On the Identification of Associations between Flow Cytometry Data, Systemic Sclerosis and Cancer

This work seeks to develop reliable biomarkers of disease activity, progression and outcomes through the identification of significant associations between high-throughput flow cytometry data and a scleroderma clinical phenotype – initially, interstitial lung disease (ILD) - which is the leading cause of morbidity and mortality in Systemic Sclerosis (SSc). A specific aim of the work involves developing a clinically useful screening tool (hereafter a filter). Such a filter could yield accurate assessments of disease state such as the risk or presence of SSc-ILD, the activity of lung involvement and the possibility to respond to therapeutic intervention. Ultimately this instrument should facilitate a refined stratification of SSc patients into clinically relevant subsets at the time of diagnosis and subsequently during the course of the disease, preventing bad outcomes from disease progression or unnecessary treatment side effects. This role could involve a scenario in which an SSc patient passes the presumptive (FVCstpp) test for ILD, but the filter indicates that their flow cytometry (FC) profile is consistent with ILD. In such a case, a physician might: 1) increase frequency of testing to detect early development of ILD; 2) implement more sophisticated diagnostic procedures (e.g., high resolution chest CT scan - HRCT) to confirm the presence of ILD; and 3) consider prophylactic disease modifying treatments. Note that the intention of this research is not to develop screening tools that merely aim at predictive accuracy, but to produce methods that also contribute to the understanding of disease mechanisms. Having used ILD as phenotype, subsequent analyses in this thesis used different phenotypes: antiTopoisomerase (ATA), antiCentromere Anti Nuclear Antibodies (these antibodies are most strongly associated with diffuse and limited systemic sclerosis respectively) and cancer. This research was based on clinical and peripheral blood flow cytometry data (Immune Response In Scleroderma, IRIS) from consented patients followed at the Johns Hopkins Scleroderma Center. Methods. The methods utilized in the work involve: (1) data mining (Conditional Random Forests - CRF) to identify subsets of FC variables that are highly effective in classifying ILD patients; (2) Gene Set Enrichment Analysis (GSEA) to further refine FC subsets; (3) stochastic simulation and Classification and Regression Trees (CART) to design, test and validate ILD filters; and (4) Stepwise Generalized Linear Model (GLM) regression and Drop-in-Deviance testing to identify minimal size, best performing models for predicting ILD status from both FC and selected clinical variables. Results. IRIS flow cytometry data provides useful information in assessing the ILD status of SSc patients. Our hybrid analysis approach proved successful in predicting SSc patient ILD status with a high degree of success (out-of-sample > 82%; training data set 79 patients, validation data set 40 patients). Pre-partitioning patients into groups using CART significantly increased validation performance to 95% successful ILD identification. When the phenotype was Cancer, FC subsets, created through ranked Student t Test scores and point-wise GLM were statistically significant (p < 0.05) using GSEA. After applying Stepwise GLM on the CRF FC subsets, four FC variables were observed to be highly associated with Cancer in SSc patients. An ILD-Cancer GSEA intercomparison was made (use the best ILD FC set with cancer as the phenotype, and vice-versa) showed that GSEA results were highly phenotype-specific. Other phenotypes including ATA and ACA were also analyzed and found to be statistically significantly associated with certain subset of FC variables, but with different FC set sizes (38 and 6 respectively) based on the CRF-GSEA-Stepwise GLM algorithm. In future research, HRCT confirmation of patient ILD status will be a critical next step in developing additional confidence with our approach (and the appropriateness of an 80% FVCstpp threshold for presumptive ILD determination)

The dimer state of GyrB is an active form: implications for the initial complex assembly and processive strand passage

In a previous study, we presented the dimer structure of DNA gyrase B′ domain (GyrB C-terminal domain) from Mycobacterium tuberculosis and proposed a ‘sluice-like’ model for T-segment transport. However, the role of the dimer structure is still not well understood. Cross-linking and analytical ultracentrifugation experiments showed that the dimer structure exists both in the B′ protein and in the full-length GyrB in solution. The cross-linked dimer of GyrB bound GyrA very weakly, but bound dsDNA with a much higher affinity than that of the monomer state. Using cross-linking and far-western analyses, the dimer state of GyrB was found to be involved in the ternary GyrA–GyrB–DNA complex. The results of mutational studies reveal that the dimer structure represents a state before DNA cleavage. Additionally, these results suggest that the dimer might also be present between the cleavage and reunion steps during processive transport

A methodology for exploring biomarker – phenotype associations: application to flow cytometry data and systemic sclerosis clinical manifestations

BACKGROUND: This work seeks to develop a methodology for identifying reliable biomarkers of disease activity, progression and outcome through the identification of significant associations between high-throughput flow cytometry (FC) data and interstitial lung disease (ILD) - a systemic sclerosis (SSc, or scleroderma) clinical phenotype which is the leading cause of morbidity and mortality in SSc. A specific aim of the work involves developing a clinically useful screening tool that could yield accurate assessments of disease state such as the risk or presence of SSc-ILD, the activity of lung involvement and the likelihood to respond to therapeutic intervention. Ultimately this instrument could facilitate a refined stratification of SSc patients into clinically relevant subsets at the time of diagnosis and subsequently during the course of the disease and thus help in preventing bad outcomes from disease progression or unnecessary treatment side effects. The methods utilized in the work involve: (1) clinical and peripheral blood flow cytometry data (Immune Response In Scleroderma, IRIS) from consented patients followed at the Johns Hopkins Scleroderma Center. (2) machine learning (Conditional Random Forests - CRF) coupled with Gene Set Enrichment Analysis (GSEA) to identify subsets of FC variables that are highly effective in classifying ILD patients; and (3) stochastic simulation to design, train and validate ILD risk screening tools. RESULTS: Our hybrid analysis approach (CRF-GSEA) proved successful in predicting SSc patient ILD status with a high degree of success (>82 % correct classification in validation; 79 patients in the training data set, 40 patients in the validation data set). CONCLUSIONS: IRIS flow cytometry data provides useful information in assessing the ILD status of SSc patients. Our new approach combining Conditional Random Forests and Gene Set Enrichment Analysis was successful in identifying a subset of flow cytometry variables to create a screening tool that proved effective in correctly identifying ILD patients in the training and validation data sets. From a somewhat broader perspective, the identification of subsets of flow cytometry variables that exhibit coordinated movement (i.e., multi-variable up or down regulation) may lead to insights into possible effector pathways and thereby improve the state of knowledge of systemic sclerosis pathogenesis. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12859-015-0722-x) contains supplementary material, which is available to authorized users

Connecting the Dots: Linking Environmental Justice Indicators to Daily Dose Model Estimates

Many different quantitative techniques have been developed to either assess Environmental Justice (EJ) issues or estimate exposure and dose for risk assessment. However, very few approaches have been applied to link EJ factors to exposure dose estimate and identify potential impacts of EJ factors on dose-related variables. The purpose of this study is to identify quantitative approaches that incorporate conventional risk assessment (RA) dose modeling and cumulative risk assessment (CRA) considerations of disproportionate environmental exposure. We apply the Average Daily Dose (ADD) model, which has been commonly used in RA, to better understand impacts of EJ indicators upon exposure dose estimates and dose-related variables, termed the Environmental-Justice-Average-Daily-Dose (EJ-ADD) approach. On the U.S. nationwide census tract-level, we defined and quantified two EJ indicators (poverty and race/ethnicity) using an EJ scoring method to examine their relation to census tract-level multi-chemical exposure dose estimates. Pollutant doses for each tract were calculated using the ADD model, and EJ scores were assigned to each tract based on poverty- or race-related population percentages. Single- and multiple-chemical ADD values were matched to the tract-level EJ scores to analyze disproportionate dose relationships and contributing EJ factors. We found that when both EJ indicators were examined simultaneously, ADD for all pollutants generally increased with larger EJ scores. To demonstrate the utility of using EJ-ADD on the local scale, we approximated ADD levels of lead via soil/dust ingestion for simulated communities with different EJ-related scenarios. The local-level simulation indicates a substantial difference in exposure-dose levels between wealthy and EJ communities. The application of the EJ-ADD approach can link EJ factors to exposure dose estimate and identify potential EJ impacts on dose-related variables

Associations between socio-demographic characteristics and chemical concentrations contributing to cumulative exposures in the United States

Association rule mining (ARM) has been widely used to identify associations between various entities in many fields. Although some studies have utilized it to analyze the relationship between chemicals and human health effects, fewer have used this technique to identify and quantify associations between environmental and social stressors. Socio-demographic variables were generated based on U.S. Census tract-level income, race/ethnicity population percentage, education level, and age information from the 2010-2014, 5-Year Summary files in the American Community Survey (ACS) database, and chemical variables were generated by utilizing the 2011 National-Scale Air Toxics Assessment (NATA) census tract-level air pollutant exposure concentration data. Six mobile- and industrial-source pollutants were chosen for analysis, including acetaldehyde, benzene, cyanide, particulate matter components of diesel engine emissions (namely, diesel PM), toluene, and 1,3-butadiene. ARM was then applied to quantify and visualize the associations between the chemical and socio-demographic variables. Census tracts with a high percentage of racial/ethnic minorities and populations with low income tended to have higher estimated chemical exposure concentrations (fourth quartile), especially for diesel PM, 1,3-butadiene, and toluene. In contrast, census tracts with an average population age of 40-50 years, a low percentage of racial/ethnic minorities, and moderate-income levels were more likely to have lower estimated chemical exposure concentrations (first quartile). Unsupervised data mining methods can be used to evaluate potential associations between environmental inequalities and social disparities, while providing support in public health decision-making contexts