Feature-agnostic metabolomics for determining effective subcytotoxic doses of common pesticides in human cells

• Published in: Toxicological sciences 2024-11, Vol.202 (1), p.85-95
• Main Authors: Rivera, Emilio S, LeBrun, Erick S, Breidenbach, Joshua D, Solomon, Emilia, Sanders, Claire K, Harvey, Tara, Tseng, Chi Yen, Thornhill, M Grace, Blackwell, Brett R, McBride, Ethan M, Luchini, Kes A, Alvarez, Marc, Williams, Robert F, Norris, Jeremy L, Mach, Phillip M, Glaros, Trevor G
• Format: Article
• Language: English
• ISSN: 1096-6080; 1096-0929; 1096-0929
• DOI: 10.1093/toxsci/kfae101

Although classical molecular biology assays can provide a measure of cellular response to chemical challenges, they rely on a single biological phenomenon to infer a broader measure of cellular metabolic response. These methods do not always afford the necessary sensitivity to answer questions of subcytotoxic effects, nor do they work for all cell types. Likewise, boutique assays such as cardiomyocyte beat rate may indirectly measure cellular metabolic response, but they too, are limited to measuring a specific biological phenomenon and are often limited to a single cell type. For these reasons, toxicological researchers need new approaches to determine metabolic changes across various doses in differing cell types, especially within the low-dose regime. The data collected herein demonstrate that LC-MS/MS-based untargeted metabolomics with a feature-agnostic view of the data, combined with a suite of statistical methods including an adapted environmental threshold analysis, provides a versatile, robust, and holistic approach to directly monitoring the overall cellular metabolomic response to pesticides. When employing this method in investigating two different cell types, human cardiomyocytes and neurons, this approach revealed separate subcytotoxic metabolomic responses at doses of 0.1 and 1 µM of chlorpyrifos and carbaryl. These findings suggest that this agnostic approach to untargeted metabolomics can provide a new tool for determining effective dose by metabolomics of chemical challenges, such as pesticides, in a direct measurement of metabolomic response that is not cell type-specific or observable using traditional assays.