mazonas state reported the first confirmed SARS-CoV-2 case in Manaus, the state capital, in March 2020 in a traveler returning from Europe 1 . By late February 2021, >306,000 laboratory-confirmed cases and more than 10,400 deaths in Amazonas had been reported 2 . The COVID-19 epidemic in Amazonas is, at the time of writing, characterized by two exponentially growing curves of cases (Fig. 1a). Epidemiological data from surveillance of severe acute respiratory illness (SARI) and burials indicate that the first wave of the epidemic started in March 2020 and peaked around early May 2020, when the number of cases dropped and then remained roughly stable from June to November 2020. However, in mid-December the number of cases started to grow exponentially, establishing the second wave of the epidemic.A new SARS-CoV-2 VOC, designated P.1 and also knowns as N501Y.V3, recently emerged in Manaus. Lineage P.1 was first detected in four travelers returning to Japan from Amazonas state on 2 January 2021 (ref. 3 ) and was soon recognized as an emergent lineage in Manaus 4 . The VOC P.1 harbors 21 lineage-defining mutations, including ten in the Spike protein (L18F, T20N, P26S, D138Y, R190S, K417T, E484K, N501Y, H655Y and T1027I). The emergence of P.1 was touted as one of the putative causes of the second wave of COVID-19 in Manaus 5 . However, the precise relationship between circulating SARS-CoV-2 variants and epidemic dynamics in Amazonas remains unclear due to the paucity of viral sequences sampled in this Brazilian state before December 2020. Results Evidence of successive SARS-CoV-2 lineage replacements in Amazonas.To acquire a more in-depth understanding of the genetic diversity of SARS-CoV-2 variants circulating in Amazonas state since the early epidemic, we generated 250 SARS-CoV-2 high-quality, whole-genome sequences from individuals living in 25 municipalities, between 16 March 2020 and 13 January 2021 (Fig. 1a,b). Viral sequences were generated at FIOCRUZ Amazônia, which is part of both the Amazonas state health genomics network (REGESAM) and the consortium FIOCRUZ COVID-19 Genomics Surveillance Network of the Brazilian Ministry of Health (http:// www.genomahcov.fiocruz.br/). Our genomic survey revealed that most sequences were classified into five lineages:

The Northern Brazilian state of Amazonas is one of the most heavily affected country regions by the COVID-19 epidemic and experienced two exponential growing waves in early and late 2020. Through a genomic epidemiology study based on 250 SARS-CoV-2 genomes from different Amazonas municipalities sampled between March 2020 and January 2021 we revealed that the first exponential growth phase was driven mostly by the dissemination of lineage B.1.195 which was gradually replaced by lineage B.1.1.28. The second wave coincides with the emergence of the variant of concern (VOC) P.1 which evolved from a local B.1.1.28 clade in late November and rapidly replaced the parental lineage in less than two months. Our findings support that successive lineage replacements in Amazonas were driven by a complex combination of variable levels of social distancing measures and the emergence of a more transmissible VOC P.1 virus. These data provide unique insights to understanding the mechanisms that underlie the COVID-19 epidemic waves and the risk of disseminating SARS-CoV-2 VOC P.1 in Brazil and potentially worldwide.

Multiple epicenters of the SARS-CoV-2 pandemic have emerged since the first pneumonia cases in Wuhan, China, such as Italy, USA, and Brazil. Brazil is the third-most affected country worldwide, but genomic sequences of SARS-CoV-2 strains are mostly restricted to states from the Southeast region. Pernambuco state, located in the Northeast region, is the sixth most affected Brazilian state, but very few genomic sequences from the strains circulating in this region are available. We sequenced 101 strains of SARS-CoV-2 from patients presenting Covid-19 symptoms that reside in Pernambuco. Phylogenetic reconstructions revealed that all genomes belong to the B lineage and most of the samples (88%) were classified as lineage B.1.1. We detected multiple viral introductions from abroad (likely from Europe) as well as six local B.1.1 clades composed by Pernambuco only strains. Local clades comprise sequences from the capital city (Recife) and other country-side cities, corroborating the community spread between different municipalities of the state. These findings demonstrate that different from Southeastern Brazilian states where the epidemics were majorly driven by one dominant lineage (B.1.1.28 or B.1.1.33), the early epidemic phase at the Pernambuco state was driven by multiple B.1.1 lineages seeded through both national and international traveling.

After the Zika virus (ZIKV) epidemic in the Americas in 2016, both Zika and dengue incidence declined to record lows in many countries in 2017–2018, but in 2019 dengue resurged in Brazil, causing ~2.1 million cases. In this study we use epidemiological, climatological and genomic data to investigate dengue dynamics in recent years in Brazil. First, we estimate dengue virus force of infection (FOI) and model mosquito-borne transmission suitability since the early 2000s. Our estimates reveal that DENV transmission was low in 2017–2018, despite conditions being suitable for viral spread. Our study also shows a marked decline in dengue susceptibility between 2002 and 2019, which could explain the synchronous decline of dengue in the country, partially as a result of protective immunity from prior ZIKV and/or DENV infections. Furthermore, we performed phylogeographic analyses using 69 newly sequenced genomes of dengue virus serotype 1 and 2 from Brazil, and found that the outbreaks in 2018–2019 were caused by local DENV lineages that persisted for 5–10 years, circulating cryptically before and after the Zika epidemic. We hypothesize that DENV lineages may circulate at low transmission levels for many years, until local conditions are suitable for higher transmission, when they cause major outbreaks.

The COVID-19 pandemic is driven by Severe Acute Respiratory Syndrome coronavirus 2 (SARS-CoV-2) that emerged in 2019 and quickly spread worldwide. Genomic surveillance has become the gold standard methodology used to monitor and study this fast-spreading virus and its constantly emerging lineages. The current deluge of SARS-CoV-2 genomic data generated worldwide has put additional pressure on the urgent need for streamlined bioinformatics workflows. Here, we describe a workflow developed by our group to process and analyze large-scale SARS-CoV-2 Illumina amplicon sequencing data. This workflow automates all steps of SARS-CoV-2 reference-based genomic analysis: data processing, genome assembly, PANGO lineage assignment, mutation analysis and the screening of intrahost variants. The pipeline is capable of processing a batch of around 100 samples in less than half an hour on a personal laptop or in less than five minutes on a server with 50 threads. The workflow presented here is available through Docker or Singularity images, allowing for implementation on laptops for small-scale analyses or on high processing capacity servers or clusters. Moreover, the low requirements for memory and CPU cores and the standardized results provided by ViralFlow highlight it as a versatile tool for SARS-CoV-2 genomic analysis.

Background: Endogenous viral elements (EVEs) are sequences of viral origin integrated into the host genome. EVEs have been characterized in various insect genomes, including mosquitoes. A large EVE content has been found in Aedes aegypti and Aedes albopictus genomes among which a recently described Chuviridae viral family is of particular interest, owing to the abundance of EVEs derived from it, the discrepancy among the chuvirus endogenized gene regions and the frequent association with retrotransposons from the BEL-Pao superfamily. In order to better understand the endogenization process of chuviruses and the association between chuvirus glycoproteins and BEL-Pao retrotransposons, we performed a comparative genomics and evolutionary analysis of chuvirus-derived EVEs found in 37 mosquito genomes. Results: We identified 428 EVEs belonging to the Chuviridae family confirming the wide discrepancy among the chuvirus genomic regions endogenized: 409 glycoproteins, 18 RNA-dependent RNA polymerases and one nucleoprotein region. Most of the glycoproteins (263 out of 409) are associated specifically with retroelements from the Pao family. Focusing only on well-assembled Pao retroelement copies, we estimated that 263 out of 379 Pao elements are associated with chuvirus-derived glycoproteins. Seventy-three potentially active Pao copies were found to contain glycoproteins into their LTR boundaries. Thirteen out of these were classified as complete and likely autonomous copies, with a full LTR structure and protein domains. We also found 116 Pao copies with no trace of glycoproteins and 37 solo glycoproteins. All potential autonomous Pao copies, contained highly similar LTRs, suggesting a recent/current activity of these elements in the mosquito genomes. Conclusion: Evolutionary analysis revealed that most of the glycoproteins found are likely derived from a single or few glycoprotein endogenization events associated with a recombination event with a Pao ancestral element. A potential functional Pao-chuvirus hybrid (named Anakin) emerged and the glycoprotein was further replicated through

Mutations at both the receptor-binding domain (RBD) and the amino (N)-terminal domain (NTD) of the SARS-CoV-2 Spike (S) glycoprotein can alter its antigenicity and promote immune escape. We identified that SARS-CoV-2 lineages circulating in Brazil with mutations of concern in the RBD independently acquired convergent deletions and insertions in the NTD of the S protein, which altered the NTD antigenic-supersite and other predicted epitopes at this region. These findings support that the ongoing widespread transmission of SARS-CoV-2 in Brazil is generating new viral lineages that might be more resistant to neutralization than parental variants of concern.

Wolbachia is an endosymbiotic bacterium that naturally infects several arthropods and nematode species. Wolbachia gained particular attention due to its impact on their host fitness and the capacity of specific Wolbachia strains in reducing pathogen vector and agricultural pest populations and pathogens transmission. Despite the success of mosquito/pathogen control programs using Wolbachia-infected mosquito release, little is known about the abundance and distribution of Wolbachia in most mosquito species, a crucial knowledge for planning and deployment of mosquito control programs and that can further improve our basic biology understanding of Wolbachia and host relationships. In this systematic review, Wolbachia was detected in only 30% of the mosquito species investigated. Fourteen percent of the species were considered positive by some studies and negative by others in different geographical regions, suggesting a variable infection rate and/or limitations of the Wolbachia detection methods employed. Eighty-three percent of the studies screened Wolbachia with only one technique. Our findings highlight that the assessment of Wolbachia using a single approach limited the inference of true Wolbachia infection in most of the studied species and that researchers should carefully choose complementary methodologies and consider different Wolbachia-mosquito population dynamics that may be a source of bias to ascertain the correct infectious status of the host species.