The evolution of strategy in bacterial warfare via the regulation of bacteriocins and antibiotics.

Bacteria inhibit and kill one another with a diverse array of compounds, including bacteriocins and antibiotics. These attacks are highly regulated, but we lack a clear understanding of the evolutionary logic underlying this regulation. Here, we combine a detailed dynamic model of bacterial competition with evolutionary game theory to study the rules of bacterial warfare. We model a large range of possible combat strategies based upon the molecular biology of bacterial regulatory networks. Our model predicts that regulated strategies, which use quorum sensing or stress responses to regulate toxin production, will readily evolve as they outcompete constitutive toxin production. Amongst regulated strategies, we show that a particularly successful strategy is to upregulate toxin production in response to an incoming competitor's toxin, which can be achieved via stress responses that detect cell damage (competition sensing). Mirroring classical game theory, our work suggests a fundamental advantage to reciprocation. However, in contrast to classical results, we argue that reciprocation in bacteria serves not to promote peaceful outcomes but to enable efficient and effective attacks

The impact of sequencing depth on the inferred taxonomic composition and AMR gene content of metagenomic samples

Shotgun metagenomics is increasingly used to characterise microbial communities, particularly for the investigation of antimicrobial resistance (AMR) in different animal and environmental contexts. There are many different approaches for inferring the taxonomic composition and AMR gene content of complex community samples from shotgun metagenomic data, but there has been little work establishing the optimum sequencing depth, data processing and analysis methods for these samples. In this study we used shotgun metagenomics and sequencing of cultured isolates from the same samples to address these issues. We sampled three potential environmental AMR gene reservoirs (pig caeca, river sediment, effluent) and sequenced samples with shotgun metagenomics at high depth (~ 200 million reads per sample). Alongside this, we cultured single-colony isolates of Enterobacteriaceae from the same samples and used hybrid sequencing (short- and long-reads) to create high- quality assemblies for comparison to the metagenomic data. To automate data processing, we developed an open- source software pipeline, ‘ResPipe’

Population-level faecal metagenomic profiling as a tool to predict antimicrobial resistance in Enterobacterales isolates causing invasive infections: an exploratory study across Cambodia, Kenya, and the UK

Background: Antimicrobial resistance (AMR) in Enterobacterales is a global health threat. Capacity for individual-level surveillance remains limited in many countries, whilst population-level surveillance approaches could inform empiric antibiotic treatment guidelines. Methods: In this exploratory study, a novel approach to population-level prediction of AMR in Enterobacterales clinical isolates using metagenomic (Illumina) profiling of pooled DNA extracts from human faecal samples was developed and tested. Taxonomic and AMR gene profiles were used to derive taxonomy-adjusted population-level AMR metrics. Bayesian modelling, and model comparison based on cross-validation, were used to evaluate the capacity of each metric to predict the number of resistant Enterobacterales invasive infections at a population-level, using available bloodstream/cerebrospinal fluid infection data. Findings: Population metagenomes comprised samples from 177, 157, and 156 individuals in Kenya, the UK, and Cambodia, respectively, collected between September 2014 and April 2016. Clinical data from independent populations included 910, 3356 and 197 bacterial isolates from blood/cerebrospinal fluid infections in Kenya, the UK and Cambodia, respectively (samples collected between January 2010 and May 2017). Enterobacterales were common colonisers and pathogens, and faecal taxonomic/AMR gene distributions and proportions of antimicrobial-resistant Enterobacterales infections differed by setting. A model including terms reflecting the metagenomic abundance of the commonest clinical Enterobacterales species, and of AMR genes known to either increase the minimum inhibitory concentration (MIC) or confer clinically-relevant resistance, had a higher predictive performance in determining population-level resistance in clinical Enterobacterales isolates compared to models considering only AMR gene information, only taxonomic information, or an intercept-only baseline model (difference in expected log predictive density compared to best model, estimated using leave-one-out cross-validation: intercept-only model = -223 [95% credible interval (CI): -330,-116]; model considering only AMR gene information = -186 [95% CI: -281,-91]; model considering only taxonomic information = -151 [95% CI: -232,-69]). Interpretation: Whilst our findings are exploratory and require validation, intermittent metagenomics of pooled samples could represent an effective approach for AMR surveillance and to predict population-level AMR in clinical isolates, complementary to ongoing development of laboratory infrastructures processing individual samples

Practical considerations for measuring the effective reproductive number, Rt.

Estimation of the effective reproductive number Rt is important for detecting changes in disease transmission over time. During the Coronavirus Disease 2019 (COVID-19) pandemic, policy makers and public health officials are using Rt to assess the effectiveness of interventions and to inform policy. However, estimation of Rt from available data presents several challenges, with critical implications for the interpretation of the course of the pandemic. The purpose of this document is to summarize these challenges, illustrate them with examples from synthetic data, and, where possible, make recommendations. For near real-time estimation of Rt, we recommend the approach of Cori and colleagues, which uses data from before time t and empirical estimates of the distribution of time between infections. Methods that require data from after time t, such as Wallinga and Teunis, are conceptually and methodologically less suited for near real-time estimation, but may be appropriate for retrospective analyses of how individuals infected at different time points contributed to the spread. We advise caution when using methods derived from the approach of Bettencourt and Ribeiro, as the resulting Rt estimates may be biased if the underlying structural assumptions are not met. Two key challenges common to all approaches are accurate specification of the generation interval and reconstruction of the time series of new infections from observations occurring long after the moment of transmission. Naive approaches for dealing with observation delays, such as subtracting delays sampled from a distribution, can introduce bias. We provide suggestions for how to mitigate this and other technical challenges and highlight open problems in Rt estimation

Evolution on the microbial battlefield

Microbes live in dense communities where strains and species compete for space and nutrients. Cells within these communities produce a large array of secretions, such as toxins or scavenging molecules, that kill and inhibit other strains. While competition is common and important for microbes, its impacts on their communities remains poorly understood. In this thesis, I study the impacts of competition on (1) the evolution of secretions and (2) horizontal gene transfer. Using a wide range of approaches, including game theory, population genetics, differential equations and individual-based models, I investigate the evolution and ecology of diverse microbial communities. First, I study the production of iron- scavenging siderophoresâa trait often assumed to be cooperativeâand show how they can function as a competitive trait that harms other strains by starving them of iron. My competitive model predicts that siderophores should be upregulated in response to competitors and I show this fits with both published and new experimental data (Chapter 2). I next study the logic of bacterial warfare proper: the evolution of strategies to produce antibiotics and bacteriocins to kill other strains. I show that the most robust strategy for using antibiotics is to attack only when detecting the attack of others, a negative form of tit-for-tat (Chapter 3). The second half of my thesis studies how competition influences another major bacterial trait, horizontal gene transfer. Firstly, I show how competition and migration combine to enable single genes to sweep horizontally through microbial communities, thus providing an explanation for a large body of data that shows that these sweeps are both common and important (Chapter 4). Finally, I ask how genetic transfer can evolve in microbial communities, despite the potential for high individual costs from taking up foreign DNA. As for the evolution of sex in eukaryotes, I show that epistasis for fitness can promote horizontal transfer under certain conditions. However, by studying regulated gene transfer, I argue that key to horizontal transfer evolution in bacteria is their ability to actively upregulate transfer in response to stress (Chapter 5). My thesis helps to map out the rules of interaction in microbial communities, and how these rules affect major phenotypes, including siderophores, antibiotics and horizontal genetic transfer. To close, I discuss how the effects of clinical antibiotics mirrors many of the effects of natural competition between microbial strains. I argue that only by understanding natural competition can we understand how the use, and overuse, of clinical antibiotics affects microbes.</p

Evolution on the microbial battlefield

Microbes live in dense communities where strains and species compete for space and nutrients. Cells within these communities produce a large array of secretions, such as toxins or scavenging molecules, that kill and inhibit other strains. While competition is common and important for microbes, its impacts on their communities remains poorly understood. In this thesis, I study the impacts of competition on (1) the evolution of secretions and (2) horizontal gene transfer. Using a wide range of approaches, including game theory, population genetics, differential equations and individual-based models, I investigate the evolution and ecology of diverse microbial communities. First, I study the production of iron- scavenging siderophores–a trait often assumed to be cooperative–and show how they can function as a competitive trait that harms other strains by starving them of iron. My competitive model predicts that siderophores should be upregulated in response to competitors and I show this fits with both published and new experimental data (Chapter 2). I next study the logic of bacterial warfare proper: the evolution of strategies to produce antibiotics and bacteriocins to kill other strains. I show that the most robust strategy for using antibiotics is to attack only when detecting the attack of others, a negative form of tit-for-tat (Chapter 3). The second half of my thesis studies how competition influences another major bacterial trait, horizontal gene transfer. Firstly, I show how competition and migration combine to enable single genes to sweep horizontally through microbial communities, thus providing an explanation for a large body of data that shows that these sweeps are both common and important (Chapter 4). Finally, I ask how genetic transfer can evolve in microbial communities, despite the potential for high individual costs from taking up foreign DNA. As for the evolution of sex in eukaryotes, I show that epistasis for fitness can promote horizontal transfer under certain conditions. However, by studying regulated gene transfer, I argue that key to horizontal transfer evolution in bacteria is their ability to actively upregulate transfer in response to stress (Chapter 5). My thesis helps to map out the rules of interaction in microbial communities, and how these rules affect major phenotypes, including siderophores, antibiotics and horizontal genetic transfer. To close, I discuss how the effects of clinical antibiotics mirrors many of the effects of natural competition between microbial strains. I argue that only by understanding natural competition can we understand how the use, and overuse, of clinical antibiotics affects microbes.</p

Implications of reducing antibiotic treatment duration for antimicrobial resistance in hospital settings: A modelling study and meta-analysis.

BackgroundReducing antibiotic treatment duration is a key component of hospital antibiotic stewardship interventions. However, its effectiveness in reducing antimicrobial resistance is uncertain and a clear theoretical rationale for the approach is lacking. In this study, we sought to gain a mechanistic understanding of the relation between antibiotic treatment duration and the prevalence of colonisation with antibiotic-resistant bacteria in hospitalised patients.Methods and findingsWe constructed 3 stochastic mechanistic models that considered both between- and within-host dynamics of susceptible and resistant gram-negative bacteria, to identify circumstances under which shortening antibiotic duration would lead to reduced resistance carriage. In addition, we performed a meta-analysis of antibiotic treatment duration trials, which monitored resistant gram-negative bacteria carriage as an outcome. We searched MEDLINE and EMBASE for randomised controlled trials published from 1 January 2000 to 4 October 2022, which allocated participants to varying durations of systemic antibiotic treatments. Quality assessment was performed using the Cochrane risk-of-bias tool for randomised trials. The meta-analysis was performed using logistic regression. Duration of antibiotic treatment and time from administration of antibiotics to surveillance culture were included as independent variables. Both the mathematical modelling and meta-analysis suggested modest reductions in resistance carriage could be achieved by reducing antibiotic treatment duration. The models showed that shortening duration is most effective at reducing resistance carriage in high compared to low transmission settings. For treated individuals, shortening duration is most effective when resistant bacteria grow rapidly under antibiotic selection pressure and decline rapidly when stopping treatment. Importantly, under circumstances whereby administered antibiotics can suppress colonising bacteria, shortening antibiotic treatment may increase the carriage of a particular resistance phenotype. We identified 206 randomised trials, which investigated antibiotic duration. Of these, 5 reported resistant gram-negative bacteria carriage as an outcome and were included in the meta-analysis. The meta-analysis determined that a single additional antibiotic treatment day is associated with a 7% absolute increase in risk of resistance carriage (80% credible interval 3% to 11%). Interpretation of these estimates is limited by the low number of antibiotic duration trials that monitored carriage of resistant gram-negative bacteria, as an outcome, contributing to a large credible interval.ConclusionsIn this study, we found both theoretical and empirical evidence that reducing antibiotic treatment duration can reduce resistance carriage, though the mechanistic models also highlighted circumstances under which reducing treatment duration can, perversely, increase resistance. Future antibiotic duration trials should monitor antibiotic-resistant bacteria colonisation as an outcome to better inform antibiotic stewardship policies

How to detect and reduce potential sources of biases in studies of SARS-CoV-2 and COVID-19

Funding: National Institute of Allergy and Infectious Diseases of the National Institutes of Health (award number T32AI007535), the National Institute of General Medical Sciences of the National Institutes of Health (award number U54GM088558), the Morris-Singer Fund, and the National Institutes of Health (cooperative agreement U01 CA261277).In response to the coronavirus disease (COVID-19) pandemic, public health scientists have produced a large and rapidly expanding body of literature that aims to answer critical questions, such as the proportion of the population in a geographic area that has been infected; the transmissibility of the virus and factors associated with high infectiousness or susceptibility to infection; which groups are the most at risk of infection, morbidity and mortality; and the degree to which antibodies confer protection to re-infection. Observational studies are subject to a number of different biases, including confounding, selection bias, and measurement error, that may threaten their validity or influence the interpretation of their results. To assist in the critical evaluation of a vast body of literature and contribute to future study design, we outline and propose solutions to biases that can occur across different categories of observational studies of COVID-19. We consider potential biases that could occur in five categories of studies: (1) cross-sectional seroprevalence, (2) longitudinal seroprotection, (3) risk factor studies to inform interventions, (4) studies to estimate the secondary attack rate, and (5) studies that use secondary attack rates to make inferences about infectiousness and susceptibility.Publisher PDFPeer reviewe

Predicting subnational incidence of COVID-19 cases and deaths in EU countries

Abstract Background Recurring COVID-19 waves highlight the need for tools able to quantify transmission risk, and identify geographical areas at risk of outbreaks. Local outbreak risk depends on complex immunity patterns resulting from previous infections, vaccination, waning and immune escape, alongside other factors (population density, social contact patterns). Immunity patterns are spatially and demographically heterogeneous, and are challenging to capture in country-level forecast models. Methods We used a spatiotemporal regression model to forecast subnational case and death counts and applied it to three EU countries as test cases: France, Czechia, and Italy. Cases in local regions arise from importations or local transmission. Our model produces age-stratified forecasts given age-stratified data, and links reported case counts to routinely collected covariates (e.g. test number, vaccine coverage). We assessed the predictive performance of our model up to four weeks ahead using proper scoring rules and compared it to the European COVID-19 Forecast Hub ensemble model. Using simulations, we evaluated the impact of variations in transmission on the forecasts. We developed an open-source RShiny App to visualise the forecasts and scenarios. Results At a national level, the median relative difference between our median weekly case forecasts and the data up to four weeks ahead was 25% (IQR: 12–50%) over the prediction period. The accuracy decreased as the forecast horizon increased (on average 24% increase in the median ranked probability score per added week), while the accuracy of death forecasts was more stable. Beyond two weeks, the model generated a narrow range of likely transmission dynamics. The median national case forecasts showed similar accuracy to forecasts from the European COVID-19 Forecast Hub ensemble model, but the prediction interval was narrower in our model. Generating forecasts under alternative transmission scenarios was therefore key to capturing the range of possible short-term transmission dynamics. Discussion Our model captures changes in local COVID-19 outbreak dynamics, and enables quantification of short-term transmission risk at a subnational level. The outputs of the model improve our ability to identify areas where outbreaks are most likely, and are available to a wide range of public health professionals through the Shiny App we developed