Chemotaxis as a navigation strategy to boost range expansion

Extending visual range of bacteria with upconversion nanoparticles and constructing NIR-responsive bio-microrobots

The motility of bacteria is crucial for navigating competitive environments and is closely linked to physiological activities essential for their survival, such as biofilm development. Precise regulation of bacterial motility enhances our understanding of these complex processes. While optogenetic tools have been used to control and investigate bacterial motility, the excitation light in most existing systems are limited to the visible light spectrum. Here, we introduce a new type of bio-microrobot comprising genetically engineered E. coli cells and orthogonally emissive upconversion nanoparticles that can respond to both 980 nm and 808 nm NIR light. This system allows toggling of bacterial states between tumbling and swimming via simply alternating the NIR light between different wavelengths. It is believed that the use of NIR light with deeper tissue penetration suggests potential applications for these bio-microrobots in areas like targeted drug delivery.

Engineered Phage Enables Efficient Control of Gene Expression upon Infection of the Host Cell

Recently, we developed a spatial phage-assisted continuous evolution (SPACE) system. This system utilizes chemotaxis coupled with the growth of motile bacteria during their spatial range expansion in soft agar to provide fresh host cells for iterative phage infection and selection pressure for preserving evolved genes of interest carried by phage mutants. Controllable mutagenesis activated only in a subpopulation of the migrating cells is essential in this system to efficiently generate mutated progeny phages from which desired individuals are selected during the directed evolution process. But, the widely adopted small molecule-dependent inducible system could hardly fulfill this purpose because it always affects all cells homogeneously. In this study, we developed a phage infection-induced gene expression system using modified Escherichia coli (E. coli) phage shock protein operon or sigma factors from Bacillus subtilis. Results showed that this system enabled efficient control of gene expression upon phage infection with dynamic output ranges from small to large using combinations of different engineered phages and corresponding promoters. This system was incorporated into SPACE to function as a phage infection-induced mutagenesis module and successfully facilitated the evolution of T7 RNA polymerase, which generated diverse mutants with altered promoter recognition specificity. We expect that phage infection-induced gene expression system could be further extended to more applications involving partial induction in a portion of a population and targeted induction in specific strains among a mixed bacterial community, which provides an important complement to small molecule-dependent inducible systems.

New insights into the efficient secretion of foreign protein in Bacillus subtilis via Ribo-seq and RNA-seq integrative analyses

BACKGROUND

As an important prokaryotic model organism, Bacillus subtilis has been widely used in the industrial production of a variety of target products. The efficient secretion of target products has always been the main purpose of industrial microbial technology. The modification of gene regulatory networks is an important technical means to construct a factory of microbial cells that efficiently secretes target products. However, the regulatory network of the efficient expression of foreign genes in B. subtilis has not been studied at the translation level.

RESULTS

In this study, Ribo-seq and RNA-seq technology were used to study the changes in differentially expressed genes during the efficient secretion of the protease PB92 by B. subtilis WB600, and the results revealed the gene regulatory network related to efficient secretion of foreign protein. The results revealed that the correlation between the differentially expressed genes of B. subtilis at the transcription and translation levels was only 0.5354. Forty-one common (transcription and translation) and 436 unique (translation) key differential gene sets that may be related to the efficient secretion of foreign proteins were revealed. KEGG enrichment analysis of these key gene sets revealed that they were involved mainly in the cell motility and central metabolic regulatory network of B. subtilis.

CONCLUSION

Our study provides important guidance for the construction of cell factories and metabolic networks for the efficient secretion of target products by B. subtilis.

Chemotaxing E. coli do not count single molecules

Understanding biological functions requires identifying the physical limits and system-specific constraints that have shaped them. In Escherichia coli chemotaxis, gradient-climbing speed is information-limited, bounded by the sensory information they acquire from real-time measurements of their environment. However, it remains unclear what limits this information. Past work conjectured that E. coli's chemosensing is limited by the physics of molecule arrivals at their sensors. Here, we derive the physical limit on behaviorally-relevant information, and then perform single-cell experiments to quantify how much information E. coli's signaling pathway encodes. We find that E. coli encode two orders of magnitude less information than the physical limit due to their stochastic signal processing. Thus, system-specific constraints, rather than the physical limit, have shaped the evolution of this canonical sensory-motor behavior.

Non-genetic adaptation by collective migration

Cell populations must adjust their phenotypic composition to adapt to changing environments. One adaptation strategy is to maintain distinct phenotypic subsets within the population and to modulate their relative abundances via gene regulation. Another strategy involves genetic mutations, which can be augmented by stress-response pathways. Here, we studied how a migrating bacterial population regulates its phenotypic distribution to traverse diverse environments. We generated isogenic Escherichia coli populations with varying distributions of swimming behaviors and observed their phenotype distributions during migration in liquid and porous environments. We found that the migrating populations became enriched with high-performing swimming phenotypes in each environment, allowing the populations to adapt without requiring mutations or gene regulation. This adaptation is dynamic and rapid, reversing in a few doubling times when migration ceases. By measuring the chemoreceptor abundance distributions during migration towards different attractants, we demonstrated that adaptation acts on multiple chemotaxis-related traits simultaneously. These measurements are consistent with a general mechanism in which adaptation results from a balance between cell growth generating diversity and collective migration eliminating under-performing phenotypes. Thus, collective migration enables cell populations with continuous, multi-dimensional phenotypes to flexibly and rapidly adapt their phenotypic composition to diverse environmental conditions.

Significance statement

Conventional cell adaptation mechanisms, like gene regulation and stochastic phenotypic switching, act swiftly but are limited to a few traits, while mutation-driven adaptations unfold slowly. By quantifying phenotypic diversity during bacterial collective migration, we discovered an adaptation mechanism that rapidly and reversibly adjusts multiple traits simultaneously. By balancing the generation of diversity through growth with the loss of phenotypes unable to keep up, this process tunes the phenotypic composition of migrating populations to the environments they traverse, without gene regulation or mutations. Given the prevalence of collective migration in microbes, cancers, and embryonic development, non-genetic adaptation through collective migration may be a universal mechanism for populations to navigate diverse environments, offering insights into broader applications across various fields.

Influence of Flagella on Salmonella Enteritidis Sedimentation, Biofilm Formation, Disinfectant Resistance, and Interspecies Interactions

Flagella are essential for bacterial motility and biofilm formation by aiding bacterial attachment to surfaces. However, the impact of flagella on bacterial behavior, particularly biofilm formation, remains unclear. This study constructed two flagellar mutation strains of Salmonella Enteritidis (SE), namely, SE-ΔflhD and SE-ΔflgE, and confirmed the loss of flagellar structures and motility in these strains. The mutant strains exhibited growth comparable with the wild-type (WT) strain but had higher sedimentation rates. Biofilm biomass did not differ significantly between the WT and mutant strains, except for SE-ΔflgE at 3 d. SE-ΔflgE showed increased susceptibility to sodium hypochlorite compared to the WT. The co-sedimentation rate of flagella-deficient strains was lower than the WT, and the biomass of dual-species biofilm formed by Bacillus paramycoides B5 with SE-ΔflhD or SE-ΔflgE was significantly lower than with the WT. These findings emphasize the significance of SE flagella in biofilm formation and interspecies interactions, offering insights into targeted biofilm prevention and control measures.

Data-driven discovery of chemotactic migration of bacteria via coordinate-invariant machine learning

BACKGROUND

E. coli chemotactic motion in the presence of a chemonutrient field can be studied using wet laboratory experiments or macroscale-level partial differential equations (PDEs) (among others). Bridging experimental measurements and chemotactic Partial Differential Equations requires knowledge of the evolution of all underlying fields, initial and boundary conditions, and often necessitates strong assumptions. In this work, we propose machine learning approaches, along with ideas from the Whitney and Takens embedding theorems, to circumvent these challenges.

RESULTS

Machine learning approaches for identifying underlying PDEs were (a) validated through the use of simulation data from established continuum models and (b) used to infer chemotactic PDEs from experimental data. Such data-driven models were surrogates either for the entire chemotactic PDE right-hand-side (black box models), or, in a more targeted fashion, just for the chemotactic term (gray box models). Furthermore, it was demonstrated that a short history of bacterial density may compensate for the missing measurements of the field of chemonutrient concentration. In fact, given reasonable conditions, such a short history of bacterial density measurements could even be used to infer chemonutrient concentration.

CONCLUSION

Data-driven PDEs are an important modeling tool when studying Chemotaxis at the macroscale, as they can learn bacterial motility from various data sources, fidelities (here, computational models, experiments) or coordinate systems. The resulting data-driven PDEs can then be simulated to reproduce/predict computational or experimental bacterial density profile data independent of the coordinate system, approximate meaningful parameters or functional terms, and even possibly estimate the underlying (unmeasured) chemonutrient field evolution.

Position-dependent roles of somatic cells in phototaxis of Volvox

A spherical green alga, Volvox, achieves phototaxis via a simple on/off switch of flagellar beating in response to changes in light intensity, without the need for complex signal transduction between cells. Moreover, the alga can change its susceptibility to light in order to adapt to its environment. To identify the mechanisms of susceptibility regulation, experiments were conducted at three different levels: population, individual, and cellular. The light intensity dependence of the average velocity at the population level and that of the change in flow speed obtained at the individual level were consistent, indicating that susceptibility regulation occurred in each Volvox colony. Furthermore, by measuring the probability of stopping flagellar beating when the light intensity was changed, susceptibility regulation was found to result from the properties of somatic cells as differential and adaptive photosensors. These photosensing properties deteriorated from the anterior to the posterior regions of the colony. Considering the mechanical motion of a Volvox colony, the position-dependent ability of somatic cells indicates that the anterior cells play the role of a rudder, whereas the posterior cells play the role of a rower. The position-dependent properties of somatic cells imply an early stage of cell differentiation that allows for an efficient response to changes in the circumstances.

Role of Signal Degradation in Directional Chemosensing

Directional chemosensing is ubiquitous in cell biology, but some cells such as mating yeast paradoxically degrade the signal they aim to detect. While the data processing inequality suggests that such signal modification cannot increase the sensory information, we show using a reaction-diffusion model and an exactly solvable discrete-state reduction that it can. We identify a non-Markovian step in the information chain allowing the system to evade the data processing inequality, reflecting the nonlocal nature of diffusion. Our results apply to any sensory system in which degradation couples to diffusion. Experimental data suggest that mating yeast operate in the beneficial regime where degradation improves sensing.

Surface-programmed microbiome assembly in phycosphere to microplastics contamination

Recalcitrance in microplastics accounts for ubiquitous white pollution. Of special interest are the capabilities of microorganisms to accelerate their degradation sustainably. Compared to the well-studied pure cultures in degrading natural polymers, the algal-bacterial symbiotic system is considered as a promising candidate for microplastics removal, cascading bottom-up impacts on ecosystem-scale processes. This study selected and enriched the algae-associated microbial communities hosted by the indigenous isolation Desmodesmus sp. in wastewater treatment plants with micro-polyvinyl chloride, polyethylene terephthalate, polyethylene, and polystyrene contamination. Results elaborated that multiple settled and specific affiliates were recruited by the uniform algae protagonist from the biosphere under manifold microplastic stress. Alteration of distinct chemical functionalities and deformation of polymers provide direct evidence of degradation in phycosphere under illumination. Microplastic-induced phycosphere-derived DOM created spatial gradients of aromatic protein, fulvic and humic acid-like and tryptophan components to expanded niche-width. Surface thermodynamic analysis was conducted to simulate the reciprocal and reversible interaction on algal-bacterial and phycosphere-microplastic interface, revealing the enhancement of transition to stable and irreversible aggregation for functional microbiota colonization and microplastics capture. Furthermore, pangenomic analysis disclosed the genes related to the chemotaxis and the proposed microplastics biodegradation pathway in enriched algal-bacterial microbiome, orchestrating the evidence for common synthetic polymer particles and ultimately to confirm the effectiveness and potential. The present study emphasizes the necessity for future endeavors aimed at fully leveraging the potential of algal-bacterial mutualistic systems within sustainable bioremediation strategies targeting the eradication of microplastic waste.

Spatiotemporal control of structure and dynamics in a polar active fluid

We apply optimal control theory to a model of a polar active fluid (the Toner-Tu model), with the objective of driving the system into particular emergent dynamical behaviors or programming switching between states on demand. We use the effective self-propulsion speed as the control parameter (i.e. the means of external actuation). We identify control protocols that achieve outcomes such as relocating asters to targeted positions, forcing propagating solitary waves to reorient to a particular direction, and switching between stationary asters and propagating fronts. We analyze the solutions to identify generic principles for controlling polar active fluids. Our findings have implications for achieving spatiotemporal control of active polar systems in experiments, particularly in vitro cytoskeletal systems. Additionally, this research paves the way for leveraging optimal control methods to engineer the structure and dynamics of active fluids more broadly.

Collective condensation and auto-aggregation of Escherichia coli in uniform acidic environments

Chemotaxis-the movement of cells along chemical gradients-leads to collective behaviors when cells coordinate their movements. Here, using Escherichia coli as a model, we demonstrate a distinct type of bacterial collective response in acidic environments containing organic acids. Bacterial populations immersed in such environments collectively condensed into millimeter-sized focal points. Furthermore, this bacterial condensation fostered the formation of small, tightly packed cell aggregates, resembling non-surface-attached biofilms. These cell aggregates were physically displaced by the free-swimming condensing cells, leading to the segregation of the two cell populations. Bacterial condensation relied on feedback between the tendency of these bacteria to neutralize the pH and their chemotactic repulsion from low pH. Sustained cell condensation occurred when the bacteria occupied only part of the acidic environment, either dynamically or due to physical constraints. Such condensed bacterial populations can mitigate acid stress more efficiently, a principle that may be applicable to other stress conditions.

In-situ enrichment of ARGs and their carriers in soil by hydroxamate siderophore: A promising biocontrol approach for source reduction

Pathogenic microorganisms with antibiotic resistance genes (ARGs) pose a serious threat to public health and soil ecology. Although new drugs and available antibacterial materials can kill ARG carriers but accidentally kill beneficial microorganisms. Therefore, the rapid enrichment and separation of ARGs and their carriers from soil is becoming an important strategy for controlling the diffusion of ARGs. Hydroxamate siderophore (HDS) has gained widespread attentions for its involvement in trace element transfer among microorganisms in the soil environment, we thus explored an in-situ trapping-enrichment method for ARGs and their carriers via a small molecular HDS secreted by Pseudomonas fluorescens HMP01. In this study, we demonstrate that HDS significantly in-situ traps and enriches certain ARGs, including chloramphenicol, MLS, rifamycin, and tetracycline resistance genes in the soil environment. The enrichment efficiencies were 1473-fold, 38-fold, 17-fold, and 5-fold, respectively, higher than those in the control group. Specifically, the primary enriched ARGs were rpoB, mphL, catB2, and tetA(60), and Bacillus, Rhizobium, Rossellomorea, and Agrobacterium were hosts for these ARGs. This enrichment was caused by the upregulation of chemotaxis genes (e.g., cheW, cheC, and cheD) and rapid biofilm formation within the enriched bacterial population. Notably, representative ARGs such as cat, macB, and rpoB were significantly reduced by 36%, 85.7%, and 72%, respectively, in the paddy soil after HDS enrichment. Our research sheds light on the potential application of siderophore as a rapping agent for the eco-friendly reduction of ARGs and their carriers in soil environments.

E. coli do not count single molecules

Organisms must perform sensory-motor behaviors to survive. What bounds or constraints limit behavioral performance? Previously, we found that the gradient-climbing speed of a chemotaxing Escherichia coli is near a bound set by the limited information they acquire from their chemical environments (1). Here we ask what limits their sensory accuracy. Past theoretical analyses have shown that the stochasticity of single molecule arrivals sets a fundamental limit on the precision of chemical sensing (2). Although it has been argued that bacteria approach this limit, direct evidence is lacking. Here, using information theory and quantitative experiments, we find that E. coli's chemosensing is not limited by the physics of particle counting. First, we derive the physical limit on the behaviorally-relevant information that any sensor can get about a changing chemical concentration, assuming that every molecule arriving at the sensor is recorded. Then, we derive and measure how much information E. coli's signaling pathway encodes during chemotaxis. We find that E. coli encode two orders of magnitude less information than an ideal sensor limited only by shot noise in particle arrivals. These results strongly suggest that constraints other than particle arrival noise limit E. coli's sensory fidelity.

Bacterial Electrophysiology

Bacterial ion fluxes are involved in the generation of energy, transport, and motility. As such, bacterial electrophysiology is fundamentally important for the bacterial life cycle, but it is often neglected and consequently, by and large, not understood. Arguably, the two main reasons for this are the complexity of measuring relevant variables in small cells with a cell envelope that contains the cell wall and the fact that, in a unicellular organism, relevant variables become intertwined in a nontrivial manner. To help give bacterial electrophysiology studies a firm footing, in this review, we go back to basics. We look first at the biophysics of bacterial membrane potential, and then at the approaches and models developed mostly for the study of neurons and eukaryotic mitochondria. We discuss their applicability to bacterial cells. Finally, we connect bacterial membrane potential with other relevant (electro)physiological variables and summarize methods that can be used to both measure and influence bacterial electrophysiology.

Advancements in bacterial chemotaxis: Utilizing the navigational intelligence of bacteria and its practical applications

The fascinating world of microscopic life unveils a captivating spectacle as bacteria effortlessly maneuver through their surroundings with astonishing accuracy, guided by the intricate mechanism of chemotaxis. This review explores the complex mechanisms behind this behavior, analyzing the flagellum as the driving force and unraveling the intricate signaling pathways that govern its movement. We delve into the hidden costs and benefits of this intricate skill, analyzing its potential to propagate antibiotic resistance gene while shedding light on its vital role in plant colonization and beneficial symbiosis. We explore the realm of human intervention, considering strategies to manipulate bacterial chemotaxis for various applications, including nutrient cycling, algal bloom and biofilm formation. This review explores the wide range of applications for bacterial capabilities, from targeted drug delivery in medicine to bioremediation and disease control in the environment. Ultimately, through unraveling the intricacies of bacterial movement, we can enhance our comprehension of the intricate web of life on our planet. This knowledge opens up avenues for progress in fields such as medicine, agriculture, and environmental conservation.

Smart active particles learn and transcend bacterial foraging strategies

Throughout evolution, bacteria and other microorganisms have learned efficient foraging strategies that exploit characteristic properties of their unknown environment. While much research has been devoted to the exploration of statistical models describing the dynamics of foraging bacteria and other (micro-) organisms, little is known, regarding the question of how good the learned strategies actually are. This knowledge gap is largely caused by the absence of methods allowing to systematically develop alternative foraging strategies to compare with. In the present work, we use deep reinforcement learning to show that a smart run-and-tumble agent, which strives to find nutrients for its survival, learns motion patterns that are remarkably similar to the trajectories of chemotactic bacteria. Strikingly, despite this similarity, we also find interesting differences between the learned tumble rate distribution and the one that is commonly assumed for the run and tumble model. We find that these differences equip the agent with significant advantages regarding its foraging and survival capabilities. Our results uncover a generic route to use deep reinforcement learning for discovering search and collection strategies that exploit characteristic but initially unknown features of the environment. These results can be used, e.g., to program future microswimmers, nanorobots, and smart active particles for tasks like searching for cancer cells, micro-waste collection, or environmental remediation.

Microbes in porous environments: from active interactions to emergent feedback

Microbes thrive in diverse porous environments-from soil and riverbeds to human lungs and cancer tissues-spanning multiple scales and conditions. Short- to long-term fluctuations in local factors induce spatio-temporal heterogeneities, often leading to physiologically stressful settings. How microbes respond and adapt to such biophysical constraints is an active field of research where considerable insight has been gained over the last decades. With a focus on bacteria, here we review recent advances in self-organization and dispersal in inorganic and organic porous settings, highlighting the role of active interactions and feedback that mediates microbial survival and fitness. We discuss open questions and opportunities for using integrative approaches to advance our understanding of the biophysical strategies which microbes employ at various scales to make porous settings habitable.

Adaptive Space-Aware Infotaxis II as a Strategy for Odor Source Localization

Mobile robot olfaction of toxic and hazardous odor sources is of great significance in anti-terrorism, disaster prevention, and control scenarios. Aiming at the problems of low search efficiency and easily falling into a local optimum of the current odor source localization strategies, the paper proposes the adaptive space-aware Infotaxis II algorithm. To improve the tracking efficiency of robots, a new reward function is designed by considering the space information and emphasizing the exploration behavior of robots. Considering the enhancement in exploratory behavior, an adaptive navigation-updated mechanism is proposed to adjust the movement range of robots in real time through information entropy to avoid an excessive exploration behavior during the search process, which may lead the robot to fall into a local optimum. Subsequently, an improved adaptive cosine salp swarm algorithm is applied to confirm the optimal information adaptive parameter. Comparative simulation experiments between ASAInfotaxis II and the classical search strategies are carried out in 2D and 3D scenarios regarding the search efficiency and search behavior, which show that ASAInfotaxis II is competent to improve the search efficiency to a larger extent and achieves a better balance between exploration and exploitation behaviors.

Integrated control of bacterial growth and stress response by (p)ppGpp in Escherichia coli: A seesaw fashion

To thrive in nature, bacteria have to reproduce efficiently under favorable conditions and persist during stress. The global strategy that integrates the growth control and stress response remains to be explored. Here, we find that a moderate induction of (p)ppGpp reduces growth rate but significantly enhances the stress tolerance of E. coli, resulting from a global resource re-allocation from ribosome synthesis to the synthesis of stress-responsive proteins. Strikingly, the activation of stress response by (p)ppGpp is still largely retained in the absence of RpoS. In addition, (p)ppGpp induction could activate the catabolism of alanine and arginine, facilitating the adaption of bacteria to nutrient downshift. Our work demonstrates that the activation of stress response by (p)ppGpp could occur in an RpoS-independent manner and (p)ppGpp enables bacteria to integrate the control of growth and stress response in a seesaw fashion, thus acting as an important global regulator of the bacterial fitness landscape.

Direct measurement of dynamic attractant gradients reveals breakdown of the Patlak-Keller-Segel chemotaxis model

Chemotactic bacteria not only navigate chemical gradients, but also shape their environments by consuming and secreting attractants. Investigating how these processes influence the dynamics of bacterial populations has been challenging because of a lack of experimental methods for measuring spatial profiles of chemoattractants in real time. Here, we use a fluorescent sensor for aspartate to directly measure bacterially generated chemoattractant gradients during collective migration. Our measurements show that the standard Patlak-Keller-Segel model for collective chemotactic bacterial migration breaks down at high cell densities. To address this, we propose modifications to the model that consider the impact of cell density on bacterial chemotaxis and attractant consumption. With these changes, the model explains our experimental data across all cell densities, offering insight into chemotactic dynamics. Our findings highlight the significance of considering cell density effects on bacterial behavior, and the potential for fluorescent metabolite sensors to shed light on the complex emergent dynamics of bacterial communities.

Root colonization by beneficial rhizobacteria

Rhizosphere microbes play critical roles for plant's growth and health. Among them, the beneficial rhizobacteria have the potential to be developed as the biofertilizer or bioinoculants for sustaining the agricultural development. The efficient rhizosphere colonization of these rhizobacteria is a prerequisite for exerting their plant beneficial functions, but the colonizing process and underlying mechanisms have not been thoroughly reviewed, especially for the nonsymbiotic beneficial rhizobacteria. This review systematically analyzed the root colonizing process of the nonsymbiotic rhizobacteria and compared it with that of the symbiotic and pathogenic bacteria. This review also highlighted the approaches to improve the root colonization efficiency and proposed to study the rhizobacterial colonization from a holistic perspective of the rhizosphere microbiome under more natural conditions.

Ecological relevance of flagellar motility in soil bacterial communities

Flagellar motility is a key bacterial trait as it allows bacteria to navigate their immediate surroundings. Not all bacteria are capable of flagellar motility, and the distribution of this trait, its ecological associations, and the life history strategies of flagellated taxa remain poorly characterized. We developed and validated a genome-based approach to infer the potential for flagellar motility across 12 bacterial phyla (26 192 unique genomes). The capacity for flagellar motility was associated with a higher prevalence of genes for carbohydrate metabolism and higher maximum potential growth rates, suggesting that flagellar motility is more prevalent in environments with higher carbon availability. To test this hypothesis, we applied a method to infer the prevalence of flagellar motility in whole bacterial communities from metagenomic data and quantified the prevalence of flagellar motility across four independent field studies that each captured putative gradients in soil carbon availability (148 metagenomes). We observed a positive relationship between the prevalence of bacterial flagellar motility and soil carbon availability in all datasets. Since soil carbon availability is often correlated with other factors that could influence the prevalence of flagellar motility, we validated these observations using metagenomic data from a soil incubation experiment where carbon availability was directly manipulated with glucose amendments. This confirmed that the prevalence of bacterial flagellar motility is consistently associated with soil carbon availability over other potential confounding factors. This work highlights the value of combining predictive genomic and metagenomic approaches to expand our understanding of microbial phenotypic traits and reveal their general environmental associations.

Plasticity of growth laws tunes resource allocation strategies in bacteria

Bacteria like E. coli grow at vastly different rates on different substrates, however, the precise reason for this variability is poorly understood. Different growth rates have been attributed to 'nutrient quality', a key parameter in bacterial growth laws. However, it remains unclear to what extent nutrient quality is rooted in fundamental biochemical constraints like the energy content of nutrients, the protein cost required for their uptake and catabolism, or the capacity of the plasma membrane for nutrient transporters. Here, we show that while nutrient quality is indeed reflected in protein investment in substrate-specific transporters and enzymes, this is not a fundamental limitation on growth rate, at least for certain 'poor' substrates. We show that it is possible to turn mannose, one of the 'poorest' substrates of E. coli, into one of the 'best' substrates by reengineering chromosomal promoters of the mannose transporter and metabolic enzymes required for mannose degradation. This result falls in line with previous observations of more subtle growth rate improvement for many other carbon sources. However, we show that this faster growth rate comes at the cost of diverse cellular capabilities, reflected in longer lag phases, worse starvation survival and lower motility. We show that addition of cAMP to the medium can rescue these phenotypes but imposes a corresponding growth cost. Based on these data, we propose that nutrient quality is largely a self-determined, plastic property that can be modulated by the fraction of proteomic resources devoted to a specific substrate in the much larger proteome sector of catabolically activated genes. Rather than a fundamental biochemical limitation, nutrient quality reflects resource allocation decisions that are shaped by evolution in specific ecological niches and can be quickly adapted if necessary.

Non-reciprocity across scales in active mixtures

In active matter, particles typically experience mediated interactions, which are not constrained by Newton's third law and are therefore generically non-reciprocal. Non-reciprocity leads to a rich set of emerging behaviors that are hard to account for starting from the microscopic scale, due to the absence of a generic theoretical framework out of equilibrium. Here we consider bacterial mixtures that interact via mediated, non-reciprocal interactions (NRI) like quorum-sensing and chemotaxis. By explicitly relating microscopic and macroscopic dynamics, we show that, under conditions that we derive explicitly, non-reciprocity may fade upon coarse-graining, leading to large-scale equilibrium descriptions. In turn, this allows us to account quantitatively, and without fitting parameters, for the rich behaviors observed in microscopic simulations including phase separation, demixing, and multi-phase coexistence. We also derive the condition under which non-reciprocity survives coarse-graining, leading to a wealth of dynamical patterns. Again, our analytical approach allows us to predict the phase diagram of the system starting from its microscopic description. All in all, our work demonstrates that the fate of non-reciprocity across scales is a subtle and important question.

Motility mediates satellite formation in confined biofilms

Bacteria have spectacular survival capabilities and can spread in many, vastly different environments. For instance, when pathogenic bacteria infect a host, they expand by proliferation and squeezing through narrow pores and elastic matrices. However, the exact role of surface structures-important for biofilm formation and motility-and matrix density in colony expansion and morphogenesis is still largely unknown. Using confocal laser-scanning microscopy, we show how satellite colonies emerge around Escherichia coli colonies embedded in semi-dense hydrogel in controlled in vitro assays. Using knock-out mutants, we tested how extra-cellular structures, (e.g., exo-polysaccharides, flagella, and fimbria) control this morphology. Moreover, we identify the extra-cellular matrix' density, where this morphology is possible. When paralleled with mathematical modelling, our results suggest that satellite formation allows bacterial communities to spread faster. We anticipate that this strategy is important to speed up expansion in various environments, while retaining the close interactions and protection provided by the community.

Accessing nutrients as the primary benefit arising from chemotaxis

About half of the known bacterial species perform chemotaxis that gains them access to sites that are optimal for growth and survival. The motility apparatus and chemotaxis signaling pathway impose a large energetic and metabolic burden on the cell. There is almost no limit to the type of chemoeffectors that are recognized by bacterial chemoreceptors. For example, they include hormones, neurotransmitters, quorum-sensing molecules, and inorganic ions. However, the vast majority of chemoeffectors appear to be of metabolic value. We review here the experimental evidence indicating that accessing nutrients is the main selective force that led to the evolution of chemotaxis.

Chemotactic Motility-Induced Phase Separation

Collectives of actively moving particles can spontaneously separate into dilute and dense phases-a fascinating phenomenon known as motility-induced phase separation (MIPS). MIPS is well-studied for randomly moving particles with no directional bias. However, many forms of active matter exhibit collective chemotaxis, directed motion along a chemical gradient that the constituent particles can generate themselves. Here, using theory and simulations, we demonstrate that collective chemotaxis strongly competes with MIPS-in some cases, arresting or completely suppressing phase separation, or in other cases, generating fundamentally new dynamic instabilities. We establish principles describing this competition, thereby helping to reveal and clarify the rich physics underlying active matter systems that perform chemotaxis, ranging from cells to robots.

Plasticity of growth laws tunes resource allocation strategies in bacteria

Bacteria like E. coli grow at vastly different rates on different substrates, however, the precise reason for this variability is poorly understood. Different growth rates have been attributed to 'nutrient quality', a key parameter in bacterial growth laws. However, it remains unclear to what extent nutrient quality is rooted in fundamental biochemical constraints like the energy content of nutrients, the protein cost required for their uptake and catabolism, or the capacity of the plasma membrane for nutrient transporters. Here, we show that while nutrient quality is indeed reflected in protein investment in substrate-specific transporters and enzymes, this is not a fundamental limitation on growth rate. We show that it is possible to turn mannose, one of the 'poorest' substrates of E. coli, into one of the 'best' substrates by reengineering chromosomal promoters of the mannose transporter and metabolic enzymes required for mannose degradation. However, we show that this faster growth rate comes at the cost of diverse cellular capabilities, reflected in longer lag phases, worse starvation survival and lower motility. We show that addition of cAMP to the medium can rescue these phenotypes but imposes a corresponding growth cost. Based on these data, we propose that nutrient quality is largely a self-determined, plastic property that can be modulated by the fraction of proteomic resources devoted to a specific substrate in the much larger proteome sector of catabolically activated genes. Rather than a fundamental biochemical limitation, nutrient quality reflects resource allocation decisions that are shaped by evolution in specific ecological niches and can be quickly adapted if necessary.

CCR7 acts as both a sensor and a sink for CCL19 to coordinate collective leukocyte migration

Immune responses rely on the rapid and coordinated migration of leukocytes. Whereas it is well established that single-cell migration is often guided by gradients of chemokines and other chemoattractants, it remains poorly understood how these gradients are generated, maintained, and modulated. By combining experimental data with theory on leukocyte chemotaxis guided by the G protein-coupled receptor (GPCR) CCR7, we demonstrate that in addition to its role as the sensory receptor that steers migration, CCR7 also acts as a generator and a modulator of chemotactic gradients. Upon exposure to the CCR7 ligand CCL19, dendritic cells (DCs) effectively internalize the receptor and ligand as part of the canonical GPCR desensitization response. We show that CCR7 internalization also acts as an effective sink for the chemoattractant, dynamically shaping the spatiotemporal distribution of the chemokine. This mechanism drives complex collective migration patterns, enabling DCs to create or sharpen chemotactic gradients. We further show that these self-generated gradients can sustain the long-range guidance of DCs, adapt collective migration patterns to the size and geometry of the environment, and provide a guidance cue for other comigrating cells. Such a dual role of CCR7 as a GPCR that both senses and consumes its ligand can thus provide a novel mode of cellular self-organization.

Multiplexed microfluidic screening of bacterial chemotaxis

Microorganism sensing of and responding to ambient chemical gradients regulates a myriad of microbial processes that are fundamental to ecosystem function and human health and disease. The development of efficient, high-throughput screening tools for microbial chemotaxis is essential to disentangling the roles of diverse chemical compounds and concentrations that control cell nutrient uptake, chemorepulsion from toxins, and microbial pathogenesis. Here, we present a novel microfluidic multiplexed chemotaxis device (MCD) which uses serial dilution to simultaneously perform six parallel bacterial chemotaxis assays that span five orders of magnitude in chemostimulant concentration on a single chip. We first validated the dilution and gradient generation performance of the MCD, and then compared the measured chemotactic response of an established bacterial chemotaxis system (Vibrio alginolyticus) to a standard microfluidic assay. Next, the MCD's versatility was assessed by quantifying the chemotactic responses of different bacteria (Psuedoalteromonas haloplanktis, Escherichia coli) to different chemoattractants and chemorepellents. The MCD vastly accelerates the chemotactic screening process, which is critical to deciphering the complex sea of chemical stimuli underlying microbial responses.

Inverted Sedimentation of Active Particles in Unbiased ac Fields

Gaining control over the motion of active particles is crucial for applications ranging from targeted cargo delivery to nanomedicine. While much progress has been made recently to control active motion based on external forces, flows, or gradients in concentration or light intensity, which all have a well-defined direction or bias, little is known about how to steer active particles in situations where no permanent bias can be realized. Here, we show that ac fields with a vanishing time average provide an alternative route to steering active particles. We exemplify this route for inertial active particles in a gravitational field, observing that a substantial fraction of them persistently travels in the upward direction upon switching on the ac field, resulting in an inverted sedimentation profile at the top wall of a confining container. Our results offer a generic control principle that could be used in the future to steer active motion, direct collective behaviors, and purify mixtures.

How Do You Build a Cognitive Map? The Development of Circuits and Computations for the Representation of Space in the Brain

In mammals, the activity of neurons in the entorhinal-hippocampal network is modulated by the animal's position and its movement through space. At multiple stages of this distributed circuit, distinct populations of neurons can represent a rich repertoire of navigation-related variables like the animal's location, the speed and direction of its movements, or the presence of borders and objects. Working together, spatially tuned neurons give rise to an internal representation of space, a cognitive map that supports an animal's ability to navigate the world and to encode and consolidate memories from experience. The mechanisms by which, during development, the brain acquires the ability to create an internal representation of space are just beginning to be elucidated. In this review, we examine recent work that has begun to investigate the ontogeny of circuitry, firing patterns, and computations underpinning the representation of space in the mammalian brain.

Proliferating active matter

The fascinating patterns of collective motion created by autonomously driven particles have fuelled active-matter research for over two decades. So far, theoretical active-matter research has often focused on systems with a fixed number of particles. This constraint imposes strict limitations on what behaviours can and cannot emerge. However, a hallmark of life is the breaking of local cell number conservation by replication and death. Birth and death processes must be taken into account, for example, to predict the growth and evolution of a microbial biofilm, the expansion of a tumour, or the development from a fertilized egg into an embryo and beyond. In this Perspective, we argue that unique features emerge in these systems because proliferation represents a distinct form of activity: not only do the proliferating entities consume and dissipate energy, they also inject biomass and degrees of freedom capable of further self-proliferation, leading to myriad dynamic scenarios. Despite this complexity, a growing number of studies document common collective phenomena in various proliferating soft-matter systems. This generality leads us to propose proliferation as another direction of active-matter physics, worthy of a dedicated search for new dynamical universality classes. Conceptual challenges abound, from identifying control parameters and understanding large fluctuations and nonlinear feedback mechanisms to exploring the dynamics and limits of information flow in self-replicating systems. We believe that, by extending the rich conceptual framework developed for conventional active matter to proliferating active matter, researchers can have a profound impact on quantitative biology and reveal fascinating emergent physics along the way.

Scaling up and down: movement ecology for microorganisms

Movement is critical for the fitness of organisms, both large and small. It dictates how individuals acquire resources, evade predators, exchange genetic material, and respond to stressful environments. Movement also influences ecological and evolutionary dynamics at higher organizational levels, such as populations and communities. However, the links between individual motility and the processes that generate and maintain microbial diversity are poorly understood. Movement ecology is a framework linking the physiological and behavioral properties of individuals to movement patterns across scales of space, time, and biological organization. By synthesizing insights from cell biology, ecology, and evolution, we expand theory from movement ecology to predict the causes and consequences of microbial movements.

Spontaneous vortex formation by microswimmers with retarded attractions

Collective states of inanimate particles self-assemble through physical interactions and thermal motion. Despite some phenomenological resemblance, including signatures of criticality, the autonomous dynamics that binds motile agents into flocks, herds, or swarms allows for much richer behavior. Low-dimensional models have hinted at the crucial role played in this respect by perceived information, decision-making, and feedback, implying that the corresponding interactions are inevitably retarded. Here we present experiments on spherical Brownian microswimmers with delayed self-propulsion toward a spatially fixed target. We observe a spontaneous symmetry breaking to a transiently chiral dynamical state and concomitant critical behavior that do not rely on many-particle cooperativity. By comparison with the stochastic delay differential equation of motion of a single swimmer, we pinpoint the delay-induced effective synchronization of the swimmers with their own past as the key mechanism. Increasing numbers of swimmers self-organize into layers with pro- and retrograde orbital motion, synchronized and stabilized by steric, phoretic, and hydrodynamic interactions. Our results demonstrate how even most simple retarded interactions can foster emergent complex adaptive behavior in small active-particle ensembles.

Emergent behaviour and neural dynamics in artificial agents tracking odour plumes

Tracking an odour plume to locate its source under variable wind and plume statistics is a complex task. Flying insects routinely accomplish such tracking, often over long distances, in pursuit of food or mates. Several aspects of this remarkable behaviour and its underlying neural circuitry have been studied experimentally. Here we take a complementary in silico approach to develop an integrated understanding of their behaviour and neural computations. Specifically, we train artificial recurrent neural network agents using deep reinforcement learning to locate the source of simulated odour plumes that mimic features of plumes in a turbulent flow. Interestingly, the agents' emergent behaviours resemble those of flying insects, and the recurrent neural networks learn to compute task-relevant variables with distinct dynamic structures in population activity. Our analyses put forward a testable behavioural hypothesis for tracking plumes in changing wind direction, and we provide key intuitions for memory requirements and neural dynamics in odour plume tracking.

Structured foraging of soil predators unveils functional responses to bacterial defenses

Predators and their foraging strategies often determine ecosystem structure and function. Yet, the role of protozoan predators in microbial soil ecosystems remains elusive despite the importance of these ecosystems to global biogeochemical cycles. In particular, amoebae-the most abundant soil protozoan predator of bacteria-remineralize soil nutrients and shape the bacterial community. However, their foraging strategies and their role as microbial ecosystem engineers remain unknown. Here, we present a multiscale approach, connecting microscopic single-cell analysis and macroscopic whole ecosystem dynamics, to expose a phylogenetically widespread foraging strategy, in which an amoeba population spontaneously partitions between cells with fast, polarized movement and cells with slow, unpolarized movement. Such differentiated motion gives rise to efficient colony expansion and consumption of the bacterial substrate. From these insights, we construct a theoretical model that predicts how disturbances to amoeba growth rate and movement disrupt their predation efficiency. These disturbances correspond to distinct classes of bacterial defenses, which allows us to experimentally validate our predictions. All considered, our characterization of amoeba foraging identifies amoeba mobility, and not amoeba growth, as the core determinant of predation efficiency and a key target for bacterial defense systems.

Agent-Based Modeling of Microbial Communities

Microbial communities are complex living systems that populate the planet with diverse functions and are increasingly harnessed for practical human needs. To deepen the fundamental understanding of their organization and functioning as well as to facilitate their engineering for applications, mathematical modeling has played an increasingly important role. Agent-based models represent a class of powerful quantitative frameworks for investigating microbial communities because of their individualistic nature in describing cells, mechanistic characterization of molecular and cellular processes, and intrinsic ability to produce emergent system properties. This review presents a comprehensive overview of recent advances in agent-based modeling of microbial communities. It surveys the state-of-the-art algorithms employed to simulate intracellular biomolecular events, single-cell behaviors, intercellular interactions, and interactions between cells and their environments that collectively serve as the driving forces of community behaviors. It also highlights three lines of applications of agent-based modeling, namely, the elucidation of microbial range expansion and colony ecology, the design of synthetic gene circuits and microbial populations for desired behaviors, and the characterization of biofilm formation and dispersal. The review concludes with a discussion of existing challenges, including the computational cost of the modeling, and potential mitigation strategies.

Individual-Based Modeling of Spatial Dynamics of Chemotactic Microbial Populations

One important direction of synthetic biology is to establish desired spatial structures from microbial populations. Underlying this structural development process are different driving factors, among which bacterial motility and chemotaxis serve as a major force. Here, we present an individual-based, biophysical computational framework for mechanistic and multiscale simulation of the spatiotemporal dynamics of motile and chemotactic microbial populations. The framework integrates cellular movement with spatial population growth, mechanical and chemical cellular interactions, and intracellular molecular kinetics. It is validated by a statistical comparison of single-cell chemotaxis simulations with reported experiments. The framework successfully captures colony range expansion of growing isogenic populations and also reveals chemotaxis-modulated, spatial patterns of a two-species amensal community. Partial differential equation-based models subsequently validate these simulation findings. This study provides a versatile computational tool to uncover the fundamentals of microbial spatial ecology as well as to facilitate the design of synthetic consortia for desired spatial patterns.

A one-dimensional three-state run-and-tumble model with a 'cell cycle'

We study a one-dimensional three-state run-and-tumble model motivated by the bacterium Caulobacter crescentus which displays a cell cycle between two non-proliferating mobile phases and a proliferating sedentary phase. Our model implements kinetic transitions between the two mobile and one sedentary states described in terms of their number densities, where mobility is allowed with different running speeds in forward and backward direction. We start by analyzing the stationary states of the system and compute the mean and squared-displacements for the distribution of all cells, as well as for the number density of settled cells. The latter displays a surprising super-ballistic scaling [Formula: see text] at early times. Including repulsive and attractive interactions between the mobile cell populations and the settled cells, we explore the stability of the system and employ numerical methods to study structure formation in the fully nonlinear system. We find traveling waves of bacteria, whose occurrence is quantified in a non-equilibrium state diagram.

Influence of Rosaceous Species and Driving Factors on Differentiation of Rhizospheric Bacteria in a Deciduous Broad-Leaved Forest

Understanding plant-microbe interactions could provide the basis for improved phytoremediation, microbial resource utilization, and secondary metabolite production. Rhizosphere bacterial communities are strongly influenced by abiotic factors such as soil nutrient availability and the composition of such communities exhibits differentiation under different host plants. In a deciduous broad-leaved forest in Anhui Province, eastern China, the rhizospheric bacteria of three different tree species of the Rosaceae family (Sorbus alnifolia, Cerasus serrulata, and Photinia beauverdiana) were studied, with the bacteria of the bulk soil as controls. Bacterial community composition was determined using the Illumina platform for high-throughput sequencing of 16S rRNA genes. The results showed that the bacterial community composition varied between rhizospheric and bulk soils, and dominant bacterial phyla as Proteobacteria, Actinobacteria, and Acidobacteria were found in both soils. Information on predicted functional genes and pathways revealed significant differences between rhizospheric and bulk soil bacteria. It provided ample evidence for the different metabolic characteristics of the rhizosphere bacterial communities of the three tree species. Electrical conductivity (22.72%), total phosphorus concentration (21.89%), and urease activity (22%) were the main drivers for changes in the composition of the rhizosphere bacterial communities from the three tree species.

Steady-state running rate sets the speed and accuracy of accumulation of swimming bacteria

We study the chemotaxis of a population of genetically identical swimming bacteria undergoing run and tumble dynamics driven by stochastic switching between clockwise and counterclockwise rotation of the flagellar rotary system, where the steady-state rate of the switching changes in different environments. Understanding chemotaxis quantitatively requires that one links the measured steady-state switching rates of the rotary system, as well as the directional changes of individual swimming bacteria in a gradient of chemoattractant/repellant, to the efficiency of a population of bacteria in moving up/down the gradient. Here we achieve this by using a probabilistic model, parametrized with our experimental data, and show that the response of a population to the gradient is complex. We find the changes to the steady-state switching rate in the absence of gradients affect the average speed of the swimming bacterial population response as well as the width of the distribution. Both must be taken into account when optimizing the overall response of the population in complex environments.

Coordination of gene expression with cell size enables Escherichia coli to efficiently maintain motility across conditions

To swim and navigate, motile bacteria synthesize a complex motility machinery involving flagella, motors, and a sensory system. A myriad of studies has elucidated the molecular processes involved, but less is known about the coordination of motility expression with cellular physiology: In Escherichia coli, motility genes are strongly up-regulated in nutrient-poor conditions compared to nutrient-replete conditions; yet a quantitative link to cellular motility has not been developed. Here, we systematically investigated gene expression, swimming behavior, cell growth, and available proteomics data across a broad spectrum of exponential growth conditions. Our results suggest that cells up-regulate the expression of motility genes at slow growth to compensate for reduction in cell size, such that the number of flagella per cell is maintained across conditions. The observed four or five flagella per cell is the minimum number needed to keep the majority of cells motile. This simple regulatory objective allows E. coli cells to remain motile across a broad range of growth conditions, while keeping the biosynthetic and energetic demands to establish and drive the motility machinery at the minimum needed. Given the strong reduction in flagella synthesis resulting from cell size increases at fast growth, our findings also provide a different physiological perspective on bacterial cell size control: A larger cell size at fast growth is an efficient strategy to increase the allocation of cellular resources to the synthesis of those proteins required for biomass synthesis and growth, while maintaining processes such as motility that are only needed on a per-cell basis.

Exploiting spatial dimensions to enable parallelized continuous directed evolution

Current strategies to improve the throughput of continuous directed evolution technologies often involve complex mechanical fluid-controlling system or robotic platforms, which limits their popularization and application in general laboratories. Inspired by our previous study on bacterial range expansion, in this study, we report a system termed SPACE for rapid and extensively parallelizable evolution of biomolecules by introducing spatial dimensions into the landmark phage-assisted continuous evolution system. Specifically, M13 phages and chemotactic Escherichia coli cells were closely inoculated onto a semisolid agar. The phages came into contact with the expanding front of the bacterial range, and then comigrated with the bacteria. This system leverages competition over space, wherein evolutionary progress is closely associated with the production of spatial patterns, allowing the emergence of improved or new protein functions. In a prototypical problem, SPACE remarkably simplified the process and evolved the promoter recognition of T7 RNA polymerase (RNAP) to a library of 96 random sequences in parallel. These results establish SPACE as a simple, easy to implement, and massively parallelizable platform for continuous directed evolution in general laboratories.

Direct measurement of the aerotactic response in a bacterial suspension

Aerotaxis is the ability of motile cells to navigate toward oxygen. A key question is the dependence of the aerotactic velocity with the local oxygen concentration c. Here we combine simultaneous bacteria tracking and local oxygen concentration measurements using Ruthenium encapsulated in micelles to characterize the aerotactic response of Burkholderia contaminans, a motile bacterium ubiquitous in the environment. In our experiments, an oxygen gradient is produced by the bacterial respiration in a sealed glass capillary permeable to oxygen at one end, producing a bacterial band traveling toward the oxygen source. We compute the aerotactic response χ(c) both at the population scale, from the drift velocity in the bacterial band, and at the bacterial scale, from the angular modulation of the run times. Both methods are consistent with a power-law χ∝c^{-2}, in good agreement with existing models based on the biochemistry of bacterial membrane receptors.

4-bit adhesion logic enables universal multicellular interface patterning

Multicellular systems, from bacterial biofilms to human organs, form interfaces (or boundaries) between different cell collectives to spatially organize versatile functions1,2. The evolution of sufficiently descriptive genetic toolkits probably triggered the explosion of complex multicellular life and patterning3,4. Synthetic biology aims to engineer multicellular systems for practical applications and to serve as a build-to-understand methodology for natural systems5-8. However, our ability to engineer multicellular interface patterns2,9 is still very limited, as synthetic cell-cell adhesion toolkits and suitable patterning algorithms are underdeveloped5,7,10-13. Here we introduce a synthetic cell-cell adhesin logic with swarming bacteria and establish the precise engineering, predictive modelling and algorithmic programming of multicellular interface patterns. We demonstrate interface generation through a swarming adhesion mechanism, quantitative control over interface geometry and adhesion-mediated analogues of developmental organizers and morphogen fields. Using tiling and four-colour-mapping concepts, we identify algorithms for creating universal target patterns. This synthetic 4-bit adhesion logic advances practical applications such as human-readable molecular diagnostics, spatial fluid control on biological surfaces and programmable self-growing materials5-8,14. Notably, a minimal set of just four adhesins represents 4 bits of information that suffice to program universal tessellation patterns, implying a low critical threshold for the evolution and engineering of complex multicellular systems3,5.

Collective behavior and nongenetic inheritance allow bacterial populations to adapt to changing environments

Collective behaviors require coordination among a group of individuals. As a result, individuals that are too phenotypically different from the rest of the group can be left out, reducing heterogeneity, but increasing coordination. If individuals also reproduce, the offspring can have different phenotypes from their parent(s). This raises the question of how these two opposing processes-loss of diversity by collective behaviors and generation of it through growth and inheritance-dynamically shape the phenotypic composition of an isogenic population. We examine this question theoretically using collective migration of chemotactic bacteria as a model system, where cells of different swimming phenotypes are better suited to navigate in different environments. We find that the differential loss of phenotypes caused by collective migration is environment-dependent. With cell growth, this differential loss enables migrating populations to dynamically adapt their phenotype compositions to the environment, enhancing migration through multiple environments. Which phenotypes are produced upon cell division depends on the level of nongenetic inheritance, and higher inheritance leads to larger composition adaptation and faster migration at steady state. However, this comes at the cost of slower responses to new environments. Due to this trade-off, there is an optimal level of inheritance that maximizes migration speed through changing environments, which enables a diverse population to outperform a nondiverse one. Growing populations might generally leverage the selection-like effects provided by collective behaviors to dynamically shape their own phenotype compositions, without mutations.

Turnover in Life-Strategies Recapitulates Marine Microbial Succession Colonizing Model Particles

Particulate organic matter (POM) in the ocean sustains diverse communities of bacteria that mediate the remineralization of organic complex matter. However, the variability of these particles and of the environmental conditions surrounding them present a challenge to the study of the ecological processes shaping particle-associated communities and their function. In this work, we utilize data from experiments in which coastal water communities are grown on synthetic particles to ask which are the most important ecological drivers of their assembly and associated traits. Combining 16S rRNA amplicon sequencing with shotgun metagenomics, together with an analysis of the full genomes of a subset of isolated strains, we were able to identify two-to-three distinct community classes, corresponding to early vs. late colonizers. We show that these classes are shaped by environmental selection (early colonizers) and facilitation (late colonizers) and find distinctive traits associated with each class. While early colonizers have a larger proportion of genes related to the uptake of nutrients, motility, and environmental sensing with few pathways enriched for metabolism, late colonizers devote a higher proportion of genes for metabolism, comprising a wide array of different pathways including the metabolism of carbohydrates, amino acids, and xenobiotics. Analysis of selected pathways suggests the existence of a trophic-chain topology connecting both classes for nitrogen metabolism, potential exchange of branched chain amino acids for late colonizers, and differences in bacterial doubling times throughout the succession. The interpretation of these traits suggests a distinction between early and late colonizers analogous to other classifications found in the literature, and we discuss connections with the classical distinction between r- and K-strategists.