In Silico Systems Biology Approach for Prioritization of Candidate Genes Linked to Lipid Metabolism in the Context of Cardiovascular Disease Susceptibility in a Serbian Cohort

Background: The population of Serbia faces a significant burden from cardiovascular diseases (CVDs). This study aimed to computationally investigate genetic factors that contribute to the prevalence of these diseases by examining the possible involvement of common variants on lipid metabolism. Methods: We examined how a variant prevalent in the Serbian population, chr7:g.56019730G>A in the PSPH gene, affects the phosphoserine phosphatase (PSP) protein interaction network, particularly involved in lipid metabolism. The Information Spectrum Method (ISM), method for the analysis of protein sequence based on amplitude changes, was applied to single out the top 10 affected interactors. Their further functional annotation identified the pathways in which they jointly participate with PSP. An additional strategy encompassed the investigation of variant combinations in all analyzed genes and potential relevance of prevalent variant combinations on lipid metabolism. Results: The PSP interactions affected by the R49W variant, such as SHMT1/2, were primarily in pathways associated with serine, glycine, and sphingolipid metabolism, highly relevant for CVD etiology. Further, we identified frequent variant combinations within the LRCH1, CEP126, PIK3CG, and PIKFYVE genes in the Serbian cohort. Conclusions: This study underscores the importance of investigating genetic variant combinations in complex diseases, and provides a hypothesis generating foundation for future research into the relationship between these genes and cardiovascular diseases.