Network-based analysis of genome-wide scans of natural selection

The genomic era brought by recent advances in the next-generation sequencing technology makes the genome-wide scans of natural selection a reality. Currently, almost all the statistical tests and analytical methods for identifying genes under selection was performed on the individual gene basis. Although these methods have the power of identifying gene subject to strong selection, they have limited power in discovering genes targeted by moderate or weak selection forces, which are crucial for understanding the molecular mechanisms of complex phenotypes and diseases. Recent availability and rapid completeness of many gene network and protein-protein interaction databases accompanying the genomic era open the avenues of exploring the possibility of enhancing the power of discovering genes under natural selection. The aim of the thesis is to explore and develop normal mixture model based methods for leveraging gene network information to enhance the power of natural selection target gene discovery. The results show that the developed statistical method, which combines the posterior log odds of the standard normal mixture model and the Guilt-By-Association score of the gene network in a naïve Bayes framework, has the power to discover moderate/weak selection gene which bridges the genes under strong selection and it helps our understanding the biology under complex diseases and related natural selection phenotypes.^

Dynamic Chemical Shift Imaging for Image-Guided Thermal Therapy

Magnetic resonance temperature imaging (MRTI) is recognized as a noninvasive means to provide temperature imaging for guidance in thermal therapies. The most common method of estimating temperature changes in the body using MR is by measuring the water proton resonant frequency (PRF) shift. Calculation of the complex phase difference (CPD) is the method of choice for measuring the PRF indirectly since it facilitates temperature mapping with high spatiotemporal resolution. Chemical shift imaging (CSI) techniques can provide the PRF directly with high sensitivity to temperature changes while minimizing artifacts commonly seen in CPD techniques. However, CSI techniques are currently limited by poor spatiotemporal resolution. This research intends to develop and validate a CSI-based MRTI technique with intentional spectral undersampling which allows relaxed parameters to improve spatiotemporal resolution. An algorithm based on autoregressive moving average (ARMA) modeling is developed and validated to help overcome limitations of Fourier-based analysis allowing highly accurate and precise PRF estimates. From the determined acquisition parameters and ARMA modeling, robust maps of temperature using the k-means algorithm are generated and validated in laser treatments in ex vivo tissue. The use of non-PRF based measurements provided by the technique is also investigated to aid in the validation of thermal damage predicted by an Arrhenius rate dose model.

DUAL ENERGY COMPUTED TOMOGRAPHY FOR PROTON THERAPY TREATMENT PLANNING

Proton radiation therapy is gaining popularity because of the unique characteristics of its dose distribution, e.g., high dose-gradient at the distal end of the percentage-depth-dose curve (known as the Bragg peak). The high dose-gradient offers the possibility of delivering high dose to the target while still sparing critical organs distal to the target. However, the high dose-gradient is a double-edged sword: a small shift of the highly conformal high-dose area can cause the target to be substantially under-dosed or the critical organs to be substantially over-dosed. Because of that, large margins are required in treatment planning to ensure adequate dose coverage of the target, which prevents us from realizing the full potential of proton beams. Therefore, it is critical to reduce uncertainties in the proton radiation therapy. One major uncertainty in a proton treatment is the range uncertainty related to the estimation of proton stopping power ratio (SPR) distribution inside a patient. The SPR distribution inside a patient is required to account for tissue heterogeneities when calculating dose distribution inside the patient. In current clinical practice, the SPR distribution inside a patient is estimated from the patient’s treatment planning computed tomography (CT) images based on the CT number-to-SPR calibration curve. The SPR derived from a single CT number carries large uncertainties in the presence of human tissue composition variations, which is the major drawback of the current SPR estimation method. We propose to solve this problem by using dual energy CT (DECT) and hypothesize that the range uncertainty can be reduced by a factor of two from currently used value of 3.5%. A MATLAB program was developed to calculate the electron density ratio (EDR) and effective atomic number (EAN) from two CT measurements of the same object. An empirical relationship was discovered between mean excitation energies and EANs existing in human body tissues. With the MATLAB program and the empirical relationship, a DECT-based method was successfully developed to derive SPRs for human body tissues (the DECT method). The DECT method is more robust against the uncertainties in human tissues compositions than the current single-CT-based method, because the DECT method incorporated both density and elemental composition information in the SPR estimation. Furthermore, we studied practical limitations of the DECT method. We found that the accuracy of the DECT method using conventional kV-kV x-ray pair is susceptible to CT number variations, which compromises the theoretical advantage of the DECT method. Our solution to this problem is to use a different x-ray pair for the DECT. The accuracy of the DECT method using different combinations of x-ray energies, i.e., the kV-kV, kV-MV and MV-MV pair, was compared using the measured imaging uncertainties for each case. The kV-MV DECT was found to be the most robust against CT number variations. In addition, we studied how uncertainties propagate through the DECT calculation, and found general principles of selecting x-ray pairs for the DECT method to minimize its sensitivity to CT number variations. The uncertainties in SPRs estimated using the kV-MV DECT were analyzed further and compared to those using the stoichiometric method. The uncertainties in SPR estimation can be divided into five categories according to their origins: the inherent uncertainty, the DECT modeling uncertainty, the CT imaging uncertainty, the uncertainty in the mean excitation energy, and SPR variation with proton energy. Additionally, human body tissues were divided into three tissue groups – low density (lung) tissues, soft tissues and bone tissues. The uncertainties were estimated separately because their uncertainties were different under each condition. An estimate of the composite range uncertainty (2s) was determined for three tumor sites – prostate, lung, and head-and-neck, by combining the uncertainty estimates of all three tissue groups, weighted by their proportions along typical beam path for each treatment site. In conclusion, the DECT method holds theoretical advantages in estimating SPRs for human tissues over the current single-CT-based method. Using existing imaging techniques, the kV-MV DECT approach was capable of reducing the range uncertainty from the currently used value of 3.5% to 1.9%-2.3%, but it is short to reach our original goal of reducing the range uncertainty by a factor of two. The dominant source of uncertainties in the kV-MV DECT was the uncertainties in CT imaging, especially in MV CT imaging. Further reduction in beam hardening effect, the impact of scatter, out-of-field object etc. would reduce the Hounsfeld Unit variations in CT imaging. The kV-MV DECT still has the potential to reduce the range uncertainty further.

Network-based genome-wide association study of Crohn's disease

Genome-wide association studies (GWAS) have rapidly become a standard method for disease gene discovery. Many recent GWAS indicate that for most disorders, only a few common variants are implicated and the associated SNPs explain only a small fraction of the genetic risk. The current study incorporated gene network information into gene-based analysis of GWAS data for Crohn's disease (CD). The purpose was to develop statistical models to boost the power of identifying disease-associated genes and gene subnetworks by maximizing the use of existing biological knowledge from multiple sources. The results revealed that Markov random field (MRF) based mixture model incorporating direct neighborhood information from a single gene network is not efficient in identifying CD-related genes based on the GWAS data. The incorporation of solely direct neighborhood information might lead to the low efficiency of these models. Alternative MRF models looking beyond direct neighboring information are necessary to be developed in the future for the purpose of this study.^

Gene-gene interaction and gene-pathway analysis of genome-wide association for schizophrenia

Schizophrenia (SZ) is a complex disorder with high heritability and variable phenotypes that has limited success in finding causal genes associated with the disease development. Pathway-based analysis is an effective approach in investigating the molecular mechanism of susceptible genes associated with complex diseases. The etiology of complex diseases could be a network of genetic factors and within the genes, interaction may occur. In this work we argue that some genes might be of small effect that by itself are neither sufficient nor necessary to cause the disease however, their effect may induce slight changes to the gene expression or affect the protein function, therefore, analyzing the gene-gene interaction mechanism within the disease pathway would play crucial role in dissecting the genetic architecture of complex diseases, making the pathway-based analysis a complementary approach to GWAS technique. ^ In this study, we implemented three novel linkage disequilibrium based statistics, the linear combination, the quadratic, and the decorrelation test statistics, to investigate the interaction between linked and unlinked genes in two independent case-control GWAS datasets for SZ including participants of European (EA) and African (AA) ancestries. The EA population included 1,173 cases and 1,378 controls with 729,454 genotyped SNPs, while the AA population included 219 cases and 288 controls with 845,814 genotyped SNPs. We identified 17,186 interacting gene-sets at significant level in EA dataset, and 12,691 gene-sets in AA dataset using the gene-gene interaction method. We also identified 18,846 genes in EA dataset and 19,431 genes in AA dataset that were in the disease pathways. However, few genes were reported of significant association to SZ. ^ Our research determined the pathways characteristics for schizophrenia through the gene-gene interaction and gene-pathway based approaches. Our findings suggest insightful inferences of our methods in studying the molecular mechanisms of common complex diseases.^

PREPARATION AND CHARACTERIZATION OF COVALENTLY LINKED ENZYME-ANTIBODY CONJUGATES AND THEIR USE IN MEASURING MINUTE QUANTITIES OF PROTEIN ANTIGENS

The discovery and characterization of oncofetal proteins have led to significant advances in early cancer diagnosis and therapeutic monitoring of patients undergoing cancer chemotherapy. These tumor-associated antigens are presently measured by sensitive, specific immunoassay techniques based on the detection of minute amounts of labeled antigen or antibody incorporated into immune complexes, which must be isolated from free antigen and antibody.^ Since there are several disadvantages with using radioisotopes, the most common immunolabel, one major objective was to prepare covalently coupled enzyme-antibody conjugates and evaluate their use as a practical alternative to radiolabeled immune reagents. An improved technique for the production of enzyme-antibody conjugates was developed that involves oxidizing the carbohydrate moieties on a glycoprotein enzyme, then introducing antibody in the presence of polyethylene glycol (PEG). Covalent enzyme-antibody conjugates involving alkaline phosphatase and amyloglucosidase were produced and characterized.^ In order to increase the sensitivity of detecting the amyloglucosidase-antibody conjugate, an enzyme cycling assay was developed that measures glucose, the product of maltose cleavage by amyloglucosidase, in the picomole range. The increased sensitivity obtained by combined usage of the amyloglucosidase-antibody conjugate and enzyme cycling assay was then compared to that of conventional enzyme immunoassay (EIA).^ For immune complex isolation, polystyrene tubes and protein A-bearing Staphylococcus aureus were evaluated as solid phase matrices, upon which antibodies can be immobilized. A sandwich-type EIA, using antibody-coated S. aureus, was developed that measures human albumin (HSA) in the nanogram range. The assay, using an alkaline phosphatase-anti-HSA conjugate, was applied to the determination of HSA in human urine and evaluated extensively for its clinical applicability.^ Finally, in view of the clinical significance of alpha-fetoprotein (AFP) as an oncofetal antigen and the difficulty with its purification for use as an immunogen and assay standard, a chemical purification protocol was developed that resulted in a high yield of immunochemically pure AFP. ^

Evaluation of epidemiological and clinical factors in survival of patients with de novo myelodysplastic syndrome at the University of Texas MD Anderson cancer center

Little is known about epidemiological markers that are associated with survival of patients with myelodysplastic syndromes (MDS). We conducted a secondary case-based analysis of 465 de novo MDS patients from the University of Texas MD Anderson Cancer Center (UTMDACC). We investigated the association between demographic as well as occupational exposure markers and survival while incorporating known clinical markers of prognosis. In our patient population, 60.6% were men and the majority were white (93.1%). The distribution of MDS subtypes by the French–American–British (FAB) classification was 81 (19%) refractory anemia (RA), 46 (9.9%) refractory anemia with ringed sideroblasts (RARS), 57 (12.3%) chronic myelomonocytic leukemia (CMML), 173 (37.2%) RA with excess blasts (RAEB), and 86 (18.5%) RAEB in transformation (RAEBT). We found that those older at diagnosis (> 60 years of age) (HR = 1.68, CI = 1.26-2.25) were at a higher risk of dying compared to younger patients. Similarly, high pack years of smoking (>= 30 pack years of smoking) (HR = 1.34, CI = 1.02-1.74), and agricultural chemical exposure (HR = 1.61, CI = 1.05-2.46) were significantly associated with overall lower survival when compared to patients with none or medium exposures. Among clinical markers, greater than 5% bone marrow blasts (HR = 1.81 CI = 1.27-2.56), poor cytogenetics (HR = 3.20, CI = 2.37-4.33)), and platelet cytopenias (<100000/ul) (HR = 1.46, CI = 1.11-1.92) were also significantly associated with overall MDS survival.^ The identification of epidemiological markers could help physicians stratify patients and customize treatment strategies to improve the outcome of MDS based on patient lifestyle information such as smoking exposure and agrochemical exposure. We hope that this study highlights the impact of these exposures in MDS prognosis.^

Systems Biology Approaches to Probe Gene Regulation in Bacteria

Mechanisms that allow pathogens to colonize the host are not the product of isolated genes, but instead emerge from the concerted operation of regulatory networks. Therefore, identifying components and the systemic behavior of networks is necessary to a better understanding of gene regulation and pathogenesis. To this end, I have developed systems biology approaches to study transcriptional and post-transcriptional gene regulation in bacteria, with an emphasis in the human pathogen Mycobacterium tuberculosis (Mtb). First, I developed a network response method to identify parts of the Mtb global transcriptional regulatory network utilized by the pathogen to counteract phagosomal stresses and survive within resting macrophages. As a result, the method unveiled transcriptional regulators and associated regulons utilized by Mtb to establish a successful infection of macrophages throughout the first 14 days of infection. Additionally, this network-based analysis identified the production of Fe-S proteins coupled to lipid metabolism through the alkane hydroxylase complex as a possible strategy employed by Mtb to survive in the host. Second, I developed a network inference method to infer the small non-coding RNA (sRNA) regulatory network in Mtb. The method identifies sRNA-mRNA interactions by integrating a priori knowledge of possible binding sites with structure-driven identification of binding sites. The reconstructed network was useful to predict functional roles for the multitude of sRNAs recently discovered in the pathogen, being that several sRNAs were postulated to be involved in virulence-related processes. Finally, I applied a combined experimental and computational approach to study post-transcriptional repression mediated by small non-coding RNAs in bacteria. Specifically, a probabilistic ranking methodology termed rank-conciliation was developed to infer sRNA-mRNA interactions based on multiple types of data. The method was shown to improve target prediction in Escherichia coli, and therefore is useful to prioritize candidate targets for experimental validation.

Exploration of genetic susceptibility to pancreatic cancer: A GWAS approach

Pancreatic cancer is the 4th most common cause for cancer death in the United States, accompanied by less than 5% five-year survival rate based on current treatments, particularly because it is usually detected at a late stage. Identifying a high-risk population to launch an effective preventive strategy and intervention to control this highly lethal disease is desperately needed. The genetic etiology of pancreatic cancer has not been well profiled. We hypothesized that unidentified genetic variants by previous genome-wide association study (GWAS) for pancreatic cancer, due to stringent statistical threshold or missing interaction analysis, may be unveiled using alternative approaches. To achieve this aim, we explored genetic susceptibility to pancreatic cancer in terms of marginal associations of pathway and genes, as well as their interactions with risk factors. We conducted pathway- and gene-based analysis using GWAS data from 3141 pancreatic cancer patients and 3367 controls with European ancestry. Using the gene set ridge regression in association studies (GRASS) method, we analyzed 197 pathways from the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. Using the logistic kernel machine (LKM) test, we analyzed 17906 genes defined by University of California Santa Cruz (UCSC) database. Using the likelihood ratio test (LRT) in a logistic regression model, we analyzed 177 pathways and 17906 genes for interactions with risk factors in 2028 pancreatic cancer patients and 2109 controls with European ancestry. After adjusting for multiple comparisons, six pathways were marginally associated with risk of pancreatic cancer ( P < 0.00025): Fc epsilon RI signaling, maturity onset diabetes of the young, neuroactive ligand-receptor interaction, long-term depression (Ps < 0.0002), and the olfactory transduction and vascular smooth muscle contraction pathways (P = 0.0002; Nine genes were marginally associated with pancreatic cancer risk (P < 2.62 × 10−5), including five reported genes (ABO, HNF1A, CLPTM1L, SHH and MYC), as well as four novel genes (OR13C4, OR 13C3, KCNA6 and HNF4 G); three pathways significantly interacted with risk factors on modifying the risk of pancreatic cancer (P < 2.82 × 10−4): chemokine signaling pathway with obesity ( P < 1.43 × 10−4), calcium signaling pathway (P < 2.27 × 10−4) and MAPK signaling pathway with diabetes (P < 2.77 × 10−4). However, none of the 17906 genes tested for interactions survived the multiple comparisons corrections. In summary, our current GWAS study unveiled unidentified genetic susceptibility to pancreatic cancer using alternative methods. These novel findings provide new perspectives on genetic susceptibility to and molecular mechanisms of pancreatic cancer, once confirmed, will shed promising light on the prevention and treatment of this disease. ^

Strange bedfellows: The ethics and politics of medical futility in Texas

Background: Futile medical treatments are interventions that are not associated with a benefit to the patient. The definition and concept of medical futility are controversial. The Texas Advance Directives Act (TADA) was passed in 1999 to address medically inappropriate interventions by allowing providers to withdraw inappropriate interventions against a surrogate decision maker's wishes following a review, attempt to transfer the patient, and 10-day waiting period. The original legislation was a negotiated compromise by players across the political spectrum. However, in recent years there has been increasing controversy regarding TADA and attempts to alter its applicability in Texas. ^ Purpose: The purpose of this project was to apply Paul Sabatier's advocacy coalition framework (ACF) to gain understanding into the historical, ethical, and political basis of the initial compromise, and determine the sources of conflict that have led to increased opposition to TADA. ^ Methods: Using the ACF model, key actors within the medical futility policy debate in Texas were aggregated into coalitions based on shared beliefs. A narrative summary based analysis identified the core elements of the policy subsystem, as well as the constraints and resources of the subsystem actors. Externalities that promoted adjustments to coalition beliefs and tactics used by coalition participants were analyzed. Data sources included review of the published literature regarding medical futility, as well as analysis of published newspaper accounts and editorials regarding the medical futility issue in Texas, legislative testimony, and review of weblogs and online commentaries dealing with the issue. ^ Results: Primary coalition participants in developing compromise legislation in 1999 were the Providers and Vitalists, with Autonomists gaining a prominent role starting in 2006. Internal factors associated with the breakdown of consensus included changes to the makeup of the governing coalition and changes in individual case information available to the Vitalist coalition. Externalities related to the intertwining of the Sun Hudson case and the Terri Schiavo case generated negative publicity for the TADA from progressive and conservative viewpoints. Dissemination of information in various venues regarding contentious cases was associated with more polarization of viewpoints, and realignment of coalition alliances. ^ Conclusions: The ACF provided an outline for the initial compromise over the creation of the Texas Advance Directives Act as well as the eventual loss of consensus. The debate between the Provider, Vitalist, and Autonomist coalitions has been affected by internal policy evolution, changes in the governing coalition, and important externalities. The debate over medical futility in Texas has had much broader implications in the dispute over Health Care Reform.^