Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Selective presynaptic inhibition of leg proprioception in behaving Drosophila

Nature (2025)Cite this article

Subjects

Abstract

Controlling arms and legs requires feedback from the proprioceptive sensory neurons that detect joint position and movement1,2. Proprioceptive feedback must be tuned for different behavioural contexts3,4,5,6, but the underlying circuit mechanisms remain poorly understood. Here, using calcium imaging in behaving Drosophila, we find that the axons of position-encoding leg proprioceptors are active across a range of behaviours, whereas the axons of movement-encoding leg proprioceptors are suppressed during walking and grooming. Using connectomics7,8,9, we identify a specific class of interneurons that provide GABAergic presynaptic inhibition to the axons of movement-encoding proprioceptors. These interneurons receive input from parallel excitatory and inhibitory descending pathways that are positioned to drive the interneurons in a context-specific and leg-specific manner. Calcium imaging from both the interneurons and their descending inputs confirms that their activity is correlated with self-generated but not passive leg movements. Taken together, our findings reveal a neural circuit that suppresses specific proprioceptive feedback signals during self-generated movements.

Access through your institution
Buy or subscribe

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

Access options

Access through your institution

Buy this article

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

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

Fig. 1: Proprioceptor axons from the Drosophila leg are positioned to receive presynaptic inhibition.
Fig. 2: The axons of position-encoding proprioceptors are not suppressed during active leg movements.
Fig. 3: The axons of movement-encoding proprioceptors are suppressed during active leg movements.
Fig. 4: GABAergic interneurons provide presynaptic inhibition to movement-encoding proprioceptor axons.
Fig. 5: GABAergic interneurons receive descending input from the brain.
Fig. 6: Summary of neural circuit for selectively suppressing proprioceptive movement feedback.

Similar content being viewed by others

Data availability

Calcium imaging and behavioural data generated for this study are available from Dryad at https://doi.org/10.5061/dryad.gqnk98t16 (ref. 84). FANC data were analysed from CAVE materialization v.840, timestamp 2024-01-17T08:10:01.179472. MANC data were analysed from v.1.0. FAFB/FlyWire data were analysed from v.783 with synapse predictions from ref. 85.

Code availability

Analyses were done in Matlab 2023a and Python 3.9. Code to recreate the figures is available at GitHub (https://github.com/chrisjdallmann/feco-inhibition). Python code made use of CAVEclient, neuPrint and FAFBseg to interact with the connectomes, NAVis and flybrains to visualize neurons, Brian 2 to simulate neural activity, SciPy for statistics, and Matplotlib, seaborn, NumPy and pandas for general computation and data visualization.

References

  1. Proske, U. & Gandevia, S. C. The proprioceptive senses: their roles in signaling body shape, body position and movement, and muscle force. Physiol. Rev. 92, 1651–1697 (2012).

    Article  CAS  PubMed  Google Scholar 

  2. Tuthill, J. C. & Azim, E. Proprioception. Curr. Biol. 28, R194–R203 (2018).

    Article  CAS  PubMed  Google Scholar 

  3. Azim, E. & Seki, K. Gain control in the sensorimotor system. Curr. Opin. Physiol. 8, 177–187 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  4. Wolf, H. & Burrows, M. Proprioceptive sensory neurons of a locust leg receive rhythmic presynaptic inhibition during walking. J. Neurosci. 15, 5623–5636 (1995).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Fink, A. J. P. et al. Presynaptic inhibition of spinal sensory feedback ensures smooth movement. Nature 509, 43–48 (2014).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  6. Koch, S. C. et al. RORβ spinal interneurons gate sensory transmission during locomotion to secure a fluid walking gait. Neuron 96, 1419–1431 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Azevedo, A. et al. Connectomic reconstruction of a female Drosophila ventral nerve cord. Nature 631, 360–368 (2024).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Takemura, S. et al. A connectome of the male Drosophila ventral nerve cord. eLife 13, RP97769 (2024).

    Google Scholar 

  9. Marin, E. C. et al. Systematic annotation of a complete adult male Drosophila nerve cord connectome reveals principles of functional organisation. eLife 13, RP97766 (2024).

    Google Scholar 

  10. Rossignol, S., Dubuc, R. & Gossard, J.-P. Dynamic sensorimotor interactions in locomotion. Physiol. Rev. 86, 89–154 (2006).

    Article  PubMed  Google Scholar 

  11. Dallmann, C. J., Karashchuk, P., Brunton, B. W. & Tuthill, J. C. A leg to stand on: computational models of proprioception. Curr. Opin. Physiol. 22, 100426 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  12. Frigon, A., Akay, T. & Prilutsky, B. I. Control of mammalian locomotion by somatosensory feedback Compr. Physiol. 12, 2877–2947 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  13. McComas, A. J. Hypothesis: Hughlings Jackson and presynaptic inhibition: is there a big picture? J. Neurophysiol. 116, 41–50 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Crapse, T. B. & Sommer, M. A. Corollary discharge across the animal kingdom. Nat. Rev. Neurosci. 9, 587–600 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Straka, H., Simmers, J. & Chagnaud, B. P. A new perspective on predictive motor signaling. Curr. Biol. 28, R232–R243 (2018).

    Article  CAS  PubMed  Google Scholar 

  16. Cullen, K. E. Sensory signals during active versus passive movement. Curr. Opin. Neurobiol. 14, 698–706 (2004).

    Article  CAS  PubMed  Google Scholar 

  17. Daly, K. C. & Dacks, A. The self as part of the sensory ecology: how behavior affects sensation from the inside out. Curr. Opin. Insect Sci. 58, 101053 (2023).

    Article  PubMed  Google Scholar 

  18. Clarac, F. & Cattaert, D. Invertebrate presynaptic inhibition and motor control. Exp. Brain Res. 112, 163–180 (1996).

    Article  CAS  PubMed  Google Scholar 

  19. Rudomin, P. & Schmidt, R. F. Presynaptic inhibition in the vertebrate spinal cord revisited. Exp. Brain Res. 129, 1–37 (1999).

    Article  CAS  PubMed  Google Scholar 

  20. Kuan, A. T. et al. Dense neuronal reconstruction through X-ray holographic nano-tomography. Nat. Neurosci. 23, 1637–1643 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Mamiya, A., Gurung, P. & Tuthill, J. C. Neural coding of leg proprioception in Drosophila. Neuron 100, 636–650 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Mamiya, A. et al. Biomechanical origins of proprioceptor feature selectivity and topographic maps in the Drosophila leg. Neuron 111, 3230–3243 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Agrawal, S. et al. Central processing of leg proprioception in Drosophila. eLife 9, e60299 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Chen, C. et al. Functional architecture of neural circuits for leg proprioception in Drosophila. Curr. Biol. 31, 5163–5175 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Chockley, A. S. et al. Subsets of leg proprioceptors influence leg kinematics but not interleg coordination in Drosophila melanogaster walking. J. Exp. Biol. 225, jeb244245 (2022).

    Article  PubMed  Google Scholar 

  26. Lee, S.-Y. J., Dallmann, C. J., Cook, A., Tuthill, J. C. & Agrawal, S. Divergent neural circuits for proprioceptive and exteroceptive sensing of the Drosophila leg. Nat. Commun. 16, 4105 (2025).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Hooper, S. L. et al. Neural control of unloaded leg posture and of leg swing in stick insect, cockroach, and mouse differs from that in larger animals. J. Neurosci. 29, 4109–4119 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Ache, J. M. & Matheson, T. Passive joint forces are tuned to limb use in insects and drive movements without motor activity. Curr. Biol. 23, 1418–1426 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Harris, R. M., Pfeiffer, B. D., Rubin, G. M. & Truman, J. W. Neuron hemilineages provide the functional ground plan for the Drosophila ventral nervous system. eLife 4, e04493 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  30. Lacin, H. et al. Neurotransmitter identity is acquired in a lineage-restricted manner in the Drosophila CNS. eLife 8, e43701 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  31. Tuthill, J. C. & Wilson, R. I. Mechanosensation and adaptive motor control in insects. Curr. Biol. 26, R1022–R1038 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Sapkal, N. et al. Neural circuit mechanisms underlying context-specific halting in Drosophila. Nature 634, 191–200 (2024).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Yang, H. H. et al. Fine-grained descending control of steering in walking Drosophila. Cell 187, 6290–6308 (2024).

    Article  CAS  PubMed  Google Scholar 

  34. Rayshubskiy, A. et al. Neural circuit mechanisms for steering control in walking Drosophila. eLife 13, RP102230 (2025).

    Article  PubMed  PubMed Central  Google Scholar 

  35. Guo, L., Zhang, N. & Simpson, J. H. Descending neurons coordinate anterior grooming behavior in Drosophila. Curr. Biol. 32, 823–833 (2022).

    Article  CAS  PubMed  Google Scholar 

  36. Cheong, H. S. J. et al. Transforming descending input into behavior: the organization of premotor circuits in the Drosophila male adult nerve cord connectome. eLife 13, RP96084 (2024).

    Google Scholar 

  37. Sterne, G. R., Otsuna, H., Dickson, B. J. & Scott, K. Classification and genetic targeting of cell types in the primary taste and premotor center of the adult Drosophila brain. eLife 10, e71679 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Zheng, Z. et al. A complete electron microscopy volume of the brain of adult Drosophila melanogaster. Cell 174, 730–743 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Dorkenwald, S. et al. Neuronal wiring diagram of an adult brain. Nature 634, 124–138 (2024).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Schlegel, P. et al. Whole-brain annotation and multi-connectome cell typing of Drosophila. Nature 634, 139–152 (2024).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Emanuel, S., Kaiser, M., Pflueger, H.-J. & Libersat, F. On the role of the head ganglia in posture and walking in insects. Front. Physiol. 11, 135 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  42. Shiu, P. K. et al. A Drosophila computational brain model reveals sensorimotor processing. Nature 634, 210–219 (2024).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Burrows, M. & Laurent, G. Synaptic potentials in the central terminals of locust proprioceptive afferents generated by other afferents from the same sense organ. J. Neurosci. 13, 808–819 (1993).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Burrows, M. & Matheson, T. A presynaptic gain control mechanism among sensory neurons of a locust leg proprioceptor. J. Neurosci. 14, 272–282 (1994).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Sauer, A. E., Büschges, A. & Stein, W. Role of presynaptic inputs to proprioceptive afferents in tuning sensorimotor pathways of an insect joint control network. J. Neurobiol. 32, 359–376 (1997).

    Article  CAS  PubMed  Google Scholar 

  46. Gebehart, C. & Büschges, A. The processing of proprioceptive signals in distributed networks: insights from insect motor control. J. Exp. Biol. 227, jeb246182 (2024).

    Article  PubMed  Google Scholar 

  47. Ramirez, J.-M., Büschges, A. & Kittmann, R. Octopaminergic modulation of the femoral chordotonal organ in the stick insect. J. Comp. Physiol. A 173, 209–219 (1993).

    Article  Google Scholar 

  48. Matheson, T. Octopamine modulates the responses and presynaptic inhibition of proprioceptive sensory neurones in the locust Schistocerca gregaria. J. Exp. Biol. 200, 1317–1325 (1997).

    Article  CAS  PubMed  Google Scholar 

  49. Bässler, U. The femur-tibia control system of stick insects: a model system for the study of the neural basis of joint control. Brain Res. Rev. 18, 207–226 (1993).

    Article  PubMed  Google Scholar 

  50. Dean, J. Control of leg protraction in the stick insect: a targeted movement showing compensation for externally applied forces. J. Comp. Physiol. A 155, 771–781 (1984).

    Article  Google Scholar 

  51. Takeoka, A., Vollenweider, I., Courtine, G. & Arber, S. Muscle spindle feedback directs locomotor recovery and circuit reorganization after spinal cord injury. Cell 159, 1626–1639 (2014).

    Article  CAS  PubMed  Google Scholar 

  52. Mayer, W. P. & Akay, T. The role of muscle spindle feedback in the guidance of hindlimb movement by the ipsilateral forelimb during locomotion in mice. eNeuro 8, ENEURO.0432-21.2021 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  53. Mackrous, I., Carriot, J. & Cullen, K. E. Context-independent encoding of passive and active self-motion in vestibular afferent fibers during locomotion in primates. Nat. Commun. 13, 120 (2022).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  54. Seki, K., Perlmutter, S. I. & Fetz, E. E. Sensory input to primate spinal cord is presynaptically inhibited during voluntary movement. Nat. Neurosci. 6, 1309–1316 (2003).

    Article  CAS  PubMed  Google Scholar 

  55. Tomatsu, S., Kim, G., Kubota, S. & Seki, K. Presynaptic gating of monkey proprioceptive signals for proper motor action. Nat. Commun. 14, 6537 (2023).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  56. Pichler, P. & Lagnado, L. Motor behavior selectively inhibits hair cells activated by forward motion in the lateral line of zebrafish. Curr. Biol. 30, 150–157 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Odstrcil, I. et al. Functional and ultrastructural analysis of reafferent mechanosensation in larval zebrafish. Curr. Biol. 32, 176–189 (2022).

    Article  CAS  PubMed  Google Scholar 

  58. Wallach, A. & Sawtell, N. B. An internal model for canceling self-generated sensory input in freely behaving electric fish. Neuron 111, 2570–2582 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Poulet, J. F. A. & Hedwig, B. The cellular basis of a corollary discharge. Science 311, 518–522 (2006).

    Article  ADS  CAS  PubMed  Google Scholar 

  60. Gisselmann, G., Plonka, J., Pusch, H. & Hatt, H. Drosophila melanogaster GRD and LCCH3 subunits form heteromultimeric GABA‐gated cation channels. Br. J. Pharmacol. 142, 409–413 (2004).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Bogovic, J. A. et al. An unbiased template of the Drosophila brain and ventral nerve cord. PLoS ONE 15, e0236495 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Schindelin, J. et al. Fiji: an open-source platform for biological-image analysis. Nat. Methods 9, 676–682 (2012).

    Article  CAS  PubMed  Google Scholar 

  63. Jenett, A. et al. A GAL4-driver line resource for Drosophila neurobiology. Cell Rep. 2, 991–1001 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Meissner, G. W. et al. A searchable image resource of Drosophila GAL4 driver expression patterns with single neuron resolution. eLife 12, e80660 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Davie, K. et al. A single-cell transcriptome atlas of the aging Drosophila brain. Cell 174, 982–998 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. Chen, C.-L. et al. Imaging neural activity in the ventral nerve cord of behaving adult Drosophila. Nat. Commun. 9, 4390 (2018).

    Article  ADS  PubMed  PubMed Central  Google Scholar 

  67. Hermans, L. et al. Microengineered devices enable long-term imaging of the ventral nerve cord in behaving adult Drosophila. Nat. Commun. 13, 5006 (2022).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  68. Moore, R. J. D. et al. FicTrac: a visual method for tracking spherical motion and generating fictive animal paths. J. Neurosci. Methods 225, 106–119 (2014).

    Article  PubMed  Google Scholar 

  69. Mathis, A. et al. DeepLabCut: markerless pose estimation of user-defined body parts with deep learning. Nat. Neurosci. 21, 1281–1289 (2018).

    Article  CAS  PubMed  Google Scholar 

  70. Karashchuk, P. et al. Anipose: a toolkit for robust markerless 3D pose estimation. Cell Rep. 36, 109730 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  71. Guizar-Sicairos, M., Thurman, S. T. & Fienup, J. R. Efficient subpixel image registration algorithms. Opt. Lett. 33, 156–158 (2008).

    Article  ADS  PubMed  Google Scholar 

  72. Weir, P. T. & Dickinson, M. H. Functional divisions for visual processing in the central brain of flying Drosophila. Proc. Natl Acad. Sci. USA 112, E5523–E5532 (2015).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  73. Azevedo, A. W. et al. A size principle for recruitment of Drosophila leg motor neurons. eLife 9, e56754 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  74. Klapoetke, N. C. et al. Independent optical excitation of distinct neural populations. Nat. Methods 11, 338–346 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  75. Mohammad, F. et al. Optogenetic inhibition of behavior with anion channelrhodopsins. Nat. Methods 14, 271–274 (2017).

    Article  CAS  PubMed  Google Scholar 

  76. Govorunova, E. G., Sineshchekov, O. A., Janz, R., Liu, X. & Spudich, J. L. Natural light-gated anion channels: a family of microbial rhodopsins for advanced optogenetics. Science 349, 647–650 (2015).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  77. Pratt, B. G., Lee, S.-Y. J., Chou, G. M. & Tuthill, J. C. Miniature linear and split-belt treadmills reveal mechanisms of adaptive motor control in walking Drosophila. Curr. Biol. 34, 4368–4381 (2024).

    Article  CAS  PubMed  Google Scholar 

  78. Phelps, J. S. et al. Reconstruction of motor control circuits in adult Drosophila using automated transmission electron microscopy. Cell 184, 759–774 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  79. Dorkenwald, S. et al. CAVE: Connectome Annotation Versioning Engine. Nat. Methods 22, 1112–1120 (2025).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  80. Eckstein, N. et al. Neurotransmitter classification from electron microscopy images at synaptic sites in Drosophila melanogaster. Cell 187, 2574–2594 (2024).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  81. Stürner, T. et al. Comparative connectomics of Drosophila descending and ascending neurons. Nature 643, 158–172 (2025).

    Article  PubMed  PubMed Central  Google Scholar 

  82. Lesser, E. et al. Synaptic architecture of leg and wing premotor control networks in Drosophila. Nature 631, 369–377 (2024).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  83. Plaza, S. M. et al. neuPrint: an open access tool for EM connectomics. Front. Neuroinform. 16, 896292 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  84. Dallmann, C. J. et al. Data from: Selective presynaptic inhibition of leg proprioception in behaving Drosophila. Dryad https://doi.org/10.5061/dryad.gqnk98t16 (2025).

  85. Buhmann, J. et al. Automatic detection of synaptic partners in a whole-brain Drosophila electron microscopy data set. Nat. Methods 18, 771–774 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

We thank members of the Tuthill laboratory for technical assistance and feedback on the manuscript, in particular S.-Y. Lee for help with pilot experiments; K. Eichler and G. Jefferis for help identifying descending neurons; J. M. Ache, O. M. Ahmed, E. Azim, E. M. Chiappe and L. Zhang for feedback on the manuscript; J. S. Phelps, W.-C. A. Lee and the FANC community for contributions to proofreading the FANC connectome; and J. W. Truman and S. S. Bidaye for sharing Drosophila stocks. We used stocks obtained from the Bloomington Drosophila Stock Center (NIH P40OD018537). This work was supported by a postdoctoral research fellowship from the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) project 432196121 to C.J.D; NIH grant K99NS117657 to S.A.; and a Searle Scholar Award, a Klingenstein-Simons Fellowship, a Pew Biomedical Scholar Award, a McKnight Scholar Award, a Sloan Research Fellowship, the New York Stem Cell Foundation and NIH grants R01NS102333 and U19NS104655 to J.C.T. J.C.T. is a New York Stem Cell Foundation Robertson Investigator.

Author information

Author notes

  1. Chris J. Dallmann

    Present address: Neurobiology and Genetics, Julius-Maximilians-University of Würzburg, Würzburg, Germany

  2. Sweta Agrawal

    Present address: School of Neuroscience, Virginia Tech, Blacksburg, VA, USA

Authors and Affiliations

  1. Department of Neurobiology and Biophysics, University of Washington, Seattle, WA, USA

    Chris J. Dallmann, Yichen Luo, Sweta Agrawal, Akira Mamiya, Grant M. Chou, Andrew Cook, Anne Sustar & John C. Tuthill

  2. Department of Biology, University of Washington, Seattle, WA, USA

    Bingni W. Brunton

Authors
  1. Chris J. Dallmann
    View author publications

    Search author on:PubMed Google Scholar

  2. Yichen Luo
    View author publications

    Search author on:PubMed Google Scholar

  3. Sweta Agrawal
    View author publications

    Search author on:PubMed Google Scholar

  4. Akira Mamiya
    View author publications

    Search author on:PubMed Google Scholar

  5. Grant M. Chou
    View author publications

    Search author on:PubMed Google Scholar

  6. Andrew Cook
    View author publications

    Search author on:PubMed Google Scholar

  7. Anne Sustar
    View author publications

    Search author on:PubMed Google Scholar

  8. Bingni W. Brunton
    View author publications

    Search author on:PubMed Google Scholar

  9. John C. Tuthill
    View author publications

    Search author on:PubMed Google Scholar

Contributions

C.J.D. and J.C.T. conceived the study. C.J.D. developed the set-up and analysis tools for calcium imaging in behaving animals. C.J.D. collected and analysed calcium imaging data for sensory and 9A neurons in behaving animals. Y.L. collected and analysed calcium imaging data for DNg74 neurons. S.A. collected and analysed calcium imaging data for hook neurons during passive leg movements. G.M.C. collected and analysed optogenetic data from flies on the treadmill. A.M. collected and analysed calcium imaging data for hook neurons during optogenetic activation of descending neurons. C.J.D., A.M. and A.S. made genetic reagents. A.S. acquired confocal images. C.J.D., S.A. and A.C. reconstructed neurons in FANC. C.J.D. and B.W.B. developed computational models. C.J.D. analysed connectome and transcriptome data. C.J.D. visualized the results. C.J.D., S.A. and J.C.T. acquired funding. J.C.T. supervised the project. C.J.D. and J.C.T. wrote the manuscript with input from other authors.

Corresponding author

Correspondence to John C. Tuthill.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature thanks the anonymous reviewers for their contribution to the peer review of this work.

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 Cell-type specific genetic driver lines.

Confocal images showing the expression patterns of the GAL4 and split-GAL4 driver lines used to target FeCO neurons, 9A neurons, and DNg74 neurons. Green, GFP or mVenus; magenta, neuropil stain (nc82). Images of the claw line, the hook flexion line 1, and the DNg74 line are from FlyLight. In the neuromere of each front leg, the claw line targets about 25 axons, each hook line targets about 5 axons, the club line targets about 40 axons, and the 9A line targets about 10 neurons. The DNg74 line targets a single pair of descending neurons. Claw, hook, and club neurons are cholinergic21; 9A neurons30 and DNg74 neurons80 are GABAergic.

Extended Data Fig. 2 Computational models for predicting calcium signals in neurons.

a, Activation functions for claw, hook flexion, hook extension, and club and 9A neurons. b, Measured and fitted calcium signals of claw axons in response to applied ramp-and-hold movements of the femur-tibia joint (N = 10 flies per ramp-and-hold stimulus). Lines, mean of animal means; shadings, s.e.m. c, Cross-correlation between measured and fitted calcium signals at a time lag of zero (N = 10 flies, n = 20 trials in total). Black line, median. d, Same as b but for hook flexion axons (N = 14 flies). e, Same as c but for hook flexion axons (N = 14 flies, n = 28 trials in total). f, Same as b but for club axons (N = 14 flies). g, Same as c but for club axons (N = 14 flies, n = 28 trials in total). h, Response of the hook flexion model to an artificial femur-tibia joint angle without tracking noise. When the velocity threshold is larger than −50 deg/s, the model starts to predict strong calcium signals in response to slow flexions, as seen in the measured calcium signals. In b–g, experimental data are from ref. 21.

Extended Data Fig. 3 Calcium signals in claw axons.

a, Median predicted and measured calcium signals in claw axons during resting, walking, and grooming on the treadmill (N = 8–11 flies, n = 133–480 bouts in total); bouts are at least 1 s in duration. Note that the calcium signals are smaller than 1 because they are normalized to the largest signal in the dataset, which occurred when the treadmill was removed (data not shown here). b, Difference between the absolute mean measured calcium signals and the absolute mean predicted calcium signal in the 0.5 s following transitions into and out of walking or grooming. c, Example of calcium imaging of claw axons in the neuromere of the left front leg without the treadmill. d, Cross-correlation between predicted and measured calcium signals per trial at a time lag of zero (N = 7 flies, n = 42 trials in total). Black line, median; black dot, trial shown in c. In a and b, distributions show kernel density estimations; boxes show IQR and median, whiskers extend up to 1.5 × IQR.

Extended Data Fig. 4 Calcium signals in hook flexion axons.

a, Example trial of calcium imaging of hook flexion axons on the treadmill in which the animal did not transition often between moving and not moving, resulting in a high cross-correlation between predicted and measured calcium signals. b, Median predicted and measured calcium signals during active and passive movement bouts on the platform (N = 5 flies, n = 264–536 bouts in total); bouts are at least 0.5 s in duration. Distributions, kernel density estimations; boxes, IQR and median, whiskers extend up to 1.5 × IQR. c, Example of calcium imaging of hook flexion axons without the treadmill. d, Cross-correlation between predicted and measured calcium signals per trial at a time lag of zero (N = 8 flies, n = 58 trials in total). Black line, median; black dot, trial shown in c. e, Predicted and measured calcium signals aligned to the transitions into and out of movement (N = 8 flies). Signals are baseline subtracted (mean from −0.5 to 0 s). Thin lines, animal means; thick lines, mean of means; shadings, s.e.m. f, Example calcium imaging of hook flexion axons (second driver line) during behavior on the treadmill. g, Same as d but for the second hook flexion driver line imaged on the treadmill (N = 6 flies, n = 64 trials in total). h, Same as e but for the second hook flexion driver line imaged on the treadmill (N = 6 flies). Movement includes walking and grooming. i, Example of calcium imaging of hook flexion axons (second driver line) without the treadmill. j, Same as d but for the second hook flexion driver line imaged without the treadmill (N = 7 flies, n = 40 trials in total). k, Same as e but for the second hook flexion driver line imaged without the treadmill (N = 7 flies).

Extended Data Fig. 5 Calcium signals in hook extension axons.

a, Left, confocal image of hook extension axons in the neuromere of the left front leg. Black box, imaging region; green, GFP; gray, neuropil stain (nc82); A, anterior; L, left. Right, mean tdTomato signal of a representative trial. Expression was consistent across animals (N = 15). b, Example of calcium imaging of hook extension axons during behavior on the treadmill. c, Cross-correlation between predicted and measured calcium signals per trial at a time lag of zero (N = 10 flies, n = 95 trials in total). Black line, median; black dot, trial shown in b. d, Predicted and measured calcium signals aligned to the transitions into and out of movement (N = 10 flies). Movement includes walking and grooming. Signals are baseline subtracted (mean from −0.5 to 0 s). Thin lines, animal means; thick lines, mean of means; shadings, s.e.m. e, Example of calcium imaging of hook extension axons without the treadmill. f, Same as c but for hook extension axons imaged without the treadmill (N = 9 flies, n = 77 trials in total). g, Same as d but for hook extension axons imaged without the treadmill (N = 9 flies). h, Example of calcium imaging of hook extension axons during behavior on the platform. i, Same as c but for hook extension axons imaged during platform trials (N = 5 flies, n = 51 trials in total). Active movements were excluded for the cross-correlation. j, Same as d but for hook extension axons imaged during platform trials (N = 5 flies). k, Median predicted and measured calcium signals during active and passive movement bouts on the platform (N = 5 flies, n = 246–556 bouts in total); bouts are at least 0.5 s in duration. Distributions, kernel density estimations; boxes, IQR and median, whiskers extend up to 1.5 × IQR.

Extended Data Fig. 6 Calcium signals in hook axons in response to passive leg movements resembling walking and grooming.

a, Experimental setup for two-photon calcium imaging from neurons in the neuromere of the left front leg while a front leg tibia is passively moved with a magnet-motor system. b, Probability distributions of walking and grooming kinematics recorded in the hook flexion neuron dataset and the walking and grooming kinematics used for passive replay with the setup shown in a. c, Example of calcium imaging of hook flexion axons during passive tibia movements. d, Predicted and measured calcium signals aligned to the transition into passive movement (N = 7 flies). Movement includes passive walking and grooming. Signals are baseline subtracted (mean from −0.5 to 0 s). Thin lines, animal means; thick lines, mean of means; shadings, s.e.m. e, Cross-correlation between predicted and measured calcium signals per trial at a time lag of zero (N = 7 flies, n = 12 trials in total). Trials are either walking or grooming replay. Black line, median. f, Same as c but for hook extension axons. g, Same as d but for hook extension axons (N = 4 flies). h, Same as e but for hook extension axons (N = 4 flies, n = 7 trials in total).

Extended Data Fig. 7 RNA-seq data for club neurons and calcium signals in club axons.

a, Expression levels of receptor genes in club neurons. b, Left, confocal image of club axons in the neuromere of the left front leg. Black box, imaging region; green, GFP; gray, neuropil stain (nc82); A, anterior; L, left. Right, mean tdTomato signal of a representative trial. Expression was consistent across animals (N = 6). c, Example of calcium imaging of club axons during behavior on the treadmill. d, Cross-correlation between predicted and measured calcium signals per trial at a time lag of zero (N = 6 flies, n = 48 trials in total). Black line, median; black dot, trial shown in c. e, Predicted and measured calcium signals aligned to the transitions into and out of movement (N = 6 flies). Movement includes walking and grooming. Signals are baseline subtracted (mean from −0.5 to 0 s). Thin lines, animal means; thick lines, mean of means; shadings, s.e.m. f, Example of calcium imaging of club axons without the treadmill. g, Same as d but for club axons imaged without the treadmill (N = 5 flies, n = 41 trials in total). h, Same as e but for club axons imaged without the treadmill (N = 5 flies). i, Example of calcium imaging of club axons in which the treadmill was lowered out of reach for the fly after 5 s. j, Median calcium signals during resting bouts on and off the treadmill (N = 6 flies, n = 68–111 bouts in total); bouts are at least 1 s in duration. Distributions, kernel density estimations; boxes, IQR and median, whiskers extend up to 1.5 × IQR.

Extended Data Fig. 8 Calcium imaging and optogenetic manipulation of 9A neurons.

a, Example of calcium imaging of 9A neurons in the neuromere of the left front leg without the treadmill. b, Cross-correlation between predicted and measured calcium signals per trial at a time lag of zero (N = 9 flies, n = 56 trials in total). Black line, median; black dot, trial shown in a. c, Predicted and measured calcium signals aligned to the transitions into and out of movement (N = 9 flies). Signals are baseline subtracted (mean from −0.5 to 0 s). Thin lines, animal means; thick lines, mean of means; shadings, s.e.m. d, Examples of calcium imaging of 9A neurons during hind leg grooming. e, Experimental setup for optogenetic activation or silencing of neurons in the neuromere of the left front leg. f, Examples of 9A silencing (top) and activation (bottom) in experimental flies walking on the treadmill. g, Mean forward velocity of experimental and control flies during the 2 s trials (66.6 ms bins; N = 10–12 flies). Thick lines, means of animal means; shadings, s.e.m. h, Femur-tibia angle of the left front leg of experimental and control flies normalized in time to the step cycle (0.04 bins; N = 8–12 flies). Thick lines, mean of animal means; shadings, s.e.m. i, Mean differences in joint angle amplitudes between steps during (laser on) and before (pre) the optogenetic manipulation.

Extended Data Fig. 9 Connectivity of neurons of interest in the VNC and the brain.

a, Hook axons, chief 9A neurons presynaptic to hook axons, and the top two descending neurons presynaptic to the chief 9A neurons in the male VNC connectome (MANC). A, anterior; L, left. b, Top two descending neurons presynaptic to the chief 9A neuron in the female VNC connectome (FANC). c, Outputs of chief 9A neurons onto different neuron types (MANC connectome). Black bars, output onto hook axons; L, left side of VNC; R, right side of VNC; 1, front leg neuromere; 2, middle leg neuromere; 3, hind leg neuromere. d, Connectivity of descending neurons with GABAergic neurons presynaptic to claw and hook axons. Descending neurons known to drive walking and grooming are indicated. Neurotransmitter predictions are from matched descending neurons in MANC81. e, Connectivity between descending neurons and 9A neurons. Numbers indicate connection strength to 9A as percent input synapses (top two rows) or absolute synapses (bottom two rows). Synapses from all DNg12 were summed. For “Other 9A” neurons, percent input synapses were averaged and absolute synapses were summed. f, Outputs of DNg74 in the VNC (MANC connectome). g, Inputs of DNg74 in the brain (FlyWire connectome). GNG, gnathal ganglia; AVLP, anterior ventrolateral protocerebrum; SAD, saddle; ICL, inferior clamp.

Extended Data Fig. 10 Calcium signals in hook axons in response to DNg100 activation and calcium signals in DNg74.

a, Expected indirect inhibitory effect of DNg100 activation onto hook axons. b, Examples showing hook flexion responses with and without optogenetic activation of DNg100. During the activation (gray, left), the PMT of the microscope was switched off, as indicated by a sudden drop in fluorescence. c, Calcium signals from hook flexion axons aligned to the offset of the LED and the equivalent time point in control trials (N = 5 flies, n = 5 transitions per fly). Thin lines, animal means; thick lines, mean of means; shadings, s.e.m. Note that the “spike” seen in some calcium signals directly after the offset is an artifact. d, Mean change in calcium signals per animal within 2 s after the offset of the LED or the equivalent time point in control trials (two-sided paired t-test, t(4) = 4.54, p = 0.01). Positive values indicate that calcium signals increased after the offset of the LED. e, GFP signals from FeCO axons aligned to switching on the PMT (N = 3 flies, n = 12 transitions per fly). f, Example of calcium imaging of DNg74 in the neuromere of the left front leg without the treadmill.

Supplementary information

Supplementary Discussion

Discussion of the behavioural function of 9A-mediated presynaptic inhibition and the DNg100 (BDN2) activation experiments.

Reporting Summary

Supplementary Table 1

Genotypes of flies used in this study with source and identifier, grouped by figure.

Supplementary Table 2

Connectome identifiers, names and predicted neurotransmitters of neurons of interest in the FANC, MANC and FAFB/FlyWire connectomes.

Supplementary Video 1

Neurons of interest in the FANC connectome.

Supplementary Video 2

Imaging of claw axons. Example trials of two-photon calcium imaging of claw axons and behaviour tracking with and without the treadmill (2× speed).

Supplementary Video 3

Imaging of hook axons. Example trials of two-photon calcium imaging of hook flexion axons and behaviour tracking with and without the treadmill and the platform (2× speed).

Supplementary Video 4

Imaging of club axons. Example trials of two-photon calcium imaging of club axons and behaviour tracking with and without the treadmill (2× speed).

Supplementary Video 5

Imaging of 9A axons. Example trials of two-photon calcium imaging of 9A axons and behaviour tracking with and without the treadmill and the platform (2× speed).

Supplementary Video 6

Imaging of DNg74 axons. Example trials of two-photon calcium imaging of DNg74 (web) axons and behaviour tracking with and without the treadmill (2× speed).

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Dallmann, C.J., Luo, Y., Agrawal, S. et al. Selective presynaptic inhibition of leg proprioception in behaving Drosophila. Nature (2025). https://doi.org/10.1038/s41586-025-09554-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1038/s41586-025-09554-2

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing