Work Package 1 progress - Development of mathematical algorithms for modulation of gut microbiota through diet

Feb 28th, 2020   /   0 COMMENTS   /  A+ | a-
The objective of work package 1 is to develop novel algorithms and mathematical models to modulate the gut microbiome through diet. In order to address this issue, we are testing different strategies. One promising approach is the construction of complex metabolic models based on Genome-Scale Metabolic Networks (GSMNs) of the different bacterial species present in the gut microbiota. These models allow us to predict the interaction of diet and gut microbiota in order to produce key healthy metabolites, such as short-chain fatty acids.  Nevertheless, these GSMNs still require further development, since they do not include the degradation pathways of different important diet-derived nutrients, for example, phenolic compounds.
In this context, we have employed AGORA, a relevant repository of well-curated GSMNs for 818 bacterial species. When we integrated AGORA with iDiet nutrition software, provided by the Stance4Health partner Gestión de Salud y Nutrición S.L., we noticed that 546 out of 649 metabolites from the iDiet nutrition software were not included in AGORA. Among these missing metabolites, we found an important set of phenolic compounds, which are of particular relevance for STANCE4HEALTH given their beneficial antioxidant properties.
In order to overcome this issue, we developed a bioinformatics pipeline to integrate AGORA database with the information of other biochemical repositories, such as the Model SEED and KEGG, exhaustive genomic annotation of present bacterial species, literature-based analysis of phenolic compounds and enzyme promiscuity methods. As a result, we were able to capture the degradation of more than 250 additional diet-derived nutrients not included in AGORA, including more than 1800 additional reactions. We are meant to publish this new repository, which will provide the scientific community with a more accurate metabolic model to predict the effect of diet in the gut microbiota.
With this more complete knowledge base, we are developing different algorithms, based on 16S sequencing and metabolomics, to predict how diet alters the gut microbiome and, in consequence, the metabolome secreted by different bacterial species. These analyses are being performed for several lean, obese, celiac and allergic patients under a variety of different nutritional scenarios. We hope to reach clear dietetic strategies to reach a healthier gut microbiome.
Work Package 1 progress - Development of mathematical algorithms for modulation of gut microbiota through diet
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"This project has received funding from the European Union 's Horizon 2020 research and innovation programme under grant agreement No 816303 "
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