Immunochemical interactions of peptides with site-directed polyclonal immunoglobulin G

The objective of this study was to investigate the immunochemical nature of the polyclonal immune response to the 14mer peptide TINKEDDESPGLYG and to identify interactions among antibodies to more than one epitope. Two groups of rabbits were immunized with the 14mer peptide and a Keyhole Limpet hemocyanin (KLH) carrier, but with KLH attached either to the 14mer's N- or C-terminus. Two approximate epitopes were mapped by an antibody-capture enzyme-linked immunosorbent assay method using antiserum obtained when KLH was oriented on the C-terminus of the 14mer. A precise mapping of the epitopes performed with inhibition enzyme immunoassays (iEIAs) resulted in an N-terminal 6mer epitope TINKED and a C-terminal 10mer epitope EDDESPGLYG. The epitopes overlapped by two amino acids. IEIAs and iEIAs incorporating antibody-blocking peptides indicated that the two anti-epitope antibody fractions did not interfere with one anothers' epitope binding. It was postulated that the anti-TINKED and anti-EDDESPGLYG antibody fractions individually bind their respective hydrophobic epitope "core" region at the N- or C-terminal of peptide TINKEDDESPGLYG, while sharing the two hydrophilic overlap amino acids. This antibody "lap joint" binding interaction can be accomplished by each of the anti-epitope antibodies binding an opposite side of the epitope overlap region in the shallow periphery of its binding site. ^

Development of a Bayesian Joint Logistic Model to Better Study the Association between Haplotypes and Disease

In 2011, there will be an estimated 1,596,670 new cancer cases and 571,950 cancer-related deaths in the US. With the ever-increasing applications of cancer genetics in epidemiology, there is great potential to identify genetic risk factors that would help identify individuals with increased genetic susceptibility to cancer, which could be used to develop interventions or targeted therapies that could hopefully reduce cancer risk and mortality. In this dissertation, I propose to develop a new statistical method to evaluate the role of haplotypes in cancer susceptibility and development. This model will be flexible enough to handle not only haplotypes of any size, but also a variety of covariates. I will then apply this method to three cancer-related data sets (Hodgkin Disease, Glioma, and Lung Cancer). I hypothesize that there is substantial improvement in the estimation of association between haplotypes and disease, with the use of a Bayesian mathematical method to infer haplotypes that uses prior information from known genetics sources. Analysis based on haplotypes using information from publically available genetic sources generally show increased odds ratios and smaller p-values in both the Hodgkin, Glioma, and Lung data sets. For instance, the Bayesian Joint Logistic Model (BJLM) inferred haplotype TC had a substantially higher estimated effect size (OR=12.16, 95% CI = 2.47-90.1 vs. 9.24, 95% CI = 1.81-47.2) and more significant p-value (0.00044 vs. 0.008) for Hodgkin Disease compared to a traditional logistic regression approach. Also, the effect sizes of haplotypes modeled with recessive genetic effects were higher (and had more significant p-values) when analyzed with the BJLM. Full genetic models with haplotype information developed with the BJLM resulted in significantly higher discriminatory power and a significantly higher Net Reclassification Index compared to those developed with haplo.stats for lung cancer. Future analysis for this work could be to incorporate the 1000 Genomes project, which offers a larger selection of SNPs can be incorporated into the information from known genetic sources as well. Other future analysis include testing non-binary outcomes, like the levels of biomarkers that are present in lung cancer (NNK), and extending this analysis to full GWAS studies.