Reduction of butyrate- and methane-producing microorganisms in patients with Irritable Bowel Syndrome

Marta Pozuelo, Suchita Panda, Alba Santiago, Sara Mendez, Anna Accarino, Javier Santos, Francisco Guarner, Fernando Azpiroz, Chaysavanh Manichanh, Marta Pozuelo, Suchita Panda, Alba Santiago, Sara Mendez, Anna Accarino, Javier Santos, Francisco Guarner, Fernando Azpiroz, Chaysavanh Manichanh

Abstract

The pathophysiology of irritable bowel syndrome (IBS) remains unclear. Here we investigated the microbiome of a large cohort of patients to identify specific signatures for IBS subtypes. We examined the microbiome of 113 patients with IBS and 66 healthy controls. A subset of these participants provided two samples one month apart. We analyzed a total of 273 fecal samples, generating more than 20 million 16S rRNA sequences. In patients with IBS, a significantly lower microbial diversity was associated with a lower relative abundance of butyrate-producing bacteria (P = 0.002; q < 0.06), in particular in patients with IBS-D and IBS-M. IBS patients who did not receive any treatment harboured a lower abundance of Methanobacteria compared to healthy controls (P = 0.005; q = 0.05). Furthermore, significant correlations were observed between several bacterial taxa and sensation of flatulence and abdominal pain (P < 0.05). Altogether, our findings showed that IBS-M and IBS-D patients are characterized by a reduction of butyrate producing bacteria, known to improve intestinal barrier function, and a reduction of methane producing microorganisms a major mechanism of hydrogen disposal in the human colon, which could explain excess of abdominal gas in IBS.

Figures

Figure 1
Figure 1
Unweighted UniFrac data redundancy analysis on the first time point samples constrained by (A) controls and IBS patients groups, and (B) constrained by the four groups of participants: controls (n = 66), IBS-C (n = 32), IBS-D (n = 54) and IBS-M (n = 27).
Figure 2. Higher relative abundance of Bacteroidetes.
Figure 2. Higher relative abundance of Bacteroidetes.
Proportion of Firmicutes and Bacteroidetes are plotted for each healthy subject (A) and for each IBS patient (B). Patients are characterized by a relatively higher proportion of Bacteroidetes than healthy controls (P = 0.02, q = 0.09).
Figure 3. Higher relative abundance of two…
Figure 3. Higher relative abundance of two bacterial families and higher alpha-diversity in healthy controls compared to IBS patients.
(A) Erysipelotrichaceae and Ruminococcaceae were found in significantly higher abundance in healthy subjects (n = 66) compared to IBS patients, regardless of their IBS subtype (Kruskal Wallis test, P = 4.7 e-5, q = 0.002 and P = 0.002, q = 0.06, respectively). The two bacterial families belong to the Firmicutes phylum. (B) The Chao1 index based on species-level OTUs was estimated for healthy controls, IBS, IBS-M, IBS-C and IBS-D. Significance (*P = 0.04, **P < 0.003) was determined by Monte Carlo permutations, a non-parametric test.
Figure 4. Dysbiosis at the family and…
Figure 4. Dysbiosis at the family and genus level in IBS subtypes.
(A) Four microbial families and one genus discriminate the 66 healthy controls from the 54 patients with IBS-D (Kruskal Wallis test, P < 0.006; q < 0.06). (B) One bacterial family was found in a lower proportion in the 27 IBS-M patients compared to the 66 controls (P = 0.0001, q = 0.006). (C) Comparing the healthy control group with the three IBS subtypes, four genera were enriched in healthy subjects and IBS-C patients compared to IBS-M and IBS-D patients (Kruskal Wallis test, P < 0.003, q ≤ 0.05).
Figure 5
Figure 5
Unweighted UniFrac data redundancy analysis (dbRDA) on the first time point samples constrained by (A) the controls and IBS patients, and constrained by (B) the four groups of participants: controls (n = 66), IBS-C (n = 14), IBS-D (n = 24) and IBS-M (n = 6), discarding patients under treatment.
Figure 6. Methanobacteria from the Euryarchaeota phylum…
Figure 6. Methanobacteria from the Euryarchaeota phylum is enriched in controls (n = 66) compared to IBS patients (n = 44) who did not follow any medical treatment (Kruskal Wallis test, P = 0.005, q = 0.05).
Figure 7. Summary of the findings of…
Figure 7. Summary of the findings of this study.
= Higher abundance of; = Lower abundance of; = positive correlation with; = negative correlation with.

References

    1. Canavan C., West J. & Card T. The epidemiology of irritable bowel syndrome. Clin. Epidemiol. 6, 71–80 (2014).
    1. Longstreth G. F. et al. Functional bowel disorders. Gastroenterology 130, 1480–1491 (2006).
    1. Carbonero F., Benefiel A. C. & Gaskins H. R. Contributions of the microbial hydrogen economy to colonic homeostasis. Nat. Rev. Gastroenterol. Hepatol. 9, 504–518 (2012).
    1. Zaleski A., Banaszkiewicz A. & Walkowiak J. Butyric acid in irritable bowel syndrome. Prz Gastroenterol. 8, 350–353 (2013).
    1. Simren M. et al. Intestinal microbiota in functional bowel disorders: a Rome foundation report. Gut 62, 159–176 (2013).
    1. Krogius-Kurikka L. et al. Microbial community analysis reveals high level phylogenetic alterations in the overall gastrointestinal microbiota of diarrhoea-predominant irritable bowel syndrome sufferers. BMC Gastroenterol. 9, 95–230X-9-95 (2009).
    1. Saulnier D. M. et al. Gastrointestinal microbiome signatures of pediatric patients with irritable bowel syndrome. Gastroenterology 141, 1782–1791 (2011).
    1. Malinen E. et al. Analysis of the fecal microbiota of irritable bowel syndrome patients and healthy controls with real-time PCR. Am. J. Gastroenterol. 100, 373–382 (2005).
    1. Tana C. et al. Altered profiles of intestinal microbiota and organic acids may be the origin of symptoms in irritable bowel syndrome. Neurogastroenterol. Motil. 22, 512-9, e114–5 (2010).
    1. Rigsbee L. et al. Quantitative profiling of gut microbiota of children with diarrhea-predominant irritable bowel syndrome. Am. J. Gastroenterol. 107, 1740–1751 (2012).
    1. Carroll I. M. et al. Molecular analysis of the luminal- and mucosal-associated intestinal microbiota in diarrhea-predominant irritable bowel syndrome. Am. J. Physiol. Gastrointest. Liver Physiol. 301, G799–807 (2011).
    1. Lyra A. et al. Diarrhoea-predominant irritable bowel syndrome distinguishable by 16S rRNA gene phylotype quantification. World J. Gastroenterol. 15, 5936–5945 (2009).
    1. Rajilic-Stojanovic M. et al. Global and deep molecular analysis of microbiota signatures in fecal samples from patients with irritable bowel syndrome. Gastroenterology 141, 1792–1801 (2011).
    1. Kerckhoffs A. P. et al. Lower Bifidobacteria counts in both duodenal mucosa-associated and fecal microbiota in irritable bowel syndrome patients. World J. Gastroenterol. 15, 2887–2892 (2009).
    1. Duboc H. et al. Increase in fecal primary bile acids and dysbiosis in patients with diarrhea-predominant irritable bowel syndrome. Neurogastroenterol. Motil. 24, 513–20, e246-7 (2012).
    1. Carroll I. M., Ringel-Kulka T., Siddle J. P. & Ringel Y. Alterations in composition and diversity of the intestinal microbiota in patients with diarrhea-predominant irritable bowel syndrome. Neurogastroenterol. Motil. 24, 521–30, e248 (2012).
    1. Chassard C. et al. Functional dysbiosis within the gut microbiota of patients with constipated-irritable bowel syndrome. Aliment. Pharmacol. Ther. 35, 828–838 (2012).
    1. Jeffery I. B. et al. An irritable bowel syndrome subtype defined by species-specific alterations in faecal microbiota. Gut 61, 997–1006 (2012).
    1. Jalanka-Tuovinen J. et al. Faecal microbiota composition and host-microbe cross-talk following gastroenteritis and in postinfectious irritable bowel syndrome. Gut 63, 1737–1745 (2014).
    1. Maukonen J. et al. Prevalence and temporal stability of selected clostridial groups in irritable bowel syndrome in relation to predominant faecal bacteria. J. Med. Microbiol. 55, 625–633 (2006).
    1. Parkes G. C. et al. Distinct microbial populations exist in the mucosa-associated microbiota of sub-groups of irritable bowel syndrome. Neurogastroenterol. Motil. 24, 31–39 (2012).
    1. Faith J. J. et al. The long-term stability of the human gut microbiota. Science 341, 1237439 (2013).
    1. Manichanh C. et al. Anal gas evacuation and colonic microbiota in patients with flatulence: effect of diet. Gut 63, 401–408 (2014).
    1. Lozupone C. A. et al. Meta-analyses of studies of the human microbiota. Genome Res. 23, 1704–1714 (2013).
    1. Santiago A. et al. Processing faecal samples: a step forward for standards in microbial community analysis. BMC Microbiol. 14, 112-2180-14-112 (2014).
    1. Attaluri A., Jackson M., Valestin J. & Rao S. S. Methanogenic flora is associated with altered colonic transit but not stool characteristics in constipation without IBS. Am. J. Gastroenterol. 105, 1407–1411 (2010).
    1. Sahakian A. B., Jee S. R. & Pimentel M. Methane and the gastrointestinal tract. Dig. Dis. Sci. 55, 2135–2143 (2010).
    1. Pimentel M. et al. Methane, a gas produced by enteric bacteria, slows intestinal transit and augments small intestinal contractile activity. Am. J. Physiol. Gastrointest. Liver Physiol. 290, G1089–95 (2006).
    1. Franzosa E. A. et al. Relating the metatranscriptome and metagenome of the human gut. Proc. Natl. Acad. Sci. USA. 111, E2329–38 (2014).
    1. Dunlop S. P. et al. Abnormal intestinal permeability in subgroups of diarrhea-predominant irritable bowel syndromes. Am. J. Gastroenterol. 101, 1288–1294 (2006).
    1. Klinkenberg-Knol E. C. et al. Long-term omeprazole treatment in resistant gastroesophageal reflux disease: efficacy, safety, and influence on gastric mucosa. Gastroenterology 118, 661–669 (2000).
    1. Wu G. D. et al. Linking long-term dietary patterns with gut microbial enterotypes. Science 334, 105–108 (2011).
    1. Cotillard A. et al. Dietary intervention impact on gut microbial gene richness. Nature 500, 585–588 (2013).
    1. Rajilic-Stojanovic M. et al. Intestinal microbiota and diet in IBS: causes, consequences, or epiphenomena? Am. J. Gastroenterol. 110, 278–287 (2015).
    1. Godon J. J., Zumstein E., Dabert P., Habouzit F. & Moletta R. Molecular microbial diversity of an anaerobic digestor as determined by small-subunit rDNA sequence analysis. Appl. Environ. Microbiol. 63, 2802–2813 (1997).
    1. Walters W. A. et al. PrimerProspector: de novo design and taxonomic analysis of barcoded polymerase chain reaction primers. Bioinformatics 27, 1159–1161 (2011).
    1. Navas-Molina J. A. et al. Advancing our understanding of the human microbiome using QIIME. Methods Enzymol. 531, 371–444 (2013).
    1. Caporaso J. G. et al. Ultra-high-throughput microbial community analysis on the Illumina HiSeq and MiSeq platforms. ISME J. 6, 1621–1624 (2012).
    1. Edgar R. C. Search and clustering orders of magnitude faster than BLAST. Bioinformatics 26, 2460–2461 (2010).
    1. Haas B. J. et al. Chimeric 16S rRNA sequence formation and detection in Sanger and 454-pyrosequenced PCR amplicons. Genome Res. 21, 494–504 (2011).
    1. Price M. N., Dehal P. S. & Arkin A. P. FastTree: computing large minimum evolution trees with profiles instead of a distance matrix. Mol. Biol. Evol. 26, 1641–1650 (2009).
    1. Chao A. Non parametric estimation of the number of classes in a population. Scand J Stat 11, 265–270 (1984).
    1. Shaphiro S. & Wilk M. An analysis of variance test for normality (complete samples). Biometrika 52, 591–611 (1965).
    1. Kruskal W. & Wallis W. Use of ranks in one-criterion variance analysis. J. Am. Statist Assoc. 47(260), 583–611 (1952).

Source: PubMed

3
Abonneren