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Regulating the regulators : emerging roles of cellular microRNAs during influenza A virus infection Loveday, Emma-Kate 2014

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REGULATING THE REGULATORS: EMERGING ROLES OF CELLULAR MICRORNAS DURING INFLUENZA A VIRUS INFECTION  by  Emma-Kate Loveday B.Sc., Suffolk University, 2008  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)  August 2014  © Emma-Kate Loveday, 2014   ii Abstract Over the past twenty years small endogenous non-coding RNAs known as microRNAs have emerged as potent regulators of gene expression during virus infection.  During influenza A virus infection the role of miRNAs and their impact on the virus lifecycle is relatively unknown.  With seasonal strains that can result in annual epidemics, to newly emerging subtypes that have the potential to cause a worldwide pandemic, influenza A remains a major threat to global human health.  Here we aimed to determine the role miRNAs play in the host-pathogen interactions associated with varying pathogenesis during infection with different influenza A virus strains.   In Chapter 2, we tested the hypothesis that human cellular miRNA expression would vary between a low pathogenic swine-origin H1N1 influenza A virus strain and a highly pathogenic avian-origin H7N7 influenza A virus strain.  Utilizing high throughput microarray analysis, we identified differentially expressed miRNA and mRNA profiles during H1N1 and H7N7 influenza A infection and found strain specific expression patterns that were associated with specific cellular pathways.     One of the miRNAs identified in Chapter 2 was miR-24 that targets the proprotein convertase furin, which is responsible for cleaving the hemagglutinin glycoprotein on the surface of highly pathogenic influenza A viruses.  In Chapter 3, we hypothesized that synthetic miR-24 could inhibit highly pathogenic H5N1 influenza A infection.  Addition of exogenous miR-24 during H5N1 infection resulted in a significant decrease in furin mRNA expression and enzymatic activity as well as reduced infectious virus released and virus spread.   In Chapter 4, we hypothesized that novel miRNAs could target the proprotein convertase furin, along with two additional human proprotein convertases: PCSK9 and SKI-1/S1P, that have   iii significant roles during the lifecycles of other enveloped viruses.  We identified a novel miRNA, miR-17, that reduced furin mRNA and enzymatic activity.  Furthermore, miR-24 was shown to target PCSK9, potentially contributing to the regulation of lipid metabolism.    MiRNAs are now recognized as important players during virus infections, especially during the influenza A virus lifecycle.  By exploiting the targets of specific miRNAs, we have identified new potential therapeutic options that could be applied to numerous enveloped viruses.   iv Preface All of the work presented henceforth was conducted in the Life Sciences Center at the University of British Columbia, Point Grey campus, and the National Centre for Foreign Animal Disease, CFIA in Winnipeg, Manitoba.  Dr. Jean and Dr. Pasick were the supervisory authors on this thesis and were involved throughout for all the projects in concept formation and manuscript composition. A version of the research presented in Chapter 2 has been published (Loveday EK, Svinti V, Diederich S, Pasick J, Jean F. 2012.  Temporal- and strain-specific host microRNA molecular signatures associated with swine-origin H1N1 and avian-origin H7N7 influenza A virus infection.  J Virol 86:6109-6122.).  I was responsible for designing all the experiments described in this manuscript.  The co-author of the manuscript, Dr. Victoria Svinti, was responsible for data analysis and provided all bioinformatics based figures.  Dr. Sandra Diederich provided technical assistance and guidance in experimental design for this project.  Together, Dr. Svinti and I wrote the first drafts of the manuscript, which was revised together with Dr. François Jean and Dr. John Pasick.   For the work presented in Chapter 3, I was the lead investigator, responsible for experimental design, data collection and analysis.  I wrote the manuscript for this project, which was revised together with Dr. François Jean and Dr. John Pasick.  Dr. Diederich and Dr. Pasick provided technical assistance and guidance in experimental design for this project.   I was the lead investigator for the research presented in Chapter 4 where I was responsible for all experimental design and data collection and analysis.  All reagents provided by external research groups are indicated in the materials and methods.   v  This study was supported by Canadian Institutes of Health Research team grant TPA-90195 and contract 4500231387 from the Public Health Agency of Canada to F.J.  Graduate student funding was also provided by the National Science Foundation Graduate Research Fellowship and the University of British Columbia Four Year Fellowship.     vi Table of Contents Abstract .......................................................................................................................................... ii	  Preface ........................................................................................................................................... iv	  Table of Contents .......................................................................................................................... vi	  List of Tables ................................................................................................................................. xi	  List of Figures .............................................................................................................................. xii	  List of Abbreviations ................................................................................................................... xv	  Acknowledgements ..................................................................................................................... xxi	  Dedication .................................................................................................................................. xxiii	  Chapter 1: Introduction ................................................................................................................ 1	  1.1	   MicroRNAs ........................................................................................................................ 1	  1.1.1	   Discovery of miRNAs ................................................................................................. 1	  1.1.2	   MiRNA biogenesis ...................................................................................................... 2	  1.1.3	   Post-transcriptional repression by miRNAs ................................................................ 3	  1.1.4	   Emerging role of miRNAs in human diseases ............................................................ 5	  1.2	   MiRNAs and virus infection .............................................................................................. 6	  1.2.1	   DNA viruses and miRNAs .......................................................................................... 7	  1.2.2	   RNA viruses and miRNAs .......................................................................................... 8	  1.2.3	   Influenza A and miRNAs .......................................................................................... 10	  1.2.4	   MiRNA-based antiviral therapeutics ......................................................................... 11	  1.3	   Influenza A virus biology ................................................................................................. 11	  1.3.1	   The influenza A virus lifecycle ................................................................................. 12	  1.3.2	   Influenza A virus genes ............................................................................................. 14	    vii 1.3.3	   Antivirals and vaccines against influenza A virus .................................................... 19	  1.3.4	   Influenza A virus evolution ....................................................................................... 21	  1.3.5	   Influenza A virus pandemics ..................................................................................... 22	  1.4	   Molecular determinants of influenza A virus pathogenesis ............................................. 24	  1.4.1	   Highly pathogenic avian influenza A virus infection in humans .............................. 25	  1.4.2	   2009 H1N1 influenza A virus infection in humans ................................................... 27	  1.4.3	   Processing of the influenza A HA glycoprotein by host proteases ........................... 28	  1.5	   Proprotein convertases ...................................................................................................... 30	  1.5.1	   Processing of viral envelope glycoproteins by proprotein convertases ..................... 30	  1.5.2	   Furin .......................................................................................................................... 31	  1.5.3	   Proposed regulation of furin by miRNAs .................................................................. 32	  1.6	   Research hypothesis and rationale .................................................................................... 33	  1.6.1	   Aim 1 ......................................................................................................................... 34	  1.6.2	   Aim 2 ......................................................................................................................... 34	  1.6.3	   Aim 3 ......................................................................................................................... 35	  Chapter 2: Temporal- and strain-specific host microRNA molecular signatures associated with swine-origin H1N1 and avian-origin H7N7 influenza A virus infection ........................ 53	  2.1	   Introduction ...................................................................................................................... 53	  2.2	   Materials and methods ...................................................................................................... 55	  2.3	   Results .............................................................................................................................. 62	  2.3.1	   Cellular miRNAs signatures in response to pandemic S-OIV H1N1 (2009) infection in human epithelial A549 cells .............................................................................................. 62	    viii 2.3.2	   Dynamic changes in the host cell miRNA-mRNA interactome induced by the 2009 pandemic influenza (H1N1) virus ......................................................................................... 66	  2.3.3	   Cellular miRNAs signatures in response to highly pathogenic A-OIV H7N7 (2003) infection in human epithelial A549 cells ............................................................................... 68	  2.3.4	   Common and distinct host cell miRNA signatures associated with pandemic S-OIV H1N1 and highly pathogenic A-OIV H7N7 infections ......................................................... 70	  2.4	   Discussion ......................................................................................................................... 71	  2.4.1	   Temporal host miRNA molecular signatures associated with pandemic S-OIV H1N1 and highly pathogenic A-OIV H7N7 infection ..................................................................... 72	  2.4.2	   Strain-specific host miRNA molecular signatures associated with pandemic S-OIV H1N1 and highly pathogenic A-OIV H7N7 infection .......................................................... 74	  Chapter 3: Human microRNA-24 modulates highly pathogenic avian-origin H5N1 influenza A virus infection in A549 cells by targeting secretory pathway furin ................... 89	  3.1	   Introduction ...................................................................................................................... 89	  3.2	   Materials and methods ...................................................................................................... 92	  3.3	   Results .............................................................................................................................. 97	  3.3.1	   Down-regulation of mir-24 with a concomitant up-regulation of furin mRNA during the HP H5N1 viral lifecycle .................................................................................................. 97	  3.3.2	   MiR-24 overexpression reduces furin mRNA and its enzymatic activity in human A549 cells .............................................................................................................................. 98	  3.3.3	   Treatment with synthetic exogenous miR-24 reduces H5N1 virus spread in human A549 cells .............................................................................................................................. 99	  3.3.4	   MiR-24 does not affect 2009 pandemic H1N1 virus infection ............................... 102	    ix 3.4	   Discussion ....................................................................................................................... 102	  Chapter 4: Identification of a microRNA landscape targeting the secretory pathways proprotein convertases .............................................................................................................. 114	  4.1	   Introduction .................................................................................................................... 114	  4.2	   Materials and methods .................................................................................................... 116	  4.3	   Results ............................................................................................................................ 118	  4.3.1	   Bioinformatics analysis reveals multiple miRNA binding sites in the 3’UTR of three proprotein convertases (e.g., furin, SKI-1/S1P, PCSK9) .................................................... 118	  4.3.2	   PCSK9 is a novel target of miR-24 ......................................................................... 119	  4.3.3	   Multiple miRNAs can reduce furin mRNA expression in HeLa cells .................... 120	  4.4	   Discussion ....................................................................................................................... 123	  4.4.1	   Regulating PCSK9 by miR-24 in HeLa cells .......................................................... 123	  4.4.2	   The role of novel miRNAs that target the human proprotein convertase furin ....... 124	  Chapter 5: Conclusions and future directions ........................................................................ 131	  5.1	   Discussion ....................................................................................................................... 131	  5.1.1	   Host cell miRNA expression during influenza A virus infection ............................ 132	  5.1.2	   MiR-24 and influenza A HA processing ................................................................. 134	  5.1.3	   Novel miRNAs targeting proprotein convertases .................................................... 137	  5.2	   Future Directions: Further dissecting the role of miRNAs during influenza A infection and their impact on host cell proteases expression .................................................................. 139	  5.2.1	   Investigating the role of individual influenza A proteins in miRNA and host cell protease expression .............................................................................................................. 140	  5.2.2	   Investigate other miR-24 targets during H5N1 infection ........................................ 141	    x 5.2.3	   Investigate the role of miR-24 for other enveloped viruses .................................... 142	  5.2.4	   Further explore the regulation of PCSK9 and lipid metabolism by miR-24 ........... 143	  5.3	   Conclusions .................................................................................................................... 145	  Bibliography ............................................................................................................................... 153	  Appendices ................................................................................................................................. 184	  Appendix A Chapter 2 supplementary figures and tables ....................................................... 184	  Appendix B Chapter 3 supplementary figures ........................................................................ 189	  Appendix C Chapter 4 supplementary figures ........................................................................ 191	     xi List of Tables Table 1.1.  Influenza A hemagglutinin consensus cleavage sites .................................................. 36	  Table 1.2. Approved and investigational antiviral agents for influenza A virus ........................... 37	  Table 2.1. Comparison of qRT-PCR-validated miRNAs between pandemic 2009 H1N1 and highly pathogenic H7N7 avian influenza A virus infection .......................................................... 88	   Table A.1 Differentially expressed miRNAs during infection with 2009 pandemic H1N1 influenza A virus compared to mock-infected control ................................................................ 187	  Table A.2 Differentially expressed miRNAs during infection with highly pathogenic avian H7N7 influenza A virus compared to mock-infected control ................................................................ 188	     xii List of Figures Figure 1.1 MiRNA biogenesis pathway ........................................................................................ 39	  Figure 1.2. Influenza A virus lifecycle .......................................................................................... 41	  Figure 1.3. Influenza A virus glycoproteins: hemagglutinin (HA) and neuraminidase (NA) ....... 42	  Figure 1.4. Influenza A virus polymerase proteins: PB2, PB1, PA, and NP ................................ 44	  Figure 1.5. Influenza A virus matrix proteins: M1 and M2 .......................................................... 45	  Figure 1.6. Influenza A virus nonstructural proteins: NS1 and NEP ............................................ 46	  Figure 1.7. Schematic representation and structural features of the HA glycoprotein .................. 48	  Figure 1.8. Alternative processing of HA glycoprotein in the trans-Golgi network by furin ....... 49	  Figure 1.9. Schematic representation and structural features of furin ........................................... 50	  Figure 1.10. Predicted miRNA binding sites in the furin 3’UTR ................................................. 52	  Figure 2.1. Time-point-specific regulation of miRNAs during pandemic 2009 H1N1 influenza A ....................................................................................................................................................... 78	  Figure 2.2. qRT-PCR analysis of miRNA expression during pandemic 2009 H1N1 influenza A virus infection ................................................................................................................................ 79	  Figure 2.3. Complexity of the miRNA-mRNA interactome network at 8 hours after infection with pandemic 2009 H1N1 influenza A virus ............................................................................... 80	  Figure 2.4. Canonical pathways implicated in infection with pandemic 2009 H1N1 influenza A virus from dual expression studies ................................................................................................ 82	  Figure 2.5. Time-specific regulation of miRNAs during infection with highly pathogenic H7N7 avian influenza A virus .................................................................................................................. 84	  Figure 2.6. Validation of miRNAs expressed during highly pathogenic H7N7 avian influenza A virus infection using comparative qRT-PCR ................................................................................ 85	    xiii Figure 2.7. Pathways predicted to be affected by the deregulated miRNAs during highly pathogenic H7N7 avian influenza A virus infection ..................................................................... 86	  Figure 2.8. Comparison of miRNAs expressed during infection with pandemic 2009 H1N1 and highly pathogenic H7N7 avian influenza A viruses ...................................................................... 87	  Figure 3.1. MiR-24 and furin RNA expression is inversely correlated during H5N1 Influenza A virus infection .............................................................................................................................. 106	  Figure 3.2. MiR-24 targets furin in A549 cells ........................................................................... 107	  Figure 3.3. Overexpression of miR-24 reduces furin mRNA expression and enzymatic activity ..................................................................................................................................................... 108	  Figure 3.4. Overexpression of miR-24 during H5N1 infection reduces furin mRNA expression and infectious virus released ....................................................................................................... 110	  Figure 3.5. Overexpression of miR-24 reduces spread of infectious virus ................................. 112	  Figure 3.6. Overexpression of miR-24 does not affect H1N1 influenza A virus ........................ 113	  Figure 4.1. Predicted miRNA binding sites in the 3’UTR of the proprotein convertases furin, PCSK9 and SKI-1/S1P ................................................................................................................ 126	  Figure 4.2. miR-24 targets both furin and PCSK9 but not SKI-1/S1P ........................................ 127	  Figure 4.3. Furin mRNA and enzymatic activity is reduced by overexpression of miR-17 ....... 128	  Figure 4.4. SKI-1/S1P mRNA levels are reduced following transfection with a combination of miRNA mimics ............................................................................................................................ 129	  Figure 4.5. PCSK9 mRNA levels are reduced following transfection with a combination of miR-24 and miR-17 or miR-106b mimics ........................................................................................... 130	  Figure 5.1. H5N1 miR24 predicted binding info ........................................................................ 149	    xiv Figure 5.2 Proprotein convertase and miR-23 cluster expression data following dengue virus 2 (DENV2) infection of human Huh7.5.1 hepatoma cells ............................................................. 150	  Figure 5.3 MicroRNA associated host-pathogen interactions during virus infection ................. 152	   Figure A.1 Complexity of the miRNA-mRNA network after infection with pandemic 2009 H1N1 influenza A virus. .............................................................................................................. 185	  Figure A.2 Time-point-specific comparison of miRNAs expressed during pandemic 2009 H1N1 and highly pathogenic avian H7N7 influenza A virus infection ................................................. 186	   Figure B.1 Growth curves of H5N1 in A549 cells at an MOI of 0.0001 .................................... 189	  Figure B.2 HA protein expression following infection with the H5N1 influenza A virus ......... 190	   Figure C.1 MRNA expression of furin, PCSK9, and SKI-1/S1P following overexpression of miR-22, 192, 93, and 100 miRNA mimics .................................................................................. 191	  Figure C.2 Furin mRNA expression of at 24, 48 and 72 hours post transfection with miR-20a, 17 and 106b mimics .......................................................................................................................... 192	  Figure C.3 PCSK9 and SKI-1/S1P mRNA expression of at 24 hours post transfection with miR-20a, 17 and 106b mimics ............................................................................................................. 193	  Figure C.4 MiR-17 targets furin in HeLa cells ............................................................................ 194	  Figure C.5 Furin enzymatic activity following transfection with miR-24 miRNA mimic ......... 195	     xv List of Abbreviations β-LPH – Beta-lipotropic hormone β-MSH – Beta-melanocyte-stimulating hormone γ-LPH – Gamma-lipotropic hormone A-OIV – Avian-origin influenza A virus Ago2 – Argonaute 2 ANOVA – Analysis of variance BART – BamHI A rightward transcripts BC – Before christ Bcl-2 – B-cell CLL/lymphoma 2 BSL – Biosafety level CCNE – Cyclin E1 CCR4 – Chemokine receptor 4 CD81 – Cluster of differentiation 81 CDC25A – Cell division cycle 25A CDK – Cyclin dependent kinase cDNA – Copy DNA CDT1 – Chromatin licensing and DNA replication factor 1  CMK – Chloromethylketones COOH – Carboxy terminus CPE – Cytopathic effect CRM1 – Chromosome region maintenance 1   xvi cRNA – Copy RNA DAA – Direct acting antiviral DENV – Dengue virus DGCR8 – DiGeorge syndrome critical region gene 8 DMEM – Dulbecco’s modified eagle medium DNA – Deoxyribonucleic acid dsRNA – Double stranded RNA EBV – Epstein Barr virus eIF – Elongation initiation factor ER – Endoplasmic reticulum EV71 – Enterovirus 71 FBS – Fetal bovine serum FI – Furin inhibitor FP – Fusion peptide GDP – Guanosine-5’-diphosphate GRTA – Geniom RT analyzer GTP – Guanosine-5’-triphosphate H2AX – H2A histone family, member X HA – Hemagglutinin HCMV – Human cytomegalovirus HCV – Hepatitis C virus HDAC1 – Histone deacetylase 1    xvii HIV-1 – Human immunodeficiency virus-1 HP – Highly pathogenic HPAI – Highly pathogenic avian influenza A  Hpi – Hours post infection Hpt – Hours post transfection HPV – Human papilloma virus HSV – Herpes simplex virus IAA – Indirect acting antiviral IFITM – Interferon induced transmembrane protein IL – Interluekin InfA – Influenza A  INSIG1 – Insulin induced gene 1 IRAK1 – Interleukin-1 receptor-associated kinase 1  IRES – Internal ribosome entry site KO – Knockout KSHV – Karposi’s sarcoma herpes virus L4 – Fourth larval stage LAT – Latency associated transcript LDLR – Low-density lipoprotein receptor LNA – Locked nucleic acid LP – Low pathogenic M – Matrix MBAA – Multiple basic amino acids   xviii MCM – Minichromosome maintenance complex component  MDCK – Madin darby canine kidney miRISC – MicroRNA induced silencing complex miRNA – MicroRNA MMP – matrix metalloproteinase MOI – Multiplicity of infection mRNA – Messenger RNA MYC – V-myc avian myelocytomatosis viral oncogene homolog MyD88 – Myeloid differentiation primary response 88 NA – Neuraminidase ncRNA – Non-coding RNA NEP – Nuclear export protein NF-κB –Nuclear factor kappa B ng – Nano grams NH2 – Amino terminus NOT1 – Negative on TATA-less NP – Nucleoprotein NS – Nonstructural P bodies – Processing bodies P/S – Penicillin/streptomycin PA – Polymerase acidic protein PABP1 – Polyadenylate-binding protein 1 Pan2/3 – Poly(A) nuclease 2/3   xix PARN – Poly(A) specific ribonuclease PAZ – Piwi/Argonaute/Zwille PB1 - Polymerase basic protein 1 PB2 – Polymerase basic protein 2 PBMC – Peripheral blood mononuclear cells PC – Proprotein convertase PCSK9 – Proprotein convertase subtilisin/kexin isozyme type 9 PI3K – Phosphatidylinositol-3-kinase PIWI – P-element induced wimpy testis PLK4 – Polo-like kinase 4  Pri-miRNA – Primary microRNA PUMA – P53 Up-regulated mediator of apoptosis qRT-PCR – Quantitative reverse transcription polymerase chain reaction RAKE – RNA-primed, array-based, klenow extension Rbl1 – Retinoblastoma-like 1 RIPA – Radioimmunoprecipitation assay RISC – RNA induced silencing complex RLU – Relative light unit RNA – Ribonucleic acid RNAi – RNA interference Rrm2 – Ribonucleotide reductase M2 RT – Room temperature S-OIV – Swine-origin influenza A virus   xx SA – Sialic acid SEM – Standard error of the mean Serpin – Serine protease inhibitors siRNA – Small interfering RNA SKI-1/S1P – Subtilisin kexin isozyme-1/site-1 protease SMAD – Small body size mothers against decapentaplegic SREBP – Sterol regulatory element binding protein STING – Stimulator of interferon genes  TGF-ß – Transforming growth factor beta TGN –Trans-golgi network TMPRSS – Transmembrane protease serine TNF-α – Tumor necrosis factor alpha TPCK – L -1-Tosylamide-2-phenylethyl chloromethyl ketone TRAF6 – TNF receptor-associated factor 6  TRBP – Trans-activating response RNA binding protein UTR – Untranslated region vRNA – Viral RNA vRNP – Viral ribonucleoprotein VSN – Variance stabilizing normalization WNV – West Nile virus   xxi Acknowledgements  To my supervisor Dr. François Jean, thank you for providing wonderful opportunities to learn and grow as a scientist and for letting me explore numerous paths during this process.  You have been incredibly supportive and one of my biggest champions, believing in my work and me before I even did.  To my co-supervisor Dr. John Pasick, thank you for providing the space and time and opportunity to train me in Winnipeg.  It was an honor to be able to work with you and your team at NCFAD.    To all the current and former Jean lab members, thank you for all of your help and support over the years.  To all the members at NCFAD, your help was always greatly appreciated and I could never thank any of you enough for your time and support.  In particular, thank you to Dr. Sandra Diederich for your guidance and training and the numerous discussions and experiences we had together both in Vancouver and in Winnipeg.  I will never forget the fun we had both in the lab and out of it.  I could not have asked for a better person to start my PhD with.  To Dr. Victoria Svinti, who provided the bioinformatics expertise that made this work possible.  You not only got this project through a major hurdle, but you inspired me to further my knowledge and understanding of computer programming and bioinformatics in a way I never thought possible.  Thank you so much for all of your help and for putting up with me and all the silly requests over the years.  To Dr. Yohannes Berhane, who constantly challenged me along the way at NCFAD, but was always available when I needed help, or reagents!   To everyone in the Abraham, Harder, Johnson, and Horwitz labs (you know who you are!)-thank you for being there when I needed a beer on a Friday (or Tuesday!) afternoon.  The lengthy discussions that often resulted were always inspiring.    xxii Thank you to my committee members Dr. Marc Horwitz, Dr. Erin Gaynor and Dr. Eric Jan for your support, suggestions, and challenges over the years.  A thank you to my parents who were always so supportive and gave me the opportunities and strength to become the independent, confident woman I am today.  To Dianne, Paul, Amy, Jason, and the rest of the Remillard/Contois family, thank you for all your love and support and for making me a part of your family.  To my sister, who is the most caring, sweetest and best human being I have ever known.  I am so proud of you and all you have done.   To Jaime for always sticking by my side-through thick and thin.  It has been a crazy 20 years.  Who would have thought that we both would be “doctors” one day! To Adam-my husband and best friend.  I could not have done any of this without you.  There are not enough words to express my love for you.  I look forward to our next adventure together and the many more that will follow.     xxiii Dedication To Adam - for your constant, never ending support, your ability to always make me laugh, and your sense of adventure, because without it we would not have gotten to where we are now.    Most importantly, thank you for constantly reminding me to never take life to seriously and always have fun J  To Eloise - You may have just arrived but this was all done for you.  1 Chapter 1: Introduction 1.1 MicroRNAs MicroRNAs (miRNAs) are small, endogenous, non-coding RNAs (ncRNAs) approximately 19-25 nucleotides in length that regulate gene expression post-transcriptionally by binding to target messenger RNAs (mRNAs) (1-3).  The human genome encodes over 2000 unique miRNAs that play key roles in diverse regulatory pathways, most notably in development and oncogenic processes, forming a complex network that is predicted to regulate over 50% of protein coding genes (4, 5).  1.1.1 Discovery of miRNAs The existence of regulatory ncRNAs was unknown until the discovery of lin-4 by the Ambros and Ruvkun laboratories (6, 7).  In C. elegans, lin-4 activity affects larval development at all stages and in diverse cell types.  lin-4 loss of function mutants display reiterations of cell fate at inappropriate developmental stages resulting in an absence of adult structures and the prevention of egg laying (8).  In contrast, lin-14 null mutations result in a phenotype opposite to that seen with lin-4 loss of function mutants (9).  Both the Ambros and Ruvkun laboratories discovered that the lin-4 gene does not encode for a protein but instead produces two small RNA transcripts of 61 and 22 nucleotides in length (6, 7).  In addition, both labs found that the lin-4 sequence is complementary to an element repeated seven times in the lin-14 3’untranslated region and proposed that lin-4 encodes for a small RNA, now recognized as the first miRNA, that binds to the lin-14 3’UTR to regulate its expression post-transcriptionally (6, 7, 10).  The second miRNA discovered came seven years later in 2000, when the Ruvkun laboratory identified let-7 as a regulator of lin-41 during larval development from L4 to adult worms (11).    2 Unlike lin-4, the discovery of let-7 led to recognition of miRNAs in animals across many phyla due to its highly conserved sequence (12).  The discovery of let-7 opened the door for identification of hundreds of small ncRNAs, known as miRNAs, in fruit flies, mice and humans.  Since the discovery of let-7, there have been over 24,000 mature miRNA sequences that have been deposited into the miRBase database (www.mirbase.org) (13-15).  Of these, 2555 are unique mature human miRNAs identified as of June 2013 (miRBase v. 20) 1.1.2 MiRNA biogenesis MiRNAs are transcribed as precursor or primary miRNAs (pri-miRNAs) by RNA polymerase II (Figure 1.1).  The pri-miRNAs can be located within intergenic or intronic regions of known genes or transcribed as clusters of pri-miRNAs.  The pri-mRNAs are 5’ capped and polyadenylated primary miRNAs (16, 17).  The pri-miRNAs are several kilobases long and are processed by the nuclear RNase III enzyme Drosha to release the precursor miRNAs (pre-miRNA) (18, 19).  A large 160 kDa protein, Drosha is part of the microprocessor complex where it interacts with its cofactor, the DiGeorge syndrome critical region gene 8 (DGCR8) protein to process the pri-miRNAs into pre-miRNAs (19-21).  Following nuclear processing, the pre-miRNA is exported from the nucleus to the cytoplasm via nuclear pore complexes.  Nuclear export is mediated by exportin-5 and its co-factor Ran, which hydrolyzes GTP to GDP to facilitate release of the miRNA cargo into the cytoplasm (22-25).  Further processing takes place in the cytoplasm where a second RNase III enzyme, Dicer, cleaves the ~70 nucleotide pre-miRNA into its mature form, an RNA duplex ~22 nucleotides in length with two overhanging nucleotides at each 3’ end (26-30).  The Dicer family members are highly conserved and contain a helicase domain, one or two dsRNA binding domains, two RNase type III domains and a PAZ domain and multiple   3 isotypes can occur in certain animals (27-30).  Following cleavage of the pre-miRNA into its mature form, Dicer dissociates from the complex and the duplex mature miRNA is separated into the functional guide strand and passenger strand (31).  The guide strand, chosen due to reduced thermodynamic stability at the 5’ end, is then incorporated into the miRNA Induced Silencing Complex (miRISC) while the passenger strand is degraded (32, 33). The miRISC in humans is comprised of Dicer, TRBP (the human immunodeficiency virus trans-activating response RNA binding protein) and Argonaute 2 (Ago2) (34, 35).  Argonaute proteins are highly conserved and specialized binding partners of small RNAs.  They are characterized by amino-terminal (N), PAZ, MID, and PIWI domains in which the N domain is required for small RNA loading and facilitates duplex unwinding, the PAZ domain anchors the 3’end of the small RNA, the MID domain anchors the 5’ end of the small RNA, and the PIWI domain can function as an endonuclease similar to RNase H (36, 37).  In mammals, while only one Dicer exists, there are four Argonaute proteins, Ago1-4, with only Ago2 being capable of cleaving mRNAs that have high homology to the guide strand (38, 39).  Following incorporation into miRISC, miRNAs guide the complex to the target mRNAs where binding of the miRNA results in the mRNA being translationally silenced or degraded (40-42).    1.1.3 Post-transcriptional repression by miRNAs Base-pairing between the miRNA and target mRNA in the miRISC mediates translational repression or mRNA degradation.  Plants generally produce miRNAs that have perfect complementarity to the target mRNA resulting in endonucleolytic cleavage, while the majority of metazoan miRNAs have imperfect base pairing to the target mRNA resulting in translational repression (43).  For metazoan miRNAs, interaction with the target mRNA follows a set of rules determined by experimental and bioinformatic analyses.  The most strict requirement is   4 contiguous perfect base pairing between nucleotides 2 through 8 of the miRNA to the target mRNA, also known as the ‘seed sequence’ (44-48).  In addition to the seed sequence, miRNA-mRNA pairs are usually found with bulges in the central region of the miRNA-mRNA duplex and reasonable complementarity to the 3’ half of the miRNA that stabilizes the reaction (44-48).  The majority of metazoan miRNAs bind the 3’UTR of target mRNAs through multiple binding sites for effective repression of translation, although miRNA-based repression can also occur when miRISC binds to the 5’UTR or coding regions (49-51).   The mechanism for miRNA induced translational repression is still not completely elucidated but is thought to mainly occur at the translation initiation step (43).  Translation of mRNAs is divided into three main steps: initiation, elongation and termination (52).  Initiation requires a large number of proteins, known as initiation factors, that recognize the 5’-terminal 7-methylguanosine (m7Gppp) cap structure (53).  Interactions between the initiation factors and the polyadenylate-binding protein 1 (PABP1) circularize the mRNA, resulting in recruitment of the 40S and 60S ribosome subunits, and allows the elongation phase to begin (52, 54, 55).  Translational repression by miRNAs is not limited to a single mechanism.  The strongest experimental evidence suggests that miRISC inhibits translation at the initiation step by interfering with eIF4F-cap recognition and 40S small ribosomal subunit recruitment or by antagonizing 60S subunit joining and preventing 80S ribosomal complex formation (43, 56-58).  However, translational repression at the initiation step does not appear to be the only mechanism of action for metazoan miRNAs with supporting experimental evidence for repression of the elongation step of translation by inducing ribosome drop-off, or facilitating proteolysis of nascent polypeptides although the mechanism of action for these processes is still unknown (43, 58).     5 In addition to repression of protein production, miRNAs are also capable of mediating mRNA decay and deadenylation as demonstrated by a number of studies that show a reduction in target mRNA levels (43, 58-64).  In the Drosophila system, a recent study has established that miRNA-mediated translational repression occurs at the initiation or early elongations phase of protein synthesis and is then followed by mRNA deadenylation and decay, indicating that the miRNA machinery most likely utilizes both mechanisms to mediate gene silencing (65).  mRNA decay frequently begins with the removal of the poly(A) tail by the 3’-5’ exonucleases CCR4 (carbon catabolite repression-4)-NOT1 (negative on TATA-less) complex, PARN (poly(A) specific ribonuclease), or Pan2/3 (poly(A) nuclease) and the mRNA is then further degraded in a 3’-5’ direction (66-68).  mRNAs can also undergo 5’-decapping followed by degradation by the exonuclease Xrn1 in a 5’-3’ direction (65, 67, 69).  MiRNA mediated deadenylation and translational repression requires the Ago and the GW182 protein components of the miRISC complex and can result in target mRNAs accumulating in cytoplasmic foci known as processing (P) bodies (70-72).  P bodies are cytoplasmic aggregates of translationally repressed mRNAs and are enriched in proteins involved in mRNA decapping, deadenylation and degradation, yet they are not essential for miRNA mediated translational repression (70, 73-75).  With multiple mechanisms proposed and investigated it is well understood that there is not one concise pathway for post-transcriptional repression by miRNAs.  Further advances in molecular biology will undoubtedly uncover novel mechanisms and clarify working hypotheses. 1.1.4 Emerging role of miRNAs in human diseases With miRNAs controlling multiple aspects of cellular protein expression, deregulation of miRNAs is associated with numerous human pathologies, including cancers, genetic disorders, skeletal and muscular diseases and viral infections (76, 77).   Within two years of the discovery   6 of let-7, the first miRNAs associated with cancer, miR-15 and miR-16, were found to be deleted or frequently down-regulated in chronic lymphocytic leukemia (78).  Most cancers have since been found to be associated with altered miRNA expression, in which miRNAs can act as oncogenes or tumor suppressors and can either promote or inhibit metastasis (79-81).  Deregulation of miRNAs is a result of multiple mechanisms including deletions, amplification, mutation, deregulation of transcription factors, as well as DNA methylation and histone modifications (82).  The relationship between deregulated miRNAs and human disease has led to the use of miRNAs as biomarkers and their use as a potential therapeutic option.  By evaluating blood and serum samples from patients, miRNA expression profiles can be analyzed for tumor diagnosis, prognosis, and disease outcomes (83, 84).   1.2 MiRNAs and virus infection  Given their prevalence and ability to regulate a wide variety of cellular processes, it is not surprising that miRNAs have been identified as key players in host-pathogen interactions.  Numerous miRNAs, such as miR-155, miR-146 and the miR-17-92 cluster, are associated with regulation of cell development and function in both the innate and adaptive immune response (85-87).  However, invertebrates and plants rely solely on their innate immune system and utilize RNA interference (RNAi) to control viral infections (88, 89).  Similar to the miRNA pathway, RNAi utilizes small interfering RNAs (siRNA) that are produced by DNA or RNA-dependent synthesis, are processed and incorporated in the RNAi Induced Silencing Complex (RISC) by the same enzymes utilized by the miRNA biogenesis pathway (90).  In contrast to miRNAs, siRNAs share perfect sequence homology with the target mRNA, resulting in mRNA cleavage and degradation upon binding of the 3’UTR with RISC (38).  This process has since been exploited by researchers, whereby exogenous siRNAs can be introduced into cells, to silence specific gene   7 targets (91).  As such, the RNAi pathway is an evolutionarily conserved process that is also utilized by vertebrates to combat virus infection (92).   Of those studied to date, miRNA expression can alter cellular gene expression during virus infection, resulting in beneficial and/or detrimental effect to the viral lifecycle (77, 93).  1.2.1 DNA viruses and miRNAs Human DNA viruses, most notably the herpesviruses, encode numerous viral miRNAs that can control viral gene expression and/or modulate cellular gene expression to allow for immune evasion and establishment of latency (77, 93).  The first virally encoded miRNAs were discovered in 2003 in B-cells infected with Epstein-Barr virus (EBV) (94, 95).  To date the majority of virally encoded miRNAs have been found in EBV and Kaposi’s sarcoma-associated herpesvirus (KSHV), along with human cytomegalovirus (HCMV) and the herpes simplex viruses (HSV) (77, 96).  For other DNA viruses, virally encoded miRNAs are not as prevalent.  For example, human and animal polyomaviruses only encode one miRNA while adenoviruses encode two (94).  Surprisingly, no virally encoded miRNAs are found in papillomaviruses or the large poxviruses, although they have been shown to modulate cellular miRNAs during infection (97-99).   EBV encodes 25 known miRNAs that are located in two clusters (95, 100, 101).  The first cluster, BHRFI encodes three miRNAs while the second cluster, BART, encodes twenty-two.  The miRNAs in these clusters are expressed at different levels depending on the latency state.  In latency III, the BHRFI cluster is expressed at high levels with the BART cluster moderately expressed, while in latency II, the BART cluster is highly expressed and there is a complete lack of expression from the BHRFI cluster (100).  EBV miRNAs target a number of cellular mRNAs to prevent apoptosis and are linked not only to latency but also to oncogenesis   8 (102, 103).  MiR-BART5 and 19 target p53 Up-regulated Mediator of Apoptosis (PUMA) to prevent apoptosis in response to DNA damage, while miR-BART4 and 15 target BCL2L11/BIM, again suppressing apoptosis (104-107).  EBV encodes miRNAs that also target viral mRNAs, such as miR-BART2 that inhibits the viral DNA polymerase BALF5 (95).  In addition to virally encoded miRNAs, EBV infection strongly induces the cellular miRNAs, miR-155 and miR-146a, which are also associated with the virally induced B-cell lymphomas (102, 103, 108).   The other gamma herpesvirus, KSHV, encodes 12 miRNAs that are expressed in high levels in latent B-cells from a single intron (109, 110).  The virally encoded KSHV miRNAs target numerous cellular mRNAs to help facilitate latency, such as IRAK-1, caspase-3 and TGF-ß, which are associated with apoptosis and immune evasion (111-113).  Similar to KSHV, both HSV-1 and -2 encode numerous miRNAs that are associated with latency-associated transcripts (LATs) and are thought to facilitate latency (114-116).  HSV-1 and -2 also encode viral miRNAs that lie antisense to specific viral genes and regulate their expression, inhibiting lytic infection and viral pathogenicity in neurons (114, 116).  In contrast to EBV, KSHV, and HSV-1 and -2, the beta herpesvirus HCMV encodes 12 miRNAs that are scattered throughout the genome and are highly expressed during lytic infection instead of latency (110, 117).   1.2.2 RNA viruses and miRNAs For RNA viruses, the smaller genome size and error prone nature of RNA-dependent RNA polymerases may constrain the incorporation of viral miRNAs.  However, there is substantial evidence for modulation of cellular miRNAs that both enhance and restrict viral replication.  For the human RNA viruses human immunodeficiency virus-1 (HIV-1) and hepatitis C virus (HCV), modulation of host miRNAs influence viral pathogenesis (93) (118).  During   9 HIV-1 infection silencing of Dicer or Drosha, key components of the RNAi pathway, leads to faster virus replication in peripheral blood mononuclear cells (PBMC) indicating that these proteins may play a key antiviral role during HIV-1 infection (119).  HIV-1 has also been shown to suppress the miR-17/92 cluster as it targets a cofactor for Tat transactivation, negatively influencing HIV-1 production (119).  A number of miRNAs (miR-28, 29a 150, 223, and 382) have also been shown to target the 3’UTR of HIV-1 mRNA and are differentially expressed between monocytes and CD4+ T-cells, restricting HIV-1 infection and possibly contributing to latency (120, 121).  In T-lymphocytes, miR-29a could directly target the HIV-1 mRNA 3’ UTR to P-bodies, leading to mRNA degradation (122).  In contrast to most miRNAs identified during HIV-1 infection, miR-132 enhances HIV-1 replication but the specific mechanism remains unknown (123).  Unique to the RNA viruses, numerous reports indicate that HIV-1 may encode viral miRNAs, but the data supporting these claims remain controversial.  Picornaviruses also manipulate cellular miRNAs during their lifecycle.  Upon enterovirus 71 (EV71) infection miR-141 is induced and targets eIF4E, a key element in cap-dependent translation (124).  Since picornaviruses contain an internal ribosome entry site (IRES) that does not require cap dependent factors such as eIF4E, up-regulation of miR-141 results in reduced cap-dependent and enhanced cap-independent translation, favoring viral protein production.  Additionally, antagonizing miR-141 during EV71 infection dramatically reduced virus production, strongly suggesting that expression of this cellular miRNA is important to the virus lifecycle (124).   During HCV infection, a liver specific miRNA, miR-122, interacts with the virus genome (125).  There are two miR-122 binding sites found in the 5’UTR of the HCV genome, close to   10 the IRES.  Binding of miR-122 enhances RNA abundance, translation, and virus production (125-128).  A proposed mechanism for miR-122 is the recruitment of Ago2 to the HCV genomic RNA, preventing or slowing RNA decay (129).  In addition to miR-122, the HCV genome also contains binding sites for miR-199a in the IRES and let-7b in the nonstructural 5B (NS5B) gene and 5’UTR, resulting in inhibition of HCV replication and significantly suppressing HCV infection, respectively (130, 131).  Not all miRNAs associated with HCV infection are directed towards the virus genome.  Infection of human hepatoma cells enhances miR-130 and miR-21, which inhibits interferon induced antiviral protein IFITM, MyD88, and IRAK1, respectively, reducing the innate immune response during infection (132, 133). HCV is not the only member of the Flaviviridae family that modulates miRNAs upon infection.  Both West Nile virus (WNV) and dengue virus (DENV) modulate cellular miRNAs.  In the case of DENV, there is enhanced replication in mosquitos following depletion of the RNAi factors Dicer, Drosha and Ago2 by the nonstructural 4B (NS4B) protein (134).  In humans, infection of PBMC’s with DENV also results in modulation of cellular miRNAs and may contribute to the cytokine storm and shock syndrome associated with severe disease (135).  Additionally, there is evidence that DENV up-regulates miR-146a to evade interferon inhibition by suppressing TRAF6 (136).  In the case of WNV, expression of miRNA Hs_154 is increased and is found to suppress a number of anti-apoptotic factors (137).     1.2.3 Influenza A and miRNAs The differential expression of miRNAs during infection with multiple strains of influenza A (infA) virus has been studied in mice, pigs, and chickens (138-141).  Microarray analysis of mouse miRNA expression during infection with the reconstructed 1918 (r1918) virus and a   11 seasonal H1N1 virus (Tx/91) established that the microRNAome was modulated during infA infection (139).  Down-regulation of specific miRNAs, such as miR-200a, which is predicted to target type I interferon genes, could contribute to the unconstrained inflammatory response associated with the r1918 virus (139).  While no miRNAs have been identified in the infA virus genome, the virus can be engineered to encode functioning cellular miRNAs.  This does not interfere with miRNA biogenesis, and can be attenuated when a cellular miRNA binding site is engineered into the viral RNA (142, 143).   1.2.4 MiRNA-based antiviral therapeutics As research into virus and miRNA interactions continues to grow, the potential for new therapeutics based on their expression is already bearing fruit.  Unique cellular miRNA profiles during infA infection were generated from PBMC’s of critically ill patients (144).  These miRNAs may serve as biomarkers for patients who are at risk of developing severe disease in response to the virus.  However, the most promising use of miRNAs as potential therapeutics comes from the relationship between miR-122 and HCV.  Using locked nucleic acid (LNA) technology, HCV infected chimpanzees were treated with LNA-modified oligonucleotides targeting miR-122, thereby inhibiting its interaction with the HCV genome (145).  Viral levels were significantly reduced and led to development of Miravirsen by Santaris pharmaceuticals (146, 147).  The anti-miR-122 Miravirsen is the first miRNA therapeutic agent to enter Phase II clinical trials in humans and shows a lot of promise for treatment of HCV (148).  As more research is done, there is no doubt more miRNA based therapeutics will be utilized to combat viral infection.   1.3 Influenza A virus biology A member of the Orthomyxoviridae family, infA is an enveloped virus with a segmented   12 negative sense RNA genome that consists of 8 gene segments: 3 polymerase genes (PB2, PB1, and PA), hemagglutinin (HA), nucleoprotein (NP), neuraminidase (NA), matrix (M), and nonstructural gene (NS) that encode at least 11 protein products (Figure 1.3-1.6) (149).  InfA has a complex life cycle and is one of the few RNA viruses that replicates in the nucleus of infected cells (Figure 1.2) (149, 150).  InfA viruses are capable of evolving rapidly through mutations and reassortment events that result in the emergence of new strains, a subset of which may have epidemic or pandemic potential.  1.3.1 The influenza A virus lifecycle One of the first major events in the infA virus lifecycle is viral entry which is mediated by the binding of the HA glycoprotein to terminal sialic acid (SA) residues on the host cell glycoproteins, initiating internalization via endocytosis (Figure 1.2) (151-153).  HA binding can only occur after the HA0 precursor protein has been cleaved by host trypsin-like proteases into its mature form consisting of the HA1 and HA2 subunits that are covalently linked via a disulfide bridge (154-157).  The acidic environment of the endosome results in a conformational change of the mature HA glycoprotein that mediates fusion with the endosomal membrane by the fusion peptide (FP), located at the amino terminus of the HA2 subunit (153, 154, 157, 158).   Before fusion of the viral and target membrane takes place the actions of a third transmembrane protein, M2, is required.  The M2 protein, which belongs to the viroporin family, forms a tetrameric transmembrane ion channel that is activated at low pH values   The M2 ion channel allows H+ ions to move into the nucleocapsid resulting in the depolymerization of the M1 protein and dissociation of the M1 and the viral ribonucleoproteins (vRNP) (150, 159).  The acidification of the nucleocapsid and fusion of the viral and target membranes results in the   13 release of the vRNPs into the cytoplasm where they are imported into the nucleus through the nuclear pore complex (150).  Each of the eight vRNPs consists of viral RNA (vRNA) associated with nucleoprotein and the three-polymerase subunits.  The 5’ and 3’ ends of the vRNA are bound to the PB1 protein and resemble a twisted rod that is coiled and folded back on itself (160).  Transcription of the viral proteins requires the use of 5’ capped oligonucleotide primers obtained from host pre-mRNAs.  The capped oligonucleotide is recognized and bound by the PB2 protein of the polymerase complex and cleaved by the PB1 protein to generate an 11-13 nucleotide primer (161, 162).  PB1 is also the RNA-dependent RNA polymerase and elongation of the viral mRNA chain proceeds until the polymerase reaches a series of uracil resides at the end of the vRNA and stutters creating a poly(A) tail (163).  Once viral mRNAs are generated they are exported out of the nucleus to the cytoplasm and translated.   In contrast to transcription, replication proceeds in a cap-independent manner and generates full-length cRNA that will serve as a template for progeny vRNA.  A new model has suggested that cRNA is produced but is quickly degraded by cellular RNases until it is stabilized by both newly synthesized polymerase and NP proteins that have been imported back into the nucleus after translation (164, 165).  These stabilized cRNA molecules can then serve as efficient templates for large amounts of progeny vRNA that will eventually be incorporated into new virions (164, 165).  The export of newly synthesized vRNPs requires both M1 and NEP (nuclear export protein, previously NS2).  NEPs interactions with the vRNP are mediated by M1 and are required for transport out of the nucleus using the CRM1 nuclear export pathway (166).  Assembly and morphogenesis of infA virions requires a number of essential steps that include assembly of viral components at the apical membrane in an orderly fashion, bud   14 formation at the plasma membrane, and release of the virus particle by cleavage of SA residues by NA (167).  The M1 protein is the most abundant protein in influenza virions and is the key protein in recruiting, concentrating, and assembling viral and host components required for budding at the assembly site of the plasma membrane (168).  M1 interacts with both the vRNPs and the envelope proteins HA and NA to facilitate the final steps of the virus lifecycle.  The envelope proteins HA, NA, and M2 preferentially accumulate at the apical membrane after being synthesized in membrane bound polyribosomes and transported together through the ER and TGN (secretory pathway) where they undergo extensive post-translational modifications (167).  After the virion has been assembled with all eight gene segments in association with the envelope proteins the virion can still be attached to the cell via SAs which are cleaved by NA to fully release the new virion.   1.3.2 Influenza A virus genes InfA glycoproteins HA and NA: Of the eight gene segments, the infA virus glycoproteins are significant in host preference and are amongst many viral factors that contribute to pathogenesis (Figure 1.3).  The hemagglutinin (HA) glycoprotein, encoded on gene segment 4, exists as 18 subtypes (H1-H18), with only subtypes H1, H2 and H3 forming stable lineages in humans.  Human infections with subtypes H5, H7, and H9 have occurred rarely and are associated with severe disease.  The H17 and H18 subtypes were discovered in bats by next-generation sequencing, although studies on the HA and NA glycoproteins revealed that they do not have canonical influenza virus functions or structures, leading them to be influenza-like at most (169, 170).  HA glycoproteins are members of two phylogenetic groups that are characterized based on specific structural features with H1, 2, 5, 6, 8, 9, 11, 12, 13, 16, 17, and 18 belonging to group 1 and H3, 4, 7, 10, 14, and 15 belonging to group 2 (171).  The HA   15 glycoprotein plays three important roles in the virus lifecycle: binding of the SA, mediating entry via membrane fusion, and acting as the major surface antigen from which neutralizing antibodies are formed (172).  The HA glycoprotein is a type I viral fusion protein which contains a single chain precursor that is assembled into trimers that are anchored into the virus envelope via the COOH terminus (154, 173).  Each HA monomer contains a receptor-binding site made up of conserved amino acids at the membrane distal tip and the stem region that is responsible for membrane fusion (171, 174).  The HA glycoprotein assists initial viral infection by binding a host cell receptor containing specific isomers of terminal SA residues (151, 175).  The sequence of amino acid residues within the HA receptor binding domain binds to α-2,3-linked SA receptors in avian hosts, which are found in their gut and intestinal tissues, while human infA viruses prefer α-2,6-linked SA receptors found in the epithelium of the upper respiratory tract  (176-181).  The cleavage of the HA precursor protein HA0 into covalently linked HA1 and HA2 subunits is necessary for infection as it results in exposure of the FP, or hydrophobic amino terminus of the HA2, facilitating fusion between the endosome and virus (153-155, 158, 182, 183). Unlike low pathogenic (LP) infA subtypes, the H5 and H7 influenza viruses can contain a multi-basic HA cleavage sequence, which is associated with high virulence in domestic poultry (Table 1.1) (184).  While HA is responsible for viral entry and uptake into a host cell via a SA receptor, the second influenza glycoprotein, neuraminidase (NA), encoded on gene segment 6, mediates virus release from the host cell.  The NA, also known as a sialidase, is a Type II single pass transmembrane protein that is a tetramer made up of four identical peptides that includes a hydrophobic transmembrane domain that anchors the protein via its NH2 terminus, a thin stalk of   16 variable length, and a globular head that contains the enzyme’s active site (Figure 1.3) (173, 185-188).  Of the 11 individual subtypes (N1 to N11) known to infect animals, two (N1 and N2) are responsible for known human outbreaks.  Similar to the HA glycoprotein, the different NA subtypes are classified into three phylogenetically distinct groups based on structure.  Group 1 consists on N1, 4, 5 and 8 while group 2 contains N2, 3, 6, 7, and 9 and group 3 is made up of the two new NA proteins (N10 and N11) that were discovered in bats along with H17 and H18 (170, 189).  NA acts as a sialidase, cleaving terminal SA residues from viral and cellular glycoproteins and glycolipids, assisting in viral replication and spread (167, 188, 190).  With the removal of SAs from host cell receptors, NA prevents viral self-aggregation and releases the new virions into the extracellular matrix (173).  The cleavage of SAs from upper airway epithelial cells is thought to contribute to and enhance viral infection in a host’s upper airways (191).  InfA polymerase proteins:  The three largest gene segments of infA encode the polymerase complex that consists of PB2 (gene segment 1), PB1 (gene segment 2) and PA (gene segment 3) (Figure 1.4) (149). This trimeric polymerase complex is required for vRNA transcription and replication of the influenza genome in infected cells (192, 193).  With such highly specialized activity, the polymerase genes are generally well conserved but specific amino acid mutations are associated with host adaptation and viral virulence (194-196).    PB2 functions during initial viral mRNA transcription as the protein that recognizes and binds the 5’cap structures of host cell mRNAs (161, 197, 198).  The caps are cleaved from the cellular mRNAs by PB1 and are used as primers for viral mRNA transcription (162, 193, 197).  The PB1 protein functions as a classic RNA-dependent RNA polymerase responsible for polymerization and endonuclease cleavage (199).  The smallest influenza protein, PB1-F2, is encoded in an alternate reading frame of the PB1 gene that consists of amino acids 40-127 (200).    17 The PB1-F2 protein was discovered during a search for novel peptides presented to CD8+ T cells during influenza infection (200).  The PB1-F2 protein induces apoptosis by localizing to the mitochondria, punching holes in the inner and outer mitochondrial membrane and releasing cytochrome c (201-203).  PB1-F2 appears to be a major determinant of pathogenesis and virulence but is attenuated in most seasonal human strains (204-209).  The PA protein, the third gene segment, is a multi-functional protein that is required for viral RNA replication and is the least well characterized polymerase protein.  PA has been shown to be involved in transcription, replication, and proteolysis of both viral and host proteins, endonuclease activity, cap and promoter binding capabilities and is phosphorylated (210-217).  InfA nucleoprotein: The fifth gene segment encodes the nucleoprotein (NP) (Figure 1.4), one of the most abundant viral proteins in the virion due to its interaction with the viral RNA genome (218).  Newly translated NP proteins are transported back to the nucleus during infection and accumulate around newly synthesized vRNA genome segments.  This action not only protects the newly synthesized viral RNA from degradation by host cell nucleases, but facilitates the switch between production of viral mRNA to genomic vRNA (218).  The NP accumulates around the viral RNA and associates with the polymerase complex forming the vRNPs that are then exported out of the nucleus for assembly into new virions (219). Along with viral proteins, the NP has also been shown to interact with proteins associated with nuclear import and export, including importin-α and CRM1, helping to facilitate the movement of vRNPs into and out of the nucleus (218).  InfA matrix proteins M1 and M2:  The seventh gene segment of infA virus encodes two M proteins, M1 and M2, the latter of which is generated by alternative splicing in the nucleus (Figure 1.5) (220).  Both of these proteins are essential for virus infection yet play very   18 different roles during the virus lifecycle.  M1 is the most abundant viral protein in mature virions and forms a nucleocapsid between the lipid bilayer and vRNPs, while the M2 protein forms an ion channel that is responsible for the pH change inside the nucleocapsid during virus entry (150, 159).  M1 has an essential role in virus assembly, associates with the vRNPs, and is involved in transcription inhibition, and control of RNP nuclear import and export (159, 167, 168, 221).  The M2 protein, a 97-amino acid tetramer, forms a pH-gated proton channel that is very well characterized as one of the smallest actual ion channels (viroporins) (222).  The four transmembrane helices form a channel that contains a pH sensor and gate with amino acids His37 and Tyr41, respectively (222).  During virus entry via endocytosis, the low pH of the endosome activates M2 before HA fusion (222).  Opening of the proton channel acidifies the interior of the virion resulting in dissociation of the M protein and vRNPs.  M2 also prevents premature arrangement of newly synthesized HA during virus assembly (222).   InfA nonstructural proteins NS1 and NEP (NS2): The last RNA segment implicated in increased pathogenesis and virulence is the nonstructural gene (NS), which also encodes two different protein products, nonstructural protein 1 (NS1) and the NEP (Figure 1.6).  The NS proteins are the only ones not incorporated into the virion but both play a major role during infection.  The NS1 protein is translated from the full-length gene segment, while NEP is generated via splicing of the NS mRNA.  NS1 is a multi-functional protein that has many roles during the virus lifecycle.  One of the main functions attributed to NS1 is suppression of innate immune responses, specifically interferon-alpha and beta (223-227).  Additionally, the NS1 protein regulates vRNA, enhances viral mRNA translation, and activates the phosphatidylinositol-3-kinase (PI3K)/Akt pathway to suppress apoptosis (227-233).  The NS1 protein is also widely considered a virulence factor and may contribute to strain specific   19 pathogenesis (227, 234, 235).  The NEP is known for its role in mediating the export of newly synthesized vRNPs from the nucleus of the host cell (166). This is mediated by CRM1, which recognizes the nuclear export signal on the N-terminus of the NEP and utilizes Ran-GTP nuclear export.  The NEP is proposed to bind CRM1 at its N-terminus and the M1 protein at its C-terminus, with the M1 protein binding the vRNPs via the NP protein (236, 237).  In addition to export of vRNPs, the NEP may regulate transcription and replication of the virus genome (236, 238).   1.3.3 Antivirals and vaccines against influenza A virus Antivirals:  There are currently two types of antiviral drugs on the market that are specifically active against infA viruses: the adamantanes (amantadine and rimantadine) and the NA inhibitors (oseltamivir and zanamivir) (Table 1.2) (239).  M2 is the target of the adamantane-based drugs that generally work by binding to the proton channel and stabilizing the closed conformation, which prevents acidification of the virion interior and dissociation of the vRNPs from the M1 protein (222).  Multiple mutations in the M2 protein, that include L26F, V27A, A30T, S31N, G34E, and L38F, confer resistance to adamantane-based drugs such as amantadine and rimantadine and because of this they are no longer used to treat human seasonal influenza virus infections (240, 241).  Similar to the adamantane-based drugs, a single mutation in the NA glycoprotein (H275Y) can result in resistance to oseltamivir and zanamivir limiting their effectiveness (242).  Since 1999, no new antivirals have been approved against influenza, although a number of therapeutics are under investigation, including those that target the virus specifically (direct-acting antiviral: DAA) and those targeted to specific host factors (indirect-acting antivirals: IAA) (239).  Virus targets for new antivirals include NA inhibitors (Laninamivir (CS-8958)), polymerase inhibitors (Favipiravir (T-705)), combination   20 chemotherapy, and HA neutralization, while the host targets include influenza virus receptor inactivators (Fludase (DAS181)), corticosteroids, and cholesterol biosynthesis pathway inhibitors (Rosuvastatin) (Table 1.2) (239) (243-247).      Vaccines:  Every year trivalent influenza vaccines are reformulated and produced based on worldwide surveillance of the most prevalent circulating strains (248).  It is most common for the vaccine to include two infA strains, usually an H1 and an H3 subtype, as well as an influenza B strain.  There are currently two different types of vaccines produced: a killed inactivated vaccine and a live attenuated vaccine and both are designed primarily around the HA glycoprotein.  The constant antigenic drift of the HA glycoprotein in response to normal immune responses during infection and vaccine use undercuts the effectiveness of neutralizing antibodies produced against the HA glycoprotein and requires the reformulation and revaccination of individuals each year (249, 250).  The vaccines are also produced in an antiquated method by growing and passaging virus stocks in embryonated chicken eggs (248, 251).  A major shortfall of the current influenza vaccine production method is a manufacturing process that takes several months to produce vaccine stocks, leaving the population at risk in the event of a pandemic or if the dominant circulating virus has shifted and is not included in the current years trivalent formulation (252).  Additionally, egg based vaccines cannot be utilized by those with an egg allergy and the HPAI viruses are often “egg lethal” leading to the challenges in growing sufficient virus stocks for use in potential vaccines against these strains.  Research into a universal vaccine or neutralizing antibody as a potential therapeutic is ongoing and may be required for stemming a major pandemic on the scale of the one seen in 1918 (252, 253).    21 1.3.4 Influenza A virus evolution With a multi segmented RNA genome, infA viruses have a large genetic diversity, with most selective pressure exerted upon, but not limited to, the surface glycoproteins HA and NA due to host immune responses (149).  A number of mechanisms result in molecular changes to the gene segments and contribute to evolution of the virus, including point mutations (antigenic drift), gene reassortment (genetic shift), RNA recombination, deletions, and insertions (149).  For aquatic wild birds, the virus is fully adapted to the intestinal epithelium and rarely causes disease yet genetic reassortment and other evolutionary processes can lead to viruses capable of infecting humans, pigs, horses, and other animal species (149, 254).  New human pandemic strains generally arise from reassortment events, resulting in viruses that can contain novel glycoproteins (ex: H1N1 → H1N2) and may also involve reassortment of the internal gene segments as well.  With a large number of genetic variants, only a few subtypes and reassortment viruses have crossed the species barrier from their natural host, aquatic birds, to other animals, pigs and humans.  This host range restriction is not fully understood at this point, but a number of mutations and specific gene combinations are associated with the ability or inability of the virus to infect new hosts.  Most attention is focused on the HA due to its receptor binding restrictions yet it is not the sole determinant for infection in new hosts (149).   In pigs, while a large number of infA subtypes have been isolated, only three subtypes, H1N1, H1N2, and H3N2, have been maintained and form stable lineages (255).  Pigs have been recognized as a reservoir of influenza viruses that are capable of transmission to humans and exhibit similar disease symptoms.  Pig influenza viruses have become a major source of potential new human infA strains making them an important player in the evolution of the virus and in the generation of potential new pandemic strains of human influenza (149).     22 1.3.5 Influenza A virus pandemics  Pandemics prior to 1918: The word pandemic comes from the Greek for all (pan) people (demos) with the Dictionary of Epidemiology definition of a pandemic being 'an epidemic occurring worldwide, or over a very wide area, crossing international boundaries and usually affecting a large number of people' (256).  Outbreaks of influenza, as determined by medical historians, date back to the 5th century BC in ancient Greece, with records of disease that were most likely influenza recorded throughout the Middle Ages (257-259).  With greater documentation the first recorded influenza pandemic occurred in 1510 with recurring pandemics in 1557/58 and 1580, all of which spread throughout Europe from Asia or Africa (257, 258). Throughout the 18th and 19th centuries multiple infA pandemics were recorded, all originating in Asia or Russia and spreading westward (257, 258).  In 1889 to 1893 an influenza pandemic that began in Russia was attributed at the time to the newly discovered bacterium, Bacillus influenzae, now known as Haemophilus influenzae, and was the first pandemic recorded during the beginning of modern microbiology (257, 258). Subsequent research during the early 20th century on survivors from this pandemic have suggested that the virus responsible was of an H3 subtype (258).  1918 ‘Spanish flu’ pandemic:  Over the last century, four infA pandemics (1918, 1958, 1968, 2009) have resulted in the loss of over 50 million lives (259).  The 1918 ‘Spanish flu’ pandemic is the most infamous and is thought to have caused between 20 and 50 million deaths worldwide between 1918 and 1920 (260).  The origin of the 1918 pandemic is difficult to place, however there are reports of severe influenza cases in early 1918 in the United States (257, 260).  With troops heading to Europe during the final months of the first World War, the virus spread quickly and is now associated with three waves (260).  The first wave, while severe, did not   23 result in unusual mortality.  The second wave occurred during the fall of 1918 and began to display the characteristics now associated with the pandemic, most notably the high mortality rates, especially in health young adults that do not usually succumb to infA infections (260).  While a large number of deaths can be attributed to secondary bacterial infections due to a lack of antibiotics, the virus also caused a violent viral pneumonia associated with acute pulmonary hemorrhage or pulmonary edema and killed in as quickly as 5 days (260).  The third wave occurred during early 1919 and was less severe, yet still resulted in an unusually high number of deaths in young adults (257).  The virus was associated with multiple seasonal epidemics in the years that followed until the next pandemic in 1957 where it disappeared entirely before reemerging in 1977 and has continued to circulate endemically in humans since (259).     1957 and 1968 pandemics: In 1957, the 1918 H1N1 virus underwent a reassortment event that replaced the surface glycoproteins and one polymerase protein with novel avian-like gene segments resulting in a novel H2N2 subtype virus (257-259).  The virus emerged in Southeast Asia and spread over the course of the year worldwide, resulting in high morbidity but not unusually high mortality rates.  The virus circulated seasonally for just over 10 years following the pandemic before disappearing entirely with the emergence of the third pandemic of the 20th century (257-259).  In 1968, another reassortment event led to a novel H3N2 subtype of infA that spread worldwide from its origin in Hong Kong (257-259).  The H3N2 virus caused relatively mild illness and mortality and has since become endemic, circulating the globe seasonally for the past 46 years (257-259). 2009 pandemic H1N1:  The most recent pandemic resulted from a novel triple reassortment infA virus that emerged in humans in Mexico and the United States in early 2009,   24 although the virus may have circulated in people as early as 2008 (259, 261).  The virus appears to have originated from swine and includes genes from avian (PB2 and PA), human H3N2 (PB1) and classical swine (HA, NP, and NS) origins with the NA and M gene segments originating from the Eurasian avian-like swine H1N1 lineage (261, 262).  Like previous virus strains (1957, 1968), the classical swine lineage genes found in the 2009 H1N1 pandemic virus are descendent from the 1918 infA virus (259, 261).  While the virus itself resulted in a high number of infections and had a relatively low mortality rate, subpopulations with underlying medical conditions accounted for more than 70% of hospital admissions with a high mortality rate (263-266).  The 2009 H1N1 virus has since circulated endemically and become the predominant seasonal influenza virus.   1.4 Molecular determinants of influenza A virus pathogenesis InfA viruses can infect and adapt to a wide variety of hosts including but not limited to: humans, pigs, birds, and horses.  Phylogenetic analysis has identified aquatic birds as the reservoir and source of all infA subtypes with virus isolates found in over 105 species such as geese, ducks, gulls, and swans (149, 267).  InfA infections in birds can vary but most are subclinical, with few exceptions (149).  In wild ducks, the virus replicates in the intestinal tract causing no disease and is shed in the feces in high concentration resulting in contaminated water and food through which the virus is passed to other ducks (149, 267).  In domestic poultry, such as chickens and turkeys, two groups of infA viruses have been isolated.  The first group consists of the H5 or H7 subtypes, and the second group is comprised of other various HA subtypes.  With multiple passages, the H5 and H7 subtypes can acquire specific mutations that result in LP strains becoming highly pathogenic (HP) resulting in up to 100% mortality in domestic poultry (149, 267).  These viruses are designated as HP avian influenza (HPAI) viruses.  The main   25 mutation associated with HPAI viruses is the accumulation of multiple basic amino acid residues in the cleavage site of the HA glycoprotein (157, 184).  HPAI viruses possess multiple basic amino acids (MBAA) at their HA0 cleavage site (Table 1.1).  This unique MBAA motif is recognized by ubiquitously expressed proteases and allows for the virus to disseminate systemically (268-270).  LP H5 and H7 virus strains circulating in poultry can acquire the multi-basic cleavage site by a variety of mechanisms, potentially transforming the LP viruses into HP strains.  These mechanisms include spontaneous duplication events, nucleotide substitution, nonhomologous recombination between the HA and another infA gene (namely the M or NP protein) (271-273).  Some signs associated with HPAI viruses include ruffled feathers, decreased egg production, respiratory signs, edema of the face and head and hemorrhages due to the systemic, instead of localized, infection.  The remaining HA subtypes that infect domestic poultry can cause mild disease but rarely death (149).   1.4.1 Highly pathogenic avian influenza A virus infection in humans For the past 50 years, HPAI viruses have resulted in outbreaks in domestic poultry in Europe, North America, Australia, and Asia (274).  In 1996 a HPAI H5N1 virus (A/Goose/Guangdong/1/96) caused a large outbreak in domestic geese in Guangdong, China with a 40% mortality rate in the birds (275).  This virus was then detected in domestic poultry in Hong Kong in 1997, which resulted in the direct infection of 18 people, six of whom subsequently died from the infection (275-277).  Since 2003, HPAI H5N1 virus strains have spread across three continents leading to multiple outbreaks in domestic poultry and huge economic losses (240).  Furthermore, the recent increase in the spread of HPAI viruses in poultry has been associated with sporadic transmission to humans resulting in over 370 deaths (275).  The zoonotic transmission of HPAI strains results in severe disease characterized by a systemic   26 instead of local infection and major cytokine deregulation (265, 278-283).  Due to the nature of avian vs human infA viruses in terms of host restrictions, there has been no sustained evidence of human-to-human transmission of HPAI viruses, therefore limiting a potential pandemic.  The limitations were recently tested using serial passage in ferrets to identify mutations that would facilitate airborne transmission of the HPAI H5N1 virus and assist in monitoring wild-type H5N1 strains for pandemic potential (284, 285).  A total of 5 mutations, 4 in the HA (Q222L, G224S, H103Y, T156A) and 1 in the PB2 (E627K) genes were identified as sufficient to confer airborne transmission of HPAI H5N1 between ferrets although they were not associated with increased mortality (284, 285). In addition to H5N1, a number of other avian origin infA (A-OIV) strains, including H7N7, H9N2, H7N3, H7N2, H10N7, H7N9, H6N1, and H10N8 have been transmitted directly to humans resulting in disease (240, 286-288).  Of these, H7N9, H7N2, H10N7, H6N1, H9N2, and H10N8 are considered to be a LP strains due to the lack of the multi-basic cleavage site in the HA protein.  The H9N2 infections have remained limited to Hong Kong and Guangdong, China resulting in mild, flu-like illness only (289-292).  The H7N3 subtype was associated with a large outbreak in poultry farms in British Columbia in 2004 and resulted in conjunctivitis and flu-like illness in two workers (272, 293, 294).  In 2003, a large outbreak of H7N7 in Dutch poultry resulted in 89 laboratory confirmed human cases, resulting in one death (295-297).  In early 2013, an outbreak of a LP avian H7N9 virus in China resulted in a large number of infections and fatalities (298-302).  Phylogenetic analysis identified the H7 HA and N9 NA to be from an avian source, while the remaining genes are closely related to the H9N2 viruses circulating in poultry (299).  The number of cases dropped during the summer months, however onset of cooler weather in the fall resulted in a new spike in H7N9 cases along with the first   27 reported human infections involving H6N1 and H10N8 subtypes (286-288, 303).  While the H10N8 and H6N1 infections resulted in single cases, the former of which was fatal, the H7N9 virus continues to circulate with over 350 confirmed cases and at least 112 deaths, resulting in a mortality rate of ~30% (286, 287, 303).  Transmission of the H7N9 virus is most likely due to contact with infected poultry or birds, although infection within family groups in which members have had no known contact with poultry suggests possible human-to-human transmission (303).   1.4.2 2009 H1N1 influenza A virus infection in humans In contrast to infections from avian infA viruses, the swine origin H1N1 virus that emerged in Mexico in early 2009 and resulted in the first infA pandemic since 1968, was highly transmissible with the majority of infections resulting in uncomplicated upper respiratory tract illness and recovery within a week (304, 305).  However, many of the molecular markers that are associated with adaptations to a human host are not found in the 2009 H1N1 viruses (262).  No other amino acid has been implicated in host adaptation more than amino acid 627 of the PB2 protein.  The amino acid at position 627 is almost always a glutamate in avian influenza virus isolates and a lysine in mammalian isolates (195).  The role of amino acid 627 and how it affects the polymerase complex is unknown although a lysine at position 627 the virus was shown to replicate better at 33°C, the average temperature of the human airway versus the 41°C found in avian intestinal tracks (195).  The 2009 H1N1 viruses all contain a glutamate at position 627, complicating our understanding of how this mutation affects host adaptation.  The PB1-F2 is also a major determinant of pathogenesis and virulence in the HP H5N1 virus strains but is attenuated in the novel 2009 H1N1 strain due to stop codons introduced at position 12 (262).  Multiple mutations in the M2 protein confer resistance to adamantine-based drugs such as amantadine and rimantadine and included L26F, V27A, A30T, S31N, G34E, and L38F (241).  The 2009 H1N1   28 strain also contains the S31N mutation, a genetic marker for adamantane resistance, while the H275Y mutation for oseltamivir resistance was not widespread in the NA protein (262).  The 2009 H1N1 virus continued to circulate after the pandemic waned in 2010, and has established itself as one of the seasonal influenza strains, along with H3N2 and the influenza B strains (306).  During the 2013 to 2014 flu season, the 2009 H1N1 strain reemerged as the dominant strain with an increase in severe disease among young adults and those with underlying medical issues (307).   1.4.3 Processing of the influenza A HA glycoprotein by host proteases The HA glycoprotein is synthesized on membrane bound polysomes, glycosylated in the ER as HA0, where it is assembled into a homo-trimer that consists of two disulfide-linked subunits, HA1 and HA2, before transport to the plasma membrane via the trans-Golgi network (308).  Cleavage of the HA glycoprotein by cellular proteases is not a requirement for virus assembly but is required for the virus to be infectious and results in conversion of HA0 into HA1 and HA2 (155, 182, 309, 310).  Cleavage of the HA0 generates the COOH- terminus of HA1 and NH2 terminus of HA2.  The NH2-terminus of HA2 consists of 10 highly conserved hydrophobic amino acids (NH2-GLFGAIAGFI) which is recognized as the FP and make up the most conserved segment of the HA glycoprotein (Figure 1.7) (158, 309).   For all infA subtypes, except select H5 and H7 strains, the HA1 and HA2 subunits are processed at a single arginine (R) at the consensus cleavage site motif Q(E)-T/X-R (Table 1.1) (156, 311).  The endoproteolytic cleavage of HA0 into the HA1 and HA2 subunits by host cell protease(s) is a critical step in the infA lifecycle.  Over the years, numerous cellular proteases have been examined both in vitro and in vivo studies and the list of host cell candidates involved in this process continue to grow (156, 310, 312, 313).  In rat lung tissue, the serine protease,   29 Tryptase Clara, was found to preferentially recognize a single arginine cleavage site, and was capable of cleaving the HA0 precursor protein of the A/Aichi/2/68 (H3N2) virus strain (314).  In addition to Tryptase Clara, proteases secreted from specific bacterial strains such as Staphylococcus aureus (S. aureus), that are commonly found during co-infection with infA leading to a combined viral-bacterial pneumonia, also resulted in proteolytic cleavage of the HA glycoprotein (315, 316).  Additionally, the serine proteases TMPRSS2, HAT, MSPL and its splice variant TMPRSS13, and matriptase have all been found to proteolytically cleave HA0 in the human airway following virus assembly (311, 317-319). In contrast to the LP infA virus strains, HP H5 and H7 subtypes contain a multi-basic cleavage site, represented by the consensus motif R-X-K/R-R and K-X-K/R-R, that are processed by ubiquitously expressed subtilisin-like proteases, such as furin, resulting in systemic infection in domestic poultry (Table 1.1) (311, 320, 321).  A multi-basic cleavage site is not the sole determinant of pathogenicity in domestic poultry for infA viruses and other factors can influence the virulence and pathogenicity of infA viruses with one major factor being the presence or absence of carbohydrate moieties close to the cleavage loop (322, 323).  Cleavage of the HP H5 and H7 subtypes is mediated by at least two cellular proteases, furin and proprotein convertase (PC) 5/6 in the trans-Golgi network (Figure 1.8) (268, 269, 324-327).  Recent studies have demonstrated that MSPL/TMPRSS13 and matriptase can recognize the consensus motif K-X-K/R-R, with a preference for the K in position P4 (328) for both proteases, indicating that other proteases may be involved in processing of the HP infA HA protein, although this has yet to be fully explored (311, 318, 319, 329).    30 1.5 Proprotein convertases  Furin and PC 5/6 are members of the PC family of serine proteases (330).  There are nine members of the PC family in humans, PC1/3, PC2, furin, PC4, PACE4, PC5/6, PC7, SKI-1/S1P, and PCSK9, discovered over twenty years ago due to shared homology with bacterial subtilases and yeast kexin that contain a distinctive “serine-histidine-aspartic acid” catalytic triad (331, 332).  Seven members, PC1/3, PC2, furin, PC4, PACE4, PC5/6 and PC7 cleave proproteins after basic residues, while the remaining two members, SKI-1/S1P and PCSK9, cleave after non-basic residues.  All nine members undergo post-translational modifications in which the N-terminal prodomain of the enzyme is autocatalytically cleaved but remains bound to the mature protein, acting as a molecular chaperone and inhibitor (331).  Full enzymatic activity of the PCs is achieved outside of the ER by a second autocatalytic cleavage event that is spatially and temporally controlled, releasing the prodomain (333, 334).  PCs play crucial roles in normal host cell physiology with in vivo knockouts (KO) of furin, PC5/6, and SKI-1/S1P resulting in a lethal phenotype in mice, while PC4 KO mice have partial lethality and PC1/3, PC2, and PC7 show multiple defective phenotypes (333, 335).   1.5.1 Processing of viral envelope glycoproteins by proprotein convertases In addition to processing numerous cellular proproteins (330), host PCs are hijacked by a variety of human enveloped viruses for proteolytic processing of viral glycoproteins (336).  In addition to infA HA processing, one of the first viral glycoproteins found to be cleaved by furin was the HIV-1 gp160 glycoprotein, which is processed into gp120 and gp41 at the furin cleavage consensus sequence R-X-K/R-R (337).  Furin has also been shown to process glycoproteins on paramyxoviruses (cleavage of F0 into F1 and F2 subunits), alphaviruses (p62 maturation), DENV (cleavage of the prM protein from the E protein), and filoviruses (cleavage of the GP   31 protein), making it an important player in a number of human pathogens (269, 326, 337-342).  In contrast to furin and furin like PCs (usually PC5/6) that cleave at multi-basic residues, SKI-1/S1P recognizes the consensus cleavage site: (R/K)-X-(V/L/I)-(K,F,L)↓ and has been shown to process the glycoprotein precursor of arenaviruses and bunyaviruses (336, 343, 344).  The crucial role of PCs during the lifecycle of a large number of human pathogens not only make PCs an important determinant of viral pathogenesis, but also a potential target for the development of IAAs (345).  1.5.2 Furin Discovered in 1990, furin is a ubiquitously expressed Type I transmembrane protein that catalyzes the maturation of a large number of proproteins and plays an essential role in embryogenesis (333).  The 794-amino acid human preproprotein contains numerous domains that play roles in trafficking, processing and activation of the zymogen through the secretory pathway (Figure 1.9) (333, 346).  A 24 residue signal peptide directs the protein through the ER while the 83 residue prodomain is autocatalytically cleaved in the ER and remains associated as a chaperone and inhibitor until the furin-proprotein complex reaches the mildly acidic trans-Golgi network leading to a second autocatalytic cleavage event producing the mature enzyme (334).  The 330 residue catalytic domain contains the catalytic triad Asp153-His194-Ser368 that hydrolyzes the peptide bond at the consensus motif R-X-K/R-R and K-X-K/R-R, and oxyanion-hole residue Asn295 that stabilizes the reaction intermediate, which defines furin as a member of the subtilase family of serine proteases (332, 333, 347, 348).  The 140 residue P domain is necessary for enzymatic activity while the 115 residue cysteine (Cys) rich region has no established role (349).  It is followed by the 23 residue trans-membrane domain and 56 residue cytosolic domain that contains sorting information directing furin through the trans-Golgi   32 network to endosomal compartments and the cell surface (346).  Furin has been implicated in the cleavage of a number of proproteins from serum proteins, hormone and growth factors, cell surface receptors, extracellular matrix proteins, bacterial toxins and viral envelope proteins (346).  Due to its role in numerous disease-related processes, furin is a potential drug target, especially during virus infection.  Inhibition of furin using irreversible chloromethylketones (CMKs) was investigated for both HIV-1 and HP infA (269, 337).  In addition to the CMKs, α1-antitrypsin, serine protease inhibitors (serpins) isolated from Drosphila melanogaster, and additional peptide-based inhibitors have been characterized as potent furin inhibitors with the potential to treat a number of infectious diseases (345, 350-352).   1.5.3 Proposed regulation of furin by miRNAs Whereas the identification of naturally occurring furin inhibitors in the human genome is still illusive, recent research has demonstrated that miR-24 plays a role in the feedback loop associated with TGF-ß processing in HeLa and human trabecular meshwork cell cultures by targeting furin (353, 354).  TGF-ß is a cytokine from a large family of receptors and ligands that regulate a number of cellular processes.  TGF-ß is regulated in a cell type and stimulation specific manner and is initially expressed as a proprotein that is comprised of a mature form and latency associated peptide (353). Cleavage of the proprotein by furin-like proteases is a required processing step before secretion out of the cell (355, 356).  MiR-24 levels are indirectly maintained by high levels of latent TGF-ß to reduce furin expression and accumulation of latent TGF-ß precursors (353).  Our bioinformatics analysis suggests that miR-24 is not the only miRNA that can target furin, with over 10 unique miRNAs that can potentially bind within the furin 3’UTR (Figure 1.10).  Further investigation of these miRNAs may provide important new   33 molecular insights into the regulatory mechanism of furin activity in host cells and provide addition tools for modulating furin and targeting specific infectious diseases. 1.6 Research hypothesis and rationale MiRNAs repress the expression levels of genes by binding to mRNA transcripts, resulting in translational repression and acting as master regulators of cellular processes.  Differential expression of miRNAs occurs during infection with numerous human pathogens and adds an additional layer of complexity to understanding host-pathogen interactions (77).  As discussed, for some viruses, the role of specific cellular or virally encoded miRNAs has been well characterized.  For example, the herpes virus family contains numerous virally encoded miRNAs that are associated with the establishment of latency, immune evasion, and can contribute to virally induced cancers (77).  Additionally, for HCV, the cellular miRNA miR-122 interacts directly with the viruses RNA genome to enhance viral translation and replication of the virus, and inhibition of miR-122 during HCV infection is being studied as a possible antiviral therapeutic against the virus (125, 127, 147).  In contrast, limited biological and molecular information has been reported on the potential role of cellular miRNAs in the lifecycle of infA viruses.  In the past 15 years, HP avian infA viruses have been frequently transmitted to humans with a mortality rate that can reach 60%, and may have the potential to cause a new infA pandemic.  Additionally, yearly epidemics of seasonal human infA viruses (H1N1 and H3N2) overload public health systems.  To date, we still do not fully understand the underlying mechanism causing the extreme virulence and pathogenesis seen in avian infA infection, compared to the seasonal virus strains, although it is partly due to the genetic makeup of the virus.  We therefore hypothesized that miRNAs play a significant role during infection with infA   34 virus by influencing the expression of specific cellular or viral mRNAs in a strain-dependent manner.   1.6.1 Aim 1 During a virus infection, cellular miRNA expression is extensively altered due to both antiviral defenses and viral factors that modify the cellular microenvironment.  Our approach began with investigating the role of host cell miRNAs in human lung epithelial cells (A549 cells) during pandemic 2009 H1N1 and HP avian H7N7 infA virus infection.  In Chapter 2, we hypothesized that elucidating the miRNA expression signatures induced by the low-pathogenic pandemic H1N1 (2009) and HP H7N7 (2003) infA strains infections could reveal temporal and strain-specific miRNA fingerprints during the viral lifecycle, shedding important insights into the potential role of cellular miRNAs in host-infA interactions.  By identifying the specific and dynamic molecular phenotypic changes (microRNAome) triggered by LP and HP infA infection in human cells, we aimed to identify strain-specific host molecular responses involving different combinatorial contributions of multiple cellular miRNAs.   1.6.2 Aim 2 For infA viruses, cleavage of the hemagglutinin precursor glycoprotein HA0 into covalently joined HA1 and HA2 subunits is necessary for infectivity.  Unlike most infA viruses, HP H5 and H7 influenza viruses contain a multi-basic (R-X-R/K-R) HA cleavage site that is processed intracellularly by the PC furin before virus assembly.  Furin is a validated target of the cellular miRNA, miR-24, and we sought to explore this relationship further in the context of HP H5N1 infA infection.  Up-regulation of miR-24 resulted in a decrease in furin mRNA and protein levels in HeLa cells.  Our data from Chapter 2 demonstrated that miR-24 was down-regulated post infection with the HP H7N7 strain but not the LP H1N1 strain.  This indicated a   35 possible connection between the requirement for furin mediated cleavage of the HA precursor and miRNA expression during infection.  In Chapter 3 we hypothesized that overexpressing miR-24 in A549 cells during infection with a HP H5N1 strain would reduce furin levels and block processing of the furin-mediated activation of the HA protein, leading to a reduction in infectious virus released from the cell.  With furin also mediating cleavage of numerous other viral glycoproteins, the role of miR-24 may be an important player in numerous virus lifecycles.   1.6.3 Aim 3 With the potential for multiple miRNAs to regulate a single mRNA target, we were interested in investigating novel human miRNAs that may target furin.  Our bioinformatics approaches identified additional human miRNA binding sites within the human furin 3’UTR.  Additionally, we identified miRNA binding sites in the 3’UTR’s of two additional human PCs that play important roles in the lifecycles of other viruses: PCSK9 and SKI-1/S1P.  In Chapter 4, we hypothesized that these human miRNAs could act as novel regulators of furin, PCSK9 or SKI-1/S1P.  Regulation of host proteases by novel miRNAs could identify additional mechanisms of miRNA-mediated control of cellular genes during viral infection and provide new insight into host-pathogen interactions and alternative therapeutic avenues for antiviral therapy.        36 Table 1.1.  Influenza A hemagglutinin consensus cleavage sites             Virus  Strains   Cleavage  site  sequences  (HA1/HA2)  H1N1  2009  human  consensus  sequence   ATGLRNVPSIQSR/GLFGAIAGFI    H5N1  2005  avian  consensus  sequence   ATGLRNSPQRERGRRKKR/GLFGAIAGFI    H7N7  2003  avian  consensus  sequence   ATGMKNVPEIPKRRRR/GLFGAIAGFI    H7N9  2013  human  consensus  sequence   KNVPEIPKGR/GLFGAIAGFI    H7N3  human  consensus  sequence   VPENPKDQASQHRMTR/GLFGAIAGFI    H7N3  avian  consensus  sequence   VPENPKQAYQKRMTR/GLFGAIAGFI    H9N2  human  consensus  sequence   RNVPARSSR/GLFGAIAGFI    H9N2  avian  consensus  sequence   LAVGLRNVPSRSSR/GLFGAIAGFI      37 Table 1.2. Approved and investigational antiviral agents for influenza A virusa  Drug name  Target Stage of development Company Relenzab (Zanamivir)  NA inhibitor Approved GlaxoSmithKline, UK Tamifluc (Oseltamivir)  NA inhibitor Approved Hoffman-LaRoche, Switzerland Flumadined (Rimantadine)  M2 inhibitor Approved Forest Pharmaceuticals, USA Symmetrele (Amantadine)  M2 inhibitor Approved Endo Pharmaceuticals, USA Favipiravir (T-705)  Polymerase inhibitor Phase 2 in Japan Toyama Chemical, Japan Peramivirf  NA inhibitor Licensed in Japan, Phase 2/3 clinical trials in the US BioCryst Pharmaceuticals, USA Laninamivirg  (CS-8958)  NA inhibitor (long lasting) Phase 3 completed Biota Triple combination therapy  Combination chemotherapy Phase 1-2 Adamas Pharmaceuticals, Inc., USA Hyper immune serum  HA neutralization Phase 1-2 Various Fludase (DAS181)  Influenza receptor inactivator Phase 1 NexBio, Inc., USA Corticosteroids  Anti-inflammatory Phase 3 (suspended) University of Versailles, France Rosuvastatinh  Cholesterol biosynthesis pathway inhibitor Phase 3 Vanderbilt University, USA                                                  a Table adapted from Govorkova et al. b This image is in the public domain (http://commons.wikimedia.org/wiki/File:Zanamivir.svg) c This image is in the public domain (http://commons.wikimedia.org/wiki/File:Strukt_vzorec_oseltamivir.PNG) d This image is in the public domain (http://commons.wikimedia.org/wiki/File:Rimantadine_no_stereo.svg) e This image is in the public domain (http://commons.wikimedia.org/wiki/File:Amantadine_stereo.png) f This image is in the public domain (http://commons.wikimedia.org/wiki/File:Peramivir.svg) g This image is in the public domain (http://commons.wikimedia.org/wiki/File:Laninamivir.svg) h This image is in the public domain (http://commons.wikimedia.org/wiki/File:Rosuvastatin-Formulae_V_1.png) OO OHN N NN OHO OHHO HH HHHH  38    RNA Pol IIm7G AAAAnPri-miRNADGCR8DroshaPre-miRNATRBPDicerAgo2exportin 5Ran GTPGDPmiRNA:miRNA*duplexunwindTRBPDicer Ago2ORFTRBPDicer Ago2TRBPDicer Ago2miRISC complexRibosomeTranslational repressionTRBPDicer Ago2miRISC miRISC71234 568NucleusCytoplasm  39 Figure 1.1 MiRNA biogenesis pathway 1. MiRNAs are transcribed from the genome by RNA polymerase II into pri-miRNAs that contain both a 5’cap and poly(A) tail.  2. The pri-miRNA is processed by the nuclear RNase Drosha and its co-factor DGCR8 to produce pre-miRNAs, smaller hairpin RNA structures that lack both a 5’ cap and poly(A) tail.  3. The pre-miRNAs are exported out of the nucleus to the cytoplasm by exportin-5 and its co-factor Ran by hydrolyzing GTP to GDP.  4. In the cytoplasm, the pre-miRNA is processed by the cytoplasmic RNase Dicer, Ago2, and TRBP into a miRNA-miRNA* duplex.  5. The miRNA duplex is unwound and the guide strand remains associated with Dicer, Ago2 and TRBP, while the passenger strand (miRNA*) is degraded.  6. The guide strand, in association with Dicer, Ago2 and TRBP make up the miRNA Induced Silencing Complex (miRISC).  7. MiRISC binds to the 3’UTR of target mRNAs with partial homology.  8. Binding of miRISC to a target mRNA results in translational repression.       40    NHOHOHCOOHOOC3HHOOHOHN HOHOH COOHOOC3HHOOHOHNHOHOHCOOHOOC3HHOOHOH1 2345678910EndosomeNucleusCytoplasmTrans-golgi networkMatrix protein (M1)Viral Ribonucleoproteins (vRNP)M2 ion channelHA glycoproteinNA glycoproteinPolymerase Complex (PB1, PB2, PA)  41 Figure 1.2. Influenza A virus lifecycle 1. InfA virus entry is mediated by the HA glycoproteins binding to host cell sialic acid residues, triggering endocytosis.  2. Endocytosis of the virion and acidification of the endosome results in a conformational change to the HA glycoprotein, mediating fusion of the virus membrane to the endosome membrane, releasing viral RNA into the cytoplasm.  3. The viral RNA is transported to the nucleus.  4. The viral RNA is transcribed into viral mRNA by the associated viral polymerase complex.  5. Viral mRNA is translated by cellular ribosomes.  6. Newly synthesized viral polymerase proteins along with nonstructural proteins, and nucleoprotein is transported back to the nucleus.  7. The structural proteins, HA NA, and M2 are transported through the secretory system (trans-Golgi network) where they acquire post-translational modifications (glycosylation, etc.).  8. Accumulation of polymerase complex and NP proteins in the nucleus initiates a switch from transcription of viral mRNA to replication of new vRNPs.  9. The newly synthesized vRNPs are transported out of the nucleus by the NEP.  10. The vRNPs and structural proteins accumulate at the apical membrane for assembly and budding.      42  Figure 1.3. Influenza A virus glycoproteins: hemagglutinin (HA) and neuraminidase (NA) The HA glycoprotein is a 566 amino acid (aa) protein and is the most abundant glycoprotein on the surface of the virion.  HA is post-translationally modified by numerous glycosylation sites (indicated by G).  The glycoprotein is comprised of two subunits, HA1 (blue) and HA2 (pink) that make up the single chain HA0 precursor protein that is assembled into trimers.  The glycoprotein is cleaved at aa 344, with the fusion peptide making up the N-terminus of the HA2 subunit.  The 10 aa fusion peptide (yellow) is highly conserved among all infA HA glycoproteins.  The NA glycoprotein is assembled into a tetramer and contains numerous glycosylation sites (indicated by G).  The glycoprotein is anchored to the virion at the NH2 terminus via the trans membrane domain (TM) (light blue-aa 7-34).  The stalk region (green) is thin and can be of variable length.  A single mutation at aa 275 from a histidine to a tyrosine (H275Y) can confer resistance to oseltamivir, one of the approved infA antiviral agents.  HA1 HA2N CGFusion PeptideGLFGAIAGFIaa 345-3551 56620 546HAHA0GG G G G GCytoplasmic Tailaa 1-6TM1Apical Signalaa11-337-3435         83Stalk Neuraminidase469H275YT-Cell Epitope456NAGGGGGGG G  43    PB1 Subunit Binding PB1 Subunit BindingNLSNLSNP Subunit Interaction aa 1-269 NP Subunit Interaction  aa 580-6831 124 242-252 449-495 538 577736-739759Cap Binding DomainCap Binding DomainPB2627PB1-F2 aa 40-127PA Binding NLS1 48 187-211 249-256 444-446 757508-522 571-572 659PB2 Binding PB1vRNA Binding Domainaa 1-83 vRNA Binding Domainaa 493-7573’ vRNA Binding5’ vRNA BindingSDD MotifEndonuclease Site1 716124-139 186-247 668NLS PB1 BindingNLSPANP1NLSRNA Binding SiteNP-NP Interactionaa 185-336NP-NP Interactionaa 371-465NAS13 180198-216336-345PB2 Interaction Domain498NLS  44 Figure 1.4. Influenza A virus polymerase proteins: PB2, PB1, PA, and NP The infA polymerase complex is made up of the three largest gene segments: PB2, PB1 and PA.  PB2 functions during initial viral mRNA transcription as the protein that recognizes and binds the 5’cap structures of host cell mRNAs (cap binding domains – purple).  The aa at position 627 plays a role in host range determination and adaptation.  The PB1 protein functions as a classic RNA dependent RNA polymerase responsible for polymerization and endonuclease cleavage.  The smallest influenza protein, PB1-F2, is encoded in an alternate reading frame of the PB1 gene that consists of amino acids 40-127.  The PA protein, the third gene segment, is involved in transcription, replication, and proteolysis of both viral and host proteins.  The NP protein, segment 5, binds the individual RNA segments and associates with the polymerase complex to form the viral ribonucleoprotein (vRNP) complex.  All four gene segments have nuclear localization signals (NLS) and subunit binding or interaction sites to associate with other members of the complex.      45  Figure 1.5. Influenza A virus matrix proteins: M1 and M2 The seventh gene segment of infA virus encodes the M proteins, M1 and M2, generated by alternative splicing in the nucleus.  M1 is the most abundant viral protein in mature virions and forms a nucleocapsid between the lipid bilayer and vRNPs.  The M2 protein, a 97 amino acid tetramer, forms a pH gated proton channel.  The four trans membrane helices form a channel that contains a pH sensor and gate with amino acids H37 and T41, respectively.    M1aa 101-105164 252NLS/NEP Binding Site1vRNP Binding Domain1 97TM Domainaa 25-43pH Sensor H37Gate W41M2  46  Figure 1.6. Influenza A virus nonstructural proteins: NS1 and NEP The smallest gene, segment 8, is the nonstructural gene that encodes two different protein products, NS1 and NEP.  The NS proteins are the only ones not incorporated into the virion.  Both NS1 and NEP contain NLS domains along with nuclear export signals (NES).  The NS1 also contains a nucleolar localization signal (NoLS) at the C-terminus.  Residues in the RNA binding domain are implicated in inhibition of RIG-I, while the effector domain predominantly mediates interactions with host-cell proteins such as PKR, ultimately helping to suppress innate immune responses (227).  The NEP protein mediates export of the vRNPs out of the nucleus via the M1 protein using the CRM1 nuclear export pathway.       NLSaa35-41 NLS aa 231-232NLS        aa 219-220RNA Binding Domainaa 1-73E!ector            Domainaa 224-229NoLSaa 138-147NES207 234791NS1W87 M1 Binding Site1NES11-23121E!ector Domainaa 1-30NEP  47    Fusin PeptideHA2-stalk region(blue)HA1-globular head region(yellow)Cleavage SiteN CG GGG G G G GFusion PeptideGLFGAIAGFIaa 345-3551 56620 546HA0HA1NG GGG G G1 20HA2 CG GFusion PeptideNH2-GLFGAIAGFIaa 345-355566546COOH NH2S - SHA Cleavage Site  48 Figure 1.7. Schematic representation and structural features of the HA glycoproteina A protein map demonstrating cleavage of the HA0 precursor into the HA1 and HA2 subunits and in silico homology model of the infA HA0 precursor protein (PDB ID: 1HA0) displaying the basic structural features associated with the individual HA trimers (357-359).  HA1 (yellow) forms the globular head of the individual trimer, while HA2 (blue) forms the stalk region.  The fusion peptide is located at the N-terminus of the HA2 subunit and contains the highly conserved 10 aa sequence GLFGAIAGFI.  The two subunits remain associated via a disulfide bond.                                                  a Advanced permission provided by http://www.rcsb.org/pdb/static.do?p=general_information/about_pdb/policies_references.html   49  Figure 1.8. Alternative processing of HA glycoprotein in the trans-Golgi network by furin Select H5 and H7 infA strains that contain a multi-basic cleavage site within the HA glycoprotein are processed by intracellular furin-like proteases in the trans-Golgi network.  This is in contrast to LP viruses that are assembled prior to HA cleavage and rely on extra cellular and membrane bound proteases for HA processing following budding of the new virions.  Cleaved HA trimers of HP viruses accumulate at the cell surface and are incorporated onto newly assembled virions before budding.  Cleavage of the HA0 precursor protein into the activated HA1 and HA2 subunits, revealing the fusion peptide, prior to assembly, can result in systemic infection and is a hallmark of HP infA virus infection.    FURINTrans-Golgi NetworkHA Cleavage in the TGN  50  Figure 1.9. Schematic representation and structural features of furina A protein map and in silico homology model of the PC furin (PDB ID: 1P8J) displaying the basic structural features associated with the peptide (357, 358, 360).  Furin encodes an N-terminal signal peptide (aqua) followed by a propeptide (orange), which acts as a chaperone and inhibitor of enzymatic activity.  Removal of the propeptide by an autocatalytic cleavage event is necessary for full enzymatic activity.  The catalytic domain (blue) contains the catalytic triad (D153-H194-S368) and oxyanion hole (N295) residues.  The P domain (pink) is critical for enzymatic activity while the Cys-rich domain (yellow) has no defined role.                                                    a Advanced permission provided by http://www.rcsb.org/pdb/static.do?p=general_information/about_pdb/policies_references.html Catalytic TriadAsp153-His194-Ser368Oxyanion-hole residueAsn295Catalytic Domain(Blue)P Domain(Pink)7941Signal Peptide24 107PropeptideTransmembraneDomain aa 716-738Cys-rich domainTrans-Golgi Network Signalaa 773-779570P Domain442Catalytic DomainD705H S153 194 368N295  51    1    UGAGCCCACUGCCCACCCCCUCAAGCCAAUCCCCUCCUUGGGCACUUUUUAAUUCACCAAAGUAUUUUUUUAUCUUGGGACUGGGUUUGGACCCCAGCUG 100101  GGAGGCAAGAGGGGUGGAGACUGCUUCCCAUCCUACCCUCGGGCCCACCUGGCCACCUGAGGUGGGCCCAGGACCAGCUGGGGCGUGGGGAGGGCCGUAC 200201  CCCACCCUCAGCACCCCUUCCAUGUGGAGAAAGGAGUGAAACCUUUAGGGCAGCUUGCCCCGGCCCCGGCCCCAGCCAGAGUUCCUGCGGAGUGAAGAGG 300301  GGCAGCCCUUGCUUGUUGGGAUUCCUGACCCAGGCCGCAGCUCUUGCCCUUCCCUGUCCCUCUAAAGCAAUAAUGGUCCCAUCCAGGCAGUCGGGGGCUG 400401  GCCUAGGAGAUAUCUGAGGGAGGAGGCCACCUCUCCAAGGGCUUCUGCACCCUCCACCCUGUCCCCCAGCUCUGGUGAGUCUUGGCGGCAGCAGCCAUCA 500501  UAGGAAGGGACCAAGGCAAGGCAGGUGCCUCCAGGUGUGCACGUGGCAUGUGGCCUGUGGCCUGUGUCCCAUGACCCACCCCUGUGCUCCGUGCCUCCAC 600601  CACCACUGGCCACCAGGCUGGCGCAGCCAAGGCCGAAGCUCUGGCUGAACCCUGUGCUGGUGUCCUGACCACCCUCCCCUCUCUUGCACCCGCCUCUCCC 700701  GUCAGGGCCCAAGUCCCUGUUUUCUGAGCCCGGGCUGCCUGGGCUGUUGGCACUCACAGACCUGGAGCCCCUGGGUGGGUGGUGGGGAGGGGCGCUGGCC 800miR-103||||||| ||||||||||||| ||||||miR-125b|||||| miR-125b|||||| ||||||||||| miR-20amiR-17miR-22||||||| miR-22||||||miR-22||||||miR-24|||||| miR-24|||||| miR-24||||||  miR-24|||||||||||||||  miR-31|||||| miR-31Predictions from overlap of 3 algorithms in mirnabodymapmiR-20amiR-17 miR-20amiR-17miR-106a,106bmiR-93miR-20a,20bmiR-17Target sites of converved miRNAs with good mirSVR scores - microRNA.org||||||||| miR-519dmiR-9||||||| miR-137|||||||| |||||||miR-424miR-497miR-15a,15bmiR-195miR-16||||||||miR-125bmiR-125a-5p miR-371-5p|||||||miR-519dmiR-106a,106bmiR-93miR-20a,20bmiR-17||||||miR-203miR-495||||||miR-192miR-506|||||||||||||||miR-124miR-19a,19b|||||||miR-219-5p||||||||miR-338-3p||||||||miR-153|||||||||miR-2151501 CAUUGCUGGUUCUAUUUAAUGGACAUGAGAUAAUGUUAGAGGUUUUAAAGUGAUUAAACGUGCAGACUAUGCAAACCAG 15791401 UCCUCUCCCAUCCCCCUGCCCUCGUGGCCAGCCCGGCUGGUUUUGUAAGAUGCUGGGUUGGUGCACAGUGAUUUUUUUCUUGUAAUUUAAACAGGCCCAG 15001301 CCUCCCAUUAGGACAAUCAGUCCCCUCCCAUCUGGGAGUCCCCUUUUCUUUUCUACCCUAGCCAUUCCUGGUACCCAGCCAUCUGCCCAGGGGUGCCCCC 14001201 GGUGGUGGCACUGAGCCCCCCCAACACUGUGCCCUGGUGGAGAAAGCACUGACCUGUCAUGCCCCCCUCAAACCUCCUCUUCUGACGUGCCUUUUGCACC 13001101 GGUCCCAGUGGGAGGGGCAGGCUGACAUCUGUGUUUCAAGUGGGGCUCGCCAUGCCGGGGGUUCAUAGGUCACUGGCUCUCCAAGUGCCAGAGGUGGGCA 12001001 CCCUGAGGUGUGGGGGCUGCAGCAUGUUGCUGAGGAGUGAGGAAUAGUUGAGCCCCAAGUCCUGAAGAGGCGGGCCAGCCAGGCGGGCUCAAGGAAAGGG 1100901  GAUGCUGAUGAUUUGUUUUUGUAUUUUUAAUGGGGGUAGCAGCUGGACUACCCACGUUCUCACACCCACCGUCCGCCCUGCUCCUCCCUGGCUGCCCUGG 1000801  CAGCCGGCCUCUCUGGCCUCCCACCCGAUGCUGCUUUCCCCUGUGGGGAUCUCAGGGGCUGUUUGAGGAUAUAUUUUCACUUUGUGAUUAUUUCACUUUA 900miR-192 Target sites from microCosmmiR-125b||||||||  miR-135a*|||||miR-215 miR-483-3p||||||||  ||||||||miR-200a*miR-328|||||||  miR-220b||||||||  miR-483-3p|||||miR-602|||||miR-137||||||||  miR-635miR-133a,133b||||||||  miR-153miR-219-5p||||||||miR-183*|||||||||miR-18b*  miR-192  52 Figure 1.10. Predicted miRNA binding sites in the furin 3’UTR Utilizing 3 different online miRNA prediction databases: mirnabodymap (www.mirnabodymap.org) (blue), microRNA.org (ww.microrna.org) (red), and microCosm (http://www.ebi.ac.uk/enright-srv/microcosm/htdocs/targets/v5/) (green), a large number of miRNAs were identified with predicted binding sites in the 3’UTR of the PC furin.    53 Chapter 2: Temporal- and strain-specific host microRNA molecular signatures associated with swine-origin H1N1 and avian-origin H7N7 influenza A virus infection 2.1 Introduction MicroRNAs (miRNAs) are small endogenous, non-coding RNAs that are highly conserved and that have been recognized as a powerful tool for regulating gene expression through the RNA interference pathway (1, 2).  The human genome encodes over 1000 miRNAs (miRBase V.16) that play key roles in diverse regulatory pathways, forming a complex network that is predicted to regulate over 50% of protein coding genes (40). With the ability of one miRNA to bind and regulate numerous mRNAs and the potential for a single mRNA to be targeted by multiple miRNAs, it is possible to fine-tune the expression of proteins within the cell in a very precise manner (60).  The deregulation of miRNA expression profoundly alters the gene expression in the cell and has been associated with many human pathologies (76).   In the case of viral infections, altered miRNA expression can be beneficial and/or detrimental to the viral lifecycle, and it can also influence disease progression and outcome (77, 93).  Human DNA viruses, most notably the herpesviruses, encode over 200 viral miRNAs that can control viral gene expression and modulate cellular gene expression to allow for immune evasion and establishment of latency (77, 93).  For the human RNA viruses such as hepatitis C virus (HCV) and human immunodeficiency virus (HIV)-1, modulation of host miRNAs influences viral pathogenesis (93).  Throughout HCV infection, a liver-specific miRNA, miR-122, increases the accumulation and translation of HCV RNA by binding to the 5’ UTR of the virus genome (125-128).  During HIV-1 infection, cellular miRNAs expressed in resting CD4+ T   54 lymphocytes were shown to negatively impact viral protein production and possibly contribute to HIV-1 latency, while miR-29a has been shown to directly target HIV-1 mRNAs leading to accumulation of viral mRNA at P-bodies for translational suppression (120, 122).  Viruses also modulate the stability of host miRNA levels in order to interfere with host-cell gene expression.  Such is the case with the non-coding uridine-rich RNA HSUR1 produced by herpesvirus saimiri, which has been shown to base pair extensively with the cellular miR-27, resulting in reduced stability and decreased levels of this miRNA within virally transformed T cells (361).  Most recently, it was demonstrated that miR-141 was induced upon enterovirus 71 (EV71) infection and found to target eIF4E, a key element in cap-dependent translation.  Antagonizing miR-141 during EV71 infection dramatically reduced virus production, suggesting that expression of this cellular miRNA is important for the virus lifecycle (124).   In contrast, the available research to date provides limited information on the role that miRNAs play during infection with influenza A (infA) viruses.  A microarray study of miRNA expression in mice during infection with the reconstructed 1918 (r1918) H1N1 virus and a seasonal H1N1 virus (Tx/91) was the first to report that the miRNA-ome was modulated during infA infection (139).  Subsequent work on the HP avian H5N1 infA strain, which can result in severe disease characterized by a systemic, rather than local, infection and major cytokine deregulation, further suggests that infA viruses of varying pathogenicity elicit distinct miRNA expression patterns during infection (141, 280).  In this study, we hypothesize that elucidating the miRNA expression signatures induced by swine-origin infA virus (S-OIV) pandemic H1N1 (2009) and avian-origin (A-OIV) H7N7 (2003), the latter associated with multiple cases of conjunctivitis and one case of acute respiratory distress syndrome in humans (295) could reveal temporal and strain-specific miRNA   55 fingerprints during the viral lifecycle.  Using a high throughput microfluidic microarray platform, we profiled cellular miRNA expression in human epithelial A549 cells infected with S- and A-OIVs at multiple time points during the viral lifecycle, including global gene expression profiling during S-OIV infection.  We performed quantitative reverse transcription PCR (qRT-PCR) to validate the miRNA chip results.  We used target prediction and pathway enrichment analyses to identify key cellular pathways associated with the differentially expressed miRNAs and inversely correlated mRNAs during pandemic S-OIV H1N1 and HP A-OIV H7N7 infection.  Our identification of specific and dynamic molecular phenotypic changes (microRNAome) triggered by S- and A-OIV infection in human cells provides robust experimental evidence demonstrating a series of strain-specific host molecular responses involving different combinatorial contributions of multiple cellular miRNAs.  Overall, our results demonstrate a series of complex temporal- and strain-specific host microRNA molecular signatures associated with swine-origin H1N1 and avian-origin H7N7 infA virus infection, and they also identify novel potential secretory miRNA biomarkers associated with pandemic S-OIV and deadly A-OIV-host infections. 2.2 Materials and methods Virus strains and cell lines – All experiments with live influenza virus were performed at the National Center for Foreign Animal Diseases under biosafety level 3 (BSL3+) conditions.  The infA virus strains used in this study were A/Mexico/InDRE4487/2009 (H1N1) and A/Chicken/Germany/R28/2003 (H7N7).  A/Mexico/InDRE4487/2009 (H1N1) stocks were propagated on MDCK (Madin Darby canine kidney) cells and A/Chicken/Germany/R28/2003 (H7N7) virus stocks were propagated on QT-35 cells.    56 Virus infections – A549 cells were infected with the H1N1 and H7N7 viruses at a multiplicity of infection (MOI) of 0.1.  In contrast to HP A-OIV H7N7 (23, 58), infection with S-OIV 2009 H1N1 was performed in the presence of 1 µg/ml TPCK-trypsin (268, 327, 362).  Cell supernatant and RNA were collected at 0, 4, 8, 24, 48, and 72 hours post-infection (hpi).  The 0-hour time point corresponds to samples collected immediately after the 1-hour virus incubation with additional time points numbered with regard to the end of viral incubation.  Mock-infected A549 cells were propagated for each experiment with samples collected at the 72-hour time point.  H1N1 virus titration was done by plaque assays on (MDCK) cells.  RNA isolation – Total RNA was isolated using the miRvana miRNA isolation kit (Ambion) following the instructions of the supplier.  The concentration of RNA was determined by a Nanodrop ND-1000 Spectrophotometer (Thermo).   miRNA expression profiling – MiRNA expression profiling was analyzed with the Geniom Real-Time Analyzer (GRTA, Febit GMBH, Heidelberg, Germany) using the Geniom miRNA Homo sapiens biochip.  Each array contained 7 replicates of 904 miRNAs and miRNA star sequences as annotated in the Sanger miRBase 14.0 (13-15).  With a total of 8 arrays per chip, the mock-infected RNA was run in duplicate (arrays 1 and 2) with the subsequent time points loaded in order (0-72) into arrays 3-8.  Sample labeling with biotin was carried out by microfluidic-based enzymatic on-chip labeling of miRNAs (MPEA) (363).  In brief, following hybridization of the miRNA with the Geniom biochip for 14 hours at 42°C, the biochip was washed automatically and a program for signal enhancement was processed with the GRTA.  The detection pictures were evaluated using the Geniom Wizard software.  The raw data values were background-corrected using the median of blank controls.  One intensity value was retained   57 for each miRNA by calculating the median for all the corresponding replicates.  Variance stabilizing normalization (VSN) (364) was applied to normalize the data across different arrays. mRNA expression profiling – Illumina Direct Hybridization assays were performed at the Vancouver Prostate Centre Laboratory for Advanced Genome Analysis, Vancouver, BC, Canada.  Total RNA quality was assessed with the Agilent 2100 bioanalyzer, and samples with a RIN value of greater than or equal to 8.0 were deemed suitable for microarray analysis.  An input of 200 ng of total RNA was used to generate biotin-labeled cRNA following the Illumina TotalPrep RNA Amplification Kit (Ambion, Inc.).  Samples were hybridized on Illumina HumanHT-12 v3 BeadChips following the Illumina Whole-Genome Gene Expression Direct Hybridization Assay Guide (11286331).  BeadChips were imaged and quantified with the Illumina iScan scanner.  Illumina GenomeStudio v2010.2 was used for data processing, which included averaging signal intensities for each unique Beadtype.  GeneSpring 7.3.1 (Agilent Technologies) was used to median-normalize data to the 25th percentile.  Statistical analysis – Quality assessment for the normalized array data was carried out using the package arrayQualityMetrics (365) in the R statistical software (366). The H1N1 RNA in the uninfected samples in chip 1 and the hour 4 samples in chip 2 were found to be degraded during the mRNA profiling; therefore, they were excluded from the statistical analysis.  The limma (367) R package was used to identify differentially expressed miRNAs or mRNAs by using a fold change of 2 and a nominal p-value of 0.05 as the filtering criteria. Hierarchical clustering was performed using average linkage clustering with Euclidean distances, treating samples independently of each other.  Identification of miRNA-mRNA binding pairs – MiRNA target prediction studies were carried out using the miRanda (version 3.3a) algorithm (368, 369), restricting the search to   58 miRNA 5’ seed pairing and using a score cut off of 100.  Additionally, we applied the GenMiR++ algorithm (370) to the H1N1 miRNA and mRNA expression profiles to obtain confidence scores for each miRNA-mRNA prediction (details in the appendix).  An interaction network was generated for each time point, where an edge in the network fulfills the following criteria: 1) it connects two nodes that correspond to a differentially expressed miRNA or mRNA at that time point, 2) the interaction is predicted by miRanda, 3) the interaction is ranked in the top 50% of the highest GenMiR++ scores, and 4) the regulation of the nodes linked by the interaction is antagonistic at that time point.  The miRNA-mRNA relationships were visualized using Cytoscape (371).  Pathway enrichment analysis – Pathway analysis for predicted mRNA targets from the H1N1 miRNA-mRNA networks was carried out using the InnateDB database (372).  A nominal p-value of 0.01 was used to determine significantly enriched pathways.  Pathway enrichment for the H7N7 miRNAs was carried out using the R package miRNApath (373) with pathways from the InnateDB database.  A p-value threshold of 0.01 was used to determine enriched predicted pathways.  Pathway ontology was used to cluster pathways into categories.  For H1N1, Fisher’s exact test was used to calculate the overlap between the starting list of mRNAs and the set of mRNA molecules present in a pathway category.  For H7N7, the overlap was calculated between predicted miRNA-mRNA interactions for differentially expressed miRNAs and the set of miRNA-mRNA interactions present in an enriched pathway category.  This clustering facilitated the simplification and visualization of the change in pathway regulation during the time course of infection. Predicting miRNA-mRNA interactions - In order to build networks of potential interactions between the differentially expressed miRNAs and mRNAs from the microarray profiling, we   59 carried out analyses using miRanda (368, 369), GenMiR++ (370) and Cytoscape (371). Sequences for all miRNAs and mRNAs present in our microarray chips (904 miRNAs and 43062 RefSeq ids) were downloaded from miRBase and NCBI, respectively.  The miRanda algorithm was applied to all the potential miRNA-mRNA combinations, restricting the search to miRNA 5’ seed pairing (using the ‘strict’ option) with a score threshold of 100.  For each time point, miRNA-mRNA pairs were extracted from the miRanda predictions for all the deregulated miRNAs and mRNAs.  Additionally, we have applied the GenMiR++ algorithm to further filter the miRanda predictions.  The algorithm was applied using the following parameters: the H1N1 miRNA expression matrix (904 miRNAs x 40 samples), the H1N1 mRNA expression matrix (43062 unique RefSeq ids x 40 samples), a binary matrix of predicted interactions from miRanda (43062 x 904), individual lists with miRNA names (904), mRNA RefSeq names (43062) and sample names (40).  The analysis was carried out on Westgrid Compute Canada clusters (~12.4 hours).  Only predicted interactions that were found in the top 50% GenMiR++ scores were retained for each time point.  We have found that some interactions that were highly scored involved positively regulated miRNA-mRNA pairs and we chose to retain only the inversely correlated miRNA-mRNA pairs for further analysis.  The number of interactions after GenMiR++ filtering were reduced by 18%, 35%, 46%, 100%, 27% and 38% for hour 0, 4, 8, 24, 48 and 72, respectively.  An interaction network was drawn using Cytoscape for each time point, where the color of the nodes correlates with the degree of regulation (red: up-regulated, blue: down-regulated) and the thickness of edges is determined by GenMiR++ scores.  Pathway analysis and visualization - The predicted mRNA targets from interaction networks were subjected to pathway analysis, using InnateDB (372), in order to determine which pathways   60 are enriched in our studies.  Although these mRNAs satisfied three filtering criteria (fold change, p-value and target prediction analyses), InnateDB permits only two data columns to determine the significant set of mRNAs.  Since InnateDB uses the proportion of significant mRNAs on the entire array to calculate which pathways are enriched, it is recommended that all data from a microarray experiment is analyzed (372).  Therefore, we generated a new column such that if an mRNA fulfills all our selection criteria it was assigned a p-value of 0.01, while all other mRNAs were assigned 0.5.  Pathway analysis was carried out independently for each time point and a p-value (unadjusted) of 0.01 was used to determine significantly enriched pathways.  Pathway enrichment for the H7N7 miRNAs was carried out using the R package miRNApath (373).  The algorithm required three input tables.  First, a miRNA table was compiled for all miRNAs (904) with fold change, p-value and details of time of infection and significance.  Second, the miRNA-mRNA predictions were obtained from the interactions generated by miRanda.  Last, a table containing gene-pathway associations for all the mRNAs in our experiment was obtained from InnateDB.  The miRNApath algorithm performs enrichment analysis using the hypergeometric test.  The composite flag was used in order to treat each miRNA-mRNA combination as a separate event, without permutations.  A p-value threshold of 0.01 was used to determine enriched predicted pathways.  Examining pathway modulation over the course of six time points in order to make correlations with viral infection can be difficult when handling multiple lists of pathways.  To create an overview of enriched pathways, pathway ontology was used to cluster them into categories.  For each database source (Reactome, INOH, KEGG, NCI Biocarta), a file was obtained with information for mapping pathways to their super-pathways (e.g. Downstream signaling in naive CD8+ T cells is mapped to Immune response).  A pathway could belong to   61 more than one pathway category.  Pathway categories were drawn as a column plot, where the x-axis represents the time course of infection and the y-axis indicates the significance of a pathway category.  For H1N1, Fisher’s exact test was used to calculate the overlap between the starting list of mRNAs and the set of mRNA molecules present in a pathway category.  Pathway up/down regulation was determined by InnateDB using the mRNA fold change information from our microarray experiment.  For H7N7, the significance p-values were calculated between predicted miRNA-mRNA interactions for differentially expressed miRNAs and the set of miRNA-mRNA interactions present in an enriched pathway category (information available from miRNApath results).  In the absence of mRNA data it is not possible to map which pathways are up/down regulated, but this may be inferred from the patterns of miRNA deregulation (i.e. at early time points, the majority of pathways would be up-regulated due to the high number of down-regulated miRNAs). Quantitative real-time PCR – Real-time (RT)-PCR was carried out on total RNA collected from each time point to determine the amount of viral RNA present.  Total RNA was prepared using the miRvana miRNA isolation kit (Ambion).  Each RNA sample was evaluated for viral matrix gene transcript levels in duplicate on the Mx3005p PCR multiplex quantitative PCR instrument (Stratagene) using the Superscript III Platinum One Step Quantitative RT-PCR System (Invitrogen).  Matrix gene forward and reverse primers and probe as described by Spackman et al. were used in the H7N7 experiments (374). A modified version of this assay (362) was used to quantify viral RNA levels in pandemic H1N1 experiments.  Three samples from each time point were analyzed in triplicate.  Data are represented as the mean ± SEM. Quantitative real-time PCR (qRT-PCR) was used to validate miRNA expression changes using the Agilent Mx3005P real-time PCR system (Agilent) and TaqMan® chemistry (Applied   62 Biosystems).  Reverse transcription and qPCR primer and probe sets were performed using the TaqMan® miRNA Reverse Transcription kit and TaqMan® miRNA qPCR assays as outlined by the company (Applied Biosystems, catalogue number 4427975 with ID numbers, let-7g: 002282, miR-34c-3p: 241009_mat, miR-34b: 002102, miR-766: 001986, miR-449b*: 121215_mat, miR-30c: 000419).  Each miRNA assay was run using at least three independent RNA samples per time point in triplicate.  RNU6B was used as an endogenous control.  Data are represented as the mean ± SEM.  A two-way ANOVA/Bonferroni post-test was used to determine the interaction between time and infection as well as significance of the miRNA at each time point compared to the mock-infected control samples (p-value < 0.05).  A one-way ANOVA was used to determine significant differences between time points (e.g., 0 versus 48 hpi).  Microarray data resource – The microarray data was deposited in the Gene Expression Omnibus (GEO) database (www.ncbi.nlm.nih.gov/geo/) with the accession number: GSE36555 2.3 Results 2.3.1 Cellular miRNAs signatures in response to pandemic S-OIV H1N1 (2009) infection in human epithelial A549 cells To understand the role of human cellular miRNAs in infA infection, we profiled the expression of cellular miRNAs following infection with pandemic 2009 H1N1 infA virus.  A549 cells were infected at a multiplicity of infection (MOI) of 0.1 with total RNA samples isolated from infected cells at 0, 4, 8, 24, 48, and 72 hours post-infection (hpi).  An MOI of 0.1 was chosen to represent a physiologically relevant level of infection and allowed us to monitor cellular miRNA expression in infected cells over a long time course (from 0-72 hpi), compared to a higher MOI that would result in high cytopathic effect (CPE) in the first 24 hours, limiting the number of cells to be profiled for cellular miRNAs.  Total RNA from mock-infected samples   63 was extracted at the 72-hour time point from each of the three experimental replicates to determine basal miRNA expression.  Viral replication efficiency was determined by quantifying the expression level of the viral M gene by qRT-PCR, while infectious virus released was determined by plaque assay (Figure 2.1).  New viral RNA was detected at 4 hpi and increased 100-fold by 24 hpi.  Infectious virus particles increased by a similar amount between 8 and 24 hpi.  The amount of new viral RNA and infectious virus particles remained relatively steady after 24 hpi. Cellular miRNA expression was determined using the Febit Geniom RT Analyzer (GRTA), which uses an RNA-primed, Array-based, Klenow Extension (RAKE) assay in conjunction with microfluidic microarray technology (363, 375).  Compared to more traditional microarray platforms, the microfluidic platform on the Febit GRTA combines 8 customizable arrays on a single chip, with each array containing 7 random repeats of the 904 human miRNA and miRNA star sequences as annotated in the Sanger miRBase 14.0 (13-15, 363).  Additionally this assay uses on-chip hybridization and labeling, reducing sample quantity and handling, and it can discriminate between closely related members of the same miRNA family with significantly less cross-hybridization than other traditional microarray platforms (363).  By applying this technology to these studies, we were able to run samples from each of the 6 time points for each experiment in parallel, along with duplicate samples from the mock-infected controls.  The number of deregulated miRNAs for each of the indicated time points was determined by a fold-change threshold of 2 and a nominal p-value cut-off of 0.05 over the mock-infected RNA samples (Table A.1).  The expression of each significantly deregulated miRNA across all time points was depicted with a heatmap (Figure 2.1A).  Down-regulated miRNAs correspond to 25% of the total differentially expressed miRNAs.  At 4 hpi, 9 out of 13 miRNAs demonstrated   64 reduced expression, thereby displaying the highest number of down-regulated miRNAs compared to any other time point (Figure 2.1B).  This result contrasts drastically with up-regulated miRNAs, which make up 75% of the total differentially expressed miRNAs during pandemic 2009 H1N1 infection across all time points.  The number of up-regulated miRNAs remains relatively low for the first 24 hpi, where 9% of the total differentially expressed miRNAs are up-regulated, before dramatically increasing at 48 and 72 hpi to 91% (Figure 2.1C).  It is of importance to note that the subset of significantly down-regulated miRNAs at early time points during infection is distinct from the subset of significantly up-regulated miRNAs at late time points in infection. Comparative qRT-PCR analysis was used to further investigate the results from our microarray data.  A subset of miRNAs were selected for validation, in particular those deregulated at 72 hpi, based on pathways that were previously reported to be implicated in infA infection (e.g., cell cycle).  Using Taqman qRT-PCR miRNA arrays, we determined the fold change of multiple miRNAs over the course of infection (Figure 2.2A-F).  Each graph represents the mean absolute fold change of triplicate experiments for each miRNA at each individual time point compared to mock-infected controls collected at 72 hpi.  The significance of interaction between time and infection as well as that of specific miRNAs relative to controls was determined using two-way ANOVA with a Bonferroni post-test, while changes between time points were determined using a one-way ANOVA.  The miRNA let-7g did not show significant deregulation during H1N1 infection in this experiment, although the qRT-PCR data shows a downward trend for this miRNA at early time points (Figure 2.2A).  Significant up-regulation (p-value < 0.05, Bonferroni post-test) was observed with miR-34c-3p compared to mock-infected controls at 72 hpi (Figure 2.2B).  Additionally, while neither infection nor time   65 individually contributed to a significant source of variation, the interaction between the two was considered significant (p-value = 0.0402, two-way ANOVA), indicating that both factors are necessary for the differential expression of miR-34c-3p during H1N1 infection.  The expression level of miR-34b at 72 hpi was significantly different compared to 0, 24, and 48 hpi by one-way ANOVA; however, there was no significance attributed to either time or infection by two-way ANOVA analysis (Figure 2.2C).  Consistent up-regulation across all six time points was observed with miR-766, and two-way ANOVA confirmed that infection is the main factor in miR-766 deregulation (p-value < 0.001), accounting for approximately 56% of the total variance (Figure 2.2D).  The expression of miR-449b* changes dramatically along the course of infection (Figure 2.2E).  At early time points (0-48 hpi), the levels of miR-449b* are lower than in mock-infected cells, with 24 and 48 hpi showing significant down-regulation.  A significant shift occurs between 48 and 72 hpi, where miR-449b* becomes more abundant at 72 hpi than in uninfected cells.  The interaction between infection and time was considered significant by two-way ANOVA (p-value = 0.0001), while infection and time independently also affect the result significantly (p-values 0.0003 and 0.0001, respectively).  This is in contrast to the microarray data that showed a significant up-regulation of miR-449b* at 48 and 72 hpi.  Differences in the platforms used for analysis can lead to differences in fold changes (376), which may explain the loss of significance at certain time points in the qRT-PCR data compared to microarray data.  It has also been shown that genes can appear to be regulated in opposite directions using different platforms (376, 377).  The methods may also disagree when the miRNAs have lower expression level, due to the greater sensitivity of the qRT-PCR assay (378). In addition, different detection techniques will show variation due to mixed populations of miRNAs that can exist in the cell (363).  In light of all this, we consider the microarray results only to acquire a global picture of   66 the trends of miRNA deregulation and affected pathways, and use the qRT-PCR validations when focusing on the regulation of specific miRNAs.  The validation of miR-30c by RT-PCR did not result in significance of expression, and this was also true with the array data (Figure 2.2F).  2.3.2 Dynamic changes in the host cell miRNA-mRNA interactome induced by the 2009 pandemic influenza (H1N1) virus To study the modulation in gene expression that may be associated with specific miRNAs, we performed global transcriptome analysis in A549 cells during pandemic 2009 H1N1 infA infection with similar parameters as for the miRNA experiment.  The number and trend of deregulated genes during early time points post-infection reflected a similar pattern as that observed for the miRNAs (range 0.12-0.37%, lowest at 24 hpi).  The number of up-regulated genes starts to increase after 24 hpi and peaks at 72 hpi (1.26% out of the total genes tested).  The biological relevance of the host genes expressed during 2009 pandemic H1N1 infection was determined using gene ontology and pathway analysis from InnateDB (372) (data not shown).  As miRNAs function to suppress gene expression either by inhibiting translation or by degrading mRNA, we examined those miRNAs that satisfied the following criteria: (1) they are differentially expressed in the H1N1 miRNA microarray expression profile, (2) they are predicted to target differentially expressed mRNAs from the gene expression profile, (3) the miRNA-mRNA prediction is found in the top 50% highest scores from the GenMiR++ algorithm, and (4) the direction of their regulation is antagonistic to the predicted targets. The collection of differentially expressed mRNA and miRNA sequences was analyzed using the miRanda algorithm (version 3.3a) to determine potential miRNA-mRNA target interactions.  The miRanda algorithm identifies strong seed region base pairing between the miRNA and mRNA 3’   67 UTR (379).  High scoring targets are then filtered by predicted heteroduplex free energy (ΔG) and conservation of the predicted binding site.  The miRanda predictions were further filtered according to confidence scores obtained from GenMiR++ (370) (details in appendix). The predicted interactions between differentially expressed miRNAs and predicted target mRNAs from our microarray experiments were visualized as networks in order to depict the complex relationship associated with miRNA gene regulation, which increases dramatically from 0 to 72 hpi (Figure 2.3, Figure A.1 A-D).  The number of up-regulated miRNAs in networks increases as the infection progresses, from 0 miRNAs at 0 and 4 hpi (Figure A.1 A-B) and 3 miRNAs at 8 hpi (Figure 3) to 20 at 48 hpi (Figure A.1 C) and 28 at 72 hpi (Figure A.1 D), which correlates with targeted mRNAs being down-regulated at 48 and 72 hpi.  In contrast, the number of down-regulated miRNAs peaks at 4 hpi, with 48 and 72 hpi showing almost no down-regulation (0 and 1 miRNAs, respectively (Figure A.1 C, D)).  The 8-hpi network (Figure 2.3) shows a number of deregulated miRNAs and their predicted targets, including miR-766, that is consistently up-regulated throughout infection with H1N1 (Figure 2.2D).  A dramatic drop in differentially expressed miRNAs at 24 hpi resulted in no significant miRNA-mRNA interactions.  By 72 hpi, most miRNAs are up-regulated, correlating with down-regulated targets such as Rbl1, Cdt1, E2f7, Rrm2, Mcm4, and Mcm7.  The predicted target mRNAs from the interaction networks were explored further using InnateDB to identify pathways that are enriched in these networks and that may be relevant to infA infection (Figure 2.4).  The number of pathways and mRNAs associated with each miRNA at different time points were influenced by our prediction analysis and data published to date.  By 72 hpi, a majority of the pathways were down-regulated and were represented by genes associated with cell cycle, including but not limited to the E2F transcription factor network, CDK regulation of DNA replication, and G1/S transition (Figure   68 2.4).  Replication and repair as well as chromosome maintenance and signaling pathways were also down-regulated at 72 hpi. 2.3.3 Cellular miRNAs signatures in response to highly pathogenic A-OIV H7N7 (2003) infection in human epithelial A549 cells Since we sought to understand the impact of viruses with varying pathogenicity on host cells, we continued our study by investigating the dynamic modulation of miRNAs during infection with a HP avian H7N7 strain (Figure 2.5, Table A.2).  A549 cells were infected at an MOI of 0.1, and total RNA samples from infected cells were isolated at the indicated time points.  Viral replication efficiency was determined by quantifying the expression level of the viral M gene by qRT-PCR with new viral RNA detected by 8 hpi (Figure 2.5A).  The number of deregulated microRNAs was determined using the same parameters as for H1N1.  As with our results for the H1N1 experiment, we observed a similar trend in miRNA expression with a down-regulation of miRNA expression at early time points (0-24 hpi) and up-regulation of miRNAs at later time points (48-72 hpi).  In contrast to the H1N1 study, the number of differentially expressed miRNAs was considerably higher during H7N7 infection: a total of 121 miRNAs showed significant deregulation at some point post-infection compared with 52 miRNAs for pandemic 2009 H1N1 (Figure 2.5A).  The percentage of miRNAs that are down-regulated during infection with H7N7 reaches 54%, being distributed more densely (70% of total down-regulated miRNAs) at the early stages of infection (0-24 hpi) (Figure 2.5B).  Similar to H1N1, a stark transition is observed at 48 and 72 hpi where the percentage of up-regulated miRNAs relative to the total number of up-regulated miRNAs is higher than at earlier time points (81%)  (Figure 2.5C).    69  Comparative qRT-PCR analysis of deregulated miRNAs (up- and down-regulated) during HP H7N7 infection was used to further investigate the validity of the microarray results in the same manner as for the H1N1 study (Figure 2.6).  The miRNA let-7g was found to be significantly down-regulated at 0 and 8 hpi and infection was considered to significantly affect the miRNA deregulation (p-value < 0.0001, two-way ANOVA) (Figure 2.6A).  There was no significant up-regulation of miR-34c-3p expression at any time point by two-way ANOVA/Bonferroni post-test (p-values > 0.05) (Figure 2.6B).  While individual time points for miR-34b and miR-766 did not show differential expression relative to control by Bonferroni post-tests, the effect of time or infection on miRNA expression for miR-34b and miR-766, respectively, was considered significant by two-way ANOVA (p-values = 0.0344 and 0.0046, respectively) (Figure 2.6C, D).  Additionally, consistent up-regulation of miR-449b* across four time points (4, 8, 24, and 48 hpi) was observed (Figure 2.6E) and a significant p-value (< 0.0001, two-way ANOVA) confirmed that infection accounts for almost 59% of the total variance observed.  While not significant by two-way ANOVA, the down-regulation of miR-30c was considered significant at 8 hpi according to Bonferroni post-tests (Figure 2.6F).   Significantly expressed miRNAs during infection with H7N7 were subjected to target prediction and pathway enrichment analyses using miRanda, the InnateDB pathway database, and the miRNApath R package (373).  A p-value cut-off of 0.01 was used to determine significantly enriched predicted miRNA target pathways (Figure 2.7).  Transmembrane transport of small molecules, metabolism of proteins and carbohydrates, signaling by G Protein-Coupled Receptor (GPCR), infectious disease-related pathways, and cell cycle dominate the spectrum of enriched pathways at the early stages of infection with H7N7.  The number of enriched pathways   70 is reduced at 24 hpi when signal transduction and cell proliferation pathways are active, while metabolic pathways and chromosome maintenance are observed at 48 hpi. 2.3.4 Common and distinct host cell miRNA signatures associated with pandemic S-OIV H1N1 and highly pathogenic A-OIV H7N7 infections The comparison of differentially expressed miRNAs from each microarray experiment identified miRNAs whose expression profiles differed between H1N1 infection and H7N7 infection.  A total of 40 miRNAs were commonly differentially expressed between both infA strains, with 33 being up-regulated (82.5%)  (Figure 2.8A, Figure A.2 A) and 7 being down-regulated between both viruses (Figure 2.8B, Figure A.2 B).  Interestingly, 23 miRNAs were identified as being significantly up-regulated only during H7N7 infection by microarray analysis, compared to only 6 uniquely up-regulated miRNAs associated with H1N1 (Figure 2.8A).  Included in the list of up-regulated common miRNAs is miR-449b*, which we validated as being significantly up-regulated by qRT-PCR for both H1N1 and H7N7 (Figure 2.2E, Figure 2.6E).  Additionally, miR-449b* had a significantly higher fold change during H7N7 infection compared to that during H1N1 infection as determined by qRT-PCR (Table 2.1).  In contrast to the up-regulated miRNAs, very few common down-regulated miRNAs were identified.  A total of 72 miRNAs were found to be down-regulated during infection with both strains of infA virus, of which 59 were significantly down-regulated only during H7N7 infection by microarray analysis (Figure 2.8B).  Only 7 down-regulated miRNAs (17.5%) were found to be common between the two viruses.  At 4 hpi, a total of 6 miRNAs were shared between the two viruses, the highest for any time point (Figure A.2 B).  However, 42 down-regulated miRNAs were unique to H7N7 at the same time point, and this number increased to 49 miRNAs at 8 hpi (Figure A.2 B).  Of the common miRNAs, we previously demonstrated that   71 let-7g was significantly down-regulated during H7N7 infection (Figure 2.6A).  This miRNA also showed a trend of down-regulation across all early time points during H1N1 infection but was not found to be significant by qRT-PCR (Figure 2.2A).   qRT-PCR validation demonstrated that miR-30c was significantly down-regulated during H7N7 infection at 8 hpi (Figure 2.6F).  We confirmed that expression of miR-30c was not significantly up- or down-regulated at any of the six time points during H1N1 infection (Figure 2.2F).  We have also shown that both miR-34b and miR-34c-3p are not significantly deregulated during H7N7 infection.  This is in contrast to H1N1 where miR-34c-3p is almost 5-fold up-regulated at 72 hpi and miR-34b expression is up-regulated at 72 hpi, a significant difference from its expression at 0, 24, and 48 hpi.  All together, we have identified three miRNAs (miR-34b, miR-34c-3p, and miR-449b*) that show significantly different expression profiles between H1N1 and H7N7 infection (Table 2.1).  Taken together, our data demonstrate that a number of miRNAs are expressed in a strain-specific manner during infA infection. 2.4 Discussion The results of our study provide the first experimental evidence demonstrating the complex temporal- and strain-specific regulation of the host microRNAome by S- and A-OIV infections in human cells.  The integration of array chip technology, qRT-PCR, and target prediction and pathway enrichment analyses has allowed us to perform a robust comparative genomics and bioinformatics study to reveal the host miRNA molecular signatures associated with S- and A-OIV infections.  Our results also reveal the common and specific cellular pathways associated with the differentially expressed host miRNAs during the H1N1 and H7N7 infA lifecycles.  We identified a unique series of temporal- and strain-specific host molecular responses involving different combinatorial contributions of multiple cellular miRNAs,   72 providing, for the first time, key molecular insights into unique cellular miRNA-mRNA interactome networks dynamically and temporally regulated by S- and A-OIV infections.  2.4.1 Temporal host miRNA molecular signatures associated with pandemic S-OIV H1N1 and highly pathogenic A-OIV H7N7 infection Using a microfluidic microarray approach, we profiled the expression of all human miRNAs (miRBase version 14) across multiple time points after infection with pandemic 2009 S-OIV H1N1 and HP A-OIV H7N7 infA viruses (Figure 2.1A, Figure 2.5A).  One of the most striking observations from this study is the temporal regulation of host cell miRNAs during the course of infection.  During the early stages of infection, from 0 to 24 hpi, the majority of differentially expressed miRNAs were down-regulated (Figure 2.1B, Figure 2.5B).  The down-regulation of miRNAs was more pronounced with the HP H7N7 strain than with the low-pathogenic H1N1 strain.  The up-regulated pathways at 0-24 hpi with H1N1 include cell cycle, replication, repair, gene expression, protein synthesis, and signaling pathways (Figure 2.4).  Furthermore, the majority of down-regulated pathways at early time points (0-24 hpi) with H1N1 infection are associated with immune system and homeostasis (Figure 2.4).  In the case of H7N7, over half of the number of differentially expressed miRNAs was down-regulated, mostly at early time points (0-24 hpi) post-infection (Figure 2.5B, Table A.2).  Among these are miR-24, let-7 family, miR-30 family, miR-29 family, miR-125b, miR-192, miR-191, and miR-99b, which have been associated with a variety of important pathways, including TGF-β signaling, stress response, cell proliferation, apoptosis, oncogene activation, and the cell cycle and inflammatory response pathways in the lung due to exposure to toxins, namely LPS, formaldehyde, or cigarette smoke (380-382).  For example, miR-24 is uniquely down-regulated at 4, 8, and 24 hpi with H7N7 (Figure 2.5, Table A.2).  With the recent demonstration that miR-  73 24 targets the cellular PC furin (354), an essential enzyme involved in the proteolytic activation of HP H7 and H5 hemagglutinin precursor molecules (HA0) (23, 58), our results suggest that H7N7-mediated miR-24 down-regulation would allow host cells to rapidly biosynthesize furin molecules, which are required to cleave HA0 molecules in the secretory pathway.  It is tempting to suggest that the proposed virus-specific miRNA-dependent regulatory mechanism of furin activity could play an important role in the distinct pathogenesis associated with S- and HP A-OIV infections.  Alternately, we observed an H7N7-specific down-regulation of miR-30 family members along with an enrichment of cell death pathways at 4, 8, and 24 hpi (Figure 2.6, Figure 2.7, Table A.2).  Interestingly, down-regulation of the miR-30 family members, which includes miR-30c, was also observed in macaques infected with HP avian H5N1 infA virus, and it may be involved in regulating genes associated with cell death (141) since HP infA viruses are known to cause enhanced inflammatory responses, resulting in hypercytokinemia and severe lung damage.   At later stages of infection (48-72 hpi), the majority of miRNAs were up-regulated with both infA strains.  For H1N1, this correlated with a strong down-regulation of enriched cellular pathways associated with cell cycle, replication and repair, signaling, and chromosome maintenance (Figure 2.4).  Comparatively, at 48-72 hpi with H7N7, we observed the down-regulation of pathways associated with  metabolism, chromosome maintenance, biological oxidation, and regulatory pathways (Figure 2.7).  Our qRT-PCR data identified miR-449b* as being significantly up-regulated at 72 hpi during H1N1 infection and at 4 to 48 hpi during H7N7 infection (Figure 2.2E, Figure 2.6E).  It is known that the down-regulation of mRNAs targeted by miR-449a/b can result in cell cycle arrest at G1 and promotion of apoptosis (383, 384).  In contrast to the temporal modulation of miR-449b*, miR-766 was significantly up-regulated at all   74 time points during H1N1 infection (Figure 2.2D).  The direct targets of miR-766 have not been identified, but its predicted targets are involved in pathways associated with cell survival after chemotherapy and aging (385, 386).  The deregulation of these miRNAs, along with others, indicate that the down-regulation of cell-cycle-related pathways is an important feature of infA infection. 2.4.2 Strain-specific host miRNA molecular signatures associated with pandemic S-OIV H1N1 and highly pathogenic A-OIV H7N7 infection The expression profiles resulting from our studies provide unique insights into the miRNAs that are significantly differentially expressed during viral infection with two important strains of infA virus (pandemic H1N1 and HP H7N7) that are associated with distinct pathogenesis.  Strain-specific patterns of miRNA expression were first observed when examining the global expression signatures, where the number of differentially expressed miRNAs in the A-OIV H7N7 infected cells was double that seen during S-OIV H1N1 infection (Figure 2.8, Figure A.2 ).  Of the miRNAs that have common expression between the two viruses, up-regulated miRNAs were most represented.  In turn, a higher number of miRNAs unique to H7N7 were down-regulated indicating that the avian strain allows the enrichment of a larger number of cellular pathways (observed at early time points in Figure A.2 ).  Three miRNAs (miR-34c-3p, miR-34b, and miR-766) were up-regulated during H1N1 infection according to RT-PCR data (Figure 2.2B, C, and D), but they showed no significant up- or down-regulation during H7N7 infection (Figure 2.6B, C, and D).  Additionally, the expression of let-7g, a member of the highly conserved let-7 family, and of miR-30c were significantly down-regulated only at early time points during H7N7 infection, further establishing a possible role for these and other down-regulated miRNAs early during H7N7 infection (Figure 2.6A, F).  Even miR-449b*, which was   75 significantly up-regulated at multiple time points during H7N7 infection, differed when compared with the H1N1 expression profile (Figure 2.6E, Table 2.1).  The miR-449 and miR-34 families of miRNAs are activated by E2F1 and p53, respectively, in response to DNA damage and are found to target a number of cell-cycle-related mRNAs (383, 384, 387-389).  Targets of miR-449a/b include CDK6 and CDC25A, while the miR-34 family functions as tumor suppressors by targeting anti-apoptotic mRNAs, including CCNE2, Bcl-2, and CDK6 (383, 384, 389).  Regulation of these mRNAs by the miR-449 and miR-34 families causes cell cycle arrest at G0/G1 and induction of apoptosis, both of which have been shown to be important to the infA lifecycle (390-392).  Furthermore, profiles of leukocytes from infected patients showed that profound changes in cell cycle regulation were associated with an increase in the severity of disease caused by infA infection (393).  The specific roles of each of these miRNAs during infA infection is still to be determined and is beyond the scope of this study, but our data do suggest that the strain-specific changes observed here may be another important factor in the distinct pathogenesis associated with S- and A-OIV infections. As we move into the 21st century, drug-resistant infA viruses are continuously eroding the therapeutic armamentarium, leaving fewer alternative therapeutic agents available (394).  More than ever, exploring novel host-directed antiviral targets is important for developing novel global anti-infA strategies that will catalyze the creation of therapeutics with novel mechanisms of action (394-396).  With the recent demonstration by Lanford and collaborators that anti-miRNA molecules can be successfully used as therapeutic agents against chronic hepatitis C virus infection in chimpanzees (145), our findings on strain-specific infA virus regulation of the host-cell microRNAome will certainly catalyze the research on therapeutic silencing of cellular   76 miRNAs as indirect-acting anti-infA agents.  Finally, it will be interesting to explore if the specific molecular miRNA signatures identified in our study will translate into the identification of new diagnostic and prognostic secretory miRNA biomarkers in infA-associated diseases.      77    AHours  post  infectionmiR-­194miR-­30amiR-­29amiR-­181amiR-­30dmiR-­29cmiR-­361-­5pmiR-­151-­5pmiR-­425miR-­320dmiR-­548d-­5pmiR-­762miR-­1974miR-­300miR-­1270miR-­1207-­5pmiR-­574-­3pmiR-­138-­1*miR-­744*miR-­1227miR-­1825miR-­449b*miR-­328miR-­1908miR-­574-­5pmiR-­1228*miR-­149*miR-­658miR-­1225-­3pmiR-­1246miR-­205miR-­34bmiR-­1470miR-­483-­5pmiR-­1233miR-­2116*miR-­220bmiR-­329miR-­34c-­3pmiR-­483-­3pmiR-­766miR-­1229miR-­877*miR-­1224-­3pmiR-­1976miR-­1281miR-­634miR-­1234miR-­1228miR-­532-­3pmiR-­197Viral  RNA  (RNA  copy  number)Infectious  virus  released  (pfu/mL)0 4 8 24 48 72-­1        0          1          2          30 4 8 24 48 721050Number  of  miRNAsHours  post  infectionDown-­regulated  miRNAs0 4 8 24 48 72010203040Number  of  miRNAsHours  post  infectionUp-­regulated  miRNAsB COHWíJ1.0 1011.0 1021.0 1031.0 1041.0 1051.0 1061.0 1071.0 108  78 Figure 2.1. Time-point-specific regulation of miRNAs during pandemic 2009 H1N1 influenza A  virus infection (A) Heatmap depicting the miRNAs that are differentially expressed at any one time point after infection (total of 52).  Colors indicate log2 ratios of infected versus mock-infected control, according to the specified scale.  Red denotes up-regulation while blue indicates down-regulation, with hashed cells highlighting significantly deregulated miRNAs at the corresponding time point.  The overlaid curves represent viral replication efficiency as determined by qRT-PCR and infectious virus released as determined by plaque assay.  In-vitro transcribed RNA of the M gene was used as the standard to determine the RNA copy number.  Data are shown as the mean ± SEM. (B) The number of significantly down-regulated miRNAs and (C) up-regulated miRNAs during the course of infection with pandemic 2009 H1N1 infA virus.  Significance was determined by using a fold-change threshold of at least 2 and a nominal p-value cut-off of 0.05.  The x-axis represents the hours post-infection (0, 4, 8, 24, 48, 72).      79  Figure 2.2. qRT-PCR analysis of miRNA expression during pandemic 2009 H1N1 influenza A virus infection  qRT-PCR analyses of (A) let-7g, (B) miR-34c-3p, (C) miR-34b, (D) miR-766, (E) miR-449b*, and (F) miR-30c at six time points after infection.  Each graph represents the mean absolute fold change of triplicate experiments for each miRNA at six individual time point compared to mock-infected controls collected at 72 hpi.  All qRT-PCR data are represented as the mean ± SEM. Significance is based on one-way and two-way ANOVA/Bonferroni post-test analyses.  Notation for p-values is determined as follows:  * - p-value < 0.05, ** - p-value < 0.01, and *** - p-value < 0.001.  The notation in the top left corner of panels corresponds to significance by two-way ANOVA for § - infection, # - time, and ‡ - interaction between time and infection.   0 4 8 24 48 72-­3-­2-­101230 4 8 24 48 72012345** * *** * * * **0 4 8 24 48 72-­6-­4-­20246*0 4 8 24 48 72-­50510*0 4 8 24 48 72-­6-­4-­2024* ** ***0 4 8 24 48 72-­5.0-­2.50.02.55.07.510.0Fold  change  overmock-­infected  cellsFold  change  overmock-­infected  cellsHours  post  infection Hours  post  infection Hours  post  infectionHours  post  infection Hours  post  infection Hours  post  infectionFold  change  overmock-­infected  cellsFold  change  overmock-­infected  cellsFold  change  overmock-­infected  cellsFold  change  overmock-­infected  cellsA B CD E FH1N1  let-­7g H1N1  miR-­34c-­3p H1N1  miR-­34bH1N1  miR-­766 H1N1  miR-­449b* H1N1  miR-­30c‡§ #§ ‡  80  Figure 2.3. Complexity of the miRNA-mRNA interactome network at 8 hours after infection with pandemic 2009 H1N1 influenza A virus Temporal and global molecular phenotypic changes triggered by infection in human A549 cells at 8 hpi.  The network displays predicted interactions between deregulated miRNAs and deregulated mRNAs from two microarray experiments and were generated using a fold-change cut-off of ± 2, p-value < 0.05, miRanda target prediction, GenMiR++ scoring, and negative-correlation filtering.  Red indicates up-regulated miRNAs and mRNAs, while blue indicates down-regulation.  miRNAs are noted by squares while mRNA nodes are depicted as circles.  The thickness of the edges corresponds to GenMiR++ scores.   HOUR  8FCGBPFGBNCALDKLF2PMEPA1SH3PXD2ASLPIMAOAMTUS1 ABCG1TSC22D3ANGPTL4ANXA13FGL1 FILIP1FOSTM7SF2MUC5ACmiR−1281C10orf11STEAP1RAB4BKIAA1199C6orf117CHGBSLC16A3LRP5CA9CRELD1ITGA2AGTR1SLC29A4CPS1LGSNFGATRIM9TCN1miR−138−1*miR−766SLC45A4 FLJ31568PDE7BRHOB SCARA5STC1CDH17SMOC1TTLL6CAPN12C4BPBmiR−181aCCNB1ATE1GTSE1TPX2STX11TUBA3DmiR−30aSLC6A6CCNB2miR−30dAXLDEPDC1BMAR4KIF11RAD23B BU857004STEAP2 INCENPBIRC3OCEL1PLD1CDCA8EID3PSRC1 BIRC5ETS1  81    Negative  logarithm  of  p-­value05102520151050CellcycleDNAreplicationTF-­mediatedsignalingReplication,repair,gene  expression,protein  synthesisCellcycle0 244 8 48 72SignalingChromosomemaintenanceDNA  repairReplication,  repair,gene  expression,protein  synthesis Replication,  repair,gene  expression,protein  synthesisTF-­mediatedsignalingSignaltransductionReplication,  repair,gene  expression,protein  synthesisTF-­mediatedsignalingGeneexpressionCelldeathImmunesystemCellcycleDNAreplication  82 Figure 2.4. Canonical pathways implicated in infection with pandemic 2009 H1N1 influenza A virus from dual expression studies  Pathways affected by deregulated mRNAs involved in the miRNA-mRNA interactions (targeted mRNAs).  The pathways were clustered according to pathway ontology into categories.  Bars above the zero line depict pathways where the majority of the implicated mRNAs were up-regulated in the expression analysis, while bars below the zero line show pathways represented by down-regulated mRNAs.  The most abundant pathway categories are labeled.  The y-axis indicates the significance of overlap (in the form of negative log of p-values) between the input mRNA list and the set of mRNA molecules in a particular pathway category.  The x-axis represents hours post infection (0, 4, 8, 24, 48 and 72 hpi).     83  OHWíD0 4 8 24 48 26050403020100Number  of  mirNAsHours  post  infection'RZQUHJXODWHGPL51$V0 4 8 24 48 20102030405060Number  of  mirNAsHours  post  infection8SUHJXODWHGPL51$VAmir-­361-­5pmir-­24mir-­191mir-­100PLUDOHWLmir-­93mir-­193bmir-­106bmir-­151-­5pPLUDmir-­138OHWFOHWEmir-­16PLUEmir-­22mir-­194PLUDSPLUDOHWJOHWGPLUDmir-­15bPLUEmir-­25mir-­99bPLUGmir-­103mir-­182mir-­29bPLUDPLUDmir-­23bmir-­125bPLUDmir-­21mir-­192OHWIPLUDOHWHmir-­30bmir-­30cmir-­31PLUDPLUPLUPLUDmir-­129-­5pmir-­382mir-­1185PLUPLUDPLUPLUmir-­520c-­3pmir-­1280mir-­595mir-­1260PLUDmir-­513bmir-­455-­5pPLUmir-­548fPLUmir-­483-­3pPLUmir-­1229PLUPLUEmir-­220bmir-­1224-­3pmir-­1249mir-­532-­3pmir-­634mir-­34c-­5pPLUGmir-­300PLUSOHWEmir-­584mir-­1233mir-­1908mir-­1236PLUmir-­483-­5pPLUmir-­150mir-­1913mir-­1225-­3pPLUPLUmir-­1238PLUPLUPLUmir-­603PLUmir-­1226PLUmir-­205PLUmir-­1825mir-­664PLUPLUSPLUSmir-­329PLUPLUDPLUmir-­885-­5pPLUmir-­34c-­3pmir-­34bmir-­1234mir-­1228PLUmir-­328mir-­12810 4 8 24 48 2-­2 20VLUDO51A51A  copy  number)Hours  post  infectionB C1.0 1041.0 1051.0 1061.0 101.0 1081.0 109  84 Figure 2.5. Time-specific regulation of miRNAs during infection with highly pathogenic H7N7 avian influenza A virus  (A) Heatmap depicting the miRNAs that are differentially expressed at any one time point after infection (total of 121).  Colors indicate log2 ratios of infected versus mock-infected control, according to the specified scale.  Red denotes up-regulation while blue indicates down-regulation, with hashed cells highlighting significantly deregulated miRNAs at the specified time point.  The overlaid curve represents the viral replication efficiency as determined by qRT-PCR.  In vitro-transcribed RNA of the M gene was used as the standard to determine the RNA copy number.  Data are shown as the mean ± SEM. (B) The number of significantly down-regulated miRNAs and (C) up-regulated miRNAs during the course of infection with HP H7N7 avian infA virus.  Significance was determined by using a fold-change threshold of at least 2 and a nominal p-value cut-off of 0.05.      85  Figure 2.6. Validation of miRNAs expressed during highly pathogenic H7N7 avian influenza A virus infection using comparative qRT-PCR  qRT-PCR analyses of (A) let-7g, (B) miR-34c-3p, (C) miR-34b, (D) miR-766, (E) miR-449b*, and (F) miR-30c for each time point.  Each graph represents the mean absolute fold change of triplicate experiments for each miRNA at six individual time point compared to mock-infected controls collected at 72 hpi.  All qRT-PCR data are represented as the mean ± SEM.  Significance is based on one- and two-way ANOVA/Bonferroni post-test analyses.  Notation for p-values is determined as follows:  * - p-value< 0.05, ** - p-value < 0.01, and *** - p-value < 0.001.  The notation in the top left corner of panels corresponds to significance by two-way ANOVA for § - infection, # - time, and ‡ - interaction between time and infection.    0 4 8 24 48 72-­6-­4-­202**0 4 8 24 48 72-­3-­2-­10123*0 4 8 24 48 72-­2-­1012340 4 8 24 48 720.02.55.07.510.012.515.0**** *0 4 8 24 48 72-­2-­10123Fold  change  overmock-­infected  cellsFold  change  overmock-­infected  cellsA B CD EHours  post  infection Hours  post  infection Hours  post  infectionHours  post  infectionHours  post  infectionFold  change  overmock-­infected  cellsFold  change  overmock-­infected  cellsFold  change  overmock-­infected  cellsH7N7  miR-­30cH7N7  miR-­449b*H7N7  miR-­34bH7N7  miR-­34c-­3pH7N7  let-­7g0 4 8 24 48 72-­4-­2024Hours  post  infectionFold  change  overmock-­infected  cellsH7N7  miR-­766F#§§ §  86  Figure 2.7. Pathways predicted to be affected by the deregulated miRNAs during highly pathogenic H7N7 avian influenza A virus infection  Significantly expressed miRNAs during infection with HP H7N7 avian infA virus were subjected to target prediction and pathway enrichment analyses.  All pathways were obtained from the InnateDB database and analysis performed using miRNApath package in R.  A p-value cut-off of 0.01 was used to determine significantly enriched pathways.  Pathways were clustered into categories using pathway ontology from the relevant sources and the most abundant pathway categories are labeled.  The y-axis indicates the significance of overlap (in the form of negative log of p-values) between predicted miRNA-mRNA pairs (for deregulated miRNAs) and the set of miRNA-mRNA pairs found in a particular pathway category.   Transport  ofNegative  logarithm  of  p-­value0246810120 244 8 48 72Hours  post  infectionMetabolicpathwaysSignaling  byGPCRsmall  moleculesMetabolicpathwaysInfectiousdiseasesTransport  ofsmall  moleculesSignaltrans-­ductionCellcycleMetabolicpathwaysSignaling  byGPCRSignaling  moleculesand  interactionMetabolicpathwaysCell  proliferationSignalingChromosomemaintenanceMetabolicpathwaysChromosomemaintenanceImmunesystemImmunesystemTransport  ofsmall  moleculesCelldeathCelldeathImmunesystemRegulatorypathways  87  Figure 2.8. Comparison of miRNAs expressed during infection with pandemic 2009 H1N1 and highly pathogenic H7N7 avian influenza A viruses  (A) Venn diagram of significant up-regulated miRNAs during H1N1 and H7N7 infection relative to untreated control.  Of the 62 miRNAs found to be up-regulated during infection with both strains of infA virus, 23 miRNAs were identified by microarray analysis as being significantly up-regulated only during H7N7 infection.  The diagram displays the names of the 33 common miRNAs that were significantly up-regulated during the course of infection.  (B) Venn diagram of significant down-regulated miRNAs during H1N1 and H7N7 infection.  Of the 72 miRNAs found to be down-regulated during infection with both strains, 59 miRNAs were identified by microarray analysis as being significantly down-regulated only during H7N7 infection.  The list shows the 7 common miRNAs that were significantly down-regulated during the course of infection.  A Blet-­7gmiR-­151-­5pmiR-­194miR-­29amiR-­30amiR-­30dmiR-­361-­5p6 597H1N1 H7N7miR-­34c-­3pmiR-­449b*miR-­483-­3pmiR-­483-­5pmiR-­532-­3pmiR-­574-­3pmiR-­574-­5pmiR-­634miR-­744*miR-­766miR-­877*miR-­1207-­5pmiR-­1224-­3pmiR-­1225-­3pmiR-­1227miR-­1228miR-­1229miR-­1233miR-­1234miR-­1281miR-­138-­1*miR-­14706 2333H1N1 H7N7miR-­1825miR-­1908miR-­197miR-­1974miR-­1976miR-­205miR-­2116*miR-­220bmiR-­328miR-­329miR-­34b  88 Table 2.1. Comparison of qRT-PCR-validated miRNAs between pandemic 2009 H1N1 and highly pathogenic H7N7 avian influenza A virus infection  Time Mean fold changeaMean di!erencebMicroRNA point (h) SEM PH1N1 H7N7let-7g 0 –1.8865 –2.7819 0.8955 1.705 0.61578 –0.3756 –2.2326 1.857 1.437 0.2146miR-34b 48 –2.1247 0.9588 –3.302 2.251 0.185972 3.4129 1.1775 3.669 1.402 0.0308miR-34c-3p 24 –1.6254 1.7561 –3.382 0.6535 0.006672 5.6259 0.1398 5.486 3.083 0.1055miR-449b* 0 –3.1523 2.3164 –5.469 1.089 0.00744 –0.7556 6.1617 –6.917 2.463 0.04848 –1.7386 5.8693 –7.608 0.9904 0.001524 –3.0287 6.3018 –9.331 1.057 0.000948 –4.0572 7.2433 –11.30 3.892 0.044072 2.1478 5.0073 –2.859 1.267 0.0504miR-30c 8 0.3046 –1.4319 1.736 1.167 0.1650miR-766 0 2.4670 0.6039 1.864 0.9390 0.09444 2.2880 0.7648 1.523 1.085 0.21008 3.2420 1.5320 1.710 1.130 0.180824 2.9060 2.1260 0.7802 0.6559 0.279248 3.0120 0.9178 2.094 1.471 0.204572 1.8000 0.8046 0.9956 1.221 0.4358a The mean fold changes from specific miRNAs found to be significantly up- ordownregulated at indicated time points by qRT-PCR were compared to thecorresponding time point associated with either pandemic 2009 H1N1 or highlypathogenic H7N7 avian influenza A viruses.b A two-tailedt test determined significance with a P value cuto! of 0.05. Significantvalues are indicated in boldface.  89 Chapter 3: Human microRNA-24 modulates highly pathogenic avian-origin H5N1 influenza A virus infection in A549 cells by targeting secretory pathway furin  3.1 Introduction MicroRNAs are small, endogenous, non-coding, highly conserved RNAs and a powerful tool for regulating gene expression through the RNA interference pathway (1, 2).  Deregulation of miRNA expression profoundly changes gene expression in the cell and has been associated with many human pathologies (76).   For human RNA viruses, modulation of host miRNAs can influence viral pathogenesis (93).  During HCV infection, a liver-specific miRNA, miR-122, increases accumulation and translation of HCV RNA by binding to the 5’ UTR of the virus genome (125-128).  During HIV-1 infection, cellular miRNAs expressed in resting CD4+ T lymphocytes were shown to target the 3’ UTR of almost all HIV-1 mRNA, reducing viral protein production and possibly contributing to HIV-1 latency, while miR-29a can directly target the HIV-1 mRNA 3’ UTR to P-bodies for degradation (120, 122).  Most recently, Ho et al. demonstrated that miR-141 was induced upon enterovirus 71 (EV71) infection and found to target eIF4E, a key element in regulating cap-dependent translation initiation; antagonizing miR-141 during EV71 infection dramatically reduced virus production, suggesting that expression of this cellular miRNA is important for the virus lifecycle (124).   For influenza A virus (infA), limited information exists on the role of cellular miRNAs during infection. Microarray analysis of mouse miRNA expression during infection with the reconstructed 1918 (r1918) virus and a seasonal H1N1 virus (Tx/91) established that the   90 miRNAome was modulated during infA infection (139).  In our previous study, we profiled human miRNA expression during infection with the low-pathogenicity (LP) swine-origin 2009 pandemic H1N1 infA and the highly pathogenic (HP) avian-origin H7N7 infA (397). Our studies demonstrated temporal and strain-specific regulation of miRNAs during infA infection, especially for the HP H7N7 virus.  Of particular interest during HP H7N7 infection of human A549 cells is the down-regulation of the miR-23 cluster at 4, 8, and 24 hours post infection (hpi) in our array data (397).   The miR-23 cluster of miRNAs consists of two highly conserved paralog pri-miRNA transcripts composed of three miRNAs: miR-23a/b, miR-27a/b, and miR-24 (398).  The miR-23b~27b~24-1 cluster is localized on chromosome 9q22, while the miR-23a~27a~24-2 cluster is localized on chromosome 19p13.  The mature sequences of both miR-23a and 23b and miR-27a and 27b differ by only one nucleotide, while the miR-24-1 and miR-24-2 mature sequences are identical.  A number of human diseases are associated with deregulation of the miR-23 cluster, although not all three miRNAs are necessarily deregulated together.  MiR-23 is linked to cancer, muscular atrophy, and cardiac hypertrophy, while miR-27 is associated with oncogenesis, proliferation, and differentiation (398, 399).  MiR-24 is implicated in a number of human diseases, especially cancer, and has been shown to play a role in proliferation, cell cycle arrest, apoptosis, and differentiation; it has a number of experimentally validated mRNA targets (398).  Most recently, miR-24 has been shown to regulate the transforming growth factor (TGF)-ß pathway by targeting different mRNA targets, including furin (353, 354).  During HP infA virus infection, furin plays an important role in the virus lifecycle (184, 268, 325, 327).  The furin-mediated endoproteolytic cleavage of the HA precursor  (HA0) into   91 two disulfide bonded subunits HA1 and HA2 is necessary for infectivity as it exposes the fusion peptide (FP), or hydrophobic amino terminus, of the HA2 (153).  The FP facilitates fusion between the late endosomal membrane and virus envelope when the cleaved HA is exposed to the low pH environment of the endosome (154). Unlike LP infA viruses, HP H5- and H7- infA virus subtypes contain a multi-basic R-X-(R/K)-R HA0 cleavage sequence that furin cleaves intracellularly before the assembly of progeny virions (184). In this study we investigated the regulation of miR-24 expression in A549 cells during HP H5N1 infA virus infection and the role of mir-24 as a potential post-transcriptional regulator of the furin-mediated activation of HA0 that occurs within the host secretory pathway and is necessary for the production of fusion-competent virions in the host secretory pathway.  We hypothesized that viral hijacking of the furin pathway by HP infA to promote cleavage of HA0 during the viral lifecycle could represent a novel molecular mechanism controlling the dynamic production of fusion-competent infectious virions during the viral lifecycle.  As hypothesized, we observed a down-regulation of mir-24 with a concomitant up-regulation of furin mRNA during the H5N1 viral lifecycle.  Transfection of exogenous synthetic mir-24 mimics in A549 cells results in a strong decrease in both furin mRNA level and intracellular furin activity.  Importantly, treatment of A549 cells with exogenous mir-24 mimics results in a robust decrease in production of H5N1 infectious virions and a complete block of H5N1 virus spread, that was not observed in during infection with the LP swine-origin infA H1N1 virus.  The results of our studies suggest that viral-specific regulation of furin-directed microRNAs such as mir-24 during the lifecycle of HP infA viruses may represent a novel regulatory mechanism that governs furin-mediated proteolytic activation of HA0 glycoproteins and production of infectious virions.   92 3.2 Materials and methods Cell lines and viruses – All in vitro experiments were performed under the guidance of the National Centre for Foreign Animal Disease, CFIA in a negative pressure HEPA-filtered enhanced biosafety level 3 laboratory (BSL-3+) with the use of a powered air-purifying respirator (PAPR), according to Biomedical Microbiological and Biomedical Laboratory procedures (CFIA Laboratory Safety Guidelines).  Influenza viruses A/Mexico/InDRE4487/2009 (H1N1) and A/Chicken/Vietnam/14/2005 (H5N1) were propagated on MDCK (Madin-Darby Canine Kidney) cells in Dulbecco’s modified Eagle’s medium (GIBCO), supplemented with 10% heat-inactivated FBS, 1% glutamax, and 1% penicillin/streptomycin (P/S; GIBCO).  Viruses were titrated on MDCK cells to determine plaque-forming units (pfus) per ml (pfu/ml).  The human type II pulmonary epithelial cell line A549 was used for all subsequent infections, and cells were propagated in F-12 medium supplemented with 1% P/S and 10% heat-inactivated fetal bovine serum (FBS).  A549 cells were infected with the H1N1 and H5N1 viruses at a multiplicity of infection (MOI) of 0.0001.  In contrast to highly pathogenic A-OIV H5N1, infection with S-OIV 2009 H1N1 was performed in the presence of 1 µg/ml TPCK-trypsin (362).  Cell supernatant and RNA were collected up to 72 hours post infection.  0-hour time-points correspond to samples collected immediately after the 1-hour virus adsorption with additional time-points numbered with regard to the end of virus adsorption.  Mock-infected A549 cells were propagated for each experiment with samples collected at the 72-hour time-point.  RNA isolation – Total RNA was isolated using TRIzol (Invitrogen Life Technologies) following the instructions of the supplier.  The concentration of total isolated RNA was measured by using a Nanodrop ND-1000 spectrophotometer (Thermo Fisher Scientific).     93 Quantitative real-time PCR – Real-time (RT)-PCR was carried out on total RNA collected from each time-point to determine the amount of viral RNA present.  Viral RNA levels were evaluated for viral matrix (M) gene transcript levels in duplicate on the Stratagene Mx3005P PCR multiplex quantitative PCR instrument (Agilent Technologies - Stratagene) using the Quantitect Probe RT-PCR kit (Qiagen).  Matrix gene forward and reverse primers and probe as described by Spackman et al. were used in the H5N1 experiments (374). A modified version of this assay (362) was used to quantify viral RNA levels in pandemic H1N1 experiments.  Samples from each time-point were analyzed in duplicate.  Data are represented as the mean ± SEM. Quantitative real-time PCR (qRT-PCR) was used to validate mRNA and miRNA expression changes using the Stratagene Mx3005P real-time PCR system (Agilent Technologies - Stratagene).  MRNA reverse transcription reactions were performed using a High Capacity cDNA RT kit (Applied Biosystems), according to the manufacturer’s instructions.  For each reaction, 200 ng total RNA was used.  Using the Stratagene Brilliant IIIqPCR master mix, quantitative RT-PCR (qRT-PCR) was used to determine furin mRNA expression using TaqMan® gene expression assays, and data were normalized to beta-actin expression using the 2ΔΔCt (2^[-Delta Delta C(T)]) method (Applied Biosystems, catalogue number 4331182 (furin) and IDT (Beta Actin)). Primer Sequences for Beta Actin assay-probe: ACTCCATGCCCAGGAAGGAAGGC, Primer 1: GCCCTGAGGCACTCTTCC, Primer 2: GGATGTCCACGTCACACTTC.  Data are represented as the mean ± SEM.  MiRNA reverse transcription reactions were performed using the Universal cDNA Synthesis kit and RNA Spike-In Kit (Exiqon).  For each reaction, 20 ng total RNA was used; and all samples were spiked with a synthetic miR-39-3p of Caenorhabditis elegans (cel-miR-39-3p) as a standard for qPCR control.  MiRCURY LNA PCR primer sets (Exiqon) for miR-24-3p and miR-39-3p were used in   94 conjunction with the SYBR® Green master mix (Qiagen) for qRT-PCR to determine miR-24 expression levels.  Data were normalized to miR-39-3p expression using the 2ΔΔCt method; data are represented as the mean ± SEM. Transfections – A549 cells were seeded at 1 x 105 cells per well in 24-well plates and transfected with miR-24-3p miRNA or All star negative control siRNA-AF 555 (neg-miR) mimics (Qiagen) at 30, 60, 120, or 240 nM using X-tremeGENE siRNA transfection reagent (Roche).  Following transfection, cells were incubated 24 h before infection with H5N1 or H1N1 influenza A virus.  Select wells left untreated until infection were treated with 20 µM of the furin inhibitor (FI) [decanoyl-Arg-Val-Lys-Arg-CH2Cl Calbiochem # 344930) post-virus adsorption (352, 400).  Supernatant from the infected cells was collected at indicated time-points, and viral yield was determined by plaque assay (see below).   Spread assays and plaque assays – A549 cells were seeded at 3 x 104 cells per well in 96-well plates and transfected with the miR-24 or neg-miR mimic.  Cells were incubated for 24 h before infection with H5N1 at an MOI of 0.0001.  20 µM of the small molecule FI was added to appropriate wells following virus adsorption.  Normal infected wells were untreated until infected with the virus, and mock-infected wells received no treatment or virus.  The cells were then incubated for an additional 24 h before removing media and fixing with 10% buffered formalin.  The cells were permeabilized with acetone at 37°C for 1 h before blocking for 1 h at 37°C.  The permeabilized cells were then incubated with an anti-NP monoclonal antibody (1:1000; H5: F26NP-9-2-1; CFIA) for 1 h at RT.  A secondary horseradish peroxidase (HRP)–conjugated anti-mouse antibody (Santa Cruz) was used at 1:2000, and virus spread was visualized following staining with True Blue peroxidase substrate (Mandel Scientific).  Plaque assays for H5N1 were performed on confluent MDCK cells and stained as described above for   95 viral NP expression at 3 days post-infection.  Plaque assays for H1N1 were performed on confluent MDCK cells and were fixed and stained with crystal violet at 5 days post-infection.  All plaque assays were done in FBS-free DMEM supplemented with 1% P/S.  H1N1 plaque assay medium was supplemented with 2-µg ml-1 TPCK trypsin.  A 3% carboxy methylcellulose overlay was diluted to 1.5% with 2x MEM and supplemented with 0.3% BSA and 1% P/S. 2 µg ml-1 TPCK trypsin was added to overlay for H1N1-infected cells.  All cells were fixed with 10% buffered formalin.   Furin enzymatic assay – The furin enzymatic assay was performed as described previously in using the pyroGlu-Arg-Thr-Lys-Arg-4-methylcoumaryl-7-amide (pERTKR-MCA) fluorogenic substrate (100 µM) (352, 400, 401).  Briefly, A549 cells were seeded at 1 x 105 cells per well and transfected with the miR-24 or neg-miR mimic.  At the same point, 20 µM of the FI (decanoyl-Arg-Val-Lys-Arg-CH2Cl) was added to select wells.  At 24, 48, and 72 hpt, cell lysates were harvested and whole cell lysates were assayed for furin-like enzyme activity by using a SpectraMax Gemini XS spectrofluorometer equipped with a temperature-controlled 96-well plate reader (Molecular Devices) at excitation and emission wavelengths of 370 and 460 nm to measure release of 7-amino-4-methylcoumarin (351). Luciferase assay – Firefly luciferase reporter genes containing repeat miR-24 binding sites or full-length human 3’ UTR from furin (S209837) were from SwitchGear Genomics (Menlo Park).  For luciferase assays, A549 cells were grown in black 96-well plates; 50 ng of plasmid DNAs were transfected per well along with miR-24 or neg-miR mimics at various concentrations using DharmaFECT Duo reagents according to the manufacturer’s instructions (Thermo Scientific).  After 24 hours, cells were treated with LightSwitch luciferase assay reagents as described by the   96 manufacturer (SwitchGear Genomics).  Luminescence signals were measured on a Varioskan Flash Multimode microtiter plate reader (Thermo Scientific).   Western blot analysis - HA expression was confirmed via western blot analysis.  Briefly, at 24 and 48 hours post infection, the cells were washed with PBS.  Cells were harvested by scraping in 0.14 ml RIPA buffer (Sigma-Aldrich) containing 1X protease inhibitor cocktail (EDTA-free Complete Protease Inhibitor; Roche) and 0.06 ml 4X NuPAGE LDS Sample Buffer (Invitrogen). After boiling  (10 min, 95°C), 20-50 ul of sample was resolved on a 10% SDS-polyacrylamide gel (120 V, 60 min) and transferred to a nitrocellulose membrane (25V, 60 min) using a semi-dry electrophoretic transfer system (Bio-Rad Laboratories, Mississauga, ON, Canada).  Membranes were blocked for 1 hour at room temperature (RT) with Odyssey Blocking Buffer (LI-COR Biosciences, Lincoln NE).  The membranes were probed according to Odyssey Infrared Imaging System Western blot analysis protocol (LI-COR Biosciences).   Primary and secondary antibodies were diluted in Odyssey Blocking Buffer (LI-COR Biosciences) containing 0.1% v/v Tween-20 (Sigma-Aldrich).  Membranes were probed overnight at 4C with primary mouse monoclonal antibody against H5N1 HA (1:1000) (kindly provided by Dr. Yang at CFIA, Winnipeg, MB) and ß-tubulin (1:2000) as a loading control.  Membranes were probed with secondary IRDye 680-conjugated goat anti-rabbit polyclonal antibody and IRDye 800-conjugated goat anti-mouse polyclonal antibody (1:10 000, LI-COR Biosciences) (30 min, RT).  Membranes were washed with PBS containing 0.1% v/v Tween-20, and then imaged using an Odyssey Infrared Imaging System (LI-COR Biosciences).   97 3.3 Results 3.3.1 Down-regulation of mir-24 with a concomitant up-regulation of furin mRNA during the HP H5N1 viral lifecycle  With furin and furin-like proteases playing a significant role in the processing of the HA precursor HA0 during HPAI infections, we hypothesized that down-regulation of miR-24 by HP infA would allow for increased expression of furin leading to enhanced HA0 processing into the HA1 and HA2 subunits (Figure 3.1A).  We focused on the role of miR-24 during H5N1 infection due to the pandemic potential of this virus and the optimal furin cleavage sequence R-X-(K/R)-R between the HA1 and HA2 subunits (184, 402, 403) (Figure 3.1A). After inoculating A549 cells with H5N1 at a MOI of 0.0001, we confirmed infection by qRT-PCR and titration of the culture supernatant at 24, 48, and 72 hpi (Figure B.1).  A low MOI allowed for multiple rounds of replication and reflected clinical viral loads providing us with a good assessment for treatment of the cells with miR-24 (404).  To assess any changes in miR-24 or furin expression in H5N1-infected A549 cells, total RNA was isolated at multiple time points (0, 24, 48, and 72 h) post infection and analyzed for miR-24 and furin expression by qRT-PCR.  RNA expression was determined using the ΔΔCt method and normalized to mock-infected cells for H5N1 samples.  MiR-24 expression was significantly reduced at 72 hpi, while furin mRNA expression in H5N1-infected A549 cells increased significantly at 48 and 72 hpi (Figure 3.1B, C).  These results demonstrate that a decrease in miR-24 expression is concomitant with increased furin mRNA levels at 72 h post-infection with H5N1.    98 3.3.2 MiR-24 overexpression reduces furin mRNA and its enzymatic activity in human A549 cells  The fur gene, which codes for furin, is predicted to contain four miR-24 binding sites in its 3’ UTR based on the analysis of three different databases in the mirnabodymap database (http://www.mirnabodymap.org/).  To verify that miR-24 can reduce furin expression in A549 cells, miR-24 mimics were transfected into A549 cells at concentrations of 30, 60, 120, and 240 nM.  Luciferase reporter constructs bearing various full-length 3’ UTRs were co-transfected with miR-24 or control (neg-miR) mimics into A549 cells to establish whether miR-24 contributes to regulation of furin.  Luciferase activity from a luciferase reporter that contained three perfect miR-24 binding sites was significantly reduced by over 80% compared to the reporter only, indicating that the miR-24 mimics function in A549 cells and that neg-miR mimics do not target miR-24 binding sites (Figure 3.2A).  In luciferase constructs containing the full-length furin 3’ UTR, luciferase activity was significantly reduced ~50% (Figure 3.2B) following transfection with at 60, 120 and 240 nM of the miR-24 mimics while luciferase activity in constructs without 3’ UTR modifications were not affected (Figure 3.2C).  Our data show similar levels of reduced luciferase activity with the furin 3’ UTR constructs as seen in HeLa cells, although our system required higher concentrations of miR-24 to be co-transfected (353).   To confirm that miR-24 expression was maintained for at least 72 h post-transfection (hpt), total RNA was isolated for analysis by qRT-PCR.  Additionally, we inhibited furin enzymatic activity with 20 µM of decanoyl-Arg-Val-Lys-Arg-CH2Cl (FI), a potent peptide-based irreversible inhibitor of furin-like enzymes (352, 400), which was added to cells at the time of transfection.  Levels of miR-24 increased over 100-fold post-transfection relative to untreated cells and were maintained up to 72 hpt (Figure 3.3A).  No increase of miR-24 was observed   99 following transfection with the neg-miRs or the FI.  The increase in levels of the miR-24 mimic was reflected by a significant decrease in furin mRNA expression by 72 hpt, further confirming that furin mRNA is targeted by miR-24 in A549 cells (Figure 3.3B).  As expected, the FI had no affect on furin mRNA levels.  While furin mRNA expression was reduced in A549 cells following addition of the miR-24 mimic, reflecting the published data for HeLa cells, a large number of miR-24 targets are only regulated at the translational level, with no effect being observed on levels of mRNA expression (405-408).  We therefore utilized an in vitro enzymatic assay to look at furin protein activity (351, 352, 400, 401).  At 72 hpt, furin enzyme activity was reduced in cells treated with 240 nM of the miR-24 mimic and a small molecule furin inhibitor compared to untreated cells and controls (Figure 3.3C).  Our data therefore confirmed that miR-24 targets furin in A549 cells, resulting in a decrease in furin mRNA expression and enzymatic activity. 3.3.3 Treatment with synthetic exogenous miR-24 reduces H5N1 virus spread in human A549 cells With evidence that miR-24 is down-regulated during H5N1 infection at multiple time points, we next determined whether miR-24 could reduce furin expression during H5N1 infection, thereby limiting HA0 activation and infectivity of the virus.  We began by transfecting A549 cells with the miR-24 or neg-miR mimics at 30, 60, 120, and 240 nM for 24 hours before infecting with H5N1 at an MOI of 0.0001.  Upon infection, cells in select wells (with no previous treatment) were treated with 20 µM of the synthetic FI.  At 24 and 48 hpi, cells and supernatant were harvested.  Using qRT-PCR to quantify furin expression from infected cells, A549 cells, to which exogenous miR-24 was added, demonstrated a significant reduction in furin mRNA at 24 hpi compared to untreated, infected cells (48 hpt) (Figure 3.4A).  Reduction in   100 furin mRNA expression by the neg-miR at the highest concentrations may be a result of off target affects although this did not translate into a significant reduction in infectious virus released or virus spread.  By 48 hpi, furin mRNA expression was still lower than controls although not significantly (Figure 3.4B).  Since the FI acts as a catalytic-site directed inhibitor of furin-like enzymes (352, 400), no reduction in furin mRNA levels was expected or observed following treatment with the FI at both 24 and 48 hpi.    In addition to furin mRNA expression, cells harvested following treatment with the miR-24 or neg-miR mimics, as described previously, were assayed for viral RNA by qRT-PCR.  Interestingly, there was no reduction in the amount of viral RNA produced at 24 or 48 hpi (Figure 3.4C).  These results indicate that overexpressing miR-24 does not have an effect on viral replication.  To look at the release of infectious virus, plaque assays using supernatant collected at 24 and 48 hpi were performed on MDCK cells (Figure 3.4D).  A two-log significant decrease in infectious virus was observed at 24 hpi following treatment with FI.  Treatment with the miR-24 mimic led to a one-log significant decrease in infectious virus released.  Treatment with the transfection reagent by itself resulted in no significant reduction of infectious virus production.  By 48 hpi, multiple rounds of infection, resulting in a high viral load, the effect from the exogenously added miR-24 mimic was abrogated and no significant reduction was observed from any treatment.    Cell extracts were probed for HA expression via Western blot analysis following treatment with the synthetic mimics and FI.  At 24 hpi, detection of HA cleavage was limited due to low protein expression, whereas at 48 hpi, HA protein expression was greatly increased and untreated cells were utilized as a positive control for HA0 and HA1 expression (Figure B.2).  We observed a difference in HA1 levels in cells treated with the miR-24 mimic and FI compared   101 to the neg-miR and untreated cells suggesting that HA cleavage is inhibited when miR-24 is overexpressed (Figure B.2). In addition to pre-treating A549 cells with the miR-24 and neg-miR mimics, we looked at the effect of mir-24 on infected cells post-infection.  A549 cells were infected with H5N1 at an MOI of 0.0001 and 24 hpi transfected with 240 nM miR-24 or neg-miR mimics.  At 24 hpi, selected wells of infected cells were also treated with 20 µM of the FI.  Cells and supernatant were harvested at 24 hpi to establish a baseline for infection and at 48 and 72 hpi, corresponding to 24 and 48 hpt, respectively.  Viral RNA was quantified by qRT-PCR and similar to previous results, there was no effect on virus replication (Figure 3.4E).  Supernatant was titered by plaque assays on MDCK cells, and no reduction in infectious virus was observed at 48 and 72 hpi, even following treatment with the FI that resulted in a two-log drop in infectious virus released when added at the time of virus inoculation (Figure 3.4F).  These data suggest that blocking HA0 cleavage by targeting furin will require a potent protease inhibitor that can overcome the induction of furin expression during infection with HP avian infA strains once the infection has been established.   To look at virus spread, A549 cells were transfected with the various concentrations of miR-24 or neg-miR mimics or transfection reagent only for 24 hours before being infected with H5N1 at an MOI of 0.0001 (Figure 3.5A).  Following infection, media in select wells was treated with 20µM of the FI.  24 hpi, the cells were fixed and probed for expression of the viral nucleoprotein (NP) (Figure 3.5A).  Virus spread from individually infected cells is observed as a comet-like formation of NP staining (Figure 3.5B).  Treatment with the FI completely blocked any virus spread and only individually infected cells were visible.  Compared to the neg-miR   102 mimic, miR-24 completely blocked virus spread at 240 nM as only individually infected cells were visible, similar to the wells treated with the FI.   3.3.4 MiR-24 does not affect 2009 pandemic H1N1 virus infection To further investigate the role of miR-24 during infA infection, the effects of exogenous miR-24 on the 2009 pandemic H1N1 virus strain was assessed.  Unlike the highly pathogenic H5N1 virus, H1N1 does not require furin for HA0 cleavage.  As previously described, A549 cells were transfected with 240 nM of miR-24 mimic or neg-miR mimic.  At 24 hpi, the cells were then infected with the LP 2009 H1N1 influenza A virus at an MOI of 0.0001.  Select wells were treated with 20 µM of the FI, and cells and culture supernatant were harvested at 24 and 48 hpi.  Viral RNA was quantified by qRT-PCR and as with previous experiments, no effect was seen on virus replication (Figure 3.6A).  Supernatant from 24 and 48 hpi was titered by plaque assay on MDCK cells (Figure 3.6B).  At 24 and 48 hpi, no significant reduction in infectious virus was observed, suggesting that miR-24 acts in a strain-specific manner and is most likely affecting HA0 cleavage by down-regulating furin.  Since furin is not required for HA0 cleavage of LP infA viruses, miR-24 modulation of furin would not be expected to result in inhibition of HA0 cleavage of LP infA viruses (409, 410). 3.4 Discussion In our previous study of microRNA expression during infA infection, the miR-23 cluster was significantly down-regulated during infection with the HP avian-origin H7N7 strain only (397).  The miR-23 cluster was not deregulated during infection with the LP 2009 H1N1 pandemic strain, suggesting that deregulation of the miR-23 cluster is a strain-specific effect.  MiR-24, a member of the miR-23 cluster, is a well-conserved microRNA found in several vertebrates and is ubiquitously expressed in multiple different tissues (398, 399).  Validated   103 targets include p14ARF in retinoblastoma cell lines, the aryl hydrocarbon receptor nuclear translocator in liver cell lines, stimulator of interferon genes (STING) in rats, the histone variant H2AX in CD8+CD28- T cells along with E2F2, MYC and a number of other cell cycle genes associated with proliferation, cell cycle arrest, apoptosis, and differentiation (398, 405, 411-415).  Recent research has demonstrated that miR-24 also plays a role in the feedback loop associated with TGF-ß processing in HeLa and human trabecular meshwork cell cultures by targeting the proprotein convertase (PC) furin (353, 354).  TGF-ß is a cytokine from a large family of receptors and ligands that regulates a number of cellular processes.  TGF-ß itself is regulated in a cell type- and stimulation-specific manner and is initially expressed as a pro-peptide that is composed of a mature form and latency associated peptide (353). Cleavage of the pro-peptide by furin is a required processing step before secretion out of the cell.  MiR-24 levels are indirectly maintained by high levels of latent TGF-ß to reduce furin expression and accumulation of latent TGF-ß precursors.  With increased furin expression during H5N1 infection it is possible that TGF-ß is also processed, resulting in release of more TGF-ß into the extracellular matrix.  This could result in induction of cytokines and recruitment of inflammatory cells, all of which are also associated with the severe disease observed during H5N1 infection (282, 416-418).   Virus-mediated miR-24 down-regulation would allow host cells to rapidly biosynthesize furin molecules to cleave HA0 in the trans-Golgi network.  To date, we are the first to demonstrate that furin is also up-regulated during infA infection.  InfA infection has also been shown to up-regulate cellular trypsin-like proteases in the heart, brain, and vascular endothelial cells (419-421).  The up-regulation of specific cytokines (IL-6, IL-1β, and TNF-α) may induce transcriptional factors that play a role in up-regulating the trypsin-like proteases during infection and contribute to increased vascular permeability and infA replication in various organs (281,   104 419, 420).  Infection with the HP H5N1 elicits a stronger host inflammatory response with induction of IL-1β, TNF-α, and other type I interferon cytokines and chemokines in a NF-κB dependent manner (281, 416, 422).  Kumar et al. provided evidence for p38-dependent NF-κB activation leading to furin expression in cervical cancer, suggesting that induction of NF-κB during H5N1 infA infection, along with reduction of miR-24 expression could result in the observed increase in furin expression (423).    We have shown that treating H5N1 infected A549 cells with exogenous miR-24 reduces infectious virus production and spread, and we have demonstrated that this impact is concomitant with reduced furin expression.  For HP H5- and H7- avian viruses, the multi-basic cleavage site is primarily acted on by furin or furin-like proteases, such as PC6 (184, 324, 424).  Recent reports have also identified MSPL and its splice variant TMPRSS13 as transmembrane proteases capable of cleaving the HP avian HA0 precursors that contain a polybasic cleavage site (329, 409, 410).  MSPL prefers the K-K-K-R/ cleavage motif, with a K in the P4 position, compared to furin, which prefers the R-K-K-R/ recognition motif (329).  Additionally, matriptase was recently identified as another transmembrane protease that can cleave the human H1 and H3 HA subtypes (318, 319).  Interestingly, although matriptase has an optimal cleavage sequence of RQRR/VVGG that resembles that of furin, neither MSPL nor matriptase are expressed in A549 cells (319).  Interestingly, the impact of miR-24 on H5N1 infectivity appeared to be greater in the virus-spread assay when the cell culture was pretreated with miR-24 mimics, unlike the plaque assay where virus recovered from the supernatant of miR-24 treated cells was assayed on naïve cells.  This could indicate that miR-24 affects more than just HA0 cleavage and activation and it may leave neighboring cells non-permissive to maintaining virus infection or virus spread.    105 Altogether, with the plaque assay results, our data demonstrates that high levels of cellular miR-24 negatively impacts H5N1 virus infection.    In conclusion, we showed a viral-specific up-regulation of furin mRNA in A549 cells during the HP H5N1 infA lifecycle.  The viral-associated deregulation of furin expression inversely correlated with the expression of miR-24, a novel regulator of furin activity in A549 cells.  Using miR-24 mimics, we demonstrated that the expression level of miR-24 and furin in A549 cells are two important molecular determinants of HP H5N1 infectious virions production and virus spread in host cells.  Since many human enveloped viruses such as HP H5N1 share this absolute requirement for a selective proteolytic cleavage of the viral spike glycoprotein by furin for yielding fusion-competent infectious virions, the finding of our studies on human mir-24, an important regulator of furin activity and HP H5N1 viral spread, provide new insights into host-virus interactions and potential new therapeutic avenues to develop a novel class of broad-spectrum indirect-acting antivirals.     106  Figure 3.1. MiR-24 and furin RNA expression is inversely correlated during H5N1 Influenza A virus infection (A) Model of proposed regulation of miR-24 and furin during H5N1 infection.  H5N1 modulation of miR-24 results in an increase in furin expression leading to an increase in HA processing.  (B) A549 cells were infected with A/Ck/Vietnam/14/2005/H5N1.  Cells were harvested at multiple intervals (0, 24, 48 and 72) post infection.  MiR-24 expression was assessed by qRT-PCR compared to mock-infected controls collected at 72 hpi.  (C) Furin mRNA expression was assessed by qRT-PCR at the same time points compared to mock-infected controls collected at 72 hpi.  Data points represent the mean ± SEM of two experiments.  Significance is based on two-tailed Student t-test.  (* - p-value < 0.05) 0 24 48 72012345Hours Post InfectionFurin mRNA expression levels relative to mock-infected cells * *HP H5N1miR-24 FURIN [ ]HA0 HA1/HA20 24 48 720246Hours Post InfectionmiR-24 expression levels relative to mock-infected cells*2ABC  107  Figure 3.2. MiR-24 targets furin in A549 cells   (A-C) 3’UTR luciferase assays with miR-24 and neg-miR mimics at concentrations of 30, 60, 120 and 240 nM.  (A) Cells transfected with miR-24 or neg-miR mimics and a luciferase expression plasmid with 3 repeated miR-24 binding sites in the 3’-UTR.  (B) Cells transfected with miR-24 or neg-miR mimics and a luciferase expression plasmid containing the furin 3’-UTR.  (C) Cells transfected with miR-24 or neg-miR mimics and an empty 3’-UTR reporter plasmid.  Luciferase activity was measured 24 hours post transfection.  Relative luciferase activity, as determined by relative light units (RLU), is displayed as the mean of triplicate transfections as a percentage of reporter only ± SEM.  Significance is based on two-tailed Student t-test. (* - p-value < 0.05, *** - p-value < 0.001, and **** - p-value < 0.0001)   0.00.51.01.52.02.5TreatmentNormalized RLUmiR-24 30-240 nM neg-miR30-240 nMReporterOnly0.00.51.01.52.0Normalized RLU****miR-24 30-240 nM neg-miR30-240 nMReporterOnly** **** ****A B0.00.51.01.5TreatmentNormalized RLUmiR-24 30-240 nM neg-miR30-240 nMReporterOnlyC* *  108   Figure 3.3. Overexpression of miR-24 reduces furin mRNA expression and enzymatic activity (A, B)  A549 cells were transfected with a miR-24 mimic or neg-miR mimic (neg-miR) at 30, 60, 120 and 240 nM, treated with a small molecule chloromethylketone furin inhibitor (FI) at 20 µM, or treated with the transfection reagent only (Trans Rgt Only).  RNA levels of miR-24 (A) or furin (B) were assessed at 72 hours post transfection.  Data points represent the mean ± SEM of three experiments.  Significance is based on two-tailed Student t-test.  (* - p-value < 0.05, and ** - p-value < 0.01, *** - p-value < 0.001)  (C) Initial rate curves (0-10 minutes) for furin processing of a flurogenic substrate in A549 cells treated with miR-24 mimics, control mimics, or 20 µM FI 72 hours post transfection.    100101102103miR-24 expression levels relative to untreated cellsmiR-24 30-240 nM neg-miR30-240 nMFI20 µMTransRgt Only0.00.51.01.52.02.5Furin mRNA expression levels relative to untreated cells*****miR-24 30-240 nM neg-miR30-240 nMFI20 µMTransRgt Only100806040200Furin enzymatic activity(percent of neg-miR)miR-24 120 nM neg-miR 120 nM FI 20 µMA B C******  109     0.00.51.01.5Furin mRNA expression levels relative to mock-infected cells** ** ** *miR-24 30-240 nM neg-miR30-240 nMFI20 µMTransRgt OnlyUntreated0.01.02.03.04.0Furin mRNA expression levels relative to mock-infected cellsmiR-24 30-240 nM neg-miR30-240 nMFI20 µMTransRgt OnlyUntreated24 hpi 48 hpi1001011021031041051061071081091010H5N1 RNA copy numbermiR-24 240 nMneg-miR 240 nMFI 20 µMTrans Rgt Only UntreatedMOI 0.000124 hpi 48 hpi100101102103104105106107pfu/mL***neg-miR 240 nMmiR-24 240 nMFI 20 µMTrans Rgt Only UntreatedMOI 0.000124 hpi 48 hpi/24 hpt 72 hpi/48 hpt100101102103104105106107108109H5N1 RNA copy numbermiR-24 240 nMneg-miR 240 nMFI 20 µMTrans Rgt Only UntreatedMOI 0.000124 hpi 48 hpi/24 hpt 72 hpi/48 hpt100101102103104105106pfu/mLmiR-24 240 nMneg-miR 240 nMFI 20 µMTrans Rgt Only UntreatedMOI 0.0001ABCDEF  110 Figure 3.4. Overexpression of miR-24 during H5N1 infection reduces furin mRNA expression and infectious virus released QRT-PCR analysis of furin mRNA expression at 24 (A) and 48 (B) hpi and treatment with miR-24 mimic, neg-miR mimic, furin inhibitor, transfection reagent only (Trans Rgt Only), or normal infection (no additional treatment) compared to mock infected cells.  Data points represent the mean ± SEM of five experiments.  Significance is based on one-way ANOVA with Bonferroni post test.  (* - p-value < 0.05 and ** - p-value < 0.01).  A549 cells were transfected with 240 nM miR-24 mimic, 240 nM neg-miR mimic, or transfection reagent alone before infecting with H5N1 24 hours later.  Upon infection select wells were treated with 20 µM of the furin inhibitor.  Cells and supernatant were harvested at 24 and 48 hours post infection.  Total RNA was assessed for viral RNA (C) while supernatant was titrated by plaque assay on MDCK cells (D).  Data points represent the mean ± SEM of five experiments.  Significance is based on one-way ANOVA with Bonferroni post test.  (* - p-value < 0.05, and ** - p-value < 0.01).  Treatment with miR-24 following H5N1 infection does not reduce viral titers.  A549 cells were infected with H5N1 at an MOI of 0.0001 for 24 hours before transfecting with 240 nM of miR-24 mimic, neg-miR mimic or transfection reagent only or treating with 20 µM of the furin inhibitor.  Cells and supernatant were harvested at 24 hours post infection and at 24 and 48 hours post transfection (48 and 72 hours post infection, respectively).  (E) Viral RNA was assessed by qRT-PCR and no changes were observed between the different treatments compared to normally infected cells.  (F) Supernatant was titered by plaque assay on MDCK cells and again no significant difference was observed with any of the various treatments.  Data points represent the mean ± SEM of three experiments.     111    AB  112 Figure 3.5. Overexpression of miR-24 reduces spread of infectious virus (A) Model of virus spread assay in which A549 cells are transfected with miR-24 mimics or neg-miR mimics at 30-240 nM for 24 hours before infecting with H5N1 at an MOI of 0.0001.  At 24 hpi the cells are fixed and probed for the viral NP protein via immunostaining to assess virus spread.  (B) Virus spread assay following transfection with the miR-24 or neg-miR mimics at various concentrations.  Controls included FI, transfection reagent only (Trans Rgt Only) and untreated cells.  Individual infected cells are visible in the FI and 240 nM miR-24 treated cells, whereas virus spread in the form of a comet like appearance is readily apparent in the other treated cells.    113  Figure 3.6. Overexpression of miR-24 does not affect H1N1 influenza A virus  A549 cells were transfected with 240 nM of miR-24 mimic or neg-miR mimic for 24 hours before infecting with H1N1 at an MOI’s of 0.0001.  Upon infection select wells were treated with 20 µM of the furin inhibitor.  Cells and supernatant were harvested at 24 and 48 hours post infection.  (A) Viral RNA levels were assessed by qRT-PCR at 24 and 48 hours post infection.  (B) Supernatant was titrated by plaque assay on MDCK cells at 24 and 48 hours post infection.  Data points represent the mean ± SEM of three experiments.  Significance is based on one-way ANOVA with Bonferroni post test.  100101102103104105MOI 0.0001H1N1 RNA copy numberUntreatedneg-miR 240 nMFI 20 µMmiR-24 240 nM24 hpi 48 hpi100101102103104MOI 0.0001pfu/mLmiR-24 240 nMneg-miR 240 nMFI 20 µMUntreated24 hpi 48 hpiAB  114 Chapter 4: Identification of a microRNA landscape targeting the secretory pathways proprotein convertases 4.1 Introduction A large number of polyproteins, including neurotrophins, growth factors, transcription factors, bacterial toxins and virus glycoproteins, require post-translational endoproteolytic activation to become active molecules (402).  It was discovered in 1967 that insulin and three pituitary hormones (β-LPH, γ-LPH, and β-MSH) were produced as inactive precursors and required processing by endoproteases at pairs of basic amino acids (425, 426).  Over twenty years later, proprotein convertases (PC), a family of calcium-dependent serine endoproteases, were discovered based on structural homology to bacterial subtilases and yeast Kexin (427).  The PC family consists of nine members: PC1/3, PC2, furin, PC4, PACE4, PC5/6, PC7, SKI-1/S1P, and PCSK9, of which the first seven cleave proproteins at basic amino acids in the trans-Golgi network, cell surface, endosomes, or extracellular matrix (331).  Of these, furin has been shown to be an important protease in the processing of numerous virus glycoproteins that contain multi-basic amino acid cleavage sites (269, 336).    The remaining two PCs, SKI-1/S1P and PCSK9 have unique functions with SKI-1/S1P processing precursor proteins at the consensus cleavage site (R/K)-X-(V/L/I)-(K,F,L)↓in the cis/medial-Golgi or endosomes, and PCSK9 acting in a non-enzymatic function to regulate cholesterol and lipid homeostasis, predominantly by recycling the low-density lipoprotein receptor (LDLR) via endosomal and lysosomal degradation (343, 428, 429).  Additionally, both SKI-1/S1P and PCSK9 play important roles in cholesterol and fatty acid synthesis and also play important roles during HCV infection (331).   During infection, HCV depends heavily on host   115 lipid metabolism, from entry to replication and assembly (430, 431).  This results in one of the hallmarks of disease associated with HCV infection, the development of steatosis, or fatty liver (432). Recent research has demonstrated that members of the miR-23 cluster, made up of miR-23a/b, miR-27a/b and miR-24 may contribute to regulating cholesterol metabolism and steatosis (433-435).  However, of the nine members of the PC family, only furin has been reported to be targeted by cellular miRNAs, specifically miR-24, as part of the TGF-β regulatory feedback loop (353).  We previously established a role for miR-24 during HP H5N1 infA infection (see section 3.3).  Our previous data demonstrated that miR-24 and furin expression were inversely correlated during HP H5N1 infection in a human lung epithelial cell line (A549).  Overexpression of miR-24 resulted in a significant decrease in infectious virus particles and greatly reduced virus spread (Figure 3.5).  We proposed that the mechanism of this inhibition was reduced proteolytic cleavage of the HA0 precursor by furin, therefore preventing the virus from being activated and capable of fusing with the endosomal membrane upon virus entry.  With a large number of other human pathogens dependent on furin for cleavage of their viral glycoproteins or lipid metabolism via SKI-1/S1P or PCSK9, the ability of miRNAs to target these proteases, may highlight an important role for them during a number of virus infections (336).  We therefore decided to investigate whether any human miRNAs had predicted binding sites within the 3’UTR of the human PCs furin, SKI-1/S1P or PCSK9.  Including the known binding sites in the furin 3’UTR, we identified a single binding site for miR-24 in the 3’UTR of PCSK9 mRNAs.  Additionally, binding sites for the miR-17-92 cluster were found in the 3’UTR of both furin and SKI-1/S1P.  We investigated the ability of these miRNAs, along with others to target the mRNAs of three PCs, furin, SKI-1/S1P and PCSK9.  Based on mRNA expression following   116 overexpression of specific miRNAs, our data demonstrates novel targeting by miR-24 and miR-17 to the PC’s PCKS9 and furin, respectively.  4.2 Materials and methods Bioinformatics analysis – Utilizing 3 different online miRNA prediction databases: mirnabodymap (www.mirnabodymap.org), microRNA.org (ww.microrna.org), and microCosm (http://www.ebi.ac.uk/enright-srv/microcosm/htdocs/targets/v5/), we identified a large number of human miRNAs that have predicted binding sites in the 3’UTR of three human PCs (furin, PCSK9, and SKI-1/S1P).   Cell culture – HeLa cells were maintained in DMEM (GIBCO) supplemented with 10% FBS (Invitrogen) and 1% penicillin/streptomycin (Invitrogen) (complete media) and incubated at 37°C and 5% CO2. In vitro transfection of miRNA mimic or siRNA – HeLa cells were seeded at 1 x 105 cells per well in 24-well plates in complete media for 24 hours prior to transfection.  The cells were washed with PBS and antibiotic free media was added prior to addition of miRNA mimics and transfection reagent.  The cells were transfected with miScript miR-24-3p miRNA mimic (Qiagen), miRIDIAN miR-20a, miR-17 and miR-106b miRNA mimics (Dharmacon) at 5, 15 or 45 nM using Xtremegene siRNA transfection reagent (Roche) per the manufacturers instructions.  All star negative control siRNA-AF 555 (neg-miR) mimic (Qiagen) was used for control transfections.  At 24, 48 and 72 hours post transfection (hpt) total RNA and cell lysates were collected for quantification of mRNA expression and furin enzymatic activity.  Transfection of combinations of miRNA mimics (miR-24 + miR-20a, 17 or 106b) was performed with 45nM of each mimic (90 nM total) or 90 nM of the neg-miR control mimic using the Xtremegene siRNA   117 transfection reagent.  At 24 hpt total RNA and cell lysates were collected for quantification of mRNA expression and furin enzymatic activity. RNA isolation – Total RNA was isolated using Trizol (Invitrogen) following the instructions of the supplier.  The concentration of RNA was determined by a Nanodrop ND-1000 Spectrophotometer (Thermo).   Quantitative real-time PCR – Quantitative real-time PCR (qRT-PCR) was used to validate mRNA expression changes using the Agilent Mx3005P real-time PCR system (Agilent).  MRNA reverse transcription reactions were performed using the Applied Biosystems High Capacity cDNA RT Kit.  For each reaction 200 ng total RNA was used for generating mRNA cDNA.  Using the Stratagene Brilliant III qPCR master mix, quantitative real time PCR (qRT-PCR) was used to determine furin, PCSK9, or SKI-1/S1P mRNA expression using TaqMan® gene expression assays and data was normalized to beta-actin expression using the 2ΔΔCt (2^[-Delta Delta C(T)]) method (Applied Biosystems (assay ID Hs00965485_g1 (furin), Hs00545399_m1 (PCSK9), Hs00921626_m1 (SKI-1/S1P)) and IDT (Beta Actin)).  Primer Sequences for Beta Actin assay-probe: ACTCCATGCCCAGGAAGGAAGGC, Primer 1: GCCCTGAGGCACTCTTCC, Primer 2: GGATGTCCACGTCACACTTC.  Data are represented as the mean ± SEM.   Furin enzymatic assay – The furin enzymatic assay was performed as described previously in using the pyroGlu-Arg-Thr-Lys-Arg-4-methylcoumaryl-7-amide (pERTKR-MCA) fluorogenic substrate (100 µM) (352, 400, 401). Briefly, HeLa cells were seeded at 1 x 105 cells per well and transfected with the miR-20a, miR-17, miR-106b or neg-miR mimics at 45 nM, or a combination of miR-24 plus miR-20a, miR-17 or miR-106b at 45 nM each or the neg-miR mimic.  At 24, 48, and 72 hpt, cell lysates were harvested and whole cell lysates were assayed for furin-like enzyme   118 activity by using a SpectraMax Gemini XS spectrofluorometer equipped with a temperature-controlled 96-well plate reader (Molecular Devices) at excitation and emission wavelengths of 370 and 460 nm to measure release of 7-amino-4-methylcoumarin (351).   Luciferase assay – Firefly luciferase reporter genes containing the full length human 3’UTR from furin (S209837) was obtained from SwitchGear Genomics.  For luciferase assays, HeLa cells were grown in black 96-well plates, 50 ng of plasmid DNAs were transfected per well along with miR-24, miR-20a, miR-17, miR-106b or neg-miR mimics at 5, 15 or 45 nM, or a combination of miR-24 plus miR-20a, miR-17 or miR-106b at 45 nM each using Dharmafect Duo per manufacturer’s instructions (T-2010 ThermoScientific).  After 24 hours, cells were treated with LightSwitch Luciferase Assay Reagents as described by the manufacturer (LS100 SwitchGear Genomics).  Luminescence signals were measured on a Varioskan Flash Multimode microtiter plate reader (Thermo Scientific).   4.3 Results 4.3.1 Bioinformatics analysis reveals multiple miRNA binding sites in the 3’UTR of three proprotein convertases (e.g., furin, SKI-1/S1P, PCSK9) With our additional experimental evidence that miR-24 is capable of targeting furin in a lung epithelial cell line (A549) (Figure 3.2), we were interested in other novel human miRNAs that may be capable of targeting human furin as well as other members of the PC family.  Starting with miR-24, which is predicted to bind to four different sites in the 3’UTR of furin, we used three separate online miRNA prediction databases to find putative miR-24 binding sites in PCSK9 and SKI-1/S1P (Figure 4.1).  Both of these PCs have been extensively studied in our laboratory in the context of HCV infection and regulation of cholesterol homeostasis (436).  Additionally, arenaviruses, such as Lassa fever virus, utilize SKI-1/S1P to process their envelope   119 glycoprotein (344).  We identified one miR-24 binding site (orange) in the 3’UTR of PCSK9, but none in the 3’UTR of SKI-1/S1P (Figure 4.1).  Using the same online prediction databases, we further explored the possibility of novel miRNAs that may target any of the three human PCs.  Binding sites for members of the miR-17-92 cluster (light blue), miR-106b-25 cluster (light blue), and miR-22 (dark blue) were identified in the furin and SKI-1/S1P 3’UTRs (Figure 4.1).  Binding sites for unique miRNAs associated with the individual PCs were also mapped to each of the 3’UTRs and include, but are not limited to miR-15a/b, miR-192, miR 100 and miR-211 (grey) (Figure 4.1).  4.3.2 PCSK9 is a novel target of miR-24 The work presented in Chapter 3 has demonstrated that miR-24 targets furin in A549 cells, a lung epithelial cell line, at concentrations ranging from 120-240 nM (Figure 3.2).  While our studies with miR-24 in A549 cells demonstrated that furin mRNA is targeted and reduced, addition of exogenous miR-24 in HeLa cells leads to a greater reduction in furin mRNA levels using lower concentrations of the miR-24 mimic (353).  We began by confirming that overexpression of miR-24 at 5, 15 and 45 nM reduced furin mRNA levels in HeLa cells.  Data presented in Dogar et al. (353) showed furin mRNA levels at 72 hours post transfection (hpt) only.  At 24, and 48 hpt, total RNA was isolated and probed for furin mRNA expression.  We show a significant reduction in furin mRNA at 24 and 48 hpt as determined by qRT-PCR at 45 nM and 15 and 45 nM, respectively (Figure 4.2A).  Furthermore, the reduction at 15 and 45 nM was still observed at 72 hpt and corroborated the data published in 2011 (Figure 4.2A) (353).  We also looked at mRNA levels for PCSK9 and SKI-1/S1P via qRT-PCR at 24, 48 and 72 hpt with 5, 15 and 45 nM of miR-24.  With a single miR-24 binding site in its 3’UTR, PCSK9 mRNA was significantly reduced at 48 and 72 hpt at the highest concentration (45 nM) only   120 (Figure 4.2B).  In agreement with our bioinformatics data, in which no miR-24 binding sites are found in the 3’UTR of SKI-1/S1P, there was no significant reduction at any time point, or at any concentration following overexpression of miR-24 (Figure 4.2C).  This is the first report to demonstrate that miR-24 can target PCSK9 and significantly reduce mRNA levels, up to 72 hpt.   4.3.3 Multiple miRNAs can reduce furin mRNA expression in HeLa cells Following on the verification that miR-24 could significantly reduce furin mRNA levels; we decided to investigate the ability of other miRNAs identified in our prediction analysis to do the same.  We transfected HeLa cells with miR-22, miR-192, miR-93, miR-100, miR-106b, miR-20a and miR-17 using the same concentrations (5, 15 and 45 nM) as in the miR-24 experiments.  At 24 hpt, we isolated total RNA and using qRT-PCR, quantified mRNA levels for all three PCs.  Preliminary data showed no significant reduction in furin, PCSK9 or SKI-1/S1P mRNA levels following transfection with miR-22, 192, 93 or 100 (Figure C.1 A-C).  However, overexpression of miR-20a, 17, and 106b all significantly reduced furin mRNA levels at 24 hpt at 45 nM (Figure 4.3A, Figure C.2A).  Similar to miR-24, furin mRNA levels remained significantly reduced at 48 hpt following overexpression with 45 nM of miR-20a and 106b, and at 72 hours with 45 nM of miR-17 (Figure 4.3A, Figure C.2 B and C).  The reduction of furin mRNA at 72 hpt by miR-17 was around 50% and the strongest observed in comparison to miR-20a and 106b.  There was no effect on PCSK9 (Figure C.3A), and only a minor reduction in SKI-1/S1P following overexpression of miR-17 (Figure C.3B).   To further characterize the impact these novel miRNAs may have on furin, we assayed furin enzymatic activity and luciferase reporter assays as previously described (see section 3.3.2).  Using the furin 3’UTR luciferase reporter vector, we transfected HeLa cells with 5, 15 or 45 nM of miR-20a, 17 or 106b along with a control miR and assayed for luciferase activity.  Our   121 preliminary data shows that miR-17 was the only miRNA to reduce luciferase activity in HeLa cells (Figure C.4A).  In addition to luciferase assays, we looked at furin enzymatic activity following transfection with multiple miRNAs.  Following transfection with miR-24, preliminary data does not show any significant affect on furin activity at 24, 48 or 72 hpt (Figure C.5).  However, the data published in Dogar et al. demonstrates a reduction in furin protein levels following transfection with 45 nM of the miR-24 mimic (353).  The only miRNA to significantly reduce furin enzymatic activity was again miR-17 (Figure 4.3B).  At 72 hpt, miR-24 reduces furin mRNA levels by up to 70% while miR-17 reduces furin mRNA levels by only 50%.  With only preliminary data (one experiment), to assess furin enzymatic activity following miR-24 transfection, it is difficult to make any definitive conclusions from the data presented thus far.  From our bioinformatics data, there are two binding sites in the furin 3’UTR shared by miR-20a, 106b and 17, whereas there are four separate miR-24 binding sites that are not shared with any other miRNAs (Figure 4.1).  We therefore investigated the ability of miR-24 to act in combination with miR-20a, miR-17 or miR-106b to target furin, and possibly reduce mRNA levels to an even lower level than we have already observed.  HeLa cells were transfected with miR-24 along with miR-20a, 17 or 106b at 45 nM each (90 nM total) or a control miR at 90 nM and total RNA was collected at 24 hpt and probed for furin mRNA expression by qRT-PCR.  Compared to overexpression of a single miRNA, a combination of miR-24 with miR-17 and miR-106b significantly reduced furin mRNA by about 25%, while no significant reduction was observed for miR-24 + miR-20a (Figure 4.3C).  A similar experimental approach was used with the furin 3’UTR luciferase reporter assay and again the miR-24 + miR17 and miR-24 + miR-106b showed a reduction in luciferase activity 24 hpt compared to miR-24 alone and the negative miRNA control (Figure C.4B).   122 In addition to looking at furin mRNA levels, we also looked at PCSK9 and SKI-1/S1P mRNA expression following overexpression of miR-20a, 17 and 106b.  HeLa cells were transfected with 45 nM of each miRNA and total RNA isolated at 24, 48 and 72 hpt.  QRT-PCR analysis showed no significant reduction in SKI-1/S1P mRNA compared to controls with the exception of miR-106b at 24 hours  (Figure 4.4A).  Similar results were obtained for PCSK9, with the exception of miR-17 at 24 hours (Figure 4.5A), although the reduction observed with miR-106b and miR-17 was minimal.  With no miR-20a, 17 or 106b biding sites in the 3’UTR of PCSK9, it was not surprising to see limited affects from these miRNAs.  As with furin, we also looked at mRNA levels of SKI-1/S1P and PCSK9 following overexpression with a combination of miR-24 and miR-20a, 17 or 106b.  HeLa cells were again transfected with 45 nM of each miRNA, or 90 nM of the control miR and 24 hpt total RNA was isolated.  Surprisingly, all three combinations significantly reduced SKI-1/S1P mRNA at 24 hpt, even though no miR-24 binding site exists in its 3’UTR and could be just the affects from miR-20a, 17 and 106b alone, since the reductions were only around 10-20% (Figure 4.4B).  PCSK9 mRNA levels were also significantly reduced upon transfection with miR-24 + miR-17 or miR-106b compared to the control miR (neg-miR), although no miR-17 or 106b sites exist within the 3’UTR of the gene and the reduction could be due solely to miR-24 binding (Figure 4.5B).  Taken together, we have demonstrated that furin is a target of miR-17 and PCSK9 is a target of miR-24.  Further experiments, including protein expression and follow-up experiments with additional luciferase reporter assay controls and repeats will be necessary to better understand the relationship between these miRNA and the PC’s.     123 4.4 Discussion 4.4.1 Regulating PCSK9 by miR-24 in HeLa cells The multifaceted role of PCs in cellular processes and the numerous human pathogens that rely on them as part of their virus lifecycle, make PCs an interesting therapeutic target.  Additionally, a better understanding of how the PCs are regulated in the cell, especially during virus infection, could lead to novel approaches for IAAs, as well as highlighting additional roles for miRNAs in host-pathogen interactions.  Our data demonstrates that PCSK9 is a novel target of miR-24 in HeLa cells (Figure 4.2).  The targeting of miR-24 to PCSK9, a key player in regulating hepatocyte cholesterol levels by recycling the LDLR, does not represent the first instance of miR-24s involvement in lipid metabolism.  The expression of miR-24 is increased in the livers of mice fed high fat diets and in hepatocytes supplemented with fatty acids (433).  In human hepatocytes, miR-24 has been shown to target Insig1, an inhibitor of lipogenesis (433).  Inhibition of miR-24 in hepatocytes results in an up-regulation of Insig1 and inhibition of SREBP processing leading to a decrease in hepatic lipid accumulation (433).  In contrast, overexpression of miR-24 in human hepatocytes enhances SREBP processing and increase lipid accumulation (433).  Along with our data that miR-24 can target PCSK9, there appears to be an important role for miR-24 in the regulation of lipid metabolism.  An overexpression of miR-24 in hepatocytes, leading to enhanced SREBP processing and reduced PCSK9 is also of significance to the HCV lifecycle since lower PCSK9 levels and HCV-mediated activation of SREBPs supports the virus lifecycle (436, 437).  The targeting of furin by miR-24 may also work to reduce PCSK9 levels, therefore adding an additional role for regulating lipids, since furin has been shown to inactivate PCSK9 by proteolytic cleavage at arginine 218 (438).  It remains to be seen if miR-24 is up-regulated during HCV infection, although other members of the miR-24   124 cluster, namely miR-27a/b, are up-regulated and associated with hepatic steatosis (434, 435).  Along with HCV, other members of the Flaviviridae family may also be reliant on lipid metabolism during their lifecycles.  DENV infection is associated with an increase in lipid droplets in human hepatoma cells, suggesting a link between lipid droplet metabolism and virus replication (439).  Inhibition of SREBP processing also leads to a decrease in lipid droplets and inhibits HCV infection (436), further highlighting the potential for miR-24 regulation of furin, PCSK9 and Insig1 to play a critical role in both HCV and DENV infection.  To date our data is the first to demonstrate a role for miR-24 in regulating PCSK9 and further emphasizes the important role miR-24 appears to play in regulating lipid metabolism.   4.4.2 The role of novel miRNAs that target the human proprotein convertase furin  Along with investigating the putative role of miR-24 in regulating other human PCs, we also explored the possibility that other miRNAs could target furin.  Based on our bioinformatics prediction results we tested multiple miRNAs for their ability to reduce furin mRNA levels in HeLa cells (Figure 4.1).  Our data showed that three miRNAs, miR-20a, miR-17 and miR-106b, all resulted in a significant reduction of furin mRNA compared to cells transfected with the neg-miR control (Figure 4.3A).  However, while all three reduced furin mRNA, only miR-17 was able to significantly reduce furin enzymatic activity at 72 hpt (Figure 4.3B).  At 72 hpt, furin mRNA expression was lowest following transfection with miR-17, suggesting a threshold of mRNA reduction that must be achieved to observe a significant decrease in enzymatic activity (Figure 4.3A and B).  While SKI-1/S1P has no predicted miR-24 binding sites and PCSK9 has no predicted miR-20a, 17 or 106b binding sites, miRNAs, specifically miR-24 have been shown to bind to “seedless” 3’UTRs (414).  For SKI-1/S1P, the only reduction in mRNA expression was observed when miR-24 was co-transfected with the other three miRNAs, with similar results   125 observed for PCSK9, suggesting that there are no “seedless” binding sites for miR-24 or the miR-17, 20a and 106b miRNAs in the 3’UTRs of SKI-1/S1P or PCSK9, respectively, that we hypothesize would more significantly reduce mRNA expression  (Figure 4.4A and Figure 4.5A).   The miR-17-92 cluster and its paralog the miR-106b-25 cluster are some of the best-characterized polycistronic miRNAs (440).  The miR-17-92 cluster encodes six miRNAs: miR-17, 18a, 19a, 20a, 19b, and 92, while the miR-106b-25 cluster encodes three miRNAs: miR-106b, 93 and 25 (440).  Both clusters have been extremely well characterized for their roles as potent oncogenes in multiple cancers (440, 441).  Interestingly, the miR-17-92 cluster has been shown to regulate the TGF-ß pathway by way of multiple mRNA targets, resulting in a loss of response to TGF-ß apoptotic signals, leading to tumor formation (440, 442-444).  The initial role for miR-24 in targeting furin was also demonstrated to be associated with regulating TGF- ß (353, 354), suggesting a possible synergistic affect by both miR-24 and miR-17 in regulating TGF- ß, possibly by targeting furin.  In our data presented in Chapter 2, miR-17 is down-regulated at 4, 8, 24, and 48 hours post infection with the HP H7N7 virus only and was not deregulated at all during infection with the LP H1N1 strain (Table A.2).  This is similar to the data we presented in Chapter 3 for miR-24 expression during H5N1 infA infection.  It would therefore be interesting to further investigate miR-17 expression during infection with the HP and LP infA strains, and further explore whether HA processing is affected following addition of exogenous miR-17, as observed with miR-24 in Chapter 3.  Taken together, we hypothesize that miR-17 is another important regulator of the human PC furin and that it also plays an important role during infA infection.   126  Figure 4.1. Predicted miRNA binding sites in the 3’UTR of the proprotein convertases furin, PCSK9 and SKI-1/S1P Utilizing three separate miRNA prediction databases, mirnabodymap (www.mirnabodymap.org), microRNA.org (ww.microrna.org), and microCosm (http://www.ebi.ac.uk/enright-srv/microcosm/htdocs/targets/v5/), unique miRNA binding sites were identified in the 3’UTR of three PCs, furin, PCSK9, and SKI-1/S1P.  Furin is a validated target of miR-24 with four binding sites in its 3’UTR.  MiR-24 was also found to have one predicted binding site in the PCSK9 3’UTR and none in the SKI-1/S1P 3’UTR.  In contrast, miR-17, 106a/b, 20a/b, 93, and 22 have predicted binding sites in both the furin and SKI-1/S1P 3’UTR and none on PCSK9.  In addition to the shared miRNAs, there are unique sites for a number of additional miRNAs on all three PCs. Furin 3’ UTRPCSK9 3’ UTRSKI-1/S1P 3’ UTRGCAGCUmiR-22CACUUUmiR-17/106ab/20ab/93(C)UGAGCCmiR-24miR-192miR-15ab, 16, 103miR-125a-5p,125bmiR-31miR-31miR-125bmiR-100,99bmiR-191miR-211miR-221,222miR-24miR-22miR-17/106ab/20ab/93not shared by PCs  127  Figure 4.2. miR-24 targets both furin and PCSK9 but not SKI-1/S1P MiR-24 overexpression (5-45 nM) reduced the expression of both furin and PCSK9 mRNA levels post transfection.  (A) Furin mRNA expression levels were significantly reduced by 24 hours post transfection at 45 nM, and by 48 and 72 hours post transfection, mRNA expression levels were significantly reduced at 15 and 45 nM compared to untreated cells.  (B) PCSK9 mRNA expression levels were significantly reduced at 48 and 72 hours post transfection at 45 nM only compared to untreated HeLa cells.  (C) SKI-1/S1P mRNA expression levels were not significantly reduced at any time point post transfection, or at any concentration of the miR-24 mimic compared to untreated cells.  Data points represent the mean ± SEM of at least two experiments.  Significance is based on two-tailed student t-test.  (* - p-value < 0.05, ** - p-value < 0.01, and *** - p-value < 0.001)   0 5 15 45 0 5 15 45 0 5 15 450.00.51.01.5Furin expression relative to untreated cells 24 hr72 hr48 hr* *********0 5 15 45 0 5 15 45 0 5 15 450.00.51.01.52.0PCSK9 expression relative to untreated cells 24 hrnM of miR-2448 hr72 hr***nM of miR-240 5 15 45 0 5 15 45 0 5 15 450.00.51.01.52.0SKI-1/S1P expression relative to untreated cells 24 hr72 hr48 hrnM of miR-24A B C  128   Figure 4.3. Furin mRNA and enzymatic activity is reduced by overexpression of miR-17 (A) Overexpression of miR-20a and miR-106b significantly reduced furin mRNA expression levels at 24 and 48 hours post transfection compared to cells treated with the neg-miR, while miR-17 significantly reduced furin mRNA expression at 24 and 72 hours post transfection.  (B) Furin enzymatic activity (0-20 minutes) based on processing of a flurogenic substrate in HeLa cells treated with miR-20a, 17 or 106b mimics, as a percentage of the neg-miR control mimic at 24 hours post transfection.  (C) Furin mRNA expression levels following transfection with a combination of miR-24 at 45 nM and miR-20a, 17 or 106b at 45nM each or neg-miR control mimic at 90 nM.  All qRT-PCR data was normalized to untreated cells.  Data points represent the mean ± SEM of three experiments with significance determined between the miRNA mimics and the neg-miR expression data for each time point.  Significance is based on two-tailed student t-test.  (* - p-value < 0.05, ** - p-value < 0.01, and *** - p-value < 0.001)    0.00.51.01.5Hours Post TransfectionFurin mRNA expression levels relative to untreated cells24 48 72 24 48 72 24 48 72 24 48 72miR-20amiR-17miR-106bneg-miR***** **** *24 48 72 24 48 72 24 48 72 24 48 720.00.51.01.52.0Hours Post TransfectionFurin enzymatic activity (percent of neg-miR)miR-20amiR-17miR-106bneg-miR*miR-24 + miR-20amiR-24 + miR-17miR-24 + miR-106bneg-miR0.00.51.01.524 hptFurin mRNA expression levels relative to untreated cells* *A B C  129  Figure 4.4. SKI-1/S1P mRNA levels are reduced following transfection with a combination of miRNA mimics (A) Overexpression of miR-106b significantly reduced SKI-1/S1P mRNA expression levels at 24 hours post transfection compared to cells treated with the neg-miR.  (B) MRNA expression levels were reduced at 24 hours post transfection following transfection with a combination of miR-24 at 45 nM and miR-20a, 17 or 106b at 45 nM each.  All qRT-PCR data was normalized to untreated cells.  Data points represent the mean ± SEM of three experiments with significance determined between the miRNA mimics and the neg-miR expression data for each time point.  Significance is based on two-tailed student t-test.  (* - p-value < 0.05, and ** - p-value < 0.01)   0.00.51.01.52.0Hours Post TransfectionSKI-1/S1P mRNA expression levels relative to untreated cells miR-20amiR-106bneg-miRmiR-1724 48 72 24 48 72 24 48 72 24 48 72*miR-24 + miR-20amiR-24 + miR-17miR-24 + miR-106bneg-miR0.00.51.01.524 hptSKI-1/S1P mRNA expression levels relative to untreated cells*****AB  130  Figure 4.5. PCSK9 mRNA levels are reduced following transfection with a combination of miR-24 and miR-17 or miR-106b mimics (A) Overexpression of miR-17 significantly reduced PCSK9 mRNA expression levels at 24 hours post transfection compared to cells treated with the neg-miR.  (B) MRNA expression levels were reduced at 24 hours post transfection following transfection with a combination of miR-24 at 45 nM and miR-17 or 106b at 45 nM each.  All qRT-PCR data was normalized to untreated cells.  Data points represent the mean ± SEM of three experiments with significance determined between the miRNA mimics and the neg-miR expression data for each time point.  Significance is based on two-tailed student t-test.  (* - p-value < 0.05, ** - p-value < 0.01, and *** - p-value < 0.001)miR-24 + miR-20amiR-24 + miR-17miR-24 + miR-106bneg-miR0.00.51.01.524 hptPCSK9 mRNA expression levels relative to untreated cells*** **0.00.51.01.52.02.5Hours Post TransfectionPCSK9 mRNA expression levels relative to untreated cells24 48 72 24 48 72 24 48 72 24 48 72miR-20amiR-17miR-106bneg-miR*AB  131 Chapter 5: Conclusions and future directions 5.1 Discussion The aim of this work was to identify novel host factors associated with infA virus pathogenesis, with a focus on the differences between HP and LP infA virus strains.  With the appearance of H5N1 (276) and the reconstruction of the 1918 pandemic H1N1 strain (445) in 1997, research has focused on the molecular and immunological aspects of different infA virus strains in an attempt to understand the varied responses during infection in humans and predict the potential virulence and pathogenesis of emerging infA viruses (260, 279, 280, 282, 446-448).  The majority of research has focused on viral determinants and the effects these have on virus-host interactions.  These include the roles of the HA glycoprotein and the viral polymerase complex.  However, major gaps in our knowledge exist in how the complex interplay between virus and host contribute to enhanced virulence and pathogenesis (446).  A better understanding of host-pathogen interactions during infA infection could reveal cellular targets or pathways that are associated with increased pathogenesis as well as potential targets for development of novel antivirals.  With the discovery of human miRNAs in 2000, and their ability to regulate up to 50% of protein coding genes, another potential layer of complexity relating to host-pathogen interactions was introduced (93).  We chose to explore strain specific host-pathogen interactions during infA virus infection by investigating the expression of human cellular miRNAs.  The studies presented herein provided evidence that human cellular miRNAs were regulated in both a temporal and strain specific manner during infA infection.  We also investigated the role of specific miRNAs that are regulated during infection with HP infA strains, to target the host protease furin.  A member of the PC family, furin is associated with the processing of numerous enveloped virus glycoproteins, making it a potential target for the development of novel   132 antivirals.  In addition, investigative studies revealed novel miRNAs that target furin as well as other members of the PC family (PCSK9).  Overall the research presented here has revealed the complex role that miRNAs play during infA virus infection, as well as potential new targets for development of therapeutics against emerging viral diseases.  5.1.1 Host cell miRNA expression during influenza A virus infection In Chapter 2, we investigated the expression signatures of human cellular miRNAs during infA infection.  A variety of methods, including array chip technology, qRT-PCR, and target prediction and pathway enrichment analyses, were integrated to compare miRNA expression patterns during infection with the HP A-OIV H7N7 and LP S-OIV H1N1 in human lung epithelial cells (A549).  A number of time points post infection were investigated to compare miRNA expression between the initial and subsequent rounds of infection.  Temporal regulation of miRNAs was observed for both viruses with a majority of down-regulated miRNAs found in the first 24 hours post infection and up-regulated miRNAs found at 48 and 72 hours post infection (Figure 2.1, Figure 2.5, and Figure A.2).  QRT-PCR analyses of specific miRNAs validated the microarray data and highlighted strain specific differences in expression (Figure 2.2, Figure 2.6, and Table 2.1).  In addition to profiling miRNA expression during H1N1 infection, we profiled the transcriptome and used target prediction analysis to identify miRNA-mRNA interactions that had inverse expression patterns during infection (up-regulated miRNA predicted to bind a down-regulated mRNA at a specific time point) (Figure 2.3, Figure A.1).  The predicted mRNAs from these interactions were subject to further analysis using InnateDB to identify enriched pathways that may be relevant to infA infection (Figure 2.4).  At early time points, up-regulated mRNAs were associated with pathways such as DNA replication, gene expression, and protein synthesis, all of which are likely hijacked by the virus to replicate   133 its own genome and produce viral proteins.  One of the few down-regulated pathways at the early time points (8 hpi) was that of the immune system.  InfA has a variety of mechanisms to interfere with the innate immune system, with miRNAs possibly playing a role (449).  By later time points a majority of predicted target mRNAs are down-regulated and are associated with the cell cycle, DNA replication, and other synthesis pathways.  Whether the mRNAs associated with these pathways are being reduced to promote replication of viral RNA and proteins over those of the cell or the cell is establishing an antiviral state by shutting down specific processes is still to be determined.   In contrast to H1N1, the majority of miRNAs that were deregulated during H7N7 infection were down-regulated and associated with the early time points (0-24 hpi) although the same temporal regulation was observed (Figure 2.5, Figure A.2).  Furthermore, the down-regulated miRNAs associated with HP H7N7 infection were unique to this virus strain, whereas a majority of miRNAs that were up-regulated at later time points were common between the two strains (Figure 2.8).  Prediction analysis also identified a different subset of mRNA targets and pathways associated with the deregulated miRNAs, suggesting more strain specific differences associated with miRNA expression during infA infection (Figure 2.7).   While large data sets provide a wealth of data, there are limits to exploring the role that every miRNA or pathway identified plays during the virus lifecycle.  However, specific miRNAs have been identified by other groups that were initially identified in our research, highlighting the important impact this work has had in the field of miRNAs and influenza A.  For example, our data showed that let-7c, a member of the let-7 family of miRNAs, which play an important role in cell differentiation, was down-regulated at 8 and 24 hpi during H7N7 infection.  By   134 contrast let-7c was found to be up-regulated during H1N1 infection and inhibited the M1 protein by binding to the M1 3’UTR, thus reducing viral titers over 10-fold in human lung epithelial cells (450, 451).  While in contrast to our expression data for H7N7, we also saw a difference in let-7g expression between the two strains, highlighting, again, the strain specific differences in let-7 miRNA expression during infA infection.  In addition to let-7c, both miR-34c and 449b were up-regulated during H1N1 and H7N7 infection and have since been identified as being associated with the host cell cycle related genes polo-like kinase 4 (PLK4) and HDAC-1, respectively (452, 453).   As more research into the role of miRNAs during infA virus infection is published, there will undoubtedly be reports further confirming our data.  The use of different strains highlights the difficulty in fully understanding the role that one miRNA may play during infection.  In addition to looking at cellular mRNA targets, there is limited data available on miRNAs targeting the virus mRNAs.  While a few reports have demonstrated that miRNAs may bind to specific influenza A genes, only a limited reduction in the amount of virus produced was observed, suggesting that miRNAs may have a limited ability to directly control infA infection (454).  Constant pressure and a low fidelity RNA-dependent RNA polymerase and a short UTR at the 3’ end may prevent the virus from being efficiently targeted by host miRNAs.  Viruses engineered with a miR-93 site within a conserved site within the NP suggests that miRNA-mediated repression of infA remains a possibility (143). 5.1.2 MiR-24 and influenza A HA processing	  	  A common critical cellular event many human enveloped viruses share is the requirement for proteolytic cleavage of the viral glycoprotein by furin in the host secretory pathway (336).  For example, the furin-dependent proteolytic activation of HP infA H5 and H7 hemagglutinin   135 precursor (HA0) subtypes is critical for yielding fusion-competent infectious virions (153, 268).  In Chapter 3, we hypothesized that viral hijacking of the furin pathway by HP infA viruses to permit cleavage of HA0 could represent a novel molecular mechanism controlling the dynamic production of fusion-competent infectious virus particles during the virus lifecycle (Figure 3.1A).  We explored the biological role of a newly identified furin-directed human microRNA, miR-24 (353), as a potential post-transcriptional regulator of the furin-mediated activation of HA0 and the production of fusion-competent virions in the host secretory pathway (Figure 3.1A). We observed that miR-24 and furin are differentially expressed in human A549 cells infected with HP avian-origin infA H5N1 (Figure 3.1B, C).  This represents the first reported instance of HP infA viruses inducing the expression of furin during infection.  We then utilized miR-24 mimics and demonstrated a robust decrease in both furin mRNA level and intracellular furin activity in A549 cells, confirming that miR-24 targets furin in our cell-based system (Figure 3.3).  Importantly, treatment of A549 cells with miR-24 mimics resulted in a robust decrease in the production of H5N1 infectious virions compared to untreated infected cells, although RNA levels remained unchanged suggesting that miR-24 overexpression did not affect virus replication (Figure 3.4C, D).  Additionally, a complete block of H5N1 virus spread (Figure 3.5) was observed only during H5N1 infection and was not seen in A549 cells infected with the LP H1N1 virus (Figure 3.6).  With no requirement for furin-mediated cleavage of the HA0 precursor in the H1N1 lifecycle, our data further suggests that miR-24 plays a specific role during HP infA virus infection only.  Our results indicate that viral-specific down-regulation of furin-directed microRNAs such as miR-24 during the lifecycle of HP infA viruses represents a   136 novel regulatory mechanism that governs furin-mediated proteolytic activation of HA0 glycoproteins and production of infectious virions.  One of the roles miR-24 plays by targeting furin is the regulation of TGF-ß, a member of a major family of cytokines that have diverse functions ranging from cell growth, apoptosis, inflammation, and embryogenesis (355, 455).  Production of mature TGF-β starts with cleavage of the precursor TGF-β prodomain dimer, known as the latency associated peptide (LAP) by furin in the trans-Golgi network.  The LAP remains associated with the TGF-β protein, functioning as an inhibitor until it is dissociated via further proteolytic activation via a number of proteases such as plasmin, MMP2/9 and thrombospondin-1, resulting in the mature and active TGF-β protein (355, 456).  Mature TGF-ß binds to the TGF-ß receptors at the cell surface leading to phosphorylation of proteins in the SMAD family, which translocate to the nucleus and bind DNA, recruiting either transcriptional co-activators or repressors (455, 457).  Along with inducing secretion of additional cytokines, such as TNF, IL-1 and IL-6, TGF-β recruits inflammatory cells and during infA virus infection these actions are associated with increased pathogenesis (417, 418).  Infection with the HP H5N1 infA strain, the induction of a strong cytokine response, also known as a cytokine storm, often leads to acute respiratory distress and is a hallmark of the severe disease associated with this particular infA strain (282, 416).  TGF-β’s role in stimulating a pro-inflammatory response suggests that it may also play an important role during infA infection.  Mice infected with infA exhibited an increase in serum levels of mature TGF-β and it was observed that the infA neuraminidase of most infA strains could also activate latent TGF-β (458).  Interestingly, the NA of HP H5N1 viruses failed to activate latent TGF-β both in vitro and in vivo (459).  As miR-24 is reduced during H5N1 infection, and an increase in furin levels is observed, resulting in the possibility of increased processing of latent TGF-β, it is   137 possible that the reduced ability of HP H5N1 NA to activate latent TGF-β prevents excessive release of TGF-β into the extracellular matrix, where it can be processed by additional proteases and stimulate pro-inflammatory and immune responses.  The exact role of TGF-β during infA infection is still unknown, although it is an intriguing target to study further.   In addition to regulating furin as part of the TGF-β pathway, miR-24 targets a number of other mRNAs, some associated with cell cycle control, such as E2F2 and MYC (414).  In our previous data, cell cycle associated transcripts were targeted by multiple miRNAs during both LP H1N1 and HP H7N7 infection (397).  The role of these cell cycle genes during infA infection remains relatively unexplored although He et al. has shown that infA induces cell cycle arrest at the G0/G1 phase possibly to provide favorable conditions for both protein production and genome replication (390).  In addition to cell cycle associated genes, bioinformatics prediction analysis reveals four potential miR-24 binding sites in the PB2 and PA genes of the H5N1 infA genome (Figure 5.1).  The role of miR-24 during HP infA infection is most likely not limited to a single mechanism and further research is warranted to determine any other functions it may have during infection.   5.1.3 Novel miRNAs targeting proprotein convertases In Chapter 4, we investigated the ability of multiple miRNAs to target three human PCs that are involved in the lifecycles of numerous human pathogens.  By utilizing similar approaches employed in Chapter 3, we identified novel miRNAs that target both furin and PCSK9 in HeLa cells.  Using bioinformatics prediction analysis, three specific miRNA clusters, miR-23-27-24, miR-17-92 and miR-106b-25, were found to target furin, PCSK9 and/or SKI-1/S1P (Figure 4.1).  Of these, the interaction between furin and miR-24 has been experimentally validated (353).  We confirmed that miR-24 targets furin, not only in HeLA cells as   138 demonstrated in Dogar et al. but also in A549 cells, as observed in Chapter 3.  We also looked at the ability of miR-24 to reduce furin mRNA levels at earlier time points (24 and 48 hpt) revealing a time dependent reduction in furin mRNA expression (Figure 4.2).  Additionally, we looked at the ability of miR-24 to target either PCSK9, which contains a single miR-24 binding site in its 3’UTR, or SKI-1/S1P, which contains no miR-24 binding sites.  With only one binding site, PCSK9 mRNA expression levels were significantly reduced at the highest miR-24 concentration (45 nM) at 48 and at 72 hpt with a greater than 50% reduction by 72 hours (Figure 4.2).  The mRNA levels for SKI-1/S1P were not reduced at any concentration or at any time point.  This data represents the first miRNA (miR-24) that targets PCSK9.  One of the most important cholesterol regulatory pathways, the SREBP pathway, is hijacked by HCV, which enhances SREBP activation and expression leading to synthesis and uptake of lipids, and is a likely contributor to HCV induced steatosis (460).  MiR-24 regulation of furin, PCSK9 and Insig1, which also leads to an increase in SREBP processing and lipid accumulation, may therefore play an important role in lipid metabolism and the lifecycles of viruses that depend on lipids, such as HCV and possibly other flaviviruses.  Along with miR-24, miR-27a/b, which is also a member of the miR-23 cluster, regulates lipid metabolism and is deregulated during HCV infection (434, 461).  Deregulation of miR-27a/b was shown to be associated with HCV core and NS4B expression (434).  With other flaviviruses, particularly DENV and even WNV, also associated with aberrant cholesterol metabolism, there could be significant roles for miR-24 and other members of the miR-23 cluster, such as miR-27a/b during their lifecycles (439, 462).  This is in addition to other human viruses that exploit furin as part of their lifecycle, some of which are emerging pathogens that present major threats to human health, such as members of the Filoviridae, Arenavirivdae, and Bunyaviridae families (336).     139 Additional miRNAs were evaluated for their ability to target the three PCs based on our initial bioinformatics analysis (Figure 4.3, 4.4, 4.5, C1.1, C1.2 and C1.3).  Of these, miR-17 was found to not only significantly reduce furin mRNA expression in HeLa cells, but also significantly reduce furin enzymatic activity (Figure 4.3).  MiR-17 was also the only miRNA found to reduce luciferase in cells expressing a luciferase reporter vector containing the furin 3’UTR (Figure C.4).  The role of miR-17, similar to miR-24, is extensive and well characterized, with targets associated with the TGF-ß pathway and cell cycle related genes (440, 442-444, 463, 464).  The down-regulation of both miRNAs (miR-24 and miR-17) during HP infA infection only further supports their role in regulating furin during infection (397).  Deregulation of both miR-17 and miR-24, was also observed in DNA viruses, such as HPV, (465, 466) .  Upon entry into the cell, conformational changes expose the minor capsid protein L2, which is cleaved by furin, a process that is required for infectivity (467).  However, during infection miR-24 is upregulated by the E6 and E7 HPV onco-proteins in differentiated keratinocytes.  In this instance miR-24 was shown to target the cell cycle inhibit p27 (465).  However, the role of miR-17 during infection with HPV infection is unexplored.  With our data demonstrating that miR-17 targets furin, we hypothesize that there are many RNA and DNA viruses that utilize furin regulation during virus infection for a variety of processes.  Further study is needed to determine the extent to which miR-17 is deregulated and the role it plays in regulating furin during infection.   5.2 Future Directions: Further dissecting the role of miRNAs during influenza A infection and their impact on host cell proteases expression The research presented in Chapters 2, 3 and 4 demonstrates that miRNAs play a complex role during infA virus infection.  We have also presented evidence of novel miRNA-mediated   140 regulation of host cell proteases, which could impact a broader range of pathogens.  Our studies have also left open a number of questions that need to be explored further to better understand the impact of specific miRNAs during virus infection. 5.2.1 Investigating the role of individual influenza A proteins in miRNA and host cell protease expression Upon infection with the H5N1 infA virus, we observed a significant increase in furin mRNA expression (Figure 3.1).  Up-regulation of ectopic host cell proteases following infA infection has been noted and may lead to an increase in HA processing, virus spread, and tissue damage (419-421).  Our data represents the first indication that secretory pathway proteases may also be induced upon infection yet the nature of the underlying mechanism remains unknown.  Although the increase in furin mRNA levels correlated with a significant decrease in miR-24 expression (Figure 3.1), details of the mechanism driving this increase remain unknown.  Since infA replicates in the nucleus of cells, it is possible that increased transcription of furin and reduced transcription of specific miRNAs, such as miR-24, could be controlled by specific viral proteins.   With eight gene segments encoding at least 11 proteins, we propose utilizing both an individual vector based expression system and the eight-plasmid system for producing recombinant viruses to determine the role that individual virus proteins may have in inducing or repressing expression of specific genes and miRNAs (465, 468, 469).  We hypothesize that the most likely protein responsible for changes in furin or miR-24 transcription is the NS1 protein.  The NS1 protein is present in both the nucleus and the cytoplasm and has multiple functions that includes but is not limited to, binding dsRNA, initiating translation of viral proteins, and limiting induction of innate immune responses (227, 230, 470).  The ability of NS1 to exert control on so   141 many cellular processes may be extended to inhibiting specific miRNAs, such as miR-24, or inducing expression specific genes necessary for the virus lifecycle.  Further examining the role of NS1 along with the other viral proteins could shed light on an important unknown aspect of infA host-pathogen interactions.    5.2.2 Investigate other miR-24 targets during H5N1 infection In the data presented in Chapter 3, we observed a significant decrease in H5N1 infectious virus released at 24 hpi following treatment with 240 nM of miR-24, while the small molecule furin inhibitor resulted in a reduction greater than 2 logs (Figure 3.4).  However, the data from our spread assays revealed that miR-24 treatment could prevent spread of H5N1 at levels comparable to the small molecule furin inhibitor (Figure 3.5).  The main difference between the plaque assay and spread assay performed in Chapter 3 was the fact that cells in the spread assay were exposed to the miR-24 mimic for 24 hours prior to infection, while the cells in the plaque assay were untreated prior to infection.  This suggests that in addition to reducing furin expression, miR-24 may also be acting upon additional cellular targets that result in a cellular microenvironment that is unfavorable for virus infection. MiR-24 is implicated in a number of diseases, especially cancer and has been shown to play a role in proliferation, cell cycle arrest, apoptosis, and differentiation and has a high number of experimentally validated mRNA targets that are tissue and cell type specific (399, 414, 415).  Investigating the expression of additional validated targets by qRT-PCR in our samples collected during infection with H5N1 may reveal unique cellular proteins associated with inhibiting infA virus infection.   An alternative experimental approach may be to look at the potential for miR-24 to target the virus itself.  Using the miRanda algortithm, we identified four potential binding sites in the   142 genome of the H5N1 strain, A/chicken/Viet Nam/17/2005, a similar strain to the one utilized in our studies (for which the sequence was not available) (Figure 5.1).  The potential for miR-24 to target the virus directly could account for the increased inhibition of virus spread.  Utilizing a similar approach mentioned in section 5.2.1, the ability of miR-24 to reduce expression of specific viral genes and proteins via a plasmid-based expression system, or luciferase-based reporter system could identify a novel mechanism of action for miR-24 during H5N1 infection and may also contribute to the strain specific affects also observed in Chapter 3.   5.2.3 Investigate the role of miR-24 for other enveloped viruses InfA is not the only virus to exploit furin during its lifecycle.  A number of other pathogens, including some highly virulent emerging pathogens, depend on the PCs for processing of their viral glycoproteins (336).  One of the first virus glycoproteins identified, as a substrate for furin was the HIV-1 gp160 glycoprotein.  Cleavage of gp160 into gp120 and gp41 by furin at a multi-basic cleavage site is required for fusion of the viral envelope with the cell membrane (337).  Likewise, the DENV prM is cleaved to M by furin resulting in mature virus particles (341, 471).  This maturation process is required for the virus to be infectious (471).  We believe that miR-24 can be utilized during HIV-1 and DENV virus infection to inhibit processing of the gp120 and prM, respectively, similar to our approach to infA H5N1 infections.   We propose using HeLa cells that express the CD4 and CCR5 receptors and can be infected with HIV-1 pseudovirus to investigate the ability of miR-24 to inhibit gp120 cleavage.  A similar approach would be used to look at DENV prM processing in primary hepatocytes.  Initial data from DENV infected human hepatoma cells (Huh7.5.1) indicates that furin is significantly up-regulated during DENV infection (Figure 5.2A).  Briefly, Huh7.5.1 cells were infected with DENV 2 at an MOI of 0.01.  Total RNA was collected from cells at 72 hpi and   143 probed for mRNA and miRNA expression as described in section 4.2.  While expression of miR-24 was not significantly changed during infection (Figure 5.2D), the additional role of miR-17 identified in Chapter 4 as another regulator of furin could also be investigated for its potential to reduce furin and inhibit cleavage of both gp120 and DENV prM glycoproteins.  Along with inhibiting glycoprotein cleavage, it would be interesting to probe the expression of miR-24 following infection with both viruses to see if a similar reduction in mir-24 expression is observed as shown in our data for H5N1.  Similar experimental approaches extended to mir-17 could also be considered.  Overall, miR-24 could prove to be a very important player in a number of virus lifecycles and should be investigated further. 5.2.4 Further explore the regulation of PCSK9 and lipid metabolism by miR-24 In Chapter 4, our data demonstrates that miR-24 can reduce PCSK9 mRNA expression levels, and is the first report indicating miRNA regulation of PCSK9.  Further validation of this interaction will be necessary to fully understand the impact of miR-24 on PCSK9 and its role in the liver and on hypercholesterolemia via the LDLR.  We propose using both a hepatoma (Huh7 or Huh7.5.1 cells) cell line and primary human hepatocytes, to investigate the expression levels of LDLR and PCSK9 following addition of exogenous miR-24.  Luciferase reporter assays containing the PCSK9 3’UTR can further confirm the ability of miR-24 to specifically target PSCK9.   In addition to validating PCSK9 as a target of miR-24, the role of miR-24 should be further investigated during HCV and DENV infection.  Increased levels of PCSK9 results in turnover of the LDLR and reduced CD81 on the surface of hepatocytes, both of which impede HCV entry into the cell (437).  Addition of exogenous miR-24 would hypothetically reduce PCSK9 levels, increasing the amount of LDLR and CD81 that is available for the virus to bind to   144 upon entry (437).  Our data from DENV infected Huh7.5.1 also indicates that SKI-1/S1P is significantly up-regulated (Figure 5.2B) while PCSK9 is unchanged (Figure 5.2C).  Interestingly, miR-27 a and b are significantly down-regulated during DENV infection suggesting a role for these other members of the miR-23 cluster during DENV infection which has been shown to be associated with steatosis in HCV infected cells (Figure 5.2D) (434).  MiR-24 has also been shown to enhance SREBP processing via inhibition of Insig1 (433), while increased SREBP processing is also mediated by HCV and results in accumulation of cellular lipid levels supporting the virus lifecycle (436).  The role of lipids is not limited to HCV infection, with recent reports suggesting that lipid droplets play an important role during DENV infection as well (439).  We propose further investigating the role of specific miRNAs, such as miR-24, and the PC’s during HCV and DENV infection.  Increased furin and SKI-1/S1P each play their own role in lipid metabolism and in the virus lifecycle, with increased SKI-1/S1P leading to enhanced SREBP processing and lipid accumulation and furin playing a role in processing the virus glycoprotein resulting in mature infectious virions (436, 471).  By utilizing similar techniques described in Chapter 3, along with assays measuring lipid droplets and intracellular lipid levels, we can begin to determine the role that miRNAs play in regulating lipid metabolism and their role during the HCV and DENV lifecycles.  Furthermore, inhibiting miR-24 could be utilized as a therapeutic against both hypercholesterolemia and possible even HCV and DENV infection.  The liver is already a prime target for the use of oligonucleotide therapeutics to tackle HCV infection (Miravirsen) hypercholesterolemia, and liver cancer among others (472).  Further exploring the role of miR-24 during HCV and DENV infection would help to elucidate its therapeutic potential.   145 5.3 Conclusions It is not surprising that miRNAs play profound roles during the lifecycles of numerous human viruses.  Herpesviruses encode their own miRNAs that can target both viral and cellular mRNAs, helping the viruses to establish latency (77, 93, 111).  For RNA viruses, the role of miRNAs during HCV infection is the best characterized, with miR-122 binding the 5’UTR of the viral genome, promoting transcription of viral mRNAs and replication of new viral RNA (125, 126, 128).  Our studies have focused on the function of miRNAs during infA infection.  With the emergence of both avian and swine-origin infA virus strains in humans in the past 15 years, understanding the host-pathogen interactions associated with pathogenesis for different strains and how this will translate during infection of a human host remains elusive.  The ability of miRNAs to target both cellular and viral genes adds complexity to better understanding these host-pathogen interactions during infA infection (Figure 5.3).  We attempted to shed some light on the role miRNAs play by comparing the miRNA-ome following infection of human lung epithelial cells (A549) with a LP H1N1 infA strain and a HP avian H7N7 infA strain.  By investigating the expression of all known human miRNAs at multiple time points post infection with both infA strains, we identified changes that occur upon initial infection and the continuous changes to both cellular mRNA and miRNA expression as the virus proceeds through multiple rounds of replication.  This data has highlighted important cellular pathways that have yet to be fully explored in the context of infA infection, most notably, the role of cell cycle associated genes and pathways that have only recently been identified as a potentially important player during infA infection (390, 393).  With extensive data stemming from large screens such as those presented in Chapter 2, there are numerous deregulated miRNAs that warrant further investigation in the context of infA infection.  Further exploring the specific miRNAs miR-24   146 and miR-17 in primary cell cultures or in vivo would be preferable to cancer based cell lines, especially due to the large number of miRNAs that are already deregulated due to cancer growth.  However, there are restrictions associated with working with HP avian infA viruses that limits our research capabilities.  Our data has provided a framework to move forward to a better understanding of the complexities associated with miRNAs during infA infection (Figure 5.3).    Within our data sets presented in Chapter 2, two deregulated miRNAs, miR-24 and miR-17, were associated only with HP infA infection and further characterized.  Recently published work demonstrated that miR-24 targets the PC furin (353, 354), an important protease associated with cleaving the HA glycoprotein of HP infA viruses, which is required to produce infectious virions (268, 327, 424).  We further demonstrated that exogenous miR-24 could significantly reduce furin mRNA levels and enzymatic activity in A549 cells.  Following infection with a HP H5N1 infA virus, overexpression of miR-24 significantly reduced furin expression and production of infectious virions and virus spread (Figure 5.3).  Our data also demonstrated an induction of furin mRNA levels following infection, a phenotype found with other proteases during infA infection (409, 410), but not shown previously for furin during infection with a HP infA strain.  Along with miR-24, bioinformatics analysis identified a number of additional miRNAs that potentially targeted furin, and we confirmed that the addition of an exogenous synthetic miR-17 mimic significantly reduced not only furin mRNA levels, but also furin enzymatic activity.  Additional experimental analysis in the context of infA infection is required to determine if miR-17 plays a similar role as observed for miR-24, and also if any synergistic effects can be achieved to further reduce furin levels within the cell.  Both miRNAs could potentially be utilized as therapeutics against not only infA, but also a number of other human enveloped viruses that rely on furin for glycoprotein processing.  With the lungs being an easily   147 accessible tissue for delivery of RNA based therapeutics, there is great potential for using miRNA based approaches for targeting specific virus infections (473).  The success of Miravirsen so far in clinical trials thus far also demonstrates the feasibility of miRNAs as an IAA (146).  Care will need to be taken to ensure that addition of exogenous miRNAs in humans does not lead to off target effects resulting in cancer, since miR-24 and miR-17 have been well studied for their roles in human cancer.   In addition to identifying miR-17 as a novel miRNA that can target furin, recently published data, observed that miR-17 is significantly elevated in the serum of patients infected with the LP H7N9 infA strain (474).  This could be the result of miRNAs being excreted from the cells via exosomes, in which their levels would be reduced, as reflected in our data.  Exosomes are multifunctional bioactive vesicles secreted by both normal and pathological cells, and they can be used for intercellular communication (475, 476).  Exosomes contain different types of functional RNA molecules (mRNA and miRNA) and biologically active polypeptides and proteins that can be released into recipient cells (477). Specific and dynamic miRNA changes triggered by infA infection in individual cells could be communicated to by-stander cells using the host cell exosome pathway (Figure 5.3).  The intercellular communication between infected and non-infected by-stander cells would involve newly biosynthesized and strain-specific exosome-associated miRNAs (475).  Since secretory exosomes provide a rich source for discovering potential blood-based biomarkers through non-invasive blood tests (478), our findings also raise the possibility of identifying in the very near future circulating miRNA biomarkers to predict clinical progression in infA-associated diseases linked with serious illness, such as the acute respiratory complications previously reported from pandemic H1N1 (2009) infection (394).    148  The last role associated with miR-24 from our research, has been the ability of miR-24 to target PCSK9, another member of the PC family.  PCSK9 acts in a non-enzymatic function to regulate cholesterol and lipid homeostasis, predominantly by recycling the low-density lipoprotein receptor (LDLR) via endosomal and lysosomal degradation (343, 428).  Along with the regulation of furin and Insig1, the targeting of PCSK9 makes miR-24 a potentially big player in regulating lipid metabolism (Figure 5.3).  Lipids are an important component of the lifecycles of viruses in the Flaviviridae family, and by regulating one miRNA; these viruses may be able to control multiple aspects of the lipid biosynthesis pathways.  The importance of lipid homeostasis is also reflected in the number of diseases associated with increased lipids, such as non-alcoholic fatty liver disease and hyperlipidemia (433).  By further exploring this relationship, valuable new tools may be developed to treat, not only virus infections that rely on lipid metabolism, but numerous other human diseases.  It will be important to explore the role of miR-24 and possibly miR-17 in primary hepatocytes and even humanized mice as the liver is a complicated organ and many liver specific viruses are limited to primates and humans only, making research in normal mouse models unavailable.   We have demonstrated the ability of infA virus to regulate miRNA expression so that the necessary host proteins are available during the virus lifecycle.  Additional roles for these miRNAs have yet to be determined in the context of infA infection and during the lifecycles of numerous other human enveloped viruses that utilized similar host proteases and pathways.  Overall, our data has demonstrated a profound role for miRNAs during infA virus infection.     149  Figure 5.1. H5N1 miR24 predicted binding info Four miR-24 binding sites in the genome of A/chicken/Viet Nam/17/2005.  Two sites were identified in both the PB2 and PA genes.  The miRanda algorithm was applied to all eight gene segments, restricting the search to miRNA 5’ seed pairing (using the ‘strict’ option) and a score threshold of 100.   miranda v3.3a    microRNA Target Scanning Algorithm=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=miR-24 CY016882 A/chicken/Viet Nam/11/2005 2005// 1 (PB2)   Forward:Score: 140.000000  Q:3 to 13  R:2134 to 2155 Align Len (10) (100.00%) (100.00%)   Query:    3' gacaaggacgACTTGACTCGgt 5'                          ||||||||||     Ref:      5' tgagcatcaaTGAACTGAGCaa 3'   Energy:  -18.290001 kCal/MolScores for this hit:>hsa-miR-24CY016882 140 -18.29 3 13 2134 2155 10 100.00% 100.00%miR-24 CY016880 A/chicken/Viet Nam/11/2005 2005// 3 (PA)   Forward:Score: 151.000000  Q:2 to 21  R:758 to 780 Align Len (20) (75.00%) (80.00%)   Query:    3' gaCAAGGACGA-CTTGACTCGGt 5'                  ||   |||| |||:||||||    Ref:      5' aaGTGAATGCTAGAATTGAGCCa 3'   Energy:  -26.420000 kCal/MolScores for this hit:>hsa-miR-24CY016880 151 -26.42 2 21 758 780 20 75.00% 80.00%miR-24 CY017066 A/chicken/Viet Nam/17/2005 2005// 1 (PB2)   Forward:Score: 140.000000  Q:3 to 13  R:2133 to 2154 Align Len (10) (100.00%) (100.00%)   Query:    3' gacaaggacgACTTGACTCGgt 5'                          ||||||||||     Ref:      5' tgagcatcaaTGAACTGAGCaa 3'   Energy:  -18.290001 kCal/MolScores for this hit:>hsa-miR-24CY017066 140 -18.29 3 13 2133 2154 10 100.00% 100.00%miR-24 CY017064 A/chicken/Viet Nam/17/2005 2005// 3 (PA)   Forward:Score: 151.000000  Q:2 to 21  R:758 to 780 Align Len (20) (75.00%) (80.00%)   Query:    3' gaCAAGGACGA-CTTGACTCGGt 5'                  ||   |||| |||:||||||    Ref:      5' agGTGAATGCTAGAATTGAGCCa 3'   Energy:  -26.590000 kCal/MolScores for this hit:>hsa-miR-24CY017064 151 -26.59 2 21 758 780 20 75.00% 80.00%  150  Figure 5.2 Proprotein convertase and miR-23 cluster expression data following dengue virus 2 (DENV2) infection of human Huh7.5.1 hepatoma cells  (A) Furin mRNA expression 72 hours post infection with DENV-2  (B) SKI-1/S1P mRNA expression 72 hours post infection with DENV-2.  (C) PCSK9 mRNA expression 72 hours post infection with DENV-2.  (D) MiR-23 cluster expression following infection with DENV-2 at 72 hours post infection.  All qRT-PCR data was normalized to mock-infected cells.  Significance is based on two-tailed student t-test.  (* - p-value < 0.05, ** - p-value < 0.01)  72 hours post infection, MOI 0.01Furin mRNA expression levels relative to mock-infected cellsmock DENV20.00.51.01.5 **72 hours post infection, MOI 0.01PCSK9 mRNA expression levels relative to mock-infected cellsmock DENV20.00.51.01.572 hours post infection, MOI 0.01SKI-1/S1P mRNA expression levels relative to mock-infected cellsmock DENV20.00.51.01.5**mock miR-24 miR-23a miR-23b miR-27a miR-27b0.00.51.01.5Relative miRNA expression normalized to miR-3972 hours post infection MOI 0.01* *A BC D  151  Temporal and strain-specific microRNA modulationModulation of furin and miR-24 by HP infAMicroRNA targeting of host PC’sRegulation of PC’s and miRNAS by human enveloped virusesHost CellVirus Host - Pathogen InteractionsFurin3’ UTRUGAGCCmiR-24miR-17-9217 18a 19a 20a 19b-1 92a-1miR-24-123b 27b 24-1miR-24-223a 27a 24-2Furin miR-24BlockFurin InhibitHIV-1/InfA/DENVPCSK9 miR-24miR-17Lipid Metabolism??ExosomesmiR-24 miR-17/92clusterFurin 3’ UTRPCSK9 3’ UTRSKI-1/S1P 3’ UTRGCAGCUmiR-22CACUUUmiR-17/106ab/20ab/93(C)UGAGCCmiR-24CDCA8SERPINE1TNFAIP3FOSmiR-194NUF2ETS1CENPFCXCL2HMGB2CYR61CCNB1KLF6TPX2CYP26A1BIRC5ADMSTARD13TUBA3DmiR-181aEID3KIF2CC15orf48KLF15KLF4miR-29aCPA4NPTX1KIF20AKRT80SLC30A1PIM1CTGFFOSL1NR4A2miR-30dANTXR1DIO2CCNB2RRM2LP infAHP infA  152 Figure 5.3 MicroRNA associated host-pathogen interactions during virus infection  MicroRNA expression is modulated in a strain specific and temporal manner by infA upon infection (green).  One consequence of this regulation is the reduction of miR-24, leading to an increase in furin expression, which we exploited to reduce virus spread during H5N1 infection (green).  The roles of specific miRNAs expressed by the host cell target human proprotein convertases and have been shown to be associated with exosomes (blue).  They may also be important players in lipid metabolism, with implications for multiple human enveloped viruses (blue).   153 Bibliography 1. Ambros V. 2004. The functions of animal microRNAs. Nature 431:350-355. 2. Bartel DP. 2004. MicroRNAs: genomics, biogenesis, mechanism, and function. Cell 116:281-297. 3. Cullen BR. 2004. Transcription and processing of human microRNA precursors. Mol Cell 16:861-865. 4. Almeida MI, Reis RM, Calin GA. 2011. MicroRNA history: discovery, recent applications, and next frontiers. Mutat Res 717:1-8. 5. Krol J, Loedige I, Filipowicz W. 2010. The widespread regulation of microRNA biogenesis, function and decay. Nat Rev Genet 11:597-610. 6. Lee RC, Feinbaum RL, Ambros V. 1993. The C. elegans Heterochronic Gene lin-4 Encodes Small RNAs with Antisense Complementarity to &II-14. Cell 75:843-854. 7. Wightman B, Ha l, Ruvkun G. 1993. Posttranscriptional Regulation of the Heterochronic Gene lin-14 by W-4 Mediates Temporal Pattern Formation in C. elegans. Cell 75:855-862. 8. Chalfie M, Horvitz HR, Sulston JE. 1981. Mutations That Lead to Reiterations in the Cell Lineages of C. elegans. Cell 24:59-69. 9. Ferguson EL, Sternberg PW, Horvitz HR. 1987. A genetic pathway for the specification of the vulval cell lineages of Caenorhabditis elegans. Nature 326:259-267. 10. Lee RC, Feinbaum RL, Ambros V. 2004. A Short History of a Short RNA. Cell S116. 11. Reinhart BJ, Slack FJ, Basson M, Pasquinelli AE, Bettinger JC, Rougvie AE, Horvitz HR, Ruvkun G. 2000. The 21-nucleotide let-7 RNA regulates developmental timing in Caenorhabditis elegans. Nature 403:901-906. 12. Pasquinelli AE, Reinhart BJ, Slack F, Martindale MQ, Kuroda MI, Maller B, Hayward DC, Ball EE, Degnan B, Muller P, Spring J, Srinivasan A, Fishman M, Finnerty J, Corbo J, Levine M, Leahy P, Davidson E, Ruvkun G. 2000. Conservation of the sequence and temporal expression of let-7 heterochronic regulatory RNA. Nature 408:86-89. 13. Griffiths-Jones S. 2004. The microRNA Registry. Nucleic Acids Res 32:D109-111. 14. Griffiths-Jones S, Grocock RJ, van Dongen S, Bateman A, Enright AJ. 2006. miRBase: microRNA sequences, targets and gene nomenclature. Nucleic Acids Res 34:D140-144. 15. Griffiths-Jones S, Saini HK, van Dongen S, Enright AJ. 2008. miRBase: tools for microRNA genomics. Nucleic Acids Res 36:D154-158. 16. Cai X, Hagedorn CH, Cullen BR. 2004. Human microRNAs are processed from capped, polyadenylated transcripts that can also function as mRNAs. RNA 10:1957-1966. 17. Lee Y, Kim M, Han J, Yeom K-H, Lee S, Baek SH, Kim VN. 2004. MicroRNA genes are transcribed by RNA polymerase II. The EMBO Journal 23:4051–4060  18. Lee Y, Ahn C, Han J, Choi H, Kim J, Yim J, Lee J, Provost P, ̊dmark OR, Kim S, Kim VN. 2003. The nuclear RNase III Drosha initiates microRNA processing. Nature 425. 19. Han J, Lee Y, Yeom KH, Kim YK, Jin H, Kim VN. 2004. The Drosha-DGCR8 complex in primary microRNA processing. Genes Dev 18:3016-3027.   154 20. Denli AM, Tops BB, Plasterk RH, Ketting RF, Hannon GJ. 2004. Processing of primary microRNAs by the Microprocessor complex. Nature 432:231-235. 21. Gregory RI, Yan KP, Amuthan G, Chendrimada T, Doratotaj B, Cooch N, Shiekhattar R. 2004. The Microprocessor complex mediates the genesis of microRNAs. Nature 432:235-240. 22. Bohnsack MT. 2004. Exportin 5 is a RanGTP-dependent dsRNA-binding protein that mediates nuclear export of pre-miRNAs. Rna 10:185-191. 23. Kim VN. 2004. MicroRNA precursors in motion: exportin-5 mediates their nuclear export. Trends in Cell Biology 14:156-159. 24. Lund E, Guttinger S, Calado A, Dahlberg JE, Kutay U. 2004. Nuclear export of microRNA precursors. Science 303:95-98. 25. Yi R, Qin Y, Macara IG, Cullen BR. 2003. Exportin-5 mediates the nuclear export of pre-microRNAs and short hairpin RNAs. Genes Dev 17:3011-3016. 26. Bernstein E, Caudy AA, Hammond SM, Hannon GJ. 2001. Role for a bidentate ribonuclease in the initiation step of RNA interference. Nature 409:363-366. 27. Grishok A, Pasquinelli AE, Conte D, Li N, Parrish S, Ha I, Baillie DL, Fire A, Ruvkun G, Mello CC. 2001. Genes and mechanisms related to RNA interference regulate expression of the small temporal RNAs that control C. elegans developmental timing. Cell 106:23-34. 28. Hutvagner G, McLachlan J, Pasquinelli AE, Balint E, Tuschl T, Zamore PD. 2001. A cellular function for the RNA-interference enzyme Dicer in the maturation of the let-7 small temporal RNA. Science 293:834-838. 29. Ketting RF, Fischer SE, Bernstein E, Sijen T, Hannon GJ, Plasterk RH. 2001. Dicer functions in RNA interference and in synthesis of small RNA involved in developmental timing in C. elegans. Genes Dev 15:2654-2659. 30. Knight SW, Bass BL. 2001. A role for the RNase III enzyme DCR-1 in RNA interference and germ line development in Caenorhabditis elegans. Science 293:2269-2271. 31. Winter J, Jung S, Keller S, Gregory RI, Diederichs S. 2009. Many roads to maturity: microRNA biogenesis pathways and their regulation. Nat Cell Biol 11:228-234. 32. Schwarz DS, Hutvagner G, Du T, Xu Z, Aronin N, Zamore PD. 2003. Asymmetry in the assembly of the RNAi enzyme complex. Cell 115:199-208. 33. Khvorova A, Reynolds A, Jayasena SD. 2003. Functional siRNAs and miRNAs exhibit strand bias. Cell 115:209-216. 34. Chendrimada TP, Gregory RI, Kumaraswamy E, Norman J, Cooch N, Nishikura K, Shiekhattar R. 2005. TRBP recruits the Dicer complex to Ago2 for microRNA processing and gene silencing. Nature 436:740-744. 35. Gregory RI, Chendrimada TP, Cooch N, Shiekhattar R. 2005. Human RISC couples microRNA biogenesis and posttranscriptional gene silencing. Cell 123:631-640. 36. Jinek M, Doudna JA. 2009. A three-dimensional view of the molecular machinery of RNA interference. Nature 457:405-412. 37. Kwak PB, Tomari Y. 2012. The N domain of Argonaute drives duplex unwinding during RISC assembly. Nat Struct Mol Biol 19:145-151.   155 38. Liu J, Carmell MA, Rivas FV, Marsden CG, Thomson JM, Song JJ, Hammond SM, Joshua-Tor L, Hannon GJ. 2004. Argonaute2 is the catalytic engine of mammalian RNAi. Science 305:1437-1441. 39. Meister G, Landthaler M, Patkaniowska A, Dorsett Y, Teng G, Tuschl T. 2004. Human Argonaute2 mediates RNA cleavage targeted by miRNAs and siRNAs. Mol Cell 15:185-197. 40. Bartel DP. 2009. MicroRNAs: target recognition and regulatory functions. Cell 136:215-233. 41. Meister G. 2013. Argonaute proteins: functional insights and emerging roles. Nat Rev Genet 14:447-459. 42. Meister G, Tuschl T. 2004. Mechanisms of gene silencing by double-stranded RNA. Nature 431:343-349. 43. Filipowicz W, Bhattacharyya SN, Sonenberg N. 2008. Mechanisms of post-transcriptional regulation by microRNAs: are the answers in sight? Nat Rev Genet 9:102-114. 44. Brennecke J, Stark A, Russell RB, Cohen SM. 2005. Principles of microRNA-target recognition. PLoS Biol 3:e85. 45. Doench JG, Sharp PA. 2004. Specificity of microRNA target selection in translational repression. Genes Dev 18:504-511. 46. Grimson A, Farh KK, Johnston WK, Garrett-Engele P, Lim LP, Bartel DP. 2007. MicroRNA targeting specificity in mammals: determinants beyond seed pairing. Mol Cell 27:91-105. 47. Lewis BP, Burge CB, Bartel DP. 2005. Conserved seed pairing, often flanked by adenosines, indicates that thousands of human genes are microRNA targets. Cell 120:15-20. 48. Nielsen CB, Shomron N, Sandberg R, Hornstein E, Kitzman J, Burge CB. 2007. Determinants of targeting by endogenous and exogenous microRNAs and siRNAs. RNA 13:1894-1910. 49. Grey F, Tirabassi R, Meyers H, Wu G, McWeeney S, Hook L, Nelson JA. 2010. A viral microRNA down-regulates multiple cell cycle genes through mRNA 5'UTRs. PLoS Pathog 6:e1000967. 50. Lytle JR, Yario TA, Steitz JA. 2007. Target mRNAs are repressed as efficiently by microRNA-binding sites in the 5' UTR as in the 3' UTR. Proc Natl Acad Sci U S A 104:9667-9672. 51. Reczko M, Maragkakis M, Alexiou P, Grosse I, Hatzigeorgiou AG. 2012. Functional microRNA targets in protein coding sequences. Bioinformatics 28:771-776. 52. Kapp LD, Lorsch JR. 2004. The molecular mechanics of eukaryotic translation. Annu Rev Biochem 73:657-704. 53. Merrick WC. 2004. Cap-dependent and cap-independent translation in eukaryotic systems. Gene 332:1-11. 54. Wells SE, Hillner PE, Vale RD, Sachs AB. 1998. Circularization of mRNA by eukaryotic translation initiation factors. Mol Cell 2:135-140. 55. Derry MC, Yanagiya A, Martineau Y, Sonenberg N. 2006. Regulation of poly(A)-binding protein through PABP-interacting proteins. Cold Spring Harb Symp Quant Biol 71:537-543.   156 56. Humphreys DT, Westman BJ, Martin DI, Preiss T. 2005. MicroRNAs control translation initiation by inhibiting eukaryotic initiation factor 4E/cap and poly(A) tail function. Proc Natl Acad Sci U S A 102:16961-16966. 57. Pillai RS, Bhattacharyya SN, Artus CG, Zoller T, Cougot N, Basyuk E, Bertrand E, Filipowicz W. 2005. Inhibition of translational initiation by Let-7 MicroRNA in human cells. Science 309:1573-1576. 58. Fabian MR, Sonenberg N, Filipowicz W. 2010. Regulation of mRNA translation and stability by microRNAs. Annu Rev Biochem 79:351-379. 59. Bagga S, Bracht J, Hunter S, Massirer K, Holtz J, Eachus R, Pasquinelli AE. 2005. Regulation by let-7 and lin-4 miRNAs results in target mRNA degradation. Cell 122:553-563. 60. Lim LP, Lau NC, Garrett-Engele P, Grimson A, Schelter JM, Castle J, Bartel DP, Linsley PS, Johnson JM. 2005. Microarray analysis shows that some microRNAs downregulate large numbers of target mRNAs. Nature 433:769-773. 61. Behm-Ansmant I, Rehwinkel J, Doerks T, Stark A, Bork P, Izaurralde E. 2006. mRNA degradation by miRNAs and GW182 requires both CCR4:NOT deadenylase and DCP1:DCP2 decapping complexes. Genes Dev 20:1885-1898. 62. Giraldez AJ, Mishima Y, Rihel J, Grocock RJ, Van Dongen S, Inoue K, Enright AJ, Schier AF. 2006. Zebrafish MiR-430 promotes deadenylation and clearance of maternal mRNAs. Science 312:75-79. 63. Guo H, Ingolia NT, Weissman JS, Bartel DP. 2010. Mammalian microRNAs predominantly act to decrease target mRNA levels. Nature 466:835-840. 64. Wu L, Fan J, Belasco JG. 2006. MicroRNAs direct rapid deadenylation of mRNA. Proc Natl Acad Sci U S A 103:4034-4039. 65. Djuranovic S, Nahvi A, Green R. 2012. miRNA-mediated gene silencing by translational repression followed by mRNA deadenylation and decay. Science 336:237-240. 66. Parker R, Song H. 2004. The enzymes and control of eukaryotic mRNA turnover. Nat Struct Mol Biol 11:121-127. 67. Meyer S, Temme C, Wahle E. 2004. Messenger RNA turnover in eukaryotes: pathways and enzymes. Critical reviews in biochemistry and molecular biology 39:197-216. 68. Yamashita A, Chang TC, Yamashita Y, Zhu W, Zhong Z, Chen CY, Shyu AB. 2005. Concerted action of poly(A) nucleases and decapping enzyme in mammalian mRNA turnover. Nat Struct Mol Biol 12:1054-1063. 69. Coller J, Parker R. 2004. Eukaryotic mRNA decapping. Annu Rev Biochem 73:861-890. 70. Eulalio A, Behm-Ansmant I, Schweizer D, Izaurralde E. 2007. P-body formation is a consequence, not the cause, of RNA-mediated gene silencing. Mol Cell Biol 27:3970-3981. 71. Eulalio A, Tritschler F, Izaurralde E. 2009. The GW182 protein family in animal cells: new insights into domains required for miRNA-mediated gene silencing. RNA 15:1433-1442. 72. Liu J, Valencia-Sanchez MA, Hannon GJ, Parker R. 2005. MicroRNA-dependent localization of targeted mRNAs to mammalian P-bodies. Nat Cell Biol 7:719-723.   157 73. Balagopal V, Parker R. 2009. Polysomes, P bodies and stress granules: states and fates of eukaryotic mRNAs. Curr Opin Cell Biol 21:403-408. 74. Eulalio A, Behm-Ansmant I, Izaurralde E. 2007. P bodies: at the crossroads of post-transcriptional pathways. Nat Rev Mol Cell Biol 8:9-22. 75. Parker R, Sheth U. 2007. P bodies and the control of mRNA translation and degradation. Mol Cell 25:635-646. 76. Zimmerman AL, Wu S. 2011. MicroRNAs, cancer and cancer stem cells. Cancer Lett 300:10-19. 77. Skalsky RL, Cullen BR. 2010. Viruses, microRNAs, and host interactions. Annu Rev Microbiol 64:123-141. 78. Calin GA, Dumitru CD, Shimizu M, Bichi R, Zupo S, Noch E, Aldler H, Rattan S, Keating M, Rai K, Rassenti L, Kipps T, Negrini M, Bullrich F, Croce CM. 2002. Frequent deletions and down-regulation of micro- RNA genes miR15 and miR16 at 13q14 in chronic lymphocytic leukemia. Proc Natl Acad Sci U S A 99:15524-15529. 79. Nicoloso MS, Spizzo R, Shimizu M, Rossi S, Calin GA. 2009. MicroRNAs--the micro steering wheel of tumour metastases. Nat Rev Cancer 9:293-302. 80. Garzon R, Calin GA, Croce CM. 2009. MicroRNAs in Cancer. Annual review of medicine 60:167-179. 81. Li C, Feng Y, Coukos G, Zhang L. 2009. Therapeutic microRNA strategies in human cancer. The AAPS journal 11:747-757. 82. Chuang JC, Jones PA. 2007. Epigenetics and microRNAs. Pediatric research 61:24R-29R. 83. Cortez MA, Calin GA. 2009. MicroRNA identification in plasma and serum: a new tool to diagnose and monitor diseases. Expert Opin Biol Ther 9:703-711. 84. Wittmann J, Jack HM. 2010. Serum microRNAs as powerful cancer biomarkers. Biochim Biophys Acta 1806:200-207. 85. Lindsay MA. 2008. microRNAs and the immune response. Trends Immunol 29:343-351. 86. O'Connell RM, Rao DS, Chaudhuri AA, Baltimore D. 2010. Physiological and pathological roles for microRNAs in the immune system. Nat Rev Immunol 10:111-122. 87. Contreras J, Rao DS. 2012. MicroRNAs in inflammation and immune responses. Leukemia 26:404-413. 88. Sabin LR, Zhou R, Gruber JJ, Lukinova N, Bambina S, Berman A, Lau CK, Thompson CB, Cherry S. 2009. Ars2 regulates both miRNA- and siRNA- dependent silencing and suppresses RNA virus infection in Drosophila. Cell 138:340-351. 89. Ding SW, Voinnet O. 2007. Antiviral immunity directed by small RNAs. Cell 130:413-426. 90. Filipowicz W. 2005. RNAi: the nuts and bolts of the RISC machine. Cell 122:17-20. 91. John M, Constien R, Akinc A, Goldberg M, Moon YA, Spranger M, Hadwiger P, Soutschek J, Vornlocher HP, Manoharan M, Stoffel M, Langer R, Anderson DG, Horton JD, Koteliansky V, Bumcrot D. 2007. Effective RNAi-mediated gene silencing without interruption of the endogenous microRNA pathway. Nature 449:745-747. 92. Voinnet O. 2005. Induction and suppression of RNA silencing: insights from viral infections. Nat Rev Genet 6:206-220. 93. Gottwein E, Cullen BR. 2008. Viral and cellular microRNAs as determinants of viral pathogenesis and immunity. Cell Host Microbe 3:375-387.   158 94. Grundhoff A, Sullivan CS. 2011. Virus-encoded microRNAs. Virology 411:325-343. 95. Pfeffer S, Zavolan M, Grasser FA, Chien M, Russo JJ, Ju J, John B, Enright AJ, Marks D, Sander C, Tuschl T. 2004. Identification of virus-encoded microRNAs. Science 304:734-736. 96. Cullen BR. 2013. MicroRNAs as mediators of viral evasion of the immune system. Nat Immunol 14:205-210. 97. Greco D, Kivi N, Qian K, Leivonen SK, Auvinen P, Auvinen E. 2011. Human papillomavirus 16 E5 modulates the expression of host microRNAs. PLoS ONE 6:e21646. 98. Backes S, Shapiro JS, Sabin LR, Pham AM, Reyes I, Moss B, Cherry S, tenOever BR. 2012. Degradation of host microRNAs by poxvirus poly(A) polymerase reveals terminal RNA methylation as a protective antiviral mechanism. Cell Host Microbe 12:200-210. 99. Cai X, Li G, Laimins LA, Cullen BR. 2006. Human papillomavirus genotype 31 does not express detectable microRNA levels during latent or productive virus replication. J Virol 80:10890-10893. 100. Cai X, Schafer A, Lu S, Bilello JP, Desrosiers RC, Edwards R, Raab-Traub N, Cullen BR. 2006. Epstein-Barr virus microRNAs are evolutionarily conserved and differentially expressed. PLoS Pathog 2:e23. 101. Zhu JY, Pfuhl T, Motsch N, Barth S, Nicholls J, Grasser F, Meister G. 2009. Identification of novel Epstein-Barr virus microRNA genes from nasopharyngeal carcinomas. J Virol 83:3333-3341. 102. Lu F, Weidmer A, Liu CG, Volinia S, Croce CM, Lieberman PM. 2008. Epstein-Barr virus-induced miR-155 attenuates NF-kappaB signaling and stabilizes latent virus persistence. J Virol 82:10436-10443. 103. Yin Q, McBride J, Fewell C, Lacey M, Wang X, Lin Z, Cameron J, Flemington EK. 2008. MicroRNA-155 is an Epstein-Barr virus-induced gene that modulates Epstein-Barr virus-regulated gene expression pathways. J Virol 82:5295-5306. 104. Choy EY, Siu KL, Kok KH, Lung RW, Tsang CM, To KF, Kwong DL, Tsao SW, Jin DY. 2008. An Epstein-Barr virus-encoded microRNA targets PUMA to promote host cell survival. J Exp Med 205:2551-2560. 105. Marquitz AR, Mathur A, Nam CS, Raab-Traub N. 2011. The Epstein-Barr Virus BART microRNAs target the pro-apoptotic protein Bim. Virology 412:392-400. 106. Riley KJ, Rabinowitz GS, Yario TA, Luna JM, Darnell RB, Steitz JA. 2012. EBV and human microRNAs co-target oncogenic and apoptotic viral and human genes during latency. EMBO J 31:2207-2221. 107. Skalsky RL, Corcoran DL, Gottwein E, Frank CL, Kang D, Hafner M, Nusbaum JD, Feederle R, Delecluse HJ, Luftig MA, Tuschl T, Ohler U, Cullen BR. 2012. The viral and cellular microRNA targetome in lymphoblastoid cell lines. PLoS Pathog 8:e1002484. 108. Cameron JE, Yin Q, Fewell C, Lacey M, McBride J, Wang X, Lin Z, Schaefer BC, Flemington EK. 2008. Epstein-Barr virus latent membrane protein 1 induces cellular MicroRNA miR-146a, a modulator of lymphocyte signaling pathways. J Virol 82:1946-1958.   159 109. Cai X, Lu S, Zhang Z, Gonzalez CM, Damania B, Cullen BR. 2005. Kaposi's sarcoma-associated herpesvirus expresses an array of viral microRNAs in latently infected cells. Proc Natl Acad Sci U S A 102:5570-5575. 110. Pfeffer S, Sewer A, Lagos-Quintana M, Sheridan R, Sander C, Grasser FA, van Dyk LF, Ho CK, Shuman S, Chien M, Russo JJ, Ju J, Randall G, Lindenbach BD, Rice CM, Simon V, Ho DD, Zavolan M, Tuschl T. 2005. Identification of microRNAs of the herpesvirus family. Nat Methods 2:269-276. 111. Abend JR, Ramalingam D, Kieffer-Kwon P, Uldrick TS, Yarchoan R, Ziegelbauer JM. 2012. Kaposi's sarcoma-associated herpesvirus microRNAs target IRAK1 and MYD88, two components of the toll-like receptor/interleukin-1R signaling cascade, to reduce inflammatory-cytokine expression. J Virol 86:11663-11674. 112. Lei X, Zhu Y, Jones T, Bai Z, Huang Y, Gao SJ. 2012. A Kaposi's sarcoma-associated herpesvirus microRNA and its variants target the transforming growth factor beta pathway to promote cell survival. J Virol 86:11698-11711. 113. Suffert G, Malterer G, Hausser J, Viiliainen J, Fender A, Contrant M, Ivacevic T, Benes V, Gros F, Voinnet O, Zavolan M, Ojala PM, Haas JG, Pfeffer S. 2011. Kaposi's sarcoma herpesvirus microRNAs target caspase 3 and regulate apoptosis. PLoS Pathog 7:e1002405. 114. Tang S, Patel A, Krause PR. 2009. Novel less-abundant viral microRNAs encoded by herpes simplex virus 2 latency-associated transcript and their roles in regulating ICP34.5 and ICP0 mRNAs. J Virol 83:1433-1442. 115. Jurak I, Kramer MF, Mellor JC, van Lint AL, Roth FP, Knipe DM, Coen DM. 2010. Numerous conserved and divergent microRNAs expressed by herpes simplex viruses 1 and 2. J Virol 84:4659-4672. 116. Umbach JL, Kramer MF, Jurak I, Karnowski HW, Coen DM, Cullen BR. 2008. MicroRNAs expressed by herpes simplex virus 1 during latent infection regulate viral mRNAs. Nature 454:780-783. 117. Stark TJ, Arnold JD, Spector DH, Yeo GW. 2012. High-resolution profiling and analysis of viral and host small RNAs during human cytomegalovirus infection. J Virol 86:226-235. 118. Swaminathan G, Martin-Garcia J, Navas-Martin S. 2013. RNA viruses and microRNAs: challenging discoveries for the 21st century. Physiol Genomics 45:1035-1048. 119. Triboulet R, Mari B, Lin YL, Chable-Bessia C, Bennasser Y, Lebrigand K, Cardinaud B, Maurin T, Barbry P, Baillat V, Reynes J, Corbeau P, Jeang KT, Benkirane M. 2007. Suppression of microRNA-silencing pathway by HIV-1 during virus replication. Science 315:1579-1582. 120. Huang J, Wang F, Argyris E, Chen K, Liang Z, Tian H, Huang W, Squires K, Verlinghieri G, Zhang H. 2007. Cellular microRNAs contribute to HIV-1 latency in resting primary CD4+ T lymphocytes. Nat Med 13:1241-1247. 121. Wang X, Ye L, Hou W, Zhou Y, Wang YJ, Metzger DS, Ho WZ. 2009. Cellular microRNA expression correlates with susceptibility of monocytes/macrophages to HIV-1 infection. Blood 113:671-674. 122. Nathans R, Chu CY, Serquina AK, Lu CC, Cao H, Rana TM. 2009. Cellular microRNA and P bodies modulate host-HIV-1 interactions. Mol Cell 34:696-709.   160 123. Chiang K, Liu H, Rice AP. 2013. miR-132 enhances HIV-1 replication. Virology 438:1-4. 124. Ho BC, Yu SL, Chen JJ, Chang SY, Yan BS, Hong QS, Singh S, Kao CL, Chen HY, Su KY, Li KC, Cheng CL, Cheng HW, Lee JY, Lee CN, Yang PC. 2011. Enterovirus-induced miR-141 contributes to shutoff of host protein translation by targeting the translation initiation factor eIF4E. Cell Host Microbe 9:58-69. 125. Jopling CL, Yi M, Lancaster AM, Lemon SM, Sarnow P. 2005. Modulation of hepatitis C virus RNA abundance by a liver-specific MicroRNA. Science 309:1577-1581. 126. Henke JI, Goergen D, Zheng J, Song Y, Schuttler CG, Fehr C, Junemann C, Niepmann M. 2008. microRNA-122 stimulates translation of hepatitis C virus RNA. EMBO J 27:3300-3310. 127. Jopling CL, Schutz S, Sarnow P. 2008. Position-dependent function for a tandem microRNA miR-122-binding site located in the hepatitis C virus RNA genome. Cell Host Microbe 4:77-85. 128. Jangra RK, Yi M, Lemon SM. 2010. Regulation of hepatitis C virus translation and infectious virus production by the microRNA miR-122. J Virol 84:6615-6625. 129. Conrad KD, Giering F, Erfurth C, Neumann A, Fehr C, Meister G, Niepmann M. 2013. MicroRNA-122 dependent binding of Ago2 protein to hepatitis C virus RNA is associated with enhanced RNA stability and translation stimulation. PLoS One 8:e56272. 130. Murakami Y, Aly HH, Tajima A, Inoue I, Shimotohno K. 2009. Regulation of the hepatitis C virus genome replication by miR-199a. J Hepatol 50:453-460. 131. Cheng JC, Yeh YJ, Tseng CP, Hsu SD, Chang YL, Sakamoto N, Huang HD. 2012. Let-7b is a novel regulator of hepatitis C virus replication. Cell Mol Life Sci 69:2621-2633. 132. Chen Y, Chen J, Wang H, Shi J, Wu K, Liu S, Liu Y, Wu J. 2013. HCV-induced miR-21 contributes to evasion of host immune system by targeting MyD88 and IRAK1. PLoS Pathog 9:e1003248. 133. Bhanja Chowdhury J, Shrivastava S, Steele R, Di Bisceglie AM, Ray R, Ray RB. 2012. Hepatitis C virus infection modulates expression of interferon stimulatory gene IFITM1 by upregulating miR-130A. J Virol 86:10221-10225. 134. Kakumani PK, Ponia SS, S RK, Sood V, Chinnappan M, Banerjea AC, Medigeshi GR, Malhotra P, Mukherjee SK, Bhatnagar RK. 2013. Role of RNA interference (RNAi) in dengue virus replication and identification of NS4B as an RNAi suppressor. J Virol 87:8870-8883. 135. Qi Y, Li Y, Zhang L, Huang J. 2013. microRNA expression profiling and bioinformatic analysis of dengue virusinfected peripheral blood mononuclear cells. Molecular medicine reports 7:791-798. 136. Wu S, He L, Li Y, Wang T, Feng L, Jiang L, Zhang P, Huang X. 2013. miR-146a facilitates replication of dengue virus by dampening interferon induction by targeting TRAF6. The Journal of infection 67:329-341. 137. Smith JL, Grey FE, Uhrlaub JL, Nikolich-Zugich J, Hirsch AJ. 2012. Induction of the cellular microRNA, Hs_154, by West Nile virus contributes to virus-mediated apoptosis through repression of antiapoptotic factors. J Virol 86:5278-5287. 138. Wang Y, Brahmakshatriya V, Zhu H, Lupiani B, Reddy SM, Yoon BJ, Gunaratne PH, Kim JH, Chen R, Wang J, Zhou H. 2009. Identification of differentially expressed   161 miRNAs in chicken lung and trachea with avian influenza virus infection by a deep sequencing approach. BMC Genomics 10:512. 139. Li Y, Chan EY, Li J, Ni C, Peng X, Rosenzweig E, Tumpey TM, Katze MG. 2010. MicroRNA expression and virulence in pandemic influenza virus-infected mice. J Virol 84:3023-3032. 140. Skovgaard K, Cirera S, Vasby D, Podolska A, Breum SO, Durrwald R, Schlegel M, Heegaard PM. 2013. Expression of innate immune genes, proteins and microRNAs in lung tissue of pigs infected experimentally with influenza virus (H1N2). Innate immunity 19:531-544. 141. Li Y, Li J, Belisle S, Baskin CR, Tumpey TM, Katze MG. 2011. Differential microRNA expression and virulence of avian, 1918 reassortant, and reconstructed 1918 influenza A viruses. Virology 421:105-113. 142. Varble A, Chua MA, Perez JT, Manicassamy B, Garcia-Sastre A, tenOever BR. 2010. Engineered RNA viral synthesis of microRNAs. Proc Natl Acad Sci U S A 107:11519-11524. 143. Perez JT, Pham AM, Lorini MH, Chua MA, Steel J, tenOever BR. 2009. MicroRNA-mediated species-specific attenuation of influenza A virus. Nat Biotechnol 27:572-576. 144. Song H, Wang Q, Guo Y, Liu S, Song R, Gao X, Dai L, Li B, Zhang D, Cheng J. 2013. Microarray analysis of microRNA expression in peripheral blood mononuclear cells of critically ill patients with influenza A (H1N1). BMC Infect Dis 13:257. 145. Lanford RE, Hildebrandt-Eriksen ES, Petri A, Persson R, Lindow M, Munk ME, Kauppinen S, Orum H. 2010. Therapeutic silencing of microRNA-122 in primates with chronic hepatitis C virus infection. Science 327:198-201. 146. Janssen HL, Kauppinen S, Hodges MR. 2013. HCV infection and miravirsen. N Engl J Med 369:878. 147. Janssen HL, Reesink HW, Lawitz EJ, Zeuzem S, Rodriguez-Torres M, Patel K, van der Meer AJ, Patick AK, Chen A, Zhou Y, Persson R, King BD, Kauppinen S, Levin AA, Hodges MR. 2013. Treatment of HCV infection by targeting microRNA. N Engl J Med 368:1685-1694. 148. Lindow M, Kauppinen S. 2012. Discovering the first microRNA-targeted drug. J Cell Biol 199:407-412. 149. Webster RG, Bean WJ, Gorman OT, Chambers TM, Kawaoka Y. 1992. Evolution and ecology of influenza A viruses. Microbiol Rev 56:152-179. 150. Boulo S, Akarsu H, Ruigrok RW, Baudin F. 2007. Nuclear traffic of influenza virus proteins and ribonucleoprotein complexes. Virus Res 124:12-21. 151. Daniels PS, Jeffries S, Yates P, Schild GC, Rogers GN, Paulson JC, Wharton SA, Douglas AR, Skehel JJ, Wiley DC. 1987. The receptor-binding and membrane-fusion properties of influenza virus variants selected using anti-haemagglutinin monoclonal antibodies. EMBO J 6:1459-1465. 152. Weis W, Brown JH, Cusack S, Paulson JC, Skehel JJ, Wiley DC. 1988. Structure of the influenza virus haemagglutinin complexed with its receptor, sialic acid. Nature 333:426-431. 153. Skehel JJ, Wiley DC. 2000. Receptor binding and membrane fusion in virus entry: the influenza hemagglutinin. Annu Rev Biochem 69:531-569.   162 154. Colman PM, Lawrence MC. 2003. The structural biology of type I viral membrane fusion. Nat Rev Mol Cell Biol 4:309-319. 155. Skehel JJ, Waterfield MD, McCauley JW, Elder K, Wiley DC. 1980. Studies on the Structure of the Haemagglutinin. Philosophical Transactions of the Royal Society B: Biological Sciences 288:335-339. 156. Garten W, Klenk HD. 1983. Characterization of the carboxypeptidase involved in the proteolytic cleavage of the influenza haemagglutinin. J Gen Virol 64 (Pt 10):2127-2137. 157. Webster RG, Rott R. 1987. Influenza virus A pathogenicity: the pivotal role of hemagglutinin. Cell 50:665-666. 158. Skehel JJ, Bayley PM, Brown EB, Martin SR, Waterfield MD, White JM, Wilson IA, Wiley DC. 1982. Changes in the conformation of influenza virus hemagglutinin at the pH optimum of virus-mediated membrane fusion. Proc Natl Acad Sci U S A 79:968-972. 159. Lanzrein M, Schlegel A, Kempf C. 1994. Entry and uncoating of enveloped viruses. Biochem J 302 ( Pt 2):313-320. 160. Li ML, Ramirez BC, Krug RM. 1998. RNA-dependent activation of primer RNA production by influenza virus polymerase: different regions of the same protein subunit constitute the two required RNA-binding sites. EMBO J 17:5844-5852. 161. Li ML, Rao P, Krug RM. 2001. The active sites of the influenza cap-dependent endonuclease are on different polymerase subunits. EMBO J 20:2078-2086. 162. Fechter P, Mingay L, Sharps J, Chambers A, Fodor E, Brownlee GG. 2003. Two aromatic residues in the PB2 subunit of influenza A RNA polymerase are crucial for cap binding. J Biol Chem 278:20381-20388. 163. Nagata K, Kawaguchi A, Naito T. 2008. Host factors for replication and transcription of the influenza virus genome. Rev Med Virol 18:247-260. 164. Vreede FT, Jung TE, Brownlee GG. 2004. Model suggesting that replication of influenza virus is regulated by stabilization of replicative intermediates. J Virol 78:9568-9572. 165. Jorba N, Coloma R, Ortin J. 2009. Genetic trans-complementation establishes a new model for influenza virus RNA transcription and replication. PLoS Pathog 5:e1000462. 166. O'Neill RE, Talon J, Palese P. 1998. The influenza virus NEP (NS2 protein) mediates the nuclear export of viral ribonucleoproteins. EMBO J 17:288-296. 167. Nayak DP, Hui EK, Barman S. 2004. Assembly and budding of influenza virus. Virus Res 106:147-165. 168. Ali A, Avalos RT, Ponimaskin E, Nayak DP. 2000. Influenza virus assembly: effect of influenza virus glycoproteins on the membrane association of M1 protein. J Virol 74:8709-8719. 169. Tong S, Zhu X, Li Y, Shi M, Zhang J, Bourgeois M, Yang H, Chen X, Recuenco S, Gomez J, Chen LM, Johnson A, Tao Y, Dreyfus C, Yu W, McBride R, Carney PJ, Gilbert AT, Chang J, Guo Z, Davis CT, Paulson JC, Stevens J, Rupprecht CE, Holmes EC, Wilson IA, Donis RO. 2013. New world bats harbor diverse influenza A viruses. PLoS Pathog 9:e1003657. 170. Wu Y, Wu Y, Tefsen B, Shi Y, Gao GF. 2014. Bat-derived influenza-like viruses H17N10 and H18N11. Trends Microbiol 22:183-191.   163 171. Gamblin SJ, Skehel JJ. 2010. Influenza hemagglutinin and neuraminidase membrane glycoproteins. J Biol Chem 285:28403-28409. 172. Wiley DC, Skehel JJ. 1987. The structure and function of the hemagglutinin membrane glycoprotein of influenza virus. Annu Rev Biochem 56:365-394. 173. Jones LV, Compans RW, Davis AR, Bos TJ, Nayak DP. 1985. Surface expression of influenza virus neuraminidase, an amino-terminally anchored viral membrane glycoprotein, in polarized epithelial cells. Mol Cell Biol 5:2181-2189. 174. Vanderlinden E, Naesens L. 2014. Emerging antiviral strategies to interfere with influenza virus entry. Medicinal research reviews 34:301-339. 175. Rogers GN, Paulson JC. 1983. Receptor determinants of human and animal influenza virus isolates: differences in receptor specificity of the H3 hemagglutinin based on species of origin. Virology 127:361-373. 176. Ito T. 2000. Interspecies transmission and receptor recognition of influenza A viruses. Microbiology and immunology 44:423-430. 177. Chandrasekaran A, Srinivasan A, Raman R, Viswanathan K, Raguram S, Tumpey TM, Sasisekharan V, Sasisekharan R. 2008. Glycan topology determines human adaptation of avian H5N1 virus hemagglutinin. Nat Biotechnol 26:107-113. 178. Matrosovich M, Tuzikov A, Bovin N, Gambaryan A, Klimov A, Castrucci MR, Donatelli I, Kawaoka Y. 2000. Early alterations of the receptor-binding properties of H1, H2, and H3 avian influenza virus hemagglutinins after their introduction into mammals. J Virol 74:8502-8512. 179. Baum LG, Paulson JC. 1990. Sialyloligosaccharides of the respiratory epithelium in the selection of human influenza virus receptor specificity. Acta histochemica. Supplementband 40:35-38. 180. Shinya K, Ebina M, Yamada S, Ono M, Kasai N, Kawaoka Y. 2006. Avian flu: influenza virus receptors in the human airway. Nature 440:435-436. 181. Guo CT, Takahashi N, Yagi H, Kato K, Takahashi T, Yi SQ, Chen Y, Ito T, Otsuki K, Kida H, Kawaoka Y, Hidari KI, Miyamoto D, Suzuki T, Suzuki Y. 2007. The quail and chicken intestine have sialyl-galactose sugar chains responsible for the binding of influenza A viruses to human type receptors. Glycobiology 17:713-724. 182. Wilson IA, Skehel JJ, Wiley DC. 1981. Structure of the haemagglutinin membrane glycoprotein of influenza virus at 3 A resolution. Nature 289:366-373. 183. Steinhauer DA, Wharton SA, Skehel JJ, Wiley DC. 1995. Studies of the membrane fusion activities of fusion peptide mutants of influenza virus hemagglutinin. J Virol 69:6643-6651. 184. Kawaoka Y, Webster RG. 1988. Sequence requirements for cleavage activation of influenza virus hemagglutinin expressed in mammalian cells. Proc Natl Acad Sci U S A 85:324-328. 185. Laver WG. 1978. Crystallization and peptide maps of neuraminidase "heads" from H2N2 and H3N2 influenza virus strains. Virology 86:78-87. 186. Blok J, Air GM. 1982. Variation in the membrane-insertion and "stalk" sequences in eight subtypes of influenza type A virus neuraminidase. Biochemistry 21:4001-4007. 187. Wiley DC. 1983. Neuraminidase of influenza virus reveals a flower-like head. Nature 303:19-20.   164 188. Gottschalk A. 1956. Neuraminic acid; the functional group of some biologically active mucoproteins. The Yale journal of biology and medicine 28:525-537. 189. Air GM. 2012. Influenza neuraminidase. Influenza and other respiratory viruses 6:245-256. 190. Chong AK, Pegg MS, Taylor NR, von Itzstein M. 1992. Evidence for a sialosyl cation transition-state complex in the reaction of sialidase from influenza virus. European journal of biochemistry / FEBS 207:335-343. 191. Matrosovich MN, Matrosovich TY, Gray T, Roberts NA, Klenk HD. 2004. Neuraminidase is important for the initiation of influenza virus infection in human airway epithelium. J Virol 78:12665-12667. 192. Masunaga K, Mizumoto K, Kato H, Ishihama A, Toyoda T. 1999. Molecular mapping of influenza virus RNA polymerase by site-specific antibodies. Virology 256:130-141. 193. Hara K, Schmidt FI, Crow M, Brownlee GG. 2006. Amino acid residues in the N-terminal region of the PA subunit of influenza A virus RNA polymerase play a critical role in protein stability, endonuclease activity, cap binding, and virion RNA promoter binding. J Virol 80:7789-7798. 194. Miotto O, Heiny A, Tan TW, August JT, Brusic V. 2008. Identification of human-to-human transmissibility factors in PB2 proteins of influenza A by large-scale mutual information analysis. BMC Bioinformatics 9 Suppl 1:S18. 195. Steel J, Lowen AC, Mubareka S, Palese P. 2009. Transmission of influenza virus in a mammalian host is increased by PB2 amino acids 627K or 627E/701N. PLoS Pathog 5:e1000252. 196. Bussey KA, Bousse TL, Desmet EA, Kim B, Takimoto T. 2010. PB2 residue 271 plays a key role in enhanced polymerase activity of influenza A viruses in mammalian host cells. J Virol 84:4395-4406. 197. Guilligay D, Tarendeau F, Resa-Infante P, Coloma R, Crepin T, Sehr P, Lewis J, Ruigrok RW, Ortin J, Hart DJ, Cusack S. 2008. The structural basis for cap binding by influenza virus polymerase subunit PB2. Nat Struct Mol Biol 15:500-506. 198. Honda A, Mizumoto K, Ishihama A. 1999. Two separate sequences of PB2 subunit constitute the RNA cap-binding site of influenza virus RNA polymerase. Genes Cells 4:475-485. 199. Jung TE, Brownlee GG. 2006. A new promoter-binding site in the PB1 subunit of the influenza A virus polymerase. J Gen Virol 87:679-688. 200. Chen W, Calvo PA, Malide D, Gibbs J, Schubert U, Bacik I, Basta S, O'Neill R, Schickli J, Palese P, Henklein P, Bennink JR, Yewdell JW. 2001. A novel influenza A virus mitochondrial protein that induces cell death. Nat Med 7:1306-1312. 201. Zamarin D, Garcia-Sastre A, Xiao X, Wang R, Palese P. 2005. Influenza virus PB1-F2 protein induces cell death through mitochondrial ANT3 and VDAC1. PLoS Pathog 1:e4. 202. Coleman JR. 2007. The PB1-F2 protein of Influenza A virus: increasing pathogenicity by disrupting alveolar macrophages. Virol J 4:9. 203. Varga ZT, Grant A, Manicassamy B, Palese P. 2012. Influenza virus protein PB1-F2 inhibits the induction of type I interferon by binding to MAVS and decreasing mitochondrial membrane potential. J Virol 86:8359-8366.   165 204. Conenello GM, Palese P. 2007. Influenza A virus PB1-F2: a small protein with a big punch. Cell Host Microbe 2:207-209. 205. Conenello GM, Zamarin D, Perrone LA, Tumpey T, Palese P. 2007. A single mutation in the PB1-F2 of H5N1 (HK/97) and 1918 influenza A viruses contributes to increased virulence. PLoS Pathog 3:1414-1421. 206. McAuley JL, Hornung F, Boyd KL, Smith AM, McKeon R, Bennink J, Yewdell JW, McCullers JA. 2007. Expression of the 1918 influenza A virus PB1-F2 enhances the pathogenesis of viral and secondary bacterial pneumonia. Cell Host Microbe 2:240-249. 207. Chen CJ, Chen GW, Wang CH, Huang CH, Wang YC, Shih SR. 2010. Differential localization and function of PB1-F2 derived from different strains of influenza A virus. J Virol 84:10051-10062. 208. Hai R, Schmolke M, Varga ZT, Manicassamy B, Wang TT, Belser JA, Pearce MB, Garcia-Sastre A, Tumpey TM, Palese P. 2010. PB1-F2 expression by the 2009 pandemic H1N1 influenza virus has minimal impact on virulence in animal models. J Virol 84:4442-4450. 209. Krumbholz A, Philipps A, Oehring H, Schwarzer K, Eitner A, Wutzler P, Zell R. 2011. Current knowledge on PB1-F2 of influenza A viruses. Medical microbiology and immunology 200:69-75. 210. Dias A, Bouvier D, Crepin T, McCarthy AA, Hart DJ, Baudin F, Cusack S, Ruigrok RW. 2009. The cap-snatching endonuclease of influenza virus polymerase resides in the PA subunit. Nature 458:914-918. 211. Fodor E, Smith M. 2004. The PA subunit is required for efficient nuclear accumulation of the PB1 subunit of the influenza A virus RNA polymerase complex. J Virol 78:9144-9153. 212. Hara K, Shiota M, Kido H, Ohtsu Y, Kashiwagi T, Iwahashi J, Hamada N, Mizoue K, Tsumura N, Kato H, Toyoda T. 2001. Influenza virus RNA polymerase PA subunit is a novel serine protease with Ser624 at the active site. Genes Cells 6:87-97. 213. Huarte M, Sanz-Ezquerro JJ, Roncal F, Ortin J, Nieto A. 2001. PA subunit from influenza virus polymerase complex interacts with a cellular protein with homology to a family of transcriptional activators. J Virol 75:8597-8604. 214. Sanz-Ezquerro JJ, de la Luna S, Ortin J, Nieto A. 1995. Individual expression of influenza virus PA protein induces degradation of coexpressed proteins. J Virol 69:2420-2426. 215. Sanz-Ezquerro JJ, Fernandez Santaren J, Sierra T, Aragon T, Ortega J, Ortin J, Smith GL, Nieto A. 1998. The PA influenza virus polymerase subunit is a phosphorylated protein. J Gen Virol 79 ( Pt 3):471-478. 216. Sanz-Ezquerro JJ, Zurcher T, de la Luna S, Ortin J, Nieto A. 1996. The amino-terminal one-third of the influenza virus PA protein is responsible for the induction of proteolysis. J Virol 70:1905-1911. 217. Wang Q, Zhang S, Jiang H, Wang J, Weng L, Mao Y, Sekiguchi S, Yasui F, Kohara M, Buchy P, Deubel V, Xu K, Sun B, Toyoda T. 2012. PA from an H5N1 highly pathogenic avian influenza virus activates viral transcription and replication and induces apoptosis and interferon expression at an early stage of infection. Virol J 9:106. 218. Portela A, Digard P. 2002. The influenza virus nucleoprotein: a multifunctional RNA-binding protein pivotal to virus replication. J Gen Virol 83:723-734.   166 219. York A, Fodor E. 2013. Biogenesis, assembly, and export of viral messenger ribonucleoproteins in the influenza A virus infected cell. RNA Biol 10:1274-1282. 220. Valcarcel J, Portela A, Ortin J. 1991. Regulated M1 mRNA splicing in influenza virus-infected cells. J Gen Virol 72 ( Pt 6):1301-1308. 221. Fan S, Deng G, Song J, Tian G, Suo Y, Jiang Y, Guan Y, Bu Z, Kawaoka Y, Chen H. 2009. Two amino acid residues in the matrix protein M1 contribute to the virulence difference of H5N1 avian influenza viruses in mice. Virology 384:28-32. 222. Schnell JR, Chou JJ. 2008. Structure and mechanism of the M2 proton channel of influenza A virus. Nature 451:591-595. 223. Fernandez-Sesma A, Marukian S, Ebersole BJ, Kaminski D, Park MS, Yuen T, Sealfon SC, Garcia-Sastre A, Moran TM. 2006. Influenza virus evades innate and adaptive immunity via the NS1 protein. J Virol 80:6295-6304. 224. Haye K, Burmakina S, Moran T, Garcia-Sastre A, Fernandez-Sesma A. 2009. The NS1 protein of a human influenza virus inhibits type I interferon production and the induction of antiviral responses in primary human dendritic and respiratory epithelial cells. J Virol 83:6849-6862. 225. Jia D, Rahbar R, Chan RW, Lee SM, Chan MC, Wang BX, Baker DP, Sun B, Peiris JS, Nicholls JM, Fish EN. 2010. Influenza virus non-structural protein 1 (NS1) disrupts interferon signaling. PLoS ONE 5:e13927. 226. Li W, Wang G, Zhang H, Xin G, Zhang D, Zeng J, Chen X, Xu Y, Cui Y, Li K. 2010. Effects of NS1 variants of H5N1 influenza virus on interferon induction, TNFalpha response and p53 activity. Cell Mol Immunol 7:235-242. 227. Hale BG, Randall RE, Ortin J, Jackson D. 2008. The multifunctional NS1 protein of influenza A viruses. J Gen Virol 89:2359-2376. 228. Ehrhardt C, Wolff T, Pleschka S, Planz O, Beermann W, Bode JG, Schmolke M, Ludwig S. 2007. Influenza A virus NS1 protein activates the PI3K/Akt pathway to mediate antiapoptotic signaling responses. J Virol 81:3058-3067. 229. Enami K, Sato TA, Nakada S, Enami M. 1994. Influenza virus NS1 protein stimulates translation of the M1 protein. Journal of virology 68:1432-1437. 230. Luna Sdl, Fortes P, Beloso A, Ortín J. 1995. Influenza virus NS1 protein enhances the rate of translation initiation of viral mRNAs. Journal of Virology 69:2427-2433. 231. Matsuda M, Suizu F, Hirata N, Miyazaki T, Obuse C, Noguchi M. 2010. Characterization of the interaction of influenza virus NS1 with Akt. Biochem Biophys Res Commun 395:312-317. 232. Min JY, Li S, Sen GC, Krug RM. 2007. A site on the influenza A virus NS1 protein mediates both inhibition of PKR activation and temporal regulation of viral RNA synthesis. Virology 363:236-243. 233. Zhirnov OP, Konakova TE, Wolff T, Klenk HD. 2002. NS1 protein of influenza A virus down-regulates apoptosis. J Virol 76:1617-1625. 234. Li Z, Jiang Y, Jiao P, Wang A, Zhao F, Tian G, Wang X, Yu K, Bu Z, Chen H. 2006. The NS1 gene contributes to the virulence of H5N1 avian influenza viruses. J Virol 80:11115-11123. 235. Zhang C, Yang Y, Zhou X, Liu X, Song H, He Y, Huang P. 2010. Highly pathogenic avian influenza A virus H5N1 NS1 protein induces caspase-dependent apoptosis in human alveolar basal epithelial cells. Virol J 7:51.   167 236. Paterson D, Fodor E. 2012. Emerging roles for the influenza A virus nuclear export protein (NEP). PLoS Pathog 8:e1003019. 237. Neumann G, Hughes MT, Kawaoka Y. 2000. Influenza A virus NS2 protein mediates vRNP nuclear export through NES-independent interaction with hCRM1. EMBO J 19:6751-6758. 238. Robb NC, Smith M, Vreede FT, Fodor E. 2009. NS2/NEP protein regulates transcription and replication of the influenza virus RNA genome. J Gen Virol 90:1398-1407. 239. Govorkova EA, McCullers JA. 2013. Therapeutics against influenza. Curr Top Microbiol Immunol 370:273-300. 240. Peiris JS, de Jong MD, Guan Y. 2007. Avian influenza virus (H5N1): a threat to human health. Clin Microbiol Rev 20:243-267. 241. Pielak RM, Schnell JR, Chou JJ. 2009. Mechanism of drug inhibition and drug resistance of influenza A M2 channel. Proc Natl Acad Sci U S A 106:7379-7384. 242. Das K, Aramini JM, Ma LC, Krug RM, Arnold E. 2010. Structures of influenza A proteins and insights into antiviral drug targets. Nat Struct Mol Biol 17:530-538. 243. Furuta Y, Takahashi K, Fukuda Y, Kuno M, Kamiyama T, Kozaki K, Nomura N, Egawa H, Minami S, Watanabe Y, Narita H, Shiraki K. 2002. In vitro and in vivo activities of anti-influenza virus compound T-705. Antimicrob Agents Chemother 46:977-981. 244. Honda T, Kubo S, Masuda T, Arai M, Kobayashi Y, Yamashita M. 2009. Synthesis and in vivo influenza virus-inhibitory effect of ester prodrug of 4-guanidino-7-O-methyl-Neu5Ac2en. Bioorganic & medicinal chemistry letters 19:2938-2940. 245. Koyama K, Takahashi M, Oitate M, Nakai N, Takakusa H, Miura S, Okazaki O. 2009. CS-8958, a prodrug of the novel neuraminidase inhibitor R-125489, demonstrates a favorable long-retention profile in the mouse respiratory tract. Antimicrob Agents Chemother 53:4845-4851. 246. Malakhov MP, Aschenbrenner LM, Smee DF, Wandersee MK, Sidwell RW, Gubareva LV, Mishin VP, Hayden FG, Kim DH, Ing A, Campbell ER, Yu M, Fang F. 2006. Sialidase fusion protein as a novel broad-spectrum inhibitor of influenza virus infection. Antimicrob Agents Chemother 50:1470-1479. 247. O'Keefe BR, Smee DF, Turpin JA, Saucedo CJ, Gustafson KR, Mori T, Blakeslee D, Buckheit R, Boyd MR. 2003. Potent anti-influenza activity of cyanovirin-N and interactions with viral hemagglutinin. Antimicrob Agents Chemother 47:2518-2525. 248. Palese P. 2006. Making better influenza virus vaccines? Emerg Infect Dis 12:61-65. 249. Carrat F, Flahault A. 2007. Influenza vaccine: the challenge of antigenic drift. Vaccine 25:6852-6862. 250. de Jong JC, Beyer WE, Palache AM, Rimmelzwaan GF, Osterhaus AD. 2000. Mismatch between the 1997/1998 influenza vaccine and the major epidemic A(H3N2) virus strain as the cause of an inadequate vaccine-induced antibody response to this strain in the elderly. J Med Virol 61:94-99. 251. Kilbourne ED, Schulman JL, Schild GC, Schloer G, Swanson J, Bucher D. 1971. Related studies of a recombinant influenza-virus vaccine. I. Derivation and characterization of virus and vaccine. J Infect Dis 124:449-462.   168 252. Donis RO, Cox NJ. 2011. Prospecting the influenza hemagglutinin to develop universal vaccines. Clin Infect Dis 52:1010-1012. 253. Pica N, Palese P. 2013. Toward a universal influenza virus vaccine: prospects and challenges. Annual review of medicine 64:189-202. 254. Shoham D. 2006. Review: molecular evolution and the feasibility of an avian influenza virus becoming a pandemic strain--a conceptual shift. Virus Genes 33:127-132. 255. Richt JrA, Webby RJ. 2012. Swine Influenza, vol. 370. Springer Verlag Berlin Heidelberg  256. Porta M. 2008. A Dictionary of Epidemiology., 5th ed. Oxford University Press, New York, NY. 257. Beveridge WIB. 1991. The Chronicle of Influenza Epidemics. History and Philosophy of the Life Sciences 13:223-234. 258. Taubenberger J, Morens D. 2009. Pandemic influenza – including a risk assessment of H5N1. Rev Sci Tech. 28:187-202. 259. Taubenberger JK, Morens DM. 2010. Influenza: the once and future pandemic. Public Health Rep 125 Suppl 3:16-26. 260. Watanabe T, Kawaoka Y. 2011. Pathogenesis of the 1918 pandemic influenza virus. PLoS Pathog 7:e1001218. 261. Smith GJ, Vijaykrishna D, Bahl J, Lycett SJ, Worobey M, Pybus OG, Ma SK, Cheung CL, Raghwani J, Bhatt S, Peiris JS, Guan Y, Rambaut A. 2009. Origins and evolutionary genomics of the 2009 swine-origin H1N1 influenza A epidemic. Nature 459:1122-1125. 262. Garten RJ, Davis CT, Russell CA, Shu B, Lindstrom S, Balish A, Sessions WM, Xu X, Skepner E, Deyde V, Okomo-Adhiambo M, Gubareva L, Barnes J, Smith CB, Emery SL, Hillman MJ, Rivailler P, Smagala J, de Graaf M, Burke DF, Fouchier RA, Pappas C, Alpuche-Aranda CM, Lopez-Gatell H, Olivera H, Lopez I, Myers CA, Faix D, Blair PJ, Yu C, Keene KM, Dotson PD, Jr., Boxrud D, Sambol AR, Abid SH, St George K, Bannerman T, Moore AL, Stringer DJ, Blevins P, Demmler-Harrison GJ, Ginsberg M, Kriner P, Waterman S, Smole S, Guevara HF, Belongia EA, Clark PA, Beatrice ST, Donis R, Katz J, Finelli L, Bridges CB, Shaw M, Jernigan DB, Uyeki TM, Smith DJ, Klimov AI, Cox NJ. 2009. Antigenic and genetic characteristics of swine-origin 2009 A(H1N1) influenza viruses circulating in humans. Science 325:197-201. 263. Mangtani P, Mak TK, Pfeifer D. 2009. Pandemic H1N1 infection in pregnant women in the USA. Lancet 374:429-430. 264. Perez-Padilla R, de la Rosa-Zamboni D, Ponce de Leon S, Hernandez M, Quinones-Falconi F, Bautista E, Ramirez-Venegas A, Rojas-Serrano J, Ormsby CE, Corrales A, Higuera A, Mondragon E, Cordova-Villalobos JA. 2009. Pneumonia and respiratory failure from swine-origin influenza A (H1N1) in Mexico. N Engl J Med 361:680-689. 265. Korteweg C, Gu J. 2010. Pandemic influenza A (H1N1) virus infection and avian influenza A (H5N1) virus infection: a comparative analysis. Biochem Cell Biol 88:575-587. 266. Shieh WJ, Blau DM, Denison AM, Deleon-Carnes M, Adem P, Bhatnagar J, Sumner J, Liu L, Patel M, Batten B, Greer P, Jones T, Smith C, Bartlett J,   169 Montague J, White E, Rollin D, Gao R, Seales C, Jost H, Metcalfe M, Goldsmith CS, Humphrey C, Schmitz A, Drew C, Paddock C, Uyeki TM, Zaki SR. 2010. 2009 pandemic influenza A (H1N1): pathology and pathogenesis of 100 fatal cases in the United States. Am J Pathol 177:166-175. 267. Wahlgren J. 2011. Influenza A viruses: an ecology review. Infection ecology & epidemiology 1. 268. Stieneke-Grober A, Vey M, Angliker H, Shaw E, Thomas G, Roberts C, Klenk HD, Garten W. 1992. Influenza virus hemagglutinin with multibasic cleavage site is activated by furin, a subtilisin-like endoprotease. EMBO J 11:2407-2414. 269. Garten W, Hallenberger S, Ortmann D, Schafer W, Vey M, Angliker H, Shaw E, Klenk HD. 1994. Processing of viral glycoproteins by the subtilisin-like endoprotease furin and its inhibition by specific peptidylchloroalkylketones. Biochimie 76:217-225. 270. Walker JA, Molloy SS, Thomas G, Sakaguchi T, Yoshida T, Chambers TM, Kawaoka Y. 1994. Sequence specificity of furin, a proprotein-processing endoprotease, for the hemagglutinin of a virulent avian influenza virus. J Virol 68:1213-1218. 271. Perdue ML, Garcia M, Senne D, Fraire M. 1997. Virulence-associated sequence duplication at the hemagglutinin cleavage site of avian influenza viruses. Virus Res 49:173-186. 272. Hirst M, Astell CR, Griffith M, Coughlin SM, Moksa M, Zeng T, Smailus DE, Holt RA, Jones S, Marra MA, Petric M, Krajden M, Lawrence D, Mak A, Chow R, Skowronski DM, Tweed SA, Goh S, Brunham RC, Robinson J, Bowes V, Sojonky K, Byrne SK, Li Y, Kobasa D, Booth T, Paetzel M. 2004. Novel avian influenza H7N3 strain outbreak, British Columbia. Emerg Infect Dis 10:2192-2195. 273. Suarez DL, Senne DA, Banks J, Brown IH, Essen SC, Lee CW, Manvell RJ, Mathieu-Benson C, Moreno V, Pedersen JC, Panigrahy B, Rojas H, Spackman E, Alexander DJ. 2004. Recombination resulting in virulence shift in avian influenza outbreak, Chile. Emerg Infect Dis 10:693-699. 274. Lupiani B, Reddy SM. 2009. The history of avian influenza. Comp Immunol Microbiol Infect Dis 32:311-323. 275. Kaplan BS, Webby RJ. 2013. The avian and mammalian host range of highly pathogenic avian H5N1 influenza. Virus Res 178:3-11. 276. Shortridge KF, Zhou NN, Guan Y, Gao P, Ito T, Kawaoka Y, Kodihalli S, Krauss S, Markwell D, Murti KG, Norwood M, Senne D, Sims L, Takada A, Webster RG. 1998. Characterization of avian H5N1 influenza viruses from poultry in Hong Kong. Virology 252:331-342. 277. Xu X, Subbarao, Cox NJ, Guo Y. 1999. Genetic characterization of the pathogenic influenza A/Goose/Guangdong/1/96 (H5N1) virus: similarity of its hemagglutinin gene to those of H5N1 viruses from the 1997 outbreaks in Hong Kong. Virology 261:15-19. 278. Chan M, Chan R, Tsao G, Peiris J. 2013. Replication and pathogenesis of avian influenza A (H5N1) virus infection in polarised human bronchial and alveolar epithelium. Hong Kong Med J 19:24-28. 279. Katz JM, Lu X, Tumpey TM, Smith CB, Shaw MW, Subbarao K. 2000. Molecular correlates of influenza A H5N1 virus pathogenesis in mice. J Virol 74:10807-10810. 280. Korteweg C, Gu J. 2008. Pathology, molecular biology, and pathogenesis of avian influenza A (H5N1) infection in humans. Am J Pathol 172:1155-1170.   170 281. Lee SM, Gardy JL, Cheung CY, Cheung TK, Hui KP, Ip NY, Guan Y, Hancock RE, Peiris JS. 2009. Systems-level comparison of host-responses elicited by avian H5N1 and seasonal H1N1 influenza viruses in primary human macrophages. PLoS ONE 4:e8072. 282. Szretter KJ, Gangappa S, Lu X, Smith C, Shieh WJ, Zaki SR, Sambhara S, Tumpey TM, Katz JM. 2007. Role of host cytokine responses in the pathogenesis of avian H5N1 influenza viruses in mice. J Virol 81:2736-2744. 283. Uiprasertkul M, Kitphati R, Puthavathana P, Kriwong R, Kongchanagul A, Ungchusak K, Angkasekwinai S, Chokephaibulkit K, Srisook K, Vanprapar N, Auewarakul P. 2007. Apoptosis and pathogenesis of avian influenza A (H5N1) virus in humans. Emerg Infect Dis 13:708-712. 284. Herfst S, Schrauwen EJ, Linster M, Chutinimitkul S, de Wit E, Munster VJ, Sorrell EM, Bestebroer TM, Burke DF, Smith DJ, Rimmelzwaan GF, Osterhaus AD, Fouchier RA. 2012. Airborne transmission of influenza A/H5N1 virus between ferrets. Science 336:1534-1541. 285. Russell CA, Fonville JM, Brown AE, Burke DF, Smith DL, James SL, Herfst S, van Boheemen S, Linster M, Schrauwen EJ, Katzelnick L, Mosterin A, Kuiken T, Maher E, Neumann G, Osterhaus AD, Kawaoka Y, Fouchier RA, Smith DJ. 2012. The potential for respiratory droplet-transmissible A/H5N1 influenza virus to evolve in a mammalian host. Science 336:1541-1547. 286. Yuan J, Zhang L, Kan X, Jiang L, Yang J, Guo Z, Ren Q. 2013. Origin and molecular characteristics of a novel 2013 avian influenza A(H6N1) virus causing human infection in Taiwan. Clin Infect Dis 57:1367-1368. 287. Chen H, Yuan H, Gao R, Zhang J, Wang D, Xiong Y, Fan G, Yang F, Li X, Zhou J, Zou S, Yang L, Chen T, Dong L, Bo H, Zhao X, Zhang Y, Lan Y, Bai T, Dong J, Li Q, Wang S, Zhang Y, Li H, Gong T, Shi Y, Ni X, Li J, Zhou J, Fan J, Wu J, Zhou X, Hu M, Wan J, Yang W, Li D, Wu G, Feng Z, Gao GF, Wang Y, Jin Q, Liu M, Shu Y. 2014. Clinical and epidemiological characteristics of a fatal case of avian influenza A H10N8 virus infection: a descriptive study. Lancet 383:714-721. 288. To KK, Tsang AK, Chan JF, Cheng VC, Chen H, Yuen KY. 2014. Emergence in China of human disease due to avian influenza A(H10N8) - Cause for concern? The Journal of infection 68:205-215. 289. Guo Y, Li J, Cheng X. 1999. Discovery of men infected by avian influenza A (H9N2) virus. Zhonghua Shi Yan He Lin Chuang Bing Du Xue Za Zhi 13:105-108. 290. Peiris M, Yuen KY, Leung CW, Chan KH, Ip PL, Lai RW, Orr WK, Shortridge KF. 1999. Human infection with influenza H9N2. Lancet 354:916-917. 291. Lin YP, Shaw M, Gregory V, Cameron K, Lim W, Klimov A, Subbarao K, Guan Y, Krauss S, Shortridge K, Webster R, Cox N, Hay A. 2000. Avian-to-human transmission of H9N2 subtype influenza A viruses: relationship between H9N2 and H5N1 human isolates. Proc Natl Acad Sci U S A 97:9654-9658. 292. Butt KM, Smith GJ, Chen H, Zhang LJ, Leung YH, Xu KM, Lim W, Webster RG, Yuen KY, Peiris JS, Guan Y. 2005. Human infection with an avian H9N2 influenza A virus in Hong Kong in 2003. J Clin Microbiol 43:5760-5767. 293. Belser JA, Davis CT, Balish A, Edwards LE, Zeng H, Maines TR, Gustin KM, Martinez IL, Fasce R, Cox NJ, Katz JM, Tumpey TM. 2013. Pathogenesis,   171 Transmissibility, and Ocular Tropism of a Highly Pathogenic Avian Influenza A (H7N3) Virus Associated with Human Conjunctivitis. J Virol 87:5746-5754. 294. Tweed SA, Skowronski DM, David ST, Larder A, Petric M, Lees W, Li Y, Katz J, Krajden M, Tellier R, Halpert C, Hirst M, Astell C, Lawrence D, Mak A. 2004. Human illness from avian influenza H7N3, British Columbia. Emerg Infect Dis 10:2196-2199. 295. Fouchier RA, Schneeberger PM, Rozendaal FW, Broekman JM, Kemink SA, Munster V, Kuiken T, Rimmelzwaan GF, Schutten M, Van Doornum GJ, Koch G, Bosman A, Koopmans M, Osterhaus AD. 2004. Avian influenza A virus (H7N7) associated with human conjunctivitis and a fatal case of acute respiratory distress syndrome. Proc Natl Acad Sci U S A 101:1356-1361. 296. de Wit E, Munster VJ, van Riel D, Beyer WE, Rimmelzwaan GF, Kuiken T, Osterhaus AD, Fouchier RA. 2010. Molecular determinants of adaptation of highly pathogenic avian influenza H7N7 viruses to efficient replication in the human host. J Virol 84:1597-1606. 297. Koopmans M, Wilbrink B, Conyn M, Natrop G, van der Nat H, Vennema H, Meijer A, van Steenbergen J, Fouchier R, Osterhaus A, Bosman A. 2004. Transmission of H7N7 avian influenza A virus to human beings during a large outbreak in commercial poultry farms in the Netherlands. Lancet 363:587-593. 298. Belser JA, Gustin KM, Pearce MB, Maines TR, Zeng H, Pappas C, Sun X, Carney PJ, Villanueva JM, Stevens J, Katz JM, Tumpey TM. 2013. Pathogenesis and transmission of avian influenza A (H7N9) virus in ferrets and mice. Nature 501:556-559. 299. Watanabe T, Kiso M, Fukuyama S, Nakajima N, Imai M, Yamada S, Murakami S, Yamayoshi S, Iwatsuki-Horimoto K, Sakoda Y, Takashita E, McBride R, Noda T, Hatta M, Imai H, Zhao D, Kishida N, Shirakura M, de Vries RP, Shichinohe S, Okamatsu M, Tamura T, Tomita Y, Fujimoto N, Goto K, Katsura H, Kawakami E, Ishikawa I, Watanabe S, Ito M, Sakai-Tagawa Y, Sugita Y, Uraki R, Yamaji R, Eisfeld AJ, Zhong G, Fan S, Ping J, Maher EA, Hanson A, Uchida Y, Saito T, Ozawa M, Neumann G, Kida H, Odagiri T, Paulson JC, Hasegawa H, Tashiro M, Kawaoka Y. 2013. Characterization of H7N9 influenza A viruses isolated from humans. Nature 501:551-555. 300. Morens DM, Taubenberger JK, Fauci AS. 2013. H7N9 avian influenza A virus and the perpetual challenge of potential human pandemicity. MBio 4:e00445-00413. 301. Poovorawan Y, Pyungporn S, Prachayangprecha S, Makkoch J. 2013. Global alert to avian influenza virus infection: from H5N1 to H7N9. Pathogens and global health 107:217-223. 302. Wiwanitkit V. 2013. H7N9 Influenza: The Emerging Infectious Disease. North American journal of medical sciences 5:395-398. 303. To KK, Chan JF, Yuen KY. 2014. Viral lung infections: epidemiology, virology, clinical features, and management of avian influenza A(H7N9). Current opinion in pulmonary medicine 20:225-232. 304. Dawood FS, Jain S, Finelli L, Shaw MW, Lindstrom S, Garten RJ, Gubareva LV, Xu X, Bridges CB, Uyeki TM. 2009. Emergence of a novel swine-origin influenza A (H1N1) virus in humans. N Engl J Med 360:2605-2615.   172 305. Neumann G, Noda T, Kawaoka Y. 2009. Emergence and pandemic potential of swine-origin H1N1 influenza virus. Nature 459:931-939. 306. Hui DS, Hayden FG. 2014. Editorial commentary: Host and viral factors in emergent influenza virus infections. Clin Infect Dis 58:1104-1106. 307. CDC. 2013. Notice to Clinicians: Early Reports of pH1N1-Associated Illnesses for the 2013-14 Influenza Season, vol. 2014. CDC Health Alert Network. 308. Copeland CS, Doms RW, Bolzau EM, Webster RG, Helenius A. 1986. Assembly of influenza hemagglutinin trimers and its role in intracellular transport. J Cell Biol 103:1179-1191. 309. Skehel JJ, Waterfield MD. 1975. Studies on the primary structure of the influenza virus hemagglutinin. Proc Natl Acad Sci U S A 72:93-97. 310. Klenk HD, Rott R, Orlich M, Blodorn J. 1975. Activation of influenza A viruses by trypsin treatment. Virology 68:426-439. 311. Kido H, Okumura Y, Takahashi E, Pan HY, Wang S, Chida J, Le TQ, Yano M. 2008. Host envelope glycoprotein processing proteases are indispensable for entry into human cells by seasonal and highly pathogenic avian influenza viruses. J Mol Genet Med 3:167-175. 312. Lazarowitz SG, Compans RW, Choppin PW. 1971. Influenza virus structural and nonstructural proteins in infected cells and their plasma membranes. Virology 46:830-843. 313. Garten W, Bosch FX, Linder D, Rott R, Klenk HD. 1981. Proteolytic activation of the influenza virus hemagglutinin: The structure of the cleavage site and the enzymes involved in cleavage. Virology 115:361-374. 314. Kido H, Yokogoshi Y, Sakai K, Tashiro M, Kishino Y, Fukutomi A, Katunuma N. 1992. Isolation and characterization of a novel trypsin-like protease found in rat bronchiolar epithelial Clara cells. A possible activator of the viral fusion glycoprotein. J Biol Chem 267:13573-13579. 315. Tashiro M, Ciborowski P, Klenk HD, Pulverer G, Rott R. 1987. Role of Staphylococcus protease in the development of influenza pneumonia. Nature 325:536-537. 316. Tashiro M, Ciborowski P, Reinacher M, Pulverer G, Klenk HD, Rott R. 1987. Synergistic role of staphylococcal proteases in the induction of influenza virus pathogenicity. Virology 157:421-430. 317. Bottcher E, Matrosovich T, Beyerle M, Klenk HD, Garten W, Matrosovich M. 2006. Proteolytic activation of influenza viruses by serine proteases TMPRSS2 and HAT from human airway epithelium. J Virol 80:9896-9898. 318. Hamilton BS, Gludish DW, Whittaker GR. 2012. Cleavage activation of the human-adapted influenza virus subtypes by matriptase reveals both subtype and strain specificities. J Virol 86:10579-10586. 319. Beaulieu A, Gravel E, Cloutier A, Marois I, Colombo E, Desilets A, Verreault C, Leduc R, Marsault E, Richter MV. 2013. Matriptase proteolytically activates influenza virus and promotes multicycle replication in the human airway epithelium. J Virol 87:4237-4251. 320. Bosch FX, Garten W, Klenk HD, Rott R. 1981. Proteolytic cleavage of influenza virus hemagglutinins: primary structure of the connecting peptide between HA1 and HA2   173 determines proteolytic cleavability and pathogenicity of Avian influenza viruses. Virology 113:725-735. 321. Kawaoka Y, Nestorowicz A, Alexander DJ, Webster RG. 1987. Molecular analyses of the hemagglutinin genes of H5 influenza viruses: origin of a virulent turkey strain. Virology 158:218-227. 322. Deshpande KL, Fried VA, Ando M, Webster RG. 1987. Glycosylation affects cleavage of an H5N2 influenza virus hemagglutinin and regulates virulence. Proc Natl Acad Sci U S A 84:36-40. 323. Steinhauer DA. 1999. Role of Hemagglutinin Cleavage for the Pathogenicity of Influenza Virus. Virology 258:1-20. 324. Guo XL, Li L, Wei DQ, Zhu YS, Chou KC. 2008. Cleavage mechanism of the H5N1 hemagglutinin by trypsin and furin. Amino Acids 35:375-382. 325. Walker JA, Molloy SS, Thomas G, Sakaguchi T, Yoshida T, Chambers TM, Kawaoka Y. 1994. Sequence specificity of furin, a proprotein-processing endoprotease, for the hemagglutinin of a virulent avian influenza virus. Journal of Virology 68. 326. Klenk HD, Garten W. 1994. Host cell proteases controlling virus pathogenicity. Trends Microbiol 2:39-43. 327. Horimoto T, Nakayama K, Smeekens SP, Kawaoka Y. 1994. Proprotein-processing endoproteases PC6 and furin both activate hemagglutinin of virulent avian influenza viruses. J Virol 68:6074-6078. 328. Schechter I, Berger A. 1967. On the size of the active site in proteases. I. Papain. Biochem Biophys Res Commun 27:157-162. 329. Okumura Y, Takahashi E, Yano M, Ohuchi M, Daidoji T, Nakaya T, Bottcher E, Garten W, Klenk HD, Kido H. 2010. Novel type II transmembrane serine proteases, MSPL and TMPRSS13, Proteolytically activate membrane fusion activity of the hemagglutinin of highly pathogenic avian influenza viruses and induce their multicycle replication. J Virol 84:5089-5096. 330. Seidah NG, Sadr MS, Chretien M, Mbikay M. 2013. The multifaceted proprotein convertases: their unique, redundant, complementary, and opposite functions. J Biol Chem 288:21473-21481. 331. Seidah NG, Prat A. 2012. The biology and therapeutic targeting of the proprotein convertases. Nature Reviews Drug Discovery 11:367-383. 332. Siezen RJ, Leunissen JA. 1997. Subtilases: the superfamily of subtilisin-like serine proteases. Protein science : a publication of the Protein Society 6:501-523. 333. Thomas G. 2002. Furin at the cutting edge: from protein traffic to embryogenesis and disease. Nature reviews. Molecular cell biology 3:753-766. 334. Anderson ED, Molloy SS, Jean F, Fei H, Shimamura S, Thomas G. 2002. The ordered and compartment-specfific autoproteolytic removal of the furin intramolecular chaperone is required for enzyme activation. J Biol Chem 277:12879-12890. 335. Artenstein AW, Opal SM. 2011. Proprotein Convertases in Health and Disease. N Engl J Med 365. 336. Pasquato A, Ramos da Palma J, Galan C, Seidah NG, Kunz S. 2013. Viral envelope glycoprotein processing by proprotein convertases. Antiviral Res 99:49-60.   174 337. Hallenberger S, Bosch V, Angliker H, Shaw E, Klenk HD, Garten W. 1992. Inhibition of furin-mediated cleavage activation of HIV-1 glycoprotein gp160. Nature 360:358-361. 338. Gotoh B, Ohnishi Y, Inocencio NM, Esaki E, Nakayama K, Barr PJ, Thomas G, Nagai Y. 1992. Mammalian subtilisin-related proteinases in cleavage activation of the paramyxovirus fusion glycoprotein: superiority of furin/PACE to PC2 or PC1/PC3. J Virol 66:6391-6397. 339. Zhang X, Fugere M, Day R, Kielian M. 2003. Furin processing and proteolytic activation of Semliki Forest virus. J Virol 77:2981-2989. 340. Li L, Lok SM, Yu IM, Zhang Y, Kuhn RJ, Chen J, Rossmann MG. 2008. The flavivirus precursor membrane-envelope protein complex: structure and maturation. Science 319:1830-1834. 341. Yu IM, Zhang W, Holdaway HA, Li L, Kostyuchenko VA, Chipman PR, Kuhn RJ, Rossmann MG, Chen J. 2008. Structure of the immature dengue virus at low pH primes proteolytic maturation. Science 319:1834-1837. 342. Volchkov VE, Feldmann H, Volchkova VA, Klenk HD. 1998. Processing of the Ebola virus glycoprotein by the proprotein convertase furin. Proc Natl Acad Sci U S A 95:5762-5767. 343. Seidah NG, Mowla SJ, Hamelin J, Mamarbachi AM, Benjannet S, Toure BB, Basak A, Munzer JS, Marcinkiewicz J, Zhong M, Barale JC, Lazure C, Murphy RA, Chretien M, Marcinkiewicz M. 1999. Mammalian subtilisin/kexin isozyme SKI-1: A widely expressed proprotein convertase with a unique cleavage specificity and cellular localization. Proc Natl Acad Sci U S A 96:1321-1326. 344. Lenz O, ter Meulen J, Klenk HD, Seidah NG, Garten W. 2001. The Lassa virus glycoprotein precursor GP-C is proteolytically processed by subtilase SKI-1/S1P. Proc Natl Acad Sci U S A 98:12701-12705. 345. Becker GL, Lu Y, Hardes K, Strehlow B, Levesque C, Lindberg I, Sandvig K, Bakowsky U, Day R, Garten W, Steinmetzer T. 2012. Highly potent inhibitors of proprotein convertase furin as potential drugs for treatment of infectious diseases. J Biol Chem 287:21992-22003. 346. Molloy SS, Anderson ED, Jean F, Thomas G. 1999. Bi-cycling the furin pathway: from TGN localization to pathogen activation and embryogenesis. Trends Cell Biol 9:28-35. 347. Wells JA, Estell DA. 1988. Subtilisin--an enzyme designed to be engineered. Trends Biochem Sci 13:291-297. 348. Creemers JW, Siezen RJ, Roebroek AJ, Ayoubi TA, Huylebroeck D, Van de Ven WJ. 1993. Modulation of furin-mediated proprotein processing activity by site-directed mutagenesis. J Biol Chem 268:21826-21834. 349. Ueda K, Lipkind GM, Zhou A, Zhu X, Kuznetsov A, Philipson L, Gardner P, Zhang C, Steiner DF. 2003. Mutational analysis of predicted interactions between the catalytic and P domains of prohormone convertase 3 (PC3/PC1). Proc Natl Acad Sci U S A 100:5622-5627. 350. Gagnon H, Beauchemin S, Kwiatkowska A, Couture F, D'Anjou F, Levesque C, Dufour F, Desbiens AR, Vaillancourt R, Bernard S, Desjardins R, Malouin F, Dory YL, Day R. 2014. Optimization of Furin Inhibitors To Protect against the Activation of Influenza Hemagglutinin H5 and Shiga Toxin. J Med Chem 57:29-41.   175 351. Richer MJ, Keays CA, Waterhouse J, Minhas J, Hashimoto C, Jean F. 2004. The Spn4 gene of Drosophila encodes a potent furin-directed secretory pathway serpin. Proc Natl Acad Sci U S A 101:10560-10565. 352. Jean F, Stella K, Thomas L, Liu G, Xiang Y, Reason AJ, Thomas G. 1998. alpha1-Antitrypsin Portland, a bioengineered serpin highly selective for furin: application as an antipathogenic agent. Proc Natl Acad Sci U S A 95:7293-7298. 353. Dogar AM, Towbin H, Hall J. 2011. Suppression of latent transforming growth factor (TGF)-beta1 restores growth inhibitory TGF-beta signaling through microRNAs. J Biol Chem 286:16447-16458. 354. Luna C, Li G, Qiu J, Epstein DL, Gonzalez P. 2011. MicroRNA-24 regulates the processing of latent TGFbeta1 during cyclic mechanical stress in human trabecular meshwork cells through direct targeting of FURIN. Journal of cellular physiology 226:1407-1414. 355. Annes JP. 2003. Making sense of latent TGFbeta activation. Journal of Cell Science 116:217-224. 356. Shi Y, Massagué J. 2003. Mechanisms of TGF-β Signaling from Cell Membrane to the Nucleus. Cell 113:685-700. 357. Moreland JL, Gramada A, Buzko OV, Zhang Q, Bourne PE. 2005. The Molecular Biology Toolkit (MBT): a modular platform for developing molecular visualization applications. BMC Bioinformatics 6:21. 358. Xu D, Zhang Y. 2009. Generating triangulated macromolecular surfaces by Euclidean Distance Transform. PLoS One 4:e8140. 359. Chen J, Lee KH, Steinhauer DA, Stevens DJ, Skehel JJ, Wiley DC. 1998. Structure of the hemagglutinin precursor cleavage site, a determinant of influenza pathogenicity and the origin of the labile conformation. Cell 95:409-417. 360. Henrich S, Cameron A, Bourenkov GP, Kiefersauer R, Huber R, Lindberg I, Bode W, Than ME. 2003. The crystal structure of the proprotein processing proteinase furin explains its stringent specificity. Nat Struct Biol 10:520-526. 361. Cazalla D, Yario T, Steitz JA. 2010. Down-regulation of a host microRNA by a Herpesvirus saimiri noncoding RNA. Science 328:1563-1566. 362. Weingartl HM, Berhane Y, Hisanaga T, Neufeld J, Kehler H, Emburry-Hyatt C, Hooper-McGreevy K, Kasloff S, Dalman B, Bystrom J, Alexandersen S, Li Y, Pasick J. 2010. Genetic and pathobiologic characterization of pandemic H1N1 2009 influenza viruses from a naturally infected swine herd. Journal of virology 84:2245-2256. 363. Vorwerk S, Ganter K, Cheng Y, Hoheisel J, Stahler PF, Beier M. 2008. Microfluidic-based enzymatic on-chip labeling of miRNAs. N Biotechnol 25:142-149. 364. Huber W, von Heydebreck A, Sultmann H, Poustka A, Vingron M. 2002. Variance stabilization applied to microarray data calibration and to the quantification of differential expression. Bioinformatics 18 Suppl 1:S96-104. 365. Kauffmann A, Gentleman R, Huber W. 2009. arrayQualityMetrics--a bioconductor package for quality assessment of microarray data. Bioinformatics 25:415-416. 366. Team R. 2009. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria.   176 367. Smyth GK. 2004. Linear models and empirical bayes methods for assessing differential expression in microarray experiments. Statistical applications in genetics and molecular biology 3:Article3. 368. Enright AJ, John B, Gaul U, Tuschl T, Sander C, Marks DS. 2003. MicroRNA targets in Drosophila. Genome biology 5:R1. 369. John B, Enright AJ, Aravin A, Tuschl T, Sander C, Marks DS. 2004. Human MicroRNA targets. PLoS Biol 2:e363. 370. Huang JC, Babak T, Corson TW, Chua G, Khan S, Gallie BL, Hughes TR, Blencowe BJ, Frey BJ, Morris QD. 2007. Using expression profiling data to identify human microRNA targets. Nat Methods 4:1045-1049. 371. Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, Amin N, Schwikowski B, Ideker T. 2003. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res 13:2498-2504. 372. Lynn DJ, Winsor GL, Chan C, Richard N, Laird MR, Barsky A, Gardy JL, Roche FM, Chan TH, Shah N, Lo R, Naseer M, Que J, Yau M, Acab M, Tulpan D, Whiteside MD, Chikatamarla A, Mah B, Munzner T, Hokamp K, Hancock RE, Brinkman FS. 2008. InnateDB: facilitating systems-level analyses of the mammalian innate immune response. Mol Syst Biol 4:218. 373. Cogswell JP, Ward J, Taylor IA, Waters M, Shi Y, Cannon B, Kelnar K, Kemppainen J, Brown D, Chen C, Prinjha RK, Richardson JC, Saunders AM, Roses AD, Richards CA. 2008. Identification of miRNA changes in Alzheimer's disease brain and CSF yields putative biomarkers and insights into disease pathways. J Alzheimers Dis 14:27-41. 374. Spackman E, Senne DA, Myers TJ, Bulaga LL, Garber LP, Perdue ML, Lohman K, Daum LT, Suarez DL. 2002. Development of a Real-Time Reverse Transcriptase PCR Assay for Type A Influenza Virus and the Avian H5 and H7 Hemagglutinin Subtypes. Journal of Clinical Microbiology 40:3256-3260. 375. Nelson PT, Baldwin DA, Scearce LM, Oberholtzer JC, Tobias JW, Mourelatos Z. 2004. Microarray-based, high-throughput gene expression profiling of microRNAs. Nat Methods 1:155-161. 376. Baum M, Bielau S, Rittner N, Schmid K, Eggelbusch K, Dahms M, Schlauersbach A, Tahedl H, Beier M, Guimil R, Scheffler M, Hermann C, Funk JM, Wixmerten A, Rebscher H, Honig M, Andreae C, Buchner D, Moschel E, Glathe A, Jager E, Thom M, Greil A, Bestvater F, Obermeier F, Burgmaier J, Thome K, Weichert S, Hein S, Binnewies T, Foitzik V, Muller M, Stahler CF, Stahler PF. 2003. Validation of a novel, fully integrated and flexible microarray benchtop facility for gene expression profiling. Nucleic Acids Res 31:e151. 377. Gan L, Schwengberg S, Denecke B. 2011. MicroRNA profiling during cardiomyocyte-specific differentiation of murine embryonic stem cells based on two different miRNA array platforms. PLoS ONE 6:e25809. 378. Camarillo C, Swerdel M, Hart RP. 2011. Comparison of microarray and quantitative real-time PCR methods for measuring MicroRNA levels in MSC cultures. Methods Mol Biol 698:419-429. 379. Thomas M, Lieberman J, Lal A. 2010. Desperately seeking microRNA targets. Nat Struct Mol Biol 17:1169-1174.   177 380. Izzotti A, Calin GA, Arrigo P, Steele VE, Croce CM, De Flora S. 2009. Downregulation of microRNA expression in the lungs of rats exposed to cigarette smoke. FASEB J 23:806-812. 381. Schmidt WM, Spiel AO, Jilma B, Wolzt M, Muller M. 2009. In vivo profile of the human leukocyte microRNA response to endotoxemia. Biochem Biophys Res Commun 380:437-441. 382. Rager JE, Smeester L, Jaspers I, Sexton KG, Fry RC. 2011. Epigenetic Changes Induced by Air Toxics: Formaldehyde Exposure Alters miRNA Expression Profiles in Human Lung Cells. Environ Health Perspect 119:494-500. 383. Lize M, Pilarski S, Dobbelstein M. 2010. E2F1-inducible microRNA 449a/b suppresses cell proliferation and promotes apoptosis. Cell Death Differ 17:452-458. 384. Yang X, Feng M, Jiang X, Wu Z, Li Z, Aau M, Yu Q. 2009. miR-449a and miR-449b are direct transcriptional targets of E2F1 and negatively regulate pRb-E2F1 activity through a feedback loop by targeting CDK6 and CDC25A. Genes Dev 23:2388-2393. 385. Hummel R, Wang T, Watson DI, Michael MZ, Van der Hoek M, Haier J, Hussey DJ. 2011. Chemotherapy-induced modification of microRNA expression in esophageal cancer. Oncol Rep 26:1011-1017. 386. Yu JM, Wu X, Gimble JM, Guan X, Freitas MA, Bunnell BA. 2011. Age-related changes in mesenchymal stem cells derived from rhesus macaque bone marrow. Aging Cell 10:66-79. 387. Ginsberg D. 2002. E2F1 pathways to apoptosis. FEBS Lett 529:122-125. 388. Zuckerman V, Wolyniec K, Sionov RV, Haupt S, Haupt Y. 2009. Tumour suppression by p53: the importance of apoptosis and cellular senescence. J Pathol 219:3-15. 389. Hermeking H. 2010. The miR-34 family in cancer and apoptosis. Cell Death Differ 17:193-199. 390. He Y, Xu K, Keiner B, Zhou J, Czudai V, Li T, Chen Z, Liu J, Klenk HD, Shu YL, Sun B. 2010. Influenza A virus replication induces cell cycle arrest in G0/G1 phase. J Virol 84:12832-12840. 391. Lowy RJ. 2003. Influenza virus induction of apoptosis by intrinsic and extrinsic mechanisms. Int Rev Immunol 22:425-449. 392. Lyles DS. 2000. Cytopathogenesis and inhibition of host gene expression by RNA viruses. Microbiol Mol Biol Rev 64:709-724. 393. Parnell G, McLean A, Booth D, Huang S, Nalos M, Tang B. 2011. Aberrant cell cycle and apoptotic changes characterise severe influenza A infection--a meta-analysis of genomic signatures in circulating leukocytes. PLoS One 6:e17186. 394. Medina RA, Garcia-Sastre A. 2011. Influenza A viruses: new research developments. Nat Rev Microbiol 9:590-603. 395. Cullen BR. 2011. Viruses and microRNAs: RISCy interactions with serious consequences. Gene Dev 25:1881-1894. 396. Govorkova EA, Webster RG. 2010. Combination chemotherapy for influenza. Viruses-Basel 2:1510-1529. 397. Loveday EK, Svinti V, Diederich S, Pasick J, Jean F. 2012. Temporal- and strain-specific host microRNA molecular signatures associated with swine-origin H1N1 and avian-origin H7N7 influenza A virus infection. J Virol 86:6109-6122.   178 398. Chhabra R, Dubey R, Saini N. 2010. Cooperative and individualistic functions of the microRNAs in the miR-23a~27a~24-2 cluster and its implication in human diseases. Molecular cancer 9:232. 399. Bang C, Fiedler J, Thum T. 2012. Cardiovascular importance of the microRNA-23/27/24 family. Microcirculation 19:208-214. 400. Jean F, Boudreault A, Basak A, Seidah NG, Lazure C. 1995. Fluorescent peptidyl substrates as an aid in studying the substrate specificity of human prohormone convertase PC1 and human furin and designing a potent irreversible inhibitor. J Biol Chem 270:19225-19231. 401. Bourne GL, Grainger DJ. 2011. Development and characterisation of an assay for furin activity. Journal of immunological methods 364:101-108. 402. Seidah NG. 2011. The proprotein convertases, 20 years later. Methods Mol Biol 768:23-57. 403. Basak A, Zhong M, Munzer JS, Chretien M, Seidah NG. 2001. Implication of the proprotein convertases furin, PC5 and PC7 in the cleavage of surface glycoproteins of Hong Kong, Ebola and respiratory syncytial viruses: a comparative analysis with fluorogenic peptides. Biochem J 353:537-545. 404. Ng EK, Cheng PK, Ng AY, Hoang TL, Lim WW. 2005. Influenza A H5N1 detection. Emerg Infect Dis 11:1303-1305. 405. Oda Y, Nakajima M, Mohri T, Takamiya M, Aoki Y, Fukami T, Yokoi T. 2012. Aryl hydrocarbon receptor nuclear translocator in human liver is regulated by miR-24. Toxicol Appl Pharmacol 260:222-231. 406. Guo Y, Fu W, Chen H, Shang C, Zhong M. 2012. miR-24 functions as a tumor suppressor in Hep2 laryngeal carcinoma cells partly through down-regulation of the S100A8 protein. Oncol Rep 27:1097-1103. 407. Lal A, Kim HH, Abdelmohsen K, Kuwano Y, Pullmann R, Jr., Srikantan S, Subrahmanyam R, Martindale JL, Yang X, Ahmed F, Navarro F, Dykxhoorn D, Lieberman J, Gorospe M. 2008. p16(INK4a) translation suppressed by miR-24. PLoS ONE 3:e1864. 408. Hatziapostolou M, Polytarchou C, Aggelidou E, Drakaki A, Poultsides GA, Jaeger SA, Ogata H, Karin M, Struhl K, Hadzopoulou-Cladaras M, Iliopoulos D. 2011. An HNF4alpha-miRNA inflammatory feedback circuit regulates hepatocellular oncogenesis. Cell 147:1233-1247. 409. Foucault ML, Moules V, Rosa-Calatrava M, Riteau B. 2011. Role for proteases and HLA-G in the pathogenicity of influenza A viruses. J Clin Virol 51:155-159. 410. Kido H, Okumura Y, Takahashi E, Pan HY, Wang S, Yao D, Yao M, Chida J, Yano M. 2012. Role of host cellular proteases in the pathogenesis of influenza and influenza-induced multiple organ failure. Biochim Biophys Acta 1824:186-194. 411. To KH, Pajovic S, Gallie BL, Theriault BL. 2012. Regulation of p14ARF expression by miR-24: a potential mechanism compromising the p53 response during retinoblastoma development. BMC Cancer 12:69. 412. Huang Z, Chen X, Yu B, Chen D. 2012. Cloning and functional characterization of rat stimulator of interferon genes (STING) regulated by miR-24. Dev Comp Immunol 37:414-420.   179 413. Brunner S, Herndler-Brandstetter D, Arnold CR, Wiegers GJ, Villunger A, Hackl M, Grillari J, Moreno-Villanueva M, Burkle A, Grubeck-Loebenstein B. 2012. Upregulation of miR-24 is associated with a decreased DNA damage response upon etoposide treatment in highly differentiated CD8(+) T cells sensitizing them to apoptotic cell death. Aging Cell 11:579-587. 414. Lal A, Navarro F, Maher CA, Maliszewski LE, Yan N, O'Day E, Chowdhury D, Dykxhoorn DM, Tsai P, Hofmann O, Becker KG, Gorospe M, Hide W, Lieberman J. 2009. miR-24 Inhibits cell proliferation by targeting E2F2, MYC, and other cell-cycle genes via binding to "seedless" 3'UTR microRNA recognition elements. Mol Cell 35:610-625. 415. Srivastava N, Manvati S, Srivastava A, Pal R, Kalaiarasan P, Chattopadhyay S, Gochhait S, Dua R, Bamezai RN. 2011. miR-24-2 controls H2AFX expression regardless of gene copy number alteration and induces apoptosis by targeting antiapoptotic gene BCL-2: a potential for therapeutic intervention. Breast Cancer Res 13:R39. 416. Cheung CY, Poon LL, Lau AS, Luk W, Lau YL, Shortridge KF, Gordon S, Guan Y, Peiris JS. 2002. Induction of proinflammatory cytokines in human macrophages by influenza A (H5N1) viruses: a mechanism for the unusual severity of human disease? Lancet 360:1831-1837. 417. Wahl SM. 1992. Transforming growth factor beta (TGF-beta) in inflammation: a cause and a cure. Journal of clinical immunology 12:61-74. 418. Julkunen I, Sareneva T, Pirhonen J, Ronni T, Melen K, Matikainen S. 2001. Molecular pathogenesis of influenza A virus infection and virus-induced regulation of cytokine gene expression. Cytokine & growth factor reviews 12:171-180. 419. Pan HY, Yamada H, Chida J, Wang S, Yano M, Yao M, Zhu J, Kido H. 2011. Up-regulation of ectopic trypsins in the myocardium by influenza A virus infection triggers acute myocarditis. Cardiovasc Res 89:595-603. 420. Wang S, Le TQ, Kurihara N, Chida J, Cisse Y, Yano M, Kido H. 2010. Influenza virus-cytokine-protease cycle in the pathogenesis of vascular hyperpermeability in severe influenza. J Infect Dis 202:991-1001. 421. Le TQ, Kawachi M, Yamada H, Shiota M, Okumura Y, Kido H. 2006. Identification of trypsin I as a candidate for influenza A virus and Sendai virus envelope glycoprotein processing protease in rat brain. Biol Chem 387:467-475. 422. Droebner K, Reiling SJ, Planz O. 2008. Role of hypercytokinemia in NF-kappaB p50-deficient mice after H5N1 influenza A virus infection. J Virol 82:11461-11466. 423. Kumar V, Behera R, Lohite K, Karnik S, Kundu GC. 2010. p38 kinase is crucial for osteopontin-induced furin expression that supports cervical cancer progression. Cancer Res 70:10381-10391. 424. Horimoto T, Kawaoka Y. 1995. The hemagglutinin cleavability of a virulent avian influenza virus by subtilisin-like endoproteases is influenced by the amino acid immediately downstream of the cleavage site. Virology 210:466-470. 425. Chretien M, Li CH. 1967. Isolation, purification, and characterization of gamma-lipotropic hormone from sheep pituitary glands. Canadian journal of biochemistry 45:1163-1174.   180 426. Steiner DF, Cunningham D, Spigelman L, Aten B. 1967. Insulin biosynthesis: evidence for a precursor. Science 157:697-700. 427. Steiner DF. 1998. The proprotein convertases. Current Opinion in Chemical Biology 2:31-39. 428. Maxwell KN, Breslow JL. 2004. Adenoviral-mediated expression of Pcsk9 in mice results in a low-density lipoprotein receptor knockout phenotype. Proc Natl Acad Sci U S A 101:7100-7105. 429. Pullikotil P, Benjannet S, Mayne J, Seidah NG. 2007. The proprotein convertase SKI-1/S1P: alternate translation and subcellular localization. J Biol Chem 282:27402-27413. 430. Alvisi G, Madan V, Bartenschlager R. 2011. Hepatitis C virus and host cell lipids: an intimate connection. RNA Biol 8:258-269. 431. Ye J. 2007. Reliance of host cholesterol metabolic pathways for the life cycle of hepatitis C virus. PLoS Pathog 3:e108. 432. Patel JH, Cobbold JF, Thomas HC, Taylor-Robinson SD. 2010. Hepatitis C and hepatic steatosis. QJM : monthly journal of the Association of Physicians 103:293-303. 433. Ng R, Wu H, Xiao H, Chen X, Willenbring H, Steer CJ, Song G. 2014. Inhibition of miR-24 expression in liver prevents hepatic lipid accumulation and hyperlipidemia. Hepatology. 434. Singaravelu R, Chen R, Lyn RK, Jones DM, O'Hara S, Rouleau Y, Cheng J, Srinivasan P, Nasheri N, Russell RS, Tyrrell DL, Pezacki JP. 2014. Hepatitis C virus induced up-regulation of microRNA-27: a novel mechanism for hepatic steatosis. Hepatology 59:98-108. 435. Shirasaki T, Honda M, Shimakami T, Horii R, Yamashita T, Sakai Y, Sakai A, Okada H, Watanabe R, Murakami S, Yi M, Lemon SM, Kaneko S. 2013. MicroRNA-27a regulates lipid metabolism and inhibits hepatitis C virus replication in human hepatoma cells. J Virol 87:5270-5286. 436. Olmstead AD, Knecht W, Lazarov I, Dixit SB, Jean F. 2012. Human subtilase SKI-1/S1P is a master regulator of the HCV Lifecycle and a potential host cell target for developing indirect-acting antiviral agents. PLoS Pathog 8:e1002468. 437. Labonte P, Begley S, Guevin C, Asselin MC, Nassoury N, Mayer G, Prat A, Seidah NG. 2009. PCSK9 impedes hepatitis C virus infection in vitro and modulates liver CD81 expression. Hepatology 50:17-24. 438. Benjannet S, Rhainds D, Hamelin J, Nassoury N, Seidah NG. 2006. The proprotein convertase (PC) PCSK9 is inactivated by furin and/or PC5/6A: functional consequences of natural mutations and post-translational modifications. J Biol Chem 281:30561-30572. 439. Samsa MM, Mondotte JA, Iglesias NG, Assuncao-Miranda I, Barbosa-Lima G, Da Poian AT, Bozza PT, Gamarnik AV. 2009. Dengue virus capsid protein usurps lipid droplets for viral particle formation. PLoS Pathog 5:e1000632. 440. Petrocca F, Vecchione A, Croce CM. 2008. Emerging role of miR-106b-25/miR-17-92 clusters in the control of transforming growth factor beta signaling. Cancer Research 68:8191-8194. 441. Mendell JT. 2008. miRiad roles for the miR-17-92 cluster in development and disease. Cell 133:217-222. 442. Dews M, Fox JL, Hultine S, Sundaram P, Wang W, Liu YY, Furth E, Enders GH, El-Deiry W, Schelter JM, Cleary MA, Thomas-Tikhonenko A. 2010. The myc-miR-  181 17~92 axis blunts TGF{beta} signaling and production of multiple TGF{beta}-dependent antiangiogenic factors. Cancer Research 70:8233-8246. 443. Mestdagh P, Bostrom AK, Impens F, Fredlund E, Van Peer G, De Antonellis P, von Stedingk K, Ghesquiere B, Schulte S, Dews M, Thomas-Tikhonenko A, Schulte JH, Zollo M, Schramm A, Gevaert K, Axelson H, Speleman F, Vandesompele J. 2010. The miR-17-92 microRNA cluster regulates multiple components of the TGF-beta pathway in neuroblastoma. Mol Cell 40:762-773. 444. Li L, Shi JY, Zhu GQ, Shi B. 2012. MiR-17-92 cluster regulates cell proliferation and collagen synthesis by targeting TGFB pathway in mouse palatal mesenchymal cells. J Cell Biochem 113:1235-1244. 445. Taubenberger JK, Reid AH, Krafft AE, Bijwaard KE, Fanning TG. 1997. Initial genetic characterization of the 1918 "Spanish" influenza virus. Science 275:1793-1796. 446. Basler CF, Aguilar PV. 2008. Progress in identifying virulence determinants of the 1918 H1N1 and the Southeast Asian H5N1 influenza A viruses. Antiviral Res 79:166-178. 447. Kobasa D, Takada A, Shinya K, Hatta M, Halfmann P, Theriault S, Suzuki H, Nishimura H, Mitamura K, Sugaya N, Usui T, Murata T, Maeda Y, Watanabe S, Suresh M, Suzuki T, Suzuki Y, Feldmann H, Kawaoka Y. 2004. Enhanced virulence of influenza A viruses with the haemagglutinin of the 1918 pandemic virus. Nature 431:703-707. 448. Watanabe T, Tisoncik-Go J, Tchitchek N, Watanabe S, Benecke AG, Katze MG, Kawaoka Y. 2013. 1918 Influenza virus hemagglutinin (HA) and the viral RNA polymerase complex enhance viral pathogenicity, but only HA induces aberrant host responses in mice. J Virol 87:5239-5254. 449. Ehrhardt C, Seyer R, Hrincius ER, Eierhoff T, Wolff T, Ludwig S. 2010. Interplay between influenza A virus and the innate immune signaling. Microbes Infect 12:81-87. 450. Roush S, Slack FJ. 2008. The let-7 family of microRNAs. Trends Cell Biol 18:505-516. 451. Ma YJ, Yang J, Fan XL, Zhao HB, Hu W, Li ZP, Yu GC, Ding XR, Wang JZ, Bo XC, Zheng XF, Zhou Z, Wang SQ. 2012. Cellular microRNA let-7c inhibits M1 protein expression of the H1N1 influenza A virus in infected human lung epithelial cells. J Cell Mol Med 16:2539-2546. 452. Bakre A, Andersen LE, Meliopoulos V, Coleman K, Yan X, Brooks P, Crabtree J, Tompkins SM, Tripp RA. 2013. Identification of Host Kinase Genes Required for Influenza Virus Replication and the Regulatory Role of MicroRNAs. PLoS One 8:e66796. 453. Buggele WA, Krause KE, Horvath CM. 2013. Small RNA Profiling of Influenza A Virus-Infected Cells Identifies miR-449b as a Regulator of Histone Deacetylase 1 and Interferon Beta. PLoS One 8:e76560. 454. Song L, Liu H, Gao S, Jiang W, Huang W. 2010. Cellular microRNAs inhibit replication of the H1N1 influenza A virus in infected cells. J Virol 84:8849-8860. 455. Constam DB. 2014. Regulation of TGFbeta and related signals by precursor processing. Seminars in cell & developmental biology. 456. Jenkins G. 2008. The role of proteases in transforming growth factor-beta activation. Int J Biochem Cell Biol 40:1068-1078. 457. Massague J. 2000. How cells read TGF-beta signals. Nat Rev Mol Cell Biol 1:169-178.   182 458. Schultz-Cherry S, Hinshaw VS. 1996. Influenza virus neuraminidase activates latent transforming growth factor beta. Journal of Virology 70. 459. Carlson CM, Turpin EA, Moser LA, O'Brien KB, Cline TD, Jones JC, Tumpey TM, Katz JM, Kelley LA, Gauldie J, Schultz-Cherry S. 2010. Transforming growth factor-beta: activation by neuraminidase and role in highly pathogenic H5N1 influenza pathogenesis. PLoS Pathog 6:e1001136. 460. Chang ML, Yeh CT, Chen JC, Huang CC, Lin SM, Sheen IS, Tai DI, Chu CM, Lin WP, Chang MY, Liang CK, Chiu CT, Lin DY. 2008. Altered expression patterns of lipid metabolism genes in an animal model of HCV core-related, nonobese, modest hepatic steatosis. BMC Genomics 9:109. 461. Vickers KC, Shoucri BM, Levin MG, Wu H, Pearson DS, Osei-Hwedieh D, Collins FS, Remaley AT, Sethupathy P. 2013. MicroRNA-27b is a regulatory hub in lipid metabolism and is altered in dyslipidemia. Hepatology 57:533-542. 462. Martin-Acebes MA, Blazquez AB, Jimenez de Oya N, Escribano-Romero E, Saiz JC. 2011. West Nile virus replication requires fatty acid synthesis but is independent on phosphatidylinositol-4-phosphate lipids. PLoS One 6:e24970. 463. Trompeter HI, Abbad H, Iwaniuk KM, Hafner M, Renwick N, Tuschl T, Schira J, Muller HW, Wernet P. 2011. MicroRNAs MiR-17, MiR-20a, and MiR-106b act in concert to modulate E2F activity on cell cycle arrest during neuronal lineage differentiation of USSC. PLoS ONE 6:e16138. 464. Luo H, Zou J, Dong Z, Zeng Q, Wu D, Liu L. 2012. Up-regulated miR-17 promotes cell proliferation, tumour growth and cell cycle progression by targeting the RND3 tumour suppressor gene in colorectal carcinoma. Biochem J 442:311-321. 465. McKenna DJ, Patel D, McCance DJ. 2014. miR-24 and miR-205 expression is dependent on HPV onco-protein expression in keratinocytes. Virology 448:210-216. 466. Zheng ZM, Wang X. 2011. Regulation of cellular miRNA expression by human papillomaviruses. Biochim Biophys Acta 1809:668-677. 467. Wang JW, Roden RB. 2013. L2, the minor capsid protein of papillomavirus. Virology 445:175-186. 468. Lara-Sampablo A, Flores-Alonso JC, De Jesus-Ortega N, Santos-Lopez G, Vallejo-Ruiz V, Rosas-Murrieta N, Reyes-Carmona S, Herrera-Camacho I, Reyes-Leyva J. 2014. Transfection of influenza A virus nuclear export protein induces the expression of tumor necrosis factor alpha. Virus Res 185C:1-9. 469. Hoffmann E, Neumann G, Kawaoka Y, Hobom G, Webster RG. 2000. A DNA transfection system for generation of influenza A virus from eight plasmids. Proc Natl Acad Sci U S A 97:6108-6113. 470. Cheng A, Wong SM, Yuan YA. 2009. Structural basis for dsRNA recognition by NS1 protein of influenza A virus. Cell Res 19:187-195. 471. Zybert IA, van der Ende-Metselaar H, Wilschut J, Smit JM. 2008. Functional importance of dengue virus maturation: infectious properties of immature virions. J Gen Virol 89:3047-3051. 472. Sehgal A, Vaishnaw A, Fitzgerald K. 2013. Liver as a target for oligonucleotide therapeutics. J Hepatol 59:1354-1359. 473. Soifer HS, Rossi JJ, Saetrom P. 2007. MicroRNAs in disease and potential therapeutic applications. Mol Ther 15:2070-2079.   183 474. Zhu Z, Qi Y, Ge A, Zhu Y, Xu K, Ji H, Shi Z, Cui L, Zhou M. 2014. Comprehensive characterization of serum microRNA profile in response to the emerging avian influenza A (H7N9) virus infection in humans. Viruses 6:1525-1539. 475. Silverman JM, Reiner NE. 2011. Exosomes and other microvesicles in infection biology: organelles with unanticipated phenotypes. Cellular microbiology 13:1-9. 476. Simons M, Raposo G. 2009. Exosomes - vesicular carriers for intercellular communication. Curr Opin Cell Biol 21:575-581. 477. Mathivanan S, Ji H, Simpson RJ. 2010. Exosomes: Extracellular organelles important in intercellular communication. Journal of Proteomics 73:1907-1920. 478. Gilad S, Meiri E, Yogev Y, Benjamin S, Lebanony D, Yerushalmi N, Benjamin H, Kushnir M, Cholakh H, Melamed N, Bentwich Z, Hod M, Goren Y, Chajut A. 2008. Serum microRNAs are promising novel biomarkers. PLoS One 3:e3148.     184 Appendices Appendix A  Chapter 2 supplementary figures and tables  A BDCmiR−30dNR4A2DIO2KIF20AKLF4CCNB2SERPINE1CTGFCDCA8STARD13CYP26A1TUBA3DCXCL2HMGB2miR−181aCCNB1CYR61FOSTNFAIP3KLF6FOSL1miR−29aNPTX1CPA4SLC30A1PIM1ADMTPX2EID3ETS1BIRC5NUF2CENPFmiR−194UBE2D4TFF1CCNE2miR−532−3pmiR−34bmiR−1825CDCA7miR−1229TRIOBPmiR−1207−5pSKP2ID1miR−1228CYP1B1PTGESMGC4172miR−1234UNGMUC5ACDTLLOC652846miR−197miR−634POLE2IGFBP3RARRES1miR−1224−3pGAL3ST1miR−1974KIAA0514 miR−1976miR−2116*miR−220bADAmiR−449b*miR−138−1*miR−1281miR−34c−3pmiR−877*ALDH1A3 miR−766Hour 0 Hour 4Hour 48Hour 72FAM83DSTX11miR−181aSOCS2CRY1miR−151−5pALDH1A3SDC4ETNK1ETS1CTNNAL1miR−361−5pRAD23BmiR−30dNA_BU857004BIRC3CCNB2miR−30aCDCA8ATE1 TPX2THBS1OLR1OKL38SH2D5HBEGFNAV3CDK6MAR4FOSL1miR−194AXLKIF20AmiR−29aAP2B1BIRC5EID3C4BPBCCNB1TRIML2MAP3K14EREGCPA4 SLC30A1miR−29cNRG1CENPFRHBDL3miR−220bmiR−766MCM7SCARA3miR−1207−5pNPTX1miR−1470KIF11DCBLD2POLECENPMRANBP1TM4SF4HMG1L1RAD54LBIRC5ORC1LSLC25A10 TMEM149CENPHHMMRBLMMNS1FAM83DADAHMGB2AURKAMCM3HELLSFANCImiR−877*DTLLIG1ZDHHC12DDX46miR−1234RBL1miR−574−5pCDT1miR−1976KIAA1524miR−34c−3pmiR−1224−3pCDCA8BARD1NDC80LOC728037CDC2TUBA3DMAP2K6NA_AF131834TFF1CDCA7EXO1NUDT1SLC2A4RGPDSS1CDC25CRAD51DEPDC1BSFRS3NUSAP1SRISIVALOC652846GINS3LOC389816CSTF3CYP26A1TRIP13HNRNPUC1QTNF6GTSE1HSPA2MCM2ATAD2TPX2PAQR4CCNE1MCM4miR−483−5pKIF15miR−1825BRI3BPTIPINLOC375295VGLL3CDC20CCDC77LOC653874SETMARMETTL7BANXA8L2GMNNSFRS6miR−205TUBBSFRS7miR−1246PLK4POLQBRCA1miR−2116*miR−574−3pKNTC1KIF23TGM2miR−532−3pC16orf75ALDH1A3CCNE2SUV39H1UHRF1E2F7MCM5 ANLNFLJ31568LMNB2XRCC3BCAT1C5orf41PMP22RP5−1022P6.2MMP7CD14ARHGDIBSKP2GREM1FAM129ACENPAGPRC5ATMEM39ABEXL1SC5DLULBP1TTKIGFBP1SNTB1PTGS2CYP4F3COL4A3miR−300RBMS1INSIG1LOC648517 C1orf24SLCO4C1DDIT3SYVN1HMGCS1WIPI1GEMDNAJB2GFPT1FLJ32810KLF4LOC647859MTHFD2XTP3TPAFAM111ACSE1LCEP55LOC731314GINS4STRA13ST3GAL5POLA2PRIM1RFC4MELKOIP5BUB1DUS2LUNGLOC647000POLE2DHFRID3C9orf100CKAP2NEK2AURKBC13orf3RGS2CKAP2LFABP5FBN2RPL29C14orf143TUBB6EME1CDCA4TUBB3DSCC1C1orf112LXNSGOL1CRIP2CHAF1AHSPA14DLG7YWHABSGOL2PRPS2MCM6ERCC6L SMC2NCAPGTTF2miR−138−1*NCAPD3LMNB1FAR1NASPMGC4172TACC3CHAF1BmiR−762C21orf58miR−1225−3p FSTL3miR−634miR−1228POLA1SPC24TROAPWDHD1MLF1IPKIF20AUCHL5IPKIFC1C1orf41FEN1PSME3H2AFXHNRNPABZWINTUBE2CTYMSPOLR2LMGC39900ASPMLOC727761TOP2ACLDN2FASTKD2LOC653110BCL7CTRIOBPRAD51AP1miR−449b*RBM14CDCA5LOC399942LOC339766DPYSL3TUBB4QFAM64ATMPOmiR−1281C15orf42NCAPG2APOBEC3BC14orf80DSN1RACGAP1DUTSPC25ZWILCHIGFBP6ID1MND1CDKN3FIGNL1HNRNPA1C16orf33CDK2RARRES1RMI1PBKFOXM1PKMYT1miR−34bmiR−1229CDCA2miR−197STRA6E2F2miR−328CDC25ARFC5 FAM54AORC6LFANCGC12orf48ABCG2KIF14NEIL3  185 Figure A.1 Complexity of the miRNA-mRNA network after infection with pandemic 2009 H1N1 influenza A virus. Temporal and global molecular phenotypic changes triggered by infection in A549 cells.  Red indicates up-regulated miRNAs and mRNAs, while blue indicates down-regulation.  The networks display predicted interactions between deregulated miRNAs and mRNAs from the microarray experiments (determined by a fold change of 2, p-value of 0.05, miRanda target prediction, GenMiR++ scoring, and negative-correlation filtering) at 0 (A), 4 (B), 48 (C), and 72 (D) hours post-infection.  The thickness of the edges corresponds to GenMiR++ scores.      186  Figure A.2 Time-point-specific comparison of miRNAs expressed during pandemic 2009 H1N1 and highly pathogenic avian H7N7 influenza A virus infection Overlap between the two circles represents common miRNAs that were significantly deregulated at the indicated time point.  The number of unique miRNAs at each time point is shown on the left and right of each overlapping set of circles.  (A) Venn diagram of significant up-regulated miRNAs during H1N1 and H7N7 infection at each time point post-infection.  (B) Venn diagram of significant down-regulated miRNAs during H1N1 and H7N7 infection.   UP-REGULATED MIRNASDOWN-REGULATED MIRNAS2 132H1N1 H7N73 426H1N1 H7N72 492H1N1 H7N72 361H1N1 H7N70 90H1N1 H7N71 20H1N1 H7N70 80H1N1 H7N71 70H1N1 H7N75 50H1N1 H7N71 60H1N1 H7N74 1019H1N1 H7N75 1932H1N1 H7N7h24              h48          h72h0               h4            h8 h24              h48          h72h0               h4            h8 AB  187 Table A.1 Differentially expressed miRNAs during infection with 2009 pandemic H1N1 influenza A virus compared to mock-infected control        Table A1. Differentially expressed miRNAs during infection with 2009 pandemic H1N1 influenza A virus compared to mock-infected controlHour 0 Fold Change (log2)P-value Hour 4 Fold Change (log2)P-value Hour 8 Fold Change (log2)P-value Hour 24 Fold Change (log2)P-value Hour 48 Fold Change (log2)P-value Hour 72 Fold Change (log2)P-valuemiR-181a -1.227 2.86E-04 miR-181a -1.289 3.21E-04 miR-30d -1.169 1.40E-03 miR-548d-5p -1.183 9.15E-04 miR-766 2.434 1.51E-03 miR-1908 1.833 3.12E-06miR-30d -1.187 1.21E-03 miR-361-5p -1.361 2.80E-03 miR-181a -1.035 1.79E-03 miR-320d -1.167 7.05E-03 miR-1974 1.460 1.63E-03 miR-766 3.036 1.20E-04miR-194 -1.100 3.67E-02 miR-29c -1.287 4.57E-03 miR-744* 1.146 8.23E-03 miR-1227 1.200 3.50E-02 miR-744* 1.321 2.62E-03 miR-574-3p 2.201 2.80E-04miR-29a -1.018 3.85E-02 miR-30d -1.071 5.17E-03 miR-1227 1.408 1.42E-02 let-7g -1.142 3.86E-02 miR-1234 2.142 3.06E-03 miR-1234 2.665 3.26E-04miR-425 -1.016 6.72E-03 miR-30a -1.062 1.44E-02 miR-574-3p 1.628 5.47E-03 miR-300 -1.048 3.78E-04miR-30a -1.166 1.15E-02 miR-138-1* 1.036 2.76E-02 miR-138-1* 1.324 5.63E-03 miR-1229 3.272 6.28E-04miR-151-5p -1.051 1.29E-02 let-7g -1.215 2.83E-02 miR-1976 2.313 8.93E-03 miR-877* 2.521 2.27E-03miR-1270 1.083 2.36E-02 miR-766 1.497 4.33E-02 miR-2116* 1.052 9.61E-03 miR-197 2.542 2.37E-03miR-194 -1.222 2.90E-02 miR-1281 1.686 4.52E-02 miR-220b 1.206 1.11E-02 miR-1470 1.303 3.25E-03miR-29a -1.103 3.46E-02 miR-1229 2.329 1.20E-02 miR-2116* 1.181 3.95E-03miR-877* 1.976 1.47E-02 miR-762 1.078 3.96E-03miR-34c-3p 1.046 1.56E-02 miR-1281 2.479 4.09E-03miR-197 1.868 2.22E-02 miR-532-3p 2.267 4.27E-03miR-1227 1.300 2.29E-02 miR-634 2.550 4.32E-03miR-634 1.977 2.42E-02 miR-1976 2.529 4.53E-03miR-532-3p 1.741 2.53E-02 miR-329 1.040 4.60E-03miR-34b 1.349 2.76E-02 miR-1207-5p 1.934 4.91E-03miR-449b* 1.603 2.92E-02 miR-1228 2.315 5.59E-03miR-1224-3p 2.255 3.21E-02 miR-744* 1.162 7.43E-03miR-1228 1.714 3.64E-02 miR-1227 1.524 8.32E-03miR-1281 1.763 3.67E-02 miR-1233 1.009 9.06E-03miR-1825 1.542 3.76E-02 miR-1225-3p 1.009 1.02E-02miR-1207-5p 1.326 4.83E-02 miR-149* 1.585 1.28E-02miR-449b* 1.833 1.34E-02miR-34b 1.507 1.45E-02miR-220b 1.155 1.47E-02miR-205 1.618 1.49E-02miR-483-3p 2.663 1.64E-02miR-34c-3p 1.032 1.70E-02miR-138-1* 1.096 2.02E-02miR-1246 1.256 2.06E-02miR-1224-3p 2.446 2.06E-02miR-483-5p 1.261 2.33E-02miR-658 1.229 2.77E-02miR-574-5p 1.658 2.96E-02miR-1228* 1.009 3.51E-02miR-328 1.897 3.82E-02miR-1825 1.523 3.99E-02  188 Table A.2 Differentially expressed miRNAs during infection with highly pathogenic avian H7N7 influenza A virus compared to mock-infected control     Table A2. Differentially expressed miRNAs during infection with highly pathogenic avian H7N7 influenza A virus compared to mock-infected controlHour 0 Fold Change (log2)P-value Hour 4 Fold Change (log2)P-value Hour 8 Fold Change (log2)P-value Hour 24 Fold Change (log2)P-value Hour 48 Fold Change (log2)P-value Hour 72 Fold Change (log2)P-valuemiR-877* 1.475 4.16E-03 miR-192 -2.180 9.67E-05 let-7d -1.788 3.67E-05 miR-220b 1.403 1.32E-04 miR-532-3p 2.479 3.04E-07 miR-220b 2.863 3.82E-11let-7g -1.195 6.42E-03 miR-361-5p -1.533 1.24E-04 miR-567 -1.446 8.98E-05 miR-320a -1.872 5.05E-04 miR-220b 1.975 4.11E-07 miR-34c-3p 2.130 5.51E-10miR-1249 1.277 7.08E-03 miR-191 -1.508 1.28E-04 miR-30b -2.036 1.84E-04 miR-192 -1.757 1.26E-03 miR-483-3p 3.043 5.14E-06 miR-532-3p 3.073 1.96E-09miR-483-3p 1.662 7.36E-03 miR-1274a -1.380 1.49E-04 miR-129-5p -1.174 3.15E-04 miR-31 -1.765 1.26E-03 miR-34c-3p 1.397 6.43E-06 miR-34b 2.489 5.35E-09miR-192 -1.434 7.43E-03 miR-30b -1.945 3.26E-04 miR-361-5p -1.382 4.55E-04 miR-361-5p -1.235 1.52E-03 miR-877* 2.385 1.25E-05 miR-483-3p 3.941 2.53E-08miR-1224-3p 1.337 7.68E-03 let-7d -1.509 3.63E-04 miR-300 1.024 5.03E-04 let-7d -1.299 1.81E-03 miR-1249 2.147 1.97E-05 miR-877* 3.200 3.73E-08miR-361-5p -1.016 8.03E-03 let-7a -2.606 5.10E-04 miR-192 -1.805 9.52E-04 miR-21 -1.708 2.02E-03 miR-634 2.240 3.18E-05 miR-634 3.185 3.74E-08miR-634 1.312 9.85E-03 miR-31 -1.899 5.76E-04 miR-1274b -1.446 1.02E-03 miR-151-5p -1.393 2.30E-03 miR-1224-3p 2.077 7.62E-05 miR-2116* 1.865 4.57E-08miR-138 -1.149 1.02E-02 miR-182 -1.080 6.35E-04 miR-30a* -1.348 1.05E-03 let-7g -1.260 4.21E-03 miR-329 1.201 9.18E-05 miR-1249 2.893 6.41E-08miR-194 -1.337 1.04E-02 miR-21 -1.907 6.56E-04 let-7g -1.436 1.27E-03 miR-1185 -1.061 4.94E-03 miR-1229 2.297 1.10E-04 miR-1229 3.411 9.92E-08miR-382 -1.029 1.26E-02 miR-220b 1.215 7.51E-04 miR-1974 -2.280 1.65E-03 miR-106b -1.256 5.14E-03 miR-449b* 1.805 1.71E-04 miR-449b* 2.635 2.90E-07miR-548f -1.006 1.43E-02 miR-34c-5p 1.068 9.25E-04 miR-106b -1.428 1.65E-03 miR-194 -1.439 6.06E-03 miR-766 2.132 3.11E-04 miR-603 1.215 3.07E-07miR-26a -1.118 1.67E-02 miR-30a -1.134 9.65E-04 miR-26a -1.497 1.72E-03 miR-877* 1.351 8.27E-03 miR-34b 1.346 3.76E-04 miR-1281 2.705 3.48E-07miR-30b -1.177 2.34E-02 miR-27a -2.200 1.15E-03 miR-191 -1.201 1.75E-03 miR-23b -1.744 8.81E-03 miR-1975 1.161 3.98E-04 miR-138-1* 1.485 6.96E-07miR-449b* 1.007 2.76E-02 miR-194 -1.726 1.20E-03 miR-1274a -1.065 2.61E-03 miR-29a -1.538 9.27E-03 miR-455-5p -1.284 7.62E-04 miR-1224-3p 2.720 7.95E-07miR-31 -1.135 3.25E-02 miR-23b -2.188 1.26E-03 miR-151-5p -1.348 3.08E-03 miR-138 -1.163 9.42E-03 miR-2116* 1.024 8.64E-04 miR-1975 1.722 8.45E-07miR-29a -1.246 3.29E-02 miR-138 -1.459 1.39E-03 let-7a -2.175 3.15E-03 miR-106a -1.552 1.01E-02 miR-1281 1.618 9.37E-04 miR-664 2.110 1.64E-06miR-766 1.188 3.54E-02 miR-720 -1.836 1.43E-03 miR-224* -1.120 3.16E-03 miR-103 -1.154 1.04E-02 miR-197 1.741 1.07E-03 miR-1470 2.220 1.78E-06let-7e -1.652 3.66E-02 miR-106b -1.423 1.70E-03 miR-194 -1.529 3.72E-03 miR-30a* -1.025 1.08E-02 miR-1227 1.417 1.19E-03 miR-329 1.526 1.84E-06miR-30c -1.204 4.06E-02 miR-151-5p -1.430 1.79E-03 miR-220b 1.029 3.75E-03 miR-483-3p 1.526 1.34E-02 miR-1976 1.657 4.71E-03 miR-766 2.926 2.52E-06let-7b -1.198 4.28E-02 miR-29a -1.868 1.86E-03 miR-520c-3p -1.010 3.80E-03 miR-125b -1.751 1.34E-02 miR-129* 1.022 5.23E-03 miR-197 2.636 3.11E-06let-7a -1.453 4.31E-02 miR-320a -1.622 2.23E-03 miR-21 -1.570 4.27E-03 miR-17 -1.218 1.36E-02 miR-574-3p 1.307 6.29E-03 miR-220a 1.634 3.79E-06miR-1229 1.124 4.49E-02 miR-22 -1.677 2.25E-03 miR-31 -1.541 4.38E-03 miR-26a -1.154 1.37E-02 miR-1470 1.166 6.39E-03 miR-1226 1.557 5.95E-06miR-548f -1.267 2.42E-03 miR-382 -1.167 5.07E-03 miR-100 -1.199 1.37E-02 miR-1234 1.512 7.09E-03 miR-129* 1.740 8.47E-06miR-17 -1.494 2.86E-03 miR-185* -1.001 7.00E-03 miR-27a -1.622 1.41E-02 miR-17 -1.268 1.04E-02 miR-1227 2.024 1.03E-05miR-1274b -1.297 2.92E-03 miR-26a-1* -1.079 7.14E-03 miR-16 -1.554 1.50E-02 miR-1228 1.554 1.04E-02 miR-1228 2.693 2.87E-05let-7b -1.799 3.00E-03 let-7c -1.706 7.36E-03 miR-532-3p 1.051 1.53E-02 miR-320a -1.327 1.11E-02 miR-1234 2.462 3.25E-05miR-26a -1.398 3.21E-03 miR-29a -1.549 8.79E-03 miR-30d -1.037 1.58E-02 miR-597 1.016 1.39E-02 miR-205 2.110 3.53E-05let-7f -2.174 3.41E-03 miR-23b -1.733 9.19E-03 let-7b -1.437 1.60E-02 miR-720 -1.366 1.52E-02 miR-1976 2.511 4.38E-05miR-100 -1.427 3.75E-03 miR-17 -1.198 1.51E-02 miR-449b* 1.098 1.67E-02 miR-328 1.587 1.55E-02 miR-574-3p 2.048 4.57E-05miR-27b -1.877 3.85E-03 miR-320a -1.264 1.52E-02 miR-23a -1.846 1.74E-02 miR-138 -1.060 1.73E-02 miR-885-5p 2.159 5.38E-05miR-30c -1.720 4.17E-03 miR-634 1.223 1.57E-02 miR-30c -1.376 2.00E-02 miR-106b -1.011 2.24E-02 miR-1233 1.668 6.57E-05miR-99b -1.290 4.59E-03 miR-22 -1.297 1.60E-02 miR-22 -1.201 2.51E-02 miR-885-5p 1.125 2.54E-02 miR-629* 1.071 7.40E-05miR-23a -2.150 6.07E-03 miR-93 -1.352 1.68E-02 miR-1249 1.034 2.72E-02 miR-20a -1.038 2.61E-02 miR-328 2.727 7.82E-05miR-125b -1.952 6.20E-03 miR-27a -1.566 1.75E-02 miR-1974 -1.547 2.83E-02 miR-205 1.044 2.88E-02 let-7b* 1.641 8.13E-05miR-15b -1.366 6.38E-03 miR-181d 1.029 1.81E-02 miR-30b -1.129 2.93E-02 miR-31 -1.133 3.27E-02 miR-1225-3p 1.473 1.68E-04miR-103 -1.234 6.39E-03 miR-106a -1.409 1.89E-02 let-7f -1.559 3.20E-02 miR-1974 1.493 3.39E-02 miR-1236 1.959 2.07E-04miR-24 -1.555 7.61E-03 miR-30d -1.006 1.90E-02 miR-93 -1.197 3.32E-02 miR-100 -1.015 3.52E-02 miR-584 1.295 4.76E-04let-7e -2.133 7.83E-03 miR-30c -1.383 1.94E-02 miR-27b -1.307 3.97E-02 miR-550* 1.140 6.64E-04let-7g -1.155 8.31E-03 miR-99b -1.049 1.94E-02 miR-24 -1.167 4.18E-02 miR-1825 2.183 7.63E-04miR-532-3p 1.149 8.36E-03 miR-100 -1.128 2.00E-02 miR-92a -1.301 4.43E-02 miR-513b -1.009 1.24E-03miR-877* 1.337 8.91E-03 miR-595 -1.215 2.06E-02 let-7c -1.245 4.66E-02 miR-483-5p 1.292 3.00E-03miR-16 -1.613 1.17E-02 miR-1260 -1.018 2.06E-02 let-7a -1.414 4.89E-02 miR-675* 1.038 4.23E-03miR-30d -1.059 1.39E-02 miR-193b -1.521 2.08E-02 miR-150 1.135 4.45E-03miR-25 -1.235 2.00E-02 let-7f -1.685 2.10E-02 miR-1908 1.687 6.14E-03miR-29b -1.135 2.11E-02 let-7e -1.806 2.29E-02 miR-1979 1.029 7.92E-03miR-193b -1.515 2.12E-02 miR-16 -1.431 2.44E-02 miR-1238 1.041 9.92E-03miR-1974 -1.575 2.56E-02 miR-23a -1.728 2.54E-02 miR-574-5p 1.774 1.13E-02miR-92a -1.446 2.61E-02 miR-483-3p 1.369 2.56E-02 miR-744* 1.025 1.62E-02miR-93 -1.236 2.80E-02 miR-27b -1.423 2.57E-02 miR-1913 1.336 1.92E-02miR-1224-3p 1.033 3.67E-02 let-7b -1.322 2.61E-02 miR-1207-5p 1.539 1.94E-02miR-106a -1.233 3.87E-02 miR-125b -1.559 2.67E-02 miR-577 -1.021 2.22E-02miR-634 1.024 4.13E-02 miR-25 -1.139 3.11E-02 miR-765 1.122 3.45E-02miR-125a-5p -1.558 4.60E-02 miR-24 -1.224 3.32E-02miR-483-3p 1.208 4.76E-02 let-7i -1.488 4.60E-02miR-1280 -1.100 4.95E-02  189  Appendix B  Chapter 3 supplementary figures   Figure B.1 Growth curves of H5N1 in A549 cells at an MOI of 0.0001 (A) H5N1 viral RNA at 24, 48 and 72 hours post infection in A549 cells.  (B) H5N1 infectious virus released (pfu/mL) at 24, 48 and 72 hours post infection in A549 cells.    A B24 48 72104105106Hours Post Infectionpfu/mL24 48 72106107108109Hours Post InfectionRNA Copy Number  190    Figure B.2 HA protein expression following infection with the H5N1 influenza A virus The cellular expression of the H5N1 infA HA glycoprotein was examined in A549 cells at 24 hpi.  Cells treated with the FI were used as a positive control for inhibition of HA cleavage while cells treated with the transfection reagent only (X gene only) or untreated (infected only) were used as negative controls.  Cell extracts lysed in RIPA buffer were analyzed by western blot and probed with a mouse anti-H5N1 HA antibody and a rabbit anti-ß-tubulin antibody as a loading control.  Cell extracts obtained from untreated cells at 48 hpi were used as a positive control for HA expression.    miR-24240 nMneg-miR240 nMFI20 uMX gene onlyUntreatedcellsMock inf cells  Untreated cells48 hpi HA0HA1B-tubulin7055705524 hpi  191 Appendix C  Chapter 4 supplementary figures  Figure C.1 MRNA expression of furin, PCSK9, and SKI-1/S1P following overexpression of miR-22, 192, 93, and 100 miRNA mimics (A) Furin mRNA expression 24 hpt following transfection with different miRNAs at 5-45 nM.  (B) PCSK9 mRNA expression 24 hpt following transfection with different miRNAs at 5-45 nM.  (C) SKI-1/S1P mRNA expression 24 hpt following transfection with different miRNAs at 5-45 nM.  All qRT-PCR data was normalized to untreated cells.     0 5 15 45 0 5 15 45 0 5 15 45 0 5 15 45 0 5 15 450.00.51.01.5Furin expression relative to untreated cellsmiR-22miR-192miR-93miR-100neg-miR0 5 15 45 0 5 15 45 0 5 15 45 0 5 15 45 0 5 15 450.00.51.01.52.0PCSK9 expression relative to untreated cellsmiR-22miR-192miR-93miR-100neg-miR0 5 15 45 0 5 15 45 0 5 15 45 0 5 15 45 0 5 15 450.00.51.01.5S1P expression relative to untreated cellsmiR-22miR-192miR-93miR-100neg-miRABCnM miRNA mimicnM miRNA mimicnM miRNA mimic  192  Figure C.2 Furin mRNA expression of at 24, 48 and 72 hours post transfection with miR-20a, 17 and 106b mimics  (A) Furin mRNA expression 24 hpt following transfection with miR-20a, 17, 106b or neg-miR at 5-45 nM.  (B) Furin mRNA expression 48 hpt following transfection with miR-20a, 17, 106b or neg-miR at 5-45 nM.  (C) Furin mRNA expression 72 hpt following transfection with miR-20a, 17, 106b or neg-miR at 5-45 nM.  All qRT-PCR data was normalized to untreated cells and is representative of a single experiment.    0 5 15 45 0 5 15 45 0 5 15 45 0 5 15 450.00.51.01.52.0Furin expression relative to untreated cellsmiR-20amiR-17miR-106bneg-miR0 5 15 45 0 5 15 45 0 5 15 45 0 5 15 450.00.51.01.5Furin expression relative to untreated cellsmiR-20amiR-17miR-106bneg-miR0 5 15 45 0 5 15 45 0 5 15 45 0 5 15 450.00.51.01.5Furin expression relative to untreated cellsmiR-20amiR-17miR-106bneg-miRnM miRNA mimicnM miRNA mimicABCnM miRNA mimic  193  Figure C.3 PCSK9 and SKI-1/S1P mRNA expression of at 24 hours post transfection with miR-20a, 17 and 106b mimics (A) PCSK9 mRNA expression 24 hpt following transfection with miR-20a, 17, 106b or neg-miR at 5-45 nM.  (B) SKI-1/S1P mRNA expression 24 hpt following transfection with miR-20a, 17, 106b or neg-miR at 5-45 nM.  All qRT-PCR data was normalized to untreated cells and is representative of a single experiment.    0 5 15 45 0 5 15 45 0 5 15 45 0 5 15 450.00.51.01.5S1P expression relative to untreated cellsneg-miRmiR-20amiR-17miR-106b0 5 15 45 0 5 15 45 0 5 15 45 0 5 15 450.00.51.01.52.0PCSK9 expression relative to untreated cellsneg-miRmiR-20amiR-17miR-106bABnM miRNA mimicnM miRNA mimic  194  Figure C.4 MiR-17 targets furin in HeLa cells (A) 3’UTR luciferase assays with miR-20a, 17, 106b and neg-miR mimics.  HeLa cells were transfected with miR-20a, 17, 106b or neg-miR mimics at 5-45 nM and 50 ng of a luciferase expression plasmid containing the furin 3’-UTR.  (B) HeLa cells were transfected with a combination of miR-24 (45 nM) and miR-20a, 17, or 106b (45 nM each) and neg-miR mimics (90 nM) and 50 ng of a luciferase expression plasmid containing the furin 3’-UTR.  Luciferase activity was measured 24 hours post transfection.  Relative luciferase activity is displayed as the mean of triplicate transfections as a percentage of reporter only.  Each data point represents one experiment.   5 15 45 5 15 45 5 15 45 5 15 450.00.20.40.60.81.0RLU LuciferasemiR-20a miR-17miR-106bNeg miRABnM miRNA mimicmiR-24 miR-24 + miR-20amiR-24 + miR-17miR-24 + miR-106bneg-miR0.00.20.40.60.81.0MiRNA mimicsRLU Luciferase  195   Figure C.5 Furin enzymatic activity following transfection with miR-24 miRNA mimic Initial rate curves (0-20 minutes) for furin processing of a flurogenic substrate in HeLa cells treated with miR-24 mimics at 24, 48 and 72 hours post transfection.  Data is representative of one experiment and shown as a percent of untreated (0 nM) cells.   	  HeLa Furin Assay miR-24 initial Rate (20 min)0 5 15 45 0 5 15 45 0 5 15 450.00.51.01.52.0Furin enzymatic activity (percent of untreated)24 hpt48 hpt72 hptmiRNA mimic concentration (nM)

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