The oviduct is a part of mammalian female reproductive tract, connecting the ovary to the uterus, that plays an invaluable role in female fertility. It is involved in gamete and embryo transport, supports fertilization and provides an optimal environment for gamete survival and preimplantation embryo development. The oviductal epithelium consists of two types of epithelial cells: ciliated and non-ciliated secretory cells, the latter producing components of oviductal fluid, which participates in gamete guidance and embryo cleavage [1].
Oxygen metabolism is particularly important in establishing a successful pregnancy, with both gametes and embryos producing reactive oxygen species (ROS). Porcine preimplantation embryos produce ROS via oxidative phosphorylation and inhibition of this process results in better outcome in
Taking everything into consideration, there is no doubt that defense mechanisms against oxidative stress must be employed to ensure gamete and embryo survival and proper development. Except for the antioxidant enzymes stored as mRNA in the oocyte, there is also an external protection through the oviductal environment. Several antioxidant enzymes, such as glutathione peroxidase, Cu-Zn-superoxide dismutase, or catalase, have been found to be expressed in oviducts of humans and mice [3]. Moreover, oviductal epithelial cell (OECs) membrane proteins bind to the human spermatozoa, which results in its protection against oxidative stress, as indicated by Huang et al. [4].
OEC
In this study, crossbred gilts (n=45) at the age of about nine months and which displayed at least two regular estrous cycles were collected from a commercial herd. All the animals were checked daily for estrus behavior and were slaughtered after reaching the anestrus phase of the estrus cycle. The uteri were then transported to the laboratory within 30 min at 38 °C.
Oviducts were washed twice in Dulbecco’s phosphate buffered saline (PBS) (137 mM NaCl, 27 mM KCl, 10 mM Na2HPO4, 2 mM KH2PO4, pH 7.4). Epithelial cells were removed using sterile surgical blades. Then, the epithelium was incubated with collagenase I (Sigma Aldrich, Madison, USA), 1mg/mL in Dubecco’s modified Eagle’s medium (DMEM; Sigma Aldrich, Madison, USA) for 1 h at 37oC. The cell suspension obtained from this digestion was filtered through 40 μm pore size strainer to remove blood and single cells. The residue was collected by rinsing the strainer with DMEM. The cells were then centrifuged (200 x g, 10 min.). Next, they were washed in PBS and centrifuged again. Later, they were incubated with 0.5% Trypsin/EDTA (Sigma Aldrich, Madison, USA) at 37oC for 10 min. The reaction was stopped with fetal calf serum (FCS; Sigma Aldrich, Madison, USA). After incubation, the cells were filtered and centrifuged for the last time. The final cell pellet was suspended in DMEM, supplemented with 10% FCS, 100U/mL penicillin, 100 μg/mL streptomycin and 1μg/mL amphotericin B. The cells were cultured at 37˚C in a humidified atmosphere of 5% CO2. Once the OEC cultures attained 70–80% confluency, they were passaged by washing with PBS, digested with 0.025% Trypsin/EDTA, neutralized by a 0.0125% trypsin inhibitor (Cascade Biologics, Portland, USA), centrifuged, and resuspended at a seeding density of 2*104cells/cm2. The culture medium was changed every three days. The culture was maintained for30 days.
Oviductal epithelial cell were pooled and harvested 24h, 7 days, 15 days and 30 days after the beginning of culture. Total RNA was extracted from the samples using TRI Reagent (Sigma, St Louis, MO, USA) and RNeasy MinElute cleanup Kit (Qiagen, Hilden, Germany). The total mRNA amount was determined from the optical density at 260 nm, and the RNA purity was estimated using the 260/280 nm absorption ratio (higher than 1.8) (NanoDrop spectrophotometer, Thermo Scientific, ALAB, Poland). The RNA integrity and quality were checked on a Bioanalyzer 2100 (Agilent Technologies, Inc., Santa Clara, CA, USA). The resulting RNA integrity numbers (RINs) were between 8.5 and 10 with an average of 9.2 (Agilent Technologies, Inc., Santa Clara, CA, USA). The RNA in each sample was diluted to a concentration of 100 ng/μl with an OD260/OD280 ratio of 1.8/2.0. From each RNA sample, 100 ng of RNA was taken for microarray expression assays.
Total RNA (100 ng) from each pooled sample was subjected to two rounds of sense cDNA amplification (Ambion® WT Expression Kit). The obtained cDNA was used for biotin labeling and fragmentation usingAffymetrix GeneChip® WT Terminal Labeling and Hybridization (Affymetrix, Santa Clara, CA, USA). Biotin-labeled fragments of cDNA (5.5 μg) were hybridized to the Affymetrix® Porcine Gene 1.1 ST Array Strip (48°C/20 h). Microarrays were then washed and stained, according to the technical protocol, using the Affymetrix GeneAtlas Fluidics Station. The array strips were scanned employing the Imaging Station of the GeneAtlas System. Preliminary analysis of the scanned chips was performed using Affymetrix GeneAtlasTM Operating Software. The quality of gene expression data was confirmed according to the quality control criteria provided by the software. The obtained CEL files were imported into downstream data analysis software.
All of the presented analyses and graphs were compiled using Bioconductor and R programming languages. Each CEL file was merged with a description file. To correct background, normalize, and summarize results, we used the Robust Multiarray Averaging (RMA) algorithm. To determine the statistical significance of the analyzed genes, moderated t-statistics from the empirical Bayes method were performed. The obtained p-value was corrected for multiple comparisons using Benjamini and Hochberg’s false discovery rate. 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 selection by examination of genes involved in oxygen metabolism. 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) [6], where genes belonging to the terms of all four Gene Ontologies (GOs) of interest were extracted. Expression data of these genes was also subjected to a hierarchical clusterization procedure, with their expression values presented as a heat map.
Subsequently, we analyzed the relation between the genes belonging to the chosen GO terms using the GO plot package [7]. The Go plot package had calculated the z-score: the number of up- regulated genes minus the number of down- regulated genes divided by the square root of the count. This information allowed to estimate the change course of each gene-ontology term.
Interactions between differentially expressed genes/proteins belonging to the studied gene ontology groups were investigated by the STRING10 software (Search Tool for the Retrieval of Interacting Genes) [8]. The list of gene names was used as a query for interaction prediction. The search criteria were based on co-occurrences of genes/proteins in scientific texts (text mining), co-expression, and experimentally observed interactions. The results of such analyses generated a gene/protein interaction network where the intensity of the edges reflected the strength of the interaction score.
Finally, the functional interactions between genes that belongs to the chosen GO BP terms were investigated by the REACTOME FIViz application to the Cytoscape 3.6.0 software. The Reactome FIViz 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 perform 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, manually curated pathway-based protein functional interaction network covering over 60% of human proteins.
The research related to animal use has been complied with all the relevant national regulations and instructional policies for the care and use of animals. Bioethical Committee approval no. 32/2012.
Whole transcriptome profiling with Affymetrix microarrays allows us to analyze the gene expression changes between 7, 15 and 30 days of porcine oviductal epithelial cell culture. Using Affymetrix® Porcine Gene 1.1 ST Array Strip, we have examined the expression of 12257 transcripts. Genes with fold change higher than abs (2) and with corrected p-value lower than 0.05 were considered as differentially expressed. This set of genes consists of 2533 different transcripts.
DAVID (Database for Annotation, Visualization and Integrated Discovery) software was used for extraction of gene ontology biological process terms (GO BP) that contain differently expressed transcripts. Up and down regulated gene sets were subjected to the DAVID search separately and only gene sets with adj. p-value lower than 0.05 were selected. The DAVID software analysis showed that the differently expressed genes belonged to 657 Gene ontology terms. In this paper, we focused on 166 genes that belong to “cellular response to oxidative stress”, “cellular response to oxygen-containing compound”, “cellular response to oxygen levels” and “cellular response to reactive oxygen species” 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 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 | MEAN RATIO |
---|---|---|---|---|---|---|---|
-4,009 | -7,510 | -69,247 | 2,57E-04 | 2,80E-05 | 5,88E-07 | -26,922 | |
-4,154 | -28,786 | -47,147 | 4,67E-05 | 9,76E-07 | 2,44E-07 | -26,695 | |
-2,719 | -11,232 | -53,971 | 7,06E-04 | 6,74E-06 | 4,60E-07 | -22,641 | |
-11,995 | -16,079 | -14,006 | 9,50E-06 | 3,21E-06 | 1,76E-06 | -14,027 | |
-6,704 | -3,398 | -25,263 | 2,69E-05 | 1,36E-04 | 7,91E-07 | -11,788 | |
-4,866 | -11,123 | -17,404 | 4,71E-05 | 3,93E-06 | 1,01E-06 | -11,131 | |
-2,278 | -7,702 | -14,754 | 8,42E-04 | 7,24E-06 | 1,14E-06 | -8,245 | |
-5,442 | -7,434 | -7,688 | 4,11E-04 | 1,29E-04 | 7,85E-05 | -6,854 | |
-2,576 | -3,932 | -5,953 | 4,96E-03 | 6,50E-04 | 1,17E-04 | -4,154 | |
-3,201 | -4,110 | -4,254 | 6,44E-05 | 1,52E-05 | 8,37E-06 | -3,855 | |
6,313 | 5,557 | 10,336 | 3,95E-05 | 3,12E-05 | 4,36E-06 | 7,402 | |
6,573 | 8,352 | 8,773 | 1,83E-05 | 4,91E-06 | 2,42E-06 | 7,899 | |
4,336 | 9,495 | 16,755 | 1,66E-05 | 1,63E-06 | 2,93E-07 | 10,195 | |
7,630 | 6,655 | 16,828 | 8,57E-06 | 5,17E-06 | 5,84E-07 | 10,371 | |
5,014 | 11,115 | 19,107 | 1,73E-05 | 1,68E-06 | 3,42E-07 | 11,745 | |
5,878 | 20,359 | 24,539 | 7,45E-06 | 7,45E-07 | 2,31E-07 | 16,925 | |
11,056 | 21,128 | 32,071 | 4,90E-06 | 9,73E-07 | 2,31E-07 | 21,418 | |
3,692 | 27,547 | 62,849 | 1,51E-04 | 1,63E-06 | 3,07E-07 | 31,363 | |
8,169 | 22,945 | 73,134 | 7,45E-06 | 9,73E-07 | 2,31E-07 | 34,749 | |
15,573 | 56,376 | 61,329 | 3,63E-06 | 7,45E-07 | 2,31E-07 | 44,426 |
The enrichment of each GO BP term was calculated as a z-score and shown on the circle diagram (
The chosen GO BP terms contain 166 differently expressed genes. Therefore, we calculated the mean fold change ratio value of each gene between 7, 15 and 30 days of culture. Based on that criteria, we chose the 10 most downregulated and 10 most upregulated genes for further analysis.
In Gene Ontology database, genes that form one particular GO can also belong to other GO term categories. For this reason, we explored the gene intersections between the selected GO BP terms. The relation between those GO BP terms was presented as circle plot (
STRING interaction network was generated among the differentially expressed genes belonging to each of the selected GO BP terms. Using such prediction method provided us with a molecular interaction network formed between protein products of the studied genes (
Oviductal epithelial cells are known for their crucial role in providing an optimal environment for gamete survival, supporting fertilization and early embryo development [1]. Therefore, the aim of this study was to gain a better insight into molecular events that influence these cells’ functioning. We utilized a microarray approach to determine differentially expressed genes in primary long-term
The group of ten most downregulated genes includes:
Another downregulated gene,
Another downregulated gene,
Apart from previously described downregulated genes, we have also observed a significant increase in several genes’ expression. In this paper we would like to focus on a group of ten most upregulated ones, which consist of:
In the course of this study we have also observed a significant increase in expression of alpha chains of two types of collagen, namely collagen I and V. Collagens are a group of proteins engaged in maintaining the structural integrity of connective tissue and types I, III and V are usually co-expressed, with their level depending on the cell type [30]. Type I is mostly expressed in skin, bone, tendon and placenta, whereas little is known about the type V presence. Based on our results, as well as previous reports by other investigators, both COL5A2 and COL1A2 interact with each other (
The results obtained in this study indicate that both COL5A2 and COL1A2 interact with FBN1 and MMP2 (
Earlier mentioned FBN1 is also known to interact with another two proteins upregulated in this study, namely GAS6 and TNC, which interact with each other as well (
Another upregulated gene was
The last of the ten most upregulated genes in our study,
Summing up, our current results revealed a set of genes differentially expressed during porcine OECs long term