1. Computational and Systems Biology
  2. Immunology and Inflammation

Computer-guided design of optimal microbial consortia for immune system modulation

  1. Richard R Stein  Is a corresponding author
  2. Takeshi Tanoue
  3. Rose L Szabady
  4. Shakti K Bhattarai
  5. Bernat Olle
  6. Jason M Norman
  7. Wataru Suda
  8. Kenshiro Oshima
  9. Masahira Hattori
  10. Georg K Gerber
  11. Chris Sander
  12. Kenya Honda
  13. Vanni Bucci  Is a corresponding author
  1. Dana-Farber Cancer Institute, United States
  2. RIKEN Institute, Japan
  3. Vedanta Biosciences, Inc, United States
  4. University of Massachusetts, United States
  5. Keio University School of Medicine, Japan
  6. The University of Tokyo, Japan
  7. Brigham and Women's Hospital, United States
https://doi.org/10.7554/eLife.30916
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Cite this article
  1. Richard R Stein
  2. Takeshi Tanoue
  3. Rose L Szabady
  4. Shakti K Bhattarai
  5. Bernat Olle
  6. Jason M Norman
  7. Wataru Suda
  8. Kenshiro Oshima
  9. Masahira Hattori
  10. Georg K Gerber
  11. Chris Sander
  12. Kenya Honda
  13. Vanni Bucci
(2018)
Computer-guided design of optimal microbial consortia for immune system modulation
eLife 7:e30916.
https://doi.org/10.7554/eLife.30916
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Abstract

Manipulation of the gut microbiota holds great promise for the treatment of diseases. However, a major challenge is the identification of therapeutically potent microbial consortia that colonize the host effectively while maximizing immunologic outcome. Here, we propose a novel workflow to select optimal immune-inducing consortia from microbiome composition and immune effectors measurements. Using published and newly generated microbial and regulatory T-cell (Treg) data from germ-free mice, we estimate the contribution of twelve Clostridia strains with known immune-modulating effect to Treg induction. Combining this with a longitudinal data-constrained ecological model, we predict the ability of every attainable and ecologically stable subconsortium in promoting Treg activation and rank them by the Treg Induction Score (TrIS). Experimental validation of selected consortia indicates a strong and statistically significant correlation between predicted TrIS and measured Treg. We argue that computational indexes, such as the TrIS, are valuable tools for the systematic selection of immune-modulating bacteriotherapeutics.

Data availability

The following previously published data sets were used

Article and author information

Author details

  1. Richard R Stein

    Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, United States
    For correspondence
    stein@jimmy.harvard.edu
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-5110-6863

  2. Takeshi Tanoue

    Center for Integrative Medical Sciences, RIKEN Institute, Yokohama City, Japan
    Competing interests
    Takeshi Tanoue, Has received support from Vedanta Biosciences, Inc. under research agreement with his institution.

  3. Rose L Szabady

    Vedanta Biosciences, Inc, Cambridge, United States
    Competing interests
    Rose L Szabady, Is employee of Vedanta Biosciences, Inc.

  4. Shakti K Bhattarai

    Engineering and Applied Sciences PhD Program, University of Massachusetts, Dartmouth, United States
    Competing interests
    No competing interests declared.

  5. Bernat Olle

    Vedanta Biosciences, Inc, Cambridge, United States
    Competing interests
    Bernat Olle, Is the Chief Executive Officer of Vedanta Biosciences, Inc.

  6. Jason M Norman

    Vedanta Biosciences, Inc, Cambridge, United States
    Competing interests
    Jason M Norman, Is employee of Vedanta Biosciences, Inc.

  7. Wataru Suda

    Department of Microbiology and Immunology, Keio University School of Medicine, Tokyo, Japan
    Competing interests
    No competing interests declared.

  8. Kenshiro Oshima

    Graduate School of Frontier Sciences, The University of Tokyo, Tokyo, Japan
    Competing interests
    No competing interests declared.

  9. Masahira Hattori

    Graduate School of Frontier Sciences, The University of Tokyo, Tokyo, Japan
    Competing interests
    No competing interests declared.

  10. Georg K Gerber

    Massachusetts Host-Microbiome Center, Department of Pathology, Brigham and Women's Hospital, Boston, United States
    Competing interests
    Georg K Gerber, Is a member of the Scientific Advisory Board of Kaleido, Inc.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-9149-5509

  11. Chris Sander

    Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, United States
    Competing interests
    No competing interests declared.

  12. Kenya Honda

    Center for Integrative Medical Sciences, RIKEN Institute, Yokohama City, Japan
    Competing interests
    Kenya Honda, Is a Co-Founder and Scientific Advisory Board Member of Vedanta Biosciences, Inc. Has received support from Vedanta Biosciences, Inc. under research agreements with his institution.

  13. Vanni Bucci

    Engineering and Applied Sciences PhD Program, University of Massachusetts, Dartmouth, United States
    For correspondence
    vanni.bucci@umassd.edu
    Competing interests
    Vanni Bucci, Has received support from Vedanta Biosciences, Inc. under research agreement with his institution.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-3257-2922

Funding

National Institutes of Health (P41 GM103504)

  • Richard R Stein
  • Chris Sander

Brigham and Women's Hospital (Precision Medicine Initiative)

  • Georg K Gerber

Defense Advanced Research Projects Agency (BRICS award HR0011-15-C-0094)

  • Georg K Gerber

Human Frontier Science Program (RGP00055/2015)

  • Chris Sander

Takeda Science Foundation

  • Kenya Honda

National Institute of General Medical Sciences (5R01 GM106303)

  • Chris Sander

Japan Agency for Medical Research and Development

  • Kenya Honda

National Institute of Allergy and Infectious Diseases

  • Vanni Bucci

National Science Foundation

  • Vanni Bucci

Core Research for Evolutional Science and Technology

  • Kenya Honda

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

Reviewing Editor

  1. Rob Knight, University of California, San Diego, United States

Ethics

Animal experimentation: 11-strain time-series mouse experiments were performed under ethical approval by RIKEN, Keio and Azabu Universities under protocol H24-9(14) (RIKEN). 4-strain validation mouse work was performed at Brigham and Women's Hospital in Boston, MA in the Massachusetts Host Microbiome Center under IACUC protocol 2016N000141.

Version history

  1. Received: August 1, 2017
  2. Accepted: March 31, 2018
  3. Accepted Manuscript published: April 17, 2018 (version 1)
  4. Version of Record published: May 11, 2018 (version 2)

Copyright

© 2018, Stein 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.

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  1. Richard R Stein
  2. Takeshi Tanoue
  3. Rose L Szabady
  4. Shakti K Bhattarai
  5. Bernat Olle
  6. Jason M Norman
  7. Wataru Suda
  8. Kenshiro Oshima
  9. Masahira Hattori
  10. Georg K Gerber
  11. Chris Sander
  12. Kenya Honda
  13. Vanni Bucci
(2018)
Computer-guided design of optimal microbial consortia for immune system modulation
eLife 7:e30916.
https://doi.org/10.7554/eLife.30916

Share this article

https://doi.org/10.7554/eLife.30916

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