Impact of oestrus synchronization devices on ewes vaginal microbiota and artificial insemination outcome.

Introduction: The low pregnancy rate by artificial insemination in sheep represents a fundamental challenge for breeding programs. In this species, oestrus synchronization is carried out by manipulating hormonal regimens through the insertion of progestogen intravaginal devices. This reproductive strategy may alter the vaginal microbiota affecting the artificial insemination outcome. Methods: In this study, we analyzed the vaginal microbiome of 94 vaginal swabs collected from 47 ewes with alternative treatments applied to the progesterone-releasing intravaginal devices (probiotic, maltodextrin, antibiotic and control), in two sample periods (before placing and after removing the devices). To our knowledge, this is the first study using nanopore-based metagenome sequencing for vaginal microbiome characterization in livestock. Results: Our results revealed a significant lower abundance of the genera Oenococcus (Firmicutes) and Neisseria (Proteobacteria) in pregnant compared to non-pregnant ewes. We also detected a significant lower abundance of Campylobacter in the group of samples treated with the probiotic. Discussion: Although the use of probiotics represents a promising practice to improve insemination results, the election of the suitable species and concentration requires further investigation. In addition, the use of progestogen in the synchronization devices seemed to increase the alpha-diversity and decrease the abundance of harmful microorganisms belonging to Gammaproteobacteria and Fusobacteriia classes, suggesting a beneficial effect of their use.

Genome-Enabled Prediction Methods Based on Machine Learning.

Growth of artificial intelligence and machine learning (ML) methodology has been explosive in recent years. In this class of procedures, computers get knowledge from sets of experiences and provide forecasts or classification. In genome-wide based prediction (GWP), many ML studies have been carried out. This chapter provides a description of main semiparametric and nonparametric algorithms used in GWP in animals and plants. Thirty-four ML comparative studies conducted in the last decade were used to develop a meta-analysis through a Thurstonian model, to evaluate algorithms with the best predictive qualities. It was found that some kernel, Bayesian, and ensemble methods displayed greater robustness and predictive ability. However, the type of study and data distribution must be considered in order to choose the most appropriate model for a given problem.