Dane rzeczywiste o wariantach genetycznych i fenotypach rzadkich monogenowych epilepsji oraz geny o nieznanym znaczeniu dla epilepsji

PubMedEpilepsia Open

Real-world-data for phenotypes and genotypes of rare monogenic genetic epilepsies and genes of uncertain significance for epilepsy

W skrócie

Naukowcy utworzyli bazę danych 167 pacjentów z epilepsją i odkryli przyczynę genetyczną u 66 osób, identyfikując 44 różne rzadkie formy epilepsji. Przy użyciu zaawansowanych narzędzi komputerowych do analizy struktury białek, badacze znaleźli 19 genów, które mogą być związane z epilepsją, ale wymagają dalszych badań laboratoryjnych. Jeśli te geny potwierdzą swój związek z epilepsją, liczba przypadków, które uda się wyjaśnić genetycznie, może wzrosnąć o 17 procent.

Oryginalny abstract (angielski)

OBJECTIVES: The objectives of this study were to develop a real-world-data (RWD) database for patients with epilepsy to provide further real-world-evidence (RWE) for monogenic genetic epilepsies; to assess the usefulness of a diagnostic algorithm in epilepsy; and to examine protein 3D structures using in silico tools to predict variant pathogenicity. METHODS: We stratified patients into Group 1 (with genetic diagnoses) and Group 2 (with no genetic diagnoses). We performed protein 3D modeling of variants of uncertain significance (VUS) in genes. RESULTS: We included 167 patients in our RWD database. We report the genotypes and phenotypes of 44 distinct monogenic genetic epilepsies from 66 patients. The diagnostic yield of clinical exome sequencing (ES) was 31%. Developmental delay, developmental brain malformation, movement disorder, and infantile-onset epilepsy (seizure onset <2 years of age) were more common (p < 0.05) in Group 1. The diagnostic yield of biochemical investigations was 0%. Compared to wild type protein structures, there were predicted conformational changes to protein structure including changes in α-helices and/or β-sheets and losses in loops in the protein structures expressed by variants of uncertain significance (VUS) for ARHGAP35, BACH2, BAZ2B, DOP1B, EPHA5, FRYL, HMGXB4, MTDH, NHSL2, NLGN2, NRXN2, P2RX4, PRICKLE2, PRRC2A, RALGAPB, RNF157, SREBF2, ZNF532, and ZNF536, all of which are genes of uncertain significance (GUS). We calculated the estimated diagnostic yield of ES to be 48% by using a 3D protein structure prediction tool, and the functional characterization of a LARP7 VUS, as well as previous case series of GUS. SIGNIFICANCE: We report 19 GUS that accounts 10% of the cohort with GUS. If those GUS would have been proven to cause genetic epilepsies, the diagnostic yield of ES would have been increased 17%. The GUS and VUS in monogenic epilepsy genes will require further functional characterization. International collaborations are needed to increase our understanding of these rare epilepsies. PLAIN LANGUAGE SUMMARY: We wanted to create a database for people with epilepsy. We did genetic tests in people with epilepsy to find out if they have a genetic problem. We looked at their information. We added 167 people with epilepsy and diagnosed a genetic problem in 66 people with epilepsy. We applied genetic tools to find out if genetic changes were important. We still need to the do more laboratory tests to see if those genetic changes explain a genetic problem in >100 people that we did not diagnose a genetic problem.

Metadane publikacji

Journal
Epilepsia Open
Data publikacji
01.06.2026
PMID
42220229
DOI
10.1002/epi4.70269
Autorzy
Morris H, Mathew E, Bahl S, Villa-Lopez M, Mercimek-Andrews S
Słowa kluczowe
3D protein structure prediction tool, chromosomal microarray, exome sequencing, gene of uncertain significance, monogenic genetic epilepsy, targeted next‐generation sequencing panel for epilepsy
Źródło
PubMed