The U2AF1-S34F mutation alters the level of translation initiation factors

Using RIP-seq, we previously reported that U2AF1 binds mRNA in the cytoplasm enriched for functional Gene Ontology (GO) categories that include “translation initiation” and “peptide biosynthetic process.” Here, we systematically address the changes in translational efficiency and total protein for those mRNAs using quantitative analysis of polysome profiles and mass spectrometry. To dissect the molecular mechanisms perturbed by the U2AF1-S34F mutation, we employed 3 human bronchial epithelial cell (HBEC) lines [22] that were generated by gene editing: 1) biallelic wt U2AF1 (herein referred to as wt/wt), 2) wt/U2AF1-S34F heterozygous (wt/S34F), and 3) a frameshift of the U2AF1-S34F allele (wt/S34F-) as previously described [3,6].

By analyzing polysome profiling data from wt/wt, wt/S34F, and wt/S34F- cells (previously published in [6]), we sought to determine whether the S34F mutation altered the translational regulation of mRNA in the GO category “translation initiation” (GO:0006413). This category consists of 185 genes, overlaps many of the other categories we observed as U2AF1-bound in RIP-seq, and contains ribosomal subunits, eukaryotic initiation factors, and some ribosome biogenesis factors. Our analysis is based on a weighted estimator of translation efficiency as determined by RNA abundance in the polysome profile. Briefly, this analysis attempts to infer the number of ribosomes on an mRNA by comparing the quantitative polysome absorbance profile, the normalized counts in each fraction from RNA sequencing (RNA-seq), and the theoretical expectation for the sedimentation velocity. We applied this estimator for 20,997 RNAs that were present in each fraction of each sample in our polysome analysis (24 total measurements, S1 Table). Out of 185 mRNA targets in the translation initiation GO category, 175 met these criteria, and we performed principal component analysis and hierarchical clustering on the translational efficiency computed from this estimator (Fig 1A and 1B). We also carried out the identical analysis using a simpler estimator based only on polysome/monosome ratio (S1 Fig). These data indicate that the S34F mutation results in widespread changes in translation efficiency in mRNA in the GO category of translation initiation. However, these changes are almost completely rescued by frameshifting the mutant allele, as indicated by the observation that the S34F mutation accounts for >90% of the variation in the samples (Fig 1B). Translation efficiency both increases and decreases for genes in this family, with 2 distinct subgroups evident in the clustering analysis. Increases in translation efficiency occur for a number of eukaryotic initiation factors (EIF5, EIF4E, EIF3E, EIF3H, EIF3J, EIF3M), DEAD-box (DDX) and DEAH-box (DHX) helicases (DDX18, DDX1, DDX3X, DHX29), and NPM1. Decreases in efficiency occur for some negative regulators (EIF4EBP1), members of the mechanistic target of rapamycin kinase (mTOR) pathway (regulatory associated protein of MTOR complex 1 [RPTOR]; late endosomal/lysosomal adaptor, MAPK and MTOR activators [LAMTOR2 and LAMTOR4]), and a subset of ribosomal subunits. Notably, ribosomal subunits were either unchanged or decreased, and several genes implicated in Diamond-Blackfan Anemia (TSR2 ribosome maturation factor [TSR2], RPS26, RPS17) clustered in the group showing the largest decreases in translation efficiency. We also plotted mean polysome profiles for the top, middle, and bottom quintiles to demonstrate the extent of the changes in translational efficiency (S2 Fig). Thus, a single amino acid change in the splicing factor U2AF1 results in changes in translation efficiency across messages coding for proteins that are also involved in translation initiation.

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Fig 1. The U2AF1-S34F mutation up-regulates a subset of translation and ribosome biogenesis factors and increases nascent polypeptide production.

(A) Heat map and hierarchical clustering of the weighted estimator applied to RNA-seq from polysome fractions. The genes are the 175 translation initiation genes present in all 8 fractions of each sample (wt/wt, wt/S34F, wt/S34F-) cells (24 total measurements). Rows are centered; no scaling is applied to rows. Rows are clustered using Euclidean distance and Ward linkage. Columns are clustered using correlation distance and average linkage. 175 rows, 3 columns. The underlying data are in S1 Table. (B) Principal component analysis of weighted estimator applied to RNA-seq from polysome fractions. No scaling is applied to rows; SVD with imputation is used to calculate principal components. X and Y axis show PC 1 and PC 2, which explain 92.6% and 7.4% of the total variance, respectively. N = 3 data points. The underlying data are in S1 Table. (C) Comparison of FC for polysome measurement to mass spectrometry measurements for genes/proteins in the “translation initiation” GO category (152 genes, S2 Table). FC is wt/S34F over wt/wt. Regression line is reduced major axis regression with slope = 0.32 ± 0.17, intercept = 0.59 ± 0.04. The underlying data are in S2 Table. (D) Comparison of FC for polysome measurement to mass spectrometry measurements for genes/proteins with similar abundance to proteins in Fig 1C. FC is wt/S34F over wt/wt. Regression line is reduced major axis regression with slope = 0.04 ± 0.04, intercept = 0.98 ± 0.10. The underlying data are in S2 Table. (E) GSEA based on MS data. Analysis was carried out on preranked lists using GSEA 4.0.3. Translation initiation is negatively enriched in wt/S34F cells compared with wt/wt cells. Normalized enrichment score = −2.20. FDR q-value = 0.008. FWER p-value = 0.027. Full analysis is shown in S3 Fig. The underlying data are in S2 Table. (F) Puromycin treatment abrogates the nascent polypeptide fluorescence signal. Scale bar = 10 μm. (G) The U2AF1-S34F mutation up-regulates nascent polypeptide production. Scale bar = 10 μm. (H) Total cellular fluorescence intensity was quantified. Each bar represents the average and standard error of 4 independent experiments. The underlying data are in S1 Data. (I) Cumulative fluorescence intensity of the AHA signal. The underlying data are in S1 Data. AHA, azido-homoalanine; ATF4, activating transcription factor 4; CTIF, cap = binding-complex–dependent translation initiation factor; DDX, DEAD-box helicase; DENR, density = regulated reinitiation and release factor; DHX, DEAH-box helicase; EIF, eukaryotic initiation factor; FC, fold change; FDR, false discovery rate; FWER, family-wise error rate; GLE1, GLFG lethal 1; GO, Gene Ontology; GSEA, gene set enrichment analysis; LAMTOR, late endosomal/lysosomal adaptor, MAPK and MTOR activator; LARP1, La ribonucleoprotein 1, translational regulator; MCTS1, Malignant T-cell–amplified sequence 1 reinitiation and release factor; METTL3, methyltransferase-like 3; MIF4GD, MIF4G domain containing; MS, mass spectrometry; MTOR, mechanistic target of rapamycin kinase; NCBP, nuclear cap binding protein; NPM1, Nucleophosmin 1; PABPC1, poly(A) binding protein cytoplasmic 1; PAIP2, poly(A) binding protein interacting protein 2; PC, principal component; PPP1CA, protein phosphatase 1 catalytic subunit alpha; RCL1, RNA terminal phosphate cyclase like-1; RIOK1, RIO kinase 1; RNA-seq, RNA sequencing; RPL, ribosomal protein large subunit; RPLP2, ribosomal protein lateral stalk subunit P2; RPS, ribosomal protein small subunit; RPSA, ribosomal protein SA; RPTOR, regulatory associated protein of MTOR complex 1; RRBP1, ribosome binding protein 1; RRP, ribosomal RNA processing 1; RRS, ribosome biogenesis regulator 1 homolog; SBDS, Shwachman-Bodian-Diamond syndrome ribosome maturation factor; SVD, singular value decomposition; S34F, serine-34 to phenylalanine substitution; TSR1/2, TSR 1/2 ribosome maturation factors; UBA52, ubiquitin A-52 residue ribosomal protein fusion product 1; U2AF1, U2 Small Nuclear RNA Auxiliar Factor 1; wt, wild-type; YTHDF, YTH N6-methyladenosine RNA binding protein.

https://doi.org/10.1371/journal.pbio.3000920.g001

To independently interrogate changes in protein levels resulting from the S34F mutation, we used quantitative mass spectrometry (MS). wt/wt and wt/S34F cells were harvested and lysed, and total protein was quantified and digested. The peptides were conjugated to tandem mass tags (TMT), and MS was performed as previously described [23,24]. We detected 4,167 proteins present in 3 replicates of isobaric mass tag-MS in wt/wt and wt/S34F cells (6 total measurements). Comparing the fold change (FC) measured from MS to the FC measured from polysome profiling across all proteins that are above the threshold in both studies (3,953 proteins, S2 Table) revealed little to no correlation between the 2 measures. The difficulty in comparing translation efficiency as determined through sequencing approaches and MS has been reported previously and can be due to technical artifacts and also the relative contributions of protein synthesis and decay in determining total protein abundance [25]. Nevertheless, for the translation initiation GO category (152 genes above threshold in polysome and MS measurements), there was a linear relationship between polysome FC and MS FC (Fig 1C and S2 Table). Using reduced major axis regression, which effectively handles errors in both the x- and y-variable [26], gives a regression coefficient of 0.32 ± 0.17. This slope indicates that changes in translation efficiency are in fact resulting in changes in protein abundance, albeit with a smaller dynamic range. In contrast, a randomly selected group of proteins with similar abundance shows no correlation between polysome FC and MS FC (Fig 1D, regression coefficient = 0.04).

Next, we used our MS data in wt/wt and wt/S34F as a more faithful measure of gene expression changes that arise through the S34F mutation. Because changes in translation efficiency and/or protein abundance would not be reflected in RNA abundance or isoform changes measured through RNA-seq, we performed gene set enrichment analysis (GSEA) on protein abundance [27]. Positive enrichment (proteins that are increased in the wt/S34F cells compared with wt/wt cells) did not result in any gene sets with a false discovery rate (FDR) < 0.2 or family-wise error rate (FWER) < 0.2. However, analyzing the negatively enriched gene sets (proteins that are decreased in the wt/S34F cells compared with the wt/wt cells) uniquely identified genes/proteins involved in translation initiation (FDR = 0.008, FWER = 0.027, normalized enrichment score = −2.20) (Fig 1E and S3 Fig). Thus, MS identifies changes in translation initiation proteins as the dominant molecular phenotype for the U2AF1-S34F mutation.

Finally, we sought to examine the functional consequences of misregulation of the translation initiation machinery at the single-cell level. Our genome-wide assays pointed in multiple directions; translation initiation as a category was negatively enriched in wt/S34F cells compared with wt/wt cells, but this category encompasses both positive and negative regulators of initiation. To measure the integrated output of these changes, we compared mRNA translation rates between the wt/wt and the wt/S34F cells using fluorescent noncanonical amino acid tagging (FUNCAT) as previously described [28]. Cells were pulsed with the methionine analog azido-homoalanine (AHA) for 1 hour. Subsequently, cells were fixed and permeabilized, and the AHA-containing nascent polypeptides were labeled for microscopy using click chemistry. First, we confirmed that puromycin treatment abolished the AHA fluorescence signal (Fig 1F). Next, we found that the S34F mutation caused an up-regulation in translation (Fig 1G, quantification in Fig 1H). Comparing fluorescence intensity at the single-cell level indicated that the elevated level of mRNA translation in the wt/S34F cells was representative of the whole cell population rather than a few outlier cells (Fig 1I). Taken together, we conclude that the misregulation of the translation initiation machinery in wt/S34F cells results in a net increase in mRNA translation.

Because MS reports primarily on relative abundance changes and AHA labeling measures amino acid incorporation rates, we sought an intermediate method that would report on the nature of the proteins being up-regulated in wt/S34F cells. We probed for nascent polypeptides using puromycin labeling as described in the previously published SUnSet protocol [29]. This approach involves treating cells with puromycin for 1 hour. Puromycin is incorporated into nascent polypeptides and the nascent polypeptides are detected by western blot using an anti-puromycin antibody. Puromycin labeling showed higher levels of nascent polypeptides in wt/S34F cells over wt/wt cells (S4 Fig), confirming our single-cell AHA labeling. Surprisingly, the wt/S34F cellular proteome had a different mobility in the gel in comparison to wt/wt cells, suggesting widespread proteomic alterations due to the S34F mutation. Thus, increased translation in wt/S34F is not simply an amplification of the translation program in wt/wt cells.

In summary, a single missense mutation in U2AF1 results in widespread changes to translation as measured both by polysome sequencing and mass spectrometry. Proteins involved in translation initiation—including eukaryotic initiation factors, ribosome subunits, positive and negative translational regulators, and ribosome biogenesis factors—are significantly affected. Despite a decrease in protein abundance for the GO category of translation initiation as a whole in wt/S34F cells compared with wt/wt cells, protein synthesis is elevated in these cells, recapitulating the phenotype seen in human MDS stem cells [10].

wt/S34F cells require NPM1 for survival in a p53-independent manner

Because NPM1 was identified first as a target of U2AF1 binding in the cytosol, is included in the translation initiation gene set, shows concordant increase in translational efficiency by polysome sequencing and protein abundance by MS (S2 Table), and is frequently altered in myeloid malignancy, we chose to focus on this mRNA/protein for in depth characterization. We first confirmed the up-regulation of NPM1 translation in wt/S34F cells using a dual-luciferase reporter. The first exon of NPM1 was fused upstream to Renilla luciferase. To normalize for differences in cell number and copy number, the reporter also included a Firefly luciferase downstream of an Internal Ribosome Entry Site (IRES) sequence derived from the Cricket Paralysis Virus (CrPV) [30,31] (Fig 2A). The wt/S34F cells had an 5-fold higher Renilla to Firefly luciferase activity than wt/wt or wt/S34F- cells (Fig 2B), further confirming the up-regulation of NPM1 in cells harboring the U2AF1-S34F mutation and additionally indicating that the 5ʹ end of the mRNA is sufficient for increased translation. All cell lines had comparable luciferase reporter mRNA levels using reverse transcription–quantitative PCR (RT-qPCR) (S5 Fig).

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Fig 2. NPM1 and IPO7 are required for the viability of the wt/S34F cells.

(A) A schematic diagram of the NPM1-luciferase reporter. The reporter consists of NPM1 exon 1, Renilla luciferase, CrPV IRES, and Firefly luciferase. (B) Normalized Renilla to Firefly luciferase activity demonstrates elevated translation levels of the NPM1-luciferase reporter. *p < 0.05 using a paired 2-sided t test. (C) Cells treated with siRNA directed against NPM1 were lysed, and proteins were separated on SDS-PAGE and analyzed by immunoblot using antibodies against NPM1 and α-tubulin. (D) Cells treated with the indicated siRNAs were imaged 144 hours post-siRNA transfection. Scale bar = 50 μm. (E) Quantification of cell viability using the WST-1 reagent. Each bar represents the average and standard error of at least 3 independent experiments. *p < 0.05 using a paired 2-sided t test. (F) Cells that express either NPM1^r-2A-GFP or empty vector were treated with siRNA directed against NPM1 and were lysed, and proteins were separated on SDS-PAGE and analyzed by immunoblot using antibodies against NPM1 and β-actin. (G) Quantification of cell viability using the WST-1 reagent. Each bar represents the average and standard error of at least 3 independent experiments. *p < 0.05 using a paired 2-sided t test. (H) Cells treated with siRNA directed against IPO7 were lysed, and proteins were separated on SDS-PAGE and analyzed by immunoblot using antibodies against IPO7 and β-actin. (I) Quantification of cell viability using the WST-1 reagent. Each bar represents the average and standard error of 3 independent experiments. *p < 0.05 using a paired 2-sided t test. (J) Cells treated with siRNA directed against RBM10 were lysed, and proteins were separated on SDS-PAGE and analyzed by immunoblot using antibodies against RBM10 and β-actin. The underlying data are in S1 Data. E1, exon 1; GFP, green fluorescent protein; IPO7, Importin 7; IRES, Internal Ribosome Entry Site; NPM1: Nucleophosmin 1; RBM10, RNA binding motif protein 10; siRNA, small interfering RNA; S34F, serine-34 to phenylalanine substitution; WST-1, water-soluble tetrazolium-1; wt, wild-type.

https://doi.org/10.1371/journal.pbio.3000920.g002

We sought to examine whether increased NPM1 translation was a downstream mediator of the pro-tumorigenic phenotype of the U2AF1-S34F mutation. NPM1 was depleted by RNA interference (RNAi) in wt/wt, wt/S34F, and wt/S34F- cells (Fig 2C). We noticed a marked decrease in the viability of the wt/S34F cells upon NPM1 depletion (Fig 2D). No defects were observed for the wt/wt or wt/S34F- cells (Fig 2D and 2E). To quantitatively assay cell viability, we employed the water-soluble tetrazolium-1 (WST-1) assay, in which tetrazolium salt is added to the cells. Mitochondrial dehydrogenases cleave the tetrazolium salts to formazan, and the level of formazan is measured colorimetrically. A higher number of cells is accompanied by a higher dehydrogenase activity and more formazan salt is formed. We confirmed the decreased viability of wt/S34F cells depleted of NPM1 using the WST-1 assay (Fig 2E). We generated cell lines that express an RNAi-resistant NPM1 construct (NPM1^r-2A-green fluorescent protein [GFP]) using lentiviral-mediated integration. A slower mobility band was detected for NPM1^r-2A-GFP, which was unaffected by NPM1 siRNA treatment (Fig 2F). The viability of the wt/S34F cells that express NPM1^r-2A-GFP was partially restored (Fig 2G). The partial rescue is likely due to the low expression levels of NPM1^r-2A-GFP in comparison to endogenous NPM1 levels (Fig 2F).

We sought to corroborate these findings by depletion of a second ribosome biogenesis factor, Importin 7 (IPO7). IPO7 mRNA is also a direct U2AF1 target in the cytoplasm [6] and is responsible for the import of the large ribosomal subunit proteins RPL5 and RPL11 to the nucleus [32]. As for NPM1, IPO7 silencing by RNAi was also not reported to cause cell death [32]. Yet, IPO7 depletion also specifically impaired the survival of wt/S34F, but not wt/wt, cells (Fig 2D, 2H and 2I). Neither a scrambled control siRNA nor siRNA to RBM10, which is a splicing factor in the nucleus, had any effect on cell viability in any cell line (Fig 2D, 2E and 2J). Taken together, NPM1 and IPO7, both targets of U2AF1 binding and translational regulation (S1 Table) and both involved in transport and/or ribosome biogenesis, are essential for viability in the wt/S34F cells, but not in the wt/wt cells.

It is known that disruption of ribosome biogenesis and/or alteration of the stoichiometry of ribosome subunits can trigger a p53 response in cells [33,34]. Specifically, NPM1 down-regulates the activity of p53 by sequestering the Alternate Reading Frame (ARF, also known as human p14) in the nucleolus and preventing the ARF–mouse 3T3 cell double minute 2 proto-oncogene (MDM2) interaction, thus enabling MDM2-mediated degradation of p53 [19,35,36]. We therefore investigated whether the viability defect of the wt/S34F cells depleted of NPM1 was dependent on p53 activity. First, we compared p53 levels between wt/wt and wt/S34F cells depleted of NPM1. NPM1 depletion caused the up-regulation of p53 in both the wt/wt and wt/S34F cells to a comparable extent (Fig 3A). Moreover, the synergistic depletion of NPM1 and p53 did not rescue the viability defect of the wt/S34F cells caused by the absence of NPM1 (Fig 3B and 3C). Next, we sought to assess whether increasing p53 levels in an NPM1-independent mechanism would specifically impair the viability of the wt/S34F cells. p53 levels were induced by treating cells with 10 μM Nutlin3A [37] (Fig 3D). p53 activation by increasing Nutlin3A concentration impaired the viability of wt/wt, wt/S34F, and wt/S34F- cells to a similar extent (Fig 3E, quantification in Fig 3F). Thus, we conclude that the depletion of NPM1 impairs the viability of the wt/S34F cells through a p53-independent pathway.

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Fig 3. The requirement of wt/S34F cells for NPM1 is independent of p53.

(A) Cells treated with siRNA directed against NPM1 were lysed and proteins separated on SDS-PAGE and analyzed by immunoblot using antibodies against NPM1, p53, and α-tubulin. (B) Cells treated with the indicated siRNAs were imaged 144 hours post-siRNA transfection. Scale bar = 50 μm. (C) Quantification of cell viability using the WST-1 reagent. Each bar represents the average and standard error of 3 independent experiments. *p < 0.05 using a paired 2-sided t test. (D) Cells treated with 10 μM Nutlin3A for 36 hours were lysed, and proteins were separated on SDS-PAGE and analyzed by immunoblot using antibodies against NPM1, p53, and β-actin. (E) Nutlin3A treatment impairs the viability of wt/wt, wt/S34F, and wt/S34F- cells. Scale bar = 50 μm. (F) Cell viability was assessed 36 hours after treatment with the indicated doses of Nutlin3A using the WST-1 reagent. The underlying data are in S1 Data. DMSO, dimethyl sulfoxide; NPM1, Nucleophosmin 1; p53: protein 53; siRNA, small interfering RNA; S34F, serine-34 to phenylalanine substitution; WST-1, water-soluble tetrazolium-1; wt, wild-type.

https://doi.org/10.1371/journal.pbio.3000920.g003

wt/S34F cells depleted of NPM1 exhibit cycle progression defects and changes in ribosome biogenesis factors

The viability defect observed in wt/S34F cells lacking NPM1 can be explained by either an activation of apoptotic pathways or a defect in cell cycle progression. To distinguish between these possibilities, we first examined cellular viability over the time course of the knock-down experiment. First, wt/wt cells transfected with control siRNA had comparable growth with wt/wt cells depleted of either NPM1 or IPO7. In contrast, wt/S34F cells depleted of NPM1 or IPO7 had a drastically lower viability than wt/S34F cells treated with control siRNA (Fig 4A), consistent with our earlier observations (Fig 2D). Importantly, we observed a monotonic increase in the metabolic activity of the wt/S34F cells depleted of NPM1 or IPO7 over the time period of the measurement using the WST-1 assay. These findings are more consistent with slowed growth rates and not an induction of apoptotic pathways. We further confirmed the lack of apoptosis by comparing cleaved Caspase-3 levels. We chose Caspase-3 because it is one of the late apoptotic factors and thus is activated by both the extrinsic and intrinsic apoptotic pathways. We did not observe a difference in cleaved Caspase-3 levels between wt/wt and wt/S34F cells treated with either control or NPM1 siRNA, suggesting that apoptosis is not the cause of decreased viability in the wt/S34F cells lacking NPM1 activity (Fig 4B and S6A Fig). In contrast, actinomycin-D treatment (5 μg/mL) caused a robust up-regulation in cleaved Caspase-3 levels.

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Fig 4. NPM1 or IPO7 silencing slows the proliferation of wt/S34F cells.

(A) The viability of cells treated with siRNAs directed against NPM1 or IPO7 was assessed at the indicated time points post-siRNA transfection. Error bars represent the standard error of 3 independent experiments. (B) Quantification of the active Caspase-3 levels. Each bar represents the average and standard error of 3 independent experiments. (C) Cells that express either NPM1^r-2A-GFP or empty vector were treated with siRNA directed against NPM1. 144 hours post-siRNA transfection, cells were pulsed with EdU for 10 minutes, and EdU incorporation was quantified. The fraction of cells in each phase of the cell cycle was calculated. Each bar represents the average and standard error of 4 independent experiments. *p < 0.05 using a paired 2-sided t test. (D) Cells were treated with siRNA directed against IPO7. 144 hours post-siRNA transfection, cells were pulsed with EdU for 10 minutes, and EdU incorporation was quantified. The fraction of cells in each phase of the cell cycle was calculated. Each bar represents the average and standard error of 4 independent experiments. *p < 0.05 using a paired 2-sided t test. (E) Cells treated with siRNA directed against NPM1 were lysed, and proteins were separated on SDS-PAGE and analyzed by immunoblot using antibodies against CCNA2, NPM1, and β-actin. (F) Cells treated with siRNA directed against IPO7 were lysed, and proteins were separated on SDS-PAGE and analyzed by immunoblot using antibodies against CCNA2, IPO7, and β-actin. The underlying data are in S1 Data. CCNA2, Cyclin A2; EdU, 5-ethynyl-2ʹ-deoxyuridine; GFP, green fluorescent protein; IPO7, Importin 7; NPM1, Nucleophosmin 1; siRNA, small interfering RNA; S34F, serine-34 to phenylalanine substitution; wt, wild-type.

https://doi.org/10.1371/journal.pbio.3000920.g004

Next, we examined whether wt/S34F cells depleted of NPM1 had cell cycle progression defects. Cells were treated with 5-ethynyl-2ʹ-deoxyuridine (EdU) for 10 minutes to label the S-phase population [38]. The G1 and G2 cell populations were identified by quantifying the DNA fluorescence intensity using Hoechst (Fig 4C and S6B Fig). The depletion of NPM1 in wt/wt cells did not have a measurable effect on cell cycle progression. In contrast, the absence of NPM1 from the wt/S34F cells led to an increase in the fraction of S-phase cells and a concomitant decrease in the G1 population (Fig 4C). These results indicate that wt/S34F cells depleted of NPM1 take a longer time to complete the S phase. Expression of NPM1^r-2A-GFP in wt/S34F when NPM1 is silenced restored the S-phase population to control siRNA levels (Fig 4C). We also observed elevated levels of the S-phase population in wt/S34F cells when IPO7 was silenced (Fig 4D and S6C Fig). We further confirmed the accumulation of cells in the S phase by examining the levels of Cyclin A2 (CCNA2), which is up-regulated in the S, G2, and early M phases. Indeed, CCNA2 levels were specifically up-regulated in wt/S34F cells depleted of NPM1 (Fig 4E) or IPO7 (Fig 4F). Silencing NPM1 or IPO7 expression in wt/wt cells did not have a detectable effect on CCNA2 levels. In summary, the absence of NPM1 or IPO7 caused the accumulation of wt/S34F cells in the S phase, leading to slower growth and a proliferative disadvantage.

In order to dissect the molecular pathways that are perturbed in wt/S34F cells depleted of NPM1, we repeated our quantitative MS assays in wt/wt and wt/S34F cells after NPM1 knock-down. Of a total of 4,380 proteins detected in 3 replicates across 2 samples (6 total measurements), NPM1 depletion in wt/wt cells had a minimal effect on the cellular proteome. Only 7 and 13 proteins were up- and down-regulated, respectively, above an FC of 1.5. Importantly, these factors were not significantly enriched in any GO categories. In contrast, NPM1 silencing in wt/S34F caused 26 and 152 proteins to be up- and down-regulated, respectively, with an FC greater than 1.5 (S3 Table). A GO term analysis at an FDR p-value < 0.05 indicated that the down-regulated factors were statistically enriched for 2 major GO categories: “DNA replication coupled to cell cycle” and “ribosome biogenesis” (Fig 5A). The down-regulation of factors that couple DNA replication to cell cycle progression corroborates the accumulation of wt/S34F cells depleted of NPM1 in the S phase (Fig 4C and 4D). Furthermore, the negative enrichment for ribosome biogenesis factors suggested a direct mechanistic link to NPM1 function and the translational misregulation that arises from the S34F mutation, spurring us to investigate this observation in greater detail.

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Fig 5. Silencing NPM1 impairs rRNA precursor levels in wt/S34F cells.

(A) Statistically enriched GO terms using an FDR p-value < 0.05 threshold for down-regulated proteins in wt/S34F cells depleted of NPM1 in comparison to wt/wt cells. (B) The levels of the ITS2-containing rRNA precursors are decreased in wt/S34F cells depleted of NPM1. C) Overexpression of the RNAi-resistant NPM1^r-2A-GFP restores the levels of the ITS2-containing rRNA precursors. (D) NPM1 silencing in wt/S34F decreases the level of the 47S rRNA and is rescued upon expression of the RNAi-resistant NPM1^r-2A-GFP construct. The underlying data are in S1 Data. FDR, false discovery rate; GAPDH, glyceraldehyde-3-phosphate dehydrogenase; GFP, green fluorescent protein; GO, Gene Ontology; ITS2, internal transcribed spacer 2; NPM1, Nucleophosmin 1; RNAi, RNA interference; rRNA, ribosomal RNA; siRNA, small interfering RNA; S34F, serine-34 to phenylalanine substitution; wt, wild-type.

https://doi.org/10.1371/journal.pbio.3000920.g005

Processing of the 28S rRNA precursors is impaired in wt/S34F cells depleted of NPM1

Ribosome biogenesis is initiated when RNA polymerase I transcribes ribosomal DNA (rDNA) into the 47S preribosomal RNA (rRNA) transcript that encompasses the 18S, 5.8S, and 28S flanked by 2 external (5ʹ external transcribed spacer [5ʹ ETS] and 3ʹ ETS) and interspersed by 2 internal spacers (internal transcribed spacer 1 [ITS1] and ITS2). The 18S rRNA and small subunit ribosomal proteins constitute the pre-40S ribosomal subunit. The 28S, 5.8S, and 5S (produced by RNA polymerase III), along with the large subunit ribosomal proteins, constitute the pre-60S subunit. NPM1 has been previously shown to bind the 28S rRNA [39] and regulate the processing of the 28S rRNA by promoting the localization of the ITS2-cleaving endoribonuclease Las1 like ribosome biogenesis factor (LAS1L) to the nucleolus [40]. Therefore, we reasoned that silencing NPM1 might cause a specific impairment in the processing of the 28S rRNA precursors.

We examined the levels of the 28S rRNA precursors by employing northern blot using a probe complementary in sequence to the ITS2 [41]. Silencing NPM1 in wt/wt cells did not cause a change in the levels of the ITS2-containing precursors, in agreement with previous studies in which NPM1 siRNA caused very modest changes in the 47S and 32S rRNA [14,16]. Importantly, these modest increases were not consistent for all NPM1-targeting siRNA sequences used in this study, suggesting a minimal effect for NPM1 on rRNA processing in cells that express wt U2AF1 [16]. In contrast, the levels of the ITS2-containing rRNA precursors in wt/S34F cells decreased after NPM1 was depleted (Fig 5B). The levels of glyceraldehyde-3-phosphate dehydrogenase GAPDH mRNA demonstrate equal loading across samples. However, the decrease in the ITS2 precursors did not cause a noticeable change in mature 28S and 18S rRNA levels, likely because of their long half-life ranging from 3 to 7 days [42,43]. Furthermore, the decrease in ITS2 levels in wt/S34F cells in the absence of NPM1 is rescued by expressing the RNAi-resistant NPM1^r-2A-GFP (Fig 5C). We also examined the levels of the primary 47S rRNA transcript using a probe that is complementary to the 5ʹ ETS [44]. Silencing NPM1 in wt/S34F cells caused a marked decrease in the 47S rRNA (Fig 5D). In contrast, 47S rRNA was comparable between wt/wt cells, wt/S34F cells, and wt/wt cells depleted of NPM1. Quantification of band intensities is shown (S7 Fig). These results indicate that the wt/S34F cells are dependent on NPM1 for proper ribosome biogenesis, as shown from the quantitative MS (Fig 5A) and the rRNA precursor levels (Fig 5B, 5C and 5D).

The U2AF1-S34F mutation up-regulates mRNA translation in mouse myeloid progenitors and is mutually exclusive with NPM1 mutations in human patients

Our previous studies were carried out in HBECs. These cells have the benefit of being immortalized non-transformed human cell lines, which might better phenocopy the context in which initiating mutations such as U2AF1-S34F occur. However, U2AF1 mutations (like all splicing factor mutations) occur most frequently in the context of myeloid malignancy, so we examined the functional relevance of these findings with 2 additional approaches: experimental perturbation of immortalized mouse myeloid progenitor cells in tissue culture and analysis of human patient somatic mutation data.

First, we sought to examine whether the U2AF1-S34F mutation up-regulates mRNA translation in myeloid cells. We employed Homeobox B8 (Hoxb8) overexpression to immortalize myeloid progenitor cells as previously described [45]. These myeloid progenitors were derived from bone marrow of transgenic mice that express the human cDNA of either U2AF1-wt or U2AF1-S34F from the mouse collagen, type I, alpha 1 (Col1a1) locus [46]. Hoxb8 is expressed as a fusion to the estrogen-binding domain of the estrogen receptor (ER). In the presence of estrogen, Hoxb8 arrests myeloid differentiation and maintains the cells at the granulocyte/macrophage progenitor (GMP) stage. Upon estrogen withdrawal, the cells differentiate into neutrophils and macrophages that express the panmyeloid marker CD11b [45]. Strikingly, we observed that the U2AF1-S34F mutation impairs myeloid differentiation and maintains the mutant cells in a less differentiated state. In the absence of estrogen, i.e., under differentiation conditions, about 66% of wt cells expressed the panmyeloid differentiation marker CD11b. In contrast, only 47% of S34F cells expressed CD11b (Fig 6A and 6B). Thus, S34F GMP cells are not able to differentiate to the same extent as wt cells, indicating that the S34F missense mutation is sufficient to recapitulate aspects of the MDS cytopenia phenotype in vitro.

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Fig 6. The U2AF1-S34F mutation up-regulates mRNA translation in immortalized myeloid cells.

(A) The U2AF1-S34F mutation impairs the ability of myeloid progenitor cells to differentiate. (B) Quantification of A. Each bar represents the average and standard error of at least 4 independent experiments. *p < 0.05 using a paired 2-sided t test. The underlying data are in S1 Data. (C) The U2AF1-S34F mutation up-regulates mRNA translation. (D) Quantification of C. Each bar represents the average and standard error of 4 independent experiments. *p < 0.05 using a paired 2-sided t test. The underlying data are in S1 Data. (E) Absorbance at 254 nm of sucrose density gradient fractions from wt and S34F myeloid cells. The underlying data are in S1 Data. (F) Correlation matrix showing co-occurring and mutually exclusive mutations of the indicated factors. The underlying data are in S4 Table. U2AF1 and NPM1 mutations are mutually exclusive in MDS and AML patients (q-value < 0.001). AML, Acute Myeloid Leukemia; ASXL1, Additional sex combs like transcriptional regulator 1; CD, cluster of differentiation; CHX, cycloheximide; IDH, isocitrate dehydrogenase; IPO7, Importin 7; MDS, Myelodysplastic Syndrome; NPM1, Nucleophosmin 1; OPP, O-propargyl-puromycin; RUNX1, Runt-related transcription factor 1; SF3B1, splicing factor 3 subunit 1; SRSF2, serine and arginine rich splicing factor 2; SSC-A, side scatter-Area; S34F, serine-34 to phenylalanine substitution; TET2, Tet methylcytosine dioxygenase 2; TP53, tumor protein 53; U2AF1, U2 Small Nuclear RNA Auxiliary Factor 1; wt, wild-type; ZRSR2, zinc finger CCCH-type, RNA binding motif and serine/arginine rich 2.

https://doi.org/10.1371/journal.pbio.3000920.g006

Next, we examined nascent polypeptide biosynthesis in wt and S34F myeloid progenitor cells. We treated the myeloid progenitors with the puromycin analog, O-propargyl-puromycin (OPP), which incorporates into nascent polypeptide chains [8]. The OPP-containing polypeptides are then chemically reacted to a fluorophore and detected by flow cytometry as previously described [8]. This assay is functionally similar to the AHA assay used previously but works better for suspension cells using flow cytometry. Similar to HBECs, S34F myeloid progenitors had higher mRNA translation levels in comparison to wt cells (Fig 6C and 6D). However, polysome profiles were unchanged between the cell lines (Fig 6E). Cells pretreated with 100 μg/mL cycloheximide (CHX) for 1 hour before adding OPP had a lower signal, confirming that the OPP signal corresponds to nascent polypeptides. Again, the elevated mRNA translation levels in S34F cells shows that the U2AF1-S34F mutation in progenitor cells in vitro is sufficient to recapitulate the elevated levels of polypeptide production observed in hematopoietic stem cells derived from MDS patients [10].

Second, because the S34F cells require NPM1 for their viability, we hypothesized that the S34F mutation should not co-occur in cancer patients with NPM1 loss of function mutations. Both U2AF1 and NPM1 are frequently mutated in patients with malignancies of the myeloid lineage. According to the cBioPortal database [47,48] reporting of 4 studies of adult MDS and AML patients [4,49–51], U2AF1 mutations occur in 5% and NPM1 is mutated in 22% of all myeloid malignancy patients. Despite the high frequency of these mutations, we found that U2AF1 and NPM1 mutations co-occurred at a probability lower than that expected by chance (Fig 6F), likely because of the deleterious viability defect caused by abolishing NPM1 function in cells harboring U2AF1 mutations. Statistical significance was based on q-values computed by the cBioPortal database. Raw values from the cBioPortal database are shown (S4 Table). This analysis recapitulated other previously observed mutual dependencies. For example, serine and arginine rich splicing factor 2 (SRSF2) and isocitrate dehydrogenase 2 (IDH2) are more likely to co-occur [52]. Notably, NPM1 mutations were mutually exclusive with U2AF1 mutations, but not with other commonly mutated splicing factors such as splicing factor 3 subunit 1 (SF3B1), SRSF2, and zinc finger CCCH-type, RNA binding motif, and serine/arginine rich 2 (ZRSR2). Taken together, these analyses indicate that both the role of U2AF1-S34F in translation regulation and the interaction between U2AF1 and NPM1 are relevant in a disease context.

Plasmids and cells lines

The plasmid expressing the NPM1^r-2A-GFP reporter was generated by fusing the cDNA of NPM1 amplified from HBEC cDNA library upstream of a T2A-GFP synthetic construct (IDT, Coralville, IA, USA) using Superscript III reverse transcriptase according to the manufacturer’s instructions (Thermo Scientific, Waltham, MA, USA). RNAi resistance was conferred by introducing silent mutations in the siRNA-targeting sequence. This construct was subsequently inserted into a lentiviral transfer plasmid that has the ubiquitin C promoter. The NPM1 luciferase reporter was generated by fusing 2 synthetic constructs (IDT) into the ubiquitin C lentiviral transfer plasmid. The first construct was composed of human NPM1 exon 1 sequence fused to Renilla luciferase. The second construct was composed of CrPV IRES fused to firefly luciferase [30]. The lentiviral plasmids were packaged into lentiviral particles using HEK293T cells. Virus titer was measured, and the viruses were transduced into HBECs using 8 μg/mL polybrene. HBEC lines were cultured in keratinocyte serum-free medium supplemented with bovine pituitary extract and epidermal growth factor (Gibco, Gaithersburg, MD, USA). Wt/wt, wt/S34F and wt/S34F- HBEC cells were previously reported [3]. For translation inhibition experiments, cells were incubated with puromycin (100 μg/mL) for 30 minutes prior to AHA treatment. Nutlin3A treatment was for 36 hours at the indicated doses. For Caspase-3 experiments, cells were treated with actinomcycin D (5 μg/mL) for 12 hours. For siRNA experiments, cells were treated with 25 pmol smartpool siRNA (Dharmacon, Lafayette, CO, USA) and Lipofectamine RNAiMax transfection reagent (Thermo Scientific). Knock-down efficiency was assessed after 6 days by western blotting. The following siRNA pools were used: NPM1 (M-015737-01-0005), IPO7 (L-012255-00-0005), TP53 (L-003329-00-0005), and RBM10 (E-009065-00-0005) (Dharmacon). Hoxb8-immortalized myeloid cells were generated as previously described [45]. Briefly, bone marrow mononuclear cells were harvested from murine models [46] and stimulated in RPMI supplemented with 10 ng/mL IL6, IL3, murine stem cell factor (mSCF), 10% fetal bovine serum (FBS), and 1% penicillin/streptomycin for 36 hours. Cells were then transduced in a 12-well plate coated with fibronectin (250,000 cells in 500 μL opti-MEM) with 1 mL of HoxB8-ER retrovirus and 8 μg/mL polybrene by spinfection. After spinfection, 3 mL of RPMI supplemented with 10% FBS, 1% pen/strep, 50 ng/mL mSCF, and 0.5 μM beta-estradiol was added. The cells were cultured in RPMI 1640 supplemented with 10% FBS, 1% mSCF, and 1 μM β-estradiol (Sigma-Aldrich, St. Louis, MO, USA). The cells were grown in 1 μg/mL doxycycline for 24 hours to induce the U2AF1 transgene expression. Cells were induced to differentiate by culturing in the absence of β-estradiol for 6 days before cells were stained for CD11b (BioLegend, San Diego, CA, USA) and analyzed on the LSRII flow cytometer (BD Biosciences, San Jose, CA, USA). Data analysis was performed using FCS Express (De Novo Software).

Polysome profiling

Polysomes were frozen using 100 μg/mL CHX for 10 minutes. Cells were harvested and lysed in 20 mM Tris, 130 mM KCl, 0.5% NP40, 10% deoxycholate, 100 μg/mL CHX, and 10 μL/mL RNasin (Promega, Madison, WI, USA). Lysate concentration was measured and equal OD260 units of wt/wt and wt/S34F were loaded onto 10%–50% sucrose prepared using a BioComp Gradient Master (BioComp, Fredericton, NB, Canada). Gradients were spun at 40,000 RPM (Beckman Coulter, Brea, CA, USA), fractionated and the absorbance at 254 nm measured using a gradient fractionator (BioComp).

Polysome profile analysis

The following steps are used to compute changes in translation efficiency from polysome sequencing profiles [6]:

1. For each sample (wt/wt, wt/s34f, wt/s34f-), 12 fractions were collected from the sucrose gradient, and the bottom/heaviest 10 fractions were sequenced.
2. Fractions 10–12 were pooled to increase coverage, resulting in 8 fractions in the final analysis. Fraction 5 and 6 correspond to the monosome fractions.
3. Reads mapping to particular message are normalized by the total reads in that sample and multiplied by 1 × 10⁶, generating counts per million (CPM) for each transcript in each fraction.
4. The CPM in each fraction constitutes the polysome profile (poly[x], where “x” designates the sucrose fraction), which is now normalized for read depth across fractions/samples.
5. This profile can be further normalized by a weighting function that accounts for the nonlinear relationship between sedimentation coefficient, position in the sucrose gradient, and molecular mass: S is approximately x^3/2, where S is the sedimentation coefficient in Svedrup [62]. Here, we use the following weights for each fraction after pooling: w = [0.6, 1.0, 1.6, 2.2, 2.9, 3.7, 4.5, 19.0].
6. The final numeric value, reflecting translation efficiency, is ∑w_ipoly(x_i). This number, generated for each transcript in each of the 3 cell lines, is the basis for clustering in Fig 1A. Hierarchial clustering was done using ClustVis [63].
7. Alternatively, a simpler measure of translational efficiency is reported (S1 Fig), consisting only of the polysome/monosome ratio, i.e., without a weighted estimator. The monosome fractions are taken as w₂ and w₃, and the polysome fractions are w₅, w₆, w₇, and w₈.

Average polysome profiles were generated from quintiles consisting of 35 genes. Error bars are generated with bootstrapping over 30 random replicates.

GSEA

GSEA analysis was done with GSEA v. 4.0.3 from The Broad Institute using the “GSEA on a pre-ranked gene list” option [27]. Gene set database is c5.bp.v7.0.symbols.gmt run with 1,000 perturbations. The enrichment statistic is “weighted.” Accepted gene sets contained between 15 and 500 members.

Luciferase assay

Luciferase plasmids were introduced by lentiviral transduction. Equal number of cells were plated in a 96-well dish. After 24 hours, cells were treated with the Dual-Luciferase Reporter Assay System as per manufacturer’s instructions (Promega). Luciferase activity was measured by Victor plate reader (Perkin Elmer, Waltham, MA, USA). To quantify luciferase mRNA levels, RNA was isolated by Trizol (Thermo Scientific), treated with DNaseI and reverse transcribed using Superscript III (Thermo Scientific) according to the manufacturer’s instructions using random hexamers. qPCR was done using iTaq SYBR Green master mix (Bio-Rad, Hercules, CA, USA) and run on a CFX96 PCR detection system (Bio-Rad). Luciferase mRNA levels were normalized to tubulin mRNA. Primer sequences for luciferase are TAACGCGGCCTCTTCTTATTT and GATTTGCCTGATTTGCCCATAC. Primer sequences for Tubulin mRNA are CGATATTGAGCGTCCAACCTAT and TTCAGGGCTCCATCAAATCTC.

AHA metabolic labeling

Protein biosynthesis was assessed by AHA pulse labeling as previously described [28] with the following modifications. Cells were plated on coverslips (VWR, Radnor, PA, USA). After 24 hours, cells were pulsed with AHA for 1 hour. Cells were then fixed in 4% paraformaldehyde in PBS, permeabilized in 0.1% TritonX in PBS, and blocked in blocking reagent that contains 2% BSA and 5% donkey serum in PBS for 3 hours. Cells were washed once in PBS. The click chemistry reaction was composed of Tris(2-carboxyethyl)phosphine (TCEP) (Thermo Scientific) and Tris[(1-benzyl-1H-1,2,3-triazol-4-yl)methyl]amine (TBTA) (Sigma-Aldrich). The reaction was allowed to proceed for 1 hour. Coverslips were washed in 0.1% TritonX in PBS 3 times, 15 minutes each and mounted on microscopy slides using DAPI-containing mounting solution.

Quantitative MS

Quantitative MS was done according to the NCI MS core standard protocol. Briefly, cells were lysed, and total protein was quantified using bicinchoninic acid (BCA) reagent (Thermo Scientific). The samples were reduced using DTT (Thermo Scientific), alkylated with iodoacetamide (Thermo Fisher Scientific), and trypsinized (Promega) overnight. TMT tags were conjugated to the peptides and cleaned up using peptide desalting columns (Thermo Scientific). The first dimensional separation of the peptides was performed using a Waters Acquity UPLC system coupled with a fluorescence detector (Waters, Milford, MA, USA). The dried peptide fractions were reconstituted in 0.1% TFA and subjected to nanoflow liquid chromatography (Thermo Easy nLC 1000; Thermo Scientific) coupled to high-resolution tandem MS (Q Exactive, HF; Thermo Scientific). Peptides were separated using a second-dimension low pH gradient using a 2%–40% ACN over 120 minutes in mobile phase, containing 0.1% formic acid at 300 nl/min flow rate. MS scans were performed in the Orbitrap analyzer at a resolution of 120,000 with an ion accumulation target set at 3 × 10⁶ and max IT set at 50 ms over a mass range of 200–1,800 m/z, followed by MS/MS analysis at a resolution of 45,000 with an ion accumulation target set at 1 × 10⁵, max IT set at 120 ms and first fixed mass set at 105 m/z. MS2 precursor isolation width was setup at 0.7 m/z, normalized collision energy was 29, and charge state 1 and unassigned charge states were excluded.

MS data analysis

Acquired MS/MS spectra were searched against a human uniprot protein database along with a contaminant protein database, using an SEQUEST and percolator validator algorithms in the Proteome Discoverer 2.2 software (Thermo Scientific,). The precursor ion tolerance was set at 10 ppm, and the fragment ions tolerance was set at 0.02 Da along with methionine oxidation included as dynamic modification and TMT6 plex (229.163 Da) set as a static modification of lysine and the N-termini of the peptide.

Image acquisition and analysis

Slides were imaged using a custom epifluorescence microscope equipped with a Plan-Apochromatic 40× (NA 1.4) objective (Carl Zeiss, Oberkochen, Germany), illuminated with a LED light source (Model Spectra-6LCR-SA; Lumencor, Beaverton, OR, USA) and the emitted fluorescence collected with a CMOS camera (Hamamatsu ORCA-Flash 4.0; Hamamatsu, Hamamatsu City, Japan). The microscope was controlled using μManager [64]. A custom pipeline was employed to compute total fluorescence intensity per cell using Cellprofiler (Broad Institute) [65].

Western blotting

Cells were lysed in 1× RIPA buffer, protein lysate quantified using the BCA kit (Thermo Scientific), boiled in SDS, and run on SDS-PAGE. Proteins were transferred onto nitrocellulose membrane, blocked in 5% milk, and probed with the following antibodies: NPM1 (1:1,000; Abcam, Cambridge, UK), IPO7 (1:1,000; Abcam), p53 (1:500; Bethyl Laboratories, Montgomery, TX, USA), CCNA2 (1:1,000; BD Biosciences), RBM10 (1:1,000; Sigma-Aldrich), puromycin (1:1,000; Sigma-Aldrich), tubulin (1:1,000; Abcam), and actin (1:10,000; Sigma-Aldrich). For SUnSET, cells were treated with 10 μg/mL puromycin for 1 hour before lysis. The CHX-puromycin sample was treated first with 100 μg/mL CHX for 1 hour. Subsequently, fresh medium containing 10 μg/mL puromycin and 100 μg/mL CHX was added to the cells for another 1 hour before cells were lysed.

Viability assay

To assess changes in cell viability, cells were counted using an automated cell counter (Bio-Rad), and an equal number of cells were plated in a 96-well plate. On the sixth day of the siRNA experiment, fresh medium was added that contained 1:10 dilution of the WST-1 reagent (Sigma-Aldrich). Cells were incubated for different time points before the absorbance was measured at 450 nm using a plate reader (Bio-Rad).

Apoptosis assay

Apoptotic induction was examined by checking for cleaved Caspase-3 levels using a PE-conjugated anti-Caspase-3 antibody per the manufacturer’s instructions (BD Biosciences). PE levels were examined using an LSRII flow cytometer (BD Biosciences). Data analysis was performed using FCS Express (De Novo Software, Pasadena, CA, USA).

Cell cycle analysis

Cell cycle progression was examined using the Click-it EdU Alexa Fluor 647 kit (Thermo Scientific). Briefly, cells were plated on cover slips. After 24 hours, cells were pulsed in EdU for 10 minutes and then fixed in 4% paraformaldehyde in PBS, permeabilized in 0.1% TritonX in PBS. Cells were then washed twice in 2% BSA in PBS. The EdU was reacted with azide-conjugated fluorophore in the presence of copper sulfate. The click chemistry reaction was allowed to proceed for 1 hour. Cells were washed twice in 2% BSA in PBS. DNA was labeled using Hoechst at a dilution of 1:10,000 mounted on Prolong Gold mounting solution (Thermo Scientific). The fraction of cells in each stage of the cell cycle was quantified using a previously published MATLAB (The MathWorks, Natick, MA, USA) analysis pipeline [66] and is available at https://github.com/scappell/Cell_tracking.

GO term analysis

The genes that were down-regulated at an FC of 1.5 in wt/S34F cells depleted of NPM1 in comparison to wt/wt treated with control siRNA were uploaded into the Panther Database (www.geneontology.org) [67]. The Panther overrepresentation test was used with the test type = FISHER and the correction = FDR.

Northern blotting

Equal numbers of cells were harvested for all conditions using the Luna Automated Cell Counter (Logos Biosystems). Total RNA was extracted using Trizol (Thermo Scientific) according to the manufacturer’s instructions. The RNA was separated on a denaturing gel and transferred to nylon membrane (GE Healthcare, Chicago, IL, USA) overnight as previously described [68]. The membrane was hybridized to a ³²P-labeled probe overnight and imaged using a Typhoon phosphoimager (GE Healthcare). To probe for the ITS2, an oligonucleotide was 5ʹ-labeled using PNK enzyme (New England Biolabs, Ipswich, MA, USA). The ITS2 probe was previously reported [41], and its sequence is GAGGGAGGAACCCGGACCGCAGGCGGCGGCCACGGGAACTCGGCCCGAGCCGGCTCTCTC. The 5ʹ ETS probe sequence was previously reported [44], and its sequence is GGCGAGCGACCGGCAAGGCGGAGGTCGACCCACGCCACACGTCGCACGAACGCCTGTC. The pET30-2-GAPDH plasmid,a gift from David Sabatini (Addgene plasmid # 83910; Addgene, Watertown, MA, USA), was used to generate the GAPDH probe[69], and gene-body labeling was performed using the Prime-A-Gene kit (Promega). rRNA precursor band intensity was measured using FIJI [70] and normalized to GAPDH mRNA band intensity.

OPP labeling

Immortalized myeloid cells were treated with 10 μM OPP (Click Chemistry Tools, Scottsdale, AZ, USA) for 1 hour. Cells were fixed and permeabilized. Click chemistry was employed to conjugate a fluorophore to the OPP-containing polypeptides according to the manufacturer’s instructions (Thermo Scientific). Cells were washed 5 times in PBS and analyzed on the LSRII flow cytometer (BD Biosciences). Data analysis was performed using FCS Express (De Novo Software). For CHX-OPP samples, cells were treated first with 100 μg/mL CHX for 1 hour. Subsequently, fresh medium containing 10 μM OPP and 100 μg/mL CHX for another 1 hour before cells were fixed.

Mutual exclusivity of U2AF1 and NPM1 mutations

Cancer patient data were retrieved from cBioPortal [47,48] (www.cbioportal.org) as of October 4th, 2019. All adult AML and MDS patient studies were selected. These include “Acute myeloid leukemia or myelodysplastic syndromes (Wash U, 2016)” [49], “Acute myeloid leukemia (OHSU, 2018)” [50], “Acute myeloid leukemia (TCGA, PanCancer Atlas)” [51], and “Myelodysplasia (UTokyo, Nature 2011)” [4]. Statistical significance was based on q-values computed by the cBioPortal website. Raw values from the cBioPortal website are shown in S4 Table.