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. 2023 Oct 20;14(1):6546.
doi: 10.1038/s41467-023-42112-w.

Disease-specific loss of microbial cross-feeding interactions in the human gut

Vanessa R Marcelino  1   2   3   4 , Caitlin Welsh  5 , Christian Diener  6 , Emily L Gulliver  7   8 , Emily L Rutten  7   8 , Remy B Young  7   8 , Edward M Giles  8   9 , Sean M Gibbons  6   10   11   12 , Chris Greening  5 , Samuel C Forster  13   14
Affiliations

Disease-specific loss of microbial cross-feeding interactions in the human gut

Vanessa R Marcelino et al. Nat Commun. .

Abstract

Many gut microorganisms critical to human health rely on nutrients produced by each other for survival; however, these cross-feeding interactions are still challenging to quantify and remain poorly characterized. Here, we introduce a Metabolite Exchange Score (MES) to quantify those interactions. Using metabolic models of prokaryotic metagenome-assembled genomes from over 1600 individuals, MES allows us to identify and rank metabolic interactions that are significantly affected by a loss of cross-feeding partners in 10 out of 11 diseases. When applied to a Crohn's disease case-control study, our approach identifies a lack of species with the ability to consume hydrogen sulfide as the main distinguishing microbiome feature of disease. We propose that our conceptual framework will help prioritize in-depth analyses, experiments and clinical targets, and that targeting the restoration of microbial cross-feeding interactions is a promising mechanism-informed strategy to reconstruct a healthy gut ecosystem.

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Conflict of interest statement

S.C.F. is an inventor on patents and has acted as an advisor to BiomeBank and Microbiotica. R.B.Y. has acted as an advisor to BiomeBank. All other authors have no competing interests to declare.

Figures

Fig. 1
Fig. 1. Overview of the Metabolite Exchange Score (MES) calculation and application.
a MES is the harmonic mean between the number of potential producers (P) and consumers (C) inferred from metagenome-informed metabolic models. b Comparative analysis of MES between healthy and diseased cohorts can help identify the species and metabolites required to restore cross-feeding interactions, which may be promising targets of microbiome therapies.
Fig. 2
Fig. 2. Global analysis reveals most common metabolic exchanges among healthy gut microbes and disease-specific loss of cross-feeding partners.
a Prevalence of species-level MAGs across all samples. b Top 15 metabolites with the highest MESs in healthy individuals, which are expected to be central to sustain a healthy microbial community structure. c Metabolites with significantly reduced MES in diseased microbiomes when compared to the healthy group (one-sided Kruskal–Wallis’ p < 0.05/number of comparisons within each disease category), suggesting significant loss of microbial cross-feeding partners for those metabolites. The panel of metabolites shown here include the top 5 metabolites with the highest MES differences between healthy and diseased groups for each disease (metabolites with increased MES in diseased microbiomes are not included). No significant difference in MES was found in patients with schizophrenia (n = 87) after accounting for multiple comparisons. Sample sizes and Bonferroni-corrected p-value thresholds: IBD inflammatory bowel disease (n = 63, p < 1.27 × 10−4), liver cirrhosis (n = 54, p < 1.30 × 10−4), Ank ankylosing spondylitis (n = 72, p < 1.32 × 10−4), NAFLD non-alcoholic fatty liver disease (n = 71, p < 1.25 × 10−4), Behcet’s disease (n = 18, p < 2.21 × 10−4), ME/CSF myalgic encephalomyelitis/chronic fatigue syndrome (n = 17, p < 2.99 × 10−4), T2D type 2 diabetes (n = 32, p < 1.37 × 10−4), Athero atherosclerosis (n = 98, p < 1.18 × 10−4), CRC colorectal cancer (n = 143, p < 1.17 × 10−4), Arthritis rheumatoid arthritis (n = 135, p < 1.18 × 10−4). Colours in b, c represent metabolite Sub Classes according to the Human Metabolome Database.
Fig. 3
Fig. 3. Producer to consumer dynamics is affected by species richness for most metabolites.
a Significant differences between the slopes of the species richness vs producers or consumers correlations were observed for the majority of metabolites, with producers having a steeper slope in 24% of the metabolites, and consumers having a steeper slope in 55% of the metabolites analysed. b–p Representation of the correlation between species diversity vs producers or consumers for the top 15 metabolites with the highest MESs in healthy microbiomes. Analyses included all samples from our dataset (n = 1661, including healthy and diseased cohorts), and only metabolites exchanged within at least 50 microbiomes. Each subplot contains two points for each sample to represent the diversity of producers (brown circles) and consumers (blue triangles). Asterisks indicate a significant p value of the t-test associated with the linear regression model (two-sided) after Bonferroni correction (i.e., p < 0.00011).
Fig. 4
Fig. 4. Shift in hydrogen sulfide production-consumption equilibrium associated with Crohn’s disease.
a The number of species with potential to produce or consume H2S is significantly reduced in microbiomes associated with CD when compared to healthy controls. b The total estimated consumption of H2S is depleted in CD, while production was not significantly affected (fluxes estimated in millimoles per hour per gram of dry weight). A significant increase in the ratio of number of producers to consumers (c) and in the total estimated H2S production to consumption (d) was found in microbiomes associated with CD. e Species involved in the exchange of H2S that are most altered in CD, which might be promising targets of microbiome therapy. The network shows the H2S producers with increased production (brown), and the consumers with reduced H2S consumption (blue) in CD when compared to healthy controls. The 10 species contributing most to H2S production or consumption are highlighted. The thickness of the nodes and edges are proportional to the species’ weighted flux sum of H2S within the consumer or producer categories. Statistical tests in a–d were performed with a one-sided Kruskal-Wallis test, degrees of freedom = 1, p < 0.05 were considered significant. Box-plot elements in a–d: centre line = median; box limits = upper and lower quartiles; whiskers = 1.5× interquartile range; points = samples (n = 84 biologically independent samples).
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