A homozygous loss-of-function (LoF) variant in POC5 was previously described in an individual with retinitis pigmentosa. We identified POC5 variants in 12 probands with a syndromic phenotype. We aim t Show more
A homozygous loss-of-function (LoF) variant in POC5 was previously described in an individual with retinitis pigmentosa. We identified POC5 variants in 12 probands with a syndromic phenotype. We aim to define the phenotype spectrum and molecular mechanism associated with biallelic POC5 LoF variants. We studied a cohort of 12 families with bi-allelic LoF POC5 variants and performed detailed phenotype analysis. POC5 localization studies were performed in 3 proband-derived fibroblast cell lines. Detailed phenotyping of probands with POC5 variants expands the phenotype spectrum beyond ocular manifestations. This syndrome causes not only rod-cone dystrophy but also diabetes mellitus with severe insulin resistance and partial lipodystrophy, kidney disease, and muscle cramps. The POC5 protein plays an essential role during cell cycle and cilium formation. Interestingly, POC5 localization studies in 3 proband-derived fibroblast cell lines show aberrant localization suggesting a ciliary defect. The phenotypes of the 12 families in this study fit well within the ciliopathy phenotype spectrum, except for lipodystrophy, which is not common in ciliopathies. We describe a multiorgan syndrome caused by bi-allelic LoF variants in POC5. This underscores the pleiotropic effects of POC5 variants and highlights the significance of adipose tissue and metabolic dysfunction in ciliopathies. Show less
Hereditary disorders are frequently caused by genetic variants that affect pre-messenger RNA splicing. Though genetic variants in the canonical splice motifs are almost always disrupting splicing, the Show more
Hereditary disorders are frequently caused by genetic variants that affect pre-messenger RNA splicing. Though genetic variants in the canonical splice motifs are almost always disrupting splicing, the pathogenicity of variants in the noncanonical splice sites (NCSS) and deep intronic (DI) regions are difficult to predict. Multiple splice prediction tools have been developed for this purpose, with the latest tools employing deep learning algorithms. We benchmarked established and deep learning splice prediction tools on published gold standard sets of 71 NCSS and 81 DI variants in the ABCA4 gene and 61 NCSS variants in the MYBPC3 gene with functional assessment in midigene and minigene splice assays. The selection of splice prediction tools included CADD, DSSP, GeneSplicer, MaxEntScan, MMSplice, NNSPLICE, SPIDEX, SpliceAI, SpliceRover, and SpliceSiteFinder-like. The best-performing splice prediction tool for the different variants was SpliceRover for ABCA4 NCSS variants, SpliceAI for ABCA4 DI variants, and the Alamut 3/4 consensus approach (GeneSplicer, MaxEntScacn, NNSPLICE and SpliceSiteFinder-like) for NCSS variants in MYBPC3 based on the area under the receiver operator curve. Overall, the performance in a real-time clinical setting is much more modest than reported by the developers of the tools. Show less