38 research outputs found
Towards the prediction of essential genes by integration of network topology, cellular localization and biological process information
<p>Abstract</p> <p>Background</p> <p>The identification of essential genes is important for the understanding of the minimal requirements for cellular life and for practical purposes, such as drug design. However, the experimental techniques for essential genes discovery are labor-intensive and time-consuming. Considering these experimental constraints, a computational approach capable of accurately predicting essential genes would be of great value. We therefore present here a machine learning-based computational approach relying on network topological features, cellular localization and biological process information for prediction of essential genes.</p> <p>Results</p> <p>We constructed a decision tree-based meta-classifier and trained it on datasets with individual and grouped attributes-network topological features, cellular compartments and biological processes-to generate various predictors of essential genes. We showed that the predictors with better performances are those generated by datasets with integrated attributes. Using the predictor with all attributes, i.e., network topological features, cellular compartments and biological processes, we obtained the best predictor of essential genes that was then used to classify yeast genes with unknown essentiality status. Finally, we generated decision trees by training the J48 algorithm on datasets with all network topological features, cellular localization and biological process information to discover cellular rules for essentiality. We found that the number of protein physical interactions, the nuclear localization of proteins and the number of regulating transcription factors are the most important factors determining gene essentiality.</p> <p>Conclusion</p> <p>We were able to demonstrate that network topological features, cellular localization and biological process information are reliable predictors of essential genes. Moreover, by constructing decision trees based on these data, we could discover cellular rules governing essentiality.</p
The Gene Ontology resource: enriching a GOld mine
The Gene Ontology Consortium (GOC) provides the most comprehensive resource currently available for computable knowledge regarding the functions of genes and gene products. Here, we report the advances of the consortium over the past two years. The new GO-CAM annotation framework was notably improved, and we formalized the model with a computational schema to check and validate the rapidly increasing repository of 2838 GO-CAMs. In addition, we describe the impacts of several collaborations to refine GO and report a 10% increase in the number of GO annotations, a 25% increase in annotated gene products, and over 9,400 new scientific articles annotated. As the project matures, we continue our efforts to review older annotations in light of newer findings, and, to maintain consistency with other ontologies. As a result, 20 000 annotations derived from experimental data were reviewed, corresponding to 2.5% of experimental GO annotations. The website (http://geneontology.org) was redesigned for quick access to documentation, downloads and tools. To maintain an accurate resource and support traceability and reproducibility, we have made available a historical archive covering the past 15 years of GO data with a consistent format and file structure for both the ontology and annotations
Unravelling the Neospora caninum secretome through the secreted fraction (ESA) and quantification of the discharged tachyzoite using high-resolution mass spectrometry-based proteomics
Background The apicomplexan parasite Neospora caninum causes neosporosis, a disease that leads to abortion or stillbirth in cattle, generating an economic impact on the dairy and beef cattle trade. As an obligatory intracellular parasite, N. caninum needs to invade the host cell in an active manner to survive. The increase in parasite cytosolic Ca2+ upon contact with the host cell mediates critical events, including the exocytosis of phylum-specific secretory organelles and the activation of the parasite invasion motor. Because invasion is considered a requirement for pathogen survival and replication within the host, the identification of secreted proteins (secretome) involved in invasion may be useful to reveal interesting targets for therapeutic intervention. Methods To chart the currently missing N. caninum secretome, we employed mass spectrometry-based proteomics to identify proteins present in the N. caninum tachyzoite using two different approaches. The first approach was identifying the proteins present in the tachyzoite-secreted fraction (ESA). The second approach was determining the relative quantification through peptide stable isotope labelling of the tachyzoites submitted to an ethanol secretion stimulus (discharged tachyzoite), expecting to identify the secreted proteins among the down-regulated group. Results As a result, 615 proteins were identified at ESA and 2,011 proteins quantified at the discharged tachyzoite. We have analysed the connection between the secreted and the down-regulated proteins and searched for putative regulators of the secretion process among the up-regulated proteins. An interaction network was built by computational prediction involving the up- and down-regulated proteins. The mass spectrometry proteomics data have been deposited to the ProteomeXchange with identifier PXD000424. Conclusions The comparison between the protein abundances in ESA and their measure in the discharged tachyzoite allowed for a more precise identification of the most likely secreted proteins. Information from the network interaction and up-regulated proteins was important to recognise key proteins potentially involved in the metabolic regulation of secretion. Our results may be helpful to guide the selection of targets to be investigated against Neospora caninum and other Apicomplexan organisms
Eosinophilic pneumonitis induced by aerosol-administered diesel oil and pyrethrum to mice Neumonía eosinofílica inducida por aerosol de aceite diésel y piretroide en ratones
OBJECTIVE: To confirm the episode of eosinophilic pneumonitis that occurred in March 2001 in Manaus, Amazon, northern Brazil, as secondary to home aerosolization with 2% cypermethrin diluted in diesel compared with the more conventional 1% cypermethrin and soybean solution used in prophylaxis of dengue. METHODS: Four groups of Swiss mice were kept in polycarbonate cages aerosolized with one of the following solutions: diesel, diesel and cypermethrin, soy oil and cypermethrin, and saline. Three and 6 days after exposure, resistance and compliance of the respiratory system and white cell kinetics in peripheral blood and lung tissue were analyzed. RESULTS: The group exposed to diesel and cypermethrin showed higher respiratory system resistance (p OBJETIVO: Confirmar el episodio de neumonía eosinofílica ocurrido en marzo de 2001 en Manaus, Amazonas, en el norte de Brasil, secundario al uso de aerosol de cipermetrina diluida al 2% en aceite diésel en las viviendas en comparación con la profilaxis más convencional contra el dengue, basada en cipermetrina al 1% con aceite de soya. MÉTODOS: Se mantuvieron cuatro grupos de ratones suizos en jaulas de policarbonato y se aplicó aerosol con una de las siguientes soluciones: aceite diésel, aceite diésel y cipermetrina, aceite de soya y cipermetrina, y solución salina. Se analizaron la resistencia y el funcionamiento del sistema respiratorio y la cinética de leucocitos en sangre periférica y tejido pulmonar a los tres y seis días después de la exposición. RESULTADOS: El grupo expuesto a aceite diésel y cipermetrina mostró mayor resistencia del sistema respiratorio (P < 0,001), peor funcionamiento (P = 0,03) y más eosinófilos en sangre (P = 0,03) y tejido pulmonar (P = 0,005) que los otros grupos. Se observó un aumento de neutrófilos en sangre en todos los grupos experimentales al tercer día después de la exposición (P < 0,001). CONCLUSIONES: El aceite diésel con cipermetrina indujo una hiperrespuesta pulmonar en este modelo experimental y se asoció con un aumento en las células polimorfonucleares (eosinófilos y neutrófilos) en sangre y tejido pulmonar. Este efecto es mayor al tercer día después de la exposición. Estos efectos son similares a los observados en el episodio ocurrido en Manaus en 2001 e indican que se debe reevaluar el uso de aerosol de aceite diésel con cipermetrina para la profilaxis de arbovirus en las viviendas
Eosinophilic pneumonitis induced by aerosol-administered diesel oil and pyrethrum to mice
CausalTAB: The PSI-MITAB 2.8 updated format for signalling data representation and dissemination
Motivation: Combining multiple layers of information underlying biological complexity into a structured framework represent a challenge in systems biology. A key task is the formalization of such information in models describing how biological entities interact to mediate the response to external and internal signals. Several databases with signalling information, focus on capturing, organizing and displaying signalling interactions by representing them as binary, causal relationships between biological entities. The curation efforts that build these individual databases demand a concerted effort to ensure interoperability among resources.Results: Aware of the enormous benefits of standardization efforts in the molecular interaction research field, representatives of the signalling network community agreed to extend the PSI-MI controlled vocabulary to include additional terms representing aspects of causal interactions. Here, we present a common standard for the representation and dissemination of signalling information: the PSI Causal Interaction tabular format (CausalTAB) which is an extension of the existing PSI-MI tab-delimited format, now designated PSI-MITAB 2.8. We define the new term 'causal interaction', and related child terms, which are children of the PSI-MI 'molecular interaction' term. The new vocabulary terms in this extended PSI-MI format will enable systems biologists to model large-scale signalling networks more precisely and with higher coverage than before