Genome-Scale Characterization of Toxicity-Induced Metabolic Alterations in Primary Hepatocytes

Context-specific Genome-scale Metabolic Network Reconstructions (GENREs) provide a means to understand cellular metabolism at a deeper level of physiological detail. Here, we use transcriptomics data from chemically exposed rat hepatocytes to constrain a GENRE of rat hepatocyte metabolism and predict biomarkers of liver toxicity using the Transcriptionally Inferred Metabolic Biomarker Response (TIMBR) algorithm. We profiled alterations in cellular hepatocyte metabolism following in vitro exposure to three toxicants (acetaminophen, 2,3,7,8-tetrachlorodibenzodioxin, and trichloroethylene) for six hours. TIMBR predictions were compared with paired metabolomics data from the same exposure conditions. Agreement between computational model predictions and experimental data led to the identification of specific metabolites and thus metabolic pathways associated with toxicant exposure; where predictions and experimental data disagreed, we identified testable hypotheses to reconcile differences between the model predictions and experimental data. The presented pipeline for using paired transcriptomics and metabolomics data provides a framework for interrogating multiple omics datasets to generate mechanistic insight of metabolic changes associated with toxicological responses.组织特异性基因组规模代谢网络重构（Context-specific Genome-scale Metabolic Network Reconstructions, GENREs）为在更高精度的生理细节层面解析细胞代谢提供了有效途径。本研究采用经化学物质暴露的大鼠肝细胞转录组学数据，对大鼠肝细胞代谢的GENRE进行约束，并借助转录推断代谢生物标志物响应（Transcriptionally Inferred Metabolic Biomarker Response, TIMBR）算法预测肝毒性生物标志物。我们对体外暴露于三种毒物（对乙酰氨基酚、2,3,7,8-四氯二苯并对二噁英、三氯乙烯）6小时后的肝细胞代谢变化开展了谱分析，将TIMBR算法的预测结果与相同暴露条件下获取的配对代谢组学数据进行比对。计算模型预测与实验数据的一致性可助力我们鉴定出与毒物暴露相关的特定代谢物及代谢通路；而在预测结果与实验数据存在偏差的场景中，我们也提出了可验证的假说以弥合模型预测与实验数据间的差异。本研究提出的整合配对转录组学与代谢组学数据的分析流程，为解析多组学数据集以获取毒理响应相关代谢变化的机制性见解提供了通用框架。