Abstract

BackgroundSimultaneous infection with multiple malaria parasite strains is common in high transmission areas. Quantifying the number of strains per host, or the multiplicity of infection (MOI), provides additional parasite indices for assessing transmission levels but it is challenging to measure accurately with current tools. This paper presents new laboratory and analytical methods for estimating the MOI of Plasmodium falciparum.MethodsBased on 24 single nucleotide polymorphisms (SNPs) previously identified as stable, unlinked targets across 12 of the 14 chromosomes within P. falciparum genome, three multiplex PCRs of short target regions and subsequent next generation sequencing (NGS) of the amplicons were developed. A bioinformatics pipeline including B4Screening pathway removed spurious amplicons to ensure consistent frequency calls at each SNP location, compiled amplicons by SNP site diversity, and performed algorithmic haplotype and strain reconstruction. The pipeline was validated by 108 samples generated from cultured-laboratory strain mixtures in different proportions and concentrations, with and without pre-amplification, and using whole blood and dried blood spots (DBS). The pipeline was applied to 273 smear-positive samples from surveys conducted in western Kenya, then providing results into StrainRecon Thresholding for Infection Multiplicity (STIM), a novel MOI estimator.ResultsThe 24 barcode SNPs were successfully identified uniformly across the 12 chromosomes of P. falciparum in a sample using the pipeline. Pre-amplification and parasite concentration, while non-linearly associated with SNP read depth, did not influence the SNP frequency calls. Based on consistent SNP frequency calls at targeted locations, the algorithmic strain reconstruction for each laboratory-mixed sample had 98.5% accuracy in dominant strains. STIM detected up to 5 strains in field samples from western Kenya and showed declining MOI over time (q < 0.02), from 4.32 strains per infected person in 1996 to 4.01, 3.56 and 3.35 in 2001, 2007 and 2012, and a reduction in the proportion of samples with 5 strains from 57% in 1996 to 18% in 2012.ConclusionThe combined approach of new multiplex PCRs and NGS, the unique bioinformatics pipeline and STIM could identify 24 barcode SNPs of P. falciparum correctly and consistently. The methodology could be applied to field samples to reliably measure temporal changes in MOI.

Highlights

  • Simultaneous infection with multiple malaria parasite strains is common in high transmission areas

  • The methodology could be applied to field samples to reliably measure temporal changes in multiplicity of infection (MOI)

  • This study described the development of laboratory assays with multiplex Polymerase chain reaction (PCR) followed by next generation sequencing (NGS), a unique bioinformatic process with B4Screening pathway and a novel threshold-calibrated MOI estimation method capable of detecting multiple-strain infections of P. falciparum parasites in artificially mixed laboratory strains and field isolates

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Summary

Introduction

Simultaneous infection with multiple malaria parasite strains is common in high transmission areas. Quantifying the number of strains per host, or the multiplicity of infection (MOI), provides additional parasite indices for assessing transmission levels but it is challenging to measure accurately with current tools. EIR is not suitable for obtaining estimates of transmission level rapidly and its accuracy has been questioned in some studies [3,4,5] Epidemiological approaches, such as cohort infection incidence studies, parasite prevalence surveys and passive case incidence data, are frequently used to measure transmission levels, but cohort incidence studies with relatively high precision are expensive and time consuming, and other malaria metrics may be subject to a number of biases [3]. The MOI metric, assuming adequate precision, can uncover parasite strain populations that may enhance understanding of transmission dynamics [3]

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