The Therapeutic Potential of Natural Products from \u3ci\u3eVaccinium\u3c/i\u3e Berries for Viral and Lung Diseases Through an Improved LC-MS-Based Chemometric Approach

Smoking is a global epidemic that creates serious health and economic burden. It is the primary, preventative factor for the majority of causes of death worldwide. Analysis of publicly available data revealed that smoking prevalence rates among the youth in developing countries, especially in Bulgaria, are alarmingly high. The Bulgarian population has one of the highest percentages of smokers and the second highest rate among teenage girls. Consequently, chronic diseases affected by smoking, such as chronic obstructive pulmonary disorder (COPD), have been increasing there in the last five years. Poland, on the other hand, is an exemplary outlier for the region with much lower smoking prevalence rates and COPD incidence growth like those in the developed countries, such as the United States. A survey among college-aged people at Lehman College, City University of New York, determined that there were behavioral sensitivities predictive of cigarette use. Experimenters were highly sensitive to fun seeking and reward responsiveness. A larger portion of the experimenters knew people that have COPD but were not related to them. Most experimenters were not interested in learning more about smoke-related diseases. Hispanics were the majority within the experimenter group and they seemed to eat antioxidant-rich foods less often than others. Therefore, tailored anti-tobacco efforts focused on developing countries are needed to reduce country disparities of smoking prevalence and chronic diseases, such as COPD. COPD is a disease for which there is no cure and current therapies only temporarily control symptoms. Still, evidence suggests a diet rich in polyphenol-rich foods, such as apples and berries, may hold a promise in the treatment of the disease. Antioxidant-rich blueberries, cranberries, and lingonberries are temperate species in the Vaccinium genus, which produce several classes of secondary metabolites potent against aging and chronic diseases, such as COPD. A literature review of their chemistry, bioavailability, and bioactivity was conducted. Growing consumer awareness of the health-promoting effects of cranberry and blueberry compounds combined with trends towards organic farming may offer further areas of growth and development for these crops. Vaccinium berries have been studied for centuries, but their full potential to ameliorate lung and viral diseases remains to be established. These studies focused on a class of berry compounds that have not been studied extensively – the oligomeric procyanidins. Procyanidin dimer B2 and an A-type trimer were found to counteract smoke-induced responses in airway epithelial cells by decreasing an inflammatory marker interleukin 8 (IL-8) and downregulating proteolytic enzyme collagenase matrix metalloproteinase-1 (MMP-1). In contrast, procyanidin dimer A2 induced inflammation alone. Furthermore, jaboticabin, an anti-inflammatory depside, was found in several Vaccinium berries for the first time. Procyanidins have low bioavailability and upon ingestion most of these compounds pass unabsorbed through the small intestine as they enter the colon where they get metabolized by the gut microflora. Consumption of foods rich in procyanidins has been found to impact the concentration of certain microbial metabolites. The study investigated four of these metabolites for their anti-inflammatory potency in small airway epithelium, and hippuric acid was determined to significantly inhibit smoke-induced IL-8 levels. Other indirect, favorable health effects of procyanidin B2 were explored in relation to its ability to modify gene expression of antioxidant enzymes and epigenetics factors. The tested procyanidins were selected with an improved natural product discovery approach whose success was further measured by searching for the most antiviral compounds from three highbush blueberry cultivars. The approach builds on previous chemometric methods which prioritized markers with higher relative abundance in the bioactive samples. The improved approach utilized a vast chemical data set from state-of-the-art LC-MS techniques, but reduced the number of highest-ranked, potentially active markers. This first improvement was achieved by employing very similar samples, such as parallel fractions from the three cultivars. Additionally, the approach helped prioritize compounds with strong anti-HSV-1 activity while decreasing the priority of markers with weaker antiviral potency. This second improvement accounted for different levels of fractions’ antiviral activity by making two separate chemometric comparisons among the three fractions and considering only the overlapping markers. Out of the tested compounds, quercetin 3-β-D-glucoside was determined to have the strongest viricidal effect against acyclovir resistant HSV-1 strain. This study also led to the identification of antiviral cinchonain in the fruits of blueberry species for the first time. The novel chemometric approach integrates improvements in the analysis of big chemical data sets that could make the natural products drug discovery process more targeted and efficient. Procyanidin dimer B2 and the A-type trimers, prioritized through this approach, merit additional studies of their pharmacological potential for chronic lung diseases. Dark-colored berries should be further investigated for other potent antimicrobial compounds

Mechanisms of glucocorticoid insensitivity in asthma

Asthma and COPD are obstructive pulmonary diseases, which can be treated with anti-inflammatory drugs like glucocorticoids. This thesis aimed to investigate the circumstances which made that these glucocorticoids sometimes do not work like they should. We started by looking which factors predicted whether a subject with asthma would react properly to glucocorticoids. This study showed that some factor associated with eosinophilic inflammation was associated with a better response to glucocorticoid, unlike a more neutrophilic inflammation. Afterwards, we investigated possible mechanisms which could play role. Firstly, we determined that Interleukin-17A reduced the sensitivity of pulmonary epithelial cells to glucocorticoids. Therefore, the attraction of neutrophils remained present for a longer period. We showed the mechanism through which this occurred, which could be a target for future therapies in glucocorticoid insensitive disease. Th17 cells are one of the celltypes producing IL-17A and they are attracted by the protein CCL20. Therefore, we investigated the effect of glucocorticoids on CCL20 production. We found that glucocorticoids enhance the amount of CCL20 released by pulmonary epithelial cells. Possibly, this leads to a larger influx of Th17 cells in the lung. Furthermore we studied the effect of the damage that cigarette smoke does on inflammation. We found that the cell death caused by cigarette smoke leads to the release of molecules which can promote inflammation. The inhibition of a certain type of cell death, necroptosis, lead to the presence of less inflammatory cells in the lungs of mice after cigarette smoke exposition

Pipeline design to identify key features and classify the chemotherapy response on lung cancer patients using large-scale genetic data

Background: During the last decade, the interest to apply machine learning algorithms to genomic data has increased in many bioinformatics applications. Analyzing this type of data entails difficulties for managing high-dimensional data, class imbalance for knowledge extraction, identifying important features and classifying individuals. In this study, we propose a general framework to tackle these challenges with different machine learning algorithms and techniques. We apply the configuration of this framework on lung cancer patients, identifying genetic signatures for classifying response to drug treatment response. We intersect these relevant SNPs with the GWAS Catalog of the National Human Genome Research Institute and explore the Regulomedb, GTEx databases for functional analysis purposes. Results: The machine learning based solution proposed in this study is a scalable and flexible alternative to the classical uni-variate regression approach to analyze large-scale data. From 36 experiments executed using the machine learning framework design, we obtain good classification performance from the top 5 models with the highest cross-validation score and the smallest standard deviation. One thousand two hundred twenty four SNPs corresponding to the key features from the top 20 models (cross validation F1 mean >= 0.65) were compared with the GWAS Catalog finding no intersection with genome-wide significant reported hits. From these, new genetic signatures in MAE, CEP104, PRKCZ and ADRB2 show relevant biological regulatory functionality related to lung physiology. Conclusions: We have defined a machine learning framework using data with an unbalanced large data-set of SNP-arrays and imputed genotyping data from a pharmacogenomics study in lung cancer patients subjected to first-line platinum-based treatment. This approach found genome signals with no genome-wide significance in the uni-variate regression approach (GWAS Catalog) that are valuable for classifying patients, only few of them with related biological function. The effect results of these variants can be explained by the recently proposed omnigenic model hypothesis, which states that complex traits can be influenced mostly by genes outside not only by the “core genes”, mainly found by the genome-wide significant SNPs, but also by the rest of genes outside of the “core pathways” with apparent unrelated biological functionality.Peer ReviewedPostprint (published version

Bayesian Inference of Networks Across Multiple Sample Groups and Data Types

In this paper, we develop a graphical modeling framework for the inference of networks across multiple sample groups and data types. In medical studies, this setting arises whenever a set of subjects, which may be heterogeneous due to differing disease stage or subtype, is profiled across multiple platforms, such as metabolomics, proteomics, or transcriptomics data. Our proposed Bayesian hierarchical model first links the network structures within each platform using a Markov random field prior to relate edge selection across sample groups, and then links the network similarity parameters across platforms. This enables joint estimation in a flexible manner, as we make no assumptions on the directionality of influence across the data types or the extent of network similarity across the sample groups and platforms. In addition, our model formulation allows the number of variables and number of subjects to differ across the data types, and only requires that we have data for the same set of groups. We illustrate the proposed approach through both simulation studies and an application to gene expression levels and metabolite abundances on subjects with varying severity levels of Chronic Obstructive Pulmonary Disease (COPD)

Effects of OGG1 activation on mitochondrial function in response to oxidative stress

Previously held under moratorium in Chemistry Department (GSK) from 27th May 2016 to 18th June 2021.Chronic obstructive pulmonary disease (COPD) is characterized by progressive airflow limitation, loss of the alveolar unit, and increased levels of oxidative damage to macromolecules, including DNA. 8-oxoguanine (8-OG) is the most common oxidative DNA lesion and its removal and repair is executed through the base excision repair pathway. The purpose of these studies was to determine whether enhancing the activity of the DNA glycosylase, OGG1, would benefit epithelial cell health by maintaining mitochondrial function following an oxidative challenge. Cigarette smoke exposure, a risk factor for COPD, enhanced oxidative DNA damage content. However, paraquat was chosen as a more specific stimulus as it intercalates within the inner mitochondrial membrane to produce excessive amounts of mitochondrial reactive oxygen species. Levels of 8-OG were measured in A549 cells using immunofluorescence and single cell phenotypic analysis was undertaken by a high content imaging platform. Transduction of A549 cells with full length-OGG1 baculovirus lowered the maximal levels of 8-OG in mtDNA compared to null virus control cells. Conversely, administration of OGG1 siRNA rendered the cells more vulnerable to paraquat, increasing 8-OG content. Exemplars of small molecule OGG1 activators identified through a high-throughput-screen were shown to reduce paraquat-induced 8-OG formation. Moreover, OGG1 activators improved paraquat-induced loss of mitochondrial membrane potential, while paraquat-induced cytochrome c translocation to the nucleus was blocked. The paraquat-stimulated decline in the cellular energy state, i.e., the ATP/ADP ratio, was prevented in the presence of the OGG1 activators. Paraquat-induced changes in mitochondrial dynamics were blocked with the OGG1 activators, but did not affect mitochondrial mass. These data provide evidence of cytoprotection from oxidant-induced mtDNA damage when OGG1 protein is increased or activated, suggesting that the base excision repair pathway may be a target for potential small molecule intervention in COPD.Chronic obstructive pulmonary disease (COPD) is characterized by progressive airflow limitation, loss of the alveolar unit, and increased levels of oxidative damage to macromolecules, including DNA. 8-oxoguanine (8-OG) is the most common oxidative DNA lesion and its removal and repair is executed through the base excision repair pathway. The purpose of these studies was to determine whether enhancing the activity of the DNA glycosylase, OGG1, would benefit epithelial cell health by maintaining mitochondrial function following an oxidative challenge. Cigarette smoke exposure, a risk factor for COPD, enhanced oxidative DNA damage content. However, paraquat was chosen as a more specific stimulus as it intercalates within the inner mitochondrial membrane to produce excessive amounts of mitochondrial reactive oxygen species. Levels of 8-OG were measured in A549 cells using immunofluorescence and single cell phenotypic analysis was undertaken by a high content imaging platform. Transduction of A549 cells with full length-OGG1 baculovirus lowered the maximal levels of 8-OG in mtDNA compared to null virus control cells. Conversely, administration of OGG1 siRNA rendered the cells more vulnerable to paraquat, increasing 8-OG content. Exemplars of small molecule OGG1 activators identified through a high-throughput-screen were shown to reduce paraquat-induced 8-OG formation. Moreover, OGG1 activators improved paraquat-induced loss of mitochondrial membrane potential, while paraquat-induced cytochrome c translocation to the nucleus was blocked. The paraquat-stimulated decline in the cellular energy state, i.e., the ATP/ADP ratio, was prevented in the presence of the OGG1 activators. Paraquat-induced changes in mitochondrial dynamics were blocked with the OGG1 activators, but did not affect mitochondrial mass. These data provide evidence of cytoprotection from oxidant-induced mtDNA damage when OGG1 protein is increased or activated, suggesting that the base excision repair pathway may be a target for potential small molecule intervention in COPD

Post-Genomic Approaches to Personalized Medicine: Applications in Exome Sequencing, Microbiome, and COPD

Since the completion of the sequencing of the human genome at the turn of the century, genomics has revolutionized the study of biology and medicine by providing high-throughput and quantitative methods for measuring molecular activities. Microarray and next generation sequencing emerged as important inflection points where the rate of data generation skyrocketed. The high dimensionality nature and the rapid growth in the volume of data precipitated a unique computational challenge in massive data analysis and interpretation. Noise and signal structure in the data varies significantly across types of data and technologies; thus, the context of the data generation process itself plays an important role in detecting key and oftentimes subtle signals. In this dissertation, we discuss four areas where contextualizing the data aids discoveries of disease-causing variants, complex relationships in the human microecology, interplay between gene and environment, and genetic regulation of gene expression. These studies, each in its own unique way, have helped made possible discoveries and expanded the horizon of our understanding of the human body, in health and disease

Determinants of exacerbation frequency in COPD

Background: Chronic obstructive pulmonary disease (COPD) is a heterogeneous collection of conditions characterized by irreversible expiratory airflow limitation. The disease is interspersed with exacerbations; periods of acute symptomatic, physiological and functional deterioration. There are large differences in yearly exacerbation incidence rates between patients of similar COPD severity giving rise to the concept of two distinct phenotypes; frequent and infrequent exacerbators. This thesis hypothesizes that frequent exacerbators are a distinct phenotype of COPD, and identifies some of the factors that influence exacerbation frequency. Method: 356 individuals from the London COPD cohort were included in the analyses in different subgroups. All patients completed daily diary cards and reported exacerbations to the study team for sampling and treatment. Blood and sputum were collected in the stable state and at exacerbation. Samples were processed for cytokines, genetic polymorphisms and viruses. A subset of patients also had endobronchial biopsies for epithelial cell work and immunohistochemistry. Results: Patient reported exacerbation frequency can be used to accurately stratify patients into frequent and infrequent exacerbators groups in subsequent years. Frequent exacerbators were more depressed and more likely to be female then infrequent exacerbators. There was no difference in social contacts, HRV positivity or load in sputum, Vitamin D levels, or cytokine variability between frequent and infrequent exacerbators. No differences in genetic polymorphisms (ICAM-1, IL-6, IL-8, VDR, Taq1 α1 –antitrypsin) were identified between the two groups. Conclusions: The frequent exacerbator phenotype exists. There is not one single determinant of exacerbation frequency, and determinants vary with underlying disease severity