We explored spatial variation in the prevalence of established molecular markers of antimalarial resistance across a geographically diverse, highland region of western Uganda.Weidentified Plasmodium falciparumCQresistance transporter 76T mutations in all pools, but there was no evidence of spatial differences across village-based strata defined by either altitude or river valley. In contrast, we identified a significant inverse association between altitude and the prevalence of Plasmodium falciparum multidrug resistance 1 mutations with the largest proportion of Y184F mutations observed in the low-elevation, high-transmission villages. These results demonstrate the substantial heterogeneity in resistance markers observed across geographic settings, even at relatively small scales, but highlight the complex nature of these ecological relationships

Boyce, Ross M.

Brazaeu, Nicholas

Fulton, Travis

Hathaway, Nick

Juliano, Jonathan

Matte, Michael

Mulogo, Edgar

Ntaro, Moses

Carolina Digital Repository

Am. J. Trop. Med. Hyg., 101(4), 2019, pp. 799–802doi:10.4269/ajtmh.19-0081Prevalence of Molecular Markers of Antimalarial Drug Resistance across Altitudinal TransmissionZones in Highland Western UgandaRoss M. Boyce,1,2* Nicholas Brazeau,3 Travis Fulton,1 Nick Hathaway,4 Michael Matte,2 Moses Ntaro,2 Edgar Mulogo,2 andJonathan J. Juliano1,3,51Division of Infectious Diseases, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina; 2Department of Community Health, Mbarara University of Science and Technology, Mbarara, Uganda; 3Division of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina; 4Program in Bioinformatics and Integrative Biology, University of Massachusetts, Worcester, Massachusetts; 5Curriculum in Genetics and Microbiology, University of North Carolina at Chapel Hill, Chapel Hill, North CarolinaAbstract. We explored spatial variation in the prevalence of established molecular markers of antimalarial resistanceacross a geographically diverse, highland region ofwesternUganda.We identifiedPlasmodium falciparumCQ resistancetransporter 76Tmutations in all pools, but therewasno evidenceof spatial differences across village-based strata definedby either altitude or river valley. In contrast, we identified a significant inverse association between altitude and theprevalence of Plasmodium falciparum multidrug resistance 1 mutations with the largest proportion of Y184F mutationsobserved in the low-elevation, high-transmission villages. These results demonstrate the substantial heterogeneity inresistancemarkers observed across geographic settings, even at relatively small scales, but highlight the complex natureof these ecological relationships.The emergence and spread of drug resistance are majorthreats to global public health with the most severe conse-quences residing in sub-Saharan Africa, where the burden ofPlasmodium falciparum malaria is greatest.1,2 Although arte-misinin combination therapies (ACTs) remain effective, on-going surveillance is required to identify and contain theemergenceof resistant parasites.3 The level of drug resistancein a given area canbe inferred by the studyof knownmolecularmarkers, which may provide an early warning of decreasingefficacy.4In Uganda, like most of sub-Saharan Africa, chloroquine(CQ) was the mainstay of treatment for uncomplicatedmalaria. High rates of treatment failure, accompanied by thenear universal prevalence of established CQ resistancemutations led to the adoption of CQ plus sulfadoxine–pyrimethamine (SP) as first-line therapy in June 2000.5 Theefficacy of CQ–SP combination therapy was short-lived,resulting in a change to artemether–lumefantrine (AL) in June2004, although the policy launch did not take place for an-other 2 years.6With thewithdrawal of CQ from routine use, reduction in theprevalence of the P. falciparum CQ resistance transporter(pfcrt) 76T mutation has been reported in Uganda, consistentwith the trends observed in landmark studies from Malawi.7,8Re-emergence of CQ-sensitive alleles depends greatly on theduration of time following the removal of selection pressureand local factors, such as transmission intensity.9 Therefore,we sought to examine spatial variation in the prevalence ofestablished molecular markers of antimalarial resistanceacross a geographically diverse, highland region of westernUganda.The study area, located in Bugoye subcounty of the KaseseDistrict, experiences year-roundmalaria transmissionmarkedby semiannual transmission peaks typically following the endof the rainy seasons. The geography of the subcounty is highlyvaried. The westernmost villages, which border the RwenzoriMountains National Park, are characterized by steep hillsideswith elevations up to 2,000 m, whereas villages located to theeast are defined by low-lying, level terrain (Figure 1A). As wehave previously demonstrated, this altitudinal gradient resultsin heterogenous malaria transmission intensity over a rela-tively small geographic area.10 The subcounty is also definedby three river valleys that flow from west to east converging inthe low-lying areas near the health center. Artemether–lumefantrine was introduced to the area in 2008, but thesupply available at public health facilities did not stabilize until2011.We collected malaria rapid diagnostic test (RDT) samplesfrom theBugoyeLevel III HealthCenter fromMay toNovember2015 as part of a prospective cohort study of individualspresenting with undifferentiated fever.11 DNA was extractedfrom each RDT sample using the Chelex method, as pre-viously described.10 Extracted P. falciparum DNA from 219RDT-positive samples was pooled in an isovolumetric man-ner, stratified by the river valley and elevation of the individu-al’s village of residence. The nine resulting pools (Figure 1B)then underwent PCR amplification targeting specific regionsassociated with putative drug resistance, including 1) pfcrt, 2)Kelch protein K13 (Kelch), and 3) P. falciparum multidrugresistance (pfmdr1) genes. The PCR conditions and theanalyzed genomic regions are summarized in SupplementalTable 1. Amplicons were prepared for sequencing using theKAPA Hyper Prep Kit (Roche Sequencing, Pleasanton, CA)and were sequenced on MiSeq 300-base pair paired-endchemistry with a Phi-X spike to increase diversity (Illumina,San Diego, CA). The raw data can be downloaded from se-quence read archive (PRJNA555552). All sequence analysisscriptsneeded to recreatehaplotypesare available fromGitHub(nickbrazeau/RDTSM_DrugRes_ManuscriptAnalyses).Sequences were demultiplexed and trimmed using the ex-tractor module in SeekDeep (Version 2.6.0) with the paired-end feature allowing for one barcode error and shortenedbarcodes.12 Default options were used for the cluster module,which performs initial clustering and removes chimerichaplotypes. Final haplotypes were determined with processcluster for haplotypes with a minimum frequency of 5%.*Address correspondence to Ross Boyce, Division of InfectiousDiseases, University of NorthCarolina at Chapel Hill, 130Mason FarmRd., Chapel Hill, NC 27599. E-mail: roboyce@med.unc.eduSample pools were processed with a single PCR, whichwarranted the higher, more conservative haplotype fre-quency cutoff. The resulting haplotypes were then evaluatedfor putative drug resistance mutations and amino acidchanges using custom R scripts (R Project, Version 3.5.1,www.r-project.org).We evaluated the effect of altitude and river valley on thefrequencyof theputativedrug resistancephenotypes (i.e., 74I/75E/76T and N86/184F for pfcrt and pfmdr1-early, re-spectively) using simple linear regression weighted by thenumber of samples in each pool. Elevation and location weremodeled as disjoint indicator variables based on the cate-gories described in Figure 1. Models were fit a priori, andcovariates were pruned using backward selection with an al-pha value of 0.05.Ethical approval of the study was provided by the in-stitutional review boards of the Mbarara University of Scienceand Technology, the University of North Carolina at ChapelHill, and the Uganda National Council for Science and Tech-nology. Written informed consent was obtained from all theadult study participants and the caregivers of children.Sequencing of pfcrt showed the presence of resistant 76Thaplotypes in all pools. The fraction of mutant haplotypesranged from16.8% to91.3%, although six of nine of pools hadfractions between 40%and 65% (Figure 2). Overall, there wasno trend between elevation or river valley and the frequency ofpfcrtmutations.In the early pfmdr1 PCR fragment, the Y184F mutation,which is associated with amodiaquine exposure, was identi-fied in all pools except for the one high-elevation pool thatcomprised only three clinical samples (Pool 1). In contrast tothe pfcrt analysis, there was evidence for a trend between thefraction of pfmdr1 mutations and elevation. The largest frac-tion of Y184 mutations was observed in the three low-elevation pools (pools 3, 6, and 9), whereas the smallestfraction was found in two of the three highest elevationpools (pools 1 and 7). The association was robust in thelinear regressionmodel with the fraction of N86/184F allelesbeing lower in both the medium (β = ‒33.04%, ‒65.59% to‒0.49%, P = 0.05) and high (β = ‒29.85, ‒70.04% to 10.34%,P = 0.11) elevation pools compared with the low-elevationpools. No trends were seen in regard to the river valley ofresidence.No mutations were observed in the pfmdr1 N86Y poly-morphism, which is more consistently associated with thedegree of lumefantrine susceptibility than the Y184F poly-morphism. Similarly, no resistance mutations were identifiedin the Kelch propeller domain in any of the nine pools(Supplemental Table 2).Overall, the results of our study provide further insight intothe microepidemiological patterns of antimalarial drug re-sistance. Most notably, we observed a significant, inversecorrelation between altitude and the frequency of pfmdr1Y184 mutations. The ecological factors underlying thisfinding are, however, complex. For example, although ele-vation can be a surrogate measure of transmission intensity,it may also be a proxy for access to care, with the higherelevation villages to the west of the subcounty being fartherfrom public health facilities. Residents of these lower trans-mission villages are likely to have less acquired immunity,be exposed to increasing clonal infections, and developsymptoms thatwill promptcare-seekingand treatment—factorsthat some models have suggested may contribute to therapid spread of resistance in lower transmission set-tings.13 Yet without access to public facilities, residentsare likely to seek care in the private sector where first-lineACTs such as AL are less available.14 Unfortunately, ourstudy is not designed to disentangle these factors, andFIGURE 1. (A) Topographic map of study area showing the rapid change in altitude and major river valleys. Contour lines demarcate elevationchanges of 200 m. (B) Table of sample pools stratified by elevation and river valley, which can be roughly overlaid on map to left, showing numberof samples and median and interquartile range of parasitemia. This figure appears in color at www.ajtmh.org.further investigation will be required. At a minimum, these re-sults suggest that there are not clear associations betweenaltitude of residence and the prevalence of drug resistancepolymorphisms.Our finding that approximately half of alleles in each poolwere wild-type pfcrt is consistent with the well-establishedphenomenon of wild-type re-emergence following the with-drawal of CQ as first-line therapy. We do not have historicaldata from the site to assess the rate of wild-type pfcrt re-emergence, but our estimates are similar to those from con-temporaneous studies fromother areas of Uganda.15 The lackof an association between the frequency of pfcrt mutationsand elevation may be due to the fact that alleles associatedwith drug resistance saturated the country at the time of theantimalarial policy change.Our study has a number of strengths, including the uniquegeographic setting and use of a validated, high-throughputpooling technique. To our knowledge, this is the first study toexamine differences in the frequency of putative drug re-sistance mutations over such a small, yet geographically di-verse area. Our study also has limitations. First, our sampleswere collected from a single clinical site. This approach re-stricted the number of samples from the more distant, higherelevation villages. This sampling limitation also could havecreated a selection bias with patients from distant villagespresenting with more severe disease. Second, the pools werenot performed in replicate,which could impact the accuracy ofour frequency estimations. We attempted to account for thislimitation by using a very conservative minimum cutoff of 5%to determine the final haplotypes.In conclusion, by pooling samples from a geographicallydiverse area, we were able to identify an association betweenaltitude, which often serves as a surrogate marker of trans-mission intensity, and the frequency of the Y184F mutation,but this association was not seen across multiple drugresistance–conferring alleles, suggesting that the relationshipinvolves factors other than transmission intensity.Received January 26, 2019. Accepted for publication July 23, 2019.Published online August 19, 2019.Note: Supplemental tables appear at www.ajtmh.org.Acknowledgments: We wish to thank the clinical staff and patients ofthe Bugoye Health Center for their continued support. We acknowl-edge the cartography support provided by Corianna Keeler from theUNC Department of Geography.Financial support: R. M. B. (T32 AI007151) and J. J. J. (K24AI134990,R01AI121558, and R21AI121465) received support from the NationalInstitutes of Health. Funding for the prospective cohort study wasprovided by a Thrasher Research Foundation Early Career Award toRMB.Authors’ addresses: Ross Boyce and Travis Fulton, Division of In-fectious Diseases, University of North Carolina at Chapel Hill, ChapelHill, NC, E-mails: roboyce@med.unc.edu and travismfulton@gmail.com. Nicholas Brazeau, Medicine, University of North Carolinaat Chapel Hill, Chapel Hill, NC, E-mail: nbrazeau1@gmail.com. NickHathaway, University of Massachusetts Medical School, School ofMedicine, Worcester, MA, E-mail: nickjhathaway@gmail.com. Mi-chael Matte, Moses Ntaro, and Edgar Mulogo, Department of Com-munity Health, Mbarara University of Science and Technology,Mbarara, Uganda, E-mails: mattemichael18@gmail.com, ntaro2001@gmail.com, and emulogo2000@gmail.com. Jonathan J. Juliano, Di-vision of Infectious Diseases, Department of Medicine, University ofNorth Carolina at Chapel Hill, Chapel Hill, NC, E-mail: jonathan_juliano@med.unc.edu.REFERENCES1. Talisuna AO et al., 2012. Mitigating the threat of artemisinin re-sistance in Africa: improvement of drug-resistance surveillanceand response systems. Lancet Infect Dis 12: 888–896.2. Packard RM, 2014. The origins of antimalarial-drug resistance.N Engl J Med 371: 397–399.3. WHO, 2011. Global Plan for Artemisinin Resistance Containment(GPARC). Geneva, Switzerland: World Health Organization.FIGURE 2. Frequency of wild-type and nonsynonymous putative drug-resistant haplotypes by river valley (y axis) and elevation category (x axis)with respect to targeted amplicon. This figure appears in color at www.ajtmh.org.4. Plowe CV, 2009. The evolution of drug-resistant malaria. Trans RSoc Trop Med Hyg 103 (Suppl 1): S11–S14.5. Kamya MR, Bakyaita NN, Talisuna AO, Were WM, Staedke SG,2002. Increasing antimalarial drug resistance in Uganda andrevision of the national drug policy. Trop Med Int Health 7:1031–1041.6. NanyunjaM, NabyongaOrem J, Kato F, KaggwaM, KatureebeC,Saweka J, 2011. Malaria treatment policy change and imple-mentation: the case of Uganda.Malar Res Treat 2011: 683167.7. Kublin JG, Cortese JF, Njunju EM, Mukadam RA, Wirima JJ,Kazembe PN, Djimde AA, Kouriba B, Taylor TE, Plowe CV,2003. Reemergence of chloroquine-sensitive Plasmodium fal-ciparum malaria after cessation of chloroquine use in Malawi.J Infect Dis 187: 1870–1875.8. Mbogo GW et al., 2014. Temporal changes in prevalence of mo-lecular markersmediating antimalarial drug resistance in a highmalaria transmission setting inUganda.AmJTropMedHyg91:54–61.9. TalisunaAO,ErhartA,SamarasingheS,VanOvermeirC,SpeybroeckN, D’Alessandro U, 2006. Malaria transmission intensity and therate of spread of chloroquine resistant Plasmodium falciparum:why have theoretical models generated conflicting results? InfectGenet Evol 6: 241–248.10. Boyce RM et al., 2018. Reuse of malaria rapid diagnostic tests foramplicon deep sequencing to estimatePlasmodium falciparumtransmission intensity in western Uganda. Scientific Rep 8:10159.11. Boyce R, Reyes R, Matte M, Ntaro M, Mulogo E, Siedner MJ,2017. Use of a dual-antigen rapid diagnostic test to screenchildren for severe Plasmodium falciparum malaria in a high-transmission, resource-limited setting. Clin Infect Dis 65:1509–1515.12. Hathaway NJ, Parobek CM, Juliano JJ, Bailey JA, 2018. Seek-Deep: single-base resolution de novo clustering for amplicondeep sequencing. Nucleic Acids Res 46: e21.13. PongtavornpinyoW,YeungS,Hastings IM,DondorpAM,DayNP,White NJ, 2008. Spread of anti-malarial drug resistance:mathematical model with implications for ACT drug policies.Malar J 7: 229.14. Wang LT, Bwambale R, Keeler C, Reyes R, Muhindo R, Matte M,Ntaro M, Mulogo E, Sundararajan R, Boyce RM, 2018. Privatesector drugshops frequentlydispenseparenteral anti-malarialsin a rural region of western Uganda.Malar J 17: 305.15. Tumwebaze P et al., 2017. Changing antimalarial drug resistancepatterns identified by surveillance at three sites in Uganda.J Infect Dis 215: 631–635.

Prevalence of molecular markers of antimalarial drug resistance across altitudinal transmission zones in Highland Western Uganda

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Prevalence of molecular markers of antimalarial drug resistance across altitudinal transmission zones in Highland Western Uganda

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