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el Bouhaddani S, Höllerhage M, Uh HW, Moebius C, Bickle M, Höglinger G, Houwing-Duistermaat J. Statistical integration of multi-omics and drug screening data from cell lines. PLoS Comput Biol 2024; 20:e1011809. [PMID: 38295113 PMCID: PMC10878536 DOI: 10.1371/journal.pcbi.1011809] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2023] [Revised: 02/20/2024] [Accepted: 01/08/2024] [Indexed: 02/02/2024] Open
Abstract
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.
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Affiliation(s)
| | | | - Hae-Won Uh
- Dept. Data science & Biostatistics, UMC Utrecht, Utrecht, Netherlands
| | - Claudia Moebius
- Max Planck Institute of Molecular Cell Biology and Genetics, Dresden, Germany
| | - Marc Bickle
- Roche Institute for Translational Bioengineering, Basel, Switzerland
| | - Günter Höglinger
- 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
| | - Jeanine Houwing-Duistermaat
- Dept. Data science & Biostatistics, UMC Utrecht, Utrecht, Netherlands
- Dept. of Mathematics, Radboud University, Nijmegen, Netherlands
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Ivanisevic T, Sewduth RN. Multi-Omics Integration for the Design of Novel Therapies and the Identification of Novel Biomarkers. Proteomes 2023; 11:34. [PMID: 37873876 PMCID: PMC10594525 DOI: 10.3390/proteomes11040034] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2023] [Revised: 10/13/2023] [Accepted: 10/19/2023] [Indexed: 10/25/2023] Open
Abstract
Multi-omics is a cutting-edge approach that combines data from different biomolecular levels, such as DNA, RNA, proteins, metabolites, and epigenetic marks, to obtain a holistic view of how living systems work and interact. Multi-omics has been used for various purposes in biomedical research, such as identifying new diseases, discovering new drugs, personalizing treatments, and optimizing therapies. This review summarizes the latest progress and challenges of multi-omics for designing new treatments for human diseases, focusing on how to integrate and analyze multiple proteome data and examples of how to use multi-proteomics data to identify new drug targets. We also discussed the future directions and opportunities of multi-omics for developing innovative and effective therapies by deciphering proteome complexity.
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Affiliation(s)
| | - Raj N. Sewduth
- VIB-KU Leuven Center for Cancer Biology (VIB), 3000 Leuven, Belgium;
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Downing T, Angelopoulos N. A primer on correlation-based dimension reduction methods for multi-omics analysis. J R Soc Interface 2023; 20:20230344. [PMID: 37817584 PMCID: PMC10565429 DOI: 10.1098/rsif.2023.0344] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2023] [Accepted: 09/19/2023] [Indexed: 10/12/2023] Open
Abstract
The continuing advances of omic technologies mean that it is now more tangible to measure the numerous features collectively reflecting the molecular properties of a sample. When multiple omic methods are used, statistical and computational approaches can exploit these large, connected profiles. Multi-omics is the integration of different omic data sources from the same biological sample. In this review, we focus on correlation-based dimension reduction approaches for single omic datasets, followed by methods for pairs of omics datasets, before detailing further techniques for three or more omic datasets. We also briefly detail network methods when three or more omic datasets are available and which complement correlation-oriented tools. To aid readers new to this area, these are all linked to relevant R packages that can implement these procedures. Finally, we discuss scenarios of experimental design and present road maps that simplify the selection of appropriate analysis methods. This review will help researchers navigate emerging methods for multi-omics and integrating diverse omic datasets appropriately. This raises the opportunity of implementing population multi-omics with large sample sizes as omics technologies and our understanding improve.
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Affiliation(s)
- Tim Downing
- Pirbright Institute, Pirbright, Surrey, UK
- Department of Biotechnology, Dublin City University, Dublin, Ireland
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Gill SK, Karwath A, Uh HW, Cardoso VR, Gu Z, Barsky A, Slater L, Acharjee A, Duan J, Dall'Olio L, el Bouhaddani S, Chernbumroong S, Stanbury M, Haynes S, Asselbergs FW, Grobbee DE, Eijkemans MJC, Gkoutos GV, Kotecha D. Artificial intelligence to enhance clinical value across the spectrum of cardiovascular healthcare. Eur Heart J 2023; 44:713-725. [PMID: 36629285 PMCID: PMC9976986 DOI: 10.1093/eurheartj/ehac758] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/16/2022] [Revised: 11/22/2022] [Accepted: 12/05/2022] [Indexed: 01/12/2023] Open
Abstract
Artificial intelligence (AI) is increasingly being utilized in healthcare. This article provides clinicians and researchers with a step-wise foundation for high-value AI that can be applied to a variety of different data modalities. The aim is to improve the transparency and application of AI methods, with the potential to benefit patients in routine cardiovascular care. Following a clear research hypothesis, an AI-based workflow begins with data selection and pre-processing prior to analysis, with the type of data (structured, semi-structured, or unstructured) determining what type of pre-processing steps and machine-learning algorithms are required. Algorithmic and data validation should be performed to ensure the robustness of the chosen methodology, followed by an objective evaluation of performance. Seven case studies are provided to highlight the wide variety of data modalities and clinical questions that can benefit from modern AI techniques, with a focus on applying them to cardiovascular disease management. Despite the growing use of AI, further education for healthcare workers, researchers, and the public are needed to aid understanding of how AI works and to close the existing gap in knowledge. In addition, issues regarding data access, sharing, and security must be addressed to ensure full engagement by patients and the public. The application of AI within healthcare provides an opportunity for clinicians to deliver a more personalized approach to medical care by accounting for confounders, interactions, and the rising prevalence of multi-morbidity.
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Affiliation(s)
- Simrat K Gill
- Institute of Cardiovascular Sciences, University of Birmingham, Vincent Drive, B15 2TT Birmingham, UK
- Health Data Research UK Midlands, University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK
| | - Andreas Karwath
- Health Data Research UK Midlands, University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK
- Institute of Cancer and Genomic Sciences, University of Birmingham, Vincent Drive, B15 2TT Birmingham, UK
| | - Hae-Won Uh
- Julius Center for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht, The Netherlands
| | - Victor Roth Cardoso
- Institute of Cardiovascular Sciences, University of Birmingham, Vincent Drive, B15 2TT Birmingham, UK
- Health Data Research UK Midlands, University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK
- Institute of Cancer and Genomic Sciences, University of Birmingham, Vincent Drive, B15 2TT Birmingham, UK
| | - Zhujie Gu
- Julius Center for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht, The Netherlands
| | - Andrey Barsky
- Health Data Research UK Midlands, University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK
- Institute of Cancer and Genomic Sciences, University of Birmingham, Vincent Drive, B15 2TT Birmingham, UK
| | - Luke Slater
- Health Data Research UK Midlands, University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK
- Institute of Cancer and Genomic Sciences, University of Birmingham, Vincent Drive, B15 2TT Birmingham, UK
| | - Animesh Acharjee
- Health Data Research UK Midlands, University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK
- Institute of Cancer and Genomic Sciences, University of Birmingham, Vincent Drive, B15 2TT Birmingham, UK
| | - Jinming Duan
- School of Computer Science, University of Birmingham, Birmingham, UK
- Alan Turing Institute, London, UK
| | - Lorenzo Dall'Olio
- Department of Physics and Astronomy, University of Bologna, Bologna, Italy
| | - Said el Bouhaddani
- Julius Center for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht, The Netherlands
| | - Saisakul Chernbumroong
- Health Data Research UK Midlands, University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK
- Institute of Cancer and Genomic Sciences, University of Birmingham, Vincent Drive, B15 2TT Birmingham, UK
| | | | | | - Folkert W Asselbergs
- Amsterdam University Medical Center, Department of Cardiology, University of Amsterdam, Amsterdam, The Netherlands
- Health Data Research UK and Institute of Health Informatics, University College London, London, UK
| | - Diederick E Grobbee
- Julius Center for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht, The Netherlands
| | - Marinus J C Eijkemans
- Julius Center for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht, The Netherlands
| | - Georgios V Gkoutos
- Health Data Research UK Midlands, University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK
- Institute of Cancer and Genomic Sciences, University of Birmingham, Vincent Drive, B15 2TT Birmingham, UK
| | - Dipak Kotecha
- Institute of Cardiovascular Sciences, University of Birmingham, Vincent Drive, B15 2TT Birmingham, UK
- Health Data Research UK Midlands, University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK
- Department of Cardiology, Division Heart and Lungs, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
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Hall RD, D'Auria JC, Silva Ferreira AC, Gibon Y, Kruszka D, Mishra P, van de Zedde R. High-throughput plant phenotyping: a role for metabolomics? TRENDS IN PLANT SCIENCE 2022; 27:549-563. [PMID: 35248492 DOI: 10.1016/j.tplants.2022.02.001] [Citation(s) in RCA: 24] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/06/2021] [Revised: 01/18/2022] [Accepted: 02/02/2022] [Indexed: 05/17/2023]
Abstract
High-throughput (HTP) plant phenotyping approaches are developing rapidly and are already helping to bridge the genotype-phenotype gap. However, technologies should be developed beyond current physico-spectral evaluations to extend our analytical capacities to the subcellular level. Metabolites define and determine many key physiological and agronomic features in plants and an ability to integrate a metabolomics approach within current HTP phenotyping platforms has huge potential for added value. While key challenges remain on several fronts, novel technological innovations are upcoming yet under-exploited in a phenotyping context. In this review, we present an overview of the state of the art and how current limitations might be overcome to enable full integration of metabolomics approaches into a generic phenotyping pipeline in the near future.
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Affiliation(s)
- Robert D Hall
- BU Bioscience, Wageningen University & Research, 6700 AA, Wageningen, The Netherlands; Laboratory of Plant Physiology, Wageningen University, 6700 AA, Wageningen, The Netherlands; Netherlands Metabolomics Centre, Einsteinweg 55, Leiden, The Netherlands.
| | - John C D'Auria
- Department of Molecular Genetics, Leibniz Institute of Plant Genetics and Crop Plant Research (IPK Gatersleben), Gatersleben, Corrensstraße 3, 06466 Seeland, Germany
| | - Antonio C Silva Ferreira
- Universidade Católica Portuguesa, CBQF-Centro de Biotecnologia e Química Fina-Laboratório Associado, Escola Superior de Biotecnologia, Rua Arquiteto Lobão Vital, Apartado 2511, 4202-401 Porto, Portugal; Faculty of AgriSciences, University of Stellenbosch, Matieland 7602, South Africa; Cork Supply Portugal, S.A., Rua Nova do Fial, 4535, Portugal
| | - Yves Gibon
- UMR 1332 Biologie du Fruit et Pathologie, INRAE, Univ. Bordeaux, INRAE Nouvelle Aquitaine - Bordeaux, Avenue Edouard Bourlaux, Villenave d'Ornon, France; Bordeaux Metabolome, MetaboHUB, INRAE, Univ. Bordeaux, Avenue Edouard Bourlaux, Villenave d'Ornon, France PMB-Metabolome, INRAE, Centre INRAE de Nouvelle, Aquitaine-Bordeaux, Villenave d'Ornon, France
| | - Dariusz Kruszka
- Institute of Plant Genetics, Polish Academy of Sciences, 60-479 Poznan, Poland
| | - Puneet Mishra
- Food and Biobased Research, Wageningen University & Research, 6708 WE, Wageningen, The Netherlands
| | - Rick van de Zedde
- Plant Sciences Group, Wageningen University & Research, 6700 AA, Wageningen, The Netherlands
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