High carrier frequency of pathogenic PATL2 gene mutations predicted in population: a bioinformatics-based approach.

Topoisomerase II homologue 2 () has been confirmed to be a key gene that contributes to oocyte maturation. However, the allele distribution and carrier frequency of these mutations remain uncharacterized. So a bioinformatics subcategory analysis of mutations from outcome data and Single Nucleotide Polymorphism (SNP) databases was conducted. Altogether, the causative mutation number detected in patients with oocyte maturation defects in the clinical studies and pathogenic mutation sites predicted by software based on the database was approximately 53. The estimated carrier frequency of pathogenic mutation sites was at least 1.14‰ based on the gnomAD and ExAC database, which was approximately 1/877. The highest frequency of mutations detected in the independent patients was c.223-14_223-2del13. The carrier frequency of this mutation in the population was 0.25‰, which may be a potential threat to fertility. Estimated allele and carrier frequency are relatively higher than those predicted previously based on clinical ascertainment. A review of mutation lineage identified in 34 patients showed that 53.81%, 9.22% and 14.72% of the oocytes with mutations were arrested at the germinal vesicle (GV) stage, metaphase I (MI) stage and first polar body stage, respectively. Oocytes that could develop to the first polar body stage were extremely rare to fertilise, and their ultimate fate was early embryonic arrest. Phenotypic variability is related to the function of the regions and degree of loss of function of PATL2 protein. A 3D protein structure changes predicted by online tools, AlphaFold, showed aberrations at the mutation sites, which may explain partially the function loss. When the mutated and wild-type proteins are not in the same amino acid category, the protein structure will be considerably unstable. The integration of additional mutation sites with phenotypes is helpful in drawing a complete picture of the disease. Bioinformatics analysis of mutations will help reveal molecular epidemiological characteristics and provide an important reference for new mutation assessment, genetic counselling and drug research.