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Herpesvirus of turkeys: microarray analysis of host gene responses to infection
Virology 318 (2004) 102 – 111 www.elsevier.com/locate/yviro
Herpesvirus of turkeys: microarray analysis of host gene responses to infection Gamze Karaca, Jonathan Anobile, Danielle Downs, Joan Burnside, and Carl J. Schmidt * Department of Animal and Food Sciences, University of Delaware, Newark, DE 19717-2150, USA Received 19 May 2003; returned to author for revision 8 September 2003; accepted 10 September 2003
Abstract Herpesvirus of turkeys (HVT) provides an economically important live vaccine for prevention of Marek’s disease (MD) of chickens. MD, characterized by both immunosuppression and T-cell lymphoma, is caused by another herpesvirus termed Marek’s disease virus (MDV). Microarrays were used to investigate the response of chicken embryonic fibroblasts (CEF) to infection with HVT. Genes responding to HVT infection include several induced by interferon along with others modulating signal transduction, transcription, scaffolding proteins, and the cytoskeleton. Results are compared with earlier studies examining the responses of CEF cells to infection with MDV. D 2003 Elsevier Inc. All rights reserved. Keywords: Herpesvirus of turkeys; Microarray; Chicken; Fibroblast; Infection
Introduction Herpesvirus of turkeys (HVT) provides an important live vaccine for the prevention of Marek’s disease (MD) in chickens. MD is characterized by both immunosuppression and T-cell lymphoma with the causative agent being another herpesvirus: Marek’s disease virus (MDV). MDV is endemic in facilities designed for high production of chickens with significant losses incurred by MD if the disease is not controlled. Vaccination with HVT does not prevent infection with MDV, but does block the onset of lymphoma. Because the virus is not eradicated, vaccination apparently drives selection of MDV strains with increased virulence, thus necessitating new vaccines or new vaccination regimens to control MD. Currently, little is known about the interactions between HVT and the host that gives rise to an immune response that controls MDV infection. Ultimately, understanding the mechanisms regulating host response to vaccination with HVT should increase our ability to control this economically important disease (Djeraba et al., 2002). HVT is ubiquitous in turkey flocks (Witter and Solomon, 1971), yet appears to have little or no impact upon the health of either turkeys or chickens (Fabricant et al., 1982). Of
* Corresponding author. Fax: +1-302-831-2822. E-mail address: [email protected] (C.J. Schmidt). 0042-6822/$ - see front matter D 2003 Elsevier Inc. All rights reserved. doi:10.1016/j.virol.2003.09.025
particular significance, HVT is not oncogenic in either species. The overall genomic sequence of HVT is closely related to that of MDV with recognizable conservation of genes involved in lytic infection (Afonso et al., 2001; Kingham et al., 2001). A significant difference exists between HVT and MDV, however, in that MDV contains a block of 10 genes that are absent from HVT. Included in this block is the meq gene, a member of the fos/jun family of transcription activators, which may play an important role in MDV-induced lymphoma. Other genes located in this region may also play a role in MDV-induced pathology (Liu et al., 1999; Parcells et al., 2001). Hence, the absence of this block of 10 genes from HVT is a likely reason for HVT’s failure to induce T-cell lymphoma. To initiate studies aimed at better understanding host responses to HVT, microarrays were used to monitor gene expression patterns in chicken cells in response to HVT infection. Microarrays provide a high-throughput method to monitor gene expression patterns in response to many different stimuli. High-density gene arrays have been used to monitor host responses to infection with a variety of viruses, including HIV (de la Fuente et al., 2002; Geiss et al., 2000; Sun et al., 2001; van’t Wout et al., 2003), coxsackieviruses (Taylor et al., 2000), hepatitis (Otsuka et al., 2003), and herpesviruses (Carter et al., 2002; Jones and Arvin, 2003; Mossman et al., 2001), including MDV (Liu et al., 2001; Morgan et al., 2001). In this study, microarrays
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were used to examine chicken embryonic fibroblast (CEF) gene expression responses to infection with HVT. These microarrays contained a total of 1126 non-redundant chicken cDNAs that focus largely upon genes identified in an activated T-cell EST database (Morgan et al., 2001). Genes were identified that were either up- or down-regulated at least twofold in response to HVT infection and some responses were verified by quantitative polymerase chain reaction (Q-PCR).
Results and discussion Three independent infections were used in microarray experiments to identify genes that consistently responded to infection with HVT. The data were first normalized using global normalization (Morgan et al., 2001), in which the output from each array is multiplied by a normalization factor such that the average signal intensities of all arrays are equivalent. Initial results were screened to identify genes that were up- or down-regulated at least twofold at any time point, in all three experiments. In addition, cluster analysis was used to characterize the data, grouping genes that responded to infection in a similar pattern. To provide greater insight into the function of the responsive gene products, Gene Ontology (Ashburner and Lewis, 2002; Ashburner et al., 2000; Lewis et al., 2000) annotation terms were extracted (Table 1) using GoFigure (Khan et al., in press). Because of the cell-associated nature of HVT, it is difficult to achieve a high multiplicity of infection (m.o.i.); the m.o.i. of these experiments was 10 3 infectious particles per cell. This is important to consider as results are discussed, as the detected gene responses undoubtedly reflect a mixture of effects from both infected and uninfected cells. In total, 56 genes exhibited reproducible responses in all three experiments (Fig. 1). Also, 4 genes that responded to HVT infection were further analyzed by Q-PCR: g interferon inducible lysosomal thiol reductase, apolipoprotein AI, a actinin, and CXCR4 (Fig. 2). The absolute fold change in mRNA abundance was not identical in all cases when the microarray results were compared with those from Q-PCR, but the temporal trends were similar between the two results. For example, both the microarrays and Q-PCR detected a threefold increase in apolipoprotein AI levels in infected relative to control cells at 24 and 48 h postinfection. At 72 h post-infection, the level of apolipoprotein AI mRNA detected by the microarrays was equivalent to uninfected cells. However, at 72 h post-infection, the level of this mRNA detected by Q-PCR in infected cells was onehalf that seen in control cells. Given the distinctly different nature of these methods, it is not surprising that the absolute fold differences were not identical at all time points. What is significant is that the general trends revealed by the two methods are similar, suggesting that the results from the microarray analysis are good indicators of overall changes in gene expression.
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Inspection of the Gene Ontology terms associated with these gene products provides direct insight into the types of processes modulated by HVT. Almost half of these responsive genes are involved in processes associated with either signal transduction or transcription. This is consistent with modulating gene products that function at critical steps in either host resistance to infection, or the successful growth of the virus. For example, the mRNA levels for the tyrosine kinase JAK1 are increased following infection. Since JAK1 functions to couple cytokine receptors to cellular effectors (Aringer et al., 1999; Kohlhuber et al., 1997; Kotenko and Pestka, 2000), this may serve to prime cells to receive cytokine signals and hasten the appropriate response to herpesvirus infection. Another receptor pathway affected by HVT infection is regulated by CXCR4. Both CXCR4 and Lyn, a tyrosine kinase that couples CXCR4 to intracellular pathways (Chernock et al., 2001; Ptasznik et al., 2002), are induced in response to infection. CXCR4 functions as a receptor for stromal derived factor 1a and has been implicated in cell migration (Bachelder et al., 2002), hematopoiesis, and angiogenesis (Ma et al., 1998; Tachibana et al., 1998). If this response is manifested in lymphocytes, one possible role of increased CXCR4 signaling may be to promote migration of immune cells into the region of herpesvirus infection. Transcription factors that respond to HVT infection include genes that directly regulate gene transcription, such as Hypoxia inducible factor, which was down-regulated during the time course. Two other transcription factors, BATF and Lmo2, were also modulated by HVT infection. BATF contains a leucine zipper, but lacks an activation domain and functions as a negative regulator of AP-1 function by forming an inactive heterodimer with members of the Jun family (Dorsey et al., 1995). Lmo2 is part of an oligomeric transcription complex (Wadman et al., 1997), that functions in both hematopoiesis and angiogenesis (Yamada et al., 1998, 2000). The gene Sirtuin has more global effects upon transcription and is down-regulated by HVT infection. Sirtuin is a member of the SIR gene family, which encodes deacetylase activity (Imai et al., 2000) and is involved in silencing gene activity at rRNA loci and telomeres (Guarente, 2000). Several other gene products responding to HVT infection function as scaffolding or adaptor proteins. Such proteins play critical roles in co-localizing interacting signaling proteins in the cellular compartments appropriate to their combined function. For example RACK (receptor for Ckinase) functions as a scaffold to co-localize protein kinase C (PKC) with a variety of receptors and effectors including PLC-1, Src, and cAMP-specific phosphodiesterase (Hermanto et al., 2002). During HVT infection, RACK expression was down-regulated. Since receptors and effectors exhibit different affinities for RACK, altering the level of this scaffold would be predicted to shift the interaction between PKC and these various signaling molecules. Even subtle changes in the level of a scaffolding protein may
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Table 1 Genes identified as responding to HVT infection in CEF cells Gene product name
Molecular function
Biological process
Cellular apoptosis susceptibility protein Protein tyrosine phosphatase TD14 Caspase 7 Interleukin-activated receptor WASP interactor protein RAB-2 Transcription elongation factor B polypeptide 1-like *Cathepsin C *h-2-Microglobulin Lyn protein tyrosine kinase JAK1 RasGAP-associated protein p56dok-2 G protein pathway suppressor 1 Apolipoprotein AI (APO-AI) RNase L inhibitor Stem cell antigen 2 60S Ribosomal protein L7A Gamma-interferon inducible lysosomal thiol reductase Receptor for activated C-kinase (RACK) Translation elongation factor eEF-1 delta chain Laminin-binding protein Cyclophilin 18 Regulator of G-protein signaling 2 60S Ribosomal protein L3 e3B1 a-Actinin Protein phosphatase 4, regulatory subunit 1 MYD118 Platelet activating receptor TNF receptor-associated factor 1 Rab-GDP dissociation inhibitor *DEAD-box RNA helicase TATA box binding protein (TBP)associated factor Interferon regulatory factor 3 Hypoxia inducible factor Lmo2 B-ATF Interferon-dependent positive-acting transcription factor ISGF-3 Sirtuin Translation initiation factor eIF-4A Ubiquitin Thymocyte activation and developmental protein Zinc finger protein Proline-rich protein with nuclear targeting signal KIAA0242 KIAA0085 Amyloid beta A4 KIAA1027 KIAA1195 Integral membrane protein 2B PBP1 scaffold protein Leucine-rich nuclear protein KIAA0288 Mitogen inducible gene mig-2 Zinc finger protein 64 Annexin II
protein binding protein tyrosine phosphatase initiator caspase receptor vinculin binding GTPase ubiquitin-protein ligase
apoptosis apoptosis apoptosis apoptosis chemotaxis ER to Golgi transport G1/S transition of mitotic cell cycle immune response immune response intracellular signaling cascade intracellular signaling cascade JAK – STAT cascade JNK cascade lipid transport; steroid biosynthesis
cysteine-type endopeptides receptor protein tyrosine kinase protein tyrosine kinase protein serine/threonine kinase GTPase inhibitor lipid transporter enzyme inhibitor lymphocyte antigen RNA binding enzyme
Cluster 9 4 3 3 7 4 7
post-translational membrane targeting protein biosynthesis protein degradation
11 8 11 4 6 5 7 1 4 11 7
protein kinase C binding protein guanyl-nucleotide exchange factor laminin binding protein chaperone GTPase activator structural protein of ribosome mitogenic signalling anchoring actin to membrane regulation of protein phosphatase apoptosis G protein-coupled receptor chemotaxis tumor necrosis factor intracellular signalling transcription general RNA polymerase II transcription factor transcription factor transcription factor transcription factor transcription transcription factor
protein kinase C activation protein synthesis protein translation; laminin receptor protein folding regulation of G-protein coupled receptors ribosomal large subunit assembly signal transducer signal transducer signal transducer signal transducer signal transducer signal transducer signal transducer transcription co-regulator transcription initiation
9 3 7 4 4 7 9 9 9 6 4 4 2 10 7
transcription transcription transcription transcription transcription
10 9 9 7 4
transcription translation initiation protein degradation tagging unknown unknown unknown unknown unknown unknown unknown unknown unknown unknown unknown unknown unknown unknown calcium-dependent phospholipid binding
transcription silencing translation ubiquitin cycle unknown unknown unknown unknown unknown unknown unknown unknown unknown unknown unknown unknown unknown unknown vesicle transport
regulation regulation regulation regulation regulation
9 5 11 11 11 11 10 10 9 7 7 7 7 6 4 1 1 7
For each gene, the Molecular Function and Biological Process Gene Ontology terms are provided. The cluster number is derived from cluster analysis and refers to the groupings indicated in Fig. 1. An asterisk indicates those genes that exhibited a similar response in fibroblasts infected with MDV (Morgan et al., 2001).
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Fig. 1. Genes responding to HVT infection. Histograms showing the ratio of infected divided by control levels of gene expression as detected by microarray analysis either 24, 48, or 72 h after infection. For inclusion in this analysis, each gene must have displayed similar changes in all three microarray experiments and a minimum change of twofold (up or down) from the control value.
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Fig. 1 (continued ).
significantly alter the responsiveness of signal transduction pathways. The specific effect of RACK down-regulation in the context of HVT infection is unclear, although other
herpesviruses, such as Epstein-Barr virus, inhibit PKC activity by affecting RACK (Baumann et al., 2000; Tardif et al., 2002). Additional scaffolding proteins modulated by
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Fig. 2. Quantitative PCR analysis of g-interferon, g-interferon lysosomal thiol reductase, apolipoprotein A1, a-actinin, CXCR4, and MIP-1a. At each time point post-infection, the ratio mRNA level detected in infected cells divided by control level is indicated. Note that the Y-axes are not identical.
HVT infection include: E3B1, which participates in coupling the Ras to Rac pathway (Scita et al., 1999), TNF receptor associated factor 1 (Wajant et al., 2001), which couples TNF receptors to intracellular regulators of apoptosis, and the putative scaffold protein PBP1, whose function is unknown. Not surprisingly, several of the genes responding to HVT infection are homologous to mammalian genes known to respond to interferon (de Veer et al., 2001). Because no chicken interferon cDNAs were included in this array, we used Q-PCR to examine the expression pattern of a, h, and g interferon mRNAs during infection. As seen in Fig. 2, g interferon mRNA in HVT-infected cells peaked at 48 h postinfection, while a and h interferon mRNA levels in infected cells were similar to controls (not shown). Several of the candidate interferon responsive genes also peaked at 48 h including: g interferon inducible lysosomal thiol reductase, h-2 microglobulin, ISGF-3, IRF3, PbP1, eIF-4A, and dead box RNA helicase. However, not all of these are found in the same cluster, perhaps reflecting different kinetic responses to interferon. One gene, TNF receptor associated factor-1, which is known to be interferon-regulated, peaked 72 h post-infection. This may merely reflect normal kinetics of interferon response for this gene. Neither MHC class I nor class II genes, which are typically induced by interferon g (Song et al., 1997; Zoeller et al., 1998; Zoller et al., 1992), made this final list of genes that responded to HVT infection in all three experiments.
Inspection of the primary data indicated that the MHC class I gene expression was increased at 48 h post-infection in two out of three experiments, while MHC class II gene was induced in only one experiment (not shown). Regulation of MHC in the context of infection by MD-like viruses is known to be complex. Cells actively infected with either MDV or HVT reduce MHC class I protein levels (Hunt et al., 2001; Levy et al., 2003), while surrounding uninfected cells have elevated MHC class I protein. Evidence indicates that the level of surface MHC protein in infected cells is regulated by post-translational processes (Hunt et al., 2001) while uninfected cells elevate transcription of MHC genes in response to interferon (Zoeller et al., 1998). The microarray results presented here integrate mRNA levels in both infected and uninfected cells. Possibly, HVT infection also down-regulates the transcription of MHC genes, leading to an apparently diminished interferon effect upon MHC transcription. The level of several gene products predicted to control cell morphology responded to HVT infection. For example, a-actinin mRNA levels decreased during the course of infection and a-actinin serves to anchor actin stress fibers to focal adhesion plaques (Rajfur et al., 2002). Stress fibers play an important role in generating the flat, extended morphology of fibroblasts (Baschong et al., 1997; Lombardi et al., 1990). The levels of E3B1 transcription are also decreased during HVT infection (see above), and this is predicted to decrease activation of the Rac GTPase. Acti-
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vated Rac GTPase promotes extension of the cell into lamellipodia and membrane ruffles (Nobes and Hall, 1995a, 1995b; Ridley and Hall, 1992; Ridley et al., 1992). Finally, decreased expression of the RACK in fibroblasts by antisense oligonucleotides reduces cell spreading (Hermanto et al., 2002) and RACK expression appears reduced in HVT-infected cells (see above). Taken together, decreased expression of a-actinin, E3B1, and RACK may be predicted to reduce contact adhesion and be responsible for rounding up of infected cells, a phenomenon associated with infection by HVT and other herpesviruses. HVT plays an important economic role as a vaccine preventing MD in chickens. Studies examining host cell responses to MDV infection using similar microarrays have been published (Morgan et al., 2001). Comparing the results obtained here for HVT, with those for MDV, reveals both overlaps, and differences in host cell responses. Both HVT and MDV generate an interferon response, as indicated by induction of genes responsive to this cytokine. In addition, both viruses increased expression of the Cathepsin C gene. This protease is essential in activating cytotoxic T-cellmediated apoptosis (McGuire et al., 1997), which may play an important role in recognizing and eliminating cell-associated viruses such as HVT and MDV. Several genes induced by MDV infection were not detectably altered in these microarray experiments using HVT. Many of these were induced less than threefold during MDV infection. One gene product not detectably affected in the microarray experiments by HVT, but induced greater than fivefold by MDV infection, was chicken MIP-1a (Morgan et al., 2001). This chemokine mediates inflammatory responses to virus infection, and may play a role in recruiting T cells to such sites. To further evaluate MIP-1a, Q-PCR was conducted to evaluate more rigorously the expression of this gene in the context of HVT infection. As shown in Fig. 2, MIP-1a was induced approximately fourfold in one of the three infection experiments used for the microarray analysis. Hence HVT may induce MIP-1a expression, but this response may not be as reproducible as with MDV infection.
Genomic differences between HVT and MDV may play a role in causing the distinct cellular responses to infection by these two viruses. For example, the long terminal repeat (LTR) of MDV contains at least 10 genes that are absent from HVT. Included in this region of MDV is the meq oncogene, a member of the jun/fos family of transcription regulators (Jones et al., 1992). In addition, the MDV-LTR also contains v-IL8, a viral homolog of a cellular cytokine (Liu et al., 1999). As both meq and v-IL8 are likely to alter the expression of cellular genes, they probably allow MDV to affect cellular pathways that are not modulated by HVT. These studies have revealed that HVT infection modulates genes that affect different pathways and functions in the cell. As previously mentioned, the m.o.i. of these experiments was 10 3 , which means that only a small portion of the cells is actively infected during the early stages of the experiment. Many of the genes that respond to infection are associated with functions that limit viral spread, such as those involved in interferon responses or cytokine signaling. As interferons and cytokines are secreted products, they affect surrounding, uninfected cells, and alter gene expression patterns throughout the culture. Other responses such as those affecting MHC levels may be mixed; combining effects from both infected and uninfected cells. Finally, the changes in expression of genes that control the cytoskeleton may reflect responses solely from infected cells. An important future objective will be to sort the responses that promote survival of the organism, from those that promote the survival of the virus. This will help us better understand host responses to herpesvirus infection, and may provide insight into the mechanisms by which HVT provides protection against the oncogenic effects of MDV.
Materials and methods To identify cellular responses to infection with HVT, CEF were prepared from 10-day-old embryos. Secondary cultures were plated at a density of 1 107 cells per 100-mm dish and
Table 2 Primers used for Q-PCR Gene
Accession
Amplicon
Primer
Primer sequence
Apolipoprotein AI
M17961
91
MIP-1a
AY037860
69
INF gamma
L39766
63
g IFN inducible thiol reductase
XM_214298
72
a-Actinin
M74143
97
CXCR4
AF294794
66
Forward Reverse Forward Reverse 871F 934R Forward Reverse Forward Reverse Forward Reverse
TGAAGGACACCGAGGCTCTG GCGGAGAACTGGTCCAGGA ATTGCCTCCGCCTACATCAC CCCTTTCTTGGTCACCAGGA GGGAAAGGCTTCTTCACCAAC CTTGTAGCCAAACGTGCTGC GCAAGCCTGCCTGATGCAC CCCGACTCCATGCAGAAGAT GCCTCTGCTCCGCTCTTTT GGTAGGCGGTGTCATGTCAAT GTTTGGATCTGTCCTCTGGCA CTGAGCCAATCTCCTCCGAG
The column at left indicates the chicken genes further analyzed by Q-PCR and at right the nucleotide sequence of the forward and reverse primers. The NCBI accession number, along with the size of the amplicon is also provided.
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then either mock infected or infected with cell associated HVT strain Fc126 (1 104 plaque forming units). Total RNA was isolated at 24, 48, and 72 h post-infection using the Qiagen RNA purification kit. Ten micrograms of total RNA was incubated with 400 units of Superscript II reverse transcriptase (Invitrogen), 0.8 mM dATP, dGTP and dTTP, 100 ACi of [32P] dCTP and 3 mM DTT in a final volume of 30 Al at 42 jC for 1 h. Labeled cDNA was purified by centrifugation through a G-50 column (Amersham). cDNA was labeled to a specific activity of approximately 108 cpm/Ag of input total RNA. For hybridization to nylon arrays, filters were first pre-hybridized in hybridization solution (20 mM Tris pH 7.5, 50% formamide, 4 SSC, 5 Denhardt’s solution, 10% dextran sulfate, 0.5% SDS) containing 50 Ag/ml of denatured chicken genomic DNA. Subsequently, 32P-labelled probe was denatured for 3 min at 98 jC and then added to the pre-hybridization solution. Hybridization was carried out overnight at 48 jC, then filters were washed first in 1 SSC, 0.1% SDS at 62 jC for 10 min, then in two changes of 0.1 SSC, 0.1% SDS at 62 jC. Filters were wrapped in Saran wrap, exposed to a phosphorimager screen overnight and scanned using a Storm PhosphorImager (Molecular Dynamics, Sunnyvale, CA). Images were imported as GIF files for density extraction in the program Snakes (Srinark and Kambhamettu, 2001). Output files were then transferred to the statistical package JMP (SAS Institute, Cary, NC). For Q-PCR, 200 ng of total RNA from infected or control CEF cells were reverse transcribed using Invitrogen’s SuperScript II RNase H Reverse Transcriptase according to the manufacturer’s instructions. Negative controls for genomic contamination were prepared for each sample by processing the RNA as described above, without the addition of the SuperScript II Reverse Transcriptase. Primers for the genes of interest were designed with the aid of the ABI Primer Express 2.0 software (Table 2). To test primer accuracy and amplicon fragment size, 30 cycle PCR reactions were set up using control cDNA. Amplification of the correct sized product was verified by gel electrophoresis on 4 –20% TBE polyacrylamide gels. Q-PCR reactions were carried out according to the kit instructions (Stratagene Brilliant SYBR Green QPCR Mix). The ABI Prism 7900HT Sequence Detection System default cycling conditions were used for quantification. Cycling conditions were as follows: 95 jC for 10 min; 40 cycles of 95 jC for 30 s; and 60 jC for 1 min. Raw fluorescence data were analyzed with the ABI Prism SDS 2.0 software. The number of cycles required to cross the default threshold (Ct) for each of the five replicates for each sample was exported to and further analyzed by SAS Institute’s JMP package.
Acknowledgments We thank Dr. Robin Morgan for her encouragement and advice during the course of these experiments. This
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work was supported by USDA award 99-35205-8828 to C.J.S.
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Herpesvirus of turkeys: microarray analysis of host gene responses to infection Gamze Karaca, Jonathan Anobile, Danielle Downs, Joan Burnside, and Carl J. Schmidt * Department of Animal and Food Sciences, University of Delaware, Newark, DE 19717-2150, USA Received 19 May 2003; returned to author for revision 8 September 2003; accepted 10 September 2003
Abstract Herpesvirus of turkeys (HVT) provides an economically important live vaccine for prevention of Marek’s disease (MD) of chickens. MD, characterized by both immunosuppression and T-cell lymphoma, is caused by another herpesvirus termed Marek’s disease virus (MDV). Microarrays were used to investigate the response of chicken embryonic fibroblasts (CEF) to infection with HVT. Genes responding to HVT infection include several induced by interferon along with others modulating signal transduction, transcription, scaffolding proteins, and the cytoskeleton. Results are compared with earlier studies examining the responses of CEF cells to infection with MDV. D 2003 Elsevier Inc. All rights reserved. Keywords: Herpesvirus of turkeys; Microarray; Chicken; Fibroblast; Infection
Introduction Herpesvirus of turkeys (HVT) provides an important live vaccine for the prevention of Marek’s disease (MD) in chickens. MD is characterized by both immunosuppression and T-cell lymphoma with the causative agent being another herpesvirus: Marek’s disease virus (MDV). MDV is endemic in facilities designed for high production of chickens with significant losses incurred by MD if the disease is not controlled. Vaccination with HVT does not prevent infection with MDV, but does block the onset of lymphoma. Because the virus is not eradicated, vaccination apparently drives selection of MDV strains with increased virulence, thus necessitating new vaccines or new vaccination regimens to control MD. Currently, little is known about the interactions between HVT and the host that gives rise to an immune response that controls MDV infection. Ultimately, understanding the mechanisms regulating host response to vaccination with HVT should increase our ability to control this economically important disease (Djeraba et al., 2002). HVT is ubiquitous in turkey flocks (Witter and Solomon, 1971), yet appears to have little or no impact upon the health of either turkeys or chickens (Fabricant et al., 1982). Of
* Corresponding author. Fax: +1-302-831-2822. E-mail address: [email protected] (C.J. Schmidt). 0042-6822/$ - see front matter D 2003 Elsevier Inc. All rights reserved. doi:10.1016/j.virol.2003.09.025
particular significance, HVT is not oncogenic in either species. The overall genomic sequence of HVT is closely related to that of MDV with recognizable conservation of genes involved in lytic infection (Afonso et al., 2001; Kingham et al., 2001). A significant difference exists between HVT and MDV, however, in that MDV contains a block of 10 genes that are absent from HVT. Included in this block is the meq gene, a member of the fos/jun family of transcription activators, which may play an important role in MDV-induced lymphoma. Other genes located in this region may also play a role in MDV-induced pathology (Liu et al., 1999; Parcells et al., 2001). Hence, the absence of this block of 10 genes from HVT is a likely reason for HVT’s failure to induce T-cell lymphoma. To initiate studies aimed at better understanding host responses to HVT, microarrays were used to monitor gene expression patterns in chicken cells in response to HVT infection. Microarrays provide a high-throughput method to monitor gene expression patterns in response to many different stimuli. High-density gene arrays have been used to monitor host responses to infection with a variety of viruses, including HIV (de la Fuente et al., 2002; Geiss et al., 2000; Sun et al., 2001; van’t Wout et al., 2003), coxsackieviruses (Taylor et al., 2000), hepatitis (Otsuka et al., 2003), and herpesviruses (Carter et al., 2002; Jones and Arvin, 2003; Mossman et al., 2001), including MDV (Liu et al., 2001; Morgan et al., 2001). In this study, microarrays
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were used to examine chicken embryonic fibroblast (CEF) gene expression responses to infection with HVT. These microarrays contained a total of 1126 non-redundant chicken cDNAs that focus largely upon genes identified in an activated T-cell EST database (Morgan et al., 2001). Genes were identified that were either up- or down-regulated at least twofold in response to HVT infection and some responses were verified by quantitative polymerase chain reaction (Q-PCR).
Results and discussion Three independent infections were used in microarray experiments to identify genes that consistently responded to infection with HVT. The data were first normalized using global normalization (Morgan et al., 2001), in which the output from each array is multiplied by a normalization factor such that the average signal intensities of all arrays are equivalent. Initial results were screened to identify genes that were up- or down-regulated at least twofold at any time point, in all three experiments. In addition, cluster analysis was used to characterize the data, grouping genes that responded to infection in a similar pattern. To provide greater insight into the function of the responsive gene products, Gene Ontology (Ashburner and Lewis, 2002; Ashburner et al., 2000; Lewis et al., 2000) annotation terms were extracted (Table 1) using GoFigure (Khan et al., in press). Because of the cell-associated nature of HVT, it is difficult to achieve a high multiplicity of infection (m.o.i.); the m.o.i. of these experiments was 10 3 infectious particles per cell. This is important to consider as results are discussed, as the detected gene responses undoubtedly reflect a mixture of effects from both infected and uninfected cells. In total, 56 genes exhibited reproducible responses in all three experiments (Fig. 1). Also, 4 genes that responded to HVT infection were further analyzed by Q-PCR: g interferon inducible lysosomal thiol reductase, apolipoprotein AI, a actinin, and CXCR4 (Fig. 2). The absolute fold change in mRNA abundance was not identical in all cases when the microarray results were compared with those from Q-PCR, but the temporal trends were similar between the two results. For example, both the microarrays and Q-PCR detected a threefold increase in apolipoprotein AI levels in infected relative to control cells at 24 and 48 h postinfection. At 72 h post-infection, the level of apolipoprotein AI mRNA detected by the microarrays was equivalent to uninfected cells. However, at 72 h post-infection, the level of this mRNA detected by Q-PCR in infected cells was onehalf that seen in control cells. Given the distinctly different nature of these methods, it is not surprising that the absolute fold differences were not identical at all time points. What is significant is that the general trends revealed by the two methods are similar, suggesting that the results from the microarray analysis are good indicators of overall changes in gene expression.
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Inspection of the Gene Ontology terms associated with these gene products provides direct insight into the types of processes modulated by HVT. Almost half of these responsive genes are involved in processes associated with either signal transduction or transcription. This is consistent with modulating gene products that function at critical steps in either host resistance to infection, or the successful growth of the virus. For example, the mRNA levels for the tyrosine kinase JAK1 are increased following infection. Since JAK1 functions to couple cytokine receptors to cellular effectors (Aringer et al., 1999; Kohlhuber et al., 1997; Kotenko and Pestka, 2000), this may serve to prime cells to receive cytokine signals and hasten the appropriate response to herpesvirus infection. Another receptor pathway affected by HVT infection is regulated by CXCR4. Both CXCR4 and Lyn, a tyrosine kinase that couples CXCR4 to intracellular pathways (Chernock et al., 2001; Ptasznik et al., 2002), are induced in response to infection. CXCR4 functions as a receptor for stromal derived factor 1a and has been implicated in cell migration (Bachelder et al., 2002), hematopoiesis, and angiogenesis (Ma et al., 1998; Tachibana et al., 1998). If this response is manifested in lymphocytes, one possible role of increased CXCR4 signaling may be to promote migration of immune cells into the region of herpesvirus infection. Transcription factors that respond to HVT infection include genes that directly regulate gene transcription, such as Hypoxia inducible factor, which was down-regulated during the time course. Two other transcription factors, BATF and Lmo2, were also modulated by HVT infection. BATF contains a leucine zipper, but lacks an activation domain and functions as a negative regulator of AP-1 function by forming an inactive heterodimer with members of the Jun family (Dorsey et al., 1995). Lmo2 is part of an oligomeric transcription complex (Wadman et al., 1997), that functions in both hematopoiesis and angiogenesis (Yamada et al., 1998, 2000). The gene Sirtuin has more global effects upon transcription and is down-regulated by HVT infection. Sirtuin is a member of the SIR gene family, which encodes deacetylase activity (Imai et al., 2000) and is involved in silencing gene activity at rRNA loci and telomeres (Guarente, 2000). Several other gene products responding to HVT infection function as scaffolding or adaptor proteins. Such proteins play critical roles in co-localizing interacting signaling proteins in the cellular compartments appropriate to their combined function. For example RACK (receptor for Ckinase) functions as a scaffold to co-localize protein kinase C (PKC) with a variety of receptors and effectors including PLC-1, Src, and cAMP-specific phosphodiesterase (Hermanto et al., 2002). During HVT infection, RACK expression was down-regulated. Since receptors and effectors exhibit different affinities for RACK, altering the level of this scaffold would be predicted to shift the interaction between PKC and these various signaling molecules. Even subtle changes in the level of a scaffolding protein may
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Table 1 Genes identified as responding to HVT infection in CEF cells Gene product name
Molecular function
Biological process
Cellular apoptosis susceptibility protein Protein tyrosine phosphatase TD14 Caspase 7 Interleukin-activated receptor WASP interactor protein RAB-2 Transcription elongation factor B polypeptide 1-like *Cathepsin C *h-2-Microglobulin Lyn protein tyrosine kinase JAK1 RasGAP-associated protein p56dok-2 G protein pathway suppressor 1 Apolipoprotein AI (APO-AI) RNase L inhibitor Stem cell antigen 2 60S Ribosomal protein L7A Gamma-interferon inducible lysosomal thiol reductase Receptor for activated C-kinase (RACK) Translation elongation factor eEF-1 delta chain Laminin-binding protein Cyclophilin 18 Regulator of G-protein signaling 2 60S Ribosomal protein L3 e3B1 a-Actinin Protein phosphatase 4, regulatory subunit 1 MYD118 Platelet activating receptor TNF receptor-associated factor 1 Rab-GDP dissociation inhibitor *DEAD-box RNA helicase TATA box binding protein (TBP)associated factor Interferon regulatory factor 3 Hypoxia inducible factor Lmo2 B-ATF Interferon-dependent positive-acting transcription factor ISGF-3 Sirtuin Translation initiation factor eIF-4A Ubiquitin Thymocyte activation and developmental protein Zinc finger protein Proline-rich protein with nuclear targeting signal KIAA0242 KIAA0085 Amyloid beta A4 KIAA1027 KIAA1195 Integral membrane protein 2B PBP1 scaffold protein Leucine-rich nuclear protein KIAA0288 Mitogen inducible gene mig-2 Zinc finger protein 64 Annexin II
protein binding protein tyrosine phosphatase initiator caspase receptor vinculin binding GTPase ubiquitin-protein ligase
apoptosis apoptosis apoptosis apoptosis chemotaxis ER to Golgi transport G1/S transition of mitotic cell cycle immune response immune response intracellular signaling cascade intracellular signaling cascade JAK – STAT cascade JNK cascade lipid transport; steroid biosynthesis
cysteine-type endopeptides receptor protein tyrosine kinase protein tyrosine kinase protein serine/threonine kinase GTPase inhibitor lipid transporter enzyme inhibitor lymphocyte antigen RNA binding enzyme
Cluster 9 4 3 3 7 4 7
post-translational membrane targeting protein biosynthesis protein degradation
11 8 11 4 6 5 7 1 4 11 7
protein kinase C binding protein guanyl-nucleotide exchange factor laminin binding protein chaperone GTPase activator structural protein of ribosome mitogenic signalling anchoring actin to membrane regulation of protein phosphatase apoptosis G protein-coupled receptor chemotaxis tumor necrosis factor intracellular signalling transcription general RNA polymerase II transcription factor transcription factor transcription factor transcription factor transcription transcription factor
protein kinase C activation protein synthesis protein translation; laminin receptor protein folding regulation of G-protein coupled receptors ribosomal large subunit assembly signal transducer signal transducer signal transducer signal transducer signal transducer signal transducer signal transducer transcription co-regulator transcription initiation
9 3 7 4 4 7 9 9 9 6 4 4 2 10 7
transcription transcription transcription transcription transcription
10 9 9 7 4
transcription translation initiation protein degradation tagging unknown unknown unknown unknown unknown unknown unknown unknown unknown unknown unknown unknown unknown unknown calcium-dependent phospholipid binding
transcription silencing translation ubiquitin cycle unknown unknown unknown unknown unknown unknown unknown unknown unknown unknown unknown unknown unknown unknown vesicle transport
regulation regulation regulation regulation regulation
9 5 11 11 11 11 10 10 9 7 7 7 7 6 4 1 1 7
For each gene, the Molecular Function and Biological Process Gene Ontology terms are provided. The cluster number is derived from cluster analysis and refers to the groupings indicated in Fig. 1. An asterisk indicates those genes that exhibited a similar response in fibroblasts infected with MDV (Morgan et al., 2001).
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Fig. 1. Genes responding to HVT infection. Histograms showing the ratio of infected divided by control levels of gene expression as detected by microarray analysis either 24, 48, or 72 h after infection. For inclusion in this analysis, each gene must have displayed similar changes in all three microarray experiments and a minimum change of twofold (up or down) from the control value.
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Fig. 1 (continued ).
significantly alter the responsiveness of signal transduction pathways. The specific effect of RACK down-regulation in the context of HVT infection is unclear, although other
herpesviruses, such as Epstein-Barr virus, inhibit PKC activity by affecting RACK (Baumann et al., 2000; Tardif et al., 2002). Additional scaffolding proteins modulated by
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Fig. 2. Quantitative PCR analysis of g-interferon, g-interferon lysosomal thiol reductase, apolipoprotein A1, a-actinin, CXCR4, and MIP-1a. At each time point post-infection, the ratio mRNA level detected in infected cells divided by control level is indicated. Note that the Y-axes are not identical.
HVT infection include: E3B1, which participates in coupling the Ras to Rac pathway (Scita et al., 1999), TNF receptor associated factor 1 (Wajant et al., 2001), which couples TNF receptors to intracellular regulators of apoptosis, and the putative scaffold protein PBP1, whose function is unknown. Not surprisingly, several of the genes responding to HVT infection are homologous to mammalian genes known to respond to interferon (de Veer et al., 2001). Because no chicken interferon cDNAs were included in this array, we used Q-PCR to examine the expression pattern of a, h, and g interferon mRNAs during infection. As seen in Fig. 2, g interferon mRNA in HVT-infected cells peaked at 48 h postinfection, while a and h interferon mRNA levels in infected cells were similar to controls (not shown). Several of the candidate interferon responsive genes also peaked at 48 h including: g interferon inducible lysosomal thiol reductase, h-2 microglobulin, ISGF-3, IRF3, PbP1, eIF-4A, and dead box RNA helicase. However, not all of these are found in the same cluster, perhaps reflecting different kinetic responses to interferon. One gene, TNF receptor associated factor-1, which is known to be interferon-regulated, peaked 72 h post-infection. This may merely reflect normal kinetics of interferon response for this gene. Neither MHC class I nor class II genes, which are typically induced by interferon g (Song et al., 1997; Zoeller et al., 1998; Zoller et al., 1992), made this final list of genes that responded to HVT infection in all three experiments.
Inspection of the primary data indicated that the MHC class I gene expression was increased at 48 h post-infection in two out of three experiments, while MHC class II gene was induced in only one experiment (not shown). Regulation of MHC in the context of infection by MD-like viruses is known to be complex. Cells actively infected with either MDV or HVT reduce MHC class I protein levels (Hunt et al., 2001; Levy et al., 2003), while surrounding uninfected cells have elevated MHC class I protein. Evidence indicates that the level of surface MHC protein in infected cells is regulated by post-translational processes (Hunt et al., 2001) while uninfected cells elevate transcription of MHC genes in response to interferon (Zoeller et al., 1998). The microarray results presented here integrate mRNA levels in both infected and uninfected cells. Possibly, HVT infection also down-regulates the transcription of MHC genes, leading to an apparently diminished interferon effect upon MHC transcription. The level of several gene products predicted to control cell morphology responded to HVT infection. For example, a-actinin mRNA levels decreased during the course of infection and a-actinin serves to anchor actin stress fibers to focal adhesion plaques (Rajfur et al., 2002). Stress fibers play an important role in generating the flat, extended morphology of fibroblasts (Baschong et al., 1997; Lombardi et al., 1990). The levels of E3B1 transcription are also decreased during HVT infection (see above), and this is predicted to decrease activation of the Rac GTPase. Acti-
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vated Rac GTPase promotes extension of the cell into lamellipodia and membrane ruffles (Nobes and Hall, 1995a, 1995b; Ridley and Hall, 1992; Ridley et al., 1992). Finally, decreased expression of the RACK in fibroblasts by antisense oligonucleotides reduces cell spreading (Hermanto et al., 2002) and RACK expression appears reduced in HVT-infected cells (see above). Taken together, decreased expression of a-actinin, E3B1, and RACK may be predicted to reduce contact adhesion and be responsible for rounding up of infected cells, a phenomenon associated with infection by HVT and other herpesviruses. HVT plays an important economic role as a vaccine preventing MD in chickens. Studies examining host cell responses to MDV infection using similar microarrays have been published (Morgan et al., 2001). Comparing the results obtained here for HVT, with those for MDV, reveals both overlaps, and differences in host cell responses. Both HVT and MDV generate an interferon response, as indicated by induction of genes responsive to this cytokine. In addition, both viruses increased expression of the Cathepsin C gene. This protease is essential in activating cytotoxic T-cellmediated apoptosis (McGuire et al., 1997), which may play an important role in recognizing and eliminating cell-associated viruses such as HVT and MDV. Several genes induced by MDV infection were not detectably altered in these microarray experiments using HVT. Many of these were induced less than threefold during MDV infection. One gene product not detectably affected in the microarray experiments by HVT, but induced greater than fivefold by MDV infection, was chicken MIP-1a (Morgan et al., 2001). This chemokine mediates inflammatory responses to virus infection, and may play a role in recruiting T cells to such sites. To further evaluate MIP-1a, Q-PCR was conducted to evaluate more rigorously the expression of this gene in the context of HVT infection. As shown in Fig. 2, MIP-1a was induced approximately fourfold in one of the three infection experiments used for the microarray analysis. Hence HVT may induce MIP-1a expression, but this response may not be as reproducible as with MDV infection.
Genomic differences between HVT and MDV may play a role in causing the distinct cellular responses to infection by these two viruses. For example, the long terminal repeat (LTR) of MDV contains at least 10 genes that are absent from HVT. Included in this region of MDV is the meq oncogene, a member of the jun/fos family of transcription regulators (Jones et al., 1992). In addition, the MDV-LTR also contains v-IL8, a viral homolog of a cellular cytokine (Liu et al., 1999). As both meq and v-IL8 are likely to alter the expression of cellular genes, they probably allow MDV to affect cellular pathways that are not modulated by HVT. These studies have revealed that HVT infection modulates genes that affect different pathways and functions in the cell. As previously mentioned, the m.o.i. of these experiments was 10 3 , which means that only a small portion of the cells is actively infected during the early stages of the experiment. Many of the genes that respond to infection are associated with functions that limit viral spread, such as those involved in interferon responses or cytokine signaling. As interferons and cytokines are secreted products, they affect surrounding, uninfected cells, and alter gene expression patterns throughout the culture. Other responses such as those affecting MHC levels may be mixed; combining effects from both infected and uninfected cells. Finally, the changes in expression of genes that control the cytoskeleton may reflect responses solely from infected cells. An important future objective will be to sort the responses that promote survival of the organism, from those that promote the survival of the virus. This will help us better understand host responses to herpesvirus infection, and may provide insight into the mechanisms by which HVT provides protection against the oncogenic effects of MDV.
Materials and methods To identify cellular responses to infection with HVT, CEF were prepared from 10-day-old embryos. Secondary cultures were plated at a density of 1 107 cells per 100-mm dish and
Table 2 Primers used for Q-PCR Gene
Accession
Amplicon
Primer
Primer sequence
Apolipoprotein AI
M17961
91
MIP-1a
AY037860
69
INF gamma
L39766
63
g IFN inducible thiol reductase
XM_214298
72
a-Actinin
M74143
97
CXCR4
AF294794
66
Forward Reverse Forward Reverse 871F 934R Forward Reverse Forward Reverse Forward Reverse
TGAAGGACACCGAGGCTCTG GCGGAGAACTGGTCCAGGA ATTGCCTCCGCCTACATCAC CCCTTTCTTGGTCACCAGGA GGGAAAGGCTTCTTCACCAAC CTTGTAGCCAAACGTGCTGC GCAAGCCTGCCTGATGCAC CCCGACTCCATGCAGAAGAT GCCTCTGCTCCGCTCTTTT GGTAGGCGGTGTCATGTCAAT GTTTGGATCTGTCCTCTGGCA CTGAGCCAATCTCCTCCGAG
The column at left indicates the chicken genes further analyzed by Q-PCR and at right the nucleotide sequence of the forward and reverse primers. The NCBI accession number, along with the size of the amplicon is also provided.
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then either mock infected or infected with cell associated HVT strain Fc126 (1 104 plaque forming units). Total RNA was isolated at 24, 48, and 72 h post-infection using the Qiagen RNA purification kit. Ten micrograms of total RNA was incubated with 400 units of Superscript II reverse transcriptase (Invitrogen), 0.8 mM dATP, dGTP and dTTP, 100 ACi of [32P] dCTP and 3 mM DTT in a final volume of 30 Al at 42 jC for 1 h. Labeled cDNA was purified by centrifugation through a G-50 column (Amersham). cDNA was labeled to a specific activity of approximately 108 cpm/Ag of input total RNA. For hybridization to nylon arrays, filters were first pre-hybridized in hybridization solution (20 mM Tris pH 7.5, 50% formamide, 4 SSC, 5 Denhardt’s solution, 10% dextran sulfate, 0.5% SDS) containing 50 Ag/ml of denatured chicken genomic DNA. Subsequently, 32P-labelled probe was denatured for 3 min at 98 jC and then added to the pre-hybridization solution. Hybridization was carried out overnight at 48 jC, then filters were washed first in 1 SSC, 0.1% SDS at 62 jC for 10 min, then in two changes of 0.1 SSC, 0.1% SDS at 62 jC. Filters were wrapped in Saran wrap, exposed to a phosphorimager screen overnight and scanned using a Storm PhosphorImager (Molecular Dynamics, Sunnyvale, CA). Images were imported as GIF files for density extraction in the program Snakes (Srinark and Kambhamettu, 2001). Output files were then transferred to the statistical package JMP (SAS Institute, Cary, NC). For Q-PCR, 200 ng of total RNA from infected or control CEF cells were reverse transcribed using Invitrogen’s SuperScript II RNase H Reverse Transcriptase according to the manufacturer’s instructions. Negative controls for genomic contamination were prepared for each sample by processing the RNA as described above, without the addition of the SuperScript II Reverse Transcriptase. Primers for the genes of interest were designed with the aid of the ABI Primer Express 2.0 software (Table 2). To test primer accuracy and amplicon fragment size, 30 cycle PCR reactions were set up using control cDNA. Amplification of the correct sized product was verified by gel electrophoresis on 4 –20% TBE polyacrylamide gels. Q-PCR reactions were carried out according to the kit instructions (Stratagene Brilliant SYBR Green QPCR Mix). The ABI Prism 7900HT Sequence Detection System default cycling conditions were used for quantification. Cycling conditions were as follows: 95 jC for 10 min; 40 cycles of 95 jC for 30 s; and 60 jC for 1 min. Raw fluorescence data were analyzed with the ABI Prism SDS 2.0 software. The number of cycles required to cross the default threshold (Ct) for each of the five replicates for each sample was exported to and further analyzed by SAS Institute’s JMP package.
Acknowledgments We thank Dr. Robin Morgan for her encouragement and advice during the course of these experiments. This
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work was supported by USDA award 99-35205-8828 to C.J.S.
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