Systems biology, genomics and proteomics of pulmonary fibrosis

Description of projects available to graduate students:
Our main interest is to understand the regulatory networks that underlie the lung phenotype in chronic fibrotic lung diseases. The main disease that we study is Idiopathic pulmonary fibrosis, a chronic progressive lung disease with an unknown etiology or treatment. The hallmarks of the disease are progressive fibrosis, change in fibroblasts and epithelial cell phenotype and a relative lack of inflammatory response. Models that propose chronic inflammation or repeated epithelial injury as the initial events have been proposed. Using microarrays we have identified a role for disregulated activation of metalloproteases, distinct key regulatory events in IPF lungs and evidence for cellular plasticity in the lung. The research projects entail learning the basics of an integrated use of innovative genomics and proteomics experiments, traditional molecular biology follow-up studies, as well as computational biology tools.
Available projects include:
• Analysis of human and mouse fibrotic lungs using gene expression microarrays
• Analysis of shifting cellular phenotypes in fibrotic lungs using laser capture microdissection
• High throughput functional analysis of fibrosis relevant transcription factors using methods that allow detection of all the promoters that bind to a transcription factor in a certain genome (CHIP on Chip) , current studied transcription factors include NFKB (inflammatory pathways), SMAD (TGFB, fibrosis pathway) and FOXA2 (epithelial cell differentiation)
• Construction of fibrosis module networks

Techniques graduate student will learn:
RNA expression and sample preparation for Expression microarrays, Chromatin immunoprecipitation and location array analysis (CHIP on Chip), laser capture microdissection, Western blotting, Real time quantitative RT-PCR, traditional cell culture methods, DIGE 2d-gel analysis of protein expression, Bioinformatic and computational approaches to microarray data analysis.