Monitoramento da evolução viral e aprimoramento de estratégias de sequenciamento para o vírus da dengue no Brasil

Abstract

The genetic diversity of the dengue virus (DENV) in Brazil poses challenges for epidemiological surveillance and disease control. This study investigated the genetic variability of DENV serotypes I and II, aiming to develop genomic sequencing panels optimized for the Brazilian context and to assess the impact of this diversity on surveillance strategies. The results showed significant regional genetic variability, affecting the compatibility of sequencing primers. This genetic analysis further revealed specific mutations across different regions and time periods, highlighting the importance of considering the local context when interpreting results. Panels specifically developed for Brazil, considering this diversity, demonstrated superior in silico performance compared to international panels. Additionally, the choice of amplicon size (400, 600, or 800 bp) proved to be a crucial factor, requiring a balance between sample quality and panel durability, especially for degraded samples. The panels achieved average genomic coverages of 97.7%, 97.3%, and 96.6%, respectively, with complete failures observed in two samples and low coverage in another, regardless of the protocol used. The analysis also identified a small number of defective primers, with no clear pattern among the different amplicon sizes. The introduction of the cosmopolitan genotype (DENV2-II) may have contributed to the increase in cases, although the possibility of reinfection by the same serotype still needs to be explored in future studies. As part of this study, the Hydra software was developed to monitor sequencing quality. Hydra proved to be a fundamental tool for analyzing coverage, detecting deletions, and identifying variations in primer regions, making it essential for continuous genomic surveillance of DENV. Finally, the integration of epidemiological, sociodemographic, environmental, and economic data proved vital for the effective control of dengue. Although predictive outbreak modeling still presents challenges, this study reinforces the importance of optimizing sequencing panels and continuously monitoring data quality, which are essential for improving dengue response efforts in Brazil.