Small RNAs present in bovine ejaculate can be linked to sperm abnormalities and fertility disorders. At present, quality parameters routinely used in semen evaluation are not fully reliable to predict bull fertility. In order to provide additional quality measurements for cryopreserved semen used for breeding, a method based on deep sequencing of sperm microRNA (miRNA) and Piwi-interacting RNA (piRNA) from individual bulls was developed. To validate our method, two populations of spermatozoa isolated from high and low motile fractions separated by Percoll were sequenced, and their small RNAs content characterized. Sperm cells from frozen thawed semen samples of 4 bulls were successfully separated in two fractions. We identified 83 miRNAs and 79 putative piRNAs clusters that were differentially expressed in both fractions. Gene pathways targeted by 40 known differentially expressed miRNAs were related to apoptosis. Dysregulation of miR-17-5p, miR-26a-5p, miR-486-5p, miR-122-5p, miR-184 and miR-20a-5p was found to target three pathways (PTEN, PI3K/AKT and STAT). Small RNAs sequencing data obtained from single bulls are consistent with previous findings. Specific miRNAs are differentially represented in low versus high motile sperm, suggesting an alteration of cell functions and increased germ cell apoptosis in the low motile fraction.

Within recent years, there has been growing interest in the prediction of bull fertility through in vitro assessment of semen quality. A model for fertility prediction based on early evaluation of semen quality parameters, to exclude sires with potentially low fertility from breeding programs, would therefore be useful. The aim of the present study was to identify the most suitable parameters that would provide reliable prediction of fertility. Frozen semen from 18 Italian Holstein-Friesian proven bulls was analyzed using computer-assisted semen analysis (CASA) (motility and kinetic parameters) and flow cytometry (FCM) (viability, acrosomal integrity, mitochondrial function, lipid peroxidation, plasma membrane stability and DNA integrity). Bulls were divided into two groups (low and high fertility) based on the estimated relative conception rate (ERCR). Significant differences were found between fertility groups for total motility, active cells, straightness, linearity, viability and percentage of DNA fragmented sperm. Correlations were observed between ERCR and some kinetic parameters, and membrane instability and some DNA integrity indicators. In order to define a model with high relation between semen quality parameters and ERCR, backward stepwise multiple regression analysis was applied. Thus, we obtained a prediction model that explained almost half (R 2=0.47, P<0.05) of the variation in the conception rate and included nine variables: five kinetic parameters measured by CASA (total motility, active cells, beat cross frequency, curvilinear velocity and amplitude of lateral head displacement) and four parameters related to DNA integrity evaluated by FCM (degree of chromatin structure abnormality Alpha-T, extent of chromatin structure abnormality (Alpha-T standard deviation), percentage of DNA fragmented sperm and percentage of sperm with high green fluorescence representative of immature cells). A significant relationship (R 2=0.84, P<0.05) was observed between real and predicted fertility. Once the accuracy of fertility prediction has been confirmed, the model developed in the present study could be used by artificial insemination centers for bull selection or for elimination of poor fertility ejaculates.

Fat supplementation plays an important role in defining milk fatty acids (FA) composition of ruminant products. The use of sources rich in linoleic and α-linolenic acid favors the accumulation of conjugated linoleic acids isomers, increasing the healthy properties of milk. Ruminal microbiota plays a pivotal role in defining milk FA composition, and its profile is affected by diet composition. The aim of this study was to investigate the responses of rumen FA production and microbial structure to hemp or linseed supplementation in diets of dairy goats. Ruminal microbiota composition was determined by 16S amplicon sequencing, whereas FA composition was obtained by gas-chromatography technique. In all, 18 pluriparous Alpine goats fed the same pre-treatment diet for 40±7 days were, then, arranged to three dietary treatments consisting of control, linseed and hemp seeds supplemented diets. Independently from sampling time and diets, bacterial community of ruminal fluid was dominated by Bacteroidetes (about 61.2%) and Firmicutes (24.2%) with a high abundance of Prevotellaceae (41.0%) and Veillonellaceae (9.4%) and a low presence of Ruminococcaceae (5.0%) and Lachnospiraceae (4.3%). Linseed supplementation affected ruminal bacteria population, with a significant reduction of biodiversity; in particular, relative abundance of Prevotella was reduced (−12.0%), whereas that of Succinivibrio and Fibrobacter was increased (+50.0% and +75.0%, respectively). No statistically significant differences were found among the average relative abundance of archaeal genera between each dietary group. Moreover, the addition of linseed and hemp seed induced significant changes in FA concentration in the rumen, as a consequence of shift from C18 : 2n-6 to C18 : 3n-3 biohydrogenation pathway. Furthermore, dimethylacetal composition was affected by fat supplementation, as consequence of ruminal bacteria population modification. Finally, the association study between the rumen FA profile and the bacterial microbiome revealed that Fibrobacteriaceae is the bacterial family showing the highest and significant correlation with FA involved in the biohydrogenation pathway of C18 : 3n-3.

Sperm epigenetics is an emerging area of study supported by observations reporting that abnormal sperm DNA methylation patterns are associated with infertility. Here, we explore cytosine-guanine dinucleotides (CpGs) methylation in high (HM) and low motile (LM) Bos taurus sperm populations separated by Percoll gradient. HM and LM methylation patterns were investigated by bisulfite sequencing. Comparison between HM and LM sperm populations revealed that methylation variation affects genes involved in chromatin organization. CpG Islands (CGIs), were highly remodelled. A high proportion of CGIs was found to be methylated at low/intermediate level (20–60%) and associated to the repetitive element BTSAT4 satellite. The low/intermediate level of methylation in BTSAT4 was stably maintained in pericentric regions of chromosomes. BTSAT4 was hypomethylated in HM sperm populations. The characterization of the epigenome in HM and LM Bos taurus sperm populations provides a first step towards the understanding of the effect of methylation on sperm fertility. Methylation variation observed in HM and LM populations in genes associated to DNA structure remodelling as well as in a repetitive element in pericentric regions suggests that maintenance of chromosome structure through epigenetic regulation is probably crucial for correct sperm functionality.

Predicting bull fertility is one of the main challenges for the dairy breeding industry and artificial insemination (AI) centers. Semen evaluation performed in the AI center is not fully reliable to determine the level of bull fertility. Spermatozoa are rich in active miRNA. Specific sperm-borne miRNAs can be linked to fertility. The aim of our study is to propose a combined flow cytometric analysis and miRNA profiling of semen bulls with different fertility to identify markers that can be potentially used for the prediction of field fertility. Sperm functions were analyzed in frozen-thawed semen doses (CG: control group) and high-quality sperm (HQS) fraction collected from bulls with different field fertility levels (estimated relative conception rate or ERCR) by using advanced techniques, such as the computer-assisted semen analysis system, flow cytometry, and small RNA-sequencing. Fertility groups differ for total and progressive motility and in the abnormality degree of the chromatin structure (P< 0.05). A backward, stepwise, multiple regression analysis was applied to define a model with high relation betweenin vivo(e.g., ERCR) andin vitro(i.e., semen quality and DE-miRNA) fertility data. The analysis produced two models that accounted for more than 78% of the variation of ERCR (CG:R²= 0.88; HQS:R²= 0.78), identifying a suitable combination of parameters useful to predict bull fertility. The predictive equation on CG samples included eight variables: four kinetic parameters and four DNA integrity indicators. For the HQS fraction, the predictive equation included five variables: three kinetic parameters and two DNA integrity indicators. A significant relationship was observed between real and predicted fertility in CG (R²= 0.88) and HQS fraction (R²= 0.82). We identified 15 differentially expressed miRNAs between high- and low-fertility bulls, nine of which are known (miR-2285n, miR-378, miR-423-3p, miR-191, miR-2904, miR-378c, miR-431, miR-486, miR-2478) while the remaining are novel. The multidimensional preference analysis model partially separates bulls according to their fertility, clustering three semen quality variable groups relative to motility, DNA integrity, and viability. A positive association between field fertility, semen quality parameters, and specific miRNAs was revealed. The integrated approach could provide a model for bull selection in AI centers, increasing the reproductive efficiency of livestock.