Biology subjects

Prokopenko, I.

Publications and source records attributed to Prokopenko, I..

7 recordsLinked to original sources

bioRxiv2024

An inter-organelle contact between endosomal GLP-1R, ER VAP-B, and the mitochondrial AKAP SPHKAP triggers PKA-dependent MIC19 phosphorylation and β-cell mitochondrial remodelling

Glucagon-like peptide-1 receptor (GLP-1R) agonists (GLP-1RAs) ameliorate mitochondrial health by increasing mitochondrial turnover in metabolically relevant tissues. Mitochondrial adaptation to metabolic stress is crucial to maintain pancreatic {beta}-cell function and prevent type 2 diabetes (T2D) progression. While the GLP-1R is well-known to stimulate cAMP production leading to Protein Kinase A (PKA) and Exchange Protein Activated by cyclic AMP 2 (Epac2) activation, there is a lack of understanding of the molecular mechanisms linking GLP-1R signalling with mitochondrial and {beta}-cell functional adaptation. Here, we present a comprehensive study in {beta}-cell lines and primary islets that demonstrates that, following GLP-1RA stimulation, GLP-1R-positive endosomes associate with the endoplasmic reticulum (ER) membrane contact site (MCS) tether VAPB at ER-mitochondria MCSs (ERMCSs), where active GLP-1R engages with SPHKAP, an A-kinase anchoring protein (AKAP) previously linked to T2D and adiposity risk in genome-wide association studies (GWAS). The inter-organelle complex formed by endosomal GLP-1R, ER VAPB and SPHKAP triggers a pool of ERMCS-localised cAMP/PKA signalling via the formation of a PKA-RI biomolecular condensate which leads to changes in mitochondrial contact site and cristae organising system (MICOS) complex phosphorylation, mitochondrial remodelling, and {beta}-cell functional adaptation, with important consequences for the regulation of {beta}-cell insulin secretion and survival to stress.

cell biology↗

bioRxiv2019

Bifidobacterium is enriched in gut microbiome of Kashmiri women with polycystic ovary syndrome

Polycystic ovary syndrome (PCOS) is a common endocrine condition in women of reproductive age understudied in non-European populations. In India, PCOS affects the life of up to 19.4 million women of age 14-25 years. Gut microbiome composition might contribute to PCOS susceptibility. We profiled the microbiome in DNA isolated from faecal samples by 16S rRNA sequencing in 19/20 women with/without PCOS from Kashmir, India. We assigned genera to sequenced species with an average 121k reads depth and included bacteria detected in at least 1/3 of the subjects or with average relative abundance [≥]0.1%. We compared the relative abundances of 40/58 operational taxonomic units in family/genus level between cases and controls, and in relation to 33 hormonal and metabolic factors, by multivariate analyses adjusted for confounders, and corrected for multiple testing. Seven genera were significantly enriched in PCOS cases: Sarcina, Alkalibacterium and Megasphaera, and previously reported for PCOS Bifidobacterium, Collinsella, Paraprevotella and Lactobacillus. We identified significantly increased relative abundance of Bifidobacteriaceae (median 6.07% vs. 2.77%) and Aerococcaceae (0.03% vs. 0.004%), whereas we detected lower relative abundance Peptococcaceae (0.16% vs. 0.25%) in PCOS cases. For the first time, we identified a significant direct association between butyrate producing Eubacterium and follicle-stimulating hormone levels. We observed increased relative abundance of Collinsella and Paraprevotella with higher fasting blood glucose levels, and Paraprevotella and Alkalibacterium with larger hip and waist circumference, and weight. We show a relationship between gut microbiome composition and PCOS linking it to specific reproductive health metabolic and hormonal predictors in Indian women.

genomics↗

bioRxiv2019

methylSCOPA and META-methylSCOPA: software for the analysis and aggregation of epigenome-wide association studies of multiple correlated phenotypes

BackgroundMulti-phenotype genome-wide association studies (MP-GWAS) of correlated traits have greater power to detect genotype-phenotype associations than single-trait GWAS. However, no multi-phenotype analysis method exists for epigenome-wide association studies (EWAS).\n\nResultsWe extended the SCOPA approach developed by us to \"methylSCOPA\" software in C++ by reversely regressing DNA hyper/hypo-methylation information on a linear combination of phenotypes. We evaluated two models of association between DNA methylation and fasting glucose (FG) and insulin (FI) levels: Model 1, including FG, FI, and three measured potential confounders (body mass index [BMI], fasting serum triglyceride levels [TG], and waist/hip ratio [WHR]), and Model 2, including FG and FI corrected for the effects of BMI, TG, and WHR. Both models were additionally corrected for participant sex and smoking status (current/ever/never). We meta-analyzed the cohort-specific MP-EWAS results with our novel software META-methylSCOPA, mapped genomic locations to CGCh37/hg19, and adopted P<1x10-7 to denote epigenome-wide significance. We used the Illumina Infinium HumanMethylation450K BeadChip array data from the Northern Finland Birth Cohorts (NFBC) 1966/1986. We quality-controlled the data, regressed out the effects of measured potential confounders, and normalized the methylation signal intensity and FI data. The MP-EWAS included data for 643/457 individuals from NFBC1966 and NFBC1986, respectively (total N=1,100).\n\nIn Model 1, we detected epigenome-wide significant association in the MP-EWAS meta-analysis at cg13708645 (chr12:121,974,305; P=1.2x10-8) within KDM2B gene. Single-trait effects within KDM2B were on FI, BMI, and WHR. Model with effect on BMI and WHR showed the strongest association at this locus, while effect on FI in single-phenotype analysis was driven by the effect of adiposity. In Model 2, the strongest association was at cg05063096 (chr3:143,689,810; P=2.3x10-7) annotated to C3orf58 with strongest effect on FI in single-trait analysis and multi-phenotype effect on FI and WHI within Model 1.\n\nWe characterized the effects of established EWAS loci for diabetes and its risk factors and detected suggestive (p<0.01) associations at six markers including PHGDH, TXNIP, SLC7A11, CPT1A, MYO5C and ABCG1, through the dissection of the multi-phenotype effects in Model 1.\n\nConclusionsWe implemented MP-EWAS in methylSCOPA and demonstrated its enhanced power over single-trait EWAS for correlated phenotypes in large-scale data.

bioinformatics↗

bioRxiv2019

Risk of recurrent pregnancy loss in the Ukrainian population using a combined effect of genetic variants

Recurrent pregnancy loss (RPL) affects nearly 5% of the women of reproductive age. Its heterogeneous and multifactorial nature complicate both diagnosis and treatment, as well as identification of the genetic contribution to RPL. Evidence about the aetiology of RPL is controversial; however, several biological mechanisms have been proposed. Given the current knowledge about the genetic susceptibility to idiopathic RPL, we aimed to evaluate the predictive ability of a combined variant panel to the risk of RPL in the Ukrainian sample of 114 cases and 106 healthy controls. We genotyped variants within the 12 genetic loci reflecting the main biological pathways involved in pregnancy maintenance: blood coagulation (F2, F5, F7, GP1A), hormonal regulation (ESR1, ADRB2), endometrium and placental function (ENOS, ACE), folate metabolism (MTHFR) and inflammatory response (IL6, IL8, IL10). We showed that a genetic risk score (GRS) calculated from the 12 variants was associated with an increased risk of RPL (odds ratio 1.56, 95% CI: 1.21,2.04, P=8.7x10-4). The receiver operator characteristic (ROC) analysis resulted in the area under the curve (AUC) of 0.64 (95% CI: 0.57, 0.72), indicating an improved ability of the GRS to classify women with and without RPL. In summary, implementation of the GRS approach can help defining women at higher risk to complex multifactorial conditions such as RPL. Future well-powered genome-wide association studies will help in the dissection of biological pathways not hypothesised previously for RPL and further improve the prediction and identification of those at risk for RPL.

genetics↗

bioRxiv2018

Genetics of fasting indices of glucose homeostasis using GWIS unravels tight relationships with inflammatory markers

PurposeHomeostasis Model Assessment of {beta}-cell function and Insulin Resistance (HOMA-B/-IR) indices are informative about the pathophysiological processes underlying type 2 diabetes (T2D). Data on both fasting glucose and insulin levels are required to calculate HOMA-B/-IR, leading to underpowered Genome-Wide Association studies (GWAS) of these traits.\n\nMethodsWe overcame such power loss issues by implementing Genome-Wide Inferred Statistics (GWIS) approach and subsequent dense genome-wide imputation of HOMA-B/-IR summary statistics with SS-imp to 1000 Genomes project variant density, reaching an analytical sample size of 75,240 European individuals without diabetes. We dissected mechanistic heterogeneity of glycaemic trait/T2D loci effects on HOMA-B/-IR and their relationships with 36 inflammatory and cardiometabolic phenotypes.\n\nResultsWe identified one/three novel HOMA-B (FOXA2)/HOMA-IR (LYPLAL1, PER4, PPP1R3B) loci. We detected novel strong genetic correlations between HOMA-IR/-B and Plasminogen Activator Inhibitor 1 (PAI-1, rg=0.92/0.78, P=2.13x10-4/2.54x10-3). HOMA-IR/-B were also correlated with C-Reactive Protein (rg=0.33/0.28, P=4.67x10-3/3.65x10-3). HOMA-IR was additionally correlated with T2D (rg=0.56, P=2.31x10-9), glycated haemoglobin (rg=0.28, P=0.024) and adiponectin (rg=-0.30, P=0.012).\n\nConclusionUsing innovative GWIS approach for composite phenotypes we report novel evidence for genetic relationships between fasting indices of insulin resistance/beta-cell function and inflammatory markers, providing further support for the role of inflammation in T2D pathogenesis.

genetics↗

bioRxiv2018

Human pancreatic islet 3D chromatin architecture provides insights into the genetics of type 2 diabetes

Genetic studies promise to provide insight into the molecular mechanisms underlying type 2 diabetes (T2D). Variants associated with T2D are often located in tissue-specific enhancer regions (enhancer clusters, stretch enhancers or super-enhancers). So far, such domains have been defined through clustering of enhancers in linear genome maps rather than in 3D-space. Furthermore, their target genes are generally unknown. We have now created promoter capture Hi-C maps in human pancreatic islets. This linked diabetes-associated enhancers with their target genes, often located hundreds of kilobases away. It further revealed sets of islet enhancers, super-enhancers and active promoters that form 3D higher-order hubs, some of which show coordinated glucose-dependent activity. Hub genetic variants impact the heritability of insulin secretion, and help identify individuals in whom genetic variation of islet function is important for T2D. Human islet 3D chromatin architecture thus provides a framework for interpretation of T2D GWAS signals.

genomics↗

bioRxiv2018

Machine Learning in Multi-Omics Data to Assess Longitudinal Predictors of Glycaemic Trait Levels

Type 2 diabetes (T2D) is a global health burden that will benefit from personalised risk prediction and targeted prevention programmes. Omics data have enabled more detailed risk prediction; however, most studies have focussed on directly on the ability of DNA variants predicting T2D onset with less attention given to epigenetic regulation and glycaemic trait variability. By applying machine learning to the longitudinal Northern Finland Birth Cohort 1966 (NFBC 1966) at 31 (T1) and 46 (T2) years old, we predicted fasting glucose (FG) and insulin (FI), glycated haemoglobin (HbA1c) and 2-hour glucose and insulin from oral glucose tolerance test (2hGlu, 2hIns) at T2 in 513 individuals from 1,001 variables at T1 and T2, including anthropometric, metabolic, metabolomic and epigenetic variables. We further tested whether the information obtained by the machine learning models in NFBC could be used to predict glycaemic traits in the independent French study with 48 matching predictors (DESIR, N=769, age range 30-65 years at recruitment, interval between data collections: 9 years). In this study, FG and FI were best predicted, with average R2 values of 0.38 and 0.53. Sex, branched-chain and aromatic amino acids, HDL-cholesterol, glycerol, ketone bodies, blood pressure at T2 and measurements of adiposity at T1, as well as multiple methylation marks at both time points were amongst the top predictors. In the validation analysis, we reached R2 values of 0.41/0.55 for FG/FI when trained and tested in NFBC1966 and 0.17/0.30 when trained in NFBC1966 and tested in DESIR. We identified clinically relevant sets of predictors from a large multi-omics dataset and highlighted the potential of methylation markers and longitudinal changes in prediction.

genomics↗