Quantitative high-throughput screening for chemical toxicity in a population-based in vitro model

Abstract

A shift in toxicity testing from in vivo to in vitro may efficiently prioritize compounds, reveal new mechanisms, and enable predictive modeling. Quantitative high-throughput screening (qHTS) is a major source of data for computational toxicology, and our goal in this study was to aid in the development of predictive in vitro models of chemical-induced toxicity, anchored on interindividual genetic variability. Eighty-one human lymphoblast cell lines from 27 Centre d'Etude du Polymorphisme Humain trios were exposed to 240 chemical substances (12 concentrations, 0.26nM-46.0μM) and evaluated for cytotoxicity and apoptosis. qHTS screening in the genetically defined population produced robust and reproducible results, which allowed for cross-compound, cross-assay, and cross-individual comparisons. Some compounds were cytotoxic to all cell types at similar concentrations, whereas others exhibited interindividual differences in cytotoxicity. Specifically, the qHTS in a population-based human in vitro model system has several unique aspects that are of utility for toxicity testing, chemical prioritization, and high-throughput risk assessment. First, standardized and high-quality concentration-response profiling, with reproducibility confirmed by comparison with previous experiments, enables prioritization of chemicals for variability in interindividual range in cytotoxicity. Second, genome-wide association analysis of cytotoxicity phenotypes allows exploration of the potential genetic determinants of interindividual variability in toxicity. Furthermore, highly significant associations identified through the analysis of population-level correlations between basal gene expression variability and chemical-induced toxicity suggest plausible mode of action hypotheses for follow-up analyses. We conclude that as the improved resolution of genetic profiling can now be matched with high-quality in vitro screening data, the evaluation of the toxicity pathways and the effects of genetic diversity are now feasible through the use of human lymphoblast cell lines.

FIG. 1.

Intraexperimental reproducibility for cytotoxicity (panels a and c) and caspase-3/7 (panels b and d) assays. Panels a and b show log (curve P) values for randomly selected pairs of replicate plates within each chemical and cell line (240 chemicals × 81 cell lines = 19,440 replicate pairs displayed). Panels c and d show side-by-side boxplots for eight duplicate compounds that were tested in two independent wells on each plate.

FIG. 2.

Distribution of cytotoxicity across chemicals for cytotoxicity (panels a and c) and caspase-3/7 (panels b and d) assays. Panels a and b give the percentage of chemicals classified as “active,” “nonactive,” or “inconclusive” for each cell line. Panels c and d give the range of potency (curve P) for active chemicals in each cell line.

FIG. 3.

The percent of cell lines exhibiting activity for each chemical for cytotoxicity (panel a) and caspase-3/7 (panel b) assays. Panel c displays the rank of the mean ATP curve P value versus the mean caspase curve P value for each chemical. Panel d shows a heatmap of the correlations between log (curve P) values for all chemical-assay combinations.

FIG. 4.

Boxplots of curve P values for each of the 240 chemicals (arranged by mean activity) across the 81 cell lines are shown for cytotoxicity (panel a) and caspase-3/7 (panel b) assays. For cytotoxicity (panel c) and caspase-3/7 (panel d) assays, −log (p values, Kruskal-Wallis test) were plotted against mean curve P (micromolar). The blue line gives a FDR-adjusted significance threshold (FDR = 0.05). Chemicals colored in red had a significant correlation between activity and basal metabolic rate (ATP level in vehicle-treated cells) across the panel of cell lines (Spearman rank correlation; FDR < 0.05).

FIG. 5.

Toxicity-genotype relationships were assessed using GWAS analysis for the 240 chemicals on both cytotoxicity (panels a and c) and caspase-3/7 (panels b and d) assays. Panels a and b give p values (-log10 scale) for the most significant SNP associated with toxicity for each chemical. The inset in the diagram gives −log10 (p values) for SNP-toxicity associations across the entire genome, for progesterone (cytotoxicity assay, inset in panel a) and Guggulsterones Z (caspase-3/7 assay, inset in panel b). Panels c and d provide a zoomed-in look at the locus with the most significant p value for each of the two compounds, respectively. Correlation between SNPs is identified with colors. SNP and gene tracks are also shown. Inset: box and whisker plots for each compound’s curve P.

FIG. 6.

Panel a, a population concentration response was modeled using in vitro qHTS data using cycloheximide data (cytotoxicity assay) as an example. Logistic dose-response modeling was performed for each individual to the values shown in gray, providing individual 10% effect concentration values (EC₁₀). The EC₁₀ obtained by performing the modeling on average assay values for each concentration (see frequency distribution) are shown in the inset. Panel b, a heatmap of clustered FDRs (q values, see color bar) for association of the data from caspase-3/7 assay with publicly available RNA-Seq expression data on a subset of cell lines. A sample subcluster is shown.