Effects of glyceollin on mRNA expression in the female mouse brain.

Glyceollins (Glys), produced by soy plants in response to stress, have anti-estrogenic activity in breast and ovarian cancer cell lines in vitro and in vivo. In addition to known anti-estrogenic effects, Glys exhibit mechanisms of action not involving estrogen receptor (ER) signaling. To date, effects of Glys on brain physiology and function are unknown. The purpose of the experiments summarized in this dissertation was to gain an understanding of the effects of Gly on brain-related functions in the female mouse brain through the observation of changes in gene expression. For our initial studies, we treated ovariectomized Swiss Webster (CFW) mice with 17-β estradiol (E2) or placebo pellets, followed by 11 days of exposure to Glys or vehicle i.p. injections. We then performed microarray (Chapter 2) and RNA-sequencing analyses (Chapter 3) on total RNA extracted from whole brain hemispheres and identified differentially expressed genes (DEGs) between our treatment groups. Our results suggested that Glys, when in combination with E2 (E2+Gly), can oppose the E2 effects on gene expression and vice versa, can regulate genes similarly to E2, and can also have E2-independent effects on gene expression in the female brain. However, the whole brain experiments did not take into account the heterogeneity of the brain. Different brain regions perform unique and distinct functions and can differ markedly in terms of gene expression, so we wanted to determine if Glys had any brain region-specific effects on gene expression. Additionally, as the whole brain studies only included a single time point of exposure to Glys, we evaluated the effects of a single acute dose of Glys (2, 24 and 48 hr) as well as chronic exposure to Glys (multiple doses of Glys for 7 consecutive days) on gene expression in distinct brain regions. Therefore, in Chapter 4, we evaluated the effects of acute vs. chronic doses of Glys alone and in combination with E2 on gene expression in the hypothalamus, hippocampus, and cortex of the female mouse brain. Our results suggest that Glys can rapidly upregulate the expression of genes like growth hormone (Gh) in the hypothalamus, hippocampus and cortex and prolactin (Prl) in the hypothalamus and cortex, 2h or 24h after administration of a single acute dose. Thus Glys may potentially affect neuronal processes like food intake, stress and cognition through its effects on Gh and Prl gene expression in the female mouse brain. As all of the above chapters involve a peripheral administration of Glys (intraperitoneal injections), it was unclear if Glys affect gene expression through direct action at the neuron or through some indirect peripheral effect. To address this issue, in Chapter 5 we screened five immortalized neural cell lines derived from the adult female mouse hypothalamus (mHypoA-50, 51, 55, 59 and 63) for the presence of our genes of interest and E2 responsiveness. Based on consistency of mRNA transcript detection and E2 responsivity, we selected two cell lines (mHypoA-55 and 63) that may be suitable for future experiments to determine the direct effect of Glys on gene expression at the neuron. Together this work provides novel information on the effects of Glys in the brain, which is important in order to develop its use as a dietary supplement and/or therapeutic agent

Bayesian nonparametric clusterings in relational and high-dimensional settings with applications in bioinformatics.

Recent advances in high throughput methodologies offer researchers the ability to understand complex systems via high dimensional and multi-relational data. One example is the realm of molecular biology where disparate data (such as gene sequence, gene expression, and interaction information) are available for various snapshots of biological systems. This type of high dimensional and multirelational data allows for unprecedented detailed analysis, but also presents challenges in accounting for all the variability. High dimensional data often has a multitude of underlying relationships, each represented by a separate clustering structure, where the number of structures is typically unknown a priori. To address the challenges faced by traditional clustering methods on high dimensional and multirelational data, we developed three feature selection and cross-clustering methods: 1) infinite relational model with feature selection (FIRM) which incorporates the rich information of multirelational data; 2) Bayesian Hierarchical Cross-Clustering (BHCC), a deterministic approximation to Cross Dirichlet Process mixture (CDPM) and to cross-clustering; and 3) randomized approximation (RBHCC), based on a truncated hierarchy. An extension of BHCC, Bayesian Congruence Measuring (BCM), is proposed to measure incongruence between genes and to identify sets of congruent loci with identical evolutionary histories. We adapt our BHCC algorithm to the inference of BCM, where the intended structure of each view (congruent loci) represents consistent evolutionary processes. We consider an application of FIRM on categorizing mRNA and microRNA. The model uses latent structures to encode the expression pattern and the gene ontology annotations. We also apply FIRM to recover the categories of ligands and proteins, and to predict unknown drug-target interactions, where latent categorization structure encodes drug-target interaction, chemical compound similarity, and amino acid sequence similarity. BHCC and RBHCC are shown to have improved predictive performance (both in terms of cluster membership and missing value prediction) compared to traditional clustering methods. Our results suggest that these novel approaches to integrating multi-relational information have a promising future in the biological sciences where incorporating data related to varying features is often regarded as a daunting task

The Obesity Epidemic in Turkey: A System Dynamics and Behavioral Economics Approach in the context of an Obesogenic System

Obesity is an increasing problem across the world, and it has risen dramatically in the last decades. It is a major risk factor for noncommunicable diseases which are the world’s leading cause of death. In Turkey, the obesity epidemic is becoming a growing concern. Policies against obesity have had minimal success thus far. Given this issue, the aim of this study is to analyze the underlying structure of the obesity problem from a system’s perspective since the obesogenic system is a complex adaptive system. Therefore, this study uncovers the dynamic interactions within this system and resulting behavior patterns by developing a system dynamics simulation model. Furthermore, behavioral economics and reinforcement pathology frameworks are integrated into the model to provide policymakers with more robust insights. This thesis employs a system dynamics methodology to analyze aggregated level interactions between system components to understand complex systems. Combining system dynamics with behavioral economics and reinforcement pathology frameworks provides a guide to this complex adaptive system to understand how the obesogenic environment shapes individual decision-making. A theoretical model developed to show how reinforcement pathology occurs within the obesogenic environment, as well as the feedback loop analysis to identify important feedbacks within the system. Thereafter, the theoretical model quantified into a system dynamics simulation model that generates the behavior pattern and trend from endogenous interactions for further analysis of the system. According to the findings, the obesogenic environment is a complex adaptive system where ingestive behavior is shaped by the environment as well as the environment is influenced by the ingestive behavior. It was found that this system is dominated by many uncontrolled powerful reinforcing feedback loops at various levels interacting with each other. In addition, the study found that reinforcement pathology framework integrated to system dynamics methodology shows how environmental factors are making food consumption more valuable, more reinforcing within this adaptive system, hence affecting individual behavior. Additionally, the study also identified several leverage points to intervene obesogenic system namely intervening reinforcement pathology feedback loop by creating substitutes for food, the weak balancing feedback loop that fails to balance the relative reinforcing value of food and lack of rules within the system especially mechanisms that reward individuals with healthier lifestyle. In conclusion, the study showed that without a clear understanding feedback mechanisms working within an obesogenic environment and interventions that aim to address those feedback processes may result in less effective policies. This research sheds some light into understanding the obesity problem as a complex adaptive system and how the system can be leveraged to help reduce obesity rates.Master's Thesis in System DynamicsGEO-SD351INTL-KMDINTL-JUSINTL-MEDINTL-MNMASV-SYSDYINTL-SVINTL-HFINTL-PSY