Reconstruction and analysis of human heart-specific metabolic network based on transcriptome and proteome data.

The availability and utility of genome-scale metabolic networks have exploded with modern genome-sequencing capabilities. However, these generic models overlooked actual physiological states of the tissues and included all the reactions implied by the genome annotations. To address this problem, we reconstructed a human heart-specific metabolic network based on transcriptome and proteome data. The resulting model consists of 2803 reactions and 1880 metabolites, which correspond to 1721 active enzymes in human heart. Using the model, we detected 24 epistatic interactions in human heart, which are useful in understanding both the structure and function of cardiovascular systems. In addition, a set of 776 potential biomarkers for cardiovascular disease (CVD) has been successfully explored, whose concentration is predicted to be either elevated or reduced because of 278 possible dysfunctional cardiovascular-associated genes. The model could also be applied in predicting selective drug targets for eight subtypes of CVD. The human heart-specific model provides valuable information for the studies of cardiac activity and development of CVD.

[1]  Jason A. Papin,et al.  Applications of genome-scale metabolic reconstructions , 2009, Molecular systems biology.

[2]  Pornpimol Charoentong,et al.  ClueGO: a Cytoscape plug-in to decipher functionally grouped gene ontology and pathway annotation networks , 2009, Bioinform..

[3]  M. Yaffe,et al.  Exploiting synthetic lethal interactions for targeted cancer therapy , 2009, Cell cycle.

[4]  Ronan M. T. Fleming,et al.  Quantitative prediction of cellular metabolism with constraint-based models: the COBRA Toolbox v2.0 , 2007, Nature Protocols.

[5]  S. Tyagi,et al.  Attenuation of beta2-adrenergic receptors and homocysteine metabolic enzymes cause diabetic cardiomyopathy. , 2010, Biochemical and biophysical research communications.

[6]  Albert J R Heck,et al.  Proteome-wide protein concentrations in the human heart. , 2010, Molecular bioSystems.

[7]  M. Xiong,et al.  A systems biology approach to genetic studies of complex diseases , 2005, FEBS letters.

[8]  M. Monti,et al.  Puzzle of protein complexes in vivo: a present and future challenge for functional proteomics , 2009, Expert review of proteomics.

[9]  S. Hanash Progress in mining the human proteome for disease applications. , 2011, Omics : a journal of integrative biology.

[10]  O. Demin,et al.  The Edinburgh human metabolic network reconstruction and its functional analysis , 2007, Molecular systems biology.

[11]  Alan F. Scott,et al.  Online Mendelian Inheritance in Man (OMIM), a knowledgebase of human genes and genetic disorders , 2004, Nucleic Acids Res..

[12]  E. Ruppin,et al.  Predicting metabolic biomarkers of human inborn errors of metabolism , 2009, Molecular systems biology.

[13]  R. Mahadevan,et al.  The effects of alternate optimal solutions in constraint-based genome-scale metabolic models. , 2003, Metabolic engineering.

[14]  G. Church,et al.  Analysis of optimality in natural and perturbed metabolic networks , 2002 .

[15]  Aldons J. Lusis,et al.  Metabolic syndrome: from epidemiology to systems biology , 2008, Nature Reviews Genetics.

[16]  Markus J. Herrgård,et al.  Reconstruction and validation of Saccharomyces cerevisiae iND750, a fully compartmentalized genome-scale metabolic model. , 2004, Genome research.

[17]  Paul Webb,et al.  Selective activation of thyroid hormone signaling pathways by GC-1: a new approach to controlling cholesterol and body weight , 2004, Trends in Endocrinology & Metabolism.

[18]  E. Ingelsson,et al.  Impact of Body Mass Index and the Metabolic Syndrome on the Risk of Cardiovascular Disease and Death in Middle-Aged Men , 2010, Circulation.

[19]  Michael L. Creech,et al.  Integration of biological networks and gene expression data using Cytoscape , 2007, Nature Protocols.

[20]  K. Cooper,et al.  Relation of the number of metabolic syndrome risk factors with all-cause and cardiovascular mortality. , 2008, The American journal of cardiology.

[21]  Thomas J. Wang,et al.  The search for new cardiovascular biomarkers , 2008, Nature.

[22]  Ian C. Hsu,et al.  Identification of Human Housekeeping Genes and Tissue-Selective Genes by Microarray Meta-Analysis , 2011, PloS one.

[23]  Cathy H. Wu,et al.  The Universal Protein Resource (UniProt): an expanding universe of protein information , 2005, Nucleic Acids Res..

[24]  Monica L. Mo,et al.  Global reconstruction of the human metabolic network based on genomic and bibliomic data , 2007, Proceedings of the National Academy of Sciences.

[25]  Bernhard O. Palsson,et al.  Context-Specific Metabolic Networks Are Consistent with Experiments , 2008, PLoS Comput. Biol..

[26]  Jeffrey D Orth,et al.  What is flux balance analysis? , 2010, Nature Biotechnology.

[27]  M. W. Anders Putting bioactivation reactions to work: Targeting antioxidants to mitochondria. , 2011, Chemico-biological interactions.

[28]  E. Ruppin,et al.  Computational reconstruction of tissue-specific metabolic models: application to human liver metabolism , 2010, Molecular systems biology.

[29]  A. Sima,et al.  Association of APOA5 and APOC3 gene polymorphisms with plasma apolipoprotein A5 level in patients with metabolic syndrome. , 2010, Biochemical and biophysical research communications.

[30]  C. López-Otín,et al.  Protease degradomics: A new challenge for proteomics , 2002, Nature Reviews Molecular Cell Biology.

[31]  Alicia Oshlack,et al.  Gene Regulation in Primates Evolves under Tissue-Specific Selection Pressures , 2008, PLoS genetics.