MCAM/MUC18 REGULATES MELANOMA PROGRESSION BY MODULATING THE EXPRESSION OF ID-1

The acquisition of the metastatic melanoma phenotype is associated with increased expression of the melanoma cell adhesion molecule MCAM/MUC18 (CD146). However, the mechanism by which MUC18 contributes to melanoma metastasis remains unclear. Herein, we stably silenced MUC18 expression utilizing lentivirus-incorporated small hairpin RNA, in two metastatic melanoma cell lines, A375SM and C8161, and conducted cDNA microarray analysis. We identified and validated that the transcriptional regulator, Inhibitor of DNA Binding-1 (Id-1), previously shown to function as an oncogene in several malignancies, was downregulated by 5.6-fold following MUC18 silencing. Additionally, we found that MUC18 regulated Id-1 expression at the transcriptional level via ATF-3. Interestingly, ATF-3 was upregulated by 6.9 fold in our cDNA microarray analysis following MUC18 silencing. ChIP analysis showed increased binding of ATF-3 to the Id-1 promoter after MUC18 silencing, while mutation of the ATF-3 binding site on the Id-1 promoter increased Id-1 promoter activity in MUC18-silenced cells. These Data suggest that MUC18 silencing promotes inhibition of Id-1 expression by increasing ATF-3 expression and binding to the Id-1 promoter. Rescue of MUC18 reverted the expression of Id-1 and ATF-3, thus validating that they are not off-target effects of MUC18. To further assess the role of Id-1 in melanoma invasion and metastasis, we overexpressed Id-1 in MUC18-silenced cells. Overexpression of Id-1 in MUC18-silenced cells resulted in increased cell invasion, as well as increased expression and activity of MMP-2. Our data further reveal that Id-1 regulates MMP-2 at the transcriptional level through Sp1 and Ets-1. This is the first report to demonstrate that MUC18 does not act exclusively in cell adherence, but is also involved in cell signaling that regulates the expression of genes, such as Id-1 and ATF-3, thus contributing to the metastatic melanoma phenotype.

Integrating sequence information in microarray data analysis by free energy modeling

Microarray technology is a high-throughput method for genotyping and gene expression profiling. Limited sensitivity and specificity are one of the essential problems for this technology. Most of existing methods of microarray data analysis have an apparent limitation for they merely deal with the numerical part of microarray data and have made little use of gene sequence information. Because it's the gene sequences that precisely define the physical objects being measured by a microarray, it is natural to make the gene sequences an essential part of the data analysis. This dissertation focused on the development of free energy models to integrate sequence information in microarray data analysis. The models were used to characterize the mechanism of hybridization on microarrays and enhance sensitivity and specificity of microarray measurements. ^ Cross-hybridization is a major obstacle factor for the sensitivity and specificity of microarray measurements. In this dissertation, we evaluated the scope of cross-hybridization problem on short-oligo microarrays. The results showed that cross hybridization on arrays is mostly caused by oligo fragments with a run of 10 to 16 nucleotides complementary to the probes. Furthermore, a free-energy based model was proposed to quantify the amount of cross-hybridization signal on each probe. This model treats cross-hybridization as an integral effect of the interactions between a probe and various off-target oligo fragments. Using public spike-in datasets, the model showed high accuracy in predicting the cross-hybridization signals on those probes whose intended targets are absent in the sample. ^ Several prospective models were proposed to improve Positional Dependent Nearest-Neighbor (PDNN) model for better quantification of gene expression and cross-hybridization. ^ The problem addressed in this dissertation is fundamental to the microarray technology. We expect that this study will help us to understand the detailed mechanism that determines sensitivity and specificity on the microarrays. Consequently, this research will have a wide impact on how microarrays are designed and how the data are interpreted. ^