Literature DB >> 26296523

Use of combinations of in vitro quality assessments to predict fertility of bovine semen.

E Sellem1, M L W J Broekhuijse2, L Chevrier3, S Camugli3, E Schmitt3, L Schibler4, E P C Koenen2.   

Abstract

Predicting in vivo fertility of bull ejaculates using in vitro-assessed semen quality criteria remains challenging for the breeding industry. New technologies such as computer-assisted semen analysis (CASA) and flow cytometry may provide accurate and objective methods to improve semen quality control. The aim of this study was to evaluate the relationship between semen quality parameters and field fertility of bull ejaculates. A total of 153 ejaculates from 19 Holstein bulls have been analyzed using CASA (postthawing semen motility and morphology) and several flow cytometric tests, including sperm DNA integrity, viability (estimated by membrane integrity), acrosomal integrity, mitochondria aerobic functionality and oxidation. Samples were analyzed both immediately after thawing and after 4 hours at 37 °C. A fertility value (FV), based on nonreturn rate at 56 days after insemination and adjusted for environment factors, was calculated for each ejaculate. Simple and multiple regressions have been used to correlate FV with CASA and flow cytometric parameters. Significant simple correlations have been observed between some parameters and FV (e.g., straight line velocity [μm/s], r(2) = -0.12; polarized mitochondria sperm (%), r(2) = 0.07), but the relation between simple parameter and FV was too week to predict the fertility. Partial least square procedure identified several mathematical models combining flow cytometer and CASA variables and had better correlations with FV (adjusted r(2) ranging between 0.24 and 0.40 [P < 0.0001], depending on the number of included variables). In conclusion, this study suggests that quality assessment of thawed bull sperm using CASA and flow cytometry may provide a reasonable prediction of bovine semen fertility. Additional work will be required to increase the prediction reliability and promote this technology in routine artificial insemination laboratory practice.
Copyright © 2015 Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Bovine semen; Computer-assisted semen analysis system; Fertility prediction; Flow cytometry; Sperm assessment

Mesh:

Year:  2015        PMID: 26296523     DOI: 10.1016/j.theriogenology.2015.07.035

Source DB:  PubMed          Journal:  Theriogenology        ISSN: 0093-691X            Impact factor:   2.740


  11 in total

1.  A dual targeted β-defensin and exome sequencing approach to identify, validate and functionally characterise genes associated with bull fertility.

Authors:  Ronan Whiston; Emma K Finlay; Matthew S McCabe; Paul Cormican; Paul Flynn; Andrew Cromie; Peter J Hansen; Alan Lyons; Sean Fair; Patrick Lonergan; Cliona O' Farrelly; Kieran G Meade
Journal:  Sci Rep       Date:  2017-09-25       Impact factor: 4.379

2.  High-throughput characterisation of bull semen motility using differential dynamic microscopy.

Authors:  Alys Jepson; Jochen Arlt; Jonathan Statham; Mark Spilman; Katie Burton; Tiffany Wood; Wilson C K Poon; Vincent A Martinez
Journal:  PLoS One       Date:  2019-04-10       Impact factor: 3.240

3.  Sperm-borne miR-216b modulates cell proliferation during early embryo development via K-RAS.

Authors:  Maíra Bianchi Rodrigues Alves; Rubens Paes de Arruda; Tiago Henrique Camara De Bem; Shirley Andrea Florez-Rodriguez; Manoel Francisco de Sá Filho; Clémence Belleannée; Flávio Vieira Meirelles; Juliano Coelho da Silveira; Felipe Perecin; Eneiva Carla Carvalho Celeghini
Journal:  Sci Rep       Date:  2019-07-17       Impact factor: 4.379

4.  Relationship between the Length of Sperm Tail Mitochondrial Sheath and Fertility Traits in Boars Used for Artificial Insemination.

Authors:  Karl Kerns; Jennifer Jankovitz; Julie Robinson; Amanda Minton; Chris Kuster; Peter Sutovsky
Journal:  Antioxidants (Basel)       Date:  2020-10-23

5.  Relationship of frozen-thawed semen quality with the fertility rate after being distributed in the Brahman Cross Breeding Program.

Authors:  Berlin Pandapotan Pardede; Muhammad Agil; Yudi Yudi; Iman Supriatna
Journal:  Vet World       Date:  2020-12-14

6.  Autosomal recessive loci contribute significantly to quantitative variation of male fertility in a dairy cattle population.

Authors:  Maya Hiltpold; Naveen Kumar Kadri; Fredi Janett; Ulrich Witschi; Fritz Schmitz-Hsu; Hubert Pausch
Journal:  BMC Genomics       Date:  2021-03-30       Impact factor: 3.969

7.  The effect of adjusting settings within a Computer-Assisted Sperm Analysis (CASA) system on bovine sperm motility and morphology results.

Authors:  Ciara O'Meara; Emilie Henrotte; Kasia Kupisiewicz; Catherine Latour; Marleen Broekhuijse; Agnes Camus; Lucie Gavin-Plagne; Eli Sellem
Journal:  Anim Reprod       Date:  2022-02-04       Impact factor: 1.807

8.  Predicting male fertility from the sperm methylome: application to 120 bulls with hundreds of artificial insemination records.

Authors:  Valentin Costes; Aurélie Chaulot-Talmon; Eli Sellem; Jean-Philippe Perrier; Anne Aubert-Frambourg; Luc Jouneau; Charline Pontlevoy; Chris Hozé; Sébastien Fritz; Mekki Boussaha; Chrystelle Le Danvic; Marie-Pierre Sanchez; Didier Boichard; Laurent Schibler; Hélène Jammes; Florence Jaffrézic; Hélène Kiefer
Journal:  Clin Epigenetics       Date:  2022-04-27       Impact factor: 7.259

9.  Sperm Nuclei Analysis and Nuclear Organization of a Fertile Boar-Pig Hybrid by 2D FISH on Both Total and Motile Sperm Fractions.

Authors:  Viviana Genualdo; Federica Turri; Flavia Pizzi; Bianca Castiglioni; Donata Marletta; Alessandra Iannuzzi
Journal:  Animals (Basel)       Date:  2021-03-08       Impact factor: 2.752

10.  Carryover effects of feeding bulls with an omega-3-enriched-diet-From spermatozoa to developed embryos.

Authors:  Dorit Kalo; Dan Reches; Noam Netta; Alisa Komsky-Elbaz; Yoel Zeron; Uzi Moallem; Zvi Roth
Journal:  PLoS One       Date:  2022-03-24       Impact factor: 3.240

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