Calreticulin; Receptors, Thrombopoietin; Janus Kinase 2; Humans; Dimerization; Receptors, Thrombopoietin/metabolism; Frameshift Mutation; Mutation; Janus Kinase 2/metabolism; Calreticulin/metabolism; Myeloproliferative Disorders/genetics; Myeloproliferative Disorders; Chemistry (all); Biochemistry, Genetics and Molecular Biology (all); Physics and Astronomy (all); General Physics and Astronomy; General Biochemistry, Genetics and Molecular Biology; General Chemistry; Multidisciplinary
Abstract :
[en] Calreticulin (CALR) frameshift mutations represent the second cause of myeloproliferative neoplasms (MPN). In healthy cells, CALR transiently and non-specifically interacts with immature N-glycosylated proteins through its N-terminal domain. Conversely, CALR frameshift mutants turn into rogue cytokines by stably and specifically interacting with the Thrombopoietin Receptor (TpoR), inducing its constitutive activation. Here, we identify the basis of the acquired specificity of CALR mutants for TpoR and define the mechanisms by which complex formation triggers TpoR dimerization and activation. Our work reveals that CALR mutant C-terminus unmasks CALR N-terminal domain, rendering it more accessible to bind immature N-glycans on TpoR. We further find that the basic mutant C-terminus is partially α-helical and define how its α-helical segment concomitantly binds acidic patches of TpoR extracellular domain and induces dimerization of both CALR mutant and TpoR. Finally, we propose a model of the tetrameric TpoR-CALR mutant complex and identify potentially targetable sites.
Disciplines :
Genetics & genetic processes
Author, co-author :
Papadopoulos, Nicolas ; Ludwig Institute for Cancer Research Brussels, Brussels, Belgium ; Université catholique de Louvain and de Duve Institute, Brussels, Belgium
Nédélec, Audrey; Ludwig Institute for Cancer Research Brussels, Brussels, Belgium ; Université catholique de Louvain and de Duve Institute, Brussels, Belgium
Derenne, Allison; Spectralys Biotech SRL, rue Auguste Piccard 48, 6041, Gosselies, Belgium
Şulea, Teodor Asvadur; Department of Bioinformatics and Structural Biochemistry, Institute of Biochemistry of the Romanian Academy, Splaiul Independentei 296, Bucharest, 060031, Romania
Pecquet, Christian ; Ludwig Institute for Cancer Research Brussels, Brussels, Belgium ; Université catholique de Louvain and de Duve Institute, Brussels, Belgium
Chachoua, Ilyas; Ludwig Institute for Cancer Research Brussels, Brussels, Belgium ; Université catholique de Louvain and de Duve Institute, Brussels, Belgium ; Department of Molecular Biology and Genetics, Bilkent University, Ankara, Turkey
Vertenoeil, Gaëlle ; Centre Hospitalier Universitaire de Liège - CHU > > Service d'hématologie clinique ; Ludwig Institute for Cancer Research Brussels, Brussels, Belgium ; Université catholique de Louvain and de Duve Institute, Brussels, Belgium
Tilmant, Thomas ; Université de Liège - ULiège > Molecular Systems (MolSys)
Petrescu, Andrei-Jose; Department of Bioinformatics and Structural Biochemistry, Institute of Biochemistry of the Romanian Academy, Splaiul Independentei 296, Bucharest, 060031, Romania
Mazzucchelli, Gabriel ; Université de Liège - ULiège > Département de chimie (sciences) > Laboratoire de spectrométrie de masse (L.S.M.)
Iorga, Bogdan I ; Université Paris-Saclay, CNRS, Institut de Chimie des Substances Naturelles, UPR 2301, Gif-sur-Yvette, France
Vertommen, Didier ; Université catholique de Louvain and de Duve Institute, Brussels, Belgium ; de Duve Institute and MASSPROT platform, Brussels, Belgium
Constantinescu, Stefan N ; Ludwig Institute for Cancer Research Brussels, Brussels, Belgium. Stefan.constantinescu@bru.licr.org ; Université catholique de Louvain and de Duve Institute, Brussels, Belgium. Stefan.constantinescu@bru.licr.org ; Walloon Excelence in Life Sciences and Biotechnology, WELBIO, avenue Pasteur, 6, 1300, Wavre, Belgium. Stefan.constantinescu@bru.licr.org ; Ludwig Institute for Cancer Research, Nuffield Department of Medicine, Oxford University, Oxford, UK. Stefan.constantinescu@bru.licr.org
F.R.S.-FNRS - Fonds de la Recherche Scientifique LICR - Ludwig Institute for Cancer Research
Funding text :
We thank Dr. Didier Colau for his outstanding work in the production and purification of recombinant proteins used in this study, Lidvine Genet and Céline Mouton for expert technical support, and Dr. Nicolas Dauguet for flow cytometry assistance. We also thank Raphaël Frédérick from Louvain Drug Research Institute (LDRI) for his guidance and expertise in microscale thermophoresis experiments. We also thank Jean-François Collet and Steve O. Smith for their sound advices in the writing process of the manuscript. Funding to S.N.C. is acknowledged from Ludwig Institute for Cancer Research, Fondation contre le cancer, Salus Sanguinis and Fondation “Les avions de Sébastien”, projects Action de recherché concertée (ARC) 16/21-073 and WELBIO F 44/8/5 - MCF/UIG – 10955, avenue Pasteur, 6, 1300 Wavre (Belgium). Funding to G.M. for the HDx-MS platform creation is acknowledged to the FRS-FNS (Appel Grands Equipement 2018, ref:32938497). G.V. has received an Aspirant PhD fellowship from the FRS-FNRS, Belgium. N.P. has received an FSR PhD Fellowship from Université catholique de Louvain and an Aspirant PhD Fellowship from the FRS-FNRS, Belgium.We thank Dr. Didier Colau for his outstanding work in the production and purification of recombinant proteins used in this study, Lidvine Genet and Céline Mouton for expert technical support, and Dr. Nicolas Dauguet for flow cytometry assistance. We also thank Raphaël Frédérick from Louvain Drug Research Institute (LDRI) for his guidance and expertise in microscale thermophoresis experiments. We also thank Jean-François Collet and Steve O. Smith for their sound advices in the writing process of the manuscript. Funding to S.N.C. is acknowledged from Ludwig Institute for Cancer Research, Fondation contre le cancer, Salus Sanguinis and Fondation “Les avions de Sébastien”, projects Action de recherché concertée (ARC) 16/21-073 and WELBIO F 44/8/5 - MCF/UIG – 10955, avenue Pasteur, 6, 1300 Wavre (Belgium). Funding to G.M. for the HDx-MS platform creation is acknowledged to the FRS-FNS (Appel Grands Equipement 2018, ref:32938497). G.V. has received an Aspirant PhD fellowship from the FRS-FNRS, Belgium. N.P. has received an FSR PhD Fellowship from Université catholique de Louvain and an Aspirant PhD Fellowship from the FRS-FNRS, Belgium.
Constantinescu, S. N., Vainchenker, W., Levy, G. & Papadopoulos, N. Functional consequences of mutations in myeloproliferative neoplasms. Hemasphere 5, e578 (2021). DOI: 10.1097/HS9.0000000000000578
Klampfl, T. et al. Somatic mutations of calreticulin in myeloproliferative neoplasms. N. Engl. J. Med. 369, 2379–2390 (2013). DOI: 10.1056/NEJMoa1311347
Nangalia, J. et al. Somatic CALR mutations in myeloproliferative neoplasms with nonmutated JAK2. N. Engl. J. Med. 369, 2391–2405 (2013). DOI: 10.1056/NEJMoa1312542
Araki, M. et al. Activation of the thrombopoietin receptor by mutant calreticulin in CALR-mutant myeloproliferative neoplasms. Blood 127, 1307–1316 (2016). DOI: 10.1182/blood-2015-09-671172
Chachoua, I. et al. Thrombopoietin receptor activation by myeloproliferative neoplasm associated calreticulin mutants. Blood 127, 1325–1335 (2016). DOI: 10.1182/blood-2015-11-681932
Marty, C. et al. Calreticulin mutants in mice induce an MPL-dependent thrombocytosis with frequent progression to myelofibrosis. Blood 127, 1317–1324 (2016). DOI: 10.1182/blood-2015-11-679571
Nivarthi, H. et al. Thrombopoietin receptor is required for the oncogenic function of CALR mutants. Leukemia 30, 1759–1763 (2016). DOI: 10.1038/leu.2016.32
Pecquet, C. et al. Calreticulin mutants as oncogenic rogue chaperones for TpoR and traffic-defective pathogenic TpoR mutants. Blood 133, 2669–2681 (2019). DOI: 10.1182/blood-2018-09-874578
Jia, R. et al. Hematoxylin binds to mutant calreticulin and disrupts its abnormal interaction with thrombopoietin receptor. Blood 137, 1920–1931 (2021). DOI: 10.1182/blood.2020006264
Varricchio, L. et al. Calreticulin: challenges posed by the intrinsically disordered nature of calreticulin to the study of its function. Front. Cell Dev. Biol. 5, 96 (2017). DOI: 10.3389/fcell.2017.00096
Elf, S. et al. Defining the requirements for the pathogenic interaction between mutant calreticulin and MPL in MPN. Blood 131, 782–786 (2018). DOI: 10.1182/blood-2017-08-800896
Elf, S. et al. Mutant calreticulin requires both its mutant c-terminus and the thrombopoietin receptor for oncogenic transformation. Cancer Discov. 6, 368–381 (2016). DOI: 10.1158/2159-8290.CD-15-1434
Pecquet, C. et al. Secreted mutant calreticulins as rogue cytokines trigger thrombopoietin receptor activation specifically in CALR mutated cells: perspectives for MPN therapy. Blood 132, 4–4 (2018). DOI: 10.1182/blood-2018-99-118348
Liu, P. et al. Immunosuppression by mutated calreticulin released from malignant cells. Mol. Cell 77, 748–760.e749 (2020). DOI: 10.1016/j.molcel.2019.11.004
Kapoor, M. et al. Mutational analysis provides molecular insight into the carbohydrate-binding region of calreticulin: pivotal roles of tyrosine-109 and aspartate-135 in carbohydrate recognition. Biochemistry 43, 97–106 (2004). DOI: 10.1021/bi0355286
Ellgaard, L. et al. NMR structure of the calreticulin P-domain. Proc. Natl Acad. Sci. USA 98, 3133–3138 (2001). DOI: 10.1073/pnas.051630098
Chouquet, A. et al. X-ray structure of the human calreticulin globular domain reveals a peptide-binding area and suggests a multi-molecular mechanism. PLoS ONE 6, e17886 (2011). DOI: 10.1371/journal.pone.0017886
Jumper, J. et al. Highly accurate protein structure prediction with AlphaFold. Nature 596, 583–589 (2021).
Thomson, S. P. & Williams, D. B. Delineation of the lectin site of the molecular chaperone calreticulin. Cell Stress Chaperones 10, 242–251 (2005). DOI: 10.1379/CSC-126.1
Wood, T. J. et al. Specificity of transcription enhancement via the STAT responsive element in the serine protease inhibitor 2.1 promoter. Mol. Cell Endocrinol. 130, 69–81 (1997). DOI: 10.1016/S0303-7207(97)00075-0
Gopalakrishnapai, J. et al. Isothermal titration calorimetric study defines the substrate binding residues of calreticulin. Biochem. Biophys. Res. Commun. 351, 14–20 (2006). DOI: 10.1016/j.bbrc.2006.09.164
Kozlov, G. et al. Structural basis of carbohydrate recognition by calreticulin. J. Biol. Chem. 285, 38612–38620 (2010). DOI: 10.1074/jbc.M110.168294
Dominguez, C., Boelens, R. & Bonvin, A. M. HADDOCK: a protein-protein docking approach based on biochemical or biophysical information. J. Am. Chem. Soc. 125, 1731–1737 (2003). DOI: 10.1021/ja026939x
Xue, L. C., Rodrigues, J. P., Kastritis, P. L., Bonvin, A. M. & Vangone, A. PRODIGY: a web server for predicting the binding affinity of protein-protein complexes. Bioinformatics 32, 3676–3678 (2016). DOI: 10.1093/bioinformatics/btw514
Kollman, P. A. et al. Calculating structures and free energies of complex molecules: combining molecular mechanics and continuum models. Acc. Chem. Res. 33, 889–897 (2000). DOI: 10.1021/ar000033j
Araki, M. et al. Homomultimerization of mutant calreticulin is a prerequisite for MPL binding and activation. Leukemia 33, 122–131 (2019). DOI: 10.1038/s41375-018-0181-2
Wilmes, S. et al. Mechanism of homodimeric cytokine receptor activation and dysregulation by oncogenic mutations. Science 367, 643–652 (2020). DOI: 10.1126/science.aaw3242
Staerk, J. et al. Orientation-specific signalling by thrombopoietin receptor dimers. EMBO J. 30, 4398–4413 (2011). DOI: 10.1038/emboj.2011.315
Seubert, N. et al. Active and inactive orientations of the transmembrane and cytosolic domains of the erythropoietin receptor dimer. Mol. Cell 12, 1239–1250 (2003). DOI: 10.1016/S1097-2765(03)00389-7
Rivera, J. F. et al. Zinc-dependent multimerization of mutant calreticulin is required for MPL binding and MPN pathogenesis. Blood Adv. 5, 1922–1932 (2021). DOI: 10.1182/bloodadvances.2020002402
Lyskov, S. & Gray, J. J. The RosettaDock server for local protein-protein docking. Nucleic Acids Res. 36, W233–W238 (2008). DOI: 10.1093/nar/gkn216
Cui, L. et al. Tuning MPL signaling to influence hematopoietic stem cell differentiation and inhibit essential thrombocythemia progenitors. Proc. Natl Acad. Sci. USA 118, e2017849118 (2021). DOI: 10.1073/pnas.2017849118
Venkatesan, A. et al. Mechanism of mutant calreticulin-mediated activation of the thrombopoietin receptor in cancers. J. Cell Biol. 220, e202009179 (2021).
Masubuchi, N. et al. Mutant calreticulin interacts with MPL in the secretion pathway for activation on the cell surface. Leukemia 34, 499–509 (2020). DOI: 10.1038/s41375-019-0564-z
Lau, A. M., Claesen, J., Hansen, K. & Politis, A. Deuteros 2.0: peptide-level significance testing of data from hydrogen deuterium exchange mass spectrometry. Bioinformatics 37, 270–272 (2021). DOI: 10.1093/bioinformatics/btaa677
Masson, G. R. et al. Recommendations for performing, interpreting and reporting hydrogen deuterium exchange mass spectrometry (HDX-MS) experiments. Nat. Methods 16, 595–602 (2019). DOI: 10.1038/s41592-019-0459-y
Oberg, K. A., Ruysschaert, J. M. & Goormaghtigh, E. Rationally selected basis proteins: a new approach to selecting proteins for spectroscopic secondary structure analysis. Protein Sci. 12, 2015–2031 (2003). DOI: 10.1110/ps.0354703
Goormaghtigh, E., Ruysschaert, J. M. & Raussens, V. Evaluation of the information content in infrared spectra for protein secondary structure determination. Biophys. J. 90, 2946–2957 (2006). DOI: 10.1529/biophysj.105.072017
Drozdetskiy, A., Cole, C., Procter, J. & Barton, G. J. JPred4: a protein secondary structure prediction server. Nucleic Acids Res. 43, W389–W394 (2015). DOI: 10.1093/nar/gkv332
Jones, D. T. Protein secondary structure prediction based on position-specific scoring matrices. J. Mol. Biol. 292, 195–202 (1999). DOI: 10.1006/jmbi.1999.3091
Yachdav, G. et al. PredictProtein—an open resource for online prediction of protein structural and functional features. Nucleic Acids Res. 42, W337–W343 (2014). DOI: 10.1093/nar/gku366
Petersen, B., Petersen, T. N., Andersen, P., Nielsen, M. & Lundegaard, C. A generic method for assignment of reliability scores applied to solvent accessibility predictions. BMC Struct. Biol. 9, 51 (2009). DOI: 10.1186/1472-6807-9-51
Rost, B. & Sander, C. Prediction of protein secondary structure at better than 70% accuracy. J. Mol. Biol. 232, 584–599 (1993). DOI: 10.1006/jmbi.1993.1413
Ishida, T. & Kinoshita, K. PrDOS: prediction of disordered protein regions from amino acid sequence. Nucleic Acids Res. 35, W460–W464 (2007). DOI: 10.1093/nar/gkm363
Romero, P. et al. Sequence complexity of disordered protein. Proteins 42, 38–48 (2001). DOI: 10.1002/1097-0134(20010101)42:1<38::AID-PROT50>3.0.CO;2-3
Barik, A. et al. DEPICTER: intrinsic disorder and disorder function prediction server. J. Mol. Biol. 432, 3379–3387 (2020). DOI: 10.1016/j.jmb.2019.12.030
Kelley, L. A., Mezulis, S., Yates, C. M., Wass, M. N. & Sternberg, M. J. The Phyre2 web portal for protein modeling, prediction and analysis. Nat. Protoc. 10, 845–858 (2015). DOI: 10.1038/nprot.2015.053
Webb, B. & Sali, A. Comparative protein structure modeling using MODELLER. Curr. Protoc. Bioinforma. 54, 5.6.1–5.6.37 (2016). DOI: 10.1002/cpbi.3
van Zundert, G. C. P. et al. The HADDOCK2.2 web server: user-friendly integrative modeling of biomolecular complexes. J. Mol. Biol. 428, 720–725 (2016). DOI: 10.1016/j.jmb.2015.09.014
Eastman, P. et al. OpenMM 7: Rapid development of high performance algorithms for molecular dynamics. PLoS Comput Biol. 13, e1005659 (2017). DOI: 10.1371/journal.pcbi.1005659
Maier, J. A. et al. ff14SB: improving the accuracy of protein side chain and backbone parameters from ff99SB. J. Chem. Theory Comput 11, 3696–3713 (2015). DOI: 10.1021/acs.jctc.5b00255
Case D. A. et al. AMBER 2020 [computer program] (University of California, San Francisco, 2020).
Scherer, M. K. et al. PyEMMA 2: a software package for estimation, validation, and analysis of Markov models. J. Chem. Theory Comput. 11, 5525–5542 (2015). DOI: 10.1021/acs.jctc.5b00743
Hunter, J. D. Matplotlib: A 2D graphics environment. Comput. Sci. Eng. 9, 90–95 (2007). DOI: 10.1109/MCSE.2007.55
Westerlund, A. M. & Delemotte, L. InfleCS: clustering free energy landscapes with gaussian mixtures. J. Chem. Theory Comput. 15, 6752–6759 (2019). DOI: 10.1021/acs.jctc.9b00454
Paduraru, C. et al. An N-linked glycan modulates the interaction between the CD1d heavy chain and beta 2-microglobulin. J. Biol. Chem. 281, 40369–40378 (2006). DOI: 10.1074/jbc.M608518200
Petrescu, A. J. et al. The solution NMR structure of glucosylated N-glycans involved in the early stages of glycoprotein biosynthesis and folding. EMBO J. 16, 4302–4310 (1997). DOI: 10.1093/emboj/16.14.4302
Petrescu, A. J., Petrescu, S. M., Dwek, R. A. & Wormald, M. R. A statistical analysis of N- and O-glycan linkage conformations from crystallographic data. Glycobiology 9, 343–352 (1999). DOI: 10.1093/glycob/9.4.343
Petrescu, A. J., Wormald, M. R. & Dwek, R. A. Structural aspects of glycomes with a focus on N-glycosylation and glycoprotein folding. Curr. Opin. Struct. Biol. 16, 600–607 (2006). DOI: 10.1016/j.sbi.2006.08.007
Jo, S., Kim, T., Iyer, V. G. & Im, W. CHARMM-GUI: A web-based graphical user interface for CHARMM. J. Comput. Chem. 29, 1859–1865 (2008). DOI: 10.1002/jcc.20945
Phillips, J. C. et al. Scalable molecular dynamics on CPU and GPU architectures with NAMD. J. Chem. Phys. 153, 044130 (2020). DOI: 10.1063/5.0014475
Guvench, O. et al. CHARMM additive all-atom force field for carbohydrate derivatives and its utility in polysaccharide and carbohydrate–protein modeling. J. Chem. Theory Comput. 7, 3162–3180 (2011). DOI: 10.1021/ct200328p
Huang, J. & MacKerell, A. D. Jr CHARMM36 all-atom additive protein force field: validation based on comparison to NMR data. J. Comput. Chem. 34, 2135–2145 (2013). DOI: 10.1002/jcc.23354
Pastor, R. W. & MacKerell, A. D. Development of the CHARMM force field for lipids. J. Phys. Chem. Lett. 2, 1526–1532 (2011). DOI: 10.1021/jz200167q
Palacios, R. & Steinmetz, M. Il-3-dependent mouse clones that express B-220 surface antigen, contain Ig genes in germ-line configuration, and generate B lymphocytes in vivo. Cell 41, 727–734 (1985). DOI: 10.1016/S0092-8674(85)80053-2
