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mass spectrometry-based quantitative metabolomics, metabolomic features were annotated to metabolites with Level 1 of confidence by comparing them to the standard metabolites.MetaboLightsPublicLiquid Chromatography MS -A ultra-performance liquid chromatography coupled to tandem mass spectrometry (UPLC-MS/MS) system (ACQUITY UPLC-Xevo TQ-S, Waters Corp., Milford, MA, USA) was used to quantitate all targeted metabolites in this project.Microbiome and metabolome features in inflammatory bowel disease via multi-omics integration analyses across cohorts. 10.1038/s41467-023-42788-0. PMID:37932270Lijun NingShanghai Jiao Tong University School of Medicinefecesmass spectrometry assayTargeted metabolomics profiling was conducted using the Q300 Metabolite Array Kit from Metabo-Profile Biotechnology of China[1]. In brief, to extract metabolites from lyophilized feces, a homogenate was prepared using 10 mg of feces with 25 μL of water. The mixture was then extracted with 185 μL of cold ACN-Methanol (8/2, v/v) and centrifuged. Next, 30 μL of the supernatant was derivatized with 20 μL of freshly prepared derivative reagents on a Biomek 4000 workstation. Internal standards were added to the derivatized samples, which were then randomly analyzed and quantitated using an ultra-performance liquid chromatography coupled to tandem mass spectrometry (UPLC-MS/MS) system. A total of 310 standard substances, including 12 subclasses, were obtained from Sigma-Aldrich, Steraloids Inc, and TRC Chemicals. To ensure the quality of the metabolomics platform, three types of quality control samples were routinely used: test mixtures, internal standards, and pooled biological samples. The derivatized pooled quality control samples were injected every 14 test samples (Supplementary Fig. 4a). Ref: [1] Xie, G., Wang, L., Chen, T., Zhou, K., Zhang, Z., Li, J., Sun, B., Guo, Y., Wang, X., Wang, Y. and Zhang, H., 2021. A metabolite array technology for precision medicine. Analytical chemistry, 93(14), pp.5709-5717.Homo sapienshttps://www.ebi.ac.uk/metabolights/MTBLS8713Jie Hong. Shanghai Jiao Tong University. jiehong97@sjtu.edu.cn.The raw data generated by UPLC-MS/MS were processed using the QuanMET software (v2.0, Metabo-Profile, Shanghai, China) for peak integration, calibration and quantification of each metabolite.Diseaserenjixiaohua@gmail.comIn this study, we recruited two IBD cohorts from Renji Hospital, Shanghai, including the Puxi and Pudong campuses, for the discovery and validation cohorts, between between January 1, 2019, and December 31, 2022, respectively. We also recruited a group of healthy control subjects who were carefully matched by age and gender across two hospital campuses.  For the metabolomic cohorts (Fig. 4a of paper associated to study), a total of 178 participants were included, with 135 individuals diagnosed with IBD and 43 healthy control subjects, carefully matched for age and gender.  All participants are required to provide a minimum of 3 g of stool sample upon enrollment. Samples are to be collected in a sterile specimen collector (Thermo Scientific, USA, R21922) provided by the investigator in advance. After collection, the samples must be promptly transferred to a -80 °C ultra-low temperature cryogenic freezer for storage within 4 h, pending further processing in 3 months.Metabolomicsultra-performance liquid chromatography-mass spectrometryinflammatory bowel diseasetargeted metabolitesultra-performance liquid chromatography-mass spectrometryinflammatory bowel diseasetargeted metabolitesA ultra-performance liquid chromatography coupled to tandem mass spectrometry (UPLC-MS/MS) system (ACQUITY UPLC-Xevo TQ-S, Waters Corp., Milford, MA, USA) was used to quantitate all targeted metabolites in this project.The perturbations of the gut microbiota and metabolites are closely associated with the progression of inflammatory bowel disease (IBD). However, inconsistent findings across studies impede a comprehensive understanding of their roles in IBD and their potential as reliable diagnostic biomarkers. To address this challenge, here we comprehensively analyze 9 metagenomic and 4 metabolomics cohorts of IBD from different populations. Through cross-cohort integrative analysis (CCIA), we identify a consistent characteristic of commensal gut microbiota. Especially, three bacteria, namely Asaccharobacter celatus, Gemmiger formicilis, and Erysipelatoclostridium ramosum, which are rarely reported in IBD. Metagenomic functional analysis reveals that essential gene of Two-component system pathway, linked to fecal calprotectin, are implicated in IBD. Metabolomics analysis shows 36 identified metabolites with significant differences, while the roles of these metabolites in IBD are still unknown. To further elucidate the relationship between gut microbiota and metabolites, we construct multi-omics biological correlation (MOBC) maps, which highlights gut microbial biotransformation deficiencies and significant alterations in aminoacyl-tRNA synthetases. Finally, we identify multi-omics biomarkers for IBD diagnosis, validated across multiple global cohorts (AUROC values ranging from 0.92 to 0.98). Our results offer valuable insights and a significant resource for developing mechanistic hypotheses on host-microbiome interactions in IBD.Microbiome and metabolome features in inflammatory bowel disease via multi-omics integration analyses across cohorts.Ning Lijun L, Zhou Yi-Lu YL, Sun Han H, Zhang Youwei Y, Shen Chaoqin C, Wang Zhenhua Z, Xuan Baoqin B, Zhao Ying Y, Ma Yanru Y, Yan Yuqing Y, Tong Tianying T, Huang Xiaowen X, Hu Muni M, Zhu Xiaoqiang X, Ding Jinmei J, Zhang Yue Y, Cui Zhe Z, Fang Jing-Yuan JY, Chen Haoyan H, Hong Jie JfalseMicrobiome and metabolome features in inflammatory bowel disease via multi-omics integration analyses across cohortsThe perturbations of the gut microbiota and metabolites are closely associated with the progression of inflammatory bowel disease (IBD). However, inconsistent findings across studies impede a comprehensive understanding of their roles in IBD and their potential as reliable diagnostic biomarkers. To address this challenge, here we comprehensively analyze 9 metagenomic and 4 metabolomics cohorts of IBD from different populations. Through cross-cohort integrative analysis (CCIA), we identify a consistent characteristic of commensal gut microbiota. Especially, three bacteria, namely Asaccharobacter celatus, Gemmiger formicilis and Erysipelatoclostridium ramosum, which are rarely reported in IBD. Metagenomic functional analysis reveals that essential gene of Two-component system pathway, linked to fecal calprotectin, are implicated in IBD. Metabolomics analysis shows 36 identified metabolites with significant differences, while the roles of these metabolites in IBD are still unknown. To further elucidate the relationship between gut microbiota and metabolites, we construct multi-omics biological correlation (MOBC) maps, which highlights gut microbial biotransformation deficiencies and significant alterations in aminoacyl-tRNA synthetases. Finally, we identify multi-omics biomarkers for IBD diagnosis, validated across multiple global cohorts (AUROC values ranging from 0.92 to 0.98). Our results offer valuable insights and a significant resource for developing mechanistic hypotheses on host-microbiome interactions in IBD.2026-01-282023-10-09MTBLS871337932270