1. Neuroscience

Deconstruction of the Beaten Path-Sidestep interaction network provides insights into neuromuscular system development

  1. Hanqing Li
  2. Ash Watson
  3. Agnieszka Olechwier
  4. Michael Anaya
  5. Siamak K Sorooshyari
  6. Dermott P Harnett
  7. Hyung-Kook (Peter) Lee
  8. Jost Vielmetter
  9. Mario A Fares
  10. K Christopher Garcia
  11. Engin Özkan
  12. Juan-Pablo Labrador  Is a corresponding author
  13. Kai Zinn  Is a corresponding author
  1. California Institute of Technology, United States
  2. Trinity College Dublin, Ireland
  3. University of Chicago, United States
  4. Ellipsis Health, United States
  5. Howard Hughes Medical Institute, Stanford University School of Medicine, United States
https://doi.org/10.7554/eLife.28111
Share this article
Cite this article
  1. Hanqing Li
  2. Ash Watson
  3. Agnieszka Olechwier
  4. Michael Anaya
  5. Siamak K Sorooshyari
  6. Dermott P Harnett
  7. Hyung-Kook (Peter) Lee
  8. Jost Vielmetter
  9. Mario A Fares
  10. K Christopher Garcia
  11. Engin Özkan
  12. Juan-Pablo Labrador
  13. Kai Zinn
(2017)
Deconstruction of the Beaten Path-Sidestep interaction network provides insights into neuromuscular system development
eLife 6:e28111.
https://doi.org/10.7554/eLife.28111
  2. Download BibTeX
  3. Download .RIS

Abstract

An "interactome" screen of all Drosophila cell-surface and secreted proteins containing immunoglobulin superfamily (IgSF) domains discovered a network formed by paralogs of Beaten Path (Beat) and Sidestep (Side), a ligand-receptor pair that is central to motor axon guidance. Here we describe a new method for interactome screening, the Bio-Plex Interactome Assay (BPIA), which allows identification of many interactions in a single sample. Using the BPIA, we "deorphanized" four more members of the Beat-Side network. We confirmed interactions using surface plasmon resonance. The expression patterns of beat and side genes suggest that Beats are neuronal receptors for Sides expressed on peripheral tissues. side-VI is expressed in muscle fibers targeted by the ISNb nerve, as well as at growth cone choice points and synaptic targets for the ISN and TN nerves. beat-V genes, encoding Side-VI receptors, are expressed in ISNb and ISN motor neurons.

Article and author information

Author details

  1. Hanqing Li

    Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, United States
    Competing interests
    The authors declare that no competing interests exist.

  2. Ash Watson

    Smurfit Institute of Genetics, Trinity College Dublin, Dublin, Ireland
    Competing interests
    The authors declare that no competing interests exist.

  3. Agnieszka Olechwier

    Department of Biochemistry and Molecular Biology, University of Chicago, Chicago, United States
    Competing interests
    The authors declare that no competing interests exist.

  4. Michael Anaya

    Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, United States
    Competing interests
    The authors declare that no competing interests exist.

  5. Siamak K Sorooshyari

    Ellipsis Health, San Francisco, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-1172-6291

  6. Dermott P Harnett

    Smurfit Institute of Genetics, Trinity College Dublin, Dublin, Ireland
    Competing interests
    The authors declare that no competing interests exist.

  7. Hyung-Kook (Peter) Lee

    Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, United States
    Competing interests
    The authors declare that no competing interests exist.

  8. Jost Vielmetter

    Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, United States
    Competing interests
    The authors declare that no competing interests exist.

  9. Mario A Fares

    Smurfit Institute of Genetics, Trinity College Dublin, Dublin, Ireland
    Competing interests
    The authors declare that no competing interests exist.

  10. K Christopher Garcia

    Department of Molecular and Cellular Physiology, Howard Hughes Medical Institute, Stanford University School of Medicine, Stanford, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-9273-0278

  11. Engin Özkan

    Department of Biochemistry and Molecular Biology, University of Chicago, Chicago, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-0263-6729

  12. Juan-Pablo Labrador

    Smurfit Institute of Genetics, Trinity College Dublin, Dublin, Ireland
    For correspondence
    labradoj@tcd.ie
    Competing interests
    The authors declare that no competing interests exist.

  13. Kai Zinn

    Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, United States
    For correspondence
    zinnk@caltech.edu
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-6706-5605

Funding

NIH (R37)

  • Kai Zinn

SFI

  • Juan-Pablo Labrador

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

Copyright

© 2017, Li 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

  • 3,742
    views
  • 604
    downloads
  • 44
    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. Hanqing Li
  2. Ash Watson
  3. Agnieszka Olechwier
  4. Michael Anaya
  5. Siamak K Sorooshyari
  6. Dermott P Harnett
  7. Hyung-Kook (Peter) Lee
  8. Jost Vielmetter
  9. Mario A Fares
  10. K Christopher Garcia
  11. Engin Özkan
  12. Juan-Pablo Labrador
  13. Kai Zinn
(2017)
Deconstruction of the Beaten Path-Sidestep interaction network provides insights into neuromuscular system development
eLife 6:e28111.
https://doi.org/10.7554/eLife.28111

Share this article

https://doi.org/10.7554/eLife.28111

Categories and tags

Research organism

Further reading

    1. Neuroscience

    A deep learning approach for automated scoring of the Rey–Osterrieth complex figure

    Nicolas Langer, Maurice Weber ... Ce Zhang
    Tools and Resources

    Memory deficits are a hallmark of many different neurological and psychiatric conditions. The Rey–Osterrieth complex figure (ROCF) is the state-of-the-art assessment tool for neuropsychologists across the globe to assess the degree of non-verbal visual memory deterioration. To obtain a score, a trained clinician inspects a patient’s ROCF drawing and quantifies deviations from the original figure. This manual procedure is time-consuming, slow and scores vary depending on the clinician’s experience, motivation, and tiredness. Here, we leverage novel deep learning architectures to automatize the rating of memory deficits. For this, we collected more than 20k hand-drawn ROCF drawings from patients with various neurological and psychiatric disorders as well as healthy participants. Unbiased ground truth ROCF scores were obtained from crowdsourced human intelligence. This dataset was used to train and evaluate a multihead convolutional neural network. The model performs highly unbiased as it yielded predictions very close to the ground truth and the error was similarly distributed around zero. The neural network outperforms both online raters and clinicians. The scoring system can reliably identify and accurately score individual figure elements in previously unseen ROCF drawings, which facilitates explainability of the AI-scoring system. To ensure generalizability and clinical utility, the model performance was successfully replicated in a large independent prospective validation study that was pre-registered prior to data collection. Our AI-powered scoring system provides healthcare institutions worldwide with a digital tool to assess objectively, reliably, and time-efficiently the performance in the ROCF test from hand-drawn images.

    1. Neuroscience

    Evaluating hippocampal replay without a ground truth

    Masahiro Takigawa, Marta Huelin Gorriz ... Daniel Bendor
    Research Article

    During rest and sleep, memory traces replay in the brain. The dialogue between brain regions during replay is thought to stabilize labile memory traces for long-term storage. However, because replay is an internally-driven, spontaneous phenomenon, it does not have a ground truth - an external reference that can validate whether a memory has truly been replayed. Instead, replay detection is based on the similarity between the sequential neural activity comprising the replay event and the corresponding template of neural activity generated during active locomotion. If the statistical likelihood of observing such a match by chance is sufficiently low, the candidate replay event is inferred to be replaying that specific memory. However, without the ability to evaluate whether replay detection methods are successfully detecting true events and correctly rejecting non-events, the evaluation and comparison of different replay methods is challenging. To circumvent this problem, we present a new framework for evaluating replay, tested using hippocampal neural recordings from rats exploring two novel linear tracks. Using this two-track paradigm, our framework selects replay events based on their temporal fidelity (sequence-based detection), and evaluates the detection performance using each event's track discriminability, where sequenceless decoding across both tracks is used to quantify whether the track replaying is also the most likely track being reactivated.

    1. Neuroscience

    Brain Activity: Unifying networks of a rhythm

    Mohsen Alavash
    Insight

    Combining electrophysiological, anatomical and functional brain maps reveals networks of beta neural activity that align with dopamine uptake.