Browsing by Author "Lewis, Stephanie N."

Now showing 1 - 18 of 18
  • Thumbnail Image
    Adderall and Academia: How Amphetamine binds in the Human Norepinephrine Transporter Protein
    Bell, Ian; Jachimowski, Lindsey; Lewis, Stephanie N. (2019-05-07)
    Recently, there has been a drastic increase in the use of prescription stimulants by healthy individuals in academia – specifically with undergraduate college students. We wanted to answer why this was phenomenon was occurring. Are there cognitive benefits from taking stimulants when there is no medical need and are these benefits why students are drawn to them? Amphetamine or AdderallTM is a popular misused stimulant and serves as an example to explore this issue. The first question to answer was how amphetamine is processed in the brain. Our chosen transporter was the human norepinephrine transporter (hNET) protein. This transporter controls the uptake and reuptake of both dopamine (DA) and norepinephrine (NE). The unbalance of these two neurotransmitters are believed to play a major role in Attention-Deficit Hyperactivity Disorder (ADHD). hNET is often a main target in research studies because of this. To analyze the interaction of amphetamine and NET we built a human 3D model through a process known as homology modeling and docked amphetamine, NE, and DA into it. We found that amphetamine successfully binds in the hNET binding cavity. In impaired individuals this means that amphetamine does in fact have positive benefits. However, the effect on healthy individuals is still unknown. Further research needs to be done to determine whether or not healthy individuals experience any benefits before we can answer why undergraduate college students are misusing the drug.
  • Thumbnail Image
    Analyzing the Presence of Unmet Need and Depressive Symptoms in Older Adults
    Butynes, Amanda; Tarr, Nina; Thompson, Caleb; Lewis, Stephanie N. (2019-12-11)
    This paper works to examine and determine a relationship or association between unmet need from disability and depressive symptoms in older adults. The older adult population is extremely vulnerable to deteriorating quality of life in the presence of unmet need or disability. Older adults are significant members of the population and deserve the right to a healthy, positive quality of life. Data from the National Health and Aging Trends Study (NHATS) provided quantitative data for both unmet need and depressive symptoms for beneficiaries of Medicare. The data was processed using descriptive statistics and basic statistical analysis. Dividing up data into subpopulations based on their unmet need and depressive symptoms across time points allowed the team to understand how the behaviors of the participants changed over time. The results of this analysis showed that those with a higher unmet need due to disability score also show more depressive symptoms. The data suggest that depressive symptoms and disability in adults are closely associated. Currently, there are several forms of governance helping this population out with basic needs, but there is substantial room for improvement. The results of this study have the potential to spread awareness and amend government policies to provide older adults with the services they need.
  • Thumbnail Image
    AT-121 as a Potential Opioid Replacement
    Colgan, Grant; Patel, Kethan; Lewis, Stephanie N. (2020-05-05)
    The mu opioid Receptor (μ-Receptor) is the neural structure involved in interpreting pain signals. An opioid acts as an agonist that provides pain relief by binding to a large number of these receptors and preventing pain signals from being processed by the brain. Over prescription of addictive opioids in America has led to a rise in addiction in recent decades. To reduce addiction rates, we sought to research a new drug that has the potential to block pain signals without causing dependence and see what sets it apart from common opioids. A ligand supposedly matching this description has been identified in AT-121. We used computational docking methods and structural analysis to determine if AT-121 poses a legitimate solution to opioid addiction. To determine if docking was successful, we relied on a complementary study to identify key ligands, and their residues involved with neurochemical opioid interactions. Our results indicate that AT-121 interacted with the residue that is essential for a conformational change to the binding cavity. Given this, human testing should be carried out to further assess the agonist’s effectiveness at reducing addiction to opioids. If testing results show positive results, AT-121 could pose as a beneficial drug for helping to cease the US opioid epidemic.
  • Thumbnail Image
    Binding Interactions of Psilocin and Serotonin in the 5-HT2A Receptor
    Barnes, Katie; Lewis, Stephanie N. (2020-05-05)
    Psilocin is a molecule found in psilocybin mushrooms, which are typically consumed recreationally for their hallucinogenic effects. Recently, studies have shown that psilocin can have almost immediate antidepressant effects in patients who are treatment-resistant to medications that increase serotonin levels in the synapse. Researchers believe that the molecule works by suppressing activity in the medial prefrontal cortex and amygdala, which are both brain structures involved in the emotional aspect of depression. However, psilocin’s exact mechanism of action and binding characteristics in the body remain unknown. Using Chimera for visualization and AutoDock Tools and AutoDock Vina for docking, psilocin and serotonin were separately docked in a crystallized 5-HT2A receptor. Key residues were identified using existing information in the RCSB database. Once the ligands were docked, the lengths of the potential bonds between atoms of the ligands and the key residues within the receptor were measured to determine if they were close enough to each other to interact. Serotonin had multiple possible hydrogen bonds and hydrophobic interactions; however, psilocin only had one potential hydrophobic interaction. The main structural difference between psilocin and serotonin is the presence of the phosphate group in psilocin; therefore, studies of phosphate’s binding properties within the 5-HT2A receptor could potentially provide insight on the efficacy of psilocin.
  • Thumbnail Image
    Compare and contrast mode of action of penicillin and vancomycin: Why penicillin is still an effective antibiotic today
    Kim, Vincent; Lewis, Stephanie N. (2020-05-05)
    Penicillin is a group of antibiotics that contains β-lactam, which prevents peptidoglycan crosslinking and indirectly bursts bacterial cell walls. It is widely used today against many infections caused by staphylococci and streptococci bacteria. Although antibiotics were effective at treating disease in the early development of these treatments, the late 20th century has seen an increase in antibiotic resistance. However, penicillin-derived antibiotics are still used today through generations and we see fewer cases of resistance to this antibiotic. Understanding the interactions between penicillin and bacterial proteins would be useful for studies on counteracting antibiotic resistance. Other antibiotic called Vancomycin was compared with penicillin because vancomycin resistance is arising in late 20th century like Vancomycin-resistant Enterococcus. Computational methods were used to propose interactions between 6I1E and comparable ligands to understand what the mode of action of penicillin is. It was found that SER294 likely interacts with the carboxylic acid functional group. Additionally, assessment of vancomycin resistance provided a case study for understanding how resistance happens. Comparison of interactions between ligands and residue suggested that GLN67 and ALA88 were the key residues and mutations from Δ110 to 115 showed the significant loss of activity against substrate. This paper highlighted that each antibiotic reacts with hydrogen bond interaction between ligand and residues. In penicillin, amoxicillin and carbenicillin interacted through hydrogen bond. In vancomycin, it likely interacts through hydrogen bonding in D-Ala-D-Ala. Further steps would be choosing antibiotics that work through the same function as penicillin and comparing the structural differences and ligand interactions.
  • Thumbnail Image
    Computational Modeling-Based Discovery of Novel Classes of Anti-Inflammatory Drugs That Target Lanthionine Synthetase C-Like Protein 2
    Lu, Pinyi; Hontecillas, Raquel; Horne, William T.; Carbo, Adria; Viladomiu, Monica; Pedragosa, Mireia; Bevan, David R.; Lewis, Stephanie N.; Bassaganya-Riera, Josep (PLOS, 2012-04-11)
    Background: Lanthionine synthetase component C-like protein 2 (LANCL2) is a member of the eukaryotic lanthionine synthetase component C-Like protein family involved in signal transduction and insulin sensitization. Recently, LANCL2 is a target for the binding and signaling of abscisic acid (ABA), a plant hormone with anti-diabetic and anti-inflammatory effects. Methodology/Principal Findings: The goal of this study was to determine the role of LANCL2 as a potential therapeutic target for developing novel drugs and nutraceuticals against inflammatory diseases. Previously, we performed homology modeling to construct a three-dimensional structure of LANCL2 using the crystal structure of lanthionine synthetase component C-like protein 1 (LANCL1) as a template. Using this model, structure-based virtual screening was performed using compounds from NCI (National Cancer Institute) Diversity Set II, ChemBridge, ZINC natural products, and FDA-approved drugs databases. Several potential ligands were identified using molecular docking. In order to validate the anti-inflammatory efficacy of the top ranked compound (NSC61610) in the NCI Diversity Set II, a series of in vitro and pre-clinical efficacy studies were performed using a mouse model of dextran sodium sulfate (DSS)-induced colitis. Our findings showed that the lead compound, NSC61610, activated peroxisome proliferator-activated receptor gamma in a LANCL2- and adenylate cyclase/cAMP dependent manner in vitro and ameliorated experimental colitis by down-modulating colonic inflammatory gene expression and favoring regulatory T cell responses. Conclusions/Significance: LANCL2 is a novel therapeutic target for inflammatory diseases. High-throughput, structure-based virtual screening is an effective computational-based drug design method for discovering anti-inflammatory LANCL2-based drug candidates.
  • Thumbnail Image
    Defining Novel Clusters of PPAR gamma Partial Agonists for Virtual Screening
    Collins, Erin Taylor (Virginia Tech, 2022-06-03)
    Peroxisome proliferator-activated receptor γ (PPARγ) is associated with a wide range of diseases, including type 2 diabetes mellitus (T2D). Thiazolidinediones (TZDs) are agonists of PPARγ which have an insulin sensitizing effect, and are therefore used as a treatment for T2D. However, TZDs cause negative side effects in patients, such as weight gain, edema, and increased risk of bone fracture. Partial agonists could be an alternative to TZD-based drugs with fewer side effects. However, there is a lack of understanding of the types of PPARγ partial agonists and how they differ from full agonists. In silico techniques, like virtual screening, molecular docking, and pharmacophore modeling, allow us to determine and characterize markers of varying levels of agonism. An extensive search of the RCSB Protein Data Bank found 62 structures of PPARγ resolved with partial agonists. Cross-docking was performed and found that two PDB structures, 3TY0 and 5TWO, would be effective as receptor structures for virtual screening. By clustering known partial agonists by common pharmacophore features, we found several distinct groups of partial agonists. Interaction and pharmacophore models were created for each group of partial agonists. Virtual screening of FDA-approved compounds showed that the models were able to predict potential partial agonists of PPARγ. This study provides additional insight into the different binding modes of partial agonists of PPARγ and their characteristics. These models can be used to assist drug discovery efforts for intelligently designing novel therapeutics for T2D which have fewer negative side effects.
  • Thumbnail Image
    Designing Transdisciplinarity: Exploring Institutional Barriers and Drivers of Collaborative Transdisciplinary Teaching
    Velez, Anne-Lise K.; Hall, Ralph P.; Lewis, Stephanie N. (Informa, 2021-01-01)
    Employers increasingly desire new graduates to work across boundaries, in teams, and with developed soft skills, especially in public affairs. Likewise, students increasingly seek academic experiences for learning, practicing, and honing transferable, competency-based skills. This suggests instructors should explore alternative pedagogy engaging problem definition and transdisciplinary teamwork. We describe institutional drivers and barriers to collaborative transdisciplinarity in undergraduate teaching and the structure and processes involved in developing a co-taught studio-based capstone involving public affairs students and varied other unrelated majors. We describe the structure through which the “SuperStudio” (1) combines topic concentrations with a shared policy context allowing students to apply disciplinary knowledge to define transdisciplinary problems and (2) fosters collaborative teaching and strategic exploration of overarching issues like problem framing, equity, and effective communication. We then offer lessons learned regarding the drivers and barriers to such efforts, and advice from institutional decision-makers on designing such courses at other institutions.
  • Thumbnail Image
    Dietary alpha-Eleostearic Acid Ameliorates Experimental Inflammatory Bowel Disease in Mice by Activating Peroxisome Proliferator-Activated Receptor-gamma
    Lewis, Stephanie N.; Brannan, Lera; Guri, Amir J.; Lu, Pinyi; Hontecillas, Raquel; Bassaganya-Riera, Josep; Bevan, David R. (PLOS, 2011-08-31)
    Background: Treatments for inflammatory bowel disease (IBD) are modestly effective and associated with side effects from prolonged use. As there is no known cure for IBD, alternative therapeutic options are needed. Peroxisome proliferator-activated receptor-gamma (PPARγ) has been identified as a potential target for novel therapeutics against IBD. For this project, compounds were screened to identify naturally occurring PPARγ agonists as a means to identify novel anti-inflammatory therapeutics for experimental assessment of efficacy. Methodology/Principal Findings: Here we provide complementary computational and experimental methods to efficiently screen for PPARγ agonists and demonstrate amelioration of experimental IBD in mice, respectively. Computational docking as part of virtual screening (VS) was used to test binding between a total of eighty-one compounds and PPARγ. The test compounds included known agonists, known inactive compounds, derivatives and stereoisomers of known agonists with unknown activity, and conjugated trienes. The compound identified through VS as possessing the most favorable docked pose was used as the test compound for experimental work. With our combined methods, we have identified α-eleostearic acid (ESA) as a natural PPARγ agonist. Results of ligand-binding assays complemented the screening prediction. In addition, ESA decreased macrophage infiltration and significantly impeded the progression of IBD-related phenotypes through both PPARγ-dependent and –independent mechanisms in mice with experimental IBD. Conclusions/Significance: This study serves as the first significant step toward a large-scale VS protocol for natural PPARγ agonist screening that includes a massively diverse ligand library and structures that represent multiple known target pharmacophores.
  • Thumbnail Image
    Increasing Equity and Decreasing Costs for Medicare Access and Treatment
    Bourret, Kira; Cironi, Kate; Guzinski, Max; Huaman, Jonathan; Jones, P'trice; Mitchell, Shannon; Scott, Reilly; Slinde, Neil; Smith, Brett; Wear, Robert; Lewis, Stephanie N. (2018-05)
    In 1965, President Lyndon B. Johnson signed into law a bill that established Medicare. Since then, the medical needs and structure of our society have changed in many ways. But does our current medical care and payment system afford quality, affordable medical care to those who need it the most? What does data tell us is the story of our national health? Who is left behind as advancements are developed to serve our numerous health needs? To tackle these questions, national and global data on the status of health are summarized. Conclusions drawn from the data were connected to provide recommendations on adjustments to and priorities for the U.S. Medicare system.
  • Thumbnail Image
    A Pedagogical Approach to Create and Assess Domain-Specific Data Science Learning Materials in the Biomedical Sciences
    Chen, Daniel (Virginia Tech, 2022-02-01)
    This dissertation explores creating a set of domain-specific learning materials for the biomedical sciences to meet the educational gap in biomedical informatics, while also meeting the call for statisticians advocating for process improvements in other disciplines. Data science educational materials are plenty enough to become a commodity. This provides the opportunity to create domain-specific learning materials to better motivate learning using real-world examples while also capturing intricacies of working with data in a specific domain. This dissertation shows how the use of persona methodologies can be combined with a backwards design approach of creating domain-specific learning materials. The work is divided into three (3) major steps: (1) create and validate a learner self-assessment survey that can identify learner personas by clustering. (2) combine the information from persona methodology with a backwards design approach using formative and summative assessments to curate, plan, and assess domain-specific data science workshop materials for short term and long term efficacy. (3) pilot and identify at how to manage real-time feedback within a data coding teaching session to drive better learner motivation and engagement. The key findings from this dissertation suggests using a structured framework to plan and curate learning materials is an effective way to identify key concepts in data science. However, just creating and teaching learning materials is not enough for long-term retention of knowledge. More effort for long-term lesson maintenance and long-term strategies for practice will help retain the concepts learned from live instruction. Finally, it is essential that we are careful and purposeful in our content creation as to not overwhelm learners and to integrate their needs into the materials as a primary focus. Overall, this contributes to the growing need for data science education in the biomedical sciences to train future clinicians use and work with data and improve patient outcomes.
  • Thumbnail Image
    Phosphorylation of PPAR gamma Affects the Collective Motions of the PPAR gamma-RXR alpha-DNA Complex
    Lemkul, Justin A.; Lewis, Stephanie N.; Bassaganya-Riera, Josep; Bevan, David R. (PLOS, 2015-05-08)
    Peroxisome-proliferator activated receptor-γ (PPARγ) is a nuclear hormone receptor that forms a heterodimeric complex with retinoid X receptor-α (RXRα) to regulate transcription of genes involved in fatty acid storage and glucose metabolism. PPARγ is a target for pharmaceutical intervention in type 2 diabetes, and insight into interactions between PPARγ, RXRα, and DNA is of interest in understanding the function and regulation of this complex. Phosphorylation of PPARγ by cyclin-dependent kinase 5 (Cdk5) has been shown to dysregulate the expression of metabolic regulation genes, an effect that is counteracted by PPARγ ligands. We applied molecular dynamics (MD) simulations to study the relationship between the ligand-binding domains of PPARγ and RXRα with their respective DNA-binding domains. Our results reveal that phosphorylation alters collective motions within the PPARγ-RXRα complex that affect the LBD-LBD dimerization interface and the AF-2 coactivator binding region of PPARγ.
  • Thumbnail Image
    Potential Opioid Addiction Therapeutics
    Parras, Isabel; Kidd, Rachel; Merten, Eric; Lewis, Stephanie N. (2019-05-07)
    Throughout the last thirty years, a severe opioid epidemic has arisen due to the excessive consumption and abuse of these addictive narcotics. Opioids are currently the best analgesic known to man, however the effects of opioids are not all beneficial; they are extremely addictive and are deadly when taken in high doses. Since opioids began rising in popularity in the 1990’s as a prescribed pain-reliever, opioid deaths have skyrocketed. These circumstances have caused the need for the development of both a potent, non-addictive pain reliever and also a way to treat patients with an opioid addiction. To solve this problem, we used computational methods and structural analysis to investigate the µ-opioid receptor binding cavity and its unique interactions with four different ligands: morphine, heroin, fentanyl, and naloxone. From the results, we have created a criterion of interactions that a potential opioid therapeutic should have.
  • Thumbnail Image
    Prediction of Disease and Phenotype Associations from Genome-Wide Association Studies
    Lewis, Stephanie N.; Nsoesie, Elaine O.; Weeks, Charles; Qiao, Dan; Zhang, Liqing (PLOS, 2011-11-04)
    Background Genome wide association studies (GWAS) have proven useful as a method for identifying genetic variations associated with diseases. In this study, we analyzed GWAS data for 61 diseases and phenotypes to elucidate common associations based on single nucleotide polymorphisms (SNP). The study was an expansion on a previous study on identifying disease associations via data from a single GWAS on seven diseases. Methodology/Principal Findings Adjustments to the originally reported study included expansion of the SNP dataset using Linkage Disequilibrium (LD) and refinement of the four levels of analysis to encompass SNP, SNP block, gene, and pathway level comparisons. A pair-wise comparison between diseases and phenotypes was performed at each level and the Jaccard similarity index was used to measure the degree of association between two diseases/phenotypes. Disease relatedness networks (DRNs) were used to visualize our results. We saw predominant relatedness between Multiple Sclerosis, type 1 diabetes, and rheumatoid arthritis for the first three levels of analysis. Expected relatedness was also seen between lipid- and blood-related traits. Conclusions/Significance The predominant associations between Multiple Sclerosis, type 1 diabetes, and rheumatoid arthritis can be validated by clinical studies. The diseases have been proposed to share a systemic inflammation phenotype that can result in progression of additional diseases in patients with one of these three diseases. We also noticed unexpected relationships between metabolic and neurological diseases at the pathway comparison level. The less significant relationships found between diseases require a more detailed literature review to determine validity of the predictions. The results from this study serve as a first step towards a better understanding of seemingly unrelated diseases and phenotypes with similar symptoms or modes of treatment.
  • Thumbnail Image
    Refinement of the Docking Component of Virtual Screening for PPAR
    Lewis, Stephanie N. (Virginia Tech, 2013-07-31)
    Exploration of peroxisome proliferator-activated receptor-gamma (PPAR") as a drug target holds applications for treating a wide variety of chronic inflammation-related diseases. Type 2 diabetes (T2D), which is a metabolic disease influenced by chronic inflammation, is quickly reaching epidemic proportions. Although some treatments are available to control T2D, more efficacious compounds with fewer side effects are in great demand. Drugs targeting PPAR" typically are compounds that function as agonists toward this receptor, which means they bind to and activate the protein. Identifying compounds that bind to PPAR" (i.e. binders) using computational docking methods has proven difficult given the large binding cavity of the protein, which yields a large target area and variations in ligand positions within the binding site. We applied a combined computational and experimental concept for characterizing PPAR" and identifying binders. The goal was to establish a time- and cost-effective way to screen a large, diverse compound database potentially containing natural and synthetic compounds for PPAR" agonists that are more efficacious and safer than currently available T2D treatments. The computational molecular modeling methods used include molecular docking, molecular dynamics, steered molecular dynamics, and structure- and ligand-based pharmacophore modeling. Potential binders identified in the computational component funnel into wet-lab experiments to confirm binding, assess activation, and test preclinical efficacy in a mouse model for T2D and other chronic inflammation diseases. The initial process used provided "-eleostearic acid as a compound that ameliorates inflammatory bowel disease in a pre-clinical trial. Incorporating pharmacophore analyses and binding interaction information improved the method for use with a diverse ligand database of thousands of compounds. The adjusted methods showed enrichment for full agonist binder identification. Identifying lead compounds using our method would be an efficient means of addressing the need for alternative T2D treatments.
  • Thumbnail Image
    Researcher Identity: Active Learning Pedagogy for STEM Learners
    MacDonald, Amanda B.; Brown, Anne M.; Lewis, Stephanie N. (2020-09-28)
    Undergraduate research experiences (UREs) are a known predictor of successful outcomes for STEM students, and are often essential technical and theoretical training opportunities. However, intrinsic and extrinsic barriers result in deterrents for engagement. To broaden the STEM workforce, it is necessary to lower barriers of entry to research fields and UREs. We have implemented an introductory, interdisciplinary research practices course for first-year students where they ideate and explore projects in their domains of interest. Students produce formal research proposals, present research posters, and reflect on their learning experience. Students gain skills in research and data literacy while networking with professionals on campus. This course provides a structured, active-learning experience for students to explore and reflect on development of knowledge in a variety of STEM fields.
  • Thumbnail Image
    US Healthcare Reform in a Green New Deal World
    Bonnes, Caroline; Harley, Diana; Koppler, Natalie; Phan, Jenna; Lewis, Stephanie N. (2020-05-09)
    National health insurance has been a topic of discussion in the United States for over a century, yet even in 2020, this is a widely controversial and argued topic. There are disagreements about who should provide healthcare, who should be responsible for providing insurance, and what role, if any, the government should have in the process. One thing remains clear, however: access to healthcare in the United States is inherently unstable. Through an analysis of the Green New Deal, the current healthcare system, health expenditures and outcomes, private insurance in the US, and a survey of healthcare in other countries, this report aims to answer the following research question: Would a renovation of the current healthcare system following the initiatives outlined within the GND allow for the effective and efficient provision of equitable quality healthcare to all individuals living within the US? This report was developed as the final project deliverable for the Honors StudioStudio course "Data Analysis for Health Reform".
  • Thumbnail Image
    Virtual Screening as a Technique for PPAR Modulator Discovery
    Lewis, Stephanie N.; Bassaganya-Riera, Josep; Bevan, David R. (Hindawi, 2010-01-01)
    Virtual screening (VS) is a discovery technique to identify novel compounds with therapeutic and preventive efficacy against disease. Our current focus is on the in silico screening and discovery of novel peroxisome proliferator-activated receptor-gamma (PPARγ) agonists. It is well recognized that PPARγagonists have therapeutic applications as insulin sensitizers in type 2 diabetes or as anti-inflammatories. VS is a cost- and time-effective means for identifying small molecules that have therapeutic potential. Our long-term goal is to devise computational approaches for testing the PPARγ-binding activity of extensive naturally occurring compound libraries prior to testing agonist activity using ligand-binding and reporter assays. This review summarizes the high potential for obtaining further fundamental understanding of PPARγ biology and development of novel therapies for treating chronic inflammatory diseases through evolution and implementation of computational screening processes for immunotherapeutics in conjunction with experimental methods for calibration and validation of results.