I have pursued a breadth of research that explored the functional genomic study of eukaryotic transcriptional regulation. I have utilized two model organisms, many experimental methodologies, and have developed a suite of computational resources to study the interaction of transcription factors with regulated targets. In Saccharomyces cerevisiae I worked with my collaborator Dr. Zhanzhi (Mike) Hu to characterize the whole-genome transcriptional response of 263 individual transcription factor deletions. We utilized a sophisticated error model and directed-weighted graphs to model a network of high-confidence targets for each transcription factor profiled. We then used regulatory epistasis to elucidate the true set of primary KO-regulated targets and construct a functional transcriptional regulatory network. This network was analyzed for ontological and sequence motif enrichment in order to gain insight into the biological functions represented by transcription factors studied. Functional validation was performed to evaluate the probability of novel functional characterizations. Significant insight was gained from this study with regard to the nature of regulatory cascades and the inability for DNA binding events to predict regulation. This set of analysis was performed with a novel bioinformatic server called ArrayPlex. ArrayPlex is a software package that centrally provides a large number of flexible toolsets useful for functional genomics including microarray data storage, quality assessments, data visualization, gene annotation retrieval, statistical tests, genomic sequence retrieval and motif analysis. It uses a client-server architecture based on open source components, provides graphical, command-line, as well as programmatic access to all needed resources, and is extensible by virtue of a documented API. Using many of the techniques and computational resources developed, I pursued the study of microRNA transcriptional abundance and targeting in H. sapiens cell cultures. Utilizing custom-fabricated microarrays, I measured the whole-genome response of both mRNAs and microRNAs under serum stimulation, c-Myc overexpression, and c-Myc siRNA-mediated knockdown. I then characterized the regulatory interactions between the sets of regulated microRNAs and coordinately regulated transcription factors. Using analytical methods sensitive to regulatory directionality of both populations I was able to determine high-confidence relationships between transcription factors and regulated microRNAs as well as microRNAs and regulated gene targets.

published_or_final_version / abstract / toc / Zoology / Doctoral / Doctor of Philosophy

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The yeast Saccharomyces cerevisiae is a prevalent system for studying gene regulation because of the ease of experimental methods and the simplicity of its gene structure. Here, I describe my work that aims to identify the sequences and factors responsible for demarcating genes within the genome sequence. With comparative genomics and RNA-Seq, we are quite adept at identifying gene structure. However, the cell does not have access to this kind of information. Instead, it uses the specificities of DNA- and RNA-binding proteins to read and interpret the sequence of the genome; it is this process that I have studied in my thesis. In the first chapter, I describe my work collecting yeast transcription factor specificities. I evaluated these specificities using available confirmatory data to determine which one best represents the transcription factor; this gave me a high-confidence description of what DNA sequences yeast transcription factors recognize. Next, I look for over- and under-represented DNA words within and surrounding gene structures and attempt to explain these in terms of the specificities of known factors or other known biological phenomena. I found that the sequences in the 5' and 3' gene ends are very similar and can often be explained by similar phenomena. I also provide evidence that several factors may be involved in regulating transcription in non-canonical ways. In the final chapter, I describe my efforts to build a model that uses my collection of transcription factor specificities as well as DNA structural features to identify gene structure as we think the cell would. This model is comprised of two classifiers that identify mRNA initiation and termination sites, and these are used to provide evidence to a hidden Markov model that predicts gene structure. I test that the predicted determinants of promoter structure are sufficient to initiate transcription, and that the transcription arising from randomly-generated DNA is correctly predicted. Overall, my work demonstrates that the sequence elements demarcating yeast genes are relatively simple in nature, which has implications for how transcription is regulated and how genes evolve.

Understanding the binding specificity of transcription factors is an important step towards accurate computational prediction of regulatory sequences governing gene expression. Higher-throughput binding site characterization methods have long been available in the laboratory for the study of protein-DNA interactions in solution or upon a surface. In this thesis a new method is introduced for characterization of inducible regulatory sequences in living cells based on construction and analysis of promoter-reporter gene plasmids. Spiked oligonucleotides are used to generate libraries of regulatory sequences with subtle variations from a known regulatory element. Screening of the library in cell culture for the capacity of the mutated sequences to mediate expression provides a diverse collection of responsive and non-responsive sequences to aid in understanding the sequence requirement for an inducible transcription factor binding site. We apply the methodology to the study of antioxidant responsive elements, the target sites of the Nfe212 transcription factor. These target sequences commonly found in the promoters of detoxification genes modulate gene expression in response to a diverse array of chemicals. The variants serve as a primary screen for future targeted mutational analysis to further characterize context-specific sequence requirement in the ARE and/or interdependence between positions. Moreover, a transcription factor binding profile can be generated from functional ARE variants in the library screen. Such an ARE profile performs as well as standard profiles based on bona fide ARE sequences drawn from the scientific literature.

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