The utility of transcriptomic data for interpreting cellular modes-of-action (MOA) after exposure to chemicals has been established for over a decade. This is conventionally performed by whole transcriptome gene expression tools such as microarray and next-generation sequencing (RNA-seq). However, for the toxicity assessment and testing prioritization of tens of thousands of chemicals, there is a pressing need for efficient alternatives such as high-throughput transcriptomics (HTT). The goal of this study was to develop suitable HTT computational models for evaluating the diverse cellular modes of action of environmental, industrial, and agricultural compounds.<br/>As a proof of concept, we used Genometry’s L1000 platform to demonstrate the possibility for establishing statistical relationships between genes and to infer whole genome transcriptional profiles. Then we developed a qualitative MOA-oriented strategy to predict relevant cellular pathway information and applied it to Affymetrix array data collected from three human cell lines (HepaRG, MCF7, and A673) exposed to three agrichemicals (imazalil, fenbuconazole, and 2,4-dichlorophenoxyacetic acid) over nine concentrations. To avoid commonly encountered pitfalls from comparing different technologies and to increase prediction accuracy, we built three-class categorical models predicting up-regulated, down-regulated and unchanged genes essential to determining the cellular MOA.<br/>This work was then extended to predict gene expression changes resulting from exposure to heterogenous chemicals from a wide range of classes. The models were developed using the Open TG-GATEs database providing toxicogenomics data of multiple cell lines for over 900 samples covering ~170 compounds. Different machine learning techniques were used to identify a set of landmark genes which can predict differential expression changes of the genomic profile. A 10-fold cross validation balanced accuracy of ~65% was reached compared to a correlation coefficient of 42% with continuous models.  The predicted and actual Affymetrix pathway enrichment profiles for HepaRG cell line in response to 0.01 μM Fenbuconazole showed the 22 (out of 39) highest enriched pathways in common. Such models can be used for MOA prediction and pathway analysis to prioritize large libraries of chemicals starting from gene expression data of representative genes.

Pathway analysis and mode-of-action prediction based on computational modeling of high-throughput toxicogenomics

255th National Meeting (2018)

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