Biology subjects

Ashley, E. A.

Publications and source records attributed to Ashley, E. A..

At least 19 recordsLinked to original sources

bioRxiv2026

Damaging RBM20 E-rich domain variants are not rescued by gene replacement

The promise of precision therapeutics in genetic cardiomyopathies relies on linking specific therapies to variant mechanisms. Missense variants in the cardiac splice regulator RBM20 cause a highly penetrant and arrhythmogenic dilated cardiomyopathy. Disease-causing variants in RBM20s arginine-serine rich (RS) domain act via formation of toxic gain of function cytoplasmic granules, but this is not true for a small number of clinically adjudicated pathogenic variants in its glutamate(E)-rich domain. To better define the effects of E-rich domain variants, we developed a scalable screen based on induced pluripotent stem cell (iPSC) cardiomyocyte differentiation that identified several additional damaging variants. Several of these reduced RBM20 protein abundance and stability. We therefore hypothesized that, unlike RS domain variants, these E-rich variants might be rescued by RBM20 overexpression. To test this hypothesis, we generated induced pluripotent stem cells (iPSCs) from a patient with a pathogenic E-rich domain variant (p.E913K), and confirmed reduced RBM20 protein expression in these RBM20+/p.E913K cells after differentiation to iPSC-derived cardiomyocytes (iPSC-CM, vs. engineered isogenic RBM20+/+). These iPSC-CMs also displayed aberrant transcriptional splicing, reduced contractility, increased calcium-induced calcium release, and nuclear localization of RBM20 protein, often with more than the two expected RBM20-centric splice factories. AAV-based overexpression of RBM20 reversed some, but not all of the mis-splicing events identified in RBM20+/p.E913K iPSC-CMs, and did not improve their abnormal contractility, calcium handling or supernumerary RBM20 nuclear granules. In summary, our data indicate that pathogenic E-rich domain variants reduce RBM20 protein abundance, but that their mechanism is unlikely to be explained by haploinsufficiency alone.

genetics↗

bioRxiv2026

Multi-Omic, Multi-Tissue Responses to Acute Exercise in Sedentary Adults: Findings from the Molecular Transducers of Physical Activity Consortium

Regular physical activity represents one of the greatest mechanisms for maintaining human health, yet the underlying molecular transducers of these benefits remain incompletely understood. Multi-omic assays now provide new opportunities to study the coordinated molecular responses of body tissues to different exercise modalities. The Molecular Transducers of Physical Activity Consortium (MoTrPAC) was established to address this need by creating a molecular map of the response to physical activity. Described here is the first human cohort of MoTrPAC: sedentary adults enrolled prior to study suspension during the COVID-19 pandemic (N=175) randomized to either endurance or resistance exercise, or non-exercise control. From these participants, we detail their global acute molecular response in skeletal muscle, adipose tissue, and blood, integrated at multiple levels: tissue, exercise modality, timepoint, and omic category. These analyses characterize key molecular pathways, identify central regulators, and implicate novel candidate exerkines in mediating multi-organ exercise effects.

systems biology↗

bioRxiv2025

Multiparametric Assessment of TNNI3 Variant Phenotypes in Human iPSC-Cardiomyocytes Correlates with Disease Severity in Patients

BackgroundThe routine genetic testing of cardiomyopathy patients has significantly accelerated the identification of causative cardiomyopathy variants. However, translating these genetic insights into effective patient management poses significant challenges, since the impact of gene variants on physiological function and clinical outcomes is not yet fully understood. Therefore, there is an urgent need for large-scale methods to assess the effects of genetic variants on cardiomyocyte physiology and to establish correlations between functional phenotypes and clinical severity. MethodsWe developed a high throughput imaging platform to measure force generation and calcium handling throughout the cardiac cycle of human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs). By expressing variants of a sarcomeric protein [cardiac Troponin-I (TNNI3)] in a healthy genetic background, we were able to assess sarcomeric calcium sensitivity as well as systolic and diastolic function. Analysis of these parameters distinguished subgroups of variants, and permitted the correlation of in vitro physiological effects with a measure of disease severity in a single-center cardiomyopathy cohort. ResultsCombining contractile force and calcium cycling measurements accurately distinguished known pathogenic from non-pathogenic TNNI3 variants and also revealed pathogenicity of two variants of unknown significance (VUS) that occurred in two families, suggesting the ability to prospectively discern pathogenicity. Clustering of TNNI3 variants based on quantitative physiological phenotypes identified subgroups that correlated with age of disease onset across a well-characterized cardiomyopathy patient cohort, showing clinical relevance of the in vitro phenotypes. Interestingly, normalized measures of in vitro diastolic function correlated with age of onset (R2 = 0.6), but calcium sensitivity, which accurately predicted pathogenicity, did not translate into disease severity. ConclusionsA high throughput in vitro platform that measures multidimensional cardiomyocyte function can link subgroups of human genetic variants in TNNI3 with differential patient outcomes. Comprehensive determination of variant effects on disease-relevant cardiomyocyte function will help classify variants into different pathogenic mechanisms leading to variable disease severity, and potentially lead to class-targeted ameliorative strategies.

genetics↗

bioRxiv2025

The Long Non-coding RNA Landscape of Endurance Exercise Training

Long non-coding RNAs (lncRNAs) regulate multiple cellular processes. However, knowledge of the responses and regulatory functions of lncRNAs in physical exercise and training remains limited. As part of the Molecular Transducers of Physical Activity Consortium (MoTrPAC), we conducted a comprehensive analysis of lncRNA expression patterns in 18 tissues after an 8-week progressive endurance training program in rats. The lncRNA expression pattern was largely tissue-specific. In total, 759 unique lncRNAs were found to be differentially expressed across all tissues, generally displaying lower abundance, shorter transcript length, and reduced GC content compared with protein-coding genes. The most pronounced changes were observed in white and brown adipose tissues, the hypothalamus, and the adrenal gland. In the two skeletal muscle tissues investigated, only two lncRNAs were commonly differentially expressed. White and brown adipose tissues revealed a correlation between upregulated differentially expressed lncRNAs and coding genes associated with immune regulation. We identified substantial sex differences in the lncRNA regulatory landscape in response to exercise training. This comprehensive tissue-specific characterization of exercise-responsive lncRNAs opens new avenues for understanding exercise as molecular medicine and may inform the development of lncRNA-targeted therapeutics that harness the beneficial effects of exercise.

physiology↗

bioRxiv2025

Scaled multidimensional assays of variant effect identify sequence-function relationships in hypertrophic cardiomyopathy

BackgroundAn estimated 1 in 500 people live with hypertrophic cardiomyopathy (HCM), a disease for which genetic diagnosis can identify family members at risk, and increasingly guide therapy. Mutations in the myosin binding protein C3 (MYBPC3) gene account for a significant proportion of HCM cases. However, many of these variants are classified as variants of uncertain significance (VUS), complicating clinical decision-making. Scalable methods for variant interpretation in disease-specific cell types are crucial for understanding variant impact and uncovering disease mechanisms. MethodsWe developed a scaled multidimensional mapping strategy to evaluate the functional impact of variants across a critical domain of MYBPC3. We incorporate saturation base editing at the native MYBPC3 locus, a long-read RNA sequencing-enabled assay of variant splice effects, and measurements of HCM-relevant phenotypes, including MYBPC3 abundance, hypertrophic signaling, and ubiquitin-proteasome function in human induced pluripotent stem cell-derived cardiomyocytes (iPSC-CMs). ResultsOur multidimensional mapping strategy enabled high-resolution functional analysis of MYBPC3 variants in iPSC-CMs. Targeted transient base editing generated a comprehensive variant library at the native locus, capturing diverse variant effects on cellular HCM-relevant phenotypes. Our massively parallel splicing assay identified novel splice-disrupting variants. Integration of functional assays revealed that decreased MYBPC3 abundance is a key driver of HCM-related phenotypes. In parallel, downregulation of protein degradation was observed as a compensatory response to MYBPC3 loss of function, and novel disease mechanisms were identified for missense variants near a critical binding domain, underscoring their contribution to pathogenesis. Bayesian estimates of variant effects enable the reclassification of clinical variants. ConclusionsThis work provides a platform for extending genome engineering in iPSCs to multiplexed assays of variant effects across diverse disease-relevant cellular phenotypes, enhancing the understanding of variant pathogenicity and uncovering novel biological mechanisms that could inform therapeutic strategies.

genetics↗

bioRxiv2024

A Novel Approach for Accurate Sequence Assembly Using de Bruijn graphs

Sequence assembly methods are valuable for reconstructing genomes from shorter read fragments. Modern nucleic acid sequencing instruments produce quality scores associated with each reported base; however, these quality scores are not generally used as a core part of sequence assembly or alignment algorithms. Here, we leverage weighted de Bruijn graphs as graphical probability models representing the relative abundances and qualities of kmers within FASTQ-encoded observations. We then utilize these weighted de Bruijn graphs to identify alternate, higher-likelihood candidate sequences compared to the original observations, which are known to contain errors. By improving the original observations with these resampled paths, iteratively across increasing k-lengths, we can use this expectation-maximization approach to "polish" read sets from any sequencing technology according to the mutual information shared in the reads. We use this polishing approach to probabilistically correct simulated short- and long-read datasets of lower coverages and higher error rates than some algorithms can produce satisfactory assemblies for. We find that this approach corrects sequencing errors at rates that are able to produce error-free and nearly-error-free de Bruijn assembly graphs for simulated read-set challenges.

bioinformatics↗

bioRxiv2023

Leveraging whole genome sequencing to promote genetic diversity and population health in zoo-housed western lowland gorillas

The sustainability of zoo populations is dependent on maintaining genetic diversity and controlling heritable disease. Here, we explore the integration of whole genome sequencing data in the management of the international zoological population of western lowland gorillas, focusing on genetic diversity and heritable diseases. By comparing kinship values derived from classical pedigree mapping and whole genome sequencing, we demonstrate that genomic data provides a more sensitive measure of relatedness. Our analysis reveals a decrease in genetic diversity due to closed breeding, emphasizing the potential for genetic intervention to mitigate negative impact on population fitness. We identify contributing factors to the decreasing genetic diversity including breeding within a closed population, unknown kinship among potential mates, and disproportionate genetic contributions from individual founders. Additionally, we highlight idiopathic myocardial fibrosis (IMF), a common cardiovascular pathology observed in zoologically housed gorillas, and identify a novel genetic variant in the TNNI3K gene that appears to be associated with this condition. These findings underscore the importance of incorporating molecular data into ex-situ population management strategies, and advocate for the adoption of advanced genomic techniques to optimize the genetic health and diversity of zoologically housed western lowland gorillas.

zoology↗

bioRxiv2023

Local read haplotagging enables accurate long-read small variant calling

Long-read sequencing technology has enabled variant detection in difficult-to-map regions of the genome and enabled rapid genetic diagnosis in clinical settings. Rapidly evolving third-generation sequencing platforms like Pacific Biosciences (PacBio) and Oxford nanopore technologies (ONT) are introducing newer platforms and data types. It has been demonstrated that variant calling methods based on deep neural networks can use local haplotyping information with long-reads to improve the genotyping accuracy. However, using local haplotype information creates an overhead as variant calling needs to be performed multiple times which ultimately makes it difficult to extend to new data types and platforms as they get introduced. In this work, we have developed a local haplotype approximate method that enables state-of-the-art variant calling performance with multiple sequencing platforms including PacBio Revio system, ONT R10.4 simplex and duplex data. This addition of local haplotype approximation makes DeepVariant a universal variant calling solution for long-read sequencing platforms.

bioinformatics↗

bioRxiv2022

Influences of rare protein-coding genetic variants on the human plasma proteome in 50,829 UK Biobank participants

Combining human genomics with proteomics is becoming a powerful tool for drug discovery. Associations between genetic variants and protein levels can uncover disease mechanisms, clinical biomarkers, and candidate drug targets. To date, most population-level proteogenomic studies have focused on common alleles through genome-wide association studies (GWAS). Here, we studied the contribution of rare protein-coding variants to 1,472 plasma proteins abundances measured via the Olink Explore 1536 assay in 50,829 UK Biobank human exomes. Through a variant-level exome-wide association study (ExWAS), we identified 3,674 rare and significant protein quantitative trait loci (pQTLs), of which 76% were undetected in a prior GWAS performed on the same cohort, and we found that rare pQTLs are less likely to be random in their variant effect annotation. In gene-based collapsing analyses, we identified an additional 166 significant gene-protein pQTL signals that were undetected through single-variant analyses. Of the total 456 protein-truncating variant (PTV)-driven cis-pQTLs in the gene-based collapsing analysis, 99.3% were associated with decreased protein levels. We demonstrate how this resource can identify allelic series and propose biomarkers for several candidate therapeutic targets, including GRN, HSD17B13, NLRC4, and others. Finally, we introduce a new collapsing analysis framework that combines PTVs with missense cis-pQTLs that are associated with decreased protein abundance to bolster genetic discovery statistical power. Our results collectively highlight a considerable role for rare variation in plasma protein abundance and demonstrate the utility of plasma proteomics in gene discovery and unravelling mechanisms of action.

genomics↗

bioRxiv2022

Realtime morphological characterization and sorting of unlabeled viable cells using deep learning

Phenotyping of single cells has dramatically lagged advances in molecular characterization and remains a manual, subjective, and destructive process. We introduce COSMOS, a platform for phenotyping and enrichment of cells based on deep learning interpretation of high-content morphology data in realtime. By training models on an atlas of >1.5 billion images, we demonstrate enrichment of unlabeled cells up to 33,000 fold. We apply COSMOS to multicellular tissue biopsy samples demonstrating that it can identify malignant cells with similar accuracy to molecular approaches while enriching viable cells for functional evaluation. We show high-dimensional embedding vectors of morphology generated by COSMOS without any need for complex sample pre-processing, gating, or bioinformatics capabilities, which enables discovery of cellular phenotypes, and integration of morphology into multi-dimensional analyses. One sentence summaryA novel platform capable of high-throughput imaging and gently sorting cells using deep morphological assessment.

cell biology↗

bioRxiv2021

Cell-specific chromatin landscape of human coronary artery resolves regulatory mechanisms of disease risk

Coronary artery disease (CAD) is a complex inflammatory disease involving genetic influences across several cell types. Genome-wide association studies (GWAS) have identified over 170 loci associated with CAD, where the majority of risk variants reside in noncoding DNA sequences impacting cis-regulatory elements (CREs). Here, we applied single-cell ATAC-seq to profile 28,316 cells across coronary artery segments from 41 patients with varying stages of CAD, which revealed 14 distinct cellular clusters. We mapped ~320,000 accessible sites across all cells, identified cell type-specific elements, transcription factors, and prioritized functional CAD risk variants via quantitative trait locus and sequence-based predictive modeling. We identified a number of candidate mechanisms for smooth muscle cell transition states and identified putative binding sites for risk variants. We further employed CRE to gene linkage to nominate disease-associated key driver transcription factors such as PRDM16 and TBX2. This single cell atlas provides a critical step towards interpreting cis-regulatory mechanisms in the vessel wall across the continuum of CAD risk.

genomics↗

bioRxiv2021

The mechanism of artemisinin resistance of Plasmodium falciparum malaria parasites originates in their initial transcriptional response.

The emergence and spread of artemisinin resistant Plasmodium falciparum, first in the Greater Mekong Subregion (GMS), and now in East Africa, is a major threat to global malaria eliminations ambitions. To investigate the artemisinin resistance mechanism, transcriptome analysis was conducted of 577 P. falciparum isolates collected in the GMS between 2016-2018. A specific artemisinin resistance-associated transcriptional profile was identified that involves a broad but discrete set of biological functions related to proteotoxic stress, host cytoplasm remodeling and REDOX metabolism. The artemisinin resistance-associated transcriptional profile evolved from initial transcriptional responses of susceptible parasites to artemisinin. The genetic basis for this adapted response is likely to be complex. One sentence summaryThe transcriptional profile that characterize artemisinin resistant infections with malaria parasites Plasmodium falciparum originates in the initial transcriptional response to the drug.

systems biology↗

bioRxiv2020

Multi-omic profiling reveals widespread dysregulation of innate immunity and hematopoiesis in COVID-19

Our understanding of protective vs. pathologic immune responses to SARS-CoV-2, the virus that causes Coronavirus disease 2019 (COVID-19), is limited by inadequate profiling of patients at the extremes of the disease severity spectrum. Here, we performed multi-omic single-cell immune profiling of 64 COVID-19 patients across the full range of disease severity, from outpatients with mild disease to fatal cases. Our transcriptomic, epigenomic, and proteomic analyses reveal widespread dysfunction of peripheral innate immunity in severe and fatal COVID-19, with the most profound disturbances including a prominent neutrophil hyperactivation signature and monocytes with anti-inflammatory features. We further demonstrate that emergency myelopoiesis is a prominent feature of fatal COVID-19. Collectively, our results reveal disease severity-associated immune phenotypes in COVID-19 and identify pathogenesis-associated pathways that are potential targets for therapeutic intervention. One Sentence SummarySingle-cell profiling demonstrates multifarious dysregulation of innate immune phenotype associated with COVID-19 severity.

immunology↗

bioRxiv2020

Computational prediction of protein subdomain stability in MYBPC3 enables clinical risk stratification in hypertrophic cardiomyopathy and enhances variant interpretation.

PurposeVariants in MYBPC3 causing loss-of-function are the most common cause of HCM. However, a substantial number of patients carry missense variants of uncertain significance (VUS) in MYBPC3. We hypothesize that a structural-based algorithm, STRUM, which estimates the effect of missense variants on protein folding, will improve clinical risk stratification of patients with HCM and a MYBPC3 VUS. MethodsAmong 7,963 patients in the multi-center Sarcomeric Human Cardiomyopathy Registry, 120 unique missense VUSs in MYBPC3 were identified. Variants were evaluated for their effect on subdomain folding and a stratified time-to-event analysis for an overall composite endpoint (first occurrence of ventricular arrhythmia, heart failure, all-cause mortality, atrial fibrillation, and stroke) was performed for patients with HCM and a MYBPC3 missense VUS. ResultsWe demonstrated that patients carrying a MYBPC3 VUS predicted to cause subdomain misfolding (STRUM +, {Delta}{Delta}G [≤]-1.2 kcal/mol) exhibited a higher rate of adverse events compared to those with a STRUM-VUS (Hazard Ratio=2.29, P=0.0282). In silico saturation mutagenesis of MYBPC3 identified 4,943/23,427 (21%) missense variants that were predicted to cause subdomain misfolding. ConclusionsSTRUM enables clinical risk stratification of patients with HCM and a MYBPC3 VUS and has the capacity to improve prognostic predictions and clinical decision making.

genetics↗

bioRxiv2020

A Bayesian phase 2 model based adaptive design to optimise antivenom dosing: application to a dose-finding trial for a novel Russell's viper antivenom in Myanmar

For most antivenoms there is little information from clinical studies to infer the relationship between dose and efficacy or dose and toxicity. Antivenom dose-finding studies usually recruit too few patients (e.g. less than 20) relative to clinically significant event rates (e.g. 5%). Model based adaptive dose-finding studies make efficient use of accrued patient data by using information across dosing levels, and converge rapidly to the contextually defined optimal dose. Adequate sample sizes for adaptive dose-finding trials can be determined by simulation studies. We propose a model based, Bayesian phase 2 type, adaptive clinical trial design for the characterisation of optimal initial antivenom doses in contexts where both efficacy and toxicity are measured as binary endpoints. This design is illustrated in the context of dose-finding for Daboia siamensis (Eastern Russells viper) envenoming in Myanmar. The design formalises the optimal initial dose of antivenom as the dose closest to that giving a pre-specified desired efficacy, but resulting in less than a pre-specified maximum toxicity. For Russells viper efficacy is defined as the restoration of blood coagulability within six hours, and toxicity is defined as anaphylaxis. Comprehensive simulation studies compared the expected behaviour of the model based design to a simpler rule based design (a modified 3+3 design). The model based design can identify the optimal dose after fewer patients than the rule based design. Open source code for the simulations can be used to calculate sample sizes under a priori beliefs of efficacy and toxicity. Antivenom dose-finding trials would benefit from using standard model based adaptive designs. Dose-finding trials where rare events (e.g. 5% occurrence) are of clinical importance necessitate larger sample sizes than current practice. We will apply the model based design to determine a safe and efficacious dose for a novel lyophilised antivenom to treat Daboia siamensis envenoming in Myanmar. Author summarySnakebite envenoming is one of the most neglected tropical diseases relative to its mortality and morbidity. Antivenoms are the only known effective treatment for snake-bite envenoming but are frequently responsible for high rates of adverse reactions. Clinical development of antivenoms rarely follows the iterative phases of clinical development applied to other drugs. Dosing is typically based on pre-clinical testing. Here we propose a Bayesian model based adaptive design for clinical trials aiming to determine the optimal dose of antivenom needed. Optimality is defined using safety and efficacy thresholds contextual to the study. This design can be applied to all antivenoms which have binary efficacy and toxicity endpoints. Our design formally specifies a desired efficacy and a maximum tolerated toxicity. We use simulation studies to characterise the sample size necessary to determine the optimal dose in different scenarios. The simulation studies highlight the advantages of a model based design over simpler rule based alternatives. We intend to use this design to determine an effective and safe dose of the new lyophilised viper antivenom currently in use to treat Russells viper envenoming in Myanmar.

pharmacology and toxicology↗

bioRxiv2020

Molecular Choreography of Acute Exercise

Exercise testing is routinely used in clinical practice to assess fitness - a strong predictor of survival - as well as causes of exercise limitations. While these studies often focus on cardiopulmonary response and selected molecular pathways, the dynamic system-wide molecular response to exercise has not been fully characterized. We performed a longitudinal multi-omic profiling of plasma and peripheral blood mononuclear cells including transcriptome, immunome, proteome, metabolome and lipidome in 36 well-characterized volunteers before and after a controlled bout of acute exercise (2, 15, 30 min and 1 hour in recovery). Integrative analysis revealed an orchestrated choreography of biological processes across key tissues. Most of these processes were dampened in insulin resistant participants. Finally, we discovered biological pathways involved in exercise capacity and developed prediction models revealing potential resting blood-based biomarkers of fitness.

systems biology↗

bioRxiv2019

Deep Learning Interpretation of Echocardiograms

Echocardiography uses ultrasound technology to capture high temporal and spatial resolution images of the heart and surrounding structures and is the most common imaging modality in cardiovascular medicine. Using convolutional neural networks on a large new dataset, we show that deep learning applied to echocardiography can identify local cardiac structures, estimate cardiac function, and predict systemic phenotypes that modify cardiovascular risk but not readily identifiable to human interpretation. Our deep learning model, EchoNet, accurately identified the presence of pacemaker leads (AUC = 0.89), enlarged left atrium (AUC = 0.85), normal left ventricular wall thickness (AUC = 0.75), left ventricular end systolic and diastolic volumes(R2 = 0.73 and R2 = 0.68), and ejection fraction (R2 = 0.48) as well as predicted systemic phenotypes of age (R2 = 0.46), sex (AUC = 0.88), weight (R2 = 0.56), and height (R2 = 0.33). Interpretation analysis validates that EchoNet shows appropriate attention to key cardiac structures when performing human-explainable tasks and highlight hypothesis-generating regions of interest when predicting systemic phenotypes difficult for human interpretation. Machine learning on echocardiography images can streamline repetitive tasks in the clinical workflow, standardize interpretation in areas with insufficient qualified cardiologists, and more consistently produce echocardiographic measurements.

bioengineering↗

bioRxiv2019

Machine learning-based detection of insertions and deletions in the human genome

Insertions and deletions (indels) make a critical contribution to human genetic variation. While indel calling has improved significantly, it lags dramatically in performance relative to single-nucleotide variant calling, something of particular concern for clinical genomics where larger scale disruption of the open reading frame can commonly cause disease. Here, we present a machine learning-based approach to the detection of indel breakpoints called Scotch. This novel approach improves sensitivity to larger variants dramatically by leveraging sequencing metrics and signatures of poor read alignment. We also introduce a meta-analytic indel caller, called Metal, that performs a "smart intersection" of Scotch and currently available tools to be maximally sensitive to large variants. We use new benchmark datasets and Sanger sequencing to compare Scotch and Metal to current gold standard indel callers, achieving unprecedented levels of precision and recall. We demonstrate the impact of these improvements by applying this tool to a cohort of patients with undiagnosed disease, generating plausible novel candidates in 21 out of 26 undiagnosed cases. We highlight the diagnosis of one patient with a 498-bp deletion in HNRNPA1 missed by traditional indel-detection tools.

bioinformatics↗