Deciphering microbial interactions in synthetic human gut microbiome communities

Cross-feeding in the gut microbiome: Ecology and mechanisms

Microbial communities are shaped by positive and negative interactions ranging from competition to mutualism. In the context of the mammalian gut and its microbial inhabitants, the integrated output of the community has important impacts on host health. Cross-feeding, the sharing of metabolites between different microbes, has emergent roles in establishing communities of gut commensals that are stable, resistant to invasion, and resilient to external perturbation. In this review, we first explore the ecological and evolutionary implications of cross-feeding as a cooperative interaction. We then survey mechanisms of cross-feeding across trophic levels, from primary fermenters to H2 consumers that scavenge the final metabolic outputs of the trophic network. We extend this analysis to also include amino acid, vitamin, and cofactor cross-feeding. Throughout, we highlight evidence for the impact of these interactions on each species' fitness as well as host health. Understanding cross-feeding illuminates an important aspect of microbe-microbe and host-microbe interactions that establishes and shapes our gut communities.

Navigation between initial and desired community states using shortcuts

Ecological management problems often involve navigating from an initial to a desired community state. We ask whether navigation without brute-force additions and deletions of species is possible via: adding/deleting a small number of individuals of a species, changing the environment, and waiting. Navigation can yield direct paths (single sequence of actions) or shortcut paths (multiple sequences of actions with lower cost than a direct path). We ask (1) when is non-brute-force navigation possible?; (2) do shortcuts exist and what are their properties?; and (3) what heuristics predict shortcut existence? Using a state diagram framework applied to several empirical datasets, we show that (1) non-brute-force navigation is only possible between some state pairs, (2) shortcuts exist between many state pairs; and (3) changes in abundance and richness are the strongest predictors of shortcut existence, independent of dataset and algorithm choices. State diagrams thus unveil hidden strategies for manipulating species coexistence and efficiently navigating between states.

A Single Intranasal Dose of Bacterial Therapeutics to Calves Confers Longitudinal Modulation of the Nasopharyngeal Microbiota: a Pilot Study

Bovine respiratory disease (BRD) remains the most significant health challenge affecting the North American beef cattle industry and results in $3 billion in economic losses yearly. Current BRD control strategies mainly rely on antibiotics, with metaphylaxis commonly employed to mitigate BRD incidence in commercial feedlots.

Acidity drives volatile metabolites in the spontaneous fermentation of sesame flavor-type baijiu

Environmental factors play an important role in contributing to intricate compositional dynamics and volatile metabolites in food fermentation. However, our understanding of which and how environmental factors affect volatile metabolites during sesame flavor-type baijiu fermentation is poor. Here, we examined the effects of environmental factors on the bacterial and fungal community to determine how changes in representative factors impact the microbial structure, diversity, and volatile metabolites in three fermentations. Results showed that bacterial community (ANOSIM: R = 0.79, P = 0.001), fungal community (ANOSIM: R = 0.65, P = 0.001), and volatile metabolites (ANOSIM: R = 0.84, P = 0.001) were significantly different in three fermentations. Acidity, ethanol, and moisture negatively impacted bacterial composition and diversity (P < 0.05). The fungal diversity and structure were positively and significantly affected by acidity (path coefficient, b = 0.54 for diversity, b = 0.35 for structure, P < 0.05). The fungal community rather than the bacterial community was the strongest driver of volatile metabolites. Fungal structure and diversity were equally important for the composition and content of volatile metabolites (structure: b = 0.50, diversity: b = 0.56, P < 0.05). 66 % of variations in volatile metabolites could be explained. Altogether these results indicated that acidity strongly drove volatile metabolites by modulating fungal structure and diversity. This work provides insights into managing volatile metabolites by regulating initial acidity in sesame flavor-type baijiu fermentation.

Synthetic microbial communities (SynComs) of the human gut: design, assembly, and applications

The human gut harbors native microbial communities, forming a highly complex ecosystem. Synthetic microbial communities (SynComs) of the human gut are an assembly of microorganisms isolated from human mucosa or fecal samples. In recent decades, the ever-expanding culturing capacity and affordable sequencing, together with advanced computational modeling, started a ''golden age'' for harnessing the beneficial potential of SynComs to fight gastrointestinal disorders, such as infections and chronic inflammatory bowel diseases. As simplified and completely defined microbiota, SynComs offer a promising reductionist approach to understanding the multispecies and multikingdom interactions in the microbe-host-immune axis. However, there are still many challenges to overcome before we can precisely construct SynComs of designed function and efficacy that allow the translation of scientific findings to patients' treatments. Here, we discussed the strategies used to design, assemble, and test a SynCom, and address the significant challenges, which are of microbiological, engineering, and translational nature, that stand in the way of using SynComs as live bacterial therapeutics.

Non-gene-editing microbiome engineering of spontaneous food fermentation microbiota-Limitation control, design control, and integration

Non-gene-editing microbiome engineering (NgeME) is the rational design and control of natural microbial consortia to perform desired functions. Traditional NgeME approaches use selected environmental variables to force natural microbial consortia to perform the desired functions. Spontaneous food fermentation, the oldest kind of traditional NgeME, transforms foods into various fermented products using natural microbial networks. In traditional NgeME, spontaneous food fermentation microbiotas (SFFMs) are typically formed and controlled manually by the establishment of limiting factors in small batches with little mechanization. However, limitation control generally leads to trade-offs between efficiency and the quality of fermentation. Modern NgeME approaches based on synthetic microbial ecology have been developed using designed microbial communities to explore assembly mechanisms and target functional enhancement of SFFMs. This has greatly improved our understanding of microbiota control, but such approaches still have shortcomings compared to traditional NgeME. Here, we comprehensively describe research on mechanisms and control strategies for SFFMs based on traditional and modern NgeME. We discuss the ecological and engineering principles of the two approaches to enhance the understanding of how best to control SFFM. We also review recent applied and theoretical research on modern NgeME and propose an integrated in vitro synthetic microbiota model to bridge gaps between limitation control and design control for SFFM.

Aging by Near-Extinctions in Many-Variable Interacting Populations

Models of many-species ecosystems, such as the Lotka-Volterra and replicator equations, suggest that these systems generically exhibit near-extinction processes, where population sizes go very close to zero for some time before rebounding, accompanied by a slowdown of the dynamics (aging). Here, we investigate the connection between near-extinction and aging by introducing an exactly solvable many-variable model, where the time derivative of each population size vanishes at both zero and some finite maximal size. We show that aging emerges generically when random interactions are taken between populations. Population sizes remain exponentially close (in time) to the absorbing values for extended periods of time, with rapid transitions between these two values. The mechanism for aging is different from the one at play in usual glassy systems: At long times, the system evolves in the vicinity of unstable fixed points rather than marginal ones.

Distinct dynamic phases observed in bacterial microcosms

Predicting biodiversity and dynamics of complex communities is a fundamental challenge in ecology. Leveraging bacterial microcosms with well-controlled laboratory conditions, Hu et al. recently performed a direct test of theory predicting that two community-level parameters (i.e., species pool size and inter-species interaction strength) dictate transitions between three dynamical phases: stable full coexistence, stable partial coexistence, and persistent fluctuations. Generally, communities experience species extinctions before they lose stability as either of the two parameters increases.

Competitive interactions between culturable bacteria are highly non-additive

Microorganisms are found in diverse communities whose structure and function are determined by interspecific interactions. Just as single species seldom exist in isolation, communities as a whole are also constantly challenged and affected by external species. Though much work has been done on characterizing how individual species affect each other through pairwise interactions, the joint effects of multiple species on a single (focal) species remain underexplored. As such, it is still unclear how single-species effects combine to a community-level effect on a species of interest. To explore this relationship, we assayed thousands of communities of two, three, and four bacterial species, measuring the effect of single, pairs of, and trios of 61 affecting species on six different focal species. We found that when multiple species each have a negative effect on a focal species, their joint effect is typically not given by the sum of the effects of individual affecting species. Rather, they are dominated by the strongest individual-species effect. Therefore, while joint effects of multiple species are often non-additive, they can still be derived from the effects of individual species, making it plausible to map complex interaction networks based on pairwise measurements. This finding is important for understanding the fate of species introduced into an occupied environment and is relevant for applications in medicine and agriculture, such as probiotics and biocontrol agents, as well as for ecological questions surrounding migrating and invasive species.

Gut-on-a-Chip for the Analysis of Bacteria-Bacteria Interactions in Gut Microbial Community: What Would Be Needed for Bacterial Co-Culture Study to Explore the Diet-Microbiota Relationship?

Bacterial co-culture studies using synthetic gut microbiomes have reported novel research designs to understand the underlying role of bacterial interaction in the metabolism of dietary resources and community assembly of complex microflora. Since lab-on-a-chip mimicking the gut (hereafter "gut-on-a-chip") is one of the most advanced platforms for the simulative research regarding the correlation between host health and microbiota, the co-culture of the synthetic bacterial community in gut-on-a-chip is expected to reveal the diet-microbiota relationship. This critical review analyzed recent research on bacterial co-culture with perspectives on the ecological niche of commensals, probiotics, and pathogens to categorize the experimental approaches for diet-mediated management of gut health as the compositional and/or metabolic modulation of the microbiota and the control of pathogens. Meanwhile, the aim of previous research on bacterial culture in gut-on-a-chip has been mainly limited to the maintenance of the viability of host cells. Thus, the integration of study designs established for the co-culture of synthetic gut consortia with various nutritional resources into gut-on-a-chip is expected to reveal bacterial interspecies interactions related to specific dietary patterns. This critical review suggests novel research topics for co-culturing bacterial communities in gut-on-a-chip to realize an ideal experimental platform mimicking a complex intestinal environment.

Interactions between Culturable Bacteria Are Predicted by Individual Species' Growth

Predicting interspecies interactions is a key challenge in microbial ecology given that interactions shape the composition and functioning of microbial communities. However, predicting microbial interactions is challenging because they can vary considerably depending on species' metabolic capabilities and environmental conditions. Here, we employ machine learning models to predict pairwise interactions between culturable bacteria based on their phylogeny, monoculture growth capabilities, and interactions with other species. We trained our models on one of the largest available pairwise interactions data set containing over 7,500 interactions between 20 species from two taxonomic groups that were cocultured in 40 different carbon environments. Our models accurately predicted both the sign (accuracy of 88%) and the strength of effects (R² of 0.87) species had on each other's growth. Encouragingly, predictions with comparable accuracy could be made even when not relying on information about interactions with other species, which are often hard to measure. However, species' monoculture growth was essential to the model, as predictions based solely on species' phylogeny and inferred metabolic capabilities were significantly less accurate. These results bring us one step closer to a predictive understanding of microbial communities, which is essential for engineering beneficial microbial consortia. IMPORTANCE In order to understand the function and structure of microbial communities, one must know all pairwise interactions that occur between the different species within the community, as these interactions shape the community's structure and functioning. However, measuring all pairwise interactions can be an extremely difficult task especially when dealing with big complex communities. Because of that, predicting interspecies interactions is a key challenge in microbial ecology. Here, we use machine learning models in order to accurately predict the type and strength of interactions. We trained our models on one of the largest available pairwise interactions data set, containing over 7,500 interactions between 20 different species that were cocultured in 40 different environments. Our results show that, in general, accurate predictions can be made, and that the ability of each species to grow on its own in the given environment contributes the most to predictions. Being able to predict microbial interactions would put us one step closer to predicting the functionality of microbial communities and to rationally microbiome engineering.

Controlling the human microbiome

We coexist with a vast number of microbes that live in and on our bodies. Those microbes and their genes are collectively known as the human microbiome, which plays important roles in human physiology and diseases. We have acquired extensive knowledge of the organismal compositions and metabolic functions of the human microbiome. However, the ultimate proof of our understanding of the human microbiome is reflected in our ability to manipulate it for health benefits. To facilitate the rational design of microbiome-based therapies, there are many fundamental questions to be addressed at the systems level. Indeed, we need a deep understanding of the ecological dynamics associated with such a complex ecosystem before we rationally design control strategies. In light of this, this review discusses progress from various fields, e.g., community ecology, network science, and control theory, that are helping us make progress toward the ultimate goal of controlling the human microbiome.

Community diversity is associated with intra-species genetic diversity and gene loss in the human gut microbiome

How the ecological process of community assembly interacts with intra-species diversity and evolutionary change is a longstanding question. Two contrasting hypotheses have been proposed: Diversity Begets Diversity (DBD), in which taxa tend to become more diverse in already diverse communities, and Ecological Controls (EC), in which higher community diversity impedes diversification. Previously, using 16S rRNA gene amplicon data across a range of microbiomes, we showed a generally positive relationship between taxa diversity and community diversity at higher taxonomic levels, consistent with the predictions of DBD (Madi et al., 2020). However, this positive 'diversity slope' plateaus at high levels of community diversity. Here we show that this general pattern holds at much finer genetic resolution, by analyzing intra-species strain and nucleotide variation in static and temporally sampled metagenomes from the human gut microbiome. Consistent with DBD, both intra-species polymorphism and strain number were positively correlated with community Shannon diversity. Shannon diversity is also predictive of increases in polymorphism over time scales up to ~4-6 months, after which the diversity slope flattens and becomes negative - consistent with DBD eventually giving way to EC. Finally, we show that higher community diversity predicts gene loss at a future time point. This observation is broadly consistent with the Black Queen Hypothesis, which posits that genes with functions provided by the community are less likely to be retained in a focal species' genome. Together, our results show that a mixture of DBD, EC, and Black Queen may operate simultaneously in the human gut microbiome, adding to a growing body of evidence that these eco-evolutionary processes are key drivers of biodiversity and ecosystem function.

A guide for comparing microbial co-occurrence networks

The article provides a pipeline for comparing microbial co-occurrence networks based on the R microeco package and meconetcomp package. It has high flexibility and expansibility and can help users efficiently compare networks built from different groups of samples or different construction approaches.

The Effects of Drug Addiction and Detoxification on the Human Oral Microbiota

Drug addiction can powerfully and chronically damage human health. Detoxification contributes to health recovery of the body. It is well established that drug abuse is associated with poor oral health in terms of dental caries and periodontal diseases. We supposed that drug addiction and detoxification might have significant effects on the oral microbiota. To test the hypothesis, we assessed the effects of drug (heroin and methylamphetamine) addiction/detoxification on the oral microbiota based on 16S rRNA gene sequencing by an observational investigation, including 495 saliva samples from participants. The oral microbial compositions differed between non-users, current and former drug users. Lower alpha diversities were observed in current drug users, with no significant differences between non-users and former drug users. Heroin and METH addiction can cause consistent variations in several specific phyla, such as the enrichment of Acidobacteria and depletion of Proteobacteria and Tenericutes. Current drug users had significantly lower relative abundances of Neisseria subflava and Haemophilus parainfluenzae compared to non-users and former drug users. The result of random forest prediction model suggested that the oral microbiota has a powerful classification potential for distinguishing current drug users from non-users and former drug users. A cooccurrence network analysis showed that current drug users had more complex oral microbial networks and lower functional modularity. Overall, our study suggested that drug addiction may damage the balance of the oral microbiota. These results may have benefits for further understanding the effects of addiction-related oral microbiota on the health of drug users and promoting the microbiota to serve as a potential tool for accurate forensic identification. IMPORTANCE Drug addiction has serious negative consequences for human health and public security. The evidence indicates that drug abuse can cause poor oral health. In the current study, we observed that drug addiction caused oral microbial dysbiosis. Detoxication have positive effects on the recovery of oral microbial community structures to some extent. Understanding the effects of drug addiction and detoxification on oral microbial communities will promote a more rational approach for recovering the oral function and health of drug users. Furthermore, specific microbial species might be considered biomarkers that could provide information regarding drug abuse status for saliva left at crime scenes. To the best of our knowledge, this is the first report on the role of the oral microbiota in drug addiction and detoxification. Our findings give new clues to understand the association between drug addiction and oral health.

Detailed analysis of metabolism reveals growth-rate-promoting interactions between Anaerostipes caccae and Bacteroides spp

INTRODUCTION

Human gut microbiota species which are next-generation probiotics (NGPs) candidates are of high interest as they have shown the potential to treat intestinal inflammation and other diseases. Unfortunately, these species are often not robust enough for large-scale cultivation, especially in maintaining diversity in co-culture production.

OBJECTIVES

In this study, we describe interactions between human gut microbiota species in the cultivation process with unique substrates. We also demonstrated that it is possible to change the species ratio in co-culture by changing the ratio of carbon sources.

METHODS

We screened 25 different bacterial species based on their metabolic capabilities. After evaluating unique substrate possibilities, we chose Anaerostipes caccae (A. caccae), Bacteroides thetaiotaomicron (B. thetaiotaomicron), and Bacteroides vulgatus (B. vulgatus) as subjects for further study. D-sorbitol, D-xylose, and D-galacturonic acid were selected as substrates for A. caccae, B. thetaiotaomicron, and B. vulgatus respectively. All three species were cultivated as both monocultures and in co-cultures in serial batch fermentations in an isothermal microcalorimeter.

RESULTS

Positive interactions were detected between the species in both co-cultures (A. caccae + B. thetaiotaomicron; A. caccae + B. vulgatus) resulting in higher heat production compared to the sum of the monocultures. The same positive cross-feeding interactions took place in larger-scale cultivation experiments. We confirmed acetate and lactate cross-feeding between A. caccae and B. thetaiotaomicron with flux balance analysis (FBA).

CONCLUSION

Changing the ratio of the selected carbon sources in the medium changed the species ratio accordingly. Such robustness is the basis for developing more efficient industrial co-culture processes including the production of NGPs.

Assembly of gut-derived bacterial communities follows "early-bird" resource utilization dynamics

Diet can impact host health through changes to the gut microbiota, yet we lack mechanistic understanding linking nutrient availability and microbiota composition. Here, we use thousands of microbial communities cultured in vitro from human feces to uncover simple assembly rules and develop a predictive model of community composition upon addition of single nutrients from central carbon metabolism to a complex medium. Community membership was largely determined by the donor feces, whereas relative abundances were determined by the supplemental carbon source. The absolute abundance of most taxa was independent of the supplementing nutrient, due to the ability of fast-growing organisms to quickly exhaust their niche in the complex medium and then exploit and monopolize the supplemental carbon source. Relative abundances of dominant taxa could be predicted from the nutritional preferences and growth dynamics of species in isolation, and exceptions were consistent with strain-level variation in growth capabilities. Our study reveals that community assembly follows simple rules of nutrient utilization dynamics and provides a predictive framework for manipulating gut commensal communities through nutritional perturbations.

Tracking defined microbial communities by multicolor flow cytometry reveals tradeoffs between productivity and diversity

Cross feeding between microbes is ubiquitous, but its impact on the diversity and productivity of microbial communities is incompletely understood. A reductionist approach using simple microbial communities has the potential to detect cross feeding interactions and their impact on ecosystem properties. However, quantifying abundance of more than two microbes in a community in a high throughput fashion requires rapid, inexpensive assays. Here, we show that multicolor flow cytometry combined with a machine learning-based classifier can rapidly quantify species abundances in simple, synthetic microbial communities. Our approach measures community structure over time and detects the exchange of metabolites in a four-member community of fluorescent Bacteroides species. Notably, we quantified species abundances in co-cultures and detected evidence of cooperation in polysaccharide processing and competition for monosaccharide utilization. We also observed that co-culturing on simple sugars, but not complex sugars, reduced microbial productivity, although less productive communities maintained higher community diversity. In summary, our multicolor flow cytometric approach presents an economical, tractable model system for microbial ecology using well-studied human bacteria. It can be extended to include additional species, evaluate more complex environments, and assay response of communities to a variety of disturbances.

Technical versus biological variability in a synthetic human gut community

Synthetic communities grown in well-controlled conditions are an important tool to decipher the mechanisms driving community dynamics. However, replicate time series of synthetic human gut communities in chemostats are rare, and it is thus still an open question to what extent stochasticity impacts gut community dynamics. Here, we address this question with a synthetic human gut bacterial community using an automated fermentation system that allows for a larger number of biological replicates. We collected six biological replicates for a community initially consisting of five common gut bacterial species that fill different metabolic niches. After an initial 12 hours in batch mode, we switched to chemostat mode and observed the community to stabilize after 2-3 days. Community profiling with 16S rRNA resulted in high variability across replicate vessels and high technical variability, while the variability across replicates was significantly lower for flow cytometric data. Both techniques agree on the decrease in the abundance of Bacteroides thetaiotaomicron, accompanied by an initial increase in Blautia hydrogenotrophica. These changes occurred together with reproducible metabolic shifts, namely a fast depletion of glucose and trehalose concentration in batch followed by a decrease in formic acid and pyruvic acid concentrations within the first 12 hours after the switch to chemostat mode. In conclusion, the observed variability in the synthetic bacterial human gut community, as assessed with 16S rRNA gene sequencing, is largely due to technical variability. The low variability seen in HPLC and flow cytometry data suggests a highly deterministic system.

Carbohydrate complexity limits microbial growth and reduces the sensitivity of human gut communities to perturbations

Dietary fibre impacts the growth dynamics of human gut microbiota, yet we lack a detailed and quantitative understanding of how these nutrients shape microbial interaction networks and responses to perturbations. By building human gut communities coupled with computational modelling, we dissect the effects of fibres that vary in chemical complexity and each of their constituent sugars on community assembly and response to perturbations. We demonstrate that the degree of chemical complexity across different fibres limits microbial growth and the number of species that can utilize these nutrients. The prevalence of negative interspecies interactions is reduced in the presence of fibres compared with their constituent sugars. Carbohydrate chemical complexity enhances the reproducibility of community assembly and resistance of the community to invasion. We demonstrate that maximizing or minimizing carbohydrate competition between resident and invader species enhances resistance to invasion. In sum, the quantitative effects of carbohydrate chemical complexity on microbial interaction networks could be exploited to inform dietary and bacterial interventions to modulate community resistance to perturbations.

Ecological landscapes guide the assembly of optimal microbial communities

Assembling optimal microbial communities is key for various applications in biofuel production, agriculture, and human health. Finding the optimal community is challenging because the number of possible communities grows exponentially with the number of species, and so an exhaustive search cannot be performed even for a dozen species. A heuristic search that improves community function by adding or removing one species at a time is more practical, but it is unknown whether this strategy can discover an optimal or nearly optimal community. Using consumer-resource models with and without cross-feeding, we investigate how the efficacy of search depends on the distribution of resources, niche overlap, cross-feeding, and other aspects of community ecology. We show that search efficacy is determined by the ruggedness of the appropriately-defined ecological landscape. We identify specific ruggedness measures that are both predictive of search performance and robust to noise and low sampling density. The feasibility of our approach is demonstrated using experimental data from a soil microbial community. Overall, our results establish the conditions necessary for the success of the heuristic search and provide concrete design principles for building high-performing microbial consortia.

A Transcriptomic Analysis of Higher-Order Ecological Interactions in a Eukaryotic Model Microbial Ecosystem

Nonlinear ecological interactions within microbial ecosystems and their contribution to ecosystem functioning remain largely unexplored. Higher-order interactions, or interactions in systems comprised of more than two members that cannot be explained by cumulative pairwise interactions, are particularly understudied, especially in eukaryotic microorganisms. The wine fermentation ecosystem presents an ideal model to study yeast ecosystem establishment and functioning. Some pairwise ecological interactions between wine yeast species have been characterized, but very little is known about how more complex, multispecies systems function. Here, we evaluated nonlinear ecosystem properties by determining the transcriptomic response of Saccharomyces cerevisiae to pairwise versus tri-species culture. The transcriptome revealed that genes expressed during pairwise coculture were enriched in the tri-species data set but also that just under half of the data set comprised unique genes attributed to a higher-order response. Through interactive protein-association network visualizations, a holistic cell-wide view of the gene expression data was generated, which highlighted known stress response and metabolic adaptation mechanisms which were specifically activated during tri-species growth. Further, extracellular metabolite data corroborated that the observed differences were a result of a biotic stress response. This provides exciting new evidence showing the presence of higher-order interactions within a model microbial ecosystem. IMPORTANCE Higher-order interactions are one of the major blind spots in our understanding of microbial ecosystems. These systems remain largely unpredictable and are characterized by nonlinear dynamics, in particular when the system is comprised of more than two entities. By evaluating the transcriptomic response of S. cerevisiae to an increase in culture complexity from a single species to two- and three-species systems, we were able to confirm the presence of a unique response in the more complex setting that could not be explained by the responses observed at the pairwise level. This is the first data set that provides molecular targets for further analysis to explain unpredictable ecosystem dynamics in yeast.

Parallel engineering of environmental bacteria and performance over years under jungle-simulated conditions

Engineered bacteria could perform many functions in the environment, for example, to remediate pollutants, deliver nutrients to crops or act as in-field biosensors. Model organisms can be unreliable in the field, but selecting an isolate from the thousands that naturally live there and genetically manipulating them to carry the desired function is a slow and uninformed process. Here, we demonstrate the parallel engineering of isolates from environmental samples by using the broad-host-range XPORT conjugation system (Bacillus subtilis mini-ICEBs1) to transfer a genetic payload to many isolates in parallel. Bacillus and Lysinibacillus species were obtained from seven soil and water samples from different locations in Israel. XPORT successfully transferred a genetic function (reporter expression) into 25 of these isolates. They were then screened to identify the best-performing chassis based on the expression level, doubling time, functional stability in soil, and environmentally-relevant traits of its closest annotated reference species, such as the ability to sporulate and temperature tolerance. From this library, we selected Bacillus frigoritolerans A3E1, re-introduced it to soil, and measured function and genetic stability in a contained environment that replicates jungle conditions. After 21 months of storage, the engineered bacteria were viable, could perform their function, and did not accumulate disruptive mutations.

Guided by the principles of microbiome engineering: Accomplishments and perspectives for environmental use

Although the accomplishments of microbiome engineering highlight its significance for the targeted manipulation of microbial communities, knowledge and technical gaps still limit the applications of microbiome engineering in biotechnology, especially for environmental use. Addressing the environmental challenges of refractory pollutants and fluctuating environmental conditions requires an adequate understanding of the theoretical achievements and practical applications of microbiome engineering. Here, we review recent cutting-edge studies on microbiome engineering strategies and their classical applications in bioremediation. Moreover, a framework is summarized for combining both top-down and bottom-up approaches in microbiome engineering toward improved applications. A strategy to engineer microbiomes for environmental use, which avoids the build-up of toxic intermediates that pose a risk to human health, is suggested. We anticipate that the highlighted framework and strategy will be beneficial for engineering microbiomes to address difficult environmental challenges such as degrading multiple refractory pollutants and sustain the performance of engineered microbiomes in situ with indigenous microorganisms under fluctuating conditions.

Dietary fiber chemical structure determined gut microbiota dynamics

Precision modulation of gut microbiota requires elucidation of the relation between dietary fiber intake and gut microbe dynamics. However, current studies on this aspect are few due to many technical limitations. Here, we used Caenorhabditis elegans to minimize the complicated host-microbial factors and to find the relation between dietary fiber chemical structures and gut microbiota dynamics. The Allium schoenoprasum polysaccharide (AssP) structure was elucidated and used as the complex dietary fiber against the simple fiber inulin. In vitro bacterial growth and genome analysis indicated that AssP supports bacterial growth better than inulin, while in vivo gut microbiota analysis of C. elegans fed with AssP showed that microbiota richness increased significantly compared with those fed with inulin. It is concluded that the more complex the dietary fiber chemical structure, the more gut bacteria growth it supports. Together with the community bacterial interactions that alter their abundances in vivo, these factors regulate gut microbiota synergistically.

Enhancing nutritional niche and host defenses by modifying the gut microbiome

The gut microbiome is essential for processing complex food compounds and synthesizing nutrients that the host cannot digest or produce, respectively. New model systems are needed to study how the metabolic capacity provided by the gut microbiome impacts the nutritional status of the host, and to explore possibilities for altering host metabolic capacity via the microbiome. Here, we colonized the nematode Caenorhabditis elegans gut with cellulolytic bacteria that enabled C. elegans to utilize cellulose, an otherwise indigestible substrate, as a carbon source. Cellulolytic bacteria as a community component in the worm gut can also support additional bacterial species with specialized roles, which we demonstrate by using Lactobacillus plantarum to protect C. elegans against Salmonella enterica infection. This work shows that engineered microbiome communities can be used to endow host organisms with novel functions, such as the ability to utilize alternate nutrient sources or to better fight pathogenic bacteria.

Classifying Interactions in a Synthetic Bacterial Community Is Hindered by Inhibitory Growth Medium

Predicting the fate of a microbial community and its member species relies on understanding the nature of their interactions. However, designing simple assays that distinguish between interaction types can be challenging. Here, we performed spent medium assays based on the predictions of a mathematical model to decipher the interactions among four bacterial species: Agrobacterium tumefaciens, Comamonas testosteroni, Microbacterium saperdae, and Ochrobactrum anthropi. While most experimental results matched model predictions, the behavior of C. testosteroni did not: its lag phase was reduced in the pure spent media of A. tumefaciens and M. saperdae but prolonged again when we replenished our growth medium. Further experiments showed that the growth medium actually delayed the growth of C. testosteroni, leading us to suspect that A. tumefaciens and M. saperdae could alleviate this inhibitory effect. There was, however, no evidence supporting such "cross-detoxification," and instead, we identified metabolites secreted by A. tumefaciens and M. saperdae that were then consumed or "cross-fed" by C. testosteroni, shortening its lag phase. Our results highlight that even simple, defined growth media can have inhibitory effects on some species and that such negative effects need to be included in our models. Based on this, we present new guidelines to correctly distinguish between different interaction types such as cross-detoxification and cross-feeding. IMPORTANCE Communities of microbes colonize virtually every place on earth. Ultimately, we strive to predict and control how these communities behave, for example, if they reside in our guts and make us sick. But precise control is impossible unless we can identify exactly how their member species interact with one another. To find a systematic way to measure interactions, we started very simply with a small community of four bacterial species and carefully designed experiments based on a mathematical model. This first attempt accurately mapped out interactions for all species except one. By digging deeper, we understood that our method failed for that species as it was suffering in the growth medium that we chose. A revised model that considered that growth media can be harmful could then make more accurate predictions. What we have learned with these four species can now be applied to decipher interactions in larger communities.

Emergent phases of ecological diversity and dynamics mapped in microcosms

From tropical forests to gut microbiomes, ecological communities host notably high numbers of coexisting species. Beyond high biodiversity, communities exhibit a range of complex dynamics that are difficult to explain under a unified framework. Using bacterial microcosms, we performed a direct test of theory predicting that simple community-level features dictate emergent behaviors of communities. As either the number of species or the strength of interactions increases, we show that microbial ecosystems transition between three distinct dynamical phases, from a stable equilibrium in which all species coexist to partial coexistence to emergence of persistent fluctuations in species abundances, in the order predicted by theory. Under fixed conditions, high biodiversity and fluctuations reinforce each other. Our results demonstrate predictable emergent patterns of diversity and dynamics in ecological communities.

Transcriptional Changes in Bifidobacterium bifidum Involved in Synergistic Multispecies Biofilms

Bifidobacterium bifidum is part of the core microbiota of healthy infant guts where it may form biofilms on epithelial cells, mucosa, and food particles in the gut lumen. Little is known about transcriptional changes in B. bifidum engaged in synergistic multispecies biofilms with ecologically relevant species of the human gut. Recently, we reported prevalence of synergism in mixed-species biofilms formed by the human gut microbiota. This study represents a comparative gene expression analysis of B. bifidum when grown in a single-species biofilm and in two multispecies biofilm consortia with Bifidobacterium longum subsp. infantis, Bacteroides ovatus, and Parabacteroides distasonis in order to identify genes involved in this adaptive process in mixed biofilms and the influence on its metabolic and functional traits. Changes up to 58% and 43% in its genome were found when it grew in three- and four-species biofilm consortia, respectively. Upregulation of genes of B. bifidum involved in carbohydrate metabolism (particularly the galE gene), quorum sensing (luxS and pfs), and amino acid metabolism (especially branched chain amino acids) in both multispecies biofilms, compared to single-species biofilms, suggest that they may be contributing factors for the observed synergistic biofilm production when B. bifidum coexists with other species in a biofilm.

Design, construction, and in vivo augmentation of a complex gut microbiome

Efforts to model the human gut microbiome in mice have led to important insights into the mechanisms of host-microbe interactions. However, the model communities studied to date have been defined or complex, but not both, limiting their utility. Here, we construct and characterize in vitro a defined community of 104 bacterial species composed of the most common taxa from the human gut microbiota (hCom1). We then used an iterative experimental process to fill open niches: germ-free mice were colonized with hCom1 and then challenged with a human fecal sample. We identified new species that engrafted following fecal challenge and added them to hCom1, yielding hCom2. In gnotobiotic mice, hCom2 exhibited increased stability to fecal challenge and robust colonization resistance against pathogenic Escherichia coli. Mice colonized by either hCom2 or a human fecal community are phenotypically similar, suggesting that this consortium will enable a mechanistic interrogation of species and genes on microbiome-associated phenotypes.

Dynamic metabolic interactions and trophic roles of human gut microbes identified using a minimal microbiome exhibiting ecological properties

Microbe–microbe interactions in the human gut are influenced by host-derived glycans and diet. The high complexity of the gut microbiome poses a major challenge for unraveling the metabolic interactions and trophic roles of key microbes. Synthetic minimal microbiomes provide a pragmatic approach to investigate their ecology including metabolic interactions. Here, we rationally designed a synthetic microbiome termed Mucin and Diet based Minimal Microbiome (MDb-MM) by taking into account known physiological features of 16 key bacteria. We combined 16S rRNA gene-based composition analysis, metabolite measurements and metatranscriptomics to investigate community dynamics, stability, inter-species metabolic interactions and their trophic roles. The 16 species co-existed in the in vitro gut ecosystems containing a mixture of complex substrates representing dietary fibers and mucin. The triplicate MDb-MM’s followed the Taylor’s power law and exhibited strikingly similar ecological and metabolic patterns. The MDb-MM exhibited resistance and resilience to temporal perturbations as evidenced by the abundance and metabolic end products. Microbe-specific temporal dynamics in transcriptional niche overlap and trophic interaction network explained the observed co-existence in a competitive minimal microbiome. Overall, the present study provides crucial insights into the co-existence, metabolic niches and trophic roles of key intestinal microbes in a highly dynamic and competitive in vitro ecosystem.

Metabolic modeling of the International Space Station microbiome reveals key microbial interactions

BACKGROUND

Recent studies have provided insights into the persistence and succession of microbes aboard the International Space Station (ISS), notably the dominance of Klebsiella pneumoniae. However, the interactions between the various microbes aboard the ISS and how they shape the microbiome remain to be clearly understood. In this study, we apply a computational approach to predict possible metabolic interactions in the ISS microbiome and shed further light on its organization.

RESULTS

Through a combination of a systems-based graph-theoretical approach, and a constraint-based community metabolic modeling approach, we demonstrated several key interactions in the ISS microbiome. These complementary approaches provided insights into the metabolic interactions and dependencies present amongst various microbes in a community, highlighting key interactions and keystone species. Our results showed that the presence of K. pneumoniae is beneficial to many other microorganisms it coexists with, notably those from the Pantoea genus. Species belonging to the Enterobacteriaceae family were often found to be the most beneficial for the survival of other microorganisms in the ISS microbiome. However, K. pneumoniae was found to exhibit parasitic and amensalistic interactions with Aspergillus and Penicillium species, respectively. To prove this metabolic prediction, K. pneumoniae and Aspergillus fumigatus were co-cultured under normal and simulated microgravity, where K. pneumoniae cells showed parasitic characteristics to the fungus. The electron micrography revealed that the presence of K. pneumoniae compromised the morphology of fungal conidia and degenerated its biofilm-forming structures.

CONCLUSION

Our study underscores the importance of K. pneumoniae in the ISS, and its potential positive and negative interactions with other microbes, including potential pathogens. This integrated modeling approach, combined with experiments, demonstrates the potential for understanding the organization of other such microbiomes, unravelling key organisms and their interdependencies. Video Abstract.

Metabolic modeling of the International Space Station microbiome reveals key microbial interactions

BACKGROUND

Recent studies have provided insights into the persistence and succession of microbes aboard the International Space Station (ISS), notably the dominance of Klebsiella pneumoniae. However, the interactions between the various microbes aboard the ISS and how they shape the microbiome remain to be clearly understood. In this study, we apply a computational approach to predict possible metabolic interactions in the ISS microbiome and shed further light on its organization.

RESULTS

Through a combination of a systems-based graph-theoretical approach, and a constraint-based community metabolic modeling approach, we demonstrated several key interactions in the ISS microbiome. These complementary approaches provided insights into the metabolic interactions and dependencies present amongst various microbes in a community, highlighting key interactions and keystone species. Our results showed that the presence of K. pneumoniae is beneficial to many other microorganisms it coexists with, notably those from the Pantoea genus. Species belonging to the Enterobacteriaceae family were often found to be the most beneficial for the survival of other microorganisms in the ISS microbiome. However, K. pneumoniae was found to exhibit parasitic and amensalistic interactions with Aspergillus and Penicillium species, respectively. To prove this metabolic prediction, K. pneumoniae and Aspergillus fumigatus were co-cultured under normal and simulated microgravity, where K. pneumoniae cells showed parasitic characteristics to the fungus. The electron micrography revealed that the presence of K. pneumoniae compromised the morphology of fungal conidia and degenerated its biofilm-forming structures.

CONCLUSION

Our study underscores the importance of K. pneumoniae in the ISS, and its potential positive and negative interactions with other microbes, including potential pathogens. This integrated modeling approach, combined with experiments, demonstrates the potential for understanding the organization of other such microbiomes, unravelling key organisms and their interdependencies. Video Abstract.

Transcriptional programming in a Bacteroides consortium

Bacteroides species are prominent members of the human gut microbiota. The prevalence and stability of Bacteroides in humans make them ideal candidates to engineer as programmable living therapeutics. Here we report a biotic decision-making technology in a community of Bacteroides (consortium transcriptional programming) with genetic circuit compression. Circuit compression requires systematic pairing of engineered transcription factors with cognate regulatable promoters. In turn, we demonstrate the compression workflow by designing, building, and testing all fundamental two-input logic gates dependent on the inputs isopropyl-β-D-1-thiogalactopyranoside and D-ribose. We then deploy complete sets of logical operations in five human donor Bacteroides, with which we demonstrate sequential gain-of-function control in co-culture. Finally, we couple transcriptional programs with CRISPR interference to achieve loss-of-function regulation of endogenous genes-demonstrating complex control over community composition in co-culture. This work provides a powerful toolkit to program gene expression in Bacteroides for the development of bespoke therapeutic bacteria.

MicNet toolbox: Visualizing and unraveling a microbial network

Applications of network theory to microbial ecology are an emerging and promising approach to understanding both global and local patterns in the structure and interplay of these microbial communities. In this paper, we present an open-source python toolbox which consists of two modules: on one hand, we introduce a visualization module that incorporates the use of UMAP, a dimensionality reduction technique that focuses on local patterns, and HDBSCAN, a clustering technique based on density; on the other hand, we have included a module that runs an enhanced version of the SparCC code, sustaining larger datasets than before, and we couple the resulting networks with network theory analyses to describe the resulting co-occurrence networks, including several novel analyses, such as structural balance metrics and a proposal to discover the underlying topology of a co-occurrence network. We validated the proposed toolbox on 1) a simple and well described biological network of kombucha, consisting of 48 ASVs, and 2) we validate the improvements of our new version of SparCC. Finally, we showcase the use of the MicNet toolbox on a large dataset from Archean Domes, consisting of more than 2,000 ASVs. Our toolbox is freely available as a github repository (https://github.com/Labevo/MicNetToolbox), and it is accompanied by a web dashboard (http://micnetapplb-1212130533.us-east-1.elb.amazonaws.com) that can be used in a simple and straightforward manner with relative abundance data. This easy-to-use implementation is aimed to microbial ecologists with little to no experience in programming, while the most experienced bioinformatics will also be able to manipulate the source code's functions with ease.

Recurrent neural networks enable design of multifunctional synthetic human gut microbiome dynamics

Predicting the dynamics and functions of microbiomes constructed from the bottom-up is a key challenge in exploiting them to our benefit. Current models based on ecological theory fail to capture complex community behaviors due to higher order interactions, do not scale well with increasing complexity and in considering multiple functions. We develop and apply a long short-term memory (LSTM) framework to advance our understanding of community assembly and health-relevant metabolite production using a synthetic human gut community. A mainstay of recurrent neural networks, the LSTM learns a high dimensional data-driven non-linear dynamical system model. We show that the LSTM model can outperform the widely used generalized Lotka-Volterra model based on ecological theory. We build methods to decipher microbe-microbe and microbe-metabolite interactions from an otherwise black-box model. These methods highlight that Actinobacteria, Firmicutes and Proteobacteria are significant drivers of metabolite production whereas Bacteroides shape community dynamics. We use the LSTM model to navigate a large multidimensional functional landscape to design communities with unique health-relevant metabolite profiles and temporal behaviors. In sum, the accuracy of the LSTM model can be exploited for experimental planning and to guide the design of synthetic microbiomes with target dynamic functions.

Substrate Utilization and Competitive Interactions Among Soil Bacteria Vary With Life-History Strategies

Microorganisms have evolved various life-history strategies to survive fluctuating resource conditions in soils. However, it remains elusive how the life-history strategies of microorganisms influence their processing of organic carbon, which may affect microbial interactions and carbon cycling in soils. Here, we characterized the genomic traits, exometabolite profiles, and interactions of soil bacteria representing copiotrophic and oligotrophic strategists. Isolates were selected based on differences in ribosomal RNA operon (rrn) copy number, as a proxy for life-history strategies, with pairs of "high" and "low" rrn copy number isolates represented within the Micrococcales, Corynebacteriales, and Bacillales. We found that high rrn isolates consumed a greater diversity and amount of substrates than low rrn isolates in a defined growth medium containing common soil metabolites. We estimated overlap in substrate utilization profiles to predict the potential for resource competition and found that high rrn isolates tended to have a greater potential for competitive interactions. The predicted interactions positively correlated with the measured interactions that were dominated by negative interactions as determined through sequential growth experiments. This suggests that resource competition was a major force governing interactions among isolates, while cross-feeding of metabolic secretion likely contributed to the relatively rare positive interactions observed. By connecting bacterial life-history strategies, genomic features, and metabolism, our study advances the understanding of the links between bacterial community composition and the transformation of carbon in soils.

Machine learning aided construction of the quorum sensing communication network for human gut microbiota

Quorum sensing (QS) is a cell-cell communication mechanism that connects members in various microbial systems. Conventionally, a small number of QS entries are collected for specific microbes, which is far from being able to fully depict communication-based complex microbial interactions in human gut microbiota. In this study, we propose a systematic workflow including three modules and the use of machine learning-based classifiers to collect, expand, and mine the QS-related entries. Furthermore, we develop the Quorum Sensing of Human Gut Microbes (QSHGM) database ( http://www.qshgm.lbci.net/ ) including 28,567 redundancy removal entries, to bridge the gap between QS repositories and human gut microbiota. With the help of QSHGM, various communication-based microbial interactions can be searched and a QS communication network (QSCN) is further constructed and analysed for 818 human gut microbes. This work contributes to the establishment of the QSCN which may form one of the key knowledge maps of the human gut microbiota, supporting future applications such as new manipulations to synthetic microbiota and potential therapies to gut diseases.

Quantifying the impact of ecological memory on the dynamics of interacting communities

Ecological memory refers to the influence of past events on the response of an ecosystem to exogenous or endogenous changes. Memory has been widely recognized as a key contributor to the dynamics of ecosystems and other complex systems, yet quantitative community models often ignore memory and its implications.

Recent modeling studies have shown how interactions between community members can lead to the emergence of resilience and multistability under environmental perturbations. We demonstrate how memory can be introduced in such models using the framework of fractional calculus. We study how the dynamics of a well-characterized interaction model is affected by gradual increases in ecological memory under varying initial conditions, perturbations, and stochasticity.

Our results highlight the implications of memory on several key aspects of community dynamics. In general, memory introduces inertia into the dynamics. This favors species coexistence under perturbation, enhances system resistance to state shifts, mitigates hysteresis, and can affect system resilience both ways depending on the time scale considered. Memory also promotes long transient dynamics, such as long-standing oscillations and delayed regime shifts, and contributes to the emergence and persistence of alternative stable states. Our study highlights the fundamental role of memory in communities, and provides quantitative tools to introduce it in ecological models and analyse its impact under varying conditions.

An ecosystem is said to exhibit ecological memory when its future states do not only depend on its current state but also on its initial state and trajectory. Memory may arise through various mechanisms as organisms adapt to their environment, modify it, and accumulate biotic and abiotic material. It may also emerge from phenotypic heterogeneity at the population level. Despite its commonness in nature, ecological memory and its potential influence on ecosystem dynamics have been so far overlooked in many applied contexts. Here, we use modeling to investigate how memory can influence the dynamics, composition, and stability landscape of communities. We incorporate long-term memory effects into a multi-species model recently introduced to investigate alternative stable states in microbial communities. We assess the impact of memory on key aspects of model behavior and further examine our findings using a model parameterized by empirical data from the human gut microbiota. Our approach for modeling long-term memory and studying its implications has the potential to improve our understanding of microbial community dynamics and ultimately our ability to predict, manipulate, and experimentally design microbial ecosystems. It could also be applied more broadly in the study of systems composed of interacting components.

Bacterial-fungal interactions under agricultural settings: from physical to chemical interactions

Bacteria and fungi are dominant members of environmental microbiomes. Various bacterial-fungal interactions (BFIs) and their mutual regulation are important factors for ecosystem functioning and health. Such interactions can be highly dynamic, and often require spatiotemporally resolved assessments to understand the interplay which ranges from antagonism to mutualism. Many of these interactions are still poorly understood, especially in terms of the underlying chemical and molecular interplay, which is crucial for inter-kingdom communication and interference. BFIs are highly relevant under agricultural settings; they can be determinative for crop health. Advancing our knowledge related to mechanisms underpinning the interactions between bacteria and fungi will provide an extended basis for biological control of pests and pathogens in agriculture. Moreover, it will facilitate a better understanding of complex microbial community networks that commonly occur in nature. This will allow us to determine factors that are crucial for community assembly under different environmental conditions and pave the way for constructing synthetic communities for various biotechnological applications. Here, we summarize the current advances in the field of BFIs with an emphasis on agriculture.

Bacterial species rarely work together

Competition is prevalent and could be harnessed as an alternative to antibiotics.

Development of an in vitro Model of Human Gut Microbiota for Screening the Reciprocal Interactions With Antibiotics, Drugs, and Xenobiotics

Altering the gut microbiota can negatively affect human health. Efforts may be sustained to predict the intended or unintended effects of molecules not naturally produced or expected to be present within the organism on the gut microbiota. Here, culture-dependent and DNA-based approaches were combined to UHPLC-MS/MS analyses in order to investigate the reciprocal interactions between a constructed Human Gut Microbiota Model (HGMM) and molecules including antibiotics, drugs, and xenobiotics. Our HGMM was composed of strains from the five phyla commonly described in human gut microbiota and belonging to Firmicutes, Bacteroidetes, Proteobacteria, Fusobacteria, and Actinobacteria. Relevantly, the bacterial diversity was conserved in our constructed human gut model through subcultures. Uneven richness distribution was revealed and the sensitivity of the HGMM was mainly affected by antibiotic exposure rather than by drugs or xenobiotics. Interestingly, the constructed model and the individual cultured strains respond with the same sensitivity to the different molecules. UHPLC-MS/MS analyses revealed the disappearance of some native molecules in the supernatants of the HGMM as well as in those of the individual strains. These results suggest that biotransformation of molecules occurred in the presence of our gut microbiota model and the coupled approaches performed on the individual cultures may emphasize new bacterial strains active in these metabolic processes. From this study, the new HGMM appears as a simple, fast, stable, and inexpensive model for screening the reciprocal interactions between the intestinal microbiota and molecules of interest.

A Study and Modeling of Bifidobacterium and Bacillus Coculture Continuous Fermentation under Distal Intestine Simulated Conditions

The diversity and the stability of the microbial community are associated with microecological interactions between its members. Antagonism is one type of interaction, which particularly determines the benefits that probiotics bring to host health by suppressing opportunistic pathogens and microbial contaminants in food. Mathematical models allow for quantitatively predicting intrapopulation relationships. The aim of this study was to create predictive models for bacterial contamination outcomes depending on the probiotic antagonism and prebiotic concentration. This should allow an improvement in the screening of synbiotic composition for preventing gut microbial infections. The functional model (fermentation) was based on a three-stage continuous system, and the distal colon section (N₂, pH 6.8, flow rate 0.04 h^–1) was simulated. The strains Bifidobacterium adolescentis ATCC 15703 and Bacillus cereus ATCC 9634 were chosen as the model probiotic and pathogen. Oligofructose Orafti P95 (OF) was used as the prebiotic at concentrations of 2, 5, 7, 10, 12, and 15 g/L of the medium. In the first stage, the system was inoculated with Bifidobacterium, and a dynamic equilibrium (Bifidobacterium count, lactic, and acetic acids) was achieved. Then, the system was contaminated with a 3-day Bacillus suspension (spores). The microbial count, as well as the concentration of acids and residual carbohydrates, was measured. A Bacillus monoculture was studied as a control. The stationary count of Bacillus in monoculture was markedly higher. An increase (up to 8 h) in the lag phase was observed for higher prebiotic concentrations. The specific growth rate in the exponential phase varied at different OF concentrations. Thus, the OF concentration influenced two key events of bacterial infection, which together determine when the maximal pathogen count will be reached. The mathematical models were developed, and their accuracies were acceptable for Bifidobacterium (relative errors ranging from 1.00% to 2.58%) and Bacillus (relative errors ranging from 0.74% to 2.78%) count prediction.

Distinct N and C Cross-Feeding Networks in a Synthetic Mouse Gut Consortium

The complex interactions between the gut microbiome and host or pathogen colonization resistance cannot be understood solely from community composition. Missing are causal relationships, such as metabolic interactions among species, to better understand what shapes the microbiome. Here, we focused on metabolic niches generated and occupied by the Oligo-Mouse-Microbiota (OMM) consortium, a synthetic community composed of 12 members that is increasingly used as a model for the mouse gut microbiome. Combining monocultures and spent medium experiments with untargeted metabolomics revealed broad metabolic diversity in the consortium, constituting a dense cross-feeding network with more than 100 pairwise interactions. Quantitative analysis of the cross-feeding network revealed distinct C and N food webs, highlighting the two Bacteroidetes members Bacteroides caecimuris and Muribaculum intestinale as primary suppliers of carbon and a more diverse group as nitrogen providers. Cross-fed metabolites were mainly carboxylic acids, amino acids, and the so far not reported nucleobases. In particular, the dicarboxylic acids malate and fumarate provided a strong physiological benefit to consumers, presumably used in anaerobic respiration. Isotopic tracer experiments validated the fate of a subset of cross-fed metabolites, such as the conversion of the most abundant cross-fed compound succinate to butyrate. Thus, we show that this consortium is tailored to produce the anti-inflammatory metabolite butyrate. Overall, we provide evidence for metabolic niches generated and occupied by OMM members that lays a metabolic foundation to facilitate an understanding of the more complex in vivo behavior of this consortium in the mouse gut. IMPORTANCE This article maps out the cross-feeding network among 10 members of a synthetic consortium that is increasingly used as the model mouse gut microbiota. Combining metabolomics with in vitro cultivations, two dense networks of carbon and nitrogen exchange are described. The vast majority of the ∼100 interactions are synergistic in nature, in several cases providing distinct physiological benefits to the recipient species. These networks lay the groundwork toward understanding gut community dynamics and host-gut microbe interactions.

Fast quantification of gut bacterial species in cocultures using flow cytometry and supervised classification

A bottleneck for microbial community experiments with many samples and/or replicates is the fast quantification of individual taxon abundances, which is commonly achieved through sequencing marker genes such as the 16S rRNA gene. Here, we propose a new approach for high-throughput and high-quality enumeration of human gut bacteria in a defined community, combining flow cytometry and supervised classification to identify and quantify species mixed in silico and in defined communities in vitro. We identified species in a 5-species in silico community with an F1 score of 71%. In addition, we demonstrate in vitro that our method performs equally well or better than 16S rRNA gene sequencing in two-species cocultures and agrees with 16S rRNA gene sequencing data on the most abundant species in a four-species community. We found that shape and size differences alone are insufficient to distinguish species, and that it is thus necessary to exploit the multivariate nature of flow cytometry data. Finally, we observed that variability of flow cytometry data across replicates differs between gut bacterial species. In conclusion, the performance of supervised classification of gut species in flow cytometry data is species-dependent, but is for some combinations accurate enough to serve as a faster alternative to 16S rRNA gene sequencing.