Integrating multi-source information on a single network to detect disease-related clusters of molecular mechanisms

Journal of Proteomics

Volumn 188, Issue , 2018, Pages 15-29

  Zachariou, Margarita ^a   Minadakis, George ^a   Oulas, Anastasis ^a   Afxenti, Sotiroula ^a   Spyrou, George M ^a  

^a CYPRUS INSTITUTE OF NEUROLOGY AND GENETICS   (Cyprus)

Author keywords

Alzheimer's disease; Multi source integration; Network science; Predictive; Preventive and personalised medicine

Indexed keywords

ALZHEIMER DISEASE; ARTICLE; COPY NUMBER VARIATION; DATA ANALYSIS; INFORMATION PROCESSING; PERSONALIZED MEDICINE; PREVENTIVE MEDICINE; PRIORITY JOURNAL; PROTEIN PROTEIN INTERACTION; SINGLE NUCLEOTIDE POLYMORPHISM; CLUSTER ANALYSIS; HUMAN; INFORMATION SERVICE; PROCEDURES;

ALZHEIMER DISEASE; CLUSTER ANALYSIS; DATA AGGREGATION; HUMANS; INFORMATION SERVICES; PRECISION MEDICINE;

EID: 85044120386     PISSN: 18743919     EISSN: 18767737     Source Type: Journal    
DOI: 10.1016/j.jprot.2018.03.009     Document Type: Article

References (49)

1. Barabasi, A.L., Gulbahce, N., Loscalzo, J., Network medicine: a network-based approach to human disease. Nat. Rev. Genet. 12:1 (2011), 56–68.
2. Oulas, A., Minadakis, G., Zachariou, M., Sokratous, K., Bourdakou, M.M., Spyrou, G.M., Systems Bioinformatics: increasing precision of computational diagnostics and therapeutics through network-based approaches. Brief. Bioinform., 2017, 10.1093/bib/bbx151/4662946.
3. Emmert-Streib, F., Dehmer, M., Networks for systems biology: conceptual connection of data and function. IET Syst. Biol. 5:3 (2011), 185–207.
4. Najafi, A., Bidkhori, G., Bozorgmehr, J.H., Koch, I., Masoudi-Nejad, A., Genome scale modeling in systems biology: algorithms and resources. Curr. Genomics 15:2 (2014), 130–159.
5. Vidal, M., Cusick, M.E., Barabasi, A.L., Interactome networks and human disease. Cell 144:6 (2011), 986–998.
6. Ma, X., Liu, Z., Zhang, Z., Huang, X., Tang, W., Multiple network algorithm for epigenetic modules via the integration of genome-wide DNA methylation and gene expression data. BMC Bioinformatics, 18(1), 2017, 72.
7. Siavelis, J.C., Bourdakou, M.M., Athanasiadis, E.I., Spyrou, G.M., Nikita, K.S., Bioinformatics methods in drug repurposing for Alzheimer's disease. Brief. Bioinform. 17:2 (2016), 322–335.
8. Gligorijevic, V., Przulj, N., Methods for biological data integration: perspectives and challenges. J. R. Soc. Interface, 12(112), 2015.
9. Huang, S., Chaudhary, K., Garmire, L.X., More is better: recent progress in multi-omics data integration methods. Front. Genet., 8, 2017, 84.
10. Golubnitschaja, O., Baban, B., Boniolo, G., Wang, W., Bubnov, R., Kapalla, M., Krapfenbauer, K., Mozaffari, M.S., Costigliola, V., Medicine in the early twenty-first century: paradigm and anticipation - EPMA position paper 2016. EPMA J., 7, 2016, 23.
11. Amare, A.T., Schubert, K.O., Baune, B.T., Pharmacogenomics in the treatment of mood disorders: strategies and opportunities for personalized psychiatry. EPMA J. 8:3 (2017), 211–227.
12. Cheng, T., Zhan, X., Pattern recognition for predictive, preventive, and personalized medicine in cancer. EPMA J. 8:1 (2017), 51–60.
13. Lazarenko, L.M., Nikitina, O.E., Nikitin, E.V., Demchenko, O.M., Kovtonyuk, G.V., Ganova, L.O., Bubnov, R.V., Shevchuk, V.O., Nastradina, N.M., Bila, V.V., Spivak, M.Y., Development of biomarker panel to predict, prevent and create treatments tailored to the persons with human papillomavirus-induced cervical precancerous lesions. EPMA J., 5(1), 2014, 1.
14. Berliner, L., Lemke, H.U., The digital patient model and model guided therapy. Berliner, L., Lemke, H.U., (eds.) An Information Technology Framework for Predictive, Preventive and Personalised Medicine: A Use-Case with Hepatocellular Carcinoma, 2015, Springer International Publishing, Cham, 9–19.
15. Pimplikar, S.W., Multi-omics and Alzheimer's disease: a slower but surer path to an efficacious therapy?. Am. J. Physiol. Cell Physiol. 313:1 (2017), C1–C2.
16. Rappaport, N., Twik, M., Plaschkes, I., Nudel, R., Iny Stein, T., Levitt, J., Gershoni, M., Morrey, C.P., Safran, M., Lancet, D., MalaCards: an amalgamated human disease compendium with diverse clinical and genetic annotation and structured search. Nucleic Acids Res. 45:D1 (2017), D877–D887.
17. Wishart, D.S., Feunang, Y.D., Guo, A.C., Lo, E.J., Marcu, A., Grant, J.R., Sajed, T., Johnson, D., Li, C., Sayeeda, Z., Assempour, N., Iynkkaran, I., Liu, Y., Maciejewski, A., Gale, N., Wilson, A., Chin, L., Cummings, R., Le, D., Pon, A., Knox, C., Wilson, M., DrugBank 5.0: a major update to the DrugBank database for 2018. Nucleic Acids Res. 46:D1 (2018), D1074–D1082.
18. Chou, C.H., Shrestha, S., Yang, C.D., Chang, N.W., Lin, Y.L., Liao, K.W., Huang, W.C., Sun, T.H., Tu, S.J., Lee, W.H., Chiew, M.Y., Tai, C.S., Wei, T.Y., Tsai, T.R., Huang, H.T., Wang, C.Y., Wu, H.Y., Ho, S.Y., Chen, P.R., Chuang, C.H., Hsieh, P.J., Wu, Y.S., Chen, W.L., Li, M.J., Wu, Y.C., Huang, X.Y., Ng, F.L., Buddhakosai, W., Huang, P.C., Lan, K.C., Huang, C.Y., Weng, S.L., Cheng, Y.N., Liang, C., Hsu, W.L., Huang, H.D., miRTarBase update 2018: a resource for experimentally validated microRNA-target interactions. Nucleic Acids Res. 46:D1 (2018), D296–D302.
19. Griffiths-Jones, S., Saini, H.K., van Dongen, S., Enright, A.J., miRBase: tools for microRNA genomics. Nucleic Acids Res. 36:Database issue (2008), D154–8.
20. Lewis, B.P., Burge, C.B., Bartel, D.P., Conserved seed pairing, often flanked by adenosines, indicates that thousands of human genes are microRNA targets. Cell 120:1 (2005), 15–20.
21. Jiang, Q., Wang, Y., Hao, Y., Juan, L., Teng, M., Zhang, X., Li, M., Wang, G., Liu, Y., miR2Disease: a manually curated database for microRNA deregulation in human disease. Nucleic Acids Res. 37:Database (2009), D98–104.
22. Warde-Farley, D., Donaldson, S.L., Comes, O., Zuberi, K., Badrawi, R., Chao, P., Franz, M., Grouios, C., Kazi, F., Lopes, C.T., Maitland, A., Mostafavi, S., Montojo, J., Shao, Q., Wright, G., Bader, G.D., Morris, Q., The GeneMANIA prediction server: biological network integration for gene prioritization and predicting gene function. Nucleic Acids Res. 38:Web Server issue (2010), W214–20.
23. Stark, C., Breitkreutz, B.-J., Reguly, T., Boucher, L., Breitkreutz, A., Tyers, M., BioGRID: a general repository for interaction datasets. Nucleic Acids Res. 34:suppl_1 (2006), D535–D539.
24. Cerami, E.G., Gross, B.E., Demir, E., Rodchenkov, I., Babur, O., Anwar, N., Schultz, N., Bader, G.D., Sander, C., Pathway commons, a web resource for biological pathway data. Nucleic Acids Res. 39:Database (2011), D685–90.
25. Shannon, P., Markiel, A., Ozier, O., Baliga, N.S., Wang, J.T., Ramage, D., Amin, N., Schwikowski, B., Ideker, T., Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 13:11 (2003), 2498–2504.
26. Csardi, G., Nepusz, T., The igraph software package for complex network research. InterJournal Complex Systems, 2006, 1695.
27. Kuleshov, M.V., Jones, M.R., Rouillard, A.D., Fernandez, N.F., Duan, Q., Wang, Z., Koplev, S., Jenkins, S.L., Jagodnik, K.M., Lachmann, A., McDermott, M.G., Monteiro, C.D., Gundersen, G.W., Ma'ayan, A., Enrichr: a comprehensive gene set enrichment analysis web server 2016 update. Nucleic Acids Res. 44:W1 (2016), W90–7.
28. Kanehisa, M., Goto, S., KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 28:1 (2000), 27–30.
29. Kanehisa, M., Furumichi, M., Tanabe, M., Sato, Y., Morishima, K., KEGG: new perspectives on genomes, pathways, diseases and drugs. Nucleic Acids Res. 45:D1 (2017), D353–D361.
30. Kanehisa, M., Sato, Y., Kawashima, M., Furumichi, M., Tanabe, M., KEGG as a reference resource for gene and protein annotation. Nucleic Acids Res. 44:D1 (2016), D457–62.
31. Newman, M.E., Girvan, M., Finding and evaluating community structure in networks. Phys. Rev. E Stat. Nonlinear Soft Matter Phys., 69(2 Pt 2), 2004, 026113.
32. Santiago, J.A., Bottero, V., Potashkin, J.A., Dissecting the molecular mechanisms of neurodegenerative diseases through network biology. Front. Aging Neurosci., 9, 2017, 166.
33. Van Cauwenberghe, C., Van Broeckhoven, C., Sleegers, K., The genetic landscape of Alzheimer disease: clinical implications and perspectives. Genet. Med. 18:5 (2016), 421–430.
34. Chen, X.Q., Sawa, M., Mobley, W.C., Dysregulation of neurotrophin signaling in the pathogenesis of Alzheimer disease and of Alzheimer disease in Down syndrome. Free Radic. Biol. Med. 114 (2018), 52–61.
35. Johnson, G.V., Bailey, C.D., The p38 MAP kinase signaling pathway in Alzheimer's disease. Exp. Neurol. 183:2 (2003), 263–268.
36. Zhu, X., Lee, H.G., Raina, A.K., Perry, G., Smith, M.A., The role of mitogen-activated protein kinase pathways in Alzheimer's disease. Neurosignals 11:5 (2002), 270–281.
37. Wang, L., Chadwick, W., Park, S.S., Zhou, Y., Silver, N., Martin, B., Maudsley, S., Gonadotropin-releasing hormone receptor system: modulatory role in aging and neurodegeneration. CNS Neurol. Disord. Drug Targets 9:5 (2010), 651–660.
38. Rubio-Perez, J.M., Morillas-Ruiz, J.M., A review: inflammatory process in Alzheimer's disease, role of cytokines. TheScientificWorldJOURNAL, 2012, 2012, 756357.
39. Dolan, H., Crain, B., Troncoso, J., Resnick, S.M., Zonderman, A.B., Obrien, R.J., Atherosclerosis, dementia, and Alzheimer disease in the Baltimore longitudinal study of aging cohort. Ann. Neurol. 68:2 (2010), 231–240.
40. Martinez, M., Fernandez, E., Frank, A., Guaza, C., de la Fuente, M., Hernanz, A., Increased cerebrospinal fluid cAMP levels in Alzheimer's disease. Brain Res. 846:2 (1999), 265–267.
41. Li, X., Long, J., He, T., Belshaw, R., Scott, J., Integrated genomic approaches identify major pathways and upstream regulators in late onset Alzheimer's disease. Sci. Rep., 5, 2015, 12393.
42. Woods, N.K., Padmanabhan, J., Neuronal calcium signaling and Alzheimer's disease. Adv. Exp. Med. Biol. 740 (2012), 1193–1217.
43. Supnet, C., Bezprozvanny, I., Neuronal calcium signaling, mitochondrial dysfunction, and Alzheimer's disease. J. Alzheimer's Dis. 20:Suppl. 2 (2010), S487–98.
44. Mattson, M.P., Chan, S.L., Neuronal and glial calcium signaling in Alzheimer's disease. Cell Calcium 34:4–5 (2003), 385–397.
45. Soler-Lopez, M., Badiola, N., Zanzoni, A., Aloy, P., Towards Alzheimer's root cause: ECSIT as an integrating hub between oxidative stress, inflammation and mitochondrial dysfunction. Hypothetical role of the adapter protein ECSIT in familial and sporadic Alzheimer's disease pathogenesis. BioEssays 34:7 (2012), 532–541.
46. Shimohama, S., Apoptosis in Alzheimer's disease–an update. Apoptosis 5:1 (2000), 9–16.
47. Hu, L., Wang, B., Zhang, Y., Serotonin 5-HT6 receptors affect cognition in a mouse model of Alzheimer's disease by regulating cilia function. Alzheimers Res. Ther., 9(1), 2017, 76.
48. McAlpine, F.E., Tansey, M.G., Neuroinflammation and tumor necrosis factor signaling in the pathophysiology of Alzheimer's disease. J. Inflamm. Res. 1 (2008), 29–39.
49. Villalba, R.M., Smith, Y., Differential striatal spine pathology in Parkinson's disease and cocaine addiction: a key role of dopamine?. Neuroscience 251 (2013), 2–20.