Browsing by Author "Koelle, Katia"
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Item Open Access A dimensionless number for understanding the evolutionary dynamics of antigenically variable RNA viruses.(Proc Biol Sci, 2011-12-22) Koelle, Katia; Ratmann, Oliver; Rasmussen, David A; Pasour, Virginia; Mattingly, JonathanAntigenically variable RNA viruses are significant contributors to the burden of infectious disease worldwide. One reason for their ubiquity is their ability to escape herd immunity through rapid antigenic evolution and thereby to reinfect previously infected hosts. However, the ways in which these viruses evolve antigenically are highly diverse. Some have only limited diversity in the long-run, with every emergence of a new antigenic variant coupled with a replacement of the older variant. Other viruses rapidly accumulate antigenic diversity over time. Others still exhibit dynamics that can be considered evolutionary intermediates between these two extremes. Here, we present a theoretical framework that aims to understand these differences in evolutionary patterns by considering a virus's epidemiological dynamics in a given host population. Our framework, based on a dimensionless number, probabilistically anticipates patterns of viral antigenic diversification and thereby quantifies a virus's evolutionary potential. It is therefore similar in spirit to the basic reproduction number, the well-known dimensionless number which quantifies a pathogen's reproductive potential. We further outline how our theoretical framework can be applied to empirical viral systems, using influenza A/H3N2 as a case study. We end with predictions of our framework and work that remains to be done to further integrate viral evolutionary dynamics with disease ecology.Item Open Access Characterization of Influenza A Virus Infection through Analysis of Intrahost Viral Evolution and Within-host Infection Dynamics(2016) Sobel Leonard, Ashley ElizabethInfluenza A virus is a major source of morbidity and mortality, annually resulting in over 9000 deaths in the United States alone. As a segmented, RNA virus, influenza has a high mutation rate, facilitating its evolution to overcome cross protective immunity through natural selection and adapt to new host species or sources of evolutionary pressure through reassortment. The high viral mutation rate also means that these processes affect not only evolution at the population level, but also at the intrahost level. While these processes have been well characterized for population-level viral evolution, viral evolution within a single host is far less well defined. In this dissertation, I characterize influenza infection at the intrahost level with respect to viral evolution and infection dynamics. In the second chapter, I critically evaluate methods for estimating the transmission bottleneck size for influenza A virus from viral sequencing data. The transmission bottleneck describes the infecting population size, a determinant for the level of genetic diversity present at the onset of infection. I show current methods may be biased, both by the criteria used to identify sequencing variants and the presence of demographic stochasticity. In response to these biases, I introduce a new method that (1) corrects for differences in variant calling criteria and (2) accommodates demographic stochasticity. Chapters 3-5 are based on data collected from an existing human challenge study with influenza A virus. In this challenge study, volunteers were experimentally infected with a heterogeneous viral inoculum that had adapted to the conditions in which it had been generated. In chapter 3, I show that transmission was governed by a selective bottleneck and that subsequent intrahost viral evolution was dominated by purifying selection. In chapter 4, I further characterize the observed intrahost viral evolution through the reconstruction of viral haplotypes to evaluate different models of selection. These models differed by the level at which selection was acting, whether selection is focused on individual loci, multiple loci within a single gene segment, or across gene segments. Model selection favored the third model, wherein selection acted across gene segments, thereby establishing that the effective viral reassortment rate was limited in these subjects. In chapter 5, I develop a mathematical model for within-host influenza infection linking viral replication and the host immune response with the development of disease symptoms. I fit this model to experimental data collected from the challenge study. Analysis of the model fits indicated that much of the heterogeneity in the data between subjects could be explained by interindividual variation in viral infectivity. This finding echoed the results of chapters 3 and 4, that there were quantifiable differences in the infecting viral populations between the study subjects. Taken together, these observations suggest that
intrahost viral genetics may underlie the differences between the subjects’ response to infection.
Item Open Access Drivers of Dengue Within-Host Dynamics and Virulence Evolution(2016) BenShachar, RotemDengue is an important vector-borne virus that infects on the order of 400 million individuals per year. Infection with one of the virus's four serotypes (denoted DENV-1 to 4) may be silent, result in symptomatic dengue 'breakbone' fever, or develop into the more severe dengue hemorrhagic fever/dengue shock syndrome (DHF/DSS). Extensive research has therefore focused on identifying factors that influence dengue infection outcomes. It has been well-documented through epidemiological studies that DHF is most likely to result from a secondary heterologous infection, and that individuals experiencing a DENV-2 or DENV-3 infection typically are more likely to present with more severe dengue disease than those individuals experiencing a DENV-1 or DENV-4 infection. However, a mechanistic understanding of how these risk factors affect disease outcomes, and further, how the virus's ability to evolve these mechanisms will affect disease severity patterns over time, is lacking. In the second chapter of my dissertation, I formulate mechanistic mathematical models of primary and secondary dengue infections that describe how the dengue virus interacts with the immune response and the results of this interaction on the risk of developing severe dengue disease. I show that only the innate immune response is needed to reproduce characteristic features of a primary infection whereas the adaptive immune response is needed to reproduce characteristic features of a secondary dengue infection. I then add to these models a quantitative measure of disease severity that assumes immunopathology, and analyze the effectiveness of virological indicators of disease severity. In the third chapter of my dissertation, I then statistically fit these mathematical models to viral load data of dengue patients to understand the mechanisms that drive variation in viral load. I specifically consider the roles that immune status, clinical disease manifestation, and serotype may play in explaining viral load variation observed across the patients. With this analysis, I show that there is statistical support for the theory of antibody dependent enhancement in the development of severe disease in secondary dengue infections and that there is statistical support for serotype-specific differences in viral infectivity rates, with infectivity rates of DENV-2 and DENV-3 exceeding those of DENV-1. In the fourth chapter of my dissertation, I integrate these within-host models with a vector-borne epidemiological model to understand the potential for virulence evolution in dengue. Critically, I show that dengue is expected to evolve towards intermediate virulence, and that the optimal virulence of the virus depends strongly on the number of serotypes that co-circulate. Together, these dissertation chapters show that dengue viral load dynamics provide insight into the within-host mechanisms driving differences in dengue disease patterns and that these mechanisms have important implications for dengue virulence evolution.
Item Open Access Inference for nonlinear epidemiological models using genealogies and time series.(PLoS Comput Biol, 2011-08) Rasmussen, David A; Ratmann, Oliver; Koelle, KatiaPhylodynamics - the field aiming to quantitatively integrate the ecological and evolutionary dynamics of rapidly evolving populations like those of RNA viruses - increasingly relies upon coalescent approaches to infer past population dynamics from reconstructed genealogies. As sequence data have become more abundant, these approaches are beginning to be used on populations undergoing rapid and rather complex dynamics. In such cases, the simple demographic models that current phylodynamic methods employ can be limiting. First, these models are not ideal for yielding biological insight into the processes that drive the dynamics of the populations of interest. Second, these models differ in form from mechanistic and often stochastic population dynamic models that are currently widely used when fitting models to time series data. As such, their use does not allow for both genealogical data and time series data to be considered in tandem when conducting inference. Here, we present a flexible statistical framework for phylodynamic inference that goes beyond these current limitations. The framework we present employs a recently developed method known as particle MCMC to fit stochastic, nonlinear mechanistic models for complex population dynamics to gene genealogies and time series data in a Bayesian framework. We demonstrate our approach using a nonlinear Susceptible-Infected-Recovered (SIR) model for the transmission dynamics of an infectious disease and show through simulations that it provides accurate estimates of past disease dynamics and key epidemiological parameters from genealogies with or without accompanying time series data.Item Open Access Minimal within-host dengue models highlight the specific roles of the immune response in primary and secondary dengue infections.(J R Soc Interface, 2015-02-06) Ben-Shachar, Rotem; Koelle, KatiaIn recent years, the within-host viral dynamics of dengue infections have been increasingly characterized, and the relationship between aspects of these dynamics and the manifestation of severe disease has been increasingly probed. Despite this progress, there are few mathematical models of within-host dengue dynamics, and the ones that exist focus primarily on the general role of immune cells in the clearance of infected cells, while neglecting other components of the immune response in limiting viraemia. Here, by considering a suite of mathematical within-host dengue models of increasing complexity, we aim to isolate the critical components of the innate and the adaptive immune response that suffice in the reproduction of several well-characterized features of primary and secondary dengue infections. By building up from a simple target cell limited model, we show that only the innate immune response is needed to recover the characteristic features of a primary symptomatic dengue infection, while a higher rate of viral infectivity (indicative of antibody-dependent enhancement) and infected cell clearance by T cells are further needed to recover the characteristic features of a secondary dengue infection. We show that these minimal models can reproduce the increased risk of disease associated with secondary heterologous infections that arises as a result of a cytokine storm, and, further, that they are consistent with virological indicators that predict the onset of severe disease, such as the magnitude of peak viraemia, time to peak viral load, and viral clearance rate. Finally, we show that the effectiveness of these virological indicators to predict the onset of severe disease depends on the contribution of T cells in fuelling the cytokine storm.Item Open Access Phylodynamic inference for structured epidemiological models.(PLoS Comput Biol, 2014-04) Rasmussen, David A; Volz, Erik M; Koelle, KatiaCoalescent theory is routinely used to estimate past population dynamics and demographic parameters from genealogies. While early work in coalescent theory only considered simple demographic models, advances in theory have allowed for increasingly complex demographic scenarios to be considered. The success of this approach has lead to coalescent-based inference methods being applied to populations with rapidly changing population dynamics, including pathogens like RNA viruses. However, fitting epidemiological models to genealogies via coalescent models remains a challenging task, because pathogen populations often exhibit complex, nonlinear dynamics and are structured by multiple factors. Moreover, it often becomes necessary to consider stochastic variation in population dynamics when fitting such complex models to real data. Using recently developed structured coalescent models that accommodate complex population dynamics and population structure, we develop a statistical framework for fitting stochastic epidemiological models to genealogies. By combining particle filtering methods with Bayesian Markov chain Monte Carlo methods, we are able to fit a wide class of stochastic, nonlinear epidemiological models with different forms of population structure to genealogies. We demonstrate our framework using two structured epidemiological models: a model with disease progression between multiple stages of infection and a two-population model reflecting spatial structure. We apply the multi-stage model to HIV genealogies and show that the proposed method can be used to estimate the stage-specific transmission rates and prevalence of HIV. Finally, using the two-population model we explore how much information about population structure is contained in genealogies and what sample sizes are necessary to reliably infer parameters like migration rates.Item Open Access Phylodynamic Methods for Infectious Disease Epidemiology(2014) Rasmussen, David AlanIn this dissertation, I present a general statistical framework for phylodynamic inference that can be used to estimate epidemiological parameters and reconstruct disease dynamics from pathogen genealogies. This framework can be used to fit a broad class of epidemiological models, including nonlinear stochastic models, to genealogies by relating the population dynamics of a pathogen to its genealogy using coalescent theory. By combining Markov chain Monte Carlo and particle filtering methods, efficient Bayesian inference of all parameters and unobserved latent variables is possible even when analytical likelihood expressions are not available under the epidemiological model. Through extensive simulations, I show that this method can be used to reliably estimate epidemiological parameters of interest as well as reconstruct past disease dynamics from genealogies, or jointly from genealogies and other common sources of epidemiological data like time series. I then extend this basic framework to include different types of host population structure, including models with spatial structure, multiple-hosts or vectors, and different stages of infection. The later is demonstrated by using a multistage model of HIV infection to estimate stage-specific transmission rates and incidence from HIV sequence data collected in Detroit, Michigan. Finally, to demonstrate how the approach can be used more generally, I consider the case of dengue virus in southern Vietnam. I show how earlier phylodynamic inference methods fail to reliably reconstruct the dynamics of dengue observed in hospitalization data, but by deriving coalescent models that take into consideration ecological complexities like seasonality, vector dynamics and spatial structure, accurate dynamics can be reconstructed from genealogies. In sum, by extending phylodynamics to include more ecologically realistic and mechanistic models, this framework can provide more accurate estimates and give deeper insight into the processes driving infectious disease dynamics.
Item Open Access Probabilistic methods for multiscale evolutionary dynamics(2013) Luo, Shishi ZhigeEvolution by natural selection can occur at multiple biological scales. This is particularly the case for host-pathogen systems, where selection occurs both within each infected host as well as through transmission between hosts. Despite there being established mathematical models for understanding evolution at a single biological scale, fewer tractable models exist for multiscale evolutionary dynamics. Here I present mathematical approaches using tools from probability and stochastic processes as well as dynamical systems to handle multiscale evolutionary systems. The first problem I address concerns the antigenic evolution of influenza. Using a combination of ordinary differential equations and inhomogeneous Poisson processes, I study how immune selection pressures at the within-host level impact population-level evolutionary dynamics. The second problem involves the more general question of evolutionary dynamics when selection occurs antagonistically at two biological scales. In addition to host-pathogen systems, such situations arise naturally in the evolution of traits such as the production of a public good and the use of a common resource. I introduce a model for this general phenomenon that is intuitively visualized as a a stochastic ball-and-urn system and can be used to systematically obtain general properties of antagonistic multiscale evolution. Lastly, this ball-and-urn framework is in itself an interesting mathematical object which can studied as either a measure-valued process or an interacting particle system. In this mathematical context, I show that under different scalings, the measure-valued process can have either a propagation of chaos or Fleming-Viot limit.
Item Open Access Rates of coalescence for common epidemiological models at equilibrium.(J R Soc Interface, 2012-05-07) Koelle, Katia; Rasmussen, David ACoalescent theory provides a mathematical framework for quantitatively interpreting gene genealogies. With the increased availability of molecular sequence data, disease ecologists now regularly apply this body of theory to viral phylogenies, most commonly in attempts to reconstruct demographic histories of infected individuals and to estimate parameters such as the basic reproduction number. However, with few exceptions, the mathematical expressions at the core of coalescent theory have not been explicitly linked to the structure of epidemiological models, which are commonly used to mathematically describe the transmission dynamics of a pathogen. Here, we aim to make progress towards establishing this link by presenting a general approach for deriving a model's rate of coalescence under the assumption that the disease dynamics are at their endemic equilibrium. We apply this approach to four common families of epidemiological models: standard susceptible-infected-susceptible/susceptible-infected-recovered/susceptible-infected-recovered-susceptible models, models with individual heterogeneity in infectivity, models with an exposed but not yet infectious class and models with variable distributions of the infectious period. These results improve our understanding of how epidemiological processes shape viral genealogies, as well as how these processes affect levels of viral diversity and rates of genetic drift. Finally, we discuss how a subset of these coalescent rate expressions can be used for phylodynamic inference in non-equilibrium settings. For the ones that are limited to equilibrium conditions, we also discuss why this is the case. These results, therefore, point towards necessary future work while providing intuition on how epidemiological characteristics of the infection process impact gene genealogies.Item Open Access Reconciling phylodynamics with epidemiology: the case of dengue virus in southern Vietnam.(Mol Biol Evol, 2014-02) Rasmussen, David A; Boni, Maciej F; Koelle, KatiaCoalescent methods are widely used to infer the demographic history of populations from gene genealogies. These approaches-often referred to as phylodynamic methods-have proven especially useful for reconstructing the dynamics of rapidly evolving viral pathogens. Yet, population dynamics inferred from viral genealogies often differ widely from those observed from other sources of epidemiological data, such as hospitalization records. We demonstrate how a modeling framework that allows for the direct fitting of mechanistic epidemiological models to genealogies can be used to test different hypotheses about what ecological factors cause phylodynamic inferences to differ from observed dynamics. We use this framework to test different hypotheses about why dengue serotype 1 (DENV-1) population dynamics in southern Vietnam inferred using existing phylodynamic methods differ from hospitalization data. Specifically, we consider how factors such as seasonality, vector dynamics, and spatial structure can affect inferences drawn from genealogies. The coalescent models we derive to take into account vector dynamics and spatial structure reveal that these ecological complexities can substantially affect coalescent rates among lineages. We show that incorporating these additional ecological complexities into coalescent models can also greatly improve estimates of historical population dynamics and lead to new insights into the factors shaping viral genealogies.Item Open Access The impact of host immune status on the within-host and population dynamics of antigenic immune escape.(J R Soc Interface, 2012-10-07) Luo, Shishi; Reed, Michael; Mattingly, Jonathan C; Koelle, KatiaAntigenically evolving pathogens such as influenza viruses are difficult to control owing to their ability to evade host immunity by producing immune escape variants. Experimental studies have repeatedly demonstrated that viral immune escape variants emerge more often from immunized hosts than from naive hosts. This empirical relationship between host immune status and within-host immune escape is not fully understood theoretically, nor has its impact on antigenic evolution at the population level been evaluated. Here, we show that this relationship can be understood as a trade-off between the probability that a new antigenic variant is produced and the level of viraemia it reaches within a host. Scaling up this intra-host level trade-off to a simple population level model, we obtain a distribution for variant persistence times that is consistent with influenza A/H3N2 antigenic variant data. At the within-host level, our results show that target cell limitation, or a functional equivalent, provides a parsimonious explanation for how host immune status drives the generation of immune escape mutants. At the population level, our analysis also offers an alternative explanation for the observed tempo of antigenic evolution, namely that the production rate of immune escape variants is driven by the accumulation of herd immunity. Overall, our results suggest that disease control strategies should be further assessed by considering the impact that increased immunity--through vaccination--has on the production of new antigenic variants.Item Open Access The Roles of Cellular Receptor Binding Avidity and Other Viral Phenotypes in the Antigenic Drift of Influenza(2013) Yuan, HsiangYuDespite high vaccination rates and effective adaptive immune responses from the part of infected individuals, influenza A viruses cause significant morbidity and mortality annually. This is due to influenza's rapid antigenic evolution, whereby continual mutations occurring in epitope regions of the virus's hemagglutinin protein result in the diminishment of long-term antibody recognition, in a process that has been termed `antigenic drift'. Although it is clear that antigenic drift enables previously infected individuals to become reinfected, the mechanism that is responsible for influenza's antigenic drift is still under debate. As recently as 2009, a new hypothesis of antigenic drift was put forward that argues that binding avidity changes in the viral hemagglutinin result in antigenic drift as a side effect. This hypothesis stands in contrast to the traditionally accepted hypothesis that mutations in epitope regions are positively selected for their ability to evade immune recognition. This thesis focuses on the use of epidemiological models and empirical data analysis to explore different hypotheses of antigenic drift.
In the first chapter, I am asking what effects on antigenic drift rate would be produced under the new hypothesis. I mathematically formulate the hypothesis that antigenic drift is simply a side effect of cellular receptor binding avidity changes that occur as the virus is transmitted between individuals of different immune status levels. I then use this formulation to explore how influenza's rate of antigenic drift depends on different epidemiological factors, including host contact rate, host lifespan, and the duration of infection. Finally, I use the model to assess alternative vaccination strategies by the impact they have on rates of antigenic drift and therewith rates of disease incidence/
In the second chapter, I critically evaluate the binding avidity hypothesis by comparing predictions of the hypothesis against empirical data. I first use a `phylodynamic' extension of the model presented in the first chapter to determine whether the hypothesis is consistent with the ladderlike phylogeny of influenza's hemagglutinin protein. I then use viral sequence data and metadata to determine whether older aged individuals (with a higher number of previous infections) harbor viruses with higher binding avidity than younger aged individuals (with a lower number of previous infections), a prediction made by the binding avidity hypothesis. Finally, I perform a phylogenetic analysis to determine how rapidly binding avidity changes occur. From these analyses, I conclude that the binding avidity hypothesis is not well supported by empirical data.
In the third chapter, I develop an integrated viral life cycle model, in which viral replication depends on three viral phenotypes: receptor binding avidity, neuraminidase activity, and antigenicity. This integrated model recognizes that receptor binding avidity changes will influence viral replication, but also allows for antigenic evolution to be brought about directly by epitope changes. I first use this model to show how the evolutionary dynamics of these phenotypes are dependent on one another and how antigenic drift can be interpreted within this framework. I then return to some of the questions addressed in the first chapter to ask how different epidemiological factors impact influenza's rate of antigenic drift.
Together, these three chapters highlight the importance of viral phenotypes other than antigenicity in contributing to influenza's antigenic evolution, and, more generally, the importance of computational and mathematical research in understanding constraints on viral adaptation.