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. 2014 Dec 17;9(12):e115175.
doi: 10.1371/journal.pone.0115175. eCollection 2014.

Bacterial bile metabolising gene abundance in Crohn's, ulcerative colitis and type 2 diabetes metagenomes

Alain Labbé  1 , Jorge G Ganopolsky  1 , Christopher J Martoni  1 , Satya Prakash  2 , Mitchell L Jones  2
Affiliations

Bacterial bile metabolising gene abundance in Crohn's, ulcerative colitis and type 2 diabetes metagenomes (V体育2025版)

Alain Labbé et al. PLoS One. .

Abstract

We performed an analysis to determine the importance of bile acid modification genes in the gut microbiome of inflammatory bowel disease and type 2 diabetic patients. We used publicly available metagenomic datasets from the Human Microbiome Project and the MetaHIT consortium, and determined the abundance of bile salt hydrolase gene (bsh), 7 alpha-dehydroxylase gene (adh) and 7-alpha hydroxysteroid dehydrogenase gene (hsdh) in fecal bacteria in diseased populations of Crohn's disease (CD), Ulcerative Colitis (UC) and Type 2 diabetes mellitus (T2DM). Phylum level abundance analysis showed a significant reduction in Firmicute-derived bsh in UC and T2DM patients but not in CD patients, relative to healthy controls. Reduction of adh and hsdh genes was also seen in UC and T2DM patients, while an increase was observed in the CD population as compared to healthy controls. A further analysis of the bsh genes showed significant differences in the correlations of certain Firmicutes families with disease or healthy populations. From this observation we proceeded to analyse BSH protein sequences and identified BSH proteins clusters representing the most abundant strains in our analysis of Firmicute bsh genes. The abundance of the bsh genes corresponding to one of these protein clusters was significantly reduced in all disease states relative to healthy controls. This cluster includes bsh genes derived from Lachospiraceae, Clostridiaceae, Erysipelotrichaceae and Ruminococcaceae families. This metagenomic analysis provides evidence of the importance of bile acid modifying enzymes in health and disease. It further highlights the importance of identifying gene and protein clusters, as the same gene may be associated with health or disease, depending on the strains expressing the enzyme, and differences in the enzymes themselves. VSports手机版.

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"V体育ios版" Conflict of interest statement

Competing Interests: Micropharma Limited funded this research. MLJ and SP are co-founders and shareholders of Micropharma. MLJ, CJM, JGG and AL are employed by and CJM is a shareholder of Micropharma. AL, JGG and MLJ designed the research. AL collected and analyzed the data. AL, JGG, CJM and MLJ prepared the manuscript for publication V体育安卓版. Micropharma Limited holds no editorial restriction over the work published and the authors' association with it does not alter their adherence to PLOS ONE policies on sharing data and materials.

Figures

Figure 1
Figure 1. Abundance of bsh in metagenomic data from IBD and diabetic patients.
Abundance of bsh genes was obtained by searching a BSH protein database against publicly available SRA sequences from HMP and MetaHIT metagenomic datasets for Crohn's Disease (CD) and Ulcerative Colitis (UC) (A) and diabetic patients (B). Hits (≥24 amino acids and ≥75% ID) were counted (Total hits) and further searched against a bacterial genome database to assign taxonomic origins to the hits (Assigned hits). Assigned and total hits were quantified and expressed as hits per millions sequences for each individual patient. Values are median abundance and error bars are 95% confidence interval. Statistical analysis was Kruskall-wallis (A) or Mann-Whitney U (B). *p<0.05, **p<0.01, ***p<0.001.
Figure 2
Figure 2. Abundance of Firmicute derived bsh in metagenomic data.
Abundance of bsh genes was obtained by searching a BSH protein database against publicly available SRA sequences from HMP and MetaHIT metagenomic datasets for IBD and diabetic patients. Hits (≥24 amino acids and ≥75% ID) were counted and searched against a bacterial genome database to assign taxonomic origins to the hits. Hits assigned to the Firmicute phylum were counted and expressed as hits per millions sequences for each individual patient. Values are median abundance and error bars are 95% confidence interval. Statistical analysis was Kruskall-wallis (A) or Mann-Whitney U (B). *p<0.05, **p<0.01.
Figure 3
Figure 3. Abundance of adh and hsdh in bacterial phyla.
Abundance of adh genes (please refer to S3 Table for sequences assigned as adh) (A) and hsdh genes (B) was obtained by searching protein database against publicly available SRA sequences from HMP and MetaHIT metagenomic datasets for IBD (UC and CD) and diabetic patients. Hits (≥24 amino acids and ≥75% ID for hsdh and ≥60% ID for adh) were counted and searched against a bacterial genome database to assign taxonomic origins to the hits. Hits assigned to the various phylum were counted and expressed as hits per millions sequences for each individual patient. Values are median abundance and error bars are 95% confidence interval. Statistical analysis was Kruskall-wallis. ***p<0.001
Figure 4
Figure 4. Correlation between bsh abundance from bacterial families and disease.
Spearman's rank order correlations was performed and represented using a heatmap to determine the association of bacterial families associated bsh gene abundance with disease in CD, UC and diabetes. Firmicute_unassigned represents hits for which only phylum assignment was available from NCBI taxonomy database. *p<0.05, **p<0.01.
Figure 5
Figure 5. Abundance of Firmicute derived bsh by BSH protein clusters.
Abundance of bsh genes was obtained by searching a bsh protein database against publically available SRA sequences from HMP and MetaHIT metagenomic datasets for IBD and diabetic patients. Hits (≥24 amino acids and ≥75% ID) were counted and searched against a bacterial genome database to assign taxonomic origins to the hits. Hits assigned to the Firmicute phylum were subdivided based on the species and assigned to 1 of 4 clusters. Values are median abundance of each cluster and error bars are 95% confidence interval. Statistical analysis was Kruskall-wallis (A) or Mann-Whitney U (B). *p<0.05, **p<0.01, ***p<0.001.
Figure 6
Figure 6. Correlation between the abundance of bsh gene assigned to protein clusters and disease.
Spearman's rank order correlations was performed and represented using a heatmap to determine the association of bsh genes assigned to 4 BSH protein clusters with individual datasets and with the combined dataset for CD, UC or diabetes. “all_CD” and “all_UC” represent the combined datasets while “SRP015779_CD”, “SRP002423_CD”, “Metahit_CD” and “Metahit_UC” represent the correlation of the clusters within these datasets only. Sum represents the correlations of the total abundance of all 4 clusters and the dataset-associated disease. *p<0.05, **p<0.01.
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