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't Hart, L. M.

Publications and source records attributed to 't Hart, L. M..

3 recordsLinked to original sources

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↗

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↗

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↗