UNDERSTANDING THE MECHANISMS OF ONCOGENICITY BY MAREK’S DISEASE VIRUS: ROLE OF MEQ ONCOPROTEIN By SUGALESINI SUBRAMANIAM A DISSERTATION Submitted to Michigan State University in partial fulfillment of the requirements for the degree of Comparative Medicine and Integrative Biology - Doctor of Philosophy 2013 ABSTRACT UNDERSTANDING THE MECHANISMS OF ONCOGENICITY BY MAREK’S DISEASE VIRUS: ROLE OF MEQ ONCOPROTEIN By SUGALESINI SUBRAMANIAM Marek’s disease (MD) is one of the most economically significant diseases in chickens. It is caused by a highly oncogenic, α-herpesvirus named Marek’s disease virus (MDV). Currently, the main strategy to control MD is vaccination. However, accumulating evidence points to increase in virulence among MDV field isolates over time, which implicates that new strains of the virus are evolving and could break vaccine protection. This necessitates better understanding of MDV-host interactions, not only to elucidate the events in pathogenesis but also develop strategies for newer and more effective vaccines. One of the major unanswered questions in this area is the mechanism of tumor formation by MDV. The main objective of this project is to gain a comprehensive understanding of host genes that are transcriptionally regulated by Meq, the major oncoprotein of MDV and their relevance in genetic resistance to MD. MDV oncogenicity is largely attributed to the bZIP transcription factor Meq. Although it was discovered in the 1990s, only a few of host target genes have been described. This knowledge gap has impeded our understanding of Meq-induced tumorigenesis. Using a combination of state-of-the-art genomic techniques including ChIP-Seq and microarray analysis, a high confidence list of Meq binding sites and a global transcriptome of genes regulated by Meq was generated. Given the importance of Meq in MDV pathogenesis, we next explored the role of Meq in genetic resistance to MD. Two highly inbred chicken lines, varying in MD resistance, were infected with a virulent strain of MDV, Md5 or a mutant virus lacking Meq, Md5ΔMeq. Analysis of differentially expressed genes provided a list of Meq-dependent genes that are involved in MD resistance and susceptibility. Pathway analysis indicated that MD resistant lines were enriched for positive regulation of cell death whereas the susceptible cell lines were enriched for regulation of cell proliferation. In addition, some of the Meq-regulated pathways like ERK/MAPK signaling and Jak-STAT pathways were also involved in differential MD susceptibility. Taken together, our study provides a comprehensive analysis of how Meq interacts with cellular pathways involved in oncogenesis. In addition, this study forms the basis for selection of candidate genes that might be involved in genetic resistance to Marek’s disease. Dedicated to the love of my life Madhu Sirivelu and Kashvi Sirivelu iv ACKNOWLEDGEMENTS I am greatly indebted to my mentor Dr Hans Cheng who introduced and contributed his expertise and time to provide me the best possible training in this fascinating project. He taught me to be resourceful, meticulous, positive and stoic not just in the context of science but in the larger context of life. I would also like to thank Dr.Titus Brown, Dr. Jerry Dodgson and Dr. Richard Schwartz for serving on my guidance committee, for their time and useful suggestions. My special thanks to John Johnston and Likit Preeyanon for all the assistance with their expertise in computational analysis in some of the experiments. This work would have not been completed without the coordinated help from the members of the Cheng lab. I am thankful to all the people who have assisted me in accomplishing this work. Evin Hidebrandt and Dr.Sudeep Perumbakkam had been a very helpful hand during my period in the lab and we share numerous productive discussions regarding experiments and analysis. The camaraderie that I share with Evin Hildebrandt goes much beyond her help with the experiments and I greatly appreciate her friendship. Laurie Molitor has been such an asset to the lab. I am so very thankful for her assistance and managerial skills to help me accomplish all the experiments I intended to. I am very thankful to Lonnie Milan, Noah Koller and all members of Avian Disease and Oncology (ADOL) lab for all their friendship, assistance and useful suggestions in different experiments. I would also like to thank the director of the CMIB program, Dr Vilma YuzbasiyanGurkan for all the support and encouragement she provided. I am greatly appreciative of the unwavering support of my family, especially my parents who have provided tremendous energy for the fulfillment of this enormous project. Most special thanks to my husband, Madhu Sirivelu for his unconditional love and support for my professional pursuit. He has been the v electric force that helped me maintain my sanity and still run behind my dreams. Finally, special thanks to my daughter Kashvi Sirivelu, the joy of my life. vi TABLE OF CONTENTS LIST OF FIGURES…………………………………………………….…………………………………………….....viii KEY TO ABBREVIATIONS………………………………………….………………………………………….......x CHAPTER 1. INTRODUCTION AND LITERATURE REVIEW HISTORY OF MAREK’S DISEASE ……………………………………………………...............................1 CLASSIFICATION OF MDV...............................................................................................2 GENOMIC STRUCTURE OF MDV ………………………………………………….................................3 BIOLOGY OF MD…………………..………….............................................................................4 PATHOGENESIS OF MD..................................................................................................5 ONCOPROTEIN OF MDV: MEQ …...................................................................................8 OTHER MDV UNIQUE GENES AND THEIR ROLE IN PATHOGENESIS……………………………14 GENETIC RESISTANCE TO MD..………………………..............................................................18 CHAPTER 2. GENOME-WIDE REGULATORY NETWORK OF MEQ, THE ONCOPROTEIN OF MAREK’S DISEASE VIRUS REVEALS INTERACTION BETWEEN MULTIPLE CELLULAR PROCESSES INVOLVED IN ONCOGENESIS INTRODUCTION…………………………………………………….......................................................19 MATERIALS AND METHODS………………………………………..................................................22 RESULTS………………………………………………………………........................................................27 DISCUSSION…………………………………………………………........................................................64 CHAPTER 3. TRANSCRIPTIONAL PROFILING OF MEQ-DEPENDENT GENES IN MAREK’S DISEASE-RESISTANT AND SUSCEPTIBLE INBRED CHICKEN LINES INTRODUCTION……………………………………………………........................................................73 MATERIALS AND METHODS………………………………………...................................................75 RESULTS………………………………………………………………........................................................78 DISCUSSION…………………………………………………………........................................................98 CHAPTER 4. FUTURE DIRECTIONS………………………………………………………………………….……...103 BIBLIOGRAPHY .…………………………………………………………………………………………………………….109 vii LIST OF FIGURES Figure 2-1. Distribution of Meq binding sites within ±20kb………………………………………………..29 Figure 2-2. Distribution of Meq binding sites within ±2kb………………………………………………….30 Figure 2-3. Distribution of c-Jun binding sites within ±20kb……………………………………………….31 Figure 2-4. Distribution of c-Jun binding sites within ±2kb………………………………………………….32 Figure 2-5. Distribution of of IgG, Meq and c-Jun binding sites …………………………………………..34 Figure 2-6. ChIP-qPCR validation of a subset of Meq binding sites………………………………………36 Figure 2-7. Characterization of Meq DNA binding motifs…………………………………………………….38 Figure 2-8. Luciferase reporter assay…………………………………………………………………………………..39 Figure 2-9. Integrated analysis of expression profiling and genome occupancy by Meq……..41 Figure 2-10. Integrated analysis of expression profiling and genome occupancy of sites…….42 Figure 2-11 Pathway analysis of Meq-regulated genes………………………………………………………..44 Figure 2-12. Pathway analysis of Meq-regulated genes……………………………………………………….45 Figure 2-13. Validation of gene expression analysis..…………………………………………………………..46 Figure 2-14. qPCR validation of microarray results………………………………………………………………47 Figure 2-15. Levels of Meq and c-Jun…………………………………………………………………………………..49 Figure 2-16. Optimization of siRNA concentration……………………………………………………………….50 Figure 2-17. Gene expression changes after siRNA mediated knockdown of Meq……………….51 Figure 2-18. Gene expression changes after siRNA mediated knockdown of Meq……………….52 Figure 2-19. Differential location of Meq-binding sites in up- regulated genes……………………54 Figure 2-20. Differential location of Meq-binding sites in down regulated genes………………..55 Figure 2-21. Relative occupancy of Meq in the five genomic regions…………………………………..57 Figure 2-22. Overrepresented motifs in up- and down regulated genes………………………………59 viii Figure 2-23. Comparative enrichment of cellular pathways………………………………………………..61 Figure 2-24. Functional validation of the role of ERK/MAPK signaling…………………………………63 Figure 3-1. Schematic for analysis of experimental groups…………………………………………………80 Figure 3-2. Venn diagram of comparison between two virus-infected groups……………………81 Figure 3-3. Representation of overlap between Meq-dependent genes…………………………….83 Figure 3-4. Representation of overlap between Meq-dependent genes ……………………………84 Figure 3-5. Gene Go categorization of molecular functions ……………………………………………….86 Figure 3-6. Gene Go categorization of cellular processes …………….…………………………………….87 Figure 3-7. qPCR validation of microarray results……………………………………………………………….90 Figure 3-8. qPCR validation of microarray results……………………………………………………………….91 Figure 3-9. Validation of gene expression analysis …………………………………………………………….92 Figure 3-10. Functional validation of the role of Meq in resistance and susceptibility ……….94 Figure 4-1. Histone modifications in promoters of Meq-regulated genes………………………….102 ix KEY TO ABBREVIATIONS BAC Bacterial artificial chromosome BCR B cell receptor CEF Chicken embryo fibroblasts CTL Cytotoxic T lymphocyte EGFP Enhanced green fluorescent protein EID Embryonic incubation day ERC Epithelial reticular cells FBS Fetal bovine serum GAPDH Glyceraldehyde 3-phosphate dehydrogenase HSC Hematopoeitic stem cells HSV-1 Herpes simplex virus 1 HSV-2 Herpes simplex virus 2 IE Immediate-early genes IFN Interferon IL-2 Interleukin 2 LATs Latency-associated transcripts x CHAPTER 1. INTRODUCTION AND LITERATURE REVIEW HISTORY OF MAREK’S DISEASE Marek’s disease is named in the honor of the remarkable Hungarian veterinarian, József Marek, who first described this disease in four adult male chickens (Marek, 1907). In this report, he described the affected birds as having a paralysis of legs and wings, describing the condition to be “polyneuritis” or “neuritis interstitialis”. Gross pathologic lesions included thickening of sacral plexus and spinal cord and microscopically, a robust infiltration of mononuclear cells (lymphocytes) was noted. Subsequently, there were several reports around the world describing a similar condition. However, there was no consensus in the nomenclature. Numerous terms including “paralysis of the domestic fowl”, “fowl paralysis”, and “neurolymphomatosis gallinarium”, were used to describe this disease. There were also several differences in terms of the pathology: in addition to peripheral nerves, lymphocytic infiltration was noted in several visceral organs including ovary, liver, kidneys, adrenals and muscle in a subset of cases and the possibility of a neoplastic etiology was considered (Pappenheimer et al., 1929). Later in the 1940s, MD was confused with other lymphocytic neoplasms due to difficulty in distinguishing between the visceral lymphoma of MD and avian lymphoid leucosis, another neoplastic disease (Campbell and Biggs, 1961). Subsequently, the transmission of MD using lymphoma cells from diseased birds was described. It was noted that the transmission was dependent on whole cells as the destruction of cells resulted in loss of infectivity, leading to 1 conclusions that it was an infectious disease, and the causative agent was likely to be a cellassociated virus (Biggs and Payne, 1967; Churchill and Biggs, 1967). Two independent groups in the UK and USA discovered a cytopathic effect in cultured cells infected with the “MD agent” isolated from tumor cells or whole blood. Upon electron microscopy, they came to the conclusion that a herpes-type virus could be the etiologic agent of MD (Churchill and Biggs, 1967; Nazerian and Burmester, 1968). Later, it was demonstrated that the feather-follicle epithelium was the source of fully-enveloped virus and that cell-free virus could reproduce the disease, which provided an explanation for the contagious nature of this disease (Calnek et al., 1970; Calnek and Hitchner, 1969). Soon after, an attenuated MD herpes virus and related turkey herpes virus was shown to confer immunity towards MD in chicken. This provided conclusive evidence that the herpesvirus was the etiologic agent of MD (Okazaki et al., 1970; Purchase and Okazaki, 1971). CLASSFICATION OF MDV The causative agent of Marek’s disease, MDV, is also termed Gallid herpesvirus 2 (GaHV-2), with the following classification: order Herpesvirales, family Herpesviridae, subfamily Alphaherpesvirinae and genus Mardivirus (Davison et al., 2009). Other members of the genus Mardivirus include the non-pathogenic chicken MDVs (also termed Gallid herpesvirus 3) and the non-pathogenic turkey herpesviruses (HVTs, also termed Meleagrid herpesvirus 1). A former classification system divided this genus of viruses into three serotypes on the basis of antigenic differences. Under this prior system, Gallid herpesvirus 2, Gallid herpesvirus 3, and Meleagrid herpesvirus 1 were termed MDV serotype 1, MDV serotype 2, and MDV serotype 3, 2 respectively. Among the various serotypes, only MDV-1 causes clinical disease in chickens. Regardless of the classification system used, the A antigen is similar among the three strains (Isfort et al., 1987; Long et al., 1975; Ross et al., 1973), whereas each strain has specific B and C antigens (Bulow and Biggs, 1975; Isfort et al., 1986). Recent evidence clearly indicates that after introduction of new and more efficient vaccines, there has been an increase in virulence of MDV. Based on several factors including cytolytic infection, uncommon cell tropism, severe lymphoid organ atrophy, early mortality, severe suppression of immune system, and aggressive tumors in chickens, MDV isolates are classified into mild (m), virulent (v), very virulent (vv), and very virulent plus (vv+) pathotypes (Witter, 1997). Recently, a new pathotyping method referred to as ‘Avian Disease and Oncology Laboratory (ADOL) method’ has been proposed which is based on induction of lymphoproliferative lesions in vaccinated chickens. Using this method, many field isolates have been pathotyped and this method is the basis for the current pathotype classification of MD virus strains (Witter et al., 2005). GENOMIC STRUCTURE OF MDV The MDV genome is comprised of linear, double-stranded DNA with typical herpesviral structure consisting of a 100-nm capsid surrounded by a tegument and envelope. The initial classification of MDV was based on restriction endonuclease maps and partial DNA sequence data. Broadly, the genomic content has been divided into the following regions: two unique DNA segments called a unique long (UL) and a unique short (US), which are flanked by large inverted repeated sequences. These unique regions are flanked by inverted repeat regions: 3 terminal repeat long (TRL) and internal repeat long (IRL), terminal repeat short (TRS) and internal repeat short (IRS) (Lee et al., 2000a; Silva et al., 2001). The genes in UL and US are more conserved and exhibit a high level of homology with other α-herpesviruses like herpes simplex virus (HSV-1). In addition, some of the genes in the short repeat regions (TRS and IRS) such as ICP4, have homology to HSV-1. Unlike unique regions and the short repeat regions, genes located in the long repeat regions (TRL, and IRL) are exclusively present in MDV-1 genomes and are not found in other MDV serotypes or non-avian herpesviruses (Lee et al., 2000a; Silva et al., 2001). Many of these genes (meq, pp38, pp24, vTR, and vIL-8) are associated with MDV pathogenesis (Cui et al., 1990; Fragnet et al., 2003; Kung et al., 2001; Parcells et al., 2001). It is therefore considered that most of the viral proteins that are necessary for herpesvirus replication are present in the unique regions and the MDV-specific genes are clustered in RL regions. BIOLOGY OF MDV The life cycle of MDV is considered to be complex, since it uses both productive and nonproductive modes of replication for survival and propagation. During productive interactions (lytic phase), the virus invades the host cells leading to death of host cells and release of progeny virions. The replicative life cycle of MDV is approximately 18-20 hours. The sequence of events that occur from the viral entry to release of progeny virions can be summarized as follows: MDV attaches to host cells by its glycoproteins (gB, gC and gD), which is followed by fusion to target cell. Upon successful penetration, the 4 virion is uncoated by the action of cellular proteases leading to release of viral DNA, which subsequently enters the nucleus. Subsequently, mRNA synthesis in the nucleus and protein synthesis in the cytoplasm result in production of viral proteins. Maturation of virions takes place in the Golgi complex and their release leads to metabolic changes in the cell, resulting in a cytopathic effect and death of target cells. In non-productive interactions, the viral genome is present within the nucleus but gene expression is minimal leading to long term persistence of the virus within the host cell. From this stage, the virus either enters latency or leads to transformation of host cells. PATHOGENESIS OF MD The source of infectious virus is considered to be feather follicle epithelium, which sheds infectious virions. Horizontal spread of the virus is mostly through inhalation, upon which the virus is considered to enter the lungs and is processed by resident macrophages. According to the ‘Cornell model’ of pathogenesis, the course of the disease is divided into four phases: an early cytolytic phase, a latent phase, a late cytolytic and immunosuppressive phase, and a transformation phase (Calnek, 1986). The details of each phase are provide below: Early cytolytic phase: Initial infection occurs by natural inhalation of cell-free virus from infected dust and dander shed in the form of flakes from infected birds. The lungs are considered to be the portal of entry for the virus. Following inhalation, the phagocytic cells carry the virus to the lymphoid organs such as the bursa, thymus and spleen. The early cytolytic phase is evident in these organs between 3 to 6 days post infection (dpi), resulting in extensive loss of 5 lymphocytes, most of which are B-lymphocytes. In the spleen, 95% of MDV infected lymphocytes are B cells, while 4% are double positive CD4+ CD8+ T cells (Baigent et al., 1996). This productive phase provokes inflammation which induces an influx of many cell types such as macrophages, thymus derived T-cells and bursa derived B-cells (Baigent and Davison, 2004; Calnek, 1986; Payne, 2004). Latent phase: The virus switches from a productive cytolytic phase to latent phase by 7 to 8 dpi, evidenced by the loss of MDV antigens in the lymphoid cells. As in other herpesviruses, there is no expression of viral protein or infectious virus production during MDV latency. Unlike the early cytolytic phase, the majority of infected lymphocytes in latent phase are T lymphoyctes with only a few B-cells (Calnek et al., 1984). These T-lymphocytes retain the virus, serving as a reservoir for the virus. Infected lymphocytes disseminate the virus, which spreads to various tissues including the kidneys, adrenal gland, liver, gonads, esophagus, and feather follicle epithelium (FFE) (Baigent and Davison, 2004). The mechanisms of latency are not very well understood due to a lack of suitable experimental systems that differentiate latently infected cells from transformed cells. Another interesting aspect of this phase is that the cell-mediated immune response, rather than the humoral response, influences the development and maintenance of latency (Calnek et al., 1981). Late cytolytic phase: Dissemination of the virus initiates a second phase of cytolytic infection, termed as late cytolytic phase. The latently infected cells transfer virus to the tissues, where it is reactivated causing secondary immunosuppression. This probably results in replication and release of infectious, enveloped cell free virus which results in cell death in affected tissues 6 (Calnek et al., 1970). According to the present understanding, the FFE is considered to be a privileged site for production and release of infectious, cell-free virus. The FFE envelopes and protects the virus in cytoplasmic inclusion bodies, and gradual dekeratinization in these epithelial cells results in reduced lysosomal activity which prevents degradation of virus (Baigent and Davison, 2004). Immunosuppression is another important feature of this phase. Both humoral and cell mediated immune responses are impaired, apparently due to cytolytic infection of thymus and bursa (Calnek, 1986). Transforming phase: Latently infected lymphocytes undergo neoplastic transformation to lymphoblastoid tumor cells. The predominant site of transformation is considered to be the spleen and most of the transformed cells are found to be T-lymphocytes. These changes are likely to occur three weeks post infection (Baigent and Davison, 2004; Saif and Fadly, 2008). The phenotype of these neoplastic cells, which resembles that of activated T helper-2 cells, is CD4+, TCR αβ+, CD30 hi, CD28 lo/- , MHC class I hi, MHC class II hi, IL-2α+, MDV pp38-, and MDV gB- (Burgess and Davison, 2002). It is still unclear whether latently infected cells become transformed following infiltration, or alternatively, cells that are transformed elsewhere infiltrate these sites. Earlier reports suggested that MD tumors were monoclonal in nature based on the pattern of integration of MDV genome in the host lymphocytes collected from multiple organs (Delecluse et al., 1993). However, recent studies suggest that tumors are polyclonal in nature, which containing both TCR2+ and TCR3+ cells, products of two different chicken TCR variable beta chain genes (Burgess and Davison, 2002). 7 ONCOPROTEIN OF MDV: MEQ The rapid onset of tumors in MDV-affected birds raised the possibility of direct involvement of virus encoded transforming genes that could drive oncogenesis. Several approaches including a comparative genomic approach to identify genes unique to onco-serotypes of MDV, characterization of genes that are expressed in transformed cells, and identification of viral regions that are altered in attenuated strains of MDV have been used to explore for transforming oncogenes. Based on these strategies, genes unique to MDV were found to be encoded in the inverted repeat long region (IRL), which included Meq, vIL-8, pp38 and vTR (Cui et al., 1990; Jones et al., 1992; Parcells et al., 2001). Among the several genes unique to MDV, Meq was discovered as a potential oncoprotein. The meq gene (MDV EcoQ fragment) located in the BamI2-BamQ2/EcoQ fragments within the repeat long regions was first identified based on the observation that the EcoQ transcripts are abundantly present in the MDV transformed Tcell lines such as RP1, RP4, and MSB1 as well as in tumors (Jones et al., 1992). Subsequently, expression of Meq protein was also demonstrated in these cell lines (Liu et al., 1998). Further, among genes unique to MDV, only Meq-null recombinants alone showed no oncogenicity (Liu and Kung, 2000; Xie et al., 1996), while knockout mutants of other viral genes (e.g., pp38, vIL-8) only resulted in attenuated virulence (Parcells et al., 2001; Reddy et al., 2002). In addition, analysis of MD tumors and transformed cells revealed consistent expression of Meq as a latent and oncogenic component of MDV (Kung et al., 2001). Structure of Meq: The oncoprotein of MDV, Meq belongs to the family of bZIP transcription factors. The full length Meq protein can be divided into two functional domains: an N-terminal 8 bZIP domain, rich in basic amino acids and a proline-rich C-terminal transactivation domain (Jones et al., 1992). A heptad repeat with five leucine residues that form the leucine zipper is present next to the N-terminal region. The basic region, along with the leucine zipper directly contacts the DNA. The leucine zipper mediates the homodimerization and heterodimerization of the protein monomers(Nair and Kung, 2004). The C-terminal proline-rich region contains the Pro-Leu-Asp-Leu-Ser (PLDLS) motif that binds the C terminal-binding protein, which regulates development, proliferation and apoptosis (Brown et al., 2006; Qian et al., 1996). Additional basic regions at the N-terminus include BR1 and BR2 (Liu et al., 1997) which are nuclear localization signals (Liu et al., 1997; Nair and Kung, 2004). Transactivation: The transactivation property of Meq mainly depends on the presence of both the proline-rich domain and the C-terminal 33 amino acids (Qian et al., 1996). However, using proline-rich repeats alone in a transactivation analysis, transcriptional repression was noted (Qian et al., 1996). It has been speculated that Meq can function as a transactivator and as a repressor based on the exposure of the proline-rich repeats (Nair and Kung, 2004). Dimerization partners and DNA binding: Meq has multiple dimerization partners (Kung et al., 2001; Qian et al., 1996) and is shown to form homodimers or heterodimer with many other bZIP proteins (Levy et al., 2003). The most stable dimer formed is a Meq/c-Jun heterodimer, such that if Meq and c-Jun are mixed in equimolar concentrations, it was found that nearly all of the molecules were Meq/c-Jun heterodimers (Qian et al., 1996). Meq and c-Jun are colocalized in the nucleus and co-precipitated in nuclear extracts (Levy et al., 2003). They have also been shown to be recruited at the AP-1 site in the promoters of chicken genes (e.g., 9 interleukin 2), as well as MDV genes (e.g., Meq) (Levy et al., 2003). Chimeric proteins, bZIPMeq/TA-c-jun and bZIP-c-jun/TA-Meq, have similar properties as Meq and c-Jun, suggesting that the bZIP and transactivation domains of these proteins can complement each other in the transformation process (Liu et al., 1999a). The basic region of the Meq bZIP domain is responsible for its DNA-binding specificity. The Meq/c-Jun heterodimer preferentially binds cyclic AMP (CRE)-and 12-O- tetradecanoylphorbol 13-acetate responsive element (TRE)-like sequences, called MERE (MEq Response Element) I, such as the one in the meq promoter (Qian et al., 1996). Thus, the Meq/cJun heterodimer has stronger activity on the meq promoter when compared to Meq/Meq and Jun/Jun homodimers. The sequence of MERE I is GAGTGATGAC(G)TCATC (TRE/CRE sequence underlined) and is similar to other transcription factor binding sites such as TPA response element (TRE)/Cyclic AMP Response Element(CRE) but the MERE II motif is not shared by other bZIP proteins. Meq/c-Jun heterodimers bind to sequences that have either TRE or CRE motifs (Levy et al., 2003). In contrast, Meq/Meq homodimers, preferentially bind a consensus sequence ACACACA, called MERE II (Qian et al., 1996). It was also found that Meq/Meq homodimers and not Meq/c-Jun heterodimers suppress transcription of pp38/pp14, a bidirectional promoter located in the MDV-Ori (Levy et al., 2003). Using a chromatin precipitation assay, Meq has been shown to bind MDV promoters such as meq, ICP4, gB, and gD, which contain a MERE I sequence and MDV-Ori, which contain a MERE II sequence (Levy et al., 2003). 10 Interaction of Meq with cellular factors: Apart from c-Jun, Meq has been shown to associate with a number of cellular proteins, ranging from cell cycle regulators such as p53 and RB to corepressors such as CtBP (C-terminal binding protein) and protein kinases such as CDK2. It was found that the binding between Meq and p53 involves the bZIP domain of Meq and C-terminal tetradimerization domain of p53 (Kung et al., 2001). Additionally, Meq interacts with c-Fos, JunB, CREB, ATF-1, ATF-2, and ATF-3. Meq can be found in the cytoplasm and colocalizes with CDK2 in the coiled bodies during S phase of the cell cycle. In addition, phosphorylation of Meq by CDK2 drastically reduced the DNA binding activity of Meq (Liu et al., 1999b), suggesting that nuclear localization of Meq is dependent on its phosphorylation by cellular kinases. Interestingly, Meq can also be phosphorylated by a number of cytoplasmic kinases, including PKA, PKC and MAPK, the exact significance of which is not yet thoroughly understood. Transforming properties of Meq: Overexpression of Meq in rodent fibroblast cell lines, such as Rat-2, led to serum-independent and anchorage-independent growth accompanied by shortened G1 phase indicating the transformation of these cells (Liu et al., 1997). Additionally, Meq-transfected cells were highly resistant to apoptosis induced by serum withdrawal, TNF-α, UV-irradiation, and C-2 ceramide (Liu et al., 1998). It was suggested that the anti-apoptotic activity of Meq might be due to increased anti-apoptotic protein Bcl-2 expression, and decreased in proapoptotic protein Bax expression (Liu et al., 1998). In addition to rat fibroblasts, similar transforming properties have also been shown in chicken fibroblasts. In a spontaneously immortalized chicken fibroblast cell line, DF-1, Meq induced growth of large, anchorage-independent colonies in soft agar, serum-independent survival and induction of morphological changes similar to those induced by retroviral oncogenes like Ski (Levy et al., 11 2005; Liu et al., 1997). The role of c-Jun in this transforming ability was also well demonstrated. Further, the transforming ability of Meq was noted to be similar to v-Jun in terms of cellular morphology, anti-apoptotic features in cells and up-regulation of genes such as JTAP-1, JAC, and HB-EGF, implicated in the v-Jun transformation process (Levy et al., 2005). Down-regulation of Meq using antisense RNA in MSB-1, a MDV-transformed lymphoma cell line, resulted in loss of colony formation ability, underlining the importance of Meq in oncogenesis (Xie et al., 1996). By deleting both copies of meq from the genome of a very virulent MDV strain, a mutant virus rMd5∆Meq was developed which did not induce any tumors in infected birds, providing further proof that Meq is indispensable for transformation of lymphocytes (Lupiani et al., 2004). Additionally, mutations in the meq gene that eliminated the interaction with CtBP also prevented oncogenesis (Brown et al., 2006). Using a chimeric meq gene, which contains the leucine zipper region of the yeast transcription factor GCN4, which can only homodimerize, it was demonstrated that homodimerization alone was insufficient for transformation of lymphocytes (Suchodolski et al., 2009). In the same year, showed that ability to form homodimers is an absolute requirement and the ability to bind cJun alone is not sufficient for development of lymphomas in T-cells using used mutant meq with inability to form homodimers (Brown et al., 2009). Subsequently, it was shown that both homo and heterodimerization of Meq are required for oncogenesis (Suchodolski et al., 2010). Taken together, these observations provide compelling evidence that Meq is the transforming oncogene of MDV. 12 Variant forms of Meq: There are reports of at least three variants of meq transcripts: long form of meq (L-meq), meq/vIL-8 and ∆meq. Among these, the L-meq gene is the longer form of Meq that was reported in CVI988-infected chicken embryo fibroblast (CEF) DNA (Lee et al., 2000b). This had a 178-bp, proline-rich insertion that was absent in CEFs infected with other MDV-1 strains including GA, Md5, RB1B or MDV-2 strains including SB infected CEFs. In co-expression experiments, it was found that L-Meq protein has the ability to decrease the transactivation of Meq (Chang et al., 2002). Another form is referred to as Meq-sp or Meq/vIL-8, which was identified in the MSB-1 cell line as a splice variant of Meq (Peng and Shirazi, 1996a). This protein retained the DNA binding domain of Meq and the receptor binding portion of vIL-8 but lacked C-terminal transcriptional regulatory domains of Meq and the amino-terminal secretory signal of vIL-8 (Peng and Shirazi, 1996a; Peng and Shirazi, 1996b). It has been shown that Meq/vIL-8 has the ability to bind DNA, and form homodimers with Meq or heterodimers with c-Jun, and it is speculated that Meq/vIL-8 could act as a negative regulator of Meq (Anobile et al., 2006; Parcells et al., 2001). Another splice variant of meq, termed Δmeq, has been described in lymphoblastoid cells lines and CEFs infected with Md5 strain of MDV (Okada et al., 2007). This study also found that Δmeq is identical in sequence to Meq within the first 99 amino acids at the N-terminus but differs in the C-terminus. They also found that this protein lacks the transactivation domain of Meq, has anti-apoptotic activity, dimerizes with Meq and L-Meq, and suppresses transcription by both Meq and L-Meq. 13 OTHER MDV UNIQUE GENES AND THEIR ROLE IN PATHOGENESIS MDV phosphoprotein 38 (pp38): The pp38 gene encodes for a 290 aa protein and is located in the BamHI-H fragment in the MDV genome. The protein is expressed in MDV lymphoblastoid cell lines as well as MD tumors (Cui et al., 1990). Although pp38 is expressed in these cell lines, its expression is restricted to a small percentage of cells. This protein is a complex of two related phosphoproteins, pp24 and pp38, that are expressed in the cytolytic phase. The direct role of pp38 in early cytolytic phase has been demonstrated using a pp38-null MDV recombinant virus, with decreased replication but still retaining the ability to induce tumors in chicken, albeit at a lower level (Reddy et al., 2002). Despite these observations, at least using RNA antisense oligonucleotides in vitro, it has been shown that pp38 might play a role in proliferation of lymphoblastoid cells (Xie et al., 1996). Viral IL-8 (vIL-8): vIL-8 is another MDV gene which is located in the BamHI-L fragment of MDV-1 genome and encodes for a 134 aa CXC type chemokine that has high homology to human and chicken cellular IL-8 (Liu et al., 1999a; Parcells et al., 2001). vIL-8 is expressed as a late gene and could play a role in lytic phase of MDV infection. However, it is not essential for viral replication, latency, tumor formation and viral transmission (Cui et al., 2004; Parcells et al., 2001). In terms of biological function, secreted vIL-8 serves to attract lymphocytes and macrophages and not neutrophils (Parcells et al., 2001), unlike its cellular counterpart IL-8 which is neutrophil 14 chemoattractant. It is speculated that vIL-8 may be involved in T-cell activation and thus play a role in immunomodulation, by interfering with cellular IL-8 signal pathways. Viral-encoded telomerase RNA (vTR): During analysis of the ends of the RB1B genome, a 443-bp long novel viral telomerase RNA gene (vTR) was discovered with 88% sequence identity with the chicken telomerase RNA subunit gene (Fragnet et al., 2003). Using a vTR-null mutant virus, it was shown that tumor incidence was decreased by 60% in chickens, although lytic replication was unaffected, suggesting a potential role for vTR in tumorigenesis (Trapp et al., 2006). It was shown that the rapid onset of tumor formation by vTR is dependent on functional telomerase activity, since delay in tumor development was noted when vTR-telomerase interaction was abrogated (Kaufer et al., 2010). Further, the ability to form tumors was completely abolished when the template sequence of vTR was mutated, raising the possibility of using mutant vTR as a novel vaccine candidate (Kaufer et al., 2011). Viral lipase (vLIP): The vLIP gene is present in all three serotypes of MDV and is located in the UL region. vLIP protein is 756 aa long, and has 26% sequence identity to the α/β hydrolase fold of pancreatic lipase (Kamil et al., 2005). Despite this sequence identity, vLIP lacks enzymatic function and is speculated to have a role in lipid bonding. Using a recombinant MDV virus lacking vLIP, it was found that tumor incidence was decreased but not totally prevented, suggesting a minor role for vLIP in tumorigenesis (Kamil et al., 2005). 15 GENETIC RESISTANCE TO MD: Even before the etiologic agent for MD was well characterized, it was noted that different families of chickens had variable mortality rates to MD (Biely et al., 1933). These differences were further confirmed and extended by differences in mortality ranging from 18% to 96% in tested lines (Cole, 1968). It was also shown that using selective breeding, resistant and susceptible chicken lines could be developed, which markedly altered mortality in these lines (Hutt and Cole, 1947). Further, the first study to explore the underlying basis of resistance showed an association between the inheritance of alleles at the B blood group and increased MD resistance (Hansen et al., 1967). Many later studies have confirmed that the B locus can have an enormous effect on the response to MDV infection (Bacon et al., 2001; Schat et al., 1981). Although these studies vary widely in host genetics, sex, age and environment, pathogen strain, dose and route of infection, the involvement of MHC or the B complex in chicken is widely agreed upon as an important determinant of genetic resistance. In chickens, there are three regions in the MHC complex that control cell surface antigens: B-F (class I), B-G (class II), and B-L (class IV). An advantage to this system is the expression of the B-G locus on erythrocytes that lends itself to convenient typing of blood groups. Certain B alleles are found to be associated with genetic resistance to MD. For example, chickens with B21 were more resistant than other B haplotypes, and even vaccinal immunity is differentially affected (Bacon and Witter, 1994). This is particularly evident when comparing inbred lines 6 and 7 and their sublines (Stone, 1975), which are homozygous for the same MHC (B2) haplotype but differ greatly in their resistance to a wide range of MDV strains (Pazderka et al., 1975). 16 Despite the strong contribution of some MHC related loci in genetic resistance, it is clear that other genes are also involved in overall MD resistance. This is evident in comparing chicken lines 6 and 7 and their sublines, which are MD resistant and susceptible, developed at East Lansing but share the same major histocompatibility complex (MHC) haplotype (Stone, 1975). Though these lines are homozygous for MHC haplotype they greatly vary in resistance and susceptibility to MDV (Pazderka et al., 1975) using a wide range of MDV strains. Several approaches have been used to determine the genetic mechanism underlying the resistance properties of these lines. Functional genomic analysis using microarrays revealed several candidate genes including the growth hormone gene (Liu et al., 2001a). Some of the explanations for the differences included differential viral replication. There was a significant increase in viral replication in line7during early infection (Lee et al., 1981). Difference in viral copies in MDV infected chicken lymphocytes in line 6 and line7 suggested that there could be a difference in mounting immune response after infection (Bumstead et al., 1997). However, similarity in the levels of MDV replication in fibroblast cultures excluded the idea of differential in viral replication in line 6 and line 7 (Longenecker and Gallatin, 1978). There have been numerous studies recently that have explored for candidate genes associated with MDV resistance and susceptibility (Heidari et al., 2010; Morgan et al., 2001; Sarson et al., 2008; Yu et al., 2011). One of the candidate resistance genes, growth hormone gene, has been shown to have numerous polymorphisms associated with MD tumors in chickens (Liu et al., 2001b). Using protein-protein interaction screens followed by linkage analysis, SCA2 was discovered as a candidate resistance gene (Liu et al., 2003). In spite of these advances, there are no 17 comprehensive studies to elucidate a reliable list of candidate genes that can be used for selective breeding. 18 CHAPTER 2. GENOME-WIDE REGULATORY NETWORK OF MEQ, THE ONCOPROTEIN OF MAREK’S DISEASE VIRUS REVEALS INTERACTION BETWEEN MULTIPLE CELLULAR PROCESSES INVOLVED IN ONCOGENESIS INTRODUCTION Marek’s disease (MD) is one of the most economically significant diseases affecting poultry which is caused by a highly oncogenic α-herpesvirus, Marek's disease virus (MDV). Currently, the main strategy to control MD is vaccination (Baigent et al., 2006; Witter, 1998). Although the presently used vaccines reduce the symptoms and incidence of tumor formation, they do not confer protection from MDV replication or prevent horizontal spread of infection. Also, despite the use of MD vaccines, field strains of MDV continue to evolve with increased virulence in vaccinated birds (Okazaki et al., 1970; Witter, 1997). The concern of MD is further enhanced by the unpredictable and spontaneous vaccine breaks that can result in devastating losses to poultry farms (Gimeno, 2008). Annual losses in the U.S. by MD due to carcass condemnation and reduced egg production exceed $2 billion (Morrow and Fehler, 2004) . The severity of MD may in fact be even larger, since the figure has not been revised to reflect inflation, new disease outbreaks or MDV-induced immunosuppression. This necessitates better understanding of MDV-host interactions, not only to elucidate the events in pathogenesis but also to develop strategies to combat infection. One of the major unanswered questions in the pathogenesis of MD is the mechanism underlying tumorigenesis (Nair and Kung, 2004). Among several viral genes, null mutants for 19 Meq alone showed no oncogenicity, while knock out mutants of other viral genes only resulted in attenuated virulence (Jones et al., 1992; Liu et al., 1997). Further, knockdown of Meq using siRNA resulted in reduced colony formation in MSB-1, a MDV transformed cell line (Xie et al., 1996). In addition, analysis of MDV tumors and transformed cells revealed consistent expression of Meq as a latent and oncogenic component of MDV (Kung et al., 2001; Lupiani et al., 2004). Meq belongs to the bZIP family of transcription factors. It contains a DNA binding domain with a basic leucine zipper, similar in structure to c-jun/c-fos bZIP proteins (Jones et al., 1992). Meq has been shown to homodimerize with itself or form heterodimers with other bZIP proteins. The most stable heterodimers were found to be with c-jun (Kung et al., 2001; Qian et al., 1996). Meq has been shown to bind both to the viral and chicken genomes and regulate gene expression (Nair and Kung, 2004; Ross, 1999). However, a global understanding of the role of Meq in regulating the host gene expression by elucidation of its binding sites is lacking. Our major objective is to gain a comprehensive understanding of host genes that are regulated by Meq. Identification of the binding sites of Meq and the corresponding genes would provide valuable information regarding the biologic pathways regulated by Meq. In addition to characterizing genes regulated by Meq, we also aim to analyze genes that are coregulated by Meq and c-Jun. Using a comprehensive, genome-wide analysis to identify specific DNA binding sites and global transcriptome analyses, we have generated a high confidence list of Meq-regulated genes, as well as a list of genes that are co-regulated by both Meq and c-Jun. Our results 20 indicate that Meq controls a transcriptional program critical for transformation through both positive and negative transcriptional regulation. The severity of MD may in fact be even larger, since the figure has not been revised to reflect inflation, new disease outbreaks or MDVinduced immunosuppression. This necessitates better understanding of MDV-host interactions, not only to elucidate the events in pathogenesis but also to develop strategies to combat infection. Also, despite the use of MD vaccines, field strains of MDV continue to evolve with increased virulence in vaccinated birds. 21 MATERIALS AND METHODS Cell Culture: DF-1, a chicken embryo fibroblast cell line and Meq-DF-1 cell line (DF-1 clone with stable transfection of Meq, Clone 5G (Levy et al., 2005)) were cultured in Lebowitz's L-15 and McCoy 5A media with 15% inactivated fetal bovine serum and 100U of penicillin per ml and maintained at 37°C. These cell lines were used for ChIP and microarray experiments. Chromatin Immunoprecipitation (ChIP): 107 Meq-DF-1 cells were cross-linked with 1% formaldehyde added directly to the culture medium, and incubated for 10 min at 37°C. Culture medium was removed completely after centrifugation, and cells were washed twice with ice cold PBS containing 1% protease inhibitor. Following this, ChIP-seq was performed using the chromatin immunoprecipitation assay kit protocol (Upstate Biotechnology, 17-295). Cells were be lysed and sonicated and then pre-cleared using Agarose/Salmon Sperm to avoid non-specific background. An efficient sonication is very essential for good enrichment and resolution in ChIP experiments. At an appropriate setting for sonication, the chromatin fragments are expected to have a size between 50 to 100bp. The supernatant fraction was collected from the pelleted agarose by brief centrifugation immunoprecipitated with anti-Meq polyclonal antibody (1:100) or anti-c-Jun antibody (1:200) and then incubated overnight at 4°C. The immune complex was pulled down using salmon sperm DNA Protein A agarose beads. Protein A agarose/immune complex was then pelleted by brief centrifugation. The samples were then washed and the immune complexes were eluted. Cross-linking was reversed in a portion of this eluted sample separating the protein from DNA. DNA was recovered by phenol/chloroform extraction and its 22 isolation was verified by PCR reaction analysis with known primers of the chicken genome. Replicates from each sample were subjected to massively parallel sequencing at the Michigan State University Research Technology Support Facility (MSU RTSF) using the Solexa/Illumina platform. Two lanes were single-read for each sample yielding 4-5 million reads per sample. Analysis of ChIP-Seq Data: Peak calling: ChIP-seq data was analyzed by using the peak calling software, QUEST(Valouev et al., 2008). Peaks with high-confidence were defined by the ChIPenrichment of ≥ threefold with an FDR < 0.01. Motif Analysis: For motif analysis, 500 bp of the center of each binding peak, called the peak-associated sequences, were extracted. MEME (Bailey et al., 2009) was then used for searching de novo motifs with default parameters to yield the consensus motifs in each dataset. Identification of DNA-Binding Sites: Bowtie was employed to map the validated sequences to the chicken genome. Bowtie is an open source mapping program which is fast and memory efficient alignment tool that aligns the short DNA sequences (reads) to large genomes (Langmead et al., 2009). Bowtie uses the Burrows-Wheeler index, which allows it to align more than 25 million reads per CPU hour with a memory foot print of 1.3 gigabytes. Candidate Meq-enriched DNA fragments, along with the nearest gene were identified. RNA extraction and microarray analysis: Total RNA was extracted using the Absolutely RNA Miniprep Kit (Stratagene) according to manufacturer’s instructions. The quality and the quantity of the RNA was verified using an Agilent Bioanalyser 2100 lab-n-a-chip instrument. 23 High quality RNA was labeled using Affymetrix One Cycle Target Labeling and Control Reagents (Cat# 900493). The Affymetrix chicken and pathogen GeneChips were used. This chip has probe sets for 32,773 chicken transcripts which includes all 17,179 chicken unigenes, Ensembl predicted genes, and reporter genes. In addition to this, it has 699 probe sets corresponding to 684 transcripts from 18 different chicken pathogens, and 107 probe sets for all predicted MDV genes. Hybridisation and scanning was performed by the MSU RTSF core facility. Pathway Analysis: Identification of biologically relevant networks and biological pathways Gene accession numbers were imported into the Ingenuity Pathway Analysis version 8.0 (IPA) software (Ingenuity Systems®, Mountain View, CA, USA) along with Chicken Affymetrix identifiers and corresponding expression values (p<0.05). The ‘Core Analysis’ function included in IPA was used to interpret data in the context of biological processes, pathways, and networks. Validation of ChIP-seq data by quantitative PCR: After obtaining DNA fragments by chromatin immunoprecipitation as described above, the relative enrichment was determined by quantitative PCR using an ABI 7500 (Applied Biosystems, Foster City, CA) and Power SYBR Green Master mix (Applied Biosystems) with the following parameters: 95°C 10min followed by 40 cycles of (95°C 15sec, 60°C 1min). The fluorescence was expressed as CT values. The data is 24 expressed as % input, calculated as 2^(CTinput- CTMeq antibody), where DNA before immunoprecipitation was used as input DNA. Validation of microarray data by quantitative PCR: Total RNA was extracted from DF-1, DF1Meq cells and spleen samples from infected and uninfected birds using Absolutely RNA Miniprep Kit (Stratagene) according to manufacturer’s instructions. First strand cDNA was synthesized by reverse transcribing 250ng of total RNA using Superscript III First strand synthesis Supermix for qRT-PCR (Invitrogen, Carlsbad, CA) according to the manufacturer’s instructions. The real-time quantitative PCR consisted of SYBR Green PCR Super Mix-(Applied Biosystems, Foster city, CA) and 300 nM of forward and reverse primers. The reactions werel be performed in the 7500 Real-time PCR system (Applied Biosystems) with the following settings: 50 ° C for 2 min, 95 C for 10 min, followed by 40 cycles of 95° C for 15 sec, 60 °C for 1min, and 95° C for 15 sec. At the end of amplification, a dissociation curve analysis was performed to confirm the presence of a single amplification product. Each sample was run in duplicate to obtain average CT values. For negative controls, No-RT controls were used as template in place of single-stranded cDNA in the real-time quantitative PCR. mRNA expression of each gene was normalized using the expression of house-keeping gene, β-actin, and compared with the data obtained from the control group according to the 2-ΔΔCT method. siRNA transfection and analysis: DF-1 and Meq-DF-1 cells (30-40% confluence) were transfected with nontargeting siRNA (NT1) or siRNA-targeting Meq (Custom synthesis, Dharmacon; 25 Lafayette, CO) using TransIT-TKO® Transfection Reagent (Mirus; Madison, WI) according to manufacturer's instructions. Cells were harvested 48h after transfection and used for RNA extraction and qPCR. Knockdown (routinely 80-90%) at mRNA level was confirmed by qPCR. The presented data were derived from three independent experiments. Cell Proliferation: Cell proliferation was measured by using a CellTiter 96® AQueous NonRadioactive Cell Proliferation Assay (Promega, USA) according to the manufacturer's instructions. Briefly, cells were plated at a density of 10,000 cells/well in 96-well microtiter plates and were incubated overnight for attachment. They were then switched to serum free medium and treated with pathway inhibitors for 24hrs. PD98059 and FR180204 (Sigma; St. Louis, MO) were used to inhibit MEK and ERK. They were used at 50 µM and 1 µM respectively, based on previously published reports (Ohori et al., 2005; Scaffidi et al., 2002) . At the end of treatment period, combined MTS [3-(4,5-dimethylthiazol-2yl)-5-(3-carboxymethoxyphenyl)-2(4-sulfophenyl)-2H-tetrazolium]-phenozine methosulfate solution (20 μl/well) was added. After incubation for 40 min h at 37°C, the absorbance was measured at 490 nm by using an enzymelinked immunosorbent assay plate reader. Data presented represent the average of four wells in one experiment which was repeated twice. Luciferase assays: Custom reporter plasmids containing motif 1 (AP-1 like) and motif 2 (MERE-II like) upstream of the luciferase coding region were ordered from Genecopoeia (Rockville, MD). DF-1Meq cells were plated in 96-well plates at a density of 5000 cells/well and transfected with 26 2.5 µg of empty vector or luciferase constructs with motif 1 or 2 respectively. 48 hours after transfection, the luciferase activity was measured using Secrete-Pair™ Dual Luminescence Assay Kit according to manufacturer’s instructions (Genecopoeia). Statistical Analysis: One way ANOVA followed by Fisher’s test, t-test and Chi-square analysis were used as necessary. A p-value of 0.05 was used as a standard for statistical significance. RESULTS ChIP-Seq analysis of Meq and c-Jun binding sites in the chicken genome: To identify all the binding sites occupied by Meq and/or c-Jun within the chicken genome, ChIP was performed using polyclonal antibodies directed against Meq, c-Jun (the preferred dimerization partner for Meq), or IgG (control) in Meq-DF-1 cells, followed by massively parallel sequencing of the enriched DNA fragments. There were 23 and 21.5 million reads enriched for Meq, whereas there were 19.6 and 18.5 million reads for c-Jun from replicates 1 and 2, respectively; our control using non-specific IgG antibody had 0.75 and 0.69 million reads for the two replicates, indicating a relatively low non-specific background. Only those reads that uniquely mapped to the chicken genome were used for further analysis. Peak-calling was performed using the statistical program Quantitative Enrichment of Sequence Tags (QuEST) (Valouev et al., 2008) under high stringency conditions at 10% FDR. The degree of overlap was computed using a Python script that compared peaks in replicates and the ‘merged peaks’ were calculated by ‘union’ method. Based on this script, we noted that there was high overlap (85% and 80% for Meq and c-Jun, respectively) between the two biological replicates indicating good 27 reproducibility. For further analysis of the binding sites, ‘merged peaks’ were used to generate a total of 15,576 peaks for Meq and 8,545 peaks for c-Jun. The location of the binding sites relative to the transcriptional start site (TSS) can provide insights into how a transcription factor regulates transcription. To examine the frequency distribution of Meq and c-Jun binding sites relative to TSS, peaks located ±20 Kb relative to the TSS were organized into 1 Kb bins. Genome wide distance correlation analysis revealed that about 60% and 55% of Meq and c-Jun binding sites, respectively, were located within ±2 Kb of TSS (Fig. 2-1). To further analyze the distribution of binding sites within ±2 Kb of TSS, peaks were organized into 100 bp bins. We noted that there was a relatively greater abundance of Meq binding sites (41% of all binding sites) in the region between -300 bp to -800 bp (Fig. 2-2). Similarly, about 44% of all c-Jun binding sites were between -400 bp to -1000 bp relative to the TSS (Fig. 2-3 and 2-4). 28 Figure 2-1. Distribution of Meq binding sites within ±20kb. The distance between Meq binding sites from the TSS within ±20 kb was computed and the results were binned in 1000 bp intervals 29 Figure 2-2. Distribution of Meq binding sites within ±2kb. The distance between Meq binding sites from the TSS within ±2 kb was computed and the results were binned in 100 bp intervals. 30 Figure 2-3. Distribution of c-Jun binding sites within ±20kb. The distance between c-Jun binding sites from the TSS within ±20 kb was computed and the results were binned in 1000 bp intervals. The blue line represents polynomial line of best fit. 31 Figure 2-4. Distribution of c-Jun binding sites within ±2kb. The distance between c-Jun binding sites from the TSS within ±2 kb was computed and the results were binned in 100 bp intervals. The blue line represents polynomial line of best fit. 32 Based on the enrichment of Meq and c-Jun binding sites in promoters, we next identified genes that had Meq and/or c-Jun peaks within 2 Kb upstream of their TSS. Based on this criterion, there were a total of 1,490 and 778 genes with binding sites near the TSS for Meq and c-Jun, respectively. In our analyses, we also identified 204 genes that have both Meq and c-Jun binding sites. To verify if Meq and c-Jun binding sites were concentrated in a particular portion of the genome, the chicken genome was partitioned into the following segments: promoter (-2 Kb to TSS), 5’ or 3’ UTR, introns, exons, and intergenic regions. The peaks of Meq and c-Jun were significantly overrepresented in the promoter region (63 and 69%, respectively) and significantly underrepresented in the intergenic region (18 and 13%, respectively) compared with IgG-bound regions within the chicken genome (10% and 55% in promoter and intergenic regions, respectively) (Fig. 1E; p<0.0001 by χ2 test) (Fig. 2-5). 33 IgG-bound regions Meq-bound regions c-Jun bound regions % genomic partition 80 60 40 20 0 Promoter Exons Introns UTRs Intergenic Figure 2-5. Distribution of of IgG, Meq and c-Jun binding sites across the chicken genome. The chicken genome was partitioned into five discrete regions and the relative distribution binding sites was analyzed. 34 To support the ChIP-Seq findings, we performed quantitative PCR analysis from two independently generated ChIP experiments using primers for 15 high confidence binding sites from top biological networks and 2 negative controls regions. As shown in (Fig. 2-6), all the 15 Meq bound sites were significantly enriched compared to the IgG control. In addition, Meq binding was not different from IgG in the negative control regions, indicating the specificity of our assay in detecting Meq binding sites. 35 Fold enrichment at IP site 2500 2000 Meq Jun 1500 1000 500 150 100 50 0 PARP4 RPAP3 CXCL12 HAS2 Figure 2-6. ChIP-qPCR validation of a subset of Meq and c-Jun binding sites from ChIP-Seq data. 36 To identify consensus binding motifs in the chicken genome, the enriched Meq and cJun binding sites were analyzed using MEME (Bailey et al., 2006). The top two candidate motifs, which account for 89% of the Meq binding sites are shown in Web LOGO format (Fig. 2-7). Motif 1 had a core sequence which was 97% similar to the previously described AP-1 consensus binding site (Levy et al., 2003) as designated by JASPAR and TRANSFAC databases (PortalesCasamar et al., 2010; Wingender et al., 1996). The second motif in Meq binding sites, Motif 2 had a core sequence of CACACAGC, which is similar to a putative motif referred to as MERE-II (Levy et al., 2003; Qian et al., 1996). Analysis of c-Jun binding sites revealed AP-1 like motif was present in 72% of the binding sites. Next we analyzed the enriched peaks common to both Meq and c-Jun binding sites, which revealed that the top motif in these common peaks was identical to Motif 1 described above. This corroborates previous evidence that Meq can readily dimerize with c-Jun and this Meq-Jun heterodimer exhibit stronger binding to an AP-1 like site (Levy et al., 2003; Qian et al., 1996). Interestingly, Motif 2 (MERE-II like motif) was not present in the peaks common to both Meq and c-Jun binding sites, which supports a previous observation that Meq homodimers bind to MERE-II motif (Qian et al., 1996). To test the relevance of these de novo discovered motifs, Meq-DF-1 cells, which constitutively expressed Meq, were transfected with control vector or luciferase constructs with repeats of Motif 1 or Motif 2. As expected, there was significant increase in luciferase activity compared to control with Motif 1, suggesting transactivation. However, there was decrease in luciferase activity compared to control vector in the Motif 2 luciferase construct suggesting transcriptional repression (Fig 2-8). 37 Figure 2-7. Characterization of Meq DNA binding motifs. Top two significantly overrepresented motifs generated by MEME analysis of Meq binding sites are represented. The height of each letter is proportionate to its frequency. 38 Figure 2-8. Luciferase reporter assay depicting activity of constructs with either empty vector (control), repeats of motif 1 or motif 2. The activity was normalized to activity in transfection control (Renilla luciferase). Symbols represent statistically significant differences at p<0.05. 39 Microarray analysis of transcriptional regulation by Meq: We performed microarray analyses on DF-1 and Meq-DF-1 cell lines using Affymetrix Chicken Genome Array, which is said to provide comprehensive coverage of over 28,000 chicken genes. Only genes that showed consistent changes at a statistical significance of P < 0.001 were used for further analysis. There were 236 up-regulated genes and 549 down-regulated genes in Meq-DF-1 compared to DF-1. To further decipher the relationship between DNA binding by Meq and transcriptional regulation, were explored the ChIP-Seq data in combination with gene expression microarray data. About 70% of the differentially expressed genes had Meq binding sites in the promoter (generated from the list mentioned above), indicating a significant and direct role of Meq binding in regulating gene transcription in Meq-DF-1 cells (Fig. 2-9). Next, we compared genes with binding sites for both Meq and c-Jun in the promoter with the differentially expressed genes. Thirty-five percent of all up regulated genes had binding sites of both Meq and c-Jun but only 10% of all down regulated genes had Meq and c-Jun binding sites in the promoter (Fig. 210). 40 Figure 2-9. Integrated analysis of expression profiling and genome occupancy by Meq. Overlap between genes with Meq binding sites identified by ChIP-Seq experiments and differentially expressed genes based on microarray analysis in DF-1Meq cells. For interpretation of the references to color in this and all other figures, the reader is referred to the electronic version of this dissertation. 41 Figure 2-10. Integrated analysis of expression profiling and genome occupancy of sites common to Meq and c-Jun. Overlap between genes with binding sites common to both Meq and c-Jun and differential gene expression. For interpretation of the references to color in this and all other figures, the reader is referred to the electronic version of this dissertation. 42 To further examine the functional categories of genes and potential biologic networks regulated by Meq, we analyzed the list of differentially expressed genes with Meq binding sites, using Ingenuity Pathway Analysis. The top five cellular pathways were apoptosis, cell cycle, regulation of transcription, cell proliferation, and cell migration (Fig. 2-11). This analysis also revealed several important cellular pathways that are regulated by Meq. For example, 21 of the 95 genes (22%) in apoptosis signaling pathway and 31 of the 206 genes in ERK/MAPK pathway (15%) were differentially expressed in Meq-DF-1 cells compared to DF-1 cells (Fig 2-12). We validated the microarray findings using RNA from independent experiments from DF-1 and Meq-DF-1 cells. Eighteen genes from top biological networks were used for validation. The fold change using qPCR was highly correlated to the findings from microarray data (r2=0.91; p<0.001) (Fig. 2-13 and 14). 43 Figure 2-11. Pathway analysis of Meq-regulated genes. Functional classification based on canonical pathway analysis of differentially expressed genes with significant Meq binding sites using Ingenuity Pathway Analysis. 44 Figure 2-12. Cellular pathway analysis of Meq-regulated genes. The major cellular functions are depicted. 45 Figure 2-13. Validation of gene expression analysis. Correlation plot comparing differential gene expression using microarray analysis to qPCR data on a subset of 20 genes in DF-1Meq cell line 46 Figure 2-14. qPCR validation of microarray results. Validation by RT-qPCR of the microarray-based differentially expressed genes between DF-1 Meq cells and DF-1 cells. Beta-actin was used as internal control. *p<0.05 compared to DF-1 cells. 47 We also validated the expression of Meq and c-Jun in this model. There was a significant expression of Meq iin Meq-DF-1 cells compared to DF-1 cells (Fig 2-15). To further analyze the impact of Meq on gene expression, we employed siRNA targeting Meq. In Meq-DF-1 cells, Meqspecific siRNA resulted in at least 75% reduction in mRNA expression (Fig 2-16). Quantitative RT-PCR for 4 genes each from our up- and down regulated gene list was performed in cell lines transfected with non-targeting siRNA (NT) or siMeq. These results showed that upon Meq silencing, there was significant attenuation in genes that were up-regulated and the decrease in expression of down-regulated genes was blocked (Fig. 2-17 and 2-18). There was no detectable change in expression upon siRNA treatment in DF-1 control cells. 48 Figure 2-15. Levels of Meq and c-Jun mRNA is significantly different in DF-1 Meq cells compared to DF-1 cells. *p<0.05 compared to DF-1 cells. 49 Figure 2-16. Optimization of siRNA concentration and verifying efficiency of knockdown in DF1Meq cells. *p<0.05 compared to NT 50 Figure 2-17. Gene expression changes after siRNA mediated knockdown of Meq. A subset of up regulated genes. *P<0.05 compared to DF-1NT; #p<0.05 compared to DF-1Meq NT. For interpretation of the references to color in this and all other figures, the reader is referred to the electronic version of this dissertation. 51 Figure 2-18. Gene expression changes after siRNA mediated knockdown of Meq. A subset of down regulated genes. *P<0.05 compared to DF-1NT; #p<0.05 compared to DF-1Meq NT. For interpretation of the references to color in this and all other figures, the reader is referred to the electronic version of this dissertation. 52 The distribution of Meq binding sites of genes exhibiting up- or down-regulation was performed. Notably, in up-regulated genes (transcriptional activation), the Meq peaks were located closer to the TSS Integrated analysis of Meq binding sites and transcriptional regulation: To determine if location of Meq binding has any impact on the differential transcriptional regulation, we analyzed of Meq binding site locations within genes that were transcriptionally regulated. The distribution of Meq binding sites of genes exhibiting up- or down-regulation was performed. Notably, in up-regulated genes (transcriptional activation), the Meq peaks were located closer to the TSS and concentrated around a narrow region (Fig. 2-19). On the other hand, Meq peaks in down regulated genes were located much farther than the TSS compared to activated genes and distributed more randomly (Fig. 2-20). Indeed, about 60% of transcriptionally activated genes contained Meq peaks within 2 Kb around TSS whereas the same proportion of transcriptionally repressed genes contained peaks within 15 Kb. 53 Figure 2-19. Differential location of Meq-binding sites in up- regulated genes. Combination of ChIP-Seq data and gene expression analysis reveals a distinct localization of Meq peaks in up regulated genes. Frequency distribution of Meq binding sites relative to TSS, represented in 1 kb intervals for a distance covering 20 kb in up regulated genes. 54 Figure 2-20. Differential location of Meq-binding sites in down regulated genes. Combination of ChIP-Seq data and gene expression analysis reveals a distinct localization of Meq peaks in down regulated genes. Frequency distribution of Meq binding sites relative to TSS, represented in 1 kb intervals for a distance covering 20 kb in down regulated genes 55 In addition, we investigated whether genes that were transcriptionally regulated by Meq, and also had significant Meq binding exhibited enriched occupancy of Meq binding in any particular sub region of the chicken genome. For both up-regulated and down-regulated genes, the percentage of genes containing Meq peaks in each of the sub regions as described above were calculated. Up regulated genes showed a strong enrichment for Meq binding in the promoter region (about 70%) whereas the down-regulated genes had only modest enrichment in the same region (18%). In contrast, the region with strongest enrichment for down-regulated genes was the intergenic region (62%) (Fig. 2-21). 56 Upregulated genes Downregulated genes Figure 2-21. Relative occupancy of Meq in the five genomic regions in up regulated genes and in down regulated genes (p < 0.001; compared to relative distributions in control regions, χ2 test). For interpretation of the references to color in this and all other figures, the reader is referred to the electronic version of this dissertation. 57 To investigate the role of differential binding to motifs and transcriptional regulation, we analyzed all Meq binding sites from up-regulated and down-regulated genes for the presence of these motifs. We found that Motif 1 was overrepresented in up-regulated genes while down-regulated genes had a higher incidence of Motif 2 (Fig. 2-22). Indeed, Motif 1 was present in 85.6% of up-regulated genes whereas Motif 2 was present in 84.1% of downregulated genes. We investigated the possibility of contribution of other TFs to the ability of Meq to modulate transcription by analyzing overrepresented sequence motifs within the binding sites. The details of these TF motifs in up- and down-regulated genes along with relative abundance in differentially expressed genes are shown in (Fig. 2-22). We found binding motifs for other bZIP TFs like CREB1 and NFE2L2 are present in up-regulated genes while motifs for TFs like Myc, BRCA1 and ZEB1 were mostly found in down-regulated genes. 58 Up-regulated genes MOTIF 1 NFE2L2 CREB1 NR2F1 0 20 40 60 % of peaks 80 100 80 100 Down-regulated genes MOTIF 2 MYC BRCA1 ZEB1 0 20 40 60 % of peaks Figure 2-22. Overrepresented motifs in up- and down regulated genes. Comparison of motifs along with sequence logos in up- and down regulated genes along with percentage of genes with occurrence of each of the motifs. 59 We examined if there were differences in cellular processes and pathways based on the presence of a certain motif. All genes with Motif 1 or Motif 2 were analyzed for overrepresented cellular processes and canonical pathways. Some of the top cellular processes were common to genes with both the motifs and included Cellular growth, Cell cycle, Cell Development, and Cellular apoptosis. Similarly, ErbB signaling pathway and VEGF signaling pathway were common to both motifs. However, there were signaling pathways unique to each motif- MAPK pathway, CDK5 pathway and NF-kB pathway were unique to Motif 1 while Death receptor signaling, Jak-STAT pathway and telomerase signaling pathways were unique to Motif 2 ( Fig. 2-23). 60 Motif 1 Motif 2 Figure 2-23. Comparative enrichment of cellular pathways in genes with Motif 1.and Motif 2. Functional classification based on canonical pathway analysis of differentially expressed genes with significant Meq binding sites using Ingenuity Pathway Analysis. 61 Functional validation of cellular pathways regulated by Meq: Role of MAPK inhibitors on cellular proliferation Assessment of cell proliferation is a reliable index of transforming ability of Meq. Under serum free condition for 24 hrs, Meq-DF-1 cells had a significantly higher cell number (25% increase, n=6; p<0.05) compared to the parent cell line, DF-1. Further, to assess the biological relevance of pathway analysis, we used specific inhibitors for some of the targets in top biological networks. Since MAPK pathway was the top canonical pathways overrepresented in Meq transcriptome, we used inhibitors to two targets in this pathway. PD 98059, a selective and potent inhibitor of mitogen-activated protein kinase kinase (MAPKK/MEK) blocked the increase in cell number noted with Meq-DF-1 cells (Fig. 2-24). Similarly, inhibition of another downstream molecule, extracellular signal-regulated kinase (ERK) using a selective inhibitor, FR 180204 also blocked the proliferative effect of Meq (Fig. 2-24). This strongly supports the role of ERK/MAPK signaling in mediating the mitogenic effect of Meq. 62 Figure 2-24. Functional validation of the role of ERK/MAPK signaling in Meq-mediated transformation using cell proliferation assay. MEK inhibitor and ERK inhibitor blocked the proliferative effect of Meq. *p<0.05 compared to DF-1 control; #p<0.05 compared to DF-1Meq control. 63 DISCUSSION Understanding the mechanisms involved in oncogenesis is essential to develop strategies to prevent and combat the development of Marek’s disease, an economically important disease in poultry. Based on the genomic structure of MDV, it is similar to alphaherpesviruses (Osterrieder and Vautherot, 2004), however, its biological properties are similar to tumor-producing gamma-herpesviruses (Baigent and Davison, 2004). A comprehensive analysis of the target genes induced by its oncoprotein, Meq is an important first step in unraveling the mechanism of MDV oncogenesis. There is limited information on comprehensive genome-wide studies examining role of Meq oncoprotein in transcriptional regulation of host genes. By integrating genome-wide chromatin occupancy data with a comprehensive data set of Meq-induced gene expression changes, we have identified a high confidence list of Meq target genes by correlating in vivo occupancy and transcriptional activity in this study. Consequently, these results have the potential to provide fundamental knowledge into how viral oncoproteins involved in cellular transformation regulate host gene regulatory networks. ChIP-Seq analysis revealed several insights into transcriptional regulation by Meq. We generated a list of 1,490 genes that had a Meq binding site and ~40% of them had differential gene expression based on microarray analysis. Not all genes that with a binding site were transcriptionally regulated, suggesting that binding site information alone is an insufficient predictor of transcriptional regulation. However, among the differentially expressed genes, about 75% of them had evidence of Meq binding. In addition, Meq binding to the chicken genome was non-random and it preferentially bound to promoters in the host genome. Also, a 64 majority of Meq peaks were located within 1 Kb upstream of the TSS. This provides strong evidence that the main biochemical function of Meq is to regulate transcription. Analysis of Meq peak locations within genes that were transcriptionally regulated revealed distinct functional consequences of DNA binding by Meq. Notably, in up-regulated genes (transcriptional activation), the Meq peaks were located closer to the TSS and a majority of them were located in the promoters. This suggests that Meq acts as a transcriptional activator through short range effects. On the other hand, Meq peaks in down-regulated genes were located much farther relative to TSS, more dispersed throughout the genome and mostly located in the intergenic regions. Transcriptional repression by Meq could involve long range effects and might involve other co-repressor proteins. Taken together, these findings indicate that the distinct localization of Meq-binding in the chicken genome influences the nature of transcriptional activity. Recent studies on genome-wide binding site characterization of other TFs like Myb and PU.1 revealed similar dependence of the location of transcription factor binding and its influence on differential regulation of transcription (Ridinger-Saison et al., 2012; Zhao et al., 2011). c-Jun has been shown to be an important partner for Meq in engaging cellular pathways leading to transformation (Levy et al., 2005). However, the specific involvement of this partnership in genome-wide transcriptional regulation in the host has not been previously reported. We have generated a high confidence list of genes that are co-regulated by Meq. Close location of binding sites of Meq and c-Jun suggest cooperativity between these two factors in modulating transcription. 65 Two previously described DNA binding motifs (Levy et al., 2003; Qian et al., 1996) were identified in the Meq-enriched peak regions using position-weighted matrix analysis and motif discovery tools. Interestingly, the top motif in the genes that had peaks common to Meq and cJun as well as top motif among all up-regulated genes was Motif 1 (Fig 6). Previous studies indicate that Meq/c-Jun form heterodimers and these heterodimers bind to AP-1 like site, resulting in transcriptional activation, at least in the context of a few viral as well as host gene promoters (Levy et al., 2005; Levy et al., 2003; Qian et al., 1996). Although we have not directly tested the possibility of heterodimerization, the discovery of AP-1 like motif in genes common to both Meq and c-Jun, as well as in up-regulated genes, suggests a similar possibility. Meq has the ability to form homodimers with itself. These homodimers bind to DNA sequence referred to as MERE-II, and cause transcriptional repression (Qian et al., 1996). Motif 2 from our analysis had the core DNA binding sequence described as MERE-II and this was the top motif in all down regulated genes (Fig 6). Also, this motif was not discovered in any c-Jun peaks. Taken together, these findings support the idea of Meq homodimers binding to this sequence and causing transcriptional repression. Both heterodimers and homodimers of Meq have been shown to be necessary for transformation (Suchodolski et al., 2010). Analysis of cellular pathways in our study suggested both these motifs (that correspond to hetero- and homodimers) are involved in regulation of genes involved in key processes like cell proliferation, cell cycle and apoptosis. However, signaling pathways specific to genes with each motif suggest distinct binding motif-dependent signature in gene regulatory networks. In other TFs like ectopic viral integration site 1 (EVI1) and PAX6, two DNA binding domains are associated with distinct motifs and regulate different 66 sets of target genes involved in cellular transformation and development respectively (BardChapeau et al., 2012; Verbruggen et al., 2010). It would be interesting to further assess if the heterodimers and homodimers of Meq regulate distinct subsets of genes using modified constructs of Meq as utilized in some previous studies (Brown et al., 2009; Suchodolski et al., 2009; Suchodolski et al., 2010) The enrichment of Meq target genes for several neoplastic processes suggests that it probably acts as a multifunctional transcription factor that modulates multiple processes, including cell proliferation, apoptosis/survival, and cell migration. Some of the previous studies have described a few of the target genes modulated by Meq involved in apoptosis and cell proliferation (Levy et al., 2005). However, there is little information on the regulatory mechanisms through which Meq affects these features. Our analysis shows that Meq transcriptionally regulates many genes that are part of several signaling pathways include the ERK/MAPK, Jak-STAT, and ErbB pathways that are critical for oncogenesis (De Luca et al., 2012; Kiu and Nicholson, 2012; Platanias, 2003; Sansone and Bromberg, 2012; Yarden and Pines, 2012). Activation of ERK or MAPK by MEK is an essential event in the mitogenic growth factor signal transduction (Zheng and Guan, 1993). In our study, we found that Meq up-regulates key players in the pathway like Ras, MEK1, and MEK2, which initiate downstream signaling leading to cell proliferation. Other tyrosine kinases like Src were also up-regulated by Meq. We also found that activating signals upstream of MAPKs, like ErbB, were upregulated. We have also functionally validated the relevance of MAPK signaling in the ability of Meq to transform cells 67 by using specific inhibitors to MEK1/2 and ERK1/2. These inhibitors abrogated the proliferative effect of Meq, indicating that key mitogenic signals in MAPK pathway are involved in mediating the effects of Meq on cell proliferation. Although our study has only examined the transcriptional regulation of genes in this pathway, there is evidence from other studies that, even at protein level, ERK/MAPK pathway is significantly involved in transformation by Meq (Buza and Burgess, 2007). The activation of Ras/ERK signals is essential for cellular transformation by LMP1, a major transforming viral oncoprotein of Epstein-Barr virus, a gammaherpesvirus (Dawson et al., 2008; Roberts and Cooper, 1998). Further, LMP1/2 transforms B-lymphocytes by providing pro-survival signals through constitutive phosphorylation of ERK/MAPK and Ras (Anderson and Longnecker, 2008; Portis and Longnecker, 2004). In addition, several studies implicate the essential role of ERK/MAPK signaling in maintenance of latency (Xie et al., 2008), angiogenesis (Ye et al., 2007) and cell survival (Cai et al., 2010; Lambert et al., 2007) by Kaposi's sarcoma-associated herpesvirus. An important corollary to this observation would be to explore the possibility of convergent cellular signaling networks by which oncogenic herpesviruses regulate cellular processes and lead to transformation. One of the major modes of regulating the MAPK pathway involves restriction of the magnitude and duration of activation (Chen and Thorner, 2007). Because MAPKs are fully active only when phosphorylated, one effective mechanism of MAPK inactivation is dephosphorylation by protein phosphatases (Keyse, 2000). Interestingly, we found that Meq down-regulated the expression of dual specificity phosphatases like DUSP4 which inactivates ERK1/2 family of MAPKs (Camps et al., 2000; Owens and Keyse, 2007). Dual specificity 68 phosphatases also known as MAPK phosphatases (MKPs) act as a negative regulatory feedback in modulating MAPK signaling. It has also been proposed that the integration of multiple MAPK pathways occurs at the level of MKPs (Owens and Keyse, 2007). The role of MKPs in cancer is further underlined by the fact that decreased expression of MKPs is associated with poor prognosis in various malignancies in humans (Haagenson and Wu, 2010; Wu et al., 2011). In addition, down-regulation of serine/threonine phosphatases including PP1 and PP2, which target proteins of like MEK, Raf and Akt (Theodosiou and Ashworth, 2002; Westermarck et al., 2001), was also noted. This family of phosphatases acts as tumor suppressor genes that target the ERK/MAPK pathway (Mumby, 2007). Several viral oncoproteins have been shown to induce transformation by down-regulation of PP-2 expression or by inhibiting its activity (Westermarck et al., 2001). PP-2 has also been shown to be involved in inhibition of Cyclin E/CDK2 complex (Mochida and Hunt, 2012), which is a key mediator in cell cycle progression. Taken together, to maintain a proliferative drive, Meq activates oncogenic signaling cascades by transcriptionally activating major kinases in the ERK/MAPK pathway and simultaneously repressing phosphatases. This ability of Meq appears to be conserved mechanism of transformation by viral oncoproteins. The ability of Meq to up-regulate the expression of a mitogenic signal in a pathway and down-regulate the inhibitory signal appears to be a common strategy across multiple cellular pathways. For example, we found that a similar phenomenon occurs in the context of Jak-STAT signaling pathway as well. We noted that STAT3, an oncogenic signal was up-regulated while negative regulators of the pathway like SHP-1, SOCS2 and PIAS were down-regulated. STAT3 has been reported to oncogenic in a number of malignancies in human patients (Burke et al., 69 2001; Hodge et al., 2005; Sehgal, 2000). In contrast, SHP-1, SOCS and PIAS have been shown to act as tumor suppressors (Barclay et al., 2009; Coppola et al., 2009; Ehrmann et al., 2008; Wu et al., 2003a; Wu et al., 2003b). SOCS2 is an indispensable negative regulator of growth hormone actions (Greenhalgh and Alexander, 2004) and SHP-1 negatively modulates growth hormone (GH) mediated signal transduction (Ram and Waxman, 1997). It is interesting to note that GH is a putative MD resistance gene (Liu et al., 2001b) and transformation by MDV involves negative regulation of GH downstream signaling mediators. Another theme that emerges from pathway analysis is the transcriptional regulation of key players that are involved in the regulation of multiple cellular pathways. Meq was noted to transcriptionally activate several members of the 14-3-3 protein family, which modulate the function of a diverse array of binding partners and hence function as key regulatory components of many vital cellular processes (Freeman and Morrison, 2011; Wilker and Yaffe, 2004). Cell cycle deregulation caused by changes in 14-3-3 expression has been implicated in cancer formation (Freeman and Morrison, 2011). 14-3-3 proteins function at several key points in G1/S- and G2/M-transition by binding to regulatory proteins and modulating their function (Hermeking and Benzinger, 2006). In addition, these proteins also play a major role in regulating MAPK pathway by contributing to activation of Raf, leading to cell proliferation (Freed et al., 1994; Irie et al., 1994). Meq also up-regulated Grb2, an adaptor protein, which acts as a critical downstream intermediary in several oncogenic signaling pathways. Grb2 has also been shown to link ErbB receptor to the activation of Ras and its downstream kinases, ERK1/2 (Downward, 1994; Tari and Lopez-Berestein, 2001). Overexpression of Grb2 has been noted in several 70 malignancies including breast cancer and bladder cancer (Daly et al., 1994; Giubellino et al., 2008; Watanabe et al., 2000). Dysregulation of the cellular apoptotic pathway due to a combination of activated antiapoptotic signals and inhibition of pro-apoptotic signals is one of the hallmarks of cancer (Hanahan and Weinberg, 2000). We noted that Meq manipulates several players in the apoptotic pathway to shift cells towards an anti-apoptotic phenotype. We found increased expression in anti-apoptotic signals like Bcl-2 and Bcl-XL with a subsequent decrease in proapoptotic genes like Bid. Increased expression of anti-apoptotic genes like Bcl-2 and Bcl-XL by MDV has been well described previously (Levy et al., 2005; Liu et al., 1998; Ohashi et al., 1999). There was a decrease in expression of caspases 3 and 6; while an increase in expression of cIAP-1, an inhibitor of caspsases was noted. In MDV-transformed lymphoblastoid cell lines, decrease in IAP transcript levels was noted with induction of apoptosis (Takagi et al., 2006). There was decrease in expression of AIFM1, a proapoptotic factor that induces apoptosis in a caspase-independent manner. Interestingly, we noted a relative downregulation of FOXO1A, an important transcription factor that regulates gene expression in response to TNF-mediated apoptotic signals (Alikhani et al., 2005). Taken together, Meq controls several critical genes involved in apoptosis to promote transformation. Since there are no available T-cell models to study transformation, the present studies were conducted using a chicken fibroblast cell line stably transfected with Meq, as a model. It has been previously shown that Meq is able to transform rat and chicken fibroblasts (Levy et al., 2005; Liu and Kung, 2000; Liu et al., 1998). However, the information gained using this model 71 would likely provide a useful framework for understanding transformation by MDV, including Tcell transformation in MDV infected birds. Taken together, our study provides insights on the mechanistic basis of how Meq, and possibly other viral proteins that function as oncogenes, transform cells and cause malignancies. In addition, our study provides a comprehensive analysis of how Meq interacts with host bZIP proteins like c-Jun to regulate transcription. This data set elucidates how Meq regulates cellular ERK/MAPK and Jak-STAT pathways to induce transformation. In addition, this study forms the basis for selection of candidate genes that might be involved in genetic resistance to Marek’s disease. 72 CHAPTER 3. TRANSCRIPTIONAL PROFILING OF MEQ-DEPENDENT GENES IN MAREK’S DISEASERESISTANT AND SUSCEPTIBLE INBRED CHICKEN LINES INTRODUCTION One of the main strategies of MD control is vaccination. While vaccination reduces the symptoms and incidence of tumor formation, it does not prevent MDV replication or spread of infection. Additionally, field strains continue to evolve, with increased virulence in vaccinated birds. Losses with MD are further enhanced by the unpredictable and spontaneous outbreaks that occur even in vaccinated flocks (Gimeno, 2008; Witter, 1997). Given the problems with vaccination, there is a need to pursue other strategies to combat MD. Identifying chickens with enhanced genetic resistance to MD is an attractive alternative to vaccination. Using genomic tools to identify genetic markers associated with MD resistance genes would be highly beneficial to control of MD and can serve to augment vaccinal control. A better understanding of the mechanism of genetic resistance to MDV would therefore contribute toward strategies to control the disease. Currently, there are two lines of White Leghorn chickens that have been developed to study the mechanisms underlying resistance and susceptibility to MD (Bumstead and Kaufman, 2004; Stone, 1975): ADOL lines 6 (MD resistant) and 7 (MD susceptible). These are highly inbred (over 99%) lines that share the same MHC haplotype, a genetic locus that has been shown to have a large effect on MD incidence. Therefore, these lines enable us to focus on the remaining genes that cumulatively account for the majority of MD genetic resistance. 73 Selection for MD resistance is based on identifying the genes that are associated with MD resistance and is based on two broad strategies: genome-wide genetic screens which include QTL scans and functional genomic screens (e.g., microarray analysis of differentially expressed genes). However, it is extremely difficult to identify positional candidate genes for MD resistance using QTL scan approaches alone due to poor mapping resolution. Gene expression profiling is a powerful alternative tool to identify potential genes that are involved in resistance. Transcriptome analysis using spleens from resistant and susceptible lines identified several candidate genes related to resistance and susceptibility, most of which were related to the immune response (Yu et al., 2011). There are differences in the proportion of CD4 and CD8 T cells in MD resistant and susceptible lines (Burgess et al., 2001), and higher expression of immunoglobulin genes in MD resistant lines when compared to susceptible lines (Sarson et al., 2008). However, these studies have not specifically examined the influence of Meq and its contribution to genetic resistance. Given the important role of Meq in MDV pathogenesis, our aim was to explore the role of Meq in genetic resistance. To our knowledge, this is the first comprehensive study which explores the role of Meq in MD resistance and susceptibility. Using global transcriptome analysis, we have demonstrated for the first time that Meq is involved in MD resistance, and have identified a number of genes and pathways that are consistently associated with either MD resistance or susceptibility. 74 MATERIALS AND METHODS: Cells and culture conditions: Chicken embryo fibroblasts (CEF) from day 10 embryos were prepared from line 6 and line 7, and secondary cultures was plated at a density of 107 cells per 100-mm dish. Cells were cultured in Leibowitz’s L-15 and McCoy 5A media with 15% heat inactivated fetal bovine serum, 100U of penicillin per ml and maintained at 37°C. Each plate was infected with 104 pfu of MDV derived from either Md5B40BAC1, our BAC clone that contains the Md5 strain MDV genome and generates virulent MDV, which would be referred to as Md5 or Md5B40BAC1 that lacks both copies of Meq, developed through recombineering (Niikura et al., 2011), which would be referred to as Md5∆Meq. Total RNA was isolated at 24, 48, and 72 h from line 6 and line 7 CEFs infected with Md5-BAC or Md5∆Meq-BAC as well as uninfected CEFs as controls. RNA extraction, microarray procedure and data analysis: Total RNA for microarray hybridization was extracted using Absolutely RNA Miniprep Kit (Stratagene, Clara, CA) according to manufacturer’s instructions. RNA concentration was assessed using Nanodrop ND-1000 spectrophotometer (Thermo Scientific, Wilmington, DE) and the RNA integrity was determined using Agilent 2100 Bioanalyzer with a RNA 6000 Nano/pico Assay (Agilent Technologies, Palo Alto, CA) Affymetrix GeneChip Chicken Genome Arrays (Affymetrix, Santa Clara, CA) were used for microarray hybridization and data collection. This chip has probe sets for 32,773 chicken transcripts which includes all 17,179 chicken unigenes, Ensembl predicted genes, and reporter genes. In addition to this it has 699 probe sets 75 corresponding to 684 transcripts from 18 different chicken pathogens and 107 probe sets for all predicted MDV genes. RNA preparation, hybridization and scanning were performed following protocols recommended by Affymetrix (Santa Clara, CA) by the MSU RTSF core facility. CEL files were generated, containing the summary intensities for each probe. The expression value of each probe set was then- normalized and calibrated using open source 'R'statistical software (version 2.11.1) through Bioconductor project. The raw data files were loaded using affy package and the probe intensities and normalization were done using Limma (Linear models for microarray data) (Wettenhall et al., 2006). Quantitative RT-PCR analysis: Total RNA was extracted using RNeasy Mini Kit (Qiagen, Valencia, CA).Complementary DNA was synthesized using a Superscript III First-Strand Synthesis System (Invitrogen, Carlsbad, CA). Gene expression levels were measured using SYBR Green PCR Master Mix (Invitrogen) using a ABI 7500 Real-Time PCR System (Applied Biosystems, Foster City, CA). β-Actin gene was used for normalization. Each target gene and β-actin gene was analyzed in triplicate. The PCR conditions were as follows: 95 C for 10 min, followed by 40 cycles of 95 C for 15 sec, 60 C for 1min, and 95 C for 15sec. At the end of amplification, a melting curve analysis was done by heating the PCR products to 65–95 C and held for 15 sec at increments of 0.2 C, and the fluorescence was detected to confirm the presence of a single amplification product. For negative controls, No-RT was used as template in place of singlestranded cDNA in the real-time quantitative PCR. The data analysis was performed with the comparative ∆∆Ct relative quantification method. 76 Gene Ontology (GO) category The differentially expressed genes between the uninfected Line 6 and Line 7 were intersected with those that were differentially expressed between Line6 and Line7 infected with Md5B40BAC1 with and without Meq. The differentially expressed genes common between Line6 and Line7 MDV were eliminated as they had no information on the Meq regulated genes. The remaining sets of differentially expressed genes were compared between Line6MDV with and without Meq and Line7 MDV with and without Meq. These genes were further analyzed for inclusion in GO categories and pathways in order to examine their biological processes. The differentially expressed genes from two categories include genes involved in MD-resistance and susceptibility that are regulated by Meq. Categorization of genes based on significant biological properties was done using Gene Ontology Project (http://www.geneontology.org/). The genes were grouped categories based on common biological properties. Pathway Analysis The pathway analysis was carried out using Ingenuity Pathway Analysis software (IPA, Ingenuity systems Redwood City CA). The annotated genes were grouped into networks, functions and canonical pathways. The data containing gene IDs and expression fold change were uploaded into the software. The gene IDs were mapped into its corresponding gene object in the Ingenuity Knowledge Base (IKB). The network of focus genes were generated based on the information contained in the IKB into a global molecular network. Functional analysis generates biological functions that are significant to the genes in the data uploaded. The Canonical pathways are generated based on the differential expressed genes from the 77 data. All the analysis in the IPA were carried out based on Fisher’s exact test to determine the association between the differentially regulated genes and the network, biological function and the canonical pathway. Statistical Analysis: Two way ANOVA followed by Fisher’s test and t-test were used as indicated. P-value of 0.05 was used as a cutoff for statistical significance. RESULTS Identification of Meq-dependent genes related to genetic resistance and susceptibility: To determine the role of Meq in MD genetic resistance, we performed global transcriptome analysis in cultured chicken embryo fibroblasts (CEFs) from lines 6 and 7, two different chicken lines that differ greatly in MD incidence (Bumstead and Kaufman, 2004; Stone, 1975). The resulting information would help us determine if genes in Meq-regulated pathways are influenced by the genetic resistance status of the host. CEFs from both chicken lines were infected with MDV regenerated from either Md5B40BAC1 (Md5), a BAC clone that contains the entire virulent Md5 strain genome, or Md5B40BAC1 (Md5ΔMeq) that lacks both copies of Meq through recombineering (Niikura et al., 2011). We chose three time points: 24, 48 and 96 hrs to study the gene expression changes induced during critical phases of virus infection. We hypothesized that genes that are uniquely differentially expressed in Md5-infected groups and not Md5ΔMeq-infected indicate the genes that are directly or indirectly dependent on Meq expression. 78 We made pair-wise comparisons to generate a list of genes that are unique to lines 6 and 7, which were further classified into genes dependent on Meq and those not dependent on Meq as shown in Fig. 3-1. The details on differentially expressed genes at each time point in Md5-infected and Md5ΔMeq-infected groups and in each line are provided in Table 1. GO (Gene Ontology) categorization for pathway analysis of lines 6 and line 7 were performed using a ‘union’ of differentially expressed genes in all three time points. The number of Meqdependent genes that are involved in MD resistance and susceptibility are shown in Fig. 3-2 and 3-3, respectively. 79 Figure 3-1. Schematic for analysis of experimental groups. The differentially expressed genes (DEGs) between Md5-infected and Md5ΔMeq-infected groups compared to untreated control CEFs were obtained. Then, DEGs present in Md5-infected group and not in Md5ΔMeq-infected group were designated as Meq-dependent DEGs. These were further divided into DEGs specific to lines 6 and 7 and this set was used for GO categorization and IPA Pathway analysis. 80 Figure 3-2. Venn diagram of comparison between two virus-infected groups in and line 6 and line7. Representation of overlap between the groups- Each circle depicts the number of differentially expressed genes compared to uninfected controls. The pink portion in circle 1 in each diagram denotes Meq dependent genes. For interpretation of the references to color in this and all other figures, the reader is referred to the electronic version of this dissertation. 81 To further interpret if genes in Meq-regulated pathways are influenced by the genetic resistance status of the host. We combined information about Meq bound and regulated genes (from Subramaniam et al., paper1) with the list of differently expressed genes from line 6 and 7 at a P value of 0.01. Interestingly, about 25% of genes from line 6 and 57% of genes from line 7 were overlapping with Meq bound and regulated genes (Fig. 3-4 and 3-5). 82 Figure 3-3. Representation of overlap between Meq-dependent genes involved in MD resistance and genes with Meq ChIP-Seq peaks and transcriptionally regulated by Meq. For interpretation of the references to color in this and all other figures, the reader is referred to the electronic version of this dissertation. 83 Figure 3-4. Representation of overlap between Meq-dependent genes involved in MD susceptibility and genes with Meq ChIP-Seq peaks and transcriptionally regulated by Meq. For interpretation of the references to color in this and all other figures, the reader is referred to the electronic version of this dissertation. 84 GO categorization of Meq-dependent gene list The differentially expressed genes (DEGs) of Md5-infected and Md5ΔMeq-infected groups in each of lines 6 and 7 were classified into different functional categories according to GO for biological process. Each of the putative genes responsible for resistance and susceptibility were assigned to Molecular Function categories as designated by the GO database. Based on functional annotation clustering using highest classification stringency, there were 53 and 44 clusters respectively in DEGs related to MD resistance and susceptibility, respectively. Further, the enrichment cutoff was increased to >1.0, resulting in 21 and 23 clusters, respectively in DEGs related to MD resistance and susceptibility. The primary GO categories of significantly differentially expressed Meq-dependent genes involved in MD resistance (Fig. 3-6) were negative regulation of cell proliferation, granscription regulator activity, inflammatory cell apoptosis, immune response, and transcription factor activity whereas differentially expressed Meq-dependent genes involved in MD susceptibility (Fig. 3-7) were positive regulation of B cell receptor signaling, catalytic activity, regulation of lymphocyte differentiation, positive regulation of cellular biosynthetic process, positive regulation of cell proliferation, and positive regulation of gene expression. The categories that were common to both line 6 and line 7 Meq-dependent genes were transcription activator activity and transporter activity. 85 Figure 3-5. Gene GO categorization of molecular functions in significantly expressed genes that are Meq-dependent and involved in resistance to MD. P-value < 0.05 and FDR <0.05 were used as thresholds. 86 Figure 3-6. Gene Go categorization of cellular processes in significantly expressed genes that are Meq-dependent and involved in MD susceptibility. P-value < 0.05 and FDR <0.05 were used as thresholds. 87 Analysis of cellular pathways regulated by Meq in lines 6 and 7 To further examine the biological pathways that are altered during the host response to MDV infection in the presence of Meq, the DEGs involved in MD resistance and susceptibility were analyzed using Ingenuity Pathway Analysis (IPA). The DEGs involved in MD resistance that were Meq-dependent were significantly associated with 21 canonical pathways (Fig. 3-8); P < 0.05. Some of the top pathways included apoptosis, death receptor signaling pathway, and Myc-mediated apoptosis which plays an important role for controlled cell death. Genes involved in cell death were upregulated and genes involved in cell maintenance were down-regulated in this category. The pathways involved in DEGs involved in MD resistance that were not dependent on Meq included DNA replication pathway, tight junction, and VEGF signaling pathway. It is interesting to note that most of Meq-dependent pathways were unique and not expressed in the group of DEGs not dependent on Meq. Similar pathway analyses were performed on the DEGs involved in MD susceptibility that were Meq-dependent (18 significant canonical pathways; Fig. 3-9). Some of the top pathways in DEGs involved in MD susceptibility that were Meq-dependent group included cell cycle regulation, MAPK Signaling pathway, and Jak-STAT pathway. The pathways involved in DEGs involved in MD resistance that were not dependent on Meq included VEGF signaling pathway, DNA replication pathway, Hedgehog signaling pathway, mismatch repair, and Insulin signaling pathway. Of these analyses, there were three pathways that were common between the two groups. 88 Validation of microarray results by real-time quantitative PCR The differential gene expression changes detected by microarrays were validated by quantitative PCR for a subset of genes, selected from top biological networks (Fig. 3-10). RNA was extracted from samples that underwent the same experimental protocol as described for microarray analysis. The fold change represents normalization with house keeping control- beta actin and further normalization to uninfected controls. The fold change using qPCR was significantly correlated to the findings from microarray data (r2=0.67; p<0.05) (Fig. 3-11). 89 Figure 3-7. qPCR validation of microarray results. Validation by RT-qPCR of the microarray-based differentially expressed genes between Line 6 and Line 7 CEF’s. Betaactin was used as internal control. *p<0.05 compared to uninfected. 90 Figure 3-8. qPCR validation of microarray results. Validation by RT-qPCR of the microarray-based differentially expressed genes between Line6 and Line7 CEF’s. Beta-actin was used as internal control. *p<0.05 compared to uninfected. 91 Figure 3-9 . Validation of gene expression analysis. Correlation plot comparing differential gene expression using microarray analysis to qPCR data. 92 Proliferation Assay on Meq-dependent line 6 and line 7 CEFs Proliferation of Meq-dependent line 7 CEFs was significantly higher (p<0.05) compared to uninfected controls as well as Meqdependent line 6 CEFs (Fig. 3-12). Further, this effect was blocked in line 7 CEFs when infected with Md5 lacking Meq. No significant changes were noted in line 6 CEFs that were infected either with Md5 or Md5 lacking Meq compared to uninfected controls. 93 Figure 3-10. Functional validation of the role of Meq in resistance and susceptibility using cell proliferation assay. *P<0.05 compared to control. 94 DISCUSSION In the recent years, unpredictable and spontaneous vaccine breaks resulting in devastating losses to poultry farms (Witter, 1997), has further necessitated the need to explore alternate strategies for MD prevention. Selection for increased genetic resistance to MD is a control strategy that can augment MD vaccine protection. Over the years, several attempts have been made to identify candidate genes that determine genetic resistance to MD. Based on a variety of genetic and genomic strategies, these studies have shown various factors underlying the mechanism of resistance and susceptibility (Bacon et al., 2001; Bumstead and Kaufman, 2004; Cheng et al., 2008; Liu et al., 2001b; McElroy et al., 2005; Vallejo et al., 1998; Yu et al., 2011). It is imperative to further understand the underlying mechanisms of MD pathogenesis and further examine how this impacts genetic resistance. Being a complex disease, variability in a single gene cannot explain the basis for genetic resistance by itself. Although some previous studies have examined differential gene expression patterns after exposure to MDV in resistant and susceptible lines of chicken, there are no studies that have explored the role of Meq in determining genetic basis for resistance. Among several viral genes, null mutants for Meq alone showed no oncogenicity while knock out mutants of other viral genes only resulted in attenuated virulence (Jones et al., 1992; Liu et al., 1997). Given the importance of Meq, we for the first time have attempted to provide insights into the molecular mechanisms of MD resistance in the presence of Meq in response to MD infection. Through selective breeding and identification of phenotypic variation with respect to MD incidence, genetically resistant and susceptible lines of chicken have been developed (Bumstead and Kaufman, 2004). One of the major contributors to resistance is variable MHC 95 haplotypes, as evidenced by differential MHC haplotypes responsible for phenotypic variation in lines N and P (ref). However, there are numerous other factors that are non-MHC dependent which play an important role in genetic resistance to MD. Lines 6 and 7 share the same MHC haplotype but markedly vary in their susceptibilities to MDV (Longenecker et al., 1977). Hence we used this model to unravel non-MHC related basis for variable susceptibility to MDV. Moreover, our study adds an essential aspect, that has not previous been explored by using MDV constructs with and without Meq, its principal oncogene. Further comparisons of results from our previous findings on genome-wide regulatory network of Meq (unpublished data) should provide additional confidence in the declared list of genes influencing resistance and susceptibility. This helps us to determine if genes in Meqregulated pathways are influenced by the genetic resistance status of the host. We found that more than 21% of genes from Line 6 and 35% of genes from line 7 were overlapping with genes that had binding sites for Meq and were transcriptionally regulated by Meq. We hypothesized that the Meq-dependent differentially expressed genes would be involved in the downstream molecular pathways that might play an important for maintaining MD resistance. In order to identify the types of specific molecular functions of these genes, the Meq-dependent differentially expressed genes were annotated using the GO. Each of the genes from line 6 and line 7 were assigned to Molecular Function and Biological Process categories as designated by the GO database. The molecular functions and the biological process were significantly different between MD-resistant and susceptible lines. The resistant lines were enriched for positive regulation of cell death whereas the susceptible cell lines were enriched for regulation of cell proliferation. Further analyzing the cellular pathways involved, we found 96 that apoptosis signaling and death receptor pathways were among the significantly represented pathways. Specifically, we found an up-regulation of caspases 3, 6 and 8. Caspase 3 is an executioner caspase that is activated by both extrinsic and mitochondrial intrinsic pathways of apoptosis. Caspase 3 is activated by caspase 8 and activated caspase 3 can in turn activate other caspases like 6 and 7 which eventually leads to cell death (Estaquier et al., 2012). We also noted a down-regulation of inhibitors of apoptosis like BIRC2 and antiapoptotic proteins like Bcl-2 and Bcl-XL. Interestingly, we have shown in a previous study that most of these genes have Meq binding sites and are transcriptionally regulated by Meq (Subramaniam, unpublished). The up-regulation of these caspases and down-regulation of anti-apoptotic factors could be one of the determinants of MD resistance in line 6. We found that mitogenic signals like MAPK signaling and regulation of cell cycle involving cyclin D were overrepresented in the line 7 transcriptome. We have previously shown that one of the major cellular pathways that Meq targets to induce transformation is the MAPK signaling pathway. Specifically, we have shown that Meq up-regulates mitogenic signals like MEK1, MEK2 and Ras, which drive the cells towards proliferation. Also, Meq down-regulates inhibitory signals like phosphatases that limit the activation of MAPK signaling. In the present study, we found that at least some of these genes are involved in differential MDV resistance/susceptibility. In the resistant line 6, there was a down-regulation of MAP2K2 (MEK2) in Md5-infected group compared with or Md5ΔMeqinfected group. This highlights the role of Meq in modulating MAPK signaling and how downregulation of mitogenic signals may be involved in genetic resistance to MD. In contrast, we found an up-regulation of Ras, another mitogenic signal in line 7 infected with Md5 compared to Md5ΔMeq-infected group, raising the possibility that up-regulation of mitogenic 97 signals underlies genetic susceptibility to MD. These results suggest that the genes related to cell death and proliferation is one of the major determinants of genetic variability in resistance/susceptibility to MD. In one of our previous studies using RNA sequencing analysis, we have shown that apoptotic signaling is significantly overrepresented in spleens of birds infected with MDV (Maceachern et al., 2011). Our group has previously demonstrated that Jak-STAT is an important cellular pathway involved in genetic basis of MD resistance (Maceachern et al., 2011; Perumbakkam et al., 2013). Similarly, we also noted that genes in Jak-STAT pathway are transcriptionally regulated in susceptible line 7 and not in the resistant line 6. Activation of Jak-STAT pathway results in nuclear translocation of activated STAT dimer resulting in transcription of genes involved in cell survival and proliferation (Boudny and Kovarik, 2002). In addition to MAPK pathway, this could be another mitogenic signaling pathway that is involved in transformation by Meq. We also found down-regulation of IRG1, a putative proapoptotic factor which was recently described as a candidate susceptibility gene for MD (Smith et al., 2011) in line 7 but no expression in line 6. The fact that we noted this transcriptional response in line 7 infected with Md5 and not Md5ΔMeq-infected group raises an important and novel corollary that Meq has a role in regulating IRG1 expression. Further, we also noted transcriptional regulation of other genes (STAT1, MyD88, IFN-γ, TNF-α) proposed in the biological interaction network analysis of IRG1 in the previous study. In addition to corroborating the results form that study, this further underscores the role of Meq in modulating apoptotic signaling as a determinant of MD susceptibility. 98 In conclusion, we have made significant insights on the different sets of genes and pathways that interact to modulate MD resistance/susceptibility. Taken together, our findings adds to the current understanding of the mechanism behind Meq induced MD resistance and susceptibility. In addition, this study forms the basis for selection of candidate genes that might be involved in genetic resistance to Marek’s disease. 99 CHAPTER 4. FUTURE DIRECTIONS The results of my present study indicate that Meq controls a transcriptional program critical for transformation through both positive and negative transcriptional regulation. However, the mechanism by which Meq regulates these processes is not clearly understood. I hypothesized that modifications in the chromatin pattern could be one of the contributing mechanisms by which Meq differentially regulates transcription. As discussed below, I have some preliminary data to support this hypothesis. Chromatin modifications i.e., modifications in the DNA and the histone proteins that bind DNA, have been shown to regulate transcriptional regulation and gene expression. Also, the change in the chromatin state has implications in several important cellular processes like malignant transformation, embryonic development and stem cell biology, to name a few (Kouzarides, 2007). Differences in chromatin states or characteristic histone modifications clearly impact the functional genomic consequences and determine the outcome of transcriptional regulation. Methylation of certain histones is significantly associated with specific and often opposing effects on gene transcription. For example, trimethylation of histone H3 lysine 4 (H3K4me3) is associated with transcriptional activation of nearby genes while trimethylation of histone H3 lysine 27 (H3K27me3) is a characteristic feature of transcriptional repressed genes. Chromatin immunoprecipitation of enriched DNA fragments using antibodies to H3K4me3 and H3K27me3 in comparison to IgG control was used to examine for differences in histone modifications in a subset of differentially regulated genes by Meq. As shown in Fig 4-1, subsets of up-regulated and down-regulated genes were explored for differences in enrichment 100 for H3K4me3 and H3K27me3. Interestingly, there was a significant enrichment of H3K4me3 in the promoter region of genes that were transcriptionally up-regulated, compared to IgG while there was no difference in enrichment of H3K27me3. In contrast, repressive chromatin marks as noted by enrichment of H3K27me3 was significant in a subset of down-regulated genes. Taken together, there were differential changes in chromatin modifications, in transcriptionally active and repressed genes that were regulated by Meq. 101 Figure 4-1. Histone modifications in promoters of Meq-regulated genes. Antibodies to H3K4me3 and H3K27me3 were used to generate ChIP-enriched DNA fragments which were probed with promoter-specific primers. The gray line denotes background binding. * p<0.05 compared to IgG control. An important next step would be to examine the global changes in chromatin state in the genes that have Meq binding sites and are transcriptionally regulated by Meq. By employing ChIP-Seq technique, global analysis of histone modifications- methylation and acetylation of specific histones can be performed. There have been a few studies that have examined variable chromatin states following MDV infection in chicken tissues like thymus and 102 spleen (Luo et al., 2012; Mitra et al., 2012). However, there are no studies that have directly examined a role for Meq in mediating these modifications. The results from this experiment would enhance our understanding of the mechanism by which Meq would regulate transcription. However, it would only be a correlative study associating Meq-dependent transcription and histone modification. I propose the following set of experiments to explore a mechanistic basis for the direct involvement of Meq in regulating histone modifications, as explained below: First, it would be imperative to examine the differential expression of histone modifying enzymes due to Meq. I had performed genome-wide transcript profiling in a Meq-transformed cell line, Meq-DF-1, as explained in Chapter 2. Examining the differentially expressed genes for enrichment of pathways involvement in chromatin modification revealed that the following genes were transcriptionally regulated by Meq: EHMT-1, HDAC-1, HDAC-7, SMARCE1 and KAT2A. EHMT-1 or Euchromatic histone-lysine N-methyltransferase 1 is a histone methyltransferase involved in transcriptional repression by methylating Lys-9 position of histone H3 (Ogawa et al., 2002). The main biochemical role of HDACs or Histone deacetylases is to remove acetyl groups from histone tails so that DNA condensing is increased, thereby prevent transcription. There are four different classes of HDACs based on protein structure and tissue distribution. HDACs are involved in multiple cellular processes like cell-cycle regulation, cell proliferation, and in the development of cancer(Sengupta and Seto, 2004). SMARCE1 refers to SWI/SNF related, matrix associated, actin dependent regulator of chromatin, subfamily e, 103 member1 which is a component of the ATP-dependent chromatin remodeling complex SWI/SNF involved in transcriptional activation and repression of select genes by chromatin remodeling (Wang, 2003). This complex typically functions to remodel chromatin to allow transcription factors access to DNA binding, thereby enabling transcriptional activation. KAT2A refers to K(lysine) acetyltransferase 2A, also termed as GCN5, is a histone acetyltransferase i.e., functions to add acetyl group to lysine residues on histones, which results in transcriptional activation (Lee and Workman, 2007). It is clear that Meq regulates expression of many classes of histone modifying enzymes. The specific role of each enzyme in contributing to Meq-mediated transcription can be studies by using siRNA targeting each of the above mentioned enzymes and exploring the changes in differentially expressed genes. Another experimental strategy to investigate if chromatin modifications are necessary in cellular transformation by Meq would be to use inhibitors of this process. HDAC inhibitors are a well characterized group of compounds that prevent deacetylation. Further, these compounds are one of the promising candidates for anti-cancer therapy (Khan and La Thangue, 2012). The present studies were conducted using a chicken fibroblast cell line stably transfected with Meq, as a model since there are no available T-cell models to study transformation. It has been previously shown that Meq is able to transform rat and chicken fibroblasts (Levy et al., 2005; Liu and Kung, 2000; Liu et al., 1998). Although the information gained using this model would likely provide a useful framework for understanding transformation by MDV, investigating similar changes in transformed T-cells in MDV infected birds would be a logical next step. 104 In spite of several advances in our understanding of MD pathogenesis, it is still not clear how the virus enters latency and from latency, how it induces transformation. Another intriguing aspect of MDV pathogenesis is that 3-4 dpi, 90% of MDV infected lymphocytes are Bcells, which enter cytolytic phase; however, from 3-4 weeks post infection, there is marked expansion and neoplastic transformation of CD4+, TCRαβ+ cells which progressive form lymphoid tumors. What role does Meq play these diametrically opposite effects of MDV? Does Meq regulate induce differential transcriptional programs to mediate cytolytic effect in B-cells while inducing malignant transformation in CD4+, TCRαβ+ cells? One experimental strategy would be to express GFP-tagged Meq in a recombinant MDV virus and infect chicken. Harvesting infected lymphocytes during early infection (3-4 dpi) and at later phases of latency and from tumors and probing for GFP-positive cells would yield lymphocytes that express Meq. Further, flow cytometry-based sorting these lymphocytes into B-cells and T-cells using well characterized markers will yield subsets of lymphocytes that express Meq. Genome-wide analysis of Meq-modulated transcriptome and ChIP-Seq analysis in these subsets would yield valuable information on the role of Meq in mediating differential effects in these lymphocyte subsets. It would be interesting to investigate if similar cellular pathways (ERK/MAPK pathway, Jak-STAT pathway) as elucidated in my study are modulated by Meq in these lymphocyte subsets. Some of the specific questions I would like to explore are: Is MEK1/2 necessary in mediating cellular transformation by Meq. To mechanistically test this idea, I would propose the following experiments: • In the Meq-DF-1 transformed cell line, use of siRNA directed towards MEK1/2 would give an idea of the involvement of MEK. Specifically, examination of cell 105 proliferation, cell adhesion, cell migration, growth in soft agar in cells with reduced levels of MEK1/2 would elucidate the role of MEK1/2 in transformation by Meq. This would demonstrate the role of MEK1/2 in cellular transformation. • To test the in vivo significance of MEK1/2, a lentiviral vector can be used to knock down levels of MEK1/2. Further, challenging these birds with a virulent strain of MDV and examining for tumor formation would be performed. Further, B- and T-lymphocyte proportions in circulation at several time points can be examined. This would show if MEK1/2 is involved in transformation of T-cells in vivo. • As a complementary approach, siRNA vectors targeting MEK1/2 can be employed in lymphoblastoid cell lines. These siRNA-treated cells can be injected into susceptible mice (SCID mice or nude mice) and after solid tumor is formed, the xenografts can be collected and examined for several changes: mRNA and protein levels of downstream targets, effect of cell growth, angiogenesis and metastasis can also be examined. This would show that MEK1/2 is involved in angiogenesis and metastasis by Meq. Using all these approaches, it would be possible to determine if specific signaling proteins are directly involved in transformation by Meq. 106 BIBLIOGRAPHY 107 BIBLIOGRAPHY Alikhani, M., Alikhani, Z., Graves, D.T., 2005. FOXO1 functions as a master switch that regulates gene expression necessary for tumor necrosis factor-induced fibroblast apoptosis. J Biol Chem 280(13), 12096-12102. Anderson, L.J., Longnecker, R., 2008. EBV LMP2A provides a surrogate pre-B cell receptor signal through constitutive activation of the ERK/MAPK pathway. J Gen Virol 89(Pt 7), 15631568. Anobile, J.M., Arumugaswami, V., Downs, D., Czymmek, K., Parcells, M., Schmidt, C.J., 2006. Nuclear localization and dynamic properties of the Marek's disease virus oncogene products Meq and Meq/vIL8. J Virol 80(3), 1160-1166. Bacon, L.D., Hunt, H.D., Cheng, H.H., 2001. Genetic resistance to Marek's disease. Curr Top Microbiol Immunol 255, 121-141. Bacon, L.D., Witter, R.L., 1994. B haplotype influence on the relative efficacy of Marek's disease vaccines in commercial chickens. Poult Sci 73(4), 481-487. Baigent, S.J., Davison, F., 2004. Marek's disease virus: biology and lifecycle. In: Davison, F., Nair, V. (Eds.), Marek's Disease: An Evolving Problem. Elsevier-Academic Press, San-Diego, pp. 62-76. Baigent, S.J., Ross, L.J., Davison, T.F., 1996. A flow cytometric method for identifying Marek's disease virus pp38 expression in lymphocyte subpopulations. Avian Pathol 25(2), 255267. Baigent, S.J., Smith, L.P., Nair, V.K., Currie, R.J., 2006. Vaccinal control of Marek's disease: current challenges, and future strategies to maximize protection. Vet Immunol Immunopathol 112(1-2), 78-86. Bailey, T.L., Boden, M., Buske, F.A., Frith, M., Grant, C.E., Clementi, L., Ren, J., Li, W.W., Noble, W.S., 2009. MEME SUITE: tools for motif discovery and searching. Nucleic Acids Res 37(Web Server issue), W202-208. Bailey, T.L., Williams, N., Misleh, C., Li, W.W., 2006. MEME: discovering and analyzing DNA and protein sequence motifs. Nucleic Acids Res 34(Web Server issue), W369-373. Barclay, J.L., Anderson, S.T., Waters, M.J., Curlewis, J.D., 2009. SOCS3 as a tumor suppressor in breast cancer cells, and its regulation by PRL. Int J Cancer 124(8), 1756-1766. 108 Bard-Chapeau, E.A., Jeyakani, J., Kok, C.H., Muller, J., Chua, B.Q., Gunaratne, J., Batagov, A., Jenjaroenpun, P., Kuznetsov, V.A., Wei, C.L., D'Andrea, R.J., Bourque, G., Jenkins, N.A., Copeland, N.G., 2012. Ecotopic viral integration site 1 (EVI1) regulates multiple cellular processes important for cancer and is a synergistic partner for FOS protein in invasive tumors. Proceedings of the National Academy of Sciences of the United States of America 109(6), 2168-2173. Biely, J., Palmer, V.E., Lerner, I.M., Asmundson, V.S., 1933. Inheritance of resistance to Fowl paralysis (Neurolymphomatosis gallinarum). Science 78(2011), 42. Biggs, P.M., Payne, L.N., 1967. Studies on Marek's disease. I. Experimental transmission. J Natl Cancer Inst 39(2), 267-280. Boudny, V., Kovarik, J., 2002. JAK/STAT signaling pathways and cancer. Janus kinases/signal transducers and activators of transcription. Neoplasma 49(6), 349-355. Brown, A.C., Baigent, S.J., Smith, L.P., Chattoo, J.P., Petherbridge, L.J., Hawes, P., Allday, M.J., Nair, V., 2006. Interaction of MEQ protein and C-terminal-binding protein is critical for induction of lymphomas by Marek's disease virus. Proceedings of the National Academy of Sciences of the United States of America 103(6), 1687-1692. Brown, A.C., Smith, L.P., Kgosana, L., Baigent, S.J., Nair, V., Allday, M.J., 2009. Homodimerization of the Meq viral oncoprotein is necessary for induction of T-cell lymphoma by Marek's disease virus. Journal of virology 83(21), 11142-11151. Bulow, V.V., Biggs, P.M., 1975. Precipitating antigens associated with Marek's disease viruses and a herpesvirus of turkeys. Avian Pathol 4(2), 147-162. Bumstead, N., Kaufman, J., 2004. Genetic resistance to Marek's disease. In: Davison, F., Nair, V. (Eds.), Marek's Disease: An Evolving Problem. Elsevier-Academic Press, San Diego, pp. 112-123. Bumstead, N., Sillibourne, J., Rennie, M., Ross, N., Davison, F., 1997. Quantification of Marek's disease virus in chicken lymphocytes using the polymerase chain reaction with fluorescence detection. J Virol Methods 65(1), 75-81. Burgess, S.C., Basaran, B.H., Davison, T.F., 2001. Resistance to Marek's disease herpesvirusinduced lymphoma is multiphasic and dependent on host genotype. Vet Pathol 38(2), 129-142. Burgess, S.C., Davison, T.F., 2002. Identification of the neoplastically transformed cells in Marek's disease herpesvirus-induced lymphomas: recognition by the monoclonal antibody AV37. J Virol 76(14), 7276-7292. 109 Burke, W.M., Jin, X., Lin, H.J., Huang, M., Liu, R., Reynolds, R.K., Lin, J., 2001. Inhibition of constitutively active Stat3 suppresses growth of human ovarian and breast cancer cells. Oncogene 20(55), 7925-7934. Buza, J.J., Burgess, S.C., 2007. Modeling the proteome of a Marek's disease transformed cell line: a natural animal model for CD30 overexpressing lymphomas. Proteomics 7(8), 1316-1326. Cai, Q., Verma, S.C., Lu, J., Robertson, E.S., 2010. Molecular biology of Kaposi's sarcomaassociated herpesvirus and related oncogenesis. Adv Virus Res 78, 87-142. Calnek, B.W., 1986. Marek's disease--a model for herpesvirus oncology. Crit Rev Microbiol 12(4), 293-320. Calnek, B.W., Adldinger, H.K., Kahn, D.E., 1970. Feather follicle epithelium: a source of enveloped and infectious cell-free herpesvirus from Marek's disease. Avian Dis 14(2), 219-233. Calnek, B.W., Hitchner, S.B., 1969. Localization of viral antigen in chickens infected with Marek's disease herpesvirus. J Natl Cancer Inst 43(4), 935-949. Calnek, B.W., Schat, K.A., Ross, L.J., Shek, W.R., Chen, C.L., 1984. Further characterization of Marek's disease virus-infected lymphocytes. I. In vivo infection. Int J Cancer 33(3), 389398. Calnek, B.W., Shek, W.R., Schat, K.A., 1981. Latent infections with Marek's disease virus and turkey herpesvirus. J Natl Cancer Inst 66(3), 585-590. Campbell, J.G., Biggs, P.M., 1961. A proposed classification of the leucosis complex and fowl paralysis. Br Vet J 117, 316-334. Camps, M., Nichols, A., Arkinstall, S., 2000. Dual specificity phosphatases: a gene family for control of MAP kinase function. FASEB J 14(1), 6-16. Chang, K.S., Lee, S.I., Ohashi, K., Ibrahim, A., Onuma, M., 2002. The detection of the meq gene in chicken infected with Marek's disease virus serotype 1. J Vet Med Sci 64(5), 413-417. Chen, R.E., Thorner, J., 2007. Function and regulation in MAPK signaling pathways: lessons learned from the yeast Saccharomyces cerevisiae. Biochim Biophys Acta 1773(8), 13111340. Cheng, H., Niikura, M., Kim, T., Mao, W., MacLea, K.S., Hunt, H., Dodgson, J., Burnside, J., Morgan, R., Ouyang, M., Lamont, S., Dekkers, J., Fulton, J., Soller, M., Muir, W., 2008. 110 Using integrative genomics to elucidate genetic resistance to Marek's disease in chickens. Dev Biol (Basel) 132, 365-372. Churchill, A.E., Biggs, P.M., 1967. Agent of Marek's disease in tissue culture. Nature 215(5100), 528-530. Cole, R.K., 1968. Studies on genetic resistance to Marek's disease. Avian Dis 12(1), 9-28. Coppola, D., Parikh, V., Boulware, D., Blanck, G., 2009. Substantially reduced expression of PIAS1 is associated with colon cancer development. J Cancer Res Clin Oncol 135(9), 1287-1291. Cui, X., Lee, L.F., Reed, W.M., Kung, H.J., Reddy, S.M., 2004. Marek's disease virus-encoded vIL-8 gene is involved in early cytolytic infection but dispensable for establishment of latency. J Virol 78(9), 4753-4760. Cui, Z.Z., Yan, D., Lee, L.F., 1990. Marek's disease virus gene clones encoding virus-specific phosphorylated polypeptides and serological characterization of fusion proteins. Virus Genes 3(4), 309-322. Daly, R.J., Binder, M.D., Sutherland, R.L., 1994. Overexpression of the Grb2 gene in human breast cancer cell lines. Oncogene 9(9), 2723-2727. Davison, A.J., Eberle, R., Ehlers, B., Hayward, G.S., McGeoch, D.J., Minson, A.C., Pellett, P.E., Roizman, B., Studdert, M.J., Thiry, E., 2009. The order Herpesvirales. Arch Virol 154(1), 171-177. Dawson, C.W., Laverick, L., Morris, M.A., Tramoutanis, G., Young, L.S., 2008. Epstein-Barr virusencoded LMP1 regulates epithelial cell motility and invasion via the ERK-MAPK pathway. J Virol 82(7), 3654-3664. De Luca, A., Maiello, M.R., D'Alessio, A., Pergameno, M., Normanno, N., 2012. The RAS/RAF/MEK/ERK and the PI3K/AKT signalling pathways: role in cancer pathogenesis and implications for therapeutic approaches. Expert Opin Ther Targets 16 Suppl 2, S1727. Delecluse, H.J., Schüller, S., Hammerschmidt, W., 1993. Latent Marek's disease virus can be activated from its chromosomally integrated state in herpesvirus-transformed lymphoma cells. EMBO J 12(8), 3277-3286. Downward, J., 1994. The GRB2/Sem-5 adaptor protein. FEBS Lett 338(2), 113-117. 111 Ehrmann, J., Strakova, N., Vrzalikova, K., Hezova, R., Kolar, Z., 2008. Expression of STATs and their inhibitors SOCS and PIAS in brain tumors. In vitro and in vivo study. Neoplasma 55(6), 482-487. Estaquier, J., Vallette, F., Vayssiere, J.L., Mignotte, B., 2012. The mitochondrial pathways of apoptosis. Adv Exp Med Biol 942, 157-183. Fragnet, L., Blasco, M.A., Klapper, W., Rasschaert, D., 2003. The RNA subunit of telomerase is encoded by Marek's disease virus. J Virol 77(10), 5985-5996. Freed, E., Symons, M., Macdonald, S.G., McCormick, F., Ruggieri, R., 1994. Binding of 14-3-3 proteins to the protein kinase Raf and effects on its activation. Science 265(5179), 17131716. Freeman, A.K., Morrison, D.K., 2011. 14-3-3 Proteins: diverse functions in cell proliferation and cancer progression. Semin Cell Dev Biol 22(7), 681-687. Gimeno, I.M., 2008. Marek's disease vaccines: a solution for today but a worry for tomorrow? Vaccine 26 Suppl 3, C31-41. Giubellino, A., Burke, T.R., Bottaro, D.P., 2008. Grb2 signaling in cell motility and cancer. Expert Opin Ther Targets 12(8), 1021-1033. Greenhalgh, C.J., Alexander, W.S., 2004. Suppressors of cytokine signalling and regulation of growth hormone action. Growth Horm IGF Res 14(3), 200-206. Haagenson, K.K., Wu, G.S., 2010. Mitogen activated protein kinase phosphatases and cancer. Cancer Biol Ther 9(5), 337-340. Hanahan, D., Weinberg, R.A., 2000. The hallmarks of cancer. Cell 100(1), 57-70. Hansen, M., Van Zandt, J., Law, G., 1967. Differences in susceptibility to Marek's disease in chickens carrying two different B locus blood group alleles [abst]. Poultry Science 46, 1268. Heidari, M., Sarson, A.J., Huebner, M., Sharif, S., Kireev, D., Zhou, H., 2010. Marek's disease virus-induced immunosuppression: array analysis of chicken immune response gene expression profiling. Viral Immunol 23(3), 309-319. Hermeking, H., Benzinger, A., 2006. 14-3-3 proteins in cell cycle regulation. Semin Cancer Biol 16(3), 183-192. Hodge, D.R., Hurt, E.M., Farrar, W.L., 2005. The role of IL-6 and STAT3 in inflammation and cancer. Eur J Cancer 41(16), 2502-2512. 112 Hutt, F.B., Cole, R.K., 1947. Genetic Control of Lymphomatosis in the Fowl. Science 106(2756), 379-384. Irie, K., Gotoh, Y., Yashar, B.M., Errede, B., Nishida, E., Matsumoto, K., 1994. Stimulatory effects of yeast and mammalian 14-3-3 proteins on the Raf protein kinase. Science 265(5179), 1716-1719. Isfort, R.J., Kung, H.J., Velicer, L.F., 1987. Identification of the gene encoding Marek's disease herpesvirus A antigen. J Virol 61(8), 2614-2620. Isfort, R.J., Sithole, I., Kung, H.J., Velicer, L.F., 1986. Molecular characterization of Marek's disease herpesvirus B antigen. J Virol 59(2), 411-419. Jones, D., Lee, L., Liu, J.L., Kung, H.J., Tillotson, J.K., 1992. Marek disease virus encodes a basicleucine zipper gene resembling the fos/jun oncogenes that is highly expressed in lymphoblastoid tumors. Proceedings of the National Academy of Sciences of the United States of America 89(9), 4042-4046. Kamil, J.P., Tischer, B.K., Trapp, S., Nair, V.K., Osterrieder, N., Kung, H.J., 2005. vLIP, a viral lipase homologue, is a virulence factor of Marek's disease virus. J Virol 79(11), 6984-6996. Kaufer, B.B., Arndt, S., Trapp, S., Osterrieder, N., Jarosinski, K.W., 2011. Herpesvirus telomerase RNA (vTR) with a mutated template sequence abrogates herpesvirus-induced lymphomagenesis. PLoS Pathog 7(10), e1002333. Kaufer, B.B., Trapp, S., Jarosinski, K.W., Osterrieder, N., 2010. Herpesvirus telomerase RNA(vTR)-dependent lymphoma formation does not require interaction of vTR with telomerase reverse transcriptase (TERT). PLoS Pathog 6(8), e1001073. Keyse, S.M., 2000. Protein phosphatases and the regulation of mitogen-activated protein kinase signalling. Curr Opin Cell Biol 12(2), 186-192. Khan, O., La Thangue, N.B., 2012. HDAC inhibitors in cancer biology: emerging mechanisms and clinical applications. Immunol Cell Biol 90(1), 85-94. Kiu, H., Nicholson, S.E., 2012. Biology and significance of the JAK/STAT signalling pathways. Growth factors (Chur, Switzerland) 30(2), 88-106. Kouzarides, T., 2007. Chromatin modifications and their function. Cell 128(4), 693-705. Kung, H.J., Xia, L., Brunovskis, P., Li, D., Liu, J.L., Lee, L.F., 2001. Meq: an MDV-specific bZIP transactivator with transforming properties. Current topics in microbiology and immunology 255, 245-260. 113 Lambert, P.J., Shahrier, A.Z., Whitman, A.G., Dyson, O.F., Reber, A.J., McCubrey, J.A., Akula, S.M., 2007. Targeting the PI3K and MAPK pathways to treat Kaposi's-sarcoma-associated herpes virus infection and pathogenesis. Expert Opin Ther Targets 11(5), 589-599. Langmead, B., Trapnell, C., Pop, M., Salzberg, S.L., 2009. Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol 10(3), R25. Lee, K.K., Workman, J.L., 2007. Histone acetyltransferase complexes: one size doesn't fit all. Nat Rev Mol Cell Biol 8(4), 284-295. Lee, L.F., Powell, P.C., Rennie, M., Ross, L.J., Payne, L.N., 1981. Nature of genetic resistance to Marek's disease in chickens. J Natl Cancer Inst 66(4), 789-796. Lee, L.F., Wu, P., Sui, D., Ren, D., Kamil, J., Kung, H.J., Witter, R.L., 2000a. The complete unique long sequence and the overall genomic organization of the GA strain of Marek's disease virus. Proc Natl Acad Sci U S A 97(11), 6091-6096. Lee, S.I., Takagi, M., Ohashi, K., Sugimoto, C., Onuma, M., 2000b. Difference in the meq gene between oncogenic and attenuated strains of Marek's disease virus serotype 1. J Vet Med Sci 62(3), 287-292. Levy, A.M., Gilad, O., Xia, L., Izumiya, Y., Choi, J., Tsalenko, A., Yakhini, Z., Witter, R., Lee, L., Cardona, C.J., Kung, H.J., 2005. Marek's disease virus Meq transforms chicken cells via the v-Jun transcriptional cascade: a converging transforming pathway for avian oncoviruses. Proceedings of the National Academy of Sciences of the United States of America 102(41), 14831-14836. Levy, A.M., Izumiya, Y., Brunovskis, P., Xia, L., Parcells, M.S., Reddy, S.M., Lee, L., Chen, H.W., Kung, H.J., 2003. Characterization of the chromosomal binding sites and dimerization partners of the viral oncoprotein Meq in Marek's disease virus-transformed T cells. J Virol 77(23), 12841-12851. Liu, H.C., Cheng, H.H., Tirunagaru, V., Sofer, L., Burnside, J., 2001a. A strategy to identify positional candidate genes conferring Marek's disease resistance by integrating DNA microarrays and genetic mapping. Anim Genet 32(6), 351-359. Liu, H.C., Kung, H.J., Fulton, J.E., Morgan, R.W., Cheng, H.H., 2001b. Growth hormone interacts with the Marek's disease virus SORF2 protein and is associated with disease resistance in chicken. Proc Natl Acad Sci U S A 98(16), 9203-9208. Liu, H.C., Niikura, M., Fulton, J.E., Cheng, H.H., 2003. Identification of chicken lymphocyte antigen 6 complex, locus E (LY6E, alias SCA2) as a putative Marek's disease resistance gene via a virus-host protein interaction screen. Cytogenet Genome Res 102(1-4), 304308. 114 Liu, J.L., Kung, H.J., 2000. Marek's disease herpesvirus transforming protein MEQ: a c-Jun analogue with an alternative life style. Virus genes 21(1-2), 51-64. Liu, J.L., Lee, L.F., Ye, Y., Qian, Z., Kung, H.J., 1997. Nucleolar and nuclear localization properties of a herpesvirus bZIP oncoprotein, MEQ. Journal of virology 71(4), 3188-3196. Liu, J.L., Lin, S.F., Xia, L., Brunovskis, P., Li, D., Davidson, I., Lee, L.F., Kung, H.J., 1999a. MEQ and V-IL8: cellular genes in disguise? Acta Virol 43(2-3), 94-101. Liu, J.L., Ye, Y., Lee, L.F., Kung, H.J., 1998. Transforming potential of the herpesvirus oncoprotein MEQ: morphological transformation, serum-independent growth, and inhibition of apoptosis. J Virol 72(1), 388-395. Liu, J.L., Ye, Y., Qian, Z., Qian, Y., Templeton, D.J., Lee, L.F., Kung, H.J., 1999b. Functional interactions between herpesvirus oncoprotein MEQ and cell cycle regulator CDK2. J Virol 73(5), 4208-4219. Long, P.A., Kaveh-Yamini, P., Velicer, L.F., 1975. Marek's Disease Herpesviruses I. Production and Preliminary Characterization of Marek's Disease Herpesvirus A Antigen. J Virol 15(5), 1182-1191. Longenecker, B.M., Gallatin, W.M., 1978. Genetic control of resistance to Marek's disease. IARC Sci Publ(24 Pt 2), 845-850. Longenecker, B.M., Pazderka, F., Gavora, J.S., Spencer, J.L., Stephens, E.A., Witter, R.L., Ruth, R.F., 1977. Role of the major histocompatibility complex in resistance to Marek's disease: restriction of the growth of JMV-MD tumor cells in genetically resistant birds. Adv Exp Med Biol 88, 287-298. Luo, J., Mitra, A., Tian, F., Chang, S., Zhang, H., Cui, K., Yu, Y., Zhao, K., Song, J., 2012. Histone methylation analysis and pathway predictions in chickens after MDV infection. PLoS One 7(7), e41849. Lupiani, B., Lee, L.F., Cui, X., Gimeno, I., Anderson, A., Morgan, R.W., Silva, R.F., Witter, R.L., Kung, H.J., Reddy, S.M., 2004. Marek's disease virus-encoded Meq gene is involved in transformation of lymphocytes but is dispensable for replication. Proceedings of the National Academy of Sciences of the United States of America 101(32), 11815-11820. Maceachern, S., Muir, W.M., Crosby, S.D., Cheng, H.H., 2011. Genome-Wide Identification and Quantification of cis- and trans-Regulated Genes Responding to Marek's Disease Virus Infection via Analysis of Allele-Specific Expression. Front Genet 2, 113. Marek, J., 1907. Mutiple Nervenentzundung bei Huhnern. Dtsch. Tierarztl. Wschr 15, 417-421. 115 McElroy, J.P., Dekkers, J.C., Fulton, J.E., O'Sullivan, N.P., Soller, M., Lipkin, E., Zhang, W., Koehler, K.J., Lamont, S.J., Cheng, H.H., 2005. Microsatellite markers associated with resistance to Marek's disease in commercial layer chickens. Poult Sci 84(11), 1678-1688. Mitra, A., Luo, J., Zhang, H., Cui, K., Zhao, K., Song, J., 2012. Marek's disease virus infection induces widespread differential chromatin marks in inbred chicken lines. BMC Genomics 13, 557. Mochida, S., Hunt, T., 2012. Protein phosphatases and their regulation in the control of mitosis. EMBO Rep 13(3), 197-203. Morgan, R.W., Sofer, L., Anderson, A.S., Bernberg, E.L., Cui, J., Burnside, J., 2001. Induction of host gene expression following infection of chicken embryo fibroblasts with oncogenic Marek's disease virus. J Virol 75(1), 533-539. Morrow, C., Fehler, F., 2004. Marek's disease: a world-wide problem. In: Davison, F., Nair, V. (Eds.), Marek's Disease. Marek's disease: a world-wide problem, London, pp. 49-61. Mumby, M., 2007. PP2A: unveiling a reluctant tumor suppressor. Cell 130(1), 21-24. Nair, V., Kung, H.J., 2004. Marek's disease virus oncogenicity: molecular mechanisms. In: Davison, F., Nair, V. (Eds.), Marek's Disease: An Evolving Problem. Elsevier-Academic Press, San-Diego, pp. 32-47. Nazerian, K., Burmester, B.R., 1968. Electron microscopy of a herpes virus associated with the agent of Marek's disease in cell culture. Cancer Res 28(12), 2454-2462. Niikura, M., Kim, T., Silva, R.F., Dodgson, J., Cheng, H.H., 2011. Virulent Marek's disease virus generated from infectious bacterial artificial chromosome clones with complete DNA sequence and the implication of viral genetic homogeneity in pathogenesis. J Gen Virol 92(Pt 3), 598-607. Ogawa, H., Ishiguro, K., Gaubatz, S., Livingston, D.M., Nakatani, Y., 2002. A complex with chromatin modifiers that occupies E2F- and Myc-responsive genes in G0 cells. Science 296(5570), 1132-1136. Ohashi, K., Morimura, T., Takagi, M., Lee, S.I., Cho, K.O., Takahashi, H., Maeda, Y., Sugimoto, C., Onuma, M., 1999. Expression of bcl-2 and bcl-x genes in lymphocytes and tumor cell lines derived from MDV-infected chickens. Acta Virol 43(2-3), 128-132. Ohori, M., Kinoshita, T., Okubo, M., Sato, K., Yamazaki, A., Arakawa, H., Nishimura, S., Inamura, N., Nakajima, H., Neya, M., Miyake, H., Fujii, T., 2005. Identification of a selective ERK inhibitor and structural determination of the inhibitor-ERK2 complex. Biochem Biophys Res Commun 336(1), 357-363. 116 Okada, T., Takagi, M., Murata, S., Onuma, M., Ohashi, K., 2007. Identification and characterization of a novel spliced form of the meq transcript in lymphoblastoid cell lines derived from Marek's disease tumours. J Gen Virol 88(Pt 8), 2111-2120. Okazaki, W., Purchase, H.G., Burmester, B.R., 1970. Protection against Marek's disease by vaccination with a herpesvirus of turkeys. Avian Dis 14(2), 413-429. Osterrieder, K., Vautherot, J., 2004. The genome content of Marek's disease-like virus. In: Davison, F., Nair, V. (Eds.), Marek's Disease: An Evolving Problem. Elsevier-Academic Press, San-Diego, pp. 17-29. Owens, D.M., Keyse, S.M., 2007. Differential regulation of MAP kinase signalling by dualspecificity protein phosphatases. Oncogene 26(22), 3203-3213. Pappenheimer, A.M., Dunn, L.C., Cone, V., 1929. Studies on fowl paralysis (Neurolymphomatosis gallinarum). I. Clinical features and pathology. J Exp Med 49(1), 63-86. Parcells, M.S., Lin, S.F., Dienglewicz, R.L., Majerciak, V., Robinson, D.R., Chen, H.C., Wu, Z., Dubyak, G.R., Brunovskis, P., Hunt, H.D., Lee, L.F., Kung, H.J., 2001. Marek's disease virus (MDV) encodes an interleukin-8 homolog (vIL-8): characterization of the vIL-8 protein and a vIL-8 deletion mutant MDV. J Virol 75(11), 5159-5173. Payne, L., 2004. Pathologic responses to infection. In: Davison, F., Nair, V. (Eds.), Marek's Disease: An Evolving Problem. Elsevier-Academic Press, San-Diego, pp. 78-96. Pazderka, F., Longenecker, B., Law, G., Stone, H., Ruth, R., 1975. Histocompatibility of chicken populations selected for resistance to Marek's disease. Immunogenetics 2, 93-100. Peng, Q., Shirazi, Y., 1996a. Characterization of the protein product encoded by a splicing variant of the Marek's disease virus Eco-Q gene (Meq). Virology 226(1), 77-82. Peng, Q., Shirazi, Y., 1996b. Isolation and characterization of Marek's disease virus (MDV) cDNAs from a MDV-transformed lymphoblastoid cell line: identification of an open reading frame antisense to the MDV Eco-Q protein (Meq). Virology 221(2), 368-374. Perumbakkam, S., Muir, W.M., Black-Pyrkosz, A., Okimoto, R., Cheng, H.H., 2013. Comparison and contrast of genes and biological pathways responding to Marek's disease virus infection using allele-specific expression and differential expression in broiler and layer chickens, BMC Genomics. Vol. 14, pp. 64. Platanias, L.C., 2003. Map kinase signaling pathways and hematologic malignancies. Blood 101(12), 4667-4679. 117 Portales-Casamar, E., Thongjuea, S., Kwon, A.T., Arenillas, D., Zhao, X., Valen, E., Yusuf, D., Lenhard, B., Wasserman, W.W., Sandelin, A., 2010. JASPAR 2010: the greatly expanded open-access database of transcription factor binding profiles. Nucleic Acids Res 38(Database issue), D105-110. Portis, T., Longnecker, R., 2004. Epstein-Barr virus (EBV) LMP2A mediates B-lymphocyte survival through constitutive activation of the Ras/PI3K/Akt pathway. Oncogene 23(53), 86198628. Purchase, H.G., Okazaki, W., 1971. Effect of vaccination with herpesvirus of turkeys (HVT) on horizontal spread of Marek's disease herpesvirus. Avian Dis 15(2), 391-397. Qian, Z., Brunovskis, P., Lee, L., Vogt, P.K., Kung, H.J., 1996. Novel DNA binding specificities of a putative herpesvirus bZIP oncoprotein. J Virol 70(10), 7161-7170. Ram, P.A., Waxman, D.J., 1997. Interaction of growth hormone-activated STATs with SH2containing phosphotyrosine phosphatase SHP-1 and nuclear JAK2 tyrosine kinase. J Biol Chem 272(28), 17694-17702. Reddy, S.M., Lupiani, B., Gimeno, I.M., Silva, R.F., Lee, L.F., Witter, R.L., 2002. Rescue of a pathogenic Marek's disease virus with overlapping cosmid DNAs: use of a pp38 mutant to validate the technology for the study of gene function. Proceedings of the National Academy of Sciences of the United States of America 99(10), 7054-7059. Ridinger-Saison, M., Boeva, V., Rimmele, P., Kulakovskiy, I., Gallais, I., Levavasseur, B., Paccard, C., Legoix-Ne, P., Morle, F., Nicolas, A., Hupe, P., Barillot, E., Moreau-Gachelin, F., Guillouf, C., 2012. Spi-1/PU.1 activates transcription through clustered DNA occupancy in erythroleukemia. Nucleic acids research 40(18), 8927-8941. Roberts, M.L., Cooper, N.R., 1998. Activation of a ras-MAPK-dependent pathway by EpsteinBarr virus latent membrane protein 1 is essential for cellular transformation. Virology 240(1), 93-99. Ross, L.J., Biggs, P.M., Newton, A.A., 1973. Purification and properties of the 'A' antigen associated with Marek's disease virus infections. J Gen Virol 18(3), 291-304. Ross, N.L., 1999. T-cell transformation by Marek's disease virus. Trends Microbiol 7(1), 22-29. Saif, Y.M., Fadly, A.M., 2008. Diseases of poultry, xxiii, 1324 p., [1339] p. of plates pp. 12th ed. Blackwell Pub., Ames, Iowa. Sansone, P., Bromberg, J., 2012. Targeting the interleukin-6/Jak/stat pathway in human malignancies. J Clin Oncol 30(9), 1005-1014. 118 Sarson, A.J., Parvizi, P., Lepp, D., Quinton, M., Sharif, S., 2008. Transcriptional analysis of host responses to Marek's disease virus infection in genetically resistant and susceptible chickens, Anim Genet. Vol. 39, England, pp. 232-240. Scaffidi, A.K., Mutsaers, S.E., Moodley, Y.P., McAnulty, R.J., Laurent, G.J., Thompson, P.J., Knight, D.A., 2002. Oncostatin M stimulates proliferation, induces collagen production and inhibits apoptosis of human lung fibroblasts. Br J Pharmacol 136(5), 793-801. Schat, K.A., Calnek, B.W., Fabricant, J., Abplanalp, H., 1981. Influence of oncogenicity of Marek' disease virus on evaluation of genetic resistance. Poult Sci 60(12), 2559-2566. Sehgal, P.B., 2000. STAT-signalling through the cytoplasmic compartment: consideration of a new paradigm. Cell Signal 12(8), 525-535. Sengupta, N., Seto, E., 2004. Regulation of histone deacetylase activities. J Cell Biochem 93(1), 57-67. Silva, R.F., Lee, L., Kutish, G.F., 2001. The Genomic Structure of Marek's Disease Virus. In: Hirai, K. (Ed.), Marek's Disease. Springer-Verlag, Berlin, pp. 143-155. Smith, J., Sadeyen, J.R., Paton, I.R., Hocking, P.M., Salmon, N., Fife, M., Nair, V., Burt, D.W., Kaiser, P., 2011. Systems analysis of immune responses in Marek's disease virus-infected chickens identifies a gene involved in susceptibility and highlights a possible novel pathogenicity mechanism, J Virol. Vol. 85, United States, pp. 11146-11158. Stone, H., 1975. Use of highly inbred chickens in research. . USDA Technical Bulletin 1514. Suchodolski, P.F., Izumiya, Y., Lupiani, B., Ajithdoss, D.K., Gilad, O., Lee, L.F., Kung, H.J., Reddy, S.M., 2009. Homodimerization of Marek's disease virus-encoded Meq protein is not sufficient for transformation of lymphocytes in chickens. Journal of virology 83(2), 859869. Suchodolski, P.F., Izumiya, Y., Lupiani, B., Ajithdoss, D.K., Lee, L.F., Kung, H.J., Reddy, S.M., 2010. Both homo and heterodimers of Marek's disease virus encoded Meq protein contribute to transformation of lymphocytes in chickens. Virology 399(2), 312-321. Takagi, M., Takeda, T., Asada, Y., Sugimoto, C., Onuma, M., Ohashi, K., 2006. The presence of a short form of p53 in chicken lymphoblastoid cell lines during apoptosis. J Vet Med Sci 68(6), 561-566. Tari, A.M., Lopez-Berestein, G., 2001. GRB2: a pivotal protein in signal transduction. Semin Oncol 28(5 Suppl 16), 142-147. 119 Theodosiou, A., Ashworth, A., 2002. MAP kinase phosphatases. Genome Biol 3(7), REVIEWS3009. Trapp, S., Parcells, M.S., Kamil, J.P., Schumacher, D., Tischer, B.K., Kumar, P.M., Nair, V.K., Osterrieder, N., 2006. A virus-encoded telomerase RNA promotes malignant T cell lymphomagenesis. J Exp Med 203(5), 1307-1317. Vallejo, R.L., Bacon, L.D., Liu, H.C., Witter, R.L., Groenen, M.A., Hillel, J., Cheng, H.H., 1998. Genetic mapping of quantitative trait loci affecting susceptibility to Marek's disease virus induced tumors in F2 intercross chickens. Genetics 148(1), 349-360. Valouev, A., Johnson, D.S., Sundquist, A., Medina, C., Anton, E., Batzoglou, S., Myers, R.M., Sidow, A., 2008. Genome-wide analysis of transcription factor binding sites based on ChIP-Seq data. Nat Methods 5(9), 829-834. Verbruggen, V., Ek, O., Georlette, D., Delporte, F., Von Berg, V., Detry, N., Biemar, F., Coutinho, P., Martial, J.A., Voz, M.L., Manfroid, I., Peers, B., 2010. The Pax6b homeodomain is dispensable for pancreatic endocrine cell differentiation in zebrafish. The Journal of biological chemistry 285(18), 13863-13873. Wang, W., 2003. The SWI/SNF family of ATP-dependent chromatin remodelers: similar mechanisms for diverse functions. Curr Top Microbiol Immunol 274, 143-169. Watanabe, T., Shinohara, N., Moriya, K., Sazawa, A., Kobayashi, Y., Ogiso, Y., Takiguchi, M., Yasuda, J., Koyanagi, T., Kuzumaki, N., Hashimoto, A., 2000. Significance of the Grb2 and son of sevenless (Sos) proteins in human bladder cancer cell lines. IUBMB Life 49(4), 317-320. Westermarck, J., Li, S.P., Kallunki, T., Han, J., Kähäri, V.M., 2001. p38 mitogen-activated protein kinase-dependent activation of protein phosphatases 1 and 2A inhibits MEK1 and MEK2 activity and collagenase 1 (MMP-1) gene expression. Mol Cell Biol 21(7), 2373-2383. Wettenhall, J.M., Simpson, K.M., Satterley, K., Smyth, G.K., 2006. affylmGUI: a graphical user interface for linear modeling of single channel microarray data. Bioinformatics 22(7), 897-899. Wilker, E., Yaffe, M.B., 2004. 14-3-3 Proteins--a focus on cancer and human disease. J Mol Cell Cardiol 37(3), 633-642. Wingender, E., Dietze, P., Karas, H., Knüppel, R., 1996. TRANSFAC: a database on transcription factors and their DNA binding sites. Nucleic Acids Res 24(1), 238-241. Witter, R.L., 1997. Increased virulence of Marek's disease virus field isolates. Avian Dis 41(1), 149-163. 120 Witter, R.L., 1998. Control strategies for Marek's disease: a perspective for the future. Poultry science 77(8), 1197-1203. Witter, R.L., Calnek, B.W., Buscaglia, C., Gimeno, I.M., Schat, K.A., 2005. Classification of Marek's disease viruses according to pathotype: philosophy and methodology. Avian Pathol 34(2), 75-90. Wu, C., Guan, Q., Wang, Y., Zhao, Z.J., Zhou, G.W., 2003a. SHP-1 suppresses cancer cell growth by promoting degradation of JAK kinases. J Cell Biochem 90(5), 1026-1037. Wu, C., Sun, M., Liu, L., Zhou, G.W., 2003b. The function of the protein tyrosine phosphatase SHP-1 in cancer. Gene 306, 1-12. Wu, S., Wang, Y., Sun, L., Zhang, Z., Jiang, Z., Qin, Z., Han, H., Liu, Z., Li, X., Tang, A., Gui, Y., Cai, Z., Zhou, F., 2011. Decreased expression of dual-specificity phosphatase 9 is associated with poor prognosis in clear cell renal cell carcinoma. BMC Cancer 11, 413. Xie, J., Ajibade, A.O., Ye, F., Kuhne, K., Gao, S.J., 2008. Reactivation of Kaposi's sarcomaassociated herpesvirus from latency requires MEK/ERK, JNK and p38 multiple mitogenactivated protein kinase pathways. Virology 371(1), 139-154. Xie, Q., Anderson, A.S., Morgan, R.W., 1996. Marek's disease virus (MDV) ICP4, pp38, and meq genes are involved in the maintenance of transformation of MDCC-MSB1 MDVtransformed lymphoblastoid cells. J Virol 70(2), 1125-1131. Yarden, Y., Pines, G., 2012. The ERBB network: at last, cancer therapy meets systems biology. Nature reviews 12(8), 553-563. Ye, F.C., Blackbourn, D.J., Mengel, M., Xie, J.P., Qian, L.W., Greene, W., Yeh, I.T., Graham, D., Gao, S.J., 2007. Kaposi's sarcoma-associated herpesvirus promotes angiogenesis by inducing angiopoietin-2 expression via AP-1 and Ets1. J Virol 81(8), 3980-3991. Yu, Y., Luo, J., Mitra, A., Chang, S., Tian, F., Zhang, H., Yuan, P., Zhou, H., Song, J., 2011. Temporal transcriptome changes induced by MDV in Marek's disease-resistant and susceptible inbred chickens, BMC Genomics. Vol. 12, England, pp. 501. Zhao, L., Glazov, E.A., Pattabiraman, D.R., Al-Owaidi, F., Zhang, P., Brown, M.A., Leo, P.J., Gonda, T.J., 2011. Integrated genome-wide chromatin occupancy and expression analyses identify key myeloid pro-differentiation transcription factors repressed by Myb. Nucleic acids research 39(11), 4664-4679. Zheng, C.F., Guan, K.L., 1993. Properties of MEKs, the kinases that phosphorylate and activate the extracellular signal-regulated kinases. J Biol Chem 268(32), 23933-23939. 121