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Compressed sensing based approach identifies modular neural circuitry driving learned pathogen avoidance

View ORCID ProfileTimothy Hallacy, Abdullah Yonar, Niels Ringstad, Sharad Ramanathan
doi: https://doi.org/10.1101/2024.04.10.588911
Timothy Hallacy
1Biophysics Program, Harvard University, Cambridge, MA, USA
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  • For correspondence: [email protected] [email protected]
Abdullah Yonar
2Departments of Molecular and Cellular Biology, and of Stem Cell and Regenerative Biology, John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA
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Niels Ringstad
3Department of Cell Biology, Skirball Institute of Biomolecular Medicine, New York University Grossman School of Medicine, New York, NY 10016, USA
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Sharad Ramanathan
2Departments of Molecular and Cellular Biology, and of Stem Cell and Regenerative Biology, John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA
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  • For correspondence: [email protected] [email protected]
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Abstract

An animal’s survival hinges on its ability to integrate past information to modify future behavior. The nematode C. elegans adapts its behavior based on prior experiences with pathogen exposure, transitioning from attraction to avoidance of the pathogen. A systematic screen for the neural circuits that integrate the information of previous pathogen exposure to modify behavior has not been feasible because of the lack of tools for neuron type specific perturbations. We overcame this challenge using methods based on compressed sensing to efficiently determine the roles of individual neuron types in learned avoidance behavior. Our screen revealed that distinct sets of neurons drive exit from lawns of pathogenic bacteria and prevent lawn re-entry. Using calcium imaging of freely behaving animals and optogenetic perturbations, we determined the neural dynamics that regulate one key behavioral transition after infection: stalled re-entry into bacterial lawns. We find that key neuron types govern pathogen lawn specific stalling but allow the animal to enter nonpathogenic E. coli lawns. Our study shows that learned pathogen avoidance requires coordinated transitions in discrete neural circuits and reveals the modular structure of this complex adaptive behavioral response to infection.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • ↵4 Lead contact

  • - Added of OP50 control data to supplementary figure S1 - Fixed errors in Figure 2 captions and labels - Addition of supplementary movies 1-3 to illustrate worm evacuation behaviors - Addition of a new supplementary figure to illustrate AIY's influence on navigational dynamics - Rewording of several sections for improved readability and consistency - Addition of new details to the methods section

  • https://github.com/timhallcloud/LawnEvacuationPaper

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
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Posted October 17, 2024.
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Compressed sensing based approach identifies modular neural circuitry driving learned pathogen avoidance
Timothy Hallacy, Abdullah Yonar, Niels Ringstad, Sharad Ramanathan
bioRxiv 2024.04.10.588911; doi: https://doi.org/10.1101/2024.04.10.588911
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Compressed sensing based approach identifies modular neural circuitry driving learned pathogen avoidance
Timothy Hallacy, Abdullah Yonar, Niels Ringstad, Sharad Ramanathan
bioRxiv 2024.04.10.588911; doi: https://doi.org/10.1101/2024.04.10.588911

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