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Mitchell RM, Zhou Z, Sheth M, Sergent S, Frace M, Nayak V, Hu B, Gimnig J, Ter Kuile F, Lindblade K, Slutsker L, Hamel MJ, Desai M, Otieno K, Kariuki S, Vigfusson Y, Shi YP. Development of a new barcode-based, multiplex-PCR, next-generation-sequencing assay and data processing and analytical pipeline for multiplicity of infection detection of Plasmodium falciparum. Malar J 2021; 20:92. [PMID: 33593329 PMCID: PMC7885407 DOI: 10.1186/s12936-021-03624-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2020] [Accepted: 02/04/2021] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND 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. This paper presents new laboratory and analytical methods for estimating the MOI of Plasmodium falciparum. METHODS Based 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. RESULTS The 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. CONCLUSION The 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.
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Affiliation(s)
- Rebecca M Mitchell
- Division of Parasitic Diseases, Center for Global Health, Centers for Disease Control and Prevention (CDC), Atlanta, USA
- Department of Computer Science, Emory University, Atlanta, USA
- School of Nursing, Emory University, Atlanta, USA
| | - Zhiyong Zhou
- Division of Parasitic Diseases, Center for Global Health, Centers for Disease Control and Prevention (CDC), Atlanta, USA
| | - Mili Sheth
- Biotechnology Core Facility Branch, Division of Scientific Resources, CDC, Atlanta, USA
| | - Sheila Sergent
- Division of Parasitic Diseases, Center for Global Health, Centers for Disease Control and Prevention (CDC), Atlanta, USA
| | - Michael Frace
- Biotechnology Core Facility Branch, Division of Scientific Resources, CDC, Atlanta, USA
| | - Vishal Nayak
- Office of Infectious Diseases, National Center for Emerging and Zoonotic Infectious Diseases, CDC, Atlanta, USA
| | - Bin Hu
- Office of Infectious Diseases, National Center for Emerging and Zoonotic Infectious Diseases, CDC, Atlanta, USA
| | - John Gimnig
- Division of Parasitic Diseases, Center for Global Health, Centers for Disease Control and Prevention (CDC), Atlanta, USA
| | | | - Kim Lindblade
- Division of Parasitic Diseases, Center for Global Health, Centers for Disease Control and Prevention (CDC), Atlanta, USA
| | - Laurence Slutsker
- Division of Parasitic Diseases, Center for Global Health, Centers for Disease Control and Prevention (CDC), Atlanta, USA
| | - Mary J Hamel
- Division of Parasitic Diseases, Center for Global Health, Centers for Disease Control and Prevention (CDC), Atlanta, USA
| | - Meghna Desai
- Division of Parasitic Diseases, Center for Global Health, Centers for Disease Control and Prevention (CDC), Atlanta, USA
| | - Kephas Otieno
- Kenya Medical Research Institute, Centre for Global Health Research, Kisumu, Kenya
| | - Simon Kariuki
- Kenya Medical Research Institute, Centre for Global Health Research, Kisumu, Kenya
| | - Ymir Vigfusson
- Department of Computer Science, Emory University, Atlanta, USA.
| | - Ya Ping Shi
- Division of Parasitic Diseases, Center for Global Health, Centers for Disease Control and Prevention (CDC), Atlanta, USA.
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