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In vivo X-ray diffraction and simultaneous EMG reveal the time course of myofilament lattice dilation and filament stretch.

Sage A MalingenAnthony M AsencioJulie A CassWeikang MaThomas C IrvingThomas L Daniel
Published in: The Journal of experimental biology (2020)
Muscle function within an organism depends on the feedback between molecular and meter-scale processes. Although the motions of muscle's contractile machinery are well described in isolated preparations, only a handful of experiments have documented the kinematics of the lattice occurring when multi-scale interactions are fully intact. We used time-resolved X-ray diffraction to record the kinematics of the myofilament lattice within a normal operating context: the tethered flight of Manduca sexta As the primary flight muscles of M . sexta are synchronous, we used these results to reveal the timing of in vivo cross-bridge recruitment, which occurred 24 ms (s.d. 26) following activation. In addition, the thick filaments stretched an average of 0.75% (s.d. 0.32) and thin filaments stretched 1.11% (s.d. 0.65). In contrast to other in vivo preparations, lattice spacing changed an average of 2.72% (s.d. 1.47). Lattice dilation of this magnitude significantly affects shortening velocity and force generation, and filament stretching tunes force generation. While the kinematics were consistent within individual trials, there was extensive variation between trials. Using a mechanism-free machine learning model we searched for patterns within and across trials. Although lattice kinematics were predictable within trials, the model could not create predictions across trials. This indicates that the variability we see across trials may be explained by latent variables occurring in this naturally functioning system. The diverse kinematic combinations we documented mirror muscle's adaptability and may facilitate its robust function in unpredictable conditions.
Keyphrases
  • skeletal muscle
  • machine learning
  • high resolution
  • single molecule
  • genome wide
  • mass spectrometry
  • magnetic resonance
  • multiple sclerosis
  • ms ms
  • computed tomography
  • artificial intelligence
  • crystal structure
  • big data