Malaria is an infectious disease that is estimated to kill more than half a million people every year, mostly young children in Africa. It is spread by mosquitos that are infected with Plasmodium parasites that attack red blood cells in the human body. Plasmodium falciparum, the species that is responsible for most of these deaths, causes malaria by entering red blood cells and releasing antigens that travel to the surface of the cells, where they change the adhesion properties. This causes the infected red blood cells to accumulate in small blood vessels, surface capillaries or the brain, which can have severe consequences for the person infected.

P. falciparum is particularly dangerous because of its ability to vary the antigens displayed on the cell surface: this process, known as antigenic variation, helps to maintain infections for extended periods of time by allowing the antigens to stay one step ahead of the immune system (a process known as immune escape). The origins of antigenic variation lie in the fact that each P. falciparum genome has a repertoire of between 50 and 60 var genes that code for the variability of a major antigen that is responsible for immune escape in malaria. Molecular sequencing has shown that local parasite populations contain thousands of different types of var genes: hence, meiotic recombination in the mosquito can create a vast number of combinations of var repertoires.

Artzy-Randrup et al. have developed a computational model of this highly diverse and complex system to address the following question: is a local pathogen population composed of largely random and ephemeral repertoires of these genes, or is it structured into independently circulating strains? Their model goes beyond previous models by including interactions within the local host population that arise as a result of indirect competition between different strains of the pathogen for available hosts: this competition is influenced by the history of infection and, therefore, by the patterns of immunity within the host population. Previous models included within-host processes but not these higher, local population-level interactions.

The model simulates the dynamics of all the unique combinations of var genes in a population of hosts, and shows that even with high rates of reproduction, the parasite population self-organizes into a limited number of coexisting strains: the distinct var repertoires of these strains only weakly overlap, suggesting that the immune response of the host population has been partitioned into distinct niches. By investigating genetic variation at both antigenic sites and regions of the genome that do not code for antigens, Artzy-Randrup et al. suggest that immune selection—the selection imposed on var repertoires by the build up of specific immunity at the population level—plays a central role in structuring parasite diversity.

The new model should lead to a better understanding of the epidemiology of Plasmodium and other pathogens that work in similar ways, including Trypanosoma brucei (sleeping sickness), Borellia burgdorferi (Lyme disease) and Giardia lamblia (gastroenteritis), and help with global efforts to eliminate malaria and other diseases.

Many pathogen populations seem to be composed of several independent strains; this has important implications for disease surveillance and control, as well as for the identification of vaccine targets. In particular, the population genetic interactions between pathogen strains can have significant epidemiological consequences, influencing transmissibility, clinical immunity, and the duration and severity of infection. An extensive variety of microparasites have been studied in this context: protozoa, such as Plasmodium falciparum, Leishmania, and Trypanosoma brucei; viruses, such as Influenza, HIV and Dengue; and bacteria, such as E. coli, Bordetella spp. and Neisseria meningitides (Tibayrenc et al., 1990; Gupta et al., 1994; Tibayrenc and Ayala, 2002).

The conceptual challenges and significant human health implications associated with the pathogen population structure have led to the establishment of several theories for why we observe this structure in nature (Selander et al., 1987; Tibayrenc et al., 1990; Smith et al., 1993; Gupta and Day, 1994a, 1994b; Gupta et al., 1996; Tibayrenc, 1994). In particular, while it would seem that sufficient out-crossing should always lead to random associations between alleles (Tibayrenc et al., 1990; Tibayrenc and Ayala, 2002) and preclude the persistence of ‘independently transmitted strains’ (Tibayrenc, 1994), mathematical models of competition between pathogen strains have demonstrated that immune selection can lead to the maintenance of discrete, non-overlapping, antigenic repertoires—even when antigenic variability is driven by several unlinked genes that undergo recombination (Gupta et al., 1996; Gupta and Anderson, 1999). An inherent challenge in testing ‘strain theory’ is to determine whether observed empirical patterns in pathogen population structure result from immune selection, or alternatively, from simple demographic processes (i.e., uneven relatedness among individuals in the pathogen population that can result from random genetic drift, particularly within the complex context of transmission dynamics) (Buckee et al., 2008, 2011).

P. falciparum is responsible for the deadliest type of malaria in humans. The complexities of its population genetics, in terms of the variable and often enormous levels of sequence variation observed at antigenic sites within local populations, are intimately tied to both its pathology and its ability to escape immune detection. While this genetic complexity provides significant challenges to malaria research and control, it also provides an opportunity to extend existing strain theory in a way that can be tested empirically. Specifically, an important extension of earlier strain theory is to consider the effects of immune selection in populations of pathogens whose antigen-encoding loci are: (a) multi-copy and recombining (i.e., located on different chromosomes of a haploid genome), and (b) undergoing antigenic variation via sequential, mutually exclusive expression within an infected host. It is not clear whether a stable strain structure would emerge under the sequential expression of multi-copy genes. Consideration of this type of system provides a more complete framework for understanding antigenic structure and its epidemiological consequences in a broader variety of pathogens; it also offers the opportunity to distinguish the effects of immune selection from those of neutral demographic forces, providing testable predictions for these two alternative mechanisms.

We describe an agent-based model that extends strain theory to address the effect of immune selection acting on multi-copy antigen genes; we use the var gene family of P. falciparum as the basis for this model. The var genes encode P. falciparum erythrocyte membrane protein 1 (PfEMP1)—the major surface antigen of the parasite's blood stages that binds to host endothelial cell receptors to allow the parasite to sequester within various host tissues (Miller et al., 2002; Scherf et al., 2008). Var genes are composed of multiple Duffy-binding-like (DBL) and cysteine-interdomain-rich (CIDR) domains that encode binding functions for specific endothelial receptors. DBLα, is the only domain found in nearly all var genes (Miller et al., 2002), and hence is the most informative molecular marker of var gene diversity. Each parasite genome has up to 60 var genes, and differential sequential expression of these genes allows the parasite to evade the host immune response and establish a chronic infection. This clonal antigenic variation sensu stricto (Miller et al., 2002; Scherf et al., 2008) extends the duration of infection to facilitate transmission to the mosquito. Not only do var genes influence transmission, but specific var genes are associated with severe malarial disease and specific PfEMP1 binding phenotypes (Deitsch et al., 2009; Chan et al., 2012; Claessens et al., 2012; Lavstsen et al., 2012; Warimwe et al., 2012).

Population studies have shown that individual P. falciparum genomes have distinct repertoires of var genes (Barry et al., 2007; Scherf et al., 2008; Chen et al., 2011) that extensively recombine (Freitas-Junior et al., 2000; Taylor et al., 2000) during sexual recombination in the mosquito (Paul et al., 1995). The immense sequence diversity observed among var genes and var repertoires has been attributed to high rates of meiotic (and possibly also mitotic) recombination. These high rates of recombination presumably allow for very low linkage disequilibrium among var loci. Our modeling framework considers the dynamics shaping the enormous potential diversity of genomic repertoires of var genes by tracking all possible var repertoires, given a diverse pool of genetic variants. We explore whether structured parasite populations resulting from the selection pressures exerted by competition among distinct var repertoires for hosts, mediated by cross-immunity, can emerge.

We model the dynamics of multiple parasite genomes that simultaneously circulate in a large host population, continuously tracking the formation of new genomes through recombination, mutation and immigration events (Paul et al., 1995; Bruce et al., 2000a, 2000b). Reliable numerical results require the simulation of explicit transmission events, as well as complete knowledge of the exposure history of each individual host with respect to past and present infections by different parasite genomes. Thus, an agent-based model is formulated that follows each host from birth to death, while recording their exposure history and the specific immunity each gradually gains as a result of infections (for a detailed description of the model see the ‘Materials and methods’ section).

The simulated host population is continuously exposed to an immense diversity of distinct var repertoires, since new combinations of var genes are continuously generated through meiotic recombination events. As a conservative example, consider the number of potential configurations arising from the combinatorial possibilities of 6 loci with 50 var genes in the pool: more than 3 × 10⁷ repertoires! Despite this large potential diversity, simulation results show that only a few dominant var repertoires circulate and persist at any given time, effectively keeping var repertoire diversity orders of magnitude lower than is potentially possible given the number of var genes present in the host population (Figure 1). We find that epidemiological dynamics significantly limit the diversity of coexisting dominant genomes under a broad spectrum of transmission intensities. This pattern persists in the long-term, far out-living the influence of initial conditions, and persisting through occasional changes in the identity of the dominant var repertoires (Figure 1). Thus, on epidemiological (and longer) time scales, most of the parasite population is structured into a limited number of distinct var repertoires.

Figure 1

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We find that the parasite population structure exhibits punctuated turnover: brief episodes of restructuring result in a new set of dominant var repertoires. Despite the changing identities of the dominant var repertoires, the overall diversity of repertoires present and the individual gene frequencies remain remarkably constant (Figure 1, lower panels). These transient phenomena punctuate periods of stasis with stable population structure. In essence, although there are an effectively infinite number of niches in host immunity space, var repertoires appear to exploit this space by partitioning it into a number of discrete and partially overlapping niches. In addition, these repertoires appear to quickly adapt and achieve some optimum after perturbations arising from the stochastic nature of the underlying demographic and transmission processes. The episodes of restructuring become more frequent as recombination rates increase, but the diversity of dominant var repertoires remains low throughout these semi-stable states. Indeed, the maintenance of structure is found to be robust even under very high levels of recombination (Figure 1). The emergence of pathogen population structure is robust to alternative formulations of our multi-locus model that do not consider sequential expression, or formulations that consider randomly ordered sequential expression. In addition, episodes of restructuring can also be observed when expression is not sequential.

The level of diversity remains orders of magnitude lower than that potentially allowed by the var gene pool itself. However, as transmission intensity increases, the overlap between distinct dominant var repertoires also increases, with completely discordant genomes only appearing at the lower end of the spectrum (see Figure 2). To capture the characteristic features of different population compositions we defined four metrics: N*, measuring the average number of successful transmission events per year, V*, measuring the average number of de-novo genomes transmitted per year, R*, measuring the diversity of var repertoires, and F*, measuring the degree of var overlap between distinct repertoires (see ‘Materials and methods’). We find that as transmission intensity (biting rate) increases, all four measures increase as well, and that epidemiological dynamics clearly constrain var repertoire diversity and structure parasite populations, with the degree of overlap determined by epidemiological parameters (Figure 3A–D). Our findings are robust to different host population sizes (from 1,000 to 100,000 individuals), different gene pools sizes (from 10 to 500), and different repertoire lengths (from 2 to 30) (Figure 3).

Figure 2

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Figure 3

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The model suggests that areas that experience different transmission intensities will exhibit ‘characteristic’ epidemiological signatures (Figure 4). Importantly, the gain of immunity is found to depend on the degree of repertoire overlap (i.e., the number of shared var alleles) among the different dominant genomes and not just on total prevalence (Figure 5). This is an important difference with compartmental transmission models that consider superinfection but have no explicit treatment of var genes and repertoire diversity.

Figure 4

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Figure 5

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Two recent large-scale studies aimed at assessing the extent of local reservoirs of var genes in five epidemiologically and geographically distinct P. falciparum populations (Barry et al., 2007; Chen et al., 2011) provide us with new knowledge on the distribution of var genes in parasite genomes. These new data clearly show that the frequency distribution of distinct var genes, or sequence ‘types’ (defined by the level of sequence identity within the DBLα domain) is significantly different from what we would expect if sequence types were equal in frequency and uncorrelated (Figure 6). In addition, the data show significant nonrandom associations among many var DBLα types within the sampled isolates (Figure 6).

Figure 6

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These patterns in observed var repertoires may alternatively result from neutral demographic processes: that is, individual mosquitoes bite discrete hosts, and thus, inevitably sample the host population unevenly. This limits ‘random mating’ and thus recombination between parasite genomes. Hence, even under neutral forces, it is possible for parasites to differ in their relatedness to one another. Under this alternative assumption we expect these neutral demographic processes to shape the diversity at all loci to the same extent (after controlling for differences in physical linkage and recombination rate between loci). Evidence of more extensive ‘strain structure’ at antigenic, immune-selected sites compared to neutral sites suffices as proof that selective forces are at work, and of these, immune selection is the most parsimonious explanation.

We compare patterns of variation at immune-selected sites to neutral sites in a variety of ways (see ‘Materials and methods’), and we propose this as an effective empirical test of strain theory. Our test is conceptually similar to empirical analyses of strain theory undertaken in the past (Buckee et al., 2010), but in this system, there is the opportunity for a more powerful test since the multi-copy nature of var genes allows us to examine patterns of variation at many independent (i.e., physically unlinked) genetic sites. Currently, the dataset with the most extensive sampling of diversity at both var and microsatellite loci within a local population consists of a set of 30 single infection isolates from a local population in Papua New Guinea for which we have sequences for a portion of the var DBLα domain and the alleles for 12 microsatellite loci (Barry et al., 2007; Chen et al., 2011).

Because we expect alleles at the same locus to anti-correlate, and because it is unknown which DBLα alleles share a common locus, we only analyze positive correlations. We measure the positive linkage disequilibrium coefficients (D) for the presence/absence states for all unique pairs of microsatellite or var DBLα alleles. High D values indicate a correlation between alleles within isolates, and they are maximized at intermediate allele frequency (see and ‘Materials and methods’). For the var DBLα dataset there are 1288 distinct pairs of alleles with positive D values that exceed a two-tailed significance threshold of 0.05 (Hedrick et al., 1978). Given the number of tests performed, under the null hypothesis of no significant correlations between alleles, we would expect only 310 pairs to exceed this threshold. For the microsatellite dataset there are 146 distinct pairs of alleles with positive D values that exceed the significance threshold and under the null hypothesis we would expect only 49. Therefore, both datasets show an enrichment of positive correlations among allele pairs, and thus, significant linkage disequilibrium. The D values of the var DBLα dataset are higher than those of the microsatellite dataset (p_v<<0.0001 by Mann–Whitney test), and we argue that this cannot easily be explained by allele frequency differences or differences in the extent of physical linkage among sites (see ‘Materials and methods’).

Another important observation is that, at least at this level of resolution, var DBLα repertoire relatedness is not a function of microsatellite repertoire relatedness. Figure 6E shows the relatedness of the var DBLα repertoires of an isolate pair as a function of the relatedness of their microsatellite repertoires. The absence of any correlation suggests the possibility that var DBLα variation is shaped by other forces than the neutral demographic forces shaping microsatellite variation.

We also consider only the most informative var types in isolation by identifying highly correlated sets of var types. We then tested the relatedness of microsatellite alleles among the isolates containing these networks of correlated var types. The networks of correlated var types were constructed from pairs of var types exhibiting both high linkage disequilibrium and correlation coefficients (i.e., D and r). Figure 6D represents one of the largest of such fully connected networks (with D > 0.0748 used as the cutoff), however this complete network is only observed in two isolates in the dataset. The microsatellite repertoires of these two isolates are less related than average for the whole dataset, so at least in this case, high microsatellite relatedness does not explain the existence of these highly correlated var types. Additionally, smaller subsets of correlated var types are often found in isolates with very dissimilar microsatellite alleles. These results suggest that the existence of highly correlated var types is not always explained by high levels of underlying genome relatedness. Moreover, when we use the same cutoff D value to look for fully connected networks of correlated microsatellite alleles, the largest of such networks comprises only four alleles as opposed to six (six pairwise connections as compared to 15).

Although the sample size of this dataset is too small to identify anything but very strong correlations between intermediate frequency var types (Brown, 1975), these analyses support a possible role for immune selective forces in structuring the variation at an important class of antigen-encoding loci in P. falciparum. More extensive datasets will be needed for definitive conclusions, but these are now within reach as next generation sequencing methods make it feasible to consider sampling whole communities of parasite genomes to assess var population structure.

In accordance with predictions from the original ‘strain theory’, our model constrains total repertoire diversity to levels that are orders of magnitude lower than is potentially possible given the diversity of the gene pool. This reduction in diversity occurs because coexisting parasites are structured into a few variants with largely discordant repertoires. In a departure from previous strain theory, our model predicts that the different dominant repertoires can exhibit partial overlap; the extent of the overlap increases with transmission intensity and this, in turn, influences observed epidemiological patterns (particularly age-serology profiles).

The specific immune profile of each individual host effectively structures the population into distinct sets of ‘patches’ or ‘resources’ that define an immune environment, ultimately causing immune selection to act on the parasite population. These distinct sets of patches are formed and conserved even when immunity to the var alleles is shorter-lived. Observations of individuals becoming susceptible to severe infection at later stages of their life (see for example (Dhingra et al., 2010)), may be in part a consequence of short-lived immunity. Under this assumption, hosts in our model regain protection by exposure to one of the dominant strains circulating in the host population at high frequencies, and do so at a sufficiently high rate to preserve strain population structure except at extremely low transmission intensity. In the event that a host is infected by one of the low frequency genomes that are continuously being introduced (and equally quickly vanishing), it is highly unlikely that this successful transmission event is sufficient for invasion or even replacement of the currently dominating strain community.

Discordant repertoires that exhibit partial immunological/antigenic overlap blur the distinction between the acquisition of strictly strain-specific immunity and the acquisition of strictly strain-transcending immunity (Gupta and Day, 1994a, 1994b) (see caption of Figure 5). This underscores the role of strain diversity structure, rather than diversity per se, in determining observed epidemiological patterns and their response to intervention. By reducing antigenic overlap between strains, vector control efforts will result in infection at an older age and a more gradual acquisition of protection to infection, even in the absence of changes in var gene diversity. The model in its current form does not differentiate infection from clinical disease, thus we cannot explicitly address the impact of control on the severity of symptoms. However, if the number of var genes that a host has previously encountered relates to the degree of protection against clinical disease, we would expect more severe symptoms to occur at younger ages under high transmission settings, and in contrast, more homogeneous malaria incidents are likely to occur over a broader range of age-classes when transmission is lowered (Figure 4D). Investigation of these patterns awaits the formulation of models that include functional, non-antigenic, differences between strains, as well as more empirical understanding of the role played by var genes associated with more severe disease. Future work should thus extend our model to consider functional, non-antigen-encoding differences between var genes (Bull et al., 1999; Miller et al., 2002; Scherf et al., 2008; Buckee et al., 2009; Claessens et al., 2012; Severins et al., 2012 for an example of a recent model applied to questions on the evolution of virulence). Ultimately, ways to represent the effects of pathogen diversity implicitly within a standard epidemiological framework, that might also be more easily parameterized, should also be investigated.

The goal of malaria eradication is to reduce the reservoir of malaria infection in humans to zero. Chen et al. (2011) have pointed to the importance of var genes in malaria surveillance, due to their influence on both the duration of infection and the degree of superinfection, and they demonstrated the existence of an immense diversity of var genes and limited var repertoire overlap in Africa, even in sites with low malaria prevalence. Consequently, they have proposed that var typing be used to complement prevalence measures for molecular surveillance of malaria control and elimination efforts. Our models support this proposal as they show that decreasing transmission provokes nonlinear changes in the dominant var configurations. Importantly, our results demonstrate that there is not a simple relationship between var diversity and repertoire diversity. Indeed, the ‘var strain’ model suggests that vector control strategies that reduce transmission will not always drastically reduce var gene diversity, because a reservoir of parasite genomes with diverse (highly non-overlapping) var repertoires will remain. This insight from the model provides a plausible explanation for why it is so difficult to eliminate malaria: non-overlapping var diversity will create significant inherent difficulties in reducing the potential of the malaria parasite to reinfect the same hosts. It also suggests that var surveillance might provide a good molecular means of evaluating control. If the end game is to be achieved in malaria control, then the mechanisms creating and maintaining antigenic diversity warrant further investigation in both modeling and empirical studies.

In conclusion, the results of our model on the dynamics of population structure in P. falciparum as defined by the var multi-gene family, have broad implications for the possible emergence of similar structure in many important human and animal pathogens that have major surface antigens encoded by multigene families, including bacterial, protozoan and fungal pathogens (Deitsch et al., 2009).

1. 1. Artzy-Randrup Y
   2. Stone L

   (2005)

   Generating uniformaly distributed networks

   Physical Review E 72:.

   • Google Scholar
2. 1. Barry AE
   2. Leliwa-Syteck A
   3. Tavul L
   4. Imrie H
   5. Migot-Nabias F
   6. Brown SM

   et al. (2007)

   Population genomics of the immune evasion (var) genes of Plasmodium falciparum

   PLoS Pathog 3:e34.

   • Google Scholar
3. 1. Brown AHD

   (1975)

   Sample sizes required to detect linkage disequilibrium between two or three loci

   Theor Popul Biol 8:184–201.

   • Google Scholar
4. 1. Bruce MC
   2. Day KP

   (2002)

   Cross-species regulation of malaria parasitaemia in the human host

   Curr Opin Microbiol 5:431–437.

   • Google Scholar
5. 1. Bruce MC
   2. Day KP

   (2003)

   Cross-species regulation of Plasmodium parasitemia in semi-immune children from Papua New Guinea

   Trends Parasitol 19:271–277.

   • Google Scholar
6. 1. Bruce MC
   2. Donnelly CA
   3. Alpers MP
   4. Galinski MR
   5. Barnwell JW
   6. Walliker D

   et al. (2000a)

   Cross-species interactions between malaria parasites in humans

   Science 287:845–848.

   • Google Scholar
7. 1. Bruce MC
   2. Donnelly CA
   3. Packer M
   4. Lagog M
   5. Gibson N
   6. Narara A

   et al. (2000b)

   Age- and species-specific duration of infection in asymptomatic malaria infections in Papua New Guinea

   Parasitology 121:247–256.

   • Google Scholar
8. 1. Buckee CO
   2. Bull PC
   3. Gupta S

   (2009)

   Inferring malaria parasite population structure from serological networks

   Proc Biol Sci 276:477–485.

   • Google Scholar
9. 1. Buckee CO
   2. Gupta S
   3. Kriz P
   4. Maiden MC
   5. Jolley KA

   (2010)

   Long-term evolution of antigen repertoires among carried meningococci

   Proc Biol Sci 277:1635–1641.

   • Google Scholar
10. 1. Buckee CO
    2. Jolley KA
    3. Recker M
    4. Penman B
    5. Kriz P
    6. Gupta S

    et al. (2008)

    Role of selection in the ermergence of lineages and the evolution of virulence in Neisseria meningitidis

    Proc Natl Acad Sci USA 105:15082–15087.

    • Google Scholar
11. 1. Buckee CO
    2. Recker M
    3. Watkins ER
    4. Gupta S

    (2011)

    Role of stochastic processes in maintaining discrete strain structure in antigenically diverse pathogen populations

    Proc Natl Acad Sci USA 108:15504–15509.

    • Google Scholar
12. 1. Bull PC
    2. Kyes S
    3. Buckee CO
    4. Montgomery J
    5. Kortok MM
    6. Newbold CI
    7. Marsh K

    (2007)

    An approach to classifying sequence tags sampled from Plasmodium falciparum var genes

    Molecular Biochemistry Parasitology 154:98–102.

    • Google Scholar
13. 1. Bull PC
    2. Lowe BS
    3. Kortok M
    4. Marsh K

    (1999)

    Antibody recognition of plasmodium falciparum erythrocyte surface antigens in Kenya: evidence for rare and prevalent variants

    Infect Immun 67:733–739.

    • Google Scholar
14. 1. Bull PC
    2. Lowe BS
    3. Kortok M
    4. Molyneux CS
    5. Newbold CI
    6. Marsh K

    (1998)

    Parasite antigens on the infected red cell surface are targets for naturally acquired immunity to malaria

    Nat Med 4:358–360.

    • Google Scholar
15. 1. Chan JA
    2. Howell KB
    3. Reiling L
    4. Ataide R
    5. Mackintosh CL
    6. Fowkes FJ

    et al. (2012)

    Targets of antibodies against Plasmodium falciparum-infected erythrocytes in malaria immunity

    J Clin Invest 122:3227–3238.

    • Google Scholar
16. 1. Chen DS
    2. Barry AE
    3. Leliwa-Syteck A
    4. Smith T
    5. Peterson I
    6. Brown SM

    et al. (2011)

    A molecular epidemiological study of var gene diversity to characterize the reservoir of Plasmodium falciparum in humans in Africa

    PLoS One 6:e16629.

    • Google Scholar
17. 1. Claessens A
    2. Adams Y
    3. Ghumra A
    4. Lindergard G
    5. Buchan C
    6. Andisi C

    et al. (2012)

    A subset of group A-like var genes encodes the malaria parasite ligands for binding to human brain endothelial cells

    Proc Natl Acad Sci USA 109:E1772–E1781.

    • Google Scholar
18. 1. Conway DJ
    2. Roper C
    3. Oduola AMJ
    4. Arnots DE
    5. Kremsner PG
    6. Grobusch MP

    et al. (1999)

    High recombination rate in natural populations of Plasmodium falciparum

    Proc Natl Acad Sci USA 96:4506–4511.

    • Google Scholar
19. 1. Deitsch KW
    2. Hviid L

    (2004)

    Variant surface antigens, virulence genes and pathogenesis of malaria

    Trends Parasitol 20:562–566.

    • Google Scholar
20. 1. Deitsch KW
    2. Lukehart SA
    3. Stringer JR

    (2009)

    Common strategies for antigenic variation by bacterial, fungal and protozoan pathogens

    Nat Rev Microbiol 7:493–503.

    • Google Scholar
21. 1. Dhingra N
    2. Jha P
    3. Sharma VP
    4. Cohen AA
    5. Jotkar RM
    6. Rodriguez PS

    et al. (2010)

    Adult and child malaria mortality in India: a nationally representative mortality survey

    Lancet 376:1768–1774.

    • Google Scholar
22. 1. Doolan DL
    2. Dobaño C
    3. Baird JK

    (2009)

    Acquired immunity to malaria

    Clin Microbiol Rev 22:13–36.

    • Google Scholar
23. 1. Ewers W

    (1972)

    Robert Koch, his work in New Guinea and his contribution to malariology

    PNG Med J 15:117–124.

    • Google Scholar
24. 1. Frank SA

    (1999)

    A model for the sequential dominance of antigenic variants in African trypanosome infections

    Proc Biol Sci 266:1397–1401.

    • Google Scholar
25. 1. Frank SA
    2. Barbour AG

    (2006)

    Within-host dynamics of antigenic variation

    Infect Genet Evol 6:141–146.

    • Google Scholar
26. 1. Freitas-Junior LH
    2. Bottius E
    3. Pirrit LA
    4. Deitsch KW
    5. Scheidig C
    6. Guinet F

    et al. (2000)

    Frequent ectopic recombination of virulence factor genes in telomeric chromosome clusters of P. falciparum

    Nature 407:1018–1022.

    • Google Scholar
27. 1. Giha HA
    2. Staalsoe T
    3. Dodoo D
    4. Roper C
    5. Satti GM
    6. Arnot DE

    et al. (2000)

    Antibodies to variable Plasmodium falciparum-infected erythrocyte surface antigens are associated with protection from novel malaria infections

    Immunol Lett 71:117–126.

    • Google Scholar
28. 1. Gillespie DT

    (1977)

    Exact stochastic simulation of coupled chemical reactions

    Journal of Physical Chemistry 81:2340–2361.

    • Google Scholar
29. 1. Gupta S
    2. Anderson RM

    (1999)

    Population structure of pathogens: the role of immune selection

    Parasitol Today 12:497–501.

    • Google Scholar
30. 1. Gupta S
    2. Day KP

    (1994a)

    A strain theory of malaria transmission

    Parasitol Today 10:476–481.

    • Google Scholar
31. 1. Gupta S
    2. Day KP

    (1994b)

    A theoretical framework for the immunoepidemiology of Plasmodium falciparum malaria

    Parasite Immunol 16:361–370.

    • Google Scholar
32. 1. Gupta S
    2. Maiden MC
    3. Feavers IM
    4. Nee S
    5. May RM
    6. Anderson RM

    (1996)

    The maintenance of strain structure in populations of recombining infectious agents

    Nat Med 2:437–442.

    • Google Scholar
33. 1. Gupta S
    2. Trenholme K
    3. Anderson RM
    4. Day KP

    (1994)

    Antigenic diversity and the transmission dynamics of Plasmodium falciparum

    Science 263:961–963.

    • Google Scholar
34. 1. Hedrick P
    2. Jain S
    3. Holden L

    (1978)

    Multilocus systems in evolution

    Evolutionary Biology 11:101–184.

    • Google Scholar
35. 1. Jiang H
    2. Li N
    3. Gopalan V
    4. Zilversmit MM
    5. Varma S
    6. Nagarajan V

    et al. (2011)

    High recombination rates and hotspots in a Plasmodium falciparum genetic cross

    Genome Biol 12:R33.

    • Google Scholar
36. 1. Jiang H
    2. Yi M
    3. Mu J
    4. Zhang L
    5. Ivens A
    6. Klimczak L

    et al. (2008)

    Detection of genome-wide polymorphisms in the AT-rich Plasmodium falciparum genome using a high-density microarray

    BMC Genomics 9:398.

    • Google Scholar
37. 1. Kyes SA
    2. Kraemer SM
    3. Smith JD

    (2007)

    Antigenic variation in Plasmodium falciparum: gene organization and regulation of the var multigene family

    Eukaryotic Cell 6:1511–1520.

    • Google Scholar
38. 1. Lavstsen T
    2. Turner L
    3. Saguti F
    4. Magistrado P
    5. Rask TS
    6. Jespersen JS

    et al. (2012)

    Plasmodium falciparum erythrocyte membrane protein 1 domain cassettes 8 and 13 are associated with severe malaria in children

    Proc Natl Acad Sci USA 109:E1791–E1800.

    • Google Scholar
39. 1. Miller LH
    2. Baruch DI
    3. Marsh K
    4. Doumbo OK

    (2002)

    The pathogenic basis of malaria

    Nature 415:673–679.

    • Google Scholar
40. 1. Miller LH
    2. Good MF
    3. Milon G

    (1994)

    Malaria pathogenesis

    Science 264:1878–1883.

    • Google Scholar
41. 1. Mu J
    2. Myers RA
    3. Jiang H
    4. Liu S
    5. Ricklefs S
    6. Waisberg M

    et al. (2010)

    Plasmodium falciparum genome-wide scans for positive selection, recombination hot spots and resistance to antimalarial drugs

    Nat Genet 42:268–271.

    • Google Scholar
42. 1. Paul RE
    2. Packer MJ
    3. Walmsley M
    4. Lagog M
    5. Ranford-Cartwright LC
    6. Paru R

    et al. (1995)

    Mating patterns in malaria parasite populations of Papua New Guinea

    Science 269:1709–1711.

    • Google Scholar
43. 1. Recker M
    2. Buckee CO
    3. Serazin A
    4. Kyes S
    5. Pinches R
    6. Christodoulou Z

    et al. (2011)

    Antigenic variation in Plasmodium falciparum malaria involves a highly structured switching pattern

    PLoS Pathog 7:e1001306.

    • Google Scholar
44. 1. Recker M
    2. Nee S
    3. Bull PC
    4. Kinyanjui S
    5. Marsh K
    6. Newbold C

    et al. (2004)

    Transient cross-reactive immune responses can orchestrate antigenic variation in malaria

    Nature 429:555–558.

    • Google Scholar
45. 1. Scherf A
    2. Lopez-Rubio JJ
    3. Riviere L

    (2008)

    Antigenic variation in Plasmodium falciparum

    Annu. Rev. Microbiol 62:445–470.

    • Google Scholar
46. 1. Selander RK
    2. Musser JM
    3. Caugant DA
    4. Gilmour MN
    5. Whittam TS

    (1987)

    Population genetics of pathogenic bacteria

    Microb Pathog 3:1–7.

    • Google Scholar
47. 1. Severins M
    2. Klinkenberg D
    3. Heesterbeck H

    (2012)

    How selection forces dictate the variant surface antigens used by malaria parasites

    J R Soc Interface 9:246–260.

    • Google Scholar
48. 1. Smith M
    2. Smith NH
    3. O'Rourke M
    4. Spratt BG

    (1993)

    How clonal are bacteria?

    Proc Natl Acad Sci USA 90:4384–4388.

    • Google Scholar
49. 1. Taylor HM
    2. Kyes SA
    3. Newbold CI

    (2000)

    Var gene diversity in Plasmodium falciparum is generated by frequent recombination events

    Mol Biochem Parasitol 110:391–397.

    • Google Scholar
50. 1. Tibayrenc M

    (1994)

    Antigenic diversity and the transmission dynamics of Plasmodium falciparum: the clonality/sexuality debate revisited

    Parasitol Today 10:456–457.

    • Google Scholar
51. 1. Tibayrenc M
    2. Ayala FJ

    (2002)

    The clonal theory of parasitic protozoa: 12 years on

    Trends Parasitol 18:405–410.

    • Google Scholar
52. 1. Tibayrenc M
    2. Kjellberg F
    3. Ayala FJ

    (1990)

    A clonal theory of parasitic protozoa: the population structures of Entamoeba, Giardia, Leishmania, Naegleria, Plasmodium, Trichomonas, and Trypanosoma and their medical and taxonomical consequences

    Proc Natl Acad Sci USA 87:2414–2418.

    • Google Scholar
53. 1. Volkman S
    2. Sabeti PC
    3. DeCaprio D
    4. Neafsey DE
    5. Schaffner SF
    6. Milner J

    et al. (2006)

    A genome-wide map of diversity in Plasmodium falciparum

    Nat Genet 39:113–119.

    • Google Scholar
54. 1. Warimwe GM
    2. Fegan G
    3. Musyoki JN
    4. Newton CR
    5. Opiyo M
    6. Githinji G

    et al. (2012) Prognostic indicators of life-threatening malaria are associated with distinct parasite variant antigen profiles

    Sci Transl Med 4:129ra145.

    https://doi.org/10.1126/scitranslmed.3003247

    • Google Scholar

Author details

1. Yael Artzy-Randrup

   Department of Ecology and Evolutionary Biology, Howard Hughes Medical Institute and the University of Michigan, Ann Arbor, United States

   Contribution

   YAR: Conception and design, Analysis and interpretation of data, Drafting or revising the article

   For correspondence

   yartzy@umich.edu

   Competing interests

   No competing interests declared.

2. Mary M Rorick

   Department of Ecology and Evolutionary Biology, Howard Hughes Medical Institute and the University of Michigan, Ann Arbor, United States

   Contribution

   MMR: Conception and design, Analysis and interpretation of data, Drafting or revising the article

   Competing interests

   No competing interests declared.

3. Karen Day

   Department of Microbiology, Division of Medical Parasitology, New York University School of Medicine, New York, United States

   Contribution

   KD: Generated and provided the expertise on the empirical data, Drafting or revising the article

   Competing interests

   No competing interests declared.

4. Donald Chen

   Department of Microbiology, Division of Medical Parasitology and Department of Medicine, Division of Infectious Disease, New York University School of Medicine, New York, United States

   Contribution

   DC: Generated and provided the expertise on the empirical data

   Competing interests

   No competing interests declared.

5. Andrew P Dobson

   1. Department of Ecology and Evolutionary Biology, Princeton University, Princeton, United States
   2. Santa Fe Institute, Santa Fe, United States

   Contribution

   APD: Conception and design, Drafting or revising the article

   Competing interests

   No competing interests declared.

6. Mercedes Pascual

   1. Department of Ecology and Evolutionary Biology, Howard Hughes Medical Institute and the University of Michigan, Ann Arbor, United States
   2. Santa Fe Institute, Santa Fe, United States

   Contribution

   MP: Conception and design, Drafting or revising the article

   Competing interests

   MP: Reviewing Editor, eLife.

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