Biology subjects

Ware, J. S.

Publications and source records attributed to Ware, J. S..

15 recordsLinked to original sources

bioRxiv2026

Mapping and rewiring the MYBPC3 promoter for rescue of haploinsufficiency driven hypertrophic cardiomyopathy

Autosomal dominant loss-of-function variants in the gene MYBPC3 are, collectively, the most common genetic cause of hypertrophic cardiomyopathy (HCM) and are a prototype of haploinsufficient human disease. Typical for haploinsufficiency-associated genes, hundreds of unique loss-of-function pathogenic variants have been reported for MYBPC3 - therapeutic gene editing to correct each of these variants poses major regulatory and logistical hurdles. Upregulating wild-type allele expression could offer a generalizable therapeutic strategy, but the capacity to modulate native MYBPC3 transcription is unknown. Here, we present a variant-agnostic approach to rescue haploinsufficiency by mapping and rationally redesigning the MYBPC3 promoter. Using massively parallel reporter assays (MPRAs) in human induced pluripotent stem cell-derived cardiomyocytes, we performed saturation mutagenesis of the MYBPC3 promoter at single base-pair resolution. This defined a new class of clinically relevant noncoding loss-of-function variants while revealing essential cis-regulatory grammar anchored by key transcription factor binding sites (TFBSs). Furthermore, by systematically screening thousands of variant combinations, modular promoter elements, and heterologous TFBS insertions, we identified synergistic sequence edits that drive robust increases in MYBPC3 expression. Together, our findings improve the clinical interpretation of noncoding variants and establish a scalable blueprint for promoter editing to treat MYBPC3-associated HCM and other haploinsufficient diseases.

molecular biology↗

bioRxiv2026

Unmasking Supervillin: SVIL haploinsufficiency causes hypertrophic cardiomyopathy by impairing mechanotransduction and cellular energetics

BackgroundRare heterozygous loss-of-function (LoF) variants in SVIL, encoding the Z-disk and costameric protein supervillin, have recently been identified as a cause of hypertrophic cardiomyopathy (HCM). Although supervillin is implicated in actin-dependent mechanotransduction, the mechanisms linking SVIL deficiency to cardiomyopathy remain poorly understood. Homozygous LoF cause a novel skeletal Myofibrillar Myopathy-10 (MFM-10) while heterozygous LoF cause HCM without skeletal myopathy. In this study we use a human model system to disentangle the LoF pathomechanism of the scaffolding protein supervillin in cardiomyocytes and its clinical implications. MethodsUsing CRISPR/Cas-9 we engineered a representative pathogenic LoF variant Q255X into an isogenic induced pluripotent stem cell (iPSC) line creating the heterozygous SVILQ255X/+ and homozygous SVILQ255X/Q255X cell lines. These lines were differentiated into iPSC-derived cardiomyocytes (iPSC-CMs) and cellular phenotypes were assessed using bulk RNA-sequencing, LC-MS proteomics, electrophysiological and calcium handling analyses, contractility measurements, sarcomere organization analysis, Seahorse metabolic flux assay, and pharmacological intervention with mavacamten. ResultsThe Q255X variant resulted in SVIL haploinsufficiency at both RNA and protein levels with no evidence of a truncated protein. Compared with isogenic controls, SVILQ255X/+ iPSC-CMs demonstrated action potential shortening, calcium transient elongation, sarcomeric disorganization and hypertrophy, and impaired mitochondrial respiration. Multi-omic analyses of SVILQ255X/+ iPSC-CMs showed a profile of cellular stress and inflammation, hypertrophic and pro-fibrotic signalling, and a pseudohypoxic state driven by decreased respiration and a HIF-induced glycolytic shift. These abnormalities were not present in SVILQ255X/Q255X cardiomyocytes, consistent with a relatively limited cardiac phenotype reported in homozygous variant carriers. Mavacamten improved sarcomeric disorganization and hypertrophy in SVILQ255X/+ cells but did not rescue energetic compromise. ConclusionsPathogenic heterozygous SVIL LoF produces a distinct cellular phenotype characterized by impaired mechanotransduction, mitochondrial dysfunction, and maladaptive metabolic remodelling that promotes hypertrophic and pro-fibrotic signalling. These findings define a mechanistic basis for SVIL-associated cardiomyopathy and identify metabolic dysfunction as a potential therapeutic target beyond sarcomere-directed therapy. Clinical PerspectiveO_ST_ABSWhat Is New?C_ST_ABSO_LISVIL haploinsufficiency causes HCM through a mechanism distinct from canonical sarcomeric disease, characterized by impaired mechanotransduction, mitochondrial dysfunction, and pseudohypoxia-driven metabolic remodeling. C_LIO_LIHeterozygous SVIL loss of function produces a substantially more severe cardiomyocyte phenotype than homozygous loss of function, providing a mechanistic explanation for the predominance of cardiac disease in heterozygous variant carriers. C_LIO_LIMavacamten improves sarcomeric organization but does not restore impaired mitochondrial respiration, demonstrating that energetic dysfunction persists despite sarcomere-directed therapy. C_LI What Are the Clinical Implications?O_LIOur findings give functional evidence to support SVIL as a clinically relevant HCM disease gene and its inclusion in clinical genetic testing panels. C_LIO_LIThese findings establish SVIL-associated cardiomyopathy as a mechanistically distinct form of HCM and offer insight into the pathomechanism of Z-disk and costameric HCM C_LIO_LIThe persistence of mitochondrial dysfunction despite myosin inhibition suggests that drugs targeting mitochondrial bioenergetics may be a therapeutic strategy in patients with SVIL-associated cardiomyopathy. C_LI

cell biology↗

bioRxiv2025

Spatial genomics of the cardiac sarcomere

Structure-function mapping of proteins has improved our understanding of disease mechanisms and protein domains while human population genetics has provided a window into variational tolerance that can be modeled at a structural level. Here, to reveal novel insight into the cardiac sarcomere, the motor unit of the heart, we develop a novel analytical framework to integrate data from over 17,000 patients with hypertrophic cardiomyopathy (HCM) and combine it with two population-scale genomic databases that incorporate data from more than 800,000 individuals. We integrate in silico genetic predictions of gene variant pathogenicity with 3 dimensional integrative spatial scanning across multiple structural models of cardiac motor proteins. Results reveal both recognized and novel regions of structural variant intolerance across the critical genes of the cardiac sarcomere including novel insights into protein function. We discuss the structural relevance of variant enrichment in the context of sarcomere organization and destabilization of sequestered myosin leading to hypercontractility seen in HCM, by incorporating the recently defined high-resolution structure of human cardiac myosin filament. We extend and validate these findings through pathogenic variant class enrichment and reveal novel associations with the earlier onset of disease in a large clinical cohort. In summary, our study provides a multi-dimensional framework for integrating structural, genomic, and modeling data to reveal novel insight into the cardiac sarcomere.

genomics↗

bioRxiv2025

Loss of the alternative calcineurin variant CnAβ1 enhances brown adipocyte differentiation and drives metabolic overactivation through FoxO1 activation

The alternative calcineurin A variant CnA{beta}1 has a unique C-terminal domain that provides it with distinct subcellular localization and mechanism of action different from other calcineurin isoforms. Here, we used mice lacking CnA{beta}1s C-terminal domain (CnA{beta}1{Delta}i12) to show that the absence of this specific isoform strongly reprograms metabolism. CnA{beta}1{Delta}i12 mice on a high-fat diet showed reduced body weight, white adipose tissue (WAT) mass, and circulating triglycerides, together with enhanced insulin sensitivity. In brown adipose tissue (BAT), CnA{beta}1 deficiency increased mitochondrial content and upregulated fatty acid oxidation and thermogenic proteins, improving cold resistance. Conversely, under starvation, CnA{beta}1{Delta}i12 mice experienced rapid fat depletion and hypothermia. Importantly, BAT-specific FoxO1 knockout in CnA{beta}1{Delta}i12 mice reduced catabolism-related gene expression and partially reversed the metabolic phenotypes, increasing body weight and WAT mass. Our findings reveal a relevant role for CnA{beta}1 in orchestrating BAT metabolism, highlighting its potential as a therapeutic target for obesity and metabolic syndrome.

physiology↗

bioRxiv2023

LAVASET: Latent Variable Stochastic Ensemble of Trees. A novel ensemble method for correlated datasets.

MotivationRandom Forests (RFs) can deal with a large number of variables, achieve reasonable prediction scores, and yield highly interpretable feature importance values. As such, RFs are appropriate models for feature selection and further dimension reduction (DR). However, RFs are often not appropriate for correlated datasets due to their mode of selecting individual features for splitting. Addressing correlation relationships in high dimensional datasets is imperative for reducing the number of variables that are assigned high importance, hence making the DR most efficient. Here, we propose the LAtent VAriable Stochastic Ensemble of Trees (LAVASET) method that derives latent variables based on the distance characteristics of each feature and aims to incorporate the correlation factor in the splitting step. ResultsWithout compromising on performance in the majority of examples, LAVASET outperforms RF by accurately determining feature importance across all correlated variables and ensuring proper distribution of importance values. LAVASET yields mostly non-inferior prediction accuracies to traditional RFs when tested in simulated and real 1D datasets, as well as more complex and high-dimensional 3D datatypes. Unlike traditional RFs, LAVASET is unaffected by single important noisy features (false positives), as it considers the local neighbourhood. LAVASET, therefore, highlights neighbourhoods of features, reflecting real signals that collectively impact the models predictive ability. LAVASET is freely available as a standalone package from https://github.com/melkasapi/LAVASET.

bioinformatics↗

bioRxiv2023

Variant annotation across homologous proteins (Paralogue Annotation) identifies disease-causing missense variants with high precision, and is widely applicable across protein families

BackgroundDistinguishing pathogenic variants from those that are rare but benign remains a key challenge in clinical genetics, especially for variants not previously observed and characterised in humans. In vitro and in vivo functional characterisation are typically resource intensive, and model systems may not accurately predict influence on human disease. Many in silico tools have been developed to predict which variants are disease-causing, but typically lack precision. Here we demonstrate the applicability of a framework, called Paralogue Annotation, that draws on information from previously-characterised variants in homologous proteins to predict whether variants in a gene of interest are likely disease causing. MethodsWe assessed the performance of Paralogue Annotation through three orthogonal approaches: (1) comparison to established in silico variant prediction tools using 47,016 missense variants from ClinVar across 3,524 genes representing a broad range of diverse protein classes, by calculating precision and sensitivity; (2) evaluation against large-scale functional assays of variant effect; and (3) comparing odd ratios calculated from case-control association tests for inherited cardiac arrhythmia syndromes, and neurodevelopmental disorders with epilepsy, stratifying variants by Paralogue Annotation. ResultsParalogue Annotation correctly annotates 4,328 ClinVar pathogenic variants, with 245 false positives, yielding a precision of 0.95. This increases to 0.99 with more stringent annotation parameters (requiring greater conservation of amino acids between annotated orthologues) at the expense of sensitivity. Compared to established tools, Paralogue Annotation has higher precision for the identification of pathogenic variants, albeit with lower sensitivity across diverse test sets. Extending the technique by transferring annotations between homologous protein domains, rather than full-length protein paralogues, increases sensitivity. Rare variants predicted pathogenic by Paralogue Annotation were more strongly disease-associated (increased odds ratio) than unstratified rare variants for six out of eight genes tested with case-control cohort approaches. ConclusionsWe demonstrate that Paralogue Annotation has high precision for predicting pathogenic missense variants, providing a useful line of evidence for clinical variant interpretation that is complementary to other approaches in use. As the number of characterised variants increases in reference datasets such as ClinVar, Paralogue Annotation will further increase in sensitivity and applicability.

bioinformatics↗

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

Annotating high-impact 5'untranslated region variants with the UTRannotator

SummaryCurrent tools to annotate the predicted effect of genetic variants are heavily biased towards protein-coding sequence. Variants outside of these regions may have a large impact on protein expression and/or structure and can lead to disease, but this effect can be challenging to predict. Consequently, these variants are poorly annotated using standard tools. We have developed a plugin to the Ensembl Variant Effect Predictor, the UTRannotator, that annotates variants in 5untranslated regions (5UTR) that create or disrupt upstream open reading frames (uORFs). We investigate the utility of this tool using the ClinVar database, providing an annotation for 30.8% of all 5UTR (likely) pathogenic variants, and highlighting 31 variants of uncertain significance as candidates for further follow-up. We will continue to update the UTR annotator as we gain new knowledge on the impact of variants in UTRs. Availability and implementationUTRannotator is freely available on Github: https://github.com/ImperialCardioGenetics/UTRannotator Supplementary informationSupplementary data are available at bioRxiv.

bioinformatics↗

bioRxiv2020

Disease-specific variant pathogenicity prediction significantly improves variant interpretation in inherited cardiac conditions

BackgroundAccurate discrimination of benign and pathogenic rare variation remains a priority for clinical genome interpretation. State-of-the-art machine learning tools are useful for genome-wide variant prioritisation but remain imprecise. Since the relationship between molecular consequence and likelihood of pathogenicity varies between genes with distinct molecular mechanisms, we hypothesised that a disease-specific classifier may outperform existing genome-wide tools. MethodsWe present a novel disease-specific variant classification tool, CardioBoost, that estimates the probability of pathogenicity for rare missense variants in inherited cardiomyopathies and arrhythmias, trained with variants of known clinical effect. To benchmark against state-of-the-art genome-wide pathogenicity classification tools, we assessed classification of hold-out test variants using both overall performance metrics, and metrics of high-confidence (>90%) classifications relevant to variant interpretation. We further evaluated the prioritisation of variants associated with disease and patient clinical outcomes, providing validations that are robust to potential mis-classification in gold-standard reference datasets. ResultsCardioBoost has higher discriminating power than published genome-wide variant classification tools in distinguishing between pathogenic and benign variants based on overall classification performance measures with the highest area under the Precision-Recall Curve as 91% for cardiomyopathies and as 96% for inherited arrhythmias. When assessed at high-confidence (>90%) classification thresholds, prediction accuracy is improved by at least 120% over existing tools for both cardiomyopathies and arrhythmias, with significantly improved sensitivity and specificity. Finally, CardioBoost improves prioritisation of variants significantly associated with disease, and stratifies survival of patients with cardiomyopathies, confirming biologically relevant variant classification. ConclusionsWe demonstrate that a disease-specific variant pathogenicity prediction tool outperforms state-of-the-art genome-wide tools for the classification of rare missense variants of uncertain significance for inherited cardiac conditions. To facilitate evaluation of CardioBoost, we provide pre-computed pathogenicity scores for all possible rare missense variants in genes associated with cardiomyopathies and arrhythmias (https://www.cardiodb.org/cardioboost/). Our results also highlight the need to develop and evaluate variant classification tools focused on specific diseases and clinical application contexts. Our proposed model for assessing variants in known disease genes, and the use of application-specific evaluations, is broadly applicable to improve variant interpretation across a wide range of Mendelian diseases.

genetics↗

bioRxiv2019

Human loss-of-function variants suggest that partial LRRK2 inhibition is a safe therapeutic strategy for Parkinsons disease

Human genetic variants causing loss-of-function (LoF) of protein-coding genes provide natural in vivo models of gene inactivation, which are powerful indicators of gene function and the potential toxicity of therapeutic inhibitors targeting these genes1,2. Gain-of-kinase-function variants in LRRK2 are known to significantly increase the risk of Parkinsons disease3,4, suggesting that inhibition of LRRK2 kinase activity is a promising therapeutic strategy. Whilst preclinical studies in model organisms have raised some on-target toxicity concerns5-8, the biological consequences of LRRK2 inhibition have not been well characterized in humans. Here we systematically analyse LoF variants in LRRK2 observed across 141,456 individuals sequenced in the Genome Aggregation Database (gnomAD)9 and over 4 million participants in the 23andMe genotyped dataset, to assess their impact at a molecular and phenotypic level. After thorough variant curation, we identify 1,358 individuals with high-confidence predicted LoF variants in LRRK2, several with experimental validation. We show that heterozygous LoF of LRRK2 reduces LRRK2 protein level by ~50% but is not associated with reduced life expectancy, or with any specific phenotype or disease state. These data suggest that therapeutics that downregulate LRRK2 levels or kinase activity by up to 50% are unlikely to have major on-target safety liabilities. Our results demonstrate the value of large scale genomic databases and phenotyping of human LoF carriers for target validation in drug discovery.

genomics↗

bioRxiv2019

Characterising the loss-of-function impact of 5' untranslated region variants in whole genome sequence data from 15,708 individuals

Upstream open reading frames (uORFs) are important tissue-specific cis-regulators of protein translation. Although isolated case reports have shown that variants that create or disrupt uORFs can cause disease, genetic sequencing approaches typically focus on protein-coding regions and ignore these variants. Here, we describe a systematic genome-wide study of variants that create and disrupt human uORFs, and explore their role in human disease using 15,708 whole genome sequences collected by the Genome Aggregation Database (gnomAD) project. We show that 14,897 variants that create new start codons upstream of the canonical coding sequence (CDS), and 2,406 variants disrupting the stop site of existing uORFs, are under strong negative selection. Furthermore, variants creating uORFs that overlap the CDS show signals of selection equivalent to coding loss-of-function variants, and uORF-perturbing variants are under strong selection when arising upstream of known disease genes and genes intolerant to loss-of-function variants. Finally, we identify specific genes where perturbation of uORFs is likely to represent an important disease mechanism, and report a novel uORF frameshift variant upstream of NF2 in families with neurofibromatosis. Our results highlight uORF-perturbing variants as an important and under-recognised functional class that can contribute to penetrant human disease, and demonstrate the power of large-scale population sequencing data to study the deleteriousness of specific classes of non-coding variants.

genomics↗

bioRxiv2019

Variation across 141,456 human exomes and genomes reveals the spectrum of loss-of-function intolerance across human protein-coding genes

Genetic variants that inactivate protein-coding genes are a powerful source of information about the phenotypic consequences of gene disruption: genes critical for an organisms function will be depleted for such variants in natural populations, while non-essential genes will tolerate their accumulation. However, predicted loss-of-function (pLoF) variants are enriched for annotation errors, and tend to be found at extremely low frequencies, so their analysis requires careful variant annotation and very large sample sizes1. Here, we describe the aggregation of 125,748 exomes and 15,708 genomes from human sequencing studies into the Genome Aggregation Database (gnomAD). We identify 443,769 high-confidence pLoF variants in this cohort after filtering for sequencing and annotation artifacts. Using an improved human mutation rate model, we classify human protein-coding genes along a spectrum representing tolerance to inactivation, validate this classification using data from model organisms and engineered human cells, and show that it can be used to improve gene discovery power for both common and rare diseases.

genomics↗

bioRxiv2018

Response to Shah et al: Using high-resolution variant frequencies empowers clinical genome interpretation and enables investigation of genetic architecture

Main text Main text Supplemental Data description Declaration of Interests Web Resources References Recent work by Shah and colleagues1 demonstrated that many variants in the ClinVar database2 are misclassified, and that disease-specific allele frequency (AF) thresholds can identify wrongly classified alleles by flagging variants that are too prevalent in the population to be causative of rare penetrant disease. While we agree with the main conclusions of this work, the authors compare their AF filtering approach to our recently published method3, concluding that the method we advanced \"may be prone to removing potentially pathogenic variants\". This is incorrect. Here we demonstrate that our approach is robust, and further illustrate the power of di ...

genomics↗

bioRxiv2018

Quantitative approaches to variant classification increase the yield and precision of genetic testing in Mendelian diseases: The case of hypertrophic cardiomyopathy

BackgroundInternational guidelines for variant interpretation in Mendelian disease set stringent criteria to report a variant as (likely) pathogenic, prioritising control of false positive rate over test sensitivity and diagnostic yield. Genetic testing is also more likely informative in individuals with well-characterised variants from extensively studied European-ancestry populations. Inherited cardiomyopathies are relatively common Mendelian diseases that allow empirical calibration and assessment of this framework.\n\nResultsWe compared rare variants in large hypertrophic cardiomyopathy (HCM) cohorts to reference populations to identify variant classes with high prior likelihoods of pathogenicity, as defined by etiological fraction (EF). Analysis of variant distribution identified regions in which variants are significantly enriched in cases and variant location was a better discriminator of pathogenicity than generic computational functional prediction algorithms. Non-truncating variant classes with an EF[≥]0.95, and therefore clinically actionable, were identified in 5 established HCM genes. Applying this approach leads to an estimated 14-20% increase in cases with actionable HCM variants.\n\nConclusionsWhen found in a patient confirmed to have disease, novel variants in some genes and regions are empirically shown to have a sufficiently high probability of pathogenicity to support a \"likely pathogenic\" classification, even without additional segregation or functional data. This could increase the yield of high confidence actionable variants, consistent with the framework and recommendations of current guidelines. The techniques outlined offer a consistent, unbiased and equitable approach to variant interpretation for Mendelian disease genetic testing. We propose adaptations to ACMG/AMP guidelines to incorporate such evidence in a quantitative and transparent manner.

genetics↗

bioRxiv2017

CardioClassifier: demonstrating the power of disease- and gene-specific computational decision support for clinical genome interpretation

PurposeInternationally-adopted variant interpretation guidelines from the American College of Medical Genetics and Genomics (ACMG) are generic and require disease-specific refinement. Here we developed CardioClassifier (www.cardioclassifier.org), a semi-automated decision-support tool for inherited cardiac conditions (ICCs).\n\nMethodsCardioClassifier integrates data retrieved from multiple sources with user-input case-specific information, through an interactive interface, to support variant interpretation. Combining disease- and gene-specific knowledge with variant observations in large cohorts of cases and controls, we refined 14 computational ACMG criteria and created three ICC-specific rules.\n\nResultsWe benchmarked CardioClassifier on 57 expertly-curated variants and show full retrieval of all computational data, concordantly activating 87.3% of rules. A generic annotation tool identified fewer than half as many clinically-actionable variants (64/219 vs 156/219, Fishers P=1.1x10-18), with important false positives; illustrating the critical importance of disease and gene-specific annotations.\n\nCardioClassifier identified putatively disease-causing variants in 33.7% of 327 cardiomyopathy cases, comparable with leading ICC laboratories. Through addition of manually-curated data, variants found in over 40% of cardiomyopathy cases are fully annotated, without requiring additional user-input data.\n\nConclusionCardioClassifier is an ICC-specific decision-support tool that integrates expertly curated computational annotations with case-specific data to generate fast, reproducible and interactive variant pathogenicity reports, according to best practice guidelines.

genomics↗