Physiol. Genomics AJP: Endocrinology and Metabolism
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Physiol. Genomics 18: 51-62, 2004. First published April 6, 2004; doi:10.1152/physiolgenomics.00155.2003
1094-8341/04 $5.00
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Received 22 September 2003; accepted in final form 2 April 2004.
Physiological Genomics 18:51-62 (2004)
1094-8341/04 $5.00 © 2004 American Physiological Society

Evaluation of the host transcriptional response to human cytomegalovirus infection

Jean F. Challacombe1, Andreas Rechtsteiner2, Raphael Gottardo1, Luis M. Rocha2, Edward P. Browne3, Thomas Shenk3, Michael R. Altherr1 and Thomas S. Brettin1

1 Bioscience Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545
2 Computing and Computational Sciences Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545
3 Department of Molecular Biology, Princeton University, Princeton, New Jersey 08544-1014

Gene expression data from human cytomegalovirus (HCMV)-infected cells were analyzed using DNA-Chip Analyzer (dChip) followed by singular value decomposition (SVD) and compared with a previous analysis of the same data that employed GeneChip software and a fold change filtering approach. dChip and SVD analysis revealed two clusters of coexpressed human genes responding differently to HCMV infection: one containing some genes identified previously, and another that was largely unique to this analysis. Annotating these genes, we identified several functional categories important to host cell responses to HCMV infection. These categories included genes involved in transcriptional regulation, oncogenesis, and cell cycle regulation, which were more prevalent in cluster 1, and genes involved in immune system regulation, signal transduction, and cell adhesion, which were more prevalent in cluster 2. Within these categories, we found genes involved in the host response to HCMV infection (mainly in cluster 1), as well as genes targeted by HCMV’s immune evasion strategies (mainly in cluster 2). As the second group of genes identified by the dChip and SVD approach was statistically and biologically significant, our results point out the advantages of using different methods to analyze gene expression data.

gene expression analysis; herpesvirus; pathogenesis




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