Cvitanović Tomaš Tanja, Urlep Žiga, Moškon Miha, Mraz Miha, Rozman Damjana
Faculty of Medicine, Centre for Functional Genomics and Bio-Chips, Institute of Biochemistry, University of Ljubljana, Ljubljana, Slovenia.
Faculty of Computer and Information Science, University of Ljubljana, Ljubljana, Slovenia.
Front Physiol. 2018 Apr 12;9:360. doi: 10.3389/fphys.2018.00360. eCollection 2018.
The liver is to date the best example of a sexually dimorphic non-reproductive organ. Over 1,000 genes are differentially expressed between sexes indicating that female and male livers are two metabolically distinct organs. The spectrum of liver diseases is broad and is usually prevalent in one or the other sex, with different contributing genetic and environmental factors. It is thus difficult to predict individual's disease outcomes and treatment options. Systems approaches including mathematical modeling can aid importantly in understanding the multifactorial liver disease etiology leading toward tailored diagnostics, prognostics and therapy. The currently established computational models of hepatic metabolism that have proven to be essential for understanding of non-alcoholic fatty liver disease (NAFLD) and hepatocellular carcinoma (HCC) are limited to the description of gender-independent response or reflect solely the response of the males. Herein we present , the first sex-based multi-tissue and multi-level liver metabolic computational model. The model was constructed based on liver model and the object-oriented modeling. The crucial factor in adaptation of liver metabolism to the sex is the inclusion of estrogen and androgen receptor responses to respective hormones and the link to sex-differences in growth hormone release. The model was extensively validated on literature data and experimental data obtained from wild type C57BL/6 mice fed with regular chow and western diet. These experimental results show extensive sex-dependent changes and could not be reproduced with the uniform model . represents the first large-scale liver metabolic model, which allows a detailed insight into the sex-dependent complex liver pathologies, and how the genetic and environmental factors interact with the sex in disease appearance and progression. We used the model to identify the most important sex-dependent metabolic pathways, which are involved in accumulation of triglycerides representing initial steps of NAFLD. We identified PGC1A, PPARα, FXR, and LXR as regulatory factors that could become important in sex-dependent personalized treatment of NAFLD.
肝脏是迄今为止性别二态性非生殖器官的最佳范例。超过1000个基因在两性之间存在差异表达，这表明雌性和雄性肝脏是两个代谢上截然不同的器官。肝脏疾病谱广泛，通常在某一性别中更为普遍，且有不同的遗传和环境因素起作用。因此，很难预测个体的疾病结果和治疗方案。包括数学建模在内的系统方法对于理解导致定制化诊断、预后和治疗的多因素肝脏疾病病因至关重要。目前已建立的肝脏代谢计算模型，虽已证明对理解非酒精性脂肪性肝病（NAFLD）和肝细胞癌（HCC）至关重要，但仅限于描述性别无关的反应，或仅反映男性的反应。在此，我们提出了首个基于性别的多组织、多层次肝脏代谢计算模型。该模型是基于肝脏模型和面向对象建模构建的。肝脏代谢适应性别的关键因素是纳入雌激素和雄激素受体对各自激素的反应以及与生长激素释放性别差异的联系。该模型在文献数据以及从喂食常规饲料和西式饮食的野生型C57BL/6小鼠获得的实验数据上进行了广泛验证。这些实验结果显示出广泛的性别依赖性变化，且无法用统一模型重现。该模型代表了首个大规模肝脏代谢模型，它能够深入洞察性别依赖性复杂肝脏病理，以及遗传和环境因素在疾病发生和发展过程中如何与性别相互作用。我们使用该模型确定了最重要的性别依赖性代谢途径，这些途径参与了代表NAFLD初始步骤的甘油三酯积累。我们确定PGC1A、PPARα、FXR和LXR为调控因子，它们可能在NAFLD的性别依赖性个性化治疗中发挥重要作用。
