Biology subjects

Allouba, M.

Publications and source records attributed to Allouba, M..

4 recordsLinked to original sources

bioRxiv2026

Transcriptomic signature of Human Cardiac Fibroblast in Hypertrophic Obstructive Cardiomyopathy

BackgroundHypertrophic Cardiomyopathy (HCM) is a cardiac disorder characterized by an increased interstitial fibrosis and Extracellular Matrix (ECM) remodeling. Cardiac fibroblasts (CFs) have a crucial role in ECM remodeling as well as influencing contractility. There is accumulating evidence that the phenotype of CFs is disease-specific. Here, we investigate the transcriptome signature of CFs in Hypertrophic obstructive Cardiomyopathy (HOCM) patients and its functional significance. MethodsPrimary CFs were isolated from myectomy specimens of 12 clinically phenotyped HOCM patients and 3 controls. RNA libraries were prepared from cell isolates for whole transcriptome sequencing. Differential expression analysis was conducted using the Tuxedo pipeline. Pathway and Gene Ontology (GO) enrichment were performed using Protein-protein interaction network analysis and functional clustering were performed using Cytoscape StringApp. Expression of candidate genes and proteins was validated using quantitative PCR, Immunocytochemistry, immunohistochemistry, cytokines/chemokines profiling and western blotting, in patient-derived CFs extracts, CFs-conditioned media, and myocardial tissue sections. ResultsWhole transcriptome analysis identified 265 significant differentially expressed genes (DEGs) in HOCM fibroblasts compared to controls. The most significant GO terms identified were associated with ECM organization and inflammatory response. The most significant GO terms identified were associated with ECM organization and inflammatory response, with circos plot analysis further highlighting pathway-specific gene overlap within inflammatory and structural signaling clusters. The DEGs encompassed gene families such as collagens, proteases, fibulins, inflammatory cytokines, integrins and signaling receptors and kinases. MYC was upregulated alongside chemokine ligands and receptors, highlighting a MYC-linked chemokine signaling axis within the inflammatory HOCM-CFs phenotype. The protein expression of selected extracellular Matrix organization and inflammatory response genes confirmed the transcriptome results. ConclusionTranscriptomic profiling of patient-derived HOCM-CFs identified genes and pathways associated with inflammatory signaling, ECM remodeling, and altered cell-cell/matrix communication. These findings show that advanced HOCM-CFs acquire an inflammatory-remodeling phenotype, with ECM genes other than collagens. Novelty and SignificanceO_ST_ABSWhat Is Known?C_ST_ABSO_LIHypertrophic cardiomyopathy is characterized by myocardial hypertrophy, interstitial fibrosis, extracellular matrix remodelling, and inflammatory signalling. C_LIO_LIMost human HCM transcriptomic studies have been performed using whole myocardial tissue, which limits resolution of fibroblast-specific disease programmes. C_LI What New Information Does This Article Contribute?O_LIPatient-derived cardiac fibroblasts from patients with advanced hypertrophic obstructive cardiomyopathy exhibit a distinct transcriptomic signature enriched for inflammatory signalling, extracellular matrix organization, chemokine activity, and altered cell-matrix communication. C_LIO_LIThe HOCM-CF signature is not characterized by uniform collagen gene induction, but by selective ECM remodelling involving fibulins, proteases, integrins, matricellular genes, and downregulation of selected collagen-associated matrix components. C_LIO_LIComparison with bulk myocardial RNA-sequencing and publicly available human cardiomyopathy data supports the biological relevance of the fibroblast-derived signature while highlighting the limited sensitivity of bulk tissue transcriptomics for resolving fibroblast-specific programmes. C_LI What Is the Significance?This study identifies a disease-associated inflammatory and ECM-remodelling state in patient-derived HOCM cardiac fibroblasts. The findings extend the role of cardiac fibroblasts in HOCM beyond classical collagen deposition and suggest that fibroblast-mediated matrix remodelling, cytokine/chemokine signalling, and altered cell-matrix communication may contribute to myocardial remodelling in advanced obstructive disease. These data support the value of fibroblast-focused profiling for uncovering disease-relevant mechanisms that may be masked in whole myocardial tissue analyses and provide candidate pathways for future mechanistic and therapeutic studies.

cell biology↗

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

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

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↗