Biology subjects

Kararigas, G.

Publications and source records attributed to Kararigas, G..

3 recordsLinked to original sources

bioRxiv2025

Stabilin levels are related to atherosclerotic plaque burden in type 2 diabetes mellitus individuals

Type 2 diabetes mellitus (T2DM) is a global burgeoning health problem that increases the risk of atherosclerotic cardiovascular disease (ASCVD). Infiltration and oxidative modification of low-density lipoprotein (LDL) cholesterol in the arterial wall and chronic inflammation comprise central pathogenetic mechanisms in ASCVD. Scavenger receptors, particularly Stabilin-1 (Stab1) and Stabilin-2 (Stab2), are pivotal in the clearance of oxidized LDL (oxLDL) cholesterol and pro-atherogenic ligands from circulation. However, their role in atherosclerosis development in the spectrum of T2DM remains poorly characterized. We assessed circulating levels of Stab1, Stab2, and their ligands (TGFbI, Periostin and Reelin) in a cohort of 33 T2DM and 21 non-diabetic individuals, stratified by their atherosclerotic plaque burden as assessed by high-resolution vascular ultrasound. Associations between stabilins, their ligands and conventional cardiovascular risk factors were evaluated. Stab1 levels were significantly elevated in individuals with higher atherosclerotic plaque burden (p<0.05), while Reelin levels were marginally elevated, both in the total study cohort and among T2DM patients. Stab1 levels positively correlated with body mass index and inversely correlated with total cholesterol, LDL, and high-density lipoprotein (HDL) cholesterol levels. Our findings indicate that Stab1 may serve as a marker of dysregulated lipid metabolism and increased atherosclerotic plaque burden in individuals with T2DM. Larger prospective studies are warranted to establish the prognostic and potentially therapeutic value of Stab1 and to clarify its mechanistic role in diabetic atherosclerosis.

physiology↗

bioRxiv2018

The impact of sex on alternative splicing

Over 95% of human genes undergo alternative splicing (AS) in a developmental, tissue-specific, or signal transduction-dependent manner. Here, we present a large-scale survey of sex-biased differential alternative splicing (DAS) across 7027 samples of 39 tissues from 532 individuals (351 males and 181 females) from the Genotype-Tissue Expression project. We detected a total of 1278 statistically significant DAS events affecting 888 different genes and 4417 significant differential gene expression (DGE) events in 3221 genes. Only 267 (29.3%) of the differentially spliced genes were also differentially expressed. Genes that displayed sex-biased DGE or DAS across multiple tissues were enriched in functions related to signaling including histone demethylation. The probability of a gene showing significant differential AS varies by chromosome and is highest for the X chromosome, with differentially spliced X chromosomal genes additionally being more likely to escape X chromosomal inactivation. A small but significant association was found between sex-biased AS and transcripts that undergo physiological nonsense-mediated decay (NMD). We show a significant overlap of differential splicing and genes that display estrogen-induced alternative splicing, that are involved in estrogen response pathway. Further, we show overlap of the involved exons with estrogen-receptor bindings sites. Our results provide a comprehensive survey of sex-biased AS and its characteristics across a large collection of human tissues.

genomics↗

bioRxiv2017

DrugPattern: a web-based tool for drug set enrichment analysis

Set enrichment analysis based methods (e.g. gene set enrichment analysis) have provided great helps in mining patterns in biomedical datasets, however, tools for inferring regular patterns in drug-related datasets are still limited. For the above purpose, here we developed a web-based tool, DrugPattern. DrugPattern first collected and curated 7019 drug sets, including indications, adverse reaction, targets, pathways etc. For a list of interested drugs, DrugPattern then evaluates the significance of the enrichment of these drugs in each of the 7019 drug sets. To validate DrugPattern, we applied it to predict the potential protective roles of oxidized low-density lipoprotein (oxLDL), a widely accepted deleterious factor for the body. We predicted that oxLDL has beneficial effects on some diseases, most of which were supported by literature except type 2 diabetes (T2D), in which oxLDL was previously believed to be a risk factor. Animal experiments further validated that oxLDL indeed has beneficial effects on T2D. These data confirmed the prediction accuracy of our approach and revealed unexpected protective roles for oxLDL in various diseases including T2D. This study provides a tool to infer regular patterns in biomedical datasets based on drug set enrichment analysis.

bioinformatics↗