Mechanical torque promotes bipolarity of the mitotic spindle through multi-centrosomal clustering

Abstract

Intracellular forces shape cellular organization and function. One example is the mi-totic spindle, a cellular machine consisting of multiple chromosomes and centrosomes which interact via dynamic microtubule filaments and motor proteins, resulting in complicated spatially dependent forces. For a cell to divide properly, is important for the spindle to be bipolar, with chromosomes at the center and multiple centrosomes clustered into two ‘poles’ at opposite sides of the chromosomes. Experimental observations show that in unhealthy cells, the spindle can take on a variety of patterns. What forces drive each of these patterns? It is known that attraction between centrosomes is key to bipolarity, but what the prevents the centrosomes from collapsing into a monopolar configuration? Here, we explore the hypothesis that torque rotating chromosome arms into orientations perpendicular to the centrosome-centromere vector promotes spindle bipolarity. To test this hypothesis, we construct a pairwise-interaction model of the spindle. On a continuum version of the model, an integro-PDE system, we perform linear stability analysis and construct numerical solutions which display a variety of spatial patterns. We also simulate a discrete particle model resulting in a phase diagram that confirms that the spindle bipolarity emerges most robustly with torque. Altogether, our results suggest that rotational forces may play an important role in dictating spindle patterning.

Cortical Dynein Drives Centrosome Clustering in Cells with Centrosome Amplification

“…Previous studies of mitotic cell division utilizing different modeling approaches have been valuable in understanding and informing force-derived centrosome clustering mechanisms in cells with centrosome amplification (Chatterjee et al, 2020;Goupil et al, 2020;Miles et al, 2022). In particular, these models have provided insight into chromosome-dependent centrosome clustering mechanisms which implicated kinetochore microtubule-derived torque (Miles et al, 2022) and have highlighted that there must exist a delicate balance between attraction forces for efficient centrosome clustering to occur, including centrosome-cortex forces (Chatterjee et al, 2020). Our results further expand on this latter observation by identifying that the centrosome-cortex force must correspond to a region on the cell cortex, either fixed or dynamically changing, for efficient clustering to occur via dynein motor activation (Figure 4).…”

“…Second, chromosomes form stable interactions, via kinetochores, with bundles of microtubules that are in turn anchored at the centrosomes (DeLuca et al, 2006). The bioriented configuration, and associated forces, of paired kinetochores enforce a bipolar geometry where centrosomes are positioned along the spindle axis (Leber et al, 2010;Tanaka, 2010;Chatterjee et al, 2020;Miles et al, 2022). Similarly, cell shape and actin-dependent cortical contractility impact centrosome clustering by restricting the space within which centrosomes can move (Kwon et al, 2008;Rhys et al, 2018).…”

Computational modelling and near-complete kinetochore tracking reveal how chromosome dynamics during cell division are co-ordinated in space and time

“…Understanding this complex multi-scale mechanical system requires development of quantitative mathematical models that can capture crucial elements of the system's biophysics and regulatory properties, provide quantitative support for conceptual ideas, and generate testable predictions. Efforts in this direction have been ongoing since the 1980's with previous work focusing on microscopic models of kinetochore-microtubule attachment (Hill, 1985;Joglekar and Hunt, 2002; Civelekoglu-Scholey and Cimini, 2014), on the role of bridging fibres and spindle geometry (Kajtez et al, 2016;Miles et al, 2021), and on chromosome congression dynamics to the spindle equator (Mogilner et al, 2006;Zaytsev and Grishchuk, 2015;Blackwell et al, 2017). Careful calibration of models to experimental data is crucial to ensure model validity.…”