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ABSTRACT Bacillus subtilisis an important industrial and environmental microorganism known to occupy many niches and produce many compounds of interest. Although it is one of the best-studied organisms, much of this focus including the reconstruction of genome-scale metabolic models has been placed on a few key laboratory strains. Here, we substantially expand these prior models to pan-genome-scale, representing 481 genomes ofB. subtiliswith 2,315 orthologous gene clusters, 1,874 metabolites, and 2,239 reactions. Furthermore, we incorporate data from carbon utilization experiments for eight strains to refine and validate its metabolic predictions. This comprehensive pan-genome model enables the assessment of strain-to-strain differences related to nutrient utilization, fermentation outputs, robustness, and other metabolic aspects. Using the model and phenotypic predictions, we divideB. subtilisstrains into five groups with distinct patterns of behavior that correlate across these features. The pan-genome model offers deep insights intoB. subtilis’metabolism as it varies across environments and provides an understanding as to how different strains have adapted to dynamic habitats. IMPORTANCEAs the volume of genomic data and computational power have increased, so has the number of genome-scale metabolic models. These models encapsulate the totality of metabolic functions for a given organism.Bacillus subtilisstrain 168 is one of the first bacteria for which a metabolic network was reconstructed. Since then, several updated reconstructions have been generated for this model microorganism. Here, we expand the metabolic model for a single strain into a pan-genome-scale model, which consists of individual models for 481B. subtilisstrains. By evaluating differences between these strains, we identified five distinct groups of strains, allowing for the rapid classification of any particular strain. Furthermore, this classification into five groups aids the rapid identification of suitable strains for any application. 

Abstract The bacteriumBacillus subtilisundergoes asymmetric cell division during sporulation, producing a mother cell and a smaller forespore connected by the SpoIIQ-SpoIIIA (or Q-A) channel. The two cells differentiate metabolically, and the forespore becomes dependent on the mother cell for essential building blocks. Here, we investigate the metabolic interactions between mother cell and forespore using genome-scale metabolic and expression models as well as experiments. Our results indicate that nucleotides are synthesized in the mother cell and transported in the form of nucleoside di- or tri-phosphates to the forespore via the Q-A channel. However, if the Q-A channel is inactivated later in sporulation, then glycolytic enzymes can form an ATP and NADH shuttle, providing the forespore with energy and reducing power. Our integrated in silico and in vivo approach sheds light into the intricate metabolic interactions underlying cell differentiation inB. subtilis, and provides a foundation for future studies of metabolic differentiation. 

Abstract Cells can sense changes in their extracellular environment and subsequently adapt their biomass composition. Nutrient abundance defines the capability of the cell to produce biomass components. Under nutrient-limited conditions, resource allocation dramatically shifts to carbon-rich molecules. Here, we used dynamic biomass composition data to predict changes in growth and reaction flux distributions using the available genome-scale metabolic models of five eukaryotic organisms (three heterotrophs and two phototrophs). We identified temporal profiles of metabolic fluxes that indicate long-term trends in pathway and organelle function in response to nitrogen depletion. Surprisingly, our calculations of model sensitivity and biosynthetic cost showed that free energy of biomass metabolites is the main driver of biosynthetic cost and not molecular weight, thus explaining the high costs of arginine and histidine. We demonstrated how metabolic models can accurately predict the complexity of interwoven mechanisms in response to stress over the course of growth. 

Abstract Microbial communities comprised of phototrophs and heterotrophs hold great promise for sustainable biotechnology. Successful application of these communities relies on the selection of appropriate partners. Here we construct four community metabolic models to guide strain selection, pairing phototrophic, sucrose-secretingSynechococcus elongatuswith heterotrophicEscherichia coliK-12,Escherichia coliW,Yarrowia lipolytica, orBacillus subtilis. Model simulations reveae metabolic exchanges that sustain the heterotrophs in minimal media devoid of any organic carbon source, pointing toS. elongatus-E. coliK-12 as the most active community. Experimental validation of flux predictions for this pair confirms metabolic interactions and potential production capabilities. Synthetic communities bypass member-specific metabolic bottlenecks (e.g. histidine- and transport-related reactions) and compensate for lethal genetic traits, achieving up to 27% recovery from lethal knockouts. The study provides a robust modelling framework for the rational design of synthetic communities with optimized growth sustainability using phototrophic partners.