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Synechocystis gathered momentum in modelling studies and biotechnological applications owing to multiple factors like fast growth, ability to fix carbon dioxide into valuable products, and the relative ease of genetic manipulation. Synechocystis physiology and metabolism, and consequently, the productivity of Synechocystisbased photobioreactors (PBRs), are heavily light modulated. Here, we set up a turbidostat-controlled lab-scale cultivation system in order to study the influence of varying orange-red light intensities on Synechocystis growth characteristics and photosynthetic activity. Synechocystis growth and photosynthetic activity were found to raise as supplied light intensity increased up to 500 mmol photons m-2 s-1 and to enter the photoinhibition state only at 800 mmol photons m-2 s-1. Interestingly, reverting the light to a non-photo-inhibiting intensity unveiled Synechocystis to be able to promptly recover. Furthermore, our characterization displayed a clear correlation between variations in growth rate and cell size, extending a phenomenon previously observed in other cyanobacteria. Further, we applied a modelling approach to simulate the effects produced by varying the incident light intensity on its local distribution within the PBR vessel. Our model simulations suggested that the photosynthetic activity of Synechocystis could be enhanced by finely regulating the intensity of the light incident on the PBR in order to prevent cells from experiencing light-induced stress and induce their exploitation of areas of different local light intensity formed in the vessel. In the latter case, the heterogeneous distribution of the local light intensity would allow Synechocystis for an optimized usage of light. [ABSTRACT FROM AUTHOR]

Experimental evolution of microbes has gained lots of interest in recent years, mainly due to the ease of strain characterisation through next-generation sequencing. While evolutionary and systems biologists use experimental evolution to address fundamental questions in their respective fields, studies with lactic acid bacteria are often more directed by applied questions. Insight into population and genome dynamics are valuable for experimental design and data interpretation, and it is becoming increasingly apparent how different constraints limit and govern the outcome of microbial adaptation to a selective environment. Examples for such constraints are the finite membrane and cellular space which can lead to trade-offs between cellular strategies. A powerful perspective is that of resource allocation, which allows cells to maximise fitness. This impacts on metabolic strategies that have different protein/resource demands. This review focuses on parameters and forces that shape cellular optimisation processes and that are determining for the outcome of laboratory evolution experiments. Phenotypic changes of experimentally evolved lactic acid bacteria will be discussed in the light of the selection conditions and the prevailing constraints. [ABSTRACT FROM AUTHOR]

We review the uses and limitations of modelling approaches that are in use in the field of Lactic Acid Bacteria (LAB). We describe recent developments in model construction and computational methods, starting from application of such models to monocultures. However, since most applications in food biotechnology involve complex nutrient environments and mixed cultures, we extend the scope to discuss developments in modelling such complex systems. With metagenomics and meta-functional genomics data becoming available, the developments in genome-scale community models are discussed. We conclude that exploratory tools are available and useful, but truly predictive mechanistic models will remain a major challenge in the field. [Copyright &y& Elsevier]

Background: For some lactic acid bacteria higher biomass production as a result of aerobic respiration has been reported upon supplementation with heme and menaquinone. In this report, we have studied a large number of species among lactic acid bacteria for the existence of this trait. Results: Heme- (and menaquinone) stimulated aerobic growth was observed for several species and genera of lactic acid bacteria. These include Lactobacillus plantarum, Lactobacillus rhamnosus, Lactobacilllus brevis, Lactobacillus paralimentarius, Streptococcus entericus and Lactococcus garviae. The increased biomass production without further acidification, which are respiration associated traits, are suitable for high-throughput screening as demonstrated by the screening of 8000 Lactococcus lactis insertion mutants. Respiration-negative insertion-mutants were found with noxA, bd-type cytochrome and menaquinol biosynthesis gene-disruptions. Phenotypic screening and in silico genome analysis suggest that respiration can be considered characteristic for certain species. Conclusion: We propose that the cyd-genes were present in the common ancestor of lactic acid bacteria, and that multiple gene-loss events best explains the observed distribution of these genes among the species. [ABSTRACT FROM AUTHOR]

Abstract: We have reported previously on the ability of Lactobacillus reuteri to produce a compound with vitamin activity. Here we report on the chemical characterisation of this corrinoid-like molecule. High performance liquid chromatography coupled to an ultraviolet diode array detector, mass spectrometry and nuclear magnetic resonance spectroscopy has enabled us to identify the compound as Co-[-(7-adenyl)]-Co-cyanocobamide or pseudovitamin . This molecule differs from cobalamin in the -ligand, where it has adenine instead of 5,6-dimethylbenzimidazole bound in a -glycosidic linkage to C-1 of ribose. L. reuteri is the first lactic acid bacterium in which the production of a cobalamin-like molecule has been identified and the first microorganism reported to produce exclusively pseudo-. [Copyright &y& Elsevier]

Background: Lactobacillus reuteri is a heterofermentative Lactic Acid Bacterium (LAB) that is commonly used for food fermentations and probiotic purposes. Due to its robust properties, it is also increasingly considered for use as a cell factory. It produces several industrially important compounds such as 1,3-propanediol and reuterin natively, but for cell factory purposes, developing improved strategies for engineering and fermentation optimization is crucial. Genome-scale metabolic models can be highly beneficial in guiding rational metabolic engineering. Reconstructing a reliable and a quantitatively accurate metabolic model requires extensive manual curation and incorporation of experimental data. Results: A genome-scale metabolic model of L. reuteri JCM 1112T was reconstructed and the resulting model, Lreuteri_530, was validated and tested with experimental data. Several knowledge gaps in the metabolism were identified and resolved during this process, including presence/absence of glycolytic genes. Flux distribution between the two glycolytic pathways, the phosphoketolase and Embden–Meyerhof–Parnas pathways, varies considerably between LAB species and strains. As these pathways result in different energy yields, it is important to include strain-specific utilization of these pathways in the model. We determined experimentally that the Embden–Meyerhof–Parnas pathway carried at most 7% of the total glycolytic flux. Predicted growth rates from Lreuteri_530 were in good agreement with experimentally determined values. To further validate the prediction accuracy of Lreuteri_530, the predicted effects of glycerol addition and adhE gene knock-out, which results in impaired ethanol production, were compared to in vivo data. Examination of both growth rates and uptake- and secretion rates of the main metabolites in central metabolism demonstrated that the model was able to accurately predict the experimentally observed effects. Lastly, the potential of L. reuteri as a cell factory was investigated, resulting in a number of general metabolic engineering strategies. Conclusion: We have constructed a manually curated genome-scale metabolic model of L. reuteri JCM 1112T that has been experimentally parameterized and validated and can accurately predict metabolic behavior of this important platform cell factory. [ABSTRACT FROM AUTHOR]