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一种系统基因组学方法,用于揭示溃疡性结肠炎中患者特异性的致病途径和蛋白质。

A systems genomics approach to uncover patient-specific pathogenic pathways and proteins in ulcerative colitis.

机构信息

Earlham Institute, Norwich Research Park, Norwich, UK.

Gut Microbes and Health Programme, The Quadram Institute Bioscience, Norwich Research Park, Norwich, UK.

出版信息

Nat Commun. 2022 Apr 28;13(1):2299. doi: 10.1038/s41467-022-29998-8.

Abstract

We describe a precision medicine workflow, the integrated single nucleotide polymorphism network platform (iSNP), designed to determine the mechanisms by which SNPs affect cellular regulatory networks, and how SNP co-occurrences contribute to disease pathogenesis in ulcerative colitis (UC). Using SNP profiles of 378 UC patients we map the regulatory effects of the SNPs to a human signalling network containing protein-protein, miRNA-mRNA and transcription factor binding interactions. With unsupervised clustering algorithms we group these patient-specific networks into four distinct clusters driven by PRKCB, HLA, SNAI1/CEBPB/PTPN1 and VEGFA/XPO5/POLH hubs. The pathway analysis identifies calcium homeostasis, wound healing and cell motility as key processes in UC pathogenesis. Using transcriptomic data from an independent patient cohort, with three complementary validation approaches focusing on the SNP-affected genes, the patient specific modules and affected functions, we confirm the regulatory impact of non-coding SNPs. iSNP identified regulatory effects for disease-associated non-coding SNPs, and by predicting the patient-specific pathogenic processes, we propose a systems-level way to stratify patients.

摘要

我们描述了一种精准医学工作流程,即整合单核苷酸多态性网络平台(iSNP),旨在确定 SNP 影响细胞调控网络的机制,以及 SNP 共同发生如何导致溃疡性结肠炎(UC)发病机制。我们使用 378 名 UC 患者的 SNP 谱,将 SNP 的调控作用映射到包含蛋白-蛋白、miRNA-mRNA 和转录因子结合相互作用的人类信号网络中。通过无监督聚类算法,我们将这些患者特异性网络分为四个不同的簇,由 PRKCB、HLA、SNAI1/CEBPB/PTPN1 和 VEGFA/XPO5/POLH 枢纽驱动。通路分析确定钙稳态、伤口愈合和细胞运动是 UC 发病机制中的关键过程。我们使用来自独立患者队列的转录组数据,并通过三种互补的验证方法,重点关注受 SNP 影响的基因、患者特异性模块和受影响的功能,证实了非编码 SNP 的调控作用。iSNP 确定了与疾病相关的非编码 SNP 的调控作用,并通过预测患者特异性的致病过程,我们提出了一种基于系统水平的患者分层方法。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f00b/9051123/8991f36bc6fa/41467_2022_29998_Fig1_HTML.jpg

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