Deciphering microbial interactions in synthetic human gut microbiome communities

Emerging strategies for engineering microbial communities

From biosynthesis to bioremediation, microbes have been engineered to address a variety of biotechnological applications. A promising direction in these endeavors is harnessing the power of designer microbial consortia that consist of multiple populations with well-defined interactions. Consortia can accomplish tasks that are difficult or potentially impossible to achieve using monocultures. Despite their potential, the rules underlying microbial community maintenance and function (i.e. the task the consortium is engineered to carry out) are not well defined, though rapid progress is being made. This limited understanding is in part due to the greater challenges associated with increased complexity when dealing with multi-population interactions. Here, we review key features and design strategies that emerge from the analysis of both natural and engineered microbial communities. These strategies can provide new insights into natural consortia and expand the toolbox available to engineers working to develop novel synthetic consortia.

Introducing THOR, a Model Microbiome for Genetic Dissection of Community Behavior

The manipulation and engineering of microbiomes could lead to improved human health, environmental sustainability, and agricultural productivity. However, microbiomes have proven difficult to alter in predictable ways, and their emergent properties are poorly understood. The history of biology has demonstrated the power of model systems to understand complex problems such as gene expression or development. Therefore, a defined and genetically tractable model community would be useful to dissect microbiome assembly, maintenance, and processes. We have developed a tractable model rhizosphere microbiome, designated THOR, containing Pseudomonas koreensis, Flavobacterium johnsoniae, and Bacillus cereus, which represent three dominant phyla in the rhizosphere, as well as in soil and the mammalian gut. The model community demonstrates emergent properties, and the members are amenable to genetic dissection. We propose that THOR will be a useful model for investigations of community-level interactions.

The quest to manipulate microbiomes has intensified, but many microbial communities have proven to be recalcitrant to sustained change. Developing model communities amenable to genetic dissection will underpin successful strategies for shaping microbiomes by advancing an understanding of community interactions. We developed a model community with representatives from three dominant rhizosphere taxa, the Firmicutes, Proteobacteria, and Bacteroidetes. We chose Bacillus cereus as a model rhizosphere firmicute and characterized 20 other candidates, including “hitchhikers” that coisolated with B. cereus from the rhizosphere. Pairwise analysis produced a hierarchical interstrain-competition network. We chose two hitchhikers, Pseudomonas koreensis from the top tier of the competition network and Flavobacterium johnsoniae from the bottom of the network, to represent the Proteobacteria and Bacteroidetes, respectively. The model community has several emergent properties, induction of dendritic expansion of B. cereus colonies by either of the other members, and production of more robust biofilms by the three members together than individually. Moreover, P. koreensis produces a novel family of alkaloid antibiotics that inhibit growth of F. johnsoniae, and production is inhibited by B. cereus. We designate this community THOR, because the members are the hitchhikers of the rhizosphere. The genetic, genomic, and biochemical tools available for dissection of THOR provide the means to achieve a new level of understanding of microbial community behavior.

Web-gLV: A Web Based Platform for Lotka-Volterra Based Modeling and Simulation of Microbial Populations

The affordability of high throughput DNA sequencing has allowed us to explore the dynamics of microbial populations in various ecosystems. Mathematical modeling and simulation of such microbiome time series data can help in getting better understanding of bacterial communities. In this paper, we present Web-gLV-a GUI based interactive platform for generalized Lotka-Volterra (gLV) based modeling and simulation of microbial populations. The tool can be used to generate the mathematical models with automatic estimation of parameters and use them to predict future trajectories using numerical simulations. We also demonstrate the utility of our tool on few publicly available datasets. The case studies demonstrate the ease with which the current tool can be used by biologists to model bacterial populations and simulate their dynamics to get biological insights. We expect Web-gLV to be a valuable contribution in the field of ecological modeling and metagenomic systems biology.

Revisit gut microbiota and its impact on human health and disease

Trillions of microbes have evolved with and continue to live on human beings. With the rapid advances in tools and technology in recent years, new knowledge and insight in cross-talk between the microbes and their hosts have gained. It is the aim of this work to critically review and summarize recent literature reports on the role of microbiota and mechanisms involved in the progress and development of major human diseases, which include obesity, hypertension, cardiovascular disease, diabetes, cancer, Inflammatory Bowel Disease (IBD), gout, depression and arthritis, as well as infant health and longevity.

Available energy fluxes drive a transition in the diversity, stability, and functional structure of microbial communities

A fundamental goal of microbial ecology is to understand what determines the diversity, stability, and structure of microbial ecosystems. The microbial context poses special conceptual challenges because of the strong mutual influences between the microbes and their chemical environment through the consumption and production of metabolites. By analyzing a generalized consumer resource model that explicitly includes cross-feeding, stochastic colonization, and thermodynamics, we show that complex microbial communities generically exhibit a transition as a function of available energy fluxes from a "resource-limited" regime where community structure and stability is shaped by energetic and metabolic considerations to a diverse regime where the dominant force shaping microbial communities is the overlap between species' consumption preferences. These two regimes have distinct species abundance patterns, different functional profiles, and respond differently to environmental perturbations. Our model reproduces large-scale ecological patterns observed across multiple experimental settings such as nestedness and differential beta diversity patterns along energy gradients. We discuss the experimental implications of our results and possible connections with disorder-induced phase transitions in statistical physics.

Understanding and Engineering Distributed Biochemical Pathways in Microbial Communities

Microbiomes impact nearly every environment on Earth by modulating the molecular composition of the environment. Temporally changing environmental stimuli and spatial organization are major variables shaping the structure and function of microbiomes. The web of interactions among members of these communities and between the organisms and the environment dictates microbiome functions. Microbial interactions are major drivers of microbiomes and are modulated by spatiotemporal parameters. A mechanistic and quantitative understanding of ecological, molecular, and environmental forces shaping microbiomes could inform strategies to control microbiome dynamics and functions. Major challenges for harnessing the potential of microbiomes for diverse applications include the development of predictive modeling frameworks and tools for precise manipulation of microbiome behaviors.

Metabolic Basis for Mutualism between Gut Bacteria and Its Impact on the Drosophila melanogaster Host

Interactions between species shape the formation and function of microbial communities. In the gut microbiota of animals, cross-feeding of metabolites between microbes can enhance colonization and influence host physiology. We examined a mutually beneficial interaction between two bacteria isolated from the gut microbiota of Drosophila, i.e., Acetobacter fabarum and Lactobacillus brevis After developing an in vitro coculture assay, we utilized a genetic screen to identify A. fabarum genes required for enhanced growth with L. brevis The screen, and subsequent genetic analyses, showed that the gene encoding pyruvate phosphate dikinase (ppdK) is required for A. fabarum to benefit fully from coculture. By testing strains with mutations in a range of metabolic genes, we provide evidence that A. fabarum can utilize multiple fermentation products of L. brevis Mutualism between the bacteria in vivo affects gnotobiotic Drosophila melanogaster; flies associated with A. fabarum and L. brevis showed >1,000-fold increases in bacterial cell density and significantly lower triglyceride storage than monocolonized flies. Mutation of ppdK decreased A. fabarum density in flies cocolonized with L. brevis, consistent with the model in which Acetobacter employs gluconeogenesis to assimilate Lactobacillus fermentation products as a source of carbon in vivo We propose that cross-feeding between these groups is a common feature of microbiota in Drosophila IMPORTANCE The digestive tracts of animals are home to a community of microorganisms, the gut microbiota, which affects the growth, development, and health of the host. Interactions among microbes in this inner ecosystem can influence which species colonize the gut and can lead to changes in host physiology. We investigated a mutually beneficial interaction between two bacterial species from the gut microbiota of fruit flies. By coculturing the bacteria in vitro, we were able to identify a metabolic gene required for the bacteria to grow better together than they do separately. Our data suggest that one species consumes the waste products of the other, leading to greater productivity of the microbial community and modifying the nutrients available to the host. This study provides a starting point for investigating how these and other bacteria mutually benefit by sharing metabolites and for determining the impact of mutualism on host health.

Characterize carbapenem-resistant Klebsiella pneumoniae isolates for nosocomial pneumonia and their Gram-negative bacteria neighbors in the respiratory tract

This study was conducted to perform an epidemiological survey of carbapenem-resistant Klebsiella pneumoniae (CRKP) isolates for nosocomial pneumonia (NP) and visit their Gram-negative bacteria neighbors (GNNs) from the respiratory tracts. Pulsed-field gel electrophoresis and multi locus sequence typing were performed to evaluate the clonal relationships of these isolates. Statistical methods were then used to understand their possible relationship between CRKP and their GNNs. Among the 63 CRKP isolates, 84.1% produced a KPC-2 carbapenemase gene, followed by blaNDM-1 and blaIMP-38 like. 13 different STs and 29 clones were identified. Less heterogeneous clone backgrounds were observed in 53 KPC-2 K. pneumoniae isolates, and 6 STs have been found to contain KPC-2, of which the predominant ST11 harbored 47 KPC-2-producing K. pneumoniae isolates. Particularly, the two infrequent isolates co-possessing blaKPC-2 and blaIMP-38 like shared a different clonal relationship. 87 GNNs isolates of CRKP were established, the analysis present that different strains showed significant or no difference with CRKP upon antibiotics susceptibility. Conclusion, ST11 harboring KPC-2-producing K. pneumoniae isolates were dominant for NP, and both clonal spread and horizontal transfer contributed to the dissemination of CRKP. However, additional studies are needed to explore the biological relationship with their GNNs.

Effects of exogenous microbial inoculum on the structure and dynamics of bacterial communities in swine carcass composting

Composting is a widely accepted method for the disposal of deceased livestock. It is a biological self-heating process during which animal carcasses are converted to fertilizer products. Additional inoculants can facilitate the composting progress. This study investigated how the addition of microbial inoculants could improve the composting effectiveness and could change the structure and dynamics of bacterial communities in the carcass composting process. Four strains of Bacillus were inoculated into the swine carcass composting piles. The groups with the additional inoculants showed a higher temperature in the thermophilic phase and higher germination indices in the composted products. The sequencing results showed that the dominant phyla were Proteobacteria, Firmicutes, and Actinobacteria, and the dominant classified genera were Brevibacterium and Bacillus. Canonical correlation analysis showed that temperature and moisture exerted a stronger influence on the bacterial community diversity. The interaction network of dominant genera and the abundance variation of the bacterial community demonstrated that the inoculated bacterial agent changed the structure of bacterial communities and enriched the diversity of the species due to antagonism and symbiosis among the dominant bacterial communities.

Multiple stable states in microbial communities explained by the stable marriage problem

Experimental studies of microbial communities routinely reveal that they have multiple stable states. While each of these states is generally resilient, certain perturbations such as antibiotics, probiotics, and diet shifts, result in transitions to other states. Can we reliably both predict such stable states as well as direct and control transitions between them? Here we present a new conceptual model-inspired by the stable marriage problem in game theory and economics-in which microbial communities naturally exhibit multiple stable states, each state with a different species' abundance profile. Our model's core ingredient is that microbes utilize nutrients one at a time while competing with each other. Using only two ranked tables, one with microbes' nutrient preferences and one with their competitive abilities, we can determine all possible stable states as well as predict inter-state transitions, triggered by the removal or addition of a specific nutrient or microbe. Further, using an example of seven Bacteroides species common to the human gut utilizing nine polysaccharides, we predict that mutual complementarity in nutrient preferences enables these species to coexist at high abundances.

Machine Learning Reveals Missing Edges and Putative Interaction Mechanisms in Microbial Ecosystem Networks

Microbes affect each other's growth in multiple, often elusive, ways. The ensuing interdependencies form complex networks, believed to reflect taxonomic composition as well as community-level functional properties and dynamics. The elucidation of these networks is often pursued by measuring pairwise interactions in coculture experiments. However, the combinatorial complexity precludes an exhaustive experimental analysis of pairwise interactions, even for moderately sized microbial communities. Here, we used a machine learning random forest approach to address this challenge. In particular, we show how partial knowledge of a microbial interaction network, combined with trait-level representations of individual microbial species, can provide accurate inference of missing edges in the network and putative mechanisms underlying the interactions. We applied our algorithm to three case studies: an experimentally mapped network of interactions between auxotrophic Escherichia coli strains, a community of soil microbes, and a large in silico network of metabolic interdependencies between 100 human gut-associated bacteria. For this last case, 5% of the network was sufficient to predict the remaining 95% with 80% accuracy, and the mechanistic hypotheses produced by the algorithm accurately reflected known metabolic exchanges. Our approach, broadly applicable to any microbial or other ecological network, may drive the discovery of new interactions and new molecular mechanisms, both for therapeutic interventions involving natural communities and for the rational design of synthetic consortia. IMPORTANCE Different organisms in a microbial community may drastically affect each other's growth phenotypes, significantly affecting the community dynamics, with important implications for human and environmental health. Novel culturing methods and the decreasing costs of sequencing will gradually enable high-throughput measurements of pairwise interactions in systematic coculturing studies. However, a thorough characterization of all interactions that occur within a microbial community is greatly limited both by the combinatorial complexity of possible assortments and by the limited biological insight that interaction measurements typically provide without laborious specific follow-ups. Here, we show how a simple and flexible formal representation of microbial pairs can be used for the classification of interactions via machine learning. The approach we propose predicts with high accuracy the outcome of yet-to-be performed experiments and generates testable hypotheses about the mechanisms of specific interactions.

Pairing off: a bottom-up approach to the human gut microbiome

The human gut microbiome has been implicated in a variety of health outcomes, and extensive research has aimed to understand its composition and function, primarily via metagenomic analyses. An examination of how the microbiome develops and interacts through interspecies competition and cooperation has been lacking so far. In their recent work, Venturelli et al (2018 ) build a synthetic gut community and accurately predict its dynamics with a simple network of pairwise interactions.