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. ^

Tp53-dependent repression of cell cycle target genes: Global and mechanistic analysis

Most studies of p53 function have focused on genes transactivated by p53. It is less widely appreciated that p53 can repress target genes to affect a particular cellular response. There is evidence that repression is important for p53-induced apoptosis and cell cycle arrest. It is less clear if repression is important for other p53 functions. A comprehensive knowledge of the genes repressed by p53 and the cellular processes they affect is currently lacking. We used an expression profiling strategy to identify p53-responsive genes following adenoviral p53 gene transfer (Ad-p53) in PC3 prostate cancer cells. A total of 111 genes represented on the Affymetrix U133A microarray were repressed more than two fold (p ≤ 0.05) by p53. An objective assessment of array data quality was carried out using RT-PCR of 20 randomly selected genes. We estimate a confirmation rate of >95.5% for the complete data set. Functional over-representation analysis was used to identify cellular processes potentially affected by p53-mediated repression. Cell cycle regulatory genes exhibited significant enrichment (p ≤ 5E-28) within the repressed targets. Several of these genes are repressed in a p53-dependent manner following DNA damage, but preceding cell cycle arrest. These findings identify novel p53-repressed targets and indicate that p53-induced cell cycle arrest is a function of not only the transactivation of cell cycle inhibitors (e.g., p21), but also the repression of targets that act at each phase of the cell cycle. The mechanism of repression of this set of p53 targets was investigated. Most of the repressed genes identified here do not harbor consensus p53 DNA binding sites but do contain binding sites for E2F transcription factors. We demonstrate a role for E2F/RB repressor complexes in our system. Importantly, p53 is found at the promoter of CDC25A. CDC25A protein is rapidly degraded in response to DNA damage. Our group has demonstrated for the first time that CDC25A is also repressed at the transcript level by p53. This work has important implications for understanding the DNA damage cell cycle checkpoint response and the link between E2F/RB complexes and p53 in the repression of target genes. ^

Nonlinear dimension reduction in pathway-based genome-wide association analysis

Pathway based genome wide association study evolves from pathway analysis for microarray gene expression and is under rapid development as a complementary for single-SNP based genome wide association study. However, it faces new challenges, such as the summarization of SNP statistics to pathway statistics. The current study applies the ridge regularized Kernel Sliced Inverse Regression (KSIR) to achieve dimension reduction and compared this method to the other two widely used methods, the minimal-p-value (minP) approach of assigning the best test statistics of all SNPs in each pathway as the statistics of the pathway and the principal component analysis (PCA) method of utilizing PCA to calculate the principal components of each pathway. Comparison of the three methods using simulated datasets consisting of 500 cases, 500 controls and100 SNPs demonstrated that KSIR method outperformed the other two methods in terms of causal pathway ranking and the statistical power. PCA method showed similar performance as the minP method. KSIR method also showed a better performance over the other two methods in analyzing a real dataset, the WTCCC Ulcerative Colitis dataset consisting of 1762 cases, 3773 controls as the discovery cohort and 591 cases, 1639 controls as the replication cohort. Several immune and non-immune pathways relevant to ulcerative colitis were identified by these methods. Results from the current study provided a reference for further methodology development and identified novel pathways that may be of importance to the development of ulcerative colitis.^