Fitting interaction network among effective transcription factors associated with ovulation rate and explored genes by using promoter analysis in cattle

Document Type : Research Paper

Authors

1 2. MSc, Department of Animal Science, Faculty of Agriculture, University of Birjand, Birjand, Iran

2 Professor, Department of Animal Science, Faculty of Agriculture, University of Birjand, Birjand, Iran

3 PhD, Department of Animal Sciences, College of Agriculture and Natural Resources, Ramin University, Khozestan, Iran

Abstract

Abstract
Introduction: Fertility traits and their improvement is the hot topics in animal studies; because these traits effect directly on profitability of livestock industry. Since ovulation rate is an important trait breeding strategies along with applying nutritional and managerial strategies must be used. Identifying and choosing the effective regulatory genes and biomarkers are the basic steps in applying molecular breeding strategies such as genomic selection and/ or marker assisted selection. It should be noted, one of the major challenges in reproductive traits is the large number of regulatory gene, which control these traits. Therefore, selection of the most effective and the best ones requires bioinformatics analysis. In this study, we hypothesized that genes, which have same transcription factor binding site, regulate by same transcription factors, express in same time and act in the same physiological processes. Therefore, transcription factor binding site on identified marker genes associated with ovulation rate was used to introduce new marker genes and then, protein-protein interaction network was applied to select the most regulatory ones and explore candidate molecular mechanisms, which control this trait.
Materials and method: In the present study, known marker genes associated with ovulation rate in cattle were searched from NCBI database. To extract promoter region of these genes, we used the link of gene to promoter of Genomatix, which is an on-line software. Link of Frameworker of Genomatix, was performed to achieve transcription factors that have binding site on promoters of the known markers. New marker genes associated with mentioned trait were investigated by searching genes throughout the genome, which have the same pattern of transcription factor binding sites on their promoter regions. To done this step, ModelInspector link of Genomatix software was used. These genes probably are potential effector genes for ovulation, because their expression regulate by same transcription factors, which regulate expression of ovulation’s genes. In order to confirm the interaction of potential marker and transcription factors, we fitted protein-protein interaction network on the last results of Genomatix by STRING database. Potential marker genes and transcription factors, which validated their interactions by STRING, were used to survey the most important cellular algorithm, which control ovulation rate. In this step, we applied comparative GO database.
Results and Discussion: Seven identified genes were found from NCBI database that associated with ovulation rate. Promoter regions of these genes was taken by Genomatix. Frameworker searched Data mining of transcription factors binding and their patterns. The results indicated the transcription factors, which regulate the expression of these genes, could induce the expression of 51 other genes in genome. These genes introduced as the potential marker genes that could control ovulation rate. Because these genes and identified genes have the same transcription factor binding sites on their promoter regions and expression by same transcription factors. Protein–protein interaction network, which is constructed between potential marker genes associated with ovulation rete and their transcription factors showed that two transcription factors (E2F1 and TFDP1) were more important in regulation of genes in ovulation rate. These two transcription factors had the most interactions with the targets. E2F1 affect the different stages of ovarian growth, follicle growth and regulate ovulation. TFDP1 play the important role in the cell cycle, growth and puberty of follicles. This transcription factor has poor activity, but it participate in formation another transcription factor complex with E2Fs families. This complex is more effective in expression of target genes of E2F. Our results indicated, two target genes (CAV1 and RANBP1) had the most interactions with transcription factors. In other words, the expression of these genes are regulate by more number of transcription factors, rather other target genes. CAV1 express in bovine granulosa cells, mature follicles and ovarian epithelial cells and it explore regulate vital processes in ovulation such as differentiation of granulosa cells and luteinization. Furthermore, gonadotropin hormones induce differentiation of granulosa cells by increasing of CAV1 expression. RANBP1 control cell cycle, transcription processes in ovum and cumulus cells. Our analysis reveals tyrosine kinase activity, JAK-STAT cascade, response to transforming growth factor beta, cell migration, JNK cascade and response to estrogen were the most important molecular mechanisms, which regulate ovulation. Tyrosine kinase activity and JAK-STAT cascade are two regulatory candidate pathways in cell differentiation and proliferation. They also induce first stage of follicle growth and associate with number of activated follicles. They effect on oogenesis by inducing progesterone secretion. Some inhibitors of JAK/STAT signaling pathway negative effect on progesterone secretion and ovulation process. Pathway of transforming growth factor beta was also bold in this study. This pathway has close relation with differentiation and maturation of ovum, induction of gonadotropins and regulate the selection of dominant follicle. This family protein regulate gonadotropin secretion by effect on anterior pituitary. Cell migration and JNK cascade are conserved processes between vertebrata in ovulation processes. Studies have shown these pathways were up-regulated during ovulation and they control other processes such as cytokines production, inflammatory response and apoptosis occurring during ovulation.
Conclusion: In conclusion, this study offers a vital basis for understanding potential marker genes and cellular algorithm, which control ovulation rate by fitting protein-protein interaction of ovulation related genes. Our data suggested E2F1 and TFDP1 as the most important transcription factors, and CAV1 and RANBP1 as the major target genes in ovulation. This network induce some biological pathways, which effect the activation of primary follicles, follicle secretion and cell migration. These findings have important implications for enhancement ovulation rate by genomic selection and/or marker assisted selection. This analysis could implications for complex traits, which control by many genes, to detect the novel and important ones.

Keywords


Baruselli P, Batista E, Vieira L and Souza A, 2015. Relationship between follicle population, AMH concentration and fertility in cattle. Animal Reproduction 12(3): 487-497.
Berkovich E and Ginsberg D, 2003. ATM is a target for positive regulation by E2F-1. Oncogene 22(2): 161.
Boscher C and Nabi IR, 2012. Caveolin-1: role in cell signaling, in Caveolins and Caveolae.  Springer. p. 29-50.
Di Fiore B, Ciciarello M, Mangiacasale R, Palena A, Tassin A-M, Cundari E and Lavia P, 2003. Mammalian RanBP1 regulates centrosome cohesion during mitosis. Journal of Cell Science 116(16): 3399-3411.
Di F, Liu J, Li S, Yao G, Hong Y, Chen Z J, Li W and Du Y, 2018. ATF4 contributes to ovulation via regulating COX2/PGE2 expression: A potential role of ATF4 in PCOS. Frontiers in Endocrinology 15(9):669.
Di Yorio MP, Bilbao MG, Biagini-Majorel AM and Faletti AG. 2013. Ovarian signalling pathways regulated by leptin during the ovulatory process. Reproduction: REP-13-0257.
Diouf MN, Lefebvre R, Silversides DW, Sirois J and Lussier JG, 2006. Induction of alpha‐caveolin‐1 (αCAV1) expression in bovine granulosa cells in response to an ovulatory dose of human chorionic gonadotropin. Molecular Reproduction and Development: Incorporating Gamete Research 73(11): 1353-1360.
Docquier A, Augereau P, Lapierre M, Harmand P-O, Badia E, Annicotte J-S, Fajas L and Cavaillès V, 2012. The RIP140 gene is a transcriptional target of E2F1. PloS One 7(5): e35839.
Gaddis KP, Dikmen S, Null D, Cole J and Hansen P, 2017. Evaluation of genetic components in traits related to superovulation, in vitro fertilization, and embryo transfer in Holstein cattle. Journal of Dairy Science 100(4): 2877-2891.
Hansen KR, Hodnett GM, Knowlton N and Craig LB, 2011. Correlation of ovarian reserve tests with histologically determined primordial follicle number. Fertility and Sterility 95(1): 170-175.
Helin K, Wu C-L, Fattaey AR, Lees JA, Dynlacht BD, Ngwu C and Harlow E, 1993. Heterodimerization of the transcription factors E2F-1 and DP-1 leads to cooperative trans-activation. Genes & Development 7(10): 1850-1861.
Holmberg C, Helin K, Sehested M and KarlstroÈm O, 1998. E2F-1-induced p53-independent apoptosis in transgenic mice. Oncogene 17(2): 143.
Kawashima C, Matsui M, Shimizu T, Kida K and Miyamoto A, 2012. Nutritional factors that regulate ovulation of the dominant follicle during the first follicular wave postpartum in high-producing dairy cows. Journal of Reproduction and Development 58(1): 10-16.
Kimble K, 2018. Transcriptome profiles of porcine oocytes and their corresponding cumulus cells reveal functional gene regulatory networks. masters thesis, Auburn University; Alabama, USA:
Kumar S, Dahiya S P, Magotra A and Kumar S, 2017. Genetic Markers Associated With Fecundity in Sheep. International Journal of Science, Environment and Technology 6(5): 3064-3074.
Liu DT, Brewer MS, Chen S, Hong W and Zhu Y, 2017. Transcriptomic signatures for ovulation in vertebrates. General and Comparative Endocrinology 247: 74-86.
Lussier JG, Diouf MN, Lévesque V, Sirois J and Ndiaye K, 2017. Gene expression profiling of upregulated mRNAs in granulosa cells of bovine ovulatory follicles following stimulation with hCG. Reproductive Biology and Endocrinology 15: 80-88.
Ma X, 2014. Context-dependent interplay between Hippo and JNK pathway in Drosophila. AIMS Genet 1: 20-33.
Mullen M and Gonzalez-Perez R, 2016. Leptin-Induced JAK/STAT signaling and cancer growth. Vaccines 4(3): 26.
Ndiaye K, Castonguay A, Benoit G, Silversides DW and Lussier JG, 2016. Differential regulation of Janus kinase 3 (JAK3) in bovine preovulatory follicles and identification of JAK3 interacting proteins in granulosa cells. Journal of Ovarian Research 9(1): 71.
Pramod RK, Sharma SK, Kumar R and Rajan A, 2013. Genetics of ovulation rate in farm animals. Veterinary World 6(11): 833.
Regan SL, Knight PG, Yovich JL, Leung Y, Arfuso F and Dharmarajan A, 2018. Involvement of Bone Morphogenetic Proteins (BMP) in the Regulation of Ovarian Function, in Vitamins and hormones.  Elsevier. p. 227-261.
Shaw L, Sneddon SF, Zeef L, Kimber SJ and Brison DR, 2013. Global gene expression profiling of individual human oocytes and embryos demonstrates heterogeneity in early development. PLoS One 8(5): e64192.
Sobinoff A, Sutherland J and McLaughlin E, 2013. Intracellular signalling during female gametogenesis. MHR: Basic Science of Reproductive Medicine 19(5): 265-278.
Sprícigo J, Morais K, Ferreira A, Machado G, Gomes A, Rumpf R, Franco M and Dode M, 2014. Vitrification of bovine oocytes at different meiotic stages using the Cryotop method: assessment of morphological, molecular and functional patterns. Cryobiology 69(2): 256-265.
Wiechen K, Diatchenko L, Agoulnik A, Scharff KM, Schober H, Arlt K, Zhumabayeva B, Siebert PD, Dietel M and Schäfer R, 2001. Caveolin-1 is down-regulated in human ovarian carcinoma and acts as a candidate tumor suppressor gene. The American Journal of Pathology 159(5): 1635-1643.
Yang F, Wang M, Zhang B, Xiang W, Zhang K, Chu M and Wang P, 2018. Identification of new progestogen-associated networks in mammalian ovulation using bioinformatics. BMC Systems Biology 12(1): 36.
Yang W, Tang K, Li S and Yang L, 2011. Association analysis between variants in bovine progesterone receptor geneand superovulation traits in Chinese Holstein cows. Reproduction in Domestic Animals 46(6): 1029-1034.
Yin M, Wang X, Yao G, Lu M, Liang M, Sun Y and Sun F, 2014. Transactivation of miR-320 by miR-383 regulates granulosa cell functions by targeting E2F1 and SF-1. Journal of Biological Chemistry 289(26): 18239-18257.