A Bioinformatics Perspective on the Links Between Tetraspanin-Enriched Microdomains and Cardiovascular Pathophysiology

Background: Tetraspanins and integrins are integral membrane proteins. Tetraspanins interact with integrins to modulate the dynamics of adhesion, migration, proliferation, and signaling in the form of membrane domains called tetraspanin-enriched microdomains (TEMs). TEMs also contain other cell adhesion proteins like immunoglobulin superfamily (IgSF) proteins and claudins. Cardiovascular functions of these TEM proteins have emerged and remain to be further revealed. Objectives: The aims of this study are to explore the roles of these TEM proteins in the cardiovascular system using bioinformatics tools and databases and to highlight the TEM proteins that may functionally associate with cardiovascular physiology and pathology. Methods: For human samples, three databases—GTEx, NCBI-dbGaP, and NCBI-GEO—were used for the analyses. The dbGaP database was used for GWAS analysis to determine the association between target genes and human phenotypes. GEO is an NCBI public repository that archives genomics data. GTEx was used for the analyses of tissue-specific mRNA expression levels and eQTL. For murine samples, GeneNetwork was used to find gene–phenotype correlations and gene–gene correlations of expression levels in mice. The analysis of cardiovascular data was the focus of this study. Results: Some integrins and tetraspanins, such as ITGA8 and Cd151, are highly expressed in the human cardiovascular system. TEM components are associated with multiple cardiovascular pathophysiological events in humans. GWAS and GEO analyses showed that human Cd82 and ITGA9 are associated with blood pressure. Data from mice also suggest that various cardiovascular phenotypes are correlated with integrins and tetraspanins. For instance, Cd82 and ITGA9, again, have correlations with blood pressure in mice. Conclusion: ITGA9 is related to blood pressure in both species. KEGG analysis also linked ITGA9 to metabolism and MAPK signaling pathway. This work provides an example of using integrated bioinformatics approaches across different species to identify the connections of structurally and/or functionally related molecules to certain categories of diseases.