Back

MicroRNA-based regulation of genomics and transcriptomics of inflammatory cytokines in COVID-19

Khokhar, M.; Tomo, S.; Purohit, P.

2021-06-12 allergy and immunology
10.1101/2021.06.08.21258565 medRxiv
Show abstract

BackgroundCoronavirus disease 2019 is characterized by the elevation of a wide spectrum of inflammatory mediators, which are associated with poor disease outcomes. We aimed at an in-silico analysis of regulatory microRNA and their transcription factors (TF) for these inflammatory genes that may help to devise potential therapeutic strategies in the future. MethodsThe cytokine regulating immune-expressed genes (CRIEG) was sorted from literature and the GEO microarray dataset. Their co-differentially expressed miRNA and transcription factors were predicted from publicly available databases. Enrichment analysis was done through mienturnet, MiEAA, Gene Ontology, and pathways predicted by KEGG and Reactome pathways. Finally, the functional and regulatory features were analyzed and visualized through Cytoscape. ResultsSixteen CRIEG were observed to have a significant protein-protein interaction network. The ontological analysis revealed significantly enriched pathways for biological processes, molecular functions, and cellular components. The search performed in the MiRNA database yielded 10 (ten) miRNAs that are significantly involved in regulating these genes and their transcription factors. ConclusionAn in-silico representation of a network involving miRNAs, CRIEGs, and TF which take part in the inflammatory response in COVID-19 has been elucidated. These regulatory factors may have potentially critical roles in the inflammatory response in COVID-19 and may be explored further to develop targeted therapeutic strategies and mechanistic validation.

Matching journals

The top 8 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.