TORC1 organized in inhibited domains (TOROIDs) regulate TORC1 activity (original) (raw)

Accession codes

Primary accessions

Electron Microscopy Data Bank

References

  1. Wullschleger, S., Loewith, R. & Hall, M. N. TOR signaling in growth and metabolism. Cell 124, 471–484 (2006)
    Article CAS PubMed Google Scholar
  2. Loewith, R. et al. Two TOR complexes, only one of which is rapamycin sensitive, have distinct roles in cell growth control. Mol. Cell 10, 457–468 (2002)
    Article CAS PubMed Google Scholar
  3. Saxton, R. A. & Sabatini, D. M. mTOR signaling in growth, metabolism, and disease. Cell 168, 960–976 (2017)
    Article CAS PubMed PubMed Central Google Scholar
  4. Urban, J. et al. Sch9 is a major target of TORC1 in Saccharomyces cerevisiae. Mol. Cell 26, 663–674 (2007)
    Article CAS PubMed Google Scholar
  5. Gaubitz, C. et al. Molecular basis of the rapamycin insensitivity of target of rapamycin complex 2. Mol. Cell 58, 977–988 (2015)
    Article CAS PubMed Google Scholar
  6. Yang, H. et al. mTOR kinase structure, mechanism and regulation. Nature 497, 217–223 (2013)
    Article CAS ADS PubMed PubMed Central Google Scholar
  7. Aylett, C. H. et al. Architecture of human mTOR complex 1. Science 351, 48–52 (2016)
    Article CAS ADS PubMed Google Scholar
  8. Reinke, A. et al. TOR complex 1 includes a novel component, Tco89p (YPL180w), and cooperates with Ssd1p to maintain cellular integrity in Saccharomyces cerevisiae. J. Biol. Chem. 279, 14752–14762 (2004)
    Article CAS PubMed Google Scholar
  9. Valbuena, N., Guan, K. L. & Moreno, S. The Vam6 and Gtr1-Gtr2 pathway activates TORC1 in response to amino acids in fission yeast. J. Cell Sci. 125, 1920–1928 (2012)
    Article CAS PubMed PubMed Central Google Scholar
  10. Jewell, J. L. et al. Differential regulation of mTORC1 by leucine and glutamine. Science 347, 194–198 (2015)
    Article CAS ADS PubMed PubMed Central Google Scholar
  11. Binda, M. et al. The Vam6 GEF controls TORC1 by activating the EGO complex. Mol. Cell 35, 563–573 (2009)
    Article CAS PubMed Google Scholar
  12. Panchaud, N., Péli-Gulli, M. P. & De Virgilio, C. Amino acid deprivation inhibits TORC1 through a GTPase-activating protein complex for the Rag family GTPase Gtr1. Sci. Signal. 6, ra42 (2013)
    Article PubMed Google Scholar
  13. Kira, S. et al. Dynamic relocation of the TORC1-Gtr1/2-Ego1/2/3 complex is regulated by Gtr1 and Gtr2. Mol. Biol. Cell 27, 382–396 (2016)
    Article CAS PubMed PubMed Central Google Scholar
  14. Hughes Hallett, J. E., Luo, X. & Capaldi, A. P. Snf1/AMPK promotes the formation of Kog1/Raptor-bodies to increase the activation threshold of TORC1 in budding yeast. eLife 4, e09181 (2015)
    Article PubMed PubMed Central Google Scholar
  15. Petrovska, I. et al. Filament formation by metabolic enzymes is a specific adaptation to an advanced state of cellular starvation. eLife (2014)
  16. Powis, K. et al. Crystal structure of the Ego1-Ego2-Ego3 complex and its role in promoting Rag GTPase-dependent TORC1 signaling. Cell Res. 25, 1043–1059 (2015)
    Article CAS PubMed PubMed Central Google Scholar
  17. Eltschinger, S. & Loewith, R. TOR complexes and the maintenance of cellular homeostasis. Trends Cell Biol. 26, 148–159 (2016)
    Article CAS PubMed Google Scholar
  18. Rust, M. J., Bates, M. & Zhuang, X. Sub-diffraction-limit imaging by stochastic optical reconstruction microscopy (STORM). Nat. Methods 3, 793–796 (2006)
    Article CAS PubMed PubMed Central Google Scholar
  19. Betzig, E. et al. Imaging intracellular fluorescent proteins at nanometer resolution. Science 313, 1642–1645 (2006)
    Article CAS ADS PubMed Google Scholar
  20. Hess, S. T., Girirajan, T. P. & Mason, M. D. Ultra-high resolution imaging by fluorescence photoactivation localization microscopy. Biophys. J. 91, 4258–4272 (2006)
    Article CAS ADS PubMed PubMed Central Google Scholar
  21. Rothbauer, U. et al. Targeting and tracing antigens in live cells with fluorescent nanobodies. Nat. Methods 3, 887–889 (2006)
    Article CAS PubMed Google Scholar
  22. Ries, J., Kaplan, C., Platonova, E., Eghlidi, H. & Ewers, H. A simple, versatile method for GFP-based super-resolution microscopy via nanobodies. Nat. Methods 9, 582–584 (2012)
    Article CAS PubMed Google Scholar
  23. Egelman, E. H. Reconstruction of helical filaments and tubes. Methods Enzymol. 482, 167–183 (2010)
    Article CAS PubMed PubMed Central Google Scholar
  24. Desfosses, A., Ciuffa, R., Gutsche, I. & Sachse, C. SPRING – an image processing package for single-particle based helical reconstruction from electron cryomicrographs. J. Struct. Biol. 185, 15–26 (2014)
    Article CAS PubMed Google Scholar
  25. Baretic´, D., Berndt, A., Ohashi, Y., Johnson, C. M. & Williams, R. L. Tor forms a dimer through an N-terminal helical solenoid with a complex topology. Nat. Commun. 7, 11016 (2016)
    Article ADS PubMed PubMed Central Google Scholar
  26. Sturgill, T. W. et al. TOR1 and TOR2 have distinct locations in live cells. Eukaryot. Cell 7, 1819–1830 (2008)
    Article CAS PubMed PubMed Central Google Scholar
  27. Egelman, E. H. Three-dimensional reconstruction of helical polymers. Arch. Biochem. Biophys. 581, 54–58 (2015)
    Article CAS PubMed PubMed Central Google Scholar
  28. Seetoh, W. G., Chan, D. S., Matak-Vinkovic´, D. & Abell, C. Mass spectrometry reveals protein kinase CK2 high-order oligomerization via the circular and linear assembly. ACS Chem. Biol. 11, 1511–1517 (2016)
    Article CAS PubMed PubMed Central Google Scholar
  29. Rispal, D. et al. Target of rapamycin complex 2 regulates actin polarization and endocytosis via multiple pathways. J. Biol. Chem. 290, 14963–14978 (2015)
    Article CAS PubMed PubMed Central Google Scholar
  30. Douglass, K. M., Sieben, C., Archetti, A., Lambert, A. & Manley, S. Super-resolution imaging of multiple cells by optimised flat-field epi-illumination. Nat. Photon. 10, 705–708 (2016)
    Article CAS ADS Google Scholar
  31. Edelstein, A ., Amodaj, N ., Hoover, K ., Vale, R. & Stuurman, N. in Current Protocols in Molecular Biology Ch. 14, Unit 14.20 (Wiley, 2010)
    Google Scholar
  32. Olivier, N., Keller, D., Gönczy, P. & Manley, S. Resolution doubling in 3D-STORM imaging through improved buffers. PLoS ONE 8, e69004 (2013)
    Article CAS ADS PubMed PubMed Central Google Scholar
  33. Huang, F. et al. Video-rate nanoscopy using sCMOS camera-specific single-molecule localization algorithms. Nat. Methods 10, 653–658 (2013)
    Article CAS PubMed PubMed Central Google Scholar
  34. Ovesný, M., Krˇížek, P., Borkovec, J., Svindrych, Z. & Hagen, G. M. ThunderSTORM: a comprehensive ImageJ plug-in for PALM and STORM data analysis and super-resolution imaging. Bioinformatics 30, 2389–2390 (2014)
    Article PubMed PubMed Central Google Scholar
  35. Kaplan, C. & Ewers, H. Optimized sample preparation for single-molecule localization-based superresolution microscopy in yeast. Nat. Protocols 10, 1007–1021 (2015)
    Article CAS PubMed Google Scholar
  36. Guichard, P., Hamel, V., Neves, A. & Gönczy, P. Isolation, cryotomography, and three-dimensional reconstruction of centrioles. Methods Cell Biol. 129, 191–209 (2015)
    Article CAS PubMed Google Scholar
  37. Tang, G. et al. EMAN2: an extensible image processing suite for electron microscopy. J. Struct. Biol. 157, 38–46 (2007)
    Article CAS PubMed Google Scholar
  38. Mindell, J. A. & Grigorieff, N. Accurate determination of local defocus and specimen tilt in electron microscopy. J. Struct. Biol. 142, 334–347 (2003)
    PubMed Google Scholar
  39. 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
  40. Navaza, J., Lepault, J., Rey, F. A., Alvarez-Rúa, C. & Borge, J. On the fitting of model electron densities into EM reconstructions: a reciprocal-space formulation. Acta Crystallogr. D 58, 1820–1825 (2002)
    Article CAS PubMed Google Scholar

Download references

Acknowledgements

We thank C. Bauer and J. Bosset at the UniGE BioImaging centre, Y. Sagon at the UniGE High Performance Computing service, I. Filipuzzi and S. Helliwell of the Novartis Institutes for BioMedical Research for CMB4563, M. Berti for technical assistance, and T. Noda for providing the GFP–TOR1 strain. A.D. and A.K.M. are grateful for the contribution of NeSI high-performance computing facilities to the results of this research. New Zealand’s national facilities are provided by the NZ eScience Infrastructure and funded jointly by NeSI’s collaborator institutions and through the Ministry of Business, Innovation & Employment’s Research Infrastructure programme (https://www.nesi.org.nz). A.D. thanks the Royal Society of New Zealand Marsden grant to A.K.M. for support of this work. P.G. acknowledges the Swiss National Science Foundation (SNSF) (PP00P3_157517). R.L. acknowledges support from the Canton of Geneva, SystemsX, and project funding from the SNSF and the European Research Council Consolidator grant program. N.L.M., C.S., S.M., and R.L. are indebted to the National Centre for Competence in Research in Chemical Biology for its support.

Author information

Authors and Affiliations

  1. Department of Molecular Biology, University of Geneva, 30 quai Ernest-Ansermet, Geneva, CH1211, Switzerland
    Manoël Prouteau, Clélia Bourgoint, Nour Lydia Mozaffari & Robbie Loewith
  2. Institute of Genetics and Genomics of Geneva (iGE3), University of Geneva, 30 quai Ernest-Ansermet, Geneva, CH1211, Switzerland
    Manoël Prouteau, Clélia Bourgoint, Nour Lydia Mozaffari & Robbie Loewith
  3. School of Biological Sciences, University of Auckland, Private Bag 92019, Auckland, 1010, New Zealand
    Ambroise Desfosses & Alok K. Mitra
  4. Institute of Physics, Laboratory of Experimental Biophysics, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, CH1015, Switzerland
    Christian Sieben, Nour Lydia Mozaffari & Suliana Manley
  5. Swiss National Centre for Competence in Research (NCCR) in Chemical Biology. University of Geneva, Sciences II, room 3-308, 30 Quai Ernest-Ansermet, Geneva,, CH1211, Switzerland
    Christian Sieben, Nour Lydia Mozaffari, Suliana Manley & Robbie Loewith
  6. Interdisciplinary Centre for Electron Microscopy, École Polytechnique Fédérale de Lausanne (EPFL), CH1015 Lausanne, Switzerland
    Davide Demurtas
  7. Department of Cell Biology, University of Geneva, 30 quai Ernest-Ansermet, Geneva, CH1211, Switzerland
    Paul Guichard

Authors

  1. Manoël Prouteau
    You can also search for this author inPubMed Google Scholar
  2. Ambroise Desfosses
    You can also search for this author inPubMed Google Scholar
  3. Christian Sieben
    You can also search for this author inPubMed Google Scholar
  4. Clélia Bourgoint
    You can also search for this author inPubMed Google Scholar
  5. Nour Lydia Mozaffari
    You can also search for this author inPubMed Google Scholar
  6. Davide Demurtas
    You can also search for this author inPubMed Google Scholar
  7. Alok K. Mitra
    You can also search for this author inPubMed Google Scholar
  8. Paul Guichard
    You can also search for this author inPubMed Google Scholar
  9. Suliana Manley
    You can also search for this author inPubMed Google Scholar
  10. Robbie Loewith
    You can also search for this author inPubMed Google Scholar

Contributions

M.P., C.B., and N.L.M. generated the yeast strains, performed the confocal imaging and analyses, and the associated western blot analyses. M.P. and N.L.M. set up the α-GFP nanobody labelling conditions. C.S. performed the STORM acquisition, analysis, and simulation and the GFP calibration, with GFP–Ypk1 purification by C.B. M.P. optimized the TORC1 purifications and performed the negative-stain electron microscopy. P.G. and M.P. set up the cryo-EM conditions. D.D. and M.P. acquired the cryo-EM micrographs. A.D. performed the helical reconstruction and the mTORC1 fitting. R.L. and M.P. designed experiments, interpreted results, and wrote the manuscript with contributions from all authors.

Corresponding author

Correspondence toRobbie Loewith.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Additional information

Reviewer Information Nature thanks M. Cardenas and the other anonymous reviewer(s) for their contribution to the peer review of this work.

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 Figure 1 Stoichiometric co-localization of TORC1 subunits to a vacuole-associated focus.

a, b, Differential interference contrast microscopy and confocal images of yeast cells exiting exponential growth. Cells express GFP- and/or mCherry-tagged TORC1 subunits as indicated. Vph1–mCherry marks the membrane of the vacuole. We note, however, that the Lst8–GFP and Tco89–mCherry strains presented major growth phenotypes, which for Lst8 were so severe that we were unable to generate the strains necessary to assess its presumptive co-localization with other TORC1 components. c, Purification of GFP–Ypk1 used for GFP calibrations, quantifications, and localizations. Left: gel filtration plot showing void volume and GFP–Ypk1 monomer peaks. Right: Coommassie stained gel of purified fractions obtained by gel filtration. d, Distribution of single GFP brightness values calculated from GFP calibration using epifluorescence images of GFP–Ypk1. e, Anti-GFP western blot analysis of the GFP-tagged TORC1 subunits expressed in the presence (+Glc) or absence (−Glc) of glucose. Hog1 is used as loading control. f, g, Boxplots of the number of GFP molecules per cell (f) and per focus (g) for the indicated GFP-tagged TORC1 subunits. Error bars, s.d. for values obtained on at least 100 cells. h, Boxplot quantifying the number of GFP molecules per WT cell expressing GFP–Kog1 in the presence (+Glc) or absence (−Glc) of glucose. i, Boxplot quantifying the number of GFP molecules per focus in WT cells expressing GFP–Kog1 starved for glucose (−Glc) or grown into stationary phase (Stat.). f–i, Error bars, s.d. for values obtained with ≥100 cells.

Extended Data Figure 2 TORC1 focus formation occurs upon glucose starvation but not nitrogen or leucine starvation independently of external pH.

a, The majority of cells grown into stationary phase display a prominent TORC1 focus as determined by confocal microscopy imaging of GFP–Kog1. Addition of glucose, but not amino acids (AA) or ammonium sulfate (AS), triggered rapid disassembly of these foci. b, TORC1 focus formation monitored by confocal microscopy using a microfluidic device demonstrates that foci can be observed within 2–3 min after glucose depletion. c–e, TORC1 focus formation monitored by confocal microscopy after glucose (c), ammonium sulfate (d), or leucine (e) starvation and subsequent re-addition. f, Confocal images of WT cells expressing GFP–Kog1 and Gln1–mCherry grown in pH-controlled medium before (+Glucose) and 1 h after (−Glucose) glucose starvation. White arrows show Gln1 foci.

Extended Data Figure 3 Snf1 does not contribute significantly to TORC1 focus dynamics.

a, Growth phenotypes on YP-dextrose or YP-glycerol plates of WT and Δ_snf1_ cells expressing GFP–Tor1. The Δ_snf1_ cells show a characteristic defect in using glycerol as a carbon source. b, Representative confocal images of TORC1 focus formation in WT and Δ_snf1_ cells following glucose depletion (− Glucose, 0 min) and subsequent re-addition (+ Glucose, 30 min). c, Percentage of cells displaying a TORC1 focus as measured from b. Data are mean ± s.d. and represent three independent experiments. d, Western blot assessing the extent of Sch9 Ser758 phosphorylation as a proxy of TORC1 activity, at the time points monitored in b. Hog1 is used as loading control.

Extended Data Figure 4 TORC1 focus dynamics, but not size, are different in WT versus Δ_gtr1_ Δ_gtr2_ cells.

a, Three-dimensional reconstruction of WT cells expressing GFP–Kog1 in the presence of glucose or after 5 and 30 min of glucose starvation. Red arrowheads show granular TORC1 localization; blue arrowheads show TORC1 foci. b, Representative confocal images of exponentially growing WT and Δ_gtr1_ Δ_gtr2_ cells expressing the indicated GFP-tagged TORC1 subunits. c, Boxplot quantifying the number of GFP–Kog1 molecules per focus in stationary-phase WT cells (Stat.) and in exponentially growing Δ_gtr1_ Δ_gtr2_ cells. Error bars, s.d. for values obtained with ≥100 cells. d, Quantitative analysis of TORC1 focus disassembly after dilution of stationary-phase WT (light grey) and Δ_gtr1_ Δ_gtr2_ (dark grey) cells expressing GFP–Kog1 into fresh CSM. Half-life of TORC1 foci in both backgrounds is indicated. Data are mean ± s.d. and represent at least three independent experiments (150–800 cells each).

Extended Data Figure 5 Gtr1GDP/Gtr2GTP conformation favours focus formation while the Gtr1GTP/Gtr2GDP conformation antagonizes focus formation.

a, TORC1 focus formation (GFP–Kog1; dark grey bars on left graph) was assessed in Δ_gtr1_ Δ_gtr2_ cells expressing all combinations of plasmid-borne WT or nucleotide-locked (GDP/GTP) variants of Gtr1 and Gtr2, both in exponentially growing or saturated cultures. Using growth state (state: exponential = −1 and saturated = 1) and nucleotide loading of the Gtrs (Gtr1 and Gtr2: GDP bound = −1, WT = 0 and GTP bound = 1) as variables, the data were analysed by multiple linear regression to obtain a model explaining the percentage of cells displaying a TORC1 focus (equation). The obtained model explains 83.7% of the total observed variance (R_2, residual error = 16.3%) and has a high statistical significance (Fisher’s test, P < 10−5; summarized in the table on the right). Simulation with the model is shown (light grey bars on left graph). The model shows that the contribution of both growth state and Gtr variables are statistically significant (β_1, β_2 and β_3: Student’s test, P < 0.05). Gtr1 and Gtr2 participate together to account for 47.3% of the variation of TORC1 focus formation and have opposing contributions: Gtr1–GTP disfavours, whereas Gtr2–GTP favours, TORC1 focus formation (β_2 = −0.076 and β_3 = 0.056). The rest of the variation in focus formation is Gtr-independent. b–f, The amount of active TORC1 is lower, but Sch9 phosphorylation is faster, in Δ_gtr1 Δ_gtr2 cells compared with WT cells. b, The proportion of cells without TOROIDs and the relative Sch9-phosphorylation in WT and Δ_gtr1 Δ_gtr2 cells. Sch9 phosphorylation is partly compromised in Δ_gtr1 Δ_gtr2 cells (~75% of WT and not 40% as would be suggested by the fraction of cells lacking focus), suggesting that the remaining active TORC1 entities in Δ_gtr1_ Δ_gtr2_ cells must be approximately twofold more active compared with the active TORC1 entities in WT cells. c, Growth curves of Δ_gtr1_ Δ_gtr2_ and WT cells treated with increasing concentrations of CMB4563 in liquid culture (estimated half-maximum effective concentrations (EC50) of 0.12 μM and 0.27 μM, respectively). d, Spot assays of WT and Δ_gtr1_ Δ_gtr2_ cells on increasing concentrations of CMB4563. On plate, the Δ_gtr1_ Δ_gtr2_ cells appear to be about twice as sensitive as WT. e, Measures of Sch9 dephosphorylation (phosphatase activity) and rephosphorylation (TORC1 activity) rates in WT and Δ_gtr1_ Δ_gtr2_ cells. To monitor dephosphorylation rates, we treated cells with 1.0 μM CMB4563 and collected At the indicated time points, over a period of 10 min for western blot analyses (0–10 min). The remaining cells were washed and resuspended into fresh medium containing varying concentrations of CMB4563. At the indicated time points, samples were collected over the next 20 min for western blot analyses (10–30 min). f, Plot of relative Sch9 rephosphorylation rates versus increasing concentrations of CMB4563 measured in WT and Δ_gtr1_ Δ_gtr2_ cells in e. In the absence of CMB4563, the rate of Sch9 rephosphorylation is 1.75 times higher in Δ_gtr1_ Δ_gtr2_ cells than in WT. However, when released into CMB4563, the rate of Sch9 rephosphorylation is more strongly affected in Δ_gtr1_ Δ_gtr2_ cells than WT (estimated EC50 of 0.1 μM and 0.2 μM, respectively). Error bars in c and e represent s.e.m.

Extended Data Figure 6 STORM supplementary data.

a, Correlation plot between Alexa Fluor 647 and GFP integrated intensities, measured for Alexa Fluor 647-conjugated anti-GFP nanobody-labelled TORC1 foci segmented from cytosolic signal. b, Representation of Alexa Fluor 647 localization precision along x and y axes, derived from Alexa Fluor 647-conjugated anti-GFP nanobody calibration. c, Gallery of reconstructed clusters classified according to their eccentricity (Ecc < 2 and Ecc > 2). Data correspond to TORC1 foci imaged in WT cells expressing GFP–Kog1 grown into stationary phase. Scale bar, 100 nm.

Extended Data Figure 7 Negative-stain electron microscopy analyses of TORC1 purification.

a, b, Assembled transmission electron micrographs showing 50 μm2 of a negative-stained TORC1 purification from exponentially growing (a) or starved (b) Kog1–TAP-expressing cells. Red stars show extended and end-on views of TORC1 helices. Scale bar, 500 nm. c, Two-dimensional class averaging analyses reveal similar TORC1 single-particle features and organized pattern of subunits in the tubular structures. Green circles correspond to the picked images used for two-dimensional classification and averaging. Red arrows highlight a repeated pattern in the two-dimensional class. Scale bar, 25 nm. d, Fourier filtration of the tubular structure suggests a helical organization. Scale bar, 50 nm.

Extended Data Figure 8 Three-dimensional TOROID reconstruction and comparison between the cryo-EM map and the mTORC1 atomic model.

a, Symmetry refinement of TORC1 filaments. The pitch of the helix (left) and the number of units per turn (right) were refined by 30 iterations of IHRSR using 29 different starting choices for the number of subunits per turn (5.1–7.9 in steps of 0.1). Numbers on the right indicate the refined solutions for the number of units per turn at the end of the refinement. For each of these solutions, the numbers of refinement cycles culminating in the converged solution are indicated in parentheses. b, Side-by-side display of experimental versus simulated power spectra from the three-dimensional reconstruction. Experimental power spectrum is the sum of the power spectrum of aligned segments after projection matching. c, Resolution assessment for the TORC1 helical reconstruction determined using SPRING: the Fourier shell correlation was computed from two reconstructions containing half of the data set each. The resolution corresponding to the 0.5 and 0.143 cut-offs is indicated in the plot. d, Image processing statistics of the reconstruction. e, Side (top) and cross-section (bottom) views of the cryo-EM maps for the three possible refined solutions for the number of units per helix turn with helix pitch of 211 Å as deduced from the analysis of the convergence points of the IHRSR refinements. The maps shown at 30 Å resolution were rendered at a threshold 1.0_σ_. The maps in the left and middle panels are inconsistent with the expected density profile of the TORC1 subunit from f. In the rightmost panel, a dashed ellipse encloses one TORC1 dimer. f, Comparison of the density models of mTORC1 dimer generated from the atomic model (Protein Data Bank accession number 5FLC) filtered to 27 Å resolution and that extracted from the final cryo-EM map. g, Estimation of the docking precision. Shown is the correlation of the fitted model with the map upon rotation of the mTORC1 dimer around its principal axes of inertia x, y, and z as indicated in the inset. h, Comparison of the cryo-EM map and electron density maps computed from the models suggests that the features of the map are in agreement with the two variants of the docked model at roughly 30 Å resolution and confirms the resolution assessment. The Fourier shell correlation (FSC) between the helically arranged models and the experimental cryo-EM map is shown together with the Fourier shell correlation used for assessing the cryo-EM map resolution.

Extended Data Figure 9 Simulation of STORM data using EM particle reconstruction data.

a, Left, end-on view of reconstructed TORC1 helix fitted with nine TORC1 particles in dark green, dark red, and purple (compare with Fig. 3). Middle, crystal structure of GFP (light green) was added to the reconstructed helix at sites identified as Raptor/Kog1 N termini. Right, crystal structure of anti-GFP nanobody (red) was added to a subset of the GFP structures, according to the labelling efficiency estimated for our experimental conditions (~20%). The localization precision is indicated as a red cloud. b, Three-dimensional representation of the simulated starting model according to a. Red and blue spheres denote GFP positions. c, Simulated STORM images. Helices of different lengths were generated on the basis of the distribution of focus sizes observed in vivo (Fig. 2 and bar graph). Orientations of these helices were randomized in three-dimensional (xyz) space and then projected in two-dimensions (xy). Coloured spheres denote GFP positions, a random fraction of which were considered labelled according to our experimentally determined labelling efficiency (see single-molecule localization microscopy simulation in Methods for more details). d, Reconstructions of individual simulations from c, classified according to their eccentricity and presence of a cavity. Scale bar, 100 nm. e, Particle averages obtained from the images in d. f, Plot of the eccentricity distribution of the simulated and experimental STORM dataset.

Extended Data Figure 10 Disruption of TORC1–TORC1 contact interfaces abolishes TOROID assembly.

a, Quantitative analysis of TORC1 focus disassembly after dilution of stationary-phase WT cells expressing GFP–Kog1 into either fresh CSM containing rapamycin, CMB4563, or drug vehicle (Mock). b, Quantitative analysis of TORC1 focus formation after treatment of late log-phase WT cells expressing of GFP–Kog1 with either rapamycin, CMB4563, or drug vehicle (Mock). b, c, Black diamonds, mock treatment; grey squares, rapamycin treatment (200 nM); grey triangles, CMB4563 treatment (800 nM). Data are mean ± s.d. and represent three independent experiments (150–800 cells each). c, Representative confocal images of WT or Δ_gtr1_ Δ_gtr2_ cells expressing GFP–Tor1 or Tor1D330::3xGFP in exponential or stationary growth phases.

Supplementary information

PowerPoint slides

Rights and permissions

About this article

Cite this article

Prouteau, M., Desfosses, A., Sieben, C. et al. TORC1 organized in inhibited domains (TOROIDs) regulate TORC1 activity.Nature 550, 265–269 (2017). https://doi.org/10.1038/nature24021

Download citation