Molecular characterization and intracellular distribution of the alpha 5 subunit of Trypanosoma cruzi 20S proteasome

Three different monoclonal antibodies were produced against Trypanosona cruzi proteasomes. These antibodies were shown to react with a single 27-kDa hand on immunoblots of purified proteasomes. Using a 7E5 monoclonal antibody (IgG1) that recognized the alpha 5 subunit of protozoan protease we have studied the intracellular distribution of the T cruzi 20S proteasome. Contrary to all cell types described to date, T cruzi 20S proteasome was found not only in the cytoplasm and nucleus but also in the kinetoplast. As revealed by confocal microscopy, the reactivity of monoclonal antibody 7E5 was highly specific for protozoan proteasome because the antibody recognized only the proteasomes from parasites and not those from the mammalian host in T. cruzi infected cells. These findings were confirmed by immunoblots or immunoprecipitations, followed by chymotrypsin-like activity detection in kinetoplasts isolated by differential centrifugation and sucrose density gradients. Proteasome 20S was present in all T cruzi stages and only slight differences in terms of relative abundance were found. the potential role of the proteasome in kinetoplast remodeling remains to be determined. (C) 2009 Published by Elsevier Ireland Ltd.Univ Antofagasta, Fac Hlth Sci, Mol Parasitol Unit, Antofagasta, ChileUniv Fed Rio de Janeiro, Inst Biofis Carlos Chagas Filho, Lab Ultraestrutura Celular Hertha Meyer, BR-21941 Rio de Janeiro, BrazilNew York Blood Ctr, New York, NY 10021 USAMem Sloan Kettering Canc Ctr, Program Mol Biol, New York, NY 10021 USAEscola Paulista Med UNIFESP, Dept Microbiol Imunol & Parasitol, São Paulo, BrazilN Catholic Univ, Ctr Biotechnol, Antofagasta, ChileEscola Paulista Med UNIFESP, Dept Microbiol Imunol & Parasitol, São Paulo, BrazilWeb of Scienc

Generating Models of Infinite-State Communication Protocols Using Regular Inference with Abstraction

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Extending automata learning to extended finite state machines

Automata learning is an established class of techniques for inferring automata models by observing how they respond to a sample of input words. Recently, approaches have been presented that extend these techniques to infer extended finite state machines (EFSMs) by dynamic black-box analysis. EFSMs model both data flow and control behavior, and their mutual interaction. Different dialects of EFSMs are widely used in tools for model-based software development, verification, and testing. This survey paper presents general principles behind some of these recent extensions. The goal is to elucidate how the principles behind classic automata learning can be maintained and guide extensions to more general automata models, and to situate some extensions with respect to these principles