Ovarian follicles are formed during the process of folliculogenesis and oogenesis. The follicle consists of the oocyte, the follicular fluid and the oocyte‑associated cells: granulosa cells (GCs). GCs provide the microenvironment necessary for the development of the follicle and the maturation of the oocyte [1]. GCs also influence the development of the female gamete in a paracrine and autocrine manner [2]. Paracrine communication leads to the establishment of bidirectional cooperation affecting the hormonal production and the expression of genes associated with follicular development [3]. GCs are associated with the functioning of the reproductive system and the maintenance of pregnancy by participating in the synthesis of steroid hormones. The physiological and morphological changes occurring in GCs during the formation of the ovarian follicle have been well characterized. In contrasts, the deep molecular mechanisms and the gene interactions of GCs are still insufficiently described.
With a large probability for application as the grafts in regenerative medicine, the establishment and characterization of
The aim of study is to furtherly characterize GCs at the molecular level and obtain more insight in the variability of the transcriptomic profile during longterm cell culture. Affymetrix microarray assays revealed the regulatory peptides and enzymes involved in the functioning of the mechanism of enzymatic reactions. The focus of the current study was to examine transcript variability of genes belonging to “coenzyme biosynthetic process”, “coenzyme metabolic process”, “cofactor biosynthetic process” and “cofactor metabolic process” gene ontology biological process terms (GO BP) during OECs longterm primary culture
A total of 40 crossbred Landrace gilts with a median age of 170 days and weight of 98 kg were used in this study. All animals were housed under identical conditions.
The ovaries (n = 80) and the reproductive tracts were recovered at slaughter and transported to the laboratory at 38°C in 0.9% NaCl within 30 min. The ovaries of each animal were placed in phosphate-buffered saline supplemented with fetal bovine serum (Sigma-Aldrich Co., St. Louis, MO). Thereafter, single preovulatory large follicles with an estimated diameter >5 mm (n = 300) were opened into a sterile Petri dish by puncturing them with a 5 mL syringe and 20-G needle, recovering the cumulus–oocyte complexes (COCs) and follicular fluid (FF). The GCs were extracted from the FF, whereas the COCs were discarded. Culture medium consisted of Dulbecco’s modified Eagle’s medium (Sigma-Aldrich) supplemented with 2% fetal calf serum (PAA, Linz, Austria) and l-glutamine. The final concentration of l-glutamine in the medium was 4 mM (diluted from the 200 mM stock solution; Invitrogen). Moreover, 10 mg/mL gentamicin (Invitrogen), 10,000 U/mL penicillin, and 10,000 mg/mL streptomycin (Invitrogen) were added to the culture medium. Cells were cultivated at 38.5°C under aerobic conditions (5% CO2). Once the adherent cells were >80% confluent, they were detached by treating them with 0.05% trypsin– ethylenediaminetetraacetic acid (Invitrogen) for 3 min and they were then counted using a Z2 counter and cell viability analyzer (ViCell XR 2.03; both Beckman Coulter). For our experiments, 3 106 cells per dish were used for culture.
Oviductal epithelial cells were pooled and harvested at 24h, 7 days, 15 days and 30 days after the beginning of the in vitro culture. The total RNA was extracted from samples using TRI Reagent (Sigma, St Louis, MO, USA) according to the method described by Chomczyński and Sacchi [8]. Then the RNA was quantified by measuring the optical density (OD) at 260 nm (NanoDrop spectrophotometer; Thermo Scientific, Waltham, MA, USA). Afterwards, 100 ng of an RNA sample were used for downstream analysis.
In the first step, the whole RNA (100 ng) from each sample was subjected to two rounds of sense cDNA amplification (Ambion® WT Expression Kit, provided by Ambion, Austin, TX, USA). The synthesis of cRNA was performed by in vitro transcription (16 h, 40 °C). Subsequently, cRNA was purified and retranscribed into cDNA. The cDNA samples were then used for biotin labeling and fragmentation using an Affymetrix GeneChip® WT Terminal Labeling and Hybridization kit (Affymetrix). These biotin-labeled samples were loaded onto and hybridized to the Affymetrix® Porcine Gene 1.1 ST Array Strip (48°C/20 h). Hybridization was conducted at 48 °C for 20 h, employing an AccuBlock™ Digital Dry Bath (Labnet International, Inc., Edison, NJ, USA) hybridization oven. Subsequently, according to technical protocol, microarrays were washed and stained using an Affymetrix GeneAtlas™ Fluidics Station (Affymetrix, Santa Clara, CA, USA). The strips were scanned using an Affymetrix GeneAtlas™ Imaging Station (Affymetrix, Santa Clara, CA, USA) and the scans of the microarrays were saved on hard drives as *.CEL files for downstream data analysis.
All of the presented analyses and graphs were performed using Bioconductor and R programming languages. Each *.CEL file was merged with a description file. The Robust Multiarray Averaging (RMA) algorithm was used in order to correct the background, and normalize and summarize the results. The statistical significance of the analyzed genes was determined by performing moderated t-statistics from the empirical Bayes method. The obtained p-value was corrected for multiple comparisons using Benjamini and Hochberg’s false discovery rate. The selection of significantly altered genes was based on a p-value beneath 0.05 and expression higher than two-fold.
Differentially expressed genes were subjected to selection by examination of genes involved in cell migration regulation. The differentially expressed gene list (separated for up- and down-regulated genes) was uploaded to the DAVID software (Database for Annotation, Visualization and Integrated Discovery) [9], where genes belonging to the terms of all three Gene Ontology (GO) domains were extracted. Expression data of these genes were also subjected to a hierarchical clusterization procedure, and their expression values were presented as a heat map.
Subsequently, we analyzed the relation between the genes belonging to chosen GO terms with the GOplot package [10]. The GoPlot package had calculated the z-score, hence the number of up- regulated genes minus the number of down- regulated genes divided by the square root of the count. This information allowed the estimation of the change course of each gene-ontology term.
The interactions between differentially expressed genes/proteins belonging to the studied gene ontology group were investigated by STRING10 software (Search Tool for the Retrieval of Interacting Genes) [11]. The list of gene names was used as a query for an interaction prediction. The search criteria were based on co-occurrences of genes/proteins, their co-expression, and their experimentally observed interactions in scientific texts (text mining). The results of such analyses generated a gene/protein interaction network in which the intensity of the edges reflected the strength of the interaction score.
Finally, the functional interaction between genes that belong to the chosen GO BP terms were investigated by REACTOME FIViz application to the Cytoscape 3.6.0 software. The ReactomeFIViz app is designed to find pathways and network patterns related to cancer and other types of diseases. This app accesses the pathways stored in the Reactome database, allowing to do pathway enrichment analysis for a set of genes, visualize hit pathways using manually laid-out pathway diagrams directly in Cytoscape, and investigate functional relationships among genes in hit pathways. The app can also access the Reactome Functional Interaction (FI) network, a highly reliable and manually curated pathway-based protein functional interaction network covering over 60% of human proteins.
The research has been complied with all the relevant national regulations, institutional policies and in accordance the tenets of the Helsinki Declaration, and has been approved by the authors’ institutional review board or equivalent committee. Poznan University of Medical Sciences Bioethics Committee approval no. 32/2012 from June 1, 2012.
Whole transcriptome profiling by Affymetrix microarray allows to analyze the gene expression changes of the oocyte after in vitro maturation in relation to freshly isolated oocyte before in vitro procedure (in Vivo). By Affymetrix® Porcine Gene 1.1 ST Array the expression of 22480 transcripts were examined. Genes were considered as differentially expressed when presented fold changes higher than abs (2) and corrected p value lower than 0.05. This set of genes consist of 2278 different transcripts. The first detailed analysis based on GO BP lead to the identification of differentially expressed genes belonging to the significantly enrichment GO BP terms.
The DAVID (Database for Annotation, Visualization and Integrated Discovery) software was used for the extraction of the genes belonging to the gene ontology biological process terms (GO BP). Up and down regulated gene sets were subjected to DAVID searching separately and only gene sets where adj. p values were lower than 0.05 were selected. The DAVID software analysis showed that differently expressed genes belong to 582 Gene ontology groups and 45 KEGG pathways. The genes of main focus were “coenzyme biosynthetic process”, “coenzyme metabolic process”, “cofactor biosynthetic process” and “cofactor metabolic process” GO BP terms. These sets of genes were subjected to hierarchical clusterization procedure and presented as heatmaps (
Gene symbols, fold change in expression ratio, Entrez gene IDs, corrected p values and mean value of fold change ratio of the 10 chosen differentially expressed, studied genes
GENE SYMBOL | RATIO D7/D1 | RATIO D15/D1 | RATIO D30/D1 | ADJUSTED P.VALUE D7/D1 | ADJUSTED P.VALUE D15/D1 | ADJUSTED P.VALUE D30/D1 | ENTREZ GENE ID | MEAN RATIO |
---|---|---|---|---|---|---|---|---|
-9,251267471 | -9,938010711 | -10,38803385 | 0,772063445 | 0,001410011 | 0,001221118 | 60481 | -9,85910401 | |
-8,02377638 | -8,20961524 | -10,82671442 | 0,076845334 | 0,001194058 | 0,001135003 | 57678 | -9,020035348 | |
-7,126693786 | -8,012261249 | -10,12683753 | 0,046856913 | 0,000599885 | 0,000492234 | 8942 | -8,421930854 | |
-7,317544673 | -7,853658138 | -7,57763343 | 0,897789877 | 0,01064549 | 0,008292358 | 6319 | -7,582945414 | |
-6,467890961 | -8,066798079 | -7,468977384 | 0,726193645 | 0,005971885 | 0,003885481 | 441024 | -7,334555475 | |
-7,606632986 | -5,292293151 | -7,422938244 | 0,155430935 | 0,004519068 | 0,007571066 | 10135 | -6,773954794 | |
-5,271754338 | -7,924819598 | -5,887363184 | 0,221016313 | 0,009871755 | 0,004441924 | 3156 | -6,361312373 | |
-4,519000381 | -5,545912723 | -5,546499245 | 0,999794541 | 0,077215834 | 0,04794247 | 79071 | -5,203804116 | |
-2,907857277 | -6,352044342 | -4,361821755 | 0,104734337 | 0,027816555 | 0,004611004 | 27010 | -4,540574458 | |
-3,286963496 | -3,973472161 | -5,442013188 | 0,019676685 | 0,002620929 | 0,001569971 | 4200 | -4,234149615 |
In the Gene Ontology database, genes that formed one particular GO group can also belong to other different GO term categories. By this meaning, the gene intersections between selected GO BP terms were explored and the relation between the GO BP terms are presented as circle plot (
The STRING software generated interaction networks among differentially expressed genes belonging to each of the selected GO BP terms. Through this method it was possible to observe the molecular interaction network formed between protein products of studied genes (
GCs are known to be closely related to mammalian oocytes. GCs surround the oocyte and they form the proper architecture of the ovarian follicle. In physiological conditions, GCs support the development and the maturation of the oocyte, and the formation of ovarian follicles [12]. Moreover, they produce gap junctions that allow a bidirectional communication with the oocyte. Thus, in addition to the hormonal secretory function, GCs also play an important role in communication with oocytes. They are associated with the functioning of the reproductive system, as well as the maintenance of pregnancy through the participation in the synthesis of steroid hormones. After ovulation, when the follicle ruptures, the GCs fill the formed gap and transform into lutein cells, whose main purpose is to produce progesterone and maintain pregnancy. The resulting structure is called the corpus luteum [13]. Understanding the molecular mechanisms regulating this process is pivotal to completely understand the regulation of oogenesis and the ovarian function.
The physiological and morphological changes that occur in GCs during the formation of the ovarian follicle have been well characterized. However, the molecular mechanisms and gene interactions of GCs are still insufficiently described, Previous studies are an important introduction to a better understanding of all key mechanisms in this cell population [13]. In addition to indicating numerous molecular markers that are key to many physiological and biochemical processes occurring in these cells [13, 14, 15], the very high plasticity of GCs and their potential to differentiate into other cell populations were demonstrated[16, 17, 18]. In the present study, by employing primary
Of all the genes showed on the heat maps in Fig. 1 and belonging to GO BP terms of interest, the greatest reduction in the level of mRNA expression has been observed in the case of the gene
The detection of the reduced mRNA expression level of another ELOVL family member, ELOVL6 is an additional argument suggesting decreased activity of elongation very long chain fatty acids (VLCFA) in GCc during
Significantly reduced
Decreasing levels of gene expression involved in the functioning of the mechanism of enzymatic reactions occur over the course of long-term cell culture. The transcriptomic profile variability for genes (