- Research article
- Open Access
Transcriptomic profiling of bovine IVF embryos revealed candidate genes and pathways involved in early embryonic development
© Huang et al; licensee BioMed Central Ltd. 2010
- Received: 24 April 2009
- Accepted: 11 January 2010
- Published: 11 January 2010
Early embryonic loss is a large contributor to infertility in cattle. Although genetic factors are known to affect early embryonic development, the discovery of such factors has been a serious challenge. The objective of this study was to identify genes differentially expressed between blastocysts and degenerative embryos at early stages of development.
Using microarrays, genome-wide RNA expression was profiled and compared for in vitro fertilization (IVF) - derived blastocysts and embryos undergoing degenerative development up to the same time point. Surprisingly similar transcriptomic profiles were found in degenerative embryos and blastocysts. Nonetheless, we identified 67 transcripts that significantly differed between these two groups of embryos at a 15% false discovery rate, including 33 transcripts showing at least a two-fold difference. Several signaling and metabolic pathways were found to be associated with the developmental status of embryos, among which were previously known important steroid biosynthesis and cell communication pathways in early embryonic development.
This study presents the first direct and comprehensive comparison of transcriptomes between IVF blastocysts and degenerative embryos, providing important information for potential genes and pathways associated with early embryonic development.
- Gene Ontology
- Early Embryonic Development
- Transcriptomic Change
- Bovine Genome Array
- GTPase Mediate Signal Transduction
The decline in reproductive efficiency in high producing dairy cows has become a worldwide challenge to the dairy industry and scientific community . Successful fertilization and normal embryonic development are two main components of fertility. There is a growing concern about early embryonic loss, which accounts for a large proportion of infertility, particularly in high-producing cows . Moreover, the bovine has become an increasingly popular animal model for studying development of human embryos because of similar biochemical processes in these species [3, 4]. However, little has been understood concerning the mechanisms underlying proper early embryonic development in cattle.
Genome-wide expression profiling by microarrays has proved a highly effective tool for high throughput analysis of transcriptomes of tissues, cell lines, or any biological mRNA pools, usually across different stages, conditions, or treatments. Indeed, a number of studies have utilized microarrays to understand the dynamics of gene expression during early embryonic development. For example, Misirlioglu et al.  and Kues et al.  investigated the dynamics of gene expression and defined subsets of genes regulated during preimplantation development of bovine embryos, particularly those related to embryonic genome activation. In addition, using a cDNA microarray consisting of 932 bovine ESTs, between in vitro- and in vivo-cultured blastocysts of varying quality, Corcoran et al.  were able to identify 384 differentially-expressed genes that were believed to affect subsequent survival and pregnancy. However, no study has directly investigated changes in gene expression associated with abnormal early embryonic development or growth retardation of embryos.
An in vitro fertilization (IVF) system has been previously established in our laboratory to identify genetic markers for fertility traits in cattle [8–10]. Specifically, the developmental status of embryos is graded based on their morphology. In contrast to blastocysts, degenerative embryos appear morphologically retarded in their development. These embryos do not exhibit a distinct inner cell mass and have no blastocoele. Using this system, single nucleotide polymorphisms in several genes have been shown to be associated with fertilization and blastocyst rates [8–10]. The aim of this study was to characterize transcriptomic differences between IVF blastocysts and degenerative embryos. This is the first direct and comprehensive comparison between in vitro-produced embryos with distinct morphological phenotypes. Although remarkably similar gene expression profiles were found between blastocysts and degenerative embryos, a total of 67 differentially expressed transcripts were identified. Results of this study may help elucidate transcriptomic changes associated with abnormal development in mammalian species and facilitate improvement of assisted reproductive technologies.
Global transcriptomic changes in degenerative embryos compared to blastocysts
Identification and clustering of differentially expressed genes
Transcripts differentially expressed by at least two-fold in degenerative embryos as compared to blastocysts (FDR <= 0.15)
pleckstrin homology-like domain, family A, member 2
chromosome 8 open reading frame 70 ortholog
carboxymethylenebutenolidase homolog (Pseudomonas)
troponin C type 2 (fast)
transforming growth factor, beta receptor III
dual adaptor of phosphotyrosine and 3-phosphoinositides
fermitin family homolog 2 (Drosophila)
peroxisomal trans-2-enoyl-CoA reductase
solute carrier family 11 (proton-coupled divalent metal ion transporters), member 2
shisa homolog 2 (Xenopus laevis)
MCF.2 cell line derived transforming sequence-like
solute carrier family 10 (sodium/bile acid cotransporter family), member 1
serpin peptidase inhibitor, clade C (antithrombin), member 1
cytochrome P450, family 11, subfamily A, polypeptide 1
similar to hCG1788238
solute carrier family 11 (proton-coupled divalent metal ion transporters), member 2
protein tyrosine phosphatase, receptor type, K
farnesyl-diphosphate farnesyltransferase 1
cytochrome P450, family 51, subfamily A, polypeptide 1
retinitis pigmentosa 2 (X-linked recessive)
similar to aminoacylase 1
similar to UPF0474 protein C5orf41
solute carrier family 25 (mitochondrial oxodicarboxylate carrier), member 21
Signaling pathways associated with abnormality of embryonic development
Gene Set Enrichment Analysis (GSEA) and GO enrichment analysis results with FDR = 0.25
Sample size (n/m)1
Enriched in degenerative embryos2
Cholesterol metabolic process
Steroid biosynthetic process
Small GTPase mediated signal transduction
GPCRs class A rhodopsin like
Endoplasmic reticulum membrane
Enriched in normal embryos3
Biosynthesis of steroids
Transferase activity, transferring alkyl or aryl groups
Linoleic acid metabolism
In the present study we report the transcriptomic profiling of blastocysts and degenerative embryos and identification of candidate genes and pathways involved in early embryonic development. While global gene expression in blastocysts and degenerative embryos was largely similar, 67 (33 with greater than 2 fold difference) transcripts were significantly different between these two groups of embryos. In addition, several signaling pathways were found to be altered in degenerative embryos as compared to blastocysts. Although there has been a number of reports on dynamics of transcriptomes in IVF embryos [5, 6], this study, to the best of our knowledge, reports the first direct and comprehensive comparison between blastocysts and degenerative embryos produced by IVF. We believe transcriptomic alteration characterized through this comparison could provide insights into mechanisms of early embryonic development and may help identify biomarkers for growth defect in IVF and for infertility in cattle.
Validity of the experimental design and microarray analysis
Pooling of embryos and amplification of RNA in this study were necessitated by scarcity of RNA present in embryos [5, 6]. These approaches have been well documented in the literature [5, 18, 19]. In this study, we pooled a relatively large number (n = 20) of independent embryos in each pool to achieve a sufficient accuracy of biological pooling. In order to validate the results of microarray experiment and analysis, a total of 16 different pools (eight for each of blastocysts and degenerative embryos) from two different sources of RNA were used as biological replications in the real-time RT-PCR experiments (Figure 3 and Additional File 1). Importantly, eight out of the nine differentially-expressed genes identified through the microarray experiment and SAM were validated in real-time RT-PCR, testifying to the validity of the experimental design and the analysis used in this study.
It is possible that some of the degenerative embryos could be in different developmental stages because of the three-day time window of embryo collection. However, we prefer not to narrow down this window into one or two days because shorter times would result in substantial disturbance to embryo culture and mischaracterization of the two groups of embryos. Thus, we assume that there is some variation in gene expression within the degenerative embryos and that this variation would be reduced by pooling the embryos for expression analysis. Poor synchronization of embryos and integrity of RNA extracted from embryos could potentially introduce errors to the experiment. RNA integrity was checked before every major step in the microarray experiment to ensure sample quality. Moreover, although pooling of embryos removes variation between individual embryos from expression measurements, correlations between gene expression of pools can be used to assess synchronization in the same group of embryos and identify outliers due to compromise of RNA integrity. In fact, correlations of gene expression between samples were 0.979-0.995 within the group of blastocysts and 0.951-0.990 among degenerative embryos. This is an evidence of synchronization and integrity of the biological samples used in this study.
A comparison between gene expression profiles of embryos in this study with embryos at the same developmental stage from other studies would also indicate whether RNA and data quality was compromised in our samples. In fact, high correlations between gene expression profiles of our samples and those of Kues et al.  using the same microarray platform were observed. The correlations between our IVF blastocysts and those of Kues at al. were 0.942-0.970, and the correlations between our degenerative embryos and IVF blastocysts of Kues et al. were 0.925-0.957. Collectively, these results suggest that blastocysts and degenerative embryos used in the present study were largely synchronized to the same stage.
Biologically sensible results
We identified 67 differentially-expressed transcripts and several candidate pathways associated with abnormal early embryonic development. The identification of previously known candidate genes or pathways is also an important aspect of microarray experiments. Interestingly, a number of genes and pathways identified in this study fall into this category of biologically sensible results. PHLDA2 (also known as TSSC3) was found to be upregulated in degenerative compared to blastocysts by both microarray and real-time RT-PCR (Table 1 and Figure 3). PHLDA2 is an apoptosis-related gene that maps to a paternally-imprinted region involved in cancer development . The imprinting status of bovine PHLDA2 is not yet known. However, two known imprinted genes H19 and IGF2 are located nearby on bovine chromosome 29 [21, 22], and the whole conserved cluster is imprinted in human and mouse [20, 23]. Interestingly, overexpression of PHLDA2 in mice caused placental growth retardation  while PHLDA2 knock-out mice showed placental overgrowth , indicating that proper PHLDA2 expression is required for normal placental growth. Thus, our result offers further support for the importance of tightly regulated expression of PHLDA2 and may indicate its involvement in earlier stages of development.
Another differentially-regulated gene in degenerative embryos versus blastocysts is TGFBR3, one of the three types of receptors for TGF beta and one that regulates ligand binding of TGF beta to type I and type II receptors . In addition, although not meeting our FDR cutoff, the TGF beta signaling pathway was significantly (p = 0.046) associated with the developmental status of embryos. Collectively, these two lines of evidence suggest an important role of TGF beta signaling pathway in normal embryonic development, which has been reported in other studies . Cell communication and steroid biosynthesis pathways identified by both GSEA and GO enrichment analyses are of particular interest (Table 2) and their roles in early embryonic development have been studied extensively. For example, gap junctions and cell communication have been well documented to have profound influence on early embryonic development [28, 29], while several steroid hormones are required for transition from morula into blastocyst stage [30, 31].
Small change, large effect
Although the phenotypic outcomes of degenerative embryos and blastocysts were distinct, we did not observe dramatic transcriptomic changes differentiating these two groups of embryos. The correlation between gene expression of blastocysts and degenerative embryos was relatively high (r = 0.986). Among the 67 differentially- expressed transcripts, 33 were changed by more than two fold while only eight of them differed by more than four-fold, and two of them by eight-fold. One may argue that the sample size in this study is not large enough to detect small changes, so that many genuine differentially-expressed genes were missed. This is true for most microarray experiments, which normally do not involve many samples. However, our pooling strategy that reduces variation between samples presumably should alleviate this problem. Indeed, about half (34/67) of the differentially-expressed genes were less than two-fold different. These results suggest that small transcriptomic changes can lead to the distinct phenotype observed in the degenerative embryos and that the high degree of similarity observed between degenerative embryos and blastocysts was a results of true effects rather than of insufficient experimental power.
The ability to detect differentially expressed genes can also be limited by completeness of transcripts manufactured on the microarray platform. The Affymetrix Bovine Genome Array has 24,128 probe sets representing over 23,000 bovine transcripts. Contents of the array were based on GenBank and UniGene databases. Although it is possible that some transcripts exclusively expressed during early development are not represented on the array, it is unlikely that too many transcripts are missed.
The size of the differences in expression may be a specific characteristic of these genes, yet small changes in gene expression can lead to pronounced phenotypic change. For example, silencing by microRNAs has been shown to be less than two-fold [32, 33], yet they have been suggested to regulate a wide range of developmental processes to a large degree. Thus, our results suggest that small transcriptomic changes could lead to the abnormal development of degenerative embryos.
Influence of in vitro culture
There have been several reports comparing genome wide mRNA profiles between IVF and in vivo blastocysts [7, 34]. These studies demonstrated that expressions of a number of genes were changed in IVF blastocysts as compared to embryos produced in vivo. Identification of genes affecting quality of IVF embryos due to culture system is undoubtedly important. Nevertheless, comparison between blastocysts and degenerative embryos in this study is also important because only 30%-35% zygotes can successfully develop to blastocyst stage in vitro, a large source of economic loss. There are likely to be various reasons for unsuccessful development in vitro but genetics seems to play an important role [8, 10]. Embryos cultured in vitro are in a unified environment; therefore significant differential expressions detected are likely to be associated with developmental defect rather than culture system. In fact, we compared our list of differentially expressed genes to the 200 genes that showed expression differences between IVF embryos and embryos produced in vivo by artificial insemination . Importantly, among the 67 differentially expressed genes identified in our study, only one gene (DAPP1) showed in vitro/in vivo difference . This result suggests that the differentially expressed genes identified in this study were likely due to developmental defect rather than culture system.
In summary, we found that the transcriptome of degenerative embryos was largely unchanged as compared to their blastocysts counterparts, yet there was a relatively small number of candidate genes that displayed differential expression between the two groups of embryos. We also found several signaling and metabolic pathways associated with bovine early embryonic development. Importantly, the results presented provide useful information in conceiving future experiments aiming at the mechanistic understanding of early embryonic development as well as improving current assisted reproductive technology. There is a growing body of studies reporting the use of the bovine as a suitable model for human infertility and embryonic development [3, 4]. As such, genes and pathways associated with early embryonic development identified in this study can be utilized to investigate similar traits in other mammalian species.
In vitro fertilization and sample preparation
Ovaries from mature cows were collected at a local abattoir and immediately followed by aspiration of oocytes from antral follicles (> 2-6 mm). Oocytes were processed, incubated in maturation media, and allowed to mature for 24 hours. Mature oocytes were combined with bull semen adjusted to a final concentration of 1 × 106/mL sperm. Frozen thawed bull semen was Percoll separated as described previously  using a discontinued 45%-95% gradient. Putative zygotes were cultured for 120 hours (5 days) before they were evaluated for evidence of compaction or cell coalescence. On day 5 of development in vitro (fertilization = day 0) embryos were viewed via light microscopy to assess morphological development (Figure 1). Embryos that exhibited compaction (cellular coalescence) were classified as compacted morula. Embryos that have not attained 16-32 cells and that did not exhibit compaction were classified as early degenerate. Early degenerative embryos might include a range of cellular development from 2 cell (initial cleavage) up to 8-16 non-compacting cells and were removed from the culture and excluded from further analysis. Embryos showing evidence of compaction were cultured for additional 72 hours (day 8 of development) until they were morphologically graded as blastocysts or degenerative. On day 8, embryos exhibiting a distinct inner cell mass and obvious blastocoele were classified as blastocysts. Embryos that did not properly transition from morula to blastocyst were classified as late degenerative embryos (Figure 1). These two groups of embryos were subjected to microarray and subsequent analysis. Blastocysts and late degenerative embryos were collected and stored in RNAlater (Ambion, TX) to preserve RNA integrity. Embryo culture conditions and media were as described . Briefly, putative zyotes were cultured in syntheic oviductal fluid (Biowhittaker, Walkersburg, MD) supplemented with 0.22 mM sodium pyruvate, 25 ug/mL gentamicin sulfate and 8 mg/mL essentially fatty acid-free BSA. Three pools consisting of 20 randomly sampled blastocysts or degenerative embryos were created. Embryos were produced from 5 bulls and 57 cows. Each pool contained embryos from 5 - 12 cows and 3 - 5 bulls. Total RNA was extracted from pools of embryos using RNaqueous Micro (Ambion, TX) and quality controlled using a RNA6000 PicoChip (Agilent Technologies, CA). The PicoChip was analyzed on an Agilent 2100 Bioanalyzer (Additional file 2) according to manufacturer's instructions. Approximately 100 ng of total RNA was purified from a pool 20 embryos.
Linear amplification and labeling of complementary RNA (cRNA)
Due to the limited amount of RNA present in embryos, a two-round linear amplification was employed to amplify and label whole polyadenylated pools of RNA  using the MessageAmp II aRNA amplification kit (Ambion, TX). Briefly, the first round of amplification was achieved by priming cDNA synthesis with a T7 promoter tagged poly-dT primer and in vitro transcription by T7 polymerase with unlabeled NTPs. Purified first-round aRNA was quality checked and then subjected to the second round amplification and labeling with biotinlyted UTP following manufacturer's protocol. In fact, for all Affymetrix arrays this second round of amplification and labeling must be performed, and total RNA is used as input .
Array hybridization and data acquisition
A total of 15 ug of the biotin-labeled cRNA was fragmented and hybridized to GeneChip Bovine Genome Array (Affymetrix, CA). After staining and washing, microarrays were scanned using a GC3000 7G scanner at the University of Wisconsin Biotechnology Center Gene Expression Center. Raw data was acquired by GeneChip® Operating Software v1.4 (GCOS) and stored as .CEL files. Data from Kues et al.  was downloaded from GEO database at NCBI with the accession number GSE12327 as .CEL files.
Analysis of microarray data
All data analysis was carried out using Bioconductor 2.3  packages implemented with R 2.8.1 . Microarray expression intensities were preprocessed using the 'GCRMA' (v2.14.1)  package in Bioconductor, which corrected backgrounds based on calculated affinities of probe sequences, quantile-quantile normalized intensities, and summarized expressions of probe sets through median-polish and log2 transformed expression values. MAS5 detection calls  were used to qualitatively classify transcript presence. Transcripts that were called "P" (present) in at least two out of the three samples were classified as "Expressed" whereas transcripts called "A" (Absent) in all three samples were classified as "Not expressed". Unclassified transcripts represented genes whose expressions were at the detection limit or at extremely low levels.
There are a total of 24,128 probe sets on the Bovine Genome Array. Probe sets that represent spike-in controls or did not vary across all samples were removed, leaving a total of 18,946 probe sets. Significance Analysis of Microarrays (SAM)  was used to identify differentially-expressed probe sets by the 'siggenes' (v1.16.0) package in Bioconductor, with the False Discovery Rate (FDR) controlled at 15%. Differentially-expressed genes were hierarchically clustered and visualized using functions from the 'gplots' (v2.6.0) package in R.
Gene Set Enrichment Analysis (GSEA)  was carried out using a desktop version of GSEA which queried 'canonical pathways' (v2.5) in the MsigDB database http://www.broad.mit.edu/gsea/msigdb/index.jsp. It is a collection of 649 gene sets curated from various sources including KEGG, GenMAPP, and gene ontology among others. Gene sets were permuted 5000 times to estimate FDRs for enrichments. In addition, 352 unique differentially-expressed Entrez genes having gene ontology (GO) annotations were identified by setting FDR to 25% in SAM. Enrichments for GO terms were tested by a hypergeometric test ('GOstats' package v2.8.0) with respect to 6598 unique GO annotated Entrez genes on the bovine array. Hypergeometric p values were corrected using the Benjamini-Hochberg method  to control FDR at 0.25 for GO enrichments. Two additional filters were applied to minimize false positives: 1) GO categories with fewer than 20 genes were dropped; 2) when a significant GO category is a parent of (contains) another significant GO category, only the child was considered. Enrichments with FDR = 0.25  were presented.
Primer sequences in real- time RT-PCR reactions and products' sizes
Sequence (5' - 3')
This study was supported by USDA Hatch grant No. WIS-142-PRJ16JH from the University of Wisconsin-Madison. The authors thank Ricky Monson and Ashley Driver for assistance with the IVF experiments.
- Royal MD, Smith RF, Friggens NC: Fertility in dairy cows: bridging the gaps. Animal. 2008, 2 (08): 1101-1103. 10.1017/S1751731108002693.PubMedView ArticleGoogle Scholar
- Diskin MG, Murphy JJ, Sreenan JM: Embryo survival in dairy cows managed under pastoral conditions. Anim Reprod Sci. 2006, 96 (3-4): 297-311. 10.1016/j.anireprosci.2006.08.008.PubMedView ArticleGoogle Scholar
- Wrenzycki C, Herrmann D, Keskintepe L, Martins A, Sirisathien S, Brackett B, Niemann H: Effects of culture system and protein supplementation on mRNA expression in pre-implantation bovine embryos. Hum Reprod. 2001, 16 (5): 893-901. 10.1093/humrep/16.5.893.PubMedView ArticleGoogle Scholar
- Menezo YJ, Herubel F: Mouse and bovine models for human IVF. Reprod Biomed Online. 2002, 4 (2): 170-175. 10.1016/S1472-6483(10)61936-0.PubMedView ArticleGoogle Scholar
- Misirlioglu M, Page GP, Sagirkaya H, Kaya A, Parrish JJ, First NL, Memili E: Dynamics of global transcriptome in bovine matured oocytes and preimplantation embryos. Proc Natl Acad Sci USA. 2006, 103 (50): 18905-18910. 10.1073/pnas.0608247103.PubMed CentralPubMedView ArticleGoogle Scholar
- Kues WA, Sudheer S, Herrmann D, Carnwath JW, Havlicek V, Besenfelder U, Lehrach H, Adjaye J, Niemann H: Genome-wide expression profiling reveals distinct clusters of transcriptional regulation during bovine preimplantation development in vivo. Proc Natl Acad Sci USA. 2008, 105 (50): 19768-19773. 10.1073/pnas.0805616105.PubMed CentralPubMedView ArticleGoogle Scholar
- Corcoran D, Fair T, Park S, Rizos D, Patel OV, Smith GW, Coussens PM, Ireland JJ, Boland MP, Evans AC: Suppressed expression of genes involved in transcription and translation in in vitro compared with in vivo cultured bovine embryos. Reproduction. 2006, 131 (4): 651-660. 10.1530/rep.1.01015.PubMedView ArticleGoogle Scholar
- Khatib H, Huang W, Wang X, Tran AH, Bindrim AB, Schutzkus V, Monson RL, Yandell BS: Single gene and gene interaction effects on fertilization and embryonic survival rates in cattle. J Dairy Sci. 2009, 92 (5): 2238-2247. 10.3168/jds.2008-1767.PubMedView ArticleGoogle Scholar
- Khatib H, Maltecca C, Monson RL, Schutzkus V, Wang X, Rutledge JJ: The fibroblast growth factor 2 gene is associated with embryonic mortality in cattle. J Ani Sci. 2008, 86 (9): 2063-2067. 10.2527/jas.2007-0791.View ArticleGoogle Scholar
- Khatib H, Monson RL, Schutzkus V, Kohl DM, Rosa GJ, Rutledge JJ: Mutations in the STAT5A gene are associated with embryonic survival and milk composition in cattle. J Dairy Sci. 2008, 91 (2): 784-793. 10.3168/jds.2007-0669.PubMedView ArticleGoogle Scholar
- Van Gelder RN, von Zastrow ME, Yool A, Dement WC, Barchas JD, Eberwine JH: Amplified RNA synthesized from limited quantities of heterogeneous cDNA. Proc Natl Acad Sci USA. 1990, 87 (5): 1663-1667. 10.1073/pnas.87.5.1663.PubMed CentralPubMedView ArticleGoogle Scholar
- Affymetrix Inc: GeneChip® Expression Analysis. 2004, Santa Clara, CA: Affymetrix IncGoogle Scholar
- Eisen MB, Spellman PT, Brown PO, Botstein D: Cluster analysis and display of genome-wide expression patterns. Proc Natl Acad Sci USA. 1998, 95 (25): 14863-14868. 10.1073/pnas.95.25.14863.PubMed CentralPubMedView ArticleGoogle Scholar
- Shi X, Ma YQ, Tu Y, Chen K, Wu S, Fukuda K, Qin J, Plow EF, Wu C: The MIG-2/integrin interaction strengthens cell-matrix adhesion and modulates cell motility. J Biol Chem. 2007, 282 (28): 20455-20466. 10.1074/jbc.M611680200.PubMedView ArticleGoogle Scholar
- Kooistra MR, Dube N, Bos JL: Rap1: a key regulator in cell-cell junction formation. J Cell Sci. 2007, 120 (Pt 1): 17-22.PubMedGoogle Scholar
- Bos JL: Linking Rap to cell adhesion. Curr Opin Cell Biol. 2005, 17 (2): 123-128. 10.1016/j.ceb.2005.02.009.PubMedView ArticleGoogle Scholar
- Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, Paulovich A, Pomeroy SL, Golub TR, Lander ES: Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci USA. 2005, 102 (43): 15545-15550. 10.1073/pnas.0506580102.PubMed CentralPubMedView ArticleGoogle Scholar
- Kendziorski C, Irizarry RA, Chen KS, Haag JD, Gould MN: On the utility of pooling biological samples in microarray experiments. Proc Natl Acad Sci USA. 2005, 102 (12): 4252-4257. 10.1073/pnas.0500607102.PubMed CentralPubMedView ArticleGoogle Scholar
- Puskas LG, Zvara A, Hackler L, Van Hummelen P: RNA amplification results in reproducible microarray data with slight ratio bias. Biotechniques. 2002, 32 (6): 1330-1340.PubMedGoogle Scholar
- Lee MP, Feinberg AP: Genomic imprinting of a human apoptosis gene homologue, TSSC3. Cancer Res. 1998, 58 (5): 1052-1056.PubMedGoogle Scholar
- Zaitoun I, Khatib H: Assessment of genomic imprinting of SLC38A4, NNAT, NAP1L5, and H19 in cattle. BMC Genetics. 2006, 7: 49-10.1186/1471-2156-7-49.PubMed CentralPubMedView ArticleGoogle Scholar
- Dindot SV, Kent KC, Evers B, Loskutoff N, Womack J, Piedrahita JA: Conservation of genomic imprinting at the XIST, IGF2, and GTL2 loci in the bovine. Mamm Genome. 2004, 15 (12): 966-974. 10.1007/s00335-004-2407-z.PubMedView ArticleGoogle Scholar
- Qian N, Frank D, O'Keefe D, Dao D, Zhao L, Yuan L, Wang Q, Keating M, Walsh C, Tycko B: The IPL gene on chromosome 11p15.5 is imprinted in humans and mice and is similar to TDAG51, implicated in Fas expression and apoptosis. Human Mol Genet. 1997, 6 (12): 2021-2029. 10.1093/hmg/6.12.2021.View ArticleGoogle Scholar
- Salas M, John R, Saxena A, Barton S, Frank D, Fitzpatrick G, Higgins MJ, Tycko B: Placental growth retardation due to loss of imprinting of Phlda2. Mech Dev. 2004, 121 (10): 1199-1210. 10.1016/j.mod.2004.05.017.PubMedView ArticleGoogle Scholar
- Frank D, Fortino W, Clark L, Musalo R, Wang W, Saxena A, Li CM, Reik W, Ludwig T, Tycko B: Placental overgrowth in mice lacking the imprinted gene Ipl. Proc Natl Acad Sci USA. 2002, 99 (11): 7490-7495. 10.1073/pnas.122039999.PubMed CentralPubMedView ArticleGoogle Scholar
- Massague J: TGF-beta signal transduction. Annu Rev Biochem. 1998, 67: 753-791. 10.1146/annurev.biochem.67.1.753.PubMedView ArticleGoogle Scholar
- Jones RL, Stoikos C, Findlay JK, Salamonsen LA: TGF-beta superfamily expression and actions in the endometrium and placenta. Reproduction. 2006, 132 (2): 217-232. 10.1530/rep.1.01076.PubMedView ArticleGoogle Scholar
- Wei CJ, Xu X, Lo CW: Connexins and cell signaling in development and disease. Annu Rev Cell Dev Biol. 2004, 20: 811-838. 10.1146/annurev.cellbio.19.111301.144309.PubMedView ArticleGoogle Scholar
- Boni R, Tosti E, Roviello S, Dale B: Intercellular communication in in vivo- and in vitro-produced bovine embryos. Biol Reprod. 1999, 61 (4): 1050-1055. 10.1095/biolreprod61.4.1050.PubMedView ArticleGoogle Scholar
- Paria BC, Sengupta J, Manchanda SK: Role of embryonic oestrogen in rabbit blastocyst development and metabolism. J Reprod Fertil. 1984, 70 (2): 429-436. 10.1530/jrf.0.0700429.PubMedView ArticleGoogle Scholar
- Niemann H, Elsaesser F: Evidence for estrogen-dependent blastocyst formation in the pig. Biol Reprod. 1986, 35 (1): 10-16. 10.1095/biolreprod35.1.10.PubMedView ArticleGoogle Scholar
- Farh KK, Grimson A, Jan C, Lewis BP, Johnston WK, Lim LP, Burge CB, Bartel DP: The widespread impact of mammalian MicroRNAs on mRNA repression and evolution. Science. 2005, 310 (5755): 1817-1821. 10.1126/science.1121158.PubMedView ArticleGoogle Scholar
- Lim LP, Lau NC, Garrett-Engele P, Grimson A, Schelter JM, Castle J, Bartel DP, Linsley PS, Johnson JM: Microarray analysis shows that some microRNAs downregulate large numbers of target mRNAs. Nature. 2005, 433 (7027): 769-773. 10.1038/nature03315.PubMedView ArticleGoogle Scholar
- Smith SL, Everts RE, Sung LY, Du F, Page RL, Henderson B, Rodriguez-Zas SL, Nedambale TL, Renard JP, Lewin HA: Gene expression profiling of single bovine embryos uncovers significant effects of in vitro maturation, fertilization and culture. Mol Reprod Dev. 2009, 76 (1): 38-47. 10.1002/mrd.20927.PubMedView ArticleGoogle Scholar
- Parrish JJ, Krogenaes A, Susko-Parrish JL: Effect of bovine sperm separation by either swim-up or Percoll method on success of in vitro fertilization and early embryonic development. Theriogenology. 1995, 44 (6): 859-869. 10.1016/0093-691X(95)00271-9.PubMedView ArticleGoogle Scholar
- Gentleman RC, Carey VJ, Bates DM, Bolstad B, Dettling M, Dudoit S, Ellis B, Gautier L, Ge Y, Gentry J: Bioconductor: open software development for computational biology and bioinformatics. Genome Biol. 2004, 5 (10): R80-10.1186/gb-2004-5-10-r80.PubMed CentralPubMedView ArticleGoogle Scholar
- R Development Core Team: R: A Language and Environment for Statistical Computing. 2008, Vienna, Austria: R Foundation for Statistical ComputingGoogle Scholar
- Wu Z, Irizarry RA, Gentleman R, Martinez-Murillo F, Spencer F: A Model-Based Background Adjustment for Oligonucleotide Expression Arrays. J Amer Stat Assoc. 2004, 99: 909-917. 10.1198/016214504000000683.View ArticleGoogle Scholar
- Liu WM, Mei R, Di X, Ryder TB, Hubbell E, Dee S, Webster TA, Harrington CA, Ho MH, Baid J: Analysis of high density expression microarrays with signed-rank call algorithms. Bioinformatics. 2002, 18 (12): 1593-1599. 10.1093/bioinformatics/18.12.1593.PubMedView ArticleGoogle Scholar
- Tusher VG, Tibshirani R, Chu G: Significance analysis of microarrays applied to the ionizing radiation response. Proc Natl Acad Sci USA. 2001, 98 (9): 5116-5121. 10.1073/pnas.091062498.PubMed CentralPubMedView ArticleGoogle Scholar
- Benjamini Y, Hochberg Y: Controlling the False Discovery Rate: a Practical and Powerful Approach to Multiple Testing. J R Statist Soc B. 1995, 57: 963-971.Google Scholar
- Goeman JJ, Buhlmann P: Analyzing gene expression data in terms of gene sets: methodological issues. Bioinformatics. 2007, 23 (8): 980-987. 10.1093/bioinformatics/btm051.PubMedView ArticleGoogle Scholar
- Vandesompele J, De Preter K, Pattyn F, Poppe B, Van Roy N, De Paepe A, Speleman F: Accurate normalization of real-time quantitative RT-PCR data by geometric averaging of multiple internal control genes. Genome Biol. 2002, 3 (7): RESEARCH0034-10.1186/gb-2002-3-7-research0034.PubMed CentralPubMedView ArticleGoogle Scholar
- Livak KJ, Schmittgen TD: Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C(T)) Method. Methods. 2001, 25 (4): 402-408. 10.1006/meth.2001.1262.PubMedView ArticleGoogle Scholar
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