Physical properties of the cytoplasm modulate the rates of microtubule polymerization and depolymerization.

The cytoplasm is a crowded, visco-elastic environment whose physical properties change according to physiological or developmental states. How the physical properties of the cytoplasm impact cellular functions in vivo remains poorly understood. Here, we probe the effects of cytoplasmic concentration on microtubules by applying osmotic shifts to fission yeast, moss, and mammalian cells. We show that the rates of both microtubule polymerization and depolymerization scale linearly and inversely with cytoplasmic concentration; an increase in cytoplasmic concentration decreases the rates of microtubule polymerization and depolymerization proportionally, whereas a decrease in cytoplasmic concentration leads to the opposite. Numerous lines of evidence indicate that these effects are due to changes in cytoplasmic viscosity rather than cellular stress responses or macromolecular crowding per se. We reconstituted these effects on microtubules in vitro by tuning viscosity. Our findings indicate that, even in normal conditions, the viscosity of the cytoplasm modulates the reactions that underlie microtubule dynamic behaviors.

Numerical Parameter Space Compression and Its Application to Biophysical Models.

Physical models of biological systems can become difficult to interpret when they have a large number of parameters. But the models themselves actually depend on (i.e., are sensitive to) only a subset of those parameters. This phenomenon is due to parameter space compression (PSC), in which a subset of parameters emerges as "stiff" as a function of time or space. PSC has only been used to explain analytically solvable physics models. We have generalized this result by developing a numerical approach to PSC that can be applied to any computational model. We validated our method against analytically solvable models of a random walk with drift and protein production and degradation. We then applied our method to a simple computational model of microtubule dynamic instability. We propose that numerical PSC has the potential to identify the low-dimensional structure of many computational models in biophysics. The low-dimensional structure of a model is easier to interpret and identifies the mechanisms and experiments that best characterize the system.

The Structure and Dynamics of C. elegans Tubulin Reveals the Mechanistic Basis of Microtubule Growth.

The dynamic instability of microtubules is a conserved and fundamental mechanism in eukaryotes. Yet microtubules from different species diverge in their growth rates, lattice structures, and responses to GTP hydrolysis. Therefore, we do not know what limits microtubule growth, what determines microtubule structure, or whether the mechanisms of dynamic instability are universal. Here, we studied microtubules from the nematode C. elegans, which have strikingly fast growth rates and non-canonical lattices in vivo. Using a reconstitution approach, we discovered that C. elegans microtubules combine intrinsically fast growth with very frequent catastrophes. We solved the structure of C. elegans microtubules to 4.8 Å and discovered sequence divergence in the lateral contact loops, one of which is ordered in C. elegans but unresolved in other species. We provide direct evidence that C. elegans tubulin has a higher free energy in solution and propose a model wherein the ordering of lateral contact loops activates tubulin for growth.