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      p180 Promotes the Ribosome-Independent Localization of a Subset of mRNA to the Endoplasmic Reticulum

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          The localization of many secretory mRNAs to the endoplasmic reticulum does not require ribosomes or translation, but is instead promoted by p180, an abundant, membrane-bound protein that likely binds directly to mRNA.


          In metazoans, the majority of mRNAs coding for secreted and membrane-bound proteins are translated on the surface of the endoplasmic reticulum (ER). Although the targeting of these transcripts to the surface of the ER can be mediated by the translation of a signal sequence and their maintenance is mediated by interactions between the ribosome and the translocon, it is becoming increasingly clear that additional ER-localization pathways exist. Here we demonstrate that many of these mRNAs can be targeted to, and remain associated with, the ER independently of ribosomes and translation. Using a mass spectrometry analysis of proteins that associate with ER-bound polysomes, we identified putative mRNA receptors that may mediate this alternative mechanism, including p180, an abundant, positively charged membrane-bound protein. We demonstrate that p180 over-expression can enhance the association of generic mRNAs with the ER. We then show that p180 contains a lysine-rich region that can directly interact with RNA in vitro. Finally, we demonstrate that p180 is required for the efficient ER-anchoring of bulk poly(A) and of certain transcripts, such as placental alkaline phosphatase and calreticulin, to the ER. In summary, we provide, to our knowledge, the first mechanistic details for an alternative pathway to target and maintain mRNA at the ER. It is likely that this alternative pathway not only enhances the fidelity of protein sorting, but also localizes mRNAs to various subdomains of the ER and thus contributes to cellular organization.

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          Messenger RNAs (mRNAs) that encode secreted or membrane-bound proteins must be delivered to, and then maintained on, the surface of the endoplasmic reticulum (ER). These mRNAs encode a short polypeptide that targets the mRNA/ribosome/nascent protein complexes to the ER surface during translation; however, recent studies support the existence of additional ER-localization signals that might be present within the mRNA molecules themselves. Here, we demonstrate that a fraction of these mRNAs, whose encoded proteins are destined for secretion, contain information that targets and anchors them to the ER independently of their encoded polypeptide or their association to ribosomes. We identify proteins on the ER that may serve as receptors for these mRNAs. We then show that one of these candidate membrane-bound receptors, p180, is required for the maintenance of certain mRNAs on the surface of the ER even after their translation into protein is disrupted. We also demonstrate that p180 contains a region that binds directly to RNA and likely mediates the anchoring of mRNA to the ER. Our study thus provides the first mechanistic details of an alternative pathway used to ensure that secretory mRNAs, and their encoded proteins, reach their proper destination in the ER.

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          Most cited references 62

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          Global analysis of mRNA localization reveals a prominent role in organizing cellular architecture and function.

          Although subcellular mRNA trafficking has been demonstrated as a mechanism to control protein distribution, it is generally believed that most protein localization occurs subsequent to translation. To address this point, we developed and employed a high-resolution fluorescent in situ hybridization procedure to comprehensively evaluate mRNA localization dynamics during early Drosophila embryogenesis. Surprisingly, of the 3370 genes analyzed, 71% of those expressed encode subcellularly localized mRNAs. Dozens of new and striking localization patterns were observed, implying an equivalent variety of localization mechanisms. Tight correlations between mRNA distribution and subsequent protein localization and function, indicate major roles for mRNA localization in nucleating localized cellular machineries. A searchable web resource documenting mRNA expression and localization dynamics has been established and will serve as an invaluable tool for dissecting localization mechanisms and for predicting gene functions and interactions.
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            Diverse RNA-Binding Proteins Interact with Functionally Related Sets of RNAs, Suggesting an Extensive Regulatory System

            Introduction Much of the regulation of eukaryotic gene expression programs is still unaccounted for. Although these programs are subject to regulation at many steps, most investigation has focused on regulation of transcription. There are clues, however, that a significant portion of undiscovered regulation might be post-transcriptional, acting to regulate mRNA processing, localization, translation, and decay [1–5]. For example, systematic phylogenetic comparison among yeast and mammalian genomes sequences have revealed that untranslated regions of many mRNAs are under purifying selection, and thus presumably carrying information important for fitness [6–8]. Biological regulation can be achieved by controlling any of a large number of steps in the lives of RNA molecules. Alternative splicing of transcripts can enable a single gene to encode numerous protein products, greatly expanding its molecular complexity [9]. Even in organisms with few introns, such as Saccharomyces cerevisiae, splicing is subject to regulation [10,11]. Notable examples of regulated RNA localization include mRNA export from the nucleus to the cytoplasm, partitioning of mRNAs to the rough endoplasmic reticulum (ER) membrane for cotranslational export, and the precise subcellular localization of thousands of specific mRNAs [12]. In a recent survey of mRNA localization in developing Drosophila embryos, more than 70% of the roughly 3,000 mRNAs examined showed distinct patterns of subcellular localization [13]. Widespread regulation of translation rates is evident in several observations. In yeast, despite extensive regulation of transcription and mRNA decay, only about 70% of the observed variance in protein abundance is accounted for by variation in mRNA abundance [14,15]. When cells are moved from rich media to minimal media, the abundance of hundreds of proteins change, but mRNA abundance changes parallel changes in the abundance for only about half of the cognate proteins [16,17]. The abundance of each RNA is determined jointly by regulated transcription and regulated degradation. Widespread, transcript-specific regulation of mRNA decay is evident from the closely matched decay rates of mRNAs encoding functionally related proteins [18–21], particularly evident in S. cerevisiae in sets of proteins that form stoichiometric complexes [19]. Increasing evidence points to extensive involvement of specific RNA-binding proteins (RBPs) in regulation of these post-transcriptional events [1–5]. Pioneering studies focusing on tens of predominantly nuclear mRNA RBPs (so-called heterogeneous ribonucleoprotein [hnRNP] proteins), revealed that these proteins recognize specific features in mRNAs, bind at overlapping, but distinct, times during RNA processing, and differentially associate with subsets of nascent transcripts [22]. Steps in RNA processing in the nucleus are functionally and physically coupled, providing an opportunity for coordinated control [23]. Investigations of regulation acting on RNA have usually focused on a few model RNAs, leaving unanswered the extent to which mRNAs are coordinated and differentially regulated, and this regulatory landscape is still largely unexplored. Recent studies have systematically identified the suite of mRNAs associated with some individual RBPs. Several RBPs implicated in RNA processing and nuclear export in S. cerevisiae were found to associate with distinct sets of hundreds of functionally related mRNAs [24,25]. Five members of the Puf family of RBPs in S. cerevisiae were each found to associate with distinct, overlapping sets of 40–250 mRNAs [26]. The specific sets of mRNAs associated with each Puf protein were significantly enriched for mRNAs encoding functionally and cytotopically related proteins. For instance, most of the approximately 220 mRNAs associated with Puf3 are transcribed from nuclear genes and encode proteins localized to the mitochondrion (p 10% FDR) to yellow (0% FDR) scale. For the most part, each RBP had a unique profile of enrichment, with a few notable exceptions, including Scp160/Bfr1 and Nrd1/Nab3, which are pairs of proteins that act together in stable stoichiometric complexes [50,51] and were correspondingly associated with similar sets of mRNAs. Altogether, we identified more than 12,000 mRNA–RBP interactions (at a 1% FDR), an average of at least 2.8 RBPs interacting with each of 4,300 distinct mRNAs; 31 proteins (including Puf1–5 and She2) reproducibly bound at least ten mRNAs (at a 1% FDR). Most mRNAs were bound by multiple RBPs (Figure 1C, black bars); 628 mRNAs were bound by five or more of this set of 31 RBPs; intriguingly, a disproportionate fraction of the mRNAs with the greatest number of identified interactions with this set of RBPs encode proteins localized to the cell wall (31, p 500) sets of mRNAs display several distinct enrichment profiles (Figure 1B), with correspondingly different GO annotations overrepresented among the most highly enriched mRNAs (Figure 2). In addition, for each of these nine RBPs, immunoaffinity enrichment of mRNAs with the RBP was significantly correlated with either ribosome occupancy [62], abundance [19], half-life [19], 3′-UTR length [63], 5′-UTR length [63], mRNA length [63], coding sequence length, or in some cases, with more than one of these features (Figure S3). Quantitative differences in the enrichment of mRNAs in association with a given RBP could result from the number or affinity of the RBP molecules bound or differences in the fraction of its lifespan that an individual mRNA spends at the specific stage during which a particular RBP plays a role (Text S5). Pab1 provides a simple and useful example of the possible functional significance of the differential enrichment; immunoaffinity enrichment of mRNAs associated with Pab1 was correlated with ribosome occupancy (Pearson correlation = 0.35). Pab1 is the major poly(A) binding protein in both the nucleus and cytoplasm [64]. In the cytoplasm, Pab1 binds to the poly(A) tails of mRNAs and interacts with eIF4-G to promote translation initiation [65]. Because longer poly(A) tails have been reported to increase translation efficiency [66], a possible interpretation of these results is that the observed enrichment could reflect the number of Pab1 proteins bound per mRNA and thus the length of the poly(A) tail [39]. In contrast, immunoaffinity enrichment with Khd1 was negatively correlated with ribosome occupancy (r = −0.26). Khd1 is implicated in repressing translation of ASH1 mRNA during the transport of the mRNA to the bud tip [67]. The negative correlation with global ribosome occupancy and the large number of mRNAs associated with Khd1 suggest that Khd1 may similarly repress translation initiation of hundreds to thousands of mRNAs, perhaps during their transport to specific cellular loci. Many RNA-Binding Proteins Appear to Bind Their Targets during Specific Stages in Their Lives Many RBPs associate with mRNAs at a particular stage in their lives [2]. For the approximately 270 intron-containing genes, the relative enrichment of introns (i.e., unspliced pre-mRNAs and possibly uncleaved excised introns) versus exons (i.e., mature mRNAs and pre-mRNAs) should reveal whether the RBP is bound specifically to intron-containing transcripts, mature mRNAs, or both, and thus indicate when and where the RBP associates with its target RNAs. Linking these data to functional information on the RBP could then provide insights into timing and duration of specific stages in the lives of mRNAs. To test this idea, we compared the enrichment of intron and exon sequences in association with RBPs. For the approximately 120 intron/exon probe pairs for which our data were most consistently reliable, the relative enrichment profiles vary greatly among RBPs (Figure 3 and Text S6). For example, Cbc2 (a component of the heterodimeric nuclear cap-binding protein) and Pab1 were preferentially associated with both intron-containing transcripts and mature mRNAs derived from intron-containing transcripts (Figure 3). Cbc2 was strongly associated with intron-containing transcripts (mean enrichment of intronic sequences = 6.8), and also, but to a considerably lesser extent, with exon sequences from intron-derived transcripts (mean enrichment of exonic sequences = 1.5). These results are consistent with Cbc2 binding during transcription, prior to splicing, and being displaced shortly after the mature mRNA reaches the cytoplasm [68,69]. The enrichment of intron-related transcripts and the paucity of significantly enriched mature mRNAs suggest that most mRNAs spend only a very small fraction of their lives in the nucleus. That Pab1, the major poly(A) binding protein, associated with intron-containing transcripts (mean enrichment of intronic sequences = 1.5), as well as sequences from exons (mean enrichment of exonic sequences = 3.9), is consistent with most splicing occurring after poly(A) tail addition [70]. Figure 3 Differential Exon/Intron Association Suggests That Certain RNA-Binding Proteins Bind Their Targets during Specific Stages in Their Lives The relative enrichment of exons and introns in association with RBPs (columns) is represented using a color scale. Results are shown for RBPs that associated substantially more or less strongly with exons or introns than with RNAs overall (mean enrichment of exons from intron-containing genes or introns 25% above or below the median IP enrichment of all RNAs, respectively). Combinatorial Interactions among RNA-Binding Proteins and mRNAs The RBPs we analyzed bound overlapping sets of mRNAs, and many individual mRNAs were bound by more than one RBP (Figure 1B and 1C). This network of interactions could support a robust and multidimensional regulatory program. To explore the relationships among the groups of RNAs bound by different RBPs, we determined the extent to which the overlaps between targets for each RBP pair differed from what would be expected by chance. The significance values from this analysis were used as a metric of similarity for hierarchical clustering to identify pairs and sets of RBPs with similar patterns of shared targets. The results are presented in Figure 4A as a heat map, in which the similarity between the target sets of each pair of RBPs is shown on a blue (significantly fewer shared targets than expected, p = 10−25) to white (p > 0.001) to red (significantly more shared targets than expected, p = 10−25) scale. At a p-value threshold of 0.001, 69 of 465 RBP pairs shared significantly more mRNA targets than expected by chance, whereas 11 RBP pairs shared significantly fewer mRNA targets than expected by chance. Several of the most significantly overlapping target sets belong to sets of RBPs that are known to physically interact, such as Scp160 and Bfr1 [50], Nrd1 and Nab3 [51], Nrd1/Nab3 and Npl3 [71], and Nrd1/Nab3 and Pab1 [72]. Figure 4 Combinatorial Interactions among RNA-Binding Proteins and mRNAs (A) The significance of the overlap between mRNA targets for each pair of RBPs (1% FDR threshold) is represented as a hierarchically clustered heat map in which the color intensity represents the negative log10 p-value, which was calculated using the hypergeometric density distribution and corrected for multiple hypothesis testing using the Bonferroni method. (B) An example of a cluster of functionally and cytotopically related mRNAs defined by their pattern of binding to multiple RBPs. The heat map represents RBPs (rows) and mRNAs (columns) color coded to reflect the certainty with which we infer that a specific mRNA is a target of a specific RBP (10% FDR [black] to 0% FDR [yellow]). These 78 mRNAs were associated (at a 1% FDR threshold) with at least four of a set of six RBPs (Ssd1, Khd1, Pub1, Ypl184c, Scp160, and Nab6) whose targets are enriched for mRNAs encoding proteins localized to the cell wall. To further explore the interrelationships among RBPs and their mRNA targets, we used a supervised method to identify smaller subsets of mRNAs that shared interactions with several RBPs. We did this by selecting mRNAs bound by a common set of RBPs whose targets, in turn, were enriched for common GO terms (Figure 2). The group of mRNAs, defined by interactions with at least four of a set of six RBPs (Pub1, Khd1, Nab6, Ssd1, Ypl184c, and Scp160), includes a significant excess of mRNAs encoding proteins localized to the cell wall (Figure 4B); indeed, 23 of the 78 mRNAs in this cluster encode cell-wall proteins (p 10−4 based on the hypergeometric distribution) in targets. “Cons” indicates the negative log10 p-value measuring whether motif sites in targets are more likely to be conserved in orthologous sequence alignments in S. bayanus than are motif sites occurring in nontargets, based on the hypergeometric distribution. Asterisks (*) denote motifs matching previously described RNA-binding elements (details in text). Exact data values and full descriptions of all motifs are presented in Table S4. The motifs we identified for Puf3, Puf4, Puf5, Pub1, Nab2, Nrd1, and Nab3 match previously described binding sites for the corresponding RBPs, validating our approach and suggesting that many of the RBP–RNA interactions we measured are likely to be directly mediated by these elements (Text S7). Interestingly, the inferred recognition element for Nrd1, Nrd1–1 (UUCUUGUW), contains both an exact match to the reported Nrd1 binding site consensus “UCUU” and a partial match to the reported Nab3 recognition site consensus “GUAR” [91,92]. As Nrd1 and Nab3 are known to act as a complex to control transcriptional termination of nonpolyadenylated RNAs [93], and a nearly identical motif was identified in Nab3 targets (Table S4), it is possible that these motifs represent a favored orientation of adjacent Nrd1 and Nab3 RNA elements that facilitates specific binding of the Nrd1–Nab3 complex. The most significant novel motif we identified, Puf2–1 (UAAUAAUUW), is enriched in the 3′-UTRs and coding sequences of Puf2 targets and demonstrates significant conservation and a forward strand bias (Figure 5). This motif is similar to a motif identified for the paralogous RBP Puf1, which associates with a subset of the Puf2 target mRNAs (Table S4). The next most significant novel motif, Ssd1–1 (AKUCAUUCCUU), is highly enriched in the 5′-UTRs of Ssd1 targets (Figure 5). Although its presence upstream of the coding sequences of Ssd1 target genes would also be consistent with a role as a transcription factor binding site, its tendency to occur within the annotated 5′-UTRs of targets (63% targets versus 19% nontargets, p < 10−6) [94], its dramatic enrichment in targets, and its forward strand bias suggest that this RNA motif is recognized by Ssd1. A selective sample of 11 mRNAs provides an unfinished, but revealing, picture of the organization of the information that specifies interactions with, and perhaps regulation by, specific RBPs examined in this study (Figure 6). For each mRNA, the location of high-confidence RNA recognition elements for RBPs that interact with the mRNA are indicated, while RBPs that interact with the mRNA, but whose binding site is uncertain, are shown to the right of the mRNA. The relative lengths of the 5′-UTR, coding sequence, and 3′-UTR are drawn to scale, and the translation start and stop codons are depicted with the corresponding “traffic signal.” Each of these mRNAs has specific interactions with overlapping, but distinct, subsets of RBPs in the study. The putative binding patterns of specific RBPs, with respect to the number and locations of sites, vary considerably among the mRNAs, which may have important functional consequences. The first five mRNAs (SUN4, DSE2, CTS1, SCW4, and EGT2) encode cell-wall enzymes (Figure 6A–6E). Each of these mRNAs associated with five to nine RBPs in this study, including all five with Pub1, Khd1, and Ypl184c, four with Ssd1 (SUN4, DSE2, CTS1, and SCW4), three with Scp160 (CTS1, SCW4, and EGT2), and two with Nab6 (CTS1 and SCW4) and Nrd1 (DSE2 and EGT2). In addition to these overlapping interactions, most of these mRNAs associated with a unique set of additional RBPs; for instance, SUN4 contains two Puf5-binding sites in its 3′-UTR and EGT2 contains eight She2-binding sites in its coding sequence. CLN2 encodes a G1 cyclin and associated with many of the same RBPs as SUN4, DSE2, CTS1, SCW4, and EGT2 (Figure 6F). PUF2 associated with several RBPs, including its cognate protein, which is common among RBPs in this study (Text S8); there are 12 Puf2-binding sites in its coding sequence (Figure 6G). PMA1 associated with a similar set of RBPs as PUF2, including Pub1 and Puf2, but the locations and numbers of binding sites for these RBPs are very different in the two mRNAs (Figure 6H). The putative binding sites for Puf4 and Puf5 in the 3′-UTR of HHT1 partially overlap, suggesting these RBPs may compete for binding to this mRNA (Figure 6J). These diagrams represent only a partial picture of the RBP interactions with these mRNAs; the mRNA targets have only been defined for a small fraction of all yeast RBPs, and the sequence elements that specify many of the interactions we have identified are not yet known. Figure 6 Diverse Combinatorial Patterns of RNA-Binding Protein Interactions with a Choice Sample of mRNAs (A–K) Putative binding sites of RBPs in target mRNAs. The relative lengths of the 5′-UTR, coding sequence, and 3′-UTR are drawn to scale. For mRNAs for which there are reliable measurements for untranslated sequence lengths (SUN4, DSE2, SCW4, CLN2, PUF2, PMA1, SUR7, and HHT1) [63], we added 50 bases onto the estimated 5′-UTR and 3′-UTR lengths, because the estimated UTR lengths are likely conservative. For mRNAs that do not have reliable untranslated region measurements (CTS1, EGT2, and MRP1), we used 250 bases upstream and downstream of the coding sequence as the 5′-UTR and 3′-UTR, respectively. The positions of the start and stop codons are indicated by stop signals. Putative binding sites for RBPs with strong evidence for association (1% FDR) are marked (Puf3-REFINE, Puf4-FIRE, Puf5-REFINE, Pub1-FIRE, Puf1/2-REFINE, Ssd1-REFINE, Nsr1-REFINE, Yll032c-REFINE, Pin4-REFINE, and Nrd1/Nab3-REFINE) (Figure 5 and Table S4). RBPs that we found to be associated with the mRNA, but for which the recognition elements are not yet known, are listed to the right of the mRNA. The number of Pab1 molecules shown bound to the poly(A) tail represents the degree of enrichment of the corresponding mRNA in the Pab1 IPs (log2 immunopurification enrichment −6 = 0, −5 = 1, etc.) and not the number of Pab1 molecules bound per mRNA. The cap-binding proteins, Cbc1/2 and eiF4e, are shown by default at the cap site. For many RBPs, our computational method did not identify any sequence motifs with statistically significant enrichment, the motifs identified significantly overlapped those associated with other RBP target sets, or the motif did not match previously reported binding preferences (Table S4 and Text S7). The large degree of motif coenrichment observed in our analysis is consistent with combinatorial regulation by a highly interconnected regulatory network and represents an important limitation of computational regulatory element identification. It is likely that some of the RBPs for which we failed to predict sequence motifs recognize RNA structural elements or features primarily present in coding sequences, which are difficult to detect with current methods for RNA motif prediction, because they are not suited to modeling structural features or handling the significant confounding sequence biases in coding sequences. Vts1 illustrates some of the limitations of current RNA motif prediction methods. Vts1 is known to bind to a structural RNA motif called the Smaug recognition element (SRE), which consists of a short hairpin with the loop consensus sequence CNGGN(0–1) [95]. SRE sites are indeed significantly enriched in the coding sequences of Vts1 targets (65% targets versus 36% nontargets, p < 10−7) in agreement with previous results [96], suggesting that SRE elements are directly responsible for these interactions in vivo. However, neither REFINE nor FIRE succeeded in identifying the SRE. Instead, both programs identified a motif, Vts1–1 (UKWCGRGGN), which is indeed enriched in the 3′-UTRs of Vts1 targets but is unrelated to the SRE (Table S4). We suspect that the Vts1–1 motif may represent a binding site for an unknown factor that regulates a set of mRNAs that overlaps extensively with the targets of Vts1. It is likely that direct high-resolution mapping of in vivo RBP binding sites and systematic in vitro characterization of binding preferences of RBPs will overcome some of the limitations in current methods for RNA motif identification [97,98]. Insights into the Functions of Specific RNA-Binding Proteins The functional and cytotopic themes represented among the specific targets of each RBP have obvious implications for their possible regulatory roles, which can be integrated with previously reported information to derive further insights, and generate new hypotheses, as illustrated here for Ssd1 and Ypl184c (see Text S9 for descriptions of Khd1 and Gbp2). Ssd1 is a large (140 kDa), ribonuclease-II domain–containing, predominantly cytoplasmic protein [99], genetically implicated in cell-wall biogenesis and function: mutant phenotypes include increased sensitivity to osmotic stress and caffeine, altered composition and structure of the cell wall, defects in germination and sporulation, premature aging, and pathogenicity [73,74,100–103]. Ssd1 physically and genetically interacts with numerous signaling proteins, many of which are genetically implicated in cell-wall function [71,102,104,105]. Ssd1 binds to the C-terminal domain of RNA polymerase II in vitro [106]. Of the 52 annotated mRNAs associated with Ssd1, 16 encode proteins localized to the cell wall (p < 10−15), and 11 encode proteins localized to the bud (p < 10−5). The proteins encoded by the Ssd1-associated transcripts have diverse functional and structural roles related to cell-wall biosynthesis, or remodeling and its regulation, cell-cycle progression, and protein trafficking. Ssd1 also appears to bind its own transcript (Text S8). For both of the Ssd1 mRNA targets encoded by intron-containing genes (PUF5 and ECM33), the intron-containing primary transcripts are also enriched by Ssd1 IP, suggesting that Ssd1 binds its RNA targets in the nucleus, perhaps while they are being transcribed. A putative RNA-recognition motif is significantly enriched in the 5′-UTRs of Ssd1 targets (Figure 5). The numbers and positions of this motif in Ssd1-bound RNAs vary widely among its targets (Figure 6A–6D and 6F). These data lead us to speculate that Ssd1 binds its targets cotranscriptionally by recognizing a specific RNA motif and prevents their translation initiation until these mRNAs reach specific locations in the cell, such as the ER membrane, bud, or sites of cell-wall biosynthesis. The multiple phosphorylation sites on Ssd1 could regulate the localization, binding, and release of its RNA targets. Although Ssd1 is a ribonuclease-II domain–containing protein, it has no discernable nuclease activity [99]. Given that Ssd1 does not contain any other known RNA-binding domains, we suggest that the ribonuclease-II domain may have evolved into a sequence-specific RNA-binding domain in this protein family. Ypl184c is a largely uncharacterized, predominantly cytoplasmic protein that contains three RNA recognition motifs (RRMs). Of the three proteins that have been found to physically interact with Ypl184c, two are among the other RBPs included in this survey: Pab1 and Nab6 [71]. A disproportionate fraction of the 321 annotated mRNAs we found to associate with Ypl184c encode proteins localized to the cell wall (38, p < 10−23), ER (50, p < 10−5), plasma membrane (32, p < 10−3), or extracellular milieu (8, p < 10−3). Transcripts encoding components of several protein complexes were associated with Ypl184c, including three of five components of the Cdc28 complex (CLB2, CLN3, and CLN2) for which we obtained high-quality measurements, three of three components of the plasma membrane H+ ATPase (PMP1, PMP2, and PMA1) for which we obtained high-quality measurements, and four of nine components of the oligosaccharyltransferase complex (OST4, SWP1, OST3, and OST5) [107]. Components of these complexes that were not defined as targets of Ypl184c (at a stringent 1% FDR) were nevertheless more likely to be overrepresented in Ypl184c IPs than expected by chance, suggesting that Ypl184c may actually associate with the mRNAs encoding most or all members of these complexes. Ypl184c associated with many mRNAs that exhibit unusual modes of translation regulation. Ypl184c bound all five of the mRNAs that have experimentally confirmed short upstream open reading frames (uORFs) (GCN4, CPA1, LEU4, SCH9, and SCO1) [108–115] in their 5′-UTRs and for which we obtained high-quality measurements; uORFs have been shown to regulate the translation of the downstream coding sequence and the stability of the mRNA [116]. Ypl184c associated with all five of the S. cerevisiae mRNAs that have been shown to have internal ribosome entry sites (IRES) (HAP4, YMR181C, GPR1, NCE102, and GIC1) in their 5′-UTRs [117,118] for which we obtained high-quality measurements; these IRESs enable cap-independent translation, often in response to environmental stresses [119]. Ypl184c also bound the unspliced HAC1 transcript, which associates with the cytosolic side of the ER membrane and is not efficiently translated until it is spliced by IRE1 as part of the unfolded protein response pathway [120,121]. Given Ypl184c's association with Pab1 and its striking association with sets of mRNAs that are known to be subject to extensive translational regulation, we speculate that Ypl184c regulates translation. The sequence motifs that we found to be significantly enriched in the mRNA targets of Ypl184c closely match the ones we found for Pub1 (Table S4). Indeed, the RNA target sets of these two proteins overlap significantly (Figures 1B and 4A). Given the absence of evidence for direct interactions between Ypl184c and Pub1, perhaps they compete for binding to overlapping groups of mRNAs. We have named YPL184C, post-transcriptional regulator of 69 kDa (PTR69). Discussion A large body of work has given us a general picture of the relationship between the several hundred transcription factors and thousands of genes in yeast (e.g., [26–29,32,35,52–60]). Among the key features of transcriptional regulation are that: (1) individual transcription factors characteristically regulate sets of genes with related biological roles, (2) transcription factors are recruited to the specific genes they regulate by binding to specific sequences in the vicinity of those genes, and (3) combinatorial regulation of individual genes by two or more distinct transcription factors provides multidimensional control and precision to their regulation. Our systematic identification of RNAs associated with each of 46 proteins in yeast suggests that a system that shares these three key features, likely involving dozens to hundreds of RBPs, may regulate the post-transcriptional fate of most or all RNAs in the yeast cell. This glimpse into the landscape of RNA–protein interactions has provided tantalizing clues to its organization and role. The mRNA targets of most of the RBPs in the survey encoded sets of proteins that were significantly associated with one or several related subcellular sites or biological processes (Figure 2 and Table S3). Although the regulatory roles and molecular mechanisms of most of these interactions remain to be elucidated, it seems unlikely that they have a purely decorative function. The selective binding of RBPs to sets of mRNAs that encode functionally and cytotopically related proteins provides strong evidence for widespread regulation at the post-transcriptional level. The functional relevance of these interactions is further supported by their relationships to phenotypes associated with mutation or altered expression of the RBP (Table S2). Many RBPs, including those examined in our survey, have mutant phenotypes only in specific physiological and developmental programs, and they have diverse gene expression patterns ( Thus, the regulatory program mediated by RBPs may be reorganized in response to specific physiological and developmental cues. The striking tendency of individual RBPs to bind to sets of mRNAs whose protein products are similarly localized in the cell hints at an important role for RBPs in establishing and maintaining spatial organization in the cell, perhaps through facilitating localized protein production and mRNA decay [13,32,122–131]. The cellular structures that were most often overrepresented among the mRNA targets of many RBPs were the cell wall, plasma membrane, and ER. Thus, in addition to the familiar role of the peptide signal sequence in mediating ER-localized translation [12], RBPs may have important roles in RNA partitioning between the cytoplasm and ER, and perhaps in localization to specific sites in the periphery of the cell, such as sites of cell-wall biogenesis, bud development, and endocytosis [32,132–135]. Two of the RBPs whose targets disproportionably encode proteins localized to the cell periphery, She2 and Khd1, have been shown to be involved in trafficking some of their mRNA targets to the bud tip during the G2/M phase of the cell cycle [32,67,136]. The particularly strong overrepresentation of RBPs that associate with mRNAs encoding cell-wall components may reflect the need for extensive multilayered regulation of the location and timing of assembly and remodeling of this dynamic subcellular structure. Identification of the information that specifies mRNA–RBP interactions is still in its earliest stages. The sequence motifs overrepresented in RBP targets, identified with the recently developed FIRE and novel REFINE methodologies, are diverse in design and location (Figures 5 and 6). Many of these RBPs recognized short linear sequences in the 3′-UTRs, 5′-UTRs, or coding sequences, or two or more of these regions. For about half of the RBPs, however, we were unable to find a sequence motif enriched among its RNA targets. Some of these RBPs may recognize structural elements. In support of this idea, we found the SRE hairpin loop, previously recognized as important for specific recognition of RNA by Vts1 [95], significantly enriched in coding sequences of Vts1 targets. Another protein in this survey, She2, is believed to recognize a three-dimensional structure in its targets [137,138]. We found promoter elements that likely specify transcription factor interactions enriched in the upstream regions of several RBP target sets, e.g., Gbp2 (Table S4). It is possible these promoter elements play an indirect role in specifying RBP interactions, perhaps by cotranscriptional recruitment of an RBP to mRNA targets via interactions with specific transcription-associated factors [22,23,139]. Identification of the large amount of still-undiscovered RNA regulatory information is an essential step in uncovering the specific regulatory program of each gene. We identified over 12,000 mRNA–RBP interactions with high confidence. Most mRNAs in the yeast transcriptome associated with at least one of the RBPs in our survey and many associated with multiple RBPs. Some of the RBPs in the survey appear to interact with most or all mRNAs at some point in their lifecycle (Figure S1 and Text S3). Naively extrapolating from our results to the estimated 600 RBPs in Saccharomyces suggests that each mRNA might interact with a dozen or more different RBPs, on average, during its lifetime. This extrapolation is highly speculative; the sample of RBPs that we investigated is biased towards RBPs that we suspected might have a regulatory function; we do not have a good estimate of the number of regulatory RBPs that bind discrete sets of mRNAs in the manner analogous to specific transcription factors; given that three of the four proteins in this survey that were not annotated as RBPs nevertheless gave reproducible interactions with specific sets of mRNAs (Bud27, Aco1, and Tdh3), the number of potential noncanonical, unannotated RBPs with regulatory roles may be large, perhaps even in the hundreds [140–144]. There is no reason to believe the system we have described is peculiar to yeast. Extensive post-transcriptional regulation by combinatorial binding of a large and diverse set of specific RBPs is likely to be a general feature of regulation in eukaryotes. Indeed, several lines of evidence suggest an even greater genomic investment in post-transcriptional regulation in humans (and other metazoans); the number and diversity of RBPs encoded by the human genome seems to far exceed that of yeast [145], untranslated regions of mRNAs are much longer in humans (∼1,300 bases on average) than in yeast (∼300 bases on average) and appear to contain much more regulatory information [6,146,147], and the architecture of animal cells is far more diverse and complex than that of the yeast cell, with a correspondingly greater potential role for specific RNA localization [13,130,148–151]. This work has provided a glimpse of a network of RBP–mRNA interactions that is likely to play an important, but still largely undiscovered, role in biological regulation. The genes and cis-regulatory elements implicated in this process represent a substantial fraction of the genome's investment in regulation, yet the specific details and molecular mechanisms of this network of RBP–mRNA interactions are still largely terra incognita—and fertile ground for further exploration and discovery. Materials and Methods RNA imunoaffinity purifications. We carried out immunopurifications of specific proteins, together with the associated RNAs, using specific strains expressing a TAP-tagged derivative of each selected protein (Open Biosystems Cat# YSC1177-OB), essentially as described in Gerber et al. [26]. After growing 1L cultures to an optical density at 600 nm (OD600) of 0.6–0.9 in YPAD, we harvested cells by centrifugation, chilled the cell pellets on ice, washed them twice with 25 ml of ice cold buffer A (20 mM Tris–HCl [pH 8.0], 140 mM KCl, 1.8 mM MgCl2, 0.1% Nonidet P-40, 0.02 mg/ml heparin), then froze them in LN2 and stored them at −80 °C. In a few instances, we proceeded to lyse the pelleted cells immediately without freezing. To lyse the cells, we first thawed the cell suspension at 4 °C, added 5 ml of buffer B (buffer A plus 0.5 mM DTT, 1 mM PMSF, 1 μg/ml leupeptin, 1 μg/ml pepstatin, 20 U/ml DNase I [Stratagene Cat# 600032], 50 U/ml Superasin [Ambion Cat# AM2696], and 0.2 mg/ml heparin), and then mechanically lysed the cells by vortexing in the presence of glass beads. We removed the beads by centrifugation at 1,000g for 5 min, then clarified the extracts by centrifuging them twice at 7,000g for 5 min each. We adjusted the volume of the extract to 5 ml with buffer B, removed a 100-μl aliquot for reference RNA isolation, and then incubated the remaining 4.9 ml with 400 μl of 50% (v/v) suspension of IgG-agarose beads (Sigma Cat# A2909) in Buffer A with gentle rotation for 2 h. We washed the beads once with 5 ml of buffer B for 15 min, and three times with 12 ml of buffer C (20 mM Tris-HCl [pH 8.0], 140 mM KCl, 1.8 mM MgCl2, 0.5 mM DTT, 0.01% NP-40, 15 U/ml Superasin, 1 μg/ml pepstatin, 1 μg/ml leupeptin, 1 mM PMSF) for 15 min with gentle rotation. We pelleted the beads by centrifugation for 5 min at 60g in a table-top centrifuge. We then transferred the beads to 1.2-ml micro-spin columns (BioRad Cat# 732-6204), centrifuged them briefly to pellet the beads, removed buffer C, and then added 1 volume of buffer C. We cleaved TAP-tagged proteins by incubation with 80 U acTEV protease (Invitrogen Cat# 12575023) or an equivalent amount of purified TEV [152] for 2 h at 15 °C. We collected the eluent by centrifugation into 2-ml tubes. We isolated reference RNA using RNeasy Mini Kit (Qiagen Cat# 74106), while we isolated RNA from the eluate by extraction with Phenol/Chloroform/Isoamyl Alcohol, 25:24:1 (Invitrogen Cat# 15593031) twice, and chloroform once, followed by ethanol precipitation with 15 μg of Glycoblue (Ambion Cat# AM9515) as carrier. Oligonucleotide microarray design. Starting with the Operon AROS 1.1 oligo set, which contains long oligonucleotides for almost all annotated S. cerevisiae nuclear and mitochondrial coding sequences, we added 3,072 additional probes designed to detect annotated noncoding RNAs, ribosomal RNA precursors, introns, exon-intron and exon-exon junctions, other sequences predicted to be expressed, additional probes for genes with high cross-hybridization potential, and hundreds of controls for array quality measurements and normalization. Details of oligonucleotide selection and probe sequences are available from the Operon Web site (; S. cerevisiae YBOX V1.0). Microarray production and prehybridization processing. Detailed methods for microarray experiments are available at the Brown lab Web site ( For oligonucleotide microarrays, we resuspended oligonucleotides in 3× SSC (1× SSC = 150 mM NaCl, 15 mM sodium citrate [pH 7.0]) at a final concentration of 25 μM and printed oligonucleotides on poly-lysine glass (Erie Scientific Cat# C41–5870-M20) ( We printed each oligonucleotide twice per array. For most arrays, the second print was in reverse orientation to the first print, such that oligonucleotide pairs were printed with different pins and thus located in different sectors of the array. Prior to hybridization, the oligonucleotides were crosslinked to the poly-lysine–coated surface with 65 mJ of UV irradiation. Slides were then incubated in a 500-ml solution containing 3× SSX and 0.2% SDS for 5 min at 50 °C. Slides were washed for 2 min in a glass chamber containing 400 ml of water, dunked in a glass chamber containing 400 ml of 95% ethanol for 15 s, and then dried by centrifugation. Free poly-lysine groups were then succinylated by incubation with 5.5 g of succinic anhydride that was dissolved in 350 ml of anhydrous 1-methyl,2-pyrolidoinone (Sigma Cat# 328634) and 15 ml of 1 M sodium borate (pH 8.0) for 20 min [53]. Slides were washed for 2 min in a glass chamber containing 400 ml of room temperature water, dunked in a glass chamber containing 400 ml of 95% ethanol for 15 s, and then dried by centrifugation. cDNA microarrays containing long double-stranded DNA (dsDNA) from PCR reactions were prepared as previously described [53]. Microarray sample preparation, hybridization, and washing. A total of 3 μg of reference RNA from extract and up to 3 μg (or 50%) of affinity-purified RNA were reverse transcribed with Superscript II (Invitrogen Cat# 18064–014) in the presence of 5-(3-aminoallyl)-dUTP (Ambion Cat# AM8439) and natural dNTPs (GE Healthcare Life Sciences Cat# US77212) with a 1:1 mixture of N9 and dT20V primers (Invitrogen). Subsequently, amino-allyl–containing cDNAs were covalently linked to Cy3 and Cy5 NHS-monoesters (GE Healthcare Life Sciences Cat# RPN5661). Dye-labeled DNA was diluted in a 20–40-μl solution containing 3× SSC, 25 mM Hepes-NaOH (pH 7.0), 20 μg of poly(A) RNA (Sigma cat # P4303), and 0.3% SDS. The sample was incubated at 95 °C for 2 min, spun at 14,000 rpm for 10 min in a microcentrifuge, and then hybridized at 65 °C for 12–16 h. For most oligonucleotide microarray experiments, we hybridized microarrays inside sealed chambers in a water bath using the M-series lifterslip to contain the probe on the microarray (Erie Scientific Cat # 22x60I-M-5522). For some oligonucleotide microarray experiments, we hybridized microarrays using the MAUI hybridization system (BioMicro), which promotes active mixing during hybridization. We hybridized cDNA microarrays inside sealed chambers in a water bath using a coverslip to contain the probe on the microarray. Following hybridization, microarrays were washed in a series of four solutions containing 400 ml of 2× SSC with 0.05% SDS, 2× SSC, 1× SSC, and 0.2× SSC, respectively. The first wash was performed for 5 min at 65 °C. The subsequent washes were performed at room temperature for 2 min each. Following the last wash, the microarrays were dried by centrifugation in a low-ozone environment (<5 ppb) to prevent destruction of Cy dyes [153,154]. Once dry, the microarrays were kept in a low-ozone environment during storage and scanning (see Microarray scanning and data processing. Microarrays were scanned using either AxonScanner 4200, 4000B, or 4000A (Molecular Devices). PMT levels were adjusted to achieve 0.1%–0.5% pixel saturation. Each element was located and analyzed using GenePix Pro 5.0 (Molecular Devices). These data were submitted to the Stanford Microarray Database [155] for further analysis. Data were filtered, as described in Text S10, to remove low-confidence measurements. Oligonucleotide pairs that both passed filtering criteria were averaged, and the data were globally normalized per array such that the mean log2 (Cy5/Cy3 fluorescence) ratio was zero after normalization. We analyzed a total of 123 IPs by microarray hybridization (Dataset S1). During the course of this work, we continued to improve and optimize our protocols. These changes and the manufacturing differences in reagents (especially in the beads used in the IPs) led to systematic differences in the background distribution of RNAs between corresponding experiments. We minimized systematic differences among sets of experiments by deriving estimates of the background separately for each set of experiments. Each group was normalized by subtracting the median log2 ratio for each molecular features across the experiments in a group from the log2 ratio of the molecular feature in each experiment. The details of the group normalization are described in Text S10, and the groups are labeled in Table S5. Microarray analyses. Hierarchical clustering was performed with Cluster 3.0 [156], and the results were visualized as heat maps with Java TreeView 1.0.12 [157]. Clustering of FDR values (Figures 1B and 4B) was performed using the centered Pearson correlation as a similarity metric. FDR values that were greater than or equal to 10 and missing values were set to 10 prior to clustering. Clustering of the significance values measuring the degree of overlap between RBP target sets (Figure 4A) was performed using the uncentered Pearson correlation as a similarity metric. For SAM, unpaired two-class t-tests were performed with default settings. FDRs were generated from up to 1,000 permutations of group normalized data. Details of SAM analysis are described in Text S11. Enrichment of specific gene lists in RBP target sets. The p-values of enrichment of specific classes of RNAs and GO terms in target sets were determined using the hypergeometric density distribution function and corrected for multiple hypothesis testing using the Bonferroni method. Enrichment of GO terms was performed with GO::TermFinder [158]. For noncoding RNAs, all RNAs for which we obtained reliable measurements on the microarray were used as background. For GO analysis, only probes that are meant to capture mature mRNAs were included in analyses. For oligonucleotide microarray experiments, this corresponds to probes that match the following regular expression: Y[A-P][RL][0–9]{3}[WC][-ABC]*_ORF (Datasets S1–S3). For cDNA microarray experiments, this corresponds to probes that match the following regular expression: Y[A-P][RL][0–9]{3}[WC][-ABC]* (Datasets S1–S3). mRNAs for which we obtained high-quality measurements were used as background. Sequences used for motif analysis. Yeast sequence files orf_genomic_1000.fasta and orf_coding.fasta were downloaded from SGD ( The 200 nucleotides upstream and downstream of coding sequences containing proper start and stop codons were extracted to create 5′-UTR and 3′-UTR databases, and the coding sequences were used for the coding sequence database. All-by-all WU-BLAST [159] ( comparisons were performed for each database against itself to identify highly similar sequences (using options -e 1e-10 -b 5000 -S 1 -F F). WU-BLAST output files were parsed to identify alignments of greater than or equal to 80% identity extending over half the length of the query sequence, and all such sequence pairs were grouped into redundant classes. One sequence from each redundant class was retained to create nonredundant databases for each region. Motif prediction. The REFINE procedure was run using hexamers with significant (p < 10−3) enrichment in RBP targets, as measured by the hypergeometric distribution (using options –ss –f 3 –g 6 –ct 3 –max 15 –dust). MEME analysis (version 3.5.1) was performed on the REFINE output sequences with options –dna –minw 6 –maxw 15 –text –maxsize 200000 –evt 10 –nmotifs 3. Motif site sequences were extracted from MEME output and used to generate position-specific log-odds scoring matrices based on the observed frequencies and 0.25 pseudocounts per base, and null frequencies based on mononucleotide composition of all sequences in the corresponding (5′-UTR or 3′- UTR) nonredundant database. Cutoff scores for motif classification were chosen to maximize the significance of association of motif sites with RBP target membership as measured by hypergeometric p-values for enrichment. All subsequences with scores above the cutoff threshold were classified as motif sites, and the final significance was measured as the negative log of the p-value of motif enrichment in RBP targets. FIRE analysis was run on the nonredundant 5′- and 3′-UTR databases using binary data indicating RBP target membership with options –exptype=discrete –seqlen_rna=200 –nodups=1 –dodna=0. Simulations to evaluate significance of predicted motifs. For both REFINE and FIRE, statistical significance of the predicted motifs was assessed by randomly generating target sets of similar size and repeating each procedure 100 times on the simulated target data. We defined a test statistic as the negative log of the p-value for motif enrichment for REFINE; the reported motif z-score was used for FIRE motifs, and we compared the observed values of these test statistics to the distributions generated by the random simulations (Table S4). Motifs were declared as significant if the observed test statistic was greater than three standard deviations above the mean, or if there was significant enrichment (p < 10−4) of the motif in targets occurring in regions from which that motif was not derived. Supporting Information Dataset S1 Normalized Data from DNA Microarray Experiments; Values from Both Pregroup Normalization and after Group Normalization Are Included (6.76 MB ZIP) Click here for additional data file. Dataset S2 Data Matrix Containing False-Discovery Rate Values for Each RNA–RBP Pair (2.5 MB ZIP) Click here for additional data file. Dataset S3 Significance Analysis of Microarray Results for Each Protein (11.8 MB ZIP) Click here for additional data file. Figure S1 Immunopurification Enrichment Profiles of Several RNA-Binding Proteins (A) Distribution of average Cy5/Cy3 fluorescence ratios from five independent microarray hybridizations analyzing Ssd1 targets. The enrichment distribution for mRNAs is shown in black, and the enrichment distribution for other annotated RNAs (i.e., nuclear introns, mitochondrion-encoded mRNAs, mitochondrial introns, snoRNAs, ribosomal RNAs, LSR1, NME1, SCR1, SRG1, and TLC1) is shown in red. The points correspond to an estimated distribution that was created by binning the average fluorescence ratios into 0.1 log2 unit bins from −7 to 7 log2 units. The lines correspond to a smoothed fit of the data [160]. We scaled the smoothed fit of the distribution to the binned data by making the maximum value of the smoothed fit data equal to the value in the bin with the largest number of RNAs. (B) Same as in (A), except for Scp160. The results are the average of three independent microarray hybridizations. (C) Same as in (A), except for Pab1. The results are the average of three independent microarray hybridizations. (D) Same as in (A), except for Pub1. The results are the average of three independent microarray hybridizations. (374 KB PDF) Click here for additional data file. Figure S2 Overrepresentation of Specific Classes of RNAs in Association with Specific RNA-Binding Proteins Enrichment of several classes of RNAs (rows) in target sets (1% FDR) of RBPs (columns). The significance of enrichment of the class of RNAs is represented as a heat map in which the color intensity corresponds to the negative log10 p-value, which was calculated using the hypergeometric density distribution function and corrected for multiple hypothesis testing using the Bonferroni method. RBPs whose targets are significantly enriched (p ≤ 0.05) for a specific class of RNAs are shown. (219 KB PDF) Click here for additional data file. Figure S3 Specific Features of Post-Transcriptional Regulation May Be Linked to Broad-Specificity RNA-Binding Proteins Pearson correlations between IP enrichment with the RBP (columns) and selected characteristics of mRNAs (rows) are represented as a heat map. mRNAs that passed quality filtering for all nine RBPs were included in this analysis. (231 KB PDF) Click here for additional data file. Table S1 Annotated and Putative RNA-Binding Proteins in Saccharomyces cerevisiae (160 KB XLS) Click here for additional data file. Table S2 Summary of RNA-Binding Proteins in the Survey (49 KB XLS) Click here for additional data file. Table S3 Gene Ontology Terms Enriched in RNA-Binding Protein Target Sets (91 KB XLS) Click here for additional data file. Table S4 RNA Motifs Identified in RNA-Binding Protein Target Sequences (46 KB XLS) Click here for additional data file. Table S5 Description of Microarray Experiments and Groups Used for Group Normalization (41 KB XLS) Click here for additional data file. Text S1 Representation of RNA-Binding Proteins in This Study (24 KB DOC) Click here for additional data file. Text S2 Comments on the Immunopurification Method (51 KB DOC) Click here for additional data file. Text S3 Diverse RNA Enrichment Profiles among RNA-Binding Proteins (29 KB DOC) Click here for additional data file. Text S4 RNA-Binding Proteins That Preferentially Associate with RNAs Other Than Mature mRNAs Encoded by Nuclear Genes (68 KB DOC) Click here for additional data file. Text S5 Specific Features of Post-Transcriptional Regulation May Be Linked to Broad-Specificity RNA-Binding Proteins (38 KB DOC) Click here for additional data file. Text S6 Many RNA-Binding Proteins Appear to Bind Their Targets during Specific Stages in Their Lives (57 KB DOC) Click here for additional data file. Text S7 Putative RNA-Recognition Motifs (48 KB DOC) Click here for additional data file. Text S8 Many RNA-Binding Proteins Associated with Their Own Transcripts (32 KB DOC) Click here for additional data file. Text S9 Insights into the Functions of Specific RNA-Binding Proteins (49 KB DOC) Click here for additional data file. Text S10 Immunopurification Group Normalization (28 KB DOC) Click here for additional data file. Text S11 Significance Analysis of Microarrays (33 KB DOC) Click here for additional data file. Accession Numbers Our microarray experiment data are publicly available from the Stanford Microarray Database and Gene Expression Omnibus.
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              Extensive Association of Functionally and Cytotopically Related mRNAs with Puf Family RNA-Binding Proteins in Yeast

              Introduction The dynamic structure and physiology of a cell depend on coordinated synthesis, assembly, and localization of its macromolecular components (Orphanides and Reinberg 2002). The timing and level of expression of the genes that encode these components are controlled by transcription factors that regulate initiation of transcription in a gene-specific manner by binding to specific DNA sequences proximal to the genes they regulate. The combinatorial binding and activity of specific transcription factors confer a distinctive program of regulation on each individual gene while enabling coherent global responses of large sets of genes in physiological and developmental programs. Much less is known about either the system architecture or molecular mechanisms that underlie regulation of the post-transcriptional steps in the gene expression program. There are approximately 15,000 mRNA molecules in each Saccharomyces cerevisiae cell during exponential growth in rich medium (Hereford and Rosbash 1977) and at least a 10-fold larger number in a typical mammalian cell (Hastie and Bishop 1976). The extent to which the location, activity, and fates of these diverse populations of mRNAs are coordinated and the post-transcriptional mechanisms that might mediate their coordinated regulation remain largely unknown. RNA-binding proteins (RBPs) have been implicated in diverse aspects of post-transcriptional gene regulation, including RNA processing, export, localization, degradation, and translational control (Dreyfuss et al. 2002; Maniatis and Reed 2002; Mazumder et al. 2003). Although there appear to be hundreds of RBPs encoded in eukaryotic genomes (Costanzo et al. 2001; Issel-Tarver et al. 2002), for only a few of these proteins have the RNA targets been systematically identified (Takizawa et al. 2000; Tenenbaum et al. 2000; Brown et al. 2001; Hieronymus and Silver 2003; Li et al. 2003; Shepard et al. 2003; Waggoner and Liebhaber 2003). For example, a recent study in S. cerevisiae found that two nuclear RNA export factors were each associated with large and distinct mRNA populations, and common functional themes were found among the 1,000 or so proteins encoded by each population (Hieronymus and Silver 2003). These observations support a role for RBPs in the coordinated regulation of mRNA subpopulations (Keene and Tenenbaum 2002; Keene 2003). Systematic identification of the mRNA targets of RBPs can be a powerful approach to understanding the cellular roles of RBPs and the mechanisms by which they might regulate the post-transcriptional lives of mRNAs. We have focused first on the Pumilio–Fem-3-binding factor (FBF) (Puf) proteins from S. cerevisiae, which belong to a structurally related family of cytoplasmic RBPs that are implicated in developmental processes in various eukaryotes (Wickens et al. 2002). Puf proteins are defined by the presence of several (typically eight) consecutive repeats of the Pumilio homology domain (Pum-HD), which confers RNA binding activity (Zamore et al. 1997; Wang et al. 2002a). The Puf proteins characterized to date have been reported to bind to 3′-untranslated region (UTR) sequences encompassing a so-called UGUR tetranucleotide motif and thereby to repress gene expression by affecting mRNA translation or stability. Despite the widespread occurrence of Puf family members, only a few mRNA targets have been identified for these RBPs (Wickens et al. 2002). For example, in Drosophila, the PUMILIO protein binds maternal hunchback mRNA and, in concert with NANOS protein, represses translation of the mRNA at the posterior pole during early embryogenesis. The Caenorhabditis elegans Puf homologs, called Fem-3-binding factors (FBFs), regulate the switch from spermatogenesis to oogenesis by repressing fem-3 translation, and they are implicated in the propagation of germline stem cells through binding and inhibition of gld-1 mRNA expression (Zhang et al. 1997; Crittenden et al. 2002). Less is known about the human homologs: PUMILIO-2 protein interacts with DAZ (deleted in azoospermia) protein and is expressed in embryonic stem cells and germ cells, whereas PUMILIO-1 is almost ubiquitously expressed (Moore et al. 2003). In S. cerevisiae, five proteins, termed Puf1p to Puf5p, bear six to eight Puf repeats (Figure 1). Little is known about the physiological function of these proteins. Mutations in either PUF4 or PUF5 result in diminished longevity (Kennedy et al. 1997). PUF1 was isolated as a multicopy suppressor of certain microtubule mutants (Machin et al. 1995), and a PUF2 null mutant displayed increased resistance to cycloheximide and paromomycin (Waskiewicz-Staniorowska et al. 1998). However, S. cerevisiae mutants lacking all five PUF genes are viable (Olivas and Parker 2000). A genome-wide analysis of mRNA expression patterns in yeast mutants lacking all five PUF genes found differential expression of 7%–8% of all mRNAs under steady-state conditions, but no common theme was found among the affected genes (Olivas and Parker 2000). Only two specific mRNA targets have been identified for yeast Puf proteins: Puf3p binds to the COX17 mRNA 3′-UTR in vitro and may regulate its turnover (Olivas and Parker 2000), and Puf5p negatively regulates expression of reporter genes substituting for the HO endonuclease (Tadauchi et al. 2001). Using DNA microarrays to identify the specific mRNAs that interact with the five S. cerevisiae Puf proteins, we have found that each Puf protein bound to a large set of distinct and functionally related mRNAs. We identified novel and conserved sequence elements in the mRNAs bound by Puf3p, Puf4p, and Puf5p. The results suggest a system for large-scale coordinated control of cytoplasmic mRNAs and provide insights into the physiological logic of the gene expression program. Results Systematic Identification of mRNAs Associated with Specific RBPs To identify RNAs associated with Puf proteins, tandem-affinity purification (TAP)-tagged proteins were purified from whole-cell extracts of S. cerevisiae (Figure 2). The TAP tag (Rigaut et al. 1999), a sequence encoding two IgG-binding units of protein A, a specific protease recognition site, and a calmodulin-binding domain, was fused in-frame at the C-terminus of the respective open reading frame (ORF) in its original chromosomal location (Ghaemmaghami et al. 2003). This design was intended to preserve normal regulation of the expression of the fusion protein. Cells of the TAP-tagged strains showed growth rates and cell morphologies similar to wild-type cells. Cells were grown to mid-log phase in rich medium, extracts were prepared, and ribonucleoprotein complexes were recovered by affinity selection on IgG beads and subsequent cleavage with tobacco etch virus (TEV) protease (see Materials and Methods). To control for nonspecifically enriched mRNAs, the same procedure was performed with wild-type cells lacking the TAP tag. TEV protease cleavage was superior to direct elution of proteins from beads, as it gave lower contamination from nonspecifically interacting RNAs in the resulting purified fractions (data not shown). RNA was isolated from the purified protein samples and from extracts. We obtained 0.8–2 μg of RNA from the Puf affinity-isolated samples gathered from 1-l cultures, but no detectable RNA (<0.1 μg) was recovered when the same procedure was applied to untagged control cells. The yield of RNA from the Puf affinity isolation procedure was sufficient to perform further labeling steps directly, without amplification of RNA by PCR, as had been required in previous studies (Takizawa et al. 2000; Hieronymus and Silver 2003). Two samples from each cell population, total RNA, and RNA isolated by the Puf affinity procedure were used to prepare cDNA probes labeled with different fluorescent dyes, which were mixed and hybridized to S. cerevisiae DNA microarrays containing all known and putative ORFs, introns, and the mitochondrial genome (see Materials and Methods). The ratio of the fluorescent hybridization signals from the two differentially labeled RNA samples, at the array element representing each specific gene, provided an assay for enrichment of the corresponding mRNA by the Puf-affinity procedure. Puf3p is the only one of the five S. cerevisiae Puf proteins for which direct in vitro interaction with an mRNA (COX17) has previously been described, thereby providing an internal positive control (Olivas and Parker 2000). COX17 mRNA was substantially and consistently enriched in four independent Puf3p affinity isolations (ratio = 10 ± 1.4; Figure 3A), but not in mock isolations (ratio = 0.8 ± 1.2). In general, after filtering for spots with high background or irregular shapes, enrichment values for the entire set of arrayed sequences were reproducible (median of standard deviations in all arrayed spots = 0.35 on a log2 scale) (see Materials and Methods). To define targets specific to each Puf protein, we first selected all sequences for which enrichment factors in the corresponding affinity isolation procedures were at least two standard deviations above the mean for all arrayed sequences (Figure S1; for samples isolated by the Puf3p-affinity procedure, this corresponded to an enrichment factor of greater than or equal to 2.5). Second, we eliminated from this selected group any sequences that were also consistently enriched in the mock procedure (see Materials and Methods). Although no cutoff can perfectly distinguish the actual physiological targets from false positives, the high reproducibility of the results (see Figure 3B), the occurrence of distinct mRNA populations associated with the different Puf proteins, and the characterization of these targets described in the subsequent sections, including the identification of distinct sequence motifs and in vivo confirmation of the role of these motifs in specific RNA–protein interactions, strongly support the validity of the majority of the targets. Finally, the list of target mRNAs did not change substantially by application of other statistical methods for selection (see Lieb et al. 2001). A large number of arrayed sequences, 818, identified transcripts associated with at least one Puf protein (see Figure 3B; Table S1), with 735 encoding distinct ORFs. This represents approximately 12% of the known and predicted protein-coding sequences in the S. cerevisiae genome. Of these, 90 transcripts interact with more than one Puf protein. The largest overlap was observed between the groups of transcripts associated with Puf1p and Puf2p—which also have the greatest overall similarity in amino acid sequence among the Puf proteins (45% identical); 36 of the 40 Puf1p targets were also associated with Puf2p. Twenty-eight mRNAs were bound by both Puf4p and Puf5p, and 16 were bound both by Puf2p and Puf5p. Seven transcripts were enriched with three different Puf proteins (DHH1 and YOL109w mRNAs with Puf1p, Puf2p, and Puf5p; NOP1 mRNA with Puf1p, Puf4p, and Puf5p; SUR7 and SFL1 mRNAs with Puf2p, Puf4p, and Puf5p; and IFM1 mRNA with Puf3p, Puf4p, and Puf5p). The remaining 645 target mRNAs were each associated with only one of the Puf proteins. Thus, each Puf protein associates with a distinct and highly specific subset of mRNAs (see Tables S3–S7). We estimated the number of Puf proteins per cell by a filter affinity blot analysis using protein A as a standard for calibration (Table S2). We found that Puf1p, Puf2p, Puf3p, and Puf5p were similar in abundance, with 350–400 molecules per cell. Puf4p was approximately twice as abundant (approximately 900 molecules per cell). The relatively low abundance of the Puf proteins is therefore comparable to that of transcription factors, protein kinases, and cell cycle proteins (Futcher et al. 1999). Moreover, our measurements imply that the intracellular concentrations of the Puf proteins range between 20 and 50 nM, approximately one order of magnitude higher than the dissociation constants for binding of their metazoan homologs to the cognate target RNAs. The number of Puf proteins per cell approximates the estimated numbers of cognate Puf target mRNA molecules present in the cell (Holstege et al. 1998; Wang et al. 2002b) (Table S2), consistent with a model in which each Puf protein molecule is associated with one mRNA molecule in the cell. Puf3p Specifically Binds mRNAs Encoding Mitochondrial Proteins As a first step toward identifying functional themes among the mRNAs associated with each Puf protein, we retrieved the Gene Ontology (GO) annotations for process, function, and compartment from the Saccharomyces Genome Database (SGD) (Issel-Tarver et al. 2002). (The target mRNAs for each Puf protein are listed in Tables S3–S7.) We then searched for significant shared GO terms in the lists of Puf mRNA targets (Table S8). Puf3p associated almost exclusively with transcripts of nuclear genes that encode mitochondrial proteins (p < 10−88; see Table S5). In particular, of the 154 Puf3p-associated transcripts for which GO annotation of subcellular localization was available, 135 (87%) were assigned to mitochondria (Figure 4A). Of the Puf3p-associated mitochondrial gene products, 80 (59%) are involved in protein biosynthesis, including structural components of the ribosome (55 genes), tRNA ligases (12 genes), and translational regulators (nine genes). Twenty-two of the Puf3p-bound transcripts are involved in mitochondrial organization and biogenesis, 17 in aerobic respiration, and 12 in mitochondrial translocation. Based on this striking cytotopic (relating to location in the cell) concordance, we suggest that the remaining 66 Puf3p mRNA substrates (30%) for which no GO annotations were available are likely to encode mitochondrial proteins. (While this paper was under review, a genome-wide analysis of protein localization in S. cerevisiae [Huh et al. 2003] reported a mitochondrial localization for 27 additional Puf3p targets, raising the total to 162 of the 220 putative Puf3p mRNA targets encoding mitochondrial proteins.) Puf1p- and Puf2p-Associated mRNAs Disproportionately Encode Membrane-Associated Proteins Of all the characterized S. cerevisiae genes for which any information about subcellular localization is available, 18% are currently classified as encoding membrane-associated proteins (Yeast Proteome Database [YPD], May 2003; see Costanzo et al. 2001). A much greater fraction of the mRNAs associated with Puf1p and Puf2p encode membrane-associated proteins: 16 of the 28 (57%) known proteins encoded by Puf1p-interacting mRNAs and 55 of 106 (52%) known proteins encoded by Puf2p-interacting mRNAs (see Figure 3B; see Tables S3 and S4). Transcripts encoding proteins associated with the plasma membrane were particularly enriched among the Puf1p- and Puf2p-bound mRNAs. Most of the mRNAs bound by Puf1p were also associated with Puf2p. However, Puf2p bound uniquely to many additional mRNAs (146 Puf2p mRNA targets versus 40 for Puf1p). In terms of cellular processes, many Puf1p- and Puf2p-associated transcripts encode proteins with roles in transmembrane transport and vesicular trafficking of proteins: 9 out of 26 (34%; p < 0.0002) of annotated Puf1p targets and 24 out of 104 (23%; p < 10−5) annotated Puf2p targets (compared to 9% of all characterized genes) (YPD, May 2003). This group includes transporters for spermine (Tpo1, Tpo2, Tpo3), proteins (Nce101, Nce102, Ast1, Vps72, Mas6, Sfk1, Mup3), vesicles (Sso2, Snc2, Yip1, Aps3, Ypr157w), and lipids (Pdr16, Ykl091c, Fps1 [glycerol]). (Tpo2 and Tpo3 may cross-hybridize on arrays because of their high sequence identity [89%], but Tpo1 does not [Shepard et al. 2003]). Puf4p and Puf5p Interact Selectively with mRNAs Encoding Nuclear Components Among the Puf5p targets (see Table S6), we found two common themes. First, a remarkable fraction encodes nuclear proteins that participate in covalent modification of histones, chromatin-remodeling complexes, or transcriptional regulation (64 of the 113 annotated genes [57%; p < 3 × 10−6]). Second, the Puf5p-associated transcripts included a substantial fraction of the mRNAs known to encode components or regulators of the mitotic spindle apparatus in yeast: 14 mRNAs that encode microtubule-based spindle components, including seven of the 25 (28%; p < 4 × 10−5) structural components of the spindle pole body (Kar1, Ccd31, Spc19, Spc42, Bbp1, Cnm67, and Nuf2) (Wigge et al. 1998). Messages encoding nuclear and cytoplasmic proteins that regulate polarized growth (Ame1, Boi2, Bsp1, Bub1, Bud9, Dad2, Elm1, Gic1, Kar9, Rax2, Ste7), some of them known to interact with spindle components, were also Puf5p targets. Transcripts encoding nucleolar proteins were highly enriched among the Puf4p-bound mRNAs: 36 of the 133 (27%) annotated genes in this group encode nucleolar proteins, as compared to 3% of all the annotated genes in the S. cerevisiae genome (p < 10−12). Of these 36, 29 are directly involved in ribosomal RNA (rRNA) synthesis, processing, and ribosome maturation (p < 10−15), major functions of the nucleolus (Fatica and Tollervey 2002; Gerbi et al. 2003) (see Tables S5 and S8). Twenty-eight transcripts were enriched in both the Puf4p and Puf5p affinity isolations, including six transcripts encoding components of the nucleosome (p < 10−11), among them the four core histone proteins (histones 2A and 2B, histone 3, and histone 4; note that histones 2A and 2B are 98% identical and therefore cross-hybridize). Diverse Functional Links among Transcripts Associated with Each Puf Protein In addition to the cytotopic relationships within each group of Puf-associated mRNAs, we were struck by the frequency with which transcripts encoding different components of protein complexes or systems of interacting proteins were bound by the Puf proteins. For example, most of the nuclear transcripts encoding components of the mitochondrial ribosome (55 out of the 77 known genes; Gan et al. 2002) were Puf3p-associated. This observation prompted us to search for other protein complexes and functional systems that shared similarly Puf-associated mRNAs. Other examples of coordinate “tagging” of transcripts encoding subunits of multiprotein complexes include Puf4p association of mRNAs encoding three of the four protein components of the H/ACA core particle (Cbf5p, Gar1p, and Nhp2p), which synthesizes pseudouridine in rRNAs (Henras et al. 1998) (Figure S2; no data were obtained for the fourth component, Nop10p). Puf5p bound mRNAs encoding histone acetylases (Ada2p, Spt8p, and Hfi1p), which are components of the Spt–Ada–Gcn5–acetyltransferase (SAGA) complex, and transcripts encoding at least four of the six members of the RSC (remodels the structure of chromatin) family of DNA-stimulated ATPases with bromodomains (Bdf1p, Bdf2p, Rsc2p, and Rsc4p; no array data were obtained for the two other members, Rsc1p and Spt7p). As mentioned above, the mRNAs encoding at least three of the four core histones were enriched in both Puf4p and Puf5p affinity isolations. We also found numerous cases in which the transcripts encoding multiple members of a functional group of proteins were bound by the same Puf protein. For example, the transcripts encoding the Tpo1, Tpo2, and Tpo3 proteins, the three known spermine transporters in the plasma membrane (Albertsen et al. 2003; see note above about cross-hybridization), and the two known genes implicated in the nonclassical protein export pathway (NCE101, NCE102) (Cleves et al. 1996) were bound by Puf1p and Puf2p and by Puf2p, respectively. Puf5p was associated with all of the histone deacetylases (HDACs) that act on histones located around coding sequences—Sin3p (a class I HDAC), Hda1p (a class II HDAC), and both components of the Set3C complex (Hst1p and Snt1p) (Kurdistani and Grunstein 2003). (Two other HDACs, Hos1p and Hos3p, which deacetylate histones around the ribosomal DNA locus, were not enriched in Puf5p affinity isolations.) Finally, we identified cases in which the mRNAs encoding multiple components of a specific regulatory system were bound by the same Puf protein. For example, Puf2p associates with mRNAs encoding diverse proteins regulating Pma1p, which is an ATP-dependent proton transporter located in the plasma membrane, and with PMA1 mRNA itself (Figure S2). All of the mRNAs encoding nucleolar glycine/arginine-rich (GAR) domain-bearing proteins (Sbp1p, Nsr1p, Nop1p, Gar1p) as well as HMT1 mRNA, encoding a dimethylase that modifies the nucleolar GAR proteins (Xu et al. 2003), were associated with Puf4p, while none of the mRNAs encoding the distinct group of nonnucleolar GAR proteins were bound by Puf4p (Figure S2). Sequence Motifs in the 3′-UTR of mRNA Targets Direct Binding by Puf Proteins The Puf homologs in Drosophila and C. elegans bind to sequences in the 3′-UTR of mRNAs (Wickens et al. 2002). We therefore examined the sets of mRNAs associated with each of the S. cerevisiae Puf proteins for the presence of common sequence motifs in 5′-UTRs and 3′-UTRs, using multiple expectation maximization for motif elicitation (MEME) as a motif discovery tool (Bailey and Elkan 1994). We identified distinct 10- or 11-nucleotide sequence motifs in the 3′-UTR among the mRNAs interacting with Puf3p, Puf4p, and Puf5p (Figure 5A, Tables S9–S11). We have thus far been unable to identify conserved sequence elements among Puf1p and Puf2p targets; these proteins may recognize structural elements in the RNA rather than simple sequence strings, possibly via their classical RNA-binding domains instead of their six-repeat Pumillio domains. The conserved motifs we identified in the Puf3p, Puf4p, and Puf5p targets each include a UGUR tetranucleotide sequence, which is a feature of all previously reported RNA targets of Puf family proteins (Wickens et al. 2002). Furthermore, in each case, the consensus sequence contains a conserved dinucleotide (UA), located two, three, or four nucleotides downstream of the UGUR motif, in the consensus sites for Puf3p, Puf4p, and Puf5p. Remarkably, the Puf3p consensus motif matches a sequence (CYUGUAAAUA) previously identified by computational tools in 3′-UTR sequences of nuclear genes coding for mitochondrial proteins (Jacobs Anderson and Parker 2000). We examined the distribution of the consensus sequence motifs in the entire S. cerevisiae genome (Table 1). Of the genes whose mRNAs were predicted by computational analysis to contain one of these three target sequences in their 3′-UTRs, 42% were identified experimentally as targets in the corresponding affinity isolation procedure (Table 1). The consensus motifs were occasionally found in the coding sequence of an experimentally identified target gene, but were much rarer in the predicted 5′-UTR sequences (Table 1). Moreover, only a few mRNAs had two copies of the motifs: five mRNAs among the Puf3p targets, six among the Puf4p targets, and one among the Puf5p targets (see Tables S5–S7). As our computational method did not detect the cognate consensus sequence elements in all the experimentally identified targets, alternative sequences or structural elements in RNAs might also allow specific interactions with Puf proteins, some mRNAs may be associated indirectly as part of larger complexes, and some of the putative mRNA targets identified by our affinity procedure are likely to be false positives. To test the in vivo function of the putative recognition elements identified by the computational analysis, we assayed RNA–protein interactions in vivo using the yeast three-hybrid system (Bernstein et al. 2002) (see Figure 5B). Puf3p, Puf4p, and Puf5p bound specifically to a sequence matching to the cognate consensus sequence, as assayed by activation of the lacZ and HIS3 reporter genes (see Figure 5C and 5D). For Puf3p and Puf4p, the Pum-HD alone was sufficient to confer specific binding (see Figure 5C and 5D), but no interaction could be seen with the Puf5p Pum-HD alone (data not shown). These interactions were specific: mutations in the UGU of the Puf3p consensus sequence disrupted binding, and each Puf protein interacted with its cognate consensus sequence in preference to the closely related consensus sequences recognized by the other Puf proteins. We detected a weak interaction between Puf3p and the Puf4p target sequence, an interaction that was not seen with the Puf3p Pum-HD alone. These results suggest that binding of the Puf proteins to these specific cis-acting elements directs their functions to specific sets of mRNAs. Subcellular Distribution of Puf Proteins We investigated the localization of the TAP-tagged Puf proteins by immunofluorescence with antibodies against the TAP tag (see Materials and Methods). All five Puf proteins were predominantly localized to multiple discrete foci in the cytoplasm (Figure 6). The predominantly cytoplasmic localization is consistent with previous reports for S. cerevisiae Puf3p and Puf5p (Tadauchi et al. 2001) and for the homologous proteins in higher eukaryotes (Lehmann and Nüsslein-Volhard 1991; Zhang et al. 1997). The distribution of the foci of Puf proteins was not obviously related to distinct cellular organelles or structures, with the exception of Puf1p and Puf2p, which localized in foci enriched near the periphery of the cell. Because of the diffuse and pleiomorphic distribution of mitochondria in the cell, we cannot exclude the possibility that Puf3p, which specifically bound transcripts of proteins destined for the mitochondria, is associated with mitochondria. Altered Levels of Puf3p-Associated mRNAs in a puf3Δ Mutant A previous study compared steady-state mRNAs levels of cells bearing deletions of all five Puf proteins and wild-type cells grown in rich media (Olivas and Parker 2000). Only 12 of the 148 (8%) mRNAs whose abundance changed by more than 2-fold were selectively enriched in our affinity isolations with Puf proteins. The lack of a simple relationship between the mRNA binding specificity we observed and the reported effects of these multiple mutations on global gene expression prompted us to design a more specific experiment to search for a possible connection between specific mRNAs levels and binding to Puf proteins. We focused on Puf3p, as its strong association with mRNA-encoding mitochondrial proteins suggested that we should look for a regulatory function for this protein in mitochondrial physiology. Indeed, we found that puf3Δ cells grew more slowly than isogenic puf3+ cells on minimal media plates with glycerol as the carbon source (Figure S3). We therefore compared mRNA levels in the puf3Δ and puf3+ cells grown under these conditions by DNA microarray hybridization. Although the magnitude of the change was small, the relative expression levels of the 220 Puf3p-associated mRNAs were selectively increased in puf3Δ cells, compared to all other mRNAs analyzed (p < 10−34) (Figure 7). Of the 16 mRNAs whose abundance was increased by more than 2-fold in the puf3Δ mutant, 11 (70%) were among the transcripts identified as Puf3p targets by our co-purification experiments, and all encode mitochondrial proteins. This result could reflect a direct effect of Puf3p on its target mRNAs, for example, by promoting mRNA decay (Olivas and Parker 2000). However, the levels of transcripts involved in respiration and mitochondrial function, including many that did not appear to be bound directly by Puf3p, were increased in the puf3Δ mutant, suggesting the possibility that the elevated abundance of Puf3p target mRNAs could instead be an indirect response to impaired mitochondrial and respiratorial function in puf3Δ cells. Discussion In an analysis of just five of the hundreds of RBPs encoded by the S. cerevisiae genome, we found that more than 700 transcripts appeared to be specifically bound by one or more RBPs, with each of the five Puf family proteins “tagging” a distinct set of mRNAs. These sets encode functionally and cytotopically related proteins. For three of the Puf proteins, we identified distinct short sequences in the associated specific set of mRNAs, typically in the 3′-UTR, which were sufficient for specific binding to the cognate Puf protein in vivo. Many sets of mRNAs encoding proteins localized to the same subcellular compartment, protein complex, or functional system were bound by the same Puf protein. Puf3p, which specifically associated with cytoplasmic mRNAs encoding mitochondrial proteins, generally affected the steady-state levels of its mRNA targets as reflected by their increased abundance in puf3 mutant cells. The selective “tagging” by sequence-specific RBPs of mRNAs that share common physiological roles suggests a general and widespread mechanism for coordinated control of their expression. Previous reports have identified coordinated regulation of small sets of functionally related mRNAs by specific RBPs. For example, mammalian stem–loop binding protein (SLBP) associates with all five classes of histone mRNAs and guides proper 3′-end formation (Dominski and Marzluff 1999). Iron regulatory proteins (IRPs) bind to and regulate translation of five different mRNAs encoding proteins involved in iron metabolism (Eisenstein and Ross 2003), and a cytoplasmic poly(A) polymerase regulates multiple mRNAs in early development (Mendez and Richter 2001). Based on these and other examples (Tenenbaum et al. 2000), Keene and Tenenbaum (2002) have suggested that messenger RBPs could define “post-transcriptional operons.” Our results provide strong support for this general idea of coordination of gene expression via RBPs and suggest that the post-transcriptional control afforded by combinatorial binding of RBPs to mRNAs could allow greater regulatory flexibility than a simple operon (see also Keene and Tenenbaum 2002). Further, we suggest that RBPs may play important roles in subcellular localization and efficient assembly of protein complexes. The RBPs encoded in eukaryotic genomes rival specific transcription factors in their numbers and diversity, raising the intriguing possibility that specific regulation of the localization, translation, and survival of mRNAs might be comparable in their richness and complexity to regulation of transcription itself. Each of the five Puf proteins interacts with a distinct large set of mRNAs, comprising more than 700 different mRNAs in total. Five other RBPs in S. cerevisiae have been subjected to a similar genome-wide survey of their mRNA targets. She2p, which plays a critical role in selective targeting of specific mRNAs to the bud tip (Shepard et al. 2003), Khd1p, which has also been implicated in localizing gene expression to the nascent bud (A. P. Gerber, unpublished data), and Scp160p, an RBP implicated in genome stability (Li et al. 2003), were each found to bind from 20 to hundreds of distinct mRNAs, and two proteins implicated in RNA export from the nucleus, Yra1p and Mex67p, were each associated with more than 1,000 mRNAs (Hieronymus and Silver 2003). Thus, just ten of the 567 S. cerevisiae proteins known or predicted from the genome sequence to have RNA binding activity (Costanzo et al. 2001) have been found to bind, in a functionally specific pattern, a total of approximately 2,500 different transcripts (approximately 40% of the transcriptome). The extent and specificity of the RNA–protein interactions represented by the proteins studied to date, extrapolated to the hundreds of putative RBPs that remain to be investigated, suggest the existence of an extensive network of RNA–protein interactions that coordinate the post-transcriptional fate of large sets of cytotopically and functionally related RNAs through each stage of its “lifecycle.” It further suggests a potential regulatory repertoire comparable in its diversity and richness to that of the DNA-binding transcription factors (Figure 8). Indeed, the combinatorial binding of mRNAs by multiple RBPs could, in principle, define a specific post-transcriptional fate for each individual mRNA (for an example, see Sonoda and Wharton 2001). Many sets of mRNAs bound by the same Puf protein encode proteins that act in the same subcellular location, form stochiometric complexes, or are implicated in the same cellular pathway. This organization is most clearly exemplified by Puf3p, which selectively bound mRNAs encoding mitochondrial proteins, including at least 70% of all mitochondrial ribosomal proteins (see Figure 4). Combinations of RBPs could specify smaller sets of RNAs encoding more precisely defined functional groups of proteins. For example, the mRNAs encoding the core histone proteins were among the small set of mRNAs that were associated with both Puf4p and Puf5p. These results therefore hint that networks of functional and physical interactions among proteins could be reflected in a corresponding network of mRNA–protein interactions that coordinate post-transcriptional control of their expression and fate. For three of the Puf proteins, we found that RNA–protein interactions were directed by compact sequence elements, usually located in the 3′-UTR of the mRNA (see Figure 5). Interactions with 3′-UTR sequences have been described for many cytoplasmic RBPs involved in post-transcriptional regulation (Mazumder et al. 2003). Our analysis has revealed that such recognition elements are probably much more widespread than previously recognized. Sequence and structural elements in mRNAs that are related to the function or cellular localization of the encoded proteins may be a general feature of eukaryotic genes, paralleling the role of the DNA sequences that direct specific transcription factors to promoters and enhancers (Cliften et al. 2003). The multifocal cytoplasmic distribution of Puf proteins raises the possibility that the mRNAs associated with each Puf protein are colocalized (see Figure 6). In mammalian cells, specific mRNA molecules and specific messenger RBPs have also been found to be localized to specific “granular” subcytoplasmic loci, although the generality of this phenomenon has not been established (Andersen and Kedersha 2002; Eystathioy et al. 2002; Farina et al. 2003). One function of the Puf proteins and related proteins that bind specific families of mRNAs could be to localize functionally related mRNAs to specific cytoplasmic loci. Physical clustering of functionally related groups of mRNAs could aid the assembly of complexes and the coordinated control of translation or RNA turnover. In support of this idea, it has recently been suggested that mRNA decay in the cytoplasm of S. cerevisiae occurs in distinct loci (Sheth and Parker 2003) and, further, that mRNAs encoding different subunits of stoichiometric complexes do indeed have concordant decay rates (Wang et al. 2002b). We propose that the location in the cell at which any mRNA is translated or degraded is not left to chance. Instead, every mRNA that leaves the nucleus may be delivered, in a process directed by specific protein–RNA interactions, to one of a limited number of specific foci in the cytoplasm, designated as destinations for a specific functionally related family of mRNAs. These foci could serve to colocalize and coregulate synthesis of proteins that need to assemble or act together, thereby facilitating efficient and rapid assembly and localization of the proteins. The number of distinct families of functionally specialized foci may be quite large. The locations of these foci need not correspond to recognizable cellular features, but may simply be ad hoc sites for localized, coordinated translation of proteins that are to be assembled into a complex or a functional unit. Specific predictions of this hypothesis, such as colocalized translation of the subunits of stoichiometric complexes, should be amenable to direct experimental tests. Combinatorial binding of mRNAs by specific regulatory proteins, linking their post-transcriptional regulation to specific signal transduction pathways, could allow rapid and efficient reprogramming of gene expression during development or in response to changing physiological conditions. Indeed, regulation of specific genes by external signals via RPBs has been described in higher eukaryotes (Lasko 2003). For example, the signal transduction and activation of RNA (STAR) proteins contain RNA-binding motifs combined with protein–protein interaction domains and phosphorylation sites, which could allow integration of stimuli conducted by signal transduction cascades (Lasko 2003). Similarly, the Puf proteins contain numerous putative phosphorylation motifs, as well as domains with characteristics often implicated in protein–protein interactions, such as glutamine/arginine-rich regions (Michelitsch and Weissman 2000) (see Figure 1). Coordination of cellular processes has long been thought to be mediated primarily at the transcriptional and post-translational level. Our results join a growing body of studies (Tenenbaum et al. 2000; Eystathioy et al. 2002; Wang et al. 2002b; Hieronymus and Silver 2003; Shepard et al. 2003; see also Keene and Tenenbaum 2002) that suggest that the localization, translation, and stability of mRNAs are subject to extensive and important regulation and coordination by interaction with a diverse set of RBPs. Systematic mapping of these interactions and deciphering their roles, molecular mechanisms, and coordination will undoubtedly yield important new insights into biological regulation and the gene expression program. Materials and Methods Oligonucleotide primers Restriction sites are in italics: Puf3-F1, 5′-cgggatccATGGAAATGAACATGGATATGGATATGG-3′; Puf3-R1, 5′-ggaattcTCACACCTCCGCATTTTCAACCAATG-3′; Puf3-F6nco, 5′-cCATGgCACTAAAAGACATCTTTGG-3′; Puf4-F2nco, 5′-ccatgGCGGACGCAGTTTTAGACCAATA-3′; Puf4-R1eco, 5′-gaattcgTGAATCTAAATGTAACATTCCG-3′; Puf5-F2nco, 5′-ccATGGTCGAAATCAGCGCACTACC-3′; Puf5-R1xho, 5′-ctcgagcACTTGGAAGTAATTCTTTTGTA-3′; M16-1, 5′-GGGCTCGAGtagggaataccttgtaaatatcctatgaaaGCATG-3′; M16-2, 5′-CtttcataggatatttacaaggtattccctaCTCGAGCCC-3′; M16-1mut, 5′-GGGCTCGAGtagggaatacctacaaaatatcctatgaaaGCATG-3′; M16-2mut, 5′-CtttcataggatattttgtaggtattccctaCTCGAGCCC-3′; Caf-1, 5′-GGGCTCGAGtgggcacgattgtaataatacttcatgataaGCATG-3′; Caf-2, 5′-CttatcatgaagtattattacaatcgtgcccaCTCGAGCCC-3′; Yor-1, 5′-GGGCTCGAGgctttcatcatctgtataatatttatatgtcGCATG-3′; and Yor-2, 5′-CgacatataaatattatacagatgatgaaagcCTCGAGCCC-3′. Strains and plasmid construction The TAP-tagged Puf3p strain (SC1249) was obtained from Cellzome (Heidelberg, Germany) (Gavin et al. 2002). TAP-tagged Puf1p, Puf2p, Puf4p, and Puf5p strains were a gift from Dr. Erin O'Shea (Ghaemmaghami et al. 2003). Correct genomic integration of each tag was verified by PCR and by immunoblot analysis of cell extracts (data not shown). Strain BY4741 was used for mock-control affinity isolations of RNA, and deletions of the PUF3 and PUF4 genes in this strain were obtained from Dr. Ron Davis (Winzeler et al. 1999). The ORF of PUF3 was amplified by PCR with primers Puf3-F1 and Puf3-R1 from S. cerevisiae genomic DNA and cloned into pCR2.1 using the TOPO TA Cloning Kit (Invitrogen, San Diego, California, United States). The PUF3 ORF was sequenced and subcloned into pACTII via NcoI and EcoRI restriction sites, resulting in plasmid pACTII-Puf3. A full-length Puf5p construct pGAD-MPT5 was a gift from Dr. Kenji Irie (Tadauchi et al. 2001). Sequences encoding the Pum-HD domains of Puf3p (amino acids 535–879), Puf4p (amino acids 557–888), and Puf5p (amino acids 202–578) were PCR-amplified from genomic DNA with oligo pairs Puf3-F6nco/Puf3-R1, Puf4-F2nco/Puf4-R1eco, and Puf5-F2nco/Puf5-R1xho, respectively. Products were ligated into pCR2.1-TOPO, sequenced, and further cloned into pACTII via restriction sites present in the oligonucleotides used for amplification. The RNA consensus sequences interacting with Puf proteins plus ten nucleotides of flanking sequences were cloned into the SmaI and SphI sites of the vector pIIIA/MS2-2 (Bernstein et al. 2002) using annealed synthetic oligonucleotides. The PUF3 RNA consensus sequence spanning nucleotides 24–33 in the 3′-UTR of YBL038w/MRPL16 was constructed with oligonucleotides M16-1 and M16-2. In M16mut the conserved UGU motif was changed to ACA. The PUF4 consensus (nucleotides 24–34 in the 3′-UTR of YOR145c) was constructed with oligonucleotides Yor-1 and Yor-2. The PUF5 consensus (nucleotides 105–114 in the 3′-UTR of YNL278w/CAF120) was constructed with oligonucleotides Caf-1 and Caf-2. Isolating RNAs specifically associated with selected RBPs For a detailed protocol, see the Supporting Information on our Web site. In brief, 1 l of cells were cultured in YPAD medium (yeast–peptone–dextrose [YPD] supplemented with 20 mg/ml adenine–sulfate) at 30°C and collected during exponential growth by centrifugation. Cells were washed twice with ice-cold buffer A (20 mM Tris–HCl [pH 8.0], 140 mM KCl, 1.8 mM MgCl2, 0.1% Nonidet P-40 [NP-40], 0.02 mg/ml heparin) and resuspended in 5 ml of buffer B (buffer A plus 0.5 mM dithiothreitol [DTT], 1 mM phenylmethylsulfonylfluoride, 0.5 μg/ml leupeptin, 0.8 μg/ml pepstatin, 20 U/ml DNase I, 100 U/ml RNasin [Promega, Madison, Wisconsin, United States], and 0.2 mg/ml heparin). Cells were broken mechanically with glass beads, and extracts were incubated with 400-μl slurry (50% [v/v]) IgG–agarose beads (Sigma, St. Louis, Missouri, United States) for 2 h at 4°C. The beads were washed four times for 15 min at 4°C with buffer C (20 mM Tris–HCl [pH 8.0], 140 mM KCl, 1.8 mM MgCl2, 0.5 mM DTT, 0.01% NP-40, 10 U/ml RNasin). Puf proteins were released from the beads by incubation with 80 U of TEV protease (Invitrogen) for 2 h at 15°C. RNA was isolated from the TEV eluates, which corresponds to the purified fraction and from extracts (input) by extraction with phenol/chloroform and isopropanol precipitation. Microarray analysis and data selection Equal amounts of a pool of five synthetically prepared Bacillus subtilis RNAs were added to each RNA sample prior to labeling and served as a control for the labeling procedure (Wang et al. 2002b). Total RNA (3 μg) derived from the extract and 300 ng of affinity-isolated RNA (or up to 40% of isolated RNA) were labeled with Cy3 and Cy5 fluorescent dyes, respectively, following cDNA synthesis with amino-allyl dUTP in addition to the four natural dNTPs using a 1:1 mixture of oligo(dT) and random nonamer primers. The Cy3- and Cy5-labeled cDNA samples were mixed and competitively hybridized to DNA microarrays representing all S. cerevisiae ORFs, introns, and the mitochondrial genome (see Microarrays were scanned with an Axon Instruments (Foster City, California, United States) Scanner 4000. Scanning parameters were adjusted to give similar fluorescent intensities for B. subtilis spots in both channels. Data were collected with the GENEPIX 3.0 Program (Axon Instruments), and spots with abnormal morphology were excluded from further analysis. Arrays were computer normalized by the Stanford Microarray Database (SMD) (Gollub et al. 2003). Log2 median ratios were retrieved from SMD and exported into Microsoft (Redmond, Washington, United States) Excel after filtering for regression correlation of greater than 0.6 (filters for large variations in the ratios of pixels within each spot), CH1I/CH1B of greater than 1.8 (signal over background in the channel measuring total RNA from extract), and CH2I/CH2B of greater than 1.0 (affinity-isolated RNA signal greater than background) and for data from at least two independent measurements. Average log2 ratios were calculated for each gene across the four independent experiments performed for each Puf protein (microarrays and raw data can be downloaded from our supporting Web sites [ and http://genome-www5.stanford.MicroArray/SMD/]). Genes for which the enrichment ratios were at least two standard deviations above the median across all genes were selected. A total of 923 genes were selected in this way. To eliminate nonspecifically enriched RNAs from this gene list, the results from the affinity enrichments for each of the Puf proteins and the data obtained from four independent mock affinity enrichments were clustered by the Pearson correlation algorithm (Eisen et al. 1998). Transcripts of 84 genes were enriched beyond the two standard deviation threshold in all the Puf affinity isolations as well as in the mock procedure. These were presumed to represent RNAs whose enrichment was unrelated to specific interactions with Puf proteins and therefore were excluded from further analysis. Among the finally selected target mRNAs (see Tables S3–S7), most were represented in the four independent measurements: PUF1, 98%; PUF2, 97%; PUF3, 82%; PUF4, 93%; PUF5, 97%. Gene expression profiling puf3 mutant and wild-type cells were cultured in minimal media supplemented with 3% glycerol and harvested during exponential growth (OD600 = 0.5). Total RNA (8 μg) isolated from wild-type and mutant cells were used to prepare Cy3 and Cy5 fluorescently labeled cDNA as described above, except that only an oligo(dT) primer was used. The two differentially labeled cDNAs were mixed together and hybridized to yeast DNA microarrays. Arrays were scanned and the data were collected, entered into SMD, and computer normalized (Gollub et al. 2003). Log2 median ratios were retrieved from SMD after filtering for regression correlation of greater than 0.6 and signal over background of greater than 1.5. Results from three independent experiments were averaged for this analysis (raw data can be retrieved from our Web site). Motif searches As the exact 5′- and 3′-UTR lengths are unknown for most of the Puf target mRNAs, we used the estimated average lengths from yeast (Mignone et al. 2002). Hence, the coding 237 nucleotides of predicted 3′-UTR and 134 nucleotides of predicted 5′-UTR sequences were retrieved from SGD for the Puf target genes. The sequences were searched for motifs in the sense strand with the program MEME under the proposed default settings ( (Bailey and Elkan 1994) (see Tables S9–S11). The number and location of consensus motifs in the S. cerevisiae genome was obtained by searching “Pattern Match” in the SGD (Issel-Tarver et al. 2002). Thereby, nucleotides that were at least 19% conserved among the MEME selected sequences were used to compile the Consensus Motif that was searched for. Three-hybrid assays Three-hybrid assays were performed as described elsewhere (Bernstein et al. 2002). Immunofluorescence Immunofluorescence was performed as described at Fixed and permeabilized cells were treated with 5 μg/ml purified rabbit immunoglobulin (Sigma) for 1 h at room temperature. After washing, cells were incubated with Cy3 goat anti-rabbit antibodies (1:400). Images were obtained on a Zeiss (Oberkochen, Germany) Axioplan-2 microscope using an Axiocam HRC camera. Supporting Information Full microarray results and other supporting information can be viewed at and at http://genome-www5.stanford.MicroArray/SMD/. Figure S1 Distribution of Average Cy5/Cy3 Fluorescence Ratios from Quadruplicate Microarray Hybridizations Analyzing mRNA Targets for Puf1p, Puf2p, Puf4p, and Puf5p See Figure 3A for Puf3p. (167 KB EPS). Click here for additional data file. Figure S2 Examples of Groups of mRNAs Associated with the Same Puf Protein and Encoding Related Proteins (A) Puf2p-bound mRNAs encode diverse proteins involved in regulation of ATP-dependent proton transport. PMA1 and PMA2 encode plasma membrane proteins that comprise the major ATP-dependent proton transporters and regulate cellular pH levels. Pmp1p, Pmp2p, and Pmp3p are small isoproteolipids, which are present in a physical complex with Pma1p and act as regulators of its activity upon stress conditions (Navarre et al. 1994). Hrk1p is a protein histidine kinase, which activates Pma1p in response to glucose (Goossens et al. 2000). Ast1p is implicated in proper delivery of Pma1p to plasma membranes (Bagnat et al. 2001). (B) Puf4p-bound mRNAs encode the nucleolar GAR proteins (blue), members of the H/ACA core complex (boxed), and Hmt1p, a dimethylase acting on GAR proteins. Nop1p performs 2′-O-ribose methylation of pre-rRNA, a process guided by small nucleolar RNAs (snoRNAs) of the box C/D family. Cbf5p catalyzes pseudouridine formation with box H/ACA snoRNAs, and three of the four components of the H/ACA core complex were Puf4p-associated (Cbf5, Gar1, and Nhp2 [Henras et al. 1998]; no data were obtained for the fourth component, Nop10, shown in gray). All transcripts encoding nucleolar proteins of the GAR repeats family (Gar1p, Sbp1p, Nop1p, Nsr1p) were Puf4p-bound. The GAR domain is dimethylated at arginine residues. Remarkably, several mRNAs coding for S-adenosylmethionine-dependent methyltransferases were Puf4p-bound including Hmt1p, the major protein arginine-methyltransferase in yeast (Gary et al. 1996). Hmt1p has recently been shown to dimethylate arginines of the proteins Gar1p, Nop1p, and Nsr1p (Xu et al. 2003). (38 KB EPS). Click here for additional data file. Figure S3 Phenotypic Analysis of puf3Δ Cells Serial dilutions (1:10) of cells were spotted on plates supplemented with the indicated media. Plates were incubated for 3 d at 30°C. Abbreviations: YPD, yeast–peptone–dextrose; YPGE, yeast–peptone–3% glycerol–2% ethanol; SC, synthetic complete. (264 KB PDF). Click here for additional data file. Table S1 Number of mRNA Targets Shared between Puf Proteins (15 KB XLS). Click here for additional data file. Table S2 Protein Copy Number Determination of Puf Proteins Cells were grown to mid-log phase in YPAD medium and the number of cells was counted. Whole-cell extracts were prepared as described previously (Hoffman et al. 2002). In brief, cells were resuspended in 1× SDS-PAGE sample buffer, incubated at 100°C for 10 min, and vortexed for 2 min with glass beads. After a short centrifugation, eight dilutions of cell extracts and protein A (Amersham, Little Chalfont, United Kingdom), which served as a reference standard, were spotted on a nitrocellulose filter. Expression of IgG-binding domains was monitored with rabbit peroxidase–anti-peroxidase soluble complex at 1:5,000 (Sigma). Chemiluminescence was measured with a Typhoon 8600 Imager (Molecular Dynamics, Sunnyvale, California, United States) and quantified with the ImageQuant 5.2 software. Averaged numbers from two independent measurements were used for calculations. The total number of mRNA copies in the pool associated with each Puf protein was estimated as follows: copy numbers for individual mRNAs were retrieved from two independent genome-wide measurements (Holstege et al. 1998; Wang et al. 2002b). For genes with no data, we added the median value for copy numbers of all mRNAs in the respective pool. (30 KB XLS). Click here for additional data file. Table S3 List of Puf1p Target mRNAs Columns indicate the following (from left to right): ORF; gene name; GO annotations; classification of gene products (soluble/membrane-associated); average log2 ratios of enrichment across four independent Puf affinity isolations; standard deviations; association of mRNA with other Puf proteins; mRNA copy numbers. (28 KB XLS). Click here for additional data file. Table S4 List of Puf2p Target mRNAs Notations are as in Table S3. (52 KB XLS). Click here for additional data file. Table S5 List of Puf3p Target RNAs Columns indicate the following (from left to right): ORF; gene name; GO annotations; classification of gene products (soluble/membrane-associated); average log2 ratios of enrichment across four independent Puf affinity isolations; standard deviations; association of mRNA with other Puf proteins; location of consensus motif identified by MEME; mRNA copy numbers. (70 KB XLS). Click here for additional data file. Table S6 List of Puf4p Target mRNAs Notations are as in Table S5. (61 KB XLS). Click here for additional data file. Table S7 List of Puf5p Target mRNAs Notations are as in Table S5. (64 KB XLS). Click here for additional data file. Table S8 Significant Shared GO Annotations among Puf mRNA Targets Only annotations with p values of less than 0.001 are indicated. GO annotations were retrieved from the SGD with GO Finder ( on May 21, 2003. Respective p values are indicated in a column next to the names of the GO term. (30 KB XLS). Click here for additional data file. Table S9 Results of MEME Motif Searches: Motifs among Puf3p mRNA Targets (63 KB XLS). Click here for additional data file. Table S10 Results of MEME Motif Searches: Motifs among Puf4p mRNA Targets (55 KB XLS). Click here for additional data file. Table S11 Results of MEME Motif Searches: Motifs among Puf5p mRNA Targets (34 KB XLS). Click here for additional data file. Accession Numbers All accession numbers for human, Drosophila, or C. elegans proteins are from the SwissProt database ( CPEB (Q18317), GLD1 (Q17339), DAZL (Q92904), FBF-1 (Q9N5M6), FEM3 (P34691), IRP (P21399), NANOS (P25724), Drosophila PUMILIO (P25822), human PUMILIO-1 (Q14671), human PUMILIO-2 (Q9HAN2), and SLBP (P97330). The accession numbers for S. cerevisiae genes are from SGD ( (ORF/SGD identification number): ADA2 (YDR448W/S0002856), AME1 (YBR211C/S0000415), APS3 YJL024C/S0003561), AST1 (YBL069W/S0000165), BBP1 (YPL255W/S0006176), BDF1 (YLR399C/S0004391), BDF2 (YDL070W/S0002228), BOI2 (YER114C/S0000916), BSP1 (YPR171W/S0006375), BUB1 (YGR188C/S0003420), BUD9 (YGR041W/S0003273), CBF5 (YLR175W/S0004165), CDC31 (YOR257W/S0005783), CNM67 (YNL225C/S0005169), COX17 (YLL009C/S0003932), DAD2 (YKR083C/S0001791), DHH1 (YDL160C/S0002319), ELM1 (YKL048C/S0001531), FPS1 (YLL043W/S0003966), GAR1 (YHR089C/S0001131), GIC1 (YHR061C/S0001103), HDA1 (YNL021W/S0004966), HFI1 (YPL254W/S0006175), HMT1 (YBR034C/S0000238), HOS1 (YPR068C/S0006272), HOS3 (YPL116W/S0006037), HST1 (YOL068C/S0005429), HTA1 (YDR225W/S0002633), IFM1 (YOL023W/S0005383), KAR1 (YNL188W/S0005132), KAR9 (YPL269W/S0006190), KHD1 (YBL032W/S0000128), MAS6 (YNR017W/S0005300), MEX67 (YPL169C/S0006090), MUP3 (YHL036W/S0001028), NCE101 (YJL205C/S0003742), NCE102 (YPR149W/S0006353), NHP2 (YDL208W/S0002367), NOP1 (YDL014W/S0002172), NSR1 (YGR159C/S0003391), NUF2 (YOL069W/S0005430), PDR16 (YNL231C/S0005175), PMA1 (YGL008C/S0002976), PUF1 (YJR091C/S0003851), PUF2 (YPR042C/S0006246), PUF3 (YLL013C/S0003936), PUF4 (YGL014W/S0002982), PUF5 (YGL178W/S0003146), RAX2 (YLR084C/S0004074), RSC1 (YGR056W/S0003288), RSC2 (YLR357W/S0004349), RSC4 (YKR008W/S0001716), SBP1 (YHL034C/S0001026), SCP160 (YJL080C/S0003616), SFK1 (YKL051W/S0001534), SFL1 (YOR140W/S0005666), SHE2 (YKL130C/S0001613), SIN3 (YOL004W/S0005364), SNC2 (YOR327C/S0005854), SNT1 (YCR033W/S0000629), SPC19 (YDR201W/S0002609), SPC42 (YKL042W/S0001525), SPT7 (YBR081C/S0000285), SPT8 (YLR055C/S0004045), SSO2 (YMR183C/S0004795), STE7 (YDL159W/S0002318), SUR7 (YML052W/S0004516), TPO1 (YLL028W/S0003951), TPO2 (YGR138C/S0003370), TPO3 (YPR156C/S0006360), VPS72 (YDR485C/S0002893), YIP1 (YGR172C/S0003404), YKL091c (YKL091C/S0001574), YPR157w (YPR157W/S0006361), and YRA1 (YDR381W/S0002789).

                Author and article information

                Role: Academic Editor
                PLoS Biol
                PLoS Biol
                PLoS Biology
                Public Library of Science (San Francisco, USA )
                May 2012
                May 2012
                29 May 2012
                06 June 2012
                : 10
                : 5
                Department of Biochemistry, University of Toronto, Toronto, Ontario, Canada
                Medical University of Vienna, Austria
                Author notes

                The author(s) have made the following declarations about their contributions: Conceived and designed the experiments: AFP XAC. Performed the experiments: AFP XAC HZ. Analyzed the data: AFP XAC HZ. Contributed reagents/materials/analysis tools: AFP XAC. Wrote the paper: AFP XAC.

                Cui et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
                Page count
                Pages: 18
                Research Article
                Molecular Cell Biology
                Cellular Structures
                Subcellular Organelles
                Gene Expression
                Protein Translation
                RNA transport
                Nucleic Acids
                RNA transport
                Membranes and Sorting

                Life sciences


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