The nucleolus as a polarized coaxial cable in which the rDNA axis is surrounded by dynamic subunit-specific phases

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Growth of Mycobacterium tuberculosis biofilms containing free mycolic acids and harbouring drug-tolerant bacteria

Successful treatment of human tuberculosis requires 6–9 months' therapy with multiple antibiotics. Incomplete clearance of tubercle bacilli frequently results in disease relapse, presumably as a result of reactivation of persistent drug-tolerant Mycobacterium tuberculosis cells, although the nature and location of these persisters are not known. In other pathogens, antibiotic tolerance is often associated with the formation of biofilms – organized communities of surface-attached cells – but physiologically and genetically defined M. tuberculosis biofilms have not been described. Here, we show that M. tuberculosis forms biofilms with specific environmental and genetic requirements distinct from those for planktonic growth, which contain an extracellular matrix rich in free mycolic acids, and harbour an important drug-tolerant population that persist despite exposure to high levels of antibiotics.     

Whole-genome metabolic model of <Emphasis Type="Italic">Trichoderma reesei</Emphasis> built by comparative reconstruction

Background

Trichoderma reesei is one of the main sources of biomass-hydrolyzing enzymes for the biotechnology industry. There is a need for improving its enzyme production efficiency. The use of metabolic modeling for the simulation and prediction of this organism’s metabolism is potentially a valuable tool for improving its capabilities. An accurate metabolic model is needed to perform metabolic modeling analysis.

Results

A whole-genome metabolic model of T. reesei has been reconstructed together with metabolic models of 55 related species using the metabolic model reconstruction algorithm CoReCo. The previously published CoReCo method has been improved to obtain better quality models. The main improvements are the creation of a unified database of reactions and compounds and the use of reaction directions as constraints in the gap-filling step of the algorithm. In addition, the biomass composition of T. reesei has been measured experimentally to build and include a specific biomass equation in the model.

Conclusions

The improvements presented in this work on the CoReCo pipeline for metabolic model reconstruction resulted in higher-quality metabolic models compared with previous versions. A metabolic model of T. reesei has been created and is publicly available in the BIOMODELS database. The model contains a biomass equation, reaction boundaries and uptake/export reactions which make it ready for simulation. To validate the model, we dem1onstrate that the model is able to predict biomass production accurately and no stoichiometrically infeasible yields are detected. The new T. reesei model is ready to be used for simulations of protein production processes.