Comparative analysis of targeted next-generation sequencing for Plasmodium falciparum drug resistance markers

Drug resistance profiling of asymptomatic and low-density Plasmodium falciparum malaria infections on Ngodhe island, Kenya, using custom dual-indexing next-generation sequencing

Malaria control initiatives require rapid and reliable methods for the detection and monitoring of molecular markers associated with antimalarial drug resistance in Plasmodium falciparum parasites. Ngodhe island, Kenya, presents a unique malaria profile, with lower P. falciparum incidence rates than the surrounding region, and a high proportion of sub-microscopic and low-density infections. Here, using custom dual-indexing and Illumina next generation sequencing, we generate resistance profiles on seventy asymptomatic and low-density P. falciparum infections from a mass drug administration program implemented on Ngodhe island between 2015 and 2016. Our assay encompasses established molecular markers on the Pfcrt, Pfmdr1, Pfdhps, Pfdhfr, and Pfk13 genes. Resistance markers for sulfadoxine-pyrimethamine were identified at high frequencies, including a quintuple mutant haplotype (Pfdhfr/Pfdhps: N51I, C59R, S108N/A437G, K540E) identified in 62.2% of isolates. The Pfdhps K540E biomarker, used to inform decision making for intermittent preventative treatment in pregnancy, was identified in 79.2% of isolates. Several variants on Pfmdr1, associated with reduced susceptibility to quinolones and lumefantrine, were also identified (Y184F 47.1%; D1246Y 16.0%; N86 98%). Overall, we have presented a low-cost and extendable approach that can provide timely genetic profiles to inform clinical and surveillance activities, especially in settings with abundant low-density infections, seeking malaria elimination.

The many definitions of multiplicity of infection

The presence of multiple genetically different pathogenic variants within the same individual host is common in infectious diseases. Although this is neglected in some diseases, it is well recognized in others like malaria, where it is typically referred to as multiplicity of infection (MOI) or complexity of infection (COI). In malaria, with the advent of molecular surveillance, data is increasingly being available with enough resolution to capture MOI and integrate it into molecular surveillance strategies. The distribution of MOI on the population level scales with transmission intensities, while MOI on the individual level is a confounding factor when monitoring haplotypes of particular interests, e.g., those associated with drug-resistance. Particularly, in high-transmission areas, MOI leads to a discrepancy between the likelihood of a haplotype being observed in an infection (prevalence) and its abundance in the pathogen population (frequency). Despite its importance, MOI is not universally defined. Competing definitions vary from verbal ones to those based on concise statistical frameworks. Heuristic approaches to MOI are popular, although they do not mine the full potential of available data and are typically biased, potentially leading to misinferences. We introduce a formal statistical framework and suggest a concise definition of MOI and its distribution on the host-population level. We show how it relates to alternative definitions such as the number of distinct haplotypes within an infection or the maximum number of alleles detectable across a set of genetic markers. It is shown how alternatives can be derived from the general framework. Different statistical methods to estimate the distribution of MOI and pathogenic variants at the population level are discussed. The estimates can be used as plug-ins to reconstruct the most probable MOI of an infection and set of infecting haplotypes in individual infections. Furthermore, the relation between prevalence of pathogenic variants and their frequency (relative abundance) in the pathogen population in the context of MOI is clarified, with particular regard to seasonality in transmission intensities. The framework introduced here helps to guide the correct interpretation of results emerging from different definitions of MOI. Especially, it excels comparisons between studies based on different analytical methods.