University Of California, San Diego

NSF Awards · FY 2024 · 2024-07

Solving today's societal challenges requires transdisciplinary research and educational models that prepare undergraduates for collaborative, cross-disciplinary work. This project investigates how computing can bridge disciplines while supporting student persistence and identity formation in STEM. Using participatory design-based research, it pursues two objectives: (1) examining how students develop computing identities and computational thinking through transdisciplinary experiences, and (2) designing and evaluating curriculum that uses computing as an integrative bridge. The research identifies curriculum design principles and pedagogies that support computational thinking; examines how collaborative experiences shape identity and career trajectories; and analyzes factors enabling effective transdisciplinary teamwork. It contributes to computer science education and design-based research by advancing computing identity frameworks and understanding how students navigate computing across disciplinary contexts. Outputs include evidence-based curriculum, design principles, assessment tools, and models for scalable, collaborative learning environments. 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 · 2024-07

Glycans consist of long branching chains of different sugars. They adorn proteins of all types. Some viruses use glycans to enhance the probability of infection. Some tumors hide from the immune system by using “stealth” glycoproteins on their surfaces. Most of the top drugs by revenue are glycoproteins, and their glycans affect drug safety and efficacy. This places a premium on making sure that their glycans are well-characterized and carefully managed. The complexity of glycan structures makes it difficult to control their structure on therapeutic drugs. Conventional glycan characterization methods have steadily advanced, but many challenges continue to hinder efforts to study and engineer these critical molecules. Here, an approach is being developed to rapidly and inexpensively sequence and quantify glycan structures. It will be first applied to accelerate the characterization and design of critical glycans required in biotherapeutics. A high-school outreach program on biological machine learning will introduce high school students to concepts underlying data science and its application to biological and biomedical questions. State-of-the-art technologies for glycan sequencing remain limited in their throughput and accessibility. They rely on methods with expensive, specialized equipment (e.g., mass spectrometry, NMR) or complex biochemistry (e.g., lectin arrays, exoglycosidase treatment). This research project aims to develop Glycosequencing, a technology that determines glycan structures using Next-Generation Sequencing (NGS) technologies. Using NGS to sequence and quantify DNA-barcoded lectins, Glycosequencing will measure a wide array of glycan features. The mapping of lectin binding patterns to glycan structures will be predicted using AI, trained on a large panel of recombinant glycoproteins with well-defined glycosylation patterns. This project will first identify the optimal set of lectins and biochemically characterize them. The lectin barcoding will be prepared, and lectin pooling will be optimized for NGS. To improve the AI accuracy, a training dataset will be built using 5 different recombinant proteins, transiently produced in a panel of >30 glycoengineered Chinese hamster ovary cell lines. To demonstrate the power of this technology, it will first be deployed to rapidly determine the structure of glycans on recombinant protein drugs. It will also be used to simultaneously profile glycans and the mRNA of the mammalian production host cells when cultured on 92 different media. This will allow the rapid characterization of the impact of culture conditions on protein glycosylation of monoclonal antibody drugs and a candidate hepatitis C vaccine. 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 · 2024-07

RNA localization is a fundamental process that enables cells to compartmentalize gene expression, allowing for precise control of protein production in specific subcellular regions where they are needed. For instance, many messenger RNA transcripts encoding mitochondrial proteins are localized to the mitochondrial membrane through mechanisms that remain poorly understood. This project will develop novel technologies to identify key determinants influencing RNA subcellular localization on a genome-wide scale. By merging chemical biology and genomics approaches to create a novel tool for studying cellular function, the research aims to bridge the growing knowledge gap between the identification of spatially localized RNAs and the mechanisms directing their localization. The research will not only accelerate understanding of RNA localization to diverse cellular compartments but also provide opportunities for interdisciplinary scientific training for undergraduate students, graduate students and postdoctoral fellows in RNA chemical biology, biochemistry, and genomics. Furthermore, the project will promote RNA research and scientific communication through participation in the Center for RNA Technologies and Therapeutics at University of California San Diego. Connections made through this work will foster collaboration and provide career development opportunities for students from diverse backgrounds. The proposed Localization (Loc)-Seq technology developed in this project will enable high-throughput screening of factors affecting mRNA localization, initially focusing on mitochondrial localization in yeast and later adapting to mammalian cells. The project will establish the feasibility of Loc-Seq technology in yeast by combining pooled barcoded expression libraries with mRNA reporters and a mitochondria-localized polyuridylation enzyme. For mammalian cells, novel RNA modification strategies using genetically encoded enzymes targeted to specific subcellular locations will be implemented to identify and enrich localized RNAs. These approaches will overcome limitations of current genome-wide screening methods for RNA localization mediators, offering a more accessible and cost-effective alternative that expands the pool of targetable genes. This research has the potential to significantly advance our understanding of RNA localization mechanisms, which play crucial roles in various cellular functions including embryonic development, cell differentiation, and protein synthesis regulation. 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 · 2024-07

ABSTRACT Hematopoietic stem cells (HSCs) regenerate blood and immune cells throughout life. However, aging HSCs exhibit reduced self-renewal activity, diminished regenerative capacity, and myeloid-biased differentiation that contribute to an increased incidence of immune dysfunction, bone marrow failure, anemia, clonal hematopoiesis, and cancer in older adults. Translational control has emerged as fundamentally and preferentially important for stem cells. Young adult HSCs have lower protein synthesis rates than other hematopoietic cells and increases in protein synthesis impair HSC function by increasing the biogenesis of misfolded proteins and disrupting protein homeostasis (proteostasis). In preliminary studies, we found that old HSCs preserve low protein synthesis rates, but increase the biogenesis of misfolded proteins, raising the possibility that translation fidelity declines in HSCs during aging and contributes to age-related HSC dysfunction by disrupting proteostasis. The goal of this proposal is to test if young adult HSCs have elevated translation fidelity as compared to restricted progenitors and old HSCs, and to determine if enhancing translation fidelity increases HSC fitness and longevity. To our knowledge, translation fidelity has not yet been studied in a cell-type- or age-specific manner in vivo, and interventions that specifically boost translation fidelity in mammals have not been reported. To overcome these limitations, we developed a single cell assay to quantify relative translation fidelity, and developed a new genetic mouse model with high-fidelity ribosomes (Rps23K60R) that enhances translation fidelity and reduces proteostasis stress in aging HSCs. In Aim 1, we will use translation fidelity reporter mice to quantify relative translation fidelity in HSCs and progenitors in vivo, and will determine if genetically or environmentally increasing protein synthesis reduces fidelity in HSCs. Furthermore, we will test if enhancing translation fidelity in Rps23K60R mice protects HSCs from the proteostasis disrupting effects associated with increased protein synthesis. In Aim 2, we will test if translation fidelity declines in HSCs during aging, and if enhancing fidelity delays/mitigates age-related declines in HSC function. We will also determine if enhancing translation fidelity delays/prevents stem cell exhaustion in serial transplantation assays and extends organismal healthspan and lifespan. Finally, in Aim 3, we will determine the effects of enhancing translation fidelity on proteostasis using a suite of single cell assays to investigate proteome quality and quantitative proteomics to investigate proteome content throughout life. We will also test if enhancing fidelity alleviates the need to activate proteotoxic stress response pathways to preserve HSC fitness during aging. These studies will elucidate if cell-type-specific differences in translation fidelity are employed to confer stem cell longevity and will reveal if modulating translation fidelity is a potential therapeutic strategy to enhance stem cell fitness and organismal longevity by preventing age-related proteostasis disruption. Overall, this work will open new directions for extending human healthspan and longevity.

NIH Research Projects · FY 2025 · 2024-07

PROJECT SUMMARY Alpha Protein Kinase 3 (ALPK3) is an atypical protein kinase belonging to the alpha (α)-kinase family. Loss-of- function mutations in ALPK3 result in human cardiomyopathy. Currently, no specific treatments are available for patients diagnosed with ALPK3 cardiomyopathy. To understand the in vivo role of ALPK3, we generated Alpk3 global knockout (gKO) mice, which displayed a sequential progression from early-onset dilated cardiomyopathy (DCM) to late-stage atypical hypertrophic cardiomyopathy (HCM). These mice accurately mimic the pathological development of cardiac abnormalities observed in humans carrying loss-of-function ALPK3 variants. In addition, we generated Alpk3 constitutive (cKO) and inducible (icKO) cardiac-specific knockout mouse models. Our data revealed that, like Alpk3 gKO mice, Alpk3 cKO mice develop early-onset DCM leading to premature lethality, suggesting that the primary cause of the cardiac phenotypes observed in Alpk3 gKO mice is due to loss of ALPK3 in cardiomyocytes. We also observed that adult Alpk3 icKO mice develop DCM and heart failure. Gene therapies offer an attractive approach for addressing a wide range of human diseases by directly targeting the underlying molecular causes. Adeno-associated virus (AAV)-based gene therapies have demonstrated remarkable efficacy in gene therapies for several cardiovascular diseases. We aimed to utilize a robust muscle-tropic adeno-associated virus (MyoAAV) vector to deliver Alpk3 cDNA into the myocardium of Alpk3 gKO mice, with the goal of treating ALPK3 cardiomyopathy. However, the coding sequence of the wild-type Alpk3 cDNA, encodes 1,608 amino acids (AAs), spanning 4,824 base pairs, which exceeds the 4.7 kb AAV packaging limit. Consequently, we developed a compact yet functional Alpk3 variant, named mini-ALPK3, designed for efficient AAV packaging and potential ALPK3 cardiomyopathy treatment. To validate the functionality of the mini-ALPK3 gene, we generated a mouse model in which mini-ALPK3 replaced the normal Alpk3 allele. Remarkably, mice homozygous for mini-ALPK3 exhibit normal cardiac function throughout adulthood, suggesting that mini-ALPK3 can fully compensate for the full-length ALPK3 in terms of heart functionality. Based on these compelling findings, we propose the hypothesis that mini-ALPK3 gene replacement therapy, delivered via MyoAAV vector, can effectively rescue cardiac phenotypes and premature mortality in Alpk3 knockout mice. Accordingly, our specific aims are: 1. To ascertain whether mini-ALPK3 gene replacement in Alpk3 knockout mice, administered on postnatal day 1 prior to the manifestation of cardiac abnormalities, can prevent cardiac phenotypes and rescue premature mortality; 2. To evaluate whether mini- ALPK3 gene replacement in Alpk3 knockout mice, administered at one month of age when DCM phenotypes become evident, can reverse DCM phenotypes; and 3. To investigate the capability of mini-ALPK3 gene replacement in Alpk3 knockout mice, administered at two months of age when atypical HCM phenotypes are evident, to effectively reverse late-stage HCM phenotypes.

NIH Research Projects · FY 2025 · 2024-07

Project Summary Anorexia nervosa (AN) is a severe psychiatric disorder. However, we lack neurobiological models and interventions to explain and treat the core characteristics of food restriction, feeling fat, and body size overestimation. While research has made progress in understanding brain function involved in AN pathophysiology, translating those results into biological therapies has been challenging. Studies have suggested that metabolic factors contribute to developing and maintaining AN pathophysiology. Specifically, brain glucose utilization and metabolism may be altered in AN, interfere with brain energy homeostasis, and contribute to illness development and maintenance. A small study indicated that ketosis might be therapeutic for AN core behaviors such as eating and shape concerns. In this application, we will study individuals weight recovered from AN, establish biological targets as diet-related metabolic markers for AN (R61 phase), and replicate and link those targets to AN-specific behaviors (R33 phase). The weight- recovered AN group will also be compared with a healthy control sample. The R61 Phase Specific Aim of the project is to establish target engagement for a therapeutic ketogenic diet (TKD) in AN after weight recovery and establish safety and tolerability. We hypothesize that TKD will be associated with reduced brain glucose metabolism using [18F]fluorodeoxyglucose ([18F]FDG) and positron emission tomography (PET) (target engagement). Higher blood ketosis levels will be associated with a more significant reduction of the brain [18F]FDG glucose metabolism rate (dose dependency). We hypothesize that TKD will be well tolerated, that participants will remain within the normal weight range (tolerability), and that study participants will be able to adhere to TKD as indicated by regular blood ketosis measurements (treatment fidelity). There will be an initial indication that TKD and associated biological measures correlate with behavioral measures derived from eating disorder-specific assessments. The go/no-go criterion for the transition from the R61 to the R33 phase is determined by a significant change in [18F]FDG metabolism rate between before and after TKD in the frontal cortex (target engagement). The R33 Phase Specific Aim is to replicate target engagement in a larger weight-recovered AN cohort and associate brain response with AN- specific behaviors (functional outcome). Similarly to the R61 Specific Aims, we hypothesize that TKD will decrease brain [18F]FDG metabolism in the larger cohort (target engagement replication). The level of ketosis and magnitude of [18F]FDG uptake decrease will be associated with decreased eating restraint, eating and weight concerns, and clinical impairment based on clinical assessments (functional outcome).

NSF Awards · FY 2024 · 2024-07

Images are key to modern communication, whether in the form of real photographs, computer graphics movies, outputs of physical simulations, or catalogs for e-commerce. They come with a standard representation, a regular grid of image pixels called a raster pixel grid. As successful as this grid has been, it imposes severe limitations as one zooms into images. For example to see the fine details of hair, the individual glints on a complex surface, the rich wrinkles or turbulence in a physical simulation, or individual rooms in a whole-earth representation is not possible using the raster grid. Given the rapid increase in geometric complexity and simulation fidelity, images need to be higher and higher-resolution to match the power of modern displays, and to provide fidelity for applications like digital twins and the metaverse. This project seeks a transformative change to this ubiquitous pixel representation, leveraging recent developments in neural fields to build a novel resolution-independent neural image representation. Crucially, the new representation will preserve image discontinuities that are core to many applications in computer graphics and beyond, like the boundaries of objects in physical simulation, rendering, and natural images. The contributions of the project will be threefold. First, a 2D Neural Discontinuity-Preserving representation will be created. This is achieved using a feature field, with features carefully constructed to be continuous almost everywhere, and discontinuous in the correct way over line/curve and point discontinuities. The interpolated features are fed to a multilayer perceptron to obtain the final function values. Second, the project will develops a number of extensions to higher dimensions such as videos, light fields, or radiance fields, and to jointly updating the discontinuity locations (when unknown) and the representation. Techniques will be developed to fit signals efficiently, and to significantly accelerate training convergence and memory efficiency. Third, the project will generate the applications of the representations. This will include image rendering, diffusion curves, physics-informed neural networks, view synthesis, and super-resolution. Beyond this, images are central to almost everything humans observe. The representation is expected to dramatically transform generation of computer-generated imagery and computational photography in a variety of different domains. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2026 · 2024-07

PROJECT SUMMARY/ABSTRACT Pulmonary arterial hypertension (PAH) is a progressive and incurable disease involving pathological signaling between pulmonary arterial smooth muscle cells and endothelial cells. The goal of this project is to study the NOTCH3 extracellular domain (NOTCH3 ECD) as a serum biomarker for this disease, with the objective of applying these discoveries and enabling the translation of our findings into clinical practice. The NOTCH3 receptor on vascular smooth muscle cells is activated by Jagged-1 ligand binding, which induces cleavage of the receptor into two peptide fragments: the NOTCH3 ECD and the intracellular domain (NOTCH3 ICD). The NOTCH3 ICD translocates to the nucleus and stimulates a signaling cascade that results in PASMC proliferation, anti-apoptosis, and marks cells destined to become pulmonary neointimal cells. The fate of the NOTCH3 ECD in the lung is not known and will be the focus of this grant. In preliminary work, we have shown that human PAH is characterized by the overexpression and increased cleavage of NOTCH3. We have found that the severity of PAH in humans and pulmonary hypertension in rodents correlates with the amount of NOTCH3 ICD in the lung. We have demonstrated that mice with homozygous deletion of Notch3 do not develop pulmonary hypertension in response to hypoxia or SU5416/hypoxic stimulation and that pulmonary hypertension can be successfully treated in mice and rats by administration of a monoclonal antibody that blocks Jagged-1 binding to Notch3 in small pulmonary artery smooth muscle cells. We hypothesize that after NOTCH3 cleavage, NOTCH3 ECD is released into the serum and can serve as a useful non-invasive biomarker for PAH. To this end, we have developed a novel bioassay to measure NOTCH3 ECD levels in the serum of humans and rodents. To test our hypothesis, we propose the following specific aims: 1) test whether patients with PAH have elevated levels of NOTCH3 ECD in their serum and whether this biomarker reliably predicts pulmonary vascular resistance, mean pulmonary artery pressure, and tricuspid regurgitant velocity in individuals of different races and ethnicities, sexes, and ages, both at diagnosis and in serial measurements during disease progression, 2) demonstrate that the serum blood test for NOTCH3 ECD is specific to patients with WHO Group 1 (idiopathic) PAH and can accurately predict disease progression, irrespective of patient drug regimens, and 3) elucidate the molecular mechanisms and cellular interactions in the pulmonary arterial wall that lead to release of NOTCH3 ECD into the serum in this disease. Information gained from the proposed experiments should put forth a unique, simple blood test for the diagnosis of PAH and augment our understanding of NOTCH3 signaling in this disease.

NIH Research Projects · FY 2025 · 2024-07

PROJECT SUMMARY / ABSTRACT Traumatic spinal cord injury is a devastating condition that affect about 302,000 people in the United States, with 18,000 new cases each year. The limited ability of axons to regenerate after injury in the adult central nervous system (CNS) underlies the permanent functional deficits and paralysis experienced by people with spinal cord injury. Much research remains to be done to fully understand how regeneration is controlled by molecular and cellular machinery in the neurons. This proposal builds on our recent success of applying Patch-based single cell RNA sequencing technology to interrogate the molecular mechanism of corticospinal axon regeneration after spinal cord injury. By sequencing only hundreds of neurons but at unusually high depths, we developed a regeneration classifier that can be broadly applied to predict the regenerative potential of diverse neuronal types based on their single cell profiles, the first of its kind in regenerative biology. Furthermore, this study implicates key components in mitochondrial biogenesis and antioxidant response in regulating regeneration. Here we propose to expand Patch-seq based single cell RNA sequencing approach in several ways. First, we will refine and extend the regeneration classifier by sequencing additional corticospinal neurons and other neuronal types with different regenerative capabilities. This will allow us to develop a more accurate regeneration classifier and understand its full range of capabilities and limitations. Second, we will investigate the role of antioxidant response and mitochondrial biogenesis with a comprehensive array of genetic gain and loss of function analyses on NFE2L2 and PPARGC1A, master regulators of the two biological processes and two top candidates from our Patch-seq study. Third, we will conduct deep sequencing on young versus old neurons to understand the age impact on axon regeneration, which would be required to develop therapies that are robust across age groups. Together, the proposed experiments using this unique deep single cell RNA sequencing approach will bring a greater understanding of the neuron intrinsic control of axon regeneration, providing the foundation for therapeutic development to promote repair and recovery after spinal cord injury.

NSF Awards · FY 2024 · 2024-07

This project aims to serve the national interest by redesigning an introductory computer science course (CS1) to teach students how to write computer code with the aid of an artificial intelligence (AI) assistant. The rise of Large Language Model (LLM) tools such as ChatGPT and GitHub Copilot has created new challenges and opportunities for education. In computer science education, both enthusiasm and concern exist regarding how to best teach students to write code in the presence of these AI assistants. The proposed course called CS1-LLM intends to teach students skills different from the skills taught in traditional CS1 courses. In the proposed CS1-LLM course, less emphasis will be placed on syntax and writing code from scratch, but more emphasis and attention will be given to code reading, code testing, problem decomposition, and writing software in meaningful contexts. This proposed project will be built upon early results from a pilot course offered at UCSD in Fall 2023. This proposal seeks to build on the initial findings by: 1) redesigning the course to improve student success across a wide range of metrics, 2) studying how students will learn programming with the aid of an LLM, 3) comparing how well students can write software a year after completing a traditional CS1 course vs a CS1-LLM course, 4) examining how the inclusion of LLMs in a CS1 course will impact learning outcomes of the students from underrepresented groups, and 5) engaging instructors worldwide in this study to understand their perspectives of adopting such a course. The NSF IUSE: EDU Program supports research and development projects to improve the effectiveness of STEM education for all students. Through its Engaged Student Learning track, the program supports the creation, exploration, and implementation of promising practices and tools. 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 · 2024-07

Project Summary Oral squamous cell carcinoma (OSCC) is the sixth most common epithelial cancer worldwide with 50,000 new cases/year in US. Despite being less frequent, survival rates are relative lower. Clinically, OSCC presents as a necrotic lesion with stiffened borders; surgical resection requires significant tissue loss up to 2 cm surround the lesion, owing to the fact that these tumors are poorly margined and require significant resection to ensure all metastatic disease is removed. Even with aggressive surgical approaches, significant morbidity and mortality is associated with OSCC, and this dramatically reduces patient quality of life. While stiffened borders are a hallmark of the disease, it is unclear to what extent microenvironmental properties, e.g., mechanical changes in extracellular matrix (ECM) stiffness, contribute to tumor progression and invasion. The role of stiffness has been well established in other epithelial tumors, e.g., breast, but our understanding in OSCC is relatively nascent; less than 10 studies have even peripherally addressed the issue. Preliminary data from our groups is the first to demonstrate that a stiffer environment induces epithelial-to-mesenchymal transition (EMT) of oral epithelial cells and that they adopt a more aggressive behavior of invasive OSCC (Matte et al 2019; Moon et al 2023). EMT of these oral tissues might contribute to frequent tumor recurrence observed in patients with excessively stiff tumor margins. Since OSCC is exceedingly invasive, learned behaviors within the stiffened tumor must be remembered and recalled once in softer adjacent oral tissues to facilitate the establishment of secondary disease. Thus, we hypothesize that tumor stiffness induces the acquisition and consolidation of mechanical memory, i.e., EMT and migration, which can be recalled later when the resulting OSCC cells disseminate and contribute to tumor metastasis, invasion, and recurrence. To test this hypothesis, we propose the following specific aims: Specific Aim 1: Elucidate the mechanisms involved in mechanical sensing leading to “memory” acquisition and recall Specific Aim 2: Determine to what extent epigenetic changes in OSCC induce “memory” consolidation

NIH Research Projects · FY 2024 · 2024-07

Project Summary/Abstract Neurodevelopmental disorders (NDD) are a broad category of congenital neurological disease characterized by a wide range of cognitive and behavioral impairments. The etiology of NDD remains elusive, with genetic and epigenetic factors playing crucial roles in its pathogenesis. Recent advances in genomics have provided valuable insights into the role of dynamic chromatin reorganization in neurodevelopment and neurodevelopmental disorders. One key protein involved in chromatin organization is CTCF, which is known for its pivotal role in regulating gene expression by facilitating long-range chromatin interactions and global chromatin organization, and was recently identified as the causative gene for a rare NDD referred to as CTCF-related disorder (CRD). However, the precise mechanisms by which CTCF contributes to neural differentiation-dependent chromatin organization and NDD pathogenesis are poorly understood. The central aim of this project is to elucidate the function of CTCF in chromatin organization and transcriptional dynamics during neural differentiation by studying the molecular and cellular consequence of two CRD-associated CTCF mutations in human pluripotent stem cells (hPSCs), hPSC-derived neural progenitor cells (NPCs), and NPC-derived neural organoids. Preliminary data indicate that CTCF is a critical regulator of the dynamic transcriptional changes that occur during neural differentiation. Our research will employ a multi-pronged approach, utilizing both 2D and 3D models of human neural differentiation. We will use state-of-the-art chromatin conformation capture techniques such as Hi-ChIP and lamina-associated domain (LAD) mapping to examine the role of CTCF in shaping neural differentiation- dependent long-range chromatin interactions and nuclear architecture. Moreover, we will utilize neural organoids to investigate the impact of CTCF mutations on neuronal differentiation, maturation, and function. The outcomes of this research have significant implications for our understanding of CRD pathogenesis. By uncovering the role of CTCF in neural differentiation-dependent chromatin organization, we hope to identify potential mechanisms that contribute to the development of CRD and other neurodevelopmental disorders that stem from dysfunctional chromatin regulation. In summary, this pilot project grant application seeks funding to investigate the role of CTCF in neural differentiation and neurodevelopment. Through an integrated approach combining genomics, epigenomics, and functional analyses, this research aims to shed light on the intricate molecular mechanism underlying CRD and ultimately contribute to the development of targeted therapies and diagnostic tools for this complex neurodevelopmental disorder.

NIH Research Projects · FY 2024 · 2024-07

PROJECT SUMMARY The objective of this proposal is to illuminate the fundamental mechanisms by which ubiquitin ligases recognize and ubiquitinate substrates within protein complexes. Our interest in this topic arose after our discovery that the multifunctional ubiquitin ligase Anaphase-Promoting Complex (APC) is mutated in inherited neurodevelopmental disorders. Studies from the Ferguson and Brown labs in APC mutant in vivo and in vitro systems demonstrated a previously unknown role for APC-mediated ubiquitin signaling in the regulation of the composition of neuronal heterochromatin through clearance of specific protein substrates. In post-mitotic neurons of the developing APC mutant brain, we found that the most significantly dysregulated target of the APC in neurons was the Chromosome-Passenger Complex (CPC), which includes the kinase Aurora B and the scaffold INCENP. Imaging analysis showed that Aurora B and its product phosphorylated Histone 3 (p-H3, H3S10ph) accumulate within heterochromatin in APC mutant neurons during post-mitotic terminal differentiation. Through the proposed aims, we will examine the interaction between the APC, its target the CPC, and the CPC product H3S10ph, to generate critical structural insight into the molecular pathogenesis of neurodevelopmental disorders. In Aim 1, we will build upon past successes in similar experiments by producing the CPC and the APC as purified recombinant protein complexes and dissect their interaction using in vitro enzyme assays and mass spectrometry. In Aim 2, we will perform cryo-EM to produce a structural map of the APC interacting with the CPC. We predict that the completion of these aims will shed light on the molecular interactions required for the recognition and ubiquitination of the CPC by the APC, providing key mechanistic insight into chromatin regulation by ubiquitin signaling and the pathogenesis of APC-related neurodevelopmental disorders. Through the proposed combination of hypothesis-driven and unbiased experiments, we will build upon our discovery of the neurodevelopmentally essential APC-CPC-H3S10ph axis. Insight gained from these aims will likely serve as the foundation for us to expand in a multitude of long-term experimental directions in vivo and in vitro. The molecular understanding gained from these lines of inquiry would provide critical insight that will be necessary for the development of rational interventions for a class of debilitating diseases that represents a major unmet medical need.

NIH Research Projects · FY 2025 · 2024-07

PROJECT SUMMARY ABSTRACT Cellular Senescence is as an acquired state cells enter in response to environmental stressors or tumor evasion. Senescent cells cease their proliferative capacity and enhance their ability to respond and regulate the microenvironment through the senescence-associated secretory phonotype (SASP). Another shared characteristic of cellular senescence is the upregulation of the tumor suppressor cyclin inhibitor p16INK4a. With age there’s accumulation of senescence cells in tissues along with the upregulation of p16INK4a expression. While removal of p16INK4a expressing cells using genetic mouse tools slows down aging, it also adversely impacts wound healing during injury repair, suggesting contradictory roles of p16INK4a during homeostasis and injury response. We generated an ultra-sensitive p16INK4a reporter mouse line name, named INK4A H2B-GFP Reporter-In-Tandem, or INKBRITE to further understand in vivo p16INK4+ cells. In INKBRITE adult lungs, p16INK4+ cells are predominantly within immune and fibroblasts populations. p16INK4+ fibroblasts express features of senescence including polyploidy, increase in cell size, low proliferation capacity and ability to promote airway epithelial cell growth after injury. My main goal is to determine if the capacity to promote regeneration is restricted to airway p16INK4+ and epithelium or other regionally specific p16INK4 expressing fibroblast can support epithelial growth. Whether the capacity to promote epithelial regeneration is restricted to airway p16INK4+ fibroblasts or other spatially defined fibroblast subpopulations such as alveolar fibroblast can promote epithelial growth through SASP, remains unknown. To fill this knowledge gap, I will isolate alveolar p16INK4a+ fibroblasts and determine their capacity to promote epithelial growth after acute epithelial injury by 1) ex vivo 3D co-culture assay, 2) identify the transcribed SASP through RNA sequencing, and 3) in vivo using known senolytics Dasatinib and Quercetin (D&Q). Our p16INK4a induction and knockdown studies showed the requirement of p16INK4a for expression of known SASP factors such as IL6, Ereg, Ccl8. Another aspect that has been largely underexplored in the identity and dependence p16INK4 expression of SASP factors in vivo. For the R00 phase of my proposed work, I will further explore how the expression of p16INK4 is able to reprogram the SASP to support tissue repair, specifically epithelial regeneration. I will with our tools to functionally induce and remove p16INK4 in fibroblasts and 1) asses epithelial growth, 2) transcriptome analysis, and 3) proteomics to capture secreted proteins to identify p16INK4a-dependent SASP factors. I have extensive knowledge on working with INKBRITE and the tools to manipulate p16 INK4a expression which will allow me to pursue the proposed work with ease. With additional training from Drs. Peng and Sheppard I will expand my current knowledge of lung biology and cellular senescence while establishing a new protocol of identifying secreted proteins in vivo that will serve as a unique skillset for my transition to my own lab. These studies will lay foundation for our understanding of diverse roles of SASP factors during homeostasis and injury.

NSF Awards · FY 2024 · 2024-07

Over the last decade, machine learning (ML) and artificial intelligence (AI) have often been in the news, thanks to striking achievements in areas such as self-driving cars and AI-enabled chat services. Much of the progress that has led to these breakthroughs can be attributed to the development of deep learning methods (deep neural networks). While these methods have developed very quickly to address a large number of tasks, training them often involves manual tuning and requires enormous amounts of data, resulting in significant costs, including both manpower and power consumption. More sustainable, lower-cost approaches for developing and deploying complex ML and AI systems are needed to ensure that these methods can be widely deployed to accelerate scientific progress and benefit society. For this purpose, it is important to develop a better understanding of why and how certain neural networks outperform others. This is critical for faster prototyping, reduced training times, lower complexity, and better interpretability of deep learning models. The investigators will test their methods through external collaborations and they plan to develop multiple activities to broaden participation in computing, including new course development, REU programs, and K-12 activities. This project focuses on developing techniques for a mathematical understanding of deep learning intrinsic to the given task and data being considered. In this project, neural networks are viewed as operators whose behavior is characterized in terms of their action on the training (or testing) data and, more specifically, on the geometry of the data. One major benefit of this approach is that it can be applied to a wide variety of networks since it abstracts the architecture and considers only how a specific system converts inputs into outputs. This project has three main research objectives. First, when both the model and data are fixed, the goal is to compare different networks and understand how they learn and generalize to unseen data. Second, this project investigates the situation where the data is fixed, but the network model can change. This work focuses on clustering, dimensionality reduction, and importance sampling to reduce the size of the model. Finally, the geometric analysis developed in this project is applied in settings where both the model and data change. This makes analyzing domain adaptation and graph neural network transfer possible, providing efficient algorithms and generalization error guarantees. 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 · 2024-07

Project Summary The heart is composed of a broad range of diverse cardiac cell types, which organize into distinct cardiac structures that coordinately regulate cardiac function and circulation throughout the body. Despite efforts devoted toward understanding how these cardiac cell types are created in order to develop potential human cardiac therapies and/or illuminate underlying mechanisms/etiologies of congenital heart disease (CHD), our understanding of how diverse cardiac cell types emerge from developmental heart field progenitors, and how they organize into heart structures remains to be fully defined. Thus, we propose to identify and investigate the cardiovascular developmental regulators and gene regulatory networks that direct the development of Juxta- Cardiac Field (JCF)-derived cardiovascular cell types creating the mammalian heart. Toward this end, a multi- disciplinary experimental and computational systems biology approach will be employed to: (1) examine the contribution of JCF progenitors to the developing heart, (2) investigate how JCF progenitors differentiate into specific cell types contributing to heart development and (3) investigate whether mechanisms in JCF development are conserved in human heart development.

NIH Research Projects · FY 2025 · 2024-07

Project Summary Atypical teratoid rhabdoid tumors (ATRTs) are aggressive pediatric brain cancers that lack standardized treatment regimens. After radiotherapy treatments, survivors suffer from long-term neurocognitive defects. Thus, there is a critical need for dissecting the underlying biology of ATRTs to identify novel therapeutic strategies. ATRTs are driven by a differentiation block caused by biallelic inactivation of SMARCB1 with no other recurrent mutations, resulting in dysregulated cells that fail to terminally differentiate and consequently acquire oncogenic states. Naturally, neural progenitor cells (NPCs) are suspected to be the cells of origin for ATRTs, as their stalled differentiation has been implicated in pediatric brain tumorigenesis. However, ATRTs present unique features that imply a cell of origin that is not restricted to the central nervous system (CNS). Despite being driven by only SMARCB1 loss, ATRTs are comprised of three molecular subgroups, with different clinical outcomes, of which only one displays neural features: ATRT-Sonic hedgehog (SHH). Furthermore, ATRTs are molecularly identical to extracranial malignant rhabdoid tumors (MRTs). As a potential explanation for these nonneural features, the neural crest cell (NCC) is a putative cell of origin for both ATRTs and extracranial MRTs, as it emerges from the neuroectoderm but then migrates throughout the embryo. Therefore, the hypotheses of this proposal are that SMARCB1 loss interacts with cell identity and anatomical location during tumorigenesis, and that there are targetable vulnerabilities that can enable SMARCB1-depleted cells to overcome their differentiation block. Previously, the Furnari lab engineered human induced pluripotent stem cells (hiPSCs) with DOX-inducible SMARCB1 knockdown (KD hiPSCs). NPCs derived from these hiPSCs, that were differentiated without SMARCB1 expression (KD NPCs), exhibited an ATRT-SHH transcriptome, presented a block in neuronal differentiation, and formed orthotopic tumors. This hiPSC-derived SMARCB1 knockdown platform will be leveraged to investigate the hypotheses of this proposal. To unveil interactions between cell identity and SMARCB1 loss, RNAseq analyses will be performed on hiPSCs that were differentiated, with or without SMARCB1 expression, into NPCs and NCCs. To characterize interactions between anatomical location and SMARCB1 loss during tumorigenesis, KD NPCs and NCCs will be engrafted intracranially and subcutaneously. Resulting tumors will be analyzed via RNAseq. Since KD NPCs are unable to differentiate further into NCAM+ neurons, a high-throughput, pooled CRISPR screen will be performed on KD NPCs to identify targetable vulnerabilities that can overcome their differentiation block. Candidate genes will be validated in vitro via IPTG inducible knockdown and drug treatment studies, followed by in vivo validation by treating orthotopically engrafted KD NPC brain tumors with candidate drugs. If successful, this proposal will provide novel insights into the underlying biology that drives intertumoral heterogeneity of ATRTs and identify novel drug targets that overcome stalled differentiation, a fundamental feature of ATRTs.

NSF Awards · FY 2024 · 2024-07

With the support of the CLP program in the Division of Chemistry, Professor Ferguson from the University of California, San Diego is developing experimental and theoretical frameworks for targeted protein degrader optimization. Targeted protein degradation is a strategy that employs bifunctional small molecules to recruit a target-of-interest to an E3-ligase, triggering target ubiquitination and proteasomal degradation. Despite being a valuable technique for creating tools and therapeutics, guiding principles for the optimization of degrader starting points into potent, selective and fast-acting compounds are lacking. This project seeks to develop the requisite chemistry, proteomic methodology and mathematical frameworks to evaluate current hypotheses at scale, and in doing so, uncover the rules of molecular recognition that govern efficient targeted protein degradation. In parallel, an integrative educational outreach component will be developed, giving high school students hands-on experience with TPD in the classroom. This research project seeks to evaluate the relative contributions of targeted protein degrader kinetic parameters across the kinome via the development of a ‘Kinetic Scout Degrader’ platform. To enable high-throughput and unbiased evaluation of these molecules, new chemoproteomic methods will be developed and used to measure in situ target engagement, ternary complex abundance and ternary complex co-operativity. Finally, these large datasets will be used to evaluate and train theoretical models of targeted protein degrader kinetics, that may be applied to any target-of-interest. 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 · 2024-07

This Research Advanced by Interdisciplinary Science and Engineering (RAISE) award is made in response to Dear Colleague Letter 23-109, as part of the NSF-wide Clean Energy Technology initiative. Catalysts are used to drive the chemical reactions that make most of the materials that society uses. While catalysts are well developed for the processing of petrochemicals, the deployment of clean energy technologies demands innovation to develop new catalysts. In particular, the production of hydrogen from splitting water using electrical energy can be catalyzed, but today hydrogen production from water typically uses catalysts such as platinum that are rare and expensive. This research will advance fundamental knowledge to enhance the rate with which hydrogen can be produced from water splitting reactions. A potentially critical benefit of the work is to enable the use of catalysts that are not precious metals, so that if the project is successful technologies built upon it will not be limited by costly, scarce catalysts. Beyond hydrogen conversion, the underlying knowledge gained regarding how catalysts facilitate chemical reactions may find wide application in catalysis for clean energy technologies. Because hydrogen involves forming a single bond between just two atoms, it can serve as a model catalytic system for study while also being critical to future clean energy technologies. This work on the catalytic production of hydrogen will provide research training to students on the project in combining computational and experimental methods and in soft-skills scientific communication bridging between theory and experiment. Throughout, the project will integrate research and education about reaction dynamics into undergraduate and graduate curricula and promote the development of a diverse research workforce. A small annual workshop will provide an extended forum to share project research on dynamic catalysis with a mix of junior scientists and researchers from the broader materials design and catalysis community. The workshop’s goals include the training and professional development of a diverse set of researchers in computational and experimental materials and catalyst design. This project develops catalysts that exhibit multiple adsorbate binding energies to enable the systematic investigation of dynamic catalysis, with a focus on hydrogen evolving catalysts. Dynamic catalysis of aqueous hydrogen evolution is a distinct fundamental approach to electrochemical hydrogen production, a clean energy technology critical to a future hydrogen economy. The project evolves a closed-loop cycle of learning that integrates first-principles mechanistic understanding and catalyst design, modeling of time-dependent surface reactions, surface-sensitive spectroscopy, and experimental electrocatalysis to design, test, and analyze the operation of catalysts surfaces in the dynamic catalysis paradigm. The project will use well-defined, epitaxial thin film catalysts for dynamic electrocatalysis experiments, providing realistic constructs for evaluating the ab-initio modeling predictions and model surfaces for surface spectroscopy, maximizing learning between theory and experiment. Hydrogen evolution also serves as a model electrochemical reaction, providing the best opportunity to gain mechanistic understanding of dynamic effects on activity. Developing new pathways to enable active and potentially precious-metal-free electrocatalysis of hydrogen is societally important for bolstering the deployment of electrolyzers and fuel cells. 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 · 2024-07

Collective Intelligence offers profound insights into how groups, whether they be cells, animals, or even machines, can work together to accomplish tasks more effectively than individuals alone. Originating in biology and now influencing fields as varied as management science, artificial intelligence, and robotics, this concept underscores the potential of collaborative efforts in solving complex challenges. On the other hand, the quest for finding global minimizers of nonconvex optimization problems arises in physics and chemistry, as well as in machine learning due to the widespread adoption of deep learning. Building the bridge between these two seemingly disparate realms, this project will utilize Collective Intelligence to leverage the interacting particle systems as a means to address the formidable challenge of finding global minimizers in nonconvex optimization problems. Graduate students will also be integrated within the research team as part of their professional training. This project will focus on a gradient-free optimization method inspired by a consensus-based interacting particle system to solve different types of nonconvex optimization problems. Effective communication and cooperation among particles within the system play pivotal roles in efficiently exploring the landscape and converging to the global minimizer. Aim 1 targets nonconvex optimization with equality constraints; and Aim 2 addresses nonconvex optimization on convex sets; while Aim 3 applies to Clustered Federated Learning. Additionally, convergence guarantees will be provided for nonconvex and nonsmooth objective functions. Theoretical analyses, alongside practical implementations, will provide valuable insights and tools for addressing different types of nonconvex optimization challenges. 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 · 2024-07

This grant is intended to increase student and early career researcher participation at the Model Reduction and Surrogate Modeling Conference 2024 (MORe24) in La Jolla, California, 9-13 September 2024. The funds will cover conference registration and accommodation at the conference hotel. This award will be particularly beneficial for students with financial needs. The 5-day conference will bring together computational scientists, engineers, mathematicians, and domain experts from industry, national laboratories, and academia to discuss model reduction and surrogate modeling for high-dimensional complex systems. The event will feature over 125 presentations, including plenaries, regular talks, and poster presentations. Given the conference's location and financial constraints, the grant aims to promote access within the engineering community. By prioritizing students who lack sufficient financial support from other sources, such as their advisors or departments, the grant ensures broader access to valuable learning, professional development, and networking opportunities. The grant will also facilitate networking with academics, researchers from national laboratories, and industry. This opportunity will improve career prospects of scientists and engineers working in model reduction and surrogate modeling who otherwise would not have been exposed to this professional development experience. 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 · 2024-07

While artificial intelligence (AI) is making fast progress and impacting society, the cost of running AI systems is also becoming prohibitively high. To be sustainable, next-generation AI needs to be much more efficient in power consumption and speed. In-memory computing is a highly promising solution to this challenge; however, its many noise mechanisms require effective error correction schemes to make its computing reliable. This project will explore new error-correcting codes for in-memory computing in next-generation AI systems. The codes can be integrated with AI systems that directly use analog values for computing, which will help AI achieve much higher efficiency. The codes will focus on the correction of significant errors in computing that are most likely to affect the performance of AI, thus help AI systems achieve an optimal tradeoff between efficiency and reliability. By making in-memory computing more reliable, the project can help AI systems overcome the "von Neumann bottleneck" and become more scalable. The project will also develop on-line course materials related to the research topic, and organize workshops to bring together researchers and practitioners in the field. This project proposes Quantized-Analog Error-Correcting Code (QA-ECC), a new type of code for reliable in-memory computing. It focuses on the dominant operation in deep neural networks---the vector-matrix multiplication---and accommodates various practical constraints of analog AI systems. The project will conduct a comprehensive study of QA-ECCs, including their theoretical foundations, practical constructions, and efficient integration with AI systems. It will explore a new paradigm for error correction codes, where redundancy is initially added to the input data for computing, while error correction is performed on the result of computing, making it different from conventional error-correcting codes used in data storage and communications. It will consider multiple resolutions for analog data, and focus on the correction of the most significant errors in computing, making it practical for in-memory computing. The project will develop new theoretical foundations for the new error-correcting codes, including maximum code rates, analytical tools for measuring their error-correction capabilities, and the impact of various parameter settings on the codes' performance. The project will develops new techniques for building error correction codes for analog computing, new algorithms for code searching and error correction, and new methodologies for integrating the codes closely with various aspects of AI systems. 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 · 2024-07

Minority languages spoken by small communities preserve a wealth of linguistic and cultural knowledge, which is often threatened by larger regional and global languages. This doctoral dissertation project focuses on the production of a grammar, dictionary, and texts that describe one such under-documented minority language. The language has many properties that are of interest, including a complex tone system and a typologically rare vowel inventory – understanding these features contributes to an understanding of linguistic diversity and language more broadly. Linguistic documentation is valuable outside the field of linguistics, as minority languages, through their vocabularies and oral literature, contain ecological and cultural knowledge about local flora and fauna, history, and anthropological practices. For the community who speaks the language, documentation provides data to support resources on language planning and the development of educational materials. This project also benefits society by providing training in language documentation and by depositing data into a publicly accessible archive. This doctoral dissertation project creates a reference grammar that comprehensively details the properties of this language, including its sound system (phonology), the structure of its words (morphology), and the way that the words are organized (syntax). This project includes two directed studies: (1) an analysis of the phonetic properties of a series of pharyngealized vowels and (2) a sociolinguistic survey of variation in pronoun use. Most of the data comes from audio and video recordings of the language. A selection of the recordings, made in naturalistic settings, are edited into short documentary videos, capturing language use during cultural practices like the planting and harvesting of crops, and oral literature like stories, histories, and narratives. In doing so, this project provides insights into the structure of the language. 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 · 2024-07

Project Summary Shear stresses resulting from distinct blood flow patterns impact endothelial cell (EC) functions. ECs under atheroprone flow exhibit increased proliferation, inflammation, and glycolysis, compared to those under atheroprotective flow. Our recent studies have uncovered a novel role of epitranscriptional regulation (i.e., functional changes to RNAs without altered nucleotide sequence) in EC mechanobiology. Thus, atheroprone flow induces METTL3, a methyltransferase that catalyzes N6-methyladenosine (m6A) to result in the most abundant RNA modification found in eukaryotes. Our newly conducted preliminary studies indicate that METTL3 inhibition in ECs suppresses the atheroprone flow-induced pro-inflammatory response and glycolysis. Through transcriptome and epitranscriptome mapping, we found that METTL3 caused hypermethylation of the mRNAs encoding the key enzymes involved in glycolysis and pentose phosphate pathway (PPP, a branch of glycolysis). These findings led to the guiding hypothesis that atheroprone flow upregulates METTL3 to modulate m6A epitranscriptomes and hence promote glycolysis and PPP in ECs, thus contributing to EC dysfunction and atherosclerosis. The four specific aims proposed to test this hypothesis are: Aim 1. To delineate the dynamics of METTL3-regulated epitranscriptomes in ECs under atheroprone vs. atheroprotective flow patterns; Aim 2. To identify the METTL3-modulated m6A RNA targets that drive the atheroprone flow enhancement of EC glycolysis and PPP; Aim 3. To elucidate the effects of the flow-regulated EC m6A epitranscriptomes on the phenotypic changes of co-cultured SMCs and EC glycolysis by SRS imaging; Aim 4. To validate flow regulation of METTL3 and m6A epitranscriptomes in mouse atherosclerosis models. This interdisciplinary research will unveil novel epitranscriptional mechanisms underlying EC mechanobiology and atherosclerotic diseases.

NIH Research Projects · FY 2025 · 2024-07

ABSTRACT Age-related macular degeneration (AMD) is the leading cause of blindness in developed countries. This devastating disease affects 196 million individuals and is predicted to increase to 288 million by 2050. Most of these patients suffer from the early and intermediate “dry” AMD and are currently without treatment options due to an incomplete mechanistic understanding of this complex disease. In early and intermediate AMD, large diffuse lipoprotein-rich deposits, named drusen, are deposited in the macula, and impair vision. Although drusen are a hallmark feature of AMD, the mechanism of drusenogenesis is unresolved. Drusen form between the retinal pigmented epithelium basal lamina (RPE BLam) and the inner collagenous layer (ICL) of Bruch’s membrane (BrM). RPE BLam and BrM are composed of glycosaminoglycans (GAG), including heparan sulfate (HS). Modification of GAG side chain residues creates binding sites for numerous growth factors, enzymes, and lipoproteins. This structural variation makes GAGs one of the most complex macromolecules found in nature and has been shown to be altered with aging and disease, including as an initiating event in subendothelial deposition of lipoproteins in atherosclerosis. Our understanding of GAGs in general, their role in BrM health, and how they change with aging and diseases, such as AMD, has lagged far behind other macromolecules despite their central role in biology. Our preliminary data show that HS is increased in AMD BrM compared to age-matched controls. In addition, AMD macula is rich in highly sulfated HS disaccharides (N-, 2-O and 6-O sulfation). Notably, Apolipoprotein E (APOE), a critical component of both lipoproteins and drusen, has binding sites to N-, 2-O and 6-O sulfated HS. In this proposal, we propose to test the hypothesis that an age-related increase in HS sulfation (N-, 2-O and 6-O) induces lipoprotein retention in BrM in early AMD. To address this hypothesis and elucidate the pharmacologic potential of this pathway, we propose the following aims: Aim 1: Determine what AMD risk factors are associated with BrM GAG composition. Aim 2: Determine if BrM HS structure or content promote lipoprotein binding in AMD. Aim 3: Determine if HS sulfation regulates lipoprotein retention in vivo in mice. If successful, the proposed experiments will establish alterations in HS sulfation as an initiating event in drusenogenesis and identify therapeutic targets for HS and lipoprotein binding for AMD. The mentored training program is designed to acquire new skills to support a career as a physician-scientist with expertise in glycobiology and lipoprotein biology. Towards this goal, a team of mentors have been assembled at UC San Diego, with expertise in heparan sulfate and lipoprotein metabolism.