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Structure and function of a broad-range thermal receptor in myriapods

Nature Structural & Molecular Biology volume 32, pages 1081–1090 (2025)Cite this article

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Abstract

Broad-range thermal receptor 1 (BRTNaC1), activated by heat at low extracellular pH, was recently identified in myriapods. Although the overexpression of BRTNaC1 leads to robust heat-activated current with a cation selectivity profile, the structure of this receptor and how it is gated by proton and heat remain to be investigated. Here we determine cryogenic electron microscopy structures of BRTNaC1 in the apo, proton-induced and heated states. Based on these structures, patch-clamp recordings and molecular dynamic simulations, we found that a ‘twist the wrist’ mechanism is used for proton activation of BRTNaC1, while heat induces broad conformational changes in BRTNaC1, including rotation and shift in the transmembrane helices to open this channel. Moreover, as testosterone inhibited BRTNaC1 activation, we identified four clustered residues important for such inhibition. Therefore, our study has established the structural basis for ligand and temperature gating in the BRTNaC1 ion channel.

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Fig. 1: The overall structure of BRTNaC1 in the apo state.
Fig. 2: Comparison of BRTNaC1, ASIC1 and ENaC channel structures.
Fig. 3: Extracellular proton-activated BRTNaC1.
Fig. 4: Mechanisms of proton activation in BRTNaC1.
Fig. 5: Structures of BRTNaC14°C and BRTNaC140°C.
Fig. 6: Potential mechanisms underlying BRTNaC1 inhibition by TEST.

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Data availability

The structure coordinates and cryo-EM density maps have been deposited in the PDB under accession numbers 8YMR and EMD-39405 for BRTNaC1pH8, 8YMS and EMD-39406 for BRTNaC1pH4, 8YMU and EMD-39408 for BRTNaC1MβCD_pH4, 8YMT and EMD-39407 for BRTNaC1MβCD_pH4_TEST_bound, 8YMW and EMD-39409 for BRTNaC14°C, 8YMX and EMD-39410 for BRTNaC140°C. The materials and data can be obtained from the corresponding authors upon request. Source data are provided with this paper.

References

  1. Julius, D. & Nathans, J. Signaling by sensory receptors. Cold Spring Harb. Perspect. Biol. 4, a005991 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  2. Dhaka, A., Viswanath, V. & Patapoutian, A. Trp ion channels and temperature sensation. Annu. Rev. Neurosci 29, 135–161 (2006).

    Article  CAS  PubMed  Google Scholar 

  3. Qu, Z. et al. Millipede genomes reveal unique adaptations during myriapod evolution. PLoS Biol. 18, e3000636 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Yao, Z. et al. A thermal receptor for nonvisual sunlight detection in myriapods. Proc. Natl Acad. Sci. USA 120, e2218948120 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Boscardin, E., Alijevic, O., Hummler, E., Frateschi, S. & Kellenberger, S. The function and regulation of acid-sensing ion channels (ASICs) and the epithelial Na+ channel (ENaC): IUPHAR Review 19. Br. J. Pharmacol. 173, 2671–2701 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Jasti, J., Furukawa, H., Gonzales, E. B. & Gouaux, E. Structure of acid-sensing ion channel 1 at 1.9 Å resolution and low pH. Nature 449, 316–323 (2007).

    Article  CAS  PubMed  Google Scholar 

  7. Yoder, N., Yoshioka, C. & Gouaux, E. Gating mechanisms of acid-sensing ion channels. Nature 555, 397–401 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Sun, D. et al. Structural insights into human acid-sensing ion channel 1a inhibition by snake toxin mambalgin1. eLife 9, e57096 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Noreng, S., Bharadwaj, A., Posert, R., Yoshioka, C. & Baconguis, I. Structure of the human epithelial sodium channel by cryo-electron microscopy. eLife 7, e39340 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  10. Sun, D. et al. Cryo-EM structure of the ASIC1a–mambalgin-1 complex reveals that the peptide toxin mambalgin-1 inhibits acid-sensing ion channels through an unusual allosteric effect. Cell Discov. 4, 27 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  11. 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).

    Article  CAS  PubMed  Google Scholar 

  12. Yang, J. et al. PAC, an evolutionarily conserved membrane protein, is a proton-activated chloride channel. Science 364, 395–399 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Sherwood, T. W., Frey, E. N. & Askwith, C. C. Structure and activity of the acid-sensing ion channels. Am. J. Physiol. Cell Physiol. 303, C699–C710 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Li, T., Yang, Y. & Canessa, C. M. Outlines of the pore in open and closed conformations describe the gating mechanism of ASIC1. Nat. Commun. 2, 399 (2011).

    Article  PubMed  Google Scholar 

  15. Yao, Z. et al. A thermal receptor for nonvisual sunlight detection in myriapods. Proc. Natl Acad. Sci. USA, 120, e2218948120 (2023).

    Article  CAS  PubMed  Google Scholar 

  16. Baconguis, I., Bohlen, C. J., Goehring, A., Julius, D. & Gouaux, E. X-ray structure of acid-sensing ion channel 1-snake toxin complex reveals open state of a Na+-selective channel. Cell 156, 717–729 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Cristofori-Armstrong, B. & Rash, L. D. Acid-sensing ion channel (ASIC) structure and function: insights from spider, snake and sea anemone venoms. Neuropharmacology 127, 173–184 (2017).

    Article  CAS  PubMed  Google Scholar 

  18. Olsson, M. H. M., Søndergaard, C. R., Rostkowski, M. & Jensen, J. H. PROPKA3: consistent treatment of internal and surface residues in empirical pKa predictions. J. Chem. Theory Comput. 7, 525–537 (2011).

    Article  CAS  PubMed  Google Scholar 

  19. Korkosh, V. S. & Tikhonov, D. B. Analysis of residue-residue interactions in the structures of ASIC1a suggests possible gating mechanisms. Eur. Biophys. J. 52, 111–119 (2023).

    Article  CAS  PubMed  Google Scholar 

  20. Zhou, H. X. & Pang, X. Electrostatic interactions in protein structure, folding, binding, and condensation. Chem. Rev. 118, 1691–1741 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Su, N. et al. Structural mechanisms of TRPV2 modulation by endogenous and exogenous ligands. Nat. Chem. Biol. 19, 72–80 (2023).

    Article  CAS  PubMed  Google Scholar 

  22. Yoder, N. & Gouaux, E. The His–Gly motif of acid-sensing ion channels resides in a reentrant ‘loop’ implicated in gating and ion selectivity. eLife 9, e56527 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Mahammad, S. & Parmryd, I. in Methods in Membrane Lipids (ed. Owen, D. M.) 91–102 (Springer, 2015).

  24. Gonzales, E. B., Kawate, T. & Gouaux, E. Pore architecture and ion sites in acid-sensing ion channels and P2X receptors. Nature 460, 599–604 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Rook, M. L., Williamson, A., Lueck, J. D., Musgaard, M. & Maclean, D. M. β11-12 linker isomerization governs acid-sensing ion channel desensitization and recovery. eLife 9, e51111 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Askwith, C. C., Benson, C. J., Welsh, M. J. & Snyder, P. M. DEG/ENaC ion channels involved in sensory transduction are modulated by cold temperature. Proc. Natl Acad. Sci. USA 98, 6459–6463 (2001).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Cao, E., Liao, M., Cheng, Y. & Julius, D. TRPV1 structures in distinct conformations reveal activation mechanisms. Nature 504, 113–118 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Liao, M., Cao, E., Julius, D. & Cheng, Y. Structure of the TRPV1 ion channel determined by electron cryo-microscopy. Nature 504, 107–112 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Tominaga, M. et al. The cloned capsaicin receptor integrates multiple pain-producing stimuli. Neuron 21, 531–543 (1998).

    Article  CAS  PubMed  Google Scholar 

  30. Zheng, J. Molecular mechanism of TRP channels. Compr. Physiol. 3, 221–242 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  31. Yang, F., Cui, Y., Wang, K. & Zheng, J. Thermosensitive TRP channel pore turret is part of the temperature activation pathway. Proc. Natl Acad. Sci. USA 107, 7083–7088 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Kwon, D. H. et al. Heat-dependent opening of TRPV1 in the presence of capsaicin. Nat. Struct. Mol. Biol. 28, 554–563 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Nadezhdin, K. D. et al. Structural mechanism of heat-induced opening of a temperature-sensitive TRP channel. Nat. Struct. Mol. Biol. 28, 564–572 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Kim, J., Won, J., Chung, D. K. & Lee, H. H. FRET analysis of the temperature-induced structural changes in human TRPV3. Sci. Rep. 13, 10108 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Clapham, D. E. & Miller, C. A thermodynamic framework for understanding temperature sensing by transient receptor potential (TRP) channels. Proc. Natl Acad. Sci. USA 108, 19492–19497 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Liu, R. et al. Choline kinase alpha 2 acts as a protein kinase to promote lipolysis of lipid droplets. Mol. Cell 81, 2722–2735.e9 (2021).

    Article  CAS  PubMed  Google Scholar 

  37. Ma, Q. et al. Coupling HDAC4 with transcriptional factor MEF2D abrogates SPRY4-mediated suppression of ERK activation and elicits hepatocellular carcinoma drug resistance. Cancer Lett. 520, 243–254 (2021).

    Article  CAS  PubMed  Google Scholar 

  38. Caterina, M. J. et al. The capsaicin receptor: a heat-activated ion channel in the pain pathway. Nature 389, 816–824 (1997).

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  40. Li, Z. et al. Uni-Fold: an open source platform for developing protein folding models beyond AlphaFold. Preprint at bioRxiv https://doi.org/10.1101/2022.08.04.502811 (2022).

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

    Article  CAS  PubMed  Google Scholar 

  42. PyMOL, v. 1.8. (Schrödinger, 2015).

  43. 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 

  44. Laguerre, M., Saux, M., Dubost, J. P. & Carpy, A. MLPP: a program for the calculation of molecular lipophilicity potential in proteins. Pharm. Pharmacol. Commun. 3, 217–222 (1997).

    CAS  Google Scholar 

  45. Emsley, P., Lohkamp, B., Scott, W. G. & Cowtan, K. Features and development of Coot. Acta Crystallogr. D 66, 486–501 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Schneider, C. A., Rasband, W. S. & Eliceiri, K. W. NIH Image to ImageJ: 25 years of image analysis. Nat. Methods 9, 671–675 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Jo, S., Kim, T., Iyer, V. G. & Im, W. CHARMM-GUI: a web-based graphical user interface for CHARMM. J. Comput. Chem. 29, 1859–1865 (2008).

    Article  CAS  PubMed  Google Scholar 

  48. Lee, J. et al. CHARMM-GUI input generator for NAMD, GROMACS, AMBER, OpenMM, and CHARMM/OpenMM simulations using the CHARMM36 additive force field. J. Chem. Theory Comput. 12, 405–413 (2016).

    Article  CAS  PubMed  Google Scholar 

  49. Páll, S. et al. Heterogeneous parallelization and acceleration of molecular dynamics simulations in GROMACS. J. Chem. Phys. 153, 134110 (2020).

    Article  PubMed  Google Scholar 

  50. 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 

  51. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

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

    Article  PubMed  Google Scholar 

  55. Parrinello, M. & Rahman, A. Polymorphic transitions in single crystals: a new molecular dynamics method. J. Appl. Phys. 52, 7182–7190 (1981).

    Article  CAS  Google Scholar 

  56. Hess, B., Bekker, H., Berendsen, H. J. C. & Fraaije, J. G. E. M. LINCS: a linear constraint solver for molecular simulations. J. Comput. Chem. 18, 1463–1472 (1997).

    Article  CAS  Google Scholar 

  57. Michaud-Agrawal, N., Denning, E. J., Woolf, T. B. & Beckstein, O. MDAnalysis: a toolkit for the analysis of molecular dynamics simulations. J. Comput. Chem. 32, 2319–2327 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

Single-particle cryo-EM data were collected at the Center of Cryo-Electron Microscopy at Zhejiang University. We thank X. Zhang and C. Ma for their support in facility access and data acquisition. We thank S. Chang and L. Wu in the Center of Cryo-Electron Microscopy, Zhejiang University for their technical assistance on cryo-EM. We thank J. Guo for the constructive suggestions. This work was supported by the National Key R&D Program of China (2023YFF1304903 to S.Y.), the National Natural Science Foundation of China (32122040, 31971040 and 32421001 to F.Y.; 32170486 and 32430012 to S.Y.; 81571916, 81372079 and 81201478 to Y.X.), National Forestry and Grassland Administration (2020132610 to S.Y.), Heilongjiang Province (JQ2021C001 to S.Y.), China Postdoctoral Science Foundation (2021M692818 to N.S.; 2023M743097 to X.C.), the Ministry of Science and Technology of China (2022YFF1201901 to H.W.), STI2030-Major Project (2022ZD0212700 to H.W.) and Zhejiang Provincial Natural Science Foundation (LR20C050002 to F.Y.). F.Y. was supported by MOE Frontier Science Center for Brain Science and Brain-Machine Integration, Zhejiang University. This work was supported by the Fundamental Research Funds for the Central Universities. This work was also supported by the Core Facilities in Zhejiang University School of Medicine, including the Zeiss LSM800 confocal fluorescence imaging microscope.

Author information

Author notes

  1. These authors contributed equally: Xiaoying Chen, Licheng Yuan, Han Wen.

Authors and Affiliations

  1. Kidney Disease Center of the First Affiliated Hospital and Department of Biophysics, Zhejiang University School of Medicine, Hangzhou, China

    Xiaoying Chen & Fan Yang

  2. Liangzhu Laboratory, Zhejiang University Medical Center, Hangzhou, China

    Xiaoying Chen & Fan Yang

  3. Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou, China

    Xiaoying Chen & Fan Yang

  4. College of Wildlife and Protected Area, Northeast Forestry University, Harbin, China

    Licheng Yuan, Zhihao Yao, Yunfei Wang & Shilong Yang

  5. DP Technology, Beijing, China

    Han Wen

  6. Institute for Advanced Algorithms Research, Shanghai, China

    Han Wen

  7. State Key Laboratory of Medical Proteomics, Beijing, China

    Han Wen

  8. AI for Science Institute, Beijing, China

    Han Wen

  9. National Key Laboratory of Lead Druggability Research, Beijing, China

    Han Wen

  10. Cancer Institute, The Affiliated Hospital of Qingdao University and Qingdao Cancer Institute, Qingdao, China

    Qingxia Ma

  11. School of Pharmaceutical Sciences, Peking University, Beijing, China

    Zhenfeng Deng

  12. Department of Emergency Medicine, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China

    Yongan Xu

  13. Center for Membrane receptors and Brain Medicine, the Fourth Affiliated Hospital of School of Medicine, and International School of Medicine, International Institutes of Medicine, Zhejiang University, Yiwu, China

    Nannan Su

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Contributions

F.Y., N.S. and S.Y. conceived and supervised the project. X.C. and L.Y. performed sample preparation, data acquisition and structure determination. X.C., L.Y., Q.M. and Z.Y. did functional studies. H.W. and Z.D. performed the MD simulation. F.Y., N.S., S.Y., Y.W. and Y.X. performed data analysis. All authors participated in paper preparation.

Corresponding authors

Correspondence to Shilong Yang, Nannan Su or Fan Yang.

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Extended data

Extended Data Fig. 1 Validation of PAC-KO cells.

a, Western blot of the expression of PAC in HEK293T cells and PAC-KO HEK293T cells. b, Representative whole-cell recording of PAC current induced by extracellular acidification in HEK293T cells. c, PAC current induced by extracellular acidification was abolished in PAC-KO cells.

Source data

Extended Data Fig. 2 Representative whole-cell current recordings of BRTNaC1 mutants.

High concentration of proton (pH3.5) were applied to activate the mutants in the patch-clamp recordings. Whole-cell mode recordings were conducted at ±80 mV for 450 ms, respectively. A dashed line in black indicated zero current level.

Source data

Extended Data Fig. 3 The confocal imaging of BRTNaC1 mutant and cell membrane.

The expression pattern of BRTNaC1 and its mutants with cell membranes. Representative bright-field (first row), fluorescent of green fluorescent protein (GFP)-tagged WT BRTNaC1 and mutants (second row), fluorescent of cell membrane labeled by DiD dye (third row) and merged images of GFP tag and DiD signals (forth row) were showed. The fluorescence intensity profile from a line scan (indicated by a dashed line) is showed on the right side, which indicated WT and mutant BRTNaC1 exhibited similar expression patterns.

Extended Data Fig. 4 G392R mutant is anion permeable.

a, Voltage ramps (800 ms) ranging from −80 to +80 mV were used to record current in different extracellular solutions of G392R mutant transfected cells. Extracellular solution (140 mM NaCl, 5 mM MES, 5 mM EDTA, pH 3.5) was substituted with sucrose-supplemented extracellular solution (14 mM NaCl, 5 mM MES, 5 mM EDTA, pH 3.5, osmolarity constant is adjust by sucrose), respectively in a. b, The reversal potentials tested in hPAC-KO cells expressing G392R (n = 9 biological replicates). Data points and bars represent mean ± sem.

Source data

Extended Data Fig. 5 Inhibition of BRTNaC1 by amiloride.

a, Representative whole-cell current recording showing that amiloride inhibited BRTNaC1 channel in a concentration-dependent manner. b, Concentration dependent curve of amiloride inhibition of BRTNaC1 in whole-cell patch recordings (IC50: 168.4 ± 7.96 µM, n = 3 biological replicates). Data points and bars represent mean ± sem.

Source data

Extended Data Fig. 6 Inhibition of BRTNaC1 by cholesterol.

a, Representative whole-cell current recording of BRTNaC1, where cholesterol up to 300 µM exhibited little inhibitory effect. b, Concentration dependent curve of cholesterol inhibition of BRTNaC1 in whole-cell patch recordings (n = 3 biological replicates). Data points and bars represent mean ± sem.

Source data

Extended Data Fig. 7 Cation selectivity pattern of I400A and C401A.

a and c, Voltage ramps (500 ms) ranging from −60 to +60 mV were used to record current in different extracellular solutions of I400A or C401A transfected cells, respectively. Bath solution (140 mM NaCl, 5 mM MES, pH 3.5) was substituted with different cationic compositions (140 mM KCl, 140 mM CsCl, 140 mM LiCl, 110 mM CaCl2, 110 mM MgCl2, or 110 mM BaCl2). b, Cation selectivity in I400A transfected cells (n = 6 biological replicates; Mg2+, P = 0.3487; Ba2+, P = 0.6575; Ca2+, P = 0.6855; Cs+, P = 0.9; Li+, P = 0.0001; K+, P = 0.0002). d, Cation selectivity in C401A transfected cells (n = 9 biological replicates; Mg2+, P = 0.8969; Ba2+, P = 0.6443; Ca2+, P = 0.1272; Cs+, P = 0.0992; Li+, P = 0.0015; K+, P = 0.114). Data points and bars represent mean ± sem (two-tailed t-test. *p < 0.05; **p < 0.01; ***p < 0.001; ****p < 0.0001; N.S, not statistically significant).

Source data

Extended Data Table 1 Protonation states predicted by PROPKA3 for BRTNaC1 at pH 3.5 (pH 3.5) for the open state and 8.0 (pH 8.0), 3.5 (pH 8.0 to 3.5) for the closed state, in CHARMM format
Full size table
Extended Data Table 2 Inhibition of BRTNaC1 WT and mutants by testosterone
Full size table
Extended Data Table 3 Primers used for site-directed mutagenesis
Full size table

Supplementary information

Supplementary Information

Supplementary Figs. 1–7 and corresponding figure legends.

Reporting Summary

Supplementary Video 1

Proton-induced closed-to-open conformational changes in BRTNaC1 channel simulated by MDs. Residue G392 is highlighted in green.

Source Data for Supplementary figures

The source data and unprocessed gels for supplementary figures.

Source Data for Extended Data Fig. 3

Unprocessed confocal images for Extended Data Fig. 3.

Source data

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Source Data Extended Data Fig. 1

Unprocessed western blots and statistical source data.

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Source Data Extended Data Fig. 7

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Source Data Extended Data Table 2

Statistical source data.

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Chen, X., Yuan, L., Wen, H. et al. Structure and function of a broad-range thermal receptor in myriapods. Nat Struct Mol Biol 32, 1081–1090 (2025). https://doi.org/10.1038/s41594-025-01495-8

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