Genome-Scale Characterization of Toxicity-Induced Metabolic Alterations in Primary Hepatocytes. 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. Overall design: Examination of RNA from compound-treated primary rat hepatocytes for 5 conditions (including the control) with 3-4 replicates for each condition.

上下文特异性基因组规模代谢网络重构（Genome-scale Metabolic Network Reconstructions, GENREs）可为在更精细的生理细节层面深入解析细胞代谢提供可行路径。
本研究利用经化学物暴露的大鼠肝细胞转录组学（transcriptomics）数据，对大鼠肝细胞代谢的GENRE模型进行约束，并通过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。
本研究分析了体外暴露于三种毒物（对乙酰氨基酚、2,3,7,8-四氯二苯并二噁英、三氯乙烯）6小时后大鼠肝细胞代谢的变化特征。
将TIMBR的预测结果与相同暴露条件下获取的配对代谢组学（metabolomics）数据进行比对。
计算模型预测与实验数据的一致性可帮助鉴定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分歧之处，我们提出可验证的假说以调和模型预测与实验数据间的差异。
本研究提出的利用配对转录组学与代谢组学数据的分析流程，可为多组学数据集的解析提供框架，以阐明与毒理学响应相关的代谢变化的机制。
整体实验设计：对经化合物处理的原代大鼠肝细胞的RNA进行检测，共设置5种条件（含对照组），每种条件设置3-4个生物学重复。