Abstract

The μ-opioid receptor (μOR) is a G-protein-coupled receptor (GPCR) and the target of most clinically and recreationally used opioids. The induced positive effects of analgesia and euphoria are mediated by μOR signalling through the adenylyl cyclase-inhibiting heterotrimeric G protein Gi. Here we present the 3.5 Å resolution cryo-electron microscopy structure of the μOR bound to the agonist peptide DAMGO and nucleotide-free Gi. DAMGO occupies the morphinan ligand pocket, with its N terminus interacting with conserved receptor residues and its C terminus engaging regions important for opioid-ligand selectivity. Comparison of the μOR–Gi complex to previously determined structures of other GPCRs bound to the stimulatory G protein Gs reveals differences in the position of transmembrane receptor helix 6 and in the interactions between the G protein α-subunit and the receptor core. Together, these results shed light on the structural features that contribute to the Gi protein-coupling specificity of the µOR.

Access optionsAccess options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Additional information

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

References

  1. 1.

    Matthes, H. W. et al. Loss of morphine-induced analgesia, reward effect and withdrawal symptoms in mice lacking the mu-opioid-receptor gene. Nature 383, 819–823 (1996).

  2. 2.

    Barnett, M. L., Olenski, A. R. & Jena, A. B. Opioid-prescribing patterns of emergency physicians and risk of long-term use. N. Engl. J. Med. 376, 663–673 (2017).

  3. 3.

    Connor, M. & Christie, M. D. Opioid receptor signalling mechanisms. Clin. Exp. Pharmacol. Physiol. 26, 493–499 (1999).

  4. 4.

    Raffa, R. B., Martinez, R. P. & Connelly, C. D. G-protein antisense oligodeoxyribonucleotides and μ-opioid supraspinal antinociception. Eur. J. Pharmacol. 258, R5–R7 (1994).

  5. 5.

    Raehal, K. M., Walker, J. K. L. & Bohn, L. M. Morphine side effects in β-arrestin 2 knockout mice. J. Pharmacol. Exp. Ther. 314, 1195–1201 (2005).

  6. 6.

    Schmid, C. L. et al. Bias factor and therapeutic window correlate to predict safer opioid analgesics. Cell 171, 1165–1175.e13 (2017).

  7. 7.

    DeWire, S. M. et al. A G protein-biased ligand at the μ-opioid receptor is potently analgesic with reduced gastrointestinal and respiratory dysfunction compared with morphine. J. Pharmacol. Exp. Ther. 344, 708–717 (2013).

  8. 8.

    Manglik, A. et al. Structure-based discovery of opioid analgesics with reduced side effects. Nature 537, 185–190 (2016).

  9. 9.

    Rasmussen, S. G. F. et al. Crystal structure of the β2 adrenergic receptor-Gs protein complex. Nature 477, 549–555 (2011).

  10. 10.

    Zhang, Y. et al. Cryo-EM structure of the activated GLP-1 receptor in complex with a G protein. Nature 546, 248–253 (2017).

  11. 11.

    Liang, Y.-L. et al. Phase-plate cryo-EM structure of a class B GPCR–G-protein complex. Nature 546, 118–123 (2017).

  12. 12.

    Huang, W. et al. Structural insights into µ-opioid receptor activation. Nature 524, 315–321 (2015).

  13. 13.

    Ballesteros, J. A. & Weinstein, H. in Receptor Molecular Biology Vol. 25 (ed. Sealfon, S. C.) Ch. 19 (Elsevier, 1995).

  14. 14.

    Manglik, A. et al. Crystal structure of the µ-opioid receptor bound to a morphinan antagonist. Nature 485, 321–326 (2012).

  15. 15.

    Granier, S. et al. Structure of the δ-opioid receptor bound to naltrindole. Nature 485, 400–404 (2012).

  16. 16.

    Wu, H. et al. Structure of the human κ-opioid receptor in complex with JDTic. Nature 485, 327–332 (2012).

  17. 17.

    Che, T. et al. Structure of the nanobody-stabilized active state of the kappa opioid receptor. Cell 172, 55–67.e15 (2018).

  18. 18.

    Fenalti, G. et al. Structural basis for bifunctional peptide recognition at human δ-opioid receptor. Nat. Struct. Mol. Biol. 22, 265–268 (2015).

  19. 19.

    Emmerson, P. J., Liu, M. R., Woods, J. H. & Medzihradsky, F. Binding affinity and selectivity of opioids at mu, delta and kappa receptors in monkey brain membranes. J. Pharmacol. Exp. Ther. 271, 1630–1637 (1994).

  20. 20.

    Minami, M. et al. DAMGO, a μ-opioid receptor selective ligand, distinguishes between μ-and κ-opioid receptors at a different region from that for the distinction between μ- and δ-opioid receptors. FEBS Lett. 364, 23–27 (1995).

  21. 21.

    DiMaio, J. & Schiller, P. W. A cyclic enkephalin analog with high in vitro opiate activity. Proc. Natl Acad. Sci. USA 77, 7162–7166 (1980).

  22. 22.

    Rasmussen, S. G. F. et al. Structure of a nanobody-stabilized active state of the β2 adrenoceptor. Nature 469, 175–180 (2011).

  23. 23.

    Wall, M. A. et al. The structure of the G protein heterotrimer Giα1β1γ2. Cell 83, 1047–1058 (1995).

  24. 24.

    Dror, R. O. et al. Signal transduction. Structural basis for nucleotide exchange in heterotrimeric G proteins. Science 348, 1361–1365 (2015).

  25. 25.

    Van Eps, N. et al. Interaction of a G protein with an activated receptor opens the interdomain interface in the alpha subunit. Proc. Natl Acad. Sci. USA 108, 9420–9424 (2011).

  26. 26.

    Kaya, A. I. et al. A conserved phenylalanine as a relay between the α5 helix and the GDP binding region of heterotrimeric Gi protein α subunit. J. Biol. Chem. 289, 24475–24487 (2014).

  27. 27.

    Chung, K. Y. et al. Conformational changes in the G protein Gs induced by the β2 adrenergic receptor. Nature 477, 611–615 (2011).

  28. 28.

    Choe, H. W. et al. Crystal structure of metarhodopsin II. Nature 471, 651–655 (2011).

  29. 29.

    Conklin, B. R., Farfel, Z., Lustig, K. D., Julius, D. & Bourne, H. R. Substitution of three amino acids switches receptor specificity of Gqα to that of Giα. Nature 363, 274–276 (1993).

  30. 30.

    Isberg, V. et al. Generic GPCR residue numbers—aligning topology maps while minding the gaps. Trends Pharmacol. Sci. 36, 22–31 (2015).

  31. 31.

    Isberg, V. et al. GPCRdb: an information system for G protein-coupled receptors. Nucleic Acids Res. 42, D422–D425 (2014).

  32. 32.

    Ravindranathan, A. et al. Functional characterization of human variants of the mu-opioid receptor gene. Proc. Natl Acad. Sci. USA 106, 10811–10816 (2009).

  33. 33.

    Skorpen, F. et al. The rare Arg181Cys mutation in the μ opioid receptor can abolish opioid responses. Acta Anaesthesiol. Scand. 60, 1084–1091 (2016).

  34. 34.

    Chaipatikul, V., Loh, H. H. & Law, P. Y. Ligand-selective activation of μ-opioid receptor: demonstrated with deletion and single amino acid mutations of third intracellular loop domain. J. Pharmacol. Exp. Ther. 305, 909–918 (2003).

  35. 35.

    West, R. E., Moss, J., Vaughan, M., Liu, T. & Liu, T. Y. Pertussis toxin-catalyzed ADP-ribosylation of transducin. Cysteine 347 is the ADP-ribose acceptor site. J. Biol. Chem. 260, 14428–14430 (1985).

  36. 36.

    Okude, J. et al. Identification of a conformational equilibrium that determines the efficacy and functional selectivity of the μ-opioid receptor. Angew. Chem. Int. Edn Engl. 54, 15771–15776 (2015).

  37. 37.

    Gregorio, G. G. et al. Single-molecule analysis of ligand efficacy in β2AR-G-protein activation. Nature 547, 68–73 (2017).

  38. 38.

    Qin, K., Dong, C., Wu, G. & Lambert, N. A. Inactive-state preassembly of Gq-coupled receptors and Gq heterotrimers. Nat. Chem. Biol. 7, 740–747 (2011).

  39. 39.

    Maeda, S. et al. Crystallization scale preparation of a stable GPCR signaling complex between constitutively active rhodopsin and G-protein. PLoS One 9, e98714 (2014).

  40. 40.

    Westfield, G. H. et al. Structural flexibility of the Gαs α-helical domain in the β2-adrenoceptor Gs complex. Proc. Natl Acad. Sci. USA 108, 16086–16091 (2011).

  41. 41.

    Zheng, S. Q., Palovcak, E., Armache, J.-P., Verba, K. A., Cheng, Y. & Agard, D. A. MotionCor2: anisotropic correction of beam-induced motion for improved cryo-electron microscopy. Nat. Methods 14, 331–332 (2016).

  42. 42.

    Zhang, K. Gctf: Real-time CTF determination and correction. J. Struct. Biol. 193, 1–12 (2016).

  43. 43.

    Scheres, S. H. W. RELION: implementation of a Bayesian approach to cryo-EM structure determination J. Struct. Biol. 180, 519–530 (2012).

  44. 44.

    Grigorieff, N. FREALIGN: high-resolution refinement of single particle structures. J. Struct. Biol. 157, 117–125 (2007).

  45. 45.

    Penczek, P. A., Grassucci, R. A. & Frank, J. The ribosome at improved resolution: new techniques for merging and orientation refinement in 3D cryo-electron microscopy of biological particles. Ultramicroscopy 53, 251–270 (1994).

  46. 46.

    Grigorieff, N. Frealign: an exploratory tool for single-particle Cryo-EM. Methods Enzymol. 579, 191–226 (2016).

  47. 47.

    Adams, P. D. et al. The Phenix software for automated determination of macromolecular structures. Methods 55, 94–106 (2011).

  48. 48.

    Heymann, J. B. & Belnap, D. M. Bsoft: image processing and molecular modeling for electron microscopy. J. Struct. Biol. 157, 3–18 (2007).

  49. 49.

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

  50. 50.

    Wang, R. Y.-R. et al. Automated structure refinement of macromolecular assemblies from cryo-EM maps using Rosetta. eLife 5, 352 (2016).

  51. 51.

    Williams, C. J. et al. MolProbity: More and better reference data for improved all-atom structure validation. Protein Sci. 27, 293–315 (2018).

  52. 52.

    Schüttelkopf, A. W. & van Aalten, D. M. F. PRODRG: a tool for high-throughput crystallography of protein-ligand complexes. Acta Crystallogr. D 60, 1355–1363 (2004).

  53. 53.

    Friesner, R. A. et al. Glide: a new approach for rapid, accurate docking and scoring. 1. Method and assessment of docking accuracy. J. Med. Chem. 47, 1739–1749 (2004).

  54. 54.

    Lomize, M. A., Lomize, A. L., Pogozheva, I. D. & Mosberg, H. I. OPM: orientations of proteins in membranes database. Bioinformatics 22, 623–625 (2006).

  55. 55.

    Betz, R. Dabble (v.2.6.3). https://doi.org/10.5281/zenodo.836914 (2017).

  56. 56.

    Best, R. B., Mittal, J., Feig, M. & MacKerell, A. D. Inclusion of many-body effects in the additive CHARMM protein CMAP potential results in enhanced cooperativity of α-helix and β-hairpin formation. Biophys. J. 103, 1045–1051 (2012).

  57. 57.

    Best, R. B. et al. Optimization of the additive CHARMM all-atom protein force field targeting improved sampling of the backbone φ, ψ and side-chain χ1 and χ2 dihedral angles. J. Chem. Theory Comput. 8, 3257–3273 (2012).

  58. 58.

    Huang, J. & MacKerell, A. D. CHARMM36 all-atom additive protein force field: validation based on comparison to NMR data. J. Comput. Chem. 34, 2135–2145 (2013).

  59. 59.

    Klauda, J. B. et al. Update of the CHARMM all-atom additive force field for lipids: validation on six lipid types. J. Phys. Chem. B 114, 7830–7843 (2010).

  60. 60.

    Huang, J. et al. CHARMM36m: an improved force field for folded and intrinsically disordered proteins. Nat. Methods 14, 71–73 (2016).

  61. 61.

    Salomon-Ferrer, R., Götz, 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).

  62. 62.

    Case, D. A. et al. Amber (v.16). http://ambermd.org (2018).

  63. 63.

    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).

  64. 64.

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

  65. 65.

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

  66. 66.

    Nguyen, C. N., Young, T. K. & Gilson, M. K. Grid inhomogeneous solvation theory: hydration structure and thermodynamics of the miniature receptor cucurbit[7]uril. J. Chem. Phys. 137, 044101 (2012).

  67. 67.

    Nguyen, C., Gilson, M. K. & Young, T. Structure and thermodynamics of molecular hydration via grid inhomogeneous solvation theory. Preprint at https://arXiv.org/abs/1108.4876 (2011).

Download references

Acknowledgements

We thank J.-P. Carralot (F. Hoffmann–La Roche) for help in antibody generation, C. Yoshioka and C. Lopez for assistance with data collection, M. Siegrist, G. Schmid, B. Rutten, D. Zulauf, S. Kueng (Roche Non-Clinical Biorepository) and R. Thoma for technical assistance with biomass and cell line generation. We also acknowledge N. Moriarty (Lawrence Berkeley National Laboratories) for help with generation of parameters for DAMGO and general advice for refinement of our model. The work is supported by NIH grant R37DA036246 (B.K.K., S.G. and G.S.) and NIH grant R01GM083118 (B.K.K.). Research reported in this publication was supported by the National Institute of General Medical Sciences of the National Institutes of Health under award number T32GM007276 (A.K.). The content is solely the responsibility of the authors and does not necessarily represent the official view of the National Institutes of Health. S.M. was supported by the Roche Postdoctoral Fellowship (RPF ID: 113). G.F.X.S. acknowledges the Swiss National Science Foundation for grants 310030_153145 and 310030B_17335 and long-term financial support from the Paul Scherrer Institute. B.K.K. is a Chan–Zuckerberg Biohub Investigator.

Reviewer information

Nature thanks L. Shi, D. Wacker and the other anonymous reviewer(s) for their contribution to the peer review of this work.

Author information

Author notes

  1. These authors contributed equally: Antoine Koehl, Hongli Hu, Shoji Maeda.

Affiliations

  1. Department of Structural Biology, Stanford University School of Medicine, Stanford, CA, USA

    • Antoine Koehl
    • , Hongli Hu
    • , Yan Zhang
    • , Qianhui Qu
    • , Joseph M. Paggi
    • , Naomi R. Latorraca
    • , William I. Weis
    • , Ron O. Dror
    •  & Georgios Skiniotis
  2. Department of Molecular and Cellular Physiology, Stanford University School of Medicine, Stanford, CA, USA

    • Hongli Hu
    • , Shoji Maeda
    • , Yan Zhang
    • , Qianhui Qu
    • , Joseph M. Paggi
    • , Naomi R. Latorraca
    • , Daniel Hilger
    • , William I. Weis
    • , Ron O. Dror
    • , Georgios Skiniotis
    •  & Brian K. Kobilka
  3. Department of Computer Science, Stanford University, Stanford, CA, USA

    • Joseph M. Paggi
    • , Naomi R. Latorraca
    •  & Ron O. Dror
  4. Institute for Computational and Mathematical Engineering, Stanford University, Stanford, CA, USA

    • Joseph M. Paggi
    • , Naomi R. Latorraca
    •  & Ron O. Dror
  5. Biophysics Program, Stanford University, Stanford, CA, USA

    • Naomi R. Latorraca
    •  & Ron O. Dror
  6. Roche Pharma Research and Early Development, Therapeutic Modalities, Roche Innovation Center Basel, F.Hoffmann–La Roche, Basel, Switzerland

    • Roger Dawson
    •  & Hugues Matile
  7. Laboratory of Biomolecular Research, Paul Scherrer Institute, Villigen, Switzerland

    • Gebhard F. X. Schertler
  8. Department of Biology, ETH Zürich, Zürich, Switzerland

    • Gebhard F. X. Schertler
  9. Institut de Génomique Fonctionnelle, INSERM, Montpellier, France

    • Sebastien Granier
  10. Department of Pharmaceutical Chemistry, University of California San Francisco, San Francisco, CA, USA

    • Aashish Manglik
  11. Department of Anesthesia and Perioperative Care, University of California San Francisco, San Francisco, CA, USA

    • Aashish Manglik

Authors

  1. Search for Antoine Koehl in:

  2. Search for Hongli Hu in:

  3. Search for Shoji Maeda in:

  4. Search for Yan Zhang in:

  5. Search for Qianhui Qu in:

  6. Search for Joseph M. Paggi in:

  7. Search for Naomi R. Latorraca in:

  8. Search for Daniel Hilger in:

  9. Search for Roger Dawson in:

  10. Search for Hugues Matile in:

  11. Search for Gebhard F. X. Schertler in:

  12. Search for Sebastien Granier in:

  13. Search for William I. Weis in:

  14. Search for Ron O. Dror in:

  15. Search for Aashish Manglik in:

  16. Search for Georgios Skiniotis in:

  17. Search for Brian K. Kobilka in:

Contributions

A.K. prepared the µOR–Gi complex and refined the structure from cryo-EM density maps. H.H. obtained and processed cryo-EM data with the assistance of Y.Z. and Q.Q. S.M. identified and prepared scFv16 with assistance from R.D. and H.M and under supervision of G.F.X.S. A.M., D.H. S.G. and A.K. developed the procedure for forming the µOR–Gi complex. N.R.L and J.M.P performed molecular dynamics simulations under supervision of R.O.D. W.I.W. aided in map interpretation and model refinement. A.K., A.M., B.K.K. and G.S. wrote the manuscript. A.M., G.S. and B.K.K. supervised the project.

Competing interests

B.K. is a founder of and consultant for ConfometRx.

Corresponding authors

Correspondence to Aashish Manglik or Georgios Skiniotis or Brian K. Kobilka.

Extended data figures and tables

  1. Extended Data Fig. 1 Binding characteristics of scFv.

    a, b, scFv 16 does not perturb the interfaces between Gα and Gβ at its binding epitope (a) or the switch II region located ~40 Å away (b). Our structure is coloured by chain, whereas the structure of GDP-bound Gi1 heterotrimer (PDB code 1GP2) is coloured grey. c, In the nucleotide-free state (coloured by subunit), there is a ~7° rotation of Gβγ relative to the Gαs switch II domain when compared to the GDP-bound form. d, This rotated conformation is similar to that observed in nucleotide-free Gs coupled to the β2AR (PDB code 3SN6).

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

    a, Representative cryo-EM image of the μOR–Gi complex. Scale bar, 20 nm. b, Representative 2D averages showing distinct secondary structure features from different views of the complex. c, Flow chart of cryo-EM data processing. The unmasked map in the middle of the chart has been coloured by subunit. The inset shows the fit of the crystal structure of the α-helical domain in the corresponding density of the unmasked reconstruction. 3D density maps coloured according to local resolution. d, ‘Gold standard’ FSC curves from Phenix indicate overall nominal resolutions of 3.5 Å and 3.6 Å using the FSC = 0.143 criterion for the scFv-subtracted map (green curve) and scFv-retained maps (purple curve), respectively.

  3. Extended Data Fig. 3 Cryo-EM map versus refined structure.

    a, Cryo-EM density map (scFv subtracted) and model are shown for all seven transmembrane α-helices of the μOR, DAMGO, and Gα helices α5 and αN. b, c, Cross-validation of model to cryo-EM density map. The model was refined against one half map after displacement of atoms by 0.2 Å, and FSC curves were calculated between this model and the final cryo-EM map (full dataset, black) of the outcome of model refinement with a half map versus the same map (red), and of the outcome of model refinement with a half map versus the other half map (green). The results of the scFv-retained model versus map (b) and of scFv subtracted model versus map (c) are shown.

  4. Extended Data Fig. 4 Selected cryo-EM densities of μOR–Gi complex.

    ad, Cryo-EM density (displayed as mesh) surrounding residues involved in DAMGO binding (a), μOR–Gαi interaction around ICL2 (b), ICL3 (c), and cytoplasmic ends of the μOR transmembrane helices (d). These figures accompany the models shown in Figs. 1e, 4b, 5a and 5c, respectively.

  5. Extended Data Fig. 5 Stability of DAMGO in molecular dynamics simulations.

    a, Over the course of molecular dynamics simulations, the positions of the first four residues of DAMGO do not significantly change, while the fifth residue (Gly-ol) shows significant variability in position. Frames from the first and last 100 ns are shown with an intermediate to highlight both the relative stability of the first four amino acids and the flexibility of the fifth. Arrows show the extent of motion in the N- and C-terminal residues over the course of simulation. Cryo-EM density for DAMGO is shown as mesh. b, Root mean standard deviations (RMSDs) from the modelled pose of DAMGO to the pose during molecular dynamics simulations. The RMSD calculations include heavy atoms on the peptide backbone. Data from three independent simulations are plotted. The RMSDs for residues 1 to 4 (black) and the C-terminal Gly-ol (blue) are plotted separately to highlight their stability and mobility, respectively.

  6. Extended Data Fig. 6 Water occupancy in orthosteric binding site.

    Left, water occupancy in molecular dynamics simulations of DAMGO-bound µOR overlaid with a representative conformation from molecular dynamics simulations. Occupancy relative to bulk solvent is the ratio of the rate at which water is observed in a given volume to the rate at which water is expected to be observed in an equivalent volume in the bulk solvent. For example, blue regions (occupancy ratio = 2) are occupied by water twice as often as an equivalent region in the bulk solvent. Right, crystallographic waters in the BU72-bound µOR binding pocket (PDB code 5C1M). Waters are shown as black spheres, BU72 is shown as yellow sticks, and hydrogen bonds are shown as dashed lines.

  7. Extended Data Fig. 7 Comparison of the C termini of Gαs and Gαi.

    The C terminus of Gαs is bulkier than that of Gαi owing to substitution of small amino acids C (−4 position) and G (−3 position) in Gαi to Y and E, respectively, in Gαs. This leads to steric clashes with TM3 and TM7 of the μOR. Top, ribbon view of μOR (green) with wild-type Gαi (gold, left) and a Gαis model (right) created by substituting C and G for Y and E based on the β2AR–Gs crystal structure. Substituted positions are coloured in light purple. The −4 to −2 positions have their side chains shown as spheres, and the rest are shown as a ribbon. Bottom, space-filling view of the μOR showing the steric clashes that result from these substitutions.

  8. Extended Data Fig. 8 Comparison of Gαi C-terminal peptide binding modes.

    ac, Side (top) and cytoplasmic (bottom) views of the μOR (green) with the last 11 residues of Gαi (gold) alone (a), compared to the β2AR(orange) with the last 11 residues of Gαs (light purple) (PDB code 3SN6) (b), or compared to metarhodopsin II (pink) in complex with an 11-residue Gtransducin (Gt) C-terminal peptide (dark purple) (PDB code 3PQR) (c). The μOR–Gi complex aligns best with the metarhodopsin II–Gt complex in terms of both TM6 displacement and position of the α5 peptide.

  9. Extended Data Table 1 Cryo-EM data collection, refinement and validation statistics
  10. Extended Data Table 2 Sequence alignment of residues that form the interaction interface between μOR and Gi

Supplementary information

  1. Supplementary Information

    This file contains the sequences used in the study and Supplementary Figures 1-3.

  2. Reporting Summary

About this article

Publication history

Received

Accepted

Published

DOI

https://doi.org/10.1038/s41586-018-0219-7

Further reading

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.