Leveraging 16S rRNA Microbiome Sequencing Data to Identify Bacterial Signatures for Irritable Bowel Syndrome - Liu, Yuxia; Li, Wenhui; Yang, Hongxia; Zhang, Xiaoying; Wang, Wenxiu; Jia, Sitong; Xiang, Beibei; Wang, Yi; Miao, Lin; Zhang, Han; Wang, Lin; Wang, Yujing; et al. (Frontiers in Cellular and Infection Microbiology, 2021)
Irritable bowel syndrome (IBS) is one of gastrointestinal disorder chronic disease, which 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 researches based on various methods were almost incomparable, and the reproducible microorganism signatures was still uncertain. To cope with this problem, previously published 16S rRNA gene sequencing data from 439 feces samples, including 253 IBS samples and 186 controls samples, were collected and processed with a uniform bioinformatic pipeline. We found no significant differences in community structure between IBS and healthy controls at ASV level, while machine learning approach allowed us to discriminate IBS from healthy controls at genus level. 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 machine learning approach using meta-analysis. Pooled results showed that nine genera had significant SMD, in which showed high abundance of the genera Lachnoclostridium, Dorea, Erysipelatoclostridium, Prevotella 9, Clostridium sensu stricto 1 in IBS, while health control group had a dominant abundance of 4 genera Ruminococcaceae UCG-005, Holdemanella, Coprococcus 2 and Eubacterium coprostanoligenes group. In summary, this combined analysis of worldwide studies identified nine novel microbiome biomarkers which might be a basis for understanding the key gut microbiome associated with IBS, and could be taken as potential targets for microbiome-based diagnostics and therapeutics.