Abstract
The Amazon basin is home to numerous arthropod-borne viral pathogens that cause febrile disease in humans. Among these,
(OROV) is a relatively understudied member of the genus
, family
, that causes periodic outbreaks in human populations in Brazil and other South American countries. Although several studies have described the genetic diversity of the virus, the evolutionary processes that shape the OROV genome remain poorly understood. Here, we present a comprehensive study of the genomic dynamics of OROV that encompasses phylogenetic analysis, evolutionary rate estimates, inference of natural selective pressures, recombination and reassortment, and structural analysis of OROV variants. Our study includes all available published sequences, as well as a set of new OROV genome sequences obtained from patients in Ecuador, representing the first set of genomes from this country. Our results show differing evolutionary processes on the three segments that comprise the viral genome. We infer differing times of the most recent common ancestors of the genome segments and propose that this can be explained by cryptic reassortment. We also present the discovery of previously unobserved putative N-linked glycosylation sites, as well as codons that evolve under positive selection on the viral surface proteins, and discuss the potential role of these features in the evolution of OROV through a combined phylogenetic and structural approach.
The emergence and reemergence of pathogens such as Zika virus, chikungunya virus, and yellow fever virus have drawn attention toward other cocirculating arboviruses in South America. Oropouche virus (OROV) is a poorly studied pathogen responsible for over a dozen outbreaks since the early 1960s and represents a public health burden to countries such as Brazil, Panama, and Peru. OROV is likely underreported since its symptomatology can be easily confounded with other febrile illnesses (e.g., dengue fever and leptospirosis) and point-of-care testing for the virus is still uncommon. With limited data, there is a need to optimize the information currently available. Analysis of OROV genomes can help us understand how the virus circulates in nature and can reveal the evolutionary forces that shape the genetic diversity of the virus, which has implications for molecular diagnostics and the design of potential vaccines.