U2af1S34F and U2af1Q157R myeloid neoplasm-associated hotspot mutations induce distinct hematopoietic phenotypes in mice

U2af1S34F and U2af1Q157R myeloid neoplasm-associated hotspot mutations induce distinct hematopoietic phenotypes in mice | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article U2af1 S34F and U2af1 Q157R myeloid neoplasm-associated hotspot mutations induce distinct hematopoietic phenotypes in mice Michael O. Alberti, Sridhar Nonavinkere Srivatsan, Jin Shao, Dennis L. Fei, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6377810/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 06 May, 2026 Read the published version in Leukemia → Version 1 posted 11 You are reading this latest preprint version Abstract Recurrent somatic mutations in the spliceosome genes SF3B1 , SRSF2 , and U2AF1 are frequently identified in patients with myeloid neoplasms, such as myelodysplastic syndromes. We characterized the in vivo consequences of expressing two hotspot mutations in U2AF1 that code for the S34F and Q157R substitutions. Our results indicate that the two mutations induce distinct hematopoietic phenotypes in mice, suggesting that the U2AF1 S 34 F and U2AF1 Q157R mutations should not be conflated as they may impact disease pathogenesis differently in patients. Mice expressing U2af1 S 34 F have a more severe reduction in their blood and bone marrow cell counts and reduced stem cell repopulating ability, compared to mice expressing U2af1 Q157R . The expression and splicing of target genes are largely unique between the mutations, in both mouse and human samples, potentially driving the phenotypic differences induced by either mutation. The two mutations co-occur with different gene mutations in patients and are not equally represented across myeloid neoplasms, suggesting that multiple mechanisms likely drive U2AF1-mutant disease pathogenesis. Collectively, our results support that U2AF1 S 34 F and U2AF1 Q157R mutations induce distinct hematopoietic, gene expression, and RNA splicing phenotypes in vivo . Larger population studies will be needed to determine if these phenotypic changes translate into clinico-pathologic differences in patients warranting separate classification. Biological sciences/Cancer/Cancer genetics Biological sciences/Cancer/Cancer models Biological sciences/Cancer/Haematological cancer/Myelodysplastic syndrome Biological sciences/Cancer/Haematological cancer/Leukaemia/Acute myeloid leukaemia Biological sciences/Cancer/Haematological cancer/Myeloproliferative disease Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 INTRODUCTION Recurrent somatic mutations in a subset of spliceosome genes ( SF3B1 , SRSF2 , and U2AF1 ) are frequently identified (30–60% depending on disease phenotype) in patients afflicted with myelodysplastic syndromes (MDS), myeloproliferative neoplasms (MPN) such as myelofibrosis (MF), MDS/MPN overlap disorders such as chronic myelomonocytic leukemia (CMML), and secondary acute myeloid leukemia (sAML). 1 – 8 These heterozygous and mutually exclusive mutations are enriched in hotspot codons in these 3’ splicing factor proteins resulting in aberrant alternative mRNA splicing in hematopoietic cells. However, each mutant protein predominantly affects a distinct set of alternatively spliced downstream target genes suggesting that common downstream pathway alterations or cellular response to mutation expression, rather than specific shared splicing targets, may be responsible for MDS phenotypes, including dysplasia, ineffective hematopoiesis, and cytopenias. 9 – 11 U2AF1 provides a unique opportunity to address this question because it has two hotspot positions (serine 34 [S34] and glutamine 157 [Q157]) that are each commonly mutated in MDS and are associated with unique mRNA splicing consequences. 12 In addition, U2AF1 S34 and Q157 codon mutations co-occur with mutations in different genes (e.g., BCOR and ASXL1 , respectively) and patients with these mutations may have different hematopoietic phenotypes—highlighting that these mutations may induce distinct phenotypes. 13 – 16 We asked if the splicing differences resulting from S34F and Q157R mutations were thus associated with different or similar effects on hematopoiesis. To do so, we characterized and compared an established conditional S34F knock-in mouse model 17 to a new Cre/ lox conditional mouse model with the Q157R mutation knocked-in to the endogenous U2af1 locus, in order to directly study the hematopoietic phenotype, transcriptional, and mRNA splicing consequences of individual U2AF1 gene mutations in vivo . MATERIALS AND METHODS Animal models and experimental details Experiments were performed per institutional guidelines for care and use of laboratory animals and approved by the Institutional Animal Care and Use Committee of Washington University in St. Louis (WUSTL). U2af1 Q157R/+ ( M ini G ene Q157R or ‘MGQ157R’) conditional knock-in mice were generated by Biocytogen (Waltham, MA). A full description of the targeting construct is described in the Supplementary Methods . U2af1 S34F/+ (‘MGS34F’) conditional knock-in (Jackson Laboratory [JAX] Stock #032638, Bar Harbor, ME), 17 U2af1 fl/+ conditional knockout (JAX Stock #037015), 18 and Mx1 - Cre (JAX Stock #003556) 19 mice are described elsewhere. B6.SJL- Ptprc a Pepc b /BoyCrCrl or ‘CD45.1’ recipient mice were purchased from Charles River Laboratories (Stock #564). Heterozygous CD45.1/CD45.2 mice were bred by crossing C57BL/6J (B6; JAX Stock #000664) to B6.SJL- Ptprc a Pepc b /BoyJ (JAX Stock #002014). All mouse lines were on a B6 background. Genotyping primers are listed in Supplementary Table 1 . Details pertaining to bone marrow (BM) transplant, peripheral blood (PB) sampling and analysis, and flow cytometry setup and population gating are described in the Supplementary Methods . mRNA-sequencing (RNA-seq) and bioinformatics BM myeloid progenitor (c-kit + Lineage − Sca-1 − ; KL) cells were sorted into FACS buffer and gDNA-depleted total RNA were purified from cell pellets using the NucleoSpin RNA Plus XS Micro Kit (Macherey-Nagel, Allentown, PA) in RNase-free water. RNA concentration and RIN were measured by Bioanalyzer (Agilent, Santa Clara, CA) and then cDNA libraries for RNA-seq were prepared by KAPA RNA HyperPrep Kit with RiboErase (Cat #KK8560/61; Roche, Indianapolis, IN). Detailed library preparation and bioinformatic analyses are described in Supplementary Methods . Details pertaining to the bioinformatics and reanalysis of published MDS and AML RNA-seq datasets, analysis of U2AF1 hotspot mutation co-occurrence in myeloid malignancies, and confirmation of splicing changes in mouse KL cell and MDS/sAML patient samples are also described in the Supplementary Methods . All patients provided written consent on a protocol approved by the WUSTL Human Studies Committee. Clinical characteristics of patients who donated research samples are listed in Supplementary Table 2 . Statistics Data were analyzed and visualized using GraphPad Prism 10 software (Boston, MA). Statistical tests are described in each figure legend. P < 0.05 was considered statistically significant. RESULTS Establishing a mouse model with conditional knock-in of the Q157R mutation at the U2af1 locus. A conditional (Cre/ lox -mediated) knock-in of the S34F mutation at the U2af1 locus (MGS34F or U2af1 S34F/+ ) was previously generated (Fig. 1 A,B and Supplementary Fig. 1A ). 17 To allow for direct comparison with the MGS34F mouse, a similar strategy was used to generate a conditional (Cre/ lox -mediated) Q157R mutant allele at the endogenous U2af1 locus (MGQ157R or U2af1 Q157R/+ ) of B6 mice (Fig. 1 C and Supplementary Fig. 1B ). Successful introduction of the targeting vector at the U2af1 locus was confirmed by Southern blot and Sanger sequencing ( Supplementary Fig. 1C ). To confirm Cre/ lox -mediated hematopoietic expression of U2af1 Q157R mRNA and assess the short-term effects of U2AF1 Q157R in a non-transplant model (i.e., native hematopoiesis), we crossed heterozygous U2af1 Q157R/+ mice to Mx1-Cre transgenic mice (Fig. 1 D-G and Supplementary Fig. 1D-I ). Mx1-Cre is expressed in hematopoietic lineage cells following administration of polyinosinic-polycytidylic acid (pIpC). 19 Four weeks after pIpC treatment of U2af1 Q157R/+ ; Mx1-Cre mice, the U2af1 wild-type (WT) and Q157R alleles were expressed at similar levels in BM myeloid progenitor (KL) cells by targeted NGS amplicon sequencing of cDNA (Fig. 1 E). As expected, the WT and S34F alleles were also expressed at similar levels in BM KL cells from U2af1 S34F/+ ; Mx1-Cre mice and only the WT allele was detected in U2af1 +/+ ; Mx1-Cre control mice (Fig. 1 E). It was previously reported that the Q157R mutation in U2AF1 creates an alternative 5’ splice site that leads to expression of a minor U2AF1 isoform (termed ‘Q157Rdel’) with in-frame deletion of four amino acids immediately following the Q157R mutant codon. The U2af1 Q157R/+ mouse model recapitulates expression of the Q157Rdel isoform in BM KL cells (Fig. 1 E and Supplementary Fig. 1D ). U2AF1 S 34 F and U2AF1 Q157R cause different hematopoietic changes in mice. To determine if S34F and Q157R result in similar short-term effects on native hematopoiesis, we performed complete blood counts and flow cytometric analysis on PB samples from U2af1 Q157R/+ , U2af1 S34F/+ , and U2af1 +/+ control mice (all Mx1-Cre + ) four weeks after pIpC treatment. Consistent with previous characterization, 17 U2af1 S34F/+ mice had no change in platelet counts, modestly reduced red blood cell (RBC) counts and hemoglobin levels (with elevated mean corpuscular volume [MCV]), and markedly reduced white blood cell counts compared to U2af1 +/+ mice (Fig. 1 F). Flow cytometric analysis of U2af1 S34F/+ PB and BM demonstrated significant reductions in both myeloid and lymphoid lineages ( Supplementary Fig. 1E,F ). In contrast, U2af1 Q157R/+ mice had no significant PB or BM changes except for elevated MCV (Fig. 1 F and Supplementary Fig. 1E,F ). Assessment of BM hematopoietic stem and progenitor cells (HSPC) four weeks after pIpC treatment revealed that U2af1 S34F/+ mice had significantly reduced numbers of short-term hematopoietic stem cell (ST-HSC), KL, and common myeloid progenitor (CMP) populations with increased numbers of multipotent progenitor (MPP)2 and MPP3 populations compared with control mice (Fig. 1 G). U2af1 Q157R/+ mice also had significantly reduced numbers of ST-HSC and KL populations and non-significant reductions in both CMP and megakaryocyte-erythroid progenitor (MEP) cells compared with control mice (Fig. 1 G). U2af1 S34F/+ mice had a significant block in erythroid development in the BM and spleen, with an increased proportion of immunophenotypically defined nucleated erythroblasts (Ter119 lo/hi CD71 hi ) and a decreased proportion of enucleated erythrocytes (Ter119 hi CD71 lo ). In contrast, U2af1 Q157R/+ mice had a smaller but non-significant, increase in Ter119 hi CD71 hi cells in the spleen ( Supplementary Fig. 1G-I ). To better evaluate the cell-intrinsic effects of both mutants on hematopoiesis, we transplanted BM from U2af1 Q157R/+ , U2af1 S34F/+ , or U2af1 +/+ control mice (CD45.2 + ; all Mx1-Cre + ) into lethally irradiated WT congenic recipient mice (CD45.1 + ). Following engraftment, we treated mice (including controls) with pIpC to induce expression of S34F and Q157R in donor-derived cells (Fig. 2 A). Four weeks after pIpC treatment, PB (Fig. 2 B,C) and BM changes ( Supplementary Fig. 2A,C ) reflected similar overall trends observed in native hematopoiesis (Fig. 1 F and Supplementary Fig. 1E ) for both mutant mice. At 24 weeks, both mutant mice had significantly reduced PB RBC counts with increased MCV, as well as decreased hemoglobin in U2af1 S34F/+ mice. U2af1 Q157R/+ mice also had mildly increased platelet counts (Fig. 2 B). U2af1 S34F/+ mice had significantly reduced PB and BM myeloid and lymphoid lineage cells, while U2af1 Q157R/+ mice had significantly decreased PB monocytes and a non-significant increase in BM monocytes (Fig. 2 C and Supplementary Fig. 2D ). Although myeloid and lymphoid lineages were significantly decreased in U2af1 S34F/+ mouse spleens at 4 weeks, there were no significant changes at 24 weeks ( Supplementary Fig. 2B,E ). HSPC populations reflected similar significant overall trends at 24 weeks compared to 4 weeks for U2af1 S34F/+ mice (Fig. 2 D and Supplementary Fig. 2C ). U2af1 Q157R/+ mice also showed similar, but non-significant, trends in HSPC population numbers at 24 weeks compared to 4 weeks (Fig. 2 D and Supplementary Fig. 2C ). U2af1 S34F/+ HSCs are significantly more impaired than U2af1 Q157R/+ HSCs in BM repopulation assays. To compare the effects of S34F or Q157R expression on HSC reconstitution capacity, we performed competitive BM transplantation experiments. Lethally irradiated WT congenic recipient mice (CD45.1 + ) were transplanted with whole BM ‘test’ cells from U2af1 Q157R/+ , U2af1 S34F/+ , or U2af1 +/+ control mice (CD45.2 + ; all Mx1-Cre + ) mixed with an equal number of competitor BM cells from WT congenic mice (CD45.1 + /CD45.2 + ). Following engraftment, we treated mice (including controls) with pIpC to induce expression of S34F and Q157R in donor-derived cells (Fig. 3 A). Consistent with previous characterization, 17 we observed significant multi-lineage reductions in PB, BM, and spleen donor cell chimerism (CD45.2 + ) for U2af1 S34F/+ compared to U2af1 +/+ test cells (Fig. 3 B-D and Supplementary Fig. 3 ). In contrast, the reduction in overall and multilineage PB donor cell chimerism for U2af1 Q157R/+ compared to U2af1 +/+ test cells was less severe relative to U2af1 S34F/+ test cells (Fig. 3 B,C). In addition, there were variable reductions in donor cell chimerism of PB, BM, and spleen myeloid lineages for U2af1 Q157R/+ compared to U2af1 +/+ test cells (Fig. 3 C,D and Supplementary Fig. 3 ). Donor cell chimerism for all BM HSPC populations were significantly reduced for U2af1 S34F/+ compared to U2af1 +/+ test cells (Fig. 3 E). However, reduced U2af1 Q157R/+ donor cell chimerism was restricted to the HSC and MPP2 populations, but not to the same degree as for U2af1 S34F/+ (Fig. 3 E). Hemizygous U2af1 Q157R/− and U2af1 S34F/− HSCs are both severely impaired in BM repopulation assays. We previously demonstrated that cell survival and reconstitution capacity are severely reduced for HSCs that express mutant U2AF1 S 34 F without WT U2AF1 expression (hemizygous U2af1 S34F/− ). 18 Given the mild reconstitution defect observed for U2af1 Q157R/+ cells, we hypothesized that mutant U2AF1 Q157R cells may not require the expression of WT U2AF1 for cell survival. To test this, we performed competitive BM transplantation experiments using test cells from three additional genotypes of mice: U2af1 Q157R/− , U2af1 S34F/− , and U2af1 +/− mice (all Mx1-Cre + ; Fig. 4 A). Hemizygous conditional knock-in mice were generated by crossing heterozygous floxed mutant (S34F or Q157R) mice to heterozygous floxed knockout mice. Consistent with previous characterization, 18 we noted a rapid and significant loss in mature cell and HSPC donor cell chimerism (CD45.2 + ) in the PB, BM, and spleen for hemizygous U2af1 S34F/− (but not U2af1 +/− ) compared to U2af1 +/+ test cells following administration of pIpC (Fig. 4 B-D and Supplementary Fig. 4A,B ). We also observed an identical rapid loss in mature cell and HSPC chimerism for hemizygous U2af1 Q157R/− compared to U2af1 +/+ test cells (Fig. 4 B-D and Supplementary Fig. 4A,B ). This indicates that the expression of WT U2AF1 is required for the viability of either U2AF1 S 34 F or U2AF1 Q157R mutant expressing HSCs, consistent with U2AF1 being a haplo-essential gene, 18 and reinforcing that the U2af1 Q157R allele impairs U2AF1 function despite the less severe phenotype compared to U2af1 S 34 F . U2AF1 S 34 F and U2AF1 Q157R induce distinct gene expression changes in mouse myeloid progenitor cells. To characterize the effects of mutant U2AF1 on gene expression in vivo , we performed RNA-seq of total RNA (rRNA-depleted) from BM myeloid progenitor (KL) cells from U2af1 Q157R/+ , U2af1 S34F/+ , or U2af1 +/+ control mice (all Mx1-Cre + ) under native hematopoiesis conditions (as in Fig. 1 D). KL cells were isolated by FACS at 4 weeks after completion of pIpC injections and the variant allele frequencies of the S34F and Q157R mutations were near 50% ( Supplementary Fig. 5A ). Unsupervised principal component analysis of gene expression values (N = 19312 genes) segregated U2af1 Q157R/+ , U2af1 S34F/+ , and U2af1 +/+ KL cells (Fig. 5 A and Supplementary Table 3 ). Reanalysis of U2af1 S34F/+ Native KL RNA-seq data published by Fei et al . 17 demonstrated a strong concordance in gene expression changes with our U2af1 S34F/+ KL data ( Supplementary Fig. 5B,C ). In our dataset, we identified 185 differentially expressed genes (DEGs; FDR 1) in U2af1 S34F/+ compared to U2af1 +/+ control mice (Fig. 5 B) and 77 DEGs in U2af1 Q157R/+ KL cells (Fig. 5 C). There were only 12 DEGs shared between U2af1 S34F/+ and U2af1 Q157R/+ KL cells (4.8%; Fig. 5 D) with no overlap in gene ontology (GO) analysis ( Supplementary Fig. 5D,E and Supplementary Table 4 ). Gene set enrichment analysis (GSEA) revealed significant positive enrichment of the p53 pathway in U2af1 S34F/+ KL cells and negative enrichment of immune response related Hallmark pathways in both U2af1 S34F/+ and U2af1 Q157R/+ KL cells compared to U2af1 +/+ KL cells (Fig. 5 E). U2AF1 S 34 F and U2AF1 Q157R induce distinct alternative pre-mRNA splicing changes in myeloid progenitor cells. Using the same bulk RNA-seq data, we next characterized the effects of mutant U2AF1 on alternative mRNA splicing in vivo . We employed replicate multivariate analysis of transcript splicing (rMATS) 20 to assess differential alternative pre-mRNA splicing of five different types of annotated splicing events (alternative 3’ or 5’ splice sites [A3SS, A5SS], mutually exclusive exons [MXE], retained introns [RI], and skipped exons [SE]) in U2af1 Q157R/+ , U2af1 S34F/+ , and U2af1 +/+ KL cells. Unsupervised principal component analysis of inclusion ratios (referred to as ‘percent spliced-in’ or ‘PSI’) for all annotated alternative splicing events (N = 11580) revealed that global alternative pre-mRNA splicing is distinct between U2af1 Q157R/+ , U2af1 S34F/+ , and U2af1 +/+ KL cells (Fig. 6 A and Supplementary Tables 5–7 ). We then applied rMATS to identify 1048 and 580 differentially spliced events (DSEs; FDR 0.05 vs U2af1 +/+ ) in U2af1 S34F/+ and U2af1 Q157R/+ KL cells, respectively (Fig. 6 B and Supplementary Table 8 ). We also applied our rMATS analysis pipeline to the U2af1 S34F/+ Native KL RNA-seq dataset published by Fei et al . 17 ( Supplementary Fig. 6A,B ) and observed a strong concordance (i.e., unidirectional ΔPSI values) between DSEs shared between the two U2af1 S34F/+ KL datasets ( Supplementary Fig. 6C ). Thus, rMATS analysis of independent RNA-seq data demonstrates that the U2af1 S34F/+ mouse model produces robust and reproducible gene expression and alternative pre-mRNA splicing changes in hematopoietic cells in vivo ( Supplementary Figs. 5B,6C ). In line with previous studies of U2AF1 mutant cell lines and patient HSPC, SE events represented the majority of DSEs identified in U2AF1 mutant mouse KL cells (Fig. 6 B and Supplementary Fig. 6A ). 17 , 21 , 22 U2af1 S34F/+ SE DSEs also favored exon exclusion (‘skipping’) over exon inclusion. 23 Of note, U2af1 Q157R/+ DSEs were more equally distributed between RI and SE events (Fig. 6 B). The overlap of DSE shared between U2af1 S34F/+ and U2af1 Q157R/+ KL cells was low (125 events or 8.3%; Fig. 6 C). Conversion of DSE to differentially spliced genes (DSG) revealed 196 genes (17.5%) aberrantly spliced in common between the two mutants (Fig. 6 D). GO analysis revealed that DSGs from U2af1 S34F/+ KL cells were most significantly enriched in mRNA binding and metabolism gene sets, as well as histone post-translational modification and stress granule 23 related gene sets ( Supplementary Fig. 6D and Supplementary Table 9 ). DSGs from U2af1 Q157R/+ KL cells were enriched in mRNA gene sets to a weaker extent than U2af1 S34F/+ ( Supplementary Fig. 6D and Supplementary Table 9 ). Analysis of consensus 3’ splice site (3’SS) sequences from differentially spliced SE events in U2af1 S34F/+ and U2af1 Q157R/+ KL cells confirmed previous dependencies identified in U2AF1 mutant cell lines and patient HSPC. Specifically, exon inclusion favored a C and exon exclusion favored a T at the − 3 position of the 3’SS in U2af1 S34F/+ cells (Fig. 6 E, middle). In contrast, exon inclusion favored a G and exon exclusion favored an A at the + 1 position of the 3’SS in U2af1 Q157R/+ cells (Fig. 6 E, right). Overall, these findings highlight that the U2AF1 S 34 F and U2AF1 Q157R mutants induce significant but distinct changes to alternative mRNA splicing in vivo . U2af1 S34F/+ and U2af1 Q157R/+ mouse models recapitulate alternative pre-mRNA splicing changes found in MDS and AML patients. To assess how well alternative splicing changes in mouse KL cells recapitulate changes seen in MDS and AML patient hematopoietic cells, we performed a meta-analysis using publicly available RNA-seq data from three published studies. 9 , 11 , 24 Each study included 2–10 U2AF1 S 34 F and only 1–2 U2AF1 Q157R patients. Therefore, U2AF1 R156H and U2AF1 Q157(P/R) patient samples were grouped together (N = 4–5 U2AF1 R156H/Q157(P/R) patients per study; Fig. 7 A) consistent with previous studies demonstrating similar 3’SS sequence dependencies. 21 , 22 In each study, samples from MDS/AML patients who did not have identifiable mutations in SF3B1 or SRSF2 were used as a comparator (Splicing Factor [SF] WT ). To allow for a more rigorous analysis of differential splicing, we reanalyzed the FASTQ files for each study using the same analysis workflows and applied the same significance thresholds (FDR 0.05 vs SF WT ) as used for the analysis of mouse KL cells. Using this approach, we credentialed each of the three datasets (referred to as Madan, 11 Pellagatti, 9 and Beat AML 24 ) (Fig. 7 A and Supplementary Fig. 7A-I and Supplementary Tables 10–15 ). Specifically, SE events were the most frequent DSE type identified in each study for S34F and R156/Q157 ( Supplementary Fig. 7A-C ) and these events favored the characteristic consensus 3’SS sequence dependencies identified previously ( Supplementary Fig. 7G-I ). 9 , 17 , 21 , 22 , 25 , 26 To increase rigor of our meta-analysis we prioritized only the DSEs that were shared between at least two of the three MDS/AML datasets for either U2AF1 S 34 F or U2AF1 R156/Q157 (Fig. 7 B and Supplementary Table 16 ). The distribution of these DSEs was similar to each individual dataset with SE events still representing the majority event type in U2AF1 mutant MDS/AML cells (Fig. 7 C). As in the mice, the overlap of DSE shared between U2AF1 S 34 F and U2AF1 R156/Q157 MDS/AML cells was low (144 of 1978 events or 7.3%; Fig. 7 D). Conversion of DSE to DSG revealed a total of 284 of 1305 genes (21.8%) aberrantly spliced in common between the two mutants (Fig. 7 E). The overlap of DSG identified in human and mouse cells revealed that approximately 20% of aberrantly spliced genes in KL (mouse) cells were also mis-spliced in MDS/AML (human) cells for both U2AF1 S 34 F (17.6% shared) and U2AF1 Q157R (19.7% shared) mutants (Fig. 7 F,G). GO analysis revealed that shared S34F DSGs were most significantly enriched in mRNA binding and metabolism gene sets as well as stress granule 23 and mRNA translation related gene sets (Fig. 7 H and Supplementary Table 17 ). Shared Q157R DSGs were less significantly enriched in mRNA gene sets than S34F. Histone binding and DNA damage response gene sets were among some of the significantly enriched gene sets for Q157R DSGs (Fig. 7 H and Supplementary Table 17 ). We validated several of these putatively shared aberrant splicing changes identified by rMATS analysis by performing RT-PCR followed by gel electrophoresis of RNA isolated from additional mouse KL cell samples (N = 4 per genotype) and MDS patient samples (N = 4–9 per genotype). Consistent with previous observations, 17 , 25 – 28 we confirmed aberrant splicing of functionally relevant transcripts ( H2AFY and GNAS ) in U2AF1 S 34 F mutant mouse KL and MDS cells (Fig. 7 I,J). We also demonstrate that aberrantly spliced transcripts ( MPHOSPH9 , SETD5 , ATP6V0A1 , and CLIP1 ) in U2AF1 Q157R mutant MDS patient cells are similarly mis-spliced in KL cells from U2af1 Q157R/+ mice (Fig. 7 I,K-L). Aberrant splicing of CLIP1 is one example of an SE event that is differentially spliced in opposite directions by U2AF1 S 34 F (increased exon inclusion) and U2AF1 Q157R (increased exon skipping/exclusion) in mouse and human cells (Fig. 7 L), further highlighting the distinct splicing differences induced by these two U2AF1 mutants. U2AF1 R156/Q157 mutations are enriched in patients with CMML and MPN compared to U2AF1 S 34 F mutations. Given the differences in gene expression, splicing, and hematopoietic phenotypes induced by U2af1 S34F/+ and U2af1 Q157R/+ mutations in mice, we asked if the two hotspot mutations were differentially enriched in various myeloid neoplasms. We identified 487 patients with a diagnosis of AML, sAML (from MDS), MDS, CMML, or MPN who had a U2AF1 mutation based on available sequencing data and calculated the proportion of patients with U2AF1 R156/Q157 or U2AF1 S 34 mutations (see Supplementary Methods ). We observed that U2AF1 R156/Q157 mutations were more common in CMML and MPN patients, U2AF1 S 34 mutations more common in sAML and AML patients, and a similar proportion of both mutations occurred in MDS (Fig. 8 A and Supplementary Table 18 ). The co-occurrence of U2AF1 and signaling gene mutations also differed across myeloid neoplasms, with NRAS and FLT3 mutations being more common with S34 mutations and CBL , PTPN11 and CSF3R mutations more common with R156/Q157 mutations. Similar to previous reports by our group and others, we also observed preferential co-occurrence of other gene mutations with U2AF1 R156/Q157 (e.g., ASXL1 ) or U2AF1 S 34 F (e.g., BCOR ) mutations in MDS patients (Fig. 8 B and Supplementary Tables 19–20 ). 15 , 16 DISCUSSION In this study, we characterized the in vivo consequences of expressing two myeloid neoplasm-associated hotspot mutations in U2AF1 that code for S34F and Q157R substitutions. Our results indicate that the two mutations induce distinct hematopoietic phenotypes in mice, suggesting that the U2af1 S 34 F and U2af1 Q157R mutations should not be conflated as they may impact disease pathogenesis differently in patients. Mice expressing U2af1 S 34 F have a more severe reduction in their PB and BM cell counts, and reduced HSPCs repopulating ability, compared to mice expressing U2af1 Q157R . The expression and splicing of the majority of target genes are unique between the mutations, in both mouse and human samples, potentially driving the phenotypic differences induced by the two mutations. The two mutations co-occur with different gene mutations and are not equally represented in various myeloid neoplasms, suggesting that multiple mechanisms are likely to drive the pathogenesis of U2AF1 mutant myeloid diseases. Our results add to the growing body of literature highlighting the paradigm that different hotspot mutations in a specific cancer gene can lead to distinct functional consequences and should, therefore, not necessarily be conflated. In one of the more well studied examples, different KRAS hotspot mutations (e.g., G12, G13, Q61) lead to varying levels of KRAS activation through modulation of distinct biochemical properties of KRAS. 29 In turn, different KRAS hotspot mutations confer different prognostic value in various cancers (e.g., colorectal cancer) and are predictive of response to chemotherapy and/or targeted therapies. 29 Similarly, the prognostic significance of distinct SF3B1 mutations can be different in MDS, including their impact on overall survival. 30 , 31 With respect to U2AF1 , a prognostic scoring model for MF (MIPSS70 + v2.0) now incorporates the negative impact of Q157 (but not S34) codon mutations. 32 U2AF1 S34 and Q157 codon-specific clinical characteristics have also been reported in MDS. 33 , 34 Further studies are needed to fully understand the functional impact of each U2AF1 hotspot mutation in patients and determine whether these differences confer consistent prognostic or therapeutic value across the spectrum of myeloid malignancies. Enrichment of U2AF1 S 34 vs U2AF1 Q157 mutations in different myeloid diseases suggest that mutations may contribute to the disease phenotype by differences in the target genes that they dysregulate and/or cooperating gene mutations. Identifying and validating the key target genes that are dysregulated and confer mutation-specific cellular phenotypes will require future in vivo functional studies. Additionally, based on differences in hotspot mutations in other cancers, the cellular ‘soil’ that a S34 or Q157 mutation occurs in likely also matters. In U2AF1 -mutated solid tumors, particularly lung adenocarcinomas and endometrial cancers, S34 codon mutations are highly enriched compared to Q157 codon mutations. 35 This observation is not specific to U2AF1 , as SF3B1 R625 codon mutations are enriched in uveal and cutaneous melanomas, whereas K700 mutations are more common in breast cancer and chronic lymphoid leukemia specimens. 35 The subtle phenotype in the U2af1 Q157R/+ mouse, including lack of severe cytopenias and possibly a slight increase in platelets, could make Q157R cells more permissive to transformation with a MPN-associated cooperating mutation (e.g., CSF3R ) resulting in higher blood counts, something that will require future studies. In contrast, S34F induces cytopenias in mice and may contribute to cytopenias seen in MDS. In addition, S34 and Q157 do not cooperate with the same mutations in MDS (e.g., BCOR with S34 > Q157 and ASXL1 with Q157 > S34), 36 and this could impact mutation-associated phenotypes in patients. 15 , 37 Finally, U2AF1 hotspot mutation phenotypes could be influenced by the order of cooperating gene mutation acquisition (i.e., U2AF1 mutation occurring before or after a cooperating gene mutation) or the presence of hematopoietic stressors, requiring future experiments. Collectively, our results support that U2AF1 S 34 F and U2AF1 Q157R mutations induce distinct hematopoietic, gene expression, and RNA splicing phenotypes in vivo . Larger population studies will be needed to determine if these phenotypic changes translate into clinico-pathologic differences in patients warranting separate classification. Declarations ACKNOWLEDGEMENTS The authors thank Harold E. Varmus for the gift of the MGQ157R mice; William C. Eades, Daniel Schweppe, Michael Savio, and Matt Patana of the the Alvin J. Siteman Cancer Center at Washington University School of Medicine (WUSM) and Barnes-Jewish Hospital (St. Louis, MO) for the use of the Siteman Flow Cytometry Core and sorting assistance; Zev J. Greenberg for sorting assistance; Nichole M. Helton for help with RNA-seq preparation; the McDonnell Genome Institute (MGI) for RNA-seq; Jessica Hoisington-Lopez and MariaLynn Crosby of the DNA Sequencing Innovation Lab (DSIL) at WUSM Center for Genome Sciences & Systems Biology for help with amplicon sequencing; Eric J. Duncavage and Kiran Vij for hematopathology expertise; John D. Pfeifer for critical support; and Timothy J. Ley, Daniel C. Link, and members of the Walter Lab for useful discussions. This work was supported by grants to M.J.W. from the Edward P. Evans Foundation, Taub Foundation, Lottie Caroline Hardy Trust, Foundation for Barnes-Jewish Hospital Cancer Frontier Fund, the National Cancer Institute (NCI) of the National Institutes of Health (NIH) (P01 CA101937, Timothy J. Ley, PI; P50 CA171963, Daniel C. Link, PI), and the Leukemia & Lymphoma Society (7024-21). M.O.A. was supported by career development awards from the Edward P. Evans Foundation and NCI and National Heart, Lung, and Blood Institutes (NHLBI) of the NIH under the award numbers K12 CA167540 (Washington University Paul Calabresi) and K08 HL159354. M.Z. was supported by an American Society of Hematology (ASH) Physician-Scientist Career Development Award. C.P. was supported by an ASH Minority Hematology Graduate Award (MHGA) and a NIH/NCI award (F31 CA284751). O.A.-W. is supported by the Neil S. Hirsch Foundation, Edward P. Evans Foundation, Break Through Cancer, NIH/NCI (R01 CA251138, R01 CA242020, R01 CA283364, and P50 CA254838), and NIH/NHLBI (R01 HL128239), and the Leukemia & Lymphoma Society. T.A.G. is supported by the Edward P. Evans Foundation, NIH/NCI (P50 CA171963), and the Leukemia & Lymphoma Society (7024-21). Sample banking was provided by the Genomics of Acute Myeloid Leukemia Program Project Grant (P01 CA101937, Timothy J. Ley, PI) and Specialized Program of Research Excellence in Acute Myeloid Leukemia (P50 CA171963, Daniel C. Link, PI). We thank the Alvin J. Siteman Cancer Center at WUSM and Barnes-Jewish Hospital, as well as the Institute of Clinical and Translational Sciences (ICTS) at Washington University in St. Louis, for the use of the Siteman Flow Cytometry Core, the Tissue Procurement Core, and the Genome Technology Access Center. The Siteman Cancer Center is supported in part by an NCI Cancer Center Support Grant (P30 CA091842) and the ICTS is funded by the NIH NCATS Clinical and Translational Science Award (CTSA) program (UL1 TR002345). The content of this publication is solely the responsibility of the authors and does not necessarily represent the official views of the NIH. AUTHOR CONTRIBUTIONS M.O.A., D.L.F., T.A.G., O.A.-W., and M.J.W. were responsible for conceptualization; M.O.A., S.N.S., D.L.F., and M.J.W. were responsible for methodology; M.O.A., S.N.S., J.S., M.Z., C.C., and S.G. performed investigation; M.O.A., S.N.S., and M.J.W. wrote the original draft manuscript; M.O.A., S.N.S., and M.J.W. reviewed and edited the manuscript; M.J.W. was responsible for funding acquisition; and M.J.W. provided supervision. COMPETING INTERESTS O.A.-W. is a founder and scientific advisor of Codify Therapeutics, holds equity, and receives research funding from this company. O.A.-W. has served as a consultant for Amphista Therapeutics, and MagnetBio, and is on scientific advisory boards of Envisagenics Inc. and Harmonic Discovery Inc. O.A.-W. has received research funding from Astra Zeneca, Nurix Therapeutics, and Minovia Therapeutics, unrelated to this study. The remaining authors declare no competing interests.I ADDITIONAL INFORMATION Supplementary information The online version contains supplementary material. DATA AVAILABILITY Data are available on request to the corresponding author. RNA-seq data generated from this study have been deposited in the National Center for Biotechnology Information (NCBI) Gene Expression Omnibus (GEO) database (accession # GSE282060). References Damm F, Kosmider O, Gelsi-Boyer V, et al. Mutations affecting mRNA splicing define distinct clinical phenotypes and correlate with patient outcome in myelodysplastic syndromes. Blood . 2012;119(14):3211–3218. Haferlach T, Nagata Y, Grossmann V, et al. Landscape of genetic lesions in 944 patients with myelodysplastic syndromes. Leukemia . 2014;28(2):241–247. Papaemmanuil E, Gerstung M, Malcovati L, et al. Clinical and biological implications of driver mutations in myelodysplastic syndromes. Blood . 2013;122(22):3616–3627; quiz 3699. Barraco D, Elala YC, Lasho TL, et al. Molecular correlates of anemia in primary myelofibrosis: a significant and independent association with U2AF1 mutations. Blood Cancer J. 2016;6(4):e415. Kandarpa M, Robinson D, Wu Y-M, et al. Broad next generation integrated sequencing of myelofibrosis identifies disease-specific and age-related genomic alterations. Clin. Cancer Res. Off. J. Am. Assoc. Cancer Res. 2024; Palomo L, Meggendorfer M, Hutter S, et al. Molecular landscape and clonal architecture of adult myelodysplastic/myeloproliferative neoplasms. Blood . 2020;136(16):1851–1862. Patel BJ, Przychodzen B, Thota S, et al. Genomic determinants of chronic myelomonocytic leukemia. Leukemia . 2017;31(12):2815–2823. Lindsley RC, Mar BG, Mazzola E, et al. Acute myeloid leukemia ontogeny is defined by distinct somatic mutations. Blood . 2015;125(9):1367–1376. Pellagatti A, Armstrong RN, Steeples V, et al. Impact of spliceosome mutations on RNA splicing in myelodysplasia: dysregulated genes/pathways and clinical associations. Blood . 2018;132(12):1225–1240. Hershberger CE, Moyer DC, Adema V, et al. Complex landscape of alternative splicing in myeloid neoplasms. Leukemia . 2021;35(4):1108–1120. Madan V, Li J, Zhou S, et al. Distinct and convergent consequences of splice factor mutations in myelodysplastic syndromes. Am. J. Hematol. 2020;95(2):133–143. Yoshida K, Sanada M, Shiraishi Y, et al. Frequent pathway mutations of splicing machinery in myelodysplasia. Nature . 2011;478(7367):64–69. Tefferi A, Mudireddy M, Finke CM, et al. U2AF1 mutation variants in myelodysplastic syndromes and their clinical correlates. Am. J. Hematol. 2018;93(6):E146–E148. Tefferi A, Finke CM, Lasho TL, et al. U2AF1 mutation types in primary myelofibrosis: phenotypic and prognostic distinctions. Leukemia . 2018;32(10):2274–2278. Badar T, Vanegas YAM, Nanaa A, et al. 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Shen S, Park JW, Lu Z, et al. rMATS: robust and flexible detection of differential alternative splicing from replicate RNA-Seq data. Proc. Natl. Acad. Sci. U. S. A. 2014;111(51):E5593-5601. Pangallo J, Kiladjian J-J, Cassinat B, et al. Rare and private spliceosomal gene mutations drive partial, complete, and dual phenocopies of hotspot alterations. Blood . 2020;135(13):1032–1043. Ilagan JO, Ramakrishnan A, Hayes B, et al. U2AF1 mutations alter splice site recognition in hematological malignancies. Genome Res. 2015;25(1):14–26. Biancon G, Joshi P, Zimmer JT, et al. Precision analysis of mutant U2AF1 activity reveals deployment of stress granules in myeloid malignancies. Mol. Cell . 2022;82(6):1107-1122.e7. Tyner JW, Tognon CE, Bottomly D, et al. Functional genomic landscape of acute myeloid leukaemia. Nature . 2018;562(7728):526–531. Shirai CL, Ley JN, White BS, et al. Mutant U2AF1 Expression Alters Hematopoiesis and Pre-mRNA Splicing In Vivo. Cancer Cell . 2015;27(5):631–643. Yip BH, Steeples V, Repapi E, et al. The U2AF1S34F mutation induces lineage-specific splicing alterations in myelodysplastic syndromes. J. Clin. Invest. 2017;127(6):2206–2221. Kim SP, Srivatsan SN, Chavez M, et al. Mutant U2AF1-induced alternative splicing of H2afy (macroH2A1) regulates B-lymphopoiesis in mice. Cell Rep. 2021;36(9):109626. Wheeler EC, Vora S, Mayer D, et al. Integrative RNA-omics Discovers GNAS Alternative Splicing as a Phenotypic Driver of Splicing Factor-Mutant Neoplasms. Cancer Discov. 2022;12(3):836–855. Haigis KM. KRAS Alleles: The Devil Is in the Detail. Trends Cancer . 2017;3(10):686–697. Dalton WB, Helmenstine E, Pieterse L, et al. The K666N mutation in SF3B1 is associated with increased progression of MDS and distinct RNA splicing. Blood Adv. 2020;4(7):1192–1196. Kanagal-Shamanna R, Montalban-Bravo G, Sasaki K, et al. Only SF3B1 mutation involving K700E independently predicts overall survival in myelodysplastic syndromes. Cancer . 2021;127(19):3552–3565. Tefferi A, Guglielmelli P, Lasho TL, et al. MIPSS70+ Version 2.0: Mutation and Karyotype-Enhanced International Prognostic Scoring System for Primary Myelofibrosis. J. Clin. Oncol. Off. J. Am. Soc. Clin. Oncol. 2018;36(17):1769–1770. Li B, Liu J, Jia Y, et al. Clinical features and biological implications of different U2AF1 mutation types in myelodysplastic syndromes. Genes. Chromosomes Cancer . 2018;57(2):80–88. Wang H, Guo Y, Dong Z, et al. Differential U2AF1 mutation sites, burden and co-mutation genes can predict prognosis in patients with myelodysplastic syndrome. Sci. Rep. 2020;10(1):18622. Seiler M, Peng S, Agrawal AA, et al. Somatic Mutational Landscape of Splicing Factor Genes and Their Functional Consequences across 33 Cancer Types. Cell Rep. 2018;23(1):282-296.e4. Bernard E, Hasserjian RP, Greenberg PL, et al. Molecular taxonomy of myelodysplastic syndromes and its clinical implications. Blood . 2024;144(15):1617–1632. Pritzl SL, Gurney M, Badar T, et al. Clinical and molecular spectrum and prognostic outcomes of U2AF1 mutant clonal hematopoiesis- a prospective mayo clinic cohort study. Leuk. Res. 2023;125:107007. Supek F, Bošnjak M, Škunca N, Šmuc T. REVIGO summarizes and visualizes long lists of gene ontology terms. PloS One . 2011;6(7):e21800. Additional Declarations Yes there is potential conflict of interest. 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As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6377810","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":439785727,"identity":"060b228e-daa9-436a-bd59-12513ee3602a","order_by":0,"name":"Michael O. 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Cre-mediated recombination of the floxed MGS34F or MGQ157R alleles results in removal of the WT MG cassette and conditional expression of \u003cem\u003eU2af1\u003c/em\u003e\u003csup\u003eS34F\u003c/sup\u003e or \u003cem\u003eU2af1\u003c/em\u003e\u003csup\u003eQ157R\u003c/sup\u003e, respectively, from the mouse endogenous locus. 3XpA, three repeats of the SV40 late polyadenylation signal. See \u003cstrong\u003eSupplementary Fig. 1A\u003c/strong\u003e-\u003cstrong\u003eC\u003c/strong\u003e for targeting vectors and additional locus detail. (\u003cstrong\u003eD\u003c/strong\u003e) Non-transplant (native hematopoiesis) assay design. \u003cem\u003eU2af1\u003c/em\u003e\u003csup\u003e+/+\u003c/sup\u003e, \u003cem\u003eU2af1\u003c/em\u003e\u003csup\u003eS34F/+\u003c/sup\u003e, or \u003cem\u003eU2af1\u003c/em\u003e\u003csup\u003eQ157R/+\u003c/sup\u003e mice (all \u003cem\u003eMx1\u003c/em\u003e-\u003cem\u003eCre\u003c/em\u003e\u003csup\u003e+\u003c/sup\u003e) were treated with three doses of pIpC at 6-12 weeks of age. (\u003cstrong\u003eE\u003c/strong\u003e) Assessment of S34F and Q157R mRNA expression levels in BM KL cells at 4 weeks post-pIpC treatment. cDNA was prepared from KL cells for targeted NGS amplicon sequencing of the S34 (left) and Q157 (right) codons. The fraction of reads matching either WT or mutated alleles is plotted. \u003cem\u003eU2af1\u003c/em\u003e\u003csup\u003e+/+\u003c/sup\u003e mice were assessed for both S34F and Q157R/Q157Rdel alleles. The Q157R mutation in \u003cem\u003eU2af1\u003c/em\u003e creates an alternative 5’ splice site that leads to expression of a minor \u003cem\u003eU2af1\u003c/em\u003e isoform (termed “Q157Rdel”) with in-frame deletion of four amino acids immediately following the Q157R mutant codon. See also \u003cstrong\u003eSupplementary Fig. 1D\u003c/strong\u003e. N=3 mice per genotype. (\u003cstrong\u003eF\u003c/strong\u003e) Complete blood count analysis (white blood cell [WBC], red blood cell [RBC], and platelet [PLT] counts, Hb [hemoglobin], and RBC mean corpuscular volume [MCV]) of PB samples from mice at 4 weeks post-pIpC. N=18-26 mice per genotype pooled from five independent experiments. (\u003cstrong\u003eG\u003c/strong\u003e) Absolute cell counts of BM HSPC populations (KLS [c-kit\u003csup\u003e+\u003c/sup\u003eLineage\u003csup\u003e−\u003c/sup\u003eSca-1\u003csup\u003e+\u003c/sup\u003e], KL [c-kit\u003csup\u003e+\u003c/sup\u003eLineage\u003csup\u003e−\u003c/sup\u003eSca-1\u003csup\u003e−\u003c/sup\u003e], long- and short-term HSC [LT-HSC and ST-HSC], multipotent progenitors [MPP2, MPP3, and MPP4], common myeloid progenitors [CMP], granulocyte-macrophage progenitors [GMP], and megakaryocyte-erythrocyte progenitors [MEP]) were determined by flow cytometric analysis at 4 weeks post-pIpC. N=4 mice per genotype. See also \u003cstrong\u003eSupplementary Fig. 1E-G\u003c/strong\u003e. Results represent the mean ± standard deviation (SD) (\u003cstrong\u003eE\u003c/strong\u003e-\u003cstrong\u003eG\u003c/strong\u003e). One-way analysis of variance (ANOVA) with Tukey multiple comparison correction (\u003cstrong\u003eF\u003c/strong\u003e-\u003cstrong\u003eG\u003c/strong\u003e) was used for the comparison of groups. *\u003cem\u003eP\u003c/em\u003e\u0026nbsp;\u0026lt; 0.05; **\u003cem\u003eP\u003c/em\u003e\u0026nbsp;\u0026lt; 0.01; ***\u003cem\u003eP\u003c/em\u003e\u0026nbsp;\u0026lt; 0.001; ****\u003cem\u003eP\u003c/em\u003e\u0026nbsp;\u0026lt; 0.0001. ns, not significant (or labeled if \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.10).\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6377810/v1/6db9fdd1768b821e81b19643.png"},{"id":82133963,"identity":"715bdc60-4a0b-4dfd-a598-ebd9624d68ee","added_by":"auto","created_at":"2025-05-07 06:01:35","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":85666,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eU2AF1\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003eS34F\u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003e and U2AF1\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003eQ157R\u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003e cause different cell-intrinsic effects on hematopoiesis. \u003c/strong\u003e(\u003cstrong\u003eA\u003c/strong\u003e)\u003cstrong\u003e \u003c/strong\u003eTransplant assay design. CD45.2\u003csup\u003e+\u003c/sup\u003e donor BM cells from \u003cem\u003eU2af1\u003c/em\u003e\u003csup\u003e+/+\u003c/sup\u003e, \u003cem\u003eU2af1\u003c/em\u003e\u003csup\u003eS34F/+\u003c/sup\u003e, or \u003cem\u003eU2af1\u003c/em\u003e\u003csup\u003eQ157R/+\u003c/sup\u003e mice (all \u003cem\u003eMx1\u003c/em\u003e-\u003cem\u003eCre\u003c/em\u003e\u003csup\u003e+\u003c/sup\u003e) were transplanted into lethally irradiated WT congenic (CD45.1\u003csup\u003e+\u003c/sup\u003e) recipient mice. Recipient mice were treated with pIpC at 6 weeks post-transplant. (\u003cstrong\u003eB\u003c/strong\u003e) Complete blood counts of PB samples from recipient mice before (−1 week) and up to 24 weeks post-pIpC. (\u003cstrong\u003eC\u003c/strong\u003e) Flow cytometric analysis of PB samples was performed before and after pIpC to determine absolute counts of lymphoid (B-cells or T-cells) and myeloid (Neutrophils or Monocytes) cells. For \u003cstrong\u003eB\u003c/strong\u003e-\u003cstrong\u003eC\u003c/strong\u003e, N=28-30 recipient mice per genotype pooled from two independent experiments. (\u003cstrong\u003eD\u003c/strong\u003e) Absolute cell counts of BM HSPC populations in recipient mice were determined by flow cytometric analysis at 24 weeks post-pIpC. N=5-8 recipient mice per genotype pooled from two independent experiments. See also \u003cstrong\u003eSupplementary Fig. 2\u003c/strong\u003e. Results represent the mean ± standard error of the mean (SEM) (\u003cstrong\u003eB\u003c/strong\u003e-\u003cstrong\u003eC\u003c/strong\u003e) or mean ± SD (\u003cstrong\u003eD\u003c/strong\u003e). A mixed effects analysis with repeated measures and Tukey multiple comparison correction (\u003cstrong\u003eB\u003c/strong\u003e-\u003cstrong\u003eC\u003c/strong\u003e) or one-way ANOVA with Tukey multiple comparison correction (\u003cstrong\u003eD\u003c/strong\u003e) were used for the comparison of groups. *\u003cem\u003eP\u003c/em\u003e\u0026nbsp;\u0026lt; 0.05; **\u003cem\u003eP\u003c/em\u003e\u0026nbsp;\u0026lt; 0.01; ***\u003cem\u003eP\u003c/em\u003e\u0026nbsp;\u0026lt; 0.001; ****\u003cem\u003eP\u003c/em\u003e\u0026nbsp;\u0026lt; 0.0001. ns, not significant (or labeled if \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.10). Symbols (\u003cem\u003eU2af1\u003c/em\u003e\u003csup\u003e+/+\u003c/sup\u003e vs \u003cem\u003eU2af1\u003c/em\u003e\u003csup\u003eS34F/+\u003c/sup\u003e [*]; \u003cem\u003eU2af1\u003c/em\u003e\u003csup\u003e+/+\u003c/sup\u003e vs \u003cem\u003eU2af1\u003c/em\u003e\u003csup\u003eQ157R/+\u003c/sup\u003e [\u003csup\u003e#\u003c/sup\u003e]) are used to differentiate comparisons in \u003cstrong\u003eB\u003c/strong\u003e-\u003cstrong\u003eC\u003c/strong\u003e.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6377810/v1/0308672bb00ca79692b0a6cc.png"},{"id":82133971,"identity":"23a9e3f1-63fe-42cc-99e7-173261648450","added_by":"auto","created_at":"2025-05-07 06:01:37","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":82081,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eU2af1\u003c/strong\u003e\u003c/em\u003e\u003csup\u003e\u003cstrong\u003eS34F/+\u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003e HSCs are significantly more impaired than \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eU2af1\u003c/strong\u003e\u003c/em\u003e\u003csup\u003e\u003cstrong\u003eQ157R/+\u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003e HSCs in BM repopulation assays. \u003c/strong\u003e(\u003cstrong\u003eA\u003c/strong\u003e) Competitive transplant assay design. CD45.2\u003csup\u003e+\u003c/sup\u003e (test) donor BM cells from \u003cem\u003eU2af1\u003c/em\u003e\u003csup\u003e+/+\u003c/sup\u003e, \u003cem\u003eU2af1\u003c/em\u003e\u003csup\u003eS34F/+\u003c/sup\u003e, or \u003cem\u003eU2af1\u003c/em\u003e\u003csup\u003eQ157R/+\u003c/sup\u003e mice (all \u003cem\u003eMx1\u003c/em\u003e-\u003cem\u003eCre\u003c/em\u003e\u003csup\u003e+\u003c/sup\u003e) were each mixed 1:1 with CD45.1\u003csup\u003e+\u003c/sup\u003e/CD45.2\u003csup\u003e+\u003c/sup\u003e competitor BM cells and transplanted into lethally irradiated WT congenic (CD45.1\u003csup\u003e+\u003c/sup\u003e) recipient mice. Recipient mice were treated with pIpC at 6 weeks post-transplant. (\u003cstrong\u003eB\u003c/strong\u003e-\u003cstrong\u003eC\u003c/strong\u003e) Donor cell chimerism (CD45.2\u003csup\u003e+\u003c/sup\u003e) was assessed on PB from recipient mice before (−1 week) and up to 16 weeks post-pIpC. Input (−7 weeks) refers to the 1:1 BM cell mixtures transplanted into recipient mice. (\u003cstrong\u003eB\u003c/strong\u003e) Overall chimerism of PB leukocytes. (\u003cstrong\u003eC\u003c/strong\u003e) Chimerism of lymphoid (B-cells or T-cells) and myeloid (Neutrophils or Monocytes) cell populations. (\u003cstrong\u003eD\u003c/strong\u003e-\u003cstrong\u003eE\u003c/strong\u003e) Donor cell chimerism (CD45.2\u003csup\u003e+\u003c/sup\u003e) was assessed on BM from recipient mice at 16 weeks post-pIpC. (\u003cstrong\u003eD\u003c/strong\u003e) Chimerism of lymphoid (B-cells or T-cells) and myeloid (PMNs or Monos) cell populations. (\u003cstrong\u003eE\u003c/strong\u003e) Chimerism of HSPC populations. N=8-10 recipient mice per genotype pooled from two independent experiments (\u003cstrong\u003eB\u003c/strong\u003e-\u003cstrong\u003eE\u003c/strong\u003e). See also \u003cstrong\u003eSupplementary Fig. 3\u003c/strong\u003e. Results represent the mean ± SEM (\u003cstrong\u003eB\u003c/strong\u003e-\u003cstrong\u003eC\u003c/strong\u003e) or mean ± SD (\u003cstrong\u003eD\u003c/strong\u003e-\u003cstrong\u003eE\u003c/strong\u003e). A two-way ANOVA with repeated measures and Tukey multiple comparison correction (\u003cstrong\u003eB\u003c/strong\u003e-\u003cstrong\u003eC\u003c/strong\u003e) or one-way ANOVA with Tukey multiple comparison correction (\u003cstrong\u003eD\u003c/strong\u003e-\u003cstrong\u003eE\u003c/strong\u003e) were used for the comparison of groups. *\u003cem\u003eP\u003c/em\u003e\u0026nbsp;\u0026lt; 0.05; **\u003cem\u003eP\u003c/em\u003e\u0026nbsp;\u0026lt; 0.01; ***\u003cem\u003eP\u003c/em\u003e\u0026nbsp;\u0026lt; 0.001; ****\u003cem\u003eP\u003c/em\u003e\u0026nbsp;\u0026lt; 0.0001. ns, not significant (or labeled if \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.10). Symbols (\u003cem\u003eU2af1\u003c/em\u003e\u003csup\u003e+/+\u003c/sup\u003e vs \u003cem\u003eU2af1\u003c/em\u003e\u003csup\u003eS34F/+\u003c/sup\u003e [*]; \u003cem\u003eU2af1\u003c/em\u003e\u003csup\u003e+/+\u003c/sup\u003e vs \u003cem\u003eU2af1\u003c/em\u003e\u003csup\u003eQ157R/+\u003c/sup\u003e [\u003csup\u003e#\u003c/sup\u003e]) are used to differentiate comparisons in \u003cstrong\u003eB\u003c/strong\u003e-\u003cstrong\u003eC\u003c/strong\u003e.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6377810/v1/c3ccfd691973cd8bf839a213.png"},{"id":82132528,"identity":"c675fc3f-b733-4858-a394-a0054f3c88a7","added_by":"auto","created_at":"2025-05-07 05:44:08","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":66809,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eHemizygous \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eU2af1\u003c/strong\u003e\u003c/em\u003e\u003csup\u003e\u003cstrong\u003eQ157R/−\u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003e and \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eU2af1\u003c/strong\u003e\u003c/em\u003e\u003csup\u003e\u003cstrong\u003eS34F/−\u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003e HSCs are severely impaired in BM repopulation assays. \u003c/strong\u003e(\u003cstrong\u003eA\u003c/strong\u003e) Competitive transplant assay design. CD45.2\u003csup\u003e+\u003c/sup\u003e (test) donor BM cells from \u003cem\u003eU2af1\u003c/em\u003e\u003csup\u003e+/+\u003c/sup\u003e, \u003cem\u003eU2af1\u003c/em\u003e\u003csup\u003e+/−\u003c/sup\u003e, \u003cem\u003eU2af1\u003c/em\u003e\u003csup\u003eS34F/+\u003c/sup\u003e, \u003cem\u003eU2af1\u003c/em\u003e\u003csup\u003eQ157R/+\u003c/sup\u003e, \u003cem\u003eU2af1\u003c/em\u003e\u003csup\u003eS34F/−\u003c/sup\u003e, or \u003cem\u003eU2af1\u003c/em\u003e\u003csup\u003eQ157R/−\u003c/sup\u003e mice (all \u003cem\u003eMx1-Cre\u003c/em\u003e\u003csup\u003e+\u003c/sup\u003e) were each mixed 1:1 with CD45.1\u003csup\u003e+\u003c/sup\u003e/CD45.2\u003csup\u003e+\u003c/sup\u003e competitor BM cells and transplanted into lethally irradiated WT congenic (CD45.1\u003csup\u003e+\u003c/sup\u003e) recipient mice. Recipient mice were treated with pIpC at 5 weeks post-transplant. (\u003cstrong\u003eB\u003c/strong\u003e) Donor cell chimerism (CD45.2\u003csup\u003e+\u003c/sup\u003e) was assessed on PB from recipient mice before (-1 week) and up to 16 weeks post-pIpC. Input (−6 weeks) refers to the 1:1 BM cell mixtures transplanted into recipient mice. Overall, Myeloid (CD11b\u003csup\u003e+\u003c/sup\u003e cells), and Lymphoid (B-cells and T-cells) PB chimerism are shown. (\u003cstrong\u003eC\u003c/strong\u003e-\u003cstrong\u003eD\u003c/strong\u003e) Donor cell chimerism (CD45.2\u003csup\u003e+\u003c/sup\u003e) was assessed on BM from recipient mice at 16 weeks post-pIpC. (\u003cstrong\u003eC\u003c/strong\u003e) Chimerism of Myeloid (CD11b\u003csup\u003e+\u003c/sup\u003e cells) and lymphoid (B-cells or T-cells) cell populations. (\u003cstrong\u003eD\u003c/strong\u003e) Chimerism of HSPC populations. For \u003cstrong\u003eB\u003c/strong\u003e-\u003cstrong\u003eD\u003c/strong\u003e, Data are from a single experiment in which a pool of competitor BM cells (N=3 donors) was individually mixed with test BM cells from N=15 different donors (N=2-4 per genotype) prior to transplant into N=80 recipients (N=5-8 recipient mice per BM cell mixture and N=10-20 total recipient mice per genotype group). BM analysis was performed on a subset (N=6-12 randomized mice) of each genotype group. Data from one \u003cem\u003eU2af1\u003c/em\u003e\u003csup\u003eS34F/−\u003c/sup\u003e mouse was identified as a significant outlier (Grubb’s test, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05) and removed from final analysis. See also \u003cstrong\u003eSupplementary Fig. 4\u003c/strong\u003e. Results represent the mean ± SEM (\u003cstrong\u003eB\u003c/strong\u003e) or mean ± SD (\u003cstrong\u003eC\u003c/strong\u003e). A two-way ANOVA with repeated measures and Tukey multiple comparison correction (\u003cstrong\u003eB\u003c/strong\u003e) or one-way ANOVA with Tukey multiple comparison correction (\u003cstrong\u003eC\u003c/strong\u003e-\u003cstrong\u003eD\u003c/strong\u003e) were used for the comparison of groups. *\u003cem\u003eP\u003c/em\u003e\u0026nbsp;\u0026lt; 0.05; **\u003cem\u003eP\u003c/em\u003e\u0026nbsp;\u0026lt; 0.01; ***\u003cem\u003eP\u003c/em\u003e\u0026nbsp;\u0026lt; 0.001; ****\u003cem\u003eP\u003c/em\u003e\u0026nbsp;\u0026lt; 0.0001. ns, not significant (or labeled if \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.10). Symbols (\u003cem\u003eU2af1\u003c/em\u003e\u003csup\u003e+/+\u003c/sup\u003e vs \u003cem\u003eU2af1\u003c/em\u003e\u003csup\u003eS34F/+\u003c/sup\u003e [*]; \u003cem\u003eU2af1\u003c/em\u003e\u003csup\u003e+/+\u003c/sup\u003e vs \u003cem\u003eU2af1\u003c/em\u003e\u003csup\u003eQ157R/+\u003c/sup\u003e [#]; \u003cem\u003eU2af1\u003c/em\u003e\u003csup\u003e+/+\u003c/sup\u003e vs \u003cem\u003eU2af1\u003c/em\u003e\u003csup\u003eS34F/−\u003c/sup\u003e [§]; \u003cem\u003eU2af1\u003c/em\u003e\u003csup\u003e+/+\u003c/sup\u003e vs \u003cem\u003eU2af1\u003c/em\u003e\u003csup\u003eQ157R/−\u003c/sup\u003e [+]) are used to differentiate comparisons in \u003cstrong\u003eB\u003c/strong\u003e-\u003cstrong\u003eD\u003c/strong\u003e.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6377810/v1/27eac8dd8efc0e701f562a76.png"},{"id":82132531,"identity":"c8105acf-798f-4b2d-9dfd-f2f47c7ff4ea","added_by":"auto","created_at":"2025-05-07 05:44:09","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":59408,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eU2AF1\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003eS34F\u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003e and U2AF1\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003eQ157R\u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003e induce distinct gene expression changes in myeloid progenitor cells. \u003c/strong\u003e(\u003cstrong\u003eA\u003c/strong\u003e-\u003cstrong\u003eE\u003c/strong\u003e) Assessment of differential gene expression by RNA-seq in BM KL cells from \u003cem\u003eU2af1\u003c/em\u003e\u003csup\u003e+/+\u003c/sup\u003e, \u003cem\u003eU2af1\u003c/em\u003e\u003csup\u003eS34F/+\u003c/sup\u003e, and \u003cem\u003eU2af1\u003c/em\u003e\u003csup\u003eQ157R/+\u003c/sup\u003e mice under native hematopoiesis conditions (as in \u003cstrong\u003eFigure 1D\u003c/strong\u003e). N=3 KL cell samples per genotype. (\u003cstrong\u003eA\u003c/strong\u003e) Unsupervised principal component (PC) analysis of gene expression levels in KL cells. (\u003cstrong\u003eB\u003c/strong\u003e-\u003cstrong\u003eC\u003c/strong\u003e) Volcano plot of differentially expressed genes (DEG; FDR\u0026lt;0.05 and |log\u003csub\u003e2\u003c/sub\u003e FC|\u0026gt;1 vs \u003cem\u003eU2af1\u003c/em\u003e\u003csup\u003e+/+\u003c/sup\u003e) in KL cells from \u003cem\u003eU2af1\u003c/em\u003e\u003csup\u003eS34F/+\u003c/sup\u003e (\u003cstrong\u003eB\u003c/strong\u003e) or \u003cem\u003eU2af1\u003c/em\u003e\u003csup\u003eQ157R/+\u003c/sup\u003e (\u003cstrong\u003eC\u003c/strong\u003e) mice. The numbers of up- (▲) and down- (▼) regulated DEG are listed. (\u003cstrong\u003eD\u003c/strong\u003e) Overlap of upregulated (top) and downregulated (bottom) DEG in \u003cem\u003eU2af1\u003c/em\u003e\u003csup\u003eS34F/+\u003c/sup\u003e and \u003cem\u003eU2af1\u003c/em\u003e\u003csup\u003eQ157R/+\u003c/sup\u003e KL cells. (\u003cstrong\u003eE\u003c/strong\u003e) Gene set enrichment analysis (GSEA) for Hallmark gene sets that were significantly enriched (FDR\u0026lt;0.05) in \u003cem\u003eU2af1\u003c/em\u003e\u003csup\u003eS34F/+\u003c/sup\u003e vs \u003cem\u003eU2af1\u003c/em\u003e\u003csup\u003eQ157R/+\u003c/sup\u003e KL cells (column 4). Normalized enrichment scores (NES) for \u003cem\u003eU2af1\u003c/em\u003e\u003csup\u003eS34F/+\u003c/sup\u003e vs \u003cem\u003eU2af1\u003c/em\u003e\u003csup\u003e+/+\u003c/sup\u003e (column 2) and \u003cem\u003eU2af1\u003c/em\u003e\u003csup\u003eQ157R/+\u003c/sup\u003e vs \u003cem\u003eU2af1\u003c/em\u003e\u003csup\u003e+/+\u003c/sup\u003e (column 3) KL cells are also shown. Reanalyzed RNA-seq data (\u003ca href=\"https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE112174\"\u003eGSE112174\u003c/a\u003e) from \u003cem\u003eU2af1\u003c/em\u003e\u003csup\u003e+/+\u003c/sup\u003e and \u003cem\u003eU2af1\u003c/em\u003e\u003csup\u003eS34F/+\u003c/sup\u003e KL cells under native hematopoiesis conditions in Fei \u003cem\u003eet al\u003c/em\u003e.\u003csup\u003e17\u003c/sup\u003e (column 1) is also included. Circle color indicates the NES score for each term and size is proportional to the magnitude of the FDR (q-value).\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-6377810/v1/395d828addafa05f1477dd5a.png"},{"id":82133965,"identity":"4ea7217c-ba7a-4344-8869-d121e737fbc5","added_by":"auto","created_at":"2025-05-07 06:01:36","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":50166,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eU2AF1\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003eS34F\u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003e and U2AF1\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003eQ157R\u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003e induce distinct alternative pre-mRNA splicing changes in myeloid progenitor cells. \u003c/strong\u003e(\u003cstrong\u003eA\u003c/strong\u003e-\u003cstrong\u003eE\u003c/strong\u003e) Assessment of differential alternative pre-mRNA splicing by RNA-seq in BM KL cells from \u003cem\u003eU2af1\u003c/em\u003e\u003csup\u003e+/+\u003c/sup\u003e, \u003cem\u003eU2af1\u003c/em\u003e\u003csup\u003eS34F/+\u003c/sup\u003e, and \u003cem\u003eU2af1\u003c/em\u003e\u003csup\u003eQ157R/+\u003c/sup\u003e mice under native hematopoiesis conditions (\u003cstrong\u003eFig. 1D\u003c/strong\u003e). N=3 KL cell samples per genotype. (\u003cstrong\u003eA\u003c/strong\u003e) Unsupervised principal component (PC) analysis of exon-inclusion ratios (referred to as ‘percent spliced-in’ or ‘PSI’) for all annotated alternative splicing events in KL cells. (\u003cstrong\u003eB\u003c/strong\u003e) Number and type (alternative 3’ or 5’ splice sites [A3SS, A5SS], mutually exclusive exons [MXE], retained introns [RI], and skipped exons [SE]) of differentially spliced events (DSE; FDR\u0026lt;0.05 and |ΔPSI|\u0026gt;0.05 vs \u003cem\u003eU2af1\u003c/em\u003e\u003csup\u003e+/+\u003c/sup\u003e) in KL cells from \u003cem\u003eU2af1\u003c/em\u003e\u003csup\u003eS34F/+\u003c/sup\u003e (left bars) or \u003cem\u003eU2af1\u003c/em\u003e\u003csup\u003eQ157R/+\u003c/sup\u003e (right bars) mice. (\u003cstrong\u003eC\u003c/strong\u003e) Overlap of DSE in \u003cem\u003eU2af1\u003c/em\u003e\u003csup\u003eS34F/+\u003c/sup\u003e and \u003cem\u003eU2af1\u003c/em\u003e\u003csup\u003eQ157R/+\u003c/sup\u003e KL cells. (\u003cstrong\u003eD\u003c/strong\u003e) Overlap of differentially spliced genes (DSG) in \u003cem\u003eU2af1\u003c/em\u003e\u003csup\u003eS34F/+\u003c/sup\u003e and \u003cem\u003eU2af1\u003c/em\u003e\u003csup\u003eQ157R/+\u003c/sup\u003e KL cells. DSE from \u003cstrong\u003eC\u003c/strong\u003e were converted to DSG for analysis. (\u003cstrong\u003eE\u003c/strong\u003e) Analysis of consensus 3’ splice site (3’SS) sequences from control (i.e., no change in mutant vs \u003cem\u003eU2af1\u003c/em\u003e\u003csup\u003e+/+\u003c/sup\u003e) and differentially spliced SE events in \u003cem\u003eU2af1\u003c/em\u003e\u003csup\u003eS34F/+\u003c/sup\u003e (middle) or \u003cem\u003eU2af1\u003c/em\u003e\u003csup\u003eQ157R/+\u003c/sup\u003e (right) KL cells. The highlighted −3 and +1 positions of the 3’SS recapitulate the aberrant consensus 3’SS sequence dependencies identified previously in \u003cem\u003eU2AF1\u003c/em\u003e\u003csup\u003eS34F\u003c/sup\u003e and \u003cem\u003eU2AF1\u003c/em\u003e\u003csup\u003eQ157R\u003c/sup\u003e MDS patients. See also \u003cstrong\u003eSupplementary Fig. 6\u003c/strong\u003e.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-6377810/v1/ee38e7043a37de196cd314d1.png"},{"id":82133646,"identity":"77348942-14ef-45ae-b63a-7c999a03fa5e","added_by":"auto","created_at":"2025-05-07 06:00:14","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":172944,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eU2af1\u003c/strong\u003e\u003c/em\u003e\u003csup\u003e\u003cstrong\u003eS34F/+\u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003e and \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eU2af1\u003c/strong\u003e\u003c/em\u003e\u003csup\u003e\u003cstrong\u003eQ157R/+\u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003e mouse models recapitulate alternative pre-mRNA splicing changes found in MDS and AML patients. \u003c/strong\u003e(\u003cstrong\u003eA\u003c/strong\u003e-\u003cstrong\u003eE\u003c/strong\u003e) Assessment of differential alternative pre-mRNA splicing in BM cells from splicing factor WT [SF\u003csup\u003eWT\u003c/sup\u003e] MDS and AML patients and those harboring \u003cem\u003eU2AF1\u003c/em\u003e\u003csup\u003eS34F\u003c/sup\u003e (S34F) or \u003cem\u003eU2AF1\u003c/em\u003e\u003csup\u003eR156H/Q157(P/R)\u003c/sup\u003e (R156/Q157) mutations in three publicly available RNA-seq datasets (Madan \u003cem\u003eet al\u003c/em\u003e.,\u003csup\u003e11\u003c/sup\u003e Pellagatti \u003cem\u003eet al\u003c/em\u003e.,\u003csup\u003e9\u003c/sup\u003e and Beat AML\u003csup\u003e24\u003c/sup\u003e). RNA-seq data (\u003ca href=\"https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE128429\"\u003eGSE128429\u003c/a\u003e, \u003ca href=\"https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE114922\"\u003eGSE114922\u003c/a\u003e, and \u003ca href=\"https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs001657.v1.p1\"\u003ephs001657.v1.p1\u003c/a\u003e) were reanalyzed for this study. N=2-10 samples per mutant genotype per study. N=8 (Madan), 40 (Pellagatti), or 279 (Beat AML) SF\u003csup\u003eWT\u003c/sup\u003e samples. (\u003cstrong\u003eA\u003c/strong\u003e) BM cell variant allele frequencies (VAF) of S34F, R156H, and Q157(P/R) mutations in \u003cem\u003eU2AF1\u003c/em\u003e mRNA from MDS and AML patients harboring \u003cem\u003eU2AF1\u003c/em\u003e mutations in Madan, Pellagatti, and Beat AML. (\u003cstrong\u003eB\u003c/strong\u003e) Total number and intersection of differentially spliced events (DSE; FDR\u0026lt;0.05 and |ΔPSI|\u0026gt;0.05 vs SF\u003csup\u003eWT\u003c/sup\u003e patients) in BM cells from MDS and AML patients harboring S34F or R156/Q157 mutations in Madan, Pellagatti, and Beat AML. See also \u003cstrong\u003eSupplementary Fig. 7\u003c/strong\u003e. DSE shared (∩) between at least two datasets are underlined and bolded. (\u003cstrong\u003eC\u003c/strong\u003e) Number and type (alternative 3’ or 5’ splice sites [A3SS, A5SS], mutually exclusive exons [MXE], retained introns [RI], and skipped exons [SE]) of DSE (∩≥2 MDS/AML datasets) in BM cells from patients harboring S34F (left bars) or R156/Q157 (right bars) mutations. (\u003cstrong\u003eD\u003c/strong\u003e) Overlap of DSE (∩≥2 MDS/AML datasets) in BM cells from MDS and AML patients harboring S34F or R156/Q157 mutations. (\u003cstrong\u003eE\u003c/strong\u003e) Overlap of differentially spliced genes (DSG) in BM cells from MDS and AML patients harboring S34F or R156/Q157 mutations. DSE from \u003cstrong\u003eD\u003c/strong\u003e were converted to DSG for analysis. (\u003cstrong\u003eF\u003c/strong\u003e-\u003cstrong\u003eG\u003c/strong\u003e) Overlap of DSG from \u003cstrong\u003eE\u003c/strong\u003e (MDS-AML) with DSG from \u003cstrong\u003eFig. 6D\u003c/strong\u003e (Mouse KL) for S34F (\u003cstrong\u003eF\u003c/strong\u003e) or R156/Q157 (\u003cstrong\u003eG\u003c/strong\u003e) mutations. (\u003cstrong\u003eH\u003c/strong\u003e) GO analysis of S34F (left) and R156/Q157 (right) shared DSGs from \u003cstrong\u003eF\u003c/strong\u003e-\u003cstrong\u003eG\u003c/strong\u003e. Circle size is proportional to the gene count for each term and the color indicates the magnitude of the FDR (q-value). REVIGO was used to consolidate 51 (S34F) or 40 (R156/Q157) gene sets into a representative subset of GO terms.\u003csup\u003e38\u003c/sup\u003e All significant GO terms are listed in \u003cstrong\u003eSupplementary Table 17\u003c/strong\u003e. (\u003cstrong\u003eI\u003c/strong\u003e-\u003cstrong\u003eL\u003c/strong\u003e) RT-PCR orthogonal confirmation of S34F or Q157R aberrantly spliced transcripts in mutant mouse KL and MDS/s-AML patient cells. (\u003cstrong\u003eI\u003c/strong\u003e) Representative RT-PCR/polyacrylamide gel results for \u003cem\u003eH2afy\u003c/em\u003e/\u003cem\u003eH2AFY\u003c/em\u003e (aberrantly spliced by S34F, left) and \u003cem\u003eSetd5\u003c/em\u003e/\u003cem\u003eSETD5\u003c/em\u003e (aberrantly spliced by Q157R, right) prior to gel densitometry quantification. N=4 samples per genotype. (\u003cstrong\u003eJ\u003c/strong\u003e-\u003cstrong\u003eL\u003c/strong\u003e) Quantification of aberrantly spliced transcripts in S34F (\u003cstrong\u003eJ\u003c/strong\u003e), Q157P/R (\u003cstrong\u003eK\u003c/strong\u003e), or both (\u003cstrong\u003eL\u003c/strong\u003e). Results represent the mean ± SD (\u003cstrong\u003eJ\u003c/strong\u003e-\u003cstrong\u003eL\u003c/strong\u003e). A one-way ANOVA with Tukey multiple comparison correction (\u003cstrong\u003eJ\u003c/strong\u003e-\u003cstrong\u003eL\u003c/strong\u003e) was used for the comparison of groups. *\u003cem\u003eP\u003c/em\u003e\u0026nbsp;\u0026lt; 0.05; **\u003cem\u003eP\u003c/em\u003e\u0026nbsp;\u0026lt; 0.01; ***\u003cem\u003eP\u003c/em\u003e\u0026nbsp;\u0026lt; 0.001; ****\u003cem\u003eP\u003c/em\u003e\u0026nbsp;\u0026lt; 0.0001. ns, not significant (or labeled if \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.10).\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-6377810/v1/ea8a006db56ec3288c05cecb.png"},{"id":82132529,"identity":"345dd329-7b00-4d98-800a-2f2bce2c0942","added_by":"auto","created_at":"2025-05-07 05:44:08","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":29530,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFrequency of \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eU2AF1\u003c/strong\u003e\u003c/em\u003e\u003csup\u003e\u003cstrong\u003eS34\u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003e and \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eU2AF1\u003c/strong\u003e\u003c/em\u003e\u003csup\u003e\u003cstrong\u003eR156/Q157\u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003e hotspot mutations and co-occurrence with other gene mutations differ in myeloid malignancies.\u003c/strong\u003e (\u003cstrong\u003eA\u003c/strong\u003e) Frequency of \u003cem\u003eU2AF1\u003c/em\u003e hotspot mutations in myeloid malignancy patients. Patients with a \u003cem\u003eU2AF1\u003c/em\u003e mutation(s) (i.e., S34[F/Y], R156H/Q157[P/R], both S34 and R156/Q157, or ‘other’ rare variants) and a diagnosis of AML (N= 50 patients), sAML (from MDS; N=51 patients), MDS (N=271 patients), CMML (N=47 patients), and MPN (N=68 patients), were identified from 21 published studies (see \u003cstrong\u003eSupplementary Methods\u003c/strong\u003e and \u003cstrong\u003eSupplementary Table 18\u003c/strong\u003e). (\u003cstrong\u003eB\u003c/strong\u003e) Analysis of \u003cem\u003eU2AF1\u003c/em\u003e hotspot mutation co-occurrence and mutual exclusivity in myeloid malignancies. Mutation data for patients with a diagnosis of AML (N=1857 patients), sAML (from MDS; N=458 patients), MDS (N=3159 patients), CMML (N=430 patients), and MPN (N=1551 patients) were included from 20 published studies that performed \u003cem\u003eU2AF1\u003c/em\u003e sequencing and had patient-level mutation data available for a common set of 23 (MPN) or 31 (AML, sAML, MDS, and CMML) genes sequenced across all studies (see \u003cstrong\u003eSupplementary Methods\u003c/strong\u003e and \u003cstrong\u003eSupplementary Table 19\u003c/strong\u003e). cBioPortal was used for the co-occurrence and mutual exclusivity of genomic alteration analysis within each disease group using the default settings. Genes with significant interactions (FDR\u0026lt;0.1) with \u003cem\u003eU2AF1\u003c/em\u003e are shown (for complete analysis see \u003cstrong\u003eSupplementary Fig. 8 \u003c/strong\u003eand\u003cstrong\u003e Supplementary Table 20\u003c/strong\u003e). Circle color indicates the log\u003csub\u003e2\u003c/sub\u003e odds ratio (OR) for each gene pair and size is proportional to the magnitude of the FDR (q-value).\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-6377810/v1/5345043ac343b7b2fd6db443.png"},{"id":108668287,"identity":"7702ce37-27a1-417a-8d54-1d78979d40cb","added_by":"auto","created_at":"2026-05-07 07:07:50","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1267037,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6377810/v1/bca288f9-3d84-4e13-9de4-37f832474227.pdf"},{"id":82132526,"identity":"408abcc9-7389-4adc-ace7-846cd621d567","added_by":"auto","created_at":"2025-05-07 05:44:08","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":2033385,"visible":true,"origin":"","legend":"Supplementary Methods and Figures","description":"","filename":"SupplementaryInformationv02.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6377810/v1/47a3c1be89521246536b51f7.pdf"},{"id":82132550,"identity":"109a58a9-784c-43b5-857c-f72dd7008b0f","added_by":"auto","created_at":"2025-05-07 05:44:12","extension":"zip","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":187030416,"visible":true,"origin":"","legend":"Supplementary Tables 1-20","description":"","filename":"SupplementaryTables120.zip","url":"https://assets-eu.researchsquare.com/files/rs-6377810/v1/2385d23cbd01394dec3e0096.zip"}],"financialInterests":"\u003cb\u003eYes\u003c/b\u003e there is potential conflict of interest.","formattedTitle":"\u003ci\u003eU2af1\u003c/i\u003e\u003csup\u003eS34F\u003c/sup\u003e and \u003ci\u003eU2af1\u003c/i\u003e\u003csup\u003eQ157R\u003c/sup\u003e myeloid neoplasm-associated hotspot mutations induce distinct hematopoietic phenotypes in mice","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eRecurrent somatic mutations in a subset of spliceosome genes (\u003cem\u003eSF3B1\u003c/em\u003e, \u003cem\u003eSRSF2\u003c/em\u003e, and \u003cem\u003eU2AF1\u003c/em\u003e) are frequently identified (30\u0026ndash;60% depending on disease phenotype) in patients afflicted with myelodysplastic syndromes (MDS), myeloproliferative neoplasms (MPN) such as myelofibrosis (MF), MDS/MPN overlap disorders such as chronic myelomonocytic leukemia (CMML), and secondary acute myeloid leukemia (sAML).\u003csup\u003e\u003cspan additionalcitationids=\"CR2 CR3 CR4 CR5 CR6 CR7\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e These heterozygous and mutually exclusive mutations are enriched in hotspot codons in these 3\u0026rsquo; splicing factor proteins resulting in aberrant alternative mRNA splicing in hematopoietic cells. However, each mutant protein predominantly affects a distinct set of alternatively spliced downstream target genes suggesting that common downstream pathway alterations or cellular response to mutation expression, rather than specific shared splicing targets, may be responsible for MDS phenotypes, including dysplasia, ineffective hematopoiesis, and cytopenias.\u003csup\u003e\u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e \u003cem\u003eU2AF1\u003c/em\u003e provides a unique opportunity to address this question because it has two hotspot positions (serine 34 [S34] and glutamine 157 [Q157]) that are each commonly mutated in MDS and are associated with unique mRNA splicing consequences.\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e In addition, \u003cem\u003eU2AF1\u003c/em\u003e S34 and Q157 codon mutations co-occur with mutations in different genes (e.g., \u003cem\u003eBCOR\u003c/em\u003e and \u003cem\u003eASXL1\u003c/em\u003e, respectively) and patients with these mutations may have different hematopoietic phenotypes\u0026mdash;highlighting that these mutations may induce distinct phenotypes.\u003csup\u003e\u003cspan additionalcitationids=\"CR14 CR15\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e We asked if the splicing differences resulting from S34F and Q157R mutations were thus associated with different or similar effects on hematopoiesis. To do so, we characterized and compared an established conditional S34F knock-in mouse model\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e to a new Cre/\u003cem\u003elox\u003c/em\u003e conditional mouse model with the Q157R mutation knocked-in to the endogenous \u003cem\u003eU2af1\u003c/em\u003e locus, in order to directly study the hematopoietic phenotype, transcriptional, and mRNA splicing consequences of individual \u003cem\u003eU2AF1\u003c/em\u003e gene mutations \u003cem\u003ein vivo\u003c/em\u003e.\u003c/p\u003e"},{"header":"MATERIALS AND METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eAnimal models and experimental details\u003c/h2\u003e \u003cp\u003e Experiments were performed per institutional guidelines for care and use of laboratory animals and approved by the Institutional Animal Care and Use Committee of Washington University in St. Louis (WUSTL). \u003cem\u003eU2af1\u003c/em\u003e\u003csup\u003eQ157R/+\u003c/sup\u003e (\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eM\u003c/span\u003eini\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eG\u003c/span\u003eene Q157R or \u0026lsquo;MGQ157R\u0026rsquo;) conditional knock-in mice were generated by Biocytogen (Waltham, MA). A full description of the targeting construct is described in the \u003cb\u003eSupplementary Methods\u003c/b\u003e. \u003cem\u003eU2af1\u003c/em\u003e\u003csup\u003eS34F/+\u003c/sup\u003e (\u0026lsquo;MGS34F\u0026rsquo;) conditional knock-in (Jackson Laboratory [JAX] Stock #032638, Bar Harbor, ME),\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e\u003cem\u003eU2af1\u003c/em\u003e\u003csup\u003efl/+\u003c/sup\u003e conditional knockout (JAX Stock #037015),\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e and \u003cem\u003eMx1\u003c/em\u003e-\u003cem\u003eCre\u003c/em\u003e (JAX Stock #003556)\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e mice are described elsewhere. B6.SJL-\u003cem\u003ePtprc\u003c/em\u003e\u003csup\u003e\u003cem\u003ea\u003c/em\u003e\u003c/sup\u003e\u003cem\u003ePepc\u003c/em\u003e\u003csup\u003e\u003cem\u003eb\u003c/em\u003e\u003c/sup\u003e/BoyCrCrl or \u0026lsquo;CD45.1\u0026rsquo; recipient mice were purchased from Charles River Laboratories (Stock #564). Heterozygous CD45.1/CD45.2 mice were bred by crossing C57BL/6J (B6; JAX Stock #000664) to B6.SJL-\u003cem\u003ePtprc\u003c/em\u003e\u003csup\u003e\u003cem\u003ea\u003c/em\u003e\u003c/sup\u003e\u003cem\u003ePepc\u003c/em\u003e\u003csup\u003e\u003cem\u003eb\u003c/em\u003e\u003c/sup\u003e/BoyJ (JAX Stock #002014). All mouse lines were on a B6 background. Genotyping primers are listed in \u003cb\u003eSupplementary Table\u0026nbsp;1\u003c/b\u003e.\u003c/p\u003e \u003cp\u003eDetails pertaining to bone marrow (BM) transplant, peripheral blood (PB) sampling and analysis, and flow cytometry setup and population gating are described in the \u003cb\u003eSupplementary Methods\u003c/b\u003e.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003emRNA-sequencing (RNA-seq) and bioinformatics\u003c/h3\u003e\n\u003cp\u003eBM myeloid progenitor (c-kit\u003csup\u003e+\u003c/sup\u003eLineage\u003csup\u003e\u0026minus;\u003c/sup\u003eSca-1\u003csup\u003e\u0026minus;\u003c/sup\u003e; KL) cells were sorted into FACS buffer and gDNA-depleted total RNA were purified from cell pellets using the NucleoSpin RNA Plus XS Micro Kit (Macherey-Nagel, Allentown, PA) in RNase-free water. RNA concentration and RIN were measured by Bioanalyzer (Agilent, Santa Clara, CA) and then cDNA libraries for RNA-seq were prepared by KAPA RNA HyperPrep Kit with RiboErase (Cat #KK8560/61; Roche, Indianapolis, IN). Detailed library preparation and bioinformatic analyses are described in \u003cb\u003eSupplementary Methods\u003c/b\u003e.\u003c/p\u003e \u003cp\u003eDetails pertaining to the bioinformatics and reanalysis of published MDS and AML RNA-seq datasets, analysis of \u003cem\u003eU2AF1\u003c/em\u003e hotspot mutation co-occurrence in myeloid malignancies, and confirmation of splicing changes in mouse KL cell and MDS/sAML patient samples are also described in the \u003cb\u003eSupplementary Methods\u003c/b\u003e. All patients provided written consent on a protocol approved by the WUSTL Human Studies Committee. Clinical characteristics of patients who donated research samples are listed in \u003cb\u003eSupplementary Table\u0026nbsp;2\u003c/b\u003e.\u003c/p\u003e\n\u003ch3\u003eStatistics\u003c/h3\u003e\n\u003cp\u003eData were analyzed and visualized using GraphPad Prism 10 software (Boston, MA). Statistical tests are described in each figure legend. \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cp\u003e \u003cb\u003eEstablishing a mouse model with conditional knock-in of the Q157R mutation at the\u003c/b\u003e \u003cb\u003eU2af1\u003c/b\u003e \u003cb\u003elocus.\u003c/b\u003e\u003c/p\u003e \u003cp\u003eA conditional (Cre/\u003cem\u003elox\u003c/em\u003e-mediated) knock-in of the S34F mutation at the \u003cem\u003eU2af1\u003c/em\u003e locus (MGS34F or \u003cem\u003eU2af1\u003c/em\u003e\u003csup\u003eS34F/+\u003c/sup\u003e) was previously generated (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA,B and \u003cb\u003eSupplementary Fig.\u0026nbsp;1A\u003c/b\u003e).\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e To allow for direct comparison with the MGS34F mouse, a similar strategy was used to generate a conditional (Cre/\u003cem\u003elox\u003c/em\u003e-mediated) Q157R mutant allele at the endogenous \u003cem\u003eU2af1\u003c/em\u003e locus (MGQ157R or \u003cem\u003eU2af1\u003c/em\u003e\u003csup\u003eQ157R/+\u003c/sup\u003e) of B6 mice (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC and \u003cb\u003eSupplementary Fig.\u0026nbsp;1B\u003c/b\u003e). Successful introduction of the targeting vector at the \u003cem\u003eU2af1\u003c/em\u003e locus was confirmed by Southern blot and Sanger sequencing (\u003cb\u003eSupplementary Fig.\u0026nbsp;1C\u003c/b\u003e). To confirm Cre/\u003cem\u003elox\u003c/em\u003e-mediated hematopoietic expression of \u003cem\u003eU2af1\u003c/em\u003e\u003csup\u003eQ157R\u003c/sup\u003e mRNA and assess the short-term effects of U2AF1\u003csup\u003eQ157R\u003c/sup\u003e in a non-transplant model (i.e., native hematopoiesis), we crossed heterozygous \u003cem\u003eU2af1\u003c/em\u003e\u003csup\u003eQ157R/+\u003c/sup\u003e mice to \u003cem\u003eMx1-Cre\u003c/em\u003e transgenic mice (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD-G and \u003cb\u003eSupplementary Fig.\u0026nbsp;1D-I\u003c/b\u003e). \u003cem\u003eMx1-Cre\u003c/em\u003e is expressed in hematopoietic lineage cells following administration of polyinosinic-polycytidylic acid (pIpC).\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFour weeks after pIpC treatment of \u003cem\u003eU2af1\u003c/em\u003e\u003csup\u003eQ157R/+\u003c/sup\u003e;\u003cem\u003eMx1-Cre\u003c/em\u003e mice, the \u003cem\u003eU2af1\u003c/em\u003e wild-type (WT) and Q157R alleles were expressed at similar levels in BM myeloid progenitor (KL) cells by targeted NGS amplicon sequencing of cDNA (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE). As expected, the WT and S34F alleles were also expressed at similar levels in BM KL cells from \u003cem\u003eU2af1\u003c/em\u003e\u003csup\u003eS34F/+\u003c/sup\u003e;\u003cem\u003eMx1-Cre\u003c/em\u003e mice and only the WT allele was detected in \u003cem\u003eU2af1\u003c/em\u003e\u003csup\u003e+/+\u003c/sup\u003e;\u003cem\u003eMx1-Cre\u003c/em\u003e control mice (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE). It was previously reported that the Q157R mutation in \u003cem\u003eU2AF1\u003c/em\u003e creates an alternative 5\u0026rsquo; splice site that leads to expression of a minor \u003cem\u003eU2AF1\u003c/em\u003e isoform (termed \u0026lsquo;Q157Rdel\u0026rsquo;) with in-frame deletion of four amino acids immediately following the Q157R mutant codon. The \u003cem\u003eU2af1\u003c/em\u003e\u003csup\u003eQ157R/+\u003c/sup\u003e mouse model recapitulates expression of the Q157Rdel isoform in BM KL cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE and \u003cb\u003eSupplementary Fig.\u0026nbsp;1D\u003c/b\u003e).\u003c/p\u003e \u003cp\u003e \u003cb\u003eU2AF1\u003c/b\u003e \u003csup\u003e \u003cb\u003eS\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003eF\u003c/b\u003e \u003c/sup\u003e \u003cb\u003eand U2AF1\u003c/b\u003e\u003csup\u003e\u003cb\u003eQ157R\u003c/b\u003e\u003c/sup\u003e \u003cb\u003ecause different hematopoietic changes in mice.\u003c/b\u003e\u003c/p\u003e \u003cp\u003eTo determine if S34F and Q157R result in similar short-term effects on native hematopoiesis, we performed complete blood counts and flow cytometric analysis on PB samples from \u003cem\u003eU2af1\u003c/em\u003e\u003csup\u003eQ157R/+\u003c/sup\u003e, \u003cem\u003eU2af1\u003c/em\u003e\u003csup\u003eS34F/+\u003c/sup\u003e, and \u003cem\u003eU2af1\u003c/em\u003e\u003csup\u003e+/+\u003c/sup\u003e control mice (all \u003cem\u003eMx1-Cre\u003c/em\u003e\u003csup\u003e+\u003c/sup\u003e) four weeks after pIpC treatment. Consistent with previous characterization,\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e \u003cem\u003eU2af1\u003c/em\u003e\u003csup\u003eS34F/+\u003c/sup\u003e mice had no change in platelet counts, modestly reduced red blood cell (RBC) counts and hemoglobin levels (with elevated mean corpuscular volume [MCV]), and markedly reduced white blood cell counts compared to \u003cem\u003eU2af1\u003c/em\u003e\u003csup\u003e+/+\u003c/sup\u003e mice (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eF). Flow cytometric analysis of \u003cem\u003eU2af1\u003c/em\u003e\u003csup\u003eS34F/+\u003c/sup\u003e PB and BM demonstrated significant reductions in both myeloid and lymphoid lineages (\u003cb\u003eSupplementary Fig.\u0026nbsp;1E,F\u003c/b\u003e). In contrast, \u003cem\u003eU2af1\u003c/em\u003e\u003csup\u003eQ157R/+\u003c/sup\u003e mice had no significant PB or BM changes except for elevated MCV (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eF and \u003cb\u003eSupplementary Fig.\u0026nbsp;1E,F\u003c/b\u003e). Assessment of BM hematopoietic stem and progenitor cells (HSPC) four weeks after pIpC treatment revealed that \u003cem\u003eU2af1\u003c/em\u003e\u003csup\u003eS34F/+\u003c/sup\u003e mice had significantly reduced numbers of short-term hematopoietic stem cell (ST-HSC), KL, and common myeloid progenitor (CMP) populations with increased numbers of multipotent progenitor (MPP)2 and MPP3 populations compared with control mice (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eG). \u003cem\u003eU2af1\u003c/em\u003e\u003csup\u003eQ157R/+\u003c/sup\u003e mice also had significantly reduced numbers of ST-HSC and KL populations and non-significant reductions in both CMP and megakaryocyte-erythroid progenitor (MEP) cells compared with control mice (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eG). \u003cem\u003eU2af1\u003c/em\u003e\u003csup\u003eS34F/+\u003c/sup\u003e mice had a significant block in erythroid development in the BM and spleen, with an increased proportion of immunophenotypically defined nucleated erythroblasts (Ter119\u003csup\u003elo/hi\u003c/sup\u003eCD71\u003csup\u003ehi\u003c/sup\u003e) and a decreased proportion of enucleated erythrocytes (Ter119\u003csup\u003ehi\u003c/sup\u003eCD71\u003csup\u003elo\u003c/sup\u003e). In contrast, \u003cem\u003eU2af1\u003c/em\u003e\u003csup\u003eQ157R/+\u003c/sup\u003e mice had a smaller but non-significant, increase in Ter119\u003csup\u003ehi\u003c/sup\u003eCD71\u003csup\u003ehi\u003c/sup\u003e cells in the spleen (\u003cb\u003eSupplementary Fig.\u0026nbsp;1G-I\u003c/b\u003e).\u003c/p\u003e \u003cp\u003eTo better evaluate the cell-intrinsic effects of both mutants on hematopoiesis, we transplanted BM from \u003cem\u003eU2af1\u003c/em\u003e\u003csup\u003eQ157R/+\u003c/sup\u003e, \u003cem\u003eU2af1\u003c/em\u003e\u003csup\u003eS34F/+\u003c/sup\u003e, or \u003cem\u003eU2af1\u003c/em\u003e\u003csup\u003e+/+\u003c/sup\u003e control mice (CD45.2\u003csup\u003e+\u003c/sup\u003e; all \u003cem\u003eMx1-Cre\u003c/em\u003e\u003csup\u003e+\u003c/sup\u003e) into lethally irradiated WT congenic recipient mice (CD45.1\u003csup\u003e+\u003c/sup\u003e). Following engraftment, we treated mice (including controls) with pIpC to induce expression of S34F and Q157R in donor-derived cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). Four weeks after pIpC treatment, PB (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB,C) and BM changes (\u003cb\u003eSupplementary Fig.\u0026nbsp;2A,C\u003c/b\u003e) reflected similar overall trends observed in native hematopoiesis (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eF and \u003cb\u003eSupplementary Fig.\u0026nbsp;1E\u003c/b\u003e) for both mutant mice. At 24 weeks, both mutant mice had significantly reduced PB RBC counts with increased MCV, as well as decreased hemoglobin in \u003cem\u003eU2af1\u003c/em\u003e\u003csup\u003eS34F/+\u003c/sup\u003e mice. \u003cem\u003eU2af1\u003c/em\u003e\u003csup\u003eQ157R/+\u003c/sup\u003e mice also had mildly increased platelet counts (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). \u003cem\u003eU2af1\u003c/em\u003e\u003csup\u003eS34F/+\u003c/sup\u003e mice had significantly reduced PB and BM myeloid and lymphoid lineage cells, while \u003cem\u003eU2af1\u003c/em\u003e\u003csup\u003eQ157R/+\u003c/sup\u003e mice had significantly decreased PB monocytes and a non-significant increase in BM monocytes (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC and \u003cb\u003eSupplementary Fig.\u0026nbsp;2D\u003c/b\u003e). Although myeloid and lymphoid lineages were significantly decreased in \u003cem\u003eU2af1\u003c/em\u003e\u003csup\u003eS34F/+\u003c/sup\u003e mouse spleens at 4 weeks, there were no significant changes at 24 weeks (\u003cb\u003eSupplementary Fig.\u0026nbsp;2B,E\u003c/b\u003e). HSPC populations reflected similar significant overall trends at 24 weeks compared to 4 weeks for \u003cem\u003eU2af1\u003c/em\u003e\u003csup\u003eS34F/+\u003c/sup\u003e mice (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD and \u003cb\u003eSupplementary Fig.\u0026nbsp;2C\u003c/b\u003e). \u003cem\u003eU2af1\u003c/em\u003e\u003csup\u003eQ157R/+\u003c/sup\u003e mice also showed similar, but non-significant, trends in HSPC population numbers at 24 weeks compared to 4 weeks (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD and \u003cb\u003eSupplementary Fig.\u0026nbsp;2C\u003c/b\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eU2af1\u003c/b\u003e \u003csup\u003e \u003cb\u003eS34F/+\u003c/b\u003e \u003c/sup\u003e \u003cb\u003eHSCs are significantly more impaired than\u003c/b\u003e \u003cb\u003eU2af1\u003c/b\u003e\u003csup\u003e\u003cb\u003eQ157R/+\u003c/b\u003e\u003c/sup\u003e \u003cb\u003eHSCs in BM repopulation assays.\u003c/b\u003e\u003c/p\u003e \u003cp\u003eTo compare the effects of S34F or Q157R expression on HSC reconstitution capacity, we performed competitive BM transplantation experiments. Lethally irradiated WT congenic recipient mice (CD45.1\u003csup\u003e+\u003c/sup\u003e) were transplanted with whole BM \u0026lsquo;test\u0026rsquo; cells from \u003cem\u003eU2af1\u003c/em\u003e\u003csup\u003eQ157R/+\u003c/sup\u003e, \u003cem\u003eU2af1\u003c/em\u003e\u003csup\u003eS34F/+\u003c/sup\u003e, or \u003cem\u003eU2af1\u003c/em\u003e\u003csup\u003e+/+\u003c/sup\u003e control mice (CD45.2\u003csup\u003e+\u003c/sup\u003e; all \u003cem\u003eMx1-Cre\u003c/em\u003e\u003csup\u003e+\u003c/sup\u003e) mixed with an equal number of competitor BM cells from WT congenic mice (CD45.1\u003csup\u003e+\u003c/sup\u003e/CD45.2\u003csup\u003e+\u003c/sup\u003e). Following engraftment, we treated mice (including controls) with pIpC to induce expression of S34F and Q157R in donor-derived cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). Consistent with previous characterization,\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e we observed significant multi-lineage reductions in PB, BM, and spleen donor cell chimerism (CD45.2\u003csup\u003e+\u003c/sup\u003e) for \u003cem\u003eU2af1\u003c/em\u003e\u003csup\u003eS34F/+\u003c/sup\u003e compared to \u003cem\u003eU2af1\u003c/em\u003e\u003csup\u003e+/+\u003c/sup\u003e test cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB-D and \u003cb\u003eSupplementary Fig.\u0026nbsp;3\u003c/b\u003e). In contrast, the reduction in overall and multilineage PB donor cell chimerism for \u003cem\u003eU2af1\u003c/em\u003e\u003csup\u003eQ157R/+\u003c/sup\u003e compared to \u003cem\u003eU2af1\u003c/em\u003e\u003csup\u003e+/+\u003c/sup\u003e test cells was less severe relative to \u003cem\u003eU2af1\u003c/em\u003e\u003csup\u003eS34F/+\u003c/sup\u003e test cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB,C). In addition, there were variable reductions in donor cell chimerism of PB, BM, and spleen myeloid lineages for \u003cem\u003eU2af1\u003c/em\u003e\u003csup\u003eQ157R/+\u003c/sup\u003e compared to \u003cem\u003eU2af1\u003c/em\u003e\u003csup\u003e+/+\u003c/sup\u003e test cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC,D and \u003cb\u003eSupplementary Fig.\u0026nbsp;3\u003c/b\u003e). Donor cell chimerism for all BM HSPC populations were significantly reduced for \u003cem\u003eU2af1\u003c/em\u003e\u003csup\u003eS34F/+\u003c/sup\u003e compared to \u003cem\u003eU2af1\u003c/em\u003e\u003csup\u003e+/+\u003c/sup\u003e test cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE). However, reduced \u003cem\u003eU2af1\u003c/em\u003e\u003csup\u003eQ157R/+\u003c/sup\u003e donor cell chimerism was restricted to the HSC and MPP2 populations, but not to the same degree as for \u003cem\u003eU2af1\u003c/em\u003e\u003csup\u003eS34F/+\u003c/sup\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eHemizygous\u003c/b\u003e \u003cb\u003eU2af1\u003c/b\u003e\u003csup\u003e\u003cb\u003eQ157R/\u0026minus;\u003c/b\u003e\u003c/sup\u003e \u003cb\u003eand\u003c/b\u003e \u003cb\u003eU2af1\u003c/b\u003e\u003csup\u003e\u003cb\u003eS34F/\u0026minus;\u003c/b\u003e\u003c/sup\u003e \u003cb\u003eHSCs are both severely impaired in BM repopulation assays.\u003c/b\u003e\u003c/p\u003e \u003cp\u003eWe previously demonstrated that cell survival and reconstitution capacity are severely reduced for HSCs that express mutant U2AF1\u003csup\u003eS\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003eF\u003c/sup\u003e without WT U2AF1 expression (hemizygous \u003cem\u003eU2af1\u003c/em\u003e\u003csup\u003eS34F/\u0026minus;\u003c/sup\u003e).\u003csup\u003e18\u003c/sup\u003e Given the mild reconstitution defect observed for \u003cem\u003eU2af1\u003c/em\u003e\u003csup\u003eQ157R/+\u003c/sup\u003e cells, we hypothesized that mutant U2AF1\u003csup\u003eQ157R\u003c/sup\u003e cells may not require the expression of WT U2AF1 for cell survival. To test this, we performed competitive BM transplantation experiments using test cells from three additional genotypes of mice: \u003cem\u003eU2af1\u003c/em\u003e\u003csup\u003eQ157R/\u0026minus;\u003c/sup\u003e, \u003cem\u003eU2af1\u003c/em\u003e\u003csup\u003eS34F/\u0026minus;\u003c/sup\u003e, and \u003cem\u003eU2af1\u003c/em\u003e\u003csup\u003e+/\u0026minus;\u003c/sup\u003e mice (all \u003cem\u003eMx1-Cre\u003c/em\u003e\u003csup\u003e+\u003c/sup\u003e; Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). Hemizygous conditional knock-in mice were generated by crossing heterozygous floxed mutant (S34F or Q157R) mice to heterozygous floxed knockout mice. Consistent with previous characterization,\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e we noted a rapid and significant loss in mature cell and HSPC donor cell chimerism (CD45.2\u003csup\u003e+\u003c/sup\u003e) in the PB, BM, and spleen for hemizygous \u003cem\u003eU2af1\u003c/em\u003e\u003csup\u003eS34F/\u0026minus;\u003c/sup\u003e (but not \u003cem\u003eU2af1\u003c/em\u003e\u003csup\u003e+/\u0026minus;\u003c/sup\u003e) compared to \u003cem\u003eU2af1\u003c/em\u003e\u003csup\u003e+/+\u003c/sup\u003e test cells following administration of pIpC (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB-D and \u003cb\u003eSupplementary Fig.\u0026nbsp;4A,B\u003c/b\u003e). We also observed an identical rapid loss in mature cell and HSPC chimerism for hemizygous \u003cem\u003eU2af1\u003c/em\u003e\u003csup\u003eQ157R/\u0026minus;\u003c/sup\u003e compared to \u003cem\u003eU2af1\u003c/em\u003e\u003csup\u003e+/+\u003c/sup\u003e test cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB-D and \u003cb\u003eSupplementary Fig.\u0026nbsp;4A,B\u003c/b\u003e). This indicates that the expression of WT U2AF1 is required for the viability of either U2AF1\u003csup\u003eS\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003eF\u003c/sup\u003e or U2AF1\u003csup\u003eQ157R\u003c/sup\u003e mutant expressing HSCs, consistent with \u003cem\u003eU2AF1\u003c/em\u003e being a haplo-essential gene,\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e and reinforcing that the \u003cem\u003eU2af1\u003c/em\u003e\u003csup\u003eQ157R\u003c/sup\u003e allele impairs U2AF1 function despite the less severe phenotype compared to \u003cem\u003eU2af1\u003c/em\u003e\u003csup\u003eS\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003eF\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eU2AF1\u003c/b\u003e \u003csup\u003e \u003cb\u003eS\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003eF\u003c/b\u003e \u003c/sup\u003e \u003cb\u003eand U2AF1\u003c/b\u003e\u003csup\u003e\u003cb\u003eQ157R\u003c/b\u003e\u003c/sup\u003e \u003cb\u003einduce distinct gene expression changes in mouse myeloid progenitor cells.\u003c/b\u003e\u003c/p\u003e \u003cp\u003eTo characterize the effects of mutant U2AF1 on gene expression \u003cem\u003ein vivo\u003c/em\u003e, we performed RNA-seq of total RNA (rRNA-depleted) from BM myeloid progenitor (KL) cells from \u003cem\u003eU2af1\u003c/em\u003e\u003csup\u003eQ157R/+\u003c/sup\u003e, \u003cem\u003eU2af1\u003c/em\u003e\u003csup\u003eS34F/+\u003c/sup\u003e, or \u003cem\u003eU2af1\u003c/em\u003e\u003csup\u003e+/+\u003c/sup\u003e control mice (all \u003cem\u003eMx1-Cre\u003c/em\u003e\u003csup\u003e+\u003c/sup\u003e) under native hematopoiesis conditions (as in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD). KL cells were isolated by FACS at 4 weeks after completion of pIpC injections and the variant allele frequencies of the S34F and Q157R mutations were near 50% (\u003cb\u003eSupplementary Fig.\u0026nbsp;5A\u003c/b\u003e). Unsupervised principal component analysis of gene expression values (N\u0026thinsp;=\u0026thinsp;19312 genes) segregated \u003cem\u003eU2af1\u003c/em\u003e\u003csup\u003eQ157R/+\u003c/sup\u003e, \u003cem\u003eU2af1\u003c/em\u003e\u003csup\u003eS34F/+\u003c/sup\u003e, and \u003cem\u003eU2af1\u003c/em\u003e\u003csup\u003e+/+\u003c/sup\u003e KL cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA and \u003cb\u003eSupplementary Table\u0026nbsp;3\u003c/b\u003e). Reanalysis of \u003cem\u003eU2af1\u003c/em\u003e\u003csup\u003eS34F/+\u003c/sup\u003e Native KL RNA-seq data published by Fei \u003cem\u003eet al\u003c/em\u003e.\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e demonstrated a strong concordance in gene expression changes with our \u003cem\u003eU2af1\u003c/em\u003e\u003csup\u003eS34F/+\u003c/sup\u003e KL data (\u003cb\u003eSupplementary Fig.\u0026nbsp;5B,C\u003c/b\u003e). In our dataset, we identified 185 differentially expressed genes (DEGs; FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and |log\u003csub\u003e2\u003c/sub\u003eFC|\u0026gt;1) in \u003cem\u003eU2af1\u003c/em\u003e\u003csup\u003eS34F/+\u003c/sup\u003e compared to \u003cem\u003eU2af1\u003c/em\u003e\u003csup\u003e+/+\u003c/sup\u003e control mice (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB) and 77 DEGs in \u003cem\u003eU2af1\u003c/em\u003e\u003csup\u003eQ157R/+\u003c/sup\u003e KL cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC). There were only 12 DEGs shared between \u003cem\u003eU2af1\u003c/em\u003e\u003csup\u003eS34F/+\u003c/sup\u003e and \u003cem\u003eU2af1\u003c/em\u003e\u003csup\u003eQ157R/+\u003c/sup\u003e KL cells (4.8%; Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD) with no overlap in gene ontology (GO) analysis (\u003cb\u003eSupplementary Fig.\u0026nbsp;5D,E\u003c/b\u003e and \u003cb\u003eSupplementary Table\u0026nbsp;4\u003c/b\u003e). Gene set enrichment analysis (GSEA) revealed significant positive enrichment of the p53 pathway in \u003cem\u003eU2af1\u003c/em\u003e\u003csup\u003eS34F/+\u003c/sup\u003e KL cells and negative enrichment of immune response related Hallmark pathways in both \u003cem\u003eU2af1\u003c/em\u003e\u003csup\u003eS34F/+\u003c/sup\u003e and \u003cem\u003eU2af1\u003c/em\u003e\u003csup\u003eQ157R/+\u003c/sup\u003e KL cells compared to \u003cem\u003eU2af1\u003c/em\u003e\u003csup\u003e+/+\u003c/sup\u003e KL cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eE).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eU2AF1\u003c/b\u003e \u003csup\u003e \u003cb\u003eS\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003eF\u003c/b\u003e \u003c/sup\u003e \u003cb\u003eand U2AF1\u003c/b\u003e\u003csup\u003e\u003cb\u003eQ157R\u003c/b\u003e\u003c/sup\u003e \u003cb\u003einduce distinct alternative pre-mRNA splicing changes in myeloid progenitor cells.\u003c/b\u003e\u003c/p\u003e \u003cp\u003eUsing the same bulk RNA-seq data, we next characterized the effects of mutant U2AF1 on alternative mRNA splicing \u003cem\u003ein vivo\u003c/em\u003e. We employed replicate multivariate analysis of transcript splicing (rMATS)\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e to assess differential alternative pre-mRNA splicing of five different types of annotated splicing events (alternative 3\u0026rsquo; or 5\u0026rsquo; splice sites [A3SS, A5SS], mutually exclusive exons [MXE], retained introns [RI], and skipped exons [SE]) in \u003cem\u003eU2af1\u003c/em\u003e\u003csup\u003eQ157R/+\u003c/sup\u003e, \u003cem\u003eU2af1\u003c/em\u003e\u003csup\u003eS34F/+\u003c/sup\u003e, and \u003cem\u003eU2af1\u003c/em\u003e\u003csup\u003e+/+\u003c/sup\u003e KL cells. Unsupervised principal component analysis of inclusion ratios (referred to as \u0026lsquo;percent spliced-in\u0026rsquo; or \u0026lsquo;PSI\u0026rsquo;) for all annotated alternative splicing events (N\u0026thinsp;=\u0026thinsp;11580) revealed that global alternative pre-mRNA splicing is distinct between \u003cem\u003eU2af1\u003c/em\u003e\u003csup\u003eQ157R/+\u003c/sup\u003e, \u003cem\u003eU2af1\u003c/em\u003e\u003csup\u003eS34F/+\u003c/sup\u003e, and \u003cem\u003eU2af1\u003c/em\u003e\u003csup\u003e+/+\u003c/sup\u003e KL cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA and \u003cb\u003eSupplementary Tables\u0026nbsp;5\u0026ndash;7\u003c/b\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWe then applied rMATS to identify 1048 and 580 differentially spliced events (DSEs; FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and |ΔPSI|\u0026gt;0.05 vs \u003cem\u003eU2af1\u003c/em\u003e\u003csup\u003e+/+\u003c/sup\u003e) in \u003cem\u003eU2af1\u003c/em\u003e\u003csup\u003eS34F/+\u003c/sup\u003e and \u003cem\u003eU2af1\u003c/em\u003e\u003csup\u003eQ157R/+\u003c/sup\u003e KL cells, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB and \u003cb\u003eSupplementary Table\u0026nbsp;8\u003c/b\u003e). We also applied our rMATS analysis pipeline to the \u003cem\u003eU2af1\u003c/em\u003e\u003csup\u003eS34F/+\u003c/sup\u003e Native KL RNA-seq dataset published by Fei \u003cem\u003eet al\u003c/em\u003e.\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e (\u003cb\u003eSupplementary Fig.\u0026nbsp;6A,B\u003c/b\u003e) and observed a strong concordance (i.e., unidirectional ΔPSI values) between DSEs shared between the two \u003cem\u003eU2af1\u003c/em\u003e\u003csup\u003eS34F/+\u003c/sup\u003e KL datasets (\u003cb\u003eSupplementary Fig.\u0026nbsp;6C\u003c/b\u003e). Thus, rMATS analysis of independent RNA-seq data demonstrates that the \u003cem\u003eU2af1\u003c/em\u003e\u003csup\u003eS34F/+\u003c/sup\u003e mouse model produces robust and reproducible gene expression and alternative pre-mRNA splicing changes in hematopoietic cells \u003cem\u003ein vivo\u003c/em\u003e (\u003cb\u003eSupplementary Figs.\u0026nbsp;5B,6C\u003c/b\u003e). In line with previous studies of U2AF1 mutant cell lines and patient HSPC, SE events represented the majority of DSEs identified in U2AF1 mutant mouse KL cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB and \u003cb\u003eSupplementary Fig.\u0026nbsp;6A\u003c/b\u003e).\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e,\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e,\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e \u003cem\u003eU2af1\u003c/em\u003e\u003csup\u003eS34F/+\u003c/sup\u003e SE DSEs also favored exon exclusion (\u0026lsquo;skipping\u0026rsquo;) over exon inclusion.\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e Of note, \u003cem\u003eU2af1\u003c/em\u003e\u003csup\u003eQ157R/+\u003c/sup\u003e DSEs were more equally distributed between RI and SE events (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB). The overlap of DSE shared between \u003cem\u003eU2af1\u003c/em\u003e\u003csup\u003eS34F/+\u003c/sup\u003e and \u003cem\u003eU2af1\u003c/em\u003e\u003csup\u003eQ157R/+\u003c/sup\u003e KL cells was low (125 events or 8.3%; Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC). Conversion of DSE to differentially spliced genes (DSG) revealed 196 genes (17.5%) aberrantly spliced in common between the two mutants (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eD). GO analysis revealed that DSGs from \u003cem\u003eU2af1\u003c/em\u003e\u003csup\u003eS34F/+\u003c/sup\u003e KL cells were most significantly enriched in mRNA binding and metabolism gene sets, as well as histone post-translational modification and stress granule\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e related gene sets (\u003cb\u003eSupplementary Fig.\u0026nbsp;6D\u003c/b\u003e and \u003cb\u003eSupplementary Table\u0026nbsp;9\u003c/b\u003e). DSGs from \u003cem\u003eU2af1\u003c/em\u003e\u003csup\u003eQ157R/+\u003c/sup\u003e KL cells were enriched in mRNA gene sets to a weaker extent than \u003cem\u003eU2af1\u003c/em\u003e\u003csup\u003eS34F/+\u003c/sup\u003e (\u003cb\u003eSupplementary Fig.\u0026nbsp;6D\u003c/b\u003e and \u003cb\u003eSupplementary Table\u0026nbsp;9\u003c/b\u003e).\u003c/p\u003e \u003cp\u003eAnalysis of consensus 3\u0026rsquo; splice site (3\u0026rsquo;SS) sequences from differentially spliced SE events in \u003cem\u003eU2af1\u003c/em\u003e\u003csup\u003eS34F/+\u003c/sup\u003e and \u003cem\u003eU2af1\u003c/em\u003e\u003csup\u003eQ157R/+\u003c/sup\u003e KL cells confirmed previous dependencies identified in U2AF1 mutant cell lines and patient HSPC. Specifically, exon inclusion favored a C and exon exclusion favored a T at the \u0026minus;\u0026thinsp;3 position of the 3\u0026rsquo;SS in \u003cem\u003eU2af1\u003c/em\u003e\u003csup\u003eS34F/+\u003c/sup\u003e cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eE, middle). In contrast, exon inclusion favored a G and exon exclusion favored an A at the +\u0026thinsp;1 position of the 3\u0026rsquo;SS in \u003cem\u003eU2af1\u003c/em\u003e\u003csup\u003eQ157R/+\u003c/sup\u003e cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eE, right). Overall, these findings highlight that the U2AF1\u003csup\u003eS\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003eF\u003c/sup\u003e and U2AF1\u003csup\u003eQ157R\u003c/sup\u003e mutants induce significant but distinct changes to alternative mRNA splicing \u003cem\u003ein vivo\u003c/em\u003e.\u003c/p\u003e \u003cp\u003e \u003cb\u003eU2af1\u003c/b\u003e \u003csup\u003e \u003cb\u003eS34F/+\u003c/b\u003e \u003c/sup\u003e \u003cb\u003eand\u003c/b\u003e \u003cb\u003eU2af1\u003c/b\u003e\u003csup\u003e\u003cb\u003eQ157R/+\u003c/b\u003e\u003c/sup\u003e \u003cb\u003emouse models recapitulate alternative pre-mRNA splicing changes found in MDS and AML patients.\u003c/b\u003e\u003c/p\u003e \u003cp\u003eTo assess how well alternative splicing changes in mouse KL cells recapitulate changes seen in MDS and AML patient hematopoietic cells, we performed a meta-analysis using publicly available RNA-seq data from three published studies.\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e Each study included 2\u0026ndash;10 U2AF1\u003csup\u003eS\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003eF\u003c/sup\u003e and only 1\u0026ndash;2 U2AF1\u003csup\u003eQ157R\u003c/sup\u003e patients. Therefore, U2AF1\u003csup\u003eR156H\u003c/sup\u003e and U2AF1\u003csup\u003eQ157(P/R)\u003c/sup\u003e patient samples were grouped together (N\u0026thinsp;=\u0026thinsp;4\u0026ndash;5 \u003cem\u003eU2AF1\u003c/em\u003e\u003csup\u003eR156H/Q157(P/R)\u003c/sup\u003e patients per study; Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA) consistent with previous studies demonstrating similar 3\u0026rsquo;SS sequence dependencies.\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e,\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e In each study, samples from MDS/AML patients who did not have identifiable mutations in \u003cem\u003eSF3B1\u003c/em\u003e or \u003cem\u003eSRSF2\u003c/em\u003e were used as a comparator (Splicing Factor [SF]\u003csup\u003eWT\u003c/sup\u003e). To allow for a more rigorous analysis of differential splicing, we reanalyzed the FASTQ files for each study using the same analysis workflows and applied the same significance thresholds (FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and |ΔPSI|\u0026gt;0.05 vs SF\u003csup\u003eWT\u003c/sup\u003e) as used for the analysis of mouse KL cells. Using this approach, we credentialed each of the three datasets (referred to as Madan,\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e Pellagatti,\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e and Beat AML\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e) (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA and \u003cb\u003eSupplementary Fig.\u0026nbsp;7A-I\u003c/b\u003e and \u003cb\u003eSupplementary Tables\u0026nbsp;10\u0026ndash;15\u003c/b\u003e). Specifically, SE events were the most frequent DSE type identified in each study for S34F and R156/Q157 (\u003cb\u003eSupplementary Fig.\u0026nbsp;7A-C\u003c/b\u003e) and these events favored the characteristic consensus 3\u0026rsquo;SS sequence dependencies identified previously (\u003cb\u003eSupplementary Fig.\u0026nbsp;7G-I\u003c/b\u003e).\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e,\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e,\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e,\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e,\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e To increase rigor of our meta-analysis we prioritized only the DSEs that were shared between at least two of the three MDS/AML datasets for either \u003cem\u003eU2AF1\u003c/em\u003e\u003csup\u003eS\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003eF\u003c/sup\u003e or \u003cem\u003eU2AF1\u003c/em\u003e\u003csup\u003eR156/Q157\u003c/sup\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eB and \u003cb\u003eSupplementary Table\u0026nbsp;16\u003c/b\u003e). The distribution of these DSEs was similar to each individual dataset with SE events still representing the majority event type in U2AF1 mutant MDS/AML cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eC). As in the mice, the overlap of DSE shared between \u003cem\u003eU2AF1\u003c/em\u003e\u003csup\u003eS\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003eF\u003c/sup\u003e and \u003cem\u003eU2AF1\u003c/em\u003e\u003csup\u003eR156/Q157\u003c/sup\u003e MDS/AML cells was low (144 of 1978 events or 7.3%; Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eD). Conversion of DSE to DSG revealed a total of 284 of 1305 genes (21.8%) aberrantly spliced in common between the two mutants (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eE).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe overlap of DSG identified in human and mouse cells revealed that approximately 20% of aberrantly spliced genes in KL (mouse) cells were also mis-spliced in MDS/AML (human) cells for both U2AF1\u003csup\u003eS\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003eF\u003c/sup\u003e (17.6% shared) and U2AF1\u003csup\u003eQ157R\u003c/sup\u003e (19.7% shared) mutants (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eF,G). GO analysis revealed that shared S34F DSGs were most significantly enriched in mRNA binding and metabolism gene sets as well as stress granule\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e and mRNA translation related gene sets (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eH and \u003cb\u003eSupplementary Table\u0026nbsp;17\u003c/b\u003e). Shared Q157R DSGs were less significantly enriched in mRNA gene sets than S34F. Histone binding and DNA damage response gene sets were among some of the significantly enriched gene sets for Q157R DSGs (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eH and \u003cb\u003eSupplementary Table\u0026nbsp;17\u003c/b\u003e).\u003c/p\u003e \u003cp\u003eWe validated several of these putatively shared aberrant splicing changes identified by rMATS analysis by performing RT-PCR followed by gel electrophoresis of RNA isolated from additional mouse KL cell samples (N\u0026thinsp;=\u0026thinsp;4 per genotype) and MDS patient samples (N\u0026thinsp;=\u0026thinsp;4\u0026ndash;9 per genotype). Consistent with previous observations,\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e,\u003cspan additionalcitationids=\"CR26 CR27\" citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e we confirmed aberrant splicing of functionally relevant transcripts (\u003cem\u003eH2AFY\u003c/em\u003e and \u003cem\u003eGNAS\u003c/em\u003e) in U2AF1\u003csup\u003eS\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003eF\u003c/sup\u003e mutant mouse KL and MDS cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eI,J). We also demonstrate that aberrantly spliced transcripts (\u003cem\u003eMPHOSPH9\u003c/em\u003e, \u003cem\u003eSETD5\u003c/em\u003e, \u003cem\u003eATP6V0A1\u003c/em\u003e, and \u003cem\u003eCLIP1\u003c/em\u003e) in U2AF1\u003csup\u003eQ157R\u003c/sup\u003e mutant MDS patient cells are similarly mis-spliced in KL cells from \u003cem\u003eU2af1\u003c/em\u003e\u003csup\u003eQ157R/+\u003c/sup\u003e mice (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eI,K-L). Aberrant splicing of \u003cem\u003eCLIP1\u003c/em\u003e is one example of an SE event that is differentially spliced in opposite directions by U2AF1\u003csup\u003eS\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003eF\u003c/sup\u003e (increased exon inclusion) and U2AF1\u003csup\u003eQ157R\u003c/sup\u003e (increased exon skipping/exclusion) in mouse and human cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eL), further highlighting the distinct splicing differences induced by these two U2AF1 mutants.\u003c/p\u003e \u003cp\u003e \u003cb\u003eU2AF1\u003c/b\u003e \u003csup\u003e \u003cb\u003eR156/Q157\u003c/b\u003e \u003c/sup\u003e \u003cb\u003emutations are enriched in patients with CMML and MPN compared to\u003c/b\u003e \u003cb\u003eU2AF1\u003c/b\u003e\u003csup\u003e\u003cb\u003eS\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003eF\u003c/b\u003e\u003c/sup\u003e \u003cb\u003emutations.\u003c/b\u003e\u003c/p\u003e \u003cp\u003eGiven the differences in gene expression, splicing, and hematopoietic phenotypes induced by \u003cem\u003eU2af1\u003c/em\u003e\u003csup\u003eS34F/+\u003c/sup\u003e and \u003cem\u003eU2af1\u003c/em\u003e\u003csup\u003eQ157R/+\u003c/sup\u003e mutations in mice, we asked if the two hotspot mutations were differentially enriched in various myeloid neoplasms. We identified 487 patients with a diagnosis of AML, sAML (from MDS), MDS, CMML, or MPN who had a \u003cem\u003eU2AF1\u003c/em\u003e mutation based on available sequencing data and calculated the proportion of patients with \u003cem\u003eU2AF1\u003c/em\u003e\u003csup\u003eR156/Q157\u003c/sup\u003e or \u003cem\u003eU2AF1\u003c/em\u003e\u003csup\u003eS\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e mutations (see \u003cb\u003eSupplementary Methods\u003c/b\u003e). We observed that \u003cem\u003eU2AF1\u003c/em\u003e\u003csup\u003eR156/Q157\u003c/sup\u003e mutations were more common in CMML and MPN patients, \u003cem\u003eU2AF1\u003c/em\u003e\u003csup\u003eS\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e mutations more common in sAML and AML patients, and a similar proportion of both mutations occurred in MDS (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eA and \u003cb\u003eSupplementary Table\u0026nbsp;18\u003c/b\u003e). The co-occurrence of \u003cem\u003eU2AF1\u003c/em\u003e and signaling gene mutations also differed across myeloid neoplasms, with \u003cem\u003eNRAS\u003c/em\u003e and \u003cem\u003eFLT3\u003c/em\u003e mutations being more common with S34 mutations and \u003cem\u003eCBL\u003c/em\u003e, \u003cem\u003ePTPN11\u003c/em\u003e and \u003cem\u003eCSF3R\u003c/em\u003e mutations more common with R156/Q157 mutations. Similar to previous reports by our group and others, we also observed preferential co-occurrence of other gene mutations with \u003cem\u003eU2AF1\u003c/em\u003e\u003csup\u003eR156/Q157\u003c/sup\u003e (e.g., \u003cem\u003eASXL1\u003c/em\u003e) or \u003cem\u003eU2AF1\u003c/em\u003e\u003csup\u003eS\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003eF\u003c/sup\u003e (e.g., \u003cem\u003eBCOR\u003c/em\u003e) mutations in MDS patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eB and \u003cb\u003eSupplementary Tables\u0026nbsp;19\u0026ndash;20\u003c/b\u003e).\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e,\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eIn this study, we characterized the \u003cem\u003ein vivo\u003c/em\u003e consequences of expressing two myeloid neoplasm-associated hotspot mutations in \u003cem\u003eU2AF1\u003c/em\u003e that code for S34F and Q157R substitutions. Our results indicate that the two mutations induce distinct hematopoietic phenotypes in mice, suggesting that the \u003cem\u003eU2af1\u003c/em\u003e\u003csup\u003eS\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003eF\u003c/sup\u003e and \u003cem\u003eU2af1\u003c/em\u003e\u003csup\u003eQ157R\u003c/sup\u003e mutations should not be conflated as they may impact disease pathogenesis differently in patients. Mice expressing \u003cem\u003eU2af1\u003c/em\u003e\u003csup\u003eS\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003eF\u003c/sup\u003e have a more severe reduction in their PB and BM cell counts, and reduced HSPCs repopulating ability, compared to mice expressing \u003cem\u003eU2af1\u003c/em\u003e\u003csup\u003eQ157R\u003c/sup\u003e. The expression and splicing of the majority of target genes are unique between the mutations, in both mouse and human samples, potentially driving the phenotypic differences induced by the two mutations. The two mutations co-occur with different gene mutations and are not equally represented in various myeloid neoplasms, suggesting that multiple mechanisms are likely to drive the pathogenesis of \u003cem\u003eU2AF1\u003c/em\u003e mutant myeloid diseases.\u003c/p\u003e \u003cp\u003eOur results add to the growing body of literature highlighting the paradigm that different hotspot mutations in a specific cancer gene can lead to distinct functional consequences and should, therefore, not necessarily be conflated. In one of the more well studied examples, different \u003cem\u003eKRAS\u003c/em\u003e hotspot mutations (e.g., G12, G13, Q61) lead to varying levels of KRAS activation through modulation of distinct biochemical properties of KRAS.\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e In turn, different \u003cem\u003eKRAS\u003c/em\u003e hotspot mutations confer different prognostic value in various cancers (e.g., colorectal cancer) and are predictive of response to chemotherapy and/or targeted therapies.\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e Similarly, the prognostic significance of distinct \u003cem\u003eSF3B1\u003c/em\u003e mutations can be different in MDS, including their impact on overall survival.\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e,\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e With respect to \u003cem\u003eU2AF1\u003c/em\u003e, a prognostic scoring model for MF (MIPSS70\u0026thinsp;+\u0026thinsp;v2.0) now incorporates the negative impact of Q157 (but not S34) codon mutations.\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e U2AF1 S34 and Q157 codon-specific clinical characteristics have also been reported in MDS.\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e,\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e Further studies are needed to fully understand the functional impact of each \u003cem\u003eU2AF1\u003c/em\u003e hotspot mutation in patients and determine whether these differences confer consistent prognostic or therapeutic value across the spectrum of myeloid malignancies.\u003c/p\u003e \u003cp\u003eEnrichment of \u003cem\u003eU2AF1\u003c/em\u003e\u003csup\u003eS\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e vs \u003cem\u003eU2AF1\u003c/em\u003e\u003csup\u003eQ157\u003c/sup\u003e mutations in different myeloid diseases suggest that mutations may contribute to the disease phenotype by differences in the target genes that they dysregulate and/or cooperating gene mutations. Identifying and validating the key target genes that are dysregulated and confer mutation-specific cellular phenotypes will require future \u003cem\u003ein vivo\u003c/em\u003e functional studies. Additionally, based on differences in hotspot mutations in other cancers, the cellular \u0026lsquo;soil\u0026rsquo; that a S34 or Q157 mutation occurs in likely also matters. In \u003cem\u003eU2AF1\u003c/em\u003e-mutated solid tumors, particularly lung adenocarcinomas and endometrial cancers, S34 codon mutations are highly enriched compared to Q157 codon mutations.\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e This observation is not specific to \u003cem\u003eU2AF1\u003c/em\u003e, as \u003cem\u003eSF3B1\u003c/em\u003e R625 codon mutations are enriched in uveal and cutaneous melanomas, whereas K700 mutations are more common in breast cancer and chronic lymphoid leukemia specimens.\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e The subtle phenotype in the \u003cem\u003eU2af1\u003c/em\u003e\u003csup\u003eQ157R/+\u003c/sup\u003e mouse, including lack of severe cytopenias and possibly a slight increase in platelets, could make Q157R cells more permissive to transformation with a MPN-associated cooperating mutation (e.g., \u003cem\u003eCSF3R\u003c/em\u003e) resulting in higher blood counts, something that will require future studies. In contrast, S34F induces cytopenias in mice and may contribute to cytopenias seen in MDS. In addition, S34 and Q157 do not cooperate with the same mutations in MDS (e.g., \u003cem\u003eBCOR\u003c/em\u003e with S34\u0026thinsp;\u0026gt;\u0026thinsp;Q157 and \u003cem\u003eASXL1\u003c/em\u003e with Q157\u0026thinsp;\u0026gt;\u0026thinsp;S34),\u003csup\u003e36\u003c/sup\u003e and this could impact mutation-associated phenotypes in patients.\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e,\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e Finally, \u003cem\u003eU2AF1\u003c/em\u003e hotspot mutation phenotypes could be influenced by the order of cooperating gene mutation acquisition (i.e., \u003cem\u003eU2AF1\u003c/em\u003e mutation occurring before or after a cooperating gene mutation) or the presence of hematopoietic stressors, requiring future experiments.\u003c/p\u003e \u003cp\u003eCollectively, our results support that \u003cem\u003eU2AF1\u003c/em\u003e\u003csup\u003eS\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003eF\u003c/sup\u003e and \u003cem\u003eU2AF1\u003c/em\u003e\u003csup\u003eQ157R\u003c/sup\u003e mutations induce distinct hematopoietic, gene expression, and RNA splicing phenotypes \u003cem\u003ein vivo\u003c/em\u003e. Larger population studies will be needed to determine if these phenotypic changes translate into clinico-pathologic differences in patients warranting separate classification.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eACKNOWLEDGEMENTS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors thank Harold E. Varmus for the gift of the MGQ157R mice; William C. Eades, Daniel Schweppe, Michael Savio, and Matt Patana of the the Alvin J. Siteman Cancer Center at Washington University School of Medicine (WUSM) and Barnes-Jewish Hospital (St. Louis, MO) for the use of the Siteman Flow Cytometry Core and sorting assistance; Zev J. Greenberg for sorting assistance; Nichole M. Helton for help with RNA-seq preparation; the McDonnell Genome Institute (MGI) for RNA-seq; Jessica Hoisington-Lopez and MariaLynn Crosby of the DNA Sequencing Innovation Lab (DSIL) at WUSM Center for Genome Sciences \u0026amp; Systems Biology for help with amplicon sequencing; Eric J. Duncavage and Kiran Vij for hematopathology expertise; John D. Pfeifer for critical support; and Timothy J. Ley, Daniel C. Link, and members of the Walter Lab for useful discussions.\u003c/p\u003e\n\u003cp\u003eThis work was supported by grants to M.J.W. from the Edward P. Evans Foundation, Taub Foundation, Lottie Caroline Hardy Trust, Foundation for Barnes-Jewish Hospital Cancer Frontier Fund, the National Cancer Institute (NCI) of the National Institutes of Health (NIH) (P01 CA101937, Timothy J. Ley, PI; P50 CA171963, Daniel C. Link, PI), and the Leukemia \u0026amp; Lymphoma Society (7024-21). M.O.A. was supported by career development awards from the Edward P. Evans Foundation and NCI and National Heart, Lung, and Blood Institutes (NHLBI) of the NIH under the award numbers K12 CA167540 (Washington University Paul Calabresi) and K08 HL159354. M.Z. was supported by an American Society of Hematology (ASH) Physician-Scientist Career Development Award. C.P. was supported by an ASH Minority Hematology Graduate Award (MHGA) and a NIH/NCI award (F31 CA284751). O.A.-W. is supported by the Neil S. Hirsch Foundation, Edward P. Evans Foundation,\u0026nbsp;Break Through Cancer, NIH/NCI (R01 CA251138, R01 CA242020, R01 CA283364, and P50 CA254838), and\u0026nbsp;NIH/NHLBI (R01 HL128239), and the Leukemia \u0026amp; Lymphoma Society.\u0026nbsp;T.A.G. is supported by the Edward P. Evans Foundation, NIH/NCI (P50 CA171963), and the Leukemia \u0026amp; Lymphoma Society (7024-21). Sample banking was provided by the Genomics of Acute Myeloid Leukemia Program Project Grant (P01 CA101937, Timothy J. Ley, PI) and Specialized Program of Research Excellence in Acute Myeloid Leukemia (P50 CA171963, Daniel C. Link, PI). We thank the Alvin J. Siteman Cancer Center at WUSM and Barnes-Jewish Hospital, as well as the Institute of Clinical and Translational Sciences (ICTS) at Washington University in St. Louis, for the use of the Siteman Flow Cytometry Core, the Tissue Procurement Core, and the Genome Technology Access Center. The Siteman Cancer Center is supported in part by an NCI Cancer Center Support Grant (P30 CA091842) and the ICTS is funded by the NIH NCATS Clinical and Translational Science Award (CTSA) program (UL1 TR002345). The content of this publication is solely the responsibility of the authors and does not necessarily represent the official views of the NIH.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAUTHOR CONTRIBUTIONS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eM.O.A., D.L.F., T.A.G., O.A.-W., and M.J.W. were responsible for conceptualization; M.O.A., S.N.S., D.L.F., and M.J.W. were responsible for methodology; M.O.A., S.N.S., J.S., M.Z., C.C., and S.G. performed investigation; M.O.A., S.N.S., and M.J.W. wrote the original draft manuscript; M.O.A., S.N.S., and M.J.W. reviewed and edited the manuscript; M.J.W. was responsible for funding acquisition; and M.J.W. provided supervision.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCOMPETING INTERESTS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eO.A.-W. is a founder and scientific advisor of Codify Therapeutics, holds equity, and receives research funding from this company. O.A.-W. has served as a consultant for Amphista Therapeutics, and MagnetBio, and is on scientific advisory boards of Envisagenics Inc. and Harmonic Discovery Inc. O.A.-W. has received research funding from Astra Zeneca, Nurix Therapeutics, and Minovia Therapeutics, unrelated to this study. The remaining authors declare no competing interests.I\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eADDITIONAL INFORMATION\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupplementary information\u003c/strong\u003e The online version contains supplementary material.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDATA AVAILABILITY\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData are available on request to the corresponding author. RNA-seq data generated from this study have been deposited in the National Center for Biotechnology Information (NCBI) Gene Expression Omnibus (GEO) database (accession # GSE282060).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eDamm F, Kosmider O, Gelsi-Boyer V, et al. Mutations affecting mRNA splicing define distinct clinical phenotypes and correlate with patient outcome in myelodysplastic syndromes. \u003cem\u003eBlood\u003c/em\u003e. 2012;119(14):3211\u0026ndash;3218.\u003c/li\u003e\n \u003cli\u003eHaferlach T, Nagata Y, Grossmann V, et al. Landscape of genetic lesions in 944 patients with myelodysplastic syndromes. \u003cem\u003eLeukemia\u003c/em\u003e. 2014;28(2):241\u0026ndash;247.\u003c/li\u003e\n \u003cli\u003ePapaemmanuil E, Gerstung M, Malcovati L, et al. 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Clinical and molecular spectrum and prognostic outcomes of U2AF1 mutant clonal hematopoiesis- a prospective mayo clinic cohort study. \u003cem\u003eLeuk. Res.\u003c/em\u003e 2023;125:107007.\u003c/li\u003e\n \u003cli\u003eSupek F, Bo\u0026scaron;njak M, \u0026Scaron;kunca N, \u0026Scaron;muc T. REVIGO summarizes and visualizes long lists of gene ontology terms. \u003cem\u003ePloS One\u003c/em\u003e. 2011;6(7):e21800.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"leukemia","isNatureJournal":false,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"leu","sideBox":"Learn more about [Leukemia](http://www.nature.com/leu/)","snPcode":"41375","submissionUrl":"https://mts-leu.nature.com/cgi-bin/main.plex","title":"Leukemia","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-6377810/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6377810/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eRecurrent somatic mutations in the spliceosome genes \u003cem\u003eSF3B1\u003c/em\u003e, \u003cem\u003eSRSF2\u003c/em\u003e, and \u003cem\u003eU2AF1\u003c/em\u003e are frequently identified in patients with myeloid neoplasms, such as myelodysplastic syndromes. We characterized the \u003cem\u003ein vivo\u003c/em\u003e consequences of expressing two hotspot mutations in \u003cem\u003eU2AF1\u003c/em\u003e that code for the S34F and Q157R substitutions. Our results indicate that the two mutations induce distinct hematopoietic phenotypes in mice, suggesting that the \u003cem\u003eU2AF1\u003c/em\u003e\u003csup\u003eS\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003eF\u003c/sup\u003e and \u003cem\u003eU2AF1\u003c/em\u003e\u003csup\u003eQ157R\u003c/sup\u003e mutations should not be conflated as they may impact disease pathogenesis differently in patients. Mice expressing \u003cem\u003eU2af1\u003c/em\u003e\u003csup\u003eS\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003eF\u003c/sup\u003e have a more severe reduction in their blood and bone marrow cell counts and reduced stem cell repopulating ability, compared to mice expressing \u003cem\u003eU2af1\u003c/em\u003e\u003csup\u003eQ157R\u003c/sup\u003e. The expression and splicing of target genes are largely unique between the mutations, in both mouse and human samples, potentially driving the phenotypic differences induced by either mutation. The two mutations co-occur with different gene mutations in patients and are not equally represented across myeloid neoplasms, suggesting that multiple mechanisms likely drive U2AF1-mutant disease pathogenesis. Collectively, our results support that \u003cem\u003eU2AF1\u003c/em\u003e\u003csup\u003eS\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003eF\u003c/sup\u003e and \u003cem\u003eU2AF1\u003c/em\u003e\u003csup\u003eQ157R\u003c/sup\u003e mutations induce distinct hematopoietic, gene expression, and RNA splicing phenotypes \u003cem\u003ein vivo\u003c/em\u003e. Larger population studies will be needed to determine if these phenotypic changes translate into clinico-pathologic differences in patients warranting separate classification.\u003c/p\u003e","manuscriptTitle":"U2af1S34F and U2af1Q157R myeloid neoplasm-associated hotspot mutations induce distinct hematopoietic phenotypes in mice","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-07 05:44:04","doi":"10.21203/rs.3.rs-6377810/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"revise","date":"2025-04-29T09:09:14+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"This content is not available.","date":"2025-04-28T14:55:15+00:00","index":2,"fulltext":"This content is not available."},{"type":"editorInvitedReview","content":"This content is not available.","date":"2025-04-28T05:01:23+00:00","index":3,"fulltext":"This content is not available."},{"type":"editorInvitedReview","content":"This content is not available.","date":"2025-04-23T10:44:27+00:00","index":1,"fulltext":"This content is not available."},{"type":"reviewerAgreed","content":"This content is not available.","date":"2025-04-11T02:34:53+00:00","index":3,"fulltext":"This content is not available."},{"type":"reviewerAgreed","content":"This content is not available.","date":"2025-04-08T18:29:51+00:00","index":2,"fulltext":"This content is not available."},{"type":"reviewerAgreed","content":"This content is not available.","date":"2025-04-08T07:07:14+00:00","index":1,"fulltext":"This content is not available."},{"type":"reviewersInvited","content":"","date":"2025-04-08T01:00:48+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-04-07T11:14:24+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-04-07T11:08:48+00:00","index":"","fulltext":""},{"type":"submitted","content":"Leukemia","date":"2025-04-04T16:40:11+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"leukemia","isNatureJournal":false,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"leu","sideBox":"Learn more about [Leukemia](http://www.nature.com/leu/)","snPcode":"41375","submissionUrl":"https://mts-leu.nature.com/cgi-bin/main.plex","title":"Leukemia","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"d00d1cc3-fb3e-4aca-b209-43c45bddafc2","owner":[],"postedDate":"May 7th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":46818915,"name":"Biological sciences/Cancer/Cancer genetics"},{"id":46818916,"name":"Biological sciences/Cancer/Cancer models"},{"id":46818917,"name":"Biological sciences/Cancer/Haematological cancer/Myelodysplastic syndrome"},{"id":46818918,"name":"Biological sciences/Cancer/Haematological cancer/Leukaemia/Acute myeloid leukaemia"},{"id":46818919,"name":"Biological sciences/Cancer/Haematological cancer/Myeloproliferative disease"}],"tags":[],"updatedAt":"2026-05-07T07:07:40+00:00","versionOfRecord":{"articleIdentity":"rs-6377810","link":"https://doi.org/10.1038/s41375-026-02974-7","journal":{"identity":"leukemia","isVorOnly":false,"title":"Leukemia"},"publishedOn":"2026-05-06 04:00:00","publishedOnDateReadable":"May 6th, 2026"},"versionCreatedAt":"2025-05-07 05:44:04","video":"","vorDoi":"10.1038/s41375-026-02974-7","vorDoiUrl":"https://doi.org/10.1038/s41375-026-02974-7","workflowStages":[]},"version":"v1","identity":"rs-6377810","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6377810","identity":"rs-6377810","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}