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Rudy Celeghin

Publications and source records attributed to Rudy Celeghin.

2 recordsLinked to original sources

An Updated Evidence Assessment of the Genetic Causes of Dilated Cardiomyopathy.

BACKGROUND: Evidence of the diverse genetic architecture of dilated cardiomyopathy (DCM) continues to emerge and requires reassessment of the clinical relevance of implicated disease genes. Building on the 2019-2020 Clinical Genome Resource evaluation, the DCM gene curation expert panel reconvened in 2024-2025 to conduct a reassessment of genes in DCM. METHODS: The Clinical Genome Resource semiquantitative clinical validity classification framework was applied with specifications to DCM to classify genes into categories on the basis of strength of published evidence for a DCM phenotype. Previously curated genes were reassessed, and newly reported gene-disease-mode of inheritance (MOI) relationships, termed "curations," were evaluated. RESULTS: Sixty-eight genes were evaluated, inclusive of 72 unique gene-disease-MOI relationships across 51 previously evaluated and 17 newly assessed genes. Thirty-five curations were classified as high evidence (16 Definitive, 10 Strong, 9 Moderate), increasing by 16 from the prior assessment. Nine newly assessed genes were classified as high evidence: BAG5, FLII, LMOD2, MYLK3, MYZAP, NRAP, PPA2, PPP1R13L, and RPL3L. Twelve genes (11 newly appraised) were rated as high evidence with an autosomal recessive (AR) MOI. Five reevaluated genes from 2019-2020 had clinically significant changes in classification. Except for JPH2, for which curation was modified to separate autosomal dominant and AR MOI curations, clinically significant changes involved upgrades from low- to high-evidence categories (PLEKHM2, PRDM16, TBX20, TNNI3K), demonstrating the robustness of the Clinical Genome Resource gene curation process over time. An additional 29 gene-disease-MOI curations were classified as Limited, including 6 newly evaluated genes and 1 new MOI for a previously evaluated gene, MYBPC3-AR; 4 were classified as No Known Disease Relationship, and remained Disputed. Four previously evaluated genes were curated for both AD and AR MOIs: JPH2 (AD-Strong, AR-Limited), LDB3 (AD-Limited, AR-Strong), MYBPC3 (AD-Limited, AR-Limited), and TNNI3 (AD- and AR- Strong). CONCLUSIONS: With substantial new evidence, the genetic architecture of DCM has rapidly expanded. This updated assessment of genes reported in DCM yielded 35 high-evidence curations, an increase from 19 only 5 years ago. The results of this evidence-based evaluation process inform clinical interpretation of genetic information in the care of DCM patients and families.

dilated cardiomyopathy

Tackling non-canonical splicing in arrhythmogenic cardiomyopathy to reduce the uncertain significance variants burden.

BACKGROUND: Splice-altering variants (SAVs), particularly those outside canonical splice sites, are an underappreciated contributor to inherited cardiovascular diseases. In arrhythmogenic cardiomyopathy (ACM), these variants frequently remain classified as of uncertain significance (VUS) due to limited predictive power and lack of transcript-level evidence, constraining genetic yield and clinical management. Our study aimed to determine the functional impact of SAVs in ACM genes and refine their classification using ACMG/AMP and ClinGen SVI criteria. METHODS: SAVs identified in 200 ACM probands underwent SpliceAI prediction, GTEx cardiac exon-usage annotation, and functional assessment using pSPL3-based minigene assays. Aberrant transcripts were quantified using Percent Splicing Alteration (PSA). Segregation data and ACMG/AMP criteria refined by ClinGen SVI were applied to integrate functional and clinical evidence for classification. RESULTS: Aberrant splicing was confirmed in 9/20 variants (45%), including synonymous, missense, and non-canonical intronic changes. SpliceAI scores correlated strongly with PSA values (R²=0.86). Case-control burden testing revealed significant enrichment of splice-altering variants in DSP, DSG2, DSC2 and FLNC. Integrating predictive algorithms with experimental validation and segregation analysis markedly enhances reclassification of 16/20 variants (80%). CONCLUSION: Splicing defects beyond canonical sites significantly shape ACM genetic landscape. Integrating predictive models with experimental validation clarifies uncertain variants bridging the gap between genomic uncertainty and clinical decision-making.

Humans