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

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.

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.

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.

Metrics

  • 8,022
    views
  • 1,373
    downloads
  • 71
    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. 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

Further reading

    1. Computational and Systems Biology
    Yang Tan, Bingxin Zhou ... Liang Hong
    Research Article

    Protein engineering is a pivotal aspect of synthetic biology, involving the modification of amino acids within existing protein sequences to achieve novel or enhanced functionalities and physical properties. Accurate prediction of protein variant effects requires a thorough understanding of protein sequence, structure, and function. Deep learning methods have demonstrated remarkable performance in guiding protein modification for improved functionality. However, existing approaches predominantly rely on protein sequences, which face challenges in efficiently encoding the geometric aspects of amino acids’ local environment and often fall short in capturing crucial details related to protein folding stability, internal molecular interactions, and bio-functions. Furthermore, there lacks a fundamental evaluation for developed methods in predicting protein thermostability, although it is a key physical property that is frequently investigated in practice. To address these challenges, this article introduces a novel pre-training framework that integrates sequential and geometric encoders for protein primary and tertiary structures. This framework guides mutation directions toward desired traits by simulating natural selection on wild-type proteins and evaluates variant effects based on their fitness to perform specific functions. We assess the proposed approach using three benchmarks comprising over 300 deep mutational scanning assays. The prediction results showcase exceptional performance across extensive experiments compared to other zero-shot learning methods, all while maintaining a minimal cost in terms of trainable parameters. This study not only proposes an effective framework for more accurate and comprehensive predictions to facilitate efficient protein engineering, but also enhances the in silico assessment system for future deep learning models to better align with empirical requirements. The PyTorch implementation is available at https://github.com/ai4protein/ProtSSN.

    1. Computational and Systems Biology
    2. Microbiology and Infectious Disease
    Saugat Poudel, Jason Hyun ... Bernhard O Palsson
    Research Article

    The Staphylococcus aureus clonal complex 8 (CC8) is made up of several subtypes with varying levels of clinical burden; from community-associated methicillin-resistant S. aureus USA300 strains to hospital-associated (HA-MRSA) USA500 strains and ancestral methicillin-susceptible (MSSA) strains. This phenotypic distribution within a single clonal complex makes CC8 an ideal clade to study the emergence of mutations important for antibiotic resistance and community spread. Gene-level analysis comparing USA300 against MSSA and HA-MRSA strains have revealed key horizontally acquired genes important for its rapid spread in the community. However, efforts to define the contributions of point mutations and indels have been confounded by strong linkage disequilibrium resulting from clonal propagation. To break down this confounding effect, we combined genetic association testing with a model of the transcriptional regulatory network (TRN) to find candidate mutations that may have led to changes in gene regulation. First, we used a De Bruijn graph genome-wide association study to enrich mutations unique to the USA300 lineages within CC8. Next, we reconstructed the TRN by using independent component analysis on 670 RNA-sequencing samples from USA300 and non-USA300 CC8 strains which predicted several genes with strain-specific altered expression patterns. Examination of the regulatory region of one of the genes enriched by both approaches, isdH, revealed a 38-bp deletion containing a Fur-binding site and a conserved single-nucleotide polymorphism which likely led to the altered expression levels in USA300 strains. Taken together, our results demonstrate the utility of reconstructed TRNs to address the limits of genetic approaches when studying emerging pathogenic strains.