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Copyright © 2007 The Author(s) The abundance of RNPS1, a protein component of the exon junction complex, can determine the variability in efficiency of the Nonsense Mediated Decay pathway 1Department of Pediatric Oncology, Hematology and Immunology, Children's Hospital, University of Heidelberg, Im Neuenheimer Feld 150, 69120 Heidelberg, Germany, 2Molecular Medicine Partnership Unit (University of Heidelberg and European Molecular Biology Laboratory) and 3European Molecular Biology Laboratory, Gene Expression Unit, Meyerhofstr 1, 69117 Heidelberg, Germany *To whom correspondence should be addressed. Phone: +49 6221 56 2303, Fax: +49 6221 56 4559, Email: andreas.kulozik/at/med.uni-heidelberg.de Correspondence may also be addressed to Matthias W. Hentze. Phone: +49 6221 387 501, Fax: +49 6221 387 518, Email: hentze/at/embl.de Received February 13, 2007; Revised May 9, 2007; Accepted May 27, 2007. This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. This article has been cited by other articles in PMC.Abstract Nonsense-mediated mRNA decay (NMD) is a molecular pathway of mRNA surveillance that ensures rapid degradation of mRNAs containing premature translation termination codons (PTCs) in eukaryotes. NMD has been shown to also regulate normal gene expression and thus emerged as one of the key post-transcriptional mechanisms of gene regulation. Recently, NMD efficiency has been shown to vary between cell types and individuals thus implicating NMD as a modulator of genetic disease severity. We have now specifically analysed the molecular mechanism of variable NMD efficiency and first established an assay system for the quantification of NMD efficiency, which is based on carefully validated cellular NMD target transcripts. In a HeLa cell model system, NMD efficiency is shown to be remarkably variable and to represent a stable characteristic of different strains. In one of these strains, low NMD efficiency is shown to be functionally related to the reduced abundance of the exon junction component RNPS1. Furthermore, restoration of functional RNPS1 expression, but not of NMD-inactive mutant proteins, also restores efficient NMD in this model. We conclude that cellular concentrations of RNPS1 can modify NMD efficiency and propose that cell type specific co-factor availability represents a novel principle that controls NMD. INTRODUCTION Nonsense mediated decay (NMD) is a surveillance pathway by which cells recognize and limit the expression of mRNAs containing premature stop codons (PTCs) and thus reduce the expression of potentially harmful truncated proteins (1–4). Originally, NMD was thought to represent a control mechanism to limit the expression of faulty transcripts with frameshift or nonsense mutations, which originate from point mutations or from aberrant splicing. The finding of NMD being involved in negative feedback loops regulating normal gene expression foreshadowed a wider role of NMD as a basic post-transcriptional cellular process (5–7). More recently, microarray analyses of yeast (8,9), Drosophila (10) and human cells (11–13) have revealed that NMD modulates the levels of a large number of normal transcripts. Furthermore, NMD has been suggested to vary in its efficiency. In Saccharomyces cerevisiae, the degradation of the pre-mRNA of CYH2 (an endogenous NMD target) has been reported to vary in different strains (14). In humans, the expression of dystrophin and JARID1C genes carrying identical nonsense mutations has been reported to differ and to modulate disease severity (15,16). Moreover, tissue-specific differences of NMD efficiency for nonsense-mutated collagen X have been suggested in a patient with Schmid metaphyseal chondrodysplasia (17). More recently, intertissue and interindividual variations in NMD efficiency have been proposed in the study of two fetuses diagnosed with Roberts syndrome and carrying a homozygous frameshift mutation in the ESCO2 gene (18). These observations led to the hypothesis that variations of NMD efficiency may contribute to the phenotypic variability of hereditary disorders (19,20). However, it has so far been difficult to quantify NMD efficiency. Here, we have developed an assay system that estimates differences of NMD efficiency based on an internally controlled measurement of the expression of cellular NMD targets. Applying this assay in a HeLa cell model system we demonstrate variable NMD efficiency between strains. Functionally, these differences are shown to be caused by a deficiency of RNPS1, a key protein in at least one of the known NMD pathways (12). We thus propose that cell type specific co-factor availability represents a novel principle that controls NMD. MATERIALS AND METHODS Cell culture, transfections, RNA isolation and analysis HeLa cells were grown in DMEM supplemented with 10% fetal calf serum (FCS) and 1% penicillin/streptomycin at 37°C and 5% CO2. HeLa strain A has been used by our laboratory for many years (21,22). Strain B (ACC 57) was purchased at the German Repository of Cell lines (DSMZ). Strain C was kindly provided by Dr Elisa Izaurralde (EMBL, Heidelberg). For plasmid and siRNA transfections, we used previously described methods (23). We isolated RNA according to standard protocols with TRIzol reagent (Invitrogen, CA, USA) and performed northern blot analysis as described previously (23) using 2–3 µg RNA per lane. Target sequences of siRNAs for luciferase, UPF1 and UPF2 were described previously (23). For estimations of mRNA half-life, actinomycinD (5 μg/ml) was added to the growth medium 48 h after siRNA treatment and RNA was collected every hour. Transcript abundance was quantified by quantitative RT-PCR as in the other cases. The half-life of the FOS transcript was used to monitor efficient inhibition of transcription.Complementary RNA preparation and Microarray hybridization and analysis We assessed the integrity of total cytoplasmic RNA from the cultured cells using a Agilent 2100 Bioanalyzer (Agilent, Palo Alto, CA, USA). We performed preparation, processing and hybridization of labelled and fragmented cRNA targets to Affymetrix HG_U133A GeneChips™ according to the manufacturer's protocols (Affymetrix Inc., Santa Clara, CA, USA). Oligonucleotide arrays were scanned using a confocal laser scanner (GeneArray™, Hewlett Packard, Palo Alto, CA, USA). Statistical analysis of microarray data Three independent experiments with UPF1 siRNA or Luciferase siRNA as a negative control were analysed. We used the Affymetrix GeneChip Suite 5.0 software (MAS 5.0) to calculate raw expression values for each of the 22 283 probe sets on the U133A oligonucleotide array. Signal intensities were calculated as average intensity difference (AID) between perfect and mismatch probes. Approximately 8800 probe sets continuously resulting in absent calls were excluded from the analyses. Next, we used GeneSpring 4.2.1 (Silicon Genetics, Redwood City, CA, USA) for scaling, normalization and background correction of all genes and arrays. We performed Student's t-test on normalized relative expression ratios to identify significant differentially expressed genes with a minimum factor of difference of >2-fold, within the 95% confidence interval (P < 0.05). Full data sets are available in the Supplementary Data and on the Gene Expression Omnibus (GEO) repository (GSE7009).Quantitative Real-Time PCR (LightCycler) We synthesized first strand cDNA using MuMLV RNaseH- Reverse Transcriptase (MBI Fermentas) according to the manufacturer's protocol using 4 µg of RNA. We carried out real-time PCR, using the LightCycler system (Roche Diagnostics, Mannheim, Germany), as an independent method to assess differences of gene expression and to validate the microarray expression data. We performed expression analyses of selected genes with single-stranded cDNA and gene-specific primers (primer sequences are available on request). We used the FastStart DNA Master SYBR Green kit (Roche Diagnostics) to quantify the mRNA levels by measuring real-time fluorimetric intensity of SYBR green I incorporation. The working concentrations of gene-specific primer, MgCl2, enzyme and SYBR green as well as cycling parameters were optimized according to the LightCycler protocol (LightCycler Operator's Manual, Version 3.5). For the experiments done in exclusively in strain A cells, we used the concentration of glyceraldehyde-3-phosphate dehydrogenase (GAPDH) to normalize all other genes tested from identical cDNA samples. For the other experiments also the ribosomal protein L32 (RPL32), hypoxanthine phosphoribosyltransferase 1 (HPRT1) and core-binding factor-beta subunit (CBFB) were included as standard controls. The ratio of each analysed cDNA was determined as the mean of 4 or 5 experiments. Melting curves of the PCR products were performed for quality control. The primer sequences of SC35 and GAPDH were described previously (12). For TBL2: gcagtcatttaccacatgc/tattgtttctgcttcttggat, for GADD45B: gagtgagactgactgcaagc/tcttattaattcgcaaactgg, for NAT9: attgtgctggatgccgaga/acctagcgtggtcactccgta, for RPL32: ttgacaacagggttcgtag/ttcttggaggaaacattgtg, for HPRT1: gaccagtcaacaggggacat/aacacttcgtggggtccttttc and for CBFB: gcccatctttacatacaca/acttcaaattat tactggctac.Protein isolation and immunoblot analysis We prepared protein lysates with an isotonic lysis buffer as described previously (23). For total extracts, the buffer composition was 50 mM Tris-HCl, pH 7.5, 150 mM NaCl,1 mM EDTA,1% Triton X-100, 0.5% Deoxycholate, 0.1% SDS,1× Complete protease inhibitor (Roche). For the cytoplasmic fraction, the buffer was 50 mM Tris-HCl, pH 7.2, 150 mM NaCl, 0.5% (v/v) NP-40, 0.1% Deoxycholate, 5 mM Vanadyl-Ribosyl-complex, 1 mM Dithiothreitol, 0.5 mM PMSF, 1× Complete protease inhibitor (Roche). We performed immunoblot analysis of protein samples using 10–15 µg of total protein per lane as previously described (22).RESULTS Identification of bona fide cellular NMD targets We aimed at developing an assay to estimate differences in NMD efficiency based on the expression levels of physiological NMD transcripts. To identify a panel of endogenous direct NMD targets in human cells, HeLa cells were treated with siRNA against the NMD-key factor UPF1 (11,23,24) or Luciferase as a negative control. UPF1-specific immunoblotting showed that this protein was efficiently depleted to a level of <10% (Figure 1 283 probe sets, representing ~14 500 human genes, 9336 transcripts were expressed at a level of more than two SDs above background and were thus included in the analysis. A total of 265 probe sets (2.8%) representing 227 genes were up-modulated more than 2-fold, while 248 probe sets (2.6%) representing 202 genes were down-modulated more than 2-fold (Supplementary Data, Tables 1 and 2). These data indicate that a substantial number of genes are affected directly or indirectly by UPF1 activity.
In order to exclude transcripts that are affected by UPF1 depletion in an NMD-independent, non-post-transcriptional fashion, we analysed mRNA and pre-mRNA levels in a subset of 16 transcripts, chosen because of their strong differential expression in the microarray analyses. In several independent experiments performed on UPF1-depleted HeLa cells that showed efficiently inhibited NMD function (see Figure 1
Transcripts that are targeted by NMD are expected to be stabilized by an inhibition of this pathway. We thus analysed the decay rates of the KCNJ12, NAT9, SEPW1 and TBL2 mRNAs. We also included the GADD45B transcript, which has previously been suggested to represent an endogenous NMD target by in silico analysis (25) and is experimentally shown to be up-modulated by UPF1 depletion here (see below). Actinomycin D was added to cells pre-treated with siRNA against UPF1 or Luciferase. The short-lived FOS transcript was used as a positive control to assess the block of transcription (Figure 3
The role of NMD in directly modulating the abundance of the TBL2, NAT9 and GADD45B transcripts was further analysed by depleting UPF2, which interacts with UPF1 in the NMD pathway (29,30). The efficient depletion of UPF2 to ~10% was confirmed by immunoblotting (Figure 4
Different HeLa strains display remarkable variations in NMD efficiency Unpublished observations in our laboratory have previously suggested that different strains of HeLa cells may differ in their NMD capacity. We have now used these HeLa strains as a model system to quantify subtle differences in NMD efficiency and to gain mechanistic insight into this variability. The panel of five validated cellular NMD target transcripts (SC35 A+B, TBL2, NAT9 and GADD45B) was used to systematically analyse the NMD efficiency of three different HeLa cell strains (referred to as A, B and C). To avoid a potential bias of quantification against a single housekeeping gene, we selected four different transcripts (HPRT1, CBFB, GAPDH and RPL32) for normalisation purposes (32,33). This group of control transcripts was selected because (1) they showed <10% variability in all of our microarray experiments (data not shown); (2) they were expressed at different steady-state levels and (3) they belong to different metabolic pathways and are thus unlikely to be co-regulated. The comparison of the degree of up-modulation following UPF1 depletion showed similar results for all transcripts that were used for normalisation (Supplementary Figure 2), which indicated that all of these housekeeping genes can be used as standards. Quantification of the five endogenous NMD targets (SC35 A+B combined, GADD45B, NAT9 and TBL2) against the four standards in these strains gave reproducible results in four independent experiments (Figure 5
To validate our analysis, we estimated NMD efficiency by a direct comparison of the down-modulation of transfected, nonsense mutated β-globin (NS39) reporter in four independent experiments (Figure 5 These data demonstrate that differences of NMD efficiency between human cell lines can be estimated by measuring the abundance of transfected NMD-sensitive reporters and by analysing the abundance of a carefully validated panel of cellular NMD target transcripts. RNPS1 abundance modulates NMD efficiency Subsequently, we aimed at gaining insight into the mechanism of variable NMD efficiency in these HeLa strains. As a starting point, we analysed the abundance of the key NMD proteins UPF1, UPF2 and UPF3b and of the functionally critical exon junction complex components Y14, Magoh and RNPS1 by immunoblotting in both, total and cytoplasmic lysates (Figure 6
We next functionally analysed if RNPS1 might be the limiting factor for NMD in these cells and over-expressed functional RNPS1 (12) in cells that were transfected with β-globin reporter genes. We confirmed that the transfection of pCI-NEO-Flag has no effect on the abundance of endogenous RNPS1 in any strain (Figure 7
DISCUSSION NMD has recently emerged as one of the critical post-transcriptional processes that regulate gene expression by targeting transcripts with truncated reading frames (9). While the phenomenon of variable NMD efficiency has been observed by many groups studying NMD (15–18,34), we document here that NMD efficiency can be systematically analysed by quantifying bona fide cellular NMD targets. Such cellular NMD targets have previously been thought to represent ~1–10% of the total transcriptome of human cells and yeast (9,11). However, our simultaneous analysis of pre-mRNA and mRNA abundance and of mRNA stability of selected transcripts (Figures 2 The five cellular NMD target mRNAs that were analysed (SC35A, SC35B and the identified TBL2, GADD45B and NAT9) here were also shown to be UPF2-sensitive (Figure 4 NMD variability has previously been studied systematically only in yeast (14). The analysis of the yeast NMD substrate CYH2 pre-mRNA in strain crosses suggested that the variable efficiency of NMD is pleiotropic in this organism. Although we cannot discard a multi-gene effect to also be important in human cells, the findings reported here document that the abundance and functional availability of a single NMD co-factor can be limiting for NMD efficiency. NMD is thought to require the interaction of the exon junction complex (EJC) with the SURF complex that is recruited to the ribosome at the site of translation termination (30). The EJC is recruited to the RNA by the spliceosome and is remodelled during nucleo-cytoplasmic export (37). Structural analyses have shown that the EJC is anchored to the RNA by a core that consists of the proteins eIF4AIII and MNL51 (BTZ) and the Y14/Magoh heterodimer (38,39). At the periphery of the complex, a number of other proteins are thought to establish the interaction of the EJC with other protein networks and different cellular functions (40,41). The protein RNPS1 is one of these peripheral EJC proteins that have previously been shown to activate the NMD pathway following tethering to a NMD competent position of the mRNA (23,42) and to be an important component of one of two pathways implicated in NMD (12). Interestingly, the data reported here now functionally link the reduced abundance of this protein in one of the cell lines to low NMD efficiency, thus for the first time implicating the natural abundance of an EJC protein to the efficiency of NMD. SUPPLEMENTARY DATA Supplementary Data are available at NAR Online. [Supplementary Material]
ACKNOWLEDGEMENTS We thank Dr. Elisa Izaurralde for providing HeLa cells (strain C). This work was funded by grants from the Fritz Thyssen Stiftung and the Deutsche Forschungsgemeinschaft (DFG). Conflict of interest statement. None declared. REFERENCES 1. Mango SE. Stop making nonSense: the C. elegans smg genes. Trends Genet. 2001;17:646–53. [PubMed] 2. Gonzalez CI, et al. Nonsense-mediated mRNA decay in Saccharomyces cerevisiae. Gene. 2001;274:15–25. [PubMed] 3. Gatfield D, et al. Nonsense-mediated mRNA decay in Drosophila: at the intersection of the yeast and mammalian pathways. EMBO J. 2003;22:3960–3970. [PubMed] 4. Maquat LE. Nonsense-mediated mRNA decay: splicing, translation and mRNP dynamics. Nat. Rev. Mol. Cell Biol. 2004;5:89–99. [PubMed] 5. Sureau A, et al. SC35 autoregulates its expression by promoting splicing events that destabilize its mRNAs. EMBO J. 2001;20:1785–1796. [PubMed] 6. Wollerton MC, et al. Autoregulation of polypyrimidine tract binding protein by alternative splicing leading to nonsense-mediated decay. Mol. Cell. 2004;13:91–100. [PubMed] 7. Mitrovich QM, Anderson P. Unproductively spliced ribosomal protein mRNAs are natural targets of mRNA surveillance in C. elegans. Genes Dev. 2000;14:2173–2184. [PubMed] 8. Lelivelt MJ, Culbertson MR. Yeast upf proteins required for RNA surveillance affect global expression of the yeast transcriptome. Mol. Cell. Biol. 1999a;19:6710–6719. [PubMed] 9. He F, et al. Genome-wide analysis of mRNAs regulated by the nonsense-mediated and 5′ to 3′ mRNA decay pathways in yeast. Mol. Cell. 2003;12:1439–1452. [PubMed] 10. Rehwinkel J, et al. Nonsense-mediated mRNA decay factors act in concert to regulate common mRNA targets. RNA. 2005;11:1530–1544. [PubMed] 11. Mendell JT, et al. Nonsense surveillance regulates expression of diverse classes of mammalian transcripts and mutes genomic noise. Nat. Genet. 2004;36:1073–1078. [PubMed] 12. Gehring NH, et al. Exon-junction complex components specify distinct routes of nonsense-mediated mRNA decay with differential cofactor requirements. Mol. Cell. 2005;20:65–75. [PubMed] 13. Wittmann J, Hol EM, Jack HM. hUPF2 silencing identifies physiologic substrates of mammalian nonsense-mediated mRNA decay. Mol. Cell Biol. 2006;26:1272–1287. [PubMed] 14. Kebaara B, et al. Genetic background affects relative nonsense mRNA accumulation in wild-type and upf mutant yeast strains. Curr. Genet. 2003;43:171–177. [PubMed] 15. Kerr TP, et al. Long mutant dystrophins and variable phenotypes: evasion of nonsense-mediated decay? Hum. Genet. 2001;109:402–407. [PubMed] 16. Jensen LR, et al. Mutations in the JARID1C gene, which is involved in transcriptional regulation and chromatin remodeling, cause X-linked mental retardation. Am. J. Hum. Genet. 2005;76:227–236. [PubMed] 17. Bateman JF, et al. Tissue-specific RNA surveillance? Nonsense-mediated mRNA decay causes collagen X haploinsufficiency in Schmid metaphyseal chondrodysplasia cartilage. Hum. Mol. Genet. 2003;12:217–225. [PubMed] 18. Resta N, et al. A homozygous frameshift mutation in the ESCO2 gene: evidence of intertissue and interindividual variation in Nmd efficiency. J. Cell Physiol. 2006;209:67–73. [PubMed] 19. Frischmeyer PA, Dietz HC. Nonsense-mediated mRNA decay in health and disease. Hum. Mol. Genet. 1999;8:1893–1900. [PubMed] 20. Holbrook JA, et al. Nonsense-mediated decay approaches the clinic. Nat. Genet. 2004;36:801–808. [PubMed] 21. Enssle J, et al. Determination of mRNA fate by different RNA polymerase II promoters. Proc. Natl Acad. Sci. USA. 1993;90:10091–10095. [PubMed] 22. Thermann R, et al. Binary specification of nonsense codons by splicing and cytoplasmic translation. EMBO J. 1998;17:3484–3494. [PubMed] 23. Gehring NH, et al. Y14 and hUpf3b form an NMD-activating complex. Mol. Cell. 2003;11:939–949. [PubMed] 24. Mendell JT, ap Rhys CM, Dietz HC. Separable roles for rent1/hUpf1 in altered splicing and decay of nonsense transcripts. Science. 2002;298:419–422. [PubMed] 25. Hillman RT, Green RE, Brenner SE. An unappreciated role for RNA surveillance. Genome Biol. 2004;5:R8. [PubMed] 26. Leeds P, et al. Gene products that promote mRNA turnover in Saccharomyces cerevisiae. Mol. Cell. Biol. 1992;12:2165–2177. [PubMed] 27. Guan Q, et al. Impact of nonsense-mediated mRNA decay on the global expression profile of budding yeast. PLoS Genet. 2006;2:e203. [PubMed] 28. Ishigaki Y, et al. Evidence for a pioneer round of mRNA translation: mRNAs subject to nonsense-mediated decay in mammalian cells are bound by CBP80 and CBP20. Cell. 2001;106:607–617. [PubMed] 29. Singh G, Lykke-Andersen J. New insights into the formation of active nonsense-mediated decay complexes. Trends Biochem. Sci. 2003;28:464–466. [PubMed] 30. Kashima I, et al. Binding of a novel SMG-1-Upf1-eRF1-eRF3 complex (SURF) to the exon junction complex triggers Upf1 phosphorylation and nonsense-mediated mRNA decay. Genes Dev. 2006;20:355–367. [PubMed] 31. Nagy E, Maquat L. A rule for termination-codon position within intron-containing genes: when nonsense affects RNA abundance. Trends Biochem. Sci. 1998;23:198–199. [PubMed] 32. Jin P, et al. Selection and validation of endogenous reference genes using a high throughput approach. BMC Genomics. 2004;5:55. [PubMed] 33. Zhang X, Ding L, Sandford AJ. Selection of reference genes for gene expression studies in human neutrophils by real-time PCR. BMC Mol. Biol. 2005;6:4. [PubMed] 34. Hutchinson S, et al. Allelic variation in normal human FBN1 expression in a family with Marfan syndrome: a potential modifier of phenotype? Hum. Mol. Genet. 2003;12:2269–276. Epub 2003 Jul 22. [PubMed] 35. Kaygun H, Marzluff WF. Regulated degradation of replication-dependent histone mRNAs requires both ATR and Upf1. Nat. Struct. Mol. Biol. 2005;12:794–800. [PubMed] 36. Azzalin CM, Lingner J. The human RNA surveillance factor UPF1 is required for S phase progression and genome stability. Curr. Biol. 2006;16:433–439. [PubMed] 37. Ballut L, et al. The exon junction core complex is locked onto RNA by inhibition of eIF4AIII ATPase activity. Nat. Struct. Mol. Biol. 2005;12:861–869. Epub 2005 Sep 18. [PubMed] 38. Bono F, et al. The crystal structure of the exon junction complex reveals how it maintains a stable grip on mRNA. Cell. 2006;126:713–725. [PubMed] 39. Andersen CB, et al. Structure of the exon junction core complex with a trapped DEAD-box ATPase bound to RNA. Science. 2006;313:1968–1972. [PubMed] 40. Shibuya T, et al. eIF4AIII binds spliced mRNA in the exon junction complex and is essential for nonsense-mediated decay. Nat. Struct. Mol. Biol. 2004;11:346–351. Epub 2004 Mar 21. [PubMed] 41. Tange TO, et al. Biochemical analysis of the EJC reveals two new factors and a stable tetrameric protein core. RNA. 2005;11:1869–1883. [PubMed] 42. Lykke-Andersen J, Shu MD, Steitz JA. Communication of the position of exon-exon junctions to the mRNA surveillance machinery by the protein RNPS1. Science. 2001;293:1836–1839. [PubMed] |
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[Mol Cell. 2005]Proc Natl Acad Sci U S A. 1993 Nov 1; 90(21):10091-5.
[Proc Natl Acad Sci U S A. 1993]EMBO J. 1998 Jun 15; 17(12):3484-94.
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[EMBO J. 1998]EMBO J. 1998 Jun 15; 17(12):3484-94.
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[Nat Genet. 2004]Mol Cell. 2003 Apr; 11(4):939-49.
[Mol Cell. 2003]Science. 2002 Oct 11; 298(5592):419-22.
[Science. 2002]Genome Biol. 2004; 5(2):R8.
[Genome Biol. 2004]Nat Genet. 2004 Oct; 36(10):1073-8.
[Nat Genet. 2004]Mol Cell Biol. 1992 May; 12(5):2165-77.
[Mol Cell Biol. 1992]PLoS Genet. 2006 Nov 24; 2(11):e203.
[PLoS Genet. 2006]Cell. 2001 Sep 7; 106(5):607-17.
[Cell. 2001]Trends Biochem Sci. 2003 Sep; 28(9):464-6.
[Trends Biochem Sci. 2003]Genes Dev. 2006 Feb 1; 20(3):355-67.
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[Nat Genet. 2004]Trends Biochem Sci. 1998 Jun; 23(6):198-9.
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[BMC Genomics. 2004]BMC Mol Biol. 2005 Feb 18; 6(1):4.
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