Transcriptional Networks – Control of Lung Maturation

Abstract

AbstractThis chapter provides examples of the application of tools of bioinformatics and functional genomics to integration of large-scale gene expression data with array independent genomic information to reveal transcriptional regulatory networks. The focus is on networks that control lung maturation and surfactant homeostasis, which can serve as a prototype for study of other complex biological processes. Prenatal maturation of the respiratory system is fundamentally important for the transition to airbreathing at birth. Lung immaturity is a major cause of morbidity and mortality in newborn infants and underlies the pathogenesis of acute respiratory failure (respiratory distress syndrome) and chronic respiratory dysfunction (bronchopulmonary dysplasia), associated with preterm birth. The immaturity of type II alveolar epithelial cells and the lack of pulmonary surfactant lipids and proteins that are needed to reduce surface tension in the alveolar saccules cause atelectasis and respiratory insufficiency after preterm birth. Lung maturation includes diverse structural, cellular, and biochemical changes in lung architecture and function that are precisely coordinated by genetic and environmental factors that synchronize the length of gestation with the process of lung maturation. Analysis of lung specific gene deletion and mutation mouse models, using tools of functional genomics, permits the identification of new genes and pathways controlling surfactant lipid homeostasis and lung maturation. The signaling and transcriptional mechanisms that influence lung growth and maturation needed to support the abrupt adaptation to airbreathing at birth are of considerable interest, and will provide a rational basis for the design of new treatment strategies for neonatal pulmonary disease.KeywordsLung DevelopmentTranscriptional NetworkTranscriptional Regulatory NetworkLiterature MiningLung MaturationThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.