Skip to main content
NCBI home page
Scientific Reports logoLink to Scientific Reports
. 2024 Nov 5;14:26789. doi: 10.1038/s41598-024-76442-6

Temporal genomics in Southern Zambia shows rising prevalence of Plasmodium falciparum mutations linked to delayed clearance after artemisinin-lumefantrine treatment

Abebe A Fola 1, Tamaki Kobayashi 2, Harry Hamapumbu 3, Michael Musonda 3, Ben Katowa 3, Japhet Matoba 3, Jennifer C Stevenson 3, Douglas E Norris 4, Philip E Thuma 3, Amy Wesolowski 2, William J Moss 2,4, Jonathan J Juliano 5,6,7,8,#, Jeffrey A Bailey 1,✉,#
PMCID: PMC11538544  PMID: 39500918

Abstract

The emergence of antimalarial drug resistance is an impediment to malaria control and elimination in Africa. Analysis of temporal trends in molecular markers of resistance is critical to inform policy makers and guide malaria treatment guidelines. In a low and seasonal transmission region of southern Zambia, we successfully genotyped 85.5% (389/455) of Plasmodium falciparum samples collected between 2013 and 2018 from 8 spatially clustered health centres using molecular inversion probes (MIPs) targeting key drug resistance genes. Aside from one sample from 2016 carrying K13 622I, no other World Health Organization-validated or candidate artemisinin partial resistance (ART-R) mutations were observed. However, in the more recent years (2016–2017) five novel K13-propeller-domain mutations, C532S, A578S, Q613E, D680N and G718S were identified at low prevalence. Moreover, 13% (CI, 9.6–17.2) of isolates had the AP2MU 160N mutation, which has been associated with delayed clearance following artemisinin combination therapy in Africa. This mutation increased in prevalence between 2015 and 2018 and bears a genomic signature of selection. During this time period, there was an increase in the MDR1 NFD haplotype that is associated with reduced susceptibility to lumefantrine. Sulfadoxine-pyrimethamine polymorphisms were near fixation. While validated ART-R mutations are rare, a mutation associated with slow parasite clearance in Africa appears to be under selection in southern Zambia.

Keywords: Plasmodium falciparum, Antimalarial resistance, Kelch13, Genomics, Zambia

Subject terms: Malaria, Parasite genomics

Introduction

Plasmodium falciparum malaria remains an overwhelming problem in Africa, where approximately 90% of global cases and deaths occur, affecting primarily children younger than five years of age and pregnant women1. Effective antimalarial drugs are a cornerstone for both malaria treatment and prevention. The emergence, continued evolution, and spread of antimalarial drug resistance will undermine ongoing control and elimination efforts2–4. Of particular concern is evolving artemisinin and artemisinin combination therapy (ACT) resistance in eastern Africa5–8. Resistance phenotyping of parasites ex vivo and clinical therapeutic efficacy studies (TESs) are useful tools for assessing resistance9; however, phenotypic characterization is technically challenging, costly, labor-intensive, and not as scalable as widespread genomic surveillance. Importantly, parasite clearance curves in TES are less informative in African regions where most infections consist of multiple parasite strains10,11, such that resistant parasites are likely to share their host with sensitive parasites, thus obscuring detection, particularly early during the spread of resistance11,12. Molecular surveillance of P. falciparum parasites is a powerful tool for early detection of emergence and monitoring the spread of antimalarial drug resistance for known resistance mutations and genes.

Currently, the World Health Organization (WHO) recommends artemisinin combination therapy (ACT) as first-line treatment in all African countries4. Sulfadoxine-pyrimethamine (SP) is the recommended first line drug for the intermittent preventive treatment for pregnant women (IPTp) and infants (IPTi) living in high-transmission areas of Africa. However, studies provide evidence that P. falciparum has developed resistance to most available antimalarial drugs including SP and ACTs3,12–15. For ACTs, TES from several African countries have shown early signs of treatment failure of the ACT partner drugs by day 28 or 4216,17. Moreover, recent studies showed increased frequency of WHO-validated/candidate mutations (R561H, A675V and C469Y) in the pfkelch13 (K13) gene in Rwanda, Tanzania and Uganda6,19,20 and 622I in Eritrea and Ethiopia5,7. Decreased lumefantrine susceptibility has been documented in northern Uganda and in the context of clinical failures8,21. The emergence of artemisinin partial resistance (ART-R) and decreasing lumefantrine sensitivity in Africa likely heralds the future accelerated evolution of partner drug resistance and the eventual clinical failure of ACTs, as seen in Southeast Asia22,23.

In Zambia, despite intensified control interventions, P. falciparum malaria remains endemic with high heterogeneity in transmission at the provincial level24. Chloroquine was the frontline drug along with SP until 2002 when ACTs were adopted, with artemisinin-lumefantrine (AL) the predominant first-line treatment nationwide. SP is still employed for IPTp25. The emergence and spread of antimalarial drug resistance in neighbouring countries, particularly artemisinin partial resistance19,26, is an additional threat to national malaria control and elimination. In areas of low transmission, premunition will decrease27 and may enhance the emergence of drug resistance, as a greater proportion of infected individuals seek treatment thereby exposing the parasite population to greater drug pressure28. And once evolved, drug resistant strains that can survive treatment can potentiate malaria resurgence and epidemics29,30. Strong molecular malaria surveillance using multiplexed molecular genotyping and data analysis tools is essential to track the early emergence and spread of antimalarial drug resistance mutations to mitigate its impact.

Despite these concerns, temporally informative surveillance of antimalarial drug resistance in Zambia is limited. While validated mutations associated with ART-R in the K13 gene have not been detected, parasites carrying mutations that confer SP resistance are highly prevalent in the country25,31. Therefore, serial or continuous collections of samples can help us better understand trends in antimalarial drug resistance. Here, we successfully genotyped 389 P. falciparum samples collected from 2012 to 2018 from 8 health centres in Choma District, a low malaria transmission zone in Southern Province, Zambia, using 815 molecular inversion probes (MIPs) targeting 14 key P. falciparum drug resistance genes to determine temporal trends in resistance mutations. In addition, we conducted whole genome sequencing (WGS) on 28 samples collected in 2019 from the same region to evaluate genomic signatures of selection across important antimalarial resistance polymorphisms. Together, these findings suggest that P. falciparum parasites in Zambia are under strong selective pressure with an increase in prevalence and genomic evidence of positive selection at loci that may have an impact on ACT effectiveness.

Results

K13 gene polymorphism

From the 389 (85%) samples successfully sequenced, we first assessed the prevalence of WHO validated or candidate mutations associated with ART-R. One sample from Mangunza Health Centre in 2016 carried the validated ART-R K13 622I pure mutant. This mutation has become common in the Horn of Africa and was recently validated in TES in Eritrea5,7,32. Five novel mutations, C532S, A578S, Q613E, D680N and G718S, within the Kelch 13 propeller domain were identified from samples collected in more recent years (mainly in 2016 and 2017) and 6 non-synonymous (NS) mutations outside the Kelch 13 propeller domain were found at low frequencies (Supplementary Figure S1A). The A578S mutation is close to the validated mutation C580Y, which is associated with ART-R in South east Asia. We also identified the commonly found K189T polymorphism in Africa that is not associated with resistance in 13.2% (95% CI, 9.5–17.4) of samples. The Kelch 13 mutations R561H, A675V, and C469Y recently reported in Rwanda, Tanzania, and Uganda33 were not identified. None of the samples carried mutations that were associated with the genetic backbone for ART-R in Southeast Asia such as apicoplast ribosomal protein s10 (ARPS10 V127M), ferredoxin (FD D193Y), P. falciparum multidrug resistance 2 transporter (MDR2 T484I), putative phosphoinositide-binding protein (PIB7 C1484F), P. falciparum protein phosphatase (PPH V1157L), and P. falciparum chloroquine resistance transporter (CRT N326S and CRT I356T). The prevalence of other key mutations identified in this study can be found in Supplementary Figure S1B, Tables S1 and S2.

Emerging S160N mutation in Plasmodium falciparum protein complex 2 mu subunit (AP2MU) gene

We assessed polymorphisms in the AP2MU gene from samples collected from the Macha Hospital catchment area within Choma District, Southern Province, Zambia with variable epidemiology and seasonal dynamics of malaria (Fig. 1A). The analysis revealed a significant increasing trend in prevalence of 160N (Jonckheere-Terpstra test, p-value = 0.017), a mutation putatively associated with ACT delayed clearance in Africa34,35, from 0% in 2012 and 2013 to 16.3% in 2018 (Fig. 1B). The 160N mutation was detected in samples from all health facilities with spatial heterogeneity (range 6.6% in Kabanze to 17.6% in Kamwanu) (Fig. 1C). All parasites carrying the 160N mutation also carried the CRT wild-type (C72, V73, M74, N75, K76) haplotype suggesting reversal to chloroquine sensitive strains in Zambia.

Fig. 1.

Fig. 1

Open in a new tab

Malaria and spatio-temporal trends of AP2MU S160N mutations. (A) The top panel shows trends of RDT positive cases at the 8 health centres over the course of the study (blue dotted line) and the number of dried blood spot (DBS) samples collected by month over the course of the study (grey bars). Note that only 8 samples were collected in 2012 and are not shown in the figure. (B) Temporal prevalence of S160N mutations in Choma District, Southern Province, Zambia. (C) Prevalence of the S160N mutation at the health facility level. Each bar in the barplot represents the mean prevalence values while the error bars indicate the standard deviation (SD).

P. falciparum ATPase 6 (ATP6) and P. falciparum ubiquitin-specific protease 1 (UBP1) gene polymorphisms

Of the four mutations L263E, E431K, A623E, S769N in the ATPase 6 gene previously associated with increased artemether IC50, 13.1% (CI, 9.4–16.5) of genotyped samples had the E431K mutation. One additional mutation, N569E, was found at a high frequency (31.1%, CI, 26.5–37.2) with less temporal variation. We detected additional nonsynonymous (NS) mutations in the ATPase 6 gene at low frequency (Fig. 2A). In UBP1, two key mutations D1525E, and E1528D, also are associated with delayed clearance of parasites in Africa35,36. These mutations were found at 4.7% (CI, 2.4–7.4) and 8.7% (CI, 5.4–11.9) prevalence, respectively. Genotyped samples also had an additional 30 different NS mutations in UBP1 gene with variable prevalence (range 2% to 100%) (Fig. 2B).

Fig. 2.

Fig. 2

Open in a new tab

Non-synonymous polymorphisms in ATPase 6 (A) and UBP1 (B) genes. The bar plots show the prevalence and colours whether the mutation was present in the artemisinin resistant marker within each gene.

Increased prevalence of multi drug resistance 1 (MDR1) NFD haplotype

Mutations in the P. falciparum multi-drug resistance gene 1 (MDR1), particularly MDR1 N86, 184F, and D1246 (NFD haplotype), are associated with decreased sensitivity to lumefantrine37,38. Overall, 41% (CI, 34.4–45.1) of genotyped samples carried the Y184F mutant allele (Fig. 3A, Supplementary Table S1) with some spatial variation (Fig. S2). Overall, 38.5% (121/314) of samples carried the NFD haplotype with a statistically significant (Jonckheere-Terpstra test, p-value = 0.027) increasing prevalence over time (Fig. 3B). Only 3 genotyped samples carried the N86Y mutation that has been associated with enhanced resistance to chloroquine, further supporting reversal to chloroquine-sensitive parasites in southern Zambia.

Fig. 3.

Fig. 3

Open in a new tab

Temporal trends of MDR1 N86, 184F and D1246 mutations in Choma District, Southern Province, Zambia. Colours indicate genotypes (mutant or wildtype) (A) and haplotypes combination of N86/184F/D1246 at MDR1 gene (B).

Spatio-temporal trend of DHFR and DHPS mutations

Resistance to sulfadoxine-pyrimethamine (SP) in P. falciparum is determined by multiple mutations in two genes encoding dihydrofolate reductase (DHFR) related to pyrimethamine and dihydropteroate synthase (DHPS) related to sulfadoxine (Supplementary Table S1). Overall, 91.8% (290/316) of genotyped samples carried point mutations at codons DHFR 51I, 59R, and 108N (IRN-triple mutant). Also, 80.8% (237/293) of genotyped samples carried point mutations at codons DHPS 437G and 540E (GE-double mutant). Overall, 78.8% (220/279) of samples carried five mutations at DHFR 51I/59R/108N and DHPS 437G/540E (IRNGE), the quintuple mutant (Fig. 4A), which remained stable at a high prevalence over the study period (Fig. 4B). Only two sequenced samples (one from Nalube Rural Health Center in 2016 and the other from Mbabala Rural Health Center in 2017) carried the DHPS 581G mutation, which increases SP resistance in combination with the DHFR-DHPS quintuple mutant haplotype. None of the isolates had the DHFR 164L mutation that confers high-level resistance to SP.

Fig. 4.

Fig. 4

Open in a new tab

Prevalence of DHFR/DHPS genotypes in Choma District, Southern Province, Zambia. (A) UpSet plots showing the number of times each combination of mutations (all monoclonals or major strain in cases of polyclonal infections) were seen for Pfdhfr and Pfdhps genes with IRNGE quintuple mutant parasites the most common. (B) Temporal trends of IRNGE quintuple mutant across Choma District, Southern Province, Zambia. IRNGE = DHFR 51I/59R/108N and DHPS 437G/540E genotypes.

Spatio-temporal trend of S258L mutation in putative P. falciparum amino acid transporter (AAT1) gene

The 258L mutation in the AAT1 gene was found at high frequency (range 89.4–100%) across different health facilities and increased in frequency at end of study period (Fig. 5). There was no statistically significant trend in prevalence of the 258L mutation (Jonckheere-Terpstra test, p-value = 0.23). However, none of the isolates carried AAT1 313S, a key mutation that augments CQ resistance when it occurs together with 258L39. Importantly, all isolates carried CRT K76 wild-type across the study period, suggesting CQ sensitivity was maintained in Zambia.

Fig. 5.

Fig. 5

Open in a new tab

Spatio-temporal trends of the AAT1 S258L mutation in Choma District, Southern Province, Zambia. (A) Temporal prevalence S258L mutation in Choma District, Southern Province, Zambia. (B) Prevalence of the S258L mutation at the health facility level. Each bar in barplot represents the mean values while the error bars indicate the standard deviation (SD) of the data.

Haplotype-based signatures of selection in southern Zambia

Using WGS from 2019 and integrated haplotype score (iHS) statistics analysis, we identified several loci under positive selection in the parasite population (Fig. 6A). Except for the AP2MU gene, no selection signals were observed around the five known drug resistance genes that include CRT, MDR1, DHFR, DHPS, and K13. The extended haplotype homozygosity (EHH) analysis detected significantly increased EHH associated with the mutant allele AP2MU 160N (Fig. 6B,C, red line), which is indicative of a a selective sweep. Moreover, the iHS analysis identified the selection signals for important genes such PfEMP1 (P. falciparum erythrocyte membrane protein ) that has been shown to be involved in sequestration and antigenic variation, other genes implicated in roles for immune evasion, aggregation, or cytoadherence to microvasculature repetitive interspersed families of polypeptides (RIFINs), subtelomeric variable open reading frame (STEVOR), vaccine candidate P. falciparum apical membrane antigen 1 (AMA1) and other genes (see list of genes under selection in Supplementary Table S3). Signals across these loci may be indicative of decreasing effective population size and increasing clonal spread in the population.

Fig. 6.

Fig. 6

Open in a new tab

Genome-wide scans for selection signals (A) Integrated Haplotype Score (iHS) Manhattan plot with individual chromosomes identified by alternate coloring of their SNPs. Genes with high scoring SNPs (iHS > 2) suggest recent positive directional selection. (B) Extended haplotype homozygosity (EHH) plot showing the homozygosity of the most frequent extended haplotype around the SNP 160N. X-axis of the EHH plot shows the upstream and downstream genomic coordinates from the locus of interest. Homozygosity scale is shown on the y-axis, ranging from 0 to 1 (0 implying no homozygosity and 1 complete homozygosity). Haplotype bifurcation diagrams in panel (C) showing breakdown of linkage disequilibrium in individuals carrying the ancestral and derived allele of the polymorphism 160N. This is bidirectional with the root representing a core SNP (at 160N) depicted by a vertical dotted line. The thickness of the line corresponds to the number of samples with shared haplotype.

Discussion

P. falciparum is continuously evolving to become resistant to antimalarials. There is a particular concern for this selection to be accelerated in low transmission regions, where decreased immunity may lead to an increase in symptomatic cases which eventually increase drug treatment and selective pressure on the parasite population27,28. In low transmission settings, the high number of monogenomic infections and clonal recombination of closely related parasites reduce competition with wildtype strains. In contrast, high transmission settings feature increased within-host competition, polyclonality, and genetic diversity, which slows the spread of drug resistance40,41. Choma District, Southern Province, Zambia is such a low transmission region yet little longitudinal data exist on molecular markers of antimalarial drug resistance or genomic signatures of how the parasite population may be changing in the face of control efforts. Here, we provide evidence through longitudinal molecular surveillance, supported by genomic analysis of selection, of temporal changes in antimalarial drug resistance that are concerning for both antimalarial therapies with ACTs and chemoprevention with antifolate drugs. As endemic countries like Zambia successfully approach pre-elimination, implementation of genomic surveillance can clarify how current control methods affect parasite populations and monitor for the emergence and spread of antimalarial drug resistance42.

Zambia was the first African country to adopt the use of ACTs with the use of artemether-lumefantrine (AL) in 2002, highlighting the importance of monitoring ART-R mutations that arise de novo, in addition potential importation of resistant mutations43, as well as resistance mutations to the partner drugs leading to treatment failure. The country is landlocked and shares a border with countries where malaria transmission is high, which could contribute to the potential for parasite importation43. Another concern is that the practice of using different antimalarial drugs for mass drug administration (MDA)44 could lead to the natural selection of drug-resistant P. falciparum strains45. Genotyping drug resistance markers from longitudinally collected samples from low transmission settings provides the opportunity to assess how past and current intensified malaria control have affected the emergence and spread of drug resistant strains, providing an early warning system.

None of isolates in this study carried WHO validated or candidate mutations R561H, A675V and C469Y found commonly in east Africa19,46. Interestingly, one sample had the WHO validated artemisinin partial resistance marker Kelch 13 R622I common in the Horn of Africa. Identification of a Kelch 13 R622I mutation in this low transmission setting could mean one of two things: 1) parasites have emerged in the region bearing ART-R mutations; or 2) parasites have been imported that have this mutation. This study cannot answer that question but supports the need for intensified surveillance. A recent study from Ethiopia5,32 and Eritrea7 have also shown that the Kelch 13 622I mutation can co-exist with diagnostic-resistant histidine-rich proteins 2 and 3 (Pfhrp2/3) deleted parasite strains in low transmission settings. Moreover, we found five novel de novo mutations C532S, A578S, Q613E, D680N and G718S within the Kelch 13 propeller domain suggesting that parasites are under selective pressure due to continued use of ACT. This warrants close monitoring of the emergence and spread of mutations associated with ART-R.

Also of concern for artemisinin efficacy in Zambia are the findings of increasing AP2MU mutations. The 160N mutation was associated with artemisinin resistance in Africa35,36. Our data suggest an increasing prevalence in the community (Fig. 1), and genomic signatures of positive selection at this site are consistent with selection due to drug pressure (Fig. 6). This supports the finding of directional selection at this locus in the African P. falciparum population47. This combination of findings suggests that current ACT practices are impacting the parasite population, although further investigation into the phenotypic association between this mutation and antimalarial resistance is needed. Other posited markers of ART-R, such as UBP1 and ATP6, did not have concerning signatures in our analysis.

Our genomic analysis of P. falciparum parasites from southern Zambia identified known resistance mutations in DHFR, DHPS, MDR1, and other genes. This was not surprising since mutations in these genes have been previously documented within Zambia48. While AL retains its efficacy against uncomplicated P. falciparum malaria in Zambia, the increase in recent years in the MDR1 NFD haplotype associated with decreased lumefantrine susceptibility calls for close monitoring for partner drug resistance49. We did not find any evidence of mutations that would affect amodiaquine efficacy. Given the alternating selective pressure between lumefantrine and amodiaquine in neighbouring countries like Tanzania, the persistence of amodiaquine sensitivity is important for Zambia’s malaria control program.

The effectiveness of antimalarials used for chemoprevention is also compromised by resistance. SP is a widely used drug for IPTp and IPTi in southern Zambia25. Persistence and potential increases in the IRNGE genotypes threaten the utility of this drug for chemoprevention. Our findings support other studies in Africa that have also found that the prevalence of SP resistance mutations is high14,50. However, none of the parasites in the current study carried DHFR 164L and only 2 samples carried the DHPS 581G mutation that confers high-level resistance to sulfadoxine. The WHO-recommended thresholds for the withdrawal of SP for IPTp are when DHPS 540E > 95% and 581G > 10%, which this area of Zambia has not yet reached51.

Interestingly, WGS showed evidence of recent positive selection among variable antigen genes often involved in immune evasion, which may be indicative of soft sweeps related to balancing selection. We previously showed that there is a high degree of clonal transmission in this area through evaluation of identity by descent (IBD)52. Reductions in effective population size and increasing clonal transmission may also impact these hypervariable genes identified under selection in the iHS analysis (Fig. 6A). These signatures of selection may indicate strong selective pressure on the parasite population, contributing to the increased prevalence and emergence of different mutations in southern Zambia. However, the absence of detailed information on the relationship between the observed mutations and treatment outcomes represents a significant gap in our analysis. We suggest that future research focus on this critical area, including in vivo and in vitro phenotypes such as ACT failure and susceptibility to artemisinin/lumefantrine, to provide a more comprehensive understanding of drug resistance dynamics.

Our study provides insights into the prevalence of drug resistant genetic markers and how current and past control efforts have affected malaria parasite populations through longitudinal molecular surveillance and genomic analysis for signatures of selection. The presence of mutations associated with reduced efficacy for malaria therapy with ACTs and malaria chemoprevention with SP are concerning for malaria control efforts in the region. Coordinated efforts for widespread longitudinal molecular surveillance, combined with appropriate sampling design and collection of travel histories, will be essential for achieving malaria elimination. This study provides additional data for developing a starting point to help engage stakeholders and provide preliminary data and hypotheses for ramping up malaria molecular surveillance in low transmission settings like southern Zambia. Therefore, continued monitoring of temporal changes in frequency and distribution of antimalarial drug resistance across Zambia is warranted.

Methods

Sample collection

Samples that underwent MIP genotyping were dried blood spots (DBS, n = 455) collected between 2012 and 2018 from symptomatic RDT-positive cases at 8 health centres within the catchment area of Macha Hospital in Choma District, Southern Province that encompasses 2,000 km2. In addition, 28 samples from persons presenting with uncomplicated malaria in 2019 to Mapanza Rural Health Center within the catchment areas of Macha Hospital underwent WGS.

MIP sequencing of drug resistance loci

All methods were carried out in accordance with relevant guidelines and regulations in Brown University (RI, USA). We used previously approved and published MIP capturing and sequencing protocols approved by Bailey Laboratory, Department of Pathology and Laboratory Medicine, Brown University. In brief, DNA was extracted from each of the 455 DBS specimens with a Chelex-Tween protocol53. Parasitemia was assessed using quantitative PCR with probes targeting the P. falciparum ldh sequence54. All samples were then genotyped using a MIP panel (n = 815) targeting known drug resistant SNP mutations and select coding sequences in 14 genes across the P. falciparum genome55. MIP capture and library preparation were done as previously described56. Sequencing was conducted using an Illumina NextSeq 550 instrument (150 bp paired-end reads) at Brown University (RI, USA). All methods were carried out in accordance with relevant guidelines and regulations. All experimental protocols were approved by a named institutional and/or licensing committee.

MIP data analysis and estimating drug resistance prevalence

Processing of sequencing data and variant calling was done using MIPtools (v0.19.12.13; https://github.com/bailey-lab/MIPTools), a suite of computational tools designed to process MIP Illumina sequencing and provide haplotype and variant calls. Briefly, after demultiplexing samples, raw reads from each captured MIP, identifiable using unique molecular identifiers (UMIs), were used to reconstruct sequences using MIPWrangler, and variant calling was performed on these samples using freebayes57. Biallelic, variant SNP positions were retained for analysis. Variants were annotated using the 3D7 v3 reference genome. To reduce false positives due to PCR and sequencing errors, the alternative allele (SNP) must have been supported by more than one UMI within a sample with minimum coverage of 10x, and the alternative allele must have been represented by at least 10 UMIs across the entire population.

The prevalence of point mutations for each drug resistance markers was calculated as p = x/n*100, where p = prevalence, x = number of infections with mutant alleles, n = number of successfully genotyped infections. Mutant combinations were plotted and visualized using UpSet Package in R58. Where appropriate, all outputs were visualized using the ggplot2 package in R.

Whole genome sequencing and tests for signatures of selection

The 28 P. falciparum DBS samples were punched and extracted using a QIAcube HT instrument and QIAmp 96 DNA kit (Qiagen: Hilden, Germany) using an optimized high throughput genomic DNA (gDNA) extraction protocol59. gDNA quantity and quality were assessed using Qubit 1 × dsDNA High Sensitivity Assay (ThermoFisher Scientific: Waltham, MA) and ScreenTape on the Agilent TapeStation 4150 (Agilent Technologies: Santa Clara, CA), respectively. A 4-plex hybrid capture method using SeqCap EZ custom probes60 was then used to selectively enrich P. falciparum DNA from DBS samples containing host DNA due to the low parasitaemia in these samples. All WGS samples were sequenced using NovaSeq 6000 technology system (Illumina: San Diego, CA), and paired-end reads were mapped to the P. falciparum 3D7 reference genome using BWA-MEM 0.7.17. Variants and down streaming filtering were called using GATK v4.1.4.121 following best practices (https://software.broadinstitute.org/gatk/best-practices). From the unphased VCF file, we calculated selection using the standardized integrated haplotype score (iHS) and estimated integrated extended-haplotype homozygosity (EHH) values between alleles at a given SNP. All associated iHS and EHH calculations were carried out using the R-package rehh (version 2.0.4). (https://cran.r-project.org/web/packages/rehh/index.html).

Statistical analysis

All references to an analysis in a ‘package’ indicate the analysis was performed in R version 4.2.2 software. Where appropriate, all outputs were visualized using the ggplot2 package in R. The Jonckheere-Terpstra test used to assess whether there is a significant trend in the prevalence of resistance markers of P. falciparum over time using `jonckheere.test` from the `DescTools` from R package. A P-value of ≤ 0.05 was considered statistically significant.

Ethics approval

This work was approved as part of the Southern and Central Africa International Center of Excellence for Malaria Research (ICEMR) by the Tropical Diseases Research Centre, Ndola Ethics Review Committee (Ref No: TDRC/ERC/2010/14/11) and the Johns Hopkins Bloomberg School of Public Health Institutional Review Board (IRB # 3467).

The study adhered to the ethical principles outlined in the Declaration of Helsinki. All participants provided written informed consent, and for individuals falling below the age of 18 years but exceeding three months, informed assent and consent from a parent or guardian were obtained. Participants had the right to voluntarily withdraw from the study and have their samples removed at any time. Sequence analysis using parasite genomes from de-identified samples and data were deemed nonhuman subjects of research at the University of North Carolina at Chapel Hill (NC, USA) and Brown University (RI, USA).

Supplementary Information

Supplementary Information 1. (388.3KB, docx)
Supplementary Information 2. (109.6KB, xlsx)
Supplementary Information 3. (1MB, xlsx)
Supplementary Information 4. (19.7KB, csv)

Acknowledgements

We thank the participants of the study.

Author contributions

AAF, JJJ and JAB conceived the study. TK, TS and HH were responsible for DNA extraction and molecular diagnosis of Plasmodium falciparum infections. AAF did MIP initial data analyses. AAF, JJJ and JAB wrote the manuscript and undertook final data analyses and interpretations. All authors contributed to the writing of the manuscript and approved the final version.

Funding

This work was supported by the National Institute for Allergy and Infectious Diseases (U19AI089680 to WJM, K24AI134990 to JJJ, R01AI139520 to JAB).

Data availability

All MIP sequencing data available under Accession no. SAMN29983042—SAMN29983315 at the Sequence Read Archive (SRA) (http://www.ncbi.nlm.nih.gov/sra), and the associated BioProject alias is PRJNA862735.

Declarations

Competing interests

The authors declare no competing interests.

Footnotes

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

These authors jointly supervised this work: Jonathan J. Juliano and Jeffrey A. Bailey.

Supplementary Information

The online version contains supplementary material available at 10.1038/s41598-024-76442-6.

References

  • 1.WHO. World malaria report 2023 (World Health Organization, 2023).
  • 2.Hamilton, W. L. et al. Evolution and expansion of multidrug-resistant malaria in southeast Asia: A genomic epidemiology study. Lancet Infect. Dis.19, 943–951 (2019). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 3.Imwong, M. et al. The spread of artemisinin-resistant Plasmodium falciparum in the Greater Mekong subregion: A molecular epidemiology observational study. Lancet Infect. Dis.17, 491–497 (2017). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 4.Conrad, M. D. & Rosenthal, P. J. Antimalarial drug resistance in Africa: The calm before the storm?. Lancet Infect. Dis.19, e338–e351 (2019). [DOI] [PubMed] [Google Scholar]
  • 5.Fola, A. A. et al. Plasmodium falciparum resistant to artemisinin and diagnostics have emerged in Ethiopia. Nat Microbiol10.1038/s41564-023-01461-4 (2023). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 6.Conrad, M. D. et al. Evolution of partial resistance to artemisinins in malaria parasites in Uganda. N. Engl. J. Med.389, 722–732 (2023). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 7.Mihreteab, S. et al. Increasing prevalence of artemisinin-resistant HRP2-negative malaria in eritrea. N. Engl. J. Med.389, 1191–1202 (2023). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 8.van Schalkwyk, D. A. et al. Treatment failure in a UK malaria patient harboring genetically variant Plasmodium falciparum from Uganda with reduced in vitro susceptibility to artemisinin and lumefantrine. Clin. Infect. Dis.78, 445–452 (2024). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 9.White, N. J. The parasite clearance curve. Malar. J.10, 278 (2011). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 10.Bushman, M. et al. Within-host competition and drug resistance in the human malaria parasite Plasmodium falciparum. Proc. Biol. Sci.283, 20153038 (2016). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 11.Mideo, N. et al. Ahead of the curve: Next generation estimators of drug resistance in malaria infections. Trends Parasitol.29, 321–328 (2013). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 12.Orwa, T. O., Mbogo, R. W. & Luboobi, L. S. Multiple-strain malaria infection and its impacts on Plasmodium falciparum resistance to antimalarial therapy: A mathematical modelling perspective. Comput. Math. Methods Med.2019, 9783986 (2019). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 13.Wellems, T. E. & Plowe, C. V. Chloroquine-resistant malaria. J. Infect. Dis.184, 770–776 (2001). [DOI] [PubMed] [Google Scholar]
  • 14.Amimo, F. et al. Plasmodium falciparum resistance to sulfadoxine-pyrimethamine in Africa: a systematic analysis of national trends. BMJ Glob Health5, e003217 (2020). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 15.Phyo, A. P. et al. Emergence of artemisinin-resistant malaria on the western border of Thailand: A longitudinal study. Lancet379, 1960–1966 (2012). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 16.Roberts, L. Malaria wars. Science352(398–402), 404–405 (2016). [DOI] [PubMed] [Google Scholar]
  • 17.Silva-Pinto, A. et al. Artemether-lumefantrine treatment failure of uncomplicated Plasmodium falciparum malaria in travellers coming from Angola and Mozambique. Int. J. Infect. Dis.110, 151–154 (2021). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 18.van Loon, W. et al. In vitro confirmation of artemisinin resistance in Plasmodium falciparum from patient isolates, Southern Rwanda, 2019. Emerg. Infect. Dis.28, 852–855 (2022). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 19.Uwimana, A. et al. Emergence and clonal expansion of in vitro artemisinin-resistant Plasmodium falciparum kelch13 R561H mutant parasites in Rwanda. Nat. Med.26, 1602–1608 (2020). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 20.Moser, K. A. et al. Describing the current status of Plasmodium falciparum population structure and drug resistance within mainland Tanzania using molecular inversion probes. Mol. Ecol.30, 100–113 (2021). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 21.Tumwebaze, P. K. et al. Decreased susceptibility of Plasmodium falciparum to both dihydroartemisinin and lumefantrine in northern Uganda. Nat. Commun.13, 6353 (2022). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 22.Phyo, A. P. et al. Declining efficacy of artemisinin combination therapy against P. falciparum malaria on the Thai-Myanmar Border (2003–2013): The role of parasite genetic factors. Clin. Infect. Dis.63, 784–791 (2016). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 23.van der Pluijm, R. W. et al. Determinants of dihydroartemisinin-piperaquine treatment failure in Plasmodium falciparum malaria in Cambodia, Thailand, and Vietnam: a prospective clinical, pharmacological, and genetic study. Lancet Infect. Dis.19, 952–961 (2019). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 24.Lubinda, J. et al. Spatio-temporal monitoring of health facility-level malaria trends in Zambia and adaptive scaling for operational intervention. Commun. Med.2, 79 (2022). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 25.Sitali, L. et al. Surveillance of molecular markers for antimalarial resistance in Zambia: Polymorphism of Pfkelch 13, Pfmdr1 and Pfdhfr/Pfdhps genes. Acta Trop.212, 105704 (2020). [DOI] [PubMed] [Google Scholar]
  • 26.Kirby, R. et al. Examining the early distribution of the artemisinin-resistant Plasmodium falciparum kelch13 R561H mutation in areas of higher transmission in Rwanda. Open Forum Infect Dis10, ofad149 (2023). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 27.Fowkes, F. J. I., Boeuf, P. & Beeson, J. G. Immunity to malaria in an era of declining malaria transmission. Parasitology143, 139–153 (2016). [DOI] [PubMed] [Google Scholar]
  • 28.Whitlock, A. O. B., Juliano, J. J. & Mideo, N. Immune selection suppresses the emergence of drug resistance in malaria parasites but facilitates its spread. PLoS Comput. Biol.17, e1008577 (2021). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 29.Volkman, S. K. et al. Application of genomics to field investigations of malaria by the international centers of excellence for malaria research. Acta Trop.121, 324–332 (2012). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 30.Klein, E. Y. Antimalarial drug resistance: A review of the biology and strategies to delay emergence and spread. Int. J. Antimicrob. Agents41, 311–317 (2013). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 31.Ippolito, M. M. et al. Therapeutic efficacy of artemether-lumefantrine for uncomplicated falciparum malaria in Northern Zambia. Am. J. Trop. Med. Hyg.103, 2224–2232 (2020). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 32.Bayih, A. G. et al. A unique Plasmodium falciparum K13 gene mutation in Northwest Ethiopia. Am. J. Trop. Med. Hyg.94, 132–135 (2016). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 33.Young, N. W. et al. High frequency of artemisinin partial resistance mutations in the great lake region revealed through rapid pooled deep sequencing. medRxiv10.1101/2024.04.29.24306442 (2024). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 34.Henrici, R. C., van Schalkwyk, D. A. & Sutherland, C. J. Modification of pfap2μ and pfubp1 Markedly Reduces Ring-Stage Susceptibility of Plasmodium falciparum to Artemisinin In Vitro. Antimicrob. Agents Chemother.10.1128/AAC.01542-19 (2019). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 35.Adams, T. et al. Prevalence of Plasmodium falciparum delayed clearance associated polymorphisms in adaptor protein complex 2 mu subunit (pfap2mu) and ubiquitin specific protease 1 (pfubp1) genes in Ghanaian isolates. Parasit. Vectors11, 175 (2018). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 36.Feng, J. et al. Characterization of pfmdr1, pfcrt, pfK13, pfubp1, and pfap2mu in Travelers Returning from Africa with Plasmodium falciparum Infections Reported in China from 2014 to 2018. Antimicrob. Agents Chemother.65, e0271720 (2021). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 37.Happi, C. T. et al. Selection of Plasmodium falciparum multidrug resistance gene 1 alleles in asexual stages and gametocytes by artemether-lumefantrine in Nigerian children with uncomplicated falciparum malaria. Antimicrob. Agents Chemother.53, 888–895 (2009). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 38.Adamu, A. et al. Plasmodium falciparum multidrug resistance gene-1 polymorphisms in Northern Nigeria: Implications for the continued use of artemether-lumefantrine in the region. Malar. J.19, 439 (2020). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 39.Amambua-Ngwa, A. et al. Chloroquine resistance evolution in Plasmodium falciparum is mediated by the putative amino acid transporter AAT1. Nat. Microbiol.8, 1213–1226 (2023). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 40.Camponovo, F., Buckee, C. O. & Taylor, A. R. Measurably recombining malaria parasites. Trends Parasitol.39, 17–25 (2023). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 41.Bushman, M., Antia, R., Udhayakumar, V. & de Roode, J. C. Within-host competition can delay evolution of drug resistance in malaria. PLoS Biol.16, e2005712 (2018). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 42.Neafsey, D. E., Taylor, A. R. & MacInnis, B. L. Advances and opportunities in malaria population genomics. Nat. Rev. Genet.22, 502–517 (2021). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 43.Su, X.-Z., Lane, K. D., Xia, L., Sá, J. M. & Wellems, T. E. Plasmodium genomics and genetics: New insights into malaria pathogenesis, drug resistance, epidemiology, and evolution. Clin. Microbiol. Rev.32(4), 10–128 (2019). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 44.Finn, T. P. et al. Treatment coverage estimation for mass drug administration for malaria with dihydroartemisinin-piperaquine in Southern Province. Zambia. Am. J. Trop. Med. Hyg.103, 19–27 (2020). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 45.White, N. J. Does antimalarial mass drug administration increase or decrease the risk of resistance?. Lancet Infect. Dis.17, e15–e20 (2017). [DOI] [PubMed] [Google Scholar]
  • 46.Balikagala, B. et al. Evidence of artemisinin-resistant malaria in Africa. N. Engl. J. Med.385, 1163–1171 (2021). [DOI] [PubMed] [Google Scholar]
  • 47.Henriques, G. et al. Directional selection at the pfmdr1, pfcrt, pfubp1, and pfap2mu loci of Plasmodium falciparum in Kenyan children treated with ACT. J. Infect. Dis.210, 2001–2008 (2014). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 48.Sitali, L. et al. Data on selected antimalarial drug resistance markers in Zambia. Data Brief34, 106650 (2021). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 49.Duraisingh, M. T. & Cowman, A. F. Contribution of the pfmdr1 gene to antimalarial drug-resistance. Acta Trop.94, 181–190 (2005). [DOI] [PubMed] [Google Scholar]
  • 50.Roux, A. T. et al. Chloroquine and sulfadoxine-pyrimethamine resistance in sub-saharan Africa-A review. Front. Genet.12, 668574 (2021). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 51.World Health Organisation & Others. WHO Policy Brief for the Implementation of Intermittent Preventive Treatment of Malaria in Pregnancy Using Sulfadoxine-Pyrimethamine (IPTp-SP). https://apps.who.int/iris/bitstream/handle/10665/338350/WHO-HTM-GMP-2014.4-eng.pdf (2014).
  • 52.Fola, A. A. et al. Temporal and spatial analysis of Plasmodium falciparum genomics reveals patterns of parasite connectivity in a low-transmission district in Southern Province, Zambia. Malar. J.22, 208 (2023). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 53.Teyssier, N. B. et al. Optimization of whole-genome sequencing of Plasmodium falciparum from low-density dried blood spot samples. Malar. J.20, 116 (2021). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 54.Pickard, A. L. et al. Resistance to antimalarials in Southeast Asia and genetic polymorphisms in pfmdr1. Antimicrob. Agents Chemother.47, 2418–2423 (2003). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 55.Verity, R. et al. The impact of antimalarial resistance on the genetic structure of Plasmodium falciparum in the DRC. Nat. Commun.11, 2107 (2020). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 56.Aydemir, O. et al. Drug-resistance and population structure of Plasmodium falciparum across the democratic republic of congo using high-throughput molecular inversion probes. J. Infect. Dis.218, 946–955 (2018). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 57.Garrison, E. & Marth, G. Haplotype-based variant detection from short-read sequencing. arXiv [q-bio.GN] (2012).
  • 58.Conway, J. R., Lex, A. & Gehlenborg, N. UpSetR: an R package for the visualization of intersecting sets and their properties. Bioinformatics33, 2938–2940 (2017). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 59.A. Fola, A., Dorman, J., Levy, M., Ciubotariu, I. & Carpi, G. Optimized HT gDNA extraction from dried blood spot using QIAcube HT for malaria genomic epidemiology studies v1. (2020) 10.17504/protocols.io.bh69j9h6.
  • 60.Carpi, G. et al. Whole genome capture of vector-borne pathogens from mixed DNA samples: A case study of Borrelia burgdorferi. BMC Genom.16, 434 (2015). [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary Information 1. (388.3KB, docx)
Supplementary Information 2. (109.6KB, xlsx)
Supplementary Information 3. (1MB, xlsx)
Supplementary Information 4. (19.7KB, csv)

Data Availability Statement

All MIP sequencing data available under Accession no. SAMN29983042—SAMN29983315 at the Sequence Read Archive (SRA) (http://www.ncbi.nlm.nih.gov/sra), and the associated BioProject alias is PRJNA862735.

Articles from Scientific Reports are provided here courtesy of Nature Publishing Group

RESOURCES