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Decoding the diagnostic and therapeutic potential of microbiota using pan-body pan-disease microbiomics

Author

Listed:
  • Georges P. Schmartz

    (Saarland University)

  • Jacqueline Rehner

    (Saarland University)

  • Madline P. Gund

    (Saarland University)

  • Verena Keller

    (Saarland University Medical Center)

  • Leidy-Alejandra G. Molano

    (Saarland University)

  • Stefan Rupf

    (Saarland University
    Saarland University)

  • Matthias Hannig

    (Saarland University)

  • Tim Berger

    (Saarland University Medical Center)

  • Elias Flockerzi

    (Saarland University Medical Center)

  • Berthold Seitz

    (Saarland University Medical Center)

  • Sara Fleser

    (Saarland University)

  • Sabina Schmitt-Grohé

    (Saarland University)

  • Sandra Kalefack

    (Saarland University)

  • Michael Zemlin

    (Saarland University)

  • Michael Kunz

    (Saarland University Hospital)

  • Felix Götzinger

    (Saarland University Hospital)

  • Caroline Gevaerd

    (and Allergology)

  • Thomas Vogt

    (and Allergology)

  • Jörg Reichrath

    (and Allergology)

  • Lisa Diehl

    (Saarland University)

  • Anne Hecksteden

    (Saarland University
    Medical University of Innsbruck)

  • Tim Meyer

    (Saarland University)

  • Christian Herr

    (Saarland University)

  • Alexey Gurevich

    (Helmholtz Institute for Pharmaceutical Research Saarland
    Saarland Informatics Campus)

  • Daniel Krug

    (Helmholtz Institute for Pharmaceutical Research Saarland)

  • Julian Hegemann

    (Helmholtz Institute for Pharmaceutical Research Saarland
    Saarland University)

  • Kenan Bozhueyuek

    (Helmholtz Institute for Pharmaceutical Research Saarland)

  • Tobias A. M. Gulder

    (Helmholtz Institute for Pharmaceutical Research Saarland
    Saarland University)

  • Chengzhang Fu

    (Helmholtz Institute for Pharmaceutical Research Saarland)

  • Christine Beemelmanns

    (Helmholtz Institute for Pharmaceutical Research Saarland)

  • Jörn M. Schattenberg

    (Saarland University Medical Center)

  • Olga V. Kalinina

    (Helmholtz Institute for Pharmaceutical Research Saarland)

  • Anouck Becker

    (Saarland University Medical Center)

  • Marcus Unger

    (Saarland University Medical Center)

  • Nicole Ludwig

    (Saarland University)

  • Martina Seibert

    (Saarland University Medical Center)

  • Marie-Louise Stein

    (Saarland University Medical Center)

  • Nikolas Loka Hanna

    (Saarland University)

  • Marie-Christin Martin

    (Saarland University Medical Center)

  • Felix Mahfoud

    (Saarland University Hospital)

  • Marcin Krawczyk

    (Saarland University Medical Center)

  • Sören L. Becker

    (Saarland University)

  • Rolf Müller

    (Helmholtz Institute for Pharmaceutical Research Saarland
    PharmaScienceHub)

  • Robert Bals

    (Saarland University
    PharmaScienceHub)

  • Andreas Keller

    (Saarland University
    Helmholtz Institute for Pharmaceutical Research Saarland
    PharmaScienceHub)

Abstract

The human microbiome emerges as a promising reservoir for diagnostic markers and therapeutics. Since host-associated microbiomes at various body sites differ and diseases do not occur in isolation, a comprehensive analysis strategy highlighting the full potential of microbiomes should include diverse specimen types and various diseases. To ensure robust data quality and comparability across specimen types and diseases, we employ standardized protocols to generate sequencing data from 1931 prospectively collected specimens, including from saliva, plaque, skin, throat, eye, and stool, with an average sequencing depth of 5.3 gigabases. Collected from 515 patients, these samples yield an average of 3.7 metagenomes per patient. Our results suggest significant microbial variations across diseases and specimen types, including unexpected anatomical sites. We identify 583 unexplored species-level genome bins (SGBs) of which 189 are significantly disease-associated. Of note, the existence of microbial resistance genes in one specimen was indicative of the same resistance genes in other specimens of the same patient. Annotated and previously undescribed SGBs collectively harbor 28,315 potential biosynthetic gene clusters (BGCs), with 1050 significant correlations to diseases. Our combinatorial approach identifies distinct SGBs and BGCs, emphasizing the value of pan-body pan-disease microbiomics as a source for diagnostic and therapeutic strategies.

Suggested Citation

  • Georges P. Schmartz & Jacqueline Rehner & Madline P. Gund & Verena Keller & Leidy-Alejandra G. Molano & Stefan Rupf & Matthias Hannig & Tim Berger & Elias Flockerzi & Berthold Seitz & Sara Fleser & Sa, 2024. "Decoding the diagnostic and therapeutic potential of microbiota using pan-body pan-disease microbiomics," Nature Communications, Nature, vol. 15(1), pages 1-13, December.
  • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-52598-7
    DOI: 10.1038/s41467-024-52598-7
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