Longitudinal surveillance of kelch13 identifies C469Y, P553L, R561H and A675V mutations associated with artemisinin resistance in Western Kenya

Longitudinal surveillance of kelch13 identifies C469Y, P553L, R561H and A675V mutations associated with artemisinin resistance in Western Kenya | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Longitudinal surveillance of kelch13 identifies C469Y, P553L, R561H and A675V mutations associated with artemisinin resistance in Western Kenya Victor Osoti, Kevin Wamae, Moses M. Musau, John B. Magudha, Leonard Ndwiga, and 12 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6890493/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 03 Nov, 2025 Read the published version in Scientific Reports → Version 1 posted 10 You are reading this latest preprint version Abstract Background Recent reports from East African countries indicate the emergence and spread of artemisinin partial resistance (ART-R), posing a significant threat to malaria control efforts in the region. The presence of critical Plasmodium falciparum kelch13 (k13) resistance markers, including C469Y , P553L and A675V , have been detected in Kenya, although their clinical significance remains unclear. This highlights an urgent need to closely monitor the prevalence of these mutations. Methods A total of 24,227 dried blood spot (DBS) samples were collected from 82 primary schools across eight counties in Western Kenya during repeated cross-sectional surveys conducted in 2019 (n = 7,941), 2022 (n = 8,086), and 2023 (n = 8,200). Initial screening was performed using a rapid diagnostic test (RDT), and DNA extraction was conducted on RDT-positive samples. These samples were further analyzed using a Pf 18S qPCR assay to quantify the Plasmodium falciparum DNA. Amplicons from malaria-positive samples were sequenced using a previously established amplicon deep sequencing pipeline to analyze mutations in Pfk13 . A total of 500, 920 and 1058 samples from 2019, 2022, and 2023, respectively were successfully processed, enabling a temporal assessment of the changes in k13 mutations in the region. Results Four mutations that have previously been associated with artemisinin resistance were found. The A675V mutation was the most prevalent, being found in all 8 counties. It was absent in 2019 and increased from 0.9% in 2022 to 5% in 2023. In contrast, the C469Y mutation declined from 4% in 2022 to 1% in 2023, maintaining a presence in 3 counties. The P553L mutation was only detected in 2022 in 1.2% of the samples across 5 counties. The R561H mutation was not detected in 2019 and 2022 but emerged at a low frequency (0.5%) in 2023 in 2 counties. Siaya and Kisumu were the only counties with all 4 validated mutations between 2022 and 2023. Conclusion The rising prevalence and geographical presence of the A675V mutation and the new detection of R561H in 2023 highlights the critical need for robust molecular surveillance systems to track the frequency and geographic spread of resistance markers. School-based sampling presents a practical and scalable approach for molecular surveillance, providing early warning signals for potential resistance hotspots. Additionally, the detection of the four WHO validated PfK13 artemisinin resistance mutations in Western Kenya underscores the urgency of conducting regular Therapeutic Efficacy Studies (TES) to assess the continued efficacy of frontline antimalarial treatments. Integrating molecular surveillance with TES will generate important data to inform national treatment policies and support the long-term effectiveness of malaria control strategies in Kenya. Biological sciences/Genetics Biological sciences/Microbiology Biological sciences/Molecular biology Malaria Drug Resistance Artemisinin R561H A675V C469Y P553L Amplicon Deep-sequencing Kenya Figures Figure 1 Figure 2 Introduction As malaria cases decline across the African continent (WHO 2024 ), the emergence of artemisinin resistance is one of several biological threats for future reductions in the malaria burden. The emergence of confirmed clinical artemisinin resistance (ART-R) in Africa, currently documented in four countries (Eritrea, Rwanda, Uganda and United Republic of Tanzania) and is suspected in Ethiopia, the Sudan, Namibia and Zambia presents a significant challenge for malaria control efforts on the continent, which bears over 95% of the global malaria burden (WHO 2024 ). Countries in East Africa and the Horn of Africa, including Eritrea, Ethiopia, Rwanda, Tanzania and Uganda, are grappling with a high prevalence of kelch13 ( Pfk13 ) mutations, a molecular marker of artemisinin resistance, across multiple sites (Bwire et al. 2020 ; Conrad et al. 2023 ; Loon et al. 2023 ; Mihreteab et al. 2023 ). Neighboring nations, such as Kenya (Jeang et al. 2024 ; Makau et al. 2024 ; Osoti et al. 2025 ), Democratic Republic of Congo (van Loon, Bisimwa, et al. 2024; Mesia Kahunu et al. 2024) and Zambia (Martin et al. 2025 ), have also recently identified these mutations, albeit at lower prevalence, highlighting a growing regional concern. Artemisinin resistance in Plasmodium falciparum malaria was first suspected in western Cambodia in the early 2000s, with clinical impact becoming evident by 2004 (Dondorp et al. 2009 ; Noedl et al. 2008). This resistance was largely driven by the emergence of the C580Y mutation in the Pfk13 gene (Ashley et al. 2014 ). Since then, it has either spread or independently emerged in various parts of Cambodia, Thailand, Vietnam, Myanmar, and Laos (Takala-Harrison et al. 2015) In contrast, studies from East Africa have identified distinct k13 mutations as drivers of artemisinin partial resistance, independent of the Asian mutations. In Rwanda, the R561H mutation has been implicated (Uwimana et al. 2020 ), while Uganda has reported C469Y and A675V mutations (Balikagala et al. 2021 ). In Tanzania, nationwide malaria molecular surveillance revealed a high prevalence of R561H mutation, a validated artemisinin resistance k13 mutation, in the Kagera region of northwestern Tanzania. Supporting these findings, a Therapeutic Efficacy Study (TES) conducted in Karagwe District found the mutation in over 20% of patients, with a strong association with delayed parasite clearance. Additionally, day 3 parasitemia exceeded the World Health Organization (WHO) 5% threshold for suspected artemisinin resistance (ART-R), underscoring growing concerns about emerging resistance in the region (Ishengoma et al. 2024 ). Haplotype analysis suggested that some of these parasites are related to isolates that were collected in Rwanda in 2015, supporting the regional spread of the R561H mutation. Additionally, other validated k13 resistance polymorphisms, including A675V and R622I, have also been identified (Juliano 2023 ). R561H, C469Y, and P441L mutations have been detected at low frequencies in the Democratic Republic of Congo (DRC), specifically in regions bordering Uganda and Rwanda (Mesia Kahunu et al. 2024). Conrad et al. (2022) previously reported a significant increase in the prevalence of PfK13 mutations in Uganda. In Northern Uganda, the prevalence of the C469Y and A675V mutations reached up to 50%, while sites in Southern Uganda documented a 40% prevalence of the C469F mutation in 2018. Pfk13 mutations (469F, 561H, and 675V) associated with artemisinin resistance, are increasingly prevalent in southern Rwanda. The prevalence of these mutations rose from 9.1% in 2019 to 17.5% by 2023, indicating a concerning upward trend in resistance markers in Rwanda (van Loon, Schallenberg, et al. 2024) Cross-sectional surveys are valuable tools for the surveillance of antimalarial drug resistance, enabling the monitoring of various Plasmodium falciparum resistance markers to different antimalarial therapies (Mesia Kahunu et al. 2024). Cross-sectionals surveys should be repeated in the same locations to examine temporal changes in emerging resistance markers. Single nucleotide polymorphisms (SNPs) in the Pfk13 gene are linked to varying levels of susceptibility to components of artemisinin-based combination therapies (ACTs) (Ariey et al. 2014 ; Okell et al. 2018 ). The current, pressing concern of artemisinin-resistant mutations across the Horn and East Africa, necessitates a scaled molecular surveillance in the region. School surveys are a convenient sample set to determine the circulating parasite genotypes in the population. We conducted a longitudinal analysis of samples collected from Western Kenya in 2019, 2022, and 2023 to assess the frequency and temporal changes of Pfk13 mutations associated with antimalarial drug resistance. Materials and Methods Study area and sampling Kenya exhibits a range of malaria transmission patterns, with the highest levels of perennial transmission occurring in the densely populated eight counties around Lake Victoria in Western Kenya (Alegana et al. 2021 ). Since 2010, these counties have been prioritized for targeted malaria control interventions. Since 2019, the Kenya Medical Research Institute Eastern and Southern Africa Centre of International Parasite Control (KEMRI ESACIPAC) in collaboration with KEMRI-Wellcome Trust Research Programme (KWTRP) has led efforts to assess national school-based malaria parasite prevalence surveys to augment periodic national malaria indicator surveys and support modelling of transmission intensity over time. A total of 82 primary schools were selected for sampling across eight counties in Western Kenya, namely Migori, Homa Bay, Kisumu, Siaya, Busia, Kakamega, Vihiga, and Bungoma (Fig. 1 ). Cross-sectional surveys were conducted in the same schools across three time-points (2019, 2022, and 2023) to allow for meaningful comparisons over time. At each of these schools, the study team randomly selected 20 students per class aged between 5 and 15 years comprising 10 boys and 10 girls from classes ranging from Pre-Primary 2 (PP2) to Grade 6, representing approximately 100 children per school for each survey year. Each child provided a finger-prick blood sample for a rapid diagnostic test (RDT) using CareStart™ Malaria kits, along with a ~ 50µL blood sample collected on Whatman™ CF12 filter papers, (Cytiva, USA). The filter papers were air-dried for at least one hour, sealed in zip-lock bags and shipped to the KWTRP laboratories for further analysis. The study received ethical approval from the KEMRI Scientific and Ethics Review Unit (SERU) (Approval Number: KEMRI/SERU/ESACIPAC/11/3822). Consent was obtained from selected county ministries of health and education, heads of schools briefed students and parents who were given the opportunity to opt out and children could refuse participation on the day of the survey or were excluded if sick. Children who tested positive for malaria were treated with artemether-lumefantrine, following the national treatment guidelines, and written instructions on dosing were provided to both the child and their class teacher (Osoti et al 2022 ). DNA extraction, sequencing and data analysis. Parasite DNA was extracted from RDT-positive DBS samples using a previously published protocol (Osoti et al., 2022 ). Four punches, 6mm each, were punched from two locations (at the center and periphery) of the DBS into a 1.5ml Eppendorf tube. The DBS punching was performed with a BSD600 Ascent A2 Semi-Automated Puncher (BSD Robotics, Australia) that was cleaned using a cotton swab soaked in 100% ethanol between each run and cross-contamination between samples was mitigated by punching a blank filter card four times after each sample. DNA extraction was done using the Chelex saponin method (Osoti et al. 2022 ). Parasite DNA was amplified using 18S rRNA Plasmodium falciparum qPCR assay (Hermsen et al. 2001 ) to determine the cycling threshold (Ct) cutoff for amplicons to proceed on to antimalarial drug resistance marker PCR amplification and subsequent sequencing. From previous experience (Osoti et al., 2022 ), this varied per county, taking into consideration the overall sample size, i.e. for counties where a higher number of amplicons were generated, a more stringent Ct cutoff was applied. The Pfk13 amplicons were generated in a 5-amplicon sequencing panel assay utilized in the KWTRP lab and thus also included Pfdhfr , Pfdhps , Pfmdr1 and Pfama1 amplicons using primers labeled with molecular identifiers (MIDs), as described by Osoti et al. ( 2022 ). PCR amplicons with non-overlapping molecular identifiers (MIDs) were combined to form distinct amplicon pools. Each pool underwent purification using the Zymo ZR-96 DNA Clean & Concentrator-5 Kit (ZR D4014, Zymo Research) following the manufacturer's instructions. The purified amplicons were then eluted in 30 µl of PCR-grade water. To assess the DNA concentration, we used the Qubit™ 1X dsDNA High Sensitivity (HS) Assay Kit (Invitrogen) according to the manufacturer’s instructions. Subsequently, the purified PCR amplicons were normalized to a final concentration of 200 ng/µl each using PCR-grade water. Library preparation was carried out using the KAPA kit, followed by size selection using 1X AMPure XP beads. Adapter-ligated libraries were amplified with Illumina primers and underwent a second clean-up using 0.8X AMPure beads. The resulting libraries were quantified with the Qubit™ 1X dsDNA High Sensitivity (HS) Assay Kit (Invitrogen) and size-verified using the Agilent 2200 TapeStation system (Agilent Technologies). Libraries were then pooled in equimolar ratios, denatured, and spiked with 15% PhiX DNA for quality control. The amplicon libraries were sequenced at the International Livestock Research Institute using the MiSeq reagent kit v3 (Illumina) on the MiSeq. The entire protocol has been outlined by Osoti et al. ( 2022 ). Data analysis was performed using SeekDeep v3.0.1, as described in Osoti et al. ( 2025 ). Due to the expected high frequency of the well-described Pfdhfr , fdhps and Pfmdr1 mutations, these genes also served as a control in the bioinformatic pipeline analysis. However, this manuscript only presents the Pfk13 data. All statistical analyses were conducted in R (version 4.0.3; R Core Team, 2022), comparisons were conducted with samples from Western Kenya collected in 2019 (Osoti et al., 2022 ) and 2022 (Osoti et al., 2025 ). Results Detection of WHO validated mutations Between March and April in 2019, 2022, and 2023, repeated cross-sectional surveys were conducted to assess malaria prevalence among primary school children across eight malaria-endemic counties in Western Kenya: Migori, Homa Bay, Kisumu, Siaya, Busia, Bungoma, Vihiga, and Kakamega. A total of 24,227 children were sampled and screened for Plasmodium falciparum infection RDTs. In 2019, 8,111 children aged 4–18 years were screened, of whom 2,247 (27.7%) tested positive by RDT. Subsequent 18S Pf qPCR analysis detected P. falciparum DNA in 1,263 samples (56.2% of RDT-positive cases) and 500 samples were selected for genotyping. In 2022, 8,086 children aged 5–14 years were sampled, with 1,573 (19.5%) testing positive by RDT. P. falciparum DNA was detected in 1,260 of the RDT positive samples (80.1%) using 18S Pf qPCR, and 920 samples were selected for genotyping to investigate drug resistance markers. In 2023, 8,200 children between 5 and 14 years of age were screened, 2,226 (27.1%) children were RDT positive for malaria while 1370 (61.5%) of the RDT positive samples had detectable DNA material using 18s Pf qPCR assay, and 1,058 qPCR-confirmed samples were selected for genotyping to assess P. falciparum drug resistance markers. For samples collected during the 2019, 2022, and 2023 surveys, sequencing of the k13 gene (covering amplicon fragments 2156bp–2645bp) was successfully completed for 196 samples (39.2%) in 2019, 523 samples (57%) in 2022, and 727 samples (69%) in 2023. For the second fragment (2509bp–3020bp), sequencing was successfully performed on 110 samples (12%) in 2022 and 965 samples (91%) in 2023. The second fragment was not amplified in samples collected in 2019 (Table 1 ). Several mutations were detected in the pfk13 gene, including validated markers of artemisinin partial resistance (Table 1 ). Four validated k13 mutations associated with artemisinin resistance were identified: C469Y; P553L; R561H; and A675V. All these mutations were identified in mixed genotype infections. Prevalence of the WHO validated mutations The C469Y mutation was observed in a total of 8 samples (1.1%) and present in 4 counties in 2023, with Bungoma having the largest number (3) of mutant samples (Fig. 1 ). The overall proportion of this mutation decreased from 4% in 2022 to 1% in 2023. However, a county level analysis revealed an increase in Bungoma to 7.7% and a reduction in Migori, 1%, and Siaya, 3%, from 5.9%, 2.1% and 6.9% in 2022, respectively. It was only observed in Kakamega at 1.3% in 2023. P553L was only observed in 2022 in 5 counties, with no mutations in 2023 (Fig. 1 ). The R561H/C mutation was not present in 2019 and 2022 but appeared in 2023 at a frequency of 0.9% (Table 1 ). Both R561H and R561C mutations were only identified in Kisumu and Siaya counties (Fig. 1 ), while Bungoma and Busia were the only counties with the R561C mutation. The R561H mutation was found in 5 samples (0.5%) in 2023. All 8 counties harbored the A675V (Fig. 2 mutation in 2023, as the dominant mutation in 48 samples (5%), with Homa Bay (which in prior years had no mutations) topping the list with 11 mutant samples followed by Siaya (10) and thereafter Busia (7). Frequency of other k13 mutations The prevalence of most Pfk13 SNPs was low (Table 1 ), while the frequencies of the wild type remained high (> 96%) across the years, suggesting limited accumulation of k13 SNPs. An additional twenty non-synonymous mutations (Table 1 ) were detected. A578S was a consistent SNP that fluctuated over time as mixed infections, increasing from 7.7% (2019) to 15.3% (2022) and declining to 6% (2023). Mutations such as S522C and P667A/S (not significantly associated k13 mutations) were identified in both 2022 and 2023, and P441A (a candidate k13 mutation) was only observed in 2022. E691D was only observed in 2022 at a high prevalence in mixed infections (89.1%). There were a higher number of k13 mutation loci (codons) in 2022 of 18 compared to 2 in 2019 and 13 in 2023, (Table 1 ) across 315, 17 and 208 samples, respectively (Table 2 ). Bungoma, Busia, Siaya and Kisumu counties contained Pfk13 SNPs in all 3 years. In 2023, the highest proportion of Pfk13 SNPs was observed in Migori (21%), which also contained the third largest number of Pfk13 SNPs over the entire sampling period, Siaya (16%), Homa Bay (15%) and Kakamega (14%). Notably, all 3 counties except for Siaya showed an increase in SNP frequency over the 3-year sampling period (Supplementary table 1 ). Similar to Siaya, Busia and Bungoma counties had less Pfk13 SNPs by 2023, however overall, Siaya and Busia had the highest number of Pfk13 SNPs in the population. Vihiga was unique as it consistently recorded low SNP frequencies (≤ 4%) across all years (Table 2 ) with a reduction by half in the number of unique Pfk13 mutations between 2022 and 2023 (Table 3 ). The proportion of unique Pfk13 SNPs per county was maintained in Kisumu, with all other counties showing a reduction in 2023. (Table 3 ). Table 1 The frequency of k13mutations from the schools’ surveys conducted in 2019, 2022 and 2023 in Western Kenya Non-reference allele frequencies Codon Position Reference Allele Non-Reference Allele 2019 2022 2023 k13 441 P A 0 6/523 (1.1%) 0 453 G C 0 7/523 (1.3%) 0 469 C Y 0 21/523 (4%) 8/727 (1.1%) 471 R C 0 0 8/727 (1.1%) 489 N K 0 12/523 (2.3%) 24/727 (3.3%) 499 N S 0 5/523 (1%) 0 501 D Y 0 0 6/727 (0.8%) 504 A V 0 6/523 (1.1%) 0 517 V I 0 9/523 (1.7%) 0 522 S C 0 18/523 (3.4%) 13/727 (1.8%) 537 N S 0 2/523 (0.4%) 0 553 P S 0 6/523 (1.2%) 0 P L 0 6/523 (1.2%) 0 558 Y C 0 14/523 (2.7%) 0 561 R H 0 0 5/965 (0.5%) R C 0 0 4/965 (0.4%) 569 A T 0 11/523 (2.1%) 7/965 (0.7%) A S 2/196 (1%) 0 5/965 (0.5%) 578 A S 15/196 (7.7%) 80/523 (15.3%) 61/965 (6%) 667 P A 0 8/110 (7.3%) 1/965 (0.1%) P S 0 0 38/965 (3.9%) 675 A V 0 1/110 (0.9%) 48/965 (5%) 679 S T 0 3/110 (2.7%) 0 691 E D 0 98/110 (89.1%) 0 *110 (2022) and 965 (2023) samples were successfully sequenced using primers targeting the second k13 fragment. Codons in bold are the WHO validated artemisinin resistance mutations. Table 2 Total number of individuals with k13 mutations identified in 2019, 2022 and 2023 Total number of k13 mutations 2019 2022 2023 TOTAL Vihiga 0 19 (6.0) 7 (3.4) 26 Kakamega 0 15 (4.7) 29 (13.9) 43 Homa Bay 0 18 (5.7) 32 (15.4) 44 Migori 0 41 (13.0) 44 (21.2) 50 Kisumu 1 (5.9) 26 (8.3) 24 (11.5) 51 Bungoma 3 (17.6) 22 (7.0) 18 (8.7) 85 Busia 5 (29.4) 76 (24.1) 21 (10.1) 102 Siaya 8 (47.1) 98 (31.1) 33 (15.9) 139 TOTAL 17 315 208 Table 3 Total number of unique K13 mutations identified in 2019, 2022 and 2023 Total number of unique k13 mutations 2019 2022 2023 Vihiga 0 9 (37.5) 4 (16.7) Kakamega 0 9 (37.5) 7 (29.2) Homa Bay 0 8 (33.3) 7 (29.2) Migori 0 13 (54.2) 8 (33.3) Kisumu 1 (4.2) 10 (41.7) 10 (41.7) Bungoma 1 (4.2) 8 (33.3) 7 (29.2) Busia 1 (4.2) 14 (58.3) 7 (29.2) Siaya 2 (8.3) 15 (62.5) 9 (37.5) The percentages were calculated over the total number (24) of segregating sites identified between 2019 and 2023 Discussion In 2023 in Western Kenya, three WHO validated Pfk13 mutations were identified, including an emerging R561H/C mutation not previously observed in this area. In addition, there was a five-fold increase and increasing geographical spread in the A675V mutation over a one-year period in this among asymptomatic malaria population. This alarming rise in the A675V frequency in a school-aged population that is a reservoir for malaria transmission signals a rapid need for scaling surveillance to health facilities to determine their frequency in the symptomatic malaria population that will receive artemisinin-based combination therapies for treatment. The C469Y mutation continues to be maintained though it was at a higher prevalence than A675V in 2022, and reduced in frequency in 2023, comparable to observations made in Uganda (Asua et al 2021; Balikagala et al 2021 ). The frequency of A675V rapidly rose to become the major Pfk13 variant in Uganda. In addition to the low C469Y prevalence, the emergent R561H mutation in the Kenyan population requires continuous monitoring to assess whether these low frequency mutations continue to be maintained in prospective studies. R561H has previously been documented in neighboring countries at high prevalence in symptomatic infections in Uganda (Conrad et al., 2023 ), Rwanda (Uwimana et al., 2020 ), and Tanzania’s region bordering Rwanda (Juliano et al., 2024), highlighting its regional significance. What is intriguing is the reduction in prevalence of C469Y, although our limited sample sizes may have led to errors in measuring rare SNPs. Longitudinal surveillance remains an important approach to track the emergence and spread of mutations, notably counties bordering western Uganda, northwestern Tanzania and those with high volume human population movement with neighboring countries with high mutation rates. Our findings highlight evolving patterns in both the prevalence and diversity of Pf k13 mutations in Western Kenya, potentially signaling emerging or declining selection pressures in different regions. Further whole genome analyses are required to determine the origins of these Pfk13 WHO validated mutations. The data provided here corroborates the history of challenges in antimalarial drug use in malaria-endemic regions, clearly demonstrating that following the introduction of a new antimalarial, the emergence and spread of P. falciparum resistance mutations is inevitable. This was not previously possible with chloroquine resistance (CQR) since the causal variant in the genetic marker P. falciparum chloroquine resistance transporter was described in 2001(Bdoulaye Jimdé et al. 2001 ) years after CQR was widespread globally (Wongsrichanalai et al. 2002 ). Similarly, for SP resistance the early use (in the 1930s) of sulpha based drugs exacerbated the development of SP resistance development (Triglia and Cowman 1999). The advent of whole genome sequencing (Gardner et al. 2002 ) hastened the process of identifying genetic markers of resistance, i.e. Pfk13 (Ariey et al., 2014 ). All these mutations are now better tracked and are rapidly informing malaria treatment strategies. Based on this current data, ongoing and more rapid surveillance of resistance markers becomes essential and requires support from public health policy makers to ensure it is sustainable and it immediately feeds back into public health interventions. As a matter of urgency, the efficacy of the ACTs currently in use in Kenya needs to be examined. Declarations Supplementary Information Supplementary Table 1 Ethics approval and consent to participate This study was conducted in accordance with the ethical principles of the Declaration of Helsinki. Ethical approval was sourced from the Kenya Medical Research Institute (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 Consent for publication Not applicable. Availability of data and materials For data access inquiries, please contact the KEMRI-Wellcome Trust Research Programme Data Governance Committee at [email protected] . Specific requests related to school-based mRDT data should be directed to RWS, while SNP data inquiries can be addressed to LIO. The datasets from the longitudinal surveillance of kelch13 mutations linked to artemisinin resistance in Western Kenya are publicly available. For the 2023 survey, FASTQ files for the K13_469 and K13_675 fragments can be accessed via the Harvard Dataverse (Kenyansa, Victor. 2025. https://doi.org/10.7910/DVN/QVHDSR). The 2022 survey data, also available on Harvard Dataverse, include FASTQ files for the k13-675 fragment (https://doi.org/10.7910/DVN/Q7PD5P), FASTA files (https://doi.org/10.7910/DVN/O82JXI) and FASTQ files for k13-469 fragment https://doi.org/10.7910/DVN/2TJH9F, with nucleotide sequences for k13-469 submitted to GenBank under accession numbers PQ283632–PQ283660. Additionally, amplicon sequence data from the 2019 survey are available in GenBank under accession numbers OM370918–OM370923 for Pfk13 .All data are shared under the Creative Commons Attribution 4.0 International License (CC-BY 4.0) Competing interests The authors declare no competing interests. Clinical Trial Number Not applicable 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, SK, PG, CM, CO supervised field collection of samples. VO, JBM, and LN performed laboratory experiments; VO and KW did the data analysis while MMM generated the maps. RWS and CM secured funds for field sample collection. LIO secured funding for the laboratory experiments. VO, PB, RWS, LIO drafted the manuscript, KW, MMM, JBM, LN, PMG, CO, KR, SM, SA, RK, KK, SK, and CM 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. We sincerely appreciate the invaluable support and collaboration from national and county authorities, especially the Ministry of Education, Ministry of Health, County and Sub-County Directors of Education and Health, as well as the school head teachers. Their commitment has been instrumental to the success of this study. 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. References Alegana, Victor A., Peter M. Macharia, Samuel Muchiri, Eda Mumo, Elvis Oyugi, Alice Kamau, Frank Chacky, et al. 2021. “Plasmodium Falciparum Parasite Prevalence in East Africa: Updating Data for Malaria Stratification.” PLOS Global Public Health 1(12): e0000014. doi:10.1371/JOURNAL.PGPH.0000014. 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Namuganga, et al. 2023. “Evolution of Partial Resistance to Artemisinins in Malaria Parasites in Uganda.” New England Journal of Medicine 389(8): 722–32. doi:10.1056/nejmoa2211803. Dondorp, Arjen M., François Nosten, Poravuth Yi, Debashish Das, Aung Phae Phyo, Joel Tarning, Khin Maung Lwin, et al. 2009. “ Artemisinin Resistance in Plasmodium Falciparum Malaria .” New England Journal of Medicine 361(5): 455–67. doi:10.1056/nejmoa0808859. Gardner, Malcolm J., Neil Hall, Eula Fung, Owen White, Matthew Berriman, Richard W. Hyman, Jane M. Carlton, et al. 2002. “Genome Sequence of the Human Malaria Parasite Plasmodium Falciparum.” Nature 419(6906): 498–511. doi:10.1038/nature01097. Hermsen, Cornelus C., Denise S.C. Telgt, Ellen H.P. Linders, Louis A.T.F. Van De Locht, Wijnand M.C. Eling, Ewald J.B.M. Mensink, and Robert W. Sauerwein. 2001. “Detection of Plasmodium Falciparum Malaria Parasites in Vivo by Real-Time Quantitative PCR.” Molecular and Biochemical Parasitology 118(2): 247–51. doi:10.1016/S0166-6851(01)00379-6. Ishengoma, Deus S., Celine I. Mandara, Catherine Bakari, Abebe A. Fola, Rashid A. Madebe, Misago D. Seth, Filbert Francis, et al. 2024. “Evidence of Artemisinin Partial Resistance in Northwestern Tanzania: Clinical and Molecular Markers of Resistance.” The Lancet Infectious Diseases 24(11): 1225–33. doi:10.1016/S1473-3099(24)00362-1. Jeang, Brook, Daibin Zhong, Ming Chieh Lee, Harrysone Atieli, Delenasaw Yewhalaw, and Guiyun Yan. 2024. “Molecular Surveillance of Kelch 13 Polymorphisms in Plasmodium Falciparum Isolates from Kenya and Ethiopia.” Malaria Journal 23(1): 36. doi:10.1186/S12936-023-04812-Y. Juliano, Jonathan. 2023. “NOTE: This Preprint Reports New Research That Has Not Been Certified by Peer Review and Should Not Be Used to Guide Clinical Practice.” van Loon, Welmoed, Bertin C. Bisimwa, Valery Byela, Rebecca Kirby, Patrick M. Bugeme, Aime Balagizi, David Lupande, et al. 2024. “Detection of Artemisinin Resistance Marker Kelch-13 469Y in Plasmodium Falciparum, South Kivu, Democratic Republic of the Congo, 2022.” American Journal of Tropical Medicine and Hygiene 110(4): 653–55. doi:10.4269/ajtmh.23-0740. van Loon, Welmoed, Emma Schallenberg, Clement Igiraneza, Felix Habarugira, Djibril Mbarushimana, Fabian Nshimiyimana, Christian Ngarambe, et al. 2024. “Escalating Plasmodium Falciparum K13 Marker Prevalence Indicative of Artemisinin Resistance in Southern Rwanda.” Antimicrobial Agents and Chemotherapy 68(1). doi:10.1128/AAC.01299-23. Loon, Welmoed Van, Emma Schallenberg, Clement Igiraneza, Felix Habarugira, Djibril Mbarushimana, Fabian Nshimiyimana, Christian Ngarambe, et al. 2023. “Indicative of Artemisinin Resistance in Southern Rwanda.” : 1–5. Makau, Mark, Bernard N Kanoi, Calvin Mgawe, Michael Maina, Hussein Abkallo, Harrison Waweru, Ferdinand Adung’, and Jesse Gitaka. 2024. “Evidence of Partial Artemisinin Resistance in Malaria Endemic Lake Region, Busia County, Western, Kenya.” doi:10.21203/RS.3.RS-4538408/V1. Martin, Authors Anne C, Jacob M Sadler, Alfred Simkin, Michael Musonda, Ben Katowa, Jessica Schue, Edgar Simulundu, et al. 2025. “Emergence and Rising Prevalence of Artemisinin Partial Resistance Marker Kelch13 P441L in a Low Malaria Transmission Setting in Southern Zambia.” Mesia Kahunu, Gauthier, Sarah Wellmann Thomsen, Louise Wellmann Thomsen, Hypolite Muhindo Mavoko, Patrick Mitashi Mulopo, Emma Filtenborg Hocke, Papy Mandoko Nkoli, et al. 2024. “Identification of the PfK13 Mutations R561H and P441L in the Democratic Republic of Congo.” International journal of infectious diseases : IJID : official publication of the International Society for Infectious Diseases 139: 41–49. doi:10.1016/J.IJID.2023.11.026. Mihreteab, Selam, Lucien Platon, Araia Berhane, Barbara H. Stokes, Marian Warsame, Pascal Campagne, Alexis Criscuolo, et al. 2023. “Increasing Prevalence of Artemisinin-Resistant HRP2-Negative Malaria in Eritrea.” New England Journal of Medicine 389(13): 1191–1202. doi:10.1056/nejmoa2210956. Noedl, Harald, Youry Se, Kurt Schaecher, Bryan L Smith, Duong Socheat, Mark M Fukuda, and Artemisinin Resistance in Cambodia 1 (ARC1) Study Consortium. 2008. “Evidence of Artemisinin-Resistant Malaria in Western Cambodia.” The New England journal of medicine 359(24): 2619–20. doi:10.1056/NEJMc0805011. Okell, Lucy C., Lisa Malene Reiter, Lene Sandø Ebbe, Vito Baraka, Donal Bisanzio, Oliver J. Watson, Adam Bennett, et al. 2018. “Emerging Implications of Policies on Malaria Treatment: Genetic Changes in the Pfmdr-1 Gene Affecting Susceptibility to Artemether–Lumefantrine and Artesunate–Amodiaquine in Africa.” BMJ Global Health 3(5): 1–12. doi:10.1136/bmjgh-2018-000999. Osoti, Victor, Mercy Akinyi, Kevin Wamae, Kelvin M. Kimenyi, Zaydah De Laurent, Leonard Ndwiga, Paul Gichuki, et al. 2022. “Targeted Amplicon Deep Sequencing for Monitoring Antimalarial Resistance Markers in Western Kenya.” Antimicrobial Agents and Chemotherapy 66(4). doi:10.1128/aac.01945-21. Osoti, Victor, Kevin Wamae, Leonard Ndwiga, Paul M. Gichuki, Collins Okoyo, Stella Kepha, Kibor Keitany, et al. 2025. “Detection of Low Frequency Artemisinin Resistance Mutations, C469Y, P553L and A675V, and Fixed Antifolate Resistance Mutations in Asymptomatic Primary School Children in Kenya.” BMC Infectious Diseases 2025 25:1 25(1): 1–10. doi:10.1186/S12879-025-10462-Z. Takala-Harrison, Shannon, Christopher G. Jacob, Cesar Arze, Michael P. Cummings, Joana C. Silva, Arjen M. Dondorp, Mark M. Fukuda, et al. 2015. “Independent Emergence of Artemisinin Resistance Mutations among Plasmodium Falciparum in Southeast Asia.” Journal of Infectious Diseases 211(5): 670–79. doi:10.1093/infdis/jiu491. Triglia, Tony, and Alan F. Cowman. 1999. “The Mechanism of Resistance to Sulfa Drugs in Plasmodium Falciparum.” Drug resistance updates : reviews and commentaries in antimicrobial and anticancer chemotherapy 2(1): 15–19. doi:10.1054/DRUP.1998.0060. Uwimana, Aline, Eric Legrand, Barbara H. Stokes, Jean Louis Mangala Ndikumana, Marian Warsame, Noella Umulisa, Daniel Ngamije, et al. 2020. “Emergence and Clonal Expansion of in Vitro Artemisinin-Resistant Plasmodium Falciparum Kelch13 R561H Mutant Parasites in Rwanda.” Nature Medicine . doi:10.1038/s41591-020-1005-2. WHO. 2024. World Malaria World Malaria Report Report . https://www.wipo.int/amc/en/mediation/%0Ahttps://www.who.int/teams/global-malaria-programme/reports/world-malaria-report-2023. Wongsrichanalai, Chansuda, Amy L Pickard, Walther H Wernsdorfer, and Steven R Meshnick. 2002. “Reviews Epidemiology of Drug-Resistant Malaria.” 2: 209–18. Additional Declarations No competing interests reported. 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The red dots indicate the schools from which malaria-positive children were identified by a malaria rapid diagnostic test, and a dried blood spot was collected from each child.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-6890493/v1/914f50e53197e521fd13223e.png"},{"id":85428951,"identity":"c35d8d5d-df16-4330-9434-233772086127","added_by":"auto","created_at":"2025-06-25 17:42:30","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":696993,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial distribution of k13 validated resistance mutations by village in Western Kenya. The 8 counties have been demarcated and labelled, and each dot represents the village of the child harboring the parasite with the mutation. The size of the dot varies depending on the number of mutations identified in that village. A-D illustrates mutations that were identified in 2022 while E-H shows mutations that were identified in 2023. Each color shows a different mutation. No mutations were identified in 2019.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-6890493/v1/a7a0e4a4470a22ee8e1cf929.png"},{"id":95564171,"identity":"40c61aeb-f067-49c0-821d-2fac803f8568","added_by":"auto","created_at":"2025-11-10 16:08:38","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1848979,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6890493/v1/cb3bca0d-b80c-44e9-a013-98b586800409.pdf"},{"id":85429139,"identity":"0588cb07-2dd5-4f6e-b7e2-54ec4a89db0e","added_by":"auto","created_at":"2025-06-25 17:50:30","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":27229,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable1SSWK202312052025.docx","url":"https://assets-eu.researchsquare.com/files/rs-6890493/v1/df202125ab7e9a601399ada5.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Longitudinal surveillance of kelch13 identifies C469Y, P553L, R561H and A675V mutations associated with artemisinin resistance in Western Kenya","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAs malaria cases decline across the African continent (WHO \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), the emergence of artemisinin resistance is one of several biological threats for future reductions in the malaria burden. The emergence of confirmed clinical artemisinin resistance (ART-R) in Africa, currently documented in four countries (Eritrea, Rwanda, Uganda and United Republic of Tanzania) and is suspected in Ethiopia, the Sudan, Namibia and Zambia presents a significant challenge for malaria control efforts on the continent, which bears over 95% of the global malaria burden (WHO \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Countries in East Africa and the Horn of Africa, including Eritrea, Ethiopia, Rwanda, Tanzania and Uganda, are grappling with a high prevalence of kelch13 (\u003cem\u003ePfk13\u003c/em\u003e) mutations, a molecular marker of artemisinin resistance, across multiple sites (Bwire et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Conrad et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Loon et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Mihreteab et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Neighboring nations, such as Kenya (Jeang et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Makau et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Osoti et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), Democratic Republic of Congo (van Loon, Bisimwa, et al. 2024; Mesia Kahunu et al. 2024) and Zambia (Martin et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), have also recently identified these mutations, albeit at lower prevalence, highlighting a growing regional concern.\u003c/p\u003e \u003cp\u003eArtemisinin resistance in \u003cem\u003ePlasmodium falciparum\u003c/em\u003e malaria was first suspected in western Cambodia in the early 2000s, with clinical impact becoming evident by 2004 (Dondorp et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Noedl et al. 2008). This resistance was largely driven by the emergence of the C580Y mutation in the \u003cem\u003ePfk13\u003c/em\u003e gene (Ashley et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Since then, it has either spread or independently emerged in various parts of Cambodia, Thailand, Vietnam, Myanmar, and Laos (Takala-Harrison et al. 2015)\u003c/p\u003e \u003cp\u003eIn contrast, studies from East Africa have identified distinct \u003cem\u003ek13\u003c/em\u003e mutations as drivers of artemisinin partial resistance, independent of the Asian mutations. In Rwanda, the R561H mutation has been implicated (Uwimana et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), while Uganda has reported C469Y and A675V mutations (Balikagala et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In Tanzania, nationwide malaria molecular surveillance revealed a high prevalence of R561H mutation, a validated artemisinin resistance \u003cem\u003ek13\u003c/em\u003e mutation, in the Kagera region of northwestern Tanzania. Supporting these findings, a Therapeutic Efficacy Study (TES) conducted in Karagwe District found the mutation in over 20% of patients, with a strong association with delayed parasite clearance. Additionally, day 3 parasitemia exceeded the World Health Organization (WHO) 5% threshold for suspected artemisinin resistance (ART-R), underscoring growing concerns about emerging resistance in the region (Ishengoma et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Haplotype analysis suggested that some of these parasites are related to isolates that were collected in Rwanda in 2015, supporting the regional spread of the R561H mutation. Additionally, other validated k13 resistance polymorphisms, including A675V and R622I, have also been identified (Juliano \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). R561H, C469Y, and P441L mutations have been detected at low frequencies in the Democratic Republic of Congo (DRC), specifically in regions bordering Uganda and Rwanda (Mesia Kahunu et al. 2024). Conrad et al. (2022) previously reported a significant increase in the prevalence of \u003cem\u003ePfK13\u003c/em\u003e mutations in Uganda. In Northern Uganda, the prevalence of the C469Y and A675V mutations reached up to 50%, while sites in Southern Uganda documented a 40% prevalence of the C469F mutation in 2018. \u003cem\u003ePfk13\u003c/em\u003e mutations (469F, 561H, and 675V) associated with artemisinin resistance, are increasingly prevalent in southern Rwanda. The prevalence of these mutations rose from 9.1% in 2019 to 17.5% by 2023, indicating a concerning upward trend in resistance markers in Rwanda (van Loon, Schallenberg, et al. 2024)\u003c/p\u003e \u003cp\u003eCross-sectional surveys are valuable tools for the surveillance of antimalarial drug resistance, enabling the monitoring of various \u003cem\u003ePlasmodium falciparum\u003c/em\u003e resistance markers to different antimalarial therapies (Mesia Kahunu et al. 2024). Cross-sectionals surveys should be repeated in the same locations to examine temporal changes in emerging resistance markers.\u003c/p\u003e \u003cp\u003eSingle nucleotide polymorphisms (SNPs) in the \u003cem\u003ePfk13\u003c/em\u003e gene are linked to varying levels of susceptibility to components of artemisinin-based combination therapies (ACTs) (Ariey et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Okell et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The current, pressing concern of artemisinin-resistant mutations across the Horn and East Africa, necessitates a scaled molecular surveillance in the region. School surveys are a convenient sample set to determine the circulating parasite genotypes in the population. We conducted a longitudinal analysis of samples collected from Western Kenya in 2019, 2022, and 2023 to assess the frequency and temporal changes of \u003cem\u003ePfk13\u003c/em\u003e mutations associated with antimalarial drug resistance.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy area and sampling\u003c/h2\u003e \u003cp\u003eKenya exhibits a range of malaria transmission patterns, with the highest levels of perennial transmission occurring in the densely populated eight counties around Lake Victoria in Western Kenya (Alegana et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Since 2010, these counties have been prioritized for targeted malaria control interventions. Since 2019, the Kenya Medical Research Institute Eastern and Southern Africa Centre of International Parasite Control (KEMRI ESACIPAC) in collaboration with KEMRI-Wellcome Trust Research Programme (KWTRP) has led efforts to assess national school-based malaria parasite prevalence surveys to augment periodic national malaria indicator surveys and support modelling of transmission intensity over time. A total of 82 primary schools were selected for sampling across eight counties in Western Kenya, namely Migori, Homa Bay, Kisumu, Siaya, Busia, Kakamega, Vihiga, and Bungoma (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Cross-sectional surveys were conducted in the same schools across three time-points (2019, 2022, and 2023) to allow for meaningful comparisons over time. At each of these schools, the study team randomly selected 20 students per class aged between 5 and 15 years comprising 10 boys and 10 girls from classes ranging from Pre-Primary 2 (PP2) to Grade 6, representing approximately 100 children per school for each survey year. Each child provided a finger-prick blood sample for a rapid diagnostic test (RDT) using CareStart\u0026trade; Malaria kits, along with a\u0026thinsp;~\u0026thinsp;50\u0026micro;L blood sample collected on Whatman\u0026trade; CF12 filter papers, (Cytiva, USA). The filter papers were air-dried for at least one hour, sealed in zip-lock bags and shipped to the KWTRP laboratories for further analysis.\u003c/p\u003e \u003cp\u003eThe study received ethical approval from the KEMRI Scientific and Ethics Review Unit (SERU) (Approval Number: KEMRI/SERU/ESACIPAC/11/3822). Consent was obtained from selected county ministries of health and education, heads of schools briefed students and parents who were given the opportunity to opt out and children could refuse participation on the day of the survey or were excluded if sick. Children who tested positive for malaria were treated with artemether-lumefantrine, following the national treatment guidelines, and written instructions on dosing were provided to both the child and their class teacher (Osoti et al \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eDNA extraction, sequencing and data analysis.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eParasite DNA was extracted from RDT-positive DBS samples using a previously published protocol (Osoti et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Four punches, 6mm each, were punched from two locations (at the center and periphery) of the DBS into a 1.5ml Eppendorf tube. The DBS punching was performed with a BSD600 Ascent A2 Semi-Automated Puncher (BSD Robotics, Australia) that was cleaned using a cotton swab soaked in 100% ethanol between each run and cross-contamination between samples was mitigated by punching a blank filter card four times after each sample. DNA extraction was done using the Chelex saponin method (Osoti et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Parasite DNA was amplified using 18S rRNA \u003cem\u003ePlasmodium falciparum\u003c/em\u003e qPCR assay (Hermsen et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2001\u003c/span\u003e) to determine the cycling threshold (Ct) cutoff for amplicons to proceed on to antimalarial drug resistance marker PCR amplification and subsequent sequencing. From previous experience (Osoti et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), this varied per county, taking into consideration the overall sample size, i.e. for counties where a higher number of amplicons were generated, a more stringent Ct cutoff was applied.\u003c/p\u003e \u003cp\u003eThe \u003cem\u003ePfk13\u003c/em\u003e amplicons were generated in a 5-amplicon sequencing panel assay utilized in the KWTRP lab and thus also included \u003cem\u003ePfdhfr\u003c/em\u003e, \u003cem\u003ePfdhps\u003c/em\u003e, \u003cem\u003ePfmdr1\u003c/em\u003e and \u003cem\u003ePfama1\u003c/em\u003e amplicons using primers labeled with molecular identifiers (MIDs), as described by Osoti et al. (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). PCR amplicons with non-overlapping molecular identifiers (MIDs) were combined to form distinct amplicon pools. Each pool underwent purification using the Zymo ZR-96 DNA Clean \u0026amp; Concentrator-5 Kit (ZR D4014, Zymo Research) following the manufacturer's instructions. The purified amplicons were then eluted in 30 \u0026micro;l of PCR-grade water. To assess the DNA concentration, we used the Qubit\u0026trade; 1X dsDNA High Sensitivity (HS) Assay Kit (Invitrogen) according to the manufacturer\u0026rsquo;s instructions. Subsequently, the purified PCR amplicons were normalized to a final concentration of 200 ng/\u0026micro;l each using PCR-grade water.\u003c/p\u003e \u003cp\u003eLibrary preparation was carried out using the KAPA kit, followed by size selection using 1X AMPure XP beads. Adapter-ligated libraries were amplified with Illumina primers and underwent a second clean-up using 0.8X AMPure beads. The resulting libraries were quantified with the Qubit\u0026trade; 1X dsDNA High Sensitivity (HS) Assay Kit (Invitrogen) and size-verified using the Agilent 2200 TapeStation system (Agilent Technologies). Libraries were then pooled in equimolar ratios, denatured, and spiked with 15% PhiX DNA for quality control. The amplicon libraries were sequenced at the International Livestock Research Institute using the MiSeq reagent kit v3 (Illumina) on the MiSeq.\u0026nbsp;The entire protocol has been outlined by Osoti et al. (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Data analysis was performed using SeekDeep v3.0.1, as described in Osoti et al. (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Due to the expected high frequency of the well-described \u003cem\u003ePfdhfr\u003c/em\u003e, \u003cem\u003efdhps\u003c/em\u003e and \u003cem\u003ePfmdr1\u003c/em\u003e mutations, these genes also served as a control in the bioinformatic pipeline analysis. However, this manuscript only presents the \u003cem\u003ePfk13\u003c/em\u003e data. All statistical analyses were conducted in R (version 4.0.3; R Core Team, 2022), comparisons were conducted with samples from Western Kenya collected in 2019 (Osoti et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) and \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2022\u003c/span\u003e (Osoti et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eDetection of WHO validated mutations\u003c/h2\u003e \u003cp\u003eBetween March and April in 2019, 2022, and 2023, repeated cross-sectional surveys were conducted to assess malaria prevalence among primary school children across eight malaria-endemic counties in Western Kenya: Migori, Homa Bay, Kisumu, Siaya, Busia, Bungoma, Vihiga, and Kakamega. A total of 24,227 children were sampled and screened for \u003cem\u003ePlasmodium falciparum\u003c/em\u003e infection RDTs.\u003c/p\u003e \u003cp\u003eIn 2019, 8,111 children aged 4\u0026ndash;18 years were screened, of whom 2,247 (27.7%) tested positive by RDT. Subsequent 18S \u003cem\u003ePf\u003c/em\u003e qPCR analysis detected \u003cem\u003eP. falciparum\u003c/em\u003e DNA in 1,263 samples (56.2% of RDT-positive cases) and 500 samples were selected for genotyping. In 2022, 8,086 children aged 5\u0026ndash;14 years were sampled, with 1,573 (19.5%) testing positive by RDT. \u003cem\u003eP. falciparum\u003c/em\u003e DNA was detected in 1,260 of the RDT positive samples (80.1%) using 18S \u003cem\u003ePf\u003c/em\u003e qPCR, and 920 samples were selected for genotyping to investigate drug resistance markers. In 2023, 8,200 children between 5 and 14 years of age were screened, 2,226 (27.1%) children were RDT positive for malaria while 1370 (61.5%) of the RDT positive samples had detectable DNA material using 18s \u003cem\u003ePf\u003c/em\u003e qPCR assay, and 1,058 qPCR-confirmed samples were selected for genotyping to assess \u003cem\u003eP. falciparum\u003c/em\u003e drug resistance markers.\u003c/p\u003e \u003cp\u003eFor samples collected during the 2019, 2022, and 2023 surveys, sequencing of the \u003cem\u003ek13\u003c/em\u003e gene (covering amplicon fragments 2156bp\u0026ndash;2645bp) was successfully completed for 196 samples (39.2%) in 2019, 523 samples (57%) in 2022, and 727 samples (69%) in 2023. For the second fragment (2509bp\u0026ndash;3020bp), sequencing was successfully performed on 110 samples (12%) in 2022 and 965 samples (91%) in 2023. The second fragment was not amplified in samples collected in 2019 (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Several mutations were detected in the \u003cem\u003epfk13\u003c/em\u003e gene, including validated markers of artemisinin partial resistance (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Four validated \u003cem\u003ek13\u003c/em\u003e mutations associated with artemisinin resistance were identified: C469Y; P553L; R561H; and A675V. All these mutations were identified in mixed genotype infections.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003ePrevalence of the WHO validated mutations\u003c/h3\u003e\n\u003cp\u003eThe C469Y mutation was observed in a total of 8 samples (1.1%) and present in 4 counties in 2023, with Bungoma having the largest number (3) of mutant samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The overall proportion of this mutation decreased from 4% in 2022 to 1% in 2023. However, a county level analysis revealed an increase in Bungoma to 7.7% and a reduction in Migori, 1%, and Siaya, 3%, from 5.9%, 2.1% and 6.9% in 2022, respectively. It was only observed in Kakamega at 1.3% in 2023. P553L was only observed in 2022 in 5 counties, with no mutations in 2023 (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The R561H/C mutation was not present in 2019 and 2022 but appeared in 2023 at a frequency of 0.9% (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Both R561H and R561C mutations were only identified in Kisumu and Siaya counties (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), while Bungoma and Busia were the only counties with the R561C mutation. The R561H mutation was found in 5 samples (0.5%) in 2023.\u003c/p\u003e \u003cp\u003eAll 8 counties harbored the A675V (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e mutation in 2023, as the dominant mutation in 48 samples (5%), with Homa Bay (which in prior years had no mutations) topping the list with 11 mutant samples followed by Siaya (10) and thereafter Busia (7).\u003c/p\u003e\n\u003ch3\u003eFrequency of other k13 mutations\u003c/h3\u003e\n\u003cp\u003eThe prevalence of most \u003cem\u003ePfk13\u003c/em\u003e SNPs was low (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), while the frequencies of the wild type remained high (\u0026gt;\u0026thinsp;96%) across the years, suggesting limited accumulation of k13 SNPs. An additional twenty non-synonymous mutations (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) were detected. A578S was a consistent SNP that fluctuated over time as mixed infections, increasing from 7.7% (2019) to 15.3% (2022) and declining to 6% (2023). Mutations such as S522C and P667A/S (not significantly associated k13 mutations) were identified in both 2022 and 2023, and P441A (a candidate k13 mutation) was only observed in 2022. E691D was only observed in 2022 at a high prevalence in mixed infections (89.1%). There were a higher number of k13 mutation loci (codons) in 2022 of 18 compared to 2 in 2019 and 13 in 2023, (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) across 315, 17 and 208 samples, respectively (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Bungoma, Busia, Siaya and Kisumu counties contained \u003cem\u003ePfk13\u003c/em\u003e SNPs in all 3 years.\u003c/p\u003e \u003cp\u003eIn 2023, the highest proportion of \u003cem\u003ePfk13\u003c/em\u003e SNPs was observed in Migori (21%), which also contained the third largest number of \u003cem\u003ePfk13\u003c/em\u003e SNPs over the entire sampling period, Siaya (16%), Homa Bay (15%) and Kakamega (14%). Notably, all 3 counties except for Siaya showed an increase in SNP frequency over the 3-year sampling period (Supplementary table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Similar to Siaya, Busia and Bungoma counties had less \u003cem\u003ePfk13\u003c/em\u003e SNPs by 2023, however overall, Siaya and Busia had the highest number of \u003cem\u003ePfk13\u003c/em\u003e SNPs in the population. Vihiga was unique as it consistently recorded low SNP frequencies (\u0026le;\u0026thinsp;4%) across all years (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) with a reduction by half in the number of unique \u003cem\u003ePfk13\u003c/em\u003e mutations between 2022 and 2023 (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The proportion of unique \u003cem\u003ePfk13\u003c/em\u003e SNPs per county was maintained in Kisumu, with all other counties showing a reduction in 2023. (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe frequency of k13mutations from the schools\u0026rsquo; surveys conducted in 2019, 2022 and 2023 in Western Kenya\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eNon-reference allele frequencies\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCodon\u003c/p\u003e \u003cp\u003ePosition\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReference Allele\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNon-Reference Allele\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2019\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2022\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2023\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ek13\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e441\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6/523 (1.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e453\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7/523 (1.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e469\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e21/523 (4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e8/727 (1.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e471\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e8/727 (1.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e489\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12/523 (2.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e24/727 (3.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e499\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5/523 (1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e501\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6/727 (0.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e504\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6/523 (1.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e517\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9/523 (1.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e522\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e18/523 (3.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e13/727 (1.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e537\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2/523 (0.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e553\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6/523 (1.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6/523 (1.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e558\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e14/523 (2.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e561\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5/965 (0.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4/965 (0.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e569\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11/523 (2.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7/965 (0.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2/196 (1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5/965 (0.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e578\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15/196 (7.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e80/523 (15.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e61/965 (6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e667\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8/110 (7.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1/965 (0.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e38/965 (3.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e675\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1/110 (0.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e48/965 (5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e679\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3/110 (2.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e691\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e98/110 (89.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003e*110 (2022) and 965 (2023) samples were successfully sequenced using primers targeting the second k13 fragment. Codons in bold are the WHO validated artemisinin resistance mutations.\u003c/em\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTotal number of individuals with k13 mutations identified in 2019, 2022 and 2023\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003eTotal number of k13 mutations\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e2019\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e2022\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e2023\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eTOTAL\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVihiga\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19 (6.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7 (3.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKakamega\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15 (4.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29 (13.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e43\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHoma Bay\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18 (5.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e32 (15.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMigori\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e41 (13.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e44 (21.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKisumu\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (5.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26 (8.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24 (11.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e51\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBungoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (17.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22 (7.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18 (8.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e85\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBusia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5 (29.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e76 (24.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21 (10.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e102\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSiaya\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8 (47.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e98 (31.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e33 (15.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e139\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTOTAL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e315\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e208\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTotal number of unique K13 mutations identified in 2019, 2022 and 2023\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eTotal number of unique k13 mutations\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e2019\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e2022\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e2023\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eVihiga\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9 (37.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4 (16.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eKakamega\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9 (37.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7 (29.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHoma Bay\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8 (33.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7 (29.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMigori\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13 (54.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8 (33.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eKisumu\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (4.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10 (41.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10 (41.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBungoma\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (4.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8 (33.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7 (29.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBusia\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (4.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14 (58.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7 (29.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSiaya\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (8.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15 (62.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9 (37.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe percentages were calculated over the total number (24) of segregating sites identified between 2019 and 2023\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn 2023 in Western Kenya, three WHO validated \u003cem\u003ePfk13\u003c/em\u003e mutations were identified, including an emerging R561H/C mutation not previously observed in this area. In addition, there was a five-fold increase and increasing geographical spread in the A675V mutation over a one-year period in this among asymptomatic malaria population. This alarming rise in the A675V frequency in a school-aged population that is a reservoir for malaria transmission signals a rapid need for scaling surveillance to health facilities to determine their frequency in the symptomatic malaria population that will receive artemisinin-based combination therapies for treatment. The C469Y mutation continues to be maintained though it was at a higher prevalence than A675V in 2022, and reduced in frequency in 2023, comparable to observations made in Uganda (Asua et al 2021; Balikagala et al \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The frequency of A675V rapidly rose to become the major \u003cem\u003ePfk13\u003c/em\u003e variant in Uganda. In addition to the low C469Y prevalence, the emergent R561H mutation in the Kenyan population requires continuous monitoring to assess whether these low frequency mutations continue to be maintained in prospective studies. R561H has previously been documented in neighboring countries at high prevalence in symptomatic infections in Uganda (Conrad et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), Rwanda (Uwimana et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), and Tanzania\u0026rsquo;s region bordering Rwanda (Juliano et al., 2024), highlighting its regional significance. What is intriguing is the reduction in prevalence of C469Y, although our limited sample sizes may have led to errors in measuring rare SNPs. Longitudinal surveillance remains an important approach to track the emergence and spread of mutations, notably counties bordering western Uganda, northwestern Tanzania and those with high volume human population movement with neighboring countries with high mutation rates. Our findings highlight evolving patterns in both the prevalence and diversity of \u003cem\u003ePf\u003c/em\u003ek13 mutations in Western Kenya, potentially signaling emerging or declining selection pressures in different regions. Further whole genome analyses are required to determine the origins of these \u003cem\u003ePfk13\u003c/em\u003e WHO validated mutations.\u003c/p\u003e \u003cp\u003eThe data provided here corroborates the history of challenges in antimalarial drug use in malaria-endemic regions, clearly demonstrating that following the introduction of a new antimalarial, the emergence and spread of \u003cem\u003eP. falciparum\u003c/em\u003e resistance mutations is inevitable. This was not previously possible with chloroquine resistance (CQR) since the causal variant in the genetic marker \u003cem\u003eP. falciparum\u003c/em\u003e chloroquine resistance transporter was described in 2001(Bdoulaye Jimd\u0026eacute; et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2001\u003c/span\u003e) years after CQR was widespread globally (Wongsrichanalai et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). Similarly, for SP resistance the early use (in the 1930s) of sulpha based drugs exacerbated the development of SP resistance development (Triglia and Cowman 1999). The advent of whole genome sequencing (Gardner et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2002\u003c/span\u003e) hastened the process of identifying genetic markers of resistance, i.e. \u003cem\u003ePfk13\u003c/em\u003e (Ariey et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). All these mutations are now better tracked and are rapidly informing malaria treatment strategies. Based on this current data, ongoing and more rapid surveillance of resistance markers becomes essential and requires support from public health policy makers to ensure it is sustainable and it immediately feeds back into public health interventions. As a matter of urgency, the efficacy of the ACTs currently in use in Kenya needs to be examined.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eSupplementary Information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSupplementary Table 1\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was conducted in accordance with the ethical principles of the Declaration of Helsinki. Ethical approval was sourced from the Kenya Medical Research Institute (KEMRI) and National Ethics Review Committee (number KEMRI/SERU/ESACIPAC/11/3822). The student\u0026rsquo;s parents or guardians provided informed consent while each child assented. Additional approval was provided by the county health and education authorities\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFor data access inquiries, please contact the KEMRI-Wellcome Trust Research Programme Data Governance Committee at [email protected]. Specific requests related to school-based mRDT data should be directed to RWS, while SNP data inquiries can be addressed to LIO. The datasets from the longitudinal surveillance of \u003cem\u003ekelch13\u003c/em\u003e mutations linked to artemisinin resistance in Western Kenya are publicly available. For the 2023 survey, FASTQ files for the K13_469 and K13_675 fragments can be accessed via the Harvard Dataverse (Kenyansa, Victor. 2025. https://doi.org/10.7910/DVN/QVHDSR). The 2022 survey data, also available on Harvard Dataverse, include FASTQ files for the k13-675 fragment (https://doi.org/10.7910/DVN/Q7PD5P), FASTA files (https://doi.org/10.7910/DVN/O82JXI) and FASTQ files for k13-469 fragment https://doi.org/10.7910/DVN/2TJH9F, with nucleotide sequences for k13-469 submitted to GenBank under accession numbers PQ283632\u0026ndash;PQ283660. Additionally, amplicon sequence data from the 2019 survey are available in GenBank under accession numbers OM370918\u0026ndash;OM370923 for \u003cem\u003ePfk13\u003c/em\u003e.All data are shared under the Creative Commons Attribution 4.0 International License (CC-BY 4.0)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical Trial Number\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFunding 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.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLIO and RWS conceptualized the study, VO, SK, PG, CM, CO supervised field collection of samples. VO, JBM, and LN performed laboratory experiments; VO and KW did the data analysis while MMM generated the maps. RWS and CM secured funds for field sample collection. LIO secured funding for the laboratory experiments. VO, PB, RWS, LIO drafted the manuscript, KW, MMM, JBM, LN, PMG, CO, KR, SM, SA, RK, KK, SK, and CM interpreted the data and contributed to writing of the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe 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. We sincerely appreciate the invaluable support and collaboration from national and county authorities, especially the Ministry of Education, Ministry of Health, County and Sub-County Directors of Education and Health, as well as the school head teachers. Their commitment has been instrumental to the success of this study. 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.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAlegana, Victor A., Peter M. Macharia, Samuel Muchiri, Eda Mumo, Elvis Oyugi, Alice Kamau, Frank Chacky, et al. 2021. \u0026ldquo;Plasmodium Falciparum Parasite Prevalence in East Africa: Updating Data for Malaria Stratification.\u0026rdquo; \u003cem\u003ePLOS Global Public Health\u003c/em\u003e 1(12): e0000014. doi:10.1371/JOURNAL.PGPH.0000014.\u003c/li\u003e\n\u003cli\u003eAriey, Fr\u0026eacute;d\u0026eacute;ric, Benoit Witkowski, Chanaki Amaratunga, Johann Beghain, Anne-Claire Claire Langlois, Nimol Khim, Saorin Kim, et al. 2014. \u0026ldquo;A Molecular Marker of Artemisinin-Resistant Plasmodium Falciparum Malaria.\u0026rdquo; \u003cem\u003eNature\u003c/em\u003e 505(7481): 50\u0026ndash;55. doi:10.1038/nature12876.\u003c/li\u003e\n\u003cli\u003eAshley, Elizabeth A., Mehul Dhorda, Rick M. 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Carlton, et al. 2002. \u0026ldquo;Genome Sequence of the Human Malaria Parasite Plasmodium Falciparum.\u0026rdquo; \u003cem\u003eNature\u003c/em\u003e 419(6906): 498\u0026ndash;511. doi:10.1038/nature01097.\u003c/li\u003e\n\u003cli\u003eHermsen, Cornelus C., Denise S.C. Telgt, Ellen H.P. Linders, Louis A.T.F. Van De Locht, Wijnand M.C. Eling, Ewald J.B.M. Mensink, and Robert W. Sauerwein. 2001. \u0026ldquo;Detection of Plasmodium Falciparum Malaria Parasites in Vivo by Real-Time Quantitative PCR.\u0026rdquo; \u003cem\u003eMolecular and Biochemical Parasitology\u003c/em\u003e 118(2): 247\u0026ndash;51. doi:10.1016/S0166-6851(01)00379-6.\u003c/li\u003e\n\u003cli\u003eIshengoma, Deus S., Celine I. Mandara, Catherine Bakari, Abebe A. Fola, Rashid A. Madebe, Misago D. 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Bugeme, Aime Balagizi, David Lupande, et al. 2024. \u0026ldquo;Detection of Artemisinin Resistance Marker Kelch-13 469Y in Plasmodium Falciparum, South Kivu, Democratic Republic of the Congo, 2022.\u0026rdquo; \u003cem\u003eAmerican Journal of Tropical Medicine and Hygiene\u003c/em\u003e 110(4): 653\u0026ndash;55. doi:10.4269/ajtmh.23-0740.\u003c/li\u003e\n\u003cli\u003evan Loon, Welmoed, Emma Schallenberg, Clement Igiraneza, Felix Habarugira, Djibril Mbarushimana, Fabian Nshimiyimana, Christian Ngarambe, et al. 2024. \u0026ldquo;Escalating Plasmodium Falciparum K13 Marker Prevalence Indicative of Artemisinin Resistance in Southern Rwanda.\u0026rdquo; \u003cem\u003eAntimicrobial Agents and Chemotherapy\u003c/em\u003e 68(1). doi:10.1128/AAC.01299-23.\u003c/li\u003e\n\u003cli\u003eLoon, Welmoed Van, Emma Schallenberg, Clement Igiraneza, Felix Habarugira, Djibril Mbarushimana, Fabian Nshimiyimana, Christian Ngarambe, et al. 2023. \u0026ldquo;Indicative of Artemisinin Resistance in Southern Rwanda.\u0026rdquo; : 1\u0026ndash;5.\u003c/li\u003e\n\u003cli\u003eMakau, Mark, Bernard N Kanoi, Calvin Mgawe, Michael Maina, Hussein Abkallo, Harrison Waweru, Ferdinand Adung\u0026rsquo;, and Jesse Gitaka. 2024. \u0026ldquo;Evidence of Partial Artemisinin Resistance in Malaria Endemic Lake Region, Busia County, Western, Kenya.\u0026rdquo; doi:10.21203/RS.3.RS-4538408/V1.\u003c/li\u003e\n\u003cli\u003eMartin, Authors Anne C, Jacob M Sadler, Alfred Simkin, Michael Musonda, Ben Katowa, Jessica Schue, Edgar Simulundu, et al. 2025. \u0026ldquo;Emergence and Rising Prevalence of Artemisinin Partial Resistance Marker Kelch13 P441L in a Low Malaria Transmission Setting in Southern Zambia.\u0026rdquo;\u003c/li\u003e\n\u003cli\u003eMesia Kahunu, Gauthier, Sarah Wellmann Thomsen, Louise Wellmann Thomsen, Hypolite Muhindo Mavoko, Patrick Mitashi Mulopo, Emma Filtenborg Hocke, Papy Mandoko Nkoli, et al. 2024. \u0026ldquo;Identification of the PfK13 Mutations R561H and P441L in the Democratic Republic of Congo.\u0026rdquo; \u003cem\u003eInternational journal of infectious diseases : IJID : official publication of the International Society for Infectious Diseases\u003c/em\u003e 139: 41\u0026ndash;49. doi:10.1016/J.IJID.2023.11.026.\u003c/li\u003e\n\u003cli\u003eMihreteab, Selam, Lucien Platon, Araia Berhane, Barbara H. Stokes, Marian Warsame, Pascal Campagne, Alexis Criscuolo, et al. 2023. \u0026ldquo;Increasing Prevalence of Artemisinin-Resistant HRP2-Negative Malaria in Eritrea.\u0026rdquo; \u003cem\u003eNew England Journal of Medicine\u003c/em\u003e 389(13): 1191\u0026ndash;1202. doi:10.1056/nejmoa2210956.\u003c/li\u003e\n\u003cli\u003eNoedl, Harald, Youry Se, Kurt Schaecher, Bryan L Smith, Duong Socheat, Mark M Fukuda, and Artemisinin Resistance in Cambodia 1 (ARC1) Study Consortium. 2008. \u0026ldquo;Evidence of Artemisinin-Resistant Malaria in Western Cambodia.\u0026rdquo; \u003cem\u003eThe New England journal of medicine\u003c/em\u003e 359(24): 2619\u0026ndash;20. doi:10.1056/NEJMc0805011.\u003c/li\u003e\n\u003cli\u003eOkell, Lucy C., Lisa Malene Reiter, Lene Sand\u0026oslash; Ebbe, Vito Baraka, Donal Bisanzio, Oliver J. Watson, Adam Bennett, et al. 2018. \u0026ldquo;Emerging Implications of Policies on Malaria Treatment: Genetic Changes in the Pfmdr-1 Gene Affecting Susceptibility to Artemether\u0026ndash;Lumefantrine and Artesunate\u0026ndash;Amodiaquine in Africa.\u0026rdquo; \u003cem\u003eBMJ Global Health\u003c/em\u003e 3(5): 1\u0026ndash;12. doi:10.1136/bmjgh-2018-000999.\u003c/li\u003e\n\u003cli\u003eOsoti, Victor, Mercy Akinyi, Kevin Wamae, Kelvin M. Kimenyi, Zaydah De Laurent, Leonard Ndwiga, Paul Gichuki, et al. 2022. \u0026ldquo;Targeted Amplicon Deep Sequencing for Monitoring Antimalarial Resistance Markers in Western Kenya.\u0026rdquo; \u003cem\u003eAntimicrobial Agents and Chemotherapy\u003c/em\u003e 66(4). doi:10.1128/aac.01945-21.\u003c/li\u003e\n\u003cli\u003eOsoti, Victor, Kevin Wamae, Leonard Ndwiga, Paul M. Gichuki, Collins Okoyo, Stella Kepha, Kibor Keitany, et al. 2025. \u0026ldquo;Detection of Low Frequency Artemisinin Resistance Mutations, C469Y, P553L and A675V, and Fixed Antifolate Resistance Mutations in Asymptomatic Primary School Children in Kenya.\u0026rdquo; \u003cem\u003eBMC Infectious Diseases 2025 25:1\u003c/em\u003e 25(1): 1\u0026ndash;10. doi:10.1186/S12879-025-10462-Z.\u003c/li\u003e\n\u003cli\u003eTakala-Harrison, Shannon, Christopher G. Jacob, Cesar Arze, Michael P. Cummings, Joana C. Silva, Arjen M. Dondorp, Mark M. Fukuda, et al. 2015. \u0026ldquo;Independent Emergence of Artemisinin Resistance Mutations among Plasmodium Falciparum in Southeast Asia.\u0026rdquo; \u003cem\u003eJournal of Infectious Diseases\u003c/em\u003e 211(5): 670\u0026ndash;79. doi:10.1093/infdis/jiu491.\u003c/li\u003e\n\u003cli\u003eTriglia, Tony, and Alan F. Cowman. 1999. \u0026ldquo;The Mechanism of Resistance to Sulfa Drugs in Plasmodium Falciparum.\u0026rdquo; \u003cem\u003eDrug resistance updates : reviews and commentaries in antimicrobial and anticancer chemotherapy\u003c/em\u003e 2(1): 15\u0026ndash;19. doi:10.1054/DRUP.1998.0060.\u003c/li\u003e\n\u003cli\u003eUwimana, Aline, Eric Legrand, Barbara H. Stokes, Jean Louis Mangala Ndikumana, Marian Warsame, Noella Umulisa, Daniel Ngamije, et al. 2020. \u0026ldquo;Emergence and Clonal Expansion of in Vitro Artemisinin-Resistant Plasmodium Falciparum Kelch13 R561H Mutant Parasites in Rwanda.\u0026rdquo; \u003cem\u003eNature Medicine\u003c/em\u003e. doi:10.1038/s41591-020-1005-2.\u003c/li\u003e\n\u003cli\u003eWHO. 2024. \u003cem\u003eWorld Malaria World Malaria Report Report\u003c/em\u003e. https://www.wipo.int/amc/en/mediation/%0Ahttps://www.who.int/teams/global-malaria-programme/reports/world-malaria-report-2023.\u003c/li\u003e\n\u003cli\u003eWongsrichanalai, Chansuda, Amy L Pickard, Walther H Wernsdorfer, and Steven R Meshnick. 2002. \u0026ldquo;Reviews Epidemiology of Drug-Resistant Malaria.\u0026rdquo; 2: 209\u0026ndash;18.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Malaria, Drug Resistance, Artemisinin, R561H, A675V, C469Y, P553L, Amplicon, Deep-sequencing, Kenya","lastPublishedDoi":"10.21203/rs.3.rs-6890493/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6890493/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eRecent reports from East African countries indicate the emergence and spread of artemisinin partial resistance (ART-R), posing a significant threat to malaria control efforts in the region. The presence of critical \u003cem\u003ePlasmodium falciparum\u003c/em\u003e kelch13 (k13) resistance markers, including \u003cem\u003eC469Y\u003c/em\u003e, \u003cem\u003eP553L\u003c/em\u003e and \u003cem\u003eA675V\u003c/em\u003e, have been detected in Kenya, although their clinical significance remains unclear. This highlights an urgent need to closely monitor the prevalence of these mutations.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA total of 24,227 dried blood spot (DBS) samples were collected from 82 primary schools across eight counties in Western Kenya during repeated cross-sectional surveys conducted in 2019 (n\u0026thinsp;=\u0026thinsp;7,941), 2022 (n\u0026thinsp;=\u0026thinsp;8,086), and 2023 (n\u0026thinsp;=\u0026thinsp;8,200). Initial screening was performed using a rapid diagnostic test (RDT), and DNA extraction was conducted on RDT-positive samples. These samples were further analyzed using a \u003cem\u003ePf\u003c/em\u003e18S qPCR assay to quantify the \u003cem\u003ePlasmodium falciparum\u003c/em\u003e DNA. Amplicons from malaria-positive samples were sequenced using a previously established amplicon deep sequencing pipeline to analyze mutations in \u003cem\u003ePfk13\u003c/em\u003e. A total of 500, 920 and 1058 samples from 2019, 2022, and 2023, respectively were successfully processed, enabling a temporal assessment of the changes in k13 mutations in the region.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eFour mutations that have previously been associated with artemisinin resistance were found. The A675V mutation was the most prevalent, being found in all 8 counties. It was absent in 2019 and increased from 0.9% in 2022 to 5% in 2023. In contrast, the C469Y mutation declined from 4% in 2022 to 1% in 2023, maintaining a presence in 3 counties. The P553L mutation was only detected in 2022 in 1.2% of the samples across 5 counties. The R561H mutation was not detected in 2019 and 2022 but emerged at a low frequency (0.5%) in 2023 in 2 counties. Siaya and Kisumu were the only counties with all 4 validated mutations between 2022 and 2023.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThe rising prevalence and geographical presence of the A675V mutation and the new detection of R561H in 2023 highlights the critical need for robust molecular surveillance systems to track the frequency and geographic spread of resistance markers. School-based sampling presents a practical and scalable approach for molecular surveillance, providing early warning signals for potential resistance hotspots. Additionally, the detection of the four WHO validated PfK13 artemisinin resistance mutations in Western Kenya underscores the urgency of conducting regular Therapeutic Efficacy Studies (TES) to assess the continued efficacy of frontline antimalarial treatments. Integrating molecular surveillance with TES will generate important data to inform national treatment policies and support the long-term effectiveness of malaria control strategies in Kenya.\u003c/p\u003e","manuscriptTitle":"Longitudinal surveillance of kelch13 identifies C469Y, P553L, R561H and A675V mutations associated with artemisinin resistance in Western Kenya","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-25 17:42:25","doi":"10.21203/rs.3.rs-6890493/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-08-06T04:56:25+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-18T00:04:01+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"316984956300223067754815501726594020283","date":"2025-07-14T19:11:36+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-03T21:02:26+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"285859251651175180718583028209529853624","date":"2025-06-25T16:56:15+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-06-23T15:58:55+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-06-23T15:50:58+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-06-16T17:04:04+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-06-14T05:40:52+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-06-13T19:25:49+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"7bee6aae-348c-4001-a551-8dbc6fc383af","owner":[],"postedDate":"June 25th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":50514628,"name":"Biological sciences/Genetics"},{"id":50514629,"name":"Biological sciences/Microbiology"},{"id":50514630,"name":"Biological sciences/Molecular biology"}],"tags":[],"updatedAt":"2025-11-10T16:05:26+00:00","versionOfRecord":{"articleIdentity":"rs-6890493","link":"https://doi.org/10.1038/s41598-025-22286-7","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-11-03 15:57:57","publishedOnDateReadable":"November 3rd, 2025"},"versionCreatedAt":"2025-06-25 17:42:25","video":"","vorDoi":"10.1038/s41598-025-22286-7","vorDoiUrl":"https://doi.org/10.1038/s41598-025-22286-7","workflowStages":[]},"version":"v1","identity":"rs-6890493","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6890493","identity":"rs-6890493","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}