The INO80 chromatin remodeling complex is involved in the nucleotide excision repair pathway

The genomic DNA of eukaryotic cells is well organized into chromatin structures. However, these repressed structures present barriers that block the access of regulatory factors to the genome during various nuclear events. To overcome the obstacle, two major cellular processes, post-modification of histone tails and ATP-dependent chromatin remodeling, are involved in reconfiguring chromatin structure and creating accessible DNA. Despite the current research progress, much remains to be explored concerning the relationship between chromatin remodeling and DNA repair. Recently, one member of the ATP-dependent chromatin remodeling complexes, INO80, has been found to play a crucial role in DNA damage repair. However, the functions of this complex in higher eukaryotes have yet to be determined. The goal of my study is to generate a human somatic INO80 conditional knockout model and investigate the functions of Ino80 in damage repair.^ By homologous targeting of the INO80 locus in human HCT116 colon epithelial cells, I established a human somatic INO80 conditional knockout model. I have demonstrated that the conditional INO80 cells exhibited a sufficiently viable period when the INO80 protein is removed. Moreover, I found that loss of INO80 resulted in deficient UV lesion repair in response to UV while the protein levels of the NER factors such as XPC, XPA, XPD were not affected. And in vitro repair synthesis assay showed that the NER incision and repair synthesis activities were intact in the absence of INO80. Examination on the damage recognition factor XPC showed its recruitment to damage sites was impaired in the INO80 mutant cells. Loss of INO80 also led to reduced enrichment of XPA at the site of UV lesions. Despite the reduced recruitment of XPC and XPA observed in INO80 mutants, no direct interaction was detected. Meanwhile, direct interaction between INO80 and DDB1, the initial UV lesion detector, was detected by coimmunoprecipitation. UV-induced chromosome relaxation was reduced in cells devoid of INO80. These results demonstrate the INO80 complex may participates in the NER by interacting with DDB1 and having a critical role of in creating DNA accessibility for the nucleotide excision pathway. ^

A Bayesian analysis for spatial processes with application to mapping

In geographical epidemiology, maps of disease rates and disease risk provide a spatial perspective for researching disease etiology. For rare diseases or when the population base is small, the rate and risk estimates may be unstable. Empirical Bayesian (EB) methods have been used to spatially smooth the estimates by permitting an area estimate to "borrow strength" from its neighbors. Such EB methods include the use of a Gamma model, of a James-Stein estimator, and of a conditional autoregressive (CAR) process. A fully Bayesian analysis of the CAR process is proposed. One advantage of this fully Bayesian analysis is that it can be implemented simply by using repeated sampling from the posterior densities. Use of a Markov chain Monte Carlo technique such as Gibbs sampler was not necessary. Direct resampling from the posterior densities provides exact small sample inferences instead of the approximate asymptotic analyses of maximum likelihood methods (Clayton & Kaldor, 1987). Further, the proposed CAR model provides for covariates to be included in the model. A simulation demonstrates the effect of sample size on the fully Bayesian analysis of the CAR process. The methods are applied to lip cancer data from Scotland, and the results are compared. ^

DETECTION OF MALARIAL SPOROZOITES BY THE ENZYME LINKED IMMUNOSORBENT ASSAY (ELISA)

Detection of malarial sporozoites by a double antibody sandwich enzyme linked immunosorbent assay (ELISA) is described. This investigation utilized the Anopheles stephensi-Plasmodium berghei malaria model for the generation of sporozoites. Anti-sporozoite antibody was obtained from the sera of rats which had been bitten by An. stephensi with salivary gland sporozoites. Mosquitoes were irradiated prior to feeding on the rats to render the sporozoites non-viable.^ The assay employed microtiter plates coated with their rat anti-sporozoite antiserum or rat anti-sporozoite IgG. Intact and sonicated sporozoites were used as antigens. Initially, sporozoites were detected by an ELISA using staphylococcal protein A conjugated with alkaline phosphatase. Sporozoites were also detected using alkaline phosphatase or horseradish peroxidase conjugated to anti-sporozoite IgG. Best results were obtained using the alkaline phosphatase conjugate.^ This investigation included the titration of antigen, coating antibody and labelled antibody as well as studies of various incubation times. A radioimmunoassay (RIA) was also developed and compared with the ELISA for detecting sporozoites. Finally, the detection of a single infected mosquito in pools of 5 to 10 whole, uninfested ones was studied using both ELISA and RIA.^ Sonicated sporozoites were more readily detected than intact sporozoites. The lower limit of detection was approximately 500 sporozoites per ml. Results using ELISA or RIA were similar. The ability of the ELISA to detect a single infected mosquito in a pool of uninfected ones indicates that this technique has potential use in entomological field studies which aim at determining the vector status of anopheline mosquitoes. The potential of the ELISA for identifying sporozoites of different species of malaria is discussed. ^

Molecular Mechanisms of Vascular Disease in Patients with Rare Variants in MYH11

Thoracic aortic aneurysms and dissections (TAAD) are the primary disease affecting the thoracic ascending aorta, with an incidence rate of 10.4/100,000. Although about 20% of patients carry a mutation in a single gene that causes their disease, the remaining 80% of patients may also have genetic factors that increase their risk for developing TAAD. Many of the genes that predispose to TAAD encode proteins involved in smooth muscle cell (SMC) contraction and the disease-causing mutations are predicted to disrupt contractile function. SMCs are the predominant cell type in the ascending aortic wall. Mutations in MYH11, encoding the smooth muscle specific myosin heavy chain, are a rare cause of inherited TAAD. However, rare but recurrent non-synonymous variants in MYH11 are present in the general population but do not cause inherited TAAD. The goal of this study was to assess the potential role of these rare variants in vascular diseases. Two distinct variants were selected: the most commonly seen rare variant, MYH11 R247C, and a duplication of the chromosomal region spanning the MYH11 locus at 16p13.1. Genetic analyses indicated that both of these variants were significantly enriched in patients with TAAD compared with controls. A knock-in mouse model of the Myh11 R247C rare variant was generated, and these mice survive and reproduce normally. They have no structural abnormalities of the aorta or signs of aortic disease, but do have decreased aortic contractility. Myh11R247C/R247C mice also have increased proliferative response to vascular injury in vivo and increased proliferation of SMCs in vitro. Myh11R247C/R247C SMCs have decreased contractile gene and protein expression and are dedifferentiated. In fibroblasts, myosin force generation is required for maturation of focal adhesions, and enhancers of RhoA activity replace enhancers of Rac1 activity as maturation occurs. Consistent with these previous findings, focal adhesions are smaller in Myh11R247C/R247C SMCs, and there is decreased RhoA activation. A RhoA activator (CN03) rescues the dedifferentiated phenotype of Myh11R247C/R247C SMCs. Myh11R247C/R247C mice were bred with an existing murine model of aneurysm formation, the Acta2-/- mouse. Over time, mice carrying the R247C allele in conjunction with heterozygous or homozygous loss of Acta2 had significantly increased aortic diameter, and a more rapid accumulation of pathologic markers. These results suggest that the Myh11 R247C rare variant acts as a modifier gene increasing the risk for and severity of TAAD in mice. In patients with 16p13.1 duplications, aortic MYH11 expression is increased, but there is no corresponding increase in smooth muscle myosin heavy chain protein. Using SMCs that overexpress Myh11, we identified alterations in SMC phenotype leading to excessive protein turnover. All contractile proteins, not just myosin, are affected, and the proteins are turned over by autophagic degradation. Surprisingly, these cells are also more contractile compared with wild-type SMCs. The results described in this dissertation firmly establish that rare variants in MYH11 significantly affect the phenotype of SMCs. Further, the data suggests that these rare variants do increase the risk of TAAD via pathways involving altered SMC phenotype and contraction. Therefore, this study validates that these rare genetic variants alter vascular SMCs and provides model systems to explore the contribution of rare variants to disease.

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.

The assumptions and effects of conservative procedures in low -dose risk assessment for setting safety standards

Conservative procedures in low-dose risk assessment are used to set safety standards for known or suspected carcinogens. However, the assumptions upon which the methods are based and the effects of these methods are not well understood.^ To minimize the number of false-negatives and to reduce the cost of bioassays, animals are given very high doses of potential carcinogens. Results must then be extrapolated to much smaller doses to set safety standards for risks such as one per million. There are a number of competing methods that add a conservative safety factor into these calculations.^ A method of quantifying the conservatism of these methods was described and tested on eight procedures used in setting low-dose safety standards. The results using these procedures were compared by computer simulation and by the use of data from a large scale animal study.^ The method consisted of determining a "true safe dose" (tsd) according to an assumed underlying model. If one assumed that Y = the probability of cancer = P(d), a known mathematical function of the dose, then by setting Y to some predetermined acceptable risk, one can solve for d, the model's "true safe dose".^ Simulations were generated, assuming a binomial distribution, for an artificial bioassay. The eight procedures were then used to determine a "virtual safe dose" (vsd) that estimates the tsd, assuming a risk of one per million. A ratio R = ((tsd-vsd)/vsd) was calculated for each "experiment" (simulation). The mean R of 500 simulations and the probability R $<$ 0 was used to measure the over and under conservatism of each procedure.^ The eight procedures included Weil's method, Hoel's method, the Mantel-Byran method, the improved Mantel-Byran, Gross's method, fitting a one-hit model, Crump's procedure, and applying Rai and Van Ryzin's method to a Weibull model.^ None of the procedures performed uniformly well for all types of dose-response curves. When the data were linear, the one-hit model, Hoel's method, or the Gross-Mantel method worked reasonably well. However, when the data were non-linear, these same methods were overly conservative. Crump's procedure and the Weibull model performed better in these situations. ^

BIOMEDICAL LANGUAGE UNDERSTANDING AND EXTRACTION (BLUE-TEXT): A MINIMAL SYNTACTIC, SEMANTIC METHOD

Clinical text understanding (CTU) is of interest to health informatics because critical clinical information frequently represented as unconstrained text in electronic health records are extensively used by human experts to guide clinical practice, decision making, and to document delivery of care, but are largely unusable by information systems for queries and computations. Recent initiatives advocating for translational research call for generation of technologies that can integrate structured clinical data with unstructured data, provide a unified interface to all data, and contextualize clinical information for reuse in multidisciplinary and collaborative environment envisioned by CTSA program. This implies that technologies for the processing and interpretation of clinical text should be evaluated not only in terms of their validity and reliability in their intended environment, but also in light of their interoperability, and ability to support information integration and contextualization in a distributed and dynamic environment. This vision adds a new layer of information representation requirements that needs to be accounted for when conceptualizing implementation or acquisition of clinical text processing tools and technologies for multidisciplinary research. On the other hand, electronic health records frequently contain unconstrained clinical text with high variability in use of terms and documentation practices, and without commitmentto grammatical or syntactic structure of the language (e.g. Triage notes, physician and nurse notes, chief complaints, etc). This hinders performance of natural language processing technologies which typically rely heavily on the syntax of language and grammatical structure of the text. This document introduces our method to transform unconstrained clinical text found in electronic health information systems to a formal (computationally understandable) representation that is suitable for querying, integration, contextualization and reuse, and is resilient to the grammatical and syntactic irregularities of the clinical text. We present our design rationale, method, and results of evaluation in processing chief complaints and triage notes from 8 different emergency departments in Houston Texas. At the end, we will discuss significance of our contribution in enabling use of clinical text in a practical bio-surveillance setting.

Temporal lobe white matter asymmetry and language laterality in epilepsy patients.

Recent studies using diffusion tensor imaging (DTI) have advanced our knowledge of the organization of white matter subserving language function. It remains unclear, however, how DTI may be used to predict accurately a key feature of language organization: its asymmetric representation in one cerebral hemisphere. In this study of epilepsy patients with unambiguous lateralization on Wada testing (19 left and 4 right lateralized subjects; no bilateral subjects), the predictive value of DTI for classifying the dominant hemisphere for language was assessed relative to the existing standard-the intra-carotid Amytal (Wada) procedure. Our specific hypothesis is that language laterality in both unilateral left- and right-hemisphere language dominant subjects may be predicted by hemispheric asymmetry in the relative density of three white matter pathways terminating in the temporal lobe implicated in different aspects of language function: the arcuate (AF), uncinate (UF), and inferior longitudinal fasciculi (ILF). Laterality indices computed from asymmetry of high anisotropy AF pathways, but not the other pathways, classified the majority (19 of 23) of patients using the Wada results as the standard. A logistic regression model incorporating information from DTI of the AF, fMRI activity in Broca's area, and handedness was able to classify 22 of 23 (95.6%) patients correctly according to their Wada score. We conclude that evaluation of highly anisotropic components of the AF alone has significant predictive power for determining language laterality, and that this markedly asymmetric distribution in the dominant hemisphere may reflect enhanced connectivity between frontal and temporal sites to support fluent language processes. Given the small sample reported in this preliminary study, future research should assess this method on a larger group of patients, including subjects with bi-hemispheric dominance.

Evaluation of therapist and client language in Motivational Interviewing (MI) sessions: A secondary analysis of data from the Southern Methodist Alcohol Research Trial (SMART) study

In the United States, “binge” drinking among college students is an emerging public health concern due to the significant physical and psychological effects on young adults. The focus is on identifying interventions that can help decrease high-risk drinking behavior among this group of drinkers. One such intervention is Motivational interviewing (MI), a client-centered therapy that aims at resolving client ambivalence by developing discrepancy and engaging the client in change talk. Of late, there is a growing interest in determining the active ingredients that influence the alliance between the therapist and the client. This study is a secondary analysis of the data obtained from the Southern Methodist Alcohol Research Trial (SMART) project, a dismantling trial of MI and feedback among heavy drinking college students. The present project examines the relationship between therapist and client language in MI sessions on a sample of “binge” drinking college students. Of the 126 SMART tapes, 30 tapes (‘MI with feedback’ group = 15, ‘MI only’ group = 15) were randomly selected for this study. MISC 2.1, a mutually exclusive and exhaustive coding system, was used to code the audio/videotaped MI sessions. Therapist and client language were analyzed for communication characteristics. Overall, therapists adopted a MI consistent style and clients were found to engage in change talk. Counselor acceptance, empathy, spirit, and complex reflections were all significantly related to client change talk (p-values ranged from 0.001 to 0.047). Additionally, therapist ‘advice without permission’ and MI Inconsistent therapist behaviors were strongly correlated with client sustain talk (p-values ranged from 0.006 to 0.048). Simple linear regression models showed a significant correlation between MI consistent (MICO) therapist language (independent variable) and change talk (dependent variable) and MI inconsistent (MIIN) therapist language (independent variable) and sustain talk (dependent variable). The study has several limitations such as small sample size, self-selection bias, poor inter-rater reliability for the global scales and the lack of a temporal measure of therapist and client language. Future studies might consider a larger sample size to obtain more statistical power. In addition the correlation between therapist language, client language and drinking outcome needs to be explored.^

A novel method for evaluating an interaction effect of correlated continuous covariates in a linear model

Interaction effect is an important scientific interest for many areas of research. Common approach for investigating the interaction effect of two continuous covariates on a response variable is through a cross-product term in multiple linear regression. In epidemiological studies, the two-way analysis of variance (ANOVA) type of method has also been utilized to examine the interaction effect by replacing the continuous covariates with their discretized levels. However, the implications of model assumptions of either approach have not been examined and the statistical validation has only focused on the general method, not specifically for the interaction effect.^ In this dissertation, we investigated the validity of both approaches based on the mathematical assumptions for non-skewed data. We showed that linear regression may not be an appropriate model when the interaction effect exists because it implies a highly skewed distribution for the response variable. We also showed that the normality and constant variance assumptions required by ANOVA are not satisfied in the model where the continuous covariates are replaced with their discretized levels. Therefore, naïve application of ANOVA method may lead to an incorrect conclusion. ^ Given the problems identified above, we proposed a novel method modifying from the traditional ANOVA approach to rigorously evaluate the interaction effect. The analytical expression of the interaction effect was derived based on the conditional distribution of the response variable given the discretized continuous covariates. A testing procedure that combines the p-values from each level of the discretized covariates was developed to test the overall significance of the interaction effect. According to the simulation study, the proposed method is more powerful then the least squares regression and the ANOVA method in detecting the interaction effect when data comes from a trivariate normal distribution. The proposed method was applied to a dataset from the National Institute of Neurological Disorders and Stroke (NINDS) tissue plasminogen activator (t-PA) stroke trial, and baseline age-by-weight interaction effect was found significant in predicting the change from baseline in NIHSS at Month-3 among patients received t-PA therapy.^