The association between metabolic interactions and co-occurrence in microbial communities has been addressed in a previous study (40). That study, based on models of binary communities, found that co-occurring communities could be distinguished from the niche-associated ones by increased competition, but not by cooperation. In contrast, we show, by simulating higher-order communities (to better represent ecological complexity) and without resorting to a growth optimality assumption (for which there is yet little evidence), that metabolic dependency is a hallmark of species co-occurrence. The distinction of co-occurring groups is evident in comparison to random assemblies as well as to habitatfiltered background. This further allowed us to carry out comprehensive simulations identifying metabolites that preferentially connect co-occurring species."The complete reference appears below. www.pnas.org/cgi

Genome-scale metabolic models are instrumental in uncovering operating principles of cellular metabolism, for model-guided re-engineering, and unraveling cross-feeding in microbial communities. Yet, the application of genome-scale models, especially to microbial communities, is lagging behind the availability of sequenced genomes. This is largely due to the time-consuming steps of manual curation required to obtain good quality models. Here, we present an automated tool, CarveMe, for reconstruction of species and community level metabolic models. We introduce the concept of a universal model, which is manually curated and simulation ready. Starting with this universal model and annotated genome sequences, CarveMe uses a top-down approach to build single-species and community models in a fast and scalable manner. We show that CarveMe models perform closely to manually curated models in reproducing experimental phenotypes (substrate utilization and gene essentiality). Additionally, we build a collection of 74 models for human gut bacteria and test their ability to reproduce growth on a set of experimentally defined media. Finally, we create a database of 5587 bacterial models and demonstrate its potential for fast generation of microbial community models. Overall, CarveMe provides an open-source and user-friendly tool towards broadening the use of metabolic modeling in studying microbial species and communities.

SummaryMany microorganisms live in communities and depend on metabolites secreted by fellow community members for survival. Yet our knowledge of interspecies metabolic dependencies is limited to few communities with small number of exchanged metabolites, and even less is known about cellular regulation facilitating metabolic exchange. Here we show how yeast enables growth of lactic acid bacteria through endogenous, multi-component, cross-feeding in a readily established community. In nitrogen-rich environments, Saccharomyces cerevisiae adjusts its metabolism by secreting a pool of metabolites, especially amino acids, and thereby enables survival of Lactobacillus plantarum and Lactococcus lactis. Quantity of the available nitrogen sources and the status of nitrogen catabolite repression pathways jointly modulate this niche creation. We demonstrate how nitrogen overflow by yeast benefits L. plantarum in grape juice, and contributes to emergence of mutualism with L. lactis in a medium with lactose. Our results illustrate how metabolic decisions of an individual species can benefit others.

Bacterial metabolism plays a fundamental role in gut microbiota ecology and host-microbiome interactions. Yet the metabolic capabilities of most gut bacteria have remained unknown. Here we report growth characteristics of 96 phylogenetically diverse gut bacterial strains across 4 rich and 15 defined media. The vast majority of strains (76) grow in at least one defined medium, enabling accurate assessment of their biosynthetic capabilities. These do not necessarily match phylogenetic similarity, thus indicating a complex evolution of nutritional preferences. We identify mucin utilizers and species inhibited by amino acids and short-chain fatty acids. Our analysis also uncovers media for in vitro studies wherein growth capacity correlates well with in vivo abundance. Further value of the underlying resource is demonstrated by correcting pathway gaps in available genome-scale metabolic models of gut microorganisms. Together, the media resource and the extracted knowledge on growth abilities widen experimental and computational access to the gut microbiota.

Resource competition and metabolic cross-feeding are among the main drivers of microbial community assembly. Yet, the degree to which these two conflicting forces are reflected in the composition of natural communities has not been systematically investigated. Here we use genome-scale metabolic modeling to assess resource competition and metabolic cooperation potential in large co-occurring groups (up to 40 members) across thousands of habitats. Our analysis revealed two distinct community types, clustering at opposite ends in a trade-off between competition and cooperation. On one end, lie highly cooperative communities, characterized by smaller genomes and multiple auxotrophies. At the other end, lie highly competitive communities, featuring larger genomes, overlapping nutritional requirements, and harboring more genes related to antimicrobial activity. While the latter are mainly present in soils, the former are found both in free-living and host-associated habitats. Community-scale flux simulations showed that, while the competitive communities can better resist species invasion but not nutrient shift, the cooperative communities are susceptible to species invasion but resilient to nutrient change. In accord, we show, through analyzing an additional dataset, that colonization by probiotic species is positively associated with the presence of cooperative species in the recipient microbiome. Together, our analysis highlights the bifurcation between competitive and cooperative metabolism in the assembly of natural communities and its implications for community modulation.

Bacteria in the gut can modulate the availability and efficacy of therapeutic drugs. Yet, the systematic mapping of the respective interactions has only started recently 1 and the main underlying mechanism proposed is chemical transformation of drugs by microbes (biotransformation). Here, we investigated the depletion of 15 structurally diverse drugs by 25 representative gut bacterial strains. This revealed 70 bacteria-drug interactions, 29 of which had not been reported before. Over half of the new interactions can be ascribed to bioaccumulation, that is bacteria storing the drug intracellularly without chemically modifying it, and in most cases without their growth being affected. As a case in point, we studied the molecular basis of bioaccumulation of the widely used antidepressant duloxetine by using clickchemistry, thermal proteome profiling and metabolomics. We find that duloxetine binds to several metabolic enzymes and changes metabolite secretion of the respective bacteria. When tested in a defined microbial community of accumulators and non-accumulators, duloxetine markedly altered the community composition through metabolic cross-feeding. We further validated our findings in an animal model, showing that bioaccumulating bacteria attenuate the behavioral response of Caenorhabditis elegans to duloxetine. Taken together, bioaccumulation by gut bacteria may be a common mechanism that alters drug availability and bacterial metabolism, with implications for microbiota composition, pharmacokinetics, side effects and drug responses, likely in an individual manner.Therapeutic drugs can have a strong impact on the gut microbiome and vice versa 2-5 . The underlying drug-bacteria interactions can reduce microbial fitness 6 or alter the drug availability through biotransformation 7-14 . The latter can have either a positive or a negative impact on drug activity and efficacy. While drugs like lovastatin and sulfasalazine are chemically transformed by gut bacteria into their active forms, bacterial metabolism can inactivate drugs such as digoxin 15,16 , or cause toxic effects as in the case of irinotecan 17 .Furthering the diversity of susceptible drugs, over one hundred molecules were recently reported to be chemically modified by gut bacteria 1 . Yet, the mechanistic view on these interactions is largely confined to drug biotransformation 12,13 . Drug accumulation without metabolizationTo expand the knowledge of bacterial effect on drug availability, we systematically profiled interactions between 15 human-targeted drugs and 25 representative human gut bacterial strains (21 species; with additional subspecies or conspecific strains of Bifidobacterium longum, Escherichia coli and Bacteroides uniformis) (Supplementary Table 1). The bacterial species were selected to cover a broad phylogenetic and metabolic diversity representative of the healthy microbiota 18 (Extended Data Fig. 1a, Supplementary Table 1). On the drug side, 12 orally administered small molecule drugs (MW<500 Da), amenable to UPLC-UV-based quantificat...

Genome-scale metabolic networks provide a comprehensive structural framework for modeling genotype-phenotype relationships through flux simulations. The solution space for the metabolic flux state of the cell is typically very large and optimization-based approaches are often necessary for predicting the active metabolic state under specific environmental conditions. The objective function to be used in such optimization algorithms is directly linked with the biological hypothesis underlying the model and therefore it is one of the most relevant parameters for successful modeling. Although linear combination of selected fluxes is widely used for formulating metabolic objective functions, we show that the resulting optimization problem is sensitive towards stoichiometry representation of the metabolic network. This undesirable sensitivity leads to different simulation results when using numerically different but biochemically equivalent stoichiometry representations and thereby makes biological interpretation intrinsically subjective and ambiguous. We hereby propose a new method, Minimization of Metabolites Balance (MiMBl), which decouples the artifacts of stoichiometry representation from the formulation of the desired objective functions, by casting objective functions using metabolite turnovers rather than fluxes. By simulating perturbed metabolic networks, we demonstrate that the use of stoichiometry representation independent algorithms is fundamental for unambiguously linking modeling results with biological interpretation. For example, MiMBl allowed us to expand the scope of metabolic modeling in elucidating the mechanistic basis of several genetic interactions in Saccharomyces cerevisiae.

The diversity of industrially important molecules for which microbial production routes have been experimentally demonstrated is rapidly increasing. The development of economically viable producer cells is, however, lagging behind, as it requires substantial engineering of the host metabolism. A chassis strain suitable for production of a range of molecules is therefore highly sought after but remains elusive. Here, we propose a genome-scale metabolic modeling approach to design chassis strains of Saccharomyces cerevisiae – a widely used microbial cell factory. For a group of 29 products covering a broad range of biochemistry and applications, we identified modular metabolic engineering strategies for re-routing carbon flux towards the desired product. We find distinct product families with shared targets forming the basis for the corresponding chassis cells. The design strategies include overexpression targets that group products by similarity in precursor and cofactor requirements, as well as gene deletion strategies for growth-product coupling that lead to non-intuitive product groups. Our results reveal the extent and the nature of flux re-routing necessary for producing a diverse range of products in a widely used cell factory and provide blueprints for constructing pre-optimized chassis strains.