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Atomic force microscopy reveals distinct protofilament-scale structural dynamics in depolymerizing microtubule arrays.

Sithara S WijeratneMichelle F MarchanJason S TresbackRadhika Subramanian
Published in: Proceedings of the National Academy of Sciences of the United States of America (2022)
The dynamic reorganization of microtubule-based cellular structures, such as the spindle and the axoneme, fundamentally depends on the dynamics of individual polymers within multimicrotubule arrays. A major class of enzymes implicated in both the complete demolition and fine size control of microtubule-based arrays are depolymerizing kinesins. How different depolymerases differently remodel microtubule arrays is poorly understood. A major technical challenge in addressing this question is that existing optical or electron-microscopy methods lack the spatial-temporal resolution to observe the dynamics of individual microtubules within larger arrays. Here, we use atomic force microscopy (AFM) to image depolymerizing arrays at single-microtubule and protofilament resolution. We discover previously unseen modes of microtubule array destabilization by conserved depolymerases. We find that the kinesin-13 MCAK mediates asynchronous protofilament depolymerization and lattice-defect propagation, whereas the kinesin-8 Kip3p promotes synchronous protofilament depolymerization. Unexpectedly, MCAK can depolymerize the highly stable axonemal doublets, but Kip3p cannot. We propose that distinct protofilament-level activities underlie the functional dichotomy of depolymerases, resulting in either large-scale destabilization or length regulation of microtubule arrays. Our work establishes AFM as a powerful strategy to visualize microtubule dynamics within arrays and reveals how nanometer-scale substrate specificity leads to differential remodeling of micron-scale cytoskeletal structures.
Keyphrases
  • atomic force microscopy
  • high density
  • high speed
  • single molecule
  • high resolution
  • high throughput
  • electron microscopy
  • transcription factor
  • machine learning
  • soft tissue