Cell Division Resets Polarity and Motility for the Bacterium Myxococcus xanthus

Links between cell division and other cellular processes are poorly understood. It is difficult to simultaneously examine division and function in most cell types. Most of the research probing aspects of cell division has experimented with stationary or immobilized cells or distinctly asymmetrical cells. Here we took an alternative approach by examining cell division events within motile groups of cells growing on solid medium by time-lapse microscopy. A total of 558 cell divisions were identified among approximately 12,000 cells. We found an interconnection of division, motility, and polarity in the bacterium Myxococcus xanthus. For every division event, motile cells stop moving to divide. Progeny cells of binary fission subsequently move in opposing directions. This behavior involves M. xanthus Frz proteins that regulate M. xanthus motility reversals but is independent of type IV pilus “S motility.” The inheritance of opposing polarity is correlated with the distribution of the G protein RomR within these dividing cells. The constriction at the point of division limits the intracellular distribution of RomR. Thus, the asymmetric distribution of RomR at the parent cell poles becomes mirrored at new poles initiated at the site of division

A Modeling Study to Characterize Microtubule Mechanisms of Dynamic Instability: Connecting Micro-Level Tip Structures to Macro-Level Phases

Microtubules (MTs) are cytoplasmic biopolymers that are common in eukaryotic cells. The MT is assembled by αβ tubulin dimer subunits that can be in either a GTPor GDP-bound nucleotide state. These dimer subunit connect with longitudinal bonds to form linear strands called protofilements (PFs). Lateral bonds connect 13 PFs together to form the tube-like structure of a MT. GTP-bound subunits collect near the MT tip region to form a GTP-cap, which helps maintain the bonds that hold the MT structure intact. Losing the GTP-cap exposes GDP-bound subunits which are more likely to break their bonds, and promote subunits to detach from the MT structure. The MT length changes in time by undergoing spontaneous switches between periods of sustained growth and rapid shortening, which characterize the behavior called dynamic instability (DI). The molecular reactions that drive MT dynamics primarily affect the tip portion of the structure. Therefore, a study of the connection between MT tip structures and macro-level phases is needed to gain a better understanding of the mechanisms that drive phase changes in DI. Laboratory conditions limit the level of detail that can be experimentally collected from MT structures. Computational models are a vital tool that provide this level of information, and they have helped understand how molecular level reactions alter the micro-level MT structure, which drives the MT length changes observed at the macro-level. The detailed 13-PF MT model was capable of running long-time simulations that display DI behavior with a low computational cost, but it made use of an approximation that skips over MT structural states. This study first develops the extended 13-PF MT model in order to simulate a biochemically exact trajectory of all the MT structural states resulting from possible reactions events. Then, the minimal MT structure that includes the lateral bond is considered to present the simplified 2-PF MT model, a novel consideration which helps make calculations of the MT tip structure features more feasible while successfully simulating DI behavior. The high frequency and low amplitude fluctuations present in simulated MT length history data make it difficult to pinpoint where DI phases begin and end, and where phase transitions occur. To this end, an unsupervised machine learning method based on K-means clustering is presented to identify, classify, and analyze macro-level phases present in MT length history data. Application of this method revealed an intermediate phase called “stutters”, during which the rate of MT length change is smaller in magnitude compared to classically recognized growth and shortening phases. Additionally, stutter phases commonly appeared as a transitional phase during catastrophe events, between growth and shortening phases. This indicated that before a catastrophe event takes place, a MT is likely to first undergo structural changes that do not alter the MT length, which result in structural configurations prone to entering a period of rapid depolymerization. The proposed DI phase classification method now can identify these periods, which in past experimental studies have been observed, but not separately considered as a unique class of behavior [21]. Furthermore, the stutter events specifically provide a target region to study the mechanisms involved with catastrophe events. Finally, a supervised machine learning approach called Random Forest was used to test the ability for micro-level tip structure features to predict their corresponding macro-level DI phases, and to forecast upcoming phase transitions. The results indicated that the GTP-cap size and it's relative position to the cracked tip region are important factors in predicting which DI phase a MT is in. In addition to the GTP-cap size, information on the PF-tip lengths and the dispersion of GTP-bound subunits in the tip region were found to be important in forecasting upcoming phase transitions. Thus, specific MT tip structures and the reaction events that create them are identified as the mechanisms that drive respective transitions between DI phases

Behaviors of individual microtubules and microtubule populations relative to critical concentrations: dynamic instability occurs when critical concentrations are driven apart by nucleotide hydrolysis.

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

Quantification of microtubule stutters: dynamic instability behaviors that are 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 DI 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) that 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 in vitro experiments and dimer-scale MT simulations, suggesting that stutters are mechanistically involved in catastrophes. Related to this idea, we show that the anticatastrophe factor CLASP2γ works by promoting the return of stuttering MTs to growth. STADIA enables more comprehensive and data-driven analysis of MT dynamics compared with 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