ArticleBMC bioinformatics2021
Examination of hydrogen cross-feeders using a colonic microbiota model.
Article in BMC bioinformatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Host-diet-gut microbiome interactions influence human energy balance: a randomized clinical trial.Nature communications · 2023Trial
- Intestinal gases as markers to study the diet-gut microbiota-host metabolism axis in humans and their relationship to metabolic health.Gut microbes · 2026Review
- Prebiotic Effects of Insoluble Konjac Glucomannan Derived from Edible "Konnyaku" on Weight Control.Microorganisms · 2025Article
- A mechanistic modelling approach of the host-microbiota interactions to investigate beneficial symbiotic resilience in the human gut.Journal of the Royal Society, Interface · 2024Article
- Community-scale models of microbiomes: Articulating metabolic modelling and metagenome sequencing.Microbial biotechnology · 2024Review
- Reprogramming the Human Gut Microbiome Reduces Dietary Energy Harvest.Research square · 2023Article
- Modeling approaches for probing cross-feeding interactions in the human gut microbiome.Computational and structural biotechnology journal · 2022Review
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundHydrogen cross-feeding microbes form a functionally important subset of the human colonic microbiota. The three major hydrogenotrophic functional groups of the colon: sulphate-reducing bacteria (SRB), methanogens and reductive acetogens, have been linked to wide ranging impacts on host physiology, health and wellbeing.
resultsAn existing mathematical model for microbial community growth and metabolism was combined with models for each of the three hydrogenotrophic functional groups. The model was further developed for application to the colonic environment via inclusion of responsive pH, host metabolite absorption and the inclusion of host mucins. Predictions of the model, using two existing metabolic parameter sets, were compared to experimental faecal culture datasets. Model accuracy varied between experiments and measured variables and was most successful in predicting the growth of high relative abundance functional groups, such as the Bacteroides, and short chain fatty acid (SCFA) production. Two versions of the colonic model were developed: one representing the colon with sequential compartments and one utilising a continuous spatial representation. When applied to the colonic environment, the model predicted pH dynamics within the ranges measured in vivo and SCFA ratios comparable to those in the literature. The continuous version of the model simulated relative abundances of microbial functional groups comparable to measured values, but predictions were sensitive to the metabolic parameter values used for each functional group. Sulphate availability was found to strongly influence hydrogenotroph activity in the continuous version of the model, correlating positively with SRB and sulphide concentration and negatively with methanogen concentration, but had no effect in the compartmentalised model version.
conclusionsAlthough the model predictions compared well to only some experimental measurements, the important features of the colon environment included make it a novel and useful contribution to modelling the colonic microbiota.
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