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

Abstract

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 in vivo 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 in vivo 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.

Literature optimized integration of gene expression for organ-specific evaluation of toxicogenomics datasets

“…While the task of aggregating results from toxicogenomics datasets is clearly not trivial [3], current research efforts show that carefully designed protocols for meta-analysis can potentially address issues of heterogeneity of samples. For example, novel (more generic) gene biomarkers for drug-induced toxicity have been identified by integration of differential expression analysis and data mining of in vitro and/or in vivo toxicogenomics data [47, 48]. Several studies exploited biclustering methods [49] for coherent coexpression gene module discovery combined with downstream functional enrichment analysis to characterize conserved pattern of drug response across human cell lines [50] and prioritize gene modules for specific chemically-induced liver [51] and kidney injury endpoints [52].…”

Prevention of antipsychotic-induced hyperglycaemia by vitamin D: a data mining prediction followed by experimental exploration of the molecular mechanism

“…In addition, even though a large number of toxicogenomics datasets are registered in GEO, the comprehensiveness of reported experimental conditions is presently insufficient. However, these shortcomings can be overcome by integration with systems pharmacology databases 32 or by meta-analysis of large-scale toxicogenomic data 33 . Despite the challenges for prediction accuracy, data mining is still useful in areas where traditional clinical investigations and animal experiments fail to provide insights into molecular mechanisms of complicated conditions, such as antipsychotic-induced hyperglycaemia.…”

Integrating the Ribonucleic Acid Sequencing Data From Various Studies for Exploring the Multiple Sclerosis-Related Long Noncoding Ribonucleic Acids and Their Functions

“…Particularly, the random effect model (REM) and fixed effect model (FEM) were used, respectively, for the lncRNAs with a significant heterogeneity or not. Using the REM in meta-analysis can reduce bias of the results (Kim et al, 2015; Szajewska and Kolodziej, 2015). We calculated standardized mean difference (SMD) with its 95% confidence interval (CI) to identify the differentially expressed lncRNA between the MS patients and controls (95% CI of SMD does not include zero, FDR adjusted P < 0.05).…”