Hiperalimentação enteral em traumatismos crânio-encefálicos agudos reposição ou dietoterapia?

Os autores apresentam revisão geral sobre a fisiopatogenia do trauma, ressaltando os estados de hipereatabolismo e hipermetabolismo, suas consequências nutricionais, com as particularidades do trauma encefálico. São feitas, também, considerações sobre as vias, composição e volumes das dietas enterais a serem administradas a pacientes com trauma agudo encefálico, assim como são apontadas questões a serem melhor elucidadas.

Disulfide Biochemistry in 2-Cys Peroxiredoxin: Requirement of Glu50 and Arg146 for the Reduction of Yeast Tsa1 by Thioredoxin

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Synergy of DNA-bending nucleoid proteins and macromolecular crowding in condensing DNA

Many prokaryotic nucleoid proteins bend DNA and form extended helical protein-DNA fibers rather than condensed structures. On the other hand, it is known that such proteins (such as bacterial HU) strongly promote DNA condensation by macromolecular crowding. Using theoretical arguments, we show that this synergy is a simple consequence of the larger diameter and lower net charge density of the protein-DNA filaments as compared to naked DNA, and hence, should be quite general. To illustrate this generality, we use light-scattering to show that the 7kDa basic archaeal nucleoid protein Sso7d from Sulfolobus solfataricus (known to sharply bend DNA) likewise does not significantly condense DNA by itself. However, the resulting protein-DNA fibers are again highly susceptible to crowding-induced condensation. Clearly, if DNA-bending nucleoid proteins fail to condense DNA in dilute solution, this does not mean that they do not contribute to DNA condensation in the context of the crowded living cell. © 2007 World Scientific Publishing Company.

Developing new radiotherapy techniques using linac based gamma radiation sources

A major challenge in cancer radiotherapy is to deliver a lethal dose of radiation to the target volume while minimizing damage to the surrounding normal tissue. We have proposed a model on how treatment efficacy might be improved by interfering with biological responses to DNA damage using exogenous electric fields as a strategy to drastically reduce radiation doses in cancer therapy. This approach is demonstrated at this Laboratory through case studies with prokaryotes (bacteria) and eukaryotes (yeast) cells, in which cellkilling rates induced by both gamma radiation and exogenous electric fields were measured. It was found that when cells exposed to gamma radiation are immediately submitted to a weak electric field, cell death increases more than an order of magnitude compared to the effect of radiation alone. This finding suggests, although does not prove, that DNA damage sites are reached and recognized by means of long-range electric DNA-protein interaction, and that exogenous electric fields could destructively interfere with this process. As a consequence, DNA repair is avoided leading to massive cell death. Here we are proposing the use this new technique for the design and construction of novel radiotherapy facilities associated with linac generated gamma beams under controlled conditions of dose and beam intensity.

Verificação da formação de complexos protéicos nos telômeros de L. amazonensis

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Identificação de proteínas ligantes de CRABP2, proteína envolvida na via de sinalização celular por ácido retinóico

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Resistência a cobre em Xanthomonas axonopodis pv. citri. Estudos de caracterização molecular e bioquímica de genes e proteínas

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Analisando a determinação sexual de vertebrados com base em redes de interação entre proteínas

Pós-graduação em Ciências Biológicas (Genética) - IBB

Papel da proteína ZapA na divisão celular e patogenicidade de Xanthomonas citri subsp. citri

Pós-graduação em Ciências Biológicas (Microbiologia Aplicada) - IBRC

Efeitos da interação e toxicidade das proteínas Cry1 e Vip3A de Bacillus thuringiensis em Diatraea saccharalis (Fabr., 1794) (Lepidoptera: Crambidae)

Pós-graduação em Agronomia (Genética e Melhoramento de Plantas) - FCAV

Determinação de comunidades em redes biológicas integradas

To understand how biological phenomena emerge, the nonlinear interactions among the components envolved in these and the correspondent connected elements, like genes, proteins, etc., can be represented by a mathematical object called graph or network, where interacting elements are represented by edges connecting pairs of nodes. The analysis of various graph-related properties of biological networks has revealed many clues about biological processes. Among these properties, the community structure, i.e. groups of nodes densely connected among themselves, but sparsely connected to other groups, are important for identifying separable functional modules within biological systems for the comprehension of the high-level organization of the cell. Communities' detection can be performed by many algorithms, but most of them are based on the density of interactions among nodes of the same community. So far, the detection and analysis of network communities in biological networks have only been pursued for networks composed by one type of interaction (e.g. protein-protein interactions or metabolic interactions). Since a real biological network is simultaneously composed by protein-protein, metabolic and transcriptional regulatory interactions, it would be interesting to investigate how communities are organized in this type of network. For this purpose, we detected the communities in an integrated biological network of the Escherichia coli and Saccharomyces cerevisiae by using the Clique Percolation Method and we veri ed, by calculating the frequency of each type of interaction and its related entropy, if components of communities... (Complete abstract click electronic access below)

Toxicidade e interação de proteínas Cry1 de Bacillus thuringiensis em Helicoverpa armigera (Hübner) (Lepidóptera: Noctuidae)

Pós-graduação em Agronomia (Genética e Melhoramento de Plantas) - FCAV

Assessing the gain of biological data integration in gene networks inference

Background: A current challenge in gene annotation is to define the gene function in the context of the network of relationships instead of using single genes. The inference of gene networks (GNs) has emerged as an approach to better understand the biology of the system and to study how several components of this network interact with each other and keep their functions stable. However, in general there is no sufficient data to accurately recover the GNs from their expression levels leading to the curse of dimensionality, in which the number of variables is higher than samples. One way to mitigate this problem is to integrate biological data instead of using only the expression profiles in the inference process. Nowadays, the use of several biological information in inference methods had a significant increase in order to better recover the connections between genes and reduce the false positives. What makes this strategy so interesting is the possibility of confirming the known connections through the included biological data, and the possibility of discovering new relationships between genes when observed the expression data. Although several works in data integration have increased the performance of the network inference methods, the real contribution of adding each type of biological information in the obtained improvement is not clear. Methods: We propose a methodology to include biological information into an inference algorithm in order to assess its prediction gain by using biological information and expression profile together. We also evaluated and compared the gain of adding four types of biological information: (a) protein-protein interaction, (b) Rosetta stone fusion proteins, (c) KEGG and (d) KEGG+GO. Results and conclusions: This work presents a first comparison of the gain in the use of prior biological information in the inference of GNs by considering the eukaryote (P. falciparum) organism. Our results indicates that information based on direct interaction can produce a higher improvement in the gain than data about a less specific relationship as GO or KEGG. Also, as expected, the results show that the use of biological information is a very important approach for the improvement of the inference. We also compared the gain in the inference of the global network and only the hubs. The results indicates that the use of biological information can improve the identification of the most connected proteins.

Discriminating Different Classes of Biological Networks by Analyzing the Graphs Spectra Distribution

The brain's structural and functional systems, protein-protein interaction, and gene networks are examples of biological systems that share some features of complex networks, such as highly connected nodes, modularity, and small-world topology. Recent studies indicate that some pathologies present topological network alterations relative to norms seen in the general population. Therefore, methods to discriminate the processes that generate the different classes of networks (e. g., normal and disease) might be crucial for the diagnosis, prognosis, and treatment of the disease. It is known that several topological properties of a network (graph) can be described by the distribution of the spectrum of its adjacency matrix. Moreover, large networks generated by the same random process have the same spectrum distribution, allowing us to use it as a "fingerprint". Based on this relationship, we introduce and propose the entropy of a graph spectrum to measure the "uncertainty" of a random graph and the Kullback-Leibler and Jensen-Shannon divergences between graph spectra to compare networks. We also introduce general methods for model selection and network model parameter estimation, as well as a statistical procedure to test the nullity of divergence between two classes of complex networks. Finally, we demonstrate the usefulness of the proposed methods by applying them to (1) protein-protein interaction networks of different species and (2) on networks derived from children diagnosed with Attention Deficit Hyperactivity Disorder (ADHD) and typically developing children. We conclude that scale-free networks best describe all the protein-protein interactions. Also, we show that our proposed measures succeeded in the identification of topological changes in the network while other commonly used measures (number of edges, clustering coefficient, average path length) failed.

Disulfide Biochemistry in 2-Cys Peroxiredoxin: Requirement of Glu50 and Arg146 for the Reduction of Yeast Tsa1 by Thioredoxin

2-Cys peroxiredoxin (Prx) enzymes are ubiquitously distributed peroxidases that make use of a peroxidatic cysteine (Cys(P)) to decompose hydroperoxides. A disulfide bond is generated as a consequence of the partial unfolding of the alpha-helix that contains Cys(P). Therefore, during its catalytic cycle, 2-Cys Prx alternates between two states, locally unfolded and fully folded. Tsa1 (thiol-specific antioxidant protein 1 from yeast) is by far the most abundant Cys-based peroxidase in Saccharomyces cerevisiae. In this work, we present the crystallographic structure at 2.8 angstrom resolution of Tsa1(C47S) in the decameric form [(alpha(2))(5)] with a DTT molecule bound to the active site, representing one of the few available reports of a 2-Cys Prx (AhpC-Prx1 subfamily) (AhpC, alkyl hydroperoxide reductase subunit C) structure that incorporates a ligand. The analysis of the Tsa1(C47S) structure indicated that G1u50 and Arg146 participate in the stabilization of the Cys(P) alpha-helix. As a consequence, we raised the hypothesis that G1u50 and Arg146 might be relevant to the Cys(P) reactivity. Therefore, Tsa1(E50A) and Tsa1(R146Q) mutants were generated and were still able to decompose hydrogen peroxide, presenting a second-order rate constant in the range of 10(6) M-1 S-1. Remarkably, although Tsa1(E50A) and Tsa1(R146Q) were efficiently reduced by the low-molecular-weight reductant DTT, these mutants displayed only marginal thioredoxin (Trx)-dependent peroxidase activity, indicating that G1u50 and Arg146 are important for the Tsa1-Trx interaction. These results may impact the comprehension of downstream events of signaling pathways that are triggered by the oxidation of critical Cys residues, such as Trx. (C) 2012 Elsevier Ltd. All rights reserved.