Evidence map›Paper›PMID 33593329›Full record

ArticleMalaria journal2021

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.

Rebecca M Mitchell, Zhiyong Zhou, Mili Sheth, Sheila Sergent, Michael Frace, Vishal Nayak, Bin Hu, John Gimnig, Feiko Ter Kuile, Kim Lindblade and 7 more

Open access · goldAbstract read
In one paragraph

Article in Malaria journal, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.6field-weighted citation impact, top 31% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed, 5 citations in OpenAlex.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

17 authors at 7 institutions in 3 countries.

Rebecca M Mitchell *Division of Parasitic Diseases, Center for Global Health, Centers for Disease Control and Prevention (CDC), Atlanta, USA.
Zhiyong Zhou *Division of Parasitic Diseases, Center for Global Health, Centers for Disease Control and Prevention (CDC), Atlanta, USA.
Mili ShethBiotechnology Core Facility Branch, Division of Scientific Resources, CDC, Atlanta, USA.
Sheila SergentDivision of Parasitic Diseases, Center for Global Health, Centers for Disease Control and Prevention (CDC), Atlanta, USA.
Michael FraceBiotechnology Core Facility Branch, Division of Scientific Resources, CDC, Atlanta, USA.
Vishal NayakOffice of Infectious Diseases, National Center for Emerging and Zoonotic Infectious Diseases, CDC, Atlanta, USA.
Bin HuOffice of Infectious Diseases, National Center for Emerging and Zoonotic Infectious Diseases, CDC, Atlanta, USA.
John GimnigDivision of Parasitic Diseases, Center for Global Health, Centers for Disease Control and Prevention (CDC), Atlanta, USA.
Feiko Ter KuileLiverpool School of Tropical Medicine, Liverpool, UK.
Kim LindbladeDivision of Parasitic Diseases, Center for Global Health, Centers for Disease Control and Prevention (CDC), Atlanta, USA.
Laurence SlutskerDivision of Parasitic Diseases, Center for Global Health, Centers for Disease Control and Prevention (CDC), Atlanta, USA.
Mary J HamelDivision of Parasitic Diseases, Center for Global Health, Centers for Disease Control and Prevention (CDC), Atlanta, USA.
Meghna DesaiDivision of Parasitic Diseases, Center for Global Health, Centers for Disease Control and Prevention (CDC), Atlanta, USA.
Kephas OtienoKenya Medical Research Institute, Centre for Global Health Research, Kisumu, Kenya.
Simon KariukiKenya Medical Research Institute, Centre for Global Health Research, Kisumu, Kenya.
Ymir VigfussonDepartment of Computer Science, Emory University, Atlanta, USA. ymir.vigfusson@emory.edu.
Ya Ping ShiDivision of Parasitic Diseases, Center for Global Health, Centers for Disease Control and Prevention (CDC), Atlanta, USA. yps0@cdc.gov.
Center for Global Health · USCenters for Disease Control and Prevention · USKenya Medical Research Institute · KEEmory University · USLiverpool School of Tropical Medicine · GBNational Center for Emerging and Zoonotic Infectious Diseases · USOffice of Infectious Diseases · US

Funding

CDC HHS AMD-132incNational Science Foundation CAREER grant #1553579
6 · The paper itself

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.

Indexed as

DNA Barcoding, TaxonomicHigh-Throughput Nucleotide SequencingMalaria, FalciparumMultiplex Polymerase Chain ReactionPlasmodium falciparumHaplotype and strainMOI estimationMultiplicity of infectionPlasmodium falciparumSTIMStrainReconTransmission

Identifiers

PMID33593329
PMCPMC7885407
OpenAlexW3130943929

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.