Can mutation and selection explain virulence in human P. falciparum infections?

BACKGROUND: Parasites incur periodic mutations which must ultimately be eliminated to maintain their genetic integrity. METHODS: It is hypothesised that these mutations are eliminated not by the conventional mechanisms of competition between parasites in different hosts but primarily by competition between parasites within the same infection. RESULTS: This process is enhanced by the production of a large number of parasites within individual infections, and this may significantly contribute to parasitic virulence. CONCLUSIONS: Several features of the most virulent human malaria parasite Plasmodium falciparum can usefully be re-interpreted in this light and lend support to this interpretation. More generally, it constitutes a novel explanation for the evolution of virulence in a wider range of microparasites

Probability of emergence of antimalarial resistance in different stages of the parasite life cycle

Understanding the evolution of drug resistance in malaria is a central area of study at the intersection of evolution and medicine. Antimalarial drug resistance is a major threat to malaria control and directly related to trends in malaria attributable mortality. Artemisinin combination therapies (ACT) are now recommended worldwide as first line treatment for uncomplicated malaria, and losing them to resistance would be a disaster for malaria control. Understanding the emergence and spread of antimalarial drug resistance in the context of different scenarios of antimalarial drug use is essential for the development of strategies protecting ACTs. In this study, we review the basic mechanisms of resistance emergence and describe several simple equations that can be used to estimate the probabilities of de novo resistance mutations at three stages of the parasite life cycle: sporozoite, hepatic merozoite and asexual blood stages; we discuss the factors that affect parasite survival in a single host in the context of different levels of antimalarial drug use, immunity and parasitaemia. We show that in the absence of drug effects, and despite very different parasite numbers, the probability of resistance emerging at each stage is very low and similar in all stages (for example per-infection probability of 10−10–10−9 if the per-parasite chance of mutation is 10−10 per asexual division). However, under the selective pressure provided by antimalarial treatment and particularly in the presence of hyperparasitaemia, the probability of resistance emerging in the blood stage of the parasite can be approximately five orders of magnitude higher than in the absence of drugs. Detailed models built upon these basic methods should allow us to assess the relative probabilities of resistance emergence in the different phases of the parasite life cycle

Modelling malaria pathogenesis

Almost 20 years after the development of models of malaria pathogenesis began, we are beyond the ‘proof-of-concept’ phase and these models are no longer abstract mathematical exercises. They have refined our knowledge of within-host processes, and have brought insights that could not easily have been obtained from experimentation alone. There is much potential that remains to be realized, however, both in terms of informing the design of interventions and health policy, and in terms of addressing lingering questions about the basic biology of malaria. Recent research has begun to iterate theory and data in a much more comprehensive way, and the use of statistical techniques for model fitting and comparison offers a promising approach for providing a quantitative understanding of the pathogenesis of such a complex disease

A Statistically Rigorous Method for Determining Antigenic Switching Networks

Many vector-borne pathogens rely on antigenic variation to prolong infections and increase their likelihood of onward transmission. This immune evasion strategy often involves mutually exclusive switching between members of gene families that encode functionally similar but antigenically different variants during the course of a single infection. Studies of different pathogens have suggested that switching between variant genes is non-random and that genes have intrinsic probabilities of being activated or silenced. These factors could create a hierarchy of gene expression with important implications for both infection dynamics and the acquisition of protective immunity. Inferring complete switching networks from gene transcription data is problematic, however, because of the high dimensionality of the system and uncertainty in the data. Here we present a statistically rigorous method for analysing temporal gene transcription data to reconstruct an underlying switching network. Using artificially generated transcription profiles together with in vitro var gene transcript data from two Plasmodium falciparum laboratory strains, we show that instead of relying on data from long-term parasite cultures, accuracy can be greatly improved by using transcription time courses of several parasite populations from the same isolate, each starting with different variant distributions. The method further provides explicit indications about the reliability of the resulting networks and can thus be used to test competing hypotheses with regards to the underlying switching pathways. Our results demonstrate that antigenic switch pathways can be determined reliably from short gene transcription profiles assessing multiple time points, even when subject to moderate levels of experimental error. This should yield important new information about switching patterns in antigenically variable organisms and might help to shed light on the molecular basis of antigenic variation

Prospective strategies to delay the evolution of anti-malarial drug resistance: weighing the uncertainty

<p>Abstract</p> <p>Background</p> <p>The evolution of drug resistance in malaria parasites highlights a need to identify and evaluate strategies that could extend the useful therapeutic life of anti-malarial drugs. Such strategies are deployed to best effect before resistance has emerged, under conditions of great uncertainty.</p> <p>Methods</p> <p>Here, the emergence and spread of resistance was modelled using a hybrid framework to evaluate prospective strategies, estimate the time to drug failure, and weigh uncertainty. The waiting time to appearance was estimated as the product of low mutation rates, drug pressure, and parasite population sizes during treatment. Stochastic persistence and the waiting time to establishment were simulated as an evolving branching process. The subsequent spread of resistance was simulated in simple epidemiological models.</p> <p>Results</p> <p>Using this framework, the waiting time to the failure of artemisinin combination therapy (ACT) for malaria was estimated, and a policy of multiple first-line therapies (MFTs) was evaluated. The models quantify the effects of reducing drug pressure in delaying appearance, reducing the chances of establishment, and slowing spread. By using two first-line therapies in a population, it is possible to reduce drug pressure while still treating the full complement of cases.</p> <p>Conclusions</p> <p>At a global scale, because of uncertainty about the time to the emergence of ACT resistance, there was a strong case for MFTs to guard against early failure. Our study recommends developing operationally feasible strategies for implementing MFTs, such as distributing different ACTs at the clinic and for home-based care, or formulating different ACTs for children and adults.</p

Interrupting Malaria Transmission: Quantifying the Impact of Interventions in Regions of Low to Moderate Transmission

Malaria has been eliminated from over 40 countries with an additional 39 currently planning for, or committed to, elimination. Information on the likely impact of available interventions, and the required time, is urgently needed to help plan resource allocation. Mathematical modelling has been used to investigate the impact of various interventions; the strength of the conclusions is boosted when several models with differing formulation produce similar data. Here we predict by using an individual-based stochastic simulation model of seasonal Plasmodium falciparum transmission that transmission can be interrupted and parasite reintroductions controlled in villages of 1,000 individuals where the entomological inoculation rate is <7 infectious bites per person per year using chemotherapy and bed net strategies. Above this transmission intensity bed nets and symptomatic treatment alone were not sufficient to interrupt transmission and control the importation of malaria for at least 150 days. Our model results suggest that 1) stochastic events impact the likelihood of successfully interrupting transmission with large variability in the times required, 2) the relative reduction in morbidity caused by the interventions were age-group specific, changing over time, and 3) the post-intervention changes in morbidity were larger than the corresponding impact on transmission. These results generally agree with the conclusions from previously published models. However the model also predicted changes in parasite population structure as a result of improved treatment of symptomatic individuals; the survival probability of introduced parasites reduced leading to an increase in the prevalence of sub-patent infections in semi-immune individuals. This novel finding requires further investigation in the field because, if confirmed, such a change would have a negative impact on attempts to eliminate the disease from areas of moderate transmission

Mathematical Modeling of Malaria Infection with Innate and Adaptive Immunity in Individuals and Agent-Based Communities

Background: Agent-based modeling of Plasmodium falciparum infection offers an attractive alternative to the conventional Ross-Macdonald methodology, as it allows simulation of heterogeneous communities subjected to realistic transmission (inoculation patterns). Methodology/Principal Findings: We developed a new, agent based model that accounts for the essential in-host processes: parasite replication and its regulation by innate and adaptive immunity. The model also incorporates a simplified version of antigenic variation by Plasmodium falciparum. We calibrated the model using data from malaria-therapy (MT) studies, and developed a novel calibration procedure that accounts for a deterministic and a pseudo-random component in the observed parasite density patterns. Using the parasite density patterns of 122 MT patients, we generated a large number of calibrated parameters. The resulting data set served as a basis for constructing and simulating heterogeneous agent-based (AB) communities of MT-like hosts. We conducted several numerical experiments subjecting AB communities to realistic inoculation patterns reported from previous field studies, and compared the model output to the observed malaria prevalence in the field. There was overall consistency, supporting the potential of this agent-based methodology to represent transmission in realistic communities. Conclusions/Significance: Our approach represents a novel, convenient and versatile method to model Plasmodiu

Quantitative Analysis of Immune Response and Erythropoiesis during Rodent Malarial Infection

Malarial infection is associated with complex immune and erythropoietic responses in the host. A quantitative understanding of these processes is essential to help inform malaria therapy and for the design of effective vaccines. In this study, we use a statistical model-fitting approach to investigate the immune and erythropoietic responses in Plasmodium chabaudi infections of mice. Three mouse phenotypes (wildtype, T-cell-deficient nude mice, and nude mice reconstituted with T-cells taken from wildtype mice) were infected with one of two parasite clones (AS or AJ). Under a Bayesian framework, we use an adaptive population-based Markov chain Monte Carlo method and fit a set of dynamical models to observed data on parasite and red blood cell (RBC) densities. Model fits are compared using Bayes' factors and parameter estimates obtained. We consider three independent immune mechanisms: clearance of parasitised RBCs (pRBC), clearance of unparasitised RBCs (uRBC), and clearance of parasites that burst from RBCs (merozoites). Our results suggest that the immune response of wildtype mice is associated with less destruction of uRBCs, compared to the immune response of nude mice. There is a greater degree of synchronisation between pRBC and uRBC clearance than between either mechanism and merozoite clearance. In all three mouse phenotypes, control of the peak of parasite density is associated with pRBC clearance. In wildtype mice and AS-infected nude mice, control of the peak is also associated with uRBC clearance. Our results suggest that uRBC clearance, rather than RBC infection, is the major determinant of RBC dynamics from approximately day 12 post-innoculation. During the first 2–3 weeks of blood-stage infection, immune-mediated clearance of pRBCs and uRBCs appears to have a much stronger effect than immune-mediated merozoite clearance. Upregulation of erythropoiesis is dependent on mouse phenotype and is greater in wildtype and reconstitited mice. Our study highlights the informative power of statistically rigorous model-fitting techniques in elucidating biological systems

Out of Africa:Origins and evolution of the human malaria parasites <i>Plasmodium falciparum</i> and <i>Plasmodium vivax</i>

AbstractPlasmodium falciparum and Plasmodium vivax account for more than 95% of all human malaria infections, and thus pose a serious public health challenge. To control and potentially eliminate these pathogens, it is important to understand their origins and evolutionary history. Until recently, it was widely believed that P. falciparum had co-evolved with humans (and our ancestors) over millions of years, whilst P. vivax was assumed to have emerged in southeastern Asia following the cross-species transmission of a parasite from a macaque. However, the discovery of a multitude of Plasmodium spp. in chimpanzees and gorillas has refuted these theories and instead revealed that both P. falciparum and P. vivax evolved from parasites infecting wild-living African apes. It is now clear that P. falciparum resulted from a recent cross-species transmission of a parasite from a gorilla, whilst P. vivax emerged from an ancestral stock of parasites that infected chimpanzees, gorillas and humans in Africa, until the spread of the protective Duffy-negative mutation eliminated P. vivax from human populations there. Although many questions remain concerning the biology and zoonotic potential of the P. falciparum- and P. vivax-like parasites infecting apes, comparative genomics, coupled with functional parasite and vector studies, are likely to yield new insights into ape Plasmodium transmission and pathogenesis that are relevant to the treatment and prevention of human malaria