Despite recent advances in sequencing and culturing, a deep knowledge of the wiring and functioning of the human gut ecosystem and its microbiota as a community is still missing. A holistic mechanistic understanding will require study of the gut microbiota as an interactive and spatially organized biological system, which is difficult to do in complex natural communities. Synthetic gut microbial ecosystems can function as model systems to further current understanding of the composition, stability and functional activities of the microbiota. In this Review, we provide an overview of the current synthetic ecology strategies that can be used towards a more comprehensive understanding of the human gut ecosystem. Such approaches that integrate in vitro experiments using cultured isolates with mathematical modelling will enable the ultimate goal: translating mechanistic and ecological knowledge into novel and effective therapies.
The human gut microbiota is a complex and dynamic microbial community that is integral to the maintenance of health1,2 and the regulation of the host immune system3,4. Recent breakthroughs in culture-independent, high-throughput profiling have enabled the rapid, large-scale quantification of the composition of the gut microbial community in health and disease (Fig. 1). Alterations of the gut microbiota (that is, dysbiosis) have been linked to many diseases and conditions, such as obesity5,6,7,8 and diabetes mellitus9,10,11,12. As a result, modulating the gut microbiota has been viewed as a potential source of novel therapeutics for treating diseases that are associated with dysbiosis13,14. Nonetheless, the clinical translation of microbiota-based therapies has been slow, with one of the main obstacles being lack of a mechanistic understanding of the metabolic and ecological interactions between microorganisms and with the host.
Although a relatively complete atlas of the taxa that make up the human gut microbiota has been compiled in the past 10 years, much more limited progress has been made in elucidating the wiring of the human gut ecosystem — that is, the distinct characteristics of individual community members and the complex web of interactions among them2,8. Sequencing data alone have been shown to be inadequate to fully predict microbial phenotypes and functions in a community. For example, a recent study revealed that 75% of metabolic models of gut bacterial species, based on genomics and literature information on growth requirements alone, failed to predict the growth of the same bacterial species in different media15. Analyses pairing complementary ‘omics’ data (that is, metatranscriptomics, metaproteomics and metabolomics) with shotgun data arguably have more predictive power and are able to assess active species, pathways and genes in an ecosystem. However, it is not yet possible to (fully) assess the complex ecological interactions using ‘omics’ data alone16. Here, we argue that a synthetic ecology approach — that is, the study of complex microbial systems using synthetic communities — could form the solution to this problem.
Synthetic microbial communities are systems of known and reduced complexity that are amenable to experimental intervention and modelling, enabling a systems-level understanding17,18,19. From a bottom-up ecological perspective (going from components to communities), synthetic communities are a way to study how microbial community structure emerges (for example, through competition and cooperation) and to identify the conditions necessary to generate specific interaction patterns (for example, cross-feeding, syntrophy and auxotrophy). From a top-down ecological perspective (starting with the system), they can answer questions about the overall function and the resistance and resilience of microbial systems18. The study of synthetic microbial communities requires (i) controlled in vitro environments, (ii) biologically relevant bacterial strains and (iii) mathematical models of the ecological interactions to simulate and test.
Work on creating human gut-specific, controlled in vitro environments, isolating intestinal bacterial strains and mathematically modelling gut bacterial communities is currently under way. Still, the use of a synthetic-ecology approach is still relatively new for the gut microbiota field. It was once thought that as few as ~20% of the human gut microorganisms were culturable20, indicating that the design of synthetic gut microbial communities would always be limited. Now, however, new methods such as culturomics21 are overturning this paradigm and have inspired a culturing renaissance21,22,23,24 (Fig. 1). Furthermore, recent efforts to sequence the genomes of these cultured gut microorganisms21,24 (see also the Human Microbiome Project)25 and to assemble complete or nearly complete metagenome-assembled genomes (MAGs) from shotgun metagenomes26,27,28 are improving our ability to identify bacterial taxa of interest, down to the strain level. The combination of these advances allows us to begin designing meaningful, synthetic gut microbial communities and study those communities in vitro.
To date and to the best of our knowledge, there have been only a handful of studies of in vitro synthetic gut microbial communities (with at least two bacterial strains), with most of the early studies exploring the inhibitory effect of bifidobacterial strains on other gut commensals or pathogens29,30,31. Many of these in vitro studies provided some of the first insights on nutritional interactions32 between certain gut bacterial taxa. However, we still know very little. If all the strains in the different in vitro studies mentioned above are counted independently, the interactions of <100 different gut microbial strains have been studied experimentally (Fig. 2). This number is only a very small fraction of the number of cultured gut microorganisms (~5%), and it pales in comparison to the number of gut microorganisms that have been detected in amplicon-based metagenomic studies (~0.002%). Currently, we are poised to begin filling the gaps between the numbers of sequenced, of cultured and of in vitro-tested members of the gut microbiota, but the challenge is substantial.
In this Review, we provide an overview of (i) the available in vitro experimental systems, (ii) current isolation methods and (iii) mathematical modelling options to study synthetic gut microbial communities. With a synthetic ecology approach, we can gain a deeper understanding of the black box of microbial interactions in the gut ecosystem. First, we can identify the functional niche and capacity of every isolated gut inhabitant, in a wide range of environmental conditions. Next, we can begin to disentangle the interactions between major gut inhabitants. Lastly, we can begin to study the bottom-up and top-down ecology of these synthetic gut microbial ecosystems through targeted perturbations of nutrients, small molecules, specific antibiotics, organisms (including genetically modified microorganisms) and/or metabolite tracking using, for example, stable isotope probing33, to identify ecological tipping points34. Such knowledge could then be used to improve our understanding of the complex gut ecosystem and how we can modulate it in order to prevent and treat different diseases and conditions associated with dysbiosis.
In vitro systems
In vitro systems are defined and/or engineered environments outside of a living organism that provide a simple, controlled system to study ecological interactions. Although in vitro experimentation has many disadvantages, in particular the lack of host interactions, the ability to automate and miniaturize environments for high-throughput analyses makes in vitro studies an excellent starting point prior to in vivo experimentation35. For the human gut, several in vitro environments have been defined and engineered to study synthetic microbial communities (Fig. 3). The most relevant systems range from the classical batch fermenter with various modifications, all the way to multistaged continuous fermenters with or without the addition of components to simulate interactions with the human host (for example, through the addition of human cells to otherwise standard microbial cultivation systems)36.
Batch fermentations are the simplest and most commonly used in vitro culture models. Batch fermentation experiments are closed systems, with all components necessary to initiate microbial growth included in a controlled environment at the beginning of an experiment. Two commonly used deviations from this closed setup, however, include the possibility of pH correction and the gradual addition of a key nutrient (also known as fed-batch). After addition of the bacterial strain or mixture of strains, the system can be monitored for overall growth and growth by strain. Overall growth is often monitored by conventional methods, such as plate counts, optical density measurements or flow cytometry. In mixed cultures, individual strain growth has also been followed using fluorescent markers, but the number of fluorescent markers previously greatly limited the number of strains that could be grown together37,38,39,40. More recently, periodic sampling of the system and high-throughput amplicon or shotgun sequencing has greatly increased the number of strains and the complexity of mixed cultures that can be used, as a limitation is no longer imposed by the number of fluorescent markers37,38,39,40. The simplicity of set-up of batch fermentations makes them the easiest in vitro culture systems to parallelize, enabling high-throughput analyses. Nonetheless, a major limitation is that, because batch fermentations are closed systems, it is impossible to quantify stable states. Examples of standard batch fermentation systems include batch reactors, test tubes, well plates and microfluidic droplets (Fig. 3, top left).
Continuous culture systems
Continuous culture systems, unlike batch fermentations, enable counterbalancing of the inflow and outflow of a medium and, thus, the maintenance of cultures in a specific growth rate and physiological state. By physically coupling several continuous reactors in sequence through overflow weirs, several different environmental circumstances can be imposed sequentially. For example, alterations to pH or residence time in the medium, as well as the addition of particular components (such as enzymes and bile salts), can be used to simulate conditions in different compartments of the intestinal tract36. However, continuous-culture models inoculated with human faecal samples have not been able to reproduce the exact in vivo community compositions or microbial community states observed in the human gut36, although progress is being made by immobilizing faecal microbiota on gel beads41. A limitation is that, although many of the kinetic aspects (that is, transit time characteristics) of the human gastrointestinal tract are incorporated (with the notable exception of microbial metabolite absorption by the human gut), the presence of the human host is largely ignored, with certain partial exceptions such as including human cell lines or mucus-coated beads42. Nonetheless, continuous-culture models are a rapid screening tool for xenobiotic transformations and the impact of nutraceuticals36. Examples of standard continuous-culture systems include chemostats and minibioreactor arrays (Fig. 3, top right).
Microtiter well plate cultures
A microtiter well plate format is a specific type of batch fermentation system that offers a cheap, high-throughput way to study multiple different microbial interactions in parallel. Using this format for mixed cultures has previously been shown to be a valuable tool when predicting community dynamics in the gut from pairwise interactions43. However, due to the small volume used in most microtiter well-plate experimental culture models, it is more difficult to control environmental conditions (such as pH and nutrient addition) than in a full-scale fermenter. Furthermore, there are limitations on the amount of sampling possible with such systems. Microtiter well plates can potentially be used as continuous-culture systems by performing serial transfers44, but it is unclear whether the community dynamics monitored in such a setting would be entirely free of the influence of fluctuating nutrient concentrations. These problems might be mitigated in more elaborate systems in the future (and potentially in a high-throughput fashion, thanks to advances in microfluidics).
Advances and challenges
Importantly, across all types of in vitro culture systems, no truly representative system exactly mimicking the in vivo microbiota has yet been developed. Steps in this direction have been achieved by the gut-on-a-chip model44,45, the HuMiX system46, the mini-bioreactor system for growth screening47 and the combinatorial growth of strains in microtiter plates, although mostly with the aim of following growth through optical density and 16S ribosomal RNA (rRNA) sequencing, with the purpose of mathematical model parameterization43. Although efforts are ongoing46, an important challenge still lies in further miniaturizing these systems and increasing their high-throughput analyses capacity. Another challenge is the integration of spatial structure. The microbial compositions of communities in the gut lumen and mucosa are known to differ48. The latter, which is in closer contact with host cells, may have a particularly important role in disease development49. The incorporation of human cell lines in high-throughput fermentation systems — for example, through a layer of differentiated Caco-2 cells, such as in the HuMiX system46 — enables the investigation of host cellular responses to particular gut microbial strains or their mixtures, as well as the reverse interactions (Fig. 3, bottom).
Overall, given the large number of gut microbiota members that still need to be assessed, currently the most viable option to start uncovering this complexity is controlled and repeatable in vitro systems. Still, it is worth mentioning that the use of living systems as the ‘container’ (that is, in vivo experiments) for microbial trials is also needed, and most likely would be the next step following in vitro experimentation. In vivo experiments are crucial for studying many aspects of ecological interactions, ranging from exploring the role of the microbiota in host development and immune responses to understanding the complex interactions between host dietary or nutritional choices and the gut ecosystem. Most of the current in vivo gut microbiota studies have used mice50,51, but many host-specific differences between mice and humans exist and should not be ignored. These differences include immunological differences (for example, type of antimicrobial peptides), which select a different microbiota in mice than in humans52,53; transit time; type of food and propensity for coprophagy; and macroscopic and microscopic structure of the gut (for example, length and relative transit time, or the presence of a functional caecum)54. Thus, current in vivo experiments are also still limited in their capacity to directly predict microbial behaviour in the human gastrointestinal tract. However, given the obvious unavailability of gnotobiotic humans, the use of mice or functionally more equivalent model animals (for example, the porcine model) remains our best available in vivo option for certain research lines. This said, work on incorporating human cell lines in model systems55 and organoids56 may fill this gap in the near future, but currently all these systems are in the developmental phase.
Isolating the human gut microbiota
After the field almost completely abandoned microbial isolation in favour of the depth and throughput offered by DNA-based approaches, one of the ironic conclusions of evaluating one and a half decades of metagenomics-based research is that microbiologists need cultures after all. For most of the challenges as well as for the synthetic ecology solutions outlined above, the availability of well-documented strain collections is a crucial prerequisite. In addition to the anaerobic reference cultures preserved and catalogued by public microbial resource centres such as the German Collection of Microorganisms and Cell Cultures (DSMZ) and the Culture Collection University of Gothenburg (CCUG), recent years have also witnessed host-specific culture repository initiatives such as the Mouse Intestinal Bacterial Collection (miBC)57, the Human Gastrointestinal Bacteria Culture Collection (HBC)21 and the Culturable Genome Reference (CGR)44 catalogue from human faecal samples (Fig. 2). Still, coverage of well-characterized gut species in these culture collections is low compared to what is predicted from metagenomic inventories. The recently reported reconstruction of >150,000 microbial genomes from >9,400 geographically widespread human metagenomes, encompassing intestinal, oral, skin and vaginal body sites, indicated that 77% of the resulting species-level genome bins have never been described before28. This highlights the continuing need for improving existing isolation methods or for repurposing cell-capturing technologies such as droplet microfluidics from other fields.
Increased insight into the reasons why a large fraction of gut microorganisms resist cultivation under standard laboratory conditions has triggered a series of simple but effective modifications to conventional isolation strategies58. The most obvious of these modifications stems from the observation that the high phylogenetic diversity of the human gut microbiota is mirrored by myriad metabolic functions and nutritional preferences. By multiplexing bacterial isolation conditions through the serial addition of specific growth promoters and/or inhibitors, coupled to high-throughput identification with MALDI-TOF MS, the so-called culturomics approach has broadened the scope of conventional single-medium strategies12. According to a recent report59, culturomics has resulted in the discovery of 232 novel human gut species since 2015. It is worth noting, however, that only a small minority (~1–2%) of these newly proposed names have so far been validated and thus have official standing in prokaryotic nomenclature60, which may compromise their future inclusion in strictly curated taxonomic reference frameworks and prevent effective progress in the annotation of metagenomic reads. Surprisingly, recent work has challenged the need for a wide range of media; a survey15 on the growth preferences of 96 phylogenetically and metabolically diverse human gut strains representing core microbiome species, species used as probiotics and enteropathogens demonstrated that 76 strains (representing 79.2% of all tested strains) were able to grow on a single, defined medium. Although this observation does not guarantee comparable recovery rates during isolation from faecal samples, it suggests that laboratories that are not able to roll out a dedicated culturomics pipeline but that do have basic anaerobic incubation infrastructure can also contribute to the discovery of new gut bacteria, although less efficiently. Next to multiplexing nutritional resources, the supplementation of isolation media with (combinations of) antimicrobials has been successfully used as a simple modification of traditional culturing approaches for the targeted selection of previously uncultured bacteria23. In this study, antibiotics capable of inhibiting or suppressing the growth of abundant taxa could select for less abundant populations, provided that these populations can grow in the presence of the antibiotic. Likely, this particular type of modification could be extended to non-antibiotic drugs, given the recent observation that some classes of human drugs such as antipsychotics may also exhibit anticommensal activities and thus selectively inhibit the growth of specific members of the gut microbiota61. Even prior to the actual isolation stage, simple modifications such as the treatment of faecal samples with ethanol, which kills ethanol-sensitive vegetative cells but does not affect spores, have been shown to unlock novel bacterial diversity, including potentially new families62.
An additional opportunity to scale up the isolation and culture collection building of human gut microorganisms comes from the implementation of miniaturized, versatile and high-throughput technologies, most of which were initially developed for other purposes in molecular and cell biology laboratories. Especially, single-cell-based approaches in which physical isolation or sorting precedes the actual growth stage seem promising63. As such, a personalized gut microbiota culture collection study64 demonstrated that the principle of dilution-to-extinction is an interesting approach to capture slow-growing and low-abundance gut microorganisms that might escape isolation during traditional plating. However, to reach the scale and depth of metagenomics, future isolation efforts will require integration in automated platforms and dedicated pipelines. Recent advances in the analysis and separation of free microbial cells by Raman microspectroscopy65 as well as in the physical stochastic confinement of individual cells in nanofabricated and microfabricated compartments and microfluidic devices66 are two prime examples that may pave the way towards the isolation of previously uncultured species and the discovery of novel microbial taxa63, as well as being a high-throughput method for inferring interactions67. Still, the often-challenging integration of such single-cell tools in strictly anaerobic laboratory systems has strongly limited the number of successful culture-based applications in the human microbiota field.
Next to experimental in vitro work, mathematical modelling can provide further system-level understanding and generate hypotheses. Models are mathematical representations of our knowledge of a system. A mathematical model can describe the behaviour of the gut community in altered conditions, and can therefore be applied to predicting the effects of perturbations such as antibiotic treatment68 or to optimizing therapeutic microbiota for functions such as pathogen suppression69 or immune system modulation70. Models also pinpoint knowledge gaps: a deviation of experimental observations from model expectations implies a lack of understanding, necessitating further experiments and adjustments. An example is the genome-scale metabolic model of a microorganism. If the model fails to describe an observed physiology (for example, growth on formate), this may point to incomplete or erroneous gene annotations71. In addition, models enable exploring ecosystem properties that are hard to gauge experimentally. For example, a mathematical analysis of interaction networks has linked community stability to the proportion of negative interactions such as competition72. Finally, models can suggest the mechanisms underlying observations. Alternative gut community types73,74 are a good case in point: model simulations have suggested multistability75,76, priority effects (the exclusion of competitors by species arriving first)77 or differences in water uptake and nutrient influx in the colon78 as possible drivers of different community compositions. Another example is a spatial colon model that explains the high diversity in the colon through a combination of cross-feeding and spatial stratification79.
Microbial communities can be modelled at different levels of resolution, ranging from metabolic models that take the internal metabolism of every community member into account, to topological models (that is, networks) that represent community members as nodes and their interactions as directed or undirected edges80,81,82 (see the overview in Table 1). Modelling approaches differ in their ability to account for the factors that shape community dynamics; for example, the generalized Lotka–Volterra (gLV) model, which describes community dynamics as a function of growth rates and species interactions, cannot predict the impact of spatial structure on the dynamics. Modelling approaches also differ in their treatment of causality: metabolic and kinetic models describe the mechanism underlying metabolite-mediated interactions such as cross-feeding, whereas the gLV model relies on generic interaction coefficients83,84. In vitro experiments are required in order to benchmark and compare different modelling approaches, to find an optimal compromise between prediction accuracy and the number of parameters.
Fitting the neutral model, which neglects species interactions, and the gLV model to faecal sequencing data led to the conclusion that gut microbial community dynamics are not neutral, but can be described by the gLV model to some extent68,69,85. Parameterizing the gLV model enables the inference of ecological interactions directly from sequencing data, but this technique faces substantial challenges. For example, interaction inference requires densely sampled time series data and is, for the moment, limited to the most abundant species86. Alternatively, ecological interactions can be obtained from metabolic models by comparing growth rates in monocultures and co-cultures in silico87; however, the accuracy of these predicted interactions has not yet been assessed systematically. Following this approach, the anoxic gut environment was predicted to induce mutualism88,89. Metabolic models require metabolic reconstructions, which can be obtained automatically from the genome within minutes90,91, but take several months of curation effort to refine71. The AGORA database offers semi-curated metabolic reconstructions for 773 gut bacteria92. Given the challenges of obtaining an accurate metabolic reconstruction, experimental validation with growth data collected for a range of substrates is essential.
When applying community models to the human gut microbiota in vivo, a number of challenges have to be overcome. Hundreds of species live in the human gut28, each of which has a specific pH optimum, particular consumption and production kinetics for a range of metabolites, and specific interactions with other gut microorganisms and the human host. Spatial structure, such as the mucus layer and food particles as well as gradients, influences community dynamics. In addition, the gut ecosystem is not static, but varies over time as conditions change and strains immigrate, are lost or evolve. However, a model has to reduce system complexity in order to be useful. In vitro experiments help pinpoint the most relevant processes while controlling confounding factors and enable systematically measuring model parameters. For example, measured growth rates of gut bacteria were used to test and subsequently to refine metabolic reconstructions15. The large species number in the human gut is challenging not only because of the amount of work to characterize their physiology, but also because the number of all possible interactions scales exponentially with the species number. Many models assume that community dynamics can be described in terms of pair-wise interactions (which scale only quadratically with species number), and thus ignore interactions involving more than two species. Two different studies tested whether this simplifying assumption holds, by modelling observed community dynamics using parameters obtained from monocultures and co-cultures38,43. The studies both revealed that the dynamics of the community could be predicted from co-cultures, thereby ruling out a strong effect of higher-order interactions. Another study reduced species number further by grouping strains into guilds based on their functional role (for example, non-butyrate-forming fibre degraders) and was able to correctly predict the response of these guilds to pH change in continuous bioreactors seeded with faecal samples83. In vitro experiments and modelling strategies such as these are an important step towards predictive modelling in vivo.
Synthetic gut microbial communities will improve our understanding of emergent properties, from bottom-up intercellular processes (such as cross-feeding, syntrophy and auxotrophy) to top-down community processes (such as community structure and resilience). So far, in vitro batch trial experiments with defined gut cultures (such as monocultures and co-cultures) have elucidated potential intercellular interactions and in vitro systems, allowing partial simulation of the conditions in the human gastrointestinal tract and yielding first insights into the full-ecosystem processes. Key to gaining a mechanistic understanding of the human gut ecosystem will be extensive, well-documented culture collections of human microbiota members used in in vitro ecology experiments feeding into and driven by mathematical models.
Specifically, for the gut ecosystem, a priority will be to repurpose existing technologies to enable parallel anaerobic fermentation and allow high-throughput analyses. Many parallel bioreactors are coming on the market to meet this demand, but few have the option to add gut-specific functions, such as the addition of human cells. In this respect, co-development between biotechnology companies and academia, could be an option. Lastly, microfluidics also holds promise as a high-throughput method, but it is in very early development67. Given the potentially wide applications of anaerobic microbiota-oriented high-throughput technologies, ranging from health therapeutics to industrial bioreactors, innovation interest is likely to increase substantially in the coming years.
Once sufficient high-throughput capacity has been developed, we envision a consistent feedback loop between mathematical modelling and culturing, in which mathematical models guide culturing efforts through hypothesis generation, and culturing results feed back to generate new models (Fig. 4). The current mathematical models are grounded by current culture collections, which are biased by the source of the microbial isolates. Efforts to study the gut microbiota have mostly focused on faecal samples collected from Western individuals. To ensure that the global human microbiota diversity is properly captured and framed, however, worldwide sampling and quantitative profiling93 of populations should be performed and coupled to metadata collection (for example, covering dietary records, immunological parameters and xenobiotic use), so that we can use these samples and sequencing data, respectively, to extend the known taxa and culture collections and to refine mathematical models. In addition to the impact of growing culture collections, mathematical models will continuously be refined through studies of ecological interactions, growth parameters, nutritional dependencies and other properties, and will be complemented by human and animal intervention and perturbation studies14 that will further inform the models and enable better incorporation of the host context.
Cycling through this loop will lead to a deeper functional and systemic understanding of the gut ecosystem and guide the development of novel modulation strategies through, for example, synthetic probiotics and small molecules. Once modulation strategies have been properly evaluated and tested in vivo in animal models, human clinical trials will be the ultimate stepping stone towards pharmaceutical use.
In summary, the human gut is a complex ecosystem for which modulation strategies can be envisaged, but a mechanistic understanding is needed of the intricate interactions that drive its ecology. Given the vast array of metabolic activities and multiple points of interaction of the gut microbiota with the human host, the opportunities for health-promoting interventions are bountiful. The key steps towards the development of a functional model of the colon ecosystem that can be used for the design of therapeutic solutions appear all to be in place, but they have yet to be aligned and combined. Synthetic ecology holds promise as the road to a renaissance in human microbiota-based therapeutics for health and well-being.
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