Periodic Reversals in Paenibacillus dendritiformis Swarming

Be’er, Avraham; Strain, Shinji; Hernández, Roberto A.; Ben‐Jacob, Eshel; Florin, Ernst‐Ludwig

doi:10.1128/jb.00080-13

Cited by 51 publications

(43 citation statements)

References 64 publications

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“…including viscoelasticity [57]). Further developments should also include the active motility and the chemomechanical interactions occurring at the microscopic scale inside the colony, which are of fundamental importance in the appearance and evolution of patterns in some bacterial strains [10,25,26].…”

Section: Discussionmentioning

confidence: 99%

“…However, we remark for sake of completeness that some bacteria also possess other motility mechanisms for colonization. For example, recent studies on Paenibacillus dendritiformis [25] and on myxobacteria [26] have shown that very long bacterial cells do not swarm with the standard dynamic patterns of whirls and jets, but they rather form long tracks in which each individual bacterium periodically reverses its direction, moving back and forth along moderately curved lines. The short time between this direction switching was found to be independent of the number of neighbours and the environmental factors, suggesting the existence of an extraordinary robust internal clock for reversal events in bacteria.…”

Section: Introductionmentioning

confidence: 99%

“…up to centimetres per hour [25]), as extensively studied for many bacterial species. However, we remark for sake of completeness that some bacteria also possess other motility mechanisms for colonization.…”

Section: Introductionmentioning

confidence: 99%

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Branching instability in expanding bacterial colonies

Giverso

Verani

Ciarletta

2015

J. R. Soc. Interface.

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Self-organization in developing living organisms relies on the capability of cells to duplicate and perform a collective motion inside the surrounding environment. Chemical and mechanical interactions coordinate such a cooperative behaviour, driving the dynamical evolution of the macroscopic system. In this work, we perform an analytical and computational analysis to study pattern formation during the spreading of an initially circular bacterial colony on a Petri dish. The continuous mathematical model addresses the growth and the chemotactic migration of the living monolayer, together with the diffusion and consumption of nutrients in the agar. The governing equations contain four dimensionless parameters, accounting for the interplay among the chemotactic response, the bacteria-substrate interaction and the experimental geometry. The spreading colony is found to be always linearly unstable to perturbations of the interface, whereas branching instability arises in finite-element numerical simulations. The typical length scales of such fingers, which align in the radial direction and later undergo further branching, are controlled by the size parameters of the problem, whereas the emergence of branching is favoured if the diffusion is dominant on the chemotaxis. The model is able to predict the experimental morphologies, confirming that compact (resp. branched) patterns arise for fast (resp. slow) expanding colonies. Such results, while providing new insights into pattern selection in bacterial colonies, may finally have important applications for designing controlled patterns.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Branching instability in expanding bacterial colonies

Giverso

Verani

Ciarletta

2015

J. R. Soc. Interface.

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“…P. aeruginosa TFP or flagella confer multiple motility modes in addition to swarming, including swimming, twitching, crawling, and walking (15)(16)(17); P. aeruginosa requires a functional flagellum to swarm (18,19). Although the fastest swarming bacteria (i.e., species of Vibrio or Proteus) transition to a hyperflagellated state or can evolve as hyperflagellated mutants (20)(21)(22)(23), for this study, we have investigated monoflagellated bacteria characteristic of P. aeruginosa swarming. Both TFP and flagella are important to P. aeruginosa biofilm formation (24) and mediate attachment to different surfaces, including eukaryotic epithelial cells (25).…”

Section: Significancementioning

confidence: 99%

Type IV pili interactions promote intercellular association and moderate swarming of Pseudomonas aeruginosa

Anyan¹,

Amiri²,

Harvey³

et al. 2014

Proc. Natl. Acad. Sci. U.S.A.

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Pseudomonas aeruginosa is a ubiquitous bacterium that survives in many environments, including as an acute and chronic pathogen in humans. Substantial evidence shows that P. aeruginosa behavior is affected by its motility, and appendages known as flagella and type IV pili (TFP) are known to confer such motility. The role these appendages play when not facilitating motility or attachment, however, is unclear. Here we discern a passive intercellular role of TFP during flagellar-mediated swarming of P. aeruginosa that does not require TFP extension or retraction. We studied swarming at the cellular level using a combination of laboratory experiments and computational simulations to explain the resultant patterns of cells imaged from in vitro swarms. Namely, we used a computational model to simulate swarming and to probe for individual cell behavior that cannot currently be otherwise measured. Our simulations showed that TFP of swarming P. aeruginosa should be distributed all over the cell and that TFP−TFP interactions between cells should be a dominant mechanism that promotes cell−cell interaction, limits lone cell movement, and slows swarm expansion. This predicted physical mechanism involving TFP was confirmed in vitro using pairwise mixtures of strains with and without TFP where cells without TFP separate from cells with TFP. While TFP slow swarm expansion, we show in vitro that TFP help alter collective motion to avoid toxic compounds such as the antibiotic carbenicillin. Thus, TFP physically affect P. aeruginosa swarming by actively promoting cell−cell association and directional collective motion within motile groups to aid their survival.he bacterium Pseudomonas aeruginosa is a ubiquitous organism that is a known opportunistic pathogen, causing both chronic and acute infections in susceptible populations, including individuals with cystic fibrosis or burn wounds, or Intensive Care Unit patients (1). Among questions that remain unanswered for nonobligate pathogens like P. aeruginosa is how these bacteria initiate infections after entering the host from the environment. Given that P. aeruginosa is among many bacteria that grow as a biofilm during infection, there is a need to understand how individual cells coordinate in space with each other to colonize new surfaces and subsequently transition to stationary biofilms.Many organisms coordinate their movement as a population, emerging as self-organized swarming groups. Even the untrained eye would note the coordinated swarming behavior of fish, birds, and insects. Many bacteria also exhibit collective motion by swarming over surfaces in a coordinated manner to move unimpeded at the same time (2-4). Our knowledge of the specific actions used by individual cells during collective motion is limited; the behavior of single cells within a dense population is difficult to discern experimentally. Previous attempts to study bacterial collective behavior have used computational models to test mechanisms hypothesized to influence collective motion, including directional r...

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“…For example, Paenibacillus dendritiformis posess internal clocks that allow changes in direction independent of environmental cues [4]. Viswanathan et al characterized the movement of Diomedea exulans [21], and Humphries et al described Thalassarche melanophrys [13] searching for pelagic prey as statistically driven, where the pattern of motion is fractal, that is the distribution of step lengths is similar at all scales, defined as a Lévy flight.…”

Section: Adaptive and Stochastic Search Strategiesmentioning

confidence: 99%

Distinguishing Adaptive Search from Random Search in Robots and T cells

Fricke¹,

Black

Hecker

et al. 2015

Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation

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In order to trigger an adaptive immune response, T cells move through lymph nodes (LNs) searching for dendritic cells (DCs) that carry antigens indicative of infection. We hypothesize that T cells adapt to cues in the LN environment to increase search efficiency. We test this hypothesis by identifying locations that are visited by T cells more frequently than a random model of search would suggest. We then test whether T cells that visit such locations have different movement patterns than other T cells. Our analysis suggests that T cells do adapt their movement in response to cues that may indicate the locations of DC targets. We test the ability of our method to identify frequently visited sites in T cells and in a swarm of simulated iAnt robots evolved to search using a suite of biologically-inspired behaviours. We compare the movement of T cells and robots that repeatedly sample the same locations in space with the movement of agents that do not resample space in order to understand whether repeated sampling alters movement. Our analysis suggests that specific environmental cues can be inferred from the movement of T cells. While the precise identity of these cues remains unknown, comparing adaptive search strategies of robots to the movement patterns of T cells lends insights into search efficiency in both systems.

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Periodic Reversals in Paenibacillus dendritiformis Swarming

Cited by 51 publications

References 64 publications

Branching instability in expanding bacterial colonies

Branching instability in expanding bacterial colonies

Type IV pili interactions promote intercellular association and moderate swarming of Pseudomonas aeruginosa

Distinguishing Adaptive Search from Random Search in Robots and T cells

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