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To support personalized diets targeting the gut microbiota, we employed an in vitro digestion-fermentation model and 16S rRNA gene sequencing to analyze the microbiota growing on representative foods of the Mediterranean and Western diets, as well as the influence of cooking methods. Plant- and animal-derived foods had significantly different impacts on the abundances of bacterial taxa. Animal and vegetable fats, fish and dairy products led to increases in many taxa, mainly within the Lachnospiraceae. In particular, fats favored increases in the beneficial bacteria Faecalibacterium, Blautia, and Roseburia. However, butter, as well as gouda cheese and fish, also resulted in the increase of Lachnoclostridium, associated to several diseases. Frying and boiling produced the most distinct effects on the microbiota, with members of the Lachnospiraceae and Ruminococcaceae responding the most to the cooking method employed. Nevertheless, cooking effects were highly individualized and food-dependent, challenging the investigation of their role in personalized diets.

Predicting the metabolic behavior of the human gut microbiota in different contexts is one of the most promising areas of constraint-based modeling. Recently, we presented a supra-organismal approach to build context-specific metabolic networks of bacterial communities using functional and taxonomic assignments of meta-omics data. In this work, this algorithm is applied to elucidate the metabolic changes induced over the first year after birth in the gut microbiota of a cohort of Spanish infants. We used metagenomics data of fecal samples and nutritional data of 13 infants at five time points. The resulting networks for each time point were analyzed, finding significant alterations once solid food is introduced in the diet. Our work shows that solid food leads to a different pattern of output metabolites that can be potentially released from the gut microbiota to the host. Experimental validation is presented for ferulate, a neuroprotective metabolite involved in the gut-brain axis.