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.

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.

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.

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.

Contemporary management of intracardiac thrombi: a tale of two clots.

Intracardiac thrombi such as pulmonary emboli represent a high risk of mortality. In this case study, we review two cases of intracardiac thrombi occurring within 24 h of one another and managed differently by the same cardiothoracic surgical team, highlighting the importance of an individualized approach as well as an understanding of current guidelines and contemporary management techniques.

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.

Spatial Syndromic Surveillance and COVID-19 in the U.S.: Local Cluster Mapping for Pandemic Preparedness.

Maps have become the de facto primary mode of visualizing the COVID-19 pandemic, from identifying local disease and vaccination patterns to understanding global trends. In addition to their widespread utilization for public communication, there have been a variety of advances in spatial methods created for localized operational needs. While broader dissemination of this more granular work is not commonplace due to the protections under Health Insurance Portability and Accountability Act (HIPAA), its role has been foundational to pandemic response for health systems, hospitals, and government agencies. In contrast to the retrospective views provided by the aggregated geographies found in the public domain, or those often utilized for academic research, operational response requires near real-time mapping based on continuously flowing address level data. This paper describes the opportunities and challenges presented in emergent disease mapping using dynamic patient data in the response to COVID-19 for northeast Ohio for the period 2020 to 2022. More specifically it shows how a new clustering tool developed by geographers in the initial phases of the pandemic to handle operational mapping continues to evolve with shifting pandemic needs, including new variant surges, vaccine targeting, and most recently, testing data shortfalls. This paper also demonstrates how the geographic approach applied provides the framework needed for future pandemic preparedness.

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.

Identification of a population of peripheral sensory neurons that regulates blood pressure.

The vasculature is innervated by a network of peripheral afferents that sense and regulate blood flow. Here, we describe a system of non-peptidergic sensory neurons with cell bodies in the spinal ganglia that regulate vascular tone in the distal arteries. We identify a population of mechanosensitive neurons, marked by tropomyosin receptor kinase C (TrkC) and tyrosine hydroxylase in the dorsal root ganglia, which projects to blood vessels. Local stimulation of TrkC neurons decreases vessel diameter and blood flow, whereas systemic activation increases systolic blood pressure and heart rate variability via the sympathetic nervous system. Ablation of the neurons provokes variability in local blood flow, leading to a reduction in systolic blood pressure, increased heart rate variability, and ultimately lethality within 48 h. Thus, a population of TrkC+ sensory neurons forms part of a sensory-feedback mechanism that maintains cardiovascular homeostasis through the autonomic nervous system.

Assessing Competence in Critical Care Echocardiography: Development and Initial Results of an Examination and Certification Processes.

OBJECTIVES: To describe the development and initial results of an examination and certification process assessing competence in critical care echocardiography. DESIGN: A test writing committee of content experts from eight professional societies invested in critical care echocardiography was convened, with the Executive Director representing the National Board of Echocardiography. Using an examination content outline, the writing committee was assigned topics relevant to their areas of expertise. The examination items underwent extensive review, editing, and discussion in several face-to-face meetings supervised by National Board of Medical Examiners editors and psychometricians. A separate certification committee was tasked with establishing criteria required to achieve National Board of Echocardiography certification in critical care echocardiography through detailed review of required supporting material submitted by candidates seeking to fulfill these criteria. SETTING: The writing committee met twice a year in person at the National Board of Medical Examiner office in Philadelphia, PA. SUBJECTS: Physicians enrolled in the examination of Special Competence in Critical Care Electrocardiography (CCEeXAM). MEASUREMENTS AND MAIN RESULTS: A total of 524 physicians sat for the examination, and 426 (81.3%) achieved a passing score. Of the examinees, 41% were anesthesiology trained, 33.2% had pulmonary/critical care background, and the majority had graduated training within the 10 years (91.6%). Most candidates work full-time at an academic hospital (46.9%). CONCLUSIONS: The CCEeXAM is designed to assess a knowledge base that is shared with echocardiologists in addition to that which is unique to critical care. The National Board of Echocardiography certification establishes that the physician has achieved the ability to independently perform and interpret critical care echocardiography at a standard recognized by critical care professional societies encompassing a wide spectrum of backgrounds. The interest shown and the success achieved on the CCEeXAM by practitioners of critical care echocardiography support the standards set by the National Board of Echocardiography for testamur status and certification in this imaging specialty area.