Data_Sheet_1_Development of a Novel Metagenomic Biomarker for Prediction of Upper Gastrointestinal Tract Involvement in Patients With Crohn’s Disease.pdf

The human gut microbiota is an important component in the pathogenesis of Crohn’s disease (CD), promoting host–microbe imbalances and disturbing intestinal and immune homeostasis. We aimed to assess the potential clinical usefulness of the colonic tissue microbiome for obtaining biomarkers for upper gastrointestinal (UGI) tract involvement in CD. We analyzed colonic tissue samples from 26 CD patients (13 with and 13 without UGI involvement at diagnosis) from the Inflammatory Bowel Disease Multi-Omics Database. QIIME1, DiTaxa, linear discriminant analysis effect size (LEfSe), and PICRUSt2 methods were used to examine microbial dysbiosis. Linear support vector machine (SVM) and random forest classifier (RF) algorithms were used to identify the UGI tract involvement-associated biomarkers. There were no statistically significant differences in community richness, phylogenetic diversity, and phylogenetic distance between the two groups of CD patients. DiTaxa analysis predicted significant association of the species Ruminococcus torques with UGI involvement, which was confirmed by the LEfSe analysis (P = 0.025). For the feature ranking method in both linear SVM and RF models, the species R. torques and age at diagnosis contributed to the combined models. The L-methionine biosynthesis III (P = 0.038) and palmitate biosynthesis II (P = 0.050) were under-represented in CD with UGI involvement. These findings suggest that R. torques might serve as a novel potential biomarker for UGI involvement in CD and its correlations, in addition to a range of bacterial species. The mechanisms of interaction between hosts and R. torques should be further investigated.人体肠道菌群是克罗恩病（CD）发病机制中的重要组成部分，它促进了宿主-微生物失衡，扰乱了肠道和免疫稳态。本研究旨在评估结肠组织微生物组在获取克罗恩病上消化道（UGI）受累的生物标志物方面的潜在临床价值。我们分析了来自炎症性肠病多组学数据库的26名CD患者（诊断时13例有UGI受累，13例无UGI受累）的结肠组织样本。我们采用了QIIME1、DiTaxa、线性判别分析效应大小（LEfSe）和PICRUSt2方法来检查微生物失调。线性支持向量机（SVM）和随机森林分类器（RF）算法被用于识别UGI受累相关的生物标志物。两组CD患者在群落丰富度、系统发育多样性和系统发育距离方面均无统计学上的显著差异。DiTaxa分析预测了物种Ruminococcus torques与UGI受累的显著关联，这一关联通过LEfSe分析得到证实（P = 0.025）。在线性SVM和RF模型的特征排序方法中，物种R. torques和诊断时的年龄对联合模型有贡献。在UGI受累的CD患者中，L-蛋氨酸生物合成III（P = 0.038）和棕榈酸生物合成II（P = 0.050）代表性不足。这些发现表明，R. torques可能作为CD UGI受累的新型潜在生物标志物，除了一系列细菌物种外，其相互作用机制也应进一步研究。