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Rai MF, Collins KH, Lang A, Maerz T, Geurts J, Ruiz-Romero C, June RK, Ramos Y, Rice SJ, Ali SA, Pastrello C, Jurisica I, Thomas Appleton C, Rockel JS, Kapoor M. Three decades of advancements in osteoarthritis research: insights from transcriptomic, proteomic, and metabolomic studies. Osteoarthritis Cartilage 2024; 32:385-397. [PMID: 38049029 DOI: 10.1016/j.joca.2023.11.019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/10/2023] [Revised: 11/22/2023] [Accepted: 11/29/2023] [Indexed: 12/06/2023]
Abstract
OBJECTIVE Osteoarthritis (OA) is a complex disease involving contributions from both local joint tissues and systemic sources. Patient characteristics, encompassing sociodemographic and clinical variables, are intricately linked with OA rendering its understanding challenging. Technological advancements have allowed for a comprehensive analysis of transcripts, proteomes and metabolomes in OA tissues/fluids through omic analyses. The objective of this review is to highlight the advancements achieved by omic studies in enhancing our understanding of OA pathogenesis over the last three decades. DESIGN We conducted an extensive literature search focusing on transcriptomics, proteomics and metabolomics within the context of OA. Specifically, we explore how these technologies have identified individual transcripts, proteins, and metabolites, as well as distinctive endotype signatures from various body tissues or fluids of OA patients, including insights at the single-cell level, to advance our understanding of this highly complex disease. RESULTS Omic studies reveal the description of numerous individual molecules and molecular patterns within OA-associated tissues and fluids. This includes the identification of specific cell (sub)types and associated pathways that contribute to disease mechanisms. However, there remains a necessity to further advance these technologies to delineate the spatial organization of cellular subtypes and molecular patterns within OA-afflicted tissues. CONCLUSIONS Leveraging a multi-omics approach that integrates datasets from diverse molecular detection technologies, combined with patients' clinical and sociodemographic features, and molecular and regulatory networks, holds promise for identifying unique patient endophenotypes. This holistic approach can illuminate the heterogeneity among OA patients and, in turn, facilitate the development of tailored therapeutic interventions.
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Affiliation(s)
- Muhammad Farooq Rai
- Department of Anatomy and Cellular Biology, College of Medicine and Health Sciences, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates
| | - Kelsey H Collins
- Department of Orthopaedic Surgery, University of California San Francisco, San Francisco, CA, USA
| | - Annemarie Lang
- Departments of Orthopaedic Surgery and Bioengineering, University of Pennsylvania, Philadelphia, PA, USA
| | - Tristan Maerz
- Department of Orthopaedic Surgery, University of Michigan, Ann Arbor, MI, USA
| | - Jeroen Geurts
- Rheumatology, Department of Musculoskeletal Medicine, Lausanne University Hospital, Lausanne, Switzerland
| | - Cristina Ruiz-Romero
- Grupo de Investigación de Reumatología (GIR), Unidad de Proteómica, INIBIC -Hospital Universitario A Coruña, SERGAS, Spain
| | - Ronald K June
- Department of Mechanical & Industrial Engineering, Montana State University, Bozeman, MT, USA
| | - Yolande Ramos
- Dept. Biomedical Data Sciences, Leiden University Medical Center, Leiden, the Netherlands
| | - Sarah J Rice
- Biosciences Institute, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Shabana Amanda Ali
- Henry Ford Health + Michigan State University Health Sciences, Detroit, MI, USA
| | - Chiara Pastrello
- Osteoarthritis Research Program, Division of Orthopedic Surgery, Schroeder Arthritis Institute, UHN, Toronto, ON, Canada
| | - Igor Jurisica
- Osteoarthritis Research Program, Division of Orthopedic Surgery, Schroeder Arthritis Institute, UHN, Toronto, ON, Canada; Departments of Medical Biophysics and Computer Science, University of Toronto, Toronto, ON, Canada
| | - C Thomas Appleton
- Department of Medicine, University of Western Ontario, London, ON, Canada
| | - Jason S Rockel
- Osteoarthritis Research Program, Division of Orthopedic Surgery, Schroeder Arthritis Institute, UHN, Toronto, ON, Canada
| | - Mohit Kapoor
- Osteoarthritis Research Program, Division of Orthopedic Surgery, Schroeder Arthritis Institute, UHN, Toronto, ON, Canada.
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Bowler-Barnett EH, Fan J, Luo J, Magrane M, Martin MJ, Orchard S. UniProt and Mass Spectrometry-Based Proteomics-A 2-Way Working Relationship. Mol Cell Proteomics 2023; 22:100591. [PMID: 37301379 PMCID: PMC10404557 DOI: 10.1016/j.mcpro.2023.100591] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2023] [Revised: 05/20/2023] [Accepted: 06/07/2023] [Indexed: 06/12/2023] Open
Abstract
The human proteome comprises of all of the proteins produced by the sequences translated from the human genome with additional modifications in both sequence and function caused by nonsynonymous variants and posttranslational modifications including cleavage of the initial transcript into smaller peptides and polypeptides. The UniProtKB database (www.uniprot.org) is the world's leading high-quality, comprehensive and freely accessible resource of protein sequence and functional information and presents a summary of experimentally verified, or computationally predicted, functional information added by our expert biocuration team for each protein in the proteome. Researchers in the field of mass spectrometry-based proteomics both consume and add to the body of data available in UniProtKB, and this review highlights the information we provide to this community and the knowledge we in turn obtain from groups via deposition of large-scale datasets in public domain databases.
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Affiliation(s)
- E H Bowler-Barnett
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, United Kingdom
| | - J Fan
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, United Kingdom
| | - J Luo
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, United Kingdom
| | - M Magrane
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, United Kingdom
| | - M J Martin
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, United Kingdom
| | - S Orchard
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, United Kingdom.
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Garlid AO, Polson JS, Garlid KD, Hermjakob H, Ping P. Equipping Physiologists with an Informatics Tool Chest: Toward an Integerated Mitochondrial Phenome. Handb Exp Pharmacol 2017; 240:377-401. [PMID: 27995389 DOI: 10.1007/164_2016_93] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
Understanding the complex involvement of mitochondrial biology in disease development often requires the acquisition, analysis, and integration of large-scale molecular and phenotypic data. An increasing number of bioinformatics tools are currently employed to aid in mitochondrial investigations, most notably in predicting or corroborating the spatial and temporal dynamics of mitochondrial molecules, in retrieving structural data of mitochondrial components, and in aggregating as well as transforming mitochondrial centric biomedical knowledge. With the increasing prevalence of complex Big Data from omics experiments and clinical cohorts, informatics tools have become indispensable in our quest to understand mitochondrial physiology and pathology. Here we present an overview of the various informatics resources that are helping researchers explore this vital organelle and gain insights into its form, function, and dynamics.
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Affiliation(s)
- Anders Olav Garlid
- The NIH BD2K Center of Excellence in Biomedical Computing at UCLA, Department of Physiology, University of California, Los Angeles, CA, 90095, USA.
| | - Jennifer S Polson
- The NIH BD2K Center of Excellence in Biomedical Computing at UCLA, Department of Physiology, University of California, Los Angeles, CA, 90095, USA.
| | - Keith D Garlid
- The NIH BD2K Center of Excellence in Biomedical Computing at UCLA, Department of Physiology, University of California, Los Angeles, CA, 90095, USA
| | - Henning Hermjakob
- The NIH BD2K Center of Excellence in Biomedical Computing at UCLA, Department of Physiology, University of California, Los Angeles, CA, 90095, USA
- Molecular Systems Cluster, European Molecular Biology Laboratory-European Bioinformatics Institute (EMBL-EBI), Cambridge, UK
| | - Peipei Ping
- The NIH BD2K Center of Excellence in Biomedical Computing at UCLA, Departments of Physiology, Medicine, and Bioinformatics, University of California, Los Angeles, CA, 90095, USA
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