Emory University

NSF Awards · FY 2025 · 2025-09

NON-TECHNICAL SUMMARY: This award supports theoretical and computational research, and associated education to investigate the consequences of light interacting with materials and matter. While classical physics views empty space as truly empty, quantum mechanics reveals the vacuum as a sea of constantly fluctuating fields. These quantum fluctuations are essential to understanding how particles of light – photons – interact with matter. When light is spatially confined, as in optical cavities, these fluctuations become amplified, giving rise to strong light-matter interactions that can produce entirely new quantum phenomena. This project explores how such interactions can be harnessed to create exotic quantum states of matter with deeply entangled components. These states are not only scientifically novel but may also serve as architectures for robust quantum information. A key goal is to understand how vacuum fluctuations and nonlocal photon correlations stabilize highly entangled quantum systems capable of robustly storing and processing quantum bits of information. The research will investigate how these light-matter systems behave when driven far from equilibrium – revealing new dynamical regimes that challenge conventional ideas about how systems relax or thermalize – and explore efficient transport of energy and information through photon-matter hybrid quantum states. By integrating research with education and outreach, the project will extend its impact beyond the scientific community. Collaborating with high schools in the metro Atlanta area, it will bring quantum science into classrooms through hands-on activities and demonstrations. It will also launch “Emory Quantum Day,” a campus-wide event that invites students, teachers, and the public to engage with modern quantum research through talks and exhibits. Undergraduate students will receive training and mentorship in theoretical quantum science, preparing them to contribute to the Nation’s future scientific and technological workforce. TECHNICAL SUMMARY: This award supports theoretical and computational research, and associated education to investigate the consequences of light interacting with materials and matter. Understanding how to control and manipulate entanglement in many-body quantum systems is a frontier challenge in modern physics, with far-reaching implications for quantum information science and materials discovery. This award supports theoretical research on hybrid platforms where light and matter interact so strongly that fundamentally new quantum states emerge – transcending the properties of either component alone. The project investigates how electromagnetic vacuum fluctuations and confined light in optical cavities generate unconventional entanglement patterns in matter. A central thrust of this project is to uncover how vacuum fluctuations and non-local photon correlations imprinted in matter contribute to stabilizing long-range entangled phases beyond traditional quantum Hall systems, including time-reversal-invariant fractional topological insulators with spin-active excitations and photon-enabled non-Abelian orders in cavity-integrated superconducting networks. The research will also investigate how strong light-matter entanglement drives novel non-equilibrium and non-ergodic quantum dynamics, and will characterize new transport regimes arising from photon–exciton hybridization in optically active two-dimensional materials. Ultimately, the project aims to classify a new generation of light-matter hybrid materials with quantum functionalities that exceed those achievable by light or matter alone. In parallel, the project integrates research with education and outreach efforts to advance scientific literacy, inspire future scientists, and expand public engagement with quantum science. Through collaboration with public high schools in the metro Atlanta area, it will introduce classroom activities and hands-on demonstrations designed to spark curiosity about physics and expand access to high-quality STEM learning. The project will also establish “Emory Quantum Day,” a campus-wide outreach event that brings students, educators, and the public together to explore advances in quantum sciences through talks, exhibits, and interactive sessions. At the undergraduate level, the project will provide research training and mentorship, preparing students to contribute to the Nation’s scientific and technological enterprise. These efforts will help cultivate a quantum-aware workforce and connect frontier research with broader educational and societal impact. 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 award supports the "Conference: Advances and Connections of Modern Geometric Function Theory", which will be held at the University of Michigan, Ann Arbor, MI, October 25-26, 2025. Geometric function theory is a core subfield of mathematical analysis, and it has deep applications in many scientific fields, including physics, chemistry, biology, engineering, material science, and computer science. This conference will feature presentations from both leading experts and rising stars in the field and is designed to enhance the education of as well as foster collaboration among the participants. This will contribute to the training of the next generation of mathematicians and also help enhance the pre-eminent position of the US at the forefront of geometric function theory. In addition to plenary talks, the program includes short talks, a poster session, and an open problem session. The wide and varied array of conference participants will allow for exceptional exchanges of ideas between early-career and more established mathematicians. More information can be found on the conference website https://lsa.umich.edu/math/seminars/conference-on-advances-and-connections-of-modern-geometric-funct.html. Over the last few decades, the impact of geometric function theory is now seen in many fields including analysis on metric spaces, mappings of finite distortion, conformal geometry, nonlinear PDE, and geometric group theory. The presentations will focus on practical applications of techniques originated in geometric function theory. The mathematics discussed will address challenges within the related fields mentioned above, as well as the reciprocal influence of these areas on geometric function theory. This collaborative spirit aims to foster deeper exploration and innovation within the mathematical community to advance research in modern geometric function theory and related areas. 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

Abstract: Aging is associated with loss of mobility and also with dysbiosis, but it is unclear what microbiota products contribute to health or frailty. We identified indole and its derivatives as molecules secreted by benevolent commensal bacteria that act across diverse phyla (C. elegans, Drosophila, mice) to augment healthspan and allow animals to live better for longer. Our preliminary and published data suggest that as animals age, indole acts via the Aryl hydrocarbon receptor [AHR] to limit sarcopenia and maintain mobility in aged animals; specifically, indoles limit the loss of critical sarcomere proteins as well as A- and I-bands within the muscle sarcomere. Citing our work as impetus, a recent metabolomic study using samples from the Osteoporotic Fractures in Men [MrOS] cohort found that loss of indole-producing bacteria reduced plasma indole levels and correlated with decreased mobility in aged humans. We hypothesize that indole promotes stability of muscle proteins essential for the contractile apparatus by promoting chaperone-mediated refolding of damaged proteins, or by regulating proteasome activity to promote loss of irreparable proteins and/or limiting aggregation of damaged proteins. We identified another indole-regulated process in muscles that may aLect motility during aging. Specifically, indole restores youthful expression of genes associated with mitochondrial respiratory function and eLiciency that fuel muscle activity. Indole also limits mitochondrial fragmentation in aging muscle, a process associated with reduced ATP production and increased reactive oxygen species [ROS] production. We hypothesize that during aging, indole (i) regulates the proteostasis machinery, including both chaperones and the ubiquitin-proteasome system [UPS], to refold or eliminate damaged sarcomere proteins and preserve myofibrils; and (ii) upregulates mitochondria respiration and reduces ROS production, to maximize ATP production and limit damage to sarcomeres. Our Aims determine how indole limits sarcomere and mitochondrial damage, maintains muscle mass and function, and promotes mobility during aging. Aim 1 determines the mechanism by which indole limits age-dependent loss of existing assembled myofibrils via upregulating the levels of the myosin chaperone UNC-45 and the UPS. Aim 2 determines how indole maintains or improves mitochondrial function and morphology during aging to fuel muscle activity and limit ROS damage to myofibrils. Aim 3 uses genetic approaches to identify additional cellular pathways regulated by indole that promote mitochondrial or sarcomere stability and motility during aging. We use genetic and biochemical analysis in the nematode Caenorhabditis elegans, whose striated muscles exhibit significant structural conservation with mammals. Importantly, aging C. elegans exhibit loss of mobility and changes in proteostasis, including loss of the contractile apparatus and mitochondrial function, characteristic features of sarcopenia in mammals that are mitigated by indole. Here, we also propose to corroborate our worm results in aged mice. Together, these studies will provide mechanistic information on how indole derived from commensal bacteria counteracts sarcopenia during aging. These studies are essential for developing indoles as biomarkers for frailty and health, and as possible therapeutics to treat sarcopenia and other forms of muscle wasting in the elderly.

NIH Research Projects · FY 2025 · 2025-09

Project Summary/Abstract Retinitis pigmentosa (RP) is an inherited, early-onset, and irreversible form of vision loss. RP can result from mutations in over 100 unique genes. The burden of developing individualized treatments for each mutation is high, so strategies that improve photoreceptor health regardless of mutation would be benefit patients. RP perturbs retina energy metabolism, and disrupted energy metabolism can cause degeneration. Metabolic reprogramming may prevent photoreceptor loss. Reprogramming efforts should be aimed at correcting changes that occur with RP, yet it is unclear which changes to correct. In RP there are disruptions in glucose transport, aerobic glycolysis and the normal expression of TCA cycle and pentose phosphate pathway (PPP) genes. Due to critical gaps in these studies, the nature and timing of these changes is unclear. The lack of comprehensive detail on how RP alters metabolic flux hampers efforts to protect photoreceptors. The aims of this application are aimed at understanding changes to metabolism in RP. Specifically, in aim 1 I will determine how the rate of metabolism changes with disease stage. I will infuse 13C-labeled glucose or lactate through catheters to probe flux of carbons through metabolic pathways in the retina and RPE-choroid. I will quantify 13C accumulation in metabolites of glycolysis, the TCA cycle, the PPP, gluconeogenesis, and glycogen. Metabolites that fuel these pathways must first be transported from circulation, through the retinal pigment epithelium (RPE), and to the retina. In Aim 2 I will determine metabolites that increase O2 consumption by ex vivo RPE-choroid, and which of these best labels intermediates in the RPE-choroid in vivo. This proposal also seeks support for my career development. My development goals include presenting my work at conferences and leveraging these presentations to form a scientific network. I will also take scientific and non-scientific courses to improve my ability to perform surgeries and mentor young scientists. These opportunities will prepare me for a position as an independent investigator, where I can continue to fulfill the aims of this proposal and further develop my research program.

NIH Research Projects · FY 2025 · 2025-09

PROJECT SUMMARY Fractures are a common traumatic injury in humans. Fracture healing is often complicated by delayed unions and non-unions, creating a need for innovative strategies for fracture healing. We reported that the intestinal microbiome and gut permeability are potent factors governing the efficiency of fracture repair. We found that microbiome depletion by antibiotic treatment impairs fracture healing in mice. We also found that optimal fracture repair occurs in the presence of segmented filamentous bacteria (SFB) in the gut microbiome. SFB are gut commensal bacteria that are potent inducers of Th17 cell generation – a population of IL-17 producing CD4+ cells. Based on published and preliminary data, we hypothesize that early after a fracture, cytokines released by γδ T cells and other inflammatory cells within the fracture site increase local and circulating levels of inflammatory cytokines. Circulating cytokines induce a leaky gut phenotype, which, together with a microbiota containing SFB, results in the heightened expansion of intestinal Th17 cells. Attracted by chemokine gradients induced by callus inflammation, intestinal Th17 cells migrate to the to the fracture callus, where they further increase local IL-17 levels. The combined production of IL-17 by γδ T cells and Th17 cells originating from the gut shortens and optimizes fracture repair. Attesting to the relevance of γδ T cells and Th17 cells for fracture repair, mice lacking γδ T cells or mice with a paucity of Th17 cells exhibit suboptimal fracture healing. Given the importance of specific bacterial taxa for the expansion of intestinal Th17 cells, and thus for fracture repair, we further hypothesize that induction of Th17 cells by specific live microbial biotherapeutics may improve fracture healing. Because the specific phenotype of the γδ T cells that function in fracture healing is unknown, that the relative contribution of Th17 cells to fracture repair is unknown, and if fracture healing is expedited by IL-17A or IL-17F is unknown, in Aim 1 we will identify the subsets of γδ T cells relevant for fracture healing, investigate the contribution of Th17 cells to fracture repair, and to determine the relevance of IL-17A and IL-17F for fracture repair. In Aim 2, we will determine the contribution of increased gut permeability to fracture repair and comprehensively characterize the mechanisms involved. This will be achieved by preventing or enhancing the increased in gut permeability induced by fractures using pharmacological agents. In Aim 3 we will determine if precision probiotic treatment using sequential supplementation with bacteria known to expand intestinal Th17 cells in humans followed by an anti-inflammatory probiotic will expedite and/or improve fracture healing. The phenotypic characterization of T cells implicated in fracture repair, the definition of mechanisms whereby fractures increase gut permeability, and an assessment of the effects of precision probiotic treatment will yield essential data to inform novel strategies for optimizing fracture healing.

NIH Research Projects · FY 2025 · 2025-09

PROJECT SUMMARY We integrate sensory signals, like smell and taste, to make decisions about the type and quantity of food to eat on a daily basis. Distinct sensory inputs converge in brain regions that regulate food intake, such as the gustatory cortex, but the neural circuit mechanisms that integrate sensory information to control food consumption remain largely unknown. The fruit fly Drosophila melanogaster provides an excellent model system to address this question, as we can genetically target individual neurons in the fly brain and use the whole-brain connectome to identify pathways linking sensory input to motor output. Just as humans use multiple sensory cues to evaluate the quality of food, flies use distinct taste inputs from their legs and feeding organ, the proboscis, to decide whether to initiate feeding. This study will determine where and how neural circuits in the fly brain integrate concurrent taste input from the legs and proboscis to regulate feeding, providing a model to more broadly understand the logic of how neural circuits integrate different food-related signals. The experiments in Aim 1 will determine how sugar inputs on the legs and proboscis are integrated to regulate the likelihood that flies will initiate feeding and the quantity of food they consume. In vivo calcium imaging experiments in Aim 2 will determine how concurrent sugar input on the legs and proboscis influences the activity of individual neurons in a neural circuit known to respond to sugar and promote feeding initiation. These recordings of neural activity, combined with an analysis of neuronal connectivity in the whole-brain connectome, will provide a neural substrate for the behavior identified in Aim 1. Together, the findings from this study will identify the neural circuit mechanisms that integrate distinct taste inputs to promote adaptive feeding behavior. The basic principles of chemosensory processing and its role in feeding regulation are conserved across species. Thus, future work can use our findings to develop hypotheses about the neural circuits that combine sensory cues to regulate food consumption— and investigate whether these circuits contribute to dysregulated eating— in rodents and humans.

NIH Research Projects · FY 2025 · 2025-09

Project Summary Intellectual disability (ID) and autism spectrum disorder (ASD) are caused by a wide variety of environmental and genetic factors. Zinc-finger proteins (ZNF) are among the most abundant proteins expressed in eukaryotes, are involved in a wide variety of cellular processes. Numerous studies have shown that many ZNF family members are linked to neurologic disorders, including ID and ASD. ZNF185 is a member of this family, comprised of an actin filament-binding domain and a single zinc-finger containing LIM domain. ZNF185 has been suggested to act as a tumor suppressor gene, but it is otherwise an understudied protein with no known function in the nervous system. Our preliminary data indicate that ZNF185 is highly expressed in the adult hippocampus, and it is enriched in the presynaptic compartment of excitatory synapses. We hypothesize that ZNF185 regulates synaptic function, and that its disruption could lead to impaired cognition and autistic behaviors. To test this hypothesis, we will use a recently developed ZNF185 knockout mouse model. In the first aim, we will use super- resolution imaging and acute slice electrophysiology to examine the role of ZNF185 in synaptic development and function. In the second aim, we will examine the effects of ZNF185 knockout on brain development and behavior. The successful completion of this proposal will determine how ZNF185 regulates neural development, synaptic function, and behavior, with important implications for our understanding of neurologic disorders such as ID and ASD.

NIH Research Projects · FY 2025 · 2025-09

The Medicare Hospice Benefit, which dictates hospice policy for most Americans, was designed based on the needs of people with cancer, a disease with a trajectory vastly different from that of heart failure (HF). Implicit in the Medicare Hospice Benefit is the assumption that people have the supports necessary to facilitate a home death. Yet, individuals with fewer financial and social resources, who experience the greatest HF morbidity and mortality, are less likely to use hospice and die at home than individuals with greater resources. Dr. Cross’s long-term career goals are 1) to use patient-centered mixed methods to identify and understand the end-of-life experiences of people with HF and 2) to develop interventions that address differences in end-of-life HF care outcomes through practice and policy change. This K01 project includes three specific aims. Aim 1 will use hierarchical multiple regression to characterize the influence of individual and hospital level factors on end-of-life HF care. Aim 2 will incorporate geographically bounded areas into hierarchical multiple regression to quantify the impact of neighborhood-based attributes on end-of-life HF care. Aim 3 will explore end-of-life HF needs and perceived opportunities to receive optimal care among adults with HF, their caregivers, and clinicians through semi-structured interviews. Dr. Cross will strengthen and address gaps in her experience through the following career development objectives: 1) acquire increased proficiency in mixed methods research, 2) develop expertise in advanced analytic methods for health outcomes research, 3) advance her understanding of the management of patients with advanced HF and the role of hospice and palliative care within the HF trajectory, and 4) acquire skills in designing, conducting, and evaluating socio-behavioral interventions. This proposal lays out a comprehensive career development plan consisting of expert mentorship, specialized coursework, and completion of a research project. Dr. Cross’s development during this four-year award will be guided by an interdisciplinary team of mentors at Emory University, led by Dio Kavalieratos, PhD, Associate Professor and Director of Research for the Division of Palliative Medicine; Neal Dickert, MD, PhD, the Thomas R. Williams Professor of Medicine in the Division of Cardiology; Shivani Patel, PhD, MPH, Associate Professor of Public Health in the Departments of Global Health and Epidemiology; and Modele Ogunniyi, MD, MPH, Professor of Medicine in the Division of Cardiology. This research training and career development plan will support Dr. Cross in achieving her goal of becoming an independent investigator in supportive cardiology and will inform the development of evidence-based solutions to improve end-of-life HF care.

NIH Research Projects · FY 2025 · 2025-09

Tribal Nations often lack accurate, granular, and comprehensive information on the true impact of mortality indicators across American Indian/Alaska Native (AI/AN) population groups needed for different use cases. Misclassification error in state death certificates is disproportionately 30% higher for the AI/AN population compared to other demographic groups due to higher proportions of persons with a range of backgrounds within AI/AN populations. Additionally, US census reported AI/AN population statistics suffer from severe under-reporting at every geographic resolution due to small sample sizes, challenges in data collection, cost, and privacy algorithms. Importantly, official mortality and population statistics do not comprehensively or accurately enumerate distinct AI/AN populations defined by (1) Self- reported AI/AN based on U.S. Census defined classification (census category), (2) Tribal citizenship with a Tribal Nation (legal category), and (3) Residence on Tribal lands (geographic category). To address this data gap, our approach combines Bayesian hierarchical small area disease mapping with measurement error methodologies to produce small area mortality estimates across AI/AN groups for both rural and non-rural areas with improved accuracy. Within the comprehensive methodological framework, model components are customized to produce population-specific mortality estimates with increased accuracy across multiple populations. We apply our methodological approach to obtain county-year trends in AI/AN opioid mortality rates (OMRs) for the 77 counties in Oklahoma between years 2015-2024 across the distinct AI/AN population groups and county-specific rural status. In Phase 1, we estimate county-year OMRs for Tribal citizens by linking data from Oklahoma state death certificates with Cherokee Nation Tribal Registry data. In Phase 2, we estimate county-year OMRs for self-affiliated AI/AN populations using misclassification rates from Phase 1 and assess trends for total populations-at-risk in counties inside and outside Tribal boundaries. Of utmost importance to the study is the independent application and use of the proposed methodology by local health departments. We will develop a data science pipeline consisting of both educational and computational resources for local health analysts to adopt and implement the proposed methodology as a mortality self-monitoring resource.

NIH Research Projects · FY 2025 · 2025-09

Project Summary Air pollution is a known environmental risk factor for lung cancer, but the detailed mechanisms driving air pollution-induced carcinogenesis and the key molecular events involved remained underexplored. Understanding the underlying molecular mechanisms and pathways is vital for the development of targeted preventive and therapeutic strategies for lung cancer induced by air pollution. Our previous metabolomics study findings have suggested the important roles of amino acids and peptides (the building blocks of proteins) in modifying the association between air pollution exposure and lung cancer risk. Comprehensive proteomics analysis will provide critical insights into how these biomolecules are perturbed in air pollution-induced lung cancer. Although high-throughput single omics approaches have shown significant potential in revealing biological responses to air pollution exposures and lung cancer development, they often overlook interconnections among omics layers. Multi-omics integration can offer a more holistic view of underlying mechanisms. Moreover, omics-based risk prediction models have emerged as promising tools to identify individuals at high risk of lung cancer, but their development and application are still lacking. During the F99 phase, I will focus on investigating key molecular signatures and pathways underlying air pollution toxicity in lung carcinogenesis. In Aim 1.1, I will determine the potential mediation role of proteins in the causal pathway from air pollution exposure to lung cancer. Using an advanced proteomics analysis with Meet- In-The-Middle and high-dimensional mediation approaches, I will comprehensively evaluate the protein profiles to understand their involvement in mediating the etiology of air pollution-induced lung cancer. In Aim 1.2, I will conduct innovative multi-omics integration across proteomics, genomics, and metabolomics to identify a highly correlated molecular network linking air pollution toxicity with elevated lung cancer risk. Using a posteriori integration and a priori integration, I expect to identify a consistent molecular network, consisting of novel and closely related omics signals including single nucleotide polymorphisms, proteins, and metabolites, that unveils air pollution's role in lung cancer development. Although low-dose computed tomography is the standard screening for lung cancer, it's primarily recommended for heavy former and current smokers. Notably, approximately 45% of lung cancer cases occur in light smokers and never-smokers falling outside the recommendation guidelines. Transitioning to my K00 phase at a world- class cancer research institute, I will focus on developing omics-based risk prediction models to enhance lung cancer risk stratification by smoking status. In Aim 2.1, I will develop genomics, proteomics, and metabolomics risk scores in ever- and never-smokers separately and evaluate the associations of individual and combined risk scores with lung cancer risk. In Aim 2.2, I will evaluate the predictive performance of individual and combined omics-based risk scores for lung cancer risk by smoking status.

NSF Awards · FY 2025 · 2025-09

The remarkable progress of Artificial Intelligence (AI) in recent years is starting to greatly influence research across a wide range of disciplines. As Numerical Linear Algebra plays a crucial role in Deep Learning models, this trend presents unprecedented opportunities for experts in numerical analysis and linear algebra to contribute to ongoing AI research. This proposal represents a step toward capitalizing on this opportunity. The focus of the proposed work is not on applying AI to solve a specific problem, but rather on enhancing AI methods themselves by exploiting insights from numerical methods to optimize the Deep Learning process. This process is time-consuming, energy-intensive, resource-demanding, and overall very costly. Therefore, any improvements that can speed up the process are likely to have a significant impact. The investigators will leverage their experience in numerical methods to develop a number of techniques for accelerating the training of large AI models. The project aims to develop techniques that exploit both accelerators and preconditioners to speed up iterative procedures used in training deep learning models. The same combination of preconditioning and acceleration techniques is central to the effectiveness of iterative solution methods for linear systems. Acceleration methods such as Anderson/Pulay mixing or the Reduced Rank Extrapolation method, among others, have had immense success across various fields of science and engineering. However, in the context of deep learning, these methods face challenges, particularly since they were not developed for stochastic sequences common in deep learning. The team will investigate the relationship between mini-batching, a technique used for sampling subfunctions in stochastic methods, and its impact on both the convergence speed and the accuracy of the resulting models. Simple diagonal preconditioning methods have already been incorporated into optimization techniques in deep learning. The research team will explore more advanced preconditioning methods based on various approximations to the Fisher information matrix, a matrix that measures the amount of information that the observed data provides about the parameters. It has been shown that replacing the Hessian in second order methods by the Fisher matrix yields a more meaningful form of scaling of the variables, leading to better convergence and generalization. The investigating team will consider various methods for obtaining inexpensive approximations of the Fisher matrix and for combining them with accelerators. The proposed work is expected to benefit research in "scientific machine learning" by promoting participation of numerical linear algebra specialists in AI research. The PIs plan on several specific activities to promote the dissemination of knowledge in machine learning, such as writing a book on the topic of numerical methods in machine learning or offering tutorials and short courses. These activities will stimulate the interest of students in a field of increasing importance and that it will help with the immersion of those students from other areas, e.g., mathematics, into data-related disciplines. 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 Data labeling and continuous AI model validation are essential elements in clinical AI projects. This RSE award will enable the applicant to further develop a scalable and generalizable platform to support labeling of multimodality data as well as supporting continuous model benchmarking. The design of the platform will be anchored at first satisfying needs of three active NIH projects that will need support of both structured and unstructured EHR data, MRI brain scans, and neurophysiological and cardiovascular signals. The applicant will also collaborate with external collaborators including PhysioNet and OHDSI to ensure the developed platform can be used to support a wide array of applications from research communities and produce data labels in standardized terminology. To support continuous model benchmarking, the applicant will integrate functionalities of an existing application ModelsMeetData(M2D) into the platform to publish AI model cards, hosting vested AI models, and testing them with labeled data.

NIH Research Projects · FY 2025 · 2025-09

Project Summary/Abstract Takotsubo syndrome (TTS), also known as stress-induced cardiomyopathy or broken heart syndrome, is a condition characterized by sudden temporary ventricle dysfunction secondary to weakening cardiac muscles. Despite considerable research, our current understanding of the underlying pathogenesis and pathophysiology of TTS remains incomplete, thus markedly hindering the development of novel pharmacologic therapies to prevent major adverse cardiovascular events. This proposal describes a research plan that aims to accomplish the following two goals at both the basic science and translational levels: 1) gain deeper understanding of the role of a critical protein phosphatase (protein phosphatase 2A, PP2A) in the pathophysiology of Takotsubo cardiomyopathies; and 2) develop novel small molecule activators of PP2A for the treatment of Takotsubo cardiomyopathies. Reversible protein phosphorylation plays a critical regulatory role in a wide variety of biological processes. The regulation of protein phosphorylation is dictated by the activity of both protein kinases and protein phosphatases. Although there is a significant understanding of how aberrant kinase activity contributes to human cardiovascular disease, the regulation and therapeutic potential of phosphatases in this area remains under- explored. PP2A, a holoenzyme with serine/threonine phosphatase activity, plays a pivotal role in a large array of cellular processes in mammalian cells. Restoration of PP2A activity has been shown to be of significant therapeutic value, however pharmaceutically tractable approaches to directly activate PP2A remain elusive. Our recent observations revealed a marked reduction in PP2A activity in cardiac tissues from isoprenaline-induced TTS mice, as well as in isoprenaline-treated cardiomyocytes. Furthermore, in a murine model of TTS, administration of an orally bioavailable small molecule activator of PP2A, strongly mitigated myocardial damage and improved cardiac function in mice. Additionally, preliminary studies support a role for PP2A in modulating both ferritinophagy and mitophagy in TTS. Collectively, these observations form the basis for the central hypotheses for this grant application: (1) Reduced PP2A activity is involved in the development of acute heart failure in TTS and (2) Reactivation of PP2A may serve as a novel therapeutic strategy to prevent/reverse TTS pathology. Studies in this proposal are designed to extend this new field of cardiac research to gain in-depth understanding of the function of this phosphatase in cardiac biology. The results will greatly enhance our understanding of the molecular and cellular processes involved in the progression of acute heart failure in TTS, potentially paving the way for new therapeutic approaches.

NSF Awards · FY 2025 · 2025-09

Invasion fronts, or the moving boundaries between unstable and stable states caused by disturbances such as invasive species or a novel disease, play a key role in the self-organized development of coherent structures in many scientific fields, including epidemiology, developmental biology, fluid dynamics, materials science, and more. Historically, these invasion processes have been poorly understood, with clear results available only for special systems of limited interest and are difficult to study computationally. This project involves the development of a broad, model-independent framework for studying complex invasion fronts both theoretically and computationally, which can be used to make efficient predictions of invasion speeds and resulting spatial structures in broad classes of physical systems. The development of computational tools will focus on making these theoretical advances available to be put into practice by a broad range of scientists. Furthering the understanding of the selection of spatial structures in the wake of invasion fronts has particular promise for semiconductor manufacturing technologies which aim to harness this self-organized pattern formation to efficiently create nanomaterials with desirable electrical and optical properties. The project will involve both undergraduate and graduate students in the research, helping to develop the next generation of applied mathematicians. Prior mathematical results on invasion fronts have been limited to models satisfying restrictive monotonicity assumptions, which do not cover invasion fronts which make up many crucial examples in fluid dynamics, materials science, and more. In the first part of the project, the investigator will prove rigorous results for the prediction of invasion speeds and selected wave numbers of patterns in partial differential equations models, under conceptual assumptions on spectral stability of coherent front solutions. In the second part, the investigator will advance the theoretical understanding of when propagating fronts are ``pulled'' along by their tail or ``pushed'' ahead by their interface, and use this understanding to develop efficient algorithms for numerically continuing fronts and predicting selected speeds and wave numbers. In the final part of the project, the investigator will extend theoretical results on front propagation from one spatial dimension to higher dimensions, and investigate related transverse instabilities which play a key role in invasion dynamics in systems modeling bacterial motion, vegetation growth, and cancer dynamics. 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 · 2025-08

From 2001 to 2015, anal cancer cases rose 2.7% annually and deaths from anal cancer rose by 3.1% annually.   Although a relatively rare cancer, more than 70,000 people worldwide are affected by this cancer; the majority of whom are female.  Solid organ transplant recipients (SOTR) and women with a history of vulvar cancer or precancer may be at especially high risk.  Providers for women’s health, infectious disease and other specialists who manage patients at increased clinical risk are the ideal clinicians to take responsibility for anal cancer screening among high-risk patients.  Further, gynecologists are well-versed in cervical cancer prevention tools, which are like those of anal cancer prevention, such as cytology and high-risk HPV testing, diagnostic procedures to obtain histology (colposcopy and high resolution anoscopy), and treatment methods (ablation and excision). With the parallels in screening, diagnosis and treatment, formal training of gynecologists in high resolution anoscopy can provide them with the tools needed to provide full-scope anal cancer prevention. Following recently published findings showing the importance of anal cancer screening, this R25 application supports the development, piloting, and evaluation of an innovative skills development program to educate current women’s health, infectious disease and colorectal surgeons and gastrointestinal providers to meet the need for anal cancer screening among individuals at increased risk due to clinical or immunologic factors. Our proposal aims to leverage our institutional strengths in online/distance-based education for mid-career professionals, access to expert faculty and resources in anal cancer screening and treatment to deliver the TPAC curriculum, and an innovative virtual educational program in anal cancer screening. Once the curriculum is developed, we intend to scale up and expand the workforce of healthcare providers needed to provide early detection for anal cancer among all populations. This novel education program has the potential to have a long-lasting impact on cancer survivorship and serves as a model educational program to train medical professionals to incorporate new clinical guidelines into their practices.

NIH Research Projects · FY 2025 · 2025-08

Pregnancy is a critical period for neurodevelopment, with the developing fetus particularly vulnerable to environmental toxicants and maternal psychosocial stressors. Prenatal pesticide exposure, an established neurotoxicant, has been linked to an increased risk of child psychopathology, including anxiety, mood disorders, and Attention-deficit/ hyperactivity disorder. Preliminary studies indicate that the harmful effects of environmental toxins are exacerbated by psychosocial stressors, which lower the developing brain's threshold for neurotoxicity. This issue is particularly pronounced in low-resource settings, defined as areas with limited access to health services or infrastructure, and experience high burden of co-occurring exposures. However, little is known about how psychosocial stressors may exacerbate the harmful effects of pesticide exposures on child psychopathology. Epigenetic age acceleration, a biomarker of biological aging, is proposed as a mechanism underlying the impact of combined environmental and psychosocial exposures on behavioral outcomes. This study aims to investigate the joint effects of prenatal pesticide exposure and psychosocial factors on trajectories of epigenetic age acceleration and subsequent child psychopathology. Utilizing advanced environmental mixture methods, the study will leverage longitudinal data from the Drakenstein Child Health Study (DCHS), a birth cohort from a low-resource setting. Aim 1: Investigate the joint effects of prenatal urinary pesticide metabolite levels and psychosocial factors on child psychopathology at 6.5 years of age. We hypothesize that these joint effects will be associated with increased child psychopathology and that their magnitude will exceed the individual effects of pesticides or psychosocial factors. Aim 2: Assess the joint effects of prenatal urinary pesticide metabolite levels and psychosocial factors on longitudinal changes in epigenetic age acceleration at 1, 3, and 5 years of age. We hypothesize that these joint exposures will affect the trajectories of epigenetic age acceleration. Aim 3: Determine the relationship between epigenetic age acceleration trajectories and child psychopathology at 6.5 years of age and explore whether epigenetic age acceleration mediates the association between prenatal exposures and child psychopathology. We hypothesize that changes in epigenetic age acceleration will be associated with increased psychopathology and that epigenetic age acceleration will mediate the effects of joint exposures on child psychopathology. This research will enhance our understanding of how environmental toxicants and psychosocial stressors interact during a sensitive developmental period, particularly in communities that experience high burden of exposure. The findings could inform early detection and prevention strategies to improve child brain health outcomes.

NIH Research Projects · FY 2025 · 2025-08

PROJECT SUMMARY/ABSTRACT Autoimmune encephalitis is a disease in which inflammation occurs in the brain and can cause high morbidity and mortality. Anti-NMDA receptor encephalitis (NMDARE) is the most common cause of encephalitis and occurs in children and adults. Those who survive NMDARE develop long-term neuropsychological sequelae, including developmental problems in children. NMDARE treatments target antibodies and B-cells but 30% do not respond to these treatments and require additional T-cell targeting therapies which have increased toxicities and side effects. A lack of a prognostic biomarker is a major gap in the treatment of NMDARE. Having a biomarker at the time of diagnosis to guide treatment optimal treatment in NMDARE can result in improved outcomes. This five-year career development plan proposes using advanced immunological assays including high-dimensional flow cytometry and single cell RNAsequencing (scRNAseq) to identify the role of T cells in NMDARE, focusing on T helper 17 cells (Th17), a subset of CD4+ T cells that is pathogenic in other autoimmune diseases. The hypothesis is that severe NMDARE is marked by elevated Th17 cells (identified through blood and CSF cytokines, flow cytometry for immune cell profiling, and transcriptomics for identifying functional elements of the immune cell’s genome) and Th17 cells could be a prognostic biomarker. The aims of this NMDARE study is to 1) characterize how timing of T cell treatments affect outcomes in a retrospective cohort of 19 sites and 362 children with NMDARE, 2) assess blood and CSF Th17 cells and related cytokines using flow cytometry and electrochemiluminescence immunoassays respectively, and 3) use scRNAseq to identify NMDA receptor specific T cells and to assess if intracellular pathways are different in Th17 cells from NMDARE compared to controls. This proposed work supports the mission of the National Institute of Neurological Disorders and Stroke in increasing knowledge about how the neuroinflammation affects the brain and to use that knowledge to reduce the disease burden in NMDARE but also other neuroinflammatory conditions. The candidate is committed to a career in the investigation of neuroinflammatory diseases to understand the pathogenic immune pathways, and to improve treatments, by identifying optimal treatment strategies by biomarkers and novel therapeutic targets. The candidate will train in performing laboratory techniques such as high-dimensional flow cytometry and scRNAseq, and advanced statistical methods. This training would equip the candidate to be a unique pediatric translational neuroimmunology clinician-scientist. Overall this project proposes a novel translational approach to understanding NMDARE. While NMDARE is rare, the candidate will leverage CONNECT, a multi-center prospective cohort to carry out this study, and the results of this study could apply to other neuroinflammatory diseases such as multiple sclerosis to improve patient outcomes, including neurodevelopmental outcomes in children.

NIH Research Projects · FY 2025 · 2025-08

PROJECT SUMMARY/ABSTRACT Fontan palliation for rare single ventricle heart defects is lifesaving but creates deranged cardiovascular physiology with eventual premature multi-organ circulatory failure. Circulatory failure after Fontan palliation may be related to a number of physiologic states which may change over time, associated with variable prognoses, and requiring development of physiology-specific treatment options. Fontan research is limited by heterogeneity of native anatomy, post-Fontan anatomy, physiologic states, and small sample sizes due to rarity. Adverse outcomes in the Fontan population begin in childhood, are common and diverse, often affecting multiple organ systems. We have previously described Fontan Failure physiologic phenotypes based on (1) Systolic Heart Failure (2) Diastolic Heart Failure (3) Hepatic and Pulmonary phenotype (normal cardiac output) and (4) Lymphatic Abnormalities. Despite the broad range of complications, treatments for Fontan patients are generally consensus based and may not address the underlying physiologic derangement. Heart transplantation can be lifesaving for this population; however, heart transplantation creates a different disease state with its own related late morbidity and mortality, and optimal timing is unknown. Using two electronic health records systems (pediatric and adult) including free text notes, for a diverse population with Fontan anatomy across the age spectrum, we propose to use natural language processing (NLP) and machine learning (ML) techniques to improve detection of multi-organ comorbid conditions in this population to define anatomic and physiologic phenotypes, and develop of an annualized risk score applicable across age, sex, race and ethnicity. Our proposed work builds on a rigorous pilot study in which we developed an NLP-based ML model for automatically identifying Fontan patients from two hospital systems representing a racially diverse cohort across the lifespan. Our pilot system achieved significantly better performance compared to ICD code-based classification of Fontan cases. In the proposed work, we will (i) advance the state of the art in biomedical NLP to improve the automatic classification of Fontan phenotypes in the cohort so that it is closer to human-level performance; (ii) develop a generalizable and interpretable pipeline so that NLP/ML outputs can be traced by domain experts from the final decision to initial data point; and (iii) implement data-driven methods to develop a risk prediction model for adverse outcomes in Fontan patients. Our innovative approach can facilitate the development of physiology- based treatments and risk stratification for advanced therapies. Public, open-sourced release of the code associated with our technological innovations will benefit the research community as a whole to accelerate rare disease research, at lower cost and with greater inclusivity.

NIH Research Projects · FY 2025 · 2025-08

Abstract To meet the current and projected future research demand (over $94 million in research grants in FY24), the Emory National Primate Research Center (EPC) is proposing to acquire a modern chlorine dioxide-based sterilizer for ensuring continuous research support. The proposed new sterilizer will provide a safer unit for personnel and a more environmentally friendly unit that adheres to both new Environmental Protection Agency (EPA) and Food and Drug Administration (FDA) recommendations. In addition, the new sterilizer has a larger capacity to hold more instruments per cycle and a significantly shorter cycle time which will allow the EPC to improve efficiency in processing. Providing sterilization support is critical to support the EPC’s robust translational research programs focusing on infectious disease, neuroscience and transplant medicine. Purchasing this chlorine dioxide-based sterilizer will allow the EPC to provide continuous support to advance NIH-funded research for both internal and external collaborators, while increasing operational efficiency, creating a safer workplace, and protecting the environment.

NSF Awards · FY 2025 · 2025-08

The interpretation of observed astrophysical data lies at the heart of modern cosmology and general relativity. Advancements in technology, including sensitive telescopes, high-energy neutrino detectors, and gravitational wave detectors, have revolutionized our ability to observe the universe. Modern astrophysics relies on mathematical properties of the initial conditions for the evolution of Einstein’s equations combined with numerical simulations used to analyze data and improve our understanding of the astrophysical systems. The objective of this research is to study inverse problems in astrophysics, which seek the underlying causes of observed astrophysical phenomena. This involves addressing mathematical questions such as uniqueness and stability for predictive models and developing efficient numerical reconstruction techniques. This research additionally has applications in fields such as medical imaging, while also offering opportunities for graduate student training and interdisciplinary collaborations. This proposal aims to investigate two challenges in cosmology and general relativity. The first project focuses on the recovery of the initial status of the universe from the Cosmic Microwave Background. The project will explore an X-ray tomography approach integrated with the physical model, especially the Einstein’s equations governing the evolution of the universe. Collaborative efforts will concentrate on advancing statistical inference techniques and developing numerical reconstruction algorithms. In addition, the project will study the mathematical properties of geodesic ray transforms in Lorentzian and pseudo-Riemannian geometry and address related rigidity questions. The second project seeks to extract information of black hole spacetimes using gravitational wave signals. The project will study the mathematical properties of black hole quasinormal modes and their relationship to black hole parameters. Furthermore, the project will study the scattering and inverse scattering problems for black hole spacetimes. The new investigations on the high energy stability and distribution of phase shifts will aim to offer insights that could be helpful for interpreting future scattering experiments. 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 · 2025-08

Project Summary: Antimicrobial resistance (AMR) is a grave public health concern resulting in millions of infections in the United States each year. These resistant infections place a high financial burden on patients and the healthcare system. At the center of these resistant infections are six opportunistic “ESKAPE” pathogens (Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, and Enterobacter spp.), which cause over half of all healthcare-associated infections. As such, there is a dire societal need to develop novel antimicrobial compounds with unique mechanisms of action (MOA) to combat these ubiquitous pathogens. Targeting a novel MOA decreases the likelihood of cross-resistance. To identify new compounds with potent biological activity and potentially a new MOA, we turn to natural products. Natural products are secondary metabolites that have been used for their medicinal properties thousands of years. Today, upwards of one-third of all FDA-approved drugs come from natural products. Taken together, natural product synthesis represents a valuable starting point for the discovery of antimicrobial compounds with a novel mechanism of action. The drimane sesquiterpenoid family of natural products has potent activity against a wide range of antimicrobial pathogens including Pseudomonas aeruginosa and Staphylococcus aureus. Drimane sesquiterpenoids are structurally distinct from traditional antibiotics, suggesting they may have a novel MOA. However, their scarce supply in nature limits our understanding of their biological activity. This application proposes the total synthesis of over 15 drimane sesquiterpenoids from a single precursor, all with underexplored or unreported biological activity. Additionally, we will use this diverted synthetic platform to conduct a hypothesis- driven analog campaign to yield unnatural analogs with superior biological properties. With this library of synthetic compounds, we will perform a series of biological assays including resistance selection to elucidate the mechanism of action of these drimane sesquiterpenoids. This interdisciplinary proposal will propel our understanding of these potent antimicrobial natural products and potentially identify a novel target for further antibiotic drug development.

NIH Research Projects · FY 2025 · 2025-08

The incidence rates of pressure injuries (PrIs) are on the rise, intensifying the workload for nurses, leading to bad outcomes for patients, and increased healthcare costs. Individuals whose pressure injuries are not detected early go on to face longer hospital stays, more severe infection, reduced wellbeing, and, in some cases, premature death. Overburdened nurses are dealing with sicker patients, more patient care, and documentation requirements on admission. This leaves less time for skin checks, making the already difficult challenge of detecting pressure injuries even more challenging. Clinical practice guidelines recommend technological supplementation of visual and manual skin inspections, but few facilities have adopted these methods. Technologies to help identify early signs of tissue damage that are easily implemented at the bedside are needed to reduce pressure injuries across all patients, especially in the context of overburdened nursing workflows. Thermal imaging (thermography) shows potential for the early detection of PrIs but there is a critical need to thoroughly evaluate its performance across all patients. Streamlined implementation of thermography is also critical to its adoption in clinical practice. The requirement for frequent image capture and asynchronous review adds to burden and creates a fragmented workflow. Without an automated bedside interpretation, there is no clear feedback loop to motivate further skin inspection or guide interventions. Our project’s overall goal is to increase early detection of PrIs using an automated, nursing-informed solution to improve the use of thermography in the usual clinical workflow. We will accomplish this by collecting and validating a robust and balanced dataset of thermal and optical images containing more than 600 PrI- susceptible patients, more than 100 of whom have PrIs. We will then build and assess an automated, balanced, and robust PrI detection solution using deep learning models to create balanced PrI detection across all patients. A Nursing Advisory Board will participate in the entire study, guiding all decisions and ensuring clinical relevance and external validity of our work.

NIH Research Projects · FY 2025 · 2025-08

The intersection of tuberculosis (TB) with diabetes mellitus and cardiovascular disease (CVD) is a critical clinical and public health obstacle. Global diabetes incidence and prevalence is expanding rapidly, particularly in the United States where latent tuberculosis infection (LTBI) affects >13 million people. At the same time CVD is the leading cause of death, including in the US and globally. An expanding body of data bidirectionally link TB to both diabetes and CVD: previous theories suggested that diabetes and CVD only predisposed persons to becoming infected with TB; however, emerging information clearly demonstrates that TB disease increases the risk of both diabetes and CVD. But to date, the notion that LTBI may increase the risk of cardiometabolic diseases like diabetes and CVD has not been well explored. This study will determine the extent to which LTBI impacts indicators of diabetes and CVD incidence. We will also assess epigenetic signatures of LTBI and whether they impact cardiometabolic disease risk. This research will improve understanding of burdens of LTBI and non-communicable disease and inform treatment guidelines for management of LTBI and prevention of cardiometabolic disease. The long-term objective of this research is to generate an evidence base to help determine whether persons with LTBI would benefit from preventive interventions for diabetes and CVD. The specific aims of this proposal are to: (1) estimate the relationship between LTBI and indicators of diabetes and CVD; (2) determine whether established DNA methylation patterns of diabetes and CVD incidence are also associated with LTBI; and (3) explore longitudinal changes in DNA methylation patterns associated with recent LTBI. The aims of this project will be achieved by enrolling a cohort of participants with and without LTBI. We will determine LTBI status among participants at enrollment and follow them prospectively for 24-months. At enrollment and follow up time points we will measure insulin resistance, glycated hemoglobin, visceral adiposity index, pulse wave velocity, blood pressure, and endothelial function. We will also measure DNA methylation at enrollment and in a subset at follow up. The analyses will include multiple modeling strategies to assess the relationship between patient and host factors and the risk of cardiometabolic disease incidence. This proposal will directly address clinical and mechanistic uncertainties related to the growing global concern of intersecting TB with diabetes and CVD epidemics. The study will characterize the extent that LTBI contributes to diabetes and CVD risk and will identify which epigenetic signatures are related to both LTBI and cardiometabolic disease risk. Elucidating mechanistic linkages between LTBI and cardiometabolic disease risk will allow our group to evaluate the utility of using existing therapies aimed at lowering diabetes and CVD risk and identify new pathways that could be targeted to improve clinical outcomes for persons with LTBI.

NIH Research Projects · FY 2025 · 2025-08

PROJECT ABSTRACT / SUMMARY Noncommunicable diseases (NCDs), including cardiovascular diseases (CVD), now represent the leading cause of death and disability in India. Between 1990 and 2021, the proportion of deaths attributable to CVD increased from 15.2% to 24.5%. Hypertension, a central risk factor for CVD, has also increased in prevalence in recent decades. As of 2021, an estimated 28.1% of Indian adults had hypertension. Management of high blood pressure through pharmacotherapy and modifications to diet and physical activity can prevent CVD. However, most Indians with hypertension do not receive effective treatment. Among adults with hypertension, only 16.5% are on treatment and only 8.7% have achieved control of their blood pressure. Reported barriers to management of hypertension occur at multiple levels, including low uptake of screening by patients, insufficient time and knowledge of health workers, and poor geographic access to facilities. Improving hypertension management in public sector facilities will require new strategies to address these barriers, and supportive health system policies. Two examples of such strategies that have yet to be evaluated are the Andhra Pradesh Family Doctor Programme and the Integrated Tracking, Referral, and Electronic Decision Support, and Care Coordination (I- TREC) study (U01HL138635, PI: Tandon). To have impact on the population need, effective strategies must be coupled with translation of evidence into policies to support implementation at scale. However, poor understanding by researchers of the policy process and information needs limits uptake of research findings to inform policy. This F31 proposal leverages existing research partnerships with two state governments to evaluate health system interventions for hypertension management and the role of research in policy in Andhra Pradesh and Punjab states. We will use the RE-AIM framework to assess the reach, effectiveness, and implementation fidelity of hypertension services delivered through the Andhra Pradesh Family Doctor Programme, a statewide health system initiative to screen and refer people with hypertension to village health clinics (Aim 1). In Aim 2, we will develop a Markov model to estimate the cost-effectiveness of the I-TREC model of hypertension care in primary health facilities in Punjab state in terms of lifetime health outcomes. Finally, we will disseminate findings of these evaluations to officials in Andhra Pradesh and Punjab and solicit feedback on how to improve the relevance and utility of these findings to inform future decisions (Aim 3). This dissertation will leverage innovative implementation science and mixed-methods approaches to evaluate population-wide hypertension programs and produce evidence that is tailored to the needs of the state government decision makers who shape health system policy in India. We expect findings from these evaluations to be primed for uptake to inform the scale up of these programs in Andhra Pradesh and Punjab and may be applicable to other Indian states and South Asian nations.

NIH Research Projects · FY 2025 · 2025-08

PROJECT SUMMARY Metabolic dysfunction-associated steatotic liver disease (MASLD), a spectrum of liver diseases from simple steatosis to, steatohepatitis, hepatic fibrosis, and cirrhosis, is the most common liver disease in the United States and worldwide. There are currently limited treatment options for MASLD because of our poor understanding of its pathogenesis. Dysfunction of the mitochondria induces disrupted lipid metabolism, oxidative stress, and hepatocyte death. While compelling evidence indicates a central role of mitochondrial dysfunction in the development and progression of MASLD, the underlying cause of mitochondrial dysfunction remains elusive. Iron is essential for cell metabolism dependent on the biogenesis of iron sulfur clusters (ISCs) and heme, essential cofactors for enzymes in the citric acid cycle and mitochondrial respiration chain. Impaired iron utilization, as seen in Friedrich’s ataxia (FRDA) due to the loss-of-function of mutations in frataxin (FXN), leads to ISC deficiency and mitochondrial free iron overload (MFIO). Nevertheless, the potential role of FXN has never been examined in metabolic diseases including the MASLD. Our preliminary study identified for the first time a markedly decreased expression of FXN in mouse and human specimens of MASLD. We have also observed hepatic MFIO and ferroptosis in a chronic mouse model of MASLD. Additional data showed a protective role of FXN against liver injury in an acute model of MASLD. We therefore hypothesize that steatosis-induced downregulation of FXN disrupts mitochondrial function and lipid metabolism and induces MFIO-dependent hepatocyte ferroptosis, which collectively promote the development of steatohepatitis. Specific Aim 1 will interrogate whether FXN deficiency induces while restoration of FXN expression prevents ferroptosis-mediated liver injury related to MASLD. We also aim to unravel a ferroptosis-specific mechanism that triggers liver inflammatory responses. Specific Aim 2 will determine the precise mechanism by which MFIO triggers lipid peroxidation in the plasma membrane ultimately leading to ferroptosis, with a focus on labile iron dynamics through real-time confocal fluorescence imaging. Specific Aim 3 will identify the critical role of steatosis- associated ER stress response in the induction of hepatic FXN protein degradation in mice and humans with MASLD. A temporospatial changes in FXN expression at the different disease phases of MASLD will be also determined. Successful completion of the proposed studies will unveil that FXN-dependent defects in mitochondrial iron metabolism is a novel mechanism of mitochondrial dysfunction and ferroptosis-mediated liver injury in MASLD, laying the foundation for developing potential new therapies.