A Qualitative Modeling Approach for Whole Genome Prediction Using High-Throughput Toxicogenomics Data and Pathway-Based Validation

Abstract

Efficient high-throughput transcriptomics (HTT) tools promise inexpensive, rapid assessment of possible biological consequences of human and environmental exposures to tens of thousands of chemicals in commerce. HTT systems have used relatively small sets of gene expression measurements coupled with mathematical prediction methods to estimate genome-wide gene expression and are often trained and validated using pharmaceutical compounds. It is unclear whether these training sets are suitable for general toxicity testing applications and the more diverse chemical space represented by commercial chemicals and environmental contaminants. In this work, we built predictive computational models that inferred whole genome transcriptional profiles from a smaller sample of surrogate genes. The model was trained and validated using a large scale toxicogenomics database with gene expression data from exposure to heterogeneous chemicals from a wide range of classes (the Open TG-GATEs data base). The method of predictor selection was designed to allow high fidelity gene prediction from any pre-existing gene expression data set, regardless of animal species or data measurement platform. Predictive qualitative models were developed with this TG-GATES data that contained gene expression data of human primary hepatocytes with over 941 samples covering 158 compounds. A sequential forward search-based greedy algorithm, combining different fitting approaches and machine learning techniques, was used to find an optimal set of surrogate genes that predicted differential expression changes of the remaining genome. We then used pathway enrichment of up-regulated and down-regulated genes to assess the ability of a limited gene set to determine relevant patterns of tissue response. In addition, we compared prediction performance using the surrogate genes found from our greedy algorithm (referred to as the SV2000) with the landmark genes provided by existing technologies such as L1000 (Genometry) and S1500 (Tox21), finding better predictive performance for the SV2000. The ability of these predictive algorithms to predict pathway level responses is a positive step toward incorporating mode of action (MOA) analysis into the high throughput prioritization and testing of the large number of chemicals in need of safety evaluation.

Peer Review #1 of "T1000: a reduced gene set prioritized for toxicogenomic studies (v0.2)"

“…It worth nothing that about 33.7% of genes are shared between both signatures. Even though some differences can be realized between L1000 and S1500, they are both strong candidates of gene expression modeling and prediction (Haider et al 2018).…”

A strategy to detect metabolic changes induced by exposure to chemicals from large sets of condition-specific metabolic models computed with enumeration techniques

“…Nevertheless, these methods rely on arbitrary definitions of the pathways and functional sets, which might differ depending on the selected database, and tend to hide functional processes spanning several pathways [ 11 , 12 ]. Many studies [ 13 – 16 ] aim to take advantage of published gene expression data available in databases such as DrugMatrix [ 17 ], Connectivity Map [ 18 , 19 ], ToxicoDB [ 20 ] and Open TG-GATEs [ 21 ] ( https://toxico.nibiohn.go.jp ) to improve chemical toxicity assessment. For instance, Heusinkveld et al [ 22 ] implemented an approach based on the comparison of Open TG-GATEs top 50 DEG signatures ranked according to their t-statistic.…”

A strategy to detect metabolic changes induced by exposure to chemicals from large sets of condition-specific metabolic models computed with enumeration techniques

“…Nevertheless, these methods rely on arbitrary definitions of the pathways and functional sets, which might differ depending on the selected database, and tend to hide functional processes spanning several pathways [11,12]. Many studies [13][14][15][16] aim to take advantage of published gene expression data available in databases such as DrugMatrix [17], Connectivity Map [18,19], ToxicoDB [20] and Open TG-GATEs [21] (https://toxico.nibiohn.go.jp) to improve chemical toxicity assessment. For instance, Heusinkveld et al [22] implemented an approach based on the comparison of Open TG-GATEs top 50 DEG signatures ranked according to their t-statistic.…”