Exceptions to the rule: When does resistance evolution not undermine antibiotic therapy in human bacterial infections?

The use of antibiotics to treat bacterial infections often imposes strong selection for antibiotic resistance. However, the prevalence of antibiotic resistance varies greatly across different combinations of pathogens and drugs. What underlies this variation? Systematic reviews, meta-analyses, and literature surveys capable of integrating data across many studies have tried to answer this question, but the vast majority of these studies have focused only on cases where resistance is common or problematic. Yet much could presumably be learned from the cases where resistance is infrequent or absent. Here we conducted a literature survey and a systematic review to study the evolution of antibiotic resistance across a wide range of pathogen-by-drug combinations (57 pathogens and 53 antibiotics from 15 drug classes). Using Akaike information criterion-based model selection and model-averaged parameter estimation we explored 14 different factors posited to be associated with resistance evolution. We find that the most robust predictors of high resistance are nosocomial transmission (i.e., hospital-acquired pathogens) and indirect transmission (e.g., vector-, water-, air-, or vehicle-borne pathogens). While the former was to be expected based on prior studies, the positive correlation between high resistance frequencies and indirect transmission is, to our knowledge, a novel insight. The most robust predictor of low resistance is zoonosis from wild animal reservoirs. We also found partial support that resistance was associated with pathogen type, horizontal gene transfer, commensalism, and human-to-human transmission. We did not find support for correlations between resistance and environmental reservoirs, mechanisms of drug action, and global drug use. This work explores the relative explanatory power of various pathogen and drug factors on resistance evolution, which is necessary to identify priority targets of stewardship efforts to slow the spread of drug-resistant pathogens.

Detecting and quantifying heterogeneity in susceptibility using contact tracing data.

The presence of heterogeneity in susceptibility, differences between hosts in their likelihood of becoming infected, can fundamentally alter disease dynamics and public health responses, for example, by changing the final epidemic size, the duration of an epidemic, and even the vaccination threshold required to achieve herd immunity. Yet, heterogeneity in susceptibility is notoriously difficult to detect and measure, especially early in an epidemic. Here we develop a method that can be used to detect and estimate heterogeneity in susceptibility given contact by using contact tracing data, which are typically collected early in the course of an outbreak. This approach provides the capability, given sufficient data, to estimate and account for the effects of this heterogeneity before they become apparent during an epidemic. It additionally provides the capability to analyze the wealth of contact tracing data available for previous epidemics and estimate heterogeneity in susceptibility for disease systems in which it has never been estimated previously. The premise of our approach is that highly susceptible individuals become infected more often than less susceptible individuals, and so individuals not infected after appearing in contact networks should be less susceptible than average. This change in susceptibility can be detected and quantified when individuals show up in a second contact network after not being infected in the first. To develop our method, we simulated contact tracing data from artificial populations with known levels of heterogeneity in susceptibility according to underlying discrete or continuous distributions of susceptibilities. We analyzed these data to determine the parameter space under which we are able to detect heterogeneity and the accuracy with which we are able to estimate it. We found that our power to detect heterogeneity increases with larger sample sizes, greater heterogeneity, and intermediate fractions of contacts becoming infected in the discrete case or greater fractions of contacts becoming infected in the continuous case. We also found that we are able to reliably estimate heterogeneity and disease dynamics. Ultimately, this means that contact tracing data alone are sufficient to detect and quantify heterogeneity in susceptibility.

Developing transmissible vaccines for animal infections.

Intrinsically safe designs and a staged transparent development process will be essential.

Death is overrated: the potential role of detection in driving virulence evolution.

A common assumption in the evolution of virulence theory literature is that pathogens transmit better when they exploit their host more heavily, but by doing so, they impose a greater risk of killing their host, thus truncating infectious periods and reducing their own opportunities for transmission. Here, I derive an equation for the magnitude of this cost in terms of the infection fatality rate, and in doing so, I show that there are many cases where mortality costs are too small to plausibly constrain increases in host exploitation by pathogens. I propose that pathogen evolution may often be constrained by detection costs, whereby hosts alter their behaviour when infection is detectable, and thus reduce pathogen opportunities for onward transmission. I then derive an inequality to illustrate when mortality costs or detection costs impose stronger constraints on pathogen evolution, and I use empirical data from the literature to demonstrate that detection costs are frequently large in both human and animal populations. Finally, I give examples of how evolutionary predictions can change depending on whether costs of host exploitation are borne out through mortality or detection.

Pathogen evolution during vaccination campaigns.

Following the initiation of the unprecedented global vaccination campaign against Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), attention has now turned to the potential impact of this large-scale intervention on the evolution of the virus. In this Essay, we summarize what is currently known about pathogen evolution in the context of immune priming (including vaccination) from research on other pathogen species, with an eye towards the future evolution of SARS-CoV-2.

Developing an empirical model for spillover and emergence: Orsay virus host range in Caenorhabditis.

A lack of tractable experimental systems in which to test hypotheses about the ecological and evolutionary drivers of disease spillover and emergence has limited our understanding of these processes. Here we introduce a promising system: Caenorhabditis hosts and Orsay virus, a positive-sense single-stranded RNA virus that naturally infects C. elegans. We assayed species across the Caenorhabditis tree and found Orsay virus susceptibility in 21 of 84 wild strains belonging to 14 of 44 species. Confirming patterns documented in other systems, we detected effects of host phylogeny on susceptibility. We then tested whether susceptible strains were capable of transmitting Orsay virus by transplanting exposed hosts and determining whether they transmitted infection to conspecifics during serial passage. We found no evidence of transmission in 10 strains (virus undetectable after passaging in all replicates), evidence of low-level transmission in 5 strains (virus lost between passage 1 and 5 in at least one replicate) and evidence of sustained transmission in 6 strains (including all three experimental C. elegans strains) in at least one replicate. Transmission was strongly associated with viral amplification in exposed populations. Variation in Orsay virus susceptibility and transmission among Caenorhabditis strains suggests that the system could be powerful for studying spillover and emergence.

The economics of managing evolution.

Humans are altering biological systems at unprecedented rates, and these alterations often have longer-term evolutionary impacts. Most obvious is the spread of resistance to pesticides and antibiotics. There are a wide variety of management strategies available to slow this evolution, and there are many reasons for using them. In this paper, we focus on the economic aspects of evolution management and ask: When is it economically beneficial for an individual decision-maker to invest in evolution management? We derive a simple dimensionless inequality showing that it is cost-effective to manage evolution when the percentage increase in the effective life span of the biological resource that management generates is larger than the percentage increase in annual profit that could be obtained by not managing evolution. We show how this inequality can be used to determine optimal investment choices for single decision-makers, to determine Nash equilibrium investment choices for multiple interacting decision-makers, and to examine how these equilibrium choices respond to regulatory interventions aimed at stimulating investment in evolution management. Our results are illustrated with examples involving Bacillus thuringiensis (Bt) crops and antibiotic use in fish farming.

What the reproductive number R0 can and cannot tell us about COVID-19 dynamics.

The reproductive number R (or R0, the initial reproductive number in an immune-naïve population) has long been successfully used to predict the likelihood of pathogen invasion, to gauge the potential severity of an epidemic, and to set policy around interventions. However, often ignored complexities have generated confusion around use of the metric. This is particularly apparent with the emergent pandemic virus SARS-CoV-2, the causative agent of COVID-19. We address some misconceptions about the predictive ability of the reproductive number, focusing on how it changes over time, varies over space, and relates to epidemic size by referencing the mathematical definition of R and examples from the current pandemic. We hope that a better appreciation of the uses, nuances, and limitations of R and R0 facilitates a better understanding of epidemic spread, epidemic severity, and the effects of interventions in the context of SARS-CoV-2.

Monitor for COVID-19 vaccine resistance evolution during clinical trials.

Although less common than the evolution of antimicrobial drug resistance, vaccine resistance can and has evolved. How likely is it that COVID-19 vaccines currently in development will be undermined by viral evolution? We argue that this can be determined by repurposing samples that are already being collected as part of clinical trials. Such information would be useful for prioritizing investment among candidate vaccines and maximizing the potential long-term impact of COVID-19 vaccines.

Molecular epidemiology of Marek's disease virus in central Pennsylvania, USA.

The evolution of Marek's disease virus (MDV, Gallid herpesvirus 2) has threatened the sustainability of poultry farming in the past and its continued evolution remains a concern. Genetic diversity is key to understanding evolution, yet little is known about the diversity of MDV in the poultry industry. Here, we investigate the diversity of MDV on 19 Pennsylvanian poultry farms over a 3-year period. Using eight polymorphic markers, we found that at least twelve MDV haplotypes were co-circulating within a radius of 40 km. MDV diversity showed no obvious spatial clustering nor any apparent clustering by bird line: all of the virus haplotypes identified on the commercial farms could be found within a single, commonly reared bird line. On some farms, a single virus haplotype dominated for an extended period of time, while on other farms the observed haplotypes changed over time. In some instances, multiple haplotypes were found simultaneously on a farm, and even within a single dust sample. On one farm, co-occurring haplotypes clustered into phylogenetically distinct clades, putatively assigned as high and low virulence pathotypes. Although the vast majority of our samples came from commercial poultry farms, we found the most haplotype diversity on a noncommercial backyard farm experiencing an outbreak of clinical Marek's disease. Future work to explore the evolutionary potential of MDV might therefore direct efforts toward farms that harbor multiple virus haplotypes, including both backyard farms and farms experiencing clinical Marek's disease.

Why the evolution of vaccine resistance is less of a concern than the evolution of drug resistance.

Vaccines and antimicrobial drugs both impose strong selection for resistance. Yet only drug resistance is a major challenge for 21st century medicine. Why is drug resistance ubiquitous and not vaccine resistance? Part of the answer is that vaccine resistance is far less likely to evolve than drug resistance. But what happens when vaccine resistance does evolve? We review six putative cases. We find that in contrast to drug resistance, vaccine resistance is harder to detect and harder to confirm and that the mechanistic basis is less well understood. Nevertheless, in the cases we examined, the pronounced health benefits associated with vaccination have largely been sustained. Thus, we contend that vaccine resistance is less of a concern than drug resistance because it is less likely to evolve and when it does, it is less harmful to human and animal health and well-being. Studies of pathogen strains that evolve the capacity to replicate and transmit from vaccinated hosts will enhance our ability to develop next-generation vaccines that minimize the risk of harmful pathogen evolution.

Effects of multiple sources of genetic drift on pathogen variation within hosts.

Changes in pathogen genetic variation within hosts alter the severity and spread of infectious diseases, with important implications for clinical disease and public health. Genetic drift may play a strong role in shaping pathogen variation, but analyses of drift in pathogens have oversimplified pathogen population dynamics, either by considering dynamics only at a single scale-such as within hosts or between hosts-or by making drastic simplifying assumptions, for example, that host immune systems can be ignored or that transmission bottlenecks are complete. Moreover, previous studies have used genetic data to infer the strength of genetic drift, whereas we test whether the genetic drift imposed by pathogen population processes can be used to explain genetic data. We first constructed and parameterized a mathematical model of gypsy moth baculovirus dynamics that allows genetic drift to act within and between hosts. We then quantified the genome-wide diversity of baculovirus populations within each of 143 field-collected gypsy moth larvae using Illumina sequencing. Finally, we determined whether the genetic drift imposed by host-pathogen population dynamics in our model explains the levels of pathogen diversity in our data. We found that when the model allows drift to act at multiple scales-including within hosts, between hosts, and between years-it can accurately reproduce the data, but when the effects of drift are simplified by neglecting transmission bottlenecks and stochastic variation in virus replication within hosts, the model fails. A de novo mutation model and a purifying selection model similarly fail to explain the data. Our results show that genetic drift can play a strong role in determining pathogen variation and that mathematical models that account for pathogen population growth at multiple scales of biological organization can be used to explain this variation.

Modeling Marek's disease virus transmission: A framework for evaluating the impact of farming practices and evolution.

Marek's disease virus (MDV) is a pathogen of chickens whose control has twice been undermined by pathogen evolution. Disease ecology is believed to be the main driver of this evolution, yet mathematical models of MDV disease ecology have never been confronted with data to test their reliability. Here, we develop a suite of MDV models that differ in the ecological mechanisms they include. We fit these models with maximum likelihood using iterated filtering in 'pomp' to data on MDV concentration in dust collected from two commercial broiler farms. We find that virus dynamics are influenced by between-flock variation in host susceptibility to virus, shedding rate from infectious birds, and cleanout efficiency. We also find evidence that virus is reintroduced to farms approximately once per month, but we do not find evidence that virus sanitization rates vary between flocks. Of the models that survive model selection, we find agreement between parameter estimates and previous experimental data, as well as agreement between field data and the predictions of these models. Using the set of surviving models, we explore how changes to farming practices are predicted to influence MDV-associated condemnation risk (production losses at slaughter). By quantitatively capturing the mechanisms of disease ecology, we have laid the groundwork to explore the future trajectory of virus evolution.

Industry-Wide Surveillance of Marek's Disease Virus on Commercial Poultry Farms.

Marek's disease virus is a herpesvirus of chickens that costs the worldwide poultry industry more than US$1 billion annually. Two generations of Marek's disease vaccines have shown reduced efficacy over the last half century due to evolution of the virus. Understanding where the virus is present may give insight into whether continued reductions in efficacy are likely. We conducted a 3-yr surveillance study to assess the prevalence of Marek's disease virus on commercial poultry farms, determine the effect of various factors on virus prevalence, and document virus dynamics in broiler chicken houses over short (weeks) and long (years) timescales. We extracted DNA from dust samples collected from commercial chicken and egg production facilities in Pennsylvania, USA. Quantitative PCR was used to assess wild-type virus detectability and concentration. Using data from 1018 dust samples with Bayesian generalized linear mixed effects models, we determined the factors that correlated with virus prevalence across farms. Maximum likelihood and autocorrelation function estimation on 3727 additional dust samples were used to document and characterize virus concentrations within houses over time. Overall, wild-type virus was detectable at least once on 36 of 104 farms at rates that varied substantially between farms. Virus was detected in one of three broiler-breeder operations (companies), four of five broiler operations, and three of five egg layer operations. Marek's disease virus detectability differed by production type, bird age, day of the year, operation (company), farm, house, flock, and sample. Operation (company) was the most important factor, accounting for between 12% and 63.4% of the variation in virus detectability. Within individual houses, virus concentration often dropped below detectable levels and reemerged later. These data characterize Marek's disease virus dynamics, which are potentially important to the evolution of the virus.

Why does drug resistance readily evolve but vaccine resistance does not?

Why is drug resistance common and vaccine resistance rare? Drugs and vaccines both impose substantial pressure on pathogen populations to evolve resistance and indeed, drug resistance typically emerges soon after the introduction of a drug. But vaccine resistance has only rarely emerged. Using well-established principles of population genetics and evolutionary ecology, we argue that two key differences between vaccines and drugs explain why vaccines have so far proved more robust against evolution than drugs. First, vaccines tend to work prophylactically while drugs tend to work therapeutically. Second, vaccines tend to induce immune responses against multiple targets on a pathogen while drugs tend to target very few. Consequently, pathogen populations generate less variation for vaccine resistance than they do for drug resistance, and selection has fewer opportunities to act on that variation. When vaccine resistance has evolved, these generalities have been violated. With careful forethought, it may be possible to identify vaccines at risk of failure even before they are introduced.

DNA from Dust: Comparative Genomics of Large DNA Viruses in Field Surveillance Samples.

The intensification of the poultry industry over the last 60 years facilitated the evolution of increased virulence and vaccine breaks in Marek's disease virus (MDV-1). Full-genome sequences are essential for understanding why and how this evolution occurred, but what is known about genome-wide variation in MDV comes from laboratory culture. To rectify this, we developed methods for obtaining high-quality genome sequences directly from field samples without the need for sequence-based enrichment strategies prior to sequencing. We applied this to the first characterization of MDV-1 genomes from the field, without prior culture. These viruses were collected from vaccinated hosts that acquired naturally circulating field strains of MDV-1, in the absence of a disease outbreak. This reflects the current issue afflicting the poultry industry, where virulent field strains continue to circulate despite vaccination and can remain undetected due to the lack of overt disease symptoms. We found that viral genomes from adjacent field sites had high levels of overall DNA identity, and despite strong evidence of purifying selection, had coding variations in proteins associated with virulence and manipulation of host immunity. Our methods empower ecological field surveillance, make it possible to determine the basis of viral virulence and vaccine breaks, and can be used to obtain full genomes from clinical samples of other large DNA viruses, known and unknown. IMPORTANCE Despite both clinical and laboratory data that show increased virulence in field isolates of MDV-1 over the last half century, we do not yet understand the genetic basis of its pathogenicity. Our knowledge of genome-wide variation between strains of this virus comes exclusively from isolates that have been cultured in the laboratory. MDV-1 isolates tend to lose virulence during repeated cycles of replication in the laboratory, raising concerns about the ability of cultured isolates to accurately reflect virus in the field. The ability to directly sequence and compare field isolates of this virus is critical to understanding the genetic basis of rising virulence in the wild. Our approaches remove the prior requirement for cell culture and allow direct measurement of viral genomic variation within and between hosts, over time, and during adaptation to changing conditions.

Potential drivers of virulence evolution in aquaculture.

Infectious diseases are economically detrimental to aquaculture, and with continued expansion and intensification of aquaculture, the importance of managing infectious diseases will likely increase in the future. Here, we use evolution of virulence theory, along with examples, to identify aquaculture practices that might lead to the evolution of increased pathogen virulence. We identify eight practices common in aquaculture that theory predicts may favor evolution toward higher pathogen virulence. Four are related to intensive aquaculture operations, and four others are related specifically to infectious disease control. Our intention is to make aquaculture managers aware of these risks, such that with increased vigilance, they might be able to detect and prevent the emergence and spread of increasingly troublesome pathogen strains in the future.

Imperfect Vaccination Can Enhance the Transmission of Highly Virulent Pathogens.

Could some vaccines drive the evolution of more virulent pathogens? Conventional wisdom is that natural selection will remove highly lethal pathogens if host death greatly reduces transmission. Vaccines that keep hosts alive but still allow transmission could thus allow very virulent strains to circulate in a population. Here we show experimentally that immunization of chickens against Marek's disease virus enhances the fitness of more virulent strains, making it possible for hyperpathogenic strains to transmit. Immunity elicited by direct vaccination or by maternal vaccination prolongs host survival but does not prevent infection, viral replication or transmission, thus extending the infectious periods of strains otherwise too lethal to persist. Our data show that anti-disease vaccines that do not prevent transmission can create conditions that promote the emergence of pathogen strains that cause more severe disease in unvaccinated hosts.

An observational study of the temporal and spatial patterns of Marek's-disease-associated leukosis condemnation of young chickens in the United States of America.

Marek's disease, a disease primarily affecting immature chickens, is a worldwide problem that has on at least three occasions threatened the poultry industry in the United States. A rich dataset to study the epidemiology of this disease is available because the United States Department of Agriculture has required mandatory inspections of all commercially sold poultry of significant scale since the mid-20th century with over 99% of all chickens inspected. This dataset includes monthly totals aggregated by state since 1961 of the number of "young chickens" inspected and the number with "leukosis", a condemnation category that is almost always associated with Marek's disease in this category of birds. The objective of this study was to analyze temporal and spatial patterns in this condemnation data to gain insight into the ecology and epidemiology of the causative virus. We extracted visual patterns in the data using seasonal trend decomposition, and we tested for statistical significance using extended linear modeling techniques. The analysis confirmed previous findings that there are differences in leukosis condemnation rates between states, across years, and within years. The analysis also revealed several patterns not previously highlighted, including spatial and temporal autocorrelations in leukosis condemnation, changes to the amplitude of seasonality over time, and increasing within-year variation in condemnation rate over time. These patterns suggest that locally shared farm practices, virus transmission between farms, or viral persistence may be important to understanding the dynamics of the disease. We also discuss the plausibility of other potential explanations for these patterns.

Pathogen growth in insect hosts: inferring the importance of different mechanisms using stochastic models and response-time data.

Pathogen population dynamics within individual hosts can alter disease epidemics and pathogen evolution, but our understanding of the mechanisms driving within-host dynamics is weak. Mathematical models have provided useful insights, but existing models have only rarely been subjected to rigorous tests, and their reliability is therefore open to question. Most models assume that initial pathogen population sizes are so large that stochastic effects due to small population sizes, so-called demographic stochasticity, are negligible, but whether this assumption is reasonable is unknown. Most models also assume that the dynamic effects of a host's immune system strongly affect pathogen incubation times or "response times," but whether such effects are important in real host-pathogen interactions is likewise unknown. Here we use data for a baculovirus of the gypsy moth to test models of within-host pathogen growth. By using Bayesian statistical techniques and formal model-selection procedures, we are able to show that the response time of the gypsy moth virus is strongly affected by both demographic stochasticity and a dynamic response of the host immune system. Our results imply that not all response-time variability can be explained by host and pathogen variability, and that immune system responses to infection may have important effects on population-level disease dynamics.