Involvement of a region of 23S ribosomal RNA of {\it E. coli\/} in decoding of termination codons

Involvement of E. coli 23S ribosomal RNA (rRNA) in decoding of termination codons was first indicated by the characterization of a 23S rRNA mutant that causes UGA-specific nonsense suppression. The work described here was begun to test the hypothesis that more 23S rRNA suppressors of specific nonsense mutations can be isolated and that they would occur non-randomly in the rRNA genes and be clustered in specific, functionally significant regions of rRNA.^ Approximately 2 kilobases of the gene for 23S rRNA were subjected to PCR random mutagenesis and the amplified products screened for suppression of nonsense mutations in trpA. All of the suppressor mutations obtained were located in a thirty-nucleotide part of the GTPase center, a conserved rRNA sequence and structure, and they and others made in that region by site-directed mutagenesis were shown to be UGA-specific in their suppression of termination codon mutations. These results proved the initial hypothesis and demonstrated that a group of nucleotides in this region are involved in decoding of the UGA termination codon. Further, it was shown that limitation of cellular availability or synthesis of L11, a ribosomal protein that binds to the GTPase center rRNA, resulted in suppression of termination codon mutations, suggesting the direct involvement of L11 in termination in vivo.^ Finally, in vivo analysis of certain site-specific mutations made in the GTPase center RNA demonstrated that (a) the G$\cdot$A base pair closing the hexanucleotide hairpin loop was not essential for normal termination, (b) the "U-turn" structure in the 1093 to 1098 hexaloop is critical for normal termination, (c) nucleotides A1095 and A1067, necessary for the binding to ribosomes of thiostrepton, an antibiotic that inhibits polypeptide release factor binding to ribosomes in vitro, are also necessary for normal peptide chain termination in vivo, and (d) involvement of this region of rRNA in termination is determined by some unique subset structure that includes particular nucleotides rather than merely by a general structural feature of the GTPase center.^ This work advances the understanding of peptide chain termination by demonstrating that the GTPase region of 23S rRNA participates in recognition of termination codons, through an associated ribosomal protein and specific conserved nucleotides and structural motifs in its RNA. ^

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.) ^

Genome-wide algorithm for detecting CNV associations with diseases

SNP genotyping arrays have been developed to characterize single-nucleotide polymorphisms (SNPs) and DNA copy number variations (CNVs). The quality of the inferences about copy number can be affected by many factors including batch effects, DNA sample preparation, signal processing, and analytical approach. Nonparametric and model-based statistical algorithms have been developed to detect CNVs from SNP genotyping data. However, these algorithms lack specificity to detect small CNVs due to the high false positive rate when calling CNVs based on the intensity values. Association tests based on detected CNVs therefore lack power even if the CNVs affecting disease risk are common. In this research, by combining an existing Hidden Markov Model (HMM) and the logistic regression model, a new genome-wide logistic regression algorithm was developed to detect CNV associations with diseases. We showed that the new algorithm is more sensitive and can be more powerful in detecting CNV associations with diseases than an existing popular algorithm, especially when the CNV association signal is weak and a limited number of SNPs are located in the CNV.^

EVALUATION OF THE EFFECTIVENESS OF ANISOTROPIC ANALYTICAL ALGORITHM IN FLATTENED AND FLATTENING-FILTER-FREE BEAMS FOR HIGH ENERGY LUNG DOSE DELIVERY USING THE RADIOLOGICAL PHYSICS CENTER LUNG PHANTOM

The effectiveness of the Anisotropic Analytical Algorithm (AAA) implemented in the Eclipse treatment planning system (TPS) was evaluated using theRadiologicalPhysicsCenteranthropomorphic lung phantom using both flattened and flattening-filter-free high energy beams. Radiation treatment plans were developed following the Radiation Therapy Oncology Group and theRadiologicalPhysicsCenterguidelines for lung treatment using Stereotactic Radiation Body Therapy. The tumor was covered such that at least 95% of Planning Target Volume (PTV) received 100% of the prescribed dose while ensuring that normal tissue constraints were followed as well. Calculated doses were exported from the Eclipse TPS and compared with the experimental data as measured using thermoluminescence detectors (TLD) and radiochromic films that were placed inside the phantom. The results demonstrate that the AAA superposition-convolution algorithm is able to calculate SBRT treatment plans with all clinically used photon beams in the range from 6 MV to 18 MV. The measured dose distribution showed a good agreement with the calculated distribution using clinically acceptable criteria of ±5% dose or 3mm distance to agreement. These results show that in a heterogeneous environment a 3D pencil beam superposition-convolution algorithms with Monte Carlo pre-calculated scatter kernels, such as AAA, are able to reliably calculate dose, accounting for increased lateral scattering due to the loss of electronic equilibrium in low density medium. The data for high energy plans (15 MV and 18 MV) showed very good tumor coverage in contrast to findings by other investigators for less sophisticated dose calculation algorithms, which demonstrated less than expected tumor doses and generally worse tumor coverage for high energy plans compared to 6MV plans. This demonstrates that the modern superposition-convolution AAA algorithm is a significant improvement over previous algorithms and is able to calculate doses accurately for SBRT treatment plans in the highly heterogeneous environment of the thorax for both lower (≤12 MV) and higher (greater than 12 MV) beam energies.

Pathway Semantics: An Algebraic Data Driven Algorithm to Generate Hypotheses about Molecular Patterns Underlying Disease Progression

The overarching goal of the Pathway Semantics Algorithm (PSA) is to improve the in silico identification of clinically useful hypotheses about molecular patterns in disease progression. By framing biomedical questions within a variety of matrix representations, PSA has the flexibility to analyze combined quantitative and qualitative data over a wide range of stratifications. The resulting hypothetical answers can then move to in vitro and in vivo verification, research assay optimization, clinical validation, and commercialization. Herein PSA is shown to generate novel hypotheses about the significant biological pathways in two disease domains: shock / trauma and hemophilia A, and validated experimentally in the latter. The PSA matrix algebra approach identified differential molecular patterns in biological networks over time and outcome that would not be easily found through direct assays, literature or database searches. In this dissertation, Chapter 1 provides a broad overview of the background and motivation for the study, followed by Chapter 2 with a literature review of relevant computational methods. Chapters 3 and 4 describe PSA for node and edge analysis respectively, and apply the method to disease progression in shock / trauma. Chapter 5 demonstrates the application of PSA to hemophilia A and the validation with experimental results. The work is summarized in Chapter 6, followed by extensive references and an Appendix with additional material.

Pencil -beam redefinition algorithm dose calculations for electron therapy treatment planning

The electron pencil-beam redefinition algorithm (PBRA) of Shiu and Hogstrom has been developed for use in radiotherapy treatment planning (RTP). Earlier studies of Boyd and Hogstrom showed that the PBRA lacked an adequate incident beam model, that PBRA might require improved electron physics, and that no data existed which allowed adequate assessment of the PBRA-calculated dose accuracy in a heterogeneous medium such as one presented by patient anatomy. The hypothesis of this research was that by addressing the above issues the PBRA-calculated dose would be accurate to within 4% or 2 mm in regions of high dose gradients. A secondary electron source was added to the PBRA to account for collimation-scattered electrons in the incident beam. Parameters of the dual-source model were determined from a minimal data set to allow ease of beam commissioning. Comparisons with measured data showed 3% or better dose accuracy in water within the field for cases where 4% accuracy was not previously achievable. A measured data set was developed that allowed an evaluation of PBRA in regions distal to localized heterogeneities. Geometries in the data set included irregular surfaces and high- and low-density internal heterogeneities. The data was estimated to have 1% precision and 2% agreement with accurate, benchmarked Monte Carlo (MC) code. PBRA electron transport was enhanced by modeling local pencil beam divergence. This required fundamental changes to the mathematics of electron transport (divPBRA). Evaluation of divPBRA with the measured data set showed marginal improvement in dose accuracy when compared to PBRA; however, 4% or 2mm accuracy was not achieved by either PBRA version for all data points. Finally, PBRA was evaluated clinically by comparing PBRA- and MC-calculated dose distributions using site-specific patient RTP data. Results show PBRA did not agree with MC to within 4% or 2mm in a small fraction (<3%) of the irradiated volume. Although the hypothesis of the research was shown to be false, the minor dose inaccuracies should have little or no impact on RTP decisions or patient outcome. Therefore, given ease of beam commissioning, documentation of accuracy, and calculational speed, the PBRA should be considered a practical tool for clinical use. ^