Please use this identifier to cite or link to this item: doi:10.22028/D291-47919
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Title: Decoding the diagnostic and therapeutic potential of microbiota using pan-body pan-disease microbiomics
Author(s): Schmartz, Georges P
Rehner, Jacqueline
Gund, Madline P
Keller, Verena
Molano, Leidy-Alejandra G
Rupf, Stefan
Hannig, Matthias
Berger, Tim
Flockerzi, Elias
Seitz, Berthold
Fleser, Sara
Schmitt-Grohé, Sabina
Kalefack, Sandra
Zemlin, Michael
Kunz, Michael
Götzinger, Felix
Gevaerd, Caroline
Vogt, Thomas
Reichrath, Jörg
Diehl, Lisa
Hecksteden, Anne
Meyer, Tim
Herr, Christian
Gurevich, Aleksei
Krug, Daniel
Hegemann, Julian
Bozhueyuek, Kenan
Gulder, Tobias A M
Fu, Chengzhang
Beemelmanns, Christine
Schattenberg, Jörn M
Kalinina, Olga V
Becker, Anouck
Unger, Marcus
Ludwig, Nicole
Seibert, Martina
Stein, Marie-Louise
Hanna, Nikolas Loka
Martin, Marie-Christin
Mahfoud, Felix
Krawczyk, Marcin
Becker, Sören L
Müller, Rolf
Bals, Robert
Keller, Andreas
Language: English
Title: Nature communications
Volume: 15
Issue: 1
Publisher/Platform: Springer Nature
Year of Publication: 2024
DDC notations: 004 Computer science, internet
Publikation type: Conference Paper
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.
DOI of the first publication: 10.1038/s41467-024-52598-7
URL of the first publication: https://www.nature.com/articles/s41467-024-52598-7
Link to this record: urn:nbn:de:bsz:291--ds-479194
hdl:20.500.11880/41924
http://dx.doi.org/10.22028/D291-47919
ISSN: 2041-1723
Date of registration: 28-May-2026
Faculty: MI - Fakultät für Mathematik und Informatik
Department: MI - Informatik
Professorship: MI - Jun.-Prof. Alexey Gurevich
Collections:SciDok - Der Wissenschaftsserver der Universität des Saarlandes

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