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Amine-containing Anthracyclines Covalently bind AP sites in DNA Created by Nitrogen Mustards, Resulting in Reduced Cell Viability and Leading to Rapid Strand Scission via Beta- and Delta-elimination
No full textThe cancer treatment regimen known as AC chemotherapy is one of the oldest and most commonly used regimens. Consisting of a combination of Adriamycin (Doxorubicin) and Cyclophosphamide, it has long been understood that the two compounds effect cytotoxicity through independent mechanisms of action with Adriamycin being a topoisomerase II inhibiting anthracycline and Cyclophosphamide being a DNA alkylating nitrogen mustard. A fortuitous discovery in the early 1990s showed that anthracycline compounds have the potential to covalently bind DNA through an electrophile-mediated interaction, in this case formaldehyde. Herein we report that amine-containing anthracyclines efficiently exhibit this reactivity with AP sites that are generated in elevated levels due to reactions of DNA with nitrogen mustards via covalent trapping through reductive amination and likely already contribute a previously unidentified mechanism of cytotoxicity. We also show that unreduced reactions of this type lead to rapid and efficient scission of the affected DNA strands via the known processes of β- and δ-elimination at AP sites
Interplay of Intrinsic and Extrinsic Factors in Gastric Metaplasia: miR-148a in Chief Cells and the Involvement of Telocytes
No full textGastric cancer presents a significant global health challenge. This thesis explores the interplay of intrinsic and extrinsic factors driving gastric metaplasia, a precursor to gastric cancer. It particularly focuses on the role of miR-148a in chief cells and the involvement of telocytes in the gastric microenvironment. Chief cell transdifferentiation and the response of the microenvironment to injury are pivotal in metaplasia development, influencing the progression toward gastric carcinogenesis. A significant discovery of this research is the identification of miR-148a as a key intrinsic factor. Highly expressed in chief cells, miR-148a undergoes significant downregulation during the transdifferentiation of chief cells into spasmolytic polypeptide-expressing metaplasia (SPEM) cells. This downregulation coincides with the onset of metaplastic changes, suggesting a crucial role for miR-148a in initiating chief cell transdifferentiation in response to gastric tissue injury and contributing to the development of metaplasia. Parallel to the examination of intrinsic cellular changes, the thesis introduces telocytes as novel extrinsic factors in the gastric microenvironment. Characterized by their expression of FOXL1 and PDGFRα, telocytes are shown to undergo notable changes in abundance and distribution in response to metaplasia development. Their strategic positioning and dynamic redistribution suggest their recruitment in shaping the metaplastic niche, particularly through WNT signaling pathways. This research provides a comprehensive understanding of the factors involved in metaplasia development in the stomach, highlighting the interplay between chief cell intrinsic mechanisms and extrinsic stromal influences. The insights gained are instrumental for advancing our understanding of gastric carcinogenesis
Epithelial Expressed B7-H4 Drives Differential Immunotherapy Response in Murine and Human Breast Cancer
No full textBreast cancer remains the second leading cause of cancer-related deaths among females. The abundance of clinical trials reveals the extent to which investigators are attempting novel therapies for patients at risk. Immune checkpoint inhibitor (ICI) therapy, including anti-PD-1/anti-PD-L1 monoclonal antibodies, has seen broad success in several cancer types, including breast cancer. However, one of the challenges in the deployment of these therapies is determining which patients will benefit. In the present studies, we investigated whether the alternate immune checkpoint ligand B7-H4 could be contributing to immunotherapy resistance in certain breast cancer patients. B7-H4 (encoded by VTCN1) is an immune checkpoint ligand in the CD28/B7 family of molecules, which includes PD-1/PD-L1. First, we identified that in triple-negative breast cancers (TNBCs), B7-H4 was highly expressed in tumors lacking an abundant immune infiltrate and was correlated with worse patient survival. Second, we discovered B7-H4 is preferentially expressed on epithelial tumor cells in mouse and human breast cancer cell lines and in TNBC. Third, we validated that, unlike the immune checkpoint ligand PD-L1, B7-H4 is regulated by PI3K signaling in human and murine breast cancers. Finally, we observed B7-H4 caused immunotherapy resistance in a murine mammary cancer model due to inhibition of pro-inflammatory immune cell activation, but conversely in human cancers, B7-H4 expression was associated with improved response to immunotherapy plus chemotherapy. These data collectively show the importance of validating murine study results in human clinical trials and suggest B7-H4 may have different functions in patient tumors. Therefore, it may not be an appropriate target for antibody blocking therapies but perhaps more suited to antibody-drug-conjugate therapies
Rediscovering Kin: The Ethical Significance of Kinship with Nature
No full textIn this dissertation, I argue that recognizing and honoring kinship with nonhuman others transforms animal ethics and environmental ethics by bringing values of connectedness, interdependence, reciprocity, and relationality to the fore of ethical thought. In order to do so, I explore some of the ways that humans stand in a kinship relation with nonhuman parts of nature, and how the recognition of this relationship requires fundamental changes to traditional Western moral theories and modes of thinking. I argue that kinship with nonhuman others is an essential part of ethical thought by examining the ways in which kinship implicitly functions as a normative ethical concept in many contemporary approaches to animal ethics and environmental ethics. In particular, I examine Christine Korsgaard’s Kantian approach and Peter Singer’s utilitarian approach to animal ethics, as well as the contributions of feminist care ethicists, and eco-phenomenologists to animal ethics and environmental ethics. Through this analysis, I draw out some of the different ways in which kinship relations establish moral obligations, shape our perception of what it means to relate ethically, and how kinship impacts our lives. Ultimately, I argue that kinship matters because seeing others as kin transforms our understanding of our relationship with our environment and our ethical obligations to other humans, nonhuman animals, and other parts of nature. Throughout, I aim to emphasize that recognizing kinship with nonhuman others requires challenging existing Western, colonial notions of kin as biological and legal relatives, and attending specifically to the relations we have with the other animals, plants, fungi, and the land around us with openness and curiosity. Finally, I conclude by gesturing toward some of the ways that kinship not only transforms ethical thought, but carries social and political implications—namely, that attention to kinship forces us to recognize the connections between humans and nonhuman parts of nature and to acknowledge the ways white supremacy, capitalism, and colonization have contributed to environmental injustice, as well as reaffirming the necessity of working to eliminate oppressive institutions
Development and Application of Antibody Discovery Technologies in HIV-1 Infection and Multivalent Vaccination
No full textHuman immunodeficiency virus (HIV) is a significant public health threat and the causative agent of acquired immunodeficiency syndrome (AIDS). Studying the antibody response in the context of HIV-1 infection and multivalent vaccination can identify factors that contribute to the elicitation of broadly neutralizing antibody responses. To this end, I carried out a vaccine study that evaluated the effect of the number of HIV-1 Env strains included in multivalent vaccinations. Guinea pigs were vaccinated with cocktails of two, three, or six diverse strains of HIV-1 Env, and the polyclonal antibody responses were evaluated for virus neutralization. I found that immunizing with more strains of HIV-1 Env is sometimes beneficial but may be detrimental when too many strains are used; immunizations with three strains outperformed immunizations with two or six strains. Next, I adapted the antibody discovery platform LIBRA-seq (Linking B cell receptor to antigen specificity through sequencing) to be compatible with guinea pig splenocytes. I carried out LIBRA-seq on two guinea pigs from our vaccine study, guinea pig 109 (gp109) and guinea pig 112 (gp112), isolating several tier 2 neutralizing antibodies. Sequence analysis of three antibodies, one from gp109 and two from gp112, revealed the existence of vaccine-elicited guinea pig public clonotypes. Additionally, I characterized a novel antibody lineage isolated from an HIV-1 infected donor that cross-reacts with diverse viral antigens including HIV-1 Env, influenza hemagglutinin (HA), and coronavirus spike. I found that at least one antibody in this lineage, antibody 2526, possess a mannose-binding pocket that confers cross-reactivity to diverse viral pathogens while showing no signs of autoreactivity. These experiments shed light on how cross-reactive antibody responses can be elicited at the polyclonal level through multivalent HIV-1 vaccination and delineate mechanisms of diverse antigen recognition at the monoclonal level
Adversarially Robust Machine Learning Approaches for Edge-based Applications
No full textLeveraging deep learning models at the edge is becoming increasingly more prevalent due to their performance as well as security, privacy, or environmental concerns. These models can be used for applications ranging from surveillance, autonomous vehicles, personal assistants, etc. Despite their success, utilizing these edge-based models comes with several challenges. First, the edge is characterized by having constraints on compute ability, storage, and power. Thus a smaller model is needed, which is typically done by either pruning the model down in size or quantizing the model's weights and biases. This needs to be done while minimizing the loss of accuracy. Second, the edge is also characterized by having heterogeneous resources. Deep learning models executing on the heterogeneous edge will have different accuracy, execution time, and energy consumption depending on the device. This makes it difficult to decide on the most optimal combination of device and model to use. Third, deep learning models are vulnerable to adversarially crafted perturbations that cause the model to be ineffective by leading it to incorrect inferences. The adversarial vulnerability of edge-based deep learning hinders their deployment, especially in safety-critical scenarios. Defending against these attacks necessitates the use of computationally expensive approaches that cannot be utilized on the edge. There has been much research into each individual area, but the intersection or combination of them still has many open problems. To address these problems, this proposal investigates ways to integrate several different edge and cloud-based deep learning approaches and other tools such as adversarial training, sensor fusion, knowledge distillation, Apache TVM, etc. Our goal is to develop novel techniques that ease the design and deployment of robust edge-based deep learning applications. The approach relies on profiling of the application using simulated and deployed statistics on model robustness and resource consumption, which is then adaptively refined based on the model's performance until the algorithm converges
Assessing the Impact on Engagement for a Director Development Program
No full textLeadership and Learning in Organizations capstone projectTractor Supply Company (TSC) launched the Leadership Acceleration Forum (LAF) pilot program to expand leadership skills and enhance middle management engagement and which has declined recently. As of 2024, TSC is the largest rural lifestyle retailer in the U.S., headquartered in Brentwood, Tennessee. The organization has $15 billion in revenue from over 2,250 stores and online operations with 50,000 employees. The company's growth plans include opening 1,000 new stores in the next five years, necessitating the development of its middle management for executive roles. Middle management engagement scores have declined by 11% from 2022 to 2023. The LAF program, initiated in 2023, aims to address this decline through a hybrid learning model combining asynchronous and synchronous instruction. The study analyzed Hogan Personality Assessments, TSC’s promotion data, and a Vanderbilt University Survey to understand the LAF program's impact on engagement. Insights were used to identify areas for improvement and provide actionable recommendations. Key engagement factors include recognition, company fit, and autonomy. Recommendations for the LAF program include enhancing strategic thinking, interpersonal skills, recognition of accomplishments, and clearer career advancement pathways
Essays on Money and Campaigns in Congressional Elections
No full textThe polarization of political elites, proliferation of campaign contributions, and importance of primary elections are three features which define the modern era of politics in the United States. This dissertation investigates the strategies of candidates and their financial contributors in congressional campaigns using new data sources and methodological approaches. The first paper investigates how the relationship between candidates’ positions and their fundraising from individual and corporate PAC donors. Using a causal inference approach, I show that nominating an extreme U.S. House candidate does not substantially increase contributions from individuals nor decrease contributions from corporate PACs compared to nominating a moderate, suggesting that contributors’ decisions are shaped by factors beyond candidates’ positions. The second and third papers introduce an original dataset of hand-collected issue platforms from the campaign websites of all available U.S. House primary candidates in 2016, 2018, 2020, and 2022. In the second paper, I combine this text data with a machine learning approach to develop unidimensional estimates of primary candidates’ stated campaign positions. Using this measure, I demonstrate that candidate rhetoric --- but not contribution networks --- varies systematically by district partisanship, suggesting that donor behavior has nationalized while candidate behavior remains district-tailored. In the third paper, I use the campaign platforms to evaluate how single-issue interest groups respond to primary candidates’ prioritization of their issue and balance a desire to obtain access to incumbents with helping to elect new issue champions. My results suggest that issue groups rely on campaign rhetoric to identify potential issue champions during the primary election stage, and continue to cultivate relationships with them once in Congress. Taken together, the findings reported in this dissertation have critical implications for how money shapes the nature of our politics: campaign contributors are more strategic than existing studies suggest, and their instrumental behavior creates less straightforward incentives for candidate extremism than currently thought
Deriving Confidence Sets for Effect Sizes Using Simultaneous Confidence Intervals
No full textThere has been a growing criticism of hypothesis testing. Neuroimaging analysis almost exclusively focuses on hypothesis testing. Recent research has developed methods to use confidence sets instead, to draw conclusions, but has only focused on particular parameters or effect sizes that do not generalize to all statistics, such as noncontinuous and nonfunctional data. Here, we use the robust effect size index (RESI) framework, which is generally defined across many different types of models. We use RESI to develop a general approach to effect size-based inference for neuroimaging data using confidence sets derived from simultaneous confidence intervals (SCI) using bootstrapping from the pbj (parametric bootstrap joint) R package. From a given effect size threshold, this approach will identify regions of the brain that belong to the null set (areas where the effect size is less than the threshold) or the target set (areas where the effect size is greater than the threshold), with a prespecified confidence level. We then conduct simulations to evaluate this approach, using a parametric standard error normalization, and one method without any normalization. The coverage, average interval width, and the maximum interval width are reported to evaluate the SCIs. This approach can be applied to areas of research that focus on multivariate outcomes, such as genomics and imaging
Coronavirus Nonstructural Protein Interactions with Endoplasmic Reticulum Proteostasis Factors Mediate Infection
No full textMy thesis work examines how coronaviruses hijack host cell proteins and stress response pathways during infection. Coronaviruses encode sixteen nonstructural proteins (nsps), which interact with host proteins to modulate host membranes and stress response pathways to mediate viral replication. An important question with the emergence of the COVID-19 pandemic was how do SARS-CoV-2 nsps interact with host proteins to mediate infection? And are these interactions similar or different to the strategies employed by previously known CoVs, like SARS-CoV, MERS-CoV, hCoV-OC43, hCoV-229E, and Murine Hepatitis Virus (MHV)? Towards this end, I used quantitative proteomics to comparatively profile the interaction network of several CoV nsp homologs (nsp2, nsp3, nsp4), discovering a diverse array of both conserved and unique host interactors. I then applied a functional genetic screen to identify interactors with important roles in CoV infection. I found several conserved, pro-viral interactors associated with protein biogenesis, including the glycoprotein quality control factor Malectin (Mlec). Further testing reveals that Mlec promotes early nsp production and mediates replicase formation through the glycoprotein biogenesis pathway. In addition, I have also characterized the role of nsps in modulating an important stress response pathway, the Unfolded Protein Response (UPR), which senses and mitigates build-up of misfolded proteins in the endoplasmic reticulum to maintain protein homeostasis. Using quantitative proteomics, I found that SARS-CoV-2 nsp3 and nsp4 work in concert to tune the UPR in a temporal manner that may increase viral protein folding capacity while limiting potentially pathogenic effects caused by chronic UPR activation. Together, my thesis work reveals new strategies by which CoV nsps can co-opt host proteins and regulatory systems to mediate viral infection. These dependencies could potentially be exploited for the design of new pan-CoV antiviral therapies in preparation for future CoV outbreaks