Locomotor deficits in a mouse model of ALS are paralleled by loss of V1-interneuron connections onto fast motor neurons

ALS is characterized by progressive inability to execute movements. Motor neurons innervating fast-twitch muscle-fibers preferentially degenerate. The reason for this differential vulnerability and its consequences on motor output is not known. Here, we uncover that fast motor neurons receive stronger inhibitory synaptic inputs than slow motor neurons, and disease progression in the SOD1G93A mouse model leads to specific loss of inhibitory synapses onto fast motor neurons. Inhibitory V1 interneurons show similar innervation pattern and loss of synapses. Moreover, from postnatal day 63, there is a loss of V1 interneurons in the SOD1G93A mouse. The V1 interneuron degeneration appears before motor neuron death and is paralleled by the development of a specific locomotor deficit affecting speed and limb coordination. This distinct ALS-induced locomotor deficit is phenocopied in wild-type mice but not in SOD1G93A mice after appearing of the locomotor phenotype when V1 spinal interneurons are silenced. Our study identifies a potential source of non-autonomous motor neuronal vulnerability in ALS and links ALS-induced changes in locomotor phenotype to inhibitory V1-interneurons.


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Policy information about availability of computer code Data collection DigiGait Imager software version 16-A6 from Mouse Specifics was used to capture videos. Tracking analysis of foot steps and limb joints was performed with DeepLabCut version 2.1.8.2. Anatomical pictures were acquired on Zeiss LSM 700 or 900 confocal microscopes using Zen Black version 14.0 and Zen Blue version 3.1 softwares, respectively.

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Fiji software version 2.1.0/1.53c was used to quantify synaptic densities, while Zen Blue software version 3.2 was used for neuron and transcript quantifications. GraphPad Prism version 8.2.0 was used for regular statistical analysis. Python version 3.7 script was used to calculate locomotor parameters and circular statistics. Code for locomotor analysis and figure generation is available on Github at https://github.com/kiehnlab/Locomotor-Allodi2021 or under DOI https://doi.org/10.5281/zenodo.4632328 For manuscripts utilizing custom algorithms or software that are central to the research but not yet described in published literature, software must be made available to editors and reviewers. We strongly encourage code deposition in a community repository (e.g. GitHub). See the Nature Research guidelines for submitting code & software for further information.

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