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ORIGINAL RESEARCH ARTICLE

Big data and systems biology for biomarker discovery in chronic hypersensitivity pneumonitis and drug repurposing using deep learning-based DTI models

Sung-Yu Lin1 ,  Chung-Kuan Liang1 ,  Bor-Sen Chen1*
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1 Department of Electrical Engineering, College of Electrical Engineering and Computer Science, National Tsing Hua University, Hsinchu, Taiwan
MI, 4620 https://doi.org/10.36922/mi.4620
Submitted: 21 August 2024 | Revised: 6 January 2025 | Accepted: 20 January 2025 | Published: 28 March 2025
© 2025 by the Author(s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution 4.0 International License ( https://creativecommons.org/licenses/by/4.0/ )
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Abstract

Chronic hypersensitivity pneumonitis (CHP) is commonly associated with repeated inhalation of organic dust-containing microbial agents. The identification and mitigation of exposure to these microbes are key steps in managing and preventing CHP. Therefore, to elucidate the pathogenetic mechanisms underlying fibrosis and identify biomarkers for targeted therapeutics, a systems biology approach integrating large-scale microarray data mining and whole-genome RNA-sequencing data is crucial. In this study, systems biology methodologies were employed to construct genome-wide genetic and epigenetic networks for fibrotic lung cells from CHP patients and healthy controls. Protein-protein interaction networks and gene/microRNA/long non-coding RNA regulatory networks were established using RNA sequencing data, followed by refinement through a system identification framework. By applying principal network projection and the Kyoto Encyclopedia of Genes and Genomes pathway annotation, core signaling pathways and their downstream cellular dysfunctions were identified, revealing key signaling pathways, such as the mitogen-activated protein kinase, phosphoinositide 3-kinase, chemokine (C-X-C motif) ligand (CXCL)/CXC chemokine receptors family, and chemokine (C-C motif) ligand (CCL) family signaling pathways. In addition, significant biomarkers of pathogenetic mechanisms of CHP, such as AKT1, CCL20, cell division cycle 23, CXCL1, nuclear factor-kappa B, and tumor necrosis factor, were identified as potential drug targets of CHP. Significant biomarkers associated with cell inhibitors, cellular dysfunction, and cellular disorders were identified as drug targets in the core disease-causing signaling pathways. Finally, using a deep neural network-based drug-target interaction model and drug design specification, three molecular drugs, azathioprine, masitinib, and primaquine, were screened and proposed as candidates for a multiple-molecular drug for the treatment of CHP.

Keywords
Chronic hypersensitivity pneumonitis
Systems biology
Genome-wide genetic and epigenetic networks
Deep neural network-based drug-target interaction model
Funding
None.
Conflict of interest
The authors declare that they do not have competing interests.
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