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<i>SLC6A1</i> variant pathogenicity, molecular function and phenotype: a genetic and clinical analysis
(2023)
Arthur Stefanski
;
Eduardo Pérez-Palma
;
Tobias Brünger
;
Ludovica Montanucci
;
Cornelius Gati
<jats:title>Abstract</jats:title> <jats:p>Genetic variants in the SLC6A1 gene can cause a broad phenotypic disease spectrum by altering the protein function. Thus, systematically curated clinically relevant genotype-phenotype associations are needed to understand the disease mechanism and improve therapeutic decision-making.</jats:p> <jats:p>We aggregated genetic and clinical data from 172 individuals with likely pathogenic/pathogenic (lp/p) SLC6A1 variants and functional data for 184 variants (14.1% lp/p). Clinical and functional data were available for a subset of 126 individuals. We explored the potential associations of variant positions on the GAT1 3D structure with variant pathogenicity, altered molecular function and phenotype severity using bioinformatic approaches.</jats:p> <jats:p>The GAT1 transmembrane domains 1, 6 and extracellular loop 4 (EL4) were enriched for patient over population variants. Across functionally tested missense variants (n = 156), the spatial proximity from the ligand was associated with loss-of-function in the GAT1 transporter activity. For variants with complete loss of in vitro GABA uptake, we found a 4.6-fold enrichment in patients having severe disease versus non-severe disease (P = 2.9 × 10−3, 95% confidence interval: 1.5–15.3).</jats:p> <jats:p>In summary, we delineated associations between the 3D structure and variant pathogenicity, variant function and phenotype in SLC6A1-related disorders. This knowledge supports biology-informed variant interpretation and research on GAT1 function. All our data can be interactively explored in the SLC6A1 portal (https://slc6a1-portal.broadinstitute.org/).</jats:p>
Scopus© Citations 3 3
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Editorial: SLC6A1: the past, present and future
(2023)
Katrine M. Johannesen
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Eduardo Pérez-Palma
;
Guido Rubboli
Scopus© Citations 2
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Identification and quantification of oligogenic loss-of-function disorders
(2022)
Arthur Stefanski
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Eduardo Pérez-Palma
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Marko Mrdjen
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Megan McHugh
;
Costin Leu
Scopus© Citations 3 1
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Incidence and prevalence of major epilepsy-associated brain lesions
(2022)
Javier A. López-Rivera
;
Victoria Smuk
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Costin Leu
;
Gaelle Nasr
;
Deborah Vegh
Scopus© Citations 15 1
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Development and Validation of a Prediction Model for Early Diagnosis of <i>SCN1A</i>-Related Epilepsies
(2022)
Andreas Brunklaus
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Eduardo Pérez-Palma
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Ismael Ghanty
;
Ji Xinge
;
Eva Brilstra
<jats:sec><jats:title>Background and Objectives</jats:title><jats:p>Pathogenic variants in the neuronal sodium channel α1 subunit gene (<jats:italic>SCN1A</jats:italic>) are the most frequent monogenic cause of epilepsy. Phenotypes comprise a wide clinical spectrum, including severe childhood epilepsy; Dravet syndrome, characterized by drug-resistant seizures, intellectual disability, and high mortality; and the milder genetic epilepsy with febrile seizures plus (GEFS+), characterized by normal cognition. Early recognition of a child's risk for developing Dravet syndrome vs GEFS+ is key for implementing disease-modifying therapies when available before cognitive impairment emerges. Our objective was to develop and validate a prediction model using clinical and genetic biomarkers for early diagnosis of <jats:italic>SCN1A</jats:italic>-related epilepsies.</jats:p></jats:sec><jats:sec><jats:title>Methods</jats:title><jats:p>We performed a retrospective multicenter cohort study comprising data from patients with <jats:italic>SCN1A</jats:italic>-positive Dravet syndrome and patients with GEFS+ consecutively referred for genetic testing (March 2001–June 2020) including age at seizure onset and a newly developed <jats:italic>SCN1A</jats:italic> genetic score. A training cohort was used to develop multiple prediction models that were validated using 2 independent blinded cohorts. Primary outcome was the discriminative accuracy of the model predicting Dravet syndrome vs other GEFS+ phenotypes.</jats:p></jats:sec><jats:sec><jats:title>Results</jats:title><jats:p>A total of 1,018 participants were included. The frequency of Dravet syndrome was 616/743 (83%) in the training cohort, 147/203 (72%) in validation cohort 1, and 60/72 (83%) in validation cohort 2. A high <jats:italic>SCN1A</jats:italic> genetic score (133.4 [SD 78.5] vs 52.0 [SD 57.5]; <jats:italic>p</jats:italic> < 0.001) and young age at onset (6.0 [SD 3.0] vs 14.8 [SD 11.8] months; <jats:italic>p</jats:italic> < 0.001) were each associated with Dravet syndrome vs GEFS+. A combined <jats:italic>SCN1A</jats:italic> genetic score and seizure onset model separated Dravet syndrome from GEFS+ more effectively (area under the curve [AUC] 0.89 [95% CI 0.86–0.92]) and outperformed all other models (AUC 0.79–0.85; <jats:italic>p</jats:italic> < 0.001). Model performance was replicated in both validation cohorts 1 (AUC 0.94 [95% CI 0.91–0.97]) and 2 (AUC 0.92 [95% CI 0.82–1.00]).</jats:p></jats:sec><jats:sec><jats:title>Discussion</jats:title><jats:p>The prediction model allows objective estimation at disease onset whether a child will develop Dravet syndrome vs GEFS+, assisting clinicians with prognostic counseling and decisions on early institution of precision therapies (<jats:ext-link xmlns:xlink="http://www.w3.org/1999/xlink" specific-use="external-ds" ext-link-type="uri" xlink:href="http://scn1a-prediction-model.broadinstitute.org/">http://scn1a-prediction-model.broadinstitute.org/</jats:ext-link>).</jats:p></jats:sec><jats:sec><jats:title>Classification of Evidence</jats:title><jats:p>This study provides Class II evidence that a combined <jats:italic>SCN1A</jats:italic> genetic score and seizure onset model distinguishes Dravet syndrome from other GEFS+ phenotypes.</jats:p></jats:sec>
9Scopus© Citations 64
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Epilepsy Genetics and Precision Medicine in Adults: A New Landscape for Developmental and Epileptic Encephalopathies
(2022)
Álvaro Beltrán-Corbellini
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Ángel Aledo-Serrano
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Rikke S. Møller
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Eduardo Pérez-Palma
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Irene García-Morales
<jats:p>This review aims to provide an updated perspective of epilepsy genetics and precision medicine in adult patients, with special focus on developmental and epileptic encephalopathies (DEEs), covering relevant and controversial issues, such as defining candidates for genetic testing, which genetic tests to request and how to interpret them. A literature review was conducted, including findings in the discussion and recommendations. DEEs are wide and phenotypically heterogeneous electroclinical syndromes. They generally have a pediatric presentation, but patients frequently reach adulthood still undiagnosed. Identifying the etiology is essential, because there lies the key for precision medicine. Phenotypes modify according to age, and although deep phenotyping has allowed to outline certain entities, genotype-phenotype correlations are still poor, commonly leading to long-lasting diagnostic odysseys and ineffective therapies. Recent adult series show that the target patients to be identified for genetic testing are those with epilepsy and different risk factors. The clinician should take active part in the assessment of the pathogenicity of the variants detected, especially concerning variants of uncertain significance. An accurate diagnosis implies precision medicine, meaning genetic counseling, prognosis, possible future therapies, and a reduction of iatrogeny. Up to date, there are a few tens of gene mutations with additional concrete treatments, including those with restrictive/substitutive therapies, those with therapies modifying signaling pathways, and channelopathies, that are worth to be assessed in adults. Further research is needed regarding phenotyping of adult syndromes, early diagnosis, and the development of targeted therapies.</jats:p>
36Scopus© Citations 24

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