Ionotropic Receptor-dependent moist and dry cells control hygrosensation in Drosophila

  1. Zachary A Knecht
  2. Ana F Silbering
  3. Joyner Cruz
  4. Ludi Yang
  5. Vincent Croset
  6. Richard Benton  Is a corresponding author
  7. Paul A Garrity  Is a corresponding author
  1. Brandeis University, United States
  2. University of Lausanne, Switzerland
  3. University of Oxford, United Kingdom

Abstract

Insects use hygrosensation (humidity sensing) to avoid desiccation and, in vectors such as mosquitoes, to locate vertebrate hosts. Sensory neurons activated by either dry or moist air ('dry cells' and 'moist cells') have been described in many insects, but their behavioral roles and the molecular basis of their hygrosensitivity remain unclear. We recently reported that Drosophila hygrosensation relies on three Ionotropic Receptors (IRs) required for dry cell function: IR25a, IR93a and IR40a (Knecht et al., 2016). Here we discover Drosophila moist cells, and show they require IR25a and IR93a together with IR68a, a conserved, but orphan IR. Both IR68a- and IR40a-dependent pathways drive hygrosensory behavior: each is important for dry-seeking by hydrated flies and together they underlie moist-seeking by dehydrated flies. These studies reveal that humidity sensing in Drosophila, and likely other insects, involves the combined activity of two molecularly related but neuronally distinct hygrosensing systems.

Article and author information

Author details

  1. Zachary A Knecht

    National Center for Behavioral Genomics, Brandeis University, Waltham, United States
    Competing interests
    The authors declare that no competing interests exist.
  2. Ana F Silbering

    Center for Integrative Genomics, Faculty of Biology and Medicine, University of Lausanne, Lausanne, Switzerland
    Competing interests
    The authors declare that no competing interests exist.
  3. Joyner Cruz

    National Center for Behavioral Genomics, Brandeis University, Waltham, United States
    Competing interests
    The authors declare that no competing interests exist.
  4. Ludi Yang

    National Center for Behavioral Genomics, Brandeis University, Waltham, United States
    Competing interests
    The authors declare that no competing interests exist.
  5. Vincent Croset

    Centre for Neural Circuits and Behaviour, University of Oxford, Oxford, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
  6. Richard Benton

    Center for Integrative Genomics, Faculty of Biology and Medicine, University of Lausanne, Lausanne, Switzerland
    For correspondence
    Richard.Benton@unil.ch
    Competing interests
    The authors declare that no competing interests exist.
  7. Paul A Garrity

    National Center for Behavioral Genomics, Brandeis University, Waltham, United States
    For correspondence
    pgarrity@brandeis.edu
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-8274-6564

Funding

National Institute on Deafness and Other Communication Disorders (F31 DC015155)

  • Zachary A Knecht

Boehringer Ingelheim Stiftung

  • Vincent Croset

H2020 European Research Council (205202)

  • Richard Benton

H2020 European Research Council (615094)

  • Richard Benton

Swiss National Science Foundation (31003A_140869)

  • Richard Benton

National Institute of General Medical Sciences (P01 GM103770)

  • Paul A Garrity

National Institute of Allergy and Infectious Diseases (R01 AI22802)

  • Paul A Garrity

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Copyright

© 2017, Knecht et al.

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.

Metrics

  • 4,170
    views
  • 589
    downloads
  • 147
    citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.

Download links

A two-part list of links to download the article, or parts of the article, in various formats.

Downloads (link to download the article as PDF)

Open citations (links to open the citations from this article in various online reference manager services)

Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)

  1. Zachary A Knecht
  2. Ana F Silbering
  3. Joyner Cruz
  4. Ludi Yang
  5. Vincent Croset
  6. Richard Benton
  7. Paul A Garrity
(2017)
Ionotropic Receptor-dependent moist and dry cells control hygrosensation in Drosophila
eLife 6:e26654.
https://doi.org/10.7554/eLife.26654

Share this article

https://doi.org/10.7554/eLife.26654

Further reading

    1. Neuroscience
    Magdalena Solyga, Georg B Keller
    Research Article

    Our movements result in predictable sensory feedback that is often multimodal. Based on deviations between predictions and actual sensory input, primary sensory areas of cortex have been shown to compute sensorimotor prediction errors. How prediction errors in one sensory modality influence the computation of prediction errors in another modality is still unclear. To investigate multimodal prediction errors in mouse auditory cortex, we used a virtual environment to experimentally couple running to both self-generated auditory and visual feedback. Using two-photon microscopy, we first characterized responses of layer 2/3 (L2/3) neurons to sounds, visual stimuli, and running onsets and found responses to all three stimuli. Probing responses evoked by audiomotor (AM) mismatches, we found that they closely resemble visuomotor (VM) mismatch responses in visual cortex (V1). Finally, testing for cross modal influence on AM mismatch responses by coupling both sound amplitude and visual flow speed to the speed of running, we found that AM mismatch responses were amplified when paired with concurrent VM mismatches. Our results demonstrate that multimodal and non-hierarchical interactions shape prediction error responses in cortical L2/3.

    1. Neuroscience
    Moritz F Wurm, Doruk Yiğit Erigüç
    Research Article

    Recognizing goal-directed actions is a computationally challenging task, requiring not only the visual analysis of body movements, but also analysis of how these movements causally impact, and thereby induce a change in, those objects targeted by an action. We tested the hypothesis that the analysis of body movements and the effects they induce relies on distinct neural representations in superior and anterior inferior parietal lobe (SPL and aIPL). In four fMRI sessions, participants observed videos of actions (e.g. breaking stick, squashing plastic bottle) along with corresponding point-light-display (PLD) stick figures, pantomimes, and abstract animations of agent–object interactions (e.g. dividing or compressing a circle). Cross-decoding between actions and animations revealed that aIPL encodes abstract representations of action effect structures independent of motion and object identity. By contrast, cross-decoding between actions and PLDs revealed that SPL is disproportionally tuned to body movements independent of visible interactions with objects. Lateral occipitotemporal cortex (LOTC) was sensitive to both action effects and body movements. These results demonstrate that parietal cortex and LOTC are tuned to physical action features, such as how body parts move in space relative to each other and how body parts interact with objects to induce a change (e.g. in position or shape/configuration). The high level of abstraction revealed by cross-decoding suggests a general neural code supporting mechanical reasoning about how entities interact with, and have effects on, each other.