Prediction of developmental chemical toxicity based on gene networks of human embryonic stem cells

Abstract

Predictive toxicology using stem cells or their derived tissues has gained increasing importance in biomedical and pharmaceutical research. Here, we show that toxicity category prediction by support vector machines (SVMs), which uses qRT-PCR data from 20 categorized chemicals based on a human embryonic stem cell (hESC) system, is improved by the adoption of gene networks, in which network edge weights are added as feature vectors when noisy qRT-PCR data fail to make accurate predictions. The accuracies of our system were 97.5-100% for three toxicity categories: neurotoxins (NTs), genotoxic carcinogens (GCs) and non-genotoxic carcinogens (NGCs). For two uncategorized chemicals, bisphenol-A and permethrin, our system yielded reasonable results: bisphenol-A was categorized as an NGC, and permethrin was categorized as an NT; both predictions were supported by recently published papers. Our study has two important features: (i) as the first study to employ gene networks without using conventional quantitative structure-activity relationships (QSARs) as input data for SVMs to analyze toxicogenomics data in an hESC validation system, it uses additional information of gene-to-gene interactions to significantly increase prediction accuracies for noisy gene expression data; and (ii) using only undifferentiated hESCs, our study has considerable potential to predict late-onset chemical toxicities, including abnormalities that occur during embryonic development.

Figure 1.

Schematic view of processes from data generation to SVM prediction. For each of the 22 toxic chemicals, 200 qRT-PCR measurements of 10 genes were conducted on cells treated with five doses at four time points. In addition, 100 BN data from 10 × 10 genes were generated. qRT-PCR data, BN data or both data types were used to train SVMs and predict each of the three toxicity categories.

Figure 2.

Pearson correlation coefficients in 100 toxic chemical experiments, constructed based on batch-adjusted qRT-PCR data. Pairwise Pearson correlation coefficients were calculated for 20 toxic chemicals administered at five doses (100 experiments).

Figure 3.

Category-dependent high-weighted edges of BN structures in circular network diagrams. (A) Neurotoxins (9 data), (B) genotoxic carcinogens (5 data) and (C) non-genotoxic carcinogens (6 data). The network for non-genotoxic carcinogens represents limited edges, indicating that the network may be specific and similar among different chemicals and thus easy to predict, whereas that for neurotoxins contains almost all edges, indicating that the network is not easy to characterize and thus difficult to predict.

Figure 4.

Analysis of BN information by Pearson correlation coefficients. Heat maps of pairwise Pearson correlation coefficients of the 20 toxic chemicals constructed based on (A) five dose-averaged qRT-PCR Pearson correlations and (B) 10 × 10 BN edge weights, where two experimental repeats were averaged for use in the calculation, are shown. (C) The qRT-PCR and BN contributions of the Pearson correlation coefficients calculated by, (|BN|) − (|qRT-PCR|), where z is Gaussian normalization, are indicated by colors: red for BN and blue for qRT-PCR contributions.

Figure 5.

Maximum accuracies for various numbers of features. (A) Neurotoxins (9 data), (B) genotoxic carcinogens (5 data) and (C) non-genotoxic carcinogens (6 data). qRT-PCR only (200 features), BN only (100 features) and qRT-PCR + BN (300 features) are shown by different lines. In the qRT-PCR + BN lines, NTs and GCs exhibit peak accuracies at specific feature numbers, whereas NGCs constantly exhibit 90.0% or higher accuracy.

Figure 6.

Pearson correlations of bisphenol-A and permethrin against toxic chemicals based on qRT-PCR and BN edge weights. (A) Pearson correlation coefficients calculated by qRT-PCR and BN edge weights. (B) Bisphenol-A BN exhibits the highest similarity to 2,3,7,8-TCDD (an NGC), with a relatively high Pearson correlation coefficient (r = 0.713), whereas (C) permethrin BN is moderately similar to both 4-OH-2′,3,3′,4′,5′-PCB (an NT; r = 0.505) and methapyrilene hydrochloride (an NGC; r = 0.456).