Biology subjects

Claringbould, A.

Publications and source records attributed to Claringbould, A..

18 recordsLinked to original sources

bioRxiv2026

Genome-scale mapping of variant, enhancer and gene function in primary human CD4+ T cells

CD4+ T cells harbor a disproportionate enrichment of immune disease risk loci and represent the primary cellular context for immune disease biology, yet the genes and regulatory programs these variants affect remain largely unknown. We combined targeted Perturb-seq of 1,032 cis-regulatory elements (CREs) overlapping 4,724 variants across 14 immune diseases with genome-wide Perturb-seq of all expressed genes in primary human CD4+ T cells, spanning 4.1 million cells. We identified 626 CRE-gene pairs, and connected CRE targets to downstream regulatory cascades. At the TYK2 and DEXI/CLEC16A loci, we resolved target genes and linked noncoding variants to inflammatory and metabolic programs. Across diseases, we revealed that dispersed variants converged on shared and disease-specific programs. Our work provides a framework for tracing variant-to-CRE-to-gene-to-network in disease-relevant primary cells.

genetics↗

bioRxiv2025

Integrative Multi-Omics Identifies CDK1 as a Key Signaling Regulator of CD4+ T Cell Effector Function

CD4 T cell differentiation is orchestrated by coordinated signaling, transcriptional, and epigenomic programs, yet how signaling connects to chromatin and genetic variation in human T cells remains unclear. Here, we generated an integrative multi-omics map of human CD4 T cell activation and differentiation, combining phosphoproteomics, transcriptomics, and chromatin accessibility under Th0, Th1, and iTreg polarization. Within 10 minutes of activation, we observed rapid phosphorylation changes of RNA-binding proteins accompanied by degradation of effector-associated transcripts, preceding chromatin remodeling and later transcriptional activation of the same genes. Moreover, our data highlights how site-specific phosphorylation refines TF activity during T cell differentiation and activation, and identifies CDK1 as a regulator of Th1 effector function. Indeed, we found that a low dose of CKD1 inhibition impairs IFN-{gamma} expression and pro-inflammatory differentiation, while preserving regulatory features in iTregs. Single-cell multi-omic profiling upon CDK1 inhibition revealed how CDK1 activity shapes subset-specific gene regulatory networks, which are enriched for genetic variants associated with immune-traits. Specifically, CDK1-sensitive TFs, including IRF8, connect immune trait heritability to enhancer accessibility at IFNG and TNF loci. Together, these results establish CDK1 as a signaling hub that couples phosphorylation to gene regulation and genetic risk, with therapeutic relevance in autoimmune disease.

genomics↗

bioRxiv2025

High-throughput single-cell CRISPRi screens stratify neurodevelopmental functions of schizophrenia-associated genes

Schizophrenia is a complex neuropsychiatric disorder with strong genetic underpinnings, yet the molecular mechanisms linking genetic risk to disrupted brain development remain poorly understood. Transcription factors (TFs) and chromatin regulators (CRs) are increasingly implicated in neuropsychiatric disorders, where their dysregulation may disrupt neurodevelopmental programs. Despite this, systematic functional interrogation in human models has been limited. Here, we combine pooled CRISPR interference (CRISPRi) screens with high- throughput single-cell multiomic profiling in hiPSC-derived neural progenitors and neurons to functionally assess 65 schizophrenia-associated genes. Based on public datasets and literature review, we selected 55 TFs and CRs, along with ten additional risk genes whose loss-of-function has been linked to schizophrenia. Our single-cell CRISPRi readouts revealed that perturbations in TFs and CRs converge on disrupting neurodevelopmental timing. CRISPRi of several factors delayed neural differentiation, whereas others, such as the knockdown of MCRS1, drove precocious neural commitment. Validation screens combined with cell cycle and metabolic indicators confirmed the differentiation-restricting or -promoting roles of these TFs and CRs. Multimodal trajectory analysis uncovered discrete transcriptional and epigenomic states representing delayed and accelerated neurodevelopment, enriched for schizophrenia GWAS loci and disease-relevant pathways. Gene regulatory network (GRN) inference identified TCF4 and ZEB1 as critical mediators opposing the neural differentiation trajectory. Functional overexpression of these TFs followed by chromatin profiling demonstrated that TCF4 restrains, while ZEB1 promotes, neural differentiation in a stage-specific and competitive manner. Furthermore, we show that MCRS1 represses ZEB1 expression, positioning MCRS1 as a key brake on premature neurodevelopment. Together, our study establishes a scalable framework that integrates genetic perturbation, single- cell multiomics, and GRN modeling to functionally annotate disease-linked genes. We reveal convergent regulatory axes that underlie altered neurodevelopmental timing in schizophrenia, offering mechanistic insights into how chromatin misregulation contributes to disease pathogenesis.

neuroscience↗

bioRxiv2023

Scalable ultra-high-throughput single-cell chromatin and RNA sequencing reveals gene regulatory dynamics linking macrophage polarization to autoimmune disease

Enhancers and transcription factors (TFs) are crucial in regulating cellular processes, including disease-associated cell states. Current multiomic technologies to study these elements in gene regulatory mechanisms lack multiplexing capability and scalability. Here, we present SUM-seq, a cost-effective, scalable Single-cell Ultra-high-throughput Multiomic sequencing method for co-assaying chromatin accessibility and gene expression in single nuclei. SUM-seq enables profiling hundreds of samples at the million cell scale and outperforms current high-throughput single-cell methods. We applied SUM-seq to dissect the gene regulatory mechanisms governing macrophage polarization and explored their link to traits from genome-wide association studies (GWAS). Our analyses confirmed known TFs orchestrating M1 and M2 macrophage programs, unveiled key regulators, and demonstrated extensive enhancer rewiring. Integration with GWAS data further pinpointed the impact of specific TFs on a set of immune traits. Notably, inferred enhancers regulated by the STAT1/STAT2/IRF9 (ISGF3) complex were enriched for genetic variants associated with Crohns disease, ulcerative colitis and multiple sclerosis, and their target genes included known drug targets. This highlights the potential of SUM-seq for dissecting molecular disease mechanisms. SUM-seq offers a cost-effective, scalable solution for ultra-high-throughput single-cell multiomic sequencing, excelling in unraveling complex gene regulatory networks in cell differentiation, responses to perturbations, and disease studies.

genomics↗

bioRxiv2022

Omics-informed CNV calls reduce false positive rate and improve power for CNV-trait associations

Copy number variations (CNV) are believed to play an important role in a wide range of complex traits but discovering such associations remains challenging. Whilst whole genome sequencing (WGS) is the gold standard approach for CNV detection, there are several orders of magnitude more samples with available genotyping microarray data. Such array data can be exploited for CNV detection using dedicated software (e.g., PennCNV), however these calls suffer from elevated false positive and negative rates. In this study, we developed a CNV quality score that weights PennCNV calls (pCNV) based on their likelihood of being true positive. First, we established a measure of pCNV reliability by leveraging evidence from multiple omics data (WGS, transcriptomics and methylomics) obtained from the same samples. Next, we built a predictor of omics-confirmed pCNVs, termed omics-informed quality score (OQS), using only PennCNV software output parameters. Promisingly, OQS assigned to pCNVs detected in close family members was up to 35% higher than the OQS of pCNVs not carried by other relatives (P < 3.0-10-90), outperforming other scores. Finally, in an association study of four anthropometric traits in 89,516 Estonian Biobank samples, the use of OQS led to a relative increase in the trait variance explained by CNVs of up to 34% compared to raw pCNVs or previous quality scores. Overall, we put forward a flexible framework to improve any CNV detection method leveraging multi-omics evidence, applied it to improve PennCNV calls and demonstrated its utility by improving the statistical power for downstream association analyses.

bioinformatics↗

bioRxiv2021

GRaNIE and GRaNPA: Inference and evaluation of enhancer-mediated gene regulatory networks applied to study macrophages

Among the biggest challenges in the post-GWAS (genome-wide association studies) era is the interpretation of disease-associated genetic variants in non-coding genomic regions. Enhancers have emerged as key players in mediating the effect of genetic variants on complex traits and diseases. Their activity is regulated by a combination of transcription factors (TFs), epigenetic changes and genetic variants. Several approaches exist to link enhancers to their target genes, and others that infer TF-gene connections. However, we currently lack a framework that systematically integrates enhancers into TF-gene regulatory networks. Furthermore, we lack an unbiased way of assessing whether inferred regulatory interactions are biologically meaningful. Here we present two methods, implemented as user-friendly R packages: GRaNIE (Gene Regulatory Network Inference including Enhancers) for building enhancer-based gene regulatory networks (eGRNs) and GRaNPA (Gene Regulatory Network Performance Analysis) for evaluating GRNs. GRaNIE jointly infers TF-enhancer, enhancer-gene and TF-gene interactions by integrating open chromatin data such as ATAC-Seq or H3K27ac with RNA-seq across a set of samples (e.g. individuals), and optionally also Hi-C data. GRaNPA is a general framework for evaluating the biological relevance of TF-gene GRNs by assessing their performance for predicting cell-type specific differential expression. We demonstrate the power of our tool-suite by investigating gene regulatory mechanisms in macrophages that underlie their response to infection and cancer, their involvement in common genetic diseases including autoimmune diseases, and identify the TF PURA as putative regulator of pro-inflammatory macrophage polarisation. Availability- GRaNIE: https://bioconductor.org/packages/release/bioc/html/GRaNIE.html - GRaNPA: https://git.embl.de/grp-zaugg/GRaNPA Graphical abstract O_FIG_DISPLAY_L [Figure 1] M_FIG_DISPLAY C_FIG_DISPLAY

genomics↗

bioRxiv2021

Enhancer-priming in ageing human bone marrow mesenchymal stromal cells contributes to immune traits

Bone marrow mesenchymal stromal cells (BMSCs) can differentiate into adipocytes and osteoblasts, and are important regulators of the haematopoietic system. Ageing associates with an increased ratio of bone marrow adipocytes to osteoblasts and immune dysregulation. Here, we carried out an integrative multiomics analysis of ATAC-Seq, RNA-Seq and proteomics data from primary human BMSCs in a healthy cohort age between 20 - 60. We identified age-sensitive elements uniquely affecting each molecular level where transcription is mostly spared, and characterised the underlying biological pathways, revealing the interplay of age-related gene expression mechanism changes spanning multiple gene regulatory layers. Through data integration with enhancer-mediated gene regulatory network analysis, we discovered that enhancers and transcription factors influence cell differentiation potential in the ageing BMSCs. By combining our results with genome-wide association study data, we found that age-specific changes could contribute to common traits related to BMSC-derived tissues such as bone and adipose tissue, and to immune-related traits on a systemic level such as asthma. We demonstrate here that a multiomics approach is crucial for unravelling complex information, providing new insights on how ageing contributes to bone marrow- and immune-related disorders.

systems biology↗

bioRxiv2021

Pathological LSD1 mutations cause HDAC-mediated aberrant gene repression during early cell differentiation

Lysine-specific demethylase 1 (LSD1/KDM1A) demethylates both histone and non-histone substrates, recruits repressive chromatin complexes, and is increased in cancers. De novo LSD1 mutations impairing protein function lead to a rare developmental disorder, but the molecular details of the pathology remains unclear. Using patient-derived fibroblasts, reprogrammed pluripotent stem cells, and differentiated cells, we found over 4000 differentially expressed genes and 68 transcription factors (TFs) whose motif accessibilities changed upon LSD1 mutation. An enhancer-mediated gene regulatory network approach identified transcriptional repressors with impaired activity in fibroblast and stem cells, leading to erroneous activation of their target genes. We also revealed overall decreases in TF target gene expression during early lineage differentiation of LSD1 mutant stem cells, likely caused by increased activity of repressive histone deacetylases (HDACs), co-factors of LSD1. Indeed, an HDACs inhibitor restored changes in gene expression including downregulation phenotype. Our findings characterize the molecular pathogenesis of LSD1 mutations and suggest potential therapeutic strategies for the developmental disorder and cancers caused by LSD1 dysregulations.

genomics↗

bioRxiv2020

Imbalanced expression for predicted high-impact, autosomal-dominant variants in a cohort of 3,818 healthy samples

BackgroundOne of the growing problems in genome diagnostics is the increasing number of variants that get identified through genetic testing but for which it is unknown what the significance for the disease is (Variants of Unknown Significance - VUS)1,2. When these variants are observed in patients, clinicians need to be able to determine their relevance for causing the patients disease. Here we investigated whether allele-specific expression (ASE) can be used to prioritize disease-relevant VUS and therefore assist diagnostics. In order to do so, we conducted ASE analysis in RNA-seq data from 3,818 blood samples (part of the the Dutch BIOS biobank consortium), to ascertain how VUS affect gene expression. We compared the effect of VUS variants to variants that are predicted to have a high impact, and variants that are predicted to be pathogenic but are either recessive or autosomal-dominant with low penetrance. ResultsFor immune and haematological disorders, we observed that 24.7% of known pathogenic variants from ClinVar show allelic imbalance in blood, as compared to 6.6% of known benign variants with matching allele frequencies. However, for other types of disorders, ASE information from blood did not distinguish (likely) pathogenic variants from benign variants. Unexpectedly, we identified 5 genes (ALOX5, COMT, PRPF8, PSTPIP1 and SH3BP2) in which seven population-based samples had a predicted high impact, autosomal-dominant variant. For these genes the imbalanced expression of the major allele compensates for the lower expression of the minor allele. ConclusionsOur analysis in a large population-based gene expression cohort reveals examples of high impact, autosomal-dominant variants that are compensated for by imbalanced expression. Additionally, we observed that ASE analyses in blood are informative for predicting pathogenic variants that are associated with immune and haematological conditions. We have made all our ASE results, including many ASE calls for rare variants (MAF < 1%), available at https://molgenis15.gcc.rug.nl/.

genomics↗

bioRxiv2020

Genetics of human gut microbiome composition

To study the effect of host genetics on gut microbiome composition, the MiBioGen consortium curated and analyzed genome-wide genotypes and 16S fecal microbiome data from 18,340 individuals (24 cohorts). Microbial composition showed high variability across cohorts: only 9 out of 410 genera were detected in more than 95% samples. A genome-wide association study (GWAS) of host genetic variation in relation to microbial taxa identified 31 loci affecting microbiome at a genome-wide significant (P<5x10-8) threshold. One locus, the lactase (LCT) gene locus, reached study-wide significance (GWAS signal P=1.28x10-20), and it showed an age-dependent association with Bifidobacterium abundance. Other associations were suggestive (1.95x10-10<P<5x10-8) but enriched for taxa showing high heritability and for genes expressed in the intestine and brain. A phenome-wide association study and Mendelian randomization identified enrichment of microbiome trait loci in the metabolic, nutrition and environment domains and suggested the microbiome has causal effects in ulcerative colitis and rheumatoid arthritis.

genetics↗

bioRxiv2020

Correction for both common and rare cell types in blood is important to identify genes that correlate with age

BackgroundAging is a multifactorial process that affects multiple tissues and is characterized by changes in homeostasis over time, leading to increased morbidity. Whole blood gene expression signatures have been associated with aging and have been used to gain information on its biological mechanisms, which are still not fully understood. However, blood is composed of many cell types whose proportions in blood vary with age. As a result, previously observed associations between gene expression levels and aging might be driven by cell type composition rather than intracellular aging mechanisms. To overcome this, previous aging studies already accounted for major cell types, but the possibility that the reported associations are false positives driven by less prevalent cell subtypes remains. ResultsHere, we compared the regression model from our previous work to an extended model that corrects for 33 additional white blood cell subtypes. Both models were applied to whole blood gene expression data from 3165 individuals belonging to the general population (age range of 18-81 years). We evaluated that the new model is a better fit for the data and it identified fewer genes associated with aging (625, compared to the 2808 of the initial model; P [≤] 2.5 x 10-6). Moreover, 511 genes ([~]18% of the 2,808 genes identified by the initial model) were found using both models, indicating that the other previously reported genes could be proxies for less abundant cell types. In particular, functional enrichment of the genes identified by the new model highlighted pathways and GO terms specifically associated with platelet activity. ConclusionsWe conclude that gene expression analyses in blood strongly benefit from correction for both common and rare blood cell types, and recommend using blood-cell count estimates as standard covariates when studying whole blood gene expression.

bioinformatics↗

bioRxiv2020

The role of gene expression on human sexual dimorphism: too early to call

The genetic underpinning of sexual dimorphism is very poorly understood. The prevalence of many diseases differs between men and women, which could be in part caused by sex-specific genetic effects. Nevertheless, only a few published genome-wide association studies (GWAS) were performed separately in each sex. The reported enrichment of expression quantitative trait loci (eQTLs) among GWAS-associated SNPs suggests a potential role of sex-specific eQTLs in the sex-specific genetic mechanism underlying complex traits. To explore this scenario, we performed a genome-wide analysis of sex-specific whole blood RNA-seq eQTLs from 3,447 individuals. Among 9 million SNP-gene pairs showing sex-combined associations, we found 18 genes with significant sex-specific cis-eQTLs (FDR 5%). Our phenome-wide association study of the 18 top sex-specific eQTLs on >700 traits unraveled that these eQTLs do not systematically translate into detectable sex-specific trait-associations. Power analyses using real eQTL- and causal effect sizes showed that millions of samples would be necessary to observe sex-specific trait associations that are fully driven by sex-specific cis-eQTLs. Compensatory effects may further hamper their detection. In line with this observation, we confirmed that the sex-specific trait-associations detected so far are not driven by sex-specific cis-eQTLs.

genetics↗

bioRxiv2020

Genomic evaluation of circulating proteins for drug target characterisation and precision medicine

Circulating proteins are vital in human health and disease and are frequently used as biomarkers for clinical decision-making or as targets for pharmacological intervention. By mapping and replicating protein quantitative trait loci (pQTL) for 90 cardiovascular proteins in over 30,000 individuals, we identified 467 pQTLs for 85 proteins. The pQTLs were used in combination with other sources of information to evaluate known drug targets, and suggest new target candidates or repositioning opportunities, underpinned by a) causality assessment using Mendelian randomization, b) pathway mapping using trans-pQTL gene assignments, and c) protein-centric polygenic risk scores enabling matching of plausible target mechanisms to sub-groups of individuals enabling precision medicine.

genetics↗

bioRxiv2020

Systematic prioritization of candidate genes in disease loci identifies TRAFD1 as a master regulator of IFNγ signalling in celiac disease

BackgroundCeliac disease (CeD) is a complex T cell-mediated enteropathy induced by gluten. Although genome-wide association studies have identified numerous genomic regions associated with CeD, it is difficult to accurately pinpoint which genes in these loci are most likely to cause CeD. ResultsWe used four different in silico approaches - Mendelian Randomization inverse variance weighting, COLOC, LD overlap and DEPICT - to integrate information gathered from a large transcriptomics dataset. This identified 118 prioritized genes across 50 CeD-associated regions. Co-expression and pathway analysis of these genes indicated an association with adaptive and innate cytokine signalling and T cell activation pathways. 51 of these genes are targets of known drug compounds and likely druggable genes, suggesting that our methods can be used to pinpoint potential therapeutic targets. In addition, we detected 172 gene-combinations that were affected by our CeD-prioritized genes in trans. Notably, 41 of these trans-mediated genes appear to be under control of one master regulator, TRAFD1, and were found to be involved in IFN{gamma} signalling and MHC I antigen processing/presentation. Finally, we performed in vitro experiments that validated the role of TRAFD1 as an immune regulator acting in trans. ConclusionsOur strategy has confirmed the role of adaptive immunity in CeD and revealed a genetic link between CeD and the IFN{gamma} signalling and MHC I antigen processing pathways, both major players of immune activation and CeD pathogenesis.

genetics↗

bioRxiv2019

A novel Mendelian randomization method identifies causal relationships between gene expression and low-density lipoprotein cholesterol levels.

Robust inference of causal relationships between gene expression and complex traits using Mendelian Randomization (MR) approaches is confounded by pleiotropy and linkage disequilibrium (LD) between gene expression quantitative loci (eQTLs). Here we propose a new MR method, MR-link, that accounts for unobserved pleiotropy and LD by leveraging information from individual-level data. In simulations, MR-link shows false positive rates close to expectation (median 0.05) and high power (up to 0.89), outperforming all other MR methods we tested, even when only one eQTL variant is present. Application of MR-link to low-density lipoprotein cholesterol (LDL-C) measurements in 12,449 individuals and eQTLs summary statistics from whole blood and liver identified 19 genes causally linked to LDL-C. These include the previously functionally validated SORT1 gene, and the PVRL2 gene, located in the APOE locus, for which a causal role in liver was yet unknown. Our results showcase the strength of MR-link for transcriptome-wide causal inferences.

genetics↗

bioRxiv2019

Deconvolution of bulk blood eQTL effects into immune cell subpopulations

Expression quantitative trait loci (eQTL) studies are used to interpret the function of disease-associated genetic risk factors. To date, most eQTL analyses have been conducted in bulk tissues, such as whole blood and tissue biopsies, which are likely to mask the cell type context of the eQTL regulatory effects. Although this context can be investigated by generating transcriptional profiles from purified cell subpopulations, the current methods are labor-intensive and expensive. Here we introduce a new method, Decon2, a statistical framework for estimating cell proportions using expression profiles from bulk blood samples (Decon-cell) and consecutive deconvolution of cell type eQTLs (Decon-eQTL). The estimated cell proportions from Decon-cell agree with experimental measurements across cohorts (R [≥] 0.77). Using Decon-cell we can predict the proportions of 34 circulating cell types for 3,194 samples from a population-based cohort. Next we identified 16,362 whole blood eQTLs and assign them to a cell type with Decon-eQTL using the predicted cell proportions from Decon-cell. Deconvoluted eQTLs show excellent allelic directional concordance with those of eQTL([≥] 96%) and chromatin mark QTL ([≥]87%) studies that used either purified cell subpopulations or single-cell RNA-seq. Our new method provides a way to assign cell type effects to eQTLs from bulk blood, which is useful in pinpointing the most relevant cell type for a certain complex disease. Decon2 is available as an R package and Java application (https://github.com/molgenis/systemsgenetics/tree/master/Decon2), and as a web tool (www.molgenis.org/deconvolution).

genomics↗

bioRxiv2018

Unraveling the polygenic architecture of complex traits using blood eQTL meta-analysis

SummaryWhile many disease-associated variants have been identified through genome-wide association studies, their downstream molecular consequences remain unclear.\n\nTo identify these effects, we performed cis- and trans-expression quantitative trait locus (eQTL) analysis in blood from 31,684 individuals through the eQTLGen Consortium.\n\nWe observed that cis-eQTLs can be detected for 88% of the studied genes, but that they have a different genetic architecture compared to disease-associated variants, limiting our ability to use cis-eQTLs to pinpoint causal genes within susceptibility loci.\n\nIn contrast, trans-eQTLs (detected for 37% of 10,317 studied trait-associated variants) were more informative. Multiple unlinked variants, associated to the same complex trait, often converged on trans-genes that are known to play central roles in disease etiology.\n\nWe observed the same when ascertaining the effect of polygenic scores calculated for 1,263 genome-wide association study (GWAS) traits. Expression levels of 13% of the studied genes correlated with polygenic scores, and many resulting genes are known to drive these traits.

genomics↗

bioRxiv2017

Genome-wide identification of directed gene networks using large-scale population genomics data

Identification of causal drivers behind regulatory gene networks is crucial in understanding gene function. We developed a method for the large-scale inference of gene-gene interactions in observational population genomics data that are both directed (using local genetic instruments as causal anchors, akin to Mendelian Randomization) and specific (by controlling for linkage disequilibrium and pleiotropy). The analysis of genotype and whole-blood RNA-sequencing data from 3,072 individuals identified 49 genes as drivers of downstream transcriptional changes (P < 7 x 10-10), among which transcription factors were overrepresented (P = 3.3 x 10-7). Our analysis suggests new gene functions and targets including for SENP7 (zinc-finger genes involved in retroviral repression) and BCL2A1 (novel target genes possibly involved in auditory dysfunction). Our work highlights the utility of population genomics data in deriving directed gene expression networks. A resource of trans-effects for all 6,600 genes with a genetic instrument can be explored individually using a web-based browser.

genomics↗