Indian Journal of Animal Research

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Research Article

Indian Journal of Animal Research, volume 55 issue 4 (april 2021) : 412-419

Identification of Potential Disease Biomarkers in the Ovaries of Dolang Sheep from Xinjiang using Transcriptomics and Bioinformatics Approaches

WH Chang1,*, ZL Cui2, JH Wang1
1College of Animal Science, Tarim University, Alar City, Xinjiang 843300, P.R. China.
2Key Laboratory of Tarim Animal Husbandry Science and Technology, Alar City, Xinjiang 843300, P.R. China.
Cite article:- Chang WH, Cui ZL, Wang JH (2020). Identification of Potential Disease Biomarkers in the Ovaries of Dolang Sheep from Xinjiang using Transcriptomics and Bioinformatics Approaches . Indian Journal of Animal Research. 55(4): 412-419. doi: 10.18805/ijar.B-1265.

ABSTRACT

Background: The Dolang sheep is a well-known indigenous breed from the Xinjiang region of China. The most important characteristics of these sheep are a year-round estrus and a strong resistance to a variety of diseases. Although the molecular regulatory mechanisms governing the year-round estrus and adaptability in health and disease are well studied in various animals, the related information is limited for sheep, particularly, the Dolang variety. 

Methods: To identify differentially expressed genes (DEGs) that might be responsible for the year-round estrus and that are expressed under different physiological conditions in Dolang sheep, samples from ovaries collected at different reproductive periods were analyzed using high-throughput sequencing and subsequent transcriptomics and bioinformatics analyses. 

Result: We identified 28,717 expressed genes by RNA-Seq analysis and from a list of 987 candidate genes, we identified 308 that were differentially expressed in the ovaries of non-pregnant Dolang sheep in estrus and anestrus phases and those in the gestation phase. The genes DQA, DQB and LOC101106374 were upregulated during the gestation period. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses showed that these three genes may improve immunity and prevent the occurrence of abortion, brucellosis, toxoplasmosis and globidiosis and can be used to monitor sheep health during pregnancy. Thus, DQA, DQB and LOC101106374 may serve as potential biomarkers for monitoring disease progression as well as abortion risk during pregnancy in sheep.

INTRODUCTION

Sheep are an important livestock in modern husbandry. They are a source of meat, milk and wool for human consumption, fur and fiber for the textile industry and are used as a model organism in the life sciences for comparative genomics, functional genomics and other omics studies (Zhang et al., 2013; Chang et al., 2015; Shukla et al., 2019). A precondition for the commercial use of sheep is that they must be healthy. Certain breeds of sheep, especially the native varieties, have a year-round estrus cycle or have a prolonged estrus and show strong adaptability in sheep husbandry. For example, Dolang sheep, which are mainly bred in the south Xinjiang region of China, are famous for their high prolificacy, strong adaptability and are perennially in estrus (Xing et al., 2019). The regulatory mechanism underlying perennial estrus and strong adaptability in sheep is unclear and research on genes regulating these aspects in Dolang sheep is inadequate. Genome-wide transcriptomics studies have been widely applied in the analysis of differentially expressed genes (DEGs), the identification of novel candidate genes, biomarker identification, metabolic pathway analysis, and forecasting the relationship between genes and target organs (Marguerat et al., 2010; Chen et al., 2011; Ramayo-Caldas et al., 2012). Additionally, next-generation sequencing technology provides a new and fast method to quantify and analyze gene expression and for finding biomarkers on a global scale. We sought to identify candidate genes that may explain the regulatory mechanisms underlying particular characteristics of Dolang sheep, such as the year-long estrus and robust immunity to diseases. Toward this end, we used transcriptomics and bioinformatics analyses to identify DEGs in ovarian tissues from Dolang sheep spanning a range of reproductive physiological states.
 
The methods of transcriptomics could be used to analyze changes to mRNA expression and are increasingly applied to the study of gene function and for identification of biomarkers (Rani and Sharma, 2017; Chang et al., 2018). Further, post-transcriptional regulation is very important for RNA splicing, mRNA stability, transfer, translation initiation, and protein stability. Chalmel and Rolland (2015) reviewed the correlation between transcription and translation during spermatogenesis by comparing the testicular transcriptome and proteome and this provided a method for addressing new questions by combining RNA-seq and MS-based proteomics. Sun et al., (2018) studied the expression level of proteins and mRNAs in the fast and slow muscles of scallops and revealed that regulatory mechanisms are essential in the maintenance of muscle structure and function at the transcriptional and post-transcriptional levels. Similar studies were performed on pollinated and parthenocarpic litchi (Liu et al., 2017), Chinese indigenous Shaziling pigs (Yang et al., 2016) and in an analysis of bacterial denitrification (Zheng et al., 2018). Thus, the methods of transcriptomics can provide deep insights into the biological processes of organisms (Conrad et al., 2018). Dolang sheep are widely bred in the south Xinjiang region of China and are famous for their high prolificacy and strong adaptability (Xing et al., 2019). However, an understanding of the gene regulatory mechanisms underlying perennial estrus and strong adaptability in these sheep is inadequate. Thus, we sought to analyze the high prolificacy and strong adaptability of Dolang sheep of Xinjiang by transcriptional methods.

MATERIALS AND METHODS

Materials and treatment
 
Eight, healthy, three-year-old female Dolang sheep were synchronized for their estrus cycle, using hormonal treatment imparted to them in 2017 at the Clinical Veterinary Laboratory of College of Animal Science, Tarim University, China. Females received a CIDR vaginal plug for 13 days, followed by an injection of 400 IU pregnant mare serum gonadotropin and 0.1 g prostaglandin at the time of vaginal plug removal. Estrus occurred 48 h after vaginal plug removal. Two non-pregnant ewes with a synchronized estrus cycle were slaughtered on the day of estrus onset; additionally, two non-pregnant ewes were slaughtered on the 10th day after the onset of estrus during the luteal phase. Further, four ewes were mated on the day of estrus onset and again on the next day. Of these, two ewes with resulting pregnancies were slaughtered at ~45 days of pregnancy based on observation of the onset of the estrus cycle and B-mode ultrasonic monitoring. Thus, three groups of ewes were analyzed: those with a synchronized estrus cycle (estrus), a synchronized luteal phase (anestrus) and pregnant ewes. The ovaries and other reproductive organs were collected after slaughter and frozen in liquid nitrogen.
 
Isolation of total RNA, cDNA library construction and transcriptome sequencing
 
Total RNA from 0.1 g of ovarian tissue from Dolang sheep was isolated using TRIzol reagent (Takara, Dalian, China), as per the manufacturer’s protocol. We performed a quality-check and quantified the total RNA obtained by using the NanoDrop1000 (Thermo Fisher Scientific, MMAS, USA) and the Agilent Bioanalyser 2100. Next, a cDNA library was generated from 10 μg of total RNA of each sample by using the TruSeq RNA Library Preparation Kit v2 (Illumina) according to the manufacturer’s instructions. Briefly, the total RNA samples were digested with DNase and purified and the RNA samples were quantified before further use. The samples were enriched for polyA-mRNAs using DynabeadsTM Oligo(dT) (Invitrogen, California, USA). The enriched mRNAs were chemically fragmented to obtain ~200 bp fragments; these were subsequently used for first-strand cDNA synthesis using random hexamer primers and the SuperScriptTM II Reverse Transcriptase (Takara, Dalian, China). The second strand cDNA synthesis was performed using buffer, DNA Polymerase I (Takara, Dalian, China), dNTPs and RNase H (Takara, Dalian, China). The cDNA was separated on a 1.5% Tris-borate-EDTA polyacrylamide gel (Takara, Dalian, China) and the separated and purified products were end-repaired and Illumina sequencing adaptors were added. The cDNA fragments were amplified by PCR and the resulting libraries were evaluated using the Agilent 2100 Bioanalyzer. The cDNA libraries were sequenced using an Illumina HiSeq2000 system (at Novogene, Beijing). The sequencing parameters used were single-end sequencing, with an average insert size of 200 bp and a read length of 50 bp.
 
Transcriptome data analysis
 
The images generated by the Illumina HiSeq2000 sequencer were converted into raw sequencing reads of nucleotide sequences using a base-calling pipeline (Illumina). The raw reads were quality-checked using the FASTQC software (http://www.bioinformatics.babraham.ac.uk/projects/fastqc/) and low quality reads, including contaminating reads, ambiguous reads and adapters sequences were removed by using the SOAPnuke software (http://www.seq500.com/uploadfile/SOAPnuke.zip). The remaining reads were rechecked for quality using the FASTQC software, and matched for Ovis aries genes (https://www.ncbi.nlm.nih.gov/genome/gdv/?org=ovis-aries, Oar_v4.0) using the SOAPnuke software with no more than a 3 bp mismatch. After counting the number of reads mapped to each gene, a normalization procedure was performed using the reads per kilobase per million reads (RPKM) method and weakly expressed genes (<5 RPKM) were filtered from each sample. To identify genes that were differentially expressed, the edgeR package was employed for calculating the false discovery rate (FDR), the log 2-fold change (log2FC) and the p-value for each gene in all experiments. We used a strict criteria for DEG identification and cutoffs for FDR< 0.05, log2FC > 1 or log2FC <0.1 and the p-value < 0.05 was used.
 
Functional analysis
 
To identify the potential functions of genes, we first re-annotated the Dolang sheep genes that were expressed in the ovaries. Briefly, the expressed Dolang sheep ovarian genes were mapped to multiple public databases, such as Swiss-Prot/UniProt, the NCBI non-redundant (NR) genes database, the Gene Ontology (GO) resource and the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. Using all detected genes as the control dataset, we used the numbers of DEGs to calculate the p-value (<0.05), which represents the significance of enriched GO terms/KEGG pathways  and the q-value (<0.05), which controls for the FDR. The p-values and q-values were calculated using the Fisher’s exact test and the R package named “q-value”, respectively. A one-way ANOVA was used to determine the significance of the difference in gene expression levels observed in the different physiological stages of Dolang sheep ovaries.

RESULTS AND DISCUSSION

Overview of the Dolang sheep ovarian transcriptome
 
The objective of this study was to identify the changes in gene expression in Dolang sheep ovaries during the estrus and anestrus (10 days after estrus) periods and at ~45 days of pregnancy. Toward this end we carried out high-throughput sequencing using the Illumina HiSeq 2000 platform, which generated ~32.99 million raw reads and ~31.69 million clean reads across all samples (Table 1). Subsequently, the clean reads from each sample were aligned to the Ovis aries genome sequence, resulting in 76.45% to 77.95% of clean reads with no more than a 3 bp mismatch.
 

Table 1: Overview of Dolang sheep ovaries transcriptome.


 
To profile ovarian gene expression, we counted the number of clean reads that aligned to the Ovis aries gene sequences and performed a normalization step using the fragments per kilobase of transcript per million reads mapped (FPKM) method. After filtering for weakly expressed genes (<5 RPKM), we identified a total of 28,717 genes across all samples. Of these, 586 genes showed differential expression. A total of 3 genes were commonly expressed across all samples, and 192, 247 and 18 genes were expressed exclusively during the estrus, anestrus, and pregnancy periods, respectively (Fig 1). Upon using a significance level cutoff of p<0.05, we identified 242 genes that were upregulated and 66 that were downregulated during the estrus period relative to their expression level in the anestrus period. Similarly, 22 genes were upregulated and 36 were downregulated during the anestrus period relative to their expression level in the pregnancy period. Further, 81 genes were upregulated and 271 were downregulated during the estrus period relative to their expression level in the pregnancy period (Fig 2). Of these, 36 DEGs were potential transcription factors and 15 candidates remained after removing duplicates (XM_101121392, bHLH; XM_101120004, zf-C2H2; XM_101120262, Homeobox; XM_101107885, HMG; XM_101113274, Transcription_Cofactors, etc.) We identified 347,659, 312,750, and 287,191 single nucleotide polymorphisms (SNPs) in ovary samples from estrus, anestrus and gestating sheep, respectively. The direct sequencing of transcriptomes coupled with downstream analysis is an effective method to discover new or important functional genes (Qian et al., 2014; Wickramasinghe et al., 2014; Fan et al., 2015) and has been applied to various organisms (Gui et al., 2013; Kordonowy et al., 2017; Ruan et al., 2015; Tian et al., 2019). Usually, only a few common genes or DEGs are identified, although the absolute numbers may be changed by using a wider grouping or different parameter settings. However, these genes can provide valuable information for further identification of genes related to the regulation of fertilization, ovulation, estrus, immunity, or disease in model organisms.
 

Fig 1: Venn diagram of genes identified in the ovaries of Dolang sheep.


 

Fig 2: Volcano plot of genes expression.


 
GO and KEGG pathway analysis
 
In the GO analysis, 16,557 of the identified genes were classified as belonging to the ontology component, with a p£1. Of these, 79.5% clustered in the cell and cellular component class and 73.2% clustered with genes related to intracellular components. Additionally, we found that 15,895 genes clustered in the molecular function class and the most enriched molecular function was molecular binding, with 12,482 annotated genes (78.5% of 15 895 genes), followed by catalytic activity (38.5% of genes). From the biological processes class, we identified 15,488 genes related to cellular process, metabolic process and primary metabolic process, representing ~77.10%, ~52.8% and ~43.8% of the genes in these groups, respectively, while ~7.3% and ~4.0% genes were involved in reproduction and embryo development processes, respectively. Partial GO annotations are shown in Fig 3.
 

Fig 3: GO Function analysis. A: Biological process; B: Molecular Function C: Cellular Component.


 
The KEGG pathway database is a powerful tool for analyzing gene and protein function in regulation networks (Kanehisa et al., 2012). To identify the biological pathways connected to the genes expressed in the ovaries at different physiological stages, all functional genes were submitted for a KEGG pathway enrichment analysis. A total of 9731 genes were assigned to 298 KEGG pathways (Fig 4) and clustered into 30 main categories, including metabolic pathways (575 genes), endocytosis (139 genes), pathways in cancer (136 genes), the PI3K-Akt signaling pathway (120 genes), Huntington’s disease (104 genes), focal adhesion (104 genes), spliceosome (104 genes) and RNA transport (101 genes). Among the 30 enriched categories, the metabolism pathway had the largest number of represented genes. The metabolic pathways with DEGs included genes for energy metabolism, carbohydrate metabolism, lipid metabolism, intracellular respiratory metabolism, and amino acid metabolism. Of these, DEGs enriched for the lipid metabolism and amino acid metabolism processes were associated with the synthesis of steroid hormones. Thus, the results of the GO and KEGG analysis provided a valuable resource for investigating the high prolificacy and year-round estrus processes.
 

Fig 4: The KEGG pathway analysis.


 
Bioinformatics analysis of the DQA, DQB and LOC101106374 genes
 
We identified the DQA, DQB and LOC101106374 genes as being upregulated during the gestation period. GO and KEGG analysis indicated that these genes may be involved in the improvement of resistance to diseases. This finding indicated that these genes play a role in maintaining the sheep’s health during pregnancy and possibly help toward preventing abortion, brucellosis, toxoplasmosis and globidiosis.
 
The 903 bp DQA gene is located on chromosome 20. In the KEGG pathway analysis, this gene was linked to various roles such as in the production of cell adhesion molecules (CAMs; ID:oas04514), graft-versus-host disease (ID:oas05332), viral myocarditis (ID:oas05416), intestinal immune network for IgA production (ID:oas04672), asthma (ID:oas05310), autoimmune thyroid disease (ID:oas05320), inflammatory bowel disease (IBD; ID:oas05321), leishmaniasis (ID:oas05140), antigen processing and presentation (ID:oas04612), Staphylococcus aureus infection (ID:oas05150), phagosome (ID:oas04145), influenza A (ID:oas05164), herpes simplex infection (ID:oas05168), systemic lupus erythematosus (ID:oas05322) and tuberculosis (ID:oas05152). A GO functional analysis revealed that DQA is associated with antigen processing and presentation (GO:0019882), the MHC protein complex (GO:0042611), the MHC class II protein complex (GO:0042613), immune system process (GO:0002376), plasma membrane protein complex (GO:0098797), immune response (GO:0006955), cell periphery (GO:0071944), protein binding (GO:0005515), protein complex (GO:0043234), membrane protein complex (GO:0098796), binding (GO:0005488), response to stimulus (GO:0050896), membrane (GO:0016020), cell (GO:0005623) and other functions. The DQA protein binds to specific epitopes and this binding is important for triggering the immune system to attack virus-infected cells through proteins (Juul-Madsen et al., 2012; Fan et al., 2019; Xie et al., 2019).
 
The 558 bp DQB gene is located on chromosome 20. In the KEGG pathway analysis, this gene was associated with several functions, including in the production of CAMs (ID:oas04514), graft-versus-host disease (ID:oas05332), allograft rejection (ID:oas05330), viral myocarditis (ID:oas05416), intestinal immune network for IgA production (ID:oas04672), asthma (ID:oas05310), autoimmune thyroid disease (ID:oas05320), inflammatory bowel disease (IBD) (ID:oas05321), leishmaniasis (ID:oas05140), antigen processing and presentation (ID:oas04612), Staphylococcus aureus infection (ID:oas05150), toxoplasmosis (ID:oas05145), phagosome (ID:oas04145), influenza A (ID:oas05164), herpes simplex infection (ID:oas05168), systemic lupus erythematosus (ID:oas05322) and tuberculosis (ID:oas05152). A GO functional analysis indicated that DQB is associated with processes for protein binding (GO:0005515), binding (GO:0005488) and molecular function (GO:0003674). In a study by Diana et al., (2016), a 172 bp fragment of exon 2 of the MHC Class II genes at the DQB locus was genotyped for 80 blue whales and 22 putatively functional DQB allotypes were identified. These results indicate that the immunogenic variation in blue whales is comparable to that in terrestrial mammals.
 
Additionally, the HLA-DQB2 gene may contribute to genetic susceptibility to tuberculosis (Wang et al., 2018) and recurrent aphthous stomatitis (Najafi et al., 2016).
 
The 792 bp LOC101106374 gene is located on chromosome 20. In the KEGG pathway analysis, this gene was associated with various functions, including in the production of CAMs (ID:oas04514), antigen processing and presentation (ID:oas04612), type I diabetes mellitus (ID: oas04940), autoimmune thyroid disease (ID:oas05320), viral myocarditis (ID:oas05416), graft-versus-host disease (ID:oas05332), allograft rejection (ID:oas05330), phagosome (ID:oas04145), herpes simplex infection (ID:oas05168), HTLV-I infection (ID:oas05166) and endocytosis (ID:oas04144). A GO functional analysis indicated that LOC101106374 is associated with the immune system process (GO:0002376), immune response (GO:0006955), binding (GO:0005488), molecular function (GO:0003674), biological_process (GO:0008150), response to stimulus (GO:0050896) and protein binding (GO:0005515). There were no reports in the literature for LOC101106374 function, but the gene was found to be related to improving immunity.
 
Collectively, these findings indicate that DQA, DQB and LOC101106374 are related to immunity or disease processes. Because all three genes were significantly upregulated in the pregnant sheep in this study, it can be concluded that these genes are involved in maintaining a high disease resistance state during gestation in sheep. Moreover, based on these and previous findings, DQA, DQB and LOC101106374 genes can be considered potential biomarkers for monitoring disease progression, brucellosis, toxoplasmosis, globidiosis and abortion risk during pregnancy in sheep.

ACKNOWLEDGEMENT

This work was supported by the principal fund of Tarim University (grant number TDZKJC201605), the open projects of Key Laboratory of Tarim Animal Husbandry Science (grant numbers HS201906, HS202006) and the research innovation project of graduate students (TDGRI201922).

REFERENCES


  1. Chang, W.H., Yong, Z*., Wang, J.H., Ma, Y.J., Zhao, X.X., Zhang, C.R. (2015). Identification of novel and differentially expressed microRNAs in ovine ovary and testis tissues using solexa sequencing and bioinformatics[J]. Journal of Integrative Agriculture. 14(8): 1604-1616.

  2. Chang, W.H., Wang, J.H., Zhang Y., Wu, J.Y. (2018). Discovery of two novel miRNAs from the Ovis aries by a combinatorial approach of experiments and bioinformatics. Indian J. Anim. Res. 52(8): 1155-1161.

  3. Chen, C., Ai, H., Ren, J., Li, W., Li, P., Ma, J., Huang, L. (2011). A global view of porcinetranscriptome in three tissues from a full-sib pair with extreme phenotypesin growth and fat deposition by pairedend RNA sequencing. BMC Genomics. 12: 448. Doi: 10.1186/1471-2164-12-448.

  4. Chalmel F. and Rolland, A.D. (2015). Linkingtranscriptomics and proteomics inspermatogenesis. Reproduction. 150: 66 R149-R157.

  5. Conrad, T., Kniemeyer, O., Henkel, S.G., Brakhage, A.A., Vlaic, S., Linde, J. (2018). Module-detection approaches for the integration of multilevel omics data highlight the comprehensive response of Aspergillus fumigatus to caspofungin. BMC Syst Biol. 12: 88. Doi: 10.1186/s 12918-018-0620-8.

  6. Diana, D.M, Eileen, A.L, Diane, G, Jorge, O. (2016). Genetic Variation at Exon 2 of the MHC Class II DQB Locus in Blue Whale (Balaenoptera musculus) from the Gulf of California. PLOS One, 1. DOI:10.1371/journal.pone.0141296.

  7. Fan, Y.X., Wu, R.B., Qiao, X., Zhang, Y.J., Li, J.Q. (2015). Hair follicle transcriptome profiles during the transition from anagen to catagen in Cashmere goat (Capra hircus). Genet. Mol. Res., 14(4): 17904-17915 

  8. Fan, Z.B., Zhang, H.F., Rong, M., Meng, D.M., Jiang, L.L., Jiang, P.H. (2019). Molecular Cloning and Bioinformatics Analysis of DQA Gene from Mink (Neovison vison). International Journal of Molecular Sciences. 20: 1037. doi:10.3390/ijms20051037

  9. Gui, D., Jia, K.T., Xia, J., Yang, L.L., Wu, Y.P., Yi, M.S. (2013). De novo assembly of the indo-pacific humpback dolphin leucocyte transcriptome to identify putative genes involved in the aquatic adaptation and immune response.PLoS One. 8. Doi:10.1371/journal.pone.0072417.

  10. Juul-Madsen, H., Dalgaard, T.S., Afanassieff, M. (2000). Molecular characterization of major and minor MHC class I and II genes in B21-like haplotypes in chickens. Anim. Genet. 31: 252-261

  11. Kanehisa, M., Goto, S., Sato, Y., Furumichi, M., Tanabe, M. (2012). KEGG for integration and interpretation of large-scale molecular data sets. Nucleic Acids Res. 40: D109-D114.

  12. Kordonowy, L. and MacManes, M. (2017). Characterizing the reproductive transcriptomic correlates of acute dehydration in males in the desert-adapted rodent, Peromyscus eremicus. BMC Genomics.18: 473. Doi: 10.1186 /s12864-017- 3840-1.

  13. Liu, W., Chen, M.S., Bai, L.J. (2017). Comprehensive transcriptomics and proteomics analyses of pollinated and parthenocarpic litchi (Litchi chinensisSonn.) fruits during early development. Scientific Reports. 7: 5401. Doi:10.1038/s41598-017-05724-z.

  14. Marguerat, S. and Bähler, J. (2010). RNA-seq: from technology to biology. Cell Mol Life Sci. 67: 569-579.

  15. Najafi, S., Mohammadzadeh, M., Bidoki, A.Z., Meighani, Rezaei, N. (2016). HLA-DRB and HLA-DQB Allele and Haplotype Frequencies in Iranian Patients with Recurrent Aphthous Stomatitis. Iran J Allergy Asthma Immunol. 15(4): 289-295.

  16. Qian, X., Ba, Y., Zhuang, Q. F., Zhong, G. F. (2014). RNA-Seq technology and its application in fish transcriptomics. OMICS. 18: 98-110.

  17. Ramayo-Caldas, Y., Mach, N., Esteve-Codina, A., Corominas, J., Folch, J.M. (2012). Liver transcriptome profile in pigs with extremephenotypes of intramuscular fatty acid omposition. BMC Genomics. 13: 547. Doi: 10.1186/1471-2164-13-547.

  18. Rani, B., Sharma, V.K. (2017). Transcriptome profiling: methods and applications-A review. Agricultural Reviews. 38(4): 271-281.

  19. Ruan, R., Guo, A.H., Hao, Y.J., Zheng, J.S., Wang, D. (2015). Novo assembly and characterization of narrow-ridged finless porpoise renal transcriptome and identification of candidate genes involved in osmoregulation. Int J Mol Sci. 16: 2220-2238.

  20. Shukla, P., Rajput, R., Kumar R., Verma, M. (2019). Biochemical composition of amniotic fluid during different stages of gestation in Gaddi sheep. Indian J. Anim. Res. 53 (2): 178-180.

  21. Sun, X.J., Liu, Z.H., Wu, B., Zhou, L.Q., Wang, Q., Wu, W., Yang, A.G. (2018). Differences between fast and slow muscles in scallops revealed through proteomics and transcriptomics. BMC Genomics. 19. Doi.org/10.1186/s12864-018-4770-2.

  22. Tian, F., Liu, S.J., Shi, J.Q., Qi, H.F., Zhao, K., Xie, B.S. (2019). Transcriptomic profiling reveals molecular regulation of seasonal reproduction in Tibetan highland fish, Gymnocypris przewalskii. BMC Genomics, 20. Doi.org/ 10.1186/s12864- 018-5358-6.

  23. Wickramasinghe, S., Cánovas, A., Rincón, G., Juan, F. (2014). Medrano. RNA-sequencing: a tool to explore new frontiers in animal genetics. Livestock Science. 166: 206-216.

  24. Wang, X., Cao, X.D., Zhang, W.J., Zhang, EI-Ashram, S., Wu, J.D., Chen, C.F.(2018). Association of human leukocyte antigens-DQB2/DPA1/DPB1 polymorphism and pulmonary tuberculosis in the Chinese Uygur population. Molecular genetics and Genomic Medicine. DOI:10.1002/mgg 3.544

  25. Xie, H.J., Zhao, J., Zhuo-Ma, D., Zhan-Dui, N., Er-Bu, A., Tserinq, T. (2019). Inhibiting tumour metastasis by DQA modified paclitaxel plus ligustrazine micelles in treatment of non-small-cell lung cancer. Artif Cells Nanomed Biotechnol. 47(1): 3465-3477.

  26. Xing, F., Gao, Q.H., Qi, X., Li, Q.J., Li, C. (2019). Cloning and expression of Lin28A gene in the onset of pugerty in Duolang sheep. Acta Veterinaria et Zootechnica Sinica. 50(01): 78-85.

  27. Yang, H., Xu, X.L., Ma, H.M., Jiang, J. (2016). Integrative analysis of transcriptomics and proteomics of skeletal muscles of the Chinese indigenous Shaziling pig compared with the Yorkshire breed. BMC Genetics. 17. Doi:10.1186 /s12863-016- 0389-y.

  28. Zhang, S.F.,Zhao, F.P.,Wei, C.H., Sheng, X.H., Ren, H.X., Du, L.X. 2013. Identification and Characterization of the miRNA Transcriptome of Ovis aries. PLOS ONE. 8(3): e58905. https://doi.org/10.1371/journal.pone.0058905.

  29. Zheng, X., Wang, J., Chen, Y.G., Wei, Y. (2018). Comprehensive analysis of transcriptional and proteomic profiling reveals silver nanoparticles-induced toxicity to bacterial denitrification. Journal of Hazardous Materials. 344: 291-298.
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