trends in transcriptional regulation between the non-phosphorylatable mutant and the cir1Δ mutant.  Overall, our study revealed that Pka1 phosphorylates Cir1 and subsequently influences iron homeostasis, transcriptional control, and the production of virulence-related factors.    138 4.2 Introduction Cryptococcus neoformans is the leading cause of fungal meningitis in immunocompromised individuals (Mitchell and Perfect, 1995).  The opportunistic pathogen causes approximately 625,000 deaths annually, particularly in patients suffering from HIV/AIDS (Park et al., 2009).  The severity of disease is influenced by the expression of fungal virulence factors including a polysaccharide capsule, melanin, and the secretion of extracellular enzymes (Bulmer et al., 1967; Kwon-Chung et al., 1982; Rhodes et al., 1982; Kwon-Chung and Rhodes, 1986; Polacheck and Kwon-Chung, 1988; Chang and Kwon-Chung, 1994).  For C. neoformans, along with most pathogens, iron sensing contributes to virulence because its availability within the host can be a limiting factor for proliferation and survival (Hentze et al., 2004; Doherty, 2007; Kronstad, 2013).  To combat limited iron sources and increase intracellular iron availability for fungal cells, C. neoformans activates a number of uptake systems including siderophore transporters and high-affinity iron uptake functions (Lian et al., 2005; Tangen et al., 2007; Jung and Kronstad, 2008).  The expression of iron acquisition functions is under the regulation of the transcription factor, Cir1, and this regulator also influences the elaboration of virulence-related factors and fungal virulence in a mouse model (Jung et al., 2006; Jung et al., 2008).   Cir1 is often designated as a master iron regulator in C. neoformans.  Its activity as a transcription factor influences the expression of genes associated with iron uptake from heme, CIG1, an iron transporter, SIT1, and the production of melanin, LAC1, in addition to other iron-related genes (Jung et al., 2006).  CIR1 deletion mutants have a defect in capsule and melanin production, as well as reduced growth at 37°C, increased sensitivity to drug treatment, and avirulence in a mouse model (Jung et al., 2006; Jung et al., 2008).  In other fungi such as   139 Schizosaccharomyces pombe, Candida albicans, and Ustilago maydis, the transcriptional repressors Fep1, Sfu1, and Urbs1, respectively, regulate the expression of iron-responsive genes (Haas, 2003).  These proteins possess conserved cysteine-rich regions and two zinc funger motifs characteristic of GATA-type transcription factors, showing sequence similarity to Cir1, but possess an additional zinc finger  (An et al., 1997; Pelletier et al., 2002; Pelletier et al., 2003). In C. neoformans, the cAMP/PKA signal transduction pathway regulates virulence-related factors (Alspaugh et al., 1997; D'Souza et al., 2001; Kozubowski et al., 2009; Kronstad et al., 2011b; McDonough and Rodriguez, 2012) (Chapters 2 and 3).  Components of the pathway include a Gα protein (Gpa1), adenylyl cyclase (Cac1), adenylyl cyclase-associated protein (Aca1), a candidate receptor (Gpr4), a phosphodiesterase (Pde1), and the catalytic (Pka1, Pka2) and regulatory (Pkr1) subunits of PKA (Kozubowski et al., 2009; Kronstad et al., 2011a).  Mutations in the genes encoding the Gpa1, Cac1, Aca1, and Pka1 proteins result in reduced capsule and melanin formation, sterility, and attenuated virulence in a mouse model of cryptococcosis (Alspaugh et al., 1997; D'Souza et al., 2001; Alspaugh et al., 2002; Bahn et al., 2004).  Activation of the pathway occurs in response to environmental signals when a G-protein coupled receptor (GPCR; Gpr4), undergoes a conformational change, activating Cacl and subsequently stimulating the production of cAMP (Granger et al., 1985; Yang et al., 2002; Mogensen et al., 2006).  Following production of cAMP, PKA is activated upon its binding to the regulatory subunit Pkr1, inducing a conformational change and releasing the active catalytic subunit which is then capable of phosphorylating downstream targets (Taylor et al., 1990).  There is also a connection between iron and cAMP/PKA signaling because it is known that the iron regulator Cir1 positively regulates the expression of Gpr4 under iron deplete and replete   140 conditions, which in turn regulates capsule size via activation of the cAMP/PKA pathway (Jung et al., 2006). Downstream phosphorylation targets of the cAMP/PKA pathway can also have a significant influence on cellular processes and virulence.  For example, Nrg1, a transcription factor and downstream target of the pathway has been shown to regulate a glucose dehydrogenase (Ugd1) involved in capsule production and growth at 37°C (Moyrand and Janbon, 2004; Cramer et al., 2006).  Additionally, cell wall attachment of the capsule and cell wall integrity are regulated in part by Pka1 phosphorylation of the pH-responsive transcription factor, Rim101 (O'Meara et al., 2010).  Furthermore, predicted targets of PKA regulation were identified by serial analysis of gene expression (SAGE) and some of these were associated with secretion and regulation of capsule formation (Hu et al., 2007).  To achieve a deeper understanding of cAMP/PKA signaling, it is clear that a comprehensive investigation of the impact of modulating PKA expression on the phosphoproteome to identify phosphorylation targets is needed for C. neoformans.  In C. neoformans, the recent construction and characterization of a galactose-inducible, glucose-repressible expression strain by inserting the GAL7 promoter upstream of PKA1 provides an opportunity to identify potential targets of Pka1 phosphorylation.  Galactose induction of PKA1 influenced capsule thickness, cell size, ploidy, vacuole enlargement, and melaninization and laccase activity, as well as the secretion of proteases and urease (Choi et al., 2012).  Recent secretome and proteome analyses using the PGAL7::PKA1 strain under repression and induction conditions identified Pka1-regulated proteins and highlighted a significant impact on translation, the proteasome, metabolism, and virulence-related factors (Chapters 2 and 3).  In the present study, I investigated the impact of modulating PKA1 expression on the   141 phosphoproteome of C. neoformans.  Using proteomics, I identified 97 and 65 phosphoproteins under Pka1 repression and Pka1 induction, respectively.  I also observed a change in the phosphoproteome profile upon Pka1 induction from proteins involved primarily in catabolic and metabolic processes to an expanded set, which included proteins for glycolysis and cellular processes, transcription, and transport.  We identified six potential targets of Pka1 phosphorylation of which three contained the Pka1 recognition sequence R/K-R/K-X-S/T-B (Gibson et al., 1997; O'Meara et al., 2010).  Of these, one phosphoprotein was Cir1, the iron regulator mentioned above with known roles in controlling the production of capsule and melanin, the ability of the fungus to grow at 37°C, iron acquisition, and virulence.  With the use of a synthetic peptide containing the phosphorylation site, our preliminary results suggest that Pka1 directly phosphorylates Cir1.  Upon construction of site-directed mutants (SDM) with substitutions at the phosphorylation site, I found that mutation of the candidate Pka1 phosphorylation target, Cir1, impacts its production of capsule and melanin, cell size, and iron homeostasis.  Additionally, I showed similar trends in transcriptional profiling between a mutant with a non-phosphorylatable amino acid at the PKA phosphorylation site and the cir1Δ mutant.  Overall, our study revealed that Pka1 phosphorylates the master iron regulator, Cir1, and this phosphorylation influences its role in iron homeostasis and the expression of virulence-related factors.  4.3 Experimental procedures  4.3.1 Fungal strains and culture conditions The Cryptococcus neoformans var. grubii wild-type strain H99 (WT) and the galactose-inducible PKA1 strain, PGAL7::PKA1 were used for phosphoproteome analyses (Alspaugh et al.,   142 1997; Choi et al., 2012).  The strains were maintained on yeast extract peptone dextrose (YPD) medium (1% yeast extract, 2% peptone, 2% dextrose, and 2% agar).  For studies involving the regulation of PKA1, cells of the WT and regulated strain were pre-grown overnight with agitation at 30°C in YPD broth, transferred to yeast nitrogen base medium with amino acids (YNB, Sigma-Aldrich) and incubated overnight with agitation at 30°C.  Cell counts were performed and 5 x 107 cells/ml were transferred to Minimal Medium (MM) (29.4 mM KH2PO4, 10 mM MgSO4y7H2O, 13 mM glycine, 3 μM thiamine, 0.27% carbon source) containing either 0.27% glucose (MM+D) or 0.27% galactose (MM+G).  Cells were incubated with agitation at 30°C in MM+D or MM+G for 16 h.  Samples were collected in triplicate for analysis. 4.3.2 Preparation of protein extracts, quantification, and trypsin digestion   Cellular fractions were processed for total protein extraction (Crestani et al., 2012).  In brief, cells were collected following removal of the supernatant by centrifugation at 3,500 rpm for 15 min at 4°C; the cells were then washed three times with either cold MM+D or MM+G and collected by centrifugation at 3,500 rpm for 15 min at 4°C.  Cells were flash frozen in liquid N2 and lyophilized overnight.  Following lyophilization, the cells were disrupted with a mortar and pestle and suspended in cold lysis buffer (50 mM Tris-HCl pH 7.5, 5 mM EDTA, 5 mM iodoacetamide (IAA), protease inhibitor cocktail (Roche)).  Solubilization of proteins was performed by vortexing for 5 min, followed by incubation on ice for 5 min, and centrifugation at 13,000 rpm for 20 min.  Supernatant fractions were collected and stored at -20°C.  The remaining cellular debris was re-suspended in a second aliquot of cold lysis buffer followed by vortexing for 5 min and sonication at power 3 in an ice bath for three 30 s cycles with 1 min intervals (Thermo Fisher Scientific).  The supernatant was collected, pooled with the supernatant fraction from the first extraction, and stored at -80°C.  Protein concentration was determined   143 using the BCA Protein assay (Pierce).  Cellular proteins in the collected fractions were subjected to in-solution trypsin digestion using ACS grade chemicals or HPLC grade solvents (Thermo Fisher Scientific and Sigma-Aldrich) (Fang et al., 2010).  In brief, digestion buffer (1% sodium deoxycholate, 50 mM NH4HCO3) was added to the supernatant and samples were incubated at 99°C for 5 min with agitation, followed by reduction (2 mM of dithiothreitol (DTT) for 25 min at 56°C), alkylation (4 mM of IAA for 30 min at room temperature in the dark), and trypsinization (0.5 μg/μl of trypsin overnight at 37°C).  4.3.3 Peptide chemical labeling and purification  Digested peptides from cellular fractions were desalted, concentrated, and filtered on C18 STop And Go Extraction (STAGE) tips (Rappsilber et al., 2003).  To normalize for possible effects associated with growing the strains under glucose or galactose conditions, PGAL7::PKA1 and WT strains were dimethyl-labeled and processed in tandem.  Reductive dimethylation using formaldehyde isotopologues was performed to differentially label peptides from the different experimental conditions.  Light formaldehyde (CH2O) and medium formaldehyde (CD2O) (Cambridge Isotope Laboratories, Andover, MA) were combined with light cyanoborohydride (NaBH3CN) (Sigma-Aldrich) to give a 4 Da difference for labeled peptides (Boersema et al., 2008).  Samples from the WT strain were routinely labeled with light formaldehyde, and PGAL7::PKA1 samples were labeled with medium formaldehyde.  Briefly, eluted and dried STAGE-tip peptides were resuspended in 100 mM triethylammonium bicarbonate, and incubated in 200 mM formaldehyde and 20 mM sodium cyanoborohydride for 90 min in the dark.  After labeling, 125 mM NH4Cl was added to react with excess formaldehyde.  Samples were incubated for 10 min, followed by the addition of acetic acid to a pH < 2.5 to degrade the sodium   144 cyanoborohydride.  For each comparison, equal amounts of labeled peptides were mixed and desalted on C18 STAGE tips prior to mass spectrometry. 4.3.4 Enrichment of phosphopeptides using TiO2 or Strong Cation Exchange (SCX) chromatography  Digested and purified peptides were enriched by TiO2 or fractionated by strong-cation exchange chromatography (SCX).  For collection of phosphopeptides using TiO2, 1.0 mg of pooled light- and medium-formaldehyde labeled peptides were enriched using 1.0 mg of TiO2 packed onto a C8 flit (Titansphere, GL Science) per tip (Rappsilber et al., 2007; Sugiyama et al., 2007; Kyono et al., 2008).  Briefly, the TiO2 tip was washed with buffer B (0.1% acetic acid, 80% acetonitrile), centrifuged at 1,500 rpm for 1 min, then the tip was washed with buffer C (300 mg lactic acid solution in buffer B, Sigma Aldrich) and centrifuged at 2,250 rpm for 2 min.  Samples were resuspended in buffer B:buffer C (1:1), loaded on the tip and centrifuged at 1,500 rpm for 3 min.  The tip was then washed with buffer C and subsequently buffer B, following centrifugation.  Phosphopeptides were eluted with 0.5% pyrrolidine (Sigma-Aldrich), followed by 5% NH4OH, and centrifuged at 1,500 rpm for 3 min.  Eluted samples were then acidified with glacial acetic acid (Fisher) and purified on C18 STAGE tips. For SCX, protein samples were collected into approximately 15 fractions based on measurable UV output.  Fractions with low peptide concentrations were pooled.  Briefly, 600 μg of pooled light and medium formaldehyde-labeled peptides were fractionated using an Agilent 1100 HPLC system coupled with a PolySULFOETHYL A SCX column 200 mm x 2.1 mm with 5 μM particles and a flow rate of 200 μl/min.  Buffer A consisted of 5 mM KH2PO4, 30% acetonitrile in water (pH 2.7), buffer B consisted of 5 mM KH2PO4, 350 mM KCl, and 30% acetonitrile in water (pH 2.7), and buffer C consisted of 0.1 M Tris, 0.5 M KCl (pH 7.0).    145 Samples were re-suspended in buffer A and loaded with the same buffer.  Three gradients were run over 90 min.  The first gradient for collection of the phosphopeptides ran over 36 min with 100% A over 5 min, then 13% B over 15 min, then increased to 100% B over a 2 min period, followed by a hold of 100% B form 15 min.  The second gradient for washing the column ran for 15 min with 100% C.  The third gradient for neutralizing the column ran at 100% B for 5 min followed by 100% A for 30 min.  All fractions were desalted on C18 STAGE tips prior to mass spectrometry. 4.3.5 Protein identification by liquid chromatography-tandem mass spectrometry (LC-MS/MS) and mass spectrometry data analysis  Purified and TiO2-enriched or SCX-fractionated peptides were analyzed using a linear-trapping quadrupole - Orbitrap mass spectrometer (LTQ-Orbitrap Velos; Thermo Fisher Scientific) on-line coupled to an Agilent 1290 Series HPLC using a nanospray ionization source (Thermo Fisher Scientific) including a 2-cm-long, 100-μm-inner diameter fused silica trap column, 50-μm-inner diameter fused silica fritted analytical column, and a 20-μm-inner diameter fused silica gold coated spray tip (6-μm-diameter opening, pulled on a P-2000 laser puller from Sutter Instruments, coated on Leica EM SCD005 Super Cool Sputtering Device).  The trap column was packed with 5 μm-diameter Aqua C-18 beads (Phenomenex, www.phenomenex.com) while the analytical column was packed with 3.0 μm-diameter Reprosil-Pur C-18-AQ beads (Dr. Maisch, www.Dr-Maisch.com).  Buffer A consisted of 0.5% aqueous acetic acid, and buffer B consisted of 0.5% acetic acid and 80% acetonitrile in water.  Samples were re-suspended in buffer A and loaded with the same buffer.  Standard 90 min gradients were run from 10% B to 32% B over 51 min, then from 32% B to 40% B in the next 5 min, then increased to 100% B over a 2 min period, held at 100% B for 2.5 min, and then dropped to 0% B   146 for another 20 min to recondition the column.  The HPLC system included an Agilent 1290 series Pump and Autosampler with the Thermostat temperature set at 6°C.  The sample was loaded on the trap column at 5 μl/min and the analysis was performed at 0.1 μl/min.  The LTQ-Orbitrap was set to acquire a full-range scan at 60,000 resolution from 350 to 1600 Th in the Orbitrap to simultaneously fragment the top ten peptide ions by CID and top 5 by HCD (resolution 7500) in each cycle in the LTQ (minimum intensity 1000 counts).  Parent ions were then excluded from MS/MS for the next 30 s.  Singly charged ions were excluded since in ESI mode peptides usually carry multiple charges.  The Orbitrap was continuously recalibrated using lock-mass function (Olsen et al., 2005).  Mass accuracy: error of mass measurement was typically within 5 ppm and was not allowed to exceed 10 ppm.    For analysis of mass spectrometry data, centroid fragment peak lists were processed with Proteome Discoverer v. 1.2 (Thermo Fisher Scientific).  The search was performed with the Mascot algorithm (v. 2.4) against a database comprised of 6,692 protein sequences from the source organism C. neoformans H99 database (C. neoformans var. grubii H99 Sequencing Project, Broad Institute of Harvard and MIT, http://www.broadinstitute.org/) using the following parameters: peptide mass accuracy 10 ppm; fragment mass accuracy 0.6 Da; trypsin enzyme specificity, 1 max missed cleavage, fixed modifications - carbamidomethyl, variable modifications - methionine oxidation, phosphorylation of serine, threonine, and tyrosine, deamidated N, Q and N-acetyl peptides, dimethyl (K), dimethyl (N-term), dimethyl 2H(4) (K), and dimethyl 2H(4) (N-term),  ESI-TRAP fragment characteristics.  Only those peptides with IonScores exceeding the individually calculated 99% confidence limit (as opposed to the average limit for the whole experiment) were considered as accurately identified.  The acceptance criteria for protein identification were as follows: only proteins containing at least one unique peptide   147 with a Mascot score > 25 were considered in the dataset.  Phosphopeptides were mapped to corresponding protein sequences to locate the position of phosphosites in the protein.  Phosphorylated proteins identified under both glucose and galactose conditions were not considered for further analysis (Table C.1; Table C.2).  Only phosphoproteins identified under Pka1-induction were considered as possible targets of Pka1 phosphorylation.   4.3.6 In vitro phosphorylation assay  We performed an in vitro assay to determine whether Pka1 phosphorylates Serine-599 of Cir1.  Initially, PKA1 was amplified from C. neoformans WT cDNA (primers listed in Table C.3).  The PKA1 PCR product was then digested with BamH1 and Xho1 (New England Biolabs) and ligated into the BamH1 and Xho1-digested pET-28a (+) vector (Novagen) using the TOPO cloning kit (Life Technologies) in Escherichia coli.  pET28a (+) is a 5369 bp plasmid carrying an N-terminal His-Tag/thrombin/T7-tag and under control of a T7 promoter.  For sample preparation, the cell pellet from 1 mM IPTG (isopropyl β-D-1-thiogalactopyranoside)-induced transformed E. coli was collected at 16 h and sonicated in protein lysis buffer as described above.  The supernatant was applied to Ni-NTA agarose beads (Qiagen) for a pull-down assay and eluted with protein lysis buffer containing 250 mM imidazole.  Next, the following synthetic peptide was synthesized in-house: RRASIDVDMADNEPR.  Five nmol of synthetic peptide was incubated in the presence of purified Pka1 from C. neoformans expressed in E. coli.  Briefly, molar ratios of 1:1 and 1:10 of synthetic peptide to purified Pka1 were incubated in the presence of an assay buffer (40 mM Tris-HCl (pH 7.4), 20 mM magnesium acetate, 0.2 mM ATP) for 30 min at 25°C.  Samples then underwent an in-solution trypsin digest followed by STAGE-tip purification and identification of phosphopeptides by mass spectrometry as described above.     148 4.3.7 Construction of Cir1 site-directed mutants (SDM) and confirmation To generate a point mutation of the putative phosphorylation site of Cir1 from TCT (Serine) to GCT (Alanine; S599A) or GAT (Aspartic acid; S599D), an overlap PCR-mediated site-directed mutagenesis was employed to prepare the constructs (Table C.4).  Briefly, P1Ala (PCR product 1 Ala) or P1Asp (PCR product 1 Asp), of 565 bp, were amplified with primers Cir1-1F and Cir1-1R-Ala or Cir1-1F and Cir1-1R-Asp, respectively, using genomic DNA (gDNA) from the WT strain as the template.  P2Ala or P2Asp, PCR products of 1793 bp, were amplified with primers Cir1-2F-Ala and Cir1-2R or Cir1-2F-Asp and Cir1-2R, respectively, using WT gDNA as the template.  P3, a PCR product of 1140 bp, was amplified with primers Cir1-3F and Cir1-3R from the plasmid pJAF1, which carries a neomycin resistance marker cassette.  P4, a PCR product of 1455 bp, was amplified with primers Cir1-4F and Cir1-4R from the plasmid pJAF1, and P5, a PCR product of 561 bp, was amplified with primers Cir1-5F and Cir1-5R using WT gDNA as a template.  The PCR products of P1Ala (or P1Asp), P2Ala (or P2Asp), and P3 were mixed as the template in an overlap PCR to generate the final construct, PA (3498 bp), using primers Cir1F and Cir1-3R.  The PCR products of P4 and P5 were mixed to generate the final construct, PB (2016 bp), using the primers Cir1-4F and Cir1-5R.  At a molar ratio of 1:1, PA and PB were mixed and biolistically transformed into the WT strain.  Transformants were selected on YPD plates containing neomycin (200 μg/ml; Clontech Takara-Bio Inc.) and screened by colony PCR.  The successful construction of a site-directed mutation was confirmed by phenotypic assays and sequencing confirmation is currently underway.  Two independent mutants for each construct were selected and used for subsequent analyses.  Table C.3 lists all primers used for the generation of the SDM.     149 4.3.8 Capsule formation, laccase production, and growth at 37°C To examine the formation of capsule in the Ala or Asp substitution mutants, a single colony from solid YPD medium was inoculated into liquid YPD medium and cultured overnight at 30°C.  The overnight culture was harvested and diluted in low iron water (chelexed dH2O), and 1 x 107 cells/ml were added into 5 ml of liquid low-iron medium (LIM) for further incubation at 30°C for 48 h.  LIM was prepared as described with 20 mM Hepes and 22 mM NaHCO3 (Vartivarian et al., 1993).  After incubation, the capsule was stained with india ink and examined by differential interference microscopy (DIC).  Measurements of capsule and cell size were performed on an average of 40 cells per strain.  To evaluate the production of laccase in the Ala or Asp substitution mutants, cells were pre-grown in YPD overnight at 30°C and harvested and diluted in sterile dH2O.  Ten-fold serial dilutions of 1 x 106 cells/ml were then plated on 10 mM L-DOPA medium and incubated at 30°C and 37°C for 3 d and photographed.  To determine the ability of the Ala and Asp substitution mutants to grow at 37°C, cells were pre-grown in YPD at 30°C, harvested and diluted in sterile dH2O.  Ten-fold serial dilutions of 1 x 105 cells/ml were then plated on YPD medium and incubated at 30°C and 37°C for 3 d and photographed. 4.3.9 Cell wall-related phenotypic assays To investigate cell wall integrity of the Ala and Asp substitution mutants, cells were pre-grown in YPD at 30°C, harvested and diluted in sterile dH2O, and challenged with cell wall stressors.  The modes of cell wall stress induction included disruption of the plasma membrane and lysis of cells with membrane defects by sodium dodecyl sulphate (SDS), targeting protein kinase complexes and DNA metabolism by caffeine, and interfering with cell wall assembly by   150 binding to cell wall glucans by Congo red (Elorza et al., 1983; Lum et al., 2004; Levin, 2005; Imai et al., 2005; Kuranda et al., 2006; Bermejo et al., 2010; Levin, 2011).  Ten-fold serial dilutions of 1 x 106 cells/ml were plated on YPD medium containing 0.0125% SDS, 0.5 mg/ml caffeine, and 0.5 mg/ml Congo red and incubated at 30°C and 37°C for 3 d and photographed.   To determine whether capsule was synthesized and shed in the SDM, a blotting technique was used with an anti-GXM antibody to probe for capsule polysaccharide in culture medium (Yoneda and Doering, 2006; Griffiths et al., 2012).  In brief, WT, cir1Δ mutant, and SDM strains were inoculated in 5 ml of LIM for induction of capsule.  Following one week of growth at 30°C, 50 μl culture aliquots were diluted to a cell density (OD600) of 0.5.  The aliquots were then boiled for 15 min, cells were pelleted, and the supernatants were removed.  Next, the supernatant samples were electrophoresed on an agarose gel and transferred to a nylon membrane using the Southern blot technique.  The polysaccharide was detected using the primary monoclonal antibody 18b7 (1:2,000 dilution) and anti-mouse peroxidase-conjugated secondary antibody (1:3,000 dilution, Jackson Labs) followed by enhanced chemiluminescence (ECL, Amersham) for visualization. 4.3.10 Analysis of growth upon iron deprivation To examine the effects of iron limitation and iron repletion on the Ala and Asp substitution mutants, cells were pre-grown in YPD at 30°C, harvested and diluted in LIM.  Ten-fold serial dilutions of 1 x 106 cells/ml were then plated on LIM medium or LIM supplemented with 100 uM of FeCl3 and incubated at 30°C and 37°C for 3 d and photographed. 4.3.11 Construction of a CIR1::GFP fusion allele The C-terminal region of the Cir1 protein was tagged with GFP (green fluorescent protein) to examine the subcellular localization of Cir1 in the presence or absence of PKA1   151 expression.  Cryptococcus spp. codon-optimized GFP (771 bp) and GAL7 terminator (380 bp) fragments were amplified from plasmid pWH091 using primers HindIII-GFP-F and SpeI-GFP-R, digested with HindIII/SpeI, and cloned into HindIII/SpeI digested pJAF15 to generate pGH022.  A modified overlapping PCR strategy was used to generate the Cir1-GFP construct (Hu et al., 2007; Hu and Kronstad, 2010).  Briefly, the left arm (836 bp) and right arm (826 bp) for the fusion construct were amplified from WT gDNA using the primer set Cir1-GFP-P1F and Cir1-GFP-P1R and the primer set Cir1-GFP-P5F and Cir1-GFP-P5R, respectively.  GFP and the hygromycin (HYG) resistance gene were amplified from the plasmid pGH022 using primers Cir1-GFP-P2F and Cir1-GFP-P3R (3,476 bp).  Overlap PCR was performed using primers Cir1-GFP-P1F and Cir1-GFP-P5R to yield the 5,138 bp construct.  The Cir1A::GFP fusion allele was then used to transform the WT, pka1Δ, PGAL7::PKA1 strains by biolistic transformation.  Following biolistic transformation, mutants were screened for resistance to HYG and proper location and orientation of GFP was determined by PCR.  Primer sequences are listed in Table C.3. 4.3.12 Cellular localization of Cir1  Cellular localization of Cir1 upon modulation of PKA1 expression was evaluated by fluorescence microscopy.  The PGAL7::PKA1 strain possessing the CIR1::GFP fusion allele was grown under Pka1-repressed (glucose-containing medium) and Pka1-induced (galactose-containing medium) conditions.  DIC and fluorescence microcopy was then performed on the cells.  DAPI (4-,6-diamidino-2-phenylindole) a fluorescent stain for DNA was used to visualize the nucleus.  An overlay image of GFP and DAPI confirmed the presence of the CIR1::GFP fusion allele in the nucleus upon both Pka1 repression and Pka1 induction (Figure C.1).    152 4.3.13 Yeast two-hybrid assay The assays were performed using the ProQuest™ Two-Hybrid System according to the manufacturer’s protocols (Invitrogen).  Briefly, CIR1 and PKA1 were PCR amplified from C. neoformans cDNA using the primer pairs cCir1-1/cCir1-2, and Pka1-00396-F-Y2H/Pka1-00396-R-Y2H, and cloned into pDEST32 (bait) and pDEST22 (prey) vectors, respectively.  Primer sequences are listed Table C.3.  Growth of MaV203 yeast expressing both bait and prey vectors was tested on synthetic complete medium (0.7% Yeast nitrogen base without amino acids (Difco), 2.0% glucose, 0.07% synthetic complete selection medium mix (Sigma), 1.7% bacto agar (Difco), pH 5.6) lacking leucine and tryptophan to select for each vector, and histidine and uracil to test for an interaction.  Empty pDEST32 and pDEST22 vectors were used as negative controls.   4.3.14 RNA isolation and qRT-PCR of Cir1-regulated genes in substitution mutants  Cells from the WT strain, and the cir1Δ and substitution mutant strains were prepared for RNA extraction by overnight growth in YNB medium followed by dilution to 5.0 x 107 cells/ml in 5 ml of MM+D or MM+G and incubation at 30°C with agitation for 16 h.  Samples were collected in triplicate for analysis.  Cells were collected at 16 hours post inoculation (hpi), flash frozen in liquid N2, and stored at -80°C.  Total RNA was extracted using an EZ-10 DNAaway RNA Miniprep kit (Bio Basic) according to the manufacturer’s protocol.  Complementary DNA was synthesized using a Verso cDNA kit (Thermo Scientific) and used for qRT-PCR.  Primers were designed using Primer3 v.4.0 (http://bioinfo.ut.ee/primer3-0.4.0/) and targeted to the 3’ regions of transcripts (Table C.3).  Relative gene expression was quantified using the Applied Biosystems 7500 Fast Real-time PCR system and fold changes were determined based on   153 comparison of gene expression to the WT strain.  Control genes CNAG_00483 (Actin) and CNAG_06699 (GAPDH) were used for data normalization.   4.4 Results 4.4.1 Profiling of the phosphoproteome of C. neoformans upon modulation of PKA1 expression  Given the effect of a pka1 deletion on the production of capsule and melanin, and on virulence, along with our observed regulation of the secretome and proteome of C. neoformans by Pka1, I hypothesized that phosphorylation targets of Pka1 may regulate the elaboration of virulence factors and influence cell wall integrity (D'Souza et al., 2001) (Chapters 2 and 3).  We therefore evaluated the effect of Pka1 regulation on the phosphoproteome by collecting cells of the WT and PGAL7::PKA1 strains grown under Pka1-repressed (glucose) and Pka1-induced (galactose) conditions at 16 hours post inoculation (hpi) and analyzing the samples using mass spectrometry.  In total, I identified 97 phosphoproteins under Pka1-repressed (glucose-containing medium) conditions, containing 104 unique phosphopeptides and 168 unique phosphorylation sites (Table C.1).  Nineteen of these phosphoproteins were present in two or more replicates upon PKA repression (Table 4.1).  Under Pka1-induced conditions, I identified 65 phosphoproteins containing 67 unique phosphopeptides and 118 unique phosphorylation sites (Table C.2).  Ten of these phosphoproteins were present in two or more replicates upon PKA induction (Table 4.2).  An analysis of the frequency of phosphorylation between the two conditions revealed a greater phosphorylation of threonine (20.2% upon Pka1 repression vs. 17.8% upon Pka1 induction) sites in the absence of Pka1; compared to a greater phosphorylation   154 of serine (79.8% upon Pka1 repression vs. 82.2% upon Pka1 induction) sites in the presence of Pka1.   A comparison of Gene Ontology (GO) term biological classifications for all phosphoproteins identified under either Pka1-repressed or Pka1-induced conditions revealed changes in the phosphoproteome profile in response to modulation of Pka1 activity (Figure 4.1).  Specifically, I observed that the majority of proteins (67%) were associated with metabolic, catabolic, and biosynthetic processes (16%), or with unknown or unclassified including hypothetical proteins (51%) under the Pka1-repressed condition.  Additional proteins were associated with glycolysis and cellular processes (4%), transcription (6%), translation and RNA processing (10%), oxidation-reduction (1%), proteolysis and response to stress (1%), transport (3%), and phosphorylation and signal transduction (8%).  A change in the phosphoproteome profile was observed upon induction of Pka1.  In this case, the majority of phosphoproteins (58%) were again associated with metabolic, catabolic, and biosynthetic processes (19%) and unknown or unclassified including hypothetical proteins (39%), although to a lesser extent.  We also observed an increase in the proportion of proteins associated with glycolysis and cellular processes (from 4% to 7%), transcription (from 6% to 7%), and transport (from 3% to 10%).  A slight decrease was observed for phosphoproteins associated with translation and RNA processing (from 10% to 9%) and phosphorylation and signal transduction (from 8% to 7%).  The emphasis on oxidation-reduction processes and proteolysis and response to stress was unchanged between the conditions.  Overall, our analysis revealed a change in the phosphoproteome profile in response to regulation by Pka1.  A greater emphasis was placed on glycolysis and cellular processes, transcriptional regulation, and transport upon induction of   155 Pka1 expression.  This approach therefore allows an appreciation of the phosphorylation role of Pka1 in a diverse array of cellular processes.    156 Table 4.1: Phosphoproteins identified upon repression of PKA1 (glucose-containing medium) in C. neoformans.    Accessiona  Description Phosphopeptide sequenceb Phosphorylated sitec  CNAG_01155  Glycerol kinase  AGsPtLPLGNEFTQAPR S3, T5,   CNAG_01182  Cytoplasmic protein  ADSSPADNLsPK S10     GGsISGPSGVSGER S3  CNAG_01744  Phosphatase  AsQSGQAGVTLDAFR S2  CNAG_01897  Bromodomain transcription factor  SEATETPIPPPSNVtEPPRsPNPLNPAtDAQGENIQPPVER S20, T28, T15     SLSNLPAEPVIAsPAPAGPSHVR S13  CNAG_02550  Conserved hypothetical protein  NNPsPPIDPSR S4     GASHAPTSPTSVPSDIATsPNAR S19  CNAG_02568  UBA/TS-N domain-containing protein RPsGAQADDIEDVK S3  CNAG_04028  RNA binding protein  IDAEAsDEAGEASQEK S6  CNAG_04484  AousoA GLEVsEDEEGEDEE S5  CNAG_04621  Glycogen synthase VGVPLsAPAsPR S6, S10     sDsLAsAISGTATPSGGR S1, S3, S6  CNAG_05301  Microtubule binding protein  GLEIEQPEEAsEDEQEEDKGPVEK S11  CNAG_05499  Conserved hypothetical protein  RDstPGYGGGEMGLGNSIGGMPR S3, T4  CNAG_05599  Conserved hypothetical protein  KANEsDIEESDAEGDV S5  CNAG_05925  Septin ring protein  ADSYYENQTHsPsPQGEQYSNDNSER S11     IAsPsGGVVSSEGYLANR S3, S5  CNAG_06103  RNA binding protein sHsPAPGQQtGGGGAGGGAGVAGPEDVDVK S3, T10, S1  CNAG_06113  Conserved hypothetical protein  VEENAPQtPAEGVVAEtEAPAAEAEAEQEPEEATK T8, T17  CNAG_06400  Plasma membrane H(+)-ATPase  FssIQAQQSGAALTR S2, S3  CNAG_06730  CMGC/GSK protein kinase ILVAGEPNVsYICsR S14, S10  CNAG_07445  Transketolase GQPVFSPLISALDDIsE S16   157  Accessiona  Description Phosphopeptide sequenceb Phosphorylated sitec  CNAG_07717  Ubiquitin carboxyl-terminal hydrolase 12 lGsDGEEDVEGDTTLR S3 aPhosphoproteins were identified by mass spectrometry in two or more biological replicates. bPhosphopeptide sequence as determined by mass spectrometry.  Lower case and bolded amino acids represent those that were phosphorylated. cPhosphorylated amino acid and its location within the peptide sequence.     158 Table 4.2: Phosphoproteins identified upon induction of PKA1 (galactose-containing medium) in C. neoformans.    Accessiona Description Phosphopeptide sequenceb Phosphorylated sitec  CNAG_01155  Glycerol kinase AGsPtLPLGNEFTQAPR S3, T5  CNAG_01318  Monovalent inorganic cation transporter  TFsGsVSDFFFSK S3, S5  CNAG_01523  CMGC/MAPK protein kinase IQDPQMtGYVSTR T7  CNAG_04028  RNA binding protein IDAEAsDEAGEASQEK S6  CNAG_04321  Yeast chs5 homolog, fibronectin type III domain-containing protein tstPPIVVEEPR T1, S2, T3  CNAG_04484  AousoA  EKGLEVsEDEEGEDEE S7     GLEVsEDEEGEDEE S5  CNAG_04864  Iron regulator 1 RAsIDVDMADNEPR S3  CNAG_05416  Pre-mRNA-processing protein 45 GPAEPPPPVLQsPPR S12  CNAG_06081  Glucose oxidase NsssFsYAAGGPGVGR S2, S3, S4, S6  CNAG_06730  CMGC/GSK protein kinase  ILVAGEPNVsYICsR S14, S10 aPhosphoproteins were identified by mass spectrometry in two or more biological replicates. bPhosphopeptide sequence as determined by mass spectrometry.  Lower case and bolded amino acids represent those that were phosphorylated. cPhosphorylated amino acid and its location within the peptide sequence.     159  Figure 4.1: Phosphoproteomic analysis of C. neoformans.  Cells grown upon A) Pka1-repression (glucose) and B) Pka1-induction (galactose) conditions.  Proteins identified in each condition (Pka1-repressed or Pka1-induced) are categorized according to their GO term biological classifications.  4.4.2 Identification of potential Pka1 phosphorylation targets  We next identified potential targets of Pka1 phosphorylation.  For this analysis, I removed phosphoproteins present under both Pka1-repressed and Pka1-induced conditions and focused only on those phosphoproteins identified upon Pka1-induction in two or more replicates.  Table 4.3 lists the six potential targets of Pka1 phosphorylation.  The MS-detected phosphopeptide sequences of the phosphoproteins are also presented.  The identified phosphoproteins covered a broad spectrum of GO term biological classifications (6 categories) Metabolic, biosynthetic, and catabolic processes Glycolysis and Cellular processes Transcription Translation and RNA processing Oxidation-reduction Proteolysis and Response to stress Transport Phosphorylation and Signal transduction Hypothetical Unknown/Unclassified Metabolic, biosynthetic, and catabolic processes Glycolysis and Cellular processes Transcription Translation and RNA processing Oxidation-reduction Proteolysis and Response to stress Transport Phosphorylation and Signal transduction Hypothetical Unknown/Unclassified A) B) Metabolic, biosynthetic, and catabolic processes Glycolysis and Cellular processes Transcription Translation and RNA processing Oxidation-reduction Proteolysis and Response to stress Transport Phosphorylation and Signal transduction Hypothetical Unknown/Unclassified   160 including transcription, translation, phosphorylation, oxidation-reduction, transport, and unknown or unclassified proteins.  We next performed a bioinformatic analysis of the identified phosphoproteins for the presence of the Pka1 recognition sequence: R/K-R/K-X-S/T-B, where X is any amino acid and B is a hydrophobic amino acid (Table 4.4).  Three of the phosphoproteins displayed one or more occurrences of the Pka1 recognition sequence: CNAG_01318 a monovalent inorganic carbon transporter, CNAG_04321 a yeast chs5 homolog, fibronectin type III domain-containing protein, and CNAG_04864 an iron regulator, Cir1.  For both the yeast chs5 and iron regulator proteins, the Pka1 recognition sequence was identified in the MS-detected phosphopeptide sequence.  A comparison of the ATCC C. neoformans gene deletion set with our identified potential Pka1 phosphorylation targets described CNAG_04321 as displaying WT levels of capsule and melanin production, along with growth at 37°C and virulence (Banks et al., 2005; Liu et al., 2008).  Interestingly, the iron regulator Cir1 was also described in the C. neoformans ATCC collection and has well-characterized influences on capsule and melanin production, along with growth at 37°C and attenuation for virulence (Jung et al., 2006; Liu et al., 2008).  Taken together, our identification of potential targets of Pka1 phosphorylation demonstrate a broad spectrum of biological processes in C. neoformans, and highlights an interesting and important connection between iron regulation and the cAMP/PKA pathway.     161 Table 4.3: Phosphoproteins of C. neoformans present upon Pka1 induction (galactose-containing medium).   GO categoriesa Accession number Protein Name Phosphopeptidesb Transcription     CNAG_04864  Iron regulator 1  RAsIDVDMADNEPR  Translation     CNAG_05416  Pre-mRNA-processing protein 45  GPAEPPPPVLQsPPR Phosphorylation     CNAG_01523  CMGC/MAPK protein kinase  IQDPQMtGYVSTR  Oxidation-reduction     CNAG_06081  Glucose oxidase  NsSSFSYAAGGPGVGR NSsSFSYAAGGPGVGR NSSsFSYAAGGPGVGR NSSSFsYAAGGPGVGR Transport     CNAG_01318  Monovalent inorganic cation transporter  TFsGSVSDFFFSK tFSGSVSDFFFSK  Unknown/unclassified      CNAG_04321  Yeast chs5 homolog, fibronectin type III domain-containing protein tSTPPIVVEEPR TsTPPIVVEEPR TStPPIVVEEPR aGO categories associated with biological classification of proteins. bSequence of the phosphopeptides detected during mass spectrometry.  Lower case and bolded amino acids represent those that were phosphorylated.    162 Table 4.4: Potential Pka1 phosphorylation sites.   Accession number Protein Name   # Sitesa   Siteb  CNAG_01318  Monovalent inorganic cation transporter  1 S618*  CNAG_01523  CMGC/MAPK protein kinase  0 -  CNAG_04321  Yeast chs5 homolog, fibronectin type III domain-containing protein 5 S14, S318, S324, S377, S493*  CNAG_04864  Iron regulator 1  6 S58, S529, S546, S599*, S751, S935  CNAG_05416  Pre-mRNA-processing protein 45  0 -  CNAG_06081  Glucose oxidase  0 - aNumber of Pka1 recognition sites; identification based on the Pka1 recognition sequence R/K-R/K-X-S/T-B, where X is any amino acid and B is a hydrophobic amino acid. bPolypeptide sequence location of the phosphorylatable amino acid contained within the Pka1 recognition site. *MS-detected Pka1 recognition site.   163 4.4.3 Pka1 phosphorylates Cir1 in vitro  Based on our identification of Cir1 as a potential Pka1 phosphorylation target, I set out to show a direct phosphorylation event between the kinase and the iron regulator.  To initiate this confirmation, I performed an in vitro phosphorylation assay for PKA on the synthetic peptide of interest (RASIDVDMADNEPR) using the C. neoformans Pka1 protein over-expressed in E.coli.  Our preliminary analysis showed that I was able to detect both CNAG_04864 (Cir1) and CNAG_00396 (Pka1) in the samples by mass spectrometry.  Figure 4.2A shows the fragmentation spectrum for the non-phosphorylated Cir1 synthetic peptide and its ion series.  The fragmentation spectrum for the Pka1-phosphorylated Cir1 shows the mass spectrometry-identified site of phosphorylation (serine) and its ion series (Figure 4.2B).  Lastly, mass spectrometry of the C. neoformans purified Pka1 enzyme identified the protein and its ion series (Figure 4.2C).  Additional experimentation, using cellular localization assays between Pka1-repressed and Pka1-induced conditions with GFP-tagged Cir1 in PGAL7::PKA1 showed no effect of Pka1 activity on the localization of Cir1 (Figure C.1).  Furthermore, yeast 2-hybrid studies with Cir1 and Pka1 did not show an interaction (data not shown).  Overall, the preliminary results support the possibility of direct phosphorylation of Cir1 by Pka1.  Further experimentation is underway to measure the activity of the Pka1 enzyme produced in E. coli, to test phosphorylation of the synthetic peptide in the presence of purified bovine PKA, and to rule out the possibility of Cir1 phosphorylation by a co-purified E. coli kinase.     164 A)  #1 b⁺  b²⁺  b³⁺  Seq. y⁺  y²⁺  y³⁺  #2 1 157.10840 79.05784 53.04098 R       14 2 228.14552 114.57640 76.72002 A 1432.63737 716.82232 478.21731 13 3 315.17755 158.09241 105.73070 S 1361.60025 681.30376 454.53827 12 4 428.26162 214.63445 143.42539 I 1274.56822 637.78775 425.52759 11 5 543.28857 272.14792 181.76771 D 1161.48415 581.24571 387.83290 10 6 642.35699 321.68213 214.79051 V 1046.45720 523.73224 349.49058 9 7 757.38394 379.19561 253.13283 D 947.38878 474.19803 316.46778 8 8 888.42444 444.71586 296.81300 M 832.36183 416.68455 278.12546 7 9 959.46156 480.23442 320.49204 A 701.32133 351.16430 234.44529 6 10 1074.48851 537.74789 358.83435 D 630.28421 315.64574 210.76625 5 11 1188.53144 594.76936 396.84866 N 515.25726 258.13227 172.42394 4 12 1317.57404 659.29066 439.86286 E 401.21433 201.11080 134.40963 3 13 1414.62681 707.81704 472.21379 P 272.17173 136.58950 91.39543 2 14       R 175.11896 88.06312 59.04450 1      165 B)   #1 b⁺  b²⁺  b³⁺  Seq. y⁺  y²⁺  y³⁺  #2 1 157.10840 79.05784 53.04098 R       14 2 228.14552 114.57640 76.72002 A 1512.60370 756.80549 504.87275 13 3 395.14388 198.07558 132.38614 S-Phospho 1441.56658 721.28693 481.19371 12 4 508.22795 254.61761 170.08083 I 1274.56822 637.78775 425.52759 11 5 623.25490 312.13109 208.42315 D 1161.48415 581.24571 387.83290 10 6 722.32332 361.66530 241.44596 V 1046.45720 523.73224 349.49058 9 7 837.35027 419.17877 279.78827 D 947.38878 474.19803 316.46778 8 8 968.39077 484.69902 323.46844 M 832.36183 416.68455 278.12546 7 9 1039.42789 520.21758 347.14748 A 701.32133 351.16430 234.44529 6 10 1154.45484 577.73106 385.48980 D 630.28421 315.64574 210.76625 5 11 1268.49777 634.75252 423.50411 N 515.25726 258.13227 172.42394 4 12 1397.54037 699.27382 466.51831 E 401.21433 201.11080 134.40963 3 13 1494.59314 747.80021 498.86923 P 272.17173 136.58950 91.39543 2 14       R 175.11896 88.06312 59.04450 1      166 C)   #1 b⁺  b²⁺  Seq. y⁺  y²⁺  #2 1 102.05496 51.53112 T     9 2 215.13903 108.07315 L 794.41557 397.71142 8 3 272.16050 136.58389 G 681.33150 341.16939 7 4 373.20818 187.10773 T 624.31003 312.65865 6 5 430.22965 215.61846 G 523.26235 262.13481 5 6 517.26168 259.13448 S 466.24088 233.62408 4 7 664.33010 332.66869 F 379.20885 190.10806 3 8 721.35157 361.17942 G 232.14043 116.57385 2 9     R 175.11896 88.06312 1 Figure 4.2: Fragmentation spectrum following in vitro Pka1 phosphorylation of a Cir1 synthetic peptide.  The synthetic peptide (RRASIDVDMADNEPR) was incubated with the Pka1 enzyme purified from E. coli expressing the C. neoformans PKA1 gene.  A) Fragmentation spectrum and ion series for the non-phosphorylated Cir1 synthetic peptide. Retention time = 59.12 min; charge = +3; monoisotopic m/z = 530.24932 Da.  B) Fragmentation spectrum and ion series for the Pka1-phosphorylated Cir1 synthetic peptide.  Retention time = 47.10 min; charge = +3; monoisotopic m/z = 556. 90517 Da.  C) Fragmentation spectrum and ion series for C. neoformans Pka1.  Retention time = 44.47 min; charge = +2; monoisotopic m/z = 448.23303 Da.  Identified ions highlighted.     167 4.4.4 Site-directed mutagenesis of the Pka1 phosphorylation site in Cir1 influences virulence factor expression Based on our identification of a potential Pka1 phosphorylation site in the MS-detected peptide of Cir1 (Figure C.2), I used site-directed mutagenesis to alter the codon for serine (TCT) to that of the non-phosphorylatable amino acid alanine (GCT; S599A) or that of the phospho-mimic aspartic acid (GAT; S599D).  As described below, the mutants had shared and distinct phenotypes compared with the cir1Δ deletion mutant indicating that the Ala and Asp mutant proteins are expressed, although confirmatory sequencing and analysis of Cir1 protein expression are currently underway for the mutants.  Our analysis of the impact of altering the phosphorylation site on virulence-related phenotypes included testing capsule and melanin production as well as several other phenotypes known to be influenced by Cir1.  As shown in Figure 4.3A, altering the residue at the Pka1 phosphorylation site of Cir1 S599A (Ala mutants; non-phosphorylatable) resulted in a smaller capsule compared to WT, with phenotypic similarity to the cir1Δ mutant.  Cell size remained unchanged in the mutants.  Interestingly, mutation of the codon from specifying S599D (Asp mutants; phospho-mimic) resulted in a capsular phenotype similar to that of WT, however, cell size increased in the mutant.  A measurement of capsule and cell size for the cir1Δ mutants confirmed a significant reduction in capsule size between WT and the two independent non-phosphorylatable mutants (designated H99 Ala 6 and H99 Ala 10) (Figure 4.3B), and a significant increase in cell size between WT and the two independent phospho-mimic mutants (designated H99 Asp 0 and H99 Asp 2) (Figure 4.3C).  Investigation into a possible effect of Cir1 phosphorylation by Pka1 on melanin production did not show a change in pigmentation at 30°C (Figure 4.3D).  However, at 37°C, the non-phosphorylatable mutants (H99 Ala 6 and H99 Ala 10) showed reduced melanin production compared to the WT   168 strain and a similar phenotype to the cir1Δ mutant.  Conversely, the phospho-mimic mutants (H99 Asp 0 and H99 Asp 2) displayed slightly darker pigmentation compared to the WT strain.  Lastly, I assessed growth defects in the mutants (Figure 4.3E).  The cir1Δ mutant displayed a growth defect at 37°C, but I did not observe a difference in growth for the non-phosphorylatable or phospho-mimic mutants at either 30°C or 37°C.  Taken together these findings suggest that phosphorylation of Cir1 by Pka1 influences the elaboration of the virulence factors, capsule and melanin, but does not hinder the ability of the fungus to grow at the host temperature.  A)      169 B)    C)    D)  WT cir1Δ H99 Cir1  Ala 6 H99 Cir1  Ala 10 0 2 4 6 8 10 12 Size (µm) Capsule thickness Cell diameter * *w w w w WT pkr1Δ H99 Cir1 Asp 0 H99 Cir1 Asp 2 0 2 4 6 8 10 12 Size (µm) Capsule thickness Cell diameter w w **  170 E)   Figure 4.3: Virulence factor expression in Cir1 site-directed mutants.  A) Capsule production of strains grown in low iron medium overnight at 30°C and stained with india ink.  Scale bar represents 10 μM at 100 X magnification.  B) Capsule and cell size measurements performed on an average of 40 cells per WT strains, and cir1' and non-phosphorylatable mutant (H99 Ala 6, H99 Ala 10) strains grown in low iron medium overnight at 30°C and stained with india ink.  Significant differences (p > 0.05) between WT and non-phosphorylatable mutants (H99 Ala 6, H99 Ala 10) (*); significant differences (p > 0.05) between cir1' and non-phosphorylatable mutants (H99 Ala 6, H99 Ala 10) (Š).  C) Capsule and cell size measurements performed on an average of 40 cells per WT strains, pkr1' and phospho-mimic mutant (H99 Asp 0, H99 Asp 2) strains grown in low iron medium overnight at 30°C and stained with india ink.  Significant differences (p > 0.05) between WT and phospho-mimic mutant (H99 Asp 0, H99 Asp 2) (*); significant differences (p > 0.05) between pkr1' and phospho-mimic mutant (H99 Asp 0, H99 Asp 2) (Š).  D) Melanin production of strains grown on medium containing 10 mM L-DOPA at 30°C for 3 d and 37°C for 2 d.  E) Growth of strains at 30°C and 37°C after incubation on YPD for 3 d.  Two independent site-directed mutants S599A (H99 Cir1 Ala 6, H99 Cir1 Ala 10) and S599D (H99 Cir1 Asp 0, H99 Cir1 Asp 2) were used for all assays.  4.4.5 Pka1 phosphorylation of Cir1 may have an influence on cell wall integrity  Based on our observations that Pka1 phosphorylation of Cir1 influences the expression of virulence factors in C. neoformans and the connection among Pka1, Rim101 and Cir1, I hypothesized that cell wall integrity of the site-directed mutants may be compromised (O'Meara et al., 2010).  To assess cell wall integrity I challenged the non-phosphorylatable and phospho-  171 mimic mutants with SDS, Congo red, and caffeine.  In the presence of SDS, no difference in growth, as a measure of cell wall integrity, was observed at 30°C between the mutants and WT (Figure 4.4A).  At 37°C, a slight growth defect of the non-phosphorylatable mutant was observed compared to WT, but this was a less severe effect than for cir1Δ mutant.  No differences were observed for the phospho-mimic mutants when compared to WT.  When challenged with Congo red, a difference in colony colour was observed for the non-phosphorylatable mutants as compared to WT, a phenotype similar to the cir1Δ mutant at 30°C; however, no changes in growth were observed for the mutants (Figure 4.4B).  Lastly, no difference was observed for the phospho-mimic mutants when compared to WT.  In the presence of caffeine, a growth defect was observed for the non-phosphorylatable mutants at 30°C (Figure 4.4C).  Notably, at 37°C, a more substantial growth defect was evident for the non-phosphorylatable mutants as compared to WT, a phenotype similar to that of the cir1Δ mutant.  Under both temperatures the phospho-mimic mutants displayed similar phenotypes to WT.  Experimentation to test cell wall stability in the presence of calcofluor white, which binds to chitin, and increased concentrations of SDS and congo red are currently underway.  To further assess a potential association with PKA phosphorylation of Cir1 and cell wall integrity, a capsule shedding immunoblot was performed (Figure 4.5).  According to the intensity of the smeared material representing shed capsule, these results showed that the non-phosphorylatable mutants shed capsular material, a phenotype similar to that of the cir1Δ mutant, although to a greater extent.  Conversely, the phospho-mimic mutants also shed capsular material, but to a lesser extent than the non-phosphorylatable strains and similar to the WT strain.. Taken together, our findings suggest that Pka1 phosphorylation of Cir1 is partially responsible for maintaining fungal cell wall integrity and contributes to capsule appearance in the culture supernatant.   172 A)   B)      173 C)  Figure 4.4: Cell wall-related phenotypic assasy for Cir1 site-directed mutants.  A) Growth of strains on YPD supplemented with 0.0125% SDS at 30°C and 37°C for 3 d.  B) Growth of strains on YPD supplemented with 0.5 mg/ml Congo red at 30°C and 37°C for 3 d.  C) Growth of strains on YPD supplemented with 0.5 mg/ml Caffeine at 30°C and 37°C for 3 d.  Two independent site-directed mutants S599A (H99 Ala 6, H99 Ala 10) or S599D (H99 Asp 0, H99 Asp 2) were used for all assays.   Figure 4.5: Capsule shedding immunoblot for Cir1 site-directed mutants.  Cells were grown in LIM at 30°C for 1 week.  Cells were diluted to an OD600 of 0.5, transferred to a nylon membrane, and probed with the primary monoclonal antibody 18b7 (1/3,000 dilution) and anti-mouse peroxidase-conjugated secondary antibody (1/3,000 dilution).   H99 pkr1Δ cir1Δ H99 Ala 6 H99 Ala 10 H99 Asp 0 H99 Asp 2   174 4.4.6 Growth on low-iron medium is influenced by mutation of the Pka1 phosphorylation site on Cir1  Given the connection between Cir1 and iron regulation, I assessed the role of Pka1 phosphorylation on iron-related growth by challenging the non-phosphorylatable and phospho-mimic mutants with iron-limited and iron-replete conditions.  As shown in Figure 4.6A, mutation of the Pka1 phosphorylation site in Cir1 resulted in a reduced ability of the non-phosphorylatable mutants to grow under iron-limited conditions at both 30°C and 37°C as compared to the WT.  Their growth patterns were representative of the cir1Δ mutant grown under the same conditions.  When an iron source was available, the non-phosphorylatable mutants recovered their growth at both temperatures similar to the cir1Δ mutant (Figure 4.6B).  On the other hand, the phospho-mimic mutants did not show a growth defect when compared to WT under iron-limited conditions at 30°C and 37°C (Figure 4.6A).  The cells continued to grow well under iron-replete conditions (Figure 4.6B).  Our results suggest that at elevated temperatures, phosphorylation of Cir1 by Pka1 influences the ability of the cells to survive under low iron conditions and may impact its role as a master iron regulator of iron uptake functions.      175 A)  B)  Figure 4.6:  Iron-related phenotypic assasy for Cir1 site-directed mutants.  A) Growth of strains under low iron conditions at 30°C and 37°C overnight.  B) Growth of strains under iron-replete conditions supplemented with 100 μM FeCl3 at 30°C and 37°C overnight.  Two independent site-directed mutants S599A (H99 Ala 6, H99 Ala 10) and S599D (H99 Asp 0, H99 Asp 2) were used for all assays.      176 4.4.7 Effects of mutations at the PKA phosphorylation site on the transcription of Cir1-regulated genes  Based on the role of Cir1 as a transcription factor and master iron regulator, I assessed the impact of mutating the identified Pka1 phosphorylation site on the transcriptional regulation of known iron regulated genes.  Specifically, I performed qRT-PCR on two genes, CIG1 and SIT1, which are positively regulated by Cir1, and one gene, LAC1, that is repressed by Cir1.  Cig1 is associated with heme uptake and virulence, and Sit1 has been characterized as a ferrioxamine B siderophore transporter (Tangen et al., 2007; Cadieux et al., 2013).  Lac1 is the laccase responsible for melanin production and contributes to virulence in C. neoformans (Kwon-Chung and Rhodes, 1986; Casadevall et al., 2000).  As shown, similar gene expression profiles for cir1Δ and the non-phosphorylatable mutants (H99 Ala 6, H99 Ala 10) were observed in that CIG1 (Figure 4.7A) and SIT1 (Figure 4.7B) transcript levels were reduced compared to WT under iron-limited conditions.  The cir1Δ mutant showed up-regulation of CIG1 and SIT1 under iron-replete conditions and, in contrast, little change was observed in the non-phosphorylatable mutants.  These results are consistent with the known activation of CIG1 and SIT1 by Cir1 in the absence of iron, and they suggest that the PKA phosphorylation plays a role in this activity.  Interestingly, the phosphorylation site does not appear to make a contribution to Cir1 activity under iron-replete conditions.  Conversely, the mutation of the phosphorylation site to Asp (phospho-mimic) had little impact on the transcription of CIG1 and SIT1 under iron-limited or iron-replete conditions.   The transcriptional regulation of LAC1 in the cir1Δ deletion mutant and the site-directed mutants showed a distinct pattern compared with the influence on CIG1 and SIT1 (Figure 4.7C).  Under iron-limited conditions, the cir1Δ and the non-phosphorylatable mutants showed elevated   177 expression (derepression) of LAC1, although the response was less robust in the non-phosphorylatable mutants.  In the presence of iron, LAC1 was also derepressed in the cir1Δ mutant strain, but an opposite effect (slight decrease) on transcript levels was observed in the non-phosphorylatable mutants.  The phospho-mimic mutants showed minimal and inconsistent regulation of LAC1 under low iron conditions, but a consistent down-regulation of gene expression was observed in the presence of iron.  For comparison, the transcript levels of CIG1, SIT1, and LAC1 in the WT strain under iron-limited and iron-replete conditions are presented in Figure 4.7D.  Taken together, the non-phosphorylatable mutants showed similar patterns of control of RNA expression to the cir1Δ mutant strain in the absence of iron.  However, with the addition of an iron source, transcriptional regulation differed between the strains, suggesting a connection between iron availability and transcription beyond control of the mutated Pka1 phosphorylation site.  Because the non-phosphorylatable and phospho-mimic mutants all showed decreased LAC1 transcription levels in the iron-replete condition, it is also possible that the phosphorylation site influences activation of LAC1 transcription in this situation.     178 A)    B)   C)     -8 -6 -4 -2 0 2 4 6 8 Fold change (log 2) Iron limited Iron replete cir1Δ H99 Cir1 Ala 6 H99 Cir1 Ala 10 H99 Cir1 Asp 0 H99 Cir1 Asp 2 CIG1 Cir1 PActivation CIG1 Cir1 PNo activation -4 -3 -2 -1 0 1 2 3 4 Fold change (log 2) Iron limited Iron replete H99 Cir1 Ala 10 H99 Cir1 Asp 0 H99 Cir1 Asp 2 cir1Δ H99 Cir1 Ala 6 SIT1 Cir1 PActivation SIT1 Cir1 PNo activation -4 -3 -2 -1 0 1 2 3 4 5 6 Fold change (log 2) Iron limited Iron replete cir1Δ H99 Cir1 Ala 6 H99 Cir1 Ala 10 H99 Cir1 Asp 0 H99 Cir1 Asp 2   179 D)  Figure 4.7:  Transcript analysis using qRT-PCR of Cir1-regulated genes.  A) Cir1-activated Cig1.  Upon PKA phosphorylation of Cir1 under iron limitation CIG1 is activated, but mutation of the phosphorylation site S599A results in an inactive gene.  B) Cir1-activated Sit1.  Upon PKA phosphorylation of Cir1 under iron limitation SIT1 is activated, but silencing of the phosphorylation site S599A results in an inactive gene.  C) Cir1-repressed LAC1.  Upon PKA phosphorylation of Cir1 under iron limitation LAC1 is repressed, but silencing of the phosphorylation site S599A results in derepression.  D) CIG1, SIT1, and LAC1 expression levels measured in the WT strain under iron-limited conditions.  Values were normalized to expression levels under iron-replete conditions.  RNA was collected at 16 hpi following cell growth under iron-limited and iron-replete conditions.  For A, B, and C, fold changes were determined based on comparison of gene expression to the WT strain.  Two independent site-directed mutants S599A (H99 Ala 6, H99 Ala 10) and S599D (H99 Asp 0, H99 Asp 2) were used for all assays.  4.5 Discussion   The phosphorylation of proteins is an important regulatory mechanism that controls protein activity, stability, localization, and interactions.  For the pathogenic yeast, C. neoformans, the cyclic-AMP/Protein Kinase A (PKA) signal transduction pathway regulates the expression of key virulence traits such as capsule and melanin, along with virulence (Alspaugh et al., 1997; D'Souza et al., 2001).  However, only Rim101 was been identified as a target of cAMP/PKA pathway phosphorylation (O'Meara et al., 2010).  We therefore used PGAL7::PKA1 strains under Pka1-repressed and Pka1-induced conditions in this study to investigate the influence of Pka1 on the phosphoproteome of C. neoformans.  This phosphoproteomics approach allowed us to successfully identify six potential targets of Pka1 phosphorylation.  Of these six -7 -6 -5 -4 -3 -2 -1 0 1 2 3 Fold change (log 2) CIG1 SIT1 LAC1   180 phosphoproteins, Cir1, a master iron regulator associated with regulating capsule, laccase, extracellular enzymes, and virulence was identified as a potential target of Pka1 phosphorylation.  Our preliminary data demonstrated that Pka1 directly phosphorylates Cir1.  In addition, I constructed site-directed mutants of Cir1 with either a non-phosphorylatable residue or an aspartic acid residue to mimic the phosphorylation state and I observed that the Pka1 phosphorylation site influences the expression of capsule and melanin, cell size, iron acquisition, and transcriptional regulation (Figure 4.8).  Interestingly, temperature sensitivity was not influenced by mutation of the phosphorylation site.  In general, this analysis highlighted the influence of Pka1 on the phosphoproteome of C. neoformans overall, and specifically the influence of Pka1 phosphorylation on Cir1 and its control of virulence-related factors.   Figure 4.8: A model depicting the impact of PKA phosphorylation on the phenotypes known to be influenced by Cir1 in C. neoformans.  Mutating the PKA phosphorylation site of Cir1 impacts the production of capsule and melanin, iron regulation, cell size, caffeine susceptibility, and transcriptional regulation.  An impact on growth was not observed, and the impact on virulence and cell wall integrity is in need of further study.   PKA1 Cir1 Capsule Melanin Virulence Growth at 37°C Cell wall integrity Iron regulation Transcriptional regulation PCaffeine susceptibility Cell size ? ?   181 4.5.1 Modulation of Pka1 expression leads to a change in the phosphoproteome  Our analysis revealed a change in the identified phosphoproteins of C. neoformans associated with glycolysis and cellular processes, transport, and transcription upon modulation of Pka1 activity.  Similar observations of PKA1 expression having an impact on the abundance of glycolytic proteins were reported in our secretome study (Chapter 2).  In addition, glycolysis is important for the persistence of C. neoformans in the cerebrospinal fluid of rabbits and for the virulence of the fungus (Price et al., 2011).  Previous transcriptional analysis also showed that PKA1 influences the expression of glycolytic genes (Hu et al., 2007).  Regarding a change in transport-associated proteins, previous transcript profiling by SAGE indicate an important role for Pka1 in regulating functions associated with iron uptake and the expression of virulence-related factors (Hu et al., 2007).  Furthermore, the change in the abundance of proteins associated with transcription was also observed in our investigation of the intracellular proteome of C. neoformans upon modulation of PKA1 (Chapter 3).  Pka1 regulation of transcription factors has been well characterized in Saccharomyces cerevisiae.  For example, PKA influences the stress-responsive transcription factors, Msn2 and Msn4, along with entry into stationary phase and high temperature resistance through Rim15 (Gorner et al., 1998; Reinders et al., 1998; Smith et al., 1998; Hasan et al., 2002).  Additionally, in Candida albicans, phosphorylation of transcription factors Efg1 and Flo8 is associated with the switching from a budding yeast form to a polarized form involved in virulence (Bockmuhl and Ernst, 2001; Cao et al., 2006).  In C. neoformans several potential PKA1 phosphorylation targets have been identified through bioinformatic and transcriptional analyses.  These include Nrg1, a transcription factor shown to regulate a glucose dehydrogenase (Ugd1) involved in capsule production and growth at 37°C, the mating-associated Ste12α, and the pH-responsive regulator of cell wall synthesis and integrity,   182 Rim101 (Chang et al., 2000; Moyrand and Janbon, 2004; Cramer et al., 2006; O'Meara et al., 2010).  Overall, the observed changes in the phosphoproteome upon Pka1 induction indicates an opportunity for identifying novel downstream phosphorylation targets associated with the elaboration of virulence-related factors and virulence. 4.5.2 Diversity of novel targets of Pka1 phosphorylation Although I did not identify known targets of Pka1 phosphorylation in our study, possibly due to phosphoprotein abundance at the time of collection, I did identify six phosphoproteins, which were only detected upon Pka1 induction.  A recent study of the C. neoformans phosphoproteome identified over 600 phosphoproteins under general kinase regulation (Selvan et al., 2014).  We observed a smaller subset of phosphoproteins probably due to our focus on their presence in the context of regulation of Pka1 activity.  Differences in phosphoproteome coverage between the two studies may be attributed to our use of a galactose-inducible, glucose-repressible PKA1 strain of C. neoformans grown in minimal medium, the timing of sample collection, and our use of TiO2 enrichment in combination with SCX fractionation, which may have selected for a more specific subset of phosphopeptides for identification.  Our identification of six phosphoproteins influenced by the presence of Pka1 provides an interesting opportunity to identify novel targets of PKA phosphorylation.  For example, our identification of a monovalent inorganic cation transporter (CNAG_01318) with roles in cation homeostasis, pH regulation, membrane potential, and virulence in C. neoformans, suggests a possible connection between regulation of pH and activation of the cAMP/PKA pathway as described in S. cerevisiae (Dechant et al., 2010; Jung et al., 2012).  In this context, it is perhaps relevant that Dechant et al. (2010) identified cytosolic pH as a second messenger for glucose by demonstrating that pH is regulated by glucose metabolism, resulting in activation of the cAMP/PKA pathway in yeast.  It   183 is possible that in C. neoformans, PKA phosphorylation of the monovalent inorganic cation transporter could play a regulatory or feedback role in activation of the cAMP/PKA pathway.   The connection between the cAMP and mitogen activated protein kinase (MAPK) pathways, due to the identification of CMGC/MAPK (Hog1) (CNAG_01523) as a potential target of Pka1 phosphorylation, is also intriguing.  A predicted PKA phosphorylation site was not identified for this protein, although PKA phosphorylation is still possible.  For example, this phenomenon was reported for the cystic fibrosis transmembrane conductance regulator (CFTR) because PKA is still capable of activating the CFTR chloride channel after mutagenesis of all 10 PKA consensus phosphorylation sites (Chang et al., 1993).  Explanations include regulation by cryptic PKA sites, which may also mediate interactions between different kinases, and the occurrence of hierarchical phosphorylation of CFTR by obvious and cryptic PKA sites.  For CMGC/MAPK, this phosphoprotein was previously identified by global phosphoproteome analysis in C. neoformans and studies have shown that dual phosphorylation is crucial for controlling the expression of virulence-related factors through the cAMP/PKA pathway (Selvan et al., 2014).  In C. neoformans, the MAPK pathway is associated with fungal survival in the host, regulates stress responses, sexual development, maintaining cell wall integrity, drug sensitivity, and virulence (Kraus et al., 2003; Bahn et al., 2006; Bahn et al., 2007; Roman et al., 2007; Gerik et al., 2008; Kozubowski and Heitman, 2012).  Despite the cAMP and MAPK pathways each having significant roles in fungal survival and virulence, crosstalk between the two pathways is not clearly evident.  Our findings present an opportunity to investigate co-regulation of Pka1-regulated downstream processes in the context of the MAPK pathway. Our phosphoproteome analysis also identified a yeast chs5 homolog, fibronectin type III domain-containing protein (CNAG_04321), which includes five potential Pka1 phosphorylation   184 sites of which one was detected in our MS detected peptide.  This protein has been well characterized in the context of C. neoformans and is associated with a role in chitosan production, cell wall integrity, and fungal growth (Banks et al., 2005).  As described for Rim101, this result highlights an interesting connection between Pka1 phosphorylation and cell wall regulation (O'Meara et al., 2010).  Furthermore, and perhaps most notably, was our identification of Cir1, the master iron regulator, associated with regulating capsule, laccase, phospholipase, and cell wall biosynthesis genes, as a potential target of Pka1 phosphorylation (Jung et al., 2006).  Cir1 possesses six potential Pka1 phosphorylation sites, one of which was contained in our MS-detected peptide.  Cir1 is capable of activating the cAMP/PKA pathway through expression of Gpr4, however identification of Cir1 as a possible phosphorylation target of Pka1 is new and may indicate a feedback loop regulating iron acquisition and virulence in C. neoformans.  The novel connection between Pka1 and iron regulation via Cir1 and its impact on virulence-related factors will be discussed in greater detail in the following sections. 4.5.3 Pka1 phosphorylation of Cir1 impacts virulence factor expression and possibly cell wall integrity: a link to Rim101  In C. neoformans, the uptake of iron, pH sensing, nutrient and stress signaling pathways, virulence factor elaboration, and cell wall biogenesis are regulated by an interconnected set of transcription factors.  These transcription factors include the GATA-type factor and master iron regulator Cir1, and the pH-responsive factor Rim101 (Jung et al., 2006; O'Meara et al., 2010).  Upon mutating the PKA phosphorylation site of Cir1 from serine to alanine, the observed reduction in capsule and melanin production were similar to that observed for the cir1Δ mutant.  Similarly, upon deletion of the PKA phosphorylation target gene, RIM101, a defect in capsule size associated with a defect in its attachment to the cell surface was reported (O'Meara et al.,   185 2010).  For melanin, deletion of CIR1 caused a derepression under both iron-limited and iron-replete conditions indicating negative regulation of LAC1 (Jung and Kronstad, 2008).  The results for the serine to alanine mutants suggest that phosphorylation of the PKA site may be required for some of the negative regulation of LAC1 upon iron limitation.  However, under the iron-replete condition, the PKA site appears to make a small and positive contribution to LAC1 transcript levels.  These findings indicate a key role for Pka1 phosphorylation of Cir1 in the elaboration of virulence factors and altering this site would be predicted to have an impact on strain virulence.   Interestingly, the ability of the fungus to grow at 37°C was not impacted by altering the PKA phosphorylation site indicating that growth regulation could be controlled by multiple sites of phosphorylation or by an alternative method of Cir1 activation.  Identification of multiple phosphorylation sites within a single protein provides an opportunity to tease apart the role of each phosphorylation site and its potential impact on cellular functions (Beretta et al., 1993).  Additionally, multiple phosphorylation sites may indicate cellular regulation by several signaling pathways and suggest potential crosstalk between such pathways.  For example, phosphorylation of the cAMP response element binding protein (CREB) at Serine 133 in response to cAMP stimulus is sufficient to induce target gene expression, but additional promoter-bound transcription factors are required for induction in response to non-cAMP signals (Mayr and Montminy, 2001).  Additionally, sequential mutation of the PKA phosphorylation sites of Cir1 may demonstrate the presence of phosphorylation redundancy, or a phosphorylation hierarchy associated with regulation.  Such a method of control by the cAMP/PKA pathway has been well documented.  For example, phosphorylation of the CREB protein by cAMP-dependent PKA and glycogen synthase kinase-3 occurs in a hierarchical manner and alters DNA binding affinity,   186 protein conformation, and increases net charge (Fiol et al., 1994; Bullock and Habener, 1998).  Additionally, activation of the CREB protein results from phosphorylation by several different protein kinases (Johannessen et al., 2007)  An important component of capsule production is the attachment of the polysaccharide to the cell surface following secretion from the cell.  Studies have shown that Rim101 plays a key role in the attachment of capsule rather than the production of polysaccharide (O'Meara et al., 2010).  The observation that deletion of CIR1 influences cell wall integrity and our findings that mutating the PKA phosphorylation site of Cir1 may play a role in the stability of the cell wall, suggest a point of crosstalk between Cir1 and Rim101 in the control of cell wall integrity.  Specifically, the observed differences in colony colour of the cir1Δ and Ala mutants when grown on Congo red suggested differential expression or composition of cell wall-associated glucans.  Investigations into cell wall enzymes regulated by Cir1 may provide insights into a connection between Pka1 phopshorylation of Cir1, capsule production or attachment, and cell-wall glucan composition.  Such crosstalk could include Cir1 regulation of Rim101 expression, as previously demonstrated, the phosphorylation of both Rim101 and Cir1 by Pka1, or the activation of common downstream transcription targets following Pka1 phosphorylation (Jung et al., 2010).  An example of the latter may include Cig1, the cytokine-inducing glycoprotein associated with iron uptake from heme and virulence in C. neoformans (Cadieux et al., 2013).  O’Meara et al. (2010) and Jung et al. (2006) showed that expression of the iron-regulated gene CIG1 is positively regulated by Rim101 and Cir1, respectively.  Our transcriptional analysis upon iron deprivation suggested that the PKA phosphorylation site influences the ability of Cir1 to participate in the transcriptional activation of CIG1.  However, the PKA phosphorylation site appears to play little or no role in iron-replete conditions, suggesting that other functions of Cir1   187 contribute to repression under this condition.  In addition, the novel protein (CNAG_05312) that was identified in proteome and secretome analyses of C. neoformans upon modulation of PKA showed similar patterns of regulation to Cig1 at the protein and transcript levels, suggesting it as a potential target for activation by Rim101 and Cir1 as well (Chapter 2 and 3).  Furthermore, construction of a double deletion mutant for CIR1 and RIM101 may highlight the crosstalk between these two pathways and their overall impact on the cell. 4.5.4 Cir1 phosphorylation by Pka1 influences adaptation to low iron medium  In S. cerevisiae the cAMP pathway influences iron uptake by controlling expression of the high affinity iron permease (Casas et al., 1997; Robertson et al., 2000; Rutherford et al., 2001).  In Ustilago maydis, a connection between cAMP and iron uptake also exists through regulation of an iron permease gene (Eichhorn et al., 2006).  Similar regulation occurs in C. neoformans because genes for reductive iron uptake are differentially expressed in mutants lacking components of the cAMP pathway (Hu et al., 2007).  The expression of two high affinity iron permeases in C. neoformans, Cft1 and Cft2, is influenced by PKA, and it is known that Cir1 positively regulates the expression of CFT1 and negatively regulates CFT2 (Jung and Kronstad, 2008).  Mutating the PKA phosphorylation site of Cir1 to alanine impacted the ability of the mutant to grow under iron limitation, a similar phenotype to that of the cir1Δ mutant.  However, the phenotype was rescued in the iron-replete condition indicating a link between phosphorylation and iron sensing in C. neoformans.  In mammalian cells, a connection between iron homeostasis and phosphorylation has been observed (Fillebeen et al., 2005; Deck et al., 2009).  For example, phosphorylation resulted in the sensitization of an iron regulatory protein (IRP1) to decreased iron availability and resulted in regulation that enhanced the responsiveness of IRP1 to changes in intracellular iron levels.  Furthermore, iron deficiency promoted the   188 phosphorylation of IRP1 by increasing availability of the protein.  It is possible that the connection between the cAMP/PKA pathway and Cir1 regulation in the presence or absence of iron is modulated by its phosphorylation and that this phosphorylation is directly associated with sensing iron conditions within the host environment.  4.6 Conclusion In this study I characterized the overall impact of PKA modulation on the phosphoproteome of C. neoformans and discovered several potential targets of Pka1 phosphorylation.  Importantly, the master iron regulator associated with iron acquisition and virulence, Cir1, was identified as a potential target of PKA phosphorylation.  Mutating the PKA phosphorylation site of Cir1 impacted its role in the elaboration of virulence-related factors, growth in iron-limited medium, and transcriptional regulation.  Additionally, a novel connection was suggested for iron regulation via Cir1, cell wall integrity via Rim101, and phosphorylation by the cAMP/PKA pathway.    189 Chapter 5: Thesis Summary and Future Directions The studies presented in this thesis established a significant influence of Pka1 on the intracellular and extracellular abundance of proteins in C. neoformans.  Specifically, the secretome analysis identified five proteins whose abundance was regulated by Pka1.  These proteins included the Cig1 and Aph1 proteins with known roles in virulence, α-amylase and glyoxal oxidase, and a novel protein encoded by the gene CNAG_05312.  We also observed a change in the secretome profile upon induction of Pka1 from proteins primarily involved in catabolic and metabolic processes to an expanded set that included proteins for translational regulation and the response to stress.  We further characterized the secretome data using enrichment analyses and by predicting conventional versus non-conventional secretion.  Targeted proteomics of Pka1-regulated proteins by multiple reaction monitoring allowed us to identify secreted proteins in biological samples at femtomolar levels suggesting their potential utility as biomarkers of infection.   Proteomic profiling of C. neoformans identified a broad and conserved regulation by Pka1 of proteins associated with translation, the proteasome, metabolism, amino acid biosynthesis, and virulence.  Intriguingly, Pka1 regulation of proteins for the ubiquitin-proteasome pathway in C. neoformans showed a striking parallel with connections between PKA and protein degradation in chronic neurodegenerative disorders and other human diseases.  This observation suggests that the regulation of the proteasome by PKA is a conserved process in eukaryotes.  Enrichment analyses of the proteome data revealed an over representation of proteins associated with metabolic and biosynthetic processes, and translation.  Additionally, an interactome analysis emphasized the impact of PKA activity on several clusters of proteins involving translation and the ribosome, the ubiquitin-proteasome pathway, and diverse metabolic   190 processes.  Finally, expression studies on up-regulated genes upon Pka1-induction revealed correlation differences between transcript levels and the proteome.   Interestingly, upon comparison of the secretome and proteome data, several proteins were identified in both datasets and displayed similar patterns of abundance upon modulation of Pka1 activity.  Such proteins included mitochondrial aconitate hydratase (CNAG_01137), malate dehydrogenase (CNAG_03225), 5-methyltetrahydropteroyltriglutamate-homocysteine S-methyltransferase (CNAG_01890), ADP, ATP carrier protein (CNAG_06101), Cig1 (CNAG_01653), a cytoplasmic protein (CNAG_02943), and three hypothetical proteins (CNAG_03007, CNAG_05312, CNAG_06109).  Notably, Cig1 and one of the novel proteins (CNAG_05312) both showed significant increases in abundance upon induction of PKA1 expression.  Similar patterns of mRNA regulation and a positive regulation by Rim101 have been reported for both of these proteins (O'Meara et al., 2010) (Chapter 2).  Additionally, the detection of the novel protein in blood using MRM suggested that future studies should investigate the function of this protein in the context of iron acquisition and virulence.  The detection of the Cig1 and CNAG_05312 proteins in both the intracellular proteome and secretome suggests their association with the cell wall or capsular material at the time of analysis.  Moreover, it highlights our ability to follow their progress through the cell from intracellular synthesis to secretion, providing a unique opportunity to track protein release from the cell and to gain further insight into the impact of Pka1 on secretion in C. neoformans. Lastly, a phosphoproteomic analysis identified six potential targets of Pka1 phosphorylation of which one phosphoprotein, Cir1, has known roles in controlling the production of virulence factors, thermotolerance, iron acquisition, and virulence (Jung et al., 2006).  Upon construction of site-directed mutants displaying a non-phosphorylatable residue or   191 an acidic residue at the phosphorylation site, I found that phosphorylation of Cir1 by Pka1 impacts the production of capsule and melanin, cell size, and growth on iron-limited medium.  Additionally, I showed similar trends in transcriptional profiling between the non-phosphorylatable mutant at the PKA site and the cir1Δ mutant.  Taken together, our study revealed that Pka1 phosphorylates the master iron regulator, Cir1, and this phosphorylation influences the production of virulence-related factors, iron homeostasis, and transcriptional control.  Further investigation into the role of Cir1 as a transcription factor and identification of its direct and indirect targets could be gleamed from experiments utilizing chromatin immunoprecipitation (ChIP) sequencing (Seq).  ChIP Seq is a powerful tool employed for determining how proteins interact with DNA to regulate gene expression.  For Cir1, such a technique could investigate its regulatory connection with CIG1, SIT1, and LAC1, along with novel gene targets.    There are considerable additional insights into PKA regulation in C. neoformans to be gleaned by comparisons of the data in the different chapters of this thesis.  As an example, I noted that a predicted pre-mRNA-processing protein 45 (CNAG_05416) was influenced by induction of PKA1 in both the proteome and phosphoproteome datasets.  This observation provides an opportunity for future investigation into the role of Pka1 phosphorylation of CNAG_05416 and its connection to cellular processing in C. neoformans.  Our identification of additional potential PKA phosphorylation targets provide further candidates for future investigations.  For example, the monovalent inorganic cation transporter (CNAG_01318) suggests a novel connection between pH regulation and the cAMP/PKA pathway.  Additionally, CMGC/MAPK (CNAG_01523) suggests a potential point of crosstalk between the MAPK and cAMP pathways and the yeast chs5 homolog, fibronectin protein (CNAG_04321) suggests a   192 connection between cell wall integrity and PKA phosphorylation.  In addition, future studies may also focus on our identification of the Pka1-regulated novel protein, CNAG_05312, in the secretome and proteome of C. neoformans.  Our phosphoproteome study also highlighted a potential connection between Cir1 and Rim101 through regulation of downstream targets, including CNAG_05312.  Given the similarity in protein abundance and transcriptional regulation as compared to Cig1, this novel protein warrants further investigation into its role associated with iron uptake and virulence.  Lastly, our identification of a connection between PKA and the ubiquitin-proteasome pathway is intriguing and characterization studies are currently underway.  For example, utilizing a proteasome inhibitor to observe differences in growth and capsule production between the Pka1-regulated strains could provide valuable information on inhibiting fungal growth and survival within the host, and potentially identify a novel therapeutic for treatment of cryptococcal infection. The work presented in this thesis is novel and provides an important characterization of modulating Pka1 activity and its impact on the intracellular and extracellular proteomes of C. neoformans, in addition to identifying new targets of Pka1 phosphorylation.  Although the components and mechanisms of the cAMP/PKA pathway have been well characterized, its ability to influence secretion and cellular organization has not been described at the protein level.  In addition, the identification of potential biomarkers based on fungal secretion during in vitro and in vivo infection studies have not been reported for C. neoformans.  Furthermore, the connection among translational regulation, the ribosome, and the proteasome has been well studied in relation to the progression of neurodegenerative disorders and human disease conditions; however, such a connection has not previously been described in C. neoformans.  Lastly, the identification of Pka1 phosphorylation targets has long been of interest in fungal   193 pathogens including C. neoformans.  By demonstrating that Cir1 activity is regulated in part by Pka1, I have identified a previously uncharacterized target of Pka1 and established an important link between the cAMP/PKA pathway and iron regulation in C. neoformans. Taken together, the increased understanding and appreciation for the role of PKA1 expression that emerged from the proteomic profiling of C. neoformans presented in this thesis provides new opportunities for diagnosing fungal infection and monitoring disease progression.  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