The positioning mechanics of microtubule asters in Drosophila embryo explants
de-Carvalho, J.; Tlili, S. L.; Saunders, T. E.; Telley, I. A.
Show abstract
Microtubule asters are essential in localizing the action of microtubules in processes including mitosis and organelle positioning. In large cells, such as the one-cell sea urchin embryo, aster dynamics are dominated by hydrodynamic pulling forces. However, in systems with more densely positioned nuclei such as the early Drosophila embryo, which packs around 6000 nuclei within the syncytium in a crystalline-like order, it is unclear what processes dominate aster dynamics. Here, we take advantage of a cell cycle regulation Drosophila mutant to generate embryos with multiple asters, independent from nuclei. We use an ex vivo assay to further simplify this biological system to explore the forces generated by and between asters. Through live imaging, drug and optical perturbations, and theoretical modelling, we demonstrate that these asters likely generate an effective pushing force over short distances. Significance StatementUsing cytosolic explants from Drosophila syncytial embryos combined with quantitative microscopy and perturbations, de-Carvalho et al., reveal the mechanical forces separating Drosophila microtubule asters. Aster separation drives precise nuclear positioning in multinucleated embryo cells, a vital process for tissue formation and gene expression during subsequent embryo development.
Matching journals
The top 4 journals account for 50% of the predicted probability mass.
Similar papers in this journal
Similar papers in this journal
Similar papers in this journal
Similar papers in this journal
- Cell-type specific mechanical response and myosin dynamics during retinal lens development in Drosophila 95%
- Intracellular connections between basal bodies promote the coordinated behavior of motile cilia 95%
- After wounding, a G-protein coupled receptor promotes the restoration of tension in epithelial cells 95%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.