University Of Texas At Austin

NSF Awards · FY 2026 · 2026-10

Cloud computing is essential to a growing number of science use cases, but configuring scientific environments for deployment in the cloud can be challenging given that it often requires specialized knowledge in system administration, networking, security, and involves numerous configuration settings. Furthermore, many scientific workloads depend on tightly coupled virtual clusters, specialized hardware, fast interconnects, accelerators, and custom drivers. Managing this complexity consumes valuable time and attention that researchers could otherwise devote to science. This project develops an AI-based conversational assistant for configuring scientific computing environments that lets researchers describe what they need in everyday language and then creates the environment and verifies its integrity on shared research cloud infrastructure. The benefits of this approach range from increasing scientific productivity and lowering the cost of using cloud computing to enabling practical reproducibility of computational experimentation. The project designs and deploys an AI-based agent framework that can plan, provision, and validate scientific computing environments on open research computing infrastructure, such as Chameleon and Jetstream2. The framework combines large language models running on open, high-performance academic hardware with a set of software tools exposed through standard interfaces that include cloud-based services for resource provisioning, hardware and software environment templates, correctness checks, as well as validation benchmark suite. Key components include planning modules with built-in checks on resource limits, timing, and hardware compatibility; state and error handling modules that track multi-step workflows and summarize system events; and search pipelines that organize information from a wide variety of sources, including documentation, logs, help desk tickets, and environment artifacts into a searchable knowledge base. 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

Quantum computers have the potential to accelerate important applications across critical domains, such as chemistry, physics, cryptanalysis, machine learning, drug discovery. By enabling us to solve these complex problems, quantum computers promise a transformative impact on our society. Today, quantum computers with hundreds to thousands of qubit devices are already available. However, despite remarkable progress in recent years, our ability to enter an era of quantum utility and advantage is severely hindered by the hardware's intrinsic vulnerability to errors. In reality, qubit devices do not retain information forever and quantum operations are inherently imperfect. These factors limit us from running most practical quantum algorithms because computational errors plague program outputs. This project aims to tackle this challenge by building quantum architectures and software methodologies that reduce the impact of errors through fault-tolerant program execution. This project is organized into three thrusts. The first thrust focuses on building automated and optimized program compilation methods. The project will primarily build application-specific instructions, optimize resource overheads of fault-tolerant operations, and develop methods for optimized program execution on large quantum systems. The second thrust focuses on advancing qubit control architectures to tackle inefficiencies in real-time instruction scheduling, error correction, and handling of complex errors. The project will design techniques to handle non-uniform operational latencies at scheduling time, build optimized error correction frameworks to detect errors in real-time with a particular focus on resource-efficient quantum error correction codes, and develop hardware-software co-design techniques to handle non-trivial errors, such as hardware defects, qubit losses. The third thrust focuses on building educational materials and engaging in research and teaching activities to train undergraduate and graduate students so that they contribute to the domestic quantum workforce. 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

Computational simulation is essential for designing electromagnetic devices, predicting radar and sonar signatures, improving medical imaging, modeling advanced materials and larger molecular systems, and testing physical systems before they are built. These applications often require solving mathematical models that are far too large for direct calculation using standard methods, especially in realistic three-dimensional geometries. A major obstacle is that the computer representation of the underlying physical system can become an enormous array of numbers (known as a "matrix"), so large that it is impractical to store or manipulate. This project develops new mathematical and computational tools that use randomization as an algorithmic engine: instead of exhaustively examining all interactions in a simulated physical system, the methods use carefully designed random probes to discover hidden structure and build compact representations. The resulting algorithms are expected to make large-scale simulations faster, more accurate, and less costly in memory, time, and energy. Potential benefits include improved tools for electromagnetic device design, radar and sonar modeling, medical imaging, nondestructive testing, materials modeling, molecular simulation, and simulations that combine several physical models or numerical methods. By strengthening a core capability of scientific computing, the project promotes the progress of science and supports national health, prosperity, and defense. The project also supports education by training a doctoral student in mathematical research, scientific computing, and high-performance computing, with connections to the Texas Advanced Computing Center. The work relates to national priorities in artificial intelligence by developing accuracy checks for AI-assisted scientific software and compact representations of physical systems that can support trustworthy machine-learning models and digital twins. The project develops fast randomized algorithms for global operators that arise in partial differential equations, wave propagation, inverse problems, sparse matrix computations, and other large-scale models in scientific computing. The central goal is to build data sparse representations of operators that are too large to form explicitly, using only the ability to apply the operator rapidly to selected input data. The research draws on numerical linear algebra, random matrix theory, computational harmonic analysis, high-dimensional probability, and rank structured matrix methods. It combines randomized probing, adaptive compression, graph-coloring strategies, structured sampling, and randomized embeddings to identify and exploit hidden low-dimensional structure in dense operators. These tools are used to accelerate direct solvers for large sparse linear systems by compressing the dense intermediate operators that arise during factorization. The project also develops stable multiplicative and unitary factorizations for rank structured matrices, randomized methods for oscillatory wave problems, and a posteriori error estimators that certify the accuracy of individual computations. A further goal is to reorganize the algorithms to reduce data movement and improve performance on modern high-performance computing platforms, including graphics processing units. The expected contributions are new algorithms that scale nearly linearly with problem size in important settings, more reliable direct solvers for difficult three-dimensional simulations, and a stronger mathematical foundation for randomized methods applied to continuum operators and large-scale scientific computing. 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

Modern agriculture increasingly relies on RNA interference (RNAi) constructs, which protect crops by silencing specific genes in pests and pathogens. These biotechnology tools are now used around the world, and the global RNAi market is on track to reach $9 billion by 2033. However, the effects of RNAi constructs on microbes in the environment are not well understood. Microbes drive key processes like nutrient cycling, soil health, and water quality. Harm to these microbial communities can threaten both ecosystem and agricultural stability. This project will study how RNAi molecules interact with environmental bacteria. It will also build new artificial intelligence (AI) tools to predict and prevent unintended harm. Project findings will help engineers design safer, more effective RNAi products. This project will also create hands-on learning modules for K-8 students, building early science literacy and inspiring the next generation of STEM leaders. This project will employ a multiscale experimental and computational framework to identify the mechanisms by which engineered RNAi biotechnologies, including small interfering RNAs (~20 bp) and double-stranded RNAs (~200-800 bp), interact with and disrupt non-target microbiomes. First, dose-dependent impacts of an array of RNAi constructs on microbial growth and the emergence of stress phenotypes will be quantified using high-throughput microcosm experiments. Variables to be tested will include microbiome composition, RNAi structure and sequence, RNAi concentration, and external matrix chemistries. Second, transcriptomic profiling will resolve primary (direct, sequence-specific gene silencing) and secondary (generalized stress response) effects of RNAi exposure in bacteria, using RNA sequencing and bioinformatic analysis to identify functional biomarkers of non-target RNAi activity. Third, chemostat experiments with a defined synthetic soil microbial community will assess how RNAi exposure reshapes microbiome composition and biogeochemical cycling, integrating 16S amplicon sequencing, fluorescence in situ hybridization, and targeted gene expression analysis. Across all objectives, AI-enabled machine learning algorithms, including explainable boosting machines and gradient-boosted ensembles, will be trained on these multimodal datasets to identify the RNAi construct features that most strongly predict microbial responses. This framework will generate quantitative toxicity and gene-based biomarkers, establish data-supported AI tools for assessing RNAi fate in bacterial communities, and produce design principles for next-generation biotechnology constructs with enhanced ecosystem compatibility. This work will advance a shift from descriptive risk assessment to mechanistic AI-informed prediction of RNAi-microbiome interactions, ultimately enabling proactive design practices for the rapidly expanding RNA biotechnology sector. To broaden the societal impact of this research, the project will develop and deploy hands-on, inquiry-based biotechnology learning modules for K-8, connecting with students through classroom instruction, regional STEM events, and after school programs. These activities will accelerate early bioliteracy and build a pipeline of future biotechnology leaders equipped to navigate the scientific, ethical, and societal dimensions of emerging RNAi technologies. 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 · 2026-06

Quantum computers promise capabilities far beyond those of today’s classical machines, with potential applications in areas such as materials discovery, secure communication, and advanced sensing. However, existing quantum processors face major engineering barriers to continued progress. Present day superconducting quantum processors are built as single, monolithic chips in which all quantum bits (qubits) reside on a single substrate. As systems grow, this architecture becomes increasingly fragile: crosstalk rises, wiring becomes unmanageable, and a single faulty component can compromise the entire processor. As a result, the monolithic approach cannot be scaled to the chip sizes or wiring densities required for future fault-tolerant machines. A promising alternative is a modular architecture in which many smaller, high quality quantum chips are interconnected to create a larger and more capable system. Realizing such modular systems requires new technologies for routing extremely weak microwave signals between chips without disturbing their delicate quantum states. This CAREER project will develop such a technology using a hybrid superconductor–semiconductor component known as the Josephson Junction Field Effect Transistor (JJFET). The JJFET marries the low loss, coherence preserving properties of superconductors with the voltage tunable control and nanoscale footprint of semiconductor transistors, enabling compact, efficient, and reconfigurable routers that can connect quantum chips on demand. Success in this project will provide a key architectural building block for scaling up future quantum computers. In parallel, the project integrates hands on training for graduate, undergraduate, and K–12 students, strengthening the nation’s quantum ready STEM workforce and broadening public engagement with emerging quantum technologies. Technically, the project will establish JJFETs as voltage controlled superconducting elements capable of routing single photon microwave signals between separate quantum modules with high speed, low loss, and low crosstalk. By leveraging a high transparency superconductor–semiconductor interface, the JJFET provides a gate tunable Josephson inductance that enables transistor like control of quantum microwave signals while preserving coherence. The research program will develop and characterize a suite of JJFET based microwave components, beginning with single pole switches optimized for switching speed, power dissipation and microwave loss. Building on this foundation, the project will demonstrate on demand routing of microwave photons between physically separated transmon qubits, with remote entanglement serving as a sensitive probe of routing fidelity and system level performance. The final phase will realize a multiport JJFET based transfer switch that provides dynamically configurable communication pathways for modular quantum processors. By replacing bulky, magnetic flux controlled circuitry with compact, voltage controlled elements compatible with semiconductor style scaling, the JJFET platform introduces a fundamentally new approach to designing routers for signals between quantum chips. This technology opens the door to dense integration of routing and signal processing elements, supports emerging quantum network architectures, and establishes a scalable framework for building the next generation of modular quantum information 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 · 2026-06

Data-driven personalized decision-making has become increasingly important across many fields, such as health sciences where tailoring treatments to individual patients can improve effectiveness and reduce adverse effects. Achieving reliable personalized decisions requires understanding cause-and-effect relationships between actions and outcomes. However, most real-world data sources, such as electronic medical records, health surveys, and social media data, are observational rather than randomized, making causal relationships difficult to establish. In these settings, hidden or unmeasured factors may influence both the actions individuals take and the outcomes they experience, leading to biased conclusions and unreliable recommendations if not properly addressed. This project will address this fundamental challenge by developing new statistical methods for learning optimal personalized decision rules from observational data when important confounding factors are not fully observed. The project will consider both single-stage and sequential decisions, with particular attention to continuous treatments such as medication dosages. A motivating application is kidney transplantation, where optimizing immunosuppressive therapy over time is essential to reduce the risk of graft failure while minimizing harmful side effects. By enabling more reliable individualized decision-making, this project will advance statistical science, machine learning, and artificial intelligence, support the training of students in modern data science, and contribute to improved health outcomes and broader societal well-being. This project aims to develop novel Bayesian causal methods for estimating treatment effects and optimizing individualized decision rules from observational data with unmeasured confounding. A Bayesian joint modeling framework will be introduced for treatment, outcome, observed covariates, and latent confounders, leveraging mild distributional assumptions to enable causal identification without relying on additional data sources required by many existing approaches, such as instrumental or proxy variables. The project will also develop a dynamic Bayesian causal modeling framework for longitudinal data, where treatment decisions and unmeasured confounders evolve over time. This framework will support the estimation of adaptive treatment regimes that respond to an individual’s evolving treatment history, outcomes, and characteristics. In addition, the project will design optimization methods for both single-stage and sequential decision-making, using posterior uncertainty to improve robustness in finite and unbalanced observational data settings. The methods will be evaluated through simulation studies and applied to large-scale real-world kidney transplantation data for studying optimal personalized and dynamic immunosuppressive dosing strategies. To facilitate broad dissemination, open-source software will be developed for implementation. The resulting framework and tools will provide a general approach to reliable personalized decision-making in biomedicine and other fields that rely on complex observational 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.

NIH Research Projects · FY 2026 · 2026-06

Project Summary/Abstract Ultra-processed foods (UPFs) now supply almost half of American toddlers' daily calories, with global sales of infant and toddler foods more than doubling to $67.9 billion over the past decade. Diets high in UPFs are increasingly implicated in cardiometabolic disease risk, yet consumers struggle to accurately identify UPFs and remain unaware of associated health risks. Mothers, who are primarily responsible for family food purchases, face the challenge of balancing preferences, health, and value under time and budgetary constraints. The food industry exploits these challenges by marketing UPFs as "kid approved" or "natural" with misleading health claims. Our multidisciplinary team proposes a comprehensive multi-method approach to understand and address the psychosocial-behavioral-marketing factors driving toddler UPF consumption in a rigorous three- aim study involving 1,200 participants. Aim 1 will survey a nationally representative panel of 800 mothers with toddlers to identify psychosocial, behavioral, child, and marketing factors associated with UPF consumption. Aim 2 will use ecological momentary assessment and lab-based tests with 200 mother-toddler dyads to identify real-time drivers of ultra-processed versus whole fruit and vegetable feeding and link consumption patterns to biological markers. Aim 3 will employ eye-tracking technology and controlled experiments with 400 mothers to evaluate how package design and promotional strategies influence attention and purchase intentions under various induced conditions. This comprehensive approach addresses critical gaps in understanding how marketing interacts with maternal psychosocial factors, child behaviors, and environmental contexts to drive toddler UPF consumption. The results will elucidate which mothers and children are most vulnerable to UPF marketing, how real-time factors influence feeding decisions, and which package elements drive purchases. Our findings will provide evidence-based targets for food and nutrition policy interventions designed to reduce UPF consumption among toddlers.

NSF Awards · FY 2026 · 2026-06

Understanding how humans interact with the physical world is a fundamental challenge in developing intelligent systems capable of assisting, learning from, and safely interacting with people. Consider a rehabilitation therapist remotely tracking patient recovery from everyday videos, novice athletes receiving feedback from expert demonstrations, or robots learning complex manipulation tasks by observing human actions. Realizing this vision requires artificial intelligence systems that understand not only visual appearance but also the underlying physics of human interactions, including how people apply forces, maintain balance, and manipulate objects in the three-dimensional space. However, current computer vision methods often produce visually plausible yet physically impossible reconstructions: objects float unsupported, bodies pass through solid surfaces, and interactions violate basic balance and force constraints. Such inaccuracies limit reliable deployment in health, safety, and robotics applications. This project develops a unified framework for learning physically grounded models of three-dimensional human-world interactions from real-world video, enabling more reliable analysis of movement, improved assistive technologies, and new tools for robotics and embodied intelligence. The project integrates research and education through student training, curriculum development, open software release, and outreach activities that connect computer vision with biomechanics, sports science and robotics. The research develops a scalable framework for physics-aware perception of human-world interactions. The work is organized in three integrated directions. First, it reconstructs physically consistent three-dimensional motion from monocular video by combining geometric reconstruction with dynamic simulation to estimate contacts, forces, and joint torques. Second, it leverages these simulation-consistent reconstructions to train generative models that capture the dynamics of human-object interaction beyond laboratory settings. Third, it incorporates physical reasoning into modern large-scale vision models, enabling systems that reason about effort, stability, and contact while preserving strong semantic understanding. By integrating established physical simulation tools with contemporary machine learning models and Internet-scale visual data, the project advances scalable approaches for understanding, predicting, and reasoning about human movement in natural environments. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2026 · 2026-06

Magnetic storms have caused major damage to communications and energy infrastructure in the past. Such storms have the potential to cause trillions of dollars of damage in the United States. Society is more vulnerable to these risks in areas where the Earth’s magnetic field is weak. Continent-scale weak regions have grown over the past century. Studying ancient magnetic fields will help society understand how magnetic storm risks change and evolve. This has been very challenging due to the time-resolution of datasets. This project determines whether coral skeletons contain records of the magnetic field with greater resolution. Corals have the potential to yield yearly records spanning tens of thousands of years. This project aims to unlock coral magnetic records by investigating how corals become magnetized in modern reefs. Extracting magnetic records from corals could provide a new proxy for the pre-historic magnetic field. Constraining past variations of the magnetic field could help predict its future behavior, and its potential impact to society. This project investigates whether corals preserve paleomagnetic records with sufficient temporal resolution to reconstruct geomagnetic field behavior on human-relevant timescales. Preliminary results demonstrate that some modern corals carry stable magnetic remanences consistent with the ambient geomagnetic field at the time of growth. The project will produce detailed paleomagnetic, rock-magnetic, and microscopic analyses of modern corals to characterize the magnetic mineral assemblages, determine the stability and origin of remanent magnetization, and assess how reliably corals record the Earth’s magnetic field during growth. These data will be used to evaluate the feasibility of extending coral paleomagnetic records into the fossil archive. Unlocking coral paleomagnetism will provide high-resolution geomagnetic time series advancing fundamental understanding of Earth’s magnetic field. Improved knowledge of geomagnetic variability will enhance societal preparedness for geomagnetic storms that threaten critical infrastructure and public health. 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 Cell-type specific recording and manipulations are powerful methods for targeting categories of neurons that have a particular behavior or disease relevance. Cutting-edge genetic engineering approaches, such as optogenetics, enable interactions with neurons in a cell type-specific manner. However, these approaches have by and large been relegated to smaller animal models, with limited success in larger animals such as nonhuman primates. This proposal seeks to address this major gap in methodology by establishing tools for interfacing with dopamine circuitry in the macaque animal model. Dopamine is a critical neurotransmitter for a suite of cognitive processes and is implicated in many neurological and neuropsychiatric conditions, making it of clear interest for neuroscientific studies and the development of neurotherapeutics. Nonhuman primates are important preclinical animal models and it is essential to develop tools and technologies that continue to advance our capabilities to interface with the nervous system in this model system. In this work we will establish and characterize the dLight sensor, a genetically encoded fluorescent dopamine indicator, in the macaque model system. We will also develop a sono-chemogenetic approach to selectively modulate signaling in this neural population. Chemogenetics has emerged as a less invasive alternative to achieve similar manipulation of neural signaling to optogenetics. However, current chemogenetic approaches typically rely on systemic drug administration, which limits the temporal and tunable control of the manipulation. Sono-chemogenetics is an innovative new area that leverages ultrasound-programmable nanoparticles for drug delivery. This approach is non-invasive and facilitates precise manipulation of specific cells and overcomes barriers of previous optogenetic and chemogenetic methods. In Aim 1, we will characterize the in vivo sensitivity of the dLight sensor to targeted interventions and behavior. We will use pharmacological techniques and electrical stimulation to mediate dopamine release to validate functional changes in the recorded fluorescent signal. Additionally, we will establish the sensitivity of the sensor to natural variations in dopamine levels during behavior. In Aim 2, we will determine the timescale over which dopamine signals are stable. These longitudinal studies are critical to verify the longevity of this methodology, which is relevant to chronic studies. In Aim 3, we establish the sono-chemogenetic approach to mediate signaling in dopamine neurons and verify the functional effects in a relevant behavioral paradigm. Together these aims will take critical steps toward refining and optimizing these tools for use in a large animal model which is a valuable platform for developing therapies and treatments for human conditions.

NIH Research Projects · FY 2026 · 2026-06

ABSTRACT Ambient air pollution is an environmental toxin encountered by 99% of world’s population that poses a risk to the development of a broad range of health effects, primarily associated with cardiovascular and pulmonary diseases. Despite the well-documented danger to human health, the molecular programs that contribute to these environmental stress-induced disease states remain to be discovered. The structure and function of RNA molecules is modulated through their post-transcriptional modifications, with >150 different modifications having been observed in RNAs. It is increasingly clear that changes in the landscape of RNA modifications (the collection of RNA modifications and their transcriptomic positions at a given time) occur in response to environmental changes. We propose that exposure to air pollution induces shifts the RNA modification landscape that modulates the cellular responses to airborne toxins. In line with this hypothesis, the most prevalent modification in protein coding messenger RNAs (mRNAs), N6-methyladenosine (m6A), is associated with air pollution, particularly particulate matter (PM2.5). However, few studies have investigated mRNA modifications beyond m6A in the context of relevant environmental exposures. Here, we will establish how the RNA modification pseudouridine (Ψ) contributes to cellular responses to air pollution. Ψ is nearly as abundant as m6A in eukaryotic mRNAs, and is primarily found in mRNA coding regions where it is reported to alter the speed and accuracy of protein synthesis. Our preliminary data reveal that Ψ-incorporating enzymes (pseudouridine synthases) can change their subcellular localization in response to toxins found in air pollution. This relocalization appears to correlate with a re-shaping of the mRNA-modification landscape and changes in cellular fitness under stress. The work proposed herein will: 1) Determine if subcellular changes in pseudouridine synthase localization is a conserved environmental stress response, 2) Establish the molecular factors that contribute to stress-dependent localization, and 3) Characterize the impact of pseudouridine synthase relocalization on Ψ landscape and cellular health. Our initial findings lead us to posit that relocalization of RNA-modifying enzymes might be an important general component of cellular stress responses by contributing to altered RNA modification patterns that benefit cellular health under environmental stress. The findings of this study have the potential to open new avenues of investigation by revealing a novel mechanism used by cells to reprogram protein expression in response to environmental stress. Furthermore, they will provide much needed molecular level mechanistic insight into how air pollution-induced disease states arise. .

NIH Research Projects · FY 2026 · 2026-05

ABSTRACT Intimate partner violence (IPV) poses significant social and health challenges, particularly affecting immigrant women who confront increased risk and adverse consequences. Chinese immigrants, the largest Asian ethnic group in the U.S., with over 4 million individuals, have received limited focus in existing IPV research and intervention efforts, despite a high prevalence of IPV reaching nearly 21% within the past year. They face substantial barriers to accessing IPV and mental health services, due to sociocultural factors such as stigma or shame, limited English proficiency, isolation from mainstream American society, unfamiliarity with available resources, and limited availability of linguistically and culturally appropriate services. Moreover, IPV’s substantial and well-documented effects on mental health further exacerbate the challenges faced by this population of women. However, there is a lack of culturally appropriate interventions that address these barriers while improving their acceptability and accessibility to address the mental health needs of abused Chinese immigrant women. To fill this gap, our proposed community-partnered intervention, Self-Compassion, Health, and Empowerment (SHE), adapts a structured safety and empowerment intervention while uniquely incorporating mental health elements, including relaxation and self-compassion meditation, to address abused Chinese immigrant women’s mental health needs. Leveraging mobile health technology, our intervention seeks to overcome barriers such as geographic dispersal, stigma, and privacy concerns that often hinder access to support. Our pilot randomized controlled trial (RCT) with 50 abused Chinese immigrant women has shown the feasibility and acceptability of the mobile-based SHE intervention, which forms the basis for this proposal of a fully powered two-arm RCT. The primary aim of the study is to test the efficacy of the mobile-based SHE intervention in improving mental health among Chinese immigrant women experiencing IPV and co-occurring symptoms of depression, anxiety, or posttraumatic stress disorder (PTSD). We will recruit 364 Chinese immigrant women and randomize them 1:1 to the intervention or attention control group. The 6-week SHE intervention consists of one phone session on IPV safety and empowerment and five weekly relaxation/self-compassion sessions via text. The attention control group will receive 6 weekly nutrition and health sessions, matched in delivery mode, timing, and contact frequency. We will compare the two groups for primary outcomes of depression, anxiety, and PTSD symptoms and the secondary outcome of IPV from baseline to 6-week, 3-month, 6-month, and 12-month follow-ups. Our findings will provide evidence for the application of the mobile-based SHE intervention to support the mental health needs of abused Chinese immigrant women. Our approach is innovative, and there is a high potential for scaling up the intervention to improve access to IPV and mental health support among Chinese immigrant women experiencing IPV.

NIH Research Projects · FY 2026 · 2026-05

PROJECT SUMMARY/ ABSTRACT Exposures to environmental endocrine-disrupting chemicals (EDCs), especially during early life, are strongly linked to adverse health outcomes including neurobehavioral, reproductive, and other endocrine dysfunctions. EDC exposures to a fetus (F1) also exposes the germline (F2) and causes heritable epigenetic changes that are passed on to future generations. The male reproductive tract is of primary interest, particularly the epididymis, where spermatozoa undergo essential maturational processes. Epididymosomes, extracellular vesicles of the epididymal epithelium, transfer bioactive cargo like small non-coding RNAs (sncRNAs) to sperm, influencing their molecular composition. SncRNAs, including micro-RNA and transfer RNA fragments (tRFs), are emerging as significant regulators of epigenetics effects. The epigenome is environmentally responsive; thus, EDC exposure can modify the selection and packing of sncRNAs into epididymosomes, which are then transferred to sperm, thus affecting reproductive outcomes. There are several limitations to prior work that I will overcome in the current F31 application. Most EDC research is limited to a single tissue type or a single mechanism with a limited number of targets. The field is also limited by a surprisingly small number of studies that address how epigenetic programming propagates from somatic cells to gametes and causes reproductive disorders. Lastly, the ability of sncRNA from epididymosomes to respond to environmental insults and communicate such changes to sperm is a fundamental question, one that (to my knowledge) has never been addressed using an EDC mixture. Therefore, this F31 application has three overarching areas of inquiry. 1) How does EDC exposure modify epididymosome cargo in the F1 and F2 generations, specially the sncRNA profile? 2) Are sperm sncRNA profiles influenced by changes to epididymosomes? 3) What are the effects of EDC exposure on reprogramming of sperm DNA methylation in the F1 and F2 generations? To address these questions, we will use our established rat EDC exposure model with human-relevant chemicals, dosages, and route, in which direct (F1) and intergenerational (F2) work will be performed in the epididymis—specifically epididymosomes and sperm. We will profile the epididymosomes and sperm at the DNA and/or sRNA level, enabling us to pinpoint sncRNAs influenced by EDCs, and how phenotypes are propagated from gametes to individuals and across generations. Novel bioinformatic pipelines established by our laboratory will inform on these mechanisms individually, as well as their relationships. Crucially, the lines of work in sRNA and DNA will be connected by relating sncRNA reads to sperm DNA methylation status, thereby determining how epigenomic marks in gametes relate to epididymosome and sperm sncRNA cargo. These data will establish definitive epigenetic profiles that will allow us to identify the origin of EDC induced epigenetic modifications and provide potential targets for therapeutics in humans, with which the mechanisms studied in rats are highly conserved. The support provided by the F31 NRSA fellowship will enable me to fully realize the promise of this likely paradigm-shifting research.

NIH Research Projects · FY 2026 · 2026-05

Project Summary Despite available pharmacotherapies, relapse rates of opioid use disorder continue to rise, suggesting targets of these treatments, such as mechanisms mediating drug craving, may not be the only factors driving relapse. A potential cognitive target that has remained underexamined, but holds significant promise, is the impaired deci- sion making and elevated risk taking observed during abstinence from opioid use. A larger concern is that indi- viduals who intentionally use fentanyl, a potent synthetic opioid, underestimate their risk of overdose. This sug- gests these individuals are impaired in their ability to evaluate options associated with rewards and risks of adverse outcomes. The neural mechanisms by which synthetic opioids impact these processes to increase risk taking, however, are unknown. This knowledge gap poses a critical barrier to understanding the factors that promote recurrent relapse. The long-term goal of our research is to identify neural mechanisms responsible for fentanyl-induced elevations in risk taking after prolonged abstinence so as to develop strategies to mitigate ef- fects of opioids on risk taking. To meet this goal, we use a rat model of risk taking that recapitulates real-life decision making in that it incorporates both reward and risk of punishment. Using this model, we have established a role for basolateral amygdala (BLA) projections to the nucleus accumbens shell (NAcS), as well as specific cell populations within the NAcS, in this form of decision making. We have also shown fentanyl self-administration causes enduring increases in risk taking after protracted abstinence from fentanyl. Our central hypothesis is that dysfunction in these neural mechanisms that subserve risk taking under drug-naïve conditions is responsible for fentanyl-induced increases in risk taking. We will test this hypothesis using a combination of optogenetics, ex vivo electrophysiology and fiber photometry in rats with a history of fentanyl self-administra- tion. Aim 1 will use optogenetics to determine if activation of BLA projections to the NAcS rescues effects of fentanyl on risk taking. We will also use ex vivo electrophysiology to identify if fentanyl-induced elevations in risk taking during abstinence are associated with altered glutamatergic transmission between BLA and NAcS cells that express either dopamine D2 receptors (NAcSD2Rs) or dopamine D1 receptors (NAcSD1Rs). Aim 2 will deter- mine the impact of fentanyl on encoding of risk- and reward-related information in NAcS during risk taking as well as on NAcSD2R long-range projections and their association with elevated risk taking. We will use fiber pho- tometry and optogenetics to record from and manipulate, respectively, NAcSD2Rs and NAcSD1Rs during risk taking in fentanyl-exposed rats. We will use ex vivo electrophysiology to assess if fentanyl-induced elevations in risk taking are associated with changes in inhibitory transmission between NAcSD2Rs and the ventral pallidum, a region heavily innervated by NAcSD2Rs and necessary for risk taking. Together, these findings will identify the neural substrates by which fentanyl increases risk taking after prolonged abstinence and thus reveal novel ther- apeutic strategies to reduce opioid-induced elevations in risk taking and, consequently, potential relapse.

NIH Research Projects · FY 2026 · 2026-05

PROJECT SUMMARY Passenger mutations serve as a valuable record of the underlying mutational forces that shape the cancer genome and their characteristic imprints can be studied through a process known as mutational signatures, which reflect the activity of endogenous mechanisms (e.g., DNA repair function) and exogenous exposures (e.g., carcinogens). Non-B DNA structures are alternative DNA conformations that arise at specific repetitive sequence contexts and represent hotspots of genome instability. These include G-quadruplexes, Z-DNA, hairpins, cruciforms, slipped DNA, and H-DNA, which are widespread in the human genome and have been implicated in transcriptional regulation, splicing modulation, and replication dynamics. Previous work by our group and others has demonstrated that non-B DNA motifs are enriched at sites of both germline and somatic variation and are drivers of localized mutability across cancer genomes. However, the precise mechanisms driving mutagenesis at non-B DNA structures are poorly understood, representing a critical gap in our knowledge. The innovation of this project lies in integrating large-scale cancer genome analyses, gene perturbation experiments, and environmental mutagen exposure data to uncover the mutational processes that cause genomic instability at non-B DNA structures. We hypothesize that distinct non-B DNA structures have different susceptibilities to mutational processes and are preferentially associated with specific mutational signatures in cancer. The significance of this project lies in providing the first comprehensive mechanistic analysis of how non-B DNA structures drive cancer mutagenesis, promoting a more holistic understanding of genome instability in oncogenesis. In Aim 1, we will characterize non-B DNA motifs in silico across 17,517 cancer genomes and extract and compare mutational signatures at non-B DNA versus matched control regions. We will quantify the extent to which specific non-B DNA motifs are associated with known mutational signatures in a cancer-type- specific and pan-cancer manner. In Aim 2, we will determine how deficiencies in key DNA repair pathways impact non-B DNA-associated mutagenesis by integrating WGS data from CRISPR-Cas9 knockout cell lines. We will also assess DNA mismatch repair- and homologous recombination-deficiency signatures in tumor data to identify clinically relevant mutational patterns at non-B DNA loci. In Aim 3, we will evaluate how non-B DNA sequences modulate the mutational impact of 79 environmental mutagens and determine whether certain mutagens preferentially induce mutations at specific non-B DNA motifs and whether these align with patterns observed in human tumors. Finally, for Aims 2 and 3, we will conduct a focused set of targeted experiments to validate and reinforce our key findings. The objective of this project is to identify and characterize the critical pathways through which non-B DNA structures promote genome instability in cancer. Our findings will directly contribute to our long-term goal of informing precision approaches to cancer risk assessment and targeted therapy development.

NIH Research Projects · FY 2026 · 2026-05

PROJECT SUMMARY The U.S. overdose crisis remains an urgent public health concern, with recent data demonstrating an increase in fatal overdoses due to polydrug use with stimulants and opioids. People living with HIV (PLWH) are at increased risk of drug-related overdose, with studies showing that HIV seropositivity increases overdose risk. There is a gap in U.S.-based studies conducted in the past 5 years examining overdose trajectories and risk factors among PLWH during the ongoing opioid crisis. Syndemics theory posits that the co-occurrence of two or more health conditions (i.e., drug use and HIV) exacerbates the negative health effects of any or all health conditions involved. Research examining syndemic risk factors for drug overdose among PLWH has been limited to cross-sectional designs, has not measured access to overdose prevention services (e.g., naloxone, medication for opioid use disorder; MOUD), or assessed risk factors across the socio-ecological model levels. Further, there is a critical gap in examining trajectories of drug overdose risk and access to services among PLWH during the most recent wave of the opioid crisis (2019-2025), characterized by polydrug use of stimulants and opioids. This Substance Use Dissertation Research Award (R36) aims to develop a syndemics framework to identify overdose risk factors among PLWH who use drugs across the levels of the socio-ecological model (individual, social, and structural). This quantitative research study will address two research aims: (Aim 1) to examine trajectories of overdose incidents, risk, and access to overdose prevention services (e.g., naloxone, MOUD) among PLWH, and (Aim 2) to examine the influence of syndemic risk factors across the levels of the socio-ecological model on trajectories of overdose incidents, risk, and access to overdose prevention services among PLWH. Longitudinal, multilevel modelling will be used to estimate trajectories of self-reported overdose incidents, polydrug use with psychostimulants and/or opioids, naloxone availability, and MOUD access over time from 2019-2025 among PLWH in the CFAR Network of Integrated Clinical Systems (CNICS) database. Syndemic risk factors at the individual-level (Hepatitis C co-infection, depression), social-level (intimate partner violence, childhood trauma), and structural-level (housing instability, recent incarceration) will be included in the modelling to identify how syndemic factors influence overdose risk among PLWH. This dissertation grant will contribute to the applicant’s goal of an academic research career and will advance an innovative research program investigating overdose risk among PLWH through a multisystemic lens. By adopting a transdisciplinary approach and collaborating with experts in syndemics theory, multilevel modeling, and social science scholarship, this research seeks to provide novel insights into the determinants of overdose risk among PLWH who use drugs. Our ultimate goal is to develop a comprehensive syndemics framework that integrates the socioecological perspective in identifying risk factors across the individual-, social-, and structural-levels. The outcomes of this research will guide targeted interventions to reduce drug overdose among a high-risk, underserved population.

NIH Research Projects · FY 2026 · 2026-05

ABSTRACT Peer workers (PWs) are essential frontline workers addressing the opioid epidemic, yet their effectiveness is impeded by high rates of occupational burnout and relapse. Approximately 70% of PWs report elevated levels of burnout, and up to 40% of addiction professionals in recovery have relapsed. Further, the United States is experiencing a substance use workforce crisis due to staff shortages and high turnover rates. Interventions tailored to the unique needs of PWs to support workforce retention and long-term recovery are urgently needed. To date, there are no interventions that prevent burnout and relapse due to occupational stress among PWs. This K01 research and training award addresses this gap by being the first to adapt and test the acceptability and effectiveness of Mindfulness-Based Relapse Prevention (MBRP) for substance use PWs. A three-phase approach to adapt, refine, and pilot test the intervention will be conducted across three study aims: Aim 1 will include in-depth qualitative interviews with PWs to understand their experiences related to occupationally triggered substance use relapse and perspectives on intervention characteristics to optimize the effectiveness of MBRP for PWs. Aim 2 will include manual development, interventionist training, field testing, and further manual refinement. Aim 3 will include a small, 2-armed (intervention vs. standard of care) pilot hybrid type 1 randomized controlled trial with 80 PWs to assess preliminary effectiveness and implementation outcomes of the adapted MBRP intervention. Feasibility, acceptability, and preliminary indication of improvement in relapse risk and burnout symptoms will be examined in 1-, 3-, and 6-month follow-ups. The long-term goal of this work is to improve the retention and effectiveness of the peer workforce by reducing occupational burnout and substance use relapse. To enable the PI to pursue this long-term research agenda, she will work with experienced mentors to build four areas of expertise: (1) skills in intervention development and testing via randomized controlled trial design and execution, (2) proficiency in workforce development for the substance use disorder peer workforce, (3) expertise in longitudinal and multi-level statistical analysis methods, and (4) proficiency in dissemination and implementation science frameworks and hybrid implementation-effectiveness designs. This K01 proposal addresses a key priority in strengthening the nation’s substance use peer recovery support workforce, and it will fully prepare the PI for an independent research career as an addiction recovery behavioral intervention scientist, leading innovative research that advances the field and ultimately improves the addiction recovery system of care. This proposal directly aligns with NIDA’s Strategic Plan of improving the implementation of evidence-based strategies in real-world settings and identifying approaches to develop personalized interventions informed by people with lived experience and workforce development.

NSF Awards · FY 2026 · 2026-05

This project supports long-term research in rangeland ecosystem to better understand the relationships between livestock, wildlife, and other stressors impacting ecosystem resilience. Researchers will study the impact of multiple factors on competition and coexistence of livestock with wildlife, and the stability and resilience in a savanna rangeland community in the face of drought, fire, and other environmental stressors. This research provides a unique and essential baseline for the conservation, management, and restoration of rangelands including those in the United States, which lost most of its large herbivores more than 10,000 years ago, but where efforts are underway to reintroduce species similar to those lost. This project will fosters the career development of a strong research team of early career researchers and graduate students and outreach to stakeholders. The use of molecular techniques and remote sensing technology to evaluate the impact of herbivory, drought, and fertilization will improve rangeland management practices from targeted approaches to the landscape scale. This proposal is to support years 31-35 of the Kenya Long-term Exclosure Experiment (KLEE), a controlled replicated experiment examining the separate and combined effects of livestock, wildlife, and fire on each other and on their shared savanna landscape. Although it is becoming increasingly clear that loss of native fauna (“defaunation”) can have far-reaching effects on ecosystems, experimental studies to evaluate these effects remain rare. KLEE uses semi-permeable barriers to create six replicated treatments comprised of different combinations of 1) cattle, 2) meso-herbivore wildlife, and 3) mega-herbivores (elephants and giraffes). This project provides a unique opportunity to understand how interactions between defaunation and multiple pulse and press disturbances affect ecosystem stability and function. After 30 years, the six herbivore treatments support distinct (but still diverging) plant communities, providing powerful opportunities to 1) analyze long-term data in the context of community and ecosystem resilience and stability, and 2) analyze new experimental layers and additional response variables that, along with our previous core long-term data, allow us to assess community resilience under multiple disturbance stressors, including herbivory (three guilds), drought, fire, fertilization, heavy grazing, and termites. The project will continue to add to and explore this rich data set. The decadal proposal also included an ambitious plan to implement experimental reversals of several KLEE treatments in the second five years to test dynamics related to the efficacy of rewilding, the reversibility of rangeland degradation, and the stability of alternative ecological states in general. 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 · 2026-05

Non-technical description: Quantum information science is on the verge of significantly improving humankind’s technology. The research project will facilitate the development of universal quantum computers with important societal impacts, including medicine development, cybersecurity, and material discovery. Quantum computing systems based on superconducting circuits are one of the most promising platforms for quantum information science. However, such superconducting quantum computers must scale far beyond current sizes to reach their full potential with groundbreaking impacts. Distributed quantum computing with modular Quantum Processing Units (QPUs) connected by optical quantum links offers a promising solution to this scaling issue. In such a hybrid distributed scheme, an efficient and low-noise quantum transducer between microwave and optical fields is pivotal. However, it is challenging to achieve high efficiency and low noise at the same time due to the incompatibility between superconducting and photonic components. In this project, we will explore a new transduction method based on itinerant acoustic fields to mediate the transduction between optical and acoustic fields, significantly improving the transduction efficiency and noise performance at the same time. The education plan will help to develop the US quantum workforce. The training of industry employees will provide immediate support for current quantum technology development. Research opportunities for undergraduates and support for high-school science teachers will help to open pathways into the future quantum workforce. Technical description: In this project, we propose to explore a new chip-scale transduction scheme – the use of acoustic waveguides to spatially separate superconducting and photonic parts in microwave-to-optical transducers. The transduction is realized by cascading the piezoelectric process and optomechanical process, which are separated over large distances. This is in sharp contrast to previous chip-scale transducers, where the superconducting and photonic parts were co-located in close proximity. This can solve the trade-off between efficiency and noise in the microwave-to-optical transduction, thus pushing the frontier of distributed quantum computing based on superconducting circuits and photonic networks. 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

Summary Therapeutic peptides have emerged as a compelling and versatile class of molecules with immense promise in the clinic. The growing interest in therapeutic peptides underscores the urgent need for robust computational methodologies to support their discovery, optimization, and application. And yet, the recently developed deep- learning language models, which have shown monumental success in analysis of proteins and small molecules, lack a reliable format for peptide discovery and design. Existing biological language models have been trained on datasets consisting of either large, multi-domain proteins or of small molecules. To date, this has produced models ill-suited for modeling peptides, which are larger than small molecules but smaller than typical proteins. Therefore, we see a critical need to develop peptide foundation models that can be fine-tuned for various downstream tasks. For maximum usefulness, such models will need to be able to encode peptide backbone modifications, hundreds of non-canonical amino acids (natural and unnatural), and various side-chain modifications and cyclizations. Here, we will develop such a modeling framework, building on and extending prior works for small molecules and adapting them to peptides. The proposing team of PI Wilke and co-I Davies jointly has extensive experience in peptide biochemistry, machine learning, and LLMs, and UT Austin is uniquely positioned to provide the computational resources required for this project, through both the Texas Advanced Computing Center and the Center for Generative AI. We have three aims, to (1) develop chemical language models for peptides, (2) validate our models on the tasks of engineering membrane diffusion and cellular entry, and (3) develop and validate language models for protein–peptide interactions. In aggregate, this project will develop a robust platform for investigating peptide biochemistry. The models we will develop will open up several avenues of discovery including natural peptide identification, biochemical characterization of functional peptides, and classification of peptide activities. Ultimately, this work will enable predictive modeling of peptide-based macromolecules, with applications in natural peptide discovery, drug development, and rational peptide design.

NIH Research Projects · FY 2026 · 2026-04

PROJECT SUMMARY The project aims to develop a robot-assisted Single Photon Emission Computed Tomography (SPECT) imaging system for adaptive and optimized imaging acquisitions. The platform integrates a lightweight Gamma Camera with a highly articulated robotic manipulator. According to the principle of adaptive SPECT imaging, the proposed imaging system will modify its data collection configuration in response to the object under evaluation, the imaging task, and the image information received, to achieve the best imaging performance. It is our central hypothesis that an adaptive and reconfigurable SPECT imaging system, designed to personalize the imaging process for each patient, will lead to improved imaging performance, shorter scan times, and lower radiation doses. In this 3-year feasibility effort, we will mainly focus on the design, development, integration, and validation of the subsystems of this novel SPECT imaging platform with the goal to deliver optimized and tailored imaging acquisitions, offering improved diagnostic accuracy and performance. In details, the project aims to: (1) Develop a dedicated lightweight Gamma Camera: the Gamma Camera will be built using an array of solid-state detectors and a customized 3D printed collimator. It will offer excellent spectroscopic performance, high sensitivity, and a lightweight design suitable for robotic manipulation. (2) Plan optimized scanning trajectories and jointly reconstruct tomographic images: Advanced optimization algorithms will be developed to determine the best camera positions and trajectories for each patient and imaging task. This will involve simulating different scanning patterns and analyzing factors like image quality, accuracy, and image enhancement. (3) Integrate the gamma camera with a robotic arm: The dedicated camera will be mounted on a highly articulated robotic arm. This will allow the camera to freely move around the patient, ensuring optimal and repeatable image acquisitions. The entire system will be rigorously tested using both computer simulations and physical imaging phantoms. We will evaluate the performance of the robot-assisted SPECT system compared to traditional scanning methods (e.g. fixed radius of rotation and uniform scan time). This technology could be used for a wide range of diagnostic applications, and we will particularly focus on organ-specific scans like breast and heart imaging. By combining robotics, advanced optimization algorithms, and specialized hardware and sensor technology, the proposed work has the potential to change how SPECT imaging systems are conceived, operated and interface with each patient.

NIH Research Projects · FY 2026 · 2026-04

Abnormal vessel growth can lead to serious health consequences for a patient including insufficient oxygenation of organs, depletion of key nutrients, and an increased risk of unexpected and severe bleeding. Placental vascular abnormalities that can develop in twins is often fatal, yet the tools needed to treat this vascular condition make surgical care prohibitively challenging. We propose to develop a flexible, robotic ablation system, integrated with laser speckle contrast imaging, to assist the surgeon in identifying and treating vascular defects.

NSF Awards · FY 2026 · 2026-04

After drought ends, forests do not always recover as quickly as rainfall does. For example, trees may continue to grow slowly, and streamflow may remain lower than expected even when wet conditions return. The uncertainty underlying these delayed responses has implications for projections of water supply and management of forests. This project will improve understanding of forest response to drought, support student research training, and inform forest management practices. Several mechanisms may explain prolonged drought impacts, including lasting physiological changes in trees and delayed replenishment of deep moisture accessible to roots. Most field measurements capture only shallow soil moisture, even though roots often rely on water stored meters belowground. This project will combine a rare multiyear record of deep moisture measurements before, during, and after drought with new tree-ring records as well as publicly available tree-ring chronologies linked to satellite-derived water balance data. Analyses of these datasets will test whether delayed deep moisture recovery explains prolonged forest impacts and the circumstances where tree-ring records show sensitivity to drought legacies. 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 · 2026-04

Project Summary Hypersensitivity to touch is common in neurodevelopmental disorders including autism spectrum disorder (ASD). Individuals with ASD can have aversive responses to certain textures or human touch, which can lead to emotional distress, social withdrawal, and difficulties accomplishing everyday tasks. In humans and rodents, aberrant touch sensitivity perinatally is predictive of ASD-linked traits later in life. The molecules and mechanisms underlying touch sensation are just being elucidated, and little is known about how they relate to ASD. Touch sensation begins with the activity of mechanically activated PIEZO2 channels that transduce forces into electrical signals. The overall objective of the proposed research is to investigate the role(s) of the Ca2+ sensing protein caldendrin in regulating PIEZO2, touch sensation, and neurodevelopmental processes controlling cognition/affective behaviors. Our project builds on our discovery that mice lacking caldendrin (Cabp1 KO) display tactile hypersensitivity and increased activity of PIEZO2 in dorsal root ganglion neurons (DRGNs). Neurite outgrowth, which depends on PIEZO2 as well as Cav1 L-type Ca2+ channels, is abnormal in Cabp1 KO DRGNs and differs in cultures from males and females. Moreover, Cabp1 KO mice exhibit ASD-like phenotypes, such as anxiety and anti-social behavior, which are more severe in males than females. Considering that genetic silencing of some ASD-related genes in DRGNs of neonatal mice causes tactile hypersensitivity and ASD-linked behaviors in adulthood, our central hypothesis is that caldendrin modulates PIEZO2, Cav1, and the structural maturation of DRGNs early in development, which has sex-specific effects in driving synaptic plasticity in the brain and cognitive, affective, and social behaviors in adulthood. We will use state of the art methods in biochemistry, electrophysiology, and optical imaging to test the following specific aims: (1) elucidate the mechanism whereby caldendrin modulates the activity of PIEZO2; (2) define the contributions of caldendrin to sex- specific patterns of neurite development; and (3) determine how loss of function of caldendrin leads to aberrant cognitive/affective behaviors.

NIH Research Projects · FY 2026 · 2026-04

Nuclear enzymes, chromatin, and chromatin modifications collectively enable cells and organisms to respond and survive in dynamic environments. Chromatin modifying enzymes play central roles in this process by utilizing cofactors derived from cellular metabolism to exert their catalytic activities. Consequently, the metabolic status of the cell can influence histone modifications and the structure, density and location of nucleosomes, thereby shaping epigenetic landscapes and gene expression programs. Mitochondria play a crucial role in cellular stress response by signaling to the nucleus through changes in metabolic state. Therefore, elucidating how mitochondria and nucleus coordinate to regulate metabolism and chromatin regulation to shape transcriptional regulation will reveal the fundamental processes that govern cellular stress responses and adaptation. Metabolic stress and hormone signaling often converge to reprogram chromatin to prioritize genes for survival, growth, or stress responses. The plant hormone ethylene signaling pathway in Arabidopsis serves as an ideal model for this research, as it integrates hormonal and stress signals to mediate plant growth, development, and responses to environmental challenges such as hypoxia, pathogen infection, and water deficiency. We have discovered that EIN2, an essential ethylene signaling factor, is also a key component of the histone modification that directly regulates H3K14Ac and H3K23Ac to mediate the transcriptional reprogramming in response to ethylene, which establishes a direct link between ethylene signaling and chromatin regulation. Additionally, we have found that alterations in the chromatin architecture in ein2-5, the ethylene insensitive mutant, prevent ethylene-induced transcriptional reprogramming. Furthermore, our latest findings provide compelling evidence showing that the PYRUVATE DEHYDROGENASE COMPLEX (PDC), the acetyl-donor of histone acetylation, can translocate from the mitochondria to the nucleus to provide acetyl CoA for histone acetylation regulation over ethylene- regulated genes. Standing on our recent groundbreaking work, we will continue to investigate how mitochondrial- nuclear coordination modulates metabolism and chromatin regulation to control transcriptional reprogramming in ethylene signaling and stress responses in three directions from three directions: (1) Elucidating pathways and the mechanisms by which ethylene signals are transmitted from the ER to nuclear chromatin for shaping cellular responses; (2) Investigating chromatin dynamics and transcriptional control mechanisms in ethylene response; (3) Integrative study of mitochondrial-nuclear communication governing metabolism and chromatin regulation in plant ethylene and stress responses. Our overarching goal is to uncover how mitochondrial-nuclear coordination modulates metabolism and chromatin regulation, ultimately controlling transcriptional responses to plant hormone and stresses. This research will provide key insights into the molecular mechanisms that govern how metabolism and chromatin interplay to drive cellular responses to stress and disease, enhancing our understanding of cellular resilience and homeostasis.