Lack of Association between the MEF2A Gene and Coronary Artery Disease in Iranian Families

Objective(s):  Coronary artery disease (CAD) which may lead to myocardial infarction (MI) is a complex one. Great effort has been devoted to identification of genes that increase susceptibility to CAD or provide protection. A 21-bp deletion in the MEF2A gene, which encodes a member of the myocyte enhancer factor 2 family of transcription factors, has been reported in patients of a single pedigree that exhibited autosomal-dominant inheritance of CAD. Subsequent analysis of genetic variants within the gene in CAD and MI case-control settings produced inconsistent results. Here, we aimed at assessing the contribution of MEF2A to CAD in a cohort of Iranian CAD patients. Materials and Methods:Exon 11 of MEF2A wherein the above mentioned 21-bp deletion and a polyglutamine (CAG)n polymorphism are positioned was sequenced by the dideoxy-nucleotide termination protocol.  In 52 CAD patients from 12 families (3-7 affected members per family) and 76 Iranian control individuals. All exons of the gene were sequenced in 10 patients and 10 controls. Results:The 21-bp deletion was observed neither among the patients nor the control individuals. Four alleles of the polyglutamine (CAG)n polymorphism were found, but there were no significant differences in allelic frequencies between patients and controls. Sequencing of all exons of MEF2A revealed the presence of 12 novel sequence variations in introns and flanking regions of MEF2A gene, not associated with disease status. Conclusion: Our data do not support a role for MEF2A in coronary artery disease in the Iranian patients studie

Identifying Gene Signature in RNA Sequencing Multiple Sclerosis Data

Objectives: Multiple Sclerosis (MS) is a complex central nervous system disease; it is the result of a combination of genetic predispositions and a nongenetic trigger. This study aims to find the gene signatures using a Pareto optimization algorithm for MS RNA sequencing (RNA-seq) data. Methods: This case-control study involved 50 samples (25 MS patients and 25 age-matched healthy individuals) and their GSE profiles (GSE123496) were selected from the National Center for Biotechnology Information Gene Expression Omnibus database. We used Pareto-optimal cluster size identification to find the gene signatures in the RNA-seq data. After prefiltering and normalizing the data, we used the Limma package to find the differentially expressed genes (DEGs). The Pareto-optimal cluster size for these DEGs was then determined using the technique, multi-objective optimization for collecting the clusters alternatives. Afterward, the RNA-seq data were clustered via k-means with suitable cluster size. The best cluster, as a signature, was found by calculating the mean of the Spearman correlation coefficients (SCCs) of whole genes in the module in a pairwise manner. All analysis was performed in the R software, 4.1.1 package, under virtual space with 100 GB RAM. Results: In total, 960 DEGs were identified by the Limma analysis. Among them, 720 were up-regulated genes and 240 were down-regulated genes. Meanwhile, 6 Pareto-optimal clusters were obtained. Two clusters that had the greatest average SCCs score (0.88 and 0.74, respectively) were chosen as the gene signatures. Discussion: A total of 9 metabolic prognostic genes and 3 biological pathways were identified. These can provide more potent prognostic information for MS patients