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Biological roles of cellular glyceraldehyde-3-phosphate dehydrogenase in the hepatitis C virus life cycle Raj, Meera 2015

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  BIOLOGICAL ROLES OF CELLULAR GLYCERALDEHYDE-3-PHOSPHATE DEHYDROGENASE IN THE HEPATITIS C VIRUS LIFE CYCLE  by  Meera Raj B.Sc., The University of British Columbia, 2005   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)  July 2015 © Meera Raj, 2015  ii  Abstract The hijacking and manipulation of host cell biosynthetic pathways by human viruses are shared molecular events that are essential for the viral life cycle. Because of increasing evidence of the importance of human glyceraldehyde-3-phosphate dehydrogenase (GAPDH) in host secretory pathway functions, I hypothesized that this multifunctional enzyme could contribute to life cycles of two Flaviviridae members, hepatitis C virus (HCV) and dengue virus (DENV).  The first aim of this project was to investigate whether GAPDH is a host factor that regulates the life cycle steps of HCV in human hepatoma Huh-7.5.1 cells. I used short interfering RNA (siRNA)-mediated silencing of GAPDH both pre- and post-HCV infection in Huh-7.5.1 cells to demonstrate that reducing GAPDH protein abundance inhibits primary HCV infection and production and/or release of infectious HCV virus particles. Exogenous expression of V5-tagged human GAPDH, pre- and post-infection, increases the viral infectivity of HCV-infected Huh-7.5.1 cell supernatants, suggesting a predominant role of GAPDH during the post-replication steps of the HCV life cycle. Finally, siRNA-mediated GAPDH suppression in human Huh-7.5.1 cells also significantly inhibited primary DENV-2 infection.  In the second aim, I further investigated the diverse functions of GAPDH in human hepatoma cells by performing differential expression profiling of total cellular proteins by quantitative proteomics in two GAPDH knockdown Huh-7-derived cell lines (67D2 and 67b3) and the parental Huh-7 cell line. First, I successfully established GAPDH knockdown Huh-7-derived cell lines using short hairpin RNA (shRNA) lentivirus particles. Second, I demonstrated that the stable shRNA-mediated GAPDH silencing in Huh-7 cells inhibits primary HCV infection and the production of infectious HCV virus particles. Using a quantitative proteomics strategy based on triplex dimethyl labeling and nano-liquid chromatography-tandem mass spectrometry, I determined the cellular proteins deregulated in 67D2 and 67B3 cells. Bio-informatic analysis of the differentially expressed proteins revealed a robust compensatory effect in molecular functions associated with enzymatic activities and “acting binding” in response to the silencing of GAPDH in 67D2 and 67D3 cells.  iii  Collectively, these results underline the important contributions of GAPDH’s “moonlighting” activities to host-Flaviviridae interactions, and they uncover novel complex molecular interplays between human GAPDH and the Flaviviridae life cycle steps.   iv  Preface In all chapters, I have generated the majority of the figures. Any contributions from other lab members are indicated below and in the figure legends. I designed all of the experiments in my thesis and suggestion and ideas were provided by my supervisor Dr. François Jean. I conducted the research, analyzed the data, wrote the first draft of the papers and thesis, and made changes as advised by my supervisor Dr. François Jean and my committee members, Dr. Robert Hancock, Dr. Eric Jan, and Dr. James Kronstad. Dr. François Jean provided guidance for improvements in the writing of abstract, and Chapter 2. In the 5th year of my PhD, I supervised an undergraduate student, Mary Langley, who helped me generate a C-terminal V5-tagged GAPDH construct that was used for the experiments in figures 2.5 and 2.6. In figure 2.7, I utilized dengue virus serotype 2 viral stocks prepared by a PhD student, Steven McArthur. In Chapters 3, I designed and prepared the samples for proteomic profiling. Mass spectrometry was performed by Dr. Julius John, a mass spectrometry specialist and a post-doctoral fellow in the Jean lab; Dr. Victoria Svinti, bioinformatician and a post-doctoral fellow in the Jean lab, processed the acquired mass spectrometry data and provided support with the data analysis. Figure 3.1 was generated by Dr. François Jean. Figure 3.8 in Chapter 3, and figures A1.1e, A1.4 and A1.5 from Appendix 1 were generated by Dr. Svinti. Sorting of samples for fluorescence-activated cell sorting (FACS) in figure 3.9 in Chapter 3, and figure A1.1d in Appendix 1 were performed with the help of Andy Johnson and Justin Wong, in the UBC Flow Cytometry & Cell Sorting Facility. In Appendix section A1.1 and A1.2, I prepared the samples for genomic profiling, which were handled by microarray specialist Anne Haegert at the Vancouver Prostate Centre (Vancouver, BC). Dr. V. Svinti processed the acquired data and provided support with data analysis. Work (Protocol licence number: B12-0106) involving HCV and DENV-2 studies (Project: Jean Lab Antiviral Research Programs) was conducted in accordance with the University of British Columbia Policies and Procedures, Biosafety Practices and Public Health Agency of Canada guidelines.  v  My research was funded by a Canadian Institutes of Health Research (CIHR) operating grant: CIHR # MOP-84462 (Dr. F. Jean (PI)). Graduate student funding was provided by the Canadian Institutes for Health Research (CIHR-UBC Translational Research in Infectious Diseases graduate trainee award) and the Michael Smith Foundation for Health Research (MSFHR junior graduate trainee award). vi  Table of Contents  Abstract ..................................................................................................................................... ii Preface...................................................................................................................................... iv Table of Contents ..................................................................................................................... vi List of Tables .............................................................................................................................x List of Figures .......................................................................................................................... xi List of Abbreviations ............................................................................................................. xiv Acknowledgements ............................................................................................................... xvii Dedication ............................................................................................................................ xviii Chapter 1: Introduction ............................................................................................................1 1.1 The hepatitis C virus—a silent killer .............................................................................1 1.1.1 Discovery and molecular biology of HCV ..............................................................1 1.1.2 The HCV life cycle ..................................................................................................6 1.1.3 HCV pathogenesis .................................................................................................11 1.1.4 HCV current standards of care ..............................................................................15 1.1.5 New antivirals under development for HCV infection ..........................................17 1.2 GAPDH – new insight into an old protein ...................................................................21 1.2.1 GAPDH in glycolysis ............................................................................................21 1.2.2 GAPDH is a multifunction protein ........................................................................25 1.2.3 GAPDH posttranslational modifications, regulation of gene transcription, and intracellular transport ........................................................................................................26 1.2.4 Roles of GAPDH in cell survival and cell death ...................................................32 1.2.5 Roles of GAPDH in cancer, neurodegenerative diseases, and viral diseases ........34 1.3 Research hypothesis, rationale, and specific aims .......................................................37 Chapter 2: The moonlighting glycolytic enzyme, glyceraldehyde-3-phosphate dehydrogenase (GAPDH) is required for efficient hepatitis C virus and dengue virus infections in human hepatoma (Huh-7.5.1) cells .....................................................................39 2.1 Summary ......................................................................................................................39 2.2 Introduction ..................................................................................................................40 vii  2.3 Materials and methods .................................................................................................42 2.4 Results and discussion .................................................................................................50 2.4.1 Human hepatoma cells with siRNA-mediated GAPDH mRNA silencing show a dramatic decrease in GAPDH protein abundance ............................................................50 2.4.2 Naïve Huh-7.5.1 with siRNA-mediated suppression show a decrease in host-cell susceptibility to primary and secondary HCV infections .................................................52 2.4.3 HCV-infected Huh-7.5.1 with siRNA-mediated suppression leads to a decrease in viral infectivity of the HCV-infected cell supernatants ....................................................55 2.4.4 HCV replicon-harbouring cells (Huh.8 and Huh.2) with siRNA-mediated GAPDH suppression has no effect on HCV replication ...................................................57 2.4.5 Huh-7.5.1 cells with exogenous upregulation of V5-tagged human GAPDH increases viral infectivity of HCV-infected Huh-7.5.1 cell supernatants .........................59 2.4.6 Huh-7.5.1 cells with siRNA-mediated GAPDH suppression show decrease in host-cell susceptibility to primary DENV-2 infection ......................................................65 Chapter 3: Novel moonlighting functions of human GAPDH revealed by quantitative proteomic profiling of GAPDH knockdown human hepatoma (Huh-7)-derived cell lines.....69 3.1 Summary ......................................................................................................................69 3.2 Introduction ..................................................................................................................70 3.3 Materials and methods .................................................................................................72 3.4 Results ..........................................................................................................................79 3.4.1 Huh-7-derived cell clones showing shRNA-mediated stable GAPDH knockdown    ..........................................................................................................................................79 3.4.2 shRNA-mediated stable GAPDH-knockdown in Huh-7-derived cell clones, 67D2 and 67B3, showed reduced susceptibility to HCV infection ............................................84 3.4.3 Proteomic analysis of Huh-7-derived stable GAPDH-knockdown clones displayed compensatory mechanisms to GAPDH depletion ............................................89 3.4.3.1. Comparison between the common and unique protein groups significantly changed in the GAPDH knockdown Huh-7-derived cell lines 67D2 and 67B3 in relation to the parental Huh-7 cells. ...........................................................................96 viii  3.4.4 Transcriptomic analysis of Huh-7-derived stable GAPDH-knockdown clone 67B3 displayed reduction in expression of HCV entry receptor, CD81 ..................................100 3.4.5 Several proteins from the differentially regulated overlapping list were exosome-associated proteins ..........................................................................................................105 3.5 Discussion ..................................................................................................................106 Chapter 4: Conclusions and future directions ......................................................................112 4.1. Discussion ..................................................................................................................112 4.1.1 GAPDH is an important host factor for several stages of the HCV life cycle ....112 4.1.2 Profiling of the GAPDH-associated interactome and discovery of cellular compensatory mechanisms in GAPDH-depleted cells ...................................................115 4.1.3 GAPDH-specific deregulation of exosome-associated proteins: Implications for HCV-hijacking of the host cell exosomal pathway ........................................................117 4.2. Future directions ........................................................................................................119 4.2.1 GAPDH binding proteins as a host-targeted antiviral agent for HCV infection .119 4.2.2 GAPDH as a host-targeted agent against HCV infection ....................................119 4.2.3 GAPDH as a potential broad-spectrum host-targeted agent against Flaviviridae members ........................................................................................................................122 4.2.4 GAPDH, exosome-associated proteins, and exosome-mediated transmission of HCV  .............................................................................................................................122 Bibliography ..........................................................................................................................126 Appendices  ............................................................................................................................150 Appendix 1: Supplementary tables and figures related to Chapter 3 ..............................150 A1.1 Generation of Huh-7-derived GAPDH-knockdown clones showing various levels of stable GAPDH reduction ............................................................................................150 A1.2 ........... Transcriptome profiling of Huh-7-derived GAPDH-knockdown clones: 67D2 and 67B3 .........................................................................................................................157 A1.1.1. The expression of GAPDH and CD81 transcripts by RT-QPCR show high correlation to microarray-determined expression profiles of Huh-7-derived stable GAPDH-knockdown clones, 67D2 and 67B3 .........................................................159 ix  A1.1.2. List of differentially expressed overlapping genes in Huh-7-derived cell clones primarily showed downregulation of GAPDH and GAPDH pseudogenes ..161 A1.1.3. Deregulation of molecular function and enriched pathways affected by GAPDH reduction ...................................................................................................163 A1.1.4. Materials and methods ..............................................................................170 A1.3 Rescuing Huh-7-derived stable GAPDH-knockdown cell line, 67cl.1 for GAPDH expression .......................................................................................................................178 A1.2.1. Materials and methods ..............................................................................179 Appendix 2: Chapter 4 supplementary figure ..................................................................180 Appendix 3: Discovery of a unique synthetic small interfering RNA (siRNA) with robust anti-HCV activity (siRNA_MR01) ...................................................................................181 A3.1 Anti-HCV property of siRNA_MR01 .................................................................181 A3.2 Computational prediction of candidates for siRNA_MR01 ................................187 A3.3 Proteomics analysis of Huh-7.5.1 cells treated with siRNA_MR01 ...................192 A3.4 Materials and methods .........................................................................................200 Appendix 4: Supplementary list of materials ..................................................................203 A4.1 Plasmid list ..........................................................................................................203 A4.2 Primer list ............................................................................................................204 A4.3 Quantitative real-time polymerase chain reaction (RT-QPCR) assay list ...........207 x  List of Tables  Table 1.1. DAAs and HTAs in clinical development at the beginning of 2014 for treating chronic hepatitis C .................................................................................................................. 20 Table 1.2. Compendium of GAPDH interactions with various binding partners ................... 29 Table 3.1. List of differentially expressed proteins in Huh-7-derived stable GAPDH-knockdown clones and fold changes (FC) compared parental Huh-7 cells ............................ 92 Table 3.3. List of exosome-associated proteins with fold changes (FC) .............................. 105 Table A1.1. List of differentially expressed genes with fold changes (FC) across Huh-7-derived cell clones 67D2 and 67B3 ...................................................................................... 162 Table A1.2.Top ten molecular functions modulated by significantly deregulated genes with the threshold p-value of 0.5 .................................................................................................. 167 Table A1.3. Enriched pathways modulated by significantly deregulated genes with the threshold p-value of 0.5 ........................................................................................................ 168 Table A3.1. Potential mRNAs predicted to be targeted by siRNA_MR01 using software miRanda against human mRNA sequences obtained from Genbank ................................... 191 Table A3.2. List of differentially expressed proteins with fold changes (FC) in Huh-7.5.1 cells treated with a pool of siRNAs targeting GAPDH ........................................................ 197 Table A4.1. List of plasmids generated in this thesis ........................................................... 203 Table A4.2. List of primers generated in this thesis ............................................................. 204 Table A4.3. List of RT-QPCR assays generated in this thesis ............................................. 207 Table A4.4. List of RT-QPCR assays designed in this thesis ............................................... 207   xi  List of Figures  Figure 1.1. Organization, processing and functions of viral proteins encoded by the HCV genome. ..................................................................................................................................... 3 Figure 1.2. The HCV life cycle. .............................................................................................. 11 Figure 1.3. Glycolysis. ............................................................................................................ 24 Figure 1.4. Schematic representation of GAPDH domains. ................................................... 28 Figure 1.5. Involvement of GAPDH in the maintenance of the cytoskeleton and retrograde transport of vesicular tubular clusters (VTC) from the ER to the Golgi. ................................31 Figure 2.1. Robust transient suppression of GAPDH by RNA interference in human Huh-7.5.1 cells. ............................................................................................................................... 51 Figure 2.2.  Short interfering RNA (siRNA)-mediated GAPDH reduction in naïve human Huh-7.5.1 cells decreases host-cell susceptibility to primary and secondary HCV infections  ................................................................................................................................................. 53 Figure 2.3. Short interfering RNA (siRNA)-mediated GAPDH suppression in HCV-infected Huh-7.5.1 cells leads to a decrease in the viral susceptibility of the HCV-infected Huh-7.5.1 cell supernatants ...................................................................................................................... 56 Figure 2.4 Short interfering RNA (siRNA)-mediated GAPDH suppression in HCV replicon-harboring cells has no effect on HCV replication ................................................................... 58 Figure 2.5 Exogenous expression of V5-tagged human GAPDH in Huh-7.5.1 cells increases GAPDH enzymatic activity .................................................................................................... 60 Figure 2.6 Exogenous expression of V5-tagged human GAPDH increases the viral infectivity of HCV-infected Huh-7.5.1 cell supernatants ......................................................................... 64 Figure 2.7 Short interfering RNA (siRNA)-mediated GAPDH suppression in naїve human Huh-7.5.1 cells decreases host-cell susceptibility to primary dengue virus infection ............ 67 Figure 3.1. Overview of the molecular tools and experimental approach used to establish the Huh-7-derived stable GAPDH-knockdown clones ................................................................. 79 Figure 3.2. Biochemical results demonstrating knockdown of Huh7-derived stable GAPDH protein abundance, enzymatic activity, and mRNA expression in Huh-7-derived stable GAPDH-knockdown clones.................................................................................................... 83 xii  Figure 3.3. Decrease in host-cell susceptibility to primary HCV infections Huh-7-derived stable GAPDH-knockdown clones and decrease in viral infectivity of the HCV-infected cell supernatants............................................................................................................................. 86 Figure 3.4. IF results demonstrating a robust decrease in host-cell susceptibility to primary HCV infection in Huh-7-derived stable GAPDH-knockdown clones .................................... 89 Figure 3.5. Schematic summary of the experimental and data analysis process of the quantitative proteomics study ................................................................................................. 90 Figure 3.6. Correlation between quantitative proteomics and quantitative western blotting for measuring intracellular GAPDH protein abundance in Huh-7-derived stable GAPDH-knockdown clones ................................................................................................................... 91 Figure 3.7. Comparison between the common and unique protein groups significantly changed in the Huh-7-derived stable GAPDH-knockdown clones, 67D2 and 67B3 in relation to the parental Huh-7 cells ...................................................................................................... 97 Figure 3.8. Clustering of differentially expressed proteins across the Huh-7-derived stable GAPDH-knockdown clones by molecular activity (functions) .............................................. 99 Figure 3.9. Differentially expressed genes display lower overlap with higher overlap of molecular functions between Huh-7-derived stable GAPDH-knockdown clones, 67D2 and 67B3 ...................................................................................................................................... 102 Figure 3.10. Characterization of the exosome-associated tetraspanin protein CD81 levels in the Huh-7-derived stable GAPDH-knockdown clones in relation to the parental Huh-7 cells: from mRNA expression profiling (RT-QPCR) to cell surface expression in live cells (FACS)............................................................................................................................................... 104 Figure 4.1. Putative roles of GAPDH in the HCV life cycle ................................................ 125 Figure A1.1. Huh-7-derived stable GAPDH-knockdown (kd) clones, 67D2 and 67B3 are closely related to parental Huh-7 cells .................................................................................. 155 Figure A1.2. Decrease in HCV core abundance in naïve Huh-7.5.1 cells infected with infectious supernatant collected from primary HCV-infected Huh-7 cells transduced with non-specific shRNA encoded by a lentiviral transduction particle (CTRL-2) ..................... 158 Figure A1.3. Expression of GAPDH and CD81 by RT-QPCR correlates with microarray results .................................................................................................................................... 160 xiii  Figure A1.4. Pathway categories affected by differentially expressed genes in Huh-7-derived cell clones, 67D2 and 67B3 .................................................................................................. 170 Figure A1.5. Variability in proteins identified between technical replicates (two) for each biological replicate (two) of Huh-7-derived cell clones, 67D2 and 67B3 showing stable reduction in GAPDH expression levels ................................................................................ 173 Figure A1.6. Rescued GAPDH expression observed in the Huh7-derived GAPDH-kd clone, 67cl.1 ..................................................................................................................................... 178 Figure A2.1. Monolayer of Huh-7.5.1 cells transfected with an in vitro transcribed JFH-1 genome shows cytopathic effects 17 days post-transfection ................................................ 180 Figure A3.1.Non-targeting short-interfering RNA (siCTRL) treated Huh-7.5.1 cells show reduced susceptibility to HCV infection. .............................................................................. 183 Figure A3.2. Synthetic siRNA_MR01 reduces the susceptibility of Huh-7.5.1 cells to HCV infection ................................................................................................................................ 187 Figure A3.3. Replication of a HCV subgenomic replicon is not compromised in siRNA_MR01 treated Huh.8 cells ........................................................................................ 189 Figure A3.4. No change in band pattern generated by coomassie staining of total protein from Huh-7.5.1 cells treated with siRNA_MR01 .......................................................................... 193 Figure A3.5. Variability in proteins identified across technical replicates (two) for each biological replicate (three) of Huh-7.5.1 cells treated with siRNA_MR01 in comparison to cells treated with a pool of NonTarg control siRNAs ........................................................... 194 Figure A3.6. List of differentially expressed proteins unique to Huh-7.5.1 cells treated with siRNA_MR01 in comparison to non-targeting siRNA treated cells (siCTRL)-treated cells using mock-treated Huh-7.5.1 cells as control...................................................................... 199   xiv  List of Abbreviations  A549 – Human lung adenocarcinoma epithelial cell line APKCι/λ –Atypical protein kinase C iota/lambda AraC –Cytosine arabinoside ARFP –Alternate reading frame protein ATG7 –Autophagy-related protein 7 CD81 –Cluster of differentiation 81  CFSE –Carboxyfluorescein diacetate, succinimidyl ester CHO –Chinese hamster ovary CICD –Caspase-independent cell death CML –Chronic myeloid leukemia CRE –Cis-acting regulatory element  CSF-1 –Colony-stimulating factor 1 CYP –Cyclophilin DENV –Dengue virus DENV-2 –Dengue virus serotype 2  DMEM –Dulbecco’s modified eagle medium DAA –Direct-acting antiviral E1 –Envelope glycoprotein 1 EmpVec –Empty vector ER –Endoplasmic reticulum ET-1 –Endothelin 1 FACS –Fluorescence-activated cell sorting  FBS –Fetal bovine serum GAPDH –Glyceraldehyde-3-phosphate dehydrogenase GOSPEL –GAPDH’s competitor of Siah protein enhances life GSTP1 –Glutathione S-transferase pi 1 HAV –Hepatitis A virus HBV –Hepatitis B virus HCC –Hepatocellular carcinoma xv  HCV – Hepatitis C virus HDV –Hepatitis delta virus HPIV3 –Human parainfluenza virus 3 HTA –Host-targeted agent Huh –Human hepatoma ICW –In cell western IDH1 –Isocitrate dehydrogenase 1 IF –Immunofluorescence IFIT1 –Interferon-induced protein with tetratricopeptide repeats 1 IFN –Interferon IL-8 –Interleukin 8 InfA –Influenza A virus IRES –Internal ribosomal entry site IRF –Interferon regulatory factor ISG –Interferon-stimulated gene JEV –Japanese encephalitis virus JFH-1 –Japanese fulminant hepatitis 1 LD –Lipid droplet L-glu –L-glutamine miRNA –MicroRNA MOI –Multiplicity of infection MOMP –Mitochondrial outer membrane permeabilization MTOR –Mechanistic target of rapamycin MYH9 –Myosin, heavy chain 9 NAD –Nicotinamide adenine dinucleotide NANBH –Non-A, non-B type hepatitis Nano-LC-MS/MS –Nano-liquid chromatography-tandem mass spectrometry  NEAA –Non-essential amino acids NK –Natural killer NLS –Nuclear localization signal NME –Non-metastatic cells (or Nucleoside diphosphate kinase) xvi  NS/nsp –Nonstructural protein ORF –Open reading frame PBS –Phosphate buffered saline pegIFN-α –Pegylated-interferon-alpha P.i. –Post-viral infection PKR –Protein kinase R (or eukaryotic translation initiation factor 2-alpha kinase 2) PRDX5 –Peroxiredoxin 5 P.t. –Post-tranfection RAB –Ras-related protein Rab Rab2 –Ras-related proteins Rab2 RC –Replicase complex RISC –RNA-induced silencing complex ROS –Reactive oxygen species RT-QPCR –Real-time quantitative polymerase chain reaction SARS-CoV –Severe acute respiratory syndrome-related coronavirus SDS –Sodium dodecyl sulfate SERBP1 –SERPINE1 mRNA-binding protein 1 shRNA –short hairpin RNA siRNA –small interfering RNA S-NO –S-nitrosylation SOC –Standard of care SR-B1 –Scavenger receptor class B type 1 Src –V-Src sarcoma (Schmidt-Ruppin A-2) viral oncogene homolog (a tyrosine kinase) UO31 –Renal carcinoma cell line UTR –Untranslated region VDAC –Voltage-dependent anion channel Vero E6 –African green monkey kidney epithelial cell line VLDL –Very low-density lipoprotein pathway VTC –Vesicular tubular cluster WB –Western blotting WNV –West Nile virus  xvii  Acknowledgements First and foremost, I would like to thank Dr. François Jean for providing me with this opportunity to pursue my career in the field of molecular virology, supervising me through the course of a relatively challenging project, and providing me a stimulating environment full of scientific discussions, learning, and growth. I would like to thank my committee members Dr. Robert Hancock, Dr. Eric Jan, and Dr. James Kronstad for following my progress, providing valuable suggestions, being available for all my committee meetings, while on sabbatical leave or requiring rescheduling of other commitments. I would like to thank Dr. Julius John for his friendship, Dr. Andrea Olmstead for sharing protocols and troubleshooting experiences, Christine Lai for suggestions on cloning issues. Dr. Victoria Svinti, I thank you for encouraging me to stay focused with writing my thesis, for providing me with the scientific support, and for listening to my endless practices for talks. Vanessa Silva, I value your friendship dearly. I appreciate you for your encouragement, understanding, help and making my experience positive at the lab. Also, thank you for grammatically editing my progress reports from the past three years. Dr. Nina Maeshima, I appreciate your help with providing editorial suggestions for my thesis. I would like to gratefully and sincerely thank Dr. Michael Murphy, Dr. Rachel Fernandez, and Darlene Birkenhead for helping me progress with my thesis. My late Gandhian grandpa, Bapuji, I have valued education highly because of your ethical and inspirational advice.  My caring family, I appreciate you for all your help, understanding, and patience.  To my son, Sohm, thank you for saying, “Study well mama, so you do not have to ever EVER study again, and we can play together”.  Finally to most import person in my life, my loving husband Dipal, I cannot thank you enough as this would not have been possible without your ongoing patience, constant encouragement and full support with the never-ending list of things I needed to achieve my goals. Thank you for always backing me up.   xviii  Dedication  To my caring mom, loving and encouraging husband, Dipal, and motivating sons,  Sohm and Arjun   1  Chapter 1: Introduction   1.1 The hepatitis C virus—a silent killer The hepatitis C virus (HCV) is a contagious human pathogen of significant public health concern, and an estimated 2-3% of the global population is chronically infected (88, 127, 158). Due to its asymptomatic nature, individuals are often unaware they are infected until complications emerge much later in life, i.e., 20-30 years after the initial infection (262). If detected early, almost 50-80% of infected individuals can be cured (5). However, updated epidemiological data suggest that many countries, including the United States of America, have underestimated the number of individuals with chronic hepatitis C (88, 118). Because of this underestimation, HCV as a silent killer (118) is predicted to become a significant economic, medical, and social burden (127). Progressive research over the past two decades has resulted in a compendium of HCV research tools that allow the screening and identification of novel potential antiviral drugs with promising leads for treatment (168). Recently, the focus of HCV research has shifted towards improving drug regimens, enhancing drug efficacy with reduced side effects, and implementing robust techniques for the early detection and the prevention of disease progression in infected individuals.   1.1.1 Discovery and molecular biology of HCV One-third of the individuals who underwent blood transfusions during the mid-twentieth century developed post-transfusion hepatitis with an unknown pathogen (168). With the discovery of hepatitis A and hepatitis B viruses, and the development of their diagnostic tests in the mid-1970s, the post-transfusion hepatitis caused by this unknown agent was termed non-A, non-B type hepatitis (NANBH) (149, 168). This led to an intensive research effort to identify and characterize an etiological agent for NANBH, which involved transmission studies using sera from acute and chronic human infections in the only primate animal model identified to contract HCV: chimpanzees (149, 168).  Analysis of sera isolated from chimpanzees revealed that the filterable pathogen contained essential lipids, i.e., an enveloped virus that can be chloroform-inactivated (168). 2  Isolated hepatocytes from infected chimpanzees also contained a characteristic cytoplasmic membranous web (due to alteration of the membrane most likely from the formation of a scaffold allowing RNA replication), thus indicative of infection with an RNA virus (168).  After almost 15 years of defining NANBH, biopanning of the cDNA library derived from the plasma of an infected chimpanzee with serum from an individual with chronic NANBH led to the identification of a foreign nucleic acid of 150 bp designated 5-1-1 (168). Hybridization experiments using this nucleic acid sequence revealed that the genome was derived from single-stranded positive-sense RNA (168). In addition, the peptide expressed from the sequence reacted with the serum from several chronically infected patients (168). In 1989, relying upon this lead discovery, Choo et al. identified the causative agent of NANBH and termed it hepatitis C virus (HCV) (41, 168).  Shortly thereafter, this team, using the original cDNA as a probe, identified additional overlapping clones in the library and assembled the full-length sequence of 9.379 kb genome along with the 5′ and the 3′ -untranslated region (UTR) (42, 168). Subsequent investigation of the genome sequence and its genetic organization revealed a distinct resemblance to flaviviruses and pestiviruses, thereby accelerating its classification (168). HCV is classified in a separate genus, Hepacivirus, within the Flaviviridae family (149). It is a relatively small enveloped virus that exists as a hybrid lipoviroparticle of 55 nm - 65 nm in diameter (21). The 9.6 kb genome contains a single open reading frame (ORF) that encodes a large protein sequentially designated as C-E1-E2-p7-NS2-NS3-NS4A-NS4B-NS5A-NS5B (Fig. 1.1) (168). HCV Core (C) and envelope glycoproteins E1 and E2 are structural proteins that are released from the polyprotein by host peptidases (166). Processing and maturation of the core from the E1 protein promotes its transport to the surface of the lipid droplet (LD), which is the site of viral assembly (239).  The 80 amino acids of core N-terminus is an intrinsically unstructured protein domain suggested to be involved in its interaction with other host proteins (59).  Also, the highly basic N-terminal half region of the core facilitates homo-oligomerization and RNA binding, which is necessary and sufficient for nucleocapsid formation (146, 239).  Due to ribosomal frame-shifting and transcription slippage in this region of the genome, alternative ORFs exist and generate a family of core-related proteins called alternate reading frame proteins (ARFPs) (26, 65). In vitro studies and analysis of 3  HCV sequences isolated from patients have provided evidence for a role of ARFPs in advanced liver diseases and liver cancer (50, 228, 259).  In addition, ARFPs may also contribute to certain core protein functions (259). Reactive sera and positive T-cell responses to ARFPs have been identified; however, the contribution of ARFPs to the HCV life cycle has not yet been determined (239). The E1 and E2 proteins are heavily glycosylated proteins with a C-terminal transmembrane domain embedded in the endoplasmic reticulum (ER) membrane (239). Of the two, E2 is mainly responsible for interacting with cell surface receptors. In serum, these C-terminal structural proteins (C, E1, and E2) are embedded in a host-derived lipid-bilayer associated with lipoproteins and viral RNA, and they constitute physical virions of low and heterogeneous densities (170).   Figure 1.1. Organization, processing and functions of viral proteins encoded by the HCV genome.  The HCV genome is a single-strand positive-sense RNA containing cis-acting regulatory elements (CRE) at the distal ends of the genome, which is important for the process of 4  translation and RNA replication. Host ribosomal machinery initiates cap-independent translation, which gives rise to a large polyprotein of ~3011 amino acids mediated by an internal ribosomal entry site (IRES) present on the 5′ end of the genome. The polyprotein is co- and post-translationally cleaved by host and viral proteases to release viral proteins. The host signal peptidase cleaves peptide bonds that release the core, glycoprotein, and viroporin. The secondary cleavage mediated by signal peptide peptidase releases the core from the ER. A non-structural (NS) protein, NS2, autocleaves near the C-terminal end to release itself from the polyprotein. With the cofactor NS4, NS3 cleaves the remainder of the polyprotein to release five NS proteins. Arrows represent polyprotein cleavage sites.  Adapted from Moradpour et al. 2007 (166).  The putative viroporin p7 is a hydrophobic protein with N- and C-terminal ends facing the lumen of the ER (13). This protein is released by signal peptidase to oligomerize and form hydrophobic pores having cationic channel activity (176). The non-structural (NS) protein, NS2 is a viral autoprotease that releases itself by cleaving at a site close to the N-terminus of the NS3 protein (166, 243). This N-terminal domain is responsible for anchoring the protein to the ER (243). Although p7 and NS2 are not required for replication, they play an essential role in viral assembly and release (13).  NS3 is a well-studied multifunctional protein with an N-terminal portion that has chymotrypsin-like serine protease activity, while the C-terminal portion has an NTPase/helicase activity (243). With the cofactor NS4A, it cleaves the remainder of the polyprotein as well as host factors that antagonize cellular antiviral responses. Apart from modulating NS3 activity, NS4A anchors NS3 to the ER facing the cytosol (243). Even though the multifaceted role of NS3-4A in processing proteins is well established (11), its helicase activity and its involvement in the early steps of viral assembly still remain unclear (243).  NS4B is an integral membrane protein that induces an ER-derived specialized membrane compartment called the membranous web, an important feature for the formation of replicase complex (RC) (62). NS4B also gets palmitoylated to form an oligomer, an event essential for HCV replication (270). Another feature of NS4B may also involve induction of membrane reorganization by interacting with a small guanine triphosphate hydrolase (GTPase) protein, Rab5, which is involved in the regulation of membrane fusion, especially in the early endosomal compartment (229). 5  NS5A is a protein tethered to the surface of the ER facing the cytosol (166). It exists in both phosphorylated and hyperphosphorylated forms and it interacts with several host proteins (66). The phosphorylated state is thought to switch its indispensable role from viral replication to a critical function in viral assembly (244). Another well-studied viral protein, NS5B is an RNA-dependent RNA polymerase that replicates viral genomes (243). Although the primary function of these five C-terminal NS proteins is replication, they have also been shown to play an essential role in viral assembly (190).  In addition to encoding large polyproteins, the HCV genome contains several highly structured RNA elements called cis-acting regulatory elements (CRE) (190). The 5′-UTR and 3′-UTR are folded into intricate secondary structures forming stem loops important for translation and replication (190). The 5′-UTR contains an internal ribosomal entry site (IRES) for cap-independent translation of HCV RNA (200). The HCV IRES is composed of four major structural domains (I-IV) with individual stem loop structures (Ia, Ib…) (200).  Besides a start codon, IRES domain IV also includes a region from an alternate reading frame (258). Mutational studies performed in the region of the alternate reading frame suggest the presence of a functionally important CRE (154, 258). The 5′-UTR also contains an essential replication signal for a negative strand RNA intermediate to serve as a template (77) and two liver-specific microRNA (miRNA), miR-122 binding-sites that can increase HCV replication in both hepatic and non-hepatic cells (107, 190). In contrast, the 3′-UTR is a tripartite structure comprising a short variable region that differs with genotype, a poly (U/UC) tract that differs in length, and a highly conserved 3′-X tail of 98 nucleotides formed into three stem loop structures (24, 119, 236, 237). Apart from directing negative strand synthesis, the 3′-UTR also enhances IRES-mediated translation (243). The 3′-X tail can base pair with an additional CRE, 5BSL3.2, found in the protein coding region of NS5B (243). This CRE region forms a “kissing loop” with a 3′-X tail and two pseudoknots with upstream regions, one in the NS5B region and the other in the C coding region, which contains two stem loops (243). These structures can also interact with host factors and modulate translation and replication processes (243).  6  1.1.2 The HCV life cycle Our knowledge of the HCV life cycle is derived from extensive research performed over the past three decades. This research involved generating infectious clones of various genotypes, expanding the panel of functional replicon systems with and without adaptive mutations, generating retroviral pseudotypes bearing unmodified HCV glycoproteins (HCV pseudoparticle (HCVpp)), and developing HCV cell culture infectious systems (168). Broadly, HCVpp and HCV replicon systems have been instrumental in unraveling the viral entry and replication stages, respectively. The development of HCV cell cultures by three independent laboratories in 2005 was a major breakthrough in HCV research. This development allowed validation and added molecular mechanisms to the tentative conceptualization of the HCV life cycle that had been initially proposed after recognition of HCV as a member of the Flaviviridae family (13, 137, 264, 276). Specifically, it was possible to investigate the viral assembly and egress stages, which had not been possible using previous model systems. Most importantly, the cell culture system generated infectious virus particles that differ in density from those found in infected chimpanzees and human (81). Although the cell culture system was useful to study the viral replication, this system was deficient to study virus assembly and budding because the virus particles generated are atypical and not secreted in sufficient amount to allow serious biophysical study on the structure of HCV (36). This lack of proper data on the biophysical property of HCV still remains a weakness in the field of HCV research.  The proposed HCV life cycle, based on experiments carried out in cultured hepatoma cells is summarized in Fig. 1.2. The current model of cell-free viral entry suggests that HCV uses numerous entry factors in a spatiotemporal manner. Briefly, HCV entry involves the virus attaching to the surface of the cell, binding with specific receptors, and interacting with tight junction proteins, thus cumulatively facilitating ingress (98, 138, 186). HCV attachment to the host cell surface may involve a low-affinity interaction of low-density lipoproteins on the surface of the lipoviroparticles with cell surface attachment molecules, such as heparin sulphate proteoglycans (a glycosaminoglycan) and low-density lipoprotein receptors (138, 186). Subsequently, the virus binds to entry receptors, such as scavenger receptor class B type I (SR-B1) and cluster of differentiation 81 (CD81) molecules (138, 186, 243). The interaction of viral E2 glycoproteins with CD81 is well defined; 7  however, the interaction with SR-B1 is unclear. The interaction with SR-B1 may involve a direct binding by E2 or an indirect binding through lipoproteins present on the viral surface (243). Based on its physiological function, SR-B1 plays several sequential roles in viral entry: 1) contributing to viral attachment by interacting with virus-associated lipoproteins; 2) influencing post-binding events by mediating lipid transfer activity; and 3) interacting with E2 hyper-variable region 1 (HVR-1) by exposing CD81-interacting E2 determinants to allow interaction with CD81 (138). The function of CD81 in HCV entry is to prime HCV glycoproteins for low pH activation and attachment to a tight junction protein, claudin (CLDN-1)(138). The recognition of entry receptors is followed by identification and interaction with additional entry cofactors, which are tight junction proteins, such as CLDN-1 and occludin (OCLN) (98). Because the key steps in HCV entry are still being investigated, including the lateral movement of CD81-bound viral particles to tight junction proteins situated at the lateral surface of the cell, the list of cellular determinants involved in viral entry will likely keep growing (138, 186, 239). For example, two receptor tyrosine kinases, epidermal growth factor receptor (EGFR) and the ephrin type A receptor 2 (EPHA2), are viral entry host cofactors recently identified by a functional RNA interference kinase screen (142). EGFR and EPHA2 have been shown to promote CD81-CLDN-1 interactions (138). Also, transferrin receptor 1 (TFR1) has been identified as a cellular entry factor with an important role in a post-CD81-binding event (151). Following interaction with numerous entry factors, the virus is internalized by CD81 and CLDN-1-induced clathrin-mediated endocytosis (20, 70, 98, 156) to RAB5A-containing endosomal compartments (138). After internalization, the pH (pH 5- 5.5) in the endocytic compartment is reduced, which results in the fusion of viral and endosomal membranes (14). Of the two glycoproteins, E1 is proposed to harbour a putative fusion peptide; however, the precise mechanism of fusion events mediated by these glycoproteins is unknown (128, 138). Recent research suggests that host receptors, such as CD81, play a role in the fusion process by priming the virus for pH-dependent conformational change (138, 220). Another receptor, the Neimann Pick C1-like 1 (NPC1L1) cholesterol absorption receptor, is also suspected to play a role in the fusion step (205).  After viral uncoating, the genome is released into the cytosol to serve as a template for the processes of translation, replication, and assembly (179). The cap-independent 8  translation of the incoming viral genome occurs at the rough ER, and it is mediated by host factors to give rise to a large polyprotein of 3011 amino acids that is co- and post-translationally cleaved by host and viral proteases to release three N-terminal structural proteins, five C-terminal non-structural proteins, p7, and NS2 (13). The released non-structural proteins—NS3, NS4A, NS4B, NS5A, and NS5B—form an RC on the membranous web that is induced by the concerted action of the replicase proteins (13, 38, 168). Host cell factors, such as cyclophilin A and phosphatidylinositol 4 kinase III α (PI4KIIIα), are also involved in this membrane remodeling (145, 198). NS5B catalyzes the accumulation of positive-sense RNA using a negative-sense RNA intermediate (13, 168). Working models derived from unified results propose that viral assembly is triggered upon the interaction of three modules at the assembly site: the core, the complex of E1E2p7NS2, and the RC (13). The cellular assembly and export of new lipoviroparticles (LVPs) occurs in tight association with the pathways of very low-density lipoprotein (VLDL) synthesis and export (209). The putative assembly site, which is a membranous microenvironment of a nascent luminal LD, is formed at the ER membrane (13).  The viral assembly process is mediated by rapid trafficking of the cleaved core, which gets targeted to the surface of a cytosolic LD (72, 138). Viral budding from the ER involves interaction of the LD-bound core with NS5A proteins, which facilitates shifting of the newly synthesized genome out of the RC and into the assembling viral particle (138). Incorporation of glycoproteins is facilitated by the p7-NS2 protein, which brings together E1-E2 and NS3-4A complexes to a budding viral core formed from LDs to produce a hybrid LVP (138). Viral egress or trafficking into an intermediate secretory compartment after the assembly steps is dependent on the endosomal sorting complex required for transport (ESCRT) pathway (138). Morphogenesis of the virus particles in the Golgi compartments involves posttranslational modification and dimerization of E1 and E2 proteins (138). This process occurs in close association with the intracellular-trafficking pathway of apoE-containing microsome-associated LDs, which allows virus particles to acquire apo-lipoproteins on their way to egress (13, 253). During the maturation and egress process, the virus particles are stabilized by oligomerzation of p7, which acts as a viroporin to form heptameric cation-specific ion channels that equilibrate pH gradients within the secretory pathway (138). Thus, HCV 9  hijacks the host lipoprotein secretion pathway to enable the export of infectious viral particles (209).10  11  Figure 1.2. The HCV life cycle.  1) The virus attaches to the surface of the cells. 2) Binding with specific receptors and post-binding interactions with tight junction proteins induce clathrin-mediated endocytosis of the virus particle. 3) Lowering of pH in the endocytic compartment facilitates fusion between the viral and endosomal membranes. 4) Uncoating allows release of the 9.6 kb genome into the cytosol that serves as a template for translation, replication, and viral assembly. 5) The genome is translated into a large polyprotein. 6) The polyprotein is co- and post-translationally processed into structural and nonstructural protein (NS) (Fig. 1. 1). 7) The NS3-NS5B proteins form a replicase complex (RC) on the membranous web. 8) The genome gets amplified by the initiation of replication at the 3′-UTR. 9) Viral assembly is initiated upon coating of lipid droplets (LD) by the core. 10) The genome gets delivered to the assembly particle, which acquires viral glycoproteins. 11) The assembled virus matures through the secretory pathway and gets released.  C= core; E= envelope glycoprotein; NS= nonstructural protein; LDLR= low density lipoprotein receptor; GAG= glycosaminoglycan; SR-B1= scavenger receptor class B type I; CD81= cell differentiation 81; CLDN1= claudin 1; OCLN= occluding; NPC1L1= Neimann Pick C1-like 1 cholesterol receptor; RC= replicase complex.  Adapted from Bethell et al. 2009. (18); Tellinghuisen et al. 2007. (243); Popescu et al. 2011. (190).  1.1.3 HCV pathogenesis HCV is primarily a hepatotropic virus with the ability to cause both acute and chronic infections (69, 127). Currently, with the blood-to-blood mode of transmission, the major risk factors contributing to the contraction of HCV infection include an increase in intravenous drug use, inappropriate screening of blood and blood transfusion products, and inadequate sterilization of medical equipment that results from a lack of stringent medical standards in developing countries (127). About 80% of acute infections, which are short-term and occur during the first six months of exposure, are asymptomatic or associated with non-specific mild symptoms, such as lethargy and myalgia that are difficult to diagnose (5, 245). As a result, most cases go unreported and affected individuals are unaware of their infection status. Apart from mild cases, the symptomatic acute disease is characterized by elevated serum alanine aminotransferase levels and jaundice within 2-14 days of exposure along with detection of the virus in the blood (6, 105, 108). This is followed by the gradual appearance of HCV-specific antibodies within 20-150 days of exposure (5, 86, 88). In 25% of acute infections, as a result of a complex interplay between the host and the virus, a spontaneous 12  viral clearance is observed, characterized by undetectable levels of viral RNA in the blood (88). Host factors involved in spontaneous clearance of acute infections include female sex, a broad and strong immune response, and genetics, such as a favourable IFN lambda 28-B gene polymorphism (88). On the other hand, the pathogenic factors associated with increased probability of spontaneous viral clearance include a reduction in generation of HCV viral quasispecies and infection with HCV genotypes other that genotype 1 (88).  In contrast to spontaneous clearance, in ~75 % of acute cases, patients are unable to clear the virus, which results in the development of chronic and persistent HCV infection (5). Again, most chronic infections show no symptoms and the infections progress slowly over 20-30 years, damaging the liver severely. As a consequence, ~20% of chronically infected individuals develop liver complications, such as fibrosis (10-15 years), steatosis, cirrhosis (20 years), and hepatocellular carcinoma (HCC) (30 years) (5, 88, 245). Host factors involved in increased risk for liver fibrosis include male sex, ethnicity, age, immunosuppression, chronic HBV co-infection, diabetes, and hepatic steatosis along with behavioral factors, such as heavy alcohol intake (88).  As these irreversible liver complications result in liver failure, HCV is the leading cause of liver transplantation in North America and Europe with 50% of individuals undergoing this procedure being HCV-positive (27). Due to immunosuppressive treatment, the recurrence of infections in allografts with accelerated progression to severe liver complications is inevitable, which makes HCV the principle cause of death from liver disease (~19-24%) (5, 88). Owing to the uninterrupted spread of HCV resulting from contaminated blood supplies and unsafe medical practices, including injection drug use that occurred prior to the identification of the virus, and with ~3-4 million new cases of chronic infection each year, HCV is currently estimated to infect 130-170 million (88) individuals globally (88, 127, 168). As surveillance systems are prone to underestimate incidence, due to the asymptomatic nature of acute infections, the true prevalence of HCV is uncertain. HCV prevalence varies around the globe with Egypt estimated to have the highest percent of cases (>15% of its population) (88), and, the high prevalence of infection is believed to be due to iatrogenic transmission during its mass campaign for parenteral treatment against schistosomiasis (a parasite infection that progressively damages the bladder, uterus, and kidney) from the 1920s to the 1980s (88, 127, 164). The cost of HCV treatment is expected to escalate in future years 13  as a rising number of infected baby boomers are predicted to develop liver complications (127). For example, the annual cost of treatment for acute and chronic hepatitis C is estimated to exceed US $600 million in the USA and CD $150 million in Canada (127). With such a growing financial burden to societies and health-care systems, this global pathogen is now considered an escalating public health concern (88, 127).  Because HCV is a positive-sense single-stranded RNA virus, it encodes its own RNA-dependent RNA polymerase (RdRp), NS5B, for replication. The HCV RdRp does not have a proofreading function; thus, it introduces mutations with high rates during replication, resulting from a lack of 3′ to 5′ exonuclease activity (69). This has led to continuous diversification of the HCV genome. As a result, in vivo, HCV circulates as a quasi-species subjected to a continuous process of genetic variation, competition, and selection (69). Consequentially, HCV has been classified into six major genotypes with 30-35% genomic heterogeneity and numerous subtypes (a, b, c ...) with 20-25% genomic heterogeneity arising from its evolving genome (168). Challenges in identifying the virus have led to its spread, its ability to persist asymptomatically, and the inability to prevent infection due to the hindered development of effective vaccines. Along with these challenges, its continual escape from effective treatment is mainly attributed to its ability to exist with remarkable heterogeneity, thus resulting in successive pathogenesis (69). In order to establish infection, HCV can evade innate immune responses by interfering with the interferon (IFN)-induced antiviral state of host cells (245). One of the established mechanisms for this involves proteolytic cleavage of adaptor molecules, such as mitochondrial antiviral signaling protein (MAVS) and TIR-domain containing adaptor-inducing IFN (TRIF) (albeit with low efficiency), by HCV NS3/4A protease (101). The cleavage of the adaptor molecules prevents sensing of double- and single-stranded viral RNA by host sensors, such as retinoic acid inducible gene (RIG-1), melanoma differentiation-associated protein 5 (Mda5), and toll-like receptors (TLR)-3,-7, and -9 (101, 114, 245). In addition, the HCV RNA genome, especially the 5′ and 3′ double-stranded regions, is suggested to only weakly induce RIG (101, 245). Prior to replication, there is also an accumulation of viral proteins in the cells, which gives a head start to viral proteases with the accumulation of NS3/4A (245).  14  Other less well-defined mechanisms of immune evasion have also been suggested. For example, expression of the core results in the upregulation of host proteins that suppress the IFN-induced pathway, including suppression of cytokine signaling (SOCS) protein and protein phosphatase PP2Ac (245). Also, inhibition of cellular translation, which includes translation of IFN and proteins involved in IFN-induced pathways, by the attenuation of IFN-stimulated gene (ISG) expression via phosphorylation of protein kinase R (PKR) is likely to play a role (245). Finally, HCV has been shown to induce autophagy by the accumulation of autophagy vesicles resulting from the inhibition of autophagosome maturation to autolysosomes (245). Because the autophagic vesicles containing viral proteins do not fuse with the lysosome, the process of signaling through lysosomal TLRs is avoided (245). Together, these mechanisms may allow the virus to establish a robust and persistent replication machinery before it is detected by the host (245). Because HCV actively inhibits the induction of an antiviral response in infected cells, it is suspected that the bystander cells, such as an infiltration of dendritic cells, produce cytokines that elicit an immune response (245). Also, natural killer (NK) cells are found to be activated in both acute and chronic infections with the ability to generate IFN that can mediate the inhibition of HCV replication  (245). NK cells can also lyse HCV-infected cells at a high effector-to-target ratio and thus they probably contribute to HCV-associated liver damage (245). Although further investigation of how HCV interferes with the action of NK cells is required, the NS5A-containing apoptotic bodies have been shown to trigger monocytes to produce two cytokines, interleukin-10 (IL-10) and IL-12, that can significantly downregulate NKG2D activation receptors via TGF-β (245). An alternative suggested mechanism is the masking of CD81 upon interaction with E2, which may also interfere with NK cell action (245). Evasion of humoral antibody responses has been implicated as occurring through multiple mechanisms. Protection from neutralizing antibodies may occur by mutation in the E2 HVR-1, by the presence of a specific glycan on E2, by interaction with lipoproteins present on the lipoviroparticle, and by cell-to-cell spread (245). The cumulative results from experimentally infected chimpanzees and chronically infected human patients reveal the presence of strong and sustained CD4+ and CD8+ T-cell responses that target multiple epitopes within the different HCV proteins (245).  Nevertheless, extensive work on the 15  characterization of adaptive immune responses over the past few years has suggested two mechanisms by which HCV may evade T-cell responses. The first suggested mechanism is viral mutational escape resulting from multiple and evolving virus variants co-circulating in an individual; the second is T-cell dysfunction, which is the inability to secrete antiviral cytokines due to T-cell exhaustion during chronic infection (245). These immune evasion strategies, while not completely defined, result in inadequate host immune responses that are a necessary part of HCV pathogenesis; however, they are not sufficient to explain the highly complex molecular mechanisms involved in hepatic transformation that occur in succession from inflammation to fibrosis, cirrhosis, steatosis, and finally to HCC (35). These mechanisms include viral-protein-evoked host responses that contribute to the multistep processes to reprogram several pathways, such as chronic inflammation (TGF-β pathway), tissue remodelling through cell growth (Raf/MAPK pathway, Wnt/β-catenin pathway), apoptosis and/or necrosis (tumor suppressor proteins (p53) pathway), and the induction of oxidative stress, especially by the HCV core, NS3 and NS5A (255) . Because these HCV proteins mainly induce oxidative stress, the resulting reactive oxygen species (ROS) and hepatic iron overload causes a genomic instability that continually contributes to HCV-induced hepatocarcinogenesis (255).  1.1.4 HCV current standards of care For many years, the development of effective antiviral agents against HCV was hindered because of HCV’s restricted tropisms (humans, chimpanzees, and tupaia), challenges with the development of an in vitro cell culture system (52, 149), and the absence of small-animal models that could mimic both acute and chronic infections for the development of HCV-associated HCC (31). In spite of these limitations, thus far, HCV infections have been successfully treated with the widely accepted standard of care (SOC) treatment, which involves a combination of an immune modulator, pegylated-IFN-α (pegIFN-α), and ribavirin, a guanosine analogue with a broad-spectrum antiviral effect. IFN was shown to be effective against HCV in 1986, well before the identification of the virus (71). The addition of ribavirin to the regimen was also a significant advancement as it doubled anti-viral responsiveness to the treatment (71). Later, by covalently conjugating polyethylene glycol (PEG) to IFN, the reduction in renal clearance of the active drug allowed 16  it to circulate longer in the plasma, which permitted sustained absorption (262). This advance was beneficial, as it led to reduced dosage and associated costs. It also encouraged acceptance of the treatment in more than 15-20% of the patients who had been otherwise unwilling to accept treatment due to the array of serious adverse effects, especially as the modified drug decreased antigenicity, immunogenicity, and proteolytic degradation (262). IFNs signal through the janus kinase/signal transducer and activator of transcription (JAK/STAT) pathway that employs kinases to phosphorylate STAT proteins (STAT-1 and STAT-2) along with recruiting the IFN regulatory factor 9 (IRF9) to form a complex known as ISG factor 3 (ISG3) (113, 245). ISG3 subsequently binds to the promoter region of ISG to induce the expression of more than 300 genes with antiviral, antiproliferative, and immunomodulatory functions. Some of the HCV-associated antiviral effector molecules include PKR, the dsRNA-specific adenosine deaminase acting on RNA 1 (ADAR1), the 2′-5′ oligoadenylate synthetases (OAS/RNaseL system), the LD binding protein viperin, and IFN-induced proteins with tetratricopeptide repeats (IFIT1) like factors (245). The upregulation of constitutively expressed proteins, such as PKR, reduces protein synthesis through the phosphorylation of a subunit of the eukaryotic translation initiation factor eIF2, ADAR1 causes an accumulation of mutation by deamination of adenosine to inosine on target dsRNA during viral replication, and 2′-5′ OAS activates latent endoribonuclease RNaseL to degrade both viral and cellular RNA (113, 245). IFIT1 inhibits initiation of viral translation through interaction with eIF3 (245). Viperin is thought to block HCV replication by interfering with LD functions that are important for the viral assembly process. The IFN-induced regulation of miRNA has also been reported as involving the downregulation of mir-122 and the expression of miRNAs that bind and block the HCV genome (245). Altogether, modulation of these factors can induce an anti-HCV state.  The mechanism by which ribavirin augments the response rate of IFN treatment is unknown (71). With evidence from several studies, it has been proposed that this guanosine analogue, once phosphorylated in the cell, forms a triphosphate, which gets misincorporated by the RNA polymerase to inhibit replication (71). The monophosphate form also competitively inhibits inosine monophosphate dehydrogenase and causes depletion of the GTP necessary for viral RNA synthesis (71). There is both in vitro and in vivo evidence that supports the concept introduced by Crotty et al. that ribavirin acts as a viral mutagen pushing 17  viruses towards the threshold of catastrophic error (69, 71).  More recently, Thomas et al. suggested that ribavirin acts via IRF7 and IRF9 to potentiate ISG (246). In conclusion, multiple mechanisms for ribavirin activity are proposed that include both direct and host-mediated responses.  One of the major limitations of SOC treatment is that only 40-50% of the treatment-naïve patients with genotype 1 and 4 infections and 65-80% of patients with genotypes 2 and 3 infections generate sustained virological responses against HCV (6). The outcome of treatment, including contraindications, is dependent on both viral and host factors (6, 101). Key viral factors include genotype, baseline viral load, and duration of the infection (6, 101). Host factors include single nucleotide polymorphism upstream of IFN lambda 28-B, dinucleotide polymorphism disrupting the ORF of the newly characterized IFNL4, age, race, gender, presence of obesity, degree of fibrosis, and extra hepatic complications (6, 101). These shortcomings have resulted in a need to develop innovative tools, such as cell-based enzyme assays, cell lines harbouring a subgenomic or a genomic replicon, infectious cell culture systems, and humanized mouse models that can be infected by HCV for the purpose of developing new and potent antiviral agents. Several drug candidates with potential antiviral activity to HCV have been identified using some of these tools and are currently under clinical trial. They have been broadly divided into direct-acting antivirals (DAAs), which directly target viral factors, and host-targeted agents (HTAs), which target non-viral factors, such as host functions and immune modulators.  1.1.5 New antivirals under development for HCV infection In principle, several stages of the HCV life cycle (Fig. 1.2), such as binding to specific host receptors, endocytosis, fusion of viral and endosomal membranes, translation, processing of polyprotein, replication, assembly, maturation, and release, can be targeted for drug development (177). These stages can be inhibited either directly by DAAs or indirectly by HTAs (Table 1.1) (178). Research into targeting viral enzymes involved in two major steps of the HCV life cycle (HCV polyprotein processing and replication) has resulted in the discovery of two-thirds of the antiviral agents that can efficiently block viral production in infected cells (178), ushering in a new era in treatment for HCV disease (6, 178). However, when administered as a monotherapy, these drugs pose a risk of propagating viral resistance 18  (177, 178). There is widespread agreement that no single agent will cure chronic infections in the forseeable future (149), so a widely accepted approach to optimizing treatment is to combine these antivirals with other drugs that will provide additive and synergistic effects against HCV (178). Thus, the ongoing research for HCV treatment focuses on improving currently available classes of inhibitors in terms of increased potency, pangenotypic antiviral activity, and high barriers to resistance (178).  In 2011, two new inhibitors were approved by the United States Food and Drug Administration (FDA): the NS3/4A serine protease inhibitor telaprevir and the linear peptidomimetic ketoamide serine protease NS3 inhibitor boceprevir. Subjects taking these inhibitors showed a notable improvement, which supported their acceptance as a new SOC treatment when used in combination with pegIFN and ribavirin against HCV genotype 1 (6, 149). Unfortunately, individuals using these first-generation inhibitors with pegIFN often stopped using them because of augmented adverse effects, higher levels of morbidity, and the complexity of the drug regimen (177). With pegIFN still the backbone of HCV treatment strategies in 2014, this treatment era is considered a transition to the ultimate goal of IFN-free regimens (6, 178). Fortunately, many second-wave first-generation protease inhibitors (Table 1.1) are active against genotypes 1, 2, and 4 with improved efficiency and high barriers to viral resistance, and they are being actively analysed in clinical phases (6, 178). The second generation of protease inhibitors, such as MK-5172 and ACH-2684 (Table 1.1), are also being analysed in clinical phases and they also show pangenotypic antiviral activity and have a relatively higher barrier to resistance. Apart from protease inhibitors, the RdRp inhibitors, such as nucleoside/nucleotide analogues, have also been shown to be active against all genotypes. In addition, these inhibitors have high barriers to viral resistance, which results from a selection of viral variants that are inefficient in replication. In addition, when the first-generation NS5A inhibitors, such as daclatasvir, were shown to be potent, it was believed they would provide a virological cure in the near future (6). However, several even newer generations of NS5A inhibitors with high barriers to resistance and active against all genotypes have now reached a late stage of clinical development (149). As of Oct 10, 2014, the United States FDA approved Gilead’s IFN- and ribavirin-free regimen, Harvoni® for treatment of patients with the most common genotype 1 hepatitis C (82). This drug is a combination of two broad-19  spectrum DAA: the NS5A inhibitor ledipasvir that has low barrier to resistance and the nucleotide analogue polymerase inhibitor sofosbuvir that has high barrier to resistance (82). The drug is taken orally as a single tablet for relatively shorter treatment durations, and it has been shown to have cure rates of 94-99% in three clinical stages (82). The treatment duration varies from 12-24 weeks depending on prior treatment history, cirrhosis status and baseline viral load. The most common adverse reactions among patient treated with Harvoni® includes fatigue, headache, nausea, diarrhea and insomnia (82). HTAs, such as cyclophilin inhibitors, are also in clinical development (178). Conversly, use of a miR-122 antagonist has raised concerns about safety in humans as it has been associated with development of hepatocellular carcinoma in a murine model (178).  Although the ongoing research focuses on improving currently available classes of inhibitors, the purpose of many studies involving various proposed host factors is to identify new targets that effectively impede the virus. These new targets may aid in generating IFN-free regimens that are oral, cost-effective, and non-toxic. They potentially have simpler dosing regimens, are compatible in an augmenting mix, and most importantly, have a high barrier to resistance (6, 177). Vast numbers of host factors directly associated with the HCV viral genome, viral proteins, and virus particles or those that indirectly play a role in putative promotion or inhibition of various viral processes have been identified by high throughput genetic- and proteomic-based approaches as well as target-specific genome-wide small interfering  RNA (siRNA) screens (186). Some of the host factors, including PI4KIIIα (16, 53), annexin 2 (68, 208, 260), a human proprotein convertase, subtilisin kexin isozyme-1/site-1 protease (SKI-1/S1P) (174), and GAPDH (43, 134, 136, 182, 268, 269, 272) have been identified this way; they may regulate pathways common across viruses and viral families. However, independent studies demonstrating the relevant interaction with HCV biology have yet to be completed for many of these identified host factors (186).    20  Table 1.1. DAAs and HTAs in clinical development at the beginning of 2014 for treating chronic hepatitis Ca  Drug Manufacturer Phase NS3-4A protease inhibitors First-wave, first-generation Telaprevir Vertex & Janssen Approved Boceprevir Merck Approved Second-wave, first-generation Simeprevir Janssen Approved Faldaprevir Boehringer-Ingelheim III Asunaprevir Bristol-Myers Squibb III ABT-450/r Abbvie III Danoprevir/r Roche II Sovaprevir Achillion IIb Vedroprevir Gilead II IDX320 Idenix II Vaniprevir Merck III (Japan) Second-generation MK-5172 Merck III ACH-2684 Achillion II Nucleoside/nucleotide analogues of HCV RNA-dependent RNA polymerase Nucleotide analogues Sofosbuvir Gilead Approved VX-135 Vertex IIc Nucleoside analogues Mericitabine Roche II Non-nucleoside/nucleotide analogue inhibitors of HCV RNA-dependent RNA polymerase Thumb domain I inhibitors BMS-791325 Bristol-Myers Squibb III TMC647055 Janssen II Thumb domain II inhibitors Lomibuvir Vertex II GS-9669 Gilead II Palm domain I inhibitors Dasabuvir Abbvie III ABT-072 Abbvie II Setrobuvir Roche II NS5A inhibitors First-generation Daclatasvir Bristol-Myers Squibb III Ledipasvir Gilead III Ombitasvir Abbvie III PPI-668 Presidio II PPI-461 Presidio II ACH-2928 Achillion II GSK2336805 GlaxoSmithKline II BMS824393 Bristol-Myers Squibb II Samatasvir Idenix II Second-generation MK-8742 Merck II ACH-3102 Achillion II GS-5816 Gilead II Cyclophilin inhibitors First-generation Alisporivir Novartis IId Antagonist of miR-122 First-generation Miravirsen Santaris II aTable adapted from Pawlostsky 2014 (178). 21   bOn clinical hold due to increases in alanine aminotransferase and high atazanavir concentrations in HIV-coinfected patients receiving this antiretroviral drug. cOn partial clinical hold at high doses due to reversible alanine aminotransferase increases. dOn clinical hold in combination with IFNα, in development with DAAs. /r, ritonavir-boosted.   1.2 GAPDH – new insight into an old protein Glyceraldehyde-3-phosphate dehydrogenase (GAPDH) is a protein well known to biochemists and molecular biologists for its role in glycolysis and as a model protein for several biochemical and molecular techniques, such as enzyme kinetic analysis, crystallographic modelling, and gene isolation and characterization (225). It is also an obligatory control for western blot and endogenous gene expression analysis (225). Using oxidized nicotinamide  adenine dinucleotide (NAD+) as an electron acceptor, GAPDH catalyzes the sixth step in glycolysis: simultaneous phosphorylation and oxidation of glyceraldehyde-3-phospate (GAP) to 1,3-bisphosphoglycerate (46).  In somatic cells, GAPDH is encoded by a single structural gene present on human chromosome 12 and is proposed to be transcribed as a single mRNA (29, 225). No alternative transcript has been identified for this gene, and the encoded protein is found to be highly conserved across the phylogenetic scale (161, 226).  The interaction of GAPDH with tubulin was the first evidence of its functional diversity (225). However, the physiological significance of this interaction was not identified at the time (225). Since then, a substantial number of publications have emerged and identified GAPDH as possessing different properties, such as an ability to fuse membranes, bind to nucleotides, and perform numerous nuclear functions, bringing this protein into new focus (46). GAPDH is now thought to be a moonlighting protein with a “gene sharing” ability, i.e., the ability of a single protein to possess alternate functions typically attributed to other proteins (213).  1.2.1 GAPDH in glycolysis Glycolysis is a metabolic pathway that converts glucose to pyruvate with the formation of adenosine triphosphate (ATP) and reduced nicotinamide adenine dinucleotide 22  (NADH). The pathway summarized in Fig. 1.3 takes place in the cytoplasm (4). Glycolysis can be divided into three phases: the energy-investment phase or priming phase, the splitting phase, and the energy-generation phase (4). During the energy-investment phase, glucose is converted to fructose 1,6-bisphosphate, a process that involves two irreversible reactions utilizing two ATP molecules and three enzymes: hexokinase, phosphoglucose/phosphohexose isomerase, and phospho-fructokinase-1 (4). During the splitting phase, aldolase divides fructose 1,6-bisphosphate into the two three-carbon compounds, GAP and dihydroxyacetone phosphate (DHAP). Triose phosphate isomerase interconverts DHAP to GAP, thereby giving two GAP molecules (4). GAPDH initiates the first reaction of the energy generation phase and generates a net mole of NADH and 1,3-bisphosphoglycerate (4). The compound 1,3-bisphosphoglycerate is converted to pyruvate, and the process yields two molecules of ATP and involves four enzymes: phosphoglycerate kinase, phosphoglycerate mutase, enolase, and pyruvate kinase (4). Under aerobic conditions, NADH produced by GAPDH is passed through the electron transport chain to synthesize six molecules of ATP by oxidative phosphorylation (4). In addition, the pyruvate generated as an end-product enters the tricarboxylic acid (TCA) cycle to generate energy in the form of ATP and provide precursors of certain amino acids along with a reducing agent, NADH (4). Under anaerobic conditions, lactate dehydrogenase reduces the pyruvate to lactate using the NADH generated earlier (4). In doing so, oxidized NAD+ is regenerated, which allows glycolysis to proceed uninterrupted. This is essential for cells of skeletal muscle during strenuous exercise when oxygen supply is limited, and for cells that lack mitochondria, such as erythrocytes, and cells from the cornea and the optic lens (4). The uninterrupted process of glycolysis is also important for cells that derive most of their energy from glycolysis, such as in the brain, retina, skin, renal medulla, and gastrointestinal tract (4). Thus, in a glycolysis reaction, during anaerobic conditions, two ATPs are synthesized, and during aerobic conditions, eight ATPs are synthesized (4). Glycolysis is a central metabolic pathway with intermediates that branch to other pathways (214). The triose phosphates, GAP and DHAP, are involved in the glycolytic pathway, gluconeogenesis, and the flow of carbon through the pentose phosphate pathway (214). Fructose and glycerol can also be used as a precursor for synthesis of triose 23  phosphates. In addition, the metabolite GAP can be directed toward triglyceride synthesis (214). Enzyme activators and inhibitors are involved in the regulation of glycolysis (4). Adenosine monophosphate (AMP)/adenosine diphosphate (ADP) are enzyme activators that regulate three glycolytic enzymes—hexokinase, phosphofructokinase and pyruvate kinase—which catalyze irreversible reactions (4). In addition, phosphofructokinase and pyruvate kinase are also activated by fructose-2,6-bisphosphate and fructose-1,6-bisphosphate, respectively (4). On the other hand, enzyme inhibitors include glucose-6-phosphate, ATP, citrate, acetyl CoA and alanine. Glucose-6-phosphate inhibits hexokinase; ATP and citrate inhibit phosphofructokinase; and ATP, acetyl CoA, and alanine inhibit pyruvate kinase (4).  The reaction catalyzed by GAPDH is one of several reversible reactions in glycolysis (214). It has been shown that addition of pyruvate elevates levels of triose phosphate in rat liver and muscle cells, which most likely results from inhibition of GAPDH (214). Hence, the reaction catalyzed by GAPDH is also considered a control point for glycolysis, which involves product inhibition of GAPDH (214). In addition, during oxidative stress, GAPDH becomes inactivated through numerous mechanisms involving chemical modifications and changes in cellular localization, which are discussed in Section 1.2.3. For example, oxidation of an active site cysteine via biologically available oxidants, such as hydrogen peroxide, results in either the formation of a disulfide linkage with deactivation of the enzyme, or conversion to cysteine sulfonate with a gain of peroxidase activity (216).  Other chemical modifications resulting from oxidative stress that cause the loss of enzymatic GAPDH function involve nitration (nitric oxide, Section 1.2.3), succination (hydrogen sulfide), glyoxidation (oxidized sugars), reaction with lipid peroxidation byproducts, and S-sulfhydration of the active site cysteine (216). Such modifications result in rerouting the carbon flow through the pentose phosphate pathway for NADPH and glycerol production (214).   24    Figure 1.3. Glycolysis.  The diagram illustrates fundamental glycolytic (glucose to pyruvate) and gluconeogenic (reverse glycolysis) pathways. In addition, various glucose precursors relevant to liver gluconeogenesis are presented.  Adapted from Bar-Even et al. 2012. (9), and Seidler, NW. 2013 (214). 25  1.2.2 GAPDH is a multifunction protein Although GAPDH is ubiquitous and abundant in almost all tissue types, its expression is tightly regulated at both transcriptional and post-translational levels (46, 92). Expression of basal GAPDH levels is positively controlled by GAPDH binding factors (GAPBF), GAPBF1 and GAPBF2, which bind to a region in the promoter sequence of GAPDH described as the A-stretch (3).   The active upregulation of GAPDH transcript expression has been observed in response to distinct stimuli, such as cell proliferation or oxygen deprivation (46). Upon an acute decrease in oxygen, or hypoxia, transcription factors, such as hypoxia inducible factor 1 (HIF-1) and p53, induce the upregulation of genes, including GAPDH (46). Besides the hypoxia-responsive elements, insulin-responsive elements have also been identified in the GAPDH promoter, suggesting its involvement in the regulation of blood sugar (46). Protein degradation is another mechanism that maintains GAPDH levels by regulating turnover of the GAPDH protein (46). GAPDH is one of the 30% of soluble cytosolic proteins that contain the KFERQ amino acid sequence (46). Cellular proteins carrying this sequence are targeted for lysosomal degradation via chaperone-mediated autophagy as a part of the cellular quality control system or in response to cellular stress (46). The most interesting feature of GAPDH is its involvement in various non-glycolytic functions, including post-transcriptional gene regulation, intracellular membrane trafficking and endocytosis, oxidative-stress response-mediated apoptosis and regulation of autophagy, maintenance of DNA integrity, and histone gene regulation. (225). Along with its involvement in these various functions, GAPDH has been shown to play a role in severe conditions, such as cancer (46), neurodegenerative diseases (45), and virulence factors associated with various pathogens. Various mechanisms, such as chemical modification, the presence of multiple active sites, oligomerization, formation of complexes, binding of cofactors, and intrinsic disorders are thought to play a role in the functional diversity of GAPDH (45, 46). This multifunctional competence of GAPDH is mostly explained by purposeful interactions with other enzymes and regulatory proteins, its enzymatic interaction with substrates and metabolites, and its co-translational and reversible modifications (46). Interestingly, GAPDH can also engage in stochastic interactions, such as those observed in neurodegenerative diseases discussed in Section 1.2.5 (213). 26  1.2.3 GAPDH posttranslational modifications, regulation of gene transcription, and intracellular transport  Post-translational events play a key role in regulating GAPDH functions (see Fig. 1.4) (46, 225, 254). GAPDH contains several posttranslational modification sites, including phosphorylation, S-nitrosylation (S-NO), S-thiolation, and sulphonation sites. These modifications have been shown to disrupt the ability of GAPDH to exist in a homotetrameric form, augment its ability to bind to either nucleic acids (Table 1.2) or to other binding partners (Table 1.2), and in turn regulate its subcellular redistribution (46, 225, 254). Consequently, these post-translational modifications allow GAPDH to perform functions apart from its conventional metabolic role (254). The ability to exist as a monomer, dimer, and tetramer provides GAPDH with an additional flexibility in regulation (225). For example, the homotetrameric form of GAPDH is necessary for it to perform its catalytic function in glycolysis, while its dissociation to the dimeric form in the presence of ATP has been shown to inhibit its catalytic function (83, 103, 265). The subcellular compartmentalization of GAPDH in the nucleus, cytosol, pre-Golgi intermediates, mitochondria, and plasma membrane is another level of regulation (254). For example, reversible GAPDH translocation to the nucleus was initially observed to be cell cycle-dependent (150, 160). Subsequent studies have shown that nuclear accumulation occurs in response to specific stimuli resulting in GAPDH post-translational modification, such as O-linked N acetylglucosamine modification (175), acetylation (261), and S-NO (91). Interestingly, GAPDH does not contain a nuclear localization signal (NLS); hence, its localization to the nucleus is strictly attributed to the NLS located on interacting proteins (91). Nevertheless, GAPDH encodes for a novel chromosome region maintenance 1(CRM1)-mediated nuclear export signal (exportin 1) (28) or exportin 1-independent signal (211), which aids in its active export from nucleus to cytosol. With the varying properties resulting from post-translational modification, GAPDH can regulate other proteins by regulating their transcription, as it has been shown to be an integral part of OCA-S, an Oct-1 multicomponent coactivator essential for S-phase-dependent histone 2B transcription (275). The GAPDH structure also possesses attachment sites, such as the Rossmann fold, an NAD/NADH-binding pocket that contribute to its nucleotide-binding property and several 27  protein interaction sites (Fig. 1.4) (46).  Posttranscriptional regulation of genes involving the nucleic acid-binding property of GAPDH is interesting because the interaction of GAPDH with different transcripts can result in diverse biological outcomes (225). GAPDH has been primarily shown to bind to AU-rich elements (ARE) of transcripts, such as an endothelial vasoconstrictor endothelin 1 (ET-1) (197, 203), macrophage colony-stimulating factor-1 (CSF-1), and angiotensin II type 1 receptor (AT1R) (8, 225). GAPDH binding to the 3′-UTR of ET-1 results in an unwinding of this region, making it more susceptible to attack by ribonucleases (197, 203). As a result, ET-1 is degraded and vascular structure and function are affected (197, 203). GAPDH’s interaction with the 3′-UTR of AT1R does not result in unwinding of the RNA structure (8); instead, it results in inhibition of mRNA translation of an important cardiovascular protein (8). This cardiovascular protein is involved in controlling blood pressure and volume in the cardiovascular system by secreting aldosterone that has a vasopressor effect. Finally, GAPDH’s interaction with 3′-UTR of CSF-1 results in maintenance of mRNA stability, thereby maintaining its protein level to regulate cell proliferation and invasive differentiation of hematopoietic cytokine-regulating macrophages (278). Apart from its function of regulating transcript stability with contradictory outcomes, other functions of GAPDH attributed to its nucleic acid binding property involve its participation in tRNA export by discriminating between the wild-type and nuclear export defective tRNA (224), the regulation of S-phase nuclear protein transcripts, binding to thioguanylated DNA to act as a structural DNA sensor (124), and protection of telomere length (54, 169, 233).  28         Figure 1.4. Schematic representation of GAPDH domains.  Schematic representation of posttranslational modification sites and binding domains on the human GAPDH protein (254). 29  Table 1.2. Compendium of GAPDH interactions with various binding partners  Structural and functional category Binding Partners Membrane transport proteins solute carrier family 4 (SLC4) anion exchangers, sodium pump, adenosine triphosphate (ATP)-sensitive potassium (KATP) channel, glucose transporters (GLUT), N-methyl-D-aspartate (NMDA) receptors, GABAA receptor, vesicular proton pump, mitochondrial voltage-dependent anionic channel (VDAC), sarco/endoplasmic reticulum calcium-transporting ATPases (SERCA), inositol triphosphate (IP3) receptor G-proteins Rab2, Rab5, Rheb, transducin-α Poly-nucleotides AU-rich regions, 5-pUpAp-3, single-stranded DNA, 5-TTAGGG-3, 5-AUUUA-3 Adenines adenosine monophosphate (AMP), cyclic AMP (cAMP), adenosine diphosphate (ADP), ATP, diadenosine tetraphosphate (Ap4A) Lipids phosphatidyl inositol, phosphatidyl serine, phosphatidate, cardiolipin, nitro-fatty acids  Carbohydrates D-galactose, N-acetyl-glucosamine Cytoskeleton actin, α-tubulin Nuclear Import-Export E3 ubiquitin ligase (siah), chromosomal maintenance 1 (CRM1), apurinic/apyrimidinic endonuclease 1 (APE1), GAPDH’s competitor of siah protein enhances life (GOSPEL), calcium and integrin binding protein 1 (CIB1) Misfolded proteins crystallins, amyloid-β-derived peptide, huntingtin, tau, α-synuclein ATPases P97 (AAA-ATPase), non-structural proteins 5 (NS5) (viral replicase) Ca2+ binding proteins P22, calsequestrin Molecular chaperones chaperonins, α-crystalline, heat shock protein A8 Extracellular binding targets lactoferrin, transferrin, plasminogen/plasmin, fibrinogen, urokinase receptor, laminin, lysozyme,fibronectin Protein kinases protein kinase B, protein kinase C, mitogen-activated protein kinase kinase kinase (Mcs4), protein kinase C iota Translational factor eukaryotic translation initiation factor 1A domain-containing protein (eIF1AD) tRNA aminoacyl synthetase tryptophanyl-tRNA synthetase (TrpRS) Transport protein vesicle-associated member protein (VAP) Transcription factor Octamer-binding protein 1 (Oct1) Adapted from Seidler, N. W. 2013. (215) and Leisner et al. 2012.(130)  30  One of the many functions of GAPDH involves its central role in intracellular membrane transport. Robbins and colleagues demonstrated that mutation in GAPDH impairs endocytosis in Chinese hamster ovary (CHO) cells (201), most likely due to altered membrane trafficking. Later, it was revealed that GAPDH plays an essential role in the retrograde transport of the vesicular tubular cluster (VTC) between the ER and the Golgi in the early secretory pathway (Fig. 1.5) (249). VTCs are transport intermediates delivering cargo that sort recycling proteins between the ER and the Golgi (248, 249, 252). Tisdale et al. showed that tyrosine kinase Src-phosphorylated GAPDH, along with Src-phosphorylated atypical protein kinase C iota/lambda (aPKCɩ/λ), were recruited by a membrane-bound small GTPase Rab2, a monomeric G protein from a Ras superfamily of proteins, to form a Rab2-Src-aPKCɩ/λ-GAPDH complex (51), which facilitates vesicle formation and transport (248, 249, 252).  Moreover, GAPDH has initially been shown to interact with tubulin and actin, where it respectively facilitates microtubule bundling and actin polymerization (83, 103, 111, 188, 265). Later, Tisdale et al. demonstrated that GAPDH recruits dynein to allow the association of cytosolic tubulin with membrane tubulin (187, 251). This provided additional evidence that GAPDH plays a central role in regulating microtubule bundling to provide cells with the cytoskeletal structures needed for membrane transport (225).   31   Figure 1.5. Involvement of GAPDH in the maintenance of the cytoskeleton and retrograde transport of vesicular tubular clusters (VTC) from the ER to the Golgi.  The interaction of GAPDH with tubulin and actin facilitates microtubule bundling and actin polymerization, which may allow the transport of GAPDH within cells via microtubule treadmilling. GAPDH also plays a necessary role in retrograde VTC transport allowing recycling of cargo proteins from the ER to the Golgi. This process is initiated by Rab2-mediated VTC formation involving recruitment of the atypical protein kinase C ι/λ (aPKCι/λ) to phosphorylate-recruited GAPDH. Subsequent phosphorylation of aPKC by tyrosine kinase Src allows the formation of a complex Rab2-Src-aPKC-GAPDH on the VTC. The VTC interacts with the microtubule and motor proteins via GAPDH.   Adapted from Tristan et al. 2011. (254).   32  1.2.4 Roles of GAPDH in cell survival and cell death A role of GAPDH in promoting cell survival was first suggested by Warburg’s observation in 1929 that cancer cells express elevated levels of glycolytic enzymes, including GAPDH (46). Since then it has been established that to maintain a high proliferation rate, tumor cells consume high amounts of nutrients along with oxygen, which generates hypoxic conditions as they exceed the support capacity of blood vessels (46). To overcome supply-limited aerobic respiration, cancer cells overexpress glycolytic enzymes, especially GAPDH, as an alternative source of energy and as a mechanism of survival (46).  More recently, Colell and associates revealed a novel role of GAPDH in cell survival, i.e., protection from caspase-independent cell death (CICD) (47). CICD is an alternative mechanism for the induction of cell death upon the inhibition of conventional apoptosis involving caspases (47). Both apoptosis and CICD are triggered by mitochondrial outer membrane permeabilization (MOMP). Upon induction of MOMP, apoptogenic effectors are released, such as cytochrome c (cyt c), apoptosis-inducing factors (AIF), Smac/Diablo, endonuclease G (end G), heat shock proteins (HSP), and the cytoplasmic apoptosis protease activating factor 1 (APAF1). Tumors frequently show defects in this pathway, such as mutation in caspases, a lack of APAF-1 or overexpression of caspase inhibitor, or an X-linked inhibitor of apoptosis (XIAP) (271). Upon screening factors that protect the cells from CICD, Colell et al. identified GAPDH as a factor (47). Furthermore, those researchers found that in cells induced for CICD, GAPDH conferred protection via two mechanisms: first, by increasing glycolysis to elevate ATP production, and second, by upregulating the autophagy gene ATG12 to induce mitophagy, i.e., the removal of damaged mitochondria by the process of autophagy (47).  In agreement with this finding, upregulation of GAPDH accompanied with an increase in autophagy has been shown in colon carcinoma cells treated with bacterial CpG (17).  In contrast, Hara et al. established a pro-apoptotic role of GAPDH (91, 92).  They found that upon activation of the inducible/neuronal nitric oxide synthase, the apoptotic pathway is stimulated by the generation of nitric oxide (91, 92). Upon synthesis of nitric oxide, GAPDH gets S-nitrosylated (S-NO) at cysteine residue 150 (Cys 152 for human GAPDH), which augments the ability of GAPDH to interact with unstable Siah1 (an E3 ubiquitin ligase) allowing the formation of a GAPDH-Siah1 complex, thereby stabilizing 33  Siah1 in the cells (91, 92). With the NLS present on Siah1, the complex translocates to the nucleus. There, Siah1 mediates the energy-requiring process of proteasome-dependent degradation of its substrate, such as the nuclear co-repressor (N-CoR), which results in apoptosis (91, 92). Sen et al. added to our understanding of the mechanism of apoptosis mediated by the GAPDH-Siah1 complex in the nucleus (218). Their team demonstrated that in the nucleus, GAPDH gets acetylated by direct interaction of the complex with an acetyltransferase E1A-binding protein p300 (EP300 or p300)/cAMP response element-binding protein (CREB)-binding protein (CBP) (218). In doing so, p300/CBP gets autoacetylated and catalytically stimulated to activate downstream targets, such as the tumor suppressor protein p53 (218). Independent of S-NO, acetylation allows translocation of GAPDH to the nucleus, where it interacts with the p300/CBP associated factor (PCAF) (218). Unlike the apoptotic property of S-nitrosylated GAPDH (S-NO GAPDH) augmenting its interaction to Siah1, Sen et al. identified a novel prosurvival protein, GOSPEL, i.e., GAPDH’s competitor Of Siah Protein Enhances Life, which binds to S-NO GAPDH. Upon S-NO, the cytoplasmic-situated GOSPEL competes and binds to S-NO GAPDH and prevents its localization to the nucleus, thereby inhibiting apoptosis (217). Conversely, Lee et al. demonstrated that a prosurvival multifunctional protein B23/nucleophosmin, a protein that primarily shuttles between nucleolus and nucleus, can interact with Siah1 upon S-NO and abrogate an E3 ligase activity post-translocation of the GAPDH-Siah1 complex to the nucleus in a non-competitive manner (129). Thus, the fate of cells to survive as opposed to undergoing apoptosis via the S-NO pathway seems to be regulated by a threshold level of oxidation resulting from oxidation stress.  In addition to the mediation of apoptosis by S-NO, GAPDH has also been suggested to play a putative pro-apoptotic role in an intrinsic pathway of cell death, i.e., mitochondrial-mediated apoptosis (46). Tarze et al. provided evidence that stimulation of apoptosis may result in an increase in GAPDH expression to a threshold that may allow GAPDH to interact with the voltage-dependent anion channel (VDAC), the main component of the permeability transition pore, most likely via a covalent sulphide linkage (241). The linking of GAPDH to VDAC results in an opening of a permeability transition pore complex on the mitochondrial surface by alignment of VDAC in the outer membrane, the adenine nucleotide translocase (ANT) in the inner membrane, and cyclophilin D in the matrix (241). This interaction results 34  in partial loss of the inner transmembrane potential, matrix swelling, and permeabilization of the inner mitochondrial membrane to release two pro-apoptotic proteins, cyt c and AIF (241). All in all, GAPDH contributes to opposing functions in a cell, i.e., cell survival and cell death, which are mainly governed by post-translational modifications that confer the ability to interact with pro-survival vs. pro-apoptotic factors. Interestingly, the pro-survival property of GAPDH is thought to contribute to carcinogenesis, including cell protection from CICD, whereas the pro-apoptotic property is thought to play a role in neurodegenerative pathologies.  1.2.5 Roles of GAPDH in cancer, neurodegenerative diseases, and viral diseases As mentioned above, GAPDH has been shown to be upregulated in many cancer cell types, including lung, renal, breast, gastric, glioma, melanoma, colorectal, pancreatic, and bladder (87). The degree to which GAPDH is upregulated has been observed to correspond with the severity of the tumor, with the exception of prostate cancer, where the relationship is reversed (87). GAPDH upregulation is proposed to augment the rate of glycolysis and maintain the transformed phenotype by promoting the expression of growth factors, such as insulin and epidermal growth factors, and by promoting tumor growth by cell cycle progression (87). Hence, a compensatory response of targeting the overexpression of GAPDH is gaining interest as a proposed treatment for cancer (87). Recently, Phadke et al. demonstrated that treating cells with an antimetabolite, cytosine arabinoside (araC) that interferes with DNA synthesis causes the accumulation of enzymatically inactive GAPDH in the nucleus, leading to cytostatic effects in cancer cells. Consequently, this confers protection from antimetabolite chemotherapy on cells, adenocarcinoma cells (A549) and renal carcinoma cells (UO31) (183). Upregulation of GAPDH is also observed in chronic myeloid leukemia (CML) cells treated with imatinib mesylate, a drug used for treating CML (126). In contrast, Lavallard et al. demonstrated that downregulation of GAPDH resensitized CML cells to imatinib mesylate treatment (126). Recently, 3-bromopyruvate propyl ester (BrOP) has also been demonstrated to downregulate the overexpressed GAPDH in human colorectal cancer (240). In summary, the targeting of GAPDH in cancer cells has been considered in order to sensitize the cells for anticancer treatment.  35  A polyglutamine track containing protein is found in several neurodegenerative diseases, including the huntingtin protein of Huntington’s disease (HD), the β-amyloid precursor proteins (AβPP) of Alzheimer’s disease (AD), the atrophin of dentatorubal-pallidoluysian atrophy (DRPLA), the ataxin of spinocerebellar ataxia type-1 (SCA-1), and androgen receptors for spinobulbar muscular atrophy (242). GAPDH binding to the polyglutamine track is what associates GAPDH with these diseases (33). Although the functional significance of this interaction is unclear, the immobilization of GAPDH upon its interaction with the polyglutamine track results in an altered polysaccharide storage state, which is believed to play a role in these diseases, especially in DRPLA (222). Evidence suggests that for AD, GAPDH involves several direct and indirect mechanisms leading to neurodegeneration (33). In general, the interaction of GAPDH with polyglutamine track-containing protein results in decreased glycolytic activity (33). In addition, the toxic gain of function resulting from pro-apoptotic activation with continual oxidative stress contributes to the disease (33). Interestingly, as neurodegenerative diseases are typically characterized by excessive apoptosis, the targeting of GAPDH’s pro-apoptotic property by R-(-)-deprenyl (rasagiline) derivative CGP 3466 (TCH 346) has been tested for treatment of Alzheimer’s and Parkinson’s diseases (163, 172). This compound targets the binding of GAPDH to Siah1 and prevents the localization of the complex to the nucleus (93). Although TCH 346 derivative did not show any beneficial effects in Alzheimer’s disease, type B monoamine oxidase inhibitors R-(-)-deprenyl and S-(-)-deprenyl (selegiline) are showing themselves to be a promising neuroprotective drugs, which, along with several other mechanisms, involve inhibition of GAPDH apoptotic function (152, 163). Most importantly, GAPDH has been shown to interact with the genome of several viruses, but its function is uncertain. For example, upon hepatitis delta virus (HDV) infection, GAPDH re-localizes from the cytoplasm to the nucleus of the host cell, where HDV replication occurs (136). It has been hypothesized that nuclear GAPDH may enhance the ribozyme activity of HDV antigenomic RNA, which suggests that GAPDH is involved in the replication of HDV (136). In the case of the hepatitis A virus (HAV), GAPDH and the polypyrimidine tract-binding protein (PTB) bind to overlapping sites in stem-loop IIIa of the internal ribosome entry site (IRES) (43). GAPDH binding destabilizes RNA secondary structure and suppresses the ability of the IRES to direct cap-independent translation, thus 36  preventing the translation-enhancing activity of PTB in African green monkey kidney cells, i.e., Vero E6 cells (56, 212, 269). Zang et al. showed that GAPDH binds to the posttranscriptional regulatory element (PRE) of the S transcript and may regulate post-transcription in hepatitis B virus (HBV), a DNA virus (272). Evidence from several studies has shown that enveloped viruses carry host proteins in order to successfully replicate (34). The incorporation of host proteins may either occur due to their close proximity to the viral assembly or budding site, or it may occur due to their direct involvement in viral processes (117). Interestingly, GAPDH has been shown to be incorporated into virus particles, such as human parainfluenza virus 3 (HPIV3) (43), human immunodeficiency virus type I (HIV-1) (117), severe acute respiratory syndrome-related coronavirus (SARS-CoV) (68), and inflluenza A virus (InfA) (221). Choudhary et al. demonstrated that a specific phosphorylated form of GAPDH interacts with the 3′-UTR of human parainfluenza virus 3 (HPIV3) and is packaged into the virus particles (43). Using the vaccinia virus expression system, their team demonstrated that the high expression of GAPDH reduces transcription, most likely to regulate replication (43). During HIV-1 assembly, cellular tRNALys3 along with lysyl-tRNA synthetase are selectively incorporated into the HIV virions (37, 147). Lysyl-tRNA synthetase is a tRNA-binding protein that specifically aminoacylates different tRNALys isoacceptors and selectively facilitates packaging of cellular tRNA Lys3 (89). Cellular tRNA Lys3 primes reverse transcriptase, which is necessary for efficient HIV infection (89, 117, 263). Kishimoto et al. showed that incorporation of GAPDH into virions negatively regulated HIV-1 infection by reducing cellular tRNA Lys3 and lysyl-tRNA synthetase packaging efficiency (117). Thus, GAPDH’s incorporation into the viral structure either on the viral surface (SARS-CoV) or within the virus (HPIV3, HIV) shows distinct functional significance. For HCV, GAPDH has been shown to bind to the untranslated region of the 3′ end of RNA (95, 182). However, the functional significance of the interaction has not been determined. With the goal to define host cofactors involved in HCV replication, Randall et al., identified GAPDH as one of the 26 genes that upon being targeted by siRNA, compromised virus production, which was suggested to result from inhibition of HCV replication (195).  37  1.3 Research hypothesis, rationale, and specific aims Several host factors have been identified that associate with HCV viral proteins, the viral genome, and viral particles. However, identifying how these factors contribute to viral pathogenesis and the successful completion of the viral life cycle has been hindered due to the lack of an HCV cell culture system.  The targeting of host factors as an antiviral approach  is of concern due to relatively low viral specificity, disruption of important host functions leading to increased side effects, and varying antiviral activities in response to host polymorphisms (199). Despite HTAs high barrier to resistance, long-term use may also pose the risk of mutational escape, which is evidenced by persistent HCV infection of human hepatoma cells coevolving at viral and cellular levels in vitro (277). Then again, as observed with the current HCV protease inhibitors, the targeting of viral factors can also augment side effects and pose a high risk of mutational escape (6). Although antiviral drugs are considered safe and well tolerated, mounting evidence suggests that some antiviral drugs, such as the non-nucleoside analogue, the highly active antiretroviral therapy (HAART) used over the past twenty years for the treatment of HIV-1, and oseltamivir used for the treatment of InfA (H1N1), are also associated with detrimental side effects (21, 230). Given these cons for DAAs and HTAs, combinations of antivirals targeting both host and viral factors are increasingly favoured in research and development, especially as the major goal of HCV therapy is shifting towards developing an IFN-free regimen involving a shorter duration of treatment with a higher barrier to resistance (199). The identification of more agents with effective antiviral properties is expected to potentially lead to combinations of antivirals that are safer and may result in a cure or may prevent progression to severe liver conditions for most HCV-infected patients (199).  Nonetheless, evaluations of host factors are important for understanding basic host-viral interactions and aids in the identification of targetable novel pathways. One such protein of interest to our lab is an important moonlighting human enzyme, GAPDH that has previously been shown to interact with the genome of HCV. The study of the multifunctional roles of GAPDH during the HCV life cycle is important to provide new insights into host-HCV interactions and reveal potential new therapeutic avenues for developing HTAs against HCV. 38  Aim 1 Here, I hypothesized that the host cell enzyme, GAPDH, may play an important role in several stages of the HCV life cycle, including effective entry and release of infectious HCV. Furthermore, I hypothesized that the demonstrated interaction of GAPDH with the 3′-UTR of the HCV genome may play an important role in the HCV life cycle where it may regulate transcription of negative-strand synthesis for the process of replication and regulation of IRES-mediated translation. To that end, I developed and optimized novel experimental approaches to stably and transiently manipulate GAPDH intracellular expression levels in HCV permissible human hepatoma cells (Huh-7) in order to study the biological role(s) of GAPDH in several stages of the HCV life cycle, such as viral entry, genome translation, genome amplification and release of infectious virus particles (see Chapter 2). In doing so, I generated for the first time two Huh-7-derived cell clones, 67D2 and 67B3, showing high levels of stable reduction in GAPDH expression.  Aim 2 GAPDH is upregulated in several cancer cells, including HCC cells, to compensate for excessive energy requirements. It is proposed that the ongoing oxidative stress resulting from viral protein expression is one of many factors that contribute to the transformation of cells leading to HCC. Hence, I hypothesized that blocking the apoptotic and glycolytic functions of GAPDH may reduce the damage resulting from HCV infection, especially in the HCC condition, which may make cells susceptible to anticancer treatment. Along with other lab members, I performed the transcriptomic and the proteomic profiling of parental cells and two Huh-7-derived cell clones, 67D2 and 67B3 (which have reduced GAPDH expression), to identify unique and common molecular signatures associated with cell survival and increased host cell resistance to HCV infection (see Chapter 3). These activities included identification of cellular functions deregulated upon depletion of GAPDH in hepatoma cells, which results in increased host cell resistance to HCV infection. While the research in Chapter 2 involved the use of siRNA-mediated silencing of GAPDH, the research in Chapter 3 utilized a systems biology approach to identify cellular targets that have the potential to inhibit HCV infection, and to perform proteomic profiling of hepatoma cells showing stable reduction in GAPDH expression. 39  Chapter 2: The moonlighting glycolytic enzyme, glyceraldehyde-3-phosphate dehydrogenase (GAPDH) is required for efficient hepatitis C virus and dengue virus infections in human hepatoma (Huh-7.5.1) cells    2.1 Summary The hijacking and manipulation of host cell biosynthetic pathways by human pathogenic viruses are shared molecular events that are essential for the viral life cycle. For Flaviviridae members such as hepatitis C virus (HCV) and dengue virus (DENV), one of the key host cell pathways manipulated is the secretory pathway. For example, specific cellular processes and related enzymes associated with the host secretory pathway are hijacked during the entry and virus maturation steps of the viral life cycle. Uncovering the molecular players involved during viral hijacking may unearth a common Achilles’ heel in the life cycles of globally important human pathogens such as HCV and DENV, leading to a better understanding of Flaviviridae biology, and host-virus interactions, and to identifying potential broad-spectrum therapeutic targets.   In this study, the accumulating evidence of the importance of human glyceraldehyde-3-phosphate dehydrogenase (GAPDH) in the host secretory pathway led us to hypothesize that this moonlighting enzyme could contribute to multiple steps in the life cycles of HCV and DENV. I used short interfering RNA (siRNA)-mediated silencing of GAPDH both pre- and post-HCV infection in naïve Huh-7.5.1 cells to demonstrate that reduction of GAPDH protein abundance inhibits primary HCV infection and production and/or release of infectious HCV virus particles in HCV-infected Huh-7.5.1 cell supernatants. SiRNA-mediated GAPDH suppression in HCV replicon-harboring cells had no effect on HCV replication. Exogenous expression of V5-tagged human GAPDH, pre- and post-infection, increased the viral infectivity of HCV-infected Huh-7.5.1 cell supernatants, suggesting a predominant role of GAPDH during the post-replication steps of the HCV life cycle. Finally, siRNA-mediated GAPDH suppression in naïve human Huh-7.5.1 cells significantly inhibited primary DENV-2 infection.   Collectively, these results demonstrate that the moonlighting enzyme, GAPDH, is required for the productive stage of the hepatitis C virus life cycle in human Huh-7.5.1 cells. 40  In addition, our preliminary results obtained with DENV-2 suggest that GAPDH could also be a new factor in the DENV life cycle.   2.2 Introduction It is now well established that the hijacking and manipulation of host cell biosynthetic pathways by human enveloped viruses are shared molecular events essential for the viral life cycle (74, 191, 234). For example, lipid metabolism and associated host cell pathways are manipulated by Flaviviridae members, such as hepatitis C virus (HCV) and dengue virus (DENV) to enhance production of viral particles (74, 189). In addition, mounting evidence implicates the manipulation by HCV and DENV of cellular functions associated with the host secretory pathway for viral entry and virus maturation (38, 174).  An important step toward understanding Flaviviridae biology and host-virus interactions is to further characterize how Flaviviridae members, HCV and DENV, are able to successfully exploit specific pathway components and associated proteins in these and other host pathways.  Moreover, uncovering the molecular players involved during viral hijacking may unearth a common Achilles’ heel in the Flaviviridae life cycle, leading to the identification of potential broad-spectrum therapeutic targets (174).  As discussed in the introduction Section 1.1.1 and Section 1.1.2, HCV, a hepacivirus member of the Flaviviridae family, is encoded by a single-stranded positive-sense RNA genome (12). Viral RNA is directly translated by the host machinery into a single polyprotein, which is cleaved by host and virus-encoded proteases to release the individual structural (core, E1, and E2) proteins and non-structural (NS) proteins (p7, NS2, NS3, NS4A, NS4B, NS5A, and NS5B) (12). HCV is a globally important human pathogen afflicting more than 170 million people worldwide (88). The four DENV serotypes (DENV-1 to -4) are members of the Flavivirus genus of the Flaviviridae family with single-stranded positive-sense RNA genomes encoding three structural (capsid [C], precursor membrane [prM], and envelope [E]) proteins and seven nonstructural (NS1, NS2A, NS2B, NS3, NS4A, NS4B, and NS5) proteins (1). Dengue virus represents a significant threat to global public health, with 50-100 million cases annually and about 2.5 billion people living in endemic countries (1). 41  Because of the increasing evidence of the importance of human glyceraldehyde-3-phosphate dehydrogenase (GAPDH) in host secretory pathway functions (30), I hypothesized that this multifunctional enzyme could contribute to multiple steps in the viral life cycles of HCV. GAPDH plays an integral role in glycolysis (9), but it is also now associated with an important number of non-glycolytic functions such as intracellular membrane trafficking (248-252), endocytosis (201), receptor-mediated cell signaling (94, 193), and translational control of gene expression (8, 203, 278), all important biological processes for the HCV and DENV life cycles (7, 58). Interestingly, GAPDH has been shown to interact with the genomes of several viruses, suggesting that GAPDH could be involved in the regulation of viral replication (56, 134, 136, 268). For HCV, GAPDH binds to the untranslated region of the 3’ end of viral RNA, but its function is uncertain (182). Together, these results underline the “moonlighting” nature of GAPDH (i.e., an enzyme with additional functional activities  (213), but the contributions of these moonlighting activities to host-Flaviviridae interactions remain poorly understood. In this study, I investigated whether the multifunctional GAPDH is a host factor that regulates the viral life cycle steps of HCV in human hepatoma Huh-7.5.1 cells. First, I demonstrated that siRNA-mediated GAPDH silencing in naïve Huh-7.5.1 cells not only inhibits primary HCV infection, but also inhibits the production and/or release of infectious HCV virus particles in HCV-infected Huh-7.5.1 cell supernatants. Second, I demonstrated that GAPDH also plays an important role during the post-replication steps of the HCV life cycle because siRNA-mediated GAPDH silencing in HCV-infected Huh-7.5.1 cells results in a significant decrease in viral infectivity of the HCV-infected cell supernatants.  Importantly, siRNA-mediated GAPDH suppression in HCV replicon-harboring cells has no effect on HCV replication.  Third, I demonstrated that exogenous expression of V5-tagged human GAPDH pre- and post-infection increases the viral infectivity of HCV-infected Huh-7.5.1 cell supernatants, suggesting a predominant role of GAPDH during post-replication steps of the HCV life cycle. Finally, siRNA-mediated GAPDH suppression in naïve human Huh-7.5.1 cells was also shown to significantly inhibit primary DENV-2 infection.   Collectively, the results of this study show that the moonlighting enzyme, GAPDH, is an important host factor that regulates pre- and post-replication life cycle steps of HCV in human Huh-7.5.1 cells. Interestingly, the preliminary results obtained with DENV-2 suggest 42  that GAPDH’s moonlighting activities may also be important for the productive stage of the DENV life cycle.   2.3 Materials and methods Antibodies and dyes Two anti-GAPDH antibodies were employed for detecting human GAPDH in cultured cells. A mouse anti-GAPDH monoclonal antibody (1:1000 for western blot (WB) and 1:100 for Immunofluorescence (IF), Cat. no. MAB374, Chemicon/Millipore, Temecula, CA, USA) and a rabbit anti-GADPH monoclonal antibody (1:1000 for western blotting (WB) or in cell western (ICW) and 1:100 for IF, Cat. no. 2118S, Cell Signaling, Danvers, MA, USA) were interchangeably used for detecting cellular GAPDH expression.  A mouse anti-HCV core monoclonal antibody (1:1000, Cat. no. ab2740, Abcam, Cambridge MA, USA) was used for detecting HCV infection. A mouse anti-DENV envelope (DENV E) glycoprotein monoclonal antibody (1:35, Cat. no. ab41349, Abcam, Cambridge MA, USA) was used for detecting DENV-2 infection. Intracellular human β-tubulin was detected by WB using a rabbit anti-β-tubulin polyclonal antibody (1:1000, Cat. no. ab6046, Abcam, Cambridge, MA, USA). A rabbit anti-V5 tag polyclonal antibody (1:1000, Cat. no. ab9116, Abcam, Cambridge, MA, USA) was used for detecting V5-tagged human GAPDH. Secondary antibodies for WB (1:10,000) and ICW (1:200 (red) or 1:800 (green)) included IRDye® 800-conjugated (Cat. no. LIC-926-32212 or LIC-926-32213( green)) or 680-conjugated (Cat. no. LIC-926-32222 or LIC-926-32221( red)) raised against mouse or rabbit (LI-COR Biosciences, Lincoln, NE, USA). Secondary antibodies include IF Alexa Fluor-488-conjugated (Cat. no. A21202 or Cat. no. A21206) raised against mouse or rabbit ( Molecular probes/ Invitrogen, Eugene, OR, USA). Dyes used for cell staining include DRAQ5TM stain (1:10,000, Cat. no. SKU-DR50200, Biostatus, Shepshed, LEC, UK) and Sapphire700TM stain (1:1000, Cat. no. LIC-928-40022, Li-COR Biosciences, Lincoln, NE, USA).  43  Cell culture and reagents Huh-7.5.1 cells were kindly provided by Dr. Francis Chisari (Scripps Research Institute, La Jolla, CA, USA) (165, 276). Huh-7 cells, Huh.8 (Con1/SG-Neo (139)) cells supporting the HCV genotype 1b subgenomic replicon, and Huh.2 (Con1/SG-Neo: S2197P (123)) cells supporting the same replicon with an NS5A adaptive mutation were provided by Dr. Charles Rice (The Rockefeller University, New York, NY, and Apath, LLC, St. Louis, MO, USA) (22, 23). Vero cells (ATCC CCL-81) were obtained from ATCC (40). Huh.8 cells and Huh.2 cells were cultured in Dulbecco’s Modified Eagle Medium (DMEM) containing 4 mM glutamine (L-glu) (Gibco/Invitrogen, Burlington, ON, Canada) and supplemented with 25 U/mL of penicillin (Gibco®/Invitrogen, Burlington, ON, Canada), 25 µg/mL of streptomycin (Gibco®/Invitrogen, Burlington, ON, Canada) , 0.5 mM non-essential amino acids (NEAA) (Gibco®/Invitrogen, Burlington, ON, Canada), and 10% fetal bovine serum (FBS) (Gibco/Invitrogen, Burlington, ON, Canada). Huh.8 and Huh.2 cells were maintained under selection with 750 µM Geneticin (Gibco®/Invitrogen, Burlington, ON, Canada). Huh-7.5.1 cells were cultured in DMEM containing 4 mM L-glu and supplemented with 50 U/mL of penicillin, 50 µg/mL of streptomycin, 1 mM NEAA, 10% FBS  and 10 mM 4-(2-hydroxyethyl)-1-piperazineethanesulfonic acid (HEPES) (Gibco®/Invitrogen, Burlington, ON, Canada). Vero cells were cultured in fresh MEM α medium supplemented with 50 U/mL of penicillin, 50 µg/mL of streptomycin, 1 mM NEAA, 10% FBS (Gibco®/Invitrogen, Burlington, ON, Canada) (155).  Cell lines were grown, maintained, or incubated at 37˚C and 5% CO2.    HCV RNA, infectious stock production, and HCV titer determination The plasmid pUCvJFH-1, a generous gift from Dr. Takaji Wakita (National Institute of Infectious Diseases, Tokyo, Japan), was used to generate HCV RNA and infectious HCV stocks as previously described (276). pUCvJFH-1 contains a cDNA of an HCV consensus clone isolated from a Japanese patient with fulminant hepatitis, Japanese fulminant hepatitis 1 (JFH-1) (GenBank accession number: AB047639) (112) cloned next to a T7 promoter). Briefly, XbaI linearized pUCvJFH-1 (phenol/chloroform extraction) was used as a template for in vitro transcription of the RNA genome using MEGAscript® T7 kit (Cat. no. AM1333, Ambion/life technologies, Carlsbad, CA, USA) in the presence of the RNA guardTM 44  Ribonuclease inhibitor (Amersham Biosciences/GE Healthcare). In vitro transcribed JFH-1 RNA (purified by phenol/chloroform extraction) was used to transfect Huh-7.5.1 cells capable of generating infectious viral particles. The cell-cultured HCV virus (HCV)-containing media was harvested on day 15 and day 17 post transfection, and the infectious supernatant was stored at –86°C after centrifugation at 4000 rpm for 5 min. To generate infectious viral stocks for experiments, infectious supernatant from day 15 and day 17, was used to infect naïve Huh-7.5.1 cells with an MOI of 0.01; the infectious media was harvested on day 8 prior to the establishment of cytopathic effect observed in infected cell, centrifuged at 4000 rpm for 5 min, aliquoted and stored at –86°C.  Viral titers were determined using a modified version of a previously described protocol (174) . To do so, 1 × 104 Huh-7.5.1 cells were seeded in a 96-well plate and infected with a series of 10-fold serial dilutions of HCV-infected cell media. After 48 hours post-infection, cells were fixed in 4% formaldehyde (diluted in PBS) and probed for HCV core as described in the ArrayScan Quantification methods section. An ArrayScan VTI High Content Screening (HCS) Reader (Thermo Scientific Inc., Waltham, MA, USA) was used to acquire images of all infected wells. Titers were determined by manually counting distinct fluorescent foci (fluorescence forming unit (FFU)) in wells with lowest dilutions with positive signal capture for HCV core expression.  DENV stock production DENV-2 (New Guinea C) was kindly provided by Dr. Mike Drebot (the National Microbiology Laboratory (NML), Winnipeg, MB, Canada). For stock production, 50 µL of DENV-2 stock samples received from NML was used to infect T-175 flasks containing 90% confluent Vero cells. For viral stock production, media containing infectious virus was harvested day 5 post-infection.  Collected media was centrifuged at 3000 rpm for 5 minutes, aliquoted, and stored at –86°C. DENV-2 titer was determined by performing a plaque assay as previously described (155). Briefly, Vero cell monolayers were seeded in 12-well plates (Falcon; Becton Dickinson, Lincoln Park, NJ, USA) and infected with supernatant from DENV and mock infected Huh-7.5.1 using 10 fold dilutions starting at 1:10 DENV-2-containing media. Vero cells were incubated at 37°C in a CO2 incubator. Plaques were visualized and counted on day 5 post-infection by staining with 4% neutral red solution (Sigma) in PBS. 45   HCV and DENV-2 infections Huh-7.5.1 cells were either infected with HCV (MOI :0.1) or DENV-2 (MOI:0.005) diluted in complete media. Twenty-four hours post-viral infection, media was replaced with fresh complete media. Thereafter, the cells were incubated for the indicated number of days (see Figs. 2.2, 2.3, 2.6, and 2.7) and cells were fixed for ICW assay. Alternately, cell supernatants were collected at various time-points post-infection for performing secondary infection of naïve Huh-7.5.1 cells and determining viral protein abundance (HCV core; DENV E) using ICW assay.  RNA isolation Total RNA was isolated (RNeasy mini kit/RNeasy plus mini kit (Cat. no. 74104/74134, Qiagen, Toronto, ON, Canada)) from cells that were lysed in buffer RLT using Qiashredder (Cat. no. 79654, Qiagen, Toronto, ON, Canada). The concentration and purity of RNA was determined by a NanoDrop ND-1000 Spectrophotometer (Thermo Scientific, Nepean, ON, Canada).  Quantitative real-time polymerase chain reaction (RT-QPCR) Purified RNA was reverse transcribed to cDNA using the qScriptTM cDNA synthesis kit (Quanta Biosciences, Gaithersburg, MD, US). RT-QPCR was performed using PerfeCTaTM Multiplex qPCR SuperMix (Quanta Biosciences, Gaithersburg, MD, US) according to the manufacturer's instructions on an Mx3005P QPCR system (Stratagene).  PrimerQuestTM (Integrated DNA Technologies (IDT), Coralville, IA, USA) was used to design RT-QPCR assays summarized in Table A5.3 for detecting (i) HCV genomic abundance [Probe:  (5′-CAGTACCACAAGGCCTTTCGCAACC-3′); forward primer (5′-CAAGACTGCTAGCCGAGT-3′), reverse primer (5′-ACTCGCAAGCGCCCTATC-3′)], (ii) GAPDH RNA levels [Probe: (5′-GCTCCTGGAAGATGGTGATGGG-3′), forward primer (5′-AGAACGGGAAGCTTGTCATC-3′), reverse primer (5′-CATCGCCCCACTTGATTTTG-3′)], and human β-actin RNA levels [Probe: (5′-ACTCCATGCCCAGGAAGGAAGGC-3′), forward primer (5′-GCCCTGAGGCACTCTTCC-3′); reverse primer (5′-GGATCTCCACGTCACACTTC-3′)]. 46  HCV RNA levels were relatively quantified across samples and normalized to beta-actin RNA levels (5′ Cy5 fluorophore and 3′ Iowa Black RQ quencher) using 300 nM primers and 150 nM double-quenched probes (5′ FAM fluorophore and 3′ Iowa Black FQ quencher with an internal ZEN quencher) in a duplex reaction.  siRNA-mediated downregulation of human GAPDH in hepatoma cells Huh-7.5.1 cells were first transfected according to the manufacturer’s protocol with a pool of non-targeting control siRNAs (siCTRL: (D-001910-02-05, D-001910-03-05, and D-001910-04-05) or a pool of siRNA targeting GAPDH (siGAPDH: D-001930-10-20) obtained from Dharmacon/Thermoscientific, (Chicago, IL, USA) using the recommended concentration of DharmaFECT 4 (Dharmacon/Thermoscientific, Chicago, IL, USA) transfection reagent.  Levels of expression of GAPDH mRNA under the experimental conditions described in Fig. 2.4 were determined using RT-QPCR. The relative level of abundance of intracellular GAPDH under the experimental conditions described in Figs. 2.1–2.3 and Figs. 2.5–2.7 were determined using WB or ICW.  Preparation of V5-tagged human GAPDH constructs Total RNA (PureLink® RNA Mini Kit, Ambion, Carlsbad, CA, USA) isolated from Huh-7 cells was reverse transcribed to cDNA (TaqMan® Reverse Transcription Reagents, Applied Biosystems, Foster City, CA, USA) using manufacturer supplied oligo d(T)16 primers [Thermal cycling parameter : 10 min @ 25 °C, 30 min @ 48 °C, and 5 min @ 95 °C] using a Mastercycler® gradient (Eppendorf Scientific, Mississauga, ON, Canada). KOD Hot Start DNA Polymerase (Toyobo/EMD4Biosciences, Billerica, MA, USA) was used to amplify GAPDH genes using reverse transcription mix with primer sets 5′-ATGGGGAAGGTGAAGGTCGG-3′ and 5′-TTACTCCTTGGAGGCCATGTGGGC-3′ [thermal cycling parameter: 2 min @ 95 °C and 40 cycles of 20 s @ 95 °C, 10 s @ 56 °C, and 20 s @ 70 °C]. The purified human GAPDH (hGAPDH) PCR fragment (PureLink® PCR purification kit, Invitrogen/Life technologies, Carlsbad, CA, USA) was cloned into a pGEM®-t vector (Cat. no. A3600, Madison, WI, USA). After confirming the DNA sequence (NAPS Unit, University of British Columbia, Vancouver, BC, Canada), the hGAPDH gene 47  was cloned with the introduction of KpnI and EcoRI restriction cloning sites on the 5′ end and the 3′ end respectively, along with the elimination of a stop codon using primer sets 5′-GCTTGGTACCATGGGGAAGGTGAAGGTC-3′ (forward) and 5′-TGCAGAATTCCCTCCTTGGAGGCCATGT-3′ (reverse) (IDT). This cloning step was performed for the purpose of generating a hGAPDH construct C-terminally tagged with a V5 tag (GAPDH-V5). The purified PCR fragment, hGAPDH, was re-cloned into the pGEM®-T vector for ease of excising out the cDNA fragment using KpnI and EcoRI restriction enzymes (New England BioLabs® Inc. (NEB), Whitby, ON, Canada). The double-digested fragment was cloned into the mammalian expression vector pcDNA3.1+ (Invitrogen, Carlsbad, CA, USA). A duplex sequence of V5-tag was designed to have restriction sites EcoRI introduced to the 5′ end and XhoI introduced to the 3′ end (5′-TAGGCCTCGAGTTACGTAGAATCGAGACCGAGGAGAGGGTTAGGGATAGGCTTACCGAATTCCGGAT-3′ (IDT, Coralville, IA, USA)) along with four additional nucleotides at both ends for the ease of restriction digestion. The duplex was double digested with EcoRI and XhoI (NEB, Whitby, ON, Canada), and was introduced into hGAPDH-containing pcDNA3.1+ to generate the GAPDH-V5 construct (pcDNA3.1-GAPDH-V5).  After the introduction of the V5 tag, the linker EcoRI site encoding -EF- was mutated to -AA- using a primer set (forward: 5′-CATGGCCTCCAAGGAGGGAGGCATGGTGTCTAAGGGCG -3′ (IDT) and reverse: 5′-CGCCCTTAGACACCATGCCTCCCTCCTTGGAGGCCATG -3′ (IDT)) designed by QuickChange primer design software . The site directed mutagenesis was performed using a QuickChange® II site-directed mutagenesis kit (Stratagene, La Jolla, CA, USA).The mini-plasmid preparation was generated using a Qiagen QIAprep® spin miniprep kit (Qiagen, Toronto, ON, Canada), and midi-plasmid preparations used for transfection were generated using a Nucleobond®  Xtra midi plasmid DNA purification kit (Macherey-Nagel/Fisher Scientific, Ottawa, ON, Canada),  or a Qiagen plasmid midi kit (Qiagen,Toronto, ON, Canada).  48  Transfection of the GAPDH-V5 construct Huh-7.5.1 cells (2.5 × 105) were transfected with 2 µg of pcDNA3.1-GAPDH-V5 constructs using 4 µL of TransIT®-LT1 ( Mirus, Madison, WI, USA) in a 6-well plate or 0.06 µg pcDNA3.1-GAPDH-V5 construct using 0.12 µL of transfection reagent in a 96-well plate according to the manufacturer’s instructions. The relative level of abundance of intracellular GAPDH-V5 under the experimental conditions described in Figs. 2.5–2.6 were determined using WB or ICW.  Western blotting analysis Cultured cells were washed with ice-cold phosphate buffered saline (PBS (pH 7.4)) and lysed in a hypotonic buffer (20 mM Tris, pH 7.4, 10 mM MgCl2, 10 mM CaCl2) containing 1X Complete (Roche, Laval, QC, Canada), an EDTA-free protease inhibitor cocktail. Whole cell lysates were vortexed vigorously and mixed with 4X sample buffer (8 % SDS, 20 % 2-βmercaptoethanol (freshly added), 40 % glycerol, 0.008 % bromophenol blue, 0.25 M Tris-HCl (pH 6.8)) and boiled for 15 min at 95°C. The samples were electrophoresed on 12-15 % SDS polyacrylamide gel and transfered to nitrocellulose membranes. The blots were blocked for 1 hour (Odyssey blocking buffer (LI-COR Biosciences, Lincoln, NE, USA), diluted 1:1 with PBS (pH 7.4)) for 1 hour and proteins of interest were detected by probing with appropriate primary and secondary antibodies diluted in blocking buffer containing 0.1 % Tween 20. Protein bands were detected by scanning blots on an Odyssey Infrared Imaging System (LI-COR Biosciences Lincoln, NE, USA) at wavelengths of 700 nm to detect IRDye 680CW -conjugate 2o antibody, and 800 nm to detect IRDye 800 -conjugate 2o antibody. Bands were quantified using Odyssey software 2.0. Beta-tubulin was always used as a loading control and for normalizing protein expression.  In-cell western assay In-cell western (ICW) is a technique that facilitates quantification of protein expression within fixed and permeabilized intact cells in a 96-well plate. After seeding 5 × 103 - 10× 103 cells in black flat-bottom 96-well plates (BD Bioscience/PerkinElmer), cells were transfected and/or infected as needed. Cells were fixed in 4% formaldehyde v/v in PBS (pH7.4) for 45 49  min after incubation post-infection. Cells were permeabilized in PBS containing 0.1 % TritonTM-X 100 (permeabilization buffer), and wells were blocked in Odyssey blocking buffer diluted 1:1 with PBS (pH7.4) (blocking buffer) for 1 hour. Cells were incubated with primary antibodies diluted in blocking buffer overnight and probed with respective secondary antibodies diluted in blocking buffer containing 0.02% Tween-20 for 1 hour. For normalization, cells were stained with a non-specific protein stain, Sapphire 700 and a DNA interactive stain, DRAQ5. The plates were scanned using the Odyssey Infrared Imaging System at 700 nm (IRDye 680CW -conjugate 2o antibody) and 800 nm (IRDye 800 -conjugate 2o antibody) wavelengths. Quantification was performed using Odyssey software 2.0.  GAPDH enzymatic activity assay GAPDH enzymatic activity was measured in ~2-12 × 104 cells seeded in a 96-well plate using the KDaltertTM GAPDH assay kit (Invitrogen TM/Life technologies, Burlington, ON, Canada). The increase in fluorescence was measured using a GeminiTM EM fluorescence microplate reader (Molecular Devices, Sunnyvale, CA, USA) with data acquisition setting on kinetic mode and gain on autoscale at room temperature using excitation and emission wavelength filters of 544 nm and 590 nm, respectively.  Statistical analysis Standard deviations (STD) are provided for an experiment (n=1) performed with three technical replicates, or for experiments (n ≥3) performed with one technical replicate. Standard error of means (SEM) is provided for experiments (n ≥3) performed with two or more technical replicates. P-values are calculated using student’s t-test on technical replicates pooled from n number of experiments.   50  2.4 Results and discussion  2.4.1 Human hepatoma cells with siRNA-mediated GAPDH mRNA silencing show a dramatic decrease in GAPDH protein abundance  To examine the effect of GAPDH suppression in naïve human Huh-7.5.1 cells on host-cell susceptibility to HCV infection, I first optimized the experimental conditions  to obtain a robust transient knockdown of GAPDH by RNA interference. Cultured Huh-7.5.1 cells were initially transfected with an siRNA-pool targeting human GAPDH (siGAPDH) or a non-targeting siRNA pool (siCTRL).  Seventy-two hours post-transfection (p.t.), cell lysates were prepared for western blot analysis and probed for human β-tubulin (red) and human GAPDH (green) (Fig. 2.1; upper panel). A ~14-fold decrease in GAPDH abundance was observed for cells treated with 15 nM of siGAPDH relative to GAPDH protein abundance in control cells (siCTRL) (Fig. 2.1; lower panel). These results confirmed that a robust transient knockdown of GAPDH is achieved by using 15 nM of the GAPDH siRNA pool in Huh-7.5.1 cells for up to 72 hours, i.e., the duration of the HCV infection examined in this study.  Taken together, these results show that the GAPDH knockdown is sequence-specific with the most robust GAPDH suppression in human Huh.7.5.1 cells observed at 15 nM, i.e., the lowest concentration of siRNA showing maximal GAPDH suppression. Importantly, no cytotoxic effects of the siRNA-pool targeting human GAPDH (siGAPDH) were observed, compared to the non-targeting siRNA pool (siCTRL), by light microscopy in siRNA-treated Huh-7.5.1 cells up to concentrations of 15 nM.  51   Figure 2.1. Robust transient suppression of GAPDH by RNA interference in human Huh-7.5.1 cells.  Huh-7.5.1 cells (~1.5 × 105 cells) were transfected with a short interfering (siRNA) pool targeting human GAPDH (siGAPDH, 15 nM) or a non-targeting pool of siRNA (siCTRL, 15 nM) using DharmaFECT 4 transfection reagent. Cell lysates were harvested after 72 hours for WB analysis and probed for human GAPDH (green) and for β-tubulin (red) (loading control used for normalization) expression as described in Materials and Methods. Upper panel: A WB representative of two independent experiments.  Lower panel: Protein abundance was quantified as described in Materials and Methods, averaged across triplicate wells, and expressed relative to protein abundance in control cells (siCTRL).  Results (mean ± SEM) are from three experiments (siRNA transfection) subjected to a two independent WB analysis. *** p < 0.0001.  52  2.4.2 Naïve Huh-7.5.1 with siRNA-mediated suppression show a decrease in host-cell susceptibility to primary and secondary HCV infections To evaluate the importance of human GAPDH abundance in Huh-7.5.1 cells to support primary and secondary HCV infection, I used siRNA-mediated GAPDH silencing as described above. First, to examine the effect of siRNA-mediated GAPDH suppression on primary HCV infection of naïve Huh-7.5.1 cells, cells were transfected with an siRNA pool targeting human GAPDH (siGAPDH, 15 nM) or with a non-targeting siRNA pool (siCTRL, 15 nM) for 24 hours. Forty-eight hours p.t., treated cells were infected with HCV (MOI: 0.1). Seventy-two hours post-viral infection (p.i.), HCV-infected Huh-7.5.1 cells were fixed for in-cell western (ICW) analysis.  Fig. 2.2a shows representative ICW wells scanned using the Odyssey infrared imaging system from our primary infection experiments. The treated cells were probed with HCV anti-core antibody (green) and anti-GAPDH antibody (green), and stained with dyes for cell density normalization [(CD); red] (Fig. 2.2a; upper panel).  HCV core and human GAPDH proteins were quantified and expressed relative to protein abundance in control cells (siCTRL) (Fig. 2.2a; lower panel). A ~2-fold reduction of HCV core abundance was measured for siGAPDH-treated cells during the primary infection compared to siCTRL (Fig. 2.2a; lower panel). Thus, siRNA-mediated GAPDH silencing in naïve Huh-7.5.1 cells robustly inhibits primary HCV infection.  Next, to examine the effect of siRNA-mediated GAPDH suppression on HCV infectious virus particle production and its spread to naive cells, I performed an ICW assay involving HCV secondary infection. Briefly, Huh-7.5.1 cells were treated as described for the primary infection with the exception that at 72 hours p.i., the HCV-infected Huh-7.5.1 cell supernatants were collected and used to perform a secondary infection on naïve Huh-7.5.1 cells.  After the secondary infection proceeded for 72 hours, the HCV-infected Huh-7.5.1 cells were fixed for ICW assays.  A ~4-fold reduction of HCV core abundance was measured for siGAPDH-treated cells during the secondary infection compared to siCTRL (Fig. 2.2a; lower panel). Thus, siRNA-mediated GAPDH silencing in naïve Huh-7.5.1 cells not only inhibits primary HCV infection but also seems to inhibit the production and/or release of infectious HCV virus particles in HCV-infected Huh-7.5.1 cell supernatants.  53   Figure 2.2.  Short interfering RNA (siRNA)-mediated GAPDH reduction in naïve human Huh-7.5.1 cells decreases host-cell susceptibility to primary and secondary HCV infections  (a) Primary infection. Huh-7.5.1 cells (~7.5 × 103 cells) were treated with transfection mix containing an siRNA pool targeting human GAPDH (siGAPDH, 15 nM) or a non-targeting siRNA pool (siCTRL, 15 nM) using DharmaFECT 4 transfection reagent for 24 hours. After 48 hours of transfection, cells were infected with HCV (MOI: 0.1). Twenty-four hours post-viral infection, media was replaced with fresh media that was used for secondary infection. Seventy-two hours post-viral infection, HCV-infected Huh-7.5.1 cell infectious supernatants were collected to perform a secondary infection of naïve Huh-7.5.1 cells (b) and HCV-infected Huh-7.5.1 cells were fixed for ICW analysis (a) as described in Materials and Methods.  Upper panel: Representative ICW wells probed with HCV anti-core antibody (green), anti-GAPDH antibody (green), and stained with two dyes for the purpose of determining cell density (CD) (red) for normalization.  Lower panel: Protein abundance was quantified as described in Materials and Methods, averaged across triplicate wells, and expressed relative to protein abundance in control cells (siCTRL).  54  (b) Secondary infection. The HCV-infected Huh-7.5.1 cell supernatants collected in (a) were used to infect naïve Huh-7.5.1 cells for 72 hours, then the cultured cells were fixed for ICW analysis as described in Materials and Methods.  Upper panel: Representative ICW wells probed with HCV anti-core antibody (green), anti-GAPDH antibody (green), and stained with two dyes for the purpose of determining cell density (CD) (red) for normalization.  Lower panel: Protein abundance was quantified as described in Materials and Methods, averaged across triplicate wells, and expressed relative to protein abundance in control cells (siCTRL).   Results (mean ± STD) are from one representative experiment done in triplicate.  At least in part, the robust decrease of HCV core abundance observed during the primary infection in siGAPDH-treated cells could be attributed to host cells compromised for viral entry and/or replication, as the measurement of intracellular HCV core abundance is not sufficient to discriminate between these possibilities.  Notably, GAPDH may play important roles in the early steps of the HCV life cycle. GAPDH is associated with tetraspanin-enriched microdomains in the host plasma membrane, and it has been shown to directly interact with putative HCV receptor CD81 in this context (180); roles for GAPDH in endocytosis (201) as well as intracellular membrane fusion (83) have also been identified. Thus, HCV attachment, entry, and fusion could all be affected by siRNA-mediated GAPDH silencing. Interestingly, the secondary infection of naïve Huh-7.5.1 cells with the HCV-infected cell supernatants collected from the primary infection resulted in an even further decrease in intracellular HCV core abundance.  These results are consistent with a compromised host-cell susceptibility to HCV infection observed during the primary infection of siGAPDH-treated cells, but they also imply that key maturation and trafficking steps leading to the production of infectious virus particles and release may also be compromised. Because of the multifunctional roles of GAPDH in intracellular membrane trafficking, an important molecular event for the biogenesis of organelles in the secretory pathway, siRNA-suppression of GAPDH may impact essential cellular biological processes required by HCV for its maturation steps in the host secretory pathway, including the formation of lipoviroparticles and their release from the infected cells. 55  2.4.3 HCV-infected Huh-7.5.1 with siRNA-mediated suppression leads to a decrease in viral infectivity of the HCV-infected cell supernatants To test the effect of siRNA-mediated GAPDH silencing in established infections, Huh-7.5.1 cells were first infected with HCV for 24 hours to allow uninterrupted HCV replication and establishment of infection. At the end of this time, Huh-7.5.1 cells were transfected with an siRNA pool targeting human GAPDH (siGAPDH, 15 nM) or with a non-targeting siRNA pool (siCTRL, 15 nM) and incubated for an additional 72 hours. The HCV-infected Huh-7.5.1 cell supernatants were collected to perform a secondary infection on naïve Huh.7.5.1 cells (Fig. 2.3b) and cells were fixed for ICW analysis (Fig. 2.3a). The results show that siRNA-mediated GAPDH suppression in Huh-7.5.1 cells when HCV infection has already been established does not have an effect on the level of intracellular HCV core abundance during the primary infection (Fig. 2.3a). However, the relative abundance of HCV core protein associated with the secondary infection is reduced ~3-fold compared to siCTRL-treated cells (Fig. 2.3b). These results suggest that GAPDH also plays an important role during the post-replication steps of the virus life cycle because siRNA-mediated GAPDH silencing in HCV-infected Huh-7.5.1 cells caused a decrease in viral infectivity of the HCV-infected cell supernatants.  In addition to its potential roles early in the HCV life cycle, GAPDH is an important component of the Rab2-Src-aPKCiota-GAPDH microtubule-bound complex, which is vital to ER-Golgi trafficking in the secretory pathway (248-252). Since HCV hijacks this pathway to promote the formation and egress of lipoviroparticles (58), it seems that the assembly and budding of infectious virions could be inhibited as a result of siRNA-mediated GAPDH silencing. Thus, silencing GAPDH may significantly impact pre- and post-replication steps of the HCV life cycle, underlining the varied and numerous functions performed by this moonlighting enzyme. 56   Figure 2.3. Short interfering RNA (siRNA)-mediated GAPDH suppression in HCV-infected Huh-7.5.1 cells leads to a decrease in the viral susceptibility of the HCV-infected Huh-7.5.1 cell supernatants  (a) Primary infection. Huh-7.5.1 cells (7.5 × 103 cells) were infected with HCV (MOI:0.1) for 24 hours. These HCV-infected cells were then transfected with an siRNA pool targeting GAPDH (siGAPDH, 15 nM) or with a non-targeting siRNA pool (siCTRL, 15 nM) using DharmaFECT 4 transfection. Twenty-four hours post-transfection, media was replaced with fresh media that was used for secondary infection. Seventy-two hours post-transfection, the HCV-infected Huh-7.5.1 cell infectious supernatants were collected to perform a secondary infection of naïve Huh-7.5.1 cells (b) and HCV-infected Huh-7.5.1 cells were fixed for ICW analysis (a) as described in the Materials and Methods.  Upper panel: Representative ICW well, probed with HCV anti-core antibody (green), anti-GAPDH antibody (green), and stained with two dyes for the purpose of determining cell density (CD) (red) for normalization. Lower panel: Protein abundance was quantified as described in Materials and Methods, averaged across triplicate wells, and expressed relative to protein abundance in control cells (siCTRL).  (b) Secondary infection. The HCV-infected Huh-7.5.1 cell supernatants collected in (a) were used to infect naïve Huh-7.5.1 cells for 72 hours, then the cultured cells were fixed for ICW analysis as described in Materials and Methods.  57  Upper panel: Representative ICW wells, probed with HCV anti-core antibody (green), anti-GAPDH antibody (green), and stained with two dyes for the purpose of determining cell density (CD) (red) for normalization.  Lower panel: Protein abundance was quantified as described in Materials and Methods, averaged across triplicate wells, and expressed relative to protein abundance in control cells (siCTRL).  Results (mean ± STD) are from a representative experiment done in triplicate.  2.4.4 HCV replicon-harbouring cells (Huh.8 and Huh.2) with siRNA-mediated GAPDH suppression has no effect on HCV replication To determine if the host-cell susceptibility to primary and secondary HCV infections observed in siGAPDH-treated cells is due to reduced viral replication, I examined the impact of siRNA-mediated GAPDH suppression on viral replication using HCV subgenomic replicons (23). Human hepatoma cells harbouring stable HCV replicons encoding wild-type NS5A (Huh.8 cells: Fig. 2.4a) or NS5A with an adaptive mutation (Huh.2 cells: Fig. 2.4b) were transfected for 72 hours with an siRNA pool targeting human GAPDH (siGAPDH, 15 nM) or with a non-targeting siRNA pool (siCTRL, 15 nM). Total RNA levels were then harvested and the levels of HCV RNA and GAPDH RNA were quantified using qRT-PCR analysis (Fig. 2.4a-b). Although a robust knockdown of GAPDH was observed in Huh.8 cells (~17-fold decrease) and Huh.2 cells (~25-fold decrease), no significant differences were observed between HCV replicon levels treated with siGAPDH and the control (siCTRL) (Fig 2.4 a-b). In contrast, Huh.8 cells treated with the positive control, BILN 2061, a viral protease inhibitor that inhibits HCV replication (125), resulted in a ~33-fold decrease in HCV RNA compared to cells treated with DMSO (Fig. 2.4a; right panel). These findings strongly suggest that the robust reduction in HCV core abundance observed during primary and secondary HCV infections in siGAPDH-treated cells (Fig. 2a-b) is not due to a defect in HCV RNA replication.  58   Figure 2.4 Short interfering RNA (siRNA)-mediated GAPDH suppression in HCV replicon-harboring cells has no effect on HCV replication  (a) Huh.8 replicon-harbouring cells. Left panel. Huh.8  cells (Huh-7 subclones expressing HCV subgenomic replicon, Con1/SG-Neo) (~1.5 × 105 cells) were transfected with an siRNA pool targeting human GAPDH (siGAPDH, 15 nM) or with a non-targeting pool of siRNA (siCTRL, 15 nM) using DharmaFECT 4 transfection reagent. Right panel. Huh.8 cells were treated with 1 µM of the HCV NS3/4A protease inhibitor, BILN 2061 (Boehringer Ingelheim Pharma) (125) or DMSO (control). After 72 hours transfection and treatment with the protease inhibitor, total RNA was harvested from Huh.8 cells.   (b) Huh.2 replicon-harbouring cells. Huh.2  cells (Huh-7 cell line expressing HCV subgenomic replicon Con1/SG-Neo bearing S2197P adaptive mutation) (~1.5 × 105 cells) were transfected with an siRNA pool targeting human GAPDH (siGAPDH, 15 nM) or a non-targeting pool of siRNA (siCTRL, 15 nM) using DharmaFECT 4 transfection reagent. Seventy-two hours post-transfection, total RNA was harvested from Huh.2 cells.  (a-b) GAPDH transcript levels and HCV RNA levels, normalized to β-actin transcript levels, were relatively quantified from 15 ng of total RNA by RT-QPCR as described in Materials and Methods. Values are plotted as relative GAPDH or HCV levels in comparison to those of control-treated cells (siCTRL or DMSO).  Results (mean ± SEM) are from three independent experiments done in triplicate. ***p < 0.001.   The author performed this experiment, analyzed its results and generated this figure.  59  Interestingly, GAPDH has been shown to interact directly with 3’-UTRs, in the context of cellular mRNAs including angiotensin II type 1 receptor and endothelin-1 (8) as well as the genomes of several RNA viruses, including hepatitis A virus (56, 212, 269) and Japanese encephalitis virus (268). These interactions generally have the effect of destabilizing the mRNA or preventing translation by other means. Despite this, it seems that any ability GAPDH may have to interact with HCV RNA does not confer an inhibitory or stimulatory effect on HCV replication. Thus, it seems that the role of GAPDH in the HCV life cycle is limited to pre- and post-replication steps while replication is GAPDH-independent.   2.4.5 Huh-7.5.1 cells with exogenous upregulation of V5-tagged human GAPDH increases viral infectivity of HCV-infected Huh-7.5.1 cell supernatants Next, I wanted to test whether over-expression of human GAPDH in Huh-7.5.1 cells would increase host-cell susceptibility to primary and secondary HCV infections. This was accomplished by first producing a plasmid encoding for the V5-tagged full-length human GAPDH (Fig. 2.5a). The resulting construct, designated (GAPDH-V5), when transfected in Huh-7.5.1 cells for 72 hours, allowed for a robust expression of enzymatically active recombinant V5-tagged GAPDH molecules (Fig. 2.5b-c). Western blot analysis of GAPDH-V5-transfected cellular lysates using anti-GAPDH antibody (GAPDH) and anti-V5 antibody (GAPDH-V5) demonstrated the successful over-expression (~2-fold increase) of the intracellular GAPDH as a result of the expression of GAPDH-V5 recombinant protein (Fig. 2.5b), which is associated with a ~2-fold increase in GAPDH intracellular enzymatic activity (Fig. 2.5c) that was only detected in GAPDH-V5-transfected cells and not in control cells (CTRL) (Fig. 2.5c).  60    Figure 2.5 Exogenous expression of V5-tagged human GAPDH in Huh-7.5.1 cells increases GAPDH enzymatic activity  (a) Schematic representation of the V5-tagged full-length human GAPDH (GAPDH-V5) construct used for exogenous upregulation of GAPDH.  (b) Huh-7.5.1 cells were transfected with pcDNA3.1-GAPDH-V5 or pcDNA3.1 (CTRL) using the TransIT®-LT1 transfection reagent for 72 hours. Cell lysates were prepared for WB analysis and probed for human GAPDH (GAPDH) and V5-tag (GAPDH-V5) as described in Materials and Methods. Representative WB of an experiment performed in duplicate and bands visualized over two independent blots.  (c) Huh-7.5.1 cells were transfected with pcDNA3.1-GAPDH-V5 or pcDNA3.1 (CTRL) using the TransIT®-LT1 transfection reagent for 72 hours. Cell lysates were prepared according to the manufacturer’s instructions for detecting intracellular GAPDH enzymatic 61  activity using KDaltertTM GAPDH assay kit as described in Materials and Methods. Values are plotted relative to GAPDH enzymatic activity detected in control cells (CTRL).  Results (mean ± SEM) are from three experiments done in triplicate. ***p < 0.0001.  To determine whether exogenous expression of GAPDH-V5 can increase primary and secondary HCV infection in Huh-7.5.1 cells, I examined the effect of over-expressing GAPDH-V5 on HCV core abundance pre- (Fig. 2.6a-b) and post-establishment (Fig. 2.6c-d) of viral infection in Huh-7.5.1 cells.   First, Huh-7.5.1 cells were transfected with pcDNA3.1-GAPDH-V5 (GAPDH-V5) or pcDNA3.1 (CTRL) for 24 hours. Treated cells were infected with HCV (MOI: 0.1).  Seventy-two hours p.i., the HCV-infected Huh-7.5.1 cell supernatants were collected to perform a secondary infection on naïve Huh.7.5.1 cells (Fig. 2.6b) and cells were fixed for ICW analysis (Fig. 2.6a). Results show that transfection of Huh-7.5.1 naïve cells with GAPDH-V5 prior to infection with HCV does not have an effect on the level of intracellular HCV core protein in primary infected cells (Fig. 2.6a), but it increases the level of HCV core protein ~2-fold in secondary infected cells (Fig. 2.6b). No changes in HCV core abundance were observed for the CTRL-treated cells (Fig. 2.6a-b). Since HCV core abundance is an indicator of viral load in the host cells, this indicates that over-expression of GAPDH and the concomitant increase in GAPDH enzymatic activity pre-establishment of HCV infection lead to an increase in the viral infectivity of HCV-infected Huh-7.5.1 cell supernatants but does not affect cell susceptibility. To test the effect of over-expression of GAPDH enzymatic activity in established infections, Huh-7.5.1 cells were first infected with HCV for 24 hours to allow uninterrupted HCV replication and establishment of infection. HCV-infected Huh-7.5.1 cells were transfected with pcDNA3.1-GAPDH-V5 (GAPDH-V5) or pcDNA3.1 (CTRL), and 72 hours p.t., the HCV-infected Huh-7.5.1 cell supernatants were collected to perform a secondary infection on naïve Huh.7.5.1 cells (Fig. 2.6d), and cells were fixed for ICW analysis (Fig 2.6c). Over-expression of GAPDH-V5 post-establishment of viral infection in Huh-7.5.1 cells resulted in a ~2-fold increase in HCV core abundance only in the secondary infected cells (Fig. 2.6d), not detected during primary infection of Huh-7.5.1 cells (Fig. 2.6c).  These results demonstrated that over-expression of GAPDH enzymatic activity post-establishment 62  of HCV infection again increases the viral infectivity of HCV-infected Huh-7.5.1 cell supernatants but does not affect cell susceptibility.  63   64   Figure 2.6 Exogenous expression of V5-tagged human GAPDH increases the viral infectivity of HCV-infected Huh-7.5.1 cell supernatants  (a-b) Effect of GAPDH-V5 on host-cell susceptibility to HCV infection when expressed prior to establishment of viral infection in Huh-7.5.1 cells.  (a) Primary infection. Huh-7.5.1 cells (~7.5 × 103 cells) were transfected with pcDNA3.1-GAPDH-V5 or pcDNA3.1 (CTRL) using TransIT®-LT1 transfection reagent. Twenty-four hours post-transfection, cells were infected with HCV (MOI: 0.1). Twenty-four hours post-viral infection, media was replaced with fresh media for use in secondary infection. Seventy-two hours post-viral infections, the HCV-infected Huh-7.5.1-cell infectious supernatants were collected to perform a secondary infection of naïve Huh-7.5.1 cells (b) and HCV-infected Huh-7.5.1 cells were fixed for ICW analysis (a) as described in Materials and Methods.  Upper panel: Representative ICW wells, probed with HCV anti-core antibody (green), anti-V5 antibody (green), and stained with two dyes for the purpose of determining cell density (CD) (red) for normalization.  Lower panel: : Protein abundance was quantified as described in Materials and Methods, averaged across triplicate wells, and expressed relative to protein abundance in control cells (siCTRL).  (b) Secondary infection. Naïve Huh-7.5.1 cells (~1.0 × 104 cells) were infected with infectious supernatants from (a) for 72 hours and were fixed for ICW analysis as described in the Materials and Methods.  Upper panel: Representative ICW wells, probed with HCV anti-core antibody (green), anti-V5 antibody (green), and stained with two dyes for the purpose of determining cell density (CD) (red) for normalization.  Lower panel: Protein abundance was quantified as described in Materials and Methods, averaged across triplicate wells, and expressed relative to protein abundance in control cells (siCTRL).  Results (mean ± STD) are from a representative experiment done in triplicate.  (c-d) Effect of GAPDH-V5 on host-cell susceptibility to HCV infection when expressed following viral infection in Huh-7.5.1 cells.  (c) Primary infection. Huh-7.5.1 cells (7.5 × 103 cells) were infected with HCV (MOI:0.1) for 24 hours. These HCV-infected Huh-7.5.1 cells were transfected with pcDNA3.1-GAPDH-V5 or pcDNA3.1 (CTRL) using TransIT®-LT1 transfection reagent. After 24 hours post-transfection, media was replaced with fresh media that was used for secondary infection. Seventy-two hours post-transfection, the HCV-infected Huh-7.5.1 cell infectious 65  supernatants were collected to perform a secondary infection of naïve Huh-7.5.1 cells (d) and HCV-infected Huh-7.5.1 cells were fixed for ICW analysis (c) as described in the Materials and Methods.  Upper panel: Representative ICW wells, probed with HCV anti-core antibody (green), anti-V5 antibody (green), and stained with two dyes for the purpose of determining cell density (CD) (red) for normalization.  Lower panel: Protein abundance was quantified as described in Materials and Methods, averaged across triplicate wells, and expressed relative to protein abundance in control cells (siCTRL).  (d) Secondary infection. Naïve Huh-7.5.1 cells (1.0 × 104 cells) were infected with infectious supernatants from (a) for 72 hours and were fixed for ICW analysis as described in the Materials and Methods.  Upper panel: Representative ICW wells, probed with HCV anti-core antibody (green), anti-V5 antibody (green), and stained with two dyes for the purpose of determining cell density (CD) (red) for normalization.  Lower panel: Protein abundance was quantified as described in Materials and Methods, averaged across triplicate wells, and expressed relative to protein abundance in control cells (siCTRL).  Results (mean ± STD) are from a representative experiment done in triplicate.   Collectively, these results suggest a novel and important role of GAPDH enzymatic activity during post-replication steps of the HCV life cycle, since over-expression of enzymatically active GAPDH molecules pre- or post-establishment of infection triggers an equivalent ~2-fold increase in viral infectivity of HCV-infected Huh-7.5.1 cell supernatants. Importantly, the role of GAPDH in intracellular membrane trafficking and the implications of silencing it for the secretory pathway may have indirect as well as direct repercussions for HCV’s post-replication steps.  2.4.6 Huh-7.5.1 cells with siRNA-mediated GAPDH suppression show decrease in host-cell susceptibility to primary DENV-2 infection Finally, to evaluate whether GAPDH is an important host factor for other Flaviviridae members, I determined the effect of siRNA-mediated GAPDH silencing in naïve human Huh-7.5.1 cells on dengue virus-2 (DENV-2) infection. Huh-7.5.1 cells were 66  transfected with an siRNA pool targeting human GAPDH (siGAPDH, 15 nM) or with a non-targeting siRNA pool (siCTRL, 15 nM). Forty-eight hours p.t., treated cells were infected with DENV-2; 72 hours p.i., the DENV-2-infected Huh-7.5.1 cells were fixed for ICW analysis. Figure 2.7 shows representative ICW wells scanned using the Odyssey infrared imaging system from our primary infection experiments. The treated cells were probed with DENV-2 anti-E antibody (green) and anti-GAPDH antibody (green), and stained with dyes for normalization [(CD); red] (Fig. 2.7; upper panel).  DENV-2 E protein and human GAPDH protein abundance were quantified and expressed relative to protein abundance in control cells (siCTRL) (Fig. 2.7; lower panel). A ~2-fold decrease in DENV E protein abundance was measured for siGAPDH-treated cells during the primary infection compared to siCTRL (Fig 2.7; lower panel). Thus, siRNA-mediated GAPDH suppression in naïve human Huh-7.5.1 cells significantly inhibits primary DENV infection.  The results suggest that GAPDH may be a new host factor for the DENV-2 life cycle. Further investigations will be needed to evaluate the specific contributions of host cells GAPDH in the DENV life cycle.  67   Figure 2.7 Short interfering RNA (siRNA)-mediated GAPDH suppression in naїve human Huh-7.5.1 cells decreases host-cell susceptibility to primary dengue virus infection  Huh-7.5.1 cells were transfected with siRNA pools targeting human GAPDH (siGAPDH, 15 nM) or a non-targeting pool of siRNA (siCTRL, 15 nM) using DharmaFECT 4 transfection reagent.  Forty-eight hours post-transfection, cells were infected with DENV-2 (MOI: ~ 0.005). Seventy-two hours post-infection, the DENV-infected Huh-7.5.1 cells were fixed for ICW analysis as described in the Materials and Methods.  Upper panel: Cells probed with DENV anti-E antibody (green), anti-GAPDH antibody (green), and stained with two dyes to provide cell densities (CD) (red) for normalization.  68  Lower panel: Protein abundance was quantified as described in Materials and Methods, averaged across triplicate wells, and expressed relative to protein abundance in control cells (siCTRL).  Results (mean ± SEM) are from four independent experiments performed in replicates of three or six. ***p < 0.0001.    69  Chapter 3: Novel moonlighting functions of human GAPDH revealed by quantitative proteomic profiling of GAPDH knockdown human hepatoma (Huh-7)-derived cell lines   3.1 Summary Persistent infections with hepatic viruses are one of the major risk factors leading to hepatocellular carcinoma (HCC). Many host factors are involved in the life cycle of hepatitis C virus (HCV), one of which is the enzyme glyceraldehyde-3-phosphate dehydrogenase. GAPDH was shown to protect carcinoma cells from caspase-independent cell death or chemotherapy and to interact directly with the genome of other hepatic viruses, such as hepatitis B virus. However, our current understanding of the relationship between GAPDH and HCV is not sufficient to hypothesize on the effects of targeting GAPDH for HCV therapy. In Chapter 3, I described the generation of two human hepatoma cell (Huh-7) clones with stable GAPDH-knockdown (kd), (67D2 and 67B3), which divided less and showed reduced susceptibility to HCV infection compared to the parental Huh-7 cell line. The aim of this chapter was to characterize these stable GAPDH-knockdown (kd) cell clones for their unique and common molecular signatures associated with cell survival and increased host cell resistance to HCV infection. Thus, these cell clones were further examined using proteomics profiling. Proteomics profiling by nano-liquid chromatography-tandem mass spectrometry revealed that 37 proteins were commonly differentially expressed in two GAPDH-kd cell clonses compared to the parental Huh-7 cell line. These include proteins involved in the reprogramming of metabolic processes, the remodelling of cytoskeletal structure, maintenance of energy homeostasis and antioxidant properties, and the regulation of signaling proteins and gene expression. This result suggested a compensatory mechanism exhibited by the cell clones for the loss of GAPDH expression.  Several proteins unique to each GAPDH-kd cell clones were also identified. Of the overlapping list of proteins, ~81.2% of proteins was identified as exosome-associated proteins. This study investigates the effect of stable GAPDH reduction, addresses challenges associated with the use of stable carcinoma 70  cell clones, highlights the ability of cells to compensate for the reduction in GAPDH, and underlines the targetable potential of GAPDH in hepatic viral-induced HCC.    3.2 Introduction Hepatocellular carcinoma (HCC) is becoming the fifth most prevalent cancer worldwide and is ranked as the third most lethal malignancy after lung and stomach cancer (63, 76, 79, 88). Along with excessive alcoholic consumption and aflatoxin B1 exposure (a mycotoxin that can severely damage the liver), the other major risk factors leading to HCC are persistent infections from HCV and HBV (64, 79, 135). Once HCC develops, only a minority of cases can be effectively treated with surgical resection and transplantation (79, 231). Despite advances in liver transplantation procedures, which include improved immunosuppression, antiviral treatment, and an increase in the number of living donors, the demand for organ transplantation outweighs the supply globally. This is due to the increase in new cases as well as the recurrence of viral infections with accelerated progression to cirrhosis in liver grafts (64). Lack of early detection markers and the asymptomatic nature of the disease result in diagnoses of HCC at advanced stages, making treatment by liver resection a less viable option and thus emphasizing the need to identify an alternative treatment (79). In search of such alternative options, HCC cells have been characterized to identify tumor-associated protein markers that can aid in the early identification of developing liver tumors or of novel targets for treatment (135, 232). Although various therapeutic targets involving different pathways have been studied, the search for effective anticancer targets remains active due to the development of resistance to chemotherapy and the recurrence of tumors (79). A growing body of evidence suggests that a tumor-specific shift is accompanied by the metabolic reprogramming of cells; in order to support the uncontrolled proliferation and invasiveness of the developing tumor, an upregulation in nutrient uptake and glucose metabolism occurs, which requires the upregulation of glycolytic enzymes, such as GAPDH (79). Apart from glycolytic support to cancer cells, the role of GAPDH in enhancing the expression of a macrophage colony stimulating factor (CSF-1) upon stabilization of CSF-1 transcript is shown to play a critical role in several malignancies, including 71  hepatocarcinogenesis (278). Overexpression of GAPDH has been observed to confer carcinoma cells with the ability to survive various cellular offenses, such as caspase-independent cell death (CICD) (47) and chemotherapy (54).   Interestingly, GAPDH has been shown to interact with hepatic viruses, including HCV (95, 182, 195) and HBV (272), which are major contributing factors to HCC (79). Many studies suggest that the expression of viral protein triggers intracellular stress, such as ER stress and oxidative stress. This results in the regulation of several pathways that contribute to chronic inflammation, which leads to oxidative DNA damage and an accumulation of oncogenic mutations. Besides being upregulated in cancer cells, GAPDH has been shown to play a pivotal role in mediating apoptosis upon sensing oxidative stress through various mechanisms, including S-NO (91) and S-glutathionylation (192).  Due to the growing interest in the novel and exhaustive list of non-glycolytic functions, involving posttranslational regulation and interaction with other cellular functions, such as vesicular transport, receptor-mediated cell signaling, endocytosis, maintenance of chromatin structure and DNA integrity, cellular response to oxidative stress, and a role in pathology (diabetes, neurodegenerative disease, malaria) (225), compensation of cellular-specific GAPDH increase in expression as opposed to complete depletion/inhibition of GAPDH has been considered as an approach for treatment (79). However, as GAPDH is ubiquitous and multifunctional in nature, systemic toxicity that may result from targeting GAPDH is of major concern (79). In order to address this concern, I rationalized that the analysis of cells showing a reduction in GAPDH expression levels can provide valuable information on cell viability, putative biological roles of GAPDH, mechanisms by which cells compensate for GAPDH reduction and changes that may contribute to severe toxicity. In this study, I successfully generated two Huh-7-derived cell clones, 67D2 and 67B3, which show stable and dramatic reduction in GAPDH expression levels with a concomitant reduction in GAPDH enzymatic activity. Analysis of the clones’ susceptibility to HCV infection showed a reduction in the efficient establishment of HCV infection indicating lower HCV permissibility. Examination of cell surface molecular determinants found that clone 67B3 showed significantly lower levels of CD81 than did parental Huh-7 cells. In addition, these cells were analysed by mass spectrometry and microarray to identify pathways affected by the depletion of GAPDH. As several studies are considering the use of 72  GAPDH as an additional target along with other anticancer treatments, in this study, I determined the proteomic profile of these GAPDH-knockdown (kd) clones to gain insight into the therapeutic potential of targeting GAPDH. The analysis of the proteome data identified a compensatory mechanism of GAPDH-associated activities and deregulation of several exosome-associated proteins.   3.3 Materials and methods  Antibodies and dyes Two anti-GAPDH antibodies were employed for detecting human GAPDH in cultured cells. A mouse anti-GAPDH monoclonal antibody (1:1000 for western blot (WB) and 1:100 for immunofluorescence (IF), Cat. no. MAB374, Chemicon/Millipore, Temecula, CA, USA) and a rabbit anti-GADPH monoclonal antibody (1:1000 for western blotting (WB) or in-cell western (ICW) and 1:100 for IF, Cat. no. 2118S, Cell Signaling, Danvers, MA, USA) were interchangeably used for detecting cellular GAPDH expression.  A mouse anti-HCV core monoclonal antibody (1:1000, Cat. no. ab2740, Abcam, Cambridge MA, USA) was used for detecting HCV infection. Intracellular human β-tubulin was detected by WB using a rabbit anti-β-tubulin polyclonal antibody (1:1000, Cat. no. ab6046, Abcam, Cambridge, MA, USA). Human CD81 was detected using a mouse anti-human CD81 (TAPA-1) monoclonal antibody (1:1000 for FACS, 55112, BD Biosciences, Mississauga, ON, Canada). Secondary antibodies for WB (1:10,000) and ICW (1:200 (red) or 1:800 (green)) included IRDye® 800-conjugated (Cat. no. LIC-926-32212 or LIC-926-32213 (green)) or 680-conjugated (Cat. no. LIC-926-32222 or LIC-926-32221 (red)) raised against mouse or rabbit (LI-COR Biosciences, Lincoln, NE, USA). Secondary antibodies include IF Alexa Fluor-488-conjugated (Cat. no. A21202 or Cat. no. A21206) raised against mouse or rabbit ( Molecular probes/ Invitrogen, Eugene, OR, USA). Dyes used for ICW cell staining include DRAQ5TM stain (1:10,000, Cat. no. SKU-DR50200, Biostatus, Shepshed, LEC, UK) and Sapphire700TM stain (1:1000, Cat. no. LIC-928-40022, Li-COR Biosciences, Lincoln, NE, USA). Hoechst stain (1 µg/mL for IF, Cat. no. 33258, Molecular probes/Invitrogen, Eugene, OR, USA) was used for staining 73  nuclei. Propidium iodide (Cat. no. P4170, Sigma-Aldrich, St. Louis, MO, USA) staining was performed to exclude dead cells for FACS analysis. Cell culture and reagents Human hepatoma Huh-7b (Huh-7) (23) cells were provided by Dr. Charles Rice (The Rockfeller University, New York, NY, and Apath LLC, St. Louis, MO, USA) (22, 23). Huh-7 cells were cultured as described in Section 2.2. Huh-7-derived cell clones showing reduction in GAPDH levels were cultured in DMEM (Gibco/Invitrogen, Burlington, ON, Canada) containing 4 mM glutamine (L-glu) and 10 mM sodium pyruvate (91); and they were supplemented with 25 U/mL of penicillin (Gibco/Invitrogen, Burlington, ON, Canada), 25 µg/mL of streptomycin (Gibco/Invitrogen, Burlington, ON, Canada) , 0.5 mM non-essential amino acids (NEAA) (Gibco/Invitrogen, Burlington, ON, Canada), and 10% fetal bovine serum (FBS) (Gibco/Invitrogen, Burlington, ON, Canada). They were maintained under selective pressure using 7.35 µM puromycin (Sigma-Aldrich, St. Louis, MO, USA). Huh-7.5.1 cells were kindly provided by Dr. Francis Chisari (Scripps Research Institute, La Jolla, CA, USA) (165, 276). Huh-7.5.1 cells were cultured in DMEM containing 4 mM L-glu and supplemented with 50 U/mL of penicillin, 50 µg/mL of streptomycin, 1 mM NEAA, 10% FBS,  and 10 mM 4-(2-hydroxyethyl)-1-piperazineethanesulfonic acid (HEPES) (Cat. no. 15630-080, Gibco/Invitrogen, Burlington, ON, Canada). Cell lines were grown and maintained at 37˚C and 5% CO2.  Generation of Huh-7-derived cell clones, 67D2 and 67B3 that show a stable reduction in GAPDH expression. The human GAPDH MISSIONTM short-hairpin RNA (hGAPDH-shRNA) sequence (Seq:  CCGGGTGGATATTGTTGCCATCAATCTCGAGATTGATGGCAACAATATCCACTTTTT, MISSIONTM TRC shRNA target (TRCN0000025867)) was custom-designed using GAPDH RefSeq: NM_002046.2. The hGAPDH-shRNA targeted the N-terminal region of the GAPDH transcript. For control MISSION® pLKO.1-puro control (SHC001V-lentiviral vector empty of shRNA (CTRL)) and MISSION® non-target shRNA control (SHC002V-lentiviral vector encoding for non-target shRNA sequence (CTRL2)) transduction particles were used as controls. Huh-7-cells (5 × 104 cells) were infected with hGAPDH-shRNA encoding lentiviral transduction particles (Sigma-Aldrich, St. Louis, MO, USA) at a 74  multiplicity of infection (MOI) of 1 in the presence of 2.5 µM hexadimethrine bromide (Cat. no. H9268, Sigma-Aldrich, St. Louis, MO, USA) for 24 hours. Seventy-two hours post-infection, transduced cells were selected and maintained in media containing 7.35 µM puromycin and supplemented with 10 mM sodium pyruvate to compensate for GAPDH activity. Over next 5-10 days post-viral infection, selection media was replaced when excessive cell death was observed. Upon repopulation (10-20 days) of transduced cells, single-cell Huh-7-derived cell clones, 67D2 and 67B3 were isolated by serially diluting transduced cells in a 96-well plate. Thereafter, these Huh-7-derived cell clones were characterized for stable GAPDH knockdown.  HCV RNA, infectious stock production, and HCV titer determination The plasmid pUCvJFH-1 (a generous gift from Dr. Takaji Wakita, National Institute of Infectious Diseases, Tokyo, Japan) was used for generating HCV RNA and infectious HCV stocks as previously described (112, 276). HCV stocks were produced and titers determined as described in Section 2.3 (174).   HCV infections Huh-7 cells were infected with HCV (MOI: 0.5) diluted in complete media. Twenty-four hours post-viral infection, media was replaced with fresh complete media. Thereafter, the cells were incubated for the indicated number of days and cells were fixed for ICW assay. Alternately, cell supernatants were collected at various time-points post-infection for performing secondary infection of naïve Huh-7.5.1 cells and determining viral protein abundance (HCV core) using ICW assay.  Immunofluorescence (IF) Huh-7-derived cell clones (2 × 104 cells) were seeded on cover slips (13 mm diameter; thickness: No. 1.5). After 24 hours of incubation, cells were fixed in 4% formaldehyde v/v diluted in 1X PBS (pH 7.4, PBS). Cells were permeabilized in PBS containing 0.05% saponin (PBS-S) and blocked in PBS-S containing 3% bovine serum albumin (BSA) (PBS-SB). Primary labelling was performed using respective antibodies diluted 1:100 in PBS-SB 75  for overnight incubation at 4°C. Secondary labelling was performed with respective Alexa Fluor-conjugated anti-mouse or anti-rabbit antibodies diluted with Hoechst stain in PBS-SB. After washes and air-drying, the cover slips were mounted on a slide using a mounting solution (2.5% 1, 4-diazabicyclo [2.2.2] octane (DABCO) in 90% glycerol and 20mM Tris pH8.8). IF images were acquired using an Olympus FluoView FV1000 inverted laser scanning confocal microscopy (Olympus, Tokyo, Japan).  Flow cytometry To detach anchored cells, accutase detachment media (Cat. no. AT-104, Innovative Cell technologies, San Diego, CA, USA) was added to cover adherent cells, and cells were incubated at room temperature. Upon observing cells rounding and lifting off from the surface of the plate, media was pipetted up and down for detachment. Thereafter, cells were washed in FACS buffer (PBS (pH 7.4), 2 % FBS, 2.5 mM EDTA, and 0.05 % sodium azide). Washes were carried out by adding 3 mL of ice cold FACS buffer and centrifuging samples at 1200 rpm for 5 min at 4ºC. Washed cells were resuspended in primary antibody diluted 1:1000 in ice cold FACS buffer and incubated on ice for 1 hour. After incubation in primary antibody, cells were washed again and resuspended in secondary antibody diluted 1:100 in ice cold FACS buffer and incubated on ice for 45 min. Finally, cells were washed and resuspended in 500 µL of 0.2 µg/mL of propidium iodide containing ice cold FACS buffer for flow cytometry analysis. FACS data were acquired using a BDTM LSR II instrument (BD Biosciences, Mississauga, ON, Canada) using FACSDiva software and analyzed using FlowJo software.  RNA extraction Total RNA was isolated from cells (RNeasy mini kit/RNeasy plus mini kit, Qiagen, Toronto, ON, Canada) lysed in buffer RLT (Qiagen, Toronto, ON, Canada) using  Qiashredder (Qiagen, Toronto, ON, Canada).   Sample preparation for Quantitative Proteomics Twenty-four hours post-seeding (1x106 cells), cells were washed with ice-cold PBS and harvested in 500 μL of radioimmunoprecipitation assay (RIPA) buffer (50 mM Tris-HCl pH 76  8.0, 150 mM sodium chloride, 1% Triton X-100, 0.5% sodium deoxycholate, and 0.1% SDS) containing 5X complete, EDTA-free, proteinase inhibitor cocktail (Roche, Laval, QC, Canada). Protein concentrations were measured by Bradford Protein Assay (Pierce/Thermoscientific, Rockford, IL, USA). Extracted proteins (25 μg) were precipitated by ice cold (-20°C) acetone as described elsewhere (25). For digestion, precipitated and air-dried protein samples were resuspended in sodium deoxycholate digestion buffer (1% sodium deoxycholate, 50mM ammonium bicarbonate, pH 8.0) and immediately heated at 99°C for 10 mins. After cooling to room temperature, the samples (50 µg) were subjected to disulfide reduction for 30 min at 37°C using dithiothreitol (1 µg), sulfhydryl alkylation for 20 min at 37°C using iodoacetamide (5 µg), and digested in-solution with 0.5 µg of trypsin (1:50 enzyme:substrate ratio) for overnight at 37°C. After digestion, samples were diluted 3-fold with a solution of 3% (v/v) acetonitrile containing 0.5% (v/v) formic acid. The resulting deoxycholate acid precipitate was pelleted by centrifugation at 16100 rcf for 10 min (67). The supernatants containing the peptides were desalted on C18 Spin columns (Pierce Biotechnology/Thermoscientific, Rockford, IL, USA), a reverse-phase resin with a 30 μg capacity, and eluted in 0.5% (v/v) formic acid for purification.   Triplex dimethyl labelling Eluted peptides were chemically labelled with stable isotope dimethyl labelling as described previously. The Huh-7 samples were labelled with formaldehyde-D2 using cyano[2]borohydride (light), 67D2 samples were labelled with formaldehyde-H2 using sodium cyanoborohydride (medium), and 67B3 samples were labelled with 13C-D2-formaldehyde using cyanoborodeuteride (heavy). For each comparison, equal amounts of peptides were mixed, and desalted on C18 Spin columns (Pierce Biotechnology/Thermoscientific, Rockford, IL, USA). Peptides were dried and resuspended in 0.1% formic acid.  Nano-liquid chromatography-tandem mass spectrometry (nano-LC-MS/MS) analysis Peptides were subjected to on-line nanoflow liquid chromatography (nLC) using an EASY-nLC system (Thermo Fischer Scientific, Billerica, MA, USA) that composed of two columns, a C18 reverse phase precolumn (Proxeon EASY-PreColumn, length = 2 cm, i.d. = 100 μm; 77  ReproSil-Pur C18-AQ, 5 μm, 120 A) and an analytical column (Proxeon EASY-Column, length = 10 cm, i.d. = 75 μm; ReproSil-Pur C18-AQ, 3 μm, 120 A). Eluent was collected with the gradient generated by 4-35% acetonitrile in 0.5% formic acid at a flow rate of 300 nL/min for 90 min, 35-45% acetonitrile in 0.5% acetic acid at a flow rate of 300 nL/min for 90 min and 64-90% acetonitrile in 0.5% acetic acid at a flow rate of 300 nL/min for 5 min as specified. The eluent was electrosprayed into a linear trap quadropole (LTQ)-Orbitrap-Velos-Electron transfer dissociation (ETD) (Thermo Fisher Scientific, Bremen, Germany) using a thermo nanoelectrospray ion source2. The LTQ fourier transform (LTQ FT) was set to acquire a high mass accuracy and MS scan resolution at 60000 , and the ten most abundant ions were further collision-induced dissociation (CID)-fragmented (normalized CID energy- 35%, and dynamic exclusion enabled: exclusion list size 500, exclusion duration 60s). Ion trap and orbitrap maximal injection times were set to 25 milliseconds and 500 milliseconds, respectively. The ion target values used were 10,000 for the ion trap, and 10,00,000 for the orbitrap were used. Here, the LTQ-Orbitrap provides sequence information with high mass accuracy and resolving power that increases the confidence of protein identification (144). The coupling of LTQ-Orbitrap with ETD is essential for complete characterization of proteins of mass >20kDa (153). With CID, further fragmentation of ions in a few channels acquires complete sequence coverage of intact protein (153).    Data analysis Raw data were processed and quantified with a Proteome Discoverer (version 1.3, Thermo Electron) using standardized workflows. For peptide identification, Mascot 2.3 (Matrix Science) and a human subset of the Uniprot/Swiss-Prot (database release date 11/7/2011, total 20238 protein entries) supplemented with frequently observed MS contaminants were used. Parameters for peptide identification included 10 parts per million (ppm) precursor mass tolerances, 0.8 Da fragment ion tolerance and up to two missed cleavages. Modification parameters included carbamidomethyl cysteine as fixed and light, intermediate, and heavy dimethylation of peptide N-termini and lysine residues: oxidized methionine as variables. A dimethyl-based quantification method workflow was chosen in Proteome Discoverer software with default parameters with the exception of 1 min retention time tolerance, and spectra with two missing channels were allowed for quantification. After identification and 78  quantification of peptides, results from the technical replicates were combined and filtered using the criteria specified to be a Mascot ion score of at least 20, peptides of a minimum 7 amino acid residues, and a Mascot search with a  position rank 1 (167). Using this criteria yielded us peptide and protein false discovery rates (FDR) of <1%. Finally, peptide ratios were normalized on the protein median specified by the Proteome Discoverer software, which is the observation of 20 minimum proteins to allow normalization. The percentage variability between technical replicates was depicted using bar plots (Fig. A1.5). These variability plots  (a measure of the precision or reliability with which MS estimated the peptide or protein of relative abundance) of technical replicates revealed > 89% of the proteins were identified with ≤ 35 % variability displaying high reliability of the generated data.  For further analysis, only those proteins that were found in both biological replicates were considered. Using a fold change threshold of 1.3, a list of proteins that were significantly differentially expressed was determined, and bar plots were generated for proteins categorized based on molecular functions using a Panther Classification System (PANTHER version7) (162). Gene ontology to determine biological processes, molecular functions, and pathway enrichment analysis were determined using InnateDB ontology tools (InnateDB) (143). A table listing differentially expressed common proteins was also generated using a very stringent biostatistical test involving the Benjamini-Hochberg method.   Statistical analysis Standard deviations (STD) are provided for an experiment (n=1) performed with three technical replicates, or for experiments (n ≥3) performed with one technical replicate. Standard error of means (SEM) is provided for experiments (n ≥3) performed with two or more technical replicates. P-values are calculated using a student’s t-test on technical replicates pooled from n number of experiments.   79  3.4 Results  3.4.1 Huh-7-derived cell clones showing shRNA-mediated stable GAPDH knockdown To investigate the role of GAPDH in Huh-7 cells, the approach of stably reducing GAPDH was applied using a short-hairpin RNA (shRNA). To do so, Huh-7 cells were infected with commercially available lentiviral transduction particles that encode for an shRNA sequence (122) targeting the N-terminal region of the GAPDH mRNA (human GAPDH-shRNA) (Fig. 3.1a and b), and a selectable marker for puromycin. For control, Huh-7 cells were transduced with empty lentiviral transduction particles (CTRL), i.e., lentiviral transduction particles that do not contain shRNA. After infection and selection of a transduced subpopulation of cells, single-cell clones, 67D2 and 67B3 were successfully isolated and analyzed for GAPDH expression (Fig. 3.1c).      Figure 3.1. Overview of the molecular tools and experimental approach used to establish the Huh-7-derived stable GAPDH-knockdown clones  80  (a) Schematic representation of a short-hairpin RNA (shRNA) targeting human GAPDH mRNA (human GAPDH-shRNA) encoded by a genome of a lentiviral transduction particle. (b) Region (blue) in the N-terminal sequence of the human GAPDH messenger RNA (mRNA), which is targeted by the human GAPDH-shRNA antisense strand (red) shown in (a).  (c) Schematic representation of the protocol for generating Huh-7-derived stable GAPDH-knockdown (kd) clones, 67D2 and 67B3.  Huh-7 cells were infected with human GAPDH-shRNA-encoding lentiviral transduction particles (GAPDH kd, MOI: 1) or empty-lentiviral transduction particles (CTRL, MOI: 1) for 24 hours as described in Materials and Methods. Seventy-two hours post-viral infection (p.i.), transduced cells were selected and maintained in selection media supplemented with sodium pyruvate to compensate for GAPDH activity. After selection and repopulation of transduced cells, two single-cell colonies of stable GAPDH knockdown clones were isolated as described in Materials and Methods, and were designated 67D2 and 67B3. A single-cell colony of control Huh-7 (CTRL) cells was also isolated.  This figure was generated by F. Jean.  Reductions in GAPDH levels were analyzed by various detection techniques allowing relative quantification of protein abundance, transcript level, and enzymatic activity in Huh-7-derived stable GAPDH-knockdown (kd) clones (GAPDH-kd clones). Western blot (WB) analysis (Fig. 3.2a; upper panel) of cell lysates revealed significant and dramatic knockdown of GAPDH protein expression in GAPDH-kd clones [~18-fold decrease (67D2) and ~11-fold decrease (67B3)] compared to control Huh-7 cells, respectively. GAPDH protein abundance was observed to be similarly decreased in these GAPDH-kd clones [~17-fold decrease (67D2) and ~5-fold decrease (67B3)] compared to control Huh-7 cells (CTRL) by in-cell western (ICW) analysis (Fig. 3.2b). To show that the residual level of GAPDH was enzymatically active, a fluorescence-based assay was used. The assay exploited the catalytic property of the GAPDH tetramer to convert NAD+ to NADH upon oxidative phosphorylation of its substrate glyceraldehyde-3-phosphate to bisphosphoglycerate (BPG). The result of this assay showed that the GAPDH enzymatic activity was decreased in these GAPDH-kd clones [~13-fold decrease (67D2) and ~5-fold decrease (67B3)] compared to control Huh-7 cells (CTRL) in support of the results of the WB and ICW analysis (Fig. 3.2c). RT-QPCR was used to determine level of GAPDH mRNA in GAPDH-kd clones, which showed a~12-fold (67D2) and ~9-fold (67B3) decrease compared to control Huh-7 cells (CTRL) (Fig. 1d). This decrease was similar to the protein abundance pattern detected by WB and ICW analysis.  81  The reduction in GAPDH mRNA levels suggested that GAPDH knockdown was from shRNA-mediated degradation of the GAPDH mRNA. A marked difference in GAPDH protein abundance, GAPDH mRNA levels, and GAPDH enzymatic activity were not observed in parental Huh-7 cells compared to control Huh-7 cells (CTRL) (Fig. 3.2a-d). 82   83  Figure 3.2. Biochemical results demonstrating knockdown of Huh7-derived stable GAPDH protein abundance, enzymatic activity, and mRNA expression in Huh-7-derived stable GAPDH-knockdown clones  (a) GAPDH protein abundance determined by WB for Huh-7-derived GAPDH-knockdown (kd) clones, 67D2 and 67B3. Cell lysates were harvested from parental Huh-7 cells (parental), control Huh-7 cells (CTRL), and Huh7-derived stable GAPDH-kd clones (67D2 and 67B3) for WB analysis (A), and probed with human GAPDH antibody (green) and β-tubulin antibody (red) (loading control used for normalization) expression as described in Materials and Methods.  Upper panel: A WB representative  Lower panel: Protein abundance was quantified as described in Materials and Methods, averaged across WB bands, and expressed relative to protein abundance in control Huh-7 cells (CTRL).  Results (mean ± STD) are for three independent WB analyses. *** p < 0.0001.  (b) GAPDH protein abundance determined by ICW for Huh-7-derived stable GAPDH-kd clones, 67D2 and 67B3. Equal numbers of parental Huh-7 cells (parental), control Huh-7 cells (CTRL) and Huh-7-derived stable GAPDH-kd clones (67D2 and 67B3) were seeded in a 96-well plate. Twenty-four hours post-seeding, cells were fixed for ICW analysis (B) as described in Materials and Methods.  Upper panel: Representative ICW wells probed with HCV anti-core antibody (green) and anti-GAPDH antibody (green), and stained with two dyes for the purpose of determining cell density (CD) (red) for normalization.  Lower panel: Protein abundance was quantified as described in Materials and Methods, averaged across triplicate wells, and expressed relative to protein abundance in control Huh-7 cells (CTRL).  Results (mean ± SEM) are for four independent ICW experiments, each performed in triplicate. *** p < 0.0001.  (c) GAPDH enzymatic activity determined for Huh-7-derived stable GAPDH-kd clones, 67D2 and 67B3. Parental Huh-7 cells (parental), control Huh-7 cells (CTRL), and Huh-7-derived stable GAPDH-kd clones (67D2 and 67B3) were seeded in a 96-well plate. Twenty-four hours post-seeding, cell lysates were prepared according to the manufacturer’s instructions for detecting intracellular GAPDH enzymatic activity using KDaltertTM GAPDH assay kit as described in Materials and Methods. Values are plotted relative to GAPDH enzymatic activity detected in control Huh-7 cells (CTRL). 84  Results (mean ± SEM) are for four independent experiments, each performed in triplicate. ***p < 0.0001.  (d) GAPDH mRNA levels determined for Huh-7-derived stable GAPDH-kd clones, 67D2 and 67B3. Total RNA was harvested from parental Huh-7 cells (parental), control Huh-7 cells (CTRL), and Huh-7-derived stable GAPDH-kd clones (67D2 and 67B3). GAPDH mRNA levels normalized to β-actin mRNA levels, were relatively quantified from 15 ng of total RNA by QPCR as described in Materials and Methods. Values are plotted as relative GAPDH mRNA levels in comparison to control Huh-7 cells (CTRL).  Results (mean ± SEM) are for two independent experiments, each performed in triplicate. ***p < 0.001.   3.4.2 shRNA-mediated stable GAPDH-knockdown in Huh-7-derived cell clones, 67D2 and 67B3, showed reduced susceptibility to HCV infection After demonstrating stable reduction in GAPDH protein abundance, mRNA levels, and GAPDH enzymatic activity, GAPDH-kd clones were tested for their susceptibility to HCV infection. Cultured parental Huh-7 cells, control Huh-7 cells, and GAPDH-kd clones (67D2 and 67B3) were infected with HCV (MOI: 0.5) for 24 hours to allow uninterrupted HCV replication and establishment of infection. Seventy-two hours post-infection, media was replaced with fresh media to allow accumulation of extracellular infectious virus. On day 5 post-infection, the HCV-infected GAPDH-kd clone supernatants were collected to perform a secondary infection of naïve Huh.7.5.1 cells (Fig. 3.3b). At the same time, the primary infection cells were fixed for ICW analysis (Fig. 3.3a). The upper panel in Fig. 3.3a shows representative ICW wells scanned using the Odyssey infrared imaging system from the primary infection experiments. Cells were probed with HCV anti-core antibody (green) and stained with dyes for cell density normalization [(CD); red].  HCV core was quantified and expressed relative to protein abundance in control Huh-7 cells (CTRL) (Fig. 3.3a; lower panel). A significant reduction of HCV core abundance was measured for GAPDH-kd clones [~4-fold decrease (67D2) and ~9-fold decrease (67B3)] during the primary infection compared to CTRL (Fig. 3.3a; lower panel).  The reduction in core protein abundance in HCV-infected GAPDH-kd clones may have resulted from either an increase in core protein turnover due to increased assembly and 85  egress of virus, which would reduce the build-up of core within the cells, or reduced production of core protein due to compromised viral susceptibility.  To distinguish between these two possibilities, I first wanted to determine whether stable GAPDH-kd clones caused an increase in HCV infectious virus particle production: to this end, supernatants from HCV-infected GAPDH-kd clones were collected and used to perform a secondary infection on naïve Huh-7.5.1 cells.  After the secondary infection proceeded for 72 hours, these supernatant-infected Huh-7.5.1 cells were fixed for ICW assays.  A significant reduction of HCV core abundance was measured for Huh-7.5.1 cells infected with GAPDH-kd clone supernatants [~13-fold decreases (67D2) and ~27-fold decrease (67B3)] during the secondary infection compared to cells treated with control Huh-7 (CTRL) supernatants (Fig. 3.3a; lower panel). Thus, shRNA-mediated knockdown of GAPDH in GAPDH-kd clones inhibit primary HCV infection, which results in inhibition of the production and/or release of infectious HCV virus particles in HCV-infected GAPDH-kd clone supernatants. The combined results of primary and secondary infections showed a reduction in extracellular infectious virus due to compromised primary HCV infection in GAPDH-kd clones.  86   Figure 3.3. Decrease in host-cell susceptibility to primary HCV infections Huh-7-derived stable GAPDH-knockdown clones and decrease in viral infectivity of the HCV-infected cell supernatants   (a) Primary infection. Parental Huh-7 cells (parental), control Huh-7 cells (CTRL), and Huh-7-derived stable GAPDH-kd clones (67D2 and 67B3) were infected with HCV (MOI: 0.5) for 24 hours. Two-thirds of the media was replaced with fresh media on day 1 post-viral infection (p.i). On day 3 p.i., media was replaced with fresh media that was used for secondary infection. On day 5 p.i., the HCV-infected Huh-7 cell infectious supernatants were collected to perform a secondary infection of naïve Huh-7.5.1 cells (b) and HCV-infected Huh-7 cells were fixed for ICW analysis (a) as described in Materials and Methods.  Upper panel: Representative ICW well, probed with HCV anti-core antibody (green) and anti-GAPDH antibody (green), and stained with two dyes for the purpose of determining cell density (CD) (red) for normalization.  Lower panel: Protein abundance was quantified as described in Materials and Methods, averaged across triplicate wells, and expressed relative to protein abundance in control Huh-7 cells (CTRL). 87  Results are the mean ± SEM for three independent experiments, each performed in triplicate. **p < 0.001 ***p < 0.0001.  (b) Secondary infection. The HCV-infected Huh-7.5.1 cell supernatants collected in (a) were used to infect naïve Huh-7.5.1 cells for 72 hours; then the cultured cells were fixed for ICW analysis as described in Materials and Methods.  Upper panel: Representative ICW wells, probed with HCV anti-core antibody (green) and anti-GAPDH antibody (green), and stained with two dyes for the purpose of determining cell density (CD) (red) for normalization.  Lower panel: Protein abundance was quantified as described in Materials and Methods, averaged across triplicate wells, and expressed relative to protein abundance in Huh-7.5.1 cells infected with HCV-infected control Huh-7 cell supernatant (S:CTRL).  Results (mean ± SEM) are for two independent experiments, each performed in triplicate. ***p < 0.0001.  HCV core protein in HCV-infected hepatoma cells have been shown to be cytosolic and associated with either ER membranes or on the surface of lipid-droplet (10).  To study effects of GAPDH-kd on core protein expression and localization, Huh-7-derived stable GAPDH-kd clones, 67D2 and 67B3, were analysed for HCV core distribution in HCV-infected cells by immunofluorescence (IF). Here, parental Huh-7 cells, control Huh-7 cells, and stable GAPDH-kd clones (67D2 and 67B3) were seeded on coverslips and were infected with HCV (MOI: 0.5) for 24 hours. Five days post-viral infection, cells were fixed for immunofluorescence analysis and probed for human β-tubulin (green) and HCV core (red) (Fig. 3.4). The IF images showed that the distribution of β-tubulin protein remained unaltered across HCV-infected parental Huh-7 cells and HCV-infected Huh-7-derived cell clones (CTRL, 67D2 and 67B3). On the other hand, the HCV core protein distribution in HCV-infected control Huh-7 cells (CTRL) and GAPDH-kd clones (67D2 and 67B3) appeared less dispersed than in HCV-infected parental Huh7 cells. In addition, I observed a reduction in HCV core protein expression in GAPDH-kd clones (67D2 and 67B3) compared to HCV-infected control Huh-7 cells (CTRL). This result suggested that reduced susceptibility to HCV infection also results from compromised HCV core abundance observed at the cellular level.  88   89  Figure 3.4. IF results demonstrating a robust decrease in host-cell susceptibility to primary HCV infection in Huh-7-derived stable GAPDH-knockdown clones  Parental Huh-7 cells (parental), control Huh-7 cells (CTRL), and Huh-7-derived stable GAPDH-kd clones (67D2 and 67B3) were seeded on coverslips. Twenty-four hours post-seeding, cells were infected with HCV (MOI: 0.5). On day 5 post-viral infection, the HCV-infected Huh-7 cells were fixed for IF analysis as described in the Materials and Methods.  Representative IF image of cells probed with HCV anti-core antibody (red) for detecting HCV infection and anti-β-tubulin antibody (green) for detecting cellular protein, and stained with Hoechst dye for detecting nuclei. .  3.4.3 Proteomic analysis of Huh-7-derived stable GAPDH-knockdown clones displayed compensatory mechanisms to GAPDH depletion To identify unique and differential changes in the proteome expression across the two GAPDH-kd clones, an advanced systems biology approach employing nano-liquid chromatography-tandem mass spectrometry (nano-LC-MS/MS) was utilized. This technology allows for relative protein quantification with high sensitivity, resolution, accuracy, and the ability to provide complete sequence coverage of identified peptides (181). Figure 3.5 shows a schematic summary of the experimental and data analysis process performed for the quantitative proteomic study. Briefly, total protein extracts were trypsin-digested and triplex dimethyl-labelled from a replicate of parental Huh-7 cells and stable GAPDH-kd clones (67D2 and 67B3). Labelled peptides were subjected to nano-LC. Following nano-LC, the samples were electrosprayed into linear trap quadropole (LTQ)-Orbitrap coupled with electron transfer dissociation (ETD) and collision-induced dissociation (CID) for tandem mass spectrometry. The approach provided sequence information with high mass accuracy and resolving power, which increases the confidence of protein identification (144). The coupling of LTQ-Orbitrap with ETD is essential for the complete characterization of proteins having a mass of > 20 kDa (153). With CID, ions are further fragmented to a few channels, which enable the acquisition of the complete sequence data coverage of intact protein (153).  90   Figure 3.5. Schematic summary of the experimental and data analysis process of the quantitative proteomics study  Upon acquisition of data, I first determined the quality of runs by graphing percent variability bar plots for each technical replicate. The graph showed ≥ 89 % of proteins with ≤ 35% variability (Fig. A1.6). A total of 161 proteins identified by nano-LC-MS/MS are listed, along with fold changes in Table 3.1.  Next, I plotted the relative GAPDH abundance measured by both WB analysis (Fig. 3.2a) and MS analysis in GAPDH-kd clones (67D2 and 67B3) and parental Huh-7 cells for comparison. As expected, the result showed a high correlation between quantitative proteomics and quantitative WB data (Fig. 3.6). 91   Figure 3.6. Correlation between quantitative proteomics and quantitative western blotting for measuring intracellular GAPDH protein abundance in Huh-7-derived stable GAPDH-knockdown clones  Comparison of the GAPDH protein abundance in Huh-7-derived cell lines as measured by (i) mass spectrometry (MS) analysis of stable isotope dimethyl-labeled samples, and (ii) quantitative western blotting (WB) analysis performed using the LI-COR Odyssey infrared imaging system.  The WB results (mean ± STD) are from 3 independent WB analyses described in Fig. 3.2a. The MS results (mean ± SEM) are for 2 independent experiments by mass spectrometry ***p < 0.0001 performed in triplicate.    92  Table 3.1. List of differentially expressed proteins in Huh-7-derived stable GAPDH-knockdown clones and fold changes (FC) compared parental Huh-7 cells 67D2 Down Up Description Gene symbol FC Description Gene symbol FC Glyceraldehyde-3-phosphate dehydrogenase GAPDH -27.40 Myc box-dependent-interacting protein 1 BIN1 3.85 Cofilin-1 CFL1 -2.40 albumin  ALB 3.58 Filamin-A FLNA -2.19 Retinal dehydrogenase 1 ALDH1A1 3.37 Lamin-A/C LMNA -2.09 Isocitrate dehydrogenase [NADP] cytoplasmic IDH1 2.14 Heat shock protein beta-1 HSPB1 -2.08 Glutamate dehydrogenase 1, mitochondrial GLUD1 2.09 RAN binding protein 1  RANBP1 -2.03 Glutamate dehydrogenase 2, mitochondrial GLUD2 2.01 high mobility group box 1  HMGB1 -2.00 CAP, adenylate cyclase-associated protein 1 (yeast)  CAP1 1.95 Putative nucleoside diphosphate kinase NME2P1 -1.93 Phosphoserine aminotransferase PSAT1 1.91 Nucleoside diphosphate kinase B NME2 -1.81 D-3-phosphoglycerate dehydrogenase PHGDH 1.89 Calponin-2 CNN2 -1.69 Ubiquitin carboxyl-terminal hydrolase isozyme L1 UCHL1 1.84 Nucleoside diphosphate kinase A NME1 -1.66 Neuroblast differentiation-associated protein AHNAK AHNAK 1.84 Filamin-B FLNB -1.66 Glutathione S-transferase P GSTP1 1.79 proliferation-associated 2G4, 38kDa  PA2G4 -1.60 Ras-related protein Rab-1B RAB1B 1.72 Filamin-C FLNC -1.60 60 kDa heat shock protein, mitochondrial HSPD1 1.71 Myosin-9 MYH9 -1.57 eukaryotic translation initiation factor 4A3  EIF4A3 1.67 Translationally-controlled tumor protein TPT1 -1.56 Nucleolin NCL 1.62  93  67D2 Down Up Description Gene symbol FC Description Gene symbol FC Profilin-1 PFN1 -1.52 Aldehyde dehydrogenase, mitochondrial ALDH2 1.59 T-complex protein 1 subunit theta CCT8 -1.52 Creatine kinase B-type CKB 1.58 Aldo-keto reductase family 1, member C1  AKR1C1 -1.51 Perilipin-2 PLIN2 1.58 Ribosomal protein L4  RPL4 -1.50 ribosomal protein S7  RPS7 1.55 T-complex protein 1 subunit beta CCT2 -1.47 Annexin A4 ANXA4 1.53 Transketolase TKT -1.45 10 kDa heat shock protein, mitochondrial HSPE1 1.53 Heterogeneous nuclear ribonucleoprotein Q SYNCRIP -1.44 Peroxiredoxin-1 PRDX1 1.53 Heat shock protein 105 kDa HSPH1 -1.43 Acyl-CoA-binding protein DBI 1.53 40S ribosomal protein S3 RPS3 -1.43 Bifunctional purine biosynthesis protein PURH ATIC 1.49 Proteasome activator complex subunit 2 PSME2 -1.39 Phosphatidylethanol-amine-binding protein 1 PEBP1 1.48 26S proteasome non-ATPase regulatory subunit 14 PSMD14 -1.38 Acetyl-CoA acetyltransferase, cytosolic ACAT2 1.47 Endoplasmin HSP90B1 -1.36 Protein dpy-30 homolog DPY30 1.45 Calponin-3 CNN3 -1.35 Peroxiredoxin-5, mitochondrial PRDX5 1.45 DNA replication licensing factor MCM3 MCM3 -1.34 Adenine phosphoribosyl-transferase APRT 1.41 Stomatin (EPB72)-like 2  STOML2 -1.34 Clathrin heavy chain 1 CLTC 1.40 Neutral alpha-glucosidase AB GANAB -1.33 Fatty acid synthase FASN 1.35 Talin-1 TLN1 -1.31 Heat shock protein HSP 90-alpha HSP90AA1 1.34 Eukaryotic initiation factor 4A-I EIF4A1 -1.31 Protein disulfide-isomerase A6 PDIA6 1.33 Eukaryotic translation initiation factor 5A-1 EIF5A -1.31 Voltage-dependent anion-selective channel protein 1 VDAC1 1.33   94  67D2 Down Up Description Gene symbol FC Description Gene symbol FC    Delta(3,5)-Delta(2,4)-dienoyl-CoA isomerase, mitochondrial ECH1 1.33 67B3 Down Up Description Gene symbol FC Description Gene symbol FC Glyceraldehyde-3-phosphate dehydrogenase GAPDH -9.66 Filamin-C FLNC 3.04 albumin  ALB -3.90 Neuroblast differentiation-associated protein AHNAK AHNAK 2.87 Vimentin VIM -2.26 Perilipin-2 PLIN2 2.52 Profilin-1 PFN1 -2.09 D-3-phosphoglycerate dehydrogenase PHGDH 2.42 Eukaryotic translation initiation factor 5A-1 EIF5A -1.92 Acetyl-CoA acetyltransferase, cytosolic ACAT2 2.17 Fatty acid-binding protein, liver FABP1 -1.84 3-hydroxyacyl-CoA dehydrogenase type-2 HSD17B10 2.16 Heat shock 70 kDa protein 1 HSPA1A -1.81 CAP, adenylate cyclase-associated protein 1 (yeast)  CAP1 2.08 L-lactate dehydrogenase A chain LDHA -1.69 Alanyl-tRNA synthetase, cytoplasmic AARS 1.90 proliferation-associated 2G4, 38kDa  PA2G4 -1.68 Ubiquitin carboxyl-terminal hydrolase isozyme L1 UCHL1 1.76 Cofilin-1 CFL1 -1.65 Myc box-dependent-interacting protein 1 BIN1 1.76 Heat shock protein 105 kDa HSPH1 -1.64 Retinal dehydrogenase 1 ALDH1A1 1.76 Myosin-9 MYH9 -1.62 GCN1 general control of amino-acid synthesis 1-like 1 (yeast)  GCN1L1 1.71 Tubulin beta-2C chain TUBB2C -1.61 basigin (Ok blood group)  BSG 1.70  95  67B3 Down Up Description Gene symbol FC Description Gene symbol FC Putative nucleoside diphosphate kinase NME2P1 -1.53 Phosphoserine aminotransferase PSAT1 1.65 Nucleoside diphosphate kinase B NME2 -1.53 Isocitrate dehydrogenase [NADP] cytoplasmic IDH1 1.64 Proteasome activator complex subunit 1 PSME1 -1.52 Creatine kinase B-type CKB 1.64 60S ribosomal protein L9 RPL9 -1.51 Glutathione S-transferase P GSTP1 1.62 Tubulin beta-4 chain TUBB4 -1.48 Peroxiredoxin-5, mitochondrial PRDX5 1.60 Translationally-controlled tumor protein TPT1 -1.45 Endoplasmic reticulum protein ERp29 ERP29 1.56 high mobility group box 1  HMGB1 -1.42 Fatty acid synthase FASN 1.56 complement component 1, q subcomponent binding protein  C1QBP -1.42 Tubulin beta-6 chain TUBB6 1.56 Eukaryotic initiation factor 4A-I EIF4A1 -1.38 Filamin-B FLNB 1.56 Proteasome activator complex subunit 2 PSME2 -1.34 Nucleolin NCL 1.55 Nucleoside diphosphate kinase A NME1 -1.32 GDP dissociation inhibitor 1  GDI1 1.50 Fructose-bisphosphate aldolase A ALDOA -1.30 Phosphatidylethanol-amine-binding protein 1 PEBP1 1.49    Delta(3,5)-Delta(2,4)-dienoyl-CoA isomerase, mitochondrial ECH1 1.48    Serpin H1 SERPINH1 1.45    Annexin A4 ANXA4 1.44    Bifunctional purine biosynthesis protein PURH ATIC 1.44    Microtubule-associated protein 4 MAP4 1.42    Phosphoglycerate kinase 1 PGK1 1.38    Clathrin heavy chain 1 CLTC 1.38    14-3-3 protein gamma YWHAG 1.34 96  67B3 Down Up Description Gene symbol FC Description Gene symbol FC    Annexin A2 ANXA2 1.32    Myristoylated alanine-rich C-kinase substrate MARCKS 1.31    Malate dehydrogenase, mitochondrial MDH2 1.31    Chloride intracellular channel protein 1 CLIC1 1.31    Protein dpy-30 homolog DPY30 1.31    Peroxiredoxin-2 PRDX2 1.30 Fold changes (FC) in gene expression in the two Huh7-derived stable GAPDH-kd clones are determined over parental Huh-7 cells. Samples were prepared and labelled for mass spectrometry by the author; mass spectrometry was performed and technical replicates (three) were merged to provide a table of biological replicates (two) by J. John. The biological replicates (two) were merged by V. Svinti to generate this table.   3.4.3.1. Comparison between the common and unique protein groups significantly changed in the GAPDH knockdown Huh-7-derived cell lines 67D2 and 67B3 in relation to the parental Huh-7 cells. Using the fold change threshold of 1.3 for upregulated proteins and -1.3 for downregulated proteins, from the list of 161 proteins identified (Table 3.1, Fig. 3.7), 36 and 39 proteins were upregulated in GAPDH-kd clones, 67D2 and 67B3, respectively. On the other hand, 35 and 25 proteins were downregulated in Huh-7-derived stable GAPDH-kd clones, 67D2 and 67B3, respectively (Table 3.1, Fig. 3.7). More importantly, 37 proteins were identified as overlapping proteins, which were differentially upregulated and downregulated across both GAPDH-kd clones as indicated in Fig. 3.7. As these proteins were upregulated or downregulated in both of the GAPDH-kd clones, I concluded that the differential regulation of these proteins was mainly the result of shRNA-mediated stable GAPDH-kd.   97   Figure 3.7. Comparison between the common and unique protein groups significantly changed in the Huh-7-derived stable GAPDH-knockdown clones, 67D2 and 67B3 in relation to the parental Huh-7 cells  This figure was generated by the author using Table 3.1.  In order to analyze the molecular functions affected in GAPDH-kd clones, the Panther Classification System was utilized to cluster differentially upregulated and downregulated proteins with respect to their general activities  (162). A bar plot of molecular activities is displayed in Fig 3.8, which was generated based on clustering of proteins with respect to their functions. In comparison to parental Huh-7 cells, the GAPDH-kd clones (67D2 abd 67B3) showed differential regulation of proteins that fall in three major categories of molecular activities, namely catalytic, binding, and structural activities Also, for several 98  upregulated activities, a compensatory downregulation of similar activities was observed that involved a different set of proteins (Fig 3.8). For example, downregulation of proteins categorized in binding activity was accompanied by upregulation of proteins also categorized in that same activity.  The proteins, along with fold change values (Table 3.1), were used to perform gene ontology (GO) and pathway enrichment analyses using InnateDB to determine common and uniquely altered molecular functions (Table 3.2). For example, the downregulation of actin-binding function or upregulation of oxidoreductase or transferase activity was observed for both of the GAPDH-kd clones (Table 3.2).  A pathway involving regulation of the actin cytoskeleton was downregulated, which may affect several other functions in the cells, such as the process of endocytosis, or intracellular vesicular trafficking. Also, binding activity related to the ability of GAPDH to form functional complexes appears to be affected. Pathways including the metabolism of several amino acids and fatty acids were upregulated, which suggests reprogramming of cellular metabolic processes in these stable GAPDH-kd clones.    99   Figure 3.8. Clustering of differentially expressed proteins across the Huh-7-derived stable GAPDH-knockdown clones by molecular activity (functions)  The figure was generated by V. Svinti using the Panther Classification System on the list of proteins in Table 3.1, and it was modified by the author. 100  Table 3.2. List of molecular functions significantly affected in Huh-7-derived stable GAPDH-knockdown clones 67D2 67B3 Down Molecular function P-value Molecular function P-value actin binding  0.000 fatty acid binding  0.013 transcription factor binding  0.016 actin binding  0.035 Up Molecular function P-value Molecular function P-value oxidoreductase activity  0.009 transferase activity  0.003 transferase activity  0.012 serine-type endopeptidase inhibitor activity  0.039 drug binding  0.022 transferase activity, transferring acyl groups other than amino-acyl groups  0.039 protein homodimerization activity  0.023 oxidoreductase activity  0.044 aldehyde dehydrogenase (NAD) activity  0.035     cell surface binding  0.035     glutamate dehydrogenase (NAD+) activity  0.035     glutamate dehydrogenase NAD(P)+] activity  0.035     leucine binding  0.035     peroxidase activity  0.035     pyridoxal phosphate binding  0.035     transferase activity, transferring acyl groups other than amino-acyl groups  0.035     antioxidant activity  0.048     Bold: commonly affected molecular functions across the two Huh-7-derived stable GAPDH-kd clones.  This table was generated by V. Svinti, using InnateDB: Gene Ontology Analysis on the list of proteins in Table 3.1.  3.4.4 Transcriptomic analysis of Huh-7-derived stable GAPDH-knockdown clone 67B3 displayed reduction in expression of HCV entry receptor, CD81 Stable GAPDH-kd clones were also analyzed by microarray (Section A1.2) to determine their transcript profiles for identification of GAPDH-related regulation of genes and pathways. Several genes were identified as being upregulated and downregulated, and 101  they are listed in Table A1.1. When transcriptomic and proteomic results were compared, no overlap was observed between the two sets of differentially regulated genes and proteins. I reasoned that the lack of overlap could in part be due to a limitation in the number of proteins that can be detected with the acquired proteomic-based approach, which involved the use of total cellular protein extract, and identified only 228-284 soluble proteins. On the other hand, the microarray detected ~31K annotated genes. Also, reprogramming of cells at the post-transcriptional level may be an important mechanism by which cells compensate for GAPDH reduction, which cannot be detected by microarray. Gene ontology analysis was carried out using the Ingenuity Pathway Analysis software. Although the number of genes differentially regulated varied between the two Huh-7-derived cell clones, with only 9 overlapping genes (Fig. 3.9a), the number of overlapping biological functions (Fig. 3.9b) was much higher, i.e. 66/72 and 66/76 for 67D2 and 67B3, respectively.  Microarray results showed that differentially expressed genes in the Huh-7-derived GAPDH-kd cell clones, 67D2 and 67B3, was varied in number with very few overlaps, i.e., only 9 (Fig. 3.9a). I reasoned that non-overlapping genes could be the result from an accumulation of changes due to the differential regulation of dissimilar genes that may compensate for GAPDH downregulation, the carcinoma-like characteristic of the parental Huh-7 cell population prior to selection of single-cell clones, and/or less likely from off-target effects resulting from shRNA, as the same sequence is used to generate both cell clones displaying similar levels of GAPDH reduction.  102    Figure 3.9. Differentially expressed genes display lower overlap with higher overlap of molecular functions between Huh-7-derived stable GAPDH-knockdown clones, 67D2 and 67B3  103  (a) Differentially up and downregulated genes depicted by Venn diagram. (b) Number of overlapping molecular functions determined from the list of differentially expressed genes (Ingenuity Pathway Analysis). The author generated this figure using the list of genes in Table A1.2 using Bioinfomatics and Evolutionary genomics web tool (http://bioinformatics.psb.ugent.be/webtools/Venn/).  From the list of differentially regulated genes, CD81, an HCV entry receptor, was identified as being downregulated in the GAPDH-kd clone, 67B3. As Huh-7-derived GAPDH-kd clones (67D2 and 67B3) showed compromised susceptibility to HCV infection (Fig. 3.3), I first sought to validate the microarray results by analysing the transcript level of CD81 in Huh-7-derived GAPDH-kd clones compared to parental Huh-7 cells using RT-QPCR (Fig. A1.3b). The mRNA level determined by RT-QPCR showed a high correlation with those determined by microarray. RT-QPCR similarly showed decreased CD81 mRNA levels in GAPDH-kd clones compared to control Huh-7 cells. The lower expression of CD81 in clone 67B3 also correlated with lower susceptibility to HCV infection, as observed in Fig. 3.3. Cell surface expression of CD81 was additionally analyzed in the two GAPDH-kd clones, and compared to control Huh-7 cells by flow cytometry. Figure 3.10 shows that the expression of CD81, measured as the percentage of CD81-positive cells was significantly lower in 67D2 and 67B3 compared to control Huh-7 cells (CTRL).      104   Figure 3.10. Characterization of the exosome-associated tetraspanin protein CD81 levels in the Huh-7-derived stable GAPDH-knockdown clones in relation to the parental Huh-7 cells: from mRNA expression profiling (RT-QPCR) to cell surface expression in live cells (FACS)  CD81 mRNA expression profile. Total RNA was harvested from parental Huh-7 cells (parental), control Huh-7 cells (CTRL), and Huh-7-derived stable GAPDH-kd clones (67D2 and 67B3). CD81 mRNA levels were normalized to β-actin mRNA levels were relatively quantified from 50 ng of total RNA by RT-QPCR as described in Materials and Methods. Values are plotted as relative CD81 mRNA levels in comparison to control Huh-7 cells (CTRL).  Results are mean ± SEM for three independent experiments, each performed in triplicate. ***p < 0.001.  CD81 cell surface expression profile. Parental Huh-7 cells (parental), control Huh-7 cells (CTRL), and Huh-7-derived stable GAPDH-kd clones (67D2 and 67B3) were detached for FACS analysis as described in Materials and Methods. Here, cells were probed with anti-human CD81 antibody and stained with propidium iodide to exclude dead cells. Results are mean ± STD for three independent experiments. **p < 0.001; ***p < 0.0001.   105  3.4.5 Several proteins from the differentially regulated overlapping list were exosome-associated proteins Because CD81 and GAPDH are exosome-associated proteins (180), an overlapping list of 33 differentially regulated proteins determined using stringent Benjamini-Hochberg correction (a statistical method to control for multiple hypothesis testing by controlling the false discovery rate, i.e., likelihood of incorrectly rejecting a hypothesis (15)) were further analysed for other exosome-associated proteins (Table 3.3) using ExoCarta (http://www.exocarta.org). ExoCarta is a database of a manually curated repository of proteins, RNAs, miRNAs and lipid entries from published and unpublished exsosomal studies (223). The results of this analysis identified that 27 proteins (81.2%) from these overlapping list of proteins were also associated with exosomes (Table 3.3). These results suggests a novel moonlighting function of GAPDH in exosome biology affecting exosome-associated protein abundance, which results from downregulation of GAPDH expression.   Table 3.3. List of exosome-associated proteins with fold changes (FC) Downregulated Gene name Gene symbol 67D2 (FC) 67B3 (FC) Cofilin-1 CFL1 -2.210 -1.622 Eukaryotic translation initiation factor 5A-1 EIF5A -1.391 -1.848 Glyceraldehyde-3-phosphate dehydrogenase GAPDH -30.244 -10.562 High mobility group protein B1 HMGB1 -2.003 -1.425 Myosin-9 MYH9 -1.492 -1.442 Nucleoside diphosphate kinase A NME1 -1.923 -1.445 Nucleoside diphosphate kinase B NME2 -1.822 -1.750 Putative nucleoside diphosphate kinase NME2P1 -1.934 -1.529 Profilin-1 PFN1 -1.432 -1.520 Proteasome activator complex subunit 2 PSME2 -1.601 -1.634 Upregulated Gene name Gene symbol 67D2 67B3 Acetyl-CoA acetyltransferase, cytosolic ACAT2 1.45 1.96 Neuroblast differentiation-associated protein AHNAK AHNAK 1.82 2.48 Retinal dehydrogenase 1 ALDH1A1 3.55 1.73 Aldehyde dehydrogenase, mitochondrial ALDH2 1.57 1.32 106  Upregulated Gene name Gene symbol 67D2 67B3 Annexin A4 ANXA4 1.55 1.54 Bifunctional purine biosynthesis protein PURH ATIC 1.66 1.50 Myc box-dependent-interacting protein 1 BIN1 5.43 1.99 Creatine kinase B-type CKB 1.46 1.56 Cytochrome b5 type B CYB5B 1.94 1.51 Elongation factor 1-alpha 1 EEF1A1 1.57 2.04 Squalene synthetase FDFT1 1.73 2.43 Farnesyl pyrophosphate synthetase FDPS 1.29 2.72 Glyoxylate reductase/hydroxypyruvate reductase GRHPR 2.77 1.97 Glutathione S-transferase P GSTP1 1.82 1.57 Hydroxymethylglutaryl-CoA synthase, cytoplasmic HMGCS1 1.86 1.97 Isocitrate dehydrogenase [NADP] cytoplasmic IDH1 2.07 1.58 Nucleolin NCL 1.72 1.46 Phosphatidylethanolamine-binding protein 1 PEBP1 1.49 1.39 D-3-phosphoglycerate dehydrogenase PHGDH 1.83 2.17 Perilipin-2 PLIN2 1.58 2.52 Peroxiredoxin-5, mitochondrial PRDX5 1.45 1.60 Phosphoserine aminotransferase PSAT1 2.03 1.71 Tubulin alpha-1C chain TUBA1C 2.25 1.79 Bold: exosome-associated proteins. Fold changes (FC) of the two Huh-7-derived stable GAPDH-kd clones are over parental Huh-7 cells.   Sample technical replicates (three) were merged to provide a table of biological replicates (three) by L. Foster. The biological replicates (three) were analyzed to generate this table using Benjamini-Hochberg correction method. The table was further edited by the author to include FCs and commonly upregulated and downregulated proteins.    3.5 Discussion Here, I report the successful generation of GAPDH-kd clones with significant reduction in GAPDH protein abundance, mRNA level and GAPDH activity levels (Fig. 3.2) using human GAPDH-shRNA-encoding lentiviral transduction particles. I reasoned that the ability of these cells to survive may be due to several factors, such as an in vitro cell culture 107  environment having relatively high glucose levels, the addition of sodium pyruvate to compensate for the process of glycolysis, and mainly, reprogramming of the cells’ proteomic profiles to compensate for GAPDH reduction. Therefore, Huh-7-derived stable GAPDH-kd clones (GAPDH-kd clones) were proteomically profiled for their ability to survive with residual levels of GAPDH to gain insight into the moonlighting functional properties of GAPDH, specifically its role in Huh-7 cells’ susceptibility to HCV. For the purpose of characterizing these GAPDH-kd clones, I first compared bright field images (Fig. A1.1c) and observed that 67D2 and 67B3 visually resembled parental and control Huh-7 cells. Although GAPDH-kd clones visually appeared unaltered, I noticed that they did not reach similar levels of confluency in the plate compared to parental and control Huh-7 cells on day 4, after seeding the same number of cells. When, I further evaluated these cells for their relative proliferation rates using a carboxyfluorescein diacetate succinimidyl ester (CFSE)-based proliferation assay, GAPDH-kd clones showed higher levels of CFSE compared to control Huh-7 cells (CTRL). This suggested that, these cell clones underwent a significantly lower number of cell division compared to parental and control cells (Fig. A1.1d) by day 3. GAPDH-kd clones were analyzed for their susceptibility to HCV infections. HCV-infected stable GAPDH-kd clones were examined for HCV core abundance and amount of extracellular infectious virus produced in cell supernatants (Fig. 3.3). The results from the HCV-infected GAPDH-kd clones revealed that HCV core abundance (Fig. 3.3a) was reduced in primary HCV infection of stable GAPDH-kd clones, which thereby compromised secondary infection of naïve Huh7.5.1 cells with HCV-infected GAPDH-kd clone supernatants (Fig. 3.3b) . These results were consistent with the work done by Randall et al., who used J6/JFH-1 cell cultured virus and Huh7.5.1 cells, and identified GAPDH as one of the 26 genes that upon being targeted by siRNA, compromised virus production resulting from reduction in HCV replication (195). These results were also consistent with my observation, in Chapter 2, that siRNA-mediated GAPDH silencing in naïve Huh-7.5.1 cells inhibited primary HCV infection (Fig. 2.2a) and inhibited the production and/or release of infectious HCV virus particles in HCV-infected Huh-7.5.1 cell supernatants (Fig. 2.2b). As GAPDH-kd clones showed reduced susceptibility to HCV infection compared to control Huh-7 cells (CTRL), and gene expression results also showed that one of the key 108  HCV entry receptors, CD81was downregulated, I tested these cell clones for expression of CD81, and observed lower levels of CD81 in GAPDH-kd clones, both in terms of CD81 mRNA levels and the percentage of cells positive for CD81 surface expression by FACS (Fig. 3.10.). Of the two clones, 67B3 showed lower expression of CD81 than 67D2. The observation that the GAPDH-kd clones expressed lower levels of CD81 was consistent with the observation that these clones have relatively low susceptibility to HCV infection when compared to control Huh-7 cells (CTRL). This also suggests that downregulation of CD81 may further contribute to the reduced susceptibility of 67B3 cells to HCV infection, especially at the entry level, and in comparison to 67D2 ( Fig. 3.3).  It has been reported that subpopulations of hepatoma cells show differential expression of various cellular receptors involved in HCV entry, especially CD81(2). In addition, Huh-7 cells grown in different laboratories have shown different levels of susceptibility to HCV infection (206). As the GAPDH-kd clones described in this study were derived from single cells, the difference in CD81 expression in these cell clones may have resulted from either an acquired phenotypic change prior to selection for reduced GAPDH, or may be an effect of GAPDH knockdown in a subpopulation of cells. In order to verify that GAPDH knockdown caused changes in CD81 expression, a rescue experiment (Fig. A1.6, Section A1.4) restoring GAPDH expression levels could be performed, and the expression level of CD81 could be determined. This study could then be extended to other genes identified as being upregulated and downregulated in the GAPDH-kd clones. For further characterization of GAPDH-kd clones, a proteomics study was performed. Clustering of differentially expressed proteins using the Panther Classification System revealed an offset of activities affected by the downregulation of proteins with upregulation of proteins having similar activities. The prominent activities affected in Huh-7-derived stable GAPDH-kd clones in comparison to parental Huh-7 cells included catalytic, structural, and binding activities (Fig. 3.8). For example, the metabolic depletion of GAPDH resulted in upregulation of several dehydrogenases involved in aldehyde metabolism (ALDH1A1, PHGDH, and IDH1). In the absence of GAPDH activity, the substrate glyceraldehyde-3-phosphate (G3P) has the potential of getting rerouted to gluconeogenesis, and the pentose phosphate pathway, an alternative pathway to glycolysis (214). Consequently, several metabolic enzymes were upregulated, such as those involved in amino acid interconversion 109  (PSAT1 (96)) where G3P from glycolysis is utilized; and lipid metabolism involving fatty acid beta-oxidation, fatty acid alcohol and steroid metabolism, and synthesis of palmitate (ACAT2, ECH1, FASN, HMGCS1, (173)) where acetyl CoA is utilized. In conclusion, the metabolic reprogramming of cells is observed to compensate for GAPDH catalytic activity.  Along with its metabolic role, GAPDH has been shown to play a particular role in the binding to and the assembly of several cytoskeletal structural components, such as the bundling of microtubules and actin filaments (213). The physical interaction of GAPDH with cytoskeleton proteins, such as soluble tubulin heterodimer, microtubule, and microtubule-associated proteins, tau and MAP1B, has been previously demonstrated (49). MAPs have been shown to increase the physical interaction of GAPDH with microtubules, including its function in promoting microtubule bundling (49). However, the presence of ATP has been shown to inhibit the bundling activity of GAPDH (103).  In both Huh-7-derived stable GAPDH-kd clones (67D2 and 67B3), I observed that structural molecules, such as CFL1, PFN1, MYH9, and TUBB4B, were downregulated. CFL1, PFN1, and MYH9 are actin-modulating proteins important in maintaining microfilament polymerization, cross-linking and network formation. GAPDH has also been shown to bind to the actin filament (187, 188, 210).  Recently, Jung et al. showed that a triazine small-molecule inhibitor of GAPDH, GAPDS inhibited GAPDH tetramerization and induced GAPDH degradation, thereby, dramatically reducing cytoplasmic GAPDH levels in cancer cells (108). In human colon carcinoma cells, HCT116, triazine-induced reduction in GAPDH levels caused reduction in α-tubulin abundance and prevented actin polymerization at the leading edge of the cell membrane (108). In my study, I reasoned that the differential regulation of actin-binding and tubulin-interacting structural proteins affecting the cellular cytoskeletal structures, such as actin and the microtubule network may be an attempt to compensate for GAPDH function. This may have also partly contributed to the survival of the cells; especially as the Jung et al. study showed that triazine-mediated robust-reduction of GAPDH resulted in nuclear localization of GAPDH, a process linked to apoptosis. (108). Thus, the differential regulation of these structural proteins in GAPDH-kd clones suggests a rebound effect related to GAPDH depletion, especially as GAPDH has been shown to interact with the actin filament and microtubule protofilament with the suggestion of 110  participating in the formation, maturation, and/or maintenance of the actin cytoskeleton (210, 213) and the microtubule network respectively (49, 103, 265). Besides the depletion of GAPDH, other multifunctional host enzymes, NME1 and NME2, were also found to be downregulated in stable GAPDH-kd clones, 67D2 and 67B3. This result is consistent with Snider et al., who found that GAPDH and NME protein levels were coregulated (227). With the siRNA-mediated downregulation of GAPDH, a reduction in NME protein levels was observed, implying the regulation of NME expression post-translationally in hepatocytes (227).  GAPDH and NME have been shown to be important in regulating levels of ROS that are generated upon induction of Mallory-Denck bodies (MDBs, a common liver injury phenotype during steatohepatitis and alcohol liver disease) using 3.5-diethoxycarbonyl-1, 4-dihydrocollidine (DDC) (227). In the two Huh-7-derived stable GAPDH-kd clones, the reduction in GAPDH caused downregulation of NME proteins, which suggests that the HCV-mediated induction of ROS generated in the cells upon expression of viral proteins, such as core, E1, and NS3, may not be mitigated. Consequentially, upregulation of xenobiotic metabolic enzymes, such as GSTP1 (85), along with several peroxiredoxins, provided additional evidence to support the role of GAPDH as a mediator of oxidative stress (267). Overlapping proteins listed in Table 3.3 included several exosome-associated proteins (81.2%). This suggested that exosomes generated by GAPDH-kd clones may exhibit changes in exosomal protein abundance or deregulation of exosomal cargo. This finding is important as several studies have shown that HCV-infected hepatoma cells generate exosomes that carry HCV viral RNA, viral proteins, and viral particles (32, 48, 57, 140, 194). The exosomes containing viral RNA and HCV particles established productive infection in recipient hepatoma cells (48, 194). This exosome-mediated HCV transmission occurred independent of viral entry receptors present in host cells (48, 194).  In addition, full-length HCV RNA-containing exosomes have been shown to trigger the production of IFN-α in plasmacytoid dendritic cells (57). Thus, differential expression of exosome-associated proteins resulting from stable GAPDH-kd may affect exosome-mediated transmission of HCV viral RNA, viral proteins, and viral particles.  111  In summary, the proteomics data revealed changes in the expression of various proteins, which I would hypothesize as an attempt by cell clones to compensate for changes in molecular functions related to GAPDH-depletion: these include the downregulation of actin-binding and the upregulation of catalytic activities, including oxidoreductase activity and transferase activity. In addition, I discovered that stable GAPDH-kd resulted in differential expression of several exosome-associated proteins, suggesting potential moonlighting functions of GAPDH in exosome biology.   However, it is important to acknowledge that some of the changes in protein expression identified may not be an effect of GAPDH depletion, and this requires further evaluation. Again, as Huh-7 cells have been shown to accumulate changes due to adaptation to changing conditions (206, 277), not all genetic variations observed may result from GAPDH downregulation. Hence, a gain of function experiment, where GAPDH is added back to GAPDH-kd cell clones, may prove to be beneficial in identifying genes that are differentially regulated with respect to GAPDH downregulation. Furthermore, the differential expression of proteins independent of GAPDH depletion requires further investigation.  One of the limitations of this research is that the hepatoma cells utilized are tumorigenic in nature. This suggests that several proteins, as mentioned above, may not have been regulated due to GAPDH reduction. One of the interesting outcomes of this research is the generation of GAPDH-kd clones that survive significant and stable reduction in GAPDH expression levels showing lower susceptibility to HCV infection, underlining the targetable potential of GAPDH in HCV-induced HCC. Furthermore, as Huh-7 cells are also used for development of cell cultures that express HBV (132), investigation of HBV susceptibility to these cell clones can be performed to evaluate the broad-spectrum potential of targeting GAPDH in HCV-induced HCC.  In contrast, using primary hepatocytes for stable GAPDH reduction may result in detrimental effects, such as cell death, but that remains to be explored. Also, the ability to compensate for high levels of GAPDH depletion upon reprogramming of protein expression profiles needs testing in other cell types to explore whether this process is not limited to hepatocytes.  Having a list of proteins that are differentially expressed in the GAPDH-depleted cells, and knowing the biological processes and cellular pathways affected, this information may aid in the screening and identification of new targets for HCC treatments.  112  Chapter 4: Conclusions and future directions   4.1. Discussion  4.1.1 GAPDH is an important host factor for several stages of the HCV life cycle The primary aim of this study was to unravel the role of a multifunctional host protein, GAPDH, in the HCV life cycle. To do so, I performed loss-of-function and gain-of-function experiments. Because of the constitutive nature of expression in almost all cell types, GAPDH is conventionally considered a housekeeping protein; however, the list of cellular mechanisms involving GAPDH functions keeps increasing (Section 1. 2. 1). Therefore, I reasoned that the complete inhibition of GAPDH might prove detrimental to human cells. As an alternative to complete depletion, I chose to downregulate GAPDH expression. For this purpose, I employed a highly potent and sequence-specific silencing method, which included stable downregulation with lentiviral-encoded shRNA and transient downregulation with siRNAs in two human hepatoma cell lines, Huh-7 and Huh-7.5.1, respectively.  After successfully downregulating GAPDH expression to a significant level (Fig. 2.1 & 3.1), I tested my hypothesis that GAPDH is important in several stages of the HCV life cycle (e.g., entry, translation, replication, and release) by investigating the effect of GAPDH reduction on core protein expression, HCV replication, and production of extracellular infectious virus particles. With the stable reduction in GAPDH expression (see Chapter 3), I observed that the susceptibility of Huh-7-derived stable GADH-kd clones was compromised (Fig. 3.3). However, experiments with transient reduction revealed that HCV replication was not compromised (Fig. 2.4) although the stages prior to the processes of viral genome translation (Fig. 2.3a & b) and post-viral replication were significantly reduced (Fig. 2.3b). The effect of GAPDH reduction on the extracellular infectious virus was also confirmed with exogenous upregulation of GAPDH (Fig. 2.5), which resulted in an inverse phenotype, i.e., an increase in extracellular infectious virus particles (Fig. 2.6a & b). Thus, I showed that GAPDH is necessary for establishing efficient HCV infection in human hepatoma cells. In 113  addition, siRNA-mediated GAPDH suppression in naïve human Huh-7.5.1 cells also significantly inhibited primary DENV infection (Fig. 2.7). This suggests that GAPDH is a host factor for the DENV-2 life cycle. The role of GAPDH was also investigated in GAPDH-kd clones in an infection experiment using another human enveloped RNA virus, a SARS-CoV (an unpublished collaboration with Dr. Richard Kao, at The University of Hong Kong ), especially because GAPDH has been shown to be present on the surface of SARS-CoV particles (68).  Media from SARS-CoV-infected GAPDH-kd clones were collected and used to infect monolayers of Vero E6 cells, kidney epithelial cells isolated from an African green monkey. However, unlike for HCV infection, variation in the ability to generate cytopathic effect in media-infected Vero E6 cells was not observed, suggesting that the generation of extracellular infectious virus particles may not have been compromised  (90). Because GAPDH plays a key role in ER-Golgi trafficking (248-252), siRNA-mediated GAPDH silencing would be expected to strongly impact the subcellular distribution and compartment-specific enzymatic activity of proprotein convertases important for HCV and DENV, such as S1P and furin in the secretory pathway. Sterol regulatory element binding proteins (SREBPs) are transcription factors that play a key role in lipid and sterol metabolism (60, 207). Immature ER-retained membrane-bound SREBPs need to translocate to the Golgi apparatus, where they undergo proteolytic maturation mediated by the proprotein convertase S1P; this event is vital to the HCV life cycle (174). GAPDH knockdown would likely abrogate SREBP trafficking, thereby indirectly inhibiting its maturation and the transcription of downstream targets, with an overall inhibitory effect on HCV production. Over-expression of GAPDH may reverse this effect by increasing ER-Golgi trafficking and potentially increasing indirectly the proteolytic maturation rate of SREBPs, leading to increased expression of target genes involved in lipid metabolism and LD biosynthesis. This could then enable faster assembly and egress of HCV particles, leading to the production of more virions and a consequent increase in infectivity.  The other enzyme, furin, is an archetypal proprotein convertase that is involved in the proteolytic maturation of a large number of viral structural glycoproteins (105), including the precursor membrane (prM) protein of DENV (184). Furin-mediated prM cleavage is required for the production of infectious DENV particles (1, 109, 116); thus, disrupting furin sorting and recycling between the ER and Golgi by GAPDH silencing would be expected to 114  produce an abundance of immature, non-infectious virions. In this way, GAPDH seems to play a key role in the post-replication steps of the DENV life cycle as well as the HCV life cycle, underlining the importance of intracellular membrane trafficking for the successful production of infectious virus particles. Interestingly, although GAPDH intracellular depletion resulted in a dramatic reduction of HCV core abundance during primary and secondary HCV infection, the decrease in HCV core abundance (~2-fold reduction) was not as robust as the levels of knockdown achieved in the GAPDH-depleted cells (~14-fold reduction). While the reason for these discrepancies is not clear, it is possible that the multifunctional nature of GAPDH, which allows the enzyme to contribute to different—and perhaps limited extents—to key host cell pathways hijacked during HCV infection, may contribute to these differences. For example, even though the central role of GAPDH in regulating intracellular membrane trafficking within cells is an attractive explanation for the reduction in HCV entry and infectious virus release as reported in this study, the dramatic decrease in GAPDH protein abundance observed in our Huh-7-derived cell clones, 67D2 and 67B3, may also interfere with other HCV-induced cellular pathways during viral infection, such as cell cycle arrest.  In a related study using HCV (J6/JFH-1, genotype 2a) infected Huh-7.5 cells, Walter et al. observed a host-transcriptional response at 48-72 hours post-infection involving genes used in cell cycle arrest, oxidative stress corresponding to DNA damage, release of cytochrome c, induction of death receptor signaling, activation of caspases, and signaling of cytokine/growth factors (266). The genes involved in cell cycle arrest were important for G1 phase arrest and prevented transition to the S-phase (266). The genes involved in apoptosis were identified as being transcriptionally regulated by p53 (266). This implies that the perturbation of the p53 signaling pathway may play a key role in HCV-induced cell cycle arrest and induction of apoptosis (266). Although HCV is considered a non-cytolytic virus, HCV cell culture displays massive cell death on day 5 post-infection in Huh-7.5.1 cells when infected with a high MOI of 1  (266, 277) or day 7 when infected with a low MOI of 0.01, and on day 17-21 post-transfection with the HCV JFH-1 RNA genome (Fig. A2.1). Walter et al. suggested that HCV-induced apoptosis was a result of oxidative stress induced by active replication in HCV-infected cells and not in neighboring cells. Again, when compared to in 115  vivo HCV replication, the rate of in vitro replication is observed to be much higher (266), which most likely contributes to in vitro cell death. As discussed earlier and shown in Figure 4.1, one of the mechanisms involved in mediating apoptosis in response to oxidative stress involves S-nitrosylation (S-NO) of GAPDH at its active site Cys150, which augments its binding to Siah1, an E3 ubiquitin ligase (91). After binding, the GAPDH-Siah1 complex translocated to the nucleus using the NLS present on Siah1 (91). Further elucidation of this pathway revealed that GAPDH was acetylated at Lys160 by p300/CREB-binding protein (CBP), an acetyltransferase, in the nucleus (218). This modification enhanced the ability of GAPDH to stimulate auto-acetylation and the catalytic property of CBP that activated downstream targets, especially p53 (218). Interestingly, both Siah1 and GAPDH have been shown to be upregulated by p53 forming an auto-amplifying loop (39, 75). Once upregulated, p53 with its two distinct transactivation domains can either drive apoptosis or cell cycle arrest as a transcriptional activator by directly regulating more than 125 genes (19). In addition, p53 signaling has been shown to involve DNA repair, metabolism, and cell migration (19), suggesting that p53 is a downstream effector of GAPDH. In summary, although I demonstrated that GAPDH-depleted cells are less susceptible to HCV infection, it remains to be determined to what extent GAPDH’s multifunctional roles in biological processes such as intracellular membrane trafficking and apoptosis contribute to host cell susceptibility to HCV infection, and if they involve the p53-signaling pathway (Fig. 4.1). Nevertheless, my results clearly underline that GAPDH is an important host factor for the HCV life cycle. In the process of unravelling the role of GAPDH in the HCV life cycle, the use of siRNA-mediated transient GAPDH suppression has also led to the discovery of a non-targeting siRNA (siRNA_MR01) that shows significant antiviral activity, which is discussed in Appendix 3.  4.1.2 Profiling of the GAPDH-associated interactome and discovery of cellular compensatory mechanisms in GAPDH-depleted cells In Chapter 3, I characterized Huh-7-derived cell clones showing a high level of stable GAPDH reduction. To our knowledge, this is the first time stable cells have been generated with more than a ~12 to ~9-fold reduction in GAPDH expression as determined by RT-116  QPCR (Fig. 3.2d). Hence, in collaboration with other lab members, I took the opportunity to characterize these cell lines for the compensatory mechanisms that enable them to survive with such low expression of an otherwise constitutively expressed housekeeping gene. In order to identify the pathways modulated by GAPDH reduction, which might explain the relative fitness of these cells, or to identify the deregulated pathways that might lead to potentially detrimental effects, gene expression studies were initially commenced with two Huh-7-derived cell clones, 67D2 and 67B3. Results from the microarray experiment revealed that these GAPDH-kd cell clones had more than 50 genes (67D2-64 genes, 67B3 -54 genes) that are differentially regulated with only 9 overlapping genes, which was determined using a stringent p-value (Fig. 3.9). I reasoned that variations in gene expression may result from the accumulation of mutations prior to the selection process as these cell lines were derived from a single cell. These cells may have started with a different gene expression profile when selected for GAPDH depletion. Consequently, they may have compensated for the GAPDH downregulation via regulation of functionally related genes, and this may explain the high number of overlapping functions (Fig. 3.9b) despite the high number of non-overlapping genes (Fig. 3.9a).  In order to link these differentially expressed genes to GAPDH reduction, a microarray analysis of these clones with rescued GAPDH expression (Fig. A1.6) is needed.  Interestingly, in the list of overlapping genes, an established HCV cell surface determinant, CD81, was identified as being downregulated, which was validated by RT-QPCR (Fig. A1.3b) and FACS (Fig. 3.10). Because a subpopulation of cells showed reduced CD81 expression in parental and Huh-7-derived cell clones, I deduced that the expression of CD81 may be actively regulated within these cells. However, it is unclear if CD81 regulation in Huh-7-derived cell clones was a result of GAPDH reduction.  Hence, an evaluation of CD81 post-long-term transient reduction in GAPDH is needed to show that CD81 expression is co-regulated with GAPDH expression. Profiling of Huh-7-derived cell clones, 67D2 and 67B3, for their protein expression revealed differential expression of 71 and 64 proteins, respectively (Fig. 3.7). Interestingly, the molecular activity-based gene ontology revealed a differential expression of proteins, which displayed a rebound effect compensated by upregulation and downregulation of proteins with similar activities (Fig. 3.8). Also, the major activities affected included binding, 117  and catalytic and structural activities, which are properties that involve GAPDH functions (Fig. 3.8). Several proteins involved in the metabolic reprogramming of cells that differentially regulates enzymes in the glycolysis and TCA cycle were also observed to be differentially regulated. In addition, the expression of several structural and cytoskeletal-binding proteins was also affected, emphasizing the central role of GAPDH in endocytosis and intracellular membrane trafficking. Importantly, several proteins that may function downstream of GAPDH were identified and need further evaluation, such as NME2, which is involved in coat protein-mediated budding and complex formation in the secretory pathway; MCM3, which maintains DNA integrity and cell cycle progression; PSME1, which is involved in chaperone-mediated degradation; and several cytoskeletal proteins, which maintain actin filament and microtubule networks. Of most interest was the identification of several proteins that explained the distinct susceptibility of the Huh-7-derived cell clones, 67D2 and 67B3 to HCV infection.  Because the differential expression of protein in the Huh-7-derived cell clones may have resulted from factors other than GAPDH depletion, such as the accumulation of changes prior to cell clone selection proposed earlier, the list may contain several proteins not associated with GAPDH reduction (Fig. 3.7). In order to distinguish these proteins from the GAPDH-regulated list, a proteomic analysis with and without rescued GAPDH (Fig. A1.6) is needed to validate the GAPDH-associated regulatory mechanism (55). Alternatively, a proteomic analysis of transient and higher levels of GAPDH reduction is needed that may reveal regulation of GAPDH-associated proteins. Uncovering mechanisms that enable these HCC cells to survive low levels of GAPDH would help to determine the effectiveness of targeting GAPDH for HCC treatment.  Upon doing so, these stable cells can be used to screen various libraries of compounds to identify novel drugs that target HCC.  4.1.3 GAPDH-specific deregulation of exosome-associated proteins: Implications for HCV-hijacking of the host cell exosomal pathway In Chapter 3, our discovery of a robust and specific modulation of exosome-associated proteins (Table 3.3) in our Huh-7-derived stable GAPDH-kd clones represents an important finding in identifying a novel potential functional role of GAPDH in the regulation of specialized microvesicle (exosome) formation and their associated proteins. 118  Our proteomics results can be explained in part by the important role played by GAPDH—as one of the key microtubule-associated proteins (MAPs)—in the maintenance of the cytoskeletal structure critical for membrane trafficking, and consequently, in the biogenesis of cellular organelles and potentially in microvesicle formation (213). For example, in concert with other cytoskeletal proteins, GAPDH provides the needed cellular infrastructure allowing trafficking, recycling, and biogenesis of intracellular organelles  (213). Delivery of cellular content through extracellular vesicles is a mode of intercellular communications along with cell-cell contact and transfer of secreted molecules (196, 204). Exosomes are extracellular vesicles formed in multivesicular endosomes (MVEs) and have been shown to involve both ESCRT-dependent and -independent mechanisms (196). Steps involve segregation of cargo molecules to the endosomal delimiting membrane followed by membrane budding to form intraluminal vesicles (196). The molecular mechanisms regulating exosome biogenesis are under intensive study. For example, it remains to be determined how the exosomal cargos (e.g., proteins, mRNAs, and miRNAs) are recruited during exosome formation (204). Our findings that GAPDH abundance impacts the steady-state expression level of exosome-associated proteins suggest an important role for GAPDH in this process. Exosomes are secreted when MVEs fuse with the surface of the cell (196); following secretion, exosomes interact with recipient cells to deliver their content (196). In this regard, it remains to be determined if our GAPDH-kd cell clones are compromised in terms of exosome abundance and functionality for the secreted exosomes. This is important in relation to the numerous recent reports supporting an important role of exosomes in vesicle-mediated disease pathogenesis  (204). For example, in cancer, circulating tumor-derived exosomes regulate tumour progression, such as invasion of cancer cells, induction of thrombosis, and modulation of immune response (204). Besides their role in cancer, exosomes have been implicated in the spread of virulence factors, pathogen-derived antigens, and numerous pathogens, including prions and viruses, such as HIV-1 (131), HAV (73) and HCV (194). Hence, GAPDH-associated deregulation of the exosome-associated proteins could impact (i) the formation of cellular exosomes and (ii) recruitment of host cargo molecules to exosomes; in addition, the viral hijacking of host cell exosome-associated proteins and pathway may 119  influence the spread by human viruses of exosome-associated viruses that utilize endolysosomal pathways (73, 196, 204).   4.2. Future directions  4.2.1 GAPDH binding proteins as a host-targeted antiviral agent for HCV infection GAPDH is ubiquitously expressed in all tissue types; thus, targeting of GAPDH, as mentioned above, poses a risk of systemic toxicity. Systemic targeting of GAPDH, however, can be avoided by localized targeting of antivirals using liposome-based delivery of RNAi to specific tissues or organs (110). Although challenging, an additional approach is to target the specific function of GAPDH without disrupting other critical functions, such as the targeting of GAPDH interaction with Siah1 using a deprenyl derivative that inhibits the acceleration of neurodegeneration. While extensive research is needed to optimize the targeting of GAPDH, along with the screening of the inhibitor for specific GAPDH function, another alternative therapeutic avenue is to target GAPDH-binding proteins. For example, in order to target retrograde transport from the ER to the Golgi, siRNA-mediated targeting of binding partners, such as Rab2, Src, or aPKCɩ/λ, can be explored (Fig. 1.5). In doing so, the precise function of GAPDH can be targeted. Also, with respect to the cell cycle inhibitory property of GAPDH, targeting of downstream proteins, such as CDK inhibitor p21, can be evaluated provided that the p53-signaling pathway via GAPDH is established during HCV infection. For this, testing the cells for p21 upregulation as well as p53 phosphorylation on Ser-15 post-HCV infection can be performed. Again, other targets, such as the process of GAPDH acetylation, can be targeted by inhibiting CBP activity. By doing so, several potential host factors can be explored against HCV.  4.2.2 GAPDH as a host-targeted agent against HCV infection In this study, I demonstrated that GAPDH is an important host factor for the HCV life cycle.  I hypothesized that the intrinsic multifunctional nature of GAPDH is an important feature that explains its important role in supporting the robust infection of human hepatoma cells by HCV. First, I postulated that the observed decrease in HCV susceptibility of human hepatoma cells depleted in GAPDH expression is in part associated with defective viral entry 120  and defective release steps in these cells. More specifically, I hypothesized that knockdown expression of GAPDH would result in a decreased capacity of these cells to regulate microtubule bundling and the needed cytoskeleton structure required for intracellular membrane trafficking of vesicles, such as endosomes (endocytosis) and VTC (retrograde recycling of proteins from the ER to the Golgi), thereby affecting viral entry and release (213, 251). To test this hypothesis, colocalization studies can be performed to show that GAPDH depletion in host cells is associated with a reduction in retrograde recycling of host/viral proteins from the ER to the Golgi and a concomitant decrease in HCV infectious viral particles. For example, colocalization of viral proteins with host VTC-associated Rab2-Src-aPKCɩ/λ-GAPDH complex (Fig. 1.5) is necessary to show that interfering with the GAPDH-associated retrograde recycling event reduces extracellular infectious virus particles. To address the compromised endocytosis of virus during GAPDH reduction, studies with HCVpp can be commenced to directly link this effect of GAPDH to reduced viral entry (20). Second, recent studies have clarified the role of GAPDH as a key mediator of oxidative stress signaling via p53-induced cell cycle arrest and apoptosis (39, 218, 273). To test the hypothesis that GAPDH plays an important role during HCV-induced oxidative stress in infected cells, we could monitor GAPDH S-NO modification and potential activation of the p53 pathway in our control and clonal infected cell lines. Monitoring the GAPDH S-NO modification can be performed using mass spectrometry (102). Testing for the activation of the p53 pathway can be performed by monitoring (i) GAPDH-Siah1 complex formation, (ii) acetylation of Lys160 on GAPDH, and (iii) autoacetylation of CBP followed by activation of apoptosis (218).  Investigation of cell cycle arrest could be performed by testing control HCV-infected cells with our Huh-7-derived knockdown GAPDH cells for upregulation of p21 and stabilization of p53 via Ser15 phosphorylation for G1/S phase cell cycle arrest (183). Third, in light of the recent reports demonstrating the importance of post-translational modification for GAPDH function, it could be of interest to investigate whether specific molecular alterations of GAPDH trigger changes in the biological properties reported in this study on GAPDH as a host factor involved in the HCV life cycle.   These studies could be performed by utilizing the tools generated by this research. In particular, studies involving mutation in GAPDH post-translational modification sites are 121  needed, such as the microtubule binding site, acetylation site, and several phosphorylation sites that are schematically presented in Fig. 1.4. Thus, a GAPDH gene with a loss of specific function achieved by site-directed mutagenesis of a specific post-translational modification site could be introduced for the purpose of rescuing GAPDH-depleted Huh-7-derived cell clones. Doing so may validate—and may provide information on—the associated GAPDH function corresponding to the targeted post-translational modification site. In addition, redistribution of GAPDH within cells during HCV infection may also occur during HCV-induced oxidative stress, which can be tested by immunofluorescence analysis (104, 120, 171, 255). Furthermore, with depletion or targeting of GAPDH, investigation of other upregulated antioxidants, such as GSTP1 and PRDX5, as detected by proteomic analysis in Chapter 3 is needed. The purpose is to investigate whether the upregulation of these antioxidants is sufficient to protect cells from HCV-mediated oxidative damage. Analysis of deregulated pathways with a depletion of GAPDH expression may also provide insight into the physiological fitness of cells, which is important for various pathobiological conditions. Finally, GAPDH has been extensively studied as a target for treating cancer and neurodegenerative diseases (33, 87, 242). For cancer treatment, the multifunctional properties of GAPDH are shown to be beneficial in inhibiting chemoresistance (126, 183, 240), metastatic potential and motility (87), protection against CICD (46), and increased proliferation and cell cycle progression (87). For example, Ganapathy-Kanniappan et al. demonstrated that targeting of GAPDH in a mouse model of HCC by percutaneous injection of GAPDH antagonists, such as 3-bromopyruvate and GAPDH-specific shRNA, induced apoptosis and blocked tumor progression (80). In this context, examining the potential of GAPDH as a therapeutic target for HCV-induced HCC may prove to be beneficial, as it may not only reduce the susceptibility of cells to the HCV infection but also inhibit tumor progression. However, before considering GAPDH as a therapeutic avenue for indirect-acting antivirals for HCV, it will be important to examine the biological effect of the knockdown of GAPDH in primary hepatocytes and human liver-uPA-SCID mice (159), a chimeric mouse grafted with primary human hepatocytes (157), along with the impact on virus production 122  In summary, the results of our virological and proteomics studies in cellulo indicate that GAPDH is an important host factor for the HCV life cycle and it represents an interesting potential host target for examining alternative therapeutic avenues for investigating HTAs. However, since our studies used transformed carcinoma cells, it will be important to first investigate the multifunctional role of GAPDH during the HCV life cycle using primary hepatocytes.  4.2.3 GAPDH as a potential broad-spectrum host-targeted agent against Flaviviridae members An interesting attribute of targeting GAPDH, in comparison to other host factors, is that GAPDH regulates cellular functions important for various other pathogens, such as the vesicular trafficking important for the intracellular pathogen Brucella abortus (78). Most importantly, endocytosis for entry into the host cells and use of the secretory pathway for assembly and release mechanisms are generally used by several human enveloped RNA viruses, especially viruses from the same Flaviviridae family (184), such as WNV (44, 274) and DENV (109, 257). Hence, the evaluation of GAPDH functions with respect to these other viruses, especially as they readily infect human hepatoma cell lines Huh-7 and Huh-7.5.1, can be performed. Hence in Chapter 2, I reported my exploration of the DENV cell culture system established in the Jean lab, showing that GAPDH downregulation results in inefficient DENV-2 infection, which can be further evaluated to show the ability of GAPDH to act as a broad-based antiviral target. However, in contrast to the liver-specific HCV, DENV presents a broad cell tropism, including immune cells (202). These properties of DENV represent extremely important challenges for targeting GAPDH as an anti-DENV strategy in terms of systemic toxicity and achieving a robust decrease in viral production.  4.2.4 GAPDH, exosome-associated proteins, and exosome-mediated transmission of HCV Proteomic profiling of GAPDH-depleted cells revealed deregulation of the proteins that were associated with exosomes. As depletion of GAPDH may alter the generation or composition of exosomes, characterization of exosomes isolated from siRNA-mediated transient GAPDH reduction in Huh-7.5.1 can be performed. In addition, transmission of V5-123  tagged GAPDH-containing exosomes isolated from hepatoma cells transfected with V5-tagged GAPDH construct can be added to the GAPDH-kd cell clones, 67D2 and 67B3, which will allow us to study changes in vesicle-uptake in these cells.  HCV possesses multiple routes of entry into the host cell, including classical cell-free virus transmission (Section 1.2.2) and cell-cell transmission (247). Recently, a third route of viral transmission has been identified: exosome-mediated transmission (32, 48, 140, 194). Ramakrishnaiah et al. showed that exosomes from HCV-infected patients and HCV-infected Huh-7.5.1 cells transmitted productive HCV infection to naïve hepatocytes (194). In addition, these hepatic exosomes were partially resistant to neutralizing antibodies suggesting exosome-mediated transmission of HCV as a mechanism of immune evasion that explains the difficulty in developing an effective vaccine and reinfection of grafts during liver transplantation (194). Bukong et al. showed that exosomes isolated from permissible cell supernatants as well as sera of chronic HCV infected patients contain HCV RNA (32). More recently, Longatti et al. showed that exosomes can also mediated transfer of subgenomic HCV RNA, which leads to establishment of subgenomic HCV replication in permissive Huh-7 cells (140). Provided that the role of GAPDH in the biogenesis of exosomes and associated proteins can be established, deregulation of exosome-mediated viral transmission can be evaluated for both HCV and DNV. In order to do this, I would propose studying exosomal-mediated viral transmission after transfection of full-length HCV genome in GAPDH-depleted cells.  124  125  Figure 4.1. Putative roles of GAPDH in the HCV life cycle  1) GAPDH may be involved in clathrin-mediated endocytosis of HCV, which may contain Rab5 or other Rab proteins. 2) Rab2-mediated retrograde transport involving GAPDH may indirectly regulate the release of infectious virus particles through the secretory pathway. 3) Oxidative stress resulting from the expression of viral proteins, such as Core, E1, and NS3, may cause oxidative DNA damage involving S-NO and nuclear translocation of GAPDH post-binding to Siah1. Furthermore induction of p53 signaling may result in cell cycle arrest and apoptosis. 4) Deregulation of exosome-associated proteins, which may affect exosome biogenesis, thereby, exosome-mediated transmission of HCV genome, proteins and viral particles.  Adapted from Bethell et al. 2009. 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Molecular cancer research : MCR 6:1375-1384.    150  Appendices  Appendix 1: Supplementary tables and figures related to Chapter 3   A1.1 Generation of Huh-7-derived GAPDH-knockdown clones showing various levels of stable GAPDH reduction At first, out of the seven Huh-7-derived stable GAPDH-knockdown (kd) clones (Fig.A1.1a) generated, 67D2 and 67B3 (described in Chapter 3) along with other Huh-7-derived GAPDH-kd clones showing stable shRNA-mediated GAPDH reduction (67cl.1, 67.7, and 67cl.2) were subjected to WB analysis for determining GAPDH protein abundance, visual inspection for any phenotypical changes, CFSE proliferation assay, and microarray. For controls, parental Huh-7 cells, Huh-7 control cells generated using empty-lentiviral transduction particle (CTRL) and non-specific shRNA-encoding lentiviral-transduction particles (CTRL-2) were included. Apart from analyzing the reduction levels of GAPDH (Fig. A1.1a), Huh-7-derived stable GAPDH-kd clones, 67cl.1, 67D2 and 67B3 were visually compared. Bright field images of the live cells were captured for visually inspecting any phenotypical changes in Huh-7-derived stable GAPDH-kd clones (Fig. A1.1b-c). The bright field images revealed that Huh-7-derived stable GAPDH-kd clones, 67D2 and 67B3 visually resembled parental Huh-7 and control Huh-7 cells (CTRL and CTRL-2) (Fig. A1.1b). In addition, the cloned cells appeared to be less confluent when compared to parental Huh-7 cells and control Huh-7 cells (CTRL and CTRL-2) on fourth day post-seeding same number of cells. However, additional assays would be needed to confirm this. With bright field images, I concluded that the lower confluency observed in Huh-7-derived cell clones may have resulted from either lower expansion rate and/or a relative low spreading of cells altering cell morphology on the tissue culture plates. Hence, next I performed cell proliferation assay to compare relative expansion rates of Huh-7-derived cell clones. In order to determine if lower confluences resulted from a low proliferation rate or a cytostatic condition, a carboxyfluorescein diacetate succinimidyl ester (CFSE)-based proliferation assay was performed. Cells stained with covalently bound CFSE stain were 151  evaluated by flow cytometry to observe dilution in fluorescence intensity upon division of cells day 3 post-treatment. By plotting % CFSE fluorescence over time (Fig. A1.1d-iii) and relative residual CFSE fluorescence (Fig. A1.1d-iv), I observed that 67cl.1divided less over 3 days to control cells (CTRL). Hence, 67cl.1 cells were not utilized for proteomic profiling. In addition, I observed that Huh-7-derived stable GAPDH-kd clones, 67D2 and 67B3, also underwent a relatively lower number of cell divisions in comparison to parental Huh-7 cells and control Huh-7 cells (CTRL and CTRL-2), with 67D2 having more reduced cell divisions of the two (Fig. A1.1d).  A heat map (Fig. A1.1e) was generated from expression profiles of Huh-7-derived cell clones with shRNA-mediated stable GAPDH knockdown: this depicted that 67D2 and 67B3 had expression profiles closer to parental Huh-7. Thus, for further experiments, 67D2 and 67B3 were characterized in Chapter 3, and gene expression profiles were re-determined using control cells (empty lentiviral-transduced Huh-7-derived stable clone (CTRL)) as follows.  152   153     154      155      Figure A1.1. Huh-7-derived stable GAPDH-knockdown (kd) clones, 67D2 and 67B3 are closely related to parental Huh-7 cells  156  (a) Huh-7-derived stable GAPDH-kd clones were generated by infecting parental Huh-7 cells with human GAPDH-targeting short-hairpin RNA (hGAPDH-shRNA) encoding lentiviral transduction particles (MOI:1) in the presence of 2.5 µM hexadimethrine bromide for 24 hours. Seventy-two hours post-viral infection (p.i.), transduced cells (cells expressing shRNA constitutively) were selected and maintained in selection media as described in Materials and Methods (Section 3.3). Upon repopulation of transduced cells, 7 single-cell Huh-7-derived cell clones (67cl.1, 67D2, 67B3, 67.7, 67.6, 67.5, and 67cl.2) were isolated by serial dilution in a 96-well plate. Thereafter, cell lysates (1 × 106 cells) were harvested for WB analysis and probed for human GAPDH (green) and for β-tubulin (red) (loading control used for normalization) expression as described in Materials and Methods Section 2.3. In parallel, Huh-7-derived control cells were generated using empty-lentiviral transduction particle (CTRL) and non-specific shRNA-encoding lentiviral-transduction particles (CTRL-2). Recombinant human GAPDH (r-hGAPDH) was also included as an anti-hGAPDH antibody control.  Upper panel: A WB representative out of three WBs.  Lower panel: Protein abundance was quantified as described in Materials and Methods (Section 2.3), averaged across triplicate wells, and expressed relative to protein abundance in control cells (CTRL).  Results (mean ± STD) are from three independent WBs. *p < 0.05; ** p < 0.001.  The author performed this experiment, analyzed its results and generated this figure.  (b) Bright field images of Huh-7-derived stable GAPDH-kd clones, 67D2 and 67B3 resemble parental Huh-7 cells and control Huh-7 (CTRL and CTRL-2) cells. Bright field images (20× magnifications) were taken on day four post-seeding (1 × 106) parental Huh-7 cells, control Huh-7 cells (CTRL and CTRL-2), and Huh-7-derived stable GAPDH-kd clones (67cl.1, 67D2, and 67B3) in 10 cm2 tissue culture plates using.  The author performed this experiment, analyzed its results and generated this figure.  (c) Bright field images showing floating cell debris for Huh-7-derived stable GAPDH-kd clones, 67cl.1. Bright field images (10× magnifications) were taken when parental Huh-7 cells, control Huh-7 cells (CTRL and CTRL-2), and Huh-7-derived stable GAPDH-kd clones (67cl.1, 67D2, and 67B3) reached 80-90% confluency in 10 cm2 tissue culture plates.  The author performed this experiment, analyzed its results and generated this figure.  (d) Huh-7-derived stable GAPDH-kd clones display relatively low proliferation rates.  Parental Huh-7, control Huh-7 (CTRL and CTRL-2) and Huh-7-derived stable GAPDH-kd clones (67cl.1, 67D2, and 67B3) stained with CFSE stain as described in Materials and Methods. On indicated days post-treatment, cells were stained with propidium iodide and analyzed by flow cytometry for residual fluorescence intensities from dilution of CFSE stain 157  upon cells division. (i) Histogram of parental Huh-7 cells showing dilution of CFSE fluorescence intensities due to increase in number of cell divisions on indicated days. (ii) Histogram of  GAPDH-kd clone, 67cl.1 showing reduction in dilution in CFSE fluorescence intensities due to reduced number of cell divisions on indicated days. (iii) % CFSE fluorescence intensities plotted on indicated days. (iv) Relative fluorescence intensities (%) of diluted CFSE stain on indicated days post-treatment were averaged across three independent experiments, and expressed relative to fluorescence intensity in control cells (CTRL).  Results (mean ± STD) are from three independent assays. *p < 0.05; *** p < 0.0001.  The author performed this experiment, analyzed its results and generated this figure.  (e) Heat map determined from gene expression profiles shows Huh-7-derived stable GAPDH-knockdown clones, 67D2 and 67B3 are closely related to parental Huh-7 cells. Total RNA was isolated from Huh-7-derived stable GAPDH-knockdown clones (67D2, 67B3, 67cl.1, 67cl.2, and 67.7) along with parental Huh-7 cells and control Huh-7 cells (CTRL and CTRL-2) as described in Materials and Methods (Section A1.1.4). A total of ~3 µg of RNA was provided to the Vancouver Prostate Center Microarray Facility (Vancouver, Canada). Gene expression profiles of Huh-7-derived stable GAPDH-knockdown clones were determined over CTRL-2.  The author prepared RNA samples. A. Haegert, a technician at the microarray facility performed microarray. V. Svinti analyzed raw microarray data provided by A. Haegert and generated this figure.   A1.2 Transcriptome profiling of Huh-7-derived GAPDH-knockdown clones: 67D2 and 67B3 In collaboration with other lab members, I performed gene expression studies on Huh-7-derived cell clones with significant and stable shRNA-mediated downregulation of GAPDH (described in Chapter 3), 67D2 and 67B3. Here, parental Huh-7 cells and Huh-7-derived cell clones transduced with empty-lentiviral transduction particle (CTRL) were included.  Huh-7 cells transduced with non-specific shRNA-encoding lentiviral-transduction particle (CTRL-2) (Fig. A1.1a) were not included in these studies as they displayed altered susceptibility to HCV infection to HCV-infected control Huh-7 cells (CTRL). Here, the clonal cells (CTRL-2) exhibited compromised expression of core in the second round of infection (Fig. A1.2b). I reasoned that the altered susceptibility to HCV in CTRL-2 cells may have resulted from an insertional mutation caused by shRNA-encoding lentiviral genome 158  incorporation into host DNA or the non-specific sequence used as a control may have offered some off-target effects that resulted in an anti-HCV activity (61).   Figure A1.2. Decrease in HCV core abundance in naïve Huh-7.5.1 cells infected with infectious supernatant collected from primary HCV-infected Huh-7 cells transduced with non-specific shRNA encoded by a lentiviral transduction particle (CTRL-2)  (a) Primary infection. Huh-7-derived subclone expressing non-specific shRNA, CTRL-2 (5 × 103 cells) (described in Fig. A1.4a) and parental Huh-7 cells were infected with HCV (MOI: 0.1). Two-third of the media was replaced with fresh media on day 1 and day 3 post-viral infection (p.i). Five days p.i., the media was replaced with fresh media that was used for secondary infection. Seven days post-viral infection, HCV-infected Huh-7-derived CTRL-2 cell infectious supernatants were collected to perform a secondary infection of naïve Huh-7.5.1 cells (b) and HCV-infected Huh-7-derived CTRL-2 cells were fixed for ICW analysis (a) as described in Materials and Methods. HCV core abundance was quantified as described in Materials and Methods, averaged across triplicate wells, and expressed relative to protein abundance in parental Huh-7 control cells (Huh-7).  (b) Secondary infection. The HCV-infected Huh-7-derived CTRL-2 cell supernatants collected in (a) were used to infect naïve Huh-7.5.1 cells (10 × 103 cells) for 72 hours, then the cultured cells were fixed for ICW analysis as described in Materials and Methods. HCV core abundance was quantified as described in Materials and Methods, averaged across triplicate wells, and expressed relative to protein abundance in control cells (Huh-7).   Results (mean ± STD) are from one representative experiment done in triplicate.  The author performed this experiment, analyzed its results and generated this figure. 159  A1.1.1. The expression of GAPDH and CD81 transcripts by RT-QPCR show high correlation to microarray-determined expression profiles of Huh-7-derived stable GAPDH-knockdown clones, 67D2 and 67B3  For microarray analysis, total RNA was extracted from cells in triplicate and analyzed on Illumina Human HT-12 Expression bead chips that targeted more than 31K annotated genes with more than 47K probes derived from the National Center of Biotechnology Information (NCBI) (Rel38). To validate the microarray results, I first determined the relative GAPDH expression from the raw microarray data set and compared it with the relative GAPDH expression determined by RT-QPCR. Although the microarray analysis is not quantitative, a high correlation was observed (Fig. A1.3a) using the two distinct techniques.  Another gene of interest used for validation of the microarray results was CD81 (Fig. A1.3b), which is one of the key receptors for HCV entry into the host cell.  160   Figure A1.3. Expression of GAPDH and CD81 by RT-QPCR correlates with microarray results  (a) Relative GAPDH transcript levels determined by RT-QPCR assay was compared to those determined from microarray data.  Results (mean ± SEM) are from 2 independent experiments performed in triplicate by RT-QPCR, and results (mean ± STD) are from a microarray experiment performed in triplicate. ***p < 0.0001.   (b) Relative CD81transcript levels determined by RT-QPCR assay was compared to CD81 transcript levels determined from microarray data. 161  RT-QPCR results (mean ± SEM) are from 3 independent experiments performed in triplicate. ***p < 0.0001. Microarray results (mean ± STD) are from an experiment performed in triplicate. **p < 0.001;***p < 0.0001.  (a-b) GAPDH transcript levels and CD81 transcript levels, normalized to β-actin transcript levels, were relatively quantified from 50 ng of total RNA by RT-QPCR as described in Materials and Methods. Values are plotted as relative GAPDH or CD81 levels in comparison to those of parental Huh-7 (Huh-7).  The author performed this experiment, analyzed its results and generated this figure.  A1.1.2. List of differentially expressed overlapping genes in Huh-7-derived cell clones primarily showed downregulation of GAPDH and GAPDH pseudogenes After validating the GAPDH expression levels from the microarray (Fig. A1.3a), the normalized data was analyzed using the limma package in the R statistical software in order to identify differentially expressed genes. The analysis was performed using parental Huh-7 cells as a control. As expected, no genes were found to be differentially regulated between parental Huh-7 cells and control cells (CTRL).  Significantly deregulated genes were determined with a fold change cut off of 2 and a stringent p-value of 0.01 (Fig. 3.9a, Table A1.1). However, 67D2 and 67B3 cell clones showed deregulation of 64 and 42 genes respectively with only 11 genes overlapping (Fig. 3.9a). All of the down regulated genes from overlapping sections included GAPDH, GAPDH pseudogene 23 (LOC391075), GAPDH pseudogene 33 (LOC644237), CDK2, solute carrier family 22 (SLC22A7), and carboxypeptidase E (CPE) transcripts. The ankyrin repeat domain 1 transcript (ANKRD1) was the only one commonly upregulated between the two; and neurotensin (NTS) and brain expressed X-linked 1 (BEX1) transcripts were inversely regulated across GAPDH-kd cell clones, which suggested that these proteins may be regulated independent of GAPDH reduction.    162  Table A1.1. List of differentially expressed genes with fold changes (FC) across Huh-7-derived cell clones 67D2 and 67B3 67D2 UP DOWN ID FC adj. P VALUE ID FC adj. P VALUE ANKRD1 2.01 3.79E-06 VIM -3.53 5.72E-13 GPX2 1.99 9.12E-07 LOC391075 -3.38 9.45E-10 S100A6 1.99 2.93E-06 LOC644237 -2.69 8.10E-12 ITPR3 1.90 2.85E-06 GAPDH -2.54 2.78E-09 LOC647169 1.62 3.36E-06 AKR1C2 -2.46 3.60E-08 BEX1 1.58 6.24E-07 NTS -2.16 1.69E-07 DCDC5 1.51 1.58E-07 ARG1 -2.05 1.84E-05 360291 1.45 9.23E-09 UGT2B4 -2.02 3.02E-08 4670332 1.41 8.00E-10 ERP27 -1.95 8.76E-08 OSTalpha 1.37 3.28E-05 A2M -1.88 2.80E-05 CYP2S1 1.28 1.29E-07 TMEM200A -1.76 3.32E-07 RELN 1.25 1.59E-06 LOC346085 -1.59 4.44E-08 LOC388564 1.21 8.73E-07 CSF3R -1.53 2.34E-05 HMGCS2 1.17 3.42E-06 SERPINA3 -1.51 2.08E-09 MSX1 1.16 2.79E-06 GAPDHL6 -1.44 2.10E-09 TYRP1 1.13 2.42E-08 RBM4 -1.35 9.88E-09 WDR33 1.11 2.45E-05 HFE2 -1.33 2.12E-07 LOC151162 1.09 4.37E-06 CDK2 -1.21 5.52E-07 LOC440585 1.08 1.24E-06 CDO1 -1.16 3.40E-06 C3orf57 1.07 1.92E-06 INSIG1 -1.16 9.40E-08 PDGFRA 1.03 1.09E-05 EDNRB -1.11 7.00E-07 GSN 1.02 4.93E-06 AP1M1 -1.11 6.07E-06 LYSMD2 1.02 2.76E-06 MLLT11 -1.06 2.43E-07 ANGPTL3 1.01 1.32E-06 GPR177 -1.06 2.81E-06 SNHG8 1.01 4.83E-06 SLC22A7 -1.04 5.70E-06 ITGAV 1.01 6.29E-06 LMNA -1.00 6.61E-07 67B3 UP DOWN ID FC adj. P VALUE ID FC adj. P VALUE NPPB 3.28 2.22E-07 LOC391075 -3.11 3E-09 ANKRD1 2.39 5.04E-07 LOC644237 -2.47 3E-11 TAGLN 2.01 6.98E-08 GAPDH -2.42 2E-09 CXCL6 1.78 2.84E-06 CD81 -2.23 6E-09 CXCL1 1.77 6.31E-07 ASGR1 -1.63 3E-07 NTS 1.66 3.79E-06 BEX1 -1.55 8E-07 CYR61 1.50 2.44E-06 LOC346085 -1.50 1E-07 CTXN1 1.39 2.39E-08 CPE -1.46 2E-07 163  67B3 UP DOWN ID FC adj. P VALUE ID FC adj. P VALUE TNFRSF12A 1.39 9.87E-06 GAPDHL6 -1.45 2E-09 GDF15 1.32 1.30E-05 HDGFRP3 -1.39 4E-09 MATN3 1.20 1.19E-05 CDK2 -1.37 1E-07 LEPREL1 1.17 2.44E-08 MAGEB2 -1.26 2E-08 ANGPTL3 1.12 4.16E-07 OTC -1.20 2E-06 TNFRSF21 1.02 3.74E-07 PPP1R3C -1.19 5E-06 BGN 1.01 8.71E-06 GHR -1.13 3E-08    SLC22A7 -1.02 7E-06    C14orf125 -1.02 6E-06 adj. - adjusted.  The author isolated RNA samples as described in Materials and Methods (RNA isolation, Section A1.1.4). A. Haegert, a technician at the microarray facility labelled samples and performed microarray experiment. V. Svinti generated this table using raw data provided by A. Haegert as described in Materials and Methods (Microarray and data analysis, Section A1.1.4).  A1.1.3. Deregulation of molecular function and enriched pathways affected by GAPDH reduction Gene ontology analysis was carried out using the Ingenuity Pathway Analysis software. The molecular functions and canonical pathways affected by the differentially expressed genes across GAPDH-kd clones, 67D2 and 67B3 are summarized in Table A1.2 and Table A1.3 respectively. Enriched pathways (Table A1.3) and pathway categories schematically displayed in Fig. A1.4 revealed that several metabolic pathways were differentially regulated as expected with GAPDH downregulation. On the other hand, the same analysis for 67B3 revealed pathways that may result in lower susceptibility to HCV infections, such as cellular immune response or pathogen-induced signaling pathways. From the list of overlapping genes, 5 out of 8 genes down regulated were the expected genes, i.e., a GAPDH gene along with four GAPDH pseudogenes (LOC346085, LOC391075, LOC644237, and GAPDHL6). Pseudogenes are dysfunctional genes that have lost their protein-coding ability, but have the ability to regulate gene expression by acting as decoys for miRNAs (121, 185). However, the functional significance of GAPDH 164  pseudogenes is unknown, as the evidence supporting the expression to miRNA from these genes has not been reported. CDK2 (Fig 3.9a) is another gene of interest observed to be down regulated in Huh-7-derived cell clones, as it is down regulated in both the , 67D2 and 67B3. CDKs are cyclin-dependent kinases that regulate cell cycle checkpoints; i.e., their role is to verify whether each phase of a cell cycle has been accurately completed prior to the advancement to the next phase (99, 133, 148). According to the widely accepted classical model for the mammalian cell cycle, the interphase CDK2 checks processes at two stages of the cell cycle, one, the G1 phase of the cell cycle where cells prepare to initiate DNA synthesis, and two, the S-phase where DNA replicates (99, 148). The other interphase CDK, i.e., CDK1 regulates the process of mitosis (99, 148). Also, CDK activity, as the name suggests, is highly dependent on its ability to complex with various cyclins specific to each phase. To allow progression through G1/S phase and S/G2 (mitosis) phase, CDK2 activation relies on its complex formation with cyclin E and cyclin A2 respectively (99, 148). Due to the variety of evidence obtained through genetic studies, the classical model of the cell cycle has now been questioned, as the studies revealed that CDK2 is not essential for the mammalian somatic cell cycle progression and their activity can be replaced by CDK1 (99, 148).  Also, unlike CDK1 that is important for all cell types, CDK2 was observed to be important for the proliferation of very few cell types, such as glioblastoma and osteosarcoma, whereas, their ablation in hepatocytes and colon carcinoma did not affect or reduce their proliferation rate (148). Hence, downregulation of CDK2 by itself does not explain the low proliferation rates observed with Huh-7-derived cell clones. Recently, Phadke et al., by abrogating GAPDH expression with RNA interference in carcinoma cells, A549 and UO31, observed an inhibition of cell proliferation with the induction of cell cycle arrest in G0 /G1 phase via p53 stabilization, and the accumulation of p53-inducible CDK inhibitor p21 (183). From our microarray results, the expression of p21 was not observed to be significantly altered; hence, the inhibition of p21 may be occurring at the post-transcriptional level. Accordingly, testing the Huh-7-derived cell clones for p53 stabilization via serine-15 phosphorylation and upregulation of p21 allowing inhibition may explain the low proliferation rate. Then again, it is unclear if downregulation of GAPDH may 165  result in downregulation of the CDK2 gene expression, which may reveal an important pathway regulated by GAPDH. Furthermore, the mechanism of how GAPDH is involved in the regulation of other intersecting genes identified to be differentially expressed, such as SLC22A7, CPE, and ANKRD1 is not known. It can be hypothesized that it may have resulted from off-target effects. Hence, performing a rescue experiment may be necessary to link all the differentially regulated genes to GAPDH reduction (Fig. A1.7). Also, analyzing proteomic reprogramming with GAPDH downregulation may prove to be beneficial, especially as the ability of GAPDH to perform diverse functions stems from its post-translational regulation discussed in Section 1.2. Analysis of molecular function (Table A1.2), canonical pathways (Table A1.3) and pathway categories (Fig. A1.4), derived from differentially expressed genes reveal several metabolic, cancer, cell cycle, cell proliferation, and cell death pathways that were affected in 67D2. Interestingly, many of these functions can be linked to GAPDH function, including its fundamental role in glycolysis that may affect other metabolic pathways, especially as GAPDH is shown to play roles in cell proliferation by regulating cell cycle progression from G1 to S phase upon nuclear localization (46, 183), sensing oxidative stress to mediate apoptosis (91), and protecting cells from CICD-induced stress (47). Again, similar pathways have been observed to differentially regulate 67B3. However, molecular functions and pathways affected in 67B3 were relatively varied in comparison to 67D2 as the list of genes that where differentially regulated were diverse. In addition to CD81 downregulation (Fig. A1.3), the genetic profile of 67B3 cells revealed differential regulation of pathway categories involved in lower susceptibility to pathogens (Fig. A1.4), such as cellular immune responses and pathogen-induced signaling effect via interleukin (IL)-17 signaling due to differentially expressed genes, IL-8 and chemokine (C-X-C motif) ligand 1 (CXCL1) (Table A1.3, Fig. A1.4). Like CXCL, IL-8 is a cytokine of the CXC chemokine subfamily mainly involved in neutrophil chemoattractant (219).  IL-8 is also routinely used as a marker for various clinical conditions (219). Recently, IL-8 has come to be considered as a marker for tumor progression, as they have been shown to be upregulated in tumor cells (219). However, it is unclear as to how lower susceptibility to HCV infection may be attributed to IL-8 upregulation in vitro. Especially as IL-8 166  dependent activation of neutrophils contributes to pathobiological conditions (219), which may have significance in HCV-induced pathobiology in vivo. However, it can be deduced that IL-8 expression affects tumor angiogenesis, which may have altered the genetic profile of the cells leading to an antiviral state.    167  Table A1.2.Top ten molecular functions modulated by significantly deregulated genes with the threshold p-value of 0.5 67D2 67B3  Cell Morphology  Renal and Urological Disease  Cellular Development  Cellular Movement  Skeletal and Muscular System Development and Function  Cancer  Cancer  Gastrointestinal Disease  Gastrointestinal Disease  Cell-To-Cell Signaling and Interaction  Lipid Metabolism  Hematological System Development and Function  Molecular Transport  Cellular Growth and Proliferation  Small Molecule Biochemistry  Hepatic System Disease  Vitamin and Mineral Metabolism  Inflammatory Disease  Dermatological Diseases and Conditions  Genetic Disorder  Cell Death  Infection Mechanism  Cell Cycle  Infectious Disease  Connective Tissue Development and Function  Respiratory Disease  Cell-To-Cell Signaling and Interaction  Cell Death  Cardiovascular System Development and Function  Cardiovascular System Development and Function This table was generated by V. Svinti. using Ingenuity Pathway analysis on Table A1.2.   168  Table A1.3. Enriched pathways modulated by significantly deregulated genes with the threshold p-value of 0.5 67D2 67B3  Metabolism of Xenobiotics by Cytochrome P450  Role of IL-17A in Psoriasis  FXR/RXR Activation  Role of Tissue Factor in Cancer  Aryl Hydrocarbon Receptor Signaling  Role of IL-17F in Allergic Inflammatory Airway Diseases  Complement System  Role of IL-17A in Arthritis  Pentose and Glucuronate Interconversions   Maturity Onset Diabetes of Young (MODY) Signaling  Neuroprotective Role of THOP1 in Alzheimer's Disease  IL-17A Signaling in Gastric Cells  Glioblastoma Multiforme Signaling  IL-17A Signaling in Airway Cells  Cell Cycle: G1/S Checkpoint Regulation  Breast Cancer Regulation by Stathmin1  Glutathione Metabolism  IL-17 Signaling  PXR/RXR Activation  Airway Pathology in Chronic Obstructive Pulmonary Disease  Acute Phase Response Signalling  IGF-1 Signalling  Endothelin-1 Signalling  Atherosclerosis Signalling  NRF2-mediated Oxidative Stress Response  Hepatic Cholestasis  Small Cell Lung Cancer Signalling  Hepatic Fibrosis / Hepatic Stellate Cell Activation 169  67D2 67B3  Androgen and Estrogen Metabolism  Cardiomyocyte Differentiation via BMP Receptors   Acute Myeloid Leukemia Signalling  Differential Regulation of Cytokine Production in Macrophages and T Helper Cells by IL-17A and IL-17F  Cleavage and Polyadenylation of Pre-mRNA  Differential Regulation of Cytokine Production in Intestinal Epithelial Cells by IL-17A and IL-17F  Synthesis and Degradation of Ketone Bodies  This table was generated by V. Svinti using Ingenuity Pathway analysis on Table A1.2.   170    Figure A1.4. Pathway categories affected by differentially expressed genes in Huh-7-derived cell clones, 67D2 and 67B3  This figure was generated by V. Svinti using Ingenuity Pathway analysis on Table A1.2.  A1.1.4. Materials and methods  Cell culture and reagents Human hepatoma Huh-7b (Huh-7) (23) were provided by Dr. Charles Rice (The Rockfeller University, New York, NY, and Apath LLC, St. Louis, MO, USA) (22, 23). Huh-7 cells were cultured as described in Section 2.2. Huh-7-derived GAPDH-kd cell clones, 67D2 and 67B3, showing stable GAPDH suppression were cultured as described in Section 3.3. Cell lines were grown and maintained at 37˚C and 5% CO2.  Generation of Huh-7-derived cell clones, 67D2 and 67B3 showing reduction in GAPDH expression. Huh-7-derived cell clones showing stable GAPDH suppression were generated as described in Section 3.3. 171  RNA isolation Total RNA was isolated from cells (RNeasy plus mini kit, Qiagen, Toronto, ON, Canada) lysed in buffer RLT (Qiagen, Toronto, ON, Canada) using  Qiashredder (Qiagen, Toronto, ON, Canada). RNA was extracted with DNase on-column digestions for the first set of experiments performed (Fig. A1.6b) as the RNeasy mini kit (Qiagen, Toronto, ON, Canada) did not facilitate the removal of DNA during the RNA isolation process. The concentration and purity of RNA was determined by a NanoDrop ND-1000 Spectrophotometer (Thermo Scientific, Nepean, ON, Canada).  PCR Complementary DNA (cDNA) was synthesized from RNA (~200 ng) using a TaqMan Reverse transcription kit (Applied Biosystems).  Oligo dT primers were used to do so in a reaction volume of 15 μL. To ensure that the RNA samples were free of genomic contamination, an end-point PCR was performed using cDNA as a template and primer sets 5′-CCAACCGCGAGAAGATGA-3′ (forward) and 5′-CCAGAGGCGTACAGGGATAG-3′ (reverse), targeting the ACTB transcript.  Microarray and data analysis For gene expression studies, RNA samples (~3μg) were supplied to the Vancouver Prostate Center Microarray Facility (Vancouver, Canada) in triplicate. The qualities of the samples were assessed using Agilent 2100 Bioanalyzer. Samples (~200ng) were biotin-labelled with an Illumina TotalPrep RNA Amplification Kit (Ambion). Hybridization was performed on Illumina Whole-Genome Gene Expression Human HT-12 v4 BeadChip and arrays were scanned using an Illumina iScan. Array quality was assessed using an Illumina GenomeStudio and the data was quantile normalized with GeneSpring software (v7.3.1, Agilent). The data was normalized for further analysis using the limma package in the R statistical software in order to identify differentially expressed genes. In addition, hierarchical clustering of genes was performed, heatmaps (R software) were generated, and gene ontology and pathway enrichment analyses (Ingenuity ®) to determine the functional role of deregulated genes were carried out. A pathway-clustering algorithm (141) was used to cluster pathways into categories, to aid the interpretation of results. 172  Carboxyfluorescein diacetate, succinimidyl ester (CFSE)-based Proliferation assay CFSE-based proliferation assay was performed as suggested in manufacturer’s protocol using CellTrace™ CFSE Cell Proliferation Kit (Cat. no. C34554, Molecular probes/ Invitrogen, Eugene, OR, USA). Briefly, 2 million cells were incubated with 2.5 µM of CFSE stain diluted in PBS (pH 7.4) for 15 min at room temperature in dark to allow passive diffusion of the stain into cells. After washing twice with PBS (pH 7.4), fresh media was added and cells were incubated for 30 min at 37ºC to allow covalent conjugation with fluorescent dye with intracellular amines. Cells were than trypsinized and split for day 0 and day 3 analysis. For flow cytometry, cells were trypsinized on respective day and washed in ice cold fluorescence-activated cell sorting (FACS) buffer (PBS (pH 7.4), 2% FBS, 2.5 mM EDTA, and 0.05% sodium azide). Washes for resuspend cells were carried out at 1200 rpm for 5 min at 4ºC. After wash, cells were resuspended in 0.2 µg/mL of propidium iodide containing ice cold FACS buffer for flow cytometry analysis. FACS data was acquired using BDTM LSR II instrument (BD Biosciences, Mississauga, ON, Canada) using FACSDiva software and analyzed using FlowJo software.    173     Figure A1.5. Variability in proteins identified between technical replicates (two) for each biological replicate (two) of Huh-7-derived cell clones, 67D2 and 67B3 showing stable reduction in GAPDH expression levels   The author prepared and labelled samples for mass spectrometry. J. John performed mass spectrometry and merged tables of technical replicates (two) to provide tables of biological replicates. The author analyzed the data in the merged biological replicate table to generate these variability plots. 174  Table A1.4. Significantly modulated pathways, biological processes, and cellular components identified using differentially expressed proteins across Huh-7-derived stable GAPDH-kd clones (67D2 and 67B3) 67D2 67B3 Down Pathways P-value Pathways P-value MAPK signaling pathway 0.002 Validated targets of C-MYC transcriptional activation 0.010 Focal adhesion 0.005 EGFR1 0.016 Rho cell motility signaling pathway 0.012 Regulation of actin cytoskeleton 0.019 Platelet degranulation 0.020 Gap junction 0.033 Cell-extracellular matrix interactions 0.023 Post-chaperonin tubulin folding pathway 0.033 E2F transcription factor network 0.023     amb2 Integrin signaling 0.023     AndrogenReceptor 0.032     Regulation of actin cytoskeleton 0.047     Up Pathways P-value Pathways P-value Arginine and Proline metabolism 0.000 Glycine and Serine metabolism 0.037 Peroxisome 0.015 Glycine, serine and threonine metabolism 0.037 Metabolic pathways 0.039 Glyoxylate and dicarboxylate metabolism 0.037 Alanine, aspartate and glutamate metabolism 0.040 Serine biosynthesis 0.037 D-Glutamine and D-glutamate metabolism 0.040 Synthesis and degradation of ketone bodies 0.037 Ethanol oxidation 0.040 Terpenoid backbone biosynthesis 0.037 FOXA2 and FOXA3 transcription factor networks 0.040     Glutamic acid and Glutamine metabolism 0.040     Glycine, serine and threonine metabolism 0.040     Histidine degradation 0.040     Propanoate metabolism 0.040     Lysine degradation 0.040     Pyruvate metabolism 0.040     175  67D2 67B3 Up Pathways P-value Pathways P-value Arginine and Proline metabolism 0.040     Nitrogen metabolism 0.040     Proximal tubule bicarbonate reclamation 0.040     Serine biosynthesis 0.040     Synthesis and degradation of ketone bodies 0.040     Terpenoid backbone biosynthesis 0.040     67D2 67B3 Down Biological Processes P-value Biological Processes P-value cellular component movement  0.003 blood coagulation  0.018 actin cytoskeleton organization  0.004 cellular component movement  0.021 actin cytoskeleton reorganization  0.028 platelet degranulation  0.026 actomyosin structure organization  0.028 platelet activation  0.035 67D2 67B3 Down Biological Processes P-value Biological Processes P-value blood vessel endothelial cell migration  0.028 actin cytoskeleton organization  0.035 cell differentiation  0.028 protein polymerization  0.035 cytoskeleton organization  0.028 positive regulation of DNA binding  0.035 induction of apoptosis  0.028 regulation of cell shape  0.035 muscle organ development  0.028 regulation of transcription from RNA polymerase II promoter  0.035 Up Biological Processes P-value Biological Processes P-value brain development  0.001 brain development  0.001 metabolic process  0.002 metabolic process  0.003 oxidation-reduction process  0.006 response to oxidative stress  0.015 response to stress  0.019 collagen fibril organization  0.039 cellular amino acid biosynthetic process  0.022 eating behavior  0.039   176  67D2 67B3 Up Biological Processes P-value Biological Processes P-value cellular amino acid metabolic process  0.035 L-serine biosynthetic process  0.039 eating behavior  0.035 negative regulation of MAP kinase activity  0.039 ethanol oxidation  0.035 negative regulation of MAPKKK cascade  0.039 glutamate biosynthetic process  0.035 neuromuscular process  0.039 glutamate catabolic process  0.035 response to amino acid stimulus  0.039  L-serine biosynthetic process  0.035 response to toxin   0.039  negative regulation of MAP kinase activity  0.035 tricarboxylic acid cycle  0.039 negative regulation of MAPKKK cascade  0.035 tRNA processing  0.039 response to toxin  0.035 mitotic cell cycle  0.047 transmembrane transport  0.035     lipid metabolic process  0.043     response to organic cyclic compound  0.048     xenobiotic metabolic process  0.048     67D2 67B3 Down Cellular components P-value Cellular components P-value ruffle membrane  0.016 blood coagulation  0.018 microsome  0.016 cellular component movement  0.021 insoluble fraction  0.028 platelet degranulation  0.026 intermediate filament  0.028 platelet activation  0.035 nuclear envelope  0.028 actin cytoskeleton organization  0.035 centrosome  0.029 protein polymerization  0.035 cytoskeleton  0.031 positive regulation of DNA binding  0.035 stress fiber  0.034 regulation of cell shape  0.035     regulation of transcription from RNA polymerase II promoter  0.035     mitotic cell cycle  0.047  177  67D2 67B3 Up Cellular components P-value Cellular components P-value mitochondrion  0.000 brain development  0.001 axon terminus  0.035 metabolic process  0.003 mitochondrial outer membrane  0.035 response to oxidative stress  0.015 synaptic vesicle  0.035 collagen fibril organization  0.039     eating behavior  0.039     L-serine biosynthetic process  0.039     negative regulation of MAP kinase activity  0.039     negative regulation of MAPKKK cascade  0.039     neuromuscular process  0.039     response to amino acid stimulus  0.039     response to toxin  0.039     tricarboxylic acid cycle  0.039     tRNA processing  0.039 Bold: commonly affected molecular functions across the GAPDH-kd cell clones This table was generated by V. Svinti. using InnateDB: Gene Ontology and Pathway Analysis on the list of proteins in Table 3.1.   178  A1.3 Rescuing Huh-7-derived stable GAPDH-knockdown cell line, 67cl.1 for GAPDH expression As Huh-7 cells have the ability to adapt to the changing conditions, such as handling and passaging in different laboratories (206), or co-evolving upon viral infection (277), all transcriptional variation observed by microarray (Table A1.1, Fig. 3.9a) and translational variation observed by mass spectrometry (Table 3.1, Fig. 3.7) may not have resulted from stable GAPDH knockdown. Hence, a rescue experiment along with loss of functions may prove to be beneficial in identifying genes that are differentially regulated with respect to GAPDH expression. Hence, a GAP67 plasmid construct was generated for the purpose of rescuing these GAPDH-kd cell clones. The construct was generated with the introduction of seven silent mutations in the shRNA-targeted region of GAPDH cDNA, such that the GAP67 transcript does not get targeted by the shRNA that is constitutively expressed in these GAPDH-kd cell clones. The results of GAP67 transfection in 67cl.1 showed that Huh-7-derived cell clones showing stable reduction in GAPDH expression can be successfully rescued (Fig. A1. 6).  Figure A1.6. Rescued GAPDH expression observed in the Huh7-derived GAPDH-kd clone, 67cl.1  Huh-7-derived stable GAPDH-kd clone, 67cl.1 cells (2 × 106) were transfected with 3 µg of pcDNA3.1 (CTRL) or GAPDH67-V5 plasmid construct. Forty-eight hours post-transfection, cells lysates were harvested for WB analysis and probed for human GAPDH (green) and for β-tubulin (red) (loading control) expression as described in Materials and Methods (Section 2.3).  The author performed this experiment, analyzed its results and generated this figure.  179  A1.2.1. Materials and methods Preparation of GAP67 and GAPV5 constructs The purified human GAPDH PCR fragment (PCR purification kit, Invitrogen) generated as described in Section 2.3 was cloned into a pGEM-t vector (Promega, Madison, WI, USA). Thereafter GAPDH67  gene was generated by introducing seven silent mutations using primer sets 5’-/5Phos/ACGATCCATTCATTGACCTCAACTACATGGTTTACATGTTCCAATATGATTC-3’ (forward) and 5’-/5Phos/TAATTGCGACGATATCCACTTTACCAGAGTTAAAAGCAGCCTTGGTGAC-3 (reverse) (IDT, Coralville, IA, USA). After confirming DNA sequence for the mutations (NAPS Unit, University of British Columbia, Vancouver, BC, Canada), the GAPDH67 gene was cloned with the introduction of KpnI and EcoRI restriction cloning sites on the 5’ end and the 3’ end respectively, along with the elimination of stop codon using the primer sets 5’-GCTTGGTACCATGGGGAAGGTGAAGGTC-3’ (forward) and 5’-TGCAGAATTCCCTCCTTGGAGGCCATGT-3’ (reverse) (IDT). This cloning step was performed for the purpose of generating hGAPDH67 construct C-terminally tagged with a V5 tag (GAPDH67-V5). The purified hGAPDH67 PCR fragments were introduced into -containing pcDNA3.1+ as described in Materials and Methods Section 2.3. GAPDH67-V5 construct (pcDNA3.1-GAPDH67-V5) was generated as described in Materials and Methods Section 2.3.  180  Appendix 2: Chapter 4 supplementary figure   Figure A2.1. Monolayer of Huh-7.5.1 cells transfected with an in vitro transcribed JFH-1 genome shows cytopathic effects 17 days post-transfection  Huh-7.5.1 cells were visualized by light microscopy for cytopathic effect over 21 days post transfection indicating production of viral particles. Cells transfected with an in vitro transcribed JFH-1 RNA genome as described in Materials and Methods (Section 2.2) show cell death after 17 days (b) post-transfection (p.t.) with massive cell death after 21 days (d) p.t., whereas control cells show no cytopathic effects (a, c).   The author performed this experiment, analyzed its results and generated this figure. 181  Appendix 3:  Discovery of a unique synthetic small interfering RNA (siRNA) with robust anti-HCV activity (siRNA_MR01)  In this section, I report the discovery of a unique synthetic siRNA, siRNA_MR01 with significant anti-HCV activity.  The sequence of siRNA_MR01 was specifically designed to not have any target in host cells, and was validated by the company (Dharmacon) using microarray analysis to show minimal off-target effects. Hence, the observation of an anti-HCV activity with this siRNA was unexpected. Due to this observation, I hypothesized that siRNA_MR01 targets a factor or factors in the cells that lead to an anti-HCV state without triggering detrimental side effects. Here, I attempt to unravel the mechanism by which siRNA_MR01 displays anti-HCV activity.  A3.1  Anti-HCV property of siRNA_MR01  A3.1.1  Reduced core protein expression in Huh-7.5.1 cells pre-treated with a non-targeting siRNA pool In an attempt to establish the role of a multifunctional host enzyme, glyceraldehyede-3-phosphate dehydrogenase (GAPDH) in the HCV life cycle, in Chapter 2, I had transiently reduced GAPDH expression levels in HCV permissible human hepatoma cells, Huh-7.5.1. This is because these cells displayed robust core expression within 72 hours of HCV infection.  To do so, I had employed a pool of siRNAs targeting GAPDH in Huh-7.5.1 cells for 24 hours. Here, a pool of 4 non-targeting siRNAs (siCTRL; siCTRL-1, -2, -3, & -4; 25 nM) and a pool of siRNAs against cyclophilin B (siCYPB; 25 nM) were used as negative controls for 24 hours. After 48 hours of transfection, treated cells were infected with HCV (MOI: 0.1). Seventy-two hours post-viral infection (p.i.), HCV-infected Huh-7.5.1 cells were fixed and processed for in-cell western (ICW) analysis (Fig. A3.1a). As a measure of HCV infection, the treated cells were probed for HCV core protein expression (green) and GAPDH expression (green), and stained with dyes to determine cell density for normalization [(CD); red) (Fig. A3.1a; upper panel). HCV core and human GAPDH proteins were quantified, and expressed as protein abundance relative to mock-182  treated cells. Here, I observed that siCTRL-treated cells displayed a significant reduction in HCV core abundance in comparison to mock-treated cells (Fig. A3.1a; lower panel). siCTRL-treated cells also showed a reduction in core abundance compared to siGAPDH- and siCYPB-treated cells (Fig. A3.1a; lower panel). Next, to examine the effect of siCTRL-mediated suppression on HCV infectious virus particle production and its spread to naive cells, I performed an ICW assay involving HCV secondary infection. As described earlier, prior to fixing HCV-infected Huh-7.5.1 cells at 72 hours p.i. for ICW (Fig. A3.1a; upper panel), the HCV-infected Huh-7.5.1 cell supernatants were collected from the primary infection and used to perform a secondary infection on naïve Huh-7.5.1 cells. After the secondary infection proceeded for 72 hours, the HCV-infected Huh-7.5.1 cells were fixed for ICW assays (Fig. A3.1b; upper panel).  A significant reduction of HCV core abundance was measured in Huh-7.5.1 cells infected with HCV-infected Huh-7.5.1 cell supernatants collected from siCTRL-treated Huh-7.5.1-treated cells from primary infection (Fig. A3.1b; lower panel). Thus, siCTRL treatment of naïve Huh-7.5.1 cells not only inhibits primary HCV infection but also seems to inhibit the production and/or release of infectious HCV virus particles in HCV-infected Huh-7.5.1 cell supernatants.  183   Figure A3.1.Non-targeting short-interfering RNA (siCTRL) treated Huh-7.5.1 cells show reduced susceptibility to HCV infection.  (a) Primary infection. Huh-7.5.1 cells (~7.5 × 103 cells) were treated with a transfection mix containing an siRNA pool targeting human GAPDH (siGAPDH, 25 nM) or a non-targeting siRNA pool (siCTRL, 25 nM) using DharmaFECT 4 transfection reagent for 24 hours. After 48 hours of transfection, cells were infected with HCV (MOI: 0.1). Twenty-four hours post-viral infection, media was replaced with fresh media that was used for secondary infection. Seventy-two hours post-viral infection, HCV-infected Huh-7.5.1 cell infectious supernatants were collected to perform a secondary infection of naïve Huh-7.5.1 (a) cells and HCV-infected Huh-7.5.1 cells were fixed for ICW analysis (b) as described in Materials and Methods.  Upper panel: Representative ICW wells probed with HCV anti-core antibody (green), anti-GAPDH antibody (green), and stained with two dyes for the purpose of determining cell density (CD) (red) for normalization.  Lower panel: Protein abundance was quantified as described in Materials and Methods, averaged across triplicate wells, and expressed relative to protein abundance in control cells (siCTRL).  (b) Secondary infection. The HCV-infected Huh-7.5.1 cell supernatants collected in (a) were used to infect naïve Huh-7.5.1 cells for 72 hours, then the cultured cells were fixed for ICW analysis as described in Materials and Methods.  184  Upper panel: Representative ICW wells probed with HCV anti-core antibody (green), anti-GAPDH antibody (green), and stained with two dyes for the purpose of determining cell density (CD) (red) for normalization.  Lower panel: Protein abundance was quantified as described in Materials and Methods, averaged across triplicate wells, and expressed relative to protein abundance in control cells (siCTRL).   Results (mean ± STD) are from one representative experiment done in triplicate.  The author performed this experiment, analyzed its results and generated this figure.  A3.1.2  Reduced core protein expression in Huh-7.5.1 cells treated with siRNA_MR01 prior to HCV infection To identify the non-targeting sequences that possess significant anti-HCV activity, from the pool of 4 non-targeting siRNAs (siCTRL), individual siRNA (siCRTL-1, -2, -3, & -4) were tested against HCV.  Due to the possibility that the antiviral activity may have resulted from the specific combination of individual siCTRL, I included the testing of various possible combinations of individual siCTRL for anti-HCV activity. As in the above experiment, Huh-7.5.1 cells were transfected with the indicated combinations of individual siCTRL (Fig. A3.2) for 24 hours. After 48 hours, treated cells were infected with HCV (MOI: 0.1). Seventy-two hours p.i., HCV-infected Huh-7.5.1 cells were fixed and processed for ICW analysis (Fig. A3.2a).  siCTRL-treated HCV-infected cells probed for HCV core (green) abundance showed reduced HCV core protein abundance when treated with siCTRL-1 or with combinations that included siCTRL-1 (Fig. A3.2a; lower panel) without displaying toxicity as observed by cells stained with dyes for cell density (CD) normalization (Fig. A3.2a; upper panel). To examine the effect of siRNA-mediated GAPDH suppression on HCV infectious virus particle production and its spread to naive cells, I performed an ICW assay involving HCV secondary infection. The HCV-infected Huh-7.5.1 cell supernatants were collected from primary infection (Fig. A3.2a) at 72 hours p.i. to perform a secondary infection of naïve Huh-7.5.1 cells.  After the secondary infection proceeded for 72 hours, the HCV-infected Huh-7.5.1 cells were fixed and processed for ICW assays (Fig. A3.2b; upper panel), as described earlier. A significant reduction of HCV core abundance was measured for cells treated with HCV-infected Huh-7.5.1 cell supernatant treated with siCTRL-1, or combinations of siRNAs that include siCTRL-1 (Fig. A3.2a; lower panel). Thus, siCTRL-1 185  treated naïve Huh-7.5.1 cells not only inhibited primary HCV infection but also inhibited the production and/or release of infectious HCV virus particles in HCV-infected Huh-7.5.1 cell supernatants. After the identification of siCTRL-1 from a pool of non-targeting siRNA having anti-HCV activity, I designated it as siRNA_MR01.   186    187  Figure A3.2. Synthetic siRNA_MR01 reduces the susceptibility of Huh-7.5.1 cells to HCV infection  (a) Primary infection. Huh-7.5.1 cells (~7.5 × 103 cells) were treated with a transfection mix containing the indicated combination of 4 non-targeting siRNAs (siCTRL-1, -2, -3, & -4; 25 nM) using DharmaFECT 4 transfection reagent for 24 hours. After 48 hours of transfection, cells were infected with HCV (MOI: 0.1). Twenty-four hours post-viral infection, media was replaced with fresh media that was used for secondary infection. Seventy-two hours post-viral infection, HCV-infected Huh-7.5.1 cell infectious supernatants were collected to perform a secondary infection of naïve Huh-7.5.1 (a) cells and HCV-infected Huh-7.5.1 cells were fixed for ICW analysis (b) as described in Materials and Methods.  Upper panel: Representative ICW wells probed with HCV anti-core antibody (green), anti-GAPDH antibody (green), and stained with two dyes for the purpose of determining cell density (CD) (red) for normalization.  Lower panel: Protein abundance was quantified as described in Materials and Methods averaged across triplicate wells, and expressed relative to protein abundance in control cells (siCTRL).  (b) Secondary infection. The HCV-infected Huh-7.5.1 cell supernatants collected in (a) were used to infect naïve Huh-7.5.1 cells for 72 hours, then the cultured cells were fixed for ICW analysis as described in Materials and Methods.  Upper panel: Representative ICW wells probed with HCV anti-core antibody (green), anti-GAPDH antibody (green), and stained with two dyes for the purpose of determining cell density (CD) (red) for normalization.  Lower panel: Protein abundance was quantified as described in Materials and Methods averaged across triplicate wells, and expressed relative to protein abundance in control cells (siCTRL).   Results (mean ± STD) are from one representative experiment done in triplicate.  The author performed this experiment, analyzed its results and generated this figure.   A3.2  Computational prediction of candidates for siRNA_MR01  A3.2.1  Replication of HCV subgenomic replicon is not compromised in siRNA_MR01-treated subgenomic HCV replicon.  As siRNA_MR01 is a non-targeting sequence designed by the manufacturer for use with a human siRNA library, I first hypothesized that it may target the HCV genome in 188  HCV-infected cells. In order to verify this, miRanda (version 3.3a) target prediction software was utilized to search for target sites on both the positive and negative strand sequences of HCV RNA. The algorithm identifies strong seed regions based on sequence pairing, conservation of the predicted binding site, and the free energy of duplex formation (106). Strong predictions, therefore, have a higher alignment score and lower energy. The program was run with the default parameters of using a score of 130, which is equivalent to a 75% complementary match between miRNA and target, which leaves the question of the number of match pairs required for translational inhibition by any mechanism (106). The results showed no predicted siRNA_MR01 sites on the HCV genome.  To test the targeting of siRNA_MR01 on the HCV genome, I used Huh.8 cells harbouring HCV genotype 1b subgenomic replicon with a selectable neomycin marker (Con1/SG-Neo) for inhibition of HCV replication. After 24 hours of treatment with siRNA_MR01, cells were further incubated for 48 hours. For controls, siCTRL pool (siCTRL-2, -3, & -4), an individual siCTRL-4 and an siCYPB were employed. As oppose to pool, a single non-targeting siRNA control (siCTRL-4) was also included as a control. This is because siCTRL-4 had no effect on core protein expression in both primary and secondary infection (Fig. A3.2a-b). The examination of the total HCV RNA levels by RT-QPCR on day 3 post-transfection from siRNA_MR01 treated cells revealed that HCV replication (Fig. A3.3) was not significantly compromised compared to siCTRL and siCTRL-4 treated cells.  As siRNA_MR01 did not compromise HCV replication and had similar HCV genomic abundance when compared to other siRNA-treated controls, I concluded that siRNA_MR01 did not target or interact with the 5′-UTR (nucleotide 1-337), NS3-NS5B, and the 3′-UTR region of HCV genotype 1b. 189   Figure A3.3. Replication of a HCV subgenomic replicon is not compromised in siRNA_MR01 treated Huh.8 cells  Huh.8 cells (Huh-7 subclones expressing HCV subgenomic replicon, Con1/SG-Neo) (~1.32 × 105 cells) were transfected with siRNA_MR01 (15 nM), or a  pool of non-targeting siRNA (siCTRL= siCTRL-2, -3, -4), or a non-targeting siCTRL-4 (15nM), or a pool of siRNA targeting CYPB (siCYPB, 15 nM) using DharmaFECT 4 transfection reagent for 24 hours. After 72 hours of transfection, total RNA was harvested from Huh.8 cells.   HCV RNA levels, normalized to β-actin transcript levels, were relatively quantified from 25 ng of total RNA by RT-QPCR as described in Materials and Methods. Values are plotted as relative HCV levels in comparison to those of control-treated cells (siCTRL).  Results (mean ± STD) are from one experiment performed in triplicate.    The author performed this experiment, analyzed its results and generated this figure.  A3.2.2  Computational prediction of candidate host-cell transcript and miRNA targets for siRNA_MR01 After showing that HCV genome expression and replication is not inhibited in siRNA_MR01-treated cells, in collaboration with other lab members, I proceeded to check whether siRNA_MR01 inhibits the expression of human transcripts. All of the available human mRNA sequences were obtained from Genbank and prediction analysis was carried out with the same miRanda target prediction software using the above parameters. The results showed 24 potential mRNA targets (Table A3.1). Pathway enrichment analysis using InnateDB (143) revealed that the genes in Table A3.1 are involved in integrin cell surface 190  interaction signaling, long-term potentiation, CXCR4-mediated signaling, pancreatic secretion, amoebiasis, metabolic pathways and chemokine signaling.  However, in order to analyze the list further, a WB of targets is necessary to show that the predicted transcripts are downregulated in the cells, especially as the prediction software leaves room for identification matches that result in translational inhibition that require scores higher than the default score parameter used.  191  Table A3.1. Potential mRNAs predicted to be targeted by siRNA_MR01 using software miRanda against human mRNA sequences obtained from Genbank Accession number UniProt Gene Name Description Score NM_004942.2 DEFB4A defensin, beta 4A  181 XM_002342811.1   181 NM_152586.3 USP54 ubiquitin specific peptidase 54  175 NM_000426.3 LAMA2 laminin, alpha 2, transcript variant 1 174 NM_001079823.1 LAMA2 laminin, alpha 2 , transcript variant 2 174 NR_024456.1   173 NR_027051.1   172 XR_112350.1   172 NM_001010942.1 RAP1B RAP1B, member of RAS oncogene family, transcript variant 2 171 NM_001143974.1 ASAH2 N-acylsphingosine amidohydrolase (non-lysosomal ceramidase) 2, transcript variant 2 171 NM_015646.4 RAP1B RAP1B, member of RAS oncogene family, transcript variant 1 171 XM_003119952.1   171 NM_177538.2 CYP20A1 cytochrome P450, family 20, subfamily A, polypeptide 1  170 NM_001193552.1   169 NM_145307.2 RTKN2 rhotekin 2  169 NM_004412.5 TRDMT1 tRNA aspartic acid methyltransferase 1  168 NM_012203.1 GRHPR glyoxylate reductase/hydroxypyruvate reductase  168 NM_001079802.1 FKTN fukutin, transcript variant 1 167 NM_006731.2 FKTN fukutin , transcript variant 2 167 NM_015192.2 PLCB1 phospholipase C, beta 1 (phosphoinositide-specific), transcript variant 1 167 NM_182734.1 PLCB1 phospholipase C, beta 1 (phosphoinositide-specific) , transcript variant 2 167 NM_020864.1 KIAA1486 neuronal tyrosine-phosphorylated phosphoinositide-3-kinase adaptor 2  164 XM_002345602.1   164 XR_113108.1   164 NM_001169117.1 STIM2 stromal interaction molecule, transcript variant 3 163 NM_001169118.1 STIM2 stromal interaction molecule 2, transcript variant 1 163 NM_014624.3 S100A6 S100 calcium binding protein A6  163 192  Accession number UniProt Gene Name Description Score NM_020860.3 STIM2 stromal interaction molecule 2, transcript variant 2 163 NM_022780.3 RMND5A required for meiotic nuclear division 5 homolog A (S. cerevisiae)  161 NM_004998.2 MYO1E myosin IE  157 V. Svinti generated this table using siRNA_MR01 sequence and miRanda target prediction software.  Next, in collaboration with other lab members, I wanted to investigate if siRNA_MR01 is complementary to any cellular miRNA.  We downloaded all of the 1921 available miRNA sequences from the miRBase (release 18) and used them for performing prediction analysis. One potential hit, miR-299-3p, was identified as having an alignment score of 145 and free energy -15.  As the functional significance of miR-299-3p has not been reported, performing target prediction against the human genome identified an extensive list of 2,976 hits (score cut off of 130 and strict energy threshold of -20). Thus, I reasoned that the targeting of any mRNA, such as miR-299-3p can alter the expression of several proteins. To identify reduction in expression of targeted proteins either directly by siRNA or indirectly through regulation of miRNA, I performed an examination of the protein expression profile of Huh-7.5.1 cells treated with siRNA_MR01.  A3.3  Proteomics analysis of Huh-7.5.1 cells treated with siRNA_MR01 Total protein expression in siRNA_MR01-treated Huh-7.5.1 cells in comparison to siCTRL-treated cells depicted in Fig. A3.5 showed no changes in the band pattern. In order to further examine any alteration in protein expression resulting from the targeting of host miRNA or a host transcript by siRNA_MR01 treatment, total protein extracts from siRNA_MR01-treated and siCTRL-treated Huh-7.5.1 cells were subjected to nano-LC-MS/MS. Protein expression profiles of siRNA_MR01-treated and NonTarg-treated cells in relation to non-treated Huh-7.5.1 cells (Table 3.2) were compared to exclude the non-specific differential expression of proteins identified, especially as NonTarg-treatment did not result in anti-HCV activity that was observed for siRNA_MR01 (Fig. 2 4 & 4.2). The percent variability plot of technical replicates showed that ≥ 95% (Fig. A3.7) of identified proteins had ≤ 35% variability. This suggested that the data was reproducible and reliable.  193   Figure A3.4. No change in band pattern generated by coomassie staining of total protein from Huh-7.5.1 cells treated with siRNA_MR01  Huh-7.5.1 cells (~1.5 × 105 cells) were transfected with a short interfering (siRNA) pool targeting human GAPDH (siGAPDH, 15 nM) or a non-targeting pool of siRNA (siCTRL, 15 nM) using DharmaFECT 4 transfection reagent. Cell lysate were harvested on day 5 post transfection for coomassie staining or for WB analysis and probed for human GAPDH (green) or human CYPB and for β-tubulin (red) (loading control used for normalization) expression as described in Materials and Methods. (a) Total protein band expression pattern observed on a coomassie stained gel scanned with an infrared scanner (LI-COR Odyssey scanner) (b) A representative WB.   The author performed this experiment, analyzed its results and generated this figure. 194   Figure A3.5. Variability in proteins identified across technical replicates (two) for each biological replicate (three) of Huh-7.5.1 cells treated with siRNA_MR01 in comparison to cells treated with a pool of NonTarg control siRNAs  195  (a) Variability plot for biological replicate 1; (b) Variability plot for biological replicate 2; (c) Variability plot for biological replicate 3.  The author prepared and labelled samples for mass spectrometry. J. John performed mass spectrometry and provided tables of biological replicates (two) merging technical tables of replicates (three) resulting from the MS experiment. The author generated variability plots using tables of biological replicates.  From the list of 38 differentially expressed proteins identified, 34 proteins were unique to siRNA_MR01. From this unique list of genes, only three proteins were observed to be downregulated, which included SERPINE1 mRNA-binding protein 1 (SERBP1), cullin-associated and neddylation-dissociated protein 1 (CAND1), and thioredoxin reductase 1 (TXNRD1). SERBP1 regulates the expression of SERPINE1/PAI-1, i.e., the interaction of SERBP1 destabilizes the SERPINE1 transcript that gets degraded, resulting in lower expression of the protein (97). CAND1/ TIP120A (TATA Box-binding protein (TBP)-interacting proteins 120 A) negatively regulates the activity of E3 type ubiquitin ligase (238). CAND1 is also regulates the interaction of TBP along with several other transcription factors to regulate the expression of several genes carrying the specific core promoter element (TATA box) recognized by them (115). TXNRD1 reduces redoxins, glutaredoxins and other substrates, and is shown to play a role in selenium metabolism involved in protection against oxidative stress (256). The list of upregulated genes involved several histone cluster 1 proteins, a few histone cluster 2 proteins, one histone cluster 3 proteins, a yeast chromosome segregation 1-like protein (CSE1L), a beta polypeptide of tyrosine 3-monooxygenase/tryptophan 5-monooxygenase activation protein (YWHAB), proteasome 26S subunit (PSMD2), and radixin (RDX). Histones are DNA binding proteins that package the DNA into nucleosomes. They are necessary components of chromatin and act as spools to allow DNA unwinding, thereby allowing regulation of gene expression.  CSE1L is an export signal for importin-alpha that carries NLSs. By exporting the nuclear-localized importin-alpha (84), CSE1L aids in efficient nuclear localization of proteins (84) in cells. PSMD2 is a regulatory subunit (not the core subunit mentioned earlier) of the proteasome involved in ATP-dependent degradation of ubiquitinated protein (235). Finally, RDX is a cytoskeletal protein that links the barb/growing end of actin to the plasma 196  membrane (100). The GenMania network of physical protein-protein interaction (Fig. A3.7) was generated by uploading the list of differentially expressed proteins from Table A3.2.    197  Table A3.2. List of differentially expressed proteins with fold changes (FC) in Huh-7.5.1 cells treated with a pool of siRNAs targeting GAPDH Description Associated gene name FC Down catenin (cadherin-associated protein), alpha 1, 102 kDa  CTNNA1 -4.22 SERPINE1 mRNA binding protein 1  SERBP1 -1.40 ribosomal protein S18  RPS18 -1.32 cullin-associated and neddylation-dissociated 1  CAND1 -1.32 thioredoxin reductase 1  TXNRD1 -1.32 Up myosin, light chain 6, alkali, smooth muscle and non-muscle  MYL6 2.44 microtubule-associated protein 4  MAP4 2.12 CSE1 chromosome segregation 1-like (yeast)  CSE1L 1.68 histone cluster 1, H1c  HIST1H1C 1.55 histone cluster 1, H2ab/ae HIST1H2AB/AE 1.54 histone cluster 1, H2ag/ai/ak-am HIST1H2AG/AI/AK-AM 1.54 histone cluster 1, H2ad  HIST1H2AD 1.54 histone cluster 2, H2ac  HIST2H2AC 1.54 histone cluster 2, H2aa3/4 HIST2H2AA3/4 1.54 histone cluster 3, H2a  HIST3H2A 1.54 histone cluster 1, H2ac  HIST1H2AC 1.54 histone cluster 1, H2ah  HIST1H2AH 1.54 histone cluster 1, H2aj  HIST1H2AJ 1.54 H2A histone family, member J  H2AFJ 1.54 histone cluster 1, H1b  HIST1H1B 1.50 tyrosine 3-monooxygenase/tryptophan 5-monooxygenase activation protein, beta polypeptide  YWHAB 1.40 histone cluster 1, H2bk  HIST1H2BK 1.39 histone cluster 1, H2bj  HIST1H2BJ 1.39 histone cluster 1, H2bo  HIST1H2BO 1.39 histone cluster 1, H2bb  HIST1H2BB 1.39 histone H2B type F-S H2BFS 1.39 histone cluster 1, H2bd  HIST1H2BD 1.39 histone cluster 1, H2bc/be-bg/ bi HIST1H2BC/BE-BG/BI 1.39 histone cluster 2, H2be  HIST2H2BE 1.39 histone cluster 2, H2bf  HIST2H2BF 1.39 histone cluster 3, H2bb  HIST3H2BB 1.39 198  Description Associated gene name FC Up histone cluster 1, H2bh  HIST1H2BH 1.39 histone cluster 1, H2bn  HIST1H2BN 1.39 histone cluster 1, H2bm  HIST1H2BM 1.39 histone cluster 1, H2bl  HIST1H2BL 1.39 proteasome (prosome, macropain) 26S subunit, non-ATPase, 2  PSMD2 1.36 histone cluster 1, H4a-f/h-l; histone cluster 2, ha/b/bc/be-bg/bi; histone cluster 4, h4 HIST1H4A-F/H-L,HIST2HA/B/BC/ BE-BG/ BI, HIST4H4 1.36 radixin  RDX 1.30 Bold: proteins unique to siRNA_MR01 treated cells. V. Svinti provided the final table by merging three tables of biological replicates. The author generated this table using the merged table of biological replicates and for converted UniProt/SwissProt ID to description and HGNC gene symbol using the Homo sapiens data set (GRCh37.p10) from ensemble gene 70 database available online (http://uswest.ensemble.org).   199    Figure A3.6. List of differentially expressed proteins unique to Huh-7.5.1 cells treated with siRNA_MR01 in comparison to non-targeting siRNA treated cells (siCTRL)-treated cells using mock-treated Huh-7.5.1 cells as control  The author generated this figure using Table A3. 2.  200  A3.4  Materials and methods  Antibodies and dyes A mouse anti-GAPDH monoclonal antibody (1:1000 for WB and 1:100 for IF, Cat. no. MAB374, Chemicon/Millipore, Temecula, CA, USA) and a rabbit anti-GADPH monoclonal antibody (1:1000 for WB or ICW and 1:100 for IF, Cat. no. 21181S, Cell Signaling, Danvers, MA, USA) were interchangeably used for detecting cellular GAPDH expression.  A mouse anti-HCV core monoclonal antibody (1:1000, Cat. no. ab2740, Abcam, Cambridge MA, USA) was used for detecting HCV infection. Secondary antibodies used for WB (1:10,000) and ICW (1:200 (red) or 1:800 (green)) include IRDye® 800-conjugated (Cat. no. LIC-926-32212 or LIC-926-32213, green) or 680-conjugated (Cat. no. LIC-926-32222 or LIC-926-32221, red) antibodies raised against mouse or rabbit (LI-COR Biosciences, Lincoln, NE, USA). Dyes to stain cells include a DNA stain, DRAQ5® (1:10,000, Cat. no. SKU-DR50200, Biostatus, Shepshed, LEC, UK), and a cellular protein stain, Sapphire700TM (1:1000, Cat. no. LIC-928-40022, Li-COR Biosciences, Lincoln, NE, USA).  Cell culture and reagents Huh-7.5.1 cells were kindly provided by Dr. Francis Chisari (Scripps Research Institute, La Jolla, CA, USA) (276). Huh-7.5.1 cells were cultured as in Section 2.3 and 3.3.    HCV RNA, infectious stock production, HCV titer determination and HCV infection The plasmid pUCvJFH-1 (a generous gift from Dr. Takaji Wakita, National Institute of Infectious Diseases) was used for generating HCV RNA and infectious HCV stocks as previously described (112, 276). HCV stocks were produced and titers determined as described in Section 2.3 and 3.3 (174).  siRNA-mediated downregulation of human GAPDH in hepatoma cells Huh-7.5.1 cells were first transfected according to the manufacturer’s protocol with a pool of non-targeting control siRNAs (siCTRL: (D-001910-02-05, D-001910-03-05, and D 001910-04-05), a pool of siRNA targeting GAPDH (siGAPDH: D-001930-10-20), or a pool of siRNA targeting CYPB (siCYPB: D-001910-10-20) obtained from 201  Dharmacon/Thermoscientific, (Chicago, IL, USA) using the recommended concentration of DharmaFECT 4 (Dharmacon/Thermoscientific, Chicago, IL, USA) transfection reagent.  Levels of expression of GAPDH mRNA under the experimental conditions described in Fig. 2.4 were determined using RT-QPCR. The relative level of abundance of intracellular GAPDH under the experimental conditions described in Figs. 2.1–2.3 and Figs. 2.5–2.7 were determined using WB or ICW.  In cell western The cells were processed for ICW as described in Section 2.3 and 3.3.  Quantitative real-time polymerase chain reaction (RT-QPCR) The cells were processed for ICW as described in Section 2.  Nano-liquid chromatography-electronspray ionization-tandem mass spectrometry (nano-LC-ESI-MS/MS) Huh-7.5.1 cells (0.165 × 106) were transfected with siRNA_MR01 (15 nM) or pool of non-targeting siRNA (siCTRL-2, -3, -4) using DharmaFECT 4 transfection reagent for 24 hours. Here, a mock transfection was performed as a control. After 48 hours of treatment, cells were washed with ice-cold PBS and harvested in 300 μL of radioimmunoprecipitation assay (RIPA) buffer (50 mM Tris-HCl pH 8.0, 150 mM sodium chloride, 1% Triton X-100, 0.5% sodium deoxycholate, and 0.1% SDS) containing 5X complete, EDTA-free, proteinase inhibitor cocktail (Roche, Laval, QC, Canada). For protein quantification, Coomassie (Bradford) Protein Assay (Cat. no. 23236, Pierce/Thermoscientific, Rockford, IL, USA) was employed. Thereafter, 25 μg of samples were prepared as mentioned in Section 3. 3. The mock-treated Huh-7.5.1 samples were labelled with formaldehyde-H2 using sodium cyanoborohydride (light), siRNA_MR01-treated samples were labelled with formaldehyde-D2 using cyano[2]borohydride (medium), and NonTarg-treated samples were labelled with 13C-D2-formaldehyde using cyanoborodeuteride (heavy). Labelled peptides were subjected to nano-LC-MS/MS as in Section 3.3.   202  Data analysis MS data were processed and quantified with a Proteome Discoverer (version 1.3, Thermo Electron) utilizing standardized workflows. For peptide identification, Mascot 2.3 (Matrix Science) and a Human Uniprot database supplemented with all the frequently observed MS contaminants was utilized as in Section 3.3. After identification and quantification, fold changes of the proteins present in both technical replicates were averaged. The percentage variability between technical replicates was depicted using bar plots (Fig. A4.7). For further analysis, proteins present in both biological replicates were considered for GO and pathway enrichment analysis using InnateDB (143). A fold change threshold of 1.3 was used to determine significantly deregulated proteins.   203  Appendix 4: Supplementary list of materials  A4.1 Plasmid list Table A4.1. List of plasmids generated in this thesis Plasmid Insert Vector Restriction site  GAPpG GAPDH pGEM-T Blunt end  GAP67pG GAPDH67 pGEM-T Blunt end  GAP67nspG GAPDH67ns pGEM-T Blunt end  KpnI-GAP67-EcoRIpG GAPDH67 pGEM-T KpnI/EcoRI  KpnI-GAP67-EcoRInspG GAPDH67ns pGEM-T KpnI/EcoRI  tagRFPpG tagRFP pGEM-T EcoRI/XhoI  GAP67pc GAPDH67 pcDNA3.1+ KpnI/EcoRI  GAP67nspc GAPDH67ns pcDNA3.1+ KpnI/EcoRI  tagRFPpc tagRFP pcDNA3.1+ EcoRI/XhoI  GAP67-EF-tagRFPpc GAPDH67-EF-tagRFP pcDNA3.1+ KpnI/EcoRI/ XhoI  GAP67-AA-tagRFPpc GAPDH67-AA-tagRFP pcDNA3.1+ KpnI/XhoI  GAP67-VV-tagRFPpc GAPDH67-VV-tagRFP pcDNA3.1+ KpnI/XhoI  GAP67-GG-tagRFPpc GAPDH67-GG-tagRFP pcDNA3.1+ KpnI/XhoI *GAP67-EF-V5pc GAPDH67-EF-V5 pcDNA3.1+ KpnI/EcoRI/ XhoI *GAPV5pc GAPDH67-AA-V5 pcDNA3.1+ KpnI/XhoI  GAP67pVQ GAPDH67 pVQAd CMV K-NpA KpnI/XhoI  GAPV5pVQ GAPDH67-AA-V5 pcDNA3.1+ KpnI/XhoI  GAP67pfm GAPDH67 pFLAG-myc-CMV-20 KpnI/XhoI *GAP67-EF-tagRFPpfm GAPDH67-EF-V5 pFLAG-myc-CMV-20 KpnI/XhoI *GAP67-AA-tagRFPpfm GAPDH67-AA-V5 pFLAG-myc-CMV-20 KpnI/XhoI *GAP67-VV-tagRFPpfm GAPDH67-VV-tagRFP pFLAG-myc-CMV-20 KpnI/XhoI *GAP67-GG-tagRFPpfm GAPDH67-GG-tagRFP pFLAG-myc-CMV-20 KpnI/XhoI *Generated by Mary Langley, a former undergraduate student. Note: All pFLAG-myc-CMV-20 constructs prepared by Mary contain a stop codon in the linker region of the tagRFP and myc tag. 204  A4.2 Primer list  Table A4.2. List of primers generated in this thesis Name Sequence  Primers for generating cDNA from reverse transcribed total RNA extract  MrGAPDHFor ATG GGG AAG GTG AAG GTC GG MrGAPDHRev TTA CTC CTT GGA GGC CAT GTG GGC   Primers for introducing silent mutations  MRshRNA1For /5Phos/ACG ATC CAT TCA TTG ACC TCA ACT ACA TGG TTT ACA TGT TCC AAT ATG ATT C MRshRNA1Rev /5Phos/TAA TTG CGACGA TAT CCA CTT TAC CAG AGT TAA AAG CAG CCC TGG TGA C   Primer for introducing restriction sites to tagRFP: 5’-EcoRI and 3’-XhoI  MRtagRFPEcoRIFor GGT GGA ATT CTT ATG GTG GTG TCT AAG GGC GAA GAG C MRtagRFPXhoIRev TAG ACT CGA GCT AAT TAA GTT TGT GCC CCA GTT TGC TA   Primer for introducing restriction sites to GAPDH: 5’-KpnI and 3’-EcoRI  MRGAPDHKpnIFor GCT TGG TAC CAT GGG GAA GGT GAA GGT C MRGAPDHEcoRIRev TGC AGA ATT CTT ACT CCT TGG AGG CCA TGT GG  205  Name Sequence  Primers for deleting the stop codon for the purpose of linking tagRFP to the C-terminal of GAPDH and introducing restriction sites: 5’-KpnI and 3’-EcoRI  MRGAPDHKpnIFor GCT TGG TAC CAT GGG GAA GGT GAA GGT C MRGAPDHEcoRInsRev TGC AGA CCC TCC TTG GAG GCC ATG T   Primers for changing linker EF  AA  E336A337FAGAPDH_For CAT GGC CTC CAA GGA GGC AGC CAT GGT GTC TAA GGG CG E336A337FAGAPFH_Rev CGC CCT AGA CAC CAT GGC TGC CTC CTT GGA GGC CAT G   Primer for changing linker EF  GG  MREcoRIGlyGly_For CAT GGC CTC CAA GGA GGT AGT CAT GGT GTC TAA GGG C MREcoRI-GlyGly_Rev GCC CTT AGA CAC CAT GAC TAC CTC CTT GGA GGC CAT G   Primer for changing linker EF  VV  MR_E336G_F CATGGCCTCCAAGGAGGGAGGCATGGTGTCTAAGGGCG MR_E336G_F CGCCCTTAGACACCATGCCTCCCTCCTTGGAGGCCATG   Primers for catalytically inactive GAPDHtagRFP  GAPDH67tagRFP_C152A_F TCATCAGCAATGCCTCCGCCACCACCAACTGCTTAG GAPDH67tagRFP_C152A_R  CTAAGCAGTTGGTGGTRGGCGGAGGCATTGCTGATGA 206  Name Sequence  A DNA duplex with desired restriction site and a stop codon for introducing tag V5 on C-terminal of GAPDH  EcoRIV5XhoI_R TAGGCCTCGAGTTACGTAGAATCGAGACCGAGGAGAGGGTTAGGGATAGGCTTACCGAATTCCGGAT   Primers for sequencing GAPDHtagRFP linker  SeqIII_F_1525 GGCTCTCCAGAACATCATCC SeqIII_R_1787 CGCTGTTGAAGTCAGAGGAG  Primers for sequencing tagRFP  Seq.RFP.560.F* CCGCTAAGAACCTCAAGATG BGH NAPS  *Designed by Christine Lai, a former Master’s student.207  A4.3 Quantitative real-time polymerase chain reaction (RT-QPCR) assay list  Table A4.3. List of RT-QPCR assays generated in this thesis Assay Sequences label ²Efficiency ¹HCV RefSeq: AB047639 CAGTACCACAAGGCCTTTCGCAACC CAAGACTGCTAGCCGAGT ACTCGCAAGCGCCCTATC 6-FAM-ZEN-IABkFQ 97.7% GAPDH RefSeq: NM_002046.3 CGCTCCTGGAAGATGGTGATGGG AGAACGGGAAGCTTGTCATC CATCGCCCCACTTGATTTTG HEX -IABkFQ 98.6% ¹ACTB RefSeq: NM_001101.3 ACTCCATGCCCAGGAAGGAAGGC GCCCTGAGGCACTCTTCC GGATCTCCACGTCACACTTC Cy5 – IAbRqSp 97.6% hCD81 RefSeq: NM_004356.3  ACCTTCCACGAGACGCTTGACTGCTGTGGC ATGATGACGCCAACAACGCCAA TGAGGTGGTCAAAGCAGTCAGTGT 6-FAM-ZEN-IABkFQ 89.7% ¹Designed by Martin Anger, a Field Application Scientist at Agilent Technologies. ²Efficiency determined using serially diluted RNA  Table A4.4. List of RT-QPCR assays designed in this thesis  Assay Sequences label hCDK2 RefSeq: NM_001798.3 ATCTTTGCTGAGATGGTGACTCGCCGGGCC AGCTGTGGACATCTGGAGCCT CCGGAAGAGCTGGTCAATCTCA 6-FAM-ZEN-IABkFQ hCYPB RefSeq: NM_000942.4 TTCTAGAGGGCATGGAGGTGGTGCGGAAGG AGATGGCAAGCATGTGGTGTTTGG TTTATCCCGGCTGTCTGTCTTGGT 6-FAM-ZEN-IABkFQ  

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