Abstract
Chemotactile receptors (CRs) are a cephalopod-specific innovation that allow octopuses to explore the seafloor via ‘taste by touch’1. CRs diverged from nicotinic acetylcholine receptors to mediate contact-dependent chemosensation of insoluble molecules that do not readily diffuse in marine environments. Here we exploit octopus CRs to probe the structural basis of sensory receptor evolution. We present the cryo-electron microscopy structure of an octopus CR and compare it with nicotinic receptors to determine features that enable environmental sensation versus neurotransmission. Evolutionary, structural and biophysical analyses show that the channel architecture involved in cation permeation and signal transduction is conserved. By contrast, the orthosteric ligand-binding site is subject to diversifying selection, thereby mediating the detection of new molecules. Serendipitous findings in the cryo-electron microscopy structure reveal that the octopus CR ligand-binding pocket is exceptionally hydrophobic, enabling sensation of greasy compounds versus the small polar molecules detected by canonical neurotransmitter receptors. These discoveries provide a structural framework for understanding connections between evolutionary adaptations at the atomic level and the emergence of new organismal behaviour.
This is a preview of subscription content, access via your institution
Access options
Access Nature and 54 other Nature Portfolio journals
Get Nature+, our best-value online-access subscription
$29.99 / 30 days
cancel any time
Subscribe to this journal
Receive 51 print issues and online access
$199.00 per year
only $3.90 per issue
Buy this article
- Purchase on Springer Link
- Instant access to full article PDF
Prices may be subject to local taxes which are calculated during checkout
Similar content being viewed by others
References
van Giesen, L., Kilian, P. B., Allard, C. A. H. & Bellono, N. W. Molecular basis of chemotactile sensation in octopus. Cell 183, 594–604 (2020).
Bargmann, C. I. Comparative chemosensation from receptors to ecology. Nature 444, 295–301 (2006).
Mollo, E., Garson, M. J., Polese, G., Amodeo, P. & Ghiselin, M. T. Taste and smell in aquatic and terrestrial environments. Nat. Prod. Rep. 34, 496–513 (2017).
Mollo, E. et al. Sensing marine biomolecules: smell, taste, and the evolutionary transition from aquatic to terrestrial life. Front. Chem. 2, 92 (2014).
Hanlon, R. T. & Messenger, J. B. Cephalopod Behaviour 2nd edn (Cambridge Univ. Press, 2018).
Wells, M. J., Freeman, N. H. & Ashburner, M. Some experiments on the chemotactile sense of octopuses. J. Exp. Biol. 43, 553–563 (1965).
Croset, V. et al. Ancient protostome origin of chemosensory ionotropic glutamate receptors and the evolution of insect taste and olfaction. PLoS Genet. 6, e1001064 (2010).
Benton, R., Vannice, K. S., Gomez-Diaz, C. & Vosshall, L. B. Variant ionotropic glutamate receptors as chemosensory receptors in Drosophila. Cell 136, 149–162 (2009).
Albertin, C. B. et al. The octopus genome and the evolution of cephalopod neural and morphological novelties. Nature 524, 220–224 (2015).
Xu, P. et al. Functional opsin retrogene in nocturnal moth. Mob. DNA 7, 18 (2016).
Jumper, J. et al. Highly accurate protein structure prediction with AlphaFold. Nature 596, 583–589 (2021).
Noviello, C. M. et al. Structure and gating mechanism of the α7 nicotinic acetylcholine receptor. Cell 184, 2121–2134 (2021).
Rahman, M. M. et al. Structural mechanism of muscle nicotinic receptor desensitization and block by curare. Nat. Struct. Mol. Biol. 29, 386–394 (2022).
Zarkadas, E. et al. Conformational transitions and ligand-binding to a muscle-type nicotinic acetylcholine receptor. Neuron 110, 1358–1370 (2022).
Giordano, G. et al. Volatile secondary metabolites as aposematic olfactory signals and defensive weapons in aquatic environments. Proc. Natl Acad. Sci. USA 114, 3451–3456 (2017).
Long, J. D. & Hay, M. E. Fishes learn aversions to a nudibranch’s chemical defense. Mar. Ecol. Prog. Ser. 307, 199–208 (2006).
Karlin, A. Emerging structure of the nicotinic acetylcholine receptors. Nat. Rev. Neurosci. 3, 102–114 (2002).
Hochner, B. An embodied view of octopus neurobiology. Curr. Biol. 22, R887–892 (2012).
Fouke, K. E. & Rhodes, H. J. Electrophysiological and motor responses to chemosensory stimuli in isolated cephalopod arms. Biol. Bull. 238, 1–11 (2020).
Kang, G. et al. Sensory specializations drive octopus and squid behaviour. Nature https://doi.org/10.1038/s41586-023-05808-z (2023).
Morales-Perez, C. L., Noviello, C. M. & Hibbs, R. E. Manipulation of subunit stoichiometry in heteromeric membrane proteins. Structure 24, 797–805 (2016).
Zivanov, J. et al. New tools for automated high-resolution cryo-EM structure determination in RELION-3. eLife 7, e42166 (2018).
Zheng, S. Q. et al. MotionCor2: anisotropic correction of beam-induced motion for improved cryo-electron microscopy. Nat. Methods 14, 331–332 (2017).
Zhang, K. Gctf: real-time CTF determination and correction. J. Struct. Biol. 193, 1–12 (2016).
Wagner, T. et al. SPHIRE-crYOLO is a fast and accurate fully automated particle picker for cryo-EM. Commun. Biol. 2, 218 (2019).
Afonine, P. V. et al. New tools for the analysis and validation of cryo-EM maps and atomic models. Acta Crystallogr. D Struct. Biol. 74, 814–840 (2018).
Kucukelbir, A., Sigworth, F. J. & Tagare, H. D. Quantifying the local resolution of cryo-EM density maps. Nat. Methods 11, 63–65 (2014).
Morin, A. et al. Collaboration gets the most out of software. eLife 2, e01456 (2013).
Waterhouse, A. et al. SWISS-MODEL: homology modelling of protein structures and complexes. Nucleic Acids Res. 46, W296–W303 (2018).
Emsley, P., Lohkamp, B., Scott, W. G. & Cowtan, K. Features and development of Coot. Acta Crystallogr. D Biol. Crystallogr. 66, 486–501 (2010).
Adams, P. D. et al. PHENIX: a comprehensive Python-based system for macromolecular structure solution. Acta Crystallogr. D Biol. Crystallogr. 66, 213–221 (2010).
Liebschner, D. et al. Macromolecular structure determination using X-rays, neutrons and electrons: recent developments in Phenix. Acta Crystallogr. D Struct. Biol. 75, 861–877 (2019).
Williams, C. J. et al. MolProbity: more and better reference data for improved all-atom structure validation. Protein Sci. 27, 293–315 (2018).
Pettersen, E. F. et al. UCSF Chimera—a visualization system for exploratory research and analysis. J. Comput. Chem. 25, 1605–1612 (2004).
Pettersen, E. F. et al. UCSF ChimeraX: structure visualization for researchers, educators, and developers. Protein Sci. 30, 70–82 (2021).
Schrödinger, L. The PyMOL Molecular Graphics System, version 2.4.1 https://github.com/schrodinger/pymol-open-source (2015).
UniProt Consortium UniProt: a worldwide hub of protein knowledge. Nucleic Acids Res. 47, D506–D515 (2019).
Pei, J., Kim, B.-H. & Grishin, N. V. PROMALS3D: a tool for multiple protein sequence and structure alignments. Nucleic Acids Res. 36, 2295–2300 (2008).
Smart, O. S., Neduvelil, J. G., Wang, X., Wallace, B. A. & Sansom, M. S. HOLE: a program for the analysis of the pore dimensions of ion channel structural models. J. Mol. Graph. 14, 354–360 (1996).
Klesse, G., Rao, S., Sansom, M. S. P. & Tucker, S. J. CHAP: a versatile tool for the structural and functional annotation of ion channel pores. J. Mol. Biol. 431, 3353–3365 (2019).
Krissinel, E. & Henrick, K. Inference of macromolecular assemblies from crystalline state. J. Mol. Biol. 372, 774–797 (2007).
Tian, W., Chen, C., Lei, X., Zhao, J. & Liang, J. CASTp 3.0: computed atlas of surface topography of proteins. Nucleic Acids Res. 46, W363–W367 (2018).
Eddy, S. R. Profile hidden Markov models. Bioinformatics 14, 755–763 (1998).
Madden, T. in The NCBI Handbook 2nd edn (eds. McEntyre J. & Ostell J.) Ch. 16 (National Center for Biotechnology Information, 2002).
Li, W. & Godzik, A. Cd-hit: a fast program for clustering and comparing large sets of protein or nucleotide sequences. Bioinformatics 22, 1658–1659 (2006).
Katoh, K. & Standley, D. M. MAFFT multiple sequence alignment software version 7: improvements in performance and usability. Mol. Biol. Evol. 30, 772–780 (2013).
Kalyaanamoorthy, S., Minh, B. Q., Wong, T. K. F., von Haeseler, A. & Jermiin, L. S. ModelFinder: fast model selection for accurate phylogenetic estimates. Nat. Methods 14, 587–589 (2017).
Minh, B. Q. et al. IQ-TREE 2: new models and efficient methods for phylogenetic inference in the genomic era. Mol. Biol. Evol. 37, 1530–1534 (2020).
Hoang, D. T., Chernomor, O., von Haeseler, A., Minh, B. Q. & Vinh, L. S. UFBoot2: improving the ultrafast bootstrap approximation. Mol. Biol. Evol. 35, 518–522 (2018).
Revell, L. J. phytools: an R package for phylogenetic comparative biology (and other things). Methods Ecol. Evol. 3, 217–223 (2012).
Suyama, M., Torrents, D. & Bork, P. PAL2NAL: robust conversion of protein sequence alignments into the corresponding codon alignments. Nucleic Acids Res. 34, W609–W612 (2006).
Pond, S. L., Frost, S. D. & Muse, S. V. HyPhy: hypothesis testing using phylogenies. Bioinformatics 21, 676–679 (2005).
Yang, Z. PAML 4: phylogenetic analysis by maximum likelihood. Mol. Biol. Evol. 24, 1586–1591 (2007).
Murrell, B. et al. Detecting individual sites subject to episodic diversifying selection. PLoS Genet. 8, e1002764 (2012).
Albertin, C. B. et al. Genome and transcriptome mechanisms driving cephalopod evolution. Nat. Commun. 13, 2427 (2022).
Wu, T. D. & Watanabe, C. K. GMAP: a genomic mapping and alignment program for mRNA and EST sequences. Bioinformatics 21, 1859–1875 (2005).
Acknowledgements
We thank C. Winkler for providing octopuses, P. Kilian and B. Walsh for assistance with behavioural experiments, L. van Giesen and S. Burke for contributions to the early stages of the project, S. Krueger for assistance with histology, J. Teng for preliminary functional tests, R. Cabuco and L. Baxter for baculovirus production, A. Grearson for photos, C. Noviello for screening grids and J. Zhou for critical reading of the manuscript. Single-particle cryo-EM grids were screened at the University of Texas Southwestern Medical Center Cryo-EM Facility, which is supported by the Cancer Prevention and Research Institute of Texas Core Facility Support Award no. RP170644. A portion of this research was supported by NIH grant no. U24GM129547 and performed at the Pacific Northwest Center at Oregon Health and Science University and accessed through the Environmental Molecular Sciences Laboratory (grid.436923.9), a Department of Energy Office of Science User Facility sponsored by the Office of Biological and Environmental Research. This research was further supported by grants to N.W.B. from the New York Stem Cell Foundation, Searle Scholars Program and NIH (nos. R35GM142697 and R01NS129060), NIH grants to R.E.H. (nos. R01NS120496 and R01NS129060), to G.K. (no. F32DA047848) and support from the American Heart Association to J.J.K. (no. 20POST35200127), and from the National Science Foundation to C.A.A. (no. NSF-PRFB 2010728).
Author information
Authors and Affiliations
Contributions
C.A.H.A., W.A.V.-M. and N.W.B. contributed to the molecular, cellular and organismal studies. G.K., J.J.K. and R.E.H. contributed to the biochemical and structural analyses. All authors were involved with writing or reviewing the manuscript.
Corresponding authors
Ethics declarations
Competing interests
The authors declare no competing interests.
Peer review
Peer review information
Nature thanks Hugues Nury, Harold Zakon and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.
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 Octopus chemotactile receptors exhibit high rates of molecular evolution.
a, Maximum likelihood tree of nicotinic receptor-like genes from Octopus bimaculoides showing the number of introns per gene. CRT1 is highlighted in the phylogeny. b, CRs are intronless genes arranged in tandem arrays in a single chromosome. The two main groups of CRs in octopus map to two distinct tight clusters in Chromosome 15. c, Alignment of the two arrays of intronless CRs in Chromosome 15 suggest differences in overall architecture between clusters. Only genes with complete coding sequences (CDS) are mapped. d, Phylogeny of acetylcholine receptor-like genes of Octopus bimaculoides depicting variation in dN/dS values for tip branches inferred under the free-ratio model and mapped to the phylogeny. e, Phylogeny from Fig. 1b depicted as ultrametric and with bootstrap values.
Extended Data Fig. 2 Antibody control and biochemistry.
a, Anti-CRT1 is specific to expressed CRT1 as it did not label another CR (CR840) expressed in HEK293 cells. Representative of 3 independent transfections. Scale bar = 30 µm. b, Fluorescence-detection size-exclusion chromatography (FSEC) trace of CRT1 pentamer and SDS-page gel of final EM sample, representative of 4 independent purifications.
Extended Data Fig. 3 EM data processing.
a-d, TMD z-slices of 3D reconstructions from CRT1 in corresponding detergent conditions. Inset number indicates overall map resolution. A cartoon in panel a represents a schematic diagram of TMD helices. In initial purifications in DDM, both in the absence and presence of the terpenoid agonist nootkatone, the transmembrane domain (TMD) was poorly resolved, and the predicted fourth TMD helix, M4, was entirely absent. We thus collected EM datasets in two other detergents, lauryl maltose neopentyl glycol (L-MNG), and glyco-diosgenin (GDN). L-MNG stabilized an asymmetric TMD conformation, while GDN resulted in a well ordered and symmetric TMD, and the best overall resolution. Attempts to reconstitute the receptor in lipid nanodiscs resulted in profound aggregation. e, Representative cryo-electron micrograph of CRT1 in GDN detergent micelle from dataset of 4043 dose-fractionated micrographs. Scale bar indicates 100 nm. f, Projection images from the final selected 2D classes. g-i, 3D classification results; good classes selected for further processing are boxed in red. j, 3D reconstructed maps from the final 3D classification, which are shown in side-view and top-view from ECD. Selected 3D classes are boxed in red to generate a final 3D map. k, Unsharpened 3D map where M1-M3 TMD helices from an individual subunit are labeled in black text. l, Sharpened map colored by local resolution. m, Half map FSC plot for masked and unmasked maps with resolutions indicated at FSC = 0.143.
Extended Data Fig. 4 Cryo-EM density of the CRT1 receptor.
a, Cryo-EM density map of the CRT1 receptor for representative adjacent subunits colored in blue and cyan. Density map of diosgenin colored in gray at a threshold level of 0.03. b, Orthosteric binding site of CRT1, where residues within 5 Å of diosgenin are shown as sticks. c, Orthosteric binding site of α7 nicotinic receptor (PDB:7KOX), where residues within 5 Å of epibatidine are shown as sticks. d, Calculated interface areas and interaction energies (ΔiG) for protein and diosgenin using PDBePISA41. Calculated solvent accessible area and volume of the binding pocket for CRT1 and α7 nicotinic receptor using CASTp3.042. e-g, Cryo-EM density segments of M1-M3 helices at a threshold level of 0.02. h-m, Cryo-EM density segments of Loop A-F in the orthosteric binding site at a threshold level of 0.02. j-l, Cryo-EM density segments of Cys-loop, M1M2 loop, and M2M3 loop at a threshold level of 0.02.
Extended Data Fig. 5 CRT1 Secondary structure prediction from AlphaFold2.
a, A subunit of CRT1 cryo-EM structure colored in dark blue. b, Predicted AlphaFold2 CRT1 monomer colored by per-residue confidence score (pLDDT). Regions of high expected accuracy are colored in blue; regions of low expected accuracy are colored in red. c, Cα r.m.s.d between the CRT1 cryo-EM structure and the AlphaFold2 model (monomer comparisons) as a function of residue number. Helical regions are shown in green boxes. Blue boxes indicate β-strands next to the most divergent β4-β5 loop and β8-β9 loop regions. Unmodeled post-M3 helix is shaded in gray. d, Sequence of CRT1 and secondary structure prediction by AlphaFold2. Helices are shown in green cylinders and strands are indicated by blue arrows. M4 helix amino acids are colored in red, blue, and black for negatively charged, positively charged, and hydrophobic residues, respectively. S-S bond2 is in the Cys-loop. e, Sequence alignment of TMD helices (M1-M4) for CRT1 and other Cys-loop receptors. Same color scheme is followed as in Panel d.
Extended Data Fig. 6 Subunit and transmembrane pore conformation of CRT1 compared to the human α7 nicotinic acetylcholine receptor.
a, Comparison of single subunit structure of CRT1 and the human α7 nicotinic receptor (PDB:7KOX); MA and MX helices generally conserved in nicotinic receptors are absent in the CRT1 structure. b, Comparison of Loop C boxed in a; Disulfide bond on Loop C of α7 is shown as spheres. c, View at 90° rotation about pore axis from a. d, Comparison of ECD boxed in c; disulfide bonds are shown as spheres. e, M2 helices of the CRT1 receptor from opposing subunits (chains A and C) with pore-lining residues shown as sticks. Colored spheres indicate the pore diameter by displaying blue spheres (pore diameter > 5.6 Å), green spheres (2.8 Å < pore diameter < 5.6 Å), and red spheres (pore diameter < 2.8 Å). f, M2 helices of the α7 nicotinic receptor in an activated state (PDB:7KOX). g, M2 helices of α7 in a desensitized state (PDB:7KOQ). h, M2 helices of α7 in a resting state (PDB:7KOO). i, Pore diameters of CRT1 and α7 in panels e-h as a function of a distance along the pore axis. Structures were aligned using the M2 helix Leu9ʹ at the midpoint of the pore, which we defined as y = 0.
Extended Data Fig. 7 CRT1 pharmacological properties.
a, CRT1 exhibited dose-dependent responses to nootkatone, zearalenone and GDN in patch-clamp experiments. Nootkatone EC50 = 16.1 μM, 95% CI = 13.1 – 18.7 μM, zearalenone EC50 = 4.8 μM, 95% CI = 4.3 – 5.4 μM, GDN EC50 = 125.1 nM, 95% CI = 91.2 – 184.6 nM, n = 8 cells per ligand. b, Minimal desensitization was measured in response to low concentrations of agonist while higher concentrations produced inhibition with large wash-off currents, consistent with moderate pore block. p < 0.0001 for concentration, two-tailed student’s t-test (n = 6 cells). c, Heat map of normalized axial nerve and arm responses to 50 μM of the indicated compound (except GDN at 5 μM to avoid micelle formation). Nootkatone and zearalenone elicited the most robust arm activity among CRT1 agonists or other molecules. Some differences in agonist efficacy in isolated receptors and arms are expected due to solubility issues at concentrations used for arm experiments, particularly with detergent molecules, unknown features of native CR signal transduction, neural integration, and differences in experimental preparations. n = 7-8 arms. d, WT CRT1 exhibited dose-dependent activity in response to nootkatone. Y78A mutant channels were insensitive to nootkatone except at higher concentrations which inhibited activity. n = 8 cells per condition. e, Current-voltage (I-V) relationships showing ligand-gated activity in WT CRT1 versus constitutive activity in agonist-binding site mutants. Ligands did not increase currents in mutant channels and all channels were sensitive to CR blocker mecamylamine (1 mM). Data are represented as mean ± SEM.
Extended Data Fig. 8 CRT1 vs. α7 sequence alignment and residues under strongly positive selective pressure.
a, Side view of CRT1 cryo-EM single subunit structure with residues under highly positive selection (LRT > 3) colored in red. b, Sequence alignment of CRT1 and human α7 nicotinic receptor (UniProt accession number: P36544). Unresolved post-M3 region of CRT1 model is colored in gray. Red boxes indicate residues with LRT >3 highlighted in panel a, and yellow boxes highlight the conserved Cys-loop disulfide bond.
Supplementary information
Supplementary Video 1
CRT1 binding site. Animation showing CRT1 pentameric ligand-gated ion channel bound to diosgenin. The zoomed in view shows the orthosteric binding pocket with ligand-coordinating residues highlighted.
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.
About this article
Cite this article
Allard, C.A.H., Kang, G., Kim, J.J. et al. Structural basis of sensory receptor evolution in octopus. Nature 616, 373–377 (2023). https://doi.org/10.1038/s41586-023-05822-1
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1038/s41586-023-05822-1
This article is cited by
-
Bridging structural and cell biology with cryo-electron microscopy
Nature (2024)
-
Welcome to the nursery
Nature Methods (2023)
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.
