Analysis of protein-protein interaction network based on transcriptome profiling of ovine granulosa cells identifies candidate genes in cyclic recruitment of ovarian follicles
2
조회
0
다운로드
0
인용
0
참고문헌
키워드
초록
After pubertal, cohort of small antral follicles enters to gonadotrophin-sensitive development, called recruited follicles. This study was aimed to identify candidate genes in follicular cyclic recruitment via analysis of protein-protein interaction (PPI) network. Differentially expressed genes (DEGs) in ovine granulosa cells of small antral follicles between follicular and luteal phases were accumulated among gene/protein symbols of the Ensembl annotation. Following directed graphs, PTPN6 and FYN have the highest indegree and outdegree, respectively. Since, these hubs being up-regulated in ovine granulosa cells of small antral follicles during the follicular phase, it represents an accumulation of blood immune cells in follicular phase in comparison with luteal phase. By contrast, the up-regulated hubs in the luteal phase including CDK1, INSRR and TOP2A which stimulated DNA replication and proliferation of granulosa cells, they known as candidate genes of the cyclic recruitment.
Abstract
After pubertal, cohort of small antral follicles enters to gonadotrophin-sensitive development, called recruited follicles. This study was aimed to identify candidate genes in follicular cyclic recruitment via analysis of protein-protein interaction (PPI) network. Differentially expressed genes (DEGs) in ovine granulosa cells of small antral follicles between follicular and luteal phases were accumulated among gene/protein symbols of the Ensembl annotation. Following directed graphs, PTPN6 and FYN have the highest indegree and outdegree, respectively. Since, these hubs being up-regulated in ovine granulosa cells of small antral follicles during the follicular phase, it represents an accumulation of blood immune cells in follicular phase in comparison with luteal phase. By contrast, the up-regulated hubs in the luteal phase including CDK1, INSRR and TOP2A which stimulated DNA replication and proliferation of granulosa cells, they known as candidate genes of the cyclic recruitment.
참고문헌 (32)
- McGee EA, "Initial and cyclic recruitment of ovarian follicles", Endocr Rev, 21, pp.200~214 (2000)
- Shimizu T, "Molecular and cellular mechanisms for the regulation of ovarian follicular function in cows", J Reprod Dev, 62, pp.323~329 (2016)[DOI]
- Strauss JF, "Yen & Jaffe's reproductive endocrinology E-book: physiology, pathophysiology, and clinical management (Expert Consult - Online and Print)" (2013), Elsevier Inc.
- Cuiling L, "Granulosa cell proliferation differentiation and its role in follicular development", Chin Sci Bull, 50, pp.2665~ (2005)[DOI]
- Beiki H, "Large-scale gene co-expression network as a source of functional annotation for cattle genes", BMC Genomics, 17, pp.846~ (2016)[DOI]
- Chindelevitch L, "Causal reasoning on biological networks: interpreting transcriptional changes", Bioinformatics, 28, pp.1114~1121 (2012)[DOI]
- Amiri M, "Atopic dermatitis associated protein interaction network lead to new insights in chronic sulfur mustard skin lesion mechanisms", Expert Rev Proteomics, 10, pp.449~460 (2013)[DOI]
- Nilsson EE, "Gene bionetwork analysis of ovarian primordial follicle development", PLoS One, 5, pp.e11637~ (2010)[DOI]
- Nilsson E, "Gene bionetworks that regulate ovarian primordial follicle assembly", BMC Genomics, 14, pp.496~ (2013)[DOI]
- Xue Z, "Genetic programs in human and mouse early embryos revealed by single-cell RNA sequencing", Nature, 500, pp.593~597 (2013)[DOI]
- Jiang Z, "Transcriptional profiles of bovine in vivo pre-implantation development", BMC Genomics, 15, pp.756~ (2014)[DOI]
- Labrecque R, "Transcriptome analysis of bovine oocytes from distinct follicle sizes: insights from correlation network analysis", Mol Reprod Dev, 83, pp.558~569 (2016)[DOI]
- Bahrami A, "miRNA-mRNA network involved in folliculogenesis interactome: systems biology approach", Reproduction, 154, pp.51~65 (2017)[DOI]
- Kotni MK, "Gene expression profiles and protein-protein interaction networks in amyotrophic lateral sclerosis patients with C9orf72 mutation", Orphanet J Rare Dis, 11, pp.148~ (2016)[DOI]
- Lin CY, "Module organization and variance in protein-protein interaction networks", Sci Rep, 5, pp.9386~ (2015)[DOI]
- Malik A, "Network analysis for the identification of differentially expressed hub genes using myogenin knock-down muscle satellite cells", PLoS One, 10, pp.e0133597~ (2015)[DOI]
- Nai W, "Identification of novel genes and pathways in carotid atheroma using integrated bioinformatic methods", Sci Rep, 6, pp.18764~ (2016)[DOI]
- Talebi R, "Parkinson's disease and lactoferrin: analysis of dependent protein networks", Gene Rep, 4, pp.177~183 (2016)[DOI]
- Xu Z, "Identification of candidate biomarkers and analysis of prognostic values in ovarian cancer by integrated bioinformatics analysis", Med Oncol, 33, pp.130~ (2016)[DOI]
- Talebi R, "Transcriptome analysis of ovine granulosa cells reveals differences between small antral follicles collected during the follicular and luteal phases", Theriogenology, 108, pp.103~117 (2018)[DOI]
- Szklarczyk D, "STRING v10: protein-protein interaction networks, integrated over the tree of life", Nucleic Acids Res, 43, pp.447~452 (2015)[DOI]
- Lotia S, "Cytoscape app store", Bioinformatics, 29, pp.1350~1351 (2013)[DOI]
- Bader GD, "An automated method for finding molecular complexes in large protein interaction networks", BMC Bioinformatics, 4, pp.2~ (2003)[DOI]
- Bindea G, "ClueGO: a Cytoscape plug-in to decipher functionally grouped gene ontology and pathway annotation networks", Bioinformatics, 15, pp.1091~1093 (2009)
- Benjamini Y, "Controlling the false discovery rate: a practical and powerful approach to multiple testing", J R Stat Soc Series B Stat Methodol, 57, pp.289~300 (1995)
- Zhuang DY, "Identification of hub subnetwork based on topological features of genes in breast cancer", Int J Mol Med, 35, pp.664~674 (2015)[DOI]
- Nimmerjahn F, "Fcgamma receptors as regulators of immune responses", Nat Rev Immunol, 8, pp.34~47 (2008)[DOI]
- Hatzirodos N, "Transcriptome profiling of granulosa cells from bovine ovarian follicles during atresia", BMC Genomics, 15, pp.40~ (2014)[DOI]
- Terenina E, "Differentially expressed genes and gene networks involved in pig ovarian follicular atresia", Physiol Genomics, 49, pp.67~80 (2017)[DOI]
- Perez-Guijarro E, "Lineage-specific roles of the cytoplasmic polyadenylation factor CPEB4 in the regulation of melanoma drivers", Nat Commun, 7, pp.13418~ (2016)[DOI]
- Tamaichi H, "Ataxia telangiectasia mutated-dependent regulation of topoisomerase II alpha expression and sensitivity to topoisomerase II inhibitor", Cancer Sci, 104, pp.178~184 (2013)[DOI]
- Baerwald AR, "Ovarian antral folliculogenesis during the human menstrual cycle: a review", Hum Reprod Update, 18, pp.73~91 (2012)[DOI]
