آشکارسازی پاسخ به عفونت آنفلوانزای H5N1: تجزیه و تحلیل مقایسه ای شبکه های بیان ژن و مسیرهای غنی شده عملکردی در جوجه ها و اردک ها

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشجوی کارشناسی ارشد، گروه علوم دامی، دانشکده علوم کشاورزی، دانشگاه گیلان

2 استادیار، گروه علوم دامی، دانشکده علوم کشاورزی، دانشگاه گیلان

3 دانش آموخته دکتری، گروه علوم دامی، دانشکده علوم کشاورزی، دانشگاه گیلان

چکیده

درک ساز و کارهای مولکولی پاسخ میزبان به عفونت H5N1 برای گسترش اقدامات کنترل موثر و کاهش خطر یک بیماری همه­گیر بالقوه بسیار مهم است. هدف مطالعه حاضر، تجزیه داده­های ریزآرایه آنفلوانزای فوق ­حاد پرندگان H5N1 جهت مقایسه شبکه ژنی در جوجه­ها و اردک­ها بود. مجموعه داده ریزآرایه GSE33389 مشتمل بر نمونه شاهد و زیر چالش H5N1 بافت ریه جوجه و اردک با بسته GEOquery نرم­افزار R دانلود شد. ژن‌های با بیان متفاوت با استفاده از بسته limma در نرم‌افزار R شناسایی شدند و سپس، ترسیم شبکه‌های ژنی با نرم افزار Cytoscape انجام شد. ژن‌های اصلی با تعاملات زیاد با افزونه Cytohubba شناسایی شدند و در نهایت، ماژول‌های اثرگذار با افزونه MCODE شناسایی شدند. تعداد 2062 و 565 ژن با بیان متفاوت بین بافت­ سالم و زیر چالش به ترتیب در جوجه‌ها و اردک‌ها شناسایی شدند (05/0P< و >2 |logFC|). نتایج تجزیه شبکه با استفاده از افزونه Cytohubba، ژن­هایBUB1 ، NDC80، CDC20 را به­­عنوان ژن­های هاب در جوجه و همچنین ژن­های کلیدی COL6A3، COL3A1 و PLOD2 را در اردک شناسایی نمود (05/0P<). مقایسه هستی­شناسی ژن‌های متفاوت بیان ­شده در جوجه و اردک نشان داد که بیشتر آن­ها در جوجه­ها در پاسخ ایمنی ذاتی و مقاومت‌های التهابی میزبان نقش دارند، ولی در اردک بیشتر در سوخت و ساز چربی و تولید انرژی برای تامین نیازمندی مقاومت میزبان در برابر بیماری نقش ایفا می‌کنند. یافته‌های این مطالعه ضمن افزایش آگاهی نسبت به چگونگی پاسخ میزبان به عفونت آنفلوانزای H5N1، ممکن است دستاوردهایی برای توسعه درمان هدفمند و راهبردهای نظارتی برای مبارزه موثر با شیوع H5N1 داشته باشد.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Unraveling the H5N1 influenza infection response: A comparative gene expression networks and functionally enriched pathways analysis in chickens and ducks

نویسندگان [English]

  • S. Golpasand 1
  • Sh. Ghovvati 2
  • Z. Pezeshkian 3
1 MSc Student, Department of Animal Science, Faculty of Agriculture, University of Guilan, Rasht, Iran
2 Assistant Professor, Department of Animal Science, Faculty of Agriculture, University of Guilan, Rasht, Iran
3 Ph.D., Department of Animal Science, Faculty of Agriculture, University of Guilan, Rasht, Iran
چکیده [English]

Introduction: In recent years, highly pathogenicity avian influenza (HPAI), especially H5N1, has emerged as a major global health concern due to its potential as a zoonotic disease and its devastating impact on poultry populations. Identifying the molecular mechanisms of response to HPAI infection is critical to control, treat, and prevent the risk of a potential pandemic. Microarray technology is becoming a standard technology used in research laboratories all across the world and it is considered as one of the centers of research in cellular processes related to the level and manner of gene expression, including gene function and cell differentiation mechanisms. By using microarray technology, it is possible to obtain a detailed view of the interaction function of genes while simultaneously studying how the genome is expressed. Using microarrays provides the analysis of gene expression in response to viral infections such as influenza, etc., the study of host-pathogen interactions, and also the identification of the effectiveness of drugs and vaccines. This study aimed to analyze the microarray data of H5N1 avian influenza to compare the gene network and analyze the functional pathway in chickens and ducks.
Materials and methods: Data mining and searching of microarray data related to Highly Pathogenic Avian Influenza infection was done in the GEO gene expression database (https://www.ncbi.nlm.nih.gov/geo). The microarray data set with accession number GSE33389 based on the GPL3213 platform was selected which contained lung tissue samples challenged with H5N1 virus in chickens and ducks. Normalization of selected microarray data was done using R software, and samples were grouped to compare between infected and control samples. Limma, Biobase, and GEOquery software packages in R software were used to determine the expression level of genes and to investigate the differentially expressed genes (DEGs) between healthy and H5N1 influenza virus-infected lung tissue samples in chickens and ducks. The criterion for selecting significant DEGs was considered as |logFC|>2 and P<0.05. DAVID online tool (https://david.ncifcrf.gov) was used to investigate biological pathways, structural and functional characteristics of genes with different expressions, and functional interpretation of upregulated and downregulated DEGs. It was evaluated and visualized separately based on biological processes (BP), molecular functions (MF), and cellular components (CC). KEGG tool (http://www.genome.jp/kegg) was used to evaluate and study metabolic pathway enrichment. To reveal interactions between proteins and analyze them, STRING database and Cytoscape software were used. While using the Cytohubba plugin to identify and display key genes, the main modules affecting the interaction of genes and proteins were also identified by the MCODE plugin.
Results and discussion: Gene expression analysis revealed 2062 and 565 differentially expressed genes between normal and infected tissue in chickens and ducks, respectively (P<0.05 and |logFC|>2). The results of bioinformatics analysis and protein-protein interaction network analysis showed BUB1, NDC80, CDC20, PLK1, PRC1, KIF11, and AURKA genes as hub genes in chicken and also COL6A3, COL3A1, COL4A3, COL18A1, PLOD2, PLOD1, and P4HA2 as highly effective genes in duck (P< 0.05). The results of the ontology comparison of DEGs proved that most of these genes in chickens are involved in the innate immune response and inflammatory resistance of the host, and the most effective genes in ducks play a role in lipid metabolism and energy production to meet the host's resistance to disease. The results of comparative gene network analysis between chickens and ducks are promising to increase our understanding of the host response to H5N1 influenza infection and the factors affecting virus pathogenesis in different avian species. Differentially expressed genes in response to H5N1 infection in chickens and ducks play critical roles in various biological processes, including immune response, inflammation, viral replication, and host-pathogen interactions.In general, gene network analysis showed that chickens and ducks use different genetic strategies to respond to avian influenza virus infection.
Conclusions: The present research was conducted to discover the response to H5N1 HPAI infection in chickens and ducks through comparative gene network analysis. It is important to note that the gene network analysis presented in this research is an initial step towards discovering the response mode of HPAI (H5N1) infection in chickens and ducks, and further functional studies, validation experiments, and integration with other omics data are needed to confirm the role of genes, pathways and hub genes in the host response to H5N1 virus. Therefore, the results of comparative gene network analysis in chickens and ducks obtained from this research can provide valuable insight into the underlying molecular mechanisms of host response to H5N1 influenza infection. Thus, by identifying differentially expressed genes, functional modules, and hub genes in this research, it can be stated that potential targets for future research have been highlighted to some extent. Undoubtedly, further studies in this field will improve our knowledge about the pathogenesis of avian influenza and will help to develop strategies for effective control and prevention of H5N1 influenza outbreaks.

کلیدواژه‌ها [English]

  • High Pathogenicity Avian influenza H5N1
  • Bioinformatics
  • Microarray
  • Gene interaction network
  • Gene ontology
Alberts, B., Johnson, A., Lewis, J., Raff, M., Roberts, K., & Walter, P. (2002). Blood vessels and endothelial cells. In Molecular Biology of the Cell. 4th edition. Garland Science.
Arrell, D., & Terzic, A. (2010). Network systems biology for drug discovery. Clinical Pharmacology & Therapeutics, 88(1), 120-125. doi: 10.1038/clpt.2010.91
Bader, G. D., & Hogue, C. W. (2003). An automated method for finding molecular complexes in large protein interaction networks. BMC Bioinformatics, 4, 2. doi: 10.1186/1471-2105-4-2
Beigel, J. (2005). Writing Committee of the World Health Organization Consultation on human influenza a/H5: avian influenza a (H5N1) infection in humans. New England Journal of Medicine, 353, 1374-1385.
Betakova, T., Kostrabova, A., Lachova, V., & Turianova, L. (2017). Cytokines induced during influenza virus infection. Current Pharmaceutical Design, 23(18), 2616-2622.
Bindea, G., Galon, J., & Mlecnik, B. (2013). CluePedia Cytoscape plugin: pathway insights using integrated experimental and in silico data. Bioinformatics,  5(29), 663-661. doi: 10.1093/bioinformatics/btt019
Bindea, G., Mlecnik, B., Hackl, H., Charoentong, P., Tosolini, M., Kirilovsky, A., Fridman, W. H., Pagès, F., Trajanoski, Z., & Galon, J. (2009). ClueGO: a Cytoscape plug-in to decipher functionally grouped gene ontology and pathway annotation networks. Bioinformatics, 25(8), 1091-1093. doi: 10.1093/bioinformatics/btp101
Cagle, C., Wasilenko, J., Adams, S. C., Cardona, C. J., To, T. L., Nguyen, T., Spackman, E., Suarez, D. L., Smith, D., & Shepherd, E. (2012). Differences in pathogenicity, response to vaccination, and innate immune responses in different types of ducks infected with a virulent H5N1 highly pathogenic avian influenza virus from Vietnam. Avian Diseases, 56(3), 479-487. doi: 10.1637/10030-120511-Reg.1
Campbell, L. K., & Magor, K. E. (2020). Pattern recognition receptor signaling and innate responses to influenza A viruses in the mallard duck, compared to humans and chickens. Frontiers in Cellular and Infection Microbiology, 209. doi: 10.3389/fcimb.2020.00209
Campos-Ferraz, P. L., Bozza, T., Nicastro, H., & Lancha Jr, A. H. (2013). Distinct effects of leucine or a mixture of the branched-chain amino acids (leucine, isoleucine, and valine) supplementation on resistance to fatigue, and muscle and liver-glycogen degradation, in trained rats. Nutrition, 29(11-12), 1388-1394. doi: 10.1016/j.nut.2013.05.003
Cañadas, O., Olmeda, B., Alonso, A., & Pérez-Gil, J. (2020). Lipid–protein and protein–protein interactions in the pulmonary surfactant system and their role in lung homeostasis. International Journal of Molecular Sciences, 21(10), 3708. doi: 10.3390/ijms21103708
Carmeliet, P., & Jain, R. K. (2011). Molecular mechanisms and clinical applications of angiogenesis. Nature, 473(7347), 298-307. doi: 10.1038/nature10144
Chen, Z., Cui, L., Xu, L., Liu, Z., Liang, Y., Li, X., Zhang, Y., Li, Y., Liu, S., & Li, H. (2022). Characterization of chicken p53 transcriptional function via parallel genome-wide chromatin occupancy and gene expression analysis. Poultry Science, 101(11), 102164. doi: 10.1016/j.psj.2022.102164
Chin, C. H., Chen, S. H., Wu, H. H., Ho, C. W., Ko, M. T., & Lin, C. Y. (2014). cytoHubba: identifying hub objects and sub-networks from complex interactome. BMC System Biology, 8(Suppl. 4), S11. doi: 10.1186/1752-0509-8-s4-s11
Chothe, S. K., Nissly, R. H., Lim, L., Bhushan, G., Bird, I., Radzio-Basu, J., Jayarao, B. M., & Kuchipudi, S. V. (2020). NLRC5 serves as a pro-viral factor during influenza virus infection in chicken macrophages. Frontiers in Cell Infection Microbiology, 10, 230. doi: 10.3389/fcimb.2020.00230
Cornelissen, J. B., Vervelde, L., Post, J., & Rebel, J. M. (2013). Differences in highly pathogenic avian influenza viral pathogenesis and associated early inflammatory response in chickens and ducks. Avian Pathology, 42(4), 347-364. doi: 10.1080/03079457.2013.807325
Davis, S., & Meltzer, P. S. (2007). GEOquery: a bridge between the Gene Expression Omnibus (GEO) and BioConductor. Bioinformatics, 23(14), 1846-1847. doi: 10.1093/bioinformatics/btm254
Dhama, K. (2013). Avian/Bird Flu virus: poultry pathogen having. Journal of Medical Science, 13(5), 301-315. doi: 10.3923/jms.2013.301.315
Dong, H.-J., Zhang, R., Kuang, Y., & Wang, X.-J. (2021). Selective regulation in ribosome biogenesis and protein production for efficient viral translation. Archives of Microbiology, 203, 1021-1032. doi: 10.1007/s00203-020-02094-5
Dubois, J., Terrier, O., & Rosa-Calatrava, M. (2014). Influenza viruses and mRNA splicing: doing more with less. MBio, 5(3), e00070-00014. doi: 10.1128/mbio.00070-14
Eierhoff, T., Hrincius, E. R., Rescher, U., Ludwig, S., & Ehrhardt, C. (2010). The epidermal growth factor receptor (EGFR) promotes uptake of influenza A viruses (IAV) into host cells. PLoS Pathogens, 6(9), e1001099. doi: 10.1371/journal.ppat.1001099
Eswarakumar, V., Lax, I., & Schlessinger, J. (2005). Cellular signaling by fibroblast growth factor receptors. Cytokine & Growth Factor Reviews, 16(2), 139-149. doi: 10.1016/j.cytogfr.2005.01.001
Evseev, D., & Magor, K. E. (2019). Innate immune responses to avian influenza viruses in ducks and chickens. Veterinary Sciences, 6(1), 5. doi: 10.3390/vetsci6010005
Grygiel-Górniak, B. (2014). Peroxisome proliferator-activated receptors and their ligands: nutritional and clinical implications--a review. Nutrition Journal, 13, 17. doi: 10.1186/1475-2891-13-17.
Guo, T., Hou, D., & Yu, D. (2019). Bioinformatics analysis of gene expression profile data to screen key genes involved in intracranial aneurysms. Molecular Medicine Reports, 20(5), 4415-4424. doi: 10.3892/mmr.2019.10696
Hasin, Y., Seldin, M., & Lusis, A. (2017) Multi-omics approaches to disease. Genome Biology, 18(1), 1-15. doi: 10.1186/s13059-017-1215-1
Hong, G., Zhang, W., Li, H., Shen, X., & Guo, Z. (2014). Separate enrichment analysis of pathways for up-and downregulated genes. Journal of the Royal Society Interface, 11(92), 20130950. doi: 10.1098/rsif.2013.0950
Huang, D. W., Sherman, B. T., Tan, Q., Kir, J., Liu, D., Bryant, D., Guo, Y., Stephens, R., Baseler, M. W., & Lane, H. C. (2007). DAVID Bioinformatics Resources: expanded annotation database and novel algorithms to better extract biology from large gene lists. Nucleic Acids Research, 35(suppl_2), W169-W175. doi: 10.1093/nar/gkm415
Huber, W., Carey, V. J., Gentleman, R., Anders, S., Carlson, M., Carvalho, B. S., Bravo, H. C., Davis, S., Gatto, L., & Girke, T. (2015). Orchestrating high-throughput genomic analysis with Bioconductor. Nature Methods, 12(2), 115-121. doi: 10.1038/nmeth.3252
Jiao, L., Liu, Y., Yu, X.-Y., Pan, X., Zhang, Y., Tu, J., Song, Y.-H., & Li, Y. (2023). Ribosome biogenesis in disease: new players and therapeutic targets. Signal Transduction and Targeted Therapy, 8(1), 15. doi:  10.1038/s41392-022-01285-4
Johnson, K. E., & Wilgus, T. A. (2014). Vascular Endothelial Growth Factor and Angiogenesis in the Regulation of Cutaneous Wound Repair. Advances in Wound Care (New Rochelle), 3(10), 647-661. doi: 10.1089/wound.2013.0517
Kanehisa, M., Goto, S., Kawashima, S., & Nakaya, A. (2002). The KEGG databases at GenomeNet. Nucleic Acids Research, 30(1), 42-46. doi: 10.1093/nar/30.1.42
Kilpatrick, A. M., Chmura, A. A., Gibbons, D. W., Fleischer, R. C., Marra, P. P., & Daszak, P. (2006). Predicting the global spread of H5N1 avian influenza. Proceedings of the National Academy of Sciences, 103(51), 19368-19373. doi: 10.1073/pnas.0609227103
Kim, D.-I., Kang, J.-H., Kim, E.-H., & Seo, Y.-J. (2021). KIF11 inhibition decreases cytopathogenesis and replication of influenza A virus. Molecular & Cellular Toxicology, 17, 201-212. doi: 10.1007/s13273-021-00126-9
Kim, W. H., Chaudhari, A. A., & Lillehoj, H. S. (2019). Involvement of T cell immunity in avian coccidiosis. Frontiers in Immunology, 10, 2732. doi: 10.3389/fimmu.2019.02732
Kim, W. K., Singh, A. K., Wang, J., & Applegate, T. (2022). Functional role of branched chain amino acids in poultry: a review. Poultry Science, 101(5), 101715. doi: 10.1016/j.psj.2022.101715
Klees, S., Schlüter, J.-S., Schellhorn, J., Bertram, H., Kurzweg, A. C., Ramzan, F., Schmitt, A. O., & Gültas, M. (2022). Comparative Investigation of Gene Regulatory Processes Underlying Avian Influenza Viruses in Chicken and Duck. Biology, 11(2), 219. doi: 10.3390/biology11020219
Kohl, M., Wiese, S., & Warscheid, B. (2011). Cytoscape: software for visualization and analysis of biological networks. Data mining in proteomics: from standards to applications, 291-303. doi: 10.1007/978-1-60761-987-1_18
Kuchipudi, S. V., Dunham, S. P., & Chang, K.-C. (2015). DNA microarray global gene expression analysis of influenza virus-infected chicken and duck cells. Genomics Data, 4, 60-64. doi: 10.1016/j.gdata.2015.03.004
Kuchipudi, S. V., Dunham, S. P., Nelli, R., White, G. A., Coward, V. J., Slomka, M. J., Brown, I. H., & Chang, K. C. (2012). Rapid death of duck cells infected with influenza: a potential mechanism for host resistance to H5N1. Immunology and Cell Biology, 90(1), 116-123. doi: 10.1038/icb.2011.17
Kuchipudi, S. V., Tellabati, M., Sebastian, S., Londt, B. Z., Jansen, C., Vervelde, L., Brookes, S. M., Brown, I. H., Dunham, S. P., & Chang, K.-C. (2014). Highly pathogenic avian influenza virus infection in chickens but not ducks is associated with elevated host immune and pro-inflammatory responses. Veterinary Research, 45(1), 1-18. doi: 10.1186/s13567-014-0118-3
Kuek, L. E., & Lee, R. J. (2020). First contact: the role of respiratory cilia in host-pathogen interactions in the airways. American Journal of Physiology-Lung Cellular and Molecular Physiology, 319(4), L603-l619. doi: 10.1152/ajplung.00283.2020
Kuivaniemi, H., & Tromp, G. (2019). Type III collagen (COL3A1): Gene and protein structure, tissue distribution, and associated diseases. Gene, 707, 151-171. doi: 10.1016/j.gene.2019.05.003
Lax, I., Johnson, A., Howk, R., Sap, J., Bellot, F., Winkler, M., Ullrich, A., Vennstrom, B., Schlessinger, J., & Givol, D. (1988). Chicken epidermal growth factor (EGF) receptor: cDNA cloning, expression in mouse cells, and differential binding of EGF and transforming growth factor alpha. Molecular and Cellular Biology, 8(5), 1970-1978. doi: 10.1128/mcb.8.5.1970-1978.1988
Lee, S., Hirohama, M., Noguchi, M., Nagata, K., & Kawaguchi, A. (2018). Influenza A virus infection triggers pyroptosis and apoptosis of respiratory epithelial cells through the type I interferon signaling pathway in a mutually exclusive manner. Journal of Virology, 92(14), e00396-00318. doi: 10.1128/JVI.00396-18
Lee, S., Hwang, N., Seok, B. G., Lee, S., Lee, S.-J., & Chung, S. W. (2023). Autophagy mediates an amplification loop during ferroptosis. Cell Death & Disease, 14(7), 464. doi: 10.1038/s41419-023-05978-8
Lee, S., Lee, R. H., Kim, S. J., Lee, H. K., Na, C. S., & Song, K. D. (2019). Transcriptional regulation of chicken leukocyte cell-derived chemotaxin 2 in response to toll-like receptor 3 stimulation. Asian-Australasian Journal of Animal Science, 32(12), 1942-1949. doi: 10.5713/ajas.19.0192
Lockhart, D. J., & Winzeler, E. A. (2000). Genomics, gene expression and DNA arrays. Nature, 405(6788), 827-836. doi: 10.1038/35015701
Lorizate, M., & Kräusslich, H. G. (2011). Role of lipids in virus replication. Cold Spring Harbor Perspectives in Biology, 3(10), a004820. doi: 10.1101/cshperspect.a004820
Lovén, J., Orlando, D. A., Sigova, A. A., Lin, C. Y., Rahl, P. B., Burge, C. B., Levens, D. L., Lee, T. I., & Young, R. A. (2012). Revisiting global gene expression analysis. Cell, 151(3), 476-482. doi: 10.1016/j.cell.2012.10.012
McCleland, M. L., Gardner, R. D., Kallio, M. J., Daum, J. R., Gorbsky, G. J., Burke, D. J., & Stukenberg, P. T. (2003). The highly conserved Ndc80 complex is required for kinetochore assembly, chromosome congression, and spindle checkpoint activity. Genes and Development, 17(1), 101-114. doi: 10.1101/gad.1040903
Mehrabadi, M. F., Ghalyanchilangeroudi, A., Tehrani, F., Hajloo, S. A., Bashashati, M., Bahonar, A., Pourjafar, H., & Ansari, F. (2022). Assessing the economic burden of multi-causal respiratory diseases in broiler farms in Iran. Tropical Animal Health and Production, 54(2), 117. doi: 10.1007/s11250-022-03110-0
Metcalfe, R. D., Putoczki, T. L., & Griffin, M. D. (2020). Structural understanding of interleukin 6 family cytokine signaling and targeted therapies: focus on interleukin 11. Frontiers in Immunology, 11, 1424. doi: 10.3389/fimmu.2020.01424
Mutryn, M. F., Brannick, E. M., Fu, W., Lee, W. R., & Abasht, B. (2015). Characterization of a novel chicken muscle disorder through differential gene expression and pathway analysis using RNA-sequencing. BMC Genomics, 16(1), 1-19. doi: 10.1186/s12864-015-1623-0
Navidshad, B., & Royan, M. (2016). Peroxisome proliferator-activated receptor alpha (PPARα), a key regulator of lipid metabolism in Avians. Critical Reviews in Eukaryotic Gene Expression, 26(4), 303-308. doi: 10.1615/CritRevEukaryotGeneExpr.2016016665
Obexer, P., Hagenbuchner, J., Unterkircher, T., Sachsenmaier, N., Seifarth, C., Böck, G., Porto, V., Geiger, K., & Ausserlechner, M. (2009). Repression of BIRC5/survivin by FOXO3/FKHRL1 sensitizes human neuroblastoma cells to DNA damage-induced apoptosis. Molecular Biology of the Cell, 20(7), 2041-2048. doi: 10.1091/mbc.e08-07-0699
Paquette, S. G., Banner, D., Huang, S. S., Almansa, R., Leon, A., Xu, L., Bartoszko, J., Kelvin, D. J., & Kelvin, A. A. (2015). Influenza transmission in the mother-infant dyad leads to severe disease, mammary gland infection, and pathogenesis by regulating host responses. PLoS Pathogens, 11(10), e1005173. doi: 10.1371/journal.ppat.1005173
Pezeshkian, Z., Mirhoseini, S. Z., & Ghovvati, S. (2022). Identification of hub genes involved in apparent metabolizable energy of chickens. Animal Biotechnology, 33(2), 242-249. doi: 10.1080/10495398.2020.1784187
Pociask, D. A., Robinson, K. M., Chen, K., McHugh, K. J., Clay, M. E., Huang, G. T., Benos, P. V., Janssen-Heininger, Y. M., Kolls, J. K., & Anathy, V. (2017). Epigenetic and transcriptomic regulation of lung repair during recovery from influenza infection. The American Journal of Pathology, 187(4), 851-863. doi: 10.1016/j.ajpath.2016.12.012
R Core Team (2023). R Foundation for Statistical Computing, version 4.2.3, Vienna, Austria.
Rehman, Z., Naz, S., Khan, R., & Tahir, M. (2017). An update on potential applications of L-carnitine in poultry. World's Poultry Science Journal, 73(4), 823-830. doi: 10.1017/S0043933917000733
Ritchie, M. E., Phipson, B., Wu, D., Hu, Y., Law, C. W., Shi, W., & Smyth, G. K. (2015). Limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Research, 43(7), e47-e47. doi: 10.1093/nar/gkv007
Stecher, B. R., Hapfelmeier, S., Müller, C., Kremer, M., Stallmach, T., & Hardt, W.-D. (2004). Flagella and chemotaxis are required for efficient induction of Salmonella enterica serovar Typhimurium colitis in streptomycin-pretreated mice. Infection and Immunity, 72(7), 4138-4150. doi: 10.1128/IAI.72.7.4138-4150.2004
Takahashi, K., & Yamanaka, S. (2016). A decade of transcription factor-mediated reprogramming to pluripotency. Nature Reviews Molecular Cell Biology, 17(3), 183-193. doi: 10.1038/nrm.2016.8
Tong, Z. W. M., Karawita, A. C., Kern, C., Zhou, H., Sinclair, J. E., Yan, L., Chew, K. Y., Lowther, S., Trinidad, L., & Challagulla, A. (2021). Primary chicken and duck endothelial cells display a differential response to infection with highly pathogenic avian influenza virus. Genes, 12(6), 901. doi: 10.3390/genes12060901
Turner, M. D., Nedjai, B., Hurst, T., & Pennington, D. J. (2014). Cytokines and chemokines: At the crossroads of cell signalling and inflammatory disease. Biochimica et Biophysica Acta (BBA)-Molecular Cell Research, 1843(11), 2563-2582. doi: 10.1016/j.bbamcr.2014.05.014
Us, D. (2008). Cytokine storm in avian influenza. Mikrobiyoloji Bulteni, 42(2), 365-380.
Vu, T. H., Hong, Y., Truong, A. D., Lee, J., Lee, S., Song, K.-D., Cha, J., Dang, H. V., Tran, H. T. T., & Lillehoj, H. S. (2022). Cytokine-cytokine receptor interactions in the highly pathogenic avian influenza H5N1 virus-infected lungs of genetically disparate Ri chicken lines. Animal Bioscience, 35(3), 367. doi: 10.5713/ab.21.0163
Wang, B., Su, Q., Luo, J., Li, M., Wu, Q., Chang, H., Du, J., Huang, C., Ma, J., & Han, S. (2021). Differences in highly pathogenic H5N6 avian influenza viral pathogenicity and inflammatory response in chickens and ducks. Frontiers in Microbiology, 12, 593202. doi: 10.3389/fmicb.2021.593202
Wang, C., Nie, G., Zhuang, Y., Hu, R., Wu, H., Xing, C., Li, G., Hu, G., Yang, F., & Zhang, C. (2020). Inhibition of autophagy enhances cadmium-induced apoptosis in duck renal tubular epithelial cells. Ecotoxicology and Environmental Safety, 205, 111188. doi:10.1016/j.ecoenv.2020.111188
Wang, J., Hu, T., Wang, Q., Chen, R., Xie, Y., Chang, H., & Cheng, J. (2021). Repression of the AURKA-CXCL5 axis induces autophagic cell death and promotes radiosensitivity in non-small-cell lung cancer. Cancer Letters, 509, 89-104. doi: 10.1016/j.canlet.2021.03.028
Yin, H.-C., Zhao, L.-L., Li, S.-Q., Niu, Y.-J., Jiang, X.-J., Xu, L.-J., Lu, T.-F., Han, L.-X., Liu, S.-W., & Chen, H.-Y. (2017). Autophagy activated by duck enteritis virus infection positively affects its replication. Journal of General Virology, 98(3), 486-495. doi: 10.1099/jgv.0.000696
Yu, W., Shi, S., Qiu, Y., Jin, Z., Zhou, J., & Zhang, H. (2023). AURKA identified as potential lung cancer marker through comprehensive bioinformatic analysis and experimental verification. Critical Reviews in Eukaryotic Gene Expression, 33(5), 39-59. doi: 10.1615/CritRevEukaryotGeneExpr.2023046830
Zhai, Y., Franco, L. M., Atmar, R. L., Quarles, J. M., Arden, N., Bucasas, K. L., Wells, J. M., Nino, D., Wang, X., & Zapata, G. E. (2015). Host transcriptional response to influenza and other acute respiratory viral infections–a prospective cohort study. PLoS Pathogens, 11(6), e1004869. doi: 10.1371/journal.ppat.1004869
Zhou, J., Law, H. K., Cheung, C. Y., Ng, I. H., Peiris, J. S., & Lau, Y. L. (2006). Differential expression of chemokines and their receptors in adult and neonatal macrophages infected with human or avian influenza viruses. Journal of Infectious Diseases, 194(1), 61-70. doi: 10.1086/504690