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.
Highlights
Vectors transmit various viral infectious diseases of great public health concern [1]
Due to the lack of studies evaluating extraction methods for viral RNA recovery in vectors by metagenomic next generation sequencing (mNGS), our study aimed to evaluate the performance of six different nucleic acid (NA) extraction methods and systematically study their effects using homogenized arthropods
We demonstrated that the selected extraction method for viral RNA recovery in homogenized arthropods determines the reliability of qPCR and mNGS results
Summary
Vectors transmit various viral infectious diseases of great public health concern [1]. Ticks and mosquitoes are known to be the most important vectors. Identification and characterization of these viral genomes is widely based on sequencing methods to determine diversity of the virus and the epidemiological relationship between isolates within the population [7,8,9]. With mounting evidence of unbiased identification of pathogens, metagenomic generation sequencing (mNGS) is a powerful tool to strengthen surveillance and rapidly respond to emerging viral vector-borne pathogens. Strengthening the identification and characterization of viral pathogens in vectors is vital in ensuring epidemic control
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