Skeletal myogenesis is under the control of a combinatorial network of transcription factors. It has been shown that the homeobox protein Six1 is required for embryonic myogenesis. Using functional genomics approaches, I determined that Six1 is required for myoblasts differentiation through direct binding to a cluster of genes that are related to muscle function and muscle structure during my Master’s studies. However, it was still not fully understood how Six1 selects its genomic targets and whether Six1 regulates the expression of Myod directly. I devoted my PhD work to study three central aspects of Six1 function: through what DNA motif it binds to DNA, how it regulates the expression of the myogenic regulatory factor MyoD, and how it might regulate chromatin structure at the enhancer regions of muscle genes. A more degenerate MEF3-like DNA sequence consensus has been identified from Six1 ChIP-on-chip experiments. This MEF3 motif was further optimized using bioinformatic methods and was proved to discover Six1 binding sites with improved specificity and sensitivity. Myod, a member of myogenic regulatory factors (MRFs), is a master regulator in the myogenic lineage. Multiple MEF3 sites were identified on the regulatory regions of Myod, including two MEF3 sites within its core enhancer region (CER). Six1 was able to bind to the CER directly through these two MEF3 sites and regulated the Myod expression in cultured primary myoblasts. Previous work has suggested that the CER is also bound by Myod in myoblasts. I demonstrated that the binding of Myod to the CER depended on the presence of Six1. Six1 was also involved in maintaining a relatively ‘open’ chromatin structure at the CER, suggesting that Six1 may play a direct or indirect role in chromatin remodeling. During my Master’s studies, I demonstrated a synergistic regulation by the Six and MRF families. This synergistic function gains potential importance by the fact that ~25% of Six1 genomic targets are also bound by Myod. I decided to study whether the co-occupancy of Six1 and Myod was essential to maintain the proper global chromatin structure at these loci. Six1 and Myod co-bound genomic regions correlated with more accessible chromatin, which was detected by the formaldehyde-assisted isolation of regulatory elements (FAIRE) assay followed by DNA deep sequencing (FAIRE-seq). When combined with small interfering RNA-mediated gene knockdown of Six1 or Myod, FAIRE-seq data suggested that Six1, but not Myod, was involved in regulating the chromatin accessibility at these co-bound DNA loci. To shed light on the mechanism by which Six1 functions, proteomics approaches were used and revealed that proteins involved in “regulation of transcription” and “chromatin organization” were enriched among Six1-bound proteins. Cdk9 and its partner cyclin T have been shown to stimulate gene expression by releasing RNA polymerase II from transcriptional pause, but they can also function at gene enhancers. I determined that Six1 and Cdk9 participated in the same protein complex, and that the Cdk9 activity appeared to mediate the effect of Six1 on the chromatin accessibility at the CER to regulate the Myod expression. Taken together, these results demonstrate that Six1 regulates the expression of Myod through its direct binding on the CER which facilitates transcriptional elongation.

Understanding the possible mechanisms of gene transcription regulation is a primary challenge for current molecular biologists. Identifying transcription factor binding sites (TFBSs), also called DNA motifs, is an important step in understanding these mechanisms. Furthermore, many human diseases are attributed to mutations in TFBSs, which makes identifying those DNA motifs significant for disease treatment. Uncertainty and variations in specific nucleotides of TFBSs present difficulties for DNA motif searching. In this project, we present an algorithm, XPRIME-EM (Eliciting EXpert PRior Information for Motif Exploration using the Expectation-Maximization Algorithm), which can discover known and de novo (unknown) DNA motifs simultaneously from a collection of DNA sequences using a modified EM algorithm and describe the variation nature of DNA motifs using position specific weight matrix (PWM). XPRIME improves the efficiency of locating and describing motifs by prevent the overlap of multiple motifs, a phenomenon termed a phase shift, and generates stronger motifs by considering the correlations between nucleotides at different positions within each motif. Moreover, a Bayesian formulation of the XPRIME algorithm allows for the elicitation of prior information for motifs of interest from literature and experiments into motif searching. We are the first research team to incorporate human genome-wide nucleosome occupancy information into the PWM based DNA motif searching.

<p>The initiation of two major processes in the eukaryotic cell, gene transcription and DNA replication, is regulated largely through interactions between proteins or protein complexes and DNA. Although a lot is known about the interacting proteins and their role in regulating transcription and replication, the specific DNA binding motifs of many regulatory proteins and complexes are still to be determined. For this purpose, many computational tools for DNA motif discovery have been developed in the last two decades. These tools employ a variety of strategies, from exhaustive search to sampling techniques, with the hope of finding over-represented motifs in sets of co-regulated or co-bound sequences. Despite the variety of computational tools aimed at solving the problem of motif discovery, their ability to correctly detect known DNA motifs is still limited. The motifs are usually short and many times degenerate, which makes them difficult to distinguish from genomic background. We believe the most efficient strategy for improving the performance of motif discovery is not to use increasingly complex computational and statistical methods and models, but to incorporate more of the biology into the computational techniques, in a principled manner. To this end, we propose a novel motif discovery algorithm: PRIORITY. Based on a general Gibbs sampling framework, PRIORITY has a major advantage over other motif discovery tools: it can incorporate different types of biological information (e.g., nucleosome positioning information) to guide the search for DNA binding sites toward regions where these sites are more likely to occur (e.g., nucleosome-free regions). </p><p>We use transcription factor (TF) binding data from yeast chromatin immunoprecipitation (ChIP-chip) experiments to test the performance of our motif discovery algorithm when incorporating three types of biological information: information about nucleosome positioning, information about DNA double-helical stability, and evolutionary conservation information. In each case, incorporating additional biological information has proven very useful in increasing the accuracy of motif finding, with the number of correctly identified motifs increasing with up to 52%. PRIORITY is not restricted to TF binding data. In this work, we also analyze origin recognition complex (ORC) binding data and show that PRIORITY can utilize DNA structural information to predict the binding specificity of the yeast ORC. </p><p>Despite the improvement obtained using additional biological information, the success of motif discovery algorithms in identifying known motifs is still limited, especially when applied to sequences bound in vivo (such as those of ChIP-chip) because the observed protein-DNA interactions are not necessarily direct. Some TFs associate with DNA only indirectly via protein partners, while others exhibit both direct and indirect binding. We propose a novel method to distinguish between direct and indirect TF-DNA interactions, integrating in vivo TF binding data, in vivo nucleosome occupancy data, and in vitro motifs from protein binding microarrays. When applied to yeast ChIP-chip data, our method reveals that only 48% of the ChIP-chip data sets can be readily explained by direct binding of the profiled TF, while 16% can be explained by indirect DNA binding. In the remaining 36%, we found that none of the motifs used in our analysis was able to explain the ChIP-chip data, either because the data was too noisy or because the set of motifs was incomplete. As more in vitro motifs become available, our method can be used to build a complete catalog of direct and indirect TF-DNA interactions.</p> / Dissertation