Biology subjects

Adelino, T.

Publications and source records attributed to Adelino, T..

4 recordsLinked to original sources

bioRxiv2025

Short-Report: First whole-genome evidence of dengue virus in field-caught mosquitoes from southern Brazil

Dengue virus (DENV) is a major global health threat whose expansion into temperate regions has been facilitated by climate change and vector adaptation. Despite recurrent epidemics in Brazil, genomic surveillance in mosquitoes remains limited, particularly in the South. To address this gap, we implemented a novel urban mosquito-trapping strategy optimized for low-density and peri-domestic environments in Porto Alegre, Rio Grande do Sul. Between April and July 2023, were collected 4,768 Aedes aegypti samples across 16 neighborhoods, generating 2,022 pools. Among these, 41 pools tested positive for DENV, of which 33 pools exhibited RNA integrity suitable for sequencing. Whole-genome sequencing revealed 33 DENV-positive pools, including 29 DENV-1 (Genotype V) and 4 DENV-2 (Genotype II). Two pools contained both serotypes, highlighting the risk of sequential infections in humans. Phylogenetic analyses indicated sustained local transmission alongside multiple introductions, while recurrent mutations, particularly in NS1, NS2A, and NS5, suggested ongoing viral adaptation. These findings represent the first vector-based genomic data for dengue in southern Brazil and demonstrate the utility of mosquito genomics for early outbreak detection, serotype monitoring, and preparedness in emerging transmission zones. Author SummaryDengue virus (DENV) continues to expand into new regions, driven by climate change and mosquito adaptation. In Brazil, dengue epidemics are recurrent, but genomic surveillance of mosquitoes is still scarce, particularly in the southern states. To fill this gap, we implemented an urban mosquito-trapping strategy designed for low-density and peri-domestic environments in Porto Alegre, Rio Grande do Sul. From April to July 2023, we collected 4,768 Aedes aegypti samples across 16 neighborhoods, generating 2,022 pools. Among these, 41 pools were positive for DENV, of which 33 pools exhibited RNA integrity suitable for sequencing, mainly DENV-1 (Genotype V) and DENV-2 (Genotype II), with two pools showing co-circulation of both serotypes, raising concern for sequential infections in humans. Phylogenetic analysis revealed sustained local transmission combined with multiple virus introductions, and mutations in NS1, NS2A, and NS5 suggested ongoing viral adaptation. Our findings provide the first mosquito-based genomic data for dengue in southern Brazil and highlight how mosquito genomics can strengthen early outbreak detection, serotype monitoring, and epidemic preparedness.

molecular biology↗

bioRxiv2025

Unraveling Dengue Serotype 3 Transmission in Brazil: Evidence for Multiple Introductions of the 3III_B.3.2 Lineage

Dengue, caused by DENV 1-4, remains a global public health concern, with Brazil experiencing some of the largest epidemics. The reemergence of DENV-3 in Brazil between 2023 and 2024 has raised concerns about new outbreaks due to the absence of sustained circulation of this serotype in recent years. This study investigates the dynamics of DENV-3 in Brazil, focusing on the spread of the 3III_B.3.2 lineage within genotype 3III and its introduction routes. We analyzed 1,536 DENV-3 genomes, all classified as genotype 3III, the dominant DENV-3 genotype in Brazil since 2001. Phylogenetic analysis identified the 3III_B.3.2 lineage in all recent Brazilian cases, with detections also reported in Central America, the United States, and Europe. At least six independent introduction events of this lineage into Brazil were identified, with the Caribbean region and Costa Rica as the primary sources. The earliest introduction likely occurred in late 2022 in Roraima, followed by introductions in Sao Paulo, Minas Gerais, and Para. While one instance of interstate transmission was detected -- from Sao Paulo to Minas Gerais -- our findings indicate that external introductions, rather than domestic spread, were the primary drivers of DENV-3 circulation during this period. These results underscore the importance of continued genomic surveillance and coordinated public health strategies to monitor and mitigate future outbreaks.

bioinformatics↗

bioRxiv2021

Machine learning models exploring characteristic single-nucleotide signatures in Yellow Fever Virus

Yellow fever virus (YFV) is the agent of the most severe mosquito-borne disease in the tropics. Recently, Brazil suffered major YFV outbreaks with a high fatality rate affecting areas where the virus has not been reported for decades, consisting of urban areas where a large number of unvaccinated people live. We developed a machine learning framework combining three different algorithms (XGBoost, random forest and regularized logistic regression). This method was applied to 56 YFV sequences from human infections and 27 from non-human primate (NHPs) infections to investigate the presence of genetic signatures possibly related to disease severity (in human related sequences) and differences in the PCR cycle threshold (Ct) values (in NHP related sequences). Our analyses reveal four non-synonymous single nucleotide variations (SNVs) on sequences from human infections, in proteins NS3 (E614D), NS4a (I69V), NS5 (R727G, V643A) and six non-synonymous SNVs on NHP sequences, in proteins E (L385F), NS1 (A171V), NS3 (I184V) and NS5 (N11S, I374V, E641D). We performed comparative protein structural analysis on these SNVs, describing possible impacts on protein function. Despite the fact that the dataset is limited in size and that this study does not consider virus-host interactions, our work highlights the use of machine learning as a versatile and fast initial approach to genomic data exploration. ImportanceYellow fever is responsible for 29-60 thousand deaths annually in South America and Africa and is the most severe mosquito-borne disease in the tropics. Given the range of clinical outcomes and the availability of YFV genomic data, the use of machine learning analysis promises to be a powerful tool in the investigation of genetic signatures that could impact disease severity and its potential of being reintroduced in an urban transmission cycle. This can assist in the search for biomarkers of severity as well as help elucidating variations in hosts Ct value. This work aims to propose a relatively fast and inexpensive computational analysis framework, which can be used as a real-time, innitial strategy associated with genomic surveillance to identify a set of single nucleotide variants putatively related to biological and clinical characteristics being observed.

microbiology↗

bioRxiv2021

Chikungunya virus ECSA lineage reintroduction in the northeasternmost region of Brazil

The Northeast region of Brazil registered the second highest incidence proportion of chikungunya fever in 2019. In that year an outbreak consisting of patients presented with febrile disease associated with joint pain were reported by the public primary health care service in the city of Natal, Rio Grande do Norte state, in March 2019. At first, the aetiological agent of the disease was undetermined. Since much is still unknown about chikungunya virus (CHIKV) genomic diversity and evolutionary history in this northeasternmost state, we used a combination of portable whole genome sequencing, molecular clock, and epidemiological analyses that revealed the re-introduction of the CHIKV East-Central-South-African (ECSA) lineage into Rio Grande do Norte. We estimated CHIKV ECSA lineage was first introduced into Rio Grande do Norte in early June 2014, while the 2019 outbreak clade diverged around April 2018 during a period of increased chikungunya incidence in the Southeast region, which might have acted as a source of virus dispersion towards the Northeast region. Together, these results confirm the ECSA lineage continues to spread across the country through interregional importation events likely mediated by human mobility. HIGHLIGHTSCHIKV ECSA lineage introduction into Rio Grande do Norte state, Northeast Brazil, was estimated to early June 2014 At least two CHIKV importation events occurred in Rio Grande do Norte state, Brazil The 2019 chikungunya outbreak in Rio Grande do Norte was likely caused by a second event of CHIKV introduction imported from Rio de Janeiro state.

genomics↗