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Eukaryotic Cell, January 2008, p. 86-101, Vol. 7, No. 1
1535-9778/08/$08.00+0 doi:10.1128/EC.00215-07
Copyright © 2008, American Society for Microbiology. All Rights Reserved.
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The Mina and Everard Goodman Faculty of Life Sciences, Bar-Ilan University, Ramat-Gan 52900, Israel
Received 21 June 2007/ Accepted 28 September 2007
| ABSTRACT |
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| INTRODUCTION |
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Fibrillarin (NOP1) is highly conserved throughout evolution from unicellular organisms such as yeast and trypanosomes to humans (19). All these proteins bear both the glycine- and arginine-rich and methyltransferase domains (9, 19). A NOP1 knockout is lethal in S. cerevisiae, and mutants of this gene exhibit defects in pre-rRNA processing, modification, and ribosome assembly (55). Studies of knockout mice suggest that fibrillarin is essential for early development and is required for accumulation of intron-encoded snoRNAs (41). The effects of the other snoRNP protein on snoRNA stability and rRNA were examined, and it was found that Nop56p is stably associated with snoRNAs only in the presence of Nop1p. In contrast, Nop58p and Nop1p associate independently with the snoRNAs (26). Nop58p is closely related to Nop56p, and each bears characteristic KKE/D repeat sequences. Depletion of Nop58p demonstrates that this protein impairs the processing of 35S pre-rRNA at the A0 and A2 cleavage sites (65).
Relatively little is known about snoRNA in trypanosomes. However, recent genome studies with Trypanosoma brucei identified 21 clusters encoding 57 C/D snoRNAs and 34 H/ACA-like RNAs, which have the potential to direct 84 methylations and 32 pseudouridinylations, respectively (35). Previous studies suggest the existence of at least 100 Nms in trypanosomatids (57). The large repertoire of Nm modification and guide RNAs in trypanosomes suggests that these modifications are likely to play a central role in these parasites. We have proposed that since many of the trypanosomatid species undergo temperature changes during their life cycle, from 26°C in the insect host to 37°C in the mammalian host, the hypermodification may be related to the need to preserve ribosomal activity during the organism's cycling between the insect and the mammalian hosts (57).
rRNA processing in trypanosomes seems to differ from maturation in most eukaryotes. The small-subunit (SSU) rRNA in trypanosomes is the largest one known so far, and the large-subunit (LSU) rRNA is processed into six fragments (6, 64). Whereas in most eukaryotes, the first cleavage of LSU takes place in the 5' external transcribed spacer (5' ETS), in T. brucei the first cleavage takes place at internal transcribed spacer 1 (ITS1 [position B1]), which separates the pre-SSU from the pre-LSU rRNA (15). Cleavages at the 5' ETS at sites A', A0, and A1 generate the 5' end of the SSU rRNA. The only trypanosome snoRNAs involved in rRNA maturation that have been identified so far are U3 and snR30 (4, 14, 15, 16, 17). Recently, the role of H/ACA RNAs in rRNA processing was examined by RNA interference (RNAi) silencing of the H/ACA RNA protein CBF5. This study identified rRNA-processing defects including the accumulation of pre-rRNA precursors, as well as reductions in the levels of the mature SSU and LSU (4). Many of the snoRNAs that have been shown to function in rRNA processing in other eukaryotes, including U22, U8, U14, and mitochondrial RNA-processing (MRP) RNAs, were not yet identified in trypanosomes. MRP RNA is structurally and functionally related to RNase P, which is involved in the processing of the 5' end of pre-tRNA (66). MRP has been shown to cleave pre-rRNA within ITS1 (7, 49).
In this study, we used RNAi silencing of NOP1 to examine the role of C/D snoRNAs in rRNA modification and processing in T. brucei. A machine-learning algorithm was used to predict the level of snoRNA with 85% accuracy. Systematic mapping of Nm sites on rRNA identified an additional 47 Nms beyond the 84 Nms previously identified, suggesting that more C/D snoRNAs than identified in the published repertoire are expected to exist in the genome (31, 35). Mapping of Nms in the two life stages of the parasite, the procyclic and bloodstream forms, revealed hypermethylation in the bloodstream form at certain positions, suggesting that this modification may help the parasite adapt to the higher temperature during cycling from the insect to the mammalian host. Inspection of the rRNA defects in cells in which NOP1 has been silenced suggests the involvement of C/D snoRNAs in trypanosome-specific rRNA-processing events. We also identified a ubiquitous snoRNA involved in rRNA processing, the MRP RNA. This study highlights the role of C/D snoRNAs in trypanosome-specific rRNA-processing events as well as in Nm modification.
| MATERIALS AND METHODS |
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Cell growth and transfection. Procyclic T. brucei strain 29-13 (obtained from the laboratory of Paul England, a gift from the laboratory of George Cross), which carries integrated genes for T7 polymerase and the tetracycline repressor, was grown in SDM-79 medium supplemented with 10% fetal calf serum in the presence of 50 µg/ml hygromycin and 15 µg/ml G418. Cells were transfected as previously described (38). For cloning, the transfected cells were diluted onto microtiter plates in the presence of the parental T. brucei 29-13, which served as feeder cells. The microtiter plate was incubated in a humid chamber at 27°C under a 5% CO2 atmosphere. After 2 to 3 weeks, clonal populations were obtained in the microtiter plates, and the cells were transferred to liquid medium for propagation. Cells from cultures that showed typical growth arrest upon tetracycline induction were grown and frozen. Every 2 weeks, a new culture was started from the original frozen stock.
Northern blot analysis.
Total RNA was prepared with TRIzol reagent (Sigma), and 20 µg/lane was fractionated on a 1.2% agarose-2.2 M formaldehyde gel. The RNA was visualized with ethidium bromide. The NOP1 mRNA and 7SL RNA were detected with randomly labeled probes (Random Primer DNA labeling mix; Biological Industries Co.). rRNA subunits and internal transcribed sequences of pre-rRNA were detected with [
-32P]ATP-labeled oligonucleotides. For analysis of small rRNAs (srRNAs) and novel C/D snoRNAs, total RNA (10 µg) was fractionated on a 10% polyacrylamide gel containing 7 M urea. The RNA was transferred to a nylon membrane (Hybond; Amersham Biosciences) and probed with [
-32P]ATP-labeled oligonucleotides.
RT-PCR. The total RNA was extensively treated with a DNase inactivation reagent (DNA-free; Ambion) to remove DNA contamination. Reverse transcription was performed on the RNA by random priming (Promega). The samples were heated for 5 min at 70°C. After chilling on ice for 2 min, 1 U of superscript III reverse transcriptase (RT) (Invitrogen) and 1 U of RNase inhibitor (Promega) were added, and the reaction mixture was incubated at 55°C for 60 min. The reaction was stopped by heating to 70°C for 15 min, and the reaction mixture was chilled on ice for 2 min. Next, the cDNA was used in PCR amplification as previously described (33).
Primer extension analysis. RNA was prepared from T. brucei cells using TRIzol reagent (Sigma). Primer extension analysis was performed as described elsewhere (32, 67) using 5'-end-labeled oligonucleotides specific to target RNAs, as indicated in the figure legends. The extension products were analyzed on 6% polyacrylamide-7 M urea gels and visualized by autoradiography.
Mapping of the modified nucleotides. Nms on rRNA were mapped using primer extension with different levels of deoxynucleoside triphosphates (dNTPs), as described by Xu et al. (67). Primers specific to the relevant region of the rRNA were chosen. In this method, the RT stops 1 nt before the modified base (67). Primer extension products were analyzed on a 6% polyacrylamide-7 M urea gel, alongside results of sequencing reactions performed using the same primer.
TAP tag purification. The T. brucei 29-13 cell line, coexpressing the Tet repressor and T7 RNA polymerase, was transfected with NotI-linearized plasmid pLew79-snu13pTAP, coding for the Snu13p-TAP-tagged protein under the control of tetracycline and carrying the phleomycin resistance gene (50). Expression of Snu13p-TAP in the transgenic cell line was induced for 72 h with tetracycline (0.1 µg/ml). Cells (1 liter; 107 cells/ml) were harvested, washed with phosphate-buffered saline, and resuspended in IPP150 buffer (10 mM Tris-HCl [pH 8], 150 mM NaCl, 0.1% Nonidet P-40, 1% bovine serum albumin, 5 µg/ml leupeptin). Next, 1% Triton X-100 was added, and the resulting lysate was incubated on ice for 20 min. The lysate was then centrifuged at 10,000 x g for 15 min, and the supernatant was subjected to affinity selection using immunoglobulin G (IgG)-Sepharose beads for 2 h. RNA was extracted from the beads with TRIzol reagent (Sigma) and was subjected to primer extension analysis with specific probes.
Western blot analysis. Whole-cell extracts (106 cell equivalents per lane) of induced and uninduced cells were separated on a 12% sodium dodecyl sulfate (SDS)-polyacrylamide gel, transferred to a Protran membrane (Whatman BioScience), and probed with appropriate antibodies. The anti-fibrillarin antibody (kindly provided by S. J. Baserga, Yale University) and IgG antibodies (Sigma) were diluted 1:1,000 (9) and 1:2,000, respectively. The bound antibodies were detected with goat anti-rabbit IgG coupled to horseradish peroxidase and were visualized by ECL (Amersham Biosciences).
Bioinformatic analysis using SVM. Support vector machine (SVM) is a machine-learning technique that is used for classification and regression. Recently, it was shown to be useful in bioinformatics studies (68). We applied the SVM algorithm (58) using SVMlight implementation in classification mode (21) to create a model to explain the different expression levels of C/D snoRNA molecules and the different levels of modifications in rRNA sites. Each SVM prediction was evaluated by a five-cross validation test, in which data were randomly divided into five equal parts. Four parts represented the training set for SVM learning, and the final part represented the test set for the SVM model. This procedure was performed 50 times, and the average percentage of successful predictions was calculated.
In vivo labeling of rRNA with [methyl-3H]methionine. For in vivo labeling of rRNA, NOP1 induced and silenced cells were grown for 2 days. Then cells were washed twice with phosphate-buffered saline and resuspended in 1 ml of methionine-free medium in the presence of 100 µCi of [methyl-3H]methionine (54, 55). Cells were incubated at 27°C for 4 h, and then RNA was extracted from the cells with TRIzol (Sigma). The RNA was separated on a 6% polyacrylamide-7 M urea gel next to a DNA marker (MspI-digested pBR322). Fluorography of the gels was performed using Amplify solution (Amersham).
Determination of 2'-O-methylated nucleotides using alkaline hydrolysis. For partial alkaline hydrolysis, 50 µg of total RNA was resuspended in 10 mM NaOH and 0.2 mM EDTA, and the samples were boiled for either 30 s or 1, 2, 5, 10, or 30 min. The RNA samples were mixed and precipitated with ethanol. One-tenth of the RNA sample was subjected to primer extension as previously described (27).
| RESULTS |
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In all the mapping experiments (Fig. 2a to c; see also Fig. S-1A to S-1C in the supplemental material) using the dNTP starvation assay, we noticed major differences in the intensity of primer extension stops and observed weaker stops in between the strong stops. Multiple factors can affect the level of modification and the stops due to the presence of Nm on a particular site. Factors that may affect the level of Nm modifications are the abundance of the snoRNA guiding the modification and the strength of the interaction between the snoRNA and its target site. In addition, the stops on the Nm may be affected by the location of Nm on the rRNA secondary structure. In the alkaline hydrolysis method as well, we observed differences between the intensity of the extension products within the gap, indicating the lack of modification on this site. However, we could not find a correlation between the intensity of the stop in the dNTP experiment and the degree of alkaline cleavage, suggesting that comparison between the two methods can be used to confirm the presence of modification but not to determine the intensity of modification, especially when different sites are compared.
Effect of NOP1 depletion on rRNA processing.
We next used the NOP1-silenced cells as a tool to decipher the complex process of rRNA processing, especially that of the LSU, aiming to identify rRNA defects that are informative as to the possible involvement of C/D snoRNA in trypanosome-specific rRNA events. Trypanosomes, as opposed to other eukaryotes, have additional rRNA-processing events that separate the LSU
and LSUβ as well as releasing the srRNA fragments (6, 64). If C/D snoRNAs are involved in these specific cleavages, we would expect to observe a decrease in the production of srRNAs and an accumulation of precursors flanking the RNAs. To identify such defects, two approaches were taken. First, the accumulation of precursors and reductions in the levels of mature RNAs were examined by Northern blot analysis (Fig. 3). RNA was extracted from cells before (–Tet) and after (+Tet) induction of silencing and was subjected to Northern blot analysis with probes specific to the SSU. The results are presented in Fig. 3Aa, and a schematic presentation of the rRNA domain and its cleavage sites is given in Fig. 3Ac. Northern blotting detected three rRNA precursors of 3.7, 3.3, and 2.6 kb. The 3.7-kb precursor results from cleavage at B1, releasing the entire pre-SSU portion of the precursor that was not cleaved at site A0, A1, or A2. The 3.3-kb species is derived by further cleavage of the 3.7-kb precursor at A2 but not at B1. The 2.6-kb species is cleaved at the A0 site but not at the A2 site. The identities of these precursors were confirmed by hybridization with ETS (data not shown) and ITS1 (Fig. 3Ab) probes. The most abundant precursor is the 3.7-kb species, which results from a major defect in cleavage at the A0, A1, or A2 sites. To precisely evaluate the rRNA defects, we mapped the precursors and the cleavages in the pre-SSU region. Primer extension was performed with oligonucleotides situated in regions downstream from the A', A0, A1, and A2 sites (indicated in Fig. 3Ba). Increases in the levels of primer extension products, obtained by using primers situated downstream of A0 (Fig. 3Bb2) and A2 (Fig. 3Bb4), suggest the accumulation of the pre-rRNA harboring these sites, since these cleavages were inhibited by silencing. The accumulation of RNA carrying the A' site (Fig. 3Bb1) suggests additional defects in removal of the ETS, which may stem from defects in cleavage at A0 and A1. Indeed, cleavage at A1 was inhibited, as can be seen in Fig. 3Bb3, demonstrating a reduction in the stop at the A1 position. The rRNA defects observed suggest the inhibition of cleavages at positions A1 and A2. The defect in rRNA processing at sites A1 and A0 may be attributed to defects in U3 function. However, other C/D snoRNAs should exist in trypanosomes to guide these cleavages. In yeast and mammals, a similar cleavage is mediated by U14 (28, 30, 39). So far, we have failed to identify a U14 homologue. However, TB11Cs2C1 might functionally replace U14 in trypanosomes (see Discussion). The most abundant precursor that accumulates in NOP1-silenced cells is the 3.7-kb transcript, which results from cleavage at the B1 site, situated near the cleavage site of MRP RNA (see below).
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and LSUβ) from the three distal small RNAs (sr2, sr6, and sr4) at the 3' end, suggesting that cleavage at ITS5 is not affected in NOP1-depleted cells (Fig. 4Aa and c). In addition, the 2.6-kb precursor was detected at low levels with an ITS2-specific probe. This intermediate spans the 5.8S precursor through LSU
and did not hybridize to the ITS3 or the ITS1 probe (Fig. 4Ab). It may represent an alternative processing intermediate whose generation does not depend on C/D snoRNA, again suggesting that cleavage at the 5' end of srRNA-2 is not mediated by C/D snoRNA. In addition, we identified two precursors that hybridized only to ITS3 (0.55 and 0.34 kb) (Fig. 4Ac), suggesting that separation of srRNA-1 from LSU
depends on C/D snoRNA. Two precursors that hybridized only to ITS7 (0.53 and 0.34 kb) (Fig. 4Af) were identified, suggesting that cleavage at srRNA-6 depends on C/D snoRNA. Probes corresponding to ITS5 to -7 identified a 3.8-kb precursor, which most probably represents an aberrant processing intermediate resulting from cleavage upstream of LSU
rRNA. The defects in rRNA processing, especially in the processing of the LSU, suggest that additional C/D snoRNAs should exist in trypanosomes to mediate the cleavages generating srRNAs 1, 4, and 6.
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was increased by 113% (Fig. 4Ca). These data are in agreement with the data presented in Fig. 4Ac, where two precursors in this area were detected, suggesting that C/D snoRNAs are likely to be involved in the processing of srRNA-1, most probably by mediating cleavages at both the 5' and 3' ends of the molecule. The other precursors whose levels were elevated are the ones situated between srRNA-2 and srRNA-6 as well as between srRNA-6 and srRNA-4 (Fig. 4Ca), suggesting that cleavages that release srRNA-6 are also dependent on C/D snoRNAs. These data are also in agreement with the detection of 0.53- and 0.43-kb precursors covering both srRNA-4 and srRNA-6 (Fig. 4Af). These results are also consistent with the rRNA precursors recently described by Jensen et al. (20). Interestingly, the levels of all the precursors were not affected to the same extent, but it was possible to detect specific defects greater than the general elevation in the level of the pre-rRNA precursor (
35%). The general increase of 30 to 40% in each of the precursors reflects the accumulation in the silenced cells of the 9.6-kb precursor, which covers the entire rRNA repeat unit. MRP RNA exists within a snoRNA cluster. Cleavage at the B1 site was not affected by silencing of either the C/D or the H/ACA pathway (4), suggesting that, as in other eukaryotes, cleavage in the vicinity of this site is most probably mediated by MRP RNA which, in yeast, cleaves at the A3 site, situated just upstream (59). Since snR30 was recently identified in a cluster carrying C/D and H/ACA RNA, we searched the snoRNA clusters for RNAs longer than the average size of snoRNAs (70 to 90 nt). Such an RNA was identified in cluster 10 as TB10Cs1. The RNA is 521 nt long. Multiple alignments of the RNAs from different species such as humans, S. cerevisiae, and D. melanogaster are presented in Fig. S-6 in the supplemental material. Primer extension was used to map the 5' end of the RNA (Fig. 5B). The secondary structure of the T. brucei RNA is presented next to the secondary structure of the S. cerevisiae RNA (Fig. 5A). The data presented in Fig. 5 suggest that TB10Cs1 is most probably the T. brucei MRP homologue. Like all MRP RNAs in nature, the trypanosome homologue is divided into two domains (Fig. 5A). Whereas subdomains P1, P2, P3, and P4 within domain 1 are highly conserved, the entire domain 2 is less conserved throughout evolution (29, 44). Interestingly, the study that identified various MRP RNAs from multiple species failed to find the trypanosomatid RNA (44). The identification of TB10Cs1 as MRP RNA is based on several criteria. CR-I (conserved region I) (Fig. 5C1) obeys the consensus sequence of MRP CR-1 but not that of RNase P; all nine of the most conserved nucleotides exist in the T. brucei sequence. The sequence of CR-V also resembles the consensus sequence and is G rich, but CR-V is less conserved than CR-I, and only 6 out of 8 nt present are conserved in the T. brucei sequence (Fig. 5C2). The sequence of CR-IV contains the three conserved nucleotides found in all MRP RNAs (Fig. 5A).
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Prediction of the cellular level of snoRNA using the SVM algorithm. This study identified two observations that require explanation: the variability in the expression level of each C/D molecule and the variability in the modification level of each Nm site, as described above and as can be clearly seen in Fig. 6 (determined by the dNTP starvation assay). Since the level of stops might be affected by the local structure and environment of the rRNA, we assigned the modifications to 11 groups, each of which represents modifications situated at a single region. Within each group, the level of each modification and the level of the snoRNA were examined by primer extension and were normalized to the level of U4 snRNA in each sample. This analysis was performed for 27 sites (Fig. 6; see also Fig. S-8 in the supplemental material). In addition, we collected other parameters for Nm modifications and the snoRNAs that guide these modifications, including the location of the modification on the secondary structure, the conservation of the modification, and the conservation of the snoRNA and its boxes (see Fig. S-8 in the supplemental material).
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However, several of the parameters described in Fig. S-8 in the supplemental material could explain the differential abundance of the snoRNAs guiding the different modifications. We decided to use a sophisticated machine-learning algorithm that can search for a combination of parameters (21, 68) to help us predict the abundance of snoRNA. To predict the abundances of C/D molecules, the SVM approach was used in the following manner. For each C/D molecule, 10 parameters that may be related to its expression level or to the function of these molecules were compiled (see Fig. S-8 in the supplemental material). For each pair of C/D molecules that guide adjacent modifications and whose expression level could be compared, we created a vector describing the differences between the 10 parameters associated with each molecule involved in these modifications. The algorithm was trained to choose the C/D molecule with the higher expression level. Clearly, a random prediction would yield a 50% success rate, and a significant deviation from that value would indicate that the algorithm was able to capture a combination of parameters that affects the expression level. When the SVM was trained on the set of parameters, the average percentage of successful predictions for the level of snoRNA was 65.5%, suggesting that indeed several or all of the parameters used can explain the differential level of snoRNA abundance. To further examine which factors were the most relevant to the prediction, we removed one parameter at a time from the list and ran the SVM with the remaining variables. When a significant deterioration in the performance of the model occurred due to the removal of a single parameter, we concluded that this variable was crucial for the system. Using this approach, we identified four features that best predicted the snoRNA level: conservation of the C box, the number of modifications guided by the snoRNA, location of the modification on a stem or loop, and the level of conservation of the Nm modification among eukaryotes. Interestingly, although the level of conservation of Nm modifications among eukaryotes has no statistically significant correlation to the abundance of the corresponding C/D molecule, somehow it is essential for the prediction model. The new SVM model was created based only on these four parameters, and the average percentage of successful prediction was 85%. The significant improvement in the prediction success rate with only 4 parameters over that for the initial set with 10 parameters is probably due to the decrease in the noise level introduced by the irrelevant parameters.
The level of modifications is increased at certain positions in bloodstream-form trypanosomes.
Based on the large amount of C/D snoRNAs and the high level of Nm modifications in trypanosomes, we previously proposed that these modifications may assist the trypanosomes in coping with the temperature shift during their cycling between the insect and mammalian hosts (57). It is well documented that in hyperthermophilic archaea, a large number of Nms stabilizes the ribosomes at high temperatures (57). We therefore systematically mapped the modifications during the two life stages of the parasites. To this end, total RNA was prepared from procyclic and bloodstream parasites and was subjected to mapping that covered
21% of the rRNA. In each experiment, the level of RNA in each sample was calibrated by primer extension using an antisense U4 snRNA probe. The results are presented in Fig. 7 and Fig. S-2A to S-2C in the supplemental material. The increase in the level of stops in RNA from bloodstream-form parasites over that for procyclic-form parasites ranged from 59 to 500%. These experiments represent only a small portion of many examples observed. However, the extent of modification did not increase for all positions, and only about 20% of the modified nucleotides seemed to increase in the bloodstream form.
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Silencing of NOP58 and TAP tagging of Snu13p homologues serve as tools to identify novel C/D snoRNAs.
We previously estimated that the collection of C/D snoRNAs that had been identified covered only 70% of the expected repertoire (35). To test this prediction, we systematically mapped
67% of the entire LSU and SSU for Nm modifications. The results, presented in Fig. S-9 in the supplemental material, summarize modifications identified based on the primer extension assay. The levels of all modifications listed in Fig. S-9 in the supplemental material were reduced in NOP1-silenced cells, indicating that these modifications are likely to be guided by C/D snoRNAs. Note that these modifications were not predicted previously on the basis of the snoRNAs identified in the T. brucei genome (35). Interestingly, most of these modifications (60%) are trypanosome specific; 30% are present only in trypanosomes and plants; and only three were found in humans, yeast, trypanosomes, and plants. These data suggest that trypanosome rRNA carries at least 131 Nms, the sum of the 84 modifications listed by Liang et al. in 2005 (35) and the 47 new modifications mapped in this study. However, the true number might be even larger, since our mapping did not cover the entire rRNA but only the domains that are also rich in modifications in other eukaryotes. These data further suggest that additional snoRNAs may exist in trypanosomes to carry out the modifications identified in this study (see below).
Since NOP1 silencing did not affect the levels of snoRNAs, we wanted to generate a tool that would enable us unequivocally identify RNA molecules as C/D snoRNAs. We therefore searched for other snoRNP proteins in the T. brucei genome. NOP58 depletion destabilizes snoRNA in yeast (25); we therefore sought to identify its trypanosome homologue. The T. brucei genome was searched with the human homologue, and a sequence alignment of T. brucei NOP58 with those of other eukaryotes is presented in Fig. S-3 in the supplemental material. The results indicate 53% identity to human, 52% identity to S. cerevisiae, and 55% identity to Drosophila NOP58. Our search also revealed a homologue to NOP56, and its sequence alignment is presented in Fig. S-4 in the supplemental material. The results indicate 49.5% identity to human, 48% identity to S. cerevisiae, and 49% identity to Drosophila NOP56. As in other eukaryotes, NOP56 and NOP58 are closely related. Like the yeast proteins, both proteins carry a KKE/D motif at the carboxy terminus. Interestingly, whereas depletion of NOP58 resulted in destabilization of snoRNAs (25), depletion of NOP56 did not affect snoRNA levels (26). To confirm that we had identified the homologue of NOP58, the expression of the putative homologue was down-regulated by RNAi. To silence the gene, T7 opposing promoters were used to produce the gene-specific double-stranded RNA (dsRNA) under tetracycline induction (61). Parasites stopped growing 3 days after dsRNA induction, suggesting the essential nature of this gene. We next examined whether silencing affects the levels of snoRNAs. RNA was extracted from cells before and 3 days after induction and was subjected to primer extension to determine the levels of snoRNAs. The results, presented in Fig. 8A, indicate specific reductions of all C/D snoRNAs by 50 to 90% in the silenced cells. These reductions are specific, since no effect on the levels of snRNA or H/ACA molecules was observed. Of special interest are two C/D snoRNAs, TB11Cs2C1 and TB11Cs2C2, which had been implicated as guide RNAs that methylate rRNA by nonconventional methylation rules (46, 47). Later it was found that the Nms predicted to be guided by these RNAs are guided by other snoRNAs based on the conventional guiding rules (9, 10). Thus, the functions of these two RNAs remained puzzling. Primer extension suggests that these two RNAs are very abundant, much more abundant than any C/D involved in methylation, suggesting that they may function in rRNA processing. The clear effect of NOP58 silencing on the levels of these two RNAs suggests that they are indeed members of the C/D snoRNA class. Interestingly, NOP58 silencing had no effect on the level of SL RNA, suggesting that, in contrast to the H/ACA pathway (4), the C/D pathway is not essential for trans-splicing. NOP58-silenced cells served as a tool to identify novel C/D snoRNAs. Extensive bioinformatic searches were employed to identify the snoRNAs in the genome that could methylate the positions that were mapped (see Fig. S-9 in the supplemental material). Surprisingly, only three novel C/D snoRNAs, which had not been identified previously (35), were found (Fig. 8B). Two of the novel C/D snoRNAs (TB10Cs2'C1 and TB7Cs1C1) are encoded by a single-copy-number snoRNA gene. Their sequences, genomic locations, and potential base pairing with their target sites are presented in Fig. S-10 in the supplemental material and in Fig. 8Ba and b. To verify that these RNAs are indeed C/D snoRNAs, their levels in NOP58-silenced cells were examined; as expected, the levels of these RNAs were reduced upon silencing (Fig. 8Bc).
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| DISCUSSION |
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rRNA processing is severely affected in the absence of functional C/D snoRNP. One of the most intriguing outcomes of this study is the finding that specific rRNA-processing defects are elicited by depletion of the C/D pathway (Fig. 4). Our data suggest that C/D snoRNAs are expected to process srRNA-1, -6, and -4. The rest of the cleavages might be generated either by H/ACA RNAs or by RNase III endonucleases. Examination of the rRNA defects generated in CBF5-silenced cells indicates a specific defect in generating srRNA-2, suggesting that this cleavage is mediated by a trypanosome-specific H/ACA RNA (our unpublished data). This H/ACA RNA, as well the C/D snoRNAs predicted to exist based on this study, has yet to be discovered. Two abundant snoRNAs that were suggested to guide rRNA by nonconventional rules, TB11Cs2C1 (76 nt) and TB11Cs2C2 (92 nt), are potential candidates for the cleavage of at least some of the sites predicted above. Our preliminary results, based on bioinformatic predictions and snoRNAi inhibition of these RNAs, suggest that TB11Cs2C1 is most probably involved in 18S rRNA processing. On the other hand, TB11Cs2C2 is predicted to function in the processing of srRNA-6 (A. Hury, Y. Ziporen, and S. Michaeli, unpublished data).
Differential levels of snoRNAs are a consequence of multiple factors. Our results demonstrate that different C/D snoRNA molecules have different abundances and that not all Nm sites are equally modified in the two life stages of the parasite.
The machine-learning algorithm was used to examine parameters that might enable us to predict the levels of snoRNAs, which seem to vary considerably. We recently made a similar attempt to understand this phenomenon using 20 Leishmania major snoRNAs (31). Transcriptional regulation cannot explain the major differences that exist among levels of snoRNAs encoded by the same cluster. Two major factors may influence the levels of snoRNAs: the extent of processing and the strength with which these snoRNAs bind their cognate binding proteins. The gene copy number, structural features of the snoRNA such as the conservation of the boxes (especially C, since D is highly conserved in all snoRNA species), the internal K-turn in the molecule, and the
G of the snoRNA-target interaction can all affect snoRNA levels. Thus, snoRNA abundance may be governed by combinatorial effects of all these factors. To test this hypothesis in a quantitative manner, we employed the machine-learning algorithm to predict the C/D snoRNA levels. Four factors were found to be important: the conservation of the C box, the number of modifications each molecule can guide, the secondary structure of the domain at which the modification is situated, and the level of conservation of Nm modification among eukaryotes. Although the last two factors cannot be rationalized as directly affecting the level of snoRNA, they do differ among different snoRNAs. The conservation level may be an indicator of the importance of the modification and thus may invoke additional mechanisms, yet to be characterized, to enhance modifications at these sites. A model based on these parameters can successfully predict the level of cellular snoRNA in 85% of cases. As we mentioned above, additional elements such as transcriptional regulation could exist for snoRNA genes (34) and might explain the 15% of predictions that were inaccurate. We tried to explore whether additional parameters such as the K-turn and the strength of the extragenic elements flanking the snoRNA gene could also contribute to snoRNA levels. Interestingly, we found no significant correlation between the level of snoRNA and the
G of the extragenic elements, such as that recently described for L. major (31). Indeed, the extragenic stems of T. brucei snoRNAs are much shorter than those found for L. major.
snoRNA expression and Nm modification are developmentally regulated; existence of additional C/D snoRNAs in the trypanosome genome. The degree of modification seems to be developmentally regulated, but why are different sites modified differently? The developmentally regulated Nms belong to different groups; a few are conserved among eukaryotes, but trypanosome-specific modifications also exist. As mentioned above, our survey for developmentally regulated Nm was limited to only 21% of the rRNA. The fact that we did not yet reveal any stage-specific modifications does not prove that no such modifications exist. A better way to identify such modifications may be to find snoRNAs whose expression is developmentally regulated. Very little is known about regulation of snoRNA expression. Most relevant to our experiments is a recent study that followed the expression of noncoding RNAs during C. elegans development. Both C/D and H/ACA RNAs were found to be poorly expressed at the egg-embryo stage, suggesting that snoRNA expression is developmentally regulated. In addition, the snoRNA level is elevated under stress, including heat shock and starvation (18). Transcriptional regulation can account for the differential expression of snoRNAs in nematodes, which are also intronic and are transcribed together with their host genes. Interestingly, however, the noncoding RNAs embedded within host genes do not always follow the pattern of the host gene expression, suggesting that RNA motifs of noncoding RNA may have a role in transcriptional activation.
We know very little about the regulation of snoRNA levels in trypanosomes. Although it is commonly believed that in trypanosomes, transcription is not the primary stage at which gene expression is regulated, it is possible that snoRNA genes are regulated by extragenic elements at the transcriptional level, as we reported for L. collosoma (34). It is also possible that transcription from these elements is activated under stress or is developmentally regulated, leading to high expression of snoRNA, as was observed for TB5Cs1C1 (Fig. 7b). It will be of great interest to perform microarray analysis on the different snoRNAs and to examine how many are developmentally regulated. Only when such snoRNAs are identified will it be possible to examine whether they guide trypanosome-specific or evolutionarily conserved modifications.
The observation that the level of Nm is increased in the bloodstream form supports our hypothesis that the high number of modifications and their level might assist the parasite in coping with the elevated temperature in the mammalian host.
The repertoire of C/D snoRNAs in trypanosomes. Based on our previous publication (35) and the mapping data presented in this study, there should be at least 131 Nms on T. brucei rRNA. Earlier studies with Crithidia fasciculata suggest the presence of at least 100 Nms on rRNA (13), and recent results for Euglena gracilis, which is closely related to trypanosomes, suggest the presence of at least 200 Nms on its rRNA (48). However, our studies identified, to date, only 57 C/D snoRNAs (31, 35) that can potentially guide 84 Nms. Our searches to find additional snoRNAs that can guide most of these additional Nms failed. Such snoRNAs are not encoded by repeated genes, since all the repeated noncoding genes were examined by us and all are described in reference 35. Although we cannot exclude the possibility that some sequence information of the T. brucei genome is incomplete, other options may also explain the "missing" snoRNAs. For instance, there may be C/D-type RNAs whose boxes deviate strongly from the consensus but that still bind fibrillarin. Another option is the existence of guide RNAs that modify these Nms under different guiding rules. Only extensive genome comparative analysis to search for noncoding RNAs, as well as preparation of noncoding-RNA libraries from trypanosomes, which also include nonabundant RNAs, is expected to solve the mystery of these missing snoRNAs.
In summary, this study highlights the role of the C/D snoRNAs in conducting trypanosome-specific rRNA cleavage events and emphasizes the role of Nm modifications as a possible mechanism of adaptation to the temperature shift experienced by the parasite in cycling between the insect and mammalian hosts. The identification of the trypanosome MRP RNA increases our understanding of the repertoire of noncoding RNAs in this organism. The fact that this RNA was previously missed by bioinformatic tools that were suitable for identifying its homologues in other organisms further emphasizes the unique properties of small RNAs in this exotic parasite.
| ACKNOWLEDGMENTS |
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| FOOTNOTES |
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Published ahead of print on 2 November 2007. ![]()
Supplemental material for this article may be found at http://ec.asm.org/. ![]()
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