Biology subjects

Theotokis, P.

Publications and source records attributed to Theotokis, P..

4 recordsLinked to original sources

bioRxiv2026

The Female Side of Autism: Sexual Dichotomies impacting the Helsmoortel-Van der Aa syndrome pathology.

Background: Helsmoortel-Van der Aa syndrome, caused by pathogenic variants in ADNP, is characterised by substantial clinical heterogeneity, but how biological sex influences disease biology and treatment response remains an emerging area of investigation. Here, we investigated whether sex modifies the behavioural and molecular consequences of Adnp deficiency, the response to the investigational drug candidate davunetide (NAP), and the clinical phenotype of Helsmoortel-Van der Aa syndrome. Methods: We studied male and female Adnp heterozygous mice harbouring the p.Leu822Hisfs*6 variant and sex-matched wild-type littermates using continuous 24-h behavioural phenotyping, hippocampal genome-wide DNA methylation and bulk RNA sequencing. NAP effects on behaviour, hippocampal ADNP protein abundance, and epi-transcriptomic responses were assessed using genotype-by-treatment models. Four Core Genotypes mice were assessed to distinguish sex-chromosome and gonadal contributions to hippocampal Adnp transcript expression, and reproductive-tissue transcriptomes were analysed in Adnp mice to unravel a possible endocrine component of the disease. Findings were compared with clinical and developmental data from 129 individuals with Helsmoortel-Van der Aa syndrome. Findings: Adnp deficiency produced a shared overall behavioural phenotype in male and female mice, but through distinct patterns of behavioural disruption, indicating that sex modifies the implementation rather than the magnitude of the phenotype. This distinction was also evident at the molecular level: hippocampal DNA methylation was predominantly determined by genotype and showed convergence between sexes, whereas transcriptional responses diverged, with mitochondrial pathways predominating in males and chromatin, RNA processing, and cell-cycle pathways in females. NAP partially shifted behavioural abnormalities towards the wild-type state in both sexes and increased hippocampal ADNP protein abundance in heterozygous mice, without reversing the underlying methylation phenotype. Its transcriptional effects were markedly broader in males, indicating a sex-dependent molecular response to treatment. In reproductive tissues, Adnp deficiency was associated with convergent alterations in steroid hormone biosynthesis, despite no evidence for direct regulation of hippocampal Adnp expression by sex-chromosome complement or gonadal hormonal state. By contrast, sex was not associated with robust differences across the clinical and developmental features assessed in 129 individuals with Helsmoortel-Van der Aa syndrome after correction for multiple testing. Interpretation: Biological sex modifies how Adnp deficiency is expressed at behavioural and molecular levels, but these differences do not necessarily define distinct clinical phenotypes. The convergence of the methylation response alongside greater divergence in transcriptional and behavioural responses identifies sex as a modifier of disease biology rather than a determinant of the core phenotype. The sex-dependent response to NAP further indicates that biological sex may influence pharmacological responses even when treatment-associated behavioural improvement is observed in both sexes. These findings support consideration of biological sex as a prespecified variable in preclinical and clinical studies of Helsmoortel-Van der Aa syndrome and suggest that systematic investigation of sex-dependent biology may also be relevant to therapeutic development across rare diseases. Keywords: Helsmoortel-Van der Aa syndrome (HVDAS), Activity-Dependent Neuroprotective Protein (ADNP), autism spectrum disorder, intellectual disability, sex, mouse model, behaviour.

genetics↗

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

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↗