Multi-omics integration of scRNA-seq time series data predicts new intervention points for Parkinson's disease.
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
14 05 2024
14 05 2024
Historique:
received:
30
01
2024
accepted:
10
05
2024
medline:
15
5
2024
pubmed:
15
5
2024
entrez:
14
5
2024
Statut:
epublish
Résumé
Parkinson's disease (PD) is a complex neurodegenerative disorder without a cure. The onset of PD symptoms corresponds to 50% loss of midbrain dopaminergic (mDA) neurons, limiting early-stage understanding of PD. To shed light on early PD development, we study time series scRNA-seq datasets of mDA neurons obtained from patient-derived induced pluripotent stem cell differentiation. We develop a new data integration method based on Non-negative Matrix Tri-Factorization that integrates these datasets with molecular interaction networks, producing condition-specific "gene embeddings". By mining these embeddings, we predict 193 PD-related genes that are largely supported (49.7%) in the literature and are specific to the investigated PINK1 mutation. Enrichment analysis in Kyoto Encyclopedia of Genes and Genomes pathways highlights 10 PD-related molecular mechanisms perturbed during early PD development. Finally, investigating the top 20 prioritized genes reveals 12 previously unrecognized genes associated with PD that represent interesting drug targets.
Identifiants
pubmed: 38744869
doi: 10.1038/s41598-024-61844-3
pii: 10.1038/s41598-024-61844-3
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
10983Subventions
Organisme : European Union's EU Framework Programme for Research and Innovation Horizon 2020
ID : 860895
Organisme : European Research Council (ERC) Consolidator Grant
ID : 770827
Organisme : Spanish State Research Agency and the Ministry of Science and Innovation MCIN grant
ID : PID2019-105500GB-I00 / AEI / 10.13039/501100011033
Organisme : Department of Research and Universities of the Generalitat de Catalunya
ID : 2021 SGR 01536
Organisme : PRIDE program of the Luxembourg National Research Fund
ID : PRIDE17/12244779/PARK-QC
Informations de copyright
© 2024. The Author(s).
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