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Biology subjects

Pahl, M.

Publications and source records attributed to Pahl, M..

7 recordsLinked to original sources

Identifying Putative Pathogenic Non-Coding Variants in Unresolved Rare Disease Patients Using Topologically Associated Domains

Unresolved rare disease is a major public health challenge affecting ~300 million people worldwide. At least 50% of these individuals remain genetically unresolved after applying exome sequencing and/or whole genome sequencing. One source of these missing diagnoses is the presence of rare variants within the non-coding genome that are detected but not interpreted by whole genome sequencing. In order to systematically evaluate candidate pathogenic non-coding variants, we created the Genomic Analysis of Variants in Unresolved Rare Disease (GAVURD) system. GAVURD leverages trio whole genome sequencing alignment data to produce a short list of putative pathogenic non-coding variants for a given proband. GAVURD uses best practices for de novo and rare inherited variant identification, links variants to human disease genes harnessing topologically associated domain (TAD) data, and rank prioritizes variants based on phenotypic overlap. As a proof-of-concept, we applied GAVURD to ten probands with unresolved rare disease and implicated six potentially causal non-coding variants based on a confluence of evidence supportive of pathogenicity. The GAVURD system serves an important role in prioritizing candidate non-coding causal variants for unresolved rare disease that can serve as the high value and informed focus of additional functional follow-up studies.

genomics↗

Predicted Effector Gene Aggregation, Standards and Unified Schema (PEGASUS): A Community Framework for Effector Gene Reporting

Genome-wide association studies (GWAS) increasingly report predicted effector genes (PEGs) - genes hypothesised to mediate the biological effects of associated variants. These function as key outputs for advancing variant-to-function research, mechanistic understanding, and therapeutic discovery. However, the rapid growth of PEG lists has not been matched by standards for organising, annotating, and reporting these predictions. As shown by recent landscape analyses, PEG lists vary widely in methodology, evidence definition, nomenclature, provenance tracking, and data structure, limiting interoperability, benchmarking, reuse, and adherence to FAIR principles. To address this gap, we convened an international multi-stakeholder community comprising method developers, data generators, resource maintainers, curators, funders, journal editors, and downstream users. Through a 2024 workshop and a 2025 working group series, we developed the Predicted Effector Gene Aggregation, Standards and Unified Schema (PEGASUS) framework. PEGASUS specifies (i) a metadata standard to capture provenance, trait and GWAS descriptors, evidence sources, and integration methods; (ii) a structured evidence matrix reporting all genes and all evidence underpinning prioritisation at each locus; and (iii) a concise PEG list that summarises author-prioritised genes linked transparently to underlying evidence. The framework balances transparency, machine readability, burden on submitters, and alignment with existing community standards. PEGASUS provides the first community-developed schema for reporting predicted effector genes and their supporting evidence. Adoption of this framework by authors will improve the comparability, reproducibility, and reusability of PEG outputs across studies, facilitating more robust biological inference, enabling cross-resource comparison of gene-prioritisation methods to support community benchmarking, and integration into downstream resources and analytical pipelines. PEGASUS-compliant data can be shared via the PEG Data Registry platform (https://kpndataregistry.org/peg), promoting re-use and establishing the basis for future integration with publicly shared GWAS data.

genomics↗

Variant-to-gene mapping identifies ARHGEF12 as a primary open-angle glaucoma effector gene

Primary open-angle glaucoma (POAG), a leading cause of irreversible blindness worldwide, has a strong genetic basis. The Primary Open-Angle African Ancestry Glaucoma Genetics study previously identified 46 risk loci associated with this disease. To pinpoint causal variants and their corresponding effector genes, we analyzed gene expression, chromatin accessibility, and conformation in two ocular cell types: trabecular meshwork cells (hTMCs) and retinal ganglion cells derived from induced pluripotent stem cells (hiPSC-RGCs). We identified 24 candidate genes in hTMCs and 56 in hiPSC-RGCs. Several prioritized genes showed altered mRNA expression, implicating pathways involved in oxidative stress, tissue homeostasis, neuronal maintenance, and retinal degeneration. Among these genes, ARHGEF12 was selected for further validation because local and distal promoter interactions nominated it in both cell types, and prior evidence reproducibly links it to POAG. While its role in hTMCs is established, its function in RGCs is unclear. hiPSC-RGCs generated from a POAG donor homozygous for the risk allele exhibited reduced ARHGEF12 expression, altered morphology, and disrupted neuronal activity. By linking POAG risk loci to candidate effector genes in ocular cell types, this study provides a framework for functional evaluation of additional POAG risk variants.

genomics↗

Notch controls early temporal factor expression to control timing of Mushroom body neuroblast apoptosis

The neurogenic period, where neural stem cells (NSCs) proliferate to produce molecularly distinct progeny in the developing brain, is a critical time of growth in many organisms. Proper brain development is crucial for survival and requires strict regulation of NSC divisions along a set developmental timeline. In Drosophila NSCs known as neuroblasts (NBs), cell intrinsic programs integrate with extrinsic cues to control periods of rapid growth through temporal patterning genes. Without regulation, NSCs can under proliferate leading to diseases like microcephaly and autism spectrum disorders, or over proliferate leading to macrocephaly and tumors. We know programs to control timing of proliferation and elimination of NSCs exist, but many elements of temporal cassettes are still unclear. What genes may be upstream to regulate known temporal programs to control when certain progeny are produced have not been fully identified, leaving a gap in our understanding. To address these questions, we carried out a large-scale RNAi screen aimed at identifying genes required for NSC elimination. We identified Notch and its ligand, Delta. When Notch pathway activity is reduced in NSCs, we found premature elimination of an important subset of neuroblasts called the mushroom body neuroblasts (MBNBs). These MBNBs produce the neurons responsible for formation of the evolutionarily conserved structure called the mushroom body (MB), which is involved in olfactory based learning and memory. MBNBs with reduced Notch pathway activity also experienced defects in MB structure. Furthermore, we determined that temporal patterning is disrupted primarily through loss of early temporal factor expression. In this work, we find that cell signaling pathways that involve the receptor Notch and its ligand. Delta function to regulate NB proliferation in Drosophila melanogaster by regulating early temporal factor expression.

developmental biology↗

Delta-dependent Notch activation closes the early neuroblast temporal program to promote lineage progression and neurogenesis termination in Drosophila

Neuroblasts in Drosophila divide asymmetrically, sequentially expressing a series of intrinsic factors to generate a diversity of neuron types. These intrinsic factors known as temporal factors dictate timing of neuroblast transitions in response to steroid hormone signaling and specify early versus late temporal fates in neuroblast neuron progeny. After completing their temporal programs, neuroblasts differentiate or die, finalizing both neuron number and type within each neuroblast lineage. From a screen aimed at identifying genes required to terminate neuroblast divisions, we identified Notch and Notch pathway components. When Notch is knocked down, neuroblasts maintain early temporal factor expression longer, delay late temporal factor expression, and continue dividing into adulthood. We find that Delta, expressed in cortex glia, neuroblasts, and after division, their GMC progeny, regulates neuroblast Notch activity. We also find that Delta in neuroblasts is expressed high early, low late, and is controlled by the intrinsic temporal program: early factor Imp promotes Delta, late factors Syp/E93 reduce Delta. Thus, in addition to systemic steroid hormone cues, forward lineage progression is controlled by local cell-cell signaling between neuroblasts and their cortex glia/GMC neighbors: Delta transactivates Notch in neuroblasts bringing the early temporal program and early temporal factor expression to a close.

developmental biology↗

Biostatistics and its impact on hazard characterization using in vitro developmental neurotoxicity assays

In the field of hazard assessment, Benchmark concentrations (BMC) and their associated uncertainty are of particular interest for regulatory decision making. The BMC estimation consists of various statistical decisions to be made, which depend largely on factors such as experimental design and assay endpoint features. In current data practice, the experimenter is often responsible for the data analysis and therefore relies on statistical software without being aware about the software default settings and how they can impact the outputs of data analysis. To provide more insight into how statistical decision making can influence the outcomes of data analysis and interpretation, we have used case studies on a large dataset produced by a developmental neurotoxicity (DNT) in vitro battery (DNT IVB). Here we focused on the BMC and its confidence interval (CI) estimation, as well as on the final hazard classification. We identified five crucial statistical decisions experimenter have to face during data analysis: choice of replicate averaging, response data normalization, regression modelling, BMC and CI estimation, as well as choice of benchmark response levels. In addition, the strength of our data evaluation platform is the integration of endpoint-specific hazard classifications, including flagging systems for uncertain cases, which none of the so far existing statistical data analysis platforms provide. The insights gained in this study demonstrate how important fit-for-purpose, internationally harmonized and accepted data evaluation and analysis procedures are for an objective hazard classification.

bioinformatics↗

The three-dimensional chromatin structure of the major human pancreatic cell types reveals lineage-specific regulatory architecture of T2D risk

Three-dimensional (3D) chromatin organization maps help to dissect cell type-specific gene regulatory programs. Furthermore, 3D chromatin maps have contributed to elucidating the pathogenesis of complex genetic diseases by connecting distal regulatory regions and genetic risk variants to their respective target genes. To understand the cell type-specific regulatory architecture of diabetes risk, we generated transcriptomic and 3D epigenomic profiles of human pancreatic acinar, alpha, and beta cells using single-cell RNA-seq, single-cell ATAC-seq, and high-resolution Hi-C of sorted cells. Comparisons of these profiles revealed differential A/B (open/closed) chromatin compartmentalization, chromatin looping, and transcriptional factor mediated control of cell type-specific gene regulatory programs. We identified a total of 4,750 putative causal-variant-target-gene pairs at 194 type 2 diabetes GWAS signals using pancreatic 3D chromatin maps. We found that the connections between candidate causal variants and their putative target effector genes are cell-type stratified and emphasize previously underappreciated roles for alpha and acinar cells in diabetes pathogenesis.

genetics↗