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A molecular marker of artemisinin-resistant Plasmodium falciparum malaria

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Abstract

Plasmodium falciparum resistance to artemisinin derivatives in southeast Asia threatens malaria control and elimination activities worldwide. To monitor the spread of artemisinin resistance, a molecular marker is urgently needed. Here, using whole-genome sequencing of an artemisinin-resistant parasite line from Africa and clinical parasite isolates from Cambodia, we associate mutations in the PF3D7_1343700 kelch propeller domain (‘K13-propeller’) with artemisinin resistance in vitro and in vivo. Mutant K13-propeller alleles cluster in Cambodian provinces where resistance is prevalent, and the increasing frequency of a dominant mutant K13-propeller allele correlates with the recent spread of resistance in western Cambodia. Strong correlations between the presence of a mutant allele, in vitro parasite survival rates and in vivo parasite clearance rates indicate that K13-propeller mutations are important determinants of artemisinin resistance. K13-propeller polymorphism constitutes a useful molecular marker for large-scale surveillance efforts to contain artemisinin resistance in the Greater Mekong Subregion and prevent its global spread.

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Figure 1: Temporal acquisition of mutations in F32-ART5.
Figure 2: Survival rates of Cambodian parasite isolates in the RSA0–3 h, stratified by K13-propeller allele.
Figure 3: Frequency of K13-propeller alleles in 886 parasite isolates in six Cambodian provinces in 2001–2012.
Figure 4: Parasite clearance half-lives.

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Acknowledgements

We thank the patients and field staff involved in clinical trials and sample collections. We are grateful to the provincial health department directors and other staff of the Cambodian Ministry of Health. Clinical trials and sample collections were supported in part by the Global Fund Grant Malaria Program Rounds 6 (CAM-607-G10M-CNM3) and 9 (CAM-S10-G14-M), the Bill and Melinda Gates Foundation and USAID (through the World Health Organization), the US DOD Global Epidemic Information System, and the Intramural Research Program, NIAID, NIH. Laboratory work was supported by grants from Banque Natixis (to O.M.-P. and D.M.) and Laboratoire d’Excellence IBEID (Agence Nationale de la Recherche, France) and Institut Pasteur, Division International (ACIP A-10-2010). B.W. was supported by a postdoctoral fellowship from Institut Pasteur, Division International; J.B. by an Institut Pasteur Paris Master-Pro fellowship; and D.M. by the French Ministry of Foreign Affairs. We are grateful to the Wellcome Trust Sanger Institute and the MalariaGEN resource centre for sequencing, genotyping and population structure analysis of some Cambodian clinical samples, funded by the Wellcome Trust (098051; 090770/Z/09/Z) and the MRC (G0600718). We thank the Rotary Club-Versailles for funding computer equipment. P.R., D.M.B. and W.O.R. are staff members of the World Health Organization and the US Navy, respectively. They alone are responsible for the views expressed in this publication, and they do not necessarily represent the decisions, policy or views of the World Health Organization or the US Navy.

Author information

Authors and Affiliations

Authors

Contributions

B.W., S.M., A.B. and F.B.-V. produced the F32-ART5 and F32-TEM clonal lines and analysed their survival rates. F.A. and J.B. developed computational components of the whole-genome sequence analysis. C.B. and L.M. performed whole-genome sequencing. F.A., C.A., S.K., V.D., P.L., R.L., S.D., Se.S., So.S., C.M.C., D.M.B., W.O.R., B.G., T.F., P.R., J.L.B., R.M.F. and D.M. conducted clinical studies and collected parasite isolates. A.-C.L., N.K., S.K., V.D., S.M. and A.B. performed PCR and sequencing analyses. B.W., F.B.-V., V.D. and D.M. performed in vitro assays (RSA0–3 h). O.M. provided genotyping and population structure data for Cambodian parasite isolates. J.-C.B. and O.M.-P. performed three-dimensional structure modelling. F.A., R.M.F., F.B.-V., O.M.-P. and D.M. conceived of the study, supervised the project, processed the data and wrote the manuscript with contributions from B.W., C.A., A.B. and J.-C.B.

Corresponding authors

Correspondence to Frédéric Ariey, Rick M. Fairhurst, Françoise Benoit-Vical, Odile Mercereau-Puijalon or Didier Ménard.

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Competing interests

The authors declare no competing financial interests.

Additional information

The following reagents have been deposited to the MR4/BEI by D.M.: MRA-1236 (Plasmodium falciparum IPC 3445 Pailin Cambodia 2010), MRA-1237 (Plasmodium falciparum IPC 3663 Pailin Cambodia 2010), MRA-1238 (Plasmodium falciparum IPC 4884 Pursat Cambodia 2011), MRA-1239 (Plasmodium falciparum IPC 5188 Ratanakiri Cambodia 2011), MRA-1240 (Plasmodium falciparum IPC 5202 Battambang Cambodia 2011) and MRA-1241 (Plasmodium falciparum IPC 4912 Mondulkiri Cambodia 2011).

Extended data figures and tables

Extended Data Figure 1 SNP-calling algorithm and sequence and coverage of SNPs.

a, SNP-calling algorithm of the whole-genome sequence comparison of F32-ART5 and F32-TEM. b, Sequence and coverage of SNPs in seven candidate genes differing in F32-TEM and F32 ART5.

Extended Data Figure 2 Geographic distribution of K13-propeller alleles in Cambodia in 2011–2012.

Pie charts show K13-propeller allele frequencies among 300 parasite isolates in ten Cambodian provinces. Pie sizes are proportional to the number of isolates and the different alleles are colour-coded as indicated. The frequencies (95% confidence interval) of mutant K13-propeller alleles are: Pailin (95%, 88–99, n = 84), Battambang (93%, 87–99, n = 71), Pursat (89%, 67–99, n = 19), Kampot (83%, 52–98, n = 12), Kampong Som (71%, 29–96, n = 7), Oddar Meanchey (76%, 58–89, n = 33), Preah Vihear (16%, 3–40, n = 19), Kratie (71%, 44–90, n = 17), Mondulkiri (67%, 9–99, n = 3) and Ratanakiri (6%, 1–19, n = 35).

Extended Data Figure 3 Correlation between the frequency of wild-type K13-propeller alleles and the prevalence of day 3 positivity after ACT treatment in eight Cambodian provinces.

The frequency of day 3 positivity is plotted against the frequency of wild-type K13-propeller alleles. Data are derived from patients treated with an ACT for P. falciparum malaria in 2010–2012 in eight Cambodian provinces (Extended Data Figure 2): Pailin (n = 86, 2011 WHO therapeutic efficacy study, artesunate-mefloquine); Pursat (n = 32, 2012 WHO therapeutic efficacy study, dihydroartemisinin-piperaquine); Oddar Meanchey (n = 32, 2010 NAMRU-2 therapeutic efficacy study, artesunate-mefloquine); Kampong Som/Speu (n = 7, 2012 WHO therapeutic efficacy study, dihydroartemisinin-piperaquine); Battambang (n = 18, 2012 WHO therapeutic efficacy study, dihydroartemisinin-piperaquine); Kratie (n = 15, 2011 WHO therapeutic efficacy study, dihydroartemisinin-piperaquine); Preah Vihear (n = 19, 2011 WHO therapeutic efficacy study, dihydroartemisinin-piperaquine); Ratanakiri (n = 32, 2010 WHO therapeutic efficacy study, dihydroartemisinin-piperaquine). Spearman’s coefficient of rank correlation (8 sites): r = −0.99, 95% confidence interval −0.99 to −0.96, P < 0.0001.

Extended Data Figure 4 Schematic representation of homology between P. falciparum K13 and human KEAP1 proteins and structural 3D model of the K13-propeller domain.

a, Schematic representation of the predicted PF3D7_1343700 protein and homology to human KEAP1. Similar to KEAP1, PF3D7_1343700 contains a BTB/POZ domain and a C-terminal 6-blade propeller, which assembles kelch motifs consisting of four anti-parallel beta sheets. b, Structural 3D model of the K13-propeller domain showing the six kelch blades numbered 1 to 6 from N to C terminus and colour-coded as in Supplementary Fig. 1. The level of amino-acid identity between the K13-propeller and kelch domains of proteins with solved 3D structures, including human KEAP146,47, enabled us to model the 3D structure of the K13-propeller and to map the mutations selected under ART pressure (Extended data Table 5). The accuracy of the K13-propeller 3D model was confirmed by Modeller-specific model/fold criteria of reliability (see Methods). We predict that the K13-propeller folds into a 6-bladed β-propeller structure48 closed by the interaction between a C-terminal beta-sheet and the N-terminal blade46,48. The first domain has three β-sheets, the fourth one being contributed by an extra C-terminal β-sheet called β'1 in Supplementary Fig. 1. The human KEAP1 kelch propeller scaffold is destabilized by a variety of mutations affecting intra- or inter-blade interactions in human lung cancer46 and hypertension47. The positions of the various mutations are indicated by a sphere, colour-coded as in Figs 24. The M476 residue mutated in F32-ART5 is indicated in dark grey. Like the mutations observed in human KEAP146,47, many K13-propeller mutations are predicted to alter the structure of the propeller or modify surface charges, and as a consequence alter the biological function of the protein. Importantly, the two major mutations C580Y (red) and R539T (blue) observed in Cambodia are both non-conservative and located in organized secondary structures: a β-sheet of blade 4 where it is predicted to alter the integrity of this scaffold and at the surface of blade 3, respectively. The kelch propeller domain of KEAP1 is involved in protein–protein interactions like most kelch containing modules43. KEAP1 is a negative regulator of the inducible Nrf2-dependent cytoprotective response, sequestering Nrf2 in the cytoplasm under steady state. Upon oxidative stress, the Nrf2/KEAP1 complex is disrupted, and Nrf2 translocates to the nucleus, where it induces transcription of cytoprotective ARE-dependent genes49,50. We speculate that similar functions may be performed by PF3D7_1343700 in P. falciparum, such that mutations of the K13-propeller impair its interactions with an unknown protein partner, resulting in a deregulated anti-oxidant/cytoprotective response. The P. falciparum anti-oxidant response is maximal during the late trophozoite stage, when haemoglobin digestion and metabolism are highest51. Its regulation is still poorly understood and no Nrf2 orthologue could be identified in the Plasmodium genome.

Extended Data Table 1 Sequence of the primers used to amplify the genes containing nonsynonymous single-nucleotide polymorphisms in F32-ART5
Extended Data Table 2 Description of the eight nonsynonymous, single-nucleotide polymorphisms acquired in the F32-ART5 compared to the F32-TEM lineage during an effective 5-year discontinuous exposure to increasing concentrations of artemisinin
Extended Data Table 3 Reported characteristics of the genes mutated in F32-ART5 parasites
Extended Data Table 4 Geographic origin and year of collection of archived blood samples studied for K13-propeller polymorphism
Extended Data Table 5 Polymorphisms observed in the K13-propeller in Cambodian P. falciparum isolates collected in 2001–2012 and in The Gambia (ref. 42)
Extended Data Table 6 Association between polymorphisms observed in the K13-propeller and KH subpopulations (ref. 15) in 150 P. falciparum isolates collected in 2009–2010 in Pursat (n = 103) and Ratanakiri (n = 47) provinces, Cambodia

Supplementary information

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Supplementary Data

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Ariey, F., Witkowski, B., Amaratunga, C. et al. A molecular marker of artemisinin-resistant Plasmodium falciparum malaria. Nature 505, 50–55 (2014). https://doi.org/10.1038/nature12876

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