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Abstract Background Thermococcus litoralis is a heterotrophic facultative sulfur dependent hyperthermophilic Archaeon, which was isolated from a shallow submarine thermal spring. It has been successfully used in a two-stage fermentation system, where various keratinaceous wastes of animal origin were converted to biohydrogen. In this system T. litoralis performed better than its close relative, P. furiosus. Therefore, new alternative enzymes involved in peptide and hydrogen metabolism were assumed in T. litoralis. Results An about 10.5 kb long genomic region was isolated and sequenced from Thermococcus litoralis. In silico analysis revealed that the region contained a putative operon consisting of eight genes: the fdhAB genes coding for a formate dehydrogenase and the mhyCDEFGH genes encoding a [NiFe] hydrogenase belonging to the group of the H2-evolving, energy-conserving, membrane-bound hydrogenases. Reverse transcription linked quantitative Real-Time PCR and Western blotting experiments showed that the expression of the fdh-mhy operon was up-regulated during fermentative growth on peptides and down-regulated in cells cultivated in the presence of sulfur. Immunoblotting and protein separation experiments performed on cell fractions indicated that the formate dehydrogenase part of the complex is associated to the membrane-bound [NiFe] hydrogenase. Conclusion The formate dehydrogenase together with the membrane-bound [NiFe] hydrogenase formed a formate hydrogenlyase (formate dehydrogenase coupled hydrogenase, FDH-MHY) complex. The expression data suggested that its physiological role is linked to the removal of formate likely generated during anaerobic peptide fermentation.

A central unresolved issue in evolutionary biology is how metabolic innovations emerge. Low-level enzymatic side activities are frequent and can potentially be recruited for new biochemical functions. However, the role of such underground reactions in adaptation toward novel environments has remained largely unknown and out of reach of computational predictions, not least because these issues demand analyses at the level of the entire metabolic network. Here, we provide a comprehensive computational model of the underground metabolism in Escherichia coli. Most underground reactions are not isolated and 45% of them can be fully wired into the existing network and form novel pathways that produce key precursors for cell growth. This observation allowed us to conduct an integrated genome-wide in silico and experimental survey to characterize the evolutionary potential of E. coli to adapt to hundreds of nutrient conditions. We revealed that underground reactions allow growth in new environments when their activity is increased. We estimate that at least ∼20% of the underground reactions that can be connected to the existing network confer a fitness advantage under specific environments. Moreover, our results demonstrate that the genetic basis of evolutionary adaptations via underground metabolism is computationally predictable. The approach used here has potential for various application areas from bioengineering to medical genetics.

The rapid rise of antibiotic resistance, combined with the increasing cost and difficulties to develop new antibiotics, calls for treatment strategies that enable more sustainable antibiotic use. The development of such strategies, however, is impeded by the lack of suitable experimental approaches that allow testing their effects under realistic epidemiological conditions. Here, we present an approach to compare the effect of alternative multidrug treatment strategies in vitro using a robotic liquidhandling platform. We use this framework to study resistance evolution and spread implementing epidemiological population dynamics for treatment, transmission, and patient admission and discharge, as may be observed in hospitals. We perform massively parallel experimental evolution over up to 40 d and complement this with a computational model to infer the underlying population-dynamical parameters. We find that in our study, combination therapy outperforms monotherapies, as well as cycling and mixing, in minimizing resistance evolution and maximizing uninfecteds, as long as there is no influx of double resistance into the focal treated community. [ABSTRACT FROM AUTHOR]

A central unresolved issue in evolutionary biology is how metabolic innovations emerge. Low-level enzymatic side activities are frequent and can potentially be recruited for new biochemical functions. However, the role of such underground reactions in adaptation toward novel environments has remained largely unknown and out of reach of computational predictions, not least because these issues demand analyses at the level of the entire metabolic network. Here, we provide a comprehensive computational model of the underground metabolism in Escherichia coli. Most underground reactions are not isolated and 45% of them can be fully wired into the existing network and form novel pathways that produce key precursors for cell growth. This observation allowed us to conduct an integrated genome-wide in silico and experimental survey to characterize the evolutionary potential of E. coli to adapt to hundreds of nutrient conditions. We revealed that underground reactions allow growth in new environments when their activity is increased. We estimate that at least ~20% of the underground reactions that can be connected to the existing network confer a fitness advantage under specific environments. Moreover, our results demonstrate that the genetic basis of evolutionary adaptations via underground metabolism is computationally predictable. The approach used here has potential for various application areas from bioengineering to medical genetics. [ABSTRACT FROM AUTHOR]