Computational identification and functional validation of regulatory motifs in cartilage-expressed genes.

Chondrocyte gene regulation is important for the generation and maintenance of cartilage tissues. Several regulatory factors have been identified that play a role in chondrogenesis, including the positive transacting factors of the SOX family such as SOX9, SOX5, and SOX6, as well as negative transacting factors such as C/EBP and delta EF1. However, a complete understanding of the intricate regulatory network that governs the tissue-specific expression of cartilage genes is not yet available. We have taken a computational approach to identify cis-regulatory, transcription factor (TF) binding motifs in a set of cartilage characteristic genes to better define the transcriptional regulatory networks that regulate chondrogenesis. Our computational methods have identified several TFs, whose binding profiles are available in the TRANSFAC database, as important to chondrogenesis. In addition, a cartilage-specific SOX-binding profile was constructed and used to identify both known, and novel, functional paired SOX-binding motifs in chondrocyte genes. Using DNA pattern-recognition algorithms, we have also identified cis-regulatory elements for unknown TFs. We have validated our computational predictions through mutational analyses in cell transfection experiments. One novel regulatory motif, N1, found at high frequency in the COL2A1 promoter, was found to bind to chondrocyte nuclear proteins. Mutational analyses suggest that this motif binds a repressive factor that regulates basal levels of the COL2A1 promoter.

Identification and characterization of virulence determinants in the Lyme disease spirochete Borrelia burgdorferi

Borrelia burgdorferi is the etiological agent of Lyme disease, the most common tick-borne disease in the United States. Although the most frequently reported symptom is arthritis, patients can also experience severe cardiac, neurologic, and dermatologic abnormalities. The identification of virulence determinants in infectious B. burgdorferi strains has been limited by their slow growth rate, poor transformability, and general lack of genetic tools. The present study demonstrates the use of transposon mutagenesis for the identification of infectivity-related factors in infectious B. burgdorferi, examines the potential role for chemotaxis in mammalian infection, and describes the development of a novel method for the analysis of recombination events at the Ids antigenic variation locus. A pool of Himar1 mutants was isolated using an infectious B. burgdorferi clone and the transposon vector pMarGent. Clones exhibiting reduced infectivity in mice possessed insertions in virulence determinants putatively involved in host survival and dissemination. These results demonstrated the feasibility of extensive transposon mutagenesis studies for the identification of additional infectivity-related factors. mcp-5 mutants were chosen for further study to determine the role of chemotaxis during infection. Animal studies indicated that mcp-5 mutants exhibited a reduced infectivity potential, and suggested a role for mcp-5 during the early stages of infection. An in vitro phenotype for an mcp-5 mutant was not detected. Genetic complementation of an mcp-5 mutant resulted in restoration of Mcp-5 expression in the complemented clone, as demonstrated by western blotting, but the organisms were not infectious in mice. We believe this result is a consequence of differences in expression between genes located on the linear chromosome and genes present on the circular plasmid used for trans-complementation. Overall, this work implicates mcp-5 as an important determinant of mammalian infectivity. Finally, the development of a computer-assisted method for the analysis of recombination events occurring at the B. burgdorferi vls antigenic variation locus has proven highly valuable for the detailed examination of vls gene conversion. The studies described here provide evidence for the importance of chemotaxis during infection in mice and demonstrate advances in both genetic and computational approaches for the further characterization of the Lyme disease spirochete. ^

RNA-SEQUENCING APPLICATIONS: GENE EXPRESSION QUANTIFICATION AND METHYLATOR PHENOTYPE IDENTIFICATION

My dissertation focuses on two aspects of RNA sequencing technology. The first is the methodology for modeling the overdispersion inherent in RNA-seq data for differential expression analysis. This aspect is addressed in three sections. The second aspect is the application of RNA-seq data to identify the CpG island methylator phenotype (CIMP) by integrating datasets of mRNA expression level and DNA methylation status. Section 1: The cost of DNA sequencing has reduced dramatically in the past decade. Consequently, genomic research increasingly depends on sequencing technology. However it remains elusive how the sequencing capacity influences the accuracy of mRNA expression measurement. We observe that accuracy improves along with the increasing sequencing depth. To model the overdispersion, we use the beta-binomial distribution with a new parameter indicating the dependency between overdispersion and sequencing depth. Our modified beta-binomial model performs better than the binomial or the pure beta-binomial model with a lower false discovery rate. Section 2: Although a number of methods have been proposed in order to accurately analyze differential RNA expression on the gene level, modeling on the base pair level is required. Here, we find that the overdispersion rate decreases as the sequencing depth increases on the base pair level. Also, we propose four models and compare them with each other. As expected, our beta binomial model with a dynamic overdispersion rate is shown to be superior. Section 3: We investigate biases in RNA-seq by exploring the measurement of the external control, spike-in RNA. This study is based on two datasets with spike-in controls obtained from a recent study. We observe an undiscovered bias in the measurement of the spike-in transcripts that arises from the influence of the sample transcripts in RNA-seq. Also, we find that this influence is related to the local sequence of the random hexamer that is used in priming. We suggest a model of the inequality between samples and to correct this type of bias. Section 4: The expression of a gene can be turned off when its promoter is highly methylated. Several studies have reported that a clear threshold effect exists in gene silencing that is mediated by DNA methylation. It is reasonable to assume the thresholds are specific for each gene. It is also intriguing to investigate genes that are largely controlled by DNA methylation. These genes are called “L-shaped” genes. We develop a method to determine the DNA methylation threshold and identify a new CIMP of BRCA. In conclusion, we provide a detailed understanding of the relationship between the overdispersion rate and sequencing depth. And we reveal a new bias in RNA-seq and provide a detailed understanding of the relationship between this new bias and the local sequence. Also we develop a powerful method to dichotomize methylation status and consequently we identify a new CIMP of breast cancer with a distinct classification of molecular characteristics and clinical features.