Mechanism for collective cell alignment in Myxococcus xanthus bacteria

Myxococcus xanthus cells self-organize into aligned groups, clusters, at various stages of their lifecycle. Formation of these clusters is crucial for the complex dynamic multi-cellular behavior of these bacteria. However, the mechanism underlying the cell alignment and clustering is not fully understood. Motivated by studies of clustering in self-propelled rods, we hypothesized that M. xanthus cells can align and form clusters through pure mechanical interactions among cells and between cells and substrate. We test this hypothesis using an agent-based simulation framework in which each agent is based on the biophysical model of an individual M. xanthus cell. We show that model agents, under realistic cell flexibility values, can align and form cell clusters but only when periodic reversals of cell directions are suppressed. However, by extending our model to introduce the observed ability of cells to deposit and follow slime trails, we show that effective trail-following leads to clusters in reversing cells. Furthermore, we conclude that mechanical cell alignment combined with slime-trail-following is sufficient to explain the distinct clustering behaviors observed for wild-type and non-reversing M. xanthus mutants in recent experiments. Our results are robust to variation in model parameters, match the experimentally observed trends and can be applied to understand surface motility patterns of other bacterial species.Comment: Added paragraph on high cell density simulations (new Supp. Figure S6) in Discussion section; Moved cell model and simulation procedure from Supplementary methods to Methods section in Main Tex

Myxococcus xanthus gliding motors are elastically coupled to the substrate as predicted by the focal adhesion model of gliding motility

Myxococcus xanthus is a model organism for studying bacterial social behaviors due to its ability to form complex multi-cellular structures. Knowledge of M. xanthus surface gliding motility and the mechanisms that coordinate it are critically important to our understanding of collective cell behaviors. Although the mechanism of gliding motility is still under investigation, recent experiments suggest that there are two possible mechanisms underlying force production for cell motility: the focal adhesion mechanism and the helical rotor mechanism which differ in the biophysics of the cell-substrate interactions. Whereas the focal adhesion model predicts an elastic coupling, the helical rotor model predicts a viscous coupling. Using a combination of computational modeling, imaging, and force microscopy, we find evidence for elastic coupling in support of the focal adhesion model. Using a biophysical model of the M. xanthus cell, we investigated how the mechanical interactions between cells are affected by interactions with the substrate. Comparison of modeling results with experimental data for cell-cell collision events pointed to a strong, elastic attachment between the cell and substrate. These results are robust to variations in the mechanical and geometrical parameters of the model. We then directly measured the motor-substrate coupling by monitoring the motion of optically trapped beads and find that motor velocity decreases exponentially with opposing load. At high loads, motor velocity approaches zero velocity asymptotically and motors remain bound to beads indicating a strong, elastic attachment

Cassava storage : post-harvest deterioration and storage of fresh cassava roots

<p>Sum of squares of the errors (SSE) between data from patients <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0014531#pone.0014531-Shankarappa1" target="_blank">[36]</a> and our predictions of viral diversity, <i>d_G</i>, and divergence, <i>d_S</i>, for different values of the population size, <i>C</i>, (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0014531#pone-0014531-g005" target="_blank">Fig. 5</a>) and the viral generation time, τ, shown for each of the nine patients. <i>C</i> and τ that yield the lowest SSE provide the best fit to the data. The best-fit value of <i>C</i> yields <i>N_e</i> (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0014531#pone-0014531-t001" target="_blank">Table 1</a>).</p

Myxobacteria: Moving, Killing, Feeding, and Surviving Together

The Supplementary Material for this article can be found online at: http://journal.frontiersin.org/article/10.3389/fmicb.2016.00781Myxococcus xanthus, like other myxobacteria, is a social bacterium that moves and feeds cooperatively in predatory groups. On surfaces, rod-shaped vegetative cells move in search of the prey in a coordinated manner, forming dynamic multicellular groups referred to as swarms. Within the swarms, cells interact with one another and use two separate locomotion systems. Adventurous motility, which drives the movement of individual cells, is associated with the secretion of slime that forms trails at the leading edge of the swarms. It has been proposed that cellular traffic along these trails contributes to M. xanthus social behavior via stigmergic regulation. However, most of the cells travel in groups by using social motility, which is cell contact-dependent and requires a large number of individuals. Exopolysaccharides and the retraction of type IV pili at alternate poles of the cells are the engines associated with social motility. When the swarms encounter prey, the population of M. xanthus lyses and takes up nutrients from nearby cells. This cooperative and highly density-dependent feeding behavior has the advantage that the pool of hydrolytic enzymes and other secondary metabolites secreted by the entire group is shared by the community to optimize the use of the degradation products. This multicellular behavior is especially observed in the absence of nutrients. In this condition, M. xanthus swarms have the ability to organize the gliding movements of 1000s of rods, synchronizing rippling waves of oscillating cells, to form macroscopic fruiting bodies, with three subpopulations of cells showing division of labor. A small fraction of cells either develop into resistant myxospores or remain as peripheral rods, while the majority of cells die, probably to provide nutrients to allow aggregation and spore differentiation. Sporulation within multicellular fruiting bodies has the benefit of enabling survival in hostile environments, and increases germination and growth rates when cells encounter favorable conditions. Herein, we review how these social bacteria cooperate and review the main cell–cell signaling systems used for communication to maintain multicellularity.This work has been funded by the Spanish Government (grants CSD2009-00006 and BFU2012-33248, 70% funded by FEDER) and Junta de Andalucía (group BIO318)

Taking Multiple Infections of Cells and Recombination into Account Leads to Small Within-Host Effective-Population-Size Estimates of HIV-1

Whether HIV-1 evolution in infected individuals is dominated by deterministic or stochastic effects remains unclear because current estimates of the effective population size of HIV-1 in vivo, Ne, are widely varying. Models assuming HIV-1 evolution to be neutral estimate Ne∼102–104, smaller than the inverse mutation rate of HIV-1 (∼105), implying the predominance of stochastic forces. In contrast, a model that includes selection estimates Ne>105, suggesting that deterministic forces would hold sway. The consequent uncertainty in the nature of HIV-1 evolution compromises our ability to describe disease progression and outcomes of therapy. We perform detailed bit-string simulations of viral evolution that consider large genome lengths and incorporate the key evolutionary processes underlying the genomic diversification of HIV-1 in infected individuals, namely, mutation, multiple infections of cells, recombination, selection, and epistatic interactions between multiple loci. Our simulations describe quantitatively the evolution of HIV-1 diversity and divergence in patients. From comparisons of our simulations with patient data, we estimate Ne∼103–104, implying predominantly stochastic evolution. Interestingly, we find that Ne and the viral generation time are correlated with the disease progression time, presenting a route to a priori prediction of disease progression in patients. Further, we show that the previous estimate of Ne>105 reduces as the frequencies of multiple infections of cells and recombination assumed increase. Our simulations with Ne∼103–104 may be employed to estimate markers of disease progression and outcomes of therapy that depend on the evolution of viral diversity and divergence

Mechanism of cell alignment in Myxococcus xanthus groups

This is predicted as a constitutive feature of a new multiphysics/mechanical model for such systems, which will be presented in this communications. In particular, the density of active receptors, the thickness changes, and the conformational changes are shown to be determined by the interplay between the work done by the lateral pressure surrounding the transmembrane domains and the surrounding lipids and the conformational energy of such domains. This competition is shown to constitutively trigger higher active receptor density regions on lipid rafts

Role of mechanical interactions in self-organization behaviors of Myxococcus xanthus bacteria

Coordinated cell movement and intercellular interactions are crucial for bacterial multicellularity and self-organization, and the mechanisms governing these processes are of active scientific interest. Individual cells interact with neighbors through various biochemical and mechanical interactions, but the role of mechanical interactions in coordination and selforganization of bacteria remains unclear. This work investigates the mechanisms underlying various multicellular patterns in Myxococcus xanthus bacteria, a model organism to study self-organization in bacteria, and the role of mechanical interactions in these self-organization behaviors using biophysical models of cell motility in an agent-based-simulation framework. Using this framework, first I studied the mechanism of gliding cell motility in M. xanthus by discriminating motility behavior of biophysical model cells during physical cell collisions from two alternative cell motility models proposed in the literature. Comparing the model cell motility behavior with experimental cell collision behavior showed that gliding cell motility in M. xanthus requires strong cell-substrate interactions supporting one of the proposed models. New predictions from this model are independently verified in direct experimentation. Next, I investigated the mechanisms responsible for formation and alignment of M. xanthus cells in groups and their collective movement in circular and spiral patterns under starvation, by simulating intercellular interactions among a large number of model cells. Results from the simulations show that these collective cell behaviors in M. xanthus can be explained through mechanical and biochemical interactions among cells and with the substrate. Finally, I investigated the mechanism for non-monotonic colony expansion behavior observed in M. xanthus motility mutants using the agent-based-simulation framework and analyzed individual cell motility behavior from experiments under similar conditions. Results from this work provide evidence that cell-stalling, a crucial assumption made by previous models to explain non-monotonic colony expansion, does not occur due to physical interactions and is not observed in experimental M. xanthus swarms. Results from this thesis work show that many self-organization behaviors in M. xanthus can be explained by a combination of mechanical interactions among cells, between the cells and the substrate and contact based biochemical signaling. This work improves our understanding of mechanisms governing various self-organization behaviors displayed by M. xanthus bacteria and provides a general framework to study self-organization behaviors in other surface motile bacteria

Clustering behavior of periodically reversing agents in simulations.

<p>(A) Snapshot of the simulation with periodically reversing agents (<i>η</i> = 0.24) at 180 min of simulation time. Reversing agents did not show significant clustering. (B) Mean cluster sizes, 〈<i>m</i>〉, in simulation as a function of cell density, <i>η</i>, for agents following slime trails (green line) and agents without slime trails (black line). Agents following slime trails showed a significant increase in mean cluster size compared to agents without slime-trail-following. (C) Snapshot of the simulation for periodically reversing cells with the slime-trail-following mechanism (<i>η</i> = 0.24, <i>L</i>_<i>s</i> = 11 <i>μm</i>, <i>ε</i>_<i>s</i> = 1.0, refer to <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004474#sec009" target="_blank">Methods</a> for details) at 180 min of simulation time. Agents show improved clustering compared to those without the slime-trail-following mechanism. (D) Orientation correlation 〈cos 2Δ<i>θ</i>_<i>r</i>〉 among agents for reversing cells (black) and reversing cells with the slime-trail-following mechanism (green). Dashed and solid lines are orientation correlation values at 1 min and 180 min of simulation time, respectively. Orientation correlation with neighbors improved for larger neighbor distances with the slime-trail-following mechanism.</p

Clustering behavior of non-reversing flexible agents in simulations.

<p>(A-D) Snapshots of the simulation region at 180 min of simulation time for different cell densities, <i>η</i>. (A) <i>η</i> = 0.08, (B) <i>η</i> = 0.16, (C) <i>η</i> = 0.24, (D) <i>η</i> = 0.32. Flexible agents formed aligned clusters at moderate to high cell densities (<i>η</i> ≥ 0.16). (E) Mean cluster sizes, 〈<i>m</i>〉, from simulation as a function of cell density, <i>η</i>. The error bars indicate the standard deviation in the data. The results are averaged over 5 independent simulation runs. The mean cluster sizes increased with increases in cell density. (F) Orientation correlation 〈cos 2Δ<i>θ</i>_<i>r</i>〉 among cells as a function of neighbor cell distance, <i>r</i>. Δ<i>θ</i>_<i>r</i> is the angle deviation between orientations (<i>θ</i>) of a pair of neighbor cells separated by a distance <i>r</i>. Orientation correlation (cos 2Δ<i>θ</i>_<i>r</i>) values from all cell pairs are binned based on <i>r</i> (bin width = 1 <i>μm</i>) and averaged. Dashed and solid lines represent orientation correlation values at 1 min and 180 min of simulation time, respectively. Agents in clusters showed higher neighbor alignment at larger distances compared to the initial randomly oriented cells. Furthermore, the alignment increases with increases in cell density.</p