Functions and intramolecular interactions of ribosomal RNA in decoding of termination codons

Two regions in the 3$\prime$ domain of 16S rRNA (the RNA of the small ribosomal subunit) have been implicated in decoding of termination codons. Using segment-directed PCR random mutagenesis, I isolated 33 translational suppressor mutations in the 3$\prime$ domain of 16S rRNA. Characterization of the mutations by both genetic and biochemical methods indicated that some of the mutations are defective in UGA-specific peptide chain termination and that others may be defective in peptide chain termination at all termination codons. The studies of the mutations at an internal loop in the non-conserved region of helix 44 also indicated that this structure, in a non-conserved region of 16S rRNA, is involved in both peptide chain termination and assembly of 16S rRNA.^ With a suppressible trpA UAG nonsense mutation, a spontaneously arising translational suppressor mutation was isolated in the rrnB operon cloned into a pBR322-derived plasmid. The mutation caused suppression of UAG at two codon positions in trpA but did not suppress UAA or UGA mutations at the same trpA positions. The specificity of the rRNA suppressor mutation suggests that it may cause a defect in UAG-specific peptide chain termination. The mutation is a single nucleotide deletion (G2484$\Delta$) in helix 89 of 23S rRNA (the large RNA of the large ribosomal subunit). The result indicates a functional interaction between two regions of 23S rRNA. Furthermore, it provides suggestive in vivo evidence for the involvement of the peptidyl-transferase center of 23S rRNA in peptide chain termination. The $\Delta$2484 and A1093/$\Delta$2484 (double) mutations were also observed to alter the decoding specificity of the suppressor tRNA lysT(U70), which has a mutation in its acceptor stem. That result suggests that there is an interaction between the stem-loop region of helix 89 of 23S rRNA and the acceptor stem of tRNA during decoding and that the interaction is important for the decoding specificity of tRNA.^ Using gene manipulation procedures, I have constructed a new expression vector to express and purify the cellular protein factors required for a recently developed, realistic in vitro termination assay. The gene for each protein was cloned into the newly constructed vector in such a way that expression yielded a protein with an N-terminal affinity tag, for specific, rapid purification. The amino terminus was engineered so that, after purification, the unwanted N-terminal tag can be completely removed from the protein by thrombin cleavage, yielding a natural amino acid sequence for each protein. I have cloned the genes for EF-G and all three release factors into this new expression vector and the genes for all the other protein factors into a pCAL-n expression vector. These constructs will allow our laboratory group to quickly and inexpensively purify all the protein factors needed for the new in vitro termination assay. (Abstract shortened by UMI.) ^

THE NATURAL AND ORTHOGONAL INTERACTION (NOIA) MODELS FOR QUANTITATIVE TRAITS (QTs) AND COMPLEX DISEASES

My dissertation focuses on developing methods for gene-gene/environment interactions and imprinting effect detections for human complex diseases and quantitative traits. It includes three sections: (1) generalizing the Natural and Orthogonal interaction (NOIA) model for the coding technique originally developed for gene-gene (GxG) interaction and also to reduced models; (2) developing a novel statistical approach that allows for modeling gene-environment (GxE) interactions influencing disease risk, and (3) developing a statistical approach for modeling genetic variants displaying parent-of-origin effects (POEs), such as imprinting. In the past decade, genetic researchers have identified a large number of causal variants for human genetic diseases and traits by single-locus analysis, and interaction has now become a hot topic in the effort to search for the complex network between multiple genes or environmental exposures contributing to the outcome. Epistasis, also known as gene-gene interaction is the departure from additive genetic effects from several genes to a trait, which means that the same alleles of one gene could display different genetic effects under different genetic backgrounds. In this study, we propose to implement the NOIA model for association studies along with interaction for human complex traits and diseases. We compare the performance of the new statistical models we developed and the usual functional model by both simulation study and real data analysis. Both simulation and real data analysis revealed higher power of the NOIA GxG interaction model for detecting both main genetic effects and interaction effects. Through application on a melanoma dataset, we confirmed the previously identified significant regions for melanoma risk at 15q13.1, 16q24.3 and 9p21.3. We also identified potential interactions with these significant regions that contribute to melanoma risk. Based on the NOIA model, we developed a novel statistical approach that allows us to model effects from a genetic factor and binary environmental exposure that are jointly influencing disease risk. Both simulation and real data analyses revealed higher power of the NOIA model for detecting both main genetic effects and interaction effects for both quantitative and binary traits. We also found that estimates of the parameters from logistic regression for binary traits are no longer statistically uncorrelated under the alternative model when there is an association. Applying our novel approach to a lung cancer dataset, we confirmed four SNPs in 5p15 and 15q25 region to be significantly associated with lung cancer risk in Caucasians population: rs2736100, rs402710, rs16969968 and rs8034191. We also validated that rs16969968 and rs8034191 in 15q25 region are significantly interacting with smoking in Caucasian population. Our approach identified the potential interactions of SNP rs2256543 in 6p21 with smoking on contributing to lung cancer risk. Genetic imprinting is the most well-known cause for parent-of-origin effect (POE) whereby a gene is differentially expressed depending on the parental origin of the same alleles. Genetic imprinting affects several human disorders, including diabetes, breast cancer, alcoholism, and obesity. This phenomenon has been shown to be important for normal embryonic development in mammals. Traditional association approaches ignore this important genetic phenomenon. In this study, we propose a NOIA framework for a single locus association study that estimates both main allelic effects and POEs. We develop statistical (Stat-POE) and functional (Func-POE) models, and demonstrate conditions for orthogonality of the Stat-POE model. We conducted simulations for both quantitative and qualitative traits to evaluate the performance of the statistical and functional models with different levels of POEs. Our results showed that the newly proposed Stat-POE model, which ensures orthogonality of variance components if Hardy-Weinberg Equilibrium (HWE) or equal minor and major allele frequencies is satisfied, had greater power for detecting the main allelic additive effect than a Func-POE model, which codes according to allelic substitutions, for both quantitative and qualitative traits. The power for detecting the POE was the same for the Stat-POE and Func-POE models under HWE for quantitative traits.