Microbial community and metabolomic comparison of irritable bowel syndrome faeces - PubMed (original) (raw)

Microbial community and metabolomic comparison of irritable bowel syndrome faeces

Kannan Ponnusamy et al. J Med Microbiol. 2011 Jun.

Abstract

Human health relies on the composition of microbiota in an individual's gut and the synthesized metabolites that may alter the gut environment. Gut microbiota and faecal metabolites are involved in several gastrointestinal diseases. In this study, 16S rRNA-specific denaturing gradient gel electrophoresis and quantitative PCR analysis showed that the mean similarity of total bacteria was significantly different (P<0.001) in faecal samples from patients with irritable bowel syndrome (IBS; n = 11) and from non-IBS (nIBS) patients (n = 8). IBS subjects had a significantly higher diversity of total bacteria, as measured by the Shannon index (H') (3.36<H'<4.37, P = 0.004), Bacteroidetes and lactobacilli; however, less diversity was observed for Bifidobacter (1.7< H'<3.08, P<0.05) and Clostridium coccoides (0.9< H'<2.98, P = 0.007). In this study, no significant difference was found in total bacterial quantity (P>0.05). GC/MS-based multivariate analysis delineated the faecal metabolites of IBS from nIBS samples. Elevated levels of amino acids (alanine and pyroglutamic acid) and phenolic compounds (hydroxyphenyl acetate and hydroxyphenyl propionate) were found in IBS. These results were highly correlated with the abundance of lactobacilli and Clostridium, which indicates an altered metabolism rate associated with these gut micro-organisms. A higher diversity of Bacteroidetes and Lactobacillus groups in IBS faecal samples also correlated with the respective total quantity. In addition, these changes altered protein and carbohydrate energy metabolism in the gut.

PubMed Disclaimer

Figures

Fig. 1.

Fig. 1.

Dice coefficient pairwise comparisons of total bacteria (using universal bacterial primers) and Bacteroidetes, Bifidobacterium, Clostridium coccoides and Lactobacillus groups in the IBS versus nIBS faecal community. *, P<0.05; **, P<0.001. White bars, nIBS; grey bars, IBS.

Fig. 2.

Fig. 2.

Diversity indices derived from DGGE fingerprinting of 16S rRNA gene-coding regions for total bacteria and the Bacteroidetes, Bifidobacterium, Clostridium coccoides and Lactobacillus groups. (a) Shannon index of general diversity (H′). (b) Simpson index of dominance (D or S). *, P<0.05; **, P<0.01. White bars, nIBS; grey bars, IBS.

Fig. 3.

Fig. 3.

Box and whisker plot showing the quantitative estimation of universal bacteria and group-specific bacteria determined by qPCR. IBS samples were compared with nIBS samples and the values were expressed as the mean log 16S rRNA gene copy number (g faeces)−1. The plot displays the following: mean (line inside the box; its position away from the centre represents the degree of skewness in the data),

sem

(±1 times large box) and

sd

(‘whiskers’ extending from the upper and lower edges). White bars, nIBS; grey bars, IBS.

Fig. 4.

Fig. 4.

(a) Dendrogram showing the DGGE profiles of the IBS and nIBS faecal bacterial communities. Community DNA was detected by PCR amplification with universal bacterial 16S rRNA gene (V3–V5 regions) primers followed by DGGE. The dendrogram was constructed using _D_sc and the UPGMA algorithm. The arrow indicates the eluted band of Eubacterium biforme. (b) PCA of DGGE fingerprints of the 16S rRNA gene of dominant bacteria in IBS (□) and nIBS (▴) samples.

Fig. 5.

Fig. 5.

PLS-DA score plots (a) and PLS-DA loading S-plots (b) of significantly different metabolites derived from GC-MS analysis datasets of faecal extracts. (a) •, nIBS; ▴, IBS. (b) •, nIBS-related biomarkers; ▴, IBS-related biomarkers; ▪, unidentified biomarkers.

Fig. 6.

Fig. 6.

Hypothetical diagram showed a comparison of the faecal metabolites found and the major gut microbiota. The relative changes in IBS and nIBS faecal metabolites were derived from the GC-MS peak area and expressed as box and whisker plots. Positive and negative correlations are indicated as solid and dashed lines, respectively.

Similar articles

Cited by

References

    1. Apajalahti J. H., Särkilahti L. K., Mäki B. R., Heikkinen J. P., Nurminen P. H., Holben W. E. (1998). Effective recovery of bacterial DNA and percent-guanine-plus-cytosine-based analysis of community structure in the gastrointestinal tract of broiler chickens. Appl Environ Microbiol 64, 4084–4088 - PMC - PubMed
    1. Benno Y., Endo K., Mizutani T., Namba Y., Komori T., Mitsuoka T. (1989). Comparison of fecal microflora of elderly persons in rural and urban areas of Japan. Appl Environ Microbiol 55, 1100–1105 - PMC - PubMed
    1. Codling C., O’Mahony L., Shanahan F., Quigley E. M., Marchesi J. R. (2010). A molecular analysis of fecal and mucosal bacterial communities in irritable bowel syndrome. Dig Dis Sci 55, 392–397 10.1007/s10620-009-0934-x - DOI - PubMed
    1. De Vos W. M. (2005). Lipotechoic acid in lactobacilli: d-alanine makes the difference. Proc Natl Acad Sci U S A 102, 10763–10764 10.1073/pnas.0504966102 - DOI - PMC - PubMed
    1. Engberg R. M., Hedemann M. S., Leser T. D., Jensen B. B. (2000). Effect of zinc bacitracin and salinomycin on intestinal microflora and performance of broilers. Poult Sci 79, 1311–1319 - PubMed

Publication types

MeSH terms

Substances

LinkOut - more resources