Decoding Immune-Metabolic Crosstalk in ARDS: A Transcriptomic Exploration of Biomarkers, Cellular Dynamics, and Therapeutic Pathways

Ting Wu¹Yuan Lu¹Qin Wang²Wei Zhou²Ming Ding¹Jing Huang¹Jingyuan Xu³Shuzhen Wei^4*Min Wang^5*

• ¹Department of Respiratory and Critical Care Medicine, Zhongda Hospital, School of Medicine, Southeast University, Nanjing, Liaoning Province, China
• ²Department of Respiratory and Critical Care Medicine, Jinling Hospital, Nanjing University School of Medicine, Nanjing, China
• ³Jiangsu Provincial Key Laboratory of Critical Care Medicine, Department of Critical Care Medicine, Zhongda Hospital, Southeast University, Nanjing, Liaoning Province, China
• ⁴Shazhou community health service center，Jianye district, Nanjing, China
• ⁵Department of Respiratory and Critical Care Medicine, The First Affiliated Hospital, and College of Clinical Medicine of Henan University of Science and Technology, Luoyang, China

Abstract Background: Metabolic reprogramming plays a critical role in various diseases, with particular emphasis on immune cell metabolism. However, the involvement of immune cells and metabolic reprogramming-related genes in acute respiratory distress syndrome (ARDS) remains underexplored. This study aimed to investigate the molecular mechanisms underlying cell and metabolic reprogramming biomarkers in ARDS. Methods: Using transcriptomic data from whole blood samples, candidate genes were identified through differential expression analysis and weighted gene co-expression network analysis (WGCNA) in conjunction with MRRGs. Machine learning techniques, expression analysis, and receiver operating characteristic (ROC) analysis were employed to identify potential biomarkers. An artificial neural network model was developed and evaluated. Additionally, functional enrichment, regulatory network, and drug prediction analyses were performed. Single-cell analysis was conducted to examine the expression of biomarkers within specific cell populations. RT-qPCR was used for biomarker validation in human whole blood samples. The functional validation of candidate biomarkers was performed in lipopolysaccharide (LPS)-induced ARDS mouse models (peripheral blood neutrophils and lung tissues) and THP-1-derived macrophages. Results: Through machine learning algorithms, RPL14, SMARCD3, and TCN1 were identified as candidate biomarkers. ROC analysis demonstrated that the ANN model, incorporating these biomarkers, exhibited strong predictive power for ARDS onset. Enrichment analysis revealed that these genes were linked to various pathways, including the chemokine signaling pathway. The regulatory network analysis suggested that KLF9 may regulate both RPL14 and SMARCD3, with these genes playing a pivotal role in ARDS progression. Furthermore, selenium and Cyclosporine A(CsA) were identified as compounds targeting RPL14 and SMARCD3. Expression levels of the biomarkers varied across different stages of cell differentiation. RT-qPCR confirmed a significant upregulation of SMARCD3 and TCN1 in ARDS samples, aligning with dataset expression analysis results. Both in vitro and in vivo experiments demonstrated that modulation of SMARCD3 and TCN1 (but not RPL14) significantly affected mitochondrial function, oxidative stress, apoptosis, glucose metabolism and inflammatory cytokine expression. Conclusion: SMARCD3 and TCN1 were identified as key biomarkers associated with immune cell and metabolic reprogramming in ARDS, while RPL14 was identified as a candidate biomarker through computational approaches, offering valuable insights for understanding the pathogenesis of the disease.