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

Dynamics of parasite growth in genetically diverse Plasmodium falciparum isolates

Multiple parasite lineages with different proliferation rates or fitness may coexist within a clinical malaria isolate, resulting in complex growth interactions and variations in phenotype. To elucidate the dynamics of parasite growth in multiclonal isolates, we measured growth rates (GRs) of three Plasmodium falciparum Cambodian isolates, including IPC_3445 (MRA-1236), IPC_5202 (MRA-1240), IPC_6403 (MRA-1285), and parasite lineages previously cloned from each of these isolates by limiting dilution. Following synchronization, in vitro cultures of each parasite line were maintained over four consecutive asexual cycles (192 h), with thin smears prepared at each 48-h cycle to estimate GR and fold change in parasitemia (FCP). Cell cycle time (CCT), the duration it takes for ring-stage parasites to develop into mature schizonts, was measured by monitoring the development of 0-3-h post-invasion rings for up to 52 h post-incubation. Laboratory lines 3D7 (MRA-102) and Dd2 (MRA-150) were used as controls. Significant differences in GR, FCP, and CCT were observed between parasite isolates and clonal lineages from each isolate. The parasite lines studied here have well-defined growth phenotypes and will facilitate basic malaria research and development of novel malaria interventions. These lines are available to malaria researchers through the MR4 collection of NIAID's BEI Resources Program.

Partitioning the influence of ecology across scales on parasite evolution

Vector-borne parasites must succeed at three scales to persist: they must proliferate within a host, establish in vectors, and transmit back to hosts. Ecology outside the host undergoes dramatic seasonal and human-induced changes, but predicting parasite evolutionary responses requires integrating their success across scales. We develop a novel, data-driven model to titrate the evolutionary impact of ecology at multiple scales on human malaria parasites. We investigate how parasites invest in transmission versus proliferation, a life-history trait that influences disease severity and spread. We find that transmission investment controls the pattern of host infectiousness over the course of infection: a trade-off emerges between early and late infectiousness, and the optimal resolution of that trade-off depends on ecology outside the host. An expanding epidemic favors rapid proliferation, and can overwhelm the evolutionary influence of host recovery rates and mosquito population dynamics. If transmission investment and recovery rate are positively correlated, then ecology outside the host imposes potent selection for aggressive parasite proliferation at the expense of transmission. Any association between transmission investment and recovery represents a key unknown, one that is likely to influence whether the evolutionary consequences of interventions are beneficial or costly for human health.

Altered life history strategies protect malaria parasites against drugs

Drug resistance has been reported against all antimalarial drugs, and while parasites can evolve classical resistance mechanisms (e.g., efflux pumps), it is also possible that changes in life history traits could help parasites evade the effects of treatment. The life history of malaria parasites is governed by an intrinsic resource allocation problem: specialized stages are required for transmission, but producing these stages comes at the cost of producing fewer of the forms required for within-host survival. Drug treatment, by design, alters the probability of within-host survival, and so should alter the costs and benefits of investing in transmission. Here, we use a within-host model of malaria infection to predict optimal patterns of investment in transmission in the face of different drug treatment regimes and determine the extent to which alternative patterns of investment can buffer the fitness loss due to drugs. We show that over a range of drug doses, parasites are predicted to adopt "reproductive restraint" (investing more in asexual replication and less in transmission) to maximize fitness. By doing so, parasites recoup some of the fitness loss imposed by drugs, though as may be expected, increasing dose reduces the extent to which altered patterns of transmission investment can benefit parasites. We show that adaptation to drug-treated infections could result in more virulent infections in untreated hosts. This work emphasizes that in addition to classical resistance mechanisms, drug treatment generates selection for altered parasite life history. Understanding how any shifts in life history will alter the efficacy of drugs, as well as any limitations on such shifts, is important for evaluating and predicting the consequences of drug treatment.

Variation in relapse frequency and the transmission potential of Plasmodium vivax malaria

There is substantial variation in the relapse frequency of Plasmodium vivax malaria, with fast-relapsing strains in tropical areas, and slow-relapsing strains in temperate areas with seasonal transmission. We hypothesize that much of the phenotypic diversity in P. vivax relapses arises from selection of relapse frequency to optimize transmission potential in a given environment, in a process similar to the virulence trade-off hypothesis. We develop mathematical models of P. vivax transmission and calculate the basic reproduction number R0 to investigate how transmission potential varies with relapse frequency and seasonality. In tropical zones with year-round transmission, transmission potential is optimized at intermediate relapse frequencies of two to three months: slower-relapsing strains increase the opportunity for onward transmission to mosquitoes, but also increase the risk of being outcompeted by faster-relapsing strains. Seasonality is an important driver of relapse frequency for temperate strains, with the time to first relapse predicted to be six to nine months, coinciding with the duration between seasonal transmission peaks. We predict that there is a threshold degree of seasonality, below which fast-relapsing tropical strains are selected for, and above which slow-relapsing temperate strains dominate, providing an explanation for the observed global distribution of relapse phenotypes.

Editorial: Mathematical modelling of infectious diseases

The field of disease ecology - the study of the spread and impact of parasites and pathogens within their host populations and communities - has a long history of using mathematical models. Dating back over 100 years, researchers have used mathematics to describe the spread of disease-causing agents, understand the relationship between host density and transmission and plan control strategies. The use of mathematical modelling in disease ecology exploded in the late 1970s and early 1980s through the work of Anderson and May (Anderson and May, 1978, 1981, 1992; May and Anderson, 1978), who developed the fundamental frameworks for studying microparasite (e.g. viruses, bacteria and protozoa) and macroparasite (e.g. helminth) dynamics, emphasizing the importance of understanding features such as the parasite's basic reproduction number (R 0) and critical community size that form the basis of disease ecology research to this day. Since the initial models of disease population dynamics, which primarily focused on human diseases, theoretical disease research has expanded hugely to encompass livestock and wildlife disease systems, and also to explore evolutionary questions such as the evolution of parasite virulence or drug resistance. More recently there have been efforts to broaden the field still further, to move beyond the standard 'one-host-one-parasite' paradigm of the original models, to incorporate many aspects of complexity of natural systems, including multiple potential host species and interactions among multiple parasite species.