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解析长新冠中 FOXP4 的作用:通过生物信息学分析探索遗传关联、进化保守性和药物鉴定。

Deciphering the role of FOXP4 in long COVID: exploring genetic associations, evolutionary conservation, and drug identification through bioinformatics analysis.

机构信息

Department of Biotechnology and Bioinformatics, Yogi Vemana University, Kadapa, Andhra Pradesh, 516005, India.

ICAR-National Rice Research Institute, Cuttack, Odisha, 753 006, India.

出版信息

Funct Integr Genomics. 2024 Sep 19;24(5):167. doi: 10.1007/s10142-024-01451-7.

Abstract

Long COVID (LC) refers to a condition characterized by a variety of lingering symptoms that persist for more than 4 to 12 weeks following the initial acute SARS-CoV-2 infection. Recent research has suggested that the FOXP4 gene could potentially be a significant factor contributing to LC. Owing to that, this study investigates FOXP4's role in LC by analyzing public datasets to understand its evolution and expression in diverse human populations and searching for drugs to reduce LC symptoms. Population genetic analysis of FOXP4 across human populations unmasks distinct genetic diversity patterns and positive selection signatures, suggesting potential population-specific susceptibilities to conditions like LC. Further, we also observed that FOXP4 experiences high expression during LC. To identify potential inhibitors, drug screening analysis identifies synthetic drugs like Glisoxepide, and natural compounds Kapurimycin A3 produced from Streptomyces sp, and Cucurbitacin B from Begonia nantoensis as promising candidates. Overall, our research contributes to understanding how FOXP4 may serve as a therapeutic target for mitigating the impact of LC.

摘要

长新冠(LC)是指在初次急性 SARS-CoV-2 感染后持续 4 至 12 周以上的多种挥之不去的症状。最近的研究表明,FOXP4 基因可能是 LC 的一个重要因素。鉴于此,本研究通过分析公共数据集来探究 FOXP4 在 LC 中的作用,以了解其在不同人群中的进化和表达,并寻找减少 LC 症状的药物。FOXP4 在人类群体中的群体遗传分析揭示了独特的遗传多样性模式和正选择特征,表明对 LC 等疾病可能存在特定人群的易感性。此外,我们还观察到 FOXP4 在 LC 期间表达水平较高。为了鉴定潜在的抑制剂,药物筛选分析确定了 Glisoxepide 等合成药物,以及来源于链霉菌的天然化合物 Kapurimycin A3 和来自 nantoensis 的 Cucurbitacin B 是有希望的候选药物。总的来说,我们的研究有助于理解 FOXP4 如何可能成为减轻 LC 影响的治疗靶点。

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