کنکاش ساختار، جزایر CPG، متیله شدن DNA و پروتئین‌ حاصل از ژن‌های کاندیدای موثر بر ورم پستان در گاو شیری

Introduction: Epigenetics is assumed as the most complex field of biological sciences. Generally, epigenetics includes mechanisms that not only affect gene expression, but also bring about functional changes in the next generation of mitotic cell division (autosomal inheritance) and meiotic cell division (generational inheritance), though DNA nucleotide structure is not changed (Barazandeh, 2016). DNA methylation refers to the enzymatic addition of a CH3 group to a cytosine residue in DNA strand, which occurs almost particularly at CpG dinucleotides (i.e., a cytosine located 5′ of a guanine) in animals. The enzymatic machinery responsible for DNA methylation includes a family of DNA methyltransferases (DNMTs), including the maintenance methyltransferase DNMT1 (responsible for copying pre-existing DNA methylation patterns to the new strand during mitosis) and the de novo methyltransferases DNMT3A/3B. DNA methylation is considered as epigenetics factor. In mammalian, DNA methylation is necessary for both embryonic development and stem cell differentiation and this phenomenon is basically occurring in CpG dinucleotide. CpG dinucleotide intends to make cluster over genome that is called CpG island. CpG islands are considered gene markers and represent an important feature of mammalian genomes. CpG islands vary greatly among mammalian genomes (Medvedeva, 2011). Some factors such as recombination rate and chromosome size might have influenced the evolution of CpG islands in the course of mammalian evolution. Whenever CpG island in gene's promoter is not methylated and suitable transcriptional factors are accessible, gene expression machine is activated and therefore gene is expressed. However, methylation of CpG islands in gene promoter with compact form of chromatin could lead to inhibition of gene expression (Shi et al., 2012). Hypermethlation of CpG islands could cause a range of diseases such as cancer. Epigenetic process could modify proteins after translation that actually refers to biochemical events inflicting on proteins. Some of these events include: methylation, phosphorylation, glycosylation, sulfation, sumoylation, and ubiquitinylation disulfide bonding.
Nutrition is seen as one of the greatest environmental determinants of an individual’s health. Although nutrient quantity and quality could impart direct effects, the interaction of nutrition with genetic and epigenetic is often overlooked despite being shown to influence different biological variation in mammals (Murdoch et al., 2016). Understating and unrevealing complex traits, such as those that are nutrition-related, to determine the genetic and epigenetic contributions toward a phenotype would be a formidable job to get it done. Similar to other epigenetic endpoints, patterns of DNA methylation could be susceptible to alterations by exposing to environmental treatments, including contaminants (Head, 2014). These sort of alterations may persist in the absence of the initial treatments as cells divide, and can even be inherited over generations provided that they occur in the germ line. Although our knowledge concerning patterns of DNA methylation in animals is increasing, a big gap remains in the literature, especially when it does come to species outside of those generally used for biomedical researches. Unlike DNA, epigenetic markers could be directly influenced by the environment factors; therefore, epigenetic markers have been shown to be pivotal mediators of phenotypic responses to environmental signals. To understand how epigenetic modifications facilitate and affect gene expression, it is crucial to understand how and when the epigenome is established (Faulk and Dolinoy, 2011). While we do not fully understand how all of the complex epigenetic genome changes occur, yet some epigenetic information is sought to retain and transmit to the next generation.
Materials and methods: In this study, first of all, some articles and resources that describe candidate genes affecting mastitis were explored and some of the genes which were considered candidate genes in bovine mastitis were chosen. These genes included 14 interleukins (in exception of interleukin 8 and interleukin 9), interferon-gamma, and tumor necrosis factor (in total 13 genes). Their DNA sequences and other features of selected genes (for example exon numbering, gene length, guanine percentage and cytosine percentage) were obtained from NCBI database and saved as FASTA format. The C++ based software and Promoter 2.0 Prediction Server were used to extract characteristics of genes and detecting gene promoters, respectively. This software is generally used to predict transcription factor-biding sites of DNA sequences in vertebrata. In order to detect CpG islands, DBCAT, SMS, and Geneinfinity softwares were used. Also, primary structure of proteins derived from these genes was extracted from NCBI database and finally protein methylation was obtained by using PMeS software. Depending on the type of algorithm used to identify the CpG islands, the number and length of the CpG islands found to be different. Predicted CpGs were used as measurements to indirectly investigate the level of methylation over DNA level of genes.
Results and discussion: The investigated genes in terms of some features like total size based on number of nucleotides, number of exons, total annotated spliced exon length, and percent of GC content were quite different. The essence of gene heterogeneity from different features made grouping of these genes a cumbersome job. In this research, our overall objective was to locate the position and number of CpG in the promoter region of genes. Referencing of applied software to detect CpG islands, number and length of detected CpGs was different. Results shown that methylation was not evenly/ uniformly occurring in considered genes and in average, less than 50 % of CpG islands were in promoter region of genes and methylated CpG was located in intergenic regions. Also, the maximum amount of methylation was seen in interleukin 1, 2, and TNF gene, which are among the most important genes in circumventing mastitis. The degree of methylation in the proteins derived from the investigated genes showed fairly non-concurrence results with their DNA methylation. However, protein derived from interleukin 11 undergoes methylation in large portions of its original protein structure. It should be noted that different algorithm architectures may give different results (Barazandeh, 2016; Medvedeva, 2011). 
At the DNA level, this gene also had the highest CpG in its promoter. This may indicate the complexity of epigenetic process with this gene. Therefore, this gene could be considered as a suitable candidate for performing demethylation experiments and proving biological drugs to circumvent mastitis. An integrative view to gene structure, DNA methylation and methylation of proteins derived from the investigated genes in this research would provide the better idea pertaining to methylation function in circumventing the mastitis in dairy cattle.
Conclusion: Out of the three software tested, the results of the two SMS and Geneinfinity software were almost similar and probably use a similar algorithmic architecture. It should be noted that the algorithms used in this study were not developed to identify specific CpG islands on the bovine genome. The genes under study had undergone a methylation process at many levels of their proteins. The results of this study depend on different algorithmic parameters and changing these parameters were diverse. As matter of fact that mastitis heritability is low in general, such studies could reveal a better role for single and group genes in the epigenetics process.