University Of California, San Diego

NSF Awards · FY 2026 · 2026-10

Engineers for Exploration is an experiential program that enables undergraduates to conduct computer systems research that addresses technology gaps in scientific exploration. These undergraduates are provided with an impactful research project, supported by a multi-tiered mentor network and structured collaboration with researchers, scientists, and explorers. Research problems they get to work on are framed by real-world, high-impact applications, providing participants with an inspiring, multidisciplinary research experience. The Engineers for Exploration Research Experience for Undergraduates Site offers research projects rooted in real-world applications and guided by mentorship from scientists and explorers. These use-inspired artificial intelligence research projects include the creation of machine learning and digital signal processing algorithms to automatically classify bird calls from acoustic wildlife sensors, the building of novel sensing strategies on the Smartfin embedded computing system to allow surfers to collect coastal oceanographic data, and the development of FishSense underwater 3D camera systems that automate the collection of fish measurements to turn recreational divers into citizen scientists aiding in fisheries management. Engineers for Exploration involves participants in multidisciplinary research projects spanning embedded systems, remote sensing, digital signal processing, computer vision, machine learning, robotics, and data analytics. The projects develop innovative computer systems that support exploration, and are deployed worldwide by scientists and explorers. For participants, the site fosters mentorship, leadership, and teamwork skills, providing a positive, memorable experience that encourages them to pursue further opportunities, such as research training or graduate education. 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 2026 · 2026-09

High-quality genome data are being produced at a pace that is changing evolutionary biology. These data can clarify how species are related, but they also bring a hard problem: evolution is not uniform across the genome. This project develops new mathematics and algorithms for estimating species histories from whole-genome data while allowing a separate history for each region. The approach builds on a recently proposed, fast, and accurate method that uses simple scores computed from patterns in the data but is not yet well understood theoretically. By explaining why its scores work, finding better scores, and extending the method to additional settings, the project will make genome-scale evolutionary analysis more accurate, scalable, and robust. The resulting tools will be distributed as open software and taught through software schools. The project will also train students at the interface of mathematics, computer science, and biology. Its long-term benefits include stronger tools for biological discovery, including work relevant to biotechnology, invasive species, and disease outbreaks, and new ideas for artificial intelligence and machine learning methods for analyzing large heterogeneous datasets. This project will develop a mathematical framework for quartet-based linear scores for species tree estimation from whole-genome alignments. The starting point is CASTER, a site-based method whose empirical accuracy and scalability come from scoring site patterns over quartets and aggregating those scores without enumerating all quartets. The theory of these scores is currently incomplete. The project will characterize valid linear scoring schemes under hierarchical sequence-evolution and gene-tree-evolution models, including the multispecies coalescent, models with multi-copy genes, and substitution-rate heterogeneity. It will analyze the algebraic structure of the score space, connections to phylogenetic invariants, and extensions to site pairs and multi-site patterns. The project will also study the probabilistic properties of score gaps, including their signal-to-noise behavior, in order to design more accurate scores and to develop site-based estimators of branch length, branch support, local histories, and genomic outliers. The resulting algorithms will be implemented by extending CASTER and tested on simulations and large empirical datasets. The work combines applied probability, statistical theory, graph-based algorithms, and algebra, while connecting to machine learning through scalable statistical inference from large heterogeneous genomic data. 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 2026 · 2026-08

Evolution is the process by which species change over time and adapt to their environment. However, understanding and predicting evolution is difficult because its course is neither completely random nor completely pre-determined. In other words, if we “rewound the tape of life” and “replayed” it, the outcomes of evolution would likely be different, but not totally random. How repeatable evolution is depends in part on how an organism interacts with other species through competition for resources, nutrient exchange, predation, etc. This project uses laboratory experiments to study how interactions between two microbial species, a yeast and an alga, affect how repeatable their evolution is. As such, this project will advance our basic understanding of evolution in the context of species interactions. It will also develop a new curriculum to help college students in California and Mississippi strengthen their scientific reasoning skills and participate firsthand in scientific research. The repeatability of evolution has been described both theoretically and experimentally across many individual species and some ecological communities, but how it depends on the type and strength of ecological interactions between species remains unclear. To address this question, the investigators will conduct a long-term evolution experiment in a tunable two-species mutualism formed by the yeast Saccharomyces cerevisiae and the alga Chlamydomonas reinhardtii under two environmental conditions. In one environment, each species can survive without the aid of the other. In the other environment, the alga can survive alone, but the yeast cannot. The first aim of the project is to characterize the evolutionary changes of this community at the genomic and ecological levels and compare repeatability across the two environments. To probe possible mechanisms underlying differences in repeatability, the second aim will measure the range of adaptive mutations available to both partners across both environments. The third aim is to test whether mutualistic partners repeatably evolve specializations to one another. A research-teaching module integrated with this aim will be created to provide research experiences for undergraduates with minimal background in evolution, ecology, or research. Overall, the project will help scientists and students better understand the process of evolution in an ecological context. It will also generate new genomic resources for C. reinhardtii and develop methods for quantifying partner specialization in mutualistic systems. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-07

The investigator studies a range of central geometric and analytic problems in complex analysis by incorporating techniques from partial differential equations, algebra, and differential geometry, together with methods from complex analysis. The project will develop substantially new methods and deepen the current understanding of several important problems in several complex variables. Through research collaboration, mentoring, seminars, and other academic activities, the proposed work will provide accessible research opportunities and valuable training experiences for graduate students and postdoctoral researchers. The research focuses on three major directions: (1) geometric and algebraic aspects of the Bergman metric and kernel; (2) Bergman logarithmic flatness and obstruction flatness; and (3) rigidity problems in several complex variables. The proposed research also has significant interactions with other areas of mathematics, including algebraic geometry, complex geometry, dynamical systems, and mathematical physics. The project will develop innovative methods for studying both the interior geometry and boundary structure of complex manifolds through Bergman and Kahler-Einstein metrics. It will also deepen the understanding of rigidity phenomena for holomorphic and CR mappings, which are among the central objects of study in several complex variables. 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 2026 · 2026-07

National Data Platform (NDP) addresses the challenge of data and computing resource integration facilitating the nation's capacity to pursue scalable AI-driven discovery and innovation by establishing a federated and interoperable ecosystem that connects distributed data repositories and computing facilities across the U.S. NDP will reduce barriers of entry for data-driven science and enable researchers, educators, and learners to accelerate their research and innovation objectives. It will expand access to advanced capabilities and unlock the nation's full research potential across regions, institutions, sectors, and disciplines. NDP will also enable hands-on experiential learning, curriculum development, data challenges, and workforce training aligned with national priorities in AI and data science. By federating existing public investments and engaging private resources into an accessible AI innovation ecosystem, NDP will strengthen U.S. competitiveness and expand both research participation and workforce engagement in an increasingly AI-driven world. NDP will integrate geographically distributed data, computing, and AI resources and services into a cohesive, federated framework that supports end-to-end, AI-enabled scientific workflows. This will be delivered through interoperable, production-grade services and middleware that streamline secure access, integration, composability, and use of data, models, and workflows across edge, cloud, and high-performance computing (HPC) resources. By enabling heterogeneous and multimodal data types to seamlessly connect with AI-optimized computing capabilities, NDP will enable new classes of multimodal, data-intensive workflows that advance scientific efficiency, reproducibility, and methodological rigor. Its scalable distributed architecture will allow users to move from data ingestion and fusion to model training, simulation, and inference, within a unified environment, reducing the barriers associated with today’s fragmented cyberinfrastructure. NDP will employ a structured build–test–validate–deploy pipeline to mature project-developed and community-contributed tools into robust, reusable, and production-ready digital assets. Through co-design with domain scientists, applications across multiple disciplines will expose shared workflow patterns that NDP will generalize into extensible, reusable services for broad adoption. Collectively, these approaches will expand capacity across the U.S. to pursue scalable, AI-enabled scientific discovery and innovation and strengthen the nation’s leadership in AI. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2026 · 2026-06

ABSTRACT The objective of this proposal is to develop conjugative methods for rapidly and reliably integrating genes into diverse native bacteria isolates from both mice and humans. This will enable the creation of engineered native bacteria (ENBs) which can be used to study the gut microbiome and develop new therapeutics for chronic diseases. Compared to lab strains of bacteria and other bacterial strains used for live bacterial therapeutics, native bacteria have the unique ability to engraft and colonize the host long term. Therefore, ENBs have great potential as a platform technology to advance our understanding of the gut microbiome from a mechanistic level and as a therapeutic for the treatment of disease. However, engineering native bacteria remains challenging due to their inherent resistance to genetic manipulation. This research aims to address these issues by developing protocols that facilitate high- throughput and site-specific genome integration in native bacterial strains. The investigators will develop two protocols to address this problem. For Aim 1 the researchers will develop a protocol using Tn7 transposon integration for high throughput gene integration in 42 strains of native bacteria (e.g. Escherichia coli, Citrobacter spp., Enterococcus spp.) isolated from the gut of mice and humans in different disease contexts. This will result in a collection of validated chassis bacteria that are ready for use in high-throughput synthetic biology-based approaches by multiple collaborating labs across several institutions. For Aim 2 the investigators will develop a protocol for iterative genome integration of genes using conjugation and phage recombinases in both mouse and human gut native E. coli. The investigators will engineer a butyrate biosynthesis pathway into native E. coli. This protocol will allow for the introduction of complex, multi-gene pathways into ENBs. The expected outcomes of this research are validated chassis strains and new techniques that will unlock high-throughput and reliable construction of ENBs. This will have a positive translational impact by advancing the development of ENBs as therapeutics for the treatment of chronic diseases. The proposed work directly aligns with the mission of the National Institute of Allergy and Infectious Diseases (NIAID), specifically the development of new tools to understand diseases and its prevention and treatment. By enabling researchers with the methods to engineer native bacterial strains more effectively, this proposal will facilitate the creation of new ENB based therapeutic approaches for chronic diseases, including metabolic and inflammatory disorders.

NIH Research Projects · FY 2026 · 2026-06

PROJECT SUMMARY Coccidioidomycosis infections cause almost a third of community-acquired pneumonia cases in Arizona, and reported cases have doubled from 2013-2021. Coccidioidomycosis is an infection caused by inhalation of fungal spores from the soil-dwelling Coccidioides genus, and can lead to chronic lung infection, meningitis, or death. The disease is a growing public health concern in the southwestern US, particularly in Arizona, which reports around 10,000 cases per year—nearly two-thirds of US cases. The disease imposes a significant public health and economic burden, with Arizona’s 2019 cases alone estimated to cost $736 million in lifetime expenses. Yet, public health responses have been hindered by critical gaps in understanding of the environmental drivers of infection, particularly the role of dust exposure. Changing hydrometeorological factors, such as increasing aridity and more frequent dust storms, may be contributing to rising incidence, as dust and dust-generating processes (e.g., wind erosion, construction, agriculture) likely promote aerosolization and dispersion of Coccidioides spores. However, the relationship between dust exposure and coccidioidomycosis incidence remains unclear, with some studies linking short-term, extreme dust events to outbreaks and others finding no impact. This project aims to fill these knowledge gaps by leveraging a novel database of spatiotemporally refined dust concentration estimates and all reported coccidioidomycosis cases (n > 126,000) in Arizona from 2005 to 2022. Our secondary data analysis study has three aims: 1) Estimate the effects of daily ambient dust exposure on coccidioidomycosis incidence and identify populations most vulnerable to these exposures, focusing on determinants of social vulnerability such as low income, poor housing quality, and racial/ethnic minorities; 2) Assess the impact of extreme dust events on coccidioidomycosis incidence, and identify populations most vulnerable to these exposures; 3) Investigate whether hydrometeorological factors, including oscillations between wet and dry conditions that may influence spore growth and dispersal, modify the relationship between dust exposures and coccidioidomycosis incidence. Our results will help guide public health messaging and target prevention strategies in Arizona to both the general population and clinicians surrounding when and where risk of coccidioidomycosis transmission is highest; provide important evidence on the contribution of environmental change to transmission; and enable identification of populations and geographies particularly sensitive to dust exposures.

NIH Research Projects · FY 2026 · 2026-06

SUMMARY. Peripartum depression (PPD) is widely prevalent, affecting about 1 in 4 birthing women worldwide, and includes the antenatal onset of depression (AND). PPD is associated with adverse clinical outcomes in both the child and mother; suicide is now a leading cause of death in the first year postpartum. Selective serotonin reuptake inhibitors (SSRI) are the preferred first-line pharmacotherapy for PPD and are effective at alleviating depressive symptoms. Despite this, the safety of SSRI use during pregnancy for both mother and fetus remains under debate, as its full spectrum of mechanisms of action remain elusive. The maternal immune system and placenta, whose regulation are both critical for successful pregnancies, are known to express serotonin signaling systems and may be affected by serotonin dysregulation in AND or by SSRIs. The immense knowledge gap of the effects of AND and SSRIs in these systems prevents informed outcome assessment for untreated and SSRI- treated AND. There is, therefore, a critical need to advance our understanding of AND and SSRI therapy effects in the immune system and placenta to improve clinical management of pregnancies affected by AND. The overall goal of this proposal is to comprehensively test the effects of AND and SSRI treatment in the maternal-placental serotonin-immune systems during pregnancy. Our central hypothesis is that pregnancy-induced flux of serotonin levels changes immune function and may unmask AND in patients at risk, which subsequently drives dysfunction of the immune system and placenta, contributing to adverse clinical outcomes. The anti-inflammatory and serotonin-enhancing properties of SSRI therapy, in contrast, restores the function of these systems. We will test our hypothesis with two aims: In Aim 1, we will determine the contribution of circulating CD4 T cells to modulating depressive symptoms during AND and antenatal SSRI therapy, analyzing longitudinally collected peripheral blood from pregnant women with untreated, or sertraline-treated AND, and at-risk controls via single-cell mass cytometry. We will build a predictive model of third trimester AND severity from first trimester profiles and validate these profiles in a demographically distinct test cohort. A preclinical non-invasive, translationally relevant model of sertraline administration will then link immune and behavioral outcomes. In Aim 2, we will determine the extent to which AND and SSRI induce molecular and functional changes in the placenta, performing whole genome and RNA sequencing as well as single-cell spatial imaging (mass cytometry) on placental tissue matched with the cohort in the first aim. Finally, we will correlate molecular alterations in placental tissue with clinical histopathologic features, maternal health parameters, and fetal outcomes. The expected outcome is a further understanding of how the immune and placental serotonergic systems are altered by AND and antenatal SSRI therapy. The positive translational impact of this project is improved outcome prediction for untreated versus SSRI-treated PPD. It will provide a framework for risk-benefit analysis of antenatal SSRI use in future clinical trials that will ultimately improve public health by reducing hesitancy toward antidepressant use in pregnancy.

NIH Research Projects · FY 2026 · 2026-06

Project Summary This is a submission of a K01 application by Dr. Armin Ahmadi, PhD, a postdoctoral fellow at University of California San Diego (UCSD). Through this proposal Dr. Ahmadi intends to establish himself as an independent investigator studying the intersection of mitochondrial dysfunction, microvascular health, and physical functioning in chronic kidney disease (CKD). This project aims to establish cell free mitochondrial DNA (cf-mtDNA) as a novel biomarker of kidney and vascular dysfunction in CKD and to evaluate nicotinamide riboside (NR) as a potential therapeutic strategy. Candidate: Dr. Ahmadi’s training objectives include 1) to become proficient in the measurement of mtDNA and noninvasive microvascular techniques and translation to patient-oriented research; 2) to gain expertise in advanced statistical methods relevant to clinical trials; and 3) to learn the necessary skills to develop an independent research program and to design and lead clinical trials. Dr. Ahmadi will accomplish these objectives through mentorship, coursework, and participation in workshops. He has assembled a multidisciplinary team of scientists including Dr. Joachim Ix, an expert clinical trialist (primary mentor), Dr. Rakesh Malhotra, a nephrologist and a leader in the field of microcirculation assessment and interpretation in humans (co-mentor); Dr. Mark Hepokoski, an expert in mitochondrial biology and a pioneer in cf-mtDNA measurements (co-mentor) and Dr. Ronit Katz, a PhD level biostatistician who focuses her work on clinical trials (statistical mentor). In addition, Dr. Mary McDermott, an expert in metabolic interventions for peripheral vascular disease and with NR trial expertise. Research: Current CKD biomarkers provide limited insight into disease progression and vascular dysfunction, two key contributors to CKD-related complications. Dr. Ahmadi’s overall hypothesis is that cf-mtDNA may be used as a biomarker of kidney and vascular dysfunction providing mechanistic insight on the relationship between the loss of kidney microvasculature integrity that lead to reduced kidney function and mitochondrial dysfunction. In Aim 1, Dr. Ahmadi will evaluate the relationship between cf-mtDNA and skin microvascular measurements among 250 individuals with CKD. In Aim 2a, Dr. Ahmadi will evaluate the association of circulating and urinary cf-mtDNA with CKD progression in 250 participants with CKD. In Aim 2b, Dr. Ahmadi will determine the associations between urinary and plasma cf-mtDNA with kidney vascular health in 75 individuals with available kidney biopsy. Lastly, in Aim 3, Dr. Ahmadi will evaluate the feasibility and efficacy of 12 weeks of nicotinamide riboside (NR) supplementation in modifying circulating cf-mtDNA levels, microvascular function, and physical function in CKD among 30 CKD subjects. Both the training and research plans will lay the groundwork for use of mitochondria-related biomarkers to assess kidney and microvascular dysfunction in CKD in clinical trials to improve CKD early detection and develop therapeutic strategies to improve clinical outcomes in the next and independent phase of Dr. Ahmadi’s career.

NSF Awards · FY 2026 · 2026-06

The San Diego Supercomputer Center (SDSC) at the University of California, San Diego (UCSD) acquires and deploys Expanse2, a multi-Petaflop/s supercomputer system consisting of the latest processors and accelerators with an advanced Ethernet-based network to support data intensive workloads. This system responds to national priorities in research and development, with the goal of enhancing national competitiveness. It supports key research areas, including electronic chip design automation, advancing drug discovery with Artificial Intelligence (AI), application of AI in digital agriculture, study of instabilities and turbulence in fusion, and open inference models. As a national resource, Expanse2 replaces Expanse and provides advanced cyberinfrastructure (CI) to continue support for the Long Tail of Science, which reflects breadth in science, researchers, and their institutions. It will also enable Artificial Intelligence and Machine Learning (AI/ML) research, as well as data-intensive analysis at ever-increasing scales and complexity to process vast and growing data sets. The project centers on delivering capabilities that increase the capacity and performance for users of batch-oriented and science gateway computing, and that enable new research in AI applications and data analytics in all areas of science and engineering, which increasingly depend on heterogeneous, distributed, and integrated CI. Expanse2 includes innovations in system software, AI/ML frameworks, operations, and support that extend its capabilities far beyond the limits of the physical system. Expanse2 is an integral part of the national CI by offering user friendly allocation and scheduling policies, supporting rapid application development and data analysis via Jupyter notebooks, providing extensive AI/ML frameworks and libraries, connecting with federated and extensible data ecosystems, and providing user support focused on delivering the tools, training, and support needed to increase productivity and decrease time-to-science results. Expanse2 features an innovative storage system that includes an all-NVMe file system ideal for AI/ML workloads, integrated with a large capacity storage system, which provides fault tolerance in terms of system availability and data integrity. Publicly accessible network interfaces on compute nodes facilitate integration with distributed data and compute sources, such as public cloud, Open Science Grid services (OSDF and OSPool), and campus systems. Expanse2 is deployed in SDSC's energy-efficient data center and connected to multiple high performance research and education networks at 400 Gbps, serving thousands of users who require high performance yet modest-scale resources. Allocation and usage policies, honed from years of experience at SDSC, are tailored to achieve fast turnaround and responsiveness. A team of experts in computational science, AI/ML, data-intensive computing, scientific workflows, and large-scale systems operations supports Expanse2 at the highest levels of utilization, reliability, and usability. The strong training and education components of the Expanse2 project are focused on domestic workforce development from universities, teaching colleges, and high school students. The system’s advanced compute and GPU architecture with high-performance storage are ideal for use by industry to foster public-private partnerships, especially startups from AI, advanced manufacturing, pharmaceutical, and medical device companies. Knowledge gained through the project leads to improvements in algorithms, software, and systems management tools, as well as a better understanding of how integrated CI can be configured to support emerging research challenges. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2026 · 2026-06

Project Summary Social dysfunction following trauma is a pervasive reality for trauma victims in the United States, with one study finding that nearly half (45.2%) of trauma patients experience social deficits after the traumatic event. Traumatic events are often experienced in social contexts, yet most preclinical studies model trauma-related disorders with stressors experienced in isolation. Therefore, there is a gap in knowledge about how the social context in which trauma is experienced affects future social behavior. The experiments outlined in this proposal will fill this gap, and address Goal 1 of the NIMH Strategic Plan for Research to “Define the Brain Mechanisms Underlying Complex Behaviors.” Human studies have reported that an interesting phenomenon following trauma is social affiliation– the tendency to come together after traumatic events. Social buffering, which describes the presence of a conspecific attenuating the biological response to a traumatic experience, is thought to be a mechanism underlying the protective effects of social support. Yet, our understanding of the neural mechanisms underlying social buffering is poor. Despite the work from the field of social buffering that has studied the impact of social support during shared trauma, no research to date has studied alterations in the neural regulation of social affiliation after shared trauma. The neurons of the anterior cingulate cortex (ACC) are poised to facilitate this phenomenon as they are known to be involved in empathy, stress regulation, and observational fear learning. Using cutting-edge techniques in behavioral pose-estimation (Aim 1), and microendoscope calcium imaging in ACC (Aim 2), this proposal will test the central hypothesis that shared trauma, as opposed to solitary trauma, alters the neurobiology of ACC to foster social affiliation. As sex is among the most significant risk factors for the development of PTSD, with females having a two to three times higher risk of developing PTSD, both aims will be conducted in male and female mice. A successful outcome of this project would provide a mechanistic understanding of how shared trauma affects social behavior, revealing a circuit-level target to develop interventions for social dysfunction in trauma-related disorders. The proposed research will take place in the laboratory of Kay Tye at the Salk institute in affiliation with the University of California, San Diego. Through graduate coursework, mentorship, and hands-on learning, Jianna will gain experience in rodent behavior and calcium imaging techniques and analysis. These skills will be valuable for the completion of the proposed research, and for Jianna’s future career as a physician-scientist.

NIH Research Projects · FY 2026 · 2026-06

Project Summary/Abstract Interferon gamma (IFN-γ) is a cytokine that plays a critical role in protective immunity by activating numerous downstream signaling cascades. Due to its important role, IFN-γ expression is tightly regulated to prevent uncontrolled activation and inflammatory signaling. Previous research revealed that IFN-γ mRNA controls its own translation through a unique autoregulatory cycle where the IFN-γ mRNA folds into a structure that activates protein kinase R (PKR). Activated PKR dimers phosphorylate eukaryotic initiation factor 2α (eIF2α), which subsequently suppresses IFN-γ translation. When PKR activation is reduced, the IFN-γ mRNA can be translated and the resulting IFN-γ protein upregulates PKR expression to maintain this finely tuned cycle. Dysregulation of these pathways has been linked to the progression of various autoimmune or neurodegenerative diseases. The IFN-γ mRNA forms a large pseudoknot-containing structure comprised of the 5' UTR and first 26 translated codons. Since the structure extends into the coding region, the mRNA is proposed to dynamically refold between conformations, accommodating a dual function as a PKR activator and translation template. While secondary structures of the alternate conformations have been proposed, there is limited knowledge about the 3D structures and their involvement in regulating IFN-γ expression. This project will integrate structural biology and biochemical approaches to characterize the IFN-γ mRNA conformational dynamics and elucidate the molecular basis of PKR activation. The first aim will employ cryo-EM and functional assays to characterize these distinct IFN-γ mRNA conformational states, and the second aim will establish the link between these conformations and PKR activation by capturing the structure of the IFN-γ-PKR complex. This research will examine the structural basis for IFN-γ mRNA's dual regulatory functions and advance the understanding of how conformational dynamics enable precise control of gene expression. Since IFN-γ overproduction and PKR dysregulation drive multiple disease states, these mechanistic insights may unveil opportunities for new RNA-targeted therapies. This project will be conducted in the Toor lab at UCSD, which provides a collaborative and innovative environment with access to many resources for physical and intellectual support. In addition to technical skills, the training plan will incorporate additional experiences for professional growth in scientific communication and mentorship. The combination of these efforts will provide the optimal foundation for cultivating the expertise needed for success as an independent researcher in RNA-based therapeutic discovery.

NIH Research Projects · FY 2026 · 2026-06

PROJECT SUMMARY In the past decade, immunotherapies have revolutionized the way many cancers are treated. By activating the immune system, these treatments enable the body's own defense mechanisms to attack cancer cells and halt tumor growth. Unfortunately, many cancers are refractory to these new therapeutics and there continues to be a desperate need for drugs with novel mechanisms of action to enhance the long-term effectiveness of cancer treatment. Type I interferons (IFN-Is) are attractive candidate drugs to fill this role since these cytokines “interfere” with the growth of tumors; indeed, manufactured IFN-Is have been used in the clinic to treat more than 10 types of cancers but the outcomes were somewhat disappointing. Due to negative feedback regulation of IFN signaling, most interferon stimulated genes (ISGs) are transiently expressed, and these feedback mechanisms ultimately suppress the anticancer effectiveness of administered IFN-Is over time. This process also limits the signaling from intrinsically produced IFN-Is, critical to the success of chemo-, radiation, and immuno-therapy. In pioneering work over many years, we unraveled the details of the negative feedback pathway and identified the ubiquitin protease family member USP18 as the central regulator driving feedback suppression of the IFN-I response. By single-cell RNA-seq analysis, we revealed that cancer stem cells are especially sensitive to USP18 depletion- triggered cell death. These novel and exciting discoveries demonstrate that targeting USP18 represents an effective and promising, yet to-date underutilized, therapeutic option for treating cancer by directly promoting immunogenic cancer stem cell death and by increasing both innate and adaptive immune responses against cancer. With the goal to discover small molecule inhibitors of the USP18-mediated IFN-I feedback loop, we conceptualized an innovative high-throughput screening (HTS) assay based on our novel IFN-I signaling biosensor cell line with CRISPR/Cas9 inserted fluorescent reporters and validated it in a pilot screen of known compounds. Since it is currently unclear which specific mechanisms for USP18 inhibition are druggable by small molecules and provide the best therapeutic window, we opted for a phenotypic discovery approach combined with target deconvolution assays. Additionally, we developed a pipeline of secondary and mechanistic assays that validates the hit compounds and provides initial insight into the targeted components of the feedback pathway. Here we propose to 1) identify inhibitors of the USP18-mediated feedback pathway through a large phenotypic HTS, 2) validate the hits in secondary assays and map their effects to the specific pathway components, and 3) evaluate the therapeutic anti-cancer potential of the final chemical probes. Successful completion of these studies will identify and validate small molecule inhibitors of USP18 that can be used to probe the therapeutic potential of the different mechanisms for inhibiting the USP18-mediated negative feedback regulation of IFN signaling. Additionally, such immunomodulators could be further development toward a new class of cancer therapeutics.

NIH Research Projects · FY 2026 · 2026-06

Summary/Abstract People with HIV (PWH) remain vulnerable to central nervous system complications (e.g., neurocognitive impairment) despite antiretroviral therapy (ART) that suppresses viral replication. While many etiologies of these complications exist, mitochondrial dysfunction and inflammation are consistently implicated yet seldom studied simultaneously. PWH also use cannabis more frequently than the general population and recent evidence by our group and others indicates that cannabis may protect PWH from mitochondrial damage by improving metabolic homeostasis and reducing inflammation through triggering receptor expressed on myeloid cells (TREM) 2. Moreover, this mechanism may be more important as PWH age, with the average age of PWH currently being >55. This proposed multidisciplinary, translational project will combine a clinical observational study with cellular and in vivo preclinical models to determine the effects of cannabis use on TREM2-mnediated changes in mitochondrial function in the brain in PWH. The preclinical models will include a) personalized ex vivo/in vitro modeling of mitochondrial toxicity in brain macrophages and neurons, and b) a mouse model for HIV-induced neurotoxicity (Eco-HIV) and age-related neuropathogenesis (TREM2*R47H). Using this multilevel approach, we will test the hypothesis that cannabis effects on TREM2-mediated changes in brain mitochondrial homeostasis vary based on patterns of use: moderate use will be associated with beneficial effects, due to the TREM2 promoting and anti-inflammatory properties of cannabis, but chronic daily use will have detrimental effects. In a cohort of aged (>50 years old) PWH across a range of cannabis use from naïve to daily users, we will measure in plasma and cerebrospinal fluid (CSF) a panel of biomarkers that reflect the mitochondrial homeostasis, TREM2 function, and inflammation. These readouts will be correlated with neurocognitive assessments and PET imaging for TSPO, a marker of neuroinflammation associated with mitochondrial function (Aim 1). We will model brain macrophages using personalized ex vivo cultures of monocyte derived microglia collected from Aim 1 study participants and culture these cells with neurons to identify mechanisms of mitochondrial dysfunction (Aim 2). Using a cross-species approach, we will investigate how different precise doses of cannabinoids interact with HIV and TREM2 variants to affect mitochondrial homeostasis in wild-type and TREM2*R47H mice infected with EcoHIV (Aim 3). This highly innovative, multidisciplinary research proposal is very likely to generate impactful translational knowledge regarding mechanisms of pathogenesis and guide future therapeutic interventions. With our combined clinical and pre-clinical expertise in HIV infection, substance abuse, imaging, and mitochondrial homeostasis, we are uniquely suited to perform the proposed research.

NIH Research Projects · FY 2026 · 2026-06

PROJECT SUMMARY Preeclampsia (PE) is a devastating hypertensive disorder of pregnancy and a leading cause of maternal and fetal mortality and morbidity worldwide. It affects 2-10% of women and accounts for 16% of maternal deaths related to childbirth in the United States. Maternal renal dysfunction is commonly associated with PE, characterized by glomerular endotheliosis and proteinuria, or appearance of protein in the urine. PE is a common cause of pregnancy-related acute kidney injury, and increases a woman’s risk of developing chronic renal, hypertensive, and cardiorenal disorders in the long term. Despite the renal manifestations of PE classically associated with this syndrome, little is known about the mechanisms of maternal kidney dam- age in PE. The placenta plays a key role in the development of PE, leading to widespread maternal endo- thelial dysfunction, hypertension, and systemic multi-organ failure in PE. Extracellular vesicles (EVs) containing RNA, protein, and lipid cargo are continuously released from the placenta directly into the ma- ternal peripheral circulation and interact with maternal organs including the kidney. Markers of proximal tubular injury are reportedly increased in severe preeclampsia, and GFP-positive placental EVs localize to the maternal proximal tubules in a mouse model. However, no studies have investigated the potential mech- anisms by which EVs from the placenta and maternal serum may interact with proximal tubules and other renal cell types, leading to kidney injury in PE. In the proposed research, we will utilize for the first time a novel, cutting-edge, validated 3D microfluidic human proximal tubule kidney-on-a-chip model (Emulate, inc.) engineered to mimic the proximal tubular lumen and peritubular vasculature to study the effects of placental and serum EVs from early and late-onset severe PE and normal pregnancy on renal cells. We will use a mouse model of EV-kidney interactions in pregnancy in tandem in order to dissect the functional effects of EVs from early and late-onset severe preeclampsia on maternal proximal tubules ex vivo. We will also investigate the effects of these EVs on the transcriptome of diverse renal cell populations including proximal tubular and glomerular cell types in this mouse model. Next Generation Sequencing techniques including bulk and single cell RNA-Seq will provide high-resolution molecular information on the effects of EVs on the maternal kidneys in PE. Relevance The only treatment option for PE remains removal of the placenta and delivery of the baby, often prema- turely. This research aims to understand the pathology of maternal kidney injury in PE in relation to EVs in pregnancy and will lead to the identification of novel molecular pathways and targets which could be ma- nipulated by therapeutics to reduce the acute and chronic renal effects of PE.

NIH Research Projects · FY 2026 · 2026-05

This project will develop and apply a novel, data-driven modeling approach to guide HIV and hepatitis C virus (HCV) prevention resource allocation among people who use drugs (PWUD) in the United States. Despite advances in HIV prevention and treatment, including long-acting injectable ART, the United States continues to fail to meet the needs of PWUD, a population central to intersecting HIV, HCV, and overdose crises. A key reason is the absence of a rapid, scalable, and comprehensive way to capture and act on the preferences and behaviors of PWUD. Revolutionary advances in large language models (LLMs) now make it possible to create highfidelity “digital twins” — artificial-intelligence powered simulations of individuals that reflect their intervention preferences and even behaviors. Using a community-based participatory process, we will train an LLM on data from multiple PWUD cohorts to generate PWUD digital twins that simulate locally-specific intervention preferences. These digital twins will be integrated into an epidemic and economic model that simulates HIV and HCV transmission, overdose, and related complications, across numerous jurisdictions in the United States. The integrated modeling framework will be used to evaluate the impact and cost-effectiveness of different HIV/HCV prevention and treatment strategies, including responses to potential funding constraints. Epidemic model outputs will be shared through an interactive dashboard designed in consultation with public health departments. Ultimately, our project aims to improve intervention implementation and reduce HIV, HCV, and overdose. Our project includes the following activities: 1) Co-design, development, and validation of a PWUD-informed LLM. 2) Digital twin simulations within a HIV/HCV transmission and overdose model to inform resource allocation. 3) Dissemination and implementation of the epidemic and resource allocation dashboard to county health departments. The proposed research will enable rapid, data-driven evaluation of prevention strategies and resource allocation options. It aligns with the NIH Office of AIDS Research priority to reduce new HIV infections and address HIV coinfections.

NIH Research Projects · FY 2026 · 2026-05

SUMMARY Reliable tools for early prediction and effective treatments of spontaneous preterm birth (sPTB) are missing. This is due to an incomplete understanding of the subclinical pathobiology preceding sPTB. The long-term goal of this project is to integrate pathological, physiological, and psychological domains preceding sPTB to define mind- body crosstalk mechanisms that enable robust sPTB prediction and reveal potential targets for therapeutic intervention. The overall objective in this application is to establish neuroimmunoendocrine pathways as critical modulatory links between maternal mental state, immune imbalance, and sPTB. The central hypotheses are that (i) catecholaminergic (i.e., dopaminergic) signaling in maternal central and peripheral systems mitigates inflammation-induced parturition, supporting pregnancy maintenance, and that (ii) disruptions in neuroendocrine signaling may underlie an immune shift in the maternal circulation as well as at the maternal-placental interface promoting sPTB. The central hypotheses will be tested by pursuing three specific aims: 1) Determine whether central dopaminergic activity mitigates inflammation-induced PTB in mice; 2) Define predictive neuroimmunoendocrine trajectories of sPTB and their placental inflammatory signatures; and 3) Determine the effect of dopamine on the human maternal-placental interface, comparing sPTB vs. TB. Under the first aim, an established murine model of PTB will be combined with targeted neuromodulation of central reward circuitry to evaluate the effect on birth timing and peripheral immune state. In the second aim, human pregnancies at risk for sPTB will be longitudinally evaluated for multiple psychological (psychometric surveys, allostatic load) and physiological (blood immune function, non-invasive urine markers, cerebrospinal fluid markers, placental pathology) domains to define mind-body crosstalk prior to and at manifestation of clinical parturition pathology. In aim three, human endometrial stromal cells will be used to identify and causally link the effect of catecholamines on inflammatory signaling, differentiation, and protein secretion at the maternal-placental interface. The research proposed in this application is innovative because it represents a substantive advancement from the status quo by defining the basic principles of the maternal brain–immune–reproductive system axis in healthy pregnancies and those complicated by sPTB. The proposed research is significant because it is expected to offer a strong scientific framework whereby new strategies for the clinical management of patients at risk for sPTB can be developed.

NSF Awards · FY 2026 · 2026-05

Why is the universe dominated by matter, while antimatter is virtually absent? Why does visible matter constitute only 15% of all matter, while the vast 85% majority—Dark Matter—has not been detected by any experiment on Earth as of today? Answering these questions requires detecting extremely rare physics events, a challenge that has driven physics for over four decades. This CAREER project aims to transform rare event search by developing Artificial Intelligence (AI) algorithms for two world-leading experiments: KamLAND-Zen and XLZD. The PI’s team will develop Large Language Model (LLM) powered AI agents to aid KamLAND-Zen in deciphering the secret of matter-antimatter asymmetry, while creating AI-accelerated simulations to optimize the design of the XLZD Dark Matter detector. To cultivate an interdisciplinary AI workforce to go beyond disciplinary silos, this project aims to train “Data Physicists” by teaching AI to physics students and sparking the interest of data science students in physics research. This will be achieved through the Rare AI for Science Ecosystem (RAISE), a student-led platform that features popular science stories and LLM-assisted AI tutorials. Technically, this project aims to leverage surrogate models and LLM to secure the scientific success of world-leading rare event search experiments over the next decade. In the near term, as the KamLAND-Zen experiment concludes its data taking, the PI proposes to integrate KamNet, a spatiotemporal neural network, into the final analysis to deliver world-leading limits on neutrinoless double-beta decay, a process that could explain the universe's matter-antimatter asymmetry. In the medium term, to support the construction of the next-generation KamLAND2-Zen detector, the PI’s team will design, test, and deploy a hardware-accelerated dual-agent AI system. This architecture leverages real-time AI for sub-millisecond event reconstruction at the detector front-end, while using LLM agents at the back-end to autonomously operate the detector and identify anomalies that eluded human’s attention. In the long term, as the ultimate Generation-3 dark matter experiment XLZD begins its simulation and design campaign, the PI proposes to develop next-generation Rare Event Surrogate Models to systematically optimize the detector's design for maximum sensitivity and discovery potential. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2026 · 2026-05

Abstract Meningomyelocele (MM, aka Spina Bifida) is the most common CNS congenital malformation, with heritability estimated at 70-75% 1,2 and a cumulative incidence of 3.72/10,000 live US births. There are more thatn 200,000 patients in the US suffering from MM. MM is a debilitating condition, the most common neural tube defect (NTD) compatible with survival, and with associated morbidity and mortality. National folic acid (FA) supplementation since 1998 has reduced incidence by approximately 30-50%, but substantial disease incidence remains, and there is little understanding of mechanism of this important Gene-Environment interaction (GXE). Here we propose to study the molecular basis of human MM through broad recruitment and analysis of trios with narrowly defined inclusion/exclusion criteria, stratified by prenatal FA exposure. We hypothesize that de novo mutations (DNMs) make a critical contribution to risk of MM, and that FA increases the mutational burden required for phenotypic expressivity. MM shares features with other severe congenital malformations such as in heart or brain that show strong DNM contributions. Our preliminary data point to a strong role for DNMs, in the form of SNPs, INDELs and SVs. To support this application, our preliminary work has: 1] Founded the Spina Bifida Sequencing Consortium (SBSC) and enrolled a cohort of 1500 MM trios to date using social media, and historic cohorts. 2] Stratified by FA exposure (+FA or -FA) based upon exposure history and detailed questionnaire. 3] Performed whole exome or whole genome sequencing on 851 previously enrolled trios, in part through the NICHD Gabriella Miller-Kids First (GMKF) program. 4] Identified a recurrent chr. 22q11.2 deletion including the CRKL gene that increases MM risk approximately 14-fold, and in animal models is influenced by FA exposure 3. 5] Identified 187 candidate MM risk genes including four recurrently mutated genes that define protein-interaction networks 6. 6] Identified a stronger DNM burden in +FA trios than -FA trios, consistent with our main hypothesis. However, these preliminary findings require confirmation and further analysis before they can be utilized in a clinical context. Here we propose: 1] To ascertain 300 carefully phenotyped new trios per year, stratified by FA supplementation status, to double the cohort size. 2] To perform complete genetic analysis for de novo and inherited variants on the entire 3000 trio cohort, compared with control trios. 3] To identify a set of high- confidence MM risk genes using a statistical framework. 4] To stratify genes based upon +FA vs -FA patient status and identify unique functional pathways. 5] To assess spatial transcriptomic differences in +FA vs -FA conditions in animals. The application proposes to build on SBSC successes, develop a multifactorial MM risk model, clarify mechanisms of FA function, and uncover high-impact disease mechanisms, that will advance the first effort towards clinical genetic testing in MM.

NIH Research Projects · FY 2026 · 2026-05

There is currently an approximate 60% success rate in diagnosing Alzheimer’s disease (AD) using cognitive tests. New FDA approved blood tests testing for amyloid plaque byproducts could soon bring this number to 90%, among patients with cognitive deficits. AD will now be detected earlier, and will consequently be more likely to be treated effectively. As amazing as these new indicators might be, effective treatments will still require an understanding of how the disease progresses, and how it affects the prefrontal and hippocampal structures, on a case-by-case basis. The ventral hippocampus (vHPC) is in bi-directional interactions with both the prefrontal cortex and the dorsal hippocampus (dHPC) and is likely to be an early locus of cognitive dysfunction. The traditional view is that vHPC and dHPC, even though they are part of the same anatomical structure, may perform different functions and that vHPC is much less involved in spatial navigation than dHPC. We propose that this view emanate from the fact that most of the studies in spatial navigation were conducted in relatively small environments where the large place fields of vHPC are not functionally relevant, because they do not discriminate between spatial locations (low position information content). Based in part on these findings, very little attention has been placed on the role of vHPC in normal or abnormal aging. In this 2-year proposal, we aim to fill this gap by characterizing the role of dHPC and vHPC in very large environments where the large place fields of vHPC are likely to be functionally relevant to spatial navigation. We further hypothesize that the encoding of space at both levels of the structure will be related to the complexity of the tasks. We propose to test these ideas by contrasting the multi-field and multi-scale neural encoding by place cells and the behavior of the animals in two tasks with low (foraging) and high (multi-goal maze) cognitive loads respectively. To measure the complexity of the multi-goal mazes, we propose to use the quantitative tools of Space Syntax, a common framework in Architectural Sciences that is rarely used in Neuroscience, if at all. We also propose to sample hippocampal activity in animals with increasing amount of experience and training, as in human aging, throughout their life span, starting from young adulthood until senescence. Whether differences in encoding between ventral and dorsal levels vary with complexity and age are observed or not, this study will likely provide important information and motivation for further studies of this and related structures not only in the context of spatial navigation in complex environments, but also in the manner in which they differentially processes simple and complex memories. Altogether, this proposal will advance our understanding to the role of the longitudinal axis of the hippocampus in complex spatial information processing and its changes as subjects age. It will shed further light of the contribution of this axis, as a whole, into the deficits observed during AD, dementia or mild Cognitive Impairments in humans.

NIH Research Projects · FY 2026 · 2026-05

Methamphetamine (METH) remains a major public health challenge for the clinical management of people with HIV (PWH) and for controlling HIV transmission. METH can increase HIV transcription, alter immune responses, and disrupt immunometabolism and the blood-brain barrier (BBB). These and other processes combine to worsen brain health, including cognition, mood, and impulsivity. Because PWH with active METH use disorder (aMUD) often struggle with medical adherence, some do not achieve durable viral suppression with oral antiretroviral therapy (ART). Advances in long-acting injectable (LAI)-ART and medication-assisted treatment (MAT) for addiction could change this. The combination of LAI-ART and MAT that includes LAI naltrexone could improve the durability of viral suppression and reduce METH use in PWH with aMUD, which creates an opportunity to investigate the brain health effects of these changes. Addressing this requires a multidisciplinary framework. We propose to leverage these advances and integrate methods from implementation science, pharmacology, virology, immunology, and clinical and basic neuroscience to understand how LAI-ART and MAT for aMUD interact to alter brain health and its biological underpinnings. The Brain-Focused Research on Antiretrovirals and Methamphetamine (Brain-FRAMe) program will pursue a single, transformative goal: to improve the clinical care and brain health of PWH with aMUD. The proposed research will be organized into three subgoals: Clinical Implementation, Brain Health Phenotyping, and Biological Mechanisms. Investigators will form four teams that will study the same participants over 52 weeks from different perspectives. The Clinical Implementation Team will deploy and evaluate the implementation of LAI-ART and MAT in PWH with aMUD in a primary care HIV clinic. The Neurobehavioral Phenotyping Team will characterize the neuromedical, neurocognitive, and psychiatric changes that result from LAI-ART and MAT. The Biophenotyping and Pharmacology Team will assess effects of METH and MAT on ART pharmacokinetics as well as changes in the HIV reservoir and proteomic and transcriptomic profiles in blood and CSF. Using biospecimens from the same participants over time, the Basic Science Mechanisms Team will interrogate targeted biological mechanisms that underlie the effects of aMUD, LAI-ART, and MAT, such as BBB integrity, immunometabolism, and immune competence. The robust and reproducible methods used by all teams will generate data that will be analyzed individually and in aggregate by the Data Integration and Analysis Group. The Scientific Program Coordinator and Contact PI will work with the MPIs and program staff to coordinate all aspects of the program, monitor progress, foster communication, curate data and biospecimens, convene advisory boards, and assure synergy and integration. Using these and other innovative methods, Brain-FRAMe will achieve within five years its goal of transforming the clinical care and brain health of this highly vulnerable group of people, which should also reduce new HIV infections in our community.

NIH Research Projects · FY 2026 · 2026-05

PROJECT SUMMARY/ABSTRACT In 2025, the American Cancer Society projects 154,270 new cases and 52,900 deaths from colorectal can- cer, making it the third most diagnosed and deadly cancer in the U.S. Current prevention strategies—such as surveillance colonoscopy and prophylactic colectomy—are invasive, costly, and poorly adhered to. Systemic chemotherapy, though standard for treatment, is limited by off-target effects, poor tolerability, and long-term complications. Microbial therapies offer targeted, self-renewing delivery and the potential to prevent adenoma- to-cancer progression, but progress has been limited by the transient engraftment of probiotic or lab-adapted strains, which fail to provide sustained tumor interaction or therapeutic delivery. This gap in knowledge reflects the absence of microbial tools and mechanistic insight needed to enable long-term, programmable col- onization in the tumor microenvironment. Additionally, clinical translation of microbial therapies requires ef- fective biocontainment to regulate colonization, persistence, and function, minimizing safety risks. There is a critical need for a microbial chassis that can stably engraft, deliver therapeutic functions over time, be genetically programmable, and be safely cleared after completing its task. Our lab addresses this need with a novel innovation: engineered native bacteria—host-derived E. coli strains that durably colonize the gut, are genetically tractable, and enable controlled luminal function. Our preliminary studies show that these bacteria can perform targeted functions and modulate tumor growth in preclinical models. Our central hypothesis is that engineered native E. coli can durably engraft in the gut to prevent or treat colorectal cancer and be selectively cleared. The overall goal is to develop a microbial platform for durable, targeted delivery of therapeutic molecules to the gut. Over the next five years, we will test this hypothesis through three aims: (1) Test whether engineered native E. coli that modify bile acids can prevent colorectal cancer in high-risk hosts. We will define how sustained microbial delivery of specific bile acid functions alters cancer risk. (2) Engineer native E. coli to activate chemotherapeutics specifically within tumors. This will establish a tumor-targeted precision-microbiome therapy using intratumoral prodrug conversion. (3) Develop a biocon- tainment system enabling selective, non-antibiotic clearance of engrafted strains. This will use an osmo- lality-targeting strategy to enhance safety and control. The expected outcome is a modular, engrafting microbial platform that prevents tumor formation, activates localized chemotherapy, and allows for controlled clearance. This approach has strong translational potential, offering a sustainable, low-toxicity therapy that addresses rising colorectal cancer rates in younger adults and aligns with national goals to reduce chronic disease through precision interventions.

NIH Research Projects · FY 2026 · 2026-05

ABSTRACT Pancreatic ductal adenocarcinoma (PDAC) accounts for ~90% of pancreatic cancers and is the 3rd leading cause of cancer-related death in the US. Most patients are diagnosed at an advanced stage due to a lack of symptoms and effective screening, and only ~15% are eligible for surgery. Standard chemotherapies offer limited survival benefits and cause substantial toxicity. The 5-year survival rate remains only ~8%. While KRAS inhibitors may offer improvement, tumors quickly develop resistance, highlighting the urgent need for new treatment approaches. A hallmark of PDAC is its desmoplastic, hypoxic tumor microenvironment (TME) generated via extracellular matrix deposition by cancer-associated fibroblasts (CAFs). Desmoplasia impairs drug delivery and suppresses antitumor immunity due to acidosis and poor vascularization. Extracellular acidification, from the “Warburg effect” and increased lactate production by cancer cells, shapes the desmoplastic TME cell interplay and drives PDAC malignant phenotypes, but is not targeted by current PDAC therapies. One of the two principal acidity-sensing G protein coupled receptors in humans, GPR68 (OGR1), is expressed in cancer cells, CAFs, and immune cells in PDAC. Its pH-induced cAMP signaling can drive cancer proliferation and immunoevasion, fibrotic and immunosuppressive phenotypes in CAFs, and terminal exhaustion of immune cells. These associations provide a compelling motivation for the development of GPR68-targeting pharmacological agents and for mechanistic studies of their effects in PDAC models: an endeavor we undertake in this proposal. We hypothesize that by simultaneously blocking GPR68 effects in cancer cells, CAFs, and T cells, pharmacological inhibitors of GPR68 can normalize the PDAC TME, slow tumor progression, and ultimately enhance the efficacy of concurrent chemo- and immunotherapies. In Aim 1, we will use GPR68 structures, artificial intelligence (AI) -powered molecular design, medicinal chemistry, and pharmacological assays to optimize the low-potency GPR68 inhibitors we identified in preliminary work. In Aim 2, we will map the effects of GPR68 inhibition on cell phenotypes and paracrine signaling in human patient-derived in vitro and ex vivo models of PDAC. This exploratory project will validate GPR68 as a therapeutic target in PDAC and yield small molecules that suppress tumor growth by blocking its acidity-induced signaling in cancer, immune, and stromal cells. This innovative approach could improve patient outcomes in this highly lethal cancer and set the stage for preclinical and clinical development. Composed of experts in GPCR pharmacology, medicinal chemistry, drug discovery, tumor biology, and clinical oncology, our multidisciplinary team is ideally positioned to succeed.

NIH Research Projects · FY 2026 · 2026-05

SUMMARY/ABSTRACT The Problem: Cancer cells often reside in environments deprived of growth factors and nutrients. Yet they thrive by rewiring their signaling through autocrine and paracrine “secrete-and-sense” circuits, enabling self- sustaining growth. This phenomenon, known as growth signaling autonomy, is one of the earliest recognized hallmarks of cancer and central to cancer stemness, tumor progression, and treatment resistance. However, the core molecular mechanisms driving these circuits remain poorly defined, limiting therapeutic progress. Central premise: Our data identify GIV (Gα-interacting vesicle-associated protein) as a master regulator of cancer cell signaling autonomy. GIV is a multimodular scaffold protein that integrates signaling across monomeric and heterotrimeric G proteins—elements typically studied in isolation—into a coherent, feed-forward signaling circuit that sustains EGF/EGFR-dependent growth. Endogenously expressed in many breast cancers, particularly triple-negative breast cancers (TNBCs), GIV enables cells to sustain tumor progression under nutrient- and growth factor-limiting conditions. In contrast, ER+ BCs, which often lack endogenous GIV, acquire it via intercellular transfer from stromal neighbors, highlighting a novel mode of proteomic exchange. We hypothesize that GIV promotes cancer stem cell-like states, tumor growth, and drug resistance under nutrient- and growth factor-limited conditions. GIV-dependent cancer cell signaling autonomy may also extend to neighboring GIV-deficient cancer cells via paracrine signaling, enhancing cooperative growth among heterogeneous cancer cell populations. Our team—experts in breast cancer biology, GIV signaling, and in the use of both animal and non-animal models (patient-derived organoids and tissue microarrays) alongside synthetic biology tools (cells with engineered circuits) and quantitative live-cell imaging—is uniquely positioned to test this model through integrated experimental and computational approaches. Our aims are to discover how GIV’s modular domains orchestrate key states of cancer cells driving tumor progression in the setting of: (1) intrinsic autonomy in GIV-expressing TNBCs or (2) intercellular transfer- dependent acquired autonomy in ER+BCs; and 3) establish the cooperative dynamics by which GIV-expressing autonomous cells support non-autonomous GIV-deficient neighbors in heterogeneous tumors. We leverage human organoids and tissue microarrays to preserve translational potential and ensure clinical relevance. Impact: This work will redefine cancer signaling by identifying the first mechanistic framework of secrete- and-sense growth factor autonomy within the EGF/EGFR pathway. It will also chart how a single intracellular hub (GIV) coordinates autocrine and paracrine signaling across diverse cell populations to drive tumor progression. By mechanistically linking cancer growth signaling autonomy to stemness, plasticity, tumor heterogeneity and therapeutic resistance, our findings will uncover new intervention points and provide a transformative conceptual advance in targeting signaling rewiring in breast cancer and beyond.

NIH Research Projects · FY 2026 · 2026-05

Summary Despite recent advances in treatment, the overall mortality for head and neck squamous cell carcinoma (HNSCC) remains high, and current treatment regimens incur significant long-term morbidity. Immune checkpoint blockade (ICB) therapies have revolutionized HNSCC treatment, but <30% of HNSCC patients respond to these immunotherapies. This highlights the urgent need to identify novel therapeutic options for HNSCC to improve mortality, reduce morbidity, and enhance the response rate of ICB. The mechanisms by which dysregulated oncogenic signaling drives HNSCC initiation, immune escape, progression, and metastasis remain incompletely defined, limiting our ability to develop effective precision prevention and treatment strategies. In this regard, our laboratory contributed to the discovery that the persistent activation of the Hippo- YAP/TEAD signaling network is one of the most frequent dysregulated oncogenic mechanisms in HNSCC. By combining the use of genetically engineered mouse models and single-cell transcriptomics, we have recently shown that when combined with p53 disruption, YAP activation is sufficient to reprogram epithelial progenitor cells into cancer-initiating cells and to establish an immune-evasive tumor microenvironment (TME), thereby promoting HNSCC progression. These findings, together with the recent development of clinically active TEAD inhibitors (TEADi) that disrupt Hippo-YAP/TEAD function, create a timely and unique opportunity to target TEAD for therapeutic benefit. Our premise is that aberrant Hippo-YAP/TEAD activation integrates proliferative signaling and immune escape programs, driving HNSCC initiation and progression. Our central hypothesis is that the Hippo-YAP/TEAD signaling network represents a major HNSCC oncogenic and immune escape driver, and that in turn, the disruption of Hippo-YAP/TEAD signaling will provide a novel therapeutic approach to reduce the growth of HNSCC lesions and dismantle their immune evasive mechanisms, thereby establishing an immune permissive TME and increasing the response to ICB. This will be explored in our two aims: 1) exploiting synthetic lethal and gene interaction networks to expose druggable systems vulnerabilities upon Hippo-YAP/TEAD blockade; and 2) to establish the impact of targeting the Hippo-YAP/TEAD signaling network on the immune TME at the single-cell and spatial resolution, aimed at increasing the response to ICB. Ultimately, we aim to develop novel rational combinations of targeted and immune therapies for HNSCC patients.