This article is part of the supplement: Proceedings of the Fourth Annual MCBIOS Conference. Computational Frontiers in Biomedicine
Systems biology approach for mapping the response of human urothelial cells to infection by Enterococcus faecalis
1 Department of Urology, College of Medicine, Oklahoma University Health Sciences Center, PO Box 26901, Oklahoma City, OK 73190, USA
2 Department of Physiology, College of Medicine, Oklahoma University Health Sciences Center, PO Box 26901, Oklahoma City, OK 73190, USA
3 Department of Biochemistry and Molecular Biology, College of Medicine, Oklahoma University Health Sciences Center, PO Box 26901, Oklahoma City, OK 73190, USA
4 Department of Pharmaceutical Sciences, College of Pharmacy, Oklahoma University Health Sciences Center, PO Box 26901, Oklahoma City, OK 73190, USA
5 Department of Environmental Health Sciences, College of Public Health, Oklahoma University Health Sciences Center, PO Box 26901, Oklahoma City, OK 73190, USA
6 Microarray Core Facility, Oklahoma Medical Research Foundation, Oklahoma City, OK 73104, USA
BMC Bioinformatics 2007, 8(Suppl 7):S2 doi:10.1186/1471-2105-8-S7-S2Published: 1 November 2007
To better understand the response of urinary epithelial (urothelial) cells to Enterococcus faecalis, a uropathogen that exhibits resistance to multiple antibiotics, a genome-wide scan of gene expression was obtained as a time series from urothelial cells growing as a layered 3-dimensional culture similar to normal urothelium. We herein describe a novel means of analysis that is based on deconvolution of gene variability into technical and biological components.
Analysis of the expression of 21,521 genes from 30 minutes to 10 hours post infection, showed 9553 genes were expressed 3 standard deviations (SD) above the system zero-point noise in at least 1 time point. The asymmetric distribution of relative variances of the expressed genes was deconvoluted into technical variation (with a 6.5% relative SD) and biological variation components (>3 SD above the mode technical variability). These 1409 hypervariable (HV) genes encapsulated the effect of infection on gene expression. Pathway analysis of the HV genes revealed an orchestrated response to infection in which early events included initiation of immune response, cytoskeletal rearrangement and cell signaling followed at the end by apoptosis and shutting down cell metabolism. The number of poorly annotated genes in the earliest time points suggests heretofore unknown processes likely also are involved.
Enterococcus infection produced an orchestrated response by the host cells involving several pathways and transcription factors that potentially drive these pathways. The early time points potentially identify novel targets for enhancing the host response. These approaches combine rigorous statistical principles with a biological context and are readily applied by biologists.