Visualizing Rare Watson-Crick-Like Tautomeric and Anionic Mismatches in DNA and RNA

The central dogma of molecular biology relies on the correct Watson-Crick (WC) geometry of canonical deoxyribonucleic acid (DNA) dG•dC and dA•dT base pairs to replicate and transcribe genetic information with speed and an astonishing level of fidelity. In addition, the Watson-Crick geometry of canonical ribonucleic acid (RNA) rG•rC and rA•rU base pairs is highly conserved to ensure that proteins are translated with high fidelity. However, numerous other potential nucleobase tautomeric and ionic configurations are possible that can give rise to entirely new pairing modes between the nucleotide bases. Very early on, James Watson and Francis Crick recognized their importance and in 1953 postulated that if bases adopted one of their less energetically disfavored tautomeric forms (and later ionic forms) during replication it could lead to the formation of a mismatch with a Watson-Crick-like geometry and could give rise to “natural mutations.”

Since this time numerous studies have provided evidence in support of this hypothesis and have expanded upon it; computational studies have addressed the energetic feasibilities of different nucleobases’ tautomeric and ionic forms in siico; crystallographic studies have trapped different mismatches with WC-like geometries in polymerase or ribosome active sites. However, no direct evidence has been given for (i) the direct existence of these WC-like mismatches in canonical DNA duplex, RNA duplexes, or non-coding RNAs; (ii) which, if any, tautomeric or ionic form stabilizes the WC-like geometry. This thesis utilizes nuclear magnetic resonance (NMR) spectroscopy and rotating frame relaxation dispersion (R1ρ RD) in combination with density functional theory (DFT), biochemical assays, and targeted chemical perturbations to show that (i) dG•dT mismatches in DNA duplexes, as well as rG•rU mismatches RNA duplexes and non-coding RNAs, transiently adopt a WC-like geometry that is stabilized by (ii) an interconnected network of rapidly interconverting rare tautomers and anionic bases. These results support Watson and Crick’s tautomer hypothesis, but additionally support subsequent hypotheses invoking anionic mismatches and ultimately tie them together. This dissertation shows that a common mismatch can adopt a Watson-Crick-like geometry globally, in both DNA and RNA, and whose geometry is stabilized by a kinetically linked network of rare tautomeric and anionic bases. The studies herein also provide compelling evidence for their involvement in spontaneous replication and translation errors.

Risk Prediction for Epithelial Ovarian Cancer in 11 United States-Based Case-Control Studies: Incorporation of Epidemiologic Risk Factors and 17 Confirmed Genetic Loci.

Previously developed models for predicting absolute risk of invasive epithelial ovarian cancer have included a limited number of risk factors and have had low discriminatory power (area under the receiver operating characteristic curve (AUC) < 0.60). Because of this, we developed and internally validated a relative risk prediction model that incorporates 17 established epidemiologic risk factors and 17 genome-wide significant single nucleotide polymorphisms (SNPs) using data from 11 case-control studies in the United States (5,793 cases; 9,512 controls) from the Ovarian Cancer Association Consortium (data accrued from 1992 to 2010). We developed a hierarchical logistic regression model for predicting case-control status that included imputation of missing data. We randomly divided the data into an 80% training sample and used the remaining 20% for model evaluation. The AUC for the full model was 0.664. A reduced model without SNPs performed similarly (AUC = 0.649). Both models performed better than a baseline model that included age and study site only (AUC = 0.563). The best predictive power was obtained in the full model among women younger than 50 years of age (AUC = 0.714); however, the addition of SNPs increased the AUC the most for women older than 50 years of age (AUC = 0.638 vs. 0.616). Adapting this improved model to estimate absolute risk and evaluating it in prospective data sets is warranted.