The ecological forces that govern the assembly and stability of the human gut microbiota remain unresolved. We developed a generalizable model‐guided framework to predict higher‐dimensional consortia from time‐resolved measurements of lower‐order assemblages. This method was employed to decipher microbial interactions in a diverse human gut microbiome synthetic community. We show that pairwise interactions are major drivers of multi‐species community dynamics, as opposed to higher‐order interactions. The inferred ecological network exhibits a high proportion of negative and frequent positive interactions. Ecological drivers and responsive recipient species were discovered in the network. Our model demonstrated that a prevalent positive and negative interaction topology enables robust coexistence by implementing a negative feedback loop that balances disparities in monospecies fitness levels. We show that negative interactions could generate history‐dependent responses of initial species proportions that frequently do not originate from bistability. Measurements of extracellular metabolites illuminated the metabolic capabilities of monospecies and potential molecular basis of microbial interactions. In sum, these methods defined the ecological roles of major human‐associated intestinal species and illuminated design principles of microbial communities.

The capability to design microbiomes with predictable functions would enable new technologies for applications in health, agriculture, and bioprocessing. Towards this goal, we develop a model-guided approach to design synthetic human gut microbiomes for production of the health-relevant metabolite butyrate. Our data-driven model quantifies microbial interactions impacting growth and butyrate production separately, providing key insights into ecological mechanisms driving butyrate production. We use our model to explore a vast community design space using a design-test-learn cycle to identify high butyrate-producing communities. Our model can accurately predict community assembly and butyrate production across a wide range of species richness. Guided by the model, we identify constraints on butyrate production by high species richness and key molecular factors driving butyrate production, including hydrogen sulfide, environmental pH, and resource competition. In sum, our model-guided approach provides a flexible and generalizable framework for understanding and accurately predicting community assembly and metabolic functions.

Understanding the principles of colonization resistance of the gut microbiome to the pathogen Clostridioides difficile will enable the design of defined bacterial therapeutics. We investigate the ecological principles of community resistance to C. difficile using a synthetic human gut microbiome. Using a dynamic computational model, we demonstrate that C. difficile receives the largest number and magnitude of incoming negative interactions. Our results show that C. difficile is in a unique class of species that display a strong negative dependence between growth and species richness. We identify molecular mechanisms of inhibition including acidification of the environment and competition over resources. We demonstrate that Clostridium hiranonis strongly inhibits C. difficile partially via resource competition. Increasing the initial density of C. difficile can increase its abundance in the assembled community, but community context determines the maximum achievable C. difficile abundance. Our work suggests that the C. difficile inhibitory potential of defined bacterial therapeutics can be optimized by designing communities featuring a combination of mechanisms including species richness, environment acidification, and resource competition.

The assembly of microbial communities and functions emerge from a complex and dynamic web of interactions. A major challenge in microbiome engineering is identifying organism configurations with community-level behaviors that achieve a desired function. The number of possible subcommunities scales exponentially with the number of species in a system, creating a vast experimental design space that is challenging to even sparsely traverse. We develop a model-guided experimental design framework for microbial communities and apply this method to explore the functional landscape of the health-relevant metabolite butyrate using a 25-member synthetic human gut microbiome community. Based on limited experimental measurements, our model accurately forecasts community assembly and butyrate production at every possible level of complexity. Our results elucidate key ecological and molecular mechanisms driving butyrate production including inter-species interactions, pH and hydrogen sulfide. Our model-guided iterative approach provides a flexible framework for understanding and predicting community functions for a broad range of applications.

Genetic competence is a process in which cells are able to take up DNA from their environment, resulting in horizontal gene transfer, a major mechanism for generating diversity in bacteria. Many bacteria carry homologs of the central DNA uptake machinery that has been well characterized in Bacillus subtilis. It has been postulated that the B. subtilis competence helicase ComFA belongs to the DEAD box family of helicases/translocases. Here, we made a series of mutants to analyze conserved amino acid motifs in several regions of B. subtilis ComFA. First, we confirmed that ComFA activity requires amino acid residues conserved among the DEAD box helicases, and second, we show that a zinc finger-like motif consisting of four cysteines is required for efficient transformation. Each cysteine in the motif is important, and mutation of at least two of the cysteines dramatically reduces transformation efficiency. Further, combining multiple cysteine mutations with the helicase mutations shows an additive phenotype. Our results suggest that the helicase and metal binding functions are two distinct activities important for ComFA function during transformation.IMPORTANCE ComFA is a highly conserved protein that has a role in DNA uptake during natural competence, a mechanism for horizontal gene transfer observed in many bacteria. Investigation of the details of the DNA uptake mechanism is important for understanding the ways in which bacteria gain new traits from their environment, such as drug resistance. To dissect the role of ComFA in the DNA uptake machinery, we introduced point mutations into several motifs in the protein sequence. We demonstrate that several amino acid motifs conserved among ComFA proteins are important for efficient transformation. This report is the first to demonstrate the functional requirement of an amino-terminal cysteine motif in ComFA.KEYWORDS ATPase, Bacillus subtilis, DEXD/DEXH box, genetic competence, helicase, natural transformation systems, transformation H orizontal gene transfer (HGT) is a widespread phenomenon in bacteria and thought to be a major driver of biodiversity. HGT in bacteria is known to be the result of three major processes: conjugation, transformation, and bacteriophage transduction (1). All of these processes appear to be dependent on motors or motor-like proteins to function. Transformation is distinct from other HGT processes, as it is the only one that does not require an active donor. Genetic competence is a physiological state in which a naturally transformable cell takes up DNA from its environment. This process has been well studied in Neisseria gonorrhoeae, Streptococcus pneumoniae, Bacillus subtilis, Staphylococcus aureus, and Helicobacter pylori (2, 3). The gene products involved in genetic competence are well conserved among these organisms, with the exception of H. pylori (4). Here, we used B. subtilis as a model system for genetic competence in bacteria.

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In the human gut, the growth of the pathogen Clostridioides difficile is impacted by a complex web of interspecies interactions with members of human gut microbiota. We investigate the contribution of interspecies interactions on the antibiotic response of C. difficile to clinically relevant antibiotics using bottom-up assembly of human gut communities. We identify 2 classes of microbial interactions that alter C. difficile’s antibiotic susceptibility: interactions resulting in increased ability of C. difficile to grow at high antibiotic concentrations (rare) and interactions resulting in C. difficile growth enhancement at low antibiotic concentrations (common). Based on genome-wide transcriptional profiling data, we demonstrate that metal sequestration due to hydrogen sulfide production by the prevalent gut species Desulfovibrio piger increases the minimum inhibitory concentration (MIC) of metronidazole for C. difficile. Competition with species that display higher sensitivity to the antibiotic than C. difficile leads to enhanced growth of C. difficile at low antibiotic concentrations due to competitive release. A dynamic computational model identifies the ecological principles driving this effect. Our results provide a deeper understanding of ecological and molecular principles shaping C. difficile’s response to antibiotics, which could inform therapeutic interventions.

Understanding the principles of colonization resistance of the gut microbiome to the pathogen Clostridioides difficile will enable the design of next generation defined bacterial therapeutics. We investigate the ecological principles of community resistance to C. difficile invasion using a diverse synthetic human gut microbiome. Our results show that species richness is a key determinant of C. difficile growth across a wide range of ecological contexts. Using a dynamic computational model, we demonstrate that C. difficile receives the largest number and magnitude of incoming negative interactions. We identify molecular mechanisms of inhibition including acidification of the environment and competition over glucose. We demonstrate that C. difficile's close relative Clostridium hiranonis strongly inhibits C. difficile via a pH-independent mechanism. While increasing the initial density of C. difficile can increase its abundance in the assembled community, the community context determines the maximum achievable C. difficile abundance. Our work suggests that the C. difficile inhibitory potential of defined bacterial therapeutics can be optimized by designing communities that feature a combination of mechanisms including species richness, environment acidification, and resource competition.