Multi-omics signatures of exposure to disinfection by-products in swimming

Abstract

Background and aims: Untargeted HRMS metabolomics provides an agnostic global profiling of small molecules in a biological system capturing an overview of exposure to exogenous compounds, and their effects on metabolic pathways, rendering an essential tool for characterising the exposome. Disinfection by-products (DBPs) are a complex mixture of halogenated organic compounds present in drinking and swimming pool water. Although several epidemiological studies have pointed their potential to induce adverse health outcomes such as bladder cancer, their mixture effect and the downstream molecules perturbed by their exposure have been poorly explored. In this work, we aim to identify molecular signatures of exposures to mixtures of DBPs in individuals with measured exposures. Methods: In PISCINA-II study, metabolomics, transcriptomics, proteomics in serum, and levels of DBPs in exhaled breaths, were measured in 60 subjects before and after swimming. Pre-processing of HRMS data from PISCINA-II study was customised to include more halogenated compounds. Multivariate normal (MVN) and partial least square (PLS) models were used to identify metabolic features associated with DBP exposures. Conditional correlation networks were constructed to understand the holistic relationship amongst the metabolic features and their relationship with proteomics and transcriptomics. Results: 98 metabolic features were identified to be associated with exposure to DBP mixture, of which 39 were not identified in the previous analysis without specific reprocessing and inclusion of halogenated compounds. In conditional network analysis, the 98 features showed cluster structures, suggesting they could contribute to different downstream biological pathways. Multi-omic networks showed that transcriptomic and proteomic signals were differentially connected to the different clusters of the metabolic signatures. Conclusions: New data processing approach allowed identification of novel metabolic signatures of DBP mixture exposure. Conditional correlation network implied the functional proximity of these signatures with other measured omic signals. Keywords: Disinfection by-products, PLS, network analysis, omics, exposome, statistical analysis