Toxicogenomic module associations with pathogenesis: A network based approach to understanding drug toxicity

Despite investment in toxicogenomics, nonclinical safety studies are still used to predict clinical liabilities for new drug candidates. Network-based approaches for genomic analysis help overcome challenges with whole-genome transcriptional profiling using limited numbers of treatments for phenotypes of interest. Herein, we apply co-expression network analysis to safety assessment using rat liver gene expression data to define 415 modules, exhibiting unique transcriptional control, organized in a visual representation of the transcriptome (the ‘TXG-MAP’). Accounting for the overall transcriptional activity resulting from treatment, we explain mechanisms of toxicity and predict distinct toxicity phenotypes using module associations. We demonstrate that early network responses compliment traditional histology-based assessment in predicting outcomes for longer studies and identify a novel mechanism of hepatotoxicity involving endoplasmic reticulum stress and Nrf2 activation. Module-based molecular subtypes of cholestatic injury derived using rat translate to human. Moreover, compared to gene-level analysis alone, combining module and gene-level analysis performed in sequence identifies significantly more phenotype-gene associations, including established and novel biomarkers of liver injury.尽管毒理基因组学（toxicogenomics）领域已投入大量研究资源，但非临床安全性研究仍被用于预测新药候选化合物的临床安全性隐患。基于网络的基因组分析方法，可有效克服针对目标表型采用有限处理方案时，全基因组转录谱分析所面临的分析难题。在此研究中，我们基于大鼠肝脏基因表达数据开展共表达网络分析，构建了415个具备独特转录调控特性的模块，并以转录组可视化图谱（TXG-MAP）的形式进行组织呈现。通过考量处理因素诱导的整体转录活性，我们阐释了毒性作用机制，并通过模块关联分析预测了不同的毒性表型。我们证实，早期网络响应能够补充传统基于组织病理学的评估手段，以预测长期毒理学研究的结局，并发现了一种涉及内质网应激（endoplasmic reticulum stress）与Nrf2激活的新型肝毒性机制。基于大鼠数据构建的胆汁淤积性损伤模块分子亚型，可跨物种推广至人类样本。此外，与单纯的基因水平分析相比，按序结合模块与基因水平分析的策略，能够识别出显著更多的表型-基因关联，包括已验证的肝损伤生物标志物与新型候选生物标志物。