University Of California Los Angeles

NIH Research Projects · FY 2025 · 2024-07

PROJECT SUMMARY Sudden cardiac death is primarily caused by ventricular arrhythmias, accounting for nearly half of all cardiovascular disease deaths in the US. However, most of the currently available antiarrhythmic drugs are proarrhythmic, and while implanted cardioverter defibrillators are believed to be the most effective therapy, their low efficacy and high cost pose significant challenges. Both implanted cardioverter defibrillators and drug therapies necessitate accurate risk stratification, and drug therapies require not only a comprehensive understanding of the mechanisms but also the identification of appropriate drug targets. The difficulties in risk stratification and identifying the right drug targets are that: 1) at the individual scale, arrhythmias have multiple and multiscale causes and mechanisms. Drugs target entities at the molecular scale but arrhythmias are fundamentally tissue-scale phenomena, with no simple one-to-one relationships due to complex multiscale nonlinear interactions. An antiarrhythmic drug may suppress one particular arrhythmia mechanism but potentiate another mechanism, unexpectedly increasing rather than decreasing mortality as shown in large clinical trials; and 2) at the population scale, a drug may be antiarrhythmic for one individual but proarrhythmic for another due to inter-individual variability/diversity and complex environmental differences, which may also account for the failure of current antiarrhythmic drug therapies. Therefore, for antiarrhythmic drug discovery, one must evaluate the effects of a molecular intervention or a drug on not just a single arrhythmia mechanism, but all possible arrhythmia mechanisms. Additionally, one must take into account inter-individual variability and complex environmental stresses. Our goal is to use mathematical modeling, computer simulation, dynamical theories, and "virtual clinical trials" in our in silico platform that includes normal and diseased human model populations, and leverage the power of computer modeling and simulation in dealing with complexity to discover novel effective antiarrhythmic drug targets for arrhythmia prevention and novel ECG markers for risk prediction. Our central hypothesis is that dynamical instabilities are the major common mechanisms of arrhythmogenesis regardless of the underlying biological causes, and suppressing dynamical instabilities by targeting the appropriate dynamical parameters can be effective unified therapies for arrhythmia prevention. There are two specific aims: 1) To discover effective antiarrhythmic drug therapies and test the hypothesis that targeting certain dynamical parameters can be effective unified therapeutic targets; 2) To discover optimal clinical markers for arrhythmia risk prediction and test the hypothesis that dynamically-sensitive ECG properties can be effective risk predictors. This is a both data-driven and hypothesis-driven proposal which integrates computational modeling and simulation, dynamical theories, concepts of evolution, experimental and clinical data to translate computational modeling to clinical medicine. The long-term goal is to generate theoretical results serving as scientific bases for future expensive and time-consuming experimental studies or clinical trials.

NIH Research Projects · FY 2026 · 2024-07

PROJECT SUMMARY R-loops are three-stranded nucleic acid structures composed of an RNA-DNA hybrid and a free single-stranded DNA. The opened DNA strands may lead to DNA damage; thus, R-loops are a risk factor for genome integrity. Improper R-loop accumulation contributes to abnormal human development and diseases. In contrast to the detrimental effects of R-loops, growing evidence suggests that they also regulate gene transcription, mitosis, and homologous recombination, thus contributing to many fundamental physiological processes. The formation and resolution of R-loops must be tightly regulated. Therefore, the goal for the next five years of my research is to investigate the mechanisms by which R-loops epigenetically regulate gene transcription in pluripotency, early development, and tissue homeostasis. My unpublished work in mouse ESCs (mESCs) strongly suggests that Zfp281 (human: ZNF281) is an R-loop-dependent transcription factor and recruits Tet1 and Brca2 for DNA demethylation and DNA damage repair, respectively. We will explore the functions of R-loops as a DNA epigenetic regulator in the mouse naive-formative-primed pluripotent state transition through pathways that include Zfp281/Tet1-mediated R-loop formation for gene activation and Zfp281/Brca2-mediated R-loop resolution for genome stability. In human development, naive human ESCs (hESCs) have a prolonged developmental plasticity and can differentiate into extraembryonic lineages. My unpublished work showed that ZNF281 is highly enriched in naive hESCs, with a widespread occupation of Polycomb repressive complex 2 (PRC2) and the repressive histone H3K27me3 mark at the extraembryonic master gene loci (e.g., GATA4, CDX2). Therefore, we will investigate the effects of R-loops and ZNF281 on PRC2-repressed low-transcription genes in naive hESCs and their differentiation into the human extraembryonic endoderm and trophectoderm lineages. Moreover, we will investigate the relationship between DNA damage and genome-wide gains of ZNF281, PRC2, and H3K27me3 in naive hESCs. And last, we will develop a research program to explore R- loop functions in tissue homeostasis and in disease settings. We will focus on B-cell homeostasis, as evidence shows that aberrant accumulation of R-loops accelerates the progression of diffuse large B-cell lymphoma (DLBCL), a disease originating from the malignant transformation of B-cells in the germinal center. My unpublished work showed that B-cell-specific Zfp281 conditional knockout mice had accumulated pre-/pro-B cells in the bone marrow and had increased IgG1-expressing activated B-cells in the spleen upon immunization. Using the DLBCL lines and the Zfp281 deficiency mouse model, we will investigate the effects of abnormal R- loop accumulation induced by Zfp281 deficiency on B-cell development and the progression of B-cell lymphoma. In summary, we aim to establish a coherent view of regulatory R-loops as DNA and histone epigenetic modifiers, as well as a potential risk factor if they are not properly resolved, in different cellular systems.

NSF Awards · FY 2024 · 2024-07

Healthcare decisions, including decisions to vaccinate, are an amalgamation of complex cultural, social, and psychological interactions, including perceptions of risk, trust in healthcare, locally relevant norms of behavior, and social learning. Understanding both the drivers of vaccine decision making is crucial to alleviating the burden of disease and increasing vaccine uptake. In particular, more work is needed from underserved communities, which tend to have disproportionate vulnerabilities and disease burden. In addition to potential impacts on public health and public policy, this study facilitates training of a diverse group of graduate and undergraduate students, including groups typically underrepresented in STEM research. This study takes a multi-modal approach to studying health care decision-making, particularly around the acceptance and uptake of vaccines. The team examines: (1) how local models of illness shape vaccination practice, (2) how individual-level factors, including medical mistrust, shape perceptions and use of the healthcare system, (3) how sociodemographic factors shape vaccine beliefs, and (4) how social learning influences individual vaccination decisions. To do this the team uses a mix of interviews, surveys and focus groups, along with innovative vignette studies designed specifically for this study. This multi-layered approach to understanding vaccination is rare in health sciences, and should highlight the value of an anthropological approach to the study of vaccination. 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

The mission of the University of California Los Angeles-California Institute of Technology Medical Scientist Training Program (UCLA-Caltech MSTP) is to recruit and train an accomplished pool of dedicated physician-scientists advancing scientific and clinical discovery and driving technological innovations to the benefit of human health and disease outcomes. The program proposes to (1) recruit gifted students with a passion for scientific knowledge and unwavering commitment to research, medicine, service, and leadership; (2) provide a safe and supportive training environment where trainees flourish academically and scientifically in their area of research interest; and (3) provide strong individualized mentorship to support the personal and professional development of all trainees. These goals will be accomplished within a program framework of strong faculty and peer mentorship, didactic and experiential learning, and professional development integrated with medical training in the UCLA David Geffen School of Medicine and doctoral research in graduate programs at UCLA and Caltech. Trainees will be inculcated with the skills and attitudes needed to pursue meaningful and impactful research in a safe, ethically responsible, rigorous, and collaborative manner. Structured faculty and peer mentorship and programming including the MSTP Tutorial, MSTP Annual Research Conference, Physician-Scientist Grand Rounds, Clinical Practicum during Research, Longitudinal Clinical Preceptorship, and further program-sponsored and student-led activities have been designed to burnish trainees’ identities as physician-scientists and prepare them to pursue wide-ranging careers in the biomedical research workforce. Program initiatives and a dedicated and collaborative team of faculty directors, administrators, and student leaders have resulted in outstanding training outcomes over the past decade as the program has substantially increased in size (118 current trainees nearing a steady state goal of 120). The current 5-year proposal requests 30 NIH-funded positions with the following intended training outcomes: 1) acquired knowledge, skills, experiences and attitudes leading to successful physician-scientist careers impacting a wide range of health-related research needs; 2) established scientific expertise and published rigorous and impactful research work guided by strong mentorship; 3) thriving and resilient graduates retained in the physician-scientist pipeline through professional and career development; 4) timely completion of training in 8 years or less facilitated by curricular innovations that streamline dual degree training and 5) recruitment of promising trainees who will address the most pressing challenges in health and disease outcomes faced by our Nation.

NSF Awards · FY 2024 · 2024-07

The development of quantum computing and the power of quantum algorithms to quickly solve prime factoring problems pose new risks for widely deployed cryptographic algorithms. Thus, it is imperative to build cryptographic schemes based on mathematical problems that are believed to be resistant to the capabilities of quantum computing. One of the leading mathematical areas under study for achieving post-quantum security is based on the hardness of solving systems of quadratic equations over a large number of variables, over finite fields. The purpose of this research project is to investigate this area from a foundational perspective, both to expand the cryptographic utility of such systems of equations, and to investigate new quantum algorithms and their impact on the security of such systems of equations. The project also continues efforts with the Prison Math project and the creation of educational videos in the area of cryptography. The primary goals of the study are: (1) to propose and study the simplest variations of solving random quadratic equations over finite fields, especially from the perspective of hardness and utility in cryptography; (2) to develop novel quantum algorithms for solving certain systems of quadratic equations over finite fields, to better understand the boundary between the kinds of problems that are hard for quantum computing and those that are easy for quantum computing; (3) to develop novel public-key cryptosystems whose security is based on solving systems of quadratic equations over finite fields; (4) to explore the possibility of constructing more advanced cryptography, such as functional encryption, using systems of quadratic equations over finite fields; and (5) to explore targets of opportunity in related research questions that arise from our investigation. 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/ABSTRACT Large-scale RNA sequencing studies have provided remarkable insight into the brain molecular processes that underlie complex neurodevelopmental disorders like autism spectrum disorder (ASD). Bulk processing of brain tissue has linked the disruption of alternative splicing, a mechanism by which a gene’s exons and introns are differentially processed into mRNA isoforms, to the transcriptomic changes seen in ASD. Furthermore, single- cell and single-nucleus analyses have localized these alterations to pro-inflammatory microglia, astrocytes, and deep excitatory neuron populations. However, prior splicing analyses were limited to well-annotated isoforms from short-read data, which account for only a small subset of all isoforms present in the brain transcriptome. Now, emerging third generation “long-read” sequencing technologies allow for the processing of full-length transcripts, revealing the full profile of introns and exons for both known and novel gene isoforms. Here, we propose single nucleus long-read sequencing of over 60 human prefrontal cortex samples, including 33 individuals with a diagnosis of ASD and 30 without such a diagnosis, to fully interrogate the biological function of isoforms in neuropsychiatric disease. Given the existing evidence of splicing alterations in ASD, and the growing body of evidence that isoform expression captures the brain’s transcriptomic diversity better than gene expression, we hypothesize that long-read sequencing will reveal broader transcriptional dysregulation than previously captured, and that we will localize these changes to highly refined cell subpopulations. Leveraging long-read technology on our dataset, we will assess the cellular distribution of isoforms in the postnatal human brain (Aim 1). Next, we will perform case-control differential expression analysis, paired with genomic enrichment, to identify isoform-level drivers of ASD pathophysiology (Aim 2). Finally, we will examine correlations between isoforms and identify systems-level regulators of expression, across both control samples and ASD samples (Aim 3). Altogether, these aims serve to systematically characterize the role of isoform diversity in the postnatal human brain across both control and ASD populations, aligning with the NIMH’s mission to uncover the neurobiological basis of brain-related disorders. This proposal will be carried out by Michael Margolis, an MD/PhD student at UCLA, who will receive comprehensive training in human genetics and genomics. Mentorship will be provided by Dr. Daniel H. Geschwind as the primary mentor, with additional mentorship from Dr. Michael J. Gandal, both of whom are experts in the fields of functional genomics and neurobehavioral genetics.

NIH Research Projects · FY 2025 · 2024-07

Project Abstract Twenty percent of US adults suffer from chronic pain, a condition characterized by reduced mental and physical well-being, and despite current treatments over half of patients experience undertreated pain symptoms. Although the most efficacious medications for pain are opioids, they possess serious side effects and long-term consequences, highlighting the importance of identifying novel therapeutics for treating pain. There is established clinical and preclinical evidence that cannabis (a plant containing over 550 chemical compounds including terpenes) possesses analgesic/anti-allodynic effects, at least partially explained by cannabis’s action on cannabinoid 1 (CB1) receptors. Cannabis strains with high terpene (odor-causing molecules of the cannabis plant) concentrations are preferred among patients who use cannabis, and preclinical work on rodents (demonstrated by the applicant) has shown terpenes can produce antiallodynic effects in pain models, which was blocked by CB1 receptor antagonists despite not directly binding to these receptors. In order to advance the understanding of how terpenes interplay with the endocannabinoid system to relieve pain, the goal of this proposed research is to elucidate how terpenes may alter eCB levels using a sophisticated and novel modified eGFP CB1 receptor sensor. The ventrolateral periaqueductal gray (vlPAG), a region involved in the descending modulation of pain, has a dense population of CB1 receptors and stimulating this area induces analgesia. This sensor paired with fiber photometry will be used to measure endocannabinoid release in the vlPAG in vivo in mouse models of chronic pain. The Cahill laboratory uses a Chronic Constriction Injury (CCI) model of neuropathic chronic pain in mice that captures the acute hypersensitivity and negative affect associated with chronic pain in humans. By naturally increasing endocannabinoids via inhibitors of their degradation (FAAH/MAGL inhibitors), Aim 1 will determine if myrcene can augment changes in hyperalgesia and negative-affect like behaviors in CCI mice, partially attenuated by knockdown or antagonism of vlPAG CB1 receptors. Aim 2 will address mechanism of action by directly measuring endocannabinoid release using a genetically modified eGFP CB1 receptor sensor in awake, freely-moving animals. This will be achieved by measuring endocannabinoid release before and after terpene administration in the vlPAG. Such findings would identify non-intoxicating, FDA-approved compounds that rapidly modulate endocannabinoid levels as a potential therapeutic strategy. UCLA contains supportive and collaborative atmosphere with personnel specializing in photometry, in-house virus packaging, and resources from an established chronic pain laboratory. This productive an integrative environment will support this trainee plan for a scientist with a disadvantaged background toward her goals of conducting her own research laboratory at an R1 institution.

NIH Research Projects · FY 2025 · 2024-07

PROJECT SUMMARY Early life adversity (ELA), such as neglect or maltreatment in childhood, is a major risk factor for the development of psychiatric disorders later in life. ELA heightens responses to threats, usually at the expense of rewarding behaviors. Excessive threat avoidance is a hallmark of anxiety, phobias, and depression. Circuits that process both rewards and threats, including the basolateral amygdala (BLA), are known to be dysregulated by ELA and have been implicated in the pathophysiology of these conditions. These circuits mature throughout early life and into early adulthood. This extended window of development, when paired with enhanced exploration in adolescence, facilitates refinement of neural circuits underlying nuanced behavioral strategies needed throughout life. Salient experiences, such as ELA, are encoded in groups of coactive neuronal ensembles throughout the brain and have the capability to impact circuit organization. Although symptoms of psychiatric disorders often manifest in adolescence, most studies focus on adult outcomes of ELA. As a result, the biological mechanisms connecting ELA to enhanced threat avoidance later in life remain largely unknown. This proposal aims to bridge this gap by determining how ELA-sensitive cells within the BLA drive brain-wide circuit reorganization underlying enhanced avoidance behavior in adolescent mice. My central hypothesis will be tested in two aims. Experiments in Aim 1 will reveal the endogenous activity of ELA-sensitive ensembles in the BLA during threat avoidance behavior and map their brain-wide connectivity. Experiments in Aim 2 will identify sensitive windows during development when ELA-sensitive neurons alter developmental circuit trajectories, leading to increased avoidance. I will gain extensive training in the neurobiology of ELA and application of systems neuroscience approaches including activity-dependent genetic labeling, chemogenetics and whole brain circuit mapping to the developing brain. This research will illuminate how early experiences can alter the trajectory of postnatal brain development and identify sensitive windows and targets for circuit-level therapeutics that can be used to prevent or treat mental health disorders in at-risk populations.

NIH Research Projects · FY 2025 · 2024-07

Project Summary/Abstract The gut microbiota influences many aspects of host physiology, spanning metabolism, gastrointestinal function, neuroactivity, and immune homeostasis. However, the molecular mechanisms that govern the symbiotic relationship between the host and gut microbiome remains unclear. Current evidence suggests that immunological tolerance in the intestine is essential for maintaining healthy host-microbe interactions by dampening immune responses to beneficial gut microbes and thus, promote commensalism. Interestingly, recent studies suggest that specific microbes that preferentially reside in the colon can promote immune tolerance by enhancing the development and function of regulatory T cells (Treg), a T helper (Th) subset important for tolerance. For example, polysaccharide A from Bacteroides fragilis can increase the suppressive capacity of Tregs, mediate the conversion of CD4+ Th cells to Tregs, and enhance microbe colonization onto host mucosa in the colon. Furthermore, Clostridial species (Clostridial spp), an abundant member of the proximal colon, produce short-chain fatty acids (SCFAs) that can also promote colonic Treg development and function. In the absence of SCFAs, Clostridial spp maintain some capacity to induce Treg development, suggesting an undescribed alternative mechanism. Interestingly, Clostridial spp are also potent inducers of host serotonin synthesis by colonic enteroendocrine cells. While commonly associated with neurotransmission in the brain regulating mood and behavior, serotonin in the periphery can also regulate diverse processes including gut motility and platelet activity. Furthermore, serotonin has immunomodulatory properties and can signal through the variety of serotonin receptors expressed by immune subsets. In the gut, serotonin receptor 7 (5-HT7) is highly expressed by innate immune cells, such as dendritic cells (DCs) and macrophages, that are important for Treg induction and maintenance. Since DCs are essential for T cell activation, we hypothesize that serotonin signaling through 5-HT7+ colonic DCs contribute to immunological tolerance that support the symbiotic relationship with gut microbes. To test this hypothesis, I will leverage novel transgenic mice and intersectional genetic approaches to determine i) the spatiotemporal regulation of 5-HT7 expression on colonic DCs, ii) the cellular consequences of 5-HT7-mediated signaling on colonic DCs, iii) the functional effects of DC-specific 5- HT7-mediated signaling on immune tolerance to the gut microbiota. Moreover, immunological tolerance is essential for dampening inflammatory responses during immune resolution to prevent chronic activation of proinflammatory pathways. In the gastrointestinal tract, chronic inflammatory disorders, such as irritable bowel disease (IBD), is associated with dysbiosis and dysregulated serotonergic signaling, suggesting a relationship between IBD, gut microbiota, and the enteric serotonergic system. Thus, results from our studies will advance our understanding of the crosstalk between the host and microbiota through serotonin-immune interactions and raise the potential for identifying novel targets that promote immune tolerance and intestinal homeostasis.

NSF Awards · FY 2024 · 2024-07

Geomagnetic disturbances (GMDs) are large localized fluctuations of Earth's magnetic field lasting 5 to 10 min, are typically related to phenomena in the near-geospace environment, and can induce electric fields within the electrically conducting Earth's crust. These electric fields drive electric currents that can flow through technological infrastructure in the form of geomagnetically induced currents and potentially damage power grids, pipelines, and other infrastructure. This project aims to understand the temporal and spatial development of significant changes in the magnetic field associated with both the equivalent ionospheric and telluric currents during GMDs and the source of these GMDs. Understanding the causes of GMDs is crucial in developing and validating models that aim to accurately and reliably predict the variations of electric fields and GICs, and they are one of the objectives of the National Space Weather Strategy and Action Plan. Typically, GMDs are associated with just ionospheric current enhancements. However, ionospheric currents can induce telluric currents that contribute to GMDs. This project aims to significantly expand the application of the spherical elementary current system (SECS) method for analyzing geomagnetic disturbances (GMDs) observed by ground magnetometers in North America and Greenlands since 2006. The goal is to estimate the spatio-temporal variations of equivalent ionospheric and telluric currents. The resulting variations in dB/dt will be examined to quantify the relative importance of equivalent ionospheric and telluric currents. Additionally, the study aims to evaluate the frequency of occurrence where dB/dts associated with each current system exceed a threshold level of concern for critical infrastructure. Event studies will be conducted in conjunction with THEMIS all-sky imagers, THEMIS and GOES spacecraft, and SWMF simulations to understand driving mechanisms and developing processes of space weather significant GMDs. 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 Visuomotor transformation (VMT), a vital process by which the brain converts vision into action, requires precise synaptic connectivity between sensory and motor neural circuits. Impaired visuomotor processing has been associated with a wide range of neurological disorders. Developmental and molecular origins of a VMT remain elusive due to the lack of experimentally tractable model systems. I address this knowledge gap by interrogating the visuomotor interface of Drosophila, where transcriptomics, connectomics and physiology can be integrated to causally link genes and molecules with circuit structure and function. My recent work uncovered a completely new wiring strategy underlying a VMT: visual space coordinates are transformed into synaptic weights. Such synaptic gradient mechanism found in Visual Projection Neurons (VPN) emerges through within-cell-type synaptic specificity. Individual neurons belonging to the same VPN cell type connect to different postsynaptic partners, which elicits multidirectional motor programs in response to differentially localized visual stimuli. Aim 1 of my research during the K99 phase will build on these findings and identify the transcriptomic signatures of synaptic gradients. I hypothesize that within-cell-type synaptic specificity is achieved through transcriptomic uniqueness of individual neurons of the same type (i.e., molecular gradients of wiring genes). In collaboration with my co-mentor Dr. Y. Kurmangaliyev, I will test this hypothesis and generate a developmental transcriptomic atlas of the fly visuomotor interface featuring 20 VPN cell types. Single-cell RNA-seq profiling will be followed by the validation of candidate gene expression patterns using genetics and spatial transcriptomics. This will generate a molecular model of synaptic gradients. Aim 2, pursued in collaboration with my co-mentor Dr. C. von Reyn, will functionally test this model using genetic perturbation screening. Candidate genes will be misexpressed in VPNs and their postsynaptic partners, and the effects on synaptic gradients will be assessed using electrophysiology. This work will provide causal relationships between molecular gradients of wiring genes and within-cell-type synaptic specificity. The mentorship I will continue to receive from Dr. Zipursky, and the training in single-cell data analysis and electrophysiology I will acquire during the K99 phase will facilitate my transition to an independent research program. Aim 3, to be pursued during the R00 phase, will investigate the gene regulatory networks of synaptic specificity in visuomotor circuits. I will examine the role of global extrinsic regulatory programs (e.g., spatially graded Wnt signaling and neural activity) in establishing synaptic gradients using a combination of Perturb-RNA-seq and chromatin accessibility (ATAC-seq) analysis in VPNs. This approach will link extracellular signals with transcription factor mediated differential gene expression in a cell-type-specific manner. I will generate models of global and local regulation of synaptic gradients. These models will be subsequently validated and refined using electrophysiology- based analysis. My work will bridge the gaps between genes, circuits, and behaviors, thereby providing fundamental insights into the molecular logic of wiring a VMT.

NSF Awards · FY 2024 · 2024-07

The Collaborative Multiracial Post-Election Survey (CMPS) is a nonpartisan post-election survey developed by academic researchers in 2008. Starting in 2016, the CMPS adopted an innovative cooperative strategy which broadened the scope of access to high-quality national survey data with large samples of racial/ethnic and underrepresented groups in the United States. The 2024 CMPS will provide essential empirical information on the state of political inclusion, democratic participation, and policy support in an increasingly diverse U.S. The 2024 CMPS will further expand access to minority data collection for scholars in large research Universities as well as those scholars in smaller teaching colleges and Minority Serving Institutions (MSIs) with fewer resources. This study continues to create a space where scholars have access to and share sociopolitical data, as well as build a network of scholars with shared research interests. The results of the survey will advance our national interests in terms of enhancing our understanding of intra-and inter-group relations, policy support, and civic and political participation among understudied groups. The project will produce a dataset that will be made publicly available within one year following data collection. The 2024 Collaborative Multiracial Post-Election Survey (CMPS) is a cooperative, multiracial, multiethnic, multilingual, post-presidential election online survey in the United States. The sample will include an estimated total of 22,000 completed interviews among various demographics of the American population. The sample includes adult, registered and non-registered voters, including non-citizens. The survey (and invitation) may be available to respondents in English, Spanish, Chinese (simplified), Chinese (traditional), Korean, Vietnamese, Arabic, Urdu, Farsi, and Haitian Creole. The study will also include a sample of youth 16–17 years old (n=1,500). The CMPS employs a cooperative research model for designing the content of the survey. Questions included on the survey are generated through contributions from a national consortium of academic researchers from across multiple academic disciplines including the social sciences, psychology, public policy, public health, education, law, and other fields. The CMPS is changing the way data is collected and shared in the social sciences by collaboratively building a diverse and inclusive academic pipeline of scholars in political science and the social sciences more broadly. 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

Large-amplitude fluid dynamic disturbances, or “gusts”, are a pervasive challenge for many energy and propulsion systems involving lifting surfaces, such as wind turbines, fixed and rotary-wing aircraft, and turbomachinery. Flow disturbances are often atmospheric, caused by terrain or weather, or introduced by the aerodynamics of other systems, as in a wind farm or a swarm of air vehicles. They become relatively stronger as the system's weight and size decrease or as weather events become more extreme. Gust encounters can significantly undermine the desired performance of the system, or at worst, cause catastrophic failure. Devising an automated strategy for large-amplitude gust mitigation is exceptionally challenging because the aerodynamic responses of the system to the gust and to actuation are highly dependent upon each other. Reinforcement learning (RL) is a promising approach for control of such complex fluid flows that circumvents many of the obstacles to previous approaches, but it is challenged by the burden of training: in a naïve application of RL, the algorithm must see a suitably large range of gust conditions and actuation responses during training to determine the best response for each encounter. It is very likely that RL training can be accelerated if the algorithm incorporates flow state information and a prediction of flow physics. The augmentation of RL with flow state information remains largely unexplored, primarily because of the challenges of practically inferring this information in real time with a small number of on-board sensors. Sensors provide a limited footprint of the flow around them, but this footprint can reveal most of the essential flow information. This program will leverage prior work in computational and experimental investigations of unsteady aerodynamics to advance the state of the art of flow state estimation from limited sensors and to close the gap on practical use of RL in fluid dynamics. The program will deploy experiments and computations to estimate coherent vortex structures in a flow during encounters of a fixed wing or rotating blade with a large-amplitude disturbance. With use of both computations and experiments with detailed flow measurements, the program will explore a wide range of crucial flow physics in gust encounters, including scaling effects across a wide range of Reynolds numbers, and to study the influence of wing/blade pitching on these encounters during RL training. This program will demonstrate, for the first time, reinforcement learning control of gust interactions in a laboratory setting. 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 Peroxisomes are ubiquitous membrane bound organelles that play a major role in the regulation of lipid metabolism. In the liver, peroxisomes contain enzymes involved in essential processes such as the detoxification of reactive oxygen species (ROS), the oxidization of fatty acids, and synthesis of bile acids, the body’s natural detergents and potent signaling molecules. The generation of peroxisomes is driven by the peroxin (herein PEX) protein family. However, the role of PEX proteins in coordinating inter-organelle crosstalk has not been explored. Together, the mitochondria, endoplasmic reticulum (ER), and peroxisomes maintain lipid homeostasis and regulate lipid metabolism within hepatocytes. How organelles involved in lipid metabolism, such as the mitochondria and the ER, respond to changes in peroxisomal function is not well understood. Here, we describe the role of two PEX family proteins, PEX6 and PEX14, in hepatic lipid metabolism. Using a panel of over 100 inbred strains of genetically diverse mice, we found that PEX6 and PEX14 protein levels positively correlated with lipid levels in the liver. We therefore hypothesized that PEX6 and PEX14 may have specific roles in regulating lipid and bile acid metabolism through the import of critical components into peroxisomes. To test this, we generated AAV-CRISPR to disrupt Pex6 and Pex14 in the liver of adult mice. Mice injected with Pex6- or Pex14-CRISPR did not put on/gain body weight when fed a standard rodent chow diet, however there was no difference in their lean mass, plasma ALT/AST, or liver histology, compared to control mice, suggesting they were not sick. In addition, we observed a significant increase of hepatic bile acids in both Pex14-CRISPR and Pex6-CRISPR animals indicating dysregulation of BA synthesis within peroxisomes. Further, lack of either PEX6 or PEX14 resulted in specific increases in ROS metabolism and fatty acid oxidation. Together, these data suggest that loss of hepatic PEX14 or PEX6 augment peroxisome activity. Here, we aim to investigate how these peroxisomal changes may impact the morphology and function of other organelles involved in lipid metabolism, specifically the mitochondria and the ER. Preliminary results suggest that in mice lacking PEX14 or PEX6, mitochondrial respiration is significantly increased, and the ER is expanded. Further, we aim to investigate whether lipid accumulation, mimicking metabolic disease states, impact these observations. The proposed studies will describe one example of how the loss of two PEX proteins affects lipid homeostasis and inter-organelle crosstalk, thus advancing our understanding of lipid metabolism in healthy and disease states.

NIH Research Projects · FY 2025 · 2024-07

PROJECT SUMMARY/ABSTRACT “Killer” (“cytotoxic”) T cells are the cells of the immune system that have the job of killing cancerous and virus-infected cells. But they do so only if they have receptors capable of recognizing antigens – expressed on the surface of target cells – that are specific to the cancer or virus involved. And often it is the absence or shortage of these antigen-specific receptors that is responsible for a patient not surviving the growth and metastasis of a cancer or the spread of a viral infection. Accordingly, immunotherapies have been developed that involve: extracting T cells from a patient; transforming them so that they express the desired receptors; “expanding” (“growing up”) the cells; and, finally, putting them back into the patient so that they can lower the load of cancerous or virus-infected tissue. Aside from the cost and danger of extraction and infusion of immune cells, this ex vivo procedure – the current state-of-the-art of immunotherapy – introduces serious risk of oncogenesis because the T-cell transformation involves the (poorly- controlled) integration of receptor genes into the T-cell chromosomes. What we are proposing is a totally different approach that avoids the risks of both the extraction/infusion and of the chromosomal transformation. More explicitly, our aim is to transform T cells in vivo, by delivering the receptor genes directly to the T cells in the blood and lymph system of the patient; further, the genes are in mRNA form so that their expression is transient, involving no change in chromosomal DNA; finally, the mRNA is protected and targeted by being self-assembled in vitro with purified viral capsid protein, so that its shell is functionalized with an antibody against proteins uniquely expressed in T cells. Our methodology involves the complementary expertises of the two PIs. Dr.Gelbart’s lab has worked for the past 15 years on the in vitro packaging of mRNA into virus-like particles (VLPs) using the capsid protein of a plant virus that is capable of self-assembling around arbitrary- sequence RNA, and on the functionalization of these particles with targeting antibodies and other protein ligands. Dr. Yang’s lab has worked for the past 25 years on the basic biology of HIV infections and specifically on the ex vivo transformation of T cells with T-cell receptors (TCRs) and/or chimeric antigen receptors (CARs) that are specific against HIV or cancer antigens and on quantifying the cell-killing activity of these transformed T cells. Together we will be developing an In vivo T-cell therapy along the lines outlined above and demonstrating its efficacy in a mouse model of HIV.

NSF Awards · FY 2024 · 2024-07

Magnetospheres form when a flowing plasma, like the solar wind, impacts a magnetic obstacle, like a planet, and are an integral part of space weather systems. Earth’s magnetosphere has been observed by spacecraft for decades, but magnetospheres can also exist on much smaller scales, such as around small moons or asteroids that are difficult to study directly. This project utilizes laboratory experiments to create and explore artificial versions of these “mini” magnetospheres. By leveraging the ability of laboratory experiments to be carried out with high repeatability in a controlled setting, the experiments will provide an unprecedented, high-resolution three-dimensional map of a dynamic magnetosphere. This will advance our fundamental understanding of space weather by investigating magnetic reconnection, a key process that can drive geomagnetic storms that pose extreme hazards to human activities in space. The project will utilize the LArge Plasma Device (LAPD) at the University of California, Los Angeles and includes a collaboration with the Instituto Superior Técnico in Lisbon, Portugal. The project also provides advanced training and mentorship opportunity to a diverse group of undergraduate and graduate students to prepare them for the STEM workforce. Magnetospheres are a ubiquitous feature of magnetized bodies embedded in a plasma flow. In planetary magnetospheres, a key process driving magnetospheric dynamics is magnetic reconnection, in which magnetic energy is explosively released when opposing magnetic field lines merge and annihilate. In space environments, this reconnection is collisionless and controlled by kinetic-scale plasma physics. Mini-magnetospheres, small ion-scale structures that are well-suited to studying kinetic-scale physics, provide a unique environment for studying magnetospheric reconnection that can be created in the laboratory. This project will create ion-scale magnetospheres by coupling a supersonic, laser-driven plasma flow with a dipole magnet embedded in the uniform, magnetized plasma of the Large Plasma Device (LAPD) at the University of California, Los Angeles. Leveraging the high-repetition and reproducible capabilities of the platform and high-fidelity 3D numerical simulations, the objectives of the project are to 1) develop a novel 3D Thomson scattering diagnostic for measuring key plasma parameters in mini-magnetosphere experiments; 2) measure for the first time the magnetic reconnection rate in both dayside and magnetotail configurations through highly-resolved, volumetric datasets; and 3) investigate how magnetic reconnection impacts the global structure of the magnetosphere. The laboratory measurements will help validate numerical simulations and magnetospheric models, as well as complement spacecraft observations of mini-magnetospheres such as those associated with small moons, comets, and lunar magnetic anomalies. 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/ABSTRACT Autism spectrum disorder (ASD) is a lifelong, common neurodevelopmental disorder (NDD) with high heritability and complex genetic architecture. Known genetic risk factors, including large effect, de novo mutations that cause approximately 10% of ASD cases, have their maximal expression and converge during early brain development to impact neurogenesis and neuronal development. Yet, multiple analyses of post-mortem brain from individuals with ASD have consistently identified pervasive microglial activation. Study of cortical development in ASD has centered around neurons. So, there is a dearth of knowledge surrounding microglia function in the developing brain in ASD, which is especially important because microglia colonize the developing fetal cortex during the epoch during in which ASD risk genes converge. Here, I propose to leverage advances in human stem cell (SC) culture, including the development and validation of 3D human cortical spheroid (hCS) models of human brain development, to assess the impact of ASD genetic risk and neuronal-microglial interactions on microglial function. Cell lines derived from control, non-ASD, SC lines and two SC lines with a haploinsufficiency of high-risk ASD-associated genes (CHD8 and SCN2A) will be utilized to derive microglia and hCS. Bulk transcriptomics, morphological analysis, and phagocytosis assays of microglia from SC lines harboring high risk ASD-associated mutations will reveal how ASD genetic risk impacts microglia physiology, independent of interactions with neuronal cell types (Aim 1). Transcriptomes will be mapped to the in vivo developmental trajectory of microglia, and differential gene expression analysis will be performed on the ASD microglia relative to controls. Perturbations to cortical neurogenesis in ASD will be assessed through single cell RNA sequencing and immunohistochemical analysis of cell type and morphology in a 3D co-culture system of human cortical spheroids (hCS) integrated with microglia to model fetal microglial colonization of the cortex (Aim 2). Cell type distribution, differential gene expression, and gene network analysis of ASD mutant hCS compared to controls at early and late neurogenesis will be performed; immunohistochemistry of cell populations, synapses, and microglial morphology will validate and extend transcriptomic findings. These experiments will help inform ASD pathophysiology by elucidating how ASD genetic risk contributes to microglial activation and neuronal- microglial signaling and provide a framework for testing other ASD risk mutations. This training plan will prepare this applicant for a successful career as a physician-scientist studying neurodevelopmental disorders (NDDs), via the following major goals: training in stem cell culture and functional genomics, rigor and ethics in scientific thinking, professional development, and translation of research into the clinical setting. Working in a collaborative, multidisciplinary laboratory specializing in functional genomic analysis alongside clinical training and mentorship from physicians practicing in NDD clinics, this applicant is well-positioned to gain the skills necessary for a productive scientific and clinical career in pediatric neurology and developmental neurobiology.

NSF Awards · FY 2024 · 2024-07

This project is funded by the Pathways to Enable Open-Source Ecosystems (POSE) Program which seeks to harness the power of open-source development for the creation of new technology solutions to problems of national and societal importance. The project aims to create a vibrant open-source ecosystem known as DriveX to advance automated driving and intelligent transportation research. This open-source ecosystem addresses a critical societal need: the safe and efficient integration of automated vehicles (AVs) into real-world transportation systems. DriveX will facilitate the design, development, and evaluation of mobility technologies for an increasingly automated future. By bridging gaps between various stakeholders, including industry, startups, academic researchers, and government agencies, the project seeks to overcome the current fragmentation in the field, unify collective efforts, and increase resource efficiency. This ecosystem not only promotes collaboration but also ensures the advancements in AV technology benefit a broader society with improved traffic flows, driving safety, and sustainability in mobility. DriveX is built upon existing open-source research and simulation platforms OpenCDA and MetaDrive, with significant enhancements and developments to create an ecosystem and tools to meet diverse stakeholders’ needs. The project will identify these needs through extensive engagement with communities in cyber-physical systems, transportation, and artificial intelligence, as well as partnerships with the AV and smart mobility industry. Planned activities include defining the ecosystem's organization and governance, executing a strategic roadmap for sustainable development, and deploying DriveX in operational settings. The project will foster a new community-driven platform, featuring an interconnected toolchain of simulators, data management systems, benchmarks, and a model zoo that evolve as the needs of the user community evolve. By implementing strategies for ecosystem discovery, community building, and sustainability, DriveX aims to facilitate a productive exchange of efforts and ideas across research communities, accelerating the pace of innovation and development in automated driving and intelligent transportation. 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 project plans to address the technological limitations of time-domain terahertz imaging systems through a cross-disciplinary and integrated research-education program. Time-domain terahertz imaging systems provide time-resolved and multispectral amplitude and phase information of an imaged object. However, existing time-domain terahertz imaging systems are slow, bulky, and complex, due to their single-pixel nature, which has prevented the use of these systems in practical applications. This project aims to investigate and develop computational imaging frameworks based on diffractive neural networks to augment the unique functionalities of a newly introduced terahertz focal-plane array (THz-FPA) based on plasmonic nanoantenna arrays. By digitally increasing the space-bandwidth product of the THz-FPA, real-time, Mega-pixel, multispectral, 3D terahertz cameras could be realized for the first time. In addition to advancing terahertz imaging science, the proposed research on computational imaging algorithms based on diffractive neural networks could potentially create ubiquitous and low-power systems at different parts of the electromagnetic spectrum that can be realized using relatively simple and compact imagers. This research will be integrated with the education and training of cross-disciplinary and diverse graduate and undergraduate students through access to resources and knowledge in computational imaging, machine learning, terahertz devices and imaging systems, as well as new course development. Public outreach activities through organizing workshops and symposia, public interviews and articles in news media and the internet, and high school seminars will complement the research activities. The proposed effort aims to explore the use of diffractive optical networks to create a spatial encoder to form a super-resolution terahertz imaging system benefiting from diffractive visual processing. This will be based on the joint optimization of a passive diffractive optical network composed of transmissive layers placed before the THz-FPA, followed by a shallow electronic neural network that post-processes the THz-FPA output. This diffractive encoder – electronic decoder pair will enable operation with limited pixel count and size at the THz-FPA, achieving super-resolution over a large field-of-view at a high framerate. The developed terahertz imaging hardware based on plasmonic nanoantenna arrays and computational imaging algorithms based on diffractive optical networks will provide a high-throughput, high-resolution, and large-field-of-view solution to fully exploit all the advantageous features of terahertz waves for imaging, sensing, and material inspection; in addition, the terahertz imaging experiments will provide a deeper understanding about the critical system specifications for real-world applications. Prototypes of the developed terahertz imaging systems during this project will be assessed for non-destructive structural evaluation and hyperspectral terahertz imaging applications. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2025 · 2024-07

Project Summary Machine learning (ML)—i.e., the use of computer algorithms that can automatically learn from sampled data and make predictions or decisions without explicit programming—is increasingly important in a wide array of applications, from image and speech recognition to product recommendation systems. Meanwhile, synthetic chemistry plays a central role in the development of medicines, agrochemicals, fine chemicals, and new materials, but the field has traditionally shown a strong aversion to adopting ML tools. A fundamental challenge in synthetic chemistry is to expedite access to high-value building blocks in a predictable and efficient manner to accelerate discovery programs. However, the development and optimization of new synthetic methodologies have traditionally relied on empirical methods. This trial-and-error approach wastes crucial time and resources, limits the likelihood of unexpected discoveries, and fails to identify reactivity cliffs or rationalize the role of additives. The goal of this proposed project is to integrate ML with synthetic chemistry to provide solutions to these longstanding challenges, particularly in the contexts of med-chem library preparation, process optimization, and rapid assembly of chiral bioactive structures. Two aims of this career development application are: (a) Mentored phase (K99): My short-term goal is to learn ML and data science tools, while developing ML workflows that reduce the number of experiments needed to obtain the desired outcome of any chemical reactions (i.e., optimization). This will be realized by undertaking three distinct types of optimization campaign, in the form of three case studies (A1, A2, and A3) that reflect those typically encountered in chemistry settings. (b) Independent phase (Roo): Armed with a better understanding of ML and data science, my long-term goal is to facilitate design and discovery of robust new asymmetric methods. This will be achieved by engaging in three different case studies (B1, B2, and B3) where stereoselectivity is currently poor or nonexistent. These projects will enable me to create my own niche in catalytic research. Integration of my established expertise (asymmetric synthesis and comp chem) with that of the host lab (ML, data science, and photoredox catalysis), together with enabling technologies from Merck and Genentech (HTE), will collectively confer the capability to accomplish these overall goals. The excellent facilities of UCLA will be augmented by close industry collaboration and the active support of the C-CAS consortium. Overall, through this fellowship, I will gain critical mentored training in both academic and industry settings, build new professional skills, and achieve distinctive academic independence in biomedical research.

NIH Research Projects · FY 2025 · 2024-07

PROJECT SUMMARY/ABSTRACT Prostate cancer is the second leading cause of male cancer death. Advanced prostate cancer, whether present at the time of diagnosis or arising after treatment of localized disease, responds to androgen deprivation, but invariably fails and recurs as castration-resistant prostate cancer (CRPC) which is the main cause of prostate cancer-associated mortality. Heavily treated tumors, particularly those treated with secondary hormone therapies, frequently acquire a neuroendocrine phenotype (NEPC), which currently accounts for 15-20% of CRPC. NEPC is commonly characterized by expression of neuroendocrine markers, an aggressive clinical course, and downregulation or loss of androgen receptor (AR) that diminishes responsiveness to androgen deprivation therapies, making it the most lethal and currently incurable subset of prostate cancer. Thus, there is an urgent unmet need to define new therapeutic strategies for adeno-CRPC and NEPC. Our recent studies identified ATAD2 as an up-regulated druggable protein in treatment-induced NEPC. Moreover, we demonstrated that ATAD2 inhibitors dramatically suppress NEPC cell and tumor growth. The goals of the study are to test the therapeutic potential of ATAD2 inhibitor alone or in combination therapy settings in pre-clinical models of adeno-CRPC and NEPC and define new mechanisms through which ATAD2 regulates prostate tumorigenesis. The goal of this proposal is based on strong preliminary results. We propose to use cell line models of adeno-CRPC and NEPC as well as patient-derived xenografts (PDXs) of adeno-CRPC and NEPC to test the proposed combination therapies in pre-clinical settings. The aims of the proposed project are: Specific Aim 1. Test the levels of ATAD2 in a large cohort of clinical specimens. Specific Aim 2. Investigate the functional role of ATAD2 in advanced prostate cancer and define the molecular mechanisms through which ATAD2 contributes to prostate tumorigenesis and NEPC. Specific Aim 3. Test the therapeutic potential of ATAD2 inhibitors alone and in combination with therapies used as a standard of care for prostate cancer in adeno-CRPC and NEPC growth and metastasis.

NIH Research Projects · FY 2025 · 2024-07

PROJECT SUMMARY/ABSTRACT There is an incredible need to find new biomarkers to improve our ability to personalize cancer therapy, to identify those that will have toxicity from treatment. This growing need has led to the request for proposals focusing on these problems and searching for solutions, entitled PAR-19-325 “Clinical Characterization of Cancer Therapy- Induced Adverse Sequelae and Mechanism-based Interventional Strategies.” Unfortunately, there are few biomarkers that have been identified that can predict toxicity radiation therapy (RT). RT is a cornerstone of cancer treatment and used to treat over 2/3 of cancer patients. This is particularly notable in the context of localized prostate cancer, which is an exceedingly common cancer for which RT is a standard of care treatment. Because survival is so high, late toxicity after RT and its impact on quality of life (QOL) is critical. In fact, prostate cancer patients have the highest cancer treatment–related years lived with disability worldwide, likely reflecting the high incidence and the high cure rates of modern therapy. RT-related adverse sequelae are related to the complex local host-specific response to therapy, indicating that germ-line biomarkers, that are present in all of a patient’s cells, will be the most likely place to find biomarkers predicting toxicity. Single nucleotide polymorphisms in microRNA, termed miR-SNPs, have been to be functional biomarkers that can identify patients with altered stress responses to therapy. However, most previous efforts studying germline DNA have largely ignored miR-SNPs, as these are not captured in most DNA evaluation platforms. Our prior work has identified a panel of these biomarkers predicting significant late genitourinary (GU) toxicity to RT for prostate cancer patients. The PROSTOX biomarker specifically predicts for late GU toxicity after stereotactic body radiotherapy (SBRT), an advanced form of RT that uses advanced technology to deliver high doses of radiation per treatment session, condensing the RT course to just 5 sessions. Our group has also shown, in a randomized trial, that increasing the physical precision of SBRT delivery reduces post-SBRT GU toxicity as well. In this proposal, the goal is to further validate the predictive power of PROSTOX in additional cohorts, to expand these biomarkers to predict those at risk of acute toxicity that can also lead to chronic toxicity in prostate cancer patients, to investigate the biological differences in the response to radiation for those that have these signatures, and finally, to evaluate whether cutting-edge adaptive radiotherapy can help reduce toxicity in patients by further increasing the precision of SBRT delivery. Results from this proposal will significantly advance our ability to prevent significant late adverse sequelae from radiation for prostate cancer patients, who are a large group of patients who currently suffer greatly from chronic toxicity from treatment.

NIH Research Projects · FY 2025 · 2024-07

PROJECT SUMMARY/ABSTRACT Malignant gliomas are the most common primary adult brain cancer, affecting over 20,000 Americans each year. While molecularly targeted therapies have demonstrated tremendous successes in other malignancies, there is a lack of effective and personalized therapeutic approaches for these deadly brain cancers. The goal of this study is to investigate the complex phenomena of phenotypic plasticity in glioblastoma (GBM), the most lethal of all brain tumors in adults, in driving resistance to targeted therapies via non-genetic mechanisms facilitated by the unique brain microenvironment. Extensive preliminary data, which incorporate a large cohort of genetically diverse preclinical glioma models and drugs in clinical development for GBM, indicate the oncogene epidermal growth factor receptor (EGFR) maintains a radial glia (RG)-like cell state in GBM, which drives glioma initiation, heterogeneity, and plasticity. Moreover, preliminary data demonstrate pharmacologic ablation of EGFR induces lineage reprogramming from RG-like to neural/oligodendrocyte progenitor (NPC/OPC)-like programs uniquely in the brain microenvironment, which is hypothesized to drive rapid adaptation and resistance to EGFR-targeted therapy. Specific Aim 1 utilizes cutting-edge single-cell RNA sequencing (scRNA-seq) combined with an innovative genetic fluorescent reporter system and high-resolution barcoded lineage tracing to elucidate the evolutionary mechanisms driving lineage transformations following targeted therapy in GBM. Through the use of state-of-the-art preclinical models that capture the unique brain microenvironment, this approach is expected to provide unprecedented insights into the influence of the brain environment in driving cellular dynamics and lineage transformations during response to therapeutic interventions. Specific Aim 2 focuses on defining the signaling pathways that facilitate brain microenvironment-dependent lineage transitions that drive resistance to oncogene ablation in GBM. Using novel, clinical stage molecularly targeted therapeutics and innovative in vitro models of the brain microenvironment, this aim will uncover the pivotal signaling pathways and brain-derived factors that drive lineage transitions and contribute to resistance against EGFR-targeted therapies in malignant gliomas. Overall, this project represents a significant step towards a deeper understanding of the molecular and cellular mechanisms underpinning tumor heterogeneity and therapy resistance in GBM. By elucidating the dynamics of cellular state transitions and the role of the brain microenvironment in these processes, this work has the potential to significantly impact the development of more effective, targeted treatment strategies for patients suffering from this devastating disease.

NIH Research Projects · FY 2026 · 2024-06

PROJECT SUMMARY Pancreatic ductal adenocarcinoma (PDAC) is one of the most aggressive forms of cancer, known for its profound immune suppression and resistance to most therapies. One strategy to overcome these barriers is to leverage the effects of standard-of-care chemotherapy to increase extracellular ATP and promote anti-tumor inflammation. However, PDACs express high levels of ectonucleotidases ENPP1, CD39, and CD73 that quickly convert extracellular ATP released by dying tumor cells to immunosuppressive adenosine. In a recent Phase IA/1B clinical trial at UCLA for patients with Borderline Resectable PDAC (NCT03970252), neoadjuvant FOLFIRINOX chemotherapy and PD-1 inhibition produced changes of a more permissive anti-tumor immune microenvironment (TME) and was associated with excellent overall survival. However, compensatory adenosine signaling increased in post-treatment tumors, potentially hindering anti-tumor immunity. To strategically address this problem, a follow-up Phase 1/2 trial (NCT05688215) was initiated at UCLA in collaboration with Arcus Biosciences that introduces a small molecule inhibitor of CD73 to neoadjuvant FOLFIRINOX and PD-1 inhibition to limit adenosine in the PDAC TME. This proposal aims to extensively evaluate pre- and post-treatment patient specimens from this trial to define the impact of CD73 inhibition on tumor adenosine metabolism, signaling and -mediated immunosuppression (Aim 1). Predictors of response and resistance to CD73 inhibition will be identified. In Aim 2, autochthonous tumor models, implantable models of metastasis and human tumor explants will be used to define the roles of ENPP1 and CD39 as regulators of ATP breakdown and whether they cooperate with CD73 to generate adenosine. In addition to ATP, the ectonucleotidase ENPP1 degrades the natural STING ligand cGAMP, also released by tumor cells after DNA damaging chemotherapy, to further reduce anti-tumor inflammation. Therefore, the proposed studies in Aim 3 will build on novel preliminary data showing that adenosine directly inhibits STING activation in myeloid cells and will explore whether ENPP1 inhibition cooperates with CD73 inhibitor-mediated adenosine depletion to enhance STING signaling in the TME. These experiments will draw on a newly developed anti-human ENPP1 antibody and ENPP1 humanized mouse model. A transdisciplinary team with expertise in PDAC biology, tumor immunology, clinical trials, bioinformatics, biostatistics and mass spectrometry with a strong track record of working together has been assembled to complete the proposed studies. This project not only will provide a comprehensive understanding of ATP and adenosine metabolism in the PDAC TME but also identify the immunologic consequences of these changes. Overall, it has the potential to advance treatment outcomes for patients with this challenging cancer type by effectively reversing adenosine-mediated immunosuppression to unleash the full potency of chemo-immunotherapy.

NIH Research Projects · FY 2026 · 2024-06

PROJECT SUMMARY/ABSTRACT Children with chronic absenteeism, defined as missing 10% or more school days, make up 13%-16% of public school students and are disproportionately from marginalized populations such as low-income, non-English speaking, Black, Latinx, and students with disabilities. Chronically absent students experience more chronic illnesses, like asthma and obesity; are more likely to have behavioral health problems like depression and substance use; and are less likely to graduate from high school, which is a key social determinant of lifelong morbidity and mortality. school-based health centers (SBHCs) may improve health equity for students with chronic absenteeism by reducing barriers to accessing care, addressing the specific health conditions associated with chronic absenteeism, avoiding the need to miss school to access healthcare and ultimately improving academic outcomes like attendance, grade point average, and graduation rates. However, few studies characterize healthcare utilization and diagnoses for students with chronic absenteeism, or whether SBHC use increases primary care utilization, reduces emergency and inpatient care, and improves academic outcomes. We propose a 5-year study to determine whether SBHCs improve healthcare utilization and academic outcomes for students with chronic absenteeism. We capitalize on unique partnerships in Los Angeles with one of the largest managed care organizations (Kaiser Permanente Southern California, KPSC), Medicaid insurance plans (LA Care), and the 2nd-largest school district in the nation (LAUSD), serving ~500,000 students a year. We propose to A) link electronic health and billing data from community clinics, managed care organizations, and 25 SBHCs in LAUSD with school district demographic and academic measures from 2015-2025. Using this unique dataset, we will B) identify children with different patterns of chronic absenteeism and characterize their diagnoses and C) healthcare utilization, so that we can better identify pediatric populations at high-risk for unmet health needs and tailor health services, such as SBHCs, to meet their needs. We will then D) test whether students in high absenteeism classes who access SBHCs have reduced emergency department visits (primary outcome) and hospitalizations, increased utilization of primary care, and improved academic performance (attendance, grade point average, and graduation rates) compared to a propensity weighted sample of chronically absent students who access community clinics or a managed care organization alone. Finally, we will use the results of our analyses to engage students with chronic absenteeism in a human centered design process to identify and prototype SBHC interventions, including the expansion of or integration of SBHCs with other health systems, to improve care for children with chronic absenteeism. This study examines whether SBHCs can improve health equity for an important marginalized population and is aligned with NIMHD’s mission to improve minority health and reduce health disparities.