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:

Structural basis for lipid transfer by the ATG2A–ATG9A complex

Nature Structural & Molecular Biology (2024)Cite this article

Subjects

Abstract

Autophagy is characterized by the formation of double-membrane vesicles called autophagosomes. Autophagy-related proteins (ATGs) 2A and 9A have an essential role in autophagy by mediating lipid transfer and re-equilibration between membranes for autophagosome formation. Here we report the cryo-electron microscopy structures of human ATG2A in complex with WD-repeat protein interacting with phosphoinositides 4 (WIPI4) at 3.2 Å and the ATG2A–WIPI4–ATG9A complex at 7 Å global resolution. On the basis of molecular dynamics simulations, we propose a mechanism of lipid extraction from the donor membranes. Our analysis revealed 3:1 stoichiometry of the ATG9A–ATG2A complex, directly aligning the ATG9A lateral pore with ATG2A lipid transfer cavity, and an interaction of the ATG9A trimer with both the N-terminal and the C-terminal tip of rod-shaped ATG2A. Cryo-electron tomography of ATG2A liposome-binding states showed that ATG2A tethers lipid vesicles at different orientations. In summary, this study provides a molecular basis for the growth of the phagophore membrane and lends structural insights into spatially coupled lipid transport and re-equilibration during autophagosome formation.

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

Access options

Buy this article

  • Purchase on SpringerLink
  • Instant access to full article PDF
Buy now

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

Fig. 1: The cryo-EM structure of ATG2A.
Fig. 2: The structural details of ATG2A’s hydrophobic cavity.
Fig. 3: The simulation of lipid translocation process by ATG2ANR.
Fig. 4: Cryo-ET reveals several membrane engagement modes of ATG2A–WIPI4.
Fig. 5: The cryo-EM structure of the ATG2A–WIPI4–ATG9A complex.
Fig. 6: Open state of ATG9A bound to ATG2A–WIPI4 complex.
Fig. 7: Proposed models of ATG2A–ATG9A-mediated lipid transfer process.

Similar content being viewed by others

Data availability

The cryo-EM density maps of ATG2A–WIPI4, ATG9A, C-terminally bound ATG9A–ATG2A–WIPI4 and N-terminally bound ATG9A–ATG2A–WIPI4 were deposited to the EM Data Bank (EMDB) under accession codes EMD-37086, EMD-37088, EMD-37091 and EMD-38839 and the coordinates of ATG2A, ATG2A–WIPI4, ATG9A, C-terminally bound ATG9A–ATG2A–WIPI4 and N-terminally bound ATG9A–ATG2A–WIPI4 were deposited to the PDB under accession codes 8KBY, 8KBX, 8KBZ, 8KC3 and 8Y1L, respectively. Cryo-ET images of ATG2A–WIPI4 with SUVs were deposited to the EMDB under accession codes EMD-50658, EMD-50659, EMD-50660, EMD-50662, EMD-50666 and EMD-50667. Previously solved structures used in this study were obtained from the PDB and EMDB under accession codes 6A9E, 7JLQ and EMD-22376. The MS proteomics data were deposited to the ProteomeXchange Consortium through the PRIDE73 partner repository with the dataset identifier PXD048906. Source data are provided with this paper.

References

  1. Levine, B. & Kroemer, G. Autophagy in the pathogenesis of disease. Cell 132, 27–42 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Rashid, H. O. et al. ER stress: autophagy induction, inhibition and selection. Autophagy 11, 1956–1977 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Mizushima, N., Yoshimori, T. & Ohsumi, Y. The role of Atg proteins in autophagosome formation. Annu. Rev. Cell Dev. Biol. 27, 107–132 (2011).

    Article  CAS  PubMed  Google Scholar 

  4. Chowdhury, S. et al. Insights into autophagosome biogenesis from structural and biochemical analyses of the ATG2A–WIPI4 complex. Proc. Natl Acad. Sci. USA 115, E9792–E9801 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Osawa, T. et al. Atg2 mediates direct lipid transfer between membranes for autophagosome formation. Nat. Struct. Mol. Biol. 26, 281–288 (2019).

    Article  CAS  PubMed  Google Scholar 

  6. Valverde, D. P. et al. ATG2 transports lipids to promote autophagosome biogenesis. J. Cell Biol. 218, 1787–1798 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Bozic, M. et al. A conserved ATG2–GABARAP family interaction is critical for phagophore formation. EMBO Rep. 21, e48412 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Kotani, T. et al. The Atg2–Atg18 complex tethers pre-autophagosomal membranes to the endoplasmic reticulum for autophagosome formation. Proc. Natl Acad. Sci. USA 115, 10363–10368 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Otomo, T., Chowdhury, S. & Lander, G. C.The rod-shaped ATG2A–WIPI4 complex tethers membranes in vitro. Contact (Thousand Oaks) https://doi.org/10.1177/2515256418819936 (2018).

    Article  PubMed  Google Scholar 

  10. Gomez-Sanchez, R. et al. Atg9 establishes Atg2-dependent contact sites between the endoplasmic reticulum and phagophores. J. Cell Biol. 217, 2743–2763 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Tamura, N. et al. Differential requirement for ATG2A domains for localization to autophagic membranes and lipid droplets. FEBS Lett. 591, 3819–3830 (2017).

    Article  CAS  PubMed  Google Scholar 

  12. Maeda, S., Otomo, C. & Otomo, T. The autophagic membrane tether ATG2A transfers lipids between membranes. eLife 8, e45777 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  13. Proikas-Cezanne, T. et al. WIPI proteins: essential PtdIns3P effectors at the nascent autophagosome. J. Cell Sci. 128, 207–217 (2015).

    CAS  PubMed  Google Scholar 

  14. Guardia, C. M. et al. Structure of human ATG9A, the only transmembrane protein of the core autophagy machinery. Cell Rep. 31, 107837 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Maeda, S. et al. Structure, lipid scrambling activity and role in autophagosome formation of ATG9A. Nat. Struct. Mol. Biol. 27, 1194–U246 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Matoba, K. et al. Atg9 is a lipid scramblase that mediates autophagosomal membrane expansion. Nat. Struct. Mol. Biol. 27, 1185–U224 (2020).

    Article  CAS  PubMed  Google Scholar 

  17. Sawa-Makarska, J. et al. Reconstitution of autophagosome nucleation defines Atg9 vesicles as seeds for membrane formation. Science 369, eaaz7714 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Olivas, T. J. et al. ATG9 vesicles comprise the seed membrane of mammalian autophagosomes. J. Cell Biol. 222, e202208088 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Bieber, A. et al. In situ structural analysis reveals membrane shape transitions during autophagosome formation. Proc. Natl Acad. Sci. USA 119, e2209823119 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Orsi, A. et al. Dynamic and transient interactions of Atg9 with autophagosomes, but not membrane integration, are required for autophagy. Mol. Biol. Cell 23, 1860–1873 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Yamamoto, H. et al. Atg9 vesicles are an important membrane source during early steps of autophagosome formation. J. Cell Biol. 198, 219–233 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Puri, C. et al. Diverse autophagosome membrane sources coalesce in recycling endosomes. Cell 154, 1285–1299 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Ghanbarpour, A. et al. A model for a partnership of lipid transfer proteins and scramblases in membrane expansion and organelle biogenesis. Proc. Natl Acad. Sci. USA 118, e2101562118 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Gotze, M. et al. A simple cross-linking/mass spectrometry workflow for studying system-wide protein interactions. Anal. Chem. 91, 10236–10244 (2019).

    Article  PubMed  Google Scholar 

  25. Dziurdzik, S. K. & Conibear, E. The Vps13 family of lipid transporters and its role at membrane contact sites. Int. J. Mol. Sci. 22, 2905 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Zhu, L. et al. TAPS: a traveling-salesman based automated path searching method for functional conformational changes of biological macromolecules. J. Chem. Phys. 150, 124105 (2019).

    Article  PubMed  Google Scholar 

  27. Xi, K. & Zhu, L. Automated path searching reveals the mechanism of hydrolysis enhancement by T4 lysozyme mutants. Int. J. Mol. Sci. 23, 14628 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Wang, L. et al. DNA deformation exerted by regulatory DNA-binding motifs in human alkyladenine DNA glycosylase promotes base flipping. J. Chem. Inf. Model. 62, 3213–3226 (2022).

    Article  PubMed  Google Scholar 

  29. Xi, K. et al. Assessing the performance of traveling-salesman based automated path searching (TAPS) on complex biomolecular systems. J. Chem. Theory Comput. 17, 5301–5311 (2021).

    Article  CAS  PubMed  Google Scholar 

  30. Torrie, G. M. & Valleau, J. P. Nonphysical sampling distributions in Monte Carlo free-energy estimation: umbrella sampling. J. Comput. Phys. 23, 187–199 (1977).

    Article  Google Scholar 

  31. Ren, J. et al. Multi-site-mediated entwining of the linear WIR-motif around WIPI β-propellers for autophagy. Nat. Commun. 11, 2702 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Watanabe, Y. et al. Structure-based analyses reveal distinct binding sites for Atg2 and phosphoinositides in Atg18. J. Biol. Chem. 287, 31681–31690 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Imai, K. et al. Atg9A trafficking through the recycling endosomes is required for autophagosome formation. J. Cell Sci. 129, 3781–3791 (2016).

    Article  CAS  PubMed  Google Scholar 

  34. Tang, Z. et al. TOM40 targets Atg2 to mitochondria-associated ER membranes for phagophore expansion. Cell Rep. 28, 1744–1757 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. van Vliet, A. R. et al. ATG9A and ATG2A form a heteromeric complex essential for autophagosome formation. Mol. Cell 82, 4324–4339 (2022).

    Article  PubMed  Google Scholar 

  36. Chumpen Ramirez, S. et al. Atg9 interactions via its transmembrane domains are required for phagophore expansion during autophagy. Autophagy 19, 1459–1478 (2023).

    Article  CAS  PubMed  Google Scholar 

  37. van Bülow, S. and Hummer, G. Kinetics of Atg2-mediated lipid transfer from the ER can account for phagophore expansion. Preprint at bioRxiv https://doi.org/10.1101/2020.05.12.090977 (2020).

  38. Nguyen, A. et al. Metamorphic proteins at the basis of human autophagy initiation and lipid transfer. Mol. Cell 83, 2077–2090 (2023).

    Article  CAS  PubMed  Google Scholar 

  39. Esfahani, E. E. Isotropic multichannel total variation framework for joint reconstruction of multicontrast parallel MRI. J. Med. Imaging (Bellingham) 9, 013502 (2022).

    PubMed  Google Scholar 

  40. Punjani, A. et al. cryoSPARC: algorithms for rapid unsupervised cryo-EM structure determination. Nat. Methods 14, 290–296 (2017).

    Article  CAS  PubMed  Google Scholar 

  41. 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 

  42. 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 

  43. 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 

  44. Tan, Y. Z. et al. Addressing preferred specimen orientation in single-particle cryo-EM through tilting. Nat. Methods 14, 793–796 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Jumper, J. et al. Highly accurate protein structure prediction with AlphaFold. Nature 596, 583–589 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Varadi, M. et al. AlphaFold Protein Structure Database: massively expanding the structural coverage of protein-sequence space with high-accuracy models. Nucleic Acids Res. 50, D439–D444 (2022).

    Article  CAS  PubMed  Google Scholar 

  47. Emsley, P. et al. Features and development of Coot. Acta Crystallogr. D Biol. Crystallogr. 66, 486–501 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. 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 

  49. Waterhouse, A. et al. SWISS-MODEL: homology modelling of protein structures and complexes. Nucleic Acids Res. 46, W296–W303 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Afonine, P. V. et al. Towards automated crystallographic structure refinement with phenix.refine. Acta Crystallogr. D Biol. Crystallogr. 68, 352–367 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Pettersen, E. F. et al. UCSF ChimeraX: structure visualization for researchers, educators, and developers. Protein Sci. 30, 70–82 (2021).

    Article  CAS  PubMed  Google Scholar 

  52. Ludtke, S. J. 3-D structures of macromolecules using single-particle analysis in EMAN. Methods Mol. Biol. 673, 157–173 (2010).

    Article  CAS  PubMed  Google Scholar 

  53. Jo, S. et al. CHARMM-GUI: a web-based graphical user interface for CHARMM. J. Comput. Chem. 29, 1859–1865 (2008).

    Article  CAS  PubMed  Google Scholar 

  54. Yesylevskyy, S. & Khandelia, H. EnCurv: simple technique of maintaining global membrane curvature in molecular dynamics simulations. J. Chem. Theory Comput. 17, 1181–1193 (2021).

    Article  CAS  PubMed  Google Scholar 

  55. Abraham, M. J. et al. GROMACS: high performance molecular simulations through multi-level parallelism from laptops to supercomputers. SoftwareX 1–2, 19–25 (2015).

    Article  Google Scholar 

  56. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Berendsen, H. J. C. et al. Molecular dynamics with coupling to an external bath. J. Chem. Phys. 81, 3684–3690 (1984).

    Article  CAS  Google Scholar 

  58. Parrinello, M. & Rahman, A. Crystal-structure and pair potentials—a molecular-dynamics study. Phys. Rev. Lett. 45, 1196–1199 (1980).

    Article  CAS  Google Scholar 

  59. Bussi, G., Donadio, D. & Parrinello, M. Canonical sampling through velocity rescaling. J. Chem. Phys. 126, 014101 (2007).

    Article  PubMed  Google Scholar 

  60. 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 

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

    Article  CAS  Google Scholar 

  62. Hess, B. et al. LINCS: a linear constraint solver for molecular simulations. J. Comput. Chem. 18, 1463–1472 (1997).

    Article  CAS  Google Scholar 

  63. Tribello, G. A. et al. PLUMED 2: new feathers for an old bird. Comput. Phys. Commun. 185, 604–613 (2014).

    Article  CAS  Google Scholar 

  64. Applegate, D. L., Bixby, R. E., Chvátal, V. & Cook, W. J. The Traveling Salesman Problem: A Computational Study (Princeton University Press, 2007).

  65. Branduardi, D., Gervasio, F. L. & Parrinello, M. From A to B in free energy space. J. Chem. Phys. 126, 054103 (2007).

    Article  PubMed  Google Scholar 

  66. Kumar, S. et al. The weighted histogram analysis method for free-energy calculations on biomolecules. I. The method. J. Comput. Chem. 13, 1011–1021 (1992).

    Article  CAS  Google Scholar 

  67. 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 

  68. Kosinski, J. et al. Xlink Analyzer: software for analysis and visualization of cross-linking data in the context of three-dimensional structures. J. Struct. Biol. 189, 177–183 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  69. Mastronarde, D. N. Automated electron microscope tomography using robust prediction of specimen movements. J. Struct. Biol. 152, 36–51 (2005).

    Article  PubMed  Google Scholar 

  70. Hagen, W. J. H., Wan, W. & Briggs, J. A. G. Implementation of a cryo-electron tomography tilt-scheme optimized for high resolution subtomogram averaging. J. Struct. Biol. 197, 191–198 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  71. Kremer, J. R., Mastronarde, D. N. & McIntosh, J. R. Computer visualization of three-dimensional image data using IMOD. J. Struct. Biol. 116, 71–76 (1996).

    Article  CAS  PubMed  Google Scholar 

  72. Liu, Y.-T. et al. Isotropic reconstruction for electron tomography with deep learning. Nat. Commun. 13, 6482 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  73. Perez-Riverol, Y. et al. The PRIDE database resources in 2022: a hub for mass spectrometry-based proteomics evidences. Nucleic Acids Res. 50, D543–D552 (2022).

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgements

We thank the cryo-EM (KEMC) and advanced MS facility (KMS) of the Kobilka Institute of Innovative Drug Discovery at the Chinese University of Hong Kong, Shenzhen for the support. The cryo-ET data were collected at the Umeå Center for EM, a SciLifeLab National Cryo-EM facility and part of the National Microscopy Infrastructure (VR-RFI 2016-00968), supported by instrumentation grants from the Knut and Alice Wallenberg Foundation and the Kempe Foundation. We are grateful to M.-Y. Su for critically reading the manuscript. This work was supported by the National Natural Science Foundation of China (31950410540 to G.S. and 31971179 to L.Z.), the Shenzhen Fundamental Research Fund (JCYJ20200109150003938 to L.Z., RCYX20200714114645019 to L.Z. and GXWD2020123115722002-20200831175432002 to L.Z.), the Foreign Young Talent Program (QN2021032004L to G.S.), the Swedish Research Council (2018–05851 and 2021–01145 to L.-A.C.) and the Guangdong Basic and Applied Basic Research Foundation (2022A1515010856 to Y.W.). L.Z. was also supported in part by a Presidential Fellowship. Y.W. and R.T. were supported by a Ganghong Young Scholar Development Fund at the Chinese University of Hong Kong, Shenzhen. The computational work was supported by the Warshel Institute for Computational Biology (funding from Shenzhen City and Longgang District, LGKCSDPT2024001) and funding from the Shenzhen–Hong Kong Cooperation Zone for Technology and Innovation (HZQB-KCZYB-2020056).

Author information

Author notes

  1. These authors contributed equally: Yang Wang, Selma Dahmane, Rujuan Ti.

Authors and Affiliations

  1. Kobilka Institute of Innovative Drug Discovery, School of Medicine, The Chinese University of Hong Kong, Shenzhen, Shenzhen, China

    Yang Wang, Xinyi Mai & Goran Stjepanovic

  2. Wallenberg Centre for Molecular Medicine, Umeå University, Umeå, Sweden

    Selma Dahmane & Lars-Anders Carlson

  3. Umeå Centre for Microbial Research (UCMR), Umeå University, Umeå, Sweden

    Selma Dahmane & Lars-Anders Carlson

  4. Laboratory for Molecular Infection Medicine Sweden (MIMS), Umeå University, Umeå, Sweden

    Selma Dahmane & Lars-Anders Carlson

  5. Department of Medical Biochemistry and Biophysics, Umeå University, Umeå, Sweden

    Selma Dahmane & Lars-Anders Carlson

  6. Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen, Shenzhen, China

    Rujuan Ti & Lizhe Zhu

  7. School of Medicine, The Chinese University of Hong Kong, Shenzhen, Shenzhen, China

    Rujuan Ti & Lizhe Zhu

Authors
  1. Yang Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Selma Dahmane
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Rujuan Ti
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Xinyi Mai
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Lizhe Zhu
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Lars-Anders Carlson
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Goran Stjepanovic
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Y.W., L.-A.C., L.Z. and G.S. designed the experiments. Y.W. cloned the constructs, expressed and purified the proteins, collected and processed the EM data, built the models, prepared the liposome and performed the lipid transfer activity assay. S.D. prepared the cryo-ET sample and collected and processed the tomography data. R.T. analyzed the MD simulations. G.S. and X.M. performed the XL–MS experiment. G.S., L.-A.C. and Y.W. wrote the paper. This work was supervised by L.Z., L.-A.C. and G.S.

Corresponding authors

Correspondence to Lizhe Zhu, Lars-Anders Carlson or Goran Stjepanovic.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Structural & Molecular Biology thanks Hector Martinez-Seara, Han Wei and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editor: Katarzyna Ciazynska, in collaboration with the Nature Structural & Molecular Biology team.

Additional information

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

Extended data

Extended Data Fig. 1 SDS-PAGE and gel filtration profile of purified proteins.

The SDS-PAGE (left) and gel filtration profile (right) of ATG2A alone (a), WIPI4 alone (b) and ATG2A-WIPI4 complex (c). In a-c, SDS-PAGEs are representative of two independent experiments.

Source data

Extended Data Fig. 2 Imaging processing procedure of the ATG2A-WIPI4 complex.

a, The representative micrograph (of 6,179 micrographs) after motion correction of ATG2A-WIPI4 complex. Scale bar equals 150 nm. b, Cryo-EM data processing flow chart of ATG2A-WIPI4 complex. c, The ATG2A-WIPI4 model fitted into the density map. d, Particle orientation distribution of ATG2A-WIPI4 map. e, The density map with corresponding structural model of ATG2A A577-D707, relative to the region shown in Fig. 2b. f, The representative density map with corresponding structural model of ATG2A.

Extended Data Fig. 3 ATG2A intramolecular crosslinks.

a, Linear plot displaying all the identified crosslink sites for ATG2A. b, Intramolecular ATG2A crosslinks mapped onto AlphFold model. ATG2A_C region (blue) crosslinks with ATG2A rod-like domain (green). Unsatisfied ( > 33 Å) and satisfied crosslinks are indicated in red and blue, respectively.

Extended Data Fig. 4 The interaction of ATG2A and WIPI4.

a, Selected 2D class averages of the ATG2A-WIPI4 complex indicate the flexible binding of WIPI4. b, XL-MS analysis of ATG2A-WIPI4 complex. Linear plots displaying all the identified intramolecular (purple lines) and intermolecular (blue lines) crosslinks. c, Satisfied intermolecular XLs mapped onto the structures of ATG2A-WIPI4 complex. d, WIPI4 interaction interface is shown on the ATG2A model (green).

Extended Data Fig. 5 The detailed process of lipid transport by ATG2ANR.

The free energy curve of 1-DOPC simulation is drawn. Along the curve, the mean values and error bars are calculated from the last 10 WHAM iterations after convergence. The error bars shown in sticks represent SEM. The energy values are presented as mean values ± SEM. a-h are the structures corresponding to the points marked on the curve. ATG2ANR is colored in pale cyan and the DOPC is colored in yellow.

Extended Data Fig. 6 Purification and in vitro reconstitution of the ATG2A-WIPI4-GABARAP-ATG9A complex.

The SDS-PAGE of GABARAP (a), ATG9A (b), and reconstituted ATG2A-WIPI4-GABARAP-ATG9A complex (c). Gel filtration profile of GABARAP (d) and ATG9A (e). In a-c, data shown are representative of two independent experiments.

Source data

Extended Data Fig. 7 Imaging processing procedure of the C-terminally bound ATG2A/WIPI4-ATG9A complex.

a, Imaging processing workflow of ATG2A/WIPI4-ATG9A complex and dissociated ATG9A for refinement and reconstruction of density maps. b, A representative cryo-EM micrograph of ATG2A/WIPI4-ATG9A complex (of 10,212 micrographs). Scale bar equals 150 nm.

Extended Data Fig. 8 Imaging processing procedure of the N-terminally bound ATG2A/WIPI4-ATG9A complex.

a, Imaging processing workflow of N-terminally bound ATG2A/WIPI4-ATG9A complex. b, A representative cryo-EM micrograph of ATG2A/WIPI4-ATG9A complex (of 21,267 micrographs). Scale bar equals 150 nm. c, Representative 2D class averages indicating that ATG9A flexibly binds to both N- and C-terminus of ATG2A. d, Model fitting of ATG9A and ATG2A-WIPI4 into the low-resolution density map.

Extended Data Fig. 9 The interaction between N-terminal mini ATG2A and ATG9A complex.

a, In vitro pull-down experiment of the full length ATG2A and N-terminal mini ATG2A (AA 1-443) interact with ATG9A. The experiment was repeated three times and visualized by SDS-PAGE. b, Western blotting of the pulldown results detected by anti-flag antibody. The experiment was repeated three times independently. c, The quantification analysis of SDS-PAGE (a) to show the interaction between full-length ATG2A and mini ATG2A (AA 1-443) with ATG9A. Vertical coordinates is calculated by the mean density of elution band divided by input and subtracted the control group. Black dots indicate three individual replicates. Graph bar was presented as mean values ± SEM.

Source data

Extended Data Fig. 10 Structure of ATG9A in the open state.

a, ATG9A model fitted into the density map, the colored local resolution map and 3D representation of angular distribution. b, The comparison of ATG9A model in this study (yellow) with the published ATG9A model (PDB 7JLQ) (cyan). The superimposed cartoon of trimer (left) and protomer. The two-helix movement of ATG9A protomers was compared by measuring the distance between T409 in each protomer. The ATG9A promoter in this study is colored in yellow, green and sky-blue, and ATG9A model (PDB 7JLQ) was colored in cyan. c, The model-to-map FSC curve and the half-map FSC curve of ATG9A.

Supplementary information

Supplementary Information

Supplementary Discussion, Figs. 1–9 and Tables 1–5.

Reporting Summary

Supplementary Video 1

The simulation of lipid extraction from DOPC micelle by spAtg2NR. spAtg2NR is colored in pink and DOPC is colored in yellow. spAtg2NR is able to spontaneously absorb one phospholipid acyl chain to the entry of the lipid-transfer channel located in its N-terminal helical region.

Supplementary Video 2

The simulation of the ATG2ANR interaction with a flat membrane. ATG2ANR is colored in pale cyan. The DOPC in the membrane is colored in yellow.

Supplementary Video 3

The simulation of the ATG2ANR interaction with DOPC. ATG2ANR is colored in cyan and DOPC is colored in yellow. ATG2ANR can stably interact with the DOPC acyl chain, similar to spAtg2NR.

Supplementary Video 4

The simulation of the lipid transfer process by ATG2ANR. ATG2ANR is colored in cyan. The simulated lipid transfer process containing the entry, flip and transfer states, related to Fig. 3d,e.

Supplementary Video 5

The simulation of the ATG2ANR interaction with one DOPC and an extra micelle. ATG2ANR is colored in pale cyan. The DOPC being transferred is colored in yellow with the extra DOPC half-entering ATG2ANR from the micelle colored in green. The remaining DOPC molecules of the micelle are shown in gray. One acyl chain of the second DOPC can keep the entrance open and assist the transfer of the first DOPC, related to Supplementary Fig. 4.

Supplementary Data 1

Unprocessed gels of Supplementary Fig. 5a.

Supplementary Data 2

Statistical source data of Supplementary Fig. 5c.

Source data

Source Data Fig. 3

Statistical source data.

Source Data Extended Data Fig. 1

Unprocessed gels.

Source Data Extended Data Fig. 6

Unprocessed gels.

Source Data Extended Data Fig. 9

Unprocessed gels and blots.

Source Data Extended Data Fig. 9

Statistical 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

Wang, Y., Dahmane, S., Ti, R. et al. Structural basis for lipid transfer by the ATG2A–ATG9A complex. Nat Struct Mol Biol (2024). https://doi.org/10.1038/s41594-024-01376-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1038/s41594-024-01376-6

Search

Advanced 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