Synaptic architecture of leg and wing premotor control networks in Drosophila

Creators: Lesser, Ellen; Azevedo, Anthony W.; Phelps, Jasper S.; Elabbady, Leila; Cook, Andrew; Syed, Durafshan Sakeena; Mark, Brandon; Kuroda, Sumiya; Sustar, Anne; Moussa, Anthony; Dallmann, Chris J.; Agrawal, Sweta; Lee, Su-Yee J.; Pratt, Brandon; Skutt-Kakaria, Kyobi; Gerhard, Stephan; Lu, Ran; Kemnitz, Nico; Lee, Kisuk; Halageri, Akhilesh; Castro, Manuel; Ih, Dodam; Gager, Jay; Tammam, Marwan; Dorkenwald, Sven; Collman, Forrest; Schneider-Mizell, Casey; Brittain, Derrick; Jordan, Chris S.; Macrina, Thomas; Dickinson, Michael¹; Lee, Wei-Chung Allen; Tuthill, John C.

1. California Institute of Technology

Abstract

Animal movement is controlled by motor neurons (MNs), which project out of the central nervous system to activate muscles¹. MN activity is coordinated by complex premotor networks that facilitate the contribution of individual muscles to many different behaviours^2,3,4,5,6. Here we use connectomics⁷ to analyse the wiring logic of premotor circuits controlling the Drosophila leg and wing. We find that both premotor networks cluster into modules that link MNs innervating muscles with related functions. Within most leg motor modules, the synaptic weights of each premotor neuron are proportional to the size of their target MNs, establishing a circuit basis for hierarchical MN recruitment. By contrast, wing premotor networks lack proportional synaptic connectivity, which may enable more flexible recruitment of wing steering muscles. Through comparison of the architecture of distinct motor control systems within the same animal, we identify common principles of premotor network organization and specializations that reflect the unique biomechanical constraints and evolutionary origins of leg and wing motor control.

Copyright and License

Acknowledgement

This work was supported by 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 a UW Innovation Award to J.C.T.; a Genise Goldenson Award to W.-C.A.L.; NIH no. U19NS104655 to J.C.T. and M.D.; 1RF1NS128785-01 to J.C.T.; and NIH no. R01MH117808 to J.C.T. and W.-C.A.L. J.C.T. is a New York Stem Cell Foundation – Robertson Investigator. We thank J. Truman, D. Shepherd and E. Marin for assistance with hemilineage identification. We thank H. Lacin, L. Marin, G. Jefferis amd G. Card for helpful discussions, and for their laboratory’s contributions to proofreading in the FANC dataset, in particular K. Eichler, P. Brooks, T. Stürner, M. Costa and G. Jefferis for sharing comprehensive proofreading and annotation of neck connective neurons including descending and ascending neurons in the FANC dataset (supported by Wellcome award 221300/Z/20/Z). We thank members of the Tuthill and Dickinson Laboratories, S. Ahmed, B. Brunton and J. Truman for comments on the manuscript.

Contributions

These authors contributed equally: Ellen Lesser, Anthony W. Azevedo

E.L., A.W.A., W.-C.A.L. and J.C.T. conceived the project. W.-C.A.L. and J.C.T. acquired funding. R.L., N.K., K.L., A.H., M.C., D.I., J.G., M.T., C.S.J., S.G., S.K. and T.M. developed and deployed the software to support proofreading of segmented electron microscopy data. S.D., F.C., C.S.-M. and D.B. deployed and supported the annotation software CAVE. A.W.A., E.L., J.S.P., B.M., L.E., A.M., D.S.S., C.J.D., S.A., S.-Y.J.L., B.P., A.C. and K.S.-K. proofread neurons in FANC and edited the paper. A.S. designed and performed light-microscopy imaging. J.S.P. organized the community guidelines and efforts to proofread and annotate FANC. M.D. and W.-C.A.L. advised the project and edited the paper. A.W.A., E.L. and L.E. analysed data. A.W.A., E.L. and J.C.T. wrote the paper, with input from all other co-authors.

Data Availability

The data presented in the paper were analysed from CAVE materialization version 840 (v.840). Annotated connectivity matrices (Fig. 2) are available as Python Pandas data frames (https://pandas.pydata.org/) at GitHub (https://github.com/tuthill-lab/Lesser_Azevedo_2023). Links to public preMN and MN segmentations are available throughout the text, as well as in Supplementary Tables 1–3.

Extended Data Fig. 1 Detailed properties of individual leg and wing MNs.

Extended Data Fig. 2 Agglomerative hierarchical clustering of leg MNs according to premotor connectivity.

Extended Data Fig. 3 Similarity of wing MNs creates separable modules through agglomerative clustering.

Extended Data Fig. 4 Local PreMNs drive MN cosine similarity and modularity, for wing and leg systems.

Extended Data Fig. 5 Example leg preMNs, their synapses onto motor modules, and impact of proportional connectivity on MN similarity.

Extended Data Fig. 6 Leg modules that include biarticular muscles have more variable module connectivity.

Extended Data Fig. 7 Example neurons from each premotor hemilineage.

Supplementary Tables. Supplementary Table 1 contains links for viewing motor neurons organized by motor module in Neuroglancer. Supplementary Table 2 contains links for viewing premotor neurons organized by motor module in Neuroglancer. Supplementary Table 3 contains Neuroglancer links for viewing premotor neurons organized by hemilineage and inferred neurotransmitter

Code Availability

Scripts to recreate the analyses and figures in the paper, as well as scripts to recreate the connectivity matrices, are available at GitHub (https://github.com/tuthill-lab/Lesser_Azevedo_2023) for users authorized to interact with the CAVEclient. All analysis was performed in Python 3.9 using custom code, making extensive use of CAVEclient (https://github.com/seung-lab/CAVEclient)⁶⁵ and CloudVolume to interact with data infrastructure, and the libraries Matplotlib, Numpy, Pandas, Scikit-learn, Scipy, stats-models and VTK for general computation, machine learning and data visualization. Additional code is available at https://github.com/htem/FANC_auto_recon, providing additional tutorials, documentation for interaction with FANC and instructions for joining the FANC community.

Conflict of Interest

The authors declare no competing interests.

Files

41586_2024_7600_Fig12_ESM.jpg

Files (3.4 MB)

Name	Size	Download all
41586_2024_7600_Fig12_ESM.jpg md5:c56c5de55f6b027e49f6783071024d46	525.9 kB	Preview Download
41586_2024_7600_Fig10_ESM.jpg md5:f37ccc524cf8398bf8712545f6638a91	532.0 kB	Preview Download
41586_2024_7600_Fig9_ESM.jpg md5:c990c3d7bb63e69ec591bd316a3c7d1a	441.7 kB	Preview Download
41586_2024_7600_Fig11_ESM.jpg md5:693788e52accf6eff8af27b81a0ecafe	546.9 kB	Preview Download
41586_2024_7600_Fig8_ESM.jpg md5:d1fa46fc91962d1ce2329e22e4bd25f8	163.2 kB	Preview Download
41586_2024_7600_Fig7_ESM.jpg md5:0ca55d7c0e6d6725f53009eea56f84af	338.5 kB	Preview Download
41586_2024_7600_Fig6_ESM.jpg md5:e1f70489c024524780b2ffedfd282ca7	246.6 kB	Preview Download
41586_2024_7600_MOESM1_ESM.pdf md5:3634264e809bbe759606ac29e4f44573	597.9 kB	Preview Download

	All versions	This version
Views	0	0
Downloads	0	0
Data volume	0 Bytes	0 Bytes