Indian Journal of Animal Research

  • Chief EditorK.M.L. Pathak

  • Print ISSN 0367-6722

  • Online ISSN 0976-0555

  • NAAS Rating 6.43

  • SJR 0.263

  • Impact Factor 0.5 (2023)

Frequency :
Monthly (January, February, March, April, May, June, July, August, September, October, November and December)
Indexing Services :
Science Citation Index Expanded, BIOSIS Preview, ISI Citation Index, Biological Abstracts, Scopus, AGRICOLA, Google Scholar, CrossRef, CAB Abstracting Journals, Chemical Abstracts, Indian Science Abstracts, EBSCO Indexing Services, Index Copernicus
Indian Journal of Animal Research, volume 56 issue 12 (december 2022) : 1492-1498

Integrative Transcriptomic and Proteomic Analysis of Ovaries at Different Physiological Periods in Dolang Sheep

Chang Wei-Hua, Ni Guo-Chao, Li Hao, Wang Juan-Hong
1College of Animal Science, Xichang University, Xichang City, Sichuan 615000, PR China.
Cite article:- Wei-Hua Chang, Guo-Chao Ni, Hao Li, Juan-Hong Wang (2022). Integrative Transcriptomic and Proteomic Analysis of Ovaries at Different Physiological Periods in Dolang Sheep. Indian Journal of Animal Research. 56(12): 1492-1498. doi: 10.18805/IJAR.BF-1465.
Background: The Dolang sheep is a well-known indigenous breed from Xinjiang, China. Two of its main advantages are year-round estrus and high prolificacy. Although the molecular regulatory mechanisms of reproductive processes have been studied in other animals and humans, relevant information on its year-round estrus and high prolificacy remains limited, particularly for Dolang sheep breeds from Xinjiang. 
Methods: To obtain differentially expressed genes (DEGs) and proteins that might be responsible for the year-round estrus and high fecundity, ovaries from Dolang sheep were studied at different periods using an integrative strategy of transcriptomics and proteomics via high-throughput sequencing and iTRAQ technologies.
Result: RNA-seq yielded 28,717 unigenes, 987 candidate genes and 308 DEGs in the ovaries (at estrus) of Dolang sheep compared with the dioestrus and gestation period (about 45 days of pregnancy). GO and KEGG analysis revealed that the gene products were mainly involved in metabolism, hormone secretion and regulation, ovulation, estrus and regulation of reproduction. At the protein level, a total of 4847 proteins and 470 differentially expressed proteins were identified. The latter were potentially associated with metabolism, endocytosis, the glycolytic pathway and skeletal muscle growth. In conclusion, the current study provides a basis and prospective understanding of the molecular regulatory mechanism underlying the high prolificacy and year-round estrus of Dolang sheep.

  1. Berean, D., Ergene, O., Blaga-Petrean, A., Bogdan, I., Bogdan, L.M. (2021). Comparative data about estrus induction and pregnancy rate on Lacaune rwes in non-breeding season after melatonin implants and intravaginal progestagen. Indian Journal of Animal Research. 55(5): 517-521.

  2. Chalmel, F.R. and Rolland, A.D. (2015). Linking transcriptomics and proteomics in spermatogenesis. Reproduction. 150: R149-R157.

  3. Chang, W.H., Cui, Z.L., Wang J.H. (2021). 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.

  4. Conrad, T., Kniemeyer, O., 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.

  5. Dabney, A. and Storey, J.D. (2010). q value: Q-value estimation for false discovery rate control Version 1.34.0. R package version 1.

  6. David, G.W., Rong, X., Alin, R., Simpson, R.J. (2017). Proteomic insights into extracellular vesicle biology defining exosomes and shed microvesicles. Expert Review of Proteomics. 14: 69-95.

  7. Deutsch, D.R, Frohlch, T, Otte, K.A. (2014). Stage-specific proteome signatures in early bovine embryo development. J. Proteome Res. 13(10): 4363-76.

  8. Getachew, T., Heather, J.H., Mwai, A.O., Sölkner, J. (2017). Identifying highly informative genetic markers for quantification of ancestry proportions in crossbred sheep populations: Implications for choosing optimum levels of admixture. BMC Genetics. 18: 80. DOI: 10.1186/s12863-017-0526-2.

  9. Hornshøj, H. andersen, P.K., Panitz, F., Bendixen, C. (2009). Transcriptomic and proteomic profiling of two porcine tissues using high throughput technologies. BMC Genomics. 10. 30. DOI: 10.1186/1471-2164-10-30.

  10. Hu, Y., Wu, X.J., Qi, J., Du, Y.J. (2019). Cellular protein profiles altered by PRRSV infection of porcine monocytes-derived dendritic cells. Veterinary Microbiology. 228: 134-142.

  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. Kim, N.K., Lee, H.C., Kim, O.H., Lee, C.S. (2010). Comparative studies of skeletal muscle proteome and transcriptome profilings between pig breeds. Mamm Genome. 21: 307- 319.

  13. Langfelder, P. and Horvath, S. (2008). WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics. 9: 559. DOI: 10.1186/1471-2105-1189-1559.

  14. Lizandra, J., Yu, H., Liu, Q., Weaver, A.M. (2019). Quantitative proteomic analysis of small and large extracellular vesicles (EVs) reveals enrichment of adhesion proteins in small EVs. Journal of proteome research. Doi: 10.1021/acs.jproteome. 8b00647.

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

  16. Mei, B.J. and Liu R. (2021). Identification of lncRNAs differentially expressed during natural and induced estrus in sheep. Indian Journal of Animal Research. 55(12): 1421-1429.

  17. Miao, X.Y., Luo, Q.M., Zhao, H.J., Qin, X.Q. (2016). Ovarian proteomic study reveals the possible molecular mechanism for hyper prolificacy of Small TailHan sheep. Scientific Reports. 08. Doi: 10.1038/srep27606.

  18. Ramayo, C.Y., Mach, N., Ballester, M., Folch, J.M. (2012). Liver transcriptome profile in pigs with extreme phenotypes of intramuscular fatty acid composition. BMC Genomics. 13: 547. Doi: 10.1186/1471-2164-13-547.

  19. Sun, X.J., Liu, Z.H., Wu, B., 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.

  20. Wu, C.C., Chang, S.C., Huang, Y.L., Liu, H.P. (2018). Proteome analyses reveal positive association of COL2A1, MPO, TYMS and IGFBP5 with canine mammary gland malignancy. Proteomics Clin Appl. Doi: 10.1002/prca.201800151.

  21. Xing, F., Gao, Q.H., 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: 78-85.

  22. 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.

  23. Yang, J.K., Yang, L.C., Li, B. (2016). iTRAQ-based proteomics identification of serum biomarkers of two chronic Hepatitis B subtypes diagnosed by traditional Chinese medicine. Biomed Res Int. Doi:10.1155/2016/3290260 (2016).

  24. 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.

  25. Zieske, L.R. (2006). A perspective on the use of iTRAQ reagent technology for protein complex and profiling studies. Journal of Experimental Botany. 57: 1501-1508.

  26. Zou, Y.C., Gong, P., Wu, X.L., Li, X., Chen, J.C. (2019). Quantitative iTRAQ-based proteomic analysis of piperine protected cerebral ischemia/reperfusion injury in rat brain. Neurochemistry International. 124: 51-61.

Editorial Board

View all (0)