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

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.