A systems biology framework integrating GWAS and RNA-seq to shed light on the molecular basis of sperm quality in swine

Abstract

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.

Seminal Plasma: Relevant for Fertility?

“…However, how are these miRNAs affecting fertility? Directly, by affecting sperm quality, of course [ 196 ]. However, for some species, such as bovine or particularly porcine, breeding males are selected for fertility and discarded when their AI-results are compromised, even when their sperm quality surpasses the established thresholds for normality [ 197 ].…”

Micrococcal nuclease sequencing of porcine sperm suggests a nucleosomal involvement on semen quality and early embryo development

“…To test this hypothesis, sperm transcriptome data from 40 Pietrain boars, including 40 total and 35 small RNA-seq datasets previously generated by our group (NCBI's BioProject PRJNA520978) was used. These datasets provided a list of 4,120 protein coding RNAs [34], 1,574 circular RNAs (circRNAs) mapping in pig chromosomes [18] and 6,729 PIWI-interacting RNAs (piRNAs) [19]. The averaged RNA abundances from all the samples were used for further analysis.…”

“…MNase profiles were also evaluated against the genomic regions showing genetic association with sperm quality traits in Pietrain in a Genome-Wide Association Study (GWAS) carried by our group and which included the two samples used in this study [34]. This GWAS provided 18 GWAS regions associated to the percentage of sperm cells with head abnormalities (7 hits), neck abnormalities (4), percentage of abnormal acrosomes after 5 minutes incubation at 37°C (2), the ratio of abnormal acrosomes after 5 minutes and 90 minutes of incubation (1), percentage of motile spermatozoa after 5 minutes incubation (2) and after 90 minutes incubation (2, one hit shared with motility after 5 minutes incubation) and the percentage of sperm cells with proximal droplets (1).…”

“…This GWAS provided 18 GWAS regions associated to the percentage of sperm cells with head abnormalities (7 hits), neck abnormalities (4), percentage of abnormal acrosomes after 5 minutes incubation at 37°C (2), the ratio of abnormal acrosomes after 5 minutes and 90 minutes of incubation (1), percentage of motile spermatozoa after 5 minutes incubation (2) and after 90 minutes incubation (2, one hit shared with motility after 5 minutes incubation) and the percentage of sperm cells with proximal droplets (1). These regions spanned in total, 16.5 Mbp (0.7%) of the porcine autosomal genome [34]. Again, to evaluate whether MNase peaks were enriched at GWAS hits, the genomic regions of the GWAS hits were iteratively randomized 100 times using BEDtools [28] shuffle.…”

Characterization of the Impact of Density Gradient Centrifugation on the Profile of the Pig Sperm Transcriptome by RNA-Seq

“…The sperm's transcriptomic landscape has been profiled in a large number of animal species including human, mice, cattle, pig, and horse (1), and several research groups focus their efforts to identify molecular markers associated to semen quality and fertility in human (2), cattle (3), horse (4) and swine (5)(6)(7)(8)(9)(10). Transcriptomic evaluations of the ejaculated spermatozoa are often preceded by a purification step to remove somatic and prokaryotic cells often by using a commercial colloidal silica suspension (e.g., PureSperm R , BoviPure TM , Nidacon, Sweden) in order to prepare density gradients and purify the sperm cells (11)(12)(13)(14). This step is considered to be necessary since spermatozoa carry small amounts of RNA that are also highly fragmented when compared with the somatic cells that could be present in the ejaculate.…”