Lee D-S, Park J, Kay K A, Christakis N A, Oltvai Z N, Barabási A-L
Center for Complex Network Research and Department of Physics, Biology, and Computer Science, Northeastern University, Boston, MA 02115, USA.
Proc Natl Acad Sci U S A. 2008 Jul 22;105(29):9880-5. doi: 10.1073/pnas.0802208105. Epub 2008 Jul 3.
Most diseases are the consequence of the breakdown of cellular processes, but the relationships among genetic/epigenetic defects, the molecular interaction networks underlying them, and the disease phenotypes remain poorly understood. To gain insights into such relationships, here we constructed a bipartite human disease association network in which nodes are diseases and two diseases are linked if mutated enzymes associated with them catalyze adjacent metabolic reactions. We find that connected disease pairs display higher correlated reaction flux rate, corresponding enzyme-encoding gene coexpression, and higher comorbidity than those that have no metabolic link between them. Furthermore, the more connected a disease is to other diseases, the higher is its prevalence and associated mortality rate. The network topology-based approach also helps to uncover potential mechanisms that contribute to their shared pathophysiology. Thus, the structure and modeled function of the human metabolic network can provide insights into disease comorbidity, with potentially important consequences for disease diagnosis and prevention.
大多数疾病是细胞过程崩溃的结果，但基因/表观遗传缺陷、其潜在的分子相互作用网络与疾病表型之间的关系仍知之甚少。为了深入了解这些关系，我们构建了一个二分人类疾病关联网络，其中节点为疾病，若与它们相关的突变酶催化相邻的代谢反应，则这两种疾病相连。我们发现，与没有代谢联系的疾病对相比，有联系的疾病对表现出更高的相关反应通量率、相应的酶编码基因共表达以及更高的共病率。此外，一种疾病与其他疾病的联系越紧密，其患病率和相关死亡率就越高。基于网络拓扑的方法也有助于揭示导致它们共同病理生理学的潜在机制。因此，人类代谢网络的结构和模拟功能可以为疾病共病提供见解，对疾病诊断和预防可能产生重要影响。
