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MLLT3 governs human haematopoietic stem-cell self-renewal and engraftment

Nature volume 576, pages 281–286 (2019)Cite this article

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Abstract

Limited knowledge of the mechanisms that govern the self-renewal of human haematopoietic stem cells (HSCs), and why this fails in culture, have impeded the expansion of HSCs for transplantation1. Here we identify MLLT3 (also known as AF9) as a crucial regulator of HSCs that is highly enriched in human fetal, neonatal and adult HSCs, but downregulated in culture. Depletion of MLLT3 prevented the maintenance of transplantable human haematopoietic stem or progenitor cells (HSPCs) in culture, whereas stabilizing MLLT3 expression in culture enabled more than 12-fold expansion of transplantable HSCs that provided balanced multilineage reconstitution in primary and secondary mouse recipients. Similar to endogenous MLLT3, overexpressed MLLT3 localized to active promoters in HSPCs, sustained levels of H3K79me2 and protected the HSC transcriptional program in culture. MLLT3 thus acts as HSC maintenance factor that links histone reader and modifying activities to modulate HSC gene expression, and may provide a promising approach to expand HSCs for transplantation.

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Fig. 1: MLLT3 regulates human HSPC expansion.
Fig. 2: MLLT3 binds to the TSSs of active genes in a cell-type-specific manner.
Fig. 3: MLLT3 protects HSC stemness program through DOT1L and H3K79me2.
Fig. 4: MLLT3-OE expands transplantable CB-HSCs in culture.

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Data availability

Sequence data that support the findings of this study have been deposited in Gene Expression Omnibus (GEO) with the accession code GSE111484. Data from published reference are available in GEO GSE8108017. All other data are either available within the paper or from the corresponding author upon reasonable request. Custom codes for data analysis are also available upon request. There is no restriction in data availability.

References

  1. Ballen, K. K., Gluckman, E. & Broxmeyer, H. E. Umbilical cord blood transplantation: the first 25 years and beyond. Blood 122, 491–498 (2013).

    Article  CAS  Google Scholar 

  2. Gritz, E. & Hirschi, K. K. Specification and function of hemogenic endothelium during embryogenesis. Cell. Mol. Life Sci. 73, 1547–1567 (2016).

    Article  CAS  Google Scholar 

  3. Prashad, S. L. et al. GPI-80 defines self-renewal ability in hematopoietic stem cells during human development. Cell Stem Cell 16, 80–87 (2014).

    Article  Google Scholar 

  4. Magnusson, M. et al. Expansion on stromal cells preserves the undifferentiated state of human hematopoietic stem cells despite compromised reconstitution ability. PLoS ONE 8, e53912 (2013).

    Article  ADS  CAS  Google Scholar 

  5. Dou, D. R. et al. Medial HOXA genes demarcate haematopoietic stem cell fate during human development. Nat. Cell Biol. 18, 595–606 (2016).

    Article  CAS  Google Scholar 

  6. He, N. et al. Human polymerase-associated factor complex (PAFc) connects the super elongation complex (SEC) to RNA polymerase II on chromatin. Proc. Natl Acad. Sci. USA 108, E636–E645 (2011).

    Article  CAS  Google Scholar 

  7. Steger, D. J. et al. DOT1L/KMT4 recruitment and H3K79 methylation are ubiquitously coupled with gene transcription in mammalian cells. Mol Cell Biol 28, 2825–2839 (2008).

    Article  CAS  Google Scholar 

  8. Li, Y. et al. AF9 YEATS domain links histone acetylation to DOT1L-mediated H3K79 methylation. Cell 159, 558–571 (2014).

    Article  CAS  Google Scholar 

  9. Bitoun, E., Oliver, P. L. & Davies, K. E. The mixed-lineage leukemia fusion partner AF4 stimulates RNA polymerase II transcriptional elongation and mediates coordinated chromatin remodeling. Hum. Mol. Genet. 16, 92–106 (2007).

    Article  CAS  Google Scholar 

  10. Li, Y. et al. Molecular coupling of histone crotonylation and active transcription by AF9 YEATS domain. Mol. Cell 62, 181–193 (2016).

    Article  CAS  Google Scholar 

  11. Schoch, C. et al. AML with 11q23/MLL abnormalities as defined by the WHO classification: incidence, partner chromosomes, FAB subtype, age distribution, and prognostic impact in an unselected series of 1897 cytogenetically analyzed AML cases. Blood 102, 2395–2402 (2003).

    Article  CAS  Google Scholar 

  12. Krivtsov, A. V. et al. Transformation from committed progenitor to leukaemia stem cell initiated by MLL-AF9. Nature 442, 818–822 (2006).

    Article  ADS  CAS  Google Scholar 

  13. Bernt, K. M. et al. MLL-rearranged leukemia is dependent on aberrant H3K79 methylation by DOT1L. Cancer Cell 20, 66–78 (2011).

    Article  CAS  Google Scholar 

  14. Wang, X., Chen, C. W. & Armstrong, S. A. The role of DOT1L in the maintenance of leukemia gene expression. Curr. Opin. Genet. Dev. 36, 68–72 (2016).

    Article  Google Scholar 

  15. Pina, C., May, G., Soneji, S., Hong, D. & Enver, T. MLLT3 regulates early human erythroid and megakaryocytic cell fate. Cell Stem Cell 2, 264–273 (2008).

    Article  CAS  Google Scholar 

  16. McKinney-Freeman, S. et al. The transcriptional landscape of hematopoietic stem cell ontogeny. Cell Stem Cell 11, 701–714 (2012).

    Article  CAS  Google Scholar 

  17. Ng, E. S. et al. Differentiation of human embryonic stem cells to HOXA+ hemogenic vasculature that resembles the aorta-gonad-mesonephros. Nat. Biotechnol. 34, 1168–1179 (2016).

    Article  CAS  Google Scholar 

  18. Boitano, A. E. et al. Aryl hydrocarbon receptor antagonists promote the expansion of human hematopoietic stem cells. Science 329, 1345–1348 (2010).

    Article  ADS  CAS  Google Scholar 

  19. Fares, I. et al. Pyrimidoindole derivatives are agonists of human hematopoietic stem cell self-renewal. Science 345, 1509–1512 (2014).

    Article  ADS  CAS  Google Scholar 

  20. Daigle, S. R. et al. Potent inhibition of DOT1L as treatment of MLL-fusion leukemia. Blood 122, 1017–1025 (2013).

    Article  CAS  Google Scholar 

  21. Horton, S. J. et al. MLL-AF9-mediated immortalization of human hematopoietic cells along different lineages changes during ontogeny. Leukemia 27, 1116–1126 (2013).

    Article  CAS  Google Scholar 

  22. Sontakke, P. et al. Modeling BCR-ABL and MLL-AF9 leukemia in a human bone marrow-like scaffold-based xenograft model. Leukemia 30, 2064–2073 (2016).

    Article  CAS  Google Scholar 

  23. Wagner, J. E. Jr et al. Phase I/II trial of stemregenin-1 expanded umbilical cord blood hematopoietic stem cells supports testing as a stand-alone graft. Cell Stem Cell 18, 144–155 (2016).

    Article  CAS  Google Scholar 

  24. Ngom, M. et al. UM171 enhances lentiviral gene transfer and recovery of primitive human hematopoietic cells. Mol. Ther. Methods Clin. Dev. 10, 156–164 (2018).

    Article  CAS  Google Scholar 

  25. Wilkinson, A. C. et al. Long-term ex vivo haematopoietic-stem-cell expansion allows nonconditioned transplantation. Nature 571, 117–121 (2019).

    Article  CAS  Google Scholar 

  26. Kataoka, K. et al. Evi1 is essential for hematopoietic stem cell self-renewal, and its expression marks hematopoietic cells with long-term multilineage repopulating activity. J. Exp. Med. 208, 2403–2416 (2011).

    Article  CAS  Google Scholar 

  27. Rentas, S. et al. Musashi-2 attenuates AHR signalling to expand human haematopoietic stem cells. Nature 532, 508–511 (2016).

    Article  ADS  Google Scholar 

  28. Komorowska, K. et al. Hepatic leukemia factor maintains quiescence of hematopoietic stem cells and protects the stem cell pool during regeneration. Cell Reports 21, 3514–3523 (2017).

    Article  CAS  Google Scholar 

  29. Hu, Y. & Smyth, G. K. ELDA: extreme limiting dilution analysis for comparing depleted and enriched populations in stem cell and other assays. J. Immunol. Methods 347, 70–78 (2009).

    Article  CAS  Google Scholar 

  30. Org, T. et al. Scl binds to primed enhancers in mesoderm to regulate hematopoietic and cardiac fate divergence. EMBO J. 34, 759–777 (2015).

    Article  CAS  Google Scholar 

  31. Heinz, S. et al. Simple combinations of lineage-determining transcription factors prime cis-regulatory elements required for macrophage and B cell identities. Mol. Cell 38, 576–589 (2010).

    Article  CAS  Google Scholar 

  32. Ramírez, F., Dündar, F., Diehl, S., Grüning, B. A. & Manke, T. deepTools: a flexible platform for exploring deep-sequencing data. Nucleic Acids Res. 42, W187-91 (2014).

    Article  Google Scholar 

  33. Zhang, Y. et al. Model-based analysis of ChIP-Seq (MACS). Genome Biol. 9, R137 (2008).

    Article  Google Scholar 

  34. Buenrostro, J. D., Giresi, P. G., Zaba, L. C., Chang, H. Y. & Greenleaf, W. J. Transposition of native chromatin for fast and sensitive epigenomic profiling of open chromatin, DNA-binding proteins and nucleosome position. Nat. Methods 10, 1213–1218 (2013).

    Article  CAS  Google Scholar 

  35. Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357–359 (2012).

    Article  CAS  Google Scholar 

  36. Trapnell, C., Pachter, L. & Salzberg, S. L. TopHat: discovering splice junctions with RNA-seq. Bioinformatics 25, 1105–1111 (2009).

    Article  CAS  Google Scholar 

  37. Quinlan, A. R. & Hall, I. M. BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics 26, 841–842 (2010).

    Article  CAS  Google Scholar 

  38. Dennis, G. Jr et al. DAVID: Database for Annotation, Visualization, and Integrated Discovery. Genome Biol. 4, 3 (2003).

    Article  Google Scholar 

Download references

Acknowledgements

We thank BSCRC flow cytometry (F. Codrea, J. Scholes and J. Calimlim) and sequencing (S. Feng) cores, TCGB (NIH P30CA016042) and CFAR cores (NIH P30AI028697-21) at UCLA. We thank J. Zhao, T. Montoya and D. Dou for assistance with experiments; V. Rezek, D. Johnson and O. Witte for help with NSG mice and D. Kohn, Z. Romero and R. Hollis for help with lentiviral vectors. This work was supported by NIH RO1 DK100959 and RO1 DK121557; Broad Stem Cell Center at UCLA, Rose Hills Foundation, Jonsson Cancer Center Foundation, and UCLA David Geffen School of Medicine Regenerative Medicine Theme Award for H.K.A.M.; LLS Special Fellow Award and BSCRC post-doctoral fellow award for V.C.; CIRM GC1R-06673B for C.M.G. and H.K.A.M; Ruth L. Kirschstein National Research Service Award T32HL069766 for A.V., LLS Fellow Award and AACR post-doctoral award for F.L.; Beckman Scholars Program for A.T.N. and T.J.B; and NIH grant CA178415 to S.K.K.

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Authors and Affiliations

  1. Department of Molecular, Cell and Developmental Biology, University of California Los Angeles, Los Angeles, CA, USA

    Vincenzo Calvanese, Andrew T. Nguyen, Timothy J. Bolan, Anastasia Vavilina, Yanling Wang, Fides D. Lay, Mattias Magnusson & Hanna K. A. Mikkola

  2. Eli and Edythe Broad Center for Regenerative Medicine and Stem Cell Research, University of California Los Angeles, Los Angeles, CA, USA

    Vincenzo Calvanese, Mattias Magnusson, Gay M. Crooks, Siavash K. Kurdistani & Hanna K. A. Mikkola

  3. Department of Biological Chemistry, University of California Los Angeles, Los Angeles, CA, USA

    Trent Su & Siavash K. Kurdistani

  4. Department of Obstetrics and Gynecology, University of California Los Angeles, Los Angeles, CA, USA

    Lydia K. Lee

  5. Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA

    Gay M. Crooks

  6. Jonsson Comprehensive Cancer Center, University of California Los Angeles, Los Angeles, CA, USA

    Gay M. Crooks, Siavash K. Kurdistani & Hanna K. A. Mikkola

  7. Molecular Biology Institute, University of California Los Angeles, Los Angeles, CA, USA

    Siavash K. Kurdistani & Hanna K. A. Mikkola

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  1. Vincenzo Calvanese
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Contributions

V.C. and H.K.A.M. designed experiments and interpreted data. V.C. performed and/or supervised all experiments and data analysis, including the generation of functional data documenting the effects of MLLT3 on HSC expansion in culture, the characterization of the expanded HSCs in transplantation and the molecular characterization of MLLT3-mediated gene regulation in HSCs. A.T.N., T.J.B. and A.V. contributed to MLLT3 knockdown and overexpression experiments. Other members of the laboratory also independently replicated these key functional data. A.V., T.J.B., L.K.L. and Y.W. contributed to the transplantation experiments in NSG mice. V.C., F.D.L. and T.S. performed bioinformatics analysis. M.M., G.M.C. and S.K.K. provided support with experimental design and discussed data analysis and results. V.C. and H.K.A.M. wrote the manuscript, which all authors edited and approved.

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Correspondence to Vincenzo Calvanese or Hanna K. A. Mikkola.

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Peer review information Nature thanks Elisa Laurenti and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Extended data figures and tables

Extended Data Fig. 1 MLLT3 expression in human haematopoietic tissues and after shRNA knockdown.

a, Candidate human HSC self-renewal factors that correlate with the self-renewal of human FL-HSCs (Fig. 1a) and are localized in the nucleus are listed. b, Bar graphs showing mean and individual probe values for MLLT3 from microarray datasets after differentiation of CD34+CD38−/loCD90+GPI80+ FL-HSCs to CD34+CD38−/loCD90+GPI80− progenitors3, in FL-HSPCs after 5-week culture on OP9M2 stroma4, and in hES cell-HSPCs5 (n = 3; n = 2 for 5-week culture and hES cell-HSPCs). c, Quantitative PCR with reverse transcription (qRT–PCR) validation of MLLT3 expression in human fetal liver, cord blood and bone marrow HSPCs and downstream progenitors (n = 2 donors per tissue). Expression is relative to GAPDH, normalized to the first sample of each plot. Mean and individual values are shown. d, UCSC genome browser tracks of RNA-seq analysis of sorted human developmental haematopoietic populations17. First trimester (5 weeks) embryonic aorta-gonad-mesonephros (AGM) endothelium (CD34+CD90+CD43−) and HSPC (CD34+CD90+CD43+) population from one donor are compared to second trimester (17-week) FL-HSPCs (CD34+CD38−/loCD90+CD45+). Expression of transcription factors involved in the development of haemogenic endothelium and HSC specification are compared to MLLT3. e, f, Flow cytometry analysis (e) and quantification (f) of GFP fluorescence in human K562 cells (haematopoietic cell line with no detectable MLLT3 expression) stably expressing MLLT3-OE vector (as in Extended Data Fig. 2a), that were transduced with five different shRNAs targeting MLLT3 (sh_90–94), representative of two transductions. shRNA-mediated knockdown targeting MLLT3 also lowers GFP that is transcribed from a single GFP-P2A-MLLT3 transcript. Median fluorescence intensity (MFI) from n = 2 transductions (mean and individual values). g, Strategy for MLLT3 lentiviral knockdown and functional analysis in FL-HSPCs. h, FACS plots 5 and 15 days after MLLT3-KD (representative of three experiments). i, j, MLLT3 qRT–PCR 5 days after transduction (i) and quantification of CD34+CD38−/lo (j) in cells transduced with empty vector or MLLT3 shRNA (KD92 and KD93) after 5 and 15 days in culture (mean and individual values, n = 3 donors). k, Representative FACS plots from NSG mouse bone marrow 12 weeks after transplantation assessing human CD45+ cells, multi-lineage haematopoietic reconstitution and HSPCs (extended from Fig. 1d, n = 10 mice, 2 independent experiments).

Extended Data Fig. 2 Effects of MLLT3-OE on HSPC expansion.

a, FUGW lentiviral construct expressing MLLT3 and GFP. P2A, cleavage sequence; UbC, ubiquitin C promoter; V5, V5 peptide tag. Representative flow cytometry plots showing persistent GFP expression in FL-HSPCs after transduction with control or MLLT3-OE vector, representative of six experiments. b, qRT–PCR quantifying MLLT3-OE in transduced HSPCs after one week (mean, n = 3). c, The effects of MLLT3-OE in GPI80+ (red) and GPI80− (blue) FL-HSPC progenitors. d, Representative FACS plots derived from GPI80+ HSPCs (top) and GPI80− progenitors (bottom) expressing control and MLLT3-OE vectors. e, Expansion of total live cells (left) and HSPCs (right) expressing control or MLLT3-OE vectors (mean and s.e.m., n = 3). f, Strategy for lentiviral overexpression of MLLT3 in FL-HSPCs seeded with or without OP9M2 stroma to assess their dependence on a supportive microenvironment. g, Representative FACS plots of FUGW empty-vector-transduced and MLLT3-OE-transduced GPI80+ HSPCs seeded on OP9M2 (top) or stroma-free culture wells. h, Expansion of total live cells (left) and CD34+CD38−/lo CD90+ HSPCs (right) transduced with control or MLLT3-OE vectors (mean and s.e.m., n = 3 independent experiments). Culture of MLLT3-OE HSPCs with small molecules UM171 and SR1. i, Expansion of total live cells (top) and CD34+CD38−/loCD90+ HSPCs (bottom) transduced with control or MLLT3-OE vectors (mean and s.e.m., n = 2 independent experiments), cultured in SFEM with SR1 and UM171. j, Expansion of total live cells (top) and CD34+CD38−/loCD90+ HSPCs (bottom) transduced with control or MLLT3-OE vectors (mean and s.e.m., n = 3 independent experiments), cultured in HSC medium on OP9M2 with SR1 and UM171. P values in e, h and j determined by two-sided t-test.

Extended Data Fig. 3 Effects of MLLT3-OE on cell cycle, viability and differentiation in culture.

a, Strategy to test cell-cycle activity, apoptosis and differentiation potential of MLLT3-OE-expanded HSPCs. b, BrdU incorporation flow cytometry analysis of FL-HSPCs cultured for 4 weeks on OP9M2 with SR1 and UM171. G0/G1 (BrdU−AADlo), S (BrdU+7AADint) and G2/M (BrdU−7AADhi) phases are quantified in FL-HSPCs (mean and s.e.m., n = 5). c, Apoptosis analysis of HSPCs cultured for 4 weeks on OP9M2 with SR1 and UM171. Early (AnnV+7AAD−) and late (AnnV+7AAD+) apoptosis are quantified by flow cytometry (mean and s.e.m., n = 2 uncultured, n = 4 control and MLLT3-OE). d, FACS analysis of in vitro differentiation (representative of three experiments). e, Quantification of the differentiation of 4-week expanded HSPCs into myeloid, erythroid, megakaryocytic, B- and T-lymphoid lineages, compared with the differentiation of uncultured FL-HSPCs (mean, n = 3 donors except for megakaryocytic n = 4 and B-lymphoid uncultured n = 2). All P values determined by two-sided t-test.

Extended Data Fig. 4 Analysis of human reconstitution and correlation of cell cycle and culture time with the engraftment of MLLT3-OE cells.

a, Transplantation strategy with ex vivo expanded FL-HSPCs transduced with control or MLLT3-OE vector. On day 5, transduced CD34+GFP+ cells derived from 1,000 HSPCs were sorted and replated in SFEM and transplanted into NSG mice at day 15. Human haematopoietic reconstitution was assessed at 24 weeks. Bone marrow from engrafted mice was transplanted to secondary recipients. b, FACS analysis showing human haematopoietic reconstitution (hCD45) (Fig. 1h) and multilineage differentiation (myeloid CD66, CD14; B-lymphoid CD19; T-lymphoid CD3, CD4, CD8). c, d, Representative FACS plots (c) and quantification (d) of human haematopoietic reconstitution from peripheral blood of transplanted NSG mice (extended from Fig. 1h). e, Quantification of human haematopoietic reconstitution from mouse spleen. Number of mice showing multi-lineage reconstitution versus total transplanted mice is shown (n = 13 or 14 mice, mean and individual values from four independent experiments). f, FACS sorting strategy for cell-cycle analysis after 100 min in vivo BrdU pulse in haematopoietic cells derived from human FL-HSPCs (8,000 GPI80+ FL-HSPCs or their progeny day 15 of culture after transduction with control or MLLT3-OE vector) transplanted in NSG mice and sorted 14 weeks after transplantation. g, h, Representative FACS plots (g) and quantification (h) of cell-cycle distribution by BrdU and DNA content (7AAD) staining. G0/G1 (BrdU−7AADlo), S (BrdU+7AADint) and G2/M (BrdU−7AADhi) phases are quantified in HSPCs, CD66b+ myeloid and CD19+ B-lymphoid cells (mean, n = 2). i, j, Cell-cycle analysis in HSPCs before transplantation, after 2-week culture in SFEM with SR1 and UM171. All P values determined by two-sided t-test. k, Transplantation strategy assessing the effect of extended culture on the reconstitution ability of MLLT3-OE FL-HSPCs. FL-HSPCs transduced with control or MLLT3-OE vector were sorted on day 5, and CD34+GFP+ cells derived from 1,000 uncultured HSPCs were transplanted directly into NSG mice or cultured in SFEM for an additional 10 days before transplantation. Numbers show mice with multilineage human engraftment, transplanted before (day 5) and after (day 15) culture expansion. l, m, Human CD45+ cells (l) and human HSPCs (m) in mouse bone marrow are quantified (n = 4 independent experiments; number of mice is indicated). *P < 0.05, two-sided t-test. n, o, Representative FACS plots (n) and quantification (o) of human haematopoietic (CD45) reconstitution and multilineage differentiation in mouse bone marrow after 18 weeks of secondary transplantation (mean, two independent experiments). Secondary mice were transplanted with 2-week-old bone marrow from primary recipients (1/2 of femur of the primary transplanted mice injected per recipient).

Extended Data Fig. 5 MLLT3 binding and correlation with epigenetic marks.

a, Heat map showing k-means clustering for MLLT3 ChIP–seq peaks and their association with ATAC-seq and histone marks in uncultured FL-HSPCs. ChIP of MLLT3 in K562 cells is shown as a control. The region surrounding the MLLT3 peak centre ± 5 kb is shown. Peak distance from the gene TSSs and representative high-ranked biological processes enriched in GREAT analysis are shown for each cluster. Cluster 6 genes, in which the MLLT3 signal does not correlate with active marks, show no significant enrichment for biological processes. b, Average profile and heat map for MLLT3 and indicated histone marks in expressed and non-expressed genes in uncultured FL-HSPCs. Metagene plot ± 2 kb is shown. This represents an extended version of Fig. 2c. c, Heat map for selected gene groups as in b. This represents an extended version of Fig. 2e. Tracks in a–c are representative of n = 3 experiments for MLLT3 ChIP in FL-HSPCs, n = 1 for H3K9cr, H3K27me3 and H3K9me3, and n = 2 for other ChIP experiments.

Extended Data Fig. 6 Cell-type specificity of MLLT3 binding.

a, UCSC genome browser tracks showing ChIP–seq of MLLT3 and epigenetic marks in representative MLLT3-bound genes in FL-HSPCs and FL-erythroblasts (n = 3 MLLT3 ChIP in FL-HSPCs, n = 1 for H3K9cr H3K27me3 and H3K9me3, and n = 2 for other ChIP experiments). This represents an extended version of Fig. 2g. b, Heat map with hierarchical clustering for genes bound by MLLT3 in FL-HSPCs (n = 2) and/or CD34−GlyA+CD71+ FL-erythroblasts (n = 2) (genes with significant MLLT3 peak < 5 kb from TSS) showing clusters of common and cell-type-specific MLLT3-bound genes. c, Distribution of MLLT3 FL- erythroblast peaks. d, GO analysis of MLLT3-bound genes in FL-erythroblasts.

Extended Data Fig. 7 MLLT3 binding and regulation of MLLT3 target genes in cultured HSPCs.

a, Heat map showing distribution of MLLT3 peaks reflecting endogenous MLLT3 and MLLT3-OE ChIP–seq signal using MLLT3 and V5 antibodies. MLLT3 from uncultured FL-HSPCs and input control, MLLT3 ChIP signal from 4 week cultured FL-HSPCs with MLLT3-OE or empty control vector, and V5 ChIP from FL-HSPCs cultured for 4 weeks with MLLT3-OE or control vector. The region surrounding the peak summit (± 2 kb) is shown for the combined MLLT3 peaks from all conditions (FL-HSPC MLLT3 ChIP n = 3, MLLT3-OE V5 ChIP n = 2 and MLLT3-OE MLLT3 ChIP n = 1). IP, immunoprecipitate. b, Heat map showing hierarchical clustering for all MLLT3-bound genes as in a. Signal from all MLLT3-bound genes (significant MLLT3 peak at < 5 kb from TSS) is shown. c, UCSC genome browser tracks of representative MLLT3-bound HSC (yellow) and erythroid (red) genes. Tracks show ChIP–seq signal for input and MLLT3 in uncultured FL-HSPCs, MLLT3 ChIP–seq on FL-HSPCs cultured for 4 weeks with control and MLLT3-OE vectors; V5-tagged MLLT3 ChIP–seq from FL-HSPCs cultured for 4 weeks with control and MLLT3-OE vectors, and MLLT3 ChIP–seq in FL-erythroblasts. d, Probe values from microarray datasets4 for MLLT3 and MLLT3-bound and upregulated HSC genes (see Fig. 3) documenting the decline of gene expression over time in culture (day 0 (n = 3) 2 weeks (n = 3) and 5 weeks (n = 2) on OP9M2, mean and individual values). e–g, Knockdown of MLLT3 targets MECOM and HLF in MLLT3-OE cells. e, FACS plots at 15 and 30 days after MECOM-KD and HLF-KD, with or without MLLT3-OE (representative of three experiments). f, Quantification of CD34+CD38−/lo CD90+ HSPCs (percentage of live cells) in MECOM-KD and HLF-KD FL-HSPCs transduced with empty vector or MLLT3-OE after 5, 15, 30 and 42 days in culture (mean, n = 3). P values determined by two-sided t-test. g, Quantification of HLF downregulation in HepG2 cells by qRT–PCR. h, Quantification of MECOM downregulation in K562 cells by qRT–PCR.

Extended Data Fig. 8 Examples of MLLT3-mediated gene regulation after MLLT3-OE, MLLT3-KD and DOT1L inhibition.

a, UCSC genome browser tracks of representative MLLT3-OE upregulated or downregulated genes. From top to bottom: MLLT3 ChIP–seq signal in FL-HSPCs cultured for 4 weeks with empty vector or MLLT3-OE; RNA-seq tracks; ChIP–seq for RNA Pol II and epigenetic marks in cultured control and MLLT3-OE FL-HSPC. Tracks are generated from the merged signal from n = 5 H3K79me2 ChIP, n = 2 other ChIPs. b, UCSC genome browser tracks of the indicated ChIP experiments in representative genes in MLLT3-KD and control vector after 5-day culture, MLLT3-OE, MLLT3-OE plus EPZ5676 (DOT1L inhibitor), or control plus EPZ5676 and control vector after 10-day culture, and MLLT3-OE versus control from 4-week culture. Genes from Fig. 3g are indicated in bold. The genomic region adjacent to MSI2 is included to show genes not bound by MLLT3 (that is, PCPT and VCL).

Extended Data Fig. 9 Effects of MLLT3 knockdown and overexpression on CB-HSPC expansion in vitro.

a, FACS analysis, 5 and 15 days after MLLT3-KD in CB-HSPCs (representative of two experiments). b, Quantification of haematopoietic subsets CD34+CD38−/lo and CD34+CD38−/loCD90+ (HSPCs) in empty vector (CTR) and MLLT3-KD-transduced cells after 5, 15 and 30 days in culture (mean, n = 2). Circles represent individual values. c, FACS analysis of CB-HSPCs transduced with control or MLLT3-OE vectors cultured on OP9M2 (representative of two experiments). d, Expansion of total live cells (left) and CD34+CD38−/lo CD90 HSPCs (right) transduced with MLLT3-OE or control vectors.

Extended Data Fig. 10 Comparison of the function of MLLT3-OE cells and uncultured cells in transplanted mice.

a, FACS analysis of total human haematopoietic reconstitution in peripheral blood from mice transplanted with uncultured CB-HSPCs (CD34+CD38−CD90+) or CB-HSPCs transduced with empty GFP vector (CTR) or MLLT3-OE vector after 15 days of culture, as in Fig. 4f (representative of three experiments). Plots show human CD45 haematopoietic reconstitution and multilineage differentiation (myeloid CD66, CD14; B-lymphoid CD19; T-lymphoid CD3, CD4, CD8), and GFP in hCD45+ cells. b, Quantification of monocytes (CD14+), granulocytes (CD66b+), B cells (CD14−CD66b−CD19+) and T cells (CD14−CD66b−CD4+ and/or CD8+) as a percentage of human CD45+ cells in engrafted NSG mice 24 weeks after transplantation (mean, n = 3 experiments). P values determined by two-sided t-test. Quantification of human haematopoietic reconstitution in the blood of NSG mice 24 weeks after transplantation in limiting dilution, as in Fig. 4f. Percentage of hCD45+ cells in peripheral blood (c) and spleen (d) (mean, n = 3 experiments). e, f, FACS plots (e) and quantification (f) of total human CD45+ haematopoietic reconstitution after 18 weeks of secondary transplantation of cord blood (uncultured, day-15-expanded control and MLLT3-OE HSPCs) in mouse bone marrow (mean, n = 2 experiments). g, h, Quantification of human reconstitution in engrafted mice after 24 weeks of primary transplantation. A panel of markers covering the classical AML blast (CD34 and CD33) and B-cell precursor markers (CD34, CD10, CD20, CD24, CD38) were tested in day 15-expanded MLLT3-OE CB-HSPCs, comparing their distribution to mice engrafted with uncultured CB-HSPCs (n = 5 mice from two independent experiments, mean and individual values). Haematopoietic markers included CD33+ myeloid, CD19+ lymphoid, and B-cell precursor markers CD34+ CD10+, CD20+, CD24+ and CD38+ within the CD19+ population.

Supplementary information

Reporting Summary

Supplementary Table

Supplementary Table 1. Effect of MLLT3-KD on human hematopoietic reconstitution. a) Hematopoietic engraftment of MLLT3-KD cultured FL-HSPC. Reconstitution by human hematopoietic cells in NSG mice transplanted with expanded progeny of 30,000 FL-CD34+ cells transduced with control (CTR) or MLLT3-KD (sh93) lentiviral vector (MLLT3). Samples are obtained from BM at week 12 from transplantation. Each row corresponds to one transplanted mouse. All numbers indicate percentage of live cells. T cells are not quantified. KD differentiated populations are not quantified as all mice were non-engrafted.

Supplementary Table

Supplementary Table 2. Effect of MLLT3-OE on human hematopoietic reconstitution. a) Hematopoietic engraftment of MLLT3-OE cultured FL-HSPC. (Top) Reconstitution by human hematopoietic cells in NSG mice transplanted at day 15 with expanded progeny of 1000 FL-HSPC transduced with control (CTR) or MLLT3-OE lentiviral vector (MLLT3). Samples are obtained from BM, blood and spleen at 24 weeks from transplantation. Each row corresponds to one engrafted mouse. All numbers indicate percentage of live cells. Multilineage reconstitution is called when human CD45+ population is detectable, expressing at least one myeloid and one lymphoid marker. (Bottom) Reconstitution by human hematopoietic cells in NSG mice transplanted at day 5 with sorted GFP+CD34+ cells that are progeny of 1000 FL-HSPC transduced with control (CTR) or MLLT3-OE lentiviral vector (MLLT3).

Supplementary Table

Supplementary Table 3. MLLT3 binding in FL-HSPC and erythroblasts. a) MLLT3 ChIPseq peaks in FL-HSPC. Peaks are called against the input with the program MACS2 (options –f BAM -g hs -n ML3_INmerge_peaksBroDef --broad) and annotated using homer annotatePeaks.pl. b) Expression of MLLT3-bound genes in FL-HSPC. Protein-coding genes having at least one MLLT3 peak within 5kb from TSS are selected. c) Selected MLLT3 bound genes grouped by the indicated functions, analyzed in Figure 2e and Extended data Fig.5c. d) MLLT3 ChIP-seq peaks in FL-erythroblasts. Peaks are called as in (a).

Supplementary Table

Supplementary Table 4. Gene expression analysis of cultured FL-HSPC upon MLLT3 overexpression. Genes upregulated (a) or downregulated (b) by MLLT3-OE in 4 weeks cultured FL-HSPC. RNAseq analysis (paired MLLT-OE vs CTR) is performed with EdgeR from n=6 experiments using homer tools. (adj p-value <0.05 and -logFC >0.322).

Supplementary Table

Supplementary Table 5. Effect of MLLT3-OE on CB-HSC hematopoietic reconstitution ability and quantification of reconstituting units (RU). Hematopoietic reconstitution of MLLT3-OE expanded CB-HSPC. Human reconstitution in NSG mice transplanted with the indicated number of HSPC (day 0, uncultured HSPC) or their progeny (day 5 or day 15) transduced with control (CTR) or MLLT3-OE lentiviral vector (MLLT3). Samples are obtained from BM, blood and spleen at 24 weeks from transplantation. Each row corresponds to one transplanted mouse. All numbers indicate percentage of live cells. Multilineage engraftment is called when human CD45+ population is detectable, expressing at least one myeloid and one lymphoid marker.

Supplementary Table

Supplementary Table 6: Primers used to study MLLT3 in human HSPC. (a) Taqman® Primers to study gene expression. (b) SyBR Green Q-RT-PCR primers. (c) Cloning Primers to generate MLLT3-OE vector.

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Calvanese, V., Nguyen, A.T., Bolan, T.J. et al. MLLT3 governs human haematopoietic stem-cell self-renewal and engraftment. Nature 576, 281–286 (2019). https://doi.org/10.1038/s41586-019-1790-2

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