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.

Repulsive guidance molecule is a structural bridge between neogenin and bone morphogenetic protein

This article has been updated

Abstract

Repulsive guidance molecules (RGMs) control crucial processes including cell motility, adhesion, immune-cell regulation and systemic iron metabolism. RGMs signal via the neogenin (NEO1) and the bone morphogenetic protein (BMP) pathways. Here, we report crystal structures of the N-terminal domains of all human RGM family members in complex with the BMP ligand BMP2, revealing a new protein fold and a conserved BMP-binding mode. Our structural and functional data suggest a pH-linked mechanism for RGM-activated BMP signaling and offer a rationale for RGM mutations causing juvenile hemochromatosis. We also determined the crystal structure of the ternary BMP2–RGM–NEO1 complex, which, along with solution scattering and live-cell super-resolution fluorescence microscopy, indicates BMP-induced clustering of the RGM–NEO1 complex. Our results show how RGM acts as the central hub that links BMP and NEO1 and physically connects these fundamental signaling pathways.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Figure 1: Structure of the BMP2–RGM complex.
Figure 2: Interaction determinants of the BMP2–RGM complex.
Figure 3: The mode of RGM-BMP2 interactions is conserved in RGMA, RGMB and RGMC.
Figure 4: RGMs and BMPR1A share a common binding site on BMP2.
Figure 5: Structure of the ternary BMP2–RGM–NEO1 complex.
Figure 6: BMP2-mediated clustering of RGM–NEO1 complexes.

Accession codes

Primary accessions

Protein Data Bank

Referenced accessions

NCBI Reference Sequence

Protein Data Bank

Change history

  • 14 May 2015

    In the version of this supplementary file originally posted online on 4 May 2015, the title and legend of Supplementary Figure 7 reproduced part of the title and legend from Supplementary Figure 6, and descriptions of panels c–f were missing. The errors have been corrected in this file as of 14 May 2015.

References

  1. 1

    Yamashita, T., Mueller, B.K. & Hata, K. Neogenin and repulsive guidance molecule signaling in the central nervous system. Curr. Opin. Neurobiol. 17, 29–34 (2007).

    CAS  PubMed  Google Scholar 

  2. 2

    Mirakaj, V. et al. Repulsive guidance molecule-A (RGM-A) inhibits leukocyte migration and mitigates inflammation. Proc. Natl. Acad. Sci. USA 108, 6555–6560 (2011).

    CAS  PubMed  Google Scholar 

  3. 3

    Muramatsu, R. et al. RGMa modulates T cell responses and is involved in autoimmune encephalomyelitis. Nat. Med. 17, 488–494 (2011).

    CAS  PubMed  Google Scholar 

  4. 4

    Li, V.S. et al. Frequent inactivation of axon guidance molecule RGMA in human colon cancer through genetic and epigenetic mechanisms. Gastroenterology 137, 176–187 (2009).

    CAS  PubMed  Google Scholar 

  5. 5

    Papanikolaou, G. et al. Mutations in HFE2 cause iron overload in chromosome 1q-linked juvenile hemochromatosis. Nat. Genet. 36, 77–82 (2004).

    CAS  Google Scholar 

  6. 6

    Monnier, P.P. et al. RGM is a repulsive guidance molecule for retinal axons. Nature 419, 392–395 (2002).

    CAS  PubMed  Google Scholar 

  7. 7

    Rajagopalan, S. et al. Neogenin mediates the action of repulsive guidance molecule. Nat. Cell Biol. 6, 756–762 (2004).

    CAS  PubMed  Google Scholar 

  8. 8

    Matsunaga, E. & Chedotal, A. Repulsive guidance molecule/neogenin: a novel ligand-receptor system playing multiple roles in neural development. Dev. Growth Differ. 46, 481–486 (2004).

    CAS  PubMed  Google Scholar 

  9. 9

    Bell, C.H. et al. Structure of the repulsive guidance molecule (RGM)-neogenin signaling hub. Science 341, 77–80 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  10. 10

    Babitt, J.L. et al. Bone morphogenetic protein signaling by hemojuvelin regulates hepcidin expression. Nat. Genet. 38, 531–539 (2006).

    CAS  Google Scholar 

  11. 11

    Samad, T.A. et al. DRAGON, a bone morphogenetic protein co-receptor. J. Biol. Chem. 280, 14122–14129 (2005).

    CAS  PubMed  Google Scholar 

  12. 12

    Babitt, J.L. et al. Repulsive guidance molecule (RGMa), a DRAGON homologue, is a bone morphogenetic protein co-receptor. J. Biol. Chem. 280, 29820–29827 (2005).

    CAS  PubMed  Google Scholar 

  13. 13

    Lee, D.H. et al. Neogenin inhibits HJV secretion and regulates BMP-induced hepcidin expression and iron homeostasis. Blood 115, 3136–3145 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  14. 14

    Zhang, A.S., Yang, F., Wang, J., Tsukamoto, H. & Enns, C.A. Hemojuvelin-neogenin interaction is required for bone morphogenic protein-4-induced hepcidin expression. J. Biol. Chem. 284, 22580–22589 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  15. 15

    Massagué, J. TGFβ signalling in context. Nat. Rev. Mol. Cell Biol. 13, 616–630 (2012).

    PubMed  PubMed Central  Google Scholar 

  16. 16

    Sieber, C., Kopf, J., Hiepen, C. & Knaus, P. Recent advances in BMP receptor signaling. Cytokine Growth Factor Rev. 20, 343–355 (2009).

    CAS  PubMed  Google Scholar 

  17. 17

    Bragdon, B. et al. Bone morphogenetic proteins: a critical review. Cell. Signal. 23, 609–620 (2011).

    CAS  PubMed  Google Scholar 

  18. 18

    Shi, Y. & Massagué, J. Mechanisms of TGF-beta signaling from cell membrane to the nucleus. Cell 113, 685–700 (2003).

    CAS  Google Scholar 

  19. 19

    Feng, X.H. & Derynck, R. Specificity and versatility in tgf-beta signaling through Smads. Annu. Rev. Cell Dev. Biol. 21, 659–693 (2005).

    CAS  PubMed  Google Scholar 

  20. 20

    Derynck, R. & Zhang, Y.E. Smad-dependent and Smad-independent pathways in TGF-β family signalling. Nature 425, 577–584 (2003).

    CAS  PubMed  Google Scholar 

  21. 21

    Di Guglielmo, G.M., Le Roy, C., Goodfellow, A.F. & Wrana, J.L. Distinct endocytic pathways regulate TGF-β receptor signalling and turnover. Nat. Cell Biol. 5, 410–421 (2003).

    CAS  PubMed  Google Scholar 

  22. 22

    Hartung, A. et al. Different routes of bone morphogenic protein (BMP) receptor endocytosis influence BMP signaling. Mol. Cell. Biol. 26, 7791–7805 (2006).

    CAS  PubMed  PubMed Central  Google Scholar 

  23. 23

    Heining, E., Bhushan, R., Paarmann, P., Henis, Y.I. & Knaus, P. Spatial segregation of BMP/Smad signaling affects osteoblast differentiation in C2C12 cells. PLoS ONE 6, e25163 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  24. 24

    Alborzinia, H. et al. Quantitative kinetics analysis of BMP2 uptake into cells and its modulation by BMP antagonists. J. Cell Sci. 126, 117–127 (2013).

    CAS  PubMed  Google Scholar 

  25. 25

    Shi, W. et al. Endofin acts as a Smad anchor for receptor activation in BMP signaling. J. Cell Sci. 120, 1216–1224 (2007).

    CAS  PubMed  Google Scholar 

  26. 26

    Brazil, D.P., Church, R.H., Surae, S., Godson, C. & Martin, F. BMP signalling: agony and antagony in the family. Trends Cell Biol. 25, 249–264 (2015).

    CAS  PubMed  Google Scholar 

  27. 27

    Xia, Y. et al. Dragon (repulsive guidance molecule b) inhibits IL-6 expression in macrophages. J. Immunol. 186, 1369–1376 (2011).

    CAS  PubMed  Google Scholar 

  28. 28

    Lee, P.L., Beutler, E., Rao, S.V. & Barton, J.C. Genetic abnormalities and juvenile hemochromatosis: mutations of the HJV gene encoding hemojuvelin. Blood 103, 4669–4671 (2004).

    CAS  PubMed  Google Scholar 

  29. 29

    Lok, C.Y. et al. Iron overload in the Asian community. Blood 114, 20–25 (2009).

    CAS  PubMed  Google Scholar 

  30. 30

    Parrow, N.L. & Fleming, R.E. Bone morphogenetic proteins as regulators of iron metabolism. Annu. Rev. Nutr. 34, 77–94 (2014).

    CAS  PubMed  Google Scholar 

  31. 31

    Yang, F., West, A.P. Jr., Allendorph, G.P., Choe, S. & Bjorkman, P.J. Neogenin interacts with hemojuvelin through its two membrane-proximal fibronectin type III domains. Biochemistry 47, 4237–4245 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  32. 32

    Wu, Q., Sun, C.C., Lin, H.Y. & Babitt, J.L. Repulsive guidance molecule (RGM) family proteins exhibit differential binding kinetics for bone morphogenetic proteins (BMPs). PLoS ONE 7, e46307 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  33. 33

    Xiong, J.P. et al. Crystal structure of the extracellular segment of integrin alpha Vbeta3 in complex with an Arg-Gly-Asp ligand. Science 296, 151–155 (2002).

    CAS  PubMed  Google Scholar 

  34. 34

    Camus, L.M. & Lambert, L.A. Molecular evolution of hemojuvelin and the repulsive guidance molecule family. J. Mol. Evol. 65, 68–81 (2007).

    CAS  PubMed  Google Scholar 

  35. 35

    Kuns-Hashimoto, R., Kuninger, D., Nili, M. & Rotwein, P. Selective binding of RGMc/hemojuvelin, a key protein in systemic iron metabolism, to BMP-2 and neogenin. Am. J. Physiol. Cell Physiol. 294, C994–C1003 (2008).

    CAS  PubMed  Google Scholar 

  36. 36

    Roetto, A. et al. Mutant antimicrobial peptide hepcidin is associated with severe juvenile hemochromatosis. Nat. Genet. 33, 21–22 (2003).

    CAS  Google Scholar 

  37. 37

    Weinstein, D.A. et al. Inappropriate expression of hepcidin is associated with iron refractory anemia: implications for the anemia of chronic disease. Blood 100, 3776–3781 (2002).

    CAS  PubMed  Google Scholar 

  38. 38

    Pagani, A., Silvestri, L., Nai, A. & Camaschella, C. Hemojuvelin N-terminal mutants reach the plasma membrane but do not activate the hepcidin response. Haematologica 93, 1466–1472 (2008).

    CAS  PubMed  Google Scholar 

  39. 39

    Allendorph, G.P., Vale, W.W. & Choe, S. Structure of the ternary signaling complex of a TGF-beta superfamily member. Proc. Natl. Acad. Sci. USA 103, 7643–7648 (2006).

    CAS  PubMed  Google Scholar 

  40. 40

    Weber, D. et al. A silent H-bond can be mutationally activated for high-affinity interaction of BMP-2 and activin type IIB receptor. BMC Struct. Biol. 7, 6 (2007).

    PubMed  PubMed Central  Google Scholar 

  41. 41

    Townson, S.A. et al. Specificity and structure of a high affinity activin receptor-like kinase 1 (ALK1) signaling complex. J. Biol. Chem. 287, 27313–27325 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  42. 42

    Heinecke, K. et al. Receptor oligomerization and beyond: a case study in bone morphogenetic proteins. BMC Biol. 7, 59 (2009).

    PubMed  PubMed Central  Google Scholar 

  43. 43

    Keller, S., Nickel, J., Zhang, J.-L., Sebald, W. & Mueller, T.D. Molecular recognition of BMP-2 and BMP receptor IA. Nat. Struct. Mol. Biol. 11, 481–488 (2004).

    CAS  PubMed  Google Scholar 

  44. 44

    Korchynskyi, O. & ten Dijke, P. Identification and functional characterization of distinct critically important bone morphogenetic protein-specific response elements in the Id1 promoter. J. Biol. Chem. 277, 4883–4891 (2002).

    CAS  PubMed  Google Scholar 

  45. 45

    Rossy, J., Owen, D.M., Williamson, D.J., Yang, Z. & Gaus, K. Conformational states of the kinase Lck regulate clustering in early T cell signaling. Nat. Immunol. 14, 82–89 (2013).

    CAS  PubMed  Google Scholar 

  46. 46

    Zhou, Z. et al. Neogenin regulation of BMP-induced canonical Smad signaling and endochondral bone formation. Dev. Cell 19, 90–102 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  47. 47

    Xia, Y., Babitt, J.L., Sidis, Y., Chung, R.T. & Lin, H.Y. Hemojuvelin regulates hepcidin expression via a selective subset of BMP ligands and receptors independently of neogenin. Blood 111, 5195–5204 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  48. 48

    Tassew, N.G. et al. Modifying lipid rafts promotes regeneration and functional recovery. Cell Reports 8, 1146–1159 (2014).

    CAS  PubMed  Google Scholar 

  49. 49

    Vieira, A.V., Lamaze, C. & Schmid, S.L. Control of EGF receptor signaling by clathrin-mediated endocytosis. Science 274, 2086–2089 (1996).

    CAS  PubMed  Google Scholar 

  50. 50

    Le Roy, C. & Wrana, J.L. Clathrin- and non-clathrin-mediated endocytic regulation of cell signalling. Nat. Rev. Mol. Cell Biol. 6, 112–126 (2005).

    CAS  PubMed  PubMed Central  Google Scholar 

  51. 51

    Bell, C.H. et al. Structure of the repulsive guidance molecule (RGM)–neogenin signaling hub. Science 341, 77–80 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  52. 52

    Aricescu, A.R., Lu, W. & Jones, E.Y. A time- and cost-efficient system for high-level protein production in mammalian cells. Acta Crystallogr. D Biol. Crystallogr. 62, 1243–1250 (2006).

    PubMed  PubMed Central  Google Scholar 

  53. 53

    Zhao, Y. et al. Automation of large scale transient protein expression in mammalian cells. J. Struct. Biol. 175, 209–215 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  54. 54

    Chang, V.T. et al. Glycoprotein structural genomics: solving the glycosylation problem. Structure 15, 267–273 (2007).

    CAS  PubMed  PubMed Central  Google Scholar 

  55. 55

    von Einem, S., Schwarz, E. & Rudolph, R. A novel TWO-STEP renaturation procedure for efficient production of recombinant BMP-2. Protein Expr. Purif. 73, 65–69 (2010).

    CAS  PubMed  Google Scholar 

  56. 56

    Trombetta, E.S. & Parodi, A.J. Quality control and protein folding in the secretory pathway. Annu. Rev. Cell Dev. Biol. 19, 649–676 (2003).

    CAS  PubMed  Google Scholar 

  57. 57

    Walter, T.S. et al. A procedure for setting up high-throughput nanolitre crystallization experiments: crystallization workflow for initial screening, automated storage, imaging and optimization. Acta Crystallogr. D Biol. Crystallogr. 61, 651–657 (2005).

    PubMed  PubMed Central  Google Scholar 

  58. 58

    Mayo, C.J. et al. Benefits of automated crystallization plate tracking, imaging, and analysis. Structure 13, 175–182 (2005).

    CAS  PubMed  Google Scholar 

  59. 59

    Otwinowski, Z. & Minor, W. Processing of X-ray diffraction data collected in oscillation mode. Methods Enzymol. 276, 307–326 (1997).

    CAS  Article  Google Scholar 

  60. 60

    Evans, P. Scaling and assessment of data quality. Acta Crystallogr. D Biol. Crystallogr. 62, 72–82 (2006).

    Article  Google Scholar 

  61. 61

    Kabsch, W. Automatic indexing of rotation diffraction patterns. J. Appl. Crystallogr. 21, 67–72 (1988).

    CAS  Google Scholar 

  62. 62

    Kabsch, W. Automatic processing of rotation diffraction data from crystals of initially unknown symmetry and cell constants. J. Appl. Crystallogr. 26, 795–800 (1993).

    CAS  Google Scholar 

  63. 63

    Leslie, A.G.W. The integration of macromolecular diffraction data. Acta Crystallogr. D Biol. Crystallogr. 62, 48–57 (2006).

    Google Scholar 

  64. 64

    Sauter, N.K., Grosse-Kunstleve, R.W. & Adams, P.D. Robust indexing for automatic data collection. J. Appl. Crystallogr. 37, 399–409 (2004).

    CAS  PubMed  PubMed Central  Google Scholar 

  65. 65

    Zhang, Z., Sauter, N.K., van den Bedem, H., Snell, G. & Deacon, A.M. Automated diffraction image analysis and spot searching for high-throughput crystal screening. J. Appl. Crystallogr. 39, 112–119 (2006).

    CAS  Google Scholar 

  66. 66

    McCoy, A.J. et al. Phaser crystallographic software. J. Appl. Crystallogr. 40, 658–674 (2007).

    CAS  PubMed  PubMed Central  Google Scholar 

  67. 67

    Scheufler, C., Sebald, W. & Hulsmeyer, M. Crystal structure of human bone morphogenetic protein-2 at 2.7 A resolution. J. Mol. Biol. 287, 103–115 (1999).

    CAS  PubMed  Google Scholar 

  68. 68

    Cowtan, K. Recent developments in classical density modification. Acta Crystallogr. D Biol. Crystallogr. 66, 470–478 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  69. 69

    Cowtan, K. The Buccaneer software for automated model building. 1. Tracing protein chains. Acta Crystallogr. D Biol. Crystallogr. 62, 1002–1011 (2006).

    PubMed  PubMed Central  Google Scholar 

  70. 70

    Emsley, P. & Cowtan, K. Coot: model-building tools for molecular graphics. Acta Crystallogr. D Biol. Crystallogr. 60, 2126–2132 (2004).

    Google Scholar 

  71. 71

    Adams, P.D. et al. PHENIX: a comprehensive Python-based system for macromolecular structure solution. Acta Crystallogr. D Biol. Crystallogr. 66, 213–221 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  72. 72

    Davis, I.W. et al. MolProbity: all-atom contacts and structure validation for proteins and nucleic acids. Nucleic Acids Res. 35, W375–W383 (2007).

    PubMed  PubMed Central  Google Scholar 

  73. 73

    Baker, N.A., Sept, D., Joseph, S., Holst, M.J. & McCammon, J.A. Electrostatics of nanosystems: application to microtubules and the ribosome. Proc. Natl. Acad. Sci. USA 98, 10037–10041 (2001).

    CAS  PubMed  PubMed Central  Google Scholar 

  74. 74

    Krissinel, E. & Henrick, K. Inference of macromolecular assemblies from crystalline state. J. Mol. Biol. 372, 774–797 (2007).

    CAS  Google Scholar 

  75. 75

    Pernot, P. et al. Upgraded ESRF BM29 beamline for SAXS on macromolecules in solution. J. Synchrotron Radiat. 20, 660–664 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  76. 76

    Petoukhov, M.V. et al. New developments in the ATSAS program package for small-angle scattering data analysis. J. Appl. Crystallogr. 45, 342–350 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  77. 77

    Rambo, R.P. & Tainer, J.A. Accurate assessment of mass, models and resolution by small-angle scattering. Nature 496, 477–481 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  78. 78

    Leaver-Fay, A. et al. ROSETTA3: an object-oriented software suite for the simulation and design of macromolecules. Methods Enzymol. 487, 545–574 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  79. 79

    Eswar, N. et al. Tools for comparative protein structure modeling and analysis. Nucleic Acids Res. 31, 3375–3380 (2003).

    CAS  PubMed  PubMed Central  Google Scholar 

  80. 80

    Weinkam, P., Pons, J. & Sali, A. Structure-based model of allostery predicts coupling between distant sites. Proc. Natl. Acad. Sci. USA 109, 4875–4880 (2012).

    CAS  PubMed  Google Scholar 

  81. 81

    Pelikan, M., Hura, G.L. & Hammel, M. Structure and flexibility within proteins as identified through small angle X-ray scattering. Gen. Physiol. Biophys. 28, 174–189 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  82. 82

    Schneidman-Duhovny, D., Hammel, M., Tainer, J.A. & Sali, A. Accurate SAXS profile computation and its assessment by contrast variation experiments. Biophys. J. 105, 962–974 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  83. 83

    Guttman, M., Weinkam, P., Sali, A. & Lee, K.K. All-atom ensemble modeling to analyze small-angle x-ray scattering of glycosylated proteins. Structure 21, 321–331 (2013).

    CAS  PubMed  Google Scholar 

  84. 84

    Johnsson, B., Löfås, S. & Lindquist, G. Immobilization of proteins to a carboxymethyldextran-modified gold surface for biospecific interaction analysis in surface plasmon resonance sensors. Anal. Biochem. 198, 268–277 (1991).

    CAS  PubMed  Google Scholar 

  85. 85

    Herrera, B. & Inman, G.J. A rapid and sensitive bioassay for the simultaneous measurement of multiple bone morphogenetic proteins: identification and quantification of BMP4, BMP6 and BMP9 in bovine and human serum. BMC Cell Biol. 10, 20 (2009).

    PubMed  PubMed Central  Google Scholar 

  86. 86

    Korchynskyi, O. & ten Dijke, P. Identification and functional characterization of distinct critically important bone morphogenetic protein-specific response elements in the Id1 promoter. J. Biol. Chem. 277, 4883–4891 (2002).

    CAS  PubMed  Google Scholar 

  87. 87

    Perry, G.L.W. SpPack: spatial point pattern analysis in Excel using Visual Basic for Applications (VBA). Environ. Model. Softw. 19, 559–569 (2004).

    Google Scholar 

  88. 88

    Rossy, J., Owen, D.M., Williamson, D.J., Yang, Z. & Gaus, K. Conformational states of the kinase Lck regulate clustering in early T cell signaling. Nat. Immunol. 14, 82–89 (2013).

    CAS  PubMed  Google Scholar 

  89. 89

    Owen, D.M. et al. PALM imaging and cluster analysis of protein heterogeneity at the cell surface. J Biophotonics 3, 446–454 (2010).

    CAS  PubMed  Google Scholar 

  90. 90

    Owen, D.M., Williamson, D., Magenau, A. & Gaus, K. Optical techniques for imaging membrane domains in live cells (live-cell palm of protein clustering). Methods Enzymol. 504, 221–235 (2012).

    CAS  PubMed  Google Scholar 

  91. 91

    Williamson, D.J. et al. Pre-existing clusters of the adaptor Lat do not participate in early T cell signaling events. Nat. Immunol. 12, 655–662 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

We thank the staff of beamlines I03, I04 and I04-1 at the Diamond Light Source (X-ray diffraction data, proposal MX-10627), BM29 at the European Synchrotron Radiation Facility (SAXS data) and the Cellular Imaging Core at the Wellcome Trust Centre for Human Genetics (TIRF and dSTORM data) for assistance; T. Walter and K. Harlos for help with crystallization; R. Robinson and G. Sutton for help with MALS; and A.R. Aricescu and D.I. Stuart for reading the manuscript. This work was supported by Cancer Research UK (C20724/A14414 (C.S.)) and the Wellcome Trust (097301/Z/11/Z (E.G.H.)). Further support from the Wellcome Trust core award grant 090532/Z/09/Z (C.S.) and the Wellcome Trust multi-user equipment grant 101584MA (S.P.-P. and C.S.) are acknowledged. E.G.H. is funded by a Wellcome Trust PhD Studentship. J.E. is supported as a Marie-Curie Postdoctoral Fellow (FP7-328531). S.P.-P. is supported as a Nuffield Department of Medicine Leadership Fellow. C.S. is supported as a Cancer Research UK Senior Research Fellow.

Author information

Affiliations

Authors

Contributions

C.S. designed and supervised the project. E.G.H. and C.H.B. cloned all RGM, NEO1 and BMP constructs. E.G.H., B.B. and C.H.B. performed protein expression and purification, and E.G.H. crystallized the proteins. E.G.H. and C.S. collected the data and solved and refined the crystal structures. E.G.H. and B.B. carried out SPR and luciferase experiments, and E.G.H. performed the MALS experiments. SAXS data were collected by J.E. and E.G.H., and J.E. conducted all subsequent SAXS data processing. E.G.H., B.B. and S.P.-P. collected the imaging data, and S.P.-P. completed the dSTORM data processing. C.S. and E.G.H. wrote the paper, and all authors discussed the results and commented on the paper.

Corresponding author

Correspondence to Christian Siebold.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Integrated supplementary information

Supplementary Figure 1 Sequence alignments of BMP and RGM family members.

(a) Sequence alignment of human BMP family members. Numbering corresponds to the full length human BMP2; residues of BMP2 molecule 1 forming hydrophilic interactions to RGMC are highlighted in dark blue and non-bonded contacts in light blue. Residues of BMP2 molecule 2 forming non-bonded contacts are shown in red. Disulfide bridges are indicated by Roman Numerals. (b) Sequence alignment of the N-terminal domain of RGM family members. Numbering is that of the full length human RGMC. Secondary structure assignments for RGMAND (blue), RGMBND (yellow) and RGMCND (orange) are displayed above the alignment. RGM residues forming hydrophilic interactions with BMP2 are highlighted in dark blue, exclusively formed by RGMA in salmon and exclusively formed by RGMC in green. Non-bonded contact residues interacting with BMP2 are depicted in magenta. Disulfide bridges are indicated by Roman Numerals. Asterisks (*) indicate disease-related residues identified in human RGMC. hRGM: human RGM, mRGM: mouse RGM, zRGM: zebrafish RGM, xRGM: Xenopus laevis RGM.

Supplementary Figure 2 Electron density maps of the RGMC–BMP2 complex.

(a) Initial electron density map, at 2.35 Å resolution, of the RGMCND–BMP2 complex after molecular replacement (using only the BMP2 dimer as search model) in PHASER (McCoy, A. J. et al. (2007) J Appl Crystallogr 40, 658-674) and density modification in PARROT (Cowtan, K. (2010) Acta Crystallogr D 66, 470-478). Contour level is 1.0 σ. The final refined RGMCND–BMP2 model is represented as ribbon (RGMC: blue and green; BMP2: yellow and magenta). (b-c) Close-up view onto the RGMC–BMP2 interface. Protein chains are depicted in stick representation. Color coding is as in (a). (b) Initial electron density map as described in (a). (c) SigmaA-weighted 2FO-FC map after the final round of refinement from autoBUSTER (BUSTER, version 2.8.0., Cambridge, 2011) contoured at 1.0 σ.

Supplementary Figure 3 RGMCND structure and comparison.

(a) Topology diagram of RGMCND (adapted from PDBSUM (http://www.ebi.ac.uk/pdbsum/)) in rainbow coloring (N-terminus: blue; C-terminus: red). N- and C-termini and residue numbers are shown. (b) Cartoon representation of the RGMCND structure. Color coding is as in (a). RGMND forms a compact three helix bundle fold, stabilized by three disulfide bonds, supported by a hydrophobic core including residues from all three helices, depicted as sticks. Right panel rotated 90° around the x-axis compared to left panel. Disulfide bridges are numbered in Roman numerals. The disordered α1–α2 loop is shown as a dotted line. (c-g) Structural comparison of RGMCND using the PDBefold server (http://www.ebi.ac.uk/msd-srv/ssm/). RGMCND ((c), cyan) shares the closest structural similarity to the human MTCP1 oncogene ((d), pdb 2HP8 (Barthe, P. et al. (1997) J Mol Biol 274, 801-815), slate; r.m.s.d. of 2.78 Å for 60 equivalent Cα positions, sequence identity: 5%), the MIT domain of VPS4-like ATPases ((e), pdb 2V6Y (Obita, T. et al. (2007) Nature 449, 735-739), magenta, r.m.s.d. of 2.47 Å for 60 equivalent Cα positions, sequence identity: 3%) and the complement inhibitor EHP from S. aureus ((f), pdb 2NOJ (Hammel, M. et al. (2007). J Biol Chem 282, 30051-30061), yellow, r.m.s.d. of 2.34 Å for 54 equivalent Cα positions, sequence identity: 6%). The position of disulfide bridge II in RGMND is conserved in MTCP1 and highlighted by an asterisk. A structural superposition is shown in (g). The two views are related by a rotation of 90° around the x-axis.

Supplementary Figure 4 SPR equilibrium binding experiments.

(a-l) Binding of different constructs and mutants of RGMA, RGMB and RGMC, respectively, to BMP2. (m-r) Binding of BMP receptor ectodomain constructs to BMP2 ligand (m-o) or to eRGMB (p-r). For (k) the eRGMB-NEO1FN56 complex was purified via size exclusion chromatography prior to SPR. Graphs show a plot of the equilibrium binding response (response units (RU)) against concentration of the used analytes. All experiments were performed in duplicate. Binding constants (Kd) are given as mean with the error representing the standard error of the mean (n=2 technical repeats) Fits are shown as lines. Sensorgrams are shown..Binding constants (Kd), Bmax and surface response units (Bsurface) are indicated below the graphs.

Supplementary Figure 5 Analysis of the effects of eBMPR1A and eRGMB on SMAD-mediated transcriptional activation by BMP-responsive reporter (BRE-LUC).

(a-c) LLC-PK1 cells (a, b) or C2C12 cells (c) were stimulated with either buffer control or different concentrations of BMP2: 25 nM (a), 6 nM BMP2 (b); or 10 nM BMP2 (c). BMP2 was pre-incubated with 0.4, 1.6, 6.3, 25 or 100 X molar excess of eBMPR1A or eRGMB. Average BRE-Luc relative response was calculated for each condition from two independent experiments. For (a): Control n = 4, +25 nM BMP2 n = 12, all others n= 8. For (b): Control and + 6 nM BMP2 n = 33, all eBMPR1A n = 41, all eRGMB n = 32. For (c): Control n= 6, + 10 nM BMP2 n = 18, all others n = 12. Where n = cell cultures. Error bars are s.e.m., dotted line indicates average increase in relative luciferase response induced by the respective BMP2 concentration in each case.

Supplementary Figure 6 Interface analysis and comparison of the ternary RGMB–NEO1–BMP2 complex to the binary RGMB–NEO1 and RGMB–BMP2 complexes.

(a) Cartoon representations of the ternary RGMB–NEO1–BMP2 complex (grey) and superimposed binary RGMB–NEO1 (orange and red) and RGMB–BMP2 (orange and blue) complexes. Superposition of the binary RGMBND–BMP2 complex from this study yields in an r.m.s.d. of 0.799 Å for 328 equivalent Cα positions. Using the previously determined structure of the binary NEO1FN56–eRGMB complex (pdb 4BQ6, site-1 interface (Bell, C. H. et al. (2013) Science 341(6141), 77-80) an r.m.s.d. of 0.511 Å for 368 equivalent Cα positions was achieved when compared with the ternary RGMB–NEO1–BMP2 complex. (b) Table showing the analysis of the interfaces highlighted in (a). Whereas the binary BMP2–BMP2, BMP2–RGMBND and NEO1FN56–RGMBCD interfaces show values expected for a physiological interaction, the BMP2-NEO1FN5 interface has a at least 3-times smaller buried interface area and significantly lower score. ashape complementary calculated with program sc (Lawrence M. C. et al. (1993) J Mol Biol 234, 946-950); btotal buried surface area (from PISA (Krissinel E. et al. (2007) J Mol Biol 372, 774-797 (1)); csolvation free energy gain upon formation of the interface1; dnumber of interfacing residues in the complex1; enumber of potential hydrogen bonds across the interface1; fcomplexation Significance Score indicating how significant the interface is for assembly formation1; gbrackets indicate the percentage of buried interface area compared to the overall surface of the free molecules; hthe BMP2-BMP2 interface also includes a intermolecular disulfide bridge that is not included in the number of interacting residues.

Supplementary Figure 7 SAXS solution structures of NEO1FN56M, eRGMB, eRGMB–NEO1FN56M and BMP2–eRGMB–NEO1FN56M.

(a-b) SEC of the BMP2-eRGMB-NEO1FN56M complex formed by saturating BMP2 and eRGMB with a molar excess of NEO1FN56M. The SEC elution profile (a) and the corresponding SDS-PAGE of the fractions under reducing conditions (b) are shown. The first peak (I) corresponds to the ternary BMP2-eRGMB-NEO1FN56M complex, the second (II) to the binary eRGMB-NEO1FN56M complex, and the third (III) to uncomplexed NEO1FN56M, which was added in a 1.5 molar excess. MW: Sigma Molecular Weight Marker S8445. The asterisks mark the two RGM products resulting from autocatalytic cleavage7. (c-f) SAXS analysis of NEO1FN56M (c), eRGMB (d), eRGMB-NEO1FN56M (e) and BMP2-eRGMB-NEO1FN56M (f) after isolation by SEC. Experimental scattering curves (black) and calculated scattering patterns (red) are shown to a maximal momentum transfer of q = 0.35 Å−1. The fitting residuals of the experimental scattering curves and calculated scattering patterns are displayed in the lower left insets. The upper right insets show the experimental (black) and calculated (red) normalized pair distance distribution (P(r)) function. The lower right insets display the experimental (black) and calculated (red) Guinier region. The shaded area indicates the range of fitting for RG analysis (RG•S ≤ 1.3). The fit of the calculated scattering pattern (χ2), the derived maximum intra-particle diameter (DMAX), the radius of gyration (RG), and the molecular weight derived from the volume of correlation metric VC (MWVc) are annotated.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–7 (PDF 32807 kb)

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Healey, E., Bishop, B., Elegheert, J. et al. Repulsive guidance molecule is a structural bridge between neogenin and bone morphogenetic protein. Nat Struct Mol Biol 22, 458–465 (2015). https://doi.org/10.1038/nsmb.3016

Download citation

Further reading

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing