Lactobacillus plantarum is a frequently encountered inhabitant of the human intestinal tract, and some strains are marketed as probiotics. Their ability to adhere to mannose residues is a potentially interesting characteristic with regard to proposed probiotic features such as colonization of the intestinal surface and competitive exclusion of pathogens. In this study, the variable capacity of 14 L. plantarum strains to agglutinate Saccharomyces cerevisiae in a mannose-specific manner was determined and subsequently correlated with an L. plantarum WCFS1-based genome-wide genotype database. This led to the identification of four candidate mannose adhesin-encoding genes. Two genes primarily predicted to code for sortase-dependent cell surface proteins displayed a complete gene-trait match. Their involvement in mannose adhesion was corroborated by the finding that a sortase (srtA) mutant of L. plantarum WCFS1 lost the capacity to agglutinate S. cerevisiae. The postulated role of these two candidate genes was investigated by gene-specific deletion and overexpression in L. plantarum WCFS1. Subsequent evaluation of the mannose adhesion capacity of the resulting mutant strains showed that inactivation of one candidate gene (lp_0373) did not affect mannose adhesion properties. In contrast, deletion of the other gene (lp_1229) resulted in a complete loss of yeast agglutination ability, while its overexpression quantitatively enhanced this phenotype. Therefore, this gene was designated to encode the mannose-specific adhesin (Msa; gene name, msa) of L. plantarum. Domain homology analysis of the predicted 1,000-residue Msa protein identified known carbohydrate-binding domains, further supporting its role as a mannose adhesin that is likely to be involved in the interaction of L. plantarum with its host in the intestinal tract.

A genome-scale metabolic model of the lactic acid bacterium Lactobacillus plantarum WCFS1 was constructed based on genomic content and experimental data. The complete model includes 721 genes, 643 reactions, and 531 metabolites. Different stoichiometric modeling techniques were used for interpretation of complex fermentation data, as L. plantarum is adapted to nutrient-rich environments and only grows in media supplemented with vitamins and amino acids. (i) Based on experimental input and output fluxes, maximal ATP production was estimated and related to growth rate. (ii) Optimization of ATP production further identified amino acid catabolic pathways that were not previously associated with free-energy metabolism. (iii) Genome-scale elementary flux mode analysis identified 28 potential futile cycles. (iv) Flux variability analysis supplemented the elementary mode analysis in identifying parallel pathways, e.g. pathways with identical end products but different co-factor usage. Strongly increased flexibility in the metabolic network was observed when strict coupling between catabolic ATP production and anabolic consumption was relaxed. These results illustrate how a genome-scale metabolic model and associated constraint-based modeling techniques can be used to analyze the physiology of growth on a complex medium rather than a minimal salts medium. However, optimization of biomass formation using the Flux Balance Analysis approach, reported to successfully predict growth rate and by product formation in Escherichia coli and Saccharomyces cerevisiae, predicted too high biomass yields that were incompatible with the observed lactate production. The reason is that this approach assumes optimal efficiency of substrate to biomass conversion, and can therefore not predict the metabolically inefficient lactate formation.Genome-scale metabolic models have become available for an increasing number of organisms (1). These models link genomic information to metabolic reaction networks and have gained considerable attention, not only within the context of metabolic engineering (2, 3), but also from the bioinformatics field (4, 5). An increasing repertoire of modeling techniques is available for exploring the capabilities of the metabolic network, and for understanding genotype-phenotype relationships (2, 6). These techniques are often referred to as constraint-based modeling techniques.So far, constraint-based modeling techniques have mainly been applied to microorganisms that grow on a minimal salt medium containing a single carbon source (1). However, in many biological niches, as well as in multicellular organisms, cells encounter more complex nutritional environments. In analogy, in many industrial and biotechnological applications of microorganisms and cell lines, complex media are used, either because the cells are auxotrophic for nutrients or such media are cheaper. Multiple inputs for the metabolic network complicate constraint-based modeling approaches considerably. Moreover, one may expect quite different metabolic ...

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