905 resultados para PEPTIDE-PROTEIN INTERACTION
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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)
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Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
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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.
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Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
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Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
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Pós-graduação em Biotecnologia - IQ
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Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
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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
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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.
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Red cell haemoglobin is the fundamental oxygen-transporting molecule in blood, but also a potentially tissue-damaging compound owing to its highly reactive haem groups. During intravascular haemolysis, such as in malaria and haemoglobinopathies(1), haemoglobin is released into the plasma, where it is captured by the protective acute-phase protein haptoglobin. This leads to formation of the haptoglobin-haemoglobin complex, which represents a virtually irreversible non-covalent protein-protein interaction(2). Here we present the crystal structure of the dimeric porcine haptoglobin-haemoglobin complex determined at 2.9 angstrom resolution. This structure reveals that haptoglobin molecules dimerize through an unexpected beta-strand swap between two complement control protein (CCP) domains, defining a new fusion CCP domain structure. The haptoglobin serine protease domain forms extensive interactions with both the alpha- and beta-subunits of haemoglobin, explaining the tight binding between haptoglobin and haemoglobin. The haemoglobin-interacting region in the alpha beta dimer is highly overlapping with the interface between the two alpha beta dimers that constitute the native haemoglobin tetramer. Several haemoglobin residues prone to oxidative modification after exposure to haem-induced reactive oxygen species are buried in the haptoglobin-haemoglobin interface, thus showing a direct protective role of haptoglobin. The haptoglobin loop previously shown to be essential for binding of haptoglobin-haemoglobin to the macrophage scavenger receptor CD163 (ref. 3) protrudes from the surface of the distal end of the complex, adjacent to the associated haemoglobin alpha-subunit. Small-angle X-ray scattering measurements of human haptoglobin-haemoglobin bound to the ligand-binding fragment of CD163 confirm receptor binding in this area, and show that the rigid dimeric complex can bind two receptors. Such receptor cross-linkage may facilitate scavenging and explain the increased functional affinity of multimeric haptoglobin-haemoglobin for CD163 (ref. 4).
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Abstract Background Propolis is a natural product of plant resins collected by honeybees (Apis mellifera) from various plant sources. Our previous studies indicated that propolis sensitivity is dependent on the mitochondrial function and that vacuolar acidification and autophagy are important for yeast cell death caused by propolis. Here, we extended our understanding of propolis-mediated cell death in the yeast Saccharomyces cerevisiae by applying systems biology tools to analyze the transcriptional profiling of cells exposed to propolis. Methods We have used transcriptional profiling of S. cerevisiae exposed to propolis. We validated our findings by using real-time PCR of selected genes. Systems biology tools (physical protein-protein interaction [PPPI] network) were applied to analyse the propolis-induced transcriptional bevavior, aiming to identify which pathways are modulated by propolis in S. cerevisiae and potentially influencing cell death. Results We were able to observe 1,339 genes modulated in at least one time point when compared to the reference time (propolis untreated samples) (t-test, p-value 0.01). Enrichment analysis performed by Gene Ontology (GO) Term finder tool showed enrichment for several biological categories among the genes up-regulated in the microarray hybridization such as transport and transmembrane transport and response to stress. Real-time RT-PCR analysis of selected genes showed by our microarray hybridization approach was capable of providing information about S. cerevisiae gene expression modulation with a considerably high level of confidence. Finally, a physical protein-protein (PPPI) network design and global topological analysis stressed the importance of these pathways in response of S. cerevisiae to propolis and were correlated with the transcriptional data obtained thorough the microarray analysis. Conclusions In summary, our data indicate that propolis is largely affecting several pathways in the eukaryotic cell. However, the most prominent pathways are related to oxidative stress, mitochondrial electron transport chain, vacuolar acidification, regulation of macroautophagy associated with protein target to vacuole, cellular response to starvation, and negative regulation of transcription from RNA polymerase II promoter. Our work emphasizes again the importance of S. cerevisiae as a model system to understand at molecular level the mechanism whereby propolis causes cell death in this organism at the concentration herein tested. Our study is the first one that investigates systematically by using functional genomics how propolis influences and modulates the mRNA abundance of an organism and may stimulate further work on the propolis-mediated cell death mechanisms in fungi.
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BACKGROUND: Ischemia and reperfusion (IR) injury remains a major cause of morbidity and mortality and multiple molecular and cellular pathways have been implicated in this injury. We determined whether acute inhibition of excessive mitochondrial fission at the onset of reperfusion improves mitochondrial dysfunction and cardiac contractility postmyocardial infarction in rats. METHODS AND RESULTS: We used a selective inhibitor of the fission machinery, P110, which we have recently designed. P110 treatment inhibited the interaction of fission proteins Fis1/Drp1, decreased mitochondrial fission, and improved bioenergetics in three different rat models of IR, including primary cardiomyocytes, ex vivo heart model, and an in vivo myocardial infarction model. Drp1 transiently bound to the mitochondria following IR injury and P110 treatment blocked this Drp1 mitochondrial association. Compared with control treatment, P110 (1 μmol/L) decreased infarct size by 28 ± 2% and increased adenosine triphosphate levels by 70+1% after IR relative to control IR in the ex vivo model. Intraperitoneal injection of P110 (0.5 mg/kg) at the onset of reperfusion in an in vivo model resulted in improved mitochondrial oxygen consumption by 68% when measured 3 weeks after ischemic injury, improved cardiac fractional shortening by 35%, reduced mitochondrial H2O2 uncoupling state by 70%, and improved overall mitochondrial functions. CONCLUSIONS: Together, we show that excessive mitochondrial fission at reperfusion contributes to long-term cardiac dysfunction in rats and that acute inhibition of excessive mitochondrial fission at the onset of reperfusion is sufficient to result in long-term benefits as evidenced by inhibiting cardiac dysfunction 3 weeks after acute myocardial infarction.
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9-hydroxystearic acid (9-HSA) is an endogenous lipoperoxidation product and its administration to HT29, a colon adenocarcinoma cell line, induced a proliferative arrest in G0/G1 phase mediated by a direct activation of the p21WAF1 gene, bypassing p53. We have previously shown that 9-HSA controls cell growth and differentiation by inhibiting histone deacetylase 1 (HDAC1) activity, showing interesting features as a new anticancer drug. The interaction of 9-HSA with the catalytic site of the 3D model has been tested with a docking procedure: noticeably, when interacting with the site, the (R)-9-enantiomer is more stable than the (S) one. Thus, in this study, (R)- and (S)-9-HSA were synthesized and their biological activity tested in HT29 cells. At the concentration of 50 M (R)-9-HSA showed a stronger antiproliferative effect than the (S) isomer, as indicated by the growth arrest in G0/G1. The inhibitory effect of (S)-9-HSA on HDAC1, HDAC2 and HDAC3 activity was less effective than that of the (R)-9-HSA in vitro, and the inhibitory activity of both the (R)- and the (S)-9-HSA isomer, was higher on HDAC1 compared to HDAC2 and HDAC3, thus demonstrating the stereospecific and selective interaction of 9-HSA with HDAC1. In addition, histone hyperacetylation caused by 9-HSA treatment was examined by an innovative HPLC/ESI/MS method. Analysis on histones isolated from control and treated HT29 confirmed the higher potency of (R)-9-HSA compared to (S)-9-HSA, severely affecting H2A-2 and H4 acetylation. On the other side, it seemed of interest to determine whether the G0/G1 arrest of HT29 cell proliferation could be bypassed by the stimulation with the growth factor EGF. Our results showed that 9-HSA-treated cells were not only prevented from proliferating, but also showed a decreased [3H]thymidine incorporation after EGF stimulation. In this condition, HT29 cells expressed very low levels of cyclin D1, that didn’t colocalize with HDAC1. These results suggested that the cyclin D1/HDAC1 complex is required for proliferation. Furthermore, in the effort of understanding the possible mechanisms of this effect, we have analyzed the degree of internalization of the EGF/EGFR complex and its interactions with HDAC1. EGF/EGFR/HDAC1 complex quantitatively increases in 9-HSA-treated cells but not in serum starved cells after EGF stimulation. Our data suggested that 9-HSA interaction with the catalytic site of the HDAC1 disrupts the HDAC1/cyclin D1 complex and favors EGF/EGFR recruitment by HDAC1, thus enhancing 9-HSA antiproliferative effects. In conclusion 9-HSA is a promising HDAC inhibitor with high selectivity and specificity, capable of inducing cell cycle arrest and histone hyperacetylation, but also able to modulate HDAC1 protein interaction. All these aspects may contribute to the potency of this new antitumor agent.