Widespread uncoupling between transcriptome and translatome variations after a stimulus in mammalian cells
© Tebaldi et al.; licensee BioMed Central Ltd. 2012
Received: 19 January 2012
Accepted: 6 June 2012
Published: 6 June 2012
The classical view on eukaryotic gene expression proposes the scheme of a forward flow for which fluctuations in mRNA levels upon a stimulus contribute to determine variations in mRNA availability for translation. Here we address this issue by simultaneously profiling with microarrays the total mRNAs (the transcriptome) and the polysome-associated mRNAs (the translatome) after EGF treatment of human cells, and extending the analysis to other 19 different transcriptome/translatome comparisons in mammalian cells following different stimuli or undergoing cell programs.
Triggering of the EGF pathway results in an early induction of transcriptome and translatome changes, but 90% of the significant variation is limited to the translatome and the degree of concordant changes is less than 5%. The survey of other 19 different transcriptome/translatome comparisons shows that extensive uncoupling is a general rule, in terms of both RNA movements and inferred cell activities, with a strong tendency of translation-related genes to be controlled purely at the translational level. By different statistical approaches, we finally provide evidence of the lack of dependence between changes at the transcriptome and translatome levels.
We propose a model of diffused independency between variation in transcript abundances and variation in their engagement on polysomes, which implies the existence of specific mechanisms to couple these two ways of regulating gene expression.
KeywordsPolysomal Profiling Transcriptome Translational Control Translatome
In the flow of genetic information, translational control is the level at which reprogramming of cell activities accesses the phenotype, ultimately shaping protein synthesis and therefore, together with the control of protein degradation, quantitative variation of the proteome. Originally studied in early stages of development in oocytes and embryos [1, 2], translational control has been increasingly recognized as a very general feature of eukaryotic cells, extensively present also in mature tissues. This process is orchestrated by incoming cell stimuli which elicit largely unknown transduction pathways, affecting primarily translation initiation, i.e. the loading of ribosomes on messenger ribonucleoprotein particles (mRNP) to form polysomes, and secondarily translation elongation [3, 4]. The ways in which these stimuli influence polysome formation involve “general” translation factors as eIF4E, eIF4G, eIF4A and PABP, allowing mRNA circularization and ribosome scanning, and more specialized factors acting on sequences found primarily in the 5’ or 3’ untranslated regions (UTRs) of mRNAs. These latter factors belong to the two classes of RNA binding proteins (RBPs) and noncoding RNAs (ncRNAs), among which microRNAs (miRNAs) are an intensively studied subclass. In the human genome the predicted genes coding for proteins involved in translational control are around a thousand and the number of miRNAs, proven to be able to modulate translation [5, 6], is estimated between one and two thousands . Furthermore, by recent transcriptome high-sensitivity sequencing scannings, the human ncRNA collection has risen to comprise around five thousands ncRNAs , to which the 18,000  processed pseudogenes have to be added because they also can interfere with gene expression . If even a small fraction of these ncRNAs was involved in modulating translation, the amount of macromolecules potentially able to operate at the interface between mRNA and proteins would be extremely high. Moreover, recent findings reveal the presence in eukaryotic cells of cytoplasmic RNA-containing granules (processing bodies, stress granules and other types) composed of aggregates of mRNPs where mRNA decay, editing and storage can take place [11–13]. These granules can generate a bidirectional flow of mRNAs with polysomes [14–16].
Given this complex layer of activities in the cytoplasm, we set the goal to estimate the relationship between fluctuations of mRNA levels in the cell and fluctuations of the fraction of mRNAs available for translation after a stimulus, which to our knowledge has never been addressed with a population-based approach. The degree of change in translation-engaged mRNAs can be estimated by extracting mRNAs organized in polysomes by a classical separation technique, velocity sedimentation by sucrose gradients, and profiling them in parallel with total mRNA .
By measuring the total mRNAs of cells (the transcriptome) and the polysomally-loaded mRNAs (the translatome) after a growth stimulus, we obtained a picture of overall mismatching between the two changes for the majority of genes, to which we refer as “uncoupling” in the mRNA behavior. This was confirmed studying a number of other available profiles coming from very diverse experiments and kinetics. The marked, general uncoupling between transcriptome and translatome gene expression changes allowed us to propose a biological model by which the machineries responsible for mRNA availability in the cytoplasm and for mRNA engagement in translation lack overall dependency, therefore questioning the notion of continuity in the control of the flow of gene expression.
Profound uncoupling between transcriptome and translatome gene expression variations upon EGF stimulation of HeLa cells
The high degree of uncoupling between transcriptome and translatome variation profiles is a general feature of the control of gene expression in mammalian cells
Description of the datasets used for the analysis
serum starvation release
erythroid EPO deprivation release
stem cell differentiation through LIF removal
macrophage LPS treatment (1 h)
macrophage LPS treatment (2 h)
macrophage LPS treatment (4 h)
differentiation of WT hepatocytes
differentiation of mTOR activated hepatocytes
testis differentiation (5d)
testis differentiation (53d)
testis differentiation (48d)
lung differentiation (3d)
lung differentiation (embrionic vs postnatal)
lung differentiation (embrionic vs postnatal)
translational machinery alteration
mTOR activation of proliferative hepatocytes
mTOR activation of differentiated hepatocytes
Ontological enrichment and pathway analysis of transcriptome and translatome variations predict very different phenotypes
Finally, we wanted to derive from the global ontological analysis those cell activities more specifically characterizing transcriptome DEGs compared to translatome DEGs and vice versa. To provide a general view, individual over-represented GO terms from all dataset pairs were mapped to GOslim , a simplified version of GO. A clear outcome was that half of the translatome-specific terms (including translation translation regulator activity translation factor activity ribosome) were exclusively translation-related (Figure 3B). This result provides additional support to the notion of independent transcriptome and translatome controls of gene expression variations.
For each dataset, lists of transcriptome and translatome DEGs were subjected to further annotation with the Ingenuity Pathway Analysis (IPA) library of canonical pathways (data available in Additional file 7). The significance of the association between the DEGs and the canonical pathways was measured with the Fisher’s exact test, and a 0.05 cut-off on the Benjamini-Hochberg corrected p-value was used to identify significantly enriched pathways. Comparing the number of pathways that resulted enriched uniquely in the transcriptome or in the translatome DEGs, we had another proof that the gene expression uncoupling between transcriptome and translatome is extended to a functional specificity between the two relative sets of enriched pathways (Additional file 1: Figure S5). Across all the dataset pairs, 97 pathways (22%) were significantly enriched only in transcriptome DEGs, 139 pathways (31%) only in translatome DEGs and 206 pathways (47%) in both transcriptome and translatome DEGs. In 14 out of the 16 datasets with at least one enriched pathway, the number of specific pathways exceeds the number of common pathways.
The Ingenuity Knowledge Base was employed to build networks from the lists of transcriptome and translatome DEGs for each dataset. Networks were generated using experimentally validated direct interactions among DEGs (data available in Additional file 8). Cellular functions associated to networks, based on the functional annotation of their genes, were ranked according to their translatome specificity ( Additional file 1: Table S3). RNA post-transcriptional modification, again an mRNA related theme, resulted as a cellular function mainly associated to translatome networks.
Transcriptome and translatome variations are globally not dependent
Past studies employing yeast and reticulocyte lysates [32–35] have claimed that mRNAs have to numerically compete to gain access to ribosomes and to form polysomes. According to this view, polysomes should generally buffer transcriptome variations, except for those transcripts associated to trans-acting factors specifically increasing their probability of access to polysomes. To verify this hypothesis in our mammalian datasets, we counted the number of translationally enhanced (polysomal mRNA fold change > total mRNA fold change) and translationally buffered (polysomal mRNA fold change < total mRNA fold change) mRNAs across all the 19 dataset pairs. Since the proportions are roughly 50% and 50% (46% with decreased polysomal access, 54% with increased polysomal access) without a significant majority of genes buffered at the polysomal level, we suggest that in mammalian cells the competition of mRNA for ribosomes is not a general driving force regulating translation, unless the action of trans factors promoting polysome formation has the same magnitude of the polysomal competition effect.
In conclusion, we suggest that by analyzing the available data with different approaches, the mRNA production/degradation and the mRNA access to translation appear to be globally regulated not in an interdependent way.
The conceptualization which framed molecular genetics studies for four decades is the so-called central dogma , representing the forward flow of gene expression from DNA to mRNA to proteins through transcription and translation. This directional flow can easily be viewed as an assembly line in which the translation step is automatically determined by the availability of mRNAs produced by the transcription step. Following this scheme, changes in the quantities of an mRNA species due to changes in its transcription and/or degradation rate after a stimulus determine changes in its translation rate.
Prompted by the recent appreciation of translational control being more widespread than originally thought , and by the discovery of universal cytoplasmic foci of mRNA accumulation , in this work we wanted to address the quantitative population changes induced by a cell stimulus between the cytoplasmic mRNAs and those mRNAs supposed to be actively engaged in translation because part of polysomes. Inspired by the results of an experiment of EGF treatment on HeLa cells, we extended the analysis to a number of available published data on mammalian systems, all obtaining the translatome measures after sucrose gradient separation of polysomes. Therefore the outcome proposed, deriving from 8 treatments and 2 developmental assays performed on 10 different types of mammalian cells, can be likely regarded as general for mammals.
We found that the degree of uncoupling between the transcriptome and the translatome was higher than the degree of coupling, both in terms of single transcripts undergoing changes in levels and in terms of the ontological enrichment of the corresponding proteins. A striking result of the ontological analysis is that the transcriptome variation profiles of two different, unrelated experiments are as diverse as each of them compared with the corresponding translatome variations of the same experiment (Figure 3A). From this result we derive a message of partiality of transcriptome data in representing cell phenotypes, no matter how much quantitatively accurate. We also observed that a general tendency to establish autoregulatory or crossregulatory loops should be a specific feature of mRNAs and proteins involved in translation. In fact, among the mRNAs that change their abundance purely at the translatome level, we observe a strong enrichment in encoded translation-related proteins. This finding extends previous observations of self-regulating translation activities, such as the well-known example of the TOP genes, that are involved in the translation basal machinery and at the same time are regulated at the translational level after cell growth stimuli . This observation provides a first clue for understanding the independence of translatome changes by the transcriptome: following cell stimuli which act on their expression, many genes coding for components of the translational machinery do not undergo any change in their mRNA levels, but only variations in the rate of polysomal loading of these mRNAs. Moreover, we know that several mammalian RBPs bind their own mRNA and the mRNAs encoding other RBPs, regulating their stability and translation . In agreement, reconstruction in the yeast of RBP expression networks suggests that at least one third of the studied proteins post-transcriptionally auto regulate themselves, acting as network hubs . In metazoan a possible strong evolutionary pressure for the wiring of these RBP-mediated post-transcriptional looping circuits could derive from the need to regulate protein synthesis of maternal mRNAs in oocytes and in early embryo development, a well-studied process in C. elegans and Drosophila. A stimulating framework to explain the complexity and the relative independence of post-transcriptional networks from transcriptional events is proposed by Keene with the concept of post-transcriptional regulons, clusters of discrete mRNAs co-regulated by the same set of RBPs in order to orchestrate complex cellular functions [43, 44].
Indirect observations sustaining the view of divergence between the transcriptome and the translatome come instead from en-masse analyses comparing absolute mRNA and protein abundance. While in many prokaryotic and eukaryotic systems mRNA levels can describe no more than 50% of protein levels [45, 46], in a recent work  on human tumor cells the matching is lower (Pearson correlation 0.29) and translation-related features (as coding sequence, 5’ UTR, 3’ UTR lengths, presence of upstream open reading frames, density of secondary structures in the 5’ UTR, amino-acid composition) contribute with 30% to the predictability of protein concentration. Similar conclusions, with a predominant role in control given to translation, were drawn from a quantitative model based on mRNA and protein abundances, synthesis rates and half-lives in mouse fibroblasts and human breast cancer cells . Protein abundances are also more conserved than total mRNA abundances among different taxa, suggesting that transcriptome networks are less affected by evolutionary pressure than proteome networks .
Direct transcriptome to translatome comparison studies after severe stresses in yeast provide a picture of a marked translational shutdown from which highly concordant homodirectional changes emerge for some genes [50–53]. Exposing yeast to mild stresses, instead, produces a response characterized by a high level of uncoupling . These last mild perturbations and their dynamic effects can be better assimilated to the stimuli analyzed here in mammalian cells. A statistical treatment of the populations of mRNAs undergoing variations in our collection of comparable dataset pairs provides the falsification of a model of straight dependency between translatome changes from transcriptome changes. As a possible alternative, a parsimonious model in line with these data could postulate a general orthogonality between the mechanisms controlling transcript levels and translation in mammalian cells. In other words, changes in abundance of a given mRNA do not determine per se any effect on changes in its polysomal engagement. While mRNA abundance is controlled in a sequence-dependent way by its rate of transcription and degradation, polysomal engagement of mRNA is determined by translation factors interacting with sequence and structural motifs present in the mRNA itself . These controls do not depend on mRNA abundance if not following titration of the trans-acting factors involved, as recently shown for miRNAs . The view proposed by this model could speculatively match that of the proposed stochastic, “burst-like” nature of transcription , characterized by variable kinetics and refractory periods , producing therefore a noisy transcriptome which could be later shaped into a more stable proteome by translational control.
Following this parsimonious model the observed degree of coupling between fluctuations of the mRNA levels in the cells and fluctuations in productive ribosome engagement should be an effect of specific mechanisms of molecular pairing between mRNA steady state determinants (i.e., controls of chromatin remodeling, transcription and mRNA decay) and regulation of translation of the same mRNA. The prediction emerging from this study is that a variety of coupling mechanisms, some of which already described [58, 59], should be active in mammalian cells to orchestrate cell and tissue primary programs.
Our study estimated the genome-wide correlation between changes in mRNA abundance and mRNA polysomal loading in an unprecedentedly large collection of mammalian cells and tissues subjected to heterogeneous stimuli. From our results we conclude that the control of gene expression at the polysomal level is pervasive with no exceptions, and genes whose expression changes homodirectionally at the transcriptome and the translatome level represent a minority of those perturbed by the stimuli. From a statistic point of view the variations in the degree of mRNA polysomal loading are, on the whole, independent from variations in mRNA abundance. This independency is further extended to the cell activities inferred from the ontological analysis of transcriptome and translatome differentially expressed genes, with a clear tendency of translation-related genes to be controlled purely at the translational level without modifications in the levels of their transcripts.
EGF treatment of HeLa cells
HeLa CCL-2 cells were cultured in DMEM supplemented with 10% FBS, 2mM glutamine, 100 units/ml penicillin, and 100 mg/ml streptomycin at 37 °C, 5% CO2. Cells were seeded on adherent plates and serum starved for 12h with DMEM, 0.5% FBS, 2mM glutamine. Cells were treated for 40 minutes with recombinant human Epidermal Growth Factor (EGF from RD Systems, Minneapolis) at the final concentration of 1 μg ml-1. Cell lysates were collected before (t=0 min) and after (t=40 min) EGF treatment. For the total and polysomal RNA extraction, 3 × 105 cells/well (6 well-plates) and 1.5 × 106 cells/dish (10mm dishes) were seeded, respectively, in order to have the same concentration of cells and the same surface density on the dishes). All experiments were run in biological triplicates.
Total RNA extraction
Total RNA was extracted using the TRIZOL reagent according to the manufacturer's protocol. RNA was quantified using a spectrophotometer and its quality was checked by agarose gel electrophoresis and by the Agilent 2100 Bioanalyzer platform, following the manifacturer’s guidelines for sample preparation and analysis of data (Agilent 2100 Bioanalyzer 2100 Expert User's Guide).
Polysomal RNA extraction
Cells were washed once with phosphate buffer saline (PBS + cycloheximide 10 μg ml-1) and treated directly on the plate with 300 μl lysis buffer [10 mM NaCl, 10 mM MgCl2, 10 mM Tris–HCl, pH 7.5, 1% Triton X-100, 1% sodium deoxycholate, 0.2 U μl-1 RNase inhibitor (Fermentas), cycloheximide 10 μg ml-1 and 1 mM dithiothreitol] and transferred to an Eppendorf tube. After a few minute incubation on ice with occasional vortexing, the extracts were centrifuged for 5 min at 12,000 g at 4 °C. The supernatant was stored at −80 °C or loaded directly onto a 15–50% linear sucrose gradient containing 30 mM Tris–HCl, pH 7.5, 100 mM NaCl, 10 mM MgCl2, and centrifuged in an Sorvall rotor for 100 min at 180,000 g. Fractions (polysomal and subpolysomal) were collected monitoring the absorbance at 254 nm and treated directly with proteinase K. After phenol–chloroform extraction and isopropanol precipitation, polysomal RNA was resuspended in 30 μl of water. RNA quality was assessed by agarose gel electrophoresis and by the Agilent 2100 Bioanalyzer platform.
Quantitative real-time RT-PCR
Reverse Transcription of RNA to produce cDNA was done on total and polysomal extracts with the Superscript® VILOTM cDNA Synthesis Kit (Invitrogen). TaqMan quantitative real-time PCR was performed in a 10-μL reaction with a KAPA PROBE FAST universal qPCR (Kapa Biosystems). Four genes were used as endogenous controls: ACTB, GADPH, HPRT1, TBP. The geometric mean of the four controls was used to calculate the ΔCT for twelve other genes: MFAP4, TSC22D2, GPM6A, PSAPL1, AG2, EGR1,PCIF1, EGR2, ZNF655, RPL27, SLC2A3, RPL10A . To compare gene expression before and after EGF, the ΔΔCT method was used. All reactions were performed in 3–9 technical replicates for each RNA purified from all the three biological replicates. TaqMan primers and probes used in analyses (purchased from Applied Biosystems) are listed in Additional file 1: Table S1.
Microarray hybridization and scanning, data acquisition and analysis
Total, polysomal and subpolysomal RNA were hybridized on the Agilent-014850 Whole Human Genome Microarray 4x44K G4112F following the manifacturer’s protocol. Hybridized microarray slides were scanned with an Agilent DNA Microarray Scanner G2505C. μm resolution with the manufacturer’s software (Agilent ScanControl 8.1.3). The scanned TIFF images were analyzed numerically and background corrected using the Agilent Feature Extraction Software version 10.7.7.1 according to the Agilent standard protocol GE1_107_Sep09. The output of Feature Extraction was analyzed with the R software environment for statistical computing (http://htpp://www.r-project.org/) and the Bioconductor library of biostatistical packages (http://www.bioconductor.org/). Low signal Agilent features (11,003), distinguished by a repeated “absent” detection call across the majority of the arrays in every condition, were filtered out from the analysis, leaving 30,075 features corresponding to 15,258 HGNC genes. Signal intensities across arrays were normalized with the quantile normalization algorithm . Signals intensities from probes associated with the same gene were averaged. DEGs were identified with the Rank Product method implemented in the Bioconductor RankProd package (pfp < 0.2 as threshold). All microarray data are available through the Gene Expression Omnibus database (http://www.ncbi.nlm.nih.gov/geo/) using the accession number GSE20277.
Cells were lysed in Ripa lysis buffer (Tris 50 mM a pH 7.4, NaCl 150 mM, Igepal CA-630 1%, EDTA 1 mM, Na deoxycholate 0.5%) containing protease and phosphatase inhibitors (Sigma-Aldrich). Total cell extracts were diluted in 2X SDS protein gel loading solution, boiled for 5 min, separated on 12% SDS–polyacrylamide gel electrophoresis (SDS–PAGE) and processed following standard procedures. The goat polyclonal antibody anti-phospo-eIF4E (Santa Cruz Biotechnology, Santa Cruz, CA) was diluted at 1:500, the rabbit anti-phospho-Akt (Cell Signaling Technology, Danrers, MA) at 1:1000, the goat anti-beta-actin (Santa Cruz Biotechnology, Santa Cruz, CA) at 1:1000 and the rabbit anti-Myc (Cell Signaling Technology, Danrers, MA) at 1:1000. The nitrocellulose membrane signals were detected by chemiluminescence. Experiments were performed at least three times for each cell preparation.
Ontological analysis of DEGs
The DAVID resource  was used for gene-annotation enrichment analysis of the transcriptome and the translatome DEG lists with categories from the following resources: PIR (http://pir.georgetown.edu/), Gene Ontology (http://www.thegeneontology.org), KEGG (http://www.genome.jp/kegg/) and Biocarta (http://www.biocarta.com/default.aspx) pathway databases, PFAM (http://pfam.sanger.ac.uk/) and COG (http://www.ncbi.nlm.nih.gov/COG/) databases. The significance of overrepresentation was determined at a false discovery rate of 5% with Benjamini multiple testing correction. Matched annotations were used to estimate the uncoupling of functional information as the proportion of annotations overrepresented in the translatome but not in the transcriptome readings and vice versa.
Data collection, pre-processing and identification of differentially expressed genes (DEGs)
High-throughput data on global changes at the transcriptome and translatome levels were gathered from public data repositories: Gene Expression Omnibus (http://www.ncbi.nlm.nih.gov/geo/), ArrayExpress (http://www.ebi.ac.uk/microarray-as/ae/), Stanford Microarray Database (http://smd.stanford.edu/). Minimum requirements we established for datasets to be included in our analysis were: full access to raw data, hybridization replicas for every experimental condition, two-group comparison (treated group vs. control group) for both transcriptome and translatome. Selected datasets are detailed in Table 1 and Additional file 4. Raw data were treated following the same procedure described in the previous section to determine DEGs in either the transcriptome or the translatome. Additionally, t-test and SAM were used as alternative DEGs selection methods applying a Benjamini Hochberg multiple test correction to the resulting p-values.
Pathway and network analysis with IPA
The IPA software (Ingenuity Systems, http://www.ingenuity.com) was used to assess the involvement of transcriptome and translatome differentially expressed genes in known pathways and networks. IPA uses the Fisher exact test to determine the enrichment of DEGs in canonical pathways. Pathways with a Bonferroni-Hochberg corrected p-value < 0.05 were considered significantly over-represented. IPA also generates gene networks by using experimentally validated direct interactions stored in the Ingenuity Knowledge Base. The networks generated by IPA have a maximum size of 35 genes, and they receive a score indicating the likelihood of the DEGs to be found together in the same network due to chance. IPA networks were generated from transcriptome and translatome DEGs of each dataset. A score of 4, used as a threshold for identifying significant gene networks, indicates that there is only a 1/10000 probability that the presence of DEGs in the same network is due to random chance. Each significant network is associated by IPA to three cellular functions, based on the functional annotation of the genes in the network. For each cellular function, the number of associated transcriptome networks and the number of associated translatome networks across all the datasets was calculated. For each function, a translatome network specificity degree was calculated as the number of associated translatome networks minus the number of associated transcriptome networks, divided by the total number of associated networks. Only cellular functions with more than five associated networks were considered.
where sem.sim. is the semantic similarity between two GO terms. Both transcriptome specificity and translatome specificity range from 0 (no specificity) to 1 (full specificity).
Calculation of the semantic transcriptome Vs translatome specificity degree associated to GOslim terms
For each GO term the transcriptome specificity degree is calculated as the ratio between the number of datasets in which it is transcriptome specifically over-represented and the number of datasets in which it is over-represented, while the translatome specificity degree is calculated as the ratio between the number of datasets in which it is translatome specifically overrepresented and the number of datasets in which it is overrepresented. According to the GO structure, terms are grouped into the parental GOslim categories and the median transcriptome and translatome specificity degrees are calculated. Within each of the three GO domains, categories were sorted from the most transcriptome specific to the most translatome specific by subtracting the transcriptome specificity degree from the translatome specificity degree.
Likelihood ratio test
The Likelihood Ratio test was used to test the null hypothesis that DEG numbers are the same between transcriptome and translatome, against the alternative hypothesis that they can be different.
Random overlap test
For each dataset, n1 and n2 genes were randomly extracted from the population of DEGs (n1 and n2 being the real numbers of observed transcriptome and translatome DEGs for the dataset). The number of common genes was calculated as the random overlap and the extraction process was repeated 1 million times. The overlap test calculates the probability of the observed overlap to be higher than the random overlap.
Mutual information test
Mutual information is used in each dataset to measure the mutual dependence between being a transcriptome DEGs and being a translatome DEG. Each of the two variables is discrete, taking the value of 1 if the gene is differentially expressed, 0 if the gene is not differentially expressed. Minimal mutual information for each dataset is calculated as the case in which the two lists of n1 transcriptome DEGs and n2 translatome DEGs have null overlap. Maximal mutual information is calculated as the case in which the two lists of DEGs are completely overlapping and have size (n1 + n2)/2. Random mutual information is calculated for each dataset from one million of random extractions, similarly as described in the previous section. The mutual information test calculates the probability of the observed mutual information to be higher than the random mutual information.
Toma Tebaldi and Angela Re equal contributors.
differentially expressed genes
multiple hypotheses testing
RNA binding protein
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