The changing landscape of Plasmodium falciparum drug resistance in the Democratic Republic of Congo

With 17 million confirmed cases in 2016, the burden of malaria in the Democratic Republic of the Congo is one of the highest in the world [1]. Understanding malaria dynamics in DRC is critical in eliminating malaria from sub-Saharan Africa. Previous studies of P. falciparum genetic diversity within the DRC have shown a mixture of both West and East African strains, indicating that the DRC serves as a nexus of regional transmission, incorporating parasites from both sides of the continent [2‐4]. Insights from genetic P. falciparum studies within the DRC therefore have important implications for reducing disease burden within the country and for Central and sub-Saharan Africa.

Efforts to halt transmission in the DRC, and across Africa, are being threatened by growing resistance to commonly used antimalarial drugs [5, 6]. Molecular markers can be used to identify resistant infections to monitor the spread of resistance [7, 8]. These markers include mutations in the dihydropteroate synthase (pfdhps) gene, which, along with mutations of the dihydrofolate reductase (pfdhfr) gene, confer resistance to sulfadoxine [7‐9]. Specifically, the pfdhps A437G, K540E, and A581G mutations are associated with sulfadoxine/pyrimethamine (SP) treatment failure [8, 9]. Co-occurrence of the K540E and A581G mutations has been associated with failure of SP for intermittent preventive therapy during pregnancy (IPTp), a program recommended for all pregnant women in the DRC [10, 11]. Additionally, mutations of the chloroquine resistance transporter (pfcrt) gene, specifically mutations in amino acids 72–76 (wild type CVMNK), confer chloroquine resistance [12, 13]. Past work has demonstrated that the K76 T mutation alone increases the risk of chloroquine treatment failure, though a K76 T-containing CVIET triple mutant haplotype has emerged in many African countries, including the DRC [5, 7, 14]. Studies have demonstrated that this haplotype is also associated with amodiaquine treatment failure; amodiaquine is used as part of the first line therapy for malaria in the DRC [10, 15‐17]. Additionally, another pfcrt haplotype, SVMNT, is also associated with resistance to amodiaquine [12, 17, 18]. Monitoring these molecular markers is critical for halting the spread of resistance.

Studies conducted in the DRC have demonstrated spatial structure of parasites resistant to SP and chloroquine [5, 6, 19]. Amongst adult respondents to the 2007 Demographic and Health Survey (DHS), those infected with parasites with a single pfdhps A437G mutation were spread throughout the country, though mostly located in the western part of the DRC [6]. Co-occurring K540E and A581G mutants were rarer, but showed geographic clustering in the northeast region of the country [6]. Past studies from the DRC have demonstrated that the pfdhfr mutations are nearly fixed within the population [20]. There was no apparent clustering of the pfcrt CVIET haplotype [21]. More recent work demonstrated similar patterns for pfdhps mutations amongst children sampled in 2013 [19]. However, the pfcrt CVIET haplotype displayed a pattern of concentrated cases on the eastern and western borders, with fewer mutations observed in the center of the DRC [19]. These findings highlight hotspots of resistance within the DRC.

Previous geospatial studies of drug resistance in the DRC often do not use population based samples and are therefore not necessarily nationally representative [22]. Better estimates of the burden and distribution of these mutations can be obtained using nationally representative surveys [19, 23]. This is the first study, to our knowledge, that uses data from a nationally representative database to evaluate risk factors for carrying a resistant infection and to study longitudinal changes in resistance.

Here, we describe the changing spatial patterns of SP and chloroquine resistance over time in the DRC by comparing samples from the 2013–2014 DHS to previously published data drawn from the 2007 DHS. We begin by describing the proportion of single and co-occurring mutations in both 2007 and in 2013. Next, we present an epidemiologic risk factor analysis to identify covariates associated with increased prevalence of resistant infections. The findings from this analysis will help identify individuals and communities that may be of higher risk for resistant infections. Finally, we use Markov chain Monte Carlo to fit a spatial-temporal model to the observed data to explore whether the geographic range of drug resistant mutations has shifted between 2007 and 2013. This model allows us to directly compare allele frequencies across space and time.

Monitoring the changing spatial distribution of drug resistance markers is necessary for developing efficient interventions to halt the spread of resistance and eliminate malaria. Here, we leverage geolocated samples from the DHS to measure resistance mutations across the DRC and map changes that occurred between 2007 and 2013 [6, 19, 21]. Studies using nationally representative samples like the DHS are less susceptible to selection bias; however, longitudinal comparisons of DHS surveys have been impeded by the fact that the individual survey clusters change between surveys. Here, we use a spatial prediction model that overcomes this by assuming a continuous surface of underlying allele frequencies, allowing us to integrate information at different points in space and time.

This study found that the 540 and 581 pfdhps mutations have increased in the DRC since 2007, both in numbers and in geographic spread. This agrees with recent findings of an increase in pfdhps mutations between 2014 and 2015 amongst individuals living in southwest DRC [45]. Evidence of geographic expansion from the eastern part of DRC is also supported by previous research that demonstrated higher prevalence of both mutations in East Africa compared to West Africa [9, 46]. This expansion is particularly concerning as these mutations are associated with SP failure during IPTp [11, 19, 46]. The risk factor analysis indicates that these increases may be in part driven by SP use, which was associated with increased prevalence of pfdhps mutations. Further, this study indicates that increasing community level drug use, not necessarily individual use, is associated with increases in resistance. This is consistent with previous work that demonstrated associations between community level interventions and malaria risk [34, 35].

Chloroquine resistance has remained relatively steady since 2007; the proportion of CVIET parasites is unchanged and the spatial distribution remains similar. These findings are troubling as the DRC halted chloroquine use as a first line treatment in 2001 due to concerns about growing resistance [47, 48]. This sustained resistance may be in part driven by demographic factors; the risk factor models results indicate that cluster-level education and wealth are associated with chloroquine resistance. There may also be unregulated chloroquine use, as has been reported in other sub-Saharan African countries [49]. Additionally, there is evidence that the CVIET haplotype is associated with amodiaquine resistance [15, 16, 50]. Since amodiaquine is used as part of the first line treatment ASAQ in the DRC, this association may explain why the prevalence of CVIET has remained steady over time [16, 48, 50]. Reported ASAQ use was too low in this study for us to evaluate this relationship statistically. However, we did not detect the SVMNT haplotype, also found to be associated with ASAQ resistance, in this population [17, 18].

The findings from this study have direct implications for malaria control programs in the DRC. As mentioned, SP is still used in the DRC as the primary drug for IPTp [47, 48]. Increasing SP resistance may threaten these preventive efforts. Additionally, though chloroquine is no longer a recommended treatment for malaria, reports from other sub-Saharan African countries show a steep drop in the proportion of resistant parasites after ending chloroquine use [48, 51, 52]. The sustained prevalence of chloroquine resistance seen in this study is alarming and warrants further investigation.

Effective monitoring of drug resistance requires sensitive molecular tools that can accommodate a large number of samples. Using MIPs to amplify resistance loci allows for highly multiplexed and efficient deep sequencing of Plasmodia. This study demonstrates the utility of MIPs for drug resistance surveillance, and the ability to answer critical epidemiological questions. This novel method can also be used to investigate questions of parasite population structure, gene flow, and selective sweeps, amongst others. The spatial-temporal approach used here also represents a step forward compared with previous mapping efforts [19]. The random Fourier features (RFF) method allows us to explore complex models in a computationally efficient way, thereby reducing the time and resources required to perform this kind of advanced spatial analysis and opening the door to much larger datasets in the future.

There are several limitations to this study. First, we only have access to a relatively small number of samples distributed over a wide geographic area, and this is reflected in the large credible intervals around our spatial-temporal predictions. We can therefore only draw large-scale conclusions about changes that have occurred over the study time period, based on patterns that are consistent over the majority of posterior draws. Second, this study compared genotype data generated using different approaches: data from 2013 to 2014 was obtained using MIPs and Illumina sequencing, while data from 2007 was obtained with standard PCR amplification and alternate sequencing methods. However, the sequencing coverage is approximately the same across studies, providing assurance that the methods are comparable. Additionally, the MIPs did not amplify across all of pfdhps in a single sequence but rather used multiple MIP probes to target the regions of interest. Therefore, we could not create true haplotypes across pfdhps.

The findings from this study indicate that the prevalence and geographic spread of SP resistance increased between 2007 and 2013. In contrast, the proportion and pattern of chloroquine resistance stayed the same, potentially a result of ASAQ use or informal chloroquine use. These findings indicate a need to continue monitoring these resistant mutations to prevent additional spread, and to further investigate the factors driving these patterns.