Microarray Technology 微陣列晶片技術

The p53 tumor suppressor and its related protein, p73, share a homologous DNA binding domain, and mouse genetics studies have suggested that they have overlapping as well as distinct biological functions. Both p53 and p73 are activated by genotoxic stress to regulate an array of cellular responses. Previous studies have suggested that p53 and p73 independently activate the cellular apoptotic program in response to cytotoxic drugs. The goal of this study was to compare the promoter-binding activity of p53 and p73 at steady state and after genotoxic stress induced by hydroxyurea.We employed chromatin immunoprecipitation, the NimbleGen promoter arrays and a model-based algorithm for promoter arrays to identify promoter sequences enriched in anti-p53 or anti-p73 immunoprecipitates, either before or after treatment with hydroxyurea, which increased the expression of both p53 and p73 in the human colon cancer cell line HCT116-3(6). We calculated a model-based algorithm for promoter array score for each promoter and found a significant correlation between the promoter occupancy profiles of p53 and p73. We also found that after hydroxyurea treatment, the p53-bound promoters were still bound by p73, but p73 became associated with additional promoters that that did not bind p53. In particular, we showed that hydroxyurea induces the binding of p73 but not p53 to the promoter of MLH3, which encodes a mismatch repair protein, and causes an up-regulation of the MLH3 mRNA.These results suggest that hydroxyurea exerts differential effects on the promoter-binding functions of p53 and p73 and illustrate the power of model-based algorithm for promoter array in the analyses of promoter occupancy profiles of highly homologous transcription factors.

Recently, microarray-based comparative genomic hybridization (array-CGH) has emerged as a very efficient technology with higher resolution for the genome-wide identification of copy number alterations (CNA). Although CNAs are thought to affect gene expression, there is no platform currently available for the integrated CNA-expression analysis. To achieve high-resolution copy number analysis integrated with expression profiles, we established human 30k oligoarray-based genome-wide copy number analysis system and explored the applicability of this system for integrated genome and transcriptome analysis using MDA-MB-231 cell line. We compared the CNAs detected by the oligoarray with those detected by the 3k BAC array for validation. The oligoarray identified the single copy difference more accurately and sensitively than the BAC array. Seventeen CNAs detected by both platforms in MDA-MB-231 such as gains of 5p15.33-13.1, 8q11.22-8q21.13, 17p11.2, and losses of 1p32.3, 8p23.3-8p11.21, and 9p21 were consistently identified in previous studies on breast cancer. There were 122 other small CNAs (mean size 1.79 mb) that were detected by oligoarray only, not by BAC-array. We performed genomic qPCR targeting 7 CNA regions, detected by oligoarray only, and one non-CNA region to validate the oligoarray CNA detection. All qPCR results were consistent with the oligoarray-CGH results. When we explored the possibility of combined interpretation of both DNA copy number and RNA expression profiles, mean DNA copy number and RNA expression levels showed a significant correlation. In conclusion, this 30k oligoarray-CGH system can be a reasonable choice for analyzing whole genome CNAs and RNA expression profiles at a lower cost.

Recently, microarray-based comparative genomic hybridization (array-CGH) has emerged as a very efficient technology with higher resolution for the genome-wide identification of copy number alterations (CNA). Although CNAs are thought to affect gene expression, there is no platform currently available for the integrated CNA-expression analysis. To achieve high-resolution copy number analysis integrated with expression profiles, we established human 30k oligoarray-based genome-wide copy number analysis system and explored the applicability of this system for integrated genome and transcriptome analysis using MDA-MB-231 cell line. We compared the CNAs detected by the oligoarray with those detected by the 3k BAC array for validation. The oligoarray identified the single copy difference more accurately and sensitively than the BAC array. Seventeen CNAs detected by both platforms in MDA-MB-231 such as gains of 5p15.33-13.1, 8q11.22-8q21.13, 17p11.2, and losses of 1p32.3, 8p23.3-8p11.21, and 9p21 were consistently identified in previous studies on breast cancer. There were 122 other small CNAs (mean size 1.79 mb) that were detected by oligoarray only, not by BAC-array. We performed genomic qPCR targeting 7 CNA regions, detected by oligoarray only, and one non-CNA region to validate the oligoarray CNA detection. All qPCR results were consistent with the oligoarray-CGH results. When we explored the possibility of combined interpretation of both DNA copy number and RNA expression profiles, mean DNA copy number and RNA expression levels showed a significant correlation. In conclusion, this 30k oligoarray-CGH system can be a reasonable choice for analyzing whole genome CNAs and RNA expression profiles at a lower cost.