Virus host shifts in Drosophila: The influences of virus genotype and coinfection on susceptibility within and across host species

Virus host shifts are a major source of outbreaks and emerging infectious diseases, and continue to cause considerable damage to public health, society, and the global economy. Predicting and preventing future virus host shifts has become a primary goal of infectious disease research, and multiple tools and approaches are being developed to work towards this goal. In this thesis, I examine three key aspects of infection that have implications for our wider understanding of virus host shifts and their predictability in natural systems: whether the outcome of infections across species is correlated between related viruses, whether the presence of a coinfecting virus can alter the outcomes of cross-species transmission, and the influence of host genetics and immunity on the outcomes of coinfection. These experiments make use of a large and evolutionarily diverse panel of Drosphilidae host species, and infections with two insect Cripaviruses: Drosophila C virus (DCV) and Cricket Paralysis virus (CrPV), with the outcomes of infection quantified throughout as viral loads via qRT-PCR. In Chapter Two, phylogenetic generalised linear mixed models are applied to data on the outcome of single infections with three isolates of DCV (DCV-C, DCV-EB, DCV-M) and one isolate of CrPV, to look for correlations in viral load across host species. Strong positive corrections were found between DCV isolates and weaker positive correlations between DCV and CrPV, with evidence of host species by virus interactions on the outcome of infection. Of the four viruses tested, the most closely related isolates tended to be the most strongly correlated, with correlation strength deteriorating with the evolutionary distance between isolates, although we lacked the diversity or sample size of viruses to properly determine any effect of evolutionary distance on correlation strength. Together, this suggests that hosts susceptible to one virus are also susceptible to closely related viruses, and that knowledge of one virus may be extrapolated to closely related viruses, at least within the range of evolutionary divergence tested here. In the remainder of this thesis, I examine the outcome of coinfection with DCV-C and CrPV across host species (Chapter Three) and across genotypes and immune mutants of Drosophila melanogaster (Chapter Four). These chapters aim to assess the potential for coinfection to alter the outcomes of cross-species transmission – and so interfere with predictions of virus host shifts – and the potential influence of host genetics and immunity on the outcome of coinfection. Chapter Three finds little evidence of systematic changes in the outcome of single and coinfection for both viruses across species, suggesting that coinfection may not be a required consideration in predictive models of every host-virus system. Effects of coinfection were found in a subset of species but were not recapitulated in a follow-up experiment looking at tissue tropism during coinfection on a subset of host species. Together, this suggests that any effects of coinfection across species with DCV and CrPV are due to stochastic effects within individual hosts. Chapter Four finds small but credible effects of coinfection across genotypes of D. melanogaster, but these effects showed little host genetic basis or effect on the genetic basis of susceptibility to each virus separately. Mutations in several immune genes caused virus-specific changes in viral load between single and coinfection, suggesting that coinfection interactions between viruses can be moderated by the host immune response. This thesis has aimed to explore several fundamental features of cross-species transmission that are relevant to our understanding – and ability to predict – virus host shifts. Both the finding that correlations exist between viruses and the approach used to characterise coinfection across and within host species would now benefit from an increased diversity of experimental pathogens, to better investigate the influence of virus evolutionary relationships on the outcomes of virus host shifts and present a broader understanding of the potential impact of coinfection on the outcomes of cross-species transmission.Natural Environment Research Council (NERC

Models of Delay Differential Equations

This book gathers a number of selected contributions aimed at providing a balanced picture of the main research lines in the realm of delay differential equations and their applications to mathematical modelling. The contributions have been carefully selected so that they cover interesting theoretical and practical analysis performed in the deterministic and the stochastic settings. The reader will find a complete overview of recent advances in ordinary and partial delay differential equations with applications in other multidisciplinary areas such as Finance, Epidemiology or Engineerin

Effect of stochasticity on coinfection dynamics of respiratory viruses

Abstract Background Respiratory viral infections are a leading cause of mortality worldwide. As many as 40% of patients hospitalized with influenza-like illness are reported to be infected with more than one type of virus. However, it is not clear whether these infections are more severe than single viral infections. Mathematical models can be used to help us understand the dynamics of respiratory viral coinfections and their impact on the severity of the illness. Most models of viral infections use ordinary differential equations (ODE) that reproduce the average behavior of the infection, however, they might be inaccurate in predicting certain events because of the stochastic nature of viral replication cycle. Stochastic simulations of single virus infections have shown that there is an extinction probability that depends on the size of the initial viral inoculum and parameters that describe virus-cell interactions. Thus the coinfection dynamics predicted by the ODE might be difficult to observe in reality. Results In this work, a continuous-time Markov chain (CTMC) model is formulated to investigate probabilistic outcomes of coinfections. This CTMC model is based on our previous coinfection model, expressed in terms of a system of ordinary differential equations. Using the Gillespie method for stochastic simulation, we examine whether stochastic effects early in the infection can alter which virus dominates the infection. Conclusions We derive extinction probabilities for each virus individually as well as for the infection as a whole. We find that unlike the prediction of the ODE model, for similar initial growth rates stochasticity allows for a slower growing virus to out-compete a faster growing virus