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

Overstretching of lung parenchyma may lead to injury, especially during mechanical ventilation. To date, there are no specific biomarkers of lung stretch, but transcriptomic signatures have not been explored. Our objective was to identify stretch-specific signatures using micro-RNA and gene expression.

Data on micro-RNA and RNA expression in response to stretch in experimental models were systematically pooled. Signatures were identified as those micro-RNAs or genes with differential expression in samples from stretched cells or tissues, and optimized using a greedy algorithm. Expression data was used to calculate transcriptomic scores. The accuracy of these scores was validated in animal models of lung injury, ex vivo mechanically ventilated human lungs, and bronchoalveolar lavage fluid (BALF, n = 7) and in serum samples (n = 31) of mechanically ventilated patients.

Six micro-RNAs (mir-383, mir-877, mir-130b; mir-146b, mir-181b, and mir-26b) were differentially expressed in stretched cell cultures (n = 24). Amongst the genes regulated by these micro-RNAs, a 451-gene signature was identified in vitro (n = 106) and refined using data from animal models (n = 143) to obtain a 6-gene signature (Lims1, Atp6v1c1, Dedd, Bclb7, Ppp1r2 and F3). Transcriptomic scores were significantly higher in samples submitted to stretch or injurious mechanical ventilation. The microRNA and RNA signatures were validated in human tissue, BALF and serum, with areas under the ROC curve between 0.7 and 1 to identify lung overdistention.

Lung cell stretch induces the expression of specific micro-RNA and genes. The potential of these signatures to identify lung stretch in a clinical setting must be explored.