SnoRNA copy regulation affects family size, genomic location and family abundance levels.

BACKGROUND: Small nucleolar RNAs (snoRNAs) are an abundant class of noncoding RNAs present in all eukaryotes and best known for their involvement in ribosome biogenesis. In mammalian genomes, many snoRNAs exist in multiple copies, resulting from recombination and retrotransposition from an ancestral snoRNA. To gain insight into snoRNA copy regulation, we used Rfam classification and normal human tissue expression datasets generated using low structure bias RNA-seq to characterize snoRNA families. RESULTS: We found that although box H/ACA families are on average larger than box C/D families, the number of expressed members is similar for both types. Family members can cover a wide range of average abundance values, but importantly, expression variability of individual members of a family is preferred over the total variability of the family, especially for box H/ACA snoRNAs, suggesting that while members are likely differentially regulated, mechanisms exist to ensure uniformity of the total family abundance across tissues. Box C/D snoRNA family members are mostly embedded in the same host gene while box H/ACA family members tend to be encoded in more than one different host, supporting a model in which box C/D snoRNA duplication occurred mostly by cis recombination while box H/ACA snoRNA families have gained copy members through retrotransposition. And unexpectedly, snoRNAs encoded in the same host gene can be regulated independently, as some snoRNAs within the same family vary in abundance in a divergent way between tissues. CONCLUSIONS: SnoRNA copy regulation affects family sizes, genomic location of the members and controls simultaneously member and total family abundance to respond to the needs of individual tissues.

Automated Single-Sperm Selection Software (SiD) during ICSI: A Prospective Sibling Oocyte Evaluation.

The computer-assisted program SiD was developed to assess and select sperm in real time based on motility characteristics. To date, there are limited studies examining the correlation between AI-assisted sperm selection and ICSI outcomes. To address this limit, a total of 646 sibling MII oocytes were randomly divided into two groups as follows: the ICSI group (n = 320): ICSI performed with sperm selected by the embryologist and the ICSI-SiD group (n = 326): ICSI performed with sperm selected using SiD software. Our results show a non-significant trend towards improved outcomes in the ICSI-SiD group across various biological parameters, including fertilization, cleavage, day 3 embryo development, blastocyst development, and quality on day 5. Similarly, we observed a non-significant increase in these outcomes when comparing both groups with sperm selection performed by a junior embryologist. Embryo development was monitored using a timelapse system. Some fertilization events happen significantly earlier when SiD is used for ICSI, but no significant difference was observed in the ICSI-SiD group for other timepoints. We observed comparable cumulative early and clinical pregnancy rates after ICSI-SiD. This preliminary investigation illustrated that employing the automated sperm selection software SiD leads to comparable biological outcomes, suggesting its efficacy in sperm selection.