Assessment of a multiplex PCR and Nanopore-based method for dengue virus sequencing in Indonesia

Abstract

Background Dengue virus (DENV) infects hundreds of thousands of people annually in Indonesia. However, DENV sequence data from the country are limited, as samples from outbreaks must be shipped across long-distances to suitably equipped laboratories to be sequenced. This approach is time-consuming, expensive, and frequently results in failure due to low viral load or degradation of the RNA genome. Methods We evaluated a method designed to address this challenge, using the ‘Primal Scheme’ multiplex PCR tiling approach to rapidly generate short, overlapping amplicons covering the complete DENV coding-region, and sequencing the amplicons on the portable Nanopore MinION device. The resulting sequence data was assessed in terms of genome coverage, consensus sequence accuracy and by phylogenetic analysis. Results The multiplex approach proved capable of producing near complete coding-region coverage from all samples tested ($$ \overline{x} $$x¯ = 99.96%, n = 18), 61% of which could not be fully amplified using the current, long-amplicon PCR, approach. Nanopore-generated consensus sequences were found to be between 99.17–99.92% identical to those produced by high-coverage Illumina sequencing. Consensus accuracy could be improved by masking regions below 20X coverage depth (99.69–99.92%). However, coding-region coverage was reduced at this depth ($$ \overline{x} $$x¯ = 93.48%). Nanopore and Illumina consensus sequences generated from the same samples formed monophyletic clades on phylogenetic analysis, and Indonesian consensus sequences accurately clustered by geographical origin. Conclusion The multiplex, short-amplicon approach proved superior for amplifying DENV genomes from clinical samples, particularly when the virus was present at low concentrations. The accuracy of Nanopore-generated consensus sequences from these amplicons was sufficient for identifying the geographic origin of the samples, demonstrating that the approach can be a useful tool for identifying and monitoring DENV clades circulating in low-resource settings across Indonesia. However, the inaccuracies in Nanopore-generated consensus sequences mean that the approach may not be appropriate for higher resolution transmission studies, particularly when more accurate sequencing technologies are available.

An investig-ation into the epidemiology of chikungunya virus across neglected regions of Indonesia

“…De-multiplexing was performed using Qcat v1.0.7 (ONT) with default settings and the de-multiplexed FASTQ sequences were aligned to the CHIKV RefSeq sequence (NC_004162.2) using BWA mem v0.7.17 (option -x ont2d) [26]. Consensus genome sequences were generated from alignment files using samtools as previously described [27]. Finally, the consensus genomes were aligned using MAFFT v7.427 [28] and any major discrepancies such as indels or multiple consecutive SNPs were manually verified or corrected by referring back to the BAM read alignment file, viewed using Tablet PLOS NEGLECTED TROPICAL DISEASES v1.19.09.03 [29].…”

A importância da utilização de técnicas moleculares no diagnóstico laboratorial de Dengue virus: uma revisão

“…Esta técnica foi utilizada por 5 (10%) estudos, nos quais foi possível identificar pacientes coinfectados com Dengue e Chikungunya virus, com evolução para manifestações atípicas de gravidade, tais como: encefalite, polineuropatia, alterações hemorrágicas, no qual uma pequena parcela dos casos evoluíram ao óbito (Sucipto et al, 2018;Suwanmanee et al,2018;Boga et al, 2019;Stubbs et al,2020).…”

InterARTIC: an interactive web application for whole-genome nanopore sequencing analysis of SARS-CoV-2 and other viruses

“…Viral whole-genome sequencing (WGS) has become a critical tool used to guide local, national and international public health responses to the ongoing COVID-19 pandemic, as well as Ebola, Zika, Dengue and other viral epidemics ( du Plessis et al , 2021; Eden et al , 2020 ; Fauver et al , 2020 ; Gonzalez-Reiche et al , 2020 ; Gudbjartsson et al , 2020 ; Lu et al , 2020 ; Meredith et al , 2020 ; Msomi et al , 2020 ; Quick et al , 2016 , 2017 ; Rockett et al , 2020 ; Stubbs et al , 2020 ). Viral WGS is used to define the phylogenetic structure of disease outbreaks in order to better understand geographical spread, resolve transmission networks and infer the origin of unknown cases ( Eden et al , 2020 ; Fauver et al , 2020 ; Gonzalez-Reiche et al , 2020 ; Rockett et al , 2020 ).…”