elham behdani

In this study, genetic parameters and (co) variance components were estimated for weights at birth, 3, 6, 9 month, daily weight gain and Keliber ratio from birth to 3 month and also form 3 month to 9 month. Records of 2544 kids, the progeny of 149 sires and 851 dams were used to analysis data by MTGSAM program. Six different animal models were fitted for all traits. The most appropriate model for weight at 6, 9 and daily weight gain and Keliber ratio form 3 to 9 month was model 2. Estimated direct heritability varied from 0.01 to 0.36, which was minimum (0.01) for daily weight gain and Keliber ratio from birth to 3 month and maximum (0.36) for birth weight. The genetic and phenotypic trends for all traits were estimated near to zero and non-significant. Generally, differences between estimated genetic trends for these traits and estimated values of other studies, is due to difference in animal breeding standard followed by different program selection, difference between models and calculation method, effects of environment, interaction between genetic and environmental, nutrition and climate conditions.

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.

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.

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.

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.

Oocyte maturity includes nuclear and cytoplasmic maturity, both of which are important for embryo fertilization. Cytoplasmic maturation involves the redistribution of a range of organelles, including mitochondria. The nuclear and cytoplasmic mammalian oocytes maturation is a complex process nuclear maturation is demonstrated by extrusion of first polar body while there may be no indication for cytoplasmic maturation According to critical role of mitochondria for energy production in oocytes, it can be considered as an indicator of cytoplasmic maturation. Oocyte maturation requires more energy. Energy reaches its peak during ovulation. Changes in the mitochondrial distribution pattern can affect the ability of embryo development from oocytes. Since fetal mitochondrial replication is not performed until the blastocyst its stage, mature Oocytes (MII), fertilized Oocytes, Energy required for fertilization, embryonic development prior to implantation and early stages of fetal development depend on the storage of mitochondria in the time of ovulation. Therefore, the location and function of mitochondria can affect the quality of the Oocyte and consequently interfere with the process of embryo development. The topic of genetic networks explores the most important genes in a physiological process. The graph theory is used to construct and reconstruct the biology network. In biology networks, genes, proteins, or any other molecule that plays a role in a cell can be considered as a node and the relationship between these nodes is considered as an edge.

 In this study, GEO access codes for this data set GSE38345 were used to determine the effect of FSH on the expression of mitochondrial genes. In the past decade, with the ability to study genetic information of the genome in a wide range, micro arrays were a high-performance method for analyzing gene expression. The data are microarray and contain the gene expression information for cow's oocyte cells, whose maturity is influenced by the FSH hormone under laboratory conditions. After data implementation, the quality of the data was analyzed and if necessary, normalization was performed using the data conversion technique. Data analysis and comparison of gene expression in two cases before maturation (20 hours after oocyte treatment with FSH in laboratory conditions) and after maturation (96 hours after oocyte treatment with FSH in laboratory conditions) using From the GEO2R software link were done. After identifying the genes and examining the different genes expressed, two genotypes included Increased and decreased expression genes. The interaction of each gene group was studied using a string database based on co-expression data. Gene ontology was performed using the comparative GO database.

 In a comparison between oocyte gene expression data in the pre-maturation stage and the post-maturation stage after treatment with FSH, it was determined that 100 mitochondrial genes in maturation compared to pre-maturation stage increased expression and 94 genes of this organ has declined. Among them, the protein interaction network has been identified in a set of increased and decreased expression genes. Of the 100 genes that have been increased expression, 68 genes are coexpression based on string information. Among decreased expression genes, 53 genes from 64 genes were reported as coexpression. In the protein interaction network of the increased expression genes, the important genes of MRPS10, MRPS18A, MRPL16 and MRPL17, which played a role in the mitochondrial destruction and translation processes of mitochondrial genes, and in the network of decreased expression genes, MRPL22, ATP5B and ATP5C1 genes, which by reducing its expression, attempted to balance in the pathways associated with mitochondrial destruction and ATP production through its role in the ATP synthase structure.

The results of this study reveal the most important genes affecting mitochondrial activity during oocyte maturation and control genes of this organ according to the network of protein interactions in the set of increased and decreased expression genes. In addition, the most important biological pathways in order to understand the mechanism of FSH effect on oocyte maturation through mitochondrial organ is investigated. Also, by comprehensive examining the gene expression network in the process of cytoplasmic oocyte maturation and showing the marker genes and different biochemical pathways, it is possible to understand the quality of oocyte during maturation, which can help improve IVM-IVF technique. Since effective mechanisms in cytoplasmic maturity are not yet fully understood, efforts to identify important regulators of mitochondria in oocyte maturation process will be effective in using fertility technology in animal production.

In most mammals, each hair follicle is a mammalian skin organ that produces hair and controls the hair growth cycle. The hair follicle is central to most economically important fiber growth in livestock. Previous studies have shown that changes in the level of genes expression play a role in the development and growth of hair follicles. With the advent of high throughput, next-generation sequencing technology measuring the level of expression of thousands of genes was possible simultaneously. As a result, many regulatory conflicts between genes can be extracted from these data. Expression profiles are generated by combining expression levels resulting from experiments in various conditions or times. Similarities and differences between expression profiles reveal many of these regulatory relationships. Despite recent advances in the identification of the molecular signaling/co-expression networks that govern the development of the skin and hair follicle, the mechanisms controlling of fiber production in Cashmere goat still remains unclear. Therefore, the present study was conducted to investigate the most important molecules controlling the growth and development of hair follicles in cashmere goats in order to decode the genes involved in the growth and development of hair follicles in cashmere goats.

 Publicly available preprocessed transcript data (accession no. SRP059481) was downloaded from the NCBI database in the SRA section. In this investigation, nine goats were selected at the same stage of gestation. Three fetuses were obtained at 55-65 days’ gestation (60-days old) and three fetuses were obtained at 105-125 days’ gestation (120 –day- old) through cesarean operation and three fetuses were also sacrificed within two hours of birth (newborn). For analysis of RNA-seq data, a random sampling from the right mid-side of the fetal skin and each time point had three replicates. The 60 days (E60) of gestation represented the initiation stage of growth, and the 120 d (E120) represented the development stage, as well as the newborn samples, represented the primary hair follicle maturation stage. The FastQC software (Version 11.7) was used to check the quality of the readings. This software check the quality of the reads, in case of low-quality sequencing, the data were edited by Trimmomatic software (Version 0.39), and filtered reads were evaluated with FastQC software, After removing low-quality reads mapping was performed on the goat reference genome (GCA_001704415.1) downloaded from the ensemble site using the Hisat2 software(Version 2.1.0). The HTSeq2 (Version 10.0) was used to create the count matrix, changes in the expression of genes between the groups (E60, E120, and newborns) was identified using the software Deseq2 (Version 1.24). In this stage, the genes between the two groups were compared to give an expression difference 1- Genes that are expressed throughout the growth process of the follicle 2. Co-Expression networks of up and down genes were created using the STRING database (Version 11) at https://string-db.org. The PPI network was constructed by Cytoscape software (Version 3.6.0). Cytoscape is commonly used for analysis and visualization of biological networks. Gene ontology analysis of effective gene related to development stage of hair follicle in Cashmere goat from fetal to birth was done using DAVID database.

After determining the level of expression of each gene at different stages of sampling, genes that increased expression in all comparisons were considered as up-regulated genes that, by increasing their expression, control the growth and development of hair follicle. On the other hand, those genes that have been shown to reduce expression in all of the follicle growth stages comparisons were considered as genes which by decreasing their expression, causes the growth and development of the hair follicle. The interaction relations were investigated between the increased and reduced genes expressed separately by STRING database. Among the up-regulated genes, the relationships between 145 genes with 203 edge were confirmed. Also, the interaction network between the down-regulated genes confirmed between 650 genes with 1302 edge. Among the genes with differential expression, the co-expression genes (genes with the most interacting) were identified based on the results of the analysis of the statistical parameters of the network using computational algorithms in Cytoscape software. These results led to the identification of seven unique genes that were SHH, KLF4, MMP9, MSX1, KRT17, COL2A1 and VEGFA, ontology analysis of these genes showed that involved the hair cycle process, hair follicle development, skin epidermis development, epidermis morphogenesis and epidermal cell differentiation that these pathways are activated in the cashmere production process. Therefore, these genes have been used as regulators of cashmere growth and development to better understand the cashmere production process and improve its quality.

The results have been able to introduce genes with major effects that regulate the expression of genes and molecular signals associated with the production of fiber that including KLF4, MMP9, MSX1, KRT17, COL2A1 and VEGFA in Cashmere goats. Based on functional analysis, these genes can play a significant role in the improvement of cashmere goats breeding. We hope that the obtained results would be beneficial toward finding the smart strategies for Cashmere production improvement.

The stress is one of main factors effects on production system. Several factors (both genetic and environmental elements) regulate immune response to stress.

In order to determine the major immune system regulatory genes underlying stress responses, a learning Bayesian network approach for those regulatory genes was applied to RNA-Seq data from a bovine leukocyte model system.

The transcriptome dataset GSE37447 was used from GEO and a Bayesian network on differentially expressed genes was learned to investigate the gene regulatory network.

Applying the method produced a strongly interconnected network with four genes (TERF2IP, PDCD10, DDX10 and CENPE) acting as nodes, suggesting these genes may be important in the transcriptome regulation program of stress response. Of these genes TERF2IP has been shown previously to regulate gene expression, act as a regulator of the nuclear factor-kappa B (NF-κB) signalling, and to activate expression of NF-κB target genes; PDCD10 encodes a conserved protein associated with cell apoptosis; DDX10 encodes a DEAD box protein and is believed to be associated with cellular growth and division; and CENPE involves unstable spindle microtubule capture at kinetochores. Together these genes are involved in DNA damage of apoptosis, RNA splicing, DNA repairing, and regulating cell division in the bovine genome. The topology of the learned Bayesian gene network indicated that the genes had a minimal interrelationship with each other. This type of structure, using the publically available computational tool, was also observed on human orthologous genes of the differentially expressed genes.

Overall, the results might be used in transcriptomic-assisted selection and design of new drug targets to treat stress-related problems in bovines.

Non-Alcoholic Fatty Liver Disease (NAFLD) is the major cause of chronic liver disease in developed countries. In this study, we identified the most important transcription factors and biological mechanisms affecting the incidence of fatty liver disease using the promoter region data mining. In this study, at first, the marker genes associated with this disease were detected and the pattern of transcription factors was examined by the Genomatix software.  In the whole of genome, genes with a similar binding pattern for transcription factors in the promoter region were identified as potentially effective genes on the fatty liver. By using the Cytoscape software (3.6.0), the network of transcription factors and target genes was mapped. Finally, the most important biological pathways associated with genes derived from fatty liver were studied using the DAVID database. The protein network fitting showed Creb1, Jun and Max transcription factors and  Sfpi1, Ddit3 and Gsk3b genes play an important role in the development of this disease. Gene ontology analysis revealed that biological pathways including apoptosis, intracellular signals, and biosynthesis of aromatic compounds and signaling pathways of circadian rhythm, non-alcoholic fatty liver, and chemokine signals contributed to the occurrence of fatty liver disease. Increasing the expression of transcription factors and genes produced can be one of the most factors affecting the onset of the disease, also, biological pathways including apoptosis, intracellular signals, and biosynthesis of aromatic compounds and signaling pathways of circadian rhythm, non-alcoholic fatty liver, and chemokine signals are important in fatty liver phenomenon.

 Oocyte maturity includes nuclear and cytoplasmic maturity, both of which are important for embryo fertilization. The development of oocyte is not limited to the period of follicular growth, and starts from the embryonic period and continues throughout life. In this study, for the purpose of evaluating the effect of the FSH hormone on the expression of genes, GEO access codes for this data set, GSE38345, were used. The data are microarray and contain the gene expression information for cow's oocyte cells, that their maturation is influenced by the FSH hormone under laboratory conditions. Data analysis was performed by using the GEO2R software link. After identifying the genes and examining the different genes expressed, two gene groups included Increased and decreased expression genes are formed. The interaction of each of the gene groups was examined by using a string database, based on the co-expression information. The meaningful sub networks were explored using the clusterone software. Gene ontology was performed using the comparative GO database. The miRNA-mRNA interaction network was also studied based on the miRWalk database. Finally, meaningful networks and subnets obtained by cytoscape software were drawn.  In a comparison between oocyte gene expression data in the pre-maturation and the post- maturation stage after treatment with FSH, 5958 increased genes and 4275 decreased genes expression were found. By examining the protein interaction network in the set of increased and decreased expression genes, based on string information, 262 increased and 147 decreased genes (high confidence (0.7) data) were found. In network of Increased expression genes in oocyte maturation, the RPS3, NUSAP1, TBL3 and ATP5H genes which are effective in the biological pathways of positive regulation of rRNA processing, cell division, mitochondrial ATP synthesis coupled proton, also in functional pathways, oxidative phosphorylation and progesterone-mediated were effective, also in decreased expression genes, WDR46 and MRPL22 genes are the most important that were effective in the biological pathways of SRP-dependent cotranslational proteins in targeting to membrane, RNA secondary structure, unwinding and functional pathways of ribosomal and RNA polymerase. The most important microarray gene in the protein network of increased and decreased expression genes bta-miR-10b-5p and miR-29b-2-5p gene were reported. In examining the genes expressed in the pathway of oocyte maturation, three groups of nuclear, mitochondria, microarray genes were determined. Increasing and decreasing gene expression helps maintain balance, which can be considered as a marker.

miRNA molecules are short sequences (With an average length of 22 nucleotides) that regulate many biological processes by suppressing gene expression. Milk production is a physiological process that influence by a large number of genes, miRNAs and signaling pathways. In this study, identification of miRNAs, their target genes and signaling pathways were done by investigation of interspecies conserved miRNAs in mouse, cattle and goat.
In this study, to investigate molecular mechanism of miRNA’s effect on milk production, firstly data were downloaded with accession number GSM1295115 for mouse species, GSM1295118 for cattle species and GSM969927 for goat species from GEO database. Identification of miRNAs was done by mirDeep2. In this study, mirwalk database was used to detect target genes of miRNA which expressed in all three species. The mirwalk database is also able to estimates target genes based on the ten other databases’ algorithms. Visualization of miRNA and their target genes’ interaction was performed by cytoscape. DAVID database was used to study target genes-related gene and signaling pathways.
According to these results miR-93-5p, miR-27b-3p, miR-27a-3p and miR-200c-3p genes are the most important miRNA and Pten, Rlim, Pdik1l and Setd5 genes are the most important target genes on the process of milk production and pathways of milk components biosynthesis. One of the most important results of this study was detected Setd5 as the novel milk production process-related gene. Gene pathway analysis showed Focal adhesion pathway, MAPK signaling pathway, mTOR signaling pathway, PI3K-Akt signaling pathway and Neurotrophin signaling pathway were the most important of gene pathways which active by target genes and have a vital role in milk production biosynthesis and development of mammary glands. Theses gene pathways could effect on milk production physiologically and biologically by influencing on the development of alveolar cells, increasing branches, developing of mammary tissue, amino acid metabolism, influencing on endocrine system, prolactin signaling pathway and influencing on the milk compounds synthesis such as fat, protein and lactose.
According to critical role of the important miRNA in the studied network and target genes of these molecules and also investigated gene pathways, it could be used in breeding programs as the most important regulators in milk production process. This information could be used to introduce and apply candidate genes for the gene assisted selection method or genomic selection. Given that milk production is along with development and differentiation of breast tissue, miRNA molecules and target genes which investigate in this study could be the good candidates in all developmental and differential processes.