Abstract
Leukaemia stem cells (LSCs) in acute myeloid leukaemia present a considerable treatment challenge due to their resistance to chemotherapy and immunosurveillance. The connection between these properties in LSCs remains poorly understood. Here we demonstrate that inhibition of tyrosine phosphatase SHP-1 in LSCs increases their glycolysis and oxidative phosphorylation, enhancing their sensitivity to chemotherapy and vulnerability to immunosurveillance. Mechanistically, SHP-1 inhibition leads to the upregulation of phosphofructokinase platelet (PFKP) through the AKT–β-catenin pathway. The increase in PFKP elevates energy metabolic activities and, as a consequence, enhances the sensitivity of LSCs to chemotherapeutic agents. Moreover, the upregulation of PFKP promotes MYC degradation and, consequently, reduces the immune evasion abilities of LSCs. Overall, our study demonstrates that targeting SHP-1 disrupts the metabolic balance in LSCs, thereby increasing their vulnerability to chemotherapy and immunosurveillance. This approach offers a promising strategy to overcome LSC resistance in acute myeloid leukaemia.
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Data availability
scRNA-seq supporting the findings of this study have been deposited at the GEO under accession code GSE244234. Clinical data were obtained from the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) and The Cancer Genome Atlas (TCGA) database of the AML project. The paired human AML scRNA-seq data were obtained from Zenodo (https://zenodo.org/,4905250). The paired human AML RNA-seq data were obtained from GEO studies GSE199451 and GSE83533. The original western blot images have been provided and are publicly available. All other data supporting the findings of this study are available from the corresponding author on reasonable request. Source data are provided with this paper.
Code availability
All codes and packages used in this study are either publicly available or are available at GitHub (https://github.com/fruitman1984/SHP1-2023).
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Acknowledgements
We acknowledge support from the National Key Research and Development Program of China (2022YFA1103300 and 2022YFA1104100 to Meng Zhao), the National Natural Science Foundation of China (82325002 to Meng Zhao, 92268205 to L.J., 32370845 and 82300181 to X.X., and 82300209 to Y.Y.), the Guangdong Innovative and Entrepreneurial Research Team Program (2019ZT08Y485 to L.J.), the Outstanding Youths Development Scheme of Nanfang Hospital, Southern Medical University (2020J001 to S.R.) and the Sanming Project of Medicine in Shenzhen (SZSM201911004 to Meng Zhao). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the paper.
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Contributions
X.X., Y.Y., W.Z., W.M. and C.H. designed and performed most of the experiments, analysed the data and generated the figures. Minyi Zhao and Qifa Liu contributed to the clinical data. G.Q. and J.X. contributed to the bioinformatic analysis. X.W. and Qiong Liu contributed to the plasmid construction and pull-down assays. F.T. contributed to ChIP–seq analysis. J.M.P., S.R. and X.K. contributed to the scientific discussion and paper preparation. L.J. and Meng Zhao supervised the study and wrote the paper.
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Extended data
Extended Data Fig. 1 SHP-1 inhibition enhances the chemotherapy efficacy in AML.
a, Quantification of western blots of SHP-1 and pSHP-1 levels in MLL-AF9 AML mice with or without chemotherapy as illustrated (n = 3 mice per group) (Related to Fig. 1b). b, Quantification of SHP-1 expression in AML patients from GEO dataset. c,d, The proportion (c) and quantification (d) of SHP-1 in human LSPCs. Data were adapted from Petti et al. PMID: 35019859 (4905250, Zenodo). e, Western blots of SHP-1 and pSHP-1 in human CD34+CD38– LSPCs from paired AML patients. β-actin was a loading control. Eight individual AML patients were presented. f, Cell viability of Shp-1Δ/Δ and Shp-1f/f AML cells at 48 hours after indicated treatments (n = 3 mice per group). g-i, The cell number (g), apoptosis rate (h) of HSCs, and the colony-forming ability (i) in the bone marrow of indicated mice (n = 3 mice per group). j, Western blots of indicated proteins in Shp-1Δ/Δ AML cells from MLL-AF9-induced AML mice with or without chemotherapy as illustrated. β-actin was a loading control. 1#, 2#, and 3# indicated three individual mice. k, Cell growth of Shp-1Δ/Δ MLL-AF9 AML cells with or without SHP-2 knock-down and chemotherapy as illustrated (n = 3 individual experiments). l, SHP-2 expression in the TARGET dataset (n = 94 in remission group, n = 38 in relapse group, n = 155 in death with disease group). m, Schematic experiment strategy for MLL-AF9-induced AML mice. Chemotherapy (DOX, doxorubicin; Ara-C, cytarabine). n-o, The percentage of GFP+ leukaemic cells in peripheral blood (n), and Kaplan-Meier survival curves (o) of AML mice with indicated treatment (n = 5 mice per group). Error bars represent the means ± s.d. The P values in o were determined using Kaplan–Meier survival analysis. The P values in b, d were determined by paired t test, The P values in f were determined by unpaired two-tailed Student’s t test.The P values in a, g, h, i k, and n were determined by a one-way ANOVA followed by Dunnett’s test.
Extended Data Fig. 2 The effects of SHP-1 in LSCs and HSCs.
a, Schematic strategy to quantify the functional LSCs in Shp-1Δ/Δ and Shp-1f/f AML mice without chemotherapy. b,c, Kaplan-Meier survival curves of AML mice that received the indicated numbers of Shp-1Δ/Δ or Shp-1f/f AML cells (b), and plot of competitive repopulating units (CRUs) calculated using ELDA (c) (n = 5 mice per group). d, Cell growth of Shp-1Δ/Δ and Shp-1f/f MLL-AF9 AML cells. e, Representative images (left) and quantification (right) of the colony-forming cells of Shp-1Δ/Δ and Shp-1f/f AML cells (n = 3 individual experiments). f, Cell viability of AML cell lines after SHP-1 knock-down at 48 hours after indicated treatments. g-l, The cell number (g), apoptosis rate (h) of HSCs, and the colony-forming ability of normal cells (i) in the bone marrow; and the frequency of T cells (j), B cells (k), and myeloid cells (l) in peripheral blood of AML mice with indicated treatments (n = 3 mice per group in g, h, i and n = 5 mice per group in j, k, l). m, Western blots (left) and quantification (right) of SHP-1 and pSHP-1 levels in HSCs from normal mice and LSCs from MLL-AF9 AML mice with or without chemotherapy (n = 4 mice per group). Western blots from 2 individual mice were presented. Error bars represent the means ± s.d. The P values of c were determined using the ELDA platform. The P values d and e were determined by unpaired two-tailed Student’s t test. The P values in f, g, h, i, j, k, l and m were determined by a one-way ANOVA followed by Dunnett’s test.
Extended Data Fig. 3 The effects of SHP-1 in mouse and human AML.
a, Western blot of SHP-1 in C1498 AML cells. β-actin was a loading control. Knock-down (KD). b,c, Cell viability of C1498 AML cells at 48 hours after indicated treatments. DOX, doxorubicin; Ara-C, cytarabine. d, Schematic depicting the strategy to analyse the function of SHP-1 in Hoxa9-Meis1-induced AML mice with or without chemotherapy (doxorubicin and cytarabine). e,f, The percentage of GFP+ leukaemic cells in peripheral blood (e) and Kaplan-Meier survival curves (f) of AML mice with indicated treatments (n = 5 mice per group). g, Western blot of SHP-1 in primary AML cells with or without SHP-1 knock-down. β-actin was a loading control. 10 individual AML patients were presented. h, Representative FACS plot of human CD45+CD33+ and CD34+CD38– cells in the bone marrow of B-NDG recipients engrafted with human AML cells at 8w after transplantation. Error bars represent the means ± s.d. The P values in f were determined by Kaplan–Meier survival analysis. The P values in b, c were determined by unpaired two-tailed Student’s t test. The P values in e were determined by a one-way ANOVA followed by Dunnett’s test.
Extended Data Fig. 4 Single-cell atlas of mouse and human AML cells.
a, Schematic strategy for scRNA-seq. b, MLL-AF9 expression in 16 cell clusters, including leukaemic cell clusters crLSCs (clusters 1, 5, and 6), cycling LSCs (clusters 0, 2, and 3), Blast leukaemic cells (clusters 4, 7, 8, and 11), and normal hematopoietic cells (clusters 9, 10, 12, 13, 14, and 15). c, Representative FACS plots (left) and the absolute number (right) of Shp-1f/f bone marrow cells from AML mice for sc-RNAseq. d, The frequency of each cell clusters. e, Pearson’s correlation (colour bar) of the average gene expression profiles for cell clusters. f, UMAP of bone marrow cells from Shp-1f/f and Shp-1Δ/Δ AML mice (n = 5 mice per group). g, Pathways enriched by upregulated genes in crLSCs compared to cycling LSCs of MLL-AF9-induced AML mice. h, Pseudotime curve analysis of MLL-AF9 AML cells. i, Hoxa9-Meis1 expression in cell clusters. j, UMAP of bone marrow cells from Hoxa9-Meis1 AML mice (n = 5 mice per group). k, The frequency of cell clusters in Hoxa9-Meis1-induced AML mice. l, Cluster of differentially expressed genes (rows) in the cell clusters (columns) in Hoxa9-Meis1-induced AML cells. m, GSEA signature enrichment plots for comparison between cycling LSCs and crLSCs in Hoxa9-Meis1-induced AML mice. The P value was derived from Fisher’s exact test. n, The expression profile of ex vivo drug sensitivity genes in crLSCs and cycling LSCs in Hoxa9-Meis1-induced AML. o, Cluster of differentially expressed genes (rows) in the cell clusters (columns) in human LSPCs and cycling-LSPCs. p, GSEA signature enrichment plots for comparison between human LSPCs and cycling-LSPCs in indicated pathways. q, The expression profile of ex vivo drug sensitivity genes of human LSPCs and cycling-LSPCs. r-t, Quantification of SHP-1 expression in crLSCs and cycling LSCs from MLL-AF9 (r), Hoxa9-Meis1(s) and human (t) AML cells. Error bars represent the means ± s.d. The P values in c, r, s and t were determined by unpaired two-tailed Student’s t test.
Extended Data Fig. 5 Functional characterization of crLSCs in mouse AML.
a-c, The reactive oxygen species (ROS) level (a), oxygen consumption rate (OCR) (b), and extracellular acidification rate (ECAR) (c) in crLSCs, cycling LSCs, and Blast leukaemic cells (n = 5 mice). d, Phagocytic index by macrophages of crLSCs, cycling LSCs, and Blast leukaemic cells phagocytosed by macrophages. (n = 4 mice per group). e, Quantification of the killing efficiency of NK cells to crLSCs, cycling LSCs, and Blast leukaemic cells (n = 3 replicates from 6 mice per group). f, Representative FACS plot of crLSCs, cycling LSCs, and Blast leukaemic cells in the bone marrow of Hoxa9-Meis1-induced AML mice. g, The heatmap of stemness and differentiation genes expression levels in crLSCs, cycling LSCs, and Blast cells of Hoxa9-Meis1-induced AML mice. h, Cell viability of crLSCs and cycling LSCs from Hoxa9-Meis1-induced AML mice at 48 hours after Ara-C or DOX treatments (n = 3- mice per group). i, Schematic treatment strategy, Kaplan-Meier survival curves, and plot of competitive repopulating units (CRUs) calculated using ELDA for crLSCs and cycling LSCs from Hoxa9-Meis1-induced AML mice with or without chemotherapy (n = 5 mice per group). j-l, The absolute number (j), apoptosis rate (k), and cell cycle (l) of crLSCs, cycling LSCs, and Blast leukaemic cells in the bone marrow of Hoxa9-Meis1-induced AML mice with or without chemotherapy (n = 3-4 mice per group). m, Pathways enriched by upregulated genes in cycling LSCs compared to crLSCs in Hoxa9-Meis1 AML model. Error bars represent the means ± s.d. The P values in a, d, e, j, k and l were determined by a one-way ANOVA followed by Dunnett’s test. The P values in h were determined by unpaired two-tailed Student’s t test. The P values of i were determined using the ELDA platform.
Extended Data Fig. 6 The effects of SHP-1 and SHP-2 in AML cells.
a, GO term analysis of downregulated genes in Shp-1Δ/Δ crLSCs compared to Shp-1f/f crLSCs from MLL-AF9-induced AML mice. b, GSEA signature enrichment plots for comparison between Shp-1f/f crLSCs and Shp-1Δ/Δ crLSCs in indicated pathways in MLL-AF9 AML model. The P value was derived from Fisher’s exact test. c, The cell cycle analysis of cycling LSCs and Blast leukaemic cells in the bone marrow of Shp-1f/f or Shp-1Δ/ΔMLL-AF9-induced AML mice. d,e, The absolute number of cycling LSCs (d) and Blast leukaemic cells (e) in Shp-1f/f or Shp-1Δ/Δ AML cells from bone marrow of MLL-AF9-induced AML mice with or without chemotherapy (n = 3-5 mice per group). f-j, The relative glucose uptake (f), intracellular ATP concentration (ATP) (g), intracellular pyruvate concentration (PA) (h), extracellular lactate level (Lactate) (i) and intracellular LDH activity (LDH) (j) in MLL-AF9 AML cells with SHP-1 and/or SHP-2 knock-down with or without chemotherapy (n = 3 mice per group). k,l, Phagocytic index by macrophages of MLL-AF9 AML cells with indicated treatments (n = 5 mice per group). m, Quantification of the killing efficiency of NK cells to MLL-AF9 AML cells with indicated treatments (n = 3 mice per group). Error bars represent the means ± s.d. The P values in c, d, e, f, g, h, i, j, k, l and m were determined by a one-way ANOVA followed by Dunnett’s test.
Extended Data Fig. 7 SHP-1 regulates the AKT-β-catenin-PFKP axis.
a, The Bayesian network plot showing the relationship between pathways enriched in Shp-1Δ/Δ crLSCs compared to Shp-1f/f crLSCs from MLL-AF9-induced AML mice. b, IC50 values of MLL-AF9 AML cells with AKT inhibitor (MK2206) and chemotherapy as indicated (n = 5 replicates). DOX, doxorubicin; Ara-C, cytarabine. c, Western blots of PFKM and PFKL in Shp-1f/f and Shp-1Δ/Δ crLSCs. β-actin was a loading control. 1#, 2#, and 3# indicated three individual mice. d, Western blots of PFKP in AML cells from MLL-AF9 AML mice with or without TPI treatment. 1#, 2# and 3# indicated three individual mice. e, Schematic of experimental strategy, western blots, and quantification of AKT, β-catenin-pSer552, and PFKP proteins in MLL-AF9 AML cells with indicated treatment. 1#, 2# and 3# indicated three individual mice. β-actin was a loading control. f, Western blot of exogenously overexpressed AKT and its mutant (T308A, S473A, 2 A) proteins in 293 T cells. β-actin was a loading control. g, Westen blots of AKT and SHP-1 in GST pulldown assay. GST-SHP-1 and His-AKT were purified from bacteria. h, Western blots of indicated proteins in in vitro de-phosphorylation assay with AKT and SHP-1. HA-SHP-1 and Flag-AKT were purified from 293 T cells. i, Western blots of indicated protein with AKT, AKT mutants, and SHP-1 overexpression in 293 T cells. j, β-catenin-pSer552 bound to Pfkp genes in the promoter region. k, Relative luciferase activity of Pfkp reporter with or without β-catenin overexpression in 293 T cells. Error bars represent the means ± s.d., and the P values in b, e and k were determined by a one-way ANOVA followed by Dunnett’s test.
Extended Data Fig. 8 SHP-1-PFKP axis regulates energy metabolism and chemosensitivity of human AML cells.
a, Western blots of indicated proteins in primary AML cells from relapsed patients with SHP-1 knock-down. β-actin was a loading control. KD1 and KD2 indicated two individual SHP-1 knock-down shRNA sequences. b, Western blot of PFKP in THP-1 cells with PFKP overexpression. β-actin was a loading control. c-h, Intracellular F1,6BP concentration (c), relative glucose uptake (d), intracellular ATP concentration (ATP) (e), intracellular pyruvate concentration (PA) (f), extracellular lactate level (g), and intracellular LDH activity (LDH) (h) in THP-1 cells after PFKP overexpression. i, The cell death rate in AML cell lines at 48 hours after indicated treatments (n = 4 independent experiments). Error bars represent the means ± s.d., and the P values in c, d, e, f, g, h were determined by unpaired two-tailed Student’s t-test.
Extended Data Fig. 9 Shp-1 deletion restores innate immunosurveillance in crLSCs in a PFKP-dependent manner.
a,b, Representative FACS plots (left) and quantification (right) of CD47 (a) and CD24 (b) expression in Shp-1Δ/Δ and Shp-1f/f crLSCs with or without PFKP knock-down (n = 4-5 mice per group). c, Representative photomicrographs of phagocytosis by macrophages (left), and quantification (right) of Shp-1Δ/Δ and Shp-1f/f crLSCs (n = 6 mice per group). Scale bars, 50 μm. d, The cell viability of sorted Shp-1Δ/Δ and Shp-1f/f crLSCs with or without PFKP knock-down (n = 3 mice per group). e, Representative photomicrographs (left) and quantification (right) of phagocytic index by macrophages of Shp-1Δ/Δ and Shp-1f/f crLSCs with or without PFKP knock-down (n = 6 mice per group). Scale bars, 50 μm. f,g, The phagocytic index by macrophages (f) and the killing efficiency of NK cells (g) to THP-1 cells after SHP-1 knock-down. KD1, and KD2 indicated two individual SHP-1 knock-down shRNA (n = 3-5 independent experiments). h,i, The phagocytic index by macrophages (h) and the killing efficiency of NK cells (i) to THP-1 cells with or without PFKP overexpression (n = 3-5 independent experiments). j, The heatmap of relative mRNA expression of immune-checkpoint genes in primary AML cells. 4#, 5# idicated two individual AML patients (n = 3 replicates for each patient). k,l, The percentage of human CD45+ leukaemic cells in peripheral blood (k), and survival curves (l) of B-NDG mice with PBMC infusion and chemotherapy treatment as indicated (n = 5 mice per group). Error bars represent the means±s.d. The P values in l were determined by Kaplan–Meier survival analysis. The P values in c, h, i were determined by unpaired two-tailed Student’s t test. The P values in a, b, d, e, f, g, k were determined by a one-way ANOVA followed by Dunnett’s test.
Extended Data Fig. 10 SHP-1-PFKP axis regulates c-MYC protein and CD47 expression in AML cells.
a, Western blots of c-MYC in crLSCs, cycling LSCs, and Blast cells from MLL-AF9 model. β-actin was a loading control. 1# and 2# indicated two individual mice. b, The quantification of indicated protein levels in Shp-1Δ/Δ and Shp-1f/f MLL-AF9 AML cells with or without PFKP knock-down (Related to Fig. 7c). β-actin was a loading control. (n = 3 independent experiments). c, The quantification of PFKP, c-MYC-pT58, c-MYC-pS62, and c-MYC protein levels in THP-1 cells with PFKP knock-down or PFKP overexpression (Related to Fig. 7d, n = 4 independent experiments). d, Western blot and quantification of c-MYC in the cytoplasm and nucleus of THP-1 cells with PFKP knock-down or PFKP overexpression. e, The relative mRNA expression of c-MYC in THP-1 cells with or without PFKP knock-down and PFKP overexpression. f, Western blots of His-PFKP, HA-GSK3β, and Flag-c-MYC in His-pulldown assay. g, Western blots of GST-c-MYC and HA-PFKP in GST-pulldown assay. h, Western blots of His-GSK3β and HA-PFKP in His-pulldown assay. i, Western blot and quantification of c-MYC in the cytoplasm and nucleus of Shp-1Δ/Δ and Shp-1f/f crLSCs. j, ChIP-qPCR of CD47 promoter region with anti-c-MYC antibody or IgG control in MLL-AF9 AML cells. k, Western blots of c-MYC and SHP-1 (left) and quantification of CD47– cells (right) in Shp-1Δ/Δ and Shp-1f/f crLSCs with or without c-MYC overexpression (n = 3 mice). l, Western blots of PFKP and c-MYC (left) and quantification of CD47– cells (right) in crLSCs with or without PFKP or c-MYC overexpression (n = 3 mice). Error bars represent the means ± s.d. The P values in b, c, d, e, i, k, and l were determined by a one-way ANOVA followed by Dunnett’s test. The P values in j were determined by unpaired two-tailed Student’s t-test.
Supplementary information
Supplementary Fig. 1
Gating strategy for Figs. 1–7 and Extended Data Figs. 1–3 and 5–9.
Supplementary Table
Clinical sample information.
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Xu, X., Yu, Y., Zhang, W. et al. SHP-1 inhibition targets leukaemia stem cells to restore immunosurveillance and enhance chemosensitivity by metabolic reprogramming. Nat Cell Biol 26, 464–477 (2024). https://doi.org/10.1038/s41556-024-01349-3
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DOI: https://doi.org/10.1038/s41556-024-01349-3
