The growth and development immature female egg cell -called the oocyte – is a complex process involving the varied expression of many proteins, metabolites and environment [1]. Our understanding of the mechanisms surrounding fertilization and
The oocyte sits in the center of the ovarian follicle, surrounded by layers of granulosa cells and then thecal cells [2]. The granulosa cells are split in two populations: cumulus cells (CCs) and mural granulosa cells (GCs). To improve fertility and health of the follicle, interest is increasingly surrounding the bi-directional communication between the oocyte and CCs, a relationship identified as the cumulus-oocyte complex (COC).
The state of the cumulus-oocyte complex directly impacts the health and competence of the oocyte by cell nuclear regulation of meiosis [3]. The bi-directional communication between the gamete and somatic cell populations are conducted through gap junctions made up of connexins, and there is numerous evidence of molecules being exchanged to regulate the other’s activity such as proteins, oocyte-derived paracrine factors or glucose metabolic enzymes [4,5]. Poor fertility is often associated with a deficiency in communication from the CC side. Despite this co-operative relationship, not much is known about the gene expression of the COC.
Micro-array analysis and RT-qPCR can be used to study the transcriptome of the oocyte during its development, and identify molecular markers involved in neuron differentiation and negative regulation of cell differentiation [6]. In addition to building our understanding of the maturation of the ovarian follicle, these possible molecular markers could be used to try and manipulate CCs for their possible stemness qualities in a similar manner as Kossowska-Tomaszczuk’s lab were able to expose
In this study, the protein expression of genes belonging to ‘neuron differentiation’ and ‘negative regulation of cell differentiation’ will be chosen and analyzed
Oocytes were collected and subjected to two Brilliant Cresyl Blue (BCB) tests and divided into two groups. The first group (“before IVM”) included oocytes graded as BCB-positive (BCB+) and directly exposed to microarray assay. The second group (“after IVM”) included BCB+ oocytes which were then
A total of 45 pubertal crossbred Landrace gilts bred on a commercial local farm were used in this study. They had a mean age of 155 days (range 140 – 170 days) and a mean weight of 100 kg (95-120 kg). All animals were bred under the same conditions and fed the same forage (depending on age and reproductive status). All experiments were approved by the Local Ethic Committee (approval no. 32/2012).
The ovaries and reproductive tracts were recovered at slaughter and transported to the laboratory within 40 min. at 38oC in 0.9% NaCl. To provide optimal conditions for subsequent oocyte maturation and fertilization
Brilliant Cresyl Blue (BCB) test was used for assessment of porcine oocytes’ quality and maturity. The glucose-6-phosphate (G6PDH) enzyme converts BCB stain from blue to colorless. In oocytes that completed the growth activity of the enzyme decreases and the stain cannot be reduced, resulting in blue oocytes (BCB+). To perform the BCB staining test, oocytes were washed twice in modified Dulbecco’s Phosphate Buffered Saline (DPBS) commercially supplemented with 0.9 mM calcium, 0.49 mM magnesium, 0.33 mM pyruvate, and 5.5 mM glucose (Sigma-Aldrich, St. Louis, MO, USA), and additionally with 50 IU/ml penicillin, 50 μg/ml streptomycin (Sigma-Aldrich, St. Louis, MO, USA), and 0.4% Bovine Serum Albumin (BSA) [w/v] (Sigma-Aldrich, St. Louis, MO, USA). They were then treated with 13 μM BCB (Sigma-Aldrich, St. Louis, MO) diluted in DPBS at 38.5°C, 5% CO2 for 90 min. After treatment, the oocytes were transferred to DPBS and washed twice. During washing, the oocytes were examined under an inverted microscope and classified as stained blue (BCB
After the first BCB test, the BCB+ COCs were subjected to IVM. The COCs were cultured in Nunclon™Δ 4-well dishes (Thermo Fisher Scientific, Waltham, MA, USA) in 500 μl standard porcine IVM culture medium: TCM-199 (tissue culture medium) with Earle’s salts and
Experiments were performed in three replicates. Total RNA (100 ng) from each pooled sample was subjected to two round 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 Affymetrix® Porcine Gene 1.1 ST Array Strip (48°C/20 h). Then, microarrays were washed and stained according to the technical protocol, using Affymetrix GeneAtlas Fluidics Station. The array strips were scanned employing Imaging Station of GeneAtlas System. The preliminary analysis of the scanned chips was performed using Affymetrix GeneAtlasTM Operating Software. Quality of gene expression data was checked according to quality control criteria provided by the software. Obtained CEL files were imported into downstream data analysis software.
All analyzes were performed using BioConductor software, based on the statistical R programming language. For background correction, normalization and summation of raw data, the Robust Multiarray Averaging (RMA) algorithm implemented in “affy” package of BioConductor was applied. Biological annotation was taken from BioConductor “oligo” package where annotated data frame object was merged with normalized data set, leading to a complete gene data table. Statistical significance of analyzed genes was performed by moderated t-statistics from the empirical Bayes method. Obtained p value was corrected for multiple comparisons using the Benjamini and Hochberg’s false discovery rate. The selection of significantly changed gene expression was based on p value beneath 0.05 and expression fold higher than |2|.
Functional annotation clustering of differentially expressed genes was performed using DAVID (Database for Annotation, Visualization and Integrated Discovery). Gene symbols for up- or down-regulated genes from each of the compared groups were loaded to DAVID by “RDAVIDWebService” BioConductor package. For further analysis we have chosen the enriched GO terms witch has at least 5 genes and p.value (Benjamini) lower than 0.05. The enriched GO terms were subjected to hierarchical clusterization algorithm and presented as a heat maps.
Subsequently we analyzed the relation between the genes belonging to chosen GO terms with GOplot package. The GoPlot 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 estimating the change course of each gene-ontology term.
Interactions between chosen differentially expressed genes/proteins belonging to ontology group were investigated by STRING10 software (Search Tool for the Retrieval of Interacting Genes). List of gene names were used as query for interaction prediction. Searching criteria based on co-occurrences of genes/proteins in scientific texts (text mining), co-expression and experimentally observed interactions. The results of such analysis generated gene/protein interaction network where the intensity of the edges reflects the strength of interaction score. Besides interaction prediction, STRING also allowed us to perform functional enrichments of GO terms based on previously uploaded gene sets.
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 from 30.06.2012.
Whole transcriptome profiling by Affymetrix microarray allowed us to analyze the gene expression changes in freshly isolated oocytes, before
DAVID (Database for Annotation, Visualization and Integrated Discovery) software was used for extraction of the genes belonging to regulation of “neuron differentiation” and “negative regulation of cell differentiation” gene ontology Biological Process term (GO BP). We found that 29 genes from these GO BP term were significantly represented in down-regulated gene sets. This set of genes was subjected to hierarchical clusterization procedure and presented as heat maps (
Set of the differentially expressed genes belonging to “neuron differentiation” and “negative regulation of cell differentiation” GO BP terms with their official gene symbols, Entrez Gene IDs, ratio and corrected p values were shown in
Gene symbols, Entrez gene IDs, ratio and corrected P values of studied genes
GENE SYMBOL | ENTREZ GENE ID | RATIO | P VALUE |
---|---|---|---|
VEGFA | 397157 | -14.34938689 | 0.001912689 |
BTG2 | 100048932 | -13.44331893 | 0.0000955 |
MCOLN3 | 100625693 | -7.203754451 | 0.000850265 |
EGR2 | 100038004 | -6.042156175 | 0.007949861 |
TGFBR3 | 397512 | -5.088482493 | 0.000405979 |
GJA1 | 100518636 | -4.833081147 | 0.000107676 |
FST | 445002 | -4.450446449 | 0.000364693 |
CTNNA2 | 100525337 | -4.349239665 | 0.000512181 |
RTN4 | 100170118 | -4.321937118 | 0.027495815 |
MDGA1 | 397529 | -4.293184001 | 0.003493167 |
SLITRK3 | 106504067 | -4.181617506 | 0.004260951 |
INHBA | 397093 | -4.144909849 | 0.000148036 |
CDK6 | 100518921 | -4.032235086 | 0.006042481 |
LAMB2 | 101101688 | -3.586055237 | 0.000187911 |
ROBO2 | 100517681 | -3.524243185 | 0.001183495 |
CUX1 | 100521258 | -3.342052921 | 0.000372663 |
IHH | 397174 | -3.278733207 | 0.000551261 |
EMX2 | 100152562 | -3.12387706 | 0.001929388 |
ZCCHC11 | 100516979 | -3.109237139 | 0.019809962 |
APP | 397663 | -3.08509997 | 0.005602323 |
WWTR1 | 100522573 | -3.056215175 | 0.000254025 |
SMARCA1 | 100188905 | -3.038211613 | 0.014758847 |
SEMA5A | 100737194 | -2.829721119 | 0.001092396 |
ITGB1 | 397019 | -2.73050204 | 0.003705215 |
SMAD4 | 397142 | -2.71885268 | 0.001238681 |
RORA | 100156637 | -2.60467925 | 0.021553766 |
NOTCH2 | 100153369 | -2.598575668 | 0.002523723 |
RYK | 100523513 | -2.370213023 | 0.00439989 |
KIT | 396810 | -2.323181414 | 0.00255635 |
The enrichment of each GO BP term as well KEGG pathway were calculated as z-score and shown on the circle diagram (
Moreover, in Gene Ontology database genes that formed one particular GO group can also belong to other different GO term categories. By this reason we explore the gene intersections between selected GO BP terms. The relation between those GO BP terms was presented as circle plot (
STRING-generated interaction network was created with differentially expressed genes belonging to the “fatty acid metabolic process” ontology group. The intensity of the edges reflects the strength of interaction score (
The cumulus-oocyte complex (COC) describes the barrier between cumulus cells (CCs) and the oocyte they surround [8]. Numerous hormonal secretions and changes in protein expression occur throughout growth and maturation of the oocyte, which makes up the inner layer of the ovarian follicle. CCs (with the higher metabolic activity) deliver energy and proteins necessary for the oocyte via gap junctions, and as the oocyte matures: gene expression, organelle distribution and the proteins present will be altered [8]. CCs are one of two populations of granulosa cells, the other being mural granulosa cells (GCs) which make up the outer layers of an ovarian follicle [9]. The essential processes for the growth and maturation of the ovarian follicle, called oogenesis and folliculogenesis, are controlled by granulosa cell and theca cell function [10]. GCs and CCs are waste material from IVF and are therefore accessible cells for studies [11].
To better understand how the growth and development of the ovarian follicle, studying the relationship between oocytes and CCs is important. One way to do this is by comparing the change of gene expression in an oocyte model before
In this study, the expression of a group of marker genes in ontology groups ‘neuron differentiation’ and ‘negative regulation of cell differentiation’ were examined before and after IVM of the oocyte to evaluate the cell model’s stemness. After IVM, we found 29 down-regulated genes belonging to our chosen ontology groups, which can be used to suggest the change in oocyte function in this stage of development and how the oocyte interacts with CCs. The 10 most negatively regulated genes were selected and analyzed:
The second most down-regulated gene was found to be
The next most negatively regulated gene that we selected and analyzed is MUCOLIPIN-3 (
The fifth most negatively regulated gene is the first one from the ‘negative regulation of cell differentiation’ ontology group, and is
Nogo proteins are encoded by
Our final gene is the ten most downregulated genes after IVM is
The least downregulated genes from our chosen GO BP terms were
Interestingly, most of the higher regulated genes are part of our ‘neuron differentiation’ GO BP terms group. These genes are
The genes belonging to our ‘neuron differentiation’ ontology group term have relatively little knowledge on how they affect processes concerning the oocyte, and even less on its interaction with cumulus cells. Therefore, more research should be done on these noted genes as insight could be given from understanding the COC microenvironment. With these results, our analyzed genes could perhaps be used for the purpose oocyte maturation biomarkers.
To summarize, we have identified the downregulation of various genes after the transition from