RESUMO
Metal contamination exerts environmental pressure on several lifeforms. Since metals are non-biodegradable and recalcitrant, they accumulate in living beings and spread through the food chain. Thus, many life forms are affected by environmental metal contamination, such as plants and microorganisms. In the case of microorganisms, scarce information is available on how metals affect them. As a highly resistant form of life, microorganisms can adapt to several environmental pressures through genetic modifications, changing their metabolism to overcome new conditions, and continuing to thrive in the same place. In this study, an Acinetobacter sp. strain was isolated from a copper mine, which presented very high resistance to copper, growing in copper concentrations of up to 7 mM. As a result of its metabolic response in the presence of 3 mM of copper, the expression of 35 proteins in total was altered. The proteins were identified to be associated with the glycolytic pathway, membrane transport, biosynthesis and two proteins directly involved in copper homeostasis (CopA and CopB).
Assuntos
Acinetobacter/metabolismo , Cobre/toxicidade , Proteômica , Acinetobacter/efeitos dos fármacos , Acinetobacter/crescimento & desenvolvimento , Acinetobacter/isolamento & purificação , Proteínas de Bactérias/metabolismo , Eletroforese em Gel Bidimensional , Amplificação de Genes , Genes Bacterianos , Testes de Sensibilidade Microbiana , Transdução de Sinais/efeitos dos fármacosRESUMO
In this study, an effective microbial consortium for the biodegradation of phenol was grown under different operational conditions, and the effects of phosphate concentration (1.4 g L(-1), 2.8 g L(-1), 4.2 g L(-1)), temperature (25 degrees C, 30 degrees C, 35 degrees C), agitation (150 rpm, 200 rpm, 250 rpm) and pH (6, 7, 8) on phenol degradation were investigated, whereupon an artificial neural network (ANN) model was developed in order to predict degradation. The learning, recall and generalization characteristics of neural networks were studied using data from the phenol degradation system. The efficiency of the model generated by the ANN was then tested and compared with the experimental results obtained. In both cases, the results corroborate the idea that aeration and temperature are crucial to increasing the efficiency ofbiodegradation.