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Background: Genetic pressure in animal breeding is sparking the interest of breeders for selecting elite boars with higher sperm quality to optimize ejaculate doses and fertility rates. However, the molecular basis of sperm quality is not yet fully understood. Our aim was to identify candidate genes, pathways and DNA variants associated to sperm quality in swine by analysing 25 sperm-related phenotypes and integrating genome-wide association studies (GWAS) and RNA-seq under a systems biology framework., Results: By GWAS, we identified 12 quantitative trait loci (QTL) associated to the percentage of head and neck abnormalities, abnormal acrosomes and motile spermatozoa. Candidate genes included CHD2, KATNAL2, SLC14A2 and ABCA1. By RNA-seq, we identified a wide repertoire of mRNAs (e.g. PRM1, OAZ3, DNAJB8, TPPP2 and TNP1) and miRNAs (e.g. ssc-miR-30d, ssc-miR-34c, ssc-miR-30c-5p, ssc-miR-191, members of the let-7 family and ssc-miR-425-5p) with functions related to sperm biology. We detected 6128 significant correlations (P-value ≤ 0.05) between sperm traits and mRNA abundances. By expression (e)GWAS, we identified three trans-expression QTL involving the genes IQCJ, ACTR2 and HARS. Using the GWAS and RNA-seq data, we built a gene interaction network. We considered that the genes and interactions that were present in both the GWAS and RNA-seq networks had a higher probability of being actually involved in sperm quality and used them to build a robust gene interaction network. In addition, in the final network we included genes with RNA abundances correlated with more than four semen traits and miRNAs interacting with the genes on the network. The final network was enriched for genes involved in gamete generation and development, meiotic cell cycle, DNA repair or embryo implantation. Finally, we designed a panel of 73 SNPs based on the GWAS, eGWAS and final network data, that explains between 5% (for sperm cell concentration) and 36% (for percentage of neck abnormalities) of the phenotypic variance of the sperm traits., Conclusions: By applying a systems biology approach, we identified genes that potentially affect sperm quality and constructed a SNP panel that explains a substantial part of the phenotypic variance for semen quality in our study and that should be tested in other swine populations to evaluate its relevance for the pig breeding sector.

The microbiome plays a key role in homeostasis and health and it has been also linked to fertility and semen quality in several animal species including swine. Despite the more than likely importance of sperm bacteria on the boar's reproductive ability and the dissemination of pathogens and antimicrobial resistance genes, the high throughput characterization of the swine sperm microbiome remains scarce. We carried RNA-seq on 40 ejaculates each from a different Pietrain boar and found that a proportion of the sequencing reads did not map to the Sus scrofa genome. The current study aimed at using these reads not belonging to pig to carry a pilot study to profile the boar sperm bacterial population and its relation with 7 semen quality traits. We found that the boar sperm contains a broad population of bacteria. The most abundant phyla were Proteobacteria (39.1%), Firmicutes (27.5%), Actinobacteria (14.9%) and Bacteroidetes (5.7%). The predominant species contaminated sperm after ejaculation from soil, faeces and water sources (Bacillus megaterium , Brachybacterium faecium , Bacillus coagulans). Some potential pathogens were also found but at relatively low levels (Escherichia coli , Clostridioides difficile , Clostridium perfringens , Clostridium botulinum and Mycobacterium tuberculosis). We also identified 3 potential antibiotic resistant genes from E. coli against chloramphenicol, Neisseria meningitidis against spectinomycin and Staphylococcus aureus against linezolid. None of these genes were highly abundant. Finally, we classified the ejaculates into categories according to their bacterial features and semen quality parameters and identified two categories that significantly differed for 5 semen quality traits and 13 bacterial features including the genera Acinetobacter , Stenotrophomonas and Rhodobacter. Our results show that boar semen contains a bacterial community, including potential pathogens and putative antibiotic resistance genes, and that these bacteria may affect its reproductive performance. • A pilot RNA-seq study in 40 Pietrain ejaculates to characterize the porcine sperm microbiome. • Most bacteria in our pig sperm samples were contaminants from soil, water and faeces. • Potential pathogens were scarce and the most abundant was Escherichia coli. • Resistance genes against chloramphenicol, spectinomycin and linezolid were predicted. • LINK-HD draw two sample clusters defined by 13 bacterial features and 5 semen traits. [ABSTRACT FROM AUTHOR]

Understanding the molecular basis of cell function and ultimate phenotypes is crucial for the development of biological markers. With this aim, several RNA-seq studies have been devoted to the characterization of the transcriptome of ejaculated spermatozoa in relation to sperm quality and fertility. Semen quality follows a seasonal pattern and decays in the summer months in several animal species. The aim of this study was to deeply profile the transcriptome of the boar sperm and to evaluate its seasonal changes. We sequenced the total and the short fractions of the sperm RNA from 10 Pietrain boars, 5 collected in summer and 5 five sampled in winter, and identified a complex and rich transcriptome with 4,436 coding genes of moderate to high abundance. Transcript fragmentation was high but less obvious in genes related to spermatogenesis, chromatin compaction and fertility. Short non-coding RNAs mostly included piwi-interacting RNAs, transfer RNAs and microRNAs. We also compared the transcriptome of the summer and the winter ejaculates and identified 34 coding genes and 7 microRNAs with a significantly distinct distribution. These genes were mostly related to oxidative stress, DNA damage and autophagy. This is the deepest characterization of the boar sperm transcriptome and the first study linking the transcriptome and the seasonal variability of semen quality in animals. The annotation described here can be used as a reference for the identification of markers of sperm quality in pigs. [ABSTRACT FROM AUTHOR]

The study of the boar sperm transcriptome by RNA-seq can provide relevant information on sperm quality and fertility and might contribute to animal breeding strategies. However, the analysis of the spermatozoa RNA is challenging as these cells harbor very low amounts of highly fragmented RNA, and the ejaculates also contain other cell types with larger amounts of non-fragmented RNA. Here, we describe a strategy for a successful boar sperm purification, RNA extraction and RNA-seq library preparation. Using these approaches our objectives were: (i) to evaluate the sperm recovery rate (SRR) after boar spermatozoa purification by density centrifugation using the non-porcine-specific commercial reagent BoviPureTM; (ii) to assess the correlation between SRR and sperm quality characteristics; (iii) to evaluate the relationship between sperm cell RNA load and sperm quality traits and (iv) to compare different library preparation kits for both total RNA-seq (SMARTer Universal Low Input RNA and TruSeq RNA Library Prep kit) and small RNA-seq (NEBNext Small RNA and TailorMix miRNA Sample Prep v2) for high-throughput sequencing. Our results show that pig SRR (~22%) is lower than in other mammalian species and that it is not significantly dependent of the sperm quality parameters analyzed in our study. Moreover, no relationship between the RNA yield per sperm cell and sperm phenotypes was found. We compared a RNA-seq library preparation kit optimized for low amounts of fragmented RNA with a standard kit designed for high amount and quality of input RNA and found that for sperm, a protocol designed to work on low-quality RNA is essential. We also compared two small RNA-seq kits and did not find substantial differences in their performance. We propose the methodological workflow described for the RNA-seq screening of the boar spermatozoa transcriptome. Abbreviations: FPKM: fragments per kilobase of transcript per million mapped reads; KRT1: keratin 1; miRNA: micro-RNA; miscRNA: miscellaneous RNA; Mt rRNA: mitochondrial ribosomal RNA; Mt tRNA: mitochondrial transference RNA; OAZ3: ornithine decarboxylase antizyme 3; ORT: osmotic resistance test; piRNA: Piwi-interacting RNA; PRM1: protamine 1; PTPRC: protein tyrosine phosphatase receptor type C; rRNA: ribosomal RNA; snoRNA: small nucleolar RNA; snRNA: small nuclear RNA; SRR: sperm recovery rate; tRNA: transfer RNA [ABSTRACT FROM AUTHOR]