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© 2024. This work is published under https://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.

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.

Details

Title
Interpretable machine learning uncovers epithelial transcriptional rewiring and a role for Gelsolin in COPD
Author
Sui, Justin; Xiao, Hanxi; Mbaekwe, Ugonna; Nai-Chun Ting; Murday, Kaley; Hu, Qianjiang; Gregory, Alyssa D; Kapellos, Theodore S; Yildirim, Ali Öender; Königshoff, Melanie; Zhang, Yingze; Sciurba, Frank; Das, Jishnu; Kliment, Corrine R
Section
Research Articles
Publication year
2024
Publication date
Nov 2024
Publisher
American Society for Clinical Investigation
e-ISSN
23793708
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
3255722550
Copyright
© 2024. This work is published under https://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.