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The implications of human metabolic network topology for disease comorbidity

  1. D.-S. Lee*?,
  2. J. Park*?,
  3. K. A. Kay?,
  4. N. A. Christakis§,
  5. Z. N. Oltvai? and
  6. A.-L. Barabási*?,?
  1. *Center for Complex Network Research and Department of Physics, Biology, and Computer Science, Northeastern University, Boston, MA 02115;
  2. ?Center for Cancer Systems Biology, Dana–Farber Cancer Institute, Boston, MA 02115;
  3. ?Department of Pathology, University of Pittsburgh, Pittsburgh, PA 15261; and
  4. §Department of Health Care Policy, Harvard Medical School, Boston, MA 02115
  1. Edited by H. Eugene Stanley, Boston University, Boston, MA, and approved May 1, 2008 (received for review March 4, 2008)

Abstract

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.

Footnotes

  • ?To whom correspondence should be addressed. E-mail: alb{at}neu.edu
  • Author contributions: D.-S.L. and A.-L.B. designed research; D.-S.L., J.P., and A.-L.B. performed research; D.-S.L., K.A.K., N.A.C., and Z.N.O. analyzed data; and D.-S.L., N.A.C., Z.N.O., and A.-L.B. wrote the paper.

  • The authors declare no conflict of interest.

  • This article is a PNAS Direct Submission.

  • See Commentary on page 9849.

  • This article contains supporting information online at www.danielhellerman.com/cgi/content/full/0802208105/DCSupplemental.

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