Machine learning reveals common transcriptomic signatures across rat brain and placenta following developmental organophosphate ester exposure

Abstract

Toxicogenomics is a critical area of inquiry for hazard identification and to identify both mechanisms of action and potential markers of exposure to toxic compounds. However, data generated by these experiments are highly dimensional and present challenges to standard statistical approaches, requiring strict correction for multiple comparisons. This stringency often fails to detect meaningful changes to low expression genes and/or eliminate genes with small but consistent changes particularly in tissues where slight changes in expression can have important functional differences, such as brain. Machine learning offers an alternative analytical approach for "omics"data that effectively sidesteps the challenges of analyzing highly dimensional data. Using 3 rat RNA transcriptome sets, we utilized an ensemble machine learning approach to predict developmental exposure to a mixture of organophosphate esters (OPEs) in brain (newborn cortex and day 10 hippocampus) and late gestation placenta of male and female rats, and identified genes that informed predictor performance. OPE exposure had sex specific effects on hippocampal transcriptome, and significantly impacted genes associated with mitochondrial transcriptional regulation and cation transport in females, including voltage-gated potassium and calcium channels and subunits. To establish if this holds for other tissues, RNAseq data from cortex and placenta, both previously published and analyzed via a more traditional pipeline, were reanalyzed with the ensemble machine learning methodology. Significant enrichment for pathways of oxidative phosphorylation and electron transport chain was found, suggesting a transcriptomic signature of OPE exposure impacting mitochondrial metabolism across tissue types and developmental epoch. Here we show how machine learning can complement more traditional analytical approaches to identify vulnerable "signature"pathways disrupted by chemical exposures and biomarkers of exposure.