Biology subjects

Clarke, B.

Publications and source records attributed to Clarke, B..

14 recordsLinked to original sources

bioRxiv2026

Toxicity of MAPT 4R RNA Contributes to Motor Neuron Degeneration in ALS

MAPT (Tau) dysregulation is implicated in several neurodegenerative diseases, but its contribution to amyotrophic lateral sclerosis (ALS) is poorly understood. Here we show that mRNA isoforms encoding 4-repeat (4R) Tau are upregulated and cytoplasmically enriched in iPSC-derived motor neurons (MNs) from VCP-mutant and sporadic ALS, without a corresponding change in Tau protein. Using splice-switching antisense oligonucleotides and isoform-specific siRNAs, we find that enhanced 4R expression reduces MN viability, whereas its selective knockdown improves survival, with kinetics more consistent with an RNA-intrinsic effect than altered protein synthesis. Exon 10-containing MAPT RNA shows increased predicted secondary structure, self-association and altered Tau biocondensation in vitro. In post-mortem ALS cervical spinal cord, increased relative exon 10 usage is associated with a higher-risk clinical phenotype and shorter disease duration These findings identify an isoform-specific contribution of MAPT to MN vulnerability in ALS and nominate 4R MAPT RNA as a therapeutic target.

neuroscience↗

bioRxiv2026

Multimodal phenotyping defines variant-to-function maps for RBM20 in dilated cardiomyopathy

Multiplex assays of variant effects have linked thousands of genotypes to fitness effects, yet we lack profound understanding of how variants impact molecular phenotypes. Here, we introduce a deep mutational scanning framework that quantifies disease-determining molecular phenotypes in human cells, allowing readouts of protein localization and splicing regulatory function at scale. Applied to the dilated cardiomyopathy (DCM)-associated protein RBM20, we profiled [~]4,300 amino acid substitutions across disease-linked protein domains. Complemented by structure-function investigations of RBM20 bound to its nuclear import receptor TNPO3, we discover new variant hotspots affecting protein function. Finally, we systematically probed nuclear relocalization to identify variants that may be amenable to this therapeutic strategy. Together, we create comprehensive variant-to-function maps that predict variant impact, enhance clinical interpretation, and stratify RBM20-mediated DCM into mechanistically distinct therapeutic classes.

genomics↗

bioRxiv2026

An Alport variant illuminates the bioactivity of the collagen IV α565- α121 scaffold in Bowman's capsule.

Alport syndrome (AS) is a major cause of chronic kidney failure and affects millions of people worldwide. Pathogenic variants in COL4A3, COL4A4, and COL4A5, which encode the collagen IV 345 scaffold, compromise glomerular basement membrane (GBM) structure and function. However, the molecular mechanisms linking the >5,000 reported variants to disease pathology remain poorly understood. To address this gap, we previously examined a distinctive variant, an 8-amino acid "Z-appendage", added to the NC1 domain of the 3 chain. Knock-in mice carrying this variant developed GBM abnormalities and proteinuria, implicating the NC1 hexamer as a critical determinant of GBM function and suggesting that the hexamer surface contains bioactive sites that may mediate signaling and/or organization of macromolecular complexes. Given that approximately 80% of AS cases are associated with COL4A5 variants, including many within the NC1 hexamer, we asked whether relocating the Z-appendage from the 3 NC1 subunit to the 5 subunit produces similar pathology. Strikingly, Col4a5-Z mice did not develop proteinuria and showed only minor changes in GBM morphology. In contrast, the variant induced marked thickening of Bowmans capsule, accompanied by increased deposition of the collagen IV 121 scaffold, increased fibrillar collagen, and cellular deposits. Structural modeling predicts that the collagen IV 565-121 scaffold bearing two Z-appendages adopts an aberrant secondary structure that may stiffen the scaffold and occlude binding sites. Together, these findings reveal a bioactive role for the collagen IV 565-121 scaffold in the Bowmans capsule basement membrane, with potential implications for other 565-121containing tissues such as the aorta and bladder.

molecular biology↗

bioRxiv2026

Mapping trans-eQTLs at single-cell resolution using Latent Interaction Variational Inference.

Single-cell expression quantitative trait loci (eQTL) studies hold promise for linking genetic variants to changes in gene expression in individual cells, thereby offering deeper insights into the genetic basis of human traits and diseases. Existing methods for mapping cis effects rely on predefined cell types, pseudobulk aggregation, or single-gene association tests, and are thus limited in their ability to detect complex trans effects that act across gene networks and cell-type subpopulations. Here, we present Latent Interaction Variational Inference (LIVI), an interpretable deep learning framework for efficient mapping of trans genetic effects at cellular resolution. LIVI employs a structured sparse variational autoencoder architecture to decompose observed gene expression profiles into cell-state- and donor-specific variation. The model enables efficient donor-level association testing while retaining single-cell resolution and interpretation. Applied to the OneK1K dataset, LIVI discovered a greater number of trans-eQTLs than alternative latent variable methods, yielded trans-eQTLs missed by conventional single-gene testing strategies and revealed the cell types and genes implicated in polygenic risk for autoimmune diseases. All in all, we demonstrate that LIVI is a powerful approach for linking genetics to gene regulation and through to human traits.

genomics↗

bioRxiv2025

Predicting functional constraints across evolutionary timescales with phylogeny-informed genomic language models

Genomic language models (gLMs) have emerged as a powerful approach for learning genome-wide functional constraints directly from DNA sequences. However, standard gLMs adapted from natural language processing often require extremely large model sizes and computational resources, yet still fall short of classical evolutionary models in predictive tasks. Here, we introduce GPN-Star (Genomic Pretrained Network with Species Tree and Alignment Representation), a biologically grounded gLM featuring a phylogeny-aware architecture that leverages whole-genome alignments and species trees to model evolutionary relationships explicitly. Trained on alignments spanning vertebrate, mammalian, and primate evolutionary timescales, GPN-Star achieves state-of-the-art performance across a wide range of variant effect prediction tasks in both coding and non-coding regions of the human genome. Analyses across timescales reveal task-dependent advantages of modeling more recent versus deeper evolution. To demonstrate its potential to advance human genetics, we show that GPN-Star substantially outperforms prior methods in prioritizing pathogenic and fine-mapped GWAS variants; yields unprecedented enrichments of complex trait heritability; and improves power in rare variant association testing. Extending beyond humans, we train GPN-Star for five model organisms - Mus musculus, Gallus gallus, Drosophila melanogaster, Caenorhabditis elegans, and Arabidopsis thaliana - demonstrating the robustness and generalizability of the framework. Taken together, these results position GPN-Star as a scalable, powerful, and flexible new tool for genome interpretation, well suited to leverage the growing abundance of comparative genomics data.

bioinformatics↗

bioRxiv2025

LICHEN: Light-chain Immunoglobulin sequence generation Conditioned on the Heavy chain and Experimental Needs

In developing therapeutic antibodies, the heavy chain is often prioritised due to its higher variability and its central role in antigen binding. An appropriate pairing of the light sequence is however important for antibody function. Here we present LICHEN, a heavy chain conditioned light sequence generation tool that enables collaborative light sequence design by leveraging computational capabilities alongside experimental expertise. LICHEN generates light sequences which are valid (antibodylike), diverse in sequence and structure, and conditioned on a specific heavy chain. LICHEN can also condition on germline and CDRs and automatically filter generated sequences for required properties. This allows LICHEN to be used across multiple antibody development use cases. We carry out experimental validation of the method conditioning only on the heavy sequence and on the heavy sequence and binding information. Our in vitro results show that sequences created by LICHEN have effective expression yields and can retain antigen-binding.

bioinformatics↗

bioRxiv2025

Accelerated plastic aging in suspension (APAS): A simple, reproducible approach for the generation of model micro- and nanoplastics through simulated environmental forces

The environmental fragmentation of plastic waste leads to the formation of micro- and nanoplastics (MNPs), which pose serious ecological and human health concerns. Despite increasing interest in their biological effects, many studies rely on artificial, uniform particles that fail to mimic the diverse physical and chemical characteristics of real-world MNPs. To address this limitation, we developed the Accelerated Plastic Aging in Suspension (APAS) system--a scalable, reproducible method that mimics natural aging processes by combining ultraviolet (UV) radiation, thermal stress, and mechanical shear to generate environmentally relevant MNPs from commonly used polymers. We used APAS to fragment polyethylene terephthalate (PET), polyamide 6 (Nylon), and polyacrylonitrile (PAN), and observed time-dependent degradation, including the spontaneous formation of nanoplastics (<100 nm). Flow cytometry revealed substantial increases in particle number and reductions in average particle size over 12 weeks. Imaging flow cytometry confirmed consistent generation of heterogeneous, irregular particles across replicate batches. High-resolution imaging via AFM, TEM, and SEM confirmed the presence of nanoplastics with textured and irregular morphologies. Chemical characterization showed APAS aging altered particle surface charge and induced polymer-specific changes in autofluorescence and Raman spectral profiles, consistent with oxidative surface modifications. Laser Direct Infrared (LDIR) imaging further confirmed structural and chemical changes in polymer spectra post-aging. Functionally, under physiologically relevant shear flow conditions, endothelial cells internalized APAS-generated PET MNPs at significantly higher levels than polystyrene (PS) beads of similar size. Uptake was enhanced particularly under oscillatory flow, highlighting the influence of particle physicochemical properties on cellular interactions. Together, these findings demonstrate the ability of the APAS system to produce complex and realistic MNPs for use in environmental and toxicological studies. The system enables generation of nanoplastics and supports more accurate modelling of biological exposure scenarios compared to conventional synthetic particles.

pharmacology and toxicology↗

bioRxiv2025

Dissociation of SYNGAP1 Enzymatic and Structural Roles: Intrinsic Excitability and Seizure Susceptibility

SYNGAP1 is a key Ras-GAP protein enriched at excitatory synapses, with mutations causing intellectual disability and epilepsy in humans. Recent studies have revealed that in addition to its role as a negative regulator of G-protein signaling through its GAP enzymatic activity, SYNGAP1 plays an important structural role through its interaction with post-synaptic density proteins. Here, we reveal that intrinsic excitability deficits and seizure phenotypes in heterozygous Syngap1 knockout (KO) mice are differentially dependent on Syngap1 GAP activity. Cortical excitatory neurons in heterozygous KO mice displayed reduced intrinsic excitability, including lower input resistance, and increased rheobase, a phenotype recapitulated in GAP-deficient Syngap1 mutants. However, seizure severity and susceptibility to pentylenetetrazol (PTZ)-induced seizures were significantly elevated in heterozygous KO mice but unaffected in GAP-deficient mutants, implicating the structural rather than enzymatic role of Syngap1 in seizure regulation. These findings highlight the complex interplay between SYNGAP1 structural and catalytic functions in neuronal physiology and disease. Significance StatementMutations in the SYNGAP1 gene are a major cause of intellectual disability, autism, and epilepsy. The SYNGAP1 protein is an important constituent of postsynaptic specializations, and two distinct functions have been characterized: a structural function, carried by its C-terminal PDZ domain, that organizes the composition of the postsynaptic density, and an enzymatic function, in which its GAP domain negatively regulates small GTPases. So far, no electrophysiological/behavioral phenotype of SYNGAP1 has been directly linked to the GAP catalytic activity. Here, we describe that while the GAP catalytic activity does not contribute to the increased seizure susceptibility seen in SYNGAP1 haploin-sufficiency, it does regulate the intrinsic excitability of upper lamina pyramidal cells.

neuroscience↗

bioRxiv2024

Finding the right words to evaluate research: An empirical appraisal of eLife's assessment vocabulary

Research articles published by the journal eLife are accompanied by short evaluation statements that use phrases from a prescribed vocabulary to evaluate research on two dimensions: importance and strength of support. Intuitively, the prescribed phrases appear to be highly synonymous (e.g., important/valuable, compelling/convincing) and the vocabularys ordinal structure may not be obvious to readers. We conducted an online repeated-measures experiment to gauge whether the phrases were interpreted as intended. We also tested an alternative vocabulary with (in our view) a less ambiguous structure. 301 participants with a doctoral or graduate degree used a 0-100% scale to rate the importance and strength of support of hypothetical studies described using phrases from both vocabularies. For the eLife vocabulary, most participants implied ranking did not match the intended ranking on both the importance (n = 59, 20% matched, 95% confidence interval [15% to 24%]) and strength of support dimensions (n = 45, 15% matched [11% to 20%]). By contrast, for the alternative vocabulary, most participants implied ranking did match the intended ranking on both the importance (n = 188, 62% matched [57% to 68%]) and strength of support dimensions (n = 201, 67% matched [62% to 72%]). eLifes vocabulary tended to produce less consistent between-person interpretations, though the alternative vocabulary still elicited some overlapping interpretations away from the middle of the scale. We speculate that explicit presentation of a vocabularys intended ordinal structure could improve interpretation. Overall, these findings suggest that more structured and less ambiguous language can improve communication of research evaluations.

scientific communication and education↗

bioRxiv2024

FGF21 acts in the brain to drive macronutrient-specific changes in behavioral motivation and brain reward signaling

Dietary protein restriction induces adaptive changes in food preference, increasing protein consumption over carbohydrates or fat. We investigated whether motivation and reward signaling underpin these preferences. In an operant task, protein-restricted male mice responded more for liquid protein rewards, but not carbohydrate, fat, or sweet rewards compared to non-restricted mice. The protein restriction-induced increase in operant responding for protein was absent in Fgf21-KO mice and mice with neuron-specific deletion of the FGF21 co-receptor beta-Klotho (KlbCam2ka) mice. Fiber photometry recording of VTA dopamine neurons revealed that oral delivery of maltodextrin triggered a larger activation as compared to casein in control-fed mice, whereas casein triggered a larger activation in protein-restricted mice. This restriction-induced shift in nutrient-specific VTA dopamine signaling was lost in Fgf21-KO mice. These data strongly suggest that the increased FGF21 during protein restriction acts in the brain to induce a protein-specific appetite by specifically enhancing the reward value of protein-containing foods and the motivation to consume them.

neuroscience↗

bioRxiv2023

Model-based inference of RNA velocity modules improves cell fate prediction

RNA velocity is a powerful paradigm that exploits the temporal information contained in spliced and unspliced RNA counts to infer transcriptional dynamics. Existing velocity models either rely on coarse biophysical simplifications or require extensive numerical approximations to solve the underlying differential equations. This results in loss of accuracy in challenging settings, such as complex or weak transcription rate changes across cellular trajectories. Here, we present cell2fate, a formulation of RNA velocity based on a linearization of the velocity ODE, which allows solving a biophysically accurate model in a fully Bayesian fashion. As a result, cell2fate decomposes the RNA velocity solutions into modules, which provides a new biophysical connection between RNA velocity and statistical dimensionality reduction. We comprehensively benchmark cell2fate in real-world settings, demonstrating enhanced interpretability and increased power to reconstruct complex dynamics and weak dynamical signals in rare and mature cell types. Finally, we apply cell2fate to a newly generated dataset from the developing human brain, where we spatially map RNA velocity modules onto the tissue architecture, thereby connecting the spatial organisation of tissues with temporal dynamics of transcription.

genomics↗

bioRxiv2023

Integration of variant annotations using deep set networks boosts rare variant association genetics

Rare genetic variants can strongly predispose to disease, yet accounting for rare variants in genetic analyses is statistically challenging. While rich variant annotations hold the promise to enable well-powered rare variant association tests, methods integrating variant annotations in a data-driven manner are lacking. Here, we propose DeepRVAT, a model based on set neural networks that learns burden scores from rare variants, annotations, and phenotypes. In contrast to existing methods, DeepRVAT yields a single, trait-agnostic, nonlinear gene impairment score, enabling both risk prediction and gene discovery in a unified framework. On 34 quantitative and 26 binary traits, using whole-exome-sequencing data from UK Biobank, we find that DeepRVAT offers substantial increases in gene discoveries and improved replication rates in held-out data. Moreover, we demonstrate that the integrative DeepRVAT gene impairment score greatly improves detection of individuals at high genetic risk. Finally, we show that pre-trained DeepRVAT scores generalize across traits, opening up the possibility to conduct highly computationally efficient rare variant tests.

bioinformatics↗

bioRxiv2022

Combining phenotypic and genomic data to improve prediction of binary traits

Plant breeders want to develop cultivars that outperform existing genotypes. Some characteristics (here main traits) of these cultivars are categorical and difficult to measure directly. It is important to predict the main trait of newly developed genotypes accurately. In addition to marker data, breeding programs often have information on secondary traits (or phenotypes) that are easy to measure. Our goal is to improve prediction of main traits with interpretable relations by combining the two data types using variable selection techniques. However, the genomic characteristics can overwhelm the set of secondary traits, so a standard technique may fail to select any phenotypic variables. We develop a new statistical technique that ensures appropriate representation from both the secondary traits and the phenotypic variables for optimal prediction. When two data types (markers and secondary traits) are available, we achieve improved prediction of a binary trait by two steps that are designed to ensure that a significant intrinsic effect of a phenotype is incorporated in the relation before accounting for extra effects of genotypes. First, we sparsely regress the secondary traits on the markers and replace the secondary traits by their residuals to obtain the effects of phenotypic variables as adjusted by the genotypic variables. Then, we develop a sparse logistic classifier using the markers and residuals so that the adjusted phenotypes may be selected first to avoid being overwhelmed by the genotypes due to their numerical advantage. This classifier uses forward selection aided by a penalty term and can be computed effectively by a technique called the one-pass method. It compares favorably with other classifiers on simulated and real data.

genomics↗

bioRxiv2019

Molecular and functional heterogeneity of cancer associated fibroblasts in high-grade serous ovarian cancer

Recent studies indicate that cancer-associated fibroblasts (CAFs) are phenotypically and functionally heterogeneous. However, little is known about CAF subtypes and the roles they play in cancer progression. Here we identify and characterize two CAF subtypes that coexist within high grade serous ovarian cancers: Fibroblast activation protein (FAP)-high (FH) CAFs resemble the classical myofibroblast-type CAF, whereas FAP-low (FL) CAFs possesses a preadipocyte-like molecular signature. Patients with predominantly FH CAFs have significantly worse outcomes than patients with predominantly FL CAFs. FH CAFs contract collagen gels and aggressively promote proliferation, invasion and therapy resistance of cancer cells, whereas FL CAFs do not. Overexpression of the FL-specific transcription factor TCF21 in FH CAFs decreases their ability to promote gel contraction, invasion, and in vivo tumor growth. Understanding CAF subtypes in more detail could lead to better patient stratification and novel therapeutic strategies.

cancer biology↗