A consistent muscle activation strategy underlies crawling and swimming in Caenorhabditis elegans

Autor: William R Schafer, Robyn Branicky, Jana F. Liewald, Rex Kerr, Alexander Gottschalk, Victoria J. Butler, Eviatar Yemini, Dmitri B. Chklovskii
Jazyk: angličtina
Rok vydání: 2015
Předmět:
Zdroj: Journal of the Royal Society Interface
ISSN: 1742-5662
1742-5689
Popis: Although undulatory swimming is observed in many organisms, the neuromuscular basis for undulatory movement patterns is not well understood. To better understand the basis for the generation of these movement patterns, we studied muscle activity in the nematodeCaenorhabditis elegans. Caenorhabditis elegansexhibits a range of locomotion patterns: in low viscosity fluids the undulation has a wavelength longer than the body and propagates rapidly, while in high viscosity fluids or on agar media the undulatory waves are shorter and slower. Theoretical treatment of observed behaviour has suggested a large change in force–posture relationships at different viscosities, but analysis of bend propagation suggests that short-range proprioceptive feedback is used to control and generate body bends. How muscles could be activated in a way consistent with both these results is unclear. We therefore combined automated worm tracking with calcium imaging to determine muscle activation strategy in a variety of external substrates. Remarkably, we observed that across locomotion patterns spanning a threefold change in wavelength, peak muscle activation occurs approximately 45° (1/8th of a cycle) ahead of peak midline curvature. Although the location of peak force is predicted to vary widely, the activation pattern is consistent with required force in a model incorporating putative length- and velocity-dependence of muscle strength. Furthermore, a linear combination of local curvature and velocity can match the pattern of activation. This suggests that proprioception can enable the worm to swim effectively while working within the limitations of muscle biomechanics and neural control.
Databáze: OpenAIRE