A pivot mutation impedes reverse evolution across an adaptive landscape for drug resistance in Plasmodium vivax
© Ogbunugafor and Hartl. 2016
Received: 12 September 2015
Accepted: 10 January 2016
Published: 25 January 2016
The study of reverse evolution from resistant to susceptible phenotypes can reveal constraints on biological evolution, a topic for which evolutionary theory has relatively few general principles. The public health catastrophe of antimicrobial resistance in malaria has brought these constraints on evolution into a practical realm, with one proposed solution: withdrawing anti-malarial medication use in high resistance settings, built on the assumption that reverse evolution occurs readily enough that populations of pathogens may revert to their susceptible states. While past studies have suggested limits to reverse evolution, there have been few attempts to properly dissect its mechanistic constraints.
Growth rates were determined from empirical data on the growth and resistance from a set of combinatorially complete set of mutants of a resistance protein (dihydrofolate reductase) in Plasmodium vivax, to construct reverse evolution trajectories. The fitness effects of individual mutations were calculated as a function of drug environment, revealing the magnitude of epistatic interactions between mutations and genetic backgrounds. Evolution across the landscape was simulated in two settings: starting from the population fixed for the quadruple mutant, and from a polymorphic population evenly distributed between double mutants.
A single mutation of large effect (S117N) serves as a pivot point for evolution to high resistance regions of the landscape. Through epistatic interactions with other mutations, this pivot creates an epistatic ratchet against reverse evolution towards the wild type ancestor, even in environments where the wild type is the most fit of all genotypes. This pivot mutation underlies the directional bias in evolution across the landscape, where evolution towards the ancestor is precluded across all examined drug concentrations from various starting points in the landscape.
The presence of pivot mutations can dictate dynamics of evolution across adaptive landscape through epistatic interactions within a protein, leaving a population trapped on local fitness peaks in an adaptive landscape, unable to locate ancestral genotypes. This irreversibility suggests that the structure of an adaptive landscape for a resistance protein should be understood before considering resistance management strategies. This proposed mechanism for constraints on reverse evolution corroborates evidence from the field indicating that phenotypic reversal often occurs via compensatory mutation at sites independent of those associated with the forward evolution of resistance. Because of this, molecular methods that identify resistance patterns via single SNPs in resistance-associated markers might be missing signals for resistance and compensatory mutation throughout the genome. In these settings, whole genome sequencing efforts should be used to identify resistance patterns, and will likely reveal a more complicated genomic signature for resistance and susceptibility, especially in settings where anti-malarial medications have been used intermittently. Lastly, the findings suggest that, given their role in dictating the dynamics of evolution across the landscape, pivot mutations might serve as future targets for therapy.
In recent years, experts have introduced several new perspectives on the management of drug resistance in malaria and other infectious diseases. These include criticisms of the aggressive use of therapeutic agents [1–3], the broader encouragement of more responsible use of antimicrobials [4–7] and the exploration of drug cycling strategies [8–13]. Drug stewardship programmes have been successful in several settings, and declines in drug resistance have been observed following changes in antibiotic use [14, 15]. There are other settings, however, where more careful use of antibiotics was not so effective, microbial populations remaining highly resistant even after removal of drug [16–19], an outcome with serious health and financial consequences. Pathogens might remain resistant to antimicrobials even after their removal for several reasons, among them compensatory mutations at other loci that counteract any fitness cost of drug resistance [18, 20, 21]. While compensatory mutations at other loci underlie many long-term fixation patterns in clinical infections, it is not fully understood why compensatory mutation is necessary, rather than the evolutionary undoing of mutations that ‘fixed’ in the process of forward resistance evolution.
The lack of a coherent understanding of reverse evolution is partly due to conceptual ambiguity: the term ‘reverse evolution’ is misleading, as it implies directionality in a process (Darwinian evolution) that is near-sighted and agnostic with regard to goal. This has spawned similarly dubious concepts, such as Dollo’s Law, asserting that evolution is intrinsically irreversible  because it would require two independent, low-probability events, occurring along the same pathway, but in opposite order . Consequently, few studies have examined the molecular pathways through which reverse evolution across an antimicrobial resistance adaptive landscape is likely to occur. One such study of cefotaxime/pipericillin resistance in Escherichia coli highlighted that epistasis may wire ‘hidden randomness’ into adaptive landscapes that prevents reverse evolution . A landmark study of reverse evolution in the vertebrate glucocorticoid receptor identified a combination of five mutations, labelled an ‘epistatic ratchet’, that precludes evolution towards the ancestral state . Studies of this sort are even less frequent as they pertain to the problem of malaria drug resistance, which remains the cause of a global pandemic complicated by widespread resistance .
Approaches utilizing all possible combinations of a suite of mutations associated with resistance can help to resolve the likelihood of adaptive evolution occurring through certain pathways [27–31]. This study uses empirical data from a combinatorial analysis of Plasmodium vivax dihydrofolate reducatase (DHFR) mutants, evolutionary theory, and individual-based simulations to uncover factors that affect the likelihood of reverse evolution across pyrimethamine (PYR) concentrations. In doing so, it proposes a method for determining whether reverse evolution will occur across an adaptive landscape. By measuring the fitness effects of individual mutations, the study uncovers the existence of a mutational pivot with potentiated genotype-by-environment (G × E) effects that may direct evolution towards or constrain evolution from areas of the landscape with high resistance or fitness. In addition, these mutations attract interactions with other mutation sites, creating an epistatic ratchet, limiting reverse evolution across a landscape. Lastly, the study discusses the implications of these findings for evolutionary theory, molecular epidemiology and in two clinically relevant contexts: (1) the use of existing drugs for resistance management in malaria, and, (2) the rational design of drugs that might target certain amino acid residues of a resistance determinant.
System of study and growth rates
The study modelled empirical growth and resistance (IC50) data developed in a prior study  in strains of transgenic Saccharomyces cerevisiae carrying P. vivax DHFR containing a set of four mutations orthologous to the resistance mutations found in Plasmodium falciparum , in all combinations, several of which have been isolated from field settings [33–46]. The combinatorial approach is an effective way to create empirical adaptive landscapes for final phenotypes when all intermediate genotypes can be reconstructed in the protein of interest, often in a transgenic setting (Saccharomyces cerevisiae in this case). Because of this, this approach is not meant to be a literal analogue for drug treatment, but does effectively test important properties of protein evolution. Results derived from prior studies of this kind have recapitulated findings from the field [29, 32], reaffirming that this approach has utility in understanding the evolution of drug resistance.
Fitness effect of mutations
This was calculated for each of the four mutations across a range of drug concentrations (between 0 and approximately 8000 μM). After calculating the fitness effect of mutations across drug concentrations, the average fitness of all whole alleles carrying each of the four mutations (1***, 1***, **1*, ***1) was measured and compared using ANOVA to determine any significant differences between mutant classes (Additional file 4).
Embedded in Fig. 3a is epistasis, or the “surprise at the phenotype when mutations are combined, given the constituent mutations’ individual effects” . Epistasis was measured by calculating the standard deviation of the total fitness effects for a mutation at a given concentration. These values were plotted in Additional file 5.
Simulations of evolution
Having identified the S117N mutation as having the greatest G x E effect, computer simulations were used to test whether these (or other) effects constrain evolution in certain directions. SimuPop was used as the simulation convention, an individual-based, Wright–Fisher, forward-time simulation model  similar to that described by Jiang et al. . In this model, generations are discrete (non-overlapping), with an effective population size of 10,000. Mutation rates were determined by the relative rate matrix for P. vivax computed from data in Neafsey et al. . For the purposes of converting the relative substitution rate matrix to a more realistic per-generation rate matrix, mutation rates were divided by 103 and then converted into amino acid substitution rates using the sum of substitution rates of nucleotides responsible for drug resistance in P. vivax DHFR.
A population fixed for the most resistant (1111) allele for 1000 generations across a range of drug concentrations (~3000, ~400, ~55, ~7 μM). This allows one to observe the general dynamics of reverse evolution, and test whether reversion towards the wild type (0000) ever occurs. The most obvious prediction would be that at the extremely high PYR concentration (~3000 μM), the population should remain trapped on the 1111 allele, as it is the most resistant allele in the set and has the highest growth rate at the highest concentration (Fig. 1b, c, Additional file 3).
A population composed equally of all six double mutants (1100, 1010, 1001, 0011, 0101, 0110), evolving in the absence of drug. Because the double mutants are in the centre of the landscape (in terms of Hamming distance between 0000 and 1111), simulations with them as a starting point would uncover any intrinsic landscape bias towards forward or reverse evolution.
The structure of reverse evolution trajectories
Analysis of the fitness effects of mutations across drug concentrations reveals a single mutation of uncommonly large positive effect
Simulations of evolution
Summary of simulations of evolution in two schemes
(a) Reverse evolution simulation summary
Starting conditions (drug and allele)
~3000 μM PYR (peak mutant = 1111)
1111 → 0111
~400 μM PYR (peak mutant = 1110)
1111 → 1110
1111 → 0111
~55 μM PYR (peak mutant = 1110)
1111 → 1110
1111 → 0111 → 0110
~7 μM PYR (peak mutant = 1110)
1111 → 1110
1111 → 1101
No drug (peak mutant = 0000)
1111 → 1110
1111 → 1101
(b) Double mutants, no drug simulation summary
Equally distributed: 1100, 1001, 1010, 0011, 0101, 1110
Polymorphic → 1110
Polymorphic → 0110
Polymorphic → 1101
Simulations demonstrate that populations fixed for the 1111 allele do not undergo reverse evolution to the ancestral allele at any drug concentration (including the drugless environment) even after a thousand generations, but are trapped at the 1110 triple mutant fitness peak at most concentrations, with a small fraction of simulations leading to the 0111 triple mutant (Fig. 4; Table 1). This finding reflects the fact that the 1110 triple mutant has a high growth rate even in the drugless environment, superior to all of its double- and single-mutant neighbours. Although the 0110, 1110 and 0111 alleles (all of which contain the S117N mutation) have a growth rate lower than the ancestor (0000) in the no-drug environment, evolving populations are unable to cross the single-mutant (1000, 0100, 0010, 0001) valley necessary to reach the ancestral genotype, precluding reverse evolution (Fig. 5). This is because the combination of the S117N mutation and the second-site mutation, S58R (*1**) has properties of an epistatic ratchet  that restricts reverse evolution: it is able to reproduce well enough at both higher drug concentrations and in the drugless environment (Fig. 1b) to limit crossing the single mutation (0010 and 0100 in this case) valley necessary to reach the 0000 absolute fitness peak in the drugless environment.
While irreversibility across an adaptive landscape for antimicrobial resistance has been observed in many pathogen types, this question has been relatively unexplored in malarial parasites and in particular, as it pertains to a mechanism underlying this constraint. In the case of malaria, several past studies from the field, involving both chloroquine and pyrimethamine, support the assertion that reverse evolution is improbable: in one instance, a population of P. falciparum resistant to chloroquine reverted to wild type only after replacement with a migrant population composed of ancestral susceptible genotypes (rather than through de novo mutation and selection) . In another, a population of P. falciparum resistant to pyrimethamine compensated through copy number variation in GTP cyclohydrolase in lieu of reversing the mutations already fixed in DHFR .
Although the study focused on P. vivax DHFR, it provides a conceptual basis for irreversibility in other resistance proteins. The findings from the field, in combination with these results, imply that modern whole-genome sequencing efforts will reveal a more complex genomic signature of resistance and reversal in settings where antimicrobial use has waxed and waned. This has implications for the practice of molecular epidemiology: while sequencing selected SNPs in resistance determinants might be sufficient for identifying resistance alleles in settings where populations of pathogen are ‘forward’ evolving resistance, the genomic picture is likely more complicated in reverse. The results, corroborated by findings from the field, suggest that re-evolution of the susceptible phenotype (without the growth defects of the more resistant phenotypes) is likely to occur either through the introduction of susceptible migrant genotypes from elsewhere or compensatory mutations at sites other than the ones originally arising during resistance evolution [57, 58].
This study dissected barriers to reverse evolution from the most resistant genotype (1111) toward the most susceptible (0000) across an adaptive landscape for drug resistance mediated by DHFR in P. vivax. Among four amino acid replacements resulting in pyrimethamine resistance, a single site, S117N (**1*) had a strong effect on the fitness of alleles in the landscape across a breadth of drug concentrations. At high drug concentrations, double and triple mutants containing the S117N mutation, and in particular those in combination with the second site mutation S58R (0110 and 1110, for example) have a reproductive advantage across most drug environments. More specifically, while these higher order alleles have lower fitness than the 0000 ancestral allele in the absence of drug, they have substantially higher fitness than the single mutant neighbours that separate the higher-order mutants carrying the S117N (**1*) mutation (1000, 0100, 0010, 0001), which explains the low likelihood of reverse evolution across this drug resistance adaptive landscape.
Simulations of evolution across the landscape demonstrate the consequences of genotype-by-environment interactions involving S117N: whereas past studies have shown that forward evolution from the 0000 ancestor to an absolute fitness peak occurs readily at drug concentrations greater than 0 , evolution starting from the population fixed for the 1111 quadruple mutant becomes trapped at the 1110 triple mutant local fitness peak, even in the drugless environment (Fig. 4). Even more, when the landscape starts with a population distributed equally between the double mutants (the centre of the landscape; 0011, 0101, 1001, 1010, 0110, 1100), the evolutionary dynamics are still driven by the S117N site (**1*), usually resulting in fixation of the 1110 triple mutant (Fig. 5; Table 1). In this sense, the S117N mutation serves as a pivot point for mutation: its arrival provides a bridge to high fitness areas of the landscape that are trapped onto local peaks through their interaction with other mutations, unable to move to other areas of the landscape.
These findings support the existence of epistatic ratchets that inhibit reverse evolution towards ancestral states, such as that observed in the evolution of the vertebrate glucocorticoid receptor . While the pivotal S117N mutation creates a ratchet through epistatic interactions, its average effect alone (across all genetic backgrounds and across environments) is larger than that of the other sites (1***, * 1**, ***1), indicating that its fitness effects are not limited to singular genetic backgrounds or certain environments. In this sense, S117N opens evolutionary ‘forks in the road’ towards higher mutation regions of the landscape (double mutants, triple mutants and the quadruple mutant), serving as the starting material for the epistatic ratchets that ultimately prevent reverse evolution.
Other than the implications for molecular epidemiology discussed above, these findings are most relevant to debates surrounding best practices for antimicrobial resistance management. The notion that ceasing use of antimicrobials is a viable strategy for decreasing resistance is based, in part, on the assumption that reverse evolution can occur across a landscape because of the fitness cost of resistance. The results suggest that such strategies may not be generally valid, and they should be tailored to the nature of the actual adaptive landscape and its G × E interactions affecting the accessible trajectories towards resistance and susceptibility. Please note that these comments apply to stepwise reverse evolution in a situation where a derived resistance allele (1111 in this manuscript) is fixed. Alternatively, if a population retains the ancestral allele at low frequency, it can increase in frequency in the absence of drug. This latter scenario is not stepwise evolution, however, but canonical selection on standing genetic variation, a different population genetic context than the one simulated in this study.
Lastly, and most provocatively, the identification of the S117N pivot mutation suggests a possible strategy for identifying targets for chemotherapeutic intervention: if a single mutation is a pivot point to large fitness effects (as found in this study), it might be an ideal drug target. Compounds that perturb the interaction between these pivot residues and others might have a destabilizing effect on protein structure or function, and diminish the evolutionary potential of alleles carrying the resistance determinant. Because this strategy would target the ability of a protein to reach high fitness areas of adaptive landscapes, it would constitute a strategy directed against the evolvability of the pathogen, an unexplored avenue for the treatment of microbial pathogens.
CBO and DLH designed the study. CBO carried out the calculations and computer simulations. CBO analysed the results. CBO and DLH wrote the manuscript. Both authors read and approved the final manuscript.
The authors would like to thank Pan Pan Jiang for discussions on the dataset, and Russell Corbett-Detig for his input on the simulations. This work was supported by NIH Grant AI106734 to DLH. Support for CBO comes from the George Washington Henderson Fellowship at the University of Vermont and by the Ford Foundation Postdoctoral Fellowship. The authors thank C Scott Wylie, Daniel Weinreich, Seth Rakoff-Nahoum and Rachel Rutishauser for helpful discussions.
The authors declare that they have no competing interests.
Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
- Read AF, Day T, Huijben S. The evolution of drug resistance and the curious orthodoxy of aggressive chemotherapy. Proc Natl Acad Sci USA. 2011;108(Suppl 2):10871–7.PubMed CentralView ArticlePubMedGoogle Scholar
- Huijben S, Bell AS, Sim DG, Tomasello D, Mideo N, Day T, et al. Aggressive chemotherapy and the selection of drug resistant pathogens. PLoS Pathog. 2013;9:e1003578.PubMed CentralView ArticlePubMedGoogle Scholar
- Kouyos RD, Metcalf CJE, Birger R, Klein EY, Abel zur Wiesch P, Ankomah P, et al. The path of least resistance: aggressive or moderate treatment? Proc Biol Sci. 2014;281:20140566.PubMed CentralView ArticlePubMedGoogle Scholar
- Carlet J, Jarlier V, Harbarth S, Voss A, Goossens H, Pittet D, et al. Ready for a world without antibiotics? The pensières antibiotic resistance call to action. Antimicrob Resist Infect Control. 2012;1:11.PubMed CentralView ArticlePubMedGoogle Scholar
- Kaki R, Elligsen M, Walker S, Simor A, Palmay L, Daneman N. Impact of antimicrobial stewardship in critical care: a systematic review. J Antimicrob Chemother. 2011;66:1223–30.View ArticlePubMedGoogle Scholar
- Dellit TH, Owens RC, McGowan JE, Gerding DN, Weinstein RA, Burke JP, et al. Infectious Diseases Society of America and the Society for Healthcare Epidemiology of America guidelines for developing an institutional program to enhance antimicrobial stewardship. Clin Infect Dis. 2007;44:159–77.View ArticlePubMedGoogle Scholar
- Antibiotic Resistance Threats in the United States. 2013. http://www.cdc.gov/drugresistance/threat-report-2013/.
- Bergstrom CT, Lo M, Lipsitch M. Ecological theory suggests that antimicrobial cycling will not reduce antimicrobial resistance in hospitals. Proc Natl Acad Sci USA. 2004;101:13285–90.PubMed CentralView ArticlePubMedGoogle Scholar
- Goulart CP, Mahmudi M, Crona KA, Jacobs SD, Kallmann M, Hall BG, et al. Designing antibiotic cycling strategies by determining and understanding local adaptive landscapes. PLoS One. 2013;8:e56040.PubMed CentralView ArticlePubMedGoogle Scholar
- Toprak E, Veres A, Michel J-B, Chait R, Hartl DL, Kishony R. Evolutionary paths to antibiotic resistance under dynamically sustained drug selection. Nat Genet. 2012;44:101–5.View ArticleGoogle Scholar
- Kim S, Lieberman TD, Kishony R. Alternating antibiotic treatments constrain evolutionary paths to multidrug resistance. Proc Natl Acad Sci. 2014;111:14494–9.PubMed CentralView ArticlePubMedGoogle Scholar
- Imamovic L, Sommer MOA. Use of collateral sensitivity networks to design drug cycling protocols that avoid resistance development. Sci Transl Med. 2013;5:204ra132.View ArticlePubMedGoogle Scholar
- AbelZurWiesch P, Kouyos R, Abel S, Viechtbauer W, Bonhoeffer S. Cycling empirical antibiotic therapy in hospitals: meta-analysis and models. PLoS Pathog. 2014;10:e1004225.View ArticleGoogle Scholar
- Mölstad S, Erntell M, Hanberger H, Melander E, Norman C, Skoog G, et al. Sustained reduction of antibiotic use and low bacterial resistance: 10-year follow-up of the Swedish Strama programme. Lancet Infect Dis. 2008;8:125–32.View ArticlePubMedGoogle Scholar
- Seppälä H, Klaukka T, Vuopio-Varkila J, Muotiala A, Helenius H, Lager K, et al. The effect of changes in the consumption of macrolide antibiotics on erythromycin resistance in group A streptococci in Finland. Finnish Study Group for Antimicrobial Resistance. N Engl J Med. 1997;337:441–6.View ArticlePubMedGoogle Scholar
- Sjölund M, Wreiber K, Andersson DI, Blaser MJ, Engstrand L. Long-term persistence of resistant Enterococcus species after antibiotics to eradicate Helicobacter pylori. Ann Intern Med. 2003;139:483–7.View ArticlePubMedGoogle Scholar
- Andersson DI, Hughes D. Persistence of antibiotic resistance in bacterial populations. FEMS Microbiol Rev. 2011;35:901–11.View ArticlePubMedGoogle Scholar
- Andersson DI, Hughes D. Antibiotic resistance and its cost: is it possible to reverse resistance? Nat Rev Microbiol. 2010;8:260–71.PubMedGoogle Scholar
- Sundqvist M, Geli P, Andersson DI, Sjölund-Karlsson M, Runehagen A, Cars H, et al. Little evidence for reversibility of trimethoprim resistance after a drastic reduction in trimethoprim use. J Antimicrob Chemother. 2010;65:350–60.View ArticlePubMedGoogle Scholar
- Handel A, Regoes RR, Antia R. The role of compensatory mutations in the emergence of drug resistance. PLoS Comput Biol. 2006;2:e137.PubMed CentralView ArticlePubMedGoogle Scholar
- Levin BR, Perrot V, Walker N. Compensatory mutations, antibiotic resistance and the population genetics of adaptive evolution in bacteria. Genetics. 2000;154:985–97.PubMed CentralPubMedGoogle Scholar
- Gould SJ. Dollo on Dollo’s law: irreversibility and the status of evolutionary laws. J Hist Biol. 1970;3:189–212.View ArticlePubMedGoogle Scholar
- Dawkins R. The blind watchmaker: why the evidence of evolution reveals a universe without design. New York: WW. Norton & Company; 1996. p 496Google Scholar
- Tan L, Serene S, Chao HX, Gore J. Hidden randomness between fitness landscapes limits reverse evolution. Phys Rev Lett. 2011;106:198102.View ArticlePubMedGoogle Scholar
- Choowongkomon K, Ortlund EA, Thornton JW. An epistatic ratchet constrains the direction of glucocorticoid receptor evolution. Nature. 2009;461:515–9.View ArticleGoogle Scholar
- WHO. World Malaria Report 2014. Geneva, World Health Organization, 2014. http://www.who.int/malaria/publications/world_malaria_report_2014/en/.
- Weinreich DM, Delaney NF, Depristo MA, Hartl DL. Darwinian evolution can follow only very few mutational paths to fitter proteins. Science. 2006;312:111–4.View ArticlePubMedGoogle Scholar
- Poelwijk FJ, Kiviet DJ, Weinreich DM, Tans SJ. Empirical fitness landscapes reveal accessible evolutionary paths. Nature. 2007;445:383–6.View ArticlePubMedGoogle Scholar
- Lozovsky ER, Chookajorn T, Brown KM, Imwong M, Shaw PJ, Kamchonwongpaisan S, et al. Stepwise acquisition of pyrimethamine resistance in the malaria parasite. Proc Natl Acad Sci USA. 2009;106:12025–30.PubMed CentralView ArticlePubMedGoogle Scholar
- Brown KM, Costanzo MS, Xu W, Roy S, Lozovsky ER, Hartl DL. Compensatory mutations restore fitness during the evolution of dihydrofolate reductase. Mol Biol Evol. 2010;27:2682–90.PubMed CentralView ArticlePubMedGoogle Scholar
- Costanzo MS, Hartl DL. The evolutionary landscape of antifolate resistance in Plasmodium falciparum. J Genet. 2011;90:187–90.PubMed CentralView ArticlePubMedGoogle Scholar
- Jiang P-P, Corbett-Detig RB, Hartl DL, Lozovsky ER. Accessible mutational trajectories for the evolution of pyrimethamine resistance in the malaria parasite Plasmodium vivax. J Mol Evol. 2013;77:81–91.View ArticlePubMedGoogle Scholar
- de Pécoulas PE, Tahar R, Ouatas T, Mazabraud A, Basco LK. Sequence variations in the Plasmodium vivax dihydrofolate reductase-thymidylate synthase gene and their relationship with pyrimethamine resistance. Mol Biochem Parasitol. 1998;92:265–73.View ArticlePubMedGoogle Scholar
- Miao M, Yang Z, Cui L, Ahlum J, Huang Y, Cui L. Different allele prevalence in the dihydrofolate reductase and dihydropteroate synthase genes in Plasmodium vivax populations from China. Am J Trop Med Hyg. 2010;83:1206–11.PubMed CentralView ArticlePubMedGoogle Scholar
- Imwong M, Pukrittakayamee S, Looareesuwan S, Pasvol G, Poirreiz J, White NJ, et al. Association of genetic mutations in Plasmodium vivax dhfr with resistance to sulfadoxine-pyrimethamine: geographical and clinical correlates. Antimicrob Agents Chemother. 2001;45:3122–7.PubMed CentralView ArticlePubMedGoogle Scholar
- Mint Lekweiry K, Ould Mohamed Salem Boukhary A, Gaillard T, Wurtz N, Bogreau H, Hafid JE, et al. Molecular surveillance of drug-resistant Plasmodium vivax using pvdhfr, pvdhps and pvmdr1 markers in Nouakchott, Mauritania. J Antimicrob Chemother. 2012;67:367–74.View ArticlePubMedGoogle Scholar
- Afsharpad M, Zakeri S, Pirahmadi S, Djadid ND. Molecular assessment of dhfr/dhps mutations among Plasmodium vivax clinical isolates after introduction of sulfadoxine/pyrimethamine in combination with artesunate in Iran. Infect Genet Evol. 2012;12:38–44.View ArticlePubMedGoogle Scholar
- Khatoon L, Baliraine FN, Bonizzoni M, Malik SA, Yan G. Prevalence of antimalarial drug resistance mutations in Plasmodium vivax and P. falciparum from a malaria-endemic area of Pakistan. Am J Trop Med Hyg. 2009;81:525–8.PubMed CentralPubMedGoogle Scholar
- Barnadas C, Musset L, Legrand E, Tichit M, Briolant S, Fusai T, et al. High prevalence and fixation of Plasmodium vivax dhfr/dhps mutations related to sulfadoxine/pyrimethamine resistance in French Guiana. Am J Trop Med Hyg. 2009;81:19–22.PubMedGoogle Scholar
- Lu F, Lim CS, Nam DH, Kim K, Lin K, Kim T-S, et al. Mutations in the antifolate-resistance-associated genes dihydrofolate reductase and dihydropteroate synthase in Plasmodium vivax isolates from malaria-endemic countries. Am J Trop Med Hyg. 2010;83:474–9.PubMed CentralView ArticlePubMedGoogle Scholar
- Schunk M, Kumma WP, Miranda IB, Osman ME, Roewer S, Alano A, et al. High prevalence of drug-resistance mutations in Plasmodium falciparum and Plasmodium vivax in southern Ethiopia. Malar J. 2006;5:54.PubMed CentralView ArticlePubMedGoogle Scholar
- Barnadas C, Ratsimbasoa A, Tichit M, Bouchier C, Jahevitra M, Picot S, et al. Plasmodium vivax resistance to chloroquine in Madagascar: clinical efficacy and polymorphisms in pvmdr1 and pvcrt-o genes. Antimicrob Agents Chemother. 2008;52:4233–40.PubMed CentralView ArticlePubMedGoogle Scholar
- Brega S, de Monbrison F, Severini C, Udomsangpetch R, Sutanto I, Ruckert P, et al. Real-time PCR for dihydrofolate reductase gene single-nucleotide polymorphisms in Plasmodium vivax isolates. Antimicrob Agents Chemother. 2004;48:2581–7.PubMed CentralView ArticlePubMedGoogle Scholar
- Valecha N, Joshi H, Eapen A, Ravinderan J, Kumar A, Prajapati SK, et al. Therapeutic efficacy of chloroquine in Plasmodium vivax from areas with different epidemiological patterns in India and their Pvdhfr gene mutation pattern. Trans R Soc Trop Med Hyg. 2006;100:831–7.View ArticlePubMedGoogle Scholar
- Hastings IM, Donnelly MJ. The impact of antimalarial drug resistance mutations on parasite fitness, and its implications for the evolution of resistance. Drug Resist Updat Rev Comment Antimicrob Anticancer Chemother. 2005;8:43–50.View ArticleGoogle Scholar
- Auliff A, Wilson DW, Russell B, Gao Q, Chen N, Le Ngoc A, et al. Amino acid mutations in Plasmodium vivax DHFR and DHPS from several geographical regions and susceptibility to antifolate drugs. Am J Trop Med Hyg. 2006;75:617–21.PubMedGoogle Scholar
- Peng B, Amos CI, Kimmel M. Forward-time simulations of human populations with complex diseases. PLoS Genet. 2007;3:e47.PubMed CentralView ArticlePubMedGoogle Scholar
- Lipka B, Milewska-Bobula B, Filipek M. Monitoring of plasma concentration of pyrimethamine (PYR) in infants with congenital Toxoplasma gondii infection—own observations. Wiad Parazytol. 2011;57:87–92.PubMedGoogle Scholar
- Dzinjalamala FK, Macheso A, Kublin JG, Taylor TE, Barnes KI, Molyneux ME, et al. Blood folate concentrations and in vivo sulfadoxine-pyrimethamine failure in Malawian children with uncomplicated Plasmodium falciparum malaria. Am J Trop Med Hyg. 2005;72:267–72.PubMedGoogle Scholar
- Dzinjalamala FK, Macheso A, Kublin JG, Taylor TE, Barnes KI, Molyneux ME, et al. Association between the pharmacokinetics and in vivo therapeutic efficacy of sulfadoxine-pyrimethamine in Malawian children. Antimicrob Agents Chemother. 2005;49:3601–6.PubMed CentralView ArticlePubMedGoogle Scholar
- Jamaludin A, Mohamad M, Navaratnam V, Yeoh PY, Wernsdorfer WH. Multiple-dose pharmacokinetic study of proguanil and cycloguanil following 12-hourly administration of 100 mg proguanil hydrochloride. Trop Med Parasitol. 1990;41:268–72.PubMedGoogle Scholar
- Weidekamm E, Plozza-Nottebrock H, Forgo I, Dubach UC. Plasma concentrations of pyrimethamine and sulfadoxine and evaluation of pharmaco-kinetic data by computerized curve fitting. Bull World Health Organ. 1982;60:115–22.PubMed CentralPubMedGoogle Scholar
- Weinreich DM, Lan Y, Wylie CS, Heckendorn RB. Should evolutionary geneticists worry about higher-order epistasis? Curr Opin Genet Dev. 2013;23:700–7.PubMed CentralView ArticlePubMedGoogle Scholar
- Neafsey DE, Galinsky K, Jiang RHY, Young L, Sykes SM, Saif S, et al. The malaria parasite Plasmodium vivax exhibits greater genetic diversity than Plasmodium falciparum. Nat Genet. 2012;44:1046–50.PubMed CentralView ArticlePubMedGoogle Scholar
- Kublin JG, Cortese JF, Njunju EM, Mukadam RAG, Wirima JJ, Kazembe PN, et al. Reemergence of chloroquine-sensitive Plasmodium falciparum malaria after cessation of chloroquine use in Malawi. J Infect Dis. 2003;187:1870–5.View ArticlePubMedGoogle Scholar
- Heinberg A, Siu E, Stern C, Lawrence EA, Ferdig MT, Deitsch KW, et al. Direct evidence for the adaptive role of copy number variation on antifolate susceptibility in Plasmodium falciparum. Mol Microbiol. 2013;88:702–12.PubMed CentralView ArticlePubMedGoogle Scholar
- Kümpornsin K, Kotanan N, Chobson P, Kochakarn T, Jirawatcharadech P, Jaru-ampornpan P, et al. Biochemical and functional characterization of Plasmodium falciparum GTP cyclohydrolase I. Malar J. 2014;13:150.PubMed CentralView ArticlePubMedGoogle Scholar
- Pelleau S, Moss EL, Dhingra SK, Volney B, Casteras J, Gabryszewski SJ, et al. Adaptive evolution of malaria parasites in French Guiana: reversal of chloroquine resistance by acquisition of a mutation in pfcrt. Proc Natl Acad Sci USA. 2015;112:11672–7.PubMed CentralView ArticlePubMedGoogle Scholar