![]() | ![]() |
Formats:
|
||||||||||||||||||||||||||||||||||||||
Copyright © 2007 Qin et al.; licensee BioMed Central Ltd. Global analyses of mRNA translational control during early Drosophila embryogenesis 1Howard Hughes Medical Institute, Department of Molecular & Cellular Biology, University of California, Berkeley, Berkeley CA 94720, USA 2InterMune, Inc., Brisbane, CA 94005, USA 3Department of Statistics, University of California, Berkeley, Berkeley CA 94720, USA 4Janelia Farm Research Campus, Howard Hughes Medical Institutes,19700 Helix Drive, Ashburn, VA 20147 Corresponding author.#Contributed equally. Xiaoli Qin: xlchin/at/gmail.com; Soyeon Ahn: ahn/at/stat.Berkeley.EDU; Terence P Speed: terry/at/stat.berkeley.edu; Gerald M Rubin: RubinG/at/jfrc.hhmi.org Received August 2, 2006; Revised January 2, 2007; Accepted April 22, 2007. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted 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 Background In many animals, the first few hours of life proceed with little or no transcription, and developmental regulation at these early stages is dependent on maternal cytoplasm rather than the zygotic nucleus. Translational control is critical for early Drosophila embryogenesis and is exerted mainly at the gene level. To understand post-transcriptional regulation during Drosophila early embryonic development, we used sucrose polysomal gradient analyses and GeneChip analysis to illustrate the translation profile of individual mRNAs. Results We determined ribosomal density and ribosomal occupancy of over 10,000 transcripts during the first ten hours after egg laying. Conclusion We report the extent and general nature of gene regulation at the translational level during early Drosophila embryogenesis on a genome-wide basis. The diversity of the translation profiles indicates multiple mechanisms modulating transcript-specific translation. Cluster analyses suggest that the genes involved in some biological processes are co-regulated at the translational level at certain developmental stages. Background In many animal species, the first few hours of life proceed with little or no transcription, and regulation of developmental events at these early stages is conferred by maternal cytoplasm rather than transcriptional activity in the zygotic nucleus. During the first two hours after fertilization, Drosophila embryos undergo 13 zygotic division cycles (Bownes' stages 1-4) and are syncytial in that the nuclei divide in a common cytoplasm without cytokenesis, except that pole cells, precursors to germline, are segregated in cycle 10. Synthesis of rRNA, tRNAs, 5S RNAs, snRNAs, poly(A)+ RNAs, and histone mRNAs is not detectable until cycle 11 or 12. Both spatial control and temporal control of mRNA translation are implemented in the early patterning of the Drosophila embryo. The basic embryonic body plan, defined by both anterior-posterior and dorso-ventral axes, as well as precursors for terminal structures, relies on the regulation of mRNA localization and coupled regulation of mRNA translation. Complete inhibition of protein synthesis with translation inhibitors, for example, cycloheximide, puromycin or pactamycin, quickly and entirely blocks mitotic cycles and arrests development [1,2]. After zygotic transcription begins at mitotic cycle 13 (about 1.5-2 hours after fertilization), the efficient use of zygotic transcripts depends on the degradation of maternal mRNA after fertilization [3]. The modulation of translation can be exerted by both general mechanisms that influence the mRNA population as a whole and selective mechanisms that influence individual mRNAs or small groups of mRNAs. In Drosophila, multiple mechanisms of translational control have been previously reported, such as control by RNA degradation, transcript localization and polyadenylation. Cis-regulatory RNA elements are generally found within the 5' or 3' untranslated regions of mRNAs (5' UTRs and 3' UTRs). For example, specific sequence elements in the 3' UTRs of Drosophila bicoid and nanos mRNAs guide these mRNAs to the anterior and posterior poles of the developing embryo, respectively. Unlocalized bicoid or nanos mRNAs are bound to translational repressor molecules, and proper localization of both mRNAs relieves the repression and permits their translation [4-8]. The sedimentation of a given mRNA when a cell extract is applied to a sucrose density gradient is determined by the number of its associated ribosomes. Changes in the size (the number of ribosomes per mRNA) and the amount (amplitude) of a specific polysome-associated transcript in a gradient can indicate regulation of protein synthesis [9]. Comparison of polysomal associated mRNA between developmental stages using microarray analysis provides an approach to a genomic-wide investigation of translation dynamics during development. This method has been successful in identifying cellular internal ribosomal entry sites (IRES) that are translated in mitotic HeLa cells and to describe the global translation profile of Saccharomyces cerevisiae [10-12]. We have taken a similar approach to a genome-level investigation of translational regulation during early embryogenesis in Drosophila. In this study, we have fractionated embryo extracts from a series of early stages by sedimentation on sucrose density gradients and analyzed the RNA components of these fractions using the Drosophila GeneChip Genome 2.0 array (Affymetrix, Santa Clara). Our analysis has focused on analyzing ribosomal density, generally and for individual transcripts, global translational activity during the first 10 hours after egg laying and coordination between transcription and translation regulation. Results and discussion We analyzed the translational status of transcripts during early embryogenesis in a genome-wide approach (Figure (Figure1).1
Analysis of ribosomal density The size of a polysome is determined by the number of associated ribosomes per mRNA transcript and, for a given mRNA, can be estimated from the position of the peak of gradient fractions containing that mRNA. The polysomes with fewer than 10 ribosomes per mRNA are well separated on 20% to 50% sucrose gradients, and the large polysomal complexes (> 10 ribosomes per mRNA) were assigned to fractions 11 and 12 by a logarithmic extrapolation [11]. As expected, we observed that the average ORF length of the mRNA species sedimenting in a given fraction increases from the low molecular weight fractions to the high molecular weight fractions (Figure (Figure3a).3a
To estimate the average ribosomal density, we used the ratio of the assigned number of associated ribosomes at each fraction to the median ORF length of all the mRNAs sedimenting in that fraction (Figure (Figure3b).3b Arava et al. [11] reported an unexpected inverse correlation in S. cerevisiae, namely that ribosomal density decreases with increasing ORF length. In a subsequent study, they concluded that the less frequent initiation of translation of mRNAs with longer ORF is responsible for the observed inverse correlation between the ORF length and ribosomal density [14]. When we analyzed our data using the methods described in Arava et al. [11], we also found an inverse correlation (Figure (Figure3c).3c The consistency of the average ribosomal density during the three developmental time periods we examined indicates that translational regulation of polysome size during early embryogenesis is exerted in a gene-specific manner, rather than at a general level. Based on available ORF lengths in Flybase, about 1.5% of transcripts have a ribosomal density lower than 200 nucleotides per ribosome at each time period and most of them stay in low-density polysomes throughout all three development periods. These mRNAs might have a lower ribosomal initiation rate due to modulation by certain cis-regulatory elements in the mRNA UTRs. However, we could not identify by computational methods any consensus features among those transcripts with a significantly low ribosomal density. Although long UTRs are likely to contain cis-regulatory RNA elements, we did not observe any correlation between the length of the ribosomal density and 5' UTR or 3' UTR length. This may result from both the limitation of computational analysis and the mixture of various cis-RNA regulatory elements within these UTRs. In addition, a prevailing feature of the polysomal profiles of individual transcripts at each of the three time periods examined is that the mRNAs are either sequestered from polysomes or fully loaded with polysomes to an extent correlated with their ORF lengths. This bimodal pattern suggests that most cis-RNA elements in the UTRs are likely to regulate the amount of a transcript associated with polysomes, instead of controlling the ribosome density of a transcript. Furthermore, we did not find significant over-presentation of any particular Gene Ontology (GO) terms among the mRNAs in the lowest 5% density. Our analysis of the 50 mRNAs with highest densities of associated ribosomes revealed that their calculated densities are derived from incorrectly predicted ORF lengths, or a possible cross-hybridization signal from their long alternatively spliced isoforms. These issues make it difficult to pinpoint the highest or lowest ribosomal densities from our analyses. However, these analyses do identify mRNAs whose ORF predictions warrant re-examination. Translation activity and ribosomal occupancy during embryonic development A key feature of translation status is the proportion of mRNAs associated with polysomes. The relative amounts of free ribosomes (fractions 1-5) to ribosomes engaged in polysomes (fractions 6-12) in the global polysomal profiles are similar at each of the three developmental time periods we examined (Figure (Figure4).4
The percentage of an individual RNA species in the polysomal fractions (fractions 6-12) is defined as the transcript's ribosomal occupancy [11]. The ribosomal occupancies of individual transcripts in each time period covered a wide range, from 20% to 100%, with most mRNA species only partially loaded on polysomes (Figure (Figure5).5
We classified mRNA species into three statistically defined groups reflecting their translational status: a preferentially translated group, a preferentially untranslated group, and the general group of remaining transcripts. Selected groups of mRNA species were defined by using a logit (see Materials and methods and Additional data file 1). Due to the dispersed distribution of ribosomal occupancy at 0-2 hours, more transcripts were found to be either preferentially translated or preferentially untranslated mRNAs than at the later stages. To understand the biological significance of translational control at each developmental stage, selected groups of mRNAs were clustered by the GO terms of biological process, molecular function and cellular component using the Affymetrix NetAffx analyses tool [15]. We identified the representative significant GO terms among the selected groups, which are listed in Tables 1 and 2 (0-2 hour old embryos), and Tables 3 and 4 (4-6 hour old embryos).
Some nuclear proteins, such as factors with general RNA polymerase II transcription activity (GO0016251) and transcription regulator activity (GO0030528) are the essential components of early zygotic transcription and embryonic pattern formation. It is perhaps unsurprising that most of their mRNAs are associated with polysomes, preferentially synthesizing their protein products in 0-2 hour old embryos (Table 2). At two hours after fertilization, the process of cellularization starts, forming mononucleate blastoderm cells and zygotic transcription begins. The active translation of proteins involved in RNA processing and metabolism in the 4-6 hour old embryos may facilitate the transition to active zygotic transcription. At this stage, nuclear proteins continue to be preferentially translated (Table 4). In contrast, synthesis of ribosomal proteins (rp) is highly inefficient in spite of the high abundance of their transcripts in 0-2 hour old and 4-6 hour old embryos (Tables 1 and 3). The selective silencing of rp-mRNAs during early embryo development is also observed in Xenopus in that mRNAs encoding ribosomal proteins are initially in mRNP particles and start to become mobilized to polysomes at a later stage [16], reflecting a need for new ribosomes [17]. A sufficient number of maternal ribosomes are stored in early Drosophila embryos before zygotic control begins (Figure (Figure4)4 From the GO cluster analyses, we found that mRNAs involved in a common process or cellular components can be specifically co-regulated over the stages of development. Sufficient maternally deposited ribosomes may result in the selective translational repression of rp-mRNAs in early Drosophila embryogenesis, and rp-mRNAs gradually move into polysomes when embryos require more ribosomes at the later developmental stages. In comparison to the general polysomal association profile, both the group of large ribosomal proteins (GO0005842) and the group of small ribosomal proteins (GO0005843) were gradually increased from early development (0-2 hour embryos) and finally reached the average level of translational activity at 8-10 hours. We performed a two-sample Kolmogorov-Smirnov test of the null hypothesis of equal distribution to confirm the significance of the polysomal-association increase of rp-mRNAs as groups (GO0005842 and GO0005843) over the three development periods. In contrast, the group of mRNAs with transcription coactivator activity (GO0016563) was preferentially associated with polysomes at 0-2 hours and their association decreased to the average level at 8-10 hours (Figure (Figure6b).6b
Regulation of mRNAs of ribosomal proteins by 5' TOP elements The rp-mRNAs of Drosophila and Xenopus embryos always appear either in the mRNPs or fully loaded with ribosomes [17,18]. In Xenopus, the translation efficiency of mRNAs encoding several protein components of the translational apparatus, including ribosome proteins, is predominantly dependent on the status of cellular growth. This mode of regulation strictly depends on the 5' terminal location of the oligopyrimidine (5' TOP) tract and sequences immediately downstream of the 5' TOP [16]. A characteristic oligopyrimidine tract starting at a C residue has been found in transcripts encoding ribosomal proteins; the 5' and 3' UTRs of these genes are significantly smaller than the genome average [19]. Ribosomal subunit 40S protein S6 phosphorylation has been implicated in an up-regulation of translation of mRNAs encoding components of the protein synthesis machinery that contain a TOP in their 5' UTR. The concomitant activation of translation of TOP-containing mRNA led to the notion that rpS6 phosphorylation increases the affinity of ribosomes for TOP-containing mRNAs and thus facilitates their initiation [16,20]. S6 knockout mice show decreased growth and cell size [21] and disruption of the Drosophila gene encoding S6 kinase leads to small body size and growth rate. Because S6 kinase regulates ribosomal protein production in mammals, loss of Drosophila S6 kinase function may have a direct impact on cell growth and proliferation [22]. Therefore, selective translational control of TOP-containing mRNAs might be a translational repression mechanism, which has been evolutionarily conserved in early embryogenesis. However, we could not confirm the existence of the TOP sequence motif in Drosophila rp-mRNA 5' UTRs due to lack of the complete 5' UTR sequences of most of these mRNAs. The selective translational repression of particular mRNA species in response to a reduced cellular need for their protein products might apply to other mRNAs sequestered in 0-2 hour embryos, particularly mRNAs encoding the components of the macromolecule biosynthesis machinery. However, there is no evidence of these mRNAs carrying 5' TOP elements or responding to TOP signaling regulation. Co-regulation of these transcripts implies that translation of these mRNAs is controlled by some shared features, which remain to be defined. Coordination of mRNA abundance and translation regulation At none of the three developmental stages we examined did we find any notable general correlation between polysome-association and mRNA abundance (data not shown). Thus, translation activity is not simply determined by level of transcript accumulation, but more likely reflects dynamic cellular requirements for particular polypeptides. Our study did reveal trends in the relationship between changes in transcript levels and ribosomal occupancies and shows that these trends can vary over the course of development. A comparison of 4-6 hour and 0-2 hour embryo samples shows that where mRNA levels decrease during very early embryogenesis, there is a tendency for ribosomal occupancies to increase (Figure (Figure7b,7b
On the other hand, comparing ribosomal occupancies in 8-10 hour and 4-6 hour embryos reveals a positive correlation with changes in transcript abundance (Figure (Figure7b,7b Using a multivariate empirical Bayes (MB) statistic [24], we ranked all the transcripts according to the difference between ribosomal occupancy between 0-2 hours and 4-6 hours, and between 4-6 hours and 8-10 hours (Additional data file 2). The higher a gene's rank, the more dramatic is the observed change in its polysomal profile. The percentile polysomal profiles of representative mRNAs with high rankings are shown in Figure Figure8.8
Polysomal profiles of localized transcripts in Drosophila embryos For the spatially localized mRNAs that have been studied, such as nanos mRNA, their spatial localization and translation control are often closely linked, with translation being repressed during mRNA translocation and activated on reaching its destination [3,25,26]. Until this study, biochemical analysis of ribosomal association to estimate mRNA translation status has been completed for only few Drosophila mRNAs. We evaluated the polysome association profiles of several known localized mRNAs during embryogenesis. Our observation that only a small proportion of nanos (5% in fraction 12) is associated with polysomes is consistent with the published estimate; it has been reported that about 4% of nanos mRNA is localized to the posterior and actively translated [27] (Figure (Figure9a).9a
Consistent with immunocytological observations that translation of hunchback transcripts and caudal transcripts is regionally repressed in the early embryo [3,26], the experiments presented here indicate that only a small proportion of these transcripts are associated with polysomes (Figure (Figure9b).9b Conclusion Translational control is critical for early Drosophila embryogenesis and we used a genomic approach to illustrate the translation profile of transcripts during this developmental period. The raw microarray data analysis tables and polysomal profiles of individual transcripts are available online at the Berkeley Drosophila Genome Project (BDGP) homepage [28]. The diversity of the polysomal profiles of maternal transcripts (0-2 hour old embryos) and later zygotic transcripts indicates multiple complex mechanisms that modulate individual gene expression, but also co-regulate the genes involved in same biological processes. The identification of consensus regulatory elements within such co-regulated mRNAs, as well as trans-acting factors that recognize them will be a fruitful area of future study. Materials and methods Synchronization of embryo collections Canton S embryos were collected in 2-hour intervals and aged to generate animals 0-2, 4-6 and 8-10 hours old. To confirm that the embryos were collected at the desired developmental stages, we examined the morphology of a small aliquot of the synchronized embryos as described previously [23]. In addition, we also validated the synchronization by comparing the variation of RNA abundance of representative mRNAs over the three time periods with the previous microarray measurements performed by Tomancak et al. [23] (data not shown). The embryos were then dechorionated and transferred to Eppendorf tubes. Preparation of RNA samples Unfractionated RNA was prepared by homogenization of dechorionated embryos with a motorized plastic pestle in RNAwiz solution (Ambion, Austin, TX 78744-1832), followed by chloroform extraction and ethanol precipitation. To prepare the polysome-associated RNAs, the dechorionated embryos were first incubated with 0.1 mg/ml cycloheximide in PBS for 10 minutes on ice, then homogenized with a motorized plastic pellet pestle in a lysis buffer (20 mM Tris-HCl, pH 7.4, 140 mM KCl, 5 mM MgCl2, 0.5 mM DTT, 1% Triton X-100, 0.1 mg/ml cycloheximide, 1 mg/ml heparin, 50 unit/ml RNasin) and incubated for 10 minutes on ice. The debris were removed by centrifugation at 12,000 × g for 10 minutes at 4°C, and supernatants were loaded onto 20% to 50% sucrose gradients with the same extraction buffer without Triton X-100. The extracts were sedimented at 35 k rpm for 160 minutes in a SW41 rotor at 4°C. Twelve fractions were collected from the tops of the gradients using an ISCO fraction collection system. RNAs were precipitated from each fraction with guanidine hydrochloride and ethanol followed by a second precipitation in 1.5 M LiCl at -20°C overnight. The RNA precipitate was washed with 70% ethanol and resuspended in an equal volume of Tris-HCl buffer (1 mM Tris-HCl, pH 8.0). Purified RNAs from individual fractions were quantified with a spectrophotometer and visualized on formaldehyde agarose gels [27,29]. EDTA-treated embryos were lysed in an EDTA extraction buffer (20 mM Tris, pH 7.4, 140 mM KCl, 15 mM EDTA, 0.5 mM DTT, 1% Triton X-100, 0.1 mg/ml cycloheximide, 1 mg/ml heparin, 50 unit/ml RNasin) and sedimented through 20% to 50% gradients prepared with the same EDTA lysis buffer, but without Triton X-100. Although most cytoskeleton-associated and endoplasmic reticulum-associated RNAs as well as ribosomes are released into the soluble extract under this buffer condition (X Qin, unpublished data), it is possible that some mRNAs are sequestered in insoluble complexes and excluded from polysomal gradient analysis. RNAs potentially in the insoluble debris were not characterized in this study. Quantitative PCR analysis Either unfractionated total RNA or equal proportions of RNA from each polysomal fraction were reverse transcribed into cDNAs with a High Capacity cDNA Archive kit (Applied Biosystems, Inc. Foster City, CA 94404). Gene-specific TaqMan® probes were designed and manufactured through Assay-by-design (Applied Biosystems). Equal proportions of cDNA samples mixed with TaqMan® Universal PCR Master Mix and gene-specific TaqMan® probes were quantified in a 96-well plate on ABI PRISM® 7000 Sequence Detection Systems as described by Applied Biosystems. Microarray hybridization and data analysis RNAs from the first five gradient fractions were pooled. An equal volume of the pooled RNAs and RNAs from the remaining seven fractions was used for cRNA labeling. Thus, the pooled RNA sample used for array labeling was the average amount of RNA from the first five fractions and each RNA sample contained at least 10 μg of RNAs. cRNA was hybridized to a GeneChip Drosophila Genome 2.0 Array using standard protocols. Thus, we collected eight GeneChip array scans of each polysomal gradient and the success of the experiments was determined by the reproducibility of the two independent replicates. Similarly, we prepared four pools of RNA from the first five fractions, from fractions 6 and 7, from fractions 8 and 9 and from fractions 10, 11 and 12. These four RNA samples were used for microarray analysis of the EDTA treated samples. Total RNA (20 μg) from unfractionated cell lysates at each time point was used for one-step labeling and GeneChip hybridization. Gene expression measures were normalized and computed using the robust multichip average (RMA) method described in [30] and implemented in the Bioconductor R package. Statistical analyses were all performed with the open-source software R, version 2.2.0 and Bioconductor 1.7 packages [31]. The following R packages were used mainly; Affy (version 1.8.1), limma (version 2.2.0) and Drosophila 2 (version 1.10.0) [32]. Moderated t-statistics were used to determine whether a transcript is released from polysomes by EDTA filtering [33]. If the amount of a particular gene's mRNA in the first fraction of EDTA-treated profiles is higher than that in the first fraction of non-EDTA-treated profiles, this transcript is releasable by EDTA since the materials dissociated by EDTA are expected to sediment at the pooled non-polysomal fraction. The false discovery rate (FDR) was controlled at p = 0.05. All data for such genes were removed for further analyses. Among all the 18,952 probes on the Drosophila Genome 2.0 GeneChips, 16,513 genes at 0-2 hours, 16,519 genes at 4-6 hours and 14,593 genes at 8-10 hours were left for further data analysis. In addition, we excluded mRNAs with low signal intensity to exclude the background noise as well as possible signal saturation of those mRNAs with extremely high intensity as described in individual analyses. Peak selection and ribosomal association assignment To determine the peak fraction of each mRNA for ribosomal density estimation, we used m as the measure for selecting genes with a sharp peak in their polysomal profiles. We first averaged the two normalized replicates of polysomal gradients on the logarithmic scale. Next, we removed the genes whose transcripts were not releasable by EDTA and whose intensities were below the median of all the probe sets. Then, we calculated m as the following:
The 3,000 genes with highest m values were expected to have a distinct peak and were selected to estimate the ribosomal density at each time interval. The number of ribosomes per transcript in fractions 6-10 was obtained directly from the peaks in the average of multiple OD254 profiles (Figure (Figure3a).3a Cluster transcripts with their translational activity We used two parameters to describe the polysomal profiles: the logarithmic ratio of the polysomal fractions to non-polysomal fractions (Logit):
and the standard deviation (SD) of the expression levels among all the gradient fractions. The preferentially translated mRNAs are expected to have a high Logit and a high SD, while unpreferentially translated mRNAs have low Logit and low SD. We examined the distribution of Logit and SD of each time point (data not shown) and decided to use a cutoff of Logit > 1.5 and SD > 0.5 to define actively translated genes, while those with log ratio < -0.15 and SD < 1 are defined to be translationally inactive genes. Any other genes not included in these two clusters were defined to be in the general translation group. Gene Ontology analyses Clustered genes were analyzed using the NetAffx Gene Ontology Mining Tool provided by Affymetrix [34]. The goal of GO analysis was to find statistically overrepresented GO terms within a group of genes [35]. Also of interest is comparison of the distribution of a statistic such as the Logit among genes associated with a certain GO term. The GOstats package of R was used to get GO-filtered data. The distributions of Logit for a set of genes such as those associated with a given GO term can be compared across time points. For this purpose, a two-sample Kolmogorov-Smirnov test of the null hypothesis of equal distribution was performed. Additional data files The following additional data are available with the online version of this paper. Additional data file 1 lists the genes that were classified by Logit into the preferentially translated group and the preferentially untranslated group at each development stage. Additional data file 2 includes the MB statistic ranking lists of the top 500 genes, showing the most significant changes of their ribosomal occupancy between the development periods. Additional data file 1 Genes that were classified by Logit into the preferentially translated group and the preferentially untranslated group at each development stage Click here for file(522K, xls) Additional data file 2 MB statistic ranking lists of the top 500 genes, showing the most significant changes of their ribosomal occupancy between the development periods Click here for file(244K, xls) Acknowledgements We thank Adina Bailey and Li Kuo Kong for critical reading of the manuscript, Pavel Tomancak for valuable advice on GO clustering of microarray data, Cyrus Harmon for helpful discussions on data analysis and Yu-Chuan Tai for the MB analysis program. Garson Tsang performed the microarray hybridization experiments. This work was supported by the Howard Hughes Medical Institute (GMR) and NIH grant LM07609 (TPS). SA is supported by PMMB and XQ is a research associate of the Howard Hughes Medical Institute. References
|
PubMed related articles
Your browsing activity is empty. Activity recording is turned off. |
|||||||||||||||||||||||||||||||||||||
Cell. 1986 Mar 28; 44(6):871-7.
[Cell. 1986]Development. 2001 Feb; 128(3):427-35.
[Development. 2001]Cell. 1989 Jun 30; 57(7):1259-73.
[Cell. 1989]Mol Cell Biol. 2001 Feb; 21(3):916-27.
[Mol Cell Biol. 2001]Proc Natl Acad Sci U S A. 1999 Nov 9; 96(23):13118-23.
[Proc Natl Acad Sci U S A. 1999]Proc Natl Acad Sci U S A. 2003 Apr 1; 100(7):3889-94.
[Proc Natl Acad Sci U S A. 2003]Proc Natl Acad Sci U S A. 2003 Apr 1; 100(7):3889-94.
[Proc Natl Acad Sci U S A. 2003]Nucleic Acids Res. 2005; 33(8):2421-32.
[Nucleic Acids Res. 2005]Proc Natl Acad Sci U S A. 2003 Apr 1; 100(7):3889-94.
[Proc Natl Acad Sci U S A. 2003]Eur J Biochem. 2000 Nov; 267(21):6321-30.
[Eur J Biochem. 2000]Genetica. 1994; 94(2-3):181-93.
[Genetica. 1994]Genetica. 1994; 94(2-3):181-93.
[Genetica. 1994]Cell. 2002 May 17; 109(4):473-83.
[Cell. 2002]Eur J Biochem. 2000 Nov; 267(21):6321-30.
[Eur J Biochem. 2000]Genome Res. 2002 Mar; 12(3):379-90.
[Genome Res. 2002]EMBO J. 1997 Jun 16; 16(12):3693-704.
[EMBO J. 1997]Genome Biol. 2002; 3(12):RESEARCH0088.
[Genome Biol. 2002]Int Rev Cytol. 2001; 211():1-31.
[Int Rev Cytol. 2001]Annu Rev Biochem. 1998; 67():335-94.
[Annu Rev Biochem. 1998]Curr Biol. 2000 Oct 19; 10(20):1311-4.
[Curr Biol. 2000]Annu Rev Biochem. 1998; 67():335-94.
[Annu Rev Biochem. 1998]Genome Biol. 2002; 3(12):RESEARCH0088.
[Genome Biol. 2002]Curr Biol. 2000 Oct 19; 10(20):1311-4.
[Curr Biol. 2000]J Biol Chem. 2004 Apr 2; 279(14):13721-8.
[J Biol Chem. 2004]Biostatistics. 2003 Apr; 4(2):249-64.
[Biostatistics. 2003]Genome Biol. 2004; 5(10):R80.
[Genome Biol. 2004]Bioinformatics. 2004 Feb 12; 20(3):307-15.
[Bioinformatics. 2004]Proc Natl Acad Sci U S A. 2003 Apr 1; 100(7):3889-94.
[Proc Natl Acad Sci U S A. 2003]Nucleic Acids Res. 2003 Jan 1; 31(1):82-6.
[Nucleic Acids Res. 2003]Nat Genet. 2000 May; 25(1):25-9.
[Nat Genet. 2000]