Identification of biomarkers for bull fertility using functional genomics

Anim Reprod. 2022 May 2;19(1):e20220004. doi: 10.1590/1984-3143-AR2022-0004. eCollection 2022.

Abstract

Prediction of bull fertility is critical for the sustainability of both dairy and beef cattle production. Even though bulls produce ample amounts of sperm with normal parameters, some bulls may still suffer from subpar fertility. This causes major economic losses in the cattle industry because using artificial insemination, semen from one single bull can be used to inseminate hundreds of thousands of cows. Although there are several traditional methods to estimate bull fertility, such methods are not sufficient to explain and accurately predict the subfertility of individual bulls. Since fertility is a complex trait influenced by a number of factors including genetics, epigenetics, and environment, there is an urgent need for a comprehensive methodological approach to clarify uncertainty in male subfertility. The present review focuses on molecular and functional signatures of bull sperm associated with fertility. Potential roles of functional genomics (proteome, small noncoding RNAs, lipidome, metabolome) on determining male fertility and its potential as a fertility biomarker are discussed. This review provides a better understanding of the molecular signatures of viable and fertile sperm cells and their potential to be used as fertility biomarkers. This information will help uncover the underlying reasons for idiopathic subfertility.

Keywords: bull fertility; bull sperm; fertility biomarkers; functional genomics.

Grants and funding

Financial support: EM received funding for this research from the Agriculture and Food Research Initiative Competitive (Grant number: 2017-67016-26507 from the USDA National Institute of Food and Agriculture, and partial funding from Mississippi Agricultural Forestry Experiment Station of Mississippi State University. AAM received funding from the Brazilian Council for Science and Technology Development – CNPq (grants # 313160/2017-1 and 438773/2018-7), and Ceará State Foundation for the Development of Science and Technology – FUNCAP (grants # DEP-0164-00351.01.00/19 and PD2 – 0175 – 00349.01.01/20).