Prediction of lung overdistension during mechanical ventilation using micro-RNA and gene expression

Area of Science:

• Pulmonary Medicine
• Molecular Biology
• Biomarker Discovery

Background:

• Lung overstretching during mechanical ventilation can cause injury.
• No specific biomarkers for lung stretch currently exist.
• Transcriptomic signatures have not been explored for lung stretch detection.

Purpose of the Study:

• To identify microRNA and gene expression signatures specific to lung stretch.
• To develop transcriptomic scores for detecting lung overstretching.

Main Methods:

• Systematic pooling of microRNA and RNA expression data from experimental models of stretch.
• Identification and optimization of differential expression signatures using a greedy algorithm.
• Validation of transcriptomic scores in animal models, ex vivo human lungs, bronchoalveolar lavage fluid (BALF), and patient serum.

Main Results:

• Six microRNAs (mir-383, mir-877, mir-130b, mir-146b, mir-181b, mir-26b) were differentially expressed in stretched cells.
• A 6-gene signature (Lims1, Atp6v1c1, Dedd, Bclb7, Ppp1r2, F3) was identified and refined.
• Transcriptomic scores accurately identified lung stretch and overventilation in various models and human samples (AUC 0.7-1).

Conclusions:

• Lung cell stretch demonstrably alters microRNA and gene expression.
• The identified transcriptomic signatures hold potential for clinical application in detecting lung stretch.