J Clin Invest. 2021 May 3;131(9). doi: 10.1172/JCI148902.
Inflammatory bowel disease (IBD) is a chronic inflammatory disease of the intestine associated with genetic susceptibility and alterations in the intestinal microbiome. Multiomics data developed and analyzed over the last several decades have yielded an unprecedented amount of genetic and microbial data. But how do we pinpoint mechanistic insight into the host-microbe relationship that will ultimately enable better care for patients with IBD? In this issue of the JCI, Grasberger et al. undertook a major decoding effort to decipher this multiomic data matrix. The authors analyzed anonymized data from more than 2800 individuals to discover a link between heterozygous carriers of deleterious DUOX2 variants and high levels of plasma IL-17C. These findings provide an example of how harnessing big data can drive mechanistic discovery to define disease biomarkers that have the potential to improve clinical care in IBD.
炎症性肠病（IBD）是一种与遗传易感性和肠道微生物组改变相关的慢性肠道炎症性疾病。在过去几十年中开发和分析的多组学数据产生了前所未有的遗传和微生物数据。但是，我们如何确定对宿主-微生物关系的机制性洞察，最终为 IBD 患者提供更好的护理？在本期 JCI 中，Grasberger 等人进行了一项重大的解码工作，以破译这个多组学数据矩阵。作者分析了超过 2800 个人的匿名数据，发现具有有害 DUOX2 变异杂合子的个体与高水平的血浆 IL-17C 之间存在关联。这些发现提供了一个例子，说明如何利用大数据来推动机制性发现，以定义疾病生物标志物，从而有可能改善 IBD 的临床护理。
