Chen Shaojun, Luo Yiyuan, Zhang Lihua
Department of Traditional Chinese Medicine, Zhejiang Pharmaceutical University, Ningbo, China.
School of Health Management, Zhejiang Pharmaceutical University, Ningbo, China.
Front Pharmacol. 2025 Aug 22;16:1562774. doi: 10.3389/fphar.2025.1562774. eCollection 2025.
Acute and long-term mental health disorders correlate with coronavirus disease 2019 (COVID-19). 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 from COVID-19 (GSE188847) or MDD (GSE101521). Subsequently, we employed two machine learning analyses-least absolute shrinkage and selection operator (LASSO) and random forest algorithms- to pinpoint shared hub gene between the two diseases. Furthermore, the LINCS L1000 characteristic direction signatures search engine (L1000CDS2) was utilized for drug repurposing studies based on the gene-expression signatures. Finally, molecular dynamics (MD) simulations were conducted to investigate the binding interactions between molecules and the target proteins.
We uncovered 60 DEGs that overlapped between the two datasets but exhibited distinct patterns of expression in each dataset. Subsequent machine learning analyses revealed EMILIN3, OPA3, and TFCP2 as potential shared hub genes underlying both diseases. Furthermore, L1000CDS2 analysis indicated that trichostatin A (TSA), a metabolite derived from Streptomyces, could potentially reverse the altered gene expression. Molecular docking and molecular dynamics simulations revealed that complexes of TSA-perturbed protein spontaneously form and are highly stable.
EMILIN3, OPA3, and TFCP2 are likely to be potential shared hub genes in both COVID-19 and depression. Meanwhile, 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.
急性和长期精神健康障碍与2019冠状病毒病（COVID-19）相关。COVID-19与抑郁症共存的潜在机制尚不清楚，需要更多研究来寻找核心基因和有效疗法。本研究的主要目的是评估基因表达谱，识别关键基因，并发现COVID-19和重度抑郁症（MDD）共病的潜在治疗药物。
首先，我们在COVID-19（GSE188847）或MDD（GSE101521）的数据集中鉴定差异表达基因（DEG）。随后，我们采用两种机器学习分析——最小绝对收缩和选择算子（LASSO）和随机森林算法——来确定两种疾病之间的共同核心基因。此外，利用LINCS L1000特征方向特征搜索引擎（L1000CDS2）基于基因表达特征进行药物再利用研究。最后，进行分子动力学（MD）模拟以研究分子与靶蛋白之间的结合相互作用。
我们发现两个数据集之间有60个DEG重叠，但在每个数据集中表现出不同的表达模式。随后的机器学习分析显示，埃米林3（EMILIN3）、视神经萎缩蛋白3（OPA3）和转录因子CP2（TFCP2）是两种疾病潜在的共同核心基因。此外，L1000CDS2分析表明，来源于链霉菌的代谢产物曲古抑菌素A（TSA）可能会逆转基因表达的改变。分子对接和分子动力学模拟表明，TSA干扰蛋白复合物能自发形成且高度稳定。
EMILIN3、OPA3和TFCP2可能是COVID-19和抑郁症潜在的共同核心基因。同时，TSA可能作为治疗COVID-19相关抑郁症的一种治疗选择。鉴于计算建模的固有局限性，进一步的生物学验证研究将有助于确定这些初步发现的意义。
