RNA-binding proteins to assess gene expression states of co-cultivated cells in response to tumor cells
© Penalva et al; licensee BioMed Central Ltd. 2004
Received: 25 June 2004
Accepted: 07 September 2004
Published: 07 September 2004
Tumors and complex tissues consist of mixtures of communicating cells that differ significantly in their gene expression status. In order to understand how different cell types influence one another's gene expression, it will be necessary to monitor the mRNA profiles of each cell type independently and to dissect the mechanisms that regulate their gene expression outcomes.
In order to approach these questions, we have used RNA-binding proteins such as ELAV/Hu, poly (A) binding protein (PABP) and cap-binding protein (eIF-4E) as reporters of gene expression. Here we demonstrate that the epitope-tagged RNA binding protein, PABP, expressed separately in tumor cells and endothelial cells can be used to discriminate their respective mRNA targets from mixtures of these cells without significant mRNA reassortment or exchange. Moreover, using this approach we identify a set of endothelial genes that respond to the presence of co-cultured breast tumor cells.
RNA-binding proteins can be used as reporters to elucidate components of operational mRNA networks and operons involved in regulating cell-type specific gene expression in tissues and tumors.
Many recent studies have described the use of microarrays to identify genes expressed in different types of cancers (reviewed in [1, 2]. Most of these transcriptomic studies monitor the steady state levels of expressed mRNAs in order to derive the "molecular signatures" of tumors . However, the gene expression profile of a whole tumor corresponds to the combined profiles of the different cell types contained within it (e.g. endothelial cells, T-cells, cancer cells, stromal cells, etc.). Moreover, the multiple cell types present in a tumor or organ are interdependent and exchange biochemical signals as a means of cell-cell communication . An important example of cell-cell communication is evident in angiogenesis, the mechanism by which new blood vessels vascularize tumors and other organs (reviewed in ). Monitoring the dynamics of gene expression in each cell type of a tumor during angiogenesis will advance understanding of tumorigenesis as well as organogenesis, in general.
Methods have been devised to generate mRNA samples from specific types of tumor cells. These include microdissection, laser capture (reviewed in [4–6], and cell sorting based on specific membrane markers . Here we demonstrate that RNA-binding proteins can be used to isolate mRNA populations representing total cell mRNA from specific types of cells, as well as discrete mRNA subpopulations that represent post-transcriptionally regulated subsets of mRNAs that encode functionally related proteins. We propose that these represent genes whose regulation is important for tumor growth and maintenance.
RNA binding proteins play a key role in post-transcriptional regulation, participating in splicing, mRNA transport and localization, mRNA stability and translation (for overview see ref. ). Our lab has devised biochemical and immunological approaches to gene expression profiling by using RNA-binding proteins as reporters of discrete mRNA subsets in metazoan cells [8–10]. For example, we identified subpopulations of mRNAs that are associated with ELAV/Hu RNA-binding proteins that are expressed in specific cell types . While we and other labs have demonstrated the isolation of mRNA subsets that are potentially co-regulated using RNA binding proteins as reporters of gene expression, methods have not been described that provide information about coordinated posttranscriptional regulation within specific types of cells during tumorigenesis and development. Moreover, because many different mRNA-binding proteins in specific cell types are known to interact with unique subpopulations of mRNAs encoding functionally related proteins [9–15] they can be informative of the dynamic effects of cells on one another. Therefore, it will be necessary to assess changes in gene expression that occur when cells such as tumor cells and endothelial cells interact in order to understand growth control and critical processes such as angiogenesis.
In this study, we define a model system for using poly (A) binding protein (PABP) to recover mRNAs from specific cell-types in mixed cell cultures. Using this approach, we were able to determine how the gene expression profiles of endothelial cells change in response to the presence of breast cancer cells. Among the advantages of this approach are: a) no manipulations or treatments are required prior to the preparation of cell extracts, b) the recovered mRNA population can be identified directly using genomic methods, and c) RNA binding proteins can be engineered for expression in different cell types using various molecular tags in order to discriminate cell-specific mRNA populations. These studies provide a methodological basis for creating mouse models in which different types of cells within a tumor express RNA binding proteins to reveal unique populations of posttranscriptionally regulated mRNAs.
Results and Discussion
The goals of these experiments are to validate procedures for the isolation and characterization of discrete mRNA sub-populations associated with RNA binding proteins expressed in specific cell types within a tumor or organ in order to assess the responses of cells to their surroundings. Earlier studies have shown that mRNA subpopulations in single cell types reflect the functions of the RNA binding proteins with which they associate and can provide key information about post-transcriptional regulatory mechanisms of gene expression [8–11, 13, 15–18]. In model organisms, such information can be obtained by expressing epitope-tagged RNA binding proteins using tissue-specific promoters  or by using virus-specific receptors (M.D.B., L.O.F.P. and J.D.K. unpublished). In this study we demonstrate the feasibility of this approach by using two different cell types in culture that each express specific RNA binding proteins as reporters of gene expression profiles.
Comparison of total mRNA of PY4.1 endothelial cells with their PABP-associated mRNA patterns
Several reports indicate that substantial differences can be found when comparing the steady state levels of mRNAs (transcriptome) with proteins (proteome) in the same cell population [28, 29]. The accumulated levels of some proteins and their corresponding mRNAs can vary by as much as 30-fold [28–30]. The differential between steady state levels of mRNA and protein are expected to be more dramatic under conditions in which post-transcriptional regulation plays a major role. For example, following T cell-activation or during neuronal differentiation, translational control is thought to affect a significant proportion of the proteomic outcome [31, 32].
It is possible that gene expression profiles obtained by immunoprecipitating mRNA-PABP complexes may reflect the functional state of protein production from these mRNAs . For the purposes of this study, PABP is used as a functionally relevant RNA-binding protein with which to compare changes in bound mRNAs across gene expression profiles.
Expression of tagged PABP does not interfere with cell growth
A potential complication for this type of analysis is that expression of a tagged-RNA binding protein, in this case PABP, could affect cell growth. While these cell lines appeared unaffected morphologically, the levels of PABP in cell lines expressing tagged-PABP and respective control cell lines were evaluated and compared by Western blotting. No substantial change in overall PABP expression was observed when expressing exogenous tagged PABP (Figure 2C). This result was expected, since PABP has been shown to inhibit the translation of its own mRNA by binding to poly (A) sequences found in the 5' UTR. This fortuitous auto-regulatory mechanism is believed to keep the level of PABP constant in the cell, thereby avoiding excessive overexpression .
No changes in cell growth or mortality of the cell lines used in this study or in other cells lines expressing tagged-PABP were observed. Moreover, the cell cycle kinetics of T98G cells expressing Flag-PABP and cells that were subsequently stimulated by serum addition were compared to those of T98G cells containing the empty vector, pCMVneo and no substantial differences were observed using fluorescent cell sorting (Figure 2D). We conclude that expression of neither the authentic PABP, nor the tagged-PABP has untoward effects on the growth and homeostasis of these cells.
PABP does not exchange between mRNAs in cell extracts
We have also addressed the potential problem of having a pool of free PABP in an extract that could be available to bind mRNA during incubation. Sucrose gradient analysis indicated that this is very unlikely since the majority of PABP was found in heavy polysomes and associated with mRNA, while only a small percentage was found in the upper portion of the sucrose gradients (H.S. and J.D.K., unpublished data). In total, these results demonstrate that reassortment of PABP in these cell extracts was not a significant limitation to using PABP RNPs for gene expression profiling of bound mRNAs.
As noted above, it has been suggested that yeast PABP uses a "hopping" mechanism in vivo by moving from RNA to RNA [27, 34]. While this experiment is not a direct test of that hypothesis, these data are not consistent with a hopping or exchange of PABP among the mRNAs in our cell extracts, but suggest instead that PABP forms a stable RNP complex with polyadenylated transcripts.
Detection of cell-specific mRNAs using epitope-tagged PABP
Analysis of PABP-associated mRNA populations using microarrays
To identify the PABP-associated mRNAs in the T98G Flag-PABP and PY4.1 G10-PABP cells, extracts were prepared as described above, followed by immunoprecipitation with either anti-Flag or anti-G10 antibodies. The mRNA populations generated by both immunoprecipitations were analyzed on human and mouse 1.2 Atlas arrays. Cross-species hybridization was monitored and genes showing cross-species reactivity were eliminated from consideration. A comparison of Flag versus G10 PABP-associated mRNAs was performed to assess the degree of enrichment. In an average experiment for the mouse genes, 91 % (184 out of 202 detected genes) were enriched at least 4 fold in the G10 PABP population when compared to the Flag PABP population. For the human genes, 82.4 % (122 out if 148) were enriched at least 4 fold in the Flag PABP population in relation to the G10 PABP population (Figure 5 and supplementary data).
Changes in gene expression induced by co-cultivation of PY4.1 endothelial cells with 4T1 breast cancer cells
Having demonstrated that the approach we described using PABP can be used to efficiently recover cell type specific mRNAs from mixed cell types, we addressed the consequences of cell-cell communication and changes in gene expression that were induced in the endothelial cells by co-cultivation with the tumor cell. The goal of these experiments is to gain insight into how endothelial cells respond to the presence of cancer cells in cell culture as a first approximation of changes in gene expression that may be involved in early stages of angiogenesis. We used two murine cell lines, the PY4.1 line described above and a 4T1 breast tumor cell line that can produce tumors and spread by metastasis in nude mice.
List of the top 20 PY4.1 genes that were upregulated in response to the presence of 4T1 tumor cells. Genes are classified according to their biological function. Gene expression regulators (GR). Genes involved in metabolism (M). Genes related to cell cycle or cell division (C). Genes encoding structural proteins (S). Other genes (O).
1-Heterogeneous nuclear ribonucleoproteinH1, (G R)
RNA binding, RNA processing and modification
2-High mobility group box 1, (G R)
DNA binding, nitric oxide biosynthesis, inflammation mediator, cell differentiation
cell proliferation, cell division
4-RIKEN cDNA2610016F04 gene, (G R)
putative DNA binding, transcritionfactor
5-ATPase, H+ transporting, (M)
hydrogen-exporting, ATPaseactivity, phosphorylativemechanism
6-RIKEN cDNA2510010F10 gene, (O)
described as a carnitinedeficiency-associated gene
7-Stem-loop binding protein, (G R)
RNA binding, histonemRNA processing
8-Quaking, (G R)
RNA binding, participates in myelination
9-RIKEN cDNA2410004I17 gene, (O)
10-Purinerich element binding protein A, (G R)
DNA and RNA binding, association with rough endoplasmic reticulum, postnatal brain development
11-Similar to isopentenyl-diphosphatedelta isomerase, (M)
cholesterol biosynthesis, steroid biosynthesis
12-P53 apoptosis effectorrelated to Pmp22, (O)
induction of apoptosis
13-Tumor differentially expressed 1, like, (S)
14-RIKEN cDNA5830409B12 gene, (S)
putative cytoskeleton associated protein
15-G7e protein, (S)
resembles viral envelope genes
16-Procollagenlysine, 2-oxoglutarate 5-dioxygenase 2, (M)
17-Receptor-like tyrosine kinase, (M)
ATP binding, kinaseactivity
18-RIKEN cDNA4930506D01 gene, (G R)
putative transcription factor
19-MusmusculusBRUL4 (Brul4) mRNA, (G R)
RNA binding, translation regulator
cell cycle, cyclin-dependent protein kinaseregulator activity
2-Gastroenterology. 2002. 123:790–802; J LeukocBiol. 2002. 72:1084–1091
3-Peptides. 2000. 21:1433–1446; IntJ Biochem Cell Biol. 1999. 31:1243–1248.
5-J BiolChem. 2002. 277:36296–36303; Gene. 2003. 302: 147–153.
6-Biochim BiophysActa. 2002. 1577:437–444.
7-J. Cell Sci. 2002. 115: 4577–4586.
8-Neuron. 2002. 36:815–829; Nucleic Acids Res. 2003.31:4616–4624
10-J BiolChem. 2002.277:37804–37810; Mol Cell Biol. 2003. 23:6857–6875
11-J Mol Evol. 2003. 57: 282–291.
12-Genes Dev. 2000. 14: 704–718.
13-J Exp Biol. 2000. 203:447–457.
15-Genomics. 1996. 15:5–12
16-J BiolChem. 2003. 278:40967–40972.
18-J Immunol. 1995. 154:1157–1166; Proc NatlAcadSciU S A. 1992. 89:11818–11822.
19-CytogenetGenome Res. 2002.97:254–260.
20-Gene. 2000. 256: 59–67.
The presence of several RNA binding proteins among the top-20 genes affected by co-cultivation may result in downstream effects on gene expression, and we plan to examine the target mRNAs of these RNA binding proteins in endothelial cells. This should help elucidate additional post-transcriptional pathways and networks regulating cell growth mechanisms and tumorigenesis .
This study describes changes in the gene expression profile of an endothelial cell when co-cultivated with a tumor cell by isolating ribonucleoprotein complexes and identifying their associated mRNAs using genomic arrays. Moreover, it presents a model system that can be used to elucidate post-transcriptional operons in specific types of cells by using various RNA binding proteins from mixed cell cultures as a novel approach to understanding how cell-cell communication affects gene expression during tumorigenesis and organogenesis.
Cell lines and media
Murine endothelial PY4.1 cells were kindly provided by Dr. Christopher Kontos, Duke University Medical Center. Human gliobastoma T98G and murine breast cancer 4T1 cells were obtained from American Type Culture Collection. All cell lines were maintained in DMEM Medium (Gibco) supplemented with 10% Fetal Bovine serum.
Constructs and stable cell lines
The ORF of human PABP I containing the Flag tag (GACTACAAGGACGACGATGACAAG) or the G10 tag (CCACCATGGCT AGCATGACTGGTGGACAGCAAATGGGT) at the 5' end was cloned into the pCMV-Neo retroviral vector . Stable lines expressing the Flag-PABP (T98G and PY 4.1 cells) and the G10-PABP (PY 4.1 cells) were obtained according the protocol described in the Pantropic Retroviral Expression System (Clontech).
Monoclonal anti-G10 antibodies were obtained as previously described . Antibodies against Flag and α-tubulin were obtained from SIGMA. A PABP carboxy-terminal (last 172 amino acids) was prepared by cloning a PCR product into the pGEXCS expression vector. The protein was purified by their affinity to glutathione beads (Amersham Biosciences). The purified proteins were dialyzed against 1 × PBS, 20% glycerol and sent to COVANCE Inc., where a rabbit was immunized.
Protein preparation and Western analysis
Protein extracts were prepared from T98G and PY4.1 cell lines by homogenization in polysomal lysis buffer . 50 μg of extract were fractionated by electrophoresis in 10% polyacrylamide-SDS Laemmli gels. Proteins were transferred to nitrocellulose membranes using a transfer cell (Bio-Rad). After blocking with 5% nonfat milk in PBS-Tween 20 buffer, the membranes were incubated with anti-PABP rabbit serum (1:10,000 dilution), anti-Flag antibody (1:1,500 dilution) or anti-G10 antibody (1:10,000 dilution). Anti-rabbit or anti-mouse HPC IgGs (Amersham Biosciences) were used as secondary antibodies at a 1:3000 dilution. Blots were developed using an ECL detection kit (Amersham Biosciences) and exposed to film.
Cell cycle experiments
Analysis of the cell cycle of T98G cells was performed as described .
Immunoprecipitation of mRNP complexes from cell lysates
Cell lysates and immunoprecipitation of mRNP complexes were essentially performed as described . Polyadenylated RNA (free poly-A) used in competition experiments was obtained from Amersham Biosciences.
RNase Protection Assay
Total or immunoprecipitated RNAs were assayed by RNase protection by using the PharMingen Riboquant assay according to the manufacturer's recommendations (45014K). mAngio-1 (mouse angiogenesis) and hTS1 (human tumor suppressor) template sets were used (551418 and 556161, respectively). Protected riboprobe fragments were visualized on a phosphorimaging screen (Molecular Dynamics). Phosphorimages were scanned by using the Molecular Dynamics STORM 860SYSTEM at 100 μm resolution and analyzed by using Molecular Dynamics IMAGE QUANT software (version 5.0).
Clontech microarrays, probing and analysis
cDNA array analysis was performed by using Atlas Mouse and Human 1.2 Arrays (CLONTECH). Probing of cDNA arrays was performed as described in the CLONTECH Atlas cDNA Expression Arrays User Manual (PT3140-1). Reverse-transcribed probes were radiolabeled with 32P α-dATP (Amersham Biosciences). After hybridization, the array membrane was washed and the results were visualized on a phosphorimaging screen (Molecular Dynamics). Phosphorimages were scanned by using the Molecular Dynamics STORM 860SYSTEM at 100 μm resolution and stored as .gel files. Images were analyzed by using ATLASIMAGE 2.01 software (CLONTECH). Global normalization was used when arrays being compared had approximately the same number of positive hits.
Printed oligo arrays, probing and analysis
Printed oligo arrays using the Operon Mouse Oligo set version 2.0 (16,423 genes) were produced by the Duke Microarray Core Facility. Protocols used for preparation of slides, labeling, amplification, hybridization and scanning are described in http://www.mgm.duke.edu/genome/dna_micro/core/protocols.htm.
GenePix data were normalized with pin-tip specific lowess normalization . Differentially expressed genes were identified with a moderated t-test, which shrinks the estimated sample variances towards a pooled estimate . This moderated t-test is more robust when the number of arrays is small. The candidate gene list is sorted by the p-value. All calculations were conducted using the bioconductor package .
List of abreviations
ELAV – embryonic lethal abnormal vision
RPA – RNase Protection Assay
PABP – Poly A binding protein
LOFP was responsible for the experimental design and performed Western blots, immunoprecipitations, RPAs and microarray experiments. MDB helped perform RPAs and microarray experiments. SML performed statistic analysis of microarray data and prepared the webpage with supplementary data. HS generated the stable cell lines used in this study and performed the cell cycle experiment. JDK conceived the project and assisted in experimental design.
We thank Amy Sims for technical help, and Juan Valcarcel and Karl Simin for comments on the manuscript, as well as S.G. Gao and his group for technical help. This work was supported by research grant CA79907 to J.D.K. from the National Institutes of Health.
- Macgregor PF, Squire JA: Application of microarrays to the analysis of gene expression in cancer. Clin Chem. 2002, 48 (8): 1170-1177.PubMedGoogle Scholar
- Ring BZ, Ross DT: Microarrays and molecular markers for tumor classification. Genome Biol. 2002, 3 (5): comment 2005Google Scholar
- Hanahan D, Weinberg RA: The hallmarks of cancer. Cell. 2000, 100 (1): 57-70. 10.1016/S0092-8674(00)81683-9View ArticlePubMedGoogle Scholar
- Folkman J: Angiogenesis in cancer, vascular, rheumatoid and other disease. Nat Med. 1995, 1 (1): 27-31. 10.1038/nm0195-27View ArticlePubMedGoogle Scholar
- Mills JC, Roth KA, Cagan RL, Gordon JI: DNA microarrays and beyond: completing the journey from tissue to cell. Nat Cell Biol. 2001, 3 (8): E175-178. 10.1038/35087108View ArticlePubMedGoogle Scholar
- Rubin MA: Use of laser capture microdissection, cDNA microarrays, and tissue microarrays in advancing our understanding of prostate cancer. J Pathol. 2001, 195 (1): 80-86. 10.1002/path.892View ArticlePubMedGoogle Scholar
- St Croix B, Rago C, Velculescu V, Traverso G, Romans KE, Montgomery E, Lal A, Riggins GJ, Lengauer C, Vogelstein B: Genes expressed in human tumor endothelium. Science. 2000, 289 (5482): 1197-1202. 10.1126/science.289.5482.1197View ArticlePubMedGoogle Scholar
- Keene JD: Ribonucleoprotein infrastructure regulating the flow of genetic information between the genome and the proteome. Proc Natl Acad Sci U S A. 2001, 98 (13): 7018-7024. 10.1073/pnas.111145598PubMed CentralView ArticlePubMedGoogle Scholar
- Keene J, Tenenbaum S: Eukaryotic mRNPs may represent posttranscriptional operons. Mol Cell. 2002, 9 (6): 1161-1167. 10.1016/S1097-2765(02)00559-2View ArticlePubMedGoogle Scholar
- Tenenbaum SA, Carson CC, Lager PJ, Keene JD: Identifying mRNA subsets in messenger ribonucleoprotein complexes by using cDNA arrays. Proc Natl Acad Sci U S A. 2000, 97 (26): 14085-14090. 10.1073/pnas.97.26.14085PubMed CentralView ArticlePubMedGoogle Scholar
- Brown V, Jin P, Ceman S, Darnell JC, O'Donnell WT, Tenenbaum SA, Jin X, Feng Y, Wilkinson KD, Keene JD: Microarray identification of FMRP-associated brain mRNAs and altered mRNA translational profiles in fragile X syndrome. Cell. 2001, 107 (4): 477-487. 10.1016/S0092-8674(01)00568-2View ArticlePubMedGoogle Scholar
- Gao FB, Carson CC, Levine T, Keene JD: Selection of a subset of mRNAs from combinatorial 3' untranslated region libraries using neuronal RNA-binding protein Hel-N1. Proc Natl Acad Sci U S A. 1994, 91 (23): 11207-11211.PubMed CentralView ArticlePubMedGoogle Scholar
- Hieronymus H, Silver PA: Genome-wide analysis of RNA-protein interactions illustrates specificity of the mRNA export machinery. Nat Genet. 2003, 33 (2): 155-161. 10.1038/ng1080View ArticlePubMedGoogle Scholar
- Grigull J, Mnaimneh S, Pootoolal J, Robinson MD, Hughes TR: Genome-wide analysis mRNA stability using transcription inhibitors and microarrays reveals posttranscriptional control of ribosome biogenesis factors. Mol Cell Biol. 2004, 24: 5534-5547. 10.1128/MCB.24.12.5534-5547.2004PubMed CentralView ArticlePubMedGoogle Scholar
- Gerber AP, Herschlag D, Brown PO: Extensive Association of Functionally and Cytotopically Related mRNAs with Puf Family RNA-Binding Proteins in Yeast. Plos. 2004, 2: 342-354. 10.1371/journal.pbio.0020079.View ArticleGoogle Scholar
- Cardinali B, Carissimi C, Gravina P, Pierandrei-Amaldi P: La protein is associated with TOP mRNAs in actively translating polysomes. J Biol Chem. 2003, 278: 35145-35151. 10.1074/jbc.M300722200View ArticlePubMedGoogle Scholar
- Figueroa A, Cuadrado A, Fan J, Atasoy U, Muscat GE, Munoz-Canoves P, Gorospe M, Munoz A: Role of HuR in skeletal myogenesis through coordinate regulation of muscle differentiation genes. Mol Cell Biol. 2003, 23 (14): 4991-5004. 10.1128/MCB.23.14.4991-5004.2003PubMed CentralView ArticlePubMedGoogle Scholar
- Intine RV, Tenenbaum SA, Sakulich AL, Keene JD, Maraia RJ: Differential phosphorylation and subcellular localization of La RNPs associated with precursor tRNAs and translation-related mRNAs. Mol Cell. 2004, 12: 1301-1307. 10.1016/S1097-2765(03)00429-5.View ArticleGoogle Scholar
- Roy PJ, Stuart JM, Lund J, Kim SK: Chromosomal clustering of muscle-expressed genes in Caenorhabditis elegans. Nature. 2002, 418 (6901): 975-979.PubMedGoogle Scholar
- Gray NK, Coller JM, Dickson KS, Wickens M: Multiple portions of poly(A)-binding protein stimulate translation in vivo. Embo J. 2000, 19 (17): 4723-4733. 10.1093/emboj/19.17.4723PubMed CentralView ArticlePubMedGoogle Scholar
- Sonenberg N: Translational Control. 2000, Cold Spring Harbor, NY: Cold Spring Harbor PressGoogle Scholar
- Gallie DR: A tale of two termini: a functional interaction between the termini of an mRNA is a prerequisite for efficient translation initiation. Gene. 1998, 216 (1): 1-11. 10.1016/S0378-1119(98)00318-7View ArticlePubMedGoogle Scholar
- Jacobson A, : Translational Control. 1996, Cold Spring Harbor, NY: Cold Spring Harbor PressGoogle Scholar
- Bernstein P, Peltz SW, Ross J: The poly(A)-poly(A)-binding protein complex is a major determinant of mRNA stability in vitro. Mol Cell Biol. 1989, 9 (2): 659-670.PubMed CentralPubMedGoogle Scholar
- Wei CC, Balasta ML, Ren J, Goss DJ: Wheat germ poly(A) binding protein enhances the binding affinity of eukaryotic initiation factor 4F and (iso)4F for cap analogues. Biochemistry. 1998, 37 (7): 1910-1916. 10.1021/bi9724570View ArticlePubMedGoogle Scholar
- Wormington M, Searfoss AM, Hurney CA: Overexpression of poly(A) binding protein prevents maturation-specific deadenylation and translational inactivation in Xenopus oocytes. Embo J. 1996, 15 (4): 900-909.PubMed CentralPubMedGoogle Scholar
- Sachs AB, Davis RW, Kornberg RD: A single domain of yeast poly(A)-binding protein is necessary and sufficient for RNA binding and cell viability. Mol Cell Biol. 1987, 7 (9): 3268-3276.PubMed CentralPubMedGoogle Scholar
- Gygi SP, Rochon Y, Franza BR, Aebersold R: Correlation between protein and mRNA abundance in yeast. Mol Cell Biol. 1999, 19 (3): 1720-1730.PubMed CentralPubMedGoogle Scholar
- Ideker T, Thorsson V, Ranish JA, Christmas R, Buhler J, Eng JK, Bumgarner R, Goodlett DR, Aebersold R, Hood L: Integrated genomic and proteomic analyses of a systematically perturbed metabolic network. Science. 2001, 292 (5518): 929-934. 10.1126/science.292.5518.929View ArticlePubMedGoogle Scholar
- Griffin TJ, Gygi SP, Ideker T, Rist B, Eng J, Hood L, Aebersold R: Complementary profiling of gene expression at the transcriptome and proteome levels in Saccharomyces cerevisiae. Mol Cell Proteomics. 2002, 1 (4): 323-333. 10.1074/mcp.M200001-MCP200View ArticlePubMedGoogle Scholar
- Garcia-Sanz JA, Mikulits W, Livingstone A, Lefkovits I, Mullner EW: Translational control: a general mechanism for gene regulation during T cell activation. Faseb J. 1998, 12 (3): 299-306.PubMedGoogle Scholar
- Mikulits W, Pradet-Balade B, Habermann B, Beug H, Garcia-Sanz JA, Mullner EW: Isolation of translationally controlled mRNAs by differential screening. Faseb J. 2000, 14 (11): 1641-1652. 10.1096/fj.14.11.1641View ArticlePubMedGoogle Scholar
- Wu J, Bag J: Negative control of the poly(A)-binding protein mRNA translation is mediated by the adenine-rich region of its 5'-untranslated region. J Biol Chem. 1998, 273 (51): 34535-34542. 10.1074/jbc.273.51.34535View ArticlePubMedGoogle Scholar
- Sachs AB, Bond MW, Kornberg RD: A single gene from yeast for both nuclear and cytoplasmic polyadenylate-binding proteins: domain structure and expression. Cell. 1986, 45 (6): 827-835. 10.1016/0092-8674(86)90557-XView ArticlePubMedGoogle Scholar
- Dominski Z, Marzluff WF: Formation of the 3' end of histone mRNA. Gene. 1999, 239: 1-14. 10.1016/S0378-1119(99)00367-4View ArticlePubMedGoogle Scholar
- Webster PJ, Liang L, Berg CA, Lasko P, Macdonald PM: Translational repressor bruno plays multiple roles in development and is widely conserved. Genes Dev. 1997, 11: 2510-2521.PubMed CentralView ArticlePubMedGoogle Scholar
- Hardy R: Molecular defects in the dysmyelinating mutant quaking. J Neurosci Res. 1998, 51: 417-422. 10.1002/(SICI)1097-4547(19980215)51:4<417::AID-JNR1>3.0.CO;2-FView ArticlePubMedGoogle Scholar
- Bender MA, Palmer TD, Gelinas RE, Miller AD: Evidence that the packaging signal of Moloney murine leukemia virus extends into the gag region. J Virol. 1987, 61 (5): 1639-1646.PubMed CentralPubMedGoogle Scholar
- Lutz-Freyermuth C, Query CC, Keene JD: Quantitative determination that one of two potential RNA-binding domains of the A protein component of the U1 small nuclear ribonucleoprotein complex binds with high affinity to stem-loop II of U1 RNA. Proc Natl Acad Sci U S A. 1990, 87 (16): 6393-6397.PubMed CentralView ArticlePubMedGoogle Scholar
- Lisztwan J, Marti A, Sutterluty H, Gstaiger M, Wirbelauer C, Krek W: Association of human CUL-1 and ubiquitin-conjugating enzyme CDC34 with the F-box protein p45(SKP2): evidence for evolutionary conservation in the subunit composition of the CDC34-SCF pathway. Embo J. 1998, 17 (2): 368-383. 10.1093/emboj/17.2.368PubMed CentralView ArticlePubMedGoogle Scholar
- Penalva LOF, Tenenbaum SA, Keene JD: Gene Expression Analysis of mRNP Complexes. In: Methods in Molecular Biology – mRNA Processing and Metabolism. Edited by: Shonenberg D. 2004, 257: 125-134. 10.1385/1-59259-750-5:125. Totowa, NJ: Human PressView ArticleGoogle Scholar
- Yang YH, Dudoit S, Luu P, Lin DM, Peng V, Ngai J, Speed TP: Normalization for cDNA microarray data: a robust composite method addressing single and multiple slide systematic variation. Nucleic Acids Res. 2002, 30 (4): e15- 10.1093/nar/30.4.e15PubMed CentralView ArticlePubMedGoogle Scholar
- Smyth GK: Linear Models and Empirical Bayes Methods for Assessing Differential Expression in Microarray Experiments. In: Statistical Applications in Genetics and Molecular Biology. 2004, 3: Article 3Google Scholar
- Dudoit S, Gentleman RC, Quackenbush J: Open source software for the analysis of microarray data. Biotechniques. 2003, Suppl: 45-51.PubMedGoogle Scholar
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