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Background Little is currently known about vaccine effectiveness (VE) for either 2 doses of Oxford-AstraZeneca (ChAdOx1) viral vector vaccine or CoronaVac (Instituto Butantan) inactivated viral vaccine followed by a third dose of mRNA vaccine (Pfizer/BioNTech) among healthcare workers (HCWs). Methods We conducted a retrospective cohort study among HCWs (aged ≥18 years) working in a private healthcare system in Brazil from January to December 2021. VE was defined as 1 – incidence rate ratio (IRR), with IRR determined using Poisson models with the occurrence of laboratory-confirmed coronavirus disease 2019 (COVID-19) infection as the outcome, adjusting for age, sex, and job type. We compared those receiving viral vector or inactivated viral primary series (2 doses) with those who received an mRNA booster. Results A total of 11 427 HCWs met the inclusion criteria. COVID-19 was confirmed in 31.5% of HCWs receiving 2 doses of CoronaVac vaccine versus 0.9% of HCWs receiving 2 doses of CoronaVac vaccine with mRNA booster (P < .001) and 9.8% of HCWs receiving 2 doses of ChAdOx1 vaccine versus 1% among HCWs receiving 2 doses of ChAdOx1 vaccine with mRNA booster (P < .001). In the adjusted analyses, the estimated VE was 92.0% for 2 CoronaVac vaccines plus mRNA booster and 60.2% for 2 ChAdOx1 vaccines plus mRNA booster, when compared with those with no mRNA booster. Of 246 samples screened for mutations, 191 (77.6%) were Delta variants. Conclusions While 2 doses of ChAdOx1 or CoronaVac vaccines prevent COVID-19, the addition of a Pfizer/BioNTech booster provided significantly more protection.

Objective:We investigated real-world vaccine effectiveness for Oxford-AstraZeneca (ChAdOx1) and CoronaVac against laboratory-confirmed severe acute respiratory coronavirus virus 2 (SARS-CoV-2) infection among healthcare workers (HCWs).Methods:We conducted a retrospective cohort study among HCWs (aged ≥18 years) working in a private healthcare system in Brazil between January 1, 2021 and August 3, 2021, to assess vaccine effectiveness. We calculated vaccine effectiveness as 1 − rate ratio (RR), with RR determined by adjusting Poisson models with the occurrence of SARS-CoV-2 infection as the outcome and the vaccination status as the main variable. We used the logarithmic link function and simple models adjusting for sex, age, and job types.Results:In total, 13,813 HCWs met the inclusion criteria for this analysis. Among them, 6,385 (46.2%) received the CoronaVac vaccine, 5,916 (42.8%) received the ChAdOx1 vaccine, and 1,512 (11.0%) were not vaccinated. Overall, COVID-19 occurred in 6% of unvaccinated HCWs, 3% of HCWs who received 2 doses of CoronaVac vaccine, and 0.7% of HCWs who received 2 doses of ChAdOx1 vaccine (P < .001). In the adjusted analyses, the estimated vaccine effectiveness rates were 51.3% for CoronaVac, and 88.1% for ChAdOx1 vaccine. Both vaccines reduced the number of hospitalizations, the length of hospital stay, and the need for mechanical ventilation. In addition, 19 SARS-CoV-2 samples from 19 HCWs were screened for mutations of interest. Of 19 samples, 18 were the γ (gamma) variant.Conclusions:Although both COVID-19 vaccines (viral vector and inactivated virus) can significantly prevent COVID-19 among HCWs, CoronaVac was much less effective. The COVID-19 vaccines were also effective against the dominant γ variant.

ObjectivesWe aimed to determine risk factors for the development of long coronavirus disease (COVID) in healthcare workers (HCWs).MethodsWe conducted a case-control study among HCWs who had confirmed COVID-19 infection working in a Brazilian healthcare system between March 1, 2020 and July 15, 2022. Cases were defined as those having long COVID per the Centers for Disease Control and Prevention definition. Controls were defined as HCWs who had documented COVID-19 infection but did not develop long COVID. Multiple logistic regression was used to assess the association between exposure variables and long COVID during 180 days of follow-up.ResultsOf 7,051 HCWs diagnosed with COVID-19 infection, 1,933 (27.4%) who developed long COVID were compared to 5,118 (72.6%) who did not. The majority of those with long COVID (51.8%) had 3 or more symptoms. Factors associated with development of long COVID were female sex (OR 1.21 [CI95 1.05-1.39]), age (OR 1.01 [CI95 1.00-1.02]), and two or more COVID-19 infections (1.27 [CI95 1.07-1.50]). Those infected with the Delta variant (OR 0.30 [CI95 0.17-0.50]) or the Omicron variant (OR 0.49 [CI95 0.30-0.78]), and those receiving four COVID-19 vaccine doses prior to infection (OR 0.05 [CI95 0.01-0.19]) were significantly less likely to develop long COVID.ConclusionsLong COVID can be prevalent among HCWs. We found that acquiring more than one COVID-19 infection was a major risk factor for long COVID, while maintenance of immunity via vaccination was highly protective.

We aimed to investigate real-world vaccine effectiveness (VE) for Oxford-AstraZeneca (ChAdOx1) and CoronaVac against laboratory-confirmed COVID-19 infection among healthcare workers (HCWs).We conducted a retrospective cohort study among HCWs (aged ≥18 years) working in a private healthcare system in Brazil between January 1, 2021 and August 3, 2021. To assess VE, we calculated VE=1-RR (rate ratio), with RR determined by adjusting Poisson models with the occurrence of COVID-19 infection as the outcome, and the vaccination status as the main exploratory variable. We used the logarithmic link function and simple models adjusting for sex, age and job types.13,813 HCWs met the inclusion criteria for this analysis. 6,385 (46.2%) received the CoronaVacvaccine, 5,916 (42.8%) received the ChAdOx1 vaccine, and 1,512 (11.0%) were not vaccinated. Overall, COVID-19 infection cases happened in 6% of unvaccinated HCWs, 3% of HCWs receiving two doses of CoronaVacvaccine, and 0.7% of HCWs receiving two doses of ChAdOx1 vaccine (p-value0.001). In the adjusted analyses, the estimated VE was 51.3% for CoronaVac, and 88.1% for ChAdOx1 vaccine. Both vaccines reduced the number of hospitalizations, the length of hospital stay, and the need of mechanical ventilation. Nineteen SARSCoV-2 samples from nineteen HCWs were screened for mutations of interest. Eighteen out of nineteen of those samples were Gamma SARS-CoV-2 variant.While both COVID-19 vaccines (viral vector and inactivated virus) can significantly prevent COVID-19 infection among HCWs, CoronaVac was much less effective. The COVID-19 vaccines were also effective even against a dominant Gamma variant.

Little is currently known about vaccine effectiveness (VE) for either two doses of Oxford-AstraZeneca (ChAdOx1) viral vector vaccine or CoronaVac inactivated viral vaccine followed by a third dose of mRNA vaccine (Pfizer/BioNTech) among healthcare workers (HCWs).We conducted a retrospective cohort study among HCWs (aged ≥18 years) working in a private healthcare system in Brazil from January to December 2021. VE was defined as 1-IRR (incidence rate ratio), with IRR determined using Poisson models with the occurrence of laboratory-confirmed COVID-19 infection as the outcome, adjusting for age, sex, and job type. We compared those receiving viral vector or inactivated viral primary series (two doses) to those who received an mRNA booster.A total of 11,427 HCWs met the inclusion criteria. COVID-19 was confirmed in 31.5% of HCWs receiving two doses of CoronaVac vaccine vs. 0.9% of HCWs receiving two doses of CoronaVac vaccine with mRNA booster (p 0.001), and 9.8% of HCWs receiving two doses of ChAdOx1 vaccine vs. 1% among HCWs receiving two doses of ChAdOx1 vaccine with mRNA booster (p 0.001). In the adjusted analyses, the estimated VE was 92.0% for two CoronaVac vaccines plus mRNA booster, and 60.2% for two ChAdOx1 vaccines plus mRNA booster, when compared to those with no mRNA booster. Of 246 samples screened for mutations, 191 (77.6%) were Delta variants.While two doses of ChAdOx1 or CoronaVac vaccines prevent COVID-19, the addition of a Pfizer/BioNTech booster provided significantly more protection.

The experimental determination of a bacteriophage host is a laborious procedure. For this reason, there is a pressing need for reliable computational predictions of bacteriophage hosts in phage research in general and in phage therapy in particular. Here, we present a new program called vHULK for phage host prediction based on 9,504 phage genome features. These features take into account alignment significance scores between predicted-protein sequences in the phage genomes and a curated database of viral protein families. The features were fed to a deep neural network, and four distinct models were trained to predict 61 different host genera and 52 host species. In random controlled test sets, the program obtained 99% and 98% accuracy values at the genus and species levels, respectively. On a validation dataset with 2,178 phage genomes, mean accuracies were 82% and 52% at the genus and species levels, respectively. When compared against other phage host prediction programs on the same validation dataset, vHULK achieved substantially better performance, therefore demonstrating that the program is an advance on the state-of-art in phage host prediction. vHULK is freely available at https://github.com/LaboratorioBioinformatica/vHULK.

International audience; Viruses are now recognized as important players in microbial dynamics and biogeochemical cycles in the oceans. Yet, compared with aquatic ecosystems, virus discovery in terrestrial ecosystems has been challenging partly due to the inherent complexity of soils. To expand our understanding of soil viruses and their putative contributions to soil microbial processes, we analysed metagenomes of community-level virus-enriched suspensions by tangential flow filtration obtained from two French agricultural soils. We found viral sequences representing a total of 239 viral operational taxonomic units that corresponded to 29.5% of the mapping reads in the metagenomic datasets. The analysis of their genomic sequences revealed novel virocell metabolic potential with implications to virus-host interactions, carbon cycling, plant-beneficial functions in the rhizosphere, horizontal gene transfer and other relevant microbial strategies applied to survive in soils.

This is the open peer reviewers comments and recommendations regarding the submitted GigaScience article and/or dataset.

Here we present MARVEL, a tool for prediction of double-stranded DNA bacteriophage sequences in metagenomic bins. MARVEL uses a random forest machine learning approach. We trained the program on a dataset with 1,247 phage and 1,029 bacterial genomes, and tested it on a dataset with 335 bacterial and 177 phage genomes. We show that three simple genomic features extracted from contig sequences were sufficient to achieve a good performance in separating bacterial from phage sequences: gene density, strand shifts, and fraction of significant hits to a viral protein database. We compared the performance of MARVEL to that of VirSorter and VirFinder, two popular programs for predicting viral sequences. Our results show that all three programs have comparable specificity, but MARVEL achieves much better performance on the recall (sensitivity) measure. This means that MARVEL should be able to identify many more phage sequences in metagenomic bins than heretofore has been possible. In a simple test with real data, containing mostly bacterial sequences, MARVEL classified 58 out of 209 bins as phage genomes; other evidence suggests that 57 of these 58 bins are novel phage sequences. Sao Paulo State Research Foundation (FAPESP)Fundacao de Amparo a Pesquisa do Estado de Sao Paulo (FAPESP) [2014/16450-8]; Brazilian Federal Agency CAPESCAPES; CNPqNational Council for Scientific and Technological Development (CNPq); FAPESPFundacao de Amparo a Pesquisa do Estado de Sao Paulo (FAPESP) [2011/50870-6]; CAPESCAPES [3385/2013] DA was supported in part by fellowship Grant No. 2014/16450-8 from the Sao Paulo State Research Foundation (FAPESP). DA and LB were supported by a fellowship from Brazilian Federal Agency CAPES. JS and AdS wish to acknowledge their respective research fellowships from CNPq. This work was supported in part by FAPESP Grant No. 2011/50870-6 and by CAPES Grant No. 3385/2013.

Phages have a major impact on microbial populations. In this work, we discuss how predation, transduction, lysogeny, and phage domestication lead to symbio-centric genomic interactions between bacteria and phages, ranging from antagonistic to mutualistic. Furthermore, these interactions influence bacterial diversification and ecotype formation. We then propose an additional consideration in the form of a symbio-centric ecological speciation framework for bacteria. Our framework builds upon classical morphological and molecular taxonomy by also considering bacteria and their phages as a unit of evolutionary selection. This framework acknowledges the considerable effect that phage interaction has on bacterial genomic content, regulation, and evolution, and will advance our understanding of bacterial evolution.

The field of viral genomic studies has experienced an unprecedented increase in data volume. New strains of known viruses are constantly being added to the GenBank database and so are completely new species with little or no resemblance to our databases of sequences. In addition to this, metagenomic techniques have the potential to further increase the number and rate of sequenced genomes. Besides, it is important to consider that viruses have a set of unique features that often break down molecular biology dogmas, e.g., the flux of information from RNA to DNA in retroviruses and the use of RNA molecules as genomes. As a result, extracting meaningful information from viral genomes remains a challenge and standard methods for comparing the unknown and our databases of characterized sequences may need to be modified. Thus, several bioinformatic approaches and tools have been created to address the challenge of analyzing viral data. In this chapter, we offer descriptions and protocols of some of the most important bioinformatic techniques for comparative analysis of viruses. We also provide comments and discussion on how viruses' unique features can affect standard analyses and how to overcome some of the major sources of problems. Topics include: (1) Clustering of related genomes, (2) Whole genome multiple sequence alignments for small RNA viruses, (3) Protein alignments for marker genes, (4) Analyses based on ortholog groups, and (5) Taxonomic identification and comparisons of viruses from environmental datasets.

The field of viral genomic studies has experienced an unprecedented increase in data volume. New strains of known viruses are constantly being added to the GenBank database and so are completely new species with little or no resemblance to our databases of sequences. In addition to this, metagenomic techniques have the potential to further increase the number and rate of sequenced genomes. Besides, it is important to consider that viruses have a set of unique features that often break down molecular biology dogmas, e.g., the flux of information from RNA to DNA in retroviruses and the use of RNA molecules as genomes. As a result, extracting meaningful information from viral genomes remains a challenge and standard methods for comparing the unknown and our databases of characterized sequences may need to be modified. Thus, several bioinformatic approaches and tools have been created to address the challenge of analyzing viral data. In this chapter, we offer descriptions and protocols of some of the most important bioinformatic techniques for comparative analysis of viruses. We also provide comments and discussion on how viruses' unique features can affect standard analyses and how to overcome some of the major sources of problems. Topics include: (1) Clustering of related genomes, (2) Whole genome multiple sequence alignments for small RNA viruses, (3) Protein alignments for marker genes, (4) Analyses based on ortholog groups, and (5) Taxonomic identification and comparisons of viruses from environmental datasets.

One-step growth curve for phage ZC03. (PDF 88 kb)

Phages ZC01, ZC03 and ZC08 lysis plaques morphology. (PDF 127 kb)

Background Among viruses, bacteriophages are a group of special interest due to their capacity of infecting bacteria that are important for biotechnology and human health. Composting is a microbial-driven process in which complex organic matter is converted into humus-like substances. In thermophilic composting, the degradation activity is carried out primarily by bacteria and little is known about the presence and role of bacteriophages in this process. Results Using Pseudomonas aeruginosa as host, we isolated three new phages from a composting operation at the Sao Paulo Zoo Park (Brazil). One of the isolated phages is similar to Pseudomonas phage Ab18 and belongs to the Siphoviridae YuA-like viral genus. The other two isolated phages are similar to each other and present genomes sharing low similarity with phage genomes in public databases; we therefore hypothesize that they belong to a new genus in the Podoviridae family. Detailed genomic descriptions and comparisons of the three phages are presented, as well as two new clusters of phage genomes in the Viral Orthologous Clusters database of large DNA viruses. We found sequences encoding homing endonucleases that disrupt a putative ribonucleotide reductase gene and an RNA polymerase subunit 2 gene in two of the phages. These findings provide insights about the evolution of two-subunits RNA polymerases and the possible role of homing endonucleases in this process. Infection tests on 30 different strains of bacteria reveal a narrow host range for the three phages, restricted to P. aeruginosa PA14 and three other P. aeruginosa clinical isolates. Biofilm dissolution assays suggest that these phages could be promising antimicrobial agents against P. aeruginosa PA14 infections. Analyses on composting metagenomic and metatranscriptomic data indicate association between abundance variations in both phage and host populations in the environment. Conclusion The results about the newly discovered and described phages contribute to the understanding of tailed bacteriophage diversity, evolution, and role in the complex composting environment. Electronic supplementary material The online version of this article (doi:10.1186/s12864-017-3729-z) contains supplementary material, which is available to authorized users.

tRNA genes in ZC03 and ZC08 genomes. (PDF 21 kb)