Biology subjects

Bosselmann, C. M.

Publications and source records attributed to Bosselmann, C. M..

3 recordsLinked to original sources

bioRxiv2024

Optimizing clinical interpretability of functional evidence in epilepsy-related ion channel variants

Variants in genes encoding the voltage-gated ion channels are among the most common monogenic causes of epilepsy and neurodevelopmental disorders. Functional effects of a variant are increasingly important for diagnosis and therapeutic decisions. To incorporate knowledge regarding functional consequences in formal clinical variant interpretation, we developed an approach for evaluating multiple functional measurements within the Bayesian framework of the modified ACMG/AMP guidelines. We analyzed 216 functional assessments of 191 variants in SCN1A (n=74), SCN2A (n=66), SCN3A (n=18), and SCN8A (n=33). Of 20 commonly measured biophysical parameters, the most frequent drivers of overall functional consequence were persistent current (f=0.54), voltage dependence of activation (f=0.51), and voltage dependence of fast inactivation (f=0.40) for gain-of-function and peak current (f=0.87) for loss-of-function. By comparing measurements of 23 benign variants, we determined thresholds by which published data on these four parameters confer Strong evidence of variant pathogenicity (likelihood ratio > 18.7) under the ACMG/AMP rubric. Similarly, we delineated evidence weights for the most common epilepsy-related potassium channel gene, KCNQ2, through reports of 80 pathogenic and 24 benign variants, accounting for heterozygous and homozygous experimental conditions. We collected the resulting categorization of functional data into FENICS, a biomedical ontology of 152 standardized terms for coherent annotation of electrophysiological results. Across 271 variants in SCN1A/2A/3A/8A and KCNQ2, 1,731 annotations are available in ClinVar, facilitating use of this evidence in variant classification. In summary, we introduce and apply an ACMG/AMP-calibrated framework for electrophysiological studies in epilepsy-related channelopathies to delineate the impact of functional evidence on clinical variant interpretation.

genetics↗

bioRxiv2022

Learning with phenotypic similarity improves the prediction of functional effects of missense variants in voltage-gated sodium channels

BackgroundMissense variants in genes encoding voltage-gated sodium channels are associated with a spectrum of severe diseases affecting neuronal and muscle cells, the so-called sodium channelopathies. Variant effects on the biophysical function of the channel correlate with clinical features and can in most cases be categorized as an overall gain- or loss-of-function. This information enables a timely diagnosis, facilitates precision therapy, and guides prognosis. Machine learning models may be able to rapidly generate supporting evidence by predicting variant functional effects. MethodsHere, we describe a novel multi-task multi-kernel learning framework capable of harmonizing functional results and structural information with clinical phenotypes. We included 62 sequence- and structure-based features such as amino acid physiochemical properties, substitution radicality, conservation, protein-protein interaction sites, expert annotation, and others. We harmonized phenotypes as human phenotype ontology (HPO) terms, and compared different measures of phenotypic similarity under simulated sparsity or noise. The final model was trained on whole-cell patch-clamp recordings of 375 unique non-synonymous missense variants each expressed in mammalian cells. ResultsOur gain- or loss-of-function classifier outperformed both conventional baseline and state-of-the-art methods on internal validation (mean accuracy 0.837 {+/-} 0.035, mean AU-ROC 0.890 {+/-} 0.023) and on an independent set of recently described variants (n = 30, accuracy 0.967, AU-ROC 1.000). Model performance was robust across different phenotypic similarity measures and largely insensitive to phenotypic noise or sparsity. Localized multi-kernel learning offered biological insight and interpretability by highlighting channels with implicit genotype-phenotype correlations or latent task similarity for downstream analysis. ConclusionsLearning with phenotypic similarity makes efficient use of clinical information to enable accurate and robust prediction of variant functional effects. Our framework extends the use of human phenotype ontology terms towards kernel-based methods in machine learning. Training data, pre-trained models, and a web-based graphical user interface for the model are publicly available.

bioinformatics↗

bioRxiv2021

Modulating effects of FGF12 variants on NaV1.2 and NaV1.6 associated with Developmental and Epileptic Encephalopathy and Autism Spectrum Disorder

ObjectiveFibroblast growth factor 12 (FGF12) may represent an important modulator of neuronal network activity and has been associated with developmental and epileptic encephalopathy (DEE). We sought to identify the underlying pathomechanism of FGF12-related disorders. MethodsPatients with pathogenic variants in FGF12 were identified through published case reports, GeneMatcher and whole exome sequencing of own case collections. The functional consequences of two missense variants and two copy number variants (CNVs) were studied by co-expression of wild-type and mutant FGF12 in neuronal-like cells (ND7/23) with the sodium channels NaV1.2 or NaV1.6, including their functional active beta-1 and beta-2 sodium channel subunits (SCN1B and SCN2B). ResultsFour variants in FGF12 were identified for functional analysis: one novel FGF12 variant in a patient with autism spectrum disorder and three variants from previously published patients affected by developmental and epileptic encephalopathy (DEE). We demonstrate the differential regulating effects of wildtype and mutant FGF12 on NaV1.2 and NaV1.6 channels. Here, FGF12 variants lead to a complex kinetic influence on Nav1.2 and Nav 1.6, including loss- as well as gain-of function changes in fast inactivation as well as loss-of function changes in slow inactivation. InterpretationFor the first time, we could demonstrate the detailed regulating effect of FGF12 on NaV1.2 and NaV1.6 and confirmed the complex effect of FGF12 on neuronal network activity. Our findings expand the phenotypic spectrum related to FGF12 variants and elucidate the underlying pathomechanism. Specific variants in FGF12-associated disorders may be amenable to precision treatment with sodium channel blockers.

neuroscience↗