TNF-α/STAT1/CXCL10 mutual inflammatory axis that contributes to the pathogenesis of experimental models of multiple sclerosis: A promising signaling pathway for targeted therapies.

[Display omitted] • CXCL10, CCL12, and GBP6 were the important upregulated mutual genes in all models. • STAT1, and CXCL10, were the most crucial hub proteins among mutual genes. • Mutual genes were involved in the TNF-α, TLRs, and complement cascade signaling. • Serum level of TNF-α/IL-1β/CXCL10 cytokines increased in all models. Identifying mutual neuroinflammatory axis in different experimental models of multiple sclerosis (MS) is essential to evaluate the de- and re-myelination processes and improve therapeutic interventions' reproducibility. The expression profile data set of EAE (GSE47900) and cuprizone (GSE100663) models were downloaded from the Gene Expression Omnibus database. The R package and GEO2R software processed these raw chip data. Gene Ontology (GO) functional analysis, KEGG pathway analysis, and protein–protein interaction network analysis were performed to investigate interactions between common differentially expressed genes (DEGs) in all models. Finally, the ELISA method assessed the protein level of highlighted mutual cytokines in serum. Results: Our data introduced 59 upregulated [CXCL10, CCL12, and GBP6 as most important] and 17 downregulated [Serpinb1a, Prr18, and Ugt8a as most important] mutual genes. The signal transducer and activator of transcription 1 (STAT1) and CXCL10 were the most crucial hub proteins among mutual upregulated genes. These mutual genes were found to be mainly involved in the TNF-α, TLRs, and complement cascade signaling, and animal models shared 26 mutual genes with MS individuals. Finally, significant upregulation of serum level of TNF-α/IL-1β/CXCL10 cytokines was confirmed in all models in a relatively similar pattern. For the first time, our study revealed the common neuroinflammatory pathway in animal models of MS and introduced candidate hub genes for better evaluating the preclinical efficacy of pharmacological interventions and designing prospective targeted therapies. [ABSTRACT FROM AUTHOR]