School of Medicine, National University of Ireland, Galway, Ireland.
Discipline of Microbiology, School of Natural Sciences, National University of Ireland, Galway, Ireland.
Mol Syst Biol. 2020 May;16(5):e8982. doi: 10.15252/msb.20198982.
Comprehensive molecular-level models of human metabolism have been generated on a cellular level. However, models of whole-body metabolism have not been established as they require new methodological approaches to integrate molecular and physiological data. We developed a new metabolic network reconstruction approach that used organ-specific information from literature and omics data to generate two sex-specific whole-body metabolic (WBM) reconstructions. These reconstructions capture the metabolism of 26 organs and six blood cell types. Each WBM reconstruction represents whole-body organ-resolved metabolism with over 80,000 biochemical reactions in an anatomically and physiologically consistent manner. We parameterized the WBM reconstructions with physiological, dietary, and metabolomic data. The resulting WBM models could recapitulate known inter-organ metabolic cycles and energy use. We also illustrate that the WBM models can predict known biomarkers of inherited metabolic diseases in different biofluids. Predictions of basal metabolic rates, by WBM models personalized with physiological data, outperformed current phenomenological models. Finally, integrating microbiome data allowed the exploration of host-microbiome co-metabolism. Overall, the WBM reconstructions, and their derived computational models, represent an important step toward virtual physiological humans.
已经在细胞水平上生成了全面的人类代谢分子水平模型。然而,由于需要新的方法学方法来整合分子和生理数据,因此尚未建立全身代谢模型。我们开发了一种新的代谢网络重建方法,该方法使用来自文献和组学数据的器官特异性信息来生成两个性别特异性的全身代谢(WBM)重建。这些重建捕获了 26 个器官和 6 种血细胞类型的代谢。每个 WBM 重建都以解剖学和生理学一致的方式代表全身器官解析代谢,其中包含超过 80,000 种生化反应。我们使用生理,饮食和代谢组学数据对 WBM 重建进行了参数化。所得的 WBM 模型可以再现已知的器官间代谢循环和能量利用。我们还说明了 WBM 模型可以预测不同生物流体中遗传代谢疾病的已知生物标志物。使用生理数据个性化的 WBM 模型预测基础代谢率的表现优于当前的现象模型。最后,整合微生物组数据可以探索宿主-微生物共代谢。总体而言,WBM 重建及其衍生的计算模型代表了迈向虚拟生理人类的重要一步。