Machine Learning Integrated Extracellular Vesicle Proteome Analysis For Early Markers Of Bronchopulmonary Dysplasia

Bronchopulmonary dysplasia (BPD) is a serious and often lethal complication of pre-term birth that typically manifests about one month after pre-term delivery. The lungs of premature infants are underdeveloped and vulnerable to mechanical damage, inflammation, and oxidative stress. Collectively, these stressors impair the normal alveolarization of the premature lungs after birth. The multifactorial pathophysiology of BPD necessitates the identification of the molecular factors that mediate cell-to-cell communication that discriminate normal lung development from progression to BPD. Extracellular vesicles (EV) mediate intercellular crosstalk by transporting functional molecules, including proteins and nucleic acids, to recipient cells through biological fluids. This feasibility study determined the utility of profiling the discarded plasma-derived EV proteome to predict BPD susceptibility risk in extremely preterm infants. Discarded plasma was obtained from routine laboratory draws from infants born at less than 32 weeks of gestation and weighing less than 1500 grams. Plasma EVs were captured using a magnetic bead-based immunoaffinity method. Subsequently, mass spectrometry and differential protein content analysis workflow identified a novel nine-EV-protein signature (APOD, HNRNPM, HMGN2, ITLN1, PRTN3, RBM4, RBMX, TAF15, TCERG1) that distinguished preterm infants who developed BPD from those who did not. Application of machine learning statistical modeling using Promor tool trained on the nine-protein signature template identified a high specificity and selectivity prognostic threshold for the development of BPD. HNRNPM emerged as the most consistent biological response component predicting development of BPD in our patient cohort. Our study suggests that circulating EVs derived from discarded plasma are a suitable “liquid biopsy” to help stratify the vulnerability risk for BPD in preterm infants.