The impact of phage and phage resistance on microbial community dynamics.

Where there are bacteria, there will be bacteriophages. These viruses are known to be important players in shaping the wider microbial community in which they are embedded, with potential implications for human health. On the other hand, bacteria possess a range of distinct immune mechanisms that provide protection against bacteriophages, including the mutation or complete loss of the phage receptor, and CRISPR-Cas adaptive immunity. While our previous work showed how a microbial community may impact phage resistance evolution, little is known about the inverse, namely how interactions between phages and these different phage resistance mechanisms affect the wider microbial community in which they are embedded. Here, we conducted a 10-day, fully factorial evolution experiment to examine how phage impact the structure and dynamics of an artificial four-species bacterial community that includes either Pseudomonas aeruginosa wild-type or an isogenic mutant unable to evolve phage resistance through CRISPR-Cas. Additionally, we used mathematical modelling to explore the ecological interactions underlying full community behaviour, as well as to identify general principles governing the impacts of phage on community dynamics. Our results show that the microbial community structure is drastically altered by the addition of phage, with Acinetobacter baumannii becoming the dominant species and P. aeruginosa being driven nearly extinct, whereas P. aeruginosa outcompetes the other species in the absence of phage. Moreover, we find that a P. aeruginosa strain with the ability to evolve CRISPR-based resistance generally does better when in the presence of A. baumannii, but that this benefit is largely lost over time as phage is driven extinct. Finally, we show that pairwise data alone is insufficient when modelling our microbial community, both with and without phage, highlighting the importance of higher order interactions in governing multispecies dynamics in complex communities. Combined, our data clearly illustrate how phage targeting a dominant species allows for the competitive release of the strongest competitor while also contributing to community diversity maintenance and potentially preventing the reinvasion of the target species, and underline the importance of mapping community composition before therapeutically applying phage.

Variation in pneumococcal invasiveness metrics is driven by serotype carriage duration and initial risk of disease.

Streptococcus pneumoniae is an opportunistic pathogen that, while usually carried asymptomatically, can cause severe invasive diseases like meningitis and bacteremic pneumonia. A central goal in S. pneumoniae public health management is to identify which serotypes (immunologically distinct strains) pose the most risk of invasive disease. The most common invasiveness metrics use cross-sectional data (i.e., invasive odds ratios (IOR)), or longitudinal data (i.e., attack rates (AR)). To assess the reliability of these metrics we developed an epidemiological model of carriage and invasive disease. Our mathematical analyses illustrate qualitative failures with the IOR metric (e.g., IOR can decline with increasing invasiveness parameters). Fitting the model to both longitudinal and cross-sectional data, our analysis supports previous work indicating that invasion risk is maximal at or near time of colonization. This pattern of early invasive disease risk leads to substantial (up to 5-fold) biases when estimating underlying differences in invasiveness from IOR metrics, due to the impact of carriage duration on IOR. Together, these results raise serious concerns with the IOR metric as a basis for public health decision-making and lend support for multiple alternate metrics including AR.

Machine learning identification of Pseudomonas aeruginosa strains from colony image data.

When grown on agar surfaces, microbes can produce distinct multicellular spatial structures called colonies, which contain characteristic sizes, shapes, edges, textures, and degrees of opacity and color. For over one hundred years, researchers have used these morphology cues to classify bacteria and guide more targeted treatment of pathogens. Advances in genome sequencing technology have revolutionized our ability to classify bacterial isolates and while genomic methods are in the ascendancy, morphological characterization of bacterial species has made a resurgence due to increased computing capacities and widespread application of machine learning tools. In this paper, we revisit the topic of colony morphotype on the within-species scale and apply concepts from image processing, computer vision, and deep learning to a dataset of 69 environmental and clinical Pseudomonas aeruginosa strains. We find that colony morphology and complexity under common laboratory conditions is a robust, repeatable phenotype on the level of individual strains, and therefore forms a potential basis for strain classification. We then use a deep convolutional neural network approach with a combination of data augmentation and transfer learning to overcome the typical data starvation problem in biological applications of deep learning. Using a train/validation/test split, our results achieve an average validation accuracy of 92.9% and an average test accuracy of 90.7% for the classification of individual strains. These results indicate that bacterial strains have characteristic visual 'fingerprints' that can serve as the basis of classification on a sub-species level. Our work illustrates the potential of image-based classification of bacterial pathogens and highlights the potential to use similar approaches to predict medically relevant strain characteristics like antibiotic resistance and virulence from colony data.

The social lives of viruses and other mobile genetic elements: a commentary on Leeks et al. 2023.

Illustration of life-histories of phages and plasmids through horizontal and vertical transmission (see Figure 1 for more information).

The impact of phage and phage resistance on microbial community dynamics.

Where there are bacteria, there will be bacteriophages. These viruses are known to be important players in shaping the wider microbial community in which they are embedded, with potential implications for human health. On the other hand, bacteria possess a range of distinct immune mechanisms that provide protection against bacteriophages, including the mutation or complete loss of the phage receptor, and CRISPR-Cas adaptive immunity. Yet little is known about how interactions between phages and these different phage resistance mechanisms affect the wider microbial community in which they are embedded. Here, we conducted a 10-day, fully factorial evolution experiment to examine how phage impact the structure and dynamics of an artificial four-species bacterial community that includes either Pseudomonas aeruginosa wild type or an isogenic mutant unable to evolve phage resistance through CRISPR-Cas. Our results show that the microbial community structure is drastically altered by the addition of phage, with Acinetobacter baumannii becoming the dominant species and P. aeruginosa being driven nearly extinct, whereas P. aeruginosa outcompetes the other species in the absence of phage. Moreover, we find that a P. aeruginosa strain with the ability to evolve CRISPR-based resistance generally does better when in the presence of A. baumannii, but that this benefit is largely lost over time as phage is driven extinct. Combined, our data highlight how phage-targeting a dominant species allows for the competitive release of the strongest competitor whilst also contributing to community diversity maintenance and potentially preventing the reinvasion of the target species, and underline the importance of mapping community composition before therapeutically applying phage.

The dynamic response of quorum sensing to density is robust to signal supplementation and individual signal synthase knockouts.

Quorum sensing (QS) is a widespread mechanism of environment sensing and behavioural coordination in bacteria. At its core, QS is based on the production, sensing and response to small signalling molecules. Previous work with Pseudomonas aeruginosa shows that QS can be used to achieve quantitative resolution and deliver a dosed response to the bacteria's density environment, implying a sophisticated mechanism of control. To shed light on how the mechanistic signal components contribute to graded responses to density, we assess the impact of genetic (AHL signal synthase deletion) and/or signal supplementation (exogenous AHL addition) perturbations on lasB reaction-norms to changes in density. Our approach condenses data from 2000 timeseries (over 74 000 individual observations) into a comprehensive view of QS-controlled gene expression across variation in genetic, environmental and signal determinants of lasB expression. We first confirm that deleting either (∆lasI, ∆rhlI) or both (∆lasIrhlI) AHL signal synthase gene attenuates QS response to density. In the ∆rhlI background we show persistent yet attenuated density-dependent lasB expression due to native 3-oxo-C12-HSL signalling. We then test if density-independent quantities of AHL signal (3-oxo-C12-HSL, C4-HSL) added to the WT either flatten or increase responsiveness to density and find that the WT response is robust to all tested concentrations of signal, alone or in combination. We then move to progressively supplementing the genetic knockouts and find that cognate signal supplementation of a single AHL signal (∆lasI +3-oxo-C12-HSL, ∆rhlI +C4HSL) is sufficient to restore the ability to respond in a density-dependent manner to increasing density. We also find that dual signal supplementation of the double AHL synthase knockout restores the ability to produce a graded response to increasing density, despite adding a density-independent amount of signal. Only the addition of high concentrations of both AHLs and PQS can force maximal lasB expression and ablate responsiveness to density. Our results show that density-dependent control of lasB expression is robust to multiple combinations of QS gene deletion and density-independent signal supplementation. Our work develops a modular approach to query the robustness and mechanistic bases of the central environmental sensing phenotype of quorum sensing.

Hygiene may attenuate selection for antibiotic resistance by changing microbial community structure.

Good hygiene, in both health care and the community, is central to containing the rise of antibiotic resistance, as well as to infection control more generally. But despite the well-known importance, the ecological mechanisms by which hygiene (or other transmission control measures) affect the evolution of resistance remain to be elucidated. Using metacommunity ecology theory, we here propose that hygiene attenuates the effect of antibiotic selection pressure. Specifically, we predict that hygiene limits the scope for antibiotics to induce competitive release of resistant bacteria within treated hosts, and that this is due to an effect of hygiene on the distribution of resistant and sensitive strains in the host population. We show this in a mathematical model of bacterial metacommunity dynamics, and test the results against data on antibiotic resistance, antibiotic treatment, and the use of alcohol-based hand rub in long-term care facilities. The data are consistent with hand rub use attenuating the resistance promoting effect of antibiotic treatment. Our results underscore the importance of hygiene, and point to a concrete way to weaken the link between antibiotic use and increasing resistance.

Defining the Benefits of Antibiotic Resistance in Commensals and the Scope for Resistance Optimization.

Antibiotic resistance is a major medical and public health challenge, characterized by global increases in the prevalence of resistant strains. The conventional view is that all antibiotic resistance is problematic, even when not in pathogens. Resistance in commensal bacteria poses risks, as resistant organisms can provide a reservoir of resistance genes that can be horizontally transferred to pathogens or may themselves cause opportunistic infections in the future. While these risks are real, we propose that commensal resistance can also generate benefits during antibiotic treatment of human infection, by promoting continued ecological suppression of pathogens. To define and illustrate this alternative conceptual perspective, we use a two-species mathematical model to identify the necessary and sufficient ecological conditions for beneficial resistance. We show that the benefits are limited to species (or strain) interactions where commensals suppress pathogen growth and are maximized when commensals compete with, rather than prey on or otherwise exploit pathogens. By identifying benefits of commensal resistance, we propose that rather than strictly minimizing all resistance, resistance management may be better viewed as an optimization problem. We discuss implications in two applied contexts: bystander (nontarget) selection within commensal microbiomes and pathogen treatment given polymicrobial infections. IMPORTANCE Antibiotic resistance is commonly viewed as universally costly, regardless of which bacterial cells express resistance. Here, we derive an opposing logic, where resistance in commensal bacteria can lead to reductions in pathogen density and improved outcomes on both the patient and public health scales. We use a mathematical model of commensal-pathogen interactions to define the necessary and sufficient conditions for beneficial resistance, highlighting the importance of reciprocal ecological inhibition to maximize the benefits of resistance. More broadly, we argue that determining the benefits as well as the costs of resistances in human microbiomes can transform resistance management from a minimization to an optimization problem. We discuss applied contexts and close with a review of key resistance optimization dimensions, including the magnitude, spectrum, and mechanism of resistance.

Collective decision-making in Pseudomonas aeruginosa involves transient segregation of quorum-sensing activities across cells.

A hallmark of bacterial sociality is that groups can coordinate cooperative actions through a cell-to-cell communication process called quorum sensing (QS). QS regulates key bacterial phenotypes such as virulence in infections and digestion of extracellular compounds in the environment. Although QS responses are typically studied as group-level phenotypes, it is unclear whether individuals coordinate their actions at the single-cell level or whether group phenotypes simply reflect the sum of their noisy members. Here, we studied the behavior of Pseudomonas aeruginosa individuals by tracking their temporal commitments to the two intertwined Las and Rhl-QS systems, from low to high population density. Using chromosomally integrated fluorescent gene reporters, we found that QS gene expression (signal, receptor, and cooperative exoproduct) was noisy with heterogeneity peaking during the build-up phase of QS. Moreover, we observed the formation of discrete subgroups of cells that transiently segregate into two gene expression states: low Las-receptor expressers that instantly activate exoproduct production and high Las-receptor expressers with delayed exoproduct production. Later, gene expression activities converged with all cells fully committing to QS. We developed general mathematical models to show that gene expression segregation can mechanistically be spurred by molecular resource limitations during the initiation phase of regulatory cascades such as QS. Moreover, our models indicate that gene expression segregation across cells can operate as a built-in brake enabling a temporary bet-hedging strategy in unpredictable environments. Altogether, our work reveals that studying the behavior of bacterial individuals is key to understanding emergent collective actions at the group level.

Antibiotics Drive Expansion of Rare Pathogens in a Chronic Infection Microbiome Model.

Chronic (long-lasting) infections are globally a major and rising cause of morbidity and mortality. Unlike typical acute infections, chronic infections are ecologically diverse, characterized by the presence of a polymicrobial mix of opportunistic pathogens and human-associated commensals. To address the challenge of chronic infection microbiomes, we focus on a particularly well-characterized disease, cystic fibrosis (CF), where polymicrobial lung infections persist for decades despite frequent exposure to antibiotics. Epidemiological analyses point to conflicting results on the benefits of antibiotic treatment yet are confounded by the dependency of antibiotic exposures on prior pathogen presence, limiting their ability to draw causal inferences on the relationships between antibiotic exposure and pathogen dynamics. To address this limitation, we develop a synthetic infection microbiome model representing CF metacommunity diversity and benchmark on clinical data. We show that in the absence of antibiotics, replicate microbiome structures in a synthetic sputum medium are highly repeatable and dominated by oral commensals. In contrast, challenge with physiologically relevant antibiotic doses leads to substantial community perturbation characterized by multiple alternate pathogen-dominant states and enrichment of drug-resistant species. These results provide evidence that antibiotics can drive the expansion (via competitive release) of previously rare opportunistic pathogens and offer a path toward microbiome-informed conditional treatment strategies. IMPORTANCE We develop and clinically benchmark an experimental model of the cystic fibrosis (CF) lung infection microbiome to investigate the impacts of antibiotic exposures on chronic, polymicrobial infections. We show that a single experimental model defined by metacommunity data can partially recapitulate the diversity of individual microbiome states observed across a population of people with CF. In the absence of antibiotics, we see highly repeatable community structures, dominated by oral microbes. Under clinically relevant antibiotic exposures, we see diverse and frequently pathogen-dominated communities, and a nonevolutionary enrichment of antimicrobial resistance on the community scale, mediated by competitive release. The results highlight the potential importance of nonevolutionary (community-ecological) processes in driving the growing global crisis of increasing antibiotic resistance.

Bacterial Quorum Sensing Allows Graded and Bimodal Cellular Responses to Variations in Population Density.

Quorum sensing (QS) is a mechanism of cell-cell communication that connects gene expression to environmental conditions (e.g., cell density) in many bacterial species, mediated by diffusible signal molecules. Current functional studies focus on qualitatively distinct QS ON/OFF states. In the context of density sensing, this view led to the adoption of a "quorum" analogy in which populations sense when they are above a sufficient density (i.e., "quorate") to efficiently turn on cooperative behaviors. This framework overlooks the potential for intermediate, graded responses to shifts in the environment. In this study, we tracked QS-regulated protease (lasB) expression and showed that Pseudomonas aeruginosa can deliver a graded behavioral response to fine-scale variation in population density, on both the population and single-cell scales. On the population scale, we saw a graded response to variation in population density (controlled by culture carrying capacity). On the single-cell scale, we saw significant bimodality at higher densities, with separate OFF and ON subpopulations that responded differentially to changes in density: a static OFF population of cells and increasing intensity of expression among the ON population of cells. Together, these results indicate that QS can tune gene expression to graded environmental change, with no critical cell mass or "quorum" at which behavioral responses are activated on either the individual-cell or population scale. In an infection context, our results indicate there is not a hard threshold separating a quorate "attack" mode from a subquorate "stealth" mode. IMPORTANCE Bacteria can be highly social, controlling collective behaviors via cell-cell communication mechanisms known as quorum sensing (QS). QS is now a large research field, yet a basic question remains unanswered: what is the environmental resolution of QS? The notion of a threshold, or "quorum," separating coordinated ON and OFF states is a central dogma in QS, but recent studies have shown heterogeneous responses at a single cell scale. Using Pseudomonas aeruginosa, we showed that populations generate graded responses to environmental variation through shifts in the proportion of cells responding and the intensity of responses. In an infection context, our results indicate that there is not a hard threshold separating a quorate "attack" mode and a subquorate "stealth" mode.

Challenges and opportunities for cheat therapy in the control of bacterial infections.

Covering: 1999 to 2021Bacterial pathogens can be highly social, communicating and cooperating within multi-cellular groups to make us sick. The requirement for collective action in pathogens presents novel therapeutic avenues that seek to undermine cooperative behavior, what we call here 'cheat therapies'. We review two broad avenues of cheat therapy: first, the introduction of genetically engineered 'cheat' strains (bio-control cheats), and second the chemical induction of 'cheat' behavior in the infecting pathogens (chemical-control cheats). Both genetically engineered and chemically induced cheats can socially exploit the cooperative wildtype infection, reducing pathogen burden and the severity of disease. We review the costs and benefits of cheat therapies, highlighting advantages of evolutionary robustness and also the challenges of low to moderate efficacy, compared to conventional antibiotic treatments. We end with a summary of what we see as the most valuable next steps, focusing on adjuvant treatments and use as alternate therapies for mild, self-resolving infections - allowing the reservation of current and highly effective antibiotics for more critical patient needs.

Bacteria can be selected to help beneficial plasmids spread.

A recent commentary raised concerns about aspects of the model and assumptions used in a previous study which demonstrated that selection can favor chromosomal alleles that confer higher plasmid donation rates. Here, the authors of that previous study respond to the concerns raised.

Causes and Consequences of COVID-19-Associated Bacterial Infections.

The COVID-19 literature highlights that bacterial infections are more common in fatal cases than recovered cases. If bacterial infections drive mortality in COVID-19, this has clear implications for patient management. However, it is possible that the enrichment of bacterial infections in COVID-19 fatalities is simply a by-product of late-stage pathology, leading to different advice for patient management. To address this question, we review current knowledge on bacterial infections in COVID-19, assess information from past viral respiratory pandemics, and simulate alternate causal models of interactions between virus, bacteria, and mortality in COVID-19. From these models, we conclude that currently available data are not sufficient to discriminate between these alternate causal pathways, and we highlight what data are required to determine the relative contribution of bacterial infection to COVID-19 morbidity and mortality. We further summarize the potential long-term consequences of SARS-CoV-2 infection.

Community lifespan, niche expansion and the evolution of interspecific cooperation.

Microbes live in dense and diverse communities where they deploy many traits that promote the growth and survival of neighbouring species, all the while also competing for shared resources. Because microbial communities are highly dynamic, the costs and benefits of species interactions change over the growth cycle of a community. How mutualistic interactions evolve under such demographic and ecological conditions is still poorly understood. Here, we develop an eco-evolutionary model to explore how different forms of helping with distinct fitness effects (rate-enhancing and yield-enhancing) affect the multiple phases of community growth, and its consequences for the evolution of mutualisms. We specifically focus on a form of yield-enhancing trait in which cooperation augments the common pool of resources, termed niche expansion. We show that although mutualisms in which cooperation increases partners growth rate are generally favoured at early stages of community growth, niche expansion can evolve at later stages where densities are high. Further, we find that niche expansion can promote the evolution of reproductive restraint, in which a focal species adaptively reduces its own growth rate to increase the density of partner species. Our findings suggest that yield-enhancing mutualisms are more prevalent in stable habitats with a constant supply of resources, and where populations typically live at high densities. In general, our findings highlight the need to integrate different components of population growth in the analysis of mutualisms to understand the composition and function of microbial communities.

Microbiome Data Enhances Predictive Models of Lung Function in People With Cystic Fibrosis.

BACKGROUND: Microbiome sequencing has brought increasing attention to the polymicrobial context of chronic infections. However, clinical microbiology continues to focus on canonical human pathogens, which may overlook informative, but nonpathogenic, biomarkers. We address this disconnect in lung infections in people with cystic fibrosis (CF). METHODS: We collected health information (lung function, age, and body mass index [BMI]) and sputum samples from a cohort of 77 children and adults with CF. Samples were collected during a period of clinical stability and 16S rDNA sequenced for airway microbiome compositions. We use ElasticNet regularization to train linear models predicting lung function and extract the most informative features. RESULTS: Models trained on whole-microbiome quantitation outperformed models trained on pathogen quantitation alone, with or without the inclusion of patient metadata. Our most accurate models retained key pathogens as negative predictors (Pseudomonas, Achromobacter) along with established correlates of CF disease state (age, BMI, CF-related diabetes). In addition, our models selected nonpathogen taxa (Fusobacterium, Rothia) as positive predictors of lung health. CONCLUSIONS: These results support a reconsideration of clinical microbiology pipelines to ensure the provision of informative data to guide clinical practice.

Author Correction: In silico bacteria evolve robust cooperation via complex quorum-sensing strategies.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Combinatorial quorum sensing in Pseudomonas aeruginosa allows for novel cheating strategies.

In the opportunistic pathogen Pseudomonas aeruginosa, quorum sensing (QS) is a social trait that is exploitable by non-cooperating cheats. Previously it has been shown that by linking QS to the production of both public and private goods, cheats can be prevented from invading populations of cooperators and this was described by Dandekar et al. (Science 2012;338:264-266) as 'a metabolic incentive to cooperate'. We hypothesized that P. aeruginosa could evolve novel cheating strategies to circumvent private goods metabolism by rewiring its combinatorial response to two QS signals (3O-C12-HSL and C4-HSL). We performed a selection experiment that cycled P. aeruginosa between public and private goods growth media and evolved an isolate that rewired its control of cooperative protease expression from a synergistic (AND-gate) response to dual-signal input to a 3O-C12-HSL-only response. We show that this isolate circumvents metabolic incentives to cooperate and acts as a combinatorial signalling cheat, with higher fitness in competition with its ancestor. Our results show three important principles: first, combinatorial QS allows for diverse social strategies to emerge; second, restrictions levied by private goods are not sufficient to explain the maintenance of cooperation in natural populations; and third, modifying combinatorial QS responses could result in important physiological outcomes in bacterial populations.

In silico bacteria evolve robust cooperaion via complex quorum-sensing strategies.

Many species of bacteria collectively sense and respond to their social and physical environment via 'quorum sensing' (QS), a communication system controlling extracellular cooperative traits. Despite detailed understanding of the mechanisms of signal production and response, there remains considerable debate over the functional role(s) of QS: in short, what is it for? Experimental studies have found support for diverse functional roles: density sensing, mass-transfer sensing, genotype sensing, etc. While consistent with theory, these results cannot separate whether these functions were drivers of QS adaption, or simply artifacts or 'spandrels' of systems shaped by distinct ecological pressures. The challenge of separating spandrels from drivers of adaptation is particularly hard to address using extant bacterial species with poorly understood current ecologies (let alone their ecological histories). To understand the relationship between defined ecological challenges and trajectories of QS evolution, we used an agent-based simulation modeling approach. Given genetic mixing, our simulations produce behaviors that recapitulate features of diverse microbial QS systems, including coercive (high signal/low response) and generalized reciprocity (signal auto-regulation) strategists - that separately and in combination contribute to QS-dependent resilience of QS-controlled cooperation in the face of diverse cheats. We contrast our in silico results given defined ecological challenges with bacterial QS architectures that have evolved under largely unknown ecological contexts, highlighting the critical role of genetic constraints in shaping the shorter term (experimental evolution) dynamics of QS. More broadly, we see experimental evolution of digital organisms as a complementary tool in the search to understand the emergence of complex QS architectures and functions.