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Ratajczak, F. ; Joblin, M.* ; Hildebrandt, M.* ; Ringsquandl, M.* ; Falter-Braun, P. ; Heinig, M.

Speos: An ensemble graph representation learning framework to predict core gene candidates for complex diseases.

Nat. Commun. 14:7206 (2023)
Verlagsversion DOI PMC
Open Access Gold
Creative Commons Lizenzvertrag
Understanding phenotype-to-genotype relationships is a grand challenge of 21st century biology with translational implications. The recently proposed "omnigenic" model postulates that effects of genetic variation on traits are mediated by core-genes and -proteins whose activities mechanistically influence the phenotype, whereas peripheral genes encode a regulatory network that indirectly affects phenotypes via core gene products. Here, we develop a positive-unlabeled graph representation-learning ensemble-approach based on a nested cross-validation to predict core-like genes for diverse diseases using Mendelian disorder genes for training. Employing mouse knockout phenotypes for external validations, we demonstrate that core-like genes display several key properties of core genes: Mouse knockouts of genes corresponding to our most confident predictions give rise to relevant mouse phenotypes at rates on par with the Mendelian disorder genes, and all candidates exhibit core gene properties like transcriptional deregulation in disease and loss-of-function intolerance. Moreover, as predicted for core genes, our candidates are enriched for drug targets and druggable proteins. In contrast to Mendelian disorder genes the new core-like genes are enriched for druggable yet untargeted gene products, which are therefore attractive targets for drug development. Interpretation of the underlying deep learning model suggests plausible explanations for our core gene predictions in form of molecular mechanisms and physical interactions. Our results demonstrate the potential of graph representation learning for the interpretation of biological complexity and pave the way for studying core gene properties and future drug development.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Schlagwörter Crohns-disease; Ulcerative-colitis; Networks; Cell; Interactome; Association; Architecture; Phenotypes; Discovery; Tnfsf15
Sprache englisch
Veröffentlichungsjahr 2023
HGF-Berichtsjahr 2023
ISSN (print) / ISBN 2041-1723
e-ISSN 2041-1723
Zeitschrift Nature Communications
Quellenangaben Band: 14, Heft: 1, Seiten: , Artikelnummer: 7206 Supplement: ,
Verlag Nature Publishing Group
Verlagsort London
Begutachtungsstatus Peer reviewed
Institut(e) Institute of Computational Biology (ICB)
Institute of Network Biology (INET)
POF Topic(s) 30205 - Bioengineering and Digital Health
30203 - Molecular Targets and Therapies
Forschungsfeld(er) Enabling and Novel Technologies
Environmental Sciences
PSP-Element(e) G-553500-001
G-506400-001
Förderungen Helmholtz Association
F.R. is supported by the Helmholtz Association under the joint research school "Munich School for Data Science-MUDS".
DOI 10.1038/s41467-023-42975-z
WOS ID 001102128500020
Scopus ID 85176014217
PubMed ID 37938585
Erfassungsdatum 2023-11-28
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