Human Molecular GeneticsGeneticsPeer-reviewed journal article
Variants in RABL2A causing male infertility and ciliopathy
Xinbao DingiD, Robert FragozaiD, Priti SinghiD, Shu ZhangiD, Haiyuan YuiD, John C. SchimentiiD
Abstract
PurposeApproximately 7% of men suffer from infertility worldwide and sperm abnormalities are the major cause. Though genetic defects are thought to underlie a substantial fraction of all male infertility cases, the actual molecular bases are usually undetermined. Because the consequences of most genetic variants in populations are unknown, this complicates genetic diagnosis even after genome sequencing of patients. Some patients with ciliopathies, including primary ciliary dyskinesia (PCD) and Bardet-Biedl syndrome (BBS), also suffer from infertility because sperm flagella, which share several characteristics with cilia, are also affected in these patients. MethodsTo identify infertility-causing genetic variants in human populations, we used in silico predictions to identify potentially deleterious SNP (single nucleotide polymorphism) alleles of RABL2A, a gene essential for normal cilia and flagella function. Candidate variants were assayed for protein stability in vitro, and the destabilizing variants were modeled in mice using CRISPR/Cas9-mediated genome editing. The resulting mice were characterized phenotypically for reproductive and developmental defects. ResultsTwo of the SNP alleles, Rabl2L119F (rs80006029) and Rabl2V158F (rs200121688), destabilized the protein. Mice bearing these alleles exhibited PCD- and BBS-associated disorders including male infertility, early growth retardation, excessive weight gain in adulthood, heterotaxia, pre-axial polydactyly, neural tube defects (NTD) and hydrocephalus. ConclusionOur study identified and validated pathogenicity of two variants causing ciliopathies and male infertility in human populations, and identified phenotypes not previously described for null alleles of Rabl2.
- organism: mouse
- assay: whole-genome sequencing
- research focus: variant effect prediction
- approach: statistical model
- approach: experimental assay
- Citations
- 16
- FWCI
- 1.17
- Field percentile
- top 23%
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