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

The within-host ecology of insects and their parasites: integrating experiments and mathematical models

The within-host ecology of hosts and their microbes involves complex feedbacks between the host immune system, energetic resources, and microbial growth and virulence, which in turn affect the probability of transmission to new hosts. This complexity can be challenging to address with experiments alone, and mathematical models have traditionally played an essential role in disentangling these processes, making new predictions, and bridging gaps across biological scales. Insect hosts serve as uniquely powerful systems for the integration of experiments and theory in disease biology. In this review, we highlight recent studies in fruit flies, moths, beetles and other invertebrates that have inspired important mathematical models, and present open questions arising from recent modeling efforts that are ripe for testing in insects.

Generation of Variability in Chrysodeixis includens Nucleopolyhedrovirus (ChinNPV): The Role of a Single Variant

The mechanisms generating variability in viruses are diverse. Variability allows baculoviruses to evolve with their host and with changes in their environment. We examined the role of one genetic variant of Chrysodeixis includens nucleopolyhedrovirus (ChinNPV) and its contribution to the variability of the virus under laboratory conditions. A mixture of natural isolates (ChinNPV-Mex1) contained two genetic variants that dominated over other variants in individual larvae that consumed high (ChinNPV-K) and low (ChinNPV-E) concentrations of inoculum. Studies on the ChinNPV-K variant indicated that it was capable of generating novel variation in a concentration-dependent manner. In cell culture, cells inoculated with high concentrations of ChinNPV-K produced OBs with the ChinNPV-K REN profile, whereas a high diversity of ChinNPV variants was recovered following plaque purification of low concentrations of ChinNPV-K virion inoculum. Interestingly, the ChinNPV-K variant could not be recovered from plaques derived from low concentration inocula originating from budded virions or occlusion-derived virions of ChinNPV-K. Genome sequencing revealed marked differences between ChinNPV-K and ChinNPV-E, with high variation in the ChinNPV-K genome, mostly due to single nucleotide polymorphisms. We conclude that ChinNPV-K is an unstable genetic variant that is responsible for generating much of the detected variability in the natural ChinNPV isolates used in this study.

Virus epidemics, plant-controlled population bottlenecks and the durability of plant resistance

Plant qualitative resistances to viruses are natural exhaustible resources that can be impaired by the emergence of resistance-breaking (RB) virus variants. Mathematical modelling can help determine optimal strategies for resistance durability by a rational deployment of resistance in agroecosystems. Here, we propose an innovative approach, built up from our previous empirical studies, based on plant cultivars combining qualitative resistance with quantitative resistance narrowing population bottlenecks exerted on viruses during host-to-host transmission and/or within-host infection. Narrow bottlenecks are expected to slow down virus adaptation to plant qualitative resistance. To study the effect of bottleneck size on yield, we developed a stochastic epidemic model with mixtures of susceptible and resistant plants, relying on continuous-time Markov chain processes. Overall, narrow bottlenecks are beneficial when the fitness cost of RB virus variants in susceptible plants is intermediate. In such cases, they could provide up to 95 additional percentage points of yield compared with deploying a qualitative resistance alone. As we have shown in previous works that virus population bottlenecks are at least partly heritable plant traits, our results suggest that breeding and deploying plant varieties exposing virus populations to narrowed bottlenecks will increase yield and delay the emergence of RB variants. This article is part of the theme issue 'Modelling infectious disease outbreaks in humans, animals and plants: approaches and important themes'. This issue is linked with the subsequent theme issue 'Modelling infectious disease outbreaks in humans, animals and plants: epidemic forecasting and control'.

An Empirical Test of the Role of Small-Scale Transmission in Large-Scale Disease Dynamics

A key assumption of epidemiological models is that population-scale disease spread is driven by close contact between hosts and pathogens. At larger scales, however, mechanisms such as spatial structure in host and pathogen populations and environmental heterogeneity could alter disease spread. The assumption that small-scale transmission mechanisms are sufficient to explain large-scale infection rates, however, is rarely tested. Here, we provide a rigorous test using an insect-baculovirus system. We fit a mathematical model to data from forest-wide epizootics while constraining the model parameters with data from branch-scale experiments, a difference in spatial scale of four orders of magnitude. This experimentally constrained model fits the epizootic data well, supporting the role of small-scale transmission, but variability is high. We then compare this model's performance to an unconstrained model that ignores the experimental data, which serves as a proxy for models with additional mechanisms. The unconstrained model has a superior fit, revealing a higher transmission rate across forests compared with branch-scale estimates. Our study suggests that small-scale transmission is insufficient to explain baculovirus epizootics. Further research is needed to identify the mechanisms that contribute to disease spread across large spatial scales, and synthesizing models and multiscale data are key to understanding these dynamics.

Understanding the Evolutionary Ecology of host--pathogen Interactions Provides Insights into the Outcomes of Insect Pest Biocontrol

The use of viral pathogens to control thepopulation size of pest insects has produced both successful and unsuccessful outcomes. Here, we investigate whether those biocontrol successes and failures can be explained by key ecological and evolutionary processes between hosts and pathogens. Specifically, we examine how heterogeneity inpathogen transmission, ecological and evolutionary tradeoffs, andpathogen diversity affect insect population density and thus successful control. Wefirst review theexisting literature and then use numerical simulations of mathematical models to further explore these processes. Our results show that thecontrol of insect densities using viruses depends strongly on theheterogeneity of virus transmission among insects. Overall, increased heterogeneity of transmission reduces theeffect of viruses on insect densities and increases thelong-term stability of insect populations. Lower equilibrium insect densities occur when transmission is heritable and when there is atradeoff between mean transmission and insect fecundity compared to when theheterogeneity of transmission arises from non-genetic sources. Thus, theheterogeneity of transmission is akey parameter that regulates thelong-term population dynamics of insects and their pathogens. Wealso show that both heterogeneity of transmission and life-history tradeoffs modulate characteristics of population dynamics such as thefrequency and intensity of ``boom--bust" population cycles. Furthermore, we show that because of life-history tradeoffs affecting thetransmission rate, theuse of multiple pathogen strains is more effective than theuse of asingle strain to control insect densities only when thepathogen strains differ considerably intheir transmission characteristics. By quantifying theeffects of ecology and evolution on population densities, we are able to offer recommendations to assess thelong-term effects of classical biocontrol.

Evaluating the Within-Host Dynamics of Ranavirus Infection with Mechanistic Disease Models and Experimental Data

Mechanistic models are critical for our understanding of both within-host dynamics (i.e., pathogen replication and immune system processes) and among-host dynamics (i.e., transmission). Within-host models, however, are not often fit to experimental data, which can serve as a robust method of hypothesis testing and hypothesis generation. In this study, we use mechanistic models and empirical, time-series data of viral titer to better understand the replication of ranaviruses within their amphibian hosts and the immune dynamics that limit viral replication. Specifically, we fit a suite of potential models to our data, where each model represents a hypothesis about the interactions between viral replication and immune defense. Through formal model comparison, we find a parsimonious model that captures key features of our time-series data: The viral titer rises and falls through time, likely due to an immune system response, and that the initial viral dosage affects both the peak viral titer and the timing of the peak. Importantly, our model makes several predictions, including the existence of long-term viral infections, which can be validated in future studies.

Small Bottleneck Size in a Highly Multipartite Virus during a Complete Infection Cycle

Multipartite viruses package their genomic segments independently and thus incur the risk of being unable to transmit their entire genome during host-to-host transmission if they undergo severe bottlenecks. In this paper, we estimated the bottleneck size during one infection cycle of Faba bean necrotic stunt virus (FBNSV), an octopartite nanovirus whose segments have been previously shown to converge to particular and unequal relative frequencies within host plants and aphid vectors. Two methods were used to derive this estimate, one based on the probability of transmission of the virus and the other based on the temporal evolution of the relative frequency of markers for two genomic segments, one frequent and one rare (segment N and S, respectively), both in plants and vectors. Our results show that FBNSV undergoes severe bottlenecks during aphid transmission. Further, even though the bottlenecks are always narrow under our experimental conditions, they slightly widen with the number of transmitting aphids. In particular, when several aphids are used for transmission, the bottleneck size of the segments is also affected by within-plant processes and, importantly, significantly differs across segments. These results indicate that genetic drift not only must be an important process affecting the evolution of these viruses but also that these effects vary across genomic segments and, thus, across viral genes, a rather unique and intriguing situation. We further discuss the potential consequences of our findings for the transmission of multipartite viruses.IMPORTANCE Multipartite viruses package their genomic segments in independent capsids. The most obvious cost of such genomic structure is the risk of losing at least one segment during host-to-host transmission. A theoretical study has shown that for nanoviruses, composed of 6 to 8 segments, hundreds of copies of each segment need to be transmitted to ensure that at least one copy of each segment was present in the host. These estimations seem to be very high compared to the size of the bottlenecks measured with other viruses. Here, we estimated the bottleneck size during one infection cycle of FBNSV, an octopartite nanovirus. We show that these bottlenecks are always narrow (few viral particles) and slightly widen with the number of transmitting aphids. These results contrast with theoretical predictions and illustrate the fact that a new conceptual framework is probably needed to understand the transmission of highly multipartite viruses.

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.

Phenotypic Variation in Overwinter Environmental Transmission of a Baculovirus and the Cost of Virulence

A pathogen's ability to persist in the environment is an ecologically important trait, and variation in this trait may promote coexistence of different pathogen strains. We asked whether naturally occurring isolates of the baculovirus that infects gypsy moth larvae varied in their overwinter environmental transmission and whether this variation was consistent with a trade-off or an upper limit to virulence that might promote pathogen diversity. We used experimental manipulations to replicate the natural overwinter infection process, using 16 field-collected isolates. Virus isolates varied substantially in the fraction of larvae infected, leading to differences in overwinter transmission rates. Furthermore, isolates that killed more larvae also had higher rates of early larval death in which no infectious particles were produced, consistent with a cost of high virulence. Our results thus support the existence of a cost that could impose an upper limit to virulence even in a highly virulent pathogen.

Effects of pathogen exposure on life-history variation in the gypsy moth (Lymantria dispar)

Investment in host defences against pathogens may lead to trade-offs with host fecundity. When such trade-offs arise from genetic correlations, rates of phenotypic change by natural selection may be affected. However, genetic correlations between host survival and fecundity are rarely quantified. To understand trade-offs between immune responses to baculovirus exposure and fecundity in the gypsy moth (Lymantria dispar), we estimated genetic correlations between survival probability and traits related to fecundity, such as pupal weight. In addition, we tested whether different virus isolates have different effects on male and female pupal weight. To estimate genetic correlations, we exposed individuals of known relatedness to a single baculovirus isolate. To then evaluate the effect of virus isolate on pupal weight, we exposed a single gypsy moth strain to 16 baculovirus isolates. We found a negative genetic correlation between survival and pupal weight. In addition, virus exposure caused late-pupating females to be identical in weight to males, whereas unexposed females were 2-3 times as large as unexposed males. Finally, we found that female pupal weight is a quadratic function of host mortality across virus isolates, which is likely due to trade-offs and compensatory growth processes acting at high and low mortality levels, respectively. Overall, our results suggest that fecundity costs may strongly affect the response to selection for disease resistance. In nature, baculoviruses contribute to the regulation of gypsy moth outbreaks, as pathogens often do in forest-defoliating insects. We therefore argue that trade-offs between host life-history traits may help explain outbreak dynamics.