Dataset Information

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

ABSTRACT: 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.

SUBMITTER: Liu Y

PROVIDER: S-EPMC8231010 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature

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

Liu Yuxia Y Li Wenhui W Yang Hongxia H Zhang Xiaoying X Wang Wenxiu W Jia Sitong S Xiang Beibei B Wang Yi Y Miao Lin L Zhang Han H Wang Lin L Wang Yujing Y Song Jixiang J Sun Yingjie Y Chai Lijuan L Tian Xiaoxuan X

Frontiers in cellular and infection microbiology 20210611

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 sampl ...[more]

PMID: 34178718

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Project description:AimTo study whether selected bacterial 16S ribosomal RNA (rRNA) gene phylotypes are capable of distinguishing irritable bowel syndrome (IBS).MethodsThe faecal microbiota of twenty volunteers with IBS, subdivided into eight diarrhoea-predominant (IBS-D), eight constipation-predominant (IBS-C) and four mixed symptom-subtype (IBS-M) IBS patients, and fifteen control subjects, were analysed at three time-points with a set of fourteen quantitative real-time polymerase chain reaction assays. All assays targeted 16S rRNA gene phylotypes putatively associated with IBS, based on 16S rRNA gene library sequence analysis. The target phylotypes were affiliated with Actinobacteria, Bacteroidetes and Firmicutes. Eight of the target phylotypes had less than 95% similarity to cultured bacterial species according to their 16S rRNA gene sequence. The data analyses were made with repeated-measures ANCOVA-type modelling of the data and principle component analysis (PCA) with linear mixed-effects models applied to the principal component scores.ResultsBacterial phylotypes Clostridium cocleatum 88%, Clostridium thermosuccinogenes 85%, Coprobacillus catenaformis 91%, Ruminococcus bromii-like, Ruminococcus torques 91%, and R. torques 93% were detected from all samples analysed. A multivariate analysis of the relative quantities of all 14 bacterial 16S rRNA gene phylotypes suggested that the intestinal microbiota of the IBS-D patients differed from other sample groups. The PCA on the first principal component (PC1), explaining 30.36% of the observed variation in the IBS-D patient group, was significantly altered from all other sample groups (IBS-D vs control, P = 0.01; IBS-D vs IBS-M, P = 0.00; IBS-D vs IBS-C, P = 0.05). Significant differences were also observed in the levels of distinct phylotypes using relative values in proportion to the total amount of bacteria. A phylotype with 85% similarity to C. thermosuccinogenes was quantified in significantly different quantities among the IBS-D and control subjects (-4.08 +/- 0.90 vs -3.33 +/- 1.16, P = 0.04) and IBS-D and IBS-M subjects (-4.08 +/- 0.90 vs -3.08 +/- 1.38, P = 0.05). Furthermore, a phylotype with 94% similarity to R. torques was more prevalent in IBS-D patients' intestinal microbiota than in that of control subjects (-2.43 +/- 1.49 vs -4.02 +/- 1.63, P = 0.01). A phylotype with 93% similarity to R. torques was associated with control samples when compared with IBS-M (-2.41 +/- 0.53 vs -2.92 +/- 0.56, P = 0.00). Additionally, a R. bromii-like phylotype was associated with IBS-C patients in comparison to control subjects (-1.61 +/- 1.83 vs -3.69 +/- 2.42, P = 0.01). All of the above mentioned phylotype specific alterations were independent of the effect of time.ConclusionSignificant phylotype level alterations in the intestinal microbiotas of IBS patients were observed, further emphasizing the possible contribution of the gastrointestinal microbiota in IBS.

Project description:An increasing body of literature suggests that both individual and collections of bacteria are associated with the progression of colorectal cancer. As the number of studies investigating these associations increases and the number of subjects in each study increases, a meta-analysis to identify the associations that are the most predictive of disease progression is warranted. We analyzed previously published 16S rRNA gene sequencing data collected from feces and colon tissue. We quantified the odds ratios (ORs) for individual bacterial taxa that were associated with an individual having tumors relative to a normal colon. Among the fecal samples, there were no taxa that had significant ORs associated with adenoma and there were 8 taxa with significant ORs associated with carcinoma. Similarly, among the tissue samples, there were no taxa that had a significant OR associated with adenoma and there were 3 taxa with significant ORs associated with carcinoma. Among the significant ORs, the association between individual taxa and tumor diagnosis was equal to or below 7.11. Because individual taxa had limited association with tumor diagnosis, we trained Random Forest classification models using only the taxa that had significant ORs, using the entire collection of taxa found in each study, and using operational taxonomic units defined based on a 97% similarity threshold. All training approaches yielded similar classification success as measured using the area under the curve. The ability to correctly classify individuals with adenomas was poor, and the ability to classify individuals with carcinomas was considerably better using sequences from feces or tissue.IMPORTANCE Colorectal cancer is a significant and growing health problem in which animal models and epidemiological data suggest that the colonic microbiota have a role in tumorigenesis. These observations indicate that the colonic microbiota is a reservoir of biomarkers that may improve our ability to detect colonic tumors using noninvasive approaches. This meta-analysis identifies and validates a set of 8 bacterial taxa that can be used within a Random Forest modeling framework to differentiate individuals as having normal colons or carcinomas. When models trained using one data set were tested on other data sets, the models performed well. These results lend support to the use of fecal biomarkers for the detection of tumors. Furthermore, these biomarkers are plausible candidates for further mechanistic studies into the role of the gut microbiota in tumorigenesis.

Dataset Information

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

Publications

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

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