Statistical integration of multi-omics and drug screening data from cell lines.

Autor: El Bouhaddani S; Dept. Data science & Biostatistics, UMC Utrecht, Utrecht, Netherlands., Höllerhage M; Department of Neurology, Hannover Medical School, Hannover, Germany., Uh HW; Dept. Data science & Biostatistics, UMC Utrecht, Utrecht, Netherlands., Moebius C; Max Planck Institute of Molecular Cell Biology and Genetics, Dresden, Germany., Bickle M; Roche Institute for Translational Bioengineering, Basel, Switzerland., Höglinger G; Department of Neurology, Hannover Medical School, Hannover, Germany.; Department of Neurology, Ludwig-Maximilians-Universität, Munich, Germany.; German Center for Neurodegenerative Diseases, Munich, Germany.; Munich Cluster for Systems Neurology (SyNergy), Munich, Germany., Houwing-Duistermaat J; Dept. Data science & Biostatistics, UMC Utrecht, Utrecht, Netherlands.; Dept. of Mathematics, Radboud University, Nijmegen, Netherlands.
Jazyk: angličtina
Zdroj: PLoS computational biology [PLoS Comput Biol] 2024 Jan 31; Vol. 20 (1), pp. e1011809. Date of Electronic Publication: 2024 Jan 31 (Print Publication: 2024).
DOI: 10.1371/journal.pcbi.1011809
Abstrakt: Data integration methods are used to obtain a unified summary of multiple datasets. For multi-modal data, we propose a computational workflow to jointly analyze datasets from cell lines. The workflow comprises a novel probabilistic data integration method, named POPLS-DA, for multi-omics data. The workflow is motivated by a study on synucleinopathies where transcriptomics, proteomics, and drug screening data are measured in affected LUHMES cell lines and controls. The aim is to highlight potentially druggable pathways and genes involved in synucleinopathies. First, POPLS-DA is used to prioritize genes and proteins that best distinguish cases and controls. For these genes, an integrated interaction network is constructed where the drug screen data is incorporated to highlight druggable genes and pathways in the network. Finally, functional enrichment analyses are performed to identify clusters of synaptic and lysosome-related genes and proteins targeted by the protective drugs. POPLS-DA is compared to other single- and multi-omics approaches. We found that HSPA5, a member of the heat shock protein 70 family, was one of the most targeted genes by the validated drugs, in particular by AT1-blockers. HSPA5 and AT1-blockers have been previously linked to α-synuclein pathology and Parkinson's disease, showing the relevance of our findings. Our computational workflow identified new directions for therapeutic targets for synucleinopathies. POPLS-DA provided a larger interpretable gene set than other single- and multi-omic approaches. An implementation based on R and markdown is freely available online.
Competing Interests: The authors have declared that no competing interests exist.
(Copyright: © 2024 el Bouhaddani et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)
Databáze: MEDLINE
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