A Motor-Gradient and Clustering Model of the Centripetal Motility of MTOCs in Meiosis I of Mouse Oocytes

Abstract

Asters nucleated by Microtubule (MT) organizing centers (MTOCs) converge on chromosomes during spindle assembly in mouse oocytes undergoing meiosis I. Time-lapse imaging suggests that this centripetal motion is driven by a biased ‘search-and-capture’ mechanism. Here, we develop a model of a random walk in a drift field to test the nature of the bias and the spatio-temporal dynamics of the search process. The model is used to optimize the spatial field of drift in simulations, by comparison to experimental motility statistics. In a second step, this optimized gradient is used to determine the location of immobilized dynein motors and MT polymerization parameters, since these are hypothesized to generate the gradient of forces needed to move MTOCs. We compare these scenarios to self-organized mechanisms by which asters have been hypothesized to find the cell-center- MT pushing at the cell-boundary and clustering motor complexes. By minimizing the error between simulation outputs and experiments, we find a model of “pulling” by a gradient of dynein motors alone can drive the centripetal motility. Interestingly, models of passive MT based “pushing” at the cortex, clustering by cross-linking motors and MT-dynamic instability gradients alone, by themselves do not result in the observed motility. The model predicts the sensitivity of the results to motor density and stall force, but not MTs per aster. A hybrid model combining a chromatin-centered immobilized dynein gradient, diffusible minus-end directed clustering motors and pushing at the cell cortex, is required to comprehensively explain the available data. The model makes experimentally testable predictions of a spatial bias and self-organized mechanisms by which MT asters can find the center of a large cell.

Spatio-temporal regulation of nuclear division by Aurora B kinase Ipl1 in Cryptococcus neoformans

“…While the MTOCs were found to be unclustered in the wild-type cells with an average ratio of the daughter /mother cell size of 0.4–0.5, MTOCs remained unclustered in Ipl1-depleted cells at an average ratio of >0.5, indicating a delay in MTOC clustering in the Ipl1-depleted cells. Further, using an in silico model, we canvassed various plausible MT-driven MTOC clustering schemes ( Fig 5A ) characterized by parameters ( Table 1 ) [41, 42]. We assumed that a single kinetochore is associated with a single MTOC prior to the clustering.…”

Dynein collective behavior in mitotic nuclear positioning ofSaccharomyces cerevisiae

“…Transitioning of aster motility from random to directed in case of centrosome nucleated asters in Xenopus oocyte extracts involve a gradient of MT stabilization that increases length asymmetrically and results in a net force in the direction of transport driven by binding to cytoplasmic dyneins (Athale et al, 2014). In contrast, in mouse oocyte meiosis a gradient of dynein but uniform MT lengths were seen to bias the positioning of MTOC asters (Khetan and Athale, 2016). However, the size scale of budding yeast cells is a few micrometers, compared to the diameter of vertebrate oocytes ranging between 10 2 to 10 3 µm.…”

“…We have used a standard method of MSD calculations as previously described (Arcizet et al, 2008;Athale et al, 2014;Khetan and Athale, 2016) based on the 2D positional coordinate of the filament (r) using a sliding-window approach of increasing time intervals (δt) based on the expression:…”

“…The first stage of this is reminiscent of 'search and capture' of dynamic MTs seen during spindle assembly (Holy and Leibler, 1994). In large cells additional directional cues such as gradients of regulatory proteins that affect MT growth dynamics in Xenopus oocyte meiosis II (Athale et al, 2008(Athale et al, , 2014 or motor gradients in mouse oocyte meiosis I (Khetan and Athale, 2016) are also thought to play a role. Once captured, cortical motors generate pulling forces on cMTs as seen in early embryonic divisions of Caenorhabditis elegans (Kozlowski et al, 2007;Grill et al, 2001).…”

‘Switch-like’ transition from random to directed motility of microtubules by a yeast dynein

“…The proportion of effective diffusion and drift velocity contributing to MT transport was estimated from MSD plots of experimental trajectories by fitting them to equations that represent (a) anomalous diffusion Equation 2 This suggests that the sharp transition in v drif t from diffusive to directed transport with increasing motors predicted in simulations, is only partially observed in experiment. Since MSD analysis is sensitive to the time of observation and numbers of data points, we also quantify the directionality of transport by tortuosity (χ), a variable that quantifies randomness in diffusion and drift processes (46). We find experimental values of χ increase above ∼10 motors ( Figure 7(A)), while simulations demonstrate a 'switch-like' transi-7 tion at ∼ 10 motors from random to directional transport ( Figure 7(B)).…”