Metabolic proximity in the order of colonization of a microbial community

Optimization and purification of bioproducts from Bacillus velezensis PhCL fermentation and their potential on industrial application and bioremediation

Bioproduction is considered a promising alternative way of obtaining useful and green chemicals. However, the downstream process of biomolecules has been one of the major difficulties in upscaling the application of bioproducts due to the high purification cost. Acid precipitation is the most common method for purifying biosurfactants from the fermentation broth with high purity. However, the use of strong acids and organic solvents in solvent extraction has limited its application. Hence, in this study, a new strain of Bacillus velezensis PhCL was isolated from phenolic waste, and its production of amylase had been optimized via response surface methodology. After that, amylase and biosurfactant were purified by sequential ammonium sulfate precipitation and the result suggested that even though the purified crude biosurfactant had a lower purification fold compared to the acid precipitation, the yield was higher and both enzymes and biosurfactant also could be recovered for lowering the purification cost. Moreover, the purified amylase and crude biosurfactant were characterized and the results suggested that the purified crude biosurfactant would have a higher emulsion activity and petroleum hydrocarbon removal rate compared to traditional surfactants. This study provided another approach for purifying bioactive compounds including enzymes and biosurfactants from the same fermentation broth and further explored the potential of the crude purified biosurfactant in the bioremediation of polycyclic aromatic hydrocarbons and petroleum hydrocarbons.

Microbial invasion of a toxic medium is facilitated by a resident community but inhibited as the community co-evolves

Predicting whether microbial invaders will colonize an environment is critical for managing natural and engineered ecosystems, and controlling infectious disease. Invaders often face competition by resident microbes. But how invasions play out in communities dominated by facilitative interactions is less clear. We previously showed that growth medium toxicity can promote facilitation between four bacterial species, as species that cannot grow alone rely on others to survive. Following the same logic, here we allowed other bacterial species to invade the four-species community and found that invaders could more easily colonize a toxic medium when the community was present. In a more benign environment instead, invasive species that could survive alone colonized more successfully when the residents were absent. Next, we asked whether early colonists could exclude future ones through a priority effect, by inoculating the invaders into the resident community only after its members had co-evolved for 44 weeks. Compared to the ancestral community, the co-evolved resident community was more competitive toward invaders and less affected by them. Our experiments show how communities may assemble by facilitating one another in harsh, sterile environments, but that arriving after community members have co-evolved can limit invasion success.

Polymicrobial biofilms related to dental implant diseases: unravelling the critical role of extracellular biofilm matrix

Biofilms are complex tri-dimensional structures that encase microbial cells in an extracellular matrix comprising self-produced polymeric substances. The matrix rich in extracellular polymeric substance (EPS) contributes to the unique features of biofilm lifestyle and structure, enhancing microbial accretion, biofilm virulence, and antimicrobial resistance. The role of the EPS matrix of biofilms growing on biotic surfaces, especially dental surfaces, is largely unravelled. To date, there is a lack of a broad overview of existing literature concerning the relationship between the EPS matrix and the dental implant environment and its role in implant-related infections. Here, we discuss recent advances in the critical role of the EPS matrix on biofilm growth and virulence on the dental implant surface and its effect on the etiopathogenesis and progression of implant-related infections. Similar to other biofilms associated with human diseases/conditions, EPS-enriched biofilms on implant surfaces promote microbial accumulation, microbiological shift, cross-kingdom interaction, antimicrobial resistance, biofilm virulence, and, consequently, peri-implant tissue damage. But intriguingly, the protagonism of EPS role on implant-related infections and the development of matrix-target therapeutic strategies has been neglected. Finally, we highlight the need for more in-depth analyses of polymicrobial interactions within EPS matrix and EPS-targeting technologies' rationale for disrupting the complex biofilm microenvironment with more outstanding translation to implant applications in the near future.

Relationships between community composition, productivity and invasion resistance in semi-natural bacterial microcosms

Common garden experiments that inoculate a standardised growth medium with synthetic microbial communities (i.e. constructed from individual isolates or using dilution cultures) suggest that the ability of the community to resist invasions by additional microbial taxa can be predicted by the overall community productivity (broadly defined as cumulative cell density and/or growth rate). However, to the best of our knowledge, no common garden study has yet investigated the relationship between microbial community composition and invasion resistance in microcosms whose compositional differences reflect natural, rather than laboratory-designed, variation. We conducted experimental invasions of two bacterial strains (Pseudomonas fluorescens and Pseudomonas putida) into laboratory microcosms inoculated with 680 different mixtures of bacteria derived from naturally occurring microbial communities collected in the field. Using 16S rRNA gene amplicon sequencing to characterise microcosm starting composition, and high-throughput assays of community phenotypes including productivity and invader survival, we determined that productivity is a key predictor of invasion resistance in natural microbial communities, substantially mediating the effect of composition on invasion resistance. The results suggest that similar general principles govern invasion in artificial and natural communities, and that factors affecting resident community productivity should be a focal point for future microbial invasion experiments.

Comparative Metagenomics Reveals Microbial Communities and Their Associated Functions in Two Types of Fuzhuan Brick Tea

Fuzhuan brick tea (FBT) is a unique post-fermented tea product, naturally co-fermented by microorganisms, and has gained global popularity due to its potential health benefits for humans. Considerable efforts have been made toward elucidating the microbial diversity within FBT, but an understanding of the underlying FBT community interactions and functions remains poorly studied. Consequently, the microbial communities of two types of FBT, originating from Hunan and Shaanxi provinces, were investigated using comparative shotgun metagenomic sequencing and functional annotations. Metagenomic analysis indicated that two communities shared similar taxonomic and functional attributes. Two samples shared 486 genera, in which Pseudomonas contributed most to the abundant functions within the two samples. The carbohydrate active enzyme functions of the communities primarily comprised GH (32.92%), GT (26.8%), CEs (20.43%), and AAs (18.04%). Furthermore, the overall metabolic pathways encoded by the metagenomes were largely associated with carbohydrate and amino acid metabolism, with nine metabolic pathways that were differential between two groups including penicillin and cephalosporin biosynthesis. Significantly, a total of 35 potential probiotics were inferred, with Pseudomonas putida being the most abundant inferred probiotic (80%) within the FBT communities. This study provides new insights into FBT microbial communities on their potential functions and roles in FBT characteristics.

A multidimensional perspective on microbial interactions

Beyond being simply positive or negative, beneficial or inhibitory, microbial interactions can involve a diverse set of mechanisms, dependencies and dynamical properties. These more nuanced features have been described in great detail for some specific types of interactions, (e.g. pairwise metabolic cross-feeding, quorum sensing or antibiotic killing), often with the use of quantitative measurements and insight derived from modeling. With a growing understanding of the composition and dynamics of complex microbial communities for human health and other applications, we face the challenge of integrating information about these different interactions into comprehensive quantitative frameworks. Here, we review the literature on a wide set of microbial interactions, and explore the potential value of a formal categorization based on multidimensional vectors of attributes. We propose that such an encoding can facilitate systematic, direct comparisons of interaction mechanisms and dependencies, and we discuss the relevance of an atlas of interactions for future modeling and rational design efforts.

Metabolic Signaling and Spatial Interactions in the Oral Polymicrobial Community

Oral supra- and subgingival biofilms are complex communities in which hundreds of bacteria, viruses, and fungi reside and interact. In these social environments, microbes compete and cooperate for resources, such as living space and nutrients. The metabolic activities of bacteria can transform their microenvironment and dynamically influence the fitness and growth of cohabitating organisms. Biofilm communities are temporally and spatially organized largely due to cell-to-cell communication, which promotes synergistic interactions. Metabolic interactions maintain biofilm homeostasis through mutualistic cross-feeding, metabolic syntrophy, and cross-respiration. These interactions include reciprocal metabolite exchanges that promote the growth of physiologically compatible bacteria, processive catabolism of complex substrates, and unidirectional interactions that are globally important for the polymicrobial community. Additionally, oral bacterial interactions can lead to detoxification of oxidative compounds, which will provide protection to the community at large. It has also been established that specific organisms provide terminal electron acceptors to partner species that result in a shift from fermentation to respiration, thus increasing ATP yields and improving fitness. Indeed, many interspecies relationships are multidimensional, and the net outcome can be spatially and temporally dependent. Cross-kingdom interactions also occur as oral yeast are antagonistic to some oral bacteria, while numerous mutualistic interactions contribute to yeast-bacterial colonization, fitness in the oral community, and the pathogenesis of caries. Consideration of this social environment reveals behaviors and phenotypes that are not apparent through the study of microbes in isolation. Here, we provide a comprehensive overview of the metabolic interactions that shape the oral microbial community.

Metabolic network percolation quantifies biosynthetic capabilities across the human oral microbiome

The biosynthetic capabilities of microbes underlie their growth and interactions, playing a prominent role in microbial community structure. For large, diverse microbial communities, prediction of these capabilities is limited by uncertainty about metabolic functions and environmental conditions. To address this challenge, we propose a probabilistic method, inspired by percolation theory, to computationally quantify how robustly a genome-derived metabolic network produces a given set of metabolites under an ensemble of variable environments. We used this method to compile an atlas of predicted biosynthetic capabilities for 97 metabolites across 456 human oral microbes. This atlas captures taxonomically-related trends in biomass composition, and makes it possible to estimate inter-microbial metabolic distances that correlate with microbial co-occurrences. We also found a distinct cluster of fastidious/uncultivated taxa, including several Saccharibacteria (TM7) species, characterized by their abundant metabolic deficiencies. By embracing uncertainty, our approach can be broadly applied to understanding metabolic interactions in complex microbial ecosystems.

Biofilms and their properties

Bacteria within the oral cavity live primarily as complex, polymicrobial biofilms. Dental biofilms are necessary etiological factors for dental caries and periodontal diseases but have also been implicated in diseases outside the oral cavity. Biofilm is the preferred lifestyle for bacteria, and biofilms are found on almost any surface in nature. Bacteria growing within a biofilm exhibit an altered phenotype. Substantial changes in gene expression occur when bacteria are in close proximity or physical contact with one another or with the host. This may facilitate nutritional co-operation, cell-cell signaling, and gene transfer, including transfer of antibiotic-resistance genes, thus rendering biofilm bacteria with properties other than those found in free-floating, planktonic bacteria. We will discuss biofilm properties and possible consequences for future prophylaxis.

Signaling Systems in Oral Bacteria

The supra- and subgingival plaque biofilm communities of plaque are composed of hundreds of different microbes. These communities are spatially and temporally structured, largely due to cell-cell communications that coordinate synergistic interactions, and intracellular signaling systems to sense changes in the surrounding environment. Homeostasis is maintained through metabolic communication, mutualistic cross-feeding, and cross-respiration. These nutritional symbioses can reciprocally influence the local microenvironments by altering the pH and by detoxifying oxidative compounds. Signal transduction mechanisms include two-component systems, tyrosine phosphorelays, quorum sensing systems, and cyclic nucleotide secondary messengers. Signaling converges on transcriptional programs and can result in synergistic or antagonistic interbacterial interactions that sculpt community development. The sum of all these interactions can be a well-organized polymicrobial community that remains in homeostasis with the host, or a dysbiotic community that provokes pathogenic responses in the host.

Dental biofilm: ecological interactions in health and disease

BACKGROUND

The oral microbiome is diverse and exists as multispecies microbial communities on oral surfaces in structurally and functionally organized biofilms.

AIM

To describe the network of microbial interactions (both synergistic and antagonistic) occurring within these biofilms and assess their role in oral health and dental disease.

METHODS

PubMed database was searched for studies on microbial ecological interactions in dental biofilms. The search results did not lend themselves to systematic review and have been summarized in a narrative review instead.

RESULTS

Five hundred and forty-seven original research articles and 212 reviews were identified. The majority (86%) of research articles addressed bacterial-bacterial interactions, while inter-kingdom microbial interactions were the least studied. The interactions included physical and nutritional synergistic associations, antagonism, cell-to-cell communication and gene transfer.

CONCLUSIONS

Oral microbial communities display emergent properties that cannot be inferred from studies of single species. Individual organisms grow in environments they would not tolerate in pure culture. The networks of multiple synergistic and antagonistic interactions generate microbial inter-dependencies and give biofilms a resilience to minor environmental perturbations, and this contributes to oral health. If key environmental pressures exceed thresholds associated with health, then the competitiveness among oral microorganisms is altered and dysbiosis can occur, increasing the risk of dental disease.

Variable habitat conditions drive species covariation in the human microbiota

Two species with similar resource requirements respond in a characteristic way to variations in their habitat—their abundances rise and fall in concert. We use this idea to learn how bacterial populations in the microbiota respond to habitat conditions that vary from person-to-person across the human population. Our mathematical framework shows that habitat fluctuations are sufficient for explaining intra-bodysite correlations in relative species abundances from the Human Microbiome Project. We explicitly show that the relative abundances of closely related species are positively correlated and can be predicted from taxonomic relationships. We identify a small set of functional pathways related to metabolism and maintenance of the cell wall that form the basis of a common resource sharing niche space of the human microbiota.

The human body is inhabited by a vast number of microorganisms comprising the human microbiota. The species composition of the microbiota varies considerably from person-to-person and the relative abundances of some species rise and fall in concert. We introduce a mathematical model where differences in habitat conditions cause most of the variability of the microbiota. A statistical analysis shows that variable habitat conditions are sufficient for explaining the patterns of variation observed across a healthy human population and, as a result, the correlation between the relative abundances of two species reflects how closely related they are rather than how they directly interact with each other.

A Genome-Scale Modeling Approach to Quantify Biofilm Component Growth of Salmonella Typhimurium

Salmonella typhimurium (S. typhimurium) is an extremely dangerous foodborne bacterium that infects both animal and human subjects, causing fatal diseases around the world. Salmonella's robust virulence, antibiotic-resistant nature, and capacity to survive under harsh conditions are largely due to its ability to form resilient biofilms. Multiple genome-scale metabolic models have been developed to study the complex and diverse nature of this organism's metabolism; however, none of these models fully integrated the reactions and mechanisms required to study the influence of biofilm formation. This work developed a systems-level approach to study the adjustment of intracellular metabolism of S. typhimurium during biofilm formation. The most advanced metabolic reconstruction currently available, STM_v1.0, was 1st extended to include the formation of the extracellular biofilm matrix. Flux balance analysis was then employed to study the influence of biofilm formation on cellular growth rate and the production rates of biofilm components. With biofilm formation present, biomass growth was examined under nutrient rich and nutrient deficient conditions, resulting in overall growth rates of 0.8675 and 0.6238 h^-1 respectively. Investigation of intracellular flux variation during biofilm formation resulted in the elucidation of 32 crucial reactions, and associated genes, whose fluxes most significantly adapt during the physiological response. Experimental data were found in the literature to validate the importance of these genes for the biofilm formation of S. typhimurium. This preliminary investigation on the adjustment of intracellular metabolism of S. typhimurium during biofilm formation will serve as a platform to generate hypotheses for further experimental study on the biofilm formation of this virulent bacterium.

Diversity in a Polymicrobial Community Revealed by Analysis of Viromes, Endolysins and CRISPR Spacers

The polymicrobial biofilm communities in Mushroom and Octopus Spring in Yellowstone National Park (YNP) are well characterized, yet little is known about the phage populations. Dominant species, Synechococcus sp. JA-2-3B'a(2–13), Synechococcus sp. JA-3-3Ab, Chloroflexus sp. Y-400-fl, and Roseiflexus sp. RS-1, contain multiple CRISPR-Cas arrays, suggesting complex interactions with phage predators. To analyze phage populations from Octopus Spring biofilms, we sequenced a viral enriched fraction. To assemble and analyze phage metagenomic data, we developed a custom module, VIRITAS, implemented within the MetAMOS framework. This module bins contigs into groups based on tetranucleotide frequencies and CRISPR spacer-protospacer matching and ORF calling. Using this pipeline we were able to assemble phage sequences into contigs and bin them into three clusters that corroborated with their potential host range. The virome contained 52,348 predicted ORFs; some were clearly phage-like; 9319 ORFs had a recognizable Pfam domain while the rest were hypothetical. Of the recognized domains with CRISPR spacer matches, was the phage endolysin used by lytic phage to disrupt cells. Analysis of the endolysins present in the thermophilic cyanophage contigs revealed a subset of characterized endolysins as well as a Glyco_hydro_108 (PF05838) domain not previously associated with sequenced cyanophages. A search for CRISPR spacer matches to all identified phage endolysins demonstrated that a majority of endolysin domains were targets. This strategy provides a general way to link host and phage as endolysins are known to be widely distributed in bacteriophage. Endolysins can also provide information about host cell wall composition and have the additional potential to be used as targets for novel therapeutics.

Metabolic modeling of a chronic wound biofilm consortium predicts spatial partitioning of bacterial species

Background

Chronic wounds are often colonized by consortia comprised of different bacterial species growing as biofilms on a complex mixture of wound exudate. Bacteria growing in biofilms exhibit phenotypes distinct from planktonic growth, often rendering the application of antibacterial compounds ineffective. Computational modeling represents a complementary tool to experimentation for generating fundamental knowledge and developing more effective treatment strategies for chronic wound biofilm consortia.

Results

We developed spatiotemporal models to investigate the multispecies metabolism of a biofilm consortium comprised of two common chronic wound isolates: the aerobe Pseudomonas aeruginosa and the facultative anaerobe Staphylococcus aureus. By combining genome-scale metabolic reconstructions with partial differential equations for metabolite diffusion, the models were able to provide both temporal and spatial predictions with genome-scale resolution. The models were used to analyze the metabolic differences between single species and two species biofilms and to demonstrate the tendency of the two bacteria to spatially partition in the multispecies biofilm as observed experimentally. Nutrient gradients imposed by supplying glucose at the bottom and oxygen at the top of the biofilm induced spatial partitioning of the two species, with S. aureus most concentrated in the anaerobic region and P. aeruginosa present only in the aerobic region. The two species system was predicted to support a maximum biofilm thickness much greater than P. aeruginosa alone but slightly less than S. aureus alone, suggesting an antagonistic metabolic effect of P. aeruginosa on S. aureus. When each species was allowed to enhance its growth through consumption of secreted metabolic byproducts assuming identical uptake kinetics, the competitiveness of P. aeruginosa was further reduced due primarily to the more efficient lactate metabolism of S. aureus. Lysis of S. aureus by a small molecule inhibitor secreted from P. aeruginosa and/or P. aeruginosa aerotaxis were predicted to substantially increase P. aeruginosa competitiveness in the aerobic region, consistent with in vitro experimental studies.

Conclusions

Our biofilm modeling approach allows the prediction of individual species metabolism and interspecies interactions in both time and space with genome-scale resolution. This study yielded new insights into the multispecies metabolism of a chronic wound biofilm, in particular metabolic factors that may lead to spatial partitioning of the two bacterial species. We believe that P. aeruginosa lysis of S. aureus combined with nutrient competition is a particularly relevant scenario for which model predictions could be tested experimentally.

Electronic supplementary material

The online version of this article (doi:10.1186/s12918-016-0334-8) contains supplementary material, which is available to authorized users.

Genome-scale modelling of microbial metabolism with temporal and spatial resolution

Most natural microbial systems have evolved to function in environments with temporal and spatial variations. A major limitation to understanding such complex systems is the lack of mathematical modelling frameworks that connect the genomes of individual species and temporal and spatial variations in the environment to system behaviour. The goal of this review is to introduce the emerging field of spatiotemporal metabolic modelling based on genome-scale reconstructions of microbial metabolism. The extension of flux balance analysis (FBA) to account for both temporal and spatial variations in the environment is termed spatiotemporal FBA (SFBA). Following a brief overview of FBA and its established dynamic extension, the SFBA problem is introduced and recent progress is described. Three case studies are reviewed to illustrate the current state-of-the-art and possible future research directions are outlined. The author posits that SFBA is the next frontier for microbial metabolic modelling and a rapid increase in methods development and system applications is anticipated.

Phenotypic differentiation of gastrointestinal microbes is reflected in their encoded metabolic repertoires

BACKGROUND

The human gastrointestinal tract harbors a diverse microbial community, in which metabolic phenotypes play important roles for the human host. Recent developments in meta-omics attempt to unravel metabolic roles of microbes by linking genotypic and phenotypic characteristics. This connection, however, still remains poorly understood with respect to its evolutionary and ecological context.

RESULTS

We generated automatically refined draft genome-scale metabolic models of 301 representative intestinal microbes in silico. We applied a combination of unsupervised machine-learning and systems biology techniques to study individual and global differences in genomic content and inferred metabolic capabilities. Based on the global metabolic differences, we found that energy metabolism and membrane synthesis play important roles in delineating different taxonomic groups. Furthermore, we found an exponential relationship between phylogeny and the reaction composition, meaning that closely related microbes of the same genus can exhibit pronounced differences with respect to their metabolic capabilities while at the family level only marginal metabolic differences can be observed. This finding was further substantiated by the metabolic divergence within different genera. In particular, we could distinguish three sub-type clusters based on membrane and energy metabolism within the Lactobacilli as well as two clusters within the Bifidobacteria and Bacteroides.

CONCLUSIONS

We demonstrate that phenotypic differentiation within closely related species could be explained by their metabolic repertoire rather than their phylogenetic relationships. These results have important implications in our understanding of the ecological and evolutionary complexity of the human gastrointestinal microbiome.

Intermicrobial Interactions as a Driver for Community Composition and Stratification of Oral Biofilms

The oral cavity is accessible to microorganisms, and biofilms are present throughout on hard and soft tissues. The shedding of epithelial cell layers is usually effective for controlling biofilm development on soft tissues. Innate immune mechanisms are not so effective against biofilms on tooth surfaces, and oral hygiene measures such as brushing and flossing are required for the periodic removal of dental plaque. Even with good oral hygiene, microbial communities accumulate on teeth in areas that are protected from mechanical abrasion forces. Changes in the composition of these biofilms are associated with oral diseases such as dental caries or periodontitis. Newly formed biofilms and more mature dental plaque each have a level of spatial organization in the horizontal and vertical planes. Communities are shaped by many varied interactions between different species and genera within the biofilm, which include physical cell-cell associations known as coaggregation, interspecies signaling, secretion and turnover of antimicrobial compounds and the sharing of an extracellular matrix. Central to these interactions is the selection for metabolic synergies and it is becoming clear that the ability of communities to extract the maximum energy from the available metabolites is a potent driver for biofilm structure and stratification. This review discusses recent advances in our understanding of intermicrobial interactions in oral biofilms and the roles that they play in determining the spatial organization of biofilm communities.

Saliva as the Sole Nutritional Source in the Development of Multispecies Communities in Dental Plaque

Dental plaque is a polymicrobial biofilm that forms on the surfaces of teeth and, if inadequately controlled, can lead to dental caries or periodontitis. Nutrient availability is the fundamental limiting factor for the formation of dental plaque, and for its ability to generate acid and erode dental enamel. Nutrient availability is also critical for bacteria to grow in subgingival biofilms and to initiate periodontitis. Over the early stages of dental plaque formation, micro-organisms acquire nutrients by breaking down complex salivary substrates such as mucins and other glycoproteins. Once dental plaque matures, dietary carbohydrates become more important for supragingival dental plaque, and gingival crevicular fluid forms the major nutrient source for subgingival microorganisms. Many species of oral bacteria do not grow in laboratory monocultures when saliva is the sole nutrient source, and it is now clear that intermicrobial interactions are critical for the development of dental plaque. This chapter aims to provide an overview of the key metabolic requirements of some well-characterized oral bacteria, and the nutrient webs that promote the growth of multispecies communities and underpin the pathogenicity of dental plaque for both dental caries and periodontitis.

Sequencing and beyond: integrating molecular 'omics' for microbial community profiling

High-throughput DNA sequencing has proven invaluable for investigating diverse environmental and host-associated microbial communities. In this Review, we discuss emerging strategies for microbial community analysis that complement and expand traditional metagenomic profiling. These include novel DNA sequencing strategies for identifying strain-level microbial variation and community temporal dynamics; measuring multiple 'omic' data types that better capture community functional activity, such as transcriptomics, proteomics and metabolomics; and combining multiple forms of omic data in an integrated framework. We highlight studies in which the 'multi-omics' approach has led to improved mechanistic models of microbial community structure and function.

Systematic prediction of health-relevant human-microbial co-metabolism through a computational framework

The gut microbiota is well known to affect host metabolic phenotypes. The systemic effects of the gut microbiota on host metabolism are generally evaluated via the comparison of germfree and conventional mice, which is impossible to perform for humans. Hence, it remains difficult to determine the impact of the gut microbiota on human metabolic phenotypes. We demonstrate that a constraint-based modeling framework that simulates "germfree" and "ex-germfree" human individuals can partially fill this gap and allow for in silico predictions of systemic human-microbial co-metabolism. To this end, we constructed the first constraint-based host-microbial community model, comprising the most comprehensive model of human metabolism and 11 manually curated, validated metabolic models of commensals, probiotics, pathogens, and opportunistic pathogens. We used this host-microbiota model to predict potential metabolic host-microbe interactions under 4 in silico dietary regimes. Our model predicts that gut microbes secrete numerous health-relevant metabolites into the lumen, thereby modulating the molecular composition of the body fluid metabolome. Our key results include the following: 1. Replacing a commensal community with pathogens caused a loss of important host metabolic functions. 2. The gut microbiota can produce important precursors of host hormone synthesis and thus serves as an endocrine organ. 3. The synthesis of important neurotransmitters is elevated in the presence of the gut microbiota. 4. Gut microbes contribute essential precursors for glutathione, taurine, and leukotrienes. This computational modeling framework provides novel insight into complex metabolic host-microbiota interactions and can serve as a powerful tool with which to generate novel, non-obvious hypotheses regarding host-microbe co-metabolism.

Anoxic Conditions Promote Species-Specific Mutualism between Gut Microbes In Silico

The human gut is inhabited by thousands of microbial species, most of which are still uncharacterized. Gut microbes have adapted to each other's presence as well as to the host and engage in complex cross feeding. Constraint-based modeling has been successfully applied to predicting microbe-microbe interactions, such as commensalism, mutualism, and competition. Here, we apply a constraint-based approach to model pairwise interactions between 11 representative gut microbes. Microbe-microbe interactions were computationally modeled in conjunction with human small intestinal enterocytes, and the microbe pairs were subjected to three diets with various levels of carbohydrate, fat, and protein in normoxic or anoxic environments. Each microbe engaged in species-specific commensal, parasitic, mutualistic, or competitive interactions. For instance, Streptococcus thermophilus efficiently outcompeted microbes with which it was paired, in agreement with the domination of streptococci in the small intestinal microbiota. Under anoxic conditions, the probiotic organism Lactobacillus plantarum displayed mutualistic behavior toward six other species, which, surprisingly, were almost entirely abolished under normoxic conditions. This finding suggests that the anoxic conditions in the large intestine drive mutualistic cross feeding, leading to the evolvement of an ecosystem more complex than that of the small intestinal microbiota. Moreover, we predict that the presence of the small intestinal enterocyte induces competition over host-derived nutrients. The presented framework can readily be expanded to a larger gut microbial community. This modeling approach will be of great value for subsequent studies aiming to predict conditions favoring desirable microbes or suppressing pathogens.

Emergent biosynthetic capacity in simple microbial communities

Microbes have an astonishing capacity to transform their environments. Yet, the metabolic capacity of a single species is limited and the vast majority of microorganisms form complex communities and join forces to exhibit capabilities far exceeding those achieved by any single species. Such enhanced metabolic capacities represent a promising route to many medical, environmental, and industrial applications and call for the development of a predictive, systems-level understanding of synergistic microbial capacity. Here we present a comprehensive computational framework, integrating high-quality metabolic models of multiple species, temporal dynamics, and flux variability analysis, to study the metabolic capacity and dynamics of simple two-species microbial ecosystems. We specifically focus on detecting emergent biosynthetic capacity--instances in which a community growing on some medium produces and secretes metabolites that are not secreted by any member species when growing in isolation on that same medium. Using this framework to model a large collection of two-species communities on multiple media, we demonstrate that emergent biosynthetic capacity is highly prevalent. We identify commonly observed emergent metabolites and metabolic reprogramming patterns, characterizing typical mechanisms of emergent capacity. We further find that emergent secretion tends to occur in two waves, the first as soon as the two organisms are introduced, and the second when the medium is depleted and nutrients become limited. Finally, aiming to identify global community determinants of emergent capacity, we find a marked association between the level of emergent biosynthetic capacity and the functional/phylogenetic distance between community members. Specifically, we demonstrate a "Goldilocks" principle, where high levels of emergent capacity are observed when the species comprising the community are functionally neither too close, nor too distant. Taken together, our results demonstrate the potential to design and engineer synthetic communities capable of novel metabolic activities and point to promising future directions in environmental and clinical bioengineering.

Syntrophic exchange in synthetic microbial communities

Metabolic crossfeeding is an important process that can broadly shape microbial communities. However, little is known about specific crossfeeding principles that drive the formation and maintenance of individuals within a mixed population. Here, we devised a series of synthetic syntrophic communities to probe the complex interactions underlying metabolic exchange of amino acids. We experimentally analyzed multimember, multidimensional communities of Escherichia coli of increasing sophistication to assess the outcomes of synergistic crossfeeding. We find that biosynthetically costly amino acids including methionine, lysine, isoleucine, arginine, and aromatics, tend to promote stronger cooperative interactions than amino acids that are cheaper to produce. Furthermore, cells that share common intermediates along branching pathways yielded more synergistic growth, but exhibited many instances of both positive and negative epistasis when these interactions scaled to higher dimensions. In more complex communities, we find certain members exhibiting keystone species-like behavior that drastically impact the community dynamics. Based on comparative genomic analysis of >6,000 sequenced bacteria from diverse environments, we present evidence suggesting that amino acid biosynthesis has been broadly optimized to reduce individual metabolic burden in favor of enhanced crossfeeding to support synergistic growth across the biosphere. These results improve our basic understanding of microbial syntrophy while also highlighting the utility and limitations of current modeling approaches to describe the dynamic complexities underlying microbial ecosystems. This work sets the foundation for future endeavors to resolve key questions in microbial ecology and evolution, and presents a platform to develop better and more robust engineered synthetic communities for industrial biotechnology.