Integrative analysis of scRNA-seq and bulk RNA-seq to identify lactylation-related gene signatures in lung ischemia-reperfusion injury after lung transplantation.

Background: Protein lactylation has been implicated in stress-responsive cellular mechanisms, yet its role in lung transplantation-associated ischemia-reperfusion injury (IRI) remains undefined.

Methods: Transcriptomic profiles from GSE145989 were analyzed through differential expression analysis (limma) and weighted gene co-expression network analysis (WGCNA). Integrating the identified genes with lactylation-related signatures uncovered key lactylation-related genes (LRGs) as potential targets. Consensus clustering stratified post-reperfusion samples into molecular subtypes with distinct lactylation dynamics. Machine learning algorithms, including least absolute shrinkage and selection operator (LASSO) and random forest, were employed to refine diagnostic biomarkers, which were subsequently incorporated into a nomogram model. External validation was performed using GSE18995 dataset, while single-cell RNA sequencing (GSE220797) was used to map cellular distributions. Lactate levels, global protein lactylation levels, and candidate gene expression were experimentally validated in a murine lung IRI model through lactic acid assay kit, western blotting, immunohistochemistry, immunofluorescence and RT-qPCR.

Results: Six LRGs were identified through differential expression patterns, co-expression networks, and lactylation signatures. Consensus clustering revealed two distinct molecular subtypes with differential IRI progression patterns. Four machine learning-optimized biomarkers (SLC2A3, MYC, NLRP3, PIGA) demonstrated robust diagnostic performance. Their differential expression was confirmed in GSE18995. Single-cell data analysis revealed their predominant expression in various cell types. Murine experiments confirmed elevated lactate concentrations in bronchoalveolar lavage fluid and plasma, accompanied by enhanced global protein lactylation and consistent hub gene expression alterations.

Conclusions: This integrative transcriptomic analysis identifies four lactylation-associated regulators of pulmonary IRI, proposing novel therapeutic targets for improving graft survival in lung transplantation.