Evolution of virulence in opportunistic pathogens: generalism, plasticity, and control

Standard virulence evolution theory assumes that virulence factors are maintained because they aid parasitic exploitation, increasing growth within and/or transmission between hosts. An increasing number of studies now demonstrate that many opportunistic pathogens (OPs) do not conform to these assumptions, with virulence factors maintained instead because of advantages in non-parasitic contexts. Here we review virulence evolution theory in the context of OPs and highlight the importance of incorporating environments outside a focal virulence site. We illustrate that virulence selection is constrained by correlations between these external and focal settings and pinpoint drivers of key environmental correlations, with a focus on generalist strategies and phenotypic plasticity. We end with a summary of key theoretical and empirical challenges to be met for a fuller understanding of OPs

Malaria parasites prepare for flight

Life in seasonal environments often means facing extreme environmental fluctuations. Many multicellular organisms have evolved strategies to cope with this lifestyle. Single-celled malaria parasites are no different. An elegant experiment reveals that they respond to the availability of mosquitoes to make the most of seasonal transmission opportunities

The Problem of Auto-Correlation in Parasitology

Explaining the contribution of host and pathogen factors in driving infection dynamics is a major ambition in parasitology. There is increasing recognition that analyses based on single summary measures of an infection (e.g., peak parasitaemia) do not adequately capture infection dynamics and so, the appropriate use of statistical techniques to analyse dynamics is necessary to understand infections and, ultimately, control parasites. However, the complexities of within-host environments mean that tracking and analysing pathogen dynamics within infections and among hosts poses considerable statistical challenges. Simple statistical models make assumptions that will rarely be satisfied in data collected on host and parasite parameters. In particular, model residuals (unexplained variance in the data) should not be correlated in time or space. Here we demonstrate how failure to account for such correlations can result in incorrect biological inference from statistical analysis. We then show how mixed effects models can be used as a powerful tool to analyse such repeated measures data in the hope that this will encourage better statistical practices in parasitology

The role of models in translating within-host dynamics to parasite evolution

Mathematical modelling provides an effective way to challenge conventional wisdom about parasite evolution and investigate why parasites ‘do what they do’ within the host. Models can reveal when intuition cannot explain observed patterns, when more complicated biology must be considered, and when experimental and statistical methods are likely to mislead. We describe how models of within-host infection dynamics can refine experimental design, and focus on the case study of malaria to highlight how integration between models and data can guide understanding of parasite fitness in three areas: (1) the adaptive significance of chronic infections; (2) the potential for tradeoffs between virulence and transmission; and (3) the implications of within-vector dynamics. We emphasize that models are often useful when they highlight unexpected patterns in parasite evolution, revealing instead why intuition yields the wrong answer and what combination of theory and data are needed to advance understanding

Adaptive plasticity in the gametocyte conversion rate of malaria parasites

<div><p>Sexually reproducing parasites, such as malaria parasites, experience a trade-off between the allocation of resources to asexual replication and the production of sexual forms. Allocation by malaria parasites to sexual forms (the conversion rate) is variable but the evolutionary drivers of this plasticity are poorly understood. We use evolutionary theory for life histories to combine a mathematical model and experiments to reveal that parasites adjust conversion rate according to the dynamics of asexual densities in the blood of the host. Our model predicts the direction of change in conversion rates that returns the greatest fitness after perturbation of asexual densities by different doses of antimalarial drugs. The loss of a high proportion of asexuals is predicted to elicit increased conversion (terminal investment), while smaller losses are managed by reducing conversion (reproductive restraint) to facilitate within-host survival and future transmission. This non-linear pattern of allocation is consistent with adaptive reproductive strategies observed in multicellular organisms. We then empirically estimate conversion rates of the rodent malaria parasite <i>Plasmodium chabaudi</i> in response to the killing of asexual stages by different doses of antimalarial drugs and forecast the short-term fitness consequences of these responses. Our data reveal the predicted non-linear pattern, and this is further supported by analyses of previous experiments that perturb asexual stage densities using drugs or within-host competition, across multiple parasite genotypes. Whilst conversion rates, across all datasets, are most strongly influenced by changes in asexual density, parasites also modulate conversion according to the availability of red blood cell resources. In summary, increasing conversion maximises short-term transmission and reducing conversion facilitates in-host survival and thus, future transmission. Understanding patterns of parasite allocation to reproduction matters because within-host replication is responsible for disease symptoms and between-host transmission determines disease spread.</p></div

A deep sequencing tool for partitioning clearance rates following antimalarial treatment in polyclonal infections

BACKGROUND AND OBJECTIVES: Current tools struggle to detect drug-resistant malaria parasites when infections contain multiple parasite clones, which is the norm in high transmission settings in Africa. Our aim was to develop and apply an approach for detecting resistance that overcomes the challenges of polyclonal infections without requiring a genetic marker for resistance. METHODOLOGY: Clinical samples from patients treated with artemisinin combination therapy were collected from Tanzania and Cambodia. By deeply sequencing a hypervariable locus, we quantified the relative abundance of parasite subpopulations (defined by haplotypes of that locus) within infections and revealed evolutionary dynamics during treatment. Slow clearance is a phenotypic, clinical marker of artemisinin resistance; we analyzed variation in clearance rates within infections by fitting parasite clearance curves to subpopulation data. RESULTS: In Tanzania, we found substantial variation in clearance rates within individual patients. Some parasite subpopulations cleared as slowly as resistant parasites observed in Cambodia. We evaluated possible explanations for these data, including resistance to drugs. Assuming slow clearance was a stable phenotype of subpopulations, simulations predicted that modest increases in their frequency could substantially increase time to cure. CONCLUSIONS AND IMPLICATIONS: By characterizing parasite subpopulations within patients, our method can detect rare, slow clearing parasites in vivo whose phenotypic effects would otherwise be masked. Since our approach can be applied to polyclonal infections even when the genetics underlying resistance are unknown, it could aid in monitoring the emergence of artemisinin resistance. Our application to Tanzanian samples uncovers rare subpopulations with worrying phenotypes for closer examination

The ghost of hosts past: impacts of host extinction on parasite specificity

A growing body of research is focused on the extinction of parasite species in response to host endangerment and declines. Beyond the loss of parasite species richness, host extinction can impact apparent parasite host specificity, as measured by host richness or the phylogenetic distances among hosts. Such impacts on the distribution of parasites across the host phylogeny can have knock-on effects that may reshape the adaptation of both hosts and parasites, ultimately shifting the evolutionary landscape underlying the potential for emergence and the evolution of virulence across hosts. Here, we examine how the reshaping of host phylogenies through extinction may impact the host specificity of parasites, and offer examples from historical extinctions, present-day endangerment, and future projections of biodiversity loss. We suggest that an improved understanding of the impact of host extinction on contemporary host–parasite interactions may shed light on core aspects of disease ecology, including comparative studies of host specificity, virulence evolution in multi-host parasite systems, and future trajectories for host and parasite biodiversity. This article is part of the theme issue ‘Infectious disease macroecology: parasite diversity and dynamics across the globe’

Resistance to natural and synthetic gene drive systems

Scientists are rapidly developing synthetic gene drive elements intended for release into natural populations. These are intended to control or eradicate disease vectors and pests, or to spread useful traits through wild populations for disease control or conservation purposes. However, a crucial problem for gene drives is the evolution of resistance against them, preventing their spread. Understanding the mechanisms by which populations might evolve resistance is essential for engineering effective gene drive systems. This review summarizes our current knowledge of drive resistance in both natural and synthetic gene drives. We explore how insights from naturally occurring and synthetic drive systems can be integrated to improve the design of gene drives, better predict the outcome of releases and understand genomic conflict in general