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Antimalarial Exposure Delays Plasmodium falciparum Intra-Erythrocytic Cycle and Drives Drug Transporter Genes Expression

  • Maria Isabel Veiga ,

    Affiliations Malaria Research Lab, Department of Medicine, Karolinska Institutet, Stockholm, Sweden, Drug Resistance and Pharmacogenetics Group, Institute of Biotechnology and Bioengineering, Centre of Molecular and Structural Biomedicine, University of Algarve, Faro, Portugal

  • Pedro Eduardo Ferreira,

    Affiliations Malaria Research Lab, Department of Medicine, Karolinska Institutet, Stockholm, Sweden, Drug Resistance and Pharmacogenetics Group, Institute of Biotechnology and Bioengineering, Centre of Molecular and Structural Biomedicine, University of Algarve, Faro, Portugal

  • Berit Aydin Schmidt,

    Affiliation Malaria Research Lab, Department of Medicine, Karolinska Institutet, Stockholm, Sweden

  • Ulf Ribacke,

    Affiliation Department of Microbiology, Tumor and Cell Biology (MTC), Karolinska Institutet, Stockholm, Sweden

  • Anders Björkman,

    Affiliation Malaria Research Lab, Department of Medicine, Karolinska Institutet, Stockholm, Sweden

  • Ales Tichopad,

    Affiliations Institute of Biotechnology AS CR, Prague, Czech Republic, Physiology Weihenstephan, Technical University Munich, Freising-Weihenstephan, Germany

  • José Pedro Gil

    Affiliations Malaria Research Lab, Department of Medicine, Karolinska Institutet, Stockholm, Sweden, Drug Resistance and Pharmacogenetics Group, Institute of Biotechnology and Bioengineering, Centre of Molecular and Structural Biomedicine, University of Algarve, Faro, Portugal, Laboratory of Molecular Anthropology and Health, Department of Anthropology, Binghamton University, Binghamton, New York, United States of America



Multi-drug resistant Plasmodium falciparum is a major obstacle to malaria control and is emerging as a complex phenomenon. Mechanisms of drug evasion based on the intracellular extrusion of the drug and/or modification of target proteins have been described. However, cellular mechanisms related with metabolic activity have also been seen in eukaryotic systems, e.g. cancer cells. Recent observations suggest that such mechanism may occur in P. falciparum.

Methodology/Principal Findings

We therefore investigated the effect of mefloquine exposure on the cell cycle of three P. falciparum clones (3D7, FCB, W2) with different drug susceptibilities, while investigating in parallel the expression of four genes coding for confirmed and putative drug transporters (pfcrt, pfmdr1, pfmrp1 and pfmrp2). Mefloquine induced a previously not described dose and clone dependent delay in the intra-erythrocytic cycle of the parasite. Drug impact on cell cycle progression and gene expression was then merged using a non-linear regression model to determine specific drug driven expression. This revealed a mild, but significant, mefloquine driven gene induction up to 1.5 fold.


Both cell cycle delay and induced gene expression represent potentially important mechanisms for parasites to escape the effect of the antimalarial drug.


Plasmodium falciparum malaria remains a major disease burden in the developing world [1], chemotherapy being the foremost available tool for its control. Efficacious malaria treatment is presently dependent on the efficacy of artemisinin combination therapy (ACT), the global replacement of the previous mainstays, chloroquine and sulfadoxine-pyrimethamine [1], [2]. Unfortunately, recent data suggests that P. falciparum resistance to ACT might be presently developing, both to its long half-life components but also – and worryingly – against the artemisinin based components [3], [4]. The latter phenomenon has been mainly defined as an in vivo significant decrease in parasite reduction rate, manifested clinically by markedly longer parasite clearance times [5]. The molecular basis of this phenomenon is unclear, starting from the fact that most of these observations are not associated with altered artemisinin IC50s in vitro [3]. It is conceivable that this phenomenon is related with other observations by us [6] and others [7], [8] showing that antimalarials can interfere with the cell cycle development of the parasite, potentially constituting a general first step towards high grade resistance to antimalarial drugs.

Drug resistance has been widely associated with the action of efflux pumps, able to transport drugs out of the relevant cellular compartment, and thereby displacing them from their molecular targets [9]. This action is expected to be potentiated through an increase in the availability of transporter proteins through enhanced gene transcriptional activity. In P. falciparum two transporters have been robustly associated with antimalarial resistance: PfCRT (chloroquine resistance transporter), a drug metabolite superfamily transporter coded by the pfcrt gene (MAL7P1.27) [10], [11], and the Pgh (P-glycoprotein homologue, coded by pfmdr1 (PFE1150w)) an ATP-binding cassette (ABC) transporter superfamily member [12]. Two other ABC transporters potentially associated with P. falciparum drug resistance can be added - the homologues of the multidrug resistance-associated protein (MRP) type [13], [14], [15] coded by the pfmrp1 (PFA0590w) and pfmrp2 (PFL1410c) genes. Polymorphisms in these drug transporters have been associated with altered in vitro and/or in vivo responses to ACT components [16], [17], [18], [19], [20], [21], indicating that their common action might constitute a basis for wide range multidrug resistance phenotypes able to manage the new combinational therapy challenges faced by the parasite. Besides single nucleotide polymorphisms (SNPs), enhanced locus expression can drive decreased drug sensitivity, a phenomenon well documented for pfmdr1, where gene duplication events are strongly associated to mefloquine (MQ) resistance [22], [23] and lumefantrine decreased sensitivity [24].

The synthetic arylaminoalcohol MQ constitutes one of the central partner drugs in artemisinin combination therapy, widely used in South East Asia and South America. In SE Asia, the in vivo delayed clearance has been observed not only in artesunate monotherapy explorative regimens, but also associated to the regular mefloquine-artesunate ACT [3].

We hypothesize that the observed clearance delay is associated with this phenomena, contributing for a survival advantage in a fraction of the parasite infection, by allowing an extended time for the induction and expression of key drug transporters, leading to a pivotal decreased intracellular drug exposure in the first phase of the treatment course.

Using MQ as a relevant and convenient reference ACT antimalarial, we have focused on monitoring changes in the cell cycle progression rate of three P. falciparum clones with different MQ susceptibilities, while in parallel detecting variations in transcript abundance of the pfmdr1, pfcrt, pfmrp1 and pfmrp2 genes.


Parasites genetic characterization

We have studied in parallel three P. falciparum clones, one MQ sensitive (W2) and two (3D7 and FCB) with decreased sensitivity. The pfmdr1, pfcrt, pfmrp1 and pfmrp2 ORF were fully sequenced to determine the genetic variability between the clones. The three clones, show two different main pfmdr1 polymorphisms: a single nucleotide polymorphism at amino acid position 86 (N86 in 3D7, 86Y in W2 and FCB) and gene copy number variation (W2 and 3D7 harbouring one copy [25], with FCB carrying two copies [23]). Numerous single nucleotide polymorphism differences were seen in the three remaining genes (GenBank accession numbers: GU797309, GU797310, GU797311, GU797312, GU797315, GU797316).

Plasmodium falciparum cell cycle progression alterations driven by mefloquine

In vitro MQ 50% inhibitory concentration (IC50) of W2, 3D7 and FCB clones were in average from 6 independent assays 10±3.3nM, 50±16.9nM and 50±14.1nM respectively. As for IC99 the average results was 44±13.9nM, 146±36.1nM, 146±50.6nM for W2, 3D7 and FCB respectively. These concentrations were used for challenging the clones in the assay performed.

Exposure to MQ induced an unexpected and unambiguous delay in the cell cycle progression of the three tested parasites, as evaluated by Giemsa staining under light microscopy for the four counted stages (Figure 1). The degree of delay effect was clone dependent.

Figure 1. Stage morphology development throughout time upon MQ exposure.

Parasite stage percentage (Y axis) of W2, 3D7 and FCB clones with continuous exposure at MQ IC50 and MQ IC99 throughout 48 hours time course (X axis). Four morphological stages were differentiated using light microscope: early rings (brown), late rings/early trophozoites (orange), trophozoites (light green) and schizonts (dark green).

At MQ IC99 a pronounced morphology arrest was seen in the three clones remaining essentially at the experimental initial ring stage with picnotic features. The quantification of end point RNA indicated the presence of viable cells, an observation supported for all tested parasites by their growth recovery 7–10 days after drug withdrawal (Figure S1).

Each gene has its own stage morphology expression profile, which once induced by drugs, it was previously shown that the expression is strictly correlated with the cell morphology [26], [27]. The MQ induced cell morphology delay was also detectable through the analysis of the transcript accumulation patterns (Figure 2, green and red curves). Applying the described non-linear regression model to the cycle (figure 3), significant differences in the degree of cell morphology progression were seen with MQ IC99 exposure in the three clones (p<0.05; figure 4: red lines). The calculated rate at IC50, compared to control was clone dependent. The effect was less pronounced in W2 parasites with approximately 15% delay (p = 0.23) vs. ∼40% for the MQ tolerant clones FCB and 3D7 (p<0.01) (Figure 4 and Table 1). Similar cell cycle morphology delay events were also observed upon quinine exposure (IC50 and IC90) in exploratory 12 hour follow up assay (Figure S2).

Figure 2. Drug transporter gene expression throughout time upon MQ exposure.

Parasites cell cycle time course of drug transporters gene expression at basal level (control, black line) and after exposure to MQ IC50 (green lines) and MQ IC99 (red lines). X axis represent time-points in hours and Y axis relative gene expression quantification using pf07_0073 as endogenous control gene. Error bars represent standard deviation from 3 in vitro replicates.

Figure 3. Non linear regression equation I (for cell morphology) and II (for gene transcripts).

y0 = offset, corresponding to the initial (experimental to) status of the parameter under analysis (e.g. number of parasites at ring stage); a = wave amplitude; b = wave period (the reciprocal wave frequency); b−1 = wave frequency; c = wave phase; x = time; d = damping constant factor (to account to dampening variations in amplitude of gene expression). cm: cell morphology; gt: gene transcripts.

Figure 4. Effect of MQ in P.falciparum stage development.

Wave form smoothing function best fitted over the early ring stage cell count (Figure 1, brown color). The wave frequency value (time needed to complete a cell cycle) for each fitted function (control, MQ IC50 and IC99) was used for the ratio calculation delay between treated and non treated parasite cultures (Table 1). Statistical comparisons (t-tests) of treatment effects (control vs. MQ IC50 and IC99) were performed with the associated error of wave frequency value of different experiments per clone compared with control.

Table 1. Cell cycle development and gene expression of parasites challenged with MQ IC50.

Basal gene expression of transporter genes in W2, 3D7 and FCB

The pfmdr1, pfcrt, pfmrp1 and pfmrp2 cell cycle patterns of relative transcript abundance from control (i.e. non drug exposed) experiments for the reference 3D7 clone were similar to the available data at PlasmoDB [28] (Figure 2, black curves), using as endogenous control seryl-tRNA synthetase gene. In brief, pfmdr1, pfcrt and pfmrp2 displayed the lowest levels of expression at approximately 24 hours (middle/late trophozoite stage) and highest amplitudes around 42–48 hours post invasion. pfmrp1 revealed the previously described clearly different expression profile [29], [30], with a peak of expression during the development of trophozoites (12–24 hours post-invasion).

Comparing relative transcript abundance (R±SD) between the three clones at onset (Figure 2, time-point 0h), pfmdr1 mRNAs showed higher levels in the FCB clone (18.15±2.84), as compared to W2 (14.18±0.99) and 3D7 (4.90±0.21) with a fold difference of 1.3 and 3.7 respectively. Pfcrt showed to be less expressed in the W2 (3.86±0.15) compared with 3D7 (7.40±1.90) and FCB (6.10±0.88) with a fold difference of 0.5 and 0.63 respectively. As for the mrp genes, pfmrp2 gene showed higher expression for 3D7 (8.40±1.95) compared with W2 (5.06±0.13) and FCB (3.81±1.03) with a fold difference of 1.7 and 2.2 respectively while pfmrp1 at time-point 0 the results was 3.5 fold difference between FCB (0.007±9E-4) and W2 (0.002±1E-4) and 1.75 fold comparing with 3D7 (0.004±6E-4).

Specific drug transporter genes induction by mefloquine

The fact that the drug exposure led to a proliferative delay in the parasites stage created a new challenge on how to differentiate gene transcription levels affected by cell morphology from the direct action of the drug on the transcript accumulation of the studied genes. We solved this challenge through two approaches: (1) From the few possible comparative time-points (control vs. MQ exposure time-points that had statistically non distinguishable parasite morphology proportion differences (p>0.05)), transcript abundance values were compared. The levels of significant gene expression modulation, within each clone upon MQ exposure, ranged from 0.6 to 5.8 fold: pfmdr1 (0.6–1.5 fold), pfcrt (0.8–1.3 fold), pfmrp2 (0.8–2.7 fold) and pfmrp1 with the highest MQ driven induction (0.7–5.8) (Table 2). (2) To complement this limited comparative point by point verification of drug direct action, which gave rise to few analyzable points, another analytical approach based on a non-linear regression analysis of gene expression over time was conducted for the MQ IC50 experiments. The calculated fold difference confirmed that the drug exposure was generally associated with mild changes in the expression of the genes (figure 5). Considering each gene at IC50 exposure: (a) The pfmdr1 gene, was significantly more induced in the less MQ sensitivity clones 3D7 (1.35 fold, p = 0.029) and FCB (1.49 fold, p<0.001), than in the more sensitive W2 (1.23 fold). Comparisons between the two less sensitive parasites showed that FCB can induce this gene significantly more than 3D7 (p = 0.003). (b) As for pfmrp1, our data show no statistical difference between the clones. (c) Concerning pfcrt gene, the W2 and 3D7 parasites appear to lack the capacity to significantly induce pfcrt upon MQ exposure (1.06 fold and 0.92 fold) compared to FCB (1.25 fold). (d) Pfmrp2, appears to significantly induce the less MQ sensitivity clones 3D7 (1.32 fold, p = 0.003) and FCB (1.46 fold, p = 0.023) compared with W2 (0.96 fold).

Figure 5. Drug transporter genes induction by MQ IC50.

Results appear as fold difference in gene expression normalized with cell cycle stage development achieved after smoothing the data with wave functions. Error bars represents SE of the difference.

Table 2. Gene transcripts time points comparison in MQ exposed parasites with identical stage proportions.


Upon continuous MQ exposure we have documented a dose dependent delay of the parasite cell cycle, particularly among the two MQ less sensitive P. falciparum clones (3D7 and FCB). This observation prompts the discussion of its biological importance in terms of drug resistance response.

Mechanistically, the phenomenon of drug resistance is essentially associated with a decrease in the number of effective opportunities for the therapeutic drug to effectively engage to its target(s). Below a certain threshold of interaction rate, the pharmacodynamic effect of the drug will be reduced to a degree that makes it clinically ineffective (hence associated to a treatment failure). This can be achieved in the cell by decreasing drug concentration in the target cellular compartment through the action of transmembrane transporters. To this a reduced availability of the target can be added, particularly through a decrease in metabolic activity, potentially related with a slow down of the cell cycle.

In several clinically relevant biological systems – notoriously in cancer cells - drug exposure leads to a noticeable slowing down in cell cycle progression, even to the point of stalling [31], [32]. In parallel, drug driven cell cycle arrest in P. falciparum has been consistently described by us and others for several antimalarial drugs [6], [7] including MQ [8] and very recently chloroquine [33]. P. falciparum dormant states are essentially refractory to antimalarial action [6] presumably due to its low metabolic activity status. Upon these observations, we speculate that the observed cell cycle delay behavior might functionally constitute a first stage towards this dormant state. The observed capacity of the different parasites clones to full recover growth, after 48 hours of intense MQ IC99 exposure, in different recrudescence time indirectly supports this suggestion.

The capacity to delay the intra-erythrocytic developmental cycle could therefore be an operational contributor for drug resistance, particularly when considering drugs with short half-lives, like artemisinins and quinine. Such a response could help the parasite to withstand the effects of the drug at its peak serum concentration, increasing the chances of survival as the drug concentration rapidly decreases in the circulatory system, due to elimination. The delay in the cycle might also be of importance for allowing time for a parallel action: the specific induction of drug transporters able to reduce the intra-cellular (or intra-compartment) concentrations of the drug. This action can in turn gain time for the process of metabolic shut off leading to a drug action refractory state based on the scarcity of drug targets.

Our results show a gene expression induction of generally less than 2 fold after MQ challenge, suggesting that these genes are actually inducible by this drug, though to a limited extent. Similar induction levels have been observed for pfmdr1 (1.7 fold, when normalized for hsp86) in a northern blot based approach, after a 6h MQ treatment [34]. This data is also coherent with a trend of low pfdhfr gene transcriptional response previously reported upon exposure to the experimental antifolate WR99210 [26], as well as to chloroquine [27]. It is to note that the observed relatively low induction of transcription seen in response to MQ does not exclude more significant responses upon exposure to other xenobiotics. Accordingly, treatment of the P. falciparum clone 3D7 with phenobarbital (a classical drug elimination inducer) for 48h was associated to an extensive increase in pfmdr1 transcripts, and to a concordant reduction in drug susceptibility [35].

These observations raise the question on how important mild changes in gene expression can be in terms of the parasite response to the drug. When drug resistance is mainly based on direct modifications of the target, as in the case with antifolates, an increase in drug quantity of the latter will probably be irrelevant, since it is unlikely that the number of drug molecules will overcome the number of target proteins (considering a regular stoichiometric relationship of action). In the case of transporter proteins - assuming that they are not themselves targets for the drug - the situation is markedly different. One protein can transport a large number of molecules per time unit, and a simple 2 fold increase in this capacity may well have an impact on the effect of the drug. This is supported by in vivo and in vitro studies showing that a two fold gene copy number amplification of pfmdr1 significantly affects the parasite response to MQ and other drugs [22], [23], [36], [37]. Interestingly, the clones herein observed with the largest induction of transporter genes transcripts in response to MQ were also the less sensitive to the drug (3D7, FCB), suggesting a role of increased expression in the overall parasite drug response.

Our observations of significant parasite cycle delay upon drug exposure points this as a significant confounding factor in the analysis of parasite drug driven gene expression. Hence, we propose a simple non-linear regression model as a novel analytical tool to distinguish drug specific gene induction from the effect of the time changed gene expression variation associated to the cell cycle. This approach could be applied in future studies, as well as revisiting previous data that did not consider the phenomenon of drug driven cell cycle delay in P. falciparum.

In conclusion, MQ is able to induce expression of pfmdr1, pfcrt, pfmrp1 and pfmrp2 which, although mild, are potentially significant in aiding the parasite to evade antimalarial drug action. This occurs in the context of a prompt MQ driven cell cycle delay that potentially constitutes a further parallel mechanism for the parasite populations to avoid, or at least delay, antimalarial action.

Materials and Methods

P. falciparum parasite clones

P. falciparum 3D7 clone was obtained from Prof. D. Walliker (Department of Animal and population genetics, University of Edinburgh, UK), while parasite clones W2 (MRA-157) and FCB (MRA-309) were obtained from Malaria Research and Reference Reagent Resource Center (MR4, ATCC Massanas Virginia). The parasite lines were selected on the basis of their sensitivity to mefloquine hydrochloride (Sigma-Aldrich®, St.Louis, MO, USA) and their genetic background (sequences submitted to GenBank accession numbers: GU797309, GU797310, GU797311, GU797312, GU797313, GU797314, GU797315, GU797316).

Drug susceptibility determinations

The inhibitory concentration of 50 and 99% for the three clones was assessed using an Histidine-Rich Protein 2 based Double-Site Sandwich Enzyme-Linked Immunosorbent Assay [38] followed by nonlinear regression analysis (

P. falciparum in vitro culture

The parasites were kept in culture using conventional methods [39] in human O+ RBCs and Malaria Culture Medium, containing RPMI 1640 culture medium supplemented with 10% L-glutamine, 0.05% gentamicine (Gibco®/Invitrogen™, Carlsbad, CA, USA) and 10% human AB+ serum. Large scale culture of each clone was produced with 3% parasitaemia in 2.5% hematocrite and synchronized in early ring stage at least twice by applying the MACS® system (Miltenyi Biotec, Bergisch Gladbach, Germany). The batch was then divided for three experiments: one control culture (no drug added) and two cultures with MQ IC50 and IC99.concentration followed by spreading it in culture plates (2mL/well). At the time of reinvasion, early rings (time-point 0), two Giemsa-stained smears were made for examination of parasites stages, while three replicate wells of parasite culture were harvested for RNA extraction. This procedure was followed for the three experiments, in six hour time-point intervals up to 48 hours (total of 9 time-points analyses including time 0). The smears were fixed with methanol and stained with 5% Giemsa and visualization under light microscopy (1000× magnification). Their examination included counting 100 parasites, twice per slide (2 slides per time point), distinguishing four main morphological stages: early rings, late rings/early trophozoites, trophozoites and schizonts. A total count of 400 parasites per time-point, per experiment was performed. The experimental design was equally applied for the three clones analyzed (W2, 3D7 and FCB).

Molecular analysis

Parasite cultures collected for RNA extraction were centrifuged at 0.8 g-force for 2 min. 1.85 mL of supernatant were removed and 150 µL of PBS plus 300 µL of Lysis buffer (Applied Biosystems™, Fresno, CA, USA) were added. The mix was kept at −20°C until RNA extraction. RNA extraction was carried out using an ABIPRISM®6100 Nucleic Acid PrepStation® (Applied Biosystems™, Fresno, CA,USA) according to the recommendations of the manufacturer. Total RNA quality and quantity was measured using the AgilentRNA 6000 Pico total RNA assay in an Agilent2100 Bioanalyser™ (Santa Clara, CA, USA). Sample concentrations with RNA Integrity Number >5 were normalized for each clone before cDNA synthesis (High-Capacity cDNA Reverse Transcription Kit (Applied Biosystems, Fresno, CA, USA)). Real-time analyses were performed in an ABIPRISM® 7900HT Sequence Detection System (Applied Biosystems™, Fresno, CA, USA) with gene specific MGB TaqMan® probes and primers. TaqMan® probes and primers for target gene pfmdr1 were as previously published [22]. New designs were developed for pfcrt, pfmrp1, pfmrp2 and for endogenous control gene [30], [40] seryl-tRNA synthetase (PF07_0073) (Table S1). Amplification reactions were done in quadruplicate in 384 well plates with 10µl containing TaqMan® Gene Expression Mastermix (Applied Biosystems™, Fresno, CA, USA), 300nM of each forward and reverse primer, 100nM of TaqMan®probe and 2 µL of amount-normalized cDNA. The thermal cycle profile was 50°C for 2 min, 95°C for 10 min and forty cycles of 95°C for 15 s and 60°C for 1 min. The amplification efficiency (E) was estimated for each gene and used as a correction factor for relative gene expression quantification. All experimental threshold cycle values (Ct) were first transformed to adjust the RNA concentration adding to the Ct value the log2 RNA concentration of each clone. Relative gene expression was calculated as the ratio (R) between the transformed Ct values of target gene and internal control reference gene (PF07_0073), taking in account the amplification efficiency for each gene [41]. Calculations were conducted using the SAS 9.1 system for Windows™.

Determination of the drug exposure specific induction effect: time-point gene expression fold differences and the non-linear regression model approach

In order to distinguish the changes in cDNA abundance associated with the parasite cell cycle progression from the ones associated with drug exposure, two different approaches were performed:

I. Direct comparisons.

Fold difference in transcript abundance was calculated in control vs. MQ exposed cultures only in time-points showing a statistically equal fractional composition of the four microscopically determined stages (Fisher test, df = 3, p>0.05).

II. Non linear regression analysis.

A wave function applied to the number of total parasites ring stage count throughout all time-points for each clone (equation Ifigure 3) was achieved (goodness of fit was R2>0.9) (SigmaPlot™ 9.0). Specific parameters of this function were used to quantify the parasite delays in cell morphology (cm) imposed by MQ exposure. The wave frequency (bcm−1) from each fitted function (control, MQ IC50 and MQ IC99 exposed), corresponds to the time needed to complete a cycle and it was used to calculate the cell cycle delay based in the observation of the morphology development of the parasite between MQ exposed (ICn) and control (Rmorphology = bcm−1ICn/bcm−1control). Statistical comparisons (t-tests) were performed with the associated error of the wave frequency parameter “bcm−1”.

A similar approach, of applying a wave function, was performed in the gene transcripts data (gt). During the 48 hours exposure to MQ IC50, the associated delay in the cycle gene expression was calculated by comparing the wave frequency parameter “bgt−1” (Rgene transcripts = bgt−1IC/bgt−1control) obtained through a best fit of the gene expression data, this time in a damped wave function (equation IIfigure 3). Statistical comparisons (t-tests) were performed with the associated error of the wave frequency parameter “bgt−1”.

The use of these equations allowed us to determine whether the expression profiles were solely stage-driven, or included a significant fraction associated with drug exposure:By its nature, this approach was only possible to apply to experimental outputs where sufficient data points were available for enabling fitting a sine function, being hence only applicable to gene transcripts data concerning IC50 and not to the IC99 drug level exposure.

Supporting Information

Figure S1.

Cell viability after MQ IC99 exposure. Parasite strains viability after challenged with continuous MQ IC99 (W2, 44nM; 3D7 and FCB, 146nM) for 48 hours analyzed by Histidine-Rich Protein 2 Double-Site Sandwich Enzyme-Linked Immunosorbent. All strains had a growth recovery of 7–10 days after drug withdrawal. Error bars are SE of rate between day0 HRP2 and collected day.

(0.06 MB PDF)

Figure S2.

Cell cycle morphology progressions upon quinine exposure (IC50 and IC90) after 12 hour follow up. Parasite stage percentage (Y axis) of W2, FCB and 3D7 strains after 12 hours (X axis) with continuous exposure of quinine IC50 and IC90. The counting of the parasites included 100 parasites, twice per slide (2 slides per different ICs exposure), distinguishing four main morphological stages using light microscope: early rings, late rings/early trophozoites, trophozoites and schizonts.

(0.01 MB PDF)

Table S1.

TaqMan® probes and primers sequence.

(0.03 MB DOC)


We greatly appreciate Prof. Ralph M Garruto for the valuable discussion and suggestions. We also thank Prof. Yngve Bergqvist and Doctor Daniel Blessborn from Högskolan, Dalarna for chromatographic measurement of mefloquine concentrations. MR4 for providing us with malaria parasites contributed by DE Kyle and TE Wellems. P. falciparum 3D7 malaria parasites were kindly supplied by the late Prof. D. Walliker.

Author Contributions

Conceived and designed the experiments: MIV PEF UER AB JPG. Performed the experiments: MIV PEF BAS JPG. Analyzed the data: MIV PEF UER AT JPG. Contributed reagents/materials/analysis tools: AB JPG. Wrote the paper: MIV PEF UER AB AT JPG.


  1. 1. WHO (2008) World Malaria Report 2008. Geneva: World Health Organization Press.
  2. 2. Bhattarai A, Ali AS, Kachur SP, Martensson A, Abbas AK, et al. (2007) Impact of artemisinin-based combination therapy and insecticide-treated nets on malaria burden in Zanzibar. PLoS Med 4: e309.
  3. 3. Dondorp AM, Nosten F, Yi P, Das D, Phyo AP, et al. (2009) Artemisinin resistance in Plasmodium falciparum malaria. N Engl J Med 361: 455–467.
  4. 4. Noedl H, Se Y, Schaecher K, Smith BL, Socheat D, et al. (2008) Evidence of artemisinin-resistant malaria in western Cambodia. N Engl J Med 359: 2619–2620. Epub 2008 Dec 2618.
  5. 5. Stepniewska K, Ashley E, Lee SJ, Anstey N, Barnes KI, et al. (2010) In vivo parasitological measures of artemisinin susceptibility. J Infect Dis 201: 570–579.
  6. 6. Thapar MM, Gil JP, Bjorkman A (2005) In vitro recrudescence of Plasmodium falciparum parasites suppressed to dormant state by atovaquone alone and in combination with proguanil. Trans R Soc Trop Med Hyg 99: 62–70.
  7. 7. Nakazawa S, Kanbara H, Aikawa M (1995) Plasmodium falciparum: recrudescence of parasites in culture. Exp Parasitol 81: 556–563.
  8. 8. Nakazawa S, Maoka T, Uemura H, Ito Y, Kanbara H (2002) Malaria parasites giving rise to recrudescence in vitro. Antimicrob Agents Chemother 46: 958–965.
  9. 9. Sauvage V, Aubert D, Escotte-Binet S, Villena I (2009) The role of ATP-binding cassette (ABC) proteins in protozoan parasites. Mol Biochem Parasitol 167: 81–94. Epub 2009 May 2021.
  10. 10. Martin RE, Marchetti RV, Cowan AI, Howitt SM, Broer S, et al. (2009) Chloroquine transport via the malaria parasite's chloroquine resistance transporter. Science 325: 1680–1682.
  11. 11. Sanchez CP, Stein WD, Lanzer M (2007) Is PfCRT a channel or a carrier? Two competing models explaining chloroquine resistance in Plasmodium falciparum. Trends Parasitol 23: 332–339. Epub 2007 May 2010.
  12. 12. Rohrbach P, Sanchez CP, Hayton K, Friedrich O, Patel J, et al. (2006) Genetic linkage of pfmdr1 with food vacuolar solute import in Plasmodium falciparum. Embo J 25: 3000–3011. Epub 2006 Jun 3022.
  13. 13. Klokouzas A, Tiffert T, van Schalkwyk D, Wu CP, van Veen HW, et al. (2004) Plasmodium falciparum expresses a multidrug resistance-associated protein. Biochem Biophys Res Commun 321: 197–201.
  14. 14. Martin RE, Henry RI, Abbey JL, Clements JD, Kirk K (2005) The ‘permeome’ of the malaria parasite: an overview of the membrane transport proteins of Plasmodium falciparum. Genome Biol 6: R26. Epub 2005 Mar 2002.
  15. 15. Mu J, Ferdig MT, Feng X, Joy DA, Duan J, et al. (2003) Multiple transporters associated with malaria parasite responses to chloroquine and quinine. Mol Microbiol 49: 977–989.
  16. 16. Johnson DJ, Fidock DA, Mungthin M, Lakshmanan V, Sidhu AB, et al. (2004) Evidence for a central role for PfCRT in conferring Plasmodium falciparum resistance to diverse antimalarial agents. Mol Cell 15: 867–877.
  17. 17. Lakshmanan V, Bray PG, Verdier-Pinard D, Johnson DJ, Horrocks P, et al. (2005) A critical role for PfCRT K76T in Plasmodium falciparum verapamil-reversible chloroquine resistance. Embo J 24: 2294–2305. Epub 2005 Jun 2299.
  18. 18. Lopes D, Rungsihirunrat K, Nogueira F, Seugorn A, Gil JP, et al. (2002) Molecular characterisation of drug-resistant Plasmodium falciparum from Thailand. Malar J 1: 12. Epub 2002 Oct 2014.
  19. 19. Reed MB, Saliba KJ, Caruana SR, Kirk K, Cowman AF (2000) Pgh1 modulates sensitivity and resistance to multiple antimalarials in Plasmodium falciparum. Nature 403: 906–909.
  20. 20. Sidhu AB, Valderramos SG, Fidock DA (2005) pfmdr1 mutations contribute to quinine resistance and enhance mefloquine and artemisinin sensitivity in Plasmodium falciparum. Mol Microbiol 57: 913–926.
  21. 21. Sidhu AB, Verdier-Pinard D, Fidock DA (2002) Chloroquine resistance in Plasmodium falciparum malaria parasites conferred by pfcrt mutations. Science 298: 210–213.
  22. 22. Price RN, Uhlemann AC, Brockman A, McGready R, Ashley E, et al. (2004) Mefloquine resistance in Plasmodium falciparum and increased pfmdr1 gene copy number. Lancet 364: 438–447.
  23. 23. Sidhu AB, Uhlemann AC, Valderramos SG, Valderramos JC, Krishna S, et al. (2006) Decreasing pfmdr1 copy number in plasmodium falciparum malaria heightens susceptibility to mefloquine, lumefantrine, halofantrine, quinine, and artemisinin. J Infect Dis 194: 528–535. Epub 2006 Jul 2011.
  24. 24. Price RN, Uhlemann AC, van Vugt M, Brockman A, Hutagalung R, et al. (2006) Molecular and pharmacological determinants of the therapeutic response to artemether-lumefantrine in multidrug-resistant Plasmodium falciparum malaria. Clin Infect Dis 42: 1570–1577. Epub 2006 Apr 1526.
  25. 25. Wilson CM, Serrano AE, Wasley A, Bogenschutz MP, Shankar AH, et al. (1989) Amplification of a gene related to mammalian mdr genes in drug-resistant Plasmodium falciparum. Science 244: 1184–1186.
  26. 26. Ganesan K, Ponmee N, Jiang L, Fowble JW, White J, et al. (2008) A genetically hard-wired metabolic transcriptome in Plasmodium falciparum fails to mount protective responses to lethal antifolates. PLoS Pathog 4: e1000214. Epub 1002008 Nov 1000221.
  27. 27. Gunasekera AM, Myrick A, Le Roch K, Winzeler E, Wirth DF (2007) Plasmodium falciparum: genome wide perturbations in transcript profiles among mixed stage cultures after chloroquine treatment. Exp Parasitol 117: 87–92. Epub 2007 Mar 2014.
  28. 28. Aurrecoechea C, Brestelli J, Brunk BP, Dommer J, Fischer S, et al. (2009) PlasmoDB: a functional genomic database for malaria parasites. Nucleic Acids Res 37: D539–543. Epub 2008 Oct 2028.
  29. 29. Bozdech Z, Ginsburg H (2005) Data mining of the transcriptome of Plasmodium falciparum: the pentose phosphate pathway and ancillary processes. Malar J 4: 17.
  30. 30. Bozdech Z, Llinas M, Pulliam BL, Wong ED, Zhu J, et al. (2003) The transcriptome of the intraerythrocytic developmental cycle of Plasmodium falciparum. PLoS Biol 1: E5. Epub 2003 Aug 2018.
  31. 31. Roninson IB (2003) Tumor cell senescence in cancer treatment. Cancer Res 63: 2705–2715.
  32. 32. Varna M, Lehmann-Che J, Turpin E, Marangoni E, El-Bouchtaoui M, et al. (2009) p53 dependent cell-cycle arrest triggered by chemotherapy in xenografted breast tumors. Int J Cancer 124: 991–997.
  33. 33. Valderramos SG, Valderramos JC, Musset L, Purcell LA, Mercereau-Puijalon O, et al. (2010) Identification of a mutant PfCRT-mediated chloroquine tolerance phenotype in Plasmodium falciparum. PLoS Pathog 6: e1000887.
  34. 34. Myrick A, Munasinghe A, Patankar S, Wirth DF (2003) Mapping of the Plasmodium falciparum multidrug resistance gene 5′-upstream region, and evidence of induction of transcript levels by antimalarial drugs in chloroquine sensitive parasites. Mol Microbiol 49: 671–683.
  35. 35. Johnson DJ, Owen A, Plant N, Bray PG, Ward SA (2008) Drug-regulated expression of Plasmodium falciparum P-glycoprotein homologue 1: a putative role for nuclear receptors. Antimicrob Agents Chemother 52: 1438–1445. Epub 2008 Jan 1414.
  36. 36. Barnes DA, Foote SJ, Galatis D, Kemp DJ, Cowman AF (1992) Selection for high-level chloroquine resistance results in deamplification of the pfmdr1 gene and increased sensitivity to mefloquine in Plasmodium falciparum. Embo J 11: 3067–3075.
  37. 37. Cowman AF, Karcz S (1993) Drug resistance and the P-glycoprotein homologues of Plasmodium falciparum. Semin Cell Biol 4: 29–35.
  38. 38. Noedl H, Bronnert J, Yingyuen K, Attlmayr B, Kollaritsch H, et al. (2005) Simple histidine-rich protein 2 double-site sandwich enzyme-linked immunosorbent assay for use in malaria drug sensitivity testing. Antimicrob Agents Chemother 49: 3575–3577.
  39. 39. Trager W, Jensen JB (1976) Human malaria parasites in continuous culture. Science 193: 673–675.
  40. 40. Gatton ML, Peters JM, Gresty K, Fowler EV, Chen N, et al. (2006) Detection sensitivity and quantitation of Plasmodium falciparum var gene transcripts by real-time RT-PCR in comparison with conventional RT-PCR. Am J Trop Med Hyg 75: 212–218.
  41. 41. Stahlberg A, Aman P, Ridell B, Mostad P, Kubista M (2003) Quantitative real-time PCR method for detection of B-lymphocyte monoclonality by comparison of kappa and lambda immunoglobulin light chain expression. Clin Chem 49: 51–59.