Predictive capacity of sperm quality parameters and sperm subpopulations on field fertility after artificial insemination in sheep

P Santolaria; S Vicente-Fiel; I Palacín; E Fantova; M E Blasco; M A Silvestre; J L Yániz

doi:10.1016/j.anireprosci.2015.10.001

Predictive capacity of sperm quality parameters and sperm subpopulations on field fertility after artificial insemination in sheep

Anim Reprod Sci. 2015 Dec:163:82-8. doi: 10.1016/j.anireprosci.2015.10.001. Epub 2015 Oct 9.

Authors

P Santolaria¹, S Vicente-Fiel¹, I Palacín¹, E Fantova², M E Blasco², M A Silvestre³, J L Yániz⁴

Affiliations

¹ TECNOGAM Research Group, Instituto Universitario de Ciencias Ambientales (IUCA), Departamento de Producción Animal y Ciencia de los Alimentos, Escuela Politécnica Superior, Universidad de Zaragoza, Huesca, Spain.
² UPRA-Grupo Pastores, Zaragoza, Spain.
³ Departamento de Biología Funcional y Antropología Física, Universidad de Valencia, Valencia, Spain.
⁴ TECNOGAM Research Group, Instituto Universitario de Ciencias Ambientales (IUCA), Departamento de Producción Animal y Ciencia de los Alimentos, Escuela Politécnica Superior, Universidad de Zaragoza, Huesca, Spain. Electronic address: jyaniz@unizar.es.

PMID: 26507945
DOI: 10.1016/j.anireprosci.2015.10.001

Abstract

This study was designed to evaluate the relevance of several sperm quality parameters and sperm population structure on the reproductive performance after cervical artificial insemination (AI) in sheep. One hundred and thirty-nine ejaculates from 56 adult rams were collected using an artificial vagina, processed for sperm quality assessment and used to perform 1319 AI. Analyses of sperm motility by computer-assisted sperm analysis (CASA), sperm nuclear morphometry by computer-assisted sperm morphometry analysis (CASMA), membrane integrity by acridine orange-propidium iodide combination and sperm DNA fragmentation using the sperm chromatin dispersion test (SCD) were performed. Clustering procedures using the sperm kinematic and morphometric data resulted in the classification of spermatozoa into three kinematic and three morphometric sperm subpopulations. Logistic regression procedures were used, including fertility at AI as the dependent variable (measured by lambing, 0 or 1) and farm, year, month of AI, female parity, female lambing-treatment interval, ram, AI technician and sperm quality parameters (including sperm subpopulations) as independent factors. Sperm quality variables remaining in the logistic regression model were viability and VCL. Fertility increased for each one-unit increase in viability (by a factor of 1.01) and in VCL (by a factor of 1.02). Multiple linear regression analyses were also performed to analyze the factors possibly influencing ejaculate fertility (N=139). The analysis yielded a significant (P<0.05) relationship between sperm viability and ejaculate fertility. The discriminant ability of the different semen variables to predict field fertility was analyzed using receiver operating characteristic (ROC) curve analysis. Sperm viability and VCL showed significant, albeit limited, predictive capacity on field fertility (0.57 and 0.54 Area Under Curve, respectively). The distribution of spermatozoa in the different subpopulations was not related to fertility.

Keywords: CASA; CASMA; Field fertility; Ovis aries; Sperm viability; Spermiogram.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Animals
Female
Insemination, Artificial / veterinary*
Male
Pregnancy
Semen Analysis / veterinary*
Sheep
Spermatozoa / classification
Spermatozoa / physiology*