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:

Methotrexate recognition by the human reduced folate carrier SLC19A1

Nature volume 609, pages 1056–1062 (2022)Cite this article

Subjects

Abstract

Folates are essential nutrients with important roles as cofactors in one-carbon transfer reactions, being heavily utilized in the synthesis of nucleic acids and the metabolism of amino acids during cell division1,2. Mammals lack de novo folate synthesis pathways and thus rely on folate uptake from the extracellular milieu3. The human reduced folate carrier (hRFC, also known as SLC19A1) is the major importer of folates into the cell1,3, as well as chemotherapeutic agents such as methotrexate4,5,6. As an anion exchanger, RFC couples the import of folates and antifolates to anion export across the cell membrane and it is a major determinant in methotrexate (antifolate) sensitivity, as genetic variants and its depletion result in drug resistance4,5,6,7,8. Despite its importance, the molecular basis of substrate specificity by hRFC remains unclear. Here we present cryo-electron microscopy structures of hRFC in the apo state and captured in complex with methotrexate. Combined with molecular dynamics simulations and functional experiments, our study uncovers key determinants of hRFC transport selectivity among folates and antifolate drugs while shedding light on important features of anion recognition by hRFC.

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: Structure of hRFC in complex with MTX.
Fig. 2: Transporter architecture and chemical environment of the hRFC central cavity.
Fig. 3: Structural determinants of folate and antifolate drug recognition by hRFC.
Fig. 4: Dynamics and transport of MTX and anions by hRFC.

Similar content being viewed by others

Data availability

Coordinates have been deposited in the Protein Data Bank with IDs 7TX6 (hRFCEM–MTX), 7XT7 (hRFCEM) and 8DEP (apo hRFCEM). The cryo-EM maps have been deposited in the Electron Microscopy Data Bank with IDs EMD-26155 (hRFCEM–MTX), EMD-26156 (hRFCEM) and EMD-27394 (apo hRFCEM). Source data are provided with this paper.

References

  1. Zheng, Y. & Cantley, L. C. Toward a better understanding of folate metabolism in health and disease. J. Exp. Med. 216, 253–266 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Clare, C. E., Brassington, A. H., Kwong, W. Y. & Sinclair, K. D. One-carbon metabolism: linking nutritional biochemistry to epigenetic programming of long-term development. Annu. Rev. Anim. Biosci. 7, 263–287 (2019).

    Article  CAS  PubMed  Google Scholar 

  3. Hou, Z. & Matherly, L. H. Biology of the major facilitative folate transporters SLC19A1 and SLC46A1. Curr. Top. Membr. 73, 175–204 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Matherly, L. H., Hou, Z. & Deng, Y. Human reduced folate carrier: translation of basic biology to cancer etiology and therapy. Cancer Metastasis Rev. 26, 111–128 (2007).

    Article  CAS  PubMed  Google Scholar 

  5. O’Connor, C. et al. Folate transporter dynamics and therapy with classic and tumor-targeted antifolates. Sci. Rep. 11, 6389 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  6. Kanarek, N. et al. Histidine catabolism is a major determinant of methotrexate sensitivity. Nature 559, 632–636 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Kobayashi, H., Takemura, Y. & Ohnuma, T. Variable expression of RFC1 in human leukemia cell lines resistant to antifolates. Cancer Lett. 124, 135–142 (1998).

    Article  CAS  PubMed  Google Scholar 

  8. Girardi, E. et al. A widespread role for SLC transmembrane transporters in resistance to cytotoxic drugs. Nat. Chem. Biol. 16, 469–478 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Zhao, R. et al. Rescue of embryonic lethality in reduced folate carrier-deficient mice by maternal folic acid supplementation reveals early neonatal failure of hematopoietic organs. J. Biol. Chem. 276, 10224–10228 (2001).

    Article  CAS  PubMed  Google Scholar 

  10. Svaton, M. et al. A homozygous deletion in the SLC19A1 gene as a cause of folate-dependent recurrent megaloblastic anemia. Blood 135, 2427–2431 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  11. Yang, R. et al. Sequence alterations in the reduced folate carrier are observed in osteosarcoma tumor samples. Clin. Cancer Res. 9, 837–844 (2003).

    CAS  PubMed  Google Scholar 

  12. Matherly, L. H. & Hou, Z. Structure and function of the reduced folate carrier a paradigm of a major facilitator superfamily mammalian nutrient transporter. Vitam. Horm. 79, 145–184 (2008).

    Article  CAS  PubMed  Google Scholar 

  13. Yee, S. W. et al. SLC19A1 pharmacogenomics summary. Pharmacogenet. Genomics 20, 708–715 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Guo, W. et al. Mechanisms of methotrexate resistance in osteosarcoma. Clin. Cancer Res. 5, 621–627 (1999).

    CAS  PubMed  Google Scholar 

  15. Luteijn, R. D. et al. SLC19A1 transports immunoreactive cyclic dinucleotides. Nature 573, 434–438 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Ritchie, C., Cordova, A. F., Hess, G. T., Bassik, M. C. & Li, L. SLC19A1 is an importer of the immunotransmitter cGAMP. Mol. Cell 75, 372–381.e5 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Alam, C., Hoque, M. T., Finnell, R. H., Goldman, I. D. & Bendayan, R. Regulation of reduced folate carrier (RFC) by vitamin D receptor at the blood-brain barrier. Mol. Pharm. 14, 3848–3858 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Hou, Z., Ye, J., Haska, C. L. & Matherly, L. H. Transmembrane domains 4, 5, 7, 8, and 10 of the human reduced folate carrier are important structural or functional components of the transmembrane channel for folate substrates. J. Biol. Chem. 281, 33588–33596 (2006).

    Article  CAS  PubMed  Google Scholar 

  19. Ganapathy, V., Smith, S. B. & Prasad, P. D. SLC19: the folate/thiamine transporter family. Pflugers Arch. 447, 641–646 (2004).

    Article  CAS  PubMed  Google Scholar 

  20. Henderson, G. B. & Zevely, E. M. Anion exchange mechanism for transport of methotrexate in L1210 cells. Biochem. Biophys. Res. Commun. 99, 163–169 (1981).

    Article  CAS  PubMed  Google Scholar 

  21. Zhao, R., Gao, F. & Goldman, I. D. Reduced folate carrier transports thiamine monophosphate: an alternative route for thiamine delivery into mammalian cells. Am. J. Physiol. Cell Physiol. 282, C1512–C1517 (2002).

    Article  CAS  PubMed  Google Scholar 

  22. Goldman, I. D. The characteristics of the membrane transport of amethopterin and the naturally occurring folates. Ann. NY Acad. Sci. 186, 400–422 (1971).

    Article  CAS  PubMed  Google Scholar 

  23. Zhao, R., Gao, F., Hanscom, M. & Goldman, I. D. A prominent low-pH methotrexate transport activity in human solid tumors: contribution to the preservation of methotrexate pharmacologic activity in HeLa cells lacking the reduced folate carrier. Clin. Cancer Res. 10, 718–727 (2004).

    Article  CAS  PubMed  Google Scholar 

  24. Desmoulin, S. K., Hou, Z., Gangjee, A. & Matherly, L. H. The human proton-coupled folate transporter: biology and therapeutic applications to cancer. Cancer Biol. Ther. 13, 1355–1373 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Parker, J. L. et al. Structural basis of antifolate recognition and transport by PCFT. Nature 595, 130–134 (2021).

    Article  CAS  PubMed  Google Scholar 

  26. Jansen, G. et al. Sulfasalazine is a potent inhibitor of the reduced folate carrier: implications for combination therapies with methotrexate in rheumatoid arthritis. Arthritis Rheum. 50, 2130–2139 (2004).

    Article  CAS  PubMed  Google Scholar 

  27. Straub, M. S., Alvadia, C., Sawicka, M. & Dutzler, R. Cryo-EM structures of the caspase-activated protein XKR9 involved in apoptotic lipid scrambling. Elife 10, e69800 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Chun, E. et al. Fusion partner toolchest for the stabilization and crystallization of G protein-coupled receptors. Structure 20, 967–976 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Gao, X. et al. Mechanism of substrate recognition and transport by an amino acid antiporter. Nature 463, 828–832 (2010).

    Article  CAS  PubMed  Google Scholar 

  30. Henderson, G. B. & Zevely, E. M. Affinity labeling of the 5-methyltetrahydrofolate/methotrexate transport protein of L1210 cells by treatment with an N-hydroxysuccinimide ester of [3H]methotrexate. J. Biol. Chem. 259, 4558–4562 (1984).

    Article  CAS  PubMed  Google Scholar 

  31. Hou, Z., Stapels, S. E., Haska, C. L. & Matherly, L. H. Localization of a substrate binding domain of the human reduced folate carrier to transmembrane domain 11 by radioaffinity labeling and cysteine-substituted accessibility methods. J. Biol. Chem. 280, 36206–36213 (2005).

    Article  CAS  PubMed  Google Scholar 

  32. Deng, Y. et al. Role of lysine 411 in substrate carboxyl group binding to the human reduced folate carrier, as determined by site-directed mutagenesis and affinity inhibition. Mol. Pharmacol. 73, 1274–1281 (2008).

    Article  CAS  PubMed  Google Scholar 

  33. Liu, X. Y. & Matherly, L. H. Functional interactions between arginine-133 and aspartate-88 in the human reduced folate carrier: evidence for a charge-pair association. Biochem. J. 358, 511–516 (2001).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Brigle, K. E., Spinella, M. J., Sierra, E. E. & Goldman, I. D. Characterization of a mutation in the reduced folate carrier in a transport defective L1210 murine leukemia cell line. J. Biol. Chem. 270, 22974–22979 (1995).

    Article  CAS  PubMed  Google Scholar 

  35. Zhao, R., Sharina, I. G. & Goldman, I. D. Pattern of mutations that results in loss of reduced folate carrier function under antifolate selective pressure augmented by chemical mutagenesis. Mol. Pharmacol. 56, 68–76 (1999).

    Article  CAS  PubMed  Google Scholar 

  36. Rothem, L. et al. Resistance to multiple novel antifolates is mediated via defective drug transport resulting from clustered mutations in the reduced folate carrier gene in human leukaemia cell lines. Biochem. J. 367, 741–750 (2002).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Tse, A., Brigle, K., Taylor, S. M. & Moran, R. G. Mutations in the reduced folate carrier gene which confer dominant resistance to 5,10-dideazatetrahydrofolate. J. Biol. Chem. 273, 25953–25960 (1998).

    Article  CAS  PubMed  Google Scholar 

  38. Gifford, A. J. et al. Role of the E45K-reduced folate carrier gene mutation in methotrexate resistance in human leukemia cells. Leukemia 16, 2379–2387 (2002).

    Article  CAS  PubMed  Google Scholar 

  39. Zhao, R., Assaraf, Y. G. & Goldman, I. D. A mutated murine reduced folate carrier (RFC1) with increased affinity for folic acid, decreased affinity for methotrexate, and an obligatory anion requirement for transport function. J. Biol. Chem. 273, 19065–19071 (1998).

    Article  CAS  PubMed  Google Scholar 

  40. Jansen, G. et al. A structurally altered human reduced folate carrier with increased folic acid transport mediates a novel mechanism of antifolate resistance. J. Biol. Chem. 273, 30189–30198 (1998).

    Article  CAS  PubMed  Google Scholar 

  41. Drori, S., Jansen, G., Mauritz, R., Peters, G. J. & Assaraf, Y. G. Clustering of mutations in the first transmembrane domain of the human reduced folate carrier in GW1843U89-resistant leukemia cells with impaired antifolate transport and augmented folate uptake. J. Biol. Chem. 275, 30855–30863 (2000).

    Article  CAS  PubMed  Google Scholar 

  42. Zhao, R., Assaraf, Y. G. & Goldman, I. D. A reduced folate carrier mutation produces substrate-dependent alterations in carrier mobility in murine leukemia cells and methotrexate resistance with conservation of growth in 5-formyltetrahydrofolate. J. Biol. Chem. 273, 7873–7879 (1998).

    Article  CAS  PubMed  Google Scholar 

  43. Rosowsky, A., Wright, J. E., Vaidya, C. M. & Forsch, R. A. The effect of side-chain, para-aminobenzoyl region, and B-ring modifications on dihydrofolate reductase binding, influx via the reduced folate carrier, and cytotoxicity of the potent nonpolyglutamatable antifolate Nα-(4-amino-4-deoxypteroyl)-Nδ-hemiphthaloyl-L-ornithine. Pharmacol. Ther. 85, 191–205 (2000).

    Article  CAS  PubMed  Google Scholar 

  44. Rhee, M. S., Galivan, J., Wright, J. E. & Rosowsky, A. Biochemical studies on PT523, a potent nonpolyglutamatable antifolate, in cultured cells. Mol. Pharmacol. 45, 783–791 (1994).

    CAS  PubMed  Google Scholar 

  45. Furst, D. E. The rational use of methotrexate in rheumatoid arthritis and other rheumatic diseases. Br. J. Rheumatol. 36, 1196–1204 (1997).

    Article  CAS  PubMed  Google Scholar 

  46. Schroder, O. & Stein, J. Low dose methotrexate in inflammatory bowel disease: current status and future directions. Am. J. Gastroenterol. 98, 530–537 (2003).

    Article  PubMed  Google Scholar 

  47. Hou, Z. et al. Dual targeting of epithelial ovarian cancer via folate receptor alpha and the proton-coupled folate transporter with 6-substituted pyrrolo[2,3-d]pyrimidine antifolates. Mol. Cancer Ther. 16, 819–830 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Aluri, S. et al. Substitutions that lock and unlock the proton-coupled folate transporter (PCFT-SLC46A1) in an inward-open conformation. J. Biol. Chem. 294, 7245–7258 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Desmoulin, S. K. et al. Targeting the proton-coupled folate transporter for selective delivery of 6-substituted pyrrolo[2,3-d]pyrimidine antifolate inhibitors of de novo purine biosynthesis in the chemotherapy of solid tumors. Mol. Pharmacol. 78, 577–587 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Drew, D., North, R. A., Nagarathinam, K. & Tanabe, M. Structures and general transport mechanisms by the major facilitator superfamily (MFS). Chem. Rev. 121, 5289–5335 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Jurrus, E. et al. Improvements to the APBS biomolecular solvation software suite. Protein Sci. 27, 112–128 (2018).

    Article  CAS  PubMed  Google Scholar 

  52. Ashkenazy, H. et al. ConSurf 2016: an improved methodology to estimate and visualize evolutionary conservation in macromolecules. Nucleic Acids Res. 44, W344–W350 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Harris, M., Firsov, D., Vuagniaux, G., Stutts, M. J. & Rossier, B. C. A novel neutrophil elastase inhibitor prevents elastase activation and surface cleavage of the epithelial sodium channel expressed in Xenopus laevis oocytes. J. Biol. Chem. 282, 58–64 (2007).

    Article  CAS  PubMed  Google Scholar 

  54. Di Francesco, V., Di Francesco, M., Decuzzi, P., Palomba, R. & Ferreira, M. Synthesis of two methotrexate prodrugs for optimizing drug loading into liposomes. Pharmaceutics 13, 332 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  55. Goehring, A. et al. Screening and large-scale expression of membrane proteins in mammalian cells for structural studies. Nat. Protoc. 9, 2574–2585 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Shinkarev, V. P., Crofts, A. R. & Wraight, C. A. Spectral analysis of the bc1 complex components in situ: beyond the traditional difference approach. Biochim. Biophys. Acta 1757, 67–77 (2006).

    Article  CAS  PubMed  Google Scholar 

  57. Zheng, S. Q. et al. MotionCor2: anisotropic correction of beam-induced motion for improved cryo-electron microscopy. Nat. Methods 14, 331–332 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Punjani, A., Rubinstein, J. L., Fleet, D. J. & Brubaker, M. A. cryoSPARC: algorithms for rapid unsupervised cryo-EM structure determination. Nat. Methods 14, 290–296 (2017).

    Article  CAS  PubMed  Google Scholar 

  59. Rohou, A. & Grigorieff, N. CTFFIND4: fast and accurate defocus estimation from electron micrographs. J. Struct. Biol. 192, 216–221 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  60. Asarnow, D., Palovcak, E., Cheng, Y. UCSF pyem v0.5. Zenodo https://doi.org/10.5281/zenodo.3576630 (2019).

  61. Zivanov, J. et al. New tools for automated high-resolution cryo-EM structure determination in RELION-3. Elife 7, e42166 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

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

    Article  PubMed  Google Scholar 

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

    Article  CAS  Google Scholar 

  64. Chen, V. B. et al. MolProbity: all-atom structure validation for macromolecular crystallography. Acta Crystallogr. D Biol. Crystallogr. 66, 12–21 (2010).

    Article  CAS  PubMed  Google Scholar 

  65. Lee, J. et al. CHARMM-GUI Input Generator for NAMD, Gromacs, Amber, Openmm, and CHARMM/OpenMM simulations using the CHARMM36 Additive Force Field. Biophys. J. 110, 641a–641a (2016).

    Article  Google Scholar 

  66. Katoh, K., Rozewicki, J. & Yamada, K. D. MAFFT online service: multiple sequence alignment, interactive sequence choice and visualization. Brief Bioinform. 20, 1160–1166 (2019).

    Article  CAS  PubMed  Google Scholar 

  67. Gabler, F. et al. Protein sequence analysis using the MPI Bioinformatics Toolkit. Curr. Protoc. Bioinformatics 72, e108 (2020).

    Article  CAS  PubMed  Google Scholar 

  68. Jo, S., Kim, T. & Im, W. Automated builder and database of protein/membrane complexes for molecular dynamics simulations. PLoS ONE 2, e880 (2007).

    Article  PubMed  PubMed Central  Google Scholar 

  69. Tian, C. et al. ff19SB: amino-acid-specific protein backbone parameters trained against quantum mechanics energy surfaces in solution. J. Chem. Theory Comput. 16, 528–552 (2020).

    Article  PubMed  Google Scholar 

  70. He, X., Man, V. H., Yang, W., Lee, T. S. & Wang, J. A fast and high-quality charge model for the next generation general AMBER force field. J. Chem. Phys. 153, 114502 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  71. Jorgensen, W. L., Chandrasekhar, J., Madura, J. D., Impey, R. W. & Klein, M. L. Comparison of simple potential functions for simulating liquid water. J. Chem. Phys. 79, 926–935 (1983).

    Article  CAS  Google Scholar 

  72. Case, D. A. et al. AMBER 2021 (Univ. California, 2021).

  73. Gotz, A. W. et al. Routine microsecond molecular dynamics simulations with AMBER on GPUs. 1. Generalized Born. J. Chem. Theory Comput. 8, 1542–1555 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  74. Salomon-Ferrer, R., Gotz, A. W., Poole, D., Le Grand, S. & Walker, R. C. Routine microsecond molecular dynamics simulations with AMBER on GPUs. 2. Explicit solvent particle mesh Ewald. J. Chem. Theory Comput. 9, 3878–3888 (2013).

    Article  CAS  PubMed  Google Scholar 

  75. Lee, J. et al. CHARMM-GUI supports the Amber force fields. J. Chem. Phys. 153, 035103 (2020).

    Article  CAS  PubMed  Google Scholar 

  76. Hopkins, C. W., Le Grand, S., Walker, R. C. & Roitberg, A. E. Long-time-step molecular dynamics through hydrogen mass repartitioning. J. Chem. Theory Comput. 11, 1864–1874 (2015).

    Article  CAS  PubMed  Google Scholar 

  77. Gao, Y. et al. CHARMM-GUI supports hydrogen mass repartitioning and different protonation states of phosphates in lipopolysaccharides. J. Chem. Inf. Model. 61, 831–839 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  78. Darden, T., York, D. & Pedersen, L. Particle mesh Ewald - an N.Log(N) method for Ewald sums in large systems. J. Chem. Phys. 98, 10089–10092 (1993).

    Article  CAS  Google Scholar 

  79. Essmann, U. et al. A smooth particle mesh Ewald method. J. Chem. Phys. 103, 8577–8593 (1995).

    Article  CAS  Google Scholar 

  80. Roe, D. R. & Cheatham, T. E. 3rd PTRAJ and CPPTRAJ: software for processing and analysis of molecular dynamics trajectory data. J. Chem. Theory Comput. 9, 3084–3095 (2013).

    Article  CAS  PubMed  Google Scholar 

  81. Pettersen, E. F. et al. UCSF Chimera-a visualization system for exploratory research and analysis. J. Comput. Chem. 25, 1605–1612 (2004).

    Article  CAS  PubMed  Google Scholar 

  82. Humphrey, W., Dalke, A. & Schulten, K. VMD: visual molecular dynamics. J. Mol. Graph. 14, 33–38 (1996).

    Article  CAS  PubMed  Google Scholar 

  83. Kaufman, Y., Ifergan, I., Rothem, L., Jansen, G. & Assaraf, Y. G. Coexistence of multiple mechanisms of PT523 resistance in human leukemia cells harboring 3 reduced folate carrier alleles: transcriptional silencing, inactivating mutations, and allele loss. Blood 107, 3288–3294 (2006).

    Article  CAS  PubMed  Google Scholar 

  84. Kaufman, Y. et al. Reduced folate carrier mutations are not the mechanism underlying methotrexate resistance in childhood acute lymphoblastic leukemia. Cancer 100, 773–782 (2004).

    Article  CAS  PubMed  Google Scholar 

  85. Roy, K., Tolner, B., Chiao, J. H. & Sirotnak, F. M. A single amino acid difference within the folate transporter encoded by the murine RFC-1 gene selectively alters its interaction with folate analogues. Implications for intrinsic antifolate resistance and directional orientation of the transporter within the plasma membrane of tumor cells. J. Biol. Chem. 273, 2526–2531 (1998).

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgements

Cryo-EM data were screened and collected at the Duke University Shared Materials Instrumentation Facility (SMIF) and at the Pacific Northwest Center for Cryo-EM (PNCC) at Oregon Health & Science University (OHSU). We thank N. Bhattacharya at SMIF and J. Myers at the PNCC for assistance with microscope operation. This research was supported by National Institutes of Health grant R01GM137421 (S.-Y.L. and J.H.), American Heart Association fellowship 20PRE35210058 (N.J.W.) and National Science Foundation grant MCB-2111728 (W.I.). A portion of this research was supported by National Institutes of Health grant U24GM129547, performed at the PNCC at OHSU and accessed through EMSL (grid.436923.9), a Department of Energy Office of Science User Facility sponsored by the Office of Biological and Environmental Research. The Duke University SMIF is affiliated with the North Carolina Research Triangle Nanotechnology Network, which is in part supported by the National Science Foundation (ECCS-2025064).

Author information

Author notes

  1. Jiho Yoo

    Present address: College of Pharmacy, Chung-Ang University, Seoul, Republic of Korea

  2. These authors contributed equally: Nicholas J. Wright, Justin G. Fedor

Authors and Affiliations

  1. Department of Biochemistry, Duke University School of Medicine, Durham, NC, USA

    Nicholas J. Wright, Justin G. Fedor, Yang Suo, Jiho Yoo & Seok-Yong Lee

  2. Departments of Biological Sciences, Chemistry and Bioengineering, Lehigh University, Bethlehem, PA, USA

    Han Zhang & Wonpil Im

  3. Department of Chemistry, Duke University, Durham, NC, USA

    Pyeonghwa Jeong & Jiyong Hong

Authors
  1. Nicholas J. Wright
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Justin G. Fedor
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Han Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Pyeonghwa Jeong
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Yang Suo
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Jiho Yoo
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Jiyong Hong
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Wonpil Im
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Seok-Yong Lee
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

J.G.F. conducted biochemical preparation, sample freezing, grid screening and surface accessibility analysis; N.J.W. performed single-particle 3D reconstruction as well as radiotracer uptake assays; and Y.S. and J.G.F. collected data and J.Y. performed initial biochemical characterization, all under the guidance of S.-Y.L. N.J.W. and S.-Y.L. performed model building and refinement. H.Z. carried out all MD simulations under the guidance of W.I. P.J. synthesized NHS–MTX under the guidance of J.H. N.J.W., J.G.F. and S.-Y.L. wrote the manuscript.

Corresponding author

Correspondence to Seok-Yong Lee.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature thanks Larry Matherly and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Additional information

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

Extended data figures and tables

Extended Data Fig. 1 Protein biochemistry, NHS-MTX protein modification and cryo-EM analysis of hRFCEM.

a, Topology diagram of hRFCEM used for structural elucidation. b, Representative gel-filtration profile for the final purification step and representative SDS-PAGE analysis (Coomassie stained) of purified protein used for cryo-EM grid preparation. Protein laddering during SDS-PAGE is common for small membrane proteins, with degree of non-specific oligomerization denoted by the number of asterisks (* monomer, ** dimer, *** trimer, **** tetramer). This purification is performed routinely with very similar results, reliably yielding pure and biochemically stable protein sample. c, Characterization of MTX modification of RFC by NHS-MTX. A representative spectral deconvolution of the MTX-RFC UV-vis spectrum into MTX and pure unlabeled RFC yields a labelling ratio of 1.1:1 MTX:RFC (graph prepared in Prism 8). d, Cryo-EM micrograph for hRFCEM sample (of representative quality for all collected cryo-EM data reported in this study) and 2D-classes of hRFCEM.

Extended Data Fig. 2 Cryo-EM data processing of hRFCEM.

a, Processing workflow for hRFCEM. b, Phenix and cryoSPARC reported Fourier shell correlations, and particle angular distribution for the final focused map. c, Local resolution analysis. d, Cryo-EM density corresponding to hRFCEM TM1-12 (map threshold = 0.20).

Extended Data Fig. 3 Cryo-EM data processing of Apo hRFCEM.

a, Processing workflow for Apo hRFCEM. b, Phenix and cryoSPARC reported Fourier shell correlations, and particle angular distribution for the final map. c, Local resolution analysis. d, Cryo-EM density corresponding to Apo hRFCEM TM1–12 (map threshold = 0.25). e, Structural superposition of the final refined coordinates for apo hRFCEM and hRFCEM f, Overlay of the final cryo-EM reconstructions (sharpened maps) for apo hRFCEM and hRFCEM (final coordinates for apo hRFCEM shown for reference, hRFCEM map resampled relative to the Apo hRFCEM map, with map threshold shown at 0.3). g, Weak, spurious cryo-EM density in the transporter central cavity present in both apo hRFCEM and hRFCEM sharpened maps (Map threshold shown at 0.15 for apo hRFCEM, 0.10 for hRFCEM).

Extended Data Fig. 4 Cryo-EM data processing of hRFCEM-MTX.

a, Processing workflow for MTX modified hRFCEM b, Fourier shell correlation and particle angular distribution for the final reconstruction c, Local resolution analysis of the final reconstruction at two different map thresholds d, Cryo-EM density corresponding to hRFCEM-MTX TM1–12 (map threshold = 0.2). e, Structural superposition of the final refined coordinates for apo hRFCEM (grey) and hRFCEM-MTX (blue), highlighting apparent ligand induced changes in the TM4 conformational state f, Cryo-EM density corresponding to R133 in the apo hRFCEM and hRFCEM-MTX reconstructions (hRFCEM map resampled relative to the hRFCEM-MTX map, with map threshold shown at 0.1) g, Ribbon depiction of superposed structures.

Extended Data Fig. 5 Human disease and drug resistance associated mutations in hRFC.

Mapping of clinically relevant mutations of full-length RFC11,35,36,37,38,39,40,41,42,83,84,85 onto the hRFCEM-MTX structure fall into two general regions. MTX-resistance associated mutations observed in tumor samples (red), in cell lines (green), or observed in both tumors and in cell lines (blue). The putative position of the megaloblastic anemia-associated mutation of ΔPhe212 is shown in purple as the model only extends to residue 211. Other residues not resolved in the structure are listed in parenthesis in the legend.

Extended Data Fig. 6 MD simulations of hRFCEM with MTX and PT523.

a, The all-atom molecular dynamics system setup for MTX-bound hRFC embedded in a POPC membrane and solvated with 150 mM KCl (Extended Data Table 3). hRFC is shown in cartoon, the N- and C- terminal domains colored in blue and yellow, respectively. MTX is shown as sticks, in pink. Lipids are depicted as spheres with glycerol-palmitoyl and -oleoyl groups colored gray, phosphates in orange, and choline in green. Red and blue spheres represent Cl− and K+, respectively. b, Timecourse traces for n = 5 replicates for MD simulations of MTX-K411 hRFC (MD system “MTX-LYS”). c, Timecourse traces for n = 5 replicates for MD simulations of hRFC with unlinked MTX (MD system “MTX”). For b and c, distances from the MTX N4 to E123 carboxylate center-of-mass (blue), and the l-Glu center-of-mass of MTX to Arg guanidiniums (R133, red; R157, purple; R373, cyan), are plotted as a function of time. d, Histogram plot of MTX N4 to E123 distances over n = 5 replicates. e. Histogram plots of MTX α- and γ- carboxylates to Arg guanidiniums over n = 5 replicates. f–h, MD simulations of PT523 docked into hRFC. f, Chemical structure of PT523 compared to MTX with the structural difference highlighted in red. g, the cryo-EM structure of MTX-labelled hRFC versus the MD simulation of docked PT523 (MD system “PT523”), with snapshots taken at 500 ns for n = 5 replicates. h, Snapshots from an MD simulation of docked PT523 sampled at various timepoints.

Extended Data Fig. 7 Evolutionary determinants of ion selectivity within the SLC19 family.

a, Select regions of a multiple sequence alignment of hSLC19A1, hSLC19A2, hSLC19A3. Numbering consistent with sequence position for hSLC19A1 b,The mutational effects of R133E and K411Q on the surface electrostatics of hRFCEM by APBS51 calculations in PyMOL. c, Ion probability densities from MD simulations of hRFCEM with and without in silico introduced mutants, becoming gradually more thiamine transporter-2 (hSLC19A3) like. Simulations performed in the presence of 150 mM KCl, with a threshold value of 25 shown for chloride or potassium for each simulation.

Extended Data Fig. 8 Antifolate drug recognition by human RFC and chicken proton-coupled folate transporter (PCFT).

a, Surface representation of the cavities for inward-facing hRFCEM-MTX and outward-facing pemetrexed (PMX)-bound PCFT. The orientation flip of the drugs in the cavities is highlighted by labelling the chemical groups. 0b, Chemical structure and immediate coordinating environment for MTX and PMX, with hydrogen bonds shown as red dashed lines and charged interactions highlighted by denoting charges. α- and γ- carboxylates are highlighted based on their extent of interactions in either system. c, The electrostatic environment of MTX and PMX as calculated by APBS61 of the hRFCEM-MTX and PCFT, respectively. Residues from b are shown as sticks with the anion binding site of RFC and selectivity pocket of PCFT as denoted.

Extended Data Table 1 Cryo-EM data collection, refinement and validation statistics
Full size table
Extended Data Table 2 MTX and antifolaxe resistance mutations in RFC
Full size table
Extended Data Table 3 Simulation system information
Full size table

Supplementary information

Supplementary Information

This file contains Supplementary Figs. 1 and 2.

Reporting Summary

Supplementary Video 1

MD simulation of hRFC–MTX. A representative movie of a 1-µs MD simulation of hRFC–MTX, where MTX is covalently linked to K411. Functionally important residues E123, R133, R157 and R373 (green) and K411–MTX (salmon) are depicted as sticks. The protein in the cartoon is coloured by the N-terminal half (blue) and C-terminal half (yellow).

Supplementary Video 2

MD simulation of hRFC with unlinked MTX. A representative movie of a 2-µs MD simulation of hRFC with MTX unlinked from K411. MTX (salmon) and functionally important residues E123, R133, R157 and R373 (green) are shown as sticks. The protein in the cartoon is coloured by its N-terminal half (blue) and C-terminal half (yellow).

Supplementary Video 3

MD simulation of hRFC with PT523. A representative movie of a 0.7-µs MD simulation of hRFC with PT523 docked into the MTX–hRFC structure (with MTX removed). PT523 (salmon) and functionally important residues E123, R133, R157 and R373 (green) are shown as sticks. The protein in the cartoon is coloured by its N-terminal half (blue) and C-terminal half (yellow).

Source data

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wright, N.J., Fedor, J.G., Zhang, H. et al. Methotrexate recognition by the human reduced folate carrier SLC19A1. Nature 609, 1056–1062 (2022). https://doi.org/10.1038/s41586-022-05168-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41586-022-05168-0

This article is cited by

Comments

By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

Search

Advanced search

Quick links

Nature Briefing: Translational Research

Sign up for the Nature Briefing: Translational Research newsletter — top stories in biotechnology, drug discovery and pharma.

Get what matters in translational research, free to your inbox weekly. Sign up for Nature Briefing: Translational Research