• PNAS Subscriptions
  • Science Sessions: The PNAS Podcast Program

Framework and resource for more than 11,000 gene-transcript-protein-reaction associations in human metabolism

  1. Sang Yup Leea,b,c,d,2
  1. aMetabolic and Biomolecular Engineering National Research Laboratory, Department of Chemical and Biomolecular Engineering (BK21 Plus Program), Institute for the BioCentury, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Republic of Korea;
  2. bBioInformatics Research Center, KAIST, Daejeon 34141, Republic of Korea;
  3. cThe Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Lyngby, Denmark;
  4. dBioProcess Engineering Research Center, KAIST, Daejeon 34141, Republic of Korea
  1. Contributed by Sang Yup Lee, September 29, 2017 (sent for review July 24, 2017; reviewed by Jens Nielsen and Nathan D. Price)

Significance

Alternative splicing is a regulatory mechanism by which multiple protein isoforms can be generated from one gene. Despite its biological importance, there has been no systematic approach that facilitates characterizing functional roles of protein isoforms in human metabolism. To this end, we present a systematic framework for the generation of gene-transcript-protein-reaction associations (GeTPRA) in human metabolism. The framework involves a generic human genome-scale metabolic model (GEM) that is an excellent framework to investigate genotype–phenotype associations. We show that a biochemically consistent and transcript-level data-compatible human GEM can be used to generate GeTPRA, which can be deployed to further upgrade the human GEM. Personal GEMs generated with GeTPRA information enabled more accurate simulation of cancer metabolism and prediction of anticancer targets.

Abstract

Alternative splicing plays important roles in generating different transcripts from one gene, and consequently various protein isoforms. However, there has been no systematic approach that facilitates characterizing functional roles of protein isoforms in the context of the entire human metabolism. Here, we present a systematic framework for the generation of gene-transcript-protein-reaction associations (GeTPRA) in the human metabolism. The framework in this study generated 11,415 GeTPRA corresponding to 1,106 metabolic genes for both principal and nonprincipal transcripts (PTs and NPTs) of metabolic genes. The framework further evaluates GeTPRA, using a human genome-scale metabolic model (GEM) that is biochemically consistent and transcript-level data compatible, and subsequently updates the human GEM. A generic human GEM, Recon 2M.1, was developed for this purpose, and subsequently updated to Recon 2M.2 through the framework. Both PTs and NPTs of metabolic genes were considered in the framework based on prior analyses of 446 personal RNA-Seq data and 1,784 personal GEMs reconstructed using Recon 2M.1. The framework and the GeTPRA will contribute to better understanding human metabolism at the systems level and enable further medical applications.

Footnotes

  • ?1J.Y.R. and H.U.K. contributed equally to this work.

  • ?2To whom correspondence should be addressed. Email: leesy{at}kaist.ac.kr.
  • Author contributions: S.Y.L. designed research; J.Y.R. and H.U.K. performed research; J.Y.R., H.U.K., and S.Y.L. analyzed data; and J.Y.R., H.U.K., and S.Y.L. wrote the paper.

  • Reviewers: J.N., Chalmers University of Technology; and N.D.P., Institute for Systems Biology.

  • Conflict of interest statement: S.Y.L. and H.U.K. have coauthored publications with J.N. and N.D.P. These were Commentary articles and did not involve any research collaboration.

  • This article contains supporting information online at www.danielhellerman.com/lookup/suppl/doi:10.1073/pnas.1713050114/-/DCSupplemental.

Published under the PNAS license.

Online Impact

  • 864971864 2018-01-22
  • 258841863 2018-01-22
  • 957295862 2018-01-22
  • 553518861 2018-01-22
  • 983792860 2018-01-22
  • 539694859 2018-01-22
  • 956115858 2018-01-22
  • 730379857 2018-01-22
  • 346624856 2018-01-22
  • 201609855 2018-01-22
  • 72549854 2018-01-21
  • 795928853 2018-01-21
  • 752345852 2018-01-21
  • 566508851 2018-01-21
  • 615722850 2018-01-21
  • 689612849 2018-01-21
  • 846903848 2018-01-21
  • 674896847 2018-01-21
  • 11197846 2018-01-21
  • 986896845 2018-01-21