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RNA sequestration in P-bodies sustains myeloid leukaemia

Nature Cell Biology (2024)Cite this article

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Abstract

Post-transcriptional mechanisms are fundamental safeguards of progenitor cell identity and are often dysregulated in cancer. Here, we identified regulators of P-bodies as crucial vulnerabilities in acute myeloid leukaemia (AML) through genome-wide CRISPR screens in normal and malignant haematopoietic progenitors. We found that leukaemia cells harbour aberrantly elevated numbers of P-bodies and show that P-body assembly is crucial for initiation and maintenance of AML. Notably, P-body loss had little effect upon homoeostatic haematopoiesis but impacted regenerative haematopoiesis. Molecular characterization of P-bodies purified from human AML cells unveiled their critical role in sequestering messenger RNAs encoding potent tumour suppressors from the translational machinery. P-body dissolution promoted translation of these mRNAs, which in turn rewired gene expression and chromatin architecture in leukaemia cells. Collectively, our findings highlight the contrasting and unique roles of RNA sequestration in P-bodies during tissue homoeostasis and oncogenesis. These insights open potential avenues for understanding myeloid leukaemia and future therapeutic interventions.

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Fig. 1: P-body regulators are AML dependencies.
Fig. 2: DDX6 is crucial for human and mouse AML progression in vivo.
Fig. 3: DDX6 plays a minor role during homoeostatic haematopoiesis but is important for regenerative haematopoiesis.
Fig. 4: P-bodies sequester translationally repressed mRNAs encoding key tumour suppressors.
Fig. 5: Loss of DDX6 impacts the chromatin architecture of AML cells.

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Data availability

RNA-seq, ATAC-seq, eCLIP-seq, polysome profiling, CUT&RUN and CUT&Tag data that support the findings of this study have been deposited in the Gene Expression Omnibus under accession codes GSE260919, GSE224858, GSE261265 and GSE224643. Raw data and the MaxQuant output for the proteomics have been deposited to the MassIVE database under the accession number MSV000090973. Previously published ChIP-seq data and polysome profiling data that were re-analysed here are available under accession code GSM1003586 (ref. 22) and GSE202227 (ref. 76). The human AML data were derived from the TCGA Research Network at http://cancergenome.nih.gov/. 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.

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Acknowledgements

We thank all members of the Sardina, Grebien and Di Stefano groups for stimulating scientific discussions. We thank S. Doulatov for the human iPS cell-derived CD34+ cells, D. Lacorazza for the THP-1 cells, D. Nakada for the MOLM-13-Cas9 cells, L. Brunetti for advice on generating the DDX6 degron AML cells, J. Yustein and M. Mamonkin for the NSG mice, M. Fabian for the LSM14 mutant plasmids, F. Stossi and A. Tostado of the BCM Integrated Microscopy Core for help with the smFISH experiments, the BCM GEMC for the generation of Ddx6 transgenic mice and Z. Ianniello for help with polysome profiling experiments. B.D.S. is a Cancer Prevention and Research Institute of Texas (CPRIT) Scholar in Cancer Research. B.D.S. is supported by the CPRIT Recruitment of First-Time, Tenure-Track Faculty Member Award RR200079, the American Society of Hematology Scholar Award, the B+ Foundation, the Worldwide Cancer Research Foundation, the Milky Way Research Foundation Investigator Award, the Nancy Chang, PhD Award for Research Excellence and the National Institutes of Health (NIH) MIRA award 1R35GM147126-01. S.K. is supported by NIH 5T32DK060445-19 and NIH 1F32CA288043-01. Research in the F.G. laboratory was supported by the European Union’s Horizon 2020 research and innovation programme (Marie Sklodowska-Curie grant agreement no. 813091) and the Austrian Science Fund (projects P-35628, P35298 to F.G.). Research in the J.L.S. laboratory was funded by Worldwide Cancer Research (20-0269) and the grant PID2019-111243RA-I00 funded by MICIU/AEI/10.13039/501100011033. J.L.S. is supported by Instituto de Salud Carlos III (CP19/00176). Research in the B.M.J. laboratory was supported by grant PID2021-125277OB-I00 funded by MICIU/AEI/10.13039/501100011033 and FEDER/EU. B.M.J. is supported by Instituto de Salud Carlos III (CP22/00127). We thank CERCA Programme/Generalitat de Catalunya for institutional support. E.V.N. is a CPRIT Scholar in Cancer Research (RR200040). This project was supported by the Cytometry and Cell Sorting Core at Baylor College of Medicine with funding from the CPRIT Core Facility Support Award (CPRIT-RP180672), the NIH (CA125123 and RR024574) and the assistance of J. M. Sederstrom. We are grateful for support on this project from NIH grant P41 GM108538 (National Center for Quantitative Biology of Complex Systems) to J.J.C.

Author information

Author notes

  1. These authors contributed equally: Srikanth Kodali, Ludovica Proietti.

Authors and Affiliations

  1. Stem Cells and Regenerative Medicine Center, Baylor College of Medicine, Houston, TX, USA

    Srikanth Kodali, Patrizia Pessina, Núria Lupión-Garcia, Yingzhi Cui, Caroline M. Sands, Rachel E. Rau & Bruno Di Stefano

  2. Center for Cell and Gene Therapy, Baylor College of Medicine, Houston, TX, USA

    Srikanth Kodali, Patrizia Pessina, Núria Lupión-Garcia, Yingzhi Cui, Caroline M. Sands, Rachel E. Rau & Bruno Di Stefano

  3. Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX, USA

    Srikanth Kodali, Patrizia Pessina, Núria Lupión-Garcia, Yingzhi Cui, Caroline M. Sands, Rachel E. Rau & Bruno Di Stefano

  4. Dan L Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, TX, USA

    Srikanth Kodali, Patrizia Pessina, Núria Lupión-Garcia, Yingzhi Cui, Caroline M. Sands & Bruno Di Stefano

  5. Center for Cancer Epigenetics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA

    Srikanth Kodali, Patrizia Pessina, Núria Lupión-Garcia, Yingzhi Cui, Caroline M. Sands & Bruno Di Stefano

  6. Institute for Medical Biochemistry, University of Veterinary Medicine Vienna, Vienna, Austria

    Ludovica Proietti, Thomas Eder, Bernhard Alber, Gabriele Manhart & Florian Grebien

  7. Josep Carreras Leukaemia Research Institute, Badalona, Spain

    Gemma Valcarcel, Anna V. López-Rubio, Ainoa Planas-Riverola, Alba Martínez, Talia Velasco-Hernandez, Laureano Tomás-Daza, Pablo Menendez, Biola M. Javierre & Jose L. Sardina

  8. Division of Biomedical Sciences, School of Medicine, University of California, Riverside, Riverside, CA, USA

    Junchao Shi

  9. Department of Biomolecular Chemistry, University of Wisconsin, Madison, WI, USA

    Annie Jen, Evgenia Shishkova & Joshua Coon

  10. Verna & Marrs McLean Department of Biochemistry & Molecular Biology and Therapeutic Innovation Center, Baylor College of Medicine, Houston, TX, USA

    Anne C. Starner, Mason D. Bartels & Eric L. Van Nostrand

  11. Institute of Pharmacology and Toxicology, University of Veterinary Medicine Vienna, Vienna, Austria

    Isabella Maria Mayer & Karoline Kollmann

  12. Department of Biology and Biotechnology ‘Charles Darwin’, Sapienza University of Rome, Rome, Italy

    Alessandro Fatica

  13. National Center for Quantitative Biology of Complex Systems, Madison, WI, USA

    Evgenia Shishkova & Joshua Coon

  14. Department of Pediatrics, Baylor College of Medicine, Texas Children’s Hospital, Houston, TX, USA

    Rachel E. Rau

  15. Department of Chemistry, University of Wisconsin, Madison, WI, USA

    Joshua Coon

  16. Morgridge Institute for Research, Madison, WI, USA

    Joshua Coon

  17. Molecular Medicine Program, Division of Urology, Department of Surgery, University of Utah School of Medicine, Salt Lake City, UT, USA

    Qi Chen

  18. St. Anna Children’s Cancer Research Institute (CCRI), Vienna, Austria

    Florian Grebien

  19. CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Vienna, Austria

    Florian Grebien

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  1. Srikanth Kodali
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Contributions

S.K., L.P., F.G. and B.D.S. conceived the study and wrote the manuscript with input from J.L.S.; S.K., L.P., N.L., P.P., T.E., Y.C., C.S., B.A. and G.M. performed experiments and analysed the data; A.V.L., G.V. and J.L.S. performed and analysed the AML patient-derived xenograft and primary CD34+ experiments; A.P.-R., L.T.-D. and B.M.J. performed liHi-C and CUT&RUN experiments in collaboration with G.V., A.V.L. and J.L.S.; J.S. and Q.C. performed and analysed the small RNA-seq experiments; A.J., E.S. and J.C. performed and analysed the proteomic data; A.S., M.B. and E.V.N. performed and analysed the eCLIP-seq; A.M., T.V. and P.M. helped with the AML patient-derived xenograft and the primary CD34+ experiments; A.F. supervised the polysome profiling experiments; I.M.M. and K.K. helped with HPC7 experiments; R.R. helped with the primary patient AML and CD34+ cells.

Corresponding authors

Correspondence to Jose L. Sardina, Florian Grebien or Bruno Di Stefano.

Ethics declarations

Competing interests

E.V.N. is co-founder, member of the Board of Directors, on the SAB, equity holder and paid consultant for Eclipse BioInnovations, on the SAB of RNAConnect and is inventor of intellectual property owned by University of California San Diego. The interests of E.V.N. have been reviewed and approved by the Baylor College of Medicine in accordance with its conflict of interest policies. P.M. is a co-founder, member of the Board of Directors, equity holder and paid consultant for OneChain Immunotherapeutics (Barcelona, Spain). This work has no connection with and is not related to the scientific interests of OneChain Immunotherapeutics. J.J.C. is a consultant for Thermo Fisher Scientific, 908 Devices and Seer. The remaining authors declare no competing interests.

Peer review

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Nature Cell Biology thanks Anthony Khong and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

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Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 CRISPR screens identify P-body regulators as a selective dependency in AML.

(a) Cytospins of HPC7 and CebpaN-mutant/C-mutant (CNC) cells. n = 3 independent experiments. (b) Schematic illustration of pooled genome-wide CRISPR/Cas9 dropout screening strategy. (c) Venn diagram showing filtering strategy for the identification of 308 leukaemia-specific genetic dependencies shown in Fig. 1a. (d, e) Competition-based proliferation assay in (d) CNC Cas9 cells or (e) HPC7 Cas9 cells, illustrated as colour-coded percentage of iRFP670+ cells transduced with indicated sgRNAs over 19 d. The non-targeting sgCTRL is used as a negative control, sgRpa3, targeting essential gene Rpa3, is used as a positive control. Results are normalized to day 5 post-infection. Two-way ANOVA with Dunnett’s post-hoc test, n = 3 biological replicates per group, mean ± s.e.m. (f) Boxplots showing the expression levels (log2 mRNA) of P-body related genes (displayed in Fig. 1c) in AML, normal HSPCs, and MDS samples. AML normal karyotype (n = 28), AML t(15;17) (n = 28), AML t(8;21) (n = 28), AML t(11q23)/MLL (n = 28), HSC (haematopoietic stem cell) (n = 6), GMP (granulocyte monocyte progenitor) (n = 7), MDS (myelodysplastic syndromes) (n = 28). Box centre line indicates median, box limits indicate upper (Q3) and lower quartiles (Q1), lower whisker is Q1 – 1.5 × interquartile range (IQR) and upper whisker is Q3 + 1.5 × IQR. Two-tailed t-test with Welch’s correction. (g) Boxplots showing normalized H3K27ac ChIP-seq signal (RPKM) in MOLM-13 cells (GSM4685439 and GSM4685440) and CD34+ HSPCs at enhancers (n = 26 per group) (left panel) and promoter (n = 214 per group) (right panel) regions (GSM772885 and GSM772894) of P-body genes. Box centre line indicates median, box limits indicate upper (Q3) and lower quartiles (Q1), lower whisker is Q1 – 1.5 × IQR and upper whisker is Q3 + 1.5 × IQR. Two-tailed t-test with Welch’s correction. (h) Significant correlation between the expression of DDX6 and EIF4ENIF1 in the TCGA-LAML dataset. Pearson’s correlation coefficients and corresponding p-values (two-tailed one-sample Student’s t-test) are indicated. (i, j) Kaplan–Meier survival analysis plots of TCGA data for AML patients with low vs high expression of (i) EIF4ENIF1 (n = 27 patients per group) or (j) DDX6 (n = 53 patients per group). (k) DDX6 mRNA expression in AML (red) compared to other cancers, based on data from TCGA database. Data are presented as mean log2 expression with range. Black dots: expression levels in normal cells; Blue dots: expression levels in cancer cells. (l) DDX6 expression in AML patient samples compared to normal HSPCs. AML: n = 48, AML t(15;17): n = 54, AML inv(16)/t(16;16): n = 47, AML t(8;21): n = 60, AML t(11q23)/MLL: n = 43 patients. HSC (haematopoietic stem cell): n = 6, GMP (granulocyte monocyte progenitor): n = 7 healthy individuals (BloodSpot data of DDX6 probe 204909_at). Unpaired two-tailed Student’s t-test. (m) qRT–PCR for DDX6 in human primary CD34+ cells (n = 7 healthy individuals) and AML patient cells (n = 10 patients). Unpaired two-tailed Student’s t-test, mean.

Extended Data Fig. 2 DDX6 is essential for AML cell proliferation and survival in vitro.

(a) Representative IF imaging of EDC4 punctae (green) in control and DDX6 overexpressing HPC7 cells. Nuclei were counterstained with DAPI (blue). Scale: 2.5 µm. (b) Quantification of EDC4 punctae in HPC7 cells overexpressing DDX6 by IF. n = 45-55 cells per group, unpaired two-tailed Student’s t-test, mean ± s.e.m. (c) Representative Western blot image of DDX6 in the nuclear and cytoplasmic fractions of MOLM-13 cells. n = 3 independent experiments. (d) Representative IF imaging of LSM14A (green) and DDX6 (red) punctae in control and DDX6 KD MOLM-13 cells. Nuclei were counterstained with DAPI. Scale: 10 µm. (e) Quantification of LSM14A+DDX6+ punctae in control and DDX6 KD MOLM-13 cells by IF. Unpaired two-tailed Student’s t-test, n = 24–30 cells per group, mean ± s.e.m. (f) Representative intracellular flow cytometry plots showing DDX6 levels in shCTRL and shDDX6 MOLM-13 cells. (g) qRT–PCR validation of DDX6 KD in MOLM-13 cells. Unpaired two-tailed Student’s t-test, n = 3 biologically independent samples per group, mean ± s.e.m. (h) Proliferation assay for shCTRL and shDDX6 AML cell lines at the indicated time points after transduction. Two-way ANOVA with Dunnett’s post-hoc test, n = 3 biological replicates per group, mean ± s.e.m. (i) Competition-based proliferation assays upon DDX6 knockdown performed in indicated cell lines. shRPL17, targeting the essential gene RPL17, is used as a positive control. Two-way ANOVA with Dunnett’s post-hoc test, n = 3 biologically independent samples per group, mean ± s.e.m. (j) Heatmap summarizing the competition-based proliferation assays upon DDX6 knockout in indicated Cas9-expressing cell lines. Data are illustrated as colour-coded percentage of iRFP670+ cells transduced with indicated sgRNAs over 17 d. sgAAVS1 is used as negative control, sgRPL17 is used as positive control. Results are normalized to day 3 post-infection, (n = 3 biologically independent samples per group, mean). (k) Proliferation assay for CTRL and DDX6 KO human AML cell lines at the indicated time points after transduction. Unpaired two-tailed Student’s t-test, n = 3 biologically independent samples per group, mean ± s.e.m. (l) Schematic of dCas9-KRAB and sgRNA vectors (upper panel). Representative intracellular flow cytometry plots for DDX6 in CRISPRi HEL cells, either untreated (UT) or treated with doxycycline (DOX) for 7 d (lower panel). (m) Representative IF imaging of EDC4 punctae (green) in CRISPRi HEL cells, either untreated (UT) or treated with doxycycline (DOX) for 4 d. Nuclei were counterstained with DAPI (blue). Scale: 2.5 µm. (n) Proliferation assay for UT or DOX-treated CRISPRi HEL cells at the indicated time points after transduction. Unpaired two-tailed Student’s t-test, n = 3 biologically independent samples per group, mean ± s.e.m. (o) Representative images of UT or DOX-treated CRISPRi HEL cells. Scale: 10 µm. (p) Megakaryocytic differentiation in CRISPRi HEL cells 7 d after DDX6 silencing was quantified by flow cytometry, using CD41 and CD61 as markers. Unpaired two-tailed Student’s t-test, n = 3 biologically independent samples per group, mean ± s.e.m.

Source data

Extended Data Fig. 3 DDX6 is essential for the proliferation and gene expression programme of AML cells.

(a) Flow cytometric analysis of cell death (Annexin V+) in shCTRL and shDDX6 AML cell lines. Unpaired two-tailed Student’s t-test, n = 3 biologically independent samples per group, mean ± s.e.m. (b) Correlation heatmap showing the correlation (r) values between MOLM-13 RNA-seq samples. Scale bar represents the range of the correlation coefficients (r) displayed. (c) Heatmap of RNA-seq data for shCTRL and shDDX6 HL-60 cells (n = 2, FC > 1.5; p < 0.05). Upregulated genes are depicted in red, while in blue are downregulated genes. (d) Correlation heatmap showing the correlation (r) values between HL-60 RNA-seq samples. Scale bar represents the range of the correlation coefficients (r) displayed. (e) GO enrichment analysis of differentially expressed genes in control vs. DDX6 KD HL-60 cells. (f) qRT–PCR validation of (left) EIF4ENIF1 KD and (right) LSM14A KD in MOLM-13 cells. Unpaired two-tailed Student’s t-test, n = 3 biologically independent samples per group, mean ± s.e.m. (g) Quantification of LSM14A+DDX6+ punctae in control and (left) EIF4ENIF1 KD or (right) LSM14A KD MOLM-13 cells by IF. Unpaired two-tailed Student’s t-test, n = 17-31 cells per group, mean ± s.e.m. (h) Representative Western blot showing HA-tagged endogenous DDX6 protein levels in DDX6-FKBP12F36V MOLM-13 cells after 2 d of dTAG-13 treatment, followed by washout and culture for 5 d. n = 3 independent experiments. (i) Representative IF imaging of EDC4 punctae (green) and DDX6 punctae (red) in DDX6-FKBP12F36V MOLM-13 cells after 2 d of dTAG-13 treatment, followed by washout and culture for 5 d. Nuclei were counterstained with DAPI (blue). Scale: 10 µm. (j) Quantification of EDC4+DDX6+ punctae in the indicated cells by IF (n = 47-87 cells per group, mean ± s.e.m.). (k) Representative Western blot showing HA-tagged endogenous DDX6 protein levels in DDX6-FKBP12F36V HL-60 cells after 2 d of dTAG-13 treatment, followed by washout and culture for 5 d. n = 3 independent experiments. (l) Proliferation of DDX6-FKBP12F36V HL-60 cells, either untreated (UT), continuously treated with dTAG-13, or treated with dTAG-13 for 2, 5, or 6 days, followed by washout and culture (n = 3 biological replicates per group, mean ± s.e.m.). (m) Percentages of control or DDX6 KD MOLM-13 cells in the bone marrow and spleens of NSG mice (25 d post-transplant) quantified by flow cytometry. Unpaired two-tailed Student’s t-test, n = 3 mice per group, mean ± s.e.m. (n) Representative intracellular flow cytometry plot for DDX6 in shCTRL and shDDX6 MOLM-13 cells in the bone marrow of NSG mice (45 d post-transplant). (o) Genotyping PCR demonstrating poly(I:C)-induced Ddx6 deletion in c-Kit+ haematopoietic progenitor cells isolated from the bone marrow of Mx1-Cre/Ddx6fl/fl mice. n = 3 independent experiments. (p) Left: Representative IF imaging of EDC4 punctae (green) and DDX6 punctae (red) in Ddx6WT and Ddx6KO c-Kit+ haematopoietic progenitor cells. Nuclei were counterstained with DAPI (blue), scale: 10 µm. Right: Quantification of EDC4+DDX6+ punctae by IF in Ddx6WT and Ddx6KO c-Kit+ haematopoietic progenitor cells. Unpaired two-tailed Student’s t-test, n = 60-63 cells per group, mean ± s.e.m. (q) Left: Representative IF imaging of LSM14A punctae (green) and DDX6 punctae (red) in Ddx6WT and Ddx6KO c-Kit+ haematopoietic progenitor cells. Nuclei were counterstained with DAPI (blue), scale: 10 µm. Right: Quantification of LSM14A+DDX6+ punctae by IF in Ddx6WT and Ddx6KO c-Kit+ haematopoietic progenitor cells. Unpaired two-tailed Student’s t-test, n = 32-33 cells per group, mean ± s.e.m.

Source data

Extended Data Fig. 4 DDX6 loss has little effect on steady-state haematopoiesis.

(a) Representative image of spleens from mice 90 d after transplantation with MLL-AF9-transduced Ddx6WT and Ddx6KO c-Kit+ cells. (b) Weights of spleens isolated from mice 90 days after transplantation with MLL-AF9-transduced Ddx6WT and Ddx6KO c-Kit+ cells. Unpaired two-tailed Student’s t-test, Ddx6WT n = 4 mice, Ddx6KO n = 6 mice, mean ± s.e.m. (c) Representative flow cytometry plots of leukaemia (GFP+) cells in bone marrow from mice 90 d after transplantation with MLL-AF9-transduced Ddx6WT and Ddx6KO c-Kit+ cells. (d) Representative intracellular flow cytometry plot for DDX6 in shCTRL and shDDX6 primary bone marrow human CD34+ cells. (e) Representative flow cytometry plots showing cell death (Annexin V+/Sytox AAD+) in shCTRL and shDDX6 primary human CD34+ cells. (f) qRT–PCR analysis to validate DDX6 KD in human iPSC-derived CD34+ HSPCs. Unpaired two-tailed t-test with Welch’s correction, n = 3 biologically independent samples per group, mean ± s.e.m. (g) Proliferation assay for shCTRL and shDDX6 human iPSC-derived HSPCs at the indicated timepoints after transduction. Unpaired two-tailed Student’s t-test, n = 3 biologically independent samples per group, mean ± s.e.m. (h) Flow cytometric analysis for myeloid differentiation (CD11b median fluorescence intensity (MFI)) in shCTRL vs shDDX6 human iPSC-derived HSPCs. Unpaired two-tailed Student’s t-test, n = 3 biologically independent samples per group. (i) Flow cytometric analysis of cell death (Annexin V+) in human iPSC-derived HPCs. Unpaired two-tailed Student’s t-test, n = 3 biologically independent samples per group, mean ± s.e.m. (j, k) Representative Western blots validating (j) LSM14A KD and (k) EIF4ENIF1 KD in primary human CD34+ cells. n = 3 independent experiments. (l) Representative flow cytometry plots and quantification for CMP, GMP, and MEP populations (gated on LK cells) in the bone marrow of Mx1-Cre and Mx1-Cre/Ddx6fl/fl mice 110 d after Ddx6 deletion. Unpaired two-tailed Student’s t-test, Ddx6WT n = 5 mice, Ddx6KO n = 5 mice, mean ± s.e.m. (m, n) Quantification of myeloid cells, T cells, and B cells as a percentage of CD45+ cells in the (m) bone marrow and (n) spleens of Mx1-Cre and Mx1-Cre/Ddx6fl/fl mice 110 d after Ddx6 deletion. Unpaired two-tailed Student’s t-test, Ddx6WT n = 5 mice, Ddx6KO n = 5 mice, mean ± s.e.m. (o, p) Frequency of erythroid cells in the (o) bone marrow and (p) peripheral blood Mx1-Cre and Mx1-Cre/Ddx6fl/fl mice 110 d after Ddx6 deletion. Unpaired two-tailed Student’s t-test, Ddx6WT n = 5 mice, Ddx6KO n = 5 mice, mean ± s.e.m. (q) Representative Western blot analysis for DDX6 in bone marrow cells of Rosa26-Cre and Rosa26-Cre/Ddx6fl/fl mice. n = 3 independent experiments. (r) Quantification of myeloid cells, T cells, and B cells as a percentage of CD45+ cells in the peripheral blood of Rosa26-Cre-ERT2 and Rosa26-Cre-ERT2/Ddx6fl/fl mice 80 d after tamoxifen treatment. Unpaired two-tailed Student’s t-test, Ddx6WT n = 3 mice, Ddx6KO n = 3 mice, mean ± s.e.m. (s) Frequency of erythroid cells in the peripheral blood of Rosa26-Cre-ERT2 and Rosa26-Cre-ERT2/Ddx6fl/fl mice 80 d after tamoxifen treatment. Unpaired two-tailed Student’s t-test, Ddx6WT n = 3 mice, Ddx6KO n = 3 mice, mean ± s.e.m. (t) Representative flow cytometry plots showing percentages of HSC, MPP1, MPP2, and MPP4 populations, gated on LSK cells, in the bone marrow of Rosa26-Cre-ERT2 and Rosa26-Cre-ERT2/Ddx6fl/fl mice after Ddx6 deletion. (u) Quantification of HSC, MPP1, MPP2, and MPP4 populations, as a percentage of LSK cells, in the bone marrow of Rosa26-Cre-ERT2 and Rosa26-Cre-ERT2/Ddx6fl/fl mice after Ddx6 deletion. Unpaired two-tailed Student’s t-test, Ddx6WT n = 5 mice, Ddx6KO n = 5 mice, mean ± s.e.m. (v) Kaplan–Meier survival curves of Rosa26-Cre-ERT2 and Rosa26-Cre-ERT2/Ddx6fl/fl mice 4 months after tamoxifen treatment. Mantel–Cox test, Ddx6WT n = 3 mice, Ddx6KO n = 3 mice.

Source data

Extended Data Fig. 5 DDX6 regulates HSC quiescence and response to stress.

(a) Unsupervised clustering of Ddx6WT and Ddx6KO HSC, MPP1, MPP2, and MPP4 populations subjected to RNA-seq. (b) Representative flow cytometry plots of (left) MitoTracker Green FM and (right) TMRM staining in Ddx6WT and Ddx6KO HSCs, 24 d after Ddx6 deletion. (c) Representative flow cytometry plots of Ddx6WT and Ddx6KO chimerism within the donor haematopoietic compartment (CD45+) in the bone marrow 187 d after primary competitive transplantation. (d) HSCs sorted from CD45.1 and Mx1-Cre/Ddx6fl/fl mice were competitively transplanted into lethally irradiated WT recipient mice, followed by poly(I:C) treatment 34 d later. Quantification is shown of Ddx6WT and Ddx6KO chimerism within the donor haematopoietic compartment (CD45+) in peripheral blood, spleen, or bone marrow, at the indicated timepoints after Ddx6 deletion. Ddx6WT n = 8 mice, Ddx6KO n = 8 mice, two-way ANOVA with Bonferroni’s multiple comparisons test, mean ± s.e.m. (e) Flow cytometric analysis for the megakaryocytic differentiation markers CD41 and CD61 in HEL cells 4 d after vehicle (DMSO) or PMA treatment. Unpaired two-tailed Student’s t-test, n = 3 biologically independent samples per group, mean ± s.e.m. (f) Representative IF imaging of EDC4 punctae (green) and DDX6 punctae (red) in vehicle-treated and PMA-treated HEL cells. Nuclei were counterstained with DAPI (blue). Scale: 10 µm. (g) Quantification of EDC4+DDX6+ punctae in vehicle-treated and PMA-treated HEL cells by IF. Unpaired two-tailed Student’s t-test, n = 24-26 cells per group, mean ± s.e.m. (h) Representative IF imaging of LSM14A punctae (green) and DDX6 punctae (red) in vehicle-treated and PMA-treated HEL cells. Nuclei were counterstained with DAPI (blue). Scale: 10 µm. (i) Quantification of LSM14A+DDX6+ punctae in vehicle-treated and PMA-treated HEL cells by IF. Unpaired two-tailed Student’s t-test, n = 25-33 cells per group, mean ± s.e.m. (j) Flow cytometric analysis for myeloid differentiation (% CD11b+) in MOLM-13 cells 5 d after treatment with the anti-leukaemic drug EPZ-5676. Unpaired two-tailed Student’s t-test, n = 3 biologically independent samples per group, mean ± s.e.m. (k) P-body numbers (EDC4+DDX6+ punctae) in MOLM-13 cells treated with EPZ-5676. Unpaired two-tailed Student’s t-test, n = 26-30 cells per group. (l) qRT–PCR analysis of DDX6 expression in DDX6 KD MOLM-13 cells rescued with DDX6 WT or DDX6 EQ. n = 3 biologically independent samples per group, mean ± s.e.m. (m) Representative IF imaging of EDC4 punctae (green) and DDX6 punctae (red) in shCTRL MOLM-13 cells with exogenous DDX6 WT and EQ expression. Nuclei were counterstained with DAPI (blue). Scale: 10 µm. (n) Representative IF imaging of LSM14A punctae (green) and DDX6 punctae (red) in shCTRL MOLM-13 cells with exogenous DDX6 WT and EQ expression. Nuclei were counterstained with DAPI (blue). (o) Quantification of LSM14A+DDX6+ punctae in shCTRL MOLM-13 cells by IF. Unpaired two-tailed Student’s t-test, n = 12-13 cells per group, mean ± s.e.m. (p) Representative IF imaging of LSM14A punctae (green) and DDX6 punctae (red) in shDDX6 MOLM-13 cells with exogenous DDX6 WT and EQ expression. Nuclei were counterstained with DAPI (blue). Scale: 10 µm. (q) Quantification of LSM14A+DDX6+ punctae in shCTRL MOLM-13 cells by IF. Unpaired two-tailed Student’s t-test, n = 13-33 cells per group, mean ± s.e.m.

Extended Data Fig. 6 Disrupting P-body assembly abrogates AML cell proliferation.

(a) Representative intracellular flow cytometry plots showing expression of FLAG-LSM14A WT, FLAG-LSM14A ∆TFG, or FLAG-LSM14A ∆FFD in control and LSM14A KD HEL cells. (b) Representative IF imaging of EDC4 punctae (green) and DDX6 punctae (red) in control HEL cells expressing LSM14A WT, LSM14A ∆TFG, or LSM14A ∆FFD. Nuclei were counterstained with DAPI (blue). (c, d) Representative IF imaging of LSM14A punctae (green) and DDX6 punctae (red) in (c) LSM14A KD and (d) control HEL cells expressing LSM14A WT, LSM14A ∆TFG, or LSM14A ∆FFD. Nuclei were counterstained with DAPI (blue). Scale: 10 µm. (e) Quantification of LSM14A+DDX6+ punctae in the indicated cells by IF. One-way ANOVA with Dunnett’s post-hoc test, n = 16-29 cells per group, mean ± s.e.m. (f) Representative intracellular flow cytometry plot showing NBDY expression (FLAG) in MOLM-13 cells. (g) Left: Representative IF imaging of LSM14A punctae (green) and FLAG (red) in control and NBDY-expressing MOLM-13 cells, scale: 10 µm. Right: quantification of LSM14A+DDX6+ punctae in control and NBDY-expressing MOLM-13 cells by IF. Unpaired two-tailed Student’s t-test, n = 24 cells per group, mean ± s.e.m. (h) HEL cell numbers 13 d after forced expression of NBDY. Unpaired two-tailed Student’s t-test, n = 3 biologically independent samples per group, mean ± s.e.m. (i) Representative flow cytometry plots showing loss of GFP-LSM14A+ P-bodies after DDX6 silencing. (j) Distribution of RNA biotypes within the P-bodies and cytoplasm of HEL and MOLM-13 cells. (k) Distribution of P-body-enriched, cytoplasm-enriched, and non-enriched genes within each expression quartile, which range from Q1 (low expression) to Q4 (high expression). (l) RNA-seq analysis showing counts per million (CPM) values for the indicated transcripts in the cytoplasm and P-bodies of MOLM-13 cells (n = 2 biologically independent samples per group, mean). (m) Representative smFISH images of (left) POLK or (right) RSRC2 mRNA molecules (red) and GFP-LSM14A+ punctae (green). Nuclei were counterstained with DAPI (blue). Scale: 5 µm (n) Quantification of the fraction of POLK or RSCR2 transcripts colocalizing with GFP-LSM14A+ punctae in individual cells (KDM5B: n = 30 cells, mean = 69.53%, RSRC2: n = 31 cells, mean = 55.05%).

Extended Data Fig. 7 DDX6 sequesters translationally repressed mRNAs in P-bodies.

(a) Venn diagrams showing overlap between (left) cytoplasmic mRNAs or (right) P-body-associated mRNAs (FC > 1.5, p < 0.05) in MOLM-13 cells and HEL cells. Shared P-body-enriched transcripts encoding genes with potential tumour-suppressive activity are listed. (b) GO enrichment analysis for shared P-body-enriched RNAs in MOLM-13 and HEL cells (n = 2042). Two-sided Fisher’s exact test. (c) Histogram of region-based FC for DDX6 eCLIP-seq read density over size-matched input (FC > 2, padj < 0.05). (d) Scatter-plot indicating correlation between region-based fold enrichment of DDX6 eCLIP-seq datasets across biological replicates (n = 2 biologically independent samples per group). (e) Venn diagram showing the overlap between DDX6 eCLIP-seq targets and P-body-enriched RNAs (n = 2 biologically independent samples per group, FC > 2, padj < 0.05, Wald test with Benjamini–Hochberg correction) in MOLM-13 cells. (f) GO pathway enrichment analysis of DDX6-bound, P-body-enriched RNAs (n = 589) identified in (e). Two-tailed Fisher’s exact test. (g, h) Scatter-plots showing lack of correlation between P-body enrichment and expression for transcripts upregulated in DDX6 KD (g) MOLM-13 or (h) HEL cells. (i) GC content and length distribution for P-body-enriched vs cytosolic mRNAs. (j) Cumulative distribution function (CDF) plot showing translation rate (log2 FC) of P-body enriched and P-body-depleted mRNAs for shDDX6 vs. shCTRL cells. Two-sided Mann–Whitney U test. (k) Dynamic changes in small RNA distribution in MOLM-13 cells following DDX6 suppression (n = 2 biologically independent samples per group). (l) Heatmap showing expression levels of selected tsRNAs in control vs DDX6 depleted MOLM-13 cells (n = 2 biologically independent samples per group).

Extended Data Fig. 8 Genome topology rewiring following DDX6 depletion.

(a) Correlation heatmap showing the correlation (r) values between proteomic samples (n = 3). Scale bar represents the range of the correlation coefficients (r) displayed. (b) Heatmap for differentially expressed proteins exhibiting a 1.5-fold or greater difference between control and DDX6 KD MOLM-13 cells (n = 3). (c) Gene ontology analysis of upregulated and downregulated proteins (FC > 1.5; p < 0.05, Wald test with Benjamini–Hochberg correction) in shDDX6 compared to shCTRL MOLM-13 cells. (d) Heatmap showing downregulation of P-body-related proteins in DDX6 KD MOLM-13 cells (n = 3 biologically independent samples per group). (e) Representative Western blots showing loss of P-body-related proteins upon DDX6 loss in DDX6-FKBP12F36V MOLM-13 cells. n = 3 independent experiments. (f, g) qRT–PCR validation of tumour suppressor gene overexpression in MOLM-13 cells. (n = 3). (h) Total number of ATAC-seq peaks detected in control and DDX6 KD (left) MOLM-13 or (right) HEL cells. (i) Genomic distribution of ATAC-seq peaks in control and DDX6 KD HEL and MOLM-13 cells. (j) Scatter-plot showing ATAC-seq data for shCTRL and shDDX6 HEL cells (n = 2 biologically independent samples per group). Blue dots indicate genomic regions showing significantly decreased chromatin accessibility in DDX6-depleted cells (FC > 1.5, p < 0.05, Wald test with Benjamini–Hochberg correction; n = 570); red dots indicate genomic regions showing significantly increased chromatin accessibility in DDX6-depleted cells (FC > 1.5, p < 0.05; n = 1044). (k) TF motif enrichment analysis on shDDX6 gained and lost ATAC-seq peaks in HEL cells. (l) Increased H3K4me1 levels at loci of genes that gained chromatin accessibility and became upregulated after DDX6 KD. Wilcoxon rank-sum test, shCTRL open (n = 2026), shDDX6 open (n = 2045), shCTRL closed (n = 1024), shDDX6 closed (n = 1035). Box centre line indicates median, box limits indicate upper (Q3) and lower quartiles (Q1), lower whisker is Q1 – 1.5 × IQR and upper whisker is Q3 + 1.5 × IQR. (m) Total number of promoter interactions detected by liCHi-C in control (n = 2) and DDX6 KD (n = 2) MOLM-13 cells. (n) Gene tracks of ATAC-seq, RNA-seq, H3K27ac CUT&Tag, and liCHi-C data for the genomic region surrounding AGO4. Blue shadow highlights the gene promoter. Arcs represent significant promoter interactions (CHiCAGO score > 5). (o) Representative intracellular flow cytometry plot for KDM5B in control and KDM5B knockout MOLM-13 cells.

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Supplementary Tables

Supplementary Table 1: all CRISPR screen data. Supplementary Table 2: top 308 CRISPR screen hits. Supplementary Table 3: AML patient mutations and cytogenetics. Supplementary Table 4: human AML cell line subtypes and mutations. Supplementary Table 5: shRNA and sgRNA oligonucleotide sequences.

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Kodali, S., Proietti, L., Valcarcel, G. et al. RNA sequestration in P-bodies sustains myeloid leukaemia. Nat Cell Biol (2024). https://doi.org/10.1038/s41556-024-01489-6

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