PDF(Pubmed)
Abstract：
Human diseases involve metabolic alterations. Metabolomic profiles have served as a vital biomarker for the early identification of high-risk individuals and disease prevention. However, current approaches can only characterize individual key metabolites, without taking into account the reality that complex diseases are multifactorial, dynamic, heterogeneous, and interdependent. Here, we leverage a statistical physics model to combine all metabolites into bidirectional, signed, and weighted interaction networks and trace how the flow of information from one metabolite to the next causes changes in health state. Viewing a disease outcome as the consequence of complex interactions among its interconnected components (metabolites), we integrate concepts from ecosystem theory and evolutionary game theory to model how the health state-dependent alteration of a metabolite is shaped by its intrinsic properties and through extrinsic influences from its conspecifics. We code intrinsic contributions as nodes and extrinsic contributions as edges into quantitative networks and implement GLMY homology theory to analyze and interpret the topological change of health state from symbiosis to dysbiosis and vice versa. The application of this model to real data allows us to identify several hub metabolites and their interaction webs, which play a part in the formation of inflammatory bowel diseases. The findings by our model could provide important information on drug design to treat these diseases and beyond.
摘要：
人类疾病涉及代谢改变。代谢组学谱已成为早期识别高危个体和疾病预防的重要生物标志物。然而,目前的方法只能表征单个关键代谢物，没有考虑到复杂疾病是多因素的现实，动态,异质，相互依存。这里,我们利用统计物理模型将所有代谢物组合成双向的，签字,和加权相互作用网络，并追踪从一种代谢物到下一种代谢物的信息流如何导致健康状况的变化。将疾病结果视为其互连成分（代谢物）之间复杂相互作用的结果，我们整合了生态系统理论和进化博弈论的概念，以模拟代谢产物的健康状态依赖性改变是如何通过其内在属性和来自其特性的外在影响而形成的。我们将内在贡献编码为节点，将外在贡献编码为边缘，并将其编码为定量网络，并实施GLMY同源性理论，以分析和解释从共生到生态失调的健康状态的拓扑变化，反之亦然。将该模型应用于实际数据，使我们能够识别出几个中心代谢物及其相互作用网，在炎症性肠病的形成中起作用。我们模型的发现可以为治疗这些疾病及其他疾病的药物设计提供重要信息。
