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Title: Learning representations of microbe–metabolite interactions

Abstract

Integrating multiomics datasets is critical for microbiome research; however, inferring interactions across omics datasets has multiple statistical challenges. We solve this problem by using neural networks (https://github.com/biocore/mmvec) to estimate the conditional probability that each molecule is present given the presence of a specific microorganism. We illustrate with known environmental (desert soil biocrust wetting) and clinical (cystic fibrosis lung) examples, our ability to recover microbe-metabolite relationships, and demonstrate how the method can discover relationships between microbially produced metabolites and inflammatory bowel disease.

Authors:
ORCiD logo [1]; ORCiD logo [1];  [1];  [2];  [3];  [4];  [5];  [5]; ORCiD logo [6];  [1];  [1];  [1];  [7]; ORCiD logo [1];  [8]; ORCiD logo [1]; ORCiD logo [1]
  1. Univ. of California, San Diego, CA (United States)
  2. Univ. of Maryland Baltimore County (UMBC), Baltimore, MD (United States)
  3. Michigan State Univ., East Lansing, MI (United States)
  4. New York Univ. (NYU), NY (United States)
  5. Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States). Environmental Genomics Division
  6. Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States). Environmental Genomics Division ; USDOE Joint Genome Institute (JGI), Walnut Creek, CA (United States)
  7. Simons Foundation, New York, NY (United States). Flatiron Inst.
  8. New York Univ. (NYU), NY (United States); Simons Foundation, New York, NY (United States). Flatiron Inst. ; Courant Inst., New York, NY (United States). Computer Science Dept.
Publication Date:
Research Org.:
Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
Sponsoring Org.:
USDOE Office of Science (SC)
OSTI Identifier:
1582030
Grant/Contract Number:  
AC02-05CH11231; P41GM103484; S10RR029121; CA211211; DGE-1144086
Resource Type:
Accepted Manuscript
Journal Name:
Nature Methods
Additional Journal Information:
Journal Volume: 16; Journal Issue: 12; Journal ID: ISSN 1548-7091
Publisher:
Nature Publishing Group
Country of Publication:
United States
Language:
English
Subject:
59 BASIC BIOLOGICAL SCIENCES; Microbial communities; Data integration; Machine learning; Metabolomics

Citation Formats

Morton, James T., Aksenov, Alexander A., Nothias, Louis Felix, Foulds, James R., Quinn, Robert A., Badri, Michelle H., Swenson, Tami L., Van Goethem, Marc W., Northen, Trent R., Vazquez-Baeza, Yoshiki, Wang, Mingxun, Bokulich, Nicholas A., Watters, Aaron, Song, Se Jin, Bonneau, Richard, Dorrestein, Pieter C., and Knight, Rob. Learning representations of microbe–metabolite interactions. United States: N. p., 2019. Web. doi:10.1038/s41592-019-0616-3.
Morton, James T., Aksenov, Alexander A., Nothias, Louis Felix, Foulds, James R., Quinn, Robert A., Badri, Michelle H., Swenson, Tami L., Van Goethem, Marc W., Northen, Trent R., Vazquez-Baeza, Yoshiki, Wang, Mingxun, Bokulich, Nicholas A., Watters, Aaron, Song, Se Jin, Bonneau, Richard, Dorrestein, Pieter C., & Knight, Rob. Learning representations of microbe–metabolite interactions. United States. doi:https://doi.org/10.1038/s41592-019-0616-3
Morton, James T., Aksenov, Alexander A., Nothias, Louis Felix, Foulds, James R., Quinn, Robert A., Badri, Michelle H., Swenson, Tami L., Van Goethem, Marc W., Northen, Trent R., Vazquez-Baeza, Yoshiki, Wang, Mingxun, Bokulich, Nicholas A., Watters, Aaron, Song, Se Jin, Bonneau, Richard, Dorrestein, Pieter C., and Knight, Rob. Mon . "Learning representations of microbe–metabolite interactions". United States. doi:https://doi.org/10.1038/s41592-019-0616-3. https://www.osti.gov/servlets/purl/1582030.
@article{osti_1582030,
title = {Learning representations of microbe–metabolite interactions},
author = {Morton, James T. and Aksenov, Alexander A. and Nothias, Louis Felix and Foulds, James R. and Quinn, Robert A. and Badri, Michelle H. and Swenson, Tami L. and Van Goethem, Marc W. and Northen, Trent R. and Vazquez-Baeza, Yoshiki and Wang, Mingxun and Bokulich, Nicholas A. and Watters, Aaron and Song, Se Jin and Bonneau, Richard and Dorrestein, Pieter C. and Knight, Rob},
abstractNote = {Integrating multiomics datasets is critical for microbiome research; however, inferring interactions across omics datasets has multiple statistical challenges. We solve this problem by using neural networks (https://github.com/biocore/mmvec) to estimate the conditional probability that each molecule is present given the presence of a specific microorganism. We illustrate with known environmental (desert soil biocrust wetting) and clinical (cystic fibrosis lung) examples, our ability to recover microbe-metabolite relationships, and demonstrate how the method can discover relationships between microbially produced metabolites and inflammatory bowel disease.},
doi = {10.1038/s41592-019-0616-3},
journal = {Nature Methods},
number = 12,
volume = 16,
place = {United States},
year = {2019},
month = {11}
}

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Cited by: 9 works
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Works referenced in this record:

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Multi-omics of the gut microbial ecosystem in inflammatory bowel diseases
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Proportionality: A Valid Alternative to Correlation for Relative Data
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Latent variable modeling for the microbiome
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A penalized matrix decomposition, with applications to sparse principal components and canonical correlation analysis
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Correlation detection strategies in microbial data sets vary widely in sensitivity and precision
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mixOmics: An R package for ‘omics feature selection and multiple data integration
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Rhamnolipids from Pseudomonas aeruginosa disperse the biofilms of sulfate-reducing bacteria
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PLS-DA for compositional data with application to metabolomics: PLS-DA for Compositional Data
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Fungi stabilize connectivity in the lung and skin microbial ecosystems
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Best practices for analysing microbiomes
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Inferring Correlation Networks from Genomic Survey Data
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Sparse and Compositionally Robust Inference of Microbial Ecological Networks
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A Winogradsky-based culture system shows an association between microbial fermentation and cystic fibrosis exacerbation
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Establishing microbial composition measurement standards with reference frames
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Metabolomics of Fecal Extracts Detects Altered Metabolic Activity of Gut Microbiota in Ulcerative Colitis and Irritable Bowel Syndrome
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Pseudomonas aeruginosa rhamnolipids: biosynthesis and potential applications
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The interaction between bacteria and bile
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Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2
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Commensal Propionibacterium strain UF1 mitigates intestinal inflammation via Th17 cell regulation
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  • DOI: 10.1172/JCI95376

How bile acids confer gut mucosal protection against bacteria
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  • DOI: 10.1073/pnas.0600780103

Immunomodulation properties of multi-species fermented milks
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Pyocyanin Production by Pseudomonas aeruginosa Induces Neutrophil Apoptosis and Impairs Neutrophil-Mediated Host Defenses In Vivo
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Biplots of compositional data
journal, October 2002

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  • DOI: 10.1111/1467-9876.00275

Interkingdom metabolic transformations captured by microbial imaging mass spectrometry
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  • Moree, W. J.; Phelan, V. V.; Wu, C. -H.
  • Proceedings of the National Academy of Sciences, Vol. 109, Issue 34
  • DOI: 10.1073/pnas.1206855109

A multi-omic future for microbiome studies
journal, April 2016


Similarity network fusion for aggregating data types on a genomic scale
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  • Wang, Bo; Mezlini, Aziz M.; Demir, Feyyaz
  • Nature Methods, Vol. 11, Issue 3
  • DOI: 10.1038/nmeth.2810

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EMPeror: a tool for visualizing high-throughput microbial community data
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  • GigaScience, Vol. 2, Issue 1
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Predictive metabolomic profiling of microbial communities using amplicon or metagenomic sequences
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Klebsiella pneumoniae: Going on the Offense with a Strong Defense
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Linking soil biology and chemistry in biological soil crust using isolate exometabolomics
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    Works referencing / citing this record:

    PLS-DA for compositional data with application to metabolomics: PLS-DA for Compositional Data
    journal, July 2014

    • Kalivodová, Alžběta; Hron, Karel; Filzmoser, Peter
    • Journal of Chemometrics, Vol. 29, Issue 1
    • DOI: 10.1002/cem.2657

    Combining selected immunomodulatory Propionibacterium freudenreichii and Lactobacillus delbrueckii strains: Reverse engineering development of an anti-inflammatory cheese
    journal, December 2015

    • Plé, Coline; Breton, Jérôme; Richoux, Romain
    • Molecular Nutrition & Food Research, Vol. 60, Issue 4
    • DOI: 10.1002/mnfr.201500580

    Canonical correspondence analysis and related multivariate methods in aquatic ecology
    journal, September 1995

    • ter Braak, Cajo J. F.; Verdonschot, Piet F. M.
    • Aquatic Sciences, Vol. 57, Issue 3
    • DOI: 10.1007/bf00877430

    Pseudomonas aeruginosa rhamnolipids: biosynthesis and potential applications
    journal, November 2000

    • Maier, R. M.; Soberón-Chávez, G.
    • Applied Microbiology and Biotechnology, Vol. 54, Issue 5
    • DOI: 10.1007/s002530000443

    Advances in Principal Balances for Compositional Data
    journal, November 2017

    • Martín-Fernández, J. A.; Pawlowsky-Glahn, V.; Egozcue, J. J.
    • Mathematical Geosciences, Vol. 50, Issue 3
    • DOI: 10.1007/s11004-017-9712-z

    The interaction between bacteria and bile
    journal, September 2005


    Immunomodulation properties of multi-species fermented milks
    journal, February 2016


    Charge competition and the linear dynamic range of detection in electrospray ionization mass spectrometry
    journal, October 2004

    • Tang, Keqi; Page, Jason S.; Smith, Richard D.
    • Journal of the American Society for Mass Spectrometry, Vol. 15, Issue 10
    • DOI: 10.1016/j.jasms.2004.04.034

    Mechanistic investigation of ionization suppression in electrospray ionization
    journal, November 2000

    • King, Richard; Bonfiglio, Ryan; Fernandez-Metzler, Carmen
    • Journal of the American Society for Mass Spectrometry, Vol. 11, Issue 11
    • DOI: 10.1016/s1044-0305(00)00163-x

    Strategies for the Assessment of Matrix Effect in Quantitative Bioanalytical Methods Based on HPLC−MS/MS
    journal, July 2003

    • Matuszewski, B. K.; Constanzer, M. L.; Chavez-Eng, C. M.
    • Analytical Chemistry, Vol. 75, Issue 13
    • DOI: 10.1021/ac020361s

    Metabolomics of Fecal Extracts Detects Altered Metabolic Activity of Gut Microbiota in Ulcerative Colitis and Irritable Bowel Syndrome
    journal, September 2011

    • Le Gall, Gwénaëlle; Noor, Samah O.; Ridgway, Karyn
    • Journal of Proteome Research, Vol. 10, Issue 9
    • DOI: 10.1021/pr2003598

    A Winogradsky-based culture system shows an association between microbial fermentation and cystic fibrosis exacerbation
    journal, December 2014

    • Quinn, Robert A.; Whiteson, Katrine; Lim, Yan-Wei
    • The ISME Journal, Vol. 9, Issue 4
    • DOI: 10.1038/ismej.2014.234

    Correlation detection strategies in microbial data sets vary widely in sensitivity and precision
    journal, February 2016

    • Weiss, Sophie; Van Treuren, Will; Lozupone, Catherine
    • The ISME Journal, Vol. 10, Issue 7
    • DOI: 10.1038/ismej.2015.235

    Inflammation-induced IgA+ cells dismantle anti-liver cancer immunity
    journal, November 2017

    • Shalapour, Shabnam; Lin, Xue-Jia; Bastian, Ingmar N.
    • Nature, Vol. 551, Issue 7680
    • DOI: 10.1038/nature24302

    Quantitative microbiome profiling links gut community variation to microbial load
    journal, November 2017

    • Vandeputte, Doris; Kathagen, Gunter; D’hoe, Kevin
    • Nature, Vol. 551, Issue 7681
    • DOI: 10.1038/nature24460

    Sharing and community curation of mass spectrometry data with Global Natural Products Social Molecular Networking
    journal, August 2016

    • Wang, Mingxun; Carver, Jeremy J.; Phelan, Vanessa V.
    • Nature Biotechnology, Vol. 34, Issue 8
    • DOI: 10.1038/nbt.3597

    Exometabolite niche partitioning among sympatric soil bacteria
    journal, September 2015

    • Baran, Richard; Brodie, Eoin L.; Mayberry-Lewis, Jazmine
    • Nature Communications, Vol. 6, Issue 1
    • DOI: 10.1038/ncomms9289

    Similarity network fusion for aggregating data types on a genomic scale
    journal, January 2014

    • Wang, Bo; Mezlini, Aziz M.; Demir, Feyyaz
    • Nature Methods, Vol. 11, Issue 3
    • DOI: 10.1038/nmeth.2810

    A multi-omic future for microbiome studies
    journal, April 2016


    Linking soil biology and chemistry in biological soil crust using isolate exometabolomics
    journal, January 2018


    Establishing microbial composition measurement standards with reference frames
    journal, June 2019


    Predictive metabolomic profiling of microbial communities using amplicon or metagenomic sequences
    journal, July 2019


    Best practices for analysing microbiomes
    journal, May 2018


    Multi-omics of the gut microbial ecosystem in inflammatory bowel diseases
    journal, May 2019


    Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2
    journal, July 2019


    How bile acids confer gut mucosal protection against bacteria
    journal, March 2006

    • Hofmann, A. F.; Eckmann, L.
    • Proceedings of the National Academy of Sciences, Vol. 103, Issue 12
    • DOI: 10.1073/pnas.0600780103

    Interkingdom metabolic transformations captured by microbial imaging mass spectrometry
    journal, August 2012

    • Moree, W. J.; Phelan, V. V.; Wu, C. -H.
    • Proceedings of the National Academy of Sciences, Vol. 109, Issue 34
    • DOI: 10.1073/pnas.1206855109

    Dimension reduction techniques for the integrative analysis of multi-omics data
    journal, March 2016

    • Meng, Chen; Zeleznik, Oana A.; Thallinger, Gerhard G.
    • Briefings in Bioinformatics, Vol. 17, Issue 4
    • DOI: 10.1093/bib/bbv108

    A penalized matrix decomposition, with applications to sparse principal components and canonical correlation analysis
    journal, April 2009


    Latent variable modeling for the microbiome
    journal, June 2018


    Matrix Factorization Techniques for Recommender Systems
    journal, August 2009

    • Koren, Yehuda; Bell, Robert; Volinsky, Chris
    • Computer, Vol. 42, Issue 8
    • DOI: 10.1109/mc.2009.263

    Biplots of compositional data
    journal, October 2002

    • Aitchison, John; Greenacre, Michael
    • Journal of the Royal Statistical Society: Series C (Applied Statistics), Vol. 51, Issue 4
    • DOI: 10.1111/1467-9876.00275

    Niche partitioning of a pathogenic microbiome driven by chemical gradients
    journal, September 2018

    • Quinn, Robert A.; Comstock, William; Zhang, Tianyu
    • Science Advances, Vol. 4, Issue 9
    • DOI: 10.1126/sciadv.aau1908

    Klebsiella pneumoniae: Going on the Offense with a Strong Defense
    journal, June 2016

    • Paczosa, Michelle K.; Mecsas, Joan
    • Microbiology and Molecular Biology Reviews, Vol. 80, Issue 3
    • DOI: 10.1128/mmbr.00078-15

    Commensal Propionibacterium strain UF1 mitigates intestinal inflammation via Th17 cell regulation
    journal, September 2017

    • Colliou, Natacha; Ge, Yong; Sahay, Bikash
    • Journal of Clinical Investigation, Vol. 127, Issue 11
    • DOI: 10.1172/jci95376

    EMPeror: a tool for visualizing high-throughput microbial community data
    journal, November 2013

    • Vázquez-Baeza, Yoshiki; Pirrung, Meg; Gonzalez, Antonio
    • GigaScience, Vol. 2, Issue 1
    • DOI: 10.1186/2047-217x-2-16

    Fungi stabilize connectivity in the lung and skin microbial ecosystems
    journal, January 2018


    Inferring Correlation Networks from Genomic Survey Data
    journal, September 2012


    Proportionality: A Valid Alternative to Correlation for Relative Data
    journal, March 2015


    Sparse and Compositionally Robust Inference of Microbial Ecological Networks
    journal, May 2015

    • Kurtz, Zachary D.; Müller, Christian L.; Miraldi, Emily R.
    • PLOS Computational Biology, Vol. 11, Issue 5
    • DOI: 10.1371/journal.pcbi.1004226

    mixOmics: An R package for ‘omics feature selection and multiple data integration
    journal, November 2017


    Microbiome Datasets Are Compositional: And This Is Not Optional
    journal, November 2017

    • Gloor, Gregory B.; Macklaim, Jean M.; Pawlowsky-Glahn, Vera
    • Frontiers in Microbiology, Vol. 8
    • DOI: 10.3389/fmicb.2017.02224

    Pyocyanin Production by Pseudomonas aeruginosa Induces Neutrophil Apoptosis and Impairs Neutrophil-Mediated Host Defenses In Vivo
    journal, March 2005


    Machine Learning Applications for Mass Spectrometry-Based Metabolomics
    journal, June 2020


    The Connection and Disconnection Between Microbiome and Metabolome: A Critical Appraisal in Clinical Research
    journal, February 2020