开发一种新的基于条码的多重聚合酶链反应-下一代测序检测方法，以及数据处理和分析流程，用于检测疟原虫感染的多重性。

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.

机构信息

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.

出版信息

Malar J. 2021 Feb 16;20(1):92. doi: 10.1186/s12936-021-03624-2.

DOI:10.1186/s12936-021-03624-2

PMID:33593329

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7885407/

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.

摘要

背景

在高传播地区，疟原虫的多重感染很常见。量化每个宿主中的寄生虫株数或感染的多重性（MOI），可以为评估传播水平提供额外的寄生虫指标，但目前的工具很难准确测量。本文提出了一种新的实验室和分析方法来估计恶性疟原虫的 MOI。

方法

基于先前在恶性疟原虫基因组的 14 条染色体中的 12 条染色体中鉴定出的 24 个稳定的、不相关的靶标单核苷酸多态性（SNP），设计了三个短靶区的多重 PCR 和随后的扩增子下一代测序（NGS）。一个包括 B4Screening 通路的生物信息学管道消除了虚假的扩增子，以确保在每个 SNP 位置上一致的频率调用，通过 SNP 位点多样性对扩增子进行编译，并进行算法单倍型和菌株重建。该管道通过在不同比例和浓度下的培养实验室菌株混合物、有和没有预扩增、使用全血和干血斑（DBS）生成的 108 个样本进行了验证。该管道应用于来自肯尼亚西部的 273 个涂片阳性样本，然后将结果输入到一种新的 MOI 估计器 StrainRecon Thresholding for Infection Multiplicity（STIM）中。

结果

在使用该管道的一个样本中，24 个条形码 SNP 成功地在恶性疟原虫的 12 条染色体上均匀地识别出来。虽然预扩增和寄生虫浓度与 SNP 读取深度呈非线性相关，但不影响 SNP 频率调用。基于在靶向位置上一致的 SNP 频率调用，每个实验室混合样本的算法菌株重建在优势菌株中具有 98.5%的准确率。STIM 在来自肯尼亚西部的现场样本中检测到多达 5 株，并且 MOI 随时间呈下降趋势（q < 0.02），从 1996 年每个感染者 4.32 株下降到 2001 年、2007 年和 2012 年的 4.01、3.56 和 3.35，5 株样本的比例从 1996 年的 57%下降到 2012 年的 18%。