ORIGINAL RESEARCH article
Front. Pharmacol.
Sec. Pharmacology of Infectious Diseases
Volume 16 - 2025 | doi: 10.3389/fphar.2025.1562774
Identification of Gene Signatures and Potential Pharmaceutical Candidates Linked to COVID-19-Related Depression Based on Gene Expression Profiles
Provisionally accepted
- 1Zhejiang Pharmaceutical College, Ningbo, China
- 2Zhejiang Pharmaceutical University, Ningbo, China
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Acute and long-term mental health disorders correlate with coronavirus disease 2019 . The underlying mechanisms responsible for the coexistence of COVID-19 and depression remain unclear, and more research is needed to find hub genes and effective therapies. The main objective of this study was to evaluate gene-expression profiles and, identify key genes, and discovery potential therapeutic agents for co-occurrence in COVID-19 and major depressive disorder (MDD).Initially, we identified differentially expressed genes (DEGs) in datasets pertaining to either COVID-19 (GSE188847) or MDD (GSE101521). We uncovered 60 DEGs that overlapped between the two datasets but exhibited distinct patterns of expression in each dataset. Subsequently, two machine learning analyses, namely least absolute shrinkage and selection operator (LASSO) and random forest algorithms, revealed EMILIN3, OPA3, and TFCP2 as potential hub genes. Furthermore, the LINCS L1000 characteristic direction signatures search engine (L1000CDS2) was employed for drug repurposing studies based on the gene-expression signatures. These drug repurposing studies indicated that trichostatin A (TSA), a metabolite derived from Streptomyces, could potentially reverse the altered gene expression. Moreover, results of molecular docking and molecular dynamics simulations revealed that complexes of TSA-perturbed protein spontaneously form and are highly stable. Therefore, TSA may serve as a therapeutic option for treating COVID-19-associated depression.Given the inherent constraints of computational modeling, further biological validation studies would help establish the significance of these preliminary findings.
Keywords: COVID-19, gene-expression signature, machine learning, Major Depressive Disorder, Trichostatin A
Received: 30 Jan 2025; Accepted: 05 Aug 2025.
Copyright: © 2025 Chen, Luo and Luo. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Shao Jun Chen, Zhejiang Pharmaceutical College, Ningbo, China
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