Vasoinhibin belongs to a family of angiogenesis inhibitors generated when the fourth α-helix (H4) of the hormone prolactin (PRL) is removed by specific proteolytic cleavage. The antiangiogenic properties are absent in uncleaved PRL, indicating that conformational changes create a new bioactive domain. However, the solution structure of vasoinhibin and the location of its bioactive domain are unknown. Molecular dynamic simulation (MD) showed that the loss of H4 exposes the hydrophobic nucleus of PRL and leads to the compression of the molecule into a three-helix bundle that buries the hydrophobic nucleus again. Compression occurs by the movement of loop 1 (L1) and its interaction with α-helix 1 (H1) generating a new L1 conformation with electrostatic and hydrophobic surfaces distinct from those of PRL, that may correspond to a bioactive domain. Consistent with this model, a recombinant protein containing the first 79 amino acids comprising H1 and L1 of human PRL inhibited the proliferation and migration of endothelial cells and upregulated the vasoinhibin target genes, IL1A and ICAM1. This bioactivity was comparable to that of a conventional vasoinhibin having the 123 residues encompassing H1, L1, Η2, L2, and Η3 of human PRL. These findings extend the vasoinhibin family to smaller proteins and provide important structural information, which will aid in antiangiogenic drug development.
Background: Bronchopulmonary dysplasia (BPD) is the most common lung disease of extreme prematurity, yet mechanisms that associate with or identify neonates with increased susceptibility for BPD are largely unknown. Combining artificial intelligence with gene expression data is a novel approach that may assist in better understanding mechanisms underpinning BPD. Objective: Develop an early peripheral blood transcriptomic signature that can predict preterm neonates at risk for developing BPD. Methods: Secondary analysis of whole blood microarray data from 97 very low birth weight neonates day of life 5 was performed. BPD was defined as positive pressure ventilation or oxygen requirement at 28 days of age. Participants were randomly assigned into a training (70%) and testing cohort (30%). Four gene-centric machine learning models were built, and their discriminatory abilities were compared to gestational age or birthweight. Results: Neonates with BPD (n=62) exhibited a lower median gestational age (26.0 weeks vs. 30.0 weeks p<0.01) and birthweight (800 grams vs 1,280 grams, p<0.01) compared to non-BPD neonates. From an initial pool (33,252 genes/patient), 4,523 genes exhibited a false discovery rate (FDR) <1%. The area under the receiver operating characteristic curve (AUC) for predicting BPD utilizing gestational age or birthweight were 87.8% and 87.2%, respectively. The machine learning models revealed AUCs ranging between 85.8% and 96.1%. Pathways integral to T cell development and differentiation were most associated with BPD. Conclusions: A derived 5-gene whole blood signature can accurately predict BPD in the first week of life.
Bronchopulmonary dysplasia (BPD) is the most common chronic lung disease of extreme prematurity. Despite more than 50 years of research, current treatments are ineffective, and clinicians are largely unable to accurately predict which neonates the condition will develop in. A deeper understanding of the molecular mechanisms underlying the characteristic arrest in lung development are warranted. Integrating high-fidelity technology from precision medicine approaches may fill this gap and provide the tools necessary to identify biomarkers and targetable pathways. In this review, we describe insights garnered from current studies using omics for BPD prediction and stratification. We conclude by describing novel programs that will integrate multi-omics in efforts to better understand and treat the pathogenesis of BPD. [ Pediatr Ann . 2022;51(10):e396–e404.]
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