| Autor: |
Mahserejian, Shant M., Scripture, Jared P., Mauro, Ava J., Lawrence, Elizabeth J., Jonasson, Erin M., Murray, Kristopher S., Li, Jun, Gardner, Melissa, Alber, Mark, Zanic, Marija, Goodson, Holly V. |
| Jazyk: |
angličtina |
| Rok vydání: |
2019 |
| DOI: |
10.1101/2019.12.16.878603 |
| Popis: |
Microtubules (MTs) are dynamic polymers with critical roles in processes ranging from membrane transport to chromosome separation. Central to MT function is dynamic instability (DI), a behavior typically assumed to consist of growth and shortening, with sharp transitions in between. However, this two-state assumption disregards details in MT behavior that are evident in high-resolution data. For example, MTs exhibit growth rate variability, and pinpointing where transitions begin can be difficult when viewed at high spatiotemporal resolution. These observations suggest that MT behavior is more complicated than implied by standard quantification methods. To address these problems, we developed STADIA (Statistical Tool for Automated Dynamic Instability Analysis). STADIA’s methods are rooted in machine learning to objectively analyze and quantify macro-level DI behaviors exhibited by MTs. Applying STADIA to MT length-history data revealed a transient, intermediate phase that we term ‘stutter’, during which the rate of MT length change is smaller in magnitude than growth or shortening phases. Significantly, most catastrophe events in both simulations and experiments are preceded by stutters, suggesting that this newly recognized phase is mechanistically involved in catastrophes. Consistent with this idea, a MT anti-catastrophe factor (CLASP2γ) increases the likelihood of growth following a stutter phase in experiments. We conclude that STADIA enables unbiased identification of DI phases including stutters, producing more complete and accurate DI measurements than possible with classical analysis methods. Identifying stutters as a distinct and quantifiable phase provides a new target for mechanistic studies regarding DI phase transitions and their regulation by MT binding proteins. SIGNIFICANCE STATEMENT Microtubules are cytoskeletal fibers that undergo dynamic instability, a remarkable process involving phases of growth and shortening separated by approximately random transitions (catastrophe and rescue). Dissecting the mechanism of dynamic instability requires first characterizing and quantifying these dynamics. We present a novel machine-learning based tool (STADIA), which shows that microtubule behavior consists not only of growth and shortening, but also a transient intermediate phase we term “stutter.” Quantifying stutter and other dynamic behaviors with STADIA shows that most catastrophes in simulations and experiments are preceded by stutters, and that the anti-catastrophe factor CLASP2γ works by increasing the fraction of stutters that revert to growth. STADIA provides new opportunities for analyzing mechanisms of microtubule dynamics and regulation by binding proteins. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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