Reduced B12 uptake and increased gastrointestinal formate are associated with archaeome-mediated breath methane emission in humans

Abstract

Background: Methane is an end product of microbial fermentation in the human gastrointestinal tract. This gas is solely produced by an archaeal subpopulation of the human microbiome. Increased methane production has been associated with abdominal pain, bloating, constipation, IBD, CRC or other conditions. Twenty percent of the (healthy) Western populations innately exhale substantially higher amounts (>5 ppm) of this gas. The underlying principle for differential methane emission and its effect on human health is not sufficiently understood.

Results: We assessed the breath methane content, the gastrointestinal microbiome, its function and metabolome, and dietary intake of one-hundred healthy young adults (female: n = 52, male: n = 48; mean age =24.1). On the basis of the amount of methane emitted, participants were grouped into high methane emitters (CH₄ breath content 5-75 ppm) and low emitters (CH₄ < 5 ppm). The microbiomes of high methane emitters were characterized by a 1000-fold increase in Methanobrevibacter smithii. This archaeon co-occurred with a bacterial community specialized on dietary fibre degradation, which included members of Ruminococcaceae and Christensenellaceae. As confirmed by metagenomics and metabolomics, the biology of high methane producers was further characterized by increased formate and acetate levels in the gut. These metabolites were strongly correlated with dietary habits, such as vitamin, fat and fibre intake, and microbiome function, altogether driving archaeal methanogenesis.

Conclusions: This study enlightens the complex, multi-level interplay of host diet, genetics and microbiome composition/function leading to two fundamentally different gastrointestinal phenotypes and identifies novel points of therapeutic action in methane-associated disorders. Video Abstract.

Keywords: Archaeome; Christensenellaceae; Gastrointestinal tract; Gut; Metabolome; Metagenome; Methane; Methanobrevibacter; Methanogens; Microbiome.

Fig. 1

Differences in alpha and beta diversity based on the ‘universal’ approach of 16S rRNA gene sequencing between high (HE) and low-methane emitters (LE). Profiles of the whole study cohort (n=100) are shown. The profiles of the matched study subset (n=30) are shown in Supplementary Figure 1. A.I. An examination of Shannon diversity index revealed significant differences in alpha diversity (RSV (ribosomal sequence variants) based; analysis of variance, ANOVA). A.II. The microbiome of HEs clustered significantly differently in the RDA plot (RSV based). B.I LEfSe (Linear Discriminant Analysis Effect Size) analysis of the 100 most abundant phyla and B.II–III. Relative abundance of selected phyla in ANOVA plots. C.I. LEfSe analysis of the 100 most abundant genera. LEfSe determines taxonomic features which are most likely to explain differences between groups by coupling tests for statistical significance with other tests for biological consistency and effect relevance [25]. C.II–VII. ANOVA plots of selected genera and statistical significance

Fig. 2

Co-occurrence networks based on Spearman’s rho correlation of selected genera in HE and LE microbiome samples. Taxa were selected based on significantly different relative abundances in both sample types and LEfSe analyses. Left, upper panel: Whole study cohort (n=100), right, upper panel: matched study subset (n=30). Lower panels show co-occurrence patterns in the HE (left) or the LE samples (right)

Fig. 3

Archaeome profile of HE and LE samples, based on the ‘archaeal’ approach of 16S rRNA gene sequencing. A Profile of the whole study cohort (n=100). B Matched study subset (n=30). I. Bar chart of the 20 most abundant taxa compared regarding their low- or high-methane emission at the phylum level and II. at the genus level. III. Shannon diversity and IV. RDA plot at RSV level

Fig. 4

Overview of the divergent functions of the HE and LE based on the shotgun metagenome analysis (subsystems). I. Shannon diversity and II. RDA plot at feature level. III. and IV. LEfSe analysis at level 1 (highest subsystem level) and level 3, respectively. (100 most abundant; n=30); ANOVA plots of selected functions are given in Supplementary Figure 7

Fig. 5

Identified keystone taxa in HE and LE subjects. A Cladogram of LE and HE keystone taxa. F Firmicutes, C Clostridia/Clostridiales, L Lachnospiraceae, and R Ruminococcaceae. Numbers in brackets indicate the number of contributing RSVs; B and C Network of keystone taxa of HE and LE at RSV and genus levels, respectively. Mbb/Mbb1 Methanobrevibacter, Ch/Ch2-4 Christensenellaceae R7 group, Eu/Eu5 Eubacterium ruminantium group, Rucl/Rucl6-7 Ruminiclostridium, Ru010/Ru010_8 Ruminococcaceaea UCG010, Bac/Bac9-12 Bacteroides, Rgna/Rgna13 Ruminococcus gnavus group, Bla/Bla14 Blautia, Rose/Rose15 Roseburia, Tyz/Tyz16 ‘Tyzzerella’, But/But17-19 Butyricicoccus, Fla/Fla20 Flavonifractor (also see Supplementary Table 3)

Fig. 6

Metabolic network of key-stone taxa in LE (left) and HE (right) microbiomes. Information on the metabolic substrates and products were derived from literature information [–54]: Lines with arrows, connecting taxa with metabolites, indicate uni-directional (grey) or bi-directional (pink) consumption and/or production. Metabolites measured in stool samples (via metabolomics; this work) are indicated by arrows in brackets following the metabolite name; respective increase (↑) or decrease (↓) of the median by >5% is displayed. For example, a substantially (>5%) increased amount of acetate, propionate and formate was measured in samples from high-methane emitters

Fig. 7

MICOM model-based flux balance analysis of keystone taxa. The 40 most predictive production fluxes (metabolites) are shown for high-methane emitters on the left and low-methane emitters to the right using L1 penalized logistic regression. Black dots underneath are used to display the categorization of each metabolite into different types of metabolites. The analysis was based on 16S rRNA gene amplicon data of the matched cohort (n=30) and the identified keystone taxa