Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Towards Whole Health Toxicology: In-Silico Prediction of Diseases Sensitive to Multi-Chemical Exposures

Version 1 : Received: 5 October 2022 / Approved: 7 October 2022 / Online: 7 October 2022 (15:09:23 CEST)

A peer-reviewed article of this Preprint also exists.

Arowolo, O., Salemme, V., & Suvorov, A. (2022). Towards whole health toxicology: in-silico prediction of diseases sensitive to multi-chemical exposures. Toxics, 10(12), 764. Arowolo, O., Salemme, V., & Suvorov, A. (2022). Towards whole health toxicology: in-silico prediction of diseases sensitive to multi-chemical exposures. Toxics, 10(12), 764.

Abstract

Chemical exposures from diverse sources merge on a limited number of molecular pathways described as toxicity pathways. Changes in the same set of molecular pathways in different cell and tissue types may generate seemingly unrelated health conditions. Today, no approaches are available to predict in an unbiased way sensitivities of different disease states and their combinations to multichemical exposures across the exposome. We propose an inductive in-silico workflow where sensitivities of genes to chemical exposures are identified based on the overlap of existing genomic datasets, and data on sensitivities of individual genes is further used to sequentially derive predictions on sensitivities of molecular pathways, disease states, and groups of disease states (syndromes). Our analysis predicts that conditions representing the most significant public health problems are among the most sensitive to cumulative chemical exposures. These conditions include six leading types of cancer in the world (prostatic, breast, stomach, lung, colorectal neoplasms, and hepatocellular carcinoma), obesity, type 2 diabetes, non-alcoholic fatty liver disease, autistic disorder, Alzheimer's disease, hypertension, heart failure, brain and myocardial ischemia, and myocardial infarction. Overall, our predictions suggest that environmental risk factors may be underestimated for the most significant public health problems.

Keywords

Syndromes; Xenobiotics; In-silico; Multichemical; Mixed exposures

Subject

Biology and Life Sciences, Biochemistry and Molecular Biology

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