A stochastic neuronal model predicts random search behaviors at multiple spatial scales in C. elegans

  1. William M Roberts
  2. Steven B Augustine
  3. Kristy J Lawton
  4. Theodore H Lindsay
  5. Tod R Thiele
  6. Eduardo J Izquierdo
  7. Serge Faumont
  8. Rebecca A Lindsay
  9. Matthew Cale Britton
  10. Navin Pokala
  11. Cornelia I Bargmann
  12. Shawn R Lockery  Is a corresponding author
  1. University of Oregon, United States
  2. University of Pennsylvania, United States
  3. Reed College, United States
  4. California Institute of Technology, United States
  5. University of Toronto, Canada
  6. Indiana University, United States
  7. Children's Hospital Los Angeles, United States
  8. University of Minnesota, United States
  9. New York Institiute of Technology, United States
  10. Howard Hughes Medical Institute, Rockefeller University, United States

Abstract

Random search is a behavioral strategy used by organisms from bacteria to humans to locate food that is randomly distributed and undetectable at a distance. We investigated this behavior in the nematode Caenorhabditis elegans, an organism with a small, well-described nervous system. Here we formulate a mathematical model of random search abstracted from the C. elegans connectome and fit to a large-scale kinematic analysis of C. elegans behavior at submicron resolution. The model predicts behavioral effects of neuronal ablations and genetic perturbations, as well as unexpected aspects of wild type behavior. The predictive success of the model indicates that random search in C. elegans can be understood in terms of a neuronal flip-flop circuit involving reciprocal inhibition between two populations of stochastic neurons. Our findings establish a unified theoretical framework for understanding C. elegans locomotion and a testable neuronal model of random search that can be applied to other organisms.

Article and author information

Author details

  1. William M Roberts

    Institute of Neuroscience, University of Oregon, Eugene, United States
    Competing interests
    The authors declare that no competing interests exist.
  2. Steven B Augustine

    School of Nursing, University of Pennsylvania, Philadelphia, United States
    Competing interests
    The authors declare that no competing interests exist.
  3. Kristy J Lawton

    Biology Department, Reed College, Portland, United States
    Competing interests
    The authors declare that no competing interests exist.
  4. Theodore H Lindsay

    Division of biology and biological engineering, California Institute of Technology, Pasadena, United States
    Competing interests
    The authors declare that no competing interests exist.
  5. Tod R Thiele

    Department of Biological Sciences, University of Toronto, Toronto, Canada
    Competing interests
    The authors declare that no competing interests exist.
  6. Eduardo J Izquierdo

    Cognitive Science Program, Indiana University, Bloomington, United States
    Competing interests
    The authors declare that no competing interests exist.
  7. Serge Faumont

    Institute of Neuroscience, University of Oregon, Eugene, United States
    Competing interests
    The authors declare that no competing interests exist.
  8. Rebecca A Lindsay

    Department of Ophthalmology, The Vision Center, Children's Hospital Los Angeles, Los Angeles, United States
    Competing interests
    The authors declare that no competing interests exist.
  9. Matthew Cale Britton

    Department of Neurology, University of Minnesota, Minneapolis, United States
    Competing interests
    The authors declare that no competing interests exist.
  10. Navin Pokala

    Department of Life Sciences, New York Institiute of Technology, Old Westbury, United States
    Competing interests
    The authors declare that no competing interests exist.
  11. Cornelia I Bargmann

    Howard Hughes Medical Institute, Rockefeller University, New York, United States
    Competing interests
    The authors declare that no competing interests exist.
  12. Shawn R Lockery

    Institute of Neuroscience, University of Oregon, Eugene, United States
    For correspondence
    shawn@uoregon.edu
    Competing interests
    The authors declare that no competing interests exist.

Copyright

© 2016, Roberts 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

  • 6,312
    views
  • 1,331
    downloads
  • 85
    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. William M Roberts
  2. Steven B Augustine
  3. Kristy J Lawton
  4. Theodore H Lindsay
  5. Tod R Thiele
  6. Eduardo J Izquierdo
  7. Serge Faumont
  8. Rebecca A Lindsay
  9. Matthew Cale Britton
  10. Navin Pokala
  11. Cornelia I Bargmann
  12. Shawn R Lockery
(2016)
A stochastic neuronal model predicts random search behaviors at multiple spatial scales in C. elegans
eLife 5:e12572.
https://doi.org/10.7554/eLife.12572

Share this article

https://doi.org/10.7554/eLife.12572

Further reading

    1. Computational and Systems Biology
    2. Genetics and Genomics
    Jia-Ying Su, Yun-Lin Wang ... Chien-Ling Lin
    Research Article

    Untranslated regions (UTRs) contain crucial regulatory elements for RNA stability, translation and localization, so their integrity is indispensable for gene expression. Approximately 3.7% of genetic variants associated with diseases occur in UTRs, yet a comprehensive understanding of UTR variant functions remains limited due to inefficient experimental and computational assessment methods. To systematically evaluate the effects of UTR variants on RNA stability, we established a massively parallel reporter assay on 6555 UTR variants reported in human disease databases. We examined the RNA degradation patterns mediated by the UTR library in two cell lines, and then applied LASSO regression to model the influential regulators of RNA stability. We found that UA dinucleotides and UA-rich motifs are the most prominent destabilizing element. Gain of UA dinucleotide outlined mutant UTRs with reduced stability. Studies on endogenous transcripts indicate that high UA-dinucleotide ratios in UTRs promote RNA degradation. Conversely, elevated GC content and protein binding on UA dinucleotides protect high-UA RNA from degradation. Further analysis reveals polarized roles of UA-dinucleotide-binding proteins in RNA protection and degradation. Furthermore, the UA-dinucleotide ratio of both UTRs is a common characteristic of genes in innate immune response pathways, implying a coordinated stability regulation through UTRs at the transcriptomic level. We also demonstrate that stability-altering UTRs are associated with changes in biobank-based health indices, underscoring the importance of precise UTR regulation for wellness. Our study highlights the importance of RNA stability regulation through UTR primary sequences, paving the way for further exploration of their implications in gene networks and precision medicine.

    1. Computational and Systems Biology
    2. Medicine
    Hong Yang, Cheng Zhang ... Adil Mardinoglu
    Research Article

    Excessive consumption of sucrose, in the form of sugar-sweetened beverages, has been implicated in the pathogenesis of metabolic dysfunction‐associated fatty liver disease (MAFLD) and other related metabolic syndromes. The c-Jun N-terminal kinase (JNK) pathway plays a crucial role in response to dietary stressors, and it was demonstrated that the inhibition of the JNK pathway could potentially be used in the treatment of MAFLD. However, the intricate mechanisms underlying these interventions remain incompletely understood given their multifaceted effects across multiple tissues. In this study, we challenged rats with sucrose-sweetened water and investigated the potential effects of JNK inhibition by employing network analysis based on the transcriptome profiling obtained from hepatic and extrahepatic tissues, including visceral white adipose tissue, skeletal muscle, and brain. Our data demonstrate that JNK inhibition by JNK-IN-5A effectively reduces the circulating triglyceride accumulation and inflammation in rats subjected to sucrose consumption. Coexpression analysis and genome-scale metabolic modeling reveal that sucrose overconsumption primarily induces transcriptional dysfunction related to fatty acid and oxidative metabolism in the liver and adipose tissues, which are largely rectified after JNK inhibition at a clinically relevant dose. Skeletal muscle exhibited minimal transcriptional changes to sucrose overconsumption but underwent substantial metabolic adaptation following the JNK inhibition. Overall, our data provides novel insights into the molecular basis by which JNK inhibition exerts its metabolic effect in the metabolically active tissues. Furthermore, our findings underpin the critical role of extrahepatic metabolism in the development of diet-induced steatosis, offering valuable guidance for future studies focused on JNK-targeting for effective treatment of MAFLD.