Effects of 17-α-ethynylestradiol on Hybrid Striped Bass Sperm

Gemstone Team FISH

The controlled delivery of hydrogen sulfide for the preservation of heart tissue

Gemstone Team Organ Storage and Hibernation

Salvianolic Acid B Inhibits Growth of Cervical Cancer Cells in vitro via Induction of Apoptosis through the Extrinsic Pathway

In 2014 alone, over 12,000 women are expected to be diagnosed with cervical cancer. Of these women who are diagnosed, about 3,909 will result in death. Despite developments in prevention methods, cervical cancer remains a major health concern for women. Growing evidence suggests that Salvianolic acid B (Sal B), a major component of the Chinese herb Danshen, may inhibit cancer cell growth and help fight against cervical cancer. This study characterizes the potential of Sal B as a cervical cancer drug through in vitro testing on HeLa cells. We hypothesized that application of Sal B to HeLa cells will result in decreased cell viability and increased apoptosis in a dose dependent manner. HeLa cells were treated with varying concentrations of Sal B: 25µM, 50µM, 100µM, and 200µM. Cell viability was determined through colony formation assay, cell death ELISA, and nuclear morphology. An inhibitor study was also conducted for further apoptosis pathway analysis. Colony formation assay demonstrated a significant decrease in cell viability with increasing concentrations of Sal B with 75% viability at 50µM down to 0% viability at 200µM. Cell death ELISA and the analysis of nuclear morphology via Hoechst staining reported significant levels of apoptosis at concentrations equal to 50µM and greater. Furthermore, experiments using caspase inhibitors indicated that Sal B’s apoptotic effects are caspase-8 dependent. In conclusion, our results demonstrate that Sal B inhibits cancer cell growth by a mechanism that involves apoptosis induction through the extrinsic pathway.

Concussion: Examining the Effect of Neuronal Oxidative Stress on the Pathophysiology of Brain and Blood Brain Barrier Cells

Neuronal stretching during concussion alters glucose transport and reduces neuronal viability, also affecting other cells in the brain and the Blood Brain Barrier (BBB). Our hypothesis is that oxidative stress (OS) generated in neurons during concussions contributes to this outcome. To validate this, we investigated: (1) whether OS independently causes alterations in brain and BBB cells, namely human neuron-like, neuroblastoma cells (NCs), astrocyte cells (ACs) and brain microvascular endothelial cells (ECs), and (2) whether OS originated in NCs (as in concussion) is responsible for causing the subsequent alterations observed in ACs and ECs. We used H2O2 treatment to mimic OS, validated by examining the resulting reactive oxygen species, and evaluated alterations in cell morphology, expression and localization of the glucose transporter GLUT1, and the overall cell viability. Our results showed that OS, either directly affecting each cell type or originally affecting NCs, caused changes in several morphological parameters (surface area, Feret diameter, circularity, inter-cellular distance), slightly varied GLUT1 expression and lowered the overall cell viability of all NCs, ACs, and ECs. Therefore, we can conclude that oxidative stress, which is known to be generated during concussion, caused alterations in NCs, ACs, and ECs whether independently originated in each cell or when originated in the NCs and could further propagate the ACs and ECs.

DATA DRIVEN APPROACHES TO IDENTIFY DETERMINANTS OF HEART DISEASES AND CANCER RESISTANCE

Cancer and cardio-vascular diseases are the leading causes of death world-wide. Caused by systemic genetic and molecular disruptions in cells, these disorders are the manifestation of profound disturbance of normal cellular homeostasis. People suffering or at high risk for these disorders need early diagnosis and personalized therapeutic intervention. Successful implementation of such clinical measures can significantly improve global health. However, development of effective therapies is hindered by the challenges in identifying genetic and molecular determinants of the onset of diseases; and in cases where therapies already exist, the main challenge is to identify molecular determinants that drive resistance to the therapies. Due to the progress in sequencing technologies, the access to a large genome-wide biological data is now extended far beyond few experimental labs to the global research community. The unprecedented availability of the data has revolutionized the capabilities of computational researchers, enabling them to collaboratively address the long standing problems from many different perspectives. Likewise, this thesis tackles the two main public health related challenges using data driven approaches. Numerous association studies have been proposed to identify genomic variants that determine disease. However, their clinical utility remains limited due to their inability to distinguish causal variants from associated variants. In the presented thesis, we first propose a simple scheme that improves association studies in supervised fashion and has shown its applicability in identifying genomic regulatory variants associated with hypertension. Next, we propose a coupled Bayesian regression approach -- eQTeL, which leverages epigenetic data to estimate regulatory and gene interaction potential, and identifies combinations of regulatory genomic variants that explain the gene expression variance. On human heart data, eQTeL not only explains a significantly greater proportion of expression variance in samples, but also predicts gene expression more accurately than other methods. We demonstrate that eQTeL accurately detects causal regulatory SNPs by simulation, particularly those with small effect sizes. Using various functional data, we show that SNPs detected by eQTeL are enriched for allele-specific protein binding and histone modifications, which potentially disrupt binding of core cardiac transcription factors and are spatially proximal to their target. eQTeL SNPs capture a substantial proportion of genetic determinants of expression variance and we estimate that 58% of these SNPs are putatively causal. The challenge of identifying molecular determinants of cancer resistance so far could only be dealt with labor intensive and costly experimental studies, and in case of experimental drugs such studies are infeasible. Here we take a fundamentally different data driven approach to understand the evolving landscape of emerging resistance. We introduce a novel class of genetic interactions termed synthetic rescues (SR) in cancer, which denotes a functional interaction between two genes where a change in the activity of one vulnerable gene (which may be a target of a cancer drug) is lethal, but subsequently altered activity of its partner rescuer gene restores cell viability. Next we describe a comprehensive computational framework --termed INCISOR-- for identifying SR underlying cancer resistance. Applying INCISOR to mine The Cancer Genome Atlas (TCGA), a large collection of cancer patient data, we identified the first pan-cancer SR networks, composed of interactions common to many cancer types. We experimentally test and validate a subset of these interactions involving the master regulator gene mTOR. We find that rescuer genes become increasingly activated as breast cancer progresses, testifying to pervasive ongoing rescue processes. We show that SRs can be utilized to successfully predict patients' survival and response to the majority of current cancer drugs, and importantly, for predicting the emergence of drug resistance from the initial tumor biopsy. Our analysis suggests a potential new strategy for enhancing the effectiveness of existing cancer therapies by targeting their rescuer genes to counteract resistance. The thesis provides statistical frameworks that can harness ever increasing high throughput genomic data to address challenges in determining the molecular underpinnings of hypertension, cardiovascular disease and cancer resistance. We discover novel molecular mechanistic insights that will advance the progress in early disease prevention and personalized therapeutics. Our analyses sheds light on the fundamental biological understanding of gene regulation and interaction, and opens up exciting avenues of translational applications in risk prediction and therapeutics.