Research Article: Circulation of chikungunya virus East/Central/South African lineage in Rio de Janeiro, Brazil

Date Published: June 11, 2019

Publisher: Public Library of Science

Author(s): Joilson Xavier, Marta Giovanetti, Vagner Fonseca, Julien Thézé, Tiago Gräf, Allison Fabri, Jaqueline Goes de Jesus, Marcos Cesar Lima de Mendonça, Cintia Damasceno dos Santos Rodrigues, Maria Angélica Mares-Guia, Carolina Cardoso dos Santos, Stephane Fraga de Oliveira Tosta, Darlan Candido, Rita Maria Ribeiro Nogueira, André Luiz de Abreu, Wanderson Kleber Oliveira, Carlos F. Campelo de Albuquerque, Alexandre Chieppe, Tulio de Oliveira, Patrícia Brasil, Guilherme Calvet, Patrícia Carvalho Sequeira, Nuno Rodrigues Faria, Ana Maria Bispo de Filippis, Luiz Carlos Junior Alcantara, Luciano Andrade Moreira.

http://doi.org/10.1371/journal.pone.0217871

Abstract

The emergence of chikungunya virus (CHIKV) has raised serious concerns due to the virus’ rapid dissemination into new geographic areas and the clinical features associated with infection. To better understand CHIKV dynamics in Rio de Janeiro, we generated 11 near-complete genomes by means of real-time portable nanopore sequencing of virus isolates obtained directly from clinical samples. To better understand CHIKV dynamics in Rio de Janeiro, we generated 11 near-complete genomes by means of real-time portable nanopore sequencing of virus isolates obtained directly from clinical samples. Our phylogenetic reconstructions indicated the circulation of the East-Central-South-African (ECSA) lineage in Rio de Janeiro. Time-measured phylogenetic analysis combined with CHIKV notified case numbers revealed the ECSA lineage was introduced in Rio de Janeiro around June 2015 (95% Bayesian credible interval: May to July 2015) indicating the virus was circulating unnoticed for 5 months before the first reports of CHIKV autochthonous transmissions in Rio de Janeiro, in November 2015. These findings reinforce that continued genomic surveillance strategies are needed to assist in the monitoring and understanding of arbovirus epidemics, which might help to attenuate public health impact of infectious diseases.

Partial Text

Chikungunya virus (CHIKV) infections have been reported worldwide since the virus was first isolated in Tanzania in 1953 [1]. Over 70 CHIKV epidemics have been reported around the world between 1959 and 2016 [2]. Only in the Americas, more than 1 million cases were notified in 2017, with Brazil reporting 185,593 cases [3, 4].

To better understand the diversity of CHIKV in some of most affected municipalities from Rio de Janeiro, we generated 11 CHIKV near-complete genomes (coverage range 62%-83%, mean = 73%) from serum samples using a nanopore sequencing approach [31]. This genome coverage obtained is considered sufficient to perform phylogenetic inferences, according to a study that showed the occurrence of a decrease in phylogenetic accuracy when genome coverage is reduced from 40% to 20% [42].

In this study, by performing real-time portable nanopore sequencing, we generated 11 new CHIKV near-complete genomic sequences from 2016–2018 collected in several municipalities in the Rio de Janeiro state. The generated genomic data allowed us to estimate the introduction date of the ECSA lineage in Rio de Janeiro to June 2015, suggesting an undetected circulation of the virus for 5 months before the first reports of CHIKV transmission in Rio de Janeiro [23, 24]. According to the Ministry of Health epidemiological bulletin, CHIKV autochthony in Rio de Janeiro was reported in the 47th epidemiological week of 2015, around November 2015. Prior to that, only imported cases had been registered. Our estimates indicate a more recent introduction event of the ECSA lineage in Rio de Janeiro compared to a recently published study [32]. In our analysis we used a larger and updated dataset including recently published 18 new CHIKV ECSA sequences from northern region of Brazil. The substantial difference (around 17 months) between Souza et al. (2019) [32] estimates and ours might reflect evolutionary models choice and the analyzed dataset. Nevertheless, our results are in agreement with Nunes et al. (2015) [15] and the epidemiological data reported by the Brazilian health system [23, 24].

 

Source:

http://doi.org/10.1371/journal.pone.0217871