@inbook{7fbcd01bc3864211a31598735a567e50,
title = "Protocol for Detecting and Sequencing Chikungunya Virus from Field-Collected Mosquitoes",
abstract = "Arboviral diseases represent a major public health challenge, especially in tropical regions where environmental conditions may favor the proliferation and spread of mosquito vectors. Thus, early and accurate detection of chikungunya virus (CHIKV) in mosquito populations can be a valuable tool for effective surveillance of circulating variants and for identifying new viral introductions. Given the challenges of detecting arboviruses in field-captured mosquitoes, we describe an integrated workflow for CHIKV molecular detection and whole-genome sequencing. This protocol includes mosquito homogenization using a bead-based mechanical disruptor, RNA extraction using TRIzol reagent with minor modifications, molecular screening using CHIKV-specific RT-qPCR, and whole-genome amplification followed by sequencing on Illumina platforms. Despite the protocol being optimized for individual mosquitoes, it results in high-quality RNA suitable for both entomological surveillance and genomic analysis.",
keywords = "Arbovirus, Chikungunya virus, RT-qPCR, Surveillance, Vector, Whole-genome sequencing",
author = "Banho, \{Cec{\'i}lia Artico\} and Nascimento, \{Olivia Borghi\} and L{\'i}via Sacchetto and Nikos Vasilakis and Nogueira, \{Mauricio Lacerda\}",
note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2027.",
year = "2027",
doi = "10.1007/978-1-0716-5496-5\_26",
language = "English (US)",
series = "Methods in Molecular Biology",
publisher = "Humana Press Inc.",
pages = "347--364",
booktitle = "Chikungunya Virus",
}