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The issue of antibiotic resistance is a critical concern for public health, prompting numerous investigations into the impact of treatment strategies on preventing or slowing down the emergence of resistance. While existing studies have predominantly focused on chromosomal resistance mutations, the consequences of often clinically more relevant plasmid-conferred resistance remain insufficiently explored. To address this gap, we conducted three extensive in vitro experiments utilizing a liquid-handling platform. These experiments evaluated the efficacy of five distinct treatment strategies using two antibiotics (tetracycline and ceftazidime) along with two horizontally transmissible clinical resistance plasmids conferring the respective resistances. Among the experimentally investigated treatment strategies, combination therapy proved to be the most effective in preventing the emergence of double resistance while minimizing the number of infections. To verify the reliability of these findings, we constructed a computational model of our experiments that we parameterized using the experimental data. We employed this model to augment the experimental data by conducting an in silico parameter sensitivity analysis. The sensitivity analysis corroborated our experimental results, demonstrating that combination therapy consistently outperformed other treatment strategies across a range of parameter values. [ABSTRACT FROM AUTHOR]

Immune responses are induced by parasite exposure and can in turn reduce parasite burden. Despite such apparently simple rules of engagement, key drivers of within-host dynamics, including dose-dependence of defense and infection duration, have proven difficult to predict. Here, we model how varied inoculating doses interact with multi-tiered host defenses at a site of inoculation, by confronting barrier, innate, and adaptive tiers with replicating and non-replicating parasites across multiple orders of magnitude of dose. We find that, in general, intermediate parasite doses generate infections of longest duration because they are sufficient in number to breach barrier defenses, but insufficient to strongly induce subsequent tiers of defense. These doses reveal "wormholes" in defense from which parasites might profit: Deviation from the hypothesis of independent action, which postulates that each parasite has an independent probability of establishing infection, may therefore be widespread. Interestingly, our model predicts local maxima of duration at two doses–one for each tier transition. While some empirical evidence is consistent with nonlinear dose-dependencies, testing the predicted dynamics will require finer-scale dose variation than experiments usually incorporate. Our results help explain varied infection establishment and duration among differentially-exposed hosts and elucidate evolutionary pressures that shape both virulence and defense. Author summary: Organisms vary in their exposure to parasites and pathogens, such that some individuals breathe in, ingest, or are inoculated (e.g., by insect vectors) with just a few parasites, while others are confronted with large doses. This variation in inoculating dose is likely to affect the odds of successful defense and the duration of infection. For example, individuals confronted with very few parasites might be completely defended, such that infection never establishes. Intuitive though such predictions are, the dose-dependence of the establishment and duration of infection is poorly understood. Here, we use a mathematical model of the three known tiers of immune defenses–i.e., barrier, innate, and adaptive tiers–to investigate how varied inoculating doses interact with immune systems. We find that although the lowest doses are indeed readily controlled, the highest doses induce potent immune responses that curtail infection. As a result, intermediate doses are the ones that can slip past barrier defense yet under the radar of induced defenses. Intermediate doses therefore generate infections of greatest duration. These findings help explain why hosts vary so much in the course of infection and suggest new research directions in epidemiology and immunology. [ABSTRACT FROM AUTHOR]

The prevalence of antibiotic-resistant pathogens has become a major threat to public health, requiring swift initiatives for discovering new strategies to control bacterial infections. Hence, antibiotic stewardship and rapid diagnostics, but also the development, and prudent use, of novel effective antimicrobial agents are paramount. Ideally, these agents should be less likely to select for resistance in pathogens than currently available conventional antimicrobials. The usage of antimicrobial peptides (AMPs), key components of the innate immune response, and combination therapies, have been proposed as strategies to diminish the emergence of resistance. Herein, we investigated whether newly developed random antimicrobial peptide mixtures (RPMs) can significantly reduce the risk of resistance evolution in vitro to that of single sequence AMPs, using the ESKAPE pathogen Pseudomonas aeruginosa (P. aeruginosa) as a model gram-negative bacterium. Infections of this pathogen are difficult to treat due the inherent resistance to many drug classes, enhanced by the capacity to form biofilms. P. aeruginosa was experimentally evolved in the presence of AMPs or RPMs, subsequentially assessing the extent of resistance evolution and cross-resistance/collateral sensitivity between treatments. Furthermore, the fitness costs of resistance on bacterial growth were studied and whole-genome sequencing used to investigate which mutations could be candidates for causing resistant phenotypes. Lastly, changes in the pharmacodynamics of the evolved bacterial strains were examined. Our findings suggest that using RPMs bears a much lower risk of resistance evolution compared to AMPs and mostly prevents cross-resistance development to other treatments, while maintaining (or even improving) drug sensitivity. This strengthens the case for using random cocktails of AMPs in favour of single AMPs, against which resistance evolved in vitro, providing an alternative to classic antibiotics worth pursuing. Antimicrobial peptides have been suggested as a solution to combat the escalating public health threat of antibiotic resistance. This study demonstrates that random peptide mixtures are significantly better than traditional single-sequence antimicrobial peptides at preventing the development of resistance in Pseudomonas aeruginosa. [ABSTRACT FROM AUTHOR]

Serosurveys are a widely used tool to estimate the cumulative incidence—the fraction of a population that has been infected by a given pathogen. These surveys rely on serological assays that measure the level of pathogen-specific antibodies. Because antibody levels are waning, the fraction of previously infected individuals that have seroreverted increases with time past infection. To avoid underestimating the true cumulative incidence, it is therefore essential to correct for waning antibody levels. We present an empirically supported approach for seroreversion correction in cumulative incidence estimation when sequential serosurveys are conducted in the context of a newly emerging infectious disease. The correction is based on the observed dynamics of antibody titers in seropositive cases and validated using several in silico test scenarios. Furthermore, through this approach we revise a previous cumulative incidence estimate relying on the assumption of an exponentially declining probability of seroreversion over time, of severe acute respiratory syndrome coronavirus 2, of 76% in Manaus, Brazil, by October 2020 to 47.6% (95% confidence region: 43.5–53.5). This estimate has implications, for example, for the proximity to herd immunity in Manaus in late 2020. [ABSTRACT FROM AUTHOR]

Author(s): Peter Rusert [1]; Roger D Kouyos [1, 2]; Claus Kadelka [1, 2]; Hanna Ebner [1]; Merle Schanz [1]; Michael Huber [1]; Dominique L Braun [1, 2]; Nathanael Hozé [3]; [...], Broadly neutralizing antibodies (bnAbs) are a focal component of HIV-1 vaccine design, yet basic aspects of their induction remain poorly understood. Here we report on viral, host and disease factors that steer bnAb evolution using the results of a systematic survey in 4,484 HIV-1-infected individuals that identified 239 bnAb inducers. We show that three parameters that reflect the exposure to antigen--viral load, length of untreated infection and viral diversity--independently drive bnAb evolution. Notably, black participants showed significantly (P = 0.0086-0.038) higher rates of bnAb induction than white participants. Neutralization fingerprint analysis, which was used to delineate plasma specificity, identified strong virus subtype dependencies, with higher frequencies of CD4-binding-site bnAbs in infection with subtype B viruses (P = 0.02) and higher frequencies of V2-glycan-specific bnAbs in infection with non-subtype B viruses (P = 1 x 10[sup.-5]). Thus, key host, disease and viral determinants, including subtype-specific envelope features that determine bnAb specificity, remain to be unraveled and harnessed for bnAb-based vaccine design.