Identification of Mitophagy-Related Genes in Sepsis

IF 2.4 3区生物学 Q3 BIOCHEMICAL RESEARCH METHODS

Current Bioinformatics Pub Date : 2024-01-05 DOI:10.2174/0115748936266722231116050255

Xiao-Yan Zeng, Min Zhang, Si-Jing Liao, Yong Wang, Ying-Bo Ren, Run Li, Tian-Mei Li, An-Qiong Mao, Guang-Zhen Li, Ying Zhang

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引用次数: 0

Abstract

Background: Numerous studies have shown that mitochondrial damage induces inflammation and activates inflammatory cells, leading to sepsis, while sepsis, a systemic inflammatory response syndrome, also exacerbates mitochondrial damage and hyperactivation. Mitochondrial autophagy eliminates aged, abnormal or damaged mitochondria to reduce intracellular mitochondrial stress and the release of mitochondria-associated molecules, thereby reducing the inflammatory response and cellular damage caused by sepsis. In addition, mitochondrial autophagy may also influence the onset and progression of sepsis, but the exact mechanisms are unclear. background: Sepsis is a critical systemic infection, a syndrome of severe inflammatory response of the organism to various pathogenic microorganisms. Methods: In this study, we mined the available publicly available microarray data in the GEO database (Home - GEO - NCBI (nih.gov)) with the aim of identifying key genes associated with mitochondrial autophagy in sepsis. objective: In this study, we used a bioinformatics approach to integrate multiple microarray data to screen for mitochondrial autophagy-related hub genes associated with sepsis onset and progression in a more scientific and systematic manner. Results: We identified four mitophagy-related genes in sepsis, TOMM20, TOMM22, TOMM40, and MFN1. method: Robust rank aggregation (RRA) Conclusion: This study provides preliminary evidence for the treatment of sepsis and may provide a solid foundation for subsequent biological studies. result: we constructed a PPI network combined with RRA analysis method to finally identify 4 key genes, namely TOMM20, TOMM22, TOMM40, and MFN1. conclusion: In this study, we used a bioinformatics analysis method, RRA, to integrate five gene microarray datasets to identify pivotal genes associated with mitochondrial autophagy in sepsis. Gene ontology (GO) functional annotation results show that these hub genes are mainly enriched in mitochondrial transport and establishment of protein localization to mitochondrion. Finally, we constructed the PPI network with the top 100 genes obtained from the rra method analysis. Based on the RRA results, the PPI results and the mitochondrial autophagy-related genes we found in the Reactome Pathway Database, we finally identified four key genes as TOMM20, TOMM22, TOMM40, and MFN1, respectively.

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鉴定败血症中与丝裂噬相关的基因

背景：大量研究表明，线粒体损伤会诱发炎症并激活炎症细胞，导致败血症，而败血症作为一种全身炎症反应综合征，也会加剧线粒体损伤和过度激活。线粒体自噬可消除老化、异常或受损的线粒体，减轻细胞内线粒体应激和线粒体相关分子的释放，从而减轻败血症引起的炎症反应和细胞损伤。此外，线粒体自噬也可能影响败血症的发生和发展，但具体机制尚不清楚：败血症是一种危重的全身性感染，是机体对各种病原微生物产生严重炎症反应的综合征。方法：在本研究中，我们挖掘了 GEO 数据库（Home - GEO - NCBI (nih.gov)）中可公开获得的微阵列数据，目的是找出与败血症中线粒体自噬相关的关键基因：在本研究中，我们采用生物信息学方法整合多种微阵列数据，以更科学、更系统的方式筛选与脓毒症发病和进展相关的线粒体自噬相关枢纽基因。结果我们发现了四个与脓毒症相关的线粒体自噬基因：TOMM20、TOMM22、TOMM40 和 MFN1：稳健秩聚合（RRA）结论：该研究为脓毒症的治疗提供了初步证据：本研究为脓毒症的治疗提供了初步证据，可为后续生物学研究奠定坚实基础。结果：我们构建了一个 PPI 网络，结合 RRA 分析方法，最终确定了 4 个关键基因，即 TOMM20、TOMM22、TOMM40 和 MFN1。结论：本研究为脓毒症的治疗提供了初步证据，可为后续生物学研究奠定坚实基础：在这项研究中，我们利用生物信息学分析方法 RRA 整合了 5 个基因芯片数据集，以确定脓毒症中与线粒体自噬相关的关键基因。基因本体论（GO）功能注释结果表明，这些枢纽基因主要富集于线粒体转运和蛋白质线粒体定位的建立。最后，我们利用 RRA 方法分析得到的前 100 个基因构建了 PPI 网络。根据RRA结果、PPI结果以及我们在Reactome Pathway数据库中发现的线粒体自噬相关基因，我们最终确定了四个关键基因，分别是TOMM20、TOMM22、TOMM40和MFN1。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Current Bioinformatics 生物-生化研究方法

CiteScore

6.60

自引率

2.50%

发文量

审稿时长

>12 weeks

期刊介绍： Current Bioinformatics aims to publish all the latest and outstanding developments in bioinformatics. Each issue contains a series of timely, in-depth/mini-reviews, research papers and guest edited thematic issues written by leaders in the field, covering a wide range of the integration of biology with computer and information science. The journal focuses on advances in computational molecular/structural biology, encompassing areas such as computing in biomedicine and genomics, computational proteomics and systems biology, and metabolic pathway engineering. Developments in these fields have direct implications on key issues related to health care, medicine, genetic disorders, development of agricultural products, renewable energy, environmental protection, etc.