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Curr Opin Microbiol. Author manuscript; available in PMC 2008 Aug 1.
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Advances in understanding the genetic basis of antimalarial drug resistance


The acquisition of drug resistance by Plasmodium falciparum has severely curtailed global efforts to control malaria. Our ability to define resistance has been greatly enhanced by recent advances in Plasmodium genetics and genomics. Sequencing and microarray studies have identified thousands of polymorphisms in the P. falciparum genome, and linkage disequilibrium analyses have exploited these to rapidly identify known and novel loci that influence parasite susceptibility to antimalarials such as chloroquine, quinine, and sulfadoxine-pyrimethamine. Genetic approaches have also been designed to predict determinants of in vivo resistance to new antimalarials such as the artemisinins. Transfection methodologies have defined the role of determinants including pfcrt, pfmdr1 and dhfr. This knowledge can be leveraged to develop more efficient methods of surveillance and treatment.


Malaria devastates the lives of millions of people each year. Eradication efforts based on the use of chloroquine (CQ) faltered in the 1960s, following the development of drug-resistant parasites [1]. Other antimalarial drug regimens, such as sulfadoxine-pyrimethamine (SP), have also selected for resistant parasites [2]. Recent genetics and genomics advances have paved the way for discoveries into the origins and spread of antimalarial drug resistance and the underlying molecular mechanisms. Researchers can now use data from genome sequencing projects to identify genetic regions linked to resistance phenotypes. The development of transfection and integration techniques permits researchers to test candidate genes for their contribution to resistance under controlled laboratory conditions. Genetic markers can also now be readily tracked in natural populations. These innovations can be used to predict drug efficacy in the field, with implications for public health policy. Here, we review how these new methodologies can expand and accelerate research into antimalarial drug resistance.

Genomic Studies

Using Polymorphisms to Identify Resistance Loci

The sequencing and annotation of the 23 Mb P. falciparum genome in 2002 provided a superb resource for localizing and identifying gene candidates within a particular locus [3]. Linking a specific locus with a given phenotype such as drug resistance, however, requires the ability to compare the genotypes of resistant and sensitive parasites. Rather than sequencing the entire genome of each resistant or sensitive clone, recent advances have exploited the presence of conserved polymorphisms in the genome as surrogate markers for the resistance determinant(s). Polymorphisms can include microsatellites (consisting of repeats of a short nucleotide sequence), single nucleotide polymorphisms (SNPs), or small insertions or deletions (indels).

A trio of papers, published in Nature Genetics in early 2007, moved the field substantially closer to a comprehensive polymorphism map for the P. falciparum genome [46]. These papers describe the sequencing of the entire genome, or selected regions, from multiple P. falciparum strains. The authors estimate the number of SNPs in the P. falciparum genome as ranging from about 25,000 to 50,000, corresponding to one SNP every 400 to 800 base pairs. In P. falciparum, as in humans, these SNPs do not segregate randomly. Instead they tend to cluster in “blocks,” called haplotype blocks, delimited by recombination hotspots. Association studies thus need only to track a signature set of SNP tags that identify a particular haplotype block. Studies indicate that recombination rates vary substantially between different strains of P. falciparum, with ones in Africa demonstrating the highest rates [7]. The number of polymorphisms varies for different gene classes and for different regions within chromosomes. This presumably reflects the influence of diversifying selection exerted on genes by factors such as host immunity and drug selection. [48]. High rates of recombination, such as that observed among African P. falciparum strains [7], will tend to obscure the linkage between ancestral traits. The phenomenon of drug resistance, however, is a relatively recent evolutionary event. Consequently, the use of linkage disequilibrium (LD) is ideally suited for tracking the spread of a resistance gene throughout a population.

Roper et al. [9] analyzed microsatellites surrounding alleles of the dihydrofolate reductase (dhfr) gene that confer resistance to pyrimethamine. They concluded that the most resistant form of dhfr commonly found in Africa, characterized by three point mutations, was associated with a set of related haplotypes that originated in Southeast Asia (Figure 1). Data collected subsequently by McCollum et al. [10], suggests that triple mutant parasites in Africa may have had additional independent origins. The findings of Roper et al. echoed the work of Wootton et al. [11] who suggested that CQ resistance spread in a selective sweep from Asia into Africa. That conclusion was based on the extensive LD among microsatellite markers surrounding the previously identified [12] Plasmodium falciparum chloroquine resistance transporter (pfcrt).

Figure 1
Identification of a selective sweep of mutant dhfr conferring pyrimethamine resistance from Asia to Africa

More recently, Volkman et al. [6] used SNPs, identified in their extensive sequencing project, to analyze 12 culture-adapted parasite lines with differing drug response profiles. They detected several selective sweeps associated with CQ resistance, including the previously described region on chromosome 7 containing pfcrt, as well as loci on chromosomes 5 (harboring the multidrug resistance gene homolog pfmdr1) and 11. Focusing on pyrimethamine resistant clones, they were able to detect two candidate selective sweeps on chromosomes 13 and 14, which were of particular interest because they demonstrated a stronger signal than the previously identified sweep at the dhfr locus on chromosome 4 [9, 13, 14].

Rapid Identification of Resistance Loci

A promising technique for exploiting polymorphisms was described by Kidgell et al. [15], who used a P. falciparum microarray to analyze genome variability. This array contained 25-mer probes covering approximately 50 percent of all coding regions. Polymorphisms were identified by measuring the loss of hybridization signal associated with mismatches between genomic DNA and the 25-mer probes. Gene amplifications were identified via their increased hybridization intensity. Using 14 cloned P. falciparum lines, a total of 23,653 single feature polymorphisms were identified, which included both SNPs and indels. This data set revealed a region on chromosome 7, encompassing pfcrt, that demonstrated extensive LD in the CQ resistant clones. They also identified numerous clones with a gene amplification of GTP-cyclohydrolase, an enzyme in the folate biosynthesis pathway (Figure 2). The authors hypothesized that this amplification might represent a novel mechanism of antifolate resistance. The power of this system, as noted by the authors, is that “tens of thousands of genetic markers can be both discovered and typed in as little as one day in any parasite isolate, potentially using only a few milliliters of infected patient blood.” The potential for rapidly identifying resistance loci from a sampling of clinical isolates stands in marked contrast to the classical approach of crossing a CQ resistant and sensitive clone, which was first reported in 1990 and culminated in the identification of pfcrt a full ten years later [12].

Figure 2
Identification of a genetic locus of variable copy number, postulated to alter parasite susceptibility to SP

Identifying Multiple Contributing Loci

While CQ sensitivity is primarily determined by pfcrt, for other drugs the situation is not always as clear. Multiple genes located at different loci may each contribute additively, or depend on pairwise interactions, to produce a resistant phenotype. Resistance to quinine, a drug used for over 350 years, exhibits such a phenotype. To explore this, Ferdig et al. [16] assessed 35 independent progeny, derived from the cross of a low level quinine resistant and a quinine sensitive clone, for their degree of quinine sensitivity. They then statistically analyzed microsatellite markers from each progeny to map the quantitative trait loci (QTL). This revealed five distinct regions with either additive or pairwise effects on resistance. Two peaks, including pfcrt and a locus on chromosome 13, dominated the sensitivity phenotype. After subtracting out the effect of these two loci, additional regions became apparent, including the region encompassing pfmdr1 (figure 3). Both pfcrt and pfmdr1 had previously been demonstrated by allelic exchange to contribute to quinine resistance [17,18], thereby confirming the authors’ approach. The genes at the three additional QTL remained indeterminate. The authors however, predicted that the pfnhe1 gene, located on chromosome 13 and encoding a putative sodium/hydrogen exchanger, might contribute to quinine resistance. Physiological studies support the idea that variant pfnhe1, in concert with other parasite determinants, contributes to quinine resistance [19].

Figure 3
Identification of multiple loci associated with quinine resistance using QTL mapping

Identifying Emerging Resistance Loci

Population studies that include drug resistant and sensitive parasites provide a powerful tool for identifying resistance loci. However, some circumstances will require alternative approaches to their identification, notably when predicting resistance to drugs used in new antimalarial regimens. Sidhu et al. [20] generated P. falciparum lines resistant to azithromycin in vitro. By taking a candidate gene approach, they were able to identify a mutation in the apicoplast-encoded ribosomal protein L4, which on the basis of structural models and literature on azithromycin-resistant bacteria was predicted to confer resistance.

Other groups have used rodent models to select for resistant parasites. While resistance to CQ arose through pfcrt independent mechanisms in the rodent parasite P. chabaudi [21], human and rodent Plasmodia do share in common their mode of resistance to atovaquone, mefloquine, and pyrimethamine (reviewed in [22]). Thus, on the balance, rodent malaria models have provided informative data on mechanisms of resistance. Using a process of gradual selection, Afonso et al. [23] recently generated artemisinin resistant lines in P. chabaudi (Figure 4a). This is the first confirmed in vivo report of Plasmodium resistance to artemisinin. Like Sidhu et al. [20] they used a candidate gene approach to identify possible mechanisms of artemisinin resistance, but failed to detect any alterations. An approach that is particularly well suited to identifying resistance genes in this case is Linkage Group Selection (LGS), which adapts a classical genetics approach related to “bulked segregant analysis” [24]. With both approaches, a resistant clone and a susceptible clone are intercrossed to generate progeny with mixed genotypes. With LGS, rather than cloning out individual progeny and testing them for their drug resistance phenotype, researchers subject the progeny to drug selection and then test the genotypes of the surviving population en masse. Genetic markers linked to the susceptible genotype are selectively lost from the population, creating a “selection valley” around the determinant (Figure 4b). Because researchers assess the genotype of the progeny in bulk, they must use techniques that allow them to assess the proportion of each haplotype within the sample (reviewed in [25]). Culleton et al. validated this technique as a method for identifying loci of resistance by crossing pyrimethamine resistant and sensitive lines of P. chabaudi and using LGS to identify a resistance locus including dhfr [26]. Applying LGS to the artemisinin pressured P. chabaudi line, Hunt et al. [27] identified a locus on chromosome 2 harboring a de-ubiquitinating enzyme that is currently a candidate.

Figure 4
Selection of artemisinin-resistant P. chabaudi and the LGS approach to identifying an in vivo determinant of resistance

Manipulating the P. falciparum Genome

Allelic Exchange

Recent advances in genomic analyses have enormously aided our ability to localize drug resistance loci. However, the regions identified with these techniques generally span several hundred Kb and may contain dozens to hundreds of predicted genes. The literature contains many examples of candidate genes that were predicted to account for a given phenotype but which proved wrong upon more extensive analysis. The gold standard for confirming the identity of a resistance gene involves allelic exchange. If a gene truly confers resistance, then replacing the sensitive allele with the putative resistant allele, on the sensitive background, should confer resistance. The stable transfection of P. falciparum parasites, first described in 1995, paved the way for gene integration and allelic exchange studies (reviewed in [28]). In an early application, Triglia and coworkers [29] demonstrated that dihydropteroate synthase (dhps) mutants conferred sulfadoxine resistance. Later, Sidhu et al. [17] definitively showed that pfcrt conferred resistance to CQ by replacing the pfcrt allele of a sensitive line with the pfcrt alleles of resistant lines from South America, Asia, and Africa. Reed et al. [18] also employed allelic exchange to demonstrate that allelic variants of pfmdr1 could modulate the degree of parasite susceptibility to mefloquine, quinine, halofantrine, CQ, and artemisinin. In some instances, such as for pfmdr1, in vitro resistance and clinical treatment failure have been attributed to gene amplification events [30]. Sidhu et al. [31] recently engineered the targeted disruption of one copy of pfmdr1 in a clone with duplicate copies. Their findings confirmed that pfmdr1 amplifications decrease sensitivity to mefloquine, lumefantrine, halofantrine, quinine, and artemisinin.

Gene Integration

Technical difficulties have hampered efforts to perform the reciprocal experiment, i.e. inserting extra copies of a putative resistance gene into the genome of sensitive parasites. Genomic integration happens inefficiently in P. falciparum. Researchers therefore have tended to rely on episomally replicating plasmids in order to express transgenes. This technique suffers however from the plasmids having low and variable numbers of copies in the transfected parasites. Balu et al. [32] described a technique for stably transfecting P. falciparum using transposable elements. While they report high transfection efficiencies, the transposable elements insert randomly at TTAA sites throughout the genome, making the system more suitable for mutagenesis studies than for generating stable integrants [32]. Another technique described recently by Nkrumah et al. [33], employs a mycobacterial integrase to transfect P. falciparum. This integrase catalyzes recombination between an attP sequence motif located on a transfected plasmid and an attB site located in the genome. This site has been introduced into three P. falciparum lines and additional lines can be generated using a classical homologous recombination strategy. While there are several applications for this site-specific integration technique, it should prove particularly useful for rapidly generating phenotypically and genetically homogeneous transgenic parasites that express putative drug resistance alleles. It also allows for the introduction of additional copies of genes that appear to confer resistance via copy number amplification.

Tracking Known Resistance Mutations

Allele Identification

Several papers have introduced interesting methods for evaluating the frequency of drug resistant genotypes within the context of heterogeneous pathogen populations [26, 34, 35]. Most techniques employ PCR-based amplification of SNP markers surrounding the resistance locus. The PCR product is then either sequenced using a quantitative sequencing technique or subjected to an oligonucleotide ligation assay. While not yet validated for Plasmodium, SNP microarrays have been used in other systems to determine the frequency of different alleles within mixed populations [36].

Field Applications for Molecular Markers of Resistance

McCollum et al. [37] assayed for the presence of dhfr and dhps mutations associated with SP resistance in Venezuela. They found that the mutations continue to persist in the population despite the fact that SP usage was discontinued in the region in 1998. Their results suggest that this drug combination may remain ineffective indefinitely within this region. More promising news was reported from Malawi where researchers found that the resistant form of pfcrt essentially disappeared less than a decade after CQ was replaced by SP as the first line of therapy [38]. A clinical study recently concurred that CQ sensitivity has returned to Malawi [39], confirming the predictive value of the genetic screening techniques. The presence of resistant lines in surrounding countries precludes the immediate return to CQ monotherapy in Malawi. However, the data suggests that CQ could potentially be used again in the future as part of a rotating arsenal of antimalarials with a rotation period of mere decades.


The development of CQ resistance has had a devastating effect on our ability to control malaria. No subsequent antimalarial regimen has contained malaria as successfully and cost effectively. As researchers develop and introduce new antimalarial drugs there is a dire need to ensure that we preserve their effectiveness for as long as possible. Clinical reports of treatment failure provide one estimate of resistance. Clinical studies, however, are generally costly, suffer from confounding factors such as poor compliance, and tend to focus on the predominant drugs utilized within a given country. Molecular studies tracking the presence of drug resistant determinants in the malarial population can thus provide critical data complementing clinical observations. New genetic tools give us an unprecedented ability to track new mutations as they arise, confirm their importance and mode of action in the laboratory, and measure their prevalence in the population. Public policy decisions should benefit from the development of these new tools to ensure that malarial eradication programs are as effective as possible.


The research of David A. Fidock, Ph.D., is supported in part by the Investigators in Pathogenesis of Infectious Disease Award from the Burroughs Wellcome Fund and by the NIH (R01 AI50234). Eric H. Ekland, Ph.D., is a Hoffman-LaRoche Fellow of the Life Sciences Research Foundation. We extend our gracious thanks to Tim Anderson, Elizabeth Winzeler, Michael Ferdig, Paul Hunt and Richard Carter for providing figures that were adapted for this review.


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References and recommended reading

Papers of particular interest, published within the period of review, have been highlighted as:

● of special interest

● ● of outstanding interest

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