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A Neuroimmune Circuit Mediates Chemo Brain Pathology
No full textChemotherapeutic agents, while effective in targeting malignant tumors, often reduce the patients’ quality of life by inducing fatigue, pain, and motivational deficits. These symptoms are collectively referred to as “chemo brain.” The mechanisms underlying these adverse effects, and specifically whether fatigue and motivational impairments share a common etiology, remain poorly understood. The neural–immune substrates of these symptoms remain obscure, in part because standard preclinical regimens either bypass key systemic signals or introduce local toxicity confounds. Here, I establish and validate a three‑day, low‑dose intravenous cisplatin protocol that avoids peritoneal inflammation and severe renal injury yet produces robust, reversible fatigue and increase in effort sensitivity. To delineate the underlying circuitry, I partnered with the WashU Neurotech Hub to develop a wireless, over‑the‑air telemetry system for continuous home‑cage monitoring using in-home-cage behavioral assays for singly housed mice. Finally, I localized activated GFRAL‑expressing and neighboring neurons under this regimen using multiplex cFos and IHC/ISH in the area postrema and nucleus tractus solitarius. Together, these results outline a cytokine‑sensing hindbrain-basal ganglia circuit driven by GDF15-GFRAL signaling, that mediates chemotherapy‑induced motivational decline and points to precise nodes for interventions to preserve patient drive during treatment
Effect of Nb, Ti, and Zr Powder Deposition Order on Directed Energy Deposition Created NbTiVZr Multi-Principal Element Alloy
No full textRefractory multi-principal Element Alloys (MPEAs) are alloys that contain high atomic percentages of multiple refractory elements. High-throughput techniques such as directed energy deposition (DED) have been utilized to study a wide range of composition for minimal resources. A Laser Engineered Net Shaping (LENS) system is utilized to study the NbTiVZr MPEA. This project studies how the order of deposition of Nb, Ti, and Zr affects the composition of the resulting deposit. The deposition was performed on a V substrate. Six bands were created to test each ordering of Nb, Ti, and Zr. With 275 W laser melting passes and 200 W laser remelt passes, the resulting deposit was shown to not be of a constant composition through scanning electron microscopy analysis (SEM) of the cross section. For most bands, cross section line scans showed three distinct melt pools with different compositions. When Nb and Zr were layered next to each other, they fully incorporated with each other into one melt pool. The general composition of the bands was much higher in Ti than the targeted equiatomic composition. Bands tended to flake off during polishing of the cross section
Becoming an Intentional Physician
No full textBecoming an Intentional Physician provides a roadmap for medical professionals to cultivate a fulfilling career rooted in empathy, resiliency, and ethical practice. Through personal stories and practical insights, the book guides readers in developing a strong professional identity and embracing the lifelong journey of self-improvement. It underscores the importance of mentorship, meaningful professional relationships, and strategic career planning for physicians to become skilled and committed to compassionate patient-centered care. Written by Dr. Tom Cox, with a foreword by Dr. Tim Bono, the work draws on the author’s extensive experience in medicine and education to offer a thoughtful guide for aspiring and early-career physicians.https://openscholarship.wustl.edu/books/1068/thumbnail.jp
DNA-damaging chemotherapy reshapes the immune landscape of the heart
Get PDFHeart failure and ischemic heart disease represent the most common causes of death among cancer survivors. With the tremendous progress in last few decades that empowered early cancer diagnosis and treatment, the population of cancer survivors has been rapidly growing and by 2040, the number is projected to be 26 million. However, despite extensive studies on chemotherapies, there exists a limited understanding of how exposure to chemotherapy reagents impacts heart function in the long term. Moreover, while the roles of immune landscape in heart diseases have recently been revealed, how chemotherapy reagents influence the cardiac immune cells is poorly understood. In this thesis, I investigated the influence of chemotherapy reagents on the cardiac macrophage population, which is the predominant immune cell type in the heart at steady state. Using experimental mice, immunostaining, flow cytometry, and in vitro cell culture experiments, I found carboplatin, a platinum-based DNA damaging drug, selectively depleted cardiac resident macrophages while bone-marrow derived cardiac macrophages survived. The selective depletion effect is due to overactivated DNA-damage response in combination with an incompetent DNA damage repair capacity in cardiac resident macrophages. Carboplatin leads to activation of p53 signaling disproportionally in the embryonic-derived cardiac resident macrophages, causing necroptosis and apoptosis in cardiac resident macrophages. Additionally, resident macrophages in the liver and the lung also demonstrated higher vulnerability to carboplatin. To study the long-term effect of carboplatin on the cardiac immune landscape and pathogenesis, carboplatin treated mice were followed during a four-week recovery period. Genetic lineage tracing, transcriptomic profiling, and functional studies revealed that recruited monocytes progressively reconstitute the cardiac resident macrophage compartment but were transcriptionally distinct from embryonic-derived cardiac resident macrophages. When exposed to hypertensive heart injury or ischemia-reperfusion injury, the reshaped resident-like macrophages conferred protection, evidenced by less cardiac fibrosis, mitigated cardiomyocyte hypertrophy, and preserved cardiac function. Mechanistically, these monocyte-derived resident-like cardiac macrophages are primed through a type-I interferon response that suppressed cardiac inflammation and attenuated adverse ventricular remodeling. Collectively, these findings uncover profound effects of chemotherapeutics on the cardiac immune landscape and highlight a type-I interferon dependent pathway in cardiac macrophages that protects the heart from subsequent adverse remodeling
Dissecting the Contribution of STMN2 to ALS Pathogenesis
Get PDFTDP-43 is required for splicing of Stathmin-2 (STMN2) transcript, so STMN2 is reduced in most ALS patients. TDP-43 does not regulate STMN2 in mice as it does in humans. Thus, mouse models of ALS do not reflect patient loss of STMN2. To test the hypothesis that a reduction in STMN2 contributes to ALS pathogenesis, we generated STMN2 knockout mice. Constitutive STMN2 loss induces an early sensory and motor neuropathy with disrupted motor behavior and dramatic distal neuromuscular junction (NMJ) denervation. This NMJ pathology occurs in fast-fatigable motor units, which are the most vulnerable in ALS. In contrast, motoneurons and axons do not degenerate. STMN2 heterozygous mice better model the partial loss of STMN2 protein occurring in ALS patients. This mild reduction in STMN2 causes a progressive, motor-selective neuropathy with functional deficits and NMJ denervation. Moreover, combining partial STMN2 depletion with a gain of function TDP-43 allele, TDP-43Q331K, resulted in a progressive motor-selective deficit. While no neurodegeneration is observed when deficits first present, mitochondria in distal axons show signs of severe pathology indicating that neurons maybe be metabolically unstable and dysfunctional. Thus, our findings strongly support the hypothesis that STMN2 reduction due to TDP-43 pathology contributes to ALS pathogenesis
Disrupting NRF2-Driven Cancer Biology through One-Carbon Metabolism Inhibition
Get PDFCancer remains a leading cause of mortality worldwide, with treatment resistance and recurrence posing major barriers to long-term survival. Despite advancements in targeted therapies, many cancers develop resistance to chemotherapy, radiation, and immunotherapy, significantly limiting their effectiveness. One key driver of therapeutic resistance is nuclear factor erythroid 2-related factor 2 (NFE2L2/NRF2), a transcription factor that regulates antioxidant responses and drug detoxification. While NRF2 protects normal cells from oxidative stress, its aberrant activation in cancer promotes tumor progression, metabolic reprogramming, and resistance to standard treatments. Alarmingly, NRF2 hyperactivation is implicated in some of the most aggressive and refractory malignancies, yet no FDA-approved selective NRF2 inhibitors currently exist. This underscores the urgent need for novel therapeutic strategies. This dissertation aims to address this challenge by investigating a new therapeutic approach to NRF2 suppression by inhibiting one-carbon metabolism. Specifically, it explores the potential of Pyrimethamine (PYR), an FDA-approved antiparasitic drug, and its more potent derivative, WCDD115, as NRF2 inhibitors through their suppression of dihydrofolate reductase (DHFR). Chapter 1 establishes NRF2\u27s critical role in cancer resistance, explores its background, highlights the lack of effective inhibitors, and introduces a rationale for targeting metabolic vulnerabilities to suppress NRF2. Chapter 2 presents original preclinical research demonstrating that PYR and WCDD115 inhibit NRF2 by targeting DHFR, linking one-carbon metabolism to NRF2 regulation. Structure-activity relationship (SAR) studies reveal that WCDD115 is a 22-fold more potent NRF2 inhibitor than PYR, with an IC50 of 57 nM compared to 1.2 μM for PYR. Metabolomic and proteomic analyses show that DHFR inhibition disrupts NRF2-dependent antioxidant responses, activates TP53-mediated DNA damage pathways, and induces tumor cell death, positioning DHFR inhibitors as a novel class of NRF2-targeting agents. Chapter 3 describes a first-in-human phase I clinical trial evaluating PYR in 18 patients with HPV-negative head and neck squamous cell carcinoma (HNSCC). The trial assesses safety, tolerability, and biological activity, focusing on tumor DHFR expression, and NRF2 suppression. Using protein quantification techniques, the study measures DHFR protein expression pre- and post-treatment as a proxy for DHFR inhibition, providing preliminary data supporting the potential of repurposing PYR to treat NRF2-driven cancer through folate pathway inhibition. Chapter 4 synthesizes the findings from previous chapters, exploring the broader implications of NRF2 suppression via DHFR inhibition. It discusses potential combination therapies with chemotherapy, radiation, and immunotherapy, the development of next-generation antifolates, and expanding NRF2-targeting strategies to other malignancies. This dissertation is significant because it identifies a novel mechanistic link between NRF2 and one-carbon metabolism, introducing DHFR inhibitors as a promising class of NRF2-targeting agents. By providing preclinical evidence and clinical trial insights into the potential of repurposing PYR for NRF2-driven cancers, this work offers a new therapeutic strategy to overcome resistance in aggressive malignancies. Furthermore, it paves the way for future combination therapies and the development of next-generation antifolates, expanding the therapeutic landscape for NRF2-driven cancers
First-Principles Studies of Excited-State Properties in Large-Scale Systems
Get PDFDensity Functional Theory (DFT) is widely used as a powerful tool for studying the electronic structure of materials. However, due to the local or semilocal nature of exchange-correlation functionals, such as the local density approximation and generalized gradient approximation functional, DFT often underestimates the electronic band gap. Many-body perturbation theory (MBPT) within the GW approximation provides more accurate quasiparticle energies calculation by incorporating many-electron screening effects. Additionally, solving the Bethe-Salpeter Equation (BSE) allows for a detailed analysis of excitonic effects in optical spectra. Despite their accuracy, GW-BSE calculations are computationally expensive, particularly for large-scale systems such as defects and substrate-supported materials, making direct calculations impractical. In this thesis, I present comprehensive first-principles studies of excited-state properties in large-scale systems, addressing the primary computational challenges while ensuring accuracy. In Chapter 1, we provide an overview of first-principles approaches, starting from DFT and extending to MBPT. We emphasize the importance of accurately predicting excited-state properties and discuss the main computational challenges associated with large-scale systems. To overcome these challenges, we introduce two methods: the defect-patched screening method for systems containing point defects, and the fractional folding method for substrate- or encapsulation-influenced systems. These methods enable the practical treatment of large-scale systems that would otherwise be computationally prohibitive. Chapter 2 presents the detailed computational framework used throughout this work. We begin by introducing the Kohn-Sham equations within DFT, followed by a description of the MBPT within GW approximation for obtaining quasiparticle energies. To investigate the optical response of materials, we first outline the single-particle transition picture and then incorporate electron-hole interactions by introducing the concept of excitons. Finally, we present the BSE, which enables the calculation of exciton excitation energies and wavefunctions, providing a comprehensive description of excitonic effects in optical spectra. In Chapter 3, we investigate the quasiparticle energies and excitonic properties of α-phase Ruthenium (III) chloride (α-RuCl3) using first principles many-body perturbation theory calculation. α-RuCl3 has garnered significant attention due to its potential realization of Kitaev quantum spin liquid. By employing the GW calculation and solving the BSE, we find enhanced many body effects that dominate quasiparticle energies and optical responses in α-RuCl3. Our calculated quasiparticle band gap of bulk structure is about 1.75 eV that agree well with recent scanning tunneling spectroscopy and angle-resolved photoemission spectroscopy measurements. Our calculated primary excitonic features show good consistency with observed the optical absorption spectrum. Moreover, we extend our investigation to monolayer α-RuCl3, examining the zigzag antiferromagnetic (AFM) and ferromagnetic (FM) phases. In addition to significant excitonic effect., the optical spectrum of the zigzag AFM phase exhibits anisotropic behavior, while the FM phase demonstrates isotropic characteristics. The different optical response behaviors provide an efficient approach to identify the energy nearly degenerate magnetic states, which can both potentially exist in fabricated samples. In Chapter 4, we focus on accelerating GW calculations for point defects in supercell systems. While GW calculation provides accurate defect energy levels, modeling realistic isolated point defects often requires large supercells, making computations prohibitively expensive. To address this challenge, we propose the “defect-patched screening method”, which reduces the simulation cost of many-electron screening calculation — the primary computational bottleneck. This method decomposes the random-phase approximation screening into two parts: intrinsic screening, computed from the unit cell of the pristine structure, and defect-induced screening, evaluated within a small energy window in the supercell. Depending on the defect type, only intrinsic screening or both contributions may be needed, significantly reducing computational cost by avoiding the summation over numerous conduction states. We apply this method to a range of neutral and charged defects in both two-dimensional and bulk materials, demonstrating results consistent with direct GW simulations. In Chapter 5, we investigate twisted homobilayer transition metal dichalcogenides (TMDs) and introduce the “fractional folding technique” to account for substrate and encapsulation screening effects in GW calculations. A fundamental challenge in pursuing topological states in twisted TMD is the relative energies between the valence band extrema at the topologically trivial Γ and nontrivial K/K\u27 valleys. We employ many-body perturbation theory within the GW approximation to investigate the energy difference of the valence band extrema in homobilayer WSe2 and MoTe2, the two most promising candidate platforms hosting various quantum phases. Notably, these quantum phases strongly prefer the K/K\u27 valley to reside at the valence band maximum (VBM) to ensure the doped hole occupies the topologically nontrivial valleys. In contrast to the results obtained from density functional theory, the GW calculation predicts quasiparticle energies of the K/K\u27 valley as the VBM above those of the Γ valley for all high-symmetry stackings. We further employ the fractional folding technique to include the substrate and encapsulation dielectric screening effects in GW simulations. We find that while environmental dielectric screening from h-BN reduces the energy difference between the K/K\u27 and Γ valley extrema, the VBM remains situated at the topologically nontrivial K/K\u27 valley. Finally, many-body effects can enhance the depth of the moiré potential, leading to a shift of the magic angle , compared to the result from density functional theory. Our study offers quasiparticle energy landscapes to guide the search for twisted homobilayers of topological interest
Characterization of Long-Noncoding RNAs in Prostate and Pancreatic Cancer Through Single Cell Transcriptomics
Get PDFMetastatic castration resistant prostate cancer (mCRPC) is a subtype of prostate cancer that develops after metastasis and resistance to hormone therapy, thereby carrying a poor prognosis. Likewise, pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal malignancies due to its propensity to present at advanced, unresectable stages in patients. Recent genomic studies have identified molecular features of mCRPC and PDAC, such as the existence of mutations in key driver genes, the extensive transcriptomic intra-tumoral heterogeneity, the role of regulatory elements in dictating driver gene expression, mechanisms of treatment resistance, and the interactions with the immunosuppressive and stromal tumor- microenvironment (TME) in facilitating disease progression. While much information has been elucidated from these analyses, they have been primarily focused on the role of protein coding genes while neglecting the importance of long non-coding RNAs. Long non-coding RNAs (lncRNAs) are RNA transcripts longer than 200 nucleotides without evidence of coding potential. lncRNAs have been demonstrated to be implicated in various steps of the metastatic cascade during tumor progression with many described mechanisms, such as through transcriptional and epigenetic regulation, as well as sequestration and modification of RNA transcripts or proteins. However, few studies have performed a systematic characterization of the lncRNA landscape in mCRPC or PDAC using single cell transcriptomic data coupled with integration of multi-omic datasets. Hence, the goal of this thesis research was to perform an integrative genomics analysis of single cell RNA sequencing data with bulk transcriptomic, genomic, epigenomic, and clinicopathologic data to better understand the mechanisms of lncRNA dysregulation in cancers. We sought to identify lncRNAs associated with genomic and regulatory features, tumor progression, intra-tumoral heterogeneity, TME cell types, histologic transformation, and treatment resistance. In the analysis of mCRPCs, we used transcriptomic data from a recently published study of 2170 cells from 14 patients and 15 biopsies of mCRPC metastatic sites with varied treatment histories and tumor pathologies, coupled with a computational pipeline for lncRNA discovery and validation. comprising various immune cells. Regulatory elements, such as hypomethylated regions and transcription factor binding sites, were enriched in these lncRNAs. Analysis of mCRPCs and localized prostate cancers revealed lncRNAs associated with tumor progression, such as the established prostate cell-enriched lncRNAs SCHLAP1 and PCAT14, and novel TME-enriched lncRNAs like MIAT and CYTOR. Prostate cell-enriched lncRNAs were correlated with Androgen Receptor (AR) mutational status, AR signaling, and demonstrated alterations during treatment with the AR signaling inhibitor, enzalutamide. In contrast, the expression of a subset of TME-enriched lncRNAs was upregulated in tumors with RB1 deletions and correlated with poor prognosis. Finally, lncRNAs identified between prostate adenocarcinomas and neuroendocrine tumors exhibited distinct expression and methylation profiles. This yielded 389 cell-enriched lncRNAs in prostate cancer cells and the TME With respect to the PDAC analysis, we used single cell transcriptomic data from a recent publication of 232,764 cells from 73 multi-region samples from a cohort of 21 PDAC patients to characterize the lncRNA landscape of this disease. We found 111 lncRNAs to be highly enriched in PDAC and TME cells, such as known PDAC-specific lncRNAs CASC8 and CRNDE, as well as novel lncRNAs associated with Treg and fibroblast cell types validated across multiple orthogonal datasets. Analysis of PDAC cells with TP53 mutations revealed several lncRNAs associated with genomic status, such as NORAD, that failed to be detected by bulk sequencing due to their expression across multiple TME cell types. In addition, we identified lncRNAs and pathways associated with resistance to treatment with FOLFIRINOX, a multi-agent chemotherapy, that were validated in treatment resistant PDAC organoids, highlighting the ability of single cell analysis to identify PDAC-specific expression changes after treatment exposure. Lastly, we performed tumor subcluster analysis of PDAC cells to identify lncRNAs commonly deregulated across patients, followed by pathway annotation which yielded 8 PDAC tumor subclusters. 6 of these tumor subclusters were subsequently validated in multiple orthogonal PDAC single cell datasets, highlighting the generalizability of our findings. These subclusters and their corresponding lncRNAs included processes related to angiogenesis, metabolic pathways, and epithelial-to-mesenchymal transition, which displayed associations with patient outcomes, thereby demonstrating the clinical relevance of these genes in PDAC. In summary, this thesis work represents the first systematic analysis of lncRNAs in mCRPC and PDAC using an integrative genomics approach with single cell and bulk multi- omics datasets. Our findings highlight the utility of single cell sequencing to ascribe lncRNA alterations to specific cell types and nominate potential mechanisms of action. This work also underscores the utility of integrative multi-omic studies for lncRNAs that can be extended to other tumor types. We hope our results will serve as a resource to inform future work on identifying the biological roles of these lncRNAs and their contributions to mCRPC, PDAC and tumor biology in general
The Role of the cGAS-STING Sensing Pathway in HIV-1 and SARS-CoV-2 Infections
Get PDFHuman immunodeficiency virus type I (HIV-1) and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) are the causative agents of two unique pandemics. For a successful replication cycle to occur, viruses need to strategically outmaneuver host cell defenses. The innate immune system is the first line of defense against viruses and other pathogens. Host pattern recognition receptors detect viral nucleic acids and initiate signaling cascades that culminate in the expression of interferon (IFN) and transcriptional upregulation of antiviral interferon-stimulated genes (ISGs). This dissertation examines the involvement of the innate immune response during early, post-entry steps of HIV-1 and SARS-CoV-2 infection. During HIV-1 maturation, the viral core is formed when the capsid (CA) protein arranges into a conical lattice around the viral RNA genome and associated proteins and replicative enzymes. Mutations in CA have been linked to innate immune sensing of HIV-1, suggesting that the CA lattice may shield viral nucleic acids from host sensor proteins that initiate antiviral responses. We utilized orthogonal genetic and chemical approaches to manipulate the stability of the CA lattice and determine the effects on innate immune recognition of HIV-1. We examined expression levels of ISGs after infection of cells with wild-type and mutant viruses containing cores of altered stability. We show that decreasing the stability of HIV-1 cores diminishes or enhances innate immune sensing in a reverse transcription-dependent manner. Surprisingly, due to the combined effects of enhanced reverse transcription and defects in nuclear entry, mutants that form hyperstable cores induce innate immune sensing more potently than destabilizing CA mutations. At low concentrations, CA-targeting compounds lenacapavir and GS-CA1 impact capsid lattice stability in cells and modestly enhance innate immune sensing of HIV-1. Innate immune activation observed with CA mutants depends on reverse transcription and the DNA-sensing cGAS-STING pathway. Overall, our findings demonstrate that CA lattice stability and reverse transcription are balanced to minimize sensing of viral DNA. Other than the cognate ACE2 receptor, host factors that determine the cellular tropism of SARS-CoV-2 are poorly defined. We sought to determine the mechanism(s) responsible for post-entry restriction of viral replication in a subset of ACE2-postive airway-derived cell lines. We show that high baseline levels of IFN pathway genes are responsible for inhibiting SARS-CoV-2 replication in infected cells. We determined that mitochondrial DNA leakage and naturally occurring cGAS and STING variants trigger constitutive activation of the cGAS-STING pathway that results in the IFN-mediated response. Notably, SARS-CoV-2 antagonizes the IFN pathway, as ISG expression is demonstrated mainly by uninfected bystander cells. Our findings suggest that constitutive activation of the cGAS-STING and IFN pathways impacts cellular tropism of SARS-CoV-2 in ACE2-expressing cell lines. Collectively, this dissertation provides insight into the involvement of the innate DNA-sensing cGAS-STING pathway during early steps of HIV-1 and SARS-CoV-2 infection. Our work contributes to the growing collection of evidence that HIV-1 CA lattice stability is critical for evasion of the host’s innate immune response. Furthermore, we provide evidence for the involvement of the host innate immune response in determining cellular tropism of SARS-CoV-2
Trustworthy Autonomy Through Robust Control and Alignment
Get PDFAs artificial intelligence systems are increasingly applied in safety critical domains such as robotics, autonomous driving, and decision making under uncertainty, ensuring their trustworthiness has become a central challenge. This dissertation addresses two major facets of trustworthy AI: reliable control through formal guarantees and alignment against adversarial manipulation. The first part of the dissertation focuses on provably stable, robust, and safe control for nonlinear systems using learning based methods. We introduce the first general framework for synthesizing neural Lyapunov controllers in discrete time systems. This method combines a sound verifier based on mixed integer linear programming with gradient based counterexample generation to efficiently learn control policies that satisfy formal stability conditions. We extend this framework to adversarial settings where state observations are perturbed, developing verification and training techniques that produce controllers robust to both persistent and intermittent attacks. We then propose a method for verified safe reinforcement learning in neural dynamical systems using finite horizon reachability and curriculum learning. Our approach achieves strong safety guarantees across multiple benchmarks while preserving task performance. To improve robustness in environments with high dimensional perceptual inputs, we develop a novel curriculum based adversarial training framework that significantly enhances deep reinforcement learning against large input perturbations. Finally, we introduce a partially supervised reinforcement learning framework that enables safety certification in partially observable environments by leveraging access to interpretable low dimensional states during training. The second part of the dissertation investigates how AI systems that learn from human preferences can be manipulated. We model election control through voter perception manipulation using spatial voting theory and characterize the computational complexity of such attacks under various assumptions. Building on this insight, we explore preference poisoning attacks on reward models, a core component of value aligned AI systems including reinforcement learning from human feedback. We develop and evaluate both gradient based and heuristic attacks that show high success even with minimal data poisoning across domains such as autonomous control and large language model alignment. Together, these contributions offer principled methods for building AI systems that are stable, robust, safe, and aligned with human values, laying a foundation for future progress in trustworthy autonomy