急性呼吸窘迫综合征的基因组图谱:根据宿主反应全基因组研究的信息含量进行的荟萃分析。

Jonathan E Millar, Sara Clohisey-Hendry, Megan McManus, Marie Zechner, Bo Wang, Nicholas Parkinson, Melissa Jungnickel, Nureen Mohamad Zaki, Erola E Pairo-Castineira, Konrad Rawlik, Joshua Rogers, Clark D Russell, Lieuwe DJ Bos, Nuala J Meyer, Carolyn Calfee, Daniel F McAuley, Manu Shankar-Hari, J Kenneth Baillie
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摘要

急性呼吸窘迫综合征(ARDS)是一种临床定义的非心源性肺水肿继发的急性低氧血症呼吸衰竭综合征。它由多种诱因引起,具有明显的生物学异质性,使开发有效疗法的工作变得更加复杂。近年来,大量研究(包括转录组学、蛋白质组学和全基因组关联研究)试图找出与 ARDS 发病机制有关的蛋白质/基因。这些不同的研究尚未得到系统的整理和解释。为了解决这个问题,我们对现有的涉及 ARDS 发病机制中宿主反应通路的 omics 数据进行了系统回顾和计算整合。我们从 6856 名 ARDS 患者中发现了 40 项报告与基因和单核苷酸多态性(SNPs)相关性、关联性和其他联系的无偏见研究。我们使用信息含量荟萃分析(MAIC)对这些数据进行了整合和评估,对 7000 多个基因和 SNP 进行了排序,并对关联的累积证据进行了加权。强支持基因的功能富集显示,胆固醇代谢、内皮功能障碍、先天性免疫激活和中性粒细胞脱颗粒是关键过程。我们确定了 51 个枢纽基因,其中大部分是潜在的治疗靶点。为了探索生物学异质性,我们对 ARDS 的严重程度/结果进行了单独分析,发现了不同的基因关联和组织特异性。我们对 ARDS 中现有的 omics 数据进行了大规模整合,通过综合来自不同来源的数十年数据,加深了对基因组状况的了解。研究结果将有助于研究人员完善假设,选择候选基因进行功能验证,并确定潜在的治疗靶点和再利用机会。我们的研究和公开可用的计算框架代表了一个开放的、不断发展的 ARDS 基因组数据解读平台。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The genomic landscape of Acute Respiratory Distress Syndrome: a meta-analysis by information content of genome-wide studies of the host response.
Acute respiratory distress syndrome (ARDS) is a clinically defined syndrome of acute hypoxaemic respiratory failure secondary to non-cardiogenic pulmonary oedema. It arises from a diverse set of triggers and encompasses marked biological heterogeneity, complicating efforts to develop effective therapies. An extensive body of recent work (including transcriptomics, proteomics, and genome-wide association studies) has sought to identify proteins/genes implicated in ARDS pathogenesis. These diverse studies have not been systematically collated and interpreted. To solve this, we performed a systematic review and computational integration of existing omics data implicating host response pathways in ARDS pathogenesis. We identified 40 unbiased studies reporting associations, correlations, and other links with genes and single nucleotide polymorphisms (SNPs), from 6,856 ARDS patients. We used meta-analysis by information content (MAIC) to integrate and evaluate these data, ranking over 7,000 genes and SNPs and weighting cumulative evidence for association. Functional enrichment of strongly-supported genes revealed cholesterol metabolism, endothelial dysfunction, innate immune activation and neutrophil degranulation as key processes. We identify 51 hub genes, most of which are potential therapeutic targets. To explore biological heterogeneity, we conducted a separate analysis of ARDS severity/outcomes, revealing distinct gene associations and tissue specificity. Our large-scale integration of existing omics data in ARDS enhances understanding of the genomic landscape by synthesising decades of data from diverse sources. The findings will help researchers refine hypotheses, select candidate genes for functional validation, and identify potential therapeutic targets and repurposing opportunities. Our study and the publicly available computational framework represent an open, evolving platform for interpretation of ARDS genomic data.
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