RESUMO
Generalized lipodystrophy is a feature of various hereditary disorders, often leading to a progeroid appearance. In the present study we identified a missense and a frameshift variant in a compound heterozygous state in SUPT7L in a boy with intrauterine growth retardation, generalized lipodystrophy, and additional progeroid features. SUPT7L encodes a component of the transcriptional coactivator complex STAGA. By transcriptome sequencing, we showed the predicted missense variant to cause aberrant splicing, leading to exon truncation and thereby to a complete absence of SUPT7L in dermal fibroblasts. In addition, we found altered expression of genes encoding DNA repair pathway components. This pathway was further investigated and an increased rate of DNA damage was detected in proband-derived fibroblasts and genome-edited HeLa cells. Finally, we performed transient overexpression of wildtype SUPT7L in both cellular systems, which normalizes the number of DNA damage events. Our findings suggest SUPT7L as a novel disease gene and underline the link between genome instability and progeroid phenotypes.
Assuntos
Mutação com Perda de Função , Humanos , Masculino , Células HeLa , Lipodistrofia Generalizada Congênita/genética , Fibroblastos/metabolismo , Dano ao DNA , Mutação de Sentido Incorreto , Reparo do DNA/genética , Lipodistrofia/genética , Fatores de Transcrição/genética , Retardo do Crescimento Fetal/genéticaRESUMO
MOTIVATION: While the identification of small variants in panel sequencing data can be considered a solved problem, the identification of larger, multi-exon copy number variants (CNVs) still poses a considerable challenge. Thus, CNV calling has not been established in all laboratories performing panel sequencing. At the same time, such laboratories have accumulated large datasets and thus have the need to identify CNVs on their data to close the diagnostic gap. RESULTS: In this article, we present our method clearCNV that addresses this need in two ways. First, it helps laboratories to properly assign datasets to enrichment kits. Based on homogeneous subsets of data, clearCNV identifies CNVs affecting the targeted regions. Using real-world datasets and validation, we show that our method is highly competitive with previous methods and preferable in terms of specificity. AVAILABILITY AND IMPLEMENTATION: The software is available for free under a permissible license at https://github.com/bihealth/clear-cnv. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
