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Farhadi, G., Fayazi, J., Allah Roshanfekr, H., Nazari, M., & Behdani, E. (2019). Use of Protein-Protein Interaction Network for Biomarker Identification in Oocyte Maturation. Research in Molecular Medicine (RMM), 6(3). https://doi.org/10.18502/rmm.v6i3.4611
https://doi.org/10.18502/rmm.v6i3.4611
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Golzar Farhadi
Golzar Farhadi
Department of Animal Science, Faculty of Animal and Food Science, Agriculture Sciences and Natural Resources University of Khuzestan, Mollasani, Ahvaz, Iran
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Jamal Fayazi
Jamal Fayazi
Department of Animal Science, Faculty of Animal and Food Science, Agriculture Sciences and Natural Resources University of Khuzestan, Mollasani, Ahvaz, Iran
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Hedayat Allah Roshanfekr
Hedayat Allah Roshanfekr
Department of Animal Science, Faculty of Animal and Food Science, Agriculture Sciences and Natural Resources University of Khuzestan, Mollasani, Ahvaz, Iran
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Mahmoud Nazari
Mahmoud Nazari
Department of Animal Science, Faculty of Animal and Food Science, Agriculture Sciences and Natural Resources University of Khuzestan, Mollasani, Ahvaz, Iran
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Elham Behdani
Elham Behdani
Department of Animal Science, Faculty of Animal and Food Science, Agriculture Sciences and Natural Resources University of Khuzestan, Mollasani, Ahvaz, Iran
Published Date
- Jun 19, 2019
Use of Protein-Protein Interaction Network for Biomarker Identification in Oocyte Maturation
Abstract
Background: Oocyte maturation begins at the embryonic stage and continues throughout life. The effect of Follicle- Stimulating hormone (FSH) on gene of genes was evaluated using GEO access codes for the data set GSE38345. Materials and
Methods: The microarray data containing the gene expression information from cow oocytes show that their maturation is influenced by FSH. Data analysis was performed using GEO2R. After identifying the genes and examining the different genes expressed, two gene groups with increased and decreased expression were identified. The interaction of each of the gene groups was examined using the 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 the Cytoscape software, were designed.
Results: Comparison of oocyte gene expression data between the pre-maturation and postmaturation stages after treatment with FSH revealed 5958 genes with increased expression and 4275 genes with decreased expression. Examination of the protein interaction network among the set of increased and decreased expression genes based on string information revealed 262 genes with increased expression and 147 genes with decreased expression (high confidence (0.7) data). RPS3, NUSAP1, TBL3, and ATP5H showed increased expression and were effective in the positive regulation of rRNA processing, cell division, mitochondrial ATP synthesis coupled proton, and in oxidative phosphorylation and progesterone-mediated functions. WDR46 and MRPL22 showed decreased expression, which were important in the regulation of SRP-dependent co-translational proteins targeting the membrane, RNA secondary structure, unwinding, and functional pathways of ribosomal and RNA polymerase. The most important miRNA genes in the protein network of increased and decreased gene expression were bta-miR-10b-5p and miR-29b-2-5p.
Conclusion: Examination of the genes expressed in the oocyte maturation pathway revealed nuclear, mitochondrial, and miRNA genes. Increasing and decreasing gene expression helps maintain equilibrium, which can be a biological marker.
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[15] Susor A, Kubelka M. Translational regulation in the mammalian oocyte. Results and probl in cell differ. 2017;63:257-95.
[16] Mayer S, Wrenzycki C, Tomek W. Inactivation of mTor arrests bovine oocytes in the metaphase-I stage, despite reversible inhibition of 4E-BP1 phosphorylation. Mol Reprod Dev. 2014;81(4):363-75.
[17] Orozco-Lucero E, Sirard MA. Molecular markers of fertility in cattle oocytes and embryos: progress and challenges. Anim. Reprod. 2014;11(3):183-94.
[18] Torner H, Ghanem N, Ambros C, Hölker M, Tomek W, Phatsara C, et al. Molecular and subcellular characterisation of oocytes screened for their developmental competence based on glucose-6-phosphate dehydrogenase activity. Reprod. 2008;135(2):197-212.
[19] Raemaekers T, Ribbeck K, Beaudouin J, Annaert W, Van Camp M, Stockmans I, et al. NuSAP, a novel microtubule-associated protein involved in mitotic spindle organization. J Cell Biol. 2003;162(6):1017-29.
[20] Ribbeck K, Aaron C, Groen AC, Santarella R, Bohnsack TB, Raemaekers T, et al. NuSAP, a mitotic RanGTP target that stabilizes and cross-links microtubules. MBoC. 2006;17(6):2646-60.
[21] Yu J, Lan X, Chen X, Yu C, Xu Y, Liu Y, et al. Protein synthesis and degradation are essential to regulate germ line stem cell homeostasis in Drosophila testes. Development. 2016;143(16):2930-45.
[22] Peddinti D, Memili E, Burgess SC. Proteomics-Based Systems Biology Modeling of Bovine Germinal Vesicle Stage Oocyte and Cumulus Cell Interaction. PLoS One. 2010;5(6):e11240.
[23] Tanghe S, Van Soom A, Nauwynck H, Coryn M, de Kruif A. Minireview: Functions of the cumulus oophorus during oocyte maturation, ovulation, and fertilization. Mol Reprod Dev. 2002;61(3):414-24.
[24] Buccione R, Schroeder AC, Eppig JJ. Interactions between somatic cells and germ cells throughout. mammalian oogenesis. Biol Reprod. 1990;43(4):543-7.
[25] Tripurani SK, Xiao C, Salem M, Yao J. Cloning and analysis of fetal ovary microRNAs in cattle. Anim Reprod Sci. 2010;120(1-4):16-22.
[26] Fleckenstein B, Daniel MD, Hunt RD, Werner RD, Falk LA, Mulder C. Tumour induction with DNA of oncogenic primate herpes viruses. Nature. 1978;274:57-9.
[27] Irvine KR, Rao JB, Rosenberg SA, Restifo NP. Cytokine enhancement of DNA immunization leads to effective treatment of established pulmonary metastases. J Immunol. 1996;156(1):238-45.
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