Gene expression profiling in leiomyosarcomas and undifferentiated pleomorphic sarcomas: SRC as a new diagnostic marker

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

Proteomic Analysis of Gastrocnemius Muscle in Rats with Streptozotocin-Induced Diabetes and Chronically Exposed to Fluoride

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

Análise de propriedades topológicas das redes biológicas integradas da Escherichia coli e da Saccharomyces cerevisiae

Biological processes are complex and possess emergent properties that can not be explained or predict by reductionism methods. To overcome the limitations of reductionism, researchers have been used a group of methods known as systems biology, a new interdisciplinary eld of study aiming to understand the non-linear interactions among components embedded in biological processes. These interactions can be represented by a mathematical object called graph or network, where the elements are represented by nodes and the interactions by edges that link pair of nodes. The networks can be classi- ed according to their topologies: if node degrees follow a Poisson distribution in a given network, i.e. most nodes have approximately the same number of links, this is a random network; if node degrees follow a power-law distribution in a given network, i.e. small number of high-degree nodes and high number of low-degree nodes, this is a scale-free network. Moreover, networks can be classi ed as hierarchical or non-hierarchical. In this study, we analised Escherichia coli and Saccharomyces cerevisiae integrated molecular networks, which have protein-protein interaction, metabolic and transcriptional regulation interactions. By using computational methods, such as MathematicaR , and data collected from public databases, we calculated four topological parameters: the degree distribution P(k), the clustering coe cient C(k), the closeness centrality CC(k) and the betweenness centrality CB(k). P(k) is a function that calculates the total number of nodes with k degree connection and is used to classify the network as random or scale-free. C(k) shows if a network is hierarchical, i.e. if the clusterization coe cient depends on node degree. CC(k) is an indicator of how much a node it is in the lesse way among others some nodes of the network and the CB(k) is a pointer of how a particular node is among several ...(Complete abstract click electronic access below)

Estudo das interações entre proteína NS1 do hRSV e flavonóides utilizando métodos biofísicos

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

Interação entre as vias de sinalização de receptores serotoninérgicos e Β-adrenérgicos em artéria femoral de ratos

Fundamento: A doença coronária tem sido amplamente estudada em pesquisas cardiovasculares. No entanto, pacientes com doença arterial periférica (DAP) têm piores resultados em comparação àqueles com doença arterial coronariana. Portanto, os estudos farmacológicos com artéria femoral são altamente relevantes para a melhor compreensão das respostas clínicas e fisiopatológicas da DAP. Objetivo: Avaliar as propriedades farmacológicas dos agentes contráteis e relaxantes na artéria femoral de ratos. Métodos: As curvas de resposta de concentração à fenilefrina contrátil (FC) e à serotonina (5-HT) e os agentes relaxantes isoproterenol (ISO) e forskolina foram obtidos na artéria femoral de ratos isolada. Para as respostas ao relaxamento, os tecidos foram contraídos com FC ou 5-HT. Resultados: A potência de classificação na artéria femoral foi de 5-HT > FC para as respostas contráteis. Em tecidos contraídos com 5-HT, as respostas de relaxamento ao isoproterenol foram praticamente abolidas em comparação aos tecidos contraídos com FC. A forskolina, um estimulante da adenilil ciclase, restaurou parcialmente a resposta de relaxamento ao ISO em tecidos contraídos com 5-HT. Conclusão: Ocorre uma interação entre as vias de sinalização dos receptores β-adrenérgicos e serotoninérgicos na artéria femoral. Além disso, esta pesquisa fornece um novo modelo para estudar as vias de sinalização serotoninérgicas em condições normais e patológicas que podem ajudar a compreender os resultados clínicos na DAP.

Formation of a ternary complex for selenocysteine biosynthesis in bacteria

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

Caracterização funcional e estrutural das proteínas RUV-1 e RUV-2 do fungo Neurospora crassa

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Caracterização funcional de um fator de transcrição hipotético no fungo Neurospora crassa

Pós-graduação em Biotecnologia - IQ

MicroRNoma do carcinoma de pâncreas

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

Caracterização biofísica da interação entre a proteína M2-1 do Vírus Sincicial Respiratório Humano (HRSV) com quercetina

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Um novo protocolo in silico para a predição de complexos flexíveis entre proteínas: estudo de caso para inibidores plasmáticos de fosfolipase A2 de serpentes

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

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.

Natural intracellular peptides can modulate the interactions of mouse brain proteins and thimet oligopeptidase with 14-3-3e and calmodulin

Protein interactions are crucial for most cellular process. Thus, rationally designed peptides that act as competitive assembly inhibitors of protein interactions by mimicking specific, determined structural elements have been extensively used in clinical and basic research. Recently, mammalian cells have been shown to contain a large number of intracellular peptides of unknown function. Here, we investigate the role of several of these natural intracellular peptides as putative modulators of protein interactions that are related to Ca2+-calmodulin (CaM) and 14-3-3 epsilon, which are proteins that are related to the spatial organization of signal transduction within cells. At concentrations of 1-50 mu M, most of the peptides that are investigated in this study modulate the interactions of CaM and 14-3-3 epsilon with proteins from the mouse brain cytoplasm or recombinant thimet oligopeptidase (EP24.15) in vitro, as measured by surface plasmon resonance. One of these peptides (VFDVELL; VFD-7) increases the cytosolic Ca2+ concentration in a dose-dependent manner but only if introduced into HEK293 cells, which suggests a wide biological function of this peptide. Therefore, it is exciting to suggest that natural intracellular peptides are novel modulators of protein interactions and have biological functions within cells.

Identification of intracellular peptides in rat adipose tissue: Insights into insulin resistance

Intracellular peptides generated by the proteasome and oligopeptidases have been suggested to function in signal transduction and to improve insulin resistance in mice fed a high-caloric diet. The aim of this study was to identify specific intracellular peptides in the adipose tissue of Wistar rats that could be associated with the physiological and therapeutic control of glucose uptake. Using semiquantitative mass spectrometry and LC/MS/MS analyses, we identified ten peptides in the epididymal adipose tissue of the Wistar rats; three of these peptides were present at increased levels in rats that were fed a high-caloric Western diet (WD) compared with rats fed a control diet (CD). The results of affinity chromatography suggested that in the cytoplasm of epididymal adipose tissue from either WD or CD rats, distinctive proteins bind to these peptides. However, despite the observed increase in the WD animals, the evaluated peptides increased insulin-stimulated glucose uptake in 3T3-L1 adipocytes treated with palmitate. Thus, intracellular peptides from the adipose tissue of Wistar rats can bind to specific proteins and facilitate insulin-induced glucose uptake in 3T3-L1 adipocytes.