Biology subjects

Ward, M.

Publications and source records attributed to Ward, M..

At least 19 recordsLinked to original sources

bioRxiv2026

TDP-43 dysfunction induces cryptic circular RNAs in ALS/FTD

Nuclear depletion of TDP-43 is a defining pathological feature of amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD), leading to widespread RNA misprocessing, including the formation of cryptic exons. Here, we identified TDP-43 as a regulator of circular RNA (circRNA) biogenesis in multiple human neuronal cell models, and showed that its dysfunction induces the de novo formation of cryptic circular RNAs (c-circRNAs). Analysis of post-mortem brain transcriptomic data identified a subset of c-circRNAs which are specific for ALS and FTD cases with TDP-43 pathology. Further, we developed highly sensitive rolling-circle amplification-based circRNA detection assays that allow to distinguish TDP-43 pathology in human CNS tissues with a 0.99 AUC. We found that c-circRNAs can co-occur with cryptic linear splicing events, uncovering complex RNA misprocessing hotspots that induce loss of disease-relevant proteins, including RPTOR and EHMT1. Notably, one of these c-circRNAs originates from UNC13A, a gene whose cryptic exon has previously been linked to one of the major GWAS hits in ALS/FTD and that is being pursued as a therapeutic target through splice-switching ASOs. We showed that c-circUNC13A is co-regulated with the linear cryptic transcript and suppression of UNC13A cryptic exon results in c-circUNC13A reduction in cultured neurons and in vivo, highlighting its potential as a target engagement biomarker for emerging UNC13A-directed therapies. Overall, this work identifies a novel molecular mechanism for TDP-43 dysfunction, opening novel avenues for understanding disease pathogenesis and developing much needed pathology biomarkers.

neuroscience↗

bioRxiv2026

iPSC Neurodegenerative Disease Initiative isogenic CAG repeat iPSC line for Huntingtons disease

STRUCTURED ABSTRACTO_ST_ABSPurpose of ResearchC_ST_ABSThe generation of iPSC lines expressing 21, 56 and 79 glutamine repeats within the HTT protein and homozygous KO of HTT in the KOLF2.1J background as an additional disease series within the iPSC Neurodegenerative Disease Initiative (iNDI) collection. Major FindingsAll iPSCs, even those expressing long repeats of 79Q or HTT KO, were capable of differentiating to striatal and cortical neurons, astrocytes and microglia using established protocols. General quality control stains and morphological analyses are described for each differentiation. A selected set of assays were carried out on differentiated cells; expanded repeat expressing astrocytes showed altered expression of astrocyte protein markers and morphological characteristics, and striatal neurons showed altered DARPP-32/CTIP2 colocalization. mRNAseq carried out for striatal neurons showed high similarities in gene expression changes between 79Q and KO lines compared to the unexpanded repeat. ConclusionsThe KOLF2.1J isogenic CAG repeat series serves as a community resource to study HD mechanisms with the potential for direct comparison across other neurodegenerative diseases through the iNDI collection.

neuroscience↗

bioRxiv2026

Efficient and scalable modelling of cotranscriptional RNA folding with deterministic and iterative RNA structure sampling

AO_SCPLOWBSTRACTC_SCPLOWRNA structure sampling is central to modelling RNA ensembles, yet stochastic sampling methods are non-exhaustive, scale poorly, and are biased towards low-free-energy structures, while current suboptimal folding approaches generate an unpredictable exponential number of structures. These limitations are particularly problematic for modelling cotranscriptional folding, where vectorial synthesis continuously reshapes the energy landscape during transcription, stabilising transient out-of-equilibrium structures. Here we introduce iterative sampling, a deterministic framework that enumerates unique RNA secondary structures in strict order of increasing free energy, enabling progressive and exhaustive exploration of the structure space up to an arbitrary stopping criterion. To implement this approach, we developed two scalable algorithms, iterative deepening and a persistent data structure approach, that incrementally traverse the expansion tree by evolving partial structures in place, avoiding redundant recomputation and fixed energy windows. Implemented in memerna, this approach achieves orders-of-magnitude speedups over existing tools (10x over ViennaRNA; 100x over RNAstructure). Integration within the sample-and-select framework (R2D2) improves structural diversity and identifies conformations with greater agreement with experimental data. Comprehensive sampling further enables direct comparison of equilibrium and cotranscriptionally restrained ensembles. Analysis of the resulting structural probability distributions uncovers kinetic traps and putative transcriptional pause sites, supporting an intuitive cotranscriptional folding mechanism in which local 3'-hairpin formation transiently stabilises upstream structure to delay large-scale rearrangement. Together, these results establish iterative sampling as a scalable and general framework for resolving out-of-equilibrium RNA cotranscriptional folding. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=80 SRC="FIGDIR/small/720041v1_ufig1.gif" ALT="Figure 1"> View larger version (30K): org.highwire.dtl.DTLVardef@dee200org.highwire.dtl.DTLVardef@c7af12org.highwire.dtl.DTLVardef@1b6f54dorg.highwire.dtl.DTLVardef@a2c97a_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2026

The complete genome of the KOLF2.1J reference iPSC line

While induced pluripotent stem cells (iPSCs) have gained popularity in studying neurodegenerative diseases, the heterogeneity of stem cells used across studies impacts cross-study comparison. The iPSC Neurodegenerative Disease Initiative (iNDI) selected the KOLF2.1J cell line and prioritized its use as a reference standard for studying the effects of pathogenic variants on cell biology due to its stability and neutral neurodegenerative disease genetic risk. This cell line, and its derivatives expressing over 100 variants related to Alzheimers disease, Parkinsons disease, and other neurological diseases, are available for academic and industry access. Current genomic data analyses are limited by the use of a human reference genome that does not capture the complete genetic background of a given iPSC line. While in the future this issue may be partially mitigated by the creation of a comprehensive human pangenome, previous work has shown that generating custom genomes is of value both to characterize the variation present and to serve as a more appropriate genomic reference. Here, we generated and characterized a custom complete genome assembly from KOLF2.1J. Mapping of sequencing reads to a personalized diploid assembly results in more comprehensive mapping compared to traditional linear references (i.e GRCh38). In addition, we provide a comprehensive custom gene annotation along with isoform expression and differential methylation analyses across multiple cell types. The assembly and all additional data is browsable and publicly available. This resource will enable more accurate investigation of the KOLF2.1J cell line and any genomics data generated compared to using traditional generalized references, while also serving as a foundational approach for establishing custom reference assemblies for other high-value iPSC lines.

genomics↗

bioRxiv2026

Statins and genetic inhibition of the mevalonate pathway activate an ATF3-STMN2 regenerative program

Loss of neuronal regenerative capacity is a common feature of neurodegenerative disease and axonal injury, yet the transcriptional programs governing this state remain poorly defined. Stathmin-2 (STMN2), a tubulin-binding protein essential for axon maintenance and repair, is profoundly depleted following loss of nuclear TDP-43 in neurodegenerative disease. Here, we identify statins as potent inducers of STMN2 expression. Pharmacological and genetic suppression of the mevalonate pathway, and subsequent prevention of protein geranylgeranylation, restored STMN2 levels in TDP-43 deficient cells and promoted neurite growth. STMN2 induction was abrogated when using a statin analogue unable to interact with HMG-CoA reductase, and through co-administration of mevalonate or geranylgeranyl diphosphate substrates. RNA-seq revealed that statins induce a coordinated pro-regenerative transcriptional response, including activation of the AP-1 transcription factor complex gene, ATF3. Loss of ATF3 attenuated STMN2 induction in vitro, and diminished injury-induced Stmn2 upregulation in spinal motor neurons in vivo. These results demonstrate statins as modulators of ATF3 and STMN2 expression and highlight their therapeutic potential in neurodegenerative disease.

neuroscience↗

bioRxiv2026

Accurate strand-specific long-read transcript isoform discovery and quantification at bulk, single-cell, and single-nucleus resolution

Recent advances in long-read transcriptome sequencing enable high-throughput profiling of full-length RNA isoforms in bulk, single-cell, and single-nucleus samples. However, long-read datasets typically contain a mixture of complete and partial transcripts, leading to pervasive ambiguity in read-to-isoform assignment and complicating accurate isoform identification and quantification, particularly in the absence of reliable reference annotations. These challenges are further amplified in single-cell and single-nucleus samples, where coverage is sparse and transcriptional heterogeneity is high. Here, we present the Long Read Alignment Assembler (LRAA), a unified and versatile computational framework for isoform identification and quantification from long-read RNA sequencing data across bulk, single-cell, and single-nucleus transcriptomic samples. LRAA combines splice-graph based structural modeling with expectation maximization based optimization to probabilistically resolve ambiguous read assignments and improve isoform abundance estimation. The framework supports quantification-only, reference-guided, and fully reference-free (de novo) modes of analysis within a single methodological paradigm. We benchmarked LRAA using both simulated and genuine long-read datasets spanning sequencing standards and whole transcriptomes. Central to this evaluation is a novel benchmarking strategy based on Multiplexed Overexpression of Regulatory Factors (MORFs), which provides biologically expressed, barcoded isoforms with unambiguous read-level ground truth. Across all benchmarks, including MORFs, synthetic spike-ins, and whole-transcriptome datasets, LRAA consistently outperformed state-of-the-art methods in isoform identification accuracy, sensitivity, and expression quantification. Finally, we demonstrate the biological utility of LRAA by resolving cell-type-specific isoform usage across peripheral blood immune cell populations and by detecting a pathogenic cryptic isoform of STMN2 with associated transcriptional changes in single-nucleus RNA-seq data from frontal cortex tissue of an individual with frontotemporal dementia (FTD). Together, these results establish LRAA as a robust and general solution for resolving transcript diversity in complex biological systems, from development to disease.

bioinformatics↗

bioRxiv2026

mRNAfold: Co-optimization of Global Stability, Local Structure, and Codon Choice via Suboptimal Folding

mRNA medicines hold great promise, but designing sequences with high translation efficiency, robust in-solution stability, and manufacturability remains a major challenge due to the vast combinatorial space of synonymous coding sequences. Computational approaches such as mRNA folding algorithms have emerged as powerful tools by co-optimizing for in-solution stability and translation efficiency, yet current methods face important limitations. Here, we present "mRNAfold", an improved mRNA folding algorithm and software package that addresses these gaps by enabling efficient exploration of diverse near-optimal solutions, incorporating untranslated regions (UTRs), parallel execution, and supporting tunable control over local structural features across the mRNA. Thermodynamically optimized mRNAs from mRNAfold were more stable ({approx} 2-fold) in-solution than those generated by simple GC maximization for the same encoded protein. In addition, mRNAs designed to vary local structure near the start codon while maintaining consistent structure and codon optimality elsewhere showed a complex relationship between local structure near the start codon and protein production in cells. We observed no impact of structure in the start codon region for a set of mRNAs with high codon optimality, but it did impact protein production for a set of mRNAs with lower codon optimality. Together, these results underscore the potential of structure-aware, multi-objective design to improve mRNA medicines and offer a framework for exploring how sequence, structure, and expression are interrelated.

bioinformatics↗

bioRxiv2025

Lysosomal escape and TMEM106B fibrillar core determine TDP-43 seeding outcomes

Frontotemporal lobar degeneration with TDP-43 inclusions (FTLD-TDP) shows striking clinical and neuropathological heterogeneity, yet a systematic analysis of subtype-specific features and inter-patient variability was missing. We treated human neurons and neuron-like cells with 30 postmortem brain samples and quantified neoaggregate formation, loss of function and changes in the TDP-43 interactome to define determinants of seeding outcomes. Potent FTLD-TDP-A seeds drove a progressive collapse of physiological TDP-43 interactions accompanied by functional loss. Beyond the burden of pathological TDP-43, we identified the fibrillar core of the lysosomal protein TMEM106B as a critical pro-seeding factor. Transient lysosomal injury markedly enhanced neoaggregation and loss of function, likely by promoting fibril interactions with native TDP-43. Our work establishes a mechanistic link between TMEM106B and TDP-43 aggregation, identifies lysosomal escape as a key driver of pathology and introduces the strongest model yet for seeded TDP-43 aggregation and loss of function, to enable discovery of disease modifiers.

neuroscience↗

bioRxiv2025

A system for high-throughput axonal imaging of induced pluripotent stem cell-derived human i3Neurons

Neurons have long, thin axons and branched dendritic processes which rely on an extensive microtubule network that functions as a cellular scaffold and substrate for cargo transport. Microtubule defects are a defining pathological feature of neurological disorders. The highly arborized, long, polarized neuronal processes pose challenges for imaging-based assays. Available methods use either dispersed cultures, which are inefficient for compartment-specific analyses, or microfluidic chambers, which allow clear separation of somatodendritic and axonal compartments but are expensive and difficult to maintain. Here, we introduce an "i3Neurosphere" culture model of induced pluripotent stem cell (iPSC)-derived human cortical i3Neurons that enables high-throughput imaging of hundreds of axons without specialized equipment. We characterize neurite outgrowth, polarization, microtubule dynamics, and motility of diverse cargo, providing a reference for future work on microtubule processes in this system. The high-throughput compartment-specific imaging we present, combined with facile genetic engineering in i3Neurons provides a powerful tool to study human neurons. SIGNIFICANCE STATEMENTHuman neurons are difficult to study due to limited access to tissue and technical challenges in existing in vitro models of axonal transport. We developed i3Neurospheres, a simple and scalable 3D culture system of human iPSC-derived neurons that enables high-throughput imaging of axonal outgrowth, microtubule dynamics, and intracellular transport. This platform provides an accessible, reproducible method for investigating neuronal function and disease mechanisms, offering broad utility for neuroscience research and preclinical drug screening.

neuroscience↗

bioRxiv2025

Deep Learning for RNA Secondary Structure Determination: Gauging Generalizability and Broadening the Scope of Traditional Methods

The diverse regulatory functions, protein production capacity, and stability of natural and synthetic RNAs are closely tied to their ability to fold into intricate structures. Determining RNA structure is thus fundamental to RNA biology and bioengineering. Among existing approaches to structure determination, computational secondary structure prediction offers a rapid and low-cost strategy and is thus widely used, especially when seeking to identify functional RNA elements in large transcriptomes or screen massive libraries of novel designs. While traditional approaches rely on detailed measurements of folding energetics and/or probabilistic modeling of structural data, recent years have witnessed a surge in deep learning methods, inspired by their tremendous success in protein structure prediction. However, the limited diversity and volume of known RNA structures can impede their ability to accurately predict structures markedly different from the ones they have seen. This is known as the generalization gap and currently poses a major barrier to progress in the field. In this Perspective article, we gauge method generalizability using a new benchmark dataset of structured RNAs we curated from the Protein Data Bank. We also discuss the emergence of deep learning methods for predicting structure probing data and use a new dataset to underscore generalization challenges unique to this domain along with directions for future improvement. Expanding beyond improving predictive accuracy, we review how advances in deep learning have recently enabled scalable and accessible optimization of traditional structure prediction methods and their seamless integration with modern neural networks.

bioinformatics↗

bioRxiv2025

Fast Fourier Transform is a training-free, ultrafast, highly efficient, and fully interpretable approach for epigenomic data compression

Improving the efficiency of data compression remains essential for feature selection and data modelling. Current approaches for compressing epigenomic/genomic data highly rely on autoencoder that requires substantial computing resources, parameter fine-tuning, training, and time. Here, we developed a training-free, Fast Fourier Transform (FFT)-based method, for data compression with high efficiency and full interpretability. Our FFT method compresses epigenomic data of histone modification up to 1,000-fold while still maintaining high reconstruction fidelity (cosine similarity, 99.7%), does not require any training and completes ultrafast within 70 milliseconds on GPU or 20 seconds on CPU opposite to extensive training in hours/days for autoencoder on GPU/CPU, and offers full interpretability of compressed features from frequency components of original signals in contrast to the uninterpretable "black box" from autoencoder. This enables high accuracy in the classification model prediction (AUC, 0.960). Thus, our novel FFT method represents a major paradigm shift in data compression.

bioinformatics↗

bioRxiv2025

A novel deep learning method using contrastive learning enables interpretable outputs from relationships between gene expression and histone modification

Current methods for predicting gene expression from histone modifications rely on arbitrary binary classification thresholds such as the median to distinguish between high and low expression for genes. This approach lacks biological justification, creates dataset-dependent classifications, and ignores the relative regulatory relationships between genes that are often more biologically meaningful than absolute cutoffs. We introduce a novel pairwise ranking approach that compares relative expression levels between gene pairs based on their histone modification patterns, eliminating arbitrary threshold selection. Evaluation using the REMC E066 and GSE76344 datasets showed that our pairwise classification framework demonstrated consistent ranking performance in both datasets. The ablation study found that only a subset of histone modification types is necessary, giving evidence of redundancy in the biological code.

bioinformatics↗

bioRxiv2025

One-step induction of human GABAergic neurons promotes presynaptic development & synapse maturation

Human induced pluripotent stem cells (iPSCs) present a powerful approach to study human brain physiology and disease, yet robust, pure GABAergic induction has remained difficult. Here we present improved, single-step, transposon-based GABAergic induction with Ascl1/Dlx2, which yields, unlike lentiviral approaches, exclusively GABAergic neurons and was validated across three independent iPSC lines. Co-seeding with Ngn2-induced excitatory neurons created stable networks of predefined excitation/inhibition ratios, with corresponding synapse ratios. Proteomic and electrophysiological characterization at different developmental time points showed that the single-step induced GABAergic neurons gain a proteomic profile that maps to different cortical interneuron subtypes and display typical GABAergic synaptic properties, producing large, synchronous and picrotoxin-sensitive currents. During early development, synaptic strength increased threefold, which was accompanied by an increase in expression of proteins exclusively enriched for presynaptic SYNGO terms. Synaptic strength continued to increase during late development but with only minor proteomic changes. Taken together, transposon-based GABAergic induction yields exclusively mature GABAergic neurons suitable for studying genes involved in synaptic maturation and to build excitation/inhibition networks for disease modelling.

neuroscience↗

bioRxiv2025

TDP-43 pathology induces CD8+ T cell activation through cryptic epitope recognition

Aggregation and nuclear depletion of the RNA binding protein TDP-43 are the crucial pathological features of amyotrophic lateral sclerosis (ALS) and inclusion body myositis (IBM), two degenerative diseases of the CNS and muscle. The loss of TDP-43 nuclear function results in the aberrant inclusion of cryptic exons in mRNA transcripts, leading to the expression of de novo proteins. Clonally expanded and highly differentiated CD8+ T cells have been observed in individuals with TDP-43 proteinopathies and therapeutics modulating the T cell response have recently been found to extend survival. However, the target antigens mediating T cell activation have remained elusive. Here, we investigate whether the de novo proteins induced by aberrant cryptic splicing due to TDP-43 nuclear loss can act as neo-antigens. We detect the HDGFL2 cryptic peptide and multiple other TDP-43 cryptic exons in IBM skeletal muscle, where their presence correlates with enrichment of T cells and class I antigen presentation pathways. Furthermore, we identify epitopes deriving from HDGFL2 and IGLON5 cryptic peptides which are recognized by clonally expanded and functionally differentiated populations of CD8+ T cells in ALS and IBM Patients. Finally, we demonstrate that T cells engineered to express the identified TCRs can bind and activate in response to the cryptic peptide derived epitopes (cryptic epitopes) and are able to kill TDP-43 deficient astrocytes. This work identifies for the first time specific T cell antigens in ALS and IBM, directly linking adaptive immune response to TDP-43 pathology.

immunology↗

bioRxiv2025

Infection and host-feeding patterns of West Nile virus vectors varies by urban greenspace composition

Greenspaces are integral to the urban environment and affect climate resilience, yet the extent to which they affect mosquito and West Nile virus (WNV) host populations and behavior is not clear. To address this question, we collected mosquitoes along a transect spanning a range of urban development. Mosquitoes were tested for West Nile virus, and the host species that were fed on determined for blood-fed specimens. Bird counts and camera traps were used to assess avian and mammal species availability. Different components that contribute to WNV transmission intensity responded to different landcover variables. Abundance of Culex mosquitoes was most strongly tied to impervious surface, while prevalence of infection was associated with increasing amounts of turf grass. The amount of turf was itself correlated with a measure of avian community reservoir competence. Blood meal analysis suggested the majority of blood meals in the ornithophagic species Cx. pipiens and Cx. restuans came from American robins and northern cardinals, with the latter in particular being overutilized relative to their abundance in sites with higher WNV prevalence. This work furthers our understanding of how the design of urban greenspaces could benefit from consideration of vector-host-virus interactions.

ecology↗

bioRxiv2025

CHIP protects lysosomes from CLN4 mutant-induced membrane damages

Understanding how cells mitigate lysosomal damage is critical for unraveling pathogenic mechanisms of lysosome-related diseases. Here we use organelle-specific proteomics in iPSC-derived neurons (i3Neuron) and an in vitro lysosome-damaging assay to demonstrate that lysosome damage, caused by the aggregation of Ceroid Lipofuscinosis Neuronal 4 (CLN4)-linked DNAJC5 mutants on lysosomal membranes, serves as a critical pathogenic linchpin in CLN4-associated neurodegeneration. Intriguingly, in non-neuronal cells, a ubiquitin-dependent microautophagy mechanism downregulates CLN4 aggregates to counteract CLN4-associated lysotoxicity. Genome-wide CRISPR screens identify the ubiquitin ligase CHIP as a central microautophagy regulator that confers ubiquitin-dependent lysosome protection. Importantly, CHIPs lysosome protection function is transferrable, as ectopic CHIP improves lysosomal function in CLN4 i3Neurons, and effectively alleviates lipofuscin accumulation and neurodegeneration in a Drosophila CLN4 disease model. Our study establishes CHIP-mediated microautophagy as a key organelle damage guardian that preserves lysosome integrity, offering new insights into therapeutic development for CLN4 and other lysosome-related neurodegenerative diseases.

cell biology↗

bioRxiv2025

Generation of C9orf72 repeat knock-in iPSC lines for modelling ALS and FTD

Induced pluripotent stem cell (iPSC) models are powerful tools for neurodegenerative disease modelling, as they allow mechanistic studies in a human genetic environment and they can be differentiated into a range of neuronal and non-neuronal cells. However, these models come with inherent challenges due to line-to-line and clonal variability. To combat this issue, the iPSC Neurodegenerative Disease Initiative (iNDI) has generated an iPSC repository using a single clonal reference line, KOLF2.1J, into which disease-causing mutations and revertants are introduced via gene editing. Here we describe the generation and validation of lines carrying the most common causative mutation for amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD), a repeat expansion in the C9orf72 gene, for the iNDI collection of neurodegenerative iPSC models. We demonstrate that these C9orf72 knock-in lines differentiate efficiently into neurons and display characteristic C9orf72-associated pathologies, including reduced C9orf72 levels and the presence of dipeptide repeat proteins (DPRs) and RNA foci, which increase in abundance over time in culture. These pathologies are not present in revertant cells lacking the repeat expansion. These repeat expansion and revertant cell lines are now available to academic and for-profit institutions through the JAX iPS cell repository and will help to facilitate and standardise iPSC-based ALS/FTD research.

neuroscience↗

bioRxiv2024

Machine learning reveals novel targets for both glioblastoma and osteosarcoma

Glioblastoma and osteosarcoma originate from the same lineage, yet patients with these two tumour types show significant differences in survival outcomes. Transcriptomic analysis comparing these tumours reveals that over 65% genes show similar expression patterns. Principal component analysis further demonstrates substantial similarities between these two tumour types, albeit with discernible differences. Deep learning analysis employing an autoencoder unveils nuanced distinctions and similarities of these two tumours at a high resolution. A classification model, leveraging gradient boosting with eXtreme Gradient Boosting (XGBoost), achieves high accuracy in distinguishing between these two tumour types. Identification of key contributors to the models performance is facilitated by SHapley Additive exPlanations (SHAP), yielding two lists of top target genes with and without considering gender. Notably, these SHAP targets tend to cluster within one or two networks of signalling pathways. Remarkably, gene expression levels of many of these SHAP targets alone can recapitulate survival differences solely based on clinical data between glioblastoma and osteosarcoma patients. Of particular interest, C2ORF72 emerges as a common target from both lists, representing an uncharacterised protein with promising potential as a novel target for diagnostic, prognostic, and therapeutic target for both glioblastoma and osteosarcoma.

genomics↗