* Southwest Foundation for Biomedical Research, San Antonio, Texas
Shoklo Malaria Research Unit (SMRU), Mae Sot, Tak, Thailand
Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand
Faculty of Medicine, National University of Laos, Vientiane, Laos
|| Nuffield Department of Medicine, John Radcliffe Hospital, Oxford, UK
¶ Wellcome Trust-Mahosot-Oxford Tropical Medicine Research Collaboration, Mahosot Hospital, Vientiane, Laos
# Epicentre (Médecins Sans Frontières-France), Paris, France
** Artsen Zonder Grenzen, Médecins Sans Frontières-Holland, Yangon, Myanmar
Hospital for Tropical Diseases, Cho Quan Hospital, Ho Chi Minh City, Vietnam
Correspondence: E-mail: tanderso{at}darwin.sfbr.org.
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Abstract |
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Key Words: Expected heterozygosity dihydrofolate reductase pyrimethamine microsatellite Plasmodium falciparum
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Introduction |
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Here, we investigate effects of strong recent selection by the antimalarial drug pyrimethamine on genomic variation on chromosome 4 of P. falciparum. This compound is the dominant component (Watkins et al. 1997) of pyrimethamine/sulfadoxine (PS) (FansidarTM [Roche]), an inexpensive drug that is used in countries where resistance has rendered chloroquine (CQ) ineffective. Pyrimethamine is a competitive inhibitor of dihydrofolate reductase (dhfr), displacing the natural folate substrate. The genetic basis of resistance to pyrimethamine is well understood (Plowe, Kublin, and Doumbo 1998). Specific point mutations in the active site of parasite dhfr (chromosome 4) alter the binding of pyrimethamine to the enzyme's active site. These mutations appear sequentially in treated populations, with the SerAsn mutation at codon 108 appearing first, followed by Asn
Ile (codon 51) or Cys
Arg (codon 59), and finally Ile
Leu (codon 164) (Plowe, Kublin, and Doumbo 1998). Single or doubly mutant dhfr have increased parasite clearance times and higher posttreatment gametocyte carriage than wild-type parasites (Mendez et al. 2002), and parasites with three or four resistance mutations are refractory to treatment (Plowe, Kublin, and Doumbo 1998). PS was introduced as the first-line antimalarial treatment on the Thailand-Myanmar border in the mid-1970s. Resistance spread to fixation in approximately 6 years (White 1992).
Since resistance spreads extremely rapidly after pyrimethamine treatment (Clyde and Shute 1957; Doumbo et al. 2000), and the mutations involved can be selected readily in the laboratory (Paget-McNicol and Saul 2001), it is generally assumed that dhfr mutations underlying resistance evolve multiple times in nature (but see Cortese et al. [2002]). In fact, given that infected people contain 1010 to 1012 parasites, and key point mutations in dhfr conferring resistance to pyrimethamine occur at frequencies as high as 2.5 x 10-9 per parasite replication in the laboratory (Paget-McNicol and Saul 2001), we might expect such mutations to arise independently in every treated malaria patient. In this case, we would expect resistant dhfr alleles to be associated with different alleles at flanking microsatellite loci and to see little evidence for diminished variation around dhfr (Doumbo et al. 2000). To test this model of resistance evolution and to investigate the use of association-based approaches to detect regions of the genome under positive selection, we examined microsatellite variation around dhfr in P. falciparum from five countries in Southeast (SE) Asia. Surprisingly, we found minimal variation around resistant dhfr alleles, suggesting a single origin of SE Asian pyrimethamine resistance. Furthermore, the pattern of reduced variation fits well with models based on independent measures of recombination, mutation, and selection intensity and provides an empirical demonstration of the potential to detect selected genes from patterns of genomic variation.
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Materials and Methods |
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DNA Preparation
Parasite DNA from Mawker-Thai was prepared from venous blood by phenol/chloroform extraction of whole blood, after removal of buffy coats. Two nanograms of DNA were used in each PCR reaction. In the case of finger-prick blood samples (50 µl of blood adsorbed and dried on a piece of filter paper) collected from other SE Asian locations, DNA was prepared from 3 mm discs removed from each blood spot using a sterile hole-punch using the Generation Card Capture Kit (Gentra Systems). Before extraction, we soaked 3 mm disks in 150 µl of DNA elution solution at 4°C overnight. Otherwise, we followed the extraction protocol suggested in the kit instructions.
Microsatellite Genotyping Methods
Primer sequences, amplification conditions, and positions of microsatellite markers in the P. falciparum chromosome 4 genome sequence are listed in table S1A of Supplementary Material online. Additional microsatellites genotyped on other chromosomes were as follows: chromosome 1: C1M38, C1M39, C1M11, C1M10, C1M4, C1M13; chromosome 2: C2M21, C2M19, C2M30, C2M33, C2M27, d_4212, d_4217, d_4227, d_4229, d_4247, d_4254, d_4265, C2M25, C2M12, C2M16, C2M17, C2M1, C2M3, C2M4, C2M6, C2M8; chromosome 3: C3M20, C3M27, C3M40, C3M86, B7M117, C3M42, C3M33, C3M81, C3M17, C3M47, C3M54, C3M43, C3M45; chromosome 12: C12M96, C12M89, C12M62, Y588M4, C12M114, C12M64, C12M105, Y588M1, C12M115, C12M81, C14M19, C12M60, Y69M2, C12M63, C12M44, Y336M1. Primer sequences and repeat array length for these loci are shown in table S1B of Supplementary Material online. Since repeat motif and array length are known to influence levels of microsatellite variation in P. falciparum (Anderson et al. 2000b), we used only microsatellite sequences containing uninterrupted arrays of greater than eight dinucleotide repeats in parasite line 3D7, for which full genome sequence data is available. Fluorescent end-labeled primers were purchased from Applied Biosystems, and PCR products were separated on an ABI 3100 capillary sequencer and length was scored using the Genotyper software. DNA from clone 3D7, for which the genome sequence is available, was run as a size control for all loci.
dhfr Genotyping Methods
Genotyping was performed by primer extension using the ABI PRISM SNaPshotTM Multiplex Kit (Applied Biosystems), and the products of the SNaPshot reactions were scored on an ABI 3100 capillary sequencer using GENESCAN and GENOTYPER software (Nair et al. 2002). We genotyped dhfr both in high-quality parasite DNA prepared from venous blood samples from Mawker-Thai and from DNA extracted from finger-prick blood samples from 10 additional locations. For the finger-prick blood samples, we used a seminested PCR strategy to amplify the template used in the SNaPshot reactions. We used the primer 5'-TTTATATTTTCTCCTTTTTA-3', combined with the reverse primer dhfr-r to preamplify DNA. Otherwise, genotyping was as described in Nair et al. (2002). We did not measure in vitro drug resistance phenotypes in this study. However, since the relationship between mutations within dhfr and both in vitro and in vivo resistance to pyrimethamine is well established (Plowe et al. 1997; Sirawaraporn et al. 1997; Plowe, Kublin, and Doumbo 1998), we refer to alleles carrying mutations in the five residues listed above as resistant dhfr alleles, whereas alleles with no mutations are referred to as sensitive dhfr alleles. To describe the different resistant dhfr alleles we indicate the numbers of mutations present relative to the sensitive dhfr allele. Throughout we write resistant and sensitive dhfr alleles in italics to emphasize that these are genotypic descriptions rather than empirically determined phenotypes.
Statistical Analysis
We measured expected heterozygosity (He) at each microsatellite locus as He = [n/(n - 1)][1 - ], where n is the number of infections sampled and pi is the frequency of the ith allele. We estimated the variance of He using a Taylor's series expansion and retaining second-order (covariance) terms, and approximate 95% confidence intervals were constructed assuming large-sample normality of the He estimates. We used He in preference to variance in repeat number because P. falciparum microsatellites frequently contain indels in the flanking regions or have complex repeat structure (Anderson et al. 2000b). Hence, inference of number of repeats from PCR product length results in frequent errors. To investigate the influence of selection on allele distributions, we used coalescent simulation (implemented using the program Bottleneck (Cornuet and Luikart 1996)) to predict He given the observed number of alleles under the assumption of mutation-drift equilibrium. This was done using both infinite alleles (IMM) and stepwise mutation models (SMM) of microsatellite mutation to give a range of expectations bounded by these two extreme mutation models. Differences between observed and predicted He were assessed using Wilcoxon's tests (Cornuet and Luikart 1996). We used F statistics to investigate levels of pairwise differentiation between parasite populations using five-amino-acid dhfr alleles and individual amino acid mutations within dhfr. Significance of FST was assessed by randomly permuting observed data sets 105 times using the program FSTAT (Goudet 2000), and table-wide significance levels were adjusted for multiple testing. We compared linkage disequilibrium around both sensitive and resistant dhfr alleles using extended haplotype homozygosity (EHH) (Sabeti et al. 2002), where EHH at a distance x from dhfr is defined as the probability that two randomly chosen haplotypes are homozygous for all microsatellites for a distance x from dhfr. We computed standard errors and 95% confidence intervals for this measure under a binomial model.
Modeling of Selective Sweep in Mawker-Thai
We modeled the pattern of reduced microsatellite variation around dhfr using minor modifications of the elegant theoretical framework described in (Wiehe 1998). The reduction in variation (He) of a microsatellite marker after a selective event (V(t1)) relative to variation preceding the selection (V(t0)) was estimated as:
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We used a recombination rate of 5.88 x 10-4 Morgans/kb/generation (Su et al. 1999) and the microsatellite mutation rate of 1.59 x 10-4 mutations/locus/generation (95% confidence interval: 6.98 x 10-5, 3.7 x 10-4) (Anderson et al. 2000a). Both of these parameters were measured using the genetic cross between parasite lines Hb3 and Dd2 (Su et al. 1999). In P. falciparum, self-fertilization frequently occurs, resulting in high levels of inbreeding and reducing the effective rate of recombination (Babiker et al. 1994; Paul et al. 1995). We estimated inbreeding coefficients (F) in the parasite population from Mawker-Thai from observed levels of multiple-clone infection (measured from the microsatellite genotype data from chromosomes 1, 2, 3, and 12). Given that approximately 60% of infections contain single clones, the minimum value of F is 0.6. However, since P. falciparum is a hermaphrodite, a proportion of the gamete fusions that occur in mosquitoes feeding on people containing multiple clone infections are expected to be self-fertilizations, and real values of F will be 0.6 to 0.9 in this population. The effective rate of recombination (r') is given by r' = r(1 - F) where r is the recombination rate and F is the inbreeding coefficient (Hill and Babiker 1995; Conway et al. 1999). We estimated e to be 1/Ne (i.e., initially there was only one resistant allele in the population), where Ne was estimated to be 103 to 105 (Anderson et al. 2000a). Selection coefficients (s) were estimated from the observed decline of clinical treatment success using PS in Thailand (Bunnag and Harinasuta 1987; White 1992). We inferred frequencies of resistant (p) and sensitive alleles (q) by assuming that the frequency of treatment failures is proportional to p and that q = 1 - p. We plotted ln(p/q) against time in generations and measured the slope to obtain s (Hartl and Dykhuizen 1981).
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Results and Discussion |
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Width of the Selective Sweep
The valley of reduced variation around dhfr spans approximately 100 kb in the Mawker-Thai parasite population (fig. 1). The valley of reduced variation around resistant dhfr alleles appears not to be symmetrical as suggested by simulation studies (Kim and Stephan 2002). dhfr diversity is restored to background levels 50 kb from the 3' end of the gene, whereas to the 5' markers 58 kb distant still show reduced variation. However, denser marker spacing and information on baseline levels of marker heterozygosity are needed to better characterize sweep asymmetry. Another selective sweep has recently been documented around the chloroquine resistance transporter (pfcrt) locus on chromosome 7 of P. falciparum (Wootton et al. 2002). The valley of reduced variation around pfcrt is larger (>200 kb) than that observed around dhfr. Heterogeneity in recombination rate across the genome may explain some of the difference observed. Time since selection is also likely to be important. The pyrimethamine selection in Thailand was imposed between 1976 and 1989 (approximately 90 parasite generations ago, assuming a 2-month generation time), whereas many of the African samples surveyed in Wootton et al. (2002) are from populations in which CQ alleles conferring resistance are currently in the process of spreading. Hence, there has been less time for recombination to break down associations between selected point mutations and flanking microsatellites in the case of the CQ sweep. Regardless of the reasons for the differences between the two sweeps, the valleys of reduced variation are quite large in both the CQ (>200 kb) and pyrimethamine (100 kb) associated selective events. If we assume similar rates of recombination across the genome, markers spaced at 50 kb (
every 3 cM) intervals should be sufficient to identify regions in which selection has removed variation around loci underlying resistance to other important antimalarial drugs. Hence antimalarial drug resistance appears to be an unusual trait in that genes may be located by genome-wide association with a relatively low density of genetic markers (Anderson et al. 2000a; Wootton et al. 2002). Encouraged by these empirical results from regions around known drug resistance loci, we are currently conducting a genome scan to locate unknown genes underlying resistance to other important antimalarial drugs such as mefloquine, quinine, and artemisinin. In this case, we will compare heterozygosity, allelic skew, and LD in parasites isolates showing high and low levels of in vitro drug resistance to locate genome regions under drug selection (Wootton et al. 2002).
We expect that both recombination rate and selection intensity will play key roles in determining the size of genomic regions affected by selection. Microsatellite mutation is expected to be of lesser importance because the selective events have occurred very recently. The recombination rate of P. falciparum has been measured in a genetic cross and is approximately 50 times greater than in the human genome. At first sight it is surprising that variation is reduced over such a distance from dhfr. The high rate of inbreeding in P. falciparum populations may help to explain this (Babiker et al. 1994; Paul et al. 1995; Anderson et al. 2000a). Malaria parasites are hermaphroditic protozoans: asexual mitotic division of haploid stages occurs in the bloodstream, and fusion of gametes and meiosis occurs in the mosquito midgut. Hence, if a mosquito feeds on an infected person containing male and female sexual stages of a single genotype, the meiotic products will result from self-fertilization. The level of inbreeding is therefore dependent on the proportion of people bearing parasites of multiple clones and varies considerably between populations, depending on levels of transmission (Anderson et al. 2000a). In the Mawker-Thai population approximately 60% of patients carried infections consisting of a single clone, suggesting a minimal inbreeding coefficient of approximately 0.6. In reality, the inbreeding coefficient in this location is probably much higher (0.6 to 0.9) because a proportion of matings that occur in mosquitoes feeding on multiply infected patients will involve self-fertilization. As a consequence of inbreeding, the effective recombination rate is considerably lower than the actual recombination rate measured in the genetic cross.
The variation in levels of inbreeding (and effective recombination rate) among P. falciparum populations may provide a valuable tool for genetic mapping and suggests a two-step strategy. First, a genome screen could be conducted in a locality where parasites are highly inbred and have a low effective recombination rate. In such a situation, selection will purge variation across a large region of the genome, allowing identification of the chromosomal regions involved with relatively low-density marker coverage. To fine-scale map the genes involved, these genomic regions could be then investigated in more detail in parasite populations with low levels of inbreeding (higher effective recombination rate). In such populations, the valleys of reduced variation are expected to be much narrower, providing more precise localization of genes targeted by selection.
The strength of selection will also determine the size of genomic regions affected by drug selection. In the selection event driven by CQ treatment on chromosome 7, selection intensity was estimated indirectly from the pattern of LD between markers flanking pfcrt (Wootton et al. 2002). Values of between 0.1 and 0.7 were consistent with the patterns observed. We were able to estimate selection intensity for resistant dhfr alleles more directly using the decline in therapeutic efficacy of PS in Thailand. A selection coefficient of 0.11 is consistent with this data if we assume a 2-month generation time for P. falciparum in Thailand (fig. 5). The selection coefficient driving the spread of resistant dhfr alleles is high. However, coefficients of comparable magnitude have been recorded in many studies of adaptive traits in natural populations of other organisms (Endler 1986). As such, drug selection of malaria parasites may also provide a useful model system for understanding genomic effects of hitchhiking in natural systems where the history of selection is poorly known (Harr, Kauer, and Schlotterer 2002).
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Implications for Malaria Parasite Biology
Removal of variation in genes linked to drug resistance loci is likely to have important influence on malaria parasite biology. Many antigen genes in P. falciparum show extreme levels of nonsynonymous mutation, and allelic distributions characteristic of balancing or frequency-dependent selection (Hughes and Hughes 1995; Polley and Conway 2001; Volkman et al. 2002). Purging of variation from antigens due to directional selection on neighboring drug resistance genes may therefore reduce mean fitness of parasites in the face of immune selection (Hill-Robertson interference [Hill and Robertson 1966]). In the approximately 100-kb region around dhfr in which variation is reduced, there are 22 predicted genes that are likely to have reduced variation resulting from selection on dhfr. In the countries studied here, the effects of selection are localized to a region encompassing approximately 8% of chromosome 4. In parasite populations where self-fertilization predominates and levels of recombination are consequently much lower such as those in South America (Anderson et al. 2000a), drug selection may result in genome-wide reduction in genome variation (fig. 6). Indeed, recurrent selection by chloroquine and PS (FansidarTM) may contribute to the genome-wide reduction in variation observed in South American parasites.
PS treatment costs are low and it is increasingly being used in Africa in regions where chloroquine has ceased to be useful. Furthermore, in SE Asian countries such as Myanmar and Laos, PS is still sometimes used. A problem with PS is that it does not kill gametocytes, the sexual stages required for mosquito transmission. This is thought to promote rapid spread of parasites bearing resistance mutations (Mendez et al. 2002). Combinations of PS with artemisinin derivatives that have strong gametocytocidal action (Price et al. 1996) have been suggested as a means to prolong the life of PS, and large-scale clinical trials are currently taking place in Africa to test these combinations (Olliaro, Taylor, and Rigal 2001). The surprising finding that extant resistant dhfr alleles have a single common ancestor in the SE Asian countries surveyed suggests that viable dhfr mutants capable of spread may evolve rather rarely. If so, treatment using PS in combination with "transmission blocking" drugs such as artemisinin may be especially valuable in reducing the rate of resistance evolution (White et al. 1999). It will be of great interest to see if similar patterns of dhfr resistance evolution are observed in sub-Saharan Africa, where the consequences of pyrimethamine resistance are likely to be the most severe.
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Note Added in Proof |
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Supplementary Material |
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Acknowledgements |
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Footnotes |
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