Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

TGF-β-induced DACT1 biomolecular condensates repress Wnt signalling to promote bone metastasis

Nature Cell Biology volume 23, pages 257–267 (2021)Cite this article

Subjects

Abstract

The complexity of intracellular signalling requires both a diversity of molecular players and the sequestration of activity to unique compartments within the cell. Recent findings on the role of liquid–liquid phase separation provide a distinct mechanism for the spatial segregation of proteins to regulate signalling pathway crosstalk. Here, we discover that DACT1 is induced by TGFβ and forms protein condensates in the cytoplasm to repress Wnt signalling. These condensates do not localize to any known organelles but, rather, exist as phase-separated proteinaceous cytoplasmic bodies. The deletion of intrinsically disordered domains within the DACT1 protein eliminates its ability to both form protein condensates and suppress Wnt signalling. Isolation and mass spectrometry analysis of these particles revealed a complex of protein machinery that sequesters casein kinase 2—a Wnt pathway activator. We further demonstrate that DACT1 condensates are maintained in vivo and that DACT1 is critical to breast and prostate cancer bone metastasis.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: TGF-β transcriptionally induces DACT1 to repress Wnt signalling.
Fig. 2: Formation of DACT1-associated biomolecular condensates.
Fig. 3: DACT1 intrinsically disordered domains drive phase separation and Wnt suppression.
Fig. 4: Isolation and characterization of DACT1 biomolecular condensates.
Fig. 5: DACT1 sequesters cytoplasmic CK2 into condensates.
Fig. 6: DACT1 is necessary for bone metastasis growth.

Similar content being viewed by others

Data availability

MS proteomics data have been deposited at ProteomeXchange Consortium through the PRIDE partner repository under the dataset identifier PXD019271; MS data are also available in Supplementary Table 3. MS data were analysed using PantherDB (http://www.pantherdb.org/) and Cytoscape (https://cytoscape.org/). The microarray data generated in this study are available at the NCBI Gene Expression Omnibus (GEO) with the accession number GSE151198. The human breast cancer data from the EMC-MSK dataset are available in Bos et al.57 and source data from this resource supporting Fig. 6g and Supplementary Table 5 are available in the source data for Fig. 6. The data from the METABRIC dataset are available in Curtis et al.56 as well as the source data for Extended Data Fig. 9. Source data are provided with this paper. All other data and image files are available from the corresponding author on reasonable request.

Code availability

Customized macro and R scripts used for colocalization analysis are available at GitHub (https://github.com/rgunaratna/Colocalization_Analysis).

References

  1. Attisano, L. & Labbe, E. TGFβ and Wnt pathway cross-talk. Cancer Metastasis Rev. 23, 53–61 (2004).

    Article  CAS  PubMed  Google Scholar 

  2. Glass, D. A. 2nd et al. Canonical Wnt signaling in differentiated osteoblasts controls osteoclast differentiation. Dev. Cell 8, 751–764 (2005).

    Article  CAS  PubMed  Google Scholar 

  3. Retting, K. N., Song, B., Yoon, B. S. & Lyons, K. M. BMP canonical Smad signaling through Smad1 and Smad5 is required for endochondral bone formation. Development 136, 1093–1104 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Takaku, K. et al. Gastric and duodenal polyps in Smad4 (Dpc4) knockout mice. Cancer Res. 59, 6113–6117 (1999).

    CAS  PubMed  Google Scholar 

  5. Fujita, K. & Janz, S. Attenuation of WNT signaling by DKK-1 and -2 regulates BMP2-induced osteoblast differentiation and expression of OPG, RANKL and M-CSF. Mol. Cancer 6, 71 (2007).

    Article  PubMed  PubMed Central  Google Scholar 

  6. Winkler, D. G. et al. Sclerostin inhibition of Wnt-3a-induced C3H10T1/2 cell differentiation is indirect and mediated by bone morphogenetic proteins. J. Biol. Chem. 280, 2498–2502 (2005).

    Article  CAS  PubMed  Google Scholar 

  7. Inkson, C. A. et al. TGF-β1 and WISP-1/CCN-4 can regulate each other’s activity to cooperatively control osteoblast function. J. Cell. Biochem. 104, 1865–1878 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Bragado, P. et al. TGF-β2 dictates disseminated tumour cell fate in target organs through TGF-β-RIII and p38α/β signalling. Nat. Cell Biol. 15, 1351–1361 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Korpal, M. et al. Imaging transforming growth factor-β signaling dynamics and therapeutic response in breast cancer bone metastasis. Nat. Med. 15, 960–966 (2009).

    Article  CAS  PubMed  Google Scholar 

  10. Weilbaecher, K. N., Guise, T. A. & McCauley, L. K. Cancer to bone: a fatal attraction. Nat. Rev. Cancer 11, 411–425 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Esposito, M., Guise, T. & Kang, Y. The biology of bone metastasis. Cold Spring Harb. Perspect. Med. 8, a031252 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  12. Xu, J. et al. 14-3-3ζ turns TGF-β’s function from tumor suppressor to metastasis promoter in breast cancer by contextual changes of Smad partners from p53 to Gli2. Cancer Cell 27, 177–192 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Sethi, N., Dai, X., Winter, C. G. & Kang, Y. Tumor-derived jagged1 promotes osteolytic bone metastasis of breast cancer by engaging notch signaling in bone cells. Cancer Cell 19, 192–205 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Pfeilschifter, J. & Mundy, G. R. Modulation of type beta transforming growth factor activity in bone cultures by osteotropic hormones. Proc. Natl Acad. Sci. USA 84, 2024–2028 (1987).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Malladi, S. et al. metastatic latency and immune evasion through autocrine inhibition of WNT. Cell 165, 45–60 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Zhuang, X. et al. Differential effects on lung and bone metastasis of breast cancer by Wnt signalling inhibitor DKK1. Nat. Cell Biol. 19, 1274–1285 (2017).

    Article  CAS  PubMed  Google Scholar 

  17. Mariz, K., Ingolf, J. B., Daniel, H., Teresa, N. J. & Erich-Franz, S. The Wnt inhibitor dickkopf-1: a link between breast cancer and bone metastases. Clin. Exp. Metastasis 32, 857–866 (2015).

    Article  CAS  PubMed  Google Scholar 

  18. Esposito, M. et al. Bone vascular niche E-selectin induces mesenchymal–epithelial transition and Wnt activation in cancer cells to promote bone metastasis. Nat. Cell Biol. 21, 627–639 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Pronobis, M. I., Rusan, N. M. & Peifer, M. A novel GSK3-regulated APC:Axin interaction regulates Wnt signaling by driving a catalytic cycle of efficient βcatenin destruction. eLife 4, e08022 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  20. Schwarz-Romond, T., Merrifield, C., Nichols, B. J. & Bienz, M. The Wnt signalling effector Dishevelled forms dynamic protein assemblies rather than stable associations with cytoplasmic vesicles. J. Cell Sci. 118, 5269–5277 (2005).

    Article  CAS  PubMed  Google Scholar 

  21. Mitrea, D. M. & Kriwacki, R. W. Phase separation in biology; functional organization of a higher order. Cell Commun. Signal. 14, 1 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  22. Yang, P. et al. G3BP1 is a tunable switch that triggers phase separation to assemble stress granules. Cell 181, 325–345 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Sanders, D. W. et al. Competing protein-RNA interaction networks control multiphase intracellular organization. Cell 181, 306–324 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Minn, A. J. et al. Genes that mediate breast cancer metastasis to lung. Nature 436, 518–524 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Kang, Y. et al. A multigenic program mediating breast cancer metastasis to bone. Cancer Cell 3, 537–549 (2003).

    Article  CAS  PubMed  Google Scholar 

  26. Forozan, F. et al. Molecular cytogenetic analysis of 11 new breast cancer cell lines. Br. J. Cancer 81, 1328–1334 (1999).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Zheng, H. et al. Therapeutic antibody targeting tumor- and osteoblastic niche-derived jagged1 sensitizes bone metastasis to chemotherapy. Cancer Cell 32, 731–747 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Shen, M. et al. Tinagl1 suppresses triple-negative breast cancer progression and metastasis by simultaneously inhibiting integrin/FAK and EGFR signaling. Cancer Cell 35, 64–80 (2019).

    Article  CAS  PubMed  Google Scholar 

  29. Koinuma, D. et al. Chromatin immunoprecipitation on microarray analysis of Smad2/3 binding sites reveals roles of ETS1 and TFAP2A in transforming growth factor β signaling. Mol. Cell. Biol. 29, 172–186 (2009).

    Article  CAS  PubMed  Google Scholar 

  30. Padua, D. et al. TGFβ primes breast tumors for lung metastasis seeding through angiopoietin-like 4. Cell 133, 66–77 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Kang, Y., Chen, C. R. & Massague, J. A self-enabling TGFβ response coupled to stress signaling: Smad engages stress response factor ATF3 for Id1 repression in epithelial cells. Mol. Cell 11, 915–926 (2003).

    Article  CAS  PubMed  Google Scholar 

  32. Cheyette, B. N. et al. Dapper, a Dishevelled-associated antagonist of β-catenin and JNK signaling, is required for notochord formation. Dev. Cell 2, 449–461 (2002).

    Article  CAS  PubMed  Google Scholar 

  33. Gloy, J., Hikasa, H. & Sokol, S. Y. Frodo interacts with Dishevelled to transduce Wnt signals. Nat. Cell Biol. 4, 351–357 (2002).

    Article  CAS  PubMed  Google Scholar 

  34. Waxman, J. S., Hocking, A. M., Stoick, C. L. & Moon, R. T. Zebrafish Dapper1 and Dapper2 play distinct roles in Wnt-mediated developmental processes. Development 131, 5909–5921 (2004).

    Article  CAS  PubMed  Google Scholar 

  35. Hikasa, H. & Sokol, S. Y. The involvement of Frodo in TCF-dependent signaling and neural tissue development. Development 131, 4725–4734 (2004).

    Article  CAS  PubMed  Google Scholar 

  36. Park, J. I. et al. Frodo links Dishevelled to the p120-catenin/Kaiso pathway: distinct catenin subfamilies promote Wnt signals. Dev. Cell 11, 683–695 (2006).

    Article  CAS  PubMed  Google Scholar 

  37. Zhang, L., Gao, X., Wen, J., Ning, Y. & Chen, Y. G. Dapper 1 antagonizes Wnt signaling by promoting dishevelled degradation. J. Biol. Chem. 281, 8607–8612 (2006).

    Article  CAS  PubMed  Google Scholar 

  38. Gao, X. et al. Dapper1 is a nucleocytoplasmic shuttling protein that negatively modulates Wnt signaling in the nucleus. J. Biol. Chem. 283, 35679–35688 (2008).

    Article  CAS  PubMed  Google Scholar 

  39. Chen, H. et al. Protein kinase A-mediated 14-3-3 association impedes human Dapper1 to promote dishevelled degradation. J. Biol. Chem. 286, 14870–14880 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Ma, B. et al. Dapper1 promotes autophagy by enhancing the Beclin1-Vps34-Atg14L complex formation. Cell Res. 24, 912–924 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Huang, Y., Wang, P., Chen, H., Ding, Y. & Chen, Y. G. Myc-interacting zinc-finger protein 1 positively regulates Wnt signalling by protecting Dishevelled from Dapper1-mediated degradation. Biochem. J. 466, 499–509 (2015).

    Article  CAS  PubMed  Google Scholar 

  42. Kivimae, S., Yang, X. Y. & Cheyette, B. N. All Dact (Dapper/Frodo) scaffold proteins dimerize and exhibit conserved interactions with Vangl, Dvl, and serine/threonine kinases. BMC Biochem. 12, 33 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Fuerer, C. & Nusse, R. Lentiviral vectors to probe and manipulate the Wnt signaling pathway. PLoS ONE 5, e9370 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  44. Biechele, T. L., Adams, A. M. & Moon, R. T. Transcription-based reporters of Wnt/β-catenin signaling. Cold Spring Harb. Protoc. 2009, pdb.prot5223 (2009).

    Article  PubMed  Google Scholar 

  45. Ma, B. et al. The Wnt signaling antagonist Dapper1 accelerates Dishevelled2 degradation via promoting its ubiquitination and aggregate-induced autophagy. J. Biol. Chem. 290, 12346–12354 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Voros, J. The density and refractive index of adsorbing protein layers. Biophys. J. 87, 553–561 (2004).

    Article  PubMed  PubMed Central  Google Scholar 

  47. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Weber, S. C. & Brangwynne, C. P. Getting RNA and protein in phase. Cell 149, 1188–1191 (2012).

    Article  CAS  PubMed  Google Scholar 

  49. Schaefer, K. N. & Peifer, M. Wnt/Beta-catenin signaling regulation and a role for biomolecular condensates. Dev. Cell 48, 429–444 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Woodruff, J. B., Hyman, A. A. & Boke, E. Organization and function of non-dynamic biomolecular condensates. Trends Biochem. Sci. 43, 81–94 (2018).

    Article  CAS  PubMed  Google Scholar 

  52. Bracha, D. et al. Mapping local and global liquid phase behavior in living cells using photo-oligomerizable seeds. Cell 175, 1467–1480 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Hubstenberger, A. et al. P-body purification reveals the condensation of repressed mRNA regulons. Mol. Cell 68, 144–157 (2017).

    Article  CAS  PubMed  Google Scholar 

  54. Gao, Y. & Wang, H. Y. Casein kinase 2 Is activated and essential for Wnt/β-catenin signaling. J. Biol. Chem. 281, 18394–18400 (2006).

    Article  CAS  PubMed  Google Scholar 

  55. Vinayagam, A. et al. A directed protein interaction network for investigating intracellular signal transduction. Sci. Signal. 4, rs8 (2011).

    Article  PubMed  Google Scholar 

  56. Curtis, C. et al. The genomic and transcriptomic architecture of 2,000 breast tumours reveals novel subgroups. Nature 486, 346–352 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Bos, P. D. et al. Genes that mediate breast cancer metastasis to the brain. Nature 459, 1005–1009 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Bracha, D. et al. Mapping local and global liquid phase behavior in living cells using photo-oligomerizable seeds. Cell 175, 1467–1480 (2019).

    Article  Google Scholar 

  59. Webb, B. D. et al. Heterozygous pathogenic variant in DACT1 causes an autosomal-dominant syndrome with features overlapping Townes-Brocks syndrome. Hum. Mutat. 38, 373–377 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Kim, K. et al. High-resolution three-dimensional imaging of red blood cells parasitized by Plasmodium falciparum and in situ hemozoin crystals using optical diffraction tomography. J. Biomed. Opt. 19, 011005 (2014).

    PubMed  Google Scholar 

  61. Popescu, G. et al. Optical imaging of cell mass and growth dynamics. Am. J. Physiol. Cell Physiol. 295, C538–C544 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Sheng, H., Stauffer, W. & Lim, H. N. Systematic and general method for quantifying localization in microscopy images. Biol. Open 5, 1882–1893 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Raj, A., van den Bogaard, P., Rifkin, S. A., van Oudenaarden, A. & Tyagi, S. Imaging individual mRNA molecules using multiple singly labeled probes. Nat. Methods 5, 877–879 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

We thank C. DeCoste and K. Rittenbach at the Molecular Biology Flow Cytometry Resource Facility of Princeton University for flow cytometry assays and G. Laevsky at the Confocal Imaging Facility of Princeton University (a Nikon Center of Excellence) for assistance with imaging; H. Cho and D. Kim for performing Tomocube image sectioning and RI analysis; and Y. Chen (Tsinghua University), B. Cheyette (UCSF), R. Moon (University of Washington) and R. Nusse (Stanford) for providing reagents. This work was supported by fellowships from the NIH (F31CA192461 to M.E. and F31AI147637 to K.C.C.), from NJCCR (DCHS19PPC029 to R.T.G.), and grants from the Brewster Foundation, NIH (R01CA212410) and the US Department of Defense (BC123187) to Y.K., and from the NIH (R01GM114141 to I.M.C. and T32GM007388 to K.C.C.).

Author information

Author notes

  1. These authors contributed equally: Cao Fang, Katelyn C. Cook.

Authors and Affiliations

  1. Department of Molecular Biology, Princeton University, Princeton, NJ, USA

    Mark Esposito, Cao Fang, Katelyn C. Cook, Nana Park, Yong Wei, Ramesh T. Gunaratna, Gary Laevsky, Christina J. DeCoste, Hannah Slabodkin, Ileana M. Cristea & Yibin Kang

  2. Osteoncology and Rare Tumors Center, Istituto Scientifico Romagnolo per lo Studio e la Cura dei Tumori (IRST) IRCCS, Meldola, Italy

    Chiara Spadazzi

  3. Department of Chemical and Biological Engineering, Princeton University, Princeton, NJ, USA

    Dan Bracha & Clifford P. Brangwynne

  4. Howard Hughes Medical Institute, Princeton University, Princeton, NJ, USA

    Clifford P. Brangwynne

  5. Ludwig Institute for Cancer Research, Princeton University, Princeton, NJ, USA

    Yibin Kang

  6. Cancer Metabolism and Growth Program, Rutgers Cancer Institute of New Jersey, New Brunswick, NJ, USA

    Yibin Kang

Authors
  1. Mark Esposito
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Cao Fang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Katelyn C. Cook
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Nana Park
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Yong Wei
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Chiara Spadazzi
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Dan Bracha
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Ramesh T. Gunaratna
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Gary Laevsky
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Christina J. DeCoste
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Hannah Slabodkin
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Clifford P. Brangwynne
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Ileana M. Cristea
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Yibin Kang
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

M.E. conceived the project and cowrote the manuscript. M.E., C.F. and N.P. designed and performed flow cytometry, xenograft, genetic, RT–qPCR, confocal and bioinformatics experiments, and analysed data with assistance from Y.W., C.S., H.S. and R.T.G.; K.C.C. and I.M.C. designed and performed MS. C.J.D. assisted with particle isolation and G.L. assisted with microscopy. D.B. and C.P.B. performed and assisted with FRAP and FUS experiments and provided expert advice. Y.K. supervised the project, cowrote the manuscript, and provided experimental advice and critical guidance.

Corresponding author

Correspondence to Yibin Kang.

Ethics declarations

Competing interests

M.E. holds equity interest in KayoThera. Y.K. holds equity interest in KayoThera and Firebrand Therapeutics.

Additional information

Peer review information: Nature Cell Biology thanks the anonymous reviewers for their contribution to the peer review of this work. Peer reviewer reports are available.

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

Extended data

Extended Data Fig. 1 Characterization of the metastatic variants of SUM159 breast cancer cell line identifies DACT1 as a highly expressed gene in the bone metastatic M1a subline.

a, Schematic summary of the establishment of a series of isogenic sublines with different primary tumor and metastasis potential from the parental SUM159 triple negative breast cancer cell line. Parental SUM159 cells were stably labeled with a retroviral triple reporter (TR) expressing GFP, thymidine kinase, and firefly luciferase (F-luc) and injected into the mammary fat pad of nude mice. A primary tumor was isolated, cultured, and re-injected by either tail-vein or mammary fat pad injection. Successful outgrowths were then isolated and cultured. b, Isolated cell lines were injected orthotopically into NSG mice and monitored for primary tumor growth. n = 5 mice/group. Student’s t-test. Representative of 2 independent experiments. c, d, e, Ex vivo imaging of bones and lungs was conducted once primary tumors reached mean diameter >1 cm. Values for lung (c) and bone (d) metastasis were thresholded to 0 at values below 104 and 105 photon/sec, respectively, to remove background noise. Individual hindlimbs were treated as independent data points. Rates of successful outgrowth were enumerated per group (e). f, Representative ex vivo bioluminescence images of spontaneous metastasis to lung and bone from each derived subline from (c-e). g, The development of bone metastasis after intracardiac injection of each derivative was monitored by bioluminescent imaging and was compared to SUM159-TR. Mann-Whitney U test. n = 6 mice/group. h,i, Representative bioluminescent (h) and X-ray images (i) at day 0 and day 25 of mice from (g). j, Gene expression values from microarray analysis were used to generate a list of genes up-regulated >4-fold in M1a compared to either SUM159PT-TR or M1L1. k, Heatmap representation of the expression levels of the 11 differentially expressed genes from (j). l, RT–qPCR analysis of DACT1 mRNA levels normalized to Gapdh in the indicated SUM159 sublines. n = 4 technical repeats, representative data from 2 independent experiments. m, n) Flow cytometry measurement of 7x-TCF-GFP Wnt reporter activity in BM2 (m) or HPL1 (n) cells with Wnt3a and Wnt inhibitor ICG-001 (25 μM) treatment. n = 3 biological replicates. Student’s t-test. Experiment independently repeated 3 times. Data represents mean ± SEM. * p < 0.05, ** p < 0.01, *** p < 0.005 in g with exact p values in Source Data. Numerical source data for b-e, g, l-n are provided.

Source data

Extended Data Fig. 2 DACT1 represses Wnt signaling.

a,b, qPCR analysis of DACT1 mRNA levels normalized to Gapdh in BM2-TGC (a) and HPL1-TGC (b) cells stably transduced with DACT1-targeting shRNA (KD#1 and KD#2) or overexpression constructs. n = 4 technical repeats, Student’s t-test. Data represents mean ± SEM. Student’s t-test. c, d, Flow cytometry assessment of 7x-TCF-GFP Wnt reporter activity in BM2 (c) or HPL1 (d) cells with DACT1 knockdown or overexpression, n = 3 (c) and n = 4 (d) biological replicates. Student’s t-test. Experiments were independently repeated >3 times (c) and 2 times (d). e, Immunofluorescence of total β-catenin in BM2 cells with DACT1 knockdown or overexpression with Wnt3a stimulation for 24 hours. Scale bars represent 50 μm. Representative of 2 independent replicates. f, Western blot of indicated proteins in BM2 cells with indicated DACT1-related constructs with Wnt3a or control media stimulation for 24 hours. Representative of 2 independent replicates. Numerical source data for a-d and uncropped blots for f, are provided.

Source data

Extended Data Fig. 3 TGF-β-induced DACT1 suppresses Wnt signaling.

a, Indirect immunofluorescence of β-catenin in the indicated DACT1-modified HPL1 cells with Wnt3a or control media for 24 h. Mean pixel in intensities for Wnt3a-treated cells were measured at 9.041 (KD#1), 5.047 (KD#2), 1.562 (Vector), 1.356 (DACT1). Scale bars represent 10 μm. Representative of 2 independent replicates. b, qPCR analysis of Axin2 mRNA in BM2 cells with stable DACT1 KD or control after treatment with Wnt3a for 24 or 48 h. mRNA levels were normalized with Gapdh level and then Axin2 levels were normalized to the respective control condition. n = 3 technical replicates, Student’s t-test. Representative of 2 independent experiments. c, 7x-TCF-GFP Wnt reporter expressing BM2 cells were pre-treated for 24 hours with TGF-β and/or the TGF-β inhibitor LY2109761 followed by stimulation with Wnt3a and flow cytometry assessment of Wnt activation. n = 4 biological replicates. Student’s t-test. d, e, BM2-TGC cells with the indicated modification of DACT1 were treated with Wnt3a or Wnt3a + TGF-β (3 h or 24 h pre-treatment) were quantified by flow cytometry. n = 4 biological replicates. Student’s t-test. f, Gating strategy for flow cytometry quantification of 7TGC Wnt reporter activity. GFP+ counts from GFP by mCherry panel corresponds to data in Figs. 1e, 3c, 3f, 4d, 4e, Extended Figs. 1m, n, 2c, d, 3c-e. Numerical source data for b-e are provided.

Source data

Extended Data Fig. 4 DACT1 bodies are proteasomally degraded and do not co-localize with known organelles.

a, DACT1-TdTomato fusion protein expression in M1a cells with or without 24 h Wnt3a treatment. Scale bar represents 10 μm. Representative of n > 5 independent replicates. b, DACT1-TdTomato c-terminal fusions imaged in HPL1 cells. Scale bar represents 5 μm. Representative of n > 5 independent replicates. c, d, Western blot analysis of DACT1 wild-type and DACT1-TdTomato fusion protein expression in both M1a and BM2 cells after treatment with cycloheximide (50 μg/mL) at the indicated times (c) or either Bafilomycin A (10 nM) or MG-132 (20 μM) for 12 h. (d). Images (c, d) representative of 3 independent experiments. * indicates expected molecular weight of fusion proteins. e, Representative images of DACT1-TdTomato fusions and the indicated organelle-associated proteins imaged by confocal sectioning. Scale bars represent 4 μm. Representative of 3 independent replicates. Uncropped blots for c, d, are provided.

Source data

Extended Data Fig. 5 DACT1 particles show biomolecular condensation properties.

a, b, DACT1 knockdown, control, overexpressing (a) or TdTomato c-terminal fusion cells (b) were probed with anti-DACT1 antibody followed by confocal imaging. Scale bars represent 5 μm. Images representative of 4 independent replicates. c, Holotomographic/epifluorescent particle sectioning analysis used to map refractive indices to 3D coordinates of TdTomato fluorescence.

Extended Data Fig. 6 Deletion and hybrid mutant analysis of DACT1 protein localization.

a, Schematic of deletions made in the DACT1 amino acid sequence with predicted intrinsically disordered domains in black background and previously reported sequence motifs labeled in multiple colors. b, c, Confocal imaging of BM2 (c) and M1a (d) cells expressing DR7-TdTomato or DR8-TdTomato fusions labeled with Hoechst. Scale bars represent 10 μm. Representative of >3 independent replicates. d, Fluorescent in situ hybridization of RNA probes for Ctnnb1 in BM2-hDACT1-TdTomato expressing cells. Scale bars represent 20 μm. Representative of 3 independent replicates.

Extended Data Fig. 7 Mass spectrometry analysis of DACT1 biomolecular condensates.

a, Isolation and analysis workflow for the characterization of DACT1-TdTomato particles. b, Proteins identified by mass spectrometry were analyzed by gene ontology using the Panther DB overrepresentation test with associated enrichment values and p-values. c, High stringency STRING analysis of identified proteins shows enriched protein interaction networks as related to biological processes. Protein background color correlates to enrichment ratio observed in Wnt3a-treated as compared to control-treated particles.

Extended Data Fig. 8 DACT1 sequesters cytoplasmic CK2 into condensates.

a, BM2 cells stably expressing CK2-GFP fusions alone or in combination with DACT1-TdTomato fusions were labeled with Hoechst and imaged via confocal microscopy. Scale bars represent 10 μm. Image representative of >5 independent replicates. b, M1a cells expressing constructs from (a) were subjected to the phase separation particle isolation protocol with the addition of GFP measurement. Experiment independently repeated 2 times.

Extended Data Fig. 9 DACT1 is essential for bone metastasis in models of breast and prostate cancer.

a, qRT-PCR analysis of DACT1 mRNA levels normalized to Gapdh in the M1a cells stably transduced with DACT1-targeting shRNA or control shRNA. n = 4 technical repeats, representative data from 2 independent mRNA isolations and qRT-PCR experiments. Student’s t-test. b, Proliferation rate over 24 hours was measured by WST-8 MTT assay and normalized to Control shRNA. n = 4 biological replicates, Student’s t-test. c, qRT-PCR analysis of DACT1 mRNA levels normalized to Gapdh in the BM2 cells stably transduced with DACT1-targeting shRNA or control shRNA. n = 4 technical repeats, representative data from 2 independent mRNA isolations and qRT-PCR experiments. Student’s t-test. d, BLI quantification of hind limb bone metastasis burden in mice injected with BM2 cells stably transduced with DACT1-targeting shRNA or control shRNA. Per-mouse signal was normalized to photon flux of the same mouse measured on Day 0 post-injection. n = 9 mice per group. Mann-Whitney U test. e, Representative bioluminescent and X-ray images from (d). f, g, The number (f) and area (g) of overt metastatic bone lesions were quantified per hindlimb per group with ImageJ. Mann-Whitney test. n = 18 (KD#1) and 14 (shCTL) hindlimbs per group. h, BLI quantification of hind limb bone metastasis burden in mice injected with DU145-ob2b cells stably transduced with DACT1-targeting shRNA or control shRNA. Per-mouse signal was normalized to photon flux of the same mouse measured on Day 0 post-injection. n = 9 mice per group. Mann-Whitney U test. i, qRT-PCR analysis of DACT1 mRNA levels normalized to Gapdh in the DU145-ob2b cells stably transduced with DACT1-targeting shRNA or control shRNA. n = 4 technical repeats. Student’s t-test. j, M1a cells stably expressing each DACT1 construct fused to TdTomato or TdTomato alone were inoculated via intracardiac injection and followed by bioluminescence. n = 9 mice per group. Experiment performed once. k, Representative images from (j). l, Correlation of DACT1 expression values to indicated GOIs from the METABRIC dataset. m, Pearson correlation coefficients for each gene correlated to DACT1 were ranked and GSEA analysis was performed to test the enrichment of the indicated gene sets in the Hallmark data set. Statistics by GSEA software. Data represents mean ± SEM. Student’s t-test. Numerical source data for a-d, f-j, m, are provided.

Source data

Supplementary information

Reporting Summary

Peer Review Information

Supplementary Tables

Supplementary Table 1: Hallmark gene sets from the Broad GSEA database enriched in genes upregulated in M1a cells versus parent Sum159 or the lung metastatic derivative M1L1 cells. Supplementary Table 2: a list of Dact1 mutants generated for the experiments in Fig. 3 and Fig. 4. Mutants were made in the DACT1–TdTomato C-terminal fusion backbone. Supplementary Table 3: MS data of proteins found in DACT1 biomolecular condensates ranked by abundance in each Wnt3a or control condition. Supplementary Table 4: GO analysis of proteins found in DACT1 biomolecular condensates ranked by abundance in each Wnt3a or control condition. Supplementary Table 5: Patient survival curves for organ-site-specific metastasis using the median DACT1 expression level to separate patients into high and low expression. Data were derived from the EMC-MSK dataset. Supplementary Table 6: the oligonucleotide sequences used in the study. Supplementary Table 7: the antibodies used in this study with accompanying dilution and application notes. Supplementary Table 8: the key characteristics of the cell lines used in this study

Supplementary Video 1

Nikon SoRA super-resolution render of HEK293T cells transfected with DACT1–tdTomato constructs.

Supplementary Video 2

Live-cell imaging of BM2 cells stably transduced with DACT1–tdTomato constructs. The blue outline highlights a cell with multiple fusion events over the course of imaging.

Supplementary Video 3

Fluorescence recovery after photobleaching of BM2 cells stably transduced with DACT1–tdTomato constructs.

Supplementary Video 4

FRAP analysis of M1a cells stably transduced with DACT1–tdTomato constructs.

Supplementary Video 5

Fluorescence confocal imaging of M1a cells stably expressing CK2α–GFP and DACT1–tdTomato with z stacks rendered into a volume view.

Source data

Source Data Fig. 1

Statistical source data.

Source Data Fig. 2

Statistical source data.

Source Data Fig. 3

Statistical source data.

Source Data Fig. 3

Unprocessed western blot.

Source Data Fig. 4

Statistical source data.

Source Data Fig. 6

Statistical source data.

Source Data Extended Data Fig. 1

Statistical source data.

Source Data Extended Data Fig. 2

Statistical source data.

Source Data Extended Data Fig. 2

Unprocessed western blot.

Source Data Extended Data Fig. 3

Statistical source data.

Source Data Extended Data Fig. 4

Unprocessed western blot.

Source Data Extended Data Fig. 9

Statistical source data.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Esposito, M., Fang, C., Cook, K.C. et al. TGF-β-induced DACT1 biomolecular condensates repress Wnt signalling to promote bone metastasis. Nat Cell Biol 23, 257–267 (2021). https://doi.org/10.1038/s41556-021-00641-w

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41556-021-00641-w

This article is cited by

Search

Advanced search

Quick links

Nature Briefing: Cancer

Sign up for the Nature Briefing: Cancer newsletter — what matters in cancer research, free to your inbox weekly.

Get what matters in cancer research, free to your inbox weekly. Sign up for Nature Briefing: Cancer