Deciphering crucial genes in multiple sclerosis pathogenesis and drug repurposing: A systems biology approach.

This study employed systems biology and high-throughput technologies to analyze complex molecular components of MS pathophysiology, combining data from multiple omics sources to identify potential biomarkers and propose therapeutic targets and repurposed drugs for MS treatment. This study analyzed GEO microarray datasets and MS proteomics data using geWorkbench, CTD, and COREMINE to identify differentially expressed genes associated with MS disease. Protein-protein interaction networks were constructed using Cytoscape and its plugins, and functional enrichment analysis was performed to identify crucial molecules. A drug-gene interaction network was also created using DGIdb to propose medications. This study identified 592 differentially expressed genes (DEGs) associated with MS disease using GEO, proteomics, and text-mining datasets. 37 DEGs were found to be important by topographical network studies, and 6 were identified as the most significant for MS pathophysiology. Additionally, we proposed six drugs that target these key genes. Crucial molecules identified in this study were dysregulated in MS and likely play a key role in the disease mechanism, warranting further research. Additionally, we proposed repurposing certain FDA-approved drugs for MS treatment. Our in silico results were supported by previous experimental research on some of the target genes and drugs. SIGNIFICANCE: As the long-lasting investigations continue to discover new pathological territories in neurodegeneration, here we apply a systems biology approach to determine multiple sclerosis's molecular and pathophysiological origin and identify multiple sclerosis crucial genes that contribute to candidating new biomarkers and proposing new medications.

Specific Regulatory Motifs Network in SARS-CoV-2-Infected Caco-2 Cell Line, as a Model of Gastrointestinal Infections.

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was primarily noted as a respiratory pathogen, but later clinical reports highlighted its extrapulmonary effects particularly on the gastrointestinal (GI) tract. The aim of the current study was the prediction of crucial genes associated with the regulatory network motifs, probably responsible for the SARS-CoV-2 effects on the GI tract. The data were obtained from a published study on the effect of SARS-CoV-2 on the Caco-2 (colon carcinoma) cell line. We used transcription factors-microRNA-gene interaction databases to find the key regulatory molecules, then analyzed the data using the FANMOD software for detection of the crucial regulatory motifs. Cytoscape software was then used to construct and analyze the regulatory network of these motifs and identify their crucial genes. Finally, GEPIA2 (Gene Expression Profiling Interactive Analysis 2) and UALCAN datasets were used to evaluate the possible relationship between crucial genes and colon cancer development. Using bioinformatics tools, we demonstrated one 3edge feed-forward loop motifs and recognized 10 crucial genes in relationship with Caco-2 cell infected by SARS-CoV-2, including SP1, TSC22D2, POU2F1, REST, NFIC, CHD7, E2F1, CEBPA, TCF7L2, and TSC22D1. The box plot analysis indicated the significant overexpression of CEBPA in colon cancer compared to normal colon tissues, while it was in contrast with the results of stage plot. However, the overall survival analysis indicated that high expression of CEBPA has positive effect on colon cancer patient survivability, verifying the results of CEBPA stage plot. We predict that the SARS-CoV-2 GI infections may cause a serious risk in colon cancer patients. However, further experimental studies are required.