Characterization of macrozoospermia-associated AURKC mutations in a mammalian meiotic system.

Aneuploidy is the leading genetic abnormality that leads to miscarriage, and it is caused by a failure of accurate chromosome segregation during gametogenesis or early embryonic divisions. Aurora kinase C (AURKC) is essential for formation of euploid sperm in humans because mutations in AURKC are correlated with macrozoospermia and these sperm are tetraploid. These mutations are currently the most frequent mutations that cause macrozoospermia and result from an inability to complete meiosis I (MI). Three of these mutations AURKC c.144delC (AURKC p.L49Wfs22), AURKC c.686G > A (AURKC p.C229Y) and AURKC c.744C > G (AURKC p.Y248*) occur in the coding region of the gene and are the focus of this study. By expressing these alleles in oocytes isolated from Aurkc-/- mice, we show that the mutations have different effects on AURKC function during MI. AURKC p.L49Wfs22 is a loss-of-function mutant that perturbs localization of the chromosomal passenger complex (CPC), AURKC p.C229Y is a hypomorph that cannot fully support cell-cycle progression, and AURKC p.Y248* fails to localize and function with the CPC to support chromosome segregation yet retains catalytic activity in the cytoplasm. Finally, we show that these variants of AURKC cause meiotic failure and polyploidy due to a failure in AURKC-CPC function that results in metaphase chromosome misalignment. This study is the first to assess the function of mutant alleles of AURKC that affect human fertility in a mammalian meiotic system.

Aligning NIH's existing data use restrictions to the GA4GH DUO standard.

It is widely accepted that large-scale genomic data (e.g., whole-genome sequencing, whole-exome sequencing, and genome-wide association study data) be shared through a controlled-access mechanism. This protects the privacy of research participants and ensures downstream uses of data align with participants' informed consent regarding future sharing of their data. In 2019, GA4GH approved the Data Use Ontology (DUO) standard to define data use terms with machine-readable representations to represent how a dataset can be used. We endeavored to determine the parity of existing data use restrictions ("Data Use Limitations" [DULs]) for datasets registered in the National Institutes of Health database for Genotypes and Phenotypes (dbGaP) with the DUO standard. We found substantial (93%) parity between the dbGaP DULs (n = 3,575) and DUO. This study demonstrates the comprehensiveness of the DUO standard and encourages data stewards to standardize data use restrictions in machine-readable formats to facilitate data sharing.