Genomic epidemiology of SARS-CoV-2 in Esteio, Rio Grande do Sul, Brazil

Spatiotemporal transmission of SARS-CoV-2 lineages during 2020-2021 in Pernambuco-Brazil

SARS-CoV-2 virus emerged as a new threat to humans and spread around the world, leaving a large death toll. As of January 2023, Brazil is among the countries with the highest number of registered deaths. Nonpharmacological and pharmacological interventions have been heterogeneously implemented in the country, which, associated with large socioeconomic differences between the country regions, has led to distinct virus spread dynamics. Here, we investigate the spatiotemporal dispersion of SARS-CoV-2 lineages in the Pernambuco state (Northeast Brazil) throughout the distinct epidemiological scenarios that unfolded in the first 2 years of the pandemic. We generated a total of 1,389 new SARS-CoV-2 genomes from June 2020 to August 2021. This sampling captured the arrival, communitary transmission, and the circulation of the B1.1, B.1.1.28, and B.1.1.33 lineages; the emergence of the former variant of interest P.2; and the emergence and fast replacement of all previous variants by the more transmissible variant of concern P.1 (Gamma). Based on the incidence and lineage spread pattern, we observed an East-to-West to inner state pattern of transmission, which is in agreement with the transmission of more populous metropolitan areas to medium- and small-size country-side cities in the state. Such transmission patterns may be partially explained by the main routes of traffic across municipalities in the state. Our results highlight that the fine-grained intrastate analysis of lineages and incidence spread can provide actionable insights for planning future nonpharmacological intervention for air-borne transmissible human pathogens.IMPORTANCEDuring the COVID-19 pandemic, Brazil was one of the most affected countries, mainly due its continental-size, socioeconomic differences among regions, and heterogeneous implementation of intervention methods. In order to investigate SARS-CoV-2 dynamics in the state of Pernambuco, we conducted a spatiotemporal dispersion study, covering the period from June 2020 to August 2021, to comprehend the dynamics of viral transmission during the first 2 years of the pandemic. Throughout this study, we were able to track three significant epidemiological waves of transmission caused by B1.1, B.1.1.28, B.1.1.33, P.2, and P.1 lineages. These analyses provided valuable insights into the evolution of the epidemiological landscape, contributing to a deeper understanding of the dynamics of virus transmission during the early years of the pandemic in the state of Pernambuco.

Online Phylogenetics with matOptimize Produces Equivalent Trees and is Dramatically More Efficient for Large SARS-CoV-2 Phylogenies than de novo and Maximum-Likelihood Implementations

Phylogenetics has been foundational to SARS-CoV-2 research and public health policy, assisting in genomic surveillance, contact tracing, and assessing emergence and spread of new variants. However, phylogenetic analyses of SARS-CoV-2 have often relied on tools designed for de novo phylogenetic inference, in which all data are collected before any analysis is performed and the phylogeny is inferred once from scratch. SARS-CoV-2 data sets do not fit this mold. There are currently over 14 million sequenced SARS-CoV-2 genomes in online databases, with tens of thousands of new genomes added every day. Continuous data collection, combined with the public health relevance of SARS-CoV-2, invites an "online" approach to phylogenetics, in which new samples are added to existing phylogenetic trees every day. The extremely dense sampling of SARS-CoV-2 genomes also invites a comparison between likelihood and parsimony approaches to phylogenetic inference. Maximum likelihood (ML) and pseudo-ML methods may be more accurate when there are multiple changes at a single site on a single branch, but this accuracy comes at a large computational cost, and the dense sampling of SARS-CoV-2 genomes means that these instances will be extremely rare because each internal branch is expected to be extremely short. Therefore, it may be that approaches based on maximum parsimony (MP) are sufficiently accurate for reconstructing phylogenies of SARS-CoV-2, and their simplicity means that they can be applied to much larger data sets. Here, we evaluate the performance of de novo and online phylogenetic approaches, as well as ML, pseudo-ML, and MP frameworks for inferring large and dense SARS-CoV-2 phylogenies. Overall, we find that online phylogenetics produces similar phylogenetic trees to de novo analyses for SARS-CoV-2, and that MP optimization with UShER and matOptimize produces equivalent SARS-CoV-2 phylogenies to some of the most popular ML and pseudo-ML inference tools. MP optimization with UShER and matOptimize is thousands of times faster than presently available implementations of ML and online phylogenetics is faster than de novo inference. Our results therefore suggest that parsimony-based methods like UShER and matOptimize represent an accurate and more practical alternative to established ML implementations for large SARS-CoV-2 phylogenies and could be successfully applied to other similar data sets with particularly dense sampling and short branch lengths.

Genomic epidemiology of SARS-CoV-2 from Uttar Pradesh, India

The various strains and mutations of SARS-CoV-2 have been tracked using several forms of genomic classification systems. The present study reports high-throughput sequencing and analysis of 99 SARS-CoV-2 specimens from Western Uttar Pradesh using sequences obtained from the GISAID database, followed by phylogeny and clade classification. Phylogenetic analysis revealed that Omicron lineages BA-2-like (55.55%) followed by Delta lineage-B.1.617.2 (45.5%) were predominantly circulating in this area Signature substitution at positions S: N501Y, S: D614G, S: T478K, S: K417N, S: E484A, S: P681H, and S: S477N were commonly detected in the Omicron variant-BA-2-like, however S: D614G, S: L452R, S: P681R and S: D950N were confined to Delta variant-B.1.617.2. We have also identified three escape variants in the S gene at codon position 19 (T19I/R), 484 (E484A/Q), and 681 (P681R/H) during the fourth and fifth waves in India. Based on the phylogenetic diversification studies and similar changes in other lineages, our analysis revealed indications of convergent evolution as the virus adjusts to the shifting immunological profile of its human host. To the best of our knowledge, this study is an approach to comprehensively map the circulating SARS-CoV-2 strains from Western Uttar Pradesh using an integrated approach of whole genome sequencing and phylogenetic analysis. These findings will be extremely valuable in developing a structured approach toward pandemic preparedness and evidence-based intervention plans in the future.

Genomic characterization and molecular evolution of SARS-CoV-2 in Rio Grande do Sul State, Brazil

SARS-CoV-2 is the virus responsible for the COVID-19 and has afflicted the world since the end of 2019. Different lineages have been discovered and the Gamma lineage, which started the second wave of infections, was first described in Brazil, one of the most affected countries by pandemic. Therefore, this study analyzed SARS-CoV-2 sequenced genomes from Esteio city in Rio Grande do Sul, Southern Brazil. We also comparatively analyzed genomes of the two first years of the pandemic from Rio Grande do Sul state for understanding their genomic and evolutionary patterns. The phylogenomic analysis showed monophyletic groups for Alpha, Gamma, Delta and Omicron, as well as for other circulating lineages in the state. Molecular evolutionary analysis identified several sites under adaptive selection in membrane and nucleocapsid proteins which could be related to a prevalent stabilizing effect on membrane protein structure, as well as majoritarily destabilizing effects on C-terminal nucleocapsid domain.

Genome Structure, Life Cycle, and Taxonomy of Coronaviruses and the Evolution of SARS-CoV-2

Coronaviruses have a broad host range and exhibit high zoonotic potential. In this chapter, we describe their genomic organization in terms of encoded proteins and provide an introduction to the peculiar discontinuous transcription mechanism. Further, we present evolutionary conserved genomic RNA secondary structure features, which are involved in the complex replication mechanism. With a focus on computational methods, we review the emergence of SARS-CoV-2 starting with the 2019 strains. In that context, we also discuss the debated hypothesis of whether SARS-CoV-2 was created in a laboratory. We focus on the molecular evolution and the epidemiological dynamics of this recently emerged pathogen and we explain how variants of concern are detected and characterised. COVID-19, the disease caused by SARS-CoV-2, can spread through different transmission routes and also depends on a number of risk factors. We describe how current computational models of viral epidemiology, or more specifically, phylodynamics, have facilitated and will continue to enable a better understanding of the epidemic dynamics of SARS-CoV-2.

SARS-CoV-2 Genomic Surveillance in Brazil: A Systematic Review with Scientometric Analysis

Several studies have monitored the SARS-CoV-2 variants in Brazil throughout the pandemic. Here, we systematically reviewed and conducted a scientometric analysis of the SARS-CoV-2 genomic surveillance studies using Brazilian samples. A Pubmed database search on October 2022 returned 492 articles, of which 106 were included. Ninety-six different strains were reported, with variant of concern (VOC) gamma (n = 35,398), VOC delta (n = 15,780), and the variant of interest zeta (n = 1983) being the most common. The top three states with the most samples in the published articles were São Paulo, Rio de Janeiro, and Minas Gerais. Whereas the first year of the pandemic presented primary circulation of B.1.1.28 and B.1.1.33 variants, consecutive replacements were observed between them and VOI zeta, VOC gamma, VOC delta, and VOC omicron. VOI mu, VOI lambda, VOC alpha, and VOC beta were also detected but failed to reach significant circulation. Co-infection, re-infection, and vaccine breakthrough reports were found. Article co-citation differed from the co-authorship structure. Despite the limitations, we expect to give an overview of Brazil's genomic surveillance studies and contribute to future research execution.

SARS-CoV-2 Spatiotemporal Genomic and Molecular Analysis of the First Wave of the COVID-19 Pandemic in Macaé, the Brazilian Capital of Oil

The SARS-CoV-2 virus infection led to millions of deaths during the COVID-19 pandemic. Hundreds of workers from several other Brazilian cities, as well as from other countries, arrive daily in Macaé to work in the oil supply chain, making this city a putative hotspot for the introduction of new viral lineages. In this study, we performed a genomic survey of SARS-CoV-2 samples from Macaé during the first outbreak of COVID-19, combined with clinical data and a molecular integrative analysis. First, phylogenomic analyses showed a high occurrence of viral introduction events and the establishment of local transmissions in Macaé, including the ingression and spread of the B.1.1.28 lineage in the municipality from June to August 2020. Second, SARS-CoV-2 mutations were identified in patients with distinct levels of COVID-19 severity. Third, molecular interactions of the mutated spike protein from three B.1.1.33 local samples and human ACE2 showed higher interactions than that of the wild-type spike protein from the ancestral virus. Altogether, these results elucidate the SARS-CoV-2 genomic profile in a strategic Brazilian city and further explore the functional aspects of SARS-CoV-2 with a characterization of emerging viral mutations associated with clinical data and the potential targets for drug development against SARS-CoV-2.

Viral isolation allows characterization of early samples of SARS-CoV-2 lineage B1.1.33 with unique mutations (S: H655Y and T63N) circulating in Southern Brazil in 2020

Different approaches are in use to improve our knowledge about the causative agent of coronavirus disease (COVID-19). Cell culture-based methods are the better way to perform viral isolation, evaluate viral infectivity, and amplify the virus. Furthermore, next-generation sequencing (NGS) have been essential to analyze a complete genome and to describe new viral species and lineages that have arisen over time. Four naso-oropharyngeal swab samples, collected from April to July of 2020, were isolated and sequenced aiming to produce viral stocks and analyze the mutational profile of the found lineage. B.1.1.33 was the lineage detected in all sequences. Although the samples belong to the same lineage, it was possible to evaluate different mutations found including some that were first described in these sequences, like the S:H655Y and T63N. The results described here can help to elicit how the pandemic started to spread and how it has been evolving in south Brazil.

Tracking the molecular evolution and transmission patterns of SARS-CoV-2 lineage B.1.466.2 in Indonesia based on genomic surveillance data

Background

As a new epi-center of COVID-19 in Asia and a densely populated developing country, Indonesia is facing unprecedented challenges in public health. SARS-CoV-2 lineage B.1.466.2 was reported to be an indigenous dominant strain in Indonesia (once second only to the Delta variant). However, it remains unclear how this variant evolved and spread within such an archipelagic nation.

Methods

For statistical description, the spatiotemporal distributions of the B.1.466.2 variant were plotted using the publicly accessible metadata in GISAID. A total of 1302 complete genome sequences of Indonesian B.1.466.2 strains with high coverage were downloaded from the GISAID’s EpiCoV database on 28 August 2021. To determine the molecular evolutionary characteristics, we performed a time-scaled phylogenetic analysis using the maximum likelihood algorithm and called the single nucleotide variants taking the Wuhan-Hu-1 sequence as reference. To investigate the spatiotemporal transmission patterns, we estimated two dynamic parameters (effective population size and effective reproduction number) and reconstructed the phylogeography among different islands.

Results

As of the end of August 2021, nearly 85% of the global SARS-CoV-2 lineage B.1.466.2 sequences (including the first one) were obtained from Indonesia. This variant was estimated to account for over 50% of Indonesia’s daily infections during the period of March–May 2021. The time-scaled phylogeny suggested that SARS-CoV-2 lineage B.1.466.2 circulating in Indonesia might have originated from Java Island in mid-June 2020 and had evolved into two disproportional and distinct sub-lineages. High-frequency non-synonymous mutations were mostly found in the spike and NSP3; the S-D614G/N439K/P681R co-mutations were identified in its larger sub-lineage. The demographic history was inferred to have experienced four phases, with an exponential growth from October 2020 to February 2021. The effective reproduction number was estimated to have reached its peak (11.18) in late December 2020 and dropped to be less than one after early May 2021. The relevant phylogeography showed that Java and Sumatra might successively act as epi-centers and form a stable transmission loop. Additionally, several long-distance transmission links across seas were revealed.

Conclusions

SARS-CoV-2 variants circulating in the tropical archipelago may follow unique patterns of evolution and transmission. Continuous, extensive and targeted genomic surveillance is essential.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12985-022-01830-1.

Online Phylogenetics using Parsimony Produces Slightly Better Trees and is Dramatically More Efficient for Large SARS-CoV-2 Phylogenies than de novo and Maximum-Likelihood Approaches

Phylogenetics has been foundational to SARS-CoV-2 research and public health policy, assisting in genomic surveillance, contact tracing, and assessing emergence and spread of new variants. However, phylogenetic analyses of SARS-CoV-2 have often relied on tools designed for de novo phylogenetic inference, in which all data are collected before any analysis is performed and the phylogeny is inferred once from scratch. SARS-CoV-2 datasets do not fit this mould. There are currently over 10 million sequenced SARS-CoV-2 genomes in online databases, with tens of thousands of new genomes added every day. Continuous data collection, combined with the public health relevance of SARS-CoV-2, invites an "online" approach to phylogenetics, in which new samples are added to existing phylogenetic trees every day. The extremely dense sampling of SARS-CoV-2 genomes also invites a comparison between likelihood and parsimony approaches to phylogenetic inference. Maximum likelihood (ML) methods are more accurate when there are multiple changes at a single site on a single branch, but this accuracy comes at a large computational cost, and the dense sampling of SARS-CoV-2 genomes means that these instances will be extremely rare because each internal branch is expected to be extremely short. Therefore, it may be that approaches based on maximum parsimony (MP) are sufficiently accurate for reconstructing phylogenies of SARS-CoV-2, and their simplicity means that they can be applied to much larger datasets. Here, we evaluate the performance of de novo and online phylogenetic approaches, and ML and MP frameworks, for inferring large and dense SARS-CoV-2 phylogenies. Overall, we find that online phylogenetics produces similar phylogenetic trees to de novo analyses for SARS-CoV-2, and that MP optimizations produce more accurate SARS-CoV-2 phylogenies than do ML optimizations. Since MP is thousands of times faster than presently available implementations of ML and online phylogenetics is faster than de novo , we therefore propose that, in the context of comprehensive genomic epidemiology of SARS-CoV-2, MP online phylogenetics approaches should be favored.

Genomic Epidemiology of SARS-CoV-2 in Tocantins State and the Diffusion of P.1.7 and AY.99.2 Lineages in Brazil

Tocantins is a state in the cross-section between the Central-West, North and Northeast regions of Brazilian territory; it is a gathering point for travelers and transportation from the whole country. In this study, 9493 genome sequences, including 241 local SARS-CoV-2 samples (collected from 21 December 2020, to 16 December 2021, and sequenced in the MinION platform) were analyzed with the following aims: (i) identify the relative prevalence of SARS-CoV-2 lineages in the state of Tocantins; (ii) analyze them phylogenetically against global SARS-CoV-2 sequences; and (iii) hypothesize the viral dispersal routes of the two most abundant lineages found in our study using phylogenetic and phylogeographic approaches. The performed analysis demonstrated that the majority of the strains sequenced during the period belong to the Gamma P.1.7 (32.4%) lineage, followed by Delta AY.99.2 (27.8%), with the first detection of VOC Omicron. As expected, there was mainly a dispersion of P.1.7 from the state of São Paulo to Tocantins, with evidence of secondary spreads from Tocantins to Goiás, Mato Grosso, Amapá, and Pará. Rio de Janeiro was found to be the source of AY.99.2 and from then, multiple cluster transmission was observed across Brazilian states, especially São Paulo, Paraiba, Federal District, and Tocantins. These data show the importance of trade routes as pathways for the transportation of the virus from Southeast to Northern Brazil.

Structural and functional insights into the spike protein mutations of emerging SARS-CoV-2 variants

Since the emergence of the first case of coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus (SARS-CoV-2), the viral genome has constantly undergone rapid mutations for better adaptation in the host system. These newer mutations have given rise to several lineages/ variants of the virus that have resulted in high transmission and virulence rates compared to the previously circulating variants. Owing to this, the overall caseload and related mortality have tremendously increased globally to > 233 million infections and > 4.7 million deaths as of Sept. 28th, 2021. SARS-CoV-2, Spike (S) protein binds to host cells by recognizing human angiotensin-converting enzyme 2 (hACE2) receptor. The viral S protein contains S1 and S2 domains that constitute the binding and fusion machinery, respectively. Structural analysis of viral S protein reveals that the virus undergoes conformational flexibility and dynamicity to interact with the hACE2 receptor. The SARS-CoV-2 variants and mutations might be associated with affecting the conformational plasticity of S protein, potentially linked to its altered affinity, infectivity, and immunogenicity. This review focuses on the current circulating variants of SARS-CoV-2 and the structure-function analysis of key S protein mutations linked with increased affinity, higher infectivity, enhanced transmission rates, and immune escape against this infection.

Reinfection cases by closely related SARS-CoV-2 lineages in Southern Brazil

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is responsible for the pandemic that started in late 2019 and still affects people's lives all over the world. Lack of protective immunity after primary infection has been involved with reported reinfection cases by SARS-CoV-2. In this study, we described two cases of reinfection caused by non-VOC (Variants of Concern) strains in southern Brazil, being one patient a healthcare worker. The four samples previously positive for SARS-CoV-2 by real-time quantitative reverse transcription polymerase chain reaction (RT-qPCR) were sequenced by a high-performance platform and the genomic analysis confirmed that lineages responsible for infections were B.1.91 and B.1.1.33 (patient 1), and B.1.1.33 and B.1.1.28 (patient 2). The interval between the two positive RT-qPCR for patients 1 and 2 was 45 and 61 days, respectively. This data shows that patients may be reinfected even by very closely related SARS-CoV-2 lineages.

SARS-CoV-2 introduction and lineage dynamics across three epidemic peaks in Southern Brazil: massive spread of P.1

Genomic surveillance of SARS-CoV-2 is paramount for understanding viral dynamics, contributing to disease control. This study analyzed SARS-CoV-2 genomic diversity in Rio Grande do Sul (RS), Brazil, including the first reported case in each Regional Health Coordination and cases from three epidemic peaks. Ninety SARS-CoV-2 genomes from RS were sequenced and analyzed through comparison with SARS-CoV-2 datasets available in GISAID for phylogenetic inference and mutation analysis. Among the first reported cases, we found the following lineages: B.1 (33.3%), B.1.1.28 (26.7%), B.1.1 (13.3%), B.1.1.33 (10.0%), and A (6.7%), evidencing SARS-CoV-2 introduction by both international origin and community-driven transmission. We found predominance of B.1.1.33 (50.0%) and B.1.1.28 (35.0%) during the first epidemic peak (July-August 2020), emergence of P.2 (55.6%) in the second peak (November-December 2020), and massive spread of P.1 and related sequences (78.4%), such as P.1-like-II, P.1.1 and P.1.2 in the third peak (February-April, 2021). Eighteen novel mutation combinations were found among P.1 genomes, and 22 different spike mutations and/or deletions among P.1 and related sequences. This study shows the dispersion of SARS-CoV-2 lineages in Southern Brazil and describes SARS-CoV-2 diversity during three epidemic peaks, highlighting the spread of P.1 and the high genetic diversity of currently circulating lineages. Genomic monitoring of SARS-CoV-2 is essential to guide health authorities' decisions to control COVID-19 in Brazil.

Insights on the SARS-CoV-2 genome variability: the lesson learned in Brazil and its impacts on the future of pandemics

Since the beginning of the SARS-CoV-2 spread in Brazil, few studies have been published analysing the variability of viral genome. Herein, we described the dynamic of SARS-CoV-2 strains circulating in Brazil from May to September 2020, to better understand viral changes that may affect the ongoing pandemic. Our data demonstrate that some of the mutations identified are currently observed in variants of interest and variants of concern, and emphasize the importance of studying previous periods in order to comprehend the emergence of new variants. From 720 SARS-CoV-2 genome sequences, we found few sites under positive selection pressure, such as the D614G (98.5 %) in the spike, that has replaced the old variant; the V1167F in the spike (41 %), identified in the P.2 variant that emerged from Brazil during the period of analysis; and I292T (39 %) in the N protein. There were a few alterations in the UTRs, which was expected, however, our data suggest that the emergence of new variants was not influenced by mutations in UTR regions, since it maintained its conformational structure in most analysed sequences. In phylogenetic analysis, the spread of SARS-CoV-2 from the large urban centres to the countryside during these months could be explained by the flexibilization of social isolation measures and also could be associated with possible new waves of infection. These results allow a better understanding of SARS-CoV-2 strains that have circulated in Brazil, and thus, with relevant infomation, provide the potential viral changes that may have affected and/or contributed to the current and future scenario of the COVID-19 pandemic.

Understanding the molecular interaction of SARS-CoV-2 spike mutants with ACE2 (angiotensin converting enzyme 2)

Covid-19 is a viral disease caused by the virus SARS-CoV-2 that spread worldwide and caused more than 4.3 million deaths. Moreover, SARS-CoV-2 still continues to evolve, and specifically the E484K, N501Y, and South Africa triple (K417N + E484K + N501Y) spike protein mutants remain as the 'escape' phenotypes. The aim of this study was to compare the interaction between the receptor binding domain (RBD) of the E484K, N501Y and South Africa triple spike variants and ACE2 with the interaction between wild-type spike RBD-ACE2 and to show whether the obtained binding affinities and conformations corraborate clinical findings. The structures of the RBDs of the E484K, N501Y and South Africa triple variants were generated with DS Studio v16 and energetically minimized using the CHARMM22 force field. Protein-protein dockings were performed in the HADDOCK server and the obtained wild-type and mutant spike-ACE2 complexes were submitted to 200-ns molecular dynamics simulations with subsequent free energy calculations using GROMACS. Based on docking binding affinities and free energy calculations the E484K, N501Y and triple mutant variants were found to interact stronger with the ACE2 than the wild-type spike. Interestingly, molecular dynamics and MM-PBSA results showed that E484K and spike triple mutant complexes were more stable than the N501Y one. Moreover, the E484K and South Africa triple mutants triggered greater conformational changes in the spike glycoprotein than N501Y. The E484K variant alone, or the combination of K417N + E484K + N501Y mutations induce significant conformational transitions in the spike glycoprotein, while increasing the spike-ACE2 binding affinity.Communicated by Ramaswamy H. Sarma.

Predominance of the SARS-CoV-2 Lineage P.1 and Its Sublineage P.1.2 in Patients from the Metropolitan Region of Porto Alegre, Southern Brazil in March 2021

Almost a year after the COVID-19 pandemic had begun, new lineages (B.1.1.7, B.1.351, P.1, and B.1.617.2) associated with enhanced transmissibility, immunity evasion, and mortality were identified in the United Kingdom, South Africa, and Brazil. The previous most prevalent lineages in the state of Rio Grande do Sul (RS, Southern Brazil), B.1.1.28 and B.1.1.33, were rapidly replaced by P.1 and P.2, two B.1.1.28-derived lineages harboring the E484K mutation. To perform a genomic characterization from the metropolitan region of Porto Alegre, we sequenced viral samples to: (i) identify the prevalence of SARS-CoV-2 lineages in the region, the state, and bordering countries/regions; (ii) characterize the mutation spectra; (iii) hypothesize viral dispersal routes by using phylogenetic and phylogeographic approaches. We found that 96.4% of the samples belonged to the P.1 lineage and approximately 20% of them were assigned as the novel P.1.2, a P.1-derived sublineage harboring signature substitutions recently described in other Brazilian states and foreign countries. Moreover, sequences from this study were allocated in distinct branches of the P.1 phylogeny, suggesting multiple introductions in RS and placing this state as a potential diffusion core of P.1-derived clades and the emergence of P.1.2. It is uncertain whether the emergence of P.1.2 and other P.1 clades is related to clinical or epidemiological consequences. However, the clear signs of molecular diversity from the recently introduced P.1 warrant further genomic surveillance.

Mutation hotspots and spatiotemporal distribution of SARS-CoV-2 lineages in Brazil, February 2020-2021

The COVID-19 pandemic has already reached more than 110 million people and is associated with 2.5 million deaths worldwide. Brazil is the third worst-hit country, with approximately 10.2 million cases and 250 thousand deaths. International efforts have been established to share information about Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) epidemiology and evolution to support the development of effective strategies for public health and disease management. We aimed to analyze the high-quality genome sequences from Brazil from February 2020-2021 to identify mutation hotspots, geographical and temporal distribution of SARS-CoV-2 lineages by using phylogenetics and phylodynamics analyses. We describe heterogeneous sequencing efforts, the progression of the different lineages along time, evaluating mutational spectra and frequency oscillations derived from the prevalence of specific lineages across different Brazilian regions. We found at least seven major (1–7) and two minor clades related to the six most prevalent lineages in the country and described its spatial distribution and dynamics. The emergence and recent frequency shift of lineages (P.1 and P.2) carrying mutations of concern in the spike protein (e. g., E484K, N501Y) draws attention due to their association with immune evasion and enhanced receptor binding affinity. Improvements in genomic surveillance are of paramount importance and should be extended in Brazil to better inform policy makers about better decisions to fight the COVID-19 pandemic.