Versatile applications of transcriptional pulsing to study mRNA turnover in mammalian cells.

Development of transcriptional pulsing approaches using the c-fos and Tet-off promoter systems greatly facilitated studies of mRNA turnover in mammalian cells. However, optimal protocols for these approaches vary for different cell types and/or physiological conditions, limiting their widespread application. In this study, we have further optimized transcriptional pulsing systems for different cell lines and developed new protocols to facilitate investigation of various aspects of mRNA turnover. We apply the Tet-off transcriptional pulsing strategy to investigate ARE-mediated mRNA decay in human erythroleukemic K562 cells arrested at various phases of the cell cycle by pharmacological inhibitors. This application facilitates studies of the role of mRNA stability in control of cell-cycle dependent gene expression. To advance the investigation of factors involved in mRNA turnover and its regulation, we have also incorporated recently developed transfection and siRNA reagents into the transcriptional pulsing approach. Using these protocols, siRNA and DNA plasmids can be effectively cotransfected into mouse NIH3T3 cells to obtain high knockdown efficiency. Moreover, we have established a tTA-harboring stable line using human bronchial epithelial BEAS-2B cells and applied the transcriptional pulsing approach to monitor mRNA deadenylation and decay kinetics in this cell system. This broadens the application of the transcriptional pulsing system to investigate the regulation of mRNA turnover related to allergic inflammation. Critical factors that need to be considered when employing these approaches are characterized and discussed.

APPLICATIONS OF EPHB4 RECEPTOR SPECIFIC PEPTIDES IN TARGETED CANCER IMAGING AND THERAPY

EphB4 receptors, a member of the largest family of receptor tyrosine kinases, are found over-expressed in a variety of tumors cells including glioma cells as well as angiogenic blood vessels. Noninvasive imaging of EphB4 could potentially increase early detection rates, monitor response to therapy directed against EphB4, and improve patient outcomes. Targeted delivery of EphB4 receptor specific peptide conjugated hollow gold nanoshells (HAuNS) into tumors has great potential in cancer imaging and photothermal therapy. In this study, we developed an EphB4 specific peptide named TNYL-RAW and labeled with radioisotope 64Cu and Cy5.5 dye. We also conjugate this specific peptide with hollow gold nanoshells (HAuNS) to evaluate targeted photothermal therapy of cancers. In vitro, 64Cu-DOTA-TNYL- RAW specifically bind to CT26 and PC-3M cells but not to A549 cells. In vivo, Small-animal PET/CT clearly showed the significant uptake of 64Cu-DOTA-TNYL-RAW in CT26 and PC-3M tumors but not in A549 tumors. Furthermore, µPET/CT and near-infrared optical imaging clearly showed the uptake of the dual labeled TNYL-RAW peptide in both U251 and U87 tumors in the brains of nude mice. In U251 tumors, Cy5.5-labeled peptide can bind to EphB4-expressing tumor blood vessels and tumors cells. But in U87 models, dual labeled peptide only could bind to tumor associated blood vessels. Also, Irradiation of PC-3M and CT-26 cell treated with TNYL-PEG-HAuNS nanopatilces with near-infrared (NIR) laser resulted in selective destruction of these cells in vitro. EphB4 targeted TNYL-PEG-HAuNS showed more photothermal killing effect on CT26 tumor model than PEG-HAuNS did. In summary, tumors with overexpression of EphB4 receptors can be noninvasively visualized by micro PET/CT with 64Cu labeled or dual labeled TNYL-RAW peptide. Targeted delivery of TNYL-RAW conjugated HAuNS into tumors can greatly improve the treatment effect of photothermal therapy. The information acquired with this study should be advantageous in improving diagnostics and future applications in photothermal ablation therapy in clinical.

The public health applications of law enforcement medical direction: A practical experience with the Dallas Police Department

In 2008, 132 law enforcement officers were killed in the line of duty in The United States. Additionally, some have explored both the public health implications of interactions with law enforcement as well as the potential benefits of the use of law enforcement officers as public health and emergency healthcare providers. By virtue of these novel analyses and techniques, professional medical direction of the emerging specialty of law enforcement medicine is needed. This paper, an analysis of law enforcement medical direction through a look at the Dallas Police Medical Direction Program, seeks to examine origins of law enforcement medicine through a comprehensive literature review, as well as begin to define to core competencies of law enforcement medical direction. ^ The unique intersection of public health, medicine and law enforcement, and the subsequent specialty that is developing to manage this interface, is in its relative infancy. An analysis of this nature is in order to begin to lay down the foundations necessary for future study and improvements in the field. ^

Bayesian and survival model for tumor relapse status and disease-specific survival, with applications to breast cancer

Breast cancer is the most common non-skin cancer and the second leading cause of cancer-related death in women in the United States. Studies on ipsilateral breast tumor relapse (IBTR) status and disease-specific survival will help guide clinic treatment and predict patient prognosis.^ After breast conservation therapy, patients with breast cancer may experience breast tumor relapse. This relapse is classified into two distinct types: true local recurrence (TR) and new ipsilateral primary tumor (NP). However, the methods used to classify the relapse types are imperfect and are prone to misclassification. In addition, some observed survival data (e.g., time to relapse and time from relapse to death)are strongly correlated with relapse types. The first part of this dissertation presents a Bayesian approach to (1) modeling the potentially misclassified relapse status and the correlated survival information, (2) estimating the sensitivity and specificity of the diagnostic methods, and (3) quantify the covariate effects on event probabilities. A shared frailty was used to account for the within-subject correlation between survival times. The inference was conducted using a Bayesian framework via Markov Chain Monte Carlo simulation implemented in softwareWinBUGS. Simulation was used to validate the Bayesian method and assess its frequentist properties. The new model has two important innovations: (1) it utilizes the additional survival times correlated with the relapse status to improve the parameter estimation, and (2) it provides tools to address the correlation between the two diagnostic methods conditional to the true relapse types.^ Prediction of patients at highest risk for IBTR after local excision of ductal carcinoma in situ (DCIS) remains a clinical concern. The goals of the second part of this dissertation were to evaluate a published nomogram from Memorial Sloan-Kettering Cancer Center, to determine the risk of IBTR in patients with DCIS treated with local excision, and to determine whether there is a subset of patients at low risk of IBTR. Patients who had undergone local excision from 1990 through 2007 at MD Anderson Cancer Center with a final diagnosis of DCIS (n=794) were included in this part. Clinicopathologic factors and the performance of the Memorial Sloan-Kettering Cancer Center nomogram for prediction of IBTR were assessed for 734 patients with complete data. Nomogram for prediction of 5- and 10-year IBTR probabilities were found to demonstrate imperfect calibration and discrimination, with an area under the receiver operating characteristic curve of .63 and a concordance index of .63. In conclusion, predictive models for IBTR in DCIS patients treated with local excision are imperfect. Our current ability to accurately predict recurrence based on clinical parameters is limited.^ The American Joint Committee on Cancer (AJCC) staging of breast cancer is widely used to determine prognosis, yet survival within each AJCC stage shows wide variation and remains unpredictable. For the third part of this dissertation, biologic markers were hypothesized to be responsible for some of this variation, and the addition of biologic markers to current AJCC staging were examined for possibly provide improved prognostication. The initial cohort included patients treated with surgery as first intervention at MDACC from 1997 to 2006. Cox proportional hazards models were used to create prognostic scoring systems. AJCC pathologic staging parameters and biologic tumor markers were investigated to devise the scoring systems. Surveillance Epidemiology and End Results (SEER) data was used as the external cohort to validate the scoring systems. Binary indicators for pathologic stage (PS), estrogen receptor status (E), and tumor grade (G) were summed to create PS+EG scoring systems devised to predict 5-year patient outcomes. These scoring systems facilitated separation of the study population into more refined subgroups than the current AJCC staging system. The ability of the PS+EG score to stratify outcomes was confirmed in both internal and external validation cohorts. The current study proposes and validates a new staging system by incorporating tumor grade and ER status into current AJCC staging. We recommend that biologic markers be incorporating into revised versions of the AJCC staging system for patients receiving surgery as the first intervention.^ Chapter 1 focuses on developing a Bayesian method to solve misclassified relapse status and application to breast cancer data. Chapter 2 focuses on evaluation of a breast cancer nomogram for predicting risk of IBTR in patients with DCIS after local excision gives the statement of the problem in the clinical research. Chapter 3 focuses on validation of a novel staging system for disease-specific survival in patients with breast cancer treated with surgery as the first intervention. ^

Biomedical informatics applications for epilepsy management: A systematic review

Epilepsy is a very complex disease which can have a variety of etiologies, co-morbidities, and a long list of psychosocial factors4. Clinical management of epilepsy patients typically includes serological tests, EEG's, and imaging studies to determine the single best antiepileptic drug (AED). Self-management is a vital component of achieving optimal health when living with a chronic disease. For patients with epilepsy self-management includes any necessary actions to control seizures and cope with any subsequent effects of the condition9; including aspects of treatment, seizure, and lifestyle. The use of computer-based applications can allow for more effective use of clinic visits and ultimately enhance the patient-provider relationship through focused discussion of determinants affecting self-management. ^ The purpose of this study is to conduct a systematic literature review on informatics application in epilepsy self-management in an effort to describe current evidence for informatics applications and decision support as an adjunct to successful clinical management of epilepsy. Each publication was analyzed for the type of study design utilized. ^ A total of 68 publications were included and categorized by the study design used, development stage, and clinical domain. Descriptive study designs comprised of three-fourths of the publications and indicate an underwhelming use of prospective studies. The vast majority of prospective studies also focused on clinician use to increase knowledge in treating patients with epilepsy. ^ Due to the chronic nature of epilepsy and the difficulty that both clinicians and patients can experience in managing epilepsy, more prospective studies are needed to evaluate applications that can effectively increase management activities. Within the last two decades of epilepsy research, management studies have employed the use of biomedical informatics applications. While the use of computer applications to manage epilepsy has increased, more progress is needed.^

RNA-SEQUENCING APPLICATIONS: GENE EXPRESSION QUANTIFICATION AND METHYLATOR PHENOTYPE IDENTIFICATION

My dissertation focuses on two aspects of RNA sequencing technology. The first is the methodology for modeling the overdispersion inherent in RNA-seq data for differential expression analysis. This aspect is addressed in three sections. The second aspect is the application of RNA-seq data to identify the CpG island methylator phenotype (CIMP) by integrating datasets of mRNA expression level and DNA methylation status. Section 1: The cost of DNA sequencing has reduced dramatically in the past decade. Consequently, genomic research increasingly depends on sequencing technology. However it remains elusive how the sequencing capacity influences the accuracy of mRNA expression measurement. We observe that accuracy improves along with the increasing sequencing depth. To model the overdispersion, we use the beta-binomial distribution with a new parameter indicating the dependency between overdispersion and sequencing depth. Our modified beta-binomial model performs better than the binomial or the pure beta-binomial model with a lower false discovery rate. Section 2: Although a number of methods have been proposed in order to accurately analyze differential RNA expression on the gene level, modeling on the base pair level is required. Here, we find that the overdispersion rate decreases as the sequencing depth increases on the base pair level. Also, we propose four models and compare them with each other. As expected, our beta binomial model with a dynamic overdispersion rate is shown to be superior. Section 3: We investigate biases in RNA-seq by exploring the measurement of the external control, spike-in RNA. This study is based on two datasets with spike-in controls obtained from a recent study. We observe an undiscovered bias in the measurement of the spike-in transcripts that arises from the influence of the sample transcripts in RNA-seq. Also, we find that this influence is related to the local sequence of the random hexamer that is used in priming. We suggest a model of the inequality between samples and to correct this type of bias. Section 4: The expression of a gene can be turned off when its promoter is highly methylated. Several studies have reported that a clear threshold effect exists in gene silencing that is mediated by DNA methylation. It is reasonable to assume the thresholds are specific for each gene. It is also intriguing to investigate genes that are largely controlled by DNA methylation. These genes are called “L-shaped” genes. We develop a method to determine the DNA methylation threshold and identify a new CIMP of BRCA. In conclusion, we provide a detailed understanding of the relationship between the overdispersion rate and sequencing depth. And we reveal a new bias in RNA-seq and provide a detailed understanding of the relationship between this new bias and the local sequence. Also we develop a powerful method to dichotomize methylation status and consequently we identify a new CIMP of breast cancer with a distinct classification of molecular characteristics and clinical features.

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.