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Sui J, Xiao H, Mbaekwe U, Ting NC, Murday K, Hu Q, Gregory AD, Kapellos TS, Yildirim AÖ, Königshoff M, Zhang Y, Sciurba F, Das J, Kliment CR. Interpretable machine learning uncovers epithelial transcriptional rewiring and a role for Gelsolin in COPD. JCI Insight 2024; 9:e180239. [PMID: 39352744 PMCID: PMC11601586 DOI: 10.1172/jci.insight.180239] [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: 02/12/2024] [Accepted: 09/23/2024] [Indexed: 11/09/2024] Open
Abstract
Transcriptomic analyses have advanced the understanding of complex disease pathophysiology including chronic obstructive pulmonary disease (COPD). However, identifying relevant biologic causative factors has been limited by the integration of high dimensionality data. COPD is characterized by lung destruction and inflammation, with smoke exposure being a major risk factor. To define previously unknown biological mechanisms in COPD, we utilized unsupervised and supervised interpretable machine learning analyses of single-cell RNA-Seq data from the mouse smoke-exposure model to identify significant latent factors (context-specific coexpression modules) impacting pathophysiology. The machine learning transcriptomic signatures coupled to protein networks uncovered a reduction in network complexity and new biological alterations in actin-associated gelsolin (GSN), which was transcriptionally linked to disease state. GSN was altered in airway epithelial cells in the mouse model and in human COPD. GSN was increased in plasma from patients with COPD, and smoke exposure resulted in enhanced GSN release from airway cells from patients with COPD. This method provides insights into rewiring of transcriptional networks that are associated with COPD pathogenesis and provides a translational analytical platform for other diseases.
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Affiliation(s)
- Justin Sui
- Division of Pulmonary, Allergy and Critical Care Medicine
- Department of Cellular and Molecular Pathology, and
| | - Hanxi Xiao
- Center for Systems Immunology, Departments of Immunology and Computational & Systems Biology, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA
| | - Ugonna Mbaekwe
- Division of Pulmonary, Allergy and Critical Care Medicine
- Department of Cellular and Molecular Pathology, and
| | - Nai-Chun Ting
- Division of Pulmonary, Allergy and Critical Care Medicine
| | - Kaley Murday
- Division of Pulmonary, Allergy and Critical Care Medicine
| | - Qianjiang Hu
- Division of Pulmonary, Allergy and Critical Care Medicine
| | | | - Theodore S. Kapellos
- Institute of Lung Health and Immunity, Comprehensive Pneumology Center (CPC), Helmholtz Zentrum München, Member of the German Center for Lung Research (DZL), Munich, Germany
- Institute of Experimental Pneumology, University Hospital, Ludwig Maximilians University (LMU) of Munich, Munich, Germany
| | - Ali Öender Yildirim
- Institute of Lung Health and Immunity, Comprehensive Pneumology Center (CPC), Helmholtz Zentrum München, Member of the German Center for Lung Research (DZL), Munich, Germany
- Institute of Experimental Pneumology, University Hospital, Ludwig Maximilians University (LMU) of Munich, Munich, Germany
| | - Melanie Königshoff
- Division of Pulmonary, Allergy and Critical Care Medicine
- Geriatric Research Education and Clinical Center (GRECC) at the VA Pittsburgh Healthcare System, Pittsburgh, Pennsylvania, USA
| | - Yingze Zhang
- Division of Pulmonary, Allergy and Critical Care Medicine
| | - Frank Sciurba
- Division of Pulmonary, Allergy and Critical Care Medicine
| | - Jishnu Das
- Center for Systems Immunology, Departments of Immunology and Computational & Systems Biology, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA
| | - Corrine R. Kliment
- Division of Pulmonary, Allergy and Critical Care Medicine
- Department of Cellular and Molecular Pathology, and
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Rahimikollu J, Xiao H, Rosengart A, Rosen ABI, Tabib T, Zdinak PM, He K, Bing X, Bunea F, Wegkamp M, Poholek AC, Joglekar AV, Lafyatis RA, Das J. SLIDE: Significant Latent Factor Interaction Discovery and Exploration across biological domains. Nat Methods 2024; 21:835-845. [PMID: 38374265 PMCID: PMC11588359 DOI: 10.1038/s41592-024-02175-z] [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: 11/23/2022] [Accepted: 01/09/2024] [Indexed: 02/21/2024]
Abstract
Modern multiomic technologies can generate deep multiscale profiles. However, differences in data modalities, multicollinearity of the data, and large numbers of irrelevant features make analyses and integration of high-dimensional omic datasets challenging. Here we present Significant Latent Factor Interaction Discovery and Exploration (SLIDE), a first-in-class interpretable machine learning technique for identifying significant interacting latent factors underlying outcomes of interest from high-dimensional omic datasets. SLIDE makes no assumptions regarding data-generating mechanisms, comes with theoretical guarantees regarding identifiability of the latent factors/corresponding inference, and has rigorous false discovery rate control. Using SLIDE on single-cell and spatial omic datasets, we uncovered significant interacting latent factors underlying a range of molecular, cellular and organismal phenotypes. SLIDE outperforms/performs at least as well as a wide range of state-of-the-art approaches, including other latent factor approaches. More importantly, it provides biological inference beyond prediction that other methods do not afford. Thus, SLIDE is a versatile engine for biological discovery from modern multiomic datasets.
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Affiliation(s)
- Javad Rahimikollu
- Center for Systems Immunology, Departments of Immunology and Computational & Systems Biology, University of Pittsburgh, Pittsburgh, PA, USA
- Joint CMU-Pitt PhD Program in Computational Biology, Pittsburgh, PA, USA
| | - Hanxi Xiao
- Center for Systems Immunology, Departments of Immunology and Computational & Systems Biology, University of Pittsburgh, Pittsburgh, PA, USA
- Joint CMU-Pitt PhD Program in Computational Biology, Pittsburgh, PA, USA
| | - AnnaElaine Rosengart
- Center for Systems Immunology, Departments of Immunology and Computational & Systems Biology, University of Pittsburgh, Pittsburgh, PA, USA
| | - Aaron B I Rosen
- Center for Systems Immunology, Departments of Immunology and Computational & Systems Biology, University of Pittsburgh, Pittsburgh, PA, USA
- Joint CMU-Pitt PhD Program in Computational Biology, Pittsburgh, PA, USA
| | - Tracy Tabib
- Division of Rheumatology and Clinical Immunology, Department of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | - Paul M Zdinak
- Center for Systems Immunology, Departments of Immunology and Computational & Systems Biology, University of Pittsburgh, Pittsburgh, PA, USA
| | - Kun He
- Department of Pediatrics, University of Pittsburgh, Pittsburgh, PA, USA
| | - Xin Bing
- Department of Statistical Sciences, University of Toronto, Toronto, Ontario, Canada
| | - Florentina Bunea
- Department of Statistics and Data Science, Cornell University, Ithaca, NY, USA
| | - Marten Wegkamp
- Department of Statistics and Data Science, Cornell University, Ithaca, NY, USA
- Department of Mathematics, Cornell University, Ithaca, NY, USA
| | - Amanda C Poholek
- Department of Pediatrics, University of Pittsburgh, Pittsburgh, PA, USA.
| | - Alok V Joglekar
- Center for Systems Immunology, Departments of Immunology and Computational & Systems Biology, University of Pittsburgh, Pittsburgh, PA, USA.
| | - Robert A Lafyatis
- Division of Rheumatology and Clinical Immunology, Department of Medicine, University of Pittsburgh, Pittsburgh, PA, USA.
| | - Jishnu Das
- Center for Systems Immunology, Departments of Immunology and Computational & Systems Biology, University of Pittsburgh, Pittsburgh, PA, USA.
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