Meta-Analysis of Large-Scale Toxicogenomic Data Finds Neuronal Regeneration Related Protein and Cathepsin D to Be Novel Biomarkers of Drug-Induced Toxicity

<div><p>Undesirable toxicity is one of the main reasons for withdrawing drugs from the market or eliminating them as candidates in clinical trials. Although numerous studies have attempted to identify biomarkers capable of predicting pharmacotoxicity, few have attempted to discover robust biomarkers that are coherent across various species and experimental settings. To identify such biomarkers, we conducted meta-analyses of massive gene expression profiles for 6,567 <i>in vivo</i> rat samples and 453 compounds. After applying rigorous feature reduction procedures, our analyses identified 18 genes to be related with toxicity upon comparisons of untreated versus treated and innocuous versus toxic specimens of kidney, liver and heart tissue. We then independently validated these genes in human cell lines. In doing so, we found several of these genes to be coherently regulated in both <i>in vivo</i> rat specimens and in human cell lines. Specifically, mRNA expression of neuronal regeneration-related protein was robustly down-regulated in both liver and kidney cells, while mRNA expression of cathepsin D was commonly up-regulated in liver cells after exposure to toxic concentrations of chemical compounds. Use of these novel toxicity biomarkers may enhance the efficiency of screening for safe lead compounds in early-phase drug development prior to animal testing.</p></div

Overview of toxicity biomarker discovery.

<p>First, we collected toxicogenomic meta-data from public resources, preprocessed gene expression array data, and assigned toxicity classes. Second, we attempted to identify differentially expressed genes (DEGs) through meta-analysis and subsequent multistage feature reductions. DEGs were subjected to systems analysis of biological pathways and networks, and an optimized set of biomarkers was used to generate and validate a prediction model. The final step involved computationally and experimentally testing the applicability of the discovered biomarkers in human cells. GEO, Gene Expression Omnibus at the National Center for Biotechnology Information; ArrayExpress, ArrayExpress at the European Bioinformatics Institute; TG-GATEs, Toxicogenomics Project-Genomics Assisted Toxicity Evaluation System of the National Institute of Health Sciences of Japan; CEBS, Chemical Effects in Biological Systems at the National Institute of Environmental Health Sciences; sPLS-DA, sparse partial least squares discriminant analysis.</p

Functional analysis of DEGs.

<p>(A) Enriched GO terms associated with DEGs from three meta-analysis comparisons. DEGs from MA5 were excluded from the analysis owing to insufficient dataset size. <i>p</i>-value: modified Fisher’s exact test implemented in the Database for Annotation, Visualization and Integrated Discovery (DAVID). (B, C) Highly interconnected subnetworks present within the individual sets of DEGs from MA3 and MA4. A circular node indicates proteins, a diamond node indicates proteins/genes, and solid lines and dashed arrows respectively indicate physical and genetic interactions reported in our input databases (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0136698#sec002" target="_blank">Methods</a> for details). Node color indicates the median expression fold-change of the training dataset (level-1/level-0).</p

Characterization of pharmacogenomics meta-data.

<p>(A) Distribution of toxicity levels for 391 compounds from single-organ studies. Compounds were rank-ordered by relative toxicity level. (B) Distribution of toxicity levels for 62 compounds from multi-organ studies. Asterisks indicate compounds showing organ-specific toxicity. (C) Distribution of toxicity levels for two selected drugs at different doses and treatment durations. The same color scale is used in all panels. Missing information is shown in grey. For each row, the sum of samples with level-0 and level-1 toxicity per each organ is 100%. See <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0136698#pone.0136698.s009" target="_blank">S5 Table</a> for the exact values used to generate this figure.</p

Number of selected genes after meta-analysis, sPLS-DA, and wrapper approaches for all five meta-analysis comparisons.

<p>Number of selected genes after meta-analysis, sPLS-DA, and wrapper approaches for all five meta-analysis comparisons.</p

Computational and experimental validations identify NREP and CTSD as biomarkers of toxicity in human cell lines.

<p>(A) Density plots comparing expression levels of <i>NREP</i> between untreated and treated samples of liver primary hepatocytes reported in TG-GATEs. (B) Boxplots display fold-changes in <i>CTSD</i> (toxic/innocuous) at each of the given cell viability thresholds measured for the liver primary hepatocytes reported in TG-GATEs. * t-test <i>p</i>-value < 0.05, ** < 0.001. (C, D) Dose-responsive viability of HEK293 (C) and HepG2 (D) cells exposed to cisplatin (C) or acetaminophen (D). DMSO was used to dissolve the compounds. Cell viability was measured by MTS assay. Error bars represent ± standard deviation of triplicate experiments. See <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0136698#pone.0136698.s004" target="_blank">S4A and S4B Fig</a> for the results with the same compounds dissolved in growth media. (E, F) <i>NREP</i> mRNA levels after exposure to the indicated concentrations of cisplatin for 72 h and acetaminophen for 48 h, respectively, determined by RT-PCR. (G-H) qRT-PCR assays for <i>NREP</i> (G) and <i>CTSD</i> (H). Y-axis indicates fold-changes in expression compared to chemically untreated samples (n = 5). Level-0 and level-1 drug concentrations for DMSO and DMEM were selected based on cell viability of > or < 60%, respectively, in C-D and <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0136698#pone.0136698.s004" target="_blank">S4A and S4B Fig</a>. *<i>p</i> < 0.05, ** <i>p</i> < 0.001; Student’s t-test. Error bars represent ± standard deviation.</p