Transcriptome profiling of granulosa cells of bovine ovarian follicles during different stages of folliculogenesis

Document Type : Research Paper

Authors

1 Ph.D. Candidate, Department of Animal Science, University College of Agriculture and Natural Resources, University of Tehran, Karaj, Iran

2 Professor, Department of Animal Science, University College of Agriculture and Natural Resources, University of Tehran, Karaj, Iran

3 Associate Professor, Department of Animal Science, University College of Agriculture and Natural Resources, University of Tehran, Karaj, Iran

4 Assistant Professor, Molecular Biology Research Center, Baqiyatallah University of Medical Sciences, Tehran, Iran

Abstract

At the later stage of folliculogenesis, the mammalian ovarian follicle contains layers of epithelial granulosa cells surrounding an antral cavity. During follicle development, granulosa cells replicate, secrete hormones and support the growth of the oocyte. In cattle, the follicle needs to grow over 10 mm in diameter to allow an oocyte release in  ovulation process, following which the granulosa cells cease dividing and differentiate into the specialized cells of the corpus luteum. To better understand the molecular basis of follicular growth and granulosa cell maturation, we undertook the transcriptome profiling of granulosa cells from small (< 5 mm; n = 10) and large (> 10 mm, n = 4) healthy bovine follicles, using data mining. In this regard, we have studied important genes that are included in folliculogenesis process using data, freely available in the different databases. In total 283 genes were identified with the comparison of transcriptome profiling of large and small granulosa cells. With construction and analysis of network, we became able to identify the interaction between them and finally we have found 6 important and functional modules using various software. The most important genes involved, were TNFα, NR1H4, LHCGR, FSHR, PTHLH, LHB, CAD, HSD3B1, CYP17A1, DICE1, MCE1, COX and Aromatase. These results suggest that identified modules can be used as markers for follicle differentiation and apoptosis process. 

Keywords

References

  1. Amsterdam, A., Gold, R. S., Hosokawa, K., Yoshida, Y., Sasson, R., Jung, Y. & Kotsuji, F. (1999). Crosstalk among multiple signaling pathways controlling ovarian cell death. Treads Endocrinology Metabolism, 10, 255-262.
  2. Andrews S. (2010). FastQC: a quality control tool for high throughput sequence data. Available online at: http://www.bioinformatics.babraham.ac.uk/projects/fastqc
  3. Assenov, Y., Ram, F., Schelhorn, S.E., Lengauer, T. & Albrecht, M. (2008). Computing topological parameters of biological networks. Bioinformatics, 24, 282-284.
  4. Austin, E. J., Mihm, M., Evans, A. C. O., Knight, P. G., Ireland, J. L. H., Ireland, J. J. & Roche, J. F. (2001). Alterations in intrafollicular regulatory factors and apoptosis during selection of follicles in the first follicular wave of the bovine estrous cycle. Biology of Reproduction, 64, 839-848.
  5. Bader, G. D. & Hogue, C. W. (2003). An automated method for finding molecular complexes in large protein interaction networks. BMC Bioinformatics, 4, 2.
  6. Baerwald, A. R., Adams, G. P. & Pierson, R. A. (2003). Characterization of ovarian follicular wave dynamics in women. Biology of Reproduction, 69, 11-31.
  7. Basini, G., Mainardi, G. L., Bussolati, S. & Tamanini, C. (2002). Steroidogenesis, proliferation and apoptosis in bovine granulosa cells: role of tumour necrosis factor-a and its possible signalling mechanisms. Reproduction and Fertility Development, 14, 141-150.
  8. Blankenberg, D., Gordon, A., Von Kuster, G., Coraor, N., Taylor, J. & Nekrutenko, A. (2010). Galaxy Team. Manipulation of FASTQ data with Galaxy. Bioinformatics, 26(14), 1783-5.
  9. Bolger, A. M., Lohse, M. & Usadel, B. (2014). Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics, 30, 2114-2120.
  10. Du, P., Kibbe, W. A. & Lin, S. M. (2008). 'lumi: a pipeline for processing Illumina microarray', Bioinformatics, 24, 1547-1548
  11. Ginther, O. J., Kastelic, J. P. & Knopf, L. (1989). Composition and characteristics of follicular waves during the bovine estrous cycle. Animal Reproduction Science, 20, 187-200.
  12. Ginther, O. J., Beg, M. A., Donadeu, F. X. & Bergfelt, D. R. (2003). Mechanism of follicle deviation in monovular farm species. Animal Reproduction Science, 78, 239-257.
  13. Goodman, A. L. & Hodgen, G. D. (1983). The ovarian triad of the primate menstrual cycle. Recent Prog Hormone Research, 39, 1-67.
  14. Hatzirodos, N., Hummitzsch, K., Irving-Rodgers, H. F., Harland, M. L. & Morris, S. E. (2014). Transcriptome profiling of granulosa cells from bovine ovarian follicles during atresia. BMC Genomics, 15, 40.
  15. Huang, D. W., Sherman, B. T. & Lempicki, R. A. (2009). Systematic and integrative analysis of large gene lists using DAVID Bioinformatics Resources. Nature Protocol, 4(1), 44-57.
  16. Ireland, J. J., Mihm, M., Austin, E., Diskin, M. G. & Roche, J. F. (2000). Historical perspective of turnover of dominant follicles during the bovine estrous cycle: key concepts, studies, advancements, and terms. Journal of Dairy Science, 83, 1648-1658.
  17. Ireland, J. J. & Richards, J. S. (1978). Acute effects of estradiol and follicle-stimulating hormone on specific binding of human [125I]iodo-follicle-stimulating hormone to rat ovarian granulosa cells in vivo and in vitro. Endocrinology, 102, 876-883.
  18. Johnson, A. L. (2003). Intracellular mechanisms regulating cell survival in ovarian follicles. Animal Reproduction Science, 78, 185-201.
  19. Johnson, A. L. & Bridgham, J. T. (2002). Caspase-mediated apoptosis in the vertebrate ovary. Reproduction, 124, 19-27.
  20. Kim, D., Pertea, G., Trapnell, C., Pimentel, H., Kelley, R. & Salzberg, S. L. (2013). TopHat2: accurate alignment of transcriptomes in the presence of insertions, deletions and gene fusions. Genome Biology, 14(4), pp. R36.
  21. Mihm, M. & Bleach, E. C. (2003). Endocrine regulation of ovarian antral follicle development in cattle. Animal Reproduction Science, 78, 217-237.
  22. Mihm, M., Austin, E. J., Good, T. E., Ireland, J. L., Knight, P. G., Roche, J. F. & Ireland, A. J. (2000). Identification of potential intrafollicular factors involved in selection of dominant follicles in heifers. Biology of Reproduction, 63, 811-819.
  23. Montojo, J., Zuberi, K. & Rodriguez, H. (2014). GeneMANIA: Fast gene network construction and function prediction for Cytoscape. F1000Research, 3, 153.
  24. Mukae, N., Enari, M., Sakahira, H., Fukuda, Y., Inazawa, J., Toh, H. & Nagata, S. (1998). Molecular cloning and characterization of human caspase-activated DNase. Proceedings of the National Academy of Sciences, 95, 9123-9128.
  25. Nepusz, T., Yu, H. & Paccanaro, A. (2012). Detecting overlapping protein complexes in protein-protein interaction networks. Nature Methods, 9, 471-472.
  26. Paul, D., Thomas, M. J., Campbell, A., Kejariwal, H. M., Brian, K., Robin, D., Karen, D., Anushya, M. & Apurva N. (2003). PANTHER: a library of protein families and subfamilies indexed by function. Genome Research, 13, 2129-2141.
  27. Pru, J. K. & Tilly, J. L. (2001). Programmed cell death in the ovary: insights and future prospects using genetic technologies. Molecular Endocrinology, 15, 845-853.
  28. Rao, J. U., Shah, K. B., Puttaiah, J. & Rudraiah, M. (2011).  Gene expression profiling of preovulatory follicle in the buffalo cow: effects of increased IGF-I concentration on periovulatory events. PLoS ONE, 6:e20754.
  29. Rice, V. M., Williams, V. R., Limback, S. D. & Terranova, P. F. (1996). Tumour necrosis factor-a inhibits follicle-stimulating hormone-induced granulosa cell oestradiol secretion in the human: dependence on size of follicle. Human Reproduction, 11, 1256-1261.
  30. Richards, J. S. (2001). Perspective: the ovarian follicle-a perspective in 2001. Endocrinology, 142, 2184-2193.
  31. Richards, J. S., Ireland, J. J., Rao, M. C., Bernath, G. A., Midgley, A. R. Jr. & Reichert, L. E. Jr. (1976). Ovarian follicular development in the rat: hormone receptor regulation by estradiol, follicle stimulating hormone and luteinizing hormone. Endocrinology, 99, 1562-1570.
  32. Ritchie, M. E., Phipson, B., Wu, D., Hu, Y., Law, C. W., Shi, W. & Smyth, G. K. (2015). Limma powers di erential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Research, 43(7), e47.
  33. Rivera, G. M. & Fortune, J. E. (2003). Proteolysis of insulin-like growth factor binding proteins-4 and -5 in bovine follicular fluid: implications for ovarian follicular selection and dominance. Endocrinology, 144, 2977-2987.
  34. Sasson, R., Winder, N., Kees, S. & Amsterdam, A. (2002). Induction of apoptosis in granulosa cells by TNFa and its attenuation by glucocorticoids involve modulation of Bcl-2. Biochemical and Biophysical Research Communications, 294, 51-59.
  35. Shannon, P., Markiel, A., Ozier, O., Baliga, N. S., Wang, J. T., Ramage, D., Amin, N., Schwikowski, B. & Ideker, T. (2003). Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Research, 13(11), 2498-504.
  36. Spicer, L. J. & Alpizar, E. (1994). Effects of cytokines on FSH-induced estradiol production by bovine granulosa cells in vitro: dependence on size of follicle. Domestic Animal Endocrinology, 11, 25-34.
  37. Tilly, J. L. (2001). Commuting the death sentence: how oocytes strive to survive. Nature Reviews Molecular Cell Biology, 2, 838-848.
  38. Terranova, P. F. (1997). Potential roles of tumor necrosis factor-a in follicular development, ovulation, and the life span of the corpus luteum. Domestic Animal Endocrinology, 14, 1-15.
  39. Trapnell, C., Williams, B. A., Pertea, G., Mortazavi, A., Kwan, G., van Baren, M. J., Salzberg, S. L., Wold, B. J. & Pachter, L. (2010). Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation. Nature Biotechnology, 28(5), 511-515.
  40. Walsh, S. W., Mehta, J. P, McGettigan, P. A. & Browne, J. A. (2012). Effect of the metabolic environment at key stages of follicle development in cattle: focus on steroid biosynthesis. Physiology Genomics, 44, 504-17.
Iranian Journal of animal Science
Volume 48, Issue 3 - Serial Number 3
December 2017
Pages 463-471

Files

History

  • Receive Date: 07 February 2017
  • Revise Date: 28 November 2017
  • Accept Date: 02 January 2018

Share

How to cite

Statistics