Biology subjects

Barton, P. J.

Publications and source records attributed to Barton, P. J..

8 recordsLinked to original sources

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

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↗

bioRxiv2023

Cardiomyocyte-restricted expression of IL11 causes cardiac fibrosis, inflammation, and dysfunction.

AbstractBackground: Cardiac fibrosis is a common pathological process in heart disease and represents a therapeutic target. TGF{beta} is the canonical driver of cardiac fibrosis and was recently shown to be dependent on IL11 for its profibrotic effects in fibroblasts. In the opposite direction, recombinant human IL11 has been reported as anti-fibrotic and also anti- inflammatory in the mouse heart. Objectives: In this study, we determined the effects of IL11 expression in cardiomyocytes on cardiac pathobiology and function. Methods: We used the Cre-loxP system to generate a tamoxifen-inducible mouse with cardiomyocyte-restricted murine Il11 expression. Using protein assays, bulk RNA-sequencing, and in vivo imaging we analysed the effects of IL11 on myocardial fibrosis, inflammation and cardiac function and challenge previous reports suggesting cardioprotective potential of IL11. Results: TGF{beta} stimulation of cardiomyocytes caused Il11 upregulation. As compared to wild-type controls, Il11 expressing hearts demonstrated severe cardiac fibrosis and inflammation that was associated with the upregulation of cytokines, chemokines, complement factors and increased inflammatory cells. IL11 expression also activated a programme of endothelial-to- mesenchymal transition and resulted in left ventricular dysfunction. Conclusion: Our data define species matched IL11 as strongly profibrotic and proinflammatory when secreted from cardiomyocytes and further establish IL11 as a disease factor.

molecular biology↗

bioRxiv2021

Cardiomyocyte BRAF and type 1 RAF inhibitors promote cardiomyocyte and cardiac hypertrophy in mice in vivo

The extracellular signal-regulated kinase 1/2 (ERK1/2) cascade promotes cardiomyocyte hypertrophy and is cardioprotective, with the three RAF kinases forming a node for signal integration. Our aims were to determine if BRAF is relevant for human heart failure, if BRAF promotes cardiomyocyte hypertrophy, and if Type 1 RAF inhibitors developed for cancer (that paradoxically activate ERK1/2 at low concentrations: the "RAF paradox") may have the same effect. BRAF was upregulated in heart samples from patients with heart failure compared with normal controls. We assessed the effects of activated BRAF in the heart using mice with tamoxifen-activated Cre for cardiomyocyte-specific knock-in of the activating V600E mutation into the endogenous gene. We used echocardiography to measure cardiac dimensions/function. Cardiomyocyte BRAFV600E induced cardiac hypertrophy within 10 d, resulting in increased ejection fraction and fractional shortening over 6 weeks. This was associated with increased cardiomyocyte size without significant fibrosis, consistent with compensated hypertrophy. The experimental Type 1 RAF inhibitor, SB590885, and/or encorafenib (a RAF inhibitor used clinically) increased ERK1/2 phosphorylation in cardiomyocytes, and promoted hypertrophy, consistent with a "RAF paradox" effect. Both promoted cardiac hypertrophy in mouse hearts in vivo, with increased cardiomyocyte size and no overt fibrosis. In conclusion, BRAF potentially plays an important role in human failing hearts, activation of BRAF is sufficient to induce hypertrophy, and Type 1 RAF inhibitors promote hypertrophy via the "RAF paradox". Cardiac hypertrophy resulting from these interventions was not associated with pathological features, suggesting that Type 1 RAF inhibitors may be useful to boost cardiomyocyte function.

biochemistry↗

bioRxiv2019

Genomics of Egyptian Healthy Volunteers: The EHVol Study

Comprehensive genomic databases offer unprecedented opportunities towards effective tailored strategies for the prevention and treatment of disease. The integration of genomic and phenotypic data from diverse ethnic populations is also key to advancements in precision medicine and novel diagnostic technologies. Current reference genomic databases, however, are not representative of the global human population, making variant interpretation challenging and uncertain, especially in underrepresented populations such as the North African population. To address this, a study of 391 Egyptian healthy volunteers (EHVols) was initiated as a milestone towards establishing the 1000 Egyptian Genomes project.

genomics↗

bioRxiv2018

Translational control of cardiac fibrosis

Abstract BackgroundFibrosis is a common pathology in many cardiac disorders and is driven by the activation of resident fibroblasts. The global post-transcriptional mechanisms underlying fibroblast-to-myofibroblast conversion in the heart have not been explored.\n\nMethodsGenome-wide changes of RNA transcription and translation during human cardiac fibroblast activation were monitored with RNA sequencing and ribosome profiling. We then used miRNA-and RNA-binding protein-based analyses to identify translational regulators of fibrogenic genes. To reveal post-transcriptional mechanisms in the human fibrotic heart, we then integrated our findings with cardiac ribosome occupancy levels of 30 dilated cardiomyopathy patients.\n\nResultsWe generated nucleotide-resolution translatome data during the TGF{beta}1-driven cellular transition of human cardiac fibroblasts to myofibroblasts. This identified dynamic changes of RNA transcription and translation at several time points during the fibrotic response, revealing transient and early-responder genes. Remarkably, about one-third of all changes in gene expression in activated fibroblasts are subject to translational regulation and dynamic variation in ribosome occupancy affects protein abundance independent of RNA levels. Targets of RNA-binding proteins were strongly enriched in post-transcriptionally regulated genes, suggesting genes such as MBNL2 can act as translational activators or repressors. Ribosome occupancy in the hearts of patients with dilated cardiomyopathy suggested an extensive post-transcriptional regulatory network underlying cardiac fibrosis. Key network hubs include RNA-binding proteins such as PUM2 and QKI that work in concert to regulate the translation of target transcripts in human diseased hearts.\n\nConclusionsWe reveal widespread translational effects of TGF{beta}1 and define novel post-transcriptional events that control the fibroblast-to-myofibroblast transition. Regulatory networks that affect ribosome occupancy in fibroblasts are paralleled in human heart disease. Our findings show the central importance of translational control in fibrosis and highlight novel pathogenic mechanisms in heart failure.

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↗