Talebi et al. Journal of Animal Science and Technology (2018) 60:11 https://doi.org/10.1186/s40781-018-0171-y SHORT REPORT Open Access Analysis of protein-protein interaction network based on transcriptome profiling of ovine granulosa cells identifies candidate genes in cyclic recruitment of ovarian follicles Reza Talebi1, Ahmad Ahmadi1* and Fazlollah Afraz2 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 infollicularphaseincomparisonwithlutealphase.Bycontrast, 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. Keywords: Protein–protein interaction network, Biomarkers, Ovarian follicles, Granulosa cells, Cyclic recruitment Introduction tools from systems biology going beyond simple gene ontol- Small antral follicles represent the fundamental develop- ogy (GO) terms like causal network modelling linking gene mental unit of the mammalian ovary and, as such, serve the expression analysis to gene interaction information are needs of the entire reproductive life span. After pubertal, sorely needed [5, 6]. Nowadays, biological networks allow a cohort of small antral follicles enters to gonadotrophin- comprehensive examination of the complex mechanisms sensitive development upon FSH (Follicle Stimulating for targeted empirical studies [7]. Numerous studies have Hormone) stimulation, called cyclic recruited follicles [1, 2]. been conducted in attempt to identify candidate genes of Cyclic recruitment, although obligatory, does not guarantee reproductive biology via gene networks analysis such as ovulation because growing follicles are vulnerable to atresia development of rat primordial follicles [8, 9], early embryo and thus may fall out from the growth trajectory [3]. development in mouse [10]andbovine[11], gene networks Among somatic cells of the antral follicles, granulosa cells of bovine oocytes [12] and bovine granulosa cells of small will undergo serious changes morphologically and physiolo- and large follicles [13]. Protein-protein interaction (PPI) net- gically during the processes of proliferation, differentiation, works are one the most known approaches for representa- ovulation, lutenization and atresia [4]. tion of candidate genes/proteins beyond of high-throughput To understand gene function of the cellular processes, studies [13–19]. genes must be studied in the context of networks. Emerging In previous study, we surprisingly have shown differ- ences in transcriptome profiles of ovine (sheep) granulosa * Correspondence: [email protected] cells between small antral follicles (1–3 mm) collected 1 Department of Animal Sciences, Faculty of Agriculture, Bu-Ali Sina during the follicular and luteal phases [20]. Therefore, the University, Hamedan, Iran Full list of author information is available at the end of the article specific purpose of the present study was to survey the © The Author(s). 2018 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. Talebi et al. Journal of Animal Science and Technology (2018) 60:11 Page 2 of 7 differentially expressed genes (DEGs) from the previous database Version. 10.0 [21] containing neighborhoods, study using the analysis of PPI networks in order to identi- gene fusion, co-occurrence, homology, co-expression, fication of candidate genes that probably those are impres- experiments, databases and text mining, were utilized with sive in cyclic recruitment of ovarian follicles. the medium confidence score (> 0.4). Materials and methods Dataset assessment Network analysis and modules selection The main dataset of the present study was belonged to Cytoscape Version 3.1.1 [22] was used to plot and 663 DEGs in ovine granulosa cells between small antral analyze the centralities, clustering and modularity of the follicles (1–3 mm in diameter) collected during the PPI network. The MCODE (Molecular Complex Detec- follicular and luteal phases [20]. These DEGs afterwards tion) v1.4.0-beta2 was performed to screen modules of were annotated using a standard Ensembl gene annota- PPI network with degree cutoff = 2, node score cutoff = tion system. 0.2, k-core = 2, and max. Depth = 100 [14]. This is a well-known automated algorithm to find highly inter- Construction of protein-protein interaction (PPI) network connected subgraphs that detects densely connected A large PPI network was reconstructed from the 646 regions in large PPI networks that may represent genes/protein symbols (Additional file 1) of the Ensembl molecular complexes [23]. Also, the functional modules annotation (Ovis_aries.Oar_v3.1.91). In order to map pair- were chosen with the number of nodes ≥16 and nodes wise interactions, all computational methods in STRING score [19]. Fig. 1 The Un-directed PPI network for total genes/proteins differentially expressed (cut-off of P < 0.05 in corrected tests of Benjamini-Hochberg) in luteal vs. follicular phases of ovine estrous cycle. The PPI network illustrates degree (node size) and betweenness centrality (node color) and layout option of edge weighted spring embedded. Up- and down-regulated genes in luteal phase vs. follicular phase are shown with triangles and V shapes, respectively Talebi et al. Journal of Animal Science and Technology (2018) 60:11 Page 3 of 7 Enrichment analysis for the functional modules from the hub genes/proteins. These important proteins Biological significance of these predicted modules were were extracted from hub genes/proteins via network inferred by ClueGO [24] plugin of Cytoscape. The statis- analysis and modularity of the network. This small, but tical significance of the biological terms analyzed was important, graph is visualized by Cytoscape Version 3.1.1 calculated with Right-sided enrichment hypergeometric [22]. Using CluePedia v.1.1.7. [24]pluginofCytoscape, test and Benjamini and Hochberg P-value correction hub genes/proteins were placed in a directed gene net- [25] to reduce false positives- and negatives. Kappa sta- work based on its molecular function. tistics were used to link and grouping of the enriched terms and functional grouping of them as described by Result and discussion Bindea et al. [24]. The minimum connectivity of the Among 646 DEGs (Additional file 1), 498 proteins were pathway network (kappa score) was set to 0.4 units. annotated on the Total PPI network. Also, 2191 edges containing neighborhoods, gene fusion, co-occurrence, Identification of hub genes/proteins and its directed homology, co-expression, experiments, databases and text interaction network mining, were interacted between such genes/proteins In this study, all networks were utilized to identify hub (Fig. 1 and Additional file 3). The statistics of network genes/proteins which important in folliculogenesis containing network density, network diameter and cluster- during the ovine estrous cycle. Hubs were detected by ing coefficient were 0.018, 9 and 0.275, respectively. The 2 calculating the node degree distribution [16] using the power law of degree distribution was followed with an R 2 Network Analyzer (http://apps.cytoscape.org/apps/net- = 0.895. Meanwhile, R is computed on logarithmized workanalyzer) plugin of Cytoscape [24]. Degree gives a values. Some proteins in this network had high values in simple count of the number of interactions of a given all degree, stress and betweenness centralities, such as node [26]. Additionally, we utilized several centrality FYN, CDK1, RAC1, ACTG2, FGR, APP, MMP2, SYK, parameters including stress, betweenness and closeness CDH1, TGFB1, PTPN6, ITGB7, FOS, ACTN1, GNAI2, instead of using degree centrality itself. The mathemat- INSRR, BMP4, BMP2, LYN and HCK (Additional file 3). ical formulas for the calculation of centrality parameters This list was utilized to identify some candidate genes including stress, betweenes and clossness, are available among hubs as major regulators in cyclic recruitment of in Additional file 2 that has been retrieved from Zhuang ovine small antral follicles. et al. [26]. By extract direction of PPI from STRING Ver- From the total PPI network, thirteen modules were ex- sion. 10.0 [21], a directed gene network was reconstructed tracted, among which five subnetworks (modules of 1, 2, Table 1 Statistics for thirteen subnetworks identified by MCODE method in PPI network