Biology subjects

Asselbergs, F. W.

Publications and source records attributed to Asselbergs, F. W..

9 recordsLinked to original sources

bioRxiv2021

LoFTK: a framework for fully automated calculation of predicted Loss-of-Function variants

MotivationLoss-of-Function (LoF) variants in human genes are important due to their impact on clinical phenotypes and frequent occurrence in the genomes of healthy individuals. Current approaches predict high-confidence LoF variants without identifying the specific genes or the number of copies they affect. Moreover, there is a lack of methods for detecting knockout genes caused by compound heterozygous (CH) LoF variants. ResultsWe have developed the Loss-of-Function ToolKit (LoFTK), which allows efficient and automated prediction of LoF variants from both genotyped and sequenced genomes. LoFTK enables the identification of genes that are inactive in one or two copies and provides summary statistics for downstream analyses. LoFTK can identify CH LoF variants, which result in LoF genes with two copies lost. Using data from parents and offspring we show that 96% of CH LoF genes predicted by LoFTK in the offspring have the respective alleles donated by each parent. Availability and implementationLoFTK is an open source software and is freely available to non-commercial users at https://github.com/CirculatoryHealth/LoFTK Contactj.vansetten@umcutrecht.nl Supplementary informationSupplementary data are available at Bioinformatics online.

bioinformatics↗

bioRxiv2020

Transcriptional regulation profiling reveals disrupted lipid metabolism in failing hearts with a pathogenic phospholamban mutation

BackgroundThe R14del mutation in the phospholamban (PLN) gene is associated with various types of cardiomyopathies and increases the risk of developing life-threatening ventricular arrhythmias. In this study, we focused on a homogeneous Dutch founder cohort of genetic cardiomyopathy due to PLN R14del mutation and aimed to study the influence of epigenetic changes from a multi-dimensional perspective. ResultsUsing cardiac tissue of PLN R14del patients and donors, we identified differentially acetylated promoters and enhancers (H3K27ac ChIPseq), annotated enriched transcription factor (TF) binding motifs located in those regions, and identified differentially expressed genes (RNA-seq). In line with the fibrofatty replacement in PLN R14del hearts at the histological level, our integrative analysis detected the downregulation of key TF regulators in fatty acid oxidation (FAO) metabolisms and their downstream target in PLN R14del hearts as compared to controls. We further examined heart tissue using immunofluorescence staining (IF) and to confirm the mitochondrial lipid abnormalities in the PLN R14del hearts. Furthermore, we observed the accumulation and deformation of lipid droplets and a disrupted morphology of mitochondria, the key organelle where FAO takes place, in PLN R14del heart using transmission electron microscopy (TEM). ConclusionUsing multi-omics approaches, we successfully obtained a unique list of chromatin regions and genes, including TF-coding genes, which played important roles in the metabolism-related signalling in PLN R14del hearts.

genomics↗

bioRxiv2020

Prediction of vascular ageing based on smartphone acquired PPG signals

Photoplethysmography (PPG) measured by smartphone has the potential for a large scale, non-invasive, and easy-to-use screening tool. Vascular aging is linked to increased arterial stiffness, which can be measured by PPG. We investigate the feasibility of using PPG to predict healthy vascular aging (HVA) based on two approaches: machine learning (ML) and deep learning (DL). We performed data preprocessing including detrending, demodulating and denoising on the raw PPG signals. For ML, ridge penalized regression has been applied to 38 features extracted from PPG, whereas for DL several convolutional neural networks (CNNs) have been applied to the whole PPG signals as input. The analysis has been conducted using the crowd-sourced Heart for Heart data. The prediction performance of ML using two features (AUC of 94.7%) - the a wave of the second derivative PPG and tpr, including four covariates, sex, height, weight, and smoking - was similar to that of the best performing CNN, 12-layer ResNet (AUC of 95.3%). Without having the heavy computational cost of DL, ML might be advantageous in finding potential biomarkers for HVA prediction. The whole workflow of the procedure is clearly described, and open software has been made available to facilitate replication of the results.

biophysics↗

bioRxiv2019

SALTClass: classifying clinical short notes using background knowledge from unlabeled data

BackgroundWith the increasing use of unstructured text in electronic health records, extracting useful related information has become a necessity. Text classification can be applied to extract patients medical history from clinical notes. However, the sparsity in clinical short notes, that is, excessively small word counts in the text, can lead to large classification errors. Previous studies demonstrated that natural language processing (NLP) can be useful in the text classification of clinical outcomes. We propose incorporating the knowledge from unlabeled data, as this may alleviate the problem of short noisy sparse text.\n\nResultsThe software package SALTClass (short and long text classifier) is a machine learning NLP toolkit. It uses seven clustering algorithms, namely, latent Dirichlet allocation, K-Means, MiniBatchK-Means, BIRCH, MeanShift, DBScan, and GMM. Smoothing methods are applied to the resulting cluster information to enrich the representation of sparse text. For the subsequent prediction step, SALTClass can be used on either the original document-term matrix or in an enrichment pipeline. To this end, ten different supervised classifiers have also been integrated into SALTClass. We demonstrate the effectiveness of the SALTClass NLP toolkit in the identification of patients family history in a Dutch clinical cardiovascular text corpus from University Medical Center Utrecht, the Netherlands.\n\nConclusionsThe considerable amount of unstructured short text in healthcare applications, particularly in clinical cardiovascular notes, has created an urgent need for tools that can parse specific information from text reports. Using machine learning algorithms for enriching short text can improve the representation for further applications.\n\nAvailabilitySALTClass can be downloaded as a Python package from Python Package Index (PyPI) website at https://pypi.org/project/saltclass and from GitHub at https://github.com/bagheria/saltclass.

bioinformatics↗

bioRxiv2019

Genetic drug target validation using Mendelian randomization

Mendelian randomisation analysis has emerged as an important tool to elucidate the causal relevance of a range of environmental and biological risk factors for human disease. However, inference on cause is undermined if the genetic variants used to instrument a risk factor of interest also associate with other traits that open alternative pathways to the disease (horizontal pleiotropy). We show how the no horizontal pleiotropy assumption in MR analysis is strengthened when proteins are the risk factors of interest. Proteins are the proximal effectors of biological processes encoded in the genome, and are becoming assayable on an-omics scale. Moreover, proteins are the targets of most medicines, so Mendelian randomization (MR) studies of drug targets are becoming a fundamental tool in drug development. To enable such studies we introduce a formal mathematical framework that contrasts MR analysis of proteins with that of risk factors located more distally in the causal chain from gene to disease. Finally, we illustrate key model decisions and introduce an analytical framework for maximizing power and elucidating the robustness of drug target MR analyses.

genetics↗

bioRxiv2019

Predicting biological age based on the BBMRI-NL 1H-NMR metabolomics repository

The blood metabolome incorporates cues from the environment as well as the hosts genetic background, potentially offering a holistic view of an individuals health status. We have compiled a vast resource of 1H-NMR metabolomics and phenotypic data encompassing over 25,000 samples derived from 26 community and hospital-based cohorts. Using this resource, we constructed a metabolomics-based age predictor (metaboAge) to calculate an individuals biological age. Exploration in independent cohorts demonstrates that being judged older by ones metabolome, as compared to ones chronological age, confers an increased risk on future cardiovascular disease, mortality and functionality in older individuals. A web-based tool for calculating metaboAge (metaboage.researchlumc.nl) allows easy incorporation in other epidemiological studies. Access to data can be requested at bbmri.nl/samples-images-data. In summary, we present a vast resource of metabolomics data and illustrate its merit by constructing a metabolomics-based score for biological age that captures aspects of current and future cardio-metabolic health.

epidemiology↗

bioRxiv2019

Common variants in OSMR contribute to carotid plaque vulnerability

Background and aimsOncostatin M (OSM) signaling is implicated in atherosclerosis, however the mechanism remains unclear. We investigated the impact of common genetic variants in OSM and its receptors, OSMR and LIFR, on overall plaque vulnerability, plaque phenotype, intraplaque OSMR and LIFR expression, coronary artery calcification burden and cardiovascular disease susceptibility. Methods and resultsWe queried Genotype-Tissue Expression data and found that rs13168867 (C allele) was associated with decreased OSMR expression and that rs10491509 (A allele) was associated with increased LIFR expression in arterial tissues. No variant was significantly associated with OSM expression. We associated these two variants with plaque characteristics from 1,443 genotyped carotid endarterectomy patients in the Athero-Express Biobank Study. After correction for multiple testing, rs13168867 was significantly associated with an increased overall plaque vulnerability ({beta}=0.118 {+/-} s.e.=0.040, p=3.00x10-3, C allele). Looking at individual plaque characteristics, rs13168867 showed strongest associations with intraplaque fat ({beta}=0.248 {+/-} s.e.=0.088, p=4.66 x 10-3, C allele) and collagen content ({beta}=-0.259 {+/-} s.e.=0.095, p=6.22 x 10-3, C allele), but these associations were not significant after correction for multiple testing. rs13168867 was not associated with intraplaque OSMR expression. Neither was intraplaque OSMR expression associated with plaque vulnerability and no known OSMR eQTLs were associated with coronary artery calcification burden, or cardiovascular disease susceptibility. No associations were found for rs10491509 in the LIFR locus. ConclusionsOur study suggests that rs1316887 in the OSMR locus is associated with increased plaque vulnerability, but not with coronary calcification or cardiovascular disease risk. It remains unclear through which precise biological mechanisms OSM signaling exerts its effects on plaque morphology. However, the OSM-OSMR/LIFR pathway is unlikely to be causally involved in lifetime cardiovascular disease susceptibility.

genetics↗

bioRxiv2018

Genome-wide association meta-analysis of PR interval identifies 47 novel loci associated with atrial and atrioventricular electrical activity

Electrocardiographic PR interval measures atrial and atrioventricular depolarization and conduction, and abnormal PR interval is a risk factor for atrial fibrillation and heart block. We performed a genome-wide association study in over 92,000 individuals of European descent and identified 44 loci associated with PR interval (34 novel). Examination of the 44 loci revealed known and novel biological processes involved in cardiac atrial electrical activity, and genes in these loci were highly over-represented in several cardiac disease processes. Nearly half of the 61 independent index variants in the 44 loci were associated with atrial or blood transcript expression levels, or were in high linkage disequilibrium with one or more missense variants. Cardiac regulatory regions of the genome as measured by cardiac DNA hypersensitivity sites were enriched for variants associated with PR interval, compared to non-cardiac regulatory regions. Joint analyses combining PR interval with heart rate, QRS interval, and atrial fibrillation identified additional new pleiotropic loci. The majority of associations discovered in European-descent populations were also present in African-American populations. Meta-analysis examining over 105,000 individuals of African and European descent identified additional novel PR loci. These additional analyses identified another 13 novel loci. Together, these findings underscore the power of GWAS to extend knowledge of the molecular underpinnings of clinical processes.

genetics↗

bioRxiv2017

Mutations in RPL3L and MYZAP increase risk of atrial fibrillation

We performed a meta-analysis of genome-wide association studies on atrial fibrillation (AF) among 14,710 cases and 373,897 controls from Iceland and 14,792 cases and 393,863 controls from the UK Biobank, focusing on low frequency coding and splice mutations, with follow-up in samples from Norway and the US. We observed associations with two missense (OR=1.19 for both) and one splice-donor mutation (OR=1.52) in RPL3L, encoding a ribosomal protein primarily expressed in skeletal muscle and heart. Analysis of 167 RNA samples from the right atrium revealed that the splice donor mutation in RPL3L results in exon skipping. AF is the first disease associated with RPL3L and RPL3L is the first ribosomal gene implicated in AF. This finding is consistent with tissue specialization of ribosomal function. We also found an association with a missense variant in MYZAP (OR=1.37), encoding a component of the intercalated discs of cardiomyocytes, the organelle harbouring most of the mutated proteins involved in arrhythmogenic right ventricular cardiomyopathy. Both discoveries emphasize the close relationship between the mechanical and electrical function of the heart.

genetics↗