Integrated culturing, modeling and transcriptomics uncovers complex interactions and emergent behavior in a three-species synthetic gut community

  1. Kevin D'hoe
  2. Stefan Vet
  3. Karoline Faust  Is a corresponding author
  4. Frédéric Moens
  5. Gwen Falony
  6. Didier Gonze
  7. Verónica Lloréns-Rico
  8. Lendert Gelens
  9. Jan Danckaert
  10. Luc De Vuyst
  11. Jeroen Raes  Is a corresponding author
  1. KU Leuven, Belgium
  2. Vrije Universiteit Brussel, Belgium
  3. Université Libre de Bruxelles, Belgium

Abstract

Whereas the composition of the human gut microbiome is well resolved, predictive understanding is still lacking. Here, we followed a bottom-up strategy to explore human gut community dynamics: we established a synthetic community composed of three representative human gut isolates (Roseburia intestinalis L1-82, Faecalibacterium prausnitzii A2-165 and Blautia hydrogenotrophica S5a33) and explored their interactions under well-controlled conditions in vitro. Systematic mono- and pair-wise fermentation experiments confirmed competition for fructose and cross-feeding of formate. We quantified with a mechanistic model how well tri-culture dynamics was predicted from mono-culture data. With the model as reference, we demonstrated that strains grown in co-culture behaved differently than in mono-culture and confirmed their altered behavior at the transcriptional level. In addition, we showed with replicate tri-cultures and simulations that dominance in tri-culture sensitively depended on initial conditions. Our work has important implications for gut microbial community modeling as well as ecological interaction detection from batch cultures.

Data availability

RNA-seq results have been deposited to the Short Read Archive under the study identifier SRP136465 (https://www.ncbi.nlm.nih.gov/sra/SRP136465). Fermentation data have been submitted to Dryad (doi:10.5061/dryad.g83f29f). Source data has been provided for Figures 3 to 6.

The following data sets were generated

Article and author information

Author details

  1. Kevin D'hoe

    Department of Microbiology and Immunology, KU Leuven, Leuven, Belgium
    Competing interests
    The authors declare that no competing interests exist.
  2. Stefan Vet

    Applied Physics Research Group, Vrije Universiteit Brussel, Brussels, Belgium
    Competing interests
    The authors declare that no competing interests exist.
  3. Karoline Faust

    Department of Microbiology and Immunology, KU Leuven, Leuven, Belgium
    For correspondence
    karoline.faust@kuleuven.be
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-7129-2803
  4. Frédéric Moens

    Research Group of Industrial Microbiology and Food Biotechnology, Vrije Universiteit Brussel, Brussels, Belgium
    Competing interests
    The authors declare that no competing interests exist.
  5. Gwen Falony

    Department of Microbiology and Immunology, KU Leuven, Leuven, Belgium
    Competing interests
    The authors declare that no competing interests exist.
  6. Didier Gonze

    Unité de Chronobiologie Théorique, Université Libre de Bruxelles, Brussels, Belgium
    Competing interests
    The authors declare that no competing interests exist.
  7. Verónica Lloréns-Rico

    Department of Microbiology and Immunology, KU Leuven, Leuven, Belgium
    Competing interests
    The authors declare that no competing interests exist.
  8. Lendert Gelens

    Laboratory of Dynamics in Biological Systems, KU Leuven, Leuven, Belgium
    Competing interests
    The authors declare that no competing interests exist.
  9. Jan Danckaert

    Applied Physics Research Group, Vrije Universiteit Brussel, Brussels, Belgium
    Competing interests
    The authors declare that no competing interests exist.
  10. Luc De Vuyst

    Research Group of Industrial Microbiology and Food Biotechnology, Vrije Universiteit Brussel, Brussels, Belgium
    Competing interests
    The authors declare that no competing interests exist.
  11. Jeroen Raes

    Department of Microbiology and Immunology, KU Leuven, Leuven, Belgium
    For correspondence
    jeroen.raes@kuleuven.vib.be
    Competing interests
    The authors declare that no competing interests exist.

Funding

Vrije Universiteit Brussel

  • Kevin D'hoe

Fonds Wetenschappelijk Onderzoek

  • Kevin D'hoe
  • Karoline Faust
  • Frédéric Moens
  • Verónica Lloréns-Rico

Interuniversity Institute of Bioinformatics in Brussels

  • Stefan Vet

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Copyright

© 2018, D'hoe et al.

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.

Metrics

  • 5,744
    views
  • 976
    downloads
  • 73
    citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.

Download links

A two-part list of links to download the article, or parts of the article, in various formats.

Downloads (link to download the article as PDF)

Open citations (links to open the citations from this article in various online reference manager services)

Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)

  1. Kevin D'hoe
  2. Stefan Vet
  3. Karoline Faust
  4. Frédéric Moens
  5. Gwen Falony
  6. Didier Gonze
  7. Verónica Lloréns-Rico
  8. Lendert Gelens
  9. Jan Danckaert
  10. Luc De Vuyst
  11. Jeroen Raes
(2018)
Integrated culturing, modeling and transcriptomics uncovers complex interactions and emergent behavior in a three-species synthetic gut community
eLife 7:e37090.
https://doi.org/10.7554/eLife.37090

Share this article

https://doi.org/10.7554/eLife.37090

Further reading

    1. Biochemistry and Chemical Biology
    2. Computational and Systems Biology
    Shinichi Kawaguchi, Xin Xu ... Toshie Kai
    Research Article

    Protein–protein interactions are fundamental to understanding the molecular functions and regulation of proteins. Despite the availability of extensive databases, many interactions remain uncharacterized due to the labor-intensive nature of experimental validation. In this study, we utilized the AlphaFold2 program to predict interactions among proteins localized in the nuage, a germline-specific non-membrane organelle essential for piRNA biogenesis in Drosophila. We screened 20 nuage proteins for 1:1 interactions and predicted dimer structures. Among these, five represented novel interaction candidates. Three pairs, including Spn-E_Squ, were verified by co-immunoprecipitation. Disruption of the salt bridges at the Spn-E_Squ interface confirmed their functional importance, underscoring the predictive model’s accuracy. We extended our analysis to include interactions between three representative nuage components—Vas, Squ, and Tej—and approximately 430 oogenesis-related proteins. Co-immunoprecipitation verified interactions for three pairs: Mei-W68_Squ, CSN3_Squ, and Pka-C1_Tej. Furthermore, we screened the majority of Drosophila proteins (~12,000) for potential interaction with the Piwi protein, a central player in the piRNA pathway, identifying 164 pairs as potential binding partners. This in silico approach not only efficiently identifies potential interaction partners but also significantly bridges the gap by facilitating the integration of bioinformatics and experimental biology.