Comparative Transcriptomic Analysis of Spermatozoa From High- and Low-Fertile Crossbred Bulls: Implications for Fertility Prediction.

Mani Arul Prakash,Arumugam Kumaresan,Elango Kamaraj,Pradeep Nag,Manish Kumar Sinha,Tirtha Kumar Datta,Ankur Sharma,John Peter Ebenezer Samuel King

doi:10.3389/fcell.2021.647717

Abstract

Crossbred bulls produced by crossing Bos taurus and Bos indicus suffer with high incidence of infertility/subfertility problems; however, the etiology remains poorly understood. The uncertain predictability and the inability of semen evaluation techniques to maintain constant correlation with fertility demand for alternate methods for bull fertility prediction. Therefore, in this study, the global differential gene expression between high- and low-fertile crossbred bull sperm was assessed using a high-throughput RNA sequencing technique with the aim to identify transcripts associated with crossbred bull fertility. Crossbred bull sperm contained transcripts for 13,563 genes, in which 2,093 were unique to high-fertile and 5,454 were unique to low-fertile bulls. After normalization of data, a total of 776 transcripts were detected, in which 84 and 168 transcripts were unique to high-fertile and low-fertile bulls, respectively. A total of 176 transcripts were upregulated (fold change > 1) and 209 were downregulated (<1) in low-fertile bulls. Gene ontology analysis identified that the sperm transcripts involved in the oxidative phosphorylation pathway and biological process such as multicellular organism development, spermatogenesis, and in utero embryonic development were downregulated in low-fertile crossbred bull sperm. Sperm transcripts upregulated and unique to low-fertile bulls were majorly involved in translation (biological process) and ribosomal pathway. With the use of RT-qPCR, selected sperm transcripts (n = 12) were validated in crossbred bulls (n = 12) with different fertility ratings and found that the transcriptional abundance of ZNF706, CRISP2, TNP2, and TNP1 genes was significantly (p < 0.05) lower in low-fertile bulls than high-fertile bulls and was positively (p < 0.05) correlated with conception rate. It is inferred that impaired oxidative phosphorylation could be the predominant reason for low fertility in crossbred bulls and that transcriptional abundance of ZNF706, CRISP2, TNP2, and TNP1 genes could serve as potential biomarkers for fertility in crossbred bulls.

Highlights

Male fertility is of great importance in dairy cattle breeding industry because semen from a single bull is utilized to breed several thousands of females
A total of 776 transcripts were detected, in which 524 transcripts were common to both high- and low-fertile bulls, 84 sperm transcripts were unique to high-fertile bulls, and 168 transcripts were unique to low-fertile bulls (Figure 2), while 176 transcripts were upregulated and 209 were downregulated in low-fertile bulls
Our study established the transcriptomic profile on high- and low-fertile crossbred bull spermatozoa using highthroughput RNA sequencing

Summary

Introduction

Male fertility is of great importance in dairy cattle breeding industry because semen from a single bull is utilized to breed several thousands of females. The most accurate method for testing the bull fertility is insemination of many fertile females, but this method is time-consuming and expensive, and only a lesser number of males can be tested at any given time (Gillan et al, 2008; Kumaresan et al, 2017). Existing semen evaluation assays estimate only few structural attributes of spermatozoa rather than their functional attributes that are having considerable correlation with fertility (Graham, 2001; Kumaresan et al, 2017), which limits the accuracy of bull fertility prediction using these assays. Bull semen consists of heterogeneous cohorts of subpopulations, as a result of different spermatogenic waves (Rodríguez-Martínez, 2006), and all the spermatozoa in a given ejaculate are not uniform in terms of their fertilizing capacity. It is essential to understand the sperm molecular differences between high- and low-fertile males so that fertility signature molecules could be identified for development of bull fertility prediction tools

Methods

Results

Discussion

Conclusion