Connecting the dots: Applications of network medicine in pharmacology and disease

Clinical Pharmacology and Therapeutics

Volumn 94, Issue 6, 2013, Pages 659-669

  Jacunski, A ^a   Tatonetti, N P ^a  

^a NewYork Presbyterian Hospital   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ATORVASTATIN; B RAF KINASE; BETA ADRENERGIC RECEPTOR BLOCKING AGENT; CYCLOOXYGENASE 2; CYTOCHROME P450 3A4; INSULIN; K RAS PROTEIN; NONSTEROID ANTIINFLAMMATORY AGENT; RITONAVIR; STAT1 PROTEIN; TAMOXIFEN;

ACUTE HEART INFARCTION; ALZHEIMER DISEASE; ARTICLE; BREAST CANCER; CANCER CHEMOTHERAPY; CANCER GROWTH; CANCER PROGNOSIS; CANCER SURVIVAL; CARCINOGENESIS; CEREBROVASCULAR ACCIDENT; CHOLERA; CLINICAL PHARMACOLOGY; COLORECTAL CANCER; DENGUE; DRUG TARGETING; ESTROGEN RECEPTOR POSITIVE BREAST CANCER; FINGER DERMATOGLYPHICS; GENE; GENE EXPRESSION; GENE P53; GENETIC ASSOCIATION; GENOTYPE PHENOTYPE CORRELATION; GLIOBLASTOMA; HAY FEVER; HUMAN; HYPOGLYCEMIC COMA; INFLUENZA; MALARIA; MEDICINE; METASTATIC COLORECTAL CANCER; MYOPATHY; NETWORK MEDICINE; NON INSULIN DEPENDENT DIABETES MELLITUS; OBESITY; OVARY CANCER; PANCREAS ADENOCARCINOMA; PANCREAS CANCER; PERSONALIZED MEDICINE; PRIORITY JOURNAL; PROSTATE CANCER; PROTEIN PROTEIN INTERACTION; RESPIRATORY SYNCYTIAL VIRUS INFECTION; RHABDOMYOLYSIS; RHINOVIRUS INFECTION; SCHIZOPHRENIA; SINGLE NUCLEOTIDE POLYMORPHISM; SURVIVAL RATE; TREATMENT RESPONSE; TRIPLE NEGATIVE BREAST CANCER; VIRUS CELL INTERACTION;

CARDIOVASCULAR DISEASES; COMMUNICABLE DISEASES; DIABETES MELLITUS; DISEASE; HUMANS; INDIVIDUALIZED MEDICINE; METABOLIC NETWORKS AND PATHWAYS; MUTATION; NEOPLASMS; OBESITY; PHARMACOLOGY; PHENOTYPE; SCHIZOPHRENIA; SYSTEMS BIOLOGY;

EID: 84888014252     PISSN: 00099236     EISSN: 15326535     Source Type: Journal    
DOI: 10.1038/clpt.2013.168     Document Type: Article

References (75)

1. Hoyert, D.L. & Xu, J. Deaths: preliminary data for 2011. Nat. Vital Stat. Rep. 61(1), 1-72 (2012).
2. Lazebnik, Y. Can a biologist fix a radio?-Or, what I learned while studying apoptosis. Cancer Cell 2, 179-182 (2002). (Pubitemid 41043973)
3. Nebert, D.W., Zhang, G. & Vesell, E.S. From human genetics and genomics to pharmacogenetics and pharmacogenomics: past lessons, future directions. Drug Metab. Rev. 40, 187-224 (2008). (Pubitemid 352000946)
4. Manolio, T.A. Genomewide association studies and assessment of the risk of disease. N. Engl. J. Med. 363, 166-176 (2010).
5. Hunter, D.J. Gene-environment interactions in human diseases. Nat. Rev. Genet. 6, 287-298 (2005). (Pubitemid 40469048)
6. Barab?i, A.L., Gulbahce, N. & Loscalzo, J. Network medicine: a network-based approach to human disease. Nat. Rev. Genet. 12, 56-68 (2011).
7. Janjic, V. & Pržulj, N. Biological function through network topology: a survey of the human diseasome. Brief. Funct. Genomics 11, 522-532 (2012).
8. Oti, M., Snel, B., Huynen, M.A. & Brunner, H.G. Predicting disease genes using protein-protein interactions. J. Med. Genet. 43, 691-698 (2006). (Pubitemid 44214908)
9. Gandhi, T.K. et al. Analysis of the human protein interactome and comparison with yeast, worm and fly interaction datasets. Nat. Genet. 38, 285-293 (2006).
10. Albert, R. Scale-free networks in cell biology. J. Cell. Sci. 118, 4947-4957 (2005). (Pubitemid 41672427)
11. Barab?i, A.L. & Oltvai, Z.N. Network biology: understanding the cell's functional organization. Nat. Rev. Genet. 5, 101-113 (2004). (Pubitemid 38160277)
12. Goh, K.I., Cusick, M.E., Valle, D., Childs, B., Vidal, M. & Barab?i, A.L. The human disease network. Proc. Natl. Acad. Sci. USA 104, 8685-8690 (2007). (Pubitemid 47175372)
13. Minguez, P. & Dopazo, J. Assessing the biological significance of gene expression signatures and co-expression modules by studying their network properties. PLoS ONE 6, e17474 (2011).
14. Paladugu, S.R., Zhao, S., Ray, A. & Raval, A. Mining protein networks for synthetic genetic interactions. BMC Bioinformatics 9, 426 (2008).
15. Ozier-Kalogeropoulos, O., Adeline, M.T., Yang, W.L., Carman, G.M. & Lacroute, F. Use of synthetic lethal mutants to clone and characterize a novel CTP synthetase gene in Saccharomyces cerevisiae. Mol. Gen. Genet. 242, 431-439 (1994). (Pubitemid 24097297)
16. Draghici, S., Khatri, P., Martins, R.P., Ostermeier, G.C. & Krawetz, S.A. Global functional profiling of gene expression. Genomics 81, 98-104 (2003). (Pubitemid 36298600)
17. Dutkowski, J. et al. A gene ontology inferred from molecular networks. Nat. Biotechnol. 31, 38-45 (2013).
18. Goh, K.I. & Choi, I.G. Exploring the human diseasome: the human disease network. Brief. Funct. Genomics 11, 533-542 (2012).
19. Barren?, F. et al. Highly interconnected genes in disease-specific networks are enriched for disease-associated polymorphisms. Genome Biol. 13, R46 (2012).
20. Zhu, J., Qin, Y., Liu, T., Wang, J. & Zheng, X. Prioritization of candidate disease genes by topological similarity between disease and protein diffusion profiles. BMC Bioinformatics 14(suppl. 5), S5 (2013).
21. American Cancer Society. Cancer Facts & Figures 2013. (American Cancer Society, Atlanta, GA, 2013).
22. Rosenblatt-Rosen et al. Interpreting cancer genomes using systematic host peturbations by tumour virus proteins. Nature 487, 491-495 (2012).
23. Adzhubei, L.A. A method and server for predicting damaging missense mutations. Nat. Methods 7, 248-249 (2010).
24. Ciriello, G., Cerami, E., Sander, C. & Schultz, N. Mutual exclusivity analysis identifies oncogenic network modules. Genome Res. 22, 398-406 (2012).
25. Howlader, N. et al. SEER Cancer Statistics Review, 1975-2010. (National Cancer Institute, Bethesda, MD, 2013).
26. The Cancer Genome Atlas Network. Comprehensive molecular portraits of breast cancer. Nature 490, 61-70 (2012).
27. van't Veer, L.J. et al. Gene expression profiling predicted clinical outcome of breast cancer. Nature 415, 530-536 (2002).
28. Venet, D., Dumont, J.E. & Detours, V. Most random gene expression signatures are significantly associated with breast cancer outcome. PLoS Comput. Biol. 7, e1002240 (2011).
29. Roy, J., Winter, C., Isik, Z. & Schroeder, M. Network information improves cancer outcome prediction. Brief Bioinform. (2012); e-pub ahead of print 18 December 2012.
30. Winter, C. et al. Google goes cancer: improving outcome prediction for cancer patients by network-based ranking of marker genes. PLoS Comput. Biol. 8, e1002511 (2012).
31. Chen, L., Xuan, J., Riggins, R.B., Wang, Y. & Clarke, R. Identifying protein interaction subnetworks by a bagging Markov random field-based method. Nucleic Acids Res. 41, e42 (2013).
32. Wei, Z. & Li, H. A Markov random field model for network-based analysis of genomic data. Bioinformatics 23, 1537-1544 (2007). (Pubitemid 47244479)
33. Li, C. et al. Subpathway-GM: identification of metabolic subpathways via joint power of interesting genes and metabolites and their topologies within pathways. Nucleic Acids Res. 41, e101 (2013).
34. Go, A.S. et al.; American Heart Association Statistics Committee and Stroke Statistics Subcommittee. Heart disease and stroke statistics-2013 update: a report from the American Heart Association. Circulation 127, e6-e245 (2013).
35. Azuaje, F., Zhang, L., Jeanty, C., Puhl, S.L., Rodius, S. & Wagner, D.R. Analysis of a gene co-expression network establishes robust association between Col5a2 and ischemic heart disease. BMC Med. Genomics 6, 13 (2013).
36. Breymaier, S. AMA Adopts New Policies on Second Day of Voting at Annual Meeting [press release]. (American Medical Association, Chicago, IL, 2013).
37. Ogden, C.L., Carroll, M.D., Kit, B.K. & Flegal, K.M. Prevalence of obesity in the United States 2009-2010. NCHS data brief, no. 92. (National Cancer for Health Statistics, Hyattsville, MD, 2012).
38. Hammond, R.A. & Levine, R. The economic impact of obesity in the United States. Diabetes. Metab. Syndr. Obes. 3, 285-295 (2010).
39. Seltzer, C.C. & Mayer, J. A review of genetic and constitutional factors in human obesity. Ann. NY Acad. Sci. 134, 688-695 (1966).
40. Mardinoglu, A. et al. Integration of clinical data with a genome-scale metabolic model of the human adipocyte. Mol. Syst. Biol. 9, 649 (2013).
41. Schultz, S.H., North, S.W. & Shields, C.G. Schizophrenia: a review. Am. Fam. Physician 75, 1821-1829 (2007). (Pubitemid 46954070)
42. Jia, P., Wang, L., Fanous, A.H., Pato, C.N., Edwards, T.L. & Zhao, Z.; International Schizophrenia Consortium. Network-assisted investigation of combined causal signals from genome-wide association studies in schizophrenia. PLoS Comput. Biol. 8, e1002587 (2012).
43. Li, X., Xia, S., Bertisch, H.C., Branch, C.A. & Delisi, L.E. Unique topology of language processing brain network: a systems-level biomarker of schizophrenia. Schizophr. Res. 141, 128-136 (2012).
44. Spellberg, B. & Taylor-Blake, B. On the exoneration of Dr. William H. Stewart: debunking an urban legend. Infect. Dis. Poverty 2, 3 (2013).
45. Navratil, V., de Chassey, B., Combe, C.R. & Lotteau, V. When the human viral infectome and diseasome networks collide: towards a systems biology platform for the aetiology of human diseases. BMC Syst. Biol. 5, 13 (2011).
46. Gulbahce, N. et al. Viral perturbations of host networks reflect disease etiology. PLoS Comput. Biol. 8, e1002531 (2012).
47. Hopkins, A.L. Network pharmacology: the next paradigm in drug discovery. Nat. Chem. Biol. 4, 682-690 (2008).
48. Huang, R. et al. The NCGC pharmaceutical collection: a comprehensive resource of clinically approved drugs enabling repurposing and chemical genomics. Sci. Transl. Med. 3, 80ps16 (2011).
49. Russ, A.P. & Lampel, S. The druggable genome: an update. Drug Discov. Today 10, 1607-1610 (2005). (Pubitemid 41817609)
50. Boran, A.D. & Iyengar, R. Systems pharmacology. Mt. Sinai J. Med. 77, 333-344 (2010).
51. Chen, X. et al. Network characteristic analysis of ADR-related proteins and identification of ADR-ADR associations. Sci. Rep. 3, 1744 (2013).
52. Cami, A., Arnold, A., Manzi, S. & Reis, B. Predicting adverse drug events using pharmacological network models. Sci. Transl. Med. 3, 114ra127 (2011).
53. Huang, L.C., Wu, X. & Chen, J.Y. Predicting adverse drug reaction profiles by integrating protein interaction networks with drug structures. Proteomics 13, 313-324 (2013).
54. Duran-Frigola, M., Mosca, R. & Aloy, P. Structural systems pharmacology: the role of 3D structures in next-generation drug development. Chem. Biol. 20, 674-684 (2013).
55. Tatonetti, N.P., Fernald, G.H. & Altman, R.B. A novel signal detection algorithm for identifying hidden drug-drug interactions in adverse event reports. J. Am. Med. Inform. Assoc. 19, 79-85 (2012).
56. Gu, Q., Dillon, C.F. & Burt, V.L. Prescription drug use continues to increase: U.S. prescription drug data for 2007-2008. NCHS Data Brief. 42, 1-8 (2010).
57. Neuvonen, P.J., Niemi, M. & Backman, J.T. Drug interactions with lipid-lowering drugs: mechanisms and clinical relevance. Clin. Pharmacol. Ther. 80, 565-581 (2006). (Pubitemid 44908269)
58. Lee, T.H. Ask the doctor. Is it okay to take a beta blocker if I have diabetes and heart failure? Harv. Heart Lett. 14, 8 (2003).
59. Cami, A., Manzi, S., Arnold, A. & Reis, B.Y. Pharmacointeraction network models predict unknown drug-drug interactions. PLoS ONE 8, e61468 (2013).
60. Xie, L., Xie, L., Kinnings, S.L. & Bourne, P.E. Novel computational approaches to polypharmacology as a means to define responses to individual drugs. Annu. Rev. Pharmacol. Toxicol. 52, 361-379 (2012).
61. Emig, D. et al. Drug target prediction and repositioning using an integrated network-based approach. PLoS ONE 8, e60618 (2013).
62. Chen, H. & Zhang, Z. A semi-supervised method for drug-target interaction prediction with consistency in networks. PloS One 8(5), 62975 (2013).
63. Gonzalez de Castro, D., Clarke, P.A., Al-Lazikani, B., Workman, P. Personalized cancer medicine: molecular diagnostics, predictive biomarkers, and drug resistance. Nature 93, 252-259 (2013).
64. Pradhan, M.P., Nagulapalli, K. & Palakal, M.J. Cliques for the identification of gene signatures for colorectal cancer across population. BMC Syst. Biol. 6 (suppl. 3), S17 (2012).
65. Kanehisa, M., Goto, S., Kawashima, S., Okuno, Y. & Hattori, M. The KEGG resource for deciphering the genome. Nucleic Acids Res. 32, D277-D280 (2004). (Pubitemid 38081656)
66. Chen, R. et al. Personal omics profiling reveals dynamic molecular and medical phenotypes. Cell 148, 1293-1307 (2012).
67. Maere, S., Heymans, K. & Kuiper, M. BiNGO: a Cytoscape plugin to assess overrepresentation of gene ontology categories in biological networks. Bioinformatics 21, 3448-3449 (2005). (Pubitemid 41222459)
68. Jeong, H., Mason, S.P., Barab?i, A.L. & Oltvai, Z.N. Lethality and centrality in protein networks. Nature 411, 41-42 (2001). (Pubitemid 32428180)
69. Coulomb, S., Bauer, M., Bernard, D. & Marsolier-Kergoat, M.C. Gene essentiality and the topology of protein interaction networks. Proc. Biol. Sci. 272, 1721-1725 (2005). (Pubitemid 41414681)
70. The UniProt Consortium. Update on activities at the Universal Protein Resource (UniProt) in 2013. Nucl. Acids Res. 41, D43-D47 (2013).
71. Yu, H. et al. Annotation transfer between genomes: protein-protein interologs and protein-DNA regulogs. Genome Res. 14, 1107-1118 (2004). (Pubitemid 38811547)
72. Jansson, L.B. & Parnas, J. Competing definitions of schizophrenia: what can be learned from polydiagnostic studies? Schizophr. Bull. 33, 1178-1200 (2007). (Pubitemid 47351232)
73. Orth, J.D., Thiele, I. & Palsson, B.?. What is flux balance analysis? Nat. Biotechnol. 28, 245-248 (2010).
74. Hucka, M. et al.; SBML Forum. The systems biology markup language (SBML): a medium for representation and exchange of biochemical network models. Bioinformatics 19, 524-531 (2003). (Pubitemid 36336519)
75. Hucka, M. et al. Evolving a lingua franca and associated software infrastructure for computational systems biology: the Systems Biology Markup Language (SBML) project. Syst. Biol. (Stevenage). 1, 41-53 (2004).