The explosion of microbiome analyses has helped identify individual microorganisms and microbial communities driving human health and disease, but how these communities function is still an open question. For example, the role for the incredibly complex metabolic interactions among microbial species cannot easily be resolved by current experimental approaches such as 16S rRNA gene sequencing, metagenomics and/or metabolomics. Resolving such metabolic interactions is particularly challenging in the context of polymicrobial communities where metabolite exchange has been reported to impact key bacterial traits such as virulence and antibiotic treatment efficacy. As novel approaches are needed to pinpoint microbial determinants responsible for impacting community function in the context of human health and to facilitate the development of novel anti-infective and antimicrobial drugs, here we review, from the viewpoint of experimentalists, the latest advances in metabolic modeling, a computational method capable of predicting metabolic capabilities and interactions from individual microorganisms to complex ecological systems. We use selected examples from the literature to illustrate how metabolic modeling has been utilized, in combination with experiments, to better understand microbial community function. Finally, we propose how such combined, cross-disciplinary efforts can be utilized to drive laboratory work and drug discovery moving forward.

All microorganisms release many metabolites, collectively known as the exometabolome. The resultant multi-way cross-feeding of metabolites among microorganisms distributes resources, thereby increasing total biomass of the microbial community, and promotes the recruitment and persistence of phylogenetically and functionally diverse taxa in microbial communities. Metabolite transfer can also select for evolutionary diversification, yielding multiple closely related but functionally distinct strains. Depending on starting conditions, the evolved strains may be auxotrophs requiring metabolic outputs from producer cells or, alternatively, display loss of complementary reactions in metabolic pathways, with increased metabolic efficiency. Metabolite cross-feeding is widespread in many microbial communities associated with animals and plants, including the animal gut microbiome, and these metabolic interactions can yield products valuable to the host. However, metabolite exchange between pairs of intracellular microbial taxa that share the same host cell or organ can be very limited compared to pairs of free-living microorganisms, perhaps as a consequence of host controls over the metabolic function of intracellular microorganisms. Priorities for future research include the development of tools for improved quantification of metabolite exchange in complex communities and greater integration of the roles of metabolic cross-feeding and other ecological processes, including priority effects and antagonistic interactions, in shaping microbial communities. This article is part of the theme issue \'Conceptual challenges in microbial community ecology\'.

In nature, microbes interact antagonistically, neutrally, or beneficially. To shed light on the effects of positive interactions in microbial consortia, we introduced metabolic dependencies and metabolite overproduction into four bacterial species. While antagonistic interactions govern the wild-type consortium behavior, the genetic modifications alleviated antagonistic interactions and resulted in beneficial interactions. Engineered cross-feeding increased population evenness, a component of ecological diversity, in different environments, including in a more complex gnotobiotic mouse gut environment. Our findings suggest that metabolite cross-feeding could be used as a tool for intentionally shaping microbial consortia in complex environments.IMPORTANCE Microbial communities are ubiquitous in nature. Bacterial consortia live in and on our body and in our environment, and more recently, biotechnology is applying microbial consortia for bioproduction. As part of our body, bacterial consortia influence us in health and disease. Microbial consortium function is determined by its composition, which in turn is driven by the interactions between species. Further understanding of microbial interactions will help us in deciphering how consortia function in complex environments and may enable us to modify microbial consortia for health and environmental benefits.

Cystic fibrosis (CF) is a fatal genetic disease characterized by chronic lung infections due to aberrant mucus production and the inability to clear invading pathogens. The traditional view that CF infections are caused by a single pathogen has been replaced by the realization that the CF lung usually is colonized by a complex community of bacteria, fungi, and viruses. To help unravel the complex interplay between the CF lung environment and the infecting microbial community, we developed a community metabolic model comprised of the 17 most abundant bacterial taxa, which account for >95% of reads across samples, from three published studies in which 75 sputum samples from 46 adult CF patients were analyzed by 16S rRNA gene sequencing. The community model was able to correctly predict high abundances of the \"rare\" pathogens Enterobacteriaceae, Burkholderia, and Achromobacter in three patients whose polymicrobial infections were dominated by these pathogens. With these three pathogens removed, the model correctly predicted that the remaining 43 patients would be dominated by Pseudomonas and/or Streptococcus. This dominance was predicted to be driven by relatively high monoculture growth rates of Pseudomonas and Streptococcus as well as their ability to efficiently consume amino acids, organic acids, and alcohols secreted by other community members. Sample-by-sample heterogeneity of community composition could be qualitatively captured through random variation of the simulated metabolic environment, suggesting that experimental studies directly linking CF lung metabolomics and 16S sequencing could provide important insights into disease progression and treatment efficacy. IMPORTANCE Cystic fibrosis (CF) is a genetic disease in which chronic airway infections and lung inflammation result in respiratory failure. CF airway infections are usually caused by bacterial communities that are difficult to eradicate with available antibiotics. Using species abundance data for clinically stable adult CF patients assimilated from three published studies, we developed a metabolic model of CF airway communities to better understand the interactions between bacterial species and between the bacterial community and the lung environment. Our model predicted that clinically observed CF pathogens could establish dominance over other community members across a range of lung nutrient conditions. Heterogeneity of species abundances across 75 patient samples could be predicted by assuming that sample-to-sample heterogeneity was attributable to random variations in the CF nutrient environment. Our model predictions provide new insights into the metabolic determinants of pathogen dominance in the CF lung and could facilitate the development of improved treatment strategies.

Due to a lack of effective immune clearance, the airways of cystic fibrosis patients are colonized by polymicrobial communities. One of the most widespread and destructive opportunistic pathogens is Pseudomonas aeruginosa; however, P. aeruginosa does not colonize the airways alone. Microbes that are common in the oral cavity, such as Rothia mucilaginosa, are also present in cystic fibrosis patient sputum and have metabolic capacities different from those of P. aeruginosa Here we examine the metabolic interactions of P. aeruginosa and R. mucilaginosa using stable-isotope-assisted metabolomics. Glucose-derived 13C was incorporated into glycolysis metabolites, namely, lactate and acetate, and some amino acids in R. mucilaginosa grown aerobically and anaerobically. The amino acid glutamate was unlabeled in the R. mucilaginosa supernatant but incorporated the 13C label after P. aeruginosa was cross-fed the R. mucilaginosa supernatant in minimal medium and artificial-sputum medium. We provide evidence that P. aeruginosa utilizes R. mucilaginosa-produced metabolites as precursors for generation of primary metabolites, including glutamate.IMPORTANCEPseudomonas aeruginosa is a dominant and persistent cystic fibrosis pathogen. Although P. aeruginosa is accompanied by other microbes in the airways of cystic fibrosis patients, few cystic fibrosis studies show how P. aeruginosa is affected by the metabolism of other bacteria. Here, we demonstrate that P. aeruginosa generates primary metabolites using substrates produced by another microbe that is prevalent in the airways of cystic fibrosis patients, Rothia mucilaginosa These results indicate that P. aeruginosa may get a metabolic boost from its microbial neighbor, which might contribute to its pathogenesis in the airways of cystic fibrosis patients.