Stanford University

NIH Research Projects · FY 2024 · 2024-08

Project Summary This High End Instrumentation grant proposal seeks to replace the 7T MRI scanner that is currently operational at the Lucas Center for Imaging (LCI) at Stanford University. The aforementioned scanner was one of the first installed in the USA and been operational since 2006. Recently, the serviceability of this system, as well as that of others sharing the same cryostat design, has been severely limited by the termination of this line of products by their former manufacturer (Agilent Technologies, which had bought Magnex Scientific, the original designer and service provider for the magnet). The proposed system will be provided by Siemens Medical Systems, which now manufactures and services its new generation of actively shielded 7T magnets. In addition, this new system is based on an FDA-approved platform, which will lessen the burden on patients participating on our clinically driven research projects.

NSF Awards · FY 2024 · 2024-08

Just as the miniaturization of electronic circuits to micro- and nano-scale has enabled the development of modern high-performance computing technology, current efforts to miniaturize optical circuits hold great promise to launch a new technology paradigm for computers and sensors. In optical circuits, signals are carried by light rather than by electrical voltages; optical circuits and large-scale networks are already being used to carry information across the globe for communication purposes. Interest in optical information technology has grown recently because optical devices and circuits can naturally span the gap between current computing architectures based on classical physics, and emerging ideas that employ novel phenomena of quantum physics to enhance computational performance. The primary aims of this research program will be to develop deeper insights into the transition regime of optical circuits, in which quantum phenomena first become significant as the optical circuits are miniaturized in both size and power consumption. The research program includes both theoretical and experimental components in order to propose new ways of understanding this mesoscopic transition regime and to test new ideas in the laboratory. The technical focus of this research encompasses emergent quantum non-Gaussian dynamics in mesoscopic nonlinear nanophotonics with characteristic energy scales on the order of tens of photons. This regime will soon become accessible using nanopatterned lithium niobate devices and ultrafast laser sources. Theoretical work will consider experimentally realistic models and develop new modeling frameworks for simulation and analysis of dissipative quantum dynamics and cascaded quantum devices in the non-Gaussian mesoscopic regime. Experimental work will implement and test a novel approach leveraging ultrafast nonlinear dynamics to characterize pulse-to-pulse quantum correlations, which could have important future applications for on-chip quantum metrology. The broader impacts of this proposal include technologically significant impact on the long-term direction of nanophotonics and quantum engineering and will advance a long-term program of assimilating quantum conceptual insights from fields such as cavity quantum electrodynamics into ultrafast nonlinear optics, which traditionally has limited its focus to semiclassical theory. 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 2024 · 2024-08

Recent progress in deep learning has demonstrated the potential of foundation models built on massive datasets, particularly in scientific discovery. In the sciences, data ranging from particles, to molecules, to cells, to brain activity can be represented by nodes on a graph or as signals on a graph substrate. Therefore, successful AI foundation models in scientific discovery are required to possess the capability of handling such graph-structured data and integrating with other types of data such as text, images, and tabular data. The proposed model can be used as a general substrate to help scientists predict and understand a variety of data that are expressed in graphs, such as molecules, proteins, and connectome. Existing methods for building graph foundation models for scientific discovery are in general severely limited in that they: 1) do not consider contexts in which the vertices of a graph can themselves be complex structures such as molecular graphs; 2) do not incorporate multimodal information in the form of knowledge graphs and text; 3) have limited forms of message passing in the form of local averaging; and 4) are not versatile and have limited performance gains due to diversity of downstream tasks and graph data distributions. This team of researchers will address these issues by developing a general foundation model framework for data represented as a graph in scientific domains by systematically addressing these key limitations. The framework incorporates novel approaches of multi-level graph neural networks, graph signal processing, multimodal graph learning, graph-specific fine-tuning, and in-context learning. By capturing human scientific knowledge and express the complexity of the natural world, our framework has the potential to dramatically transform machine learning models in scientific discovery and will allow us to tackle a wide range of complex scientific tasks, even with scarce supervision labels. 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 2024 · 2024-08

Networks and graphs are ubiquitous mathematical models to describe real world systems (social networks, transportation networks, biological systems, and so on). A network comprises a certain number of objects (referred to as `nodes') connected by links of various importance (weight). Analyzing the data of a specific large network is notoriously challenging. However, for many reasonable models of large random networks one expects the same behavior for most of their likely instances. Furthermore, such limiting behavior is often independent of the detailed definition of the model and governed by an explicit variational formula. This project aims at developing our fundamental understanding of probabilistic modeling by exploring such asymptotic descriptions and gaining both algorithmic and inference insights from them. Graduate and postdoctoral researchers will be mentored and results will be disseminated through newly developed courses and at conferences. The project focuses on various models of sparse random graphs, the simplest being the Erdos-Renyi (independent edges) random graph, with slowly growing, or bounded, average degree. Many statistical inference tasks can be addressed by suitably defined combinatorial optimization problems, whose solutions can be recovered from the ground states of suitable Gibbs measures on the underlying graph. This project will study various asymptotic properties of large random graphs as well as natural stochastic evolutions on them, thus gaining understanding of the underlying rich structure of the Gibbs measures for which they are invariant. 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 · 2024-08

PROJECT SUMMARY/ABSTRACT: Myelin is essential for rapid and precise nerve signaling, and its loss in diseases like multiple sclerosis causes profound disability. Classically, myelin was considered to be inert—hardwired during development to maximize conduction velocity. However, over the last two decades, myelin has been revealed to be surprisingly dynamic in the adult brain and to possess broader roles in plasticity and metabolic support of neurons. We recently discovered that when mice learn a new motor task, the local pattern of myelin remodels in the motor cortex, specifically on neurons activated during learning. Remodeling occurs in two stages: existing sheaths shorten during learning, then new sheaths form after learning. The role of myelin remodeling in learning remains a major knowledge gap, largely due to our lack of tools to experimentally perturb sheath dynamics. In this application, we propose to investigate the cellular mechanisms that drive learning-induced sheath remodeling, then use this knowledge to test the extent to which remodeling is required for learning. Our central hypothesis is that oligodendrocyte calcium signaling is the key mechanistic link by which neural activity is sensed and translated into sheath dynamics—sheath elongation and/or shortening. The objectives of this proposal are: (1) Determine whether calcium signaling is require for myelin sheath shortening during learning. (2) Determine whether calcium signaling is required for new myelin sheath formation after learning. (3) Determine whether calcium signaling-dependent myelin remodeling regulates motor behavior. By combining our labs’ complementary expertise in longitudinal in vivo imaging of myelin dynamics during learning, myelin cell biology, and genetic tool building, our team is uniquely positioned to address these aims and make significant contributions to our understanding of the cell biological mechanisms that control myelin remodeling and its role in learning. Approaches used in this proposal will build a platform from which additional molecular mechanisms of myelin plasticity can be evaluated. These studies will thus provide important, novel insights into mechanisms underlying brain plasticity and may lead to therapies to promote the regeneration of lost myelin and recovery of function in diseases like multiple sclerosis and after stroke.

NIH Research Projects · FY 2025 · 2024-08

PROJECT SUMMARY The prevalence of Alzheimer’s Disease and Alzheimer’s Disease-Related Dementias (AD/ADRD) is predicted to nearly triple to 152.8 million cases worldwide by 2050. Women have double the risk versus men, and as early as midlife, Black individuals have nearly double the risk versus White individuals. For women, the midlife (~40-60 yrs.) is characterized by the menopausal transition, accompanied by the loss of potentially neuro- protective and cardio-protective sex hormones. Earlier menopause means earlier exposure to decreased sex hormones and may increase AD/ADRD risk. Further, reproductive surgeries (hysterectomy and/or bilateral oophorectomy) can prematurely induce menopause, resulting in the sudden and often complete loss of hormones, and may be a modifiable risk factor for AD/ADRD. Data suggest that Black women have earlier menopause than White women – approximately 1 year for natural and up to 10 years earlier for surgical menopause. Historical and existing racial bias may contribute to higher prevalence and timing of surgical menopause in Black women. The few studies of menopause and long-term AD/ADRD risk are limited due to potential bias in the measurement of menopausal age/type, bias due to selection into later life studies, particularly affecting Black women, and little data to assess relations to disparities. The proposed project will comprehensively assess the contribution of early menopause to overall risk and Black-White disparities in AD/ADRD diagnoses (K99) and development (R00) by harmonizing data from 25,051 women (30% Black) in 3 cohorts: Study of Women’s Health Across the Nation (SWAN, menopausal transition to 75+ yrs.), Women’s Health Initiative (post-menopause to 80+ yrs.) and Cardiovascular Health Study (65+ yrs.). Harmonizing cohorts with adequate sample size to gauge disparities and “gold-standard” measurements of menopause (SWAN) and AD/ADRD (adjudicated diagnoses, neurocognitive tests over time, and neuroimaging markers of AD/ADRD) while applying methods that I previously developed to account for selection bias, will address prior limitations. This proposal will ensure I get the training I need as a lifecourse social epidemiologist to work independently with AD/ADRD data, to further address bias limitations in lifecourse data, and to estimate causal impacts on disparities. Through the K99/R00 award, I will leverage the expertise of my mentors and my prior training in single cohort longitudinal methods and lifecourse disparities to gain additional training in 1) AD/ADRD diagnoses and development, 2) multi-cohort harmonization/pooling, and 3) causal intervention modeling. Completion of this proposal will result in scientific presentations, publications, and preliminary data to successfully compete for R01 funding that examines cardio-metabolic mediators of reproductive aging and AD/ADRD disparities. Results will elucidate unique midlife targets for prevention in women; supporting a potential paradigm shift for gerontologists and clinicians, explicating the importance of maintenance of reproductive organs and reproductive aging’s role in women’s, especially Black women’s, long-term health.

NSF Awards · FY 2024 · 2024-08

Our understanding of the universe is in some respects highly mature, but in other ways just beginning to unfold. The Standard Model of particle physics has been tested to remarkable accuracy, and yet it can only explain a mere 15% of the matter in the universe. Discovering the nature of the remaining 85% of matter, called “dark matter” because it cannot be directly observed with light, is among the greatest challenges in science. One promising possibility is that dark matter consists of extremely light particles in such high abundance that they collectively act like invisible waves. This so-called ultralight wavelike dark matter is a natural prediction of many theories of physics beyond the Standard Model. Additionally, the recent observation of gravitational waves opens an entirely new modality for studying the universe. Just as it is essential to have many kinds of telescopes to cover different parts of the electromagnetic spectrum, different instruments are needed to maximize our coverage of the gravitational wave spectrum. There is currently a need to fill the gap in the 0.05 Hz to 3 Hz “mid-band” frequency range between what is covered by laser interferometric detectors such as LIGO and the planned LISA spaceborne detector. The mid-band frequency range is promising for detecting gravitational waves produced during the earliest moments of the universe, and could also give astronomers early warning to enable the simultaneous observation of extreme events such as mergers of compact objects like neutron stars and black holes by both gravitational wave detectors and electromagnetic telescopes. To pursue these discovery opportunities, the PIs and collaborators are constructing MAGIS-100, a 100-meter-tall atom interferometer detector located at Fermilab. MAGIS-100 will search for wavelike dark matter and serve as a pathfinder instrument for a future gravitational wave detector. The team of PIs will develop novel techniques at the intersection of atomic physics and quantum information science that will allow MAGIS-100 and future detectors to reach their full scientific potential. This emerging research direction is at the nexus of four fields: particle physics, gravitational wave science, atomic physics, and quantum information science. In addition to mentoring graduate students and postdocs in the MAGIS-100 collaboration, to help them navigate this interdisciplinary research area, the PIs will organize an annual summer school to provide training for both beginning graduate students and more senior researchers in the broader community interested in expanding their expertise on quantum sensing for fundamental physics. Atom interferometers use laser pulses called “atom optics” to split, recombine, and interfere the quantum-mechanical wavefunctions of atoms. The sensitivity of atom interferometer detectors can be increased by using long measurement baselines, analogous to large laser interferometers like LIGO. However, to detect gravitational waves and perform broad searches for dark matter, the signal must additionally be coherently magnified by manipulating the quantum states of the atoms with sequences of thousands of laser pulses. This requires advances beyond current state-of-the-art atom interferometers, which are limited to hundreds of pulses by the accumulation of small errors in each pulse arising from experimental imperfections. To overcome this challenge, the PIs will leverage the framework of quantum control to develop a suite of atom optics techniques for long-baseline atom interferometry that are robust against these errors, addressing several different atom interferometer modalities required by the multi-faceted science goals of MAGIS-100. Long-baseline atom interferometry is a rapidly growing field, and beyond MAGIS these techniques can directly benefit other long-baseline atom interferometers under development around the world, including AION in the UK, MIGA in France, and ZAIGA in China. The team will also work to expand the MAGIS-100 science program by applying these techniques to new types of atoms and to atom interferometer configurations suitable for measurements of the fine structure constant, paving the way for the incorporation of these capabilities into MAGIS-100. 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 2024 · 2024-08

Artificial intelligence (AI) algorithms underpin social media. Algorithms sift through a large inventory of content, deciding what appears at the top of each user's feed. These social media AI systems can shape people's beliefs, affect their well-being, and change their behaviors. These consequences accrue to individuals, but also aggregate at the societal level where the value of social media AI has been stubbornly difficult to square with the societal harms that they produce. Such issues are in part due to the individualist values embedded in how social media AI software operates, maximizing each user's individual experience–-as inferred, for example, through their likes, retweets, and surveys–-at the cost of societal preferences, such as community health and civic engagement. This project aims to shape an alternative future where social media AI software aids us in achieving societal goals, by demonstrating the feasibility of integrating such societal objectives into social media algorithms used to prioritize content in users’ feeds. The project goal is to create a method that can build translational science on top of social science and computer science, and develop engineering solutions that can be deployed at scale on social media, if desired. This project will develop techniques for encoding societal values into social media ranking algorithms. Our multi-disciplinary team of researchers aims to 1) introduce a novel method that leverages the precise language of definitions and measurements of the social science constructs to build algorithmic objective functions using large language models (LLMs), referred to as societal objective functions, which can be deployed broadly as weights in social media ranking algorithms; 2) create a pluralistic algorithmic library of such societal objective functions based on rigorous and empirically validated social science theory articulating a broad space of values; and 3) build methods to integrate multiple potentially-competing values and understand the trade-offs between them. To achieve these goals, the project will weave together social science and computer science insights. Social science research will articulate the design space of societal values at play, as well as careful definitions and measurements of each of these values. Computer science research will translate these social scientific insights into AI models that agree with community ratings on the values expressed in social media content, enabling integration into feed ranking algorithms. By conducting large-scale field experiments with diverse populations, this project will provide empirical evidence on the impact of integrating a pluralistic library of societal values into such algorithms. 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 · 2024-08

Project Summary/Abstract Heart failure is the leading cause of death in the United States and represents a major burden on the US healthcare system. Mitochondrial dysfunction and disruption of cellular energetics are heavily implicated in the pathogenesis of heart disease. A key regulator of mitochondrial function is Ca2+ uptake through the mitochondrial calcium uniporter complex (“the uniporter”). The uniporter regulates cytosolic Ca2+ transients, oxidative phosphorylation, and cell death pathways. However, the molecular mechanisms that control uniporter activation are not well resolved. Further insight into how the uniporter is regulated would increase our understanding of mitochondrial Ca2+ signaling and inform the development of therapeutics aimed at modulating uniporter activity. The Research Training Plan involves using a multidisciplinary approach to investigate how key signaling molecules and regulatory factors control uniporter activity. Understanding the molecular mechanisms of the Ca2+- dependent regulation of the uniporter and elucidating a novel gating mechanism will produce a major advance in the field. The PI will receive comprehensive training to carry out structural, biophysical, biochemical, and functional studies on membrane transport proteins. The Career Development Plan is tailored to help the PI develop skills in experimental approaches, research design, project management, mentorship, and written and oral communication. The sponsor, together with the environment, offers strong support for the PI’s training and career development. In summary, completion of the strong Research Training Plan together with the Career Development Plan will help prepare the applicant for a career as an independent scientist. The proposed research will lead to significant advances in understanding mitochondrial Ca2+ uptake and will open potential avenues for therapeutic intervention in diseases involving aberrant mitochondrial Ca2+ signaling.

NIH Research Projects · FY 2025 · 2024-08

Project summary Radiation therapy is an important component of cancer treatments, yet its usage has been hampered due to side effects to normal tissue. The current K99/R00 project seeks to investigate the molecular landscape of radiation induced coronary heart disease (RICHD), a leading cause of morbidity and mortality among cancer survivors. The complex nature of RICHD necessitates a comprehensive understanding of patient-specific risk factors and underlying genetic variations. Focusing on the pivotal role of endothelial to mesenchymal transition (EndMT) and its correlation with variations in p53 signaling due to TP53 mutations, I will utilize human-induced pluripotent stem cells (iPSCs) from cancer patients with TP53 mutations and transgenic mouse models in the mentored K99 phase. In my first aim, I will utilize 3D vessel-on-a-chip model (VoC) lined with iPSC-derived endothelial cells and smooth muscle cells from cancer patients with or without TP53 mutations for longitudinal endothelial lineage tracing analysis with vascular functional assays after exposure to 2 Gy of X-rays radiation. In my second aim, I will generate a novel endothelial lineage tracing mouse model with TP53 mutations and elucidate the functional and molecular alterations after exposure to 20 Gy of X-rays radiation on the heart. Both projects employ in-depth single cell RNA-sequencing (scRNA-seq) analysis to map the gene regulatory networks perturbations across different cell-types at varied time points post-irradiation. This project will be guided by an advisory committee constituted of researchers bringing their distinct expertise: Joseph Wu (Precision Medicine), Laura Attardi (TP53 Biology), Sharon Gerecht (Vascular Engineering), Billy Loo (Radiation Oncology), Kristy Red-Horse (Lineage Tracing), Michael Snyder (Integrative Omics Analysis). The training received under this mentorship will equip me with invaluable expertise in tissue engineering, lineage tracing and scRNA-seq analysis. Ultimately, this will foster my transition to the independent phase (R00), where I aim to establish genotype-phenotype correlations for RICHD in iPSC-derivatives. Functional radiogenomics on vascular cells will provide novel insights and a roadmap for stratifying TP53 mutations in RICHD offering a significant advancement for cancer patients undergoing radiation therapy.

NIH Research Projects · FY 2025 · 2024-08

PROJECT SUMMARY/ABSTRACT While consolidated sleep is crucial for healthy cognition and mood in older individuals, many suffer from sleep- wake fragmentation, a risk factor for developing Alzheimer's disease. One countermeasure of sleep-wake fragmentation is exposure to bright light ("phototherapy"). Studies using morning phototherapy, which targets circadian phase, to restore sleep-wake fragmentation have reported mixed results. However, both mathematical models of the circadian pacemaker and data from our lab suggest that afternoon light exposure, targeting circadian amplitude, will have greater effects on sleep-wake consolidation. Since phototherapy can be administered without significant adverse effects, it is a promising tool to reverse sleep-wake fragmentation and slow cognitive decline. Therefore, the overarching goal of the proposed studies is to slow cognitive deterioration in older individuals with mild cognitive impairment (MCI) by investigating the utility of afternoon phototherapy. The Research Training Plan will leverage state-of-the-art artificial intelligence techniques on big datasets (specific aims 1 and 3) and an intervention clinical trial (specific aim 2). In aim 1 (K99), the applicant, Dr. Lok, will train with Dr. Kochenderfer as she applies state-of-the-art machine learning techniques to determine underlying factors contributing to sleep-wake fragmentation and cognitive decline. During this time, Dr. Lok will also learn to conduct neurocognitive testing in individuals with mild cognitive impairment. Dr. Lok will conduct a clinical trial (R00), investigating the utility of afternoon phototherapy in a stepped care approach to reduce sleep- wake fragmentation and improve cognition. Finally, Dr. Lok will use machine-learning techniques to develop a personalized phototherapy model to create a prediction score calculator. These endeavors ensure that these projects' outcomes benefit the scientific and medical communities. Dr. Lok has the requisite training in machine learning and clinical trials to undertake the proposed projects. The career development plan is intricately designed to empower Dr. Lok with enhanced machine-learning skills and to facilitate a deeper understanding of gerontology and the social determinants of aging. Mentor Dr. Zeitzer is a leading expert in human translational chronobiology. Co-mentors Drs. Kochenderfer (machine learning), Fairchild (neuropsychology), and advisors Drs. Jo (statistician) and Yesavage (Alzheimer's disease) offer complementary expertise. Dr. Lok proposes to pursue these development goals and begin the proposed research with the support of the Department of Psychiatry and Behavioral Science at Stanford University, which provides an ideal environment of research support and resources to attain her training and research goals. In summary, the solid mentoring team, environment, and proposed training plan anticipate fully launching Dr. Lok's independent career. The proposed study will increase knowledge about contributory factors to sleep-wake fragmentation and cognition, as well as a scalable intervention with the potential to ameliorate cognitive decline and other concomitants of fragmented sleep, prevent Alzheimer's disease onset, delay institutionalization, and improve quality of life in older individuals.

NIH Research Projects · FY 2026 · 2024-08

PROJECT SUMMARY Biologically-grounded data-driven stratification of disease has revolutionized most of modern medicine. Yet, psychiatric disorders—a major public health concern—are still defined strictly on the basis of subjective signs and symptoms. Newly available large-scale human neurobiological data offer an unprecedented opportunity for biologically-grounded data-driven stratification of psychiatric disorders. However, conventional data analytical procedures are ill-suited for such investigations, and new computational approaches are needed. The goal of this proposal is to develop and validate a novel computational framework for identifying, validating, and characterizing biological subtypes of psychiatric disorders, and to apply the framework to the study of autism. We aim to leverage newly available large-scale open-source human brain imaging, phenotypic and transcriptomic data from consortia and repositories around the world, as well as data we have acquired at Stanford University, combined with our recent work in brain circuit analysis and modeling methods and advances in machine learning. To achieve these goals, we propose three aims. In Aim 1, we will develop and validate novel computational methods to extract individual-level neural fingerprint, in particular brain circuit dynamics, using human brain imaging data. In Aim 2, we will develop analytical procedures to stratify psychiatric disorders using neural fingerprints and apply the procedures to identify and validate biological subtypes of autism. In Aim 3, we will perform integrative analysis to determine the transcriptomic signatures of the identified biological subtypes, using neural fingerprints and human brain gene expression data. The proposed work is highly relevant to the strategic plan of the NLM to accelerate discovery and advance health through data-driven research. Through the successful completion of the work described here, the proposed studies will add new knowledge to our current understanding of the etiology of autism and, crucially, provide a new computational framework for improved stratification of other psychiatric disorders. Ultimately, these advances will lead to better diagnosis and more effective treatments for psychiatric disorders and, more broadly, advance precision medicine.

NIH Research Projects · FY 2025 · 2024-08

Project Summary A causal hallmark of brain aging and Alzheimer’s Disease (AD) is loss of proteostasis, the maintenance of proteome health through several processes, including protien degradation/clearance (hereafter, ‘turnover’). Neuronal proteostasis is exceptionally vulnerable during aging and AD as evidenced by the accumulation of dysfunctional or toxic neuronal proteins, such as pTau and Aβ, causal for neuron dysfunction and degeneration. While there is an appreciation for the role of dysregulated proteostasis in brain aging and AD, much remains to be understood about the dynamics of neuronal protein turnover (NPT), consequences of aberrant NPT, and NPT regulators during aging and AD. I hypothesize that NPT is compromised with aging and AD, ultimately worsening age and AD phenotypes, and NPT is at least partially regulated by lysosomal biology and microglia. I am uniquely positioned to address my hypotheses thanks to my establishment of a new in vivo model of bioorthogonal non-canonical amino acid tagging (BONCAT). BONCAT permits the tagging of newly synthesized proteins with an azide-bearing phenylalanine (AzF) in a cell-specific manner. Tagged proteins can be pulled down and analyzed by mass spectrometry (MS). When AzF is provided to BONCAT mice in a pulse- chase administration scheme and mice are sacrificed at different time points in the chase, tagged proteins can be pulled down and their reduction over the chase/time, representative of turnover, can be examined. I have performed this experiment in young and aged neuronal-BONCAT mice, finding >800 relatively longer-lived proteins (RLLPs) with aging, many of which are in pathways of neurodegeneration. Here, I propose to perform an identical experiment in AD models, compare the results to aging, and use an in vitro human transdifferentiated neuron model in conjunction with targeted protein degradation to determine the pathological consequences of select RLLPs common to aging and AD. I will additionally combine BONCAT with a novel lysosome immunoprecipitation (IP) method, LysoTag, to understand the causality of autophagic/lysosomal dysfunction in age/AD NPT aberrations. Lastly, I will apply BONCAT to understand how microglia regulate NPT by (1) examining neuronal protein uptake by microglia and (2) determining how deletion of Trem2, a microglia-specific AD risk factor, alters NPT. Cumulatively, the proposal will address what NPT changes occur with aging/AD, how the changes affect neuronal health and pathology, and how the lysosome and microglia act as intrinsic and extrinsic regulators of NPT. Findings will hold promise to identify new targets to promote healthy brain aging. Through continued training with the K99/R00 award, I will learn new methodologies (MS acquisition/analysis; lysosome IP; targeted protein degradation) and concepts (proteostasis; autophagy) and participate in critical professional development workshops/classes. The research and training proposed herein will empower me to be a successful and impactful independent researcher and mentor.

NIH Research Projects · FY 2025 · 2024-08

Summary Exocrine pancreas diseases like chronic pancreatitis (CP) and pancreatic ductal adenocarcinoma (PDAC) can engender type 3c diabetes mellitus (T3cD: also called pancreatogenic diabetes), a form of human diabetes that is distinct, but shares some features of type 1 and 2 diabetes. It is increasingly recognized that T3cD likely represents a substantial fraction of all diabetes in some populations, but the cellular, genetic and signaling basis of T3cD are poorly understood. To address this knowledge 'gap' we created a tissue procurement workflow focused on CP patients, and investigated primary islets from these subjects. We found evidence for deranged gene expression in islet alpha cells and excessive glucagon secretion, preceding overt T3cD. Other analysis suggests that a combination of increased pro- inflammatory and reduced anti-inflammatory signaling may lead to dysregulated incretin signaling in alpha cells. Thus, we postulate that CP could promote early stages of T3cD by impairing alpha cell regulation. To test this, we propose in Aim 1 to study gene regulation and electrophysiology in primary human islet cells procured from subjects with CP and CP with prediabetes, and to test the hypothesis that incretin-regulated glucagon secretion is enhanced in prediabetes. In Aim 2 we will use modern multiplexed imaging methods (CODEX) and in vitro islet studies to test the hypothesis that specific inflammatory signaling pathways in CP alter alpha cell gene regulation and function, leading to excessive glucagon secretion. Together, our studies could identify mechanisms of alpha cell dysfunction that occur very early in the development of T3cD. Development of T3cD in CP is thought to presage PDAC; thus, our work could strongly influence work on identifying biomarkers of PDAC at stages when surgery and other therapeutics can be applied with curative intent.

NIH Research Projects · FY 2025 · 2024-08

Project Summary/Abstract How brain circuits are modulated by sensory signals from internal organs (viscera) during ethologically relevant behaviors is a fundamental, yet largely unexplored question in neuroscience. Achieving a comprehensive understanding of viscerosensory integration in the brain of unanesthetized animals has been challenging given that neural activity measurements in the vagus nerve, the main viscerosensory input to the brain, require invasive procedures in rodents. This project will address this challenge by using the zebrafish larva. Its suitability for organism-wide neural activity measurement and genetic manipulation, as well as its well- characterized behaviors, all make it an ideal system for a comprehensive systems-level investigation of viscerosensory integration in the brain during naturalistic behaviors. In the R34 phase of the grant, this project will focus on (1) probing visceral response dynamics like heart rate, gill ventilation, and gut motility in response to environmental challenges; and (2) determining the viscerosensory inputs to the brain by studying the encoding properties of vagal sensory neurons. Accomplishing Aims 1 and 2 would set the stage for investigating the neural circuits that integrate viscerosensory information in different brain regions and their role in behavior and decision-making. Aim 1: Determine internal organ dynamics in response to environmental changes. Aim 1 of the project will measure the changes in heart rate, gill ventilation, and gut motility in addition to tracking behavioral variables like tail angle and eye position during responses to visual threat, oxygen fluctuations, or temperature fluctuations. We will use machine learning techniques to identify discrete ‘visceral states’ from measured internal organ dynamics. Then behavioral and visceral states will be merged using multidimensional embedding techniques to identify discrete ‘organismal states’ and the transitions between them. Aim 2: Probe the encoding properties of vagal sensory neurons Aim 2 will measure neural activity in the right and left vagal sensory ganglia of behaving fish while also tracking the internal organ parameters described in aim 1. These datasets will be summarized using encoding models to describe the visceral information represented in each cell of each vagal ganglion.

NIH Research Projects · FY 2025 · 2024-08

PROJECT SUMMARY Children learn language in the context of complex and multidimensional social interactions. Affective information is a prominent yet, underexplored feature of children's early learning context despite its effects on infants' attention and memory systems.1–3 Understanding how children’s affective environments shape learning is an important public health issue, as approximately a third of mothers in the US experience depression in the first three years of a child’s life.4 The proposed research aims to fill this knowledge gap by capturing theoretically important dimensions of affect (such as valence and arousal5) in children’s real-world early environments, with the ultimate translational goal of understanding the genesis of variation in language and well-being. Central to this goal will be evaluating how children adapt their use of affective information in families with depressed caregivers. We will quantify the affective context of learning events using multiple methods, including experimental studies and descriptive computer vision and machine learning analyses of a large dataset of egocentric-view videos of children’s home environment. In Aim 1, we will ask whether the structure of affective information in caregiver input predicts the age at which children produce different types of words (Aim 1a) and how children adapt to the affective cues of caregivers with depression symptoms (Aim 1b). We will use automatically coded affective features from videos across three modalities (facial, vocal, and semantic) to predict the age at which children produce different words depending on the severity of their caregivers’ depression symptoms. In Aim 2, we build on the correlational, naturalistic evidence from Aim 1 by taking an experimental approach, allowing us to uncover causal links between affect and word learning. Specifically, in three novel word-learning studies, we will disentangle which dimensions of affect (e.g., valence and arousal) are most impactful. Together, this research will provide insight into the affective mechanisms that promote word learning in child-caregiver interactions, with the ultimate goal of improving resilience and language development in households with atypical affective dynamics. Through these aims, the applicant will build on her background in affective development and gain training in computational methods and theoretical knowledge in understanding clinical variation in affect across households. The sponsor, Dr. Michael Frank, has deep expertise in early learning and extensive mentorship experience in developmental and computational methods. The co-sponsor, Dr. Ian Gotlib, is an expert in the study of caregiver depression. The unique training environment at Stanford University provides the resources necessary to successfully complete the proposed work, as well as additional opportunities for mentorship and professional development. In sum, the proposed project and training plan will support the applicant’s career goal of becoming a professor at a research university and enable her to develop a rigorous and novel research program at the intersection of cognitive, affective, and developmental science.

NIH Research Projects · FY 2025 · 2024-08

PROJECT SUMMARY/ABSTRACT Cardiovascular disease (CVD) remains among the leading causes of death worldwide. Diabetes and obesity represent two modifiable risk factors for the development of CVD, but their prevalence is on the rise. Ongoing research efforts have successfully leveraged endogenous gut hormone signaling pathways for the treatment of diabetes and obesity. Long-acting Glucagon-like peptide-1 receptor (GLP-1R) agonists have demonstrated immense therapeutic potential for the treatment of diabetes and obesity by eliciting robust reductions in body weight and hemoglobin A1C. Moreover, GLP-1R agonists have also been shown to offer cardiovascular benefits and reduce cardiovascular events in patients with diabetes and obesity. In addition to single GLP-1R agonists, novel monomolecular dual (GLP-1R and glucose-dependent insulinotropic polypeptide receptor[GIPR]) and triple (GLP-1R, GIPR, and glucagon receptor [GCGR]) agonists have recently been developed and implemented. While the predominant mechanisms of action of single, dual, and triple agonists pertaining to insulin secretion, satiety, and gastric emptying are well understood, we lack a complete understanding of their effects on the cardiovascular system. The objective of this proposal is to investigate the cardiovascular impact of these novel agonists by comprehensively profiling the transcriptomic, epigenomic, and functional responses to semaglutide (GLP-1R), tirzepatide (GLP-1R/GIPR), and retatrutide (GLP- 1R/GIPR/GCGR) both in vitro and in vivo. Using human induced pluripotent stem cell (iPSC) technology, we will pool >20 individual iPSC lines from diverse donors to generate a “cell village” of iPSC-derived endothelial cells (iPSC-ECs) and cardiomyocytes (iPSC-CMs), enabling paralleled characterization via single-nuclear RNA and ATAC-sequencing (snRNA/ATAC-seq). Simultaneously, we will attain a cardiovascular functional profile of tirzepatide and retatrutide-treated healthy and diet-induced obese (DIO) mice using echocardiography, coronary flow reserve, and myography, while cardiac and aortic tissues will be subjected to snRNA/ATAC-seq. GLP1R variants will be introduced into iPSC using CRISPR/Cas9 and drug-responses will be profiled. The fellowship training plan was assembled to maximize my potential as a future investigator and will take place in the rich academic environment of Stanford University where I will have access to state-of-the-art facilities and be afforded the opportunity to interact with leading cardiovascular scientists and clinicians. The plan encompasses three main areas 1) research (e.g. technical skills, communication), 2) education (e.g. RCR, translational research), and 3) career development (e.g. networking, acquiring K99/R00).

NIH Research Projects · FY 2025 · 2024-08

ABSTRACT Single-cell transcriptomics has revolutionized our understanding of neuronal diversity and enabled high-throughput characterization of molecular cell types across brain areas and species. We and others have pioneered multi- modal technologies such as Patch-seq and spatial transcriptomics to link molecularly-defined cell types with their physiology, cytomorphology, and anatomical features, but we still lack high-throughput, cost-effective methods that can provide comprehensive synaptic resolution wiring diagrams of entire mammalian brains and integrate these connectomes with molecularly defined cell types. We propose to further develop and validate Rabies Barcode Interaction Detection followed by sequencing (RaBID-seq) to enable high-throughput, scalable, and cost-effective mapping of brain-wide synaptic-level con- nectivity and transcriptomic profiling of the mapped neurons. We have optimized rabies virus production and packaging to achieve barcoded libraries containing more than 1.7 million unique barcodes, two orders of mag- nitude higher compared to prior studies, enough to map the inputs to thousands of post-synaptic neurons in a single animal. However, this technology still faces several experimental and computational challenges to realize its full potential. In Aim 1, we will address three potential challenges that may arise when scaling RaBID-seq to study brain-wide, densely labeled circuits: stochasticity of initial infection and spread, toxicity, and the potential for polysynaptic events when many founder cells are labeled. In addition, we will develop a new variant of Ra- bies featuring an evolvable barcode that can disambiguate monosynaptic vs polysynaptic spread in the setting of dense labeling. In Aim 2, we will benchmark RaBID-seq connectomes against other gold standard techniques measuring connectivity using multipatch-seq and spatial transcriptomics. In Aim 3, we will develop new algorithms using graph neural networks to reconstruct monosynaptic connectomes from barcoded viral datasets, assess the robustness of these algorithms under different experimental parameters in silico, and test whether an evolvable barcode can improve monosynaptic circuit reconstruction. If successful, these studies will establish RaBID-seq as a scalable, cost-effective tool for brain-wide connectivity mapping that can integrate transcriptomic cell types with their synaptic-level wiring diagram at single-cell resolution. By reducing the problem of synaptic connectivity into a problem of barcode sequencing, our approach has the potential to dramatically increase throughput, decrease costs and provide a direct link to the transcriptome of each mapped cell. RaBID-seq will transform brain-wide circuit mapping into a routine experiment that can be performed in any lab with modest resources, making it possible to explore how circuits differ between treatment conditions, in disease states, between the sexes, and across the lifespan. We will also generate pilot data in both mice and human slice cultures to demonstrate the utility of this tool across species.

NIH Research Projects · FY 2025 · 2024-08

SUMMARY Small cell lung cancer (SCLC) is a fatal form of lung cancer whose development is closely associated with tobacco smoking. Patients with SCLC have a median overall survival of only 8-10 months after initial diagnosis. SCLC development is frequently associated with mutations in TP53 (p53), RB, and related tumor suppressor genes that govern transit through the cell cycle. Disruption of these genes leads to small, rapidly dividing cells. Despite identical mutations, SCLC cells can adopt one of several distinct transcriptional/epigenetic states, including neuroendocrine and non-neuroendocrine states. How these cell states differ is poorly understood. It is also unclear how best to induce cell death in different SCLC cell states. In this research, we focus on the unique metabolism of SCLC cells to identify new and exploitable vulnerabilities that can be used to kill SCLC cells. In preliminary RNA sequencing and mass spectrometry-based metabolomics studies we find that SCLC metabolism is cell state-dependent. Our results pinpoint a cell-state-specific role for cystine/cysteine (Cys) metabolism in SCLC biology. We find that both neuroendocrine and non-neuroendocrine SCLC cells require Cys to proliferate. In the absence of Cys, non-neuroendocrine cells stop dividing whereas neuroendocrine cells die. Intriguingly, cell death responses are also cell state-dependent: ASCL1high neuroendocrine cells primarily die from apoptosis while NEUROD1high neuroendocrine cells die from ferroptosis. These findings suggest cell state- specific differences in SCLC metabolism may regulate distinct sensitivities to apoptotic versus non-apoptotic cell death. In this research, we will determine how Cys deprivation triggers cell state-specific lethal mechanisms in SCLC cells. We propose three Specific Aims. We will first determine how distinct neuroendocrine cell states dictate the choice to die by apoptosis versus ferroptosis, focusing on the role of the GCN2/ATF4 amino acid sensing pathway and polyunsaturated phospholipid metabolism. Next, we will determine how the Notch and the NRF2 signaling pathways govern the responses of neuroendocrine versus non-neuroendocrine cell states to Cys deprivation. Finally, we will develop a novel Cys-degrading enzyme therapy that selectively targets SCLC cells to induce cell death by recognizing specific cell surface antigens. These studies will test our central hypothesis, which is that Cys metabolism represent a novel targetable vulnerability for SCLC. Altogether, this research will unveil novel links between cell state, metabolism, and cell death in SCLC. Ultimately, we aim to show how targeting this metabolic network could lead to new treatments for this devastating disease.

NIH Research Projects · FY 2025 · 2024-08

PROJECT SUMMARY Type 1 diabetes mellitus (T1D) is an incurable autoimmune disease that results in the destruction of insulin producing β cells and affects nearly 10% of the global population. T1D most commonly affects young children and adolescents and can lead to irreparable and deadly complications if not properly treated. Current treatments and management strategies require time-intensive exogenous insulin administration, with incredible onus on patients and their caregivers. Islet transplantation to replace β cells is an attractive and FDA-approved alternative to insulin therapy in T1D. However, the widespread acceptance of this approach faces formidable challenges, including the absence of safe, non-toxic methods to shield transplanted allogeneic donor islets from immune rejection and a scarcity of donor material. To surmount these hurdles, the field must accomplish two critical objectives: 1) establish an effective, non-toxic strategy to induce tolerance toward donor tissues and 2) identify a readily obtainable source of donor material for transplantation. An emerging field for transplant tolerance is mixed chimerism achieved by hematopoietic cell transplant (HCT), establishing tolerance toward donor-matched tissues and correction of defects that cause autoimmunity. Unfortunately, this requires high doses of radiation and/or chemotherapy to prepare and condition the host prior to HCT, resulting in undesirable toxicities. The development of low intensity conditioning protocols with reduced toxicity, adverse effects, and risk for graft vs. host disease (GVHD) is necessary to increase access to HCT for patients without imminent fatal illness or malignancy. Additionally, pluripotent stem cell (PSC)-derived β-like cells offer an attractive and unlimited source of material for islet cell replacement therapy. However, the immunogenicity of PSC derivatives and the potential for mixed hematopoietic chimerism to induce tolerance to these derivatives remains unexplored. In recent exciting work, we show that a novel low intensity conditioning protocol using monoclonal antibodies for T cell and hematopoietic stem cell (HSC) depletion combined with a non-myeloablative dose of total body irradiation allows for stable mixed chimerism and donor-matched allogeneic islet transplant tolerance. Here, we propose to 1) optimize our conditioning protocol to significantly reduce, or eliminate, the radiation dose required for conditioning and generation of mixed hematopoietic chimerism across full MHC barriers, and 2) evaluate the immunogenicity of mouse PSC-derived β-like cells and tolerance induction in mixed hematopoietic chimeras. Collectively, this research will develop reduced intensity conditioning regimens to produce sustained mixed chimerism and islet transplant tolerance. This will allow for use of this technique beyond patients with fatal malignancy or autoimmunity, including in patients with T1D, as well as provide the necessary training for me to become an independent diabetes-focused researcher.

NSF Awards · FY 2024 · 2024-08

Stanford Institute for Theoretical Economics Summer Workshop (SITE) plays a vital role in the profession of economics, serving as a forum that attracts top scholars from around the world to bring together economic theorists and applied economists with an active interest in the theoretical foundations of their work. SITE workshops create an environment that promotes interaction, stimulates discussion, and encourages collaboration leading to research on new topics as well as new applications of economic theory. The workshops also provide an opportunity for leading senior investigators and rising junior researchers and students to present leading-edge economic research, engage in critical scientific discussion, share and strengthen research results, initiate and collaborate on new research, and promote exchanges across disciplines. SITE's support of junior-level scholars and those from underrepresented groups promotes their professional development, providing an opportunity to present their work, and receive feedback from and engage with the full array of participants – peers to senior scholars. SITE also attracts an international and interdisciplinary audience providing an opportunity for diverse scholars, who might otherwise not have worked together, to learn from one another and explore questions that relate and apply to other fields. The purpose of the SITE Summer Workshops is to advance economic science for the benefit of society. SITE contributes to the understanding and application of economic theory to processes, institutions, and systems, nationally and globally. Each year sees an exchange of organizers, which ensures broad representation for choice of topics and selection of speakers. With the increasingly interdisciplinary nature of economics research, SITE workshops contribute to and promote the rapid dissemination of the latest, topical research not only to economists but also to researchers, policymakers, and scholars in other disciplines, as well as government and private sectors. The wide range of interests and expertise of the economists involved guarantees that many topics will be offered. Many of the concepts presented at SITE have contributed to widely accepted economic theory and broadly applied economic practice. The dissemination of scientific knowledge strengthens both empirical and theoretical economic analysis and research methods and improves the understanding of the processes and institutions of the U.S. economy and of the world system of which it is a part. SITE maintains an online archive where digital versions of the presented papers are available for public viewing, which benefits those who cannot attend the sessions and facilitates the dissemination of the knowledge and research presented at the workshops. 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 · 2024-08

PROJECT SUMMARY Virtually all organisms experience osmotic imbalances from their environment, which can cause rapid changes in cell volume. Hypertonic exposure causes cell shrinkage and is thought to induce protein misfolding and aggregation in vivo. Despite these proteotoxic effects, the main paradigm for osmotic stress adaptation is still focused on osmolyte production and cell volume recovery. Little is known about the mechanisms that maintain proteome integrity during osmotic stress, and no studies have investigated the identity of proteins compromised by hypertonicity. Therefore, the goal of this project is to characterize the protein quality consequences (Aim 1) and responses (Aim 2) to hypertonic exposure. The research strategy leverages targeted and systems-level technologies, including aggregation assays, genome-editing, and mass spectrometry, to examine the molecular and systems-level aspects of proteostasis during osmotic stress. In Aim 1, the applicant Kathy Le will test the hypothesis that subsets of the proteome, including nascent proteins and peroxisomal proteins, are particularly susceptible to osmotically induced aggregation. Using targeted biochemical approaches, Kathy will quantify aggregation of pulse-labeled nascent chains and the effects of translation inhibition. She will also receive individualized training in mass spectrometry techniques to profile aggregation propensity across the yeast proteome, including peroxisomal proteins. In Aim 2, Kathy will test the hypothesis that chaperone-mediated refolding and proteasomal degradation contribute to osmotic stress resistance by acting on different sets of misfolded substrates. Leveraging yeast genetics, Kathy will measure the individual stress tolerance contributions of the chaperone system and the ubiquitin-proteasome system. She will also use a combination of biochemical aggregate assays and proteomics to analyze the protein substrate pools of each system and determine the extent of their overlap. The proposed project and training plan is tailored to enable Kathy to gain new experimental skills and concepts in proteostasis, a diverse research field led by multiple experts at Stanford. Dr. Brandman (sponsor) has extensive experience studying the heat shock response and the adaptability of chaperones using cell biological tools and CRISPR libraries in yeast. Dr. Kopito (co-sponsor) is a renowned expert in studying mammalian ER-associated degradation (ERAD) and misfolded protein substrate selection, offering a complementary perspective. In summary, the strong mentoring and training opportunities at Stanford will fully prepare Kathy for an independent research career. The proposed Aims will challenge the current paradigm of osmotic stress adaptation by investigating the protein quality aspects of hypertonicity. Understanding of the mechanisms that drive protein aggregate formation, refolding, and degradation in a variety of physiological contexts is needed to elucidate general principles for protein quality control and therapeutic strategies for protein-misfolding diseases.

NSF Awards · FY 2024 · 2024-08

Is there a way to reconcile gravity with quantum mechanics? It is well-known that quantum mechanics, the science of the very small, and gravitation do not integrate properly. Since the time of Newton, we have known that the force of gravity between two massive objects scales with the inverse of their distance squared. This law works well at planetary scales and for human scales. But we generally assume this "inverse-square law" to apply to much smaller distances, down to molecule scale, where quantum mechanics and gravity may work together. But, of course, this is a tremendous extrapolation that should be tested, at least in part, empirically. Indeed, many theories predict that gravity could deviate from the familiar inverse square law already at sub-millimeter distances. Such deviations are extremely difficult to measure experimentally due to the small strength of gravitation and the presence of residual electromagnetic interactions. This award funds the continuation of an experimental program in this area using an entirely novel technique. The team will train students in STEM research. The traditional technique to explore gravity at the meter to 0.1-millimeter scales relies on ever-improving versions of the classic Cavendish experiment, where the force of gravity is compared to the restoring force of a torsion fiber. More recent measurements have used devices obtained by photolithography but are still based on mechanical springs. This award supports an effort based on optical springs, taking advantage of the substantial progress in quantum optics and optomechanics in the last few decades. This is entirely new and holds the promise to revolutionize the field. Indeed, in addition to the primary goal, this new technique has already found applications in other fields of fundamental physics, and in technological areas as disparate as inertial guidance and vacuum measurement. 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 · 2024-08

Transcriptional elongation and mRNA processing occur simultaneously and are highly coupled to increase the efficiency and accuracy of mRNA maturation. Splicing is a step of mRNA processing where intronic regions are removed by spliceosome complexes that bind pre-mRNA. Most human genes with multiple exons are alternatively spliced generating numerous proteins with diverse functions derived from a single gene. Defects in RNA polymerase that alter the transcription elongation rate cause pervasive changes in alternative splicing. Mutations in transcriptional processing cause a variety of human diseases including retina degeneration, which is characterized by photoreceptor cell loss and visual dysfunction that can lead to blindness. Notably, the human retina harbors an astonishing splicing diversity and several retina-specific mRNA isoforms and ubiquitously expressed splicing factors are associated with retinal disease. Retinal photoreceptors, cones and rod cells, constitute over 70% of cells in the retina and initiate the transmission of visual stimuli to the brain by detecting light photons through a molecular pathway known as phototransduction. Many retinal mutations occur in phototransduction genes including rhodopsin, the only photopigment and highest expressed gene in rods. To date, the mechanisms that regulate the precise temporal and quantitative expression of rhodopsin and other phototransduction genes are poorly understood. My preliminary data suggest that the rod-specific transcription factor NRL physically interacts with splicing proteins. I hypothesize that qualitatively and quantitatively precise expression of phototransduction genes are controlled stringently by molecular interactions between the splicing and transcriptional machineries. In this proposal, genetic, biochemical and genomic approaches in combination with high throughput technologies, will be used to identify protein interactions between the transcriptional and splicing machineries. In addition, the role of these interactions will be studied in vitro and in vivo. Furthermore, genomic regions of phototransduction genes associated with RNA polymerase regulation and splicing factor binding will be identified. This grant will expose me to new technologies and computational analysis that will allow me to comprehensively study mechanisms of gene regulation and retinal homeostasis. Overall, this funding opportunity will help me become a well-rounded scientist and achieve research independence in the area of molecular genetics and vision research.

NIH Research Projects · FY 2025 · 2024-08

PROJECT SUMMARY/ABSTRACT Immune checkpoint inhibitors (ICIs) are monoclonal antibodies used as novel cancer therapeutics to release intrinsic brakes on T-cell cytotoxicity against tumor cells. While effective to treat many advanced cancers, ICIs have been reported to cause fulminant myocarditis, pathologic inflammation of the heart—a life-threatening side effect which can lead to severe arrhythmias, heart failure and death. Although our group and others have previously found effector CD8+ T-cell clonal expansion and activation in the heart in ICI myocarditis, the contribution of macrophages with respect to their effect on T-cells has yet to be fully characterized. Using single- cell RNA-seq data collected as part of my funded K08, I have found an enrichment of CXCL9/10+ macrophages and CXCR3+ effector CD8+ T-cells in the hearts of MRL/Pdcd-1/- mice with myocarditis. Furthermore, depletion of macrophages in MRL-Pdcd1-/- mice reduces cardiac CD8+ T-cell infiltration and improves mice survival. Thus, I hypothesize that recruitment of CXCR3+ T-cells to the heart by CXCL9/CXCL10 expressing macrophages plays a pathogenic role in ICI myocarditis. To test this, I will utilize a novel pharmacologically treated mouse model of ICI myocarditis developed in my laboratory, along with high throughput immunophenotyping techniques and in vitro phenotyping. Aim 1 will investigate the effects of CXCR3+ blockade in our mouse model of ICI myocarditis, while Aim 2 will investigate the mechanistic effects of blocking CXCR3 and its ligands, CXCL9/10, on macrophage-mediated T-cell migration and function in an in vitro transwell system. This proposal has been carefully designed to be fully achievable within the timespan of two years of this proposal, while also having critical and high impact in the fields of ICI myocarditis and cardiac inflammation. By completing this project, I will define key pathogenic interactions between adaptive and innate immunity which drive ICI myocarditis, bridging a major knowledge gap in the role of macrophage effects on T-cell trafficking to the heart in myocarditis. In doing so, I hope to pave the way for the development of adjuvant therapies for treatment and prevention of ICI myocarditis. This grant will be instrumental in launching the next phase of my career and prepare me to successfully compete for R01 funding in the field of cardio-immunology and cardiac inflammation.