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用于在炎症性肠病中实现精准医学的网络生物学方法。

Network Biology Approaches to Achieve Precision Medicine in Inflammatory Bowel Disease.

作者信息

Thomas John P, Modos Dezso, Korcsmaros Tamas, Brooks-Warburton Johanne

机构信息

Earlham Institute, Norwich, United Kingdom.

Quadram Institute Bioscience, Norwich, United Kingdom.

出版信息

Front Genet. 2021 Oct 21;12:760501. doi: 10.3389/fgene.2021.760501. eCollection 2021.

DOI:10.3389/fgene.2021.760501
PMID:34745229
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8566351/
Abstract

Inflammatory bowel disease (IBD) is a chronic immune-mediated condition arising due to complex interactions between multiple genetic and environmental factors. Despite recent advances, the pathogenesis of the condition is not fully understood and patients still experience suboptimal clinical outcomes. Over the past few years, investigators are increasingly capturing multi-omics data from patient cohorts to better characterise the disease. However, reaching clinically translatable endpoints from these complex multi-omics datasets is an arduous task. Network biology, a branch of systems biology that utilises mathematical graph theory to represent, integrate and analyse biological data through networks, will be key to addressing this challenge. In this narrative review, we provide an overview of various types of network biology approaches that have been utilised in IBD including protein-protein interaction networks, metabolic networks, gene regulatory networks and gene co-expression networks. We also include examples of multi-layered networks that have combined various network types to gain deeper insights into IBD pathogenesis. Finally, we discuss the need to incorporate other data sources including metabolomic, histopathological, and high-quality clinical meta-data. Together with more robust network data integration and analysis frameworks, such efforts have the potential to realise the key goal of precision medicine in IBD.

摘要

炎症性肠病(IBD)是一种慢性免疫介导性疾病,由多种遗传和环境因素之间的复杂相互作用引起。尽管近年来取得了进展,但该疾病的发病机制仍未完全明确,患者的临床结局仍不尽人意。在过去几年中,研究人员越来越多地从患者队列中获取多组学数据,以更好地表征该疾病。然而,从这些复杂的多组学数据集中得出临床可转化的终点是一项艰巨的任务。网络生物学是系统生物学的一个分支,它利用数学图论通过网络来表示、整合和分析生物数据,将是应对这一挑战的关键。在这篇叙述性综述中,我们概述了在IBD中使用的各种类型的网络生物学方法,包括蛋白质-蛋白质相互作用网络、代谢网络、基因调控网络和基因共表达网络。我们还列举了多层网络的实例,这些网络结合了各种网络类型,以更深入地了解IBD的发病机制。最后,我们讨论了纳入其他数据源的必要性,包括代谢组学、组织病理学和高质量临床元数据。与更强大的网络数据整合和分析框架一起,这些努力有可能实现IBD精准医学的关键目标。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cc0b/8566351/1942a9812637/fgene-12-760501-g006.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cc0b/8566351/2d83a2b1f21e/fgene-12-760501-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cc0b/8566351/45f499925f17/fgene-12-760501-g002.jpg
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