Due to their utmost importance, the undisturbed processes of cell life are subject to continuous genetic control, including segregation of material, correctness of subsequent events and cell division. The cell cycle is therefore an extremely complex process, with its mechanisms focused on biochemical and morphological changes of replicating cells [1]. In contrast, the permanent retention of the vital functions of cells causes their irreversible death. It can take place due to accidental damage or a physiological mechanism called apoptosis [2].
The processes associated with the femalereproductive system are very complicateddue to a vastness of biochemical changes. Therefore, they have not yet been fully understood. Proper functioning of the reproductive systems requires many hormonal changes in the body, especially the effective feedback of the hypothalamus, pituitary gland and gonads [3]. The transport of the oocyte through the fallopian tube and the interaction between the embryo and maternal tissues are of great importance in the context of effective fertilization. Morphological and biochemical changes in the oviductal cells are mainlycaused by processes occurring after ovulation, related to the influence of both oocyte and embryo on oviductal cells [4]. General changes occurring at this stage are relatively well described, but the changes on the molecular ground are not fully known. Therefore, in our study, we described the changes in expressionof genes associated with “cell cycle” and “cell death”gene ontology processes.
Recent studies indicate the possibility of short-and long-term
Current studies conducted by our team (not published data) indicate the possibility of effective
The animals in our study- crossbred gilts (n=45) at the age of around 9 months, came from a commercial breeding herd. The selected individuals expressed two regular estrus cycles. 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 surgically removed using sterile blades. Then, the epithelium was incubated with collagenase I (Sigma Aldrich, Madison, USA), 1mg/mL inDulbecco’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 cell samples 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, cells where 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 37oC in a humidified atmosphere of 5% CO2. Once the OEC cultures attained 70–80% confluency, they were passaged by washing with PBS, digestion with 0.025% Trypsin/EDTA, neutralization by a 0.0125% trypsin inhibitor (Cascade Biologics, Portland, USA), centrifugation, and resuspension at a seeding density of 2x104 cells/cm2. The culture medium was changed every three days. The culture lasted 30 days.
Oviductal epithelial cells from specific time periods were pooled into three independent samples for each experimental group. Total RNA was extracted from samples using TRI Reagent (Sigma, St Louis, MO, USA) and RNeasy MinElute cleanup Kit (Qiagen, Hilden, Germany). The amount of total mRNA 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, 500 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 by Affymetrix GeneChip® WT Terminal Labeling and Hybridization (Affymetrix). 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 with the use of 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 performed using Bioconductor and R programming languages. Each CEL file was merged with a description file. To correct background, normalize, and summarize the results, we used the Robust Multi-array 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. 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 their involvement in the gene ontologies of interest. The differentially expressed gene lists (separate for up- and down-regulated genes) were uploaded to the DAVID software (Database for Annotation, Visualization and Integrated Discovery) [6], where genes belonging to “cell cycle” and “cell death” GO BP terms were extracted. Expression data of these genes was also subjected to a hierarchical clusterization procedure, with results presented as a heat map.
Subsequently, we analyzed the relation between the genes belonging to chosen GO terms with theGO-plot package [7]. The GoPlot package 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 estimating the change course of each gene-ontology term.
Interactions between differentially expressed genes/proteins belonging to the studied gene ontology group were investigated using the STRING10 software (Search Tool for the Retrieval of Interacting Genes) [8]. 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 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 belonging to the chosen GO BP terms were investigated using the 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 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 the 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. 83/2012/DNT.
Whole transcriptome profiling using Affymetrix microarrays allowedus to analyze the gene expression changes between 7, 15 and 30 days of porcine oviductal epithelial cells culture. Using the Affymetrix® Porcine Gene 1.1 ST Array Strip, we examined the expression of 12257 transcripts. Genes with fold change higher than abs (2) and with a 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 contains differently expressed transcripts. Up and down regulated gene sets were subjected to the DAVID search separately,with only the sets of adj. p value lower than 0.05 selected. The DAVID software analysis showed that the differently expressed genes belonged to 657 Gene ontology terms. In this paper, we focused on 133 genes (61 downregulated and 72 upregulated) belonging to “cell cycle” and “cell death” GO BP terms.
These sets of genes were subjected to hierarchical clusterization procedure and presented as heat-maps (
Gene symbols, fold changes in expression, Entrez gene IDs and corrected p values of studied genes
GENE SYMBOL | FOLD. CHANGE D15/D7 | FOLD. CHANGE D30/D7 | FOLD. CHANGE D30/D15 | ADJUSTED P.VALUE D15/D7 | ADJUSTED P.VALUE D30/D7 | ADJUSTED P.VALUE D30/D15 | GENEID |
---|---|---|---|---|---|---|---|
SERPINB2 | 0,041199 | 0,012677 | 0,008239 | 4,60E-06 | 7,45E-07 | 2,31E-07 | 1,01E+08 |
CD274 | 0,04424 | 0,04443 | 0,041834 | 2,50E-06 | 7,45E-07 | 2,31E-07 | 574058 |
TXNIP | 0,082179 | 0,64453 | 0,63523 | 1,11E-05 | 0,024019 | 0,017645 | 733688 |
IL24 | 0,083371 | 0,062192 | 0,071399 | 9,50E-06 | 3,21E-06 | 1,76E-06 | 1,01E+08 |
HSH2D | 0,123575 | 0,083464 | 0,094409 | 2,25E-05 | 5,06E-06 | 3,79E-06 | 1,01E+08 |
IDO1 | 0,134544 | 0,12111 | 0,123331 | 7,22E-05 | 3,53E-05 | 2,36E-05 | 1,01E+08 |
MX1 | 0,147313 | 0,355975 | 0,489162 | 1,87E-05 | 0,000197 | 0,00096 | 397128 |
IFIT3 | 0,172101 | 0,195012 | 0,31317 | 1,87E-05 | 1,28E-05 | 4,60E-05 | 1E+08 |
SNAI2 | 0,183766 | 0,134521 | 0,130079 | 0,000411 | 0,000129 | 7,85E-05 | 641345 |
XAF1 | 0,196155 | 0,456355 | 0,66362 | 0,000162 | 0,004319 | 0,06338 | --- |
PTTG1 | 5,573407 | 4,839927 | 0,818891 | 5,27E-05 | 4,98E-05 | 0,269018 | 397015 |
CDKN2B | 5,906076 | 5,279248 | 5,031711 | 6,22E-06 | 3,39E-06 | 1,81E-06 | 397227 |
KIF20A | 6,182894 | 6,001182 | 0,588195 | 0,000319 | 0,000249 | 0,063892 | 1,01E+08 |
KIF23 | 6,49686 | 7,105965 | 7,104752 | 0,000647 | 0,000382 | 0,000271 | 1,01E+08 |
TTK | 6,595833 | 6,064142 | 0,629174 | 0,000194 | 0,000163 | 0,078507 | --- |
DLGAP5 | 7,403844 | 6,551599 | 0,699776 | 9,17E-05 | 8,22E-05 | 0,131054 | 1,01E+08 |
GAS2L3 | 7,739168 | 7,035903 | 1,559417 | 8,27E-05 | 6,71E-05 | 0,064706 | 1E+08 |
UBE2C | 8,523298 | 4,717957 | 0,372658 | 0,000914 | 0,00366 | 0,025648 | 1E+08 |
HHEX | 9,447447 | 11,82395 | 11,30739 | 2,69E-05 | 9,59E-06 | 6,25E-06 | 397232 |
CENPF | 9,506242 | 6,960497 | 0,773239 | 0,000484 | 0,000742 | 0,52224 | 1,01E+08 |
The enrichment of each GO BP term was calculated as a z-score and shown on the circle diagram (
From the differently expressed genes belonging to the studied GO BP terms, we chose 10 most downregulated and 10 most upregulated genes for further analysis. In Gene Ontology database, genes that formed one particular GO group can also belong to other different GO term categories. For this reason, we explored the gene intersections between selected GO BP terms. The relation between those GO BP terms was presented as a circle plot (
STRING interaction network was generated among 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 studied genes (
The complicated cellular processes that make up the cell cycle are focused on the progress of biochemical and morphological changes during subsequent replications. Genetic control at the level of material segregation and correctness of subsequent events, including cell division, is essential for the life of the cell and the whole organism [1]. In contrast, the cell death process is defined as the permanent cessation of all cell functions, e.g. during the loss of cell membrane integrity or the complete fragmentation of a cell including its nuclei. The cell death can be either accidental or programmed [2].
Thanks to our research on
During the in vitro culture (IVC) of OECs, we analyzed their transcriptomic profile at different time intervals: 1, 7, 15 and 30 days of cultivation. For both ontological groups, we compiled heat maps, which illustrate the distribution of expression in particular days of cultivation. As can be seen in
Within the “cell death” group, we distinguished genes typically associated with apoptotic processes (4 genes), immune response (2 genes), antiviral effect (2 genes) and metabolic mechanisms (2 genes).
The first gene, associated with apoptotic processes, is
The next two genes are related to apoptotic processes but also contribute to cell metabolism.
Another gene from the cell death group, exhibiting antiviral properties, is
The genes associated with molecular mechanisms of cells in our study are
The genes that make up the “cell cycle” group in our research showed stronger or weaker links with mitosis, meiosis or intracellular organization processes.
The first,
The
The next gene from this GO is
Another gene belonging to this group is
Thenext gene,
The network of STRING generated interactions (
Thus, our research shows groups of genes that participate in opposing cellular processes. Some of them were closely related to the reproductive system, while others were responsible for normal physiological changes of various tissues. Significant changes in the expression of the selected genes may therefore indicate their potential as markers of physiologychanges in the