Background: An impediment to the rational development of novel drugs against tuberculosis (TB) is a general paucity of knowledge concerning the metabolism of Mycobacterium tuberculosis, particularly during infection. Constraint-based modeling provides a novel approach to investigating microbial metabolism but has not yet been applied to genome-scale modeling of M. tuberculosis. Results: GSMN-TB, a genome-scale metabolic model of M. tuberculosis, was constructed, consisting of 849 unique reactions and 739 metabolites, and involving 726 genes. The model was calibrated by growing Mycobacterium bovis bacille Calmette Guérin in continuous culture and steady-state growth parameters were measured. Flux balance analysis was used to calculate substrate consumption rates, which were shown to correspond closely to experimentally determined values. Predictions of gene essentiality were also made by flux balance analysis simulation and were compared with global mutagenesis data for M. tuberculosis grown in vitro. A prediction accuracy of 78% was achieved. Known drug targets were predicted to be essential by the model. The model demonstrated a potential role for the enzyme isocitrate lyase during the slow growth of mycobacteria, and this hypothesis was experimentally verified. An interactive web-based version of the model is available. Conclusion: The GSMN-TB model successfully simulated many of the growth properties of M. tuberculosis. The model provides a means to examine the metabolic flexibility of bacteria and predict the phenotype of mutants, and it highlights previously unexplored features of M. tuberculosis metabolism.

Metabolic interactions within microbial communities are essential for the efficient degradation of complex organic compounds, and underpin natural phenomena driven by microorganisms, such as the recycling of carbon-, nitrogen-, and sulphur-containing molecules. These metabolic interactions ultimately determine the function, activity and stability of the community, and therefore their understanding would be essential to steer processes where microbial communities are involved. This is exploited in the design of microbial fuel cells (MFCs), bioelectrochemical devices that convert the chemical energy present in substrates into electrical energy through the metabolic activity of microorganisms, either single species or communities. In this work, we analysed the evolution of the microbial community structure in a cascade of microbial fuel cells (MFCs) inoculated with an anaerobic microbial community and continuously fed with a complex medium. The analysis of the composition of the anodic communities revealed the establishment of different communities in the anodes of the hydraulically connected MFCs, with a decrease in the abundance of fermentative taxa and a concurrent increase in respiratory taxa along the cascade. The analysis of the metabolites in the anodic suspension showed a metabolic shift between the first and last MFC, confirming the segregation of the anodic communities. Those results suggest a metabolic interaction mechanism between the predominant fermentative bacteria at the first stages of the cascade and the anaerobic respiratory electrogenic population in the latter stages, which is reflected in the observed increase in power output. We show that our experimental system represents an ideal platform for optimization of processes where the degradation of complex substrates is involved, as well as a potential tool for the study of metabolic interactions in complex microbial communities.

Efficient wastewater treatment monitoring is vital for addressing water scarcity. Microbial fuel cells (MFCs) have emerged as real-time biosensors for biochemical oxygen demand (BOD) in urban wastewater. Discrepancies in signal generation may arise due to changes in the composition and metabolism of mixed-culture electroactive biofilms stemming from different wastewater compositions. In this study, 3D-printed MFC-based biosensors were employed to assess the BOD of sterile complex artificial wastewater and untreated urban wastewater. Alterations in the microbial composition of the anode were evaluated using 16S rRNA sequencing and metagenomics analysis. Results show that MFC-based biosensors can be effectively recalibrated for diverse types of wastewater, maintaining consistent sensitivity (0.64 ± 0.10 mA L mg⁻¹ m⁻² with synthetic wastewater and 0.78 ± 0.13 mA L mg⁻¹ m⁻² with urban wastewater) and limit of detection (49 ± 8 mg L⁻¹ for synthetic wastewater and 44 ± 7 mg L⁻¹ for urban wastewater). Crucially, pre-sterilization, conductivity adjustments, and nitrogen purging of wastewater are not required before its introduction into the biosensor. However, the presence of native aerobic microorganisms in the wastewater might affect the current output. Metagenomics and taxonomic analyses revealed that the alterations in biofilm composition are predominantly in response to the varied chemical and microbiological compositions of different substrates. Despite variations in anodic biofilm composition, the MFC-based biosensor maintains a relative error comparable to the standard BOD test. This highlights the resilience and flexibility of the biosensor when directly used with a variety of wastewater types before full biofilm adjustment.