Discriminating between sleep and exercise-induced fatigue using computer vision and behavioral genetics

Abstract

Following prolonged swimming, Caenorhabditis elegans cycle between active swimming bouts and inactive quiescent bouts. Swimming is exercise for C. elegans and here we suggest that inactive bouts are a recovery state akin to fatigue. It is known that cGMP-dependent kinase (PKG) activity plays a conserved role in sleep, rest, and arousal. Using C. elegans EGL-4 PKG, we first validate a novel learning-based computer vision approach to automatically analyze C. elegans locomotory behavior and an edge detection program that is able to distinguish between activity and inactivity during swimming for long periods of time. We find that C. elegans EGL-4 PKG function impacts timing of exercise-induced quiescent (EIQ) bout onset, fractional quiescence, bout number, and bout duration, suggesting that previously described pathways are engaged during EIQ bouts. However, EIQ bouts are likely not sleep as animals are feeding during the majority of EIQ bouts. We find that genetic perturbation of neurons required for other C. elegans sleep states also does not alter EIQ dynamics. Additionally, we find that EIQ onset is sensitive to age and DAF-16 FOXO function. In summary, we have validated behavioral analysis software that enables a quantitative and detailed assessment of swimming behavior, including EIQ. We found novel EIQ defects in aged animals and animals with mutations in a gene involved in stress tolerance. We anticipate that further use of this software will facilitate the analysis of genes and pathways critical for fatigue and other C. elegans behaviors.

Keywords:

• Fatigue
• C. elegans
• locomotion
• swimming
• computer vision
• quiescence

Acknowledgements

The authors acknowledge the Cloud TPU hardware resources that Google made available via the TensorFlow Research Cloud (TFRC) program. Some strains were provided by the CGC, which is funded by the NIH Office of Research Infrastructure Programs (P40 OD010440).

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This research was supported by funds from Brown’s Office for the Vice-President for Research (A.C.H. and T.S.). Additional support provided by the Carney Institute for Brain Science, the Center for Vision Research (CVR) and the Center for Computation and Visualization (CCV) at Brown University and a Karen T. Romer Undergraduate Teaching and Research Awards (S.K.).