A high-throughput behavioral screening platform for measuring chemotaxis by C. elegans

PLoS Biol. 2024 Jun 27;22(6):e3002672. doi: 10.1371/journal.pbio.3002672. eCollection 2024 Jun.

Abstract

Throughout history, humans have relied on plants as a source of medication, flavoring, and food. Plants synthesize large chemical libraries and release many of these compounds into the rhizosphere and atmosphere where they affect animal and microbe behavior. To survive, nematodes must have evolved the sensory capacity to distinguish plant-made small molecules (SMs) that are harmful and must be avoided from those that are beneficial and should be sought. This ability to classify chemical cues as a function of their value is fundamental to olfaction and represents a capacity shared by many animals, including humans. Here, we present an efficient platform based on multiwell plates, liquid handling instrumentation, inexpensive optical scanners, and bespoke software that can efficiently determine the valence (attraction or repulsion) of single SMs in the model nematode, Caenorhabditis elegans. Using this integrated hardware-wetware-software platform, we screened 90 plant SMs and identified 37 that attracted or repelled wild-type animals but had no effect on mutants defective in chemosensory transduction. Genetic dissection indicates that for at least 10 of these SMs, response valence emerges from the integration of opposing signals, arguing that olfactory valence is often determined by integrating chemosensory signals over multiple lines of information. This study establishes that C. elegans is an effective discovery engine for determining chemotaxis valence and for identifying natural products detected by the chemosensory nervous system.

MeSH terms

  • Animals
  • Behavior, Animal / drug effects
  • Behavior, Animal / physiology
  • Caenorhabditis elegans* / drug effects
  • Caenorhabditis elegans* / physiology
  • Chemotaxis*
  • High-Throughput Screening Assays* / methods
  • Smell / physiology
  • Software

Grants and funding

This work was supported by the Wu Tsai Neuroscience Institutes at Stanford via the Big Ideas and Research Accelerator programs (to MBG, TRC, & SYR) and a NeURO fellowship (to HF), the National Institutes of Health (R35NS105092 to MBG; F31NS100318 to ALN; T32GM113854 to LR-H), the National Science Foundation (IOS-1546838 to SYR), Chan-Zuckerberg BioHub (Investigatorship to TRC), and Stanford University (Rise Seed Grant to LR-H, SG; BioX Interdisciplinary Fellowship to LR-H). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.