Biology subjects

Polasek, O.

Publications and source records attributed to Polasek, O..

11 recordsLinked to original sources

bioRxiv2026

N-glycome analysis of dried blood spots from different blood preparations and its potential for pre-diabetes and diabetes distinction

Dried blood spot (DBS) sampling is becoming a popular alternative to traditional blood sampling approaches, offering advantages such as convenience of collection, transportation, and storage, as well as lower biohazard risk. N-glycosylation, a major post-translational modification of proteins associated with numerous biological and pathological functions, is one area of interest for DBS analysis. In this study, we utilize a protocol for N-glycosylation profiling of DBS by ultra-high-performance liquid chromatography based on hydrophilic interactions and fluorescence detection (HILIC-UHPLC-FLR). The protocol includes DBS cutting, protein extraction and enzymatic digestion, labeling with 2-aminobenzamide, followed by cleanup and HILIC-UHPLC-FLR measurement. We compare DBS with plasma and demonstrate the stability of DBS N-glycosylation profile when DBS are prepared from fresh blood, frozen whole blood, or a combination of separated frozen blood cells and corresponding frozen plasma. Additionally, we compared DBS N-glycans from pre- and diabetic subjects. Fucosylation, bisection, and galactosylation showed a statistically non-significant increasing trend in diabetes, whereas sialylation showed a statistically non-significant decreasing trend in diabetes. The main advantage of this method is the ability to repurpose samples, which were initially not intended for biomarker N-glycan analysis, such as frozen whole blood. Additionally, DBS N-glycan profiling is the easier, cheapest and the least invasive approach to conventional plasma in pre-diabetes and diabetes patients' diagnostics and monitoring.

biochemistry↗

bioRxiv2023

Fetally-encoded GDF15 and maternal GDF15 sensitivity are major determinants of nausea and vomiting in human pregnancy

Human pregnancy is frequently accompanied by nausea and vomiting that may become severe and life-threatening, as in hyperemesis gravidarum (HG), the cause of which is unknown. Growth Differentiation Factor-15 (GDF15), a hormone known to act on the hindbrain to cause emesis, is highly expressed in the placenta and its levels in maternal blood rise rapidly in pregnancy. Variants in the maternal GDF15 gene are associated with HG. Here we report that fetal production of GDF15, and maternal sensitivity to it, both contribute substantially to the risk of HG. We found that the great majority of GDF15 in maternal circulation is derived from the feto-placental unit and that higher GDF15 levels in maternal blood are associated with vomiting and are further elevated in patients with HG. Conversely, we found that lower levels of GDF15 in the non-pregnant state predispose women to HG. A rare C211G variant in GDF15 which strongly predisposes mothers to HG, particularly when the fetus is wild-type, was found to markedly impair cellular secretion of GDF15 and associate with low circulating levels of GDF15 in the non-pregnant state. Consistent with this, two common GDF15 haplotypes which predispose to HG were associated with lower circulating levels outside pregnancy. The administration of a long-acting form of GDF15 to wild-type mice markedly reduced subsequent responses to an acute dose, establishing that desensitisation is a feature of this system. GDF15 levels are known to be highly and chronically elevated in patients with beta thalassemia. In women with this disorder, reports of symptoms of nausea or vomiting in pregnancy were strikingly diminished. Our findings support a causal role for fetal derived GDF15 in the nausea and vomiting of human pregnancy, with maternal sensitivity, at least partly determined by pre-pregnancy exposure to GDF15, being a major influence on its severity. They also suggest mechanism-based approaches to the treatment and prevention of HG.

genetics↗

bioRxiv2021

Same role but different actors: genetic regulation of post-translational modification of two distinct proteins

Post-translational modifications (PTMs) diversify protein functions and dynamically coordinate their signalling networks, influencing most aspects of cell physiology. Nevertheless, their genetic regulation or influence on complex traits is not fully understood. Here, we compare for the first time the genetic regulation of the same PTM of two proteins - glycosylation of transferrin and immunoglobulin G (IgG). By performing genome-wide association analysis of transferrin glycosylation, we identified 10 significantly associated loci, all novel. Comparing these with IgG glycosylation-associated genes, we note protein-specific associations with genes encoding glycosylation enzymes (transferrin - MGAT5, ST3GAL4, B3GAT1; IgG - MGAT3, ST6GAL1) as well as shared associations (FUT6, FUT8). Colocalisation analyses of the latter suggest that different causal variants in the FUT genes regulate fucosylation of the two proteins. We propose that they affect the binding of different transcription factors in different tissues, with fucosylation of IgG being regulated by IKZF1 in B-cells and of transferrin by HNF1A in liver.

genomics↗

bioRxiv2020

Informing epidemic (research) responses in a timely fashion by knowledge management - a Zika virus use case

The response of pathophysiological research to emerging epidemics often occurs after the epidemic and, as a consequence, has little to no impact on improving patient outcomes or on developing high-quality evidence to inform clinical management strategies during the epidemic. Rapid and informed guidance of epidemic (research) responses to severe infectious disease outbreaks requires quick compilation and integration of existing pathophysiological knowledge. As a case study we chose the Zika virus (ZIKV) outbreak that started in 2015 to develop a proof-of-concept knowledge repository. To extract data from available sources and build a computationally tractable and comprehensive molecular interaction map we applied generic knowledge management software for literature mining, expert knowledge curation, data integration, reporting and visualisation. A multi-disciplinary team of experts, including clinicians, virologists, bioinformaticians and knowledge management specialists, followed a pre-defined workflow for rapid integration and evaluation of available evidence. While conventional approaches usually require months to comb through the existing literature, the initial ZIKV KnowledgeBase (ZIKA KB) was completed within a few weeks. Recently we updated the ZIKA KB with additional curated data from the large amount of literature published since 2016 and made it publicly available through a web interface together with a step-by-step guide to ensure reproducibility of the described use case (S4). In addition, a detailed online user manual is provided to enable the ZIKV research community to generate hypotheses, share knowledge, identify knowledge gaps, and interactively explore and interpret data (S5). A workflow for rapid response during outbreaks was generated, validated and refined and is also made available. The process described here can be used for timely structuring of pathophysiological knowledge for future threats. The resulting structured biological knowledge is a helpful tool for computational data analysis and generation of predictive models and opens new avenues for infectious disease research. Availabilitywww.zikaknowledgebase.eu FundingEuropean Commissions Seventh Framework Research Programme project PREPARE (FP7-Health n{degrees}602525) and ZIKALLIANCE (MK, H2020; No 734548). Author summaryDuring the recent ZIKV outbreak there was little information about the interactions between Zika virus and the host, however, the massive research response lead to a steep increase in the number of relevant publications within a very short period of time. At the time, there was no structured and comprehensive database available for integrated molecular and physiological data and knowledge about ZIKV infection. Researchers had to manually review the literature (amounting to over 5000 articles on ZIKV during our last update of the ZIKA KB in September 2018) to extract information about host-pathogen interaction and affected molecular, cellular and organ pathways. We explored the use of automated literature analysis and a defined cooperative effort between experts from various scientific, biomedical and information-technology domains to rapidly compile existing pathophysiological knowledge as a potential tool to support investigations during an emergency. This tool is contrasted with conventional approaches that would take months to comb through the massive amount of existing literature. In addition to providing background information for research, scientific publications can be processed to transform textual information into complex networks, which can be integrated with existing knowledge resources to suggest novel hypotheses that potentially contribute to innovative infectious disease research approaches. This study shows that the knowledge extraction and mapping process required to inform clinical and research responses to an emerging epidemic can be efficiently and effectively executed with a dedicated and trained group of experts, a validated process and the necessary tools. Our results further provide an overview of ZIKV biology, allow prediction of drug efficacy and indentify specific host factors and signalling pathways affected by ZIKV.

pathology↗

bioRxiv2019

Systematic evaluation of normalization methods for glycomics data based on performance of network inference

Glycomics measurements, like all other high-throughput technologies, are subject to technical variation due to fluctuations in the experimental conditions. The removal of this non-biological signal from the data is referred to as normalization. Contrary to other omics data types, a systematic evaluation of normalization options for glycomics data has not been published so far. In this paper, we assess the quality of different normalization strategies for glycomics data with an innovative approach. It has been shown previously that Gaussian Graphical Models (GGMs) inferred from glycomics data are able to identify enzymatic steps in the glycan synthesis pathways in a data-driven fashion. Based on this finding, we here quantify the quality of a given normalization method according to how well a GGM inferred from the respective normalized data reconstructs known synthesis reactions in the glycosylation pathway. The method therefore exploits a biological measure of goodness. We analyzed 23 different normalization combinations applied to six large-scale glycomics cohorts across three experimental platforms (LC-ESI-MS, UHPLC-FLD and MALDI-FTICR-MS). Based on our results, we recommend normalizing glycan data using the Probabilistic Quotient method followed by log-transformation, irrespective of the measurement platform.

bioinformatics↗

bioRxiv2019

A strategy to incorporate prior knowledge into correlation network cutoff selection

Correlation networks are commonly used to statistically extract biological interactions between omics markers. Network edge selection is typically based on the significance of the underlying correlation coefficients. A statistical cutoff, however, is not guaranteed to capture biological reality, and heavily depends on dataset properties such as sample size. We here propose an alternative, innovative approach to address the problem of network reconstruction. Specifically, we developed a cutoff selection algorithm that maximizes the agreement to a given ground truth. We first evaluate the approach on IgG glycomics data, for which the biochemical pathway is known and well-characterized. The optimal network outperforms networks obtained with statistical cutoffs and is robust with respect to sample size. Importantly, we can show that even in the case of incomplete or incorrect prior knowledge, the optimal network is close to the true optimum. We then demonstrate the generalizability of the approach on an untargeted metabolomics and a transcriptomics dataset from The Cancer Genome Atlas (TCGA). For the transcriptomics case, we demonstrate that the optimized network is superior to statistical networks in systematically retrieving interactions that were not included in the biological reference used for the optimization. Overall, this paper shows that using prior information for correlation network inference is superior to using regular statistical cutoffs, even if the prior information is incomplete or partially inaccurate.

bioinformatics↗

bioRxiv2019

Multi-ancestry GWAS of the electrocardiographic PR interval identifies 210 loci underlying cardiac conduction

The electrocardiographic PR interval reflects atrioventricular conduction, and is associated with conduction abnormalities, pacemaker implantation, atrial fibrillation (AF), and cardiovascular mortality1,2. We performed multi-ancestry (N=293,051) and European only (N=271,570) genome-wide association (GWAS) meta-analyses for the PR interval, discovering 210 loci of which 149 are novel. Variants at all loci nearly doubled the percentage of heritability explained, from 33.5% to 62.6%. We observed enrichment for genes involved in cardiac muscle development/contraction and the cytoskeleton highlighting key regulation processes for atrioventricular conduction. Additionally, 19 novel loci harbour genes underlying inherited monogenic heart diseases suggesting the role of these genes in cardiovascular pathology in the general population. We showed that polygenic predisposition to PR interval duration is an endophenotype for cardiovascular disease risk, including distal conduction disease, AF, atrioventricular pre-excitation, non-ischemic cardiomyopathy, and coronary heart disease. These findings advance our understanding of the polygenic basis of cardiac conduction, and the genetic relationship between PR interval duration and cardiovascular disease.

genetics↗

bioRxiv2019

Global variability of the human IgG glycome

Immunoglobulin G (IgG) is the most abundant serum antibody and is a key determinant of the humoral immune response. Its structural characteristics and effector functions are modulated through the attachment of various sugar moieties called glycans. IgG N-glycome patterns change with the age of individual and in different diseases. Variability of IgG glycosylation within a population is well studied and is affected by a combination of genetic and environmental factors. However, global inter-population differences in IgG glycosylation have never been properly addressed. Here we present population-specific N-glycosylation patterns of whole IgG, analysed in 5 different populations totalling 10,482 IgG glycomes, and of IgGs fragment crystallisable region (Fc), analysed in 2,530 samples from 27 populations sampled across the world. We observed that country of residence associates with many N-glycan features and is a strong predictor of monogalactosylation variability. IgG galactosylation also strongly correlated with the development level of a country, defined by United Nations health and socioeconomic development indicators. We found that subjects from developing countries had low IgG galactosylation levels, characteristic for inflammation and ageing. Our results suggest that citizens of developing countries may be exposed to country-specific environmental factors that can cause low-grade chronic inflammation and the apparent increase in biological age.

biochemistry↗

bioRxiv2018

Genome-wide association and functional studies identify 46 novel loci for alcohol consumption and suggest common genetic mechanisms with neuropsychiatric disorders

Excessive alcohol consumption is one of the main causes of death and disability worldwide. Alcohol consumption is a heritable complex trait. We conducted a genome-wide association study (GWAS) of alcohol use in ~480,000 people of European descent to decipher the genetic architecture of alcohol intake. We identified 46 novel, common loci, and investigated their potential functional significance using magnetic resonance imaging data, gene expression and behavioral studies in Drosophila. Our results identify new genetic pathways associated with alcohol consumption and suggest common genetic mechanisms with several neuropsychiatric disorders including schizophrenia.

genetics↗

bioRxiv2018

Genome-wide Meta-analysis of 158,000 Individuals of European Ancestry Identifies Three Loci Associated with Chronic Back Pain

OBJECTIVESTo conduct a genome-wide association study (GWAS) meta-analysis of chronic back pain (CBP).\n\nMETHODSAdults of European ancestry were included from 16 cohorts in Europe and North America. CBP cases were defined as those reporting back pain present for >3-6 months; non-cases were included as comparisons (\"controls\"). Each cohort conducted genotyping using commercially available arrays followed by imputation. GWAS used logistic regression models with additive genetic effects, adjusting for age, sex, study-specific covariates, and population substructure. The threshold for genome-wide significance in the fixed-effect inverse-variance weighted meta-analysis was p<5x10-8. Suggestive (p<5x10-7) and genome-wide significant (p<5x10-8) variants were carried forward for replication or further investigation in an independent sample.\n\nRESULTSThe discovery sample was comprised of 158,025 individuals, including 29,531 CBP cases. A genome-wide significant association was found for the intronic variant rs12310519 in SOX5 (OR 1.08, p=7.2x10-10). This was subsequently replicated in an independent sample of 283,752 subjects, including 50,915 cases (OR 1.06, p=5.3x10-11), and exceeded genome-wide significance in joint meta-analysis (0R=1.07, p=4.5x10-19). We found suggestive associations at three other loci in the discovery sample, two of which exceeded genome-wide significance in joint meta-analysis: an intergenic variant, rs7833174, located between CCDC26 and GSDMC (OR 1.05, p=4.4x10-13), and an intronic variant, rs4384683, in DCC (OR 0.97, p=2.4x10-10).\n\nDISCUSSIONIn this first reported meta-analysis of GWAS for CBP, we identified and replicated a genetic locus associated with CBP (SOX5). We also identified 2 other loci that reached genome-wide significance in a 2-stage joint meta-analysis (CCDC26/GSDMC and DCC).

genomics↗

bioRxiv2017

Novel Locus Influencing Retinal Venular Tortuosity Is Also Associated With Risk Of Coronary Artery Disease

Structural variation in retinal blood vessels is associated with global vascular health in humans and may provide a readily accessible indicator of several diseases of vascular origin. Increasing evidence suggests variation in retinal vasculature is highly heritable. This study aimed to identify genetic determinants of retinal vascular traits. We reported a meta-analysis of genome-wide association studies (GWAS) for quantitative retinal vascular traits derived using semi-automatic image analysis of digital retinal photographs from the Genetics of Diabetes Audit and Research in Tayside (GoDARTS) (n=1736) and the Orkney Complex Disease Study (ORCADES) (n=1358) cohorts. We identified a novel genome-wide significant locus at 19q13 (ACTN4/CAPN12) for retinal venular tortuosity (TortV), and one at 13q34 (COL4A2) for retinal arteriolar tortuosity (TortA); these two loci were subsequently confirmed in three independent cohorts (n=1413). In the combined analysis in ACTN4/CAPN12 the lead single nucleotide polymorphism (SNP) was rs1808382 (n=4507; Beta=-0.109; standard error (SE) =0.015; P=2.39x10-13) and in COL4A2 it was rs7991229 (n=4507; Beta=0.103; SE=0.015; P=4.66x10-12). Notably, the ACTN4/CAPN12 locus associated with retinal TortV is also associated with coronary artery disease and heart rate. Our findings demonstrate the contribution of genetics in retinal tortuosity traits, and provide new insights into cardiovascular diseases.\n\nAuthor SummaryRetinal vascular features are associated with wide range of diseases related to vascular health and provide an opportunity to understand early structural changes in vasculature which may help to predict disease risk. Emerging evidence indicates that retinal tortuosity traits are both associated with vascular health and highly heritable. However, the genetic architecture of retinal vascular tortuosity has not been investigated. We therefore performed a genome-wide association study on retinal arteriolar tortuosity (TortA) and retinal venular tortuosity trait (TortV) using data from two independent discovery cohorts of 3094 individuals of European-heritage. We found a novel associations at 19q13 (ACTN4/CAPN12) for TortV, and one at 13q34 (COL4A2) for TortA at discovery stage and validated in three independent cohorts. A significant association was subsequently found between lead SNPs at 19q13 and coronary artery disease, cardiovascular vascular risk factors and heart rate. We also performed genome-wide association studies for retinal vascular calibres and optic disc radius (ODradius) and replicated previously reported locus at 10q21.3 for ODradius. Our findings highlight genetic impacts on retinal venular tortuosity and it is association with cardiovascular disease. This may provide a molecular pathophysiological foundation for use of retinal vascular traits as biomarkers for cardiovascular diseases.

genomics↗