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Intracellular signaling networks regulate host-cell responses to coxsackievirus B3 infection Sadeghi Garmaroudi, Farshid 2011

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INTRACELLULAR SIGNALING NETWORKS REGULATE HOST-CELL RESPONSES TO COXSACKIEVIRUS B3 INFECTION  by Farshid Sadeghi Garmaroudi  B.Sc., The University of Shahid Beheshti, 1997 M.Sc., The University of Shahid Beheshti, 2000  A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF  DOCTOR OF PHILOSOPHY  in THE FACULTY OF GRADUATE STUDIES (Pathology and Laboratory Medicine)  THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver)  October 2011 © Farshid Sadeghi Garmaroudi, 2011  Abstract The host-cell response to viruses such as coxsackievirus B3 (CVB3) is determined by the signal-transduction networks modified during infection. To unravel the function of signaltransduction networks, these networks can be perturbed biochemically or genetically. But at the systems-level, it is unclear how such perturbations are best implemented to extract molecular mechanisms underlying network function. Further, the modified signal-tranuction network is a cause of multiple interdependent pathophenotypes, which makes it difficult to link virus-induced signals and responses. Either pairwise perturbations of host-cells infected by CVB3 or graded doses of CVB3 will reveal causal and unappreciated network mechanisms in CVB3-induced signaling and pathogenesis. I used CVB3 infection of cardiomyocytes as a representative virus-host system. In a first study, I combined pairwise perturbations with multiparameter phosphorylation measurements to reveal causal mechanisms within the signaling-network response of cardiomyocytes to infection. In a second study, I adopted a quantitative multiparameter approach to interrelate CVB3-induced host-cell events. I built a model that quantitatively predicts cardiomyocyte responses from timedependent measurements of phosphorylation events. Cluster analysis of the dataset showed that paired-inhibitor data was required for accurate data-driven predictions of kinase-substrate links in the host network. With pairwise data, I also derived a high-confidence phospho-protein network based on partial correlations, which identified phospho-IκBα as a “hub”. This network helped connect phospho-IκBα with an autocrine feedback circuit in host cells involving the proinflammatory cytokines, TNF-α and IL1. Autocrine blockade substantially inhibited CVB3 progeny release and improved host-cell viability, implicating TNF-α and IL-1 as cell-autonomous components of CVB3-induced myocardial damage. As well, analysis of multiple-dosing data revealed a tight coupling between the ERKs (ERK1/2 and ERK5) and p38 MAPK pathways with respect to CVB3-stimulated cytotoxicity. I showed that ERKs redundantly control a caspase-9-dependent apoptotic program, whereas p38 is required for CVB3-driven necrosis. ii  I conclude that pairwise perturbations, when combined with network-level intracellular measurements, enrich for mechanisms that would be overlooked by single perturbants. Moreover, virus-host interactions may seem especially complex because multiple overlapping pathophenotypes are being executed concurrently. By monitoring these pathophenotypes experimentally and deconvolving them mathematically, the control of each could be revealed as much more simple.  iii  Preface The portion of following dissertation includes chapters which are based on two manuscripts, in that Chapter 3 is based on an article published in the Proceedings of the National Academy of Sciences of the United States of America [Garmaroudi FS, Marchant D, Si X, Khalili A, Bashashati A, Wong BW, Tabet A, Ng RT, Murphy KP, Luo H, Janes KA and McManus BM (2010). Pairwise network mechanisms in the host signaling response to coxsackievirus B3 infection. Proc. Natl. Acad. Sci., 39, 17053-17058], and Chapter 4 is based on another article that is now under review at Cell Host & Microbe [Garmaroudi FS, Holmberg KJ, Carthy JM, Bashashati A, Marchant D, Zhang M, Lin J, Murphy KP, Ng RT, Luo H, Yang D, Janes KA and McManus BM (2011). An ERK–p38 subnetwork coordinates host-cell apoptosis and necrosis during coxsackievirus B3 infection. Cell Host Microbe, in revision].  iv  Table of contents Abstract ........................................................................................................................................... ii Preface............................................................................................................................................ iv Table of contents ............................................................................................................................. v List of tables .................................................................................................................................. vii List of figures ............................................................................................................................... viii List of abbreviations ...................................................................................................................... ix Glossary ........................................................................................................................................ xii Acknowledgements ....................................................................................................................... xv Chapter 1 –Coxsackievirus B3 infection-induced signaling and pathogenesis ....................... 1 1.1 Introduction .................................................................................................................. 1 1.2 Non-polio enterovirus infections .................................................................................. 2 1.3 History of coxsackieviruses .......................................................................................... 2 1.4 Classification and properties of coxsackieviruses ........................................................ 5 1.5 Lifecycle and tissue tropism of coxsackieviruses......................................................... 5 1.6 Pathogenesis of coxsackievirus B3............................................................................... 8 1.7 Signal-transduction network in coxsackievirus B3 infection ....................................... 9 1.8 Host cell death programs in coxsackievirus B3 infection .......................................... 16 1.9 Summary of research proposal ................................................................................... 18 1.9.1 Central hypothesis ...................................................................................................... 19 1.9.2 Specific aims ............................................................................................................... 19 Chapter 2 – Material and methods............................................................................................ 20 2.1 Cells and viruses ......................................................................................................... 20 2.2 Viral infection and perturbations ................................................................................ 20 2.3 Phospho-ELISA .......................................................................................................... 21 2.4 Western blot analysis .................................................................................................. 21 2.5 Plaque assays .............................................................................................................. 22 2.6 Cell viability assays .................................................................................................... 22 2.7 Caspase-3, -8 and -9 activity assays ........................................................................... 22 2.8 Hierarchical clustering ................................................................................................ 22 2.9 Graphical gaussian modeling...................................................................................... 23 2.10 Partial least squares regression ................................................................................... 23 Chapter 3 – Pairwise network mechanisms in the host signaling response to coxsackievirus B3 infection .................................................................................................................................. 24 3.1 Introduction ................................................................................................................ 24 3.2 Results ........................................................................................................................ 26 3.2.1 Pairwise pharmacological perturbation of cardiomyocytes infected with CVB3. ..... 26 3.2.2 Hierarchical deconstruction of the CVB3-induced signaling network ...................... 35 v  Host-signaling network reconstruction using paired-inhibitor data.......................... 43 3.2.3 3.3 Summary and conclusions .......................................................................................... 48 Chapter 4 – An ERK–p38 subnetwork coordinates host-cell apoptosis and necrosis during coxsackievirus B3 infection ........................................................................................................ 50 4.1 Introduction ................................................................................................................ 50 4.2 Results ........................................................................................................................ 52 4.2.1 How CVB3-induced phospho-protein dynamics quantitatively predict host-cell responses ................................................................................................................................... 52 4.2.2 ERK-p38 crosstalk provides complex, independent control of the host-cell response in CVB3 infection .......................................................................................................................... 58 4.2.3 ERK and p38 phosphorylation are required for apoptotic responses during CVB3 infection 67 4.3 Summary and conclusions .......................................................................................... 75 Chapter 5 – Closing remarks ..................................................................................................... 77 Bibliography ................................................................................................................................ 82  vi  List of tables Table 1. Enteroviruses: Animal and human cell culture spectrum and pathogenesis ..................... 3 Table 2. Measured phospho-proteins and virus replication indicators. ........................................ 35 Table 3. Scientific support for the CVB3 partial correlation network shown in Figure 10 B. ..... 46 Table 4. Scientific support for the CVB3-induced phospho-proteins and caspases activation. ... 53  vii  List of figures Figure 1. Primary etiological agents causing myocarditis. ............................................................. 4 Figure 2. The coxsackievirus B3 (CVB3) life cycle. ...................................................................... 7 Figure 3. The experimental approach used to define pairwise network models. .......................... 27 Figure 4. Single and pairwise small-molecule perturbation of a dynamic nine-protein phosphorylation signature induced by CVB3 infection. ............................................................... 29 Figure 5. Paired-inhibitor combinations perturb CVB3-induced phosphorylation signatures nonadditively....................................................................................................................................... 30 Figure 6. Paired-inhibitor combinations cause synergistic or antagonistic inhibition of viral protein expression, progeny release, and cardiotoxicity. .............................................................. 33 Figure 7. Single and paired inhibitors specifically inhibit CVB3-induced cardiotoxicity. .......... 34 Figure 8. Time-dependent hierarchical clustering of the nine-protein signature based on inhibitor data. ............................................................................................................................................... 38 Figure 9. Pairwise-inhibitor data enable accurate, context-dependent predictions of ATF-2 and CREB kinases. .............................................................................................................................. 40 Figure 10. Data-driven interactions between phospho-protein pairs reveal a convergent autocrine circuit through TNF-α, IL-1, and phospho-IκBα that promotes CVB3 progeny release and cardiotoxcity. ................................................................................................................................ 45 Figure 11. A predictive data-driven model of CVB3-induced host-cell responses. ..................... 56 Figure 12. Model principal components identify dynamic crosstalk between ERK and p38 pathways. ...................................................................................................................................... 62 Figure 13. ERKs p38 pathways contribute individually in CVB3 pathogenesis. ......................... 65 Figure 14. Separation of p38-dependent caspase-8 activation from ERK-dependent caspase-9 activation and caspase-3-driven apoptosis. ................................................................................... 70 Figure 15. Apoptosis and p38-dependent necrosis independently contribute to CVB3 progeny release and cardiotoxicity. ............................................................................................................ 74  viii  List of abbreviations AP-1 — activator protein-1 APAF1 — apoptotic-protease activating factor 1 ASC — apoptosis-associated speck-like protein ATF-2 — activated transcription factor-2 BMK1 — big MAP kinase 1 CAR — coxsackievirus and adenovirus receptor Caspases — cysteine-dependent aspartate-specific proteases CPE — cytopathic or cytopathogenic effect CREB — cAMP responsive element binding protein CVA — coxsackievirus A CVB3 — coxsackievirus of group B3 DAF — decay-accelerating factor or CD55 DMEM — dulbecco's modified eagle's medium DCM — dilated cardiomyopathy DISC — death-inducing signaling complex DMEM — dulbecco's modified eagle's medium eIF-4 — eukaryotic initiation factor 4 ELISA — enzyme-linked-immunosorbent assays ERK — extracellular signal-regulated protein kinase FADD — FAS associated via death domain FMK — fluoromethylketone GADD45β — growth arrest and DNA-damage-inducible 45β (Gadd45b) GGM — graphical gaussian modeling GSK3 — glycogen synthase kinase 3 GTPases — GTP-binding proteins HSP — heat shock protein ICE — interleukin-1β-converting enzyme IKK — IκB kinase ix  IL-1 — interleukin-1 IL-1ra — interleukin-1 receptor antagonist ILK — integrin-linked kinase IRES — internal ribosome entry sites IκB — inhibitor of NF-κB JNK — c-Jun NH2-terminal kinase MAPKAPKs or MKs — MAPK-activated protein kinases MAPKKKs, MKKKs or MEKKs — MAPK kinase kinases MAPKKs, MKKs or MEKs — MAPK kinases MAPKs — mitogen-activated protein kinases MEF2 — myocyte enhancer factor 2 MK2 — MAPK-activated protein kinases MOI — multiplicity of infections MSK — mitogen- and stress-activated protein kinase NF-κB — nuclear factor-κB NGF — nerve growth factor ORF — open reading frame PB1 — Phox and Bem 1 PB19 — parvovirus B19 PBS — phosphate buffered saline PCR — polymerase chain reaction PDGF — platelet-derived growth factor PFU — plaque-forming unit PI3K — phosphoinositide 3-kinase PKA — protein kinase A PKB — protein kinase B PLS — partial least squares RIP — receptor-interacting protein RSK — ribosomal s6 kinase x  SAPK — stress-activated protein kinase Smac — second mitochondria-derived activator of caspase ssRNA — single stranded RNA STE20 — sterile 20 TNF-α — tumor necrosis factor alpha UTRs — untranslated regions VP1 — viral capsid protein 1 VPR — viral progeny release XIAP — X-linked inhibitor of apoptosis protein  xi  Glossary Autocrine: Describing, or relating to, a cell that produces the ligands by which it is activated. Basophilic kinases: They are serine/threonine protein kinases, including protein kinase A (PKA), PKC, PKG and calcium/calmodulin dependent protein kinase II (CaM-II). Bliss independence: In combination therapy two drugs act in a manner that neither one interferes with the other, but each contributes to a common result, called Bliss independence. The cases in which the observed effects are more or less than predicted by Bliss independence are Bliss synergism and Bliss antagonism, respectively. The Bliss independence is computed in this way: (quantities with drug1) times (quantities with drug2). Bootstrapping: Bootstrapping or resampling is a computer-based method, creating different subsets of a given dataset to test a hypothesis. Degree: The degree of a node is the number of links (edges) that connect to it. The degree of a phospho-protein could represent the number of phospho-proteins with which it interacts. Dendrogram: Dendrogram is a tree diagram which is used to illustrate the arrangement of the clusters created by hierarchical clustering. Edge (link): An edge is the interactions between the nodes of a network. In biological systems, interactions can correspond to bio-molecule interactions. Enzyme-linked-immunosorbent assays (ELISA): ELISAs involve adsorbing or coupling capture antibodies to a 96-well plate. Following protein capture, a target protein is detected, either directly (if it was labelled in the sample), or indirectly, through a labelled detection antibody. Euclidean distance: A mathematical quantity that calculates the measurable geometric distance between two vectors pointing from a common origin. Graphical gaussian modeling: Graphical gaussian modeling is a tool to study protein and gene association networks. The graphical gaussian modeling uses partial correlation algorithm to assess degree of dependency between two biomolecules with considering the effects of a set of other biomolecules within the network.  xii  Hierarchical clustering: A statistical method in which objects (phospho-proteins) are grouped into a hierarchy, which is visualized in a dendrogram. Phospho-proteins in the same cluster are more similar to each other than to those in other clusters. Module: A sub-graph on a network that often represents a set of nodes that have a joint role. In biology, a module could correspond to a group of molecules that interact with each other to achieve some common function. Node (vertex): A component that, by interacting with other components, forms a network. In biological networks, nodes can denote phospho-proteins, proteins, genes, metabolites, RNA molecules or even diseases and phenotypes. Partial correlation: A pairwise correlation that remains after considering the correlations that two variables share with other variables in the dataset. Partial least squares (PLS) regression: A statistical method that finds a linear regression model by projecting the predicted variables and the observable variables to a new space. In fact, PLS is used to find the fundamental relations between two matrices “X” (signals) and “Y” (responses). Perturbation: Any experimental condition used on a cell that causes a shift in the cell's behaviour away from the basal state. This includes extracellular stimulation by physiological ligands, inhibition of protein activities such as kinases by small-molecule inhibitors, or alterations in protein-expression levels by RNA interference or overexpression. Proline-directed protein kinases: They are serine/threonine protein kinases, including mitogen activated protein kinases (MAPKs) and cdc2. P-value (p): The probability of obtaining a test-statistic at least as extreme as the one observed, assuming that the null hypothesis is true. It is effectively the probability of wrongly rejecting the null hypothesis when it is actually true. Signal: A cue to convey information between or within cells. Signaling network: A set of biological entities that act in an integrated fashion. Typical components of biological networks are proteins, DNA and RNA. Here, we focus on the proteinprotein interaction networks. The interactions can be physical (protein A binds protein B) or correlative (perturbing protein A alters protein B's activity).  xiii  Signaling pathway: A linear set of reactions that connects an input to an output in an intracellular signaling network. Standardized data: Transforming a dataset, “X” to a centered, scaled or normally distributed version of “X” with using function of “z-score” in MATLAB. For a matrix input, “X”, z-scores are computed using the mean and standard deviation along each column of “X”. Each column of transformed dataset has mean and standard deviation zero and one, respectively. Vector: A mathematical quantity that has both length and direction. The entries of a vector specify the magnitude of its projection in different directions.  xiv  Acknowledgements I would like to thank my wife Shahla. Without her love, her unrelenting support and her understanding, this difficult venture of a graduate education would have never been achievable. It was always nice to remember that during long days and failed experiments, I could always come home to not only a sympathetic ear, but also a warm meal as well. Shahla was never one who demanded my time away from the laboratory; in contrast, she always encouraged me to get my work done and she had the foresight to see that bigger and brighter opportunities lay ahead. I dedicate this thesis to my son, Barsam, who has kept asking me “are Saturday and Sunday the weekend?” My dear parents, Houshang and Hajar, have always had more faith in me than I did myself. I can attribute to them any positive character traits I have today. Their hard work and optimism are always an example to me. Beyond all my achievements throughout my life, they only wanted for me one thing, my graduation. I would also like to acknowledge all people, including research fellows, graduate students and technicians in the Cardiovascular Research Laboratory of Dr. Bruce McManus. In particular, I would like to thank Dr. Xiaoning Si, for being a wonderful experimental collaborator and Dr. David Marchant for being supportive and for his intellectual inspiration throughout my program. I am also grateful for partnership and friendship with my labmate, Dr. Brian Wong. I really enjoyed it when we talked about science. In addition, I would like to thank Drs. Ali Bashashati and Abbas Khalili for being helpful collaborators to manage computational part of this project. I would like to thank all my thesis committee members for their discussions, comments, and contributions to my research. Particularly, I have received significant support and encouragement from Dr. Honglin Luo. She always welcomed me through an open the door to her office to answer my questions. I also appreciate the time and thoughtful guidance provided by Dr. Kevin Murphy to apply specific computational algorithms to the data. Indeed, he encouraged me to use MATLAB to analyze and annotate our large and complex datasets. I would also like to thank Dr. Raymond Ng, who has provided much welcome insight on computational modeling. xv  I thank Dr. Decheng Yang and his research assistant, Mrs. Mary Zhang, for their support and scientific guidance. I am grateful for a synergistic collaboration that I formed with Dr. Kevin A. Janes to lift this project to a higher level. He brought me happiness, hope and confidence that all together helped me to achieve my research goals. I deeply owe Kevin for the many ideas he contributed either in modeling or biology to this research project. I also consider him as my friend for life. I am enormously thankful for my supervisor Dr. Bruce M. McManus, embarking on this endeavor with me. He was integral to my development as an “independent” researcher by critically assessing my questions, techniques applied, and results, over the years of my program. He was always behind me to support me so that I could properly direct this interdisciplinary project. His “vision” and “leadership” have shaped my graduate studies and future career in a more positive way than I could have ever expected. The work presented in this thesis was supported by a Grant-in-Aid from the Heart and Stroke Foundation of British Columbia and Yukon, and a four-year graduate fellowship from Tehran of University of Medical Sciences-Iran.  xvi  Chapter 1 – Coxsackievirus B3 infection-induced signaling and pathogenesis 1.1  Introduction The term myocarditis was coined by Sobernheim in 1837 to refer to inflammation of the  myocardium1. Thereafter, gradually it became more and more appreciated that inflammation was meant to initiate the healing process and eliminate tissue damage to the injurious stimuli2. On the other hand, persistent noxious stimuli and conditions including infection and tissue injury foster maladaptive and contradictory responses that can partly explain pathophysiology of inflammation and tissue injury in the myocardium 3. Of note, the inflammatory response promotes the recruitment of leukocytes and influx of plasma proteins to the heart tissue 4, which in turn induce, a transient decline in function of the tissue, alter homeostasis of the tissue, and may further the progression of disease to its sequel, dilated cardiomyopathy (DCM). Coxsackievirus of group B3 (CVB3), a cardiotropic virus, is among numerous known etiologies for myocarditis 5. This virus has been long identified as one of the leading causes of viral myocarditis-associated heart failure. To understand the molecular mechanisms of disease, a veritable avalanche of studies have been undertaken to examine how individual host signaling pathways support virus replication. The field emerged in 1997, when it was first discovered that tyrosine phosphorylation of distinct cellular proteins occurs during the course of enterovirus infection. Thereafter, a number of investigators proposed multiple molecular mechanisms, wherein viruses manipulate host signaling pathways, supporting virus replication and promoting host-cell death. Yet, despite the known roles of individual phospho-proteins, it remains difficult to determine “network mechanisms” within phospho-protein signatures induced by CVB3 infection. The complexity is even greater when considering that signal-transduction networks composed of several signaling pathways, control multiple-but-interrelated host-cell responses during CVB3 infection. In fact, virus infection is determined by networks of interaction, taking place on different scales, including within signal-transduction networks or between signaltransduction networks and host-cell responses. The modified signal-transduction networks are probably caused or influenced by virus endocytosis, virus replication and host inflammatory responses to CVB3 infection, suggesting that virus-manipulated networks play a central role in determining disease pathogenicity. 1  1.2  Non-polio enterovirus infections The majority of enterovirus infections neither cause nor exhibit symptoms of disease, yet  10-15 million enterovirus infections are recorded in the United States each year 6. Enteroviruses are important human pathogens and have been implicated in both acute and chronic diseases, including poliomyelitis, viral myocarditis-associated heart failure, polyomyositis, dermatomyositis and diabetes mellitus. With eradication of poliovirus infections, more attention is now being focused on understanding the non-polio enteroviruses such as coxsackieviruses 7. Coxsackieviruses cause multiple human diseases, ranging from flu-like illnesses to severe diseases such as aseptic meningitis and myocarditis 8. Myocarditis or inflammation of the heart muscle usually occurs after infection. In addition to infectious agents, a broad array of etiologies has been implicated as causes of myocarditis (Figure 1), but cardiotropic viruses are the predominant cause of the disease. Both serological and molecular studies indicate coxsackieviruses of the Picornaviridae family are responsible for >50% of the cases 9, 10. However, several studies show that each continent has its own epidemiological profile for viral myocarditis 11-13. Human parvovirus B19 (PB19) has frequently been isolated from myocardial biopsies from patients with myocarditis and cardiomyopathy using gene amplification by polymerase chain reaction (PCR) 12, 13. Nevertheless, we believe that to address the diversity of epidemiological profiles of viral myocarditis, we require a comprehensive biopsy-proven study to combine both PCR and in situ hybridization to assess viral genomes in cardiac tissues.  1.3  History of coxsackieviruses Enterovirus infection are historically ancient diseases, the earliest characterized was  poliovirus. However, in 1947, certain patients in a polio epidemic in the Hudson river town of Coxsackie, New York turned out to have a different virus; this atypical outbreak of poliomyelitis like illness was reported by Gilbert Dalldorf and Grace Sickles who isolated viruses from fecal specimens of two young boys suffering from flaccid paralysis 7. Notably, these viruses differed from polioviruses that could infect only primates, in that they were able to infect newborn (suckling) mice. In the next year, they reported a virus that induced fatal disease in baby mice, but not adult mice 14. Indeed, the virus, later called coxsackievirus A (CVA) produced a 2  generalized and widespread myositis mainly affecting the striated muscles. In the following year, Edward Curnen, Ernest Shaw and Joseph Melnick discovered a new virus that was antigenically different from CVA and showed a unique pathological picture in suckling mice. This virus was termed coxsackievirus B (CVB) (Table 1) 14. CVB not only induced a focal and limited myositis in striated muscles, but also produced degeneration of brain, pancreas, heart, muscle, and embryonic fat pads under the skin in baby mice 15.  Table 1. Enteroviruses: Animal and human cell culture spectrum and pathogenesis 16 Cytopathic effect Virus  Antigenic types  Illness and pathology  Monkey tissue culture  Human tissue culture  Suckling mice  Monkey  Polioviruses  1-3  +  +  –  +  Coxsackieviruses group A  1-22 and 24  ±  ±  +  –  Coxsackieviruses group B  1-6  +  +  +  –  3  Viral  Bacterial  Parasites  Fungal  • • • • •  • • • •  • Trypanosoma cruzi • Schistosomiasis • Larva migrans  • • • • •  Coxsackievirus Adenovirus Parvovirus B19 Human immunodeficiency virus Hepatitis C virus  Mycobacterium Streptococcal species Mycoplasma pneumoniae Treponema pallidum  Aspergillus Candida Coccidioides Crytoccoccus Histoplasma  Toxins  Immunologic syndromes  Hypersensitivity  • Anthracyclines • Cocaine • Interleukin-2  • • • • • • • • •  • • • • • •  Churg-Strauss Inflammatory bowel disease Giant cell myocarditis Diabetes mellitus Sarcoidosis Systemic lupus erythematosus Thyrotoxicosis Takayasu’s arteristis Wegener’s granulomatosis  Sulfonamides Cepahlosporins Diuretics Digoxins Trycyclic antidepressants Dobutamine  Figure 1. Primary etiological agents causing myocarditis. Multiple etiologies have been identified for the myocarditis, but viral myocarditis is the most common cause of the disease. Of note, among viruses, coxsackievirus group B serotypes, are the predominant cause of disease 17.  4  1.4  Classification and properties of coxsackieviruses Coxsackieviruses belong to the Enteroviruses genus within the Picornaviridae family.  There are 218 serotypes in this family. Enteroviruses tending to infect internal organs comprise 3 polioviruses, 23 coxsackieviruses group A and 6 coxsackieviruses group B (CVB) (Table 1). Among the six serotypes of CVB, only three, CVB1, 3 and 5, are cardiotropic 18. The structure and life cycle of the Picornaviridae have been well explained 19, 20. Briefly, the virion is a nonenveloped icosahedral particle, having ~30 nm diameter. The protein capsid is composed of 4 proteins, VP1, VP2, VP3 and VP4. One molecule each of VP1, VP2, VP3 and VP4 makes a protomer. Five protomers compose a pentamer and 12 pentamers make the capsid. In fact, the capsid is composed of 60 protomers. While VP1, VP2 and VP3 appear in the outer layer of the capsid, VP4 is an internal protein. Inside the capsid exists a viral genome of ~7.5 kb. The genome is a linear molecule of single-stranded RNA (ssRNA) and is infectious owing to its positive polarity. The ssRNA comprises an open reading frame (ORF), flanked on both 3′ (~100 bases) and 5′ (~800 bases) termini untranslated regions (UTRs). The ORF contains genes, encoding 11 proteins 21. There are four capsid proteins, two viral proteases (2A and 3C), an RNA-dependent-RNA-polymerase (3D), two proteins involved in RNA synthesis (2B and 2C), a primer of initiation of RNA synthesis (3AB), and a small polypeptide (VPg) of 20-24 amino acids derived from gene 3B. The 5’ UTR of viral RNA, is not linked to eukaryotic 7methylguanosine triphosphate cap structure associated with eukaryotic mRNA, but the 5’ UTR covalently binds to VPg 22. On the other hand, the 3’ UTR of viral genome is polyadenylated.  1.5  Lifecycle and tissue tropism of coxsackieviruses Coxsackievirus infection begins by coupling of the virus to host-cell receptors.  Endocytosis of CVB3 is dependent upon binding to both CAR (coxsackievirus and adenovirus receptor) and DAF [decay-accelerating factor (DAF also known as CD55)] as the main receptor and co-receptor, respectively 23. After gaining entry into the cytoplasm, viral RNA is transcribed and translated. The viral genome is translated into viral protein, initially synthesized as a large polypeptide that is subsequently cleaved into individual structural and non-structural proteins by the virus-encoded proteases 2A and 3C 24, 25. CVB3 protein 3D, an RNA-dependent RNA 5  polymerase, is required for transcription of negative-strand viral RNA intermediate that then serves as a template for synthesis of multiple positive-strand progeny genomes 26. The positive strand RNA genome and structural proteins are then integrated to make the complete virion. Ultimately, the life cycle of the virus is completed by release of viral progeny for further infection of neighboring cells (Figure 2). With regard to tissue permissiveness, CVB3 infects multiple human organs, yet cardiac tissue is among the most pertinent targets for CVB3 infection 27. We and others have shown that the heart and exocrine pancreas are the most vulnerable mouse organs 27, 28, whereas the kidney and lung are comparatively resistant to CVB3 infection. The level of tissue permissiveness to CVB3 infection is often determined by virus-receptor (CAR and DAF) interactions 29, 30. However, the differential expression of CAR and DAF receptors alone is not able to explain tissue permissiveness to CVB3 infection 31. Indeed, expression of CAR in kidney and liver are typically not associated with severe coxsackie B viral myocarditis 27, 32. Additionally, although cardiomyocytes express moderate levels of CAR receptors, cardiac tissues are one of the most permissive tissues to virus infection 27. Conversely, CVBs have been monitored in the cells of some organs that have not been reported to express CAR at detectable levels 33-35. Hence, other anonymous determinants may play a role in tissue permissiveness and disease severity.  6  2. DAF Clustering  0.1 7 h DAF  DAF DAF DAF DAF DAF DAF  1. DAF Binding  6. Transcription 5’ SS-RNA(+)  8h  5. Translation CAR  3’ VP4 VP2 VP3 VP1 2A 2B 2C 3A  3D  Structural Proteins 5’  3’  3. CAR Binding  3B  3C 3D  Non-structural Proteins  SS-RNA(-) 3D 5’  3’ SS-RNA(+)  16 h 1h 7. Virion Assembly 4. Internalization  24 h 8. Viral Progeny Release  Figure 2. The coxsackievirus B3 (CVB3) life cycle. The CVB3 life cycle begins by binding and clustering decay accelerating factor (DAF). Coupling with its co-receptor (DAF) leads virus to the intercalated disk of the cardiac myocyte where its main receptor, coxsackievirus and adenovirus receptor (CAR), is located. Binding of virus to CAR allows virus to internalize. Now, virus injects its + ss-RNA into the cytoplasm. At this step the + RNA is either transcribed or translated. During the process RNA is translated into a polyprotein whereas in the transcription step RNA-dependent RNA polymerase (3D) converts + RNA to –RNA and vice versa, to make more +RNA to be packed into progeny virions. At the end of this step there is a pool of RNA both – and +. The +ss-RNA and structural proteins are integrated to make virions. Ultimately, the life cycle of the virus is completed by releasing viral progeny. Ultimately, virions are able to infect other cells nearby and the virus life cycle begins anew. The numbers in blue circles indicate estimated time-points when we are able to monitor a specific event in CVB3-infected cardiomyocytes that have been infected with a multiplicity of infections [(MOI)=9].  7  1.6  Pathogenesis of coxsackievirus B3 Researchers have been investigating the genetic and biochemical mechanisms underlying  the pathophysiology of myocarditis that leads to cardiomyopathy and heart failure. There is substantial evidence in support of the concept that myocardial fibrosis with heart failure stems from viral myocarditis 36. To date, several pathogenic mechanisms causing tissue injury and fibrosis have been unraveled in CVB3 infection 21. These mechanisms include direct CVB3induced damage to the heart tissue37, host-cell inflammatory responses to virus infection21, or a combination of the two which may synergistically promote cardiotoxicity. CVB3-induced acute myocarditis is most likely the consequence of direct virus-induced myocyte damage, followed by the host inflammatory response that is associated with persistent or chronic CVB3 infection. These pathological processes can be more challenging in cardiomyocytes that are end-stage or terminally differentiated because these cells are incapable of proliferation after being damaged. Several injurious stimuli, including infection, ischemia, trauma and pharmacotoxic agents can promote cell death in differentiated cardiomyocytes. In fact, such persistent insults on heart tissue can lead to cardiac dysfunction and heart failure 38. Studies have shown that CVB3 has enough virulence to induce disease in heart tissue 37. Like poliovirus, coxsackieviruses rapidly shut-off cellular RNA and protein synthesis. The question is—how can the viral RNAs compete with the multitude of cellular mRNAs for ribosomes? Three viral-encoded proteinases, 2Apro, 3Cpro and 3CDpro, can cleave virus polyproteins 39, 40. These enzymes are cysteine and serine proteinases 41, 42. Apart from the role of viral proteases in viral polyprotein processing, 2Apro degrades eukaryotic initiation factor 4 (eIF-4) 43 which plays a central role in cap-dependent translation. Indeed, 2Apro is essential for cleaving eIF-4 and, hence, for halting cellular protein synthesis 40, 44. Importantly, cleavage of eIF-4 has no effect on virus specific protein synthesis because viral RNAs are uncapped and are translated in an entirely cap-independent manner 45. There is a cis-acting sequence element in the 5’UTR of the viral genome which is called the internal ribosome entry sites (IRES). IRES elements promote cap-independent translation, leading to enhanced viral protein translation but reduced host-cell protein synthesis. Even a small level of 2Apro can disrupt host cell metabolism 24  . 8  Further, a microscopic picture of host-cells infected by CVB3, particularly in cell culture, demonstrates the cytopathic effect (CPE) that refers to degenerative change in cells associated with the replication of CVB346, 47. These morphological changes are cell rounding, followed by release of cells from the culture surface and altered membrane permeability, leading to lysis of host cells, mainly through apoptosis, necrosis, or both. Inhibition of host cell transcription and translation, and loss of cellular homeostasis due to direct viral protease cleavage of structural proteins such as dystrophin likely contribute to the cellular structure disruption and cell death of infected cells 24, 25. In addition, studies have shown that immunopathogenic or inflammatory responses can lead to tissue damage that is uncoupled from the original viral injury 36. For nearly four decades, studies in experimental animals have prompted scientists to propose mechanisms by which myocardial inflammation can be induced by specific pathologic humoral and cellular immunologic responses that are independent of virus replication, particularly during sub-acute and chronic phase of disease, wherein these responses may continue to produce myocyte injury and death 21. Pro-inflammatory cytokines play a central role in the control of chronic inflammation. For example, activation of Toll-like receptor (TLR) 4 on mast cells and macrophages by viruses may lead to release of the pro-inflammatory cytokines tumor necrosis factor (TNF)-α , interleukin (IL)-1, IL-18 and IL-4 48. It has been documented that TNF-α and IL-1 promote myocarditis caused by CVB3 49. Indeed, the levels of circulating TNF-α and IL-1 are considerably augmented upon CVB3 infection 50, while the major source of these cytokines in the heart has been thought to be infiltrating monocytes 51. Taken together, non-inflammatory lesions were observed to evolve autonomous of virus replication, indicating that persistent infection is essential for the development of chronic disease 52  . In fact, persistent infections of cardiomyoctes may lead directly to dilated cardiomyopathy.  1.7  Signal-transduction network in coxsackievirus B3 infection Host-cells have developed complex systems to detect and eradicate viruses; on the other  hand, viruses have evolved mechanisms to compromise essential cellular processes and suppress host cell defence. In this complex interplay, protein phosphorylation events control many 9  aspects of cellular function and homeostasis, and the failure of control mechanisms causes diseases 53. Most notably, these protein phosphorylation events modulate host factors crucial for the pathogen’s replication, propagation, and evasion from host immune responses 54. In fact, phosphorylation is the most significant post-translational modification regulating intracellular signaling events 55. Generally, phosphorylation is controlled by a protein kinase that modifies other proteins or biomolecules by chemically adding phosphate groups to them 56. Phosphorylation usually gives rise to a functional change of the target molecule by altering their enzymatic activity, their interaction with DNA, RNA and other proteins, their precise subcellular localization, and their susceptibility to degradation by cellular proteases or the proteasome 56. The human genome contains ~500 protein kinase genes and they constitute about 2% of all human genes 55. It was shown that ~30% of all human proteins may be modified by kinase activity, and kinases are known to regulate the majority of cellular pathways, especially those involved in signal-transduction networks 57. Several studies provide convincing evidence that CVB3 exploits the host-cell to disrupt intracellular signaling pathways underpinning its replication 58, 59. Importantly, we and others have shown the role of different signaling pathways that individually support CVB3 replication in different steps of infection. The story of the CVB3-modified signal-transduction network began in 1997 when Huber et al. reported tyrosine phosphorylation of two proteins with molecular masses of 48 and 200 kDa in CVB3 infection 60. Sub-cellular fractionation experiments, in turn, unraveled that the 48 kDa CVB3-induced phosphoprotein is a cytoplasmic protein, whereas the 200 kDa phosphoprotein is mostly associated with membrane structures. Interestingly, Herbimycin A, an inhibitor of the Src family kinases, diminishes the tyrosine phosphorylation events and in turn decreases the production of CVB3 progeny release. This suggests that the Src family kinase pathways are partly required for efficient replication of CVB3. Indeed, p56lck, a member of the Src family kinases contributes to viral replication in Tcells and virus-induced pathogenicity 61. Interestingly, mice deficient for the p56lck gene are completely protected from CVB3-induced pancreatitis, hepatitis and myocarditis. The neverending story of Src family kinases led Coyne et al. to report that the CVB3 co-receptor DAF is associated with Fyn, another member of the Src family kinases 62, and that activation of Fyn is 10  necessary for CVB3 entry through epithelial tight junctions in polarized cells 62. Moreover, we observed that Src tyrosine529 (Y529) phosphorylation decreased during CVB3 endocytosis and replication (unpublished observations). Indeed, phosphorylation at this site often is associated with a lower kinase activity of Src. In general, this observation along with previous findings, further confirms that activation of the Src family kinase-mediated signaling pathways is favorable for replication of CVB3. Progressively, these findings involving Src family kinases encouraged researchers to shift their attention to other parts of the signal-transduction network such as glycogen synthase kinase 3 (GSK3). Phosphorylation of GSK3β at serine9 (S9) inhibits its protein kinase activity 63, while phosphorylation of GSK3β at Y216 is required for GSK3β activity 64. The protein kinases that phosphorylate GSK3β at S9 are well established and include Akt and extracellular signalregulated protein kinase 1/2 (ERK1/2) 65. The pool of β-catenin, which is phosphorylated by activated GSK3β (phosphorylated GSK3β at Y216) is a signal for ubiquitination and degradation by the ubiquitin–proteasome system 66. Whereas, inactivated GSK3β (phosphorylated GSK3β at S9) is unable to phosphorylate β-catenin 66; in turn, unphosphorylated β-catenin accumulates and subsequently translocates into the nucleus 65. In the nucleus, T-cell-specific transcription factor/lymphoid enhancer binding factor 1 (TCF/LEF1) is normally maintained in a repressed state, but by association with β-catenin forms a complex that activates transcription of TCF/LEF1-regulated genes and cell survival 65. One observation from our laboratory showed how the activity of GSK3β is increased in CVB3 infection 67 via a tyrosine kinase-dependent mechanism that contributes to CVB3-induced cell death through the deregulation of β-catenin. Thereafter, almost simultaneously, multiple reports from different laboratories showed the role of another part of the network, ERK1/2 whose phosphorylation is required for CVB3 infection 68-70. ERK1/2 is a member of the mitogen-activated protein kinases (MAPKs), which are evolutionarily conserved enzymes relaying extracellular signals from cell surface receptors to critical targets within cells, and thus regulate biological events such as cell proliferation, differentiation and stress responses 71. The MAPK pathway is part of a phosphorylation-based cascade composed of three tightly and sequentially activated kinases, including MAPK kinase kinases (MAPKKKs, MKKKs or MEKKs), MAPK kinases (MAPKKs, MKKs or MEKs) and 11  MAPKs 55, 71. Of note, either sterile 20 (STE20) kinases or small GTP-binding proteins (GTPases) are required for the activation of these modules. In general, MAPKs trigger the activation of effector kinases such as MAPK-activated protein kinases (MAPKAPKs or MKs such as MK2) 72, 73. A number of studies have revealed that the early activation of ERK1/2 may stem from the engagement of CVB3 with its main receptor CAR, co-receptor DAF or both 69, 70, 74  . There is also a mechanism that partly justifies how ERK1/2 phosphorylation is associated  with replication of CVB3 at later times post-infection. At this stage, CVB3 proteases such as 3Cpro may cleave RasGAP triggering ERK1/2 phosphorylation. Cleavage of RasGAP by viral proteases leads to accumulation of the active form of Ras (GTP-bound form of Ras), which in turn activates the Ras/Raf/MEK1/2/ERK1/2 phosphorylation cascade. Interestingly, Src family kinases are likely involved in early activation of ERK1/2 because the absence of p56lck in Jurkat cells reduces ERK1/2 phosphorylation following receptor engagement 70. The importance of the ERK1/2 pathway to CVB3 infection, has been established as pan-MEK1/2–ERK1/2 inhibition is sufficient to reduce viral replication, decrease cleavage of host proteins and suppress host cell death 75. ERK5 or big MAP kinase 1 (BMK1) is a member of the MAPK family 76 and is nearly twice the size of other MAPKs 77. The amino-terminal half of ERK5 contains the kinase domain, which is similar to that of ERK1/2 and has the Threonine-Glutamate-Tyrosine activation motif, whereas the carboxy-terminal half of ERK5 has a unique structure. Activated- MEK5-ERK5 phosphorylates effector kinases such as myocyte enhancer factor 2 (MEF2) 78. It is known that the Phox and Bem 1 (PB1) domain of MEK5 interacts with ERK5 79. Importantly, these days there are tools that allow us to distinguish MEK1/2 from MEK5 pathways using either distinct stimuli or perturbations. Originally, ERK5 was a MAPK family member that was thought to be activated only by “stress stimuli” since ERK5 was activated by either oxidative stress or hyperosmolarity, but not by platelet-derived growth factor (PDGF), a strong stimulus for ERK1/2 80. However it has since been shown that PDGF can also activate the ERK5 pathway81. Several studies subsequently emerged that ERK5 can be also activated in response to serum, one of the well-known activators of ERK1/2 78. Nerve growth factor (NGF), another stimulator of ERK1/2, can also increase ERK5 activity 82. Using small-molecule inhibitors, we can separate 12  ERK1/2 from the ERK5 pathway. That is, either PD98059 or U0126 that was identified as MEK1/2-specific inhibitors, also cross-inhibit the MEK5–ERK5 pathway 82. However, the MEK5–ERK5 pathway is less sensitive to PD184352 used at low concentrations, which was also shown as a MEK1/2 inhibitor 83. Little is known about the contribution of MEK5-ERK5 in CVB3-induced myocarditis. This pathway is modified by stress stimuli and the most-commonly used MEK inhibitors (U0126 and PD98059) block MEK5 activation as potently as MEK1/2 84, suggesting the role of this pathway may have been previously overlooked in CVB3 infection. Part of the host-cell response to virus infection and pro-inflammatory stimuli, such as TNF-α and IL-1 as well as other environmental and cellular stresses, such as osmotic shock and heat shock 85, 86 are controlled by the stress-activated protein kinase (SAPK), including c-Jun NH2-terminal kinases (JNK1, JNK2, and JNK3) and p38 71, 87. The JNKs phosphorylation are required for activation of transcription factor c-Jun that is part of the activator protein (AP)-1 transcription complex responsible for the expression of many cytokine genes in response to infection 88. Importantly, phosphorylation of transcription factors such as activated transcription factor (ATF)-2 and cAMP responsive element binding protein (CREB) act as points of convergence for multiple kinase-signaling pathways 89. ATF-2 can be phosphorylated by the proline-directed JNK 90, p38 91 and ERK 92 MAPK pathways. On the other hand, CREB is phosphorylated by basophilic kinases, such as protein kinase A (PKA) 93, ribosomal s6 kinase 2 (RSK2) downstream of ERK 94, and mitogen and stress activated kinases 1 (MSK1) downstream of both ERK and p38 95. P38 and its effector kinases, MK2 and MSK are activated by inflammatory cytokines to regulate the expression of many other cytokines to stabilize mRNA transcripts of these cytokines, and play, in turn, a significant role in driving host immune responses 96. Recent studies have shown how virus infections such as CVB3 infection govern activation of both JNK and p38, and that activation of p38 is required for viral progeny release 87, 97-100  . Our laboratory has elucidated how p38 contributes to CVB3 infection 100-103, in that the  p38 pathway is required for replication of CVB3102. Nuclear factor (NF)-κB is a pivotal transcription factor in chronic inflammatory diseases and IκB kinase (IKK) has emerged as an attractive therapeutic target to diminish such conditions 104-107  . The IKK is composed of two homologous kinase subunits, IKKα (IKK1) and IKKβ 13  (IKK2), and the regulatory subunit IKKγ or NF-κB essential modifier (NEMO) 104. Studies have shown that IKK2 is an essential kinase for the production of pro-inflammatory cytokines such as IL-1 and TNF-α  108  . The IKK complex phosphorylates inhibitor of NF-κB (IκB) proteins  following activation 104. Once IκB is phosphorylated, it is rapidly ubiquitinated and degraded by the proteasome 109. The release of NF-κB from IκB allows translocation of NF-κB into the nucleus, where it binds to specific sequences in the promoter regions of target genes. Several different NF-κB proteins have been characterized 110-112. The activated form of NF-κB is a heterodimer, which usually consists of two proteins, a p65 (also called relA) subunit and a p50 subunit 113. Other subunits, such as rel, relB, v-rel, and p52, may also be part of activated NF-κB, and it is likely that the different forms of NF-κB may activate different sets of target genes. In unstimulated cells, NF-κB is found in the cytoplasm and is bound to IκBα and IκBβ, which prevent it from entering the nucleus 109-111, 114, 115. NF-κB increases the expression of genes for many cytokines, enzymes, and adhesion molecules in chronic inflammatory diseases 113such as viral myocarditis. The association of NF-κB activation with inflammation 104 and apoptosis 116, 117  has been shown in various human diseases and in animal models of disease; however NF-κB  is widely regarded as a pro-survival protein 118. In particular, inhibition of the NF-κB pathway significantly suppresses host-cell viability in CVB3 infection of HeLa cells 119. The contribution of survival pathways such as protein kinase B (PKB) or Akt, downstream of phosphoinositide 3-kinase (PI3K), was unraveled in CVB3 infection 120. Indeed, our laboratory showed that CVB3 infection leads to phosphorylation of PKB/Akt on both S473 and Threonine308 (T308) residues through a PI3K-dependent mechanism. In this study, we revealed that coupling of CVB3 with its receptor, coreceptor or both is not sufficient for PKB/Akt activation, but viral replication is remarkably associated with Akt phosphorylation. Thus, PI3K/Akt signaling is favorable to CVB3 replication. Our next observation showed the role of integrin-linked kinase (ILK) in Akt activation during CVB3 infection, in that kinase activity of ILK is required for efficient CVB3 replication121, 122. Thus, ILK controls CVB3 pathogenesis, by modulating virus replication and virus-induced cellular injury through an Aktdependent mechanism.  14  In general, the nature of host-cell responses to virus infection is tightly affected by context, such that an individual signaling pathway might be used again and again at different stages of the CVB3 life cycle, allowing the virus to progress through its life cycle. In fact, linear signaling pathways are not sufficient when host-cells need to make critical “decisions”. Thus, different signaling pathways must be collated into a higher-order network that accurately reflects the consequence of a CVB3 infection. Moreover, while the importance of alterations in the individual pathways of such a network (that is, its local properties) has been well established in CVB3 infection, the architecture and significance of the higher-level organization (that is, its global properties) remain elusive. In CVB3-host interactions, dynamic alterations of multiple host signaling pathways have led us to propose that viruses drive “signaling networks” rather than an individual pathway to support their replication. Large-scale approaches of systems biology in virus-host interactions have now emerged to monitor the network components and to propose novel and potential targets for the development of antimicrobial or anti-inflammatory drugs 123. In addressing host network complexity, combination therapeutic approaches are becoming the norm, aiming to achieve optimal efficacy with reduced mechanism-based toxicity 124  .  15  1.8  Host cell death programs in coxsackievirus B3 infection It is not completely clear how cells in tissues die when infected by viruses, but  considering that a major pathological determinant of viral myocarditis is cell death of infected cardiomyocytes, it is an important issue 125. Cell death has been classified according to morphological, biochemical or functional aspects, including apoptosis, necrosis, and autophagy. Apoptosis is required for both health and disease, yet necrosis is usually the outcome of severe injury and inflammation 126.  For a long time, apoptosis was considered the only form of  programmed cell death, yet both apoptosis and necroptosis are now known as two independent arms of regulated cell death 127. In murine enteroviral myocarditis, both necrosis and apoptosis promote death of myofibers 37. An in vitro study also showed how apoptosis may facilitate the final clearance of virus-infected cells 47. Unlike necrosis, apoptosis is a programmed cell death, playing a central role in the development and homeostasis of tissues 128. The term programmed cell death is based on the observation that dying cells endure an ordered series of morphological changes. Dysregulation of apoptosis leads to unrestricted cell death in cardiovascular diseases 129. Caspases (cysteinedependent aspartate-specific proteases) are the key components of the apoptotic response 130. In fact, caspases are cysteine proteases that cleave after an aspartate residue in their substrates 131. The first caspase, interleukin-1β-converting enzyme (ICE; also known as caspase-1), was identified in humans 132. Since then, at least 14 distinct mammalian caspases have been identified, of which there are 11 in humans 130. The phylogenetic relationship of caspases appears to correlate with their function 133. Generally, caspases are divided into two categories: initiators [caspase-1, 2, 8, 9 and 10 131, 134], and effectors [caspase-1, 3, 6 and 7] 131. Activation of multiple caspase scaffolds may occur during virus infection; in turn, they merge at a specific substrate. Thus far, several platforms were explained for apoptosis, including (i) the intrinsic pathway, to form a heptameric complex known as the apoptosome [cytochrome c, apoptotic-protease activating factor 1 (APAF1) and caspase-9)] 131; (ii) extrinsic pathway, to combine FAS associated death domain (FADD ) and caspase-8 to compose an oligomeric death-inducing signaling complex (DISC) 135 and (iii) inflammatory pathway, to assemble the inflammasome  16  which is composed of NALP [(NACHT, LRR, and pryin domain-containing proteins) ASC (apoptosis-associated speck-like protein)] and caspase-1 133. Although our knowledge about molecular mechanisms controlling apoptosis is relatively convincing, the study of non-apoptotic forms of cell death is in its infancy. In fact, the challenge of describing the precise mechanisms of necrosis and necroptosis, as well as the molecular switches between apoptosis and necrosis, has significant therapeutic implications 136. Presumably, the blocking of necrosis, the promoting of apoptotic cell death, or both may reduce inflammation, and hence halt secondary tissue damage. One study shows that necrostatin, smallmolecule inhibitor of receptor-interacting protein 1 (RIP1) kinase, has proved their therapeutic potential in a murine model of stroke 127. One of the major challenges in designing treatments for CVB3 is our disjointed understanding of how CVB3 infection interrupts the host signaling pathways that dictate hostcell responses 137. Thus, perturbation of a phospho-protein whose phosphorylation is required for regulation of a specific cell response may halt the progression of disease.  17  1.9  Summary of research proposal Although a broad array of etiologies can cause myocarditis, cardiotropic viruses are the  primary cause of the disease 5. Among viruses, CVB3 has been one of the leading causes of viral myocarditis 138, 139. Beyond infected newborns and infants, the importance of myocarditis is more as a potential precursor to dilated cardiomyopathy (DCM) than as a distinct disease entity 140, 141  . Most patients with myocarditis recuperate from a transient cardiac dysfunction and do  not manifest characteristic clinical syndromes, yet others either die or develop DCM that may lead to congestive heart failure 142, 143. Cardiotropic viruses particularly CVB3 can directly and indirectly 144-148 quicken the progression of viral myocarditis to its sequela DCM 149. Importantly, ~100,000 new cases of DCM are diagnosed in United States every year, and the 10year survival rate for the disease is ~40% 10, 150. Of note, one-third of patients that develop DCM have genome of viruses in endomyocardial biopsies, implicating a role for virus infection in myocarditis 21. The two-year mortality rate in patients testing positive for CVB3 genome is ~9 times higher than for patients testing negative for viral genome in endomyocardial biopsies 150. To date, there is neither a vaccine nor a curative treatment beyond heart transplantation for viral myocarditis-associated heart failure 37. A veritable avalanche of studies, however, has suggested that many phospho-proteins individually play roles in supporting virus replication 58. Yet despite the known roles of individual phospho-proteins in CVB3 infection, it remains difficult to determine causal and complex interactions within a “phospho-protein network” that embraces a system with redundant, convergent and distinctive signaling pathways 151. Such combinatorial properties of signaling networks may counteract the therapeutic efficacy of even highly selective drugs 152. Thus, combination therapy may be necessary to achieve efficacy with fewer side-effects. In this thesis, I focus attention on defining signaling network models composed of the interconnected signaling pathways that cooperatively regulate host-cell responses to CVB3 infection. In fact, signal-transduction networks control multiple-but-interrelated pathologic events, including viral endocytosis, viral replication and virus-induced inflammatory responses that collectively promote cell death. Due to the pivotal role of signal-transduction networks that 18  become disrupted by virus replication, studies outlined may allow us to propose novel-butunappreciated network mechanisms that may lay the foundation for new scientific and therapeutic approaches to viral myocarditis.  1.9.1  Central hypothesis Either pairwise perturbations of host-cells infected by CVB3 or graded doses of CVB3  will reveal causal and unappreciated network mechanisms in CVB3-induced signaling and pathogenesis.  1.9.2  Specific aims  1) To test whether pairwise perturbations combined with network-level intracellular measurements can enrich for mechanisms that would be overlooked by single perturbants. 2) To define a partial correlation signaling network composed of interacting phophoproteins in CVB3-infected cardiomyocytes and then to illustrate which phopho-proteins play central roles in constructing this network. 3) To determine how CVB3-modified phospho-protein networks quantitatively predict multiple-but-interrelated pathophenotypes.  19  Chapter 2 – Material and methods 2.1  Cells and viruses Mouse atrial cardiomyocyte cell line, HL1 cells are permissive cell line for CVB3  infection, wherein CVB3 can complete its life cycle in these cells 70, 153, 154. HL1 cells were obtained from Dr. William C Claycomb (Louisiana State University Medical Center, New Orleans, LA) and were maintained as described previously 155. CVB3 (Kandolf strain) was propagated in Henrietta Lacks (HeLa) cells and virus titers were determined by the plaque assay.  2.2  Viral infection and perturbations In a first study, HL1 cells were sham-infected with phosphate buffered saline (PBS) or  infected with CVB3 multiplicities of infection (MOI=9) after pre-treatment with one of 23 different experimental conditions (one control, seven single signaling inhibitors or fifteen combinations of two signaling inhibitors). HeLa cells are a well-established cell line model to study molecular mechanisms, underlying CVB3 induced signaling and pathogenesis 75. Our laboratory showed that CVB3 complete its life cycle at 9 hr p.i. either in HeLa cells infected at MOI=10 75 or HL1 cells infected at MOI=50 153. In this study, we used a moderately low MOI=9 to infect HL1 cells, but we extended time of p.i. to 24 hr when CVB3 releases their progeny into media 89. The chemical inhibitors included API-2 (reported Akt inhibitor, 1 µM), BAY11-7085 (IκB, 10 µM), SB203580 (p38, 50 µM), SB216763 (GSK3β, 10 µM), SP600125 (JNK, 50 µM) PP2 (Src-family kinases, 10 µM) and U0126 (MEK1/2, 20 µM) were obtained from Tocris Biosciences (Ellisville, MO). SL0101 (RSK, 100 µM), LY294002 (PI3K, 10 µM), JNKi (JNK, 1 µM) and SB202190 (p38, 10 µM) were purchased from Calbiochem (La Jolla, CA). PD184352 (MEK, 10 µM) were obtained from Santa Cruz Biotechnology (Santa Cruz, CA). Both anti-TNF-α (1 µM) and interleukin-1 receptor antagonist (IL-1ra) (0.5 µM) were obtained from R&D systems (Minneapolis, MN). In a second study, HL1 cells were sham-infected with PBS or infected with one of five (MOI=0.5, 1.5, 4.5, 9 and 18) and cell extracts were prepared at 0, 0.17, 1, 8, 16 and 24 hr. For perturbation experiments, the following chemical inhibitors were added one hour before infection: SB203580 (reported p38 inhibitor, 20 µM) and U0126 (MEK1/2, 10 µM) were 20  purchased from Tocris Biosciences (Ellisville, MO). PD184352 (MEK, 2 µM or 10 µM) were obtained from Santa Cruz Biotechnology (Santa Cruz, CA). Benzyloxycarbonyl- Ile-Glu(OMe)Thr-Asp(OMe)-fmk (IETD-fmk) (caspase-8, 40 µM), benzyloxycarbonyl-Leu-Glu(OMe)-HisAsp(OMe)-fmk (LEHD-fmk) (caspase-9, 20 µM) and N-benzyloxycarbonyl-Val-Ala-Asp-fmk (zVAD-fmk) (pan-caspase, 40 µM) were obtained from R&D systems (Minneapolis, MN). Necrostatin-1 (RIP1, 50 µM) was purchased from Calbiochem (La Jolla, CA).  2.3  Phospho-ELISA Cell lysates were normalized to protein concentration and analyzed by phospho-ELISA  (Biosource) for the phosphorylation levels of Akt (S473), ATF2 (T69/T71), CREB (S133), ERK1/2 (T185/Y187), GSK3β (S9), Hsp-27 (S82), IκBα (S32), JNK1/2 (T183/Y185) and p38 MAPK (T180/Y182) according to manufacturer’s instruction.  2.4  Western blot analysis Cell lysates were prepared as described previously 156. Equal amounts of protein were  subjected to sodium dodecyl sulfate-polyacrylamide gel electrophoresis and then transferred to nitrocellulose membranes (GE Healthcare). Membranes were blocked for 1 hr with non-fat dry milk solution (3% in PBS) containing 0.1% Tween-20. Blots were then incubated with one of the following primary antibodies: anti-VP1 (Dako, 1:1000), anti-phospho-ATF2 (T69/T71, Cell Signaling, 1:1000), anti-phospho-CREB (S133, Cell Signaling, 1:1000), anti-phospho-ERK1/2 (T202/Y204, Cell Signaling, 1:1000), anti-phospho-ERK5 (T218/Y220, Cell Signaling, 1:1000), antiphospho-p38 (T180/T182, Cell Signaling, 1:1000), anti-phospho-MAPKAPK (T334, Cell Signaling, 1:1000), anti-phospho-MSK (T581, Cell Signaling, 1:1000), anti-caspase-8 (Cell Signaling, 1:1000), anti-caspase-9 (Cell Signaling, 1:1000), anti-caspase-3 (Cell Signaling, 1:1000), anti-βactin (Sigma, 1:5000), or anti-tubulin (Cell Signaling, 1:5000) for 1 hr, followed by incubation for 1 hr with horseradish peroxidase-conjugated secondary antibodies (Santa Cruz). Immunoreactive bands were visualized by enhanced chemiluminescence (Pierce, Rockford, IL) on a ChemiGenius2 CCD camera-based detection system. Where indicated, band intensities were quantified by densitometry with ImageJ. 21  2.5  Plaque assays CVB3 titers in cell supernatants were determined on monolayers of HeLa cells by an agar  overlay plaque assay in triplicate 156. Briefly, samples were serially diluted 10-fold and overlaid on 90-95%-confluent monolayers of HeLa cells in six-well plates and incubated for 1 hr. Medium was aspirated, HeLa cells were washed with PBS twice, and 2 ml of complete Dulbecco's Modified Eagle's Medium (DMEM) containing 0.75% agar was overlaid onto each well. Cells were incubated at 37°C for 72 hr, fixed with Carnoy's fixative (75% ethanol-25% acetic acid) for 30 min, and stained with 1% crystal violet. Plaques were counted, and viral concentrations were calculated as plaque-forming unit (PFU) per milliliter.  2.6  Cell viability assays HL1 cells were grown in 12-well plates and infected with CVB3 (MOI=9) for 16 and 24  hr after pretreatment with inhibitors. The 3-(4,5-dimethylthiazol-2-yl)-5-(3carboxymethoxyphenyl)-2-(4-sulfophenyl)-2H-tetrazolium (MTS) solutions (1:5) were added to wells for 2.5 hr and then transferred to 96-well plates. Cell viabilities of infected cells and noninfected were assessed by MTS assay (Cell Titer 96; Promega, Inc., Madison, WI).  2.7  Caspase-3, -8 and -9 activity assays Caspase activities were measured according to the manufacturer's instruction (R&D  Systems) as described elsewhere 100. Fluorescence was measured at excitation and emission wavelengths of 485 and 535 nm, respectively, using a Tecan GENios fluorescent reader.  2.8  Hierarchical clustering Phospho-protein data were standardized as z-scores before clustering. Hierarchical  clustering was performed with the clustergram function in MATLAB using a Euclidean distance metric and average linkage. Original dendrograms— created from the dataset of DMSO, single and double inhibitors without sub-sampling. Single dendrograms—made from the dataset of DMSO and single inhibitors without sub-sampling. Double dendrograms—built from the dataset 22  of double inhibitors with sub-sampling. Single+Double dendrograms—created from the dataset of DMSO, single and double inhibitors with sub-sampling, as shown in Figures 8 & 9. For subsampled dendrograms (Figure 8 and Figure 9), 10,000 different sub-matrices were randomly generated from original data by the function and the mean Euclidean distances were used as the basis for the sub-sampled clustering with average linkage. Since the size of single datasets were 8-by-23, the size of 10,000 generated sub-matrices were the same, 8-by-23.  2.9  Graphical gaussian modeling The GeneNet package 157 was used in R to calculate significant partial correlation  coefficients for signaling molecules. Background partial correlation coefficients were determined by random sampling of 100 different sub-matrices of the original dataset.  2.10  Partial least squares regression Phospho-proteins (predictor variables) and readouts (response variables) were  standardized as z-scores, and the phospho-protein time-course was time-integrated over early (0– 8 hr) and late (8–24 hr) phases. Partial least squares regression was performed with the “plsregress” function in MATLAB by standard approaches 158, 159. The stability of the model was assessed by fivefold leave-one-out cross-validation.  23  Chapter 3 – Pairwise network mechanisms in the host signaling response to coxsackievirus B3 infection 3.1  Introduction Intracellular signal transduction is achieved through binary protein-protein interactions  that connect together to form networks 160. Signaling pathways often have multiple layers of feedback control, and crosstalk between pathways is usually extensive161. This complexity enables diverse stimuli to be integrated when key cell decisions must be made 158, 162. But conceptually, a “tangled” signaling circuit makes it difficult to assign unambiguous roles to individual proteins in a straightforward way. Viruses and other pathogens have evolved effective strategies to subvert host-signaling networks for their own purposes 163, 164. Infection is therefore equivalent to a systems-level perturbation, which can be useful for understanding network function when combined with targeted interventions. The question is – how best to deploy these perturbations in a way that takes network complexity into consideration, but also quickly identifies mechanistic connections between signals and cell outcomes? One observation that has emerged from recent studies of cell-molecular networks is the surprising richness of information that is captured when pairs of inputs are considered 151, 162, 165168  . Two-stimulus screens typically reveal functional synergies in only ~10-25% of all possible  nC2  combinations 151, 162, 169. However, pairwise stimulation and observation appears to enrich  for network states that are especially relevant to predicting function 165, 166, 168. Whether a similar approach could be used to enrich for molecular mechanisms that control network function has not been investigated. Here, I pursued this question through an in vitro model of cardiomyocyte infection with the pathogen, coxsackievirus B3 (CVB3). CVB3 is among the most common causes of viral myocarditis in infants and young children, often leading to acute heart failure and sudden death 58  . To examine the direct mechanisms whereby CVB3 disrupts the host cell signaling network  and causes tissue damage, I developed a pairwise pharmacological approach to perturb cardiomyocyte signaling during CVB3 infection. Using a panel of small-molecule kinase  24  inhibitors, the goal was to identify which protein kinases activated by CVB3 were responsible for the phosphorylation signatures and toxicity observed in host cells. I found in multiple instances that data from inhibitor pairs were required for accurate clustering-based assignments of kinase-substrate interactions. Moreover, using Graphical Gaussian Modeling to reconstruct pairwise interactions based on partial correlations within the inhibitor dataset, I was able to reveal an extracellular positive-feedback circuit for CVB3 cardiotoxicity. CVB3 drives release of the proinflammatory cytokines, TNF-α and IL-1, which act as autocrine effectors to augment virus-induced cell death. The results suggest that pairwise perturbations may be more effective at uncovering molecular mechanisms within signaling networks than equivalent numbers of single agents.  25  3.2  Results  3.2.1  Pairwise pharmacological perturbation of cardiomyocytes infected with CVB3.  To investigate the host network response to CVB3 in a uniform cell population, I used HL1 murine cardiomyocytes 155. These cells can be infected with CVB3, support viral replication, and undergo virus-induced cell death 170. I sought targeted perturbations that could be introduced rapidly into cells and combined easily. Therefore, I avoided slow overexpression or knockdown approaches that might yield adaptations in the underlying network. Focusing on small-molecule inhibitors, I targeted six kinase-signaling pathways previously implicated in different facets of CVB3 infectivity: Akt– GSK3 120, IKK–NF-κB 119, Src-family kinases 70, 171, p38–MK2 100, 101, JNK 100, and MEK–ERK 75  . Small molecule inhibitors are selective but rarely specific 172, 173. In fact, some of the  inhibitors I used have unknown mechanisms of action 174 or are known to perturb multiple kinases aside from the primary target 172, 173, 175. Consequently, the purpose of the small molecules was not to assign roles directly to the presumed target kinase. Rather, inhibitor combinations were used as a modular way to dampen the capacity of cells to signal through different branches of the overall network. I pretreated cells with single inhibitors or a paired combination and monitored the dynamics of nine phosphorylation events over a 24 hr time-course of CVB3 infection. Timepoints were selected to capture the major stages of the viral life cycle: viral docking to the host cell (~0.17 hr post-infection [p.i.]), host-cell endocytosis (~1 hr p.i.), synthesis of viral RNA (~8 hr p.i.), synthesis of viral proteins such as VP1 caspid (~16 hr p.i.), and viral progeny release (VPR ~ 24 hr p.i.). I directly tracked the phosphorylation of five kinases—Akt, GSK3β, p38, JNK, and ERK—whose activity I perturbed pharmacologically. I also measured the phosphorylation status of two proteins, Hsp-27 and IκBα, which are reliable indicators of p38– MK2 and IKK activity, respectively 73, 115, 176. Last, I quantified key phosphorylation sites on the transcription factors, CREB and ATF-2 (Figure 3). These two proteins lie downstream of multiple kinase-signaling pathways (see below) and are important for integrating the host response to CVB3 infection 177, 178.  26  Timepoints  Variables Kinases  Proteins  • Akt • Hsp-27 • GSK3β • IκBα • ERK1/2 • JNK • p38  TFs*  • ATF-2 • CREB  • Number of all possible pairrwise interactions: If N =9 N(N-1)/2 = 36 • How many of these interactions are significant? • Number of experimental conditions that we NEED > 90  1. Control (0 h p.i.) 2. Viral docking to cell (~10 min p.i.) 3. Host-cell endocytosis (~1 h p.i.)  Experimental conditions • Single Perturbations 1. API-2 (Akt) 2. BAY11-7085 (NF-κB) 3. PP2 (Src-family kinases) 4. SB203580 (p38 MAPK) 5. SP600125 (JNK) 6. U0126 (ERK1/2)  6 x 6 = 36 4. Synthesis of viral RNA (~8 h p.i.)  • Pairwise Perturbations N(N-1)/2  5. Synthesis VP1 caspid (~16 h p.i.) 6. Viral progeny release (~ 24 h p.i.)  90-36=54  6 (6-1)/2= 15  + 6 single perturbations + 15 pairwise perturbations + 1 DMSO 22 x 6 (timepoints)= 132  Figure 3. The experimental approach used to define pairwise network models. HL1 cells were pretreated with one of six small-molecule kinase inhibitors or a paired combination of these inhibitors for 0.5 hr, infected with CVB3 (MOI=9), and then assessed for the indicated variables by phospho-ELISA. The goal was to define pairwise interactions. With N (9) variables, there are N (N – 1)/2=M (36) possible interactions. I used mathematical, statistical and computational tools to verify how many of these interactions were significant. To statistically infer pairwise correlations for N (9) variables, at least 90 (N times 10) experimental conditions were required. I used 6 time-points and 6 single perturbations so I had 36 experimental conditions; however, I needed 54 more experimental conditions to meet minimum criteria, 90 experimental conditions. I perturbed system by pairwise combination of inhibitors. Using 6 inhibitors, there are 6(6-1)/2=15 additional pairwise opportunities to further characterize the systems-level properties of a network. Thus, the final calculation would be [6 (single perturbation) + 15 (double perturbation) + 1 (DMSO)] times 6 (time-points) = 132 experimental conditions. *TFs=Transcription factors. 27  I found that inhibitor pairs showed remarkably different patterns of phospho-protein dynamics in response to CVB3 infection when compared with the corresponding single inhibitors (Figure 4). For example, pretreatment of cells with any single inhibitor in the panel caused an increase in Akt phosphorylation at 1 hr p.i. (conditions 2–8) as compared to control (condition 1). However, this spike in phospho-Akt was dampened when single inhibitors were combined with the Akt inhibitor API-2 174 (conditions 9–13), even though API-2 by itself led to increased Akt phosphorylation at 1 hr p.i. (condition 2). Similarly, the nonspecific JNK inhibitor SP600125 172 and GSK3 inhibitor SB216763 173each increased Hsp-27 phosphorylation (conditions 6–7) at 24 hr p.i. as compared to control. But, combined inhibition with SP600125 and SB216763 appeared to accelerate Hsp-27 phosphorylation (condition 23), such that phosphoHsp-27 was decreased relative to control at 24 hr p.i. Overall, I found that dual-inhibitor treatments caused changes in CVB3-induced signaling that were not quantitatively predictable from single inhibitors (Figure5). This suggested that inhibitor pairs had revealed network-level behaviors that otherwise would have been missed.  28  Akt  ATF-2  CREB  ERK1/2  GSK3β  Hsp-27  IκBα  JNK  p38 1. DMSO 2. API-2 (Akt) 3. BAY11-7085 (NF-κB) 4. PP2 (Src-family kinases) 5. SB203580 (p38 MAPK) 6. SB216763 (GSK3β) 7. SP600125 (JNK) 8. U0126 (ERK1/2) 9. API-2+ BAY11-7085 10. API-2+ SB203580 11. API-2+ SB216763 12. API-2+ SP600125 13. API-2+ PP2 14. BAY11-7085+ SB203580 15. BAY11-7085+ SB216763 16. BAY11-7085+ SP600125 17. BAY11-7085+ PP2 18. PP2+ SB203580 19. PP2+ SB216763 20. PP2+ SP600125 21. SB203580+ SB216763 22. SB203580+ SP600125 23. SB216763+ SP600125  0.....24  0......24  0.....24  0.....24  0.....24  0.....24  0.....24  0, 0.17, 1, 8, 16 & 24 Hours Post-Infection  0.....24  Low  0.....24 High  Phosphorylation State  Figure 4. Single and pairwise small-molecule perturbation of a dynamic nine-protein phosphorylation signature induced by CVB3 infection. HL1 cells were pretreated with one of seven small-molecule kinase inhibitors or a paired combination of these inhibitors for 0.5 hr, infected with CVB3 (MOI=9), and then assessed for the indicated phospho-proteins by phospho-ELISA. Data are shown as mean of two (for single and double perturbation) or four (for DMSO) independent experiments and were standardized by the “z-score” function in MATLAB as described in the Methods. 29  Akt  B ATF-2 CREB ERK1/2 GSK3β Hsp-27  IκBα  JNK  p38  Akt  ATF-2 CREB ERK1/2 GSK3β Hsp-27  IκBα  JNK  p38  API-2+ BAY11-7085  0.4  API-2+ SB203580  0.8  API-2+ SB216763  0.5  API-2+ SP600125  0.4  API-2+ PP2  0.6  BAY11-7085+ SB203580  0.7  BAY11-7085+ SB216763  0.4  BAY11-7085+ SP600125  0.3  BAY11-7085+ PP2  0.7  PP2+ SB203580  0.8  PP2+ SB216763  0.7  PP2+ SP600125  0.7  SB203580+ SB216763  0.7  SB203580+ SP600125  0.4  SB216763+ SP600125  0.3  0...24 0...24 0...24 0...24 0...24 0...240...24 0...24 0...24 0, 0.17, 1, 8, 16 & 24  R 2 Goodness of Fit  A  0...24 0...24 0...24 0...24 0...24 0...240...24 0...24 0...24 Low  High Phosphorylation State  Hours Post-Infection  Figure 5. Paired-inhibitor combinations perturb CVB3-induced phosphorylation signatures non-additively. Paired-inhibitor combinations perturb CVB3-induced phosphorylation signatures non-additively. (A) Measured inhibitor pairs were reprinted from Figure 4 and compared with (B) an additive model, in which single-inhibitor time-courses from Figure 4 were used to predict paired-inhibitor signatures by adding the net perturbation of each inhibitor compared to the DMSO control. Differences between the measured and modeled signatures were evaluated by R2 goodness of fit (right). The median R2 value was 0.6.  30  Next, I complemented the intracellular-signaling dataset with three functional readouts of productive CVB3 infection: VP1 capsid protein expression, viral progeny release (VPR), and cytotoxicity determined by loss of MTS positivity (Figure 6A-C and Figure 7). I found that all single-inhibitor treatments substantially reduced VP1 expression, VPR, and toxicity caused by CVB3 (p < 0.05). When these data were used to predict dual-inhibitor responses under the assumption of Bliss independence 179, I found many instances of significant synergy or antagonism (p < 0.05). Interestingly, the pattern of non-additivity across inhibitor pairs depended on the readout. For instance, SP600125 plus SB216763 was strongly synergistic for reducing VP1 expression (Figure 6A). However, the same combination showed an additive VPR response and antagonistic cytotoxicity (Figure 6B-C). My measurements of CVB3 infectivity further support the conclusion that paired signaling inhibitors establish network states that cannot be achieved or predicted if the same inhibitors are used individually 169. To examine simple signal-response relationships between measured phospho-protein dynamics and cell phenotype, I correlated each time-integrated phosphorylation profile with VP1, VPR, and cell death across all 23 conditions (Figure 6D-F). I observed weak covariation between individual phospho-profiles and CVB3-induced cell outputs, with |R| < 0.5 for all correlations examined. This indicates that the host response is distributed across multiple signaling circuits, with no individual pathway dominating in the network as a universal control point 158. I conclude that systematic intracellular perturbations are not an effective means for observing strong links between individual signals and cell responses.  31  A  B  VP1  C  VPR  Death  1. DMSO 2. API-2 (Akt)  High  3. BAY11-7085 (NF-κB) 4. PP2 (Src)  Low  5. SB203580 (p38 MAPK)  Bliss  Bliss  8. U0126 (ERK1/2)  Bliss  7. SP600125 (JNK)  Bliss  6. SB216763 (GSK3β)  9. API-2+ BAY11-7085  Antagonistic  Additive  Antagonistic  10. API-2+ SB203580  Antagonistic  Antagonistic  Antagonistic  11. API-2+ SB216763  Synergistic  Additive  Antagonistic  12. API-2+ SP600125  Antagonistic  Synergistic  Antagonistic  13. API-2+ PP2  Antagonistic  Additive  Antagonistic  14. BAY11-7085+ SB203580  Additive  Additive  Antagonistic  15. BAY11-7085+ SB216763  Additive  Synergistic  Antagonistic  16. BAY11-7085+ SP600125  Additive  Synergistic  Antagonistic  17. BAY11-7085+ PP2  Additive  Synergistic  Antagonistic  18. PP2+ SB203580  Additive  Antagonistic  Antagonistic  19. PP2+ SB216763  Antagonistic  Antagonistic  Antagonistic  20. PP2+ SP600125  Additive  Antagonistic  Antagonistic  21. SB203580+ SB216763  Additive  Additive  Antagonistic  22. SB203580+ SP600125  Additive  Additive  Antagonistic  23. SB216763+ SP600125  Synergistic  Additive  D  VP1  E  VPR  F  0  1 -1  0  1 -1  24  16  24  16  Antagonistic  Hours Post-Infection  Death  Akt ATF-2 CREB Time- ERK1/2 Integrated GSK3β Phosphorylation Hsp-27 IκBα JNK p38 Correlation Coefficient -1  0  1  32  Figure 6. Paired-inhibitor combinations cause synergistic or antagonistic inhibition of viral protein expression, progeny release, and cardiotoxicity. HL1 cells were pretreated and infected as described in Figure 4. (A) VP1 capsid protein expression, (B) viral progeny release (VPR) and (C) cell death were measured by Western blotting, plaque assay and MTS assay, respectively (see Methods). Data are presented as the mean of three independent replicates. Both VP1 expression and VPR were normalized to the DMSO-treated control. Cell death was normalized to the CVB3-infected DMSO-treated control. Bliss predictions of independence were performed as described 179, and significant differences between each measured response and Bliss prediction was calculated by two-tailed t-test with unequal variance. Measured responses significantly below Bliss prediction were defined as synergistic inhibition (green), whereas those significantly above were antagonistic (red) and those not significantly different were additive (gray). Time-integrated phosphorylation profiles of proteins measured in Figure 4 were correlated with (D) VP1 capsid protein expression, (E) viral progeny release (VPR) and (F) cell death at 16 hr. Pearson correlation coefficients are shown ± 90% Fisher z-transformed confidence intervals.  33  A  B  Sham CVB3 1. DMSO  High  2. API-2 (Akt) 3. BAY11-7085 (NF-κB)  Low  4. PP2 (Src) 5. SB203580 (p38 MAPK) 6. SB216763 (GSK3β) 7. SP600125 (JNK) 8. U0126 (ERK1/2) 9. API-2+ BAY11-7085 10. API-2+ SB203580 11. API-2+ SB216763 12. API-2+ SP600125 13. API-2+ PP2 14. BAY11-7085+ SB203580 15. BAY11-7085+ SB216763 16. BAY11-7085+ SP600125 17. BAY11-7085+ PP2 18. PP2+ SB203580 19. PP2+ SB216763 20. PP2+ SP600125 21. SB203580+ SB216763 22. SB203580+ SP600125 23. SB216763+ SP600125  16 24 16 24  Hours Post-Infection  Figure 7. Single and paired inhibitors specifically inhibit CVB3-induced cardiotoxicity. HL1 cells were pretreated and infected as described in Figure 4. Cell death as measured by MTS assay is shown for (A) sham-infected cardiomyocytes or (B) CVB3-infected cardiomyocytes at 16 and 24 hr p.i. Data are presented as the mean of three independent replicates. Cell death was normalized to the DMSO-treated control.  34  3.2.2  Hierarchical deconstruction of the CVB3-induced signaling network  I next sought to determine whether dual-inhibitor treatments could enrich for correlations within the signaling dataset that were causal. My predictions focused on identifying the contextdependent kinase(s) responsible for the activating phosphorylation of ATF-2 on T69/T71 and of CREB on S133 (Figure 8) and (Figure 9). Active ATF-2- and CREB-mediated gene expression are important components of the antiviral and pathogenic responses to CVB3 infection 177, 178.  Table 2. Measured phospho-proteins and virus replication indicators. Variables  Assays  Akt (S473)  Phospho-ELISA  69 71  Phospho-ELISA  133  CREB (S )  Phospho-ELISA  ERK1/2 (T185Y187)  Phospho-ELISA  ATF-2 (T T )  9  GSK3β (S )  Phospho-ELISA  82  Phospho-ELISA  Hsp-27 (S ) IκBα (S32)  Phospho-ELISA 183  185  JNK1/2 (T Y ) 180  Phospho-ELISA 182  p38 MAPK (T Y )  Phospho-ELISA  Src (Y418)  Phospho-ELISA  Viral protein (CVB3 VP1)  Western blot  Virion progeny release  Plaque assay  ATF-2 and CREB phosphorylation sites act as points of convergence for multiple kinasesignaling pathways. ATF-2 can be phosphorylated by the proline-directed JNK 90, p38 91 and ERK 92 MAPK pathways. Conversely, CREB is phosphorylated by basophilic kinases, such as PKA 93, RSK2 downstream of ERK 94, and MSK1 downstream of ERK and p38 95. The pathways mediating ATF-2 and CREB phosphorylation in a given context depend on which kinases are activated and accessible during a biological event. Many of the candidate ATF-2 and CREB kinases (or their upstream activators) were measured in my dataset (Figure 8) and (Figure 9). I therefore could test whether data from the dual-inhibitor conditions were particularly important for correctly identifying the dominant ATF-2 and CREB kinases during CVB3 infection. 35  I used hierarchical clustering as a simple pairwise analysis tool for assembling groups of phospho-proteins with similar measurement patterns 180. Because different kinase pathways could be important for ATF-2 and CREB phosphorylation at different stages of CVB3 infection, I clustered the phospho-protein data for each individual time-point (Figure 9). I built clusters from four different variations of the inhibitor dataset. First, I analyzed the complete dataset comprised of 23 conditions (seven single-inhibitor treatments, 15 dual-inhibitor treatments, and one control). Then, I sub-sampled the complete dataset three different ways, using only treatments with single inhibitors (plus control), only with double inhibitors, or with mixtures of treatments involving single and double inhibitors. I controlled for differences in sample size by repeatedly taking random subsets of double- and single+double-inhibitor treatments that matched the size of the single-inhibitor treatments (eight conditions). I averaged the clustering results of many random samplings to achieve a size-adjusted clustering dendrogram for double and single+double treatments (see Methods). Last, I inspected the position of ATF-2 and CREB in the resulting dendrograms and predicted that the nearest measured kinase (or activation readout) would be the most important biochemically. These predictions were tested by adding inhibitors, which were chemically distinct from those used in the original dataset (Figure 8), shortly before the selected time-point of CVB3 infection.  36  0 h p.i.  A  Original  0.17 h p.i.  B  1 h p.i.  C  8 h p.i.  D  16 h p.i.  E  24 h p.i.  F  6  4  Single  Double  Single+Double  IkBa ERK JNK ATF-2 GSK3 B Akt CREB p38 Hsp-2 7  I kBa G SK3B E RK J NK A TF-2 A kt C REB p 38 H sp-27  Ik Ba ER K ATF-2 GS K3B JN K Ak t CR EB p3 8 Hs p-27  IkBa ERK JNK ATF-2 GSK3B Akt CREB p38 Hsp-27  p38 Hsp-2 7 CREB ERK ATF-2 GSK3B IkBa JNK Akt  G SK3B J NK I kBa A kt E RK A TF-2 C REB p 38 H sp-27  p3 8 Hs p-27 CR EB ER K ATF-2 GS K3B IkB a JN K Ak t  p38 Hsp-27 CREB ERK ATF-2 GSK3B IkBa JNK Akt  ERK ATF-2 GSK3B Akt JNK IkBa p38 Hsp-2 7 CREB  J NK G SK3B A kt E RK I kBa A TF-2 p 38 H sp-27 C REB  p3 8 ATF-2 ER K GS K3B Ak t IkB a JN K Hs p-27 CR EB  ERK GSK3B Akt JNK IkBa p38 ATF-2 Hsp-27 CREB  IkBa Akt JNK ERK ATF-2 p38 GSK3 B Hsp-2 7 CREB  IkBa Akt GSK3B JNK ERK ATF-2 Hsp-27 p38 CREB  J NK A kt I kBa H sp-27 E RK C REB A TF-2 p 38 G SK3B  Ik Ba A kt J NK E RK A TF-2 G SK3B p 38 H sp-27 C REB  ATF-2 JNK Akt Hsp-2 7 CREB ERK p38 IkBa GSK3 B  JNK ERK p38 Hsp-27 CREB ATF-2 Akt IkBa GSK3B  J NK A TF-2 A kt p 38 H sp-27 I kBa E RK G SK3B C REB  A TF-2 J NK A kt p 38 H sp-27 C REB E RK Ik Ba G SK3B  IkBa p38 Hsp-2 7 ATF-2 JNK Akt CREB GSK3 B ERK  JNK GSK3B ERK CREB IkBa Akt ATF-2 p38 Hsp-27  I kBa H sp-27 A TF-2 A kt p 38 J NK C REB G SK3B E RK  Ik Ba p 38 H sp-27 A TF-2 A kt J NK C REB G SK3B E RK  4  2  4  3  2  4  3  2  Euclidean Distances 37  Figure 8. (Previous page) Time-dependent hierarchical clustering of the nine-protein signature based on inhibitor data. HL1 cells were pretreated with one of seven small-molecule kinase inhibitors or a paired combination of these inhibitors for 0.5 hr, infected with CVB3 (MOI=9), and then assessed for the indicated phospho-proteins by phospho-ELISA. Using a Euclidean distance metric and average linkage, phospho-proteins were clustered at the indicated time-points. The dendrograms were built from the complete original dataset of single and double inhibitors (“Original”) or subsampled as single-, double-, or single+double-inhibitor subsets as described in the Methods. Phospho-protein data were standardized as z-scores before clustering. Hierarchical clustering was performed with the clustergram function in MATLAB using a Euclidean distance metric and average linkage. For sub-sampled dendrograms, 10,000 different sub-matrices were randomly generated from original data by the function and the mean Euclidean distances were used as the basis for the sub-sampled clustering with average linkage. (A) Combined single- and doubleinhibitor data uniquely predicts JNK as an ATF-2 kinase at 0 hr p.i. (B) Double-inhibitor treatments increase the measured data space to predict that CREB phosphorylation lies downstream of ERK rather than p38 at 0.17 hr p.i. (C) Single-inhibitor data predicted p38 as a CREB kinase, but double-inhibitor data depicts p38 as an ATF-2 kinase at 1 hr p.i. (D and E) There are no testable predictions at these timepoints. (F) p38 is predicted to be an ATF-2 kinase at 24 hr p.i.  38  0-24 h p.i.  H  p3 8 Hs p-27 CR EB ER K ATF-2 GS K3B Ik Ba JN K Ak t  p3 8 Hs p-27 CR EB ER K ATF-2 GS K3B IkB a JN K Ak t  E  IkB a p3 8 Hs p-27 ATF-2 JN K Ak t CR EB GS K3B ER K  JN K GS K3B ER K CR EB IkB a Ak t ATF-2 p3 8 Hs p-27  IkB a Hs p-27 ATF-2 Ak t p3 8 JN K CR EB GS K3B ER K  IkB a p3 8 Hs p-27 ATF-2 Ak t JN K CR EB GS K3B ER K  G  JN K GS K3B IkB a Ak t ER K CR EB ATF-2 p3 8 Hs p-27  JN K IkB a GS K3B Ak t ER K CR EB ATF-2 p3 8 Hs p-27  JN K ER K CR EB ATF-2 p3 8 Hs p-27 GS K3B IkB a Ak t  JN K GS K3B IkB a Ak t ER K CR EB ATF-2 p3 8 Hs p-27  I  LY294002 (PI3K)  GS K3B JN K IkB a Ak t ER K ATF-2 CR EB p38 Hs p-27  SB202190 (p38)  p3 8 Hs p-27 CR EB ER K ATF-2 GS K3B IkB a JN K Ak t  PD184352 (MEK1/2)  B JNKi (JNK)  IkB a ER K JN K ATF-2 GS K3B Ak t CR EB p3 8 Hs p-27  Vehicle  IkB a ER K ATF-2 GS K3B JN K Ak t CR EB p3 8 Hs p-27  C  Normalized p-ATF-2 Vehicle JNKi  p-ATF-2  PD184352  Tubulin  LY294002 0.5  1.0  1.5  SL0101  SB202190  JNKi  Vehicle  *  SB202190  0.0  p-CREB  PD184352  Tubulin  JNKi  24 h p.i.  F  Single+Double  IkB a GS K3B ER K JN K ATF-2 Ak t CR EB p38 Hs p-27  Vehicle  0 h p.i. 0.17 h p.i.  D  Double  IkB a ER K JN K ATF-2 GS K3B Ak t CR EB p3 8 Hs p-27  SB202190  Single  Original  A  p-ATF-2 Tubulin  0  0.17  1  8  16  24  Time (h p.i.) p-CREB Tubulin  0  100  0  100  0 100  0  100 0 100  0 100 SL0101 (µM)  39  Figure 9. (Previous page) Pairwise-inhibitor data enable accurate, context-dependent predictions of ATF-2 and CREB kinases. (A) Combined single- and double-inhibitor data uniquely predicts JNK as an ATF-2 kinase at 0 hr p.i. (B, C) JNK inhibition reduces basal ATF-2 phosphorylation in HL1 cardiomyocytes. Densitometry of phospho-ATF-2 (p-ATF-2) was quantified and is shown as the mean ± s.e.m. of three independent samples. (D) Double-inhibitor treatments increase the measured data space to predict that CREB phosphorylation lies downstream of ERK rather than p38 at 0.17 hr p.i. (E) Inhibition of the ERK substrate RSK with SL0101 abolishes CREB phosphorylation at 0.17 hr p.i. (F) p38 is predicted to be an ATF-2 kinase at 24 hr p.i. irrespective of the inhibitor data used. (G) ATF-2 phosphorylation is specifically reduced by the p38 inhibitor SB202190. (H) Doubleinhibitor treatments predict that signaling downstream of ERK globally regulates CREB phosphorylation in response to CVB3 infection. (I) Inhibition of the ERK substrate RSK with SL0101 blocks CREB phosphorylation throughout CVB3 infection. For A, D, F, and H, phospho-proteins were clustered at the indicated time-points using a Euclidean distance metric and average linkage. The dendrograms were built from the complete original dataset of single and double inhibitors (“Original”) or sub-sampled as single-, double-, or single+double-inhibitor subsets as described in the Methods. The nearest kinase(s) or activation reporter(s) to ATF-2 or CREB on the resulting dendrograms are highlighted in red and blue, respectively. For B, C, E, G, and I, HL1 cells were pretreated and infected as described in Figure 4, except that inhibitors were added one hr before the indicated time-point p.i. Inhibitors were used at the following concentrations: JNKi 1 µM, SB202190 10 µM, SL0101 100 µM, PD184352 10 µM and LY294002 10 µM. Both ATF-2 and CREB phosphorylation (p-) were measured by Western blotting with tubulin used as a loading control.  40  I found that data from paired inhibitors improved the accuracy of clustering-based predictions in several distinct ways. For example, at 0 hr p.i., double-inhibitor treatments appeared to synergize with single treatments independent of the overall sample size to link ATF2 with JNK (Figure 8A). Upon treatment of cells with a JNK peptide inhibitor (JNKi), I reproducibly detected a twofold decrease in phospho-ATF-2 (p < 0.01, Figure 8B-C). The decrease was specific to JNK inhibition, because ATF-2 phosphorylation was not significantly perturbed by inhibitors of the p38, MEK–ERK, or PI3K–Akt pathways (Figure 8B). This indicates that JNK signaling partly controls basal ATF-2 phosphorylation that precedes CVB3 infection. A second, more practical advantage of paired inhibitors related to increasing the data “space” with which to define protein clusters (Figure 8) 180. By using pairwise combinations, I could augment the breadth of experimental conditions without adding more inhibitors to the panel. Importantly, I found that the information contained in these added conditions was meaningful. For instance, at 0.17 hr p.i., phospho-CREB clustered with p38–MK2 activation for all size-adjusted, eight-sample clusters, irrespective of which inhibitor treatments were included (Figure 8D). By contrast, when the complete dataset was used, I found that phospho-CREB clustered with MEK–ERK activation, suggesting that RSK was the relevant CREB kinase 94. Treatment of cells with inhibitors of p38 181 or RSK 182 revealed that RSK inhibition abolished CREB phosphorylation, whereas p38 inhibition had no effect (Figure 9E). These results show that RSK is a key CREB kinase for the early response to CVB3 infection, which was predicted when the inhibitor training set was expanded to include paired combinations. Accurate kinase-substrate predictions did not universally require pairwise data. For example, at 24 hr p.i., all of the clustering variants grouped ATF-2 phosphorylation with p38– MK2 signaling (Figure 8F). Accordingly, p38 inhibition substantially decreased phospho-ATF-2 during the late stages of CVB3 infection, whereas JNK or MEK–ERK inhibition did not (Figure 8G). I note that the same p38 inhibitor had no effect on ATF-2 phosphorylation before CVB3 infection (Figure 8B), illustrating the context-dependent role of individual kinases on shared substrates.  41  To test the accuracy toward more global predictions of mechanism, I combined the timecourse measurements into a single dataset and sub-sampled the inhibitor conditions as before (Figure 8H). With time-aggregated data, I found that ERK specifically clustered with CREB phosphorylation because of the information contained in double-inhibitor treatments. When RSK was inhibited shortly before each time-point of CVB3 infection, I found that CREB phosphorylation was uniformly abolished (Figure 8I), as predicted by the double-inhibitor data. Thus, in contrast to the context-specific kinases of ATF-2 (Figure 8B and G), my results support RSK as the dominant CREB kinase throughout CVB3 infection.  42  3.2.3  Host-signaling network reconstruction using paired-inhibitor data The CVB3 clustering results suggested that causal connections became enriched when  paired inhibitors were included. However, clustered dendrograms focus on the most-dominant positive covariations in a dataset and do not account for shared correlations among measured variables 180. This is problematic for network reconstruction 56, because anti-correlations (as in Figure 8D-F) are important for defining inhibitory edges between nodes. Further, ignoring shared correlations makes it impossible to distinguish whether two nodes are directly connected or whether a third correlated variable intervenes between them. To accommodate these scenarios, I used Graphical Gaussian Modeling (GGM) 183 to derive a candidate host-cell signaling network from the phospho-protein dataset. GGM assigns edges between nodes based on their partial correlation—the pairwise correlation that remains after considering the correlations that two variables share with other variables in the dataset. Of 36 possible pairwise edges between the nine phosphoproteins, I identified 10 whose partial correlations were significantly above background (p < 0.05, Figure 10A). Eight of 10 partial correlations were consistent with biochemical mechanisms that have been reported in the literature (Table 3), although only one had been specifically implicated in CVB3 pathogenesis 121  . Visual inspection of the resulting GGM network revealed phospho-IκBα as a network “hub”  that was densely connected with other proteins in the dataset (Figure 10B). Notably, the assigned edges between IκBα and p38, JNK, and ATF-2 were not indicated by cluster-based analysis of the same data (Figure 8H), demonstrating the value of the GGM approach for network reconstruction.  43  A  Partial Correlation Coefficients -1.0 -0.5  0.0  0.5  B  1.0  Hsp-27-CREB JNK-IκBα p38-IκBα ERK-CREB Akt-GSK3β JNK-ATF-2 IκBα-ATF-2 GSK3β-IκBα Akt-IκBα p38-Hsp-27  Akt  ERK  p38 0.3  0.4  0.5  Hsp-27  IκBα 0.3  GSK3β  0.3 0.5  -0.5  0.5  JNK  -0.3 -0.3  CREB  ATF-2 Background  C  D  16 h p.i.  CVB3 (MOI) anti-TNF-α (µM) IL-1ra (µM)  0 0  9 0  9 1  9 0  0  0  0 0.5  Vehicle  anti-TNF-α  9 1 0.5  VP1 Tubulin  Cell Viability (%)  E  IL-1ra  anti-TNF-α+IL-1ra  100 80 60  *  *  *  1 0  0 0.5  1 0.5  40 20  anti-TNF-α(µM) 0 IL-1ra (µM) 0  44  Figure 10. (Previous page) Data-driven interactions between phospho-protein pairs reveal a convergent autocrine circuit through TNF-α, IL-1, and phospho-IκBα that promotes CVB3 progeny release and cardiotoxcity. (A) Significant partial correlation coefficients in the phospho-protein network based on singleand paired-inhibitor data. Partial correlation coefficients are shown as the mean ± sd as determined by bootstrapping. The range of background partial correlations (dashed line) was calculated by shuffling the starting dataset before performing the analysis. (B) Phospho-IκBα is a hub in the measured CVB3-induced network. An undirected graph of the partial correlations from A is shown, where the black and red lines indicate positive and negative partial correlations, respectively. (C) Autocrine TNF-α and IL-1 blockade does not affect VP1 expression. HL1 cells were pretreated with TNF-α -neutralizing antibody (anti-TNF-α 1 µM), interleukin-1 receptor antagonist (IL-1ra, 0.5 µM), or anti-TNF-α + IL-1ra for 1 hr and then infected with CVB3 (MOI=9) as described in (Figure 4). VP1 expression at 16 hr p.i. was quantified by Western blotting with tubulin used as a loading control. (D) Autocrine TNF-α and IL-1 blockade inhibits CVB3 progeny release. A digitized, representative CVB3 plaque assay is shown from HL1 cells treated with the indicated inhibitors and infected with CVB3 for 24 hr. (E) Autocrine TNF-α and IL-1 blockade significantly improves cell viability in CVB3-infected cells. Data are presented as the mean ± sd of three independent replicates. Asterisks indicate p < 0.05 by paired t-test. Treatment with anti-TNF-α or IL-1ra without CVB3 infection did not affect measured viability (not shown).  45  Table 3. Scientific support for the CVB3 partial correlation network shown in Figure 10 B. Edge  Partial  Reported interaction  References  Correlation Hsp-27–CREB  0.5  Direct phosphorylation of Hsp-27 and CREB by RSK2  184  JNK–IκBα  –0.5  Inhibition of JNK activation by NF-κB-mediated induction of GADD45B and XIAP  185, 186  p38–IκBα  0.5  p38-mediated phosphorylation of MSK1 promotes NF-κB function, which induces IKBA  95, 187  ERK–CREB  0.5  Direct phosphorylation of CREB through ERK-mediated phosphorylation of RSK  188  Akt–GSK3β  0.4  Direct phosphorylation of GSK3β by Akt  189  JNK–ATF-2  –0.3  Direct phosphorylation of ATF-2 by JNK (negative correlation unexplained)  90  IκBα–ATF-2  –0.3  Negative correlation unexplained  GSK3β–IκBα  0.3  GSK3β required for normal NF-κB function, which induces IKBA  190-192  Akt–IκBα  0.3  Direct phosphorylation of IκBα through Akt-mediated phosphorylation of IKK  119, 193  p38–Hsp-27  0.3  Direct phosphorylation of Hsp-27 through p38-mediated phosphorylation of MK2  72, 73  46  IκBα phosphorylation is a key intermediate step toward activation of NF-κB 194, a transcription factor that is critical for inflammatory gene expression 195. CVB3 infection itself did not strongly induce phospho-IκBα (Figure 10), in agreement with previous work showing that host-cell IκBα levels are reduced by a viral protease encoded by the CVB3 genome 196. However, upon pre-treatment of cells with various signaling inhibitors that disrupt CVB3 infection, I observed a spike of IκBα phosphorylation at ~8 hr p.i. IκBα phosphorylation could conceivably stem from expression of viral proteins, but many inhibitor conditions that strongly induced phospho-IκBα also blocked expression of viral proteins (e.g., conditions 2, 11, and 14; and Figure 6A). This raised the possibility that phospho-IκBα arose from stimuli that were endogenous to host cells, thereby contributing to its centrality in the GGM network (Figure 10B). Two major inducers of IκBα phosphorylation are the pro-inflammatory cytokines TNF-α and IL-1 195. Moreover, in response to pathogenic and inflammatory stimuli, both TNF-α and IL-1 can be released to signal in an autocrine manner 180, 197. I tested whether these cytokines could be involved in CVB3 pathogenesis by blocking autocrine signaling with a neutralizing antibody to TNF-α and a naturally occurring IL-1 receptor antagonist (IL-1ra). Although neither perturbation affected CVB3-induced VP1 expression (Figure 10C), I observed potent inhibition of VPR by plaque forming assay when either cytokine receptor was blocked (Figure 10D). The decrease in VPR further coincided with significant increases in cell viability upon CVB3 infection (p < 0.05, Figure 10E). Thus, autocrine TNF-α and IL-1 signaling is critical for establishing a host-cell signaling network (Figure 10B) that enables CVB3 propagation, release and cardiotoxicity. More generally, my work provides evidence that coupling intracellular measurements with pairwise pharmacological perturbations may be particularly efficient at revealing molecular mechanisms between signaling proteins.  47  3.3  Summary and conclusions Interrogating signal transduction by small-molecule pairs is a convenient method for  rapidly exploring network states and their associated cell outcomes. My results illustrate that inhibitor pairs reveal information about a signaling network that improves the prediction of biochemical mechanisms. If signaling profiles of dual inhibitors were simply a linear superposition of the individual agents, then there would be no gain in useful information. A recent report has suggested such additivity in the intracellular response to drugs 198. However, this work focused on protein expression levels, rather than post-translational modifications, and examined only three targeted signaling inhibitors (one of which showed clear non-additivity). Although gradual changes in protein expression may be roughly approximated by linear superposition, my data show that direct perturbations of kinase activity are frequently nonadditive. There are advantages to exploiting the nonlinearity of signaling inhibitors when designing experiments. For example, with ten inhibitors, there are (102 – 10)/2 = 45 additional pairwise opportunities to further characterize the systems-level properties of a network. Using this number of inhibitor pairs is easier, more cost effective, and (for some analyses) more valuable than adding 45 single inhibitors to study network function. The mechanisms revealed by such studies will depend heavily on the choice of inhibitors and their effects on the measured intracellular pathways, emphasizing the importance of experimental design when using a pairwise approach. The key is to view these interventions as general perturbations to the network rather than as experiments that will directly assign molecular mechanisms. Still-higher combinations of inhibitors could readily be tested as I did for pairs here, but it is uncertain whether these conditions would reveal additional molecular insights. Chatterjee et al. recently showed that higher-order combinations of input stimuli can be quantitatively predicted from pairwise data 199, suggesting that nonlinear information processing may normally stop at pairs 180. However, small-molecule inhibitors differ from extracellular stimuli, in that inhibitors intentionally push the signaling network into states not normally occupied physiologically. The benefit of using more than two small molecules will ultimately depend on  48  the overall connectivity of the network and the ability of the inhibitor panel to disrupt redundant or compensatory pathways within cells. My pairwise analysis of the host signaling response to CVB3 infection highlighted a central role for phospho-IκBα downstream of autocrine TNF-α and IL-1 signaling. For nearly two decades, it has been known that TNF-α and IL-1 promote myocarditis caused by CVB3 49. Circulating TNF-α and IL-1 levels rise substantially upon CVB3 infection 50, but the major source of these cytokines in the heart has been thought to be infiltrating monocytes 51. My results with cultured cells suggest that cardiomyocytes may “prime” monocyte infiltration and their own toxicity by autocrine stimulation with inflammatory cytokines. In the future, pairwiseinhibitor strategies could be applied to identify the relevant CVB3-dependent pathways that drive autocrine TNF-α and IL-1 signaling in the heart.  49  Chapter 4 – An ERK–p38 subnetwork coordinates host-cell apoptosis and necrosis during coxsackievirus B3 infection 4.1  Introduction Coxsackievirus B3 (CVB3) is among the most-common causes of viral myocarditis-  associated heart failure in infants and young children 58. A major component of CVB3 pathogenesis is cell death of infected cardiomyocytes 125. CVB3 causes cardiomyocyte cell death by disrupting host-cell metabolism 24, blocking host-cell translation 200, and explicitly activating apoptotic pathways 47, 201. Cell death via these interrelated mechanisms leads to immediate tissue damage and the subsequent release of virulent CVB3 progeny that furthers disease progression. Intervening at the early stages of CVB3 infection could potentially reduce the severity of the disease and the need for heart transplantation in patients with viral myocarditis. Throughout infection, CVB3 modulates various cell-signaling pathways that enable virus propagation 58, 89. Inhibiting these pathways may provide a therapeutic opportunity to restrict CVB3 pathogenesis. But, an important hurdle is that my understanding of how the CVB3 infection cycle intersects with the host network is fragmented. Viruses such as CVB3 have evolved to modulate cellsignaling networks in ways that allow them simultaneously to evade host defenses, promote cell entry, and undergo replication in a changing environment 54, 58, 202. Blocking individual signaling pathways in host cells often reduces CVB3 infectivity but does not prevent infection entirely 89. It remains unclear whether such “partly required” pathways converge upon a common set of host effectors or instead make independent contributions to pathogenesis 203. The challenge is that  50  CVB3 adaptively perturbs a collection of host pathways, which must be examined concurrently with time to understand how they interact and give rise to viral functions. Here, I took a multi-pathway systems approach to connect signaling and host-cell responses in an in vitro model of CVB3 infection 123, 124. I systematically monitored the dynamics of nine signaling phospho-proteins together with six CVB3-induced host-cell readouts at five different dosings of CVB3. I linked CVB3-induced signaling to host-cell readouts by building a data-driven model that predicted readouts with high accuracy using two timedependent combinations of measured phospho-proteins. The results of this analysis revealed unexpected connections between the ERK1/2, ERK5, and p38 MAPK pathways related to the control of apoptotic caspases and overall cell death induced by CVB3. Combined perturbations of these pathways validated the predictions of the model and deconstructed the CVB3 response as a mixture of apoptosis (requiring ERKs) and necrosis (requiring p38). My results illustrate how viruses such as CVB3 hijack multiple host signaling pathways simultaneously but use relatively straightforward logic to manipulate host responses.  51  4.2  Results  4.2.1  How CVB3-induced phospho-protein dynamics quantitatively predict host-cell responses CVB3 infection of cardiomyocytes activates a series of intracellular signaling pathways  that each help to support viral replication 58. To determine whether known CVB3-induced signaling events were sufficient to predict viral propagation and host-cell toxicity, I sought to build a predictive mathematical model based entirely on quantitative experiments 159. At five different CVB3 MOIs, I systematically profiled nine signaling phospho-proteins by ELISA at six time-points over 24 hr together with six CVB3-induced host-cell readouts at three time-points over 24 hr (Figures 11A and B). Each phospho-protein or host-cell readout was selected based on previous studies suggesting that these individual cell-molecular parameters were critical during the time-course of CVB3 pathogenesis (Table 4). Analyzing the information contained in this mechanism-rich signature would then allow us to examine at the network level how host-cell pathways are coordinately perturbed during CVB3 infection. I found that CVB3-induced host-cell responses showed time and dose dependencies that were expected for end-stage readouts (Figure 11B). Activation of the initiator caspases, caspase8 and caspase-9, were accelerated with increasing MOI, corresponding to more-complete activation of the effector caspase for apoptosis, caspase-3 131. Interestingly, readouts of CVB3 propagation, such as expression of the VP1 capsid protein and viral progeny release (VPR), did not accelerate appreciably as they increased with increasing CVB3 MOI.  52  Table 4. Scientific support for the CVB3-induced phospho-proteins and caspases activation. Phospho-proteins and caspases Tyrosine phosphorylation  Contributes to Effective virus replication  References 60  Caspase-2, -3, -6, -7, -8 & -9  Late-stage alterations of cellular homeostatic processes and structural integrity  47  Cleavage of RasGAP  Effective virus replication  204  Tyrosine kinase p56lck  Successful virus replication and pathogenicity  171  ERK1/2-CREB  Effective virus replication and virus-mediated changes in host cells  75, 89  Akt-GSK3β  Successful virus replication  120  p38, Hsp-27 and JNK  Effective viral progeny release, cytotoxicity and virus-induced caspase-3 activation  100  ILK-Akt  Supporting virus infection  121  IκBα-NF-κB  Promoting virus-infected host survival  119  p38-ATF-2  Underpinning virus replication  89, 101  53  This suggests intrinsic limits to the timing of the CVB3 replication cycle that are downstream of MOI-dependent rate processes, such as viral docking and internalization. The pattern of overall CVB3 cytotoxicity fell in between those of caspase and viral readouts, showing some acceleration as host-cell viability dropped with increasing CVB3 titers. Thus, CVB3 infection of cardiomyocytes elicits a collection of host-cell and viral phenotypes that are monotonic in time but differ in their kinetics and dose-dependent behaviors. By comparison, we found that the dynamic patterns of protein phosphorylation stimulated by CVB3 were substantially more complex than the associated phenotypic readouts (Figure 11A). As before, I observed accelerated phosphorylation of some CVB3-induced pathways with increasing MOI, such as p38 and Hsp-27, but not others, such as ERK (Figure 11A). In addition, biphasic activation patterns were common, and many individual activation peaks appeared or disappeared above a critical threshold of CVB3 MOI (e.g., ATF-2, CREB, IκBα, and JNK). The internal consistency of the phospho-ELISA measurements was verified by the strong correlations between phospho-Akt and phospho-GSK3β (R = 0.6), a direct substrate of Akt 189, and between phospho-p38 and phospho-Hsp-27 (R = 0.8), a direct substrate of the MK2 kinase that is a substrate of p38 72, 73. Moreover, I found that every phospho-protein was strongly correlated or anticorrelated with at-least one host-cell readout when time-integrated levels were compared across MOI (|R| ≥ 0.9). At the same time, no phospho-protein was strongly associated with all host-cell responses, and weak correlations (|R| ≤ 0.5) were common. Therefore, although the measured phospho-proteins could relay information to downstream readouts, this information transfer could not occur from one phospho-protein to all readouts.  54  A Multiplicity of Infection  Akt  ATF-2  CREB  ERK  GSK3β  Hsp-27  IκBα  JNK  p38  0.5 1.5 4.5 9.0 18 0….……....24 0…….…....24 0………....24 0………....24 0………....24 0………....24 0………....24 0………....24 0………....24 0, 0.17, 1, 8, 16 & 24 C-8  C-9  C-3  VP1  VPR  Death  C  D  100 100  0.5  4.5 9.0 18 8...24 8...24 8...24 8...24 8...24 8...24 Low High Standardized Units  8, 16 & 24 Hours Post-Infection  98  Observed Values (%)  1.5  Information Captured(%)  Multiplicity of Infection  B  Low High Phosphorylation State  Hours Post-Infection  96 94 92 90  80 60 40 8 h p.i.  20  16 h p.i. 24 h p.i.  0  11  22  Principal Components  0  50  100  Predicted Values (%)  55  Figure 11. A predictive data-driven model of CVB3-induced host-cell responses. (A) Dynamic phospho-proteins signatures measured by phospho (p)-ELISA that were used as predictor variables in the data-driven model. (B) Concurrent sequence of host-cell outcomes that were to be predicted in the data-driven model. Caspase (C)-3, -8 and -9 activities were measured by activity assays with fluorogenic substrates, VP1 capsid protein expression was measured by Western blotting, viral progeny release (VPR) was measured by plaque assay, and cell death was measured by MTS assay at the indicated time-points. (C and D) Accurate predictions of hostcell responses with a partial least squares model using two principal components. (C) Percentage of information captured with adding only one or two principal components; information was measured by the percentage of variance in host-cell outcomes that was captured by the model. Note the small-but-significant increase in information capture after inclusion of the second principal component. (D) Correlation between cross-validated predictions of biological responses by partial least squares regression (x-axis) and observed biological responses (y-axis). Marker color corresponds to the post-infection (p.i.) time-point at 8 (white), 16 (gray), and 24 (black) hours. HL1 cells were infected with CVB3 at one of five multiplicities of infection and then assessed for the indicated phospho-proteins and biological responses at six and three timepoints, respectively, over 24 hr. For (A) and (B), data are shown as mean of three independent experiments and were standardized by the “z-score” function in MATLAB as described in the Experimental Procedures. For (C), data are shown as box plots of the information captured after fivefold leave-one-out cross-validation. For (D), data are shown as the median ± range of three biological replicates (vertical) or five model cross-validation runs (horizontal).  56  One way that the observed CVB3-induced pattern of readouts could be coordinated is if each phospho-protein contributed incrementally to the pattern based on its extent of phosphorylation. Host cells would then “integrate” the intracellular state established by the level of CVB3 infection and gauge their responses accordingly. To test the feasibility of this network mechanism, I used partial-least-squares modeling to link linear combinations of measured phospho-proteins to observed CVB3-induced readouts 158, 159. In fact, using partial-least-squares modeling, we tried to predict variation of pathophenotypes induced by CVB3 infection as function of modified phospho-proteins. In a partial-least-squares model, linear combinations take the form of principal components, which are latent dimensions in the underlying dataset that are derived to be optimally efficient at predicting response outcomes. To build the model, I first subdivided the phospho-protein time-courses into early (0–8 hr) and late (8–24 hr) phases and then time-integrated each early and late phospho-protein measurement for every CVB3 MOI. This subdivision allowed us to separate biphasic activation profiles into early and late peaks. Using the phospho-protein data as a set of predictor variables, I next sought a partial-least-squares model that could predict all of the CVB3-induced readouts accurately and simultaneously. I found that a model with the two leading principal components could capture all of the measured readouts to within 97% accuracy (Figure 11C). Importantly, this model also accurately predicted readouts for individual MOI conditions that were left out of the model training during crossvalidation (Figure 11D). The success of the quantitative model thus supported a network mechanism in which multiple intracellular pathways work together by independently contributing to CVB3-induced readouts.  57  4.2.2  ERK-p38 crosstalk provides complex, independent control of the host-cell response in  CVB3 infection Principal components can be further analyzed by plotting the weighted linear combinations of the original measurements that provided the basis for accurate model predictions (Figure 12A) 158, 159. In this mapping, early and late phospho-proteins are depicted together with CVB3-induced readouts. Clusters of phospho-proteins and readouts indicate measurements with close association in principal-component space and highlight correlations in the data that are most worthy of follow-up experiments 205. Inspection of this principal-component mapping revealed that all CVB3-induced readouts were densely clustered in one region (Figure 12A, dashed box), suggesting that they were tightly coupled. Within the cluster lay the transcription factor ATF-2, which is critical for CVB3 pathogenesis in vivo 177, and p38, a MAPK that I recently showed is the dominant ATF-2 kinase during CVB3 infection 89. I also found phospho-Hsp-27 in the cluster, which was expected because of its strong concordance with phospho-p38 (see above). Conversely, I was surprised to find early ERK phosphorylation located in the cluster together with p38, because the ERK1/2 and p38 pathways are generally thought to be activated by distinct stimuli and often serve antagonistic functions 87, 206. Nevertheless, their tight association in the model suggested that ERKs and p38 might be functionally interlinked during CVB3 infection. An important consideration for this prediction was the high-throughput data upon which the model was founded 159. The commercial phospho-ELISA used to assemble the phosphoERK dataset is marketed as specific for ERK1/2 (Figure 11A and Experimental Procedures). 58  However, ERK1 and ERK2 share ~50% identity with ERK5, a third MAPK whose regulation is distinct 76. All three ERKs have a Threonine-Glutamate-Tyrosine motif that is bisphosphorylated upon activation, and the sequence surrounding this motif is so similar that many phospho-ERK1/2 antibodies will crossreact with ERK5 (K.A.J., unpublished observations). Phospho-ERK5 crossreactivity is readily distinguished from phospho-ERK1/2 during blotting (ERK5 ~ 115 kDa vs. ERK1 ~ 44 kDa, ERK2 ~ 42 kDa), but the ELISA format cannot resolve proteins by molecular weight. Because ERK5 signaling is important for cardiovascular tissues 207  , I decided to investigate the individual contributions of ERK1/2 and ERK5 by methods that  could separate the pathways. I first monitored the kinetics of ERK1/2 and ERK5 phosphorylation by blotting with antibodies that were specific for each pathway (Figure 12B). Both ERK1/2 and ERK5 were strongly phosphorylated shortly after CVB3 infection at 0.17 hr p.i. and also after host cytotoxicity was evident at 24 hr p.i. However, ERK5 showed a more-sustained phosphorylation up to 1 hr p.i. and ERK1/2 exhibited a second peak at 8 hr p.i., illustrating differences in their regulatory kinetics. Next, I used a pair of MEK inhibitors PD184352 and U0126 to separate the ERK1/2 and ERK5 contributions to the ERK phospho-ELISA. PD184352 at low concentrations selectively blocks MEK1/2 and ERK1/2 phosphorylation, whereas U0126 inhibits MEK1/2– ERK1/2 and MEK5–ERK5 equally 84 (see below). Thus, the contribution of ERK5 can be inferred from the difference between PD184352 (ERK1/2 inhibition) and U0126 (ERK1/2 + ERK5 inhibition). When cells were treated with CVB3 for 10 min after U0126 pretreatment, I found that the measured phospho-ERK ELISA signal was reduced to background levels (Figure 12C). By contrast, pretreatment with PD184352 reduced the ELISA signal by only ~30% 59  (Figure 12C), despite that ERK1/2 phosphorylation was completely inhibited (Figure 12C). This indicated that phospho-ERK ELISA data was a convolution of ERK1/2 and ERK5 pathway activities and further implied that the predicted ERK–p38 associations (Figure 12A) could be between ERK1/2 and p38 or ERK5 and p38, or both.  60  A Phospho-proteins ( 0-8 h p.i. )  JNK  2  Readouts ( 8  Principal Component #2  IκBα  1  0  IκBα  Akt  -0.5  ERK  Readouts ( 24 h p.i. )  JNK Hsp-27 ERK ATF-2 p38 p38  GSK3β  GSK3β  h p.i. )  Readouts ( 16 h p.i. )  ATF-2 Hsp-27  1.5  0.5  Phospho-proteins ( 8-24 h p.i. )  B Post-Infection (hr) p-ERK1/2 (T  202  /Y  204  0  0.17 1  8  16 24  )  p-ERK5 (T218 /Y220 )  CREB  Tubulin  Akt  C  -1  CREB  Sham  -1.5  Vehicle U0  -2  PD  -2  -1.5  -1  -0.5  0  0.5  Principal Component #1  1  1.5  2  0  0.2  0.4  0.6  0.8  1  Normalized p-ERK ELISA Signal  61  Figure 12. (Previous page) Model principal components identify dynamic crosstalk between ERK and p38 pathways. HL1 cells were infected with CVB3 at one of five multiplicities of infection and then assessed for the indicated phospho-proteins and biological responses at six and three time-points, respectively, over 24 hr. (A) Model projections of early phospho-proteins (0–8 hr p.i., blue squares), late phospho-proteins (8–24 hr p.i., gold diamonds), and host-cell responses (circles) onto the principal components derived in Figure 11C. Note that host-cell responses are clustered together with phospho-p38 and early phospho-ERK (dashed square), suggesting a strong correlation in principal-component space. (B) Dynamics of phospho (p)-ERK1/2 and phospho (p)-ERK5 over a 24 hr time-course of CVB3 infection. HL1 cells were infected with CVB3 at MOI=9, and then assessed for p-ERKs at the indicated times p.i. by Western blotting, with tubulin used as a loading control. (C) ERK phospho (p)-ELISA measurements are a convolution of p-ERK1/2 and p-ERK5. HL1 cells were pretreated with DMSO, PD184352 (PD, 2 µM) to inhibit p-ERK1/2 or U0126 (U0, 10 µM) to inhibit p-ERK1/2 and p-ERK5 and then infected with sham or CVB3 at MOI= 9. The p-ERK signals were assessed by p-ELISA at 0.17 hr p.i.. The data are shown as the mean ± s.e.m of three biological replicates.  62  I tested for crosstalk between ERKs and p38 by using PD184352 or U0126 together with SB203580, a small-molecule inhibitor of p38 181. I monitored the phosphorylation of ERK1/2, ERK5, and p38, as well as CVB3-induced pathway activation downstream of ERKs and p38 by tracking the effector kinases, MK2 and MSK. MK2 is a major in vivo substrate for p38 72, whereas MSK is a substrate for both ERK and p38 95. As expected, PD184352 potently inhibited phospho-ERK1/2 without affecting phospho-ERK5, and SB203580 blocked p38 activity as read out by phospho-MK2 (Figure 13A). I also found that SB203580 blocked the phosphorylation of ERK5, which was surprising because this cross-inhibition had not previously been reported. Thus, PD184352 and SB203580 provided two small molecules with which to examine ERK1/2 and p38 + ERK5 function during infection. At 24 hr after CVB3 infection in PD184352- or SB203580-treated cells, I observed similar perturbations to phosphorylation patterns as I did shortly after CVB3 addition (Figure 13B, compare with Figure 13A). The one notable exception was that CVB3-induced phospho-p38 itself was inhibited when cells were pretreated with a combination of SB203580+ PD184352. This suggested that SB203580- and PD184352inhibited pathways converged upon a redundant signaling event upstream of p38 activation at late stages of CVB3 infection. Concurrently, I observed a pharmacological synergy between SB203580 and PD184352 towards CVB3 pathogenicity179. PD184352 negligibly affected CVB3-induced VP1 expression, VPR, or cytotoxicity when used as a single agent but enhanced the inhibitory effects of SB203580 when used in combination (Figures 13C- E). Taken together, the molecular and phenotypic consequences of SB203580+ PD184352 indicated that p38, ERK1/2, and ERK5 are functionally interconnected during CVB3 pathogenesis.  63  Tubulin  /Y ) Tubulin  Vehicle  100  p-p38 (T180/Y182)  *  Relative Cell Death (%)  SB 0  SB+PD  SB  PD  Vehicle  Tubulin  p-p38 (T180/Y182) Tubulin  20  40  60  80  100  Vehicle  Tubulin  Tubulin p-MK2 (T ) β−actin 334  )  β−actin p-MSK (T581) Tubulin  80  J  I  p-ERK1/2 (T202/Y204)  /Y ) Tubulin  p-MK2 (T  SB+PD  SB+U0  24 h p.i.  p-ERK5 (T218 /Y220)  334  60  SB+PD  VP1 Tubulin  G  Tubulin p-ERK5 (T  40  PD  U0  SB+U0  U0  SB  Sham  Vehicle  C  220  20  SB PD  Tubulin  p-MSK (T581) Tubulin  218  Relative Cell Death (%) 0  p-MK2 (T334) β−actin  β−actin  p-ERK1/2 (T202 /Y204)  SB  Vehicle  p-p38 (T180/Y182)  p-MK2 (T334)  0.17 h p.i.  Vehicle  Tubulin  182  F  E  p-ERK5 (T218/Y220)  SB  p-p38 (T  D  p-ERK1/2 (T202/Y204) Tubulin  p-ERK5 (T218/Y220 ) Tubulin 180  Sham  SB+PD  SB  )  24 h p.i.  Vehicle  /Y  204  B  Sham  p-ERK1/2 (T  202  PD  0.17 h p.i.  Vehicle  Sham  A  H  VP1 Tubulin  SB U0  SB+U0 U0  SB+U0  64  Figure 13. (Previous page) ERKs p38 pathways contribute individually in CVB3 pathogenesis. (A and B) Early- and late-phase activation of p-ERKs, p-p38, and effector kinases upon pretreatment with SB203580 (SB), PD184352 (PD), or both and infection with CVB3. (C–D) SB and PD184352 synergize to inhibit CVB3-induced virus capsid protein 1 (VP1) expression, viral progeny release, and cytotoxicity. * p < 0.05 for the observed SB+ PD combination compared to the predicted effect of the single inhibitors based on the Bliss model of pharmacological independence 179. (F and G) Early- and late-phase activation of p-ERKs, p-p38, and effector kinases upon pretreatment with SB, U0126 (U0), or both and infection with CVB3. (H–I) SB and U0 independently inhibit CVB3-induced VP1 expression, viral progeny release, and cytotoxicity. n.s., p > 0.05 for the observed SB+U0 combination compared to the predicted effect of the single inhibitors based on the Bliss model of pharmacological independence 179. For (A–C and F–H), HL1 cells were pretreated with SB203580 (SB, 20 µM), PD184352 (PD, 2 µM), U0126 (U0, 10 µM), SB+ PD or SB+U0 for one hour, infected with sham or CVB3 at MOI=9, and then assessed for the phospho-proteins or VP1 expression at the indicated times postinfection by Western blotting, with either tubulin or β-actin used as a loading control. For (D and E) and (I and J), viral progeny release was determined by plaque assay and cell death by MTS assay. Cell death measurements were normalized to CVB3-infected cells without inhibitor and data are shown as the mean ± s.e.m of three biological replicates.  65  The synergistic inhibition of CVB3 toxicity in SB203580+ PD184352-treated cells could occur between ERK1/2 and either p38 or ERK5, which was cross-inhibited by SB203580 (Figures 13D and E). To distinguish these possibilities, I pretreated CVB3-infected cells with U0126, which inhibits ERK1/2 and ERK5 equally but not p38 (Figure 13C) 84. U0126 also provided the best pharmacological perturbation of the ERK phospho-ELISA data (Figure 12C), enabling a direct test of the ERK-p38 relationships predicted by the model (Figure 12A). Remarkably, inhibition of either p38 individually or ERKs collectively prevented CVB3-induced phosphorylation of MSK and MK2 (Figures 13F and G). Moreover, I found that long-term panMEK–ERK inhibition was sufficient to block late p38 phosphorylation, (Figure 13G). This indicated a redundant or compensatory role for ERK1/2 and ERK5 upstream of p38, because inhibition of ERK1/2 alone with PD184352 or ERK5 with SB203580 did not affect p38 phosphorylation (Figure 13B). Therefore, late-phase p38 phosphorylation and activity specifically requires early activation of an ERK pathway in CVB3-infected cells, as predicted by the model (Figure 12A). If the synergistic inhibition of CVB3 pathogenesis by SB203580+ PD184352 were similarly due to redundant signaling through ERKs, then I should observe no synergy upon SB203580+U0126 treatment. Independent contributions from ERKs and p38 would be most consistent with the model (Figure 12A), which predicts outcomes based on linear combinations of the measured signaling proteins 159. When tested against the viral readouts, I indeed found that SB203580 or U0126 pretreatment each reduced VP1 expression, progeny release, and host cytotoxicity (Figures 13H– J). However, combined SB203580+U0126 inhibition showed additive effects (Figure 13J), 66  indicating that the two inhibitors were pharmacologically independent for these readouts 179. Though both pathways are clearly interconnected (Figures 13F and G), I conclude that ERKs and p38 contribute individually to CVB3 pathogenesis without complicated synergies, as formalized in the model. 4.2.3  ERK and p38 phosphorylation are required for apoptotic responses during CVB3  infection The involvement of ERKs and p38 in CVB3-induced cytotoxicity (Figure 13J) led us to examine whether these pathways were similarly required for activation of apoptotic caspases. I found that pharmacological inhibition of ERKs collectively with U0126 or p38 and ERK5 with SB203580 blocked CVB3-induced processing of caspase-8, an initiator caspase for extrinsic apoptosis (Figure 14A) 208. Because ERK activity is required for late p38 activity following CVB3 infection (Figures 13B and G), these results suggested that either p38 or ERK5 was the more-proximal kinase that was essential for caspase-8 activation. I further excluded a role for ERK1/2 by using PD184352 pretreatment, which left CVB3-induced caspase-8 cleavage unaffected and did not enhance the inhibition provided by SB203580 (Figure 14B). A different pattern of inhibition was observed when I examined the processing of caspase-9, an initiator caspase for mitochondrial apoptosis 208. CVB3-induced cleavage of caspase-9 was almost completely blocked by pan-ERK inhibition with U0126, whereas p38 and ERK5 inhibition with SB203580 had no effect (Figure 14C). This suggested either that ERK1/2 was specifically required for caspase-9 processing or that there was a redundant signaling event upstream of caspase-9 that required ERK1/2 or ERK5. To distinguish these possibilities, I used PD184352 to inhibit ERK1/2 specifically and found that caspase-9 cleavage was unperturbed. I 67  conclude that CVB3-induced caspase-9 processing requires activation of an ERK pathway, which I independently confirmed with a higher concentration of PD184352 that cross-inhibits both ERK1/2 and ERK5. My results raised the possibility that ERKs and p38 might contribute independently to cytotoxicity by activating distinct initiator caspases en route to apoptosis. Yet, at the same time, pan-ERK inhibition with U0126 or SB203580+ PD184352 was sufficient to inhibit processing of the dominant effector caspase, caspase-3 (Figures 14E-F) 208. This indicated that ERK-mediated pathways other than p38 activation (Figures 13B and G) were critical for the execution of apoptosis. It further suggested that caspase-9, and not caspase-8, was the dominant initiator caspase responsible for CVB3-induced activation of caspase-3.  68  p17-caspase-3  p17-caspase-3  G  CVB3 + IETD (µM) 0  10 20  30 40  Sham  Caspase-3  Sham  Caspase-3  S+P S+P S+P  PD  Vehicle  SB  PD  F  PD  Sham  SB+U0  E  U0  p39-caspase-9  SB  p39-caspase-9  Vehicle  Caspase-9  Sham  Caspase-9  SB  D  SB  Sham  SB+U0  C  U0  Tubulin  SB  Tubulin  Vehicle  p43-caspase-8  Sham  p43-caspase-8  Vehicle  B  Vehicle  Sham  SB+U0  U0  SB  Vehicle  Sham  A  CVB3 + LEHD (µM) 0  10  20 30 40  p43-caspase-8 p18-caspase-8 Tubulin  H  I  U0  CVB3  ERKs Caspase-3 p17-caspase-3  SB p38  C-3 C-9  C-8 Apoptosis  69  Figure 14. Separation of p38-dependent caspase-8 activation from ERK-dependent caspase-9 activation and caspase-3-driven apoptosis. (A and B) CVB3-induced caspase-8 processing is blocked by SB203580- or U0126-mediated inhibition of p38, but not PD184352-mediated inhibition of ERK1/2. (C–F) CVB3-induced processing of (C and D) caspase-9 and (E and F) caspase-3 is blocked by U0126- or SB+ PD184352-mediated inhibition of ERK1/2 and ERK5. (G) Dose-dependent inhibition of CVB3induced caspase-8 cleavage with IETD-fmk and LEHD-fmk. Note that caspase-8 cleavage is maximally inhibited with 40 µM IETD (a caspase-8-selective inhibitor) and is unaffected by 20 µM LEHD (a caspase-9-selective inhibitor). (H) Inhibition of caspase-9 with LEHD, but not caspase-8 with IETD, abolishes caspase-3 processing in CVB3-infected HL1 cells. (I) Schematic model of the dependencies connecting ERKs, p38, caspase-8 (C-8), caspase-9 (C-9), and caspase-3 (C-3) after CVB3 infection. Dotted lines show indirect dependencies. For (A–H), HL1 cells were pretreated with SB203580 (SB, 20 µM), PD184352 (PD, 2 µM), U0126 (U0, 10 µM), SB+PD, SB+U0 or caspase inhibitors (IETD or LEHD) for one hour, infected with sham or CVB3 at MOI=9, and then analyzed for the indicated phospho-proteins or active caspase cleavage products at 24 hr p.i. by Western blotting, with tubulin or full-length caspases used as a loading control.  70  To test this latter prediction, I used fluoromethylketone (fmk) inhibitors derived from the reported substrate preferences for caspase-8 and caspase-9 209. Because fmk-based caspase inhibitors are notoriously cross-reactive 210, dosings were first titrated empirically to provide maximal selectivity. I identified the minimum concentration of IETD-fmk (a caspase-8 inhibitor) that blocked complete caspase-8 processing (40 µM, Figure 14G). I also determined the maximum concentration of LEHD-fmk (a caspase-9 inhibitor) that left caspase-8 cleavage unaffected (20 µM, Figure 14G). Then, cells were treated with the optimized dosings of each fmk inhibitor and monitored for caspase-3 cleavage following CVB3 infection. As predicted, caspase-3 processing was inhibited far more potently by caspase-9 inhibition with LEHD-fmk than by caspase-8 inhibition with IETD-fmk, despite that IETD-fmk was added at a twofold higher concentration (Figure 14H). I conclude that, although both ERK and p38 are required for early apoptotic signaling induced by CVB3, it is ERK-mediated activation of caspase-9 that drives apoptosis through effector caspases (Figure 14I). If p38 signaling was not essential for caspase-3 processing and virus capsid protein synthesis (Figures 14E and 13F), then the question remained how SB203580 treatment could inhibit VPR and overall cytotoxicity (Figures 12G–H, L–M). p38 could conceivably promote apoptosis downstream of caspase-3 cleavage, such as by inducing second mitochondria-derived activator of caspase (Smac) release from the mitochondria or by otherwise interfering with the caspase-3 inbibitor, X-linked inhibitor of apoptosis protein (XIAP) 211. To exclude these scenarios, I used the pan-caspase inhibitor, zVAD-fmk, alone or in combination with SB203580 before infecting cells with CVB3 (Figure 15A). Neither SB203580 nor zVAD-fmk affected VP1 expression (Figure 15B), but both inhibitors substantially reduced VPR and overall cytotoxicity (Figures 15C–D). Importantly, combined SB203580+zVAD-fmk pretreatment further reduced VPR and cytotoxicity in a manner that was consistent with independent modes of action (Figures 15C and D). Thus, the role of p38 in CVB3 pathogenesis is separate from that of the apoptotic cell-death program. In vivo, CVB3 infection has been reported to promote cardiomyocyte necrosis in addition to apoptosis 212, 213. p38 had not been identified at the time of these early studies, but p38 activity is important for the necrotic response of monocytes to bacterial toxins 61. Together, this raised the possibility that p38 signaling was promoting CVB3 pathogenicity via a necrosis pathway, 71  which was separable from apoptotic cell death. To test for a functional role of necrosis in CVB3 infection, I used the RIP1 kinase inhibitor, Necrostatin-1, which blocks necrotic cell death induced by various stimuli 214. RIP kinase inhibition with Necrostatin-1 reduced CVB3-induced p38 activation (Figure 15E), suggesting that p38 is partly associated with necrotic signaling. Importantly, I found that Necrostatin-1 pretreatment measurably reduced CVB3-induced VPR and cytotoxicity, and combined with zVAD-fmk to further reduce these readouts independently (Figures 15F-G). In contrast, no further reduction was observed when Necrostatin-1 was combined with SB203580, in the presence or absence of zVAD-fmk. By virtue of this pharmacological epistasis, I conclude that p38 signaling is a critical component of a necrosis pathway, which promotes CVB3 propagation independently of ERK-dependent apoptosis.  72  C SB+zVAD  zVAD  SB  Vehicle  Sham  A  Vehicle  SB  D  Relative Cell Death (%)  0  20  40  60  80  100  Vehicle  Caspase-3 SB  SB+zVAD  zVAD  SB  Sham  B  Vehicle  p17-caspase-3 zVAD  SB+zVAD zVAD  VP1 Tubulin  Vehicle  Sham  E  Necrostatin-1  SB+zVAD  G Necrostatin-1 (N)  N+SB  20  40  60  80  100  Vehicle  p-p38 (T180/Y 182) Tubulin  F  0  Relative Cell Death (%)  Necrostatin (N) Vehicle  N + zVAD  N+SB+ zVAD  N+SB N + zVAD N+SB+ zVAD  73  Figure 15. Apoptosis and p38-dependent necrosis independently contribute to CVB3 progeny release and cardiotoxicity. (A and B) zVAD-fmk blocks CVB3-induced caspase-3 processing but does not affect VP1 expression alone or in combination with SB203580 (SB). (A) Caspase-3 and (B) VP1 were measured by Western blotting with tubulin used as a loading control. (C and D) SB and zVAD independently inhibit CVB3 progeny release and host-cell cytotoxicity. n.s., p > 0.05 for the observed SB+zVAD combination compared to the predicted effect of the single inhibitors based on the Bliss model of pharmacological independence 179. (E) RIP kinase inhibition with Necrostatin-1(N) partly inhibits CVB3-induced p38 activation. p38 phosphorylation was measured by Western blotting with tubulin used as a loading control. (F and G) Necrostatin1(N) inhibits CVB3 progeny release and cytotoxicity epistatically with SB and independently of zVAD. Progeny release and cytotoxicity values for SB alone and zVAD alone were not substantially different from the data shown in (C) and (D).  74  4.3  Summary and conclusions Viruses such as CVB3 modify many host-cell signaling pathways and evoke many host-  cell responses. This study began with a holistic approach to monitor these events dynamically and as a function of CVB3 titer. By analyzing the data to make quantitative predictions of hostcell outcome, I quickly converged on ERKs and p38 as key pathways for CVB3 pathogenesis. Early-phase ERK activation stems directly from CVB3 docking to host membranes 75. Latephase p38 signaling probably lies downstream of autocrine proinflammatory cytokines, which are induced during the final stages of the viral life cycle 89. ERK signaling is required for CVB3induced processing of caspase-9, which I show is the key initiator of apoptosis through caspase3. p38 signaling is similarly essential for caspase-8 processing, but this initiator caspase is dispensable for cardiomyocyte apoptosis caused by CVB3. Rather, p38 contributes to cytotoxicity by promoting necrosis, which may act as a last-ditch antiviral strategy by the host to promote local tissue inflammation 215. One interesting result from this study is the apparent redundancy between ERK1/2 and ERK5 in mediating caspase-9 cleavage and apoptosis through caspase-3. ERK5 possesses a long PB1 protein-binding domain that confers its isoform-specific functions, but the ERK5 kinase domain with strongly homologous with ERK1/2 76. ERK1/2–ERK5 redundancy has been overlooked previously, because the most-commonly used MEK inhibitors (U0126 and PD98059) block MEK5 activation as potently as MEK1/2 84. Surprisingly, I was able to uncover a role for ERK5 in CVB3 pathogenesis by modeling a dataset that did not measure ERK5 explicitly. Elsewhere, I have shown that quantitatively accurate signaling measurements are critical for data-driven models to reflect underlying biological mechanisms 159, 167. My results here using a pan-ERK phospho-ELISA indicate that measurements of specific proteins may not be as important. This is encouraging, because many modern signaling assays increase overall throughput by relaxing the specificity constraints of traditional approaches 216. Likewise, my work shows how small molecule inhibitors with overlapping targets can be used as a set to unravel function 217. U0126, SB203580, and PD184352 provided three distinct conditions, which could be solved as a linear system for the three unknowns: the roles of 75  ERK1/2 (targeted by U0126 or PD184352), ERK5 (targeted by U0126 or SB203580), and p38 (targeted by SB203580). Thus, inhibitors do not have to be perfectly specific to unravel biological mechanisms, as long as the target profiles have been characterized and the smallmolecule sets are chosen judiciously 84, 217. Together, the approaches developed here should be generally applicable to studies of host-pathogen interactions at the systems level 123, 124.  76  Chapter 5 – Closing remarks Several studies have shown that multiple phospho-proteins individually underpin virus replication 58, yet phospho-proteins are part of intracellular signal-transduction networks, composed of several pathways. One of the major challenges in this project was to define how systems modeling could be used to determine how signal-transduction networks regulate pathophenotypes in CVB3 infection. It remains difficult to study signal-transduction networks when two or more ligands are combined simultaneously to modify multiple signaling pathways 162, 218. Interestingly, virus infection is equal to a network perturbation, in that viruses have evolved effective strategies to manipulate multiple signaling pathways all together and induce crosstalk and feedback loops among pathways to form a network. In fact, viruses drive signal-transduction networks through multiple independent events, including viral docking to receptors, viral protein synthesis, viral progeny release and virus-induced inflammatory responses 8. Thus, to define the signaltransduction network and in turn to link this network to several interrelated pathophenotypes in CVB3 infection requires a global multivariate approach  219, 220  .  To monitor components of signaling networks, researchers need new tools to systematically perturb and monitor signaling processes and functions within cells. Indeed, the emerging of high-throughput, extremely parallel technologies, have enabled biologists to monitor multiple cellular components all together 211. These tools have provided researchers the opportunity to collect comprehensive and large datasets. Nowadays, the emerging challenge in the era of “new biology” is how to organize, interpret and extract pertinent information from large and comprehensive datasets. This requires computational biologists, using mathematical, statistical and computational techniques put together the biological components into functional molecular and cellular network models in a systematic fashion 159. Causal network mechanisms were defined in primary human immune system cells, wherein Bayesian network computational methods unraveled several novel and already shown interactions in signal-transduction networks 221. To build a directed graphical model, we required to measure almost all components of a pathway. However, in Chapter 3 of this thesis, the measured phospho-proteins were not close enough to apply Bayesian network 77  modeling approach to define a causal signaling network. Thus, we used GGM to build an undirected graphical model, demonstrating partial correlations among phospho-proteins 89. Here, using high-throughput technology, pairwise perturbations and multiple dosings of CVB3 I developed network mechanisms, underlying the control of host-cell responses in CVB3 infection. Indeed, my modeling approaches along with complementary and confirmatory experiments are useful to determine connections between signaling pathways that are functionally interrelated as a network, regulating several host-cell responses during infection. Using ELISA as a high-throughput technology, I could assess the level of phosphoproteins selected based on previous studies 58, suggesting that these individual signaling pathways were important during the time-course of CVB3 pathogenesis. However, I sought to design the experiments upon which I could address network-level mechanisms in CVB3 infection, instead of studying of signaling pathways as linear step-wise processes. As discussed in chapter 3, to infer pairwise correlations among nine phospho-proteins, I required a realistic number of experimental conditions. Using 6 time-points over 24 hr and 6 small-molecule kinase inhibitors, there are only 36 experimental conditions. To achieve a practical number of experimental conditions, I could add more single inhibitors, but I decided to use all possible combinations of 6 inhibitors, providing us with 15 additional pairwise prospects to further identify network properties, wherein inhibitor pairs are more valuable than using 15 single inhibitors to study network function. I hypothesized and demonstrated how inhibitor pairs illustrated significantly different patterns of phospho-protein dynamics in response to CVB3 infection when compared with the corresponding single inhibitors. Further, cluster analysis of the pairwise dataset persistently showed that the paired-inhibitor dataset was required for accurate data-driven predictions of kinase-substrate links in the host-cell network. I next observed that blocking signal-transduction networks using pairwise perturbations in CVB3 infection unravels mechanisms that might have been ignored by using single perturbants. Moreover, using a partial correlation algorithm and the pairwise analysis of the host signaling response to CVB3 infection demonstrated a central role for phospho-IκBα downstream of autocrine TNF-α and IL-1 signaling.  78  In chapter 4, I assessed the dynamics of the same signaling phospho-proteins in chapter 3 along with six CVB3-induced host-cell outcomes at five different dosings of CVB3. In fact, I linked CVB3-modified signaling to outcomes by defining a data-driven model. The results of this analysis revealed unexpected connections between the ERKs and p38 pathways related to the control of pathophenotypes induced by CVB3. Interestingly, using a data-driven model that did not measure ERK5 overtly I showed a role for ERK5 in CVB3 pathogenesis. Indeed, in follow-up experiments I used a systematic target validation, a chemically diverse panel of inhibitors targeting a kinase, used to illustrate cryptic homologies across targets, ERK1/2 and ERK5 217. It was shown that ERK1/2 phosphorylation corresponded with CVB3 replication, in that U0126 inhibited phosphorylation of ERK1/2 required for virus replication and virus-induced cell death 75, suggesting an individual signaling pathway regulated host cellular responses. To further characterize the role of this pathway, I used an alternative small-molecule kinase inhibitor to block MEK1/2 activity more selectively, in that lower concentrations of PD184352 blocked only MEK1/2 pathway 173. It suggests that using either different inhibitors, U0126 or PD184352 targeting a kinase may reveal a network mechanism that was previously overlooked. My results support how ERK1/2, ERK5 and p38 are functionally interrelated to determine host-cell responses in CVB3 infection. These observations suggest that combination of multi-targeted inhibitors could be used to validate these signaling network mechanisms, in that ERK1/2 and ERK5 redundancy has been ignored previously. Further, complementary experiments unraveled that ERK1/2 and ERK5 redundantly control a caspase-9-dependent apoptotic program, whereas p38 is required for CVB3-driven necrosis. All in all, both approaches developed here should be generally applicable to studies of host-pathogen interactions at the systems level that may raise new opportunities for the development of novel therapeutics. To date, the therapies used in patients with myocarditis are immune serum globulin and pleconaril 222, 223, an anti-picornaviral agent. In fact, pleconaril perturbs viral uncoating and in turn blocks viral attachment to host cell receptors. Directly targeting viruses has been moderately successful to control viral disease, yet it suffers from few serious weaknesses, including to fail to eliminate viral myocarditis particularly during chronic stage, to exhibit a narrow spectrum of action, and to promote evolution of mutation associated with development of 79  drug resistance 124. Thus, the discovery of novel antiviral targets, host cell-based antiviral agents is a more promising approach and deserves more attention 224. At the cellular level, studies have shown that host cell phospho-proteins, essential for viral replication, are potentially targetable 59. It is known, however, phospho-protein network that embraces a system with redundant, convergent and distinctive signaling pathways 151. Such combinative properties of signaling networks may counteract the therapeutic efficacy of even the most selective drugs. Thus, combination therapy may be necessary to achieve efficacy with fewer side-effects 152. Motivation for this initiative, the therapeutic synergy of a combination, is tempered by concerns about introducing synergistic side effects. An in vivo study that has emerged from recent studies of the healing process in a rat asthma model showed that combining drugs performed better than single ones 225 . One work that has emerged from recent studies of networked systems in virus-infected host cells revealed ~260 host cellular factors, affecting virus infection 226. However, only a small subset of these proteins played a role in the early stage of virus infection, wherein early detection may help slow, halt or even reverse the progression of disease. Interestingly, a complementary study showed how a multi-parameter approach could unravel host-virus protein interactions that likely act as a network to facilitate the early steps of HIV-1 infection 227. Now, biologists are poised to understand the network mechanisms involved in virus infection. These networks might be used to improve patient diagnosis, monitoring, and treatment. Observations from several studies have provided compelling evidence that most patients with myocarditis recover from a transient cardiac dysfunction and do not manifest characteristic clinical syndromes, yet others either die or develop DCM that may lead to congestive heart failure 142, 143. Why do only ~30% of patients diagnosed with DCM have a poor prognosis? Considerable progress has been made in understanding the cellular and molecular events that are involved in the inflammatory response induced by stimuli including virus infection 21, 58, 228, 229. Hypothetically, unraveling the molecular mechanisms of this disease may allow us to understand the basis for disease susceptibility and propose an explanation for the different phenotypic manifestations of viral myocarditis 230. This differential phenotype in other diseases was partly  80  explained by disease-modifying genes 231 and environmental influences232, allowing us to refine disease “prognosis” with greater accuracy. One of the less-appreciated challenges in diagnosing patients with suspected myocarditis is that the existing diagnostic tools are inappropriate, insensitive and invasive. Diagnosis of myocarditis is currently based on observational association between pathological examination and clinical symptoms 233. Diagnosing myocarditis using the Dallas criteria has served pathologists well to the recent time 234, 235. Indeed, pathologists usually distinguish myocarditis from ischemia by assessing the necrotic or inflammatory area of myocardial fiber elimination to deep, patchy, non-linear groups of myocytes in myocarditis 21. Thus, in this traditional diagnostic strategy, sampling error is the norm leading to not having enough specificity and sensitivity in identifying viral myocarditis that is a subclinical disease. In addition to pathological and clinical diagnosis of the disease, cardiac magnetic resonance imaging, clinical laboratory tests, especially troponin T or I, creatine kinase and echocardiography are helpful 236. The era of “new biology” has great promise to propose novel prognostic, diagnostic and therapeutic strategies for myocarditis. In fact, biological investigators are in a transition phase to move from molecular biology to systems-level approaches. 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