INTEGRATIVE BIOMARKER IDENTIFICATION AND CLASSIFICATION USING HIGH THROUGHPUT ASSAYS

It is well accepted that tumorigenesis is a multi-step procedure involving aberrant functioning of genes regulating cell proliferation, differentiation, apoptosis, genome stability, angiogenesis and motility. To obtain a full understanding of tumorigenesis, it is necessary to collect information on all aspects of cell activity. Recent advances in high throughput technologies allow biologists to generate massive amounts of data, more than might have been imagined decades ago. These advances have made it possible to launch comprehensive projects such as (TCGA) and (ICGC) which systematically characterize the molecular fingerprints of cancer cells using gene expression, methylation, copy number, microRNA and SNP microarrays as well as next generation sequencing assays interrogating somatic mutation, insertion, deletion, translocation and structural rearrangements. Given the massive amount of data, a major challenge is to integrate information from multiple sources and formulate testable hypotheses. This thesis focuses on developing methodologies for integrative analyses of genomic assays profiled on the same set of samples. We have developed several novel methods for integrative biomarker identification and cancer classification. We introduce a regression-based approach to identify biomarkers predictive to therapy response or survival by integrating multiple assays including gene expression, methylation and copy number data through penalized regression. To identify key cancer-specific genes accounting for multiple mechanisms of regulation, we have developed the integIRTy software that provides robust and reliable inferences about gene alteration by automatically adjusting for sample heterogeneity as well as technical artifacts using Item Response Theory. To cope with the increasing need for accurate cancer diagnosis and individualized therapy, we have developed a robust and powerful algorithm called SIBER to systematically identify bimodally expressed genes using next generation RNAseq data. We have shown that prediction models built from these bimodal genes have the same accuracy as models built from all genes. Further, prediction models with dichotomized gene expression measurements based on their bimodal shapes still perform well. The effectiveness of outcome prediction using discretized signals paves the road for more accurate and interpretable cancer classification by integrating signals from multiple sources.

NOVEL THERAPEUTIC TARGETS IDENTIFIED BY HIGH-THROUGHPUT TECHNOLOGIES FOR TRIPLE-NEGATIVE BREAST CANCER

Triple-negative breast cancers (TNBC) are characterized by the lack of or reduced expression of the estrogen and progesterone receptors, and normal expression of the human epidermal growth factor receptor 2. The lack of a well-characterized target for treatment leaves only systemic chemotherapy as the mainstay of treatment. Approximately 60-70% of patients are chemosensitive, while the remaining majority does not respond. Targeted therapies that take advantage of the unique molecular perturbations found in triple-negative breast cancer are needed. The genes that are frequently amplified or overexpressed represent potential therapeutic targets for triple-negative breast cancer. The purpose of this study was to identify and validate novel therapeutic targets for triple-negative breast cancers. 681 genes showed consistent and highly significant overexpression in TNBC compared to receptor-positive cancers in 2 data sets. For two genes, 3 of the 4 siRNAs showed preferential growth inhibition in TNBC cells. These two genes were the low density lipoprotein receptor-related protein 8 (LRP8) and very low-density lipoprotein receptor (VLDLR). Exposure to their cognate ligands, reelin and apolipoprotein E isoform 4 (ApoE4), stimulated the growth of TNBC cells in vitro. Suppression of the expression of either LRP8 or VLDLR or exposure to RAP (an inhibitor of ligand binding to LRP8 and VLDLR) abolished this ligand-induced proliferation. High-throughput protein and metabolic arrays revealed that ApoE4 stimulation rescued TNBC cells from serum-starvation induced up-regulation of genes involved in lipid biosynthesis, increased protein expression of oncogenes involved in the MAPK/ERK and DNA repair pathways, and reduced the serum-starvation induction of biochemicals involved in oxidative stress response and glycolytic metabolism. shLRP8 MDA-MB-231 xenografts had reduced tumor volume, in comparison to parental and shCON xenografts. These results indicate that LRP8-APOE signaling confers survival advantages to TNBC tumors under reduced nutrient conditions and during cellular environmental stress. We revealed that the LRP8-APOE receptor-ligand system is overexpressed in human TNBC. We also demonstrated that this receptor system mediates a strong growth promoting and survival function in TNBC cells in vitro and helps to sustain the growth of MDA-MD-231 xenografts. We propose that inhibitors of LRP8-APOE signaling may be clinically useful therapeutic agents for triple-negative breast cancer.

Development of HIF-1α/HIF-1β heterodimerization inhibitors using a novel bioluminescence reporter assay system for in vitro high throughput screening and in vivo imaging

Tumor growth often outpaces its vascularization, leading to development of a hypoxic tumor microenvironment. In response, an intracellular hypoxia survival pathway is initiated by heterodimerization of hypoxia-inducible factor (HIF)-1α and HIF-1β, which subsequently upregulates the expression of several hypoxia-inducible genes, promotes cell survival and stimulates angiogenesis in the oxygen-deprived environment. Hypoxic tumor regions are often associated with resistance to various classes of radio- or chemotherapeutic agents. Therefore, development of HIF-1α/β heterodimerization inhibitors may provide a novel approach to anti-cancer therapy. To this end, a novel approach for imaging HIF-1α/β heterodimerization in vitro and in vivo was developed in this study. Using this screening platform, we identified a promising lead candidate and further chemically derivatized the lead candidate to assess the structure-activity relationship (SAR). The most effective first generation drug inhibitors were selected and their pharmacodynamics and anti-tumor efficacy in vivo were verified by bioluminescence imaging (BLI) of HIF-1α/β heterodimerization in the xenograft tumor model. Furthermore, the first generation drug inhibitors, M-TMCP and D-TMCP, demonstrated efficacy as monotherapies, resulting in tumor growth inhibition via disruption of HIF-1 signaling-mediated tumor stromal neoangiogenesis.

Systematic interpretation of molecular beacon polymerase chain reaction for identifying rpoB mutations in Mycobacterium tuberculosis isolates with mixed resistant and susceptible bacteria

Detection of multidrug-resistant tuberculosis (MDR-TB), a frequent cause of treatment failure, takes 2 or more weeks to identify by culture. RIF-resistance is a hallmark of MDR-TB, and detection of mutations in the rpoB gene of Mycobacterium tuberculosis using molecular beacon probes with real-time quantitative polymerase chain reaction (qPCR) is a novel approach that takes ≤2 days. However, qPCR identification of resistant isolates, particularly for isolates with mixed RIF-susceptible and RIF-resistant bacteria, is reader dependent and limits its clinical use. The aim of this study was to develop an objective, reader-independent method to define rpoB mutants using beacon qPCR. This would facilitate the transition from a research protocol to the clinical setting, where high-throughput methods with objective interpretation are required. For this, DNAs from 107 M. tuberculosis clinical isolates with known susceptibility to RIF by culture-based methods were obtained from 2 regions where isolates have not previously been subjected to evaluation using molecular beacon qPCR: the Texas–Mexico border and Colombia. Using coded DNA specimens, mutations within an 81-bp hot spot region of rpoB were established by qPCR with 5 beacons spanning this region. Visual and mathematical approaches were used to establish whether the qPCR cycle threshold of the experimental isolate was significantly higher (mutant) compared to a reference wild-type isolate. Visual classification of the beacon qPCR required reader training for strains with a mixture of RIF-susceptible and RIF-resistant bacteria. Only then had the visual interpretation by an experienced reader had 100% sensitivity and 94.6% specificity versus RIF-resistance by culture phenotype and 98.1% sensitivity and 100% specificity versus mutations based on DNA sequence. The mathematical approach was 98% sensitive and 94.5% specific versus culture and 96.2% sensitive and 100% specific versus DNA sequence. Our findings indicate the mathematical approach has advantages over the visual reading, in that it uses a Microsoft Excel template to eliminate reader bias or inexperience, and allows objective interpretation from high-throughput analyses even in the presence of a mixture of RIF-resistant and RIF-susceptible isolates without the need for reader training.^

Targeted Deletion Of Functionally Validated Enhancers Defines Their Role in Mouse Limb Development

Transcriptional enhancers are genomic DNA sequences that contain clustered transcription factor (TF) binding sites. When combinations of TFs bind to enhancer sequences they act together with basal transcriptional machinery to regulate the timing, location and quantity of gene transcription. Elucidating the genetic mechanisms responsible for differential gene expression, including the role of enhancers, during embryological and postnatal development is essential to an understanding of evolutionary processes and disease etiology. Numerous methods are in use to identify and characterize enhancers. Several high-throughput methods generate large datasets of enhancer sequences with putative roles in embryonic development. However, few enhancers have been deleted from the genome to determine their roles in the development of specific structures, such as the limb. Manipulation of enhancers at their endogenous loci, such as the deletion of such elements, leads to a better understanding of the regulatory interactions, rules and complexities that contribute to faithful and variant gene transcription – the molecular genetic substrate of evolution and disease. To understand the endogenous roles of two distinct enhancers known to be active in the mouse embryo limb bud we deleted them from the mouse genome. I hypothesized that deletion of these enhancers would lead to aberrant limb development. The enhancers were selected because of their association with p300, a protein associated with active transcription, and because the human enhancer sequences drive distinct lacZ expression patterns in limb buds of embryonic day (E) 11.5 transgenic mice. To confirm that the orthologous mouse enhancers, mouse 280 and 1442 (M280 and M1442, respectively), regulate expression in the developing limb we generated stable transgenic lines, and examined lacZ expression. In M280-lacZ mice, expression was detected in E11.5 fore- and hindlimbs in a region that corresponds to digits II-IV. M1442-lacZ mice exhibited lacZ expression in posterior and anterior margins of the fore- and hindlimbs that overlapped with digits I and V and several wrist bones. We generated mice lacking the M280 and M1442 enhancers by gene targeting. Intercrosses between M280 -/+ and M1442 -/+, respectively, generated M280 and M1442 null mice, which are born at expected Mendelian ratios and manifest no gross limb malformations. Quantitative real-time PCR of mutant E11.5 limb buds indicated that significant changes in transcriptional output of enhancer-proximal genes accompanied the deletion of both M280 and M1442. In neonatal null mice we observed that all limb bones are present in their expected positions, an observation also confirmed by histology of E18.5 distal limbs. Fine-scale measurement of E18.5 digit bone lengths found no differences between mutant and control embryos. Furthermore, when the developmental progression of cartilaginous elements was analyzed in M280 and M1442 embryos from E13.5-E15.5, transient development defects were not detected. These results demonstrate that M280 and M1442 are not required for mouse limb development. Though M280 is not required for embryonic limb development it is required for the development and/or maintenance of body size – adult M280 mice are significantly smaller than control littermates. These studies highlight the importance of experiments that manipulate enhancers in situ to understand their contribution to development.

Absolute quantitative real-time polymerase chain reaction for the measurement of human papillomavirus E7 mRNA in cervical cytobrush specimens.

BACKGROUND: Few reports of the utilization of an accurate, cost-effective means for measuring HPV oncogene transcripts have been published. Several papers have reported the use of relative quantitation or more expensive Taqman methods. Here, we report a method of absolute quantitative real-time PCR utilizing SYBR-green fluorescence for the measurement of HPV E7 expression in cervical cytobrush specimens. RESULTS: The construction of a standard curve based on the serial dilution of an E7-containing plasmid was the key for being able to accurately compare measurements between cervical samples. The assay was highly reproducible with an overall coefficient of variation of 10.4%. CONCLUSION: The use of highly reproducible and accurate SYBR-based real-time polymerase chain reaction (PCR) assays instead of performing Taqman-type assays allows low-cost, high-throughput analysis of viral mRNA expression. The development of such assays will help in refining the current screening programs for HPV-related carcinomas.

NETWORK TOPOLOGY IN HUMAN PROTEIN INTERACTION DATA PREDICTS FUNCTIONAL ASSOCIATION

High-throughput assays, such as yeast two-hybrid system, have generated a huge amount of protein-protein interaction (PPI) data in the past decade. This tremendously increases the need for developing reliable methods to systematically and automatically suggest protein functions and relationships between them. With the available PPI data, it is now possible to study the functions and relationships in the context of a large-scale network. To data, several network-based schemes have been provided to effectively annotate protein functions on a large scale. However, due to those inherent noises in high-throughput data generation, new methods and algorithms should be developed to increase the reliability of functional annotations. Previous work in a yeast PPI network (Samanta and Liang, 2003) has shown that the local connection topology, particularly for two proteins sharing an unusually large number of neighbors, can predict functional associations between proteins, and hence suggest their functions. One advantage of the work is that their algorithm is not sensitive to noises (false positives) in high-throughput PPI data. In this study, we improved their prediction scheme by developing a new algorithm and new methods which we applied on a human PPI network to make a genome-wide functional inference. We used the new algorithm to measure and reduce the influence of hub proteins on detecting functionally associated proteins. We used the annotations of the Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) as independent and unbiased benchmarks to evaluate our algorithms and methods within the human PPI network. We showed that, compared with the previous work from Samanta and Liang, our algorithm and methods developed in this study improved the overall quality of functional inferences for human proteins. By applying the algorithms to the human PPI network, we obtained 4,233 significant functional associations among 1,754 proteins. Further comparisons of their KEGG and GO annotations allowed us to assign 466 KEGG pathway annotations to 274 proteins and 123 GO annotations to 114 proteins with estimated false discovery rates of <21% for KEGG and <30% for GO. We clustered 1,729 proteins by their functional associations and made pathway analysis to identify several subclusters that are highly enriched in certain signaling pathways. Particularly, we performed a detailed analysis on a subcluster enriched in the transforming growth factor β signaling pathway (P<10-50) which is important in cell proliferation and tumorigenesis. Analysis of another four subclusters also suggested potential new players in six signaling pathways worthy of further experimental investigations. Our study gives clear insight into the common neighbor-based prediction scheme and provides a reliable method for large-scale functional annotations in this post-genomic era.

Delineating the molecular basis of human genetic diseases: Epigenetic and functional studies

Identifying and characterizing the genes responsible for inherited human diseases will ultimately lead to a more holistic understanding of disease pathogenesis, catalyze new diagnostic and treatment modalities, and provide insights into basic biological processes. This dissertation presents research aimed at delineating the genetic and molecular basis of human diseases through epigenetic and functional studies and can be divided into two independent areas of research. The first area of research describes the development of two high-throughput melting curve based methods to assay DNA methylation, referred to as McMSP and McCOBRA. The goal of this project was to develop DNA methylation methods that can be used to rapidly determine the DNA methylation status at a specific locus in a large number of samples. McMSP and McCOBRA provide several advantages over existing methods, as they are simple, accurate, robust, and high-throughput making them applicable to large-scale DNA methylation studies. McMSP and McCOBRA were then used in an epigenetic study of the complex disease Ankylosing spondylitis (AS). Specifically, I tested the hypothesis that aberrant patterns of DNA methylation in five AS candidate genes contribute to disease susceptibility. While no statistically significant methylation differences were observed between cases and controls, this is the first study to investigate the hypothesis that epigenetic variation contributes to AS susceptibility and therefore provides the conceptual framework for future studies. ^ In the second area of research, I performed experiments to better delimit the function of aryl hydrocarbon receptor-interacting protein-like 1 (AIPL1), which when mutated causes various forms of inherited blindness such as Leber congenital amaurosis. A yeast two-hybrid screen was performed to identify putative AIPL1-interacting proteins. After screening 2 × 106 bovine retinal cDNA library clones, 6 unique putative AIPL1-interacting proteins were identified. While these 6 AIPL1 protein-protein interactions must be confirmed, their identification is an important step in understanding the functional role of AIPL1 within the retina and will provide insight into the molecular mechanisms underlying inherited blindness. ^

Nonlinear tests for genetic studies of complex disease

Linkage and association studies are major analytical tools to search for susceptibility genes for complex diseases. With the availability of large collection of single nucleotide polymorphisms (SNPs) and the rapid progresses for high throughput genotyping technologies, together with the ambitious goals of the International HapMap Project, genetic markers covering the whole genome will be available for genome-wide linkage and association studies. In order not to inflate the type I error rate in performing genome-wide linkage and association studies, multiple adjustment for the significant level for each independent linkage and/or association test is required, and this has led to the suggestion of genome-wide significant cut-off as low as 5 × 10 −7. Almost no linkage and/or association study can meet such a stringent threshold by the standard statistical methods. Developing new statistics with high power is urgently needed to tackle this problem. This dissertation proposes and explores a class of novel test statistics that can be used in both population-based and family-based genetic data by employing a completely new strategy, which uses nonlinear transformation of the sample means to construct test statistics for linkage and association studies. Extensive simulation studies are used to illustrate the properties of the nonlinear test statistics. Power calculations are performed using both analytical and empirical methods. Finally, real data sets are analyzed with the nonlinear test statistics. Results show that the nonlinear test statistics have correct type I error rates, and most of the studied nonlinear test statistics have higher power than the standard chi-square test. This dissertation introduces a new idea to design novel test statistics with high power and might open new ways to mapping susceptibility genes for complex diseases. ^

The information bottleneck method for genome-wide association studies

In population studies, most current methods focus on identifying one outcome-related SNP at a time by testing for differences of genotype frequencies between disease and healthy groups or among different population groups. However, testing a great number of SNPs simultaneously has a problem of multiple testing and will give false-positive results. Although, this problem can be effectively dealt with through several approaches such as Bonferroni correction, permutation testing and false discovery rates, patterns of the joint effects by several genes, each with weak effect, might not be able to be determined. With the availability of high-throughput genotyping technology, searching for multiple scattered SNPs over the whole genome and modeling their joint effect on the target variable has become possible. Exhaustive search of all SNP subsets is computationally infeasible for millions of SNPs in a genome-wide study. Several effective feature selection methods combined with classification functions have been proposed to search for an optimal SNP subset among big data sets where the number of feature SNPs far exceeds the number of observations. ^ In this study, we take two steps to achieve the goal. First we selected 1000 SNPs through an effective filter method and then we performed a feature selection wrapped around a classifier to identify an optimal SNP subset for predicting disease. And also we developed a novel classification method-sequential information bottleneck method wrapped inside different search algorithms to identify an optimal subset of SNPs for classifying the outcome variable. This new method was compared with the classical linear discriminant analysis in terms of classification performance. Finally, we performed chi-square test to look at the relationship between each SNP and disease from another point of view. ^ In general, our results show that filtering features using harmononic mean of sensitivity and specificity(HMSS) through linear discriminant analysis (LDA) is better than using LDA training accuracy or mutual information in our study. Our results also demonstrate that exhaustive search of a small subset with one SNP, two SNPs or 3 SNP subset based on best 100 composite 2-SNPs can find an optimal subset and further inclusion of more SNPs through heuristic algorithm doesn't always increase the performance of SNP subsets. Although sequential forward floating selection can be applied to prevent from the nesting effect of forward selection, it does not always out-perform the latter due to overfitting from observing more complex subset states. ^ Our results also indicate that HMSS as a criterion to evaluate the classification ability of a function can be used in imbalanced data without modifying the original dataset as against classification accuracy. Our four studies suggest that Sequential Information Bottleneck(sIB), a new unsupervised technique, can be adopted to predict the outcome and its ability to detect the target status is superior to the traditional LDA in the study. ^ From our results we can see that the best test probability-HMSS for predicting CVD, stroke,CAD and psoriasis through sIB is 0.59406, 0.641815, 0.645315 and 0.678658, respectively. In terms of group prediction accuracy, the highest test accuracy of sIB for diagnosing a normal status among controls can reach 0.708999, 0.863216, 0.639918 and 0.850275 respectively in the four studies if the test accuracy among cases is required to be not less than 0.4. On the other hand, the highest test accuracy of sIB for diagnosing a disease among cases can reach 0.748644, 0.789916, 0.705701 and 0.749436 respectively in the four studies if the test accuracy among controls is required to be at least 0.4. ^ A further genome-wide association study through Chi square test shows that there are no significant SNPs detected at the cut-off level 9.09451E-08 in the Framingham heart study of CVD. Study results in WTCCC can only detect two significant SNPs that are associated with CAD. In the genome-wide study of psoriasis most of top 20 SNP markers with impressive classification accuracy are also significantly associated with the disease through chi-square test at the cut-off value 1.11E-07. ^ Although our classification methods can achieve high accuracy in the study, complete descriptions of those classification results(95% confidence interval or statistical test of differences) require more cost-effective methods or efficient computing system, both of which can't be accomplished currently in our genome-wide study. We should also note that the purpose of this study is to identify subsets of SNPs with high prediction ability and those SNPs with good discriminant power are not necessary to be causal markers for the disease.^

Integrating sequence information in microarray data analysis by free energy modeling

Microarray technology is a high-throughput method for genotyping and gene expression profiling. Limited sensitivity and specificity are one of the essential problems for this technology. Most of existing methods of microarray data analysis have an apparent limitation for they merely deal with the numerical part of microarray data and have made little use of gene sequence information. Because it's the gene sequences that precisely define the physical objects being measured by a microarray, it is natural to make the gene sequences an essential part of the data analysis. This dissertation focused on the development of free energy models to integrate sequence information in microarray data analysis. The models were used to characterize the mechanism of hybridization on microarrays and enhance sensitivity and specificity of microarray measurements. ^ Cross-hybridization is a major obstacle factor for the sensitivity and specificity of microarray measurements. In this dissertation, we evaluated the scope of cross-hybridization problem on short-oligo microarrays. The results showed that cross hybridization on arrays is mostly caused by oligo fragments with a run of 10 to 16 nucleotides complementary to the probes. Furthermore, a free-energy based model was proposed to quantify the amount of cross-hybridization signal on each probe. This model treats cross-hybridization as an integral effect of the interactions between a probe and various off-target oligo fragments. Using public spike-in datasets, the model showed high accuracy in predicting the cross-hybridization signals on those probes whose intended targets are absent in the sample. ^ Several prospective models were proposed to improve Positional Dependent Nearest-Neighbor (PDNN) model for better quantification of gene expression and cross-hybridization. ^ The problem addressed in this dissertation is fundamental to the microarray technology. We expect that this study will help us to understand the detailed mechanism that determines sensitivity and specificity on the microarrays. Consequently, this research will have a wide impact on how microarrays are designed and how the data are interpreted. ^

Targeting TRAF6 for Cancer Therapeutical Development

Tumor necrosis factor (TNF)-Receptor Associated Factors (TRAFs) are a family of signal transducer proteins. TRAF6 is a unique member of this family in that it is involved in not only the TNF superfamily, but the toll-like receptor (TLR)/IL-1R (TIR) superfamily. The formation of the complex consisting of Receptor Activator of Nuclear Factor κ B (RANK), with its ligand (RANKL) results in the recruitment of TRAF6, which activates NF-κB, JNK and MAP kinase pathways. TRAF6 is critical in signaling with leading to release of various growth factors in bone, and promotes osteoclastogenesis. TRAF6 has also been implicated as an oncogene in lung cancer and as a target in multiple myeloma. In the hopes of developing small molecule inhibitors of the TRAF6-RANK interaction, multiple steps were carried out. Computational prediction of hot spot residues on the protein-protein interaction of TRAF6 and RANK were examined. Three methods were used: Robetta, KFC2, and HotPoint, each of which uses a different methodology to determine if a residue is a hot spot. These hot spot predictions were considered the basis for resolving the binding site for in silico high-throughput screening using GOLD and the MyriaScreen database of drug/lead-like compounds. Computationally intensive molecular dynamics simulations highlighted the binding mechanism and TRAF6 structural changes upon hit binding. Compounds identified as hits were verified using a GST-pull down assay, comparing inhibition to a RANK decoy peptide. Since many drugs fail due to lack of efficacy and toxicity, predictive models for the evaluation of the LD50 and bioavailability of our TRAF6 hits, and these models can be used towards other drugs and small molecule therapeutics as well. Datasets of compounds and their corresponding bioavailability and LD50 values were curated based, and QSAR models were built using molecular descriptors of these compounds using the k-nearest neighbor (k-NN) method, and quality of these models were cross-validated.

Advanced Protein Modeling Method: Benchmarking and Applications in Computer-Aided Drug Discovery

Development of homology modeling methods will remain an area of active research. These methods aim to develop and model increasingly accurate three-dimensional structures of yet uncrystallized therapeutically relevant proteins e.g. Class A G-Protein Coupled Receptors. Incorporating protein flexibility is one way to achieve this goal. Here, I will discuss the enhancement and validation of the ligand-steered modeling, originally developed by Dr. Claudio Cavasotto, via cross modeling of the newly crystallized GPCR structures. This method uses known ligands and known experimental information to optimize relevant protein binding sites by incorporating protein flexibility. The ligand-steered models were able to model, reasonably reproduce binding sites and the co-crystallized native ligand poses of the β2 adrenergic and Adenosine 2A receptors using a single template structure. They also performed better than the choice of template, and crude models in a small scale high-throughput docking experiments and compound selectivity studies. Next, the application of this method to develop high-quality homology models of Cannabinoid Receptor 2, an emerging non-psychotic pain management target, is discussed. These models were validated by their ability to rationalize structure activity relationship data of two, inverse agonist and agonist, series of compounds. The method was also applied to improve the virtual screening performance of the β2 adrenergic crystal structure by optimizing the binding site using β2 specific compounds. These results show the feasibility of optimizing only the pharmacologically relevant protein binding sites and applicability to structure-based drug design projects.

A METAGENOMIC STUDY OF THE TICK MIDGUT

A Metagenomic Study of the Tick Midgut Daniel Yuan, B.S. Supervisory Professor : Steven J. Norris, Ph.D. Southern tick–associated rash illness (STARI) or Master’s disease is a Lyme-like illness that occurs following bites by Amblyomma americanum, the lone-star tick. Clinical symptoms include a bull’s eye rash similar to the erythema migrans lesions of Lyme disease, as well as fever and joint pains. Lyme disease is caused by Borrelia burgdorferi and related spirochetes. However, B. burgdorferi has not been detected in STARI patients, or in ticks in the South Central U.S. The causative agent of STARI has not been identified, although it was once thought to be caused by another Borrelia species, Borrelia lonestari. Furthermore, while adult A. americanum have up to a 5.6% Borrelia lonestari infection rate, the prevalence of all Borrelia species in Texas ticks as a whole is not known. Previous studies indicate that 6%-30% of Northern Ixodes scapularis ticks are infected by Borrelia burgdorferi while only 10% of Northern A. americanum and I. scapularis ticks are infected by Borrelia species. The first specific aim of this project was to determine the bacterial community that inhabits the midgut of Texas and Northeastern ticks by using high throughput metagenomic sequencing to sequence bacterial 16S rDNA. Through the use of massively parallel 454 sequencing, we were able to individually sequence hundreds of thousands of 16S rDNA regions of the bacterial flora from 133 ticks from the New York, Missouri and Texas. The presence of previously confirmed endosymbionts, specifically the Rickettsia spp. and Coxiella spp., that are commonly found in ticks were confirmed, as well as some highly prevalent genera that were previously undocumented. Furthermore, multiple pathogenic genera sequences were often found in the same tick, suggesting the possibility of co-infection of multiple pathogenic species. The second specific aim was to use Borrelia specific primers to screen 344 individual ticks from Missouri, Texas and the Northeast to determine the prevalence of Borrelia species in ticks. To screen for Borrelia species, two housekeeping genes, uvrA and recG, were selected as well as the 16S-23S rDNA intergenic spacer. Ticks from Missouri, Texas and New York were screened. None of the Missouri or Texas ticks tested positive for Borrelia spp. The rate of I. scapularis infection by B.burgdorferi is dependent on tick feeding activity as well as reservoir availability. B. burgdorferi is endemic in the Northeast, sometimes reported as highly present in over 50% of all I. scapularis ticks. 11.6% of all New York ticks were positive for a species of Borrelia, however only 6.9% of all New York ticks were positive for B. burgdorferi. Despite being significantly lower than 50%, the results still fall in line with previous reports of about the prevalence of B. burgdorferi. 1.5% of all Texas ticks were positive for a Borrelia species, specifically B. lonestari. While this study was unable to identify the causative agent for STARI, 454 sequencing was able to provide a tremendous insight into the bacterial flora and possible pathogenic species of both the I. scapularis and the A. americanum tick.

Angiotensin receptor agonistic autoantibody is highly prevalent in preeclampsia: correlation with disease severity.

Preeclampsia (PE), a syndrome affecting 5% of pregnancies, characterized by hypertension and proteinuria, is a leading cause of maternal and fetal morbidity and mortality. The condition is often accompanied by the presence of a circulating maternal autoantibody, the angiotensin II type I receptor agonistic autoantibody (AT(1)-AA). However, the prevalence of AT(1)-AA in PE remains unknown, and the correlation of AT(1)-AA titers with the severity of the disease remains undetermined. We used a sensitive and high-throughput luciferase bioassay to detect AT(1)-AA levels in the serum of 30 normal, 37 preeclamptic (10 mild and 27 severe), and 23 gestational hypertensive individuals. Here we report that AT(1)-AA is highly prevalent in PE ( approximately 95%). Next, by comparing the levels of AT(1)-AA among women with mild and severe PE, we found that the titer of AT(1)-AA is proportional to the severity of the disease. Intriguingly, among severe preeclamptic patients, we discovered that the titer of AT(1)-AA is significantly correlated with the clinical features of PE: systolic blood pressure (r=0.56), proteinuria (r=0.70), and soluble fms-like tyrosine kinase-1 level (r=0.71), respectively. Notably, only AT(1)-AA, and not soluble fms-like tyrosine kinase-1, levels are elevated in gestational hypertensive patients. These data serve as compelling clinical evidence that AT(1)-AA is highly prevalent in PE, and its titer is strongly correlated to the severity of the disease.