Biology subjects

Dwyer, G.

Publications and source records attributed to Dwyer, G..

6 recordsLinked to original sources

bioRxiv2018

Untangling the dynamics of persistence and colonization in microbial communities

A central goal of community ecology is to infer biotic interactions from observed distributions of co-occurring species. Evidence for biotic interactions, however, can be obscured by shared environmental requirements, posing a challenge for statistical inference. Here we introduce a dynamic statistical model that quantifies the effects of spatial and temporal covariance in longitudinal co-occurrence data. We separate the fixed pairwise effects of species occurrences on persistence and colonization rates, a potential signal of direct interactions, from latent pairwise correlations in occurrence, a potential signal of shared environmental responses. We apply our modeling approach to a pressing epidemiological question by examining how human papillomavirus (HPV) types coexist. Our results suggest that while HPV types respond similarly to common host traits, direct interactions are sparse and weak, so that HPV type diversity depends largely on shared environmental drivers. Our modeling approach is widely applicable to microbial communities and provides valuable insights that should lead to more directed hypothesis testing and mechanistic modeling.

ecology↗

bioRxiv2018

Using biological control data to understand host-pathogen dynamics

A key assumption of models of infectious disease is that population-scale spread is driven by transmission between host individuals at small scales. This assumption, however, is rarely tested, likely because observing disease transmission between host individuals is non-trivial in many infectious diseases. Quantifying the transmission of insect baculoviruses at a small scale is in contrast straightforward. We fit a disease model to data from baculovirus epizootics (= epidemics in animals) at the scale of whole forests, while using prior parameter distributions constructed from branch-scale experiments. Our experimentally-constrained model fits the large-scale data very well, supporting the role of small-scale transmission mechanisms in baculovirus epizootics. We further compared our experimentally-based model to an unconstrained model that ignores our experimental data, serving as a proxy for models that include large-scale mechanisms. This analysis supports our hypothesis that small-scale mechanisms are important, especially individual variability in host susceptibility to the virus. Comparison of transmission rates in the two models, however, suggests that large-scale mechanisms increase transmission compared to our experimental estimates. Our study shows that small-scale and large-scale mechanisms drive forest-wide epizootics of baculoviruses, and that synthesizing mathematical models with data collected across scales is key to understanding the spread of infectious disease.

ecology↗

bioRxiv2017

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 (within hosts, between hosts), or by making drastic simplifying assumptions (host immune systems can be ignored, transmission bottlenecks are complete). Moreover, previous studies 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.

evolutionary biology↗

bioRxiv2017

Recurring infection with ecologically distinct human papillomavirus (HPV) types explains high prevalence and diversity

The high prevalence of human papillomavirus (HPV), the most common sexually transmitted infection, arises from the coexistence of over 200 genetically distinct types. Accurately predicting the impact of vaccines that target multiple types requires understanding the factors that determine HPV diversity. The diversity of many pathogens is driven by type-specific or \"homologous\" immunity, which promotes the spread of variants to which hosts have little immunity. To test for homologous immunity and to identify mechanisms determining HPV transmission, we fitted nonlinear mechanistic models to longitudinal data on genital infections in unvaccinated men. Our results provide no evidence for homologous immunity, instead showing that infection with one HPV type strongly increases the risk of infection with that type for years afterwards. For HPV16, the type responsible for most HPV-related cancers, an initial infection increases the one-year probability of reinfection by 20-fold, and the probability of reinfection remains 14-fold higher two years later. This increased risk occurs in both sexually active and celibate men, suggesting that it arises from auto-inoculation, episodic reactivation of latent virus, or both. Overall our results show that high HPV prevalence and diversity can be explained by a combination of a lack of homologous immunity, frequent reinfections, weak competition between types, and variation in type fitness between host subpopulations. Due to the high risk of reinfection, vaccinating boys that have not yet been exposed may be crucial to reduce prevalence, but our results suggest that there may also be large benefits from vaccinating previously infected individuals.

epidemiology↗

bioRxiv2016

Eco-evolutionary theory and insect outbreaks

Eco-evolutionary theory argues that population cycles in consumer-resource interactions are partly driven by natural selection, such that changes in densities and changes in trait values are mutually reinforcing. Evidence that this theory explains cycles in nature, however, is almost nonexistent. Experimental tests of model predictions are almost always impossible because of the long time scales over which cycles occur, but for most organisms, even tests of model assumptions are logistically impractical. For insect baculoviruses in contrast, tests of model assumptions are straightforward, and baculoviruses often drive outbreaks of forest-defoliating insects, as in the gypsy moth that we study here. We therefore used field experiments with the gypsy moth baculovirus to test two key assumptions of eco-evolutionary models of host-pathogen population cycles, that reduced host infection risk is heritable and costly. Our experiments confirm the two assumptions, and inserting parameters estimated from our data into the models gives cycles closely resembling gypsy moth outbreak cycles in North America, whereas standard models predict unrealistic stable equilibria. Our work shows that eco-evolutionary models are useful for explaining outbreaks of forest insect defoliators, while widespread observations of intense selection imposed by natural enemies on defoliators, and frequent laboratory observations of heritable and costly resistance in defoliators, suggest that eco-evolutionary dynamics may play a general role in defoliator outbreaks.

ecology↗

bioRxiv2016

Disentangling the Effects of Weather and Densityon the Dynamics of a Fungal Pathogen

Nonlinear fitting algorithms have illuminated the role of weather in human diseases, by allowing for robust tests of mechanistic transmission models, but a lack of data has prevented applications to animal diseases. This is important because classical models that neglect weather predict that there will be a host density threshold, below which epidemic intensity will be slight, but models that include weather predict that this threshold will often be obliterated by weather variability. To test the applicability of thresholds to animal diseases, we estimated infection rates of the fungal pathogen Entomophaga maimaiga in the gypsy moth, by collecting larvae during epidemics at a range of host densities and weather conditions, and we estimated the pathogen's force of infection, by exposing experimental larvae to the pathogen for 24 h periods in the field. By fitting a range of models to our data, we show that epidemics of this pathogen are best explained by a model that allows for positive effects of both host density and cool, moist weather on transmission, such that weather-only and density-dependence-only models provide vastly poorer explanations for the data. Despite the effects of weather, the combined model shows that the effects of density in E. maimaiga are strong enough to ensure that the density threshold will have important effects on the probability of epidemics. Our work shows that weather and density-dependent transmission can interact in non-intuitive ways, and provides an illustration of the usefulness of nonlinear fitting for understanding animal diseases.

ecology↗