This week, the Bioinformatics Working Group meeting will feature a special presentation by Nathalie Poupin from the French National Institute for Agricultural Research – Research Center in Food Toxicology, Touluse, France. Poupin will present on deciphering metabolic modulation induced by pollutants using computational biology and omics approaches on Friday, June 9, at noon in ConocoPhillips Integrated Science Building, Room 105A.
Chemical pollutants can interfere with the homeostasis of living organisms. Much evidence has been raised for endocrine disrupting chemicals (EDC), showing that compounds like bisphenols, which can be transferred from food packaging to food-stuffs, can disrupt Human endocrine system and have lasting adverse health effects, including metabolic modulation, even at low doses of exposure. Some epidemiological studies have reported an association between xeno-estrogens such as bisphenol A, and the incidence of chronic metabolic diseases, such as type-2 diabetes and obesity, but also a positive link with the alteration of liver functions. Data obtained by omics analyses can provide knowledge on which biochemical elements (genes, proteins or metabolites) are affected by the exposure to chemicals. Transcriptomics data provide information on gene expression and thus on enzymes catalysing metabolic reactions, whereas metabolomics aims at measuring all small size molecules (metabolites) that are present in a biological system as the final endpoint of complex genetic and physiological processes. In the context of food toxicology, omics analyses enable to establish signatures of the effects of FC. However, it still remains a challenge to interpret these observed signatures and relate them with underlying mechanisms and modulation of metabolic processes. Changes observed in genes and metabolites should not be considered independently since they are connected through biochemical reactions, which turn substrate metabolites into products under the action of enzymes (gene products). In other words, this whole network of metabolic reactions constitutes the underlying architecture that coordinates metabolism modulation under chemical exposure. Genome-Scale Metabolic Network (GSMN) gathers, in an organized and mathematical format, all the biochemical reactions that can occur in a given organism, the metabolites they produce and consume, and the genes that encode for these reactions. This biochemical context, combined with omics data, allows creating snapshots of metabolism in specific environmental conditions. Presented data will focus on the integration of omics data in GSMN to decipher metabolic modulations.