Detection of Low-Frequency Artemisinin Resistance Mutations C469Y, P553L and A675V in Asymptomatic Primary School Children in Kenya

doi:10.21203/rs.3.rs-5080885/v1

Background: To understand the emergence and spread of drug-resistant parasites in malaria-endemic areas, accurate assessment and monitoring of antimalarial drug resistance markers is critical. Recent advances in next-generation sequencing (NGS) technologies have enabled the tracking of drug-resistant malaria parasites.

Methods: In this study, we used Targeted Amplicon Deep Sequencing (TADs) to characterise the genetic diversity of the Pfk13,Pfdhfr, Pfdhps, and Pfmdr1genes among primary school-going children in 15 counties in Kenya (Bungoma, Busia, Homa Bay, Migori, Kakamega, Kilifi, Kirinyaga, Kisii, Kisumu, Kwale, Siaya, Tana River, Turkana, Vihiga, and West Pokot,). A total of 920 dried blood spot (DBS) samples collected from 121 selected primary schools within the country were used to extract genomic DNA. A nested polymerase chain reaction (PCR) was used to generate amplicons that were sequenced to determine the prevalence of known and novel polymorphisms.

Results:The analysis of Pfk13 mutations associated with artemisinin resistance showed that the C469Y mutation was found in 23 samples (4%), the A675V mutation was identified in 2 samples (1.7%), and the P553L mutation was present as a mixed genotype in 7 samples (1.2%), all as mixed infections. The A578S mutation, one of the most common nonsynonymous mutations found in Africa, was also identified in mixed infections, appearing in 15.2% of the 87 samples analysed. The Pfdhfr 51I and 108N pyrimethamine-resistance mutations were at fixation (100% frequency), and the Pfmdr1 Y184F mutation that has been associated with reduced susceptibility to lumefantrine was found in 97.5% of the samples as mixed genotype infections.

Conclusion:The genomics surveillance of asymptomatic school children in Kenya provides an early warning signal of at least 1 of the 3 validated artemisinin resistance mutations circulating in all regions in Western Kenya sampled except Homa Bay and Kisii Counties. These signals in asymptomatic, mixed infections would have been missed without deep sequencing.

Plasmodium falciparum

genomic surveillance

amplicon deep sequencing

artemisinin resistance

Resistance to antimalarial drugs challenges our ability to manage acute malaria and eliminate the burden that malaria places on individuals and societies. Molecular marker studies can be used to track the prevalence of key molecular mutations that are known to confer resistance (Diakité et al., 2019), which in turn enables timely action to prevent its spread and limit public health impacts.

A high rate of ACT failure with dihydroartemisinin-piperaquine (DHA-PPQ) has been documented in the Greater Mekong subregion (GMS), and mutations associated with resistance to the ACT partner drug piperaquine have been documented (WHO, 2019)

Multiple single nucleotide polymorphisms (SNPs) in the Kelch13 (k13) gene, such as C580Y, F446I, C469Y, Y493H, R539T, I543T, P553L, R561H, P574L, M579I, A675V and R622I, have been strongly linked to clinical artemisinin resistance (Bayih et al., 2016; Conrad & Rosenthal, 2019; Uwimana et al., 2020). Further mutations in the Pfk13 propeller domain have been linked to artemisinin partial resistance in Africa. Recent work has revealed clonal expansion of the R622I and R561H mutations, which have been linked to delayed parasite clearance in ACT-treated patients in Ethiopia and Rwanda, respectively (Alemayehu et al., 2021; Uwimana et al., 2020). C469Y and A675V, ART-R candidate markers, were also found in more than 15% of Ugandan samples from 2018 to 2019 (Balikagala et al., 2021). Parasites with the C469Y mutation have been confirmed to show reduced in vitro susceptibility to artemisinin compared to wild-type controls. The haplotypic network analysis revealed that the flanking regions of the C469Y mutation share the same African genetic background, suggesting a single, indigenous origin of the mutation (Awor et al., 2024). In addition, these mutations are now more than 20% in the population, initially in Northern Uganda and recently in the East and West of the country with the inclusion of additional mutations, R561H and C469F (Conrad et al., 2023).Other validated Pfk13 mutations have been reported in Africa, although at low frequencies, these include F446I, M476I, P553L, P574L, C580Y, and C469F (Owoloye et al., 2021).

There is an urgent and growing concern about increasing artemisinin resistance mutations in the East and Horn of Africa region. This study presents malaria molecular surveillance among infected, asymptomatic school children across Kenya.

Study design and setting

Kenya is characterised by a diverse malaria ecology, ranging from semi-arid, sporadic transmission, epidemic prone highland malaria, coastal intermediary intensity transmission and high transmission areas surrounding Lake Victoria (Alegana et al., 2021). This study leveraged previous work that surveyed the prevalence and distribution of malaria infection among school children in Kenya from March to November 2022 across 45 of the 47 counties in Kenya (Alegana et al., 2021). For the current antimalarial drug-resistance surveillance study, we employed an opportunistic sampling strategy alongside this previous work whereby we included only 15 counties across malaria-endemic regions: Bungoma, Busia, Homa Bay, Kakamega, Kisumu, Migori, Siaya, and Vihiga, (lakeside), Kisii (highland), Kilifi and Kwale (Coastal), Kirinyaga (low risk), Turkana, Tana River and West Pokot (semi-arid). 121 schools were included in the current study as shown in Table 1. Dried blood spots (DBS) were collected from all participants from the 15 counties, but only the rapid diagnostic test (RDT) positive samples were processed for sequencing.

Table 1. All samples collected between March 2022 and November 2022 used for plasmodium parasite genotyping.

Study Site	Time point	Schools sampled	Samples collected	mRDT No. [%]	DNA extraction	18s Pf pos, No. [%]	No. of PCR amplicons
Bungoma	Mar/Apr	10	987	74 [8]	74	54[73]	54
Busia	Mar/Apr	10	989	322 [33]	322	276[85]	181
Homabay	Mar/Apr	10	979	174 [18]	174	86[49]	55
Kakamega	Mar/Apr	12	1195	145 [12]	145	106[73]	69
Siaya	Mar/Apr	10	993	337 [34]	337	277[82]	201
Vihiga	Mar/Apr	10	996	68 [7]	68	51[75]	51
Kisumu	Mar/Apr	10	966	161 [17]	161	126[78]	92
Migori	Mar/Apr	10	981	275 [28]	275	206[75]	137
Kisii	May/Jun	4	182	12 [7]	12	12[100]	10
Kwale	May/Jun	6	373	18 [5]	18	8[44]	8
Kilifi	May/Jun	8	541	1 [0.2]	1	1[100]	0
Kirinyaga	May/Jun	2	135	0	0	0	0
Tana River	May/Jun	4	246	0	0	0	0
Turkana	Oct/Nov	11	1024	92 [9]	92	42[46]	42
West Pokot	Oct/Nov	4	286	32 [11]	32	20[63]	20
Total		121	10873	1711	1711	1260	920

School-based surveys were undertaken using sampling methods described elsewhere (Osoti et al., 2022). In brief, schools were randomly selected from each county proportional to schools within each county and 100 children aged 5-14 years randomly selected from the school register on the survey day. An initial meeting was held with the head teacher, school committee, and parents to explain the study, and before enrolment, the student’s parents or guardians provided informed consent while each child assented. Instead of written opt-in consent, parental consent was based on passive, opt-out consent (Ellickson & Hawes, 1989). A finger prick was taken to obtain blood for malaria rapid diagnostic test (mRDT) (CareStart^TM), and a Dried Blood Spot Sample (DBS) was also collected on a Whatman CF12 filter paper (Cat No. 10535097, Cytiva, USA) for molecular diagnosis. After allowing the DBS samples to air dry for at least 1 hour, they were individually packed in zip-lock bags with a desiccant and shipped to the KEMRI-Wellcome Trust Research Programme laboratories. Children who tested mRDT positive for malaria were treated following the national malaria treatment guidelines with artemether-lumefantrine (AL) as a co-formulated tablet of 20mg of artemether and 120mg of lumefantrine. The study was approved by the KEMRI and National Ethics Review Committee (number KEMRI/SERU/ESACIPAC/11/3822). Additional approval was provided by the county health and education authorities.

DNA extraction, and amplicon deep sequencing (AmpSeq)

To extract parasite DNA from the RDT-positive DBS a previously published protocol (Osoti et al., 2022) was used. Briefly, 4 punches 6mm each were punched from two locations (at the center and periphery) of the DBS and placed into a 1.5ml Eppendorf tube using sterile tweezers. DNA extraction was done using the Chelex saponin method (Baidjoe et al., 2013). Parasite DNA was amplified using 18S rRNA Plasmodium falciparum qPCR assay (Hermsen et al., 2001). Samples from Kakamega, Busia, Homa Bay, Kisumu, Migori, and Siaya with a median cycling threshold (Ct) above 31.5 were excluded from sequencing due to the large sample size. In contrast, for samples from Bungoma, Kwale, Kisii, Vihiga, Turkana, and West Pokot, a minimum Ct threshold of 38.5 was set, as the sample sizes from these regions were smaller.

Using a previously published nested PCR approach (Osoti et al., 2022) amplicons were generated for the following genes, using molecular identifiers (MID) labelled primers (Osoti et al., 2022)for P. falciparum dihydrofolate reductase (Pfdhfr: PF3D7_0417200), P. falciparum dihydropteroate synthase (Pfdhps: PF3D7_0810800), P. falciparum kelch13 (Pfk13: PF3D7_1347700) and P. falciparum multidrug resistance 1 (Pfmdr1: PF3D7_0523000). Slight modifications were made to the initial protocol, generating PCR amplicons, and sequencing them in singlets rather than duplicates. The Pfk13 gene was split and amplified in two separate fragments. One fragment covered codon 469, while the other covered codon 675. The amplification used external PCR primers Pfk13ext675F (5’-GAAGCCTTGTTGAAAGAAGC-3’) and Pfk13ext675R (5’-CGGAGTGACCAAATCTGG-3’), and internal PCR primers Pfk13int675F (5’-GGGGGATATGATGGCTCTTCT-3’) and Pfk13int675R (5’-ACTAATAAAGATGGGCCAAGC-3’).

The PCR products were individually purified using the AMPure XP beads (Beckman Coulter, Inc.) as per the manufacturer’s instructions. Thereafter, the purified DNA was quantified using a Qubit double-strand DNA (dsDNA) high-sensitivity (HS) assay kit, according to the manufacturer’s instructions. Library preparation was done using the KAPA kit, while a size selection clean-up was done using 0.8X AMPure XP beads. The adapter-ligated libraries were amplified using Illumina primers and cleaned with 0.8X AMPure. The libraries were quantified using a Qubit dsDNA HS kit and sizes were verified by the DNA 1000 assay kit using the 2100 Bioanalyzer (Agilent). The libraries were mixed in equimolar concentrations, denatured, spiked with 8% PhiX DNA, and finally AmpSeq was done using a MiSeq reagent kit v3 (Illumina). The entire procedure has been reported by Osoti et al (2022).

Sequence data analysis.

SeekDeep v3.0.1 initially demultiplexed the sequences based on the MIDs. The paired consensus reads were trimmed and clustered to estimate the frequency of DNA clusters (referred to as microhaplotypes from here henceforth). Microhaplotypes were discarded if their relative frequency was <5%. Chimeric reads were considered PCR artefacts and discarded. The SNP and microhaplotype frequencies in the population were calculated as the number of samples containing the SNP or microhaplotype over the total number of samples genotyped to determine the number of individuals harbouring a SNP or haplotype. All statistical analysis were performed in R v4.0.3 (R Core Team, 2022).

A total of 10,873 students from 121 primary schools from the 15 counties sampled in Kenya (Table 1) were screened and 1742 were malaria positive by RDT. The dried blood spots (DBS) from RDT-positive children were screened by 18S rRNA Plasmodium falciparum qPCR assay to identify 920 samples with detectable levels of DNA to take forward for AmpSeq (Table 1). We obtained sequences for 660 samples of Pfdhfr, 756 samples of Pfdhps, 571 samples of Pfk13 at codon 469, 119 samples of Pfk13 at codon 675, and 518 samples of Pfmdr1 (Table 2).

Identification and Distribution of Artemisinin resistance mutations

We successfully amplified and sequenced 571 samples, covering all the validated Pfk13 artemisinin resistance mutations. Mutations in four different positions were detected, all seen only in mixed genotype infections. The most prevalent mutation (A578S) was detected in 87 samples (15.2%), C469Y in 23 samples (4%), A675V in 2 samples (1.7%) and the P553L mutation was detected in 7 (1.2%). The C469Y, P553L and A675V mutations are validated artemisinin resistance markers. No mutations were found at any other of the confirmed artemisinin resistance-conferring loci, but 14 other rare mutations were observed as described in Table 2.

Table 2. Frequency of mutations from each drug resistant gene in the population

			Frequency (%)
Gene	Mutation	No. successfully sequenced	Wild type	Mutant	Mixed
dhfr	N51I	660	0	99.2	0.8
	C59R		0.6	80.8	18.6
	S108N		0	99.7	0.3
	I164L		76.1	0	23.9
dhps	S436A/H	756	57.8	1.2	40.5
	A437G		0.8	93.8	5.4
	P477S		99.2	0	0.8
	K540E		0.1	95.2	4.6
	A581G		96.2	0	3.8
K13	P441A	675	98.9	0	1.1
	G453C		98.8	0	1.2
	C469Y		96	0	4
	N489K		97.7	0	2.3
	N499S		99.1	0	0.9
	A504V		98.8	0	1.2
	V517I		98.2	0	1.8
	S522C		96.7	0	3.3
	N537S		99.6	0	0.4
	P553S		97.5	0	1.2
	P553L		97.5	0	1.2
	Y558C		97.4	0	2.6
	A569T		98.1	0	1.9
	A578S		84.8	0	15.2
	P667S	119	93.3	0	6.7
	A675V		98.3	0	1.7
	S679T		97.5	0	2.5
	E691D		12.6	0	87.4
mdr1	N86Y	518	100	0	0
	Y184F		1.5	1.2	97.5
	T199S		72.2	0	27.8

The occurrence of the C469Y mutation was highest in Siaya County with 9 samples, followed by Busia with 4, Kisumu 3, and Bungoma and Migori with 2 samples each, West Pokot, Turkana, and Vihiga counties each had 1 sample with the C469Y mutation (Figure 1). The 675V mutation was detected in a sample each from Kisumu and West Pokot. Two samples with the P553L mutation were identified in Siaya County, while Busia, Kakamega, Kisumu, Migori, and West Pokot each had one sample with this mutation. An additional P553S mutation, though not associated with resistance, was observed in 1 sample each in Busia, Homa Bay, Kwale and Siaya counties, as a mixed genotype infection (Figure 1).

Figure 1: A map of Kenya showing the counties sampled and the distribution of artemisinin drug resistance mutations across the country.

In the figure, the pie chart colors correspond to specific mutations: blue represents the P553L mutation, orange denotes the A675V mutation, maroon indicates the C469Y mutation, black represents the P553S mutation, and grey signifies wild-type mutations. The circles indicate the locations of schools where samples were collected, with each circle's color showing the mutation identified at that site. All samples analyzed in this study were collected from the counties shaded in grey.

Other Genetic Markers of Antimalarial Resistance

Two SNP loci in Pfmdr1 were genotyped, resulting in two haplotypes, including wild-type infections, which were the second most common in the population. Most parasite genotypes were a mix of wild-type (i.e., NYT) and variant (NFT/NYS/NFS) while genotyping of the Pfdhfr gene identified the 164L mutation as a mixed genotype with wild-type infections in 23.9% of cases. The quadruple Pfdhfr mutant (IRNL) was found in less than 5% of the population as a mixed genotype, while the Pfdhps SGEA double mutant haplotype was the most prevalent at 88.2%, and the Pfdhps SGEG triple mutant haplotype was rare at 0.4% (Table 3). An adjusted microhaplotype frequency weighted by the within-host frequencies has been included as a supplementary Table, demonstrating a similarity in the predominant alleles in the population of the dhfr-IRNI, dhps- SGEA and mdr1-NYT/NFT microhaplotypes as the assessment of the frequencies per individual.

Table 3. Frequency of resistance haplotypes for each gene

Gene (Codons)	Haplotype	n	Frequency %
*dhfr* (51,59,108,164)	IRNI	607	92
*dhfr* (51,59,108,164)	ICNI	25	3.8
(n=660)	IRNL	15	2.3
	ICNL	11	1.7

*dhps* (436, 437,540,581)	HGEA	71	9.4
	SGEA	667	88.2
	SAKA	15	2
(n=756)	SGEG	3	0.4

*mdr1* (86,184,199)	NYT	571	45.8
*mdr1* (86,184,199)	NFT	531	42.6
(n=518)	NYS	137	11
	NFS	8	0.6

The frequency percentage of each haplotype is determined by the number of occurrences of a particular haplotype divided by the total number of haplotypes, denoted as 'n'.

Three validated Pfk13 mutations, C469Y, P553L and A675V, were detected in the Western part of Kenya. These mutations have been validated and are linked with artemisinin resistance (Balikagala et al., 2021; Owoloye et al., 2021; Uwimana et al., 2020). The A578S was the most prevalent SNP observed in our study and has been observed in other sub-Saharan African countries like Ghana, Kenya, Gabon, DRC, Uganda, Cameroon, and Mali (Kamau et al., 2015; Li et al., 2019; WWARN K13 Genotype-Phenotype Study Group, 2019). However, the A578S mutation is not associated with artemisinin resistance (Ménard et al., 2016). The A675V mutation was recently identified in Kakamega county in 3 samples from 2021, also from asymptomatic school children (Jeang et al., 2024) and C469Y in Busia, from 3 samples but from symptomatic individuals (Makau et al., 2024). Previous studies (Balikagala et al., 2021) have shown an association between prolonged parasite clearance half-lives after artemisinin monotherapy in infections containing the C469Y and A675V mutations. The continued emergence of artemisinin resistance in Africa will likely slow down the gains in malaria treatment and control.

The S522C mutation identified in the current study (3.3%) as mixed infections has previously been reported in Kenya (Schmedes et al., 2021), in two samples collected during a Therapeutic Efficacy Study (TES) between 2016-2017. The World Health Organization (WHO, 2018) has also reported this mutation to be associated with delayed parasite clearance. However, due to limited data, it was unclear if criteria for statistical significance were fully met.

Three dhfr mutations N51I, C59R, S108N and two dhps mutations A437G, K540E (i.e., the quintuple mutation associated with resistance to the antifolates pyrimethamine and sulfadoxine) are at very high prevalence and the wildtype is only seen as a minority mutation in mixed infections, similar to previous findings (Osoti et al., 2022). An increase in the wild-type S436A/H SNP was observed compared to the previous study in 2019 by Osoti et al., (2022). Similarly, the dhfr I164L mutation increased as a mixed genotype since 2019, while dhps A581G showed similar frequencies with sampling done in 2019. The Pfdhfr 164L mutation has been reported in Kilifi before (Kiara et al., 2009) and it showed high levels of resistance to pyrimethamine in vitro.

There was also an increase in the prevalence of the lumefantrine-reduced susceptibility mutation Pfmdr1 184F (Mungthin et al., 2010), from 83.7% (Osoti et al., 2022) to the current 98.5% mainly observed in mixed-genotype infections in the population.

Continuous molecular surveillance of P. falciparum drug resistance marker mutations is necessary. School children provide an effective sampling frame to act as an early warning system as they represent a broad and diverse cross-section of the community. Additionally, school children often have varied and extensive exposure to the malaria parasite. The Sulfadoxine/pyrimethamine (SP) drug resistance marker mutations (triple dhfr and double dhps) are at very high prevalence. Furthermore, the high prevalence of the Pfmdr1 NF genotype, a lumefantrine resistance marker of the commonly used artemisinin combination therapy in Kenya warrants more investigations as to the continued effectiveness of lumefantrine. The emergence of C469Y, P533L and A675V mutations in the Kenyan school children population, highlights a potential risk of drug resistance emerging to the current first-line treatment of Artemether-Lumefantrine. Therefore, increased surveillance is essential, both in school children and in health facilities, to determine the prevalence of these mutations in clinical patients and their impact on treatment efficacy. We note that many alleles were present at low frequency (either emerging drug resistance, or wild-type alleles in the face of a low prevalence of resistance alleles) would not have been detected except with deep sequencing, as they were only present as minor alleles in mixed infections.

Ethics approval and consent to participate.

The study was approved by the KEMRI and National Ethics Review Committee (number KEMRI/SERU/ESACIPAC/11/3822). The student’s parents or guardians provided informed consent while each child assented. Additional approval was provided by the county health and education authorities.

Clinical trial number: not applicable.

Consent for publication

Not applicable.

Availability of data and materials

Enquiries about using the data can be made to the KEMRI-Wellcome Trust Research Programme Data Governance Committee ([email protected]). School based mRDT data enquires directed to RWS and the SNP data to ILO. The nucleotide sequence data reported in this paper are available in the GenBank database under the accession numbers: dhfr (PQ283609 -PQ283620), dhps (PQ283621-PQ283631) k13 469 (PQ283632-PQ283660) and mdr1 (PQ283661-PQ283673)

Data are available under the terms of the Creative Commons Attribution 4.0 International license (CC-BY 4.0).

Competing interests

The authors declare no competing interests.

Funding

Funding for the study was provided by the Wellcome Trust as part of Principal Fellowship support to R.W.S. (number 103602 and 212176). VO, KW, LN, PB, RWS and LIO are grateful to the support of the Wellcome Trust to the Kenya Major Overseas Programme (number 203077). VO, KW and LIO, are supported by a Calestous Juma Leadership Fellowship, funded by BMGF (INV-036442). For open access, the authors have applied a CC-BY public copyright license to any author accepted manuscript version arising from this submission.

Author contributions

LIO and RWS conceptualized the study, VO, PG supervised field collection of samples. VO and LN performed laboratory experiments; VO and KW did the data analysis. RWS and CM secured funds for field sample collection. LIO secured funding for the laboratory experiments. VO, KW, PB, RWS, LIO drafted the manuscript, and all the authors interpreted the data and contributed to writing of the final manuscript.

Acknowledgments

We thank the school children who provided samples for this analysis. Special thanks to teachers, parents and guardians who granted us consent to collect samples for this study. VO, KW, LN, PB, RWS and LIO are grateful to the support of the Wellcome Trust to the Kenya Major Overseas Programme (number 203077). We are grateful to the research team of Dr Charles Mwandawiro and appreciate staff at KEMRI/ESACIPAC for their help with field sample collection. This manuscript is published with the permission of the Director KEMRI CGMRC.

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No competing interests reported.

SupplementaryTable.docx

Detection of Low-Frequency Artemisinin Resistance Mutations C469Y, P553L and A675V in Asymptomatic Primary School Children in Kenya

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