Mutations in splicing factors (SF) are the predominant class of mutations in myelodysplastic syndrome (MDS), but convergent downstream disease drivers remain elusive. To identify common direct targets of missplicing by mutant U2AF1 and SRSF2, we performed RNA sequencing and enhanced version of the cross-linking and immunoprecipitation assay in human hematopoietic stem/progenitor cells derived from isogenic induced pluripotent stem cell (iPSC) models. Integrative analyses of alternative splicing and differential binding converged on a long isoform of GNAS (GNAS-L), promoted by both mutant factors. MDS population genetics, functional and biochemical analyses support that GNAS-L is a driver of MDS and encodes a hyperactive long form of the stimulatory G protein alpha subunit, Gαs-L, that activates ERK/MAPK signaling. SF-mutant MDS cells have activated ERK signaling and consequently are sensitive to MEK inhibitors. Our findings highlight an unexpected and unifying mechanism by which SRSF2 and U2AF1 mutations drive oncogenesis with potential therapeutic implications for MDS and other SF-mutant neoplasms.
SF mutations are disease-defining in MDS, but their critical effectors remain unknown. We discover the first direct target of convergent missplicing by mutant U2AF1 and SRSF2, a long GNAS isoform, which activates G protein and ERK/MAPK signaling, thereby driving MDS and rendering mutant cells sensitive to MEK inhibition.
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Splicing factor (SF) gene mutations are the most common class of mutations in myelodysplastic syndrome (MDS), present in nearly 60% of patients with MDS (1–3). SF mutations are early, sometimes initiating, events in the course of the disease and can thus provide attractive therapeutic targets (4). Canonical SF mutations in MDS affect specific hotspots in three main genes that regulate splicing, SRSF2, U2AF1, and SF3B1. In the past decade since their discovery, hundreds of splicing alterations have been cataloged in MDS patient cells and in murine and cellular models of SF mutations, in search of those critical to the disease pathogenesis. Transcriptome-wide splicing analyses support that mutant SFs exhibit altered mRNA sequence binding preferences compared with the wild-type (WT) factors (5–9). However, the specific target transcripts and mechanisms by which these mutations drive MDS remain poorly understood.
SF mutations are always heterozygous and mutually exclusive to one another. Although synthetic lethality between SF mutations has also been demonstrated (10), convergence in common downstream targets at the isoform, gene, or cellular pathway level has been hypothesized to underlie their mutual exclusivity. In particular, convergence at the isoform level appears more likely in the case of SRSF2 and U2AF1 mutations, as both primarily affect exon usage (5, 11–13). Serine/arginine-rich splicing factor 2 (SRSF2) regulates splicing by promoting inclusion of exons through binding to exonic splicing enhancer (ESE) sequences. Mutant SRSF2 preferentially recognizes “CCNG” containing versus “GGNG”-containing ESEs, whereas WT SRSF2 binds to both with similar affinity in vitro (5, 6). U2 small nuclear RNA auxiliary factor 1 (U2AF1) recognizes the AG splice acceptor dinucleotide at the 3′ end of introns. Mutant U2AF1 shows preference for 3′ splice sites containing “CAG” or “AAG” versus “UAG” sequences, promoting preferential inclusion of the respective exons (7–9).
Efforts to identify common effectors of SF mutations in RNA-sequencing (RNA-seq) data sets from MDS patient cells and research models have revealed largely nonoverlapping changes (11, 12). These data sets have important limitations: primary patient cells are characterized by substantial heterogeneity due to co-occurring mutations and variable clonal composition; alternative splicing (AS) events are largely nonconserved between mouse and human (14); and cellular models engineered in aneuploid leukemia cell lines express mutant SFs at nonphysiologic levels and stoichiometry that may alter their binding and associated transcript changes.
Here, to identify splicing alterations common to SRSF2 and U2AF1 mutations that are directly caused by altered binding of the mutant factors, we developed isogenic CRISPR gene-edited induced pluripotent stem cell (iPSC) models of the two mutations that display MDS-related hematopoietic phenotypes. We characterized the transcriptome-wide splicing alterations and RNA binding [enhanced version of the cross-linking and immunoprecipitation assay (eCLIP)] of the mutant and WT factors in iPSC-hematopoietic stem/progenitor cells (HSPC). Integrated analyses revealed increased usage of a long isoform of GNAS (GNAS-L), encoding the α subunit of the stimulatory G protein (Gαs), as an altered splicing event common to mutations in both iPSC-HSPCs and MDS patient cells. We provide functional evidence for a role of GNAS-L in driving the MDS phenotype. We further show that GNAS-L encodes a more active Gαs protein (Gαs-L). Although oncogenic mutant Gαs (R201) activates the cAMP/PKA signaling pathway, we find that Gαs-L results in ERK pathway activation.
These findings reveal activation of ERK/MAPK signaling by a more active Gαs form produced by AS as a common effector of SRSF2 and U2AF1 mutations in MDS and suggest MEK inhibition as a potential therapeutic option for SF-mutant neoplasms.
Human HSPCs from Isogenic iPSC Models of SF Mutations Recapitulate Cellular and Molecular Phenotypes of SF-Mutant MDS
To interrogate the effects of U2AF1 and SRSF2 mutations and identify common downstream effectors driving MDS, we used CRISPR/Cas9 to introduce a heterozygous U2AF1S34F mutation in a normal iPSC line (N-2.12; ref. 15; Fig. 1A and B; Supplementary Fig. S1A–S1C). Multiple mutant iPSC lines were generated with two different guide RNAs (gRNA) to exclude potential confounding effects of off-target events on cellular and molecular phenotypes (Supplementary Fig. S1B; Supplementary Table S1). A heterozygous SRSF2P95L mutation was previously introduced in the same normal parental iPSC line (16). Homozygous mutant clones were not recovered, in agreement with the previously demonstrated dependency of SF-mutant cells on one WT allele (Supplementary Fig. S1D; ref. 17). To enable allele-specific immunoprecipitation (IP), an epitope tag (3xFLAG) was introduced, in a subsequent gene-editing step, at the C-terminus of the endogenous locus of either the mutant allele (in SF-mutant lines) or one WT allele (in the isogenic SF-WT iPSC lines; Fig. 1A; Supplementary Fig. S2A–S2J). We confirmed that addition of the epitope tag did not affect expression of the tagged allele and that all lines maintained a normal karyotype (Supplementary Fig. S2).
To assess the effects of the two mutations in hematopoiesis, we used a human pluripotent stem cell differentiation protocol that produces definitive-type hematopoietic progenitor cells. Directed differentiation of independent lines of each genotype revealed no defects in hematopoietic specification, as indicated by the emergence of CD34+ and CD45+ HSPCs (Supplementary Fig. S3A and S3B). In contrast, the number of hematopoietic colonies generated from SF-mutant iPSC-HSPCs in methylcellulose assays was reduced to approximately half of that generated from WT lines (Fig. 1C). SF-mutant iPSC-HSPCs showed severely impaired ability for myeloid maturation (Fig. 1D), as well as decreased proliferation (Fig. 1E). Decreased growth has previously been observed in various cell line and mouse models of SF mutations (16, 18). Additionally, SF-mutant iPSC-HSPCs had decreased viability and showed dysplastic erythroid and myeloid lineage morphologic alterations (Supplementary Fig. S3C and S3D). Collectively, these results show that SF mutations cause differentiation, proliferation, and viability defects, as well as dysplastic morphologic changes, recapitulating hallmark phenotypes previously reported by us and others in MDS patient-derived iPSCs and ex vivo primary MDS cells (15, 16, 19–21).
To evaluate the impact of SF mutations on RNA splicing, we performed RNA-seq analyses in sorted CD34+/CD45+ iPSC-HSPCs from at least three independent lines per genotype and identified AS events (Fig. 1F; Supplementary Fig. S4; Supplementary Table S1). Cassette exon events were the predominant AS event type in both genotypes, whereas alternative 3′ and 5′ splicing events (A3SS and A5SS) were detected at a lower frequency (Fig. 1G; Supplementary Fig. S5A–S5C; Supplementary Table S2). “CAG” and “AAG” 3′ splice-site sequences were enriched in flanking exons preferentially included in U2AF1-mutant cells (“S34F exons”), whereas exons preferentially skipped in U2AF1-mutant cells (“WT exons”) were enriched for “UAG” 3′ splice-site sequences (Fig. 1H and I). The same motif preferences were also identified at the 3′ splice site of A3SS events preferentially included (“CAG/AAG”) and skipped (“UAG”) in U2AF1-mutant compared with WT cells (Supplementary Fig. S6A). Exons preferentially skipped in SRSF2-mutant cells (“WT exons”) showed enrichment of 5-mers containing “GGAG” and “GGUG” sequences, whereas 5-mers containing “GCAG” and “CCAG” were enriched in exons preferentially included in SRSF2-mutant cells (“P95L exons”; Fig. 1J and K; Supplementary Fig. S6B–S6E). These sequence-specific splicing alterations are consistent with previous findings in other models and patient cells and establish that our iPSC models recapitulate the characteristic splicing alterations of SF-mutant hematopoietic cells (5–8).
Allele-Specific eCLIP Identifies Bona Fide Targets of Missplicing by Mutant SRSF2 and U2AF1
To assess the transcriptome-wide protein–RNA interaction landscape of mutant versus WT factors, eCLIP (22) was performed in sorted CD34+/CD45+ iPSC-HSPCs from two independent iPSC lines per genotype (Fig. 1F; Supplementary Table S1) with allele-specific IP of the epitope-tagged mutant (in SRSF2-mutant and U2AF1-mutant lines) or WT (in isogenic SRSF2-WT and U2AF1-WT lines) allele (Supplementary Fig. S7A–S7D). We identified ∼35,000 U2AF1 binding sites and ∼12,000 SRSF2 binding sites across the protein-coding transcriptome, the majority of which occurred within the expected RNA regions: 3′ splice sites for U2AF1 (both WT and mutant) and protein-coding exonic regions for SRSF2 (both WT and mutant; Fig. 2A; Supplementary Fig. S7E–S7G). To identify binding sequence preferences in the binding sites of each mutant or WT factor, we performed motif enrichment analyses and calculated the relative abundance of all 6-mers within peak regions (Fig. 2B–D). WT U2AF1 binding sites were enriched for 6-mers containing “UAG,” whereas U2AF1S34F binding sites were enriched for 6-mers containing “CAG” (Fig. 2B and C; Supplementary Fig. S7H). WT SRSF2 binding sites were enriched for “GC”- and “GA”-rich 6-mers, whereas peaks bound by SRSF2P95L contained no significantly enriched motifs and displayed a modest enrichment for 6-mers containing “CCUG” sequences (Fig. 2B and D; Supplementary Fig. S7I). These sequence preferences in binding between the WT and mutant forms of U2AF1 and SRSF2 mimic the sequence alterations that we found in skipped exon events by RNA-seq and support a causative link between differential RNA sequence recognition by mutant SFs and altered splicing.
To specifically interrogate differential binding in regulatory regions of skipped exons (upstream intron for U2AF1 and cassette exon for SRSF2), we generated “RNA splicing maps” to visualize position-specific SF binding (ref. 23; Fig. 2E and F; Supplementary Fig. S8A and S8B). The binding density of WT U2AF1 was higher than that of U2AF1S34F specifically at the 3′ splice site of “WT exons” (included in WT cells; Fig. 2E; Supplementary Fig. S8A, green line). Conversely, U2AF1S34F had higher binding density than WT U2AF1 at the 3′ splice site of “S34F exons” (included in mutant cells; Fig. 2E; Supplementary Fig. S8A, blue line). We also observed increased binding density of WT SRSF2 within “WT exons,” but not within upstream or downstream control exons (Fig. 2F; Supplementary Fig. S8B). These results show differences in binding density between the WT and mutant SFs specifically at the regulatory regions of skipped exons, which allow us to couple altered splicing to differential binding by mutant versus WT U2AF1 or SRSF2 at the level of individual AS events.
To this end, we grouped the binding events in regulatory regions of skipped exons (deltaPSI > 5%, FDR < 0.05) into three categories: (i) sites bound by the WT SF only (“WT peaks”); (ii) sites bound by the mutant SF only (“mutant peaks”); or (3) sites bound by both WT and mutant SF (“both peaks”; Fig. 3A). We found that “WT exons” (positive delta PSI WT–S34F) more often contained “WT peaks,” and “S34F exons” (negative delta PSI WT–S34F) more often contained “mutant peaks” (χ2 test, P = 0.003). This trend was similar, albeit less strong, in the case of SRSF2, in which the overall number of peaks detected was much lower (Fig. 3A) and was not observed in downstream, control exons (Supplementary Fig. S8C). We expanded the analysis to all observed splicing changes (not just those above significance thresholds), and observed that exons with a corresponding WT or mutant-specific binding event trended toward inclusion in cells of the genotype with a corresponding WT or mutant factor–specific binding event (Supplementary Fig. S8D and S8E).
With these data sets, we were able to assign direct, in vivo binding changes between WT and mutant SFs at both novel AS events and events previously reported in association with U2AF1 or SRSF2 mutations. For example, a skipped exon event in MED24, previously shown to be preferentially included in U2AF1- and SRSF2-mutant cells (5, 13), contains a binding site specific to both mutant SRSF2 and U2AF1 at the regulated exon (Fig. 3A; Supplementary Fig. S9A and S9B). In contrast, we did not find any direct evidence for differential binding of any factor at a previously reported EZH2 poison exon (ref. 5; Supplementary Fig. S9C and S9D).
Integrative Analysis of Missplicing and mRNA Binding Reveals a Long Isoform of GNAS, Promoted by Both Mutant Factors, as a Phenotypic Driver of MDS
Convergence in common downstream genes has been postulated to underlie the mutual exclusivity of SF mutations in patients with MDS. We thus harnessed our isogenic model to find altered splicing events common to both SF mutations. Of the 504 and 517 differential AS events that we identified in U2AF1S34F and SRSF2P95L, compared with isogenic WT cells, respectively, 41 events, affecting 40 genes, were common to both genotypes (Fig. 3B; Supplementary Table S3). We then quantified exon inclusion (delta PSI) for each of these events in a publicly available MDS patient data set (12). Twenty of the 41 events had sufficient read coverage to allow evaluation of splicing. Fifteen of those 20 were differentially spliced in the same direction in both U2AF1- and SRSF2-mutant cells (Fig. 3B and C). Of these, three AS events, in the genes GNAS, PSMA4, and ITGB3BP, also contained a differential eCLIP peak between U2AF1S34F and WT U2AF1 at the 3′ splice site of the alternatively spliced exon (Fig. 3B and C).
Of those three genes, GNAS, the gene encoding the α sub-unit of the stimulatory G protein (Gαs), is a recurrently mutated gene in MDS (24). Furthermore, G protein signaling plays important roles in multiple cellular functions and been has previously linked to oncogenesis in nonhematologic malignancies (25). Both SF mutations promoted the inclusion of exon 3, resulting in the preferential usage of a long GNAS isoform (GNAS-L), over a short isoform (GNAS-S), in both iPSC-HSPC models and in MDS patient cells, giving rise to a longer form of the Gαs protein (Gαs-L; Fig. 3D–I; Supplementary Fig. S9E; Supplementary Fig. S10A and S10B). Selective knockdown of GNAS-L with two different short hairpin RNAs (shRNA) specifically targeting exon 3 rescued the differentiation defect of SF-mutant iPSC-HSPCs and restored it to a level comparable to that of WT iPSC-H SPCs (Fig. 4A and B). This effect was specific to the SF-mutant cells, as GNAS-L knockdown had no effect in the differentiation of WT iPSC-HSPCs (Supplementary Fig. S10C). Conversely, overexpression of GNAS-L in WT iPSC-HSPCs decreased their differentiation potential and viability by approximately half, reproducing the SF-mutant phenotype (Fig. 4C; Supplementary Fig. S10D). Overexpression of GNAS-S in SF-mutant iPSC-HSPCs did not improve their colony formation potential (Supplementary Fig. S10E). These results establish that GNAS-L is a phenotypic driver of MDS.
GNAS-L Encodes a Hyperactive Form of Gαs (Gαs-L), Nonredundant to the GαsR201-Mutant Form
Gαs is a member of the heterotrimeric family of G proteins that are activated by G protein–coupled receptors (GPCR). In its inactive state, Gαs is bound to GDP. Its activation requires release of GDP and exchange for GTP. Activating GNAS mutations, most commonly involving the R201 hotspot, are found in ∼1% of patients with MDS and in other tumors (24, 25). We thus hypothesized that the GNAS-L isoform, promoted by SF mutations, also encodes a hyperactive form of Gαs (Gαs-L). Gαs contains two domains, the alpha helical domain (AHD) and the guanine-nucleotide-binding, RAS homology domain (RHD). RHD–AHD domain separation, resulting in an “open” conformation, is necessary for G protein activation, i.e., GDP dissociation and nucleotide exchange (26). Exon 3 encodes a 15 amino acid segment in Gαs-L within a hinge-like region located between the AHD and the RHD. Although well ordered on most other G protein α subunits, the hinge-like region is disordered (i.e., unstructured) in the Gαs crystal structures, whether bound to nucleotide or not (refs. 27, 28; Fig. 4D). To ascertain the role of exon 3, we analyzed the intrinsic nucleotide exchange capacity of purified Gαs-L by measuring [35S]GTPγS or Bodipy-FL GTPγS binding. Gαs-L bound GTPγS faster than Gαs-S (Fig. 4E and F; Supplementary Table S4). Furthermore, ectopic expression via transfection of Gαs-S or Gαs-L in HEK293 cells engineered to not express endogenous GNAS (29) showed that Gαs-L displays modestly higher maximal activity than Gαs-S upon stimulation with the β2-adrenergic receptor agonist isoproterenol (Fig. 4G and H; Supplementary Table S5). We speculated that differences in activity between Gαs-L and Gαs-S may be amplified in the R201-mutant background. We thus measured [35S]GTPγS and Bodipy-FL GTPγS binding and cAMP accumulation of purified R201C and R201H Gαs-L or Gαs-S forms (Fig. 4E–H; Supplementary Tables S4 and S5). These data show a significant difference in the basal levels of G protein activation and adenylyl cyclase activity of the mutant Gαs-L, compared with the Gαs-S, form. Collectively, these results demonstrate that the long Gαs form is more active than the short form.
We then hypothesized that GNAS activation by the long isoform and GNAS (GNAS-L) activation by the R201 mutation might be functionally redundant and that this redundancy may manifest as mutual exclusivity between SF mutations (promoting GNAS-L expression) and GNAS point mutations in patients with MDS. To test this, we interrogated a large cohort of 3,222 patients with MDS and acute myeloid leukemia (AML)—combined from three published cohorts (4, 30, 31)—for signals of genetic interaction between SF mutations and GNAS mutations. Unexpectedly, these analyses revealed significant co-occurrence between SRSF2 and GNAS mutations (P = 0.01; OR = 4.05; Fig. 5A), strongly suggesting that the effects of these mutations are not redundant, but potentially synergistic. To test the hypothesis that this co-mutation pattern is driven by cooperation of the GNAS-L isoform promoted by SF mutations with the GNASR201 mutation, we transduced WT iPSC-HSPCs with lentiviral vectors expressing GNAS-L, mutant GNASR201H or both (GNAS-LR201H) and quantified their effects in colony-forming ability. Both GNAS-L and the R201H mutation suppressed colony formation, and their combination (GNAS-LR201H) trended toward a more pronounced effect (Supplementary Fig. S10F). Collectively, these results show that inclusion of exon 3, giving rise to the GNAS-L isoform, produces a Gαs protein that is hyperactive. Furthermore, our data suggest that the increased activity of Gαs-L is not equivalent to that of the R201 mutant form, but is rather nonredundant and possibly synergistic to R201-mutant Gαs.
Gαs-L Activates ERK/MAPK Signaling
Activated Gαs directly activates adenylyl cyclase, the enzyme responsible for producing cAMP. cAMP is the second messenger that activates, among other things, PKA. We thus investigated the signaling pathways activated by Gαs-L in K562 cells, iPSC-HSPCs, and cord blood (CB) CD34+ cells transduced with lentiviral vectors expressing WT or R201 GNAS-L or GNAS-S. These experiments collectively showed that the R201 mutation (present on either the long or short GNAS form) activates canonical cAMP/PKA signaling. In contrast, we found no additional PKA activation by the GNAS-L form, with or without the R201 mutation (Fig. 5B andC, Supplementary Fig, S11A and S11B) or any evidence of increased PKA activation in SRSF2P95L or U2AF1S34F iPSC-HSPCs or primary cells (Fig. 5D and E).
We thus hypothesized that the Gαs-L form may result in signaling outcomes distinct from the R201-mutant form. Because many GPCRs potently regulate ERK activity (32, 33), we hypothesized that an increase in Gαs-L may affect ERK pathway activation. Indeed, we found that ectopic Gαs-L expression, with or without the R201 mutation, in WT iPSC-HSPCs and CB CD34+ cells resulted in ERK1/2 and AKT activation (Fig. 6A and B; Supplementary Fig. S11C and S11D). In contrast, the R201 mutation alone in the short isoform did not activate ERK or AKT signaling. Additionally, we found increased ERK activation in SRSF2P95L and U2AF1S34F, compared with WT, iPSC-HSPCs (Fig. 6C; Supplementary Fig. S11E), as well as increased levels of dual-specificity phosphatase 6 (DUSP6), a classic ERK target gene (Fig. 6C). Forced expression of GNAS-L activated RAF and MEK, and SRSF2P95L and U2AF1S34F iPSC-HSPCs also had increased RAF and MEK activation compared with WT iPSC-HSPCs (Fig. 6C and D; Supplementary Fig. S11F and S11G). Specific shRNA knockdown of GNAS-L resulted in a striking decrease in pERK in both SRSF2P95L and U2AF1S34F iPSC-HSPCs (Fig. 6E). Furthermore, primary MDS and AML cells with U2AF1 or SRSF2 mutations had increased Gαs-L form and ERK, but not PKA, activation, compared with SF-WT cells (Fig. 6F; Supplementary Fig. S11H and S11I; Supplementary Table S6), whereas primary MDS and AML patient cells with both SRSF2P95L and GNASR201H mutations showed increased cAMP and PKA substrate phosphorylation, compared with patient cells with SRSF2P95L alone (Fig. 5E; Supplementary Fig. S11J). Collectively, these results show that, whereas the R201 GNAS mutation activates the canonical cAMP/PKA pathway, the GNAS-L activates the ERK/MAPK pathway.
Given the ERK/MAPK activation status of SF-mutant cells we uncovered here, we next wanted to test the dependency of SF-mutant cells on ERK/MAPK signaling. MEK is immediately upstream of ERK1/2 in the MAPK pathway. Several MEK inhibitors are currently FDA-approved oncology drugs for melanoma and other solid tumors. To test the sensitivity of SF-mutant cells to MEK inhibitors, we treated U2AF1S34F, SRSF2P95L, and isogenic WT iPSC-HSPCs with four MEK inhibitors approved by the FDA or currently in clinical testing: trametinib, cobimetinib, selumenitib, and CH5126766. U2AF1- and SRSF2-mutant cells showed marked sensitivity to all four MEK inhibitors (but not to a mutant-BRAF inhibitor, vemurafenib, as control; Fig. 7A–C). Importantly, ex vivo–cultured primary cells from patients with MDS and secondary AML (sAML) with SRSF2P95 mutations were more sensitive to MEK inhibition than cells from patients without SF mutations (Fig. 7D). These results suggest that targeting the MEK/ERK pathway holds therapeutic promise for patients with MDS with SF mutations.
Here we developed a genetically faithful, human, isogenic, karyotypically normal iPSC-based model of SF-mutant MDS, with which we quantified AS and in vivo mRNA binding. We identify preferential usage of the GNAS-L isoform as a convergent consequence of U2AF1S34F and SRSF2P95L mutations. We further provide evidence that GNAS-L is an MDS phenotypic driver and that it mediates ERK/MAPK pathway activation.
Two key features of our study allowed us to pinpoint GNAS as a high-priority direct target common to both mutant factors: the isogenic conditions that empowered the identification of convergent targets of both U2AF1 and SRSF2 mutations, and the high quality eCLIP-sequencing (eCLIP-seq) data in relatively homogeneous iPSC-derived HSPC populations and faithful genomic context (diploid human genome with one SF-WT and one mutant allele), which allowed us to couple altered exon inclusion/exclusion with altered binding of the mutant factors. We were thus able to identify specific exons that are both differentially bound by the mutant SFs and preferentially included or excluded in the mutant cells. One such event, resulting in increase of the GNAS-L isoform, was functionally validated to mediate the differentiation defect observed in our MDS model. Although our differentiation protocol generates definitive-type hematopoiesis, iPSC-derived blood cells may resemble fetal cells more than adult cells. Nonetheless, we and others have shown that iPSC models of myeloid malignancies capture phenotypic and molecular characteristics of disease and can be used to discover new disease mechanisms and therapeutic vulnerabilities, as highlighted by the present study (15, 16, 19, 34–40). Studies that used previous versions of CLIP technology, without size-matched input controls, to interrogate binding of mutant SRSF2 or U2AF1 transfected into immortalized cell lines did not observe a correlation between missplicing events and binding at their regulatory regions to pinpoint specific splicing events directly coupled with altered binding (41–43). Furthermore, although the GNAS AS event we report here was present in previous RNA-seq data sets of U2AF1-mutant or SRSF2-mutant human and murine cells (13, 44–46), it was not prioritized for follow-up in previous studies, in the absence of evidence of differential binding.
Since its discovery in the 1980s, two major splice forms of Gαs were identified, but no evidence of different functional consequences contributing to disease in humans existed prior to this study. A few earlier reports suggested differences between these splice forms in terms of nucleotide affinities, receptor coupling, and cyclase activation, whereas others suggested that the two splice variants are functionally equivalent (47–50). Our biochemical and functional studies, together with MDS patient population genetics data, show that the Gαs form encoded by the long isoform is more active than the short and that this enhanced activity is nonredundant and possibly synergistic to Gαs activation by hotspot mutations that primarily activate the canonical cAMP/PKA effector pathway.
Activation of Gαs upon ligand binding to cell-surface GPCRs can result in complex signaling outcomes through interactions of Gαs with a diverse array of signaling partners. Gαs activation promotes functional dissociation of the Gαs and Gβγ subunits, which can also recruit proteins to the plasma membrane and produce signaling responses. Previous links have been made between G protein signaling and the modulation of ERK/MAPK pathway, both stimulating and inhibiting, at different cellular contexts (51). The mechanism by which G proteins modulate ERK activation is not well understood. Our data show activation of RAF and MEK upstream of ERK (Fig. 6A, C and D; Supplementary Fig. S11F and S11G). This is also consistent with our finding that SF-mutant cells are sensitive to the MEK inhibitor (MEKi) CH5126766, which inhibits RAF-bound MEK (ref. 52; Fig. 7). It is currently unclear how this activation occurs and whether it involves direct engagement of RAF–MEK by Gαs-L, indirect activation through dissociation of the Gβγ subunits, or other mechanisms. Dual activation of cAMP/PKA and ERK/MAPK signaling could mediate the cooperative effects of the R201 mutation with the long form of Gαs and provide a mechanistic basis for the co-occurrence of SRSF2 and GNAS mutations that we report. Alternatively, the contribution of other misspliced targets of mutant SRSF2 and/or U2AF1 may underlie this cooperation. Although specific knockdown of GNAS-L completely rescued the colony formation deficit of SF-mutant cells, additional splicing alterations may also contribute to the disease pathogenesis and phenotypic manifestations that cannot be detected with our system and assays. The continued study of the effects of SF mutations will shed more light into the relative contributions of different splicing alterations as drivers of MDS and other SF-mutant neoplasms.
We did not find conclusive evidence of increase in the GNAS-L isoform in MDS with SF3B1 or ZRSR2 mutations (Supplementary Fig. S10A and S10B). As SRSF2 and U2AF1 both primarily affect exon inclusion/exclusion, convergence in a common exon event was a priori more likely for SRSF2 and U2AF1 mutations. The mutual exclusivity of SF3B1 and ZRSR2 mutations could still be explained by convergence at the level of the pathway or cellular processes affected. Future studies investigating the signaling consequences and target gene-expression changes induced by increased GNAS-L in detail may point to candidate genes and isoforms that may be altered by these other mutations. Alternatively, other, as yet unidentified, targets of missplicing common to all mutant factors may exist, or SF3B1 and ZRSR2 mutations may not converge in common targets with SRSF2 and U2AF1 mutations and their mutual exclusivity may be due to common effects in cellular function that are ultimately redundant in the pathogenesis of MDS or due to synthetic lethality alone (10).
MDS is a disease with poor prognosis and few therapeutic options and thus presents a high unmet clinical need (53). Activation of MAPK signaling by other mechanisms has been documented in AML, primarily in AML subsets with RAS and FLT3 mutations (54, 55). MDS has also been linked to RAS/MAPK pathway activation, and inhibitors of RAS and MAPK signaling are in preclinical and clinical testing (56, 57). Furthermore, germline RAS pathway mutations cause inherited syndromes, such as Noonan syndrome and neurofibromatosis type 1, that predispose to development of MDS and related neoplasms (such as chronic myelomonocytic leukemia). The MEKi trametinib showed limited efficacy as a single agent in a phase I/II clinical study in relapsed/refractory AML and high-risk MDS; however, most patients enrolled in this study were heavily pretreated and preselected for RAS (NRAS and KRAS) mutations, which typically occur late in disease progression (58). Our results suggest that MEK inhibitors may be a viable therapeutic option for patients with MDS with SRSF2 and U2AF1 mutations at an earlier disease stage, a finding that could be readily translated, as these inhibitors are approved by the FDA for other cancers and could be easily repurposed (59). Because toxicity is frequently a limiting factor with MEK inhibitors, direct targeting of Gαs or even Gαs-L might provide an alternative therapeutic option with a potentially more favorable toxicity profile, although this remains to be tested. Although there are currently no small-molecule inhibitors of Gαs, approaches leveraging specific splicing modulation of GNAS exon 3 or proteolysis targeting chimeras (PROTAC) can be envisioned in the future.
The importance of the findings we report here may extend beyond MDS and AML, as U2AF1 and SRSF2 mutations are also present in chronic lymphocytic leukemia and some solid tumors (9). It remains to be seen whether other cancers with SF mutations are driven by MAPK activation through AS of GNAS. Interestingly, activating GNAS mutations in solid cancers are primarily found in low-grade tumors and benign metaplasias, which indicates a role for G protein signaling in early steps of oncogenesis (60, 61). As MDS is also a preleukemic condition with a low proliferation index akin to that of low-grade solid tumors, it is possible that Gαs activation is a common feature of low-grade malignant and premalignant cells and, as such its targeting may offer new opportunities for cancer prevention.
CRISPR/Cas9 Gene Editing of Human iPSCs
We used the previously described normal iPSC line N-2.12-D1-1 as the parental line (19). Generation of SRSF2P95L iPSC lines was previously described (15, 16). The U2AF1S34F mutation and all allele-specific 3xFLAG tags were introduced using CRISPR/Cas9-mediated homology-directed repair in two steps.
To introduce the U2AF1S34F mutation, two different gRNAs targeting the U2AF1 locus within exon 3 (cutting site between 1 and 10 bp from the 101C>T mutation site, sequences shown in Supplementary Fig. S1A) were designed, assembled by a two-step overlapping PCR reaction downstream of the U6 promoter sequence, and cloned in the gRNA/Cas9 plasmid, also expressing Cas9 driven by the CMV promoter linked to mCitrine with a P2A (15, 16). Two sets (one for each gRNA) of two donor DNA plasmids, one containing the S34F mutation and one the corresponding WT sequence, containing 5′ (1,032 bp) and 3′ (1,064 bp) homology arms consisting of nucleotides 43104347–43105378 and 43103283–43104346 (hg38 human genome assembly), respectively, were constructed. The donor plasmids also contained silent mutations (shown in Supplementary Fig. S1A) to introduce a new restriction site sequence (SphI) and to prevent further cleavage by Cas9. The entire 5′ + 3′ homology sequence was amplified from N-2.12-D1-1 genomic DNA and the c.101C>T and/or silent mutations to introduce new restriction enzyme recognition sites and to prevent cleavage by Cas9 were introduced by two-step overlapping PCR before subsequent cloning into the donor plasmid.
To generate U2AF1S34F iPSCs, the N-2.12-D1-1 iPSC line was cultured in hESC media containing 10 mmol/L Y-27632 for at least one hour before nucleofection. The cells were dissociated into single cells with accutase and 1 million cells were used for nucleofection with 5 μg of gRNA/Cas9 plasmid and 5 μg of each donor plasmid (WT and S34F) using nucleofector II (Lonza) and program B-16. Immediately after nucleofection, the cells were replated on mouse embryonic fibroblasts (MEF). mCitrine+ cells were FACS-sorted 48 hours after transfection and plated as single cells at clonal density (1,000 FACS-sorted cells per 60-mm dish). After 10 to 12 days, single colonies were picked in separate wells of a 6-well plate, allowed to grow for approximately 3 to 6 days and screened by PCR. One to three medium-sized colonies from each individual clone were picked directly into a 0.2 mL tube, pelleted, and lysed. Restriction fragment length polymorphism analysis was performed after PCR with primers F: AGGAAAGTGGAGGGGATTTG and R: CCATGGCCACTGGTTTAGTT and digestion of the product with SphI. Biallelically targeted clones were Sanger sequenced to select clones heterozygous for the U2AF1S34F mutation. Heterozygous status was confirmed by cloning the PCR product into the PCR-4 TOPO TA vector (Invitrogen) and sequencing.
Four independent heterozygous U2AF1S34F clones—one generated with gRNA1 and three with gRNA2—were confirmed to be karyotypically normal, and after preliminary phenotypic characterization to exclude potential outliers, one clone (S34F-1) was selected for the subsequent editing step, along with P95L-1 clone and the parental N-2.12 line (Supplementary Table S1). A 3xFLAG tag was inserted into the C-terminus of the mutant or WT allele with a strategy similar to the one described above. A gRNA targeting the U2AF1 locus (cutting site 12 bp 5′ to the stop codon, with sequence AGATCTTTCACGATCTCTCG) or the SRSF2 locus (cutting site 14 bp 3′ to the stop codon, with sequence TAGGGGAATGGTAATGTCTG) was designed and cloned in the gRNA/Cas9 plasmid described above. Donor templates containing 5′ (1,098 bp) and 3′ (1,023 bp) homology arms (U2AF1 locus), consisting of nucleotides 43093105–43094202 and 43092082–43093104 (hg38 human genome assembly), respectively, and 5′ (946 bp) and 3′ (851 bp) homology arms (SRSF2 locus) consisting of nucleotides 76736164–76737109 and 76735313–76736157, respectively, and the 3xFLAG sequence (GACTACAAGGACGACGATGACAAGGATTACAAAGATGACGACGATAAGGACTATAAGGACGATGATGATAAA) were constructed. Gene targeting was performed as above. Colonies were screened by PCR and confirmed by TOPO TA cloning and sequencing. Monoallelically targeted clones with the desired allele tagged were selected and confirmed to be karyotypically normal. Expression of the FLAG-tagged protein (U2AF1 or SRSF2) of the expected size was confirmed by Western blot with an anti-FLAG antibody. To ensure clonality, an additional step of single-cell cloning was performed after each step of gene editing.
Human iPSC Culture, Hematopoietic Differentiation, and Phenotypic Characterization
Culture of human iPSCs on mitotically inactivated MEFs or feeder-free conditions was performed as previously described (15, 16). All iPSC lines used in this study were tested periodically (every 2–4 weeks) and confirmed to be free of Mycoplasma contamination. Line authentication (SF genotype) was periodically performed by Sanger sequencing. All lines were confirmed to be karyotypically normal and were always maintained at a passage number not exceeding 100.
Hematopoietic differentiation was performed using a spin-EB protocol previously described (16). Briefly, cells were dissociated into single cells with accutase and plated at 3,500 cells per well in round-bottom low-attachment 96-well plates in APEL2 medium containing 5% protein-free hybridoma medium (PFHM-II), 30 ng/mL bone morphogenetic protein 4 (BMP4), and 10 μmol/L Y-27632. The plates were centrifuged at 800 rpm for 5 minutes to induce EB aggregation. After 24 hours, the medium was replaced by APEL2 medium containing 5% PFHM-II, 30 ng/mL BMP4, and 50 ng/mL FGF2. After 2 days, the cytokine cocktail was changed to 5% PFHM-II, 20 ng/mL vascular endothelial growth factor (VEGF), 10 ng/mL FGF2, 100 ng/mL stem cell factor (SCF), 20 ng/mL Flt3 ligand (FL), 20 ng/mL thrombopoietin (TPO), and 40 ng/mL IL3. On day 8, EBs were collected and resuspended in Stem Pro34 SFM medium with 1% nonessential amino acids (NEAA), 1 mmol/L l-glutamine and 0.1 mmol/L β-mercaptoethanol (β-ME), supplemented with 100 ng/mL SCF, 20 ng/mL Flt3L, 20 ng/mL TPO, and 40 ng/mL IL3. The medium was thereafter replaced every two days. At the end of the differentiation culture, the cells were collected and dissociated with accutase into single cells and used for flow cytometry or clonogenic assays, as described (15, 16). Competitive growth assays were performed using an isogenic GFP-marked iPSC line (N-2.12-GFP), as previously described (15, 16).
The following antibodies were used: CD34-PE (clone 563, BD Pharmingen, RRID:AB_393871), CD45-APC (clone HI30, BD Pharmingen, RRID:AB_398600), CD14-APC (clone M5E2, BD Pharmingen, RRID:AB_398596), CD15-BV785 (clone W6D3, BioLegend, RRID:AB_2632921), and CD16-BV510 (clone 3G8, BD Horizon, RRID:AB_2744296). Cell viability was assessed with DAPI (Life Technologies). Cells were assayed on a BD Fortessa, and data were analyzed with FlowJo software (Tree Star, RRID:SCR_008520).
At least three clones of each genotype were subjected to hematopoietic differentiation. Magnetic-activated cell sorting (MACS) with anti-CD45 MACS cell separation microbeads and reagents (Miltenyi Biotec) was performed on an empirically determined day of differentiation culture when nearly 100% of CD45+ cells were also still CD34+ (ranging from days 10 to 13, depending on the individual line and differentiation experiment). RNA was extracted with the Direct-zol RNA purification kit (Zymo R2061). Sequencing libraries were prepared using the TruSeq Stranded mRNA library prep kit (Illumina 20020594) from 500 ng input RNA. Libraries used to call AS were sequenced to a depth of ∼40 million reads in PE100 mode on an Illumina HiSeq4000. Libraries used to quantify gene expression were sequenced to a depth of ∼15 million reads in SE75 mode on the Illumina HiSeq4000.
eCLIP Library Preparation
eCLIP was performed as previously described (22, 62). Briefly, sorted CD34+/CD45+ cells were UV-cross-linked (400 mJ/cm2, 254 nm) and snap-frozen. Cross-linked cell pellets from independent differentiations were combined to obtain 10 million cells per replicate. Lysed pellets were sonicated and treated with RNaseI for RNA fragmentation. Two percent of lysate was retained for preparation of a size-matched input library, and the remaining 98% was subject to IP using an anti-FLAG antibody (Sigma F1804, RRID:AB_262044), coupled to magnetic dynabeads (Invitrogen 11203D). Bound RNA fragments were dephosphorylated and 3′-end ligated with an RNA adapter. Protein–RNA complexes from both input and IP samples were run on SDS polyacrylamide gel and transferred to nitrocellulose membrane for extraction of bound RNA fragments. Membrane regions from the size of the tagged protein to 75 kDa above the protein size were cut, and RNA was released with proteinase K. Input samples were then dephosphorylated and 3′-end ligated with an RNA adapter. Reverse transcription was performed with AffinityScript (Agilent) and cDNAs were 5′-end ligated with a DNA adapter. cDNA products were amplified with Q5 PCR mix (NEB) to obtain a sequencing library. Libraries were sequenced on the Illumina HiSeq4000 in SE75 mode to a depth of approximately 20 million reads per library.
Biotin-Based Visualization of RBP-Coupled RNA
Biotin labeling and visualization were performed as described previously (63). Briefly, the cells were UV-cross-linked (400 mJ/cm2) and snap-frozen in pellets of 1 million cells. Pellets were lysed and processed as described in eCLIP library preparation above through the IP step. Following IP, biotinylated cytidine (Thermo 20160) was ligated on-bead to the 3′ end of RNA fragments. Samples were washed and loaded on SDS-PAGE gel. Ligation of biotinylated cytidine was performed on input samples that were taken before IP and loaded directly on the gel (no postligation cleanup was performed). Samples were transferred to a nitrocellulose membrane and developed with the Chemiluminescent Nucleic Acid Kit (Thermo 89880) following the manufacturer's instructions for visualization of RNAs.
RNA-seq Data Processing
RNA-seq reads were trimmed of adapter sequences using cutadapt (v1.4.0, RRID:SCR_011841) and mapped to repetitive elements (RepBase v18.04, RRID:SCR_021169) with STAR (v2.4.0i, RRID:SCR_004463). Reads that did not map to repetitive elements were carried through and mapped to the human genome (hg19). GENCODE v19 gene annotations (RRID:SCR_01496) and featureCounts (v1.5.0, RRID:SCR_012919) were used to assign reads to genes and genic regions.
Quantification of AS
rMATS v4.0.2 (RRID:SCR_013049) was used to perform AS analysis among replicate RNA-seq data sets. Significant events were calculated as those with >5% change in isoform ratio between genotypes with an FDR < 0.05. Each event was required to contain an average of at least 10 reads supporting the inclusion and exclusion isoform across replicates in one genotype and at least an average of 10 total reads of both isoforms in the other genotype. We previously reported that rMATS often calls multiple splicing events with different flanking regions but overlapping alternatively spliced regions (23). To remove these artifacts and avoid double-counting of AS events, we used custom scripts to remove overlapping AS events for analysis (subset_rmats_junctioncountonly.py found in https://github.com/YeoLab/rbp-maps). Unprocessed fastq files of RNA-seq data of patients with MDS from Pellagatti and colleagues (12) and Madan and colleagues (64) were downloaded from the Gene Expression Omnibus (GEO; accession GSE114922 and GSE63816, respectively) and processed as described above.
Selection of Unchanged Cassette Exons
To generate a background list of cassette exons that are unchanged between genotypes, we randomly selected exons that were matched for inclusion levels. The splicing events of interest were grouped in ranges of inclusion levels from 0–0.25, 0.25–0.5, 0.5–0.75, and 0.75–1. We then calculated the total number of events that fell within each range and randomly selected that same number of exons from a list that were not differentially regulated between genotypes and were within the same range of inclusion level.
RNA-seq Motif Analyses
Weblogo (https://weblogo.berkeley.edu/logo.cgi) was used to generate nucleotide enrichment figures at 3′ splice sites flanking regulatory exons. 5-mer sequences within cassette exons were quantified with Kvector (https://github.com/olgabot/kvector). The resulting counts were summed by genotype, and enrichment was calculated using a χ2 test (Scipy v1.2.0, chi2_contingency, RRID:SCR_008058). Motif enrichment within cassette exons and downstream control exons was determined with the HOMER function (findmotifs.pl, RRID:SCR_010881) in rna mode with a sequence length of six. Sequences within “WT exons” were used as the foreground, and sequences within “P95L exons” or “S34F exons” were used as the background to calculate enrichment in “WT exons.” The reverse orientation was used to calculate sequence enrichment in “P95L exons” or “S34F exons.” P values were calculated with a cumulative binomial distribution.
eCLIP-seq Data Processing
The code used for eCLIP data processing is available on GitHub (https://github.com/YeoLab/eclip). Briefly, reads were adapter-trimmed and mapped to a database of repetitive elements (RepBase v18.04) with STAR (v2.4.0i). Reads that did not map to repetitive elements were mapped to the human genome (hg19) with STAR. Removal of PCR-duplicated reads was performed using the unique molecular identifier sequences in the 5′ adaptor, and nonduplicated reads were retained as “usable reads.” Peaks were called on “usable reads” with CLIPper and assigned to gene regions annotated in GENCODE v19 (RRID:SCR_01496) in the following order of priority: 3′splice site, 5′ splice site, coding sequence, 3′ UTR, 5′ UTR, proximal intron, distal intron, noncoding regions. Peaks with multiple annotations were assigned to the region with the highest priority. Peaks were deemed significant at ≥4-fold enrichment relative to input and P < 0.001 (χ2 or Fisher exact test). Peaks that passed significance thresholds in one of two replicate experiments were retained for analysis. Splicing maps were generated with RBP maps (ref. 23; https://github.com/YeoLab/rbp-maps). Raw sequencing data and processed files are available at GEO with accession code GSE164666.
Kmer Enrichment Analysis and Calculation of Enriched Sequences of eCLIP Peaks
Kmer sequences were counted in regions of interest using kvector (https://github.com/olgabot/kvector). The resulting counts were summed by genotype, and enrichment was calculated using a χ2 test (Scipy v1.2.0, chi2_contingency, RRID:SCR_008058) comparing the kmer frequencies observed in each genotype. FDR was calculated to correct for multiple hypothesis testing, and 6-mer sequences with FDR < 0.05 were reported as significantly enriched.
HOMER Analysis of Motif Enrichment in eCLIP Peaks
The findmotifs.pl function of HOMER (RRID:SCR_010881) was used in RNA mode with a sequence length of six to identify motifs enriched in binding sites of the mutant and WT factors. To identify sequences enriched in WT peaks, the WT binding sites were input as the foreground, and the mutant binding sites were included as the background for each genotype. The reverse comparison was performed to identify sequences enriched in the mutant factor binding sites.
RT-PCR for GNAS-L and GNAS-S
RNA was isolated with TRIzol (Life Technologies). Reverse transcription was performed with Superscript III (Life Technologies), and PCR was performed with primers F: AAGCACCATTGTGAAGCAGA, R: TTCAATCGCCTCTTTCAGGT with a varying number of amplification cycles (from 25 to 35). Products were separated on an agarose gel. The lowest cycle count in which both products were visible was used for quantification of band intensity using ImageJ (RRID:SCR_003070).
GNAS-L Knockdown and Overexpression
Two shRNAs targeting exon 3, specific to GNAS-L isoform, were designed with sequence shRNA 1: ACCCACCATAGGGCATGATTA; shRNA 2: TAAAGCCTTAAGCACAATTAA. The GNAS-L shRNA and a scrambled shRNA sequences were inserted into the 3′ UTR of the G-U6 lentiviral vector (19). Lentiviral vectors were packaged as described (19). Five hundred thousand iPSC-HSPCs on day 11 of hematopoietic differentiation were transduced with a lentiviral vector expressing either the GNAS-L shRNA or scrambled shRNA. Two days after transduction, 5,000 cells were plated on methylcellulose media for colony-forming assay, and the rest were used for quantification of GNAS-L and GNAS-S expression by RT-PCR.
For overexpression, GNAS cDNA sequences encoding the short or long isoform with or without the R201H mutation—short wild-type (S-WT), long wild-type (L-WT), short R201H (S-R201H), long R201H (L-R201H)—or GFP (empty vector) were inserted into the mP2A lentiviral vector coexpressed with mCherry through a P2A peptide (16). Five hundred thousand iPSC-HSPCs on day 11 of hematopoietic differentiation, K562 or CB CD34+ cells were transduced in parallel with each lentiviral vector. Two days after transduction (day 13 of hematopoietic differentiation) the cells were harvested for Western blots and/or cAMP ELISA assay. For cytokine starvation, cells on day 12 of hematopoietic differentiation were washed with 1× PBS and cultured for 24 hours prior to collection in Stem Pro34 medium with 1% nonessential amino acids (NEAA), 1 mmol/L l-glutamine, and 0.1 mmol/L β-ME without cytokines.
Protein Expression and Purification
The human Gαs-S and Gαs-L cDNAs, each containing TEV cleavable N-terminal hexa-histidine tag, were cloned into pQE60 vector (Qiagen, RRID:Addgene_12553). Chemically competent WK6 cells were transformed with the pQE60-Gαs plasmid, and expression was induced by 30 mmol/L isopropyl-β-D-thiogalactopyranoside for approximately 20 hours. Cell pellets were lysed and homogenized in 50 mmol/L HEPES pH 8.0, 500 mmol/L NaCl, 10 mmol/L MgCl2, 10% glycerol, protease inhibitor cocktail (35 μg/mL phenylmethanesulfonyl fluoride, 32 μg/mL tosyl phenylalanyl chloromethyl ketone, 32 μg/mL tosyl lysyl chloromethyl ketone, 3.2 μg/mL leupeptin, and 3.2 μg/mL soybean trypsin inhibitor), 6 mmol/L β-ME, and 10 mmol/L GDP and DNase (Roche Diagnostics). The lysed cell suspension was sonicated three times (with each cycle 8 minutes long and consisting of 10 seconds pulse and 10 seconds pause) and centrifuged for 40 minutes at 35.000 rpm at 4°C. Imidazole was added to the clarified supernatant at 20 mmol/L final concentration, which was then applied to a 1.5 mL Ni-NTA resin (Thermo Scientific) gravity-flow column (Bio-Rad) pre-equilibrated with 20 mmol/L HEPES pH 8.0, 500 mmol/L NaCl, 10 mmol/L MgCl2, 5% glycerol, 6 mmol/L bME, and 10 mmol/L GDP. The column was then washed with 15 column volumes (CV) of high salt wash buffer followed by a low salt wash of 10 CV (20 mmol/L HEPES pH 8.0, 20 mmol/L NaCl, 1 mmol/L MgCl2, 6 mmol/L bME, and 10 mmol/L GDP). The protein was eluted with 7 mL of elution buffer (low salt wash buffer with 200 mmol/L imidazole added). The pooled elution fractions were diluted 4× with ion-exchange buffer A (20 mmol/L HEPES pH 8.0, 5% glycerol, and 6 mmol/L bME) and immediately loaded on a 4% buffer B (ion-exchange buffer A with 1 mol/L NaCl added)-equilibrated 8 mL Q-Sepharose column (GE Healthcare). The column was washed with 5 CV of 4% buffer B and eluted with a linear gradient of 4% to 50% buffer B over 200 mL. 4 mL fractions were collected directly into tubes already containing GDP to reach a final concentration of 10 mmol/L. The Gαs-containing fractions were concentrated using a centrifugal spin concentrator with a molecular cutoff of 30 kDa (Amicon) and flash frozen until further use. The protein concentration was determined by Bradford (Bio-Rad) and the functional active protein by the [35S]GTPγS binding assay.
[35S]GTPγS Binding Assay
GTPγS binding was initiated by the addition of 100 mL of a buffer containing 2.5 mmol/L cold GTPγS and 3.5 nmol/L [35S]GTPγS to equal volumes (100 mL) of 400 nmol/L purified G protein in 20 mmol/L HEPES pH 7.7, 100 mmol/L NaCl, 10 mmol/L MgCl2, 14.3 mmol/L β-ME, for a final assay concentration of 200 nmol/L G protein. At specific time points (0, 3, 6, 10, 16, 22, 30, and 40 minutes), a 20 mL aliquot was withdrawn from the reaction and quenched in 100 μL ice-cold quenching buffer (20 mmol/L HEPES pH 7.7, 100 mmol/L NaCl, 10 mmol/L MgCl2, 2 mmol/L β-ME, and 100 mmol/L GTP). Quenched samples were immediately vacuum-filtered through PROTRAN BA-83 0.22-mm nitrocellulose filters (Whatman) presoaked in assay buffer containing 20 mmol/L HEPES pH 7.7, 100 mmol/L NaCl, 10 mmol/L MgCl2, 14.3 mmol/L β-ME, and 1 mmol/L GDP. Filters were washed twice with 4 mL of a buffer containing 20 mmol/L HEPES pH 7.7, 100 mmol/L NaCl and 2 mmol/L MgCl2 and dried at room temperature overnight. Dried filters were transferred to scintillation vials containing 5 mL of liquid scintillation cocktail (CytoScint ES, MP Biomedicals). After 1 hour of equilibration, the radioactivity was determined using an LS 6000IC Beckman counter. Association rate constants were determined by fitting the data to a single exponential using Prism 6 (GraphPad LLC; RRID:SCR_002798).
Bodipy-FL–GTPγS Binding Assay
We measured real-time GTPγS binding to purified Gas protein preparations (prepared as described above) using a fluorescent analogue, Bodipy-FL–GTPγS (Invitrogen; ref. 65), as described (66). Briefly, purified Gas protein (400 nmol/L final) was incubated with Bodipy-FL–GTPγS (100 nmol/L final) in a buffer containing 20 mmol/L HEPES pH 8.0, 200 mmol/L NaCl, 10 mmol/L MgCl2, and 7 mmol/L β-ME in a total volume of 100 μL. Fluorescence was measured in a quartz cuvette using Horiba Fluomax 4 with excitation at 480 nm (10 nm slit width) and emission at 510 nm (5 nm slit width) at 4-second recording intervals. Association rate constants were determined by fitting the data to a single exponential using Prism 6 (GraphPad LLC; RRID:SCR_002798).
The assay was performed in a clonal selected ΔGs-HEK293 cell line (29), stably expressing the cAMP biosensor pink flamido (encoded on a pcDNA4.0/TO/Zeocin plasmid). The cells were cultured in DMEM with 10% FBS (Peak Serum) and 100 mg/mL zeocin. Doxycycline (4 mg/mL) was added to the media to induce expression of the cAMP biosensor, and two days later the cells were transiently transfected with plasmid DNA encoding each G protein isoform.
Cells were harvested after 6 hours, washed in Hank's Balanced Salt Solution (HBSS; Sigma) and replated onto clear bottom poly-D-lysine coated, black 96-well polystyrene assay plates at 2 × 106 cells per well. An additional 2 × 106 cells were used for Western blotting. Isoproterenol in HBSS buffer with 20 mmol/L HEPES, 600 mmol/L 3-Isobutyl-1-methylxanthine (IBMX), and 3 mmol/L ascorbic acid was added to the cells (as a 3× stock) at concentrations 10−5, 10−6, 10−7, 10−7.5, 10−8.0, 10−8.5, and 10−9.5 mol/L, and fluorescence was read using a fluorescence plate reader (BioTek). The cAMP accumulation was monitored for 17.5 minutes. For each isoproterenol concentration the initial rates for the linear portion of the cAMP accumulation curve (30s to 330s) were fitted by linear regression. Rates were expressed as a function of isoproterenol concentration and fitted to a logistic curve using Prism (GraphPad LLC; RRID:SCR_002798).
Five hundred thousand to 1 million iPSC-HSPCs, K562, and CB CD34+ cells or 300,000 to 500,000 primary MDS or sAML patient bone marrow or peripheral blood mononuclear cells were collected and lysed with high salt buffer (0.3 mol/L KCl) supplemented with protease inhibitor and phosphatase inhibitor. Protein concentration was determined by bicinchoninic acid assay (Pierce Biotechnology Inc.), and 20 μg of protein from each extract was diluted in Laemmli SDS sample buffer and resolved by electrophoresis on Bolt 4% to 12% Bis-Tris precast gels (Invitrogen) and blotted on nitrocellulose membranes. The membranes were blocked with 5% BSA (Fischer Bioreagents) in Tris-buffered saline and incubated with primary antibody p-(Ser/Thr) PKA substrate (9621S, Cell Signaling Technologies, RRID:AB_330304), anti-FLAG (14793S, Cell Signaling Technologies), anti-P-p44/42 MAPK (Erk1/2; 4370S, Cell Signaling Technologies), p44/42 MAPK (Erk1/2; 4696S, Cell Signaling Technologies), anti-P-Raf-1 (Ser338; 05-538, Millipore), DUSP6/MKP3 (39441, Cell Signaling Technologies, RRID:AB_2246226), P-MEK1/2 (Ser217/221; 9154S, Cell Signaling Technologies), P-AKT (S473; 4060S, Cell Signaling Technologies), anti-Gαs-Subunit C-terminal (371732, Millipore), anti-mCherry (ab213511, Abcam, RRID:AB_281489), or anti-β-Actin (5125S, Cell Signaling Technologies). After washing, blots were incubated with HRP-conjugated secondary antibody and developed using ECL Western Blotting Detection Reagents (Pierce ECL Western Blotting Substrate, Thermo Scientific). Band intensity was quantified using ImageJ (RRID:SCR_003070). All samples shown in each individual figure panel were processed in parallel on the same blot. Empty space was added to indicate samples that were not run in lanes adjacent to one another. For each antibody, independent blots were used from the same cell lysate with identical loading conditions.
Primary MDS and AML Patient Cells
AML and MDS patient bone marrow or peripheral blood mononuclear cells were obtained with written informed consent under protocols approved by a local Institutional Review Board at the Icahn School of Medicine at Mount Sinai in accordance with recognized ethical guidelines. Information on cytogenetic abnormalities and mutations for each sample was obtained from the Mount Sinai tissue bank. A subset of samples was sequenced for a more extended gene panel. Specifically, a custom capture bait set was used to sequence the coding regions of 163 MDS- and AML-associated genes. Samples were sequenced with pair-end Illumina Hi-Seq at a median coverage of 600× per sample (range, 127–2480×). Variants with VAF < 2%, less than 20 total reads, or less than 5 mutant supporting reads were excluded. After prefiltering of artifactual variants, likely germline SNPs were filtered out by considering the VAF density of variants, their presence in the Genome Aggregation Database (gnomAD), their annotation in the human variation database ClinVar (RRID:SCR_006169) and their recurrence in a panel of normal samples. From the list of likely somatic variants, putative oncogenic variants were distinguished from variants of unknown significance based on the mutational consequence and their recurrence in various databases of somatic mutations in cancer. All MDS- and AML-associated mutations and chromosomal abnormalities found in the samples are listed in Supplementary Table S6.
Cryopreserved cells were thawed and cultured in X-VIVO15 containing 20% BIT, 1% nonessential amino acids (NEAA), 1 mmol/L L-glutamine, and 0.1 mmol/L β-ME supplemented with 100 ng/mL SCF, 50 ng/mL Flt3L, 50 ng/mL TPO, and 20 ng/mL IL3 for 1–3 days and collected for Western blots and cAMP ELISA assay.
cAMP ELISA Assay
Primary AML cells (2.5 million) were collected after incubation with 100 μmol/L 3-isobutyl-1-methylxanthine (IBMX) at 37°C for 30 minutes. All samples were run in duplicate according to the manufacturer's protocol (cAMP Parameter Assay, R&D Systems).
Treatment with MEK Inhibitors
iPSC-HSPCs were plated on day 11 of hematopoietic differentiation on 96-well tissue culture–treated clear flat-bottom plates (Corning, 3903 or 3603, respectively) at a density of 20,000 per well. The compounds trametinib-S2673 (GSK1120212), cobimetinib-S8041 (GDC-0973), selumetinib-S1008 (AZD6244), and vemurafenib-S1267 (PLX4032) were purchased from Selleckchem. All compounds were dissolved in DMSO for stock solutions at a concentration of 5 mmol/L or 10 mmol/L and subsequently diluted in StemPro media and added to a total volume of 50 μL of media per well at a final concentration of 10 μmol/L, 1 μmol/L, 100 nmol/L, 50 nmol/L, 10 nmol/L, 5 nmol/L, 1 nmol/L, 0.1 nmol/L, or 0.01 nmol/L in triplicate wells. After 3 days, cell viability was measured using the CellTiter-Glo Luminescent Cell Viability Assay (Promega, G7570) per the manufacturer's suggested conditions. Percent viability at each compound concentration was calculated as: (signal-blank)/(DMSO control-blank) × 100. IC50 value calculations and generation of IC50 curves were performed using the Prism 8 software (GraphPad, RRID:SCR_002798). For MEKi treatment of primary MDS and sAML patient bone marrow or peripheral blood mononuclear cells, 10,000 to 20,000 cells per well were plated and treated with the compounds, and cell viability was measured after 1 to 2 days.
Statistical analysis was performed with GraphPad Prism software (GraphPad, RRID:SCR_002798). Pairwise comparisons between different groups were performed using a two-sided unpaired unequal variance t test. For all analyses, P < 0.05 was considered statistically significant, unless otherwise stated.
RNA-seq and eCLIP-seq data of this study have been deposited in GEO with the accession code GSE164666.
J. Teruya-Feldstein reports other support from Astellas Pharma USA Inc., Blueprint Medicines, Curio Science, Histowiz, Scopio Labs Anthem Edge, and Elsevier outside the submitted work. G.W. Yeo reports personal fees from Locanabio and Eclipse Bioinnovations, and other support from NUS outside the submitted work. E.P. Papapetrou reports research support from Incyte, and personal fees from Celgene and Merck outside the submitted work. E.P. Papapetrou, G.W. Yeo, S. Vora, and E.C. Wheeler are coinventors on a patent application on the findings of this study (“Methods of treating and/or preventing cancer by modulating MAPK and ERK”; #63/181,578). No disclosures were reported by the other authors.
E.C. Wheeler: Formal analysis, investigation, methodology, writing–original draft, writing–review and editing. S. Vora: Formal analysis, investigation, methodology, writing–original draft, writing–review and editing. D. Mayer: Investigation and methodology. A.G. Kotini: Investigation and methodology. M. Olszewska: Investigation and methodology. S.S. Park: Formal analysis and methodology. E. Guccione: Resources. J. Teruya-Feldstein: Visualization and methodology. L. Silverman: Resources. R.K. Sunahara: Formal analysis, supervision, investigation, methodology, writing–review and editing. G.W. Yeo: Conceptualization, resources, formal analysis, supervision, funding acquisition, writing–original draft, project administration, writing–review and editing. E.P. Papapetrou: Conceptualization, resources, formal analysis, supervision, funding acquisition, writing–original draft, project administration, writing–review and editing.
The authors thank Elli Papaemmanuil and Elsa Bernard for help with the MDS and AML patient cohort mutational cooperation analysis. The authors also thank Poulikos Poulikakos, Arvin Dar, and Silvio Gutkind for advice. This work was supported by the US National Institutes of Health (NIH) grant R01HL137219 to E.P. Papapetrou and G.W. Yeo. Work in the Papapetrou laboratory was also supported by NIH grant R01CA225231, the New York State Stem Cell Board, the Pershing Square Sohn Cancer Research Alliance, and a Leukemia and Lymphoma Society Scholar Award. Work in the Yeo lab was also supported by NIH grants HG009889, HG004659, and HL137223. S. Vora was supported by the US NIH fellowship F31CA228295. E.C. Wheeler was supported by a National Science Foundation Graduate Research Fellowship and the NIH Ruth L. Kirschstein Institutional National Research Award T32 GM008666.
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