Phenotypic assessment and genetic validation of Plasmodium falciparum molecular markers associated with malaria chemoprevention in Senegal

Phenotypic assessment and genetic validation of Plasmodium falciparum molecular markers associated with malaria chemoprevention in Senegal | bioRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (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];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-M677548'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search New Results Phenotypic assessment and genetic validation of Plasmodium falciparum molecular markers associated with malaria chemoprevention in Senegal View ORCID Profile Katelyn Vendrely Brenneman , View ORCID Profile Wesley Wong , Mamy Yaye Die Ndiaye , Stephen Schaffner , Bassirou Ngom , Karina Bellavia , View ORCID Profile Imran Ullah , Amy Gaye , Djiby Sow , Mame Fama Ndiaye , Mariama Toure , Nogaye Gadiaga , Aita Sene , Awa Bineta Deme , Baba Dieye , Mamadou Samb Yade , Khadim Diongue , Younouss Diedhiou , Jules François Gomis , Mouhamadou Ndiaye , Mamadou Alpha Diallo , Ibrahima Mbaye Ndiaye , Bronwyn MacInnis , Dyann F. Wirth , Daouda Ndiaye , Sarah K. Volkman doi: https://doi.org/10.1101/2025.11.05.686777 Katelyn Vendrely Brenneman 1 Harvard T.H. Chan School of Public Health , Boston, MA, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Katelyn Vendrely Brenneman Wesley Wong 1 Harvard T.H. Chan School of Public Health , Boston, MA, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Wesley Wong Mamy Yaye Die Ndiaye 2 International Research & Training Center in Applied Genomics and Health Surveillance (CIGASS), Cheikh Anta Diop University , Dakar, Senegal Find this author on Google Scholar Find this author on PubMed Search for this author on this site Stephen Schaffner 3 The Broad Institute , Cambridge, MA, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Bassirou Ngom 2 International Research & Training Center in Applied Genomics and Health Surveillance (CIGASS), Cheikh Anta Diop University , Dakar, Senegal Find this author on Google Scholar Find this author on PubMed Search for this author on this site Karina Bellavia 1 Harvard T.H. Chan School of Public Health , Boston, MA, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Imran Ullah 1 Harvard T.H. Chan School of Public Health , Boston, MA, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Imran Ullah Amy Gaye 2 International Research & Training Center in Applied Genomics and Health Surveillance (CIGASS), Cheikh Anta Diop University , Dakar, Senegal Find this author on Google Scholar Find this author on PubMed Search for this author on this site Djiby Sow 2 International Research & Training Center in Applied Genomics and Health Surveillance (CIGASS), Cheikh Anta Diop University , Dakar, Senegal Find this author on Google Scholar Find this author on PubMed Search for this author on this site Mame Fama Ndiaye 2 International Research & Training Center in Applied Genomics and Health Surveillance (CIGASS), Cheikh Anta Diop University , Dakar, Senegal Find this author on Google Scholar Find this author on PubMed Search for this author on this site Mariama Toure 2 International Research & Training Center in Applied Genomics and Health Surveillance (CIGASS), Cheikh Anta Diop University , Dakar, Senegal Find this author on Google Scholar Find this author on PubMed Search for this author on this site Nogaye Gadiaga 2 International Research & Training Center in Applied Genomics and Health Surveillance (CIGASS), Cheikh Anta Diop University , Dakar, Senegal Find this author on Google Scholar Find this author on PubMed Search for this author on this site Aita Sene 2 International Research & Training Center in Applied Genomics and Health Surveillance (CIGASS), Cheikh Anta Diop University , Dakar, Senegal Find this author on Google Scholar Find this author on PubMed Search for this author on this site Awa Bineta Deme 2 International Research & Training Center in Applied Genomics and Health Surveillance (CIGASS), Cheikh Anta Diop University , Dakar, Senegal Find this author on Google Scholar Find this author on PubMed Search for this author on this site Baba Dieye 2 International Research & Training Center in Applied Genomics and Health Surveillance (CIGASS), Cheikh Anta Diop University , Dakar, Senegal Find this author on Google Scholar Find this author on PubMed Search for this author on this site Mamadou Samb Yade 2 International Research & Training Center in Applied Genomics and Health Surveillance (CIGASS), Cheikh Anta Diop University , Dakar, Senegal Find this author on Google Scholar Find this author on PubMed Search for this author on this site Khadim Diongue 2 International Research & Training Center in Applied Genomics and Health Surveillance (CIGASS), Cheikh Anta Diop University , Dakar, Senegal 4 Department of Parasitology, Faculty of Medicine and Pharmacy, Cheikh Anta Diop University , Dakar, Senegal Find this author on Google Scholar Find this author on PubMed Search for this author on this site Younouss Diedhiou 2 International Research & Training Center in Applied Genomics and Health Surveillance (CIGASS), Cheikh Anta Diop University , Dakar, Senegal Find this author on Google Scholar Find this author on PubMed Search for this author on this site Jules François Gomis 1 Harvard T.H. Chan School of Public Health , Boston, MA, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Mouhamadou Ndiaye 2 International Research & Training Center in Applied Genomics and Health Surveillance (CIGASS), Cheikh Anta Diop University , Dakar, Senegal 4 Department of Parasitology, Faculty of Medicine and Pharmacy, Cheikh Anta Diop University , Dakar, Senegal Find this author on Google Scholar Find this author on PubMed Search for this author on this site Mamadou Alpha Diallo 2 International Research & Training Center in Applied Genomics and Health Surveillance (CIGASS), Cheikh Anta Diop University , Dakar, Senegal Find this author on Google Scholar Find this author on PubMed Search for this author on this site Ibrahima Mbaye Ndiaye 2 International Research & Training Center in Applied Genomics and Health Surveillance (CIGASS), Cheikh Anta Diop University , Dakar, Senegal Find this author on Google Scholar Find this author on PubMed Search for this author on this site Bronwyn MacInnis 3 The Broad Institute , Cambridge, MA, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Dyann F. Wirth 1 Harvard T.H. Chan School of Public Health , Boston, MA, USA 3 The Broad Institute , Cambridge, MA, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Daouda Ndiaye 2 International Research & Training Center in Applied Genomics and Health Surveillance (CIGASS), Cheikh Anta Diop University , Dakar, Senegal 4 Department of Parasitology, Faculty of Medicine and Pharmacy, Cheikh Anta Diop University , Dakar, Senegal Find this author on Google Scholar Find this author on PubMed Search for this author on this site Sarah K. Volkman 1 Harvard T.H. Chan School of Public Health , Boston, MA, USA 3 The Broad Institute , Cambridge, MA, USA 6 Simmons University, School of Nursing Find this author on Google Scholar Find this author on PubMed Search for this author on this site For correspondence: svolkman{at}hsph.harvard.edu Abstract Full Text Info/History Metrics Supplementary material Preview PDF ABSTRACT Drug resistance in Plasmodium falciparum threatens to undermine malaria control and elimination efforts. Senegal is a malaria-endemic country that has implemented successive antimalarial and chemopreventive drug-based strategies for two decades. Sulfadoxine-pyrimethamine (SP) is used for chemoprevention in Senegal for intermittent preventive treatment in pregnancy (since 2004) and SP plus amodiaquine (AQ) is used for seasonal malaria chemoprevention (SMC, since 2013). Using whole genome sequence (WGS) data from malaria patient samples from health facilities across Senegal (2006 – 2022), we observed near fixation of Pfdhfr triple mutant and fluctuation in Pfdhps and Pfcrt mutation frequencies over time. It is unclear how these mutations influence drug resistance and fitness phenotypes in natural isolates; therefore, we evaluated natural parasite isolates with different Pfcrt, Pfmdr1, Pfdhps , and Pfdhfr haplotypes. Parasites were culture-adapted and phenotyped for antimalarial drug susceptibility and competitive growth (fitness). Pfcrt CVIET + A220S + Q271E + N326S + R371I and Pfcrt CVIET + A220S + Q271E + I356T + R371I mutants were significantly more resistant to monodesethyl-amodiaquine (md-AQ) compared to Pfcrt wild-type (WT) and Pfcrt CVIET + A220S + Q271E + R371I mutants. Pfdhfr triple mutants were significantly more pyrimethamine (PYR) resistant than Pfdhfr WT and revealed a range of phenotypes, but this was not explained by Pfgch1 copy-number. Pfdhps A437G parasites were significantly more sulfadoxine (SDX) resistant compared to Pfdhps wild-type and Pfdhps S436A mutants, suggesting that A437G is a key mutation for SDX resistance. Competitive growth assays between Pfdhfr-Pfdhps mutants revealed that Pfdhps mutations do not always result in fitness costs. Ongoing phenotypic assessment and genetic validation of these mutations in a Senegalese background is necessary to assess the impact of drug pressure, identify evolving genetic determinants of drug resistance, and provide molecular markers for ongoing surveillance to monitor and guide the use of drug-based interventions. AUTHOR SUMMARY Drug resistance is a major concern for both preventing and treating malaria, especially in Africa where most malaria cases and deaths occur. Since 2013, Senegal has been giving children under 10 years old a combination of sulfadoxine-pyrimethamine plus amodiaquine to prevent malaria during the transmission season, called Seasonal Malaria Chemoprevention (SMC), and plans to continue expanding its use. However, there is evidence from genetic surveillance that drug resistance mutations are present in Senegal which could render this antimalarial drug combination ineffective. Here we use natural P. falciparum isolates obtained from Senegalese patients that represent the extant parasite population to evaluate the consequences of evolving mutations on antimalarial drug resistance and fitness phenotypes. This study is one of the first to use natural parasites to assess the impact of naturally derived mutations on drug resistance and fitness phenotypes. Our results provide evidence that certain combinations of drug resistance mutations impact both parasite drug resistance and fitness, and therefore need to be closely monitored and can inform optimal antimalarial combinations for the prevention or treatment of malaria. This work informs the ongoing evolution of resistance and fitness phenotypes in malaria endemic settings that are introducing new multi first line therapies (MFTs) and SMC interventions that have been used for decades in Senegal. Our approach creates a framework for using genetic surveillance data to form a hypothesis, which can then be phenotypically tested by measuring the resistance and fitness levels of genetically diverse natural parasite isolates. INTRODUCTION Malaria remains a global health concern, with the majority of malaria cases and deaths occurring in African children under 5 years old ( 1 ). Antimalarial drugs continue to be one of the main methods to combat Plasmodium falciparum malaria; they are used to both treat malaria infections and prevent malaria infection. Many different antimalarial drugs have been used throughout history, and drug resistance has emerged to every antimalarial drug in use ( 2 ). Genetic surveillance of drug resistance mutations can identify resistant parasites and determine whether resistance has emerged locally or if resistance mutations have spread from other regions ( 3 , 4 ). By tracking drug resistance mutations and identifying their sources, genetic surveillance is a critical tool for monitoring and predicting the risk of drug resistance, controlling the spread of drug resistance, and determining whether drug-based interventions will be effective in certain regions. Typically, genetic surveillance is conducted using genotyping data of known resistance markers and therefore relies on knowledge of the molecular basis of drug resistance for antimalarial drugs of interest. For several antimalarials, the basis of drug resistance has been well established by previous studies: mutations in P. falciparum chloroquine resistance transporter ( Pfcrt ) are associated with chloroquine (CQ) resistance ( 5 , 6 ). The molecular basis of sulfadoxine (SDX) and pyrimethamine (PYR) drug resistance is associated with accumulations of point mutations in the genes encoding the targets of SDX and PYR, dihydropteroate synthase ( Pfdhps ) and dihydrofolate reductase ( Pfdhfr ), respectively ( 7 , 8 ) that encode essential enzymes involved in the folate biosynthesis pathway required for DNA synthesis. However, there are several antimalarials with unknown or partial molecular markers of resistance, including quinolines such as amodiaquine (AQ) and lumefantrine (LUM); both likely involve mutations in Pfcrt and the multi-drug resistance Pfmdr1 gene and other unidentified mutations for resistance ( 9 – 12 ). Or in the case of artemisinin, there is a major marker of resistance, Pfkelch13 ( 13 ), but there are likely other mutations that confer resistance ( 14 – 17 ). Since the genetic determinants of several antimalarial drugs have not been fully elucidated and may involve multiple genes or compensatory mutations ( 18 ), genetic surveillance of known drug resistance markers can only provide an estimate of drug resistance in the population, highlighting the need to further characterize the drug response and fitness of parasites that have evolved under drug pressure and carry different combinations of known mutations. Senegal is a malaria-endemic country that has implemented successive antimalarial drug-based strategies for treatment and prevention and has conducted genetic surveillance for several decades. CQ was used as a frontline antimalarial until 2003, when it was replaced by sulfadoxine-pyrimethamine (SP) plus AQ. SP + AQ remained the frontline antimalarial combination until Senegal transitioned to Artemisinin Combination Therapy (ACT) in 2006. ACT use began with artesunate and amodiaquine (AS-AQ) in 2006, followed by artemether-lumefantrine (AL) in 2008; both ACTs remain the current frontline antimalarials. For chemoprevention, Senegal currently uses SP for intermittent preventative treatment in pregnancy (IPTp, since 2004), and uses SP + AQ for Seasonal Malaria Chemoprevention (SMC) for children (since 2013). SMC is given by community health workers in 3-5 monthly rounds (depending on local malaria incidence and the duration of transmission season) for all children under 10 years of age ( 19 ). For the past two-decades in Senegal, genetic surveillance has been used to assess how antimalarial drug usage for treatment and prevention has affected parasite populations. The frequency of drug resistance markers of CQ (mutations in Pfcrt ), artemisinin (mutations in Pfkelch13 ), AQ (mutations in Pfmdr1 and Pfcrt ), and SP (mutations in Pfdhfr and Pfdhps ) were examined for trends of increasing drug resistance that could signal a reduction in antimalarial drug efficacy. Molecular surveillance revealed drastic changes over time; the Pfcrt K76T mutation declined from 76% to 26% prevalence after the withdrawal of CQ in 2003 but since 2014 has rebounded to 49%. The frequency of Pfdhfr mutations (N51I, C59R, S108N) have been steadily increasing from 42% since 2000 and are nearing fixation (95%). The frequency of Pfdhps A437G has been fluctuating from 17% to 72% over the last 10 years, while Pfmdr1 (N86, Y184F, D1246) appears to have stabilized at a frequency of 56% ( 19 ). These mutation frequencies from genetic surveillance strongly suggest that parasites in Senegal are rapidly evolving in response to antimalarial drug use. Because both drug resistance and parasite fitness can impact treatment efficacy and influence treatment strategies, it is important to understand the effects of known drug resistance mutations on these phenotypes in natural parasite isolates. Therefore, we performed “phenotypic surveillance”, assaying natural parasite isolates in vitro to determine their drug resistance and fitness phenotypes. Natural parasite isolates are representative of parasite genomes that are currently circulating in the field and can be culture-adapted for in vitro phenotyping. Therefore, natural parasite isolates are extremely useful for estimating the extent of drug resistance in natural populations and for identifying mutations that are driving drug resistance and fitness phenotypes. In vitro drug susceptibility assays are one approach to assess parasite drug responses. While they have limitations, they provide extremely valuable information about how certain mutations or haplotypes affect parasite drug response. A previous study assessing the in vitro drug susceptibility of 45 culture-adapted parasite isolates to 12 different antimalarials showed that these isolates exhibit a variety of drug resistance phenotypes. This study also confirmed the roles of Pfcrt, Pfdhfr, and Pfmdr1 as mediators of resistance, but also found several novel signals of PYR resistance-associated selection on chromosome 6 and 12 ( 20 ). However, since this 2012 study, there have been many changes to drug pressure in the population and SDX or SP were not included in that analysis; consequently, the role of Pfdhps and its interactions with known and novel resistance mutations was not investigated. SMC was first deployed across the Sahel subregion of Africa in 2012 for children aged 3-59 months, and as of 2023, the use of SMC has been expanded to 19 African countries that have highly seasonal malaria transmission ( 1 ). In addition, restrictions on the number of monthly cycles or age were removed so that SMC could be given to children at high risk of severe malaria during peak malaria transmission season ( 21 ). Given the expanded use of SMC (both geographically and in adding additional monthly cycles), especially in areas of Africa where drug resistance is present, and the significant increase in the prevalence of Pfdhfr triple mutants only two years after the implementation of SMC in several African countries ( 22 ), genetic surveillance has increased in an effort to monitor known drug resistance markers. However, translating this genetic surveillance data into actionable knowledge that can be used by public health professionals who implement policy changes based on this data remains a challenge. Therefore, it is important to understand how the effectiveness of the continued use and expansion of SMC and other interventions that use SP (IPTp and perennial malaria chemoprevention (PMC)) could be compromised by Pfdhfr and Pfdhps mutations. In this study, we used parasites that were collected from patients in Senegal and whole genome sequenced over the past two decades to identify major haplotypes of known drug resistance markers present in the population ( 19 , 23 ). Based on these results, we culture-adapted and phenotyped parasite isolates with certain combinations of these mutations that were all present in the natural population, including Pfcrt (codon positions 74-76, A220S, Q271E, N326S, I356T, and R371I), Pfdhfr (N51I, C59R, S108N), Pfdhps (S436A, A437G, A613S), and Pfmdr1 (N86Y, Y184F, D1246Y) to determine what role these alleles were playing in parasite fitness or drug resistance. We found that Pfcrt CVIET A220S + Q271E + N326S + R371I and Pfcrt CVIET + A220S + Q271E + I356T + R371I mutants were significantly more resistant to md-AQ compared to wild-type Pfcrt parasites. We saw a range of SDX resistance phenotypes regardless of Pfdhps genotype, but the key mutation for sulfadoxine resistance appeared to be Pfdhps A437G. There was not a clear correlation between fitness and drug resistance, but our findings provide evidence that certain combinations of drug resistance mutations are impacting both parasite drug resistance and fitness and should be closely monitored. RESULTS Analysis of drug resistance haplotypes and selection of isolates representing the overall population haplotypic diversity Plasmodium falciparum samples were collected between 2006 and 2022 from febrile patients from six sampling locations within Senegal: Pikine, Thiès, Diourbel, Kaolack, Kolda, and Kédougou. These samples were whole genome sequenced and samples that were monogenomic (single genome infection) were used for further analysis (1879 samples). To represent the genetic diversity of Senegal parasites as a whole, we selected monogenomic natural parasite isolates for culture-adaptation collected from two sites: Thiès (low transmission area; 2021 reported annual incidence was 2.8 cases per 1000) and Kédougou (high transmission area; 2021 reported annual incidence was 536.5 cases per 1000) ( 24 ). Parasites were chosen for culture-adaptation based on their combination of alleles at Pfcrt (codon positions 74-76, A220S, Q271E, N326S, I356T, and R371I), Pfmdr1 (N86Y, Y184F, D1246Y), Pfdhfr (N51I, C59R, S108N), and Pfdhps (I431V, S436A, A437G, K540E, A581G, A613S), which defined their combined Pfcrt , Pfmdr1 , Pfdhfr , and Pfdhps haplotype. Based on these 19 different genomic sites of the combined haplotype, there was a total of 524,288 unique haplotypes (2 19 ) that could possibly exist, however, we only found 239 unique haplotypes in our dataset, likely because these haplotypes are linked and not random (e.g., Pfcrt M74I, N75E, and K76T are commonly found together), some combinations are incompatible, and many genomic sites are nearing fixation in Senegal (e.g., Pfdhfr N51I, C59R, S108N). Out of the 239 haplotypes found in our dataset, 208 haplotypes had less than 10 parasites with that haplotype (134 haplotypes had only 1 parasite). Therefore, we selected 33 parasites for culture-adaptation that represented 26 haplotypes (10.9% of all haplotypes in our dataset); these 33 parasites represented 63.9% of all parasites in our dataset (supplemental table 1, supplemental figure 1). Determining Pfmdr1 and Pfgch1 copy-number variation Once culture-adapted, the 33 parasites were whole genome sequenced again to confirm their sequences; all parasite samples were confirmed to have the same genomic regions of interest pre-culture adaptation and post-culture adaptation. Parasites were also assayed for copy-number variations (CNVs) in Pfgch1 and Pfmdr1 ( table 1 ). While no parasites showed any CNVs in Pfmdr1 (associated with mefloquine resistance), some parasites had some copy-number amplifications in Pfgch1 , which encodes a GTP cyclohydrolase that is the first and rate-limiting enzyme in the folate synthesis pathway ( Pfdhfr and Pfdhps are key enzymes in the later stages of this pathway). Pfgch1 has been shown to have extensive copy-number polymorphisms with some amplifications of Pfgch1 occurring only in the gene and some amplifications only occurring in the promoter region ( 25 – 27 ); both types of amplifications are likely a result of selection by antifolate drugs SDX and PYR acting in different geographical regions ( 28 , 29 ). Therefore, we assayed parasites for amplifications in the Pfgch1 gene and the Pfgch1 promoter. We found that 3 parasites had promoter amplifications (all had triple promoter amplifications, supplemental figure 2), and 1 parasite had a Pfgch1 gene amplification (Th052.16). Studies have shown that increased copy-numbers of Pfgch1 have a variable effect on PYR EC 50 values and could also be a compensatory mechanism for the fitness cost imposed by Pfdhfr and Pfdhps ( 28 , 30 , 31 ). Further functional characterization is needed to determine whether amplifications in the Pfgch1 promoter or the Pfgch1 gene result in an increase in Pfgch1 expression. View this table: View inline View popup Download powerpoint Table 1: Genotypes and CN variations for 33 natural parasite isolates from Senegal and 2 reference lines selected for phenotypic assessment. Parasite drug susceptibility phenotyping To determine whether the different Pfcrt , Pfmdr1 , Pfdhfr , and Pfdhps combined haplotypes have an impact on drug resistance, we investigated the drug susceptibility of our culture adapted parasite isolates to a panel of antimalarial drugs that have been used for antimalarial therapy or chemoprevention in Senegal. The set of culture-adapted parasites (and 3D7 and Dd2 reference strains) were phenotyped for in vitro drug susceptibility to chloroquine (CQ), mefloquine (MQ), lumefantrine (LUM), piperaquine (PIP), dihydroartemisinin (DHA), amodiaquine (AQ), quinine (QN), monodesethyl-amodiaquine (md-AQ), pyrimethamine (PYR), sulfadoxine (SDX), and sulfadoxine-pyrimethamine (SP). No statistically significant difference in EC 50 values was seen amongst parasites for MQ, LUM, PIP, DHA, AQ, and QN, however, we did see statistically significant differences in EC 50 values for CQ, md-AQ, PYR, SDX, and SP (supplemental table 2 and supplemental figure 3). The role of Pfcrt mutations in conferring chloroquine and amodiaquine resistance One of the most well-studied relationships between drug resistance and causative mutation is CQ and Pfcrt K76T. However, Senegal has not used CQ since 2003 and yet genetic surveillance data revealed an increase in Pfcrt K76T allele frequency beginning in 2014 ( 19 ). It is also well documented that there is a fitness cost to parasites carrying Pfcrt K76T mutations ( 32 – 34 ), so why are Pfcrt K76T allele frequencies rising in Senegal? We hypothesize that AQ resistance (which likely involves mutations in Pfcrt , Pfmdr1 , and other unidentified mutations for resistance) could be driving this change. AQ is a component of both an ACT frontline antimalarial treatment (AS-AQ) and is also a component of SMC (SP + AQ) and has been extensively used throughout Senegal for the past decade; however, according to national policy, AS-AQ and SMC are not used in the same regions, AL is used as the frontline antimalarial instead. Importantly, artemisinin resistance has not yet appeared in Senegal ( 35 ). To better understand the drug susceptibility profile of parasites with the most common Pfcrt + Pfmdr1 haplotypes present in our Senegal dataset, we exposed parasites to CQ and md-AQ (the active metabolite of AQ) to determine their EC 50 values. Unsurprisingly, we found that mutant Pfcrt CVIET parasites had significantly higher CQ EC 50 values compared to Pfcrt wild-type CVMNK parasites; parasites with the Pfcrt CVIET + A220S + Q271E + I356T + R371I (Cam783) haplotype had the highest CQ EC 50 values compared to all other haplotypes ( figure 1A ), as was shown in a previous study ( 32 ). Pfcrt CVIET parasites with or without Pfmdr1 mutations did not have significantly different CQ EC 50 values, suggesting that Pfmdr1 mutations do not play a role in CQ resistance in this set of parasites. Download figure Open in new tab Figure 1. A. Chloroquine (CQ) EC 50 comparisons and B. monodesethyl-amodiaquine (md-AQ) EC 50 comparisons. (A) Parasites with Pfcrt CVIET mutations (GB4, Cam738, and FCB haplotypes), regardless of Pfmdr1 mutations, are statistically significantly more resistant to CQ. (B) Parasites with Pfcrt CVIET mutations (GB4, Cam738, and FCB haplotypes), regardless of Pfmdr1 mutations, are statistically significantly more resistant to md-AQ; GB4 parasites were 1.3-fold more md-AQ resistant than 3D7 and Cam783 parasites were 3.3-fold and FCB parasites were 3.1-fold more md-AQ resistant than 3D7. Parasite with an open green circle is Pfmdr1 NYD. We found that mutant Pfcrt CVIET parasites had increased md-AQ EC 50 values compared to Pfcrt wild-type CVMNK parasites ( figure 1B ). Interestingly, we found that Pfcrt CVIET A220S + Q271E + I356T + R371I (Cam783 haplotype, p=0.0001) and Pfcrt CVIET A220S + Q271E + N326S + R371I (FCB haplotype, p=0.001) parasites had significantly higher md-AQ EC 50 values compared to CVIET + A220S + Q271E + R371I (GB4 haplotype), which suggests that the Pfcrt N326S and I356T mutations could be playing a key role in mediating AQ susceptibility. However, none of the parasite isolates tested had elevated EC 50 values that would be considered AQ resistant (∼0.06 μM ( 36 )). Several studies have shown that Pfcrt mutations, especially the mutant Pfcrt CVIET haplotype, could be one of the potential drivers of AQ resistance ( 37 – 41 ), but it is most likely that AQ resistance is driven by multiple mutations. There is also evidence that in addition to Pfcrt mutations, Pfmdr1 mutations and copy-number variations modulate drug resistance ( 10 , 37 , 42 ). Notably, none of the parasites in our dataset have Pfmdr1 copy-number amplifications or Pfmdr1 N1042D or D1246Y mutations ( table 1 ), which means we cannot determine their contribution to drug resistance. However, in our dataset, parasites with Pfmdr1 N86Y or Y184F mutations did not have significantly greater md-AQ EC 50 values compared to Pfmdr1 wild-type parasites (either with or without Pfcrt CVIET). Therefore, these specific Pfmdr1 mutations (N86Y or Y184F) do not seem to explain the elevated md-AQ EC 50 values. However, there are likely other undiscovered contributors to AQ resistance; Pfcrt and Pfmdr1 mutations play a role and should be closely monitored, but alone do not predict AQ resistance ( 43 ). Based on these results, Pfcrt mutant parasites that include either N326S or I356T mutations should be closely monitored in Senegal for potentially indicating decreased AQ susceptibility. Interestingly, parasites with Pfcrt N326S or I356T mutations are also more resistant to CQ compared to wild-type parasites at these loci (supplemental figure 4). Pfcrt allele population frequency over time Given the decreased md-AQ susceptibility for parasites with Pfcrt N326S or I356T mutations, we wanted to determine the frequency of these mutations in Senegal over time. To do this, we investigated the full haplotypes of Pfcrt (amino acid codon positions 72-76, 220, 271, 326, 356, and 371) and Pfmdr1 (amino acid positions 86, 184, and 1246) from 2006-2022 across all sites in Senegal and found that there were four major haplotypes in the 2160 samples: wild-type parasites ( Pfmdr1 NYD + Pfcrt 3D7, black), Pfmdr1 mutant parasites (NFD + 3D7, gray), mutant Pfcrt parasites (NYD + Cam783, maroon), and mutant Pfcrt and Pfmdr1 parasites (NFD + Cam783, red) ( figure 2 ). Comparing the Pfmdr1 and Pfcrt combined haplotypes over time for Senegal and for just Thiès and just Kédougou revealed similar trends over time (supplemental figure 5). The most common haplotype found throughout Senegal in 2022 was mutant Pfmdr1 (NFD + 3D7) (21.8% [18.0, 25.6]), which we found to have no resistance to CQ or md-AQ in vitro . However, of the parasites that had Pfcrt mutations, the most common haplotype was mutant Pfcrt and Pfmdr1 (NFD + Cam783, red) (15.7% [12.4,19.0]), which has been increasing in frequency since 2012 (2.4% [0.0,7.0]) and we found to be the least susceptible to CQ and md-AQ in vitro . The Pfcrt FCB (purple) haplotype had a similarly elevated md-AQ EC 50 in vitro , however, its frequency throughout Senegal has been declining since 2014 (13.3% [3.4,2.3] in 2014 to 1.9% [0.01,0.3] in 2022). The Pfcrt Cam783 haplotype (red), along with the GB4 haplotype (green) have been reported in previous studies to be two of the most common African mutant Pfcrt haplotypes; these haplotypes were found to be susceptible to AQ and had minimal fitness costs compared to other mutant Pfcrt haplotypes ( 32 , 44 ). Based on our analysis of 10,037 samples from the MalariaGEN Pf7 dataset, the Pfcrt Cam783 haplotype is the most common mutant Pfcrt haplotype in West Africa, the GB4 haplotype is the most common mutant Pfcrt haplotype in Central and Eastern Africa, and the FCB haplotype is the most common in Northeastern Africa (supplemental figure 6). Download figure Open in new tab Figure 2. Haplotype variant allele frequencies (VAF) for Pfcrt and Pfmdr1 . Samples collected from patients throughout Senegal were whole genome sequenced, 2160 monogenomic infections with genotype calls at Pfcrt and Pfmdr1 were included in this dataset. The most common Pfcrt haplotype since 2006 is wild-type Pfcrt (3D7, gray/black), while the most common mutant Pfcrt haplotype is Cam783 (red). Pfmdr1 NYD and NFD haplotypes remained even over time, but the NFD haplotype has a slightly higher frequency (0.47 [0.44,0.51]) than NYD (0.44 [0.41,0.48]) across all years throughout Senegal. The full list of haplotypes that are included in the “other haplotypes” category can be found in supplemental table 3. The in vitro fitness of Pfcrt alleles in the absence of drug Next, we wanted to determine whether certain combinations of Pfmdr1-Pfcrt mutations had a fitness cost in these natural isolates. We determined that Pfcrt mutations have a large fitness cost in pairwise competition growth assays with Pfcrt wild-type parasites, therefore, we chose 11 Pfcrt mutant CVIET parasites that have various combinations of Pfcrt and Pfmdr1 mutations to compete in pairwise competitions. These 11 parasites were each co-grown with another parasite in all unique combinations, resulting in 55 competitions. Parasites were set up 1:1 and co-grown until one parasite outcompeted the other, or after 30 days if there was no winner, the competition was called a tie. Based on the competitive outcomes, parasites were ranked from most fit (i.e., won every competition and have a perfect winning percentage of 100%) to the least fit (i.e., lost every competition and have a winning percentage of 0%). Th080.10 ( Pfcrt Cam783 + Pfmdr1 NYD) was the most fit, while KDG047.19 ( Pfcrt GB4 + Pfmdr1 YFD) was the least fit. Perhaps unsurprisingly, a Pfmdr1 NYD wild-type parasites was the most fit parasite; the fitness cost of Pfmdr1 mutations has been previously shown ( 45 ). However, there was no fitness difference between NYD and NFD parasites; parasites with each genotype displayed a range of fitness phenotypes ( figure 3A ). Parasites from each of the different Pfcrt CVIET categories displayed a range of relative fitness (i.e., Pfmdr1 NFD or YFD + GB4 had the least fit parasite and the third-most fit parasite). Cam783 and FCB haplotypes had the top two most fit parasites, suggesting that in the particular genetic background of those two parasites, N326S and I356T do not confer a fitness cost ( figure 3B ). Download figure Open in new tab Figure 3. Relative fitness of 11 Pfcrt CVIET parasites based on A. Pfmdr1 haplotype and B. Pfcrt and Pfmdr1 haplotype. (A) Parasites with YFD mutations were the least fit compared to Pfmdr1 wild-type (NYD) and Pfdmr1 NFD parasites. (B) Amongst parasites with Pfcrt CVIET mutations, an I356T mutant parasite and a N326S mutant parasite were the two most fit. Pfmdr1 NYD parasites are indicated by open circles. Allelic determinants of pyrimethamine and sulfadoxine resistance in Senegalese parasites Parasites need folate to provide cofactors for many processes, including DNA synthesis ( 30 , 46 ). Parasites can salvage exogenous folate from the host and can also synthesize folate de novo (endogenous folate), however the efficiency of folate salvage varies between parasite strains ( 47 ). Pfdhps , the target of SDX, is an enzyme that catalyzes the conversion of endogenous folate precursors to dihydropteroate, a key intermediate in folate biosynthesis, which is then converted to tetrahydrofolate by Pfdhfr , the target of PYR. Pfdhfr can also convert exogenous folate precursors to tetrahydrofolate. The ability of Pfdhfr to convert both exogenous and endogenous folates, and therefore an ability to bypass the Pfdhps step, is likely why Pfdhfr mutations occur before Pfdhps mutations. Mutations in Pfdhfr accumulate in a step-wise fashion (S108N is often first, then N51I or C59R) and result in PYR resistance ( 48 , 49 ). Mutations in Pfdhps are selected for once parasites have at least two mutations in Pfdhfr and include I431V, S436A/F, A437G, K540E, A581G, and A613S/T. It is well known that the efficacy of SP is affected by mutations in Pfdhfr and Pfdhps , however the impact of various combinations of these mutations has not been characterized. Pyrimethamine resistance Our previous molecular surveillance study showed that the Pfdhfr triple mutant (N51I, C59R, S108N) is nearly fixed in Senegal ( 19 ). Pfdhfr is a well-studied and known driver of PYR resistance, and the accumulations of mutations in Pfdhfr are often the first steps in the evolution of SP resistance. To determine the impact of these Pfdhfr haplotypes on PYR resistance, we adapted parasites to para-aminobezoic acid (PABA)-free and folate-free media and phenotyped parasites with and without Pfdhfr triple mutations (N51I, C59R, S108N) and with and without combinations of Pfdhps mutations. In our data, we saw clearly elevated PYR EC 50 values when comparing Pfdhfr IRN mutant parasites to Pfdhfr wild-type parasites ( figure 4A ). Interestingly, there is a range of PYR EC 50 phenotypes amongst the Pfdhfr IRN mutant parasites, which suggests that other mutations or copy-number variations could also be driving these different PYR phenotypes. Download figure Open in new tab Figure 4. A. Pyrimethamine (PYR) EC 50 comparisons, B. sulfadoxine (SDX) EC 50 comparisons, and C. PYR + 100 μM SDX EC 50 comparisons. (A) Parasites with Pfdhfr IRN mutations are statistically significantly more resistant to PYR compared to wild-type parasites. (B) Parasites with Pfdhps A437 G mutations and S436 A + A437 G mutations are significantly more resistant to SDX compared to wild-type parasites and parasites with Pfdhps S436 A mutations, while parasites with Pfdhps S436 A + A437 G + A613 S mutations are significantly more resistant to SDX compared to all other parasite Pfdhps haplotypes. (C) As parasites accumulate mutations in Pfdhfr and Pfdhps , overall, they become less susceptible to SP. Parasites with Pfdhfr IRN + Pfdhps S436 A + A437 G mutations are significantly more resistant to SP compared to all other parasite haplotypes. Letters indicate significant differences between in EC 50 means between groups (p<0.05); groups labeled with the same letter (a, b, or c) are not significantly different from each other, while groups with different letters from each other are significantly different. Sulfadoxine resistance Given the near fixation of the Pfdhfr IRN triple mutation in Senegal and the continued use of SP, we next examined the effect of mutations in Pfdhps , a known driver of SDX resistance. Our set of parasites included several different Pfdhps haplotypes: Pfdhps wild-type (ISAKAA), Pfdhps S436A mutant (I A AKAA), Pfdhps A437G mutant (IS G KAA), Pfdhps S436A + A437G mutant (I AG KAA), and Pfdhps S436A + A437G + A613S mutant (I AG KA S ). While these haplotypes have been reported before in West Africa ( 22 , 50 ), the impact of these mutations on drug resistance remains unknown. Parasites were adapted to PABA-free and folate-free media and assayed for SDX susceptibility ( figure 4B ). Parasites with the Pfdhps I A AKAA haplotype had the same SDX EC 50 values as Pfdhps wild-type parasites. However, parasites with the Pfdhps IS G KAA and I AG KAA haplotype had significantly higher SDX EC 50 values compared to wild-type and I A AKAA parasites (p<0.0001), regardless of Pfdhfr haplotype. Parasites with the Pfdhps I AG KAA haplotype did not have higher SDX EC 50 values compared to IS G KAA parasites. This confirms previous findings that A437G is a key mediator of SDX resistance ( 51 ), while S436A is not a mediator of SDX resistance. The new observation in this work is that parasites with the additional A613S mutation ( Pfdhps I AG KA S haplotype) had significantly higher EC 50 values than all other Pfdhps haplotypes, suggesting that Pfdhps A613S also plays a role in mediating SDX resistance. SP resistance Given that PYR and SDX are always given in combination, we also wanted to determine the role of the full Pfdhfr and Pfdhps haplotypes in mediating resistance to SP. Parasites were assayed for SP susceptibility by testing the same concentrations of PYR as was done for the PYR EC 50 assays, but also adding 100 μM SDX to each of the tested PYR concentrations. The parasite with the highest SP EC 50 value was Pfdhfr IRN + Pfdhps I AG KAA, suggesting that the accumulation of Pfdhfr and Pfdhps mutations leads to high SP resistance ( figure 4C ). Parasites from the same Pfdhfr and Pfdhps mutation categories (i.e., Pfdhfr IRN + Pfdhps I AG KAA, light blue: Th029.10 and Th122.16) had different phenotypes, suggesting that additional differences in the genetic background other than these known mutations ( Pfcrt , Pfdhfr , Pfdhps , and Pfmdr1 ) contribute to SP phenotypes. Notably, Pfdhfr IRN + Pfdhps I AG KAA parasites did not have statistically significantly different SP EC 50 values compared to Pfdhfr IRN + Pfdhps I AG KA S mutants, whereas the SDX EC 50 data clearly showed the A613S mutation decreased SDX susceptibility. These finding raise interesting questions about how Pfdhfr and Pfdhps mutations interact to confer increasing resistance to SP, whether SDX or PYR contributes more to the effectiveness of SP, and how these mutations could affect parasite fitness. Pfdhfr and Pfdhps allele population frequency over time Given these in vitro findings about PYR, SDX, and SP susceptibility, we wanted to investigate the frequency of these different haplotypes in Senegal over time, especially since the introduction of SMC in 2014. The Pfdhfr IRN mutant haplotype has been close to fixation since 2018 and all other Pfdhfr haplotypes have remained at much lower frequencies (supplemental figure 7A). Unlike the Pfmdr1 and Pfcrt combined haplotypes, the Pfdhfr and Pfdhps combined haplotypes differed between Thiès and Kédougou, possibly due to the different number of monthly SMC rounds at each site (Thiès is a low transmission area with no SMC, while Kédougou is a high transmission area with 5 rounds of monthly SMC) ( figure 5 , Senegal-wide and Thiès and Kédougou datasets can be found in supplemental figure 7). In Thiès, the most common haplotype is IRN + I A AKAA (yellow), which has been increasing in frequency since 2012 (supplemental figure 7) and replaced IRN + IS G KAA (orange) as the most common haplotype in 2020 ( figure 5A ). In Kédougou, IRN + IS G KAA (orange) has been the most common haplotype since data began being collected in 2019 and has increased in frequency from 2019 to 2022 while the IRN + I A AKAA (yellow) haplotype has decreased in frequency ( figure 5B ). Given the continued use of SP since 2014, it is not surprising to see certain Pfdhfr and Pfdhps combined haplotyped increasing in frequency in Senegal, especially given our in vitro finding that the IRN + IS G KAA haplotype results in a marked increase in SDX EC 50 compared to wild-type and the I A AKAA haplotype. Download figure Open in new tab Figure 5. Haplotype variant allele frequencies (VAF) for A. Pfdhfr + Pfdhps for Thiès and B. Pfdhfr + Pfdhps for Kédougou. Samples collected from patients in Thiès and Kédougou from 2019-2022 were whole genome sequenced, monogenomic infections with genotype calls at Pfdhfr and Pfdhps were included in this dataset. (A) Pfdhfr IRN + Pfdhps I A AKAA is the most prevalent haplotype in Thiès while (B) IRN + IS G KAA is the most prevalent haplotype in Kédougou. The full list of haplotypes that are included in the “other haplotypes” category can be found in supplemental table 4. However, what is surprising is that the IRN + I A AKAA haplotype, which does not confer any additional in vitro SDX resistance compared to wild-type Pfdhps (ISAKAA), is increasing in frequency. Our analysis of 10,183 samples from the MalariaGEN Pf7 dataset revealed that these major haplotypes found in Senegal are also commonly found in West and Central Africa, however they are not common in East and Northeast Africa and are rarely found in Asia or South America (supplemental figure 8). The role of Pfgch1 in pyrimethamine resistance Pfgch1 copy-number amplifications have been shown to decrease susceptibility to PYR depending on the number of Pfdhfr mutations ( 31 ). In our dataset, we found 3 parasites with Pfdhfr IRN and Pfgch1 promoter copy-number amplifications (Th146.12, Th080.12, and Th015.14) and 1 parasite with Pfdhfr wild-type and Pfgch1 gene copy-number amplifications (Th052.16). There was no statistically significant PYR EC 50 difference between parasites that were Pfdhfr IRN and had Pfgch1 promoter copy-number amplifications and parasites that were Pfdhfr IRN (supplemental figure 9). Therefore, in our dataset, Pfgch1 copy-number did not explain the range of PYR EC 50 phenotypes amongst the Pfdhfr IRN mutant parasites. The in vitro fitness of Pfdhfr and Pfdhps alleles in the absence of drug To investigate the fitness costs of SP resistance, a set of 14 parasites with various Pfdhfr-Pfdhps haplotypes (all Pfcrt wild-type, CVMNK, given the known fitness costs of Pfcrt mutations) were competed in pairwise competitive growth assays for a total of 91 competitions, using the same methods described for the 11 Pfcrt CVIET mutants, resulting in a clear ranking (percentage wins) for all 14 parasites. Pfmdr1 NYD wild-type parasites and Pfmdr1 NFD mutant parasites both displayed a range of fitness phenotypes (supplemental figure 10), as was seen for the 11 Pfcrt CVIET mutant parasites. There was no difference between the relative fitness of Pfdhfr NCS wild-type parasites and Pfdhfr IRN mutants; both genotypes displayed a range of fit and unfit parasites ( figure 6A ). When considering the relative fitness of individual Pfdhps mutations and combinations of Pfdhps mutations, there was not a clear pattern. It seems that Pfmdr1 and Pfdhfr play a role in fitness ( figure 6B , open shapes are Pfmdr1 wild-type, triangles are Pfdhfr wild-type), as well as components of each parasite’s unique genetic background, making it difficult to ascertain the contribution of Pfdhps mutations to fitness. Each genotype examined (whether individual Pfdhps mutations, double, or triple) resulted in a range of fitness phenotypes. Download figure Open in new tab Figure 6. Relative fitness of 14 Pfcrt CVMNK parasites based on A. Pfdhfr haplotype and B. Pfmdr1, Pfdhr, and Pfdhps haplotype. (A) Parasites with Pfdhfr IRN mutations and Pfdhfr wild-type (NCS) parasites both display a range of fitness. Triangles are Pfdhfr NCS wild-type. (B) Amongst wild-type Pfcrt parasites, a Pfdhps I AG KAA mutant is the most fit. The least fit parasite had the Pfdhps IS G KAA genotype. Triangles show Pfdhfr NCS wild-type and open shapes show Pfmdr1 NYD wild-type. DISCUSSION In our study, we found that CVIET + A220S + Q271E + N326S + R371I (FCB) and Pfcrt CVIET + A220S + Q271E + I356T + R371I (Cam783) mutants were significantly more resistant to md-AQ compared to Pfcrt wild-type and Pfcrt CVIET + A220S + Q271E + R371I (GB4) mutants. Pfdhfr triple mutants were significantly more PYR resistant than Pfdhfr wild-type and revealed a range of resistance phenotypes. Pfdhps A437G parasites were significantly more SDX resistant compared to Pfdhps wild-type and Pfdhps S436A mutants, suggesting that A437G is a key mutation for SDX resistance. The Pfdhps triple mutant (S436A + A437G + A613S) mutation resulted in the highest SDX EC 50 values. Using competitive growth assays, we found that Pfcrt was the main determinant of parasite fitness; there was not a clear correlation between fitness and the other drug resistance markers. This study contributes to our understanding of drug resistance, which is an evolutionary balance between resistance-conferring mutations and the fitness costs of those mutations. The acquisition of drug-resistance mutations oftentimes imposes a fitness cost to the parasites, which are advantageous when parasites are under drug, but are disadvantageous when parasites are in drug-free conditions ( 52 – 54 ). There have been many examples of drug sensitive parasites returning to high frequency in the population after the discontinuation of the antimalarial drug that selected drug resistant parasites. One such example is the loss of Pfcrt mutants in places in the world that replaced CQ as the frontline antimalarial ( 55 – 57 ), and in vitro assays have clearly established the fitness cost of Pfcrt mutations ( 32 – 34 ). So why do some countries, like Senegal, still have a relatively large frequency of Pfcrt mutants (0.49 frequency in 2020 ( 19 ))? We hypothesize that these Pfcrt mutants remain in the population because they are under selection from amodiaquine use in the population. Our study presents a unique methodology to answer these questions: we have decades of molecular surveillance data that we can use to inform our hypotheses about what is happening in natural parasite isolates in the population and we can test these hypotheses using in vitro culture-adapted natural parasite isolates. Importantly, these natural parasite isolates serve as a “natural experiment”, they are a sample of the viable and successful combinations of genotypes that are circulating in the population. Our set of 33 cryopreserved natural parasite isolates represented 64% of all haplotypes present in our Senegal dataset (supplemental figure 1), thus capturing the genotypic diversity in Senegal and reflecting the consequence of decades of drug pressure in Senegal. This set of parasites allowed us to directly test our hypotheses from molecular surveillance data with in vitro phenotypes, as a type of “phenotypic surveillance”. Amodiaquine Previously, AQ resistance has been associated with mutations in Pfcrt (K76T) and Pfmdr1 (N86Y and D1246Y) ( 9 – 12 ). However, using our set of natural parasite isolates, we found that mutant Pfcrt CVIET haplotype parasites (regardless of Pfmdr1 mutation) had increased md-AQ EC 50 values compared to wild-type Pfcrt CVMNK parasites, especially parasites with the Cam783 haplotype ( figure 1B ), they were relatively fit compared to other Pfcrt CVIET parasites ( figure 3B ), and these parasites have been increasing in frequency in Senegal since 2012 ( figure 2 , supplemental figure 5). Parasites with the Pfcrt FCB haplotype had the second highest md-AQ EC 50 values ( figure 1B ) and the second highest relative fitness rating amongst Pfcrt CVIET parasites ( figure 3B ), however, they remain at low frequencies in Senegal. A previous study using isogenic lines has shown that parasites with the Cam783 haplotype had elevated md-AQ EC 50 values, but Dd2 and parasites with the FCB haplotype result in the highest md-AQ EC 50 values, suggesting that the N326S mutation is an important contributor to AQ resistance ( 32 ). Interestingly, several studies have found that parasites with the Pfcrt FCB haplotype are less fit than parasites with either the Pfcrt Cam783 haplotype or parasites with the GB4 haplotype, suggesting that the N326S mutation comes with a fitness cost while the I356T mutation comes with a fitness advantage ( 32 , 34 ), revealing interesting evolutionary trajectories for Pfcrt and AQ resistance ( 33 , 58 ). The FCB, GB4, and Cam783 haplotypes have been found to not mediate PIP resistance, but the specific Pfcrt mutations in each of these haplotypes are required for further Pfcrt mutations to confer PIP resistance ( 44 ). Furthermore, the Pfcrt N326S and I356T mutations were identified as being part of a genetic background associated with emerging artemisinin resistance ( 17 ). Given the continued use of AQ as a component of frontline antimalarials and chemoprevention and given that the major Pfcrt mutant haplotypes in Africa are GB4 and Cam783 (supplemental figure 6), it will be important to continue to monitor these Pfcrt haplotypes for additional mutations that could change their drug resistance profiles or parasite fitness. Sulfadoxine-pyrimethamine Despite the discontinuation of SP as a first-line therapy in the early 2000s due to the emergence of SP resistance, drug sensitive parasites have not returned to Senegal. This suggests that the use of SP as a chemopreventive is enough to maintain selection for SP resistance; in 2021, almost 45 million children received at least one dose of SMC and nearly 180 million doses were delivered ( 59 ). SP is currently used in several African countries in IPTp and SMC, and given the efficacy of SP, the lack of adverse effects, and a lack of suitable drugs ready to replace it, SP will likely continue to be used for years to come ( 60 ). SP efficacy is not evaluated through conventional Therapeutic Efficacy Studies (TES), therefore, studies that evaluate the contribution of mutations to resistance and parasite fitness are critical to better understand the role of mutations on parasite survival. Our study assessed the contributions of Pfdhfr , Pfdhps , and Pfgch1 to PYR, SDX, and SP resistance. By testing the susceptibility of different Pfdhfr + Pfdhps haplotypes to PYR, we confirmed that Pfdhfr triple mutations drive resistance to PYR, although we found a wide range of phenotypes, suggesting that more than just Pfdhfr is playing a role in determining susceptibility to PYR ( figure 4A ), but it is not Pfgch1 in our set of parasites (supplemental figure 9). To our knowledge, our study was the first to test the susceptibility of different combinations of Pfdhfr + Pfdhps haplotypes to SDX. We found that Pfdhps S436A does not result in increased SDX resistance, regardless of Pfdhfr mutations. However, parasites with Pfdhps A437G (regardless of Pfdhfr mutations) showed a marked increase in SDX EC 50 compared to wild-type and Pfdhps S436A mutants. Parasites with Pfdhps S436A + A437G showed similar SDX EC 50 values compared to parasites with Pfdhps A437G, which further suggested that the S436A mutation does not play a role in SDX resistance in our parasite panel. Interestingly, parasites with Pfdhps S436A + A437G + A613S mutations showed the highest SDX EC 50 values, suggesting that A613S also plays a role in SDX resistance, however EC 50 values of the Pfdhps S436A + A437G + A613S mutants were only 2.1-fold higher than Pfdhps A437G mutants ( figure 4B ). Our data from samples collected throughout Senegal shows that the Pfdhfr triple mutation is nearly fixed, the frequency of the Pfdhps IS G KAA and I A AKAA haplotypes are increasing in Kédougou and Thiès, respectively, and we are beginning to see more parasites with multiple Pfdhps mutations ( figure 5 ). A previous study reported an increase in the prevalence of Pfdhfr triple mutant parasites with the Pfdhps IS G KAA haplotype or with the double mutant Pfdhps I AG KAA haplotype after SMC implementation in Burkina Faso, suggesting that similar trends are happening in other countries ( 61 ). These mutations in Pfdhps are located in the para-aminobenzoic acid (PABA)-binding pocket, with the A437G mutation resulting in an increased affinity for PABA and a decreased binding affinity for sulfa inhibitors as well as increased enzyme activity ( 62 , 63 ), suggesting that the A437G resistance mutation does not result in a fitness cost for the parasite and could explain why the A437G mutation seems to be a key mutation in Pfdhps for further SDX resistance evolution. Our dataset included parasites with Pfdhps mutations but no Pfdhfr mutations, contrary to what may evolve most commonly. Since Pfdhfr is nearly fixed in Senegal, Pfdhfr wild-type parasites are rare, but we were able to culture-adapt several of these parasites with and without Pfcrt and Pfdhps mutations, suggesting that these parasites are in the population and viable. These parasites were fit (especially those that were Pfmdr1 NYD wild-type) and those that had Pfdhps mutations showed a decreased susceptibility for SDX, however not at the same level as their Pfdhfr triple mutant counterparts ( figure 4B ). Despite these natural isolates going against what would be expected evolutionarily, they provided important information about how Pfdhfr mutations may also impact SDX resistance. To further investigate how Pfdhfr + Pfdhps haplotypes contribute to SP resistance, we assayed parasites to varying doses of PYR with a constant 100μM of SDX. Overall, we saw that parasites with more Pfdhfr + Pfdhps mutations had increasing SP EC 50 values, however it was interesting that not all mutations resulted in big SP EC 50 value changes ( figure 4C ). Parasites with Pfdhfr IRN mutations did result in significantly increased SP EC 50 values compared to wild-type Pfdhfr parasites, but the addition of Pfdhps mutations did not always result in significantly different EC 50 values; Pfdhfr IRN + Pfdhps A437G mutants had similar SP EC 50 values to Pfdhfr IRN + Pfdhps S436A + A437G + A613S mutants. This brings up several questions about SP and its efficacy and how other drugs in the combination therapy, like AQ, contribute to the benefits provided by SMC in settings with high SP resistance. The complexity of the folate pathway, the intricate combinations of resistance mutations, and the influence of the concentration of folates and antifolates in the environment makes it difficult to study individual components, therefore, there have been few studies looking at the fitness costs associated with Pfdhfr and Pfdhps mutations and they often show conflicting results (as reviewed in ( 64 )). While the selective pressure of using SP for SMC could explain the high prevalence of SP resistant parasites, it is also possible that the use of co-trimoxazole (trimethoprim + sulfamethoxazole; there is known cross-resistance between PYR and trimethoprim ( 65 ) and SDX and sulfamethoxazole ( 66 ) in vitro ) or the acquisition of other compensatory mutations could also be playing a role in maintaining the high prevalence of SP resistant parasites. In Ethiopia, SP has not been used as a frontline treatment since 2004 and is not used for IPTp, but the high prevalence of SP mutations persists in the population, suggesting that there may be selective pressure from co-trimoxazole or no fitness costs to these SP resistance mutations ( 67 ). Using competitive growth assays, we assessed the fitness of 14 different Pfdhfr - Pfdhps haplotypes (all wild-type Pfcrt given the fitness cost of Pfcrt mutations). Interestingly, we found that the differences in fitness were not driven by Pfdhfr and Pfdhps haplotypes in the absence of drug, in contrast to mutations in Pfcrt where overall fitness is affected by Pfcrt . This implies that in the absence of drug, there will be no dramatic effect on the population. Interestingly, these haplotypes are maintained and increase in frequency indicating that the effect of drug selection on a population level is likely to be the major driver in determining the allele frequency at these loci. We plan to test this by comparing in vitro fitness in the presence of varying concentrations of therapeutic drug. While in vitro phenotypic surveillance can be a great tool, it also has some limitations; further study is needed to determine the correlations between in vitro testing and clinical outcomes of drug treatment. Clinical infections are impacted by the complexity of infection, pharmacokinetics, host immune status, and host nutritional status, especially when studying antifolates which are very sensitive to environmental conditions ( 68 ). Notably, in vitro susceptibility testing of antifolates requires sub-physiological levels of folate and PABA and therefore may not fully reflect in vivo conditions. This phenotypic surveillance work lays the foundation for future work: having used natural parasite isolates to determine in vitro fitness and resistance, the next step is use CRISPR-Cas9 to edit mutations on a single parasite background to create a set of isogenic lines to fully understand which haplotypes result in both increased resistance and fitness. These results will help us understand what the next evolutionary step could be for parasites ( e.g., additional mutations in Pfdhps such as I431V or A581G ( 61 , 69 ), or whether the East African Pfdhps K540E mutations could emerge in Senegal) and how it will impact parasite drug resistance and fitness. SMC remains effective in Senegal, as long as the timing and dosing of SMC maintains protection during the high transmission period ( 70 ). The expansion of SMC to areas such as Uganda, where moderate SP resistance mutations are prevalent, has been shown to be efficacious so far, likely because high-level SP resistance mutations ( Pfdhfr I164L and Pfdhps A581G) are rare and AQ continues to be effective ( 71 ). However, it remains unknown how each individual component of SMC contributes to its overall efficacy, especially in areas with high SP resistance. Nevertheless, as the use and expansion of SMC continues, it is critical to genotypically, phenotypically ( in vitro ), and clinically ( in vivo , via chemoprevention efficacy studies ( 72 )) monitor these resistance mutations to determine if the efficacy of SP or AQ will be compromised. In conjunction with genotypic surveillance, phenotypic surveillance of natural parasite isolates can assess the impact of drug pressure, identify evolving genetic determinants of drug resistance, and provide known and new molecular markers for ongoing genomic surveillance to monitor and guide the use of drug-based interventions. MATERIALS AND METHODS Ethics Statement Samples were obtained from febrile patients who presented at health facilities for care. Informed consent was obtained from all study participants (or from parents/guardians in the patient was a minor). The study protocol was authorized by the Ministry of Health and Social Action in Senegal (SEN 19/49) and approved by the Institutional Review Board of the Harvard T.H. Chan School of Public Health (IRB protocol 16330). Sampling Parasite samples were collected from treatment seeking patients presenting with fever or history of fever within the past 48 hours in Thiès or Kédougou between 2006 and 2022. All patients with positive tests received free malaria treatment in accordance with the National Health Development Policy in Senegal as recommended by the WHO. In addition to slide preparation and RDT, all consenting patients gave venous blood samples. These blood samples were used for whole genome sequencing and for parasite cryopreservation. Whole genome sequencing of patient isolates Samples were subjected to selective whole genome amplification (sWGA) using Phi29 polymerase followed by magnetic bead clean up and quantification, fragmented, and prepared using NEBNext Ultra II FS DNA library as described in Schaffner et al . ( 23 ). Completed libraries were shipped to the Broad Institute of MIT and Harvard in Cambridge, MA for Illumina-based short read whole genome sequencing. Variant calling was performed in accordance with the best practices established in the Pf3K project using GATK3.5.0 and Plasmodium falciparum 3D7v.3 reference assembly as described in Schaffner et al . ( 23 ). 946 whole genome sequenced monogenomic (single genome) samples were included in our final dataset and used for further analysis. Culture adaptation of parasites Parasites were culture-adapted by thawing cryopreserved Plasmodium falciparum isolates collected from patients that had been mixed with glycerolyte, with approximately 1.67 ml of glycerolyte added to every 1 ml of packed RBCs. Samples were stored overnight at -80°C, and then transferred to liquid nitrogen for long term storage. Cryopreserved samples were thawed from liquid nitrogen storage by placing them into a 37 °C water bath. Immediately upon thawing the sample was transferred to a 50 ml conical tube and volume was measured. For every 1 ml of sample volume, a volume of 0.2 ml of sterile 12% NaCl was added dropwise with gentle swirling and then incubated for 5 min at room temperature. Then, for every 1 ml of original sample volume, a total of 9 ml of sterile 1.6% NaCl solution was added with gentle swirling and incubated at room temperature for 2 min. Finally, for every 1 ml of original sample volume, a total of 9 ml of 0.9% NaCl, 0.2% Dextrose was added to the tube with gentle swirling before the tube was centrifuged (2K, 5 min). After aspirating the pellet, the sample was transferred to tissue culture dishes with O+ fresh human blood. For every 1 ml of original sample an individual tissue culture dish was established by adding 1 ml of 50% hematocrit O+ blood (freshly collected, and no more than seven days from collection) along with 10 ml HEPES buffered RPMI media containing 12.5% AB+ human serum (heat inactivated and pooled). Cultures were placed in modular incubators and gassed with 1%O 2 /5% CO 2 /balance N 2 gas (10 psi for 150 sec) and incubated with rotation (50 rpm) at 37°C. Cultures were settled for 30 min before changing media to retain the maximal amount of culture. Media was changed and smears made daily to monitor parasite growth. 100 μl of O+ RBCs were added to cultures each week while waiting for parasites to reach ∼1% parasitemia (and a first stock freeze). If parasitemia was still low 2 weeks post-thaw, cultures were split 1:2 to add fresh media and RBCs (both splits were kept) to encourage growth. ∼30 stocks of parasites grown in 12.5% AB+ human serum culture media were made by centrifuging (800 x g for 5 min) 9 ml of a predominantly ring stage culture and adding 0.3 ml AB+ human serum for every 0.2 ml of pellet and adding an equal volume of glycerolyte 57 (e.g., 1.0 ml if a 0.4 ml pellet and 0.6 ml of AB+ serum) and aliquoting into a cryotube (∼0.5 ml per aliquot, results in 3-4 stocks) and storing in liquid nitrogen. Several DNA samples (∼9 ml of parasites at 3% or higher parasitemia and late stages were centrifuged at 800 x g for 5 min) were taken and stored in -20 °C freezer until genomic DNA (gDNA) was extracted using the Qiagen QIAamp DNA Blood Mini Kit for genotypic validation. Parasites were then transitioned to a 50/50 mix of 12.5% AB+ serum culture media and 0.5% Albumax culture media (RPMI 1640 medium supplemented with 28 mM NaHCO 3 , 25 mM HEPES, 400 μM hypoxanthine, 25 μg/mL gentamicin, and 0.5% Albumax II) for 2-3 life cycles and when growing well were transitioned to only 0.5% Albumax media. ∼30 stocks of parasites grown in 0.5% Albumax media were frozen down along with several DNA samples for genotypic validation. All phenotyping was done with parasites grown in 0.5% Albumax media. Maintenance of parasites Once culture adapted, parasites were cultured by standard methods ( 73 ) in RPMI 1640 medium supplemented with 28 mM NaHCO 3 , 25 mM HEPES, 400 μM hypoxanthine, 25 μg/mL gentamicin, and 0.5% Albumax II (Life Technologies, Carlsbad, CA) at 5% hematocrit in fresh human O+ erythrocytes (Interstate Blood Band, Inc., Memphis, TN). Cultures were gassed with 1% O 2 /5% CO 2 /balance N 2 gas and incubated with rotation (50 rpm) in a 37 °C incubator. Cultures were kept below 3% parasitemia with media changes at least every 48 h. Parasite genotypic validation Parasites were whole genome sequenced when collected from the patient as described above. To confirm parasites had the same genomic regions the patient isolate did prior to culture-adaptation, gDNA collected from each natural parasite isolate after culture-adaptation to 0.5% Albumax media was whole genome sequenced. Sequencing libraries were prepared with Illumina’s Nextera XT Kit and DNA libraries were run on an Illumina NovaSeqX Plus as described in ( 74 ). All samples were confirmed to have the same genomic regions of interest pre-culture adaptation and post-culture adaptation. Parasites were also assayed for copy-number variation for Pfgch1 and Pfmdr1 by running a Comparative C T experiment using the Applied Biosystems ViiA 7 Real-time PCR system (Life Technologies). Amplification reactions were done in MicroAmp 384-well plates in 10 μl reactions with PowerUp SYBR Green Master Mix (Applied Biosystems), 150 nM of each forward and reverse primer, and 2.35 ng template. Pfmdr1 forward (5’-TGCATCTATAAAACGATCAGACAAA-3’) and Pfmdr1 reverse (5’- TCGTGTGTTCCATGTGACTGT-3’) primers were designed after Price et al. ( 75 ); Pfgch1 forward (5’-AAACACCATCTTTTACCTTTTGAA-3’) and Pfgch1 reverse (5’- AGCATCGTGCTCTTTAACTCC-3’) primers were designed after Kidgell et al. ( 29 ); primers for the endogenous control Pfβ -tubulin forward (5’-CGTGCTGGCCCCTTTG-3’) and reverse (5’-TCCTGCACCTGTTTGACCAA-3’) were designed after Ribacke et al. ( 76 ). Thermocycler conditions were as follows: Uracil-DNA glycosylase (UDG) activation at 50 °C for 2 min, activation at 95 °C for 10 min, 40 cycles of denaturation at 95 °C for 15 sec, annealing at 55 °C for 30 sec, and a final extension at 60 °C for 1 min. Amplification efficiencies were verified by testing a range of gDNA concentrations of all genes and were sufficiently close enough to obviate the need for a correction factor (supplemental figure 11). Copy-numbers were calculated using the ΔΔC T method, ΔΔCt = (Ct TE − Ct HE ) − (Ct TC − Ct HC ), where T is the test gene (either gch1 or pfmdr1 ), H is the reference gene (β- tubulin ), E is the experimental sample, and C is the control sample. Relative expression was calculated as 2 −ΔΔCt . Three biological replicates were run per parasite with 0.5 ng/μl gDNA used in each run, each with technical replicates run in quadruplicate; 3D7 and Dd2 controls were included in each run; calculated copy-numbers were averaged. Copy-numbers were considered increased (>1) when the average of the three biological replicates was above 1.6. Assays were repeated if Ct values were greater than 35. Primers were designed to determine whether parasites had amplifications in the Pfgch1 promoter: forward (5’- GATTCCATTTATTGCATTCTTG-3’) and reverse (5’-CATTTAATGGACTGGAAATT-3’). 25 μl reactions were set up using Phusion High-Fidelity PCR Master Mix with HF Buffer (New England Biolabs, cat #M0531), 0.4 nM of each forward and reverse primer, and 1 μl template. Thermocycler conditions were 98 °C for 90 sec, 30 cycles of 98 °C for 10 sec, 61.4 °C for 2 min 30 sec, and a final extension of 72 °C for 10 min. PCR products were run on a gel, parasites with Pfgch1 promoter amplifications had a PCR product size of ∼1.5 kb, parasites without Pfgch1 promoter amplifications had a PCR product of ∼750 bp (supplemental figure 2A). PCR sequencing of 3D7 and Th015.14 was performed by Plasmidsaurus using Oxford Nanopore technology, confirming that Th015.14 has a triple promoter amplification and 3D7 has no amplification (supplemental figure 2B). In vitro 72 h drug susceptibility assay by SYBR green staining In vitro drug susceptibility of asexual blood stage parasites was measured using the SYBR Green I-based cell proliferation assay as previously described ( 77 ). Twenty-four-point dilution series for several of the antimalarial drugs (chloroquine, monodesethyl-amodiaquine, pyrimethamine, and pyrimethamine + 100 μM sulfadoxine) and 12-point dilution curves for the rest of the drugs (mefloquine, lumefantrine, piperaquine, dihydroartemisinin, quinine, and amodiaquine) were carried out in triplicate and repeated with three biological replicates. Each plate included a kill control (negative control) and a no drug control (positive control). All test compounds were resuspended in dimethyl sulfoxide (except for chloroquine, which was prepared in 0.1% Triton X-100 in water and piperaquine, which was prepared in 0.1% Triton X-100 and 0.5% lactic acid in water) and were dispensed into 384-well plates by an HP D300 Digital Dispenser (Hewlett Packard Palo Alto, CA). Parasites were synchronized to the ring-stage and grown in the presence of different test compounds in 384-clear-bottom well plates at 1% hematocrit, 1% starting parasitemia, and 40 μl of folic acid- and para-aminobenzoic acid-free culture media (RPMI 1640 medium supplemented with 28 mM NaHCO 3 , 25 mM HEPES, 400 μM hypoxanthine, L-glutamate, 25 μg/mL gentamicin, no para-aminobenzoic acid, no folic acid, and 0.5% Albumax II; Gibco, custom order). Growth at 72 h was measured by SYBR Green I (Lonza, Visp, Switzerland) staining of parasite DNA. Relative fluorescence units were measured at an excitation of 494 nm and emission of 530 nm on a SpectraMax M5 (Molecular Devices Sunnyvale, CA). After background subtraction and normalization of raw fluorescence data, half-maximal effective concentration (EC 50 ) values were determined using non-linear regression curve fitting in GraphPad Prism 10 (GraphPad Software Inc., Boston, MA). Average EC 50 values were compared using an Ordinary one-way ANOVA with Tukey’s multiple comparisons test, with a single pooled variance; significance cut-off was p < 0.05. Sulfadoxine drug susceptibility In vitro drug susceptibility of asexual blood stage parasites was measured using the SYBR Green I-based cell proliferation assay as described above, with slight modifications for sulfadoxine. A twenty-four-point dilution series for sulfadoxine (resuspended in dimethyl sulfoxide) was carried out in triplicate and repeated with three biological replicates. Parasites were culture adapted to folic acid and para-aminobenzoic acid free culture media (Gibco, custom order) over several life cycles (at least 3 cycles) and were grown with RBCs that were washed 3x in folic acid- and para-aminobenzoic-free RPMI to remove as many folates from the parasite culture as possible prior to being synchronized to the ring-stage for assays. Parasites were assayed at 1% hematocrit, 1% starting parasitemia, and 40 μl of folic acid- and para-aminobenzoic acid-free culture media in 384-well plates. Parasites adapted to folic acid- and para-aminobenzoic acid-free culture media grew noticeably slower, therefore, after 72 h, parasite stage was checked by Giemsa-stained slides. Growth was measured by SYBR Green I as described above, but only when parasites were in late trophozoite/schizont stage, which was an additional ∼8-12 hours after the usual 72 h. TaqMan allelic discrimination real-time quantitative PCR-based competitive growth assays Competitive growth assays between two parasite lines were performed by co-culturing the two parasites in a 1:1 ratio. Parasites were synchronized to the ring-stage and assays were set up at 1% parasitemia (0.5% parasitemia per parasite) and 5% hematocrit. Competitive growth assays were maintained in 2 ml volumes in 12-well plates maintained in regular RPMI + albumax media and kept in a culture chamber (gassed with 1% O 2 /5% CO 2 /balance N 2 gas in a 37 °C incubator). Each assay had two biological replicates. Plates were maintained with media changes every life cycle and slides were made to monitor parasitemia; at least 75% of the culture volume was collected every life cycle (every 2 days) to isolate gDNA. Competitions were carried out for at least 15 generations. gDNA was extracted using the QIAamp DNA Blood Mini Kit (Qiagen) and quantified via Qubit Fluorometric Quantification. The percentage of each parasite in each competition was determined using TaqMan allelic discrimination real-time PCR assays on a ViiA 7 Real-Time PCR system. Previously described TaqMan primers (forward and reverse) and TaqMan fluorescence-labeled minor groove binder probes (FAM or HEX) ( 78 ) were used to differentiate between each competitor (supplemental table 7 and 8). A standard curve of mixtures of competitor A and B gDNA in fixed ratios (0:100, 20:80, 40:60, 50:50, 80:20, 100:0, and no-template negative control) was run with each set of samples. qPCR reactions for each sample were run in duplicate. 5μl reactions included 2.5μl 2x TaqMan universal PCR master mix, 0.125μl 20x TaqMan-MGB SNP assay mix primers/probes (forward and reverse), and 2.5μl 0.5 ng/μl gDNA. Amplification and detection of fluorescence were carried out using the genotyping assay mode with cycling conditions as follows: 95°C for 10 min, followed by 40 cycles of 95°C for 15 sec and 56°C for 60 sec. Cycle threshold (Ct), the number of cycles required for the fluorescent signal to cross the threshold (i.e., exceeds background level), for each probe (wild-type allele or mutant allele) for each sample was used to determine the wild-type or mutant allele frequency in each sample; Ct values of known mixtures were used to convert Ct values of unknown samples to percentages of wild-type or mutant alleles in each sample. Mixtures containing over 70% of one parasite were counted as a win for that parasite; mixtures containing 29-69% of one parasite after 15 generations were counted as a “tie” for those parasites. Competition outcomes were robust and transitive (e.g., if A beat B and B beat C, A always beat C), allowing for an unambiguous relative fitness ranking for all parasites from the most fit (100% win) to the least fit (0% win). Haplotype analysis To determine the Pfcrt + Pfmdr1 + Pfdhfr + Pfdhps combined haplotypes from our Senegal dataset, we used the WGS data to call wild-type (0), mutant (1), heterozygous (2) or missing (−1) at each of the 19 sites: Pfcrt (codon positions 74-76, A220S, Q271E, N326S, I356T, and R371I), Pfmdr1 (N86Y, Y184F, D1246Y), Pfdhfr (N51I, C59R, S108N), and Pfdhps (I431V, S436A, A437G, K540E, A581G, A613S). Based on these calls, any sample that was missing (−1) or heterozygous (2) at any of the 19 sites was removed from the analysis, resulting in 1879 samples. We then determined that our dataset contained 239 unique haplotypes; to determine the most common haplotypes, we counted how many parasites from our dataset had each of these haplotypes. Of these common haplotypes, we selected 33 parasites for culture-adaptation and phenotyping that represented some of the most common haplotypes (these parasites represent 26 different haplotypes and 63.9% of all parasites in our Senegal dataset). To determine the Pfcrt + Pfmdr1 combined haplotypes over time across Senegal and in just Thiès and Kédougou, we again used our same dataset where we called wild-type (0), mutant (1), heterozygous (2) or missing (−1) at each of the 8 genomic sites: Pfcrt (codon positions 74-76, A220S, Q271E, N326S, I356T, and R371I), Pfmdr1 (N86Y, Y184F, D1246Y). Any sample that was missing or heterozygous was removed from the analysis (resulting in 2160 samples), we then split the data into years: 2005-2006, 2007-2008, 2009, 2010, 2011, 2012, 2013-2014, 2015-2016, 2017-2018, 2019, 2020, 2021, 2022. This data was graphed to represent all samples from all sites in Senegal. The data was then split by collection site and only samples from Thiès were selected for graphing and then only samples from Kédougou were selected for graphing. Similar methodology as described for determining and graphing the Pfcrt + Pfmdr1 combined haplotypes was used for the Pfdhfr + Pfdhps combined haplotypes, except the 9 genomic sites examined were Pfdhfr (N51I, C59R, S108N) and Pfdhps (I431V, S436A, A437G, K540E, A581G, A613S), which resulted in 2269 samples. To determine the Pfcrt + Pfmdr1 combined haplotypes the MalariaGEN Pf7 dataset ( 79 ), we called wild-type (0), mutant (1), heterozygous (2) or missing (−1) at each of the 7 genomic sites: Pfcrt (codon positions 74-76, A220S, Q271E, N326S, I356T, and R371I), Pfmdr1 (Y184F). Any sample that was missing or heterozygous was removed from the analysis (resulting in 10,037 samples), we then grouped samples by region: Central Africa (Democratic Republic of Congo), East Africa (Kenya, Madagascar, Malawi, Mozambique, Tanzania), Northeast Africa (Ethiopia, Sudan, Uganda), West Africa (Benin, Burkina Faso, Cameroon, Côte d’Ivoire, Gabon, Gambia, Ghana, Guinea, Mali, Mauritania, Nigeria, Senegal), West Asia (Bangladesh, India, Myanmar), East Asia (Cambodia, Laos, Vietnam, Thailand), Oceania (Indonesia, Papua New Guinea), and South America (Colombia, Peru, Venezuela). To determine the Pfdhfr + Pfdhps combined haplotypes the MalariaGEN Pf7 dataset ( 79 ), we called wild-type (0), mutant (1), heterozygous (2) or missing (−1) at each of the 9 genomic sites: Pfdhfr (N51I, C59R, S108N) and Pfdhps (I431V, S436A, A437G, K540E, A581G, A613S). Any sample that was missing or heterozygous was removed from the analysis, and samples were grouped by region as described for the Pfcrt + Pfmdr1 combined haplotypes, resulting in 10,183 samples. SUPPORTING INFORMATION Supplemental figures 1-11. Supplemental table 1. Full haplotypes ( Pfmdr1 + Pfcrt + Pfdhfr + Pfdhps ) represented in our Senegal dataset: frequencies and haplotypes represented by 33 culture-adapted parasites. Supplemental table 2. Full set of phenotype and genotype data for 33 parasite isolates and reference lines. Supplemental table 3. Full list of Pfmdr1 + Pfcrt haplotypes from our Senegal dataset (Senegalwide, Thiès and Kédougou). Supplemental table 4. Full list of Pfdhfr + Pfdhps haplotypes from our Senegal dataset (Senegalwide, Thiès and Kédougou). Supplemental table 5. Full list of haplotypes from the Pf7 dataset ( Pfmdr1 + Pfcrt and Pfdhfr + Pfdhps ). Supplemental table 6. Fitness rankings and genotypes for 10 all-on-all pairwise Pfcrt mutant competitions (top) and 14 all-on-all pairwise Pfcrt wild-type competitions (bottom). Supplemental table 7. List of barcodes used for head-to-head competitions Supplemental table 8. 24 SNP Barcode information for the 25 parasites competed in pairwise competitive growth assays. AUTHOR CONTRIBUTIONS Conceptualization: Katelyn Vendrely Brenneman, Wesley Wong, Dyann Wirth, Sarah Volkman Funding Acquisition: Dyann Wirth, Daouda Ndiaye, Sarah Volkman Investigation: Katelyn Vendrely Brenneman, Wesley Wong, Mamy Yaye Die Ndiaye, Karina Bellavia, Imran Ullah Methodology: Katelyn Vendrely Brenneman, Wesley Wong, Stephen Schaffner, Bassirou Ngom Resources: Amy Gaye, Djiby Sow, Mame Fama Ndiaye, Mariama Toure, Nogaye Gadiaga, Aita Sene, Awa Bineta Deme, Baba Dieye, Mamadou Samb Yade, Khadim Diongue, Younouss Diedhiou, Jules François Gomis, Mouhamadou Ndiaye, Mamadou Alpha Diallo, Ibrahima Mbaye Ndiaye Supervision: Bronwyn MacInnis, Dyann Wirth, Daouda Ndiaye, Sarah Volkman Writing – original draft: Katelyn Vendrely Brenneman, Sarah Volkman Writing – review & editing: All authors ACKNOWLEDGMENTS We would like to thank the patients and their families who participated in these studies and the clinic nurses and staff involved with collecting samples from clinics. Funder Information Declared Gates Foundation, https://ror.org/0456r8d26 , INV-003442 , INV-049909 , OPP1156051 National Institute of Allergy and Infectious Diseases, https://ror.org/043z4tv69 , R21AI141843 , 5R01AI099105 , 5R01AI169892 REFERENCES 1. ↵ World Health Organization . World malaria report 2024: addressing inequity in the global malaria response . Geneva : World Health Organization ; 2024 . 2. ↵ Haldar K , Bhattacharjee S , Safeukui I . Drug resistance in Plasmodium . Nat Rev Microbiol . 2018 Jan 22; 16 : 156 – 70 . OpenUrl CrossRef PubMed 3. ↵ Ndiaye YD , Hartl DL , McGregor D , Badiane A , Fall FB , Daniels RF , et al. Genetic surveillance for monitoring the impact of drug use on Plasmodium falciparum populations . Int J Parasitol Drugs Drug Resist . 2021 Dec ; 17 : 12 – 22 . OpenUrl CrossRef PubMed 4. ↵ Neafsey DE , Taylor AR , MacInnis BL . Advances and opportunities in malaria population genomics . Nat Rev Genet . 2021 Apr 8; 22 : 502 – 17 . 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