Stanford University

NIH Research Projects · FY 2026 · 2026-02

SUMMARY There is an urgent need for new therapies for metabolic disorders, including obesity, which is associated with a significant decrease in life expectancy by 5–10 years and increased mortality resulting from diabetes and cardiovascular diseases. Peptide hormones comprise a class of small (<100 amino acids), low abundance, bioactive molecules involved in regulating physiological processes such as glucose and insulin levels, food intake, and energy expenditure, making them attractive targets for modulation of energy metabolism. However, the functional identification and characterization of small, proteolytically cleaved peptides have traditionally been challenging because of their low abundance and the difficulties in distinguishing bioactive from inactive fragments or degradation products. Recently, my lab discovered a 12-mer secreted non-incretin human peptide called BRP that controls obesity by lowering food intake through the central activation of Fos. This innovative proposal will explore the mechanisms and biology of BRP to validate the receptor target for this promising cleavage product. Based on preliminary data, we hypothesize that BRP is endogenously cleaved from the precursor BRINP2 by the proconvertase PCSK1, and degraded by proteolytic processing. Based on a receptor screen, we also hypothesize that its function is linked to its binding to a GPCR, thereby activating a CREB signaling pathway leading to Fos transcription. Our hypotheses are based on robust preliminary data generated using in vitro models, structure- activity relationships, and in vivo studies. In Aim 1 of this proposal, we will test the hypothesis that BRP is generated by cleavage of a proconvertase and that BRP is regulated by degradation. These studies will be important to understand the endogenous mechanism of regulation. In Aim 2, we determine the causal hypothalamic region and cell type activated by BRP. In Aim 3, we will validate the target receptor candidate and the signaling mechanism for BRP that is responsible for its appetite-suppressing effects. In conclusion, these studies will determine the endogenous regulation and mechanistic action of BRP, which will open up a new pathway of peptide signaling in body weight regulation.

NIH Research Projects · FY 2026 · 2026-02

PROJECT SUMMARY Developing a new drug typically takes over a decade and costs over $500 millions dollars. Despite this, 90% of drug candidates fail in clinical trials, often due to undesired off-target effects or poor pharmacokinetic (PK) properties. One promising strategy that could overcome these issues and provide a new level of control over therapeutic activity would be the in-situ assembly of a drug at the site of interest. Rather than administering a single compound, two smaller, inactive fragments can be delivered that selectively combine at the target site to furnish the active drug. Lower molecular weight compounds display enhanced PK properties, such as high membrane permeability, due to their smaller size and could avoid off-target effects by only combining at the site of disease. This approach could address PK issues that are inherently associated with the growing demand for large bifunctional molecules that break Lipinski’s rule of five, such as proteolysis-targeting chimeras. This strategy has seldom been explored as identifying a bioorthogonal reaction that will selectively unite the fragments—present only in low concentrations in a cell—remains challenging. The studies described in this proposal seek to develop strategies for the design of self-assembling pyrrole- imidazole polyamides (Py-Im PA), small molecules that can bind DNA and control gene expression, potentially stopping disease at its root. Numerous Py-Im PAs have been developed to treat different diseases yet there are no FDA-approved Py-Im PA pharmaceuticals, in part due to their poor PK properties. By leveraging the natural binding of these compounds to DNA to generate a high local concentration and template a proximity-induced bioorthogonal reaction, one could achieve their selective in situ self-assembly. In Aim 1, a self-assembling linear Py-Im PA that targets the DNA triplet repeats that cause the incurable neurodegenerative disease Friedreich’s ataxia will be designed and evaluated. This PA previously showed promise in cellulo yet suffered from poor membrane permeability due to its high molecular weight. In Aim 2, a proximity-induced azide-alkyne cycloaddition for the in situ synthesis of hairpin polyamides from their linear fragments will be developed. A hairpin Py-Im PA that binds the prostate-specific antigen promoter androgen response element for the treatment of prostate cancer will be prepared. Performing this research in Prof. Bertozzi’s group at Stanford aligns well with their success with developing bioorthogonal chemistry (2022 Nobel prize) and will augment my prior training in organic synthesis to prepare me for a future academic career as a professor. Overall, the proposed research is significant because it could unlock the therapeutic potential of Py-Im PAs for the treatment of Friedreich’s ataxia and prostate cancer by improving their PK properties. This proposal is unique as it represents the first in-situ self-assembly of PAs in cellulo from more bioavailable components. More broadly, this proposal represents a novel approach to addressing PK issues in drug design and could be expanded to solve long-standing challenges encountered by other drug candidates.

NSF Awards · FY 2026 · 2026-02

The semiconductor industry is rapidly approaching the fundamental limits of conventional scaling, creating an urgent need for transformative materials and integration strategies that can sustain progress in electronics and advanced artificial intelligence (AI) hardware. Low-dimensional (low-D) materials—metals, semiconductors, and insulators with one-dimensional features such as carbon nanotubes or atomically thin two-dimensional layers like molybdenum disulfide—offer a compelling pathway forward. With thicknesses of only a few atoms, these materials represent the ultimate size limit of electronic components and provide unique electrical, optical, and mechanical properties that surpass those of traditional semiconductor materials. However, while global investment in low-D technologies is accelerating, the United States is at risk of falling behind in both research leadership and commercialization. Addressing this challenge requires coordinated action across academia, industry, and government to identify critical scientific barriers, manufacturing needs, and integration opportunities. To catalyze this effort, a national conference will convene leading experts from universities, semiconductor companies, and national laboratories to establish a shared roadmap for accelerating and translating these materials from research labs into real technologies used by industry, thereby enabling the U.S. to secure leadership in next-generation semiconductor innovation and the hardware that will power future AI systems. Low-dimensional materials such as carbon nanotubes (CNTs) and two-dimensional (2D) transition-metal dichalcogenides (TMDs), represent the fundamental physical limits of electronic materials, with characteristic thicknesses of only a few atomic layers. These systems exhibit several advantages over conventional semiconductors: (1) high electron and hole mobilities at thicknesses below the scaling limits of bulk silicon, which suffers mobility degradation below ~3 nm; (2) compatibility with heterogeneous integration approaches, as many 2D materials can be synthesized via van der Waals epitaxy directly on amorphous substrates or transferred as isolated layers or heterostructures; and (3) an exceptionally large design space, with over a thousand known 2D compounds, hundreds of which possess electronic bandgaps between ~0.5–3 eV. Despite recent academic progress, major barriers remain in synthesis scalability, defect and doping control, interface engineering, metrology at atomic length scales, and device architectures that provide clear performance, energy-efficiency, or manufacturability advantages relative to advanced silicon electronics. These gaps must be addressed for effective lab-to-fab translation. The 2026 NSF Low-Dimensional Semiconductor Conference aims to define a national strategic roadmap by convening U.S. leaders across academia, industry, and government laboratories. The agenda will focus on synthesis challenges, stability of dopants and defects, 3D integration pathways, metrology methods compatible with industry toolsets, emerging quantum and transport phenomena relevant to device engineering, and benchmarks for evaluating system-level competitiveness. Outcomes will include a report identifying critical research directions, estimated timelines and investments required for commercialization, and recommendations for structuring a coordinated national effort to ensure U.S. leadership in low-D materials and their semiconductor applications. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2026 · 2026-02

PROJECT ABSTRACT Extreme heat exposure in the US caused over 120,000 heat-related emergency room visits in 2024; heat stroke leads to organ damage, including acute kidney injury, and even death. The health effects of recurrent, asymptomatic hyperthermia, however, remain unknown. Kidney disease disproportionately affects working-age persons living in hot regions of the world, including California’s Central Valley and Texas’ Rio Grande Valley and repetitive heat stress is posited to be the cause. If heat exposure is linked to kidney injury, a broad swath of heat-exposed workers—outdoor workers (farm workers, construction workers, and firefighters) and indoor industrial workers (warehouse workers, shipyard welders, and food truck operators)—are at risk. Since several individual-level factors contribute to heat stress—e.g., age, comorbidities, physical fitness, and acclimatization, heat stress itself can be challenging to quantify, particularly in large population studies. Consequently, systematic investigations on the causal effects of heat stress, and of mitigation strategies have also lagged. In this study, we propose two aims. In aim one, we will develop measures of heat stress using non-invasive proxies. In controlled climactic chambers experiments, we will conduct high frequency physiologic monitoring and biosampling during exercise in persons of working age (30-50 years) with varied temperature conditions. We plan to agnostically identify markers that correlate with changes in core temperature and kidney injury. In aim two, using a case-cross over randomized control trial design at the work site, we will implement and assess two active cooling interventions in 72 workers, accounting for individual response to heat and underlying comorbidities. Over sequential weeks, we will measure rate of core temperature change (primary outcome) and changes in kidney injury markers and worker acceptability (secondary outcomes). Testing and quantifying the effects of active cooling interventions on kidney injury markers not only helps to build the case for investment in a specific cooling technology, it will also provide supporting or refuting causal evidence for the link between heat stress and kidney injury. In future work, we can 1) apply a non-invasive heat stress marker in broader population-based studies on the health effects of heat, and 2) assess the cost-effectiveness of heat mitigation strategies and specifically their impact on worker productivity and satisfaction.

NIH Research Projects · FY 2026 · 2026-02

Varicella-zoster virus (VZV) is a human alphaherpesvirus of medical importance that can have severe outcomes in immunocompromised children and adults, causes debilitating diseases such as post herpetic neuralgia, and is linked to stroke. Like other herpesviruses, VZV relies on multiple glycoproteins for cell entry, tropism, and immune evasion, but their structural mechanisms remain poorly defined. Among these, gB and gH-gL constitute the core fusion machinery required for cell entry, while gE/gI plays a key role in viral replication and immune recognition. Despite their essential functions, high-resolution structures of complete glycoprotein complexes remain unresolved, limiting our understanding of herpesvirus membrane fusion and immune targeting. This study will leverage CyclApol-based membrane extraction and single-particle cryo-electron microscopy (cryo-EM) to define previously unobserved near-atomic resolution structures of VZV glycoprotein complexes in their native membranes. We will determine the structures of gB/gH-gL, providing critical insights into herpesvirus fusion, and gE/gI, defining its role in viral spread and antibody recognition. Guided by our strong preliminary data we propose to pursue two aims: (1) To determine the gB/gH-gL fusion complex structure and define its relationship to function; (2) To determine the structure of the gE/gI complex independently or interacting with antibodies. Preliminary data confirm the successful purification of both gB/gH-gL and gE/gI complexes from VZV-infected cells, demonstrating the feasibility of their structural determination. Our approach overcomes the limitations of computational modeling based on AlphaFold and similar platforms, circumventing their inability to accurately predict dynamic protein-protein interactions within membrane-associated complexes. By capturing glycoproteins in their native lipid environment, we will provide a structural framework for understanding viral entry and immune recognition at near-atomic resolution. These studies will set the stage for new approaches in herpesvirus structural biology, enabling the mechanistic understanding of membrane fusion, viral spread, and immune recognition at near-atomic scales while laying the groundwork for rational vaccine design and targeted antiviral therapies.

NIH Research Projects · FY 2026 · 2026-02

Project Summary/Abstract Metabolic dysfunction-associated steatohepatitis (MASH) is a growing public health concern in industrialized nations, with an estimated cost of $1.66 trillion in the U.S. by 2039. Despite its prevalence, therapeutic strategies remain limited due to an incomplete understanding of its pathogenesis. Emerging evidence suggests that the gut microbiota plays a critical role in modulating metabolic and inflammatory processes in MASH through the production of microbiota-dependent metabolites (MDMs). Therefore, a promising approach is to augment therapeutic MDMs in the gut by reintroducing their producers. However, current microbiome-based interventions, such as fecal microbiota transplantation (FMT), have been largely ineffective in ameliorating MASH due to critical gaps in identifying potent MDM-producing bacterial strains and elucidating the mechanisms that enable their durable engraftment in the gut. My long-term goal is to develop rationally designed microbiome therapeutics for MASH and other metabolic diseases by leading a multidisciplinary research program. This proposal aims to establish a foundational strategy to combat liver inflammation in MASH by leveraging bacterial strains with high MDM-producing capacity, focusing on Clostridia isolated from the Hadza hunter-gatherers. The Hadza harbor a highly distinct gut microbiome enriched with bacterial strains that efficiently utilize dietary polysaccharides and synthesize health-promoting MDMs, presenting promising therapeutic potential. Aim 1 will identify Hadza-derived Clostridia that produce anti-inflammatory MDMs. Aim 2 will investigate the mechanisms enabling their stable MDM production in the gut, with a focus on their polysaccharide-utilizing machinery that facilitates engraftment. Aim 3 will determine their immune and therapeutic effects in diet-induced MASH models. The successful completion of this study will enhance our understanding of gut bacterial metabolism in MASH and establish a rational framework for developing targeted microbiome therapeutics beyond current FMT approaches. Additionally, this K99/R00 award will provide essential training in both scientific and career development, facilitating my transition to becoming an interdisciplinary independent researcher. My training will be supported by a distinguished mentoring team with expertise in microbiome science (Dr. Justin Sonnenburg, primary mentor), liver biology (Dr. Natalie Torok, co-mentor), metabolomics (Dr. Michael Fischbach, advisor), gut ecology (Dr. Kerwyn Casey Huang, advisor), and immune profiling (Dr. Holden Maecker, advisor). Stanford University, a renowned institution in biomedical research, provides extensive resources, state-of-the-art equipment, and unparalleled opportunities to support my training. In summary, this K99/R00 proposal will equip me with the necessary skills to launch an independent research program in microbiome therapeutics for MASH and other metabolic diseases. The research findings will provide key insights into the role of microbiota in MASH and establish the groundwork for translational strategies aimed at improving metabolic and liver health.

NIH Research Projects · FY 2026 · 2026-02

PROJECT SUMMARY/ABSTRACT Based on clinical, epidemiological, histopathological and molecular data, the immune system appears to be an important driver of benign prostatic hyperplasia (BPH), though the details remain poorly understood. The long- term goal is to find BPH prevention and treatment approaches that counter immune-driven BPH processes. The objective of this proposal is to determine how an expansion of clonal T cells contributes to BPH pathogenesis. The central hypothesis is that T cells responding to specific microbial or self-antigen(s) drive the proliferation and activation of prostatic fibroblasts that subsequently recruit glandular epithelium. The resultant BPH nodules lead to prostate enlargement, bladder outlet obstruction and lower urinary tract symptoms. This hypothesis will be testing through three specific aims: (1) Define the oligoclonal T-cell subsets and their functionality in BPH stromal-rich nodules; (2) Identify the inciting antigens recognized by BPH oligoclonal T cells; and (3) Determine the role of lymphocyte chemoattractant CXCL13 in BPH nodular growth. The premise is based on the novel finding of T-cell oligoclonality in BPH, and we will incorporate innovative techniques including TCR repertoire profiling, single-cell spatial transcriptomics, cell-based high-throughput antigen screening, and a BPH tissue explant model system. The results will have an important impact immediately by establishing an improved understanding of how the immune system propels BPH, and longer term because they lay the foundation to develop rational approaches for the prevention and treatment of BPH.

NIH Research Projects · FY 2026 · 2026-02

SUMMARY Alzheimer’s disease (AD) is the most common form of dementia and the fifth leading cause of death among older adults in the United States. Unfortunately, available Food and Drug Administration (FDA)-approved therapeutics for treating AD only offer limited efficacy, largely due to our incomplete understanding of the underlying mechanisms that cause and drive the disease. Genome-wide association studies and immunohistochemistry both implicate neuroinflammation and immune dysfunction as key drivers of AD pathogenesis, but the availability of specific biomarkers to investigate and track the spatiotemporal dynamics and functional phenotypes of immune cells in AD remains extremely limited. Positron emission tomography (PET) is a highly sensitive molecular imaging modality well-suited for the longitudinal study of such biomarkers, with demonstrated utility for non-invasive, in vivo interrogation of biochemical processes. Existing PET biomarkers of neuroinflammation (e.g., the translocator protein 18kDa; TSPO) suffer from significant drawbacks, including non-specific expression across multiple cell types in the central nervous system (CNS) and an inability to distinguish between beneficial and harmful inflammatory processes. To address this unmet need, we identified G protein-coupled receptor 84 (GPR84) as a promising biomarker of pro-inflammatory innate immune responses in the context of AD. Specifically, my preliminary data showed that GPR84 expression is significantly upregulated in human myeloid cells following pro-inflammatory stimulation and in rodents after lipopolysaccharide challenge; upregulation is also observed in the brains of the 5xFAD AD mouse model. Excitingly, we developed two lead GPR84-specific PET radiotracers, [11C]GLPG-38 and [18F]MGX-110S, that cross the blood-brain barrier (BBB) in healthy rodents and enable sensitive detection of harmful inflammatory immune responses in vivo. In this proposal, I aim to evaluate the sensitivity and specificity of our two novel GPR84-PET radiotracers for quantifying pro-inflammatory immune responses in human cells and a mouse model of systemic and neuroinflammation (Aim 1). I will also assess radiotracer performance in the 5xFAD mouse model of AD and analyze the spatial overlap between radiotracer binding and immune cell markers in human AD postmortem brain tissues from multiple disease stages compared to healthy controls, employing in vitro autoradiography and advanced spatial biology techniques to further establish translational potential of each radiotracer (Aim 2). Successful completion of these aims will result in characterization of two novel GPR84-PET radiotracers, assessing their suitability for in vivo imaging of neuroinflammation and the specific innate immune cell populations they target. Ultimately, translation of these innovative PET radiotracers into clinical practice will significantly enhance our understanding of AD pathogenesis, facilitate targeted therapeutic development, and enable precise monitoring of treatment efficacy, ultimately paving the way for improved outcomes for patients with AD.

NIH Research Projects · FY 2026 · 2026-02

ABSTRACT. Immunotherapy has emerged as a successful therapeutic strategy for a variety of cancers. The recent success of immunotherapies in other solid tumors has sparked increased attention to treatments targeting the immune system in glioblastoma (GBM) and other brain cancers. One of the key challenges in the successful treatment of brain tumors with immunotherapy is our lack of appropriate methods to visualize and quantify the killing of cancer cells by the immune system within the brain. Currently, contrast-enhanced magnetic resonance imaging (MRI) is used to evaluate treatment response and progression in these patients. However, the accurate determination of tumor progression from treatment-associated inflammation, remains an unmet clinical challenge. The lack of a useful response assessment has complicated patient care and the clinical development of these therapies. This proposal aims to address this by developing a novel imaging strategy to visualize and quantify the specific protein, known as perforin, which immune cells utilize to gain access to kill cancer cells. Herein, we will develop a first-in-class, small molecule positron emission tomography (PET) probe which is capable of passively crossing the blood brain barrier and binding to perforin, permitting differentiation between response to immunotherapy and non-response. This strategy would permit non-invasive visualization of perforin levels with minimal off-target activity as perforin is expressed exclusively by cytotoxic cells of the immune system. We will develop a library of novel fluorine-containing small molecules targeting perforin and advance the top 10 binding molecules for radiolabeling with fluorine-18 (SA1). We will then characterize their brain penetration, biodistribution, and stability and use quantitative benchmarks to advance the top 3 performing radiotracers for treatment monitoring studies (SA2). Lastly, we will assess the utility of perforin-PET to detect therapeutic response and predict outcomes in established syngeneic orthotopic mouse models of glioblastoma following treatment with immune checkpoint blockade (SA3). Success of this approach would allow for rapid translation and incorporation into clinical studies. This would permit clinicians and researchers to visualize and have real-time information of the killing of brain cancer cells by the immune system and make informed decisions regarding the effectiveness of immunotherapy for any particular patient. The significance of the proposed research is that it demonstrates a generalizable mechanism to monitor multiple types of immunotherapy in glioblastoma and other brain tumors (including pediatric brain tumors and brain metastases). Such work has the potential to improve response determination in brain tumor immunotherapy, spare unnecessary treatment side effects, and through this eventually improve the management of this disease.

NSF Awards · FY 2026 · 2026-02

In modern financial markets and economic systems with large populations, decision-making has evolved into a multifaceted process involving various aspects such as population heterogeneity, diverse information structures, and human-AI interactions. This project aims to develop new learning frameworks and mathematical foundations that strengthen our understanding of the stability, efficiency, and fairness of societal systems with large populations. Novel frameworks developed in this research are designed to have flexible model assumptions, be able to learn from incomplete information, and accommodate heterogeneous risk preferences as well as information asymmetry. This research will involve both undergraduate and graduate students, emphasizing cross-disciplinary training in mathematics and machine learning. This project places at its core the mathematical advancement of machine learning theory for stochastic systems with many interacting agents, known as “mean-field games”. The first goal is to develop new mathematical models and learning algorithms for mean-field games under structural properties such as graphon interactions or additional summary statistics of the population distribution. This development relies on new approximation schemes and stability analyses based on the local propagation of flows. The second goal focuses on principal-agent problems, where agents have diverse risk preferences or the capability to acquire new information. These topics pose significant challenges in a dynamic setting, leading to a novel class of stochastic partial differential equations that require new developments for well-definedness and regularity theory. The final goal focuses on constructing generative models (simulators) with interactive mean-field agents, addressing the scalability issue in agent-based simulator literature. To leverage the computational power of neural networks, a key objective is to establish a universal approximation theorem in the distributional sense and the convergence of an iterative deep-learning scheme to train the simulator. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-02

High-power lasers that provide ultrashort pulses of intense light are a critical technology for fundamental science and a range of industrial, medical, and national security applications. The key constraint on laser power and performance is damage to glass and metal optics: to prevent high-power lasers from destroying themselves, their optics must be large. The size and expense of large optics make higher laser powers impractical and limit possible applications of existing systems. This project replaces optics inside high-power lasers with plasma components, allowing the control of a thousand times higher light intensity and leveraging machine learning to provide a unique, physics-based solution to a bottleneck in laser development. It also creates educational and research opportunities to train students in plasma physics and high-power laser engineering. The compact plasma-based high-power lasers developed here will both advance scientific fields like particle physics, plasma physics, quantum optics, and astrophysics and enable the use of high-intensity light for applications like nuclear fusion energy, radiotherapy for cancer treatment, x-ray imaging for sensitive material detection, and the construction of advanced accelerators and light sources for semiconductor manufacturing. This project takes advantage of recent progress in plasma optics to develop a plasma-based femtosecond laser beamline, tackling key issues in the physics of structured plasma in high-intensity light fields, the design of plasma-based chirped-pulse-amplification architectures, and the integration of plasmas into high-power laser beamlines. Theoretical, computational, and experimental study of plasma-optic integration will be combined with expanded educational and research opportunities for high-school, undergraduate, and graduate students in plasma physics and optics. Machine learning will be used to optimize high-repetition-rate laser experiments and large-scale plasma simulations, and the project will provide a generalizable platform for integrating machine-learning tools with high-power laser and plasma physics experiments. A detailed study of laser-plasma interactions under the conditions most useful for the construction of a plasma-based devices will be conducted to enable the construction of future systems. This work will produce a cohort of students and future scientists with a deep understanding of plasma physics and will lead to a small-scale prototype of a plasma-based experimental beamline that can serve as a testbed for compact, ultra-high-power, next-generation lasers. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2026 · 2026-02

Abstract. Beta-hydroxybutyrate (BHB) is an abundant metabolite and a major ketone body whose levels rise in response to low carbohydrate availability. Despite decades of research, only two biochemical pathways – ketogenesis and ketone oxidation – were thought to fully encompass the metabolism of BHB. We have recently identified a previously unknown third metabolic pathway for BHB: the “BHB shunt” (Moya-Garzon, Cell 2025). This third pathway involves enzymatic derivatization of BHB to generate a family of orphan ketone metabolites, the BHB-amino acids. We have used gain- and loss-of-function studies establish a critical role of the BHB shunt in ketosis-associated weight loss. The discovery of this BHB shunt is therefore exciting and important because it upends our textbook understanding of BHB biochemistry and uncovers an underappreciated role for anorexigenic ketone metabolites in ketosis-associated weight loss. Because of these fundamentally new insights, here in this proposal we focus entirely on additional biochemical and physiologic studies of this BHB shunt. We will test the central hypothesis that the BHB shunt constitutes a third branch of BHB metabolism linked to energy balance. In Aim 1, we will determine the cell type-specific contribution of the BHB shunt to the generation of ketone metabolites and to energy balance. This goal is enabled by the generation of a unique collection of conditional knockout mice in which the BHB shunt is specifically ablated in liver, macrophages, or the gut. In Aim 2, we turn to the molecular mechanism by which the downstream BHB-amino acid products regulate feeding and energy balance. We have identified a body weight-associated ion channel that can be directly liganded by the most abundant BHB-amino acid, BHB-Phe. We will determine how BHB-Phe modulates the activity of this ion channel, and whether related BHB-amino acids also exhibit similar effects or not. Using knockout mice, we will critically determine the requirement of this ion channel as a downstream mediator of ketone metabolites on energy balance. Lastly in Aim 3, we turn to additional downstream metabolites from the BHB shunt. In preliminary studies, we have now used in vitro biochemistry to determine an oxidative pathway of BHB-amino acid metabolism. We will test the role of known oxidoreductases in catalyzing this transformation. We will determine the endogenous levels and dynamics of the downstream AcAc-amino acid products. Successful completion of this proposal will provide a detailed and molecular understanding of biochemistry and physiology of a newly discovered ketone metabolic pathway, thereby establishing a scientific foundation for developing new therapeutics that target ketosis for obesity and associated metabolic diseases.

NIH Research Projects · FY 2026 · 2026-02

Project Summary Hemispheric high-grade gliomas (HGGs) in children are fast-growing and become rapidly widespread; the median survival rates for these diagnoses are measured in months, not years. It is therefore critical to discover more about the pathophysiology of pediatric HGGs (pHGGs), particularly their highly invasive nature. Investigating this characteristic to identify drug targets that would slow the speed of tumor invasion would significantly benefit our patients. MAP4K4 is a kinase with diverse biochemical and physiological roles in normal cell physiology. Previous data have suggested the MAP4K4 pathway may be a cornerstone in tumor invasiveness in multiple central nervous system and peripheral cancer types. We hypothesize that changes in function and expression of MAP4K4 play a significant role in the invasive ability of pHGGs. To determine whether MAP4K4 is altered in specific subtypes of hemispheric pHGG specimens from Stanford/Lucile Packard Children’s Hospital, our team will perform high- throughput methylation in combination with genomic sequencing to test for variants of MAP4K4 (and associated genes) (Aim 1). To determine whether MAP4K4 is necessary for pHGGs invasion, we will use pharmacological inhibition of (PF-06260933) and genetic (CRISPR/Cas9) silencing of MAP4K4 and downstream targets to regulate function and assess invasion in spheroid invasion assays (Aim 2). To evaluate whether inhibiting MAP4K4 reduces tumor cell invasion in vivo and prolongs survival, we will use MAP4K4 drug inhibitors in a pHGGs-luciferase orthotopic mouse model, and image the tumors at defined time points. We will also identify the cellular specificity of the MAP4K4 inhibitor in vivo with clearing-assisted tissue click chemistry (Aim 3). In conjunction with the Prolo laboratory, our team includes experts in CRISPR/Cas9 technology to probe molecular pathways genetically (Michael Bassik, PhD), leaders in neuro-oncology and xenograft mouse modeling of high-grade gliomas (Michelle Monje, MD, PhD), genomic and methylomic profiling of tumors (Matija Snuderl, MD) and click-chemistry (Li Ye, PhD). Analyzing these results will help to inform us of the molecular mechanisms underlying pHGG invasion. By creating a database detailing the profiles of our pHGG cohort, which will be published and made widely available for the broader scientific community, we hope to accelerate translational research to design treatments for this devastating cancer.

NIH Research Projects · FY 2026 · 2026-01

SUMMARY The goal of this multi-investigator proposal is to determine how the brain can act as ‘neural pacemaker’ to regulate the tempo of aging of the whole organism. Aging is a multifaceted process that alters the function of every organ and is associated with devastating diseases. An inherent limitation to understand and target aging has been the ability to perform systems level studies to identify central drivers of organismal aging. All vertebrate studies have remained limited because of the low throughput nature of studies in mice, and we miss an integrative understanding of aging. The recent pioneering of the killifish, a vertebrate with a naturally compressed lifespan, has made previously intractable challenges possible. The killifish is one of the shortest-lived vertebrates, with a compressed lifespan of only ~6 months. The conserved aging characteristics of the killifish and the availability of genomic, proteomic, and genetic tools make it an ideal model for conducting longitudinal, high-throughput studies of aging and longevity. We have recently generated transformative technologies to interrogate aging in a scalable manner in the killifish. We have performed video-tracking continuously throughout the killifish lifespan. Using artificial intelligence, we have parsed these extraordinary complex datasets and made the tantalizing observation that behavior acts as a predictive biomarker of aging. We also conducted a genetic screen for killifish lifespan, which uncovered new neuropeptides that impact organismal lifespan. Moreover, we generated ‘multi-omics’ datasets reporting molecular phenotypes in response to aging and longevity interventions. Finally, we have built new tools to test neural systems throughout lifespan to gain unbiased understanding of control centers for aging. In this multi-investigator proposal, we are combining our unprecedented ability to study high level aging markers in the killifish, uncover molecular phenotypes of aging, and identify and manipulate genetic and neural networks to test the hypothesis that the brain affects the tempo of aging. We propose to conduct the following experiments: 1: Leverage continuous behavioral recording to identify behaviors that accurately predict the tempo of aging. 2: Uncover secreted factors that connect the brain to the rest of the body for the regulation of aging. 3. Determine how molecular changes in the brain dictate organismal fitness during aging. 4: Identify new neuronal activity networks during aging and longevity. Knowledge resulting from these studies should be transformative to understand the fundamental mechanisms that control and synchronize aging and longevity, a finding that will likely have therapeutic value for extending healthy lifespan. As the median age of the human population continues to rise on all continents, these issues are directly pertinent to major medical and societal problems faced in the United States and worldwide.

NIH Research Projects · FY 2025 · 2026-01

PROJECT SUMMARY Whether due to injury, disease, or as a consequence of cancer chemotherapy, millions of Americans, both young and old, experience sensory neuropathy. This condition, characterized by numbness, tingling, and disruptions to gait, degrades the quality of life. The sheer diversity of cells that underpin the mechanical senses of touch, hearing, and proprioception pose a barrier to understanding their function in health and their dysfunction in disease. Rapid mechanosensation responsible for low-threshold touch sensation depends on the activation of specialized mechanosensitive (MS) ion channels. The proteins forming MS channels in humans and other animals have come to light over the past two decades, yet how mechanical energy causes their activation remains poorly understood. Some MS channels seem to respond to changes in membrane tension, others depend on coupling to the cytoskeleton, and still others require coupling to extracellular matrix (ECM) structures. Whereas a subset of neuropathies arise from faulty nerve transmission, many other forms involve defects in coupling MS channels to sensory stimulation or from defects in the channels themselves. This project seeks to understand how sensory stimuli is coupled to MS channel activation in vivo. Very few biological models are amenable to in vivo neuronal biophysics, including voltage-clamp of sensory neurons and to imaging MS channel complexes in living animals. The model organism C. elegans fulfills these requirements. In this research we will combine established techniques for in vivo whole-cell patch-clamp electrophysiology with cutting-edge, genetically encoded tools to visualize and measure touch-induced mechanical strain in touch receptor neurons. A large toolbox of existing mutations affecting the cytoskeleton and ECM will be used to perturb the coupling between sensory stimulation and MS channel activation, delineating the role played by each structure in this fundamental sensory process. Ample evidence indicates that the mechanics of touch are conserved even among distantly related animals, suggesting that what is learned through this research training plan has the potential to provide insight into the factors that give rise to sensory neuropathy and to other mechanosensory abnormalities.

NIH Research Projects · FY 2025 · 2026-01

PROJECT SUMMARY Coronary artery disease (CAD) remains a rising cause of morbidity and mortality in the United States. Human genetics suggest that a significant portion of the inheritable risk of CAD is related to fibroblast-specific regions of the genome. Using single cell transcriptomics, our group has identified a transcriptionally distinct adventitial fibroblast (AdvFib) population, which undergoes early phenotypic modulation during atherosclerosis development. Specific ablation of AdvFib significantly reduces plaque burden, suggesting their ability to influence atherosclerosis. To identify regulators of AdvFib, we characterized regions of altered chromatin accessibility upon AdvFib activation, revealing the transcription factor Tcf21. Tcf21 is predominantly expressed in AdvFib, and its expression declines upon AdvFib phenotypic activation. Tcf21 knockout in murine AdvFib increases vascular calcifications and upregulation of pro-atherogenic inflammatory cytokines. Given the restricted expression of Tcf21 to AdvFib and phenotypic activation of AdvFib upon Tcf21 knockdown, the central hypothesis underlying this proposal is that overexpression of Tcf21 locks AdvFib in a quiescent state, which then prevents the downstream activation of non-cell-autonomous signals that trigger plaque formation. Using an AdvFib-specific Tcf21 overexpression mouse model, I will characterize histological changes in plaque and alterations in non-AdvFib plaque cells, such as smooth muscle cells and inflammatory cells. I will then use single-cell RNA sequencing to characterize transcriptional changes of AdvFib upon Tcf21 overexpression and downstream transcriptional changes in non-AdvFib plaque cells. Given the putative effect of Tcf21 on the activation/recruitment of inflammatory cells, my goal is to characterize the precise epigenetic regions of Tcf21 binding to understand how Tcf21 regulates inflammatory cytokine production. Overall, this study will elucidate the role of AdvFib in atherosclerosis and characterize how Tcf21 regulates AdvFib.

NSF Awards · FY 2025 · 2025-11

Even though large physics experiments are able to detect complex properties of reaction rates, such foundational scientific quantities are always compressed due to limitations in statistical methods and exchange platforms. The current paradigm introduces significant barriers for scientific discovery and data reusability. Comparisons between experiments with different compression schemes is challenging. Furthermore, the compression schemes necessarily throw out potentially useful information, which may be needed to explore interesting phenomena. This information loss likewise limits the long-term utility of archived data, which may be of scientific interest long after the experiment that generated it ends. Recent advancements in machine-learning methods initiated by the PIs and others solve these issues by enabling measurements directly in the un-compressed (or minimally compressed) data. However, there is currently no standard or platform for sharing such data, and therefore, no measurements of this kind with actual data have been published to date. This project builds open source cyberinfrastructure for publishing and reusing un- or minimally compressed measurements for research and educational purposes. These tools are widely applicable across physics domains and data from electron-proton collisions are used to test and benchmark the frameworks. This project serves the national interest, as stated by NSF's mission, by promoting the progress of science. The publication of minimally processed data greatly extends the practical lifetime of experimental facilities, enabling high-quality scientific analyses well beyond the time a detector is running and the researchers who collected the data. Many analyses are simply not possible with existing protocols where only limited numerical results are published alongside academic papers. Minimally compressed data can be studied without computationally expensive and often proprietary detector simulations, and are therefore of great interest for a first exposure to research by early career scientists in training. This project builds upon recent advances by the PIs and others in the development of machine machine learning solutions to measurements of reaction rates in large physics experiments. Cyberinfrastructure is created for publishing and reusing measurements created by these machine learning algorithms. The project develops an exchange format for sharing machine learning-based measurements whose data representation is neural networks, unlike the tabular, often histogram, format of traditional measurements. This format is integrated with a software platform that enables these data to be readily findable, accessible, interoperable, and reusable (FAIR). This cyberinfrastructure is tested with a prototype science pipeline, starting from a first unbinned measurement as input into the platform and ending with an analysis that reinterprets it. In the process, practical software is developed and made available to other researchers to carry out unbinned measurements and to reuse published data. These tools are integrated with widely-used frameworks in particle, nuclear, and astrophysics in order to accelerate their adaptation. Such developments are also used to broaden participation in fundamental physics and applied machine learning for undergraduate researchers, including those who would not normally have access to large experimental and computing resources. This is enabled in part by significantly reduced computational resources required to carry out forefront analysis since computationally expensive experimental tools including detector simulations are not needed. Undergraduate researchers are involved in the development and testing of the new infrastructure. This award by the Office of Advanced Cyberinfrastructure is jointly supported by the Physics at the Information Frontier program in the Division of Physics within the Directorate for Mathematical and Physical Sciences. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2025 · 2025-11

Japanese Encephalitis virus (JEV) is a mosquito-borne virus that is a major cause of human viral encephalitis in Asia, with upwards of 65,000 cases occurring annually. JEV is found in a transmission cycle between waterbirds, swine, Culex spp. mosquitoes and incidental hosts of people and animals. JEV has recently begun to spread to new areas such as Australia. JEV risk is of great importance given the incursion of vector-borne diseases like West Nile virus, Zika virus, and Chikungunya virus into new regions. Across the globe temperature is increasing and mosquito-borne diseases are uniquely responsive to such increases. As a result, assessing transmission of vector-borne diseases across temperatures is crucial. The intersection of competent vectors, susceptible hosts, and suitable environments has been linked to emerging vector-borne diseases. Predicting the likelihood of contact and transmission between hosts found in the US is key to estimating JEV risk. To address this critical gap, I seek to develop a spillover model of JEV risk in the US. Researchers developed a framework for modeling R0 based on empirical data from mosquito life history experiments to estimate the suitability for pathogen transmission across temperatures. Such models exist for numerous mosquito-borne diseases, but data on JEV transmission across temperature is lacking. Likewise, spillover models that attempt to incorporate the full suite of ecological and demographic drivers of transmission of zoonotic vector-borne diseases have been developed for other pathogens, yet despite its spillover potential, JEV has yet to be investigated in this holistic framework. In this proposal I will address these gaps through distinct but interconnected aims, leveraging vector biology and computational modeling to: 1) Experimentally measure the effect of temperature on JEV infection and transmission in multiple US vector species and virus genotypes, 2) Evaluate temperature suitability for JEV transmission across the US, 3) Develop mechanistic spillover models to predict JEV risk using amplifying and reservoir host distributions, along with other climatic and ecological information, across the US. In summary, I propose to develop a mechanistic model of JEV spillover in the US, utilizing a One Health approach, accounting for vector, amplifying, reservoir, and incidental hosts found in the transmission cycle of JEV. The emergence of zoonotic diseases has increased. We can prepare for the emergence of future pathogens by synthesizing data across multiple scales to gain a holistic understanding of transmission dynamics by incorporating climatic and zoonotic drivers. The proposed research will expand our understanding by providing novel empirical data necessary to predict JEV risk under different temperature scenarios and identify high-risk areas in the US. Overall, this project has the potential to contribute to new insights into the transmission dynamics of zoonotic diseases and the risk of spillover.

NSF Awards · FY 2025 · 2025-10

Particle and nuclear physics (PNP) are fundamentally probabilistic due to quantum mechanics. Both fields rely on complex Monte-Carlo (MC)-based simulators that use random number sampling to make predictions for nearly all aspects of experimental design and data interpretation. In fact, most branches of science and engineering rely heavily on MC simulations for solving difficult problems, from modeling traffic flow to predicting weather patterns; in the rapidly emerging fields of machine learning and quantum computing, MC methods are essential. Progress in these areas requires developing, validating, and deploying novel and efficient MC algorithms. However, many university computer science programs focus on deterministic methods, with MC techniques covered only in passing, leading to a gap between knowledge and required skills for junior researchers. This project fills the knowledge gap by training graduate students and junior postdoctoral researchers in the development of MC models with traineeships and schools focused on real-world PNP problems. The project has three main goals. The first is to develop summer-school curricula as well as organize summer schools to train graduate students and junior postdoctoral researchers in MC generator algorithms and their applications. The second is to build on summer school material and produce online tutorials for self-guided study. The third goal is to create and run a 2-year pilot program of focused, short-term traineeships for graduate students and postdoctoral researchers, which could in the future be scaled up to include more nodes and mentors in the training network. This award by the Office of Advanced Cyberinfrastructure is jointly supported by the Division of Physics within the Directorate for Mathematical and Physical Sciences. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-10

Chemicals that are free of impurities are critical to everyday products such as electronics, medicine, and food. However, separating a chemical mixture into its pure constituents is energy intensive and expensive. This project will develop new membrane materials that can separate chemical mixtures at lower cost and use less energy. The research team will combine advanced data science with lab experiments to speed up materials discovery. The project will focus on separating a liquid mixture of small molecules called paraffins and olefins. This specific separation is especially important to the chemical industry because these molecules are used to make fuels and plastics. The results of this project will be new membrane materials, and better computer programs for finding these materials. Additional benefits to society will come from training science and engineering students in data science, undergraduate research and training, and public outreach at science festivals. This project combines researchers with expertise in polymer synthesis, materials science, chemical engineering, and data science. The goal is to discover new organic-inorganic (hybrid) membrane materials that can separate organic liquid mixtures. The research team will combine high-throughput physical experimentation with machine learning (ML) models to create new data-driven frameworks for membrane material discovery and optimization. This project will focus on using combinatorial chemistry to create structurally-tunable microporous polymers. These polymers will be combined with newly-developed inorganic vapor infiltration techniques to create a wide range of organic-inorganic hybrid membranes. These hybrid membranes will be designed for chemical stability and selectivity to achieve difficult organic liquid separations, including the separation of olefin and paraffin mixtures. Data-informed ML models will be developed to establish feasibility of the polymer synthesis, chemical stability, and permeation selectivity. The corresponding data-driven workflow will identify promising materials that can separate a given liquid mixture, and are also easy to manufacture. The most promising membrane material candidates will be tested to validate predictions. The experimental results will be fed back to improve simulation predictions. The project will also support a multi-disciplinary undergraduate research program that will train students in lab automation. Public outreach includes a demonstration module that visibly separates colored dyes using membranes. This will create awareness of how these “hidden” manufacturing processes are important to human well-being and economic security. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-10

Solid catalytic materials, such as zeolite aluminosilicates, are at the heart of petroleum and natural gas conversion. Some solid catalytically active materials are now known to be dynamic, with metal ions that move throughout the porous solid structures. While this mobility has important influences on the catalytic activity of the zeolite, and therefore the productivity of the industrial process, understanding, quantifying, and tracking this mobility is exceptionally challenging. In this emerging paradigm of dynamic catalytic materials, signals must be identified to track the mobile components and new tools developed to probe their behaviors and contributions to reactivity. This Designing Materials to Revolutionize and Engineer our Future (DMREF) project will investigate the dynamic behavior of technologically-important metal cations such as gallium and copper, dispersed in porous oxide materials. Specifically, the project will explore the spatial extent and timescale for ion mobility, the role of adsorption (determined by the size of the ion and its oxidation state, as well as the nature of the oxide support), the effect of temperature, pressure, protons, and the availability of ligands. Spectroscopic fingerprints for ion mobility will be identified and used to probe their dynamic behavior. Guided by theoretical simulations, these spectroscopic fingerprints will aid in interpreting the measurements and describing the reaction mechanisms. The data and models generated by the project will allow researchers to predict temperature regimes that mark the onset cation mobility, as well as how cation mobility influences catalytic performance, activation during start-up, and deactivation. This will provide insight into how to intentionally synthesize materials with a desired type of dynamic behavior, account for the contributions of mobility to the technological performance of materials, prolong their useful life, and regenerate them by inducing mobility to cause redispersion of the ions. Another important outcome is the training of graduate students and postdoctoral scholars in collaborative research at the intersections of synthesis, spectroscopy, theory and simulations. They will work across disciplines with researchers in the US and abroad, in academia, national labs, and industry, to tackle challenging problems in materials design. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2025 · 2025-09

PROJECT SUMMARY Family plays a central role in shaping the experiences of aging. Any health shock not only affects the patient but also reverberates throughout the family unit, taking a physical, emotional, and financial toll on family members. The effect on families is likely to be particularly pronounced for health shocks that lead to a pronounced decline in independence, as, for example, happens with the progression of Alzheimer’s Disease and Related Dementias (ADRD). The ways families manage sharp changes in health can have profound effects on both the patient’s well-being and that of their caregivers. Despite the intricate relationship between family dynamics and health outcomes at older ages, public policies and much of the existing research in the U.S. have predominantly focused on individual patients rather than the broader family context. The dearth of evidence relates not to the lack of interest, but to the absence of datasets that could allow studying the effects of health shocks on families. In this project, we propose to start closing this gap in knowledge by leveraging the new extensive Census Bureau infrastructure for linking United States survey and administrative data. We will construct and analyze a novel individual-level database that links the health trajectories of older adults to the physical, mental, and economic well-being of their family members across multiple generations, covering twenty-five years from 1999 to 2024. Using this new database, we will comprehensively examine the interdependencies between the mental, physical, and economic well-being of older adults and their families in the United States. First, we will describe the variation in family circumstances of older adults at the time of different health shocks. We will consider variation across demographic groups, local geographies, and socio- economic circumstances. Second, we will use econometric techniques for causal inference to quantify how much major health shocks affect the health and economic well-being of family members, including spouses and adult children.

NIH Research Projects · FY 2025 · 2025-09

PROJECT SUMMARY RNA molecules are essential for neuronal function, playing a key role in coordinating gene expression across different cellular compartments. Various RNAs, including messenger RNA (mRNA), microRNA (miRNA), and long non-coding RNA (lncRNA), respond rapidly to environmental cues and synaptic activity, supporting critical processes such as axonal growth, synaptic plasticity, and long-term memory formation. The spatial organization of RNAs within neurons enables localized protein synthesis at synapses, spanning distances from millimeters to meters, and is vital for nerve repair. Moreover, dysregulation of RNA-binding proteins, such as TDP-43, has been linked to various neurodegenerative diseases like amyotrophic lateral sclerosis (ALS), frontotemporal dementia (FTD), and Alzheimer’s Disease. Since these proteins are involved in alternative splicing, polyadenylation, transcription activation, translation, and RNA spatial localization4, it suggests a strong pathological link between RNA regulation and disease progression. Despite progress made in understanding other functional aspects of RNA-binding protein dysfunction (e.g., cryptic splicing), however, the functional role of spatial RNA localization in disease development remains largely underexplored, due to the lack of efficient tools for manipulating and perturbing endogenous RNA spatial localization to infer its causal physiological or pathological function. To investigate how RNA spatial mechanisms govern neuronal functions, my colleagues and I have recently developed a novel technology termed CRISPR-mediated transcriptome organization (CRISPR-TO) to perturb endogenous RNA spatial localization in neurons. I propose to use this novel CRISPR-TO tool to enable programmable control of endogenous RNA localization both in vitro and in vivo to study and create future treatment for neurodegenerative diseases. CRISPR-TO will first be applied in vitro using human induced pluripotent stem cell (hiPSC)-derived neurons from healthy individuals and neurodegenerative disease patients to identify RNA localization targets (Aim 1-2). These findings will then be translated in vivo via AAV delivery of CRISPR-TO to test how RNA spatial regulation influences disease progression and functional outcomes in preclinical models (Aim 3). If successful, this study can transform how we investigate RNA mislocalization in neurodegenerative diseases, uncovering novel mechanisms that contribute to disease progression and neuron regeneration. By enabling scalable, programmable control of endogenous RNA localization, we could pave the way for innovative spatial RNA therapeutic strategies, with implications far beyond ALS, extending to other neurological and systemic diseases where RNA localization plays a critical role.

NIH Research Projects · FY 2025 · 2025-09

Summary Aging alters the function of every organ. An inherent limitation to our understanding of aging is the complexity of the aging process. Thus, fundamental questions remain entirely unanswered: Are there mechanisms that coordinate organ function and how do they deteriorate with age? Could such mechanisms be restored to promote longevity? Previous work has revealed how immune cells and blood factors can impact organ aging and orchestrate organ communication. However, it remains entirely mysterious whether other synchronization systems could regulate organ aging. We are interested in the peripheral nervous system, which connects the brain to all organs, and whether it could coordinate the tempo of aging. This is a challenging problem because aging is a complex process and organ innervation by the peripheral nervous system is extraordinarily diverse. Our lab has recently generated paradigm-shifting methods to study complex aging biology. We have developed machine learning approaches coupled to spatial transcriptomics to determine the age of multiple cell types and to understand the impact cells have on neighboring cells within an organ. In addition, we have recently developed the first in vivo screens for brain aging in mice. Finally, we have established a new vertebrate model system, the African killifish, to perform aging studies at scale. The time is right to leverage these cutting-edge tools and develop new ones to understand how organ innervation responds to aging and to identify targeted approaches to boost the peripheral nervous system to “re-set” organ function and coordination during aging. We are interested in the following high-risk, high-reward questions: What are the age- dependent changes in the peripheral nervous system and how do they differ in diverse organs? How is the activity of peripheral neurons influenced by environmental stimuli that impact longevity? Is the peripheral nervous system functionally implicated in organ aging and how does it coordinate organ aging? Could the tempo of organ aging be “re-set” by restoring organ innervation? Addressing these questions has the potential to revolutionize how we think about aging and could lead to entirely new strategies to counter aging and diseases. More generally, solutions to this challenge could be broadly applied to other biological phenomena where time and organ coordination are critical.

NIH Research Projects · FY 2025 · 2025-09

Although intraocular pressure (IOP) is the only clinically modifiable risk factor for glaucoma, the exact role of IOP elevation in glaucomatous neurodegeneration is unclear, as the cause of glaucoma remains unknown. Even worse, glaucoma may progress in patients under controlled IOP, indicating that additional factors are pivotal to its pathogenesis. A critical barrier to progress in investigating glaucoma mechanisms is the limited comprehensive, quantitative, and non-invasive approaches for detecting and predicting the involvement of the entire visual system during glaucoma development. We recently developed these needed approaches using magnetic resonance imaging (MRI) and spectroscopy (MRS) to identify, for the first time, cholinergic dysfunction in the brains of glaucoma patients and experimental animal models. Despite the known importance of choline- containing compounds to the integrity of cell membrane and neurotransmission, very little is known about how changes in cholinergic signaling in glaucoma reflect the structural and functional damage along the visual pathway. Our primary objective is to investigate the role of this newly identified pathogenic cholinergic pathway in glaucoma patients and our novel rat models using advanced MRI and MRS techniques. We will test the central hypothesis that glaucoma is driven by impairments of the visual pathways that are mediated by the cholinergic nervous system. Aim 1: Test how cholinergic lesioning, neuromodulation, and supplementation contribute to glaucomatous damage and ameliorations in experimental rat models. Adult Long Evans rats will be induced with experimental glaucoma by chronic IOP elevation via intracameral hydrogel injection or lesioning of the basal forebrain followed by longitudinal eye, brain, and behavioral assessments. We will stimulate the cholinergic neurons of these animal models to examine their role in modulating IOP, visual brain activity, and visual function. We will also give different regimes of oral choline supplementation to determine their dose-dependency, therapeutic windows, and efficacy in ameliorating the deteriorations of glaucomatous visual pathways. Aim 2: Test the contribution of cholinergic nervous system to glaucomatous damage in patients. By examining the brains of healthy subjects and primary open-angle glaucoma patients with varying disease severity, we will determine the changes in neurochemicals relevant to the cholinergic system and how they alter with the structure and function of the visual system. To ascertain when cholinergic changes emerge in disease progression, we will define the relationships between cholinergic brain features across glaucoma stages using advanced statistical modeling and information gain assessment. Impact: Establishment and optimization of new, non-invasive glaucoma neuroimaging biomarkers and experimental modeling systems will open up opportunities to determine the neurodegenerative substrates of glaucoma in vivo and validate glaucoma neurotherapeutics. These findings will allow us to better monitor glaucoma progression and guide future studies to determine the cause or the effect of glaucoma for more targeted interventions.