The chemical interactome space between the human host and the genetically defined gut metabotypes

The bacteria that colonize the gastrointestinal tracts of mammals represent a highly selected microbiome that has a profound influence on human physiology by shaping the host's metabolic and immune system activity. Despite the recent advances on the biological principles that underlie microbial symbiosis in the gut of mammals, mechanistic understanding of the contributions of the gut microbiome and how variations in the metabotypes are linked to the host health are obscure. Here, we mapped the entire metabolic potential of the gut microbiome based solely on metagenomics sequencing data derived from fecal samples of 124 Europeans (healthy, obese and with inflammatory bowel disease). Interestingly, three distinct clusters of individuals with high, medium and low metabolic potential were observed. By illustrating these results in the context of bacterial population, we concluded that the abundance of the Prevotella genera is a key factor indicating a low metabolic potential. These metagenome-based metabolic signatures were used to study the interaction networks between bacteria-specific metabolites and human proteins. We found that thirty-three such metabolites interact with disease-relevant protein complexes several of which are highly expressed in cells and tissues involved in the signaling and shaping of the adaptive immune system and associated with squamous cell carcinoma and bladder cancer. From this set of metabolites, eighteen are present in DrugBank providing evidence that we carry a natural pharmacy in our guts. Furthermore, we established connections between the systemic effects of non-antibiotic drugs and the gut microbiome of relevance to drug side effects and health-care solutions.link_to_subscribed_fulltex

Anionic metabolic profiling of urine from antibiotic-treated rats by capillary electrophoresis–mass spectrometry

A recently developed capillary electrophoresis (CE)-negative-ionisation mass spectrometry (MS) method was used to profile anionic metabolites in a microbial-host co-metabolism study. Urine samples from rats receiving antibiotics (penicillin G and streptomycin sulfate) for 0, 4, or 8 days were analysed. A quality control sample was measured repeatedly to monitor the performance of the applied CE-MS method. After peak alignment, relative standard deviations (RSDs) for migration time of five representative compounds were below 0.4 %, whereas RSDs for peak area were 7.9–13.5 %. Using univariate and principal component analysis of obtained urinary metabolic profiles, groups of rats receiving different antibiotic treatment could be distinguished based on 17 discriminatory compounds, of which 15 were downregulated and 2 were upregulated upon treatment. Eleven compounds remained down- or upregulated after discontinuation of the antibiotics administration, whereas a recovery effect was observed for others. Based on accurate mass, nine compounds were putatively identified; these included the microbial-mammalian co-metabolites hippuric acid and indoxyl sulfate. Some discriminatory compounds were also observed by other analytical techniques, but CE-MS uniquely revealed ten metabolites modulated by antibiotic exposure, including aconitic acid and an oxocholic acid. This clearly demonstrates the added value of CE-MS for nontargeted profiling of small anionic metabolites in biological samples

Data-handling strategies for metabonomic studies: example of the UHPLC-ESI/ToF urinary signature of tetrahydrocannabinol in humans

International audienceMetabonomics has become a very valuable tool and many research fields rely on results coming out from this combination of analytical techniques, chemometric strategies, and biological interpretation. Moreover, the matrices are more and more complex and the implications of the results are often of major importance. In this context, the need for pertinent validation strategies comes naturally. The choice of the appropriate chemometric method remains nevertheless a difficult task due to particularities such as: the number of measured variables, the complexity of the matrix and the purposes of the study. Consequently, this paper presents a detailed metabonomic study on human urine with a special emphasis on the importance of assessing the data's quality. It also describes, step by step, the statistical tools currently used and offers a critical view on some of their limits. In this work, 29 urine samples among which 15 samples obtained from tetrahydrocannabinol (delta-9-tetrahydrocannabinol)-consuming athletes, 5 samples provided by volunteers, and 9 samples obtained from athletes were submitted to untargeted analysis by means of ultra high-pressure liquid chromatography-electrospray ionization-time-of-flight mass spectrometry. Next, the quality of the obtained data was assessed and the results were compared to those found in databases. Then, unsupervised (principal component analysis (PCA)) and supervised (ANOVA/PCA, partial least-square-discriminant analysis (PLS-DA), orthogonal PLS-DA) univariate and multivariate statistical methods were applied