Biology subjects

Jonasson, E. M.

Publications and source records attributed to Jonasson, E. M..

4 recordsLinked to original sources

bioRxiv2021

Characterizing interactions between the microtubule-binding protein CLIP-170 and F-actin

The cooperation between the actin and microtubule (MT) cytoskeletons is important for cellular processes such as cell migration and muscle cell development. Full understanding of how this cooperation occurs has yet to be sufficiently developed. The MT plus-end tracking protein (+TIP) CLIP-170 has been implicated in this actin-MT coordination by associating with the actin-binding signaling protein IQGAP1, and by promoting actin polymerization through binding with formins. Thus far, CLIP-170s interactions with actin were assumed to be indirect. Here, we demonstrate that CLIP-170 can bind to filamentous actin (F-actin) directly. The affinity is relatively weak, but is strong enough to be significant in the actin-rich cortex, where actin concentrations can be extremely high. Using CLIP-170 fragments and mutants, we show that the direct CLIP-170:actin interaction is independent of the FEED domain, the region that mediates formin-dependent actin polymerization, and that the CLIP-170 F-actin-binding region overlaps with the MT-binding region. Consistent with these observations, in vitro competition assays indicate that CLIP-170:F-actin and CLIP-170:MT interactions are mutually exclusive. Taken together, these observations lead us to speculate that direct CLIP-170:F-actin interactions may function to reduce the stability of MTs in actin-rich regions of the cell, as previously proposed for EB1.

biochemistry↗

bioRxiv2019

Stutter: a Transient Dynamic Instability Phase that is Strongly Associated with Catastrophe

Microtubules (MTs) are cytoskeletal fibers that undergo dynamic instability (DI), a remarkable process involving phases of growth and shortening separated by stochastic transitions called catastrophe and rescue. Dissecting dynamic instability mechanism(s) requires first characterizing and quantifying these dynamics, a subjective process that often ignores complexity in MT behavior. We present a Statistical Tool for Automated Dynamic Instability Analysis (STADIA), which identifies and quantifies not only growth and shortening, but also a category of intermediate behaviors that we term stutters. During stutters, the rate of MT length change tends to be smaller in magnitude than during typical growth or shortening phases. Quantifying stutters and other behaviors with STADIA demonstrates that stutters precede most catastrophes in our dimer-scale MT simulations and in vitro experiments, suggesting that stutters are mechanistically involved in catastrophes. Related to this idea, we show that the anti-catastrophe factor CLASP2{gamma} works by promoting the return of stuttering MTs to growth. STADIA enables more comprehensive and data-driven analysis of MT dynamics compared to previous methods. The treatment of stutters as distinct and quantifiable DI behaviors provides new opportunities for analyzing mechanisms of MT dynamics and their regulation by binding proteins.

cell biology↗

bioRxiv2019

Relationship between Dynamic Instability of Individual Microtubules and Flux of Subunits into and out of Polymer

Behaviors of dynamic polymers such as microtubules and actin are frequently assessed at one or both of two scales: (i) net assembly or disassembly of bulk polymer, (ii) growth and shortening of individual filaments. Previous work has derived various forms of an equation to relate the rate of change in bulk polymer mass (i.e., flux of subunits into and out of polymer, often abbreviated as \"J\") to individual filament behaviors. However, these versions of this \"J equation\" differ in the variables used to quantify individual filament behavior, which correspond to different experimental approaches. For example, some variants of the J equation use dynamic instability parameters, obtained by following particular individuals for long periods of time. Another form of the equation uses measurements from many individuals followed over short time steps. We use a combination of derivations and computer simulations that mimic experiments to (i) relate the various forms of the J equation to each other; (ii) determine conditions under which these J equation forms are and are not equivalent; and (iii) identify aspects of the measurements that can affect the accuracy of each form of the J equation. Improved understanding of the J equation and its connections to experimentally measurable quantities will contribute to efforts to build a multi-scale understanding of steady-state polymer behavior.

biophysics↗

bioRxiv2018

Behaviors of microtubules depend on two critical concentrations

The concept of critical concentration (CC) is central to understanding behaviors of microtubules and other cytoskeletal polymers. Traditionally, these polymers are understood to have one CC, measured multiple ways and assumed to be the subunit concentration necessary for polymer assembly. However, this framework does not incorporate dynamic instability (DI), and there is work indicating that microtubules have two CCs. We use our previously established simulations to confirm that microtubules have (at least) two experimentally relevant CCs and to clarify the behaviors of individuals and populations relative to the CCs. At free subunit concentrations above the lower CC (CCIndGrow), growth phases of individual filaments can occur transiently; above the higher CC (CCPopGrow), the populations polymer mass will increase persistently. Our results demonstrate that most experimental CC measurements correspond to CCPopGrow, meaning \"typical\" DI occurs below the concentration traditionally considered necessary for polymer assembly. We report that [free tubulin] at steady state does not equal CCPopGrow, but instead approaches CCPopGrow asymptotically as [total tubulin] increases and depends on the number of stable microtubule seeds. We show that the degree of separation between CCIndGrow and CCPopGrow depends on the rate of nucleotide hydrolysis. This clarified framework helps explain and unify many experimental observations.

cell biology↗