• View in gallery

    ama1 Haplotype diversity and sharing among site-years (A) neighbor-joining tree (right) comparing the ama1 haplotypes. Bootstrap values are shown at nodes. Nucleotides for segregating sites within the amplicon are shown for each respective haplotypes (A: red, C: green, G: yellow, and T: blue; left). Haplotypes are ordered in the same order as the tree. (B) A chord diagram showing the degree of ama1 haplotype frequency similarities among the various site-years. (C) The frequencies of each respective haplotype by site-years.

  • View in gallery

    Map of Ethiopian mdr1 N86 mutation prevalence. N86 prevalence reported in this study and previously in the literature is shown on the map. Different studies are indicated by the shading, whereas sample size is indicated by the size of the circle. The year of sample collection in each study is indicated.

  • 1.

    Ethiopian Public Health Institute, 2016. Ethiopia National Malaria Indicator Survey 2015. Addid Ababa, Ethiopia: Federal Democratic Republic of Ethiopia Ministry of Health. Available at: https://www.ephi.gov.et/images/pictures/download2009/MIS-2015-Final-Report-December-_2016.pdf.

    • Search Google Scholar
    • Export Citation
  • 2.

    Mekonnen SK, Aseffa A, Berhe N, Teklehaymanot T, Clouse RM, Gebru T, Medhin G, Velavan TP, 2014. Return of chloroquine-sensitive Plasmodium falciparum parasites and emergence of chloroquine-resistant Plasmodium vivax in Ethiopia. Malar J 13: 244.

    • Search Google Scholar
    • Export Citation
  • 3.

    Alifrangis M et al. 2014. Independent origin of Plasmodium falciparum antifolate super-resistance, Uganda, Tanzania, and Ethiopia. Emerg Infect Dis 20: 12801286.

    • Search Google Scholar
    • Export Citation
  • 4.

    Kamau E et al. 2015. K13-propeller polymorphisms in Plasmodium falciparum parasites from sub-Saharan Africa. J Infect Dis 211: 13521355.

  • 5.

    Heuchert A, Abduselam N, Zeynudin A, Eshetu T, Löscher T, Wieser A, Pritsch M, Berens-Riha N, 2015. Molecular markers of anti-malarial drug resistance in southwest Ethiopia over time: regional surveillance from 2006 to 2013. Malar J 14: 208.

    • Search Google Scholar
    • Export Citation
  • 6.

    Golassa L et al. 2015. Identification of large variation in pfcrt, pfmdr-1 and pfubp-1 markers in Plasmodium falciparum isolates from Ethiopia and Tanzania. Malar J 14: 264.

    • Search Google Scholar
    • Export Citation
  • 7.

    Tessema SK, Kassa M, Kebede A, Mohammed H, Leta GT, Woyessa A, Guma GT, Petros B, 2015. Declining trend of Plasmodium falciparum dihydrofolate reductase (dhfr) and dihydropteroate synthase (dhps) mutant alleles after the withdrawal of sulfadoxine-pyrimethamine in north western Ethiopia. PLoS One 10: e0126943.

    • Search Google Scholar
    • Export Citation
  • 8.

    Lo E et al. 2017. Transmission dynamics of co-endemic Plasmodium vivax and P. falciparum in Ethiopia and prevalence of antimalarial resistant genotypes. PLoS Negl Trop Dis 11: e0005806.

    • Search Google Scholar
    • Export Citation
  • 9.

    Taylor SM, Parobek CM, Aragam N, Ngasala BE, Mårtensson A, Meshnick SR, Juliano JJ, 2013. Pooled deep sequencing of Plasmodium falciparum isolates: an efficient and scalable tool to quantify prevailing malaria drug-resistance genotypes. J Infect Dis 208: 19982006.

    • Search Google Scholar
    • Export Citation
  • 10.

    Hathaway NJ, Parobek CM, Juliano JJ, Bailey JA, 2017. SeekDeep: single-base resolution de novo clustering for amplicon deep sequencing. Nucleic Acids Res 46: e21.

    • Search Google Scholar
    • Export Citation
  • 11.

    Tamura K, Nei M, 1993. Estimation of the number of nucleotide substitutions in the control region of mitochondrial DNA in humans and chimpanzees. Mol Biol Evol 10: 512526.

    • Search Google Scholar
    • Export Citation
  • 12.

    Paradis E, Claude J, Strimmer K, 2004. APE: analyses of phylogenetics and evolution in R language. Bioinformatics 20: 289290.

  • 13.

    Yu G, Smith DK, Zhu H, Guan Y, Lam TTY, 2016. ggtree: anrpackage for visualization and annotation of phylogenetic trees with their covariates and other associated data. Methods Ecol Evol 8: 2836.

    • Search Google Scholar
    • Export Citation
  • 14.

    Gu Z, Gu L, Eils R, Schlesner M, Brors B, 2014. Circlize implements and enhances circular visualization in R. Bioinformatics 30: 28112812.

  • 15.

    Venkatesan M et al. ASAQ Molecular Marker Study Group, 2014. Polymorphisms in Plasmodium falciparum chloroquine resistance transporter and multidrug resistance 1 genes: parasite risk factors that affect treatment outcomes for P. falciparum malaria after artemether-lumefantrine and artesunate-amodiaquine. Am J Trop Med Hyg 91: 833843.

    • Search Google Scholar
    • Export Citation
  • 16.

    Miller RH, Hathaway NJ, Kharabora O, Mwandagalirwa K, Tshefu A, Meshnick SR, Taylor SM, Juliano JJ, Stewart VA, Bailey JA, 2017. A deep sequencing approach to estimate Plasmodium falciparum complexity of infection (COI) and explore apical membrane antigen 1 diversity. Malar J 16: 490.

    • Search Google Scholar
    • Export Citation
  • 17.

    Bei AK et al. 2018. Dramatic changes in malaria population genetic complexity in dielmo and ndiop, senegal, revealed using genomic surveillance. J Infect Dis 217: 622627.

    • Search Google Scholar
    • Export Citation
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Pooled Deep Sequencing of Drug Resistance Loci from Plasmodium falciparum Parasites across Ethiopia

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  • 1 Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, North Carolina;
  • | 2 Doctor of Medicine/Doctor of Philosophy Program, School of Medicine, University of North Carolina, Chapel Hill, North Carolina;
  • | 3 Ethiopian Public Health Institute, Addis Ababa, Ethiopia;
  • | 4 Division of Infectious Diseases, School of Medicine, University of North Carolina, Chapel Hill, North Carolina;
  • | 5 Department of Geography, University of North Carolina, Chapel Hill, North Carolina;
  • | 6 School of Medicine, University of Massachusetts Medical School, Worcester, Massachusetts;
  • | 7 Division of Transfusion Medicine, Brown University, Providence, Rhode Island;
  • | 8 Curriculum in Genetics and Molecular Biology, School of Medicine, University of North Carolina, Chapel Hill, North Carolina

Although Ethiopia has an overall lower prevalence of Plasmodium falciparum among countries in Africa, the emergence of drug resistance could seriously hinder elimination efforts. Using samples collected from five therapeutic efficacy studies conducted in 2007–11, we evaluated the prevalence of putative drug resistance mutations in the pfcrt, pfmdr1, and kelch13 genes at the time of those studies, as well as the ama1 gene for genetic relatedness using a pooled amplicon deep sequencing approach. Among all sites, the kelch13 gene showed no mutations, whereas the pfcrt CVIET genotype was fixed in all populations. By contrast, the mdr1 gene demonstrated frequencies of resistant genotypes ranging from 10 to 100% at amino acid position 86 and from 0% to 57.8% at amino acid position 1246. Although we observed a low degree of haplotype sharing between sites, we did observe considerable haplotype sharing within sites over time. This suggests that P. falciparum populations in Ethiopia are isolated and able to persist through time.

Malaria remains a significant public health problem in Ethiopia despite an overall prevalence lower than in other malaria-endemic countries in Africa. About 60% of the population lives in regions susceptible to malaria, with approximately 1.2 million cases of malaria occurring in 2014–2015.1 Ethiopia uses therapeutic efficacy studies (TES) to monitor for the presence of antimalarial resistance. Current first-line therapy in Ethiopia for confirmed cases of malaria is treatment with artemether–lumefantrine (AL). However, there is limited information on the presence of the genetic mutations associated with antimalarial resistance in Plasmodium falciparum in Ethiopia, and in particular, those associated with artemisinin resistance.28 Early detection of drug resistance mutations is vital to prevent fixation of drug resistance in the parasite population, an outcome that would seriously curtail malaria elimination efforts. Here, we evaluate the frequency of polymorphisms associated with antimalarial resistance in the P. falciparum chloroquine resistance transporter (pfcrt), P. falciparum multidrug resistance 1 protein (pfmdr1), and the K13 (kelch13) genes, as well as a marker of parasite diversity in the apical membrane antigen 1 (ama1) gene, using a pooled amplicon deep sequencing approach.9 Isolates were collected during TES conducted between 2007 and 2011 (Table 1) at sites of varying transmission (Supplemental Text).

Table 1

Summary of drug resistance haplotype frequency and associated AL in vivo efficacy frequencies are displayed as percentages for each site-year for targets: kelch, crt, and mdr1

nkelch13pfcrtpfmdr1-earlypfmdr1-late28-day PCR-adjusted efficacy
Any74I/75E/76TM74/N75/K76N86/184FN86/Y18486Y/Y18486Y/184FD12461246Y
Serbo 201149ND (52,776/52,776)100 (1,609,200/1,609,200)ND (0/1,609,200)100 (17,205/17,205)NDNDND100 (48,549/48,549)ND97.6
Shele 200877ND (29,280/29,280)100 (80,109/80,109)ND (0/80,109)9.98 (767/7,682)ND56.94 (4,374/7,682)33.08 (2,541/7,682)42.2 (3,438/8,146)57.8 (4,708/8,146)92.5
Shele 201185ND (86,879/86,879)100 (188,047/188,047)ND (0/188,047)39.11 (9,470/24,215)ND21.57 (5,222/24,215)39.33 (9,523/24,215)66.53 (26,535/39,886)33.47 (13,351/39,886)96.7
Selekleka 201033ND (146,743/146,743)100 (1,405,660/1,405,660)ND (0/1,405,660)NANANANA95.48 (191,519/200,592)4.52 (9,073/200,592)100
Wondogenet 200771ND (52,668/52,668)100 (433,093/433,093)ND (0/433,093)92.18 (81,158/88,040)NDND7.82 (6,882/88,040)68.08 (22,711/33,360)31.92 (10,649/33,360)96.9
Wondogenet 201198ND (17,905/17,905)100 (171,859/171,859)ND (0/171,859)56.91 (13,104/23,024)9.36 (2,155/23,024)12.96 (2,895/23,024)20.76 (4,780/23,024)NANA98.9

NA = insufficient data; ND = not detected. Below the frequencies are the number of reads that correspond to that phenotype (numerator) divided by the total reads attributed to the sample (denominator). Coordinates of the pfmdr1-early and pfmdr1-late fragments are provided in Supplemental Table 1.

DNA was extracted from 413 clinical isolates, pooled using equal volume per sample by site-year, and amplified for specific loci associated with putative drug resistance and the ama1 hypervariable region (Supplemental Table 1). Amplicons were sequenced using the KAPA HyperPrep Kit© (Roche Sequencing, Pleasanton, CA) on an Illumina MiSeq® using 300-base pair, paired-end chemistry (Illumina, San Diego, CA).

Sequencing reads (Supplemental Table 2) were de-multiplexed and trimmed using the extractor module in SeekDeep (version 2.6.0) with the paired-end feature, allowing for one barcode error and shortened barcodes.10 Default options were used for the cluster module, which performs initial clustering and removes chimeric haplotypes. Final haplotypes were determined with processCluster for haplotypes with a minimum frequency of 1%, based on our positive controls to prevent false haplotypes. Finally, polymerase chain reaction (PCR) replicates with less than 100 reads were excluded from the analysis. As a result, not all samples had technical replicates (Supplemental Table 3). In addition, we were unable to generate a pfmdr1-1 and a pfmdr1-2 fragment for the Selekleka 2010 and Wondogenet 2011 pools, respectively. As pfmdr1 required two fragments, we were not able to assess the N86-184Y-D1246 haplotype.

Positive controls included a monoclonal 3d7 strain (MRA102G, BEI Resources, Manasas, VA) and a known mixture of 75% 3d7 (MRA-102G, BEI Resources) and 25% Dd2 (MRA-156G, BEI Resources). Both controls underwent duplicate PCR reactions for each gene target and were used to evaluate the aforementioned filters for SeekDeep (Supplemental Figure 1). The resulting haplotypes were then evaluated for putative drug resistance mutations and amino acid changes using custom R-scripts, available on GitHub (IDEELResearch/SeekDeepRANN). Haplotype fractions and read depths for all targets are provided in Supplemental Table 3.

Site-year genetic similarities were evaluated from the ama1 haplotypes using the Tamura-Nei 1993 genetic distance and summarized with neighbor-joining trees.11 To evaluate the stability of our neighbor-joining tree, we performed 1,000 bootstrap iterations (Figure 1). The Tamura-Nei 1993 model of evolution was selected to account for the AT skew in P. falciparum (unequal genotype frequencies).11 All phylogenetic analyses were performed using the R-package ape and visualized with ggtree.12,13 Based on the low support for the tree nodes, there did not appear to be distinct ama1 clusters. To determine if sites-years shared similar haplotype distributions, we first performed a logit transformation of the haplotype frequencies to return them to an unbounded scale and then calculated the distance as the inverse Euclidean distance among the transformed haplotype frequencies. The final equation was , where g(∘) was of the form . The ϵ was set to 1e-5 for all log calculations to be finite. The resulting adjacency list was then plotted with the chordDiagram from the circlize R-package.14

Figure 1.
Figure 1.

ama1 Haplotype diversity and sharing among site-years (A) neighbor-joining tree (right) comparing the ama1 haplotypes. Bootstrap values are shown at nodes. Nucleotides for segregating sites within the amplicon are shown for each respective haplotypes (A: red, C: green, G: yellow, and T: blue; left). Haplotypes are ordered in the same order as the tree. (B) A chord diagram showing the degree of ama1 haplotype frequency similarities among the various site-years. (C) The frequencies of each respective haplotype by site-years.

Citation: The American Journal of Tropical Medicine and Hygiene 101, 5; 10.4269/ajtmh.19-0142

Population-based haplotype frequencies provide insight into the distribution of antimalarial resistance mutations over space and time. Table 1 summarizes the haplotype frequency for each sequenced pool. Supplemental Table 4 shows read depth per pool. We did not detect any mutations in the gene associated with artemisinin resistance, kelch13. The key allele associated with resistance to the primary partner drug used in Ethiopia, lumefantrine, is pfmdr1 N86, which increases the hazard of recrudescence by approximately 5-fold.15 Overall, we saw variation in the frequency of the N86Y allele between site-years, ranging from around 10% to 100% of haplotypes. In the two sites where longitudinal in vivo efficacy data were available, we observed large shifts in the N86 allele frequency (nearly, a 30% increase in N86Y frequency between 2008 and 2011 in Shele and nearly a 26% decrease in N86Y frequency between 2007 and 2011 in Wondogenet), but the in vivo efficacy of AL remained high and stable. Interestingly, the frequency of resistance polymorphisms associated with chloroquine resistance remained high across all years in these sites.

Our data suggest a dynamic mutational landscape of pfmdr1 mutations, in contrast to prior reports. The pfmdr1 N86 genotype was highly prevalent in prior studies of samples collected between 2011 and 2014 across Ethiopia.5,6,8 In the Jimma zone in particular, the N86 mutation was previously shown to have increased dramatically with the introduction of AL, increasing from 14.3% prevalence in 2004 to 98.8% prevalence in 2013.5 In this study, we observed the N86 genotype at intermediate frequencies at some sites, shifting both upward (Shele) and downward (Wondogenet) over time, with fixation or near fixation at other sites as described in previous reports (Figure 2). Similarly, the 184Y mutation showed near complete fixation across multiple regions of Ethiopia in prior reports but was found at intermediate frequencies in this study.5,8 Finally, we identified shifts in both directions for the pfmdr1 D1246Y mutation in our study, whereas previous reports have suggested high prevalence of the D1246 genotype.8

Figure 2.
Figure 2.

Map of Ethiopian mdr1 N86 mutation prevalence. N86 prevalence reported in this study and previously in the literature is shown on the map. Different studies are indicated by the shading, whereas sample size is indicated by the size of the circle. The year of sample collection in each study is indicated.

Citation: The American Journal of Tropical Medicine and Hygiene 101, 5; 10.4269/ajtmh.19-0142

Our study supports the persistence of the resistant pfcrt genotype in Ethiopia, which may be due to the continued use of chloroquine in the community in response to the co-endemicity of Plasmodium vivax. Findings from previous studies of pfcrt mutations in Ethiopia have been mixed, with some studies suggesting persistence of the 76T resistant genotype and others suggesting replacement by the K76-sensitive genotype.2,5,8

Similar to other studies of kelch13 mutations in Ethiopia, we did not identify any polymorphisms associated with artemisinin resistance.4,5,8 In fact, we identified no variation from the wild type in the region sequenced and did not identify the previous mutation (N531I) found in the Jimma zone in 2013.5

The amplified segment of ama1 has previously been shown to have high heterozygosity and haplotype diversity, supporting its use to identify strains.16 Based on these ama1 haplotypes, there appeared to be limited sharing of haplotypes between sites, but a large degree of sharing within sites when longitudinal data were available (Figure 1). Among the various site-years, the unique number of ama1 haplotypes ranged from 2 to 8 and with haplotype frequencies ranging from 1.08% to 97.37%. As such, the population diversity data from the ama1 deep sequencing suggests that parasite populations differ significantly across the country but appear to persist as isolated populations through time. This has been suggested previously in a study using microsatellite genotyping.8 Overall, Ethiopia’s malaria transmission intensity remains low, with only 1.2% and 0.5% of children being malaria positive in the 2015 Malaria Indicator Survey by rapid diagnostic tests and microscopy, respectively.1 As transmission decreases, parasite populations tend to become more isolated and clonal, as has been seen in other regions of Africa.17 The overall relatively low genetic diversity in ama1 relative to other regions of Africa, such as the Democratic Republic of Congo where we found 77 ama1 haplotypes in fewer samples, suggests that there may be genetic bottlenecking in Ethiopia.16 Further studies of individual patients using molecular-barcoded amplicon sequencing, microsatellites, or whole-genome sequencing could be used to determine whether bottlenecking is occurring and further define parasite migration within- and between countries.8

The pooled deep sequencing approach used in this study has advantages and disadvantages that have been discussed previously.9 Key advantages include cost savings and speed. The total laboratory workflow for this project, from extraction to sequencing, took less than 1 month to complete. Disadvantages include the inability to look at individual patient data or to genotype select antimalarial resistance polymorphisms, such as copy number variations in pfmdr1, which have previously been reported in Ethiopia.8

The pooled deep sequencing approach used here provided a rapid, cost-effective means to determine single nucleotide polymorphism frequencies within populations and allowed the rapid exploration of antimalarial resistance mutations in Ethiopia.9 Combined with previous reports, it suggests a dynamic landscape of antimalarial resistance mutations that warrant further investigation. Population-based surveys, such as Malaria Indicator Surveys or Demographic Health Surveys, could be leveraged to shed light on country-wide patterns of antimalarial resistance and parasite population migration in Ethiopia, filling the gaps between present studies.

Supplemental text, tables, and figure

Acknowledgments:

We thank the participants in the studies and JCVD for their support. We thank the funders of the TES studies, including Ministry of Health, Global Fund, President’s Malaria Initiative, and the World Health Organization.

REFERENCES

  • 1.

    Ethiopian Public Health Institute, 2016. Ethiopia National Malaria Indicator Survey 2015. Addid Ababa, Ethiopia: Federal Democratic Republic of Ethiopia Ministry of Health. Available at: https://www.ephi.gov.et/images/pictures/download2009/MIS-2015-Final-Report-December-_2016.pdf.

    • Search Google Scholar
    • Export Citation
  • 2.

    Mekonnen SK, Aseffa A, Berhe N, Teklehaymanot T, Clouse RM, Gebru T, Medhin G, Velavan TP, 2014. Return of chloroquine-sensitive Plasmodium falciparum parasites and emergence of chloroquine-resistant Plasmodium vivax in Ethiopia. Malar J 13: 244.

    • Search Google Scholar
    • Export Citation
  • 3.

    Alifrangis M et al. 2014. Independent origin of Plasmodium falciparum antifolate super-resistance, Uganda, Tanzania, and Ethiopia. Emerg Infect Dis 20: 12801286.

    • Search Google Scholar
    • Export Citation
  • 4.

    Kamau E et al. 2015. K13-propeller polymorphisms in Plasmodium falciparum parasites from sub-Saharan Africa. J Infect Dis 211: 13521355.

  • 5.

    Heuchert A, Abduselam N, Zeynudin A, Eshetu T, Löscher T, Wieser A, Pritsch M, Berens-Riha N, 2015. Molecular markers of anti-malarial drug resistance in southwest Ethiopia over time: regional surveillance from 2006 to 2013. Malar J 14: 208.

    • Search Google Scholar
    • Export Citation
  • 6.

    Golassa L et al. 2015. Identification of large variation in pfcrt, pfmdr-1 and pfubp-1 markers in Plasmodium falciparum isolates from Ethiopia and Tanzania. Malar J 14: 264.

    • Search Google Scholar
    • Export Citation
  • 7.

    Tessema SK, Kassa M, Kebede A, Mohammed H, Leta GT, Woyessa A, Guma GT, Petros B, 2015. Declining trend of Plasmodium falciparum dihydrofolate reductase (dhfr) and dihydropteroate synthase (dhps) mutant alleles after the withdrawal of sulfadoxine-pyrimethamine in north western Ethiopia. PLoS One 10: e0126943.

    • Search Google Scholar
    • Export Citation
  • 8.

    Lo E et al. 2017. Transmission dynamics of co-endemic Plasmodium vivax and P. falciparum in Ethiopia and prevalence of antimalarial resistant genotypes. PLoS Negl Trop Dis 11: e0005806.

    • Search Google Scholar
    • Export Citation
  • 9.

    Taylor SM, Parobek CM, Aragam N, Ngasala BE, Mårtensson A, Meshnick SR, Juliano JJ, 2013. Pooled deep sequencing of Plasmodium falciparum isolates: an efficient and scalable tool to quantify prevailing malaria drug-resistance genotypes. J Infect Dis 208: 19982006.

    • Search Google Scholar
    • Export Citation
  • 10.

    Hathaway NJ, Parobek CM, Juliano JJ, Bailey JA, 2017. SeekDeep: single-base resolution de novo clustering for amplicon deep sequencing. Nucleic Acids Res 46: e21.

    • Search Google Scholar
    • Export Citation
  • 11.

    Tamura K, Nei M, 1993. Estimation of the number of nucleotide substitutions in the control region of mitochondrial DNA in humans and chimpanzees. Mol Biol Evol 10: 512526.

    • Search Google Scholar
    • Export Citation
  • 12.

    Paradis E, Claude J, Strimmer K, 2004. APE: analyses of phylogenetics and evolution in R language. Bioinformatics 20: 289290.

  • 13.

    Yu G, Smith DK, Zhu H, Guan Y, Lam TTY, 2016. ggtree: anrpackage for visualization and annotation of phylogenetic trees with their covariates and other associated data. Methods Ecol Evol 8: 2836.

    • Search Google Scholar
    • Export Citation
  • 14.

    Gu Z, Gu L, Eils R, Schlesner M, Brors B, 2014. Circlize implements and enhances circular visualization in R. Bioinformatics 30: 28112812.

  • 15.

    Venkatesan M et al. ASAQ Molecular Marker Study Group, 2014. Polymorphisms in Plasmodium falciparum chloroquine resistance transporter and multidrug resistance 1 genes: parasite risk factors that affect treatment outcomes for P. falciparum malaria after artemether-lumefantrine and artesunate-amodiaquine. Am J Trop Med Hyg 91: 833843.

    • Search Google Scholar
    • Export Citation
  • 16.

    Miller RH, Hathaway NJ, Kharabora O, Mwandagalirwa K, Tshefu A, Meshnick SR, Taylor SM, Juliano JJ, Stewart VA, Bailey JA, 2017. A deep sequencing approach to estimate Plasmodium falciparum complexity of infection (COI) and explore apical membrane antigen 1 diversity. Malar J 16: 490.

    • Search Google Scholar
    • Export Citation
  • 17.

    Bei AK et al. 2018. Dramatic changes in malaria population genetic complexity in dielmo and ndiop, senegal, revealed using genomic surveillance. J Infect Dis 217: 622627.

    • Search Google Scholar
    • Export Citation

Author Notes

Address correspondence to Jonathan J. Juliano, Division of Infectious Diseases, School of Medicine, University of North Carolina, Chapel Hill, NC 27599, E-mail: jonathan_juliano@med.unc.edu or Adugna Woyessa, Malaria and Other Vector-Borne Parasitic Diseases Research Team, Bacterial, Parasitic and Zoonotic Diseases Research Directorate, Ethiopian Public Health Institute, Addis Ababa, Ethiopia, E-mail: adugnawayessa@gmail.com.

Financial support: This project was supported by a Benjamin H Kean Travel Fellowship from the American Society of Tropical Medicine and Hygiene and the T32AI070114 to N. F. B. and K24AI134990 from the National Institute for Allergy and Infectious Diseases at the National Institutes of Health to J. J. J.

Authors’ addresses: Nicholas F. Brazeau and Steven R. Meshnick, Department of Epidemiology, University of North Carolina, Chapel Hill, NC, E-mails: nbrazeau@med.unc.edu and meshnick@email.unc.edu. Ashenafi Assefa, Hussein Mohammed, Heven Seme, Abeba G. Tsadik, Moges Kassa, and Adugna Woyessa, Ethiopian Public Health Institute, Vector Borne Diseases, Addis Ababa, Ethiopia, E-mails: ashyaega@yahoo.com, hussein_ehnri@yahoo.com, hevensime@yahoo.com, abebagtsadik@yahoo.com, eyobmk@yahoo.com, and adugnawayessa@gmail.com. Jonathan B. Parr and Jonathan J. Juliano, Division of Infectious Diseases, School of Medicine, University of North Carolina, Chapel Hill, NC, E-mails: jonathan_parr@med.unc.edu and jonathan_juliano@med.unc.edu. Corinna Keeler, Department of Geography, University of North Carolina, Chapel Hill, NC, E-mail: corinnacykeeler@live.unc.edu. Nicholas J. Hathaway, School of Medicine, University of Massachusetts Medical School, Worcester, MA, E-mail: nickjhathaway@gmail.com. Jeffrey A. Bailey, Division of Transfusion Medicine, Brown University, Providence, RI, E-mail: jeffrey_bailey@brown.edu.

These authors contributed equally to this work.

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