Spatial-temporal dynamics of a microbial cooperative behavior resistant to cheating

Spatial population dynamics of bacterial colonies with social antibiotic resistance

Bacteria frequently inhabit surface-attached communities where rich "social" interactions can significantly alter their population-level behavior, including their response to antibiotics. Understanding these collective effects in spatially heterogeneous communities is an ongoing challenge. Here, we investigated the spatial organization that emerges from antibiotic exposure in initially randomly distributed communities containing antibiotic-resistant and -sensitive strains of Enterococcus faecalis, an opportunistic pathogen. We identified that a range of complex spatial structures emerged in the population homeland-the inoculated region that microbes inhabit prior to range expansion-which depended on initial colony composition and antibiotic concentration. We found that these arrangements were explained by cooperative interactions between resistant and sensitive subpopulations with a variable spatial scale, the result of dynamic zones of protection afforded to sensitive cells by growing populations of enzyme-producing resistant neighbors. Using a combination of experiments and mathematical models, we explored the complex spatiotemporal interaction dynamics that create these patterns, and predicted spatial arrangements of sensitive and resistant subpopulations under new conditions. We illustrated how spatial population dynamics in the homeland affect subsequent range expansion, both because they modulate the composition of the initial expanding front, and through long-range cooperation between the homeland and the expanding region. Finally, we showed that these spatial constraints resulted in populations whose size and composition differed markedly from matched populations in well-stirred (planktonic) cultures. These findings underscore the importance of spatial structure and cooperation, long-studied features in theoretical ecology, for determining the fate of bacterial communities under antibiotic exposure.

Reciprocal signaling between quorum sensing mutants: A model for division of labor

Division of labor, the specialization of subsets of individuals in complementary tasks, increases population efficiency and fitness. We explored swarming motility in Pseudomonas aeruginosa quorum sensing mutants as a model for studying the division of labor. Deletion of the signal synthesis genes lasI or rhlI disrupts swarming, but co-culturing ΔlasI and ΔrhlI restores it in a density-dependent manner. This indicates a successful division of labor where ΔrhlI produces the signal necessary for the ΔlasI mutant, and the ΔlasI reciprocates. We used RNA sequencing to identify additional genes potentially involved in division of labor. Our findings underscore P. aeruginosa swarming as a tractable bacterial model for the division of labor among cells-a hallmark of differentiated multicellularity.

The collapse of cooperation during range expansion of Pseudomonas aeruginosa

Cooperation is commonly believed to be favourable in spatially structured environments, as these systems promote genetic relatedness that reduces the likelihood of exploitation by cheaters. Here we show that a Pseudomonas aeruginosa population that exhibited cooperative swarming was invaded by cheaters when subjected to experimental evolution through cycles of range expansion on solid media, but not in well-mixed liquid cultures. Our results suggest that cooperation is disfavoured in a more structured environment, which is the opposite of the prevailing view. We show that spatial expansion of the population prolongs cooperative swarming, which was vulnerable to cheating. Our findings reveal a mechanism by which spatial structures can suppress cooperation through modulation of the quantitative traits of cooperation, a process that leads to population divergence towards distinct colonization strategies.

Unraveling plant-microbe interactions: can integrated omics approaches offer concrete answers?

Advances in high throughput omics techniques provide avenues to decipher plant microbiomes. However, there is limited information on how integrated informatics can help provide deeper insights into plant-microbe interactions in a concerted way. Integrating multi-omics datasets can transform our understanding of the plant microbiome from unspecified genetic influences on interacting species to specific gene-by-gene interactions. Here, we highlight recent progress and emerging strategies in crop microbiome omics research and review key aspects of how the integration of host and microbial omics-based datasets can be used to provide a comprehensive outline of complex crop-microbe interactions. We describe how these technological advances have helped unravel crucial plant and microbial genes and pathways that control beneficial, pathogenic, and commensal plant-microbe interactions. We identify crucial knowledge gaps and synthesize current limitations in our understanding of crop microbiome omics approaches. We highlight recent studies in which multi-omics-based approaches have led to improved models of crop microbial community structure and function. Finally, we recommend holistic approaches in integrating host and microbial omics datasets to achieve precision and efficiency in data analysis, which is crucial for biotic and abiotic stress control and in understanding the contribution of the microbiota in shaping plant fitness.

Use of Individual-Based Mathematical Modelling to Understand More About Antibiotic Resistance Within-Host

To model complex systems, individual-based models (IBMs), sometimes called "agent-based models" (ABMs), describe a simplification of the system through an adequate representation of the elements. IBMs simulate the actions and interaction of discrete individuals/agents within a system in order to discover the pattern of behavior that comes from these interactions. Examples of individuals/agents in biological systems are individual immune cells and bacteria that act independently with their own unique attributes defined by behavioral rules. In IBMs, each of these agents resides in a spatial environment and interactions are guided by predefined rules. These rules are often simple and can be easily implemented. It is expected that following the interaction guided by these rules we will have a better understanding of agent-agent interaction as well as agent-environment interaction. Stochasticity described by probability distributions must be accounted for. Events that seldom occur such as the accumulation of rare mutations can be easily modeled.Thus, IBMs are able to track the behavior of each individual/agent within the model while also obtaining information on the results of their collective behaviors. The influence of impact of one agent with another can be captured, thus allowing a full representation of both direct and indirect causation on the aggregate results. This means that important new insights can be gained and hypotheses tested.

Cooperation in bioluminescence: understanding the role of autoinducers by a stochastic random resistor model

Quorum sensing is a communication mechanism adopted by different bacterial strains for the regulation of gene transcription. It takes place through the exchange of molecules called autoinducers. Bioluminescence is an emergent threshold phenomenon shown by some bacteria strains. Its precise relationship to quorum sensing is a debated topic, particularly regarding the role of the different autoinducers used by bacteria. In this paper, assuming a direct relationship between bioluminescence and quorum sensing, we investigate the role of multiple autoinducers in the bioluminescence response of Vibrio harveyi, considered as a model bioluminescent strain, due to its quorum sensing circuitry involving an array of three different autoinducers. Experiments on mutants of this bacterium, obtained by suppression of one or more autoinducers, reveal their relative non-trivial relevance and cooperative interaction patterns. The proposed analysis is implemented on a regular lattice, whose nodes represent microbial entities equipped with charges, which represent the ability to up/down regulate the gene expression. Quorum sensing results from a Coulomb-type field, produced by the charges. In analogy with random resistor network models, the lattice is permeated by an effective current which accounts for the amount and distribution of the charges. We propose that the presence of different autoinducers correspond to a different up/down regulation of gene expression, i.e., to a different way to account for the charges. Then, by introducing a modulation of the charge dependence into the current flowing within the network, we show that it is able to describe the bioluminescence exhibited by V. harveyi mutants. Furthermore, modulation of the charge dependence allows the interactions between the different autoinducers to be taken into account, providing a prediction regarding the data obtainable under specific growth conditions.

Biosurfactant from Pseudomonas fragi enhances the competitive advantage of Pseudomonas but reduces the overall spoilage ability of the microbial community in chilled meat

Biosurfactants from Pseudomonas spp. have been reported to exhibit antibacterial and anti-adhesive properties, but their role during meat spoilage remains unclear. In this study, the biosurfactant was isolated from an isolate of Pseudomonas fragi with strong spoilage potential, and its surface tension and emulsification ability were determined. The chemical and microbial characteristics of the biosurfactant-treated meat samples were periodically analyzed. The results demonstrated that the biosurfactant produced by P. fragi could reduce surface tension and showed good emulsification properties. For the in situ spoilage trials, biosurfactant from P. fragi changed the microbial diversity on meat, helping Pseudomonas establish a dominant position in the population. However, biosurfactant treatment caused chicken meat to exhibit a weaker spoilage state, as indicated by the growth of psychrophilic microorganisms, total volatile basic nitrogen (TVBN) and meat color. These results provide practical information for understanding the role of P. fragi biosurfactant during chilled meat storage.

Cooperative antibiotic resistance facilitates horizontal gene transfer

The rise of β-lactam resistance among pathogenic bacteria, due to the horizontal transfer of plasmid-encoded β-lactamases, is a current global health crisis. Importantly, β-lactam hydrolyzation by β-lactamases, not only protects the producing cells but also sensitive neighboring cells cooperatively. Yet, how such cooperative traits affect plasmid transmission and maintenance is currently poorly understood. Here we experimentally show that KPC-2 β-lactamase expression and extracellular activity were higher when encoded on plasmids compared with the chromosome, resulting in the elevated rescue of sensitive non-producers. This facilitated efficient plasmid transfer to the rescued non-producers and expanded the potential plasmid recipient pool and the probability of plasmid transfer to new genotypes. Social conversion of non-producers by conjugation was efficient yet not absolute. Non-cooperative plasmids, not encoding KPC-2, were moderately more competitive than cooperative plasmids when β-lactam antibiotics were absent. However, in the presence of a β-lactam antibiotic, strains with non-cooperative plasmids were efficiently outcompeted. Moreover, plasmid-free non-producers were more competitive than non-producers imposed with the metabolic burden of a plasmid. Our results suggest that cooperative antibiotic resistance especially promotes the fitness of replicons that transfer horizontally such as conjugative plasmids.

Agent-based methods facilitate integrative science in cancer

In this opinion, we highlight agent-based modeling as a key tool for exploration of cell-cell and cell-environment interactions that drive cancer progression, therapeutic resistance, and metastasis. These biological phenomena are particularly suited to be captured at the cell-scale resolution possible only within agent-based or individual-based mathematical models. These modeling approaches complement experimental work (in vitro and in vivo systems) through parameterization and data extrapolation but also feed forward to drive new experiments that test model-generated predictions.

Long-range alteration of the physical environment mediates cooperation between Pseudomonas aeruginosa swarming colonies

Pseudomonas aeruginosa makes and secretes massive amounts of rhamnolipid surfactants that enable swarming motility over biogel surfaces. But how these rhamnolipids interact with biogels to assist swarming remains unclear. Here, I use a combination of optical techniques across scales and genetically engineered strains to demonstrate that rhamnolipids can induce agar gel swelling over distances >10,000× the body size of an individual cell. The swelling front is on the micrometric scale and is easily visible using shadowgraphy. Rhamnolipid transport is not restricted to the surface of the gel but occurs through the whole thickness of the plate and, consequently, the spreading dynamics depend on the local thickness. Surprisingly, rhamnolipids can cross the whole gel and induce swelling on the opposite side of a two-face Petri dish. The swelling front delimits an area where the mechanical properties of the surface properties are modified: water wets the surface more easily, which increases the motility of individual bacteria and enables collective motility. A genetically engineered mutant unable to secrete rhamnolipids (ΔrhlA), and therefore unable to swarm, is rescued from afar with rhamnolipids produced by a remote colony. These results exemplify the remarkable capacity of bacteria to change the physical environment around them and its ecological consequences.

High-Throughput Gel Microbeads as Incubators for Bacterial Competition Study

Bacteria primarily live in structured environments, such as colonies and biofilms, attached to surfaces or growing within soft tissues. They are engaged in local competitive and cooperative interactions impacting our health and well-being, for example, by affecting population-level drug resistance. Our knowledge of bacterial competition and cooperation within soft matrices is incomplete, partly because we lack high-throughput tools to quantitatively study their interactions. Here, we introduce a method to generate a large amount of agarose microbeads that mimic the natural culture conditions experienced by bacteria to co-encapsulate two strains of fluorescence-labeled Escherichia coli. Focusing specifically on low bacterial inoculum (1-100 cells/capsule), we demonstrate a study on the formation of colonies of both strains within these 3D scaffolds and follow their growth kinetics and interaction using fluorescence microscopy in highly replicated experiments. We confirmed that the average final colony size is inversely proportional to the inoculum size in this semi-solid environment as a result of limited available resources. Furthermore, the colony shape and fluorescence intensity per colony are distinctly different in monoculture and co-culture. The experimental observations in mono- and co-culture are compared with predictions from a simple growth model. We suggest that our high throughput and small footprint microbead system is an excellent platform for future investigation of competitive and cooperative interactions in bacterial communities under diverse conditions, including antibiotics stress.

Eco-evolutionary modelling of microbial syntrophy indicates the robustness of cross-feeding over cross-facilitation

Syntrophic cooperation among prokaryotes is ubiquitous and diverse. It relies on unilateral or mutual aid that may be both catalytic and metabolic in nature. Hypotheses of eukaryotic origins claim that mitochondrial endosymbiosis emerged from mutually beneficial syntrophy of archaeal and bacterial partners. However, there are no other examples of prokaryotic syntrophy leading to endosymbiosis. One potential reason is that when externalized products become public goods, they incite social conflict due to selfish mutants that may undermine any mutualistic interactions. To rigorously evaluate these arguments, here we construct a general mathematical framework of the ecology and evolution of different types of syntrophic partnerships. We do so both in a general microbial and in a eukaryogenetic context. Studying the case where partners cross-feed on each other's self-inhibiting waste, we show that cooperative partnerships will eventually dominate over selfish mutants. By contrast, systems where producers actively secrete enzymes that cross-facilitate their partners' resource consumption are not robust against cheaters over evolutionary time. We conclude that cross-facilitation is unlikely to provide an adequate syntrophic origin for endosymbiosis, but that cross-feeding mutualisms may indeed have played that role.

Dynamic proteome allocation regulates the profile of interaction of auxotrophic bacterial consortia

Microbial ecosystems are composed of multiple species in constant metabolic exchange. A pervasive interaction in microbial communities is metabolic cross-feeding and occurs when the metabolic burden of producing costly metabolites is distributed between community members, in some cases for the benefit of all interacting partners. In particular, amino acid auxotrophies generate obligate metabolic inter-dependencies in mixed populations and have been shown to produce a dynamic profile of interaction that depends upon nutrient availability. However, identifying the key components that determine the pair-wise interaction profile remains a challenging problem, partly because metabolic exchange has consequences on multiple levels, from allocating proteomic resources at a cellular level to modulating the structure, function and stability of microbial communities. To evaluate how ppGpp-mediated resource allocation drives the population-level profile of interaction, here we postulate a multi-scale mathematical model that incorporates dynamics of proteome partition into a population dynamics model. We compare our computational results with experimental data obtained from co-cultures of auxotrophic Escherichia coli K12 strains under a range of amino acid concentrations and population structures. We conclude by arguing that the stringent response promotes cooperation by inhibiting the growth of fast-growing strains and promoting the synthesis of metabolites essential for other community members.