Purification, characterization and substrate specificity of a trypsin from the Amazonian fish tambaqui (Colossoma macropomum)

An enzyme was purified from the pyloric caecum of tambaqui (Colossoma macropomum) through heat treatment, ammonium sulfate fractionation, Sephadex (R) G-75 and p-aminobenzamidine-agarose affinity chromatography. the enzyme had a molecular mass of 23.9 kDa, NH(2)-terminal amino acid sequence of IVGGYECKAHSQPHVSLNI and substrate specificity for arginine at P1, efficiently hydrolizing substrates with leucine and lysine at P2 and serine and arginine at P1'. Using the substrate z-FR-MCA, the enzyme exhibited greatest activity at pH 9.0 and 50 degrees C, whereas, with BAPNA activity was higher in a pH range of 7.5-11.5 and at 70 degrees C. Moreover, the enzyme maintained ca. 60% of its activity after incubated for 3 h at 60 degrees C. the enzymatic activity significantly decreased in the presence of TLCK, benzamidine (trypsin inhibitors) and PMSF (serine protease inhibitor). This source of trypsin may be an attractive alternative for the detergent and food industry. (C) 2010 Elsevier Inc. All rights reserved.Ministry of Fisheries and AquacultureCoordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)FINEPFACEPEPETROBRASUniv Fed Pernambuco, LABENZ, Dept Bioquim CCB, BR-50670910 Recife, PE, BrazilUniv Fed Pernambuco, LIKA, BR-50670910 Recife, PE, BrazilUniversidade Federal de São Paulo, Escola Paulista Med, Dept Biofis, BR-04044020 São Paulo, BrazilUniversidade Federal de São Paulo, Escola Paulista Med, Dept Biofis, BR-04044020 São Paulo, BrazilWeb of Scienc

Yaravirus: A novel 80-nm virus infecting Acanthamoeba castellanii

Here we report the discovery of Yaravirus, a lineage of amoebal virus with a puzzling origin and evolution. Yaravirus presents 80-nm-sized particles and a 44,924-bp dsDNA genome encoding for 74 predicted proteins. Yaravirus genome annotation showed that none of its genes matched with sequences of known organ-isms at the nucleotide level; at the amino acid level, six predicted proteins had distant matches in the nr database. Complimentary prediction of three-dimensional structures indicated possible func-tion of 17 proteins in total. Furthermore, we were not able to retrieve viral genomes closely related to Yaravirus in 8,535 publicly available metagenomes spanning diverse habitats around the globe. The Yaravirus genome also contained six types of tRNAs that did not match commonly used codons. Proteomics revealed that Yaravirus particles contain 26 viral proteins, one of which potentially represent-ing a divergent major capsid protein (MCP) with a predicted double jelly-roll domain. Structure-guided phylogeny of MCP suggests that Yaravirus groups together with the MCPs of Pleurochrysis endemic viruses. Yaravirus expands our knowledge of the diversity of DNA viruses. The phylogenetic distance between Yaravirus and all other viruses highlights our still preliminary assessment of the genomic diversity of eukaryotic viruses, reinforcing the need for the isolation of new viruses of protists

Ubiquitous giants: a plethora of giant viruses found in Brazil and Antarctica

Since the discovery of giant viruses infecting amoebae in 2003, many dogmas of virology have been revised and the search for these viruses has been intensified. Over the last few years, several new groups of these viruses have been discovered in various types of samples and environments.In this work, we describe the isolation of 68 giant viruses of amoeba obtained from environmental samples from Brazil and Antarctica. Isolated viruses were identified by hemacolor staining, PCR assays and electron microscopy (scanning and/or transmission). A total of 64 viruses belonging to the Mimiviridae family were isolated (26 from lineage A, 13 from lineage B, 2 from lineage C and 23 from unidentified lineages) from different types of samples, including marine water from Antarctica, thus being the first mimiviruses isolated in this extreme environment to date. Furthermore, a marseillevirus was isolated from sewage samples along with two pandoraviruses and a cedratvirus (the third to be isolated in the world so far). Considering the different type of samples, we found a higher number of viral groups in sewage samples. Our results reinforce the importance of prospective studies in different environmental samples, therefore improving our comprehension about the circulation anddiversity of these viruses in nature

A Brief History of Giant Viruses’ Studies in Brazilian Biomes

Almost two decades after the isolation of the first amoebal giant viruses, indubitably the discovery of these entities has deeply affected the current scientific knowledge on the virosphere. Much has been uncovered since then: viruses can now acknowledge complex genomes and huge particle sizes, integrating remarkable evolutionary relationships that date as early as the emergence of life on the planet. This year, a decade has passed since the first studies on giant viruses in the Brazilian territory, and since then biomes of rare beauty and biodiversity (Amazon, Atlantic forest, Pantanal wetlands, Cerrado savannas) have been explored in the search for giant viruses. From those unique biomes, novel viral entities were found, revealing never before seen genomes and virion structures. To celebrate this, here we bring together the context, inspirations, and the major contributions of independent Brazilian research groups to summarize the accumulated knowledge about the diversity and the exceptionality of some of the giant viruses found in Brazil

Gene duplication as a major force driving the genome expansion in some giant viruses

International audienceGiant viruses with their gigantic genomes are among the most intriguing components of the virosphere. How these viruses attained such giant genomes remains unclear, despite considerable efforts to understand this phenomenon. Here, we describe the discovery of cedratvirus pambiensis, an amoebal giant virus isolated in Brazil. Although the virion morphology and replication cycle of c. pambiensis are very similar to those described for other cedratviruses, whole genome sequencing revealed the largest cedratvirus genome ever described, with 623,564 base pairs and 842 predicted protein-coding genes (among them, 76 ORFans). Genome analysis has revealed an unprecedented number of paralogous genes, with ~73% of the c. pambiensis genome being composed of genes with two or more copies. Large families of functionally diverse paralogous genes included up to >70 copies and were distributed across the genome. The in-depth investigation of the mechanisms and origins of gene duplications revealed that both tandem-like duplications and distal transfer of syntenic blocks of genes contributed to the c. pambiensis genomic expansion. Finally, a comprehensive genome analysis of viruses from all known giant virus families suggested that gene duplication is one of the key mechanisms underlying genomic gigantism across the phylum Nucleocytoviricota . The expansion of viral genomes through successive duplications followed by subfunctionalization and exaptation of the paralogous gene copies may promote the adaptation of giant viruses to a variety of niches. IMPORTANCE Giant viruses are noteworthy not only due to their enormous particles but also because of their gigantic genomes. In this context, a fundamental question has persisted: how did these genomes evolve? Here we present the discovery of cedratvirus pambiensis, featuring the largest genome ever described for a cedratvirus. Our data suggest that the larger size of the genome can be attributed to an unprecedented number of duplicated genes. Further investigation of this phenomenon in other viruses has illuminated gene duplication as a key evolutionary mechanism driving genome expansion in diverse giant viruses. Although gene duplication has been described as a recurrent event in cellular organisms, our data highlights its potential as a pivotal event in the evolution of gigantic viral genomes

Seek COVER: using a disease proxy to rapidly develop and validate a personalized risk calculator for COVID-19 outcomes in an international network

Background We investigated whether we could use influenza data to develop prediction models for COVID-19 to increase the speed at which prediction models can reliably be developed and validated early in a pandemic. We developed COVID-19 Estimated Risk (COVER) scores that quantify a patient’s risk of hospital admission with pneumonia (COVER-H), hospitalization with pneumonia requiring intensive services or death (COVER-I), or fatality (COVER-F) in the 30-days following COVID-19 diagnosis using historical data from patients with influenza or flu-like symptoms and tested this in COVID-19 patients. Methods We analyzed a federated network of electronic medical records and administrative claims data from 14 data sources and 6 countries containing data collected on or before 4/27/2020. We used a 2-step process to develop 3 scores using historical data from patients with influenza or flu-like symptoms any time prior to 2020. The first step was to create a data-driven model using LASSO regularized logistic regression, the covariates of which were used to develop aggregate covariates for the second step where the COVER scores were developed using a smaller set of features. These 3 COVER scores were then externally validated on patients with 1) influenza or flu-like symptoms and 2) confirmed or suspected COVID-19 diagnosis across 5 databases from South Korea, Spain, and the United States. Outcomes included i) hospitalization with pneumonia, ii) hospitalization with pneumonia requiring intensive services or death, and iii) death in the 30 days after index date. Results Overall, 44,507 COVID-19 patients were included for model validation. We identified 7 predictors (history of cancer, chronic obstructive pulmonary disease, diabetes, heart disease, hypertension, hyperlipidemia, kidney disease) which combined with age and sex discriminated which patients would experience any of our three outcomes. The models achieved good performance in influenza and COVID-19 cohorts. For COVID-19 the AUC ranges were, COVER-H: 0.69–0.81, COVER-I: 0.73–0.91, and COVER-F: 0.72–0.90. Calibration varied across the validations with some of the COVID-19 validations being less well calibrated than the influenza validations. Conclusions This research demonstrated the utility of using a proxy disease to develop a prediction model. The 3 COVER models with 9-predictors that were developed using influenza data perform well for COVID-19 patients for predicting hospitalization, intensive services, and fatality. The scores showed good discriminatory performance which transferred well to the COVID-19 population. There was some miscalibration in the COVID-19 validations, which is potentially due to the difference in symptom severity between the two diseases. A possible solution for this is to recalibrate the models in each location before use