Biology subjects

Hecker, D.

Publications and source records attributed to Hecker, D..

10 recordsLinked to original sources

bioRxiv2026

EpiATLAS - a reference for human epigenomic research

The sequence of the human genome provides a foundation for understanding cellular processes in health and disease1. The organisation of this primary genetic information into cell-specific structure and function is critical to understanding the cell type-specific interpretation and execution of the genome. Epigenetic processes are essential for packaging and higher-level functional organisation of the genome, and changes therein are increasingly recognised as contributors to human disease. Building on primary data generated by multinational consortia, the International Human Epigenome Consortium2 (IHEC) has uniformly processed a collection of more than 2000 comprehensive human reference epigenomes, collectively referred to as EpiATLAS. This effort involved the development of standardised molecular and bioinformatics protocols, metadata models, and analytical tools to manage, integrate, display, and share vast amounts of epigenomic data. This includes the creation of a publicly available Epigenome Reference Registry, which provides a system for accessing protected human subject datasets and facilitates open searching of de-identified samples and experimental data. The integrated EpiATLAS ecosystem and its comprehensive human reference epigenome maps provide an unprecedented resource for the biosciences, expanding the annotated epigenomic landscape while uncovering previously unappreciated relationships among regulatory layers and revealing how epigenetic inputs underpin fundamental cellular functions and disease associations.

genomics↗

bioRxiv2026

MRI-Compatible Rigid Head Holders for Artifact-Free Multimodal Imaging in Mice

PurposeHigh-resolution intravital microscopy allows cellular-scale analysis of the brain in vivo but is greatly sensitive to physiological motion. Combining optical microscopy with magnetic resonance imaging (MRI) in the same animal could relate cellular and mesoscale functional readout to whole-brain structural information, but this requires head holders that are both mechanically rigid and MRI compatible. Conventional metallic head holders introduce MRI artifacts, whereas many nonmetallic alternatives lack sufficient stability for chronic microscopy. Thus, we developed rigid, MRI-compatible head holders engineered from 3D-printed zirconia ceramics to reduce motion during microscopy while preserving MRI image quality. MethodsHead holders were designed for mouse cranial fixation and fabricated from zirconia ceramics using additive manufacturing. We quantified motion artifacts during two-photon and multimodal widefield imaging of the mouse cortex and assessed their impact on neuronal calcium activity, functional connectivity, and hemodynamic readouts. MRI compatibility was evaluated by measuring image quality in the presence of the head holder. ResultsThe ceramic head holders provided mechanical stability to reduce motion artifacts to micrometer levels during intravital imaging. The head holders produced no detectable susceptibility artifacts in MRI, and image contrast was comparable to control acquisitions performed without head holder. Sequential optical and MRI imaging of the same brain regions established artifact-minimized multimodal data acquisition within the same animal. ConclusionsNon-metallic ceramic head holders support longitudinal multimodal studies that combine high-resolution optical microscopy with whole-brain MRI measurements in the same animal.

neuroscience↗

bioRxiv2026

Identification of Parkinson's disease-associated regulatory variants in human dopaminergic neurons reveals modulators of SCARB2 and BAG3 expression

A hallmark of Parkinsons disease (PD) is the degeneration of midbrain dopaminergic neurons (mDANs). Genome-wide association studies (GWAS) have identified single nucleotide polymorphisms (SNPs) associated with PD, but causal variants and mechanisms remain unknown. Many PD-associated SNPs reside in regulatory regions, where they may disrupt transcription factor binding sites (TFBS) and alter gene expression. To assess how non-coding PD SNPs affect gene regulation in mDANs, we identify variants predicted to alter TF binding and functionally validate their effects in a cell type-specific context. We integrate time-series transcriptome and chromatin accessibility data from iPSC-derived neurons with chromatin topology and genetic variants. We profile 3D chromatin conformation in neuronal progenitors (smNPCs) and mDANs using LowC, identifying changes in A/B compartments and topologically associated domains. PD SNPs are enriched near genes expressed in mDANs, and we predict 254 regulatory variants that create or disrupt TFBS. Using chromatin conformation data, we link variants to target genes. At the BAG3 and SCARB2 loci, reporter assays in mDANs show reduced transcription driven by PD-associated alleles. Knock-down of NR2C2, a putative SCARB2 regulator, increases SCARB2 expression in differentiating neurons. The PD-associated SCARB2 allele shows reduced chromatin accessibility in mDANs and is associated with decreased expression in brain eQTL data. Insertion of PD-associated BAG3 allele by prime editing reduces chromatin accessibility across cell types, consistent with altered binding of LIM-homeodomain transcription factors. Together, these results prioritize functional PD SNPs and show that variants at SCARB2 and BAG3 modulate gene expression in mDANs, providing mechanistic insight into PD.

molecular biology↗

bioRxiv2025

Haematopoietic loss of KDM6A impairs cardiac recovery in heartfailure via epigenetic reprogramming of myeloid cells

Clonal haematopoiesis (CH) is recognized as a potent independent risk factor for cardiovascular disease (CVD). While mutations in common CH-associated genes, such as DNMT3A and TET2, have been extensively studied, the pathological roles of other CH mutations remain poorly understood. Among these is KDM6A (UTX), an X-linked histone demethylase recently found to be commonly mutated in patients with heart failure. The mechanistic implications of KDM6A mutations in cardiac dysfunction remain largely unknown. Here, using multi-omics profiling and functional characterisation of murine models and patient-derived data, we demonstrate that haematopoietic loss of KDM6A substantially impairs cardiac recovery following myocardial infarction (MI). KDM6A deficiency enhances systemic and cardiac inflammation, characterized by augmented myeloid cell infiltration into the infarcted murine heart. Single-cell chromatin accessibility and single-cell RNA sequencing analyses revealed profound epigenetic and transcriptional reprogramming in KDM6A-deficient myeloid cells, notably CCR2 recruited macrophages and neutrophils. These cells exhibited heightened inflammatory (Il1b, Nlpr3, Saa3) and chemotactic signatures (Ccr2, Mif, Cxcl12), increased activation of inflammatory transcription factor networks (AP-1, C/EBP), disrupted chromatin architecture, and enhanced glycolytic activity. Clinically, patients with heart failure harbouring KDM6A-driven CH exhibited increased pro-inflammatory monocyte signatures (CCR2, NLPR3, NFKB1, FOS, JUN, IL6R, IL32), underscoring the translational relevance. Integrative analyses further predicted pathogenic crosstalk between KDM6A-mutated monocytes and cardiac resident cells and was experimentally validated by demonstrating that KDM6A-silenced macrophages drive cardiomyocyte hypertrophy and cardiac fibroblast activation. Our findings establish a critical mechanistic link between KDM6A-driven CH, immune dysregulation, and worsened cardiac outcomes post-MI, highlighting novel avenues for personalized therapeutic strategies in heart failure.

cell biology↗

bioRxiv2025

Predicting gene-specific regulation with transcriptomic andepigenetic single-cell data

MotivationAnalysis of single cell ATAC-seq and RNA-seq data has allowed to gain unprecedented insights into gene regulation by allowing to define cell type specific regulatory regions and their effects on gene expression. While powerful, such analysis is challenging due to the inherent sparsity of single cell data. ResultsWe present the MetaFR approach to learn gene-specific models that link open-chromatin variation from scATAC-seq data to gene expression from scRNA-seq. Using efficient regression trees, we illustrate that accurate expression prediction models can be learned on the single-cell or meta-cell level. Validation was done using fine-mapped eQTLs. Meta-cell models were found to outperform single-cell models for most genes. Comparison to the SOTA method SCARlink revealed advantages of MetaFR in terms of runtime and prediction performance. MetaFR thus allows time-efficient analysis and obtains reliable models of gene expression prediction, which can be used to study gene regulation in any organism for which scRNA-seq and scATAC-seq data is available. Availability and implementationMetaFR is freely available under https://github.com/SchulzLab/MetaFR.

bioinformatics↗

bioRxiv2025

Harnessing machine learning models for epigenome to transcriptome association studies

Understanding how epigenome variation contributes to gene expression in disease and development is a fundamental challenge. Regulatory regions show cell type-specific epigenome activity and differ in their location, size, and distance to their target genes, complicating discovery and analysis. Recent machine learning models have been proposed to address these problems by learning functions for the prediction of gene expression from epigenomic data. Here, we use the large IHEC EpiATLAS dataset to benchmark state-of-the-art linear and non-linear approaches. Each approach is optimized for over 28,000 human genes, providing a comprehensive regulatory catalog of gene models. In-depth comparison reveals that gene characteristics and the epigenomic complexity of the locus influence the difficulty of predicting the epigenome-to-transcriptome association. The model performance is further evaluated using CRISPRi and eQTL validation data. Based on these models, we conduct histone-acetylation association studies in a systematic way to investigate how epigenomic variation impacts gene expression. The model-based analysis revealed genes and regulatory regions linked to B-cell leukemia in patient data with known disease-related functions. Our work provides a foundation for applications that link epigenome variation to gene expression in human cells, by benchmarking methods on a per-gene basis, illustrating their use in a disease context and making trained models available to the community.

bioinformatics↗

bioRxiv2025

Cell type-specific epigenetic regulatory circuitry of coronary artery disease loci

Coronary artery disease (CAD) is the leading cause of death worldwide. Recently, hundreds of genomic loci have been shown to increase CAD risk, however, the molecular mechanisms underlying signals from CAD risk loci remain largely unclear. We sought to pinpoint the candidate causal coding and non-coding genes of CAD risk loci in a cell type-specific fashion. We integrated the latest statistics of CAD genetics from over one million individuals with epigenetic data from 45 relevant cell types to identify genes whose regulation is affected by CAD-associated single nucleotide variants (SNVs) via epigenetic mechanisms. Applying two statistical approaches, we identified 1,580 genes likely involved in CAD, about half of which have not been associated with the disease so far. Enrichment analysis and phenome-wide association studies linked the novel candidate genes to disease-specific pathways and CAD risk factors, corroborating their disease relevance. We showed that CAD-SNVs are enriched to regulate gene expression by affecting the binding of transcription factors (TFs) with cellular specificity. Of all the candidate genes, 23.5% represented non-coding RNAs (ncRNA), which likewise showed strong cell type specificity. We conducted a proof-of-concept biological validation for the novel CAD ncRNA gene IQCH-AS1. CRISPR/Cas9-based gene knockout of IQCH-AS1, in a human preadipocyte strain, resulted in reduced preadipocyte proliferation, less adipocyte lipid accumulation, and atherogenic cytokine profile. The cellular data are in line with the reduction of IQCH-AS1 in adipose tissues of CAD patients and the negative impact of risk alleles on its expression, suggesting IQCH-AS1 to be protective for CAD. Our study not only pinpoints CAD candidate genes in a cell type-specific fashion but also spotlights the roles of the understudied ncRNA genes in CAD genetics.

bioinformatics↗

bioRxiv2024

Epigenetic control of metabolic identity across cell types

BackgroundConstraint-based network modeling is a powerful genomic-scale approach for analyzing cellular metabolism, capturing metabolic variations across tissues and cell types, and defining the metabolic identity essential for identifying disease-associated transcriptional states. ResultsUsing RNA-seq and epigenomic data from the EpiATLAS resource of the International Human Epigenome Consortium (IHEC), we reconstructed metabolic networks for 1,555 samples spanning 58 tissues and cell types. Analysis of these networks revealed the distribution of metabolic functionalities across human cell types and provides a compendium of human metabolic activity. This integrative approach allowed us to define, across tissues and cell types, i) reactions that fulfil the basic metabolic processes (core metabolism), and ii) cell type-specific functions (unique metabolism), that shape the metabolic identity of a cell or a tissue. Integration with EpiATLAS-derived cell-type-specific gene-level chromatin states and enhancer-gene interactions identified enhancers, transcription factors, and key nodes controlling core and unique metabolism. Transport and first reactions of pathways were enriched for high expression, active chromatin state, and Polycomb-mediated repression in cell types where pathways are inactive, suggesting that key nodes are targets of repression. DiscussionThis integrative analysis forms the basis for identifying regulation points that control metabolic identity in human cells.

systems biology↗

bioRxiv2022

The epigenetic modifier DOT1L regulates gene regulatory networks necessary for cardiac patterning and cardiomyocyte cell cycle withdrawal

Mechanisms by which specific histone modifications regulate distinct gene regulatory networks remain little understood. We investigated how H3K79me2, a modification catalyzed by DOT1L and previously considered a general transcriptional activation mark, regulates gene expression in mammalian cardiogenesis. Early embryonic cardiomyocyte ablation of Dot1l revealed that H3K79me2 does not act as a general transcriptional activator, but rather regulates highly specific gene regulatory networks at two critical cardiogenic junctures: left ventricle patterning and postnatal cardiomyocyte cell cycle withdrawal. Mechanistic analyses revealed that H3K79me2 in two distinct domains, gene bodies and regulatory elements, synergized to promote expression of genes activated by DOT1L. Surprisingly, these analyses also revealed that H3K79me2 in specific regulatory elements contributed to silencing genes usually not expressed in cardiomyocytes. As DOT1L mutants had increased numbers of postnatal mononuclear cardiomyocytes and prolonged cardiomyocyte cell cycle activity, controlled inhibition of DOT1L might be a strategy to promote cardiac regeneration post-injury.

developmental biology↗

bioRxiv2022

The adapted Activity-By-Contact-model for enhancer-gene assignment and its combination with transcription factor affinities in single cell data

Identifying regulatory regions in the genome is of great interest for understanding the epigenomic landscape in cells. One fundamental challenge in this context is to find the target genes whose expression is affected by the regulatory regions. A recent successful method is the Activity-By-Contact (ABC) model (Fulco et al., 2019) which scores enhancer-gene interactions based on enhancer activity and the contact frequency of an enhancer to its target gene. However, it describes regulatory interactions entirely from a genes perspective, and does not account for all the candidate target genes of an enhancer. In addition, the ABC-model requires two types of assays to measure enhancer activity, which limits the applicability. Moreover, there is no implementation available that could allow for an integration with transcription factor (TF) binding information nor an efficient analysis of single-cell data. We demonstrate that the ABC-score can yield a higher accuracy by adapting the enhancer activity according to the number of contacts the enhancer has to its candidate target genes and also by considering all annotated transcription start sites of a gene. Further, we show that the model is comparably accurate with only one assay to measure enhancer activity. We combined our generalised ABC-model (gABC) with TF binding information and illustrate an analysis of a single-cell ATAC-seq data set of the human heart, where we were able to characterise cell type-specific regulatory interactions and predict gene expression based on transcription factor affinities. All executed processing steps are incorporated into our new computational pipeline STARE. The software is available at https://github.com/schulzlab/STARE.

bioinformatics↗