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The emergence of antibiotic-resistant bacteria through mutations or the acquisition of genetic material such as resistance plasmids represents a major public health issue.sup.1,2. Persisters are subpopulations of bacteria that survive antibiotics by reversibly adapting their physiology.sup.3-10, and can promote the emergence of antibiotic-resistant mutants.sup.11. We investigated whether persisters can also promote the spread of resistance plasmids. In contrast to mutations, the transfer of resistance plasmids requires the co-occurrence of both a donor and a recipient bacterial strain. For our experiments, we chose the facultative intracellular entero-pathogen Salmonella enterica serovar Typhimurium (S. Typhimurium) and Escherichia coli, a common member of the microbiota.sup.12. S. Typhimurium forms persisters that survive antibiotic therapy in several host tissues. Here we show that tissue-associated S. Typhimurium persisters represent long-lived reservoirs of plasmid donors or recipients. The formation of reservoirs of S. Typhimurium persisters requires Salmonella pathogenicity island (SPI)-1 and/or SPI-2 in gut-associated tissues, or SPI-2 at systemic sites. The re-seeding of these persister bacteria into the gut lumen enables the co-occurrence of donors with gut-resident recipients, and thereby favours plasmid transfer between various strains of Enterobacteriaceae. We observe up to 99% transconjugants within two to three days of re-seeding. Mathematical modelling shows that rare re-seeding events may suffice for a high frequency of conjugation. Vaccination reduces the formation of reservoirs of persisters after oral infection with S. Typhimurium, as well as subsequent plasmid transfer. We conclude that--even without selection for plasmid-encoded resistance genes--small reservoirs of pathogen persisters can foster the spread of promiscuous resistance plasmids in the gut. The re-seeding of antibiotic-resistant persister subpopulations of Salmonella enterica into the gut lumen favours the transfer of resistance plasmids to gut-resident enterobacteria, showing that even small reservoirs of persister bacteria facilitate the spread of antibiotic resistance., Author(s): Erik Bakkeren [sup.1] , Jana S. Huisman [sup.2] [sup.3] , Stefan A. Fattinger [sup.1] [sup.4] , Annika Hausmann [sup.1] , Markus Furter [sup.1] , Adrian Egli [sup.5] [sup.6] , [...]

How does genetic background influence a population's evolutionary response to an environmental change? Which traits are selected for and how quickly does the population adapt? Filipow et al. (2024) address these questions by exploiting the natural genetic variation of Pseudomonas aeruginosa , a bacterium often found in the lungs of cystic fibrosis patients. They find that while genetic background influences the rate of phenotypic evolution, it does not alter the evolutionary outcome. Their findings contribute to a growing body of work that connects genetic background to future evolvability. [ABSTRACT FROM AUTHOR]

Background: Next-generation sequencing has considerably increased the number of genomes available in the public domain. However, efforts to use these genomes for surveillance of antimicrobial resistance have thus far been limited and geographically heterogeneous. We inferred global resistance trends in Escherichia coli in food animals using genomes from public databases.Methods: We retrieved 7632 E. coli genomes from public databases (NCBI, PATRIC and EnteroBase) and screened for antimicrobial resistance genes (ARGs) using ResFinder. Selection bias towards resistance, virulence or specific strains was accounted for by screening BioProject descriptions. Temporal trends for MDR, resistance to antimicrobial classes and ARG prevalence were inferred using generalized linear models for all genomes, including those not subjected to selection bias.Results: MDR increased by 1.6 times between 1980 and 2018, as genomes carried, on average, ARGs conferring resistance to 2.65 antimicrobials in swine, 2.22 in poultry and 1.58 in bovines. Highest resistance levels were observed for tetracyclines (42.2%-69.1%), penicillins (19.4%-47.5%) and streptomycin (28.6%-56.6%). Resistance trends were consistent after accounting for selection bias, although lower mean absolute resistance estimates were associated with genomes not subjected to selection bias (difference of 3.16%±3.58% across years, hosts and antimicrobial classes). We observed an increase in extended-spectrum cephalosporin ARG blaCMY-2 and a progressive substitution of tetB by tetA. Estimates of resistance prevalence inferred from genomes in the public domain were in good agreement with reports from systematic phenotypic surveillance.Conclusions: Our analysis illustrates the potential of using the growing volume of genomes in public databases to track AMR trends globally. [ABSTRACT FROM AUTHOR]

As infectious agents of bacteria and vehicles of horizontal gene transfer, plasmids play a key role in bacterial ecology and evolution. Plasmid dynamics are shaped not only by plasmid-host interactions but also by ecological interactions between plasmid variants. These interactions are complex: plasmids can co-infect the same cell and the consequences for the co-resident plasmid can be either beneficial or detrimental. Many of the biological processes that govern plasmid co-infection--from systems that exclude infection by other plasmids to interactions in the regulation of plasmid copy number--are well characterized at a mechanistic level. Modelling plays a central role in translating such mechanistic insights into predictions about plasmid dynamics and the impact of these dynamics on bacterial evolution. Theoretical work in evolutionary epidemiology has shown that formulating models of co-infection is not trivial, as some modelling choices can introduce unintended ecological assumptions. Here, we review how the biological processes that govern co-infection can be represented in a mathematical model, discuss potential modelling pitfalls, and analyse this model to provide general insights into how co-infection impacts ecological and evolutionary outcomes. In particular, we demonstrate how beneficial and detrimental effects of co-infection give rise to frequency-dependent selection on plasmid variants. [ABSTRACT FROM AUTHOR]

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. Author summary: The effective reproductive number Rt is a key epidemic parameter used to assess whether an epidemic is growing, shrinking, or holding steady. Rt estimates can be used as a near real-time indicator of epidemic growth or to assess the effectiveness of interventions. But due to delays between infection and case observation, estimating Rt in near real time, and correctly inferring the timing of changes in Rt, is challenging. Here, we provide an overview of challenges and best practices for accurate and timely Rt estimation. [ABSTRACT FROM AUTHOR]

(B-C) The case reproductive number is defined as the average number of new infections that an individual who becomes infected on day ti (green arrows in B) or symptomatic on day ts (yellow arrows in C) will eventually go on to cause (blue downward arrows in B and C). There is an error in the caption for Fig 1, "Instantaneous reproductive number as estimated by the method of Cori et al. vs. cohort reproductive number estimated by Wallinga and Teunis" sentence four. [Extracted from the article]