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Aneh Shebh Naseri, F., Bahreini Behzadi, M., Roudbari, Z., Eskandarynasab, S. (2020). Gene expression analysis of skeletal muscle to identify genes that influence bovine growth process. , 33(4), 123-131. doi: 10.22092/vj.2019.127720.1618
F. Aneh Shebh Naseri; M. R. Bahreini Behzadi; Z. Roudbari; S. Eskandarynasab. "Gene expression analysis of skeletal muscle to identify genes that influence bovine growth process". , 33, 4, 2020, 123-131. doi: 10.22092/vj.2019.127720.1618
Aneh Shebh Naseri, F., Bahreini Behzadi, M., Roudbari, Z., Eskandarynasab, S. (2020). 'Gene expression analysis of skeletal muscle to identify genes that influence bovine growth process', , 33(4), pp. 123-131. doi: 10.22092/vj.2019.127720.1618
Aneh Shebh Naseri, F., Bahreini Behzadi, M., Roudbari, Z., Eskandarynasab, S. Gene expression analysis of skeletal muscle to identify genes that influence bovine growth process. , 2020; 33(4): 123-131. doi: 10.22092/vj.2019.127720.1618

Gene expression analysis of skeletal muscle to identify genes that influence bovine growth process

تحقیقات دامپزشکی و فرآورده‌های بیولوژیک
Article 13, Volume 33, Issue 4, January 2021, Pages 123-131    PDF (858.51 K)
Document Type: Full Research Paper
DOI: 10.22092/vj.2019.127720.1618
Authors
F. Aneh Shebh Naseri1; M. R. Bahreini Behzadi* 1; Z. Roudbari2; S. Eskandarynasab1
1Department of Animal Science, Faculty of Agriculture, Yasouj University
2Department of Animal Science, Faculty of Agriculture, University of Jiroft
Abstract
The purpose of this study was to identify genes that influence bovine growth process using gene expression profile. Growth can be expressed as the change of live weight based on time unit or graph description of weight against animal age. Growth is the result of different biological processes and genetics determines the highest level that these processes can occur. In this study raw data at birth, 3, 7, 12, 20, 25, and 30 months in TXT format were obtained from ArrayExpress database with E-GEOD-2554 accession number. In the next step, the quality control assessment of the existing raw data was performed using Limma package in R environment by PCA method and then the intra array normalization was done by LOESS method and between samples by quantile method. The criteria for identifying genes with differential expression were the absolute value of fold change larger than 2 and adj P-Value < 0.05. DAVID database was used for investigation of biological pathways and reconstruction of the gene expression network were done by Cytoscape software. The results showed that there were significant differences in expression of 1030 growth-related genes at different ages. 518 genes were associated with increased expression and 512 genes with reduced expression. The Wnt, insulin and IGF-I were main signaling pathways that influences growth process. The most important genes identified in this study were FN1, SOD2, HMOX1, SERPINE1, PCK1, STAT3 and HSPA1A. FN1 gene at 20-25 and 25-30 months of age, SOD2 gene at 0-3 months of age, HMOX1 gene at 0-3 and 12-20 months of age, SERPINE1 gene at 3-7 months of age, PCK1 gene at 20-25 months of age, STAT3 gene at 20-25 months of age and HSPA1A gene at 25-30 months of age have a significant increase in expression. Therefore, the results of this study could provide additional information to better understand the relationship between genes and their biological pathways in the growth process that can be used in animal breeding programs.
Keywords
Network Analysis; Bioinformatics; Database; Microarray; Biological Pathway
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