Biogenesis of ribosomes is an essential cellular process conserved across all eukaryotes and is known to require >170 genes for the assembly, modification, and trafficking of ribosome components through multiple cellular compartments. Despite intensive study, this pathway likely involves many additional genes. Here, we employ network-guided genetics—an approach for associating candidate genes with biological processes that capitalizes on recent advances in functional genomic and proteomic studies—to computationally identify additional ribosomal biogenesis genes. We experimentally evaluated >100 candidate yeast genes in a battery of assays, confirming involvement of at least 15 new genes, including previously uncharacterized genes (YDL063C, YIL091C, YOR287C, YOR006C/TSR3, YOL022C/TSR4). We associate the new genes with specific aspects of ribosomal subunit maturation, ribosomal particle association, and ribosomal subunit nuclear export, and we identify genes specifically required for the processing of 5S, 7S, 20S, 27S, and 35S rRNAs. These results reveal new connections between ribosome biogenesis and mRNA splicing and add >10% new genes—most with human orthologs—to the biogenesis pathway, significantly extending our understanding of a universally conserved eukaryotic process.
In eukaryotic cells, the synthesis of ribosomes is a complex process involving several hundred genes whose functions span transcription of precursor ribosomal ribonucleic acids (pre-rRNAs), processing of pre-rRNAs, assembly of ribosomal proteins (r-proteins) with pre-rRNAs, and nuclear export of the ribosomal particles [1]–[6]. Ribosome biogenesis is an essential process, with mutations of ribosome biogenesis genes either causing lethality or increasing susceptibility to cancer—e. g. , bone marrow failure and leukemia [7] or breast cancer [8]. This pathway has been extensively studied over the past 30–40 y, and a broad picture of the major events is known for the yeast Saccharomyces cerevisiae. First, 35S polycistronic pre-rRNA is transcribed from the ribosomal deoxyribonucleic acid (rDNA) repeat by RNA polymerase I in the nucleolus. During transcription, the small-subunit processome and some small-subunit r-proteins assemble onto the 35S pre-rRNA to form a 90S particle. The 35S pre-rRNA is cleaved to release the pre-40S particle, which contains a 20S pre-rRNA. The pre-60S complex assembles on the rest of the transcript, and both subunits are further processed in the nucleus and independently exported through the nuclear pore complex (NPC) to the cytoplasm, where they undergo further maturation—e. g. , cleavage of 20S pre-rRNA to 18S rRNA. The mature small subunit contains 32 proteins and 18S rRNA, while the large subunit contains 46 proteins and three rRNAs: 5. 8S, 25S, both derived from the 35S precursor, and 5S, which is transcribed separately by RNA polymerase III. Ribosome biogenesis is a temporally and spatially dynamic process requiring coordination of many trans-acting factors at different stages along the pathway, including at least 170 protein factors that act to modify and cleave pre-rRNAs and help to assemble and export ribosomal particles [5], [9]. Many of these protein factors were first identified by yeast genetics. Later, biochemical purifications coupled with mass spectrometric analysis greatly expanded the number of known factors [10]–[16]. In addition, a large-scale effort using oligonucleotide microarrays identified 115 mutants that exhibited pre-rRNA processing defects, and 10 new genes were confirmed to affect pre-rRNA processing [17]. Despite these intensive studies, new ribosome biogenesis genes are still emerging, and recent computational analysis suggests that over 200 genes constitute the ribosome biogenesis regulon [18], indicating that the genes in this fundamental cellular pathway have not been completely identified. We asked if recent functional genomic and proteomic studies could be applied in a predictive fashion to identify additional ribosomal biogenesis genes. In particular, functional networks of genes have been reconstructed, incorporating literally millions of experimental observations into probabilistic networks indicating genes likely to work together in cells. The emerging technique of network-guided genetics (e. g. , [19], [20]) leverages such networks to computationally associate candidate genes with a biological process of interest, much as a genetic screen might do. We used such a probabilistic gene network [21] to predict the genes most likely to participate in yeast ribosome biogenesis based on connectivity to known ribosomal biogenesis genes, and we present here experimental confirmation of at least 15 new genes affecting ribosome biogenesis. Beyond providing new insights into ribosome biogenesis, this study therefore also represents one of the most extensive experimental studies to date of the principle of network-guided genetics, which we demonstrate to be a powerful approach for rational discovery of candidate genes, applicable to diverse biological processes.
In general, we expect genes of ribosome biogenesis to be coordinately expressed, to physically or genetically interact with each other, to show common subcellular localization, and so on. Many such associations have been observed in high-throughput experiments in yeast, but these data suffer from false-positive and false-negative observations. Nonetheless, the appropriate analyses of such data should rationally prioritize candidate ribosome biogenesis genes. We therefore constructed a computational predictor of ribosome biogenesis genes based on analysis of functional genomics, proteomics, and comparative genomics datasets that had been combined into a probabilistic gene network [21] covering about 95% of yeast proteome (Figure 1A). This network employs a probabilistic scoring scheme to quantitatively integrate heterogeneous functional genomic and proteomic datasets, including mRNA-expression data across different conditions, protein-protein interaction datasets derived from literature curation, high-throughput yeast two-hybrid assay, affinity purification coupled with mass spectrometry, genetic interaction data, and in silico interaction datasets [21]. We calculated the naïve Bayesian probability that each yeast gene will belong to the ribosome biogenesis pathway based on gene connectivity information in the gene network—i. e. , “guilt-by-association” [22], [23] with known ribosome biogenesis genes. Ribosome biogenesis genes were highly connected and predictable in this gene network, as shown by a plot of cross-validated true-positive versus false-positive prediction rates (ROC plot; Figure 1B). From the top-scoring genes, 212 candidates were manually selected based on expert knowledge for experimental validation (Table S1). The synthesis of ribosomes is essential for cell growth and survival, and most genes involved in ribosome biogenesis are either essential or required for normal growth rates. In our list of candidate ribosome biogenesis genes, 50 genes are essential, and 162 genes are nonessential under standard laboratory culture conditions [24]. We thus performed growth assays for each strain with a deletion of one of the 162 nonessential genes under three temperature conditions: 20°C, 30°C, and 37°C (Figure S1). Of these, 51 mutants with constitutive or conditional slow-growth phenotypes were identified. These mutants and 50 mutants carrying conditional essential alleles were investigated further (Figure 1A). For each of the selected 101 mutants, we tested for gross ribosome biogenesis defects by measuring the proportions of free 40S, 60S, and 80S subunits, as well as polysomes, in the mutant strains. After cleavage of the pre-40S particle from the 35S transcript, the syntheses of 40S and 60S subunits are largely independent [6]. Depletion of the factors required for the synthesis of one subunit usually does not significantly affect synthesis of the other subunit [25], resulting in a change in the ratio of 40S to 60S, which is most evident in the free subunit pools in the cell. In addition, a reduction in the amount of 60S subunits can lead to a translation initiation defect, with 40S subunits awaiting 60S subunits to form 80S ribosomes. These stalled 40S subunits are observable as halfmer polysomes in a polysome profile [26]. Polysome profiles are generated by separating the ribosomal subunits and different-sized polysomes through a continuous sucrose density gradient and monitoring the absorbance of nucleic acids along the sucrose gradient [27]. We analyzed polysome profiles for the 50 mutants carrying conditional alleles controlled by either a tetracycline-regulatable (tetO7) promoter [28] or a GAL1 promoter and for the 51 nonessential gene deletion mutants with conditional growth defects. Including controls, over 150 polysome profiles were generated. In order to compare different profiles and perform multivariate analyses such as clustering, we computationally aligned each profile to a reference wild-type profile by using a correlation-optimized warping (COW) algorithm [29], which corrects for peak shifts of ribosome subunits and polysomes due to minor variations in sucrose density gradients. Similar polysome profiles were grouped together using hierarchical clustering [30]. From the clustergram, the signals corresponding to the ribosomal subunits, monosomes, polysomes, and halfmer polysomes were clearly identifiable (Figure 2A). Importantly, nearly half of the tested mutants showed clear ribosome biogenesis defects by this analysis. This is a much higher ratio than the ∼1/30 expected by chance, indicating the strong enrichment for true ribosome biogenesis genes provided by the network-guided genetics. Several sets of mutants exhibited grossly similar biogenesis defects, detectable as coherent groups in the clustergram. Most of the profiles with high 40S to 60S ratios and halfmer peaks were in clusters 1 and 2, which represent 60S biogenesis defects (Figure 2C). Cluster 3 represents profiles from mutants showing protein translation defects (Figure 2D), some of which also affected the ratio of 40S to 60S ribosomal subunits when compared to wild-type strains (Figure 2B). It is noteworthy that the translation-initiation factor mutants, including fun12Δ, tetO7-TIF35, tetO7-TIF34, tetO7-RPG1, and tetO7-DED1, did not display the same defects, indicating that the observed ribosome biogenesis defects are not simply a general effect of inhibition of translation. The profiles with low 40S to 60S ratios were in cluster 4, which suggests 40S biogenesis defects (Figure 2E). The polysome profiles from three mutants (ypr044cΔ, tif4631Δ, and snu66Δ) were not clustered with 60S biogenesis clusters 1 and 2, although they showed halfmer polysomes (Figure 2A, 2C). Some mutants showed only subtle defects, and their profiles were interspersed among wild-type-like profiles during clustering (Figure S2). The polysome profiles provided initial suggestions about the function of these genes in ribosome biogenesis and translation. We further investigated 43 mutants that exhibited altered 40S to 60S ratios compared to wild-type strains (Table S1 and Figure 1A). Most ribosome biogenesis factors associate with pre-ribosomal particles [3]. In order to distinguish factors associated with pre-40S particles from factors associated with pre-60S particles, we applied both a classical immunoblot approach and a novel mass-spectrometry-based approach in order to assess sedimentation patterns of potential ribosome biogenesis factors in sucrose density gradients (Figure 1A). Most mutants defective for ribosome assembly display altered pre-rRNA processing [9]. The effects on pre-rRNA processing can be a direct consequence of a mutation in an enzymatic processing activity, or they can be indirect. Regardless of whether the effect is direct or indirect, the observed pre-rRNA processing defects provide valuable diagnostics for characterizing the ribosome biogenesis defects and thus the putative activity of a ribosome biogenesis candidate gene; we therefore examined pre-rRNA processing defects in each of the 43 candidate genes confirmed by polysome profiling to affect ribosome biogenesis. Several specific pre-rRNA processing events are critical to biogenesis: The 35S pre-rRNA undergoes extensive modification as well as sequential multiple endo- and exo-nuclease cleavages to give rise to the mature 18S, 5. 8S, and 25S rRNAs [2]. The 35S pre-rRNA is first cleaved at sites A0, A1, and A2 to yield 20S and 27SA2 species (Figure 5B), and the 20S pre-rRNA is further processed in the cytoplasm to form the mature 18S rRNA after cleavage at the D position. The 27SA2 pre-rRNA is processed by two different routes. The majority is cleaved at site A3, followed by exonuclease digestion to site B1S to form 27SBS, while a small amount of 27SA2 undergoes endonucleolytic cleavage at B1L to generate 27SBL. Both 27SB species are further processed at sites C1 and C2 to yield the mature 25S species and 7S species, which mature to 5. 8S by 3′-exonuclease digestion to E (Figure 5B). To examine the detailed effects of the candidate ribosome biogenesis genes on pre-rRNA processing, we used Northern blotting with oligonucleotide probes (Figure 5A) to monitor the levels of 9 different pre-rRNA and rRNA species in each of the 43 mutant strains. In order to quantitatively analyze the change of each RNA species in a mutant relative to the wild-type strain under corresponding conditions, Northern blots (Figure 5C–5E) were quantified, and the logarithm of the intensity ratio of each RNA species from a mutant strain relative to that from its corresponding wild-type strain was calculated and used for hierarchical clustering analysis (Figure 5F). We observed a dramatic increase (red signal in Figure 5F) or decrease (green signal in Figure 5F) of at least one pre-rRNA species for all of the mutants except eap1Δ and trf5Δ (Figure 5F). The mutants with 60S biogenesis defects in polysome profile analyses clustered into two groups in Northern blotting analyses (Figure 5F, green labels), and many 40S mutants in polysome profile analyses also clustered together (Figure 5F, red labels), showing general correlation between polysome profile defects and pre-rRNA processing defects. In conjunction with the polysome profile and co-sedimentation data, these defects strongly suggest function for the candidate genes in or upstream of the implicated processing steps. We thus employed the observed defects to classify the candidate genes according to their potential general roles. As a last major characterization of the candidate ribosome biogenesis genes, we investigated their possible roles in ribosome nuclear export. Nuclear export of the ribosomal subunits through NPCs depends upon the RanGTPase cycle and receptor proteins that mediate the interaction between the ribosomal subunit and the NPC. The receptors can bind to adapter proteins or to the subunits directly. In the case of the 60S subunit in yeast, export depends upon the adapter protein Nmd3p and its receptor Crm1p (Xpo1 in human), as well as the heterodimer of Mex67p/Mtr2p [63] and the specialized receptor Arx1p [64], [65]. Export of the 40S subunit also requires Crm1p, and although it has been suggested that Ltv1p acts as a Crm1p-dependent adapter, Ltv1p is not essential, indicating that additional adapters and/or receptors remain to be identified [5], [66]. To test whether the ribosome biogenesis candidates affect ribosome transport, we assayed ribosome export in the mutants by using Rps2-GFP and Rpl25-GFP as reporters for the small and large ribosomal subunits, respectively [10], [67], while monitoring the nucleolus with Sik1-mRFP [34]. In wild-type control strains cultured under various conditions, both small and large ribosomal subunits localized primarily in the cytoplasm (Figure 7A–7B, first row, and Figure S3). Upon depletion of Yrb2p, a known factor involved in small-subunit export [45], ribosomal small subunits accumulated in the nucleus (Figure 7A), while the large subunits were unaffected (Figure S4). In mutants defective in the synthesis of small subunits, including tetO7-BFR2, bud22Δ, bud23Δ, tetO7-YDR339C, ygr081cΔ, GAL1-ENP2, GAL1-NOP9, GAL1-SGD1, and GAL1-KRE33, we observed significant accumulation of the small subunit reporter in the nucleus and/or nucleolus (Figure 7A), whereas the large subunits were unaffected (Figure S4). The defective nuclear export of 40S subunits upon depletion of Kre33p is consistent with previous observation of a temperature sensitive mutant kre33-1 [10]. Because the pre-40S contains the 20S pre-rRNA as it is exported to the cytoplasm, a bona fide block in subunit export is expected to result in increased levels of 20S rRNA. This was in fact observed for the bud23Δ and ygr081cΔ mutants (Figure 5F), which suggests that they act late in the biogenesis and export pathway, whereas the other genes are involved in early ribosome biogenesis. Recently, Bud23p has also been shown to methylate G1575 of 18S rRNA [68]. We note, however, that defective pre-rRNA processing and/or ribosome assembly may also lead to the inefficient transport of ribosomes to the cytoplasm [69] or accumulation of reporter proteins in the nucleus. In mutants with defective synthesis of large ribosomal subunits, including tetO7-AFG2, tetO7-BCP1, puf6Δ, tetO7-YDR412W, and tif4631Δ, strong accumulation of the large ribosomal subunits in the nucleolus and nucleus was observed (Figure 7B), but not of the small subunits (Figure S5). Surprisingly, deletion of LSM6 or LSM7 inhibited the transport of pre-60S subunits to the cytoplasm (Figure 7B) but not the small subunits (Figure S5). Therefore, the accumulation of 20S upon deletion of LSM6 or LSM7 suggests that they act in 20S processing in the cytoplasm. In total, we identified 17 genes that affected export of either the ribosomal small or large subunits.
As expected, many genes for ribosome biogenesis are essential. However, a large number of nonessential genes are clearly involved in ribosome biogenesis, some of which show strong constitutive or conditional phenotypes (Figure S6). For example, deletion of PUF6, SAC3, or SNU66 resulted in strong defects at 20°C but only minor defects at the optimal growth temperature of 30°C. In contrast, the polysome profile of yor006cΔ showed 40S biogenesis defects at 30°C but no defects at 20°C. Several nonessential genes, including YIL096C, YCR016W, YJL122W, YNL022C, BUD20, and NOP13, form a tight cluster with known ribosome biogenesis genes in the gene network, and their encoded proteins co-sedimented with either 40S or 60S fractions, supporting them as being components of pre-ribosomes (unpublished data). However, deletion mutants for those genes did not show growth defects at 20°C, 30°C, or 37°C (Figure S1), nor were polysome profiles of the deletion mutants different from wild-type cells (unpublished data). However, lack of a mutant phenotype does not imply that these candidate genes are not part of the ribosome biogenesis pathway. In fact, Yjl122wp (Alb1p) was recently confirmed to interact directly with the known ribosome biogenesis factor Arx1p, although the deletion mutant had no observable phenotype [70]. It is therefore still likely that the remaining candidate genes participate in ribosome biogenesis but that we failed to identify a conditional phenotype or that these genes are functionally redundant with other genes. In the latter case, synthetic interaction assays might prove a useful strategy for deciphering the genes' functions. Indeed, we observed one such example: mutants with either deletion of TRF5 or depletion of Pap2p did not exhibit defects in polysome profile analyses at 30°C, but depletion of Pap2p in the trf5Δ mutant caused strong 60S biogenesis defects evident in polysome profile analysis (Figure 8), which suggests that TRF5 and its paralog PAP2 are required for efficient ribosome biogenesis, presumably by facilitating the removal of aberrant pre-rRNA molecules [71]. Thus, many of the remaining nonessential mutants without conditional phenotypes may still be involved in ribosome biogenesis. Gene network-based predictions based on binary associations between genes intrinsically help to identify genes that participate in multiple cellular processes. Correspondingly, several genes we identified have been reported to have other functions. For example, BCP1 is required for the export of Mss4p [72], Sgd1p interacts with Plc1p and is involved in osmoregulation [73], and a recent study showed that Mtr2p, known as an mRNA export receptor [74], is directly involved in ribosomal large-subunit export [63]. Similarly, we identified Sac3p, which localized to the NPC and is involved in mRNA export [75] as a ribosome biogenesis factor based on polysome profile and Northern blot analyses of the deletion mutant (Figures 2E, 5E). In addition, Sac3p co-sedimented with 40S fractions, suggesting its possible association with ribosomes (Figure 3A). It is known that Sac3p can mediate protein export [76], but we did not observe export defects for either ribosomal subunit in the sac3Δ mutant (unpublished data). Thus, SAC3 joins MTR2 and MEX67 as genes participating in both the ribosome biogenesis and mRNA export pathways. Recently, the splicing factor Prp43p was confirmed to be a ribosome biogenesis factor by several groups, which suggests coordination of ribosome biogenesis and mRNA splicing [77]–[79]. We observed that four genes associated with mRNA splicing—LSM6, LSM7, PRP4, and SNU66—also play roles in ribosome biogenesis. Although we do not exclude the possibility of indirect roles of PRP4 in ribosome biogenesis, deletion of SNU66 (a component of the tri-snRNP) not only delays 35S processing but also affects processing of the 5S rRNA precursor (Figure 5E). Thus, these data provide further evidence for shared components between these processes, which supports a general connection between ribosome biogenesis and mRNA splicing. Whether this connection is direct or indirect generally remains to be established, although the specificity of the rRNA processing defect and the observed genetic interactions (Figure 5G) suggest a direct role for SNU66 in 5S processing. In conclusion, we applied the emerging technique of network-guided genetics to computationally predict and experimentally validate at least 15 previously unreported ribosome biogenesis genes (TIF4631, SNU66, YDL063C, JIP5, TOP1, SGD1, BCP1, YOR287C, BUD22, YIL091C, YOR006C/TSR3, YOL022C/TSR4, SAC3, NEW1, FUN12) (Table 1), most of which have human orthologs and thus represent evolutionarily conserved components of this essential core cellular process. Selecting candidates with a network-guided genetics approach therefore proved to be a powerful approach for identifying new genes in a pathway, even in such a well-studied cellular process as ribosome biogenesis, with ∼40% of the tested genes in the polysome profile analyses being shown to participate in this pathway. Although considerable effort has been spent predicting and validating gene functions from diverse functional genomics and proteomics data [17], [80], to our knowledge this is one of the most extensive experimental tests of predictions from network-guided genetics. These results add >10% new genes to the ribosome biogenesis pathway, significantly extending our understanding of a universally conserved eukaryotic process.
Haploid MATa deletion mutants [81] were obtained from Research Genetics. TetO7-promoter mutants [28] and TAP-tagged strains [31] were acquired from Open Biosystems. All commercial strains in this paper were verified by PCR, and four strains found to be incorrect in commercial collections (ypr045cΔ, tetO7-SGD1, Kre33-TAP, and tetO7-KRE33) were recreated. GAL1-promoter mutants were constructed in strain BY4741 (Text S1). Haploid deletion mutants were cultured to OD600 0. 3–0. 5 in YPD (1% yeast extract, 2% peptone, 2% dextrose) at the conditional temperature (20°C, 30°C, or 37°C). TetO7-promoter mutants were cultured in YPD and then diluted into YPD with 10 ug/ml doxycycline (Fisher Scientific) for 9–20 h to OD600 0. 3–0. 5. GAL1-promoter mutants were cultured in YPGal (1% yeast extract, 2% peptone, 2% galactose) and then diluted into YPD for 12–20 h to OD600 0. 3–0. 5. Strains carrying pRS416 and pRS413 derived plasmids were cultured in synthetic complete media minus uracil and histidine supplemented with 2% dextrose to OD600 0. 3–0. 5. Detailed culture information for each individual strain is described in Table S1. Yeast cells were cultured at various conditions to OD600 0. 3–0. 5. Two hundred µg/ml cycloheximide (Sigma) was added to each culture. Cell lysate preparation and sucrose density gradient sedimentation were performed as previously described (Text S1) [65]. Each mutant' s polysome profile was aligned to the wild-type reference polysome profile using COW implemented in MATLAB [29]. Aligned polysome profiles were hierarchically clustered using Cluster and Treeview software [30]. TAP-tagged strains were cultured in YPD at 30°C to OD600 0. 3–0. 5, and subsequent steps were performed in the same manner as for the polysome profile analyses. Fractions from the sucrose density gradient were collected, and 25 µl of each fraction was deposited onto a nitrocellulose membrane using a 96-well dot-blot system (Schleicher & Schuell). The membrane was probed for the TAP-tagged proteins with the rabbit peroxidase anti-peroxidase soluble complex (Rockland Immunochemicals), using Luminol (Santa Cruz Biotechnology) as the substrate for detection. The total intensity of each dot was quantified with Quantity One 1-D Analysis software (Bio-Rad). The wild-type strain BY4741 was cultured in YPD at 30°C to OD600 0. 3–0. 5 and then lysed and fractionated on a sucrose density gradient in the same manner as for the polysome profile analyses. Proteins from each fraction were precipitated with 10% cold trichloroacetic acid, washed with cold 100% acetone, resuspended in 100 mM Tris buffer (pH 8. 0), and digested with proteomic-grade trypsin (Sigma) for 24 h at 37°C. Each digested peptide mixture was separated by a strong cation-exchange column, followed by a reverse-phase C18 column. Peptides were analyzed online with an electrospray ionization ion-trap mass spectrometer (ThermoFinnigan DecaXPplus), and proteins were identified at a 5% false-detection rate by using PeptideProphet and ProteinProphet software [82]. For each sucrose gradient fraction, the number of MS/MS spectra associated with a given protein was divided by the sum of the spectral counts across all proteins in that fraction to estimate the relative abundance of each protein within each fraction. The resulting relative abundance profiles were subjected to hierarchical clustering using the Cluster and Treeview programs. Raw mass-spectrometry data are deposited in the Open Proteomics Database as accession opd00106_YEAST. RNA was extracted by the hot acidic phenol method. The high- and low-molecular-weight RNA species were separated by 1% agarose-formaldehyde gel (NorthernMax, Ambion) and 8% polyacrylamide-TBE-urea gel, respectively. RNAs were transferred onto Zeta-Probe GT membrane (Bio-Rad) by capillary transfer for agarose gel or semi-dry electroblotting for polyacrylamide gel. After UV cross-linking of the RNAs to the membrane, 5′-P32-labeled oligonucleotide probes were sequentially hybridized, and the hybridization signals were detected by phosphorimaging and quantified using Quantity One (Bio-Rad). The logarithm ratio of total intensity of each RNA species from a mutant to that from the corresponding wild-type was calculated and used for hierarchical clustering. Wild-type strains or mutants were transformed with either pAJ907 (RPL25-GFP CEN LEU2) or pAJ1486 (RPS2-GFP CEN LEU2), and each strain was also transformed with pRS411-SIK1-mRFP (SIK1-mRFP CEN MET15). Strains were cultured in synthetic complete media minus leucine and methionine, supplemented with 2% dextrose or 2% galactose. Essential gene expression was inactivated in the same way as for the polysome profile analyses. Cells were fixed with 4% formaldehyde (Pierce) for 30 min and then washed twice with PBS (pH 7. 2). DAPI (Vector Laboratories) was used to stain DNA, and images were acquired using a Nikon E800 microscope and a Photometrics CoolSNAP ES CCD camera. The GFP median intensities within the three different compartments (cytoplasm, nucleus, and nucleolus) for each cell were determined by custom image-processing software implemented in MATLAB (Text S1). Then the relative ratio of GFP median intensity in the nucleus or nucleolus to that in the cytoplasm for each cell was calculated. For each strain, the median of this ratio for a population of cells was used as an index for the enrichment of ribosomal subunits in either the nucleus or nucleolus. To compare this enrichment in mutants to that in their corresponding wild-type strains, the index of each strain was normalized to the index of the corresponding wild-type strain.
Ribosomes are the extremely complex cellular machines responsible for constructing new proteins. In eukaryotic cells, such as yeast, each ribosome contains more than 80 protein or RNA components. These complex machines must themselves be assembled by an even more complex machinery spanning multiple cellular compartments and involving perhaps 200 components in an ordered series of processing events, resulting in delivery of the two halves of the mature ribosome, the 40S and 60S components, to the cytoplasm. The ribosome biogenesis machinery has been only partially characterized, and many lines of evidence suggest that there are additional components that are still unknown. We employed an emerging computational technique called network-guided genetics to identify new candidate genes for this pathway. We then tested the candidates in a battery of experimental assays to determine what roles the genes might play in the biogenesis of ribosomes. This approach proved an efficient route to the discovery of new genes involved in ribosome biogenesis, significantly extending our understanding of a universally conserved eukaryotic process.
molecular biology/rna-protein interactions
genetics and genomics/functional genomics
molecular biology/nucleolus and nuclear bodies
molecular biology/translation mechanisms
computational biology/molecular genetics
genetics and genomics/bioinformatics
cell biology/microbial growth and development
molecular biology/bioinformatics
genetics and genomics/gene function
biochemistry/bioinformatics
biochemistry/macromolecular assemblies and machines
computational biology/genomics
biochemistry/transcription and translation
computational biology/systems biology
Rational Extension of the Ribosome Biogenesis Pathway Using Network-Guided Genetics