Epstein-Barr virus nuclear antigen 1 (EBNA1) is the only viral product needed for replication of the latent Epstein-Barr virus genome. The latent origin of replication, oriP, consists of two cis-acting elements, the family of repeats (FR) containing twenty EBNA1 recognition sites, and the dyad symmetry element (DS) containing four recognition sites. Bidirectional DNA replication is known to occur within or near the DS. Previous studies have suggested that EBAN1 binds cooperatively to its recognition sites in the DS and have shown that EBNA1 binding induces DNA distortion within site 1 and site 4 of the DS. I have used EBNA1 mutants in electrophoretic mobility shift assays, methylation protection footprinting, and potassium permanganate reactivity analysis to examine EBAN1 assembly on the DS and the requirements for DNA distortion. I have found that: 1) EBNA1 has a 10-11 fold higher affinity for the outer two sites of the DS than the inner two sites due to DNA sequence variation, 2) the minimum region of EBNA1 necessary for site specific binding is contained within amino acids (a. A.) 470-607 but a.a. 459-470 greatly affect binding affinity, 3) EBNA1 dimers bind cooperatively on adjacent binding sites and the region responsible for this interaction is also contained between a.a. 470-607. I have also shown that EBNA1 binding to a single DS site 1 recognition site is sufficient to induce DNA distortion within that site and this distortion can be caused by a truncation mutant spanning a.a. 463-607 but not a.a. 468-607. Finally, although wild type spacing between recognition sites of the DS is critical for replication it is not crucial for EBNA1 binding or EBNA1 induced DNA distortion. / Thesis / Master of Science (MS)

This thesis consists of two related studies directed at aspects of M. tuberculosis biology. The first focuses on deciphering gene-regulatory circuits from ChIP-seq data, and the second focuses on alternative strategies for combatting drug-resistant infections. The first study describes Binding Resolution Amplifier and Cooperative Interaction Locator (BRACIL), a post-peak-caller computational method that predicts transcription-factor (TF) binding sites with high-resolution as well as cooperative TF interactions derived from ChIP-seq data. BRACIL integrates ChIP-seq coverage with motif discovery from a signal-processing perspective and uses a blind-deconvolution algorithm that predicts binding-site locations and magnitudes. BRACIL also explicitly considers a second-order signal, represented by DNA fragments with two sites bound simultaneously, and uses it to predict cooperative interaction. Cooperative interaction indicates that the binding to a first site influences the probability of binding to a second site. This method estimates the probability of a binding configuration from the ChIP-seq coverage and performs a likelihood ratio test to predict cooperative interaction. As a proof of principle, I validated this method using M. tuberculosis transcription factor DosR. The second study focuses on strategies to fight antibiotic resistance. In particular, recent reports have shown the existence of treatment conditions (called "antiR") that select against drug-resistant strains. I used a mathematical model of infection dynamics and immunity to simulate the growth of resistant and sensitive pathogens under different treatment conditions (no drugs, antibiotic present, and antiR), and could show how a precisely timed combination of treatments can defeat resistant strains. This analysis suggested that a time- scheduled, multi-treatment therapy could lead to complete elimination of both sensitive and resistant strains. Also, my results indicated that the time necessary to turn a resistant infection into a sensitive one ("tclear") depends on the experimentally measurable rates of pathogen division, growth and plasmid loss. Additionally, I estimated tclear for a specific case, using available empirical data, and found that resistance may be lost up to 15 times faster under antiR treatment as compared to a no-treatment regime. Finally, an extension of these findings to population models provides quantitative support for therapeutic plans to clear antibiotic-resistant infections, including novel drug-cycling strategies.