Biology subjects

King, A. A.

Publications and source records attributed to King, A. A..

7 recordsLinked to original sources

bioRxiv2024

Red blood cell dynamics during malaria infection violate the assumptions of mathematical models of infection dynamics

For decades, mathematical models have been used to understand the course and outcome of malaria infections (i.e., infection dynamics) and the evolutionary dynamics of the parasites that cause them. The extent to which this conclusion holds will in part depend on model assumptions about the host-mediated processes that regulate RBC availability, i.e., removal (clearance) of uninfected RBCs and supply of RBCs. Diverse mathematical functions have been used to describe host-mediated RBC supply and clearance in rodent malaria infections; however, the extent to which these functions adequately capture the dynamics of these processes has not been quantitatively interrogated, as in vivo data on these processes has been lacking. Here, we use a unique dataset, comprising time-series measurements of erythrocyte (i.e., mature RBC) and reticulocyte (i.e., newly supplied RBC) densities during Plasmodium chabaudi malaria infection, and a quantitative data-transformation scheme to elucidate whether RBC dynamics conform to common model assumptions. We found that RBC supply and clearance dynamics are not well described by mathematical functions commonly used to model these processes. Indeed, our results suggest said dynamics are not well described by a single-valued function at all. Furthermore, the temporal dynamics of both processes vary with parasite growth rate in a manner again not captured by existing models. Together, these finding suggest that new model formulations are required if we are to explain and ultimately predict the within-host population dynamics and evolution of malaria parasites.

microbiology↗

bioRxiv2023

Modeling the evolution of segment trees reveals deficiencies in current inferential methods for genomic reassortment

Reassortment is an evolutionary process common in viruses with segmented genomes. These viruses can swap whole genomic segments during cellular co-infection, giving rise to novel progeny formed from the mixture of parental segments. Because large-scale genome rearrangements have the potential to generate new phenotypes, reassortment is important to both evolutionary biology and public health research. However, statistical inference of the pattern of reassortment events from phylogenetic data is exceptionally difficult, potentially involving inference of general graphs in which individual segment trees are embedded. In this paper, we argue that, in general, the number and pattern of reassortment events are not identifiable from segment trees alone, even with theoretically ideal data. We call this fact the fundamental problem of reassortment, which we illustrate using the concept of the first-infection tree, a typically but not always counterfactual genealogy that would have been observed in the segment trees had no reassortment occurred. Further, we illustrate four additional problems that can arise logically in the inference of reassortment events and show, using simulated data, that these problems are not rare and can potentially distort our perception of reassortment even in small data sets. Finally, we discuss how existing methods can be augmented or adapted to account for not only the fundamental problem of reassortment but also the four additional situations that can complicate the inference of reassortment.

evolutionary biology↗

bioRxiv2019

Breaking down defenses: Quantitative analysis of malaria infection dynamics reveals distinct immune defense strategies

Hosts defend themselves against pathogens by mounting an immune response. Fully understanding the immune response as a driver of host disease and pathogen evolution requires a quantitative account of its impact on parasite population dynamics. Here, we use a data-driven modeling approach to quantify the birth and death processes underlying the dynamics of infections of the rodent malaria parasite, Plasmodium chabaudi, and the red blood cells (RBCs) it targets. We decompose the immune response into three components, each with a distinct effect on parasite and RBC vital rates, and quantify the relative contribution of each component to host disease and parasite density. Our analysis suggests that these components are deployed in a coordinated fashion to realize distinct resource-directed defense strategies that complement the killing of parasitized cells. Early in the infection, the host deploys a strategy reminiscent of siege and scorched-earth tactics, in which it both restricts the supply of RBCs and destroys them. Late in the infection, a juvenilization strategy, in which turnover of RBCs is accelerated, allows the host to recover from anemia while holding parasite proliferation at bay. By quantifying the impact of immunity on both parasite fitness and host disease, we reveal that phenomena often interpreted as immunopathology may in fact be beneficial to the host. Finally, we show that, across mice, the components of the host response are consistently related to each other, even when infections take qualitatively different trajectories. This suggests the existence of simple rules that govern the immune systems deployment.

ecology↗

bioRxiv2018

Rethinking the efficacy of acellular pertussis vaccines for primary immunization

BackgroundThe US has experienced a nationwide resurgence of pertussis since the mid-1970s, despite high vaccine coverage. Short-lived immunity induced by Diphtheria-Tetanus-acellular Pertussis (DTaP) vaccines in young children is widely believed to be responsible for this growing burden. However, the duration of protection conferred by DTaP vaccines remains incompletely quantified.\n\nMethods and FindingsWe employed a rigorously validated, age-structured model of pertussis transmission to explore a range of hypotheses regarding the degree of waning DTaP-derived immunity. For every hypothesis, we calculated the vaccine effectiveness and the relative increase in the odds of acquiring pertussis (or odds ratio) in children aged 5 to 9 years. We then assessed the simulated DTaP vaccine traits that best reproduced the empirical values of odds ratios from recent US epidemiological studies. We found a marked association between the degree of waning immunity, the vaccine effectiveness, and the odds ratio. Unexpectedly, the odds ratio was positively associated with the vaccine effectiveness, as a consequence of non-linear, age-assortative dynamics. Based on the empirical odds ratios, we estimated that vaccine effectiveness exceeded 75% and that more than 65% of children remained immune to pertussis 5 years after the last DTaP dose.\n\nConclusionsOur results show that temporal trends in the odds of acquiring pertussis are a seriously flawed measure of the durability of vaccine-induced protection. They further demonstrate that DTaP vaccines confer imperfect, but long-lived protection. We argue that control strategies should be based upon the best available estimates of vaccine properties and the age-structure of the transmission network.

epidemiology↗

bioRxiv2018

A simulation study on the relative role of age groups under differing pertussis transmission scenarios

Pertussis has resurged in many countries where it was once regarded as under control, with the recent outbreaks showing a shift in incidence towards teens and older individuals. Here, using an age-stratified transmission model, we tested two potential causes for underlying changes in pertussis transmission dynamics. We did so assuming hypothesized mechanisms supporting present-day pertussis epidemiology: (I) improved diagnostics, (II) acellular vaccines leading to asymptomatic transmission (III) both. We used the relative risks and odds ratio methods to examine the impact of these differing assumptions on signatures of relative roles of key age groups through time, allowing us to explore those age cohorts that disproportionately account for transmission. Our findings show that for epidemics after the vaccine switch, a scenario with increased adult reporting and no asymptomatic transmission reflect a loss of signal, where no age group appears to be key. While scenarios with asymptomatic transmission, reflect a population where children (1-10 years old) are still disproportionally at risk. These results demonstrate that understanding the underlying transmission mechanisms in a population are paramount for vaccination policies in attaining herd immunity and eventually eradication.

epidemiology↗

bioRxiv2017

Evolution-informed forecasting of seasonal influenza A (H3N2)

Inter-pandemic or seasonal influenza exacts an enormous annual burden both in terms of human health and economic impact. Incidence prediction ahead of season remains a challenge largely because of the virus antigenic evolution. We propose here a forecasting approach that incorporates evolutionary change into a mechanistic epidemiological model. The proposed models are simple enough that their parameters can be estimated from retrospective surveillance data. These models link amino-acid sequences of hemagglutinin epitopes with a transmission model for seasonal H3N2 influenza, also informed by H1N1 levels. With a monthly time series of H3N2 incidence in the United States over 10 years, we demonstrate the feasibility of prediction ahead of season and an accurate real-time forecast for the 2016/2017 influenza season.\n\nSUMMARYSkillful forecasting of seasonal (H3N2) influenza incidence ahead of the season is shown to be possible by means of a transmission model that explicitly tracks evolutionary change in the virus, integrating information from both epidemiological surveillance and readily available genetic sequences.

systems biology↗

bioRxiv2016

Infectious Disease Dynamics Inferred from Genetic Data via Sequential Monte Carlo

Genetic sequences from pathogens can provide information about infectious disease dynamics that may supplement or replace information from other epidemiological observations. Currently available methods first estimate phylogenetic trees from sequence data, then estimate a transmission model conditional on these phylogenies. Outside limited classes of models, existing methods are unable to enforce logical consistency between the model of transmission and that underlying the phylogenetic reconstruction. Such conflicts in assumptions can lead to bias in the resulting inferences. Here, we develop a general, statistically efficient, plug-and-play method to jointly estimate both disease transmission and phylogeny using genetic data and, if desired, other epidemiological observations. This method explicitly connects the model of transmission and the model of phylogeny so as to avoid the aforementioned inconsistency. We demonstrate the feasibility of our approach through simulation and apply it to estimate stage-specific infectiousness in a subepidemic of HIV in Detroit, Michigan. In a supplement, we prove that our approach is a valid sequential Monte Carlo algorithm. While we focus on how these methods may be applied to population-level models of infectious disease, their scope is more general. These methods may be applied in other biological systems where one seeks to infer population dynamics from genetic sequences, and they may also find application for evolutionary models with phenotypic rather than genotypic data.

epidemiology↗