Molecular Systems BiologySystems BiologyPeer-reviewed journal article
Metabolic modelling reveals increased autonomy and antagonism in type 2 diabetic gut microbiota
A. Samer KadibalbaniD, Axel KünstneriD, Torsten SchröderiD, Julius ZauleckiD, Oliver WittiD, Γεώργιος ΜαρίνοςiD, Christoph KaletaiD
Abstract
1.Type 2 diabetes presents a growing global health concern, with emerging evidence highlighting the pivotal role of the human gut microbiome in metabolic diseases. This study employs metabolic modelling to elucidate changes in host-microbiome interactions in type 2 diabetes. Glucose levels, dietary intake, 16S sequences and metadata were estimated and collected for a cohort of 1,866 individuals. In addition, microbial community models, as well as ecological interactions were simulated for the gut microbiomes of the cohort participants. Our findings revealed a significant decrease in the fluxes of metabolites provided by the host to the microbiome through the diet in patients with type 2 diabetes, accompanied by an increase in within-community exchanges. Moreover, the diabetic microbial community shifts towards increased exploitative ecological interactions among its member species at the expense of collaborative interactions. The reduced butyrate flux from the community to the host and reduced tryptophan acquired by the microbiome from the hosts diet further highlight the dysregulation in microbial-host interactions in diabetes. Additionally, microbiomes of type 2 diabetes patients exhibit enrichment in energy metabolism pathways, indicative of increased metabolic activity and antagonism. This study provides insights into the metabolic dynamics of the diabetic gut microbiome, shedding light on its increased autonomy and altered ecological interactions accompanying diabetes, and provides candidate metabolic targets for intervention studies and experimental validations, such as butyrate, tryptophan, H2S, several nucleotides, amino acids, and B vitamins.
- organism: human
- assay: metagenomics
- research focus: gene regulatory network inference
- approach: statistical model
- Citations
- 5
- FWCI
- 1.49
- Field percentile
- top 20%
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