Abstract:
:A greater understanding of the regulatory processes contributing to lung development could help ameliorate morbidity and mortality in premature infants and identify individuals at risk for congenital and/or chronic lung diseases. Genomics technologies have provided rich gene expression datasets for the developing lung that enable systems biology approaches for identifying large-scale molecular signatures within this complex phenomenon. Here, we applied unsupervised principal component analysis on two developing lung datasets and identified common dominant transcriptomic signatures. Of particular interest, we identify an overlying biological program we term "time-to-birth," which describes the distance in age from the day of birth. We identify groups of genes contributing to the time-to-birth molecular signature. Statistically overrepresented are genes involved in oxygen and gas transport activity, as expected for a transition to air breathing, as well as host defense function. In addition, we identify genes with expression patterns associated with the initiation of alveolar formation. Finally, we present validation of gene expression patterns across the two datasets, and independent validation of select genes by qPCR and immunohistochemistry. These data contribute to our understanding of genetic components contributing to large-scale biological processes and may be useful, particularly in animal models of abnormal lung development, to predict the state of organ development or preparation for birth.
journal_name
Am J Respir Cell Mol Biolauthors
Kho AT,Bhattacharya S,Mecham BH,Hong J,Kohane IS,Mariani TJdoi
10.1165/rcmb.2008-0048OCsubject
Has Abstractpub_date
2009-01-01 00:00:00pages
47-57issue
1eissn
1044-1549issn
1535-4989pii
2008-0048OCjournal_volume
40pub_type
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