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Resumen del póster presentado a la 37th International Conference on Animal Genetics (ISAG), celebrada en Lleida (España) del 7 al 12 de julio de 2019., Male fertility in swine has been associated with sperm motility parameters. Sperm movement is regularly assessed in Artificial Insemination studs and provides an objective and reproducible measurement of semen quality. As sperm transcripts are highly fragmented and present at low abundances we seek to investigate circular RNAs (circRNAs), a novel class of non-coding RNAs with a closed loop structure, more stable than linear transcripts and potentially acting as miRNA sponges. We have performed total and small RNA-seq from 40 boar sperm samples. The objective of this study was to characterize the porcine circRNA repertoire, assess their potential role as miRNA sponges and the correlation between their abundance and sperm motility. We identified 1,393 potential circRNAs across all the 40 samples. The majority of the circRNAs were derived from exonic regions (63.6%), formed from less than 4 exons and had an average lenght below 500 bp. circRNA abundances ranged between 0.18 and 137 CPMs. Compared with other pig tissues, the sperm circRNA repertoire resembled most to testis, even thought 75% of the sperm circRNAs were sperm-specific. Gene ontology analysis revealed that sperm circRNAs involved genes related to epigenetic regulation, spermatogenesis and cilium assembly functions. We identified 69 circRNA-miRNA interactions. For example, miR-28–5p, a miRNA that is dysregulated in human infertile patients, presented 2 different target sites in a circRNA from ZMYND10, a gene required for motile ciliary function. We identified 148 circRNAs that correlated with different sperm motility parameters. We selected 10 circRNAs and validated the closed loop structure by RT-qPCR and Sanger Sequencing. The RNA abundance of 6 cirRNAs was validated by RT-qPCR and compared in 38 additional animals presenting extreme motility parameters. Three circRNAs presented significant differences in abundance between the groups. These included circRNA from LIN7A (P-value: 0.008), LRBA (P-value: 0.04) and PAPOLA (P-value: 0.026) genes, the functions of these genes are related to generating and maintaining cell membrane channels, vesicle trafficking and RNA and ATP binding, respectively.

Resumen del póster presentado a la 37th International Conference on Animal Genetics (ISAG), celebrada en Lleida (España) del 7 al 12 de julio de 2019., For the last decades, boars have been selected for their genetic merit on carcass and meat quality traits. However, breeders and researchers are now paying attention to additional phenotypes including sperm quality. We carried a GWAS and a semen RNA-seq experiment in pigs with the aim to identify SNP markers for 25 sperm quality traits. The GWAS included 288 boars and genotypes from the Affymetrix Axiom Porcine 660K Genotyping Array. Sperm from 40 of these pigs were subjected to total and to small RNA-seq. The GWAS resulted in 345 SNPs significantly associated mostly to sperm head abnormalities and motility. The RNA-seq evidenced 4,120 protein coding genes and 95 miRNAs. 3,053 genes showed significant correlations with at least one semen quality trait. Independently for each phenotype, we then searched for eQTLs using only the GWAS SNP hits and the genes that correlated with the given semen trait. This yielded 119 eQTLs. Several of these hits involve genes with known function related to semen quality. For example, we found a hit on ACTR2 and the percentage of head abnormalities. ACTR2 has been involved in the morphogenic modeling of the sperm head. We also detected a hit between NDUFS8 and sperm motility. NDUFS8 is a known mitochondrial NADH subunit involved in the respiratory chain which is important for sperm capacitation and motility. Using the RNA-seq data, we also searched for the subset of 10 RNAs that explained the largest percentage of the variability for the semen quality phenotypes. To achieve this objective, we retrieved the genes that (i) were co-associated with traits at the SNP level by building an Associated Weight Matrix and finding correlations with PCIT, and (ii) also displayed co-abundant RNA levels (by PCIT). The resulting list of genes, together with the information of their correlated phenotypes, was used for network analysis. We identified a subset of 10 genes, which included EFHC1, ATP9A and THADA, that explained between 16 to 67% of the different sperm quality parameters.