Technical versus biological variability in a synthetic human gut community

ABSTRACT

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.

KEYWORDS:

• microbioreactor
• human gut microbiota
• Blautia
• Bacteroides
• synthetic community
• chemostat

Acknowledgments

We thank Daniel Rios Garza for helpful discussions, Anna Krzynowek for helping with the SILVA database, Raul Yhossef Tito Tadeo for processing raw 16S reads and Leen Rymenans for technical assistance during 16S library preparation. This project was supported by funding from the Research Foundation—Flanders (grant no. G0I0918N) and from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program under grant agreement no. 801747.

Data sharing statement

The 16S rRNA gene sequencing data have been submitted to ENA with accession number PRJEB51873 and will be made available upon acceptance of this work. The processed data and R code for the figures is available at: http://msysbiology.com/supplements.html#variability CellScanner is available at: http://msysbiology.com/cellscanner.html.

Disclosure statement

The authors report no conflict of interest.

Supplementary material

Supplemental data for this article can be accessed online at https://doi.org/10.1080/19490976.2022.2155019

Additional information

Funding

This work was supported by the H2020 European Research Council (ERC) [801747]; Research Foundation Flanders [G0I0918N].