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

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.

Figure 1

Methodology for identifying important pathways and associated biomarkers linking CAD, infection and infammation. CAD, coronary artery disease; HCMV, human cytomegalovirus; UMLS, Unified Medical Language System.

Figure 2

Network of 55 FCOs and 23 clinical phenotypes. The nodes indicate the FCOs and clinical phenotypes for coronary artery disease, and the edges indicate the associations between FCOs and clinical phenotypes. FCOs, functional communicator ontologies; JNK, c-Jun N-terminal kinase; MAPK, mitogen-activated protein kinase; MAPKKK, MAPK kinase kinase; JAK, Janus kinase; STAT, signal transducer and activation of transcription; NF, nuclear factor.

Figure 3

(A) Common GO terms from global gene expression analysis and 78 GO terms. (B) Potential molecular mechanism of the association between infection, inflammation and coronary artery disease. GO, gene ontology.