Evaluation of Viral RNA Recovery Methods in Vectors by Metagenomic Sequencing

Abstract

Identification and characterization of viral genomes in vectors including ticks and mosquitoes positive for pathogens of great public health concern using metagenomic next generation sequencing (mNGS) has challenges. One such challenge is the ability to efficiently recover viral RNA which is typically dependent on sample processing. We evaluated the quantitative effect of six different extraction methods in recovering viral RNA in vectors using negative tick homogenates spiked with serial dilutions of tick-borne encephalitis virus (TBEV) and surrogate Langat virus (LGTV). Evaluation was performed using qPCR and mNGS. Sensitivity and proof of concept of optimal method was tested using naturally positive TBEV tick homogenates and positive dengue, chikungunya, and Zika virus mosquito homogenates. The amount of observed viral genome copies, percentage of mapped reads, and genome coverage varied among different extractions methods. The developed Method 5 gave a 120.8-, 46-, 2.5-, 22.4-, and 9.9-fold increase in the number of viral reads mapping to the expected pathogen in comparison to Method 1, 2, 3, 4, and 6, respectively. Our developed Method 5 termed ROVIV (Recovery of Viruses in Vectors) greatly improved viral RNA recovery and identification in vectors using mNGS. Therefore, it may be a more sensitive method for use in arbovirus surveillance.

Keywords

• vector-borne viruses
• vectors
• sample processing
• NGS
• metagenomics