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Malaria Journal

Open Access

Surveillance for sulfadoxine-pyrimethamine resistant malaria parasites in the Lake and Southern Zones, Tanzania, using pooling and next-generation sequencing

  • Jeremiah M. Ngondi1Email author,
  • Deus S. Ishengoma2,
  • Stephanie M. Doctor3,
  • Kyaw L. Thwai3,
  • Corinna Keeler3,
  • Sigsbert Mkude4,
  • Oresto M. Munishi1,
  • Ritha A. Willilo1,
  • Shabbir Lalji1,
  • Naomi Kaspar5,
  • Chonge Kitojo5,
  • Lynn A. Paxton6,
  • Nicholas J. Hathaway7,
  • Jeffrey A. Bailey7,
  • Jonathan J. Juliano3,
  • Steven R. Meshnick3 and
  • Julie Gutman8Email author
Malaria Journal201716:236

https://doi.org/10.1186/s12936-017-1886-9

Received: 15 January 2017

Accepted: 30 May 2017

Published: 5 June 2017

Abstract

Background

Malaria in pregnancy (MiP) remains a major public health challenge in areas of high malaria transmission. Intermittent preventive treatment in pregnancy (IPTp) with sulfadoxine-pyrimethamine (SP) is recommended to prevent the adverse consequences of MiP. The effectiveness of SP for IPTp may be reduced in areas where the dhps581 mutation (a key marker of high level SP resistance) is found; this mutation was previously reported to be common in the Tanga Region of northern Tanzania, but there are limited data from other areas. The frequency of molecular markers of SP resistance was investigated in malaria parasites from febrile patients at health centres (HC) in seven regions comprising the Lake and Southern Zones of mainland Tanzania as part of the ongoing efforts to generate national-wide data of SP resistance.

Methods

A cross-sectional survey was conducted in the outpatient departments of 14 HCs in seven regions from April to June, 2015. 1750 dried blood spot (DBS) samples were collected (117 to 160 per facility) from consenting patients with positive rapid diagnostic tests for malaria, and no recent (within past 2 months) exposure to SP or related drugs. DNA was extracted from the DBS, pooled by HC, and underwent pooled targeted amplicon deep sequencing to yield estimates of mutated parasite allele frequency at each locus of interest.

Results

The dhps540 mutation was common across all 14 sites, ranging from 55 to 98.4% of sequences obtained. Frequency of the dhps581 mutation ranged from 0 to 2.4%, except at Kayanga HC (Kagera Region, Lake Zone) where 24.9% of sequences obtained were mutated. The dhfr164 mutation was detected only at Kanyanga HC (0.06%).

Conclusion

By pooling DNA extracts, the allele frequency of mutations in 14 sites could be directly determined on a single deep-sequencing run. The dhps540 mutant was very common at all locations. Surprisingly, the dhps581 was common at one health center, but rare in all the others, suggesting that there is geographic micro-heterogeneity in mutant distribution and that accurate surveillance requires inclusion of multiple sites. A better understanding of the effect of the dhps581 mutant on the efficacy of IPTp-SP is needed.

Keywords

Plasmodium falciparum Sulfadoxine-pyrimethamineTanzaniaMalariaResistance

Background

Malaria in pregnancy (MiP) remains a major public health challenge in areas of high malaria transmission. In pregnant women, malaria can cause mild to severe maternal anaemia, particularly in primigravid women, and placental infection can interfere with the maternal-fetal exchange of nutrients and oxygen, leading to preterm delivery and low birth weight, and consequently increasing neonatal mortality [1, 2]. In order to prevent the adverse consequences of MiP, the World Health Organization (WHO) recommends the use of intermittent preventative treatment in pregnancy (IPTp) with sulfadoxine-pyrimethamine (SP)—a full treatment dose administered to pregnant women during routine ANC visits in the 2nd and 3rd trimesters. IPTp has a protective efficacy of nearly 25% against low birth weight and 21% against neonatal mortality [3].

Plasmodium falciparum resistance to SP results from an ordered accumulation of mutations in two genes, namely P. falciparum dihydrofolate reductase (Pfdhfr) and P. falciparum dihydropteroate synthase (Pfdhps) that code for enzymes targeted by sulfadoxine and pyrimethamine, respectively. Resistance increases with the number of mutant alleles. The presence of five mutant alleles, the dhfr/dhps “quintuple mutant”, including the dhfr substitutions N51I, C59R, and S108N and the dhps substitutions A437G and K540E, are associated with a very high rate of failure when SP is used for the treatment of uncomplicated falciparum malaria [4]. However, SP remains effective for IPTp, even where the prevalence of the quintuple mutant is high, and thus its use continues to be recommended by the WHO [5, 6]. The efficacy of SP for IPTp appears to be compromised in the presence of a sixth mutation at dhps581 (sextuple mutant; A581G); infection with parasites harbouring the sextuple mutant has been associated with increased placental parasitaemia and inflammation and failure of IPTp-SP to improve birth weight [79].

Previous studies have reported that the dhps 581 mutation is common in Tanga Region of Tanzania, with up to 57% of parasites harbouring the sextuple mutant [10]; however, there are limited data from other areas of Tanzania. In light of the possibility that IPTp-SP may provide no benefit to women in areas with a high prevalence of the sextuple mutant, it is critical to define the extent of this mutation. This study aimed to investigate the prevalence of the SP resistance mutations dhps K540E (a surrogate marker for quintuple mutant [4]) and dhps A581G [9] and dhfr I164L [1] (surrogate markers for sextuple mutant parasites, with A581G being the more widely reported) in the parasite population in the Lake Zone (Mwanza, Geita, Mara, and Kagera Regions) and Southern Zone (Lindi, Mtwara, and Ruvuma Regions) of Tanzania.

Methods

Study area

From April to June, 2015, a cross-sectional survey was conducted in 14 health facilities (HFs) in seven regions of mainland Tanzania: all four regions in the Lake Zone (Mwanza, Geita, Mara, and Kagera) and all three regions in the Southern Zone (Lindi, Mtwara, and Ruvuma Regions) (Fig. 1). These regions were selected as they had high prevalence of malaria in the 2011/12 Tanzania HIV and Malaria Indicator Survey (THMIS) [11]. In each region, two health facilities were conveniently sampled from facilities with laboratory and where staff had been previously trained on collection of malaria rapid diagnostic tests (RDTs) for other projects to determine whether the dhpsA581G mutation was present in these regions.
Fig. 1

Location of sampled health facilities with frequency of dhpsA581G mutations

Sample size

It was estimated that a sample size of 100 malaria positive patients from each facility would produce a two-sided 95% confidence interval of 1.6–11.3%, assuming the true proportion of patients infected with a given mutation was 5%. To account for false positive results that may arise by use of RDTs, the target sample size was inflated by 20% to approximately 120 samples per facility.

Study population

All patients age ≥6 months presenting to the outpatient department who were diagnosed with malaria by RDT (Malaria Ag Pf/Pan, SD Bioline®) or blood smear and no self-reported history of recent exposure to SP or trimethoprim-sulfamethoxazole (within the past 2 months if not pregnant, or during the pregnancy, if pregnant) were consented. In each health facility, consenting eligible patients were consecutively enrolled until the required number of DBS samples (120) were obtained.

Sample collection procedures

Two blood spots were collected on filter paper (Whatman no. 3, GE Healthcare Life Sciences) at the time the blood sample was collected for malaria testing; if the RDT was negative or the patient did not consent or otherwise did not meet inclusion criteria, the filter paper was destroyed (Fig. 2). Blood spots from consenting individuals were air dried for 3–4 h on a flat, nonabsorbent surface, then sealed in plastic bags with a desiccant, and stored at room temperature at the study health facility. The samples from all 14 health facilities were packaged in a water-proof container, and shipped to the reference laboratory at the University of North Carolina (UNC) for pooled deep sequencing analyses at three genetic loci associated with sulfadoxine-pyrimethamine (SP) resistance, dhfr164, dhps540, and dhps581 [8, 1214].
Fig. 2

Flowchart for recruiting participants and collecting samples. BS blood smear, DBS dried blood spot, RDT malaria rapid diagnostic test

Molecular methods

Optimizing DNA extraction

120 Dried Blood Spots (DBS) from the Kharumwa health center were used in a preliminary experiment to determine if DNA from batched DBS could be extracted reliably. Each subject’s DBS was punched 3 times. One of these punches was added to a single well of a 96-well plate. The other punches were pooled so that wells would contain either three or ten punches from individuals. The plate was extracted using the Chelex method using overnight incubation at 4 °C on a shaking platform at 700 RPM (TPM2 shaker, Sarstedt, Nümbrecht, Germany) [15]. After extraction, 3, 9 and 30 ml aliquots of supernatant were removed from the n = 1, n = 3, and n = 10 extracts, respectively, and combined with aliquots from similarly extracted samples: 1×: aliquots of all 120 individually extracted samples; 3×: aliquots of all 40 of the 3-punch extracts; and 10× aliquots of all 12 of the 10-punch extracts.

Nested PCR amplification was done on each of the three DNA pools for dhps and dhfr using primers shown in Table 1 based on a previous report [16]. PCR conditions were 95 °C × 2 min, followed by 40 rounds of [95 °C × 30 s → 45 °C × 30 s → 72 °C × 1 min] and a final round of 72 °C for 10 m. Individual indexed libraries for each gene were made for each DBS pooling condition using the NEB Next Ultra DNA Library Prep Kit (New England Biolabs, Ipswich, USA) and pooled in equimolar fashion. Pooled libraries were sequenced on an Illumina MiSeq using 2 × 300 bp reads at the UNC High Throughput Sequencing Facility. Allele frequencies for single nucleotide polymorphisms (SNPs) within the demultiplexed libraries were determined using methods previously described [17, 18].
Table 1

Primers

Primer

Sequence

Descrpition

Pfdhfr-F1

TCCTTTTTATGATGGAACAAG

dhfr outer forward

Pfdhfr-R1

AGTATATACATCGCTAACAGA

dhfr outer reverse

Pfdhfr1-F

TGAGGTTTTTAATAACTACACATTTAGAGGTCT

dhfr inner forward

Pfdhfr1-R

TCGCTAACAGAAATAATTTGATACTCAT

dhfr inner reverse

Pfdhps-F1

AACCTAAACGTGCTGTTCAA

dhps outer forward

Pfdhps-R1

AATTGTGTGATTTGTCCACAA

dhps outer reverse

Pfdhps1-F

TGAAATGATAAATGAAGGTGCTAGTGT

dhps inner forward

Pfdhps1-R

GTTGTGTATTTATTACAACATTTTGATCATTC

dhps inner reverse

Sequencing reads were aligned using bowtie2 with default settings to the P. falciparum 3D7 reference strain and the minor allele frequency (MAF) at all positions across the amplicon was determined using a revised version of our previously described Minor Allele Catcher (MAC) [18]. Bases and reads were excluded from analysis if they had a mapping quality less than or equal to 10, a read length less than or equal to 200 bp, a base quality of less than or equal to 20, a base depth of less than or equal to 5000, if SNPs occurred in the first or last twenty-five bases of a read, or if 30% or fewer of the reads were on the forward or reverse strand.

Extraction and analysis of DBS from 13 other health centres

Based on the results of this pilot (Table 2), DNA from all of the remaining samples was extracted in batches of 10 by health centre. The DNA extracts from each of the health centres were then pooled yielding one amplicon pool for each site (13 pools). Library preps and analysis were performed as above. Data from the Kharumwa health centre, 10-punch extract, was re-run a second time to generate the 14-health center dataset.
Table 2

Malaria testing and positivity rates at selected health centres

Region

District

Health centre

Tested

Positive

Malaria positivity rate (%)

Lake Zone

 Kagera

Karagwe

Kayanga

331

121

37

 Kagera

Muleba

Kaigara

292

120

41

 Mara

Musoma

Murangi

219

121

55

 Mara

Tarime

Sirari

310

122

39

 Geita

Geita

Katoro

238

122

51

 Geita

Nyang′hwale

Kharumwa

230

120

52

 Mwanza

Misungwi

Misasi HC 

187

130

70

 Mwanza

Sengerema

Mwangika

332

121

36

Sub-total Lake Zone

2139

977

46

Southern Zone

 Lindi

Lindi Rural

Kitomanga

199

126

63

 Lindi

Nachingwea

Marambo

244

129

53

 Mtwara

Masasi

Nagaga

222

160

70

 Mtwara

Mtwara

Nanguruwe

215

138

64

 Ruvuma

Songea

Madaba

261

120

46

 Ruvuma

Namtumbo

Namtumbo

295

120

41

Sub-total Southern Zone

1436

793

55

Total

3575

1770

50

Results

A total of 3575 febrile patients were screened, and 1770 were found to be positive for malaria, yielding an overall positivity rate of 49.5% (Table 2). In this pilot work to determine the extraction efficiency of individual versus pooled extraction using the 120 participants from Kharumwa health centre, the proportion of mutant alleles varied only slightly as a result of batching DBS prior to extraction, with 9.4–10.7% alleles mutated at dhps540, 2.8–3.9% at dhps581, and none at dhfr164 (Table 3). As these differences in allele frequencies were considered negligible, DBS from all other health centers were extracted in pools of 10.
Table 3

Distribution of mutants using pools from individually extracted (1×), 3-punch extracted (3×) and 10-punch extracted (10×)

DNA pool

Proportion of mutant alleles

Dhps540

Dhps581

Dhfr164

88.5% (339/383)

2.8% (13/464)

0% (0/319)

88.6% (1014/1145)

3.8% (52/1380)

0.3% (2/586)

10×

89.9% (563/626)

3.3% (25/765)

0% (0/401)

A total of 1750 dried blood spot (DBS) samples were collected (117–160 samples per facility) and used in the final study analysis. Deep sequencing resulted in an average of 341,420 reads at dhfr (range 208,769–644,927) and 168,260 reads at dhps (range 49,419–313,645). The library from Kharumwa was re-sequenced during this project. Based upon this repeat sequencing (Table 4) and the 3 sequences in Table 3, the allele frequencies ±standard deviations for dhps540 and dhps581 mutants were 90.0 ± 2.1% and 3.2 ± 0.7%, respectively. The dhps540 mutation was common across all 14 sites, with allele frequency ranging from 55 to 98.4%, with higher allele frequency at sites in Lake Zone compared to Southern Zone (Table 4). Frequency of the dhps581 mutation ranged from 0 to 2.4%, with the exception of Kayanga health centre (Kagera Region, Lake Zone) where 24.9% of sequences were mutated (Fig. 1). The dhfr164 mutation was detected only at Kanyanga health centre (0.6%).
Table 4

Summary of drug resistance allele frequencies

Region

District

Health centre

Samples (N)

Mutant, % (number of mutant/total sequences)

dhps540

dhps581

dhfr164

Lake Zone

 Kagera

Karagwe

Kayanga

120

89% (154,320/173,457)

24.9% (46,056/185,117)

0.6% (3972/623,835)

 Kagera

Muleba

Kaigara

120

91.2% (270,373/296,323)

1% (31,77/313,669)

0% (123/286,155)

 Mara

Musoma

Murangi

117

86% (160,431/186,561)

0% (24/196,781)

0% (20/327,806)

 Mara

Tarime

Sirari

120

98.4% (262,461/266,632)

0% (71/285,418)

0% (17/249,213)

 Geita

Geita

Katoro

122

97.2% (254,986/262,202)

0.2% (547/280,267)

0% 19/310,706)

 Geita

Nyang’hwale

Kharumwa

120

92.9% (176,055/189,500)

2.4% (4860/201,156)

0% (23/322,015)

 Mwanza

Misungwi

Misasi

130

92.7% (168,879/182,105)

2% (3773/193,348)

0% (15/348,488)

 Mwanza

Sengerema

Mwangika

121

93.2% (177,559/190,591)

0.3% (692/202,087)

0% (24/386,750)

Mean allele frequency Lake Zone*

970

82.6%

3.9%

0.1%

Southern Zone

 Lindi

Lindi Rural

Kitomanga

124

94.4% (162,087/171,760)

0% (47/177,058)

0% (11/225,710)

 Lindi

Nachingwea

Marambo

129

77.3% (35,351/45,753)

0.1% (57/50,926)

0% (41/231,375)

 Mtwara

Masasi

Nagaga

160

68.1% (107,370/157,670)

0% (17/164,456)

0% (46/247,292)

 Mtwara

Mtwara

Nanguruwe

129

73.2% (104,952/143,305)

0.8% (1180/154,626)

0% (17/385,082)

 Ruvuma

Songea

Madaba

120

92% (71,494/77,751)

0% (11/83,706)

0% (13/324,890)

 Ruvuma

Namtumbo

Namtumbo

118

55.7% (26,134/46,901)

0% (12/52,484)

0% (22/318,552)

Mean allele frequency Southern Zone*

780

76.8%

0.2%

0%

* Mean allele frequency for each zone was calculated by averaging the percentage of mutants from each health center

Discussion

In a survey of parasites from patients at 14 health facilities in the Lake and Southern Zones of Tanzania, the dhpsK540E mutation was very common [in 10 facilities (71%) more than 85% of alleles at dhpsK540E were mutated, and nowhere were fewer than 55% mutated], while the dhpsA581G mutation remained rare and focal, with frequency greater than 2.4% in only one facility, Kayanga Health Facility, where nearly 25% of alleles carried the dhpsA581G mutation. The quintuple mutation (represented by the dhpsK540E) has been reported widely across Tanzania, with prevalence ranging from 64 to 98% in one recent report assessing seven regions [10] and 77 to 95% in another [19]. Previous reports from Tanga Region have found a high prevalence of parasites harbouring the dhpsA581G mutation (44% in Korogwe [7], 51% in Muheza [19], and 57% in Bondo [10]). Although the Lake and Southern Zones of Tanzania are not immediately adjacent to Tanga Region, the absence of the dhpsA581G mutation in the majority of sites highlights that there may be considerable geographic micro-heterogeneity. This is supported by data from Kavishe et al., who similarly found the dhpsA581G to be present at high proportion in only Tanga and Kagera Regions, though the prevalence of mutants at their sites were higher than the allele frequencies reported here [10]. This is a reassuring finding, suggesting that IPTp-SP retains efficacy in the majority of Tanzania, but highlighting the need for monitoring in multiple geographic sites.

The pooling and sequencing methodology presented here is a cost-effective alternative to individual allele-specific PCR. Using this 2-step pooling methodology, it was possible to perform 90% fewer DNA extractions and 99% fewer sequencing runs than if samples from each participant had been sequenced individually, saving a substantial amount of both time and money. The individual extraction and bi-directional Sanger sequencing of 1750 samples would cost on the order of $19,000. Here the same data has been compiled for under $4000.

Another advantage of the pooling method is that it allows direct calculation of the allele frequency, rather than prevalence. That is, it calculates the percentage of the parasite population bearing the mutation rather than the prevalence of individuals bearing parasites with mutated alleles. From an evolutionary point of view, the allele frequency of a mutation is more important than prevalence of the mutation because it approximates the likelihood that a mosquito will become infected with a mutant parasite after exposure to a given population.

Allele frequency and prevalence can be different when mixed infections are present and when individuals have infections with varying levels of parasitaemia. Traditionally, prevalence data have to be transformed by a complex equation to yield predicted allele frequencies [20]. However, in general, the allele frequency approximates the prevalence of major strain in a human population, barring any biases [16, 21].

This study has a number of limitations. Samples were collected only from the Lake and Southern Zones, neither of which are immediately adjacent to Tanga region, where the highest prevalence of the dhpsA581G have been reported, thus more studies in those areas are needed to better define the extent of the mutant. With regard to the pooling methodology employed here, while much more cost-effective and time-saving than traditional PCR, it is not possible to trace a parasite strain back to a participant. Also, some haplotypes tend to be amplified better than others, leading to PCR amplification bias. However, newer barcoding methods, such as primer ID [22], compensate for amplification bias and could allow identification of individuals (although DNA would still have to be extracted from individual DBS).

While overall, these data are reassuring with respect to the efficacy of IPTp-SP, there are select areas with a high prevalence of the sextuple mutant, where IPTp-SP may no longer provide a useful benefit against malaria. Spread of this sextuple mutant will threaten the usefulness of SP for IPTp. Given the fact that the quintuple mutant is already found throughout Tanzania, and the sextuple mutant has been found in high prevalence in several sites, continued surveillance in multiple sites, particularly in and around Tanga and Kagera, is warranted to monitor for the spread of the sextuple mutant. The pooling technique presented here provides a highly efficient and cost effective means to screen many samples from multiple sites.

Conclusion

Although the quintuple mutant was very common at all sampled facilities, dhpsA581G remains geographically restricted, suggesting that IPTp-SP remains effective in most of Tanzania. However, additional surveillance, particularly in and around Tanga and Kagera Region is warranted. This can be achieved efficiently through sequencing pooled samples as described here. Finally, a better understanding of the effect of the dhps581 mutant on the efficacy of IPTp-SP is needed.

Abbreviations

BS: 

blood slide

DBS: 

dried blood spot on filter paper

IPTp: 

intermittent preventive treatment in pregnancy

IRS: 

indoor residual spray

MFP: 

malaria focal person

MiP: 

malaria in pregnancy

OPD: 

outpatient department

RDT: 

malaria rapid diagnostic test

SP: 

sulfadoxine-pyrimethamine

WHO: 

World Health Organization

Declarations

Authors’ contributions

JG, JMN, SM, OMM, DSI, RAW designed the study. DSI, SM, OMM, RAW, SL, NK, CK, and LAP oversaw the collection of the samples. SMD, KLT, NJH, JAB, JJJ, and SRM analysed the samples. JG, JMN, and SRM drafted the manuscript which all authors edited and approved. All authors read and approved the final manuscript.

Acknowledgements

We are grateful to the staff of the HCs, NIMR Tanga team (Ezekiel Malecela and August Nyaki), and RTI staff who implemented the study and district malaria focal persons (DMFP) in Lake and Southern Zones for supervising the study.

Competing interests

The authors declare that they have no competing interests.

Availability of data and materials

The datasets generated during the current study are available from the corresponding author on reasonable request.

Disclaimer

The findings and conclusions presented in this manuscript are those of the authors and do not necessarily reflect the official position of the US President’s Malaria Initiative, US Agency for International Development, or US Centers for Disease Control and Prevention.

Ethical approval and consent to participate

The study protocol was approved by the Medical Research Coordinating Committee (MRCC) of the Tanzania National Health Institute for Medical Research (NIMR) [reference NIMR/HQ/R.8a/Vol.IX/1924]. The US Centers for Disease Control and Prevention (CDC) human research protection office reviewed the protocol and granted a non-engaged determination for CDC’s role in this project. Informed consent was obtained from all patients/parents or guardians of patients (for minors) participating in the study.

Funding

This study was made possible through support provided to the Tanzania Vector Control Scale-up Project (Cooperative Agreement 621-A-00-10-00015-00) by the President’s Malaria Initiative (PMI) via the US Agency for International Development (USAID) and an inter-agency agreement with the US Centers for Disease Control and Prevention (CDC).

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Authors’ Affiliations

(1)
RTI International, Dar es Salaam, Tanzania
(2)
National Institute for Medical Research, Tanga, Tanzania
(3)
UNC Gillings School of Global Public Health, Chapel Hill, USA
(4)
National Malaria Control Programme, Dar es Salaam, Tanzania
(5)
US President’s Malaria Initiative/United States Agency for International Development, Dar es Salaam, Tanzania
(6)
US President’s Malaria Initiative, Malaria Branch, Division of Parasitic Diseases and Malaria, US Centers for Disease Control and Prevention, Dar es Salaam, Tanzania
(7)
Program in Bioinformatics and Integrative Biology, University of Massachusetts Medical School, Worcester, USA
(8)
Malaria Branch, Division of Parasitic Diseases and Malaria, US Centers for Disease Control and Prevention, Atlanta, USA

References

  1. Rogerson SJ, Mwapasa V, Meshnick SR. Malaria in pregnancy: linking immunity and pathogenesis to prevention. Am J Trop Med Hyg. 2007;77:14–22.PubMedGoogle Scholar
  2. Desai M, ter Kuile FO, Nosten F, McGready R, Asamoa K, Brabin B, et al. Epidemiology and burden of malaria in pregnancy. Lancet Infect Dis. 2007;7:93–104.View ArticlePubMedGoogle Scholar
  3. Eisele TP, Larsen DA, Anglewicz PA, Keating J, Yukich J, Bennett A, et al. Malaria prevention in pregnancy, birthweight, and neonatal mortality: a meta-analysis of 32 national cross-sectional datasets in Africa. Lancet Infect Dis. 2012;12:942–9.View ArticlePubMedGoogle Scholar
  4. Kublin JG, Dzinjalamala FK, Kamwendo DD, Malkin EM, Cortese JF, Martino LM, et al. Molecular markers for failure of sulfadoxine-pyrimethamine and chlorproguanil-dapsone treatment of Plasmodium falciparum malaria. J Infect Dis. 2002;185:380–8.View ArticlePubMedGoogle Scholar
  5. Gutman J, Mwandama D, Wiegand RE, Ali D, Mathanga DP, Skarbinski J. Effectiveness of intermittent preventive treatment with sulfadoxine-pyrimethamine during pregnancy on maternal and birth outcomes in Machinga district, Malawi. J Infect Dis. 2013;208:907–16.View ArticlePubMedPubMed CentralGoogle Scholar
  6. Kayentao K, Garner P, van Eijk AM, Naidoo I, Roper C, Mulokozi A, et al. Intermittent preventive therapy for malaria during pregnancy using 2 vs 3 or more doses of sulfadoxine-pyrimethamine and risk of low birth weight in Africa: systematic review and meta-analysis. JAMA. 2013;309:594–604.View ArticlePubMedPubMed CentralGoogle Scholar
  7. Minja DTR, Schmiegelow C, Mmbando B, Boström S, Oesterholt M, Magistrado P, et al. Plasmodium falciparum mutant haplotype infection during pregnancy associated with reduced birthweight, Tanzania. Emerg Infect Dis. 2013;19:1446–54.View ArticlePubMed CentralGoogle Scholar
  8. Harrington WE, Mutabingwa TK, Muehlenbachs A, Sorensen B, Bolla MC, Fried M, et al. Competitive facilitation of drug-resistant Plasmodium falciparum malaria parasites in pregnant women who receive preventive treatment. Proc Natl Acad Sci USA. 2009;106:9027–32.View ArticlePubMedPubMed CentralGoogle Scholar
  9. Gutman J, Kalilani L, Taylor S, Zhou Z, Wiegand RE, Thwai KL, et al. The A581G mutation in the gene encoding Plasmodium falciparum dihydropteroate synthetase reduces the effectiveness of sulfadoxine-pyrimethamine preventive therapy in Malawian pregnant women. J Infect Dis. 2015;211:1997–2005.View ArticlePubMedPubMed CentralGoogle Scholar
  10. Kavishe RA, Kaaya RD, Nag S, Krogsgaard C, Notland JG, Kavishe AA, et al. Molecular monitoring of Plasmodium falciparum super-resistance to sulfadoxine–pyrimethamine in Tanzania. Malar J. 2016;15:335.View ArticlePubMedPubMed CentralGoogle Scholar
  11. Tanzania Commission for AIDS (TACAIDS), Zanzibar AIDS Commission (ZAC), National Bureau of, Statistics (NBS), Office of the Chief Government Statistician (OCGS), and ICF International. Tanzania HIV/AIDS and Malaria Indicator Survey 2011–12 [Internet]. Dar es Salaam, Tanzania: TACAIDS, ZAC, NBS, OCGS, and ICF International; 2013. http://www.nbs.go.tz/index.php?option=com_content&view=article&id=353:2011-12-tanzania-hivaids-and-malaria-indicator-survey-thmis-report&catid=55:this&Itemid=145.
  12. Plowe CV, Kublin JG, Doumbo OK. P. falciparum dihydrofolate reductase and dihydropteroate synthase mutations: epidemiology and role in clinical resistance to antifolates. Drug Resist Updates. 1998;1:389–96.View ArticleGoogle Scholar
  13. Gesase S, Gosling RD, Hashim R, Ord R, Naidoo I, Madebe R, et al. High resistance of Plasmodium falciparum to sulphadoxine/pyrimethamine in northern Tanzania and the emergence of dhps resistance mutation at Codon 581. PLoS ONE. 2009;4:e4569.View ArticlePubMedPubMed CentralGoogle Scholar
  14. Naidoo I, Roper C. Mapping, “partially resistant”, “fully resistant”, and “super resistant” malaria. Trends Parasitol. 2013;29:505–15.View ArticlePubMedGoogle Scholar
  15. Taylor SM, Antonia AL, Harrington WE, Goheen MM, Mwapasa V, Chaluluka E, et al. Independent lineages of highly sulfadoxine-resistant Plasmodium falciparum haplotypes, eastern Africa. Emerg Infect Dis. 2014;20:1140–8.View ArticlePubMedPubMed CentralGoogle Scholar
  16. Taylor SM, Parobek CM, Aragam N, Ngasala BE, Mårtensson A, Meshnick SR, et al. Pooled deep sequencing of Plasmodium falciparum isolates: an efficient and scalable tool to quantify prevailing malaria drug-resistance genotypes. J Infect Dis. 2013;208:1998–2006.View ArticlePubMedPubMed CentralGoogle Scholar
  17. Taylor SM, Parobek CM, DeConti DK, Kayentao K, Coulibaly SO, Greenwood BM, et al. Absence of putative artemisinin resistance mutations among Plasmodium falciparum in sub-Saharan Africa: a molecular epidemiologic study. J Infect Dis. 2015;211:680–8.View ArticlePubMedGoogle Scholar
  18. Brazeau NF, Hathaway N, Parobek CM, Lin JT, Bailey JA, Lon C, et al. Longitudinal pooled deep sequencing of the Plasmodium vivax K12 Kelch gene in Cambodia reveals a lack of selection by artemisinin. Am J Trop Med Hyg. 2016;95:1409–12.View ArticlePubMedGoogle Scholar
  19. Baraka V, Ishengoma DS, Fransis F, Minja DTR, Madebe RA, Ngatunga D, et al. High-level Plasmodium falciparum sulfadoxine-pyrimethamine resistance with the concomitant occurrence of septuple haplotype in Tanzania. Malar J. 2015;14:439.View ArticlePubMedPubMed CentralGoogle Scholar
  20. Hastings IM, Smith TA. MalHaploFreq: a computer programme for estimating malaria haplotype frequencies from blood samples. Malar J. 2008;7:130.View ArticlePubMedPubMed CentralGoogle Scholar
  21. Aragam NR, Thayer KM, Nge N, Hoffman I, Martinson F, Kamwendo D, et al. Diversity of T cell epitopes in Plasmodium falciparum circumsporozoite protein likely due to protein-protein interactions. PLoS ONE. 2013;8:e62427.View ArticlePubMedPubMed CentralGoogle Scholar
  22. Jabara CB, Jones CD, Roach J, Anderson JA, Swanstrom R. Accurate sampling and deep sequencing of the HIV-1 protease gene using a primer ID. Proc Natl Acad Sci USA. 2011;108:20166–71.View ArticlePubMedPubMed CentralGoogle Scholar

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