Translational informatics approach for identifying the functional molecular communicators linking coronary artery disease, infection and inflammation

Sharma,Ankit; Ghatge,Madankumar; Mundkur,Lakshmi; Vangala,Rajani Kanth

doi:10.3892/mmr.2016.5013

Translational informatics approach for identifying the functional molecular communicators linking coronary artery disease, infection and inflammation

Authors:
- Ankit Sharma
- Madankumar Ghatge
- Lakshmi Mundkur
- Rajani Kanth Vangala
View Affiliations

Affiliations: Proteomics and Coagulation Unit, Thrombosis Research Institute, Bangalore, Karnataka 560099, India, Molecular Immunology Unit, Thrombosis Research Institute, Bangalore, Karnataka 560099, India
Published online on: March 18, 2016 https://doi.org/10.3892/mmr.2016.5013
Pages: 3904-3912
Copyright: © Sharma et al. This is an open access article distributed under the terms of Creative Commons Attribution License.

Metrics: Total Views: 0 (Spandidos Publications: | PMC Statistics: )

Metrics: Total PDF Downloads: 0 (Spandidos Publications: | PMC Statistics: )

Cited By (CrossRef): 0 citations Loading Articles...

Abstract

Translational informatics approaches are required for the integration of diverse and accumulating data to enable the administration of effective translational medicine specifically in complex diseases such as coronary artery disease (CAD). In the current study, a novel approach for elucidating the association between infection, inflammation and CAD was used. Genes for CAD were collected from the CAD‑gene database and those for infection and inflammation were collected from the UniProt database. The cytomegalovirus (CMV)‑induced genes were identified from the literature and the CAD‑associated clinical phenotypes were obtained from the Unified Medical Language System. A total of 55 gene ontologies (GO) termed functional communicator ontologies were identified in the gene sets linking clinical phenotypes in the diseasome network. The network topology analysis suggested that important functions including viral entry, cell adhesion, apoptosis, inflammatory and immune responses networked with clinical phenotypes. Microarray data was extracted from the Gene Expression Omnibus (dataset: GSE48060) for highly networked disease myocardial infarction. Further analysis of differentially expressed genes and their GO terms suggested that CMV infection may trigger a xenobiotic response, oxidative stress, inflammation and immune modulation. Notably, the current study identified γ‑glutamyl transferase (GGT)‑5 as a potential biomarker with an odds ratio of 1.947, which increased to 2.561 following the addition of CMV and CMV‑neutralizing antibody (CMV‑NA) titers. The C‑statistics increased from 0.530 for conventional risk factors (CRFs) to 0.711 for GGT in combination with the above mentioned infections and CRFs. Therefore, the translational informatics approach used in the current study identified a potential molecular mechanism for CMV infection in CAD, and a potential biomarker for risk prediction.

View Figures

View References

1	Cho DY, Kim YA and Przytycka TM: Chapter 5: Network biology approach to complex diseases. PLOS Comput Biol. 8:e10028202012. View Article : Google Scholar
2	Loscallzo J, Kohanne I and Barabasi AL: Human disease classification in the post genomic era: A complex systems approach to human pathobiology. Mol Sys Biol. 3:1242007.
3	Epstein SE, Zhu J, Najafi AH and Burnett MS: Insights into the role of infection in atherogenesis and in plaque rupture. Circulation. 119:3133–3141. 2009. View Article : Google Scholar : PubMed/NCBI
4	Epstein SE, Zhou YF and Zhu J: Infection and atherosclerosis: Emerging mechanistic paradigms. Circulation. 100:e20–e28. 1999. View Article : Google Scholar : PubMed/NCBI
5	Rosenfield ME and Campbell LA: Pathogenesis and atherosclerosis: Update on potential contribution of multiple infectious organisms to pathogenesis of atherosclerosis. Thromb Haemost. 106:858–867. 2011. View Article : Google Scholar
6	Shah PK: Link between infection and atherosclerosis: Who are the culprits: Virises, bacteria, both, or neither? Circulation. 103:5–6. 2001. View Article : Google Scholar : PubMed/NCBI
7	Stassen FR, Vainas T and Bruggeman CA: Infection and atherosclerosis. An alternative view on an outdated hypothesis. Pharmacol Rep. 60:85–92. 2008.PubMed/NCBI
8	Mundkur LA, Rao VS, Hebbagudi S, Shanker J, Shivanandan H, Nagaraj RK and Kakkar VV: Pathogen burden, cytomegalovirus infection and inflammatory markers in the risk of premature coronary artery disease in individuals of Indian origin. Exp Clin Cardiol. 17:63–68. 2012.PubMed/NCBI
9	Gulbahce N, Yan H, Dricot A, Padi M, Byrdsong D, Franchi R, Lee DS, Rozenblatt-Rosen O, Mar JC, Calderwood MA, et al: Viral perturbations of host networks reflect disease etiology. PLoS Comput Biol. 8:e10025312012. View Article : Google Scholar : PubMed/NCBI
10	Lee DS, Park J, Kay KA, Christakis NA, Oltvai ZN and Barabási AL: The implications of human metabolic network topology for disease comorbidity. Proc Natl Acad Sci USA. 105:9880–9885. 2008. View Article : Google Scholar : PubMed/NCBI
11	Goh KI, Cusick ME, Val le D, Childs B, Vidal M and Barabási AL: The human disease network. Proc Natl Acad Sci USA. 104:8685–8690. 2007. View Article : Google Scholar : PubMed/NCBI
12	Linghu B, Snitkin ES, Hu Z, Xia Y and Delisi C: Genome-wide proirotization of disease genes and identification of disease-disease associations from an integrated human functional linkage network. Genome Biol. 10:R912009. View Article : Google Scholar
13	Janić C and Pržulj N: Biological function through network topology: A survey of the human diseasome. Brief Funct Genomics. 11:522–532. 2012. View Article : Google Scholar
14	Emmert-Streib F, Tripathi S, de Matos Simoes R, Hawwa AF and Dehmer M: The human disease network. Opportunities for classification, diagnosis and prediction of disorders and disease genes. Systems Biomedicine. 1:20–28. 2013. View Article : Google Scholar
15	Piro RM and Di Cunto F: Computational approached to disease-gene prediction: Rationale, classification and successes. FEBS J. 279:678–696. 2012. View Article : Google Scholar : PubMed/NCBI
16	Yu S, Falck T, Daemen A, Tranchevent LC, Suykens JA, De Moor B and Moreau Y: L₂-norm multiple kernel learning and its application to biomedical data fusion. BMC Bioinformatics. 11:3092010. View Article : Google Scholar
17	Chen Y, Zhu J, Lum PY, Yang X, Pinto S, MacNeil DJ, Zhang C, Lamb J, Edwards S, Sieberts SK, et al: Variations in DNA molecular networks that cause disease. Nature. 452:429–435. 2008. View Article : Google Scholar : PubMed/NCBI
18	Liu H, Liu W, Liao Y, Cheng L, Liu Q, Ren X, Shi L, Tu X, Wang QK and Guo AY: CADgene: A comprehensive database for coronary artery disease genes. Nucleic Acids Res. 39(Database issue): D991–D996. 2011. View Article : Google Scholar :
19	Wang A, Ren L and Li H: A systemic network triggered by human cytomegalovirus entry. Adv Virol. 2011:2620802011. View Article : Google Scholar
20	Suresh R, Li X, Chiriac A, Goel K, Terzic A, Perez-Terzic C and Nelson TJ: Transcriptome from circulating cells suggests dysregulated pathways associated with long-term recurrent events following frst-time myocardial infarction. J Mol Cell Cardiol. 74:13–21. 2014. View Article : Google Scholar : PubMed/NCBI
21	Shanker J, Mitra A, Rao VS, Mundkur L, Dhanalakshmi B, Hebbagodi S and Kakkar VV: Rationale, design & preliminary findings of the Indian atherosclerosis research study. Indian Heart J. 62:286–295. 2010.
22	DeLong ER, DeLong DM and Clarke-Perason DL: Comparing the areas under two or more correlated receiver operating characteristics curves: A nonparametric approach. Biometrics. 44:837–845. 1988. View Article : Google Scholar : PubMed/NCBI
23	Ji YN, An L, Zhan P and Chen XH: Cytomegalovirus infection and coronary heart disease risk: A meta-analysis. Mol Biol Rep. 39:6537–6546. 2012. View Article : Google Scholar : PubMed/NCBI
24	Span AH, Van Boven CP and Bruggeman CA: The effect of cytomegalovirus infection on the adherence of polymorphonuclear leucocytes to endothelial cells. Eur J Clin Invest. 19:542–548. 1989. View Article : Google Scholar : PubMed/NCBI
25	Naghavi M, Wyde P, Litovsky S, Madjid M, Akhtar A, Naguib S, Siadaty MS, Sanati S and Casscells W: Influenza infection exerts prominent inflammatory and thrombotic effects on the atherosclerotic plaques of apolipoprotein E-deficient mice. Circulation. 107:762–768. 2003. View Article : Google Scholar : PubMed/NCBI
26	Hsich E, Zhou YF, Paigen B, Johnson TM, Burnett MS and Epstein SE: Cytomegalovirus infection increases development of atherosclerosis in apolipoprotein-E knockout mice. Atherosclerosis. 156:23–28. 2001. View Article : Google Scholar : PubMed/NCBI
27	Burnett MS, Gaydos CA, Madico GE, Glad SM, Paigen B, Quinn TC and Epstein SE: Atherosclerosis in apoE knockout mice infected with multiple pathogens. J Infect Dis. 183:226–231. 2001. View Article : Google Scholar
28	Ezzahiri R, Neilssen-Vrancken HJ, Kurvers HA, Stassen FR, Vliegen I, Grauls GE, van Pul MM, Kitslaar PJ and Bruggeman CA: Chlamydophila pneumonia (Chlamidia pneumoniae) accelerates the formation of complex atherosclerotic lesions in apo E3-Leiden mice. Cardiovasc Res. 56:269–276. 2002. View Article : Google Scholar : PubMed/NCBI
29	Ezzahiri R, Stassen FR, Kurvers HA, van Pul MM, Kitslaar PJ and Bruggemann CA: Chlamydia pneumoniae infection induces an uns- atherosclerotic plaque phenotype in LDL-receptor, ApoE double knockout mice. Eur J Vasc Endovasc Surg. 26:88–95. 2003. View Article : Google Scholar : PubMed/NCBI
30	Janeway CA Jr, Travers P, Walport M and Shlomchik MJ: The front line of host defense. Immunobiology: The Immune System in Health and Disease. 5th edition. Garland Science; New York: pp. 295–340. 2001
31	Smeeth L, Thomas SL, Hall AJ, Hubbard R, Farrington P and Vallance P: Risk of myocardial infarction and stroke after acute infection or vaccination. N Engl J Med. 351:2611–2618. 2004. View Article : Google Scholar : PubMed/NCBI
32	Anderson JL and Muhlestein JB: Antiobiotic trials for coronary heart disease. Tex Heart Inst J. 31:33–38. 2004.
33	Dhingra R, Gona P, Wang TJ, Fox CS, D'Agostino RB Sr and Vasan RS: Serum gamma glutamyl transferase and risk of heart failure in the community. Atheroscler Thromb Vasc Biol. 30:1855–1860. 2010. View Article : Google Scholar
34	Gryaston JT: Antiobiotic treatment of atherosclerotic cardiovascular disease. Circulation. 107:1228–1230. 2003. View Article : Google Scholar