{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T01:45:39Z","timestamp":1784166339641,"version":"3.55.0"},"reference-count":64,"publisher":"Wiley","issue":"6","license":[{"start":{"date-parts":[[2024,10,11]],"date-time":"2024-10-11T00:00:00Z","timestamp":1728604800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012442","name":"Asher Space Research Institute, Technion-Israel Institute of Technology","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100012442","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Veterinary Medicine &amp; Sci"],"published-print":{"date-parts":[[2024,11]]},"abstract":"Abstract<\/jats:title>\n \n Background<\/jats:title>\n \n Salmonella enteritidis<\/jats:italic>\n (SE), a previously widespread infectious disease, is still cited as a major factor in economic losses in commercial chicken production. The host's genetic immune system determines the pathogenicity of a particular bacterium. To shed light on this topic, it was necessary to understand the key candidate genes essential for regulating susceptibility and resistance to the target disease. The field of poultry farming in particular has benefited greatly from the connection between quantitative and molecular genetics.\n <\/jats:p>\n <\/jats:sec>\n \n Objectives<\/jats:title>\n This study aims to identify the most important immune\u2010related genes and their signalling pathways (gene ontology, co\u2010expression and interactions) and to analyse their accumulation in host\u2010resistant SE diseases by combining gene expression assays with model\u2010based in silico evidence.<\/jats:p>\n <\/jats:sec>\n \n Methods<\/jats:title>\n \n A two\u2010step experimental design is followed. To start, we used free computational tools and online bioinformatics resources, including predicting gene function using a multiple association network integration algorithm (geneMania), the Kyoto Encyclopedia of Genes and Genomes, the Annotation, Visualization and Integrated Discovery (DAVID) database and the stimulator of interferon genes. Natural resistance\u2010associated macrophage protein 1 (\n NRAMP1<\/jats:italic>\n ), Toll\u2010like receptor 4 (\n TLR4<\/jats:italic>\n ), interferon\u2010\u03b3 (\n IFN\u03b3<\/jats:italic>\n ), immunoglobulin Y (\n IgY<\/jats:italic>\n ) and interleukin 8 (\n IL8<\/jats:italic>\n ) were among the five genes whose expression levels in liver, spleen, and cecum were evaluated at 1107 SE after 48\u00a0h of inoculation. This molecular study was developed in the second phase of research to validate the in silico observations. Next, we use five promising biomarkers for relative real\u2010time polymerase chain reaction (PCR) quantification: TLR4, IL8, NRAMP1, IFN\u03b3 and IgY genes in two case and control assays. The 2\n \u2212\u2206\u2206Ct<\/jats:sup>\n Livak and Schmittgen method was used to compare the expression of genes in treated and untreated samples. This method normalizes the expression of the target gene to that of actin, an internal control and estimates the change in expression relative to the untreated control. Internal control was provided by the Beta actin gene. Next, statistically, the postdoc test was used for the evaluation of treatments using SAS version 9.4, and\n p<\/jats:italic>\n values of 0.05 and 0.01 were chosen for significant level.\n <\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n \n Interestingly, the results of our study suggest the involvement of various factors in the host immune response to\n Salmonella<\/jats:italic>\n . These include inducible nitric oxide synthase,\n NRAMP1<\/jats:italic>\n , immunoglobulin light chain (\n IgL<\/jats:italic>\n ), transforming growth factor B family (\n TGFb2, TGFb3<\/jats:italic>\n and\n TGFb4<\/jats:italic>\n ), interleukin 2 (\n IL2<\/jats:italic>\n ), apoptosis inhibitor protein 1\n (IAP1<\/jats:italic>\n ),\n TLR4<\/jats:italic>\n , myeloid differentiation protein 2 (\n MD2<\/jats:italic>\n ),\n IFN\u03b3<\/jats:italic>\n , caspase 1 (\n CASP1<\/jats:italic>\n ), lipopolysaccharide\u2010induced tumour necrosis factor (\n LITAF<\/jats:italic>\n ), cluster of differentiation 28 (\n CD28<\/jats:italic>\n ) and prosaposin (\n PSAP<\/jats:italic>\n ). The summary of gene ontology and related genes found for SE resistance was surprisingly comprehensive and covered the following topics: positive regulation of endopeptidase activity, interleukin\u20108 production, chemokine production, interferon\u2010gamma production, interleukin\u20106 production, positive regulation of mononuclear cell proliferation and response to interferon\u2010gamma. The role of these promising biomarkers in our networks against SE susceptibility is essentially confirmed by these results. After 48\u00a0h, the spleen showed significant expression of the tissue\u2010specific gene expression patterns for\n NRAMP1<\/jats:italic>\n and\n IL8<\/jats:italic>\n in the cecum, spleen and liver. Based on this information, this report searches for resistance and susceptibility lineages in most genomic regions for SE.\n <\/jats:p>\n <\/jats:sec>\n \n Conclusions<\/jats:title>\n In conclusion, the development of an appropriate selection program to improve resistance to salmonellosis can be facilitated by a comprehensive understanding of the immune responses of the chicken immune system after SE exposure.<\/jats:p>\n <\/jats:sec>","DOI":"10.1002\/vms3.70006","type":"journal-article","created":{"date-parts":[[2024,10,11]],"date-time":"2024-10-11T08:39:01Z","timestamp":1728635941000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Network and systems biology approaches help investigate gene regulatory interactions between\n Salmonella<\/i>\n disease and host in chickens: Model\u2010based in silico evidence combined with gene expression assays"],"prefix":"10.1002","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4643-7836","authenticated-orcid":false,"given":"Reza","family":"Tohidi","sequence":"first","affiliation":[{"name":"Department of Animal Science Faculty of Agriculture University of Torbat\u2010e Jam Torbat\u2010e Jam Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6244-8984","authenticated-orcid":false,"given":"Hoda Javaheri","family":"Bargourooshi","sequence":"additional","affiliation":[{"name":"Department of Animal Production Management Animal Science Research Institute of Iran (ASRI) Agricultural Research Education and Extension Organization (AREEO) Karaj Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7998-9875","authenticated-orcid":false,"given":"Arash","family":"Javanmard","sequence":"additional","affiliation":[{"name":"Department of Animal Science Faculty of Agriculture University 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