Unveiling metabotype clustering in resveratrol, daidzein, and ellagic acid metabolism: Prevalence, associated gut microbiomes, and their distinctive microbial networks.

The gut microbiota (GM) produces different polyphenol-derived metabolites, yielding high interindividual variability and hampering consistent health effects. GM metabotypes associated with ellagic acid (urolithin metabotypes A (UMA), B (UMB), and 0 (UM0)), resveratrol (lunularin -producers (LP) and non-producers (LNP)), and daidzein (equol-producers (EP) and non-producers (ENP)) are known. However, individual polyphenol-related metabotypes do not occur individually. In contrast, different combinations coexist (i.e., metabotype clusters, MCs). We report here for the first time these MCs, their distribution, and their associated GM in adult humans (n = 127) after consuming for 7 days a nutraceutical (pomegranate, Polygonum cuspidatum, and red clover extracts) containing ellagitannins + ellagic acid, resveratrol, and isoflavones. Urine metabolites (UHPLC-QTOF-MS) and fecal microbiota (16S rRNA sequencing) were analyzed. Ten MCs were identified: LP + UMB + ENP (22.7%), LP + UMA + ENP (21.3%), LP + UMA + EP (16.7%), LP + UMB + EP (16%), LNP + UMA + ENP (11.3%), LNP + UMB + ENP (5.3%), LNP + UMA + EP (3.3%), LNP + UMB + EP (2%), LNP + UM0 + EP (0.7%), and LNP + UM0 + ENP (0.7%). Sex, BMI, and age did not affect the distribution of metabotypes or MCs. Multivariate analysis (MaAslin2) revealed genera differentially present in individual metabotypes and MCs. Network analysis (MENA) showed the taxa acting as module hubs and connectors. Compositional and functional profiling, alpha and beta diversities, topological network features, and GM modulation by the nutraceutical differed depending on whether the entire cohort or each MC was considered. The nutraceutical did not change the composition of LP + UMA + EP (the most robust GM with the most associated functions) but increased its network connectors. This pioneering approach, joining GM's compositional, functional, and network features in polyphenol metabolism, paves the way for identifying personalized GM-targeted strategies to improve polyphenol health benefits.

Alternative method for gas chromatography-mass spectrometry analysis of short-chain fatty acids in faecal samples.

Short-chain fatty acids are the major end products of bacterial metabolism in the large bowel. They derive mostly from the bacterial breakdown of carbohydrates and are known to have positive health benefits. Due to the biological relevance of these compounds it is important to develop efficient, cheap, fast, and sensitive analytical methods that enable the identification and quantification of the short-chain fatty acids in a large number of biological samples. In this study, a gas chromatography-mass spectrometry method was developed and validated for the analysis of short-chain fatty acids in faecal samples. These volatile compounds were extracted with ethyl acetate and 4-methyl valeric acid was used as an internal standard. No further cleanup, concentration, and derivatization steps were needed and the extract was directly injected onto the column. Recoveries ranged between 65 and 105%, and no matrix effects were observed. The proposed method has wide linear ranges, good inter- and intraday variability values (below 2.6 and 5.6%, respectively) and limits of detection between 0.49 µM (0.29 µg/g) and 4.31 µM (3.8 µg/g). The applicability of this analytical method was successfully tested in faecal samples from rats and humans.