You get what you select for: better swarming through more flagella

Swarming of P. aeruginosa: Through the lens of biophysics

Swarming is a collective flagella-dependent movement of bacteria across a surface that is observed across many species of bacteria. Due to the prevalence and diversity of this motility modality, multiple models of swarming have been proposed, but a consensus on a general mechanism for swarming is still lacking. Here, we focus on swarming by Pseudomonas aeruginosa due to the abundance of experimental data and multiple models for this species, including interpretations that are rooted in biology and biophysics. In this review, we address three outstanding questions about P. aeruginosa swarming: what drives the outward expansion of a swarm, what causes the formation of dendritic patterns (tendrils), and what are the roles of flagella? We review models that propose biologically active mechanisms including surfactant sensing as well as fluid mechanics-based models that consider swarms as thin liquid films. Finally, we reconcile recent observations of P. aeruginosa swarms with early definitions of swarming. This analysis suggests that mechanisms associated with sliding motility have a critical role in P. aeruginosa swarm formation.

Tree of motility - A proposed history of motility systems in the tree of life

Motility often plays a decisive role in the survival of species. Five systems of motility have been studied in depth: those propelled by bacterial flagella, eukaryotic actin polymerization and the eukaryotic motor proteins myosin, kinesin and dynein. However, many organisms exhibit surprisingly diverse motilities, and advances in genomics, molecular biology and imaging have showed that those motilities have inherently independent mechanisms. This makes defining the breadth of motility nontrivial, because novel motilities may be driven by unknown mechanisms. Here, we classify the known motilities based on the unique classes of movement‐producing protein architectures. Based on this criterion, the current total of independent motility systems stands at 18 types. In this perspective, we discuss these modes of motility relative to the latest phylogenetic Tree of Life and propose a history of motility. During the ~4 billion years since the emergence of life, motility arose in Bacteria with flagella and pili, and in Archaea with archaella. Newer modes of motility became possible in Eukarya with changes to the cell envelope. Presence or absence of a peptidoglycan layer, the acquisition of robust membrane dynamics, the enlargement of cells and environmental opportunities likely provided the context for the (co)evolution of novel types of motility.

The Ultimate Guide to Bacterial Swarming: An Experimental Model to Study the Evolution of Cooperative Behavior

Cooperation has fascinated biologists since Darwin. How did cooperative behaviors evolve despite the fitness cost to the cooperator? Bacteria have cooperative behaviors that make excellent models to take on this age-old problem from both proximate (molecular) and ultimate (evolutionary) angles. We delve into Pseudomonas aeruginosa swarming, a phenomenon where billions of bacteria move cooperatively across distances of centimeters in a matter of a few hours. Experiments with swarming have unveiled a strategy called metabolic prudence that stabilizes cooperation, have showed the importance of spatial structure, and have revealed a regulatory network that integrates environmental stimuli and direct cooperative behavior, similar to a machine learning algorithm. The study of swarming elucidates more than proximate mechanisms: It exposes ultimate mechanisms valid to all scales, from cells in cancerous tumors to animals in large communities.

Division of Labor during Biofilm Matrix Production

Organisms as simple as bacteria can engage in complex collective actions, such as group motility and fruiting body formation. Some of these actions involve a division of labor, where phenotypically specialized clonal subpopulations or genetically distinct lineages cooperate with each other by performing complementary tasks. Here, we combine experimental and computational approaches to investigate potential benefits arising from division of labor during biofilm matrix production. We show that both phenotypic and genetic strategies for a division of labor can promote collective biofilm formation in the soil bacterium Bacillus subtilis. In this species, biofilm matrix consists of two major components, exopolysaccharides (EPSs) and TasA. We observed that clonal groups of B. subtilis phenotypically segregate into three subpopulations composed of matrix non-producers, EPS producers, and generalists, which produce both EPSs and TasA. This incomplete phenotypic specialization was outperformed by a genetic division of labor, where two mutants, engineered as specialists, complemented each other by exchanging EPSs and TasA. The relative fitness of the two mutants displayed a negative frequency dependence both in vitro and on plant roots, with strain frequency reaching a stable equilibrium at 30% TasA producers, corresponding exactly to the population composition where group productivity is maximized. Using individual-based modeling, we show that asymmetries in strain ratio can arise due to differences in the relative benefits that matrix compounds generate for the collective and that genetic division of labor can be favored when it breaks metabolic constraints associated with the simultaneous production of two matrix components.

Bow-tie signaling in c-di-GMP: Machine learning in a simple biochemical network

Bacteria of many species rely on a simple molecule, the intracellular secondary messenger c-di-GMP (Bis-(3'-5')-cyclic dimeric guanosine monophosphate), to make a vital choice: whether to stay in one place and form a biofilm, or to leave it in search of better conditions. The c-di-GMP network has a bow-tie shaped architecture that integrates many signals from the outside world—the input stimuli—into intracellular c-di-GMP levels that then regulate genes for biofilm formation or for swarming motility—the output phenotypes. How does the ‘uninformed’ process of evolution produce a network with the right input/output association and enable bacteria to make the right choice? Inspired by new data from 28 clinical isolates of Pseudomonas aeruginosa and strains evolved in laboratory experiments we propose a mathematical model where the c-di-GMP network is analogous to a machine learning classifier. The analogy immediately suggests a mechanism for learning through evolution: adaptation though incremental changes in c-di-GMP network proteins acquires knowledge from past experiences and enables bacteria to use it to direct future behaviors. Our model clarifies the elusive function of the ubiquitous c-di-GMP network, a key regulator of bacterial social traits associated with virulence. More broadly, the link between evolution and machine learning can help explain how natural selection across fluctuating environments produces networks that enable living organisms to make sophisticated decisions.

How does evolution shape living organisms that seem so well adapted that they could be intelligently designed? Here, we address this question by analyzing a simple biochemical network that directs social behavior in bacteria; we find that it works analogously to a machine learning algorithm that learns from data. Inspired by new experiments, we derive a model which shows that natural selection—by favoring biochemical networks that maximize fitness across a series of fluctuating environments—can be mathematically equivalent to training a machine learning model to solve a classification problem. Beyond bacteria, the formal link between evolution and learning opens new avenues for biology: machine learning is a fast-moving field and its many theoretical breakthroughs can answer long-standing questions in evolution.

Evolution by flight and fight: diverse mechanisms of adaptation by actively motile microbes

Evolutionary adaptation can be achieved by mechanisms accessible to all organisms, including faster growth and interference competition, but self-generated motility offers additional possibilities. We tested whether 55 populations of the bacterium Myxococcus xanthus that underwent selection for increased fitness at the leading edge of swarming colonies adapted by swarming faster toward unused resources or by other means. Populations adapted greatly but diversified markedly in both swarming phenotypes and apparent mechanisms of adaptation. Intriguingly, although many adapted populations swarm intrinsically faster than their ancestors, numerous others do not. Some populations evolved interference competition toward their ancestors, whereas others gained the ability to facultatively increase swarming rate specifically upon direct interaction with ancestral competitors. Our results both highlight the diverse range of mechanisms by which actively motile organisms can adapt evolutionarily and help to explain the high levels of swarming-phenotype diversity found in local soil populations of M. xanthus.

Sociomicrobiology and Pathogenic Bacteria

The study of microbial pathogenesis has been primarily a reductionist science since Koch's principles. Reductionist approaches are essential to identify the causal agents of infectious disease, their molecular mechanisms of action, and potential drug targets, and much of medicine's success in the treatment of infectious disease stems from that approach. But many bacteria-caused diseases cannot be explained by a single bacterium. Several aspects of bacterial pathogenesis will benefit from a more holistic approach that takes into account social interaction among bacteria of the same species and between species in consortia such as the human microbiome. The emerging discipline of sociomicrobiology provides a framework to dissect microbial interactions in single and multi-species communities without compromising mechanistic detail. The study of bacterial pathogenesis can benefit greatly from incorporating concepts from other disciplines such as social evolution theory and microbial ecology, where communities, their interactions with hosts, and with the environment play key roles.

Probing the effect of minor groove interactions on the catalytic efficiency of DNAzymes 8-17 and 10-23

DNAzymes (Dz) 8-17 and 10-23 are two widely studied and well-characterized RNA-cleaving DNA catalysts. In an effort to further improve the understanding of the fragile interactions and dynamics of the enzymatic mechanism, this study examines the catalytic efficiency of minimally modified DNAzymes. Five single mutants of Dz8-17 and Dz10-23 were prepared by replacing the adenine residues in the corresponding catalytic cores with 3-deazaadenine units. Kinetic assays were used to assess the effect on the catalytic activity and thereby identify the importance of hydrogen bonding that arises from the N3 atoms. The results suggest that modifications at A15 and A15.0 of Dz8-17 have a significant influence and show a reduction in catalytic activity. Modification at each location in Dz10-23 results in a decrease of the observed rate constants, with A12 appearing to be the most affected with a reduction of ∼80% of kobs and ∼25% of the maximal cleavage rate compared to the wild-type DNAzyme. On the other hand, modification of A12 in Dz8-17 showed an ∼130% increase in kobs, thus unraveling a new potential site for the introduction of chemical modifications. A pH-profile analysis showed that the chemical cleavage step is rate-determining, regardless of the presence and/or location of the mutation. These findings point towards the importance of the N3-nitrogens of certain adenine nucleotides located within the catalytic cores of the DNAzymes for efficient catalytic activity and further suggest that they might directly partake in maintaining the appropriate tertiary structure. Therefore, it appears that minor groove interactions constitute an important feature of DNAzymes as well as ribozymes.

Water surface tension modulates the swarming mechanics of Bacillus subtilis

Many Bacillus subtilis strains swarm, often forming colonies with tendrils on agar medium. It is known that B. subtilis swarming requires flagella and a biosurfactant, surfactin. In this study, we find that water surface tension plays a role in swarming dynamics. B. subtilis colonies were found to contain water, and when a low amount of surfactin is produced, the water surface tension of the colony restricts expansion, causing bacterial density to rise. The increased density induces a quorum sensing response that leads to heightened production of surfactin, which then weakens water surface tension to allow colony expansion. When the barrier formed by water surface tension is breached at a specific location, a stream of bacteria swarms out of the colony to form a tendril. If a B. subtilis strain produces surfactin at levels that can substantially weaken the overall water surface tension of the colony, water floods the agar surface in a thin layer, within which bacteria swarm and migrate rapidly. This study sheds light on the role of water surface tension in regulating B. subtilis swarming, and provides insight into the mechanisms underlying swarming initiation and tendril formation.

Seeing is believing: what experiments with microbes reveal about evolution

Darwin's theory of natural selection is among the most powerful ideas in science, yet evolutionary ideas remain challenged to this day. This is in part because evolution often cannot be directly observed. Simple experiments with microbes can change that by enabling direct observation of evolutionary processes.

Hyperswarming adaptations in a bacterium improve collective motility without enhancing single cell motility

Pseudomonas aeruginosa is a monoflagellated bacterium that can use its single polar flagellum to swim through liquids and move collectively over semisolid surfaces, a behavior called swarming. Previous studies have shown that experimental evolution in swarming colonies leads to the selection of hyperswarming bacteria with multiple flagella. Here we show that the advantage of such hyperswarmer mutants cannot be explained simply by an increase in the raw swimming speed of individual bacteria in liquids. Cell tracking of time-lapse microscopy to quantify single-cell swimming patterns reveals that both wild-type and hyperswarmers alternate between forward and backward runs, rather than doing the run-and-tumble characteristic of enteric bacteria such as E. coli. High-throughput measurement of swimming speeds reveals that hyperswarmers do not swim faster than wild-type in liquid. Wild-type reverses swimming direction in sharp turns without a significant impact on its speed, whereas multiflagellated hyperswarmers tend to alternate fast and slow runs and have wider turning angles. Nonetheless, macroscopic measurement of swimming and swarming speed in colonies shows that hyperswarmers expand faster than wild-type on surfaces and through soft agar matrices. A mathematical model explains how wider turning angles lead to faster spreading when swimming through agar. Our study describes for the first time the swimming patterns in multiflagellated P. aeruginosa mutants and reveals that collective and individual motility in bacteria are not necessarily correlated. Understanding bacterial adaptations to surface motility, such as hyperswarming, requires a collective behavior approach.