Giant Amazonian fish pirarucu (Arapaima gigas): Its viscera as a source of thermostable trypsin

A trypsin was purified from pyloric caeca of pirarucu (Arapaima gigas). the effect of metal ions and protease inhibitors on its activity and its physicochemical and kinetic properties, as well its N-terminal sequence, were determined. A single band (28.0 kDa) was observed by SDS-PAGE. Optimum pH and temperature were 9.0 and 65 degrees C, respectively. the enzyme was stable after incubation for 30 min in a wide pH range (6.0-11.5) and at 55 degrees C. the kinetic parameters K-m, k(cat) and k(cat)/K-m were 0.47 +/- 0.042 mM, 1.33 s(-1) and 2.82 s(-1) mM(-1), respectively, using BApNA as substrate. This activity was shown to be very sensitive to some metal ions, such as Fe2+, Hg2+, Zn2+, Al3+, Pb2+, and was highly inhibited by trypsin inhibitors. the trypsin N-terminal sequence IVGGYECPRNSVPYQ was found. the features of this alkaline peptidase suggest that it may have potential for industrial applications (e.g. food and detergent industries). (C) 2012 Elsevier B.V. All rights reserved.Financiadora de Estudos e Projetos (FINEP/RECAR-CINE)Ministerio da Aquicultura e Pesca (MAP)Empresa brasileira de pesquisa agropecuaria (Embrapa)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Fundacao de Apoio a Ciencia e Tecnologia do Estado de Pernambuco (FACEPE)Petroleo do Brasil S/A (PETROBRAS)Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)Univ Fed Pernambuco, Lab Enzimol LABENZ, Dept Bioquim, BR-50670420 Recife, PE, BrazilUniv Fed Pernambuco, LIKA, BR-50670420 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

Metal-sensitive and thermostable trypsin from the crevalle jack (Caranx hippos) pyloric caeca: purification and characterization

Background: Over the past decades, the economic development and world population growth has led to increased for food demand. Increasing the fish production is considered one of the alternatives to meet the increased food demand, but the processing of fish leads to by-products such as skin, bones and viscera, a source of environmental contamination. Fish viscera have been reported as an important source of digestive proteases with interesting characteristics for biotechnological processes. Thus, the aim of this study was to purify and to characterize a trypsin from the processing by-products of crevalle jack (Caranx hippos) fish.Results: A 27.5 kDa trypsin with N-terminal amino acid sequence IVGGFECTPHVFAYQ was easily purified from the pyloric caeca of the crevalle jack. Its physicochemical and kinetic properties were evaluated using N-alpha-benzoyl-(DL)-arginine-p-nitroanilide (BApNA) as substrate. in addition, the effects of various metal ions and specific protease inhibitors on trypsin activity were determined. Optimum pH and temperature were 8.0 and 50 degrees C, respectively. After incubation at 50 degrees C for 30 min the enzyme lost only 20% of its activity. K-m, k(cat), and k(cat)/K-m values using BApNA as substrate were 0.689 mM, 6.9 s(-1), and 10 s(-1) mM(-1), respectively. High inhibition of trypsin activity was observed after incubation with Cd2+, Al3+, Zn2+, Cu2+, Pb2+, and Hg2+ at 1 mM, revealing high sensitivity of the enzyme to metal ions.Conclusions: Extraction of a thermostable trypsin from by-products of the fishery industry confirms the potential of these materials as an alternative source of these biomolecules. Furthermore, the results suggest that this trypsin-like enzyme presents interesting biotechnological properties for industrial applications.Financiadora de Estudos e Projetos (FINEP/RECARCINE)Petroleo do Brasil S/A (PETROBRAS)Secretaria Especial de Aquicultura e Pesca (SEAP/PR)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)Fundacao de Apoio a Ciencia e Tecnologia do Estado de Pernambuco (FACEPE)Univ Fed Pernambuco, Lab Enzimol LABENZ, Dept Bioquim CCB, BR-50670910 Recife, PE, BrazilUniv Fed Pernambuco, LIKA, BR-50670910 Recife, PE, BrazilUniversidade Federal de São Paulo, Dept Biofis, Escola Paulista Med, BR-04044020 São Paulo, BrazilUniv Fed Pernambuco, Lab Glicoprot, Dept Bioquim CCB, BR-50670910 Recife, PE, BrazilUniversidade Federal de São Paulo, Dept Biofis, Escola Paulista Med, BR-04044020 São Paulo, BrazilWeb of Scienc

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

Minimal information for studies of extracellular vesicles (MISEV2023): From basic to advanced approaches

Extracellular vesicles (EVs), through their complex cargo, can reflect the state of their cell of origin and change the functions and phenotypes of other cells. These features indicate strong biomarker and therapeutic potential and have generated broad interest, as evidenced by the steady year-on-year increase in the numbers of scientific publications about EVs. Important advances have been made in EV metrology and in understanding and applying EV biology. However, hurdles remain to realising the potential of EVs in domains ranging from basic biology to clinical applications due to challenges in EV nomenclature, separation from non-vesicular extracellular particles, characterisation and functional studies. To address the challenges and opportunities in this rapidly evolving field, the International Society for Extracellular Vesicles (ISEV) updates its 'Minimal Information for Studies of Extracellular Vesicles', which was first published in 2014 and then in 2018 as MISEV2014 and MISEV2018, respectively. The goal of the current document, MISEV2023, is to provide researchers with an updated snapshot of available approaches and their advantages and limitations for production, separation and characterisation of EVs from multiple sources, including cell culture, body fluids and solid tissues. In addition to presenting the latest state of the art in basic principles of EV research, this document also covers advanced techniques and approaches that are currently expanding the boundaries of the field. MISEV2023 also includes new sections on EV release and uptake and a brief discussion of in vivo approaches to study EVs. Compiling feedback from ISEV expert task forces and more than 1000 researchers, this document conveys the current state of EV research to facilitate robust scientific discoveries and move the field forward even more rapidly

Catálogo Taxonômico da Fauna do Brasil: setting the baseline knowledge on the animal diversity in Brazil

The limited temporal completeness and taxonomic accuracy of species lists, made available in a traditional manner in scientific publications, has always represented a problem. These lists are invariably limited to a few taxonomic groups and do not represent up-to-date knowledge of all species and classifications. In this context, the Brazilian megadiverse fauna is no exception, and the Catálogo Taxonômico da Fauna do Brasil (CTFB) (http://fauna.jbrj.gov.br/), made public in 2015, represents a database on biodiversity anchored on a list of valid and expertly recognized scientific names of animals in Brazil. The CTFB is updated in near real time by a team of more than 800 specialists. By January 1, 2024, the CTFB compiled 133,691 nominal species, with 125,138 that were considered valid. Most of the valid species were arthropods (82.3%, with more than 102,000 species) and chordates (7.69%, with over 11,000 species). These taxa were followed by a cluster composed of Mollusca (3,567 species), Platyhelminthes (2,292 species), Annelida (1,833 species), and Nematoda (1,447 species). All remaining groups had less than 1,000 species reported in Brazil, with Cnidaria (831 species), Porifera (628 species), Rotifera (606 species), and Bryozoa (520 species) representing those with more than 500 species. Analysis of the CTFB database can facilitate and direct efforts towards the discovery of new species in Brazil, but it is also fundamental in providing the best available list of valid nominal species to users, including those in science, health, conservation efforts, and any initiative involving animals. The importance of the CTFB is evidenced by the elevated number of citations in the scientific literature in diverse areas of biology, law, anthropology, education, forensic science, and veterinary science, among others

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost