Back

Molecular Surveillance Identifies Evidence of Getah Virus (GETV) in Mosquito Vectors in Alabama, USA

Li, T.; Wu, H.; McDaniel, T.; Mishra, M.; Wang, C.; Matthews, Q.; Bernard, E.; Pandit, R.

2025-12-09 microbiology
10.64898/2025.12.04.692395 bioRxiv
Show abstract

The Getah virus (GETV) is a mosquito-borne RNA virus in the Togaviridae family and the Alphavirus genus, associated with severe disease outbreaks in livestock across Asia-Pacific regions. Since its first isolation in Malaysia in 1955, GETV has been reported in Eurasia and the South Pacific, yet documented cases in the United States remain exceptionally rare, leaving major gaps in regional vector competence and surveillance data. To evaluate the potential of local mosquito populations as GETV carriers, field collections were conducted in Montgomery and Pike Road neighborhoods of Alabama from 2023 to 2024, capturing multiple mosquito species for molecular screening. Initial real-time qPCR assays on 200 pooled and individual samples suggested that approximate 30% of specimens demonstrated possible GETV carriage potential. To validate viral presence, two distinct primer sets were designed to amplify viral genome fragments. Mosquito RNA was reverse-transcribed into cDNA, followed by conventional PCR. The first PCR, targeting [~]280 bp, produced single or multiple amplicon bands in 32% of samples via gel electrophoresis. These positive cDNAs were re-amplified with a second primer pair targeting a [~]430 bp fragment from a separate genomic region, yielding confirmatory bands in 30% of specimens. Amplified products were purified and Sanger sequenced, revealing approximate 95% nucleotide similarity to the wild-type GETV reference genome. This study delivers the first field-based molecular evidence supporting GETV detection in Alabama mosquitoes, signaling either localized emergence or potential introduction of the virus. These findings underscore the need for expanded vector competence profiling, arboviral surveillance, and livestock disease preparedness in the southeastern United States, strengthening both public health readiness and state-level vector monitoring strategies.

Matching journals

The top 2 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.