Transcriptome Analysis of Chicken Embryo Fibroblast Cell Infected with Marek’s Disease Virus of GX0101 ∆ LTR

X Li S Su N Cui H Zhou X Liu Z Cui About the authors

ABSTRACT

Marek’s disease (MD), a lymphoproliferative disorder of chickens caused by the MD virus (MDV), is economically significant. The resistance/susceptibility to MD is controlled by host genetics. The host response to different virus strains varies. The pathogenicity of REV-LTR deleted GX0101∆LTR MDV has been previously reported. However, the precise molecular mechanism of the response of chickens to GX0101ΔLTR remains unclear. The current study aimed at identifying the genes and pathways involved in the response to GX0101ΔLTR virus infection in specific pathogen-free chicken embryo fibroblast cells using global transcriptome analysis. A total of 1,633 genes associated with GX0101ΔLTR infection were identified. Functional analysis showed that the cytokine-cytokine receptor interaction plays an important role in the response to GX0101ΔLTR infection.

Keywords:
Gene profiling; chicken; CEF cell; GX0101ΔLTR MDV.

INTRODUCTION

Marek’s disease (MD) is a chicken lymphoproliferative disease caused by MD virus (MDV) (Witter et al., 2005Witter RL, Calnek BW, Buscaglia C, Gimeno IM, Schat KA. Classification of Marek’s disease viruses according to pathotype: philosophy and methodology. Avian Pathology 2005;34(2):75-90.). MD causes $1-2 billion annual losses to the industry (Morrow and Fehler, 2004Morrow C, Fehler F, editors. Marek’s disease: a worldwide problem. London: Elsevier Academic Press; 2004.). Field strains continue to evolve with increasing virulence (Hunt & Dunn, 2013Hunt HD, Dunn JR. The influence of host genetics on Marek’s disease virus evolution. Avian Diseases 2013;57(2 Suppl):474-482.). We have reported the GX0101 strain of MDV, which was the first field isolate with an LTR insert of REV origin (Zhang & Cui, 2005Zhang Z, Cui Z. Isolation of recombinant field strains of Marek’s disease virus integrated with reticuloendotheliosis virus genome fragments. Science in China. Series C Lifes Sciences 2005;48(1):81-88.). The pathogenicity of the REV-LTR deleted GX0101ΔLTR virus was slightly higher than its parental GX0101 clone, based on growth retardation, immunosuppression, mortality and tumorogenicity (Sun et al., 2010Sun AJ, Xu XY, Petherbridge L, Zhao YG, Nair V, Cui ZZ. Functional evaluation of the role of reticuloendotheliosis virus long terminal repeat (LTR) integrated into the genome of a field strain of Marek’s disease virus. Virology 2010;397(2):270-276.). However, the virus-host cell interaction related to GX0101ΔLTR pathogenicity is still unclear.

The high-throughput microarray is one of the widely used technologies in transcriptome studies (Haq et al., 2010Haq K, Brisbin JT, Thanthrige-Don N, Heidari M, Sharif S. Transcriptome and proteome profiling of host responses to Marek’s disease virus in chickens. Veterinary Immunology and Immunopathology 2010;138(4):292-302.; Liu et al., 2001Liu HC, Cheng HH, Tirunagaru V, Sofer L, Burnside J. A strategy to identify positional candidate genes conferring Marek’s disease resistance by integrating DNA microarrays and genetic mapping. Animal Genetics 2001;32(6):351-359.; Sarson et al., 2008Sarson AJ, Parvizi P, Lepp D, Quinton M, Sharif S. Transcriptional analysis of host responses to Marek’s disease virus infection in genetically resistant and susceptible chickens. Animal Genetics 2008;39(3):232-240.). The microarray technology was applied to analyze the global gene expression profile of CEF following GX0101∆LTR infection in the current study.

MATERIALS AND METHODS

Chicken embryo fibroblast (CEF) cell culture

The primary CEF cells were isolated from 10-day-old specific-pathogen free (SPF) White Leghorn chicken embryos. Whole embryos were dissociated into single cell populations using 0.25% trypsin/1mM EDTA. The cells dissociated from the embryos were suspended in Dulbecco’s modified Eagle’s medium (DMEM, 0.45% glucose) plus 10% fetal bovine serum, 100 units/mL penicillin, 100 μg/mL streptomycin, and 2 mM L-glutamine in 10 cm tissue culture dishes (Corning, Shanghai, China). Cultured cells were grown at 37°C in a 5% CO2 incubator until the cells reached confluent monolayers. Frozen cell stocks were prepared and stored in liquid nitrogen for further utilization.

MDV infection and sample collection

Four chicken embryos were used in the current study. The primary CEF cells collected from each of the four embryos were seeded into two individual flasks at a density of 5 × 106/flask. The cells in one flask were infected with GX0101∆LTR (Sun et al., 2010Sun AJ, Xu XY, Petherbridge L, Zhao YG, Nair V, Cui ZZ. Functional evaluation of the role of reticuloendotheliosis virus long terminal repeat (LTR) integrated into the genome of a field strain of Marek’s disease virus. Virology 2010;397(2):270-276.), while those in the other flask were mock-infected with DMEM. The infected or non-infected CEF cells were collected at 56 h post infection and treated with RNeasy reagent (Qiagen, Valencia, CA) for total RNA extraction. A total of eight samples were collected; four of which were infected cells and four were non-infected controls.

Total RNA isolation, experimental design, sample labeling, and microarray hybridization

The total RNA was isolated using the RNeasy Mini Kit (Qiagen, Valencia, CA) from the infected and non-infected CEF cells, according to the instructions of the manufacturer. RNA concentration and integrity were checked using NanoDrop 2000 (Thermo Fisher Scientific Inc., Waltham, MA) and Agilent Bioanalyzer 2100 (Agilent Technologies, Santa Clara, CA), respectively.

A custom chicken 4 × 44 K Agilent microarray, including both chicken and MDV genes, was designed based on the chicken genome assembly galGal4 and MDV annotation using Agilent earray tool (https://earray.chem.agilent.com/earray/). A paired comparison was performed to compare the infected and the non-infected (I/N) groups. Four biological replicates were used in each group with dye balance.

A 400-ng total RNA from each sample was used for labeling. The sample labeled with Cy3 or Cy5 was hybridized with another labeled with Cy5 or Cy3 and incubated for 17h at 65°C. The slides were washed according to the manufacturer’s recommendations. All procedures were performed according to Agilent’s recommendation and described in detail previously (Li et al., 2008Li X, Chiang HI, Zhu J, Dowd SE, Zhou H. Characterization of a newly developed chicken 44K Agilent microarray. BMC Genomics 2008;9:60.).

Microarray data analysis

The signal intensity of each probe was filtered against negative controls in the microarray. Data normalization was performed using locally weighted scatter plot smoothing (Yang et al., 2002Yang YH, Dudoit S, Luu P, Lin DM, Peng V, Ngai J, et al. Normalization for cDNA microarray data: a robust composite method addressing single and multiple slide systematic variation. Nucleic Acids Research 2002;30(4):e15.). The normalized natural log intensities were analyzed using a mixed model from SAS (SAS, Cary, NC) with a fixed effect of treatment (I or N) and dye (Cy5 or Cy3) and a random effect of slide and array. A comparison between the infected (I) and non-infected (N) groups was made. Accordingly, p<0.05 and fold change >1.5 were considered as significant. The microarray information of this experiment was deposited in NCBI’s Gene Expression Omnibus (GEO) database (Barrett et al., 2013Barrett T, Wilhite SE, Ledoux P, Evangelista C, Kim IF, Tomashevsky M, et al. NCBI GEO: archive for functional genomics data sets--update. Nucleic Acids Research 2013;41:D991-5.). The accession numbers were as follows: platform: GPL18321; series: GSE59052.

The functional annotations of Gene ontology (GO) and pathway enrichment for significantly differentially up-regulated and down-regulated genes were performed using DAVID 6.7 (Huang et al., 2009Huang DW, Sherman BT, Lempicki RA. Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists. Nucleic Acids Research 2009;37(1):1-13., Huang et al., 2009). The differentially expressed genes were uploaded to the DAVID database as a gene list. The Gallus gallus whole genome was used as the background. Default setting was used. p<0.05 was considered significant.

RESULTS

Gene expression was significantly different between MDV-infected and non-infected CEF cells

A total of 1,633 genes presented significantly different expression between I and N groups (p<0.05, fold change > 1.5). Out of those genes, 952 genes were up-regulated and 681 were down-regulated. Furthermore, 15 genes of the 952 up-regulated genes presented higher than 100-fold change, and 39 a fold change higher than 20. The highest fold change (1046) was found for one chicken EST (BU246007), whereas the lowest (1.5) was found for a chicken EST (CR385211). Only two down-regulated genes presented higher than 10-fold changes [Preproinsulin and a chicken EST (BU236185)]. Fifty-six immune-related genes were differentially expressed between I and N groups, with the highest fold change (122.9) for the chicken MX gene. The lowest fold change (1.5) was found for chicken CD44 (Additional file 1).

Gene ontology annotation analysis

The Gene ontology (GO) annotation was performed for the genes with significant expression using the DAVID database. The GO biological process (BP) annotation was reported in the current study.

There were 73 enriched BP terms associated with the up-regulated genes (Table 1). These enriched BP terms were roughly divided into six groups as follows: 1) immune-related group; 2) signal transduction-related; 3) metabolism- related; 4) circulatory system; 5) cell communication; and 6) others.

Table 1
GO BP annotation for up-regulated genes

The four BP terms associated with the down-regulated genes were significantly enriched (Figure 1). Epithelial cell differentiation and epithelium development were related to epithelium function with the fold enrichments of 6.12 and 3.20, respectively. Cell adhesion and extracellular matrix organization had fold enrichments of 2.64 and 4.97, respectively.

Figure 1
GO BP annotation associated with down-regulated genes.

The enriched BP terms associated with the up-regulated genes were further clustered using the Categorizer tool (Figure 2). The enriched terms were roughly clustered into 19 groups, including 16 immune-related groups and 3 non-immune related groups. Three non-immune related groups were protein metabolism, cell adhesion, and lipid metabolism, which accounted for 7.06%, 1.18%, and 1.18% of all groups, respectively. The top eight groups were lymphocyte activation, stress response, regulation of lymphocyte activation, T-cell activation, cell death, response to external stimulus, protein metabolism, and response to biotic stimulus, which were 12.94%, 11.67%, 10.59%, 8.24%, 7.06%, 7.06%, 7.06%, and 5.88% of all groups, respectively.

Figure 2
GO term classification count using the Categorizer. The GO terms were classified based on the Immune_class classification method in Categorizer tool.

Pathway annotation for the significantly-expressed genes

The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway annotation results showed that four KEGG pathways associated with the up-regulated genes were significantly enriched (Table 2). These enriched pathways were the cytosolic DNA-sensing pathway, Toll-like receptor (TLR) signaling pathway, cytokine-cytokine receptor interaction, and focal adhesion with fold enrichments of 9.37, 3.89, 3.64, and 2.24, respectively. The enriched cytokine-cytokine receptor interaction pathway included IL6, IL1b, IL15, IL4R, IL17RA, CC5, CXCL14, TGFBR2, and the TNF family (i.e., TNFRSF21, TNFRSF15, and TNFSF10).

No enriched pathway was associated with the down-regulated genes.

Table 2
KEGG pathway annotation for up-regulated genes

Validation of microarray results using quantitative real-time PCR

The quantitative real-time PCR (qRT-PCR) was performed to validate the microarray data. The same RNA samples were used. Twelve differentially-expressed genes associated with immune response functional terms were selected for the qRT-PCR validation. The qRT-PCR results of the 11/12 validated genes were consistent with the microarray results in terms of significance and regulation direction (Table 3). The BCL6 showed up-regulation in both microarray and qRT-PCR results, but did not present significant expression by qRT-PCR. All the validated down-regulated genes presented higher fold changes in the qRT-PCR test compared with the microarray analyses. Five of the eight up-regulated genes presented higher fold changes in the microarray analysis than in the qRT-PCR test.

Table 3
Comparison of gene expression levels (fold change) between microarray and qRT-PCR results

DISCUSSION AND CONCLUSIONS

The high-throughput microarray is one of most widely-used technologies to identify the transcriptome associated with a specific trait (Chiang et al., 2008Chiang HI, Swaggerty CL, Kogut MH, Dowd SE, Li X, Pevzner IY, et al. Gene expression profiling in chicken heterophils with Salmonella enteritidis stimulation using a chicken 44 K Agilent microarray. BMC Genomics 2008;9:526.; Lee et al., 2010Lee JY, Song JJ, Wooming A, Li X, Zhou H, Bottje WG, et al. Transcriptional profiling of host gene expression in chicken embryo lung cells infected with laryngotracheitis virus. BMC Genomics 2010;11:445.; Li et al., 2010Li X, Swaggerty CL, Kogut MH, Chiang HI, Wang Y, Genovese KJ, et al. Gene expression profiling of the local cecal response of genetic chicken lines that differ in their susceptibility to Campylobacter jejuni colonization. PLoS One 2010;5(7):e11827.; Li et al., 2011; Sandford et al., 2011Sandford EE, Orr M, Balfanz E, Bowerman N, Li X, Zhou H, et al. Spleen transcriptome response to infection with avian pathogenic Escherichia coli in broiler chickens. BMC Genomics 2011;12:469., Allen et al., 2012Allen CC, Alves BR, Li X, Tedeschi LO, Zhou H, Paschal JC, et al. Gene expression in the arcuate nucleus of heifers is affected by controlled intake of high- and low-concentrate diets. Journal of Animal Science 2012:90(7):2222-2232.; Sandford et al., 2012Sandford EE, Orr M, Shelby M, Li X, Zhou H, Johnson TJ, et al. Leukocyte transcriptome from chickens infected with avian pathogenic Escherichia coli identifies pathways associated with resistance. Results in Immunology 2012;2:44-53., Subramaniam et al., 2013Subramaniam S, Preeyanon L, Cheng HH. Transcriptional profiling of mEq-dependent genes in Marek’s disease resistant and susceptible inbred chicken lines. PLoS One 2013;8(10):e78171.). Several studies have been performed to identify the gene expression profile in in-vivo and in-vitro MDV infections using microarray analysis (Yonash et al., 1999Yonash N, Bacon LD, Witter RL, Cheng HH. High resolution mapping and identification of new quantitative trait loci (QTL) affecting susceptibility to Marek’s disease. Animal Genetics 1999;30(2):126-135.; Morgan et al., 2001Morgan RW, Sofer L, Anderson AS, Bernberg EL, Cui J, Burnside J. Induction of host gene expression following infection of chicken embryo fibroblasts with oncogenic Marek’s disease virus. Journal of Virology 2001;75(1):533-9.; Sarson et al., 2006Sarson AJ, Abdul-Careem MF, Zhou H, Sharif S. Transcriptional analysis of host responses to Marek’s disease viral infection. Viral Immunology 2006;19(4):747-758.; Kano et al., 2009Kano R, Konnai S, Onuma M, Ohashi K. Microarray analysis of host immune responses to Marek’s disease virus infection in vaccinated chickens. Journal of Veterinary Medical Science 2009;71(5):603-610.; Heidari et al., 2010Heidari M, Sarson AJ, Huebner M, Sharif S, Kireev D, Zhou H. Marek’s disease virus-induced immunosuppression: array analysis of chicken immune response gene expression profiling. Viral Immunology 2010;23(3):309-319.; Smith et al., 2011Smith J, Sadeyen JR, Paton IR, Hocking PM, Salmon N, Fife M, et al. 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. Journal of Virology 2011;85(21):11146-11158.; Lian et al., 2012Lian L, Qu LJ, Sun HY, Chen YM, Lamont SJ, Liu CJ, et al. Gene expression analysis of host spleen responses to Marek’s disease virus infection at late tumor transformation phase. Poultry Science 2012;91(9):2130-2138., Subramaniam et al., 2013Subramaniam S, Preeyanon L, Cheng HH. Transcriptional profiling of mEq-dependent genes in Marek’s disease resistant and susceptible inbred chicken lines. PLoS One 2013;8(10):e78171.).. The molecular mechanism of the host response to GX0101∆LTR MDV was elucidated in the current study.

The CEF is a reasonable model for studying the reaction to MDV infection (Subramaniam et al., 2013Subramaniam S, Preeyanon L, Cheng HH. Transcriptional profiling of mEq-dependent genes in Marek’s disease resistant and susceptible inbred chicken lines. PLoS One 2013;8(10):e78171.). MDV infection includes the three following stages: early cytolytic infection starting at 2-7 dpi (day post infection), a latent phage initiated around 7-10 dpi, and a late cytolytic phase causing inflammation and transformation of latently-infected lymphocytes into tumor cells triggered between 14 and 21 dpi (Calnek, 1986Calnek BW. Marek’s disease--a model for herpesvirus oncology. Critical Reviews in Microbiology 1986;12(4):293-320., Calnek, 2001Calnek BW. Pathogenesis of Marek’s disease virus infection. Current Topics in Microbiology and Immunology 2001;255:25-55.). Various expression profiles were discovered at different stages of MDV infection. The genes related to inflammation, cell growth, and differentiation and antigen presentation (e.g., MIP, IL-13R, MHC I, and MHC II) are induced both at 2 and 4 dpi when the CEFs are infected with MDV (Morgan et al., 2001Morgan RW, Sofer L, Anderson AS, Bernberg EL, Cui J, Burnside J. Induction of host gene expression following infection of chicken embryo fibroblasts with oncogenic Marek’s disease virus. Journal of Virology 2001;75(1):533-9.). More than one thousand genes in the present study were significantly expressed in the GX0101ΔLTR-infected CEF. MDV infection induced gene expression. It has been reported that more genes are up-regulated in CEF at 24, 48, and 96 h post MDV infection in both MD-resistant and -susceptible chicken lines (Subramaniam et al., 2013Subramaniam S, Preeyanon L, Cheng HH. Transcriptional profiling of mEq-dependent genes in Marek’s disease resistant and susceptible inbred chicken lines. PLoS One 2013;8(10):e78171.). More up-regulated genes have been observed in the spleen at 2 and 5 days post MDV infection (Smith et al., 2011Smith J, Sadeyen JR, Paton IR, Hocking PM, Salmon N, Fife M, et al. 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. Journal of Virology 2011;85(21):11146-11158.). More genes are up-regulated on 5 and 10 days post infection in both MD-resistant and -susceptible chicken lines (Yu et al., 2011Yu Y, Luo J, Mitra A, Chang S, Tian F, Zhang H, et al. Temporal transcriptome changes induced by MDV in Marek’s disease-resistant and -susceptible inbred chickens. BMC Genomics 2011;12:501.). However, the opposite result has been found on 21 days post infection, where more genes were down-regulated in both MD-resistant and -susceptible lines (Yu et al., 2011Yu Y, Luo J, Mitra A, Chang S, Tian F, Zhang H, et al. Temporal transcriptome changes induced by MDV in Marek’s disease-resistant and -susceptible inbred chickens. BMC Genomics 2011;12:501.). The global gene expression associated with MDV infection shows temporal characteristics. The number of induced genes is greater at the early stage (2-10 dpi) and decreases at the late stage of MDV infection.

Cytokines are important mediators involved in cell-mediated immune responses to MDV infection and secreted as a result of antigen presentation to T cells (Haq et al., 2013Haq K, Schat KA, Sharif S. Immunity to Marek’s disease: where are we now? Developmental and Comparative Immunology 2013;41(3):439-446.). The enriched cytokine-cytokine receptor interaction pathways are associated with the up-regulated genes in the chicken CEF, which indicated that the host proinflammatory response was stimulated at the early stage but weakened in the chicken spleen at the late tumor transformation phase following MDV infection (Lian et al., 2012Lian L, Qu LJ, Sun HY, Chen YM, Lamont SJ, Liu CJ, et al. Gene expression analysis of host spleen responses to Marek’s disease virus infection at late tumor transformation phase. Poultry Science 2012;91(9):2130-2138., Smith et al., 2011Smith J, Sadeyen JR, Paton IR, Hocking PM, Salmon N, Fife M, et al. 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. Journal of Virology 2011;85(21):11146-11158.). IL-6 and IL-18 are significantly up-regulated in the MDV-infected splenocytes of genetically susceptible chickens (Kaiser et al., 2003Kaiser P, Underwood G, Davison F. Differential cytokine responses following Marek’s disease virus infection of chickens differing in resistance to Marek’s disease. Journal of Virology 2003;77(1):762-768.), whereas IL-1b and IL-8 are up-regulated in resistant birds (Jarosinski et al., 2005Jarosinski KW, Njaa BL, O’Connell P H, Schat KA. Pro-inflammatory responses in chicken spleen and brain tissues after infection with very virulent plus Marek’s disease virus. Viral Immunology 2005;18(1):148-161.). IL-6 and IL-18 seem to be associated consistently with MD susceptibility, whereas both IL-1b and IL-8 were significantly expressed in the MDV-infected SPF CEF.

The TNF receptor superfamily and their ligands are mainly expressed on immune cells. Their immunomodulatory functions include the enhancement of dendritic cell survival and priming capacity for T cells, optimal generation of effector T cells, optimal antibody responses, and amplification of inflammatory reactions (Kwon et al., 2003Kwon B, Kim BS, Cho HR, Park JE, Kwon BS. Involvement of tumor necrosis factor receptor superfamily(TNFRSF) members in the pathogenesis of inflammatory diseases. Experimental & Molecular Medicine 200335(1):8-16.). The expression of the TNF (ligand) superfamily member 10 increases at 5 days post MDV infection, while the TNF (ligand) superfamily members 11 and 13b decrease at 15 dpi (Heidari et al., 2010Heidari M, Sarson AJ, Huebner M, Sharif S, Kireev D, Zhou H. Marek’s disease virus-induced immunosuppression: array analysis of chicken immune response gene expression profiling. Viral Immunology 2010;23(3):309-319.). TNFSF10, TNFSF13b, and TNFSF 18 are up-regulated at 5 dpi (Smith et al., 2011Smith J, Sadeyen JR, Paton IR, Hocking PM, Salmon N, Fife M, et al. 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. Journal of Virology 2011;85(21):11146-11158.). TNFRSF10, TNFRSF21, and TNFRSF15 are also up-regulated post MDV infection. In conclusion, we have identified the differentially expressed genes and pathways associated with GX0101∆LTR infection in CEFs. The present findings will add to the current understanding of the mechanism behind MDV infection. The cytokine-cytokine receptor interaction plays important roles in the response to GX0101ΔLTR infection.

ACKNOWLEDGEMENTS

The authors thank Shanghai Biotechnology Corpo-ration for the microarray hybridization. This project was funded by the National Natural Science Foundation of China (31402235) and Shandong Modern Agricultural Industry & Technology System (SDAIT-11-02), Shandong Province Agricultural Seed Project.

REFERENCES

  • Allen CC, Alves BR, Li X, Tedeschi LO, Zhou H, Paschal JC, et al. Gene expression in the arcuate nucleus of heifers is affected by controlled intake of high- and low-concentrate diets. Journal of Animal Science 2012:90(7):2222-2232.
  • Barrett T, Wilhite SE, Ledoux P, Evangelista C, Kim IF, Tomashevsky M, et al. NCBI GEO: archive for functional genomics data sets--update. Nucleic Acids Research 2013;41:D991-5.
  • Calnek BW. Marek’s disease--a model for herpesvirus oncology. Critical Reviews in Microbiology 1986;12(4):293-320.
  • Calnek BW. Pathogenesis of Marek’s disease virus infection. Current Topics in Microbiology and Immunology 2001;255:25-55.
  • Chiang HI, Swaggerty CL, Kogut MH, Dowd SE, Li X, Pevzner IY, et al. Gene expression profiling in chicken heterophils with Salmonella enteritidis stimulation using a chicken 44 K Agilent microarray. BMC Genomics 2008;9:526.
  • Haq K, Brisbin JT, Thanthrige-Don N, Heidari M, Sharif S. Transcriptome and proteome profiling of host responses to Marek’s disease virus in chickens. Veterinary Immunology and Immunopathology 2010;138(4):292-302.
  • Haq K, Schat KA, Sharif S. Immunity to Marek’s disease: where are we now? Developmental and Comparative Immunology 2013;41(3):439-446.
  • Heidari M, Sarson AJ, Huebner M, Sharif S, Kireev D, Zhou H. Marek’s disease virus-induced immunosuppression: array analysis of chicken immune response gene expression profiling. Viral Immunology 2010;23(3):309-319.
  • Huang DW, Sherman BT, Lempicki RA. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nature Protocols 2009;4(1):44-57.
  • Huang DW, Sherman BT, Lempicki RA. Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists. Nucleic Acids Research 2009;37(1):1-13.
  • Hunt HD, Dunn JR. The influence of host genetics on Marek’s disease virus evolution. Avian Diseases 2013;57(2 Suppl):474-482.
  • Jarosinski KW, Njaa BL, O’Connell P H, Schat KA. Pro-inflammatory responses in chicken spleen and brain tissues after infection with very virulent plus Marek’s disease virus. Viral Immunology 2005;18(1):148-161.
  • Kaiser P, Underwood G, Davison F. Differential cytokine responses following Marek’s disease virus infection of chickens differing in resistance to Marek’s disease. Journal of Virology 2003;77(1):762-768.
  • Kano R, Konnai S, Onuma M, Ohashi K. Microarray analysis of host immune responses to Marek’s disease virus infection in vaccinated chickens. Journal of Veterinary Medical Science 2009;71(5):603-610.
  • Kwon B, Kim BS, Cho HR, Park JE, Kwon BS. Involvement of tumor necrosis factor receptor superfamily(TNFRSF) members in the pathogenesis of inflammatory diseases. Experimental & Molecular Medicine 200335(1):8-16.
  • Lee JY, Song JJ, Wooming A, Li X, Zhou H, Bottje WG, et al. Transcriptional profiling of host gene expression in chicken embryo lung cells infected with laryngotracheitis virus. BMC Genomics 2010;11:445.
  • Li X, Chiang HI, Zhu J, Dowd SE, Zhou H. Characterization of a newly developed chicken 44K Agilent microarray. BMC Genomics 2008;9:60.
  • Li X, Swaggerty CL, Kogut MH, Chiang HI, Wang Y, Genovese KJ, et al. Systemic response to Campylobacter jejuni infection by profiling gene transcription in the spleens of two genetic lines of chickens. Immunogenetics 2011;64(1):59-69.
  • Li X, Swaggerty CL, Kogut MH, Chiang HI, Wang Y, Genovese KJ, et al. Gene expression profiling of the local cecal response of genetic chicken lines that differ in their susceptibility to Campylobacter jejuni colonization. PLoS One 2010;5(7):e11827.
  • Lian L, Qu LJ, Sun HY, Chen YM, Lamont SJ, Liu CJ, et al. Gene expression analysis of host spleen responses to Marek’s disease virus infection at late tumor transformation phase. Poultry Science 2012;91(9):2130-2138.
  • Liu HC, Cheng HH, Tirunagaru V, Sofer L, Burnside J. A strategy to identify positional candidate genes conferring Marek’s disease resistance by integrating DNA microarrays and genetic mapping. Animal Genetics 2001;32(6):351-359.
  • Morgan RW, Sofer L, Anderson AS, Bernberg EL, Cui J, Burnside J. Induction of host gene expression following infection of chicken embryo fibroblasts with oncogenic Marek’s disease virus. Journal of Virology 2001;75(1):533-9.
  • Morrow C, Fehler F, editors. Marek’s disease: a worldwide problem. London: Elsevier Academic Press; 2004.
  • Sandford EE, Orr M, Balfanz E, Bowerman N, Li X, Zhou H, et al. Spleen transcriptome response to infection with avian pathogenic Escherichia coli in broiler chickens. BMC Genomics 2011;12:469.
  • Sandford EE, Orr M, Shelby M, Li X, Zhou H, Johnson TJ, et al. Leukocyte transcriptome from chickens infected with avian pathogenic Escherichia coli identifies pathways associated with resistance. Results in Immunology 2012;2:44-53.
  • Sarson AJ, Abdul-Careem MF, Zhou H, Sharif S. Transcriptional analysis of host responses to Marek’s disease viral infection. Viral Immunology 2006;19(4):747-758.
  • Sarson AJ, Parvizi P, Lepp D, Quinton M, Sharif S. Transcriptional analysis of host responses to Marek’s disease virus infection in genetically resistant and susceptible chickens. Animal Genetics 2008;39(3):232-240.
  • Smith J, Sadeyen JR, Paton IR, Hocking PM, Salmon N, Fife M, et al. 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. Journal of Virology 2011;85(21):11146-11158.
  • Subramaniam S, Preeyanon L, Cheng HH. Transcriptional profiling of mEq-dependent genes in Marek’s disease resistant and susceptible inbred chicken lines. PLoS One 2013;8(10):e78171.
  • Sun AJ, Xu XY, Petherbridge L, Zhao YG, Nair V, Cui ZZ. Functional evaluation of the role of reticuloendotheliosis virus long terminal repeat (LTR) integrated into the genome of a field strain of Marek’s disease virus. Virology 2010;397(2):270-276.
  • Yang YH, Dudoit S, Luu P, Lin DM, Peng V, Ngai J, et al. Normalization for cDNA microarray data: a robust composite method addressing single and multiple slide systematic variation. Nucleic Acids Research 2002;30(4):e15.
  • Yonash N, Bacon LD, Witter RL, Cheng HH. High resolution mapping and identification of new quantitative trait loci (QTL) affecting susceptibility to Marek’s disease. Animal Genetics 1999;30(2):126-135.
  • Yu Y, Luo J, Mitra A, Chang S, Tian F, Zhang H, et al. Temporal transcriptome changes induced by MDV in Marek’s disease-resistant and -susceptible inbred chickens. BMC Genomics 2011;12:501.
  • Witter RL, Calnek BW, Buscaglia C, Gimeno IM, Schat KA. Classification of Marek’s disease viruses according to pathotype: philosophy and methodology. Avian Pathology 2005;34(2):75-90.
  • Zhang Z, Cui Z. Isolation of recombinant field strains of Marek’s disease virus integrated with reticuloendotheliosis virus genome fragments. Science in China. Series C Lifes Sciences 2005;48(1):81-88.

  • AUTHORS’ CONTRIBUTIONS XLI designed the microarray, analyzed data and drafted the manuscript. SS and NC carried out the cell infection and sample collection. HZ participated in experiment design and microarray design. XLIU validated the sequencing data through the qRT-PCR method. ZC provided the concepts of the study. All authors read, edited and approved the final manuscript.

Publication Dates

  • Publication in this collection
    Apr-Jun 2017

History

  • Received
    Aug 2016
  • Accepted
    Dec 2016
Fundação APINCO de Ciência e Tecnologia Avícolas Av. Andrade Neves, 2501 - Castelo, 13070-001 Campinas SP - Brazil, Tel.: (55 19) 3243-6555 / Fax.: (55 19) 3243-8542 - Campinas - SP - Brazil
E-mail: rvfacta@terra.com.br