Identification of significantly expressed up and down regulated genes in categorised crossbred bulls

Introduction

Around the world, 150 million rural households (representing 750 million people) are involved in milk production, primarily in developing nations (FAO, 2018) [1] . Due to heterosis, where the crossbred animals are more robust and economically efficient in comparison to the parental breeds (Pedersen and Christensen, 1989) [2] , interest in crossbreeding has grown over the past few decades in many countries. Since the selection and upgrading of a large number of non-descript cattle could not bring about the anticipated increase at the production level in previous decades, crossbreeding of local/Indigenous cattle with exotic dairy breeds was adopted as an alternative to meet the milk requirement of India's expanding population. Globally, a significant proportion of reproductive failure is attributable to bull subfertility due to poor semen quality (DeJarnette et al., 2004) [3] . Generally, fertility is more vital in the case of bulls compared with cows, because one bull might be used for breeding nearly up to 40 cows through natural service or possibly hundreds of thousands using artificial insemination (Khan et al., 2018) [4] . Researchers investigated the physiological and molecular variations between crossbred and purebred males to comprehend the causes of the increased frequency of infertility/subfertility in crossbred males. The exact causes of the greater prevalence of infertility/subfertility in crossbred males, however, are still unknown. Analysis and thorough documentation have been carried out on the differences between crossbred and purebred males in terms of testicular histology, endocrinology, and molecular structure. To evaluate the vitality, motility, concentration, acrosomal integrity, and morphology of semen, many assays have been developed (Amann and Hammerstedt, 2004) [5] . However, bull fertility cannot be accurately predicted using the quality indicators that are frequently employed for semen examination.

Since this characteristic poses a significant economic barrier to the livestock enterprise, an improved method to evaluate male reproductive potential would be very beneficial. In light of this, this study presents a thorough examination of variations in the gene expression between spermatozoa from high and low-fertile crossbred bulls.

Materials and Methods

Cryopreserved semen straws of Frieswal bulls (n = 04) categorized into good quality bulls (with conception rate> 45%) and poor quality (with rejection rate> 65%) bulls with their satisfactory semen quality phenotypes were obtained from Institutional Semen Freezing Laboratory, ICAR-Central Institute for Research on Cattle, Meerut, Uttar Pradesh, India. All the bulls were maintained using similar management practices. Vaccination, deworming, regular check-up for communicable diseases, and other herd-health programs were imposed as per the farm schedule, and the bulls were free from major infectious diseases. Spermatozoa from high-(HF) and low-fertile (LF) bulls (n = 02 samples) were individually subjected to high-throughput RNA-seq analysis to catalogue differentially expressed transcripts as detailed below. RNA isolation from the categorized bull spermatozoa samples was done using TRIzol (Invitrogen, USA) as per the manufacturer's instructions with minor modifications (Parthipan et al., 2015) [6] . Quality and quantity of the purified RNA samples were determined using Spectrophotometer and fluorometer Qubit quotation system (Life Technologies, USA), respectively. Final libraries were quantified using a Qubit 4.0 fluorometer using a DNA HS assay kit and were sent for RNA sequencing. Paired-end sequencing protocol from the sperm samples of four bulls was carried out to generate around 50 million reads of length 150 bp per sample using NOVASeq 6000 (Neuberg Supratech Research Laboratories (NSRL), Ahmedabad, Gujrat, India).

Bioinformatics analysis

The raw data generated from the twelve samples were evaluated for the read quality using FastQC (Babraham Bioinformatics, UK) and the reads were processed with Cutadapt tool. All the processed reads were then pooled and aligned to the bovine genome (ARS-UCD2.0/Btau9) using HISAT2 and the samples were sorted with aligned sequences using Samtools (Galaxy Version 2.0.2). The mapped and properly paired sequence to the reference genome was calculated based on tabular descriptive statistics dataset tool Flagstat. The number of reads corresponding to each transcript were obtained using the FeatureCounts software. For obtaining the significantly differentially expressed genes (DEGs) along with their Log2 fold change values, DESeq2 package of R software was used. Based on the Log2 fold change values, the transcripts with absolute log2 fold change ±2 and p-value < 0.05 were considered as differentially expressed and subjected to gene ontology (GO) analysis, and pathway enrichment analysis.

Gene ontology and KEGG pathway analysis

Gene ontology (GO) classification of sperm transcripts was done using the Database for Annotation, Visualization, and Integrated Discovery (DAVID) Bioinformatics Resources v6.8 (https://david.ncifcrf.gov/) and categorized as molecular function (MF), biological process (BP), cellular component (CC), and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway. The top BPs, CCs, and MFs based for both up and downregulated genes are given in table 1 &2. All the analysis were performed using Bos taurus genome as background.

Results

With the use of Illumina NOVASeq 6000 RNA sequencing, the total reads identified for the good and poor quality bulls were 26,139 and 25,194, respectively. A total of 23,987 transcripts were differentially expressed in the categorized crossbred bull spermatozoa, of which 498 genes significantly differed (log2 FC±2, p-value < 0.05) between the groups. Of these 498 genes, 227 were significantly upregulated (log2 FC>2) and 271 were significantly downregulated (log2 FC<2) between the high and low fertile groups. The volcano plot showing significantly up and downregulated transcripts with p-value <0.05 and log2 fold change±2 is shown in Figure 1. The top 5 upregulated transcripts were OR3A4, LOC100847415, NSMCE3, LOC132346647, and CLDN4. The top 5 down regulated transcripts were TMEM92, FLCN, LOC785907, GSC2, and PARS2.

Gene Ontology Analysis

Additionally, the total transcripts of interest showing differential expression (the set of 498), including known (identified transcripts) and unknown genes (hypothetical transcripts). There were a total of 421 known transcripts and the rest were unknown. Gene ontology (GO) classification of sperm transcripts using the Database for Annotation, Visualization, and Integrated Discovery (DAVID) Bioinformatics Resources v6.8 identified 600 biological processes (BP), 212 cellular components (CC), and 239 molecular pathways (MF) for all the significantly differentially expressed transcripts. For significantly upregulated transcripts, Gene ontology analysis revealed 18 biological processes (BP), 3 cellular components (CC), and 6 molecular pathways (MF) whereas for significantly downregulated transcripts, revealed 15 biological processes (BP), 9 cellular components (CC), and 3 molecular pathways.

KEGG Pathway analysis

KEGG pathway of identified upregulated transcripts was related to Olfactory transduction, Chemical carcinogenesisreceptor activation, Chemical carcinogenesis -DNA adducts, and Viral life cycle -HIV-1. KEGG pathways related to downregulated transcripts were Phagosome, and Herpes simplex virus 1 infection. The details of the KEGG pathway of Up and down regulated transcripts are given in Table 3 and 4. Pathway enrichment of genes specific to Up and down regulated sperm transcripts are given in Table 4.7 and 4.8.

Discussion

Profiling of spermatozoal transcriptomes is an accurate tool to assess male fertility and the success of assisted reproductive technologies. Spermatozoal transcriptome profiling is a reliable method for evaluating male fertility and the efficacy of assisted reproductive technologies. Therefore, spermatozoal transcript profiling has been done in a variety of species, including humans (Sendler et al., 2013) [7] to evaluate the function of spermatozoal RNA in fertility. The quantity of differentially expressed spermatozoa transcripts in semen from crossbreds bulls was investigated in the present study. A total of 23,987 transcripts were differentially expressed in the categorized crossbred bull spermatozoa, of which 498 genes significantly differed (log2FC±2, p-value < 0.05) between the groups. Of these 498 genes, 227 were significantly upregulated (log2FC>2) and 271 were significantly downregulated (log2FC<2) between the high and low fertile groups. The number of genes differentially expressed in the categorized crossbred bull spermatozoa is comparatively higher than those received by Selvaraju et al. (2017) [8] and Raval et al. (2019) [9] . In contrast, Card et al. (2013) [11] only found 6,166 transcripts in bovine sperm. The contributing factors to these variations could be the season of sample collection (Godia et al., 2019) [12] , spermatozoa state (fresh or frozen) (Chen et al., 2015; Card et al., 2017; Singh et al., 2019) [13,10,14] RNA isolation technique (Parthipan et al., 2015; Fraser et al., 2017) [6,15] , integrity of sperm RNA, RNA sequencing device (Selvaraju et al., 2017) [8] , and library preparation techniques (Mao et al., 2014) [16] . Our analysis is limited to only gene ontology and KEGG pathways. However, identifying the relevant genes and finding their association with fertility or male reproduction can contribute to the development of molecular markers and strategies for improving sperm quality, managing spermrelated diseases, and increasing bovine fertility.

Conclusion

The proposed study was designed to generate the gene ontology profiles of the categorised bulls and to perform the comparative evaluation of the up and down regulated sperm transcripts between the groups. Identifying the significant genes that are up or down regulated between the groups can help us to identify the marker genes in the bulls which can help in the selection of superior bulls for semen quality traits. Identifying the significant UP/Down pathways can also help to determine the potential cause of reproductive problems in crossbred bulls. Therefore, they can contribute to the development of molecular markers and strategies for improving sperm quality, managing sperm-related diseases, and increasing bovine fertility in addition to providing new insights into the genetic basis of sperm cryoresistance. For more accurate results validation of the identified relevant genes can be performed using qRT-PCR. Since, the study is limited by the small sample sizes, larger sample sizes can be preferred for future validation investigations.

References

1. Pedersen J, Christensen LG. Heterosis for milk production traits by crossing Red Danish, Finnish Ayrshire and Holstein-Friesian cattle. Livest Prod Sci. 1989;23(3-4):253-266.
2. DeJarnette JM, Marshall CE, Lenz RW, Monke DR, Ayars WH, Sattler CG. Sustaining the fertility of artificially inseminated dairy cattle: the role of the artificial insemination industry. J Dairy Sci. 2004;87(Suppl. 1)
3. Khan IM, Khan RU, Qureshi MS, Usman T, Khan A, Ullah Z, et al. Cross breeding promotes deterioration of semen quality in cattle bulls. Pak J Zool. 2018;50(1):97-103.
4. Amann RP, Hammerstedt RH. Andrology lab corner*: Reflections on CASA after 25 years. J Androl. 2004;25(3):317-325. doi: 10.1002/j.1939-4640.2004.tb02793.x.
5. Parthipan S, Selvaraju S, Somashekar L, Kolte AP, Arangasamy A, Ravindra JP. Spermatozoa input concentrations and RNA isolation methods on RNA yield and quality in bull (Bos taurus). Anal Biochem. 2015;482:32-9.
6. Sendler E, Johnson GD, Mao S, Goodrich RJ, Diamond MP, Hauser R, et al. Stability, delivery and functions of human sperm RNAs at fertilization. Nucleic Acids Res. 2013;41(7):4104-4117.
7. Selvaraju S, Sivashanmugam P, Lakshminarayana S, Atul PKB, Krishnan B, Arunachalam A, et al. Occurrence and functional significance of the transcriptome in bovine (Bos taurus) spermatozoa. Sci Rep. 2017;7:42392.
8. Raval NP, Shah TM, George LB, Joshi CG. Insight into bovine (Bos indicus) spermatozoal whole transcriptome profile. Theriogenology. 2019;129:8-13.
9. Card CJ, Krieger KE, Kaproth M, Sartini BL. Oligo-dT selected spermatozoal transcript profiles differ among higher and lower fertility dairy sires. Anim Reprod Sci. 2017;177:105-23.
10. Card CJ, Anderson EJ, Zamberlan S, Krieger KE, Kaproth M, Sartini BL. Cryopreserved bovine spermatozoal transcript profile as revealed by highthroughput ribonucleic acid sequencing. Biol Reprod. 2013;88(3):49. 10.1095/biolreprod.112.103788.
11. Gòdia M, Estill M, Castelló A, Balasch S, RodríguezGil JE, Bonastre SA. RNA-seq analysis to describe the boar sperm transcriptome and its seasonal changes. Front Genet. 2019;10:299. doi: 10.3389/fgene.2019.00299.
12. Chen X, Wang Y, Zhu H, Hao H, Zhao X, Qin T, et al. Comparative transcript profiling of gene expression of fresh and frozen-thawed bull sperm. Theriogenology. 2015;83(4):504-11. doi: 10.1016/j.theriogenology.2014.10.015.
13. Singh R, Junghare V, Hazra S, Singh U, Sengar GS, Raja TV, et al. Database on spermatozoa transcriptogram of categorised Frieswal crossbred (Holstein Friesian × Sahiwal) bulls. Theriogenology. 2019;129:130-145.
14. Fraser L, Brym P, Pareek CS. Isolation of total ribonucleic acid from fresh and frozen-thawed boar semen and its relevance in transcriptome studies. S Afr J Anim Sci. 2017;47(1):56-60. doi: 10.4314/sajas.v47i1.9.
15. Mao S, Sendler E, Goodrich RJ, Hauser R, Krawetz SA. A comparison of sperm RNA-seq methods. Syst Biol Reprod Med. 2014;60(6):308-315. doi: 10.3109/19396368.2014.944318.