Stanford University

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

Project Summary Dilated cardiomyopathy (DCM) is a common cause of heart failure with a severe lack of therapeutics, creating a significant clinical burden. The gene TRAPPC11 emerged from a whole transcriptome, functional screen for therapeutic targets for DCM using patient-derived human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs), demonstrating reversion of contractile dysfunction upon knockdown in DCM hiPSC-CMs. TRAPPC11 is a modulator of endoplasmic reticulum (ER) stress. Since ER stress is recognized as a pathophysiological driver in DCM, my overarching hypothesis is that inhibition of TRAPPC11 would be therapeutic for DCM caused by TNNT2 mutations and possibly more broadly for other forms of DCM. This hypothesis will be tested through knockdown of TRAPPC11 in a mouse model of TNNT2 DCM and in myofilament and nonmyofilament induced DCM in hiPSC-CMs. Interestingly, single nucleotide polymorphisms (SNPs) in TRAPPC11 are associated with left ventricular hypertrophy (LVH) in response to pressure overload in African Americans. Therefore, my secondary hypothesis is that common mechanisms underlie TRAPPC11’s effect on hypertrophy induction and its therapeutic potential for DCM. Using CRISPR/Cas9 genome editing, I will test the effects of TRAPPC11 SNPs associated with LVH on ER/SR function in healthy hiPSC-CMs and introduce key SNPs into DCM hiPSC-CMs to assess their protective potential. Completion of this study will establish a translational and mechanistic rationale for targeting TRAPPC11 in DCM, and might warrant monitoring clinical outcomes of people carrying these SNPs for evidence supporting translatability of targeting TRAPPC11 to treat DCM. The training program proposed in this fellowship application was created to support my potential to become an independent investigator in the future. It will take place in the highly supportive, rich academic environment of Stanford University, where I will have access to state-of-the-art facilities and the opportunity to interact with leading cardiovascular researchers. The plan encompasses scientific technical skills, professional development skills, and both written and oral communication skills and will prepare me for writing my career development award.

NIH Research Projects · FY 2025 · 2025-09

PROJECT SUMMARY Torsades de Pointes (TdP) and subsequent sudden cardiac death (SCD) is a severe life-threatening arrhythmia that occurs in conditions that prolong the QT interval of the electrocardiogram (ECG). These conditions include both congenital LQT which can be caused by genetic mutations in various cardiac ion channels, as well as acquired LQT which is most commonly drug-induced, particularly through hERG potassium channel blockade. The prevention of life-threatening TdP in drug-induced acquired LQT is of particular importance as many new drug candidates fail to make it to market due to the FDA’s strict requirements for a “thorough QT study,” rejecting drug candidates that prolong the QT interval significantly. Thus, the development of both new precision diagnostics for predicting individual patient risk of acquired LQT as well as new targeted therapeutics for preventing TdP in LQT patients are greatly needed. Given the importance of QT prolongation on the FDA drug approval process, these advances would have broad implications not only on congenital LQT patients, but on the entire drug development industry. The goal of this proposal is to address these areas of need using artificial intelligence computational approaches and modern gene editing techniques. To this end, the applicant will develop a deep learning artificial intelligence ECG model to predict patient- specific risk of drug-induced LQT during the loading of Class III anti-arrhythmic drugs such as sotalol and dofetilide. These drugs are used to prevent arrhythmias but paradoxically also can increase the risk of sudden cardiac death if a patient experiences QT prolongation. Therefore, patients must be hospitalized for serial QT interval monitoring during initiation. A deep learning model that can predict in advance from the baseline ECG which individual patients will develop LQT would enable us to precisely select only patients with a high confidence of success to attempt a costly and inconvenient inpatient drug load. The applicant will also address the need for new targeted therapies to prevent sudden cardiac death in both acquired and congenital LQT. Prior work has implicated the L-type calcium channel window current in the mechanism of TdP arrhythmia initiation. The applicant will investigate these predicted arrhythmia suppression effects in patient-derived congenital LQT induced pluripotent stem cells (iPSC) using CRISPR gene editing to reduce the window current. Improved understanding of this mechanism will be important in the pursuit of a universal therapy to prevent arrhythmia in all LQT conditions. The training in this fellowship will be instrumental in the applicant’s career development as a physician-scientist in electrophysiology.

NIH Research Projects · FY 2025 · 2025-09

Project Summary The proposed project aims to enhance Stanford’s CEDAR platform for creating and managing metadata in scientific research, ensuring that datasets are Findable, Accessible, Interoperable, and Reusable (FAIR). CEDAR enables the use of metadata templates that render community-based reporting guidelines, simplifying the creation of standardized data records for users. As adoption by NIH projects continues to grow, there is a need to customize CEDAR to address the specific requirements of diverse user communities. Additionally, with the increasing number of submissions, it is essential to organize the repository’s contents in a structured and domain-specific manner to enhance searchability for domain experts. These improvements will make CEDAR more adaptable and user-friendly, supporting the varied needs of the scientific community and facilitating more efficient data management and discovery. The two primary goals include: 1. User Profiles: CEDAR will introduce three distinct user profiles to cater to the diverse needs and purposes of different user communities: a. Basic Profile: Designed for use cases where metadata consists of simple attribute-value pairs with straightforward input constraints for data validation purposes. This profile facilitates easy and efficient creation of standardized metadata without requiring specialized technical knowledge. b. Semantic Profile: Building on the Basic Profile, the Semantic Profile enables interoperability with BioPortal. This allows metadata values to be linked to standardized terms in biomedical ontologies, ensuring that users can specify the meanings of metadata values unambiguously and consistently. c. Modular Profile: Intended for use cases where metadata structures are shared across multiple projects and need to be organized into granular, reusable components. The Modular Profile supports intricate metadata arrangements, promoting consistency and reusability in collaborative and large-scale research environments. 2. Enhanced Search and Cataloging: CEDAR will provide a structured catalog of metadata templates, systematically organized using terms from standard biomedical ontologies. This organization will enable users to locate templates relevant to their specific domains efficiently. By leveraging semantic relationships, the structured catalog will significantly enhance the discoverability of templates, facilitating their use by biomedical investigators who wish to ensure that their datasets are FAIR.

NIH Research Projects · FY 2025 · 2025-09

PROJECT SUMMARY While childbirth is often viewed as a positive life event, up to one-third of women in the United States describe childbirth as traumatic. For those who experience severe maternal morbidity (SMM) or whose infants require neonatal intensive care unit (NICU) admission, the risk of traumatic childbirth developing into postpartum post- traumatic stress disorder (PTSD) is especially high, with prevalence estimates ranging from 16–20%. Postpartum PTSD is associated with significant adverse outcomes, including impaired maternal functioning, disrupted maternal-infant bonding, breastfeeding challenges, and long-term mental health morbidity. Despite growing awareness of these impacts, postpartum PTSD lacks standardized screening protocols and evidence- based early interventions that are scalable and suitable for busy perinatal care settings. This multi-site pilot study will assess the feasibility and acceptability of two innovative, low-burden, early interventions for postpartum PTSD: Written Exposure Therapy (WET) and Capnometry Guided Respiratory Intervention (CGRI). WET is a brief, 5-session trauma-focused psychosocial intervention using expressive writing, which has demonstrated non-inferiority to more intensive PTSD therapies. CGRI is an FDA-approved, home-based, digital breathing intervention designed to regulate physiological stress responses without requiring patients to process trauma content—an advantage for postpartum individuals who may prefer non-verbal, body-based therapies. Both interventions have shown efficacy in other populations but have not been tested in postpartum contexts. The study includes an initial adaptation and case series phase (n=10), followed by a randomized feasibility trial (n=60) in postpartum patients with clinically significant PTSD symptoms (PCL-5 score ≥28) within one month of a traumatic childbirth event. Participants will be randomized to WET, CGRI, or standard routine follow-up by nursing or social work (n=20 per group). Primary outcomes include feasibility of recruitment, intervention fidelity, patient adherence, and intervention acceptability using both quantitative measures and qualitative interviews. Secondary clinical outcomes include changes in PTSD symptom severity at 6 weeks and 3 months, as well as validated measures of maternal depression, anxiety, bonding, breastfeeding, and functional recovery. A novel exploratory aim will also assess the feasibility of collecting maternal hair samples during hospitalization and postpartum follow-up to measure a panel of corticosteroids (e.g., cortisol, cortisone, progesterone) using liquid chromatography-tandem mass spectrometry. These biomarkers will be evaluated in relation to PTSD symptom severity and treatment response, with the goal of informing future precision mental health strategies. This study will generate essential data to guide the development of a fully powered randomized controlled trial and support scalable, accessible early interventions for postpartum PTSD. This project aims to reduce suffering and enhance postpartum recovery outcomes in alignment with the NICHD’s mission to prevent maternal mental health disorders and promote lifelong health.

NIH Research Projects · FY 2025 · 2025-09

Project Summary Hypertension is a leading risk factor for disease and death in the US, contributing to over 690,000 deaths annually. Blood pressure (BP) measurement is an essential part of a coordinated diagnosis and management strategy. There is a longstanding need for continuous non-invasive BP measurements to reveal the daily variations between clinic visits. Wearable devices such as smartwatches are attractive platforms for health sensing. However, existing wearable sensors fall short because they use a light-based signal called photoplethysmogram (PPG). While PPG biosignals are adequate for heart rate detection, their indistinct pulse wave shapes do not encode blood pressure. A promising alternative to PPG is wearable ultrasound, which can measure high resolution waveforms from large arteries. However, wearable ultrasound’s potential remains untested. The central hypothesis is that wearable ultrasound sensors can accurately estimate central blood pressure. This work aims to evaluate the central hypothesis using both simulated and experimental data. In Specific Aim 1, the BP estimation accuracy of wearable ultrasound will be evaluated using a large simulated dataset. An existing computational model of the arterial vasculature will be used to produce a dataset of simulated wearable ultrasound from the radial artery in the wrist. This dataset will be used to train convolutional neural networks to predict central BP. In Specific Aim 2, a prototype wearable ultrasound device will be developed and used to collect an experimental dataset with human subjects. 30 healthy subjects will wear wearable ultrasound while performing BP-altering activities including postural changes, physical activity, and controlled breathing. To convert measured recordings to a format congruent with the simulated data of Specific Aim 1, the experimental waveforms’ timing and shape information will be separated using a pulse deconvolution algorithm. A convolutional neural network will then be trained to predict brachial cuff pressure. To achieve these aims, the predoctoral fellowship applicant will conduct mentored research and training in cardiovascular simulations, wearable sensor design, and statistical algorithms for biomedical time series inference. The training plan includes coursework in cardiovascular simulations and statistical learning, hands-on lab skill training, regular advising meetings, and feedback on written and oral scientific exposition. In summary, this work combines simulated and experimental data to evaluate wearable ultrasound’s potential for BP estimation, opening a new avenue of research towards the longstanding need for continuous non-invasive BP sensing. Furthermore, through technical and professional skill development, the fellowship applicant will develop as an independent researcher advancing cardiovascular health through sensors and algorithms.

NIH Research Projects · FY 2025 · 2025-09

PROJECT SUMMARY Children and adults with the inherited bleeding disorder hemophilia A are reliant on infusions of coagulation protein factor VIII (FVIII) to treat bleeding episodes. The most significant complication of FVIII replacement therapy is the formation of neutralizing antibodies called inhibitors against the infused FVIII protein. Inhibitors increases the risk of bleeding episodes that are difficult to control, which ultimately affects quality of life. However, the advent of novel non-factor therapies used to prevent bleeding has dramatically changed the treatment paradigm for persons with hemophilia A (PwHA) and inhibitors. Despite the tremendous advancements in treatment options for PwHA, none of the current therapies prevent the development of inhibitors. Moreover, PwHA and inhibitors are still dependent on bypassing agents with reduced hemostatic efficacy than FVIII for bleeding and surgical management. Although there have been extensive studies into understanding the adaptive immune response to FVIII, the mechanism(s) that drive inhibitor formation are still poorly understood. It is known that CD4+ T cells are responsible for activating B cells and plasma cells that produce FVIII antibodies. It is also established that regulatory T cells (Tregs) are an important element of central and peripheral tolerance to antigens, including FVIII tolerance achieved with successful immune tolerance induction therapy. It is postulated that FVIII inhibitor formation results from an imbalance or failure in Treg-mediated immune regulatory processes. Whether Tregs are reduced, dysfunction, or incapable of expanding in FVIII adaptive immunity warrants further investigation. The long-term goal of this work is to illuminate key mechanisms of FVIII immune responses to develop targeted and low burden therapies for inhibitor prevention during early FVIII exposure. We hypothesize that FVIII inhibitor development is associated with reduced and dysfunctional Tregs, which is prevented with Treg expansion in vivo. To test our hypothesis, we will use a combination of mouse and human model systems to understand the Treg profile in FVIII deficiency and inhibitor development. In Aim 1, we will perform rigorous Treg phenotyping in mouse models of hemophilia A with different in inhibitor responses and Tregs isolated from pediatric and adult PwHA based on inhibitor status. We will additionally generate a human-derived spleen organoid model to study the effect of FVIII on Treg responses at the primary site of FVIII interface with immune cells. In Aim 2, we will determine the effect of engaging the erythropoietin (EPO) receptor with recombinant EPO, a known Treg inducer in models of autoimmunity and solid-organ transplantation, on inhibitor responses in naive hemophilia A mice. In Aim 3, we will evaluate EPO-mediated Treg expansion capacity based on age using isolated Tregs from children with chronic kidney disease initiating EPO therapy and EPO-stimulated Tregs in vitro from PwHA naïve T cells and in the spleen organoid model. This proposal represents a new direction as an early-stage investigator that will identify mechanisms of the failed immunoregulatory responses with early FVIII exposure and investigate an innovative and alternative approach to Treg expansion for inhibitor prevention.

NIH Research Projects · FY 2025 · 2025-09

PROJECT SUMMARY/ABSTRACT Immune checkpoint inhibitors (ICIs) are cancer therapeutics that unleash T-cell cytotoxicity against tumors. While effective for up to 50% of cancer patients, ICIs can cause ICI myocarditis (ICIM), pathologic inflammation of the heart—a life-threatening side effect leading to arrhythmias, heart failure and death. Despite a high case fatality rate, there are no effective ICIM therapies; thus, there is an enormous unmet clinical need for mitigating therapies. Our group has defined immunologic drivers in ICIM using pre-clinical and patient samples. Using novel single- cell multi-omics, we found that crosstalk between T-cells and macrophages in the heart is critical for ICIM development, bringing forth a new therapeutic opportunity. This proposal is to develop a mechanism-based therapy for ICIM blocking crosstalk between macrophages expressing chemokines CXCL9/10 (CXCL9/10+MP) and antigen-specific T-cells expressing the chemokine receptor CXCR3 (CXCR3hiCD8 T-cells) in the heart. Our previous work has shown that CXCR3 blockade mitigates ICIM in a non-tumor mouse model. Since current ICIM models do not account for tumor, we have now created more realistic tumor ICIM models by inoculating C57/BL6J mice with B16F1 melanoma/MC38 colorectal tumors. Our preliminary data in these models demonstrate mitigation of ICIM with CXCR3 blockade while maintaining ICI tumor control. Through drug screening, we have discovered drug-like small molecule CXCR3 inhibitors which will increase translational opportunities. In Aim 1, we will test the hypothesis that ICIM involves a cardiac infiltration of CXCL9/10+MP/antigen-specific CXCR3hiCD8+ T-cells in ICIM distinct from tumor associated macrophages (TAMs)/antigen-specific CD8 tumor infiltrating lymphocytes (CD8 TILs) present in tumors. We will validate our findings in our tumor mouse models and patient samples. In Aim 2, we will perform CXCR3 blockade in the mouse tumor models, assessing the effect of blockade on heart/tumor phenotype and immune profile using single-cell multi-omics. In Aim 3, we will test the hypothesis that CXCR3 blockade reduces migration/cytotoxicity of cardiac CXCR3hiCD8 T-cells but not CD8 TILs. We will determine functional effects of CXCR3 blockade on cardiac CXCR3hiCD8 T-cell/TIL ex vivo with transwell assays and co-cultures with cardiomyocytes/tumor cells. These studies will provide critical and timely data on CXCR3 blockade as a novel targeted therapy for ICI myocarditis in a clinical space where treatments are urgently needed.

NIH Research Projects · FY 2025 · 2025-09

Project Summary & Abstract Neutrophils are the front-line defenders in innate immunity and are the most abundant circulating immune cells in the human body. Unfortunately, neutrophils are also major players in perpetuating autoinflammatory conditions. Neutrophils possess remarkable nuclear deformability, a necessary requisite for their migration from blood vessels through micrometer sized intercellular spaces towards sites of inflammation. The overarching goal of this proposal is to design new approaches to control neutrophil migration by manipulating nuclear deformability. Preliminary work has shown 69 differentially regulated nuclear envelope genes associated with neutrophil differentiation. Many of these are associated with the nuclear lamina and lipid homeostasis proteins. This proposal aims to use a nanobody-based protein degradation system developed by the Dassama lab, called NanoBridge, to selectively degrade these differentially produced proteins. It proposes to use a combination of microscopy and lipidomics to assess the impact of target degradation on nuclear lipid composition and how lipid composition influences neutrophil nuclear deformability. Novel aspects of this proposal include 1) development of a system for the targeted degradation of nuclear envelope proteins, 2) assessment of lipid content in the neutrophil nucleus following targeted degradation of upregulated lipid homeostasis proteins, 3) determination of neutrophil morphology and migration following targeted degradation of understudied nuclear envelope proteins and 4) development of a system to modulate neutrophil migration via manipulation of its nuclear deformability by targeted protein degradation. The project goals will allow me to develop a well-rounded skillset in cell biology and targeted protein degradation. I will further have an opportunity to gain experience in lipidomics technology. These skills will combine with my pre-existing experience in natural product biosynthesis from my PhD work to give me a well- rounded background that will facilitate my independent research career.

NIH Research Projects · FY 2025 · 2025-09

Unhealthy diet quality contributes to 1 in every 5 deaths in the US. Unhealthy diet quality is especially worrying among young adults (ages 18-29), who have worse diet quality than every other adult age group. Social media messaging campaigns can be an effective, low-cost way to improve diet quality, but need to be optimized to the needs of young adults. We propose that a counter-marketing campaign can improve diet quality among young adults—while eliciting less weight stigma and disordered eating—relative to a traditional health education campaign. Counter-marketing campaigns expose how some food companies use deceptive or harmful marketing practices to sell unhealthy foods. By exposing these practices, counter-marketing campaigns appeal to young adults’ values of autonomy and respect. Exposing these practices also emphasizes industry responsibility for diet and weight, which could minimize weight stigma and disordered eating. Counter-marketing has a long history of success in reducing smoking but is under-used and under-studied for improving diet. We propose 3 Aims to optimize and evaluate a social media counter-marketing campaign for young adults. In Aim 1, we will optimize a counter-marketing campaign to encourage young adults to eat healthier foods. We will (a) conduct qualitative interviews with young adults to identify their core values and attitudes, (b) develop counter-marketing campaign themes targeted to these values and attitudes, and (c) identify the 3 most promising themes using a ratings experiment with a nationally representative sample of young adults. Working with a marketing agency, we will develop a counter-marketing campaign using these themes to test in Aim 2. In Aim 2, we will test if a counter-marketing campaign improves diet quality more than a health education campaign or a control campaign. In a 3-arm RCT, we will randomly assign young adults to a counter-marketing campaign, a health education campaign, or an attention control campaign about safe driving. Using our social media RCT protocol, we will deliver the 3-month campaigns to young adults via Instagram. We will use social media analytics to tailor the frequency, timing, and type of campaign posts to maximize engagement. We will assess diet quality (Healthy Eating Index) at 0 months (pre-intervention), 3 months (initial campaign effects, immediately post-intervention) and 9 months (sustained campaign effects, 6 months post-intervention) using repeated 24-hour dietary recalls. In Aim 3, we will test if a counter-marketing campaign elicits less internalized weight stigma and disordered eating attitudes and behaviors than a health education campaign. We will (a) survey all young adults in the RCT at 0, 3 and 9 months and (b) conduct qualitative interviews at 9 months with a purposive sample of RCT participants. The proposed research makes a significant contribution by guiding development of a fundamentally new approach for improving diet quality while minimizing harmful unintended consequences.

NIH Research Projects · FY 2025 · 2025-09

PROJECT ABSTRACT Kidney transplantation is the best treatment for end-stage renal disease (ESRD), but most patients with ESRD have no access. In 2023, 88,763 patients were on the deceased donor kidney transplant waitlist, yet only 27,332 transplants were performed. Over 200 kidney transplant programs In the United-States (US) deliver most of the nation’s transplant-related services. Transplant programs evaluate and select individuals to be added to the waitlist, maintain the waitlist, and manage both living donor work-up and deceased donor offers from organ procurement organizations. Collective, these activities are called pretransplant operations. Much of these pretransplant operations are not well-described, and the implications of operational differences are not understood. In addition to being opaque, current pretransplant operations leave gaping holes: 15-20% kidney offers between 2008-2015 were made to dead candidates; the most common reported reason for kidney discard is “no recipient located”; 26% of patients are added to the waitlist as “status inactive” (during which they are unable to receive kidney offers), and Hispanic and black patients are less likely to have this status inactivity resolved to regain transplant access. The specific practices leading to these undesirable outcomes need to be understood in order to increase transplant access to patients with ESRD. We will comprehensively characterize all US adult kidney transplant programs in terms of their pretransplant operation strategies and local conditions. Our central hypothesis is that some pretransplant strategies arise in response to local conditions, but transplant programs in similar locations can use different pretransplant operation strategies, resulting in differences in patient outcomes and cost of care. We will define the local conditions of transplant programs in terms of: 1) descriptive characteristics of patients with ESRD serviced and 2) deceased donor organ supply (based on geospatial and match run data). For the transplant program characteristics analysis, we will use program-level and patient-level outcomes from the Scientific Registry of Transplant Recipient and US Renal Data System; we will approximate costs from our pre-created organ acquisition cost center database and Medicare claims data collected in US Renal Data System. We hope to identify pretransplant operations which are associated with better program-level and patient-level outcomes, even after accounting for local conditions. We aim to provide the evidence base for concrete, actionable recommendations to transplant programs and their governing bodies to 1) improve programmatic and population outcomes within their community and 2) reduce the programmatic and population costs of care.

NSF Awards · FY 2025 · 2025-09

Mathematical breakthroughs have historically driven advances in technology, national security, and economics. Examples of these breakthroughs include cryptography methods that protect digital infrastructure or algorithms that power modern computing, such as planning, supply chain, and navigation optimization. However, the pace of mathematical discovery is limited by the capacity of human mathematicians to explore increasingly complex problems. This project addresses a critical need by developing artificial intelligence (AI) systems that can accelerate mathematical research and theorem proving at scale. The goal is to create the first AI system capable of proving graduate-level mathematical theorems and tackling unsolved problems that challenge even world-class mathematicians. One motivating observation is that current AI systems for theorem-proving are trained in a way vastly different from how mathematicians learn. In reality, mathematicians develop their skills by working collectively within an ecosystem by taking on different roles at various times. Mathematicians also specialize in various areas and skill sets while actively collaborating across areas to build connections and transfer knowledge. The key approach is to train AI systems that imitate the evolution and interactions of mathematicians. This research would serve the national interest by accelerating scientific discovery across fields that depend on advanced mathematics, from quantum computing and cybersecurity to materials science and economics. It will also produce new techniques for training AI systems with limited high-quality data, a challenge that extends far beyond mathematics, such as scientific discovery research, medical diagnosis, and national defense applications. The project will also strengthen American competitiveness in AI by training the next generation of researchers through new coursework and mentorship programs, while making all developed tools freely available to the broader scientific community. This project addresses a fundamental limitation in automated theorem proving: while current large language models (LLMs) can solve International Mathematical Olympiad problems and college-level theorems, they remain unable to prove graduate-level theorems or tackle conjectures that challenge world-class mathematicians. The hypothesis is that existing AI systems are fundamentally different from how mathematicians develop expertise, which is through collaborative ecosystems with diverse roles and cross-field knowledge transfer. The project’s goal is to train multiple LLMs to simulate the mathematicians' learning, ideation, specialization, interaction, and cross-pollination process. Furthermore, they will enhance these neural models—which are good at capturing human intuition and insights—with software tools such as search and Python executors, in a computationally efficient manner, enabling AI to surpass human capabilities. Concretely, the project has the following three thrusts. In Thrust 1 (Learning Roles of Mathematicians), LLMs will be trained to take on various roles, such as conjecturers, provers, reviewers, agenda setters, and synthesists, while providing supervising signals to each other. In Thrust 2 (Specialization, Cross-Field Unification, and Knowledge Transfer), expert models will be trained using customized state representations and design higher-level or merged provers that address more general mathematical problems by leveraging the efficiency of the individual provers in a unified framework. Finally, in Thrust 3 (Scalable Serving and Training Systems), the project will innovate various efficiency optimization techniques for inference-time search, reward assignments, and high-throughput serving infrastructure. The expected contributions include an AI theorem-proving tool that transforms mathematical research by enabling a systematic way to explore and validate complex proofs across disciplines. This tool could accelerate mathematical discovery, providing a powerful mechanism for testing, validating, and extending mathematical knowledge. Moreover, the findings on learning from limited data and the ecosystem of AI-based mathematicians can be generalized to other adjacent domains, such as physics and computer science. 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 · 2025-09

This project aims to unlock the full scientific potential of a group of small, colorful fish commonly found in home aquariums—guppies, mollies, and swordtails. These fish have already allowed scientists to uncover important insights on human diseases such as melanoma and metabolic disorders, as well as in studies of animal behavior and development. However, scientists currently lack the tools to precisely modify their DNA, which limits how much we can learn from them. This project will develop new methods to enable DNA editing in these fish, opening the door to discoveries that could benefit both medicine and biology. In addition to advancing science, the project will provide hands-on research opportunities for undergraduate students and engage the public through outreach activities. It will also train other scientists in these new techniques, helping to build a community of researchers equipped to study health and disease using these powerful animal models. Poeciliid fishes are a key model system in integrative organismal biology, yet their utility has been constrained by the lack of DNA editing tools, largely due to their viviparous reproduction. This proposal addresses that gap by developing a suite of biotechnological tools to enable functional genomics in Poeciliids. The project will begin by establishing immortalized cell lines from multiple species and optimizing transfection protocols to enable CRISPR-based DNA editing in vitro. Building on recent success in culturing embryos ex vivo, the team will adapt these protocols for in vivo gene editing. Concurrently, the project will generate high-quality, telomere-to-telomere genome assemblies and functional annotation resources to support downstream applications. These genomic tools will be made publicly accessible via a genome browser, facilitating broad adoption. Together, these efforts will establish a robust platform for genetic manipulation in Poeciliids, enabling mechanistic studies of development, physiology, and disease. The research will result in new biotechnology. 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

This research program aims to integrate physics-based modeling with artificial intelligence to en- hance conformational sampling and design of biomolecules, with a view towards understanding self- organization dynamics in biological systems away from equilibrium. Our laboratory has made significant progress in developing generative neural networks for protein conformational sampling and designing state-of-the-art algorithms for molecular discovery. Building on this foundation, the program will pur- sue two interconnected projects over the next five years: 1) creating computational tools that combine physics and AI to improve conformational sampling and design of single molecules like intrinsically dis- ordered proteins, mRNA, and small molecules and 2) modeling and characterizing self-organization dynamics of many-molecule assemblies using nonequilibrium statistical mechanics. The research will leverage advanced Monte Carlo techniques, generative pre-trained transformers, and physics-based molecular dynamics simulations to search chemical space, model conformational heterogeneity, and bridge spatial scales. A key focus will be developing experimentally-informed minimal models of com- plex biomolecular assemblies like stress granules, and using generative machine learning to move seamlessly between spatial scales. The long-term vision is to build an effective statistical microscope that can provide detailed molec- ular insights into heterogeneous assemblies that are currently inaccessible to structural studies. This approach could enable design at the ”systems” level by combining experimentally and computationally guided small molecule, peptide, and RNA design. The program will work closely with experimental col- laborators to test predictions and develop new models. All tools developed will be made open-source to benefit the broader scientific community. Ultimately, this research aims to advance our understanding of biomolecular structure and dynamics across scales, from single molecules to mesoscale assemblies, with implications for drug discovery and the engineering of novel biomaterials.

NIH Research Projects · FY 2025 · 2025-09

PROJECT SUMMARY Optical imaging is a widely used technique in medicine and research, allowing tissue examination in a non- invasive manner. A significant limitation of optical imaging methods is that tissues scatter, reflect, and absorb light, making it difficult to obtain clear images of deeper structures. As a result, most optical imaging methods struggle to visualize tissues more than a few millimeters beneath the surface, even when using special contrast agents. Our research focuses on improving deep-tissue imaging by using shortwave infrared (SWIR) technology. SWIR imaging allows various modalities in one image, including SWIR Raman, absorption, and fluorescence imaging. SWIR imaging has better tissue penetration due to reduced scattering and provides increased contrast than near-infrared imaging when combined with clinically available dyes such as indocyanine green (ICG). One of our key goals in this proposal is to develop wide-field SWIR Raman imaging (SWIRRI), which allows us to understand the chemical composition of tissue in a label-free manner. Our preliminary studies have shown the ability to detail subcutaneous tissue composition by looking at the scattering of their molecules. In Aim 1, we will optimize the SWIRRI system for imaging above 1500 nm and quantify the depth and resolution of SWIRRI. We then apply this method in two areas: first, to study wound healing and infections, helping us distinguish between normal inflammation and bacterial infection; and second, to examine cancerous tumors, focusing on how they change surrounding tissues and help identify cancer spread to nearby lymph nodes. In our second aim, we create new fluorescent dyes that work effectively in the longer wavelengths of the SWIR spectrum above 1500 nm. While existing dyes like indocyanine green (ICG) are useful in SWIR imaging, they are inadequate for visualizing deep structures. We plan to develop new probes that are more efficient, produce higher contrast images, and can be used with current SWIR imaging systems already used in clinical trials. We will also combine our SWIRRI with these dyes to achieve higher molecular specificity and allow visualization of arterial flow not seen on SWIRI and detection of cancer metastasis. By combining Raman imaging and improved fluorescence imaging, this non-invasive modality can revolutionize medical diagnostics, surgical guidance, and disease monitoring.

NIH Research Projects · FY 2025 · 2025-09

ABSTRACT Ultrasound guides nearly all prostate biopsies. However, low contrast between cancer and normal tissue on ultrasound limits its utilization in identifying suspicious regions to target. Instead, ultrasound-only biopsies blindly sample the prostate at ∼12-14 locations, missing 52% of cancers. When combined with Magnetic Resonance Imaging (MRI), the Ultrasound-MRI fusion biopsy enables targeting of suspicious lesions for better cancer detection (88% vs. 48%). Yet, MRI is only available in 35% of biopsies due to cost and the need for interpretation expertise. Unequal access to MRI represents a major health disparity. Despite being at higher risk for prostate cancer, Black men are 50% less likely to get an MRI, leaving them to undergo sub-optimal blind biopsies, possibly delaying diagnosis. There is a clinical need to enable prostate cancer targeting in low-resource settings, using only ultrasound when MRI is inaccessible, and improve targeting in resource-rich settings using both ultrasound and MRI. We propose to develop the Ethical Multimodal AI fusion Platform (eMAP) and show proof-of-concept for prostate cancer detection. eMAP is built under careful ethical considerations (e.g, data privacy, fairness, stakeholder input) using advanced AI foundation models and knowledge transfer architectures. eMAP integrates multimodal AI training allowing MRI to inform ultrasound to enhance cancer features, but at inference, in unseen subjects, eMAP will allow improved cancer detection in both resource low and rich settings. While we show proof-of-concept in prostate cancer, we anticipate that eMAP will pave the way to ethically bridge the gap between radiology imaging modalities in prostate and beyond.

NIH Research Projects · FY 2025 · 2025-09

PROJECT SUMMARY In the United States, cardiovascular diseases (CVDs) are the leading cause of mortality, responsible for one in four deaths. While CVD typically manifests during adulthood, its risk factors -including obesity, lipid abnormalities, and subclinical atherosclerosis- begin to develop in early childhood. A significant contributor to these cardiovascular risk factors is the consumption of ultra-processed foods (UPF, foods made using industrial processing methods with added ingredients such as sugar, salt, fat, and food additives). The consumption and availability of UPF in children is alarmingly high providing 66% of the total energy intake among 2- to 5-year-old children in the U.S., and 70% of foods purchased in the U.S. market for babies or toddlers contain at least one food additive. Although there is substantial evidence of the cardiometabolic effects of UPF consumption in adults, there is a need for evidence of the effect of UPF in young children. Additionally, there is a critical need to identify interventions that can reduce UPF consumption in young children. The focus of this study is on toddlers (2-3 years old), as this is a critical age when children’s food preferences and long-term eating habits begin to develop. Establishing eating behaviors at this early age is a promising approach to improving diet quality and reducing cardiometabolic risk. The goal of this proposed research is to use a community-engaged approach to generate evidence of feasible and acceptable interventions that reduce UPF consumption in toddlers (2-3 years old) to improve cardiovascular health. To achieve this, I will 1) evaluate the association between UPF consumption and changes in adiposity, cardiometabolic risk factors, and microbiome in toddlers, 2) identify barriers and facilitators to reducing UPF consumption among parents of toddlers, and 3) pilot-test and evaluate a family-based intervention aimed at reducing UPF consumption in toddlers. This research will help me to achieve my long-term goal of generating robust evidence and partnering with communities and policy officials to co-design sustainable, rigorous, and effective interventions to reduce health disparities and improve child health through nutrition. With the support of this K01, I will build on my expertise in nutrition epidemiology and health policy nutrition, to gain training and new skills in: 1) community-engaged research, 2) qualitative research methods, 3) Design, implementation, and evaluation of clinical trials and longitudinal studies, and 4) Microbiome assessment and advanced statistical methods for its analysis. I have the support of my institution and have assembled a group of highly supportive mentors and advisors, who will provide me with the resources, expertise, and mentorship to become an independent researcher and successfully compete for R01 funding.

NIH Research Projects · FY 2025 · 2025-09

PROJECT SUMMARY Diabetic retinopathy (DR) progression varies greatly among patients, with those who develop diabetic macular edema (DME) often showing inconsistent responses to standard treatments, such as anti-VEGF therapy. Defining the molecular and cellular basis of DR in living patients remains a critical unmet need for optimizing precision health. This knowledge gap reflects the difficulty of interrogating specific cell types and proteome- wide profiling in vivo within highly complex, dynamic DR patient tissues, which hinders the development of novel and effective therapeutics. To obviate this challenge, we developed a method named TEMPO (Tracing Expression of Multiple Protein Origins; Cell (2023), PMID 37863056) a powerful tool to examine disease mechanisms at the cell level in vivo by integrating aptamer-based microvolume liquid biopsy proteomics, single-cell transcriptomics, and artificial intelligence (AI). In preliminary studies, TEMPO revealed surprising, new features of disease mechanisms by tracing the cellular origins of 5,953 proteins detected in the aqueous humor (AH): we identified immune and vascular cells as key contributors to distinct stages of DR and observed that diabetes accelerates molecular aging in the eye, even before retinopathy becomes clinically detectable. Additionally, systemic proteins, particularly liver-derived inflammatory markers, were found to play a role in DR progression, highlighting the influence of both local ocular and systemic factors. Our long-term goal is to create sensitive and patient-specific molecular diagnostics, prognostics, and treatments for retinal disease. This proposal’s objective is to apply the TEMPO proteomic platform using human liquid biopsy samples to identify protein biomarker signatures and cells driving DR stages and DME in living patients, and using our proteomic clock, determine the role of accelerated molecular aging in DR. Our central hypothesis is that the high- resolution proteomic profiling of AH will show that the diverse retinal phenotypes observed in DR and treatment-responses are driven by distinct molecular pathways and cell types, and reveal that DR-specific protein signatures accelerate cellular aging. Our specific aims will utilize three different approaches focusing on each element of DR molecular-pathology: (1) determine the differential expression of DR protein networks in living humans; (2) determine the cellular drivers of DR in living humans using TEMPO; and (3) determine the role of DR-specific protein signatures in accelerated cellular aging using AI-based proteomic models for proteomic pathways and eye clocks. Impact. This translational study will allow us to understand the mechanisms, cellular drivers, and molecular aging underlying DR pathology and DME treatment response in living patients, which will further aid in the development of therapeutics, diagnostics, prognostics, and clinical trial design.

NIH Research Projects · FY 2025 · 2025-09

PROJECT SUMMARY Hypertension is a leading modifiable risk factor for cardiovascular disease (CVD) and chronic kidney disease (CKD), contributing to over 10 million deaths worldwide each year. Although multiple classes of antihypertensive therapies (e.g., ACE inhibitors, ARBs, diuretics, CCBs) exist, fewer than half of all treated patients achieve adequate blood pressure (BP) control. Growing evidence suggests that both genetic variation (e.g., polygenic risk scores, known pharmacogenomic variants) and environmental factors (e.g., dietary sodium/potassium intake, physical activity) play key roles in shaping drug response, yet real-world data on how these elements jointly affect treatment effectiveness remain limited. This proposal aims to address these gaps by leveraging longitudinal electronic health records (EHRs) from the Kaiser Permanente Research Bank, which integrates genetic data, prescription records (including dose and strength), and lifestyle measures in a large real-world cohort. In Aim 1, we will evaluate the real-world effectiveness and dose-response relationships of both monotherapy and combination antihypertensive regimens, identifying “responders” and “non-responders” and modeling linear and non-linear dose effects. In Aim 2, we will use robust genome-wide approaches to identify genetic variants associated with antihypertensive drug response, constructing and validating polygenic risk scores (PRS) that incorporate relevant covariates. Finally, Aim 3 will map gene–environment interactions, investigating how genetic predisposition intersects with factors such as sodium intake, physical activity, and age to influence BP control and CVD risk. By focusing on hypertension as a case study, this research will advance our understanding of the complex interplay between genetics, environment, and medication use, providing insights that can be generalized to other chronic conditions and informing more reliable, data-driven precision medicine strategies.

NIH Research Projects · FY 2025 · 2025-09

ABSTRACT/SUMMARY: Fanconi Anemia (FA) is the leading cause of inherited bone marrow failure (BMF) affecting thousands of patients worldwide. While allogenic (allo)-hematopoietic stem cell transplantation (HSCT) outcomes have been improving for FA patients, these treatments currently cause significant morbidity and mortality in part due to the non-specific and genotoxic chemotherapy and/or irradiation conditioning traditionally used pre-transplant. This is especially problematic in FA patients due to their exquisite sensitivity to DNA damage which results in DNA interstrand crosslinks (ICLs) and heightened risk of malignancies in all patients. Our long-term goal is to develop alternative non-toxic FA therapies using cell and gene therapy. Towards this goal, we have generated HSC-targeted conditioning approaches using monoclonal antibodies (mAbs) and mAb-drug-conjugates against the CD117 HSC cell-surface protein that clear host HSCs without non-specific tissue damage. However, from this research we have found that host HSC depletion is not critical in the FA setting. This has been further observed in the setting of autologous FANCA lentiviral gene therapy and somatic mosaicism, which have both shown that a small number of initial functional hematopoietic stem and progenitor cells (HSPCs) can stabilize hematopoiesis in FA patients. The overall objective of this work is to leverage distinctive strategies that elude the immune system to safely establish functional HSPCs in the bone marrow that can maintain hematopoiesis in the FA setting. The preliminary data supports a central hypothesis that stable hematopoiesis with ability to repair DNA ICLs can be established in FA without the use of genotoxic chemotherapy or irradiation due to the competitive advantage of functional HSPCs over failing FA HSPCs. This hypothesis will be tested through three specific aims in FA mice: 1) Achieving and optimizing haploidentical in utero HSCT without use of any conditioning, 2) Evaluating post-natal HSCT with immune conditioning alone, and 3) Developing HSC and systemic CRISPR-based base editing correction methods using engineered virus-like-particles (eVLPs) to stabilize host FA cells. The proposed research is both innovative and significant because it will identify multiple strategies that safely enable stabilization of hematopoiesis in the FA setting that will subsequently be translated into non-genotoxic therapies for FA patients. These novel approaches that enable functional HSC correction using both cell and gene therapy strategies are aimed at treatment of FA at different stages of hematopoietic disease, with the additional capacity to uniquely correct FA mutations in non-hematopoietic tissues. This work will have a major impact on improving treatment for FA patients and findings from this research will subsequently be utilized to improve the treatment of millions of people worldwide that suffer from diverse hematopoietic and genetic diseases.

NSF Awards · FY 2025 · 2025-09

With support from the Division of Chemistry, Professors Philip Bucksbaum and Mohammed Hassan of Stanford University and the University of Arizona, respectively, along with their United Kingdom collaborators from the University College London, are investigating electron-ion entanglement in the photoionization of small molecules by extremely intense laser pulses of squeezed quantum light. Entanglement is a quantum mechanical phenomenon where two particles, such as the ion and its departing electron, exhibit correlated behavior, even when they are separated by large distances. However, entanglement is difficult to create and detect, and the ionization process happens extremely quickly. Professors Bucksbaum, Hassan, and their UK collaborators, will detect the directions and velocities of electrons emitted from atoms and molecules when they are ionized by squeezed fields derived from ultrafast laser pulses. Their discoveries could reveal quantum entanglement between the ion and its departing electron, potentially leading to a deeper understanding of the role quantum fields play in chemical dynamics. The project will also provide research opportunities for graduate students in quantum information science and thus contribute to the development of a quantum-enabled workforce. This award is made under the NSF-UKRI lead agency opportunity. The team will create bright squeezed vacuum (BSV) fields capable of affecting photoionization using near-infrared or visible coherent pulsed laser sources that then undergo nonlinear optical conversion by collinear degenerate parametric down-conversion or degenerate four-wave mixing. Nanojoule pulse energies are expected, and the nonclassical squeezing parameter will be measured using several standard methods, including autocorrelation measurements as well as pulse height histograms. These BSV fields will then be used to ionize chemical target molecules such as hydrogen, nitrogen, and water. The electron momentum distribution will be measured in a velocity-map imager (VMI). The well-known strong-field ionization patterns observed using classical fields should be altered in the presence of BSV. Holographic features, which have been observed using field ionization by classical fields, may be reduced in non-classical fields and could provide insight into ion-electron entanglement. Quantum mechanical models will be used to predict the patterns of electrons and guide the choice of targets and analysis methods. 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 Recent years have seen dramatic breakthroughs in methods for determining structures of proteins and other drug targets experimentally (e.g., cryo-EM) and predicting them computationally (e.g., AlphaFold). These advances tremendously increase the potential of structure-based drug discovery, but going from an accurate target structure to an effective drug remains a difficult problem. To do so, once must design molecules that bind tightly and selectively to the target, have appropriate (drug-like) physiochemical properties, and exert a desired effect on the target’s function. This project will make advances in several areas critical to enabling the efficient structure-based design of effective, safe drugs: 1. Creation of machine learning methods to accurately predict a chemical compound’s binding affinity, drawing simultaneously on the target’s structure and experimentally determined affinities of other compounds that do or do not bind the target. 2. Development of machine learning methods to design drug-like ligands that bind tightly and selectively, exploiting both generative artificial intelligence and iteration between computation and experiments. 3. Determination of the mechanisms by which drugs can selectively stimulate or selectively block arrestin signaling at diverse GPCRs, achieving desired therapeutic effects without dangerous side effects. This research builds on recent achievements of the Principal Investigator, Ron Dror, in two areas: (a) machine learning methods for structural biology, and (b) elucidation of the structural basis of GPCR signaling. It continues his record of collaborating with experimentalists to do innovative computational work with a strong impact on experimental biochemistry, structural biology, pharmacology, and drug design.

NIH Research Projects · FY 2025 · 2025-09

PROJECT SUMMARY/ABSTRACT The US fares poorly on international rankings of infant and maternal health. Evidence on causal policy impacts on these outcomes is necessary for improving the well-being of US mothers and children. Yet while there is abundant evidence on the effects of policies during pregnancy, less is known about policies during other important periods around childbirth: pre-conception, during delivery, and postpartum. Further, there are large maternal and infant health disparities. Previous research by the project team found that infants and mothers in families with incomes in the bottom 5% of the income distribution are two and three times more likely to die in the first year post-birth, respectively, than those with incomes in the top 5%. It also found that Black infants and mothers at the bottom of the income distribution are two and three times more likely to die, respectively, than their non-Hispanic white counterparts, and these racial gaps do not narrow as incomes rise. Reducing these gaps requires knowledge about differential policy impacts across race and income, and at different periods around childbirth. This project will use a novel linkage between multiple administrative datasets from California and natural experiment designs to deliver causal estimates of the effects of currently active and debated policies that could have disparate impacts by race and income: Medicaid coverage pre-conception and postpartum, and a California Maternal Quality Care Collaborative (CMQCC) intervention aimed at lowering cesarean section (c-section) deliveries. The research team has already linked data on the universe of California birth records, hospitalizations, emergency department (ED) visits, and death records with parental income from Internal Revenue Service tax records and Medicaid enrollment files for 2007—2019. These data allow for the calculation of women’s Medicaid eligibility in the year before conception and in the year after childbirth. Using a regression discontinuity design (RDD), the project will compare outcomes of women with incomes just above and just below the eligibility threshold. When examining impacts of preconception coverage, outcomes will include measures of pregnancy health (hospitalizations/ED visits during pregnancy, gestational diabetes and hypertension, preeclampsia), delivery characteristics (c-section, labor induction, complications), infant health (birth weight, gestation length, newborn complications, infant readmissions to the hospital, infant mortality), and postpartum health (severe maternal morbidity and mortality). Additionally, a differences-in-differences (DD) method that compares changes in outcomes in treatment and control hospitals will be used to study the CMQCC intervention, separately by maternal race and income. Outcomes will be c- section rates, birth outcomes and labor/delivery complications, maternal mortality, as well as c-section deliveries and complications at subsequent births. Results will be disseminated to key stakeholders and help policymakers, healthcare organizations, providers, and patients understand the effects of policies pre- conception, at delivery, and postpartum on maternal and infant health outcomes and disparities in the US.

NIH Research Projects · FY 2025 · 2025-09

Project Summary The evolutionary origins of complex polygenic traits remain an enduring enigma. Although introductory genetics is often taught with simple monogenic examples such as Mendel’s peas, most traits—including all common human diseases—actually result from multitudes of interacting loci. Genome-wide association studies (GWAS) have done a great deal to illustrate this, as well as the fact that variation in gene expression cis- regulation—such as promoters and enhancers—accounts for most phenotypic variation. However, uncovering the evolutionary origins of complex traits is still a major challenge. My lab has developed experimental and computational methods to pinpoint the genes contributing to complex traits, with a focus on evolutionary adaptations involving gene expression regulation. Our main focus is on two methods: high-throughput precision genome editing and allele-specific gene expression analysis in hybrids. These methods have enabled us to implicate and experimentally validate causal genes and genetic variants underlying complex traits in archaea, yeast, flies, fish, mice, and humans. Moreover, the code and genetic constructs underlying our methods are freely available to the scientific community.

NIH Research Projects · FY 2025 · 2025-09

RNA-DEPENDENT PROTEIN ASSEMBLIES IN EPIDERMAL HOMEOSTASIS Project Summary/Abstract Dysfunction of epidermal homeostasis can result in a plethora of diseases, such as psoriasis, atopic dermatitis, and skin cancer. The high incidence of skin diseases in about 25% of Americans highlights the need to expand our understanding of epidermal homeostasis mechanisms. Previous studies have shown that RNA-binding proteins (RBPs) are required to control keratinocyte proliferation and differentiation. However, how RBPs organize and collaborate in cis on RNA molecules to form functional ribonucleic protein complexes (RNPs) and how additional layers of regulation, such as post-translational modifications (PTMs), affect their roles in epidermal homeostasis, is currently unexplored. To address this, I have developed, in collaboration with postdoc fellow Dr. Brian Zarnegar, two new crosslinking immunoprecipitation (CLIP)-based methods, termed irCLIP-RNP and Re-CLIP. Both methods leverage the infrared-coupled 3’biotin adapter modification of the original irCLIP protocol to greatly enrich for RNA-dependent cis-RBP assemblies (irCLIP-RNP) and for RNA targets that are simultaneously co-bound by two RBPs of interest (Re-CLIP). These approaches represent a unique framework for studying RBP assembly code and its PTMs on co-bound RNA targets central to epidermal differentiation. Thus, this K99/R00 application aims to characterize the roles of RBP assemblies in epidermal homeostasis. In Aim I, we will test the model of RBP code in epidermal differentiation by comparing two RBPs, HNRNPC and HNRNPU, which we found essential for progenitor maintenance. Preliminary irCLIP-RNP data indicates distinct RBP codes for either HNRNPC or HNRNPU. Aim I.A will test the consequences of knocking out these distinct RBPs on progenitor RNA lifecycles. Aim I.B will analyze the extent to which these distinct combinatorial assemblies collaborate to affect the fate of co-bound RNA targets. In Aim II, we will test the RBP assembly model in untranslated regions (UTRs) in epidermal functions and how PTMs affect this process. Specifically, we will analyze the RBP code of TARDBP, a highly modified RBP, which we found essential in regulating progenitor RNA expression by binding their 3’UTRs. Aim II.A will define the identity, assembly dependencies, and functions of TARDBP and co-associated RBPs to regulate progenitor RNAs through 3’UTR binding. Aim II.B will determine how the increased phosphorylation of TARDBP during epidermal differentiation affects its functional recruitment on target RNA 3’UTRs. My long-term goal is to lead an academic research group focused on skin homeostasis mechanisms. I aim to study RNA-protein regulatory networks in epidermal differentiation, building on my expertise in RNA biology, RBP biochemistry, and bioinformatics. While training at Stanford's Department of Dermatology, I plan to gain new skills in machine learning, proteomics, genomics, and statistics. I will also participate in leadership and management seminars. This comprehensive research and training plan, supported by my mentor and collaborators, will provide me with a broad skillset crucial for a successful transition to independent investigator.