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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.

Quantification of microtubule (MT) dynamic instability (DI) is essential to mechanistic dissection of MT assembly and the activities of MT binding proteins. Typical methods for quantifying MT dynamics assume that MT behavior consists of growth and shortening phases, with instantaneous transitions (rescues and catastrophes) in between. However, examination of DI data at high temporal and spatial resolution reveals the presence of ambiguous behaviors that cannot easily fit into these categories. Failure to objectively recognize and quantify these behaviors could reduce the reproducibility of DI data and impact attempts to dissect mechanisms. To address these problems, we recently developed STADIA (Statistical Tool for Automated Dynamic Instability Analysis), a MT analysis software package that uses length-history data as input and is (presently) implemented in MATLAB. STADIA uses machine learning methods to objectively analyze and quantify macro-level DI behaviors exhibited by MTs, including variable rates of growth and shortening and a newly quantified DI phase: stutter. Here we overview the process of using STADIA to quantify MT dynamics and provide a set of concrete protocols for using STADIA to process and analyze MT length history data.

Tau is a multifaceted neuronal protein that stabilizes microtubules (MTs), but the mechanism of this activity remains poorly understood. Questions include whether Tau binds MTs laterally or longitudinally and whether Tau's binding affinity depends on the nucleotide state of tubulin. We observed that Tau binds tightly to Dolastatin-10 tubulin rings and promotes the formation of Dolastatin-10 ring stacks, implying that Tau can crosslink MT protofilaments laterally. In addition, we found that Tau prefers GDP-like tubulin conformations, which implies that Tau binding to the MT surface is biased away from the dynamic GTP-rich MT tip. To investigate the potential impact of these Tau activities on MT stabilization, we incorporated them into our previously developed dimer-scale computational model of MT dynamics. We found that lateral crosslinking activities have a much greater effect on MT stability than do longitudinal crosslinking activities, and that introducing a bias toward GDP tubulin has little impact on the observed MT stabilization. To address the question of why Tau is GDP-tubulin-biased, we tested whether Tau might affect MT binding of the +TIP EB1. We confirmed recent reports that Tau binds directly to EB1 and that Tau competes with EB1 for MT binding. Our results lead to a conceptual model where Tau stabilizes the MT lattice by strengthening lateral interactions between protofilaments. We propose that Tau's GDP preference allows the cell to independently regulate the dynamics of the MT tip and the stability of the lattice.

Quantification of microtubule (MT) dynamic instability (DI) is essential to mechanistic dissection of MT assembly and the activities of MT binding proteins. Typical methods for quantifying MT dynamics assume that MT behavior consists of growth and shortening phases, with instantaneous transitions (rescues and catastrophes) in between. However, examination of DI data at high temporal and spatial resolution reveals the presence of ambiguous behaviors that cannot easily fit into these categories. Failure to objectively recognize and quantify these behaviors could reduce the reproducibility of DI data and impact attempts to dissect mechanisms. To address these problems, we recently developed STADIA (Statistical Tool for Automated Dynamic Instability Analysis), a MT analysis software package that uses length-history data as input and is (presently) implemented in MATLAB. STADIA uses machine learning methods to objectively analyze and quantify macro-level DI behaviors exhibited by MTs, including variable rates of growth and shortening and a newly quantified DI phase: stutter. Here we overview the process of using STADIA to quantify MT dynamics and provide a set of concrete protocols for using STADIA to process and analyze MT length history data.

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 occurtransiently; above the higher CC (CCPopGrow), the population’s polymer mass will increasepersistently. 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 CCPopGrowasymptotically as [total tubulin] increases and depends on the number of stable microtubule seeds. We show that the degree of separation between CCIndGrowand CCPopGrowdepends on the rate of nucleotide hydrolysis. This clarified framework helps explain and unify many experimental observations.

Nitroxyl (HNO) has a unique, but varied, set of biological properties including beneficial effects on cardiac contractility and stimulation of glucose uptake by GLUT1. These biological effects are largely initiated by HNO's reaction with cysteine residues of key proteins. The intracellular production of HNO has not yet been demonstrated, but the small molecule, hydroxylamine (HA), has been suggested as possible intracellular source. We examined the effects of this molecule on glucose uptake in L929 fibroblast cells. HA activates glucose uptake from 2 to 5-fold within two minutes. Prior treatment with thiol-active compounds, such as iodoacetamide (IA), cinnamaldehyde (CA), or phenylarsine oxide (PAO) blocks HA-activation of glucose uptake. Incubation of HA with the peroxidase inhibitor, sodium azide, also blocks the stimulatory effects of HA. This suggests that HA is oxidized to HNO by L929 fibroblast cells, which then reacts with cysteine residues to exert its stimulatory effects. The data suggest that GLUT1 is acutely activated in L929 cells by modification of cysteine residues, possibly the formation of a disulfide bond within GLUT1 itself.

The corneal epithelial tissue is a layer of rapidly growing cells that are highly glycolytic and express GLUT1 as the major glucose transporter. It has been shown that GLUT1 in L929 fibroblast cells and other cell lines can be acutely activated by a variety agents.However, the acute regulation of glucose uptake in corneal cells has not been systematically investigated. Therefore, we examined glucose uptake in an immortalized human corneal–limbal epithelial (HCLE) cell line and compared it to glucose uptake in L929 fibroblast cells, a cell line where glucose uptake has been well characterized. We report that the expression of GLUT1 in HCLE cells is 6.6-fold higher than in L929 fibroblast cells, but the HCLE cells have a 25-fold higher basal rate of glucose uptake. Treatment with agents that interfere with mitochondrial metabolism, such as sodium azide and berberine, activate glucose uptake in L929 cells over 3-fold, but have no effect on glucose uptake HCLE cells. Also, agents known to react with thiols, such cinnamaldehyde, phenyarsine oxide and nitroxyl stimulate glucose uptake in L929 cells 3 to 4-fold, but actually inhibit glucose uptake in HCLE cells. These data suggest that in the fast growing HCLE cells, GLUT1 is expressed at a higher concentration and is already highly activated at basal conditions. These data support a model for the acute activation of GLUT1 that suggests that the activity of GLUT1 is enhanced by the formation of an internal disulfide bond within GLUT1 itself.

Nitroxyl (HNO) is a molecule of significant interest due to its unique pharmacological properties, particularly within the cardiovascular system. A large portion of HNO biological effects can be attributed to its reactivity with protein thiols, where it can generate disulfide bonds. Evidence from studies in erythrocytes suggests that the activity of GLUT1 is enhanced by the formation of an internal disulfide bond. However, there are no reports that document the effects of HNO on glucose uptake. Therefore, we examined the acute effects of Angeli’s salt (AS), a HNO donor, on glucose uptake activity of GLUT1 in L929 fibroblast cells. We report that AS stimulates glucose uptake with a maximum effective concentration of 5.0 mM. An initial 7.2-fold increase occurs within 2 min, which decreases and plateaus to a 4.0-fold activation after 10 min. About 60% of the 4.0-fold activation recovers within 10 min, and 40% remains after an hour. The activation is blocked by the pretreatment of cells with thiol-reactive compounds, iodoacetamide (0.75 mM), cinnamaldehyde (2.0 mM), and phenylarsine oxide (10 μM). The effects of AS are not additive to the stimulatory effects of other acute activators of glucose uptake in L929 cells, such as azide (5 mM), berberine (50 μM), or glucose deprivation. These data suggest that GLUT1 is acutely activated in L929 cells by the formation of a disulfide bond, likely within GLUT1 itself.