PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Gene Expression Profiles in Asbestos-exposed Epithelial and Mesothelial Lung Cell Lines
Penny Nymark, Pamela M. Lindholm, Mikko V. Korpela, Leo Lahti, Salla Ruosaari, Samuel Kaski, Jaakko Hollmen, Sisko Anttila, Vuokko L. Kinnula and Sakari Knuutila
BMC Genomics Volume 8, Number 62, 2007.

Abstract

Background Asbestos has been shown to cause chromosomal damage and DNA aberrations. Exposure to asbestos causes many lung diseases e.g. asbestosis, malignant mesothelioma, and lung cancer, but the disease-related processes are still largely unknown. We exposed the human cell lines A549, Beas-2B and Met5A to crocidolite asbestos and determined time-dependent gene expression profiles by using Affymetrix arrays. The hybridization data was analyzed by using an algorithm specifically designed for clustering of short time series expression data. A canonical correlation analysis was applied to identify correlations between the cell lines, and a Gene Ontology analysis method for the identification of enriched, differentially expressed biological processes. Results We recognized a large number of previously known as well as new potential asbestos-associated genes and biological processes, and identified chromosomal regions enriched with genes potentially contributing to common responses to asbestos in these cell lines. These include genes such as the thioredoxin domain containing gene (TXNDC) and the potential tumor suppressor, BCL2/adenovirus E1B 19kD-interacting protein gene (BNIP3L), GO-terms such as "positive regulation of I-kappaB kinase/NF-kappaB cascade" and "positive regulation of transcription, DNA-dependent", and chromosomal regions such as 2p22, 9p13, and 14q21. We present the complete data sets as Additional files. Conclusion This study identifies several interesting targets for further investigation in relation to asbestos-associated diseases.

EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Learning/Statistics & Optimisation
ID Code:3422
Deposited By:Jaakko Hollmen
Deposited On:10 February 2008