ProbMetab: an R package for Bayesian probabilistic annotation of LC-MS based metabolomics

We present ProbMetab, an R package which promotes substantial improvement in automatic probabilistic LC-MS based metabolome annotation. The inference engine core is based on a Bayesian model implemented to: (i) allow diverse source of experimental data and metadata to be systematically incorporated into the model with alternative ways to calculate the likelihood function and; (ii) allow sensitive selection of biologically meaningful biochemical reactions databases as Dirichlet-categorical prior distribution. Additionally, to ensure result interpretation by system biologists, we display the annotation in a network where observed mass peaks are connected if their candidate metabolites are substrate/product of known biochemical reactions. This graph can be overlaid with other graph-based analysis, such as partial correlation networks, in a visualization scheme exported to Cytoscape, with web and stand alone versions. ProbMetab was implemented in a modular fashion to fit together with established upstream (xcms, CAMERA, AStream, mzMatch.R, etc) and downstream R package tools (GeneNet, RCytoscape, DiffCorr, etc). ProbMetab, along with extensive documentation and case studies, is freely available under GNU license at: http://labpib.fmrp.usp.br/methods/probmetab/.Comment: Manuscript to be submitted very soon. 7 pages, 3 color figures. There is a companion material, the two case studies, which are going to be posted here together with the main text in next updated versio

Développement d’une méthode de quantification des Bisphénols-A halogénés par HPLC-MS/MS

National audienc

Quantification des Bisphénols-A halogénés par HPLC-MS/MS.

National audienc

From genomics to metabolomics, moving toward an integrated strategy for the discovery of fungal secondary metabolites

International audienceFungal secondary metabolites are defined by bioactive properties that ensure adaptation of the fungus to its environment. Although some of these natural products are promising sources of new lead compounds especially for the pharmaceutical industry, others pose risks to human and animal health. The identification of secondary metabolites is critical to assessing both the utility and risks of these compounds. Since fungi present biological specificities different from other microorganisms, this review covers the different strategies specifically used in fungal studies to perform this critical identification. Strategies focused on the direct detection of the secondary metabolites are firstly reported. Particularly, advances in high-throughput untargeted metabolomics have led to the generation of large datasets whose exploitation and interpretation generally require bioinformatics tools. Then, the genome-based methods used to study the entire fungal metabolic potential are reported. Transcriptomic and proteomic tools used in the discovery of fungal secondary metabolites are presented as links between genomic methods and metabolomic experiments. Finally, the influence of the culture environment on the synthesis of secondary metabolites by fungi is highlighted as a major factor to consider in research on fungal secondary metabolites. Through this review, we seek to emphasize that the discovery of natural products should integrate all of these valuable tools. Attention is also drawn to emerging technologies that will certainly revolutionize fungal research and to the use of computational tools that are necessary but whose results should be interpreted carefully

Disposition of benzo[c] fluorene in rats

Polycyclic aromatic hydrocarbons (PAHs) are environmental pollutants and food contaminants. Some of them are mutagenic/genotoxic and have shown clear carcinogenic effects in mammals. Among the PAHs found in food commodities, benzo[c]fluorene (B[c]F) was demonstrated to be carcinogenic in rodents and the occurrence of corresponding pulmonary adducts has been demonstrated, suggesting a bioactivation pathway. B[c]F distribution and biotransformation was studied in rats given a single oral dose of radiolabelled B[c]F (0.64 or 640 μg/kg body weight). At intervals of 2, 4, 6, 8, 12, 24 and 48 h thereafter, animals were sacrificed, excreta were collected and various tissues were sampled. Radioactivity was measured in all samples before extraction with dichloromethane/methanol mixtures. Metabolic profiles were performed by radio-HPLC and metabolites were analyzed by LC–MS/MS. Results shown that B[c]F was extensively absorbed and biotransformed in rats. The metabolic balance at 48 h indicates that 8–10% of the radioactivity was eliminated in urine while 55–69% was found in faeces, depending on administered dose. The major part of fecal radioactivity corresponded to unchanged B[c]F, whereas analysis of urine samples revealed only polar metabolites (mainly glucurono- and sulfo-conjugates of mono-, di-, tri- and tetra-hydroxylated B[c]F). The highest concentrations of radioactivity in tissues were found in liver, irrespective of the administered dose, the highest values being recovered 2 h post dosing. The characterization of the metabolic pathways of BcF in rat is in progress, with the objective to identify reactive metabolites and to better understand the mechanisms of genotoxicity of this contaminant