Spatial segregation and cooperation in radially expanding microbial colonies under antibiotic stress

Optogenetic patterning generates multi-strain biofilms with spatially distributed antibiotic resistance

Spatial organization of microbes in biofilms enables crucial community function such as division of labor. However, quantitative understanding of such emergent community properties remains limited due to a scarcity of tools for patterning heterogeneous biofilms. Here we develop a synthetic optogenetic toolkit 'Multipattern Biofilm Lithography' for rational engineering and orthogonal patterning of multi-strain biofilms, inspired by successive adhesion and phenotypic differentiation in natural biofilms. We apply this toolkit to profile the growth dynamics of heterogeneous biofilm communities, and observe the emergence of spatially modulated commensal relationships due to shared antibiotic protection against the beta-lactam ampicillin. Supported by biophysical modeling, these results yield in-vivo measurements of key parameters, e.g., molecular beta-lactamase production per cell and length scale of antibiotic zone of protection. Our toolbox and associated findings provide quantitative insights into the spatial organization and distributed antibiotic protection within biofilms, with direct implications for future biofilm research and engineering.

A growth-coupling strategy for improving the stability of terpenoid bioproduction in Escherichia coli

BACKGROUND

Achieving cost-competitiveness remains challenging for industrial biomanufacturing. With whole-cell biocatalysis, inefficiency presents when individual cells vary in their production levels. The problem exacerbates when the basis for such production heterogeneity is heritable. Here, evolution selects for the low- and non-producers, as they have lowered/abolished the cost of bioproduction to fitness. With the scale of population expansion required for industrial bioproduction, the asymmetrical enrichment can be severe enough to compromise the performance, and hence commercial viability of the bioprocess. Clearly, addressing production heterogeneity is crucial, especially in improving the stability of bioproduction across the cell generations. In this respect, we designed a growth-coupling strategy for terpenoid bioproduction in Escherichia coli. By knocking out the native 1-deoxy-D-xylulose 5-phosphate reductoisomerase (dxr) gene and introducing the heterologous mevalonate pathway, we created a chassis that relies solely on the latter for synthesis of all terpenoids. We hypothesise that the need to sustain the biosynthesis of endogenous life-sustaining terpenoids will impose a minimum level of productivity, which concomitantly improves the bioproduction of our target terpenoid.

RESULTS

Following the confirmation of lethality of a dxr knockout, we challenged the strains with a continuous plasmid-based bioproduction of linalool. The Δdxr strain achieved an improved productivity profile in the first three days post-inoculation when compared to the parental strain. Productivity of the Δdxr strain remained observable near the end of 12 days, and after a disruption in nutrient and oxygen supply in a separate run. Unlike the parental strain, the Δdxr strain did not evolve the same deleterious mutations in the mevalonate pathway, nor a viable subgroup that had lost its resistance to the antibiotic selection pressure (a plausible plasmid loss event). We believe that this divergence in the evolution trajectories is indicative of a successful growth-coupling.

CONCLUSION

We have demonstrated a proof of concept of a growth-coupling strategy that improves the performance, and stability of terpenoid bioproduction across cell generations. The strategy is relatively broad in scope, and easy to implement in the background as a 'fail-safe' against a fall in productivity below the imposed minimum. We thus believe this work will find widespread utility in our collective effort towards industrial bioproduction.

Photocatalysis Inhibits the Emergence of Multidrug-Resistant Bacteria in an Antibiotic-Resistant Bacterial Community in Aquatic Environments

Bacterial antibiotic resistance has recently attracted increasing amounts of attention. Here, an artificially antibiotic-resistant bacterial community (ARBC) combined with five different constructed antibiotic-resistant bacteria (ARB) with single antibiotic resistance, namely, kanamycin (KAN), tetracycline (TET), cefotaxime (CTX), polymyxin B (PB), or gentamicin (GEM), was studied for the stress response to photocatalysis. With photocatalytic inactivation, the transfer and diffusion of antibiotic resistance genes (ARGs) in the ARBC decreased, and fewer multidrug-resistant bacteria (MDRB) emerged in aquatic environments. After several days of photocatalytic inactivation or Luria broth cultivation, >90% ARB were transformed to antibiotic-susceptible bacteria by discarding ARGs. Bacteria with double antibiotic resistance were the dominant species (99%) of residual ARB. The changes in ARG abundance varied, decreasing for the GEM and TET resistance genes and increasing for the KAN resistance genes. The change in the antibiotic resistance level was consistent with the change in ARG abundance. Correspondingly, point mutations occurred for the KAN, CTX and PB resistance genes after photocatalytic inactivation, which might be the reason why these genes persisted longer in the studied ARBC. In summary, photocatalytic inactivation could reduce the abundance of some ARGs and inhibit the emergence of MDRB as well as block ARG transfer in the bacterial community in aquatic environments. This work highlights the advantages of long-term photocatalytic inactivation for controlling antibiotic resistance and facilitates a better understanding of bacterial communities in real aquatic environments.

Selective pressures for public antibiotic resistance

The rapid increase of antibiotic-resistant pathogens is severely limiting our current treatment possibilities. An important subset of the resistance mechanisms conferring antibiotic resistance have public effects, allowing otherwise susceptible bacteria to also survive antibiotic treatment. As susceptible bacteria can survive treatment without bearing the metabolic cost of producing the resistance mechanism, there is potential to increase their relative frequency in the population and, as such, select against resistant bacteria. Multiple studies showed that this altered selection for resistance is dependent on various environmental and treatment parameters. In this review, we provide a comprehensive overview of their most important findings and describe the main factors impacting the selection for resistance. In-depth understanding of the driving forces behind selection can aid in the design and implementation of alternative treatments which limit the risk of resistance development.

A 'rich-get-richer' mechanism drives patchy dynamics and resistance evolution in antibiotic-treated bacteria

Bacteria in nature often form surface-attached communities that initially comprise distinct subpopulations, or patches. For pathogens, these patches can form at infection sites, persist during antibiotic treatment, and develop into mature biofilms. Evidence suggests that patches can emerge due to heterogeneity in the growth environment and bacterial seeding, as well as cell-cell signaling. However, it is unclear how these factors contribute to patch formation and how patch formation might affect bacterial survival and evolution. Here, we demonstrate that a 'rich-get-richer' mechanism drives patch formation in bacteria exhibiting collective survival (CS) during antibiotic treatment. Modeling predicts that the seeding heterogeneity of these bacteria is amplified by local CS and global resource competition, leading to patch formation. Increasing the dose of a non-eradicating antibiotic treatment increases the degree of patchiness. Experimentally, we first demonstrated the mechanism using engineered Escherichia coli and then demonstrated its applicability to a pathogen, Pseudomonas aeruginosa. We further showed that the formation of P. aeruginosa patches promoted the evolution of antibiotic resistance. Our work provides new insights into population dynamics and resistance evolution during surface-attached bacterial growth.

Protective effects of antibiotic resistant bacteria on susceptibles in biofilm: Influential factors, mechanism, and modeling

In environmental biofilms, antibiotic-resistant bacteria facilitate the persistence of susceptible counterparts under antibiotic stresses, contributing to increased community-level resistance. However, there is a lack of quantitative understanding of this protective effect and its influential factors, hindering accurate risk assessment of biofilm resistance in diverse environment. This study isolated an opportunistic Escherichia coli pathogen from soil, and engineered it with plasmids conferring antibiotic resistance. Protective effects of the ampicillin resistant strain (AmpR) on their susceptible counterparts (AmpS) were observed in ampicillin-stress colony biofilms. The concentration of ampicillin delineated protective effects into 3 zones: continuous protection (<1 MIC of AmpS), initial AmpS/R dependent (1-8 MIC of AmpS), and ineffective (>8 MIC of AmpS). Intriguingly, Zone 2 exhibited a surprising "less is more" phenomenon tuned by the initial AmpS/R ratio, where biofilm with an initially lower AmpR (1:50 vs 50:1) harbored 30-90 % more AmpR after 24 h growth under antibiotic stress. Compared to AmpS, AmpR displayed superiority in adhesion, antibiotic degradation, motility, and quorum sensing, allowing them to preferentially colonize biofilm edge and areas with higher ampicillin. An agent-based model incorporating protective effects successfully simulated tempo-spatial dynamics of AmpR and AmpS influenced by antibiotic stress and initial AmpS/R. This study provides a holistic view on the pervasive but poorly understood protective effects in biofilm, enabling development of better risk assessment and precisely targeted control strategies of biofilm resistance in diverse environment.

Modeling spatial evolution of multi-drug resistance under drug environmental gradients

Multi-drug combinations to treat bacterial populations are at the forefront of approaches for infection control and prevention of antibiotic resistance. Although the evolution of antibiotic resistance has been theoretically studied with mathematical population dynamics models, extensions to spatial dynamics remain rare in the literature, including in particular spatial evolution of multi-drug resistance. In this study, we propose a reaction-diffusion system that describes the multi-drug evolution of bacteria based on a drug-concentration rescaling approach. We show how the resistance to drugs in space, and the consequent adaptation of growth rate, is governed by a Price equation with diffusion, integrating features of drug interactions and collateral resistances or sensitivities to the drugs. We study spatial versions of the model where the distribution of drugs is homogeneous across space, and where the drugs vary environmentally in a piecewise-constant, linear and nonlinear manner. Although in many evolution models, per capita growth rate is a natural surrogate for fitness, in spatially-extended, potentially heterogeneous habitats, fitness is an emergent property that potentially reflects additional complexities, from boundary conditions to the specific spatial variation of growth rates. Applying concepts from perturbation theory and reaction-diffusion equations, we propose an analytical metric for characterization of average mutant fitness in the spatial system based on the principal eigenvalue of our linear problem, λ1. This enables an accurate translation from drug spatial gradients and mutant antibiotic susceptibility traits to the relative advantage of each mutant across the environment. Our approach allows one to predict the precise outcomes of selection among mutants over space, ultimately from comparing their λ1 values, which encode a critical interplay between growth functions, movement traits, habitat size and boundary conditions. Such mathematical understanding opens new avenues for multi-drug therapeutic optimization.

Multiscale spatial segregation analysis in digital images of biofilms

Quantifying the degree of spatial segregation of two bacterial strains in mixed biofilms is an important topic in microbiology. Spatial segregation is dependent on spatial scale as two strains may appear to be well mixed if observed from a distance, but a closer look can reveal strong separation. Typically, this information is encoded in a digital image that represents the binary system, e.g., a microscopy image of a two species biofilm. To decode spatial segregation information, we have developed quantitative measures for evaluating the degree of the spatial scale-dependent segregation of two bacterial strains in a digital image. The constructed algorithm is based on the new segregation measures and overcomes drawbacks of existing approaches for biofilm segregation analysis. The new approach is implemented in a freely available software and was successfully applied to biofilms of two strains and bacterial suspensions for detection of the different spatial scale-dependent segregation levels.

Modeling spatial evolution of multi-drug resistance under drug environmental gradients

Multi-drug combinations to treat bacterial populations are at the forefront of approaches for infection control and prevention of antibiotic resistance. Although the evolution of antibiotic resistance has been theoretically studied with mathematical population dynamics models, extensions to spatial dynamics remain rare in the literature, including in particular spatial evolution of multi-drug resistance. In this study, we propose a reaction-diffusion system that describes the multi-drug evolution of bacteria, based on a rescaling approach (Gjini and Wood, 2021). We show how the resistance to drugs in space, and the consequent adaptation of growth rate is governed by a Price equation with diffusion. The covariance terms in this equation integrate features of drug interactions and collateral resistances or sensitivities to the drugs. We study spatial versions of the model where the distribution of drugs is homogeneous across space, and where the drugs vary environmentally in a piecewise-constant, linear and nonlinear manner. Applying concepts from perturbation theory and reaction-diffusion equations, we propose an analytical characterization of average mutant fitness in the spatial system based on the principal eigenvalue of our linear problem. This enables an accurate translation from drug spatial gradients and mutant antibiotic susceptibility traits, to the relative advantage of each mutant across the environment. Such a mathematical understanding allows to predict the precise outcomes of selection over space, ultimately from the fundamental balance between growth and movement traits, and their diversity in a population.

Antibiotic resistance in bacterial communities

Bacteria are single-celled organisms, but the survival of microbial communities relies on complex dynamics at the molecular, cellular, and ecosystem scales. Antibiotic resistance, in particular, is not just a property of individual bacteria or even single-strain populations, but depends heavily on the community context. Collective community dynamics can lead to counterintuitive eco-evolutionary effects like survival of less resistant bacterial populations, slowing of resistance evolution, or population collapse, yet these surprising behaviors are often captured by simple mathematical models. In this review, we highlight recent progress - in many cases, advances driven by elegant combinations of quantitative experiments and theoretical models - in understanding how interactions between bacteria and with the environment affect antibiotic resistance, from single-species populations to multispecies communities embedded in an ecosystem.

Cooperative microbial interactions drive spatial segregation in porous environments

The role of microbial interactions and the underlying mechanisms that shape complex biofilm communities are poorly understood. Here we employ a microfluidic chip to represent porous subsurface environments and show that cooperative microbial interactions between free-living and biofilm-forming bacteria trigger active spatial segregation to promote their respective dominance in segregated microhabitats. During initial colonization, free-living and biofilm-forming microbes are segregated from the mixed planktonic inoculum to occupy the ambient fluid and grain surface. Contrary to spatial exclusion through competition, the active spatial segregation is induced by cooperative interactions which improves the fitness of both biofilm and planktonic populations. We further show that free-living Arthrobacter induces the surface colonization by scavenging the biofilm inhibitor, D-amino acids and receives benefits from the public goods secreted by the biofilm-forming strains. Collectively, our results reveal how cooperative microbial interactions may contribute to microbial coexistence in segregated microhabitats and drive subsurface biofilm community succession.

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.

Advances in linking single-cell bacterial stress response to population-level survival

Stress response mechanisms can allow bacteria to survive a myriad of challenges, including nutrient changes, antibiotic encounters, and antagonistic interactions with other microbes. Expression of these stress response pathways, in addition to other cell features such as growth rate and metabolic state, can be heterogeneous across cells and over time. Collectively, these single-cell-level phenotypes contribute to an overall population-level response to stress. These include diversifying actions, which can be used to enable bet-hedging, and coordinated actions, such as biofilm production, horizontal gene transfer, and cross-feeding. Here, we highlight recent results and emerging technologies focused on both single-cell and population-level responses to stressors, and we draw connections about the combined impact of these effects on survival of bacterial communities.

Targeted Anti-Biofilm Therapy: Dissecting Targets in the Biofilm Life Cycle

Biofilm is a crucial virulence factor for microorganisms that causes chronic infection. After biofilm formation, the bacteria present improve drug tolerance and multifactorial defense mechanisms, which impose significant challenges for the use of antimicrobials. This indicates the urgent need for new targeted technologies and emerging therapeutic strategies. In this review, we focus on the current biofilm-targeting strategies and those under development, including targeting persistent cells, quorum quenching, and phage therapy. We emphasize biofilm-targeting technologies that are supported by blocking the biofilm life cycle, providing a theoretical basis for design of targeting technology that disrupts the biofilm and promotes practical application of antibacterial materials.

Biofilm neutrophils interactions under hypoxia: A mathematical modeling study

Neutrophils are important to the defense of the host against bacterial infection. Pathogens and the immune system cells create via respiration, a hypoxic environment in infected regions. Hypoxic conditions affect both the neutrophil's ability to eradicate the infection and also change the behavior of the bacterial-pathogens by eliciting the production of various virulence factors, the creation of bacterial biofilm and the initialization of anaerobic metabolism. In this work interactions of bacterial biofilm and neutrophils are studied in a domain where oxygen is diffusing into the environment and is being consumed by biofilm. Within a hypoxic environment, bacteria grow anaerobically and secrete higher levels of toxin that diffuses and lyses neutrophils. A mathematical model explicitly representing the biofilm volume fraction, oxygen, and diffusive virulence factors (toxin) as well as killing of bacteria by neutrophils is developed and studied first in 1D and then in 2D. Stability analysis and numerical simulations showing the effects of oxygen and toxin concentration on neutrophil-bacteria interactions are presented to identify different possible scenarios that can lead to elimination of the infection or its persistence as a chronic infection. Specifically, when bacteria are allowed to utilize anaerobic breathing and or to produce toxin, their fitness is enhanced against neutrophils attacks. A possible insight on how virulent bacterial colonies can synergistically resist neutrophils and survive is presented.

Contribution of the genomic and nutritional differentiation to the spatial distribution of bacterial colonies

Colony growth is a common phenomenon of structured populations dispersed in nature; nevertheless, studies on the spatial distribution of colonies are largely insufficient. Here, we performed a systematic survey to address the questions of whether and how the spatial distribution of colonies was influenced by the genome and environment. Six Escherichia coli strains carrying either the wild-type or reduced genomes and eight media of varied nutritional richness were used to evaluate the genomic and environmental impacts, respectively. The genome size and nutritional variation contributed to the mean size and total area but not the variation and shape of size distribution of the colonies formed within the identical space and of equivalent spatial density. The spatial analysis by means of the Voronoi diagram found that the Voronoi correlation remained nearly constant in common, in comparison to the Voronoi response decreasing in correlation to genome reduction and nutritional enrichment. Growth analysis at the single colony level revealed positive correlations of the relative growth rate to both the maximal colony size and the Voronoi area, regardless of the genomic and nutritional variety. This result indicated fast growth for the large space assigned and supported homeostasis in the Voronoi correlation. Taken together, the spatial distribution of colonies might benefit efficient clonal growth. Although the mechanisms remain unclear, the findings provide quantitative insights into the genomic and environmental contributions to the growth and distribution of spatially or geographically isolated populations.

Engineering microbial consortia with rationally designed cellular interactions

Synthetic microbial consortia represent a frontier of synthetic biology that promises versatile engineering of cellular functions. They are primarily developed through the design and construction of cellular interactions that coordinate individual dynamics and generate collective behaviors. Here we review recent advances in the engineering of synthetic communities through cellular-interaction programming. We first examine fundamental building blocks for intercellular communication and unidirectional positive and negative interactions. We then recap the assembly of the building blocks for creating bidirectional interactions in two-species ecosystems, which is followed by the discussion of engineering toward complex communities with increasing species numbers, under spatial contexts, and via model-guided design. We conclude by summarizing major challenges and future opportunities of engineered microbial ecosystems.

Social evolution of shared biofilm matrix components

Biofilm formation is an important and ubiquitous mode of growth among bacteria. Central to the evolutionary advantage of biofilm formation is cell-cell and cell-surface adhesion achieved by a variety of factors, some of which are diffusible compounds that may operate as classical public goods-factors that are costly to produce but may benefit other cells. An outstanding question is how diffusible matrix production, in general, can be stable over evolutionary timescales. In this work, using Vibrio cholerae as a model, we show that shared diffusible biofilm matrix proteins are indeed susceptible to cheater exploitation and that the evolutionary stability of producing these matrix components fundamentally depends on biofilm spatial structure, intrinsic sharing mechanisms of these components, and flow conditions in the environment. We further show that exploitation of diffusible adhesion proteins is localized within a well-defined spatial range around cell clusters that produce them. Based on this exploitation range and the spatial distribution of cell clusters, we constructed a model of costly diffusible matrix production and related these length scales to the relatedness coefficient in social evolution theory. Our results show that production of diffusible biofilm matrix components is evolutionarily stable under conditions consistent with natural biofilm habitats and host environments. We expect the mechanisms revealed in this study to be relevant to other secreted factors that operate as cooperative public goods in bacterial communities and the concept of exploitation range and the associated analysis tools to be generally applicable.

Microbial Interspecies Interactions and Their Impact on the Emergence and Spread of Antimicrobial Resistance

Bacteria are social organisms that commonly live in dense communities surrounded by a multitude of other species. The competitive and cooperative interactions between these species not only shape the bacterial communities but also influence their susceptibility to antimicrobials. While several studies have shown that mixed-species communities are more tolerant toward antimicrobials than their monospecies counterparts, only limited empirical data are currently available on how interspecies interactions influence resistance development. We here propose a theoretic framework outlining the potential impact of interspecies social behavior on different aspects of resistance development. We identify factors by which interspecies interactions might influence resistance evolution and distinguish between their effect on (a) the emergence of a resistant mutant and (b) the spread of this resistance throughout the population. Our analysis indicates that considering the social life of bacteria is imperative to the rational design of more effective antibiotic treatment strategies with a minimal hazard for resistance development. Expected final online publication date for the Annual Review of Microbiology, Volume 76 is September 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Spatial structure impacts adaptive therapy by shaping intra-tumoral competition

Background

Adaptive therapy aims to tackle cancer drug resistance by leveraging resource competition between drug-sensitive and resistant cells. Here, we present a theoretical study of intra-tumoral competition during adaptive therapy, to investigate under which circumstances it will be superior to aggressive treatment.

Methods

We develop and analyse a simple, 2-D, on-lattice, agent-based tumour model in which cells are classified as fully drug-sensitive or resistant. Subsequently, we compare this model to its corresponding non-spatial ordinary differential equation model, and fit it to longitudinal prostate-specific antigen data from 65 prostate cancer patients undergoing intermittent androgen deprivation therapy following biochemical recurrence.

Results

Leveraging the individual-based nature of our model, we explicitly demonstrate competitive suppression of resistance during adaptive therapy, and examine how different factors, such as the initial resistance fraction or resistance costs, alter competition. This not only corroborates our theoretical understanding of adaptive therapy, but also reveals that competition of resistant cells with each other may play a more important role in adaptive therapy in solid tumours than was previously thought. To conclude, we present two case studies, which demonstrate the implications of our work for: (i) mathematical modelling of adaptive therapy, and (ii) the intra-tumoral dynamics in prostate cancer patients during intermittent androgen deprivation treatment, a precursor of adaptive therapy.

Conclusion

Our work shows that the tumour’s spatial architecture is an important factor in adaptive therapy and provides insights into how adaptive therapy leverages both inter- and intra-specific competition to control resistance.

Cancer therapy traditionally focuses on maximising tumour cell kill with the aim of achieving a cure, but such aggressive treatment can open up space for drug-resistant cells to grow. In contrast, adaptive therapy aims to leverage competition between drug-sensitive and resistant cells by adjusting treatment to maintain the tumour at a tolerable size, whilst preserving drug-sensitive cells. This approach is being tested in trials but is not yet widely used as deeper understanding of cell-cell competition is required. Here, we used a mathematical model to investigate how strongly, and with whom, resistant cells compete during continuous and adaptive therapy, and applied our insights to hormone therapy in prostate cancer where adaptive therapy has recently been successfully trialed. Our results provide new insights into how adaptive therapy works and show that, by shaping cell competition, the tumour’s spatial architecture is important in determining therapy response.

Strobl et al. develop an agent-based spatial model of drug resistance in tumour cells under adaptive therapy. Using this model, they investigate how the tumour’s spatial architecture impacts intratumoural competitive dynamics of drug-sensitive vs. -resistant clones in response to therapy.