Biology subjects

Rumpf, L.

Publications and source records attributed to Rumpf, L..

7 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

Expectation of aversive outcomes drives multimodal integration of behavioural, physiological and neural activations during human fear retrieval and generalisation

Classical fear conditioning describes how neutral cues acquire a threat value, yet how learned associations are retrieved and generalised across similar stimuli specifically is an ongoing debate. We combined behavioural ratings, physiological measures, and fMRI in a two-day classical fear conditioning paradigm to characterize acquisition, retrieval, and generalisation across modalities. Twenty-five healthy participants completed acquisition trials on Day 1 and retrieval and generalisation trials on Day 2 using conditioned (CS+, CS-) and graded generalisation stimuli (GS). Outcomes included trial-wise US expectancy ratings, pre/post fear and arousal ratings, skin conductance responses (SCR), pupil dilation, and ROI-based fMRI (amygdala, hippocampus, insula, periaqueductal gray (PAG), locus coeruleus). Acquisition yielded robust CS+/CS-discrimination in behavioural ratings and increased BOLD responses in bilateral insula and PAG. During retrieval, US-expectancy ratings indicated early retrieval of CS contingency. The fMRI results showed greater BOLD activity during CS+ presentations than during CS-presentations in bilateral hippocampus, left insula and right PAG. Additionally, hippocampus-insula coupling increased. Critically, parametric modulation during retrieval revealed that trial-wise mean US-expectancy modulated BOLD responses in left insula and right PAG, with a trend in left hippocampus. Across generalisation, US-expectancy and pupil dilation responses followed graded profiles, which could be explained by a Gaussian model, whereas SCR generalised, but was not captured by a Gaussian model. Parametric modulation by US-expectancy correlated with BOLD activity in left PAG, with a trend in right hippocampus. Stimulus identity explained variance in bilateral insula and left PAG. Findings converge on a hippocampus-insula-PAG network that retrieves learned predictions, and scales defensive output according to similarity-based threat probability, linking subjective, physiological, and neural outcomes.

neuroscience↗

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

Resolving temporal threat uncertainty by observational learning involves the amygdala, hippocampus and anterior insula

By observing others experiences, humans learn about threats while avoiding firsthand dangers. Yet, previous neuroscience research has focused on observational threats that are predictable. In firsthand learning, temporally predictable and unpredictable threats can be mechanistically discerned. In this study, we developed a novel observational paradigm in which participants learned from predictable and unpredictable observational threats, as well as a no-threat condition. Participants encountered the same conditions during an expression phase to investigate how the brain encodes predictive and unpredictive threat cues observed in others. Participants in Experiment 1 (n = 20, 14 female, 6 male) and Experiment 2 (n = 23, 9 female, 1 non-binary, 13 male) successfully learned threat contingencies, showing heightened threat expectations for predictive cues and unpredictable condition-onsets. Behavioral findings converged with neural (fMRI, Experiment 2) responses in the anterior insula during the expression phase. Reflecting the dynamic process of learning, amygdala activation in response to predictive threat cues displayed a linear decrease across trials. Interestingly, BOLD responses to others pain that was predictable, were enhanced within the amygdala, insula and hippocampus, compared to unpredictable conditions. Our findings suggest that humans can learn to resolve temporal threat uncertainty, through the observation of others. The present work thereby contributes to understanding the social aspects of fear and anxiety disorders.

neuroscience↗

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↗

bioRxiv2023

A statistical approach to identify regulatory DNA variations

Non-coding variations located within regulatory elements may alter gene expression by modifying Transcription Factor (TF) binding sites and thereby lead to functional consequences like various traits or diseases. To understand these molecular mechanisms, different TF models are being used to assess the effect of DNA sequence variations, such as Single Nucleotide Polymorphisms (SNPs). However, few statistical approaches exist to compute statistical significance of results but they often are slow for large sets of SNPs, such as data obtained from a genome-wide association study (GWAS) or allele-specific analysis of chromatin data. ResultsWe investigate the distribution of maximal differential TF binding scores for general computational models that assess TF binding. We find that a modified Laplace distribution can adequately approximate the empirical distributions. A benchmark on in vitro and in vivo data sets showed that our new approach improves on an existing method in terms of performance and speed. In applications on large sets of eQTL and GWAS SNPs we could illustrate the usefulness of the novel statistic to highlight cell type specific regulators and TF target genes. ConclusionsOur approach allows the evaluation of DNA changes that induce differential TF binding in a fast and accurate manner, permitting computations on large mutation data sets. An implementation of the novel approach is freely available at https://github.com/SchulzLab/SNEEP. Contactmarcel.schulz@em.uni-frankfurt.de

bioinformatics↗