Constructing xenobiotic maps of metabolism to predict enzymes catalyzing metabolites capable of binding to DNA

Abstract Background The liver plays a major role in the metabolic activation of xenobiotics (drugs, chemicals such as pollutants, pesticides, food additives...). Among environmental contaminants of concern, heterocyclic aromatic amines (HAA) are xenobiotics classified by IARC as possible or probable carcinogens (2A or 2B). There exist little information about the effect of these HAA in humans. While HAA is a family of more than thirty identified chemicals, the metabolic activation and possible DNA adduct formation have been fully characterized in human liver for only a few of them (MeIQx, PhIP, A$$\alpha$$ α C). Results We have developed a modeling approach in order to predict all the possible metabolites of a xenobiotic and enzymatic profiles that are linked to the production of metabolites able to bind DNA. Our prediction of metabolites approach relies on the construction of an enriched and annotated map of metabolites from an input metabolite.The pipeline assembles reaction prediction tools (SyGMa), sites of metabolism prediction tools (Way2Drug, SOMP and Fame 3), a tool to estimate the ability of a xenobotics to form DNA adducts (XenoSite Reactivity V1), and a filtering procedure based on Bayesian framework. This prediction pipeline was evaluated using caffeine and then applied to HAA. The method was applied to determine enzymes profiles associated with the maximization of metabolites derived from each HAA which are able to bind to DNA. The classification of HAA according to enzymatic profiles was consistent with their chemical structures. Conclusions Overall, a predictive toxicological model based on an in silico systems biology approach opens perspectives to estimate the genotoxicity of various chemical classes of environmental contaminants. Moreover, our approach based on enzymes profile determination opens the possibility of predicting various xenobiotics metabolites susceptible to bind to DNA in both normal and physiopathological situations.

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Constructing Xenobiotic Maps of Metabolism to Predict the Role of Enzymes in DNA Adduct Formation

10.21203/rs.3.rs-157802/v1 ◽

2021 ◽

Author(s):

Maël Conan ◽

Nathalie Théret ◽

Sophie Langouet ◽

Anne Siegel

Keyword(s):

Dna Adducts ◽

Environmental Contaminants ◽

Food Additives ◽

Metabolic Activation ◽

Adduct Formation ◽

Dna Adduct ◽

Heterocyclic Aromatic Amines ◽

Reaction Prediction ◽

Prediction Tools

Abstract Background : The liver plays a major role in the metabolic activation of xenobiotics (drugs, chemicals such as pollutants, pesticides, food additives...). Among environmental contaminants of concern, heterocyclic aromatic amines (HAA) are xenobiotics classified as possible or probable carcinogens (2A or 2B) by IARC for which low information exist in humans. While HAA is a family of more than thirty identified chemicals, the metabolism activation and DNA adduct formation have been fully characterized in human liver for few of them (MeIQx, PhIP, AalphaC). Results: We developed a modeling approach in order to predict all the possible metabolite derivatives of a xenobiotic. Our approach relies on the construction of an enriched and annotated map of derivative metabolites from an input metabolite. The pipeline assembles reaction prediction tools (SyGMa), sites of metabolism prediction tools (Way2Drug, SOMP and Fame 3), a tool to estimate the ability of a xenobotics to form DNA adducts (XenoSite Reactivity V1), and a filtering procedure based on Bayesian framework. This prediction pipeline was evaluated using caffeine and then applied to HAAs. The method was applied to determine enzyme profiles associated with the maximization of DNA adducts formation derived from each HAA. These profiles could be very different depending on the chemicals allowing to classify HAAs which have been grouped by their associated profiles. Conclusions: Overall, such a predictive toxicological model based on a in silico systems biology approach open perspectives to estimate genotoxicity of various chemical classes of environmental contaminants. Moreover, our approach based on enzymes profile determination open the perspective to predict various xenobiotics derived metabolites susceptible to bind DNA adducts in both normal and physiopathological situations.

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