Network-based association of hypoxia-responsive genes with cardiovascular diseases

New Journal of Physics

Volumn 16, Issue , 2014, Pages

  Wang, Rui Sheng ^a   Oldham, William M ^a   Loscalzo, Joseph ^a  

^a HARVARD MEDICAL SCHOOL   (United States)

Author keywords

cardiovascular disease; human interactome; hypoxia

Indexed keywords

CARDIOLOGY; GENES; MOLECULAR OXYGEN; OXYGEN; OXYGEN SUPPLY; PROTEINS;

CARDIO-VASCULAR DISEASE; CELLULAR METABOLISM; CHRONIC LUNG DISEASE; HUMAN PROTEIN INTERACTOME; HYPOXIA; INTERACTOME; MYOCARDIAL ISCHEMIA; REGULATORY PATHWAY;

DISEASES;

EID: 84910157029     PISSN: 13672630     EISSN: None     Source Type: Journal    
DOI: 10.1088/1367-2630/16/10/105014     Document Type: Article

References (60)

1. Semenza G L 2011 Oxygen sensing, homeostasis, and disease N. Engl. J. Med. 365 537-47
2. Semenza G L 2014 Hypoxia-inducible factor 1 and cardiovascular disease Annu. Rev. Physiol. 76 39-56
3. Simon M C et al 2008 Hypoxia-induced signaling in the cardiovascular system Annu. Rev. Physiol. 70 51-71
4. Giordano F J 2005 Oxygen, oxidative stress, hypoxia, and heart failure J. Clin. Invest. 115 500-8
5. Huang Y et al 2004 Cardiac myocyte-specific HIF-1alpha deletion alters vascularization, energy availability, calcium flux, and contractility in the normoxic heart FASEB J. 18 1138-40
6. Semenza G L 2014 Oxygen sensing, hypoxia-inducible factors, and disease pathophysiology Annu. Rev. Pathol. 9 47-71
7. MacLellan W R, Wang Y and Lusis A J 2012 Systems-based approaches to cardiovascular disease Nat. Rev. Cardiol. 9 172-84
8. Lusis A J and Weiss J N 2010 Cardiovascular networks: systems-based approaches to cardiovascular disease Circulation 121 157-70
9. Ramsey S A, Gold E S and Aderem A 2010 A systems biology approach to understanding atherosclerosis EMBO Mol. Med. 2 79-89
10. Vidal M, Cusick M E and Barabasi A L 2011 Interactome networks and human disease Cell 144 986-98
11. Meng Q et al 2013 Systems biology approaches and applications in obesity, diabetes, and cardiovascular diseases Curr. Cardiovasc. Risk Rep. 7 73-83
12. Wang J et al 2009 Disease-aging network reveals significant roles of aging genes in connecting genetic diseases PLoS Comput. Biol. 5 e1000521
13. Sarajlic A et al 2013 Network topology reveals key cardiovascular disease genes PLoS One 8 e71537
14. Dewey F E et al 2011 Gene coexpression network topology of cardiac development, hypertrophy, and failure Circ. Cardiovasc. Genet. 4 26-35
15. Jensen M K et al 2011 Protein interaction-based genome-wide analysis of incident coronary heart disease Circ. Cardiovasc. Genet. 4 549-56
16. Huan T et al 2013 A systems biology framework identifies molecular underpinnings of coronary heart disease Arterioscler. Thromb. Vasc. Biol. 33 1427-34
17. Mayr M 2011 Recent highlights of metabolomics in cardiovascular research Circ. Cardiovasc. Genet. 4 463-4
18. Barabasi A L, Gulbahce N and Loscalzo J 2011 Network medicine: a network-based approach to human disease Nat. Rev. Genet. 12 56-68
19. Smyth G K 2005 Limma: linear models for microarray data Bioinformatics and Computational Biology Solutions Using R and Bioconductor ed R Gentleman et al (New York: Springer) 397-420
20. Manalo D J et al 2005 Transcriptional regulation of vascular endothelial cell responses to hypoxia by HIF-1 Blood 105 659-69
21. Schriml L M et al 2012 Disease ontology: a backbone for disease semantic integration Nucleic Acids Res. 40 D940-6 (Database issue)
22. Yu W et al 2008 A navigator for human genome epidemiology Nat. Genet. 40 124-5
23. Du P et al 2009 From disease ontology to disease-ontology lite: statistical methods to adapt a general-purpose ontology for the test of gene-ontology associations Bioinformatics 25 i63-8
24. Stelzl U et al 2005 A human protein-protein interaction network: a resource for annotating the proteome Cell 122 957-68
25. Yu H et al 2011 Next-generation sequencing to generate interactome datasets Nat. Methods 8 478-80
26. Venkatesan K et al 2009 An empirical framework for binary interactome mapping Nat. Methods. 6 83-90
27. Rual J F et al 2005 Towards a proteome-scale map of the human protein-protein interaction network Nature 437 1173-8
28. Aranda B et al 2010 The IntAct molecular interaction database in Nucleic Acids Res. 38 D525-31 2010 (Database issue)
29. Ceol A et al 2009 MINT, the molecular interaction database: 2009 update Nucleic Acids Res. 38 D532-9 2010 (Database issue)
30. Keshava Prasad T S et al 2009 Human protein reference database: 2009 update Nucleic Acids Res. 37 D767-72 (Database issue)
31. Stark C et al 2011 The BioGRID Interaction Database: 2011 update Nucleic Acids Res. 39 D698-704 (Database issue)
32. Ruepp A et al 2010 CORUM: the comprehensive resource of mammalian protein complexes - 2009 Nucleic Acids Res. 38 D497-501 (Database issue)
33. Havugimana P C et al 2012 A census of human soluble protein complexes Cell 150 1068-81
34. Matys V et al 2003 TRANSFAC: transcriptional regulation, from patterns to profiles Nucleic Acids Res. 31 374-8
35. Hornbeck P V et al 2012 PhosphoSitePlus: a comprehensive resource for investigating the structure and function of experimentally determined post-translational modifications in man and mouse Nucleic Acids Res. 40 D261-70 (Database issue)
36. Lee D S et al 2008 The implications of human metabolic network topology for disease comorbidity Proc. Natl Acad. Sci. USA 105 9880-5
37. Kanehisa M et al 2004 The KEGG resource for deciphering the genome Nucleic Acids Res. 32 D277-80 (Database issue)
38. Schellenberger J et al 2010 BiGG: a Biochemical Genetic and Genomic knowledgebase of large scale metabolic reconstructions BMC Bioinformatics 11 213
39. Zhang Q C et al 2012 Structure-based prediction of protein-protein interactions on a genome-wide scale Nature 490 556-60
40. Vinayagam A et al 2011 A directed protein interaction network for investigating intracellular signal transduction Sci. Signal 4 rs8
41. Hagberg A A, Schult D A and Swart P J 2008 Exploring network structure, dynamics, and function using NetworkX Proc. of the 7th Python in Science Conference (SciPy2008) ed G Varoquaux, T Vaught and J Millman 11-15 (Pasadena, CA, USA)
42. Freeman L C 1978 Centrality in social networks: conceptual clarification Social Netw. 1 215-39
43. Freeman L C 1977 A set of measures of centrality based on betweenness Sociometry 40 35-41
44. Barber M J 2007 Modularity and community detection in bipartite networks Phys. Rev. E 76 066102
45. Guimera R and Nunes Amaral L A 2005 Functional cartography of complex metabolic networks Nature 433 895-900
46. Blondel V D et al 2008 Fast unfolding of communities in large networks J. Stat. Mech. 10 P10008
47. Su A I et al 2004 A gene atlas of the mouse and human protein-encoding transcriptomes Proc. Natl Acad. Sci. USA 101 6062-7
48. Valente T W et al 2008 How correlated are network centrality measures? Connect (Tor) 28 16-26
49. Kamburov A et al 2013 The ConsensusPathDB interaction database: 2013 update Nucleic Acids Res. 41 D793-800 (Database issue)
50. Subramanian A et al 2005 Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles Proc. Natl Acad. Sci. USA 102 15545-50
51. Bashan A et al 2012 Network physiology reveals relations between network topology and physiological function Nat. Commun. 3 702
52. Jeong H et al 2001 Lethality and centrality in protein networks Nature 411 41-2
53. Spirin V and Mirny L A 2003 Protein complexes and functional modules in molecular networks Proc. Natl Acad. Sci. USA 100 12123-8
54. Ruths T, Ruths D and Nakhleh L 2009 GS2: an efficiently computable measure of GO-based similarity of gene sets Bioinformatics 25 1178-84
55. Sevilla J L et al 2005 Correlation between gene expression and GO semantic similarity IEEE/ACM Trans. Comput. Biol. Bioinform. 2 330-8
56. Wang J Z et al 2007 A new method to measure the semantic similarity of GO terms Bioinformatics 23 1274-81
57. Crawford D C et al 2008 A common VLDLR polymorphism interacts with APOE genotype in the prediction of carotid artery disease risk J. Lipid Res. 49 588-96
58. Florez J C et al 2006 High-density haplotype structure and association testing of the insulin-degrading enzyme (IDE) gene with type 2 diabetes in 4206 people Diabetes 55 128-35
59. Lonn J et al 2011 Hepatocyte growth factor in patients with coronary artery disease and its relation to periodontal condition. Results Immunol. 2 7-12
60. Madonna R and De Caterina R 2009 VEGF receptor switching in heart development and disease Cardiovasc. Res. 84 4-6