Biology subjects

Slieker, R.

Publications and source records attributed to Slieker, R..

5 recordsLinked to original sources

bioRxiv2025

Replication of 10 novel loci involved in human plasma protein N-glycosylation using MALDI-MS and UHPLC-FD data

N-glycans are essential components of glycoproteins, influencing their properties and functions. While biochemical pathways of glycosylation are well-characterized, their genetic regulation remains poorly understood. This study utilizes matrix-assisted laser desorption/ionization-mass spectrometry (MALDI-MS) and ultra-high performance liquid chromatography-fluorescence detection (UHPLC-FD) to strengthen replication and further characterize previously identified genome-wide association signals for the total human plasma N-glycome (TPNG). Univariate and multivariate genetic association meta-analyses involved 3,385 samples across 143 N-glycome traits from the Hoorn Diabetes Care System and DiaGene cohorts as well as 3,224 samples across 117 N-glycome traits from TwinsUK, CEDAR, QMDiab and SABRE cohorts. We successfully replicated ten previously identified but not replicated glycosylation quantitative trait loci (glyQTLs) and prioritized five high-confidence putative causal genes, including the glycosyltransferase MGAT4B and inflammation-related genes - C3 and FCGR2B. The linkage-specific sialic acid derivatization in MALDI-MS enabled delineation of genetic effects on 2,3- and 2,6-sialylation. Mass spectrometry analysis also provided evidence for glucuronic acid-containing glycans in human blood plasma. These findings advance our understanding of the genetic regulation of protein N-glycosylation and highlight the complementarity of different analytical approaches in glycomics research.

genetics↗

bioRxiv2025

Cell Type-Specific and Diabetic Kidney Disease-Associated Expression of Long Non-Coding RNAs in Human Kidneys

BackgroundLong non-coding RNAs (lncRNAs) play essential roles in cellular processes, often exhibiting cell type-specific expression and influencing kidney function. While single-cell RNA sequencing (scRNA-seq) has advanced our understanding of cellular specificity, past studies focus solely on protein-coding genes. We hypothesize that lncRNAs, due to their cell-specific nature, have crucial functions within particular renal cells and thereby play essential roles in renal cell function and disease. MethodsUsing single-nucleus RNA sequencing (snRNA-seq) data from kidney samples of five healthy individuals and six DKD patients, we explored the non-coding transcriptome. Cell type-specific lncRNAs were identified, and their differential expression in DKD was assessed. Integrative analyses included expression quantitative trait loci (eQTL), genome-wide association studies (GWAS) for estimated glomerular filtration rate (eGFR), and gene regulatory networks. Functional studies focused on TARID, a lncRNA with podocyte-specific expression, to elucidate its role in podocyte health. Resultswe identified 349 lncRNAs with cell type-specific expression across kidney cell types. Of these, 104 lncRNAs were differentially expressed in DKD. Integrative analyses, including eQTL data, GWAS results for eGFR and gene regulatory networks, pinpointed TARID, a podocyte-specific lncRNA, as a key candidate upregulated in DKD. Functional studies confirmed TARIDs podocyte-specific expression and revealed its central role in actin cytoskeleton reorganization, a critical process in podocyte health. ConclusionsOur study provides a comprehensive resource of single-cell lncRNA expression in the human kidney and highlights the importance of cell type-specific lncRNAs in kidney function and disease. Specifically, we demonstrate the functional relevance of TARID in podocyte health. This work underscores the utility of integrating scRNA-seq with functional genomics to uncover novel regulatory mechanisms in kidney biology. Key pointsO_LIThis provides a resource for kidney (cell type-specific) lncRNA expression and demonstrates the importance of lncRNAs in renal health. C_LIO_LIWe identified 349 cell type-specific lncRNAs in the human kidney, with 104 showing altered expression in diabetic kidney disease (DKD). C_LIO_LITARID, a podocyte-specific lncRNA upregulated in DKD, is crucial for actin cytoskeleton reorganization in podocytes. C_LI

bioinformatics↗

bioRxiv2023

The inactive X chromosome accumulates widespread epigenetic variability with age

BackgroundLoss of epigenetic control is a hallmark of aging. Among the most prominent roles of epigenetic mechanisms is the inactivation of one of two copies of the X chromosome in females through DNA methylation. Hence, age-related disruption of X-chromosome inactivation (XCI) may contribute to the ageing process in women. MethodsWe analyzed 9,777 CpGs on the X chromosome in whole blood samples from 2343 females and 1688 males. We replicated findings in duplicate using one whole blood and one purified monocyte data set (in total, 991/924 females/males). We used double generalized linear models (DGLM) to detect age-related differentially methylated CpGs (aDMCs), whose mean methylation level differs with age, and age-related variable methylated CpGs (aVMCs), whose methylation level becomes more variable with age. ResultsIn females, aDMCs were relatively uncommon (n=33) and preferentially occurred in regions known to escape XCI. In contrast, many CpGs (n=987) were found to display an increased variance with age (aVMCs). Of note, the replication rate of aVMCs was also high in purified monocytes (95%), indicating that their occurrence may be independent of cell composition. aVMCs accumulated in CpG islands and regions subject to XCI. Although few aVMCs were associated with X-linked genes in all females studied, an exploratory analysis suggested that such associations may be more common in old females. In males, aDMCs (n=316) were primarily driven by cell composition, while aVMCs replicated well (94%) but were infrequent (n=37). ConclusionsAge-related DNA methylation differences at the inactive X chromosome are dominated by the accumulation of variability.

genetics↗

bioRxiv2020

dsSwissKnife: An R package for federated data analysis

SummarydsSwissKnife is an R package that enables several powerful analyses to be performed on federated datasets. The package works alongside DataSHIELD and extends its functionality. We have developed and implemented dsSwissKnife in a large IMI project on type 2 diabetes, RHAPSODY, where data from 10 observational cohorts have been harmonised and federated in CDISC SDTM format and made available for biomarker discovery. Availability and implementationdsSwissKnife is freely available online at https://github.com/sib-swiss/dsSwissKnife. The package is distributed under the GNU General Public License version 3. Contactmark.ibberson@sib.swiss

bioinformatics↗

bioRxiv2020

Understanding functional consequences of type 2 diabetes risk loci using the universal data integration and visualization R package CONQUER

BackgroundNumerous large genome-wide association studies (GWASs) have been performed to understand the genetic factors of numerous traits, including type 2 diabetes. Many identified risk loci are located in non-coding and intergenic regions, which complicates the understanding how genes and their downstream pathways are influenced. An integrative data approach is required to understand the mechanism and consequences of identified risk loci. ResultsHere, we developed the R-package CONQUER. Data for SNPs of interest (build GRCh38/hg38) were acquired from static- and dynamic repositories, such as, GTExPortal, Epigenomics Project, 4D genome database and genome browsers such as ENSEMBL. CONQUER modularizes SNPs based on the underlying co-expression data and associates them with biological pathways in specific tissues. CONQUER was used to analyze 403 previously identified type 2 diabetes risk loci. In all tissues, the majority of SNPs (mean = 13.50, SD = 11.70) were linked to metabolism. A tissue-shared effect was found for four type 2 diabetes-associated SNPs (rs601945, rs1061810, rs13737, rs4932265) that were associated with differential expression of HLA-DQA2, HSD17B12, MAN2C1 and AP3S2 respectively. Seven SNPs were identified that influenced the expression of seven ribosomal proteins in multiple tissues. Finally, one SNP (rs601945) was found to influence multiple HLA genes in all twelve tissues investigated. ConclusionWe present an universal R-package that aggregates and visualizes data in order to better understand functional consequences of GWAS loci. Using CONQUER, we showed that type 2 diabetes risk loci have many tissue-shared effects on multiple pathways including metabolism, the ribosome and HLA pathway.

genomics↗