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Study Participants and Samples The whole blood samples were obtained from the Health, Well-being and Aging (Saúde, Ben-estar e Envelhecimento, SABE) study cohort. SABE is a cohort of census-withdrawn elderly from the city of São Paulo, Brazil, followed up every five years since the year 2000, with DNA first collected in 2010. Samples from 24 elderly adults were collected at two time points for a total of 48 samples. The first time point is the 2010 collection wave, performed from 2010 to 2012, and the second time point was set in 2020 in a COVID-19 monitoring project (9±0.71 years apart). The 24 individuals were 67.41±5.52 years of age (mean ± standard deviation) at time point one; and 76.41±6.17 at time point two and comprised 13 men and 11 women. All individuals enrolled in the SABE cohort provided written consent, and the ethic protocols were approved by local and national institutional review boards COEP/FSP/USP OF.COEP/23/10, CONEP 2044/2014, CEP HIAE 1263-10, University of Toronto RIS 39685. Blood Collection and Processing Genomic DNA was extracted from whole peripheral blood samples collected in EDTA tubes. DNA extraction and purification followed manufacturer’s recommended protocols, using Qiagen AutoPure LS kit with Gentra automated extraction (first time point) or manual extraction (second time point), due to discontinuation of the equipment but using the same commercial reagents. DNA was quantified using Nanodrop spectrometer and diluted to 50ng/uL. To assess the reproducibility of the EPIC array, we also obtained technical replicates for 16 out of the 48 samples, for a total of 64 samples submitted for further analyses. Whole Genome Sequencing data is also available for the samples described above. Characterization of DNA Methylation using the EPIC array Approximately 1,000ng of human genomic DNA was used for bisulphite conversion. Methylation status was evaluated using the MethylationEPIC array at The Centre for Applied Genomics (TCAG, Hospital for Sick Children, Toronto, Ontario, Canada), following protocols recommended by Illumina (San Diego, California, USA). Processing and Analysis of DNA Methylation Data The R/Bioconductor packages Meffil (version 1.1.0), RnBeads (version 2.6.0), minfi (version 1.34.0) and wateRmelon (version 1.32.0) were used to import, process and perform quality control (QC) analyses on the methylation data. Starting with the 64 samples, we first used Meffil to infer the sex of the 64 samples and compared the inferred sex to reported sex. Utilizing the 59 SNP probes that are available as part of the EPIC array, we calculated concordance between the methylation intensities of the samples and the corresponding genotype calls extracted from their WGS data. We then performed comprehensive sample-level and probe-level QC using the RnBeads QC pipeline. Specifically, we (1) removed probes if their target sequences overlap with a SNP at any base, (2) removed known cross-reactive probes (3) used the iterative Greedycut algorithm to filter out samples and probes, using a detection p-value threshold of 0.01 and (4) removed probes if more than 5% of the samples having a missing value. Since RnBeads does not have a function to perform probe filtering based on bead number, we used the wateRmelon package to extract bead numbers from the IDAT files and calculated the proportion of samples with bead number < 3. Probes with more than 5% of samples having low bead number (< 3) were removed. For the comparison of normalization methods, we also computed detection p-values using out-of-band probes empirical distribution with the pOOBAH() function in the SeSAMe (version 1.14.2) R package, with a p-value threshold of 0.05, and the combine.neg parameter set to TRUE. In the scenario where pOOBAH filtering was carried out, it was done in parallel with the previously mentioned QC steps, and the resulting probes flagged in both analyses were combined and removed from the data. Normalization Methods Evaluated The normalization methods compared in this study were implemented using different R/Bioconductor packages and are summarized in Figure 1. All data was read into R workspace as RG Channel Sets using minfi’s read.metharray.exp() function. One sample that was flagged during QC was removed, and further normalization steps were carried out in the remaining set of 63 samples. Prior to all normalizations with minfi, probes that did not pass QC were removed. Noob, SWAN, Quantile, Funnorm and Illumina normalizations were implemented using minfi. BMIQ normalization was implemented with ChAMP (version 2.26.0), using as input Raw data produced by minfi’s preprocessRaw() function. In the combination of Noob with BMIQ (Noob+BMIQ), BMIQ normalization was carried out using as input minfi’s Noob normalized data. Noob normalization was also implemented with SeSAMe, using a nonlinear dye bias correction. For SeSAMe normalization, two scenarios were tested. For both, the inputs were unmasked SigDF Sets converted from minfi’s RG Channel Sets. In the first, which we call “SeSAMe 1”, SeSAMe’s pOOBAH masking was not executed, and the only probes filtered out of the dataset prior to normalization were the ones that did not pass QC in the previous analyses. In the second scenario, which we call “SeSAMe 2”, pOOBAH masking was carried out in the unfiltered dataset, and masked probes were removed. This removal was followed by further removal of probes that did not pass previous QC, and that had not been removed by pOOBAH. Therefore, SeSAMe 2 has two rounds of probe removal. Noob normalization with nonlinear dye bias correction was then carried out in the filtered dataset. Methods were then compared by subsetting the 16 replicated samples and evaluating the effects that the different normalization methods had in the absolute difference of beta values (|β|) between replicated samples. Background The Infinium EPIC array measures the methylation status of > 850,000 CpG sites. The EPIC BeadChip uses a two-array design: Infinium Type I and Type II probes. These probe types exhibit different technical characteristics which may confound analyses. Numerous normalization and pre-processing methods have been developed to reduce probe type bias as well as other issues such as background and dye bias. Methods This study evaluates the performance of various normalization methods using 16 replicated samples and three metrics: absolute beta-value difference, overlap of non-replicated CpGs between replicate pairs, and effect on beta-value distributions. Additionally, we carried out Pearson’s correlation and intraclass correlation coefficient (ICC) analyses using both raw and SeSAMe 2 normalized data. Results The method we define as SeSAMe 2, which consists of the application of the regular SeSAMe pipeline with an additional round of QC, pOOBAH masking, was found to be the best-performing normalization method, while quantile-based methods were found to be the worst performing methods. Whole-array Pearson’s correlations were found to be high. However, in agreement with previous studies, a substantial proportion of the probes on the EPIC array showed poor reproducibility (ICC < 0.50). The majority of poor-performing probes have beta values close to either 0 or 1, and relatively low standard deviations. These results suggest that probe reliability is largely the result of limited biological variation rather than technical measurement variation. Importantly, normalizing the data with SeSAMe 2 dramatically improved ICC estimates, with the proportion of probes with ICC values > 0.50 increasing from 45.18% (raw data) to 61.35% (SeSAMe 2). We provide data on an Excel file, with absolute differences in beta values between replicate samples for each probe provided in different tabs for raw data and different normalization methods.

This registry contains raw data relating to baseline assessments of patients with sequelae after COVID-19.

Global changes in response to human encroachment into natural habitats and carbon emissions are driving the biodiversity extinction crisis and increasing disease emergence risk. Host distributions are one critical component to identify areas at risk of viral spillover, and bats act as reservoirs of diverse viruses. We developed a reproducible ecological niche modelling pipeline for bat hosts of SARS-like viruses (subgenus Sarbecovirus), given that several closely-related viruses have been discovered and sarbecovirus-host interactions have gained attention since SARS-CoV-2 emergence. We assessed sampling biases and modeled current distributions of bats based on climate and landscape relationships and project future scenarios for host hotspots. The most important predictors of species distributions were temperature seasonality and cave availability. We identified concentrated host hotspots in Myanmar and projected range contractions for most species by 2100. Our projections indicate hotspots will shift east in Southeast Asia in locations greater than 2 °C hotter in a fossil-fueled development future. Hotspot shifts have implications for conservation and public health, as loss of population connectivity can lead to local extinctions, and remaining hotspots may concentrate near human populations. Data was processed in R v. 4.1.2. We spatially predicted the occurrence of all known Sarbecovirus hosts regardless of the first viral detection location using Ecological Niche Models (ENMs) and approximating them to species distribution models (SDMs) while also assessing sampling bias. All records for bat hosts are from 1970 onwards. Please read the README html file.

O vírus da bronquite infecciosa das galinhas (IBV) é o agente causador de uma doença aviária economicamente importante. No Brasil, esta doença ocasiona problemas respiratórios, renais e reprodutivos em aves de todas as idades, apesar da vacinação constante com a cepa Massachusetts H120. Esta falha na proteção conferida pela vacina é ocasionada por mutações nos nucleotídeos do gene da glicoproteína da espícula, a qual está envolvida no processo de interação comas células do hospedeiro, a neutralização e a indução de imunidade protetora. As variantes brasileiras resultantes dessa mutação genética estão presentes desde os anos 80 e este estudo teve como objetivo analisar epidemiologicamente e caracterizar molecularmente os vírus variantes existentes durante 2010-2015 e realizar uma análise bioinformática das sequências disponíveis no GenBank em um período de 40 anos. Das 453 amostras analisadas, 61,4% foram positivas para IBV e 75,9% delas foram consideradas variantes e foram detectados em aves de todas as idades, distribuídos em todas as 5 regiões do Brasil. Um fragmento de 559-566 pb foi obtido a partir de 12 isolados, onde BR-I foi a variante predominante ao contrario que apenas um isolado pertencia ao genótipo BR-II. Análise bioinformática de 40 anos de variantes do IBV brasileiros revelou uma predominância de codões com as substituições não sinónimos no primeiro terço do gene S1 e uma relação dN / dS de 0,6757, indicando que esta porção do gene estava sob selecção negativa. Além disso a previsão de pontos de de N-glicosilação mostrou que a maioria das amostras variantes BR-I (entre o 2003 e início de 2014) apresentam um ponto adicional na posição 20, enquanto as variantes mais novas não apresentam esse ponto de nglicosilação. Estes resultados sugerem que as variantes brasileiras teriam sofrido mutações provavelmente drásticas em alguns pontos do genoma, entre os anos de 1983 a 2003 e depois de atingir uma estrutura antigênica eficaz o suficiente para a invasão e replicação em seus hospedeiros, o processo de seleção mudou para seleção negativa. Infectious bronchitis virus (IBV) is the causative agent of an economically important disease of poultry. In Brazil this disease causes respiratory, renal and reproductive problems in birds of all ages, despite constant vaccination with the Massachusetts strain H120. This lack of immunological protection is known to be due the genetic variation in the spike glycoprotein of IBV, which is involved in host cell attachment, neutralization and the induction of protective immunity. Brazilian IBV variants resulting of this genetic variation are present since the 80s and this study aimed to epidemiologicaly analyze and molecularly characterize the existing variants during 2010-2015 and perform a bioinformatics analysis of the available sequences of IBV variants in a 40 year period. Of the 453 samples tested, 61.4% were positive for IBV and 75.9% of them were considered variants and were detected in birds of all ages, distributed in all five Brazilian regions. A fragment of 559-566 bp was obtained from 12 isolates, where BR-I was the predominant variant while only one isolate belonged to the BR-II genotype. Bioinformatics analysis of the sequences of 40 years of Brazilian IBV variants was performed and the ratio of non-synonymous substitutions per non-synonymous site (dn) to synonymous substitutions per synonymous site (ds) dN/dS was calculated. It revealed a predominance of codons with non-synonymous substitutions in the first third of the S1 gene and a dN/dS ratio of 0.6757, indicating that this portion of the gene was under negative selection. Additionally prediction of N-glycosilation sites showed that most of the BR-I variants (from 2003 to early 2014) present an extra site at animoacid position 20, while the newest ones lack this feature.Together these results suggest that IBV Brazilian variants had probably suffered drastic mutations in some points between the years 1983 to 2003 and after achieving an antigenic structure effective enough for invasion and replication in their hosts, the selection processes became silent.

This registry contains raw data regarding baseline and endpoint assessments of patients with COVID-19 in hospitals and in the ICU.

Sero-surveillance can monitor and project disease burden and risk. However, SARS-CoV-2 antibody test results can produce false positive results, limiting their efficacy as a sero-surveillance tool to estimate population-level SARS-CoV-2 exposure. False positive SARS-CoV-2 antibody results have been associated with malaria exposure, and understanding this association is essential to interpret sero-surveillance results from malaria-endemic countries. Here, pre-pandemic samples from eight malaria endemic and non-endemic countries and four continents were tested by ELISA to measure SARS-CoV-2 Spike S1 subunit reactivity. Individuals with acute malaria infection generated substantial reactivity to SARS-CoV-2. Cross-reactivity was not associated with reactivity to other human coronaviruses or other SARS-CoV-2 proteins, as measured by peptide and protein arrays. ELISAs with deglycosylated and desialated Spike S1 subunits revealed that cross-reactive antibodies target sialic acid on N-linked glycans of the Spike protein. The functional activity of cross-reactive antibodies measured by neutralization assays showed that cross-reactive antibodies did not neutralize SARS-CoV-2 in vitro. Since routine use of heavily glycosylated or sialated assays could result in false positive SARS-CoV-2 antibody results in malaria endemic regions, which could overestimate exposure and population-level immunity, we explored methods to increase specificity by reducing cross-reactivity. Overestimating population-level exposure to SARS-CoV-2 could lead to underestimates of risk of continued COVID-19 transmission in sub-Saharan Africa.

This REPOSITORY contains the data used for the main figures of the manuscript. Data for main figures: Figure 1 Fig1a: Cumulative number of Covid-19 confirmed cases and deaths until the 30th of April 2020, as provided by the Brazilian ministry of Health at https://covid.saude.gov.br/; Fig1b: number of Covid-19 confirmed cases and deaths until the 30th of April 2020, as provided by the Brazilian ministry of Health at https://covid.saude.gov.br/; Fig1c: reproduction number (Rt) estimates and confidence intervals for São Paulo city. Fig1d: reproduction number (Rt) estimates and confidence intervals for Rio de Janeiro city. Figue 2 Fig2a: Dates of first confirmed Covid-19 case and death for each Brazilian state. Dates and states of collection of all 427 sequences generated in this study. Fig2b: Number of SARI Covid-19 confirmed cases and number of novel genomes generated in this study per Brazilian state. Fig3c: Number of samples per lag time (in days) between symptom onset and collection date. Figure 3 Fig3a: Maximum Clade Credibility (MCC) tree of 1,182 SARS-CoV-2 genomes, including 427 novel genomes for Brazil. For details, see supplementary data. Fig3c: Number of national and international air passengers in Brazil per flight and per day from January to 30th April 2020. Figue 4 Fig4a: Continuous phylogeography Fig4b: Discrete phylogeography Fig4c: Average distance of air travel in Brazil Brazil currently has one of the fastest growing SARS-CoV-2 epidemics in the world. Owing to limited available data, assessments of the impact of non-pharmaceutical interventions (NPIs) on virus spread remain challenging. Using a mobility-driven transmission model, we show that NPIs reduced the reproduction number from >3 to 1–1.6 in São Paulo and Rio de Janeiro. Sequencing of 427 new genomes and analysis of a geographically representative genomic dataset identified >100 international virus introductions in Brazil. We estimate that most (76%) of the Brazilian strains fell in three clades that were introduced from Europe between 22 February11 March 2020. During the early epidemic phase, we found that SARS-CoV-2 spread mostly locally and within-state borders. After this period, despite sharp decreases in air travel, we estimated multiple exportations from large urban centers that coincided with a 25% increase in average travelled distances in national flights. This study sheds new light on the epidemic transmission and evolutionary trajectories of SARS-CoV-2 lineages in Brazil, and provide evidence that current interventions remain insufficient to keep virus transmission under control in the country. Please see Materials and Methods section in Supplementary Materials. 

COVID-19 incidence in Brazil was obtained from Brasil.io (https://brasil.io/covid19/), which compiles data from all the Brazilian state health agencies and was accessed on 2020-10-06. The period considered in the analysis was from the first COVID-19 case to the 26th epidemiological week of 2020 (which ends on the 27 th of June 2020). The COVID-19 incidence in countries around the world was collected from Coronavirus COVID-19 Global Cases by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University (JHU) (https://coronavirus.jhu.edu/). State-level data were considered for Colombia and Mexico, and a country-level was considered for the other countries under investigation. Dengue epidemiological and serological data was compiled from data published regularly in the official epidemiological bulletins during 2019 and 2020 by the Brazilian Ministry of Health (Ministério da Saúde, 2020a and 2020b). The incidence available via DATASUS (2020) considered the period from the 27th epidemiological week of 2019 to the 26th epidemiological week of 2020. This incidence for Latin American countries was collected from the Pan American Health Organization (www.paho.org), which also provides dengue incidence data on a state level for Mexico. For Colombian states data was collected from bulletins made available by the Colombian Health Ministry (https://www.minsalud.gov.co). For other countries considered data was collected from disease threat reports provided by the European Centre for Disease Prevention and Control - (ECDC - www.ecdc.europa.eu). Here we investigated whether the dengue fever pandemic of 2019-2020 may have influenced COVID-19 incidence and spread around the world. In Brazil, the geographic distribution of dengue fever was highly complementary to that of COVID-19. This was accompanied by an inverse correlation between COVID-19 and dengue fever incidence that could not be explained by socioeconomic factors. This inverse correlation was observed for 5,016 Brazilian municipalities reporting COVID-19 cases, 558 micro- and 137 meso-regions, 27 states and 5 regions. Brazilian states with high population levels of dengue IgM in 2020 exhibited: (i) lower COVID-19 case and death incidence, (ii) slower infection growth rates, and (iii) took longer to accumulate COVID-19 cases. No such inverse correlations were observed for the chikungunya virus, which is also transmitted by the Aedes aegypti mosquito. The same inverse correlation between COVID-19 and dengue fever incidence was observed for 145 locations (66 countries and the 64 states of Mexico and Colombia) in Latin America, the Caribbean, and Asia. Countries with high dengue incidence took longer to accumulate COVID-19 cases than those without dengue. Although the dataset considered has quality and availability limitations, these findings raise the possibility of an immunological cross-reaction between dengue virus serotypes and SARS-CoV-2, which could have led to partial immunological protection for COVID-19 in dengue infected communities. However, further studies are necessary to better test this hypothesis. For more information about the datasets and the analysis of them please contact the authors.

This registry contains raw data regarding baseline and endpoint assessments of patients with COVID-19 in hospitals and in the ICU.