Prototypical innate immune mechanism hijacked by leukemia-initiating mutant stem cells for selective advantage and immune evasion in Ptpn11-associated juvenile myelomonocytic leukemia

Prototypical innate immune mechanism hijacked by leukemia-initiating mutant stem cells for selective advantage and immune evasion in Ptpn11-associated juvenile myelomonocytic leukemia | 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 Prototypical innate immune mechanism hijacked by leukemia-initiating mutant stem cells for selective advantage and immune evasion in Ptpn11-associated juvenile myelomonocytic leukemia Hong Zheng, Peng Zhao, Zhenya Tan, Wen-Mei Yu, Juwita Werner, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4450642/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted You are reading this latest preprint version Abstract Juvenile myelomonocytic leukemia (JMML), a clonal hematologic malignancy, originates from mutated hematopoietic stem cells (HSCs). The mechanism sustaining the persistence of mutant stem cells, leading to leukemia development, remains elusive. In this study, we conducted comprehensive examination of gene expression profiles, transcriptional factor regulons, and cell compositions/interactions throughout various stages of tumor cell development in Ptpn11 mutation-associated JMML. Our analyses revealed that leukemia-initiating Ptpn11 E76K/+ mutant stem cells exhibited de novo activation of the myeloid transcriptional program and aberrant developmental trajectories. These mutant stem cells displayed significantly elevated expression of innate immunity-associated anti-microbial peptides and pro-inflammatory proteins, particularly S100a9 and S100a8 . Biological experiments confirmed that S100a9/S100a8 conferred a selective advantage to the leukemia-initiating cells through autocrine effects and facilitated immune evasion by recruiting and promoting immune suppressive myeloid-derived suppressor cells (MDSCs) in the microenvironment. Importantly, pharmacological inhibition of S100a9/S100a8 signaling effectively impeded leukemia development from Ptpn11 E76K/+ mutant stem cells. These findings collectively suggest that JMML tumor-initiating cells exploit evolutionarily conserved innate immune and inflammatory mechanisms to establish clonal dominance. Biological sciences/Cancer/Haematological cancer Biological sciences/Stem cells/Haematopoietic stem cells JMML PTPN11 Leukemia-initiating cell Hematopoietic stem cell Innate immunity Inflammation S100a9 S100a8 Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 INTRODUCTION Juvenile myelomonocytic leukemia (JMML), a pediatric myeloproliferative neoplasm, manifests as a clonal hematopoietic disorder characterized by the excessive production of myeloid cells. This disease originates from driver mutations acquired in hematopoietic stem cells (HSCs) and is propagated and sustained by these mutated stem cells, known as leukemia-initiating cells 1–4 . JMML has limited therapeutic options. Relapse remains the primary cause of treatment failure, most likely due to the persistence of therapy-resistant, self-renewing leukemia-initiating cells 1–4 . Addressing this issue is crucial for improving treatment outcomes in JMML patients. Genetically, JMML is associated with mutations in genes encoding signaling proteins involved in the RAS/ERK pathway, including PTPN11 , RAS , NF1 , CBL , and others. 1–4 . These mutations play a causal role in driving JMML development 5–8 . JMML arises from an HSC harboring a genetic mutation, yet the mechanisms by which the initially mutated stem cell (leukemia-initiating cell) acquires a competitive advantage and evades immune surveillance remain unexplored. Additionally, the specific reasons behind the propensity of disease-associated mutations to induce myeloid malignancy are not fully understood, and the molecular mechanisms governing the aberrant repopulation of these leukemia-initiating stem cells remain elusive. Understanding these mechanisms could illuminate strategies for therapeutically targeting and eliminating JMML initiating stem cells in established disease. Of the genetic lesions identified in JMML, the protein tyrosine phosphatase PTPN11 (SHP-2), a positive regulator of RAS signaling 9,10 , is the most frequently mutated (heterozygous) 11,12 . Mutations in PTPN11 lead to a significant increase in the catalytic activity of SHP-2 12,13 . Patients carrying PTPN11 activating mutations have the worst prognosis among all subtypes of JMML 14–17 . To elucidate the mechanisms underlying the pathogenesis of PTPN11 -mutated JMML, our laboratory created a conditional Ptpn11 allele in mice with the Ptpn11 E76K mutation, the most common PTPN11 mutation found in JMML 11,12 , and developed an inducible disease model 6,18 . Induction of the Ptpn11 E76K mutation in the hematopoietic system resulted in a JMML-like myeloproliferative neoplasm with complete penetrance, affirming the causative role of this mutation in JMML 6 . In the present study, we take advantage of this unique disease model to investigate the cellular and molecular mechanisms involved in the pathological process of JMML following induction of the disease mutation. Our findings from single-cell transcriptomic profiling and experimental validations reveal an aberrant activation of innate immune responses in the mutated stem cells. These leukemia-initiating cells exploit innate immune and inflammatory mechanisms to gain a competitive advantage and evade anti-tumor immunity, ultimately leading to clonal dominance. RESULTS Aberrant activation of innate immune and inflammatory responses in leukemia-initiating Ptpn11 E76K/+ stem cells. To explore the intricate mechanisms of JMML pathogenesis, we conducted a comprehensive single-cell RNA sequencing (scRNA-seq) analysis on bone marrow (BM) cells isolated from mice with induced JMML ( Ptpn11 E76K/+ /Mx1-Cre ) 6 and wild-type (WT, Ptpn11 +/+ /Mx1-Cre ) control littermates. Utilizing gene expression pattern-based cell clustering, we identified 11 distinct cell clusters within the BM population on a t-distributed stochastic neighbor embedding (t-SNE) plot (Extended Data Fig. 1 A). Clear distinctions among these clusters were evident in the heatmap representation of the expression patterns of the top 10 differentially expressed genes (DEGs) in each cluster (Extended Data Fig. 1 B). Leveraging reference datasets 19,20 permitted the identification of various hematopoietic cell types in different developmental stages, including HSCs, granulocyte-macrophage progenitors (GMPs), megakaryocytic-erythroid progenitors (MEPs), monocytes, neutrophils, T cells, B cells, and others (Extended Data Fig. 1 C). Cell type-specific signature genes were indeed well-represented in the identified cell clusters (Extended Data Fig. 1 D). Notably, Ptpn11 E76K/+ mutant HSCs (leukemia-initiating cells) and GMPs exhibited reduced abundance, while monocytes and neutrophils displayed an increase compared to their WT ( Ptpn11 +/+ ) counterparts (Extended Data Fig. 1 C). The reduction of mutant stem cells/progenitors and the myeloid shift in hematopoietic cell development indicated hyperactivation of these leukemia-initiating cells and myeloid-committed progenitors. The decreased numbers of T cells and B cells in their hematopoietic systems suggested that the enhanced myeloid cell production resulted from skewed differentiation of Ptpn11 -mutated stem cells. Gene set enrichment analysis (GSEA) demonstrated the upregulation of genes associated with immune processes and chemokine activities, particularly through the CC chemokine receptor (CCR), in Ptpn11 E76K/+ mutant hematopoietic cells (Extended Data Fig. 1 E). Gene expression profile-based cell clustering of the stem cell population revealed two distinct clusters equivalent to long-term HSCs (LT-HSCs) and short-term HSCs (ST-HSCs) according to the reference datasets 20 . The percentage of LT-HSCs decreased while the percentage of ST-HSCs increased in Ptpn11 E76K/+ mice compared to those in WT littermates (Fig. 1 A). In our analyses we also observed that among the top 20 DEGs in LT-HSCs compared to ST-HSCs, several genes were highly expressed only in LT-HSCs (Fig. 1 B). In particular, Sdpr was predominantly expressed in LT-HSCs, indicating its potential as a distinctive marker for distinguishing LT-HSCs from ST-LT-HSCs. Notably, 177 genes in total were significantly differentially expressed in Ptpn11 E76K/+ LT-HSCs versus WT LT-HSCs (Fig. 1 C). The Gene Ontology (GO) enrichment analysis of these DEGs highlighted the predominant elevation of defense reactions to bacterial infection, innate immune response, Toll-like receptor 4 (TLR4) signaling, and inflammation-associated pathways (Fig. 1 D). Consistent with the hyperactivation of Ptpn11 E76K/+ HSCs, GSEA demonstrated a decrease in the expression of stem cell/progenitor-associated genes and upregulated/downregulated genes in HSCs versus GMPs in Ptpn11 E76K/+ HSCs (Fig. 1 E), suggesting a loss of stemness and priming towards the myeloid lineage in Ptpn11 -mutated HSCs. Similarly, 173 DEGs were identified in Ptpn11 E76K/+ ST-HSCs compared to WT ST-HSCs (Extended Data Fig. 2 A), with Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis indicating dysregulation of anti-viral immune response pathways, ribosome biogenesis, and spliceosome function. (Extended Data Fig. 2 B). Further examination of stem cell self-renewal or differentiation-associated signature genes revealed widespread deregulation in Ptpn11 E76K/+ LT-HSCs and ST-HSCs, as compared to WT counterparts (Fig. 1 F). Notable downregulated genes in Ptpn11 E76K/+ LT-HSCs included Hoxa5 , Hoxa6 , Hoxa7 , and Hoxb8 , while upregulated genes comprised Hoxb2 , Hoxb3 , and Hoxb4 . Interestingly, upregulated expression of myeloid differentiation-related genes Cebpb , Cebpe , Cebpg , and Cited2 was noticed in these mutant stem cells. In Ptpn11 E76K/+ ST-HSCs, downregulated genes included Hoxa3 , Gata1 , Klf4 , Cebpa , and Elane , while upregulated genes encompassed Mix , Irf5 , Irf8 , Ctss , Gata3 , and Csf1r . The most significant DEGs in Ptpn11 E76K/+ LT-HSCs and ST-HSCs versus WT counterparts are illustrated in Fig. 1 G. Surprisingly, myeloid cell-specific genes and genes associated with anti-pathogen and innate immune responses normally activated in myeloid cells, such as S100a9 , S100a8 , S100a6 , S100a11 , Retnlg , Ngp , Camp , Lcn2 , Lyz2 , Wfdc21 , Chil3 , and Pglyrp1 were highly expressed in Ptpn11 E76K/+ LT-HSCs. The expression levels of S100a9 and S100a8 , also known as myeloid-related proteins 9 and 8, were increased approximately 29- and 24-fold, respectively, standing out as the most strikingly upregulated among all significant DEGs in Ptpn11 E76K/+ LT-HSCs. Additionally, Cxcl2 , also known as MIP2-α , a chemokine typically secreted by monocytes/macrophages and a powerful chemoattractant for polymorphonuclear leukocytes involved in many immune responses, including wound healing, cancer metastasis, and angiogenesis, was overexpressed in these leukemia-initiating cells. Moreover, several cell surface molecules were differentially expressed in Ptpn11 E76K/+ LT-HSCs. Among the most significant DEGs, Cd52 and Cd9 were upregulated, while transcriptional expression of the early stem/progenitor cell marker Cd34 was diminished (Fig. 1 H). In addition, Cd33 , P2ry14/Gpr105 , and Gpr150 showed a marked upregulation in Ptpn11 E76K/+ LT-HSCs. These unique expression patterns of cell surface molecules in Ptpn11 mutant LT-HSCs hold promise for their utilization as therapeutic targets or biomarkers for JMML stem cells. Furthermore, Rage/Ager, the receptor for the S100a9/S100a8 heterodimer (calprotectin) 21 typically expressed on myeloid immune cells exhibited substantial upregulation in Ptpn11 E76K/+ LT-HSCs, indicating potential autocrine feedback activities in these leukemia-initiating cells. Given that S100a9 expression was significantly upregulated in Ptpn11 E76K/+ LT-HSCs (Extended Data Fig. 3 A), we sought to identify transcriptional factors potentially associated with this upregulation. To this end, we conducted Venn diagram data analysis involving the 177 DEGs in Ptpn11 E76K/+ LT-HSCs and 58 transcriptional factors related to S100a9 . This analysis revealed Spi1 and Smarca4 (Extended Data Fig. 3 B). Of the 47 dysregulated transcriptional factors in Ptpn11 E76K/+ LT-HSCs, Spi1 showed a significant upregulation, whereas Smarca4 was downregulated (Extended Data Fig. 3 C), suggesting that the elevated levels of Spi1 may have contributed to the observed overexpression of S100a9 . Profound impact on the myeloid lineage by the Ptpn11 E76K mutation. The influence of the Ptpn11 E76K mutation extended beyond the stem cell population, significantly affecting myeloid-committed GMPs. Gene expression profiling identified 4 distinct cell clusters in GMPs, revealing heterogeneity among these progenitors (Fig. 2 A). Interestingly, Ptpn11 E76K/+ GMPs exhibited a notable shift in cell composition, with Cluster 3 emerging as a unique and overrepresented subpopulation, constituting approximately 60% of the total. The heatmap representation of the top 10 DEGs in each cell cluster highlighted clear differences among these clusters, with Cluster 1 enriched in Prom1 , Clu , Mgam , Gpx3 , and Slco4c1 , and Cluster 3 marked by high expression of Fbp1 , Tmem53 , Cracr2b , and Dlg2 (Fig. 2 B). Overall, 127 genes were significantly differentially expressed in Ptpn11 E76K/+ GMPs compared to their WT counterparts (Fig. 2 C). GO enrichment analysis of the DEGs underscored enrichment in innate immune and inflammatory pathways in Ptpn11 mutant GMPs (Fig. 2 D). This included pathways related to the positive regulation of immune response, neutrophil activation, neutrophil-mediated killing of bacteria, defense response to bacteria, and innate immune response. GSEA revealed an enrichment of genes typically associated with later-stage progenitors, such as monocyte and dendritic cell progenitors, and neutrophil progenitors in Ptpn11 E76K/+ GMPs relative to WT GMPs (Fig. 2 E), indicative of enhanced differentiation activities in these mutant GMPs. Cluster 3, representing the major subpopulation within Ptpn11 E76K/+ GMPs, displayed high and unique expression of Arl11 , Fbp1 , Slc31a2 , Hnmt , Tmem53 , Cracr2b , among others (Extended Data Fig. 4 A). Differential gene expression analysis between Cluster 3 and Cluster 1, the major population in WT GMPs, revealed 114 genes with distinct expression patterns (Extended Data Fig. 4 B). KEGG pathway analyses illustrated the upregulation of genes involved in autoimmune responses, bacterial infection responses, natural killer cell-mediated cytotoxicity, neutrophil extracellular trap formation, and ribosome, whereas downregulated pathways included phagosome, ribosome, RNA transport, spliceosome, RNA degradation, oxidative phosphorylation, and thermogenesis pathways in Ptpn11 E76K/+ GMPs (Extended Data Fig. 4 C). We then examined the impact of the Ptpn11 E76K mutation on monocytes and neutrophils. Gene expression profile-based cell clustering demonstrated heterogeneity in monocytes. Seven distinct cell clusters were identified in monocytes (Fig. 3 A). The Ptpn11 E76K/+ monocyte compartment demonstrated notable changes in cell compositions. The linker histone H1 family members ( Hist1h2ab , Hist1h2af , Hist1h2bm , Hist1h2bn , Hist1h3b , and Hist1h3f ), Sirpb1c , Ms4a8a , Apoe , Slfn5 , Pla2g7 , and P2ry6 were highly expressed in Cluster 2 and Cluster 3, which were unique in the Ptpn11 mutant monocyte population (Fig. 3 B). Similarly, neutrophils also exhibited heterogeneity, with altered cell compositions in the Ptpn11 E76K/+ neutrophil compartment (Fig. 3 C). Upregulated genes in Ptpn11 E76K/+ clusters included mitochondrial protein synthesis-associated Lars2 , innate immunity-associated Chil5 , the chemokine Ccl6 , Arginase, type 2 ( Arg2) , and glycolysis-associated Ldhc , while downregulated genes comprised Lipg , Cmah , Qsox1 , Calr , Pdia6 , Sec61a1 , Prok2 , Wfdc17 , Ifitm1 , and others (Fig. 3 D). To explore whether the transcriptional landscape changes in Ptpn11 E76K/+ cells across different developmental stages shared commonality, the top 50 significant DEGs in Ptpn11 E76K/+ and WT stem cells, GMPs, monocytes, and neutrophils are shown in Fig. 4 A. Venn diagram data analysis for DEGs in the different cell populations identified 44 co-events (Fig. 4 B). Remarkably, these genes were consistently upregulated or downregulated in Ptpn11 E76K/+ cells throughout all developmental stages, without any exceptions (Fig. 4 C). This observation implies that they were cell-intrinsically dysregulated by the Ptpn11 E76K mutation. Many of these co-DEGs were associated with innate immune signaling and inflammatory pathways, including S100a11 , Retnlg , and Lyz2 . Interestingly, genes involved in ribosomal biogenesis, such as Rplp0 , Rps3 , and Rpl21 were upregulated, while Rpl41 , Rpl37a , Rps28 , Rpl38 , Rpl23a , and Rps15 were repressed. Dysregulation of ribosomal biogenesis and function can collectively contribute to cellular abnormalities, genomic instability, and the development of malignancies 22,23 . These findings underscore that the impact on ribosomal function is a common pathological effect of the Ptpn11 mutation across different cell types. Altered developmental trajectories and cell-cell communications in leukemia-initiating Ptpn11 E76K/+ stem cells. Branched expression analysis modeling (BEAM), followed by hierarchical clustering analysis, identified three distinct gene expression modules during the differentiation process from stem cells to monocytes and neutrophils. Notable differences in the dynamic changes in the expression of genes enriched in all modules were observed in the differentiation process of Ptpn11 E76K/+ stem cells (Fig. 5 A). A markedly higher number of genes showed dynamic changes in expression within Module 2, whereas fewer genes demonstrated such changes in Module 3 in the context of the Ptpn11 mutant cellular processes. Pseudotime mapping analysis, which infers the developmental trajectory or temporal progression of cells within a heterogeneous population based on gene expression profiles, revealed that leukemia-initiating Ptpn11 E76K/+ mutant stem cells gave rise to GMPs mainly in one direction as opposed to two in WT counterparts (Fig. 5 B, upper row), suggesting the impact on the mutation of GMPs. While the inferred pseudotime of neutrophil development from Ptpn11 E76K/+ stem cells remained relatively unchanged, two diverging cell fates were observed during the differentiation of these leukemia-initiating cells towards monocytes, contrasting with the essentially singular fate observed in the WT control, and the inferred pseudotime of monocyte development from the leukemia-initiating cells was prolonged (Fig. 5 B, upper row). In addition, intermediate monocytes in a transitioning state were increased in the Ptpn11 E76K/+ group, suggesting a delay or arrest in their differentiation and maturation. Further analyses focusing on specific cell compartments showed a slight difference in the diffusion trajectories within GMPs between Ptpn11 E76K/+ and WT counterparts (Fig. 5 B, lower row). No notable differences in Ptpn11 E76K/+ neutrophil diffusion maps were detected, indicating relatively normal differentiation and maturation within these two cell populations. In contrast, Ptpn11 E76K/+ monocytes exhibited two distinct developmental paths compared to the single direction observed in WT monocytes (Fig. 5 B, low row), implying the generation of various subpopulations in Ptpn11 E76K/+ monocytes along distinct developmental routes. Cell-cell communication analyses based on the expression of ligands and their cognate receptors revealed enhanced interactions between neutrophils and stem cells in Ptpn11 E76K/+ mice compared to those in WT littermates (Extended Data Fig. 5 A and 5 B). Furthermore, interactions among Ptpn11 E76K/+ stem cells were increased relative to those in WT stem cells (Extended Data Fig. 5 A and 5 B). A closer examination of neutrophil-stem cell communications indicated that interactions mediated by IL-1β, TGF-β, and Oncostatin M were enhanced in Ptpn11 E76K/+ mice compared to those in WT mice (Extended Data Fig. 5 C), providing additional evidence that leukemia-initiating Ptpn11 -mutated stem cells were situated in an inflammatory microenvironment. Leukemia-initiating Ptpn11 E76K/+ stem cells are primed by the myeloid transcriptional program. Cell identity and functional specificity are collectively governed by transcription factors and the expression levels of their target genes. The overall transcriptional activities in Ptpn11 E76K/+ stem cells were elevated compared to those in their WT counterparts (Extended Data Fig. 6 ), consistent with more active cellular processes in leukemia-initiating Ptpn11 -mutated stem cells. To further elucidate the mechanisms through which the Ptpn11 E76K mutation influences cell behavior, we conducted single cell regulatory network inference and clustering (SCENIC) analysis (transcriptional factor regulon analysis). The activities of many transcription factors in Ptpn11 E76K/+ stem cells, GMPs, monocytes, and neutrophils were altered compared to those in their WT counterparts, as indicated by regulon activity scores. In Ptpn11 E76K/+ stem cells, the transcriptional activities of Atf3 , Egr1 , Jun , Jund , Klf6 , Fos , and Gata2 were significantly decreased, while those of Irf7 , Irf8 , Maf , and Myc were increased (Fig. 6 A). Importantly, regulon specificity scores (RSS), reflecting the association between regulon activities and cellular specificity, revealed that among these differentially functioning transcription factors, the myeloid transcription factors Ets1 , Cebpe , and Nfe2 were highly associated with the identity specificity of Ptpn11 E76K/+ stem cells, as opposed to Tcf7l2 , Relb , and Irf5 for WT HSCs. At the GMP level, the activities of myeloid-specific transcription factors Cebpe , Cebpb , and Ets1 were markedly increased in Ptpn11 E76K/+ GMPs, and their cellular specificity was determined by Cebpe , Ets1 , and Myc compared to Cebpe , E2f1 , and Klf6 in WT GMPs (Fig. 6 B). Activities of transcription factors Irf7 , Cebpb , Fos , Irf5 , Irf8 , Klf4 , and Maf were significantly higher in Ptpn11 E76K/+ monocytes than those in WT cells, and the identity specificity of Ptpn11 mutant monocytes was highly associated with transcription factors Irf7 , Mafg , and Irf8 according to RSS (Fig. 6 C). Similarly, the distinction in transcriptional factor determinants influencing the specificity of Ptpn11 E76K/+ neutrophils ( Mafg , Cebpb , and Junb ) compared to those governing WT neutrophils ( Maf , Irf8 , and Junb ) was also apparent (Fig. 6 D). Consistent with the regulon results, Ptpn11 E76K/+ stem cells and GMPs demonstrated heightened cell cycling, as evidenced by the loss of quiescence (the G 0 phase in the cell cycle) and an increased number of cells in the G 2 /M phase, based on single-cell transcriptomes and a reported predictor for allocating individual cells to G 0 , G 1 /S, and G 2 /M cell cycle phases 24 (Fig. 7 A). The cell division/replication-related histone H1 family members ( Hist1h1c , Hist1h1d , Hist1h1e , and Hist1h2ae ) and CDK1 were upregulated in both Ptpn11 E76K/+ stem cells and GMPs (Fig. 7 B). Additionally, GSEA revealed a significant enrichment of cell cycling-associated gene sets in Ptpn11 E76K/+ stem cells (Fig. 7 C). Both Ptpn11 E76K/+ stem cells and GMPs exhibited a high enrichment of GM-CSF response gene sets. This observation aligns with the well-established high sensitivity of JMML cells to GM-CSF 25,26 . S100a9 and S100a8, aberrantly expressed in Ptpn11 E76K/+ stem cells, contribute significantly to leukemogenesis. Given the prominent upregulation of S100a9 and S100a8 in Ptpn11 E76K/+ mutant long-term stem cells (Fig. 1 G) and their diverse roles in various cell types 27,28 , we investigated their potential role in these tumor initiating cells. First, we confirmed a significant increase (> 8-fold) in the expression levels of S100a9 and S100a8 in mutant stem cells isolated from Ptpn11 E76K/+ mice compared to those in WT HSCs (Fig. 8 A). Importantly, expression levels of S100a9 and S100a8 were also elevated approximately 7-fold in leukemic stem/progenitor cells (CD34 + ) from PTPN11 -mutated JMML patients compared to those in normal CD34 + hematopoietic stem/progenitors (Fig. 8 B). The overexpression of S100a9 and S100a8 by Ptpn11 E76K/+ mutant stem cells appeared to promote the growth of these leukemia-initiating cells. Ptpn11 E76K/+ stem cells cultured in ex vivo expansion medium exhibited significantly accelerated proliferation compared to WT HSCs. However, this growth advantage was mitigated by tasquinimod, an inhibitor of S100a9/S100a8 that disrupts their interactions with receptors RAGE and TLR4 29,30 (Fig. 8 C), which were also highly expressed on these cells (Fig. 1 H). Additionally, the elevated differentiation capabilities of Ptpn11 E76K/+ mutant stem cells to form myeloid colonies compared to those of WT HSCs were substantially decreased by tasquinimod (Fig. 8 D). These findings suggest that S100a9 and S100a8 significantly contribute to the clonal expansion and enhanced myeloid differentiation of leukemia-initiating Ptpn11- mutated stem cells through autocrine effects. Previous studies have proposed a significant role for S100a9 and S100a8 expressed in tumor cells in recruiting MDSCs, which are known for their association with immunosuppression and inflammation 27,31,32 . These heterogeneous cells co-express CD11b, Ly6G, and Ly6C myeloid lineage markers [polymorphonuclear MDSCs (PMN-MDSCs): CD11b + Ly6G + Ly6C low ; mononuclear MDSCs (M-MDSCs): CD11b + Ly6G − Ly6C high ]. MDSCs are potent inhibitors of anti-tumor immunity, contributing to immune escape 27,31,32 . To investigate the potential interplay between Ptpn11 E76K/+ mutant stem cells and MDSCs, we conducted transwell migration assays. As displayed in Fig. 8 E, Ptpn11 E76K/+ mutant stem cells demonstrated heightened chemoattracting activities for PMN-MDSCs (CD11b + Ly6G + ) compared to WT HSCs. Notably, this effect was blocked by the S100a9/S100a8 inhibitor tasquinimod, indicating that the overproduction of S100a9 and S100a8 by leukemia-initiating Ptpn11 mutant stem cells may contribute to the recruitment of MDSCs to the microenvironment. To test this possibility and further determine the role of S100a9 and S100a8 in the leukemogenic activities of Ptpn11 E76K/+ stem cells in an in vivo setting, we evaluated the therapeutic impact of the S100a9/S100a8 inhibitor tasquinimod using a widely used transplantation leukemia model. Ptpn11 E76K/+ /Mx1-Cre/mTmG mice were generated by crossbreeding of Ptpn11 E76K/+ /Mx1-Cre mice 6 with lineage tracing mTmG transgenic mice 33 , which expressed red fluorescent protein (RFP) but transitioned to green fluorescent protein (GFP) upon the induction of Cre expression (and the Ptpn11 E76K mutation). To mimic clinical scenarios, we combined BM cells from Ptpn11 E76K/+ /Mx1-Cre/mTmG leukemic mice with WT BM cells from congenic BoyJ mice at a 10:1 ratio and transplanted mixed cells into lethally-irradiated BoyJ mice. Four weeks post-transplantation, when donor cells were engrafted, tasquinimod or vehicle was administered to mice via drinking water for 4 weeks (Fig. 8 F). Despite the high ratio of leukemic cells in the mixed donor cells, the reconstitution of leukemic cells from Ptpn11 E76K/+ mutant stem cells in the recipient mice was approximately 50% due to the hyperactivation and significant depletion of the mutant stem cell population (known as exhaustion) in the BM collected from the leukemic mice 6 . Importantly, in response to tasquinimod treatment, a notable reduction in total leukemic cells (GFP + ) in the peripheral blood (PB) was observed (Fig. 8 G). Myeloid cells (Mac-1 + ) in the GFP + leukemic cell compartment (Fig. 8 H) and the entire PB (Fig. 8 I) significantly decreased, indicating that the skewed myeloid differentiation of leukemia-initiating Ptpn11 E76K/+ stem cells was largely rectified by blocking S100a9/S100a8 function. Mice were euthanized after 4 weeks of treatment. White blood cell counts (WBCs) in the tasquinimod-treated group significantly decreased, specifically in neutrophils and monocytes, with no apparent changes in red blood cell counts (RBCs) (Fig. 8 J). Splenomegaly was also ameliorated in tasquinimod-treated mice (Fig. 8 K). Total leukemic cells (GFP + ) in the spleen, Mac-1 + myeloid cells in the GFP + leukemic compartment and the entire spleen all decreased (Fig. 8 L). Similar therapeutic effects were also observed in the BM (Fig. 8 M). Furthermore, we assessed the impact of the S100a9/S100a8 inhibitor on leukemia-initiating mutant stem cells. As shown in Fig. 8 N, GFP + Ptpn11 E76K/+ mutant stem cells in the BM and early leukemic progenitor cells (Lineage − Sca-1 + c-Kit + ) in the spleen significantly decreased in the inhibitor-treated mice. Consistently, the cell cycling of hyperactive Ptpn11 E76K/+ stem cells was reduced by the treatment (Fig. 8 O). Moreover, apoptosis in these mutant stem cells increased in the inhibitor-treated mice (Fig. 8 P), suggesting that S100a9 and S100a8 played an important role for the survival of these leukemia initiating cells. Finally, we visualized Ptpn11 E76K/+ stem cells and surrounding cells in tasquinimod- or vehicle-treated mice and found that the distance between these leukemia-initiating cells (CD150 + CD11b − Ly6G − CD3 − B220 − Ter119 − CD48 − ) (cyan) and the closest PMN-MDSCs (CD11b + Ly6G + ) (yellow) was significantly increased following tasquinimod treatment (Fig. 8 Q), confirming that the recruitment of PMN-MDSCs to the microenvironment of Ptpn11 mutant stem cells was attributed to S100a9/S100a8 overexpressed by these leukemia-initiating cells. DISCUSSION While considerable progress has been made in understanding the etiology of JMML, numerous questions remain, particularly concerning the cellular and molecular mechanisms that confer a selective advantage to the original leukemia-initiating cells. Understanding these mechanisms can illuminate how leukemia-initiating cells persist in established disease and how these tumor precursor cells may be effectively targeted and eliminated therapeutically. By undertaking a comprehensive characterization of the transcriptomic landscapes across all stages of tumor cell development in Ptpn11 mutation-associated JMML and substantiating our findings through experimental validation, we have discovered that Ptpn11 -mutated stem cells (leukemia-initiating cells) are primed by the myeloid transcriptional program and that innate immune and inflammatory responses are aberrantly activated in these cells. These mutant stem cells exhibit strikingly heightened expression of evolutionarily conserved genes that are typically activated in mature myeloid cells during pathogen defense, including anti-microbial peptides ( Camp , Lcn2 , Lyz2 , Ltf , Chil3 , and Pglyrp1 ) and essential trace metal-sequestering proteins ( S100a9 and S100a8 ), which also function as pro-inflammatory proteins triggering and amplifying innate immune responses 27,28 . The innate immune system is conventionally activated through the recognition of pathogen-associated molecular patterns (PAMPs) or damage-associated molecular patterns (DAMPs) proteins derived from host cells or damaged cells by pattern-recognition receptors, including TLRs, on myeloid immune cells. These patterns play an important role in recruiting and activating myeloid immune cells, initiating inflammation to eliminate invading microorganisms 27,28 . S100a9 and S100a8, which show the most significant overexpression in Ptpn11 E76K/+ mutant stem cells, are also categorized as DAMPs. They preferentially heterodimerize to form calprotectin, which, like their monomeric/homodimeric forms, are endogenous ligands for TLR4, Rage/Ager, and CD33 21,34,35 on myeloid effector cells, activating intracellular signaling pathways and culminating in the production of inflammatory cytokines, chemokines, and antimicrobial peptides. Interestingly, the expression of Rage/Ager and CD33 is also markedly elevated on Ptpn11 mutant stem cells, producing autocrine effects. The autocrine effects of S100a9/S100a8 indeed contributed to the expansion of these leukemia initiating cells (Fig. 8 C). Moreover, given the well-characterized detrimental effects of inflammatory challenges on normal HSCs 36,37 , the pro-tumoral inflammatory milieu provides leukemia-initiating mutant stem cells with a competitive advantage over normal counterparts, ultimately resulting in their clonal dominance. S100a9 and S100a8 may also contribute to immune evasion of JMML-initiating mutant stem cells by chemoattracting and expanding immunosuppressive MDSCs in the microenvironment. MDSCs are classically linked to immunosuppression, inflammation, and cancer, profoundly inhibiting T cell- and NK cell-mediated antitumor immunity through various mechanisms 27,31,32 . S100a9 is crucial for MDSC recruitment as MDSC accumulation in tumors is abolished in S100a9-null mice 38 , and expression of S100a9 in transgenic mice drives expansion and activation of MDSCs 35 . These immune suppressive cells can also secrete abundant S100a9/S100a8 heterodimers, bind to their own surface receptors and nurture an autocrine feedback loop that sustains MDSC recruitment, thereby maintaining immune suppression within the local microenvironment 21 . Moreover, S100a9 also contributes to anti-tumor immunity by inhibiting dendritic cell differentiation 38 . Ptpn11 E76K/+ mutant stem cells indeed demonstrate a strong ability to attract MDSCs, and this chemoattracting effect is diminished by the inhibitor of S100a9/S100a8 (Fig. 8 E and 8 Q). Furthermore, administration of the S100a9/S100a8 inhibitor impedes leukemia development from Ptpn11 E76K/+ mutant stem cells (Fig. 8 F- 8 P). These results strongly suggest that the overexpression of S100a9 and S100a8 by Ptpn11 - mutated stem cells plays a pivotal role in the initial leukemogenic process. Further investigations are necessary to elucidate how the Ptpn11 E76K mutation instigates a myeloid-specific transcriptional program and co-opts innate immune responses in the mutated stem cells. Shp-2 (encoded by Ptpn11 ) is predominantly localized to the cytosol and plays a prominent positive role in Ras signaling 9,10 . Since other genes that are mutated in JMML are also clustered in the Ras signaling pathway, it is conceivable that the Ptpn11 mutation causes pathogenic effects mainly through the Ras pathway. However, Shp-2 is also localized to the nucleus and the mitochondrion 39–41 . There is therefore a possibility that the Ptpn11 E76K mutation influences myeloid-specific transcriptomic activities through its nuclear and/or metabolic functions. The role of mutant Shp-2 in different cellular compartments may reveal novel avenues for understanding the diverse molecular mechanisms underpinning the aberrant activation of the myeloid transcriptional program in Ptpn11 -mutated stem cells. Considering the distinctive subcellular localization of Shp-2 compared to other oncoproteins associated with JMML, it is important to ascertain whether dysregulated innate immune responses are also implicated in other JMML subtypes. Another noteworthy finding of this study is the dysregulation of ribosomal biogenesis and function in Ptpn11 E76K/+ leukemic cells consistently throughout all stages, including leukemia-initiating stem cells. Several ribosomal small and large subunit proteins displayed upregulation in Ptpn11 E76K/+ leukemic cells, consistent with the elevated protein translation essential for robust tumor cell growth. Intriguingly, there was a simultaneous decrease in the expression of certain ribosomal proteins. Recent research has revealed the heterogeneity of ribosomes, with different ribosome types displaying preferences for translating specific subsets of mRNAs 22,23 . Diminished expression of ribosomal proteins has the potential to disrupt ribosome formation and function. This can also contribute to malignancies through several mechanisms. The impairment in ribosomes can impact the synthesis of crucial regulatory proteins involved in cell growth, differentiation, and maturation, such as the tumor suppressor p53 42–44 . Moreover, reduced expression of specific ribosomal proteins and perturbed ribosome function can induce chronic ribosomal stress, triggering cellular dysfunctions and genomic instability 22 . However, the precise mechanisms by which the Ptpn11 mutation selectively interferes with the expression of different ribosomal genes remain unclear. In summary, our findings reveal previously unappreciated mechanisms in the initial phase of JMML leukemogenesis, where leukemia-initiating mutant stem cells exploit innate immune signaling to gain a selective advantage and evade anti-tumor immunity. The significant dysregulation of proinflammatory proteins S100a9 and S100a8 underscores their pivotal role in orchestrating immune evasion and creating an inflammatory microenvironment conducive to leukemic progression. This insight offers new perspectives for developing therapeutic strategies to disrupt leukemia-initiating stem cells and improve treatment outcomes in JMML. MATERIALS AND METHODS Mice. Ptpn11 E76K Neo/+ conditional knock-in mice were generated in our previous study 6 . Mx1-Cre + (Strain #: 003556) 45 , mTmG dual-fluorescent reporter transgenic mice (Strain #: 007676) 33 , C57BL/6 mice (CD45.2 + ) (Strain #: 000664), and BoyJ mice (CD45.1 + ) (Strain #: 002014) were purchased from the Jackson Laboratory. All mice were kept under specific-pathogen-free conditions at Emory University Division of Animal Resources. Animal procedures complied with the NIH Guidelines for the Care and Use of Laboratory Animals and were approved by the Institutional Animal Care and Use Committee. Patient specimens. De-identified samples from PTPN11 -mutated patients with JMML and pediatric healthy controls normal BM biopsies were obtained from the University of California, San Francisco and the Aflac Cancer and Blood Disorders Center Biorepository of Children’s Healthcare of Atlanta. Samples were obtained after written, informed consent under locally approved institutional review board research protocols and in accordance with the Declaration of Helsinki. Single-cell transcriptome profiling. Fresh BM cells were collected and pooled from Ptpn11 E76K/+ /Mx1-Cre + mice and Ptpn11 +/+ /Mx1-Cre + control mice (3 mice/group), followed by the execution of the recommended protocol for the scRNA-seq 10x Genomics platform using v3 chemistry. In brief, scRNA-seq raw reads were obtained following the standard protocol for Chromium Single Cell 3ʹ Reagent Kits v3. Subsequently, the CellRanger 1 software from 10x Genomics was employed to identify cell-discriminating barcode sequence markers and unique molecular identifier (UMI) markers for different mRNA molecules within each cell. This process aimed to quantify the high-throughput single-cell transcriptome and conduct data quality statistics and comparisons against the original genome. Next, the Seurat 2 software package was utilized for further quality control (QC) and processing of the CellRanger results. In the QC step, delocalized cells were filtered by fitting a generalized linear model. Subsequently, the distribution of nUMI (unique molecular identifier counts), nGene (number of detected genes), and percent.mito (percentage of mitochondrial genes) was assessed to filter out low-quality cells, such as double cells, multiple cells, or dead cells, leaving only qualified cells for further bioinformatics analyses. t-distributed stochastic neighbor embedding (t-SNE) visualization and cell identification. The single-cell transcriptome underwent principal component analysis (PCA) for linear dimensionality reduction. Subsequently, the PCA results were visualized in a two-dimensional space using t-SNE, a non-linear dimensionality reduction technique. The Seurat platform's FindAllMarkers function was employed to identify marker genes for each cell classification relative to other cell populations. These identified genes serve as potential markers for each cell type. Visualization of the identified marker genes was carried out using the VlnPlot and FeaturePlot functions. Following the clustering process, the Single R platform was utilized to assign cell types based on published datasets 19,20 , thereby enhancing the accuracy of cell type classification. Gene set enrichment analysis (GSEA). GSEA was conducted to identify genes associated with specified cell types such as HSCs (Hematopoietic Stem Cells) and GMPs (Granulocyte-Macrophage Progenitors). The analysis utilized the GSEA platform available at http://www.broadinstitute.org/gsea/index.jsp . To prepare input data for GSEA, the top 5000 variable genes in each group were selected using the Seurat "FindVariableGenes" function. Gene sets, including those from KEGG pathways and Gene Ontology (GO), were obtained from the molecular signatures database (MSigDB). Single-cell regulatory network inference and clustering (SCENIC) analysis . SCENIC analyses were performed using version 1.1.2.2, corresponding to RcisTarget 1.2.1 and AUCell 1.4.1. The motifs database for RcisTarget and GRNboost was utilized with default parameters. In detail, the analysis involved identifying over-represented transcription factor binding motifs on a given gene list using the RcisTarget package. Subsequently, the AUCell package was employed to score the activity of each group of regulons in each cell. This process enabled the inference and clustering of regulatory networks at the single-cell level, offering insights into the regulatory landscape of the analyzed cell populations. To evaluate the cell type specificity of each predicted regulon, the regulon specificity score (RSS) was computed, employing the Jensen-Shannon divergence (JSD) as a measure of similarity between two probability distributions. Specifically, the JSD was calculated for each vector of binary regulon activity overlaps with the assignment of cells to specific cell types. The connection specificity index (CSI) for all regulons was determined using the scFunctions package, accessible at https://github.com/FloWuenne/scFunctions/ . Pseudotime analysis. We utilized the Monocle2 package (v2.9.0) for inferring cell differentiation trajectories. The specific steps were as follows: First, we employed the importCDS function from the Monocle2 package to convert the Seurat object to the CellDataSet object. Next, the differentialGeneTest function was utilized to filter out ordering genes (genes with a q-value < 0.01). Then, we used the reduceDimension function to perform dimensionality reduction clustering. Finally, we applied the orderCells function to infer the differentiation trajectory. Cell-cell communication analysis. We utilized CellPhoneDB (v2.0) to identify biologically relevant ligand-receptor interactions from single-cell transcriptomic data. We defined a ligand or receptor as 'expressed' in a particular cell type if 10% of the cells of that type exhibited non-zero read counts for the ligand/receptor encoding gene. Statistical significance was assessed by randomly shuffling the cluster labels of all cells and repeating the aforementioned steps, thereby generating a null distribution for each ligand-receptor (LR) pair in each pairwise comparison between two cell types. Following 1,000 permutations, p -values were calculated using the normal distribution curve generated from the permuted LR pair interaction scores. To delineate networks of cell-cell communication, we connected any two cell types where the ligand was expressed in the former cell type and the receptor in the latter. The R package circlize was employed for visualizing the cell-cell communication networks. Fluorescence-activated cell sorting (FACS) analysis and cell sorting. FACS analyses were performed on a Cytoflex flow cytometer (Beckman Coulter Life Sciences), following standard procedures. For HSC staining, BM cells were harvested, washed, and incubated for 30 min at 4°C in phosphate buffered saline (PBS) with 2% fetal bovine serum (FBS) containing the following antibodies: anti-Mac-1 PerCP/Cyanine5.5 (Biolegend, 101228, clone M1/70),anti-Gr-1 Pacific Blue (Biolegend,108430, clone RB6-8C5), anti-Ter119 PE (Biolegend, 116208, clone TER-119), anti-B220 PE (eBiosciences, 12-0452-83, clone RA3-6B2), anti-CD3 PE (BD Biosiences Pharmingen, 553064, clone 145-2C11), anti-Mac-1 PE (Biolegend, 101208, clone M1/70), anti-Gr-1 PE (eBiosciences, 12-5931-83, clone RB6-8C5), anti-Scal-1 PE/Cyanine7 (Biolegend, 108114, clone D7), anti-c-Kit APC/Cyanine7 (Biolegend, 105826, clone 2B8), anti-CD48 Percp (eBioscience, 46-0481-80, clone HM48-1), anti-CD150 AF647 (Biolegend, 115918, clone TC15-12F12.2). HSCs were defined as Lin − Sca-1 + c-Kit + CD150 + CD48 − . For apoptosis analyses, fresh BM cells were stained for HSCs, and then incubated with Annexin V-BV605 (BD Biosiences Pharmingen, 563974, clone Annexin V) (0.7 µg/ml) and 4',6-diamidino-2-phenylindole (DAPI) (0.3 µg/mL). For the cell cycle analysis, fresh BM cells were stained for HSCs as above, fixed and permeabilized using a Cytofix/Cytoperm kit (BD Biosciences). The samples were then stained with Ki-67 BV605 (Biolegend, 652413) and further incubated with Hoechest 33342 (20 µg/ml). Data were collected on a Beckman Coulter CytoFLEX flow cytometer and analyzed with FlowJo (Tree Star). For cell sorting, BM cells were first lineage-depleted using a lineage depletion kit. Cells were then stained with fluorochrome-labeled antibodies. Sorting of specific cell populations was conducted using BD FACSAia II following standard gating strategies. Colony-forming unit (CFU) assay. Freshly sorted HSCs (5x10 2 cells) were plated in triplicate in 35-mm dishes in 0.9% methylcellulose IMDM medium containing 15% FBS, Gln (10 − 4 M), β-mercaptoethanol (3.3x10 − 5 M), SCF (50 ng/ml), IL-3 (20 ng/ml), IL-6 (20 ng/ml), and EPO (3 Units/ml). After 12 days of incubation at 37°C in 5% CO2, myeloid colonies derived were counted under an inverted microscope. Transmigration assay. Transmigration assays were conducted with 5.0 µm pore transwells (Corning). Briefly, HSCs freshly sorted from WT and Ptpn11 E76K/+ mice were suspended in in StemSpan media ( STEMCELL Technologies) containing 20% FBS, 50 ng/mL SCF, 50 ng/mL Flt3L, 20 ng/mL IL-3, and 20 ng/mL IL-6. Six hundred microliters of cell suspension (2x10 3 cells) were loaded to lower chambers. The S100a9/S100a8 inhibitor tasquinimod was then added to the chamber (5.0 µM). CD11b + Ly6G + myeloid cells freshly sorted from normal C57BL6 mice were labeled with carboxyfluorescein succinimidyl ester (CFSE) (1.0 µM), washed and resuspended at 1x10 6 cells/ml in the same medium as that in lower chambers but without the inhibitor. One hundred microliters of cell suspension were added to upper chambers. Cells were allowed to migrate across the membrane at 37°C in 5% CO 2 for 2 hours. Both input cells, cells collected from the upper chamber, and cells collected from the lower chambers were analyzed by FACS. Migration efficiency was then calculated. Immunofluorescence staining. Tissue sections were prepared from paraffin-embedded mouse femurs, deparaffinized, and rehydrated following standard protocols. The slides were stained with the following antibodies following standard procedures: anti-CD150 AF647 (Biolegend, 115918, clone TC15-12F12.2), anti-CD11b PE (Biolegend, 101208, clone M1/70), anti-Ly-6G AF488 (Biolegend, 127625, clone 1A8), anti-Ter119 FITC (Biolegend, 116206, clone TER-119), anti-CD3 FITC (Biolegend, 100306, clone 145-2C11), anti-B220 FITC (Biolegend, 103206, clone RA3-6B2), and anti-CD48 FITC (Biolegend, 103403, clone HM48-1) antibodies. Images were acquired using Leica Stellaris 8 and processed with ImageJ 1.54f software. Statistics and reproducibility. Unless otherwise noted, data are presented as mean ± SD of biological replicates (independent animals/independent experiments) (n numbers are shown on graphics or specified in figure legends). Unpaired two-tailed Student’s t -test was used for the statistical comparison of the two groups. * p < 0.05; ** p < 0.01; *** p < 0.001, **** p < 0.0001. Declarations Data availability The raw scRNA-seq data generated in this study have been deposited in the Gene Expression Omnibus database under accession code GSE266821. Competing financial interests The authors declare no competing financial interests. Author contributions H.Z., P.Z., Z.T., W.M.Y., and J.W. conducted the research and summarized the data. E.S., C.C.P., S.C., D.S.W., and S.M.F. provided critical reagents and/or advice, discussed the work, and edited the manuscript. H.Z. and C.K.Q. designed the experiments and directed the entire study. H.Z. and C.K.Q. wrote the manuscript with input from the other authors. Acknowledgments The authors are grateful for the technical support from Pediatrics/Winship Flow Cytometry shared resources. This work was supported by the National Institutes of Health grants HL130995, HL162725 and CA275964 (to C.K.Q.). References 1. Chang, T. Y., Dvorak, C. C. & Loh, M. L. 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UK.","correspondingAuthor":false,"prefix":"","firstName":"Simon","middleName":"","lastName":"Mendez-Ferrer","suffix":""},{"id":316150749,"identity":"3ed95dbd-b73f-4551-ab22-254dcabe8bd1","order_by":10,"name":"Cheng-Kui Qu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8klEQVRIiWNgGAWjYDACCTCZwMAGoj7ARHmI1cI4gyQtIMAMV4lPC//s5mMPvzCkJfYxMG+TtqnYJmc+I4Hxwds2PJbcOZZuLMOQk9jGwFYmnXPmtrHMjQRmw7l4tBhI5JhJS/6rAGrhMZPObbudOEMigU2aF6+W/G/SEgxQLZb/wFrYf+PXksMm+QHsMKAWxgaILcz4tEjcSDOTZmBIM25jZiu27Dl221iC52Gz5JxzuLXwz0h+JvmDIVl2fnvzxhs/am7LSbAnH/zwpgy3FhCARAczgwGEK5DYgF89EDD+gPoLavEBgjpGwSgYBaNgZAEA8YhHmZX91hwAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0002-4256-8652","institution":"Department of Pediatrics, Aflac Cancer \u0026 Blood Disorders Center, Winship Cancer Institute, Children’s Healthcare of Atlanta, Emory University School of Medicine, Atlanta, USA.","correspondingAuthor":true,"prefix":"","firstName":"Cheng-Kui","middleName":"","lastName":"Qu","suffix":""}],"badges":[],"createdAt":"2024-05-20 18:21:30","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4450642/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4450642/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":61637294,"identity":"c8c561c2-5cea-4200-92d6-e41535d834c9","added_by":"auto","created_at":"2024-08-02 09:14:45","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1279387,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAberrant activation of innate immune responses in \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003ePtpn11\u003c/strong\u003e\u003c/em\u003e\u003csup\u003e\u003cem\u003e\u003cstrong\u003eE76K/+\u003c/strong\u003e\u003c/em\u003e\u003c/sup\u003e\u003cstrong\u003e mutant stem cells.\u003c/strong\u003e \u003cstrong\u003eA. \u003c/strong\u003eHSCs were segregated into 2 distinct cell clusters equivalent to LT-HSCs and ST-HSCs based on their gene expression profiles.\u003cstrong\u003e \u003c/strong\u003et-SNE plots of WT (\u003cem\u003ePtpn11\u003c/em\u003e\u003csup\u003e\u003cem\u003e+/+\u003c/em\u003e\u003c/sup\u003e) and \u003cem\u003ePtpn11\u003c/em\u003e\u003csup\u003e\u003cem\u003eE76K/+\u003c/em\u003e\u003c/sup\u003e\u003csup\u003e \u003c/sup\u003emutant HSCs are shown. \u003cstrong\u003eB.\u003c/strong\u003e Heatmap displaying the top 20 DEGs in cell clusters. Each column represents one cell, and relatively high and low expressions are represented in yellow and purple, respectively. \u003cstrong\u003eC.\u003c/strong\u003e Volcano plot depicting all DEGs in \u003cem\u003ePtpn11\u003c/em\u003e\u003csup\u003e\u003cem\u003eE76K/+\u003c/em\u003e\u003c/sup\u003e LT-HSCs compared to WT counterparts. DEGs with a fold change \u0026gt; 1.5 and a \u003cem\u003ep\u003c/em\u003e-value \u0026lt; 0.05 were considered significant, with up-regulated genes labeled in red and down-regulated genes in blue. \u003cstrong\u003eD.\u003c/strong\u003e Significant DEGs in \u003cem\u003ePtpn11\u003c/em\u003e\u003csup\u003e\u003cem\u003eE76K/+\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e \u003c/em\u003eover WT LT-HSCs were subjected to Gene Ontology (GO) enrichment analysis, sorted based on \u003cem\u003ep\u003c/em\u003e values, with \u003cem\u003ep\u003c/em\u003e\u0026lt;0.05 considered significant. The size of the dots in the plot reflects the number of enriched genes in the respective terms, and the dot shape represents different GO classifications. \u003cstrong\u003eE.\u003c/strong\u003e Gene set enrichment analysis (GSEA) plots illustrating enrichments in the gene sets of HSC and progenitor, as well as HSC versus GMP up and down regulated in WT and \u003cem\u003ePtpn11\u003c/em\u003e\u003csup\u003e\u003cem\u003eE76K/+\u003c/em\u003e\u003c/sup\u003e HSCs. The y-axis represents the enrichment score (ES), and vertical blue lines on the x-axis indicate where genes annotated to the respective pathways appear in the ranked list of genes. The colored band represents ES values, with red indicating positive enrichment and blue indicating negative enrichment. \u003cstrong\u003eF.\u003c/strong\u003e Heatmap illustrating the expression of selected signature genes in WT and \u003cem\u003ePtpn11\u003c/em\u003e\u003csup\u003e\u003cem\u003eE76K/+\u003c/em\u003e\u003c/sup\u003e ST-HSCs and LT-HSCs. \u003cstrong\u003eG.\u003c/strong\u003e Heatmap displaying the expression of most significantly up or down regulated genes in WT and \u003cem\u003ePtpn11\u003c/em\u003e\u003csup\u003e\u003cem\u003eE76K/+\u003c/em\u003e\u003c/sup\u003e ST-HSCs and LT-HSCs. \u003cstrong\u003eH.\u003c/strong\u003e Violin plots illustrating the expression levels of the indicated cell surface markers in WT and \u003cem\u003ePtpn11\u003c/em\u003e\u003csup\u003e\u003cem\u003eE76K/+\u003c/em\u003e\u003c/sup\u003e ST-HSCs and LT-HSCs.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4450642/v1/e7341d5b927f84d2cbc9fec3.png"},{"id":61637295,"identity":"2d448359-e353-4a3d-87cc-754e59b86d83","added_by":"auto","created_at":"2024-08-02 09:14:46","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":765570,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEnhanced innate immune signaling in \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003ePtpn11\u003c/strong\u003e\u003c/em\u003e\u003csup\u003e\u003cem\u003e\u003cstrong\u003eE76K/+\u003c/strong\u003e\u003c/em\u003e\u003c/sup\u003e\u003cstrong\u003e granulocyte-macrophage progenitors (GMPs). A.\u003c/strong\u003e GMPs were categorized into 4 different cell clusters based on their gene expression profiles.\u003cstrong\u003e \u003c/strong\u003et-SNE plots of WT and \u003cem\u003ePtpn11\u003c/em\u003e\u003csup\u003e\u003cem\u003eE76K/+\u003c/em\u003e\u003c/sup\u003e\u003csup\u003e \u003c/sup\u003emutant GMPs are displayed. \u003cstrong\u003eB.\u003c/strong\u003e Heatmap illustrating the expression patterns of the top 10 representative genes in each cell cluster. Each column represents one cell, and relatively high and low expressions are represented in yellow and purple, respectively.\u003cstrong\u003e C.\u003c/strong\u003e Volcano plot depicting DEGs in \u003cem\u003ePtpn11\u003c/em\u003e\u003csup\u003e\u003cem\u003eE76K/+\u003c/em\u003e\u003c/sup\u003eGMPs. The DEGs with a fold change \u0026gt; 1.5 and a \u003cem\u003ep\u003c/em\u003e-value \u0026lt; 0.05 are considered significant, with up-regulated genes labeled in red and down-regulated genes in blue. \u003cstrong\u003eD.\u003c/strong\u003e DEGs in \u003cem\u003ePtpn11\u003c/em\u003e\u003csup\u003e\u003cem\u003eE76K/+\u003c/em\u003e\u003c/sup\u003eGMPs were subjected to GO enrichment analysis, sorted by \u003cem\u003ep\u003c/em\u003e values, with \u003cem\u003ep\u003c/em\u003e\u0026lt;0.05 considered significant. The size of the dots in the plot reflects the number of enriched genes in the respective terms, and the dot shape represents different GO classifications. \u003cstrong\u003eE.\u003c/strong\u003e GSEA plots depicting enrichments in gene sets of monocyte dendritic cell (DC) progenitors, neutrophil progenitors, and late progenitors-shared pathways in \u003cem\u003ePtpn11\u003c/em\u003e\u003csup\u003e\u003cem\u003eE76K/+ \u003c/em\u003e\u003c/sup\u003eGMPs. The y-axis represents the ES, and vertical blue lines on the x-axis indicate where genes annotated to the respective pathways appear in the ranked list of genes. The colored band represents ES values, with red indicating positive enrichment and blue indicating negative enrichment.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4450642/v1/8d321b4462a2cd46c60456fb.png"},{"id":61638045,"identity":"4b516e95-9f06-40b1-b14c-6377298558d5","added_by":"auto","created_at":"2024-08-02 09:22:46","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":851921,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eImpact of the \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003ePtpn11\u003c/strong\u003e\u003c/em\u003e\u003csup\u003e\u003cem\u003e\u003cstrong\u003eE76K\u003c/strong\u003e\u003c/em\u003e\u003c/sup\u003e\u003cstrong\u003e mutation on monocytes and neutrophils. A.\u003c/strong\u003e Monocytes were divided into 7 different cell clusters based on their gene expression patterns. t-SNE plots of WT and \u003cem\u003ePtpn11\u003c/em\u003e\u003csup\u003e\u003cem\u003eE76K/+\u003c/em\u003e\u003c/sup\u003e mutant monocytes are shown. \u003cstrong\u003eB.\u003c/strong\u003e Heatmap displaying the expression patterns of the top 10 representative genes in each cluster. Each column represents one cell, and relatively high and low expressions are represented in yellow and purple, respectively. \u003cstrong\u003eC.\u003c/strong\u003e Neutrophils were segregated into 6 distinct cell clusters according to their gene expression profiles. t-SNE plots of WT and \u003cem\u003ePtpn11\u003c/em\u003e\u003csup\u003e\u003cem\u003eE76K/+\u003c/em\u003e\u003c/sup\u003e mutant neutrophils are displayed. \u003cstrong\u003eD.\u003c/strong\u003e Heatmap illustrating the expression patterns of the top 10 representative genes in each cluster.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-4450642/v1/1e83add70f1dc0d10948583a.png"},{"id":61637305,"identity":"e43b2bf8-e07a-4686-aa4c-e63dc8b2fec2","added_by":"auto","created_at":"2024-08-02 09:14:47","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":10117636,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEffects of the \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003ePtpn11\u003c/strong\u003e\u003c/em\u003e\u003csup\u003e\u003cem\u003e\u003cstrong\u003eE76K\u003c/strong\u003e\u003c/em\u003e\u003c/sup\u003e\u003cstrong\u003e mutation on ribosomes across developmental stages. A.\u003c/strong\u003e Heatmaps depicting the expression of the genes that are up- or down regulated in \u003cem\u003ePtpn11\u003c/em\u003e\u003csup\u003e\u003cem\u003eE76K/+ \u003c/em\u003e\u003c/sup\u003eHSCs, GMPs, monocytes, and neutrophils. Each column in the heatmaps represents one cell, and relatively high and low expression are represented in yellow and purple, respectively. \u003cstrong\u003eB.\u003c/strong\u003e Venn diagram data analysis was conducted on the DEGs in \u003cem\u003ePtpn11\u003c/em\u003e\u003csup\u003e\u003cem\u003eE76K/+ \u003c/em\u003e\u003c/sup\u003eHSCs, GMPs, monocytes, and neutrophils. Different colors were superimposed to represent shared genes, and the numbers of these shared genes are indicated. \u003cstrong\u003eC.\u003c/strong\u003e Bar graphs illustrating the results of the differential expression analysis for the shared DEGs (44) in \u003cem\u003ePtpn11\u003c/em\u003e\u003csup\u003e\u003cem\u003eE76K/+ \u003c/em\u003e\u003c/sup\u003eHSCs, GMPs, monocytes, and neutrophils. Up-regulated genes are marked in red, and down-regulated genes in black.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-4450642/v1/e53cf0603cb410588fe64428.png"},{"id":61637296,"identity":"7e9508e1-3b35-478e-8373-16594fabc391","added_by":"auto","created_at":"2024-08-02 09:14:46","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":4995889,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAltered developmental trajectories in \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003ePtpn11\u003c/strong\u003e\u003c/em\u003e\u003csup\u003e\u003cem\u003e\u003cstrong\u003eE76K/+\u003c/strong\u003e\u003c/em\u003e\u003c/sup\u003e\u003cstrong\u003e mutant stem cells. A.\u003c/strong\u003e Heatmaps of DEGs relevant to HSC differentiation in WT and \u003cem\u003ePtpn11\u003c/em\u003e\u003csup\u003e\u003cem\u003eE76K/+\u003c/em\u003e\u003c/sup\u003e mutant HSCs across the pseudo-time. The color gradient indicates the average expression, ranging from dark blue to red. \u003cstrong\u003eB.\u003c/strong\u003e Pseudo-time analysis using Monocle2 revealing the differentiation trajectory of WT and \u003cem\u003ePtpn11\u003c/em\u003e\u003csup\u003e\u003cem\u003eE76K/+\u003c/em\u003e\u003c/sup\u003e mutant HSCs to GMPs and further to monocytes/neutrophils (top). Pseudo-time analyses for WT and \u003cem\u003ePtpn11\u003c/em\u003e\u003csup\u003e\u003cem\u003eE76K/+\u003c/em\u003e\u003c/sup\u003e mutant GMPs, monocytes, and neutrophils were conducted separately (bottom). The directions of differentiation for each cell type are indicated by arrows.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-4450642/v1/18f85a4146257207c841988e.png"},{"id":61637304,"identity":"f32d26c7-f06e-40b6-8277-59884619ccf3","added_by":"auto","created_at":"2024-08-02 09:14:47","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":614847,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eDe novo\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e activation of the myeloid transcriptional program\u003c/strong\u003e\u003cem\u003e\u003cstrong\u003e \u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003ein \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003ePtpn11\u003c/strong\u003e\u003c/em\u003e\u003csup\u003e\u003cem\u003e\u003cstrong\u003eE76K/+\u003c/strong\u003e\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e\u003cstrong\u003e \u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003emutant stem cells.\u003c/strong\u003e \u003cstrong\u003eA-D.\u003c/strong\u003e Heatmaps displaying the results of SCENIC analysis, revealing the regulatory activities of transcription factors in WT and \u003cem\u003ePtpn11\u003c/em\u003e\u003csup\u003e\u003cem\u003eE76K/+\u003c/em\u003e\u003c/sup\u003e HSCs (\u003cstrong\u003eA\u003c/strong\u003e), GMPs (\u003cstrong\u003eB\u003c/strong\u003e), monocytes (\u003cstrong\u003eC\u003c/strong\u003e), and neutrophils (\u003cstrong\u003eD\u003c/strong\u003e) are shown on the left. Functionally up-regulated transcription factors are indicated in red, with down-regulated transcription factors in blue. The top three cell specificity-determining regulons with highest regulon specificity score (RSS) in WT and \u003cem\u003ePtpn11\u003c/em\u003e\u003csup\u003e\u003cem\u003eE76K/+\u003c/em\u003e\u003c/sup\u003e HSCs (\u003cstrong\u003eA\u003c/strong\u003e), GMPs (\u003cstrong\u003eB\u003c/strong\u003e), monocytes (\u003cstrong\u003eC\u003c/strong\u003e), and neutrophils (\u003cstrong\u003eD\u003c/strong\u003e) are shown on the right.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-4450642/v1/561d0501022b03ba9de94ca5.png"},{"id":61638046,"identity":"b70916f9-ce18-46dc-bb37-ddae7ee32c9d","added_by":"auto","created_at":"2024-08-02 09:22:46","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":1112458,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003ePtpn11\u003c/strong\u003e\u003c/em\u003e\u003csup\u003e\u003cem\u003e\u003cstrong\u003eE76K/+\u003c/strong\u003e\u003c/em\u003e\u003c/sup\u003e\u003cstrong\u003e stem cells and GMPs exhibit enhanced cell cycling.\u003c/strong\u003e \u003cstrong\u003eA.\u003c/strong\u003e t-SNE plots depicting the cell cycle distribution of HSCs and GMPs. The proportions of different cell cycle phases in WT and \u003cem\u003ePtpn11\u003c/em\u003e\u003csup\u003e\u003cem\u003eE76K/+\u003c/em\u003e\u003c/sup\u003e HSCs and GMPs are displayed on the right. \u003cstrong\u003eB.\u003c/strong\u003e GSEA plots illustrating enrichments in cell-cycling associated gene sets and GM-CSF responsive gene sets in \u003cem\u003ePtpn11\u003c/em\u003e\u003csup\u003e\u003cem\u003eE76K/+\u003c/em\u003e\u003c/sup\u003e HSCs and GMPs. The y-axis represents the ES, and vertical blue lines on the x-axis indicate where genes annotated to the respective pathways appear in the ranked list of genes. The colored band represents ES values, with red indicating positive enrichment and blue indicating negative enrichment. \u003cstrong\u003eC.\u003c/strong\u003e Violin plots showing the relative expression of the indicated genes in WT and \u003cem\u003ePtpn11\u003c/em\u003e\u003csup\u003e\u003cem\u003eE76K/+\u003c/em\u003e\u003c/sup\u003e HSCs and GMPs.\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-4450642/v1/25271d579b4ed5eb09c4708b.png"},{"id":61637303,"identity":"de6e2a51-c789-4d71-aab0-cd4aedec5792","added_by":"auto","created_at":"2024-08-02 09:14:47","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":3170303,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eS100a9 and S100a8, aberrantly expressed in \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003ePtpn11\u003c/strong\u003e\u003c/em\u003e\u003csup\u003e\u003cem\u003e\u003cstrong\u003eE76K/+\u003c/strong\u003e\u003c/em\u003e\u003c/sup\u003e\u003cstrong\u003e stem cells, contribute significantly to leukemogenesis. A.\u003c/strong\u003e mRNA levels of the indicated genes in the stem cells (Lineage\u003csup\u003e-\u003c/sup\u003eSca-1\u003csup\u003e+\u003c/sup\u003ec-Kit\u003csup\u003e+\u003c/sup\u003eCD150\u003csup\u003e+\u003c/sup\u003eCD48\u003csup\u003e-\u003c/sup\u003e) sorted from WT and \u003cem\u003ePtpn11\u003c/em\u003e\u003csup\u003e\u003cem\u003eE76K/+\u003c/em\u003e\u003c/sup\u003e mice were determined by quantitative reverse transcription PCR (qRT-PCR) (n=6 mice/genotype). \u003cstrong\u003eB.\u003c/strong\u003e CD34\u003csup\u003e+\u003c/sup\u003e early hematopoietic progenitors isolated from healthy BM samples and \u003cem\u003ePTPN11\u003c/em\u003e mutated JMML patients were analyzed for mRNA levels of the indicated genes by qRT-PCR (n=5-7 individuals/group). \u003cstrong\u003eC.\u003c/strong\u003e Stem cells freshly sorted from WT and \u003cem\u003ePtpn11\u003c/em\u003e\u003csup\u003e\u003cem\u003eE76K/+\u003c/em\u003e\u003c/sup\u003e mice were cultured (5x10\u003csup\u003e2\u003c/sup\u003e cells) in StemSpan medium supplemented with SCF (50 ng/ml), TPO (50 ng/ml), FLT3-L (50 ng/ml), and tasquinimod (5.0 µM) or vehicle. Seven days later, total cell numbers were determined (n=3 mice/group). \u003cstrong\u003eD.\u003c/strong\u003e Colonies derived from WT and \u003cem\u003ePtpn11\u003c/em\u003e\u003csup\u003e\u003cem\u003eE76K/+ \u003c/em\u003e\u003c/sup\u003emutant stem cells (5x10\u003csup\u003e2\u003c/sup\u003e cells) in the presence of tasquinimod (5.0 µM) or vehicle were determined by CFU-assays as described in Materials and Methods (n=6 mice/group). \u003cstrong\u003eE.\u003c/strong\u003e Transmigration efficiency of PMN-MDSCs (CD11b\u003csup\u003e+\u003c/sup\u003eLy6G\u003csup\u003e+\u003c/sup\u003e) toward WT\u0026nbsp;and\u0026nbsp;\u003cem\u003ePtpn11\u003c/em\u003e\u003csup\u003e\u003cem\u003eE76K/+\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003estem cells in the presence of tasquinimod (5.0 µM) or vehicle were assessed as described in Materials and Methods (n=6 mice/group). \u003cstrong\u003eF-Q.\u003c/strong\u003e BM cells collected from \u003cem\u003ePtpn11\u003c/em\u003e\u003csup\u003e\u003cem\u003eE76K/+\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e/Mx1-Cre/mTmG\u003c/em\u003e mice were mixed with WT BM cells from congenic BoyJ mice at a 10:1 ratio and transplanted them into lethally irradiated BoyJ mice (n=13 mice/group). Four weeks post-transplantation, tasquinimod or vehicle was administered to mice via drinking water (5 mg/kg body weight/day) for 4 weeks (\u003cstrong\u003eF\u003c/strong\u003e). Total leukemic cells (GFP\u003csup\u003e+\u003c/sup\u003e) in the peripheral blood \u0026nbsp;(PB) (\u003cstrong\u003eG\u003c/strong\u003e), myeloid cells (Mac-1\u003csup\u003e+\u003c/sup\u003e) in the GFP\u003csup\u003e+\u003c/sup\u003e leukemic cell compartment (\u003cstrong\u003eH\u003c/strong\u003e) and the entire PB (\u003cstrong\u003eI\u003c/strong\u003e) were determined at the indicated time points. Mice were euthanized 4 weeks after the treatment. White blood cell counts (WBCs), neutrophils, monocytes, and red blood cell counts (RBCs) in the PB were analyzed (\u003cstrong\u003eJ\u003c/strong\u003e). Spleens were weighted (\u003cstrong\u003eK\u003c/strong\u003e). Total leukemic cells (GFP\u003csup\u003e+\u003c/sup\u003e), Mac-1\u003csup\u003e+\u003c/sup\u003e myeloid cells in the GFP\u003csup\u003e+\u003c/sup\u003e leukemic compartment and the entire spleen and BM were determined (\u003cstrong\u003eL, M\u003c/strong\u003e). The pool size of GFP\u003csup\u003e+\u003c/sup\u003e stem cells (GFP\u003csup\u003e+\u003c/sup\u003eLineage\u003csup\u003e-\u003c/sup\u003eSca-1\u003csup\u003e+\u003c/sup\u003ec-Kit\u003csup\u003e+\u003c/sup\u003eCD150\u003csup\u003e+\u003c/sup\u003eCD48\u003csup\u003e-\u003c/sup\u003e) in the BM, GFP\u003csup\u003e+\u003c/sup\u003e early progenitors (GFP\u003csup\u003e+\u003c/sup\u003eLineage\u003csup\u003e-\u003c/sup\u003eSca-1\u003csup\u003e+\u003c/sup\u003ec-Kit\u003csup\u003e+\u003c/sup\u003e) in the spleen (\u003cstrong\u003eN\u003c/strong\u003e), cell cycling status (\u003cstrong\u003eO\u003c/strong\u003e) and apoptosis (\u003cstrong\u003eP\u003c/strong\u003e) in GFP\u003csup\u003e+ \u003c/sup\u003estem cells were determined. \u003cstrong\u003eQ.\u003c/strong\u003e BM sections prepared from tasquinimod- or vehicle-treated \u003cem\u003ePtpn11\u003c/em\u003e\u003csup\u003e\u003cem\u003eE76K/+\u003c/em\u003e\u003c/sup\u003e mice were immunostained for stem cells (CD150\u003csup\u003e+\u003c/sup\u003eCD3\u003csup\u003e-\u003c/sup\u003eB220\u003csup\u003e-\u003c/sup\u003eTer119\u003csup\u003e-\u003c/sup\u003eCD48\u003csup\u003e-\u003c/sup\u003e) (cyan) and PMN-MDSCs (CD11b\u003csup\u003e+\u003c/sup\u003eLy6G\u003csup\u003e+\u003c/sup\u003e) (yellow) (n=10 mice/group, with \u0026gt;76 stem cells/group examined in total). The distance between stem cells and the closest PMN-MDSCs was determined.\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-4450642/v1/9d88663a441ca208c2032d0e.png"},{"id":63650401,"identity":"da76a3f0-faaa-4553-a551-1c178041af2c","added_by":"auto","created_at":"2024-08-30 14:53:25","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":24093975,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4450642/v1/ac66d8f2-0ed2-4e69-92f7-6347c1d33f4c.pdf"},{"id":61637299,"identity":"3b57c7a2-6068-4cb3-913d-05e4a07037c0","added_by":"auto","created_at":"2024-08-02 09:14:46","extension":"pdf","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":1562134,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigures05132024.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4450642/v1/6fd695c82d950abc924995cf.pdf"},{"id":61637301,"identity":"38eb328d-64b1-448b-9872-cba33cead061","added_by":"auto","created_at":"2024-08-02 09:14:46","extension":"pdf","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":2712265,"visible":true,"origin":"","legend":"","description":"","filename":"rs.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4450642/v1/36c2747d4579f934de8c03e2.pdf"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Prototypical innate immune mechanism hijacked by leukemia-initiating mutant stem cells for selective advantage and immune evasion in Ptpn11-associated juvenile myelomonocytic leukemia","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eJuvenile myelomonocytic leukemia (JMML), a pediatric myeloproliferative neoplasm, manifests as a clonal hematopoietic disorder characterized by the excessive production of myeloid cells. This disease originates from driver mutations acquired in hematopoietic stem cells (HSCs) and is propagated and sustained by these mutated stem cells, known as leukemia-initiating cells \u003csup\u003e1\u0026ndash;4\u003c/sup\u003e. JMML has limited therapeutic options. Relapse remains the primary cause of treatment failure, most likely due to the persistence of therapy-resistant, self-renewing leukemia-initiating cells \u003csup\u003e1\u0026ndash;4\u003c/sup\u003e. Addressing this issue is crucial for improving treatment outcomes in JMML patients.\u003c/p\u003e \u003cp\u003eGenetically, JMML is associated with mutations in genes encoding signaling proteins involved in the RAS/ERK pathway, including \u003cem\u003ePTPN11\u003c/em\u003e, \u003cem\u003eRAS\u003c/em\u003e, \u003cem\u003eNF1\u003c/em\u003e, \u003cem\u003eCBL\u003c/em\u003e, and others. \u003csup\u003e1\u0026ndash;4\u003c/sup\u003e. These mutations play a causal role in driving JMML development \u003csup\u003e5\u0026ndash;8\u003c/sup\u003e. JMML arises from an HSC harboring a genetic mutation, yet the mechanisms by which the initially mutated stem cell (leukemia-initiating cell) acquires a competitive advantage and evades immune surveillance remain unexplored. Additionally, the specific reasons behind the propensity of disease-associated mutations to induce myeloid malignancy are not fully understood, and the molecular mechanisms governing the aberrant repopulation of these leukemia-initiating stem cells remain elusive. Understanding these mechanisms could illuminate strategies for therapeutically targeting and eliminating JMML initiating stem cells in established disease.\u003c/p\u003e \u003cp\u003eOf the genetic lesions identified in JMML, the protein tyrosine phosphatase \u003cem\u003ePTPN11\u003c/em\u003e (SHP-2), a positive regulator of RAS signaling \u003csup\u003e9,10\u003c/sup\u003e, is the most frequently mutated (heterozygous) \u003csup\u003e11,12\u003c/sup\u003e. Mutations in \u003cem\u003ePTPN11\u003c/em\u003e lead to a significant increase in the catalytic activity of SHP-2 \u003csup\u003e12,13\u003c/sup\u003e. Patients carrying \u003cem\u003ePTPN11\u003c/em\u003e activating mutations have the worst prognosis among all subtypes of JMML \u003csup\u003e14\u0026ndash;17\u003c/sup\u003e. To elucidate the mechanisms underlying the pathogenesis of \u003cem\u003ePTPN11\u003c/em\u003e-mutated JMML, our laboratory created a conditional \u003cem\u003ePtpn11\u003c/em\u003e allele in mice with the \u003cem\u003ePtpn11\u003c/em\u003e\u003csup\u003e\u003cem\u003eE76K\u003c/em\u003e\u003c/sup\u003e mutation, the most common \u003cem\u003ePTPN11\u003c/em\u003e mutation found in JMML \u003csup\u003e11,12\u003c/sup\u003e, and developed an inducible disease model \u003csup\u003e6,18\u003c/sup\u003e. Induction of the \u003cem\u003ePtpn11\u003c/em\u003e\u003csup\u003e\u003cem\u003eE76K\u003c/em\u003e\u003c/sup\u003e mutation in the hematopoietic system resulted in a JMML-like myeloproliferative neoplasm with complete penetrance, affirming the causative role of this mutation in JMML \u003csup\u003e6\u003c/sup\u003e. In the present study, we take advantage of this unique disease model to investigate the cellular and molecular mechanisms involved in the pathological process of JMML following induction of the disease mutation. Our findings from single-cell transcriptomic profiling and experimental validations reveal an aberrant activation of innate immune responses in the mutated stem cells. These leukemia-initiating cells exploit innate immune and inflammatory mechanisms to gain a competitive advantage and evade anti-tumor immunity, ultimately leading to clonal dominance.\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cp\u003e \u003cb\u003eAberrant activation of innate immune and inflammatory responses in leukemia-initiating\u003c/b\u003e \u003cb\u003ePtpn11\u003c/b\u003e\u003csup\u003e\u003cb\u003eE76K/+\u003c/b\u003e\u003c/sup\u003e \u003cb\u003estem cells.\u003c/b\u003e\u003c/p\u003e \u003cp\u003eTo explore the intricate mechanisms of JMML pathogenesis, we conducted a comprehensive single-cell RNA sequencing (scRNA-seq) analysis on bone marrow (BM) cells isolated from mice with induced JMML (\u003cem\u003ePtpn11\u003c/em\u003e\u003csup\u003e\u003cem\u003eE76K/+\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e/Mx1-Cre\u003c/em\u003e) \u003csup\u003e6\u003c/sup\u003e and wild-type (WT, \u003cem\u003ePtpn11\u003c/em\u003e\u003csup\u003e\u003cem\u003e+/+\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e/Mx1-Cre\u003c/em\u003e) control littermates. Utilizing gene expression pattern-based cell clustering, we identified 11 distinct cell clusters within the BM population on a t-distributed stochastic neighbor embedding (t-SNE) plot (Extended Data Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). Clear distinctions among these clusters were evident in the heatmap representation of the expression patterns of the top 10 differentially expressed genes (DEGs) in each cluster (Extended Data Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). Leveraging reference datasets \u003csup\u003e19,20\u003c/sup\u003e permitted the identification of various hematopoietic cell types in different developmental stages, including HSCs, granulocyte-macrophage progenitors (GMPs), megakaryocytic-erythroid progenitors (MEPs), monocytes, neutrophils, T cells, B cells, and others (Extended Data Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC). Cell type-specific signature genes were indeed well-represented in the identified cell clusters (Extended Data Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD). Notably, \u003cem\u003ePtpn11\u003c/em\u003e\u003csup\u003e\u003cem\u003eE76K/+\u003c/em\u003e\u003c/sup\u003e mutant HSCs (leukemia-initiating cells) and GMPs exhibited reduced abundance, while monocytes and neutrophils displayed an increase compared to their WT (\u003cem\u003ePtpn11\u003c/em\u003e\u003csup\u003e\u003cem\u003e+/+\u003c/em\u003e\u003c/sup\u003e) counterparts (Extended Data Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC). The reduction of mutant stem cells/progenitors and the myeloid shift in hematopoietic cell development indicated hyperactivation of these leukemia-initiating cells and myeloid-committed progenitors. The decreased numbers of T cells and B cells in their hematopoietic systems suggested that the enhanced myeloid cell production resulted from skewed differentiation of \u003cem\u003ePtpn11\u003c/em\u003e-mutated stem cells. Gene set enrichment analysis (GSEA) demonstrated the upregulation of genes associated with immune processes and chemokine activities, particularly through the CC chemokine receptor (CCR), in \u003cem\u003ePtpn11\u003c/em\u003e\u003csup\u003e\u003cem\u003eE76K/+\u003c/em\u003e\u003c/sup\u003e mutant hematopoietic cells (Extended Data Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eGene expression profile-based cell clustering of the stem cell population revealed two distinct clusters equivalent to long-term HSCs (LT-HSCs) and short-term HSCs (ST-HSCs) according to the reference datasets \u003csup\u003e20\u003c/sup\u003e. The percentage of LT-HSCs decreased while the percentage of ST-HSCs increased in \u003cem\u003ePtpn11\u003c/em\u003e\u003csup\u003e\u003cem\u003eE76K/+\u003c/em\u003e\u003c/sup\u003e mice compared to those in WT littermates (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). In our analyses we also observed that among the top 20 DEGs in LT-HSCs compared to ST-HSCs, several genes were highly expressed only in LT-HSCs (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). In particular, \u003cem\u003eSdpr\u003c/em\u003e was predominantly expressed in LT-HSCs, indicating its potential as a distinctive marker for distinguishing LT-HSCs from ST-LT-HSCs. Notably, 177 genes in total were significantly differentially expressed in \u003cem\u003ePtpn11\u003c/em\u003e\u003csup\u003e\u003cem\u003eE76K/+\u003c/em\u003e\u003c/sup\u003e LT-HSCs versus WT LT-HSCs (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC). The Gene Ontology (GO) enrichment analysis of these DEGs highlighted the predominant elevation of defense reactions to bacterial infection, innate immune response, Toll-like receptor 4 (TLR4) signaling, and inflammation-associated pathways (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD). Consistent with the hyperactivation of \u003cem\u003ePtpn11\u003c/em\u003e\u003csup\u003e\u003cem\u003eE76K/+\u003c/em\u003e\u003c/sup\u003e HSCs, GSEA demonstrated a decrease in the expression of stem cell/progenitor-associated genes and upregulated/downregulated genes in HSCs versus GMPs in \u003cem\u003ePtpn11\u003c/em\u003e\u003csup\u003e\u003cem\u003eE76K/+\u003c/em\u003e\u003c/sup\u003e HSCs (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE), suggesting a loss of stemness and priming towards the myeloid lineage in \u003cem\u003ePtpn11\u003c/em\u003e-mutated HSCs. Similarly, 173 DEGs were identified in \u003cem\u003ePtpn11\u003c/em\u003e\u003csup\u003e\u003cem\u003eE76K/+\u003c/em\u003e\u003c/sup\u003e ST-HSCs compared to WT ST-HSCs (Extended Data Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e2\u003c/span\u003eA), with Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis indicating dysregulation of anti-viral immune response pathways, ribosome biogenesis, and spliceosome function. (Extended Data Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e2\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFurther examination of stem cell self-renewal or differentiation-associated signature genes revealed widespread deregulation in \u003cem\u003ePtpn11\u003c/em\u003e\u003csup\u003e\u003cem\u003eE76K/+\u003c/em\u003e\u003c/sup\u003e LT-HSCs and ST-HSCs, as compared to WT counterparts (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eF). Notable downregulated genes in \u003cem\u003ePtpn11\u003c/em\u003e\u003csup\u003e\u003cem\u003eE76K/+\u003c/em\u003e\u003c/sup\u003e LT-HSCs included \u003cem\u003eHoxa5\u003c/em\u003e, \u003cem\u003eHoxa6\u003c/em\u003e, \u003cem\u003eHoxa7\u003c/em\u003e, and \u003cem\u003eHoxb8\u003c/em\u003e, while upregulated genes comprised \u003cem\u003eHoxb2\u003c/em\u003e, \u003cem\u003eHoxb3\u003c/em\u003e, and \u003cem\u003eHoxb4\u003c/em\u003e. Interestingly, upregulated expression of myeloid differentiation-related genes \u003cem\u003eCebpb\u003c/em\u003e, \u003cem\u003eCebpe\u003c/em\u003e, \u003cem\u003eCebpg\u003c/em\u003e, and \u003cem\u003eCited2\u003c/em\u003e was noticed in these mutant stem cells. In \u003cem\u003ePtpn11\u003c/em\u003e\u003csup\u003e\u003cem\u003eE76K/+\u003c/em\u003e\u003c/sup\u003e ST-HSCs, downregulated genes included \u003cem\u003eHoxa3\u003c/em\u003e, \u003cem\u003eGata1\u003c/em\u003e, \u003cem\u003eKlf4\u003c/em\u003e, \u003cem\u003eCebpa\u003c/em\u003e, and \u003cem\u003eElane\u003c/em\u003e, while upregulated genes encompassed \u003cem\u003eMix\u003c/em\u003e, \u003cem\u003eIrf5\u003c/em\u003e, \u003cem\u003eIrf8\u003c/em\u003e, \u003cem\u003eCtss\u003c/em\u003e, \u003cem\u003eGata3\u003c/em\u003e, and \u003cem\u003eCsf1r\u003c/em\u003e. The most significant DEGs in \u003cem\u003ePtpn11\u003c/em\u003e\u003csup\u003e\u003cem\u003eE76K/+\u003c/em\u003e\u003c/sup\u003e LT-HSCs and ST-HSCs versus WT counterparts are illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eG. Surprisingly, myeloid cell-specific genes and genes associated with anti-pathogen and innate immune responses normally activated in myeloid cells, such as \u003cem\u003eS100a9\u003c/em\u003e, \u003cem\u003eS100a8\u003c/em\u003e, \u003cem\u003eS100a6\u003c/em\u003e, \u003cem\u003eS100a11\u003c/em\u003e, \u003cem\u003eRetnlg\u003c/em\u003e, \u003cem\u003eNgp\u003c/em\u003e, \u003cem\u003eCamp\u003c/em\u003e, \u003cem\u003eLcn2\u003c/em\u003e, \u003cem\u003eLyz2\u003c/em\u003e, \u003cem\u003eWfdc21\u003c/em\u003e, \u003cem\u003eChil3\u003c/em\u003e, and \u003cem\u003ePglyrp1\u003c/em\u003e were highly expressed in \u003cem\u003ePtpn11\u003c/em\u003e\u003csup\u003e\u003cem\u003eE76K/+\u003c/em\u003e\u003c/sup\u003e LT-HSCs. The expression levels of \u003cem\u003eS100a9\u003c/em\u003e and \u003cem\u003eS100a8\u003c/em\u003e, also known as myeloid-related proteins 9 and 8, were increased approximately 29- and 24-fold, respectively, standing out as the most strikingly upregulated among all significant DEGs in \u003cem\u003ePtpn11\u003c/em\u003e\u003csup\u003e\u003cem\u003eE76K/+\u003c/em\u003e\u003c/sup\u003e LT-HSCs. Additionally, \u003cem\u003eCxcl2\u003c/em\u003e, also known as \u003cem\u003eMIP2-α\u003c/em\u003e, a chemokine typically secreted by monocytes/macrophages and a powerful chemoattractant for polymorphonuclear leukocytes involved in many immune responses, including wound healing, cancer metastasis, and angiogenesis, was overexpressed in these leukemia-initiating cells.\u003c/p\u003e \u003cp\u003eMoreover, several cell surface molecules were differentially expressed in \u003cem\u003ePtpn11\u003c/em\u003e\u003csup\u003e\u003cem\u003eE76K/+\u003c/em\u003e\u003c/sup\u003e LT-HSCs. Among the most significant DEGs, \u003cem\u003eCd52\u003c/em\u003e and \u003cem\u003eCd9\u003c/em\u003e were upregulated, while transcriptional expression of the early stem/progenitor cell marker \u003cem\u003eCd34\u003c/em\u003e was diminished (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eH). In addition, \u003cem\u003eCd33\u003c/em\u003e, \u003cem\u003eP2ry14/Gpr105\u003c/em\u003e, and \u003cem\u003eGpr150\u003c/em\u003e showed a marked upregulation in \u003cem\u003ePtpn11\u003c/em\u003e\u003csup\u003e\u003cem\u003eE76K/+\u003c/em\u003e\u003c/sup\u003e LT-HSCs. These unique expression patterns of cell surface molecules in \u003cem\u003ePtpn11\u003c/em\u003e mutant LT-HSCs hold promise for their utilization as therapeutic targets or biomarkers for JMML stem cells. Furthermore, Rage/Ager, the receptor for the S100a9/S100a8 heterodimer (calprotectin) \u003csup\u003e21\u003c/sup\u003e typically expressed on myeloid immune cells exhibited substantial upregulation in \u003cem\u003ePtpn11\u003c/em\u003e\u003csup\u003e\u003cem\u003eE76K/+\u003c/em\u003e\u003c/sup\u003e LT-HSCs, indicating potential autocrine feedback activities in these leukemia-initiating cells. Given that \u003cem\u003eS100a9\u003c/em\u003e expression was significantly upregulated in \u003cem\u003ePtpn11\u003c/em\u003e\u003csup\u003e\u003cem\u003eE76K/+\u003c/em\u003e\u003c/sup\u003e LT-HSCs (Extended Data Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e3\u003c/span\u003eA), we sought to identify transcriptional factors potentially associated with this upregulation. To this end, we conducted Venn diagram data analysis involving the 177 DEGs in \u003cem\u003ePtpn11\u003c/em\u003e\u003csup\u003e\u003cem\u003eE76K/+\u003c/em\u003e\u003c/sup\u003e LT-HSCs and 58 transcriptional factors related to \u003cem\u003eS100a9\u003c/em\u003e. This analysis revealed \u003cem\u003eSpi1\u003c/em\u003e and \u003cem\u003eSmarca4\u003c/em\u003e (Extended Data Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). Of the 47 dysregulated transcriptional factors in \u003cem\u003ePtpn11\u003c/em\u003e\u003csup\u003e\u003cem\u003eE76K/+\u003c/em\u003e\u003c/sup\u003e LT-HSCs, \u003cem\u003eSpi1\u003c/em\u003e showed a significant upregulation, whereas \u003cem\u003eSmarca4\u003c/em\u003e was downregulated (Extended Data Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e3\u003c/span\u003eC), suggesting that the elevated levels of \u003cem\u003eSpi1\u003c/em\u003e may have contributed to the observed overexpression of \u003cem\u003eS100a9\u003c/em\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eProfound impact on the myeloid lineage by the\u003c/b\u003e \u003cb\u003ePtpn11\u003c/b\u003e\u003csup\u003e\u003cb\u003eE76K\u003c/b\u003e\u003c/sup\u003e \u003cb\u003emutation.\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThe influence of the \u003cem\u003ePtpn11\u003c/em\u003e\u003csup\u003e\u003cem\u003eE76K\u003c/em\u003e\u003c/sup\u003e mutation extended beyond the stem cell population, significantly affecting myeloid-committed GMPs. Gene expression profiling identified 4 distinct cell clusters in GMPs, revealing heterogeneity among these progenitors (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). Interestingly, \u003cem\u003ePtpn11\u003c/em\u003e\u003csup\u003e\u003cem\u003eE76K/+\u003c/em\u003e\u003c/sup\u003e GMPs exhibited a notable shift in cell composition, with Cluster 3 emerging as a unique and overrepresented subpopulation, constituting approximately 60% of the total. The heatmap representation of the top 10 DEGs in each cell cluster highlighted clear differences among these clusters, with Cluster 1 enriched in \u003cem\u003eProm1\u003c/em\u003e, \u003cem\u003eClu\u003c/em\u003e, \u003cem\u003eMgam\u003c/em\u003e, \u003cem\u003eGpx3\u003c/em\u003e, and \u003cem\u003eSlco4c1\u003c/em\u003e, and Cluster 3 marked by high expression of \u003cem\u003eFbp1\u003c/em\u003e, \u003cem\u003eTmem53\u003c/em\u003e, \u003cem\u003eCracr2b\u003c/em\u003e, and \u003cem\u003eDlg2\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). Overall, 127 genes were significantly differentially expressed in \u003cem\u003ePtpn11\u003c/em\u003e\u003csup\u003e\u003cem\u003eE76K/+\u003c/em\u003e\u003c/sup\u003e GMPs compared to their WT counterparts (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). GO enrichment analysis of the DEGs underscored enrichment in innate immune and inflammatory pathways in \u003cem\u003ePtpn11\u003c/em\u003e mutant GMPs (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e2\u003c/span\u003eD). This included pathways related to the positive regulation of immune response, neutrophil activation, neutrophil-mediated killing of bacteria, defense response to bacteria, and innate immune response. GSEA revealed an enrichment of genes typically associated with later-stage progenitors, such as monocyte and dendritic cell progenitors, and neutrophil progenitors in \u003cem\u003ePtpn11\u003c/em\u003e\u003csup\u003e\u003cem\u003eE76K/+\u003c/em\u003e\u003c/sup\u003e GMPs relative to WT GMPs (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e2\u003c/span\u003eE), indicative of enhanced differentiation activities in these mutant GMPs. Cluster 3, representing the major subpopulation within \u003cem\u003ePtpn11\u003c/em\u003e\u003csup\u003e\u003cem\u003eE76K/+\u003c/em\u003e\u003c/sup\u003e GMPs, displayed high and unique expression of \u003cem\u003eArl11\u003c/em\u003e, \u003cem\u003eFbp1\u003c/em\u003e, \u003cem\u003eSlc31a2\u003c/em\u003e, \u003cem\u003eHnmt\u003c/em\u003e, \u003cem\u003eTmem53\u003c/em\u003e, \u003cem\u003eCracr2b\u003c/em\u003e, among others (Extended Data Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). Differential gene expression analysis between Cluster 3 and Cluster 1, the major population in WT GMPs, revealed 114 genes with distinct expression patterns (Extended Data Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). KEGG pathway analyses illustrated the upregulation of genes involved in autoimmune responses, bacterial infection responses, natural killer cell-mediated cytotoxicity, neutrophil extracellular trap formation, and ribosome, whereas downregulated pathways included phagosome, ribosome, RNA transport, spliceosome, RNA degradation, oxidative phosphorylation, and thermogenesis pathways in \u003cem\u003ePtpn11\u003c/em\u003e\u003csup\u003e\u003cem\u003eE76K/+\u003c/em\u003e\u003c/sup\u003e GMPs (Extended Data Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e4\u003c/span\u003eC).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWe then examined the impact of the \u003cem\u003ePtpn11\u003c/em\u003e\u003csup\u003e\u003cem\u003eE76K\u003c/em\u003e\u003c/sup\u003e mutation on monocytes and neutrophils. Gene expression profile-based cell clustering demonstrated heterogeneity in monocytes. Seven distinct cell clusters were identified in monocytes (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). The \u003cem\u003ePtpn11\u003c/em\u003e\u003csup\u003e\u003cem\u003eE76K/+\u003c/em\u003e\u003c/sup\u003e monocyte compartment demonstrated notable changes in cell compositions. The linker histone H1 family members (\u003cem\u003eHist1h2ab\u003c/em\u003e, \u003cem\u003eHist1h2af\u003c/em\u003e, \u003cem\u003eHist1h2bm\u003c/em\u003e, \u003cem\u003eHist1h2bn\u003c/em\u003e, \u003cem\u003eHist1h3b\u003c/em\u003e, and \u003cem\u003eHist1h3f\u003c/em\u003e), \u003cem\u003eSirpb1c\u003c/em\u003e, \u003cem\u003eMs4a8a\u003c/em\u003e, \u003cem\u003eApoe\u003c/em\u003e, \u003cem\u003eSlfn5\u003c/em\u003e, \u003cem\u003ePla2g7\u003c/em\u003e, and \u003cem\u003eP2ry6\u003c/em\u003e were highly expressed in Cluster 2 and Cluster 3, which were unique in the \u003cem\u003ePtpn11\u003c/em\u003e mutant monocyte population (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). Similarly, neutrophils also exhibited heterogeneity, with altered cell compositions in the \u003cem\u003ePtpn11\u003c/em\u003e\u003csup\u003e\u003cem\u003eE76K/+\u003c/em\u003e\u003c/sup\u003e neutrophil compartment (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). Upregulated genes in \u003cem\u003ePtpn11\u003c/em\u003e\u003csup\u003e\u003cem\u003eE76K/+\u003c/em\u003e\u003c/sup\u003e clusters included mitochondrial protein synthesis-associated \u003cem\u003eLars2\u003c/em\u003e, innate immunity-associated \u003cem\u003eChil5\u003c/em\u003e, the chemokine \u003cem\u003eCcl6\u003c/em\u003e, Arginase, type 2 (\u003cem\u003eArg2)\u003c/em\u003e, and glycolysis-associated \u003cem\u003eLdhc\u003c/em\u003e, while downregulated genes comprised \u003cem\u003eLipg\u003c/em\u003e, \u003cem\u003eCmah\u003c/em\u003e, \u003cem\u003eQsox1\u003c/em\u003e, \u003cem\u003eCalr\u003c/em\u003e, \u003cem\u003ePdia6\u003c/em\u003e, \u003cem\u003eSec61a1\u003c/em\u003e, \u003cem\u003eProk2\u003c/em\u003e, \u003cem\u003eWfdc17\u003c/em\u003e, \u003cem\u003eIfitm1\u003c/em\u003e, and others (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e3\u003c/span\u003eD).\u003c/p\u003e \u003cp\u003eTo explore whether the transcriptional landscape changes in \u003cem\u003ePtpn11\u003c/em\u003e\u003csup\u003e\u003cem\u003eE76K/+\u003c/em\u003e\u003c/sup\u003e cells across different developmental stages shared commonality, the top 50 significant DEGs in \u003cem\u003ePtpn11\u003c/em\u003e\u003csup\u003e\u003cem\u003eE76K/+\u003c/em\u003e\u003c/sup\u003e and WT stem cells, GMPs, monocytes, and neutrophils are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e4\u003c/span\u003eA. Venn diagram data analysis for DEGs in the different cell populations identified 44 co-events (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). Remarkably, these genes were consistently upregulated or downregulated in \u003cem\u003ePtpn11\u003c/em\u003e\u003csup\u003e\u003cem\u003eE76K/+\u003c/em\u003e\u003c/sup\u003e cells throughout all developmental stages, without any exceptions (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e4\u003c/span\u003eC). This observation implies that they were cell-intrinsically dysregulated by the \u003cem\u003ePtpn11\u003c/em\u003e\u003csup\u003e\u003cem\u003eE76K\u003c/em\u003e\u003c/sup\u003e mutation. Many of these co-DEGs were associated with innate immune signaling and inflammatory pathways, including \u003cem\u003eS100a11\u003c/em\u003e, \u003cem\u003eRetnlg\u003c/em\u003e, and \u003cem\u003eLyz2\u003c/em\u003e. Interestingly, genes involved in ribosomal biogenesis, such as \u003cem\u003eRplp0\u003c/em\u003e, \u003cem\u003eRps3\u003c/em\u003e, and \u003cem\u003eRpl21\u003c/em\u003e were upregulated, while \u003cem\u003eRpl41\u003c/em\u003e, \u003cem\u003eRpl37a\u003c/em\u003e, \u003cem\u003eRps28\u003c/em\u003e, \u003cem\u003eRpl38\u003c/em\u003e, \u003cem\u003eRpl23a\u003c/em\u003e, and \u003cem\u003eRps15\u003c/em\u003e were repressed. Dysregulation of ribosomal biogenesis and function can collectively contribute to cellular abnormalities, genomic instability, and the development of malignancies \u003csup\u003e22,23\u003c/sup\u003e. These findings underscore that the impact on ribosomal function is a common pathological effect of the \u003cem\u003ePtpn11\u003c/em\u003e mutation across different cell types.\u003c/p\u003e \u003cp\u003e \u003cb\u003eAltered developmental trajectories and cell-cell communications in leukemia-initiating\u003c/b\u003e \u003cb\u003ePtpn11\u003c/b\u003e\u003csup\u003e\u003cb\u003eE76K/+\u003c/b\u003e\u003c/sup\u003e \u003cb\u003estem cells.\u003c/b\u003e\u003c/p\u003e \u003cp\u003eBranched expression analysis modeling (BEAM), followed by hierarchical clustering analysis, identified three distinct gene expression modules during the differentiation process from stem cells to monocytes and neutrophils. Notable differences in the dynamic changes in the expression of genes enriched in all modules were observed in the differentiation process of \u003cem\u003ePtpn11\u003c/em\u003e\u003csup\u003e\u003cem\u003eE76K/+\u003c/em\u003e\u003c/sup\u003e stem cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). A markedly higher number of genes showed dynamic changes in expression within Module 2, whereas fewer genes demonstrated such changes in Module 3 in the context of the \u003cem\u003ePtpn11\u003c/em\u003e mutant cellular processes. Pseudotime mapping analysis, which infers the developmental trajectory or temporal progression of cells within a heterogeneous population based on gene expression profiles, revealed that leukemia-initiating \u003cem\u003ePtpn11\u003c/em\u003e\u003csup\u003e\u003cem\u003eE76K/+\u003c/em\u003e\u003c/sup\u003e mutant stem cells gave rise to GMPs mainly in one direction as opposed to two in WT counterparts (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e5\u003c/span\u003eB, upper row), suggesting the impact on the mutation of GMPs. While the inferred pseudotime of neutrophil development from \u003cem\u003ePtpn11\u003c/em\u003e\u003csup\u003e\u003cem\u003eE76K/+\u003c/em\u003e\u003c/sup\u003e stem cells remained relatively unchanged, two diverging cell fates were observed during the differentiation of these leukemia-initiating cells towards monocytes, contrasting with the essentially singular fate observed in the WT control, and the inferred pseudotime of monocyte development from the leukemia-initiating cells was prolonged (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e5\u003c/span\u003eB, upper row). In addition, intermediate monocytes in a transitioning state were increased in the \u003cem\u003ePtpn11\u003c/em\u003e\u003csup\u003e\u003cem\u003eE76K/+\u003c/em\u003e\u003c/sup\u003e group, suggesting a delay or arrest in their differentiation and maturation. Further analyses focusing on specific cell compartments showed a slight difference in the diffusion trajectories within GMPs between \u003cem\u003ePtpn11\u003c/em\u003e\u003csup\u003e\u003cem\u003eE76K/+\u003c/em\u003e\u003c/sup\u003e and WT counterparts (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e5\u003c/span\u003eB, lower row). No notable differences in \u003cem\u003ePtpn11\u003c/em\u003e\u003csup\u003e\u003cem\u003eE76K/+\u003c/em\u003e\u003c/sup\u003e neutrophil diffusion maps were detected, indicating relatively normal differentiation and maturation within these two cell populations. In contrast, \u003cem\u003ePtpn11\u003c/em\u003e\u003csup\u003e\u003cem\u003eE76K/+\u003c/em\u003e\u003c/sup\u003e monocytes exhibited two distinct developmental paths compared to the single direction observed in WT monocytes (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e5\u003c/span\u003eB, low row), implying the generation of various subpopulations in \u003cem\u003ePtpn11\u003c/em\u003e\u003csup\u003e\u003cem\u003eE76K/+\u003c/em\u003e\u003c/sup\u003e monocytes along distinct developmental routes.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eCell-cell communication analyses based on the expression of ligands and their cognate receptors revealed enhanced interactions between neutrophils and stem cells in \u003cem\u003ePtpn11\u003c/em\u003e\u003csup\u003e\u003cem\u003eE76K/+\u003c/em\u003e\u003c/sup\u003e mice compared to those in WT littermates (Extended Data Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e5\u003c/span\u003eA and \u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). Furthermore, interactions among \u003cem\u003ePtpn11\u003c/em\u003e\u003csup\u003e\u003cem\u003eE76K/+\u003c/em\u003e\u003c/sup\u003e stem cells were increased relative to those in WT stem cells (Extended Data Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e5\u003c/span\u003eA and \u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). A closer examination of neutrophil-stem cell communications indicated that interactions mediated by IL-1β, TGF-β, and Oncostatin M were enhanced in \u003cem\u003ePtpn11\u003c/em\u003e\u003csup\u003e\u003cem\u003eE76K/+\u003c/em\u003e\u003c/sup\u003e mice compared to those in WT mice (Extended Data Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e5\u003c/span\u003eC), providing additional evidence that leukemia-initiating \u003cem\u003ePtpn11\u003c/em\u003e-mutated stem cells were situated in an inflammatory microenvironment.\u003c/p\u003e \u003cp\u003e \u003cb\u003eLeukemia-initiating\u003c/b\u003e \u003cb\u003ePtpn11\u003c/b\u003e\u003csup\u003e\u003cb\u003eE76K/+\u003c/b\u003e\u003c/sup\u003e \u003cb\u003estem cells are primed by the myeloid transcriptional program.\u003c/b\u003e\u003c/p\u003e \u003cp\u003eCell identity and functional specificity are collectively governed by transcription factors and the expression levels of their target genes. The overall transcriptional activities in \u003cem\u003ePtpn11\u003c/em\u003e\u003csup\u003e\u003cem\u003eE76K/+\u003c/em\u003e\u003c/sup\u003e stem cells were elevated compared to those in their WT counterparts (Extended Data Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e6\u003c/span\u003e), consistent with more active cellular processes in leukemia-initiating \u003cem\u003ePtpn11\u003c/em\u003e-mutated stem cells. To further elucidate the mechanisms through which the \u003cem\u003ePtpn11\u003c/em\u003e\u003csup\u003e\u003cem\u003eE76K\u003c/em\u003e\u003c/sup\u003e mutation influences cell behavior, we conducted single cell regulatory network inference and clustering (SCENIC) analysis (transcriptional factor regulon analysis). The activities of many transcription factors in \u003cem\u003ePtpn11\u003c/em\u003e\u003csup\u003e\u003cem\u003eE76K/+\u003c/em\u003e\u003c/sup\u003e stem cells, GMPs, monocytes, and neutrophils were altered compared to those in their WT counterparts, as indicated by regulon activity scores. In \u003cem\u003ePtpn11\u003c/em\u003e\u003csup\u003e\u003cem\u003eE76K/+\u003c/em\u003e\u003c/sup\u003e stem cells, the transcriptional activities of \u003cem\u003eAtf3\u003c/em\u003e, \u003cem\u003eEgr1\u003c/em\u003e, \u003cem\u003eJun\u003c/em\u003e, \u003cem\u003eJund\u003c/em\u003e, \u003cem\u003eKlf6\u003c/em\u003e, \u003cem\u003eFos\u003c/em\u003e, and \u003cem\u003eGata2\u003c/em\u003e were significantly decreased, while those of \u003cem\u003eIrf7\u003c/em\u003e, \u003cem\u003eIrf8\u003c/em\u003e, \u003cem\u003eMaf\u003c/em\u003e, and \u003cem\u003eMyc\u003c/em\u003e were increased (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e6\u003c/span\u003eA). Importantly, regulon specificity scores (RSS), reflecting the association between regulon activities and cellular specificity, revealed that among these differentially functioning transcription factors, the myeloid transcription factors \u003cem\u003eEts1\u003c/em\u003e, \u003cem\u003eCebpe\u003c/em\u003e, and \u003cem\u003eNfe2\u003c/em\u003e were highly associated with the identity specificity of \u003cem\u003ePtpn11\u003c/em\u003e\u003csup\u003e\u003cem\u003eE76K/+\u003c/em\u003e\u003c/sup\u003e stem cells, as opposed to \u003cem\u003eTcf7l2\u003c/em\u003e, \u003cem\u003eRelb\u003c/em\u003e, and \u003cem\u003eIrf5\u003c/em\u003e for WT HSCs. At the GMP level, the activities of myeloid-specific transcription factors \u003cem\u003eCebpe\u003c/em\u003e, \u003cem\u003eCebpb\u003c/em\u003e, and \u003cem\u003eEts1\u003c/em\u003e were markedly increased in \u003cem\u003ePtpn11\u003c/em\u003e\u003csup\u003e\u003cem\u003eE76K/+\u003c/em\u003e\u003c/sup\u003e GMPs, and their cellular specificity was determined by \u003cem\u003eCebpe\u003c/em\u003e, \u003cem\u003eEts1\u003c/em\u003e, and \u003cem\u003eMyc\u003c/em\u003e compared to \u003cem\u003eCebpe\u003c/em\u003e, \u003cem\u003eE2f1\u003c/em\u003e, and \u003cem\u003eKlf6\u003c/em\u003e in WT GMPs (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e6\u003c/span\u003eB). Activities of transcription factors \u003cem\u003eIrf7\u003c/em\u003e, \u003cem\u003eCebpb\u003c/em\u003e, \u003cem\u003eFos\u003c/em\u003e, \u003cem\u003eIrf5\u003c/em\u003e, \u003cem\u003eIrf8\u003c/em\u003e, \u003cem\u003eKlf4\u003c/em\u003e, and \u003cem\u003eMaf\u003c/em\u003e were significantly higher in \u003cem\u003ePtpn11\u003c/em\u003e\u003csup\u003e\u003cem\u003eE76K/+\u003c/em\u003e\u003c/sup\u003e monocytes than those in WT cells, and the identity specificity of \u003cem\u003ePtpn11\u003c/em\u003e mutant monocytes was highly associated with transcription factors \u003cem\u003eIrf7\u003c/em\u003e, \u003cem\u003eMafg\u003c/em\u003e, and \u003cem\u003eIrf8\u003c/em\u003e according to RSS (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e6\u003c/span\u003eC). Similarly, the distinction in transcriptional factor determinants influencing the specificity of \u003cem\u003ePtpn11\u003c/em\u003e\u003csup\u003e\u003cem\u003eE76K/+\u003c/em\u003e\u003c/sup\u003e neutrophils (\u003cem\u003eMafg\u003c/em\u003e, \u003cem\u003eCebpb\u003c/em\u003e, and \u003cem\u003eJunb\u003c/em\u003e) compared to those governing WT neutrophils (\u003cem\u003eMaf\u003c/em\u003e, \u003cem\u003eIrf8\u003c/em\u003e, and \u003cem\u003eJunb\u003c/em\u003e) was also apparent (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e6\u003c/span\u003eD).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eConsistent with the regulon results, \u003cem\u003ePtpn11\u003c/em\u003e\u003csup\u003e\u003cem\u003eE76K/+\u003c/em\u003e\u003c/sup\u003e stem cells and GMPs demonstrated heightened cell cycling, as evidenced by the loss of quiescence (the G\u003csub\u003e0\u003c/sub\u003e phase in the cell cycle) and an increased number of cells in the G\u003csub\u003e2\u003c/sub\u003e/M phase, based on single-cell transcriptomes and a reported predictor for allocating individual cells to G\u003csub\u003e0\u003c/sub\u003e, G\u003csub\u003e1\u003c/sub\u003e/S, and G\u003csub\u003e2\u003c/sub\u003e/M cell cycle phases \u003csup\u003e24\u003c/sup\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e7\u003c/span\u003eA). The cell division/replication-related histone H1 family members (\u003cem\u003eHist1h1c\u003c/em\u003e, \u003cem\u003eHist1h1d\u003c/em\u003e, \u003cem\u003eHist1h1e\u003c/em\u003e, and \u003cem\u003eHist1h2ae\u003c/em\u003e) and CDK1 were upregulated in both \u003cem\u003ePtpn11\u003c/em\u003e\u003csup\u003e\u003cem\u003eE76K/+\u003c/em\u003e\u003c/sup\u003e stem cells and GMPs (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e7\u003c/span\u003eB). Additionally, GSEA revealed a significant enrichment of cell cycling-associated gene sets in \u003cem\u003ePtpn11\u003c/em\u003e\u003csup\u003e\u003cem\u003eE76K/+\u003c/em\u003e\u003c/sup\u003e stem cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e7\u003c/span\u003eC). Both \u003cem\u003ePtpn11\u003c/em\u003e\u003csup\u003e\u003cem\u003eE76K/+\u003c/em\u003e\u003c/sup\u003e stem cells and GMPs exhibited a high enrichment of GM-CSF response gene sets. This observation aligns with the well-established high sensitivity of JMML cells to GM-CSF \u003csup\u003e25,26\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003e \u003cb\u003eS100a9 and S100a8, aberrantly expressed in\u003c/b\u003e \u003cb\u003ePtpn11\u003c/b\u003e\u003csup\u003e\u003cb\u003eE76K/+\u003c/b\u003e\u003c/sup\u003e \u003cb\u003estem cells, contribute significantly to leukemogenesis.\u003c/b\u003e\u003c/p\u003e \u003cp\u003eGiven the prominent upregulation of \u003cem\u003eS100a9\u003c/em\u003e and \u003cem\u003eS100a8\u003c/em\u003e in \u003cem\u003ePtpn11\u003c/em\u003e\u003csup\u003e\u003cem\u003eE76K/+\u003c/em\u003e\u003c/sup\u003e mutant long-term stem cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eG) and their diverse roles in various cell types \u003csup\u003e27,28\u003c/sup\u003e, we investigated their potential role in these tumor initiating cells. First, we confirmed a significant increase (\u0026gt;\u0026thinsp;8-fold) in the expression levels of S100a9 and S100a8 in mutant stem cells isolated from \u003cem\u003ePtpn11\u003c/em\u003e\u003csup\u003e\u003cem\u003eE76K/+\u003c/em\u003e\u003c/sup\u003e mice compared to those in WT HSCs (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e8\u003c/span\u003eA). Importantly, expression levels of S100a9 and S100a8 were also elevated approximately 7-fold in leukemic stem/progenitor cells (CD34\u003csup\u003e+\u003c/sup\u003e) from \u003cem\u003ePTPN11\u003c/em\u003e-mutated JMML patients compared to those in normal CD34\u003csup\u003e+\u003c/sup\u003e hematopoietic stem/progenitors (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e8\u003c/span\u003eB). The overexpression of S100a9 and S100a8 by \u003cem\u003ePtpn11\u003c/em\u003e\u003csup\u003e\u003cem\u003eE76K/+\u003c/em\u003e\u003c/sup\u003e mutant stem cells appeared to promote the growth of these leukemia-initiating cells. \u003cem\u003ePtpn11\u003c/em\u003e\u003csup\u003e\u003cem\u003eE76K/+\u003c/em\u003e\u003c/sup\u003e stem cells cultured in \u003cem\u003eex vivo\u003c/em\u003e expansion medium exhibited significantly accelerated proliferation compared to WT HSCs. However, this growth advantage was mitigated by tasquinimod, an inhibitor of S100a9/S100a8 that disrupts their interactions with receptors RAGE and TLR4 \u003csup\u003e29,30\u003c/sup\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e8\u003c/span\u003eC), which were also highly expressed on these cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eH). Additionally, the elevated differentiation capabilities of \u003cem\u003ePtpn11\u003c/em\u003e\u003csup\u003e\u003cem\u003eE76K/+\u003c/em\u003e\u003c/sup\u003e mutant stem cells to form myeloid colonies compared to those of WT HSCs were substantially decreased by tasquinimod (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e8\u003c/span\u003eD). These findings suggest that S100a9 and S100a8 significantly contribute to the clonal expansion and enhanced myeloid differentiation of leukemia-initiating \u003cem\u003ePtpn11-\u003c/em\u003emutated stem cells through autocrine effects.\u003c/p\u003e \u003cp\u003ePrevious studies have proposed a significant role for S100a9 and S100a8 expressed in tumor cells in recruiting MDSCs, which are known for their association with immunosuppression and inflammation \u003csup\u003e27,31,32\u003c/sup\u003e. These heterogeneous cells co-express CD11b, Ly6G, and Ly6C myeloid lineage markers [polymorphonuclear MDSCs (PMN-MDSCs): CD11b\u003csup\u003e+\u003c/sup\u003eLy6G\u003csup\u003e+\u003c/sup\u003eLy6C\u003csup\u003elow\u003c/sup\u003e; mononuclear MDSCs (M-MDSCs): CD11b\u003csup\u003e+\u003c/sup\u003eLy6G\u003csup\u003e\u0026minus;\u003c/sup\u003eLy6C\u003csup\u003ehigh\u003c/sup\u003e]. MDSCs are potent inhibitors of anti-tumor immunity, contributing to immune escape \u003csup\u003e27,31,32\u003c/sup\u003e. To investigate the potential interplay between \u003cem\u003ePtpn11\u003c/em\u003e\u003csup\u003e\u003cem\u003eE76K/+\u003c/em\u003e\u003c/sup\u003e mutant stem cells and MDSCs, we conducted transwell migration assays. As displayed in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e8\u003c/span\u003eE, \u003cem\u003ePtpn11\u003c/em\u003e\u003csup\u003e\u003cem\u003eE76K/+\u003c/em\u003e\u003c/sup\u003e mutant stem cells demonstrated heightened chemoattracting activities for PMN-MDSCs (CD11b\u003csup\u003e+\u003c/sup\u003eLy6G\u003csup\u003e+\u003c/sup\u003e) compared to WT HSCs. Notably, this effect was blocked by the S100a9/S100a8 inhibitor tasquinimod, indicating that the overproduction of S100a9 and S100a8 by leukemia-initiating \u003cem\u003ePtpn11\u003c/em\u003e mutant stem cells may contribute to the recruitment of MDSCs to the microenvironment.\u003c/p\u003e \u003cp\u003eTo test this possibility and further determine the role of S100a9 and S100a8 in the leukemogenic activities of \u003cem\u003ePtpn11\u003c/em\u003e\u003csup\u003e\u003cem\u003eE76K/+\u003c/em\u003e\u003c/sup\u003e stem cells in an \u003cem\u003ein vivo\u003c/em\u003e setting, we evaluated the therapeutic impact of the S100a9/S100a8 inhibitor tasquinimod using a widely used transplantation leukemia model. \u003cem\u003ePtpn11\u003c/em\u003e\u003csup\u003e\u003cem\u003eE76K/+\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e/Mx1-Cre/mTmG\u003c/em\u003e mice were generated by crossbreeding of \u003cem\u003ePtpn11\u003c/em\u003e\u003csup\u003e\u003cem\u003eE76K/+\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e/Mx1-Cre\u003c/em\u003e mice \u003csup\u003e6\u003c/sup\u003e with lineage tracing \u003cem\u003emTmG\u003c/em\u003e transgenic mice \u003csup\u003e33\u003c/sup\u003e, which expressed red fluorescent protein (RFP) but transitioned to green fluorescent protein (GFP) upon the induction of \u003cem\u003eCre\u003c/em\u003e expression (and the \u003cem\u003ePtpn11\u003c/em\u003e\u003csup\u003e\u003cem\u003eE76K\u003c/em\u003e\u003c/sup\u003e mutation). To mimic clinical scenarios, we combined BM cells from \u003cem\u003ePtpn11\u003c/em\u003e\u003csup\u003e\u003cem\u003eE76K/+\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e/Mx1-Cre/mTmG\u003c/em\u003e leukemic mice with WT BM cells from congenic BoyJ mice at a 10:1 ratio and transplanted mixed cells into lethally-irradiated BoyJ mice. Four weeks post-transplantation, when donor cells were engrafted, tasquinimod or vehicle was administered to mice via drinking water for 4 weeks (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e8\u003c/span\u003eF). Despite the high ratio of leukemic cells in the mixed donor cells, the reconstitution of leukemic cells from \u003cem\u003ePtpn11\u003c/em\u003e\u003csup\u003e\u003cem\u003eE76K/+\u003c/em\u003e\u003c/sup\u003e mutant stem cells in the recipient mice was approximately 50% due to the hyperactivation and significant depletion of the mutant stem cell population (known as exhaustion) in the BM collected from the leukemic mice \u003csup\u003e6\u003c/sup\u003e. Importantly, in response to tasquinimod treatment, a notable reduction in total leukemic cells (GFP\u003csup\u003e+\u003c/sup\u003e) in the peripheral blood (PB) was observed (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e8\u003c/span\u003eG). Myeloid cells (Mac-1\u003csup\u003e+\u003c/sup\u003e) in the GFP\u003csup\u003e+\u003c/sup\u003e leukemic cell compartment (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e8\u003c/span\u003eH) and the entire PB (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e8\u003c/span\u003eI) significantly decreased, indicating that the skewed myeloid differentiation of leukemia-initiating \u003cem\u003ePtpn11\u003c/em\u003e\u003csup\u003e\u003cem\u003eE76K/+\u003c/em\u003e\u003c/sup\u003e stem cells was largely rectified by blocking S100a9/S100a8 function.\u003c/p\u003e \u003cp\u003eMice were euthanized after 4 weeks of treatment. White blood cell counts (WBCs) in the tasquinimod-treated group significantly decreased, specifically in neutrophils and monocytes, with no apparent changes in red blood cell counts (RBCs) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e8\u003c/span\u003eJ). Splenomegaly was also ameliorated in tasquinimod-treated mice (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e8\u003c/span\u003eK). Total leukemic cells (GFP\u003csup\u003e+\u003c/sup\u003e) in the spleen, Mac-1\u003csup\u003e+\u003c/sup\u003e myeloid cells in the GFP\u003csup\u003e+\u003c/sup\u003e leukemic compartment and the entire spleen all decreased (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e8\u003c/span\u003eL). Similar therapeutic effects were also observed in the BM (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e8\u003c/span\u003eM). Furthermore, we assessed the impact of the S100a9/S100a8 inhibitor on leukemia-initiating mutant stem cells. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e8\u003c/span\u003eN, GFP\u003csup\u003e+\u003c/sup\u003e \u003cem\u003ePtpn11\u003c/em\u003e\u003csup\u003e\u003cem\u003eE76K/+\u003c/em\u003e\u003c/sup\u003e mutant stem cells in the BM and early leukemic progenitor cells (Lineage\u003csup\u003e\u0026minus;\u003c/sup\u003eSca-1\u003csup\u003e+\u003c/sup\u003ec-Kit\u003csup\u003e+\u003c/sup\u003e) in the spleen significantly decreased in the inhibitor-treated mice. Consistently, the cell cycling of hyperactive \u003cem\u003ePtpn11\u003c/em\u003e\u003csup\u003e\u003cem\u003eE76K/+\u003c/em\u003e\u003c/sup\u003e stem cells was reduced by the treatment (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e8\u003c/span\u003eO). Moreover, apoptosis in these mutant stem cells increased in the inhibitor-treated mice (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e8\u003c/span\u003eP), suggesting that S100a9 and S100a8 played an important role for the survival of these leukemia initiating cells. Finally, we visualized \u003cem\u003ePtpn11\u003c/em\u003e\u003csup\u003e\u003cem\u003eE76K/+\u003c/em\u003e\u003c/sup\u003e stem cells and surrounding cells in tasquinimod- or vehicle-treated mice and found that the distance between these leukemia-initiating cells (CD150\u003csup\u003e+\u003c/sup\u003eCD11b\u003csup\u003e\u0026minus;\u003c/sup\u003eLy6G\u003csup\u003e\u0026minus;\u003c/sup\u003eCD3\u003csup\u003e\u0026minus;\u003c/sup\u003eB220\u003csup\u003e\u0026minus;\u003c/sup\u003eTer119\u003csup\u003e\u0026minus;\u003c/sup\u003eCD48\u003csup\u003e\u0026minus;\u003c/sup\u003e) (cyan) and the closest PMN-MDSCs (CD11b\u003csup\u003e+\u003c/sup\u003eLy6G\u003csup\u003e+\u003c/sup\u003e) (yellow) was significantly increased following tasquinimod treatment (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e8\u003c/span\u003eQ), confirming that the recruitment of PMN-MDSCs to the microenvironment of \u003cem\u003ePtpn11\u003c/em\u003e mutant stem cells was attributed to S100a9/S100a8 overexpressed by these leukemia-initiating cells.\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eWhile considerable progress has been made in understanding the etiology of JMML, numerous questions remain, particularly concerning the cellular and molecular mechanisms that confer a selective advantage to the original leukemia-initiating cells. Understanding these mechanisms can illuminate how leukemia-initiating cells persist in established disease and how these tumor precursor cells may be effectively targeted and eliminated therapeutically. By undertaking a comprehensive characterization of the transcriptomic landscapes across all stages of tumor cell development in \u003cem\u003ePtpn11\u003c/em\u003e mutation-associated JMML and substantiating our findings through experimental validation, we have discovered that \u003cem\u003ePtpn11\u003c/em\u003e-mutated stem cells (leukemia-initiating cells) are primed by the myeloid transcriptional program and that innate immune and inflammatory responses are aberrantly activated in these cells. These mutant stem cells exhibit strikingly heightened expression of evolutionarily conserved genes that are typically activated in mature myeloid cells during pathogen defense, including anti-microbial peptides (\u003cem\u003eCamp\u003c/em\u003e, \u003cem\u003eLcn2\u003c/em\u003e, \u003cem\u003eLyz2\u003c/em\u003e, \u003cem\u003eLtf\u003c/em\u003e, \u003cem\u003eChil3\u003c/em\u003e, and \u003cem\u003ePglyrp1\u003c/em\u003e) and essential trace metal-sequestering proteins (\u003cem\u003eS100a9\u003c/em\u003e and \u003cem\u003eS100a8\u003c/em\u003e), which also function as pro-inflammatory proteins triggering and amplifying innate immune responses \u003csup\u003e27,28\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe innate immune system is conventionally activated through the recognition of pathogen-associated molecular patterns (PAMPs) or damage-associated molecular patterns (DAMPs) proteins derived from host cells or damaged cells by pattern-recognition receptors, including TLRs, on myeloid immune cells. These patterns play an important role in recruiting and activating myeloid immune cells, initiating inflammation to eliminate invading microorganisms \u003csup\u003e27,28\u003c/sup\u003e. S100a9 and S100a8, which show the most significant overexpression in \u003cem\u003ePtpn11\u003c/em\u003e\u003csup\u003e\u003cem\u003eE76K/+\u003c/em\u003e\u003c/sup\u003e mutant stem cells, are also categorized as DAMPs. They preferentially heterodimerize to form calprotectin, which, like their monomeric/homodimeric forms, are endogenous ligands for TLR4, Rage/Ager, and CD33 \u003csup\u003e21,34,35\u003c/sup\u003e on myeloid effector cells, activating intracellular signaling pathways and culminating in the production of inflammatory cytokines, chemokines, and antimicrobial peptides. Interestingly, the expression of Rage/Ager and CD33 is also markedly elevated on \u003cem\u003ePtpn11\u003c/em\u003e mutant stem cells, producing autocrine effects. The autocrine effects of S100a9/S100a8 indeed contributed to the expansion of these leukemia initiating cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e8\u003c/span\u003eC). Moreover, given the well-characterized detrimental effects of inflammatory challenges on normal HSCs \u003csup\u003e36,37\u003c/sup\u003e, the pro-tumoral inflammatory milieu provides leukemia-initiating mutant stem cells with a competitive advantage over normal counterparts, ultimately resulting in their clonal dominance.\u003c/p\u003e \u003cp\u003eS100a9 and S100a8 may also contribute to immune evasion of JMML-initiating mutant stem cells by chemoattracting and expanding immunosuppressive MDSCs in the microenvironment. MDSCs are classically linked to immunosuppression, inflammation, and cancer, profoundly inhibiting T cell- and NK cell-mediated antitumor immunity through various mechanisms \u003csup\u003e27,31,32\u003c/sup\u003e. S100a9 is crucial for MDSC recruitment as MDSC accumulation in tumors is abolished in S100a9-null mice \u003csup\u003e38\u003c/sup\u003e, and expression of S100a9 in transgenic mice drives expansion and activation of MDSCs \u003csup\u003e35\u003c/sup\u003e. These immune suppressive cells can also secrete abundant S100a9/S100a8 heterodimers, bind to their own surface receptors and nurture an autocrine feedback loop that sustains MDSC recruitment, thereby maintaining immune suppression within the local microenvironment \u003csup\u003e21\u003c/sup\u003e. Moreover, S100a9 also contributes to anti-tumor immunity by inhibiting dendritic cell differentiation \u003csup\u003e38\u003c/sup\u003e. \u003cem\u003ePtpn11\u003c/em\u003e\u003csup\u003e\u003cem\u003eE76K/+\u003c/em\u003e\u003c/sup\u003e mutant stem cells indeed demonstrate a strong ability to attract MDSCs, and this chemoattracting effect is diminished by the inhibitor of S100a9/S100a8 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e8\u003c/span\u003eE and \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e8\u003c/span\u003eQ). Furthermore, administration of the S100a9/S100a8 inhibitor impedes leukemia development from \u003cem\u003ePtpn11\u003c/em\u003e\u003csup\u003e\u003cem\u003eE76K/+\u003c/em\u003e\u003c/sup\u003e mutant stem cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e8\u003c/span\u003eF-\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e8\u003c/span\u003eP). These results strongly suggest that the overexpression of S100a9 and S100a8 by \u003cem\u003ePtpn11\u003c/em\u003e- mutated stem cells plays a pivotal role in the initial leukemogenic process.\u003c/p\u003e \u003cp\u003eFurther investigations are necessary to elucidate how the \u003cem\u003ePtpn11\u003c/em\u003e\u003csup\u003e\u003cem\u003eE76K\u003c/em\u003e\u003c/sup\u003e mutation instigates a myeloid-specific transcriptional program and co-opts innate immune responses in the mutated stem cells. Shp-2 (encoded by \u003cem\u003ePtpn11\u003c/em\u003e) is predominantly localized to the cytosol and plays a prominent positive role in Ras signaling \u003csup\u003e9,10\u003c/sup\u003e. Since other genes that are mutated in JMML are also clustered in the Ras signaling pathway, it is conceivable that the \u003cem\u003ePtpn11\u003c/em\u003e mutation causes pathogenic effects mainly through the Ras pathway. However, Shp-2 is also localized to the nucleus and the mitochondrion \u003csup\u003e39\u0026ndash;41\u003c/sup\u003e. There is therefore a possibility that the \u003cem\u003ePtpn11\u003c/em\u003e\u003csup\u003e\u003cem\u003eE76K\u003c/em\u003e\u003c/sup\u003e mutation influences myeloid-specific transcriptomic activities through its nuclear and/or metabolic functions. The role of mutant Shp-2 in different cellular compartments may reveal novel avenues for understanding the diverse molecular mechanisms underpinning the aberrant activation of the myeloid transcriptional program in \u003cem\u003ePtpn11\u003c/em\u003e-mutated stem cells. Considering the distinctive subcellular localization of Shp-2 compared to other oncoproteins associated with JMML, it is important to ascertain whether dysregulated innate immune responses are also implicated in other JMML subtypes.\u003c/p\u003e \u003cp\u003eAnother noteworthy finding of this study is the dysregulation of ribosomal biogenesis and function in \u003cem\u003ePtpn11\u003c/em\u003e\u003csup\u003e\u003cem\u003eE76K/+\u003c/em\u003e\u003c/sup\u003e leukemic cells consistently throughout all stages, including leukemia-initiating stem cells. Several ribosomal small and large subunit proteins displayed upregulation in \u003cem\u003ePtpn11\u003c/em\u003e\u003csup\u003e\u003cem\u003eE76K/+\u003c/em\u003e\u003c/sup\u003e leukemic cells, consistent with the elevated protein translation essential for robust tumor cell growth. Intriguingly, there was a simultaneous decrease in the expression of certain ribosomal proteins. Recent research has revealed the heterogeneity of ribosomes, with different ribosome types displaying preferences for translating specific subsets of mRNAs \u003csup\u003e22,23\u003c/sup\u003e. Diminished expression of ribosomal proteins has the potential to disrupt ribosome formation and function. This can also contribute to malignancies through several mechanisms. The impairment in ribosomes can impact the synthesis of crucial regulatory proteins involved in cell growth, differentiation, and maturation, such as the tumor suppressor p53 \u003csup\u003e42\u0026ndash;44\u003c/sup\u003e. Moreover, reduced expression of specific ribosomal proteins and perturbed ribosome function can induce chronic ribosomal stress, triggering cellular dysfunctions and genomic instability \u003csup\u003e22\u003c/sup\u003e. However, the precise mechanisms by which the \u003cem\u003ePtpn11\u003c/em\u003e mutation selectively interferes with the expression of different ribosomal genes remain unclear.\u003c/p\u003e \u003cp\u003eIn summary, our findings reveal previously unappreciated mechanisms in the initial phase of JMML leukemogenesis, where leukemia-initiating mutant stem cells exploit innate immune signaling to gain a selective advantage and evade anti-tumor immunity. The significant dysregulation of proinflammatory proteins S100a9 and S100a8 underscores their pivotal role in orchestrating immune evasion and creating an inflammatory microenvironment conducive to leukemic progression. This insight offers new perspectives for developing therapeutic strategies to disrupt leukemia-initiating stem cells and improve treatment outcomes in JMML.\u003c/p\u003e"},{"header":"MATERIALS AND METHODS","content":"\u003cp\u003e\u003cb\u003eMice.\u003c/b\u003e\u003cem\u003ePtpn11\u003c/em\u003e\u003csup\u003e\u003cem\u003eE76K Neo/+\u003c/em\u003e\u003c/sup\u003e conditional knock-in mice were generated in our previous study \u003csup\u003e6\u003c/sup\u003e. \u003cem\u003eMx1-Cre\u003c/em\u003e\u003csup\u003e\u003cem\u003e+\u003c/em\u003e\u003c/sup\u003e (Strain #: 003556) \u003csup\u003e45\u003c/sup\u003e, mTmG dual-fluorescent reporter transgenic mice (Strain #: 007676) \u003csup\u003e33\u003c/sup\u003e, C57BL/6 mice (CD45.2\u003csup\u003e+\u003c/sup\u003e) (Strain #: 000664), and BoyJ mice (CD45.1\u003csup\u003e+\u003c/sup\u003e) (Strain #: 002014) were purchased from the Jackson Laboratory. All mice were kept under specific-pathogen-free conditions at Emory University Division of Animal Resources. Animal procedures complied with the NIH Guidelines for the Care and Use of Laboratory Animals and were approved by the Institutional Animal Care and Use Committee.\u003c/p\u003e \u003cp\u003e \u003cb\u003ePatient specimens.\u003c/b\u003e De-identified samples from \u003cem\u003ePTPN11\u003c/em\u003e-mutated patients with JMML and pediatric healthy controls normal BM biopsies were obtained from the University of California, San Francisco and the Aflac Cancer and Blood Disorders Center Biorepository of Children\u0026rsquo;s Healthcare of Atlanta. Samples were obtained after written, informed consent under locally approved institutional review board research protocols and in accordance with the Declaration of Helsinki.\u003c/p\u003e \u003cp\u003e \u003cb\u003eSingle-cell transcriptome profiling.\u003c/b\u003e Fresh BM cells were collected and pooled from \u003cem\u003ePtpn11\u003c/em\u003e\u003csup\u003e\u003cem\u003eE76K/+\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e/Mx1-Cre\u003c/em\u003e\u003csup\u003e\u003cem\u003e+\u003c/em\u003e\u003c/sup\u003e mice and \u003cem\u003ePtpn11\u003c/em\u003e\u003csup\u003e\u003cem\u003e+/+\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e/Mx1-Cre\u003c/em\u003e\u003csup\u003e\u003cem\u003e+\u003c/em\u003e\u003c/sup\u003e control mice (3 mice/group), followed by the execution of the recommended protocol for the scRNA-seq 10x Genomics platform using v3 chemistry. In brief, scRNA-seq raw reads were obtained following the standard protocol for Chromium Single Cell 3ʹ Reagent Kits v3. Subsequently, the CellRanger 1 software from 10x Genomics was employed to identify cell-discriminating barcode sequence markers and unique molecular identifier (UMI) markers for different mRNA molecules within each cell. This process aimed to quantify the high-throughput single-cell transcriptome and conduct data quality statistics and comparisons against the original genome. Next, the Seurat 2 software package was utilized for further quality control (QC) and processing of the CellRanger results. In the QC step, delocalized cells were filtered by fitting a generalized linear model. Subsequently, the distribution of nUMI (unique molecular identifier counts), nGene (number of detected genes), and percent.mito (percentage of mitochondrial genes) was assessed to filter out low-quality cells, such as double cells, multiple cells, or dead cells, leaving only qualified cells for further bioinformatics analyses.\u003c/p\u003e \u003cp\u003e \u003cb\u003et-distributed stochastic neighbor embedding (t-SNE) visualization and cell identification.\u003c/b\u003e The single-cell transcriptome underwent principal component analysis (PCA) for linear dimensionality reduction. Subsequently, the PCA results were visualized in a two-dimensional space using t-SNE, a non-linear dimensionality reduction technique. The Seurat platform's FindAllMarkers function was employed to identify marker genes for each cell classification relative to other cell populations. These identified genes serve as potential markers for each cell type. Visualization of the identified marker genes was carried out using the VlnPlot and FeaturePlot functions. Following the clustering process, the Single R platform was utilized to assign cell types based on published datasets \u003csup\u003e19,20\u003c/sup\u003e, thereby enhancing the accuracy of cell type classification.\u003c/p\u003e \u003cp\u003e \u003cb\u003eGene set enrichment analysis (GSEA).\u003c/b\u003e GSEA was conducted to identify genes associated with specified cell types such as HSCs (Hematopoietic Stem Cells) and GMPs (Granulocyte-Macrophage Progenitors). The analysis utilized the GSEA platform available at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.broadinstitute.org/gsea/index.jsp\u003c/span\u003e\u003cspan address=\"http://www.broadinstitute.org/gsea/index.jsp\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. To prepare input data for GSEA, the top 5000 variable genes in each group were selected using the Seurat \"FindVariableGenes\" function. Gene sets, including those from KEGG pathways and Gene Ontology (GO), were obtained from the molecular signatures database (MSigDB).\u003c/p\u003e \u003cp\u003e \u003cb\u003eSingle-cell regulatory network inference and clustering (SCENIC) analysis\u003c/b\u003e. SCENIC analyses were performed using version 1.1.2.2, corresponding to RcisTarget 1.2.1 and AUCell 1.4.1. The motifs database for RcisTarget and GRNboost was utilized with default parameters. In detail, the analysis involved identifying over-represented transcription factor binding motifs on a given gene list using the RcisTarget package. Subsequently, the AUCell package was employed to score the activity of each group of regulons in each cell. This process enabled the inference and clustering of regulatory networks at the single-cell level, offering insights into the regulatory landscape of the analyzed cell populations.\u003c/p\u003e \u003cp\u003eTo evaluate the cell type specificity of each predicted regulon, the regulon specificity score (RSS) was computed, employing the Jensen-Shannon divergence (JSD) as a measure of similarity between two probability distributions. Specifically, the JSD was calculated for each vector of binary regulon activity overlaps with the assignment of cells to specific cell types. The connection specificity index (CSI) for all regulons was determined using the scFunctions package, accessible at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/FloWuenne/scFunctions/\u003c/span\u003e\u003cspan address=\"https://github.com/FloWuenne/scFunctions/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cb\u003ePseudotime analysis.\u003c/b\u003e We utilized the Monocle2 package (v2.9.0) for inferring cell differentiation trajectories. The specific steps were as follows: First, we employed the importCDS function from the Monocle2 package to convert the Seurat object to the CellDataSet object. Next, the differentialGeneTest function was utilized to filter out ordering genes (genes with a q-value\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Then, we used the reduceDimension function to perform dimensionality reduction clustering. Finally, we applied the orderCells function to infer the differentiation trajectory.\u003c/p\u003e \u003cp\u003e \u003cb\u003eCell-cell communication analysis.\u003c/b\u003e We utilized CellPhoneDB (v2.0) to identify biologically relevant ligand-receptor interactions from single-cell transcriptomic data. We defined a ligand or receptor as 'expressed' in a particular cell type if 10% of the cells of that type exhibited non-zero read counts for the ligand/receptor encoding gene. Statistical significance was assessed by randomly shuffling the cluster labels of all cells and repeating the aforementioned steps, thereby generating a null distribution for each ligand-receptor (LR) pair in each pairwise comparison between two cell types. Following 1,000 permutations, \u003cem\u003ep\u003c/em\u003e-values were calculated using the normal distribution curve generated from the permuted LR pair interaction scores. To delineate networks of cell-cell communication, we connected any two cell types where the ligand was expressed in the former cell type and the receptor in the latter. The R package circlize was employed for visualizing the cell-cell communication networks.\u003c/p\u003e \u003cp\u003e \u003cb\u003eFluorescence-activated cell sorting (FACS) analysis and cell sorting.\u003c/b\u003e FACS analyses were performed on a Cytoflex flow cytometer (Beckman Coulter Life Sciences), following standard procedures. For HSC staining, BM cells were harvested, washed, and incubated for 30 min at 4\u0026deg;C in phosphate buffered saline (PBS) with 2% fetal bovine serum (FBS) containing the following antibodies: anti-Mac-1 PerCP/Cyanine5.5 (Biolegend, 101228, clone M1/70),anti-Gr-1 Pacific Blue (Biolegend,108430, clone RB6-8C5), anti-Ter119 PE (Biolegend, 116208, clone TER-119), anti-B220 PE (eBiosciences, 12-0452-83, clone RA3-6B2), anti-CD3 PE (BD Biosiences Pharmingen, 553064, clone 145-2C11), anti-Mac-1 PE (Biolegend, 101208, clone M1/70), anti-Gr-1 PE (eBiosciences, 12-5931-83, clone RB6-8C5), anti-Scal-1 PE/Cyanine7 (Biolegend, 108114, clone D7), anti-c-Kit APC/Cyanine7 (Biolegend, 105826, clone 2B8), anti-CD48 Percp (eBioscience, 46-0481-80, clone HM48-1), anti-CD150 AF647 (Biolegend, 115918, clone TC15-12F12.2). HSCs were defined as Lin\u003csup\u003e\u0026minus;\u003c/sup\u003eSca-1\u003csup\u003e+\u003c/sup\u003ec-Kit\u003csup\u003e+\u003c/sup\u003eCD150\u003csup\u003e+\u003c/sup\u003eCD48\u003csup\u003e\u0026minus;\u003c/sup\u003e. For apoptosis analyses, fresh BM cells were stained for HSCs, and then incubated with Annexin V-BV605 (BD Biosiences Pharmingen, 563974, clone Annexin V) (0.7 \u0026micro;g/ml) and 4',6-diamidino-2-phenylindole (DAPI) (0.3 \u0026micro;g/mL). For the cell cycle analysis, fresh BM cells were stained for HSCs as above, fixed and permeabilized using a Cytofix/Cytoperm kit (BD Biosciences). The samples were then stained with Ki-67 BV605 (Biolegend, 652413) and further incubated with Hoechest 33342 (20 \u0026micro;g/ml). Data were collected on a Beckman Coulter CytoFLEX flow cytometer and analyzed with FlowJo (Tree Star). For cell sorting, BM cells were first lineage-depleted using a lineage depletion kit. Cells were then stained with fluorochrome-labeled antibodies. Sorting of specific cell populations was conducted using BD FACSAia II following standard gating strategies.\u003c/p\u003e \u003cp\u003e \u003cb\u003eColony-forming unit (CFU) assay.\u003c/b\u003e Freshly sorted HSCs (5x10\u003csup\u003e2\u003c/sup\u003e cells) were plated in triplicate in 35-mm dishes in 0.9% methylcellulose IMDM medium containing 15% FBS, Gln (10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e M), β-mercaptoethanol (3.3x10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e M), SCF (50 ng/ml), IL-3 (20 ng/ml), IL-6 (20 ng/ml), and EPO (3 Units/ml). After 12 days of incubation at 37\u0026deg;C in 5% CO2, myeloid colonies derived were counted under an inverted microscope.\u003c/p\u003e \u003cp\u003e \u003cb\u003eTransmigration assay.\u003c/b\u003e Transmigration assays were conducted with 5.0 \u0026micro;m pore transwells (Corning). Briefly, HSCs freshly sorted from WT and \u003cem\u003ePtpn11\u003c/em\u003e\u003csup\u003e\u003cem\u003eE76K/+\u003c/em\u003e\u003c/sup\u003e mice were suspended in in StemSpan media ( STEMCELL Technologies) containing 20% FBS, 50 ng/mL SCF, 50 ng/mL Flt3L, 20 ng/mL IL-3, and 20 ng/mL IL-6. Six hundred microliters of cell suspension (2x10\u003csup\u003e3\u003c/sup\u003e cells) were loaded to lower chambers. The S100a9/S100a8 inhibitor tasquinimod was then added to the chamber (5.0 \u0026micro;M). CD11b\u003csup\u003e+\u003c/sup\u003eLy6G\u003csup\u003e+\u003c/sup\u003e myeloid cells freshly sorted from normal C57BL6 mice were labeled with carboxyfluorescein succinimidyl ester (CFSE) (1.0 \u0026micro;M), washed and resuspended at 1x10\u003csup\u003e6\u003c/sup\u003e cells/ml in the same medium as that in lower chambers but without the inhibitor. One hundred microliters of cell suspension were added to upper chambers. Cells were allowed to migrate across the membrane at 37\u0026deg;C in 5% CO\u003csub\u003e2\u003c/sub\u003e for 2 hours. Both input cells, cells collected from the upper chamber, and cells collected from the lower chambers were analyzed by FACS. Migration efficiency was then calculated.\u003c/p\u003e \u003cp\u003e \u003cb\u003eImmunofluorescence staining.\u003c/b\u003e Tissue sections were prepared from paraffin-embedded mouse femurs, deparaffinized, and rehydrated following standard protocols. The slides were stained with the following antibodies following standard procedures: anti-CD150 AF647 (Biolegend, 115918, clone TC15-12F12.2), anti-CD11b PE (Biolegend, 101208, clone M1/70), anti-Ly-6G AF488 (Biolegend, 127625, clone 1A8), anti-Ter119 FITC (Biolegend, 116206, clone TER-119), anti-CD3 FITC (Biolegend, 100306, clone 145-2C11), anti-B220 FITC (Biolegend, 103206, clone RA3-6B2), and anti-CD48 FITC (Biolegend, 103403, clone HM48-1) antibodies. Images were acquired using Leica Stellaris 8 and processed with ImageJ 1.54f software.\u003c/p\u003e \u003cp\u003e \u003cb\u003eStatistics and reproducibility.\u003c/b\u003e Unless otherwise noted, data are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD of biological replicates (independent animals/independent experiments) (n numbers are shown on graphics or specified in figure legends). Unpaired two-tailed Student\u0026rsquo;s \u003cem\u003et\u003c/em\u003e-test was used for the statistical comparison of the two groups. * \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05; ** \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01; *** \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, **** \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001.\u003c/p\u003e "},{"header":"Declarations","content":"\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eData availability\u003c/h2\u003e \u003cp\u003eThe raw scRNA-seq data generated in this study have been deposited in the Gene Expression Omnibus database under accession code GSE266821.\u003c/p\u003e \u003c/div\u003e\u003cp\u003e \u003ch2\u003eCompeting financial interests\u003c/h2\u003e \u003cp\u003eThe authors declare no competing financial interests.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eAuthor contributions\u003c/h2\u003e \u003cp\u003eH.Z., P.Z., Z.T., W.M.Y., and J.W. conducted the research and summarized the data. E.S., C.C.P., S.C., D.S.W., and S.M.F. provided critical reagents and/or advice, discussed the work, and edited the manuscript. H.Z. and C.K.Q. designed the experiments and directed the entire study. H.Z. and C.K.Q. wrote the manuscript with input from the other authors.\u003c/p\u003e\u003ch2\u003eAcknowledgments\u003c/h2\u003e \u003cp\u003eThe authors are grateful for the technical support from Pediatrics/Winship Flow Cytometry shared resources. This work was supported by the National Institutes of Health grants HL130995, HL162725 and CA275964 (to C.K.Q.).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003e1. Chang, T. Y., Dvorak, C. C. \u0026amp; Loh, M. L. 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Inducible gene targeting in mice. \u003cem\u003eScience\u003c/em\u003e \u003cb\u003e269\u003c/b\u003e, 1427\u0026ndash;1429 (1995).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"nature-portfolio","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Nature Portfolio","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"ejp","reportingPortfolio":"","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"JMML, PTPN11, Leukemia-initiating cell, Hematopoietic stem cell, Innate immunity, Inflammation, S100a9, S100a8","lastPublishedDoi":"10.21203/rs.3.rs-4450642/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4450642/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eJuvenile myelomonocytic leukemia (JMML), a clonal hematologic malignancy, originates from mutated hematopoietic stem cells (HSCs). The mechanism sustaining the persistence of mutant stem cells, leading to leukemia development, remains elusive. In this study, we conducted comprehensive examination of gene expression profiles, transcriptional factor regulons, and cell compositions/interactions throughout various stages of tumor cell development in \u003cem\u003ePtpn11\u003c/em\u003e mutation-associated JMML. Our analyses revealed that leukemia-initiating \u003cem\u003ePtpn11\u003c/em\u003e\u003csup\u003e\u003cem\u003eE76K/+\u003c/em\u003e\u003c/sup\u003e mutant stem cells exhibited \u003cem\u003ede novo\u003c/em\u003e activation of the myeloid transcriptional program and aberrant developmental trajectories. These mutant stem cells displayed significantly elevated expression of innate immunity-associated anti-microbial peptides and pro-inflammatory proteins, particularly \u003cem\u003eS100a9\u003c/em\u003e and \u003cem\u003eS100a8\u003c/em\u003e. Biological experiments confirmed that S100a9/S100a8 conferred a selective advantage to the leukemia-initiating cells through autocrine effects and facilitated immune evasion by recruiting and promoting immune suppressive myeloid-derived suppressor cells (MDSCs) in the microenvironment. Importantly, pharmacological inhibition of S100a9/S100a8 signaling effectively impeded leukemia development from \u003cem\u003ePtpn11\u003c/em\u003e\u003csup\u003e\u003cem\u003eE76K/+\u003c/em\u003e\u003c/sup\u003e mutant stem cells. These findings collectively suggest that JMML tumor-initiating cells exploit evolutionarily conserved innate immune and inflammatory mechanisms to establish clonal dominance.\u003c/p\u003e","manuscriptTitle":"Prototypical innate immune mechanism hijacked by leukemia-initiating mutant stem cells for selective advantage and immune evasion in Ptpn11-associated juvenile myelomonocytic leukemia","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-08-02 09:14:41","doi":"10.21203/rs.3.rs-4450642/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"nature-communications","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"NCOMMS","sideBox":"Learn more about [Nature Communications](http://www.nature.com/ncomms/)","snPcode":"","submissionUrl":"https://mts-ncomms.nature.com/","title":"Nature Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature Communications","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"3b609a8b-9262-4dc9-ad75-150b975d547f","owner":[],"postedDate":"August 2nd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":33432456,"name":"Biological sciences/Cancer/Haematological cancer"},{"id":33432457,"name":"Biological sciences/Stem cells/Haematopoietic stem cells"}],"tags":[],"updatedAt":"2024-08-02T09:14:41+00:00","versionOfRecord":[],"versionCreatedAt":"2024-08-02 09:14:41","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4450642","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4450642","identity":"rs-4450642","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}