ABSTRACT
Women are born with millions of primordial follicles which gradually decrease with increasing age and this irreversible supply of follicles completely exhausts at menopause. The fertility capacity of women diminishes in parallel with aging. The mechanisms for reproductive aging are not fully understood. We have observed a decline in Brca1 mediated DNA repair in aging rat primordial follicles. To further understand the age-related molecular changes, we performed microarray gene expression analysis using total RNA extracted from immature (18 to 20 day old) and aged (400 to 450 day old) rat primordial follicles. The results of current microarray study revealed that there were 1,011 (>1.5 fold, p<0.05) genes differentially expressed between two groups in which 422 genes were up-regulated and 589 genes were down-regulated in aged rat primordial follicles compared to immature primordial follicles. The gene ontology and pathway analysis of differentially expressed genes revealed a critical biological function such as cell cycle, oocyte meiosis, chromosomal stability, transcriptional activity, DNA replication, and DNA repair were affected by age. This considerable difference in gene expression profiles may have an adverse influence on oocyte quality. Our data provide information on the processes that may contribute to aging and age-related decline in fertility.
Introduction
The oocytes containing primordial follicles present at birth comprise the ovarian reserve [Pelosi et al. Citation2015]. The pool of these oocytes is considered to be the source for ovulation during the entire reproductive life of the mammalian female [Miao et al. Citation2009; Ottolenghi et al. Citation2004]. Females have ~500-600 thousand primordial follicles at birth [Hansen et al. Citation2008; Knowlton et al. Citation2014] and no new primordial follicles form after birth. The number of oocytes decreases gradually with increasing age through atresia with only a few thousand oocytes remaining during puberty and by menopause the number is reduced to a few hundred [Broekmans et al. Citation2009; Faddy et al. Citation1992; Govindaraj and Rao Citation2016]. During the entire female reproductive life span, only around 300-400 oocytes in human undergo ovulation [Faddy and Gosden Citation1996; Hansen et al. Citation2008]. Therefore, the follicular depletion has been accepted as a major cause of ovarian aging [Grive and Freiman Citation2015; Tiwari et al. Citation2015]. Other than the fixed number of oocytes in the adult ovaries, the existence of female germ line or oogonical stem cells (OSC) in the adult ovaries of mice, rats, and human have been reported [Grieve et al. Citation2015; White et al. Citation2012]. Although the conflicting results on isolation and confirmation of OSCs [Navaroli et al. Citation2016; Zarate-Garcia et al. Citation2016] and contrary observations on the source of these cells (native to ovary or migrated from other tissues) [Dunlop et al. Citation2013; Johnson et al. Citation2005], currently, there is no data available on the possible role of OSCs in normal physiological function of ovary including fertility. It was argued that even if OSCs can support regeneration of oocytes, his may be restricted to some extent, because the age-related decrease in follicle number is not disputed and adult neo-oogenesis may be considered insignificant due to the large number of fixed oocytes [Zarate-Garcia et al. Citation2016]. Studies have suggested that the physiological aging processes (accumulation of reactive oxygen species (ROS) and free radicals) and the action of environmental factors such as radiation and chemotherapeutic drugs in cancer patients cause DNA damage in the oocytes during long periods of dictyate arrest and if it is not repaired, the extend of DNA damage may cause genomic abnormalities (chromosomal breakages and mutations) leading to cell death [Collins and Jones Citation2016; Meldrum et al. Citation2016]. Studies from human oocytes have documented that there is an increase in aneuploidy with increasing age [MacLennan et al. Citation2015; Tatone et al. Citation2008]. Although ovarian aging is associated with a decline in the oocyte pool and an increase in aneuploidy, the underlying mechanisms remain mostly unknown. Due to the ethical issues involved in the use of human oocytes and embryos, various mammalian species are used to understand the process of age-related reproductive decline in women. Recent laboratory and clinical studies have demonstrated that BRCA1 (breast cancer type 1) related DNA repair is affected in ovarian aging [Oktay et al. Citation2010; Rzepka-Gorska et al. Citation2006; Titus et al. Citation2013]. In support of this, we recently observed a similar decrease in Brca1 expression in aged rat and buffalo primordial follicles compared to young animals [Govindaraj and Rao Citation2016; Govindaraj et al. Citation2015]. In continuation of our previous work using rat primordial follicles, the present study attempts to investigate the molecular changes during primordial follicle aging by genome-wide microarray analysis using primordial follicles from young and aged rats.
Results
Global gene expression analysis of aging rat primordial follicles
To characterize the genes that are associated with rat primordial follicle aging, we examined the gene expression profiles of immature and aged primordial follicles. The expression values of all the six samples (three samples each from immature and aged primordial follicles) were normalized using quantile and baseline to median. The results of our microarray data were made available in the public domain NCBI-GEO repository (accession ID: GSE84333). The box-whisker plot analysis of normalized data showed uniform distribution of the expression levels in both intra and inter sample manner indicating reliable hybridization (). Summary statistics showed effectiveness of quantile normalization as 50th percentile values were close to zero (0). Principal component analysis (PCA) to visualize the groups showed a high degree of reproducibility between the replicate samples within each group (). Further, unsupervised hierarchical condition tree analysis showed that all replicate samples clustered together indicating its good reproducibility (). After normalization of raw data for all three biological replicates, the volcano plot based method to identify genes that are 1.5 fold differentially expressed in aged rat primordial follicles compared to immature by using unpaired Student t-test (p<0.05) and followed by Benjamini–Hochberg based FDR (false discovery rate) test revealed that 1,011 genes were differentially expressed between the two samples, while 422 genes were up-regulated and 589 genes were down-regulated when aged primordial follicles were compared to immature. The down regulation was prominently observed in both groups (). Further, unsupervised hierarchical clustering analysis using the Pearson un-centred algorithm with average linkage rule showed distinct patterns of up- and down-regulated genes ().
Figure 1. Microarray data normalization and quality assessment. (A) Box and whisker plot (Box plot). We performed the comparison of gene expression with a total of six samples from immature (n=3) and aged (n=3) rat primordial follicles by using Illumina’s MouseWG-6 V2.0 array (45,281 genes) according to the manufacturer’s instructions. Quantile normalization method was used to eliminate the variation in the arrays from noisy data. Box plot was constructed to illustrate the distribution of normalized probe hybridization signal intensities (log ratios) for all six arrays (immature and aged rat primordial follicle). The probe distribution by 0-100% quantiles as whiskers, the 25-75% quantiles as boxes and the 50% quantile as horizontal line within the box indicated a similar range of signal intensities and confirmed perfect hybridization. (B) Principal component analysis (PCA) plot. Each data point represents one array and each array is colored differently in the online version (Blue: immature; Red: aged). The PCA analysis of all expressed genes showed clear separation of samples between immature and aged rat primordial follicles and exhibited a high degree of reproducibility among the replicate samples within each group.
Figure 2. Microarray data visualization. (A) Heat map with unsupervised hierarchical clustering condition tree. The heat map shows the expression of 45,281 mouse genes at the probe level for each sample and the cluster analysis of mRNA gene expression data separates immature and aged primordial follicles condition tree under the same branch indicate its reproducibility. (B) The fold change and statistical significance of differentially expressed genes by Volcano Scatter Plot. Volcano plot depicts mean fold change on x-axis and statistical significance (-log10 (p-value)) on the y-axis for differentially expressed genes. The top side of the plot exhibits genes that show statistical significance and high fold changes. There were 1,011 differentially expressed genes in immature versus aged rat primordial follicles, where 422 up-regulated and 589 down-regulated genes in aged rat primordial follicles at fold change of ≥ 1.5 and p value <0.05.
Figure 3. Heat map visualization for differentially expressed genes. Heat map was produced by unsupervised hierarchical-clustering analysis from microarray data for all 1,011 differentially expressed genes (422 up-regulated and 589 down-regulated genes) between immature and aged rat primordial follicles with a p value of less than 0.05 and fold change with minimum of 1.5 fold cut off. The relative expression levels of each transcript are mentioned in different colours. The darker (red) lines represent up-expression, while lighter (green) lines represents down-regulation.
Functional annotation of differentially expressed genes
The differentially expressed transcripts with at least 1.5 fold change in immature and aged rat primordial follicles was further analyzed for gene ontology (GO) and functional analysis using the DAVID online tool. As shown in and Supplementary , the results of this analysis indicated that the molecular processes (cell cycle, microtubule-based process, mitosis, reproduction, cell division, chromosome organization involved in meiosis, histone phosphorylation, and cellular component biogenesis), cellular components (nucleus, cytoskeleton, chromosome, ribo-nucleoprotein complex, microtubule, centromeric region, and condensed chromosome), molecular function (nucleotide binding, RNA binding, structural constituent of ribosome, transcription factor binding activity, chromatin binding, and translation regulator activity), and pathways (cell cycle, homologous recombination, meiosis, ribosome, DNA replication, and MAPK signaling pathway) appeared affected in aging rat primordial follicles compared to immature.
Table 1. List of primers used in the study.
Figure 4. Gene classification by PANTHER (Protein ANalysis THrough Evolutionary Relationships) analysis for differentially expressed genes in aged rat primordial follicles. The bar graph shows the number of genes and their key biological function, categories, and pathways that were affected in aged primordial follicles. The molecular processes (cell cycle, microtubule-based process, mitosis, and reproduction cell division), cellular components (nucleus, cytoskeleton, and chromosome), molecular function (structural constituent of ribosome, transcription factor binding activity, chromatin binding, and translation regulator activity), and pathways (cell cycle, homologous recombination, meiosis, DNA replication, and MAPK signaling pathway) were found to be affected in aging rat primordial follicles compared to immature.
Validation of microarray analysis by RT-PCR
The following twelve transcripts FIGN (fidgetin), CASP1(caspase1), CENP1 (CENPB DNA-binding domain-containing protein 1), SALL4 (Spalt like transcription factor 4), NUPR1 (nuclear protein 1, transcriptional regulator), REC8 (REC8 meiotic recombination protein), SLC4A1 (solute carrier family 4 member 1), GBX1 (gastrulation brain homeobox 1), PTX3 (pentraxin 3), TUBAL3 (tubulin alpha like 3), KLF5 (Kruppel like factor 5), and GRIN2B (glutamate ionotropic receptor NMDA type subunit 2B) were selected to validate the results of microarray platform data by semi-quantitative RT-PCR. These transcripts were selected from differentially expressed genes to represent different biological processes such as cell cycle, DNA repair, chromatin stability, transcriptional activity, replication, chromosome segregation, and apoptosis. The gene regulatory model, heat-map, and fold change for these 12 transcripts are presented in and . As shown in , the down-regulation of FIGN, CENTP1, SALL4, NUPR1, REC8, SLC4A1, GBX1, PTX3, TUBAL3, and KLF5 and up-regulation of Grin2b and Casp1 in aged primordial follicle by RT-PCR analysis confirms the accuracy of microarray expression data.
Figure 5. Gene regulatory network modeling for selected differentially expressed genes by using Cytoscape software (version 2.8). Gene regulatory network modeling of selected genes which were differentially expressed between immature and aged rat primordial follicles such as FIGN (fidgetin), CASP1 (caspase1), CENP1 (CENPB DNA-binding domain-containing protein 1), SALL4 (spalt like transcription factor 4), NUPR1 (nuclear protein 1, transcriptional regulator), REC8 (REC8 meiotic recombination protein), SLC4A1 (solute carrier family 4 member 1), GBX1 (gastrulation brain homeobox 1), PTX3 (pentraxin 3), TUBAL3 (tubulin alpha like 3), KLF5 (Kruppel like factor 5), and GRIN2B (glutamate ionotropic receptor NMDA type subunit 2B) showed their genetic interactions by various pathways. Circles indicate genes, red color indicates up-regulation and green color indicates down-regulation.
Figure 6. Heat map visualization and fold change for selected differentially expressed genes. (A) Heat map with hierarchical-clustering for selected differentially expressed genes in aged rat primordial follicles compared to immature. A heat map with hierarchical clustering for selected genes between immature and aged rat primordial follicles for FIGN (fidgetin), CASP1 (caspase1), CENP1 (CENPB DNA-binding domain-containing protein 1), SALL4 (spalt like transcription factor 4), NUPR1 (nuclear protein 1, transcriptional regulator), REC8 (REC8 meiotic recombination protein), SLC4A1 (solute carrier family 4 member 1), GBX1 (gastrulation brain homeobox 1), PTX3 (pentraxin 3), TUBAL3 (tubulin alpha like 3), KLF5 (Kruppel like factor 5), and GRIN2B (glutamate ionotropic receptor NMDA type subunit 2B). Dark (red) lines indicates up-expression, while light (green) lines indicate down-regulation in fold change relative to immature. (B) Fold change of selected differentially expressed genes in aged rat primordial follicles. Fold change of 12 selected differentially expressed genes with a fold change >1.5 and a p-value <0.05.
Figure 7. Validation of microarray data using semi-quantitative reverse transcription polymerase chain reaction (RT-PCR) analysis. (A) RT-PCR analysis was performed to confirm altered mRNA expression levels of FIGN (fidgetin), CASP1 (caspase1), CENP1 (CENPB DNA-binding domain-containing protein 1), SALL4 (spalt like transcription factor 4), NUPR1 (nuclear protein 1, transcriptional regulator), REC8 (REC8 meiotic recombination protein), SLC4A1 (solute carrier family 4 member 1), GBX1 (gastrulation brain homeobox 1), PTX3 (pentraxin 3), TUBAL3 (tubulin alpha like 3), KLF5 (Kruppel like factor 5), and GRIN2B (glutamate ionotropic receptor NMDA type subunit 2B) in immature and aged rat primordial follicles. The representative image of gel electrophoresis run with RT-PCR products from three independent experiments is presented. (B) The densitometric analysis of gel bands are presented in graphical form. Data from three independent experiments were presented as arbitrary densitometric units (mean ± SEM, *p<0.05 compared with immature). GAPDH served as an internal control and was used for the normalization of each gene.
Discussion
In our present study an attempt was made to compare the gene expression profiles of immature and aged rat primordial follicles by microarray analysis. The results of our study revealed more than 1,000 genes which were differentially expressed between immature and aged rat primordial follicles. The genes related to cell cycle, oocyte meiosis, transcription, chromatin stability, chromosome segregation, and DNA repair were affected in aged rat primordial follicles compared to immature. To our knowledge, ours is the first study that compared the gene expression patterns of primordial follicles in rats as a function of age. The study would have benefited had the mRNA transcripts been identified in parallel with studying the corresponding proteins to begin to resolve transcriptional and translational control. The results of our present study are in agreement with several earlier studies in other species. For example, in mice the decline in the expression of genes related to mitochondrial function, oxidative stress, genome stability, and chromatin structure in aged oocytes was similarly observed [Hamatani et al. Citation2004]. In bovine (Japanese Black cattle), a significant difference was found in signal transduction and transcriptional control related genes between pre-pubertal and adult bovine oocytes during in-vitro maturation [Dorji et al. Citation2012]. In cows, the genes that are important to oxidative phosphorylation and mitochondrial function were affected in metaphase II oocytes of aged animals [Takeo et al. Citation2013]. In the human, the gene expression microarray analysis of young and old metaphase II oocytes donated by patients undergoing IVF or controlled ovarian stimulation revealed significant changes in the genes related to spindle checkpoint regulation, DNA stability, DNA repair, and response to DNA damage and chromosome segregation were significantly affected by age [Grondahl et al. Citation2010].
Mitochondrial dysfunction has been associated with fertility decline, aging, and overall reproductive pathology [Benkhalifa et al. Citation2014]. It is often observed that the cytoplasmic levels of adenosine tri-phosphate (ATP) decreased with increasing age in the oocytes [Igarashi et al. Citation2005; Simsek-Duran et al. Citation2013]. The microtubule and other cytoskeleton proteins which are known to be involved in cellular functions like cell division and chromosomal segregation are highly dependent on energy from ATP hydrolysis [Alberts et al. Citation2002]. Failure of chromosome segregation and increase of aneuploidy is often seen in aged oocytes since these cellular functions are ATP dependent [Simsek-Duran et al. Citation2013]. In the present study, one of the down-regulated genes in aged rat primordial follicles was FIGN. FIGN is an ATP-dependent microtubule factor involved in chromosomal segregation and cell division,promoting rapid reassembly and nucleation of microtubules from the centrosome [Mukherjee et al. Citation2012]. The decrease in FIGN indicates that aging rat primordial follicles is accompanied by mitochondrial dysfunction and reduced ATP. The mutation in FIGN has also been shown to produce developmental defects like cell-cycle delay and reduced growth of the retinal neural epithelium [Konyukhov and Sazhina Citation1976; Mukherjee et al. Citation2012]. In our recent proteomic study the mice orthologue of FIGN called FIGNL1 (Fidgetin like protein 1) was down-regulated in aged rat primordial follicles compared to immature follicles [Govindaraj and Rao Citation2015]. FIGNL1 is known to be involved in DNA double-strand break (DBS) repair via homologous recombination (HR) and assembled at DSB sites BRCA2 and RAD51 paralogs in a H2AX-dependent manner [Yuan and Chen Citation2013]. Additionally, FIGNL-1 has been implicated in impairment of meiosis leading to reduced testis weight in mice [L’Hôte et al. Citation2011].
Another down-regulated gene, REC8 (REC8 meiotic recombination protein) is also a key component of the meiotic cohesion complex which controls chromosomal segregation [Watanabe and Nurse Citation1999]. Studies have demonstrated that genes involved in spindle formation and biogenesis are affected as a function of age [Battaglia et al. Citation1996]. The decrease of meiosis-specific cohesion subunits such as REC8 and SMC1B has been observed in aging mice oocytes [Tachibana-Konwalski et al. Citation2010]. It has been shown that the cohesion complex of proteins including REC8 do not undergo turnover after birth [Tsutsumi et al. Citation2014]. It is well established in humans that the age-related decline in oocyte quality has been associated with increased aneuploidy [Fu et al. Citation2014]. Similarly, in rat primordial follicles, it is possible that the gradual decrease in the cohesion levels as seen by decreased REC8 may parallel an increased risk of age related chromosomal segregation error. In the present study, TUBAL3 (tubulin, alpha like 3) is another down-regulated gene found in aged rat primordial follicles and may have a similar function as alpha tubulin, a major component of microtubule. In addition to this, down-regulation of CENP1 (centromere protein 1) was observed in aged rat primordial follicles. The protein product of CENP1 is required for the attachment of microtubules to chromosomes [Fortuno and Labarta Citation2014].
Several transcription factors involved in developmental processes are known to be affected in the oocytes by maternal age [Steuerwald et al. Citation2007]. Similarly, SALL4 (spalt-like transcription factor 4- member of SPALT gene family) was down-regulated in aged primordial follicles. The expression of SALL4 was found only in testis and ovary and this gene has been implicated mainly in organogenesis during the process of embryonic development. [Kohlhase et al. Citation2002; Miettinen et al. Citation2014]. It is very important to note that a SALL4 mutation has been associated in Han Chinese women with premature ovarian failure (POF) [Wang et al. Citation2009]. Interestingly, Sall4 was found to be drastically down-regulated in the ovaries of Nobox (newborn ovary homeobox gene) deficient mice [Choi et al. Citation2007]. NOBOX is an oocyte-specific homeobox gene and loss in NOBOX leads to rapid loss of postnatal oocytes and lack of NOBOX affects global expression of genes mostly expressed in oocytes and this has been considered as an important activator of oocyte-specific gene expression [Choi et al. Citation2007; Rajkovic et al. Citation2004]. Another transcription factor down-regulated in the present study was NUPR1 (nuclear protein-1) which is identified as chromatin protein and transcriptional activator [Mallo et al. Citation1997]. NUPR1 is known to act as either inducer or suppressor of tumor growth [Chowdhury et al. Citation2009; Grasso et al. Citation2015]. This gene acts as a chromatin remodeling protein and may modulate tumorigenesis via the regulation of cell cycle progression and various processes including apoptosis. The loss of Nupr1 in the ovaries showed delayed sexual maturation and mice lacking a corpora lutea [Million Passe et al. Citation2008]. These studies indicate that NUPR1 may play an important role not only in cell cycle and apoptosis but also in overall ovarian function. Other down-regulated transcription factors in aged primordial follicles were PTX3 and GBX2. PTX3 (pentraxin-related protein 3) has been shown to be involved in the formation of the extracellular matrix of cumulus cells and in successful in vivo fertilization. Also it has been reported that PTX3 is known to interact with oocyte-specific growth factor called GDF9 (growth differentiation factor-9) [Varani et al. Citation2002] and PTX3 (-/-) female mice are infertile [Salustri et al. Citation2004]. Fgf8 is an oocyte-specific growth factor and Fgf8 knockout mice were sub-fertile with a reduced litter size and abnormalities in estrous cycle, folliculogenesis, and ovulation were found in the ovary [Lan et al. Citation2008]. Moreover, FGF8 acts together with BMP15 (bone morphogenetic protein 15) to activate glycolysis in cumulus cells which is important for oocyte survival [Sugiura et al. Citation2009]. Fgf8 has been shown to interact with gastrulation brain homeobox 2 (Gbx2) [Joyner et al. Citation2000; Nakamura Citation2005] which is also one of the down-regulated genes in aged rat primordial follicles in the present study. The role of Gbx2 in oocyte or ovary is largely unknown.
An age-related increase in apoptosis genes such as Bcl2a1b, Casp1, and Casp11 has been reported in the ovary [Sharov et al. Citation2008]. In the present study Casp1 was up-regulated in aged rat primordial follicles compared to immature. Another up-regulated gene in aged rat primordial follicle in the present study was Grin2b also known as N-methyl-D-aspartate (Nmda) receptor 2B. This receptor is a class of ionotropic glutamate receptors expressed in brain and oocytes. NMDA receptor channel has been shown to be involved in long-term potentiation (LTP) i.e., constant increase in synaptic efficiency that is important for memory, learning, and reproductive behavior [Maffucci et al. Citation2009]. Studies have also shown that NMDA receptor activation leads to apoptotic cell death. The increase in CASP1 and GRIN2B indicate that the aging rat primordial oocytes are more prone to apoptosis.
We conclude that our data show considerable differences in gene expression patterns between immature and aged rat primordial follicles. The affected genes are central to biological functions of the oocytes such as cell cycle, oocyte meiosis, chromatin stability, chromosome segregation, spindle formation, DNA repair, transcription, and apoptosis. Our knowledge of altered genes and related transcriptional networks may be helpful for understanding the mechanism for oocyte/ovarian aging in rats.
Materials and methods
Isolation of rat primordial follicles
The isolation of rat primordial follicles was carried out as described previously [Govindaraj and Rao Citation2015; Govindaraj et al. Citation2015]. The experimental animals were maintained as per the guidelines of the Institutional Animal Ethics Committee (IAEC) with approval (IAEC approval number: CAF/Ethics/270/2012) at the Central Animal Facility at the Indian Institute of Science, Bangalore, India. The immature (~18 to 20 d old) and aged (~400 to 450 d old) female Wistar rats were sacrificed by CO2 euthanasia and ovaries were collected free of adhering fat and washed with phosphate-buffered saline (PBS). The ovaries were minced gently and transferred to a digestion medium containing Dulbecco’s Modified Eagle’s Medium (DMEM) (Catalogue number- D0422, Sigma-Aldrich, St. Louis, MO, USA) with fetal bovine serum as a supplement (Biological Industries, Catalogue number 04127-1A and 2% collagenase type IV (Calbiochem, Cat No. 234153). The suspension was incubated at 37°C for 30 min in a shaking water bath, and the follicular digest was filtered through a 70 µm nylon cell strainer (SPL Life Science, Korea). The follicular filtrate was centrifuged at 2,300×g for 5 min at 4°C and the obtained pellet was re-suspended in fresh 5 ml of PBS, and filtered through a 40 µm nylon cell strainer followed by centrifugation at 2,300×g for 5 min at 4°C. The pellet was finally resuspended in about 400–600 µl DMEM supplemented with 4% fetal calf serum and the suspension was checked for presence of the primordial follicles (oocytes surrounded by one layer of granulosa cells) under an inverted phase-contrast microscope.
RNA isolation and microarray analysis
The equal quantity of total RNA extracted from immature and aged rat primordial follicles was measured by Qubit® Fluorometer. Of the total of 6 samples, 3 replicate samples were from immature rat primordial follicles and 3 replicate samples were from aged rat primordial follicles. For primordial follicle isolation, 10-20 ovaries (5-10 rats) from aged rats and 20 ovaries (10 rats) from immature rats were used in each isolation procedure. Due to the low yield of RNA/protein, the isolated primordial follicles in each isolation were immediately stored in Trizol buffer and the primordial follicles from 4-5 isolations were pooled in order to get the required quantity of RNA. The quality of RNA was analyzed by using Agilent RNA 6000 Nano Kit and Agilent 2100 electrophoresis Bio-analyzer according to the manufacturer’s instructions by subjecting 1µl of the diluted RNA on to a Nano chip. The quality of total RNA was obtained as electropherogram profile and RNA integrity number (RIN) was obtained by the Bioanalyzer software. The samples with a RIN ≥ 8 were included in the study. For each biological replicate 50 ng of high quality total RNA was used to synthesize biotinylated cRNA using Illumina TotalPrep RNA Amplification kit (Ambion, UK) and hybridized on Illumina’s MouseWG-6 V2.0 array (45,281 genes) according to the manufacturer’s instructions. In this study, we used the strategy of cross-species hybridization by hybridizing the rat RNA with mouse cDNA microarrays to increase potential gene coverage. The use of mouse arrays with rat RNA has been validated and shown to have more than 90% correlation between these two species [Bar-Or et al. Citation2007; Farina et al. Citation1998; Wang et al. Citation2002]. The microarray experiments were performed at Sandor Lifesciences, Pvt Ltd, Hyderabad, India.
Microarray data analysis
The raw data from microarray analysis was quantile normalized and processed with GeneSpring GX 12.0.2 software (Agilent as described previously [Mruthyunjaya et al. Citation2015]. The differentially expressed genes with a fold change ≥ 1.5 and a p-value ≤ 0.05 were identified using student t-test and visualized using volcano plot. For visualization of differentially expressed genes, unsupervised hierarchical clustering was performed with the Pearson correlation and average linkage algorithm. GO and pathway analysis was performed using DAVID online tool to identify overrepresented GO-classes compared with the human whole genome, that is, molecular function, biological process, and cellular component. Whole Human Genome was used as the reference group. Statistical significance was calculated with a standard hypergeometric equation corrected by a Benjamini Yekutelli correction for multiple testing, which takes into account the dependency among the GO categories. The minimal length of considered GO-paths was 2. Significance was set at corrected p-value < 0.05.
Semi-quantitative reverse transcription-polymerase chain reaction
The RT-PCR analysis was performed as described previously. Briefly, the equal quantity of total RNA extracted from immature and aged rat primordial follicles was reverse transcribed to complementary DNA (cDNA), and PCR amplification for selected genes was carried out with 1µL of cDNA and 20 µM of gene specific forward and reverse primers (Sigma Genosys, Sigma Aldrich, India) in a 50 µL reaction using ReverseAid First Strand cDNA synthesis kit (Catalogue number 1621) and 2X PCR Master Mix (Catalogue number K0172) obtained from Thermo Scientific, USA. The sequence of oligonucleotide primers and the annealing temperature used for PCR analysis are given in . The amplified PCR product was subjected to 1% agarose gel electrophoresis and visualized as a single band of expected size under ultraviolet light by gel documentation and image analysis system (Alpha Innotech Corporation, San Leandro, CA, USA). In RT-PCR analysis, the DNA gel band intensities were further quantified by densitometric analysis using GAPDH as loading control and the data from 3 independent experiments are presented in the results section as a bar graph (mean ± SEM).
Statistical analysis
For the microarray study, the student t-test was used for qualifying differentially expressed genes and Fisher exact test was used for GO and Pathway analysis. The statistical analysis used for data normalization, quality control analysis, and PCA were performed as described previously [Kulkarni et al. Citation2015]. For RT-PCR analysis, the statistical analysis was performed (SPSS Software, Chicago; version 13.0) using arbitrary units obtained from densitometric measurements of intensity of DNA bands. The data obtained from 3 independent experiments was presented as the mean ± SEM. The difference between immature and aged primordial follicles was determined by student’s t-test and threshold p-value with 0.05 was used to identify statistical significance.
Declaration of interest
Department of Biotechnology, Government of India (Grant Number: BT/PR5487/AAQ/1/500/2012). The authors report no conflicts of interest.
IAAN_1267820_Supplementary_File.zip
Download Zip (111.9 KB)Acknowledgments
AJR wishes to thank the Indian National Science Academy (INSA) for the award of INSA Honorary Scientist Fellowship and also the Department of Biochemistry, Indian Institute of Science (IISc), Bangalore, India. The authors would like to thank the Department of Biotechnology, Government of India (Grant Number: BT/PR5487/AAQ/1/500/2012) for financial support provided during the course of the work.
Supplemental Material
Supplemental data for this article can be accessed on the publisher’s website.
Additional information
Notes on contributors
Vijayakumar Govindaraj
Designed the study, performed the experiments, analyzed the data, and wrote the paper: VG; Microarray data analysis: PC, MV, VG; Performed PCR validation of microarray data: VG, HK; Conception and design of research study and critical review of the manuscript: AJR.
Harshini Krishnagiri
Designed the study, performed the experiments, analyzed the data, and wrote the paper: VG; Microarray data analysis: PC, MV, VG; Performed PCR validation of microarray data: VG, HK; Conception and design of research study and critical review of the manuscript: AJR.
Payal Chakraborty
Designed the study, performed the experiments, analyzed the data, and wrote the paper: VG; Microarray data analysis: PC, MV, VG; Performed PCR validation of microarray data: VG, HK; Conception and design of research study and critical review of the manuscript: AJR.
Madavan Vasudevan
Designed the study, performed the experiments, analyzed the data, and wrote the paper: VG; Microarray data analysis: PC, MV, VG; Performed PCR validation of microarray data: VG, HK; Conception and design of research study and critical review of the manuscript: AJR.
A. Jagannadha Rao
Designed the study, performed the experiments, analyzed the data, and wrote the paper: VG; Microarray data analysis: PC, MV, VG; Performed PCR validation of microarray data: VG, HK; Conception and design of research study and critical review of the manuscript: AJR.
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