Leveraging 16S rRNA Microbiome Sequencing Data to Identify Bacterial Signatures for Irritable Bowel Syndrome

Frontiers in Cellular and Infection Microbiology

Beibei Xiang¹  Wenhui Li²  Hongxia Yang²  Xiaoying Zhang²  Sitong Jia²  Xiaoxuan Tian²  Yuxia Liu²  Wenxiu Wang²  Yi Wang³  Lin Miao³  Han Zhang⁴  Lijuan Chai⁴  Lin Wang⁵  Yingjie Sun⁵  Jixiang Song⁵  Yujing Wang⁵ 

[1] School of Chinese Materia Medica, Tianjin University of Traditional Chinese Medicine, Tianjin, China;State Key Laboratory of Component-Based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, China;State Key Laboratory of Component-Based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, China;Key Laboratory of Pharmacology of Traditional Chinese Medical Formulae, Ministry of Education, Tianjin University of Traditional Chinese Medicine, Tianjin, China;State Key Laboratory of Component-Based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, China;Laboratory of Pharmacology of Traditional Chinese Medical Formulae Co-Constructed by the Province-Ministry, Tianjin University of TCM, Tianjin, China;Tianjin Zhongxin Pharmaceutical Group Co., Ltd. Le Ren Tang Pharmaceutical Factory, Tianjin, China;

关键词: gut microbiome;    biomarkers;    16S rRNA;    machine learning algorithm;    irritable bowel syndrome;   

DOI  :  10.3389/fcimb.2021.645951

来源: Frontiers

【 摘 要 】

Irritable bowel syndrome (IBS) is a chronic gastrointestinal disorder characterized by abdominal pain or discomfort. Previous studies have illustrated that the gut microbiota might play a critical role in IBS, but the conclusions of these studies, based on various methods, were almost impossible to compare, and reproducible microorganism signatures were still in question. To cope with this problem, previously published 16S rRNA gene sequencing data from 439 fecal samples, including 253 IBS samples and 186 control samples, were collected and processed with a uniform bioinformatic pipeline. Although we found no significant differences in community structures between IBS and healthy controls at the amplicon sequence variants (ASV) level, machine learning (ML) approaches enabled us to discriminate IBS from healthy controls at genus level. Linear discriminant analysis effect size (LEfSe) analysis was subsequently used to seek out 97 biomarkers across all studies. Then, we quantified the standardized mean difference (SMDs) for all significant genera identified by LEfSe and ML approaches. Pooled results showed that the SMDs of nine genera had statistical significance, in which the abundance of Lachnoclostridium, Dorea, Erysipelatoclostridium, Prevotella 9, and Clostridium sensu stricto 1 in IBS were higher, while the dominant abundance genera of healthy controls were Ruminococcaceae UCG-005, Holdemanella, Coprococcus 2, and Eubacterium coprostanoligenes group. In summary, based on six published studies, this study identified nine new microbiome biomarkers of IBS, which might be a basis for understanding the key gut microbes associated with IBS, and could be used as potential targets for microbiome-based diagnostics and therapeutics.

【 授权许可】

CC BY