Global analyses of mRNA translational control during early Drosophila embryogenesis
© Qin et al.; licensee BioMed Central Ltd. 2007
Received: 2 August 2006
Accepted: 22 April 2007
Published: 22 April 2007
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.
We determined ribosomal density and ribosomal occupancy of over 10,000 transcripts during the first ten hours after egg laying.
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.
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 .
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 . 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
Analysis of ribosomal density
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 3b). It is known that many maternal mRNAs in Drosophila early embryos are sequestered in mRNP complexes and sedimentation of these mRNAs is not simply a function of ORF length. Because mRNA-ribosome associations are typically magnesium-dependent, polysome-associated mRNAs can be released from high molecular weight fractions in the presence of EDTA. Therefore, we excluded the mRNAs that cannot be released from high molecular weight fractions by EDTA. However, we found a significant number of mRNAs with long ORFs sedimenting in the disome (2 ribosomes per mRNA) in fraction 6 and trisome (3 ribosomes per mRNA) in fraction 7 (Figure 3a). We cannot distinguish between ribosome-containing mRNP complexes and translating mRNA-ribosomal complexes with the EDTA treatment assay. Furthermore, it is difficult to accurately estimate the number of ribosomes for the extremely long ORFs sedimenting in fraction 12. Therefore, we have less confidence in the accuracy of the average ribosomal density for the mRNAs sedimenting in fractions 6, 7 and 12 and excluded these three fractions from ribosomal density estimation. The average number of associated ribosomes in fractions 8 to 11 shows a linear relationship with the median ORF length of mRNAs (Figure 3b). Therefore, the average ratio of ORF lengths to the number of their associated ribosomes is the slope of this trend line. We estimate the average spacing of ribosomes on the majority of transcripts in Drosophila embryos to be about 30-32 amino acid codons, giving an average ribosomal density of about 90-100 nucleotides per ribosome. Most mRNAs have a ribosomal density in the range of 25-40 codons per ribosome at all three developmental stages.
Arava et al.  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 . When we analyzed our data using the methods described in Arava et al. , we also found an inverse correlation (Figure 3c). However, the inverse correlation may simply result from the wide range of ORF lengths seen for the transcripts sedimenting in a given fraction, while one single number of associated ribosomes is assigned to all these transcripts. Instead, we found a fairly stable ratio between the median ORF length of transcripts sedimenting within a given fraction and the assigned number of associated ribosomes to that fraction (Figure 2b). Because the exact number of associated ribosomes to individual transcripts is not clearly defined by the polysome gradient analysis used here and by Arava et al., the assertion that ribosomal density decreases with increasing ORF length may not be completely justified.
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
GO terms for mRNA transcripts preferentially excluded from polysomes at 0-2 hours(1,404 genes)
No. of genes in D. melanogaster database
No. of genes in the selected group
Biological process (918 genes*)
Amino acid activation
Membrane lipid biosynthesis
Response to endogenous stimulus
Response to external stimulus
Molecular function (933 genes*)
Structural constituent of ribosome
Nucleic acid binding
Cellular component (637 genes*)
Cytosolic large ribosomal subunit
Cytosolic small ribosomal subunint
GO terms for mRNA transcipts preferentially associated with polysomes at 0-2 hours (906 genes)
No. of genes in D. melanogaster database
No. of genes in the selected group
Biological process (535 genes*)
Embryonic pattern specification
Regulation of biological process
Regulation of cellular process
Molecular function (539 genes*)
Transcription regulator activity
RNA pol II transcription activity
General RNA pol II transcription activity
Transcriptional activator activity
Transcription cofactor activity
Transcriptional coactivator activity
Cellular component (392 genes*)
Transcription factor complexes
DNA-directed RNA polymerase II, holoenzyme
GO terms for mRNA transcripts preferentially excluded from polysomes at 4-6 hours (621 genes)
No. of genes in D. melanogaster database
No. of genes in the selected group
Biological process (333 genes*)
Cellular component (252 genes*)
Eukaryotic 43S pre-initiation complex
Eukaryotic translation initiation, factor 3 complex
Cytosolic large ribosomal subunit
Cytosolic small ribosomal subunint
GO terms for mRNA transcipts preferentially associated with polysomes at 4-6 hours (536 genes)
No. of genes in D. melanogaster database
No. of genes in the selected group
Biological process (319 genes*)
Nucleobase, nucleoside, nucleotide and nucleic acid metabolism
Transcription initiation from pol II promoter
Cellular component (216 genes*)
DNA-directed RNA polymerase II, holoenzyme
Small nuclear ribonucleoprotein complex
Proteasome regulatory particle
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 , reflecting a need for new ribosomes . A sufficient number of maternal ribosomes are stored in early Drosophila embryos before zygotic control begins (Figure 4) and the priority of ribosomal protein synthesis is low. Furthermore, many components involved in macromolecular metabolism, including lipid membrane and protein metabolism, are not observed to be actively engaged in polysomes, and we speculate that their products are also abundantly supplied in the maternal cytoplasm and are not a priority for protein synthesis. Another interesting group of preferentially untranslated mRNAs encode products involved in cell cycle progression (for example, cyclin G and cyclin T) that may also be due to the maternal storage of these products to support the rapid early mitotic cycles. At up to 8-10 hours, when organogenesis starts, we observed no significance in the molecular function or biological process among the selected groups of translated transcripts, which may indicate the complexity of differentiation at this development stage. Sixteen mRNAs encoding proteins involved in RNA binding (for example, Staufen and Smaug) are not found associated with translating polysomes. These mRNAs are known to play critical roles during oogenesis and early embryogenesis. In situ expression staining suggests these transcripts are restricted to germ cells. Due to the rapid degradation of these mRNAs after zygotic transcription begins, it is unclear whether these mRNAs are zygotic or maternal transcripts that are protected in the germ cells.
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 . 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 . 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  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 . 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.
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 7b, right). This correspondence of elevation of mRNA accumulation with increased ribosomal occupancies indicates some coordination of steady state RNA levels and translation activity; the levels of gene regulation underlying this phenomenon and how directly and generally they are integrated remain questions for future investigation.
Polysomal profiles of localized transcripts in Drosophilaembryos
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.
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 9b). The unique polysomal profiles of maternal transcripts, for example, the transcripts of nanos, oskar and bicoid, suggest the existence of multiple mechanisms controlling gene-specific translation of mRNAs during early pattern formation.
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 . 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 . 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.  (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  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 . The following R packages were used mainly; Affy (version 1.8.1), limma (version 2.2.0) and Drosophila 2 (version 1.10.0) .
Moderated t-statistics were used to determine whether a transcript is released from polysomes by EDTA filtering . 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:
m = % [peak fraction] - average% [adjacent two fractions], if peak fraction is not 6 or 12
m = % [peak fraction] - % [fraction 7] if peak = 6
m = % [peak fraction] - % [fraction 11] if peak = 12
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 3a). For fractions 11 and 12, which lacked single ribosome resolution, the number of ribosomes per transcript was estimated by a logarithmic extrapolation from the clearly defined peaks as described by Arava et al. . The assigned number of associated ribosomes for fractions 6-12 is in the order of 2, 3, 5, 7.5, 11, 17 and 26, respectively. The R2 value of the logarithmic curve over the defined region is 0.9983 (data not shown).
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):
Logit = log [percent(fraction 6-12)/percent(fraction 1-5)]
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 . The goal of GO analysis was to find statistically overrepresented GO terms within a group of genes . 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.
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.
- Edgar BA, Schubiger G: Parameters controlling transcriptional activation during early Drosophila development. Cell. 1986, 44: 871-877. 10.1016/0092-8674(86)90009-7.PubMedView ArticleGoogle Scholar
- Foe VE, Odell GM, Edgar BA: Mitosis and Morphogenesis in the Drosophila Embryo: Point and Counterpoint. 1993, Cold Spring Harbor, New York: Cold Spring Harbor Laboratory Press, 1:Google Scholar
- Wickens M, Goodwin EB, Kimble J, Strickland S, Hentze M: Translational it control of Developmental Decisions. 2000, Cold Spring Harbor, New York: Cold Spring Harbor Laboratory PressGoogle Scholar
- Bergsten SE, Huang T, Chatterjee S, Gavis ER: Recognition and long-range interactions of a minimal nanos RNA localization signal element. Development. 2001, 128: 427-435.PubMedGoogle Scholar
- Gavis ER, Lehmann R: Localization of nanos RNA controls embryonic polarity. Cell. 1992, 71: 301-313. 10.1016/0092-8674(92)90358-J.PubMedView ArticleGoogle Scholar
- Mancebo R, Zhou X, Shillinglaw W, Henzel W, Macdonald PM: BSF binds specifically to the bicoid mRNA 3' untranslated region and contributes to stabilization of bicoid mRNA. Mol Cell Biol. 2001, 21: 3462-3471. 10.1128/MCB.21.10.3462-3471.2001.PubMedPubMed CentralView ArticleGoogle Scholar
- Macdonald PM, Struhl G: Cis-acting sequences responsible for anterior localization of bicoid mRNA in Drosophila embryos. Nature. 1988, 336: 595-598. 10.1038/336595a0.PubMedView ArticleGoogle Scholar
- Struhl G, Struhl K, Macdonald PM: The gradient morphogen bicoid is a concentration-dependent transcriptional activator. Cell. 1989, 57: 1259-1273. 10.1016/0092-8674(89)90062-7.PubMedView ArticleGoogle Scholar
- Mathews MB, Sonenberg N, Hershey JWB: Origins and Principles of Translational Control. 2000, Cold Spring Harbor, New York: Cold Spring Harbor Laboratory PressGoogle Scholar
- Kuhn KM, DeRisi JL, Brown PO, Sarnow P: Global and specific translational regulation in the genomic response of Saccharomyces cerevisiae to a rapid transfer from a fermentable to a nonfermentable carbon source. Mol Cell Biol. 2001, 21: 916-927. 10.1128/MCB.21.3.916-927.2001.PubMedPubMed CentralView ArticleGoogle Scholar
- Arava Y, Wang Y, Storey JD, Liu CL, Brown PO, Herschlag D: Genome-wide analysis of mRNA translation profiles in Saccharomyces cerevisiae . Proc Natl Acad Sci USA. 2003, 100: 3889-3894. 10.1073/pnas.0635171100.PubMedPubMed CentralView ArticleGoogle Scholar
- Johannes G, Carter MS, Eisen MB, Brown PO, Sarnow P: Identification of eukaryotic mRNAs that are translated at reduced cap binding complex eIF4F concentrations using a cDNA microarray. Proc Natl Acad Sci USA. 1999, 96: 13118-13123. 10.1073/pnas.96.23.13118.PubMedPubMed CentralView ArticleGoogle Scholar
- Sonenberg N, Hershey J, Mathews MB: Translational Control of Gene Expression. 2000, Cold Spring Harbor, New York: Cold Spring Harbor Laboratory PressGoogle Scholar
- Arava Y, Boas FE, Brown PO, Herschlag D: Dissecting eukaryotic translation and its control by ribosome density mapping. Nucleic Acids Res. 2005, 33: 2421-2432. 10.1093/nar/gki331.PubMedPubMed CentralView ArticleGoogle Scholar
- Affymetrix I: NetAffx analyses tool. [http://www.Affymetrix.com]
- Meyuhas O: Synthesis of the translational apparatus is regulated at the translational level. Eur J Biochem. 2000, 267: 6321-6330. 10.1046/j.1432-1327.2000.01719.x.PubMedView ArticleGoogle Scholar
- Pierandrei-Amaldi P, Amaldi F: Aspects of regulation of ribosomal protein synthesis in Xenopus laevis. Review. Genetica. 1994, 94: 181-193. 10.1007/BF01443432.PubMedView ArticleGoogle Scholar
- Groisman I, Jung MY, Sarkissian M, Cao Q, Richter JD: Translational control of the embryonic cell cycle. Cell. 2002, 109: 473-483. 10.1016/S0092-8674(02)00733-X.PubMedView ArticleGoogle Scholar
- Yoshihama M, Uechi T, Asakawa S, Kawasaki K, Kato S, Higa S, Maeda N, Minoshima S, Tanaka T, Shimizu N, Kenmochi N: The human ribosomal protein genes: sequencing and comparative analysis of 73 genes. Genome Res. 2002, 12: 379-390. 10.1101/gr.214202.PubMedPubMed CentralView ArticleGoogle Scholar
- Jefferies HB, Fumagalli S, Dennis PB, Reinhard C, Pearson RB, Thomas G: Rapamycin suppresses 5'TOP mRNA translation through inhibition of p70s6k. EMBO J. 1997, 16: 3693-3704. 10.1093/emboj/16.12.3693.PubMedPubMed CentralView ArticleGoogle Scholar
- Ruvinsky I, Sharon N, Lerer T, Cohen H, Stolovich-Rain M, Nir T, Dor Y, Zisman P, Meyuhas O: Ribosomal protein S6 phosphorylation is a determinant of cell size and glucose homeostasis. Genes Dev. 2005, 19: 2199-2211. 10.1101/gad.351605.PubMedPubMed CentralView ArticleGoogle Scholar
- Montagne J, Stewart MJ, Stocker H, Hafen E, Kozma SC, Thomas G: Drosophila S6 kinase: a regulator of cell size. Science. 1999, 285: 2126-2129. 10.1126/science.285.5436.2126.PubMedView ArticleGoogle Scholar
- Tomancak P, Beaton A, Weiszmann R, Kwan E, Shu S, Lewis SE, Richards S, Ashburner M, Hartenstein V, Celniker SE, et al: Systematic determination of patterns of gene expression during Drosophila embryogenesis. Genome Biol. 2002, 3: research0088.1-0088.14.. 10.1186/gb-2002-3-12-research0088.View ArticleGoogle Scholar
- Tai YC, Speed TP: A multivariate empirical bayes statistic for replicated microarray time course data. Ann Statist. 2006, 34: 2387-2412. 10.1214/009053606000000759.View ArticleGoogle Scholar
- Stebbings H: Cytoskeleton-dependent transport and localization of mRNA. Int Rev Cytol. 2001, 211: 1-31.PubMedView ArticleGoogle Scholar
- Bashirullah A, Cooperstock RL, Lipshitz HD: RNA localization in development. Annu Rev Biochem. 1998, 67: 335-394. 10.1146/annurev.biochem.67.1.335.PubMedView ArticleGoogle Scholar
- Clark IE, Wyckoff D, Gavis ER: Synthesis of the posterior determinant Nanos is spatially restricted by a novel cotranslational regulatory mechanism. Curr Biol. 2000, 10: 1311-1314. 10.1016/S0960-9822(00)00754-5.PubMedView ArticleGoogle Scholar
- Berkeley Drosophila Genome Project: Supplemental Datasets. [http://www.fruitfly.org/about/pubs/index.html]
- Qin X, Sarnow P: Preferential translation of internal ribosome entry site-containing mRNAs during the mitotic cycle in mammalian cells. J Biol Chem. 2004, 279: 13721-13728. 10.1074/jbc.M312854200.PubMedView ArticleGoogle Scholar
- Irizarry RA, Hobbs B, Collin F, Beazer-Barclay YD, Antonellis KJ, Scherf U, Speed TP: Exploration, normalization, and summaries of high density oligonucleotide array probe level data. Biostatistics. 2003, 4: 249-264. 10.1093/biostatistics/4.2.249.PubMedView ArticleGoogle Scholar
- Gentleman RC, Carey VJ, Bates DM, Bolstad B, Dettling M, Dudoit S, Ellis B, Gautier L, Ge Y, Gentry J, et al: Bioconductor: open software development for computational biology and bioinformatics. Genome Biol. 2004, 5: R80-10.1186/gb-2004-5-10-r80.PubMedPubMed CentralView ArticleGoogle Scholar
- Gautier L, Cope L, Bolstad BM, Irizarry RA: affy - analysis of Affymetrix GeneChip data at the probe level. Bioinformatics. 2004, 20: 307-315. 10.1093/bioinformatics/btg405.PubMedView ArticleGoogle Scholar
- Smyth GK, Wettenhall J, Thorne N: LIMMA: Linear Models for Microarray Data User's Guide. 2005, [http://bioinf.wehi.edu.au/limma]Google Scholar
- Liu G, Loraine AE, Shigeta R, Cline M, Cheng J, Valmeekam V, Sun S, Kulp D, Siani-Rose MA: NetAffx: Affymetrix probesets and annotations. Nucleic Acids Res. 2003, 31: 82-86. 10.1093/nar/gkg121.PubMedPubMed CentralView ArticleGoogle Scholar
- Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, Davis AP, Dolinski K, Dwight SS, Eppig JT, et al: Gene Ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat Genet. 2000, 25: 25-29. 10.1038/75556.PubMedPubMed CentralView ArticleGoogle Scholar
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