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There is massive destruction of transcripts during the maturation of mouse oocytes. The objective of this project was to identify and characterize the transcripts that are degraded versus those that are stable during the transcriptionally silent germinal vesicle (GV)-stage to metaphase II (MII)-stage transition using a microarray approach. A system for oocyte transcript amplification using both internal and 3’-poly(A) priming was utilized to minimize the impact of complex variations in transcript polyadenylation prevalent during this transition. Transcripts were identified and quantified using the Affymetrix Mouse Genome 430 v2.0 GeneChip. The significantly changed and stable transcripts were analyzed using Ingenuity Pathways Analysis and GenMAPP/MAPPFinder to characterize the biological themes underlying global changes in oocyte transcripts during maturation. It was concluded that the destruction of transcripts during the GV to MII transition is a selective rather than promiscuous process in mouse oocytes. In general, transcripts involved in processes that are associated with meiotic arrest at the GV-stage and the progression of oocyte maturation, such as oxidative phosphorylation, energy production, and protein synthesis and metabolism, were dramatically degraded. In contrast, transcripts encoding participants in signaling pathways essential for maintaining the unique characteristics of the MII-arrested oocyte, such as those involved in protein kinase pathways, were the most prominent among the stable transcripts.
Fully grown mouse oocytes in Graafian follicles are transcriptionally silent during the period before the resumption of meiosis until after fertilization when most of the transcriptional reactivation occurs at the 2-cell stage (BouniolBaly et al., 1999; De La Fuente, 2006; Schultz, 1993; Telford et al., 1990). Products of the oocyte genome support oocyte growth and development before silencing, and are also stored for use during the silent period to support oocyte maturation and the early stage of preimplantation embryo development; see (De La Fuente, 2006; Telford et al., 1990) for reviews. Massive destruction of transcripts occurs during oocyte maturation. Of an estimated 85 pg of mRNA present in a GV-stage mouse oocyte, polyadenylated mRNA declines during oocyte maturation by about 50 pg; about half undergoes deadenylation and the other half, about 30% of the total mRNA, undergoes degradation (Paynton et al., 1988). However, little is known about which transcripts are degraded and which are stable during the transition from the GV-stage to MII, and whether the degradation and stabilization of certain transcripts is selective or random. Identification of the transcripts that are lost versus those that are retained during oocyte maturation could provide insights into processes occurring during oocyte maturation, and those essential to support maintenance of meiotic arrest at MII, fertilization, and early development of the embryo.
Representative linear RNA amplification and microarray technologies are powerful tools for studying global transcript profiles in oocytes and embryos (Hamatani et al., 2004a; Hamatani et al., 2004b; Pan et al., 2005; Rinaudo and Schultz, 2004; Wang et al., 2004; Zeng et al., 2004; Zeng and Schultz, 2005). However, if the length of the 3’-poly(A) tails affects efficiencies of RNA isolation or amplification, differential polyadenylation of transcripts will confound analysis of degraded or stable mRNA populations during the GV-to-MII transition. The commonly used Affymetrix standard 2-cycle amplification procedure for small sample preparation with low amounts of RNA usually employs T7-Oligo(dT) primers. The efficiency of in vitro transcription using such primers relies upon poly(A) priming, and hence products are biased toward polyadenylated mRNA species. Therefore, use of this standard protocol in a microarray study to compare the transcript profiles between GV and MII oocytes would misrepresent the relative transcript levels and the changes occurring during oocyte maturation. It is known that some transcripts become polyadenylated or deadenylated during oocyte maturation (Gebauer et al., 1994; Huarte et al., 1987; Oh et al., 2000; Paynton and Bachvarova, 1994; Sakurai et al., 2005; Sheets et al., 1994; Tay et al., 2000). These changes could distort the apparent relative steady state levels of mRNAs before and after oocyte maturation if the adenylation status differs between the GV and MII stages and if this status affects the efficiency of isolation or amplification (Wang et al., 2004). Therefore, approaches to assess the destruction or relative stability of specific mRNAs during oocyte maturation must minimize the impact of variations in adenylation.
Unlike the T7-Oligo(dT) primers used in Affymetrix’s standard 2-cycle amplification procedure, the uniquely designed Full Spectrum™ MultiStart Primers for in vitro transcription from System Biosciences (Mountain View, CA) initiates cDNA synthesis at multiple points along the mRNA with little or no bias with respect to the length of poly(A) tails (for detailed information, see http://www.systembio.com/Full_Spectrum_MultiStart_RNA_Amplification.htm). Potential profiling errors caused by a 3’-poly(A) biased amplification process can be avoided when the Full Spectrum™ system is incorporated into the Affymetrix 2-cycle amplification process instead of the original T7-Oligo(dT) primers. Transcript profiles generated from microarray studies using this modified RNA amplification protocol would provide a more accurate perspective of the global changes in populations of both degrading and stable transcripts during oocyte maturation.
Here, we used the Full Spectrum™ primers system, combined with Affymetrix Mouse Genome 430 v2.0 GeneChip® arrays, to investigate the relative changes of transcripts present in GV-stage versus MII-stage oocytes to identify transcripts that are either lost or relatively stable during the period of oocyte maturation in the mouse. We used the Ingenuity Pathways Analysis and GenMAPP/MAPPFinder bioinformatics packages to define the biological functions and pathways associated with the degraded or the stable transcripts. By these means, we determined that transcript degradation was selective and not random and hence were able to characterize the major biological themes underlying the global changes in oocyte transcripts during the process of maturation.
Immature 22–24 day old B6SJLF1 female mice were used for all experiments. Mice were produced and raised in the research colony of the investigators at The Jackson Laboratory. All animal protocols were approved by the Administrative Panel on Laboratory Animal Care at The Jackson Laboratory, and all experiments were conducted in accordance with the NIH Guide for the Care and Use of Laboratory Animals.
Ovarian follicular development was stimulated by intraperitoneal injection of 5 IU of eCG (EMD Biosciences, Inc., Calbiochem, La Jolla, CA). Immature GV-stage oocytes were collected at 44–46 h post eCG priming by puncturing large antral follicles on ovarian surface with a pair of 26 gauge needles. Released oocyte-cumulus cell complexes (OCCs) were collected and cumulus cells surrounding the oocyte were removed completely by passing OCCs several times through a hand-drawn small fine glass pipette with an inner diameter slightly narrower than the oocyte. Only those oocytes with an intact GV and no apparent sign of degeneration were collected. To collect mature oocytes, eCG-primed mice were injected with hCG (5 IU/mouse) 44–46 h after receiving eCG, and the oviducts were removed 14 h post hCG injection. Ovulated OCCs were released by gently teasing apart the ampulla of oviducts. Denuded oocytes were obtained by incubating the mass of OCCs in medium containing 1 mg/ml of hyaluronidase for about 3–5 min at 37 ºC. Mature oocytes with a visible first polar body (hereafter termed MII oocytes) and no apparent sign of degeneration were collected. Both GV and MII oocytes were washed thoroughly in the collection medium to ensure that no cumulus cells were present. Medium used for oocyte collection was MEM-α (Invitrogen Corporation, Grand Island, NY) supplemented with 3 mg/ml of crystallized lyophilized bovine serum albumin (Sigma, St. Louis, MO), 75 mg/liter of penicillin G (Sigma) and 50 mg/liter streptomycin sulfate (Sigma). Milrinone (Sigma), a selective inhibitor of oocyte-specific phosphodiesterase (PDE3), was added into the medium at the concentration of 5 μM to prevent the GV-stage oocytes from undergoing maturation. For consistency of the conditions for oocyte collection, the same concentration of milrinone was also added in the medium used for MII oocyte collection. Oocytes were finally transferred to a 1.5 ml centrifuge tube, re-suspended in the RNA extraction buffer supplied in the PicoPureTM RNA Isolation Kit (Arcturus, Mountain View, CA), vortexed, and frozen in liquid nitrogen. Oocyte samples were temporarily stored at −80°C until RNA isolation. Three sets of GV and MII oocyte samples were collected and employed for microarray study. Four additional sets of oocyte samples were collected and used for real-time RT-PCR analysis. Each sample contained 300 GV or MII oocytes that were pooled from 6 mice.
Total RNA was extracted from oocytes using the PicoPure RNA Isolation Kit according to the manufacturer’s instruction. The RNA quality and yield of each sample were determined using the Bioanalyzer 2100 and RNA 6000 Pico LabChip assay (Agilent Technologies Inc, Palo Alto, CA) in combination with Quant-iTTM RiboGreen Reagent according to supplied protocols (Invitrogen, Carlsbad, CA). Ten nanograms of total RNA from each sample was employed in the two-round cDNA synthesis and subsequent in vitro-transcription according to the Two-Cycle Eukaryotic Target Labeling Assay (Affymetrix Expression Analysis Technical Manual: “ Section 2: Eukaryotic Sample and Array Processing” (http://www.affymetrix.com/support/technical/manual/expression_manual.affx)) with the following modifications. For the first and second cycle of cDNA synthesis steps, Full Spectrum™ MultiStart Primers for in vitro transcription (SystemBiosciences, Mountain View, CA) were used, instead of Affymetrix’s standard T7-Oligo(dT) primer or random hexamers. Equal amounts, 15 μg, of fragmented and biotin-labeled cRNA from each sample were then hybridized to Affymetrix Mouse Genome 430 v2.0 GeneChip® arrays for 16 h at 45°C. Post-hybridization staining and washing were performed according to manufacturer’s protocols using the Fluidics Station 450 instrument (Affymetrix).
After the post-hybridization staining and washing, the arrays were scanned with a GeneChip™3000 laser confocal slide scanner (Affymetrix) and the images were quantified using Gene Chip Operating Software version 1.2 (GCOS, Affymetrix). Probe level data were imported into the R software environment (http://www.r-project.org) and expression values were summarized using the RMA (Robust MultiChip Average) function (Irizarry et al., 2003) in the R/affy package with rma2 background adjustment, no inter-array normalization, and median polish summarization of perfect match values (Gautier et al., 2004).
To account for the difference in starting RNA amount obtained from the same number of GV and MII oocytes, and based on the fact that there is no new transcriptional activity in the fully grown oocytes (De La Fuente, 2006), a quantile normalization was performed based on a group of non-differentially expressed genes which were sampled based on estimated normal mixture model using expectation maximization (EM) algorithm (Gautier et al., 2004; McLachlan and Krishnan, 1997). Probe sets that were considered absent in both stages using Affymetrix MAS present/absent call and Fisher’s combined p-value (Hedges and Olkin, 1985; Mah et al., 2004) were removed from further analysis. Using the R/maanova package (Wu et al., 2003a), an analysis of variance (ANOVA) model was applied to the data, and Fs test statistics were constructed along with their permutation p-values (Cui and Churchill, 2003; Cui et al., 2005). To account for potential multiple-testing problems, the Family-Wise Error-Rate (FWER) was controlled using a permutation-based one-step correction (Cui and Churchill, 2003; Wu et al., 2003a). This allows for selecting transcripts with very high statistical significance and thus reduces expected false positive rates. The transcripts in the microarray dataset that were changed significantly during the GV to MII transition were defined by the criteria of FWER p-value < 0.05. Instead of simply defining the complement of the significantly changed transcripts as the stable transcripts, stringent criteria were used for clearly identifying those that were stable on a sound statistical basis. In order to reduce false positives: (1) the 95% confidence intervals contain log2 ratio=0 (no change), (2) the lower and upper boundaries of the intervals are contained within log2 ratio=−1 (fold change=−2) and log2 ratio=1(fold change=2). This approach allows for the selection of transcripts having log2 ratios close to zero with low biological and technical variation. Results were annotated using information provided by Affymetrix (12/20/2005 release). Full data sets are available at http://www.ncbi.nlm.nih.gov/geo/.
All gene identifiers and their corresponding FWER p values, and fold changes (MII relative to GV) were uploaded into IPA 3.1 (Ingenuity Pathway Analysis, Ingenuity® System, http://www.ingenuity.com) and GenMAPP 2.0 (Gene Map Annotator and Pathway Profiler, http://www.genmapp.org)/MAPPFinder 2.0 to identify the canonical pathways and molecular functions underlying the down-regulated and relatively stable transcripts during oocyte maturation.
Real-time RT-PCR analyses were carried out using total RNA isolated from four sets of GV and MII oocyte samples, in which each sample contained 300 oocytes. RNA isolation was accomplished using the RNeasy Micro Kit (Qiagen, Valencia, CA). To normalize the potential variation between samples originated from pipetting and RNA isolation processes, rabbit ß-globin (OCBGLO) mRNA (a generous gift from Dr. Richard Schultz, University of Pennsylvania) was added into each oocyte sample at the beginning of RNA extracting process at a concentration of 0.125 pg/oocyte. In vitro transcription was carried out using QuantiTect® Reverse Transcription Kit (Qiagen) at 42 °C for 15 min. Random hexamer (Invitrogen, Grand Island, NY) was used in the RT reaction at the concentration of 100 ng/20 μl reaction to replace the original primer mix included in the RT kit. Real-time PCR was then conducted to quantify the steady-state mRNA levels of the selected genes for validation using QuantiTec® SYBR Green PCR Kits (Qiagen) on the ABI 7500 Real-time PCR System (Applied Biosystems, Foster City, CA). The threshold cycle (Ct) was used for determining the relative expression level of each gene, by normalizing to the Ct of OCBGLO. The method of ddCt was used to calculate the relative fold change of each gene. The calculating equation is: Fold change (MII vs. GV) = 2-ddCt, where ddCt = (CtMII/GOI−CtGV/GOI)−(CtMII/OCBGLO−CtGV/OCBGLO). GOI: gene of interest (the gene selected for real-time RT-PCR analysis). Because SYBR Green binding is not sequence specific, careful design and validation of each primer pair, as well as cautious manipulation of RNA were undertaken to ensure that only target gene sequence-specific, non-genomic products were amplified by real-time PCR. To achieve this, primers were designed to either span or flank introns. A dissociation curve analysis was performed at the end of the amplification process in order to verify the specificity of the PCR products. The same PCR products were also evaluated by agarose gel electrophoresis. The sequence of each PCR product was finally verified by two-direction sequencing. Information on the primers used for real-time PCR is shown in Table 1. In addition, a DNase digestion step was incorporated in the RNA isolation process, and isolated RNA was first treated with a genomic DNA wipe out buffer supplied in the RT Kit (Qiagen) before in vitro transcription. This buffer eliminates genomic DNA contamination from starting RNA samples for in vitro transcription.
Real-time RT-PCR experiments were repeated four times independently. Data are presented as mean ± SEM. Student t-test was conducted to evaluate difference between the expression levels of GV and MII oocytes. p < 0.05 was considered significantly different.
Extensive destruction of transcripts occurs in oocytes during meiotic maturation yet there is only limited information on which transcripts are degraded or relatively stable (Bachvarova et al., 1985; Bettegowda et al., 2006; Lequarre et al., 2004; Lonergan et al., 2003; Paynton et al., 1988; Stutz et al., 1998; Zheng et al., 2005a; Zheng et al., 2005b). A global perspective of the relative changes in oocyte transcript profiles during the process of maturation has been lacking. Here, using microarray and bioinformatics approaches for analysis of gene ontology and pathways, we assessed changes in transcript profiles in GV and MII mouse oocytes, and investigated the biological themes associated with the corresponding global changes. A modified RNA linear amplification procedure in which the Full Spectrum™ MultiStart Primers for in vitro transcription, a primer set uniquely designed to initiate cDNA synthesis at multiple points along the mRNA with reduced bias of the length of 3’-poly(A) tail, replaced the commonly used T7-Oligo(dT) primers whose priming efficiency is affected by the 3’-poly(A) tail. This modified amplification protocol was used to reduce potential profiling errors derived from the active mRNA polyadenylation/deadenylation that occurs in oocytes during maturation.
The Affymetrix Mouse Expression 430v2.0 GeneChip contains 45,037 probe sets that represent 14,484 full-length genes, 9,450 non-ESTs and 21,103 ESTs, according to the manufacturer. In the present study, 21,646 probe sets were detected as present in either GV or MII oocytes. The mean relative changes of these transcripts during meiotic maturation from GV to MII are shown in Fig. 1. Of all the present transcripts, 3,002 probe sets were identified to be significantly changed using the criteria of FWER p-value < 0.05, and 9,260 probe sets were identified to be relatively stable using a separate criteria for defining stable transcripts as described in Materials and Methods. Of particularly note, 2957 probe sets were significantly downregulated, and they represent the overwhelming majority (98.50%) of the changed transcripts. Moreover, 61.12% of the total changed transcripts (1838 probe sets) were downregulated more than 4 fold in MII oocytes. However, 1.5% of the changed transcripts (54 probe sets) appeared to be up-regulated in MII oocytes, but real-time PCR analysis suggested that this increase is artifactual (see below section for further discussion of this issue).
It is important to note that FWER, rather than FDR (false discovery rate), was utilized here to identify transcripts whose expression levels become changed significantly during GV-to-MII transition. Of the available methods for testing the significance of differential expression of probe sets, FWER best controls for the possibility of making one or more type I error -rejecting null hypothesis when the null hypothesis is true- in the entire experiment. While this method is quite stringent, it allows for selecting specific transcripts with very high statistical significance and thus reducing false positive rates. This conservative strategy for identification of significantly changed transcripts is essential in the present study. This is because of the difference in mRNA abundance between a GV- and a MII-oocyte, and the problem is that the normalization method employed here cannot eliminate all the artifacts inherent in the equal loading of the microarrays (see below for more discussion of this issue).
Although FWER is appropriate for identification of drastically changed transcripts with low false positive rate in the significantly changed category, its high stringency will probably leave some differentially expressed transcripts in the not significantly changed category (Cui and Churchill, 2003). Therefore, it is inappropriate to simply define the complement of the significantly changed transcripts as the stable ones. A separate strategy was therefore required to identify the stable transcripts while reducing the incidence of false positives. The criteria used in the present study for the stable transcripts met this need, and ensured the identification of the 9,260 stable transcripts with both high accuracy (their log2 ratios close to zero) and high precision (low technical and biological variation) (Woo et al., 2004). Thus, of the 21,646 probe sets that were detected in oocytes, 3,002 were determined to be significantly changed and 9,260 unchanged using the two methods of statistical evaluation. This means that 7,384 did not fall into either category with certainty and were not used in the pathway analyses.
Thirty-six transcripts were selected from the array expression profile for further analysis by real-time RT-PCR. These 36 transcripts were categorized into 5 groups consisting of: (A) transcripts normally expressed in the expanded cumulus oophorus but not in oocytes, i.e., Has2, Ptgs2, Ptx3 and Tnfaip6; (B) transcripts polyadenylated during oocyte maturation, i.e., Ccndbp1, G6pdx, Hprt1, Mos, Plat and Spin; (C) transcripts expressed only or highly in mouse oocytes, i.e., Bmp15, Fgf8, Gdf9, H1foo, Nalp5 and Zar1; (D) transcripts apparently up-regulated in MII oocytes as shown by the present microarray data, i.e., Clybl, Ina and Ly6e; (E) transcripts that were significantly downregulated as shown by the present array data and are linked to various specific biological functions and cellular processes, i.e., Calm2, Ccnh, Dedd, Dnajc2, Exosc8, Gja1, Lmna, Ndufv1, Paip2, Polr2b, Psmc2, Rpl19, Rps9, Rps17, Statip1, Tacc3 and Xrcc5.
As shown in Fig. 2, for all the transcripts subjected to validation, none were found to be up-regulated in MII oocytes by real-time PCR analysis. Of particular note, transcripts included in group D were marginally downregulated in MII oocytes as revealed by real-time PCR analysis even though they appeared to be at higher levels than in GV-stage oocytes by microarray analysis (Fig. 2D). This result of real-time PCR analysis indicates that no transcripts were up-regulated in MII oocytes, and suggests that the detection of the upregulation of these 3 transcripts shown in Fig. 2D, as well as the other 43 transcripts suggested to be up-regulated by the microarray data (Fig. 1), is an artifact. The actual levels of those up-regulated transcripts in MII oocytes were slightly below levels present in GV- oocytes. Since the amplification system used in this study minimizes the impact of polyadenylation, the cause of this artificial increase must have a different etiology. Because total mRNA in mouse oocytes decreases by at least 30% during maturation (Paynton et al., 1988), and because equal amounts of total RNA were used as input into the amplification and equivalent amounts of resulting cRNA products were applied to the arrays, some stable transcripts present at MII were probably applied to the arrays in amounts greater than those present at the GV-stage. Technically, there is no ideal way to avoid this problem by differential loading since correction for one group of transcripts would simply distort another. Although we used a normalization method to correct most of these artifacts (see Materials and Methods), some noise inevitably persists.
Group A transcripts are expressed in expanded cumulus cells and not oocytes (Fulop et al., 2003; Joyce et al., 2001; Salustri et al., 1999; Salustri et al., 2004; Varani et al., 2002), and are therefore good indicators of potential cumulus cell contamination of oocyte RNA preparations. An apparent upregulation of these transcripts would be detected in MII oocytes if there were any cumulus cells inadvertently included in the oocyte samples. As shown in Fig. 2A, none of the four cumulus cell-specific transcripts, Tnfaip6, Ptx3, Has2, or Ptgs2, was found at higher levels in MII oocytes by either microarray or real-time RT-PCR analysis. Moreover, the expression levels of all the cumulus cell-specific transcripts that were detected in oocytes by either method were extremely low as shown by their RMA expression measures or Ct values (data not shown). Thus, there was no contamination of oocyte RNA preparations with cumulus cells that could confound interpretation of the microarray results.
Transcripts in group B are normally polyadenylated during oocyte maturation (Gebauer et al., 1994; Huarte et al., 1987; Oh et al., 2000; Paynton and Bachvarova, 1994; Sheets et al., 1994), and could appear to be up-regulated in MII oocytes when poly(A)-biased mRNA quantification approaches are used to compare their expression levels in GV and MII oocytes (Wang et al., 2004). In contrast, no apparent up-regulation should be detected for any of these transcripts if the method used for mRNA quantification is unaffected by poly(A) tails as expected for the Full Spectrum™ MultiStart Primers for in vitro transcription. Indeed, as shown in Fig. 2B, none of these transcripts was found at levels higher in the MII oocytes than GV-stage by either microarray or by RT-PCR analysis. This observation provides strong validation for the use of MultiStart Primers for in vitro transcription to reduce the influence of polyadenylation. However, real-time PCR but not microarray detected that all the transcripts in this group were significantly down-regulated in MII oocytes. This could be due to the differences in sensitivity or lower limit of detection of these two methods used for mRNA quantification, or to the overloading of the MII sample to the microarrays as described above. Nevertheless, it is consistent that none of these polyadenylated transcripts was up-regulated in MII oocytes. Two other transcripts reported to be present at higher levels in MII oocytes than GV-stage oocytes are Napa (RA81) (Mann et al., 1995) and Gnai2 (Gαi2) (Rambhatla et al., 1995). The authors suggested that this apparent increase could be artifactual due to polyadenylation (Rambhatla et al., 1995). Our microarray analysis shows no change in the levels of these transcripts at MII compared with the GV-stage supporting the use of the Full Spectrum™ MultiStart Primers for amplification to avoid potential complications of polyadenylation.
Transcripts in group C encode proteins that are oocyte-specific or highly expressed in oocytes and play crucial roles in promoting the normal development and functions of oocytes and/or follicles by functioning as paracrine factors, i.e., Gdf9, Bmp15 and Fgf8 (Buratini et al., 2005; Dong et al., 1996; Eppig, 2001; Matzuk et al., 2002; Su et al., 2004; Valve et al., 1997; Yan et al., 2001), promoting oocyte-to-embryo transition as well as preimplantation development, i.e., Zar1, Nalp5 (also called Mater) (Tong et al., 2000; Wu et al., 2003b), or potentially regulating chromatin remodeling during oocyte/and or embryo development, i.e., H1foo (Gao et al., 2004; Tanaka et al., 2005; Teranishi et al., 2004). Little if anything, however, is known about the changes in the relative levels of these transcripts during mouse oocyte maturation. As shown in Fig. 2C, microarray analysis detected the significant down-regulation of Fgf8 in MII oocytes and this down-regulation was validated by real-time PCR. Although the microarray study indicated that the rest of the 5 transcripts in group C were not changed in MII oocytes, real-time RT-PCR analysis revealed that they were actually all significantly down-regulated. This discrepancy could be due to the same causes discussed in the preceding section. Nevertheless, real-time PCR analysis further confirmed no up-regulation of transcripts in MII oocytes.
Transcripts included in group E are representative of those involved in specific biological function/pathway and/or cellular process, such as protein synthesis and metabolism (e.g., Rps17, Rps9, Psmc2) and oxidative phosphorylation (e.g., Ndufv1). For all the transcripts in group E, a similar pattern of change was consistently revealed by both microarray and real-time PCR (Fig. 2E). For example, Rps17 was down-regulated as demonstrated by both microarray and real-time RT-PCR, 31.6 and 45.4 fold respectively.
In sum, real-time RT-PCR validated (1) the specificity of our array data allowing the exclusion of potential cumulus cell contamination from confounding results; (2) the use of the modified mRNA linear amplification procedure in preventing profiling errors caused by polyadenylation; (3) no up-regulation of any transcripts in MII oocytes; and (4) the overall trends of message stability or loss indicated by microarray analysis.
To determine whether there are biological themes and specificity underlying the degraded oocyte transcripts during meiotic maturation, we carried out a series of pathway analyses using both IPA and GenMAPP/MAPFinder software. IPA is a web-based software application that enables the modeling and analysis of biological systems using microarray data, while GenMAPP/MAPPFinder is a stand-alone computer program designed for viewing and analyzing gene expression data in the context of gene ontology and biological pathways. Both programs are commonly used and demonstrated to be powerful tools for identifying the biological themes of gene expression data (Calvano et al., 2005; Dahlquist et al., 2002; Doniger et al., 2003).
Canonical pathways analysis by IPA identified the pathways that were significantly associated with the degraded transcripts. Degraded transcripts as defined by FWER p < 0.05 were used for the analysis. Fisher’s exact test was used to calculate a p-value (< 0.05) determining the probability that the association between the transcripts in the dataset and the canonical pathway could be explained by chance alone. As shown in Fig. 3A, of the total of 103 metabolic and signaling pathways in the IPA canonical pathway library, 17 are significantly associated with the transcripts that were lost in MII oocytes. GenMAPP/MAPPFinder also identified most of these pathways (see Tables S1 and S2). Within these 17 canonical pathways, 13 are involved in metabolic processes. Interestingly, most of these processes are closely related. For example, 59 transcripts associated with oxidative phosphorylation were downregulated in MII oocytes. The same process, termed “electron transport chain” in GenMAPP, was identified to be among the most significantly affected MAPPs as well (see Table S2). As illustrated in Fig. 3B, oxidative phosphorylation is the major pathway for conversion of the energy of NADH oxidation into phosphate-bond energy in ATP. This process takes place in mitochondria and is catalyzed by five large respiratory enzyme complexes residing in the mitochondrial inner membrane. Most of the degraded transcripts that were involved in this pathway encode these enzyme complexes, indicating the potential for a reduced rate of ATP production in mature oocytes if the encoded proteins are rapidly turned over. The second pathway most impacted by the loss of transcripts was ubiquinone biosynthesis where 25 transcripts encoding the enzymes that convert ubiquinol into ubiquinone were involved. Interestingly, ubiquinone is an essential electron carrier in the oxidative phosphorylation process, thus the two canonical pathways having the highest significance rating by IPA are closely related. Other canonical pathways, such as pyruvate metabolism and citrate cycle, are also closely connected to the oxidative phosphorylation pathway. For example, oocytes efficiently use pyruvate but not glucose to produce energy essential for oocyte maturation (Biggers et al., 1967; Downs et al., 2002). The energy produced by pyruvate metabolism and citrate cycle is partially in a form of high-energy electrons that are transiently held by NADH. NADH is then used in the oxidative phosphorylation pathway in the mitochondrial inner membrane and the high-energy electrons are eventually used for ATP synthesis. Therefore, these two pathways are also closely related to the oxidative phosphorylation process, and the loss of transcripts involved in these two pathways could result in the lower rate of ATP synthesis. Because a high rate of oxidative phosphorylation and ATP production is always associated with cellular processes that utilizes energy, such as macromolecular synthesis (e.g., protein synthesis), protein phosphorylation, ion-transporting, etc., the loss of transcripts responsible for these processes in MII oocytes could reflect the relatively quiescent status of MII oocytes in consuming energy. Indeed, it has been found that oocyte maturation was associated with increased pyruvate consumption, hence energy utilization; and MII oocytes consumed less pyruvate than oocytes undergoing meiotic maturation (Downs et al., 2002). Therefore, it is possible that loss of relevant transcripts participates in decreased energy production and utilization apparent in MII oocytes if the amount of the encoded proteins is also reduced.
The degradation of transcripts involved in energy production process may also be associated with the relief of ATP consumption in maintenance of meiotic arrest at the GV-stage. A constant high level of cAMP maintains meiotic arrest in fully-grown oocytes, and this high level of cAMP is produced from ATP by an oocyte-specific G-protein coupled adenyl cyclase (Eppig et al., 2004; Horner et al., 2003; Mehlmann et al., 2002; Mehlmann et al., 2004). Therefore, the requirement for ATP in maintenance of meiotic arrest may explain why transcripts involved in oxidative phosphorylation were highly expressed in GV-oocyte as shown here and elsewhere (Zeng et al., 2004). Taken together, dynamic changes in the rates of oxidative phosphorylation, energy production, and consumption are tightly correlated with the meiotic status of the oocyte, and the completion of the first meiotic division is correlated with the loss of transcripts involved in these energy producing and using processes.
To further understand the biological and molecular functions represented by transcripts degraded during the GV to MII transition, an IPA Functional Analysis was carried out. This analysis identified the biological functions that were significantly associated with the set of lost transcripts. Degraded transcripts, as defined by FWER p < 0.05, and were associated with the biological functions in IPA Knowledge Base, were considered for the analysis. There were 26 molecular functions identified by this analysis to be significantly (P < 0.05) associated with the lost transcripts. The top 15 functions are shown in Fig. 4A. Interestingly, most of the functions identified by IPA analysis were also independently identified by GenMAPP/MAPPFinder (Tables S1 and S2), and were well in agreement with those aforementioned Canonical Pathways. For example, the degraded transcripts involved in oxidative phosphorylation and ubiquinone biosynthesis as identified by the IPA Canonical Pathways Analysis suggests a low rate of macromolecule biogenesis in MII oocytes, and this is consistent with the identification of protein synthesis by the IPA Functional Analysis as shown here. In addition, the identification of pyruvate metabolism and citrate cycle by IPA Canonical Pathways Analysis indicates low rate of energy production in MII oocytes, and this is in agreement with the identification of energy production as a biological function in the IPA Functional Analysis. Most interestingly, similar processes were identified simultaneously by both the IPA Canonical Pathways Analysis and Functional Analysis. For example, the nucleotide excision repair pathway was identified by the IPA Canonical Pathway analysis, and the similar process termed DNA Replication, Recombination, and Repair was identified in the IPA Functional Analysis.
IPA analysis identified protein synthesis as the function that is most significantly associated (P = 1.1 x 10−12) with the degraded transcripts. There were 109 degraded transcripts involved in this function and they were associated with 7 sub-categories of functions (Table S3). Of the degraded transcripts participating in protein synthesis, a large number of them encode cytoplasmic and mitochondrial ribosomal proteins. This function was also identified by GenMAPP (Table S1) and by MAPPFinder as the most significantly affected (Table S2). Of the 78 transcripts that encode cytoplasmic ribosomal proteins in the large and small ribosomal subunits, 68 of them were significantly degraded in MII oocytes (Fig. 4B). Coordinate with the loss of mRNAs encoding the ribosomal proteins, there is a decline of about 60 pg/oocyte in rRNA (Paynton et al., 1988). Presumably, the degradation of rRNA and transcripts encoding ribosomal proteins results in loss of ribosomes and reduction in the potential for message translation. In fact, the absolute rate of protein synthesis decreases almost 30% during oocyte maturation (Schultz and Wassarman, 1977). Coordinated with this, transcripts encoding translation initiation factors are lost; 15 transcripts encoding translation initiation and translation elongation factors were significantly degraded during oocyte maturation (Table S3). While the capacity for protein synthesis becomes reduced during oocyte maturation, so does protein degradation since many transcripts encoding the components of the ubiquitin-proteasome degradation system (12 transcripts in total) were lost.
There are high levels of expression of transcripts involved in the regulation of the meiotic cell cycle and DNA repair during oocyte growth and development (Pan et al., 2005). It is shown here that many of the transcripts associated with these functions are degraded during oocytes maturation (Fig. 4A. and Tables S1 and S2).
Taken together, results presented here show that transcripts participating in specific biological pathways and functions essential for supporting oocyte meiotic arrest, resumption, and progression are highly represented among transcripts degraded during oocyte maturation. Of particular note are transcripts associated with protein synthesis and energy production and utilization. Interestingly, transcripts associated with protein synthesis are dramatically up-regulated during zygotic gene activation at the two-cell stage (Zeng and Schultz, 2005). The explanation for this apparent inefficiency is as lost as the transcripts.
Despite the substantial loss of transcripts during oocyte maturation, a great number of transcripts are relatively stable. Using the statistical strategy described in the section of material and methods, 9,260 transcripts were categorized as stable. To understand the biological functions and processes represented by these retained transcripts in MII oocytes, this set of transcripts was subjected to IPA and GenMAPP/MAPPFinder analyses. IPA analysis revealed that 30 canonical pathways were significantly associated with the relatively stable transcripts in MII oocytes (the top 15 is shown in Fig. 5A). Twenty-five of these (83.33%) were associated with signal transduction. This was consistent with GenMAPP/MAPPFinder analysis where cell communication, signal transduction, and signal transducer activity were the most significant MAPPs and gene ontology (GO) terms associated with stable transcripts (Table S4 and S5). It is of interest to note that transcripts associated with the same GO categories were reported to be gradually degraded in mouse embryos starting from the 1-cell stage and continuing after zygotic gene activation at 2-cell stage (Zeng et al., 2004).
IPA analysis revealed that transcripts associated with the canonical pathways of ERK/MAPK and PI3/AKT signaling were highly represented in the stable group. These two signaling pathways are strongly implicated in the regulation of oocyte meiosis (Choi et al., 1996; Colledge et al., 1994; Hashimoto et al., 1994; Kalous et al., 2006; Phillips et al., 2002; Su et al., 2002b; Tomek and Smiljakovic, 2005; Verlhac et al., 1996; Vigneron et al., 2004). The ERK/MAPK signaling pathway is crucial for the maintenance of MII arrest in oocytes (Colledge et al., 1994; Hashimoto et al., 1994; Phillips et al., 2002; Su et al., 2002a). Of the 121 transcripts involved in the ERK/MAPK signaling pathway, 68 (56.19%) was identified as stable. The display of transcripts involved in MAPK signaling pathway by GenMAPP was shown in Fig. 5B. Therefore, transcripts involved in signaling pathways essential for maintaining meiotic arrest at MII are stable during the GV to MII transition.
To compare transcript profiles between GV- and MII-oocyte, one must consider the influence of mRNA polyadenylation and non-equivalency problems prevalent during GV-to-MII transition. The modified RNA linear amplification system and the data normalization method used in the present microarray study appear to be an effective way in preventing and correcting these problems. In fact, use of this system may be advantageous when conducting any transcriptome comparisons where polyadenylation issues could hamper interpretation, a situation that could be quite common.
Data presented here indicate that destruction of transcripts during the GV to MII transition in oocytes is a selective rather than promiscuous process. Transcripts associated with meiotic arrest at the GV-stage and the progression of oocyte maturation such as oxidative phosphorylation, energy production, and protein synthesis and metabolism were dramatically degraded. In contrast, transcripts encoding participants in signaling pathways essential for maintaining the unique characteristics of the MII-arrested oocyte, such as those involving protein kinase pathways, were most prominent among those retained. Aberrant degradation or maintenance of certain class of transcripts during oocyte maturation could be deleterious to oocyte quality, impacting developmental competence. Although transcriptional profiling of GV-stage mouse oocytes with decreased developmental competence did not reveal dramatic differences from oocytes with high developmental competence (Pan et al., 2005), differences may become more profound at MII.
S-Table 1. MAPPs identified by GenMAPP/MAPPFinder that are associated with degraded transcripts during oocyte maturation
S-Table 2. Gene ontology (GO) terms identified by GenMAPP/MAPFinder that are associated with degraded transcripts during oocyte maturation
S-Table 3. Summary of degraded transcripts participating in the process of protein synthesis
S-Table 4. MAPPs identified by GenMAPP/MAPFinder that are associated with stable transcripts during oocyte maturation
S-Table 5. Gene ontology (GO) terms identified by GenMAPP/MAPPFinder that are associated significantly with stable transcripts during oocyte maturation
We are grateful to Drs. Stephen Downs, Alexei Evsikov and Mary Ann Handel for their helpful comments on the preparation of this manuscript, Dr. Jin Szatkiewicz for providing the EM algorithm script for R. We also thank Dr. Keith Latham for very useful discussions that helped tremendously with the interpretation of the data presented here. This study was supported by grants HD23839 and HD21970 from the NICHD to JJE. The Gene Expression Facility at The Jackson Laboratory is supported by HL073341 Shared Microarray Facilities grant. YHW is supported by a National Science Foundation IGERT training grant (0221625) to Functional Genomics Ph.D. Program of The University of Maine (B. B. Knowles, PI).
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