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Biomolecular condensation of NUP98 fusion proteins drives leukemogenic gene expression

Abstract

NUP98 fusion proteins cause leukemia via unknown molecular mechanisms. All NUP98 fusion proteins share an intrinsically disordered region (IDR) in the NUP98 N terminus, featuring repeats of phenylalanine-glycine (FG), and C-terminal fusion partners often function in gene control. We investigated whether mechanisms of oncogenic transformation by NUP98 fusion proteins are hardwired in their protein interactomes. Affinity purification coupled to mass spectrometry (MS) and confocal imaging of five NUP98 fusion proteins expressed in human leukemia cells revealed that shared interactors were enriched for proteins involved in biomolecular condensation and that they colocalized with NUP98 fusion proteins in nuclear puncta. We developed biotinylated isoxazole-mediated condensome MS (biCon-MS) to show that NUP98 fusion proteins alter the global composition of biomolecular condensates. An artificial FG-repeat-containing fusion protein phenocopied the nuclear localization patterns of NUP98 fusion proteins and their capability to drive oncogenic gene expression programs. Thus, we propose that IDR-containing fusion proteins combine biomolecular condensation with transcriptional control to induce cancer.

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Fig. 1: NUP98−KDM5A does not operate in the context of the nuclear pore complex.
Fig. 2: Functional proteomic identification of conserved interactors of diverse NUP98 fusion proteins.
Fig. 3: The NUP98 fusion protein interactome is enriched for proteins with roles in biomolecular condensation.
Fig. 4: biCon-MS globally charts the cellular condensome.
Fig. 5: Expression of NUP98 fusion proteins dynamically alters the cellular condensome.
Fig. 6: An artificial IDR-containing KDM5A fusion protein phenocopies NUP98-fusion-induced changes in the condensome.
Fig. 7: Artificial FG-containing fusion proteins induce leukemogenic gene expression programs in hematopoietic progenitor cells.

Data availability

LC–MS/MS data have been deposited into the PRIDE database under the accession number PXD022518.

RNA-seq data have been deposited into the GEO under the accession number GSE159037. Source data are provided with this paper.

Code availability

R script implemented for group size–dependent testing statistic (GSDTS) is available at https://github.com/Edert/R-scripts

References

  1. 1.

    Mitelman, F., Johansson, B. & Mertens, F. The impact of translocations and gene fusions on cancer causation. Nat. Rev. Cancer 7, 233–245 (2007).

    CAS  PubMed  Google Scholar 

  2. 2.

    Mertens, F., Johansson, B., Fioretos, T. & Mitelman, F. The emerging complexity of gene fusions in cancer. Nat. Rev. Cancer 15, 371–381 (2015).

    CAS  PubMed  Google Scholar 

  3. 3.

    Zhao, J., Lee, S. H., Huss, M. & Holme, P. The network organization of cancer-associated protein complexes in human tissues. Sci. Rep. 3, 1583 (2013).

    PubMed  PubMed Central  Google Scholar 

  4. 4.

    Reckel, S. et al. Differential signaling networks of Bcr–Abl p210 and p190 kinases in leukemia cells defined by functional proteomics. Leukemia 31, 1502–1512 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  5. 5.

    Skucha, A. et al. MLL-fusion-driven leukemia requires SETD2 to safeguard genomic integrity. Nat. Commun. 9, 1983 (2018).

    PubMed  PubMed Central  Google Scholar 

  6. 6.

    Gough, S. M., Slape, C. I. & Aplan, P. D. NUP98 gene fusions and hematopoietic malignancies: common themes and new biologic insights. Blood 118, 6247–6257 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  7. 7.

    Struski, S. et al. NUP98 is rearranged in 3.8% of pediatric AML forming a clinical and molecular homogenous group with a poor prognosis. Leukemia 31, 565–572 (2017).

    CAS  PubMed  Google Scholar 

  8. 8.

    Radu, A., Moore, M. S. & Blobel, G. The peptide repeat domain of nucleoporin Nup98 functions as a docking site in transport across the nuclear pore complex. Cell 81, 215–222 (1995).

    CAS  PubMed  Google Scholar 

  9. 9.

    Jeganathan, K. B., Malureanu, L. & van Deursen, J. M. The Rae1–Nup98 complex prevents aneuploidy by inhibiting securin degradation. Nature 438, 1036–1039 (2005).

    CAS  PubMed  Google Scholar 

  10. 10.

    Capelson, M. et al. Chromatin-bound nuclear pore components regulate gene expression in higher eukaryotes. Cell 140, 372–383 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  11. 11.

    Capitanio, J. S., Montpetit, B. & Wozniak, R. W. Human Nup98 regulates the localization and activity of DExH/D-box helicase DHX9. Elife 6, e18825 (2017).

    PubMed  PubMed Central  Google Scholar 

  12. 12.

    Wang, G. G. et al. Haematopoietic malignancies caused by dysregulation of a chromatin-binding PHD finger. Nature 459, 847–851 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  13. 13.

    Yassin, E. R., Abdul-Nabi, A. M., Takeda, A. & Yaseen, N. R. Effects of the NUP98–DDX10 oncogene on primary human CD34+ cells: role of a conserved helicase motif. Leukemia 24, 1001–1011 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  14. 14.

    Wang, G. G., Cai, L., Pasillas, M. P. & Kamps, M. P. NUP98–NSD1 links H3K36 methylation to Hox-A gene activation and leukaemogenesis. Nat. Cell Biol. 9, 804–812 (2007).

    CAS  PubMed  Google Scholar 

  15. 15.

    Franks, T. M. et al. Nup98 recruits the Wdr82–Set1A/COMPASS complex to promoters to regulate H3K4 trimethylation in hematopoietic progenitor cells. Genes Dev. 31, 2222–2234 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  16. 16.

    Brangwynne, C. P. et al. Germline P granules are liquid droplets that localize by controlled dissolution/condensation. Science 324, 1729–1732 (2009).

    CAS  PubMed  Google Scholar 

  17. 17.

    Molliex, A. et al. Phase separation by low complexity domains promotes stress granule assembly and drives pathological fibrillization. Cell 163, 123–133 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  18. 18.

    Schmidt, H. B. & Görlich, D. Nup98 FG domains from diverse species spontaneously phase-separate into particles with nuclear pore-like permselectivity. Elife 4, e04251 (2015).

    PubMed Central  Google Scholar 

  19. 19.

    Schmoellerl, J. et al. CDK6 is an essential direct target of NUP98-fusion proteins in acute myeloid leukemia. Blood 136, 387–400 (2020).

    PubMed  PubMed Central  Google Scholar 

  20. 20.

    Fahrenkrog, B. et al. Expression of Leukemia-Associated Nup98 fusion proteins generates an aberrant nuclear envelope phenotype. PLoS ONE 11, e0152321 (2016).

    PubMed  PubMed Central  Google Scholar 

  21. 21.

    Mellacheruvu, D. et al. The CRaPome: a contaminant repository for affinity purification–mass spectrometry data. Nat Methods. 10, 730–736 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  22. 22.

    Griffis, E. R., Xu, S. & Powers, M. A. Nup98 localizes to both nuclear and cytoplasmic sides of the nuclear pore and binds to two distinct nucleoporin subcomplexes. Mol. Biol. Cell 14, 600–610 (2003).

    CAS  PubMed  PubMed Central  Google Scholar 

  23. 23.

    Ren, Y., Seo, H.-S., Blobel, G. & Hoelz, A. Structural and functional analysis of the interaction between the nucleoporin Nup98 and the mRNA export factor Rae1. Proc. Natl Acad. Sci. USA 107, 10406–10411 (2010).

    CAS  PubMed  Google Scholar 

  24. 24.

    Klein, B. J. et al. The histone-H3K4-specific demethylase KDM5B binds to its substrate and product through distinct PHD fingers. Cell Reports 6, 325–335 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  25. 25.

    Lucio-Eterovic, A. K. et al. Role for the nuclear receptor-binding SET domain protein 1 (NSD1) methyltransferase in coordinating lysine 36 methylation at histone 3 with RNA polymerase II function. Proc. Natl Acad. Sci. USA 107, 16952–16957 (2010).

    CAS  PubMed  Google Scholar 

  26. 26.

    Ramos-Mejía, V. et al. HOXA9 promotes hematopoietic commitment of human embryonic stem cells. Blood 124, 3065–3075 (2014).

    Google Scholar 

  27. 27.

    Boija, A. et al. Transcription factors activate genes through the phase-separation capacity of their activation domains. Cell 175, 1842–1855.e16 (2018).

    CAS  Google Scholar 

  28. 28.

    Patel, A. et al. A liquid-to-solid phase transition of the ALS protein FUS accelerated by disease mutation. Cell 162, 1066–1077 (2015).

    CAS  PubMed  Google Scholar 

  29. 29.

    Nott, T. J. et al. Phase transition of a disordered nuage protein generates environmentally responsive membraneless organelles. Mol. Cell 57, 936–947 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  30. 30.

    Vernon, R. M. et al. Pi-Pi contacts are an overlooked protein feature relevant to phase separation. Elife 7, e31486 (2018).

    PubMed  PubMed Central  Google Scholar 

  31. 31.

    Kato, M. et al. Cell-free formation of RNA granules: low complexity sequence domains form dynamic fibers within hydrogels. Cell 149, 753–767 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  32. 32.

    Han, T. W. et al. Cell-free formation of RNA granules: bound RNAs identify features and components of cellular assemblies. Cell 149, 768–779 (2012).

    CAS  PubMed  Google Scholar 

  33. 33.

    Schmöllerl, J. et al. CDK6 is a common transcriptional target of NUP98-fusion-proteins in acute myeloid leukemia. Blood 136, 387–400 (2020).

    Google Scholar 

  34. 34.

    Xu, H. et al. NUP98 fusion proteins interact with the NSL and MLL1 complexes to drive leukemogenesis. Cancer Cell 30, 863–878 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  35. 35.

    Feric, M. et al. Coexisting liquid phases underlie nucleolar subcompartments. Cell 165, 1686–1697 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  36. 36.

    Gall, J. G., Bellini, M., Wu, Z. & Murphy, C. Assembly of the nuclear transcription and processing machinery: Cajal bodies (coiled bodies) and transcriptosomes. Mol. Biol. Cell 10, 4385–4402 (1999).

    CAS  PubMed  PubMed Central  Google Scholar 

  37. 37.

    Sabari, B. R. et al. Coactivator condensation at super-enhancers links phase separation and gene control. Science 80, eaar3958 (2018).

    Google Scholar 

  38. 38.

    Cho, W.-K. et al. Mediator and RNA polymerase II clusters associate in transcription-dependent condensates. Science 361, 412–415 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  39. 39.

    Banani, S. F., Lee, H. O., Hyman, A. A. & Rosen, M. K. Biomolecular condensates: organizers of cellular biochemistry. Nat. Rev. Mol. Cell Biol. 18, 285–298 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  40. 40.

    Chong, S. et al. Imaging dynamic and selective low-complexity domain interactions that control gene transcription. Science 80, eaar2555.

  41. 41.

    Schürmann, M. et al. Three-dimensional correlative single-cell imaging utilizing fluorescence and refractive index tomography. J. Biophotonics 11, e201700145 (2018).

    Google Scholar 

  42. 42.

    Kasper, L. H. et al. CREB binding protein interacts with nucleoporin-specific FG repeats that activate transcription and mediate NUP98-HOXA9 oncogenicity. Mol. Cell. Biol. 19, 764–776 (1999).

    CAS  PubMed  PubMed Central  Google Scholar 

  43. 43.

    Romana, S. P. et al. NUP98 rearrangements in hematopoietic malignancies: a study of the Groupe Francophone de Cytogénétique Hématologique. Leukemia 20, 696–706 (2006).

    CAS  PubMed  Google Scholar 

  44. 44.

    Griffis, E. R., Altan, N., Lippincott-Schwartz, J. & Powers, M. A. Nup98 is a mobile nucleoporin with transcription-dependent dynamics. Mol. Biol. Cell 13, 1282–1297 (2002).

    CAS  PubMed  PubMed Central  Google Scholar 

  45. 45.

    Wang, J. et al. A molecular grammar governing the driving forces for phase separation of prion-like RNA binding proteins. Cell 174, 688–699.e16 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  46. 46.

    Shin, Y. et al. Liquid nuclear condensates mechanically sense and restructure the genome. Cell 175, 1481–1491.e13 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  47. 47.

    Soutourina, J., Wydau, S., Ambroise, Y., Boschiero, C. & Werner, M. Direct interaction of RNA polymerase II and mediator required for transcription in vivo. Science 331, 1451–1454 (2011).

    CAS  PubMed  Google Scholar 

  48. 48.

    Kuo, Y.-H. et al. Runx2 induces acute myeloid leukemia in cooperation with Cbfβ-SMMHC in mice. Blood 113, 3323–3332 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  49. 49.

    Gaidzik, V. I. et al. TET2 mutations in acute myeloid leukemia (AML): results from a comprehensive genetic and clinical analysis of the AML study group. J. Clin. Oncol. 30, 1350–1357 (2012).

    CAS  PubMed  Google Scholar 

  50. 50.

    Xu, S. & Powers, M. A. In vivo analysis of human nucleoporin repeat domain interactions. Mol. Biol. Cell 24, 1222–1231 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  51. 51.

    Xu, S. & Powers, M. A. Nup98-homeodomain fusions interact with endogenous Nup98 during interphase and localize to kinetochores and chromosome arms during mitosis. Mol. Biol. Cell 21, 1585–1596 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  52. 52.

    Yung, E. et al. Delineating domains and functions of NUP98 contributing to the leukemogenic activity of NUP98-HOX fusions. Leuk. Res. 35, 545–550 (2011).

    CAS  PubMed  Google Scholar 

  53. 53.

    Boulay, G. et al. Cancer-specific retargeting of BAF complexes by a prion-like domain. Cell 171, 163–178.e19 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  54. 54.

    Longo, P. A., Kavran, J. M., Kim, M.-S. & Leahy, D. J. Transient mammalian cell transfection with polyethylenimine (PEI). Methods Enzymol. 529, 227 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  55. 55.

    Fiji. Colocalisation_Analysis: Fiji’s plugin for colocalization analysis. https://github.com/fiji/Colocalisation_Analysis (2020).

  56. 56.

    Schneider, C. A., Rasband, W. S. & Eliceiri, K. W. NIH Image to ImageJ: 25 years of image analysis. Nat. Methods 9, 671–675 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  57. 57.

    Schindelin, J. et al. Fiji: an open-source platform for biological-image analysis. Nat. Methods 9, 676–682 (2012).

    CAS  PubMed  Google Scholar 

  58. 58.

    Wiśniewski, J. R., Zougman, A., Nagaraj, N. & Mann, M. Universal sample preparation method for proteome analysis. Nat. Methods 6, 359–362 (2009).

    PubMed  Google Scholar 

  59. 59.

    Olsen, J. V. et al. Parts per million mass accuracy on an Orbitrap mass spectrometer via lock mass injection into a C-trap. Mol. Cell. Proteom. 4, 2010–2021 (2005).

    CAS  Google Scholar 

  60. 60.

    Chambers, M. C. et al. A cross-platform toolkit for mass spectrometry and proteomics. Nat. Biotechnol. 30, 918–920 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  61. 61.

    Barsnes, H. & Vaudel, M. SearchGUI: a highly adaptable common interface for proteomics search and de novo engines. J. Proteome Res. 17, 2552–2555 (2018).

    CAS  PubMed  Google Scholar 

  62. 62.

    The Uniprot Consortium. UniProt: a worldwide hub of protein knowledge. Nucleic Acids Res. 47, D506–D515 (2019).

    Google Scholar 

  63. 63.

    Vaudel, M. et al. PeptideShaker enables reanalysis of MS-derived proteomics data sets. Nat. Biotechnol. 33, 22–24 (2015).

    CAS  PubMed  Google Scholar 

  64. 64.

    Zhang, X. et al. Proteome-wide identification of ubiquitin interactions using UbIA-MS. Nat. Protoc. 13, 530–550 (2018).

    CAS  PubMed  Google Scholar 

  65. 65.

    Shannon, P. et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 13, 2498–2504 (2003).

    CAS  PubMed  PubMed Central  Google Scholar 

  66. 66.

    Bindea, G. et al. ClueGO: a Cytoscape plug-in to decipher functionally grouped gene ontology and pathway annotation networks. Bioinformatics 25, 1091–1093 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  67. 67.

    Warnes, G. R. et al. Various R programming tools for plotting data. R package. https://cran.r-project.org/web/packages/gplots/index.html (2016).

  68. 68.

    Andrews, S. FastQC: a quality control tool for high throughput sequence data. https://www.bioinformatics.babraham.ac.uk/projects/fastqc/ (2010).

  69. 69.

    Schmieder, R. & Edwards, R. Quality control and preprocessing of metagenomic datasets. Bioinformatics 27, 863–864 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  70. 70.

    Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  71. 71.

    Li, H. et al. The sequence alignment/map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).

    PubMed  PubMed Central  Google Scholar 

  72. 72.

    Liao, Y., Smyth, G. K. & Shi, W. featureCounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics 30, 923–930 (2014).

    CAS  Google Scholar 

  73. 73.

    Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).

    PubMed  PubMed Central  Google Scholar 

  74. 74.

    Johnson, W. E., Li, C. & Rabinovic, A. Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostatistics 8, 118–127 (2007).

    PubMed  PubMed Central  Google Scholar 

  75. 75.

    Leek, J. T., Johnson, W. E., Parker, H. S., Jaffe, A. E. & Storey, J. D. The sva package for removing batch effects and other unwanted variation in high-throughput experiments. Bioinformatics 28, 882–883 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

We thank all members of the Grebien laboratory for stimulating discussions and A. Orlova for help with QUANT-seq experiments. A. Spittler, G. Hofbauer (Core Facility Flow Cytometry, Medical University of Vienna), S. Fajmann and P. Jodl (Institute of Pharmacology and Toxicology, University of Veterinary Medicine Vienna) are acknowledged for cell sorting. This research was supported using resources of the VetCore Facility (Imaging) of the University of Veterinary Medicine Vienna. Next-generation sequencing was performed at the VBCF NGS Unit (https://www.viennabiocenter.org/facilities/). In addition, we thank all members of the Proteomics and Metabolomics Facility (CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences) for the proteomics analysis. J.S. is a recipient of a DOC Fellowship of the Austrian Academy of Sciences at the University of Veterinary Medicine Vienna. This work was supported by a grant from the European Research Council under the European Union’s Horizon 2020 research and innovation programme (grant agreement no. 636855/StG) to F.G. E.M.T. was supported by a fellowship of the Austrian Science Fund (FWF, Elise Richter Fellowship V506-B28).

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Contributions

S.T.-Z, F.G.L. and F.G. designed the study; S.T.-Z, J.S., F.G.L and F.G. developed the methodology; S.T.-Z., T.H., T.E. and A.C.M. performed data analysis; S.T.-Z., T.H., J.S., N.K., E.H., G.M. and K.P. conducted experiments and collected data; T.E. performed bioinformatics analysis and developed software; S.T.-Z. and F.G. wrote the initial draft; S.T.-Z., T.H., T.E., N.K. and F.G. generated visualization of the data; F.G. supervised the project; S.T-Z. and F.G. coordinated responsibility for the research activity; E.M.T. and F.G. acquired funding; all authors revised the final manuscript prior to submission.

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Correspondence to Florian Grebien.

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The authors declare no competing interests.

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Peer review information Nature Structural & Molecular Biology thanks Christopher Slape and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available. Inês Chen was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

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Extended data

Extended Data Fig. 1 Immunoprecipitation of endogenous NUP98 and affinity purification of NUP98-KDM5A coupled to LC-MS/MS, Related to Fig. 1.

a, mRNA expression of NUP98-KDM5A in HL-60 cells and mouse AML cells expressing NUP98-KDM5A (paired, two-sided t-test, p-value: 0.33, three biological replicates). ddCt values were calculated using GAPDH/Gapdh expression for HL-60 cells and mouse leukemia cells, respectively. Graph shows individual data points, mean and s.d. n = 3 (biological replicates of the same experiment) (b) Western blot analysis of mock-transduced- and NUP98-KDM5A-expressing HL-60 cells. Control lysates were incubated with NUP98 antibody conjugated to magnetic beads and NUP98-KDM5A lysates were incubated with magnetic Strep-Tactin beads for 1 hour. Input, supernatant (sup) and pull down fractions were loaded and membranes were probed with anti-NUP98, anti-HA and anti-Tubulin antibodies. A representative blot of three independent experiments is shown. Uncropped images are available in Supplementary Fig. 1. c, String database networks of individual subcomplexes identified by Gene Ontology for Biological Processes (Fig. 1d). Identified interactors for respective GO terms were clustered using String database (cutoff 0.4). Individual proteins are highlighted (yellow to green) according to abundance in MS datasets. d, Confocal microscopy images of N-NUP98-expressing HL-60 cells stained with DAPI (green in merge) and anti-HA antibody (white in merge) for exogenous fusion proteins. Scale bar: 5 µm. Six independent experiments were performed with similar results. e, Venn diagram of identified proteins from three different affinity purifications in HL-60 cells. Protein lists from immuno-affinity purification experiments with an anti-NUP98 antibody in mock transduced cells or N-NUP98-expressing cells were intersected with each other and with proteins identified in Strep-Tactin purifications from N-NUP98-expressing cells. The overlap shows N-terminal NUP98 interactors that are conserved between different pull-down approaches. Source data

Extended Data Fig. 2 AP-MS analysis of NUP98-fusion proteins and validation of selected interaction partners, Related to Fig. 2.

a, Schematic representation of the experimental strategy. HL-60 cells were transduced with retroviral constructs encoding NUP98 fusion proteins and protein complex pulldowns were performed using Strep-Tactin followed by LC-MS/MS analysis. b, Immunofluorescence of mock transduced (top) HL-60 cells or cells expressing NUP98-KDM5A (bottom) stained with DAPI, anti-HA (background-fluorescence corrected) and anti-RAE1. Co-localization (co-loc) was determined with the ImageJ plugin ‘Colocalization’ Six independent experiments were performed with similar results. (c) Manders’ coefficient showing the co-occurrence of NUP98-KDM5A with RAE1. **** p-value < 0.0001, n = 12 cells examined over 2 independent experiments (6 mock vs. 6 NUP98-KDM5A) Two sided, paired t-test, t = -10.277, df = 5, **** p-value = 0.0001499. The box plot centre line defines the median, the box limits indicate upper and lower quartiles, whiskers indicate minima and maxima among all data points. Data behind graph are available as Source Data. d, Western blot analysis of protein lysates from control HEK293T cells or cells transfected with NUP98-KDM5A after Strep-Tactin affinity purification. Blot is representative of three independent experiments. Uncropped images are available in Supplementary Fig. 1. (e) CORUM analysis of 157 NUP98-fusion protein core interactors. Complexes are illustrated as a network highlighting top terms ranked by p-value. The network was generated with ClueGo v3.5.4. f, GO analysis (Biological Process) was performed for 157 NUP98-fusion protein core interactors. GO terms were ranked by combined score using Enrichr. Source data

Extended Data Fig. 3 PScore analysis of different protein lists, Related to Fig. 3.

a, GSDTS was performed for the Gene Ontology list ‘Nuclear Membrane’. The mean PScore was compared to a set of lists of equal length that were randomly subsampled from the human proteome. b, GSDTS was performed for the human proteome. p-values were calculated using the Kolmogorov–Smirnov test. c, 100 µM b-isox precipitation of HEK293T cells transfected with mock, flag-tagged NUP98-KDM5A or untagged NUP98-KDM5A. Endogenous NUP98 and NUP98-KDM5A proteins were detected with anti-NUP98 antibodies in input, supernatant (sup) and precipitated (b-isox) fractions. Blot is representative of three independent experiments. Uncropped images are available in Supplementary Fig. 1. Source data

Extended Data Fig. 4 b-isox precipitation of N-NUP98, related to Fig. 4.

a, Western blot analysis of protein lysates from HEK293T cells expressing N-NUP98 treated with 11 µM, 33 µM or 100 µM b-isox. N-NUP98 was detected using anti-HA antibodies. Total input, supernatant and b-isox fractions (pellet) are shown. Blot is representative of three independent experiments. Uncropped images are available in Supplementary Fig. 1.

Extended Data Fig. 5 biCon-MS for NUP98-KDM5A and NUP98-NSD1, Related to Fig. 5.

a, Normalized and scaled protein abundances for selected proteins previously implicated in the formation of biomolecular condensates identified by biCon-MS from lysates of HL-60 cells expressing NUP98-KDM5A. Graph shows individual data points, mean and s.d. for n = 4 (2 biological, 2 technical replicates). b, Normalized and scaled protein abundances for selected proteins previously implicated in the formation of biomolecular condensates identified by biCon-MS within lysates of HL-60 cells expressing NUP98-NSD1. Graph shows individual data points, mean and s.d. for n = 4 (2 biological, 2 technical replicates). c, Western blot analysis of NUP98-KDM5A-expressing NIH-3T3 cell lysates treated with 11 µM, 33 µM or 100 µM b-isox. Dose-dependent precipitation was investigated for NUP98-KDM5A and HSC70 as loading control. One representative blot of three independent experiments is shown. d, Western blot analysis of NIH-3T3 cell lysates treated with 11 µM, 33 µM or 100 µM b-isox. Dose-dependent precipitation was investigated for HSC70. One representative blot of three independent experiments is shown. Blot is representative of three independent experiments. Uncropped images for panels c and d are available in Supplementary Fig. 1. e, Schematic illustration of enriched/depleted proteins identified in fusion protein biCon-MS compared to mock-transduced HL-60 cells. f, Enrichment of proteins that exhibit dose-dependent precipitation upon expression of NUP98-KDM5A and NUP98-NSD1 as compared to mock-transduced HL-60 cells based on abundances in biCon-MS analysis. Enriched/depleted proteins in NUP98-fusion protein condensates are illustrated as nodes and are colored according to calculated fold change values. Depletion cutoff: log2(fc) < −1.5 and p-value < 0.01 Enrichment cutoff: log2(fc) > 1.5 and p-value < 0.01. P-value was calculated using a two-sided ANOVA test. g, Normalized and scaled abundances of significantly enriched (MED31) or depleted (MED15) proteins in NUP98-fusion protein condensomes as identified by biCon-MS. Graph shows individual data points, mean and s.d. for n = 4 (2 biological, 2 technical replicates). Source data

Extended Data Fig. 6 biCon-MS for artAA-KDM5A and artFG-KDM5A, Related to Fig. 6.

Volcano plot of (a) artAA-KDM5A and (b) artFG-KDM5A for 11 µM and 33 µM condensomes, generated from normalized protein abundances obtained by biCon-MS. The significance cutoff was log2(fc) < −1 or > 1 and p-value < 0.01. (c) GO (Molecular Function) analysis for 237 proteins precipitated by b-isox in artFG-KDM5A- vs. artAA-KDM5A-expressing cells. (d) Normalized and scaled abundances of selected enriched proteins in the artFG-KDM5A-induced condensome as identified by biCon-MS. Graph shows individual data points, mean and s.d. for n = 4 (2 biological and 2 technical replicates). Source data

Extended Data Fig. 7 RNA-seq of mouse fetal liver cells, Related to Fig. 7.

a, Principal component analysis based on normalized expression profiles of RNA-seq from murine wild type fetal liver cells and cells expressing NUP98-KDM5A, artAA-KDM5A or artFG-KDM5A. b, 761 genes that are differentially regulated upon expression of NUP98-KDM5A and artFG-KDM5A were grouped using DisGeNET according to related diseases. Most significant disease terms are illustrated as hexagons sized according to their p-value. Corresponding disease classes are shown as diamonds connected by edges to diseases, defined by DiGeNET. c, The mean PScore for all proteins involved in cancer gene fusions listed in COSMIC was compared to a list of 10,000 randomly subsampled lists of the human proteome with the same size. The p-value was calculated using the two-sided, two-sample Kolmogorov-Smirnov test, D = 0.26114, p-value < 2.2e-16. Source data

Supplementary information

Supplementary Information

Supplementary Table 3, Supplementary Note, Supplementary Fig. 1.

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Supplementary Table 1

Mass spectrometry of endogenous NUP98 and NUP98–KDM5A.

Supplementary Table 2

Mass spectrometry of NUP98–HOXA9, NUP98–PSIP1, NUP98–DDX10 and NUP98–NSD1 and spectrum counts for core interactors.

Supplementary Table 4

Summary of Kolmogorov–Smirnov test analyses of PScore enrichment for different protein lists.

Supplementary Table 5

Mass spectrometry of biCon-MS for HL-60 cells (mock, NUP98–KDM5A and NUP98–NSD1).

Supplementary Table 6

Mass spectrometry of biCon-MS for HL-60 cells (artAA-KDM5A and artFG-KDM5A)

Source data

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Terlecki-Zaniewicz, S., Humer, T., Eder, T. et al. Biomolecular condensation of NUP98 fusion proteins drives leukemogenic gene expression. Nat Struct Mol Biol 28, 190–201 (2021). https://doi.org/10.1038/s41594-020-00550-w

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