Periodic Reporting for period 4 - EcoBox (Ecosystem in a box: Dissecting the dynamics of a defined microbial community in vitro)
Reporting period: 2023-07-01 to 2023-12-31
The overall goal of the EcoBox project is to improve our understanding of microbial community dynamics. Although we understand quite well the behavior of bacterial species growing in monoculture in a bioreactor, it is still challenging to predict what happens if several interacting species are grown together. Which bacteria will dominate the community – or will they all take an even share of the available resources? Will their abundances fluctuate wildly, or will they reach a stable state? If the latter, will it always be the same state in a constant environment, or will small variations in initial conditions lead to big differences? How will the community respond to perturbations, such as a change in pH or irregular feeding intervals? Can we predict what is going to happen in these different scenarios? In the EcoBox project, we plan to answer these fundamental questions for an example community consisting of common human gut bacteria studied in silico and in vitro in controlled conditions, including Bacteroides thetaiotaomicron (BT), Roseburia intestinalis (RI), Blautia hydrogenotrophica (BH), Faecalibacterium prausnitzii (FP) and Prevotella copri (PC). These bacteria have the advantage that their genomes are available, that their metabolism is comparatively well known and that they are relevant to human health.
We successfully established the synthetic human gut bacterial community in an automated fermenter system in chemostat mode and recorded its variability as well as its responses to pH and feed interruptions in up to twelve replicate vessels. In each experiment, we measured pH, cell counts as well as metabolite concentrations. To count bacterial species in a high-throughput manner, we developed a new tool, CellScanner, that applies supervised classification to flow cytometry data to obtain species-specific counts from community data. In addition, we explored the interaction between BT and RI in depth in the absence and presence of mucin beads. Our experiments showed that the gut bacterial community reaches a stable composition dominated by BT and BH, which is highly reproducible across replicates in controlled conditions. We identified the technical variability of 16S rRNA gene sequencing as the main source of the remaining variation. Indeed, variability across replicates was significantly reduced when resolving community composition using flow cytometry counts obtained with CellScanner. The perturbation experiments revealed the existence of alternative stable states, i.e. the community changed its composition after the perturbation without returning to its original state. In addition, not only one, but several distinct compositions were attained after perturbation in replicate vessels, which were also reproduced in two independent experiments. Our in-depth exploration of the interaction between BT and RI, combined with viability staining, led to the discovery of a slow-growth mode in RI and highlighted the context-dependency of bacterial interactions. We investigated different modelling techniques to describe our observations and predict community dynamics in new scenarios. For this, we evaluated the performance of metabolic models of gut bacteria on previously published data, which emphasised the importance of model curation. We collected and analysed RNA-seq data for three gut bacterial species to better understand their metabolism in different conditions. However, given their greater flexibility, we opted for kinetic models to explore the dynamics of the BT/RI co-culture and to understand the mechanisms behind the alternative community states observed in vitro. In the context of this exploration, we developed the miaSim R package, which makes different microbial community modelling techniques accessible to the public.
We went beyond the state of the art by counting gut bacteria in synthetic communities with a combination of flow cytometry and machine learning. We are also to our knowledge the first to have grown synthetic gut bacterial communities in up to 24 parallel fermentation experiments in chemostat mode. Through our systematic collection of flow cytometry, targeted metabolomics and sequencing data, we gained new insights into gut bacterial ecology, such as the different ways in which RI and BT deal with the lack of nutrients and respond to the presence of mucin, how their interaction mechanisms change across conditions or the metabolic strategies that allow BH to reach the same abundance as the fast-growing primary fermenter BT at steady state. Finally, the reproducible emergence of more than one alternative steady state after perturbation is a new and exciting observation that strongly supports our hypothesis that gut microbial communities are multi-stable systems. On the theoretical side, our exploration of different microbial community modelling approaches led to a new tool that implements several of them in a user-friendly manner.
Overall, we showed that gut bacteria have flexible strategies that enable them to deal with the variable conditions they encounter in the colon. We think that the elucidation of these strategies is essential to the successful design and application of next-generation probiotics. We hope that this game-theoretical view on gut bacteria and the continued study of their strategies will lead to more effective probiotics in the future.
Overall, we showed that gut bacteria have flexible strategies that enable them to deal with the variable conditions they encounter in the colon. We think that the elucidation of these strategies is essential to the successful design and application of next-generation probiotics. We hope that this game-theoretical view on gut bacteria and the continued study of their strategies will lead to more effective probiotics in the future.