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Given the limitation of technologies, the subcellular localizations of proteins are difficult to identify. Predicting the subcellular localization and the intercellular distribution patterns of proteins in accordance with their specific biological roles, including validated functions, relationships with other proteins, and even their specific sequence characteristics, is necessary. The computational prediction of protein subcellular localizations can be performed on the basis of the sequence and the functional characteristics. In this study, the protein–protein interaction network, functional annotation of proteins and a group of direct proteins with known subcellular localization were used to construct models. To build efficient models, several powerful machine learning algorithms, including two feature selection methods, four classification algorithms, were employed. Some key proteins and functional terms were discovered, which may provide important contributions for determining protein subcellular locations. Furthermore, some quantitative rules were established to identify the potential subcellular localizations of proteins. As the first prediction model that uses direct protein annotation information (i.e., functional features) and STRING-based protein–protein interaction network (i.e., network features), our computational model can help promote the development of predictive technologies on subcellular localizations and provide a new approach for exploring the protein subcellular localization patterns and their potential biological importance Given the limitation of technologies, the subcellular localizations of proteins are difficult to identify. Predicting the subcellular localization and the intercellular distribution patterns of proteins in accordance with their specific biological roles, including validated functions, relationships with other proteins, and even their specific sequence characteristics, is necessary. The computational prediction of protein subcellular localizations can be performed on the basis of the sequence and the functional characteristics. In this study, the protein-protein interaction network, functional annotation of proteins and a group of direct proteins with known subcellular localization were used to construct models. To build efficient models, several powerful machine learning algorithms, including two feature selection methods, four classification algorithms, were employed. Some key proteins and functional terms were discovered, which may provide important contributions for determining protein subcellular locations. Furthermore, some quantitative rules were established to identify the potential subcellular localizations of proteins. As the first prediction model that uses direct protein annotation information (i.e., functional features) and STRING-based protein-protein interaction network (i.e., network features), our computational model can help promote the development of predictive technologies on subcellular localizations and provide a new approach for exploring the protein subcellular localization patterns and their potential biological importance.

The Gram-positive bacterium Bacillus subtilis encounters nutrient limitations and osmotic stress in its natural soil ecosystem. To ensure survival and sustain growth, highly integrated adaptive responses are required. Here, we investigated the system-wide response of B.subtilis to different, simultaneously imposed stresses. To address the anticipated complexity of the cellular response networks, we combined chemostat experiments under conditions of carbon limitation, salt stress and osmoprotection with multi-omics analyses of the transcriptome, proteome, metabolome and fluxome. Surprisingly, the flux through central carbon and energy metabolism is very robust under all conditions studied. The key to achieve this robustness is the adjustment of the biocatalytic machinery to compensate for solvent-induced impairment of enzymatic activities during osmotic stress. Specifically, increased production of several enzymes of central carbon metabolism compensates for their reduced activity in the presence of high salt. A major response of the cell during osmotic stress is the production of the compatible solute proline. This is achieved through the concerted adjustment of multiple reactions around the 2-oxoglutarate node, which drives metabolism towards the proline precursor glutamate. The fine-tuning of the transcriptional and metabolic networks involves functional modules that overarch the individual pathways.

Made available in DSpace on 2019-09-12T16:53:25Z (GMT). No. of bitstreams: 0 Previous issue date: 2009 Universidade Federal do Rio de Janeiro (UFRJ) Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) Ministério do Meio Ambiente (MMA) Secretariat of the Inter-Ministry Commission for Marine Resources (SeCIRM) The potential aerobic ATP-generating pathway and the argininolytic capacity of the Antarctic bivalve Laternula elliptica in its main tissues were measured by the specific activity of the enzymes malate dehydrogenase (MDH), citrate synthase (CS) and arginase. The kidney showed the major potential for aerobic ATP-generating pathway and argininolytic capacity. High levels of CS and MDH activities indicated that renal tissue can be involved in activities that require a lot of energy such as excretion of metabolic end products, amino acids catabolism or even gluconeogenic activities related to inter-tissue metabolism. The fact that kidneys are the main site for arginase activity is very unusual for mollusks and could be related to the living habits of L. elliptica. Genetic expression of the L. elliptica renal arginase could be controlling the levels of l-arginine and forming urea in the excretory organ, which may not have its physiological functions directly affected by the seasonal retraction of its siphons. Compared to the bivalve Dreissena polymorpha, renal arginase of L. elliptica is more resistant to inhibition by copper and cadmium. This could be related to naturally high levels of these metals in the Antarctic marine environment and its bioaccumulation in the renal tissue of L. elliptica, as a probable advantage to its environmental adaptation. Different from other Antarctic animals that feed on Krill, the arginase of L. elliptica is much more sensitive to fluoride inhibition. However, diet composition of L. elliptica would be expected to be variable site to site and its high sensitivity to fluoride inhibition may be a matter of concern in areas near ornithogenic soils subjected to ice-melting processes. [Rodrigues, Edson; da Silva Santos, Marcela Rosana; Rodrigues Junior, Edson; Gannabathula, Vani] Universidade de Taubaté (Unitau), Biochem Lab, Basic Inst Biosci, BR-12030180 Taubate, SP, Brazil [Lavrado, Helena Passeri] Univ Fed Rio de Janeiro, UFRJ, CCS, BR-21949900 Rio De Janeiro, Brazil