A mathematical model for bleb expansion clarifies the role of TalA in regulating blebbing
De Silva, S. H. D. S.; Movsumova, E.; Reznik, J.; Santiago, Z.; Asante-Asamani, E.; Brazill, D.
Show abstract
Eukaryotic cells, such as cancer and immune cells, migrate using either pressure-driven blebs or actin polymerization driven pseudopods, with cells preferring to bleb in confined environments where high protrusion forces are required for movement. Blebbing involves a separation of the cell membrane from the cortex, via the detachment of membrane-to-cortex linker proteins. The detached membrane then expands and stabilizes into a spherical cap as a new cortex is formed beneath the protruded membrane while the old one is completely degraded. The role of linker proteins in blebbing has mostly been associated with directing blebs to the leading edge of the cell, where linker enrichment is low, suggesting that cells devoid of linker proteins will bleb profusely. However, experimental work in this study involving talA null chemotaxing Dictyostelium discoideum cells shows the opposite effect, pointing to an alternative role for TalA. Our quantitative analysis of bleb size and frequency reveals that talA null cells produce fewer and smaller blebs in confined environments, pointing to a reduction in their intracellular pressure. A mathematical model of bleb expansion developed and validated with our experimental data supports the hypothesis that linker proteins help the cell maintain intracellular pressure during blebbing by limiting changes to its surface area when pressurized. Our model also identifies elastic and viscous properties of the cell, the assembly rate of the new cortex and disassembly rate of the old cortex as key modulators of change in bleb size induced by weakening the strength of membrane to cortex attachment. SIGNIFICANCEThis work analyzes the role of the membrane-to-cortex linker protein talA in regulating bleb size and frequency during bleb-based chemotaxis. We identified that this protein helps to regulate intracellular pressure by preventing pressure loss due to uniform membrane expansion around the cell. In particular, cells form smaller and less frequent blebs without TalA. Analysis of a mathematical model for bleb expansion demonstrates that weakening the strength of linker proteins is sufficient to reduce the size of blebs. Our model also identifies an important role for actin dynamics and the viscoelastic properties of the cell in regulating the percentage change in blebs due to weakening membrane to cortex attachment. Additionally, we find that cells can partially retract blebs without myosin II by regulating the ratio of polymerization and depolymerization of actin in the reforming cortex.
Matching journals
The top 4 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- The role of actin protrusion dynamics in cell migration through a degradable viscoelastic extracellular matrix: Insights from a computational model 97%
- Computational modelling of cell motility modes emerging from cell-matrix adhesion dynamics 97%
- Discrete mechanical model of lamellipodial actin network implements molecular clutch mechanism and generates arcs and microspikes 97%
Similar papers in this journal
- Role of Delta-Notch signalling molecules on cell-cell adhesion in determining heterogeneous chemical and cell morphological patterning 95%
- Resistive force theory and wave dynamics in swimming isolated flagellar apparatus 95%
- Curvature-driven feedback on aggregation-diffusion of proteins in lipid bilayers 95%
Similar papers in this journal
- A Predictive Model of Intercellular Tension and Cell-Matrix Mechanical Interactions in a Multicellular Geometry 96%
- Physical biology of cell-substrate interactions under cyclic stretch 96%
- Tension-area relationship in compartmentalized crumpled plasma membrane: a mechanistic model and its implications 96%
"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.