Yale University

NIH Research Projects · FY 2025 · 2025-11

PROJECT SUMMARY/ABSTRACT In progression of skin fibrosis, the pathological stiffening of skin, both pro-fibrotic biochemical signaling and mechanical signals from the stiffening extracellular environment drive hyper-activated fibroblasts to secrete excess extracellular matrix (ECM) proteins. However, how these two inputs are integrated to drive fibrosis remains poorly understood, posing a barrier for treatments that can effectively reverse the fibrotic process. Our lab’s preliminary work provides new insight to this unanswered question, having identified a novel role for SUN2, an inner nuclear membrane protein, in driving fibrosis, potentially by regulating both mechanical and biochemical signaling. We have found that SUN2 responds to fibrotic stimuli in vivo, increasing in fibrotic patient and mouse skin samples, and in fibroblasts cultured on stiff substrates modeling fibrotic tissue. Additionally, depleting SUN2 decreases expression of pro-fibrotic TGFβ pathway target genes necessary for driving excess ECM deposition, as well as chromatin accessibility at the promoters of these genes. In vivo, loss of SUN2 is protective against injury-induced skin fibrosis. Given SUN2’s well-characterized function as a Linker of Nucleoskeleton and Cytoskeleton (LINC) complex component, which transduces bi-directional force from the cytoskeleton to the nuclear interior, a compelling hypothesis is that mechanical signals from the stiffening, fibrotic ECM increases SUN2 levels at the nuclear envelope, which amplifies force transduction to the nucleus necessary for upregulation of pro-fibrotic TGFβ target genes. The goal of this proposal is to investigate this hypothesis and define a molecular mechanism for how mechanical signals alter the composition and function of the nuclear envelope to regulate pro-fibrotic biochemical signaling. To accomplish this, I will first recapitulate the mechanical conditions of skin fibrosis in vitro, culturing dermal fibroblasts on substrates of relevant stiffnesses. Then, I will define how ECM stiffness regulates SUN2 levels by performing a series of RT-qPCR and cycloheximide chase experiments, utilizing inhibitors and a CRISPR Cas9 approach to disrupt cellular degradation pathways and further probe this mechanism. To determine how biological SUN2 levels alter the mechanical environment of the nucleus I will perturb SUN2 expression via siRNA or alteration of SUN2’s phosphorylation state and utilize our lab’s Nesprin molecular tension sensor, created to measure tension on LINC complexes. Lastly, to identify how SUN2 regulates pro-fibrotic TGFβ target gene expression in response to ECM stiffness, I will perform various genomics experiments including ATAC-Seq, RNA-Seq, and CUT&RUN to determine how ECM stiffness alters chromatin accessibility and gene expression of TGFβ target genes, and the activity of TGFβ pathway effectors SMAD 2/3. This proposal will address fundamental aspects of cell biology and mechanobiology and will define a novel mechanism for how mechanical signaling and biochemical signaling are integrated to drive fibrosis. Additionally, successful completion of this proposal will provide new therapeutic targets for reversing the fibrotic process in skin.

NSF Awards · FY 2025 · 2025-11

Sponges are aquatic invertebrates with many important characteristics. Studying these animals provides insight into the evolution of complexity, specialized cell types, and interactions with microbes. Sponges also produce many natural compounds relevant to the pharmaceutical and biotechnology sectors. Yet despite their significance across these many scientific areas, research progress has been limited by the lack of tools to manipulate sponge genes and cells. This project builds on a recent technical advance that enables efficient delivery and expression of genetic material in sponges. This project will (1) develop a comprehensive library of molecular tools for visualizing and controlling gene activity in specific sponge cells, (2) establish standardized, reproducible protocols for genome editing, and (3) provide direct training opportunities to speed the adoption of these methods by researchers. All methods and tools will be made openly available to accelerate community-wide uptake. By transforming sponges into a system where genes can be readily studied, this project will empower new lines of investigation across developmental biology, immunology, cell evolution, and natural product discovery. At the core of this effort is the development of cell type-specific genetic tools for the freshwater sponge Spongilla lacustris, a widely used laboratory model in sponge biology. Building on a published single-cell atlas and a high-efficiency electroporation method, the research team will (1) optimize transfection conditions using a factorial design and quantitative reporter assays, (2) create a modular plasmid toolkit featuring cell type-specific promoters, codon-optimized fluorescent proteins, and subcellular targeting signals, and (3) implement CRISPR/Cas9 genome editing to generate stable transgenic lines. These tools will enable researchers to directly test gene function in vivo and investigate cellular mechanisms that have been inaccessible in sponges until now. The project will also serve as a model for extending genetic technologies to other experimentally challenging but scientifically important organisms. This research will develop new biotechnology that will be made available to the scientific community. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2025 · 2025-11

Influenza is a year-round public health burden, causing millions of severe illnesses and hundreds of thousands of respiratory deaths globally. A key challenge in developing more effective vaccines lies in the inherent variability of the human immune system, as vaccine responses are highly variable across individuals, with many failing to develop adequate protective immunity. Low vaccine responsiveness has been associated with specific pre- vaccination baseline immune states. The baseline immune state of an individual determines their immune function and response. We and others have linked inter-individual variations in vaccination outcomes to molecular and cellular immune components that encode the baseline state. Our group previously showed that high vaccine responsiveness is associated with a “naturally adjuvanted” baseline state characterized by enhanced innate immune response potential, a finding supported by corresponding differences in stimulation responses of immune cells from high and low vaccine responders in vitro. We also found that clinically healthy males recovered from mild COVID-19 exhibited a more “poised” baseline state and stronger immune responses to subsequent influenza vaccination. These studies suggest that variations in baseline immune states contribute to heterogenous responses to vaccination, and that prior exposures may establish new baseline states that impact future responses in an antigen-agnostic manner. It remains unclear how infection alters an individual's baseline state over time, how these changes vary across individuals, and if they have functional consequences. Using longitudinal samples from a household cohort that allows control for environmental confounders, and using influenza infection as a model, my proposal aims to address these gaps to better understand the functional impact of infection on baseline immune states. Given the antigen-nonspecific nature of innate immune cells, understanding how infection impacts their function is a key to revealing potential underlying mechanisms. I hypothesize that influenza infection induces durable antigen-agnostic transcriptional and epigenetic changes that give rise to enhanced innate immune response potential. Aim 1 will assess the impact of infection on baseline immune states and innate cell response capacity. Through single-cell multimodal immune profiling, I will assess infection-induced transcriptional and epigenetic changes in peripheral immune cells. Using the same samples, I will examine innate response capacity to in vitro stimulation. Aim 2 will elucidate how infection-induced durable changes mechanistically drive innate cell responses to stimulation. I will implement a causal network inference approach to infer immune determinants of response capacity, followed by experimental validation to establish causality. This work will advance our understanding of infection-induced antigen-agnostic immune reprogramming, potentially revealing key drivers of human immune variation and strategies to modulate baseline states for improving vaccination outcome. Rigorous scientific training will be guided by mentors with experimental and computational expertise, complemented by longitudinal clinical and professional skill development.

NSF Awards · FY 2025 · 2025-10

Deploying applications on cloud servers is increasingly popular due to their cost-efficiency and ease of management. However, these servers are controlled by companies that users may hesitate to trust with sensitive data. To keep sensitive data safe, Confidential Computing Environments (CCEs) use a mix of secure hardware and software to protect data from the cloud platform's operating system and other untrusted software. While companies like Intel, ARM, and AMD have improved the hardware for these systems, the associated software remains insufficient to protect modern cloud applications completely. This project aims to solve this problem by designing better software, studying security weaknesses, and creating new ways to block attacks from malicious software. A central idea in our project is to "hide" real data by mixing it with extra, meaningless (“noise”) data to confuse attackers. The research will test these solutions in different cloud setups for popular applications like AI training, recommendation systems, and data analysis tools. Securing cloud applications is a key challenge for computing, and our work, if successful, will positively impact the security of cloud computing platforms. Confidential Computing Environments (CCEs) leverage a combination of trusted hardware and software to protect application data confidentiality and control-flow integrity from untrusted operating systems (OSes) and hypervisors in public cloud settings. While the hardware component has already seen two generations of updates, the system software support remains immature and fails to meet the needs of emerging cloud applications for two main reasons. First, existing approaches fail to define the right boundary between the trusted software that must run within the CCE and the untrusted system software that runs outside it to manage resources and provide common system services across applications. This results in a hard tradeoff between application performance, functionality, and data confidentiality. Second, interactions across the boundary — between the trusted software within the CCE and the untrusted system software outside it — present several side channels that the untrusted software can leverage to learn sensitive application data. This project aims to overcome these challenges through principled system design, security analysis, and development of algorithmic defenses. Our research will study and establish the appropriate boundaries between software within and outside the CCE for the two primary data paths available to applications: memory and I/O. We will study the vulnerabilities via side-channel leakages available to the untrusted software by developing novel attacks and devise novel defenses against them using the principles of noise injection, i.e., by injecting spurious interactions over the side channels along with legitimate ones to obfuscate the adversarial observations. We will evaluate our approach across a range of deployment settings and real-world cloud applications. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-10

Algorithms increasingly influence the decisions people make in their everyday lives, from what clothing to buy to what movie to watch to what healthcare plan to adopt. There is a pressing need to understand how algorithms interact with human behavior in these settings. This project will develop theoretical foundations and applications for new machine learning algorithms that learn and predict human decisions descriptively from data, as they are, rather than as behavioral theories prescribe them to be. These new algorithms build on recent interpretations of choices as driven predominantly by pairwise interactions, involving new tools from graph theory to modeling human decision making. The unique practical potential of this project stems from its goal to operationalize these behavioral theories within a machine learning framework, making it possible to employ the lessons of behavioral economics to improve the design of large-scale web systems. The project builds on a set of key breakthroughs in the recent literature on machine learning for human decision making that model irrationality through interferences between alternatives in choice sets. The project aims to understand the theoretical limits of these interference-based approaches to modeling human choices, as well as to apply the methods to complex choice problems including ranking problems. The project also aims to adapt these new modeling tools to construct defensive tools capable of protecting people from malicious platform designs, measuring "irrationality" and developing a testing framework for identifying platform designs conducive to "more rational" decisions. The new tools for machine learning algorithms for modeling irrationality rely heavily on graph-theoretic pairwise analysis, and the project will therefore also build bridges between the literature on decision theory and the literature for the modeling and measurement of social networks, the birthplace of many of these graph-theoretic concepts. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-10

This project develops BrainScan, a neural interface that enables direct real-time bidirectional communication between biological neurons in the brain and the digital world. The centerpiece of BrainScan is BrainCore, a bespoke chip designed for neural interfacing. BrainCore enables an energy efficiency that is orders of magnitude superior to the state of the art. Unlike current neural interfaces, BrainScan will be highly programmable to support many neuroscience studies centered on millisecond-scale closed loops involving analysis of large volumes of brain data. BrainScan’s form factor and power dissipation will make it amenable for portable use, eschewing the need for tethering. Current neural interfaces support only a small subset of these capabilities. BrainScan does not compromise on performance, timeliness, flexibility, or power. BrainScan’s development will encompass both the hardware and software. The bulk of hardware design would target the BrainCore chip, which uses multiple hardware accelerators for neural data processing, operating, unusually, in distinct clock domains, and the integration of peripheral off-the-shelf components for storage, wireless communication, and power. Software design includes an intuitive interface in a high-level language familiar to neuroscientists along with a compiler framework. Together, BrianScan will process about a hundred megabits per second of neural data below 1 watt of power. This ability will be validated in a pilot rodent study. With a transformative redesign of neural interfaces to read and electrically stimulate orders of magnitude more neurons than is currently feasible, BrainScan will enable cutting-edge neuroscience research. BrainScan will also mark a leap in the nation’s interest in building high-precision and high-bandwidth neural interfaces that may one day help augment human cognition and decision-making. Furthermore, by designing BrainScan to be flexible, BrainScan will be the foundation for a standardized computational platform for neural interface research and industry. This will help neuroscientists and clinicians transcend the limitations of current neural interfaces that support only narrow classes of experiments, and the fragmented software ecosystem and lack of standardized and portable applications that they have lead to. BrainScan will be made available to multiple labs nationwide through an inter-institutional loan program. The PIs will devise written documentation and online video tutorials, hosted on a dedicated website, to enable use of BrainScan in a turn-key manner. BrainScan’s board design files, software, and high-level simulators for the BrainCore chip will be shared with the community to build on. This project will also train PhD students and postdoctoral scholars in neurotechnology and computer science and engineering. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-10

Understanding genetic variations and their impact on human health is essential for advancing medicine and developing new treatments. Researchers need access to comprehensive genetic databases that represent diverse populations to effectively analyze individual genetic information. Despite the growing number of large-scale genetic datasets being generated, access to these data remains limited for many researchers due to stringent data-sharing policies driven by privacy concerns. Even when access is granted, the immense size of modern genetic datasets, coupled with the growing complexity and costs of analysis, presents significant barriers for individual researchers to fully use these resources in their studies. This project addresses this challenge by developing secure methods that allow researchers to analyze their genetic data using controlled-access reference datasets without compromising the privacy of either dataset. This work serves the national interest by accelerating scientific progress and improving public health through expanded access to genetic data resources, strengthening the security infrastructure that supports genetic research, fostering public trust in genetic studies, and enhancing protection of national genetic databases. This project develops secure algorithms and deployment-ready software to support genome imputation and analytics services that preserve the confidentiality of all data involved. The research is organized into three integrated components. First, the team will develop practical algorithms and tools for genome analysis in trusted execution environments using data-oblivious techniques to prevent side-channel leakage by ensuring uniform observable behavior across all inputs. Second, the project will develop a multi-layered security framework that combines data transformation, usage monitoring, and secure testing environments to safeguard controlled-access reference datasets. Third, the research will extend these capabilities to a federated network of genomic repositories, enabling privacy-preserving, cross-institutional genome imputation and phasing through novel federated algorithms and identity management strategies. The project leverages secure hardware and related privacy technologies to enable services that allow users to analyze their genomic data using controlled-access reference datasets without compromising the confidentiality of either. These research activities will produce deployable, confidential genome analytics services and foundational techniques for future privacy-preserving workflows built on trusted execution 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 2025 · 2025-10

Nontechnical Abstract This project aims to design a new class of engineered artificial materials, commonly known as metamaterials, that exhibit a high refractive index in ways not possible with naturally occurring substances. Refractive index determines the velocity of light, with a higher index resulting in a lower velocity. A high index helps guide light better and also bends it by a larger angle. This is crucial to get better optical components, such as lenses, which need to bend light to focus. A high nonlinear index also allows one light beam to control another light beam. These capabilities can help create faster computers, build better cameras, enhance augmented reality displays, and enable high-speed communication. These materials will be created by arranging nanometer length scale artificial materials (“nanocrystals”) in a periodic structure. Additional patterning at longer length scales will enable the development of new optical hardware. While the concept of creating such artificial materials is compelling, realizing it in practice is extremely challenging. This project addresses these challenges through a unique, multi-scale inverse design approach, driven by advanced computational modeling and machine learning. The project will also empirically validate the designed material properties, creating two testbeds: thermal imaging and nonlinear optical activation for optical information processing. Along with advancing the frontiers of optical imaging and computing, the program will train a new generation of scientists and engineers through hands-on interdisciplinary research experiences that span physics, chemistry, computation, artificial intelligence (AI), and materials science. By engaging high school, undergraduate, and graduate students, the project will broaden participation in cutting-edge science. Technical Abstract Designing materials with high linear and nonlinear susceptibilities can unlock a vast range of applications in photonics. Metamaterials present a unique opportunity to realize a high index, beyond what is available in naturally occurring materials. For instance, by combining nanocrystals appropriately, it may be possible to design a composite material with record high susceptibilities. The effective susceptibility of this composite material can be further enhanced via wavelength-scale patterning. Such a multi-scale metamaterial would be the first of its kind, where the constituent meta-molecules also comprise a metamaterial. While the multi-scale design of metamaterials is conceptually simple, it is extremely challenging in practice to design the exact combination of materials to achieve a desired property, while ensuring that the designs can be synthesized or fabricated. Guided by fundamental bounds based on the causality and passivity constraints of physical materials, this project will identify new design rules. Using a multi-scale inverse design approach, including a physics-inspired artificial neural network, the optimal combination of nanocrystals and meta-molecule structures will be identified. While the design techniques will be applicable to many material systems, a few promising ones will be downselected for experimental realization. These composite nanocrystals will be chemically synthesized and subsequently patterned to create the metamaterial. Ellipsometry and nonlinear pump-probe spectroscopy will be used to validate the design. The experimental data will help refine the design assumptions and provide new insight. Combining computational electromagnetics, optimization theory, machine learning, chemical synthesis, nanofabrication, and optical characterization, three research thrusts will be pursued: (i) create high linear susceptibility composite materials; (ii) create high nonlinear susceptibility composite materials; and (iii) demonstrate metamaterials made of the composite materials for nonlinear optical activation in optical neural network accelerators and high-efficiency thermal imaging. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-10

The capture of carbon dioxide (CO2) directly from the atmosphere is emerging as a potential approach to climate change mitigation. However, the rapid separation and purification of CO2 from air can be very energy-intensive because of its low concentration (400 ppm). The development of highly energy-efficient CO2 capture technology will enhance the sustainability, affordability, and commercial appeal of capturing CO2 and converting it into useful chemicals and products. Most incumbent CO2 capture technologies run on heat, which typically requires burning fossil fuels. Heat-based CO2 capture methods also face a fundamental conversion efficiency limit in using heat for the work of separation. In contrast, CO2 capture using electrical rather than thermal energy is attractive because it does not face this efficiency limit. Moreover, it could be run using renewable energy conversion technology, which is increasingly available and inexpensive. This project will develop a new approach to energy-efficient CO2 separation that uses electro-active organic molecules attached to carbon electrodes. Polarizing these electrodes in an aqueous solution causes reversible changes in the pH of the solution, which enables CO2 separation. CO2 will be selectively absorbed in the form of carbonate ions under alkaline conditions and then released as a pure gas under acidic conditions. Experimental and modeling techniques will be used to understand how both the chemical composition of the electrode and its integration into an electrochemical flow cell influence the rate and energy efficiency of the overall separation process. Further, this project will engage Michigan residents in informal discussions and hands-on scientific demonstrations highlighting the need for and benefits of CO2 capture and utilization technology. With support from both the Interfacial Engineering and Electrochemical Systems programs, this project aims to advance a new way of using carbon electrodes, chemically functionalized with organic moieties, for energy-efficient electrochemical CO2 separation. Upon electric polarization, these electrodes can either absorb protons from or release them into solution, changing its pH, and thereby providing a mechanism for reactive CO2 capture and release. The extent and reversibility of pH changes that these electrodes can achieve are key factors controlling the energy efficiency of the separation process. These factors strongly depend on the strength of the electric field that the organic moiety experiences at the electrode/electrolyte interface. A combination of electroanalytical and in operando high-resolution x-ray spectroscopic techniques will be used to develop a mechanistic understanding of proton transfer as the chemistry of the moiety and manner of its installation on carbon vary. This knowledge will be exploited to engineer electrodes that will concentrate CO2 from the air when deployed in an aqueous electrochemical flow cell. Flow cell measurements will be used to validate a physics-based model for the separation process; the model will offer an understanding of how thermodynamic and kinetic losses dictate the overall energy input and efficiency for a given separation throughput. The education plan generates public exposure to the benefits of CO2 mitigation technology by offering face-to-face exchange between the community and researchers through discovery-based learning, informal discussion, targeted hands-on demonstrations, and a research exhibit. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2025 · 2025-09

PROJECT SUMMARY The ability to appropriately respond to threats is crucial for survival. Literature has long suggested that arousal and attention, mediated by the neuromodulators norepinephrine (NE) and acetylcholine (ACh), influence sensory processing. NE and ACh release affect behavioral states and are released in response to novelty, attentional effort, and threat. However, the unique contributions of these neuromodulators in regulating behavioral states, and circuits which might underly the release of these neuromodulators in response to salient threat stimuli, remain unclear. It is possible that salient visual stimuli alter neuromodulator release through direct projections of the superior colliculus, resulting in enhanced sensory processing in response to threat, but this circuit has not been directly tested. Height threat is an evolutionarily conserved, ethological visual threat behavior that can be used to study neural circuits underlying threat processing. The visual cliff test is an optical illusion which generates the appearance of a cliff. Both rodents and human infants avoid crossing the threshold of the visual cliff. In rodents, this behavior depends on visual perception and binocular vision. Humans exposed to virtual height threats exhibit increased physiological arousal which correlates with perceived height level. We developed a virtual visual cliff task to study the relationship between neuromodulators, attention, arousal, sensory processing, and threat-related decision-making. Mice climb down a pole towards a virtual cliff and exit to one of four quadrants, one which appears close and three which appear distant. Threat level is modulated by altering the apparent distance between the close and distant quadrants. Head-mounted eye tracking will be used to measure arousal via pupil diameter and attention via gaze and head direction, which will allow us to differentiate the roles of attention and arousal in visual processing and decision-making. This research will elucidate how neuromodulators influence sensory encoding and behavior in response to visual threats, as well as the role of sensory systems in engaging neuromodulator release to enhance sensory processing in response to salient visual threats.

NIH Research Projects · FY 2025 · 2025-09

PROJECT SUMMARY/ABSTRACT Arboviruses, viruses spread to humans through the bite of infected arthropods such as dengue, Zika, and chikungunya, represent an immense and growing burden to global health. The endo-symbiotic intra-cellular bacterium Wolbachia is a novel form of disease control that has been shown to successfully decrease the transmission of several arboviruses. Wildtype Aedes aegypti population replacement can occur after releases of transinfected female Ae. aegypti mosquitoes, driven by cytoplasmic incompatibility and maternal inheritance. Once Wolbachia introgression reaches a predetermined threshold (~50-60% prevalence), population replacement can be sustained without additional releases and reductions in arboviral transmission can be observed. However, successful delivery of Wolbachia release interventions require an understanding of the factors that drive introgression speed and stability and strategies that enable efficient implementation in a diverse range of urban environments. Achieving rapid, sustained Wolbachia introgression remains challenging in complex urban settings, particularly in areas with high arboviral burdens and socioeconomic disparities, such as Rio de Janeiro. My preliminary research has identified that the ratio of release doses to existing Ae. aegypti abundance predicts introgression success, and calibrated high-dose releases have demonstrated promising results. This study leverages these findings and unique data from Wolbachia release programs in Rio de Janeiro and Belo Horizonte, Brazil to address critical barriers to equitable, efficient, and effective implementation of Wolbachia interventions. Specifically, the proposed study aims to: (1) develop a statistical and mathematical models to identify factors influencing introgression speed and optimal Wolbachia release timing and dosing strategies based on comprehensive data from Rio de Janeiro and (2) empirically test the effects of release doses on the speed of Wolbachia introgression utilizing data from experimental releases in Belo Horizonte. By combining advanced modeling and real-world experimental data, this research will identify scalable, efficient, and equitable strategies for Wolbachia interventions. These findings will enhance global efforts to combat dengue and other arboviral diseases in diverse urban contexts.

NIH Research Projects · FY 2025 · 2025-09

The 2023 train derailment in East Palestine, Ohio, triggered a major environmental disaster involving fires, chemical spills, and a controlled burn that released numerous toxic and carcinogenic compounds into the air, water, and soil. Although extensive environmental monitoring has been conducted, our conversations with residents revealed significant gaps in the lack of: integration, accessibility, and interpretability of existing data; alignment with community priorities; information on exposure mixtures; findings on short- and long-term health impacts, especially for children; and trust. Some stakeholders expressed deep concern about persistent and worsening health symptoms and future risks, whereas others described acute symptoms that had subsided, and others expressed a desire for closure. These varied experiences underscore the urgent need for a comprehensive scientific investigation grounded in community priorities and conducted through robust academic-community partnerships. One key concern expressed by residents is the lack of information on water quality across space and time in relation to the derailment. The East Palestine Train Derailment Health Research Program: Yale University Study will contribute to overall efforts to study the health impacts of the derailment, with a focus on providing critical information on the likelihood of exposure to contaminated water. We aim to address the gap in information on water pollution through state-of-the-science hydrological modeling. This work will be grounded in community priorities with involvement from community partners to ensure that the research best meets their needs. Further, the resources generated from this work will be made available for stakeholders including the general public, decision makers, and other researchers. Our Specific Aims for Year 1, which would complete Phase I of project are to Aim 1) Conduct a water-focused community health needs assessment to identify priorities and concerns specifically on water quality issues related to the derailment, Aim 2) Perform data acquisition and integration for model inputs and model calibration and training data, and Aim 3) Initiate construction of hydrological model. These aims will provide inputs to later Phases of the Project. Overall, the Project will produce a robust body of scientific evidence, data infrastructure and resources, and meaningful community partnerships to address stakeholder concerns regarding the water quality impacts of the train derailment with spatiotemporal hydrological modeling. Ultimately, this study aims not only to understand what happened in East Palestine, but to help ensure that future communities are better protected, informed, and equipped to recover.

NIH Research Projects · FY 2025 · 2025-09

Many Americans fail to receive their high school diploma. Individuals enrolled in Adult Education classes have exited the K-12 education system without a high school diploma. This reduces their access to economic resources, heightens their risk for poverty and poor health, limits their ability to meet occupational and social expectations of adult life, and exacerbates their stress. Behavioral health (i.e., depression, anxiety, anger, substance use) is implicated in K-12 school failure, as it negatively impacts students’ acquisition of academic skills and their achievement of educational and vocational goals. Students enrolled in Adult Education Centers (AECs) are often ignored in most analyses that explore how behavioral health issues impact students’ general functioning and academic outcomes, even though behavioral health challenges in AECs may be greater than that in the general population. AECs are ill equipped to address students’ behavioral health challenges. Few evidence-based, behavioral health interventions are currently deployed in AECs that target the behavioral health challenges AEC students may experience. Screening, Brief Intervention, and Referral to Treatment (SBIRT), often informed by Motivational Interviewing (MI), positively impacts health outcomes. Positive outcomes are associated with the successful screening and referral to behavioral health services. In turn, these behavioral health improvements may also help to facilitate positive academic results for impacted AEC students. Implementation Facilitation is a promising strategy for ensuring the successful implementation of SBIRT in AECs. Guided by the integrated-Promoting Action on Research Implementation in Health Services (i-PARIHS) framework, we propose an R34 project to: 1) conduct an iterative, mixed methods formative evaluation approach to identify barriers and facilitators of SBIRT implementation in AECs and tailor an Implementation Facilitation strategy to support the delivery of SBIRT to AEC students by SRSs; and 2) examine the acceptability, feasibility, and preliminary effectiveness of Implementation Facilitation to promote the use of SBIRT by SRSs with AEC students.

NIH Research Projects · FY 2025 · 2025-09

ABSTRACT Scleroderma (SSc) is a multisystem disease characterized by cutaneous and visceral fibrosis that most commonly affects the lung. Nearly 70% of patients with SSc experience clinically significant interstitial lung disease (SSc-ILD) that in 50% will lead to death within ten years of diagnosis. For unknown reasons, women are more commonly affected, yet men have worse clinical outcomes. Defining the molecular mechanisms of SSc- ILD sex differences may eradicate these disparities and improve patient care. SSc-ILD is believed to result from immune-driven accumulation of transforming growth factor beta (TGFβ)-1 responsive, extracellular matrix (ECM)-producing inflammatory fibroblasts. Innate immune pattern recognition receptors and their endogenous ligands are implicated in this process. The endosomal innate immune receptor Toll-Like Receptor 9 (TLR9), which recognizes and responds to danger associated molecular patterns such as mitochondrial DNA (mtDNA), has a recently discovered role in SSc-ILD. We found that SSc-ILD fibroblasts exhibit spontaneous mtDNA release and TLR9 activation. Extracellular mtDNA concentrations are increased in SSc-ILD bronchoalveolar lavage and plasma where they predict disease progression. Relative to their male counterparts, women with SSc-ILD show plasma mtDNA that is more bioactive and lung explants that are enriched for TLR9-responsive inflammatory genes. In chronic SSc-ILD mouse models, females show greater mtDNA bioactivity, TLR9 activation, and a stronger therapeutic response to pharmacologic TLR9 inhibition. The heightened inflammation- promoting consequences of mtDNA-TLR9 interaction in women could explain sex differences in clinical phenotypes experienced by SSc-ILD patients. Our world class team of experts will test the hypothesis that differential interactions between extracellular mtDNA and TLR9 drive SSc-ILD sexual dimorphism. Aim 1 will advance the study of extracellular mtDNA, TLR9, and SSc-ILD sex differences by a) characterizing extracellular mtDNA and TLR9 activation in SSc-ILD BAL samples from women and men; and b) using single nuclear sequencing and protein-based assays to compare TLR9 expression/activation in archived, explanted lung tissue specimens from women and men with SSc-ILD. Aim 2 will define mechanisms of sexual dimorphism in the extracellular mtDNA-TLR9 relationship in experimental mouse models of chronic lung fibrosis using a) pharmacologic and b) genomic methods in a tamoxifen-inducible model of fibroblast-specific Tlr9 deletion and two chronic models of SSc lung fibrosis. Aim 3 will probe sex differences in fibrogenic extracellular mtDNA-TLR9 interactions in human lung fibrosis using genetic and pharmacologic strategies in primary lung fibroblasts and a next generation model of lung fibrosis based on TGFβ1-stimulated human lung cores. If successful, these studies will frame extracellular mtDNA-TLR9 interactions as drivers of SSc-ILD sexual dimorphism and identify mechanisms that can be leveraged for the betterment of human health.

NIH Research Projects · FY 2025 · 2025-09

Project Summary Advanced social cognition enables individuals to apply complex strategies when interacting with others. However, studying the neural bases of strategic social interactions has proven to be challenging. This is largely because standard laboratory animal models of neuroscience do not reliably exhibit complex interaction strategies, such as cooperative strategies, let alone reciprocity based on altruism. Here, we will build upon our newly developed automated pulling paradigms, video-based tracking of facial/body features, and wireless high-density neural recording to understand interaction strategies and their neural underpinnings. Our innovations allow studying the neural bases of interaction strategy with precise, naturalistic, behavioral data, overcoming the difficulty of using naturalistic behaviors in experimental settings and collecting neural data in observational field studies. We will determine interaction strategies used by freely moving dyads in four novel social interaction tasks with distinct payoff matrices designed to elicit diverse strategies. We will study the neural codes and population dynamics in the orbitofrontal cortex (OFC), an area that is implicated in reward-guided decision-making, and the dorsolateral prefrontal cortex (dlPFC), a major prefrontal node implicated in action-based strategy. We will apply computational modeling to test hypothesized dependencies among behavioral variables to describe partner-specific social strategies and response-specific action strategies. We hypothesize that OFC integrates task context (payoff matrix of social interaction), social relationship (sex, dominance, familiarity), and reward history (outcome from previous social interaction) to compute partner-specific social strategies. By contrast, we hypothesize that dlPFC represents response-specific action strategies that are necessary for adaptive behavioral patterns. In Aim 1, we will investigate diverse interaction strategies employed by dyads in the social interaction paradigms and use computational modeling to formally describe these strategies. In Aim 2, we will use a wireless, high-density recording approach and examine the neural code and population dynamics underlying diverse social strategies in OFC and action strategies in dlPFC. Aim 3 will investigate the OFC-dlPFC interactions that facilitate the transformation from social strategies to action strategies. Overall, this proposal will elucidate the behavioral and neural mechanisms of social interaction strategy in the prefrontal cortex for enabling diverse and adaptive social interactions.

NIH Research Projects · FY 2025 · 2025-09

Summary Explaining complex, multivariate predictive models to clinicians and life scientists is difficult, but it is necessary to increase their trust and to allow them to properly apply the model to data from their own patients or study participants. Although certain model visualizations may be helpful for data scientists, they are not always helpful for clinicians and life scientists. Explaining that similar patients get similar estimates and visualizing such patient neighborhoods in a consistent manner, regardless of the type of underlying model (e.g., logistic regression, random forests, deep neural networks, etc.) may be helpful to them. In the context of a specific predictive model (e.g., a polygenic risk score, a deterioration index), seemingly very dissimilar patients may get close estimates if many variables are considered: there could be a mismatch between a neighborhood-based explanation and model behavior. A way of preventing this mismatch is to build context- based patient neighborhoods for visualization. Context is captured by considering for the construction of patient neighborhoods only the dimensions (or variables) that are important for the predictive model. We find that context-based visualizations can work for deep neural network models based on real-world data and we propose to develop robust algorithms (using both data- and clinical-knowledge-driven approaches) and a tool that will help clinicians and life scientists understand the estimates of predictive models by looking into these patient neighborhoods built from supervised weighted distances for different clinical problems. We also propose to develop versions of the algorithms that can work with federated databases in a privacy-protecting manner. Our algorithms and tools will be developed in coordination with clinician-informaticians who will select relevant models, develop representative test cases, and help recruit clinicians for a formative evaluation. We will develop and evaluate our visualization tool under the guidance of human-computer interaction experts. We expect the neighborhood-based explanation to be intuitive and interactive, so clinicians can ask “what if” questions and explore the placing of various patients in models built for their clinical domains: emergency medicine, pulmonary medicine, pediatrics, internal medicine and endocrinology medicine. These models can be ones developed in- house by our team or by others: the privacy-protecting federated version of our algorithm and tool will allow the proposed visualizations to work even without having direct access to the database of patients used to build the models.

NIH Research Projects · FY 2025 · 2025-09

Project Summary/Abstract Alcohol-associated liver disease (ALD) progresses from fatty liver to severe stages like alcoholic steatohepatitis, fibrosis, and cirrhosis. Despite its common occurrence and significant health impact, there are no approved drugs for ALD. Our primary objective is to uncover therapeutic targets and develop effective treatments for ALD. Fatty liver in ALD is characterized by the accumulation of lipid droplets (LDs) and is closely associated with genetic variant mutations including PNPLA3-I148M variant. Our research focuses on a group of human enzymes called methyltransferase-like (METTL) proteins, notably METTL7A, is highly expressed in healthy human livers and significantly upregulated in ALD patients, particularly phosphorylated at serine 53 (hMETTL7A-S53). This elevation significantly correlates with the severity of steatosis, inflammation, and fibrosis. Furthermore, our analysis suggests an interaction between hMETTL7A-S53 and PNPLA3-I148M variant. Knockdown of hMETTL7A or inactivation of hMETTL7A-S53 reduces ethanol-induced lipid accumulation and PNPLA3 levels, supporting its role as a precision therapeutic target for ALD. We hypothesize that targeting hMETTL7A-S53 and its association with PNPLA3-I148M is an effective approach for ALD treatment. Our approaches utilize cutting- edge technology and human hepatocytes to study the protein interactions, and FDA-approved liver-specific RNAi and AAV-based gene delivery. We will assess the effectiveness of two formulations to treat ALD including GalNAc-siRNA-METTL7A conjugates to inhibit hMETTL7A, and AAV7-hMETTL7A-S53A (Serine 53 is replaced by Alanine) to deactivate hMETTL7A phosphorylation. Aim 1 Determine the mechanism of hMETTL7A interaction and stabilization of PNPLA3-I148M and evaluate the manipulation of this interaction for therapeutic benefits in ALD. Aim 2 Test the therapeutic efficacy of the two formulations in both the prevention and reversal of ALD. The goal of this project is to establish hMETTL7A as a promising therapeutic target for the precise treatment of ALD, particularly in cases associated with PNPLA3-I148M variant. This initiative represents an innovative investigation into METTL proteins, with the aim to leverage their potential for advancing clinical interventions in ALD treatment.

NIH Research Projects · FY 2025 · 2025-09

Excessive alcohol intake is the third leading cause of preventable death in the US and is the leading risk factor for death globally among people aged 15–49 years. Moreover, cirrhosis-related mortality among people aged 25–34 years has been increasing by >10% per year in recent years, and this is driven entirely by alcohol- associated liver disease (AALD). Alcohol-associated hepatitis (AAH) is a severe and potentially life-threatening complication of AALD, with a short-term mortality ranging from 20% to 50%. Mitochondrial Ca2+ signals regulate pathological events in hepatocytes that relate to alcohol metabolism, such as steatosis and cell damage, and this project will examine the pathophysiological consequences of altered mitochondrial Ca2+ signaling for the development of AAH. Our relevant previous observations are: (1) Ca2+ signals in hepatocytes are principally regulated by the inositol trisphosphate receptor (ITPR), which is an intracellular Ca2+ release channel in the endoplasmic reticulum, (2) the ability of Ca2+ signals to regulate cell function is determined by which of the three ITPR isoforms are expressed, and the subcellular regions in which they are localized, (3) hepatocytes normally express two of the three ITPR isoforms, ITPR1 and ITPR2, but not ITPR3, (4) Although ITPR2 is the most heavily expressed isoform in hepatocytes, it is excluded from the ER-mitochondrial junction, (5) In contrast, ITPR1 is concentrated in the ER-mitochondrial junction, where it regulates mitochondrial Ca2+, metabolism and apoptosis, (6) although ITPR3 is not in hepatocytes under normal conditions, it becomes expressed in AAH, and (7) like ITPR1, ITPR3 is concentrated in the ER-mitochondrial junction, where it also is positioned to modulate mitochondrial Ca2+ signals and cell metabolism. The hypothesis of this project is that hepatocellular expression and subcellular localization of ITPR3 in AAH is responsible for pathological changes in mitochondrial Ca2+ signals that result in steatosis, metabolic stress, and hepatocellular injury. We will test this hypothesis through three specific aims: (1) at a molecular level, we will compare Ca2+ release from ITPR1 and ITPR3 at the single channel level, determine why each isoform localizes to the ER-mitochondria interface in hepatocytes, and identify the proteins that modulate their Ca2+ channel activity; (2) at a cellular level, we will compare the effects of ITPR1 and ITPR3 on mitochondrial Ca2+ signals, mitochondrial physiology, hepatocyte metabolism, lipid droplet accumulation, and cell injury; and (3) at a whole-organ level, we will examine the roles of ITPR1 and ITPR3 in the development of AAH in animal models and in patients with AAH. Because mitochondrial Ca2+ overload appears to be a prerequisite for the most critical pathological events in AAH, this project has the potential to identify novel, highly specific, and effective treatment strategies, a goal which will have a very broad impact on this important health problem.

NIH Research Projects · FY 2025 · 2025-09

Project Summary/Abstract Biological functions and structures are inherently emergent, arising from vast networks of interactions at scales below. Clear examples are neural activity and chromatin structure, which, while differing in many respects, both arise from intricate webs of interactions between neurons or genomic loci. In recent years, the study of these and other complex living systems has been revolutionized by experimental advances on unprecedented scales. However, a complete understanding also requires quantitative models that are capable of bridging the gap between large-scale phenomena and fine-scale interactions. The primary roadblock in constructing these methods is the exponential explosion of possible interactions, a problem that becomes even more challenging as experiments grow. This leads to a clear question: Given mea- surements from large-scale experiments, can we infer the most important interactions within a system? Combining ideas from information theory, network science, and statistical physics, my research group has recently translated this question into a precise optimization problem. However, solving this problem in large-scale data poses fundamental computational challenges. The central goal of my research group— and the focus of this proposal—is to overcome these challenges by developing scalable methods for inferring the most important interactions within large biological networks. In turn, these methods will yield optimized models for predicting collective functions and structures from the underlying interactions. We will apply our framework to investigate two model systems: (i) neural activity and (ii) chromatin structure. Leveraging recordings of thousands of neurons in the mouse hippocampus and visual system, we will identify the networks of neural interactions that provide the best description of population-wide activity. Building upon advances in single-cell imaging and chromosome confirmation capture techniques (such as Hi-C), we will infer the interactions between genomic locations that maximally constrain the 3D or- ganization of DNA. In both contexts, our preliminary results indicate that only a small number of important interactions have an outsized effect on the system as a whole. By quantitatively testing this hypothesis in large-scale data, our research will provide critical insights into how populations of neurons encode information and how the genome folds into compact structures that are critical for cellular functions. Together, the proposed work will provide the theoretical and computational tools needed to extend principled statistical models to large living systems. This will prove indispensable in the study of neural activity, chromatin structure, and other collective phenomena that directly impact human health.

NIH Research Projects · FY 2025 · 2025-09

Project Summary (Abstract): The etonogestrel (ENG) contraceptive implant remains the most effective hormonal contraceptive method currently available. However, uptake of the ENG implant is hindered by persistent, rare insertion-related side effects including neurovascular injury and implant migration. These rare yet morbid side effects are directly related to the insertion of the implant in the non-dominant arm. An alternative insertion site in the lower scapular region not only alleviates the risk of these insertion-related side effects, but also has unique benefits for specific patient populations. The subdermal scapular insertion site is distant from any danger zones containing neurovascular structures, has a bony structure preventing deep implant insertion, and is a less-accessible area of the body. For patients with developmental delays or psychotic conditions, a less-accessible area of implant insertion is ideal to maintain reliable contraception while preventing self-removal attempts. We developed reliable insertion and removal techniques for the ENG implant at this alternative scapular site as part of a pilot study, while also establishing similar pharmacokinetic profiles for the first year of implant use. However, to support the potential clinical use of the scapular-site inserted ENG implant for its full three-year approved duration of use, we need longer-term pharmacokinetic and pharmacodynamic data from a larger cohort of participants. We aim to conduct a prospective two-year study of the ENG implant when inserted at this alternative subdermal scapular insertion site among 62 healthy, reproductive-age females. We will collect serial pharmacokinetic data from all participants measured at every 6-months over the course of two years of use (4 total measurements). We will utilize these pharmacokinetic data to test for bioequivalence between insertion of the ENG implant at this alternative subdermal scapular site and published pharmacokinetics with standard arm insertion. All participants will also complete questionnaires to assess their side effect profiles and bleeding patterns at each 6-month follow-up time point. We will compare side effect patterns and bleeding profiles from all 62 participants with scapular-inserted ENG implants to published rates from standard arm-inserted ENG implant users. Finally, we will offer all participants enrollment into our existing biobank for future pharmacogenomic and precision medicine investigations. This study will provide the first comprehensive pharmacokinetic and pharmacodynamic data for insertion of the ENG implant at this alternative subdermal scapular site out to two years of use. We will utilize these data to support this alternative insertion site as a clinically viable option for patients, especially among patient populations with contraindications or previous complications with standard arm insertion, and as preliminary data for funding applications to extend this trial out to 3-5 years of implant use. Clinicians will be able to directly incorporate the findings from this study into counseling regarding the contraceptive efficacy and side effect expectations for patients considering an ENG implant inserted at the subdermal scapular site.

NIH Research Projects · FY 2025 · 2025-09

PROJECT SUMMARY/ABSTRACT This proposal describes a rigorous training program for the career development of Dr. Sean Gu, an academic clinical pathologist specializing in hematopathology. The proposed research builds upon the candidate’s previous research and clinical experiences. Under the guidance of a multidisciplinary mentorship team led by his primary mentor, Dr. John Hwa, the comprehensive training plan outlined in this proposal will provide Dr. Gu with the knowledge and skills necessary to become a successful and independent physician-scientist. Sickle cell disease (SCD) is one of the most common inherited blood disorders with an estimated 300,000 infants born globally each year, and in the United States alone, affecting approximately 100,000 individuals. SCD is a multisystem disorder characterized by remarkable phenotypic complexity with debilitating complications. Excessive platelet activation has been consistently observed in SCD patients; however, clinical trials have failed to show significant benefit of conventional antiplatelet therapies in preventing SCD vaso-occlusive disease. Therefore, a better understanding of the cellular and molecular mechanism that drive platelet alterations in SCD can lead to more selective and effect strategies. In this proposal, the candidate proposes to employ novel high-throughput methods to characterize the heterogeneous states of platelets in SCD. By analyzing blood specimens collected from SCD patients and utilizing transgenic mouse models of SCD, the candidate will address 1) how distinct platelet subpopulations contribute to SCD vaso-occlusive disease and 2) investigate specific immunothrombotic pathways and mechanisms of platelet activation and dysfunction in SCD. The knowledge gained from these studies will advance our understanding of SCD disease pathogenesis and provide opportunities for developing novel targeted therapies.

NIH Research Projects · FY 2025 · 2025-09

Positron Emission Tomography (PET) is a powerful imaging modality that enables visualization and quantification of biological functions using radiopharmaceuticals. However, due to the short half-lives of PET isotopes (e.g., 20 minutes for C-11, 2 hours for F-18), the entire process from cyclotron to radiopharmaceutical synthesis to quality control to injection, and imaging must be streamlined to ensure efficiency and radiochemical yield. The radiopharmacy facilities at the Yale School of Medicine (YSM) are housed within the Yale PET Core which operates as a revenue-neutral inter-service provider offering full PET study services exclusively for research. The PET Core serves investigators in psychiatry, neurology, cardiology, endocrinology, and basic neuroscience. Investigators are part of a large NIH-funded community at Yale and throughout New England, as well as several institutions nationally through clinical trials recruiting subjects in the Yale PET Core. Since its inception in 2006, the PET Core has grown rapidly, performing PET imaging research studies in humans and a variety of animal species. Over 1,100 scans are projected for 2025. Despite a significant increase in the number of scanners and efficiency in the PET Core, radiochemistry production capabilities have not grown accordingly. Currently, there are 7 PET scanners available for routine use, with more expected to be installed shortly, while the radiopharmacy facilities were originally designed to support radiotracer production for 3 PET scanners. To operate available PET scanners at full capacity, the PET core will require a substantial increase to the existing radiochemistry infrastructure. This would significantly reduce the waiting time (4-6 weeks) to perform a PET scan. Furthermore, a unique opportunity exists to expand and update the PET Core facilities to bring the radiopharmacy manufacturing to 21 CFR Part 212 current Good Manufacturing Practice (cGMP) for PET drug production. Since the radiochemistry and radiopharmacy facilities have not undergone a major redesign since 2006, a significant upgrade is necessary. This will enable the production of approved compounds in the areas of oncology, neurodegeneration and cardiac imaging, while enabling shipping radiopharmaceuticals for human use outside the Yale campus. Most importantly, the expansion and upgrades to the radiochemistry facilities will enhance the investigators’ ability to efficiently work on the discovery and development of novel radiochemistry compounds focused on the detection, understanding, and treatment of disease and streamline translation to radiopharmaceutical production. The PET Core multi-phase renovation and expansion project will occur within the Magnetic Resonance Research Center building (MRC). The requested C06 funds will be used in three phases to: i) build a new radiochemistry/radiopharmacy/QC Lab cGMP suite, ii) upgrade the existing radiochemistry lab to cGMP, and iii) convert the QC lab to a research hot chemistry lab. This scope will accelerate discovery and development of novel radiochemistry compounds, enable shipping of PET radiotracers to outside entities, reduce waiting time for PET imaging and maximize Yale’s unique PET resources’ reach and accessibility.

NSF Awards · FY 2025 · 2025-09

Every living cell needs to eliminate excess electrons when metabolism converts nutrients into energy. That, in essence, is why we breathe oxygen, which acts as a terminal electron acceptor. However, to survive in harsh environments lacking such soluble, ingestible molecules, the common soil bacterium Geobacter has evolved hair-like filaments that function as “nanowires,” to export electrons to extracellular acceptors and syntrophic partner species in a process called Direct Interspecies Electron Transfer (DIET). Exactly how microbes perform DIET has remained a mystery. Understanding DIET could help mitigate environmental changes, pollution, and the energy crisis, as DIET-performing microbes drive various globally-important phenomena, such as carbon & mineral cycling, bioremediation, corrosion, and chemical or biofuel production. Nanowires and wired cells could also be used for sustainable and living electronics applications. In addition, this highly interdisciplinary project will train students from diverse research backgrounds to harness the power of electric microbes in providing environmental solutions. It will prepare the next generation of interdisciplinary scientists at the interface of biology, physics, chemistry, data science, and engineering. The PI will leverage local infrastructure to encourage the active participation of students from diverse educational backgrounds through research programs, seminars, bootcamps, local community and outreach events for students. To understand how individual species switch to syntrophic growth to build microbial communities that sustain environmental changes, this project proposes to construct a synthetic community of Geobacter metallireducens (Gm) and Geobacter sulfurreducens (Gs) that employs DIET via nanowires. These nanowires were thought to be pili proteins. However, the team of this proposal’s PI has found that Gs pili filaments remain intracellular, exhibit low electron conductivity, and display an atomic structure inconsistent with that of nanowires. Instead, they are required for the secretion of cytochrome filaments, which function as nanowires. Using novel approaches to visualize and quantify DIET in synthetic Gm-Gs communities connected via “nanowires”, the PI’s team will determine the molecular components and energy pathways responsible for the formation, maintenance, and function of these communities. In particular, this project’s goals are: (1) Identify the role of nanowires and DIET in community formation, maintenance, and function. (2) Determine the role of microbial environment in community formation, maintenance, and function by measuring its effect on nanowire conductivity and DIET. (3) Use multi-omics to quantify community response to environments to predict ecosystem behavior. (4) Examine the role of DIET beyond the model system with other environmentally important species. and (5) Assemble electronically conductive consortia for living and sustainable electronics. Fundamental insights gained from these studies in the DIET in synthetic model communities can be applied to natural communities. These insights will yield new strategies for maintaining and manipulating microbial communities that can survive in harsh environments lacking nutrients and energy, while also sustaining high fluid flow and significant temperature, pH, and pressure fluctuations. This essential knowledge will enable system and synthetic biology approaches to leverage omics and modeling approaches to understand the role of environmentally relevant microbial communities in biogeochemical cycles and the rhizosphere. This project is supported by the Systems and Synthetic Biology cluster within the Division of Molecular and Cellular Biosciences. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2025 · 2025-09

(PLEASE KEEP IN WORD, DO NOT PDF) Multipotent hematopoietic stem and progenitor cells (HSPCs) give rise to both myeloid and lymphoid cells. The ratio between myeloid and lymphoid cells is normally adjusted according to homeostatic tissue turnover and injury. Aged HSPCs, however, constitutively skew toward the myeloid, underlying diverse pathologic states ranging from hyperinflammation to cancer. Aged HSCs have reproducible and concerted changes in gene expression, with some of the highly expressed genes serving as markers for their identification (e.g. CD150hi marks myeloid biased HSC or my-HSC). However, due to the unclear nature why such marker genes are overly abundant in aged HSPCs, intervention approaches to target them are limited. Recently, antibody-targeted elimination based on some of these cell surface markers demonstrated rejuvenation of the hematopoietic and immune system. These proof-in-principal results highlight the untapped opportunities for leveraging the key intracellular mechanism to correct the myeloid bias of aged HSPCs. Chromatin organizes as DNA wrapping around nucleosomes, with linker histones (H1) binding to the nucleosome dyad. H1 binding stabilizes nucleosomes, compacts chromatin, reduces accessibility, and repress gene expression. We have generated a novel Dox-inducible H1.0 (iH1.0) mouse model, with which we found that H1.0-overexpressing HSPCs have reduced chromatin accessibility at many my-HSC genes, accompanied with their reduced expression. Functionally, Dox-treated (iH1.0+) HSPCs give rise to more lymphoid-fated progenitors and differentiated lymphoid cells. Importantly, we also found that H1.0 undergoes aspartyl protease mediated turnover. Therefore, we propose to test the hypothesis that the myeloid bias of aged HSCs could be corrected by preventing H1.0 turnover with protease inhibitors, including those in clinical use for decades. We propose to uncover translational opportunities to correct the myeloid bias of aged HSPCs by preserving the endogenous H1.0. We have imported the H1.0 KO mice and crossed them with the iH1.0 mice, thus establishing a spectrum of distinct H1.0 genetic dosages (null, WT, iH1.0). The effects of aspartyl protease inhibitors on HSPC lineage behavior in these mice will be tested. Successful completion of this project will reveal H1 insufficiency to be a molecular defect in aged HSCs amenable to pharmacologic intervention with clinically used protease inhibitors.

NIH Research Projects · FY 2025 · 2025-09

PROJECT SUMMARY Pediatric obesity is a major health burden affecting millions of children and adolescents as it predisposes to the development of cardio-metabolic diseases early in life, such as insulin resistance, fatty liver disease, and type 2 diabetes. Our lab has recently completed a series of studies to understand the relationship between intestinal microbial activity and human metabolism in youth. We observed that intestinal fermentation, a process through which fermentable carbohydrates are processed by intestinal bacteria, results in various biological responses to protect the human body from developing obesity and some of its metabolic complications, such as insulin resistance and ectopic fat accumulation. In particular, we observed that intestinal fermentation causes 1- a reduction of plasma free fatty acids (FFA) due to the inhibition of adipose tissue lipolysis (ATL); 2- a marked entero-endocrine response to reduce appetite, characterized by an increase in the production of peptide YY (PYY) and glucagon-like peptide1 (GLP-1) and a reduced production of ghrelin. In addition, we observed that some intestinal fermentation responses are impaired in youth with obesity and insulin resistance (OIR). In light of this evidence, the current proposal will address: 1- how adipose tissue lipolysis response to intestinal fermentation is affected by insulin resistance; 2- whether changes in ATL, observed when fermentation occurs, are also associated with a reduction of glycerol-derived neo-gluconeogenesis; 3- if physical activity may restore the entero-endocrine and adipose tissue response to intestinal fermentation in youth with insulin resistance. It is the first study to test the effect of insulin resistance on the relationship between intestinal microbial metabolic activity and human metabolism (namely adipose tissue lipolysis, gluconeogenesis, and entero-endocrine response). The results will provide fundamental insight into how insulin resistance occurring in youth with obesity affects the metabolic response to fermentable carbohydrates. In fact, despite the large body of literature showing an association between intestinal microbial fermentation and human metabolism, how and whether insulin resistance may modulate this association remains unknown.