Storr Liver Centre, Westmead Institute for Medical Research, Westmead Hospital and University of Sydney, Sydney, NSW, Australia; Liver and Pancreatobiliary Diseases Research Center, Digestive Disease Research Institute, Shariati Hospital, Tehran University of Medical Sciences, Tehran, Iran.
The Jane and Terry Semel Institute for Neuroscience and Human Behavior, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, USA.
Cell Syst. 2021 May 19;12(5):432-445.e7. doi: 10.1016/j.cels.2021.04.004. Epub 2021 May 5.
Findings about chronic complex diseases are difficult to extrapolate from animal models to humans. We reason that organs may have core network modules that are preserved between species and are predictably altered when homeostasis is disrupted. To test this idea, we perturbed hepatic homeostasis in mice by dietary challenge and compared the liver transcriptome with that in human fatty liver disease and liver cancer. Co-expression module preservation analysis pointed to alterations in immune responses and metabolism (core modules) in both human and mouse datasets. The extent of derailment in core modules was predictive of survival in the cancer genome atlas (TCGA) liver cancer dataset. We identified module eigengene quantitative trait loci (module-eQTL) for these predictive co-expression modules, targeting of which may resolve homeostatic perturbations and improve patient outcomes. The framework presented can be used to understand homeostasis at systems levels in pre-clinical models and in humans. A record of this paper's transparent peer review process is included in the supplemental information.
关于慢性复杂疾病的研究结果很难从动物模型推断到人类。我们推测，器官可能具有核心网络模块，这些模块在物种间是保守的，并且在体内平衡被破坏时可以被预测到发生改变。为了验证这一想法，我们通过饮食挑战来扰乱小鼠的肝脏内稳态，并将其肝脏转录组与人类脂肪肝和肝癌的转录组进行比较。共表达模块保存分析指出，在人类和小鼠数据集均存在免疫反应和代谢（核心模块）的改变。核心模块的脱轨程度可以预测癌症基因组图谱（TCGA）肝癌数据集的生存率。我们确定了这些具有预测性的共表达模块的模块特征基因定量遗传位（module-eQTL），针对这些模块的靶向治疗可能会解决体内平衡的紊乱，并改善患者的预后。所提出的框架可用于在临床前模型和人类中理解系统水平的体内平衡。本文的透明同行评审过程记录包含在补充信息中。
