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. 2019 Jan-Feb;9(1):e1376.
doi: 10.1002/wcms.1376. Epub 2018 Jul 19.

Eukaryotic Cell Dynamics from Crawlers to Swimmers

Affiliations

Eukaryotic Cell Dynamics from Crawlers to Swimmers

H G Othmer. Wiley Interdiscip Rev Comput Mol Sci. 2019 Jan-Feb.

Abstract

Movement requires force transmission to the environment, and motile cells are robustly, though not elegantly, designed nanomachines that often can cope with a variety of environmental conditions by altering the mode of force transmission used. As with humans, the available modes range from momentary attachment to a substrate when crawling, to shape deformations when swimming, and at the cellular level this involves sensing the mechanical properties of the environment and altering the mode appropriately. While many types of cells can adapt their mode of movement to their microenvironment (ME), our understanding of how they detect, transduce and process information from the ME to determine the optimal mode is still rudimentary. The shape and integrity of a cell is determined by its cytoskeleton (CSK), and thus the shape changes that may be required to move involve controlled remodeling of the CSK. Motion in vivo is often in response to extracellular signals, which requires the ability to detect such signals and transduce them into the shape changes and force generation needed for movement. Thus the nanomachine is complex, and while much is known about individual components involved in movement, an integrated understanding of motility in even simple cells such as bacteria is not at hand. In this review we discuss recent advances in our understanding of cell motility and some of the problems remaining to be solved.

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Figures

Figure 1
Figure 1
Two examples of mesenchymal motion. Top: A fibroblast in a 3D collagen matrix From Petrie et al(4). Bottom: A fish-scale keratocyte. Actin (green) myosin-II (red) and focal adhesions (blue). From www.hhmi.org/scientists/julie-theriot.
Figure 1
Figure 1
Two examples of mesenchymal motion. Top: A fibroblast in a 3D collagen matrix From Petrie et al(4). Bottom: A fish-scale keratocyte. Actin (green) myosin-II (red) and focal adhesions (blue). From www.hhmi.org/scientists/julie-theriot.
Figure 2
Figure 2
(a) Blebbing on a melanoma cell: myosin (green) localizes under the blebbing membrane (red) (b) The actin cortex of a Dd cell migrating to the lower right. Arrowheads indicate the successive blebs and arcs of the actin cortex. From Charras & Paluch(7).
Figure 2
Figure 2
(a) Blebbing on a melanoma cell: myosin (green) localizes under the blebbing membrane (red) (b) The actin cortex of a Dd cell migrating to the lower right. Arrowheads indicate the successive blebs and arcs of the actin cortex. From Charras & Paluch(7).
Figure 3
Figure 3
A schematic of a mesenchymal cell, showing the various sub-structures and protrusions. From Blanchoin et al.(20).
Figure 4
Figure 4
The molecular clutch model. From Chan & Odde(51).
Figure 5
Figure 5
How the shape of a keratocyte depends on the subtrate stiffness. From Riaz et al.(69).
Figure 6
Figure 6
(a) The normal vs the in-plane force in Dd. The inset shows the labeling. (b) The cyclic variation of the forces and the speed. (c) and (d) The tangential and normal forces in Dd. (a) and (b) taken from Delanoe-Ayari et al. (96), (c) and (d) taken from Alvarez-Gonzalez et al.(97).
Figure 6
Figure 6
(a) The normal vs the in-plane force in Dd. The inset shows the labeling. (b) The cyclic variation of the forces and the speed. (c) and (d) The tangential and normal forces in Dd. (a) and (b) taken from Delanoe-Ayari et al. (96), (c) and (d) taken from Alvarez-Gonzalez et al.(97).
Figure 6
Figure 6
(a) The normal vs the in-plane force in Dd. The inset shows the labeling. (b) The cyclic variation of the forces and the speed. (c) and (d) The tangential and normal forces in Dd. (a) and (b) taken from Delanoe-Ayari et al. (96), (c) and (d) taken from Alvarez-Gonzalez et al.(97).
Figure 7
Figure 7
(A) Some of the major components of cAMP signal transduction in Dictyostelium discoideum. CAR1: the cAMP receptor, Gαβγ: a G-protein involved in the transduction of the extracellular signal, Ras: a small G-protein, PIP2 and PIP3; components of the membrane that can be interconverted via phosphorylation and de-phosphorylation, IP3 and DAG: poducts that result from the degradation of PIP2, Ca2: calcium, GC: guanylate cyclase – the enzyme that produces cyclic GMP (cGMP), AC: adenylate cyclase – the enzyme that produces cAMP, Rac1: a small G-rotein, Myosin: a motor protein involved in contraction of the actin network. (B) The PIP2-PIP3 trio. Activated Ras activates PI3K, which phosphorylates PIP2. PIP3 provides a binding site for cytosolic PI3K, thereby creating a positive feedback loop through PI3K. Similarly, PIP2 provides a binding site for PTEN, which dephosphorylates PIP3. PIP3 levels are controlled in part by PTEN and SHIP, which dephosphorylate PIP3 at different sites. (C) The skeletal network downstream of Ras that determines the balance between dendritic network formation and myo-II assembly in Dd.
Figure 8
Figure 8
(a) A cross-section of the waves. From Gerisch(118). (b) A schematic of the network structure and molecular interactions in the model.(119) (c) Two snapshots in time of an actin wave initiated at x=2.5, showing the network density (color) as a function of space (x-axis) and network height (z-axis). From Khamviwath et al.(119) with permission.
Figure 9
Figure 9
A schematic of the major processes in the model (top), and the major steps in the network (bottom). From Cheng & Othmer (149) with permission.
Figure 9
Figure 9
A schematic of the major processes in the model (top), and the major steps in the network (bottom). From Cheng & Othmer (149) with permission.
Figure 10
Figure 10
The levels of activated Ras* at the front and back in a static cAMP gradient as a function of time measured experimentally (a) From Kortholt et al(156) and the model prediction (b)(149) (c) The average Ras* in the front and rear halves in response to a passing triangle wave. From Cheng & Othmer(149) with permission.
Figure 10
Figure 10
The levels of activated Ras* at the front and back in a static cAMP gradient as a function of time measured experimentally (a) From Kortholt et al(156) and the model prediction (b)(149) (c) The average Ras* in the front and rear halves in response to a passing triangle wave. From Cheng & Othmer(149) with permission.
Figure 10
Figure 10
The levels of activated Ras* at the front and back in a static cAMP gradient as a function of time measured experimentally (a) From Kortholt et al(156) and the model prediction (b)(149) (c) The average Ras* in the front and rear halves in response to a passing triangle wave. From Cheng & Othmer(149) with permission.
Figure 11
Figure 11
The ratio of Ras* under uniform stimulation to it’s basal level as a function of the stimulus level and time. From Cheng & Othmer (149) with permission.
Figure 12
Figure 12
The push-me-pull-you swimmer.
Figure 13
Figure 13
(a)Four time points of a swimming Dd cell showing the axial propagation of protrusions along the length. From Barry & Bretscher(17). (b) Three time frames from a computational model of a 2D swimmer using symmetric protrusions. (c) The mean velocity of two swimmers as a function of the protrusion height. (b) and (c) from Wang & Othmer(173) with permission.
Figure 13
Figure 13
(a)Four time points of a swimming Dd cell showing the axial propagation of protrusions along the length. From Barry & Bretscher(17). (b) Three time frames from a computational model of a 2D swimmer using symmetric protrusions. (c) The mean velocity of two swimmers as a function of the protrusion height. (b) and (c) from Wang & Othmer(173) with permission.
Figure 13
Figure 13
(a)Four time points of a swimming Dd cell showing the axial propagation of protrusions along the length. From Barry & Bretscher(17). (b) Three time frames from a computational model of a 2D swimmer using symmetric protrusions. (c) The mean velocity of two swimmers as a function of the protrusion height. (b) and (c) from Wang & Othmer(173) with permission.
Figure 14
Figure 14
A summary of the different modes of movement in different environments and under different substrate properties. From Petrie et al.(177).
Figure 15
Figure 15
Top: The type A1 morphology and the cortical flow rates observed in zebrafish progenitor cells. From Ruprecht et al. (9). Bottom: A schematic of the cortical and interior flow.
Figure 15
Figure 15
Top: The type A1 morphology and the cortical flow rates observed in zebrafish progenitor cells. From Ruprecht et al. (9). Bottom: A schematic of the cortical and interior flow.

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