Meta-Analysis of RNA-Seq and Microarray Expression Data to Identify Genes Effective in Sheep Muscle Growth and Development

Document Type : Research Articles

Authors

Department of Animal Science, Faculty of Agriculture, Ferdowsi University of Mashhad, Mashhad, Iran.

Abstract

Introduction: Among different sheep breeds in the world, the Texel breed is known as a meaty and muscular breed. Skeletal muscle growth is a step-by-step and exponential process from differentiation, development and maturation, which is regulated by gene networks and cell signaling pathways, and several genes and factors are involved in the process of muscle fiber formation and their growth and hypertrophy (Badday Betti et al. 2022). The study of gene expression is done with several methods, and this gene expression information is used in breeding programs as a tool to improve phenotypic choices. Databases are a large source of expression data that can be used by bioinformatics methods to integrate heterogeneous data from different studies and platforms. In this study, by integrating the microarray and RNA-Seq data available in the database belonging to the muscle tissue of Texel breed sheep, the transcriptomic profile of the muscle was compared at two ages of embryonic and adult.
Materials and Methods: Microarray data related to longissimus dorsi muscle tissue with three replicates d-70 embryos from GEO database with accession number GSE23563 and RNA-Seq data related to muscle tissue from six samples with two replicates from adult individuals from ArrayExpress database were selected. Limma, Biobase and GEOquery software packages were used to calculate the expression values of the microarray data related to the embryonic age  in the R environment, and Tuxedo, HTSeq and DESeq2 packages were used in the Linux and R environment to calculate the expression values of the RNA-Seq data (Kamali et al. 2022; Sahraei et al. 2019). Then two types of expression values were integrated and to eliminate non-biological effects, the batch effects were also removed. Next, differential genes were identified with the limma software package. In order to identify the relationship between the identified differential genes, the gene network was drawn between them by software of Cytoscape version 3.7.1 and String 1.5.1 program. next, due to the vastness of the gene network, each network was clustered with MCODE 1.6.1 and CytoCluster 2.1.0 programs (ClusterOne algorithm) and significant clusters (P-value < 0.05) were identified (Saedi et al. 2022). In order to better understand the ontology and function of the identified differential genes, the Gene Ontology of the genes was investigated using software of Cytoscape version 3.7.1 and ClueGO 2.5.9 and CluePedia 1.5.9 programs. After receiving the Gene Ontology results, significant Gene Ontology terms (P-Value < 0.05) related to functional groups were identified. Finally, the selected genes (Adj P-Value < 0.05) were identified and introduced in these two age groups.
Results and Discussion: After quality control, correcting and normalizing the microarray data, the GPL10778 platform annotation file with 1042520 Probe ID was used to calculate their expression values. After relevant analyzes of 9289 Probe ID identified related to the data of this study, 7918 Gene Symbol was identified finally. After quality control, trimming and normalizing the RNA-Seq data in total, the number of Ensembl_Genes based on which the reading values were calculated by HTSeq was 27056. After removing IDs that had zero readings in all 6 samples, 10855 IDs remained. Then, these 10855 Ensembl ID were merged with the annotation file to obtain Gene Symbol, and finally 9417 common genes were identified between the six samples of adult age. The results of differential expression analysis showed that there were significant differences in the expression of 62 genes (37 increased and 25 decreased) in the muscle tissue between adult and embryonic age. By creating a gene network between differential genes, 15 selected genes were identified, including MYH1, ACTN3, CASQ1, TMOD4, FBP2, SLC2A4, MX1, COX4I1, SOD2, MFN2, UQCRB, UCP3, PRKAB2, PHKG2, PPP1R3C. The function of these genes has been proven in cell proliferation, protein synthesis, myofibril formation, and lipid metabolism. Differential gene enrichment analysis revealed some biological processes such as Vasculogenesis, positive regulation of ossification, positive regulation of muscle tissue development, regulation of muscle contraction, contractile fiber part, calcium signaling, calcineurin-NFAT signaling cascade and regulation of receptor signaling pathway via JAK-STAT, the molecular function of regulating cation channel activity and the cellular components of the contractile fiber.
Conclusion: This study in addition to confirming the accuracy of the integration method of two types of heterogeneous data, provided a general view of the transcriptomic differences of Texel sheep muscle tissue at two important age points to be a useful source for biological investigations of genes related to muscle growth and development in sheep.

Keywords

Main Subjects


  1. Almenar-Queralt, A., Lee, A., Conley, C. A., de Pouplana, L. R., & Fowler, V. M. (1999). Identification of a novel tropomodulin isoform, skeletal tropomodulin, that caps actin filament pointed ends in fast skeletal muscle. Journal of Biological Chemistry, 274(40),28466-28475. https://doi.org/1074/jbc.274.40.28466.
  2. Badday Betti, S., Tahmoorespur, M., & Javadmanesh, (2022). Identification of lncRNAs expression and their regulatory networks associated with development and growth of skeletal muscle in sheep using RNA-Seq. Agriculture and Natural Resources, 56(2),373-386. https://doi.org/10.34044/j.anres.2022.56.2.15
  3. Bakhshalizadeh, S., Zerehdaran, S., & Javadmanesh, A. (2021). Meta-analysis of genome-wide association studies for somatic cells score trait in dairy cows. Journal of Ruminant Research, 9(3),39-58. https://doi.org/10.22069/ejrr.2021.19036.1787
  4. Beggs, A. H., Byers, T. J., Knoll, J. H., Boyce, F. M., Bruns, G. A. & Kunkel, L. M. (1992). Cloning and characterization of two human skeletal muscle alpha-actinin genes located on chromosomes 1 and 11. The Journal of Biological Chemistry, 267(13), 9281–8.
  5. Betti, S., Tahmoorespur, M. & Javadmanesh, A. (2022). Alternative Splicing Novel lncRNAs and Their Target Genes in Ovine Skeletal Muscles. Journal of Cell and Molecular Research, 13(2), 129-136. https://doi.org/10.22067/jcmr.2022.74635.1027
  6. Boss, O., Giacobino, J. P. & Muzzin, P. (1998). Genomic structure of uncoupling protein-3 (UCP3) and its assignment to chromosome 11q13. Genomics, 47 (3), 425–6. https://doi.org/1006/geno.1997.5135.
  7. Cheung, P. C., Salt, I. P., Davies, S. P., Hardie, D. G. & Carling, D. (2000). Characterization of AMP-activated protein kinase gamma-subunit isoforms and their role in AMP binding. Biochemical Journal, 346(3), 659-669. https://doi.org/10.1042/bj3460659.
  8. Doherty, M. J., Young , P. R. & Cohen , P. T. (1997). Amino acid sequence of a novel protein phosphatase 1 binding protein (R5) which is related to the liver- and muscle-specific glycogen binding subunits of protein phosphatase 1. FEBS letters, 399(3), 339-343. https://doi.org/10.1016/s0014-5793(96)01357-9.
  9. Filadi, R., Pendin, D. & Pizzo, P. (2018). Mitofusin 2: from functions to disease. Cell Death & Disease, 9(3), 330. https://doi.org/10.1038/s41419-017-0023-6.
  10. Horisberger, M. A. (1992). Interferon-induced human protein MxA is a GTPase which binds transiently to cellular proteins. Journal of Virology, 66(8), 4705-4709. https://doi.org/1128/JVI.66.8.4705-4709.1992 .
  11. Huang, C. Y., Yuan , C. J., Livanova, N. B. & Graves, D. J. (1994). Expression, purification, characterization, and deletion mutations of phosphorylase kinase gamma subunit: identification of an inhibitory domain in the gamma subunit. Reversible Protein Phosphorylation in Cell Regulation, 7–18. https://doi.org/10.1007/978-1-4615-2600-1_1.
  12. Huang, S. & Czech, M. P. (2007). The GLUT4 glucose transporter. Cell Metabolism, 5(4), 237–52. https://doi.org/1016/j.cmet.2007.03.006.
  13. Hüttemann, M., Kadenbach, B. & Grossman, L. I. (2001). Mammalian subunit IV isoforms of cytochrome c oxidase. Gene, 267(1), 111–23. https://doi.org/1016/s0378-1119(01)00385-7 .
  14. Jung, H. J., Kim, K. H., Kim, N. D., Han, G. & Kwon, H. J. (2011). Identification of a novel small molecule targeting UQCRB of mitochondrial complex III and its anti-angiogenic activity. Bioorganic & medicinal chemistry letters, 21(3), 1052-1056. https://doi.org/10.1016/j.bmcl.2010.12.002.
  15. Jung, J., Mok, C., Lee, W. & Jang, W. (2017). Meta-analysis of microarray and RNA-Seq gene expression datasets for carcinogenic risk: An assessment of Bisphenol A. Molecular & Cellular Toxicology, 13(2), 239-249. https://doi.org/1007/s13273-017-0026-5.
  16. Kamali, Javadmanesh, A., Stelinski, L, L, Kyndt, T., Seifi, A., Cheniany, M., Zaki-Aghl,, M., Hosseini, M., Heydarpour, M., Asili, J. & Karimi , J. (2022). Beneficial worm allies warn plants of parasite attack below-ground and reduce above-ground herbivore preference and performance. Molecular Ecology, 31(2), 691-712. https://doi.org/10.1111/mec.16254.
  17. Liu, N., He, J. N., Yu, W. M., Liu, K. D., Cheng, M., Liu, J. F., He, Y. H., Zhao, J. S. & Qu, X. X.. (2015). Transcriptome analysis of skeletal muscle at prenatal stages in Polled Dorset versus Small-tailed Han sheep. Genetics and Molecular Research, 14(1), 1085-1095. https://doi.org/4238/2015.February.6.12.
  18. Ma, T., Liang, F., Oesterreich, S. & Tseng, G. C. (2017). A joint bayesian model for integrating microarray and RNA sequencing transcriptomic data. Journal of Computational Biology, 24(7), 647-662. https://doi.org/1089/cmb.2017.0056.
  19. Mohammadi, F., Tahmoorespur, M. & Javadmanesh, A. (2019). Study of differentially expressed genes, related pathways and gene networks in sheep fetal muscle tissue in thin- and fat-tailed breeds, Animal Science Journal, 32(123), 301-312. https://doi.org/10.22092/asj.2018.122913.1749. (In Persian).
  20. Rakus, D., Maciaszczyk, E., Wawrzycka, D., Ułaszewski, S., Eschrich, K. & Dzugaj, A. (2005). The origin of the high sensitivity of muscle fructose 1,6-bisphosphatase towards AMP. FEBS letters, 579(25), 5577-5581. https://doi.org/1016/j.febslet.2005.09.021.
  21. Rashidian, Z., Dehdilani, N., Dehghani, H, & Javadmanesh, A. (2020). Isolation and culturing myogenic satellite cells from ovine skeletal muscle. Iranian Journal of Veterinary Science and Technology, 12(2), 36-43. https://doi.org/10.22067/veterinary.v12i2.82979. (In Persian).
  22. Sadkowski, T., Jank, M., Oprzadek, J. & Motyl, T. (2006). Agedependent changes in bovine skeletal muscle transcriptomic Journal of Physiology and Pharmacology: an Official Journal of the Polish Physiological Society, 57, 95-110.
  23. Saedi, N., Aminafshara, M., Chamania, M., Honarvar, M. & Javadmanesh, A. (2022). Expression pattern and network visualization of genes involved in milk persistency in bovine mammary tissue. Agriculture and Natural Resources, 56(1), 23-34. https://doi.org/10.34044/j.anres.2021.56.1.03
  24. Sahraei, S., Nassiri, M. R., Javadmanesh, A., Tohidi, R. & Ebrahimie, E. (2019). Investigation of gene expression and gene networks related to apoptosis in sensitive and resistant Aryan broiler breeders with RNA-Seq. Animal Production Research, 8(1), 53-66.
  25. Sun, L., Bai, M., Xiang, L., Zhang, G., Ma, W. & Jiang, H. (2016). Comparative transcriptome profiling of longissimus muscle tissues from Qianhua Mutton Merino and Small Tail Han sheep. Scientific Reports, 6, 33586.
  26. Takada, F., Vander Woude, D. L., Tong, H. Q., Thompson, T. G., Watkins, S. C., Kunkel, L. M. & Beggs, A. H. (2001). Myozenin: an alpha-actinin- and gamma-filamin-binding protein of skeletal muscle Z lines. Proceedings of the National Academy of Sciences, 98(4), 1595-1600. https://doi.org/1073/pnas.041609698.
  27. Terentyev, D., Viatchenko-Karpinski, S., Györke, I., Volpe, P., Williams, S. C. & Györke, S. (2003). Calsequestrin determines the functional size and stability of cardiac intracellular calcium stores: mechanism for hereditary arrhythmia. Proceedings of the National Academy of Sciences, 100(20), 11759-11764. https://doi.org/1073/pnas.1932318100.
  28. Weiss, A., Schiaffino, S. & Leinwand, L. A. (1999). Comparative sequence analysis of the complete human sarcomeric myosin heavy chain family: implications for functional diversity. Journal of Molecular Biology, 290 (1), 61–75. https://doi.org/1006/jmbi.1999.2865.
  29. Yan, X., Zhu, M. J., Dodson, M .V. & Du, M. (2013). Developmental programming of fetal skeletal muscle and adipose tissue development. Genomics, 1: 29–38. https://doi.org/10.7150/jgen.3930.
  30. Zelko, I. N., Mariani, T. J. & Folz, R. J. (2002). Superoxide dismutase multigene family: a comparison of the CuZn-SOD (SOD1), Mn-SOD (SOD2), and EC-SOD (SOD3) gene structures, evolution, and expression. Free Radical Biology and Medicine, 33(3), 337-349. https://doi.org/10.1016/s0891-5849(02)00905-x.
  31. Zerehdaran, S., Ghobakhloo, F., Jabbari Nooghabi, M. & Shariati, M. (2020). Meta-Analysis of studies on genetic parameters of economic traits in Iranian Holstein dairy cows. Journal of Ruminant Research, 8(2), 1-22. https://doi.org/10.22069/ejrr.2020.16994.1703. (In Persian).
  32. Zhang, Ch., Wang, G., Wang, J., Ji, Zh., Liu, Zh., Pi, Xi. & Chen, C. (2013). Characterization and Comparative Analyses of Muscle Transcriptomes in Dorper and Small-Tailed Han Sheep Using RNA-Seq Technique. PLOS ONE Journal, 8 (8), 72686.

 

 

CAPTCHA Image
Volume 15, Issue 4 - Serial Number 56
December 2023
Pages 557-569
  • Receive Date: 04 December 2022
  • Revise Date: 29 January 2023
  • Accept Date: 05 February 2023
  • First Publish Date: 05 February 2023