Wayne State University

NIH Research Projects · FY 2026 · 2026-06

PROJECT SUMMARY Electronic cigarette (e-cig) use during pregnancy has become a major health concern in recent years and is perpetuated by the perception that e-cigs are less harmful than traditional combustible cigarettes. Recent estimates show 7% of women use e-cigs in pregnancy, and 45% view them as less harmful and may help them quit or reduce combustible cigarettes in pregnancy. An extensive knowledge gap persists regarding their health impact when aerosolized, especially during pregnancy. Using our well-established pregnant rat model, we obtained preliminary data utilizing a state-of-the-art e-cig system with a commercial e-cig unit and atomizer that offered a translational inhalation delivery and generated vapor profiles directly comparable to human vaping. Our preliminary data demonstrated that a cardinal outcome of e-cig use was a significant fetal and neonatal growth deficit. Concomitant with growth restriction, our exciting preliminary data provides direct evidence that e-cig vaping significantly alters developmental brain hippocampal mTOR system. As a critical node, mTOR regulates essential brain metabolic activities, including protein synthesis and autophagy. Interestingly, our new preliminary data indicate that these mTORC1 and mTORC2 signaling adaptations were accompanied by altered fetal hippocampal dendritic morphology and hippocampal-dependent long term memory deficits. We subsequently generated critical preliminary data that demonstrated that optimizing mTORC1/C2 activity via in vivo administration of the 3rd gen mTORC1/C2 blocker (RapaLink-1) concomitant with e-cig aerosol exposure reversed specific e-cig-induced fetal developmental phenotypes. Thus, we hypothesize that the mTOR system plays a central role in e-cig induced alterations in fetal brain hippocampal adaptations. To test this hypothesis, we will (1) seek to answer fundamental questions about the impact of e-cig aerosol exposure on hippocampal mTORC1/C2 system using novel mechanistic in vivo studies, (2) assess the role of mTOR in e-cig-induced fetal brain hippocampal developmental adaptations using morphometric, stereological and behavioral approaches, and (3) identify e-cig-induced protein signal propagation pathways leading to activation of mTOR and signal propagation downstream of mTORC1/C2 utilizing mass spectrometry- based phosphoproteomics followed by stochastic optimization and reinforcement learning algorithms. We will then compare and interpret the signature pathways impacted by e-cig vaping with and without mTOR blocker using machine learning models. Upon successful completion of these aims, we will have comprehensively characterized the mTOR signaling cascade as it transduces cues from e-cig vaping into molecular action, enabling the identification of potential strategies to mitigate e-cig-induced neurodevelopmental deficits in the hippocampus. The proposed studies develop a strong etiological framework and directly address a “major research area” of developmental impacts under Theme One of NIEHS Strategic Plan 2018-2023, titled “Advancing Environmental Health Sciences”.

NIH Research Projects · FY 2026 · 2026-05

PROJECT SUMMARY Vibrio cholerae is an aquatic bacterium that causes diarrheal diseases ranging from mild to deadly. Pandemic O1/O139 serogroup strains cause cholera, a potentially deadly disease, whereas non-O1/O139 strains cause diarrhea that ranges from mild to severe. Mammalian models have been useful in characterizing major human V. cholerae virulence factors, but rabbits and mice are not natural V. cholerae hosts and require absent or damaged microbiota to enable colonization. Vertebrate fish are natural V. cholerae hosts and zebrafish are a model that recapitulates the entire infectious cycle in the presence of mature intestinal microbiota and immune responses. Fish are rapidly colonized by exposure to V. cholerae in water, develop diarrhea, excrete large numbers of hyperinfectious V. cholerae, and transmit the infection to naïve fish. Because this is a model in a natural host, it presents new opportunities for V. cholerae study that are not possible with mammalian models. However, the V. cholerae genes that are required for fish colonization and transmission are unknown and a major gap in our knowledge. The process by which V. cholerae becomes hyperinfectious when passing through a host is also not well understood. This proposal will examine the infectious processes used by pandemic O1 V. cholerae and test the following two hypotheses: 1) V. cholerae produces specific colonization factors in fish that are also important in human infections. Preliminary experiments identified genes that may be important for colonization using a Tn-Seq screen. Selected genes of interest identified by Tn-Seq will be deleted to assess colonization defects. Genes whose deletion reduces or prevents colonization will be characterized to determine how they facilitate fitness in fish. The effects of gene deletions that impact fish colonization will also be tested in the infant mouse model to assess defects in mammalian colonization. 2) Motility and chemotaxis are crucial for the transient hyperinfectious V. cholerae phenotype. RNA-Seq was used to compare differences in gene expression between actively colonizing and excreted V. cholerae. Genes particularly related to chemotaxis and motility were found to be highly upregulated in excreted V. cholerae, and a mutant that cannot produce a flagellum was found to be highly defective in transmission/hyperinfectivity. To identify the mechanisms for this, other genes important for motility and chemotaxis will be deleted and tested for defects in transmission/hyperinfectivity. Completion of the proposed work will significantly advance our understanding of the requirements for V. cholerae colonization and transmission in the environment and likely in humans as well. The long term goals of this project are to use the zebrafish model to better understand the V. cholerae life cycle in the environment that contributes to human disease, and to identify new strategies to combat V. cholerae disease and transmission.

NIH Research Projects · FY 2026 · 2026-05

Project Summary/Abstract Staphylococcus aureus is the most common invasive human pathogen and a major contributor to infection-related morbidity and mortality. Antibiotic resistance is often prevalent at baseline and can develop on therapy. Further compounding the clinical challenge, even susceptible bacteria fail to respond appropriately to first-line therapies in up to 30% of cases. In this context, antibiotic-bacteriophage combination therapies are increasingly considered as potential adjunct or salvage therapies. Unfortunately, the host range of bacteriophage can be limited by DNA restriction-modification systems that efficiently degrade “foreign” DNA, even originating from other S. aureus bacteria. Custom testing of individual isolate-phage combinations is time and labor- intensive, limiting the broad adoption of phage therapy. Additionally, an inability to trace phage particles in situ results in persistent questions about dosing frequency, phage distribution to different body tissues and penetration into infectious foci. Here we address these limitations through optimization of the phage propagation strain. We add methylation specificity proteins to the propagation host to modify the daughter phage DNA. This modified phage DNA is recognized as “self” by a wide variety of S. aureus bacteria, improving the daughter phage host range without any permanent genetic changes to the phage. As an optional enhancement, the propagation strain can make a component of phage capsids fluorescent, enabling tracking of daughter phage for pharmacokinetic and pharmacodynamic studies. This should work for any phage propagated in the enhanced propagation strain; however, subsequent generations infecting other bacteria will lose the fluorescent capsid. To address this challenge, and enable studies including phage amplification in situ and phage penetration into sequestered infection, we will modify the genome of a representative antistaphylococcal bacteriophage, Sb-1, to express the fluorescent capsid regardless of the host strain. With these new tools we will increase the likelihood of phage being able to infect a given isolate and enable visualization of bacteriophage for pharmacokinetic, pharmacodynamic, and pre-clinical analysis.

NIH Research Projects · FY 2026 · 2026-05

Project Summary Due to effective primary (human papillomavirus (HPV) vaccination) and secondary (screening) prevention methods, cervical cancer (CC) is a preventable disease. A strategy for CC elimination has been globally adopted and includes three pillars for success: (1) 90% of girls receiving the HPV vaccine; (2) 90% of women screened with an HPV test; and (3) 90% of cervical disease detected and treated. The threshold for elimination was set at an incidence rate of 4 per 100,000 women, however the incidence rate in the US is nearly double the elimination threshold. This results in approximately 14,000 new cases and 4,500 preventable deathseach year. CC is most commonly diagnosed among women aged 35-45 who may be raising families or embedded in their careers; thus, a CC diagnoses significantly impacts not only the cancer survivor, but her family and community. While the US is making progress towards the vaccination and screening goals, there have been insufficient efforts towards the detection/treatment goal, which is likely a significant driver for the persisting high rates of disease. Studies suggest that receiving an abnormal CC screen, only 25-80% of women attend diagnostic follow-up (DFU), significantly below the 90% elimination goal. A critical gap exists in our understanding of barriers and facilitators to DFU completion, without which CC prevention may be unachievable. This study aims to identify multi-level predictors of DFU completion to allow for the future development of interventions to improve the CC screening process. Specifically, this project will 1) investigate associations between individual-level reportedscreening barriers and DFU completion after an abnormal screen; 1a) model associations between clinic-level facilitators and DFU, as well as the effect moderation of these factors on associations between barriers and DFU completion; and 2) qualitatively contextualize multilevel barriers and facilitators by exploring the lived experiences of women who have received an abnormal cervical screen and staff who work at screening clinics. This study will employ a multi-level, sequentially explanatory mixed- methods design.Enrollment survey datafromunder- and uninsured women who receive free screeningthrough the tri-county Breast and Cervical Cancer Control Program (BCCCP), which serves the tri-county area of Metropolitan Detroit, will linked to electronic medical records to investigate the association between individual- level factors and DFU completion. The BCCCP enrollment survey collects data on demographics, socioeconomics, health history, and screening barriers. A survey will be con ducted with BCCCP clinics to identify clinic-level services available for participants. Data fromthis survey will be used to model a multilevel relationship between individual-level barriers and clinic-level facilitators with DFU completion. Finally, qualitative interviews will be conducted with BCCCP participants who have had an abnormal cervical screen and clinic staff to generate an in-depth understanding of multilevel barriers and facilitators along the screening process. The findings from this project will inform future interventions aimed at improving CC prevention strategies.

NSF Awards · FY 2026 · 2026-05

This project aims to transform the capabilities of modern Internet infrastructure by integrating high-performance computing directly into the network’s core. Currently, network routers are designed primarily to move data, leaving complex data analysis to be handled by remote cloud servers, which introduces significant delays. This research explores how to embed Graphics Processing Units (GPUs) directly into routers, allowing the network to analyze information in real-time as data passes through. By building this intelligence into the fabric of the network, the project seeks to create a more efficient and responsive network infrastructure capable of supporting the next generation of data-intensive applications. This project advances network intelligence by integrating GPUs directly into routers to overcome traditional computing limitations. The research follows three technical thrusts. The first thrust develops optimized memory management techniques between routers and GPUs to enable high-throughput Artificial Intelligence inference at the network core. The second thrust introduces a vision-based sketching approach to transform network traffic into vectorized formats at line rate, improving data processing efficiency. The third thrust implements specialized generative models that utilize these sketches and hardware integration for advanced threat detection. This framework will serve as a foundation for proactive network defense against complex and evolving security risks. The research goals are paired with an educational mission to train a specialized workforce in practical artificial intelligence design and secure networking under realistic computing constraints. The project will release open-source designs, tools, and workloads that bridge AI and network infrastructure, making it easier for the academic community to build deployable intelligent systems beyond simulation. This project maintains a dedicated repository at https://github.com/NIDS-LAB/Core-NI/ to provide public access to research artifacts, including network datasets, open-source code, hardware configuration files, etc. The project commits to hosting the repository for a minimum of five years, ensuring long-term availability of project outcomes and fostering a collaborative environment to advance core network intelligence. 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

Summary Enterococcus faecium (E.fcm) is a formidable multidrug-resistant (MDR) pathogen associated with high morbidity and mortality . Daptomycin (DAP) is a preferred treatment modality for serious MDR E.fcm infections and has demonstrated rapid bactericidal activity against this pathogen. Unfortunately, DAP resistance is increasing in part due to mutations in the LiaFSR pathway, a three-component regulatory system involved in cellular membrane stress response. Notably, strains harboring LiaFSR mutations have demonstrated nonresponsiveness to DAP (8-14 mg/kg/day) and beta-lactam combinations (DAP+BL) that previously displayed synergistic and bactericidal activity against other nonresponsive MDR E.fcm strains. Bacteriophages (“phages”) are an emerging anti-infective therapy for MDR infections refractory to conventional antibiotics. Given their ability to target specific pathogens, replicate within the cell, and penetrate high burden inoculum infection sites, phage therapy is increasingly sought after as an adjunct to antibiotics for patients with deep- seated MDR infections, such as infective endocarditis, that are refractory to antibiotics alone. However, little is known regarding the mechanisms for synergy observed with phages and antibiotics, which is critical to the optimizing phage-antibiotic combinations (PACs). Our central hypothesis is that we can enhance the efficacy of PACs against MDR E.fcm by analyzing the mechanisms for phage-antibiotic synergy. Our long-term goals are to: i) optimize therapy against MDR E.fcm infections by defining phage-antibiotic regimens that maximize bactericidal killing while preventing treatment-emergent resistance, and ii) determine frequency of administration, duration of therapy, and underlying synergy mechanisms for further optimization PACs. Our short-term goal is to determine the mechanisms for effective PAC synergistic bactericidal activity and prevention of antibiotic and phage resistant MDR E.fcm by conducting high inoculum modified checkerboard (CB) assays, time kill analyses (TKA), and ex-vivo SEV models to measure the impact of adjunctive therapy on E.fcm eradication. The proposed research is innovative as we will employ novel mechanistic fingerprinting techniques developed by our group and a validated ex-vivo simulated endocardial vegetation model that mimics human PK parameters to determine effective phage-antibiotic combinations against MDR E.fcm pathogens. The proposed research is expected to lay the groundwork for effective in-vivo phage-antibiotic experiments investigating the eradication of high bacterial burden MDR E.fcm while preventing treatment- emergent resistance. Attainment of this data is crucial to help guide effective clinical management of refractory MDR E.fcm due to antibiotic resistance and/or suboptimal exposure at the site of infection. This will positively impact public health by prolonging the useful life of conventional antibiotics used to treat refractory MDR E.fcm and alleviate devastating consequences endured by impacted patients.

NSF Awards · FY 2026 · 2026-04

The supported project studies how microorganisms sense and respond to carbon monoxide (CO) in the environment. CO garners a poor reputation as an imperceptible and lethal gas; however, CO is also a biological signal generated as a metabolic by-product in nearly all organisms on Earth. Such “endogenously produced” CO regulates cellular processes, including cell growth, cell death, and inflammation. CO is also a metabolic intermediate and nutrient for certain microorganisms, serving as a viable source of energy for microbes in nutrient-poor environments. Being a signal and nutrient, CO holds great promise as a potential therapeutic agent and is an underutilized energy currency. However, applications of CO are limited by a poor understanding of how CO is detected in biological systems. This project seeks to identify and characterize biological CO sensors from the microbial world to better establish how CO is detected and how CO detection triggers changes in cellular function. A complementary goal of the project is to improve scientific communication and research skills amongst STEM students while engaging the public through an integrated outreach program related to the biochemistry of blood. This project aims to uncover the molecular principles that govern CO-mediated signal transduction through the study of microbial transcription factors that regulate oxidative CO metabolism. Despite a growing appreciation for the roles of CO as a biological signal and microbial nutrient, a limited number of CO-dependent signaling pathways have been fully elucidated. A key limitation remains the identification of CO sensors proteins that trigger a downstream cellular response. Transcriptional regulators of oxidative CO metabolism offer great utility as models of CO signaling. These microbial transcription factors, which rely on strong coordinate covalent bonding between CO and metal ion-containing cofactors, are more easily expressed than their mammalian counterparts and can be readily identified through functional gene cluster analysis. Building on a combination of functional data and comparative genomics, the research team will integrate cell biology, protein biochemistry, and bioinorganic spectroscopy, to (1) establish unifying protein structural features that confer selective CO signal transduction, and (2) identify novel CO-sensing transcription factors. Completion of these research objectives will have broad-reaching impacts ranging from human health, where CO plays disparate roles as a poison and signal, to ecology and agriculture, where CO may act as an important alternative nutrient source. 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 · 2026-04

This grant fosters excellence and promotes student engagement in the fields of Dynamics, Vibration, and Acoustics by supporting participation in the 38th Conference on Mechanical Vibration and Noise (Technical Track VIB), part of the 2026 ASME International Design Engineering Technical Conferences (IDETC 2026), to be held 23–26 August 2026 in Houston, Texas. VIB is a leading technical conference covering a wide spectrum of experimental, analytical, and computational research in dynamics, vibration, and sound/acoustics engineering. Key topics include nonlinear dynamics and control of smart structures, contact dynamics, data-driven and machine learning techniques in vibration and dynamics, industrial applications, acoustic metamaterials, energy harvesting, and vibration measurement and signal processing. The conference fosters the exchange of ideas and information among engineers and researchers within the vibration and acoustics community. The primary objective of this grant is to broaden participation and strengthen the academic and professional development of K–12 students, undergraduate and graduate students, and postdoctoral scholars through targeted student-focused activities at VIB 2026. This objective will be achieved through four complementary activities. First, a K–12 outreach workshop will introduce high school students in the host city to foundational concepts in sound and vibration through hands-on demonstrations and researcher-led instruction. Second, travel support for students and postdoctoral scholars will reduce financial barriers to participation and enable early-career researchers to engage with peers and senior experts, fostering a vibrant and inclusive research community. Third, the grant will support Best Student Paper Awards that recognize outstanding student contributions in dynamics and acoustics and incentivize high-quality research. Finally, an industry–academia careers panel will provide guidance on career pathways across academia, industry, and government, linking academic research with practical engineering applications and equipping participants with insights for successful professional transitions. Through these activities, the grant strengthens technical preparation, promotes scholarly excellence, and enhances professional networking opportunities for early-career participants. Expanding access to VIB 2026 is expected to support long-term integration of students into the vibration and acoustics community and contribute to the development of a technically skilled and competent future 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.

NIH Research Projects · FY 2026 · 2026-04

Project Summary The postnatal development of the mammary gland is dependent on a multitude of cellular programs that are orchestrated by steroid and peptide hormones as well as locally produced cytokines. Two Janus kinases, JAK1 and JAK2, are obligatory intracellular signaling mediators of many peptide hormones and cytokines that have essential functions for the growth, differentiation, and survival of the mammary epithelium. Previous work from several laboratories including our own has established that JAK1 and JAK2 have non-redundant functions for the activation of specific Signal Transducers and Activators of Transcription (STATs) in normal and neoplastic epithelial cells. Important roles of these canonical JAK/STAT signaling cascades during mammogenesis are generally thought to be limited to the differentiation and remodeling of alveolar cells during the gestation cycle. In contrast to this notion, our team has discovered that JAK1 and JAK2 synergistically control the postnatal development of the mammary epithelial ductal tree. We uncovered that STAT proteins are activated in a compensatory manner in ductal epithelial cells, but the collective results from several genetically engineered mouse models revealed that the cooperative functions of JAK1 and JAK2 are not facilitated by their downstream STATs. Unlike in JAK1/2 mammary-specific double knockout mice, the growth and survival of mammary epithelial cells do not require the expression and/or activation of the seven known mammalian STAT proteins. We, therefore, propose that the biologically relevant functions of JAKs during postnatal mammary gland development are facilitated by noncanonical molecular signaling mechanisms of JAK1 and JAK2. Additional preliminary findings also raise the issue of whether the significant biological roles of JAKs are solely dependent on the functionality of their tyrosine kinase domains. To interrogate the noncanonical functions of JAK signaling, we will first establish whether the JAK1/2-dependent signaling mechanisms that govern the development of a mammary gland are dependent on the kinase and/or scaffold functions of JAKs (aim 1). Next, we will investigate the activation of JAK substrates that are currently known and use state-of-the-art genomic and proteomic approaches to identify novel targets and pathways that rely on JAK1 and JAK2 without the expression and activation of STATs (aim 2). Since JAK1/2 kinase inhibitors were clinically ineffective in treating advanced breast cancers, we will investigate the significance of noncanonical JAK signaling in mammary tumor cells and the effects of pharmacologically targeting JAK1/2 for degradation in human-relevant breast cancer models (aim 3). The collective outcomes of this project are expected to provide substantial new insights into the central roles of peptide hormone and cytokine signaling in mammary gland development. The anticipated results from the three aims will elucidate novel molecular mechanisms by which Janus kinases signal within normal and neoplastic epithelial cells beyond the activation of STAT proteins and establish whether pharmacologically targeting JAK1/2 for degradation is a suitable strategy for the treatment of breast cancer.

NIH Research Projects · FY 2026 · 2026-02

Summary/Abstract Vancomycin (VAN) is the primary antibiotic therapy for serious MRSA infections such as infective endocarditis, despite increasing failure rates and the emergence of VAN resistant strains on therapy. Daptomycin (DAP) is the alternative therapy of choice for VAN failures. However, there have been increasingly frequent reports of DAP non-susceptibility and cross resistance with VAN especially post-VAN failures. This treatment outcome has prompted the use of “off-labeled” salvage combination therapy of high-dose DAP plus ceftaroline (CPT), often with poor treatment efficacy outcomes. Bacteriophages (phages) are viruses that infect and kill bacteria and have been used with some success to treat various infections. Yet, bacterial resistance to phages has been observed when individual phage therapy is used. We have generated exciting new data that suggest phage-antibiotic combinations (PAC) may act synergistically and circumvent phage or antibiotic resistance. However, the specific mechanisms for this optimal synergy MRSA are not known. We hypothesize that phage cocktails plus DAP alone or in combination with CPT exert strong efficacy against MRSA and prevent resistance to both phage and DAP. To define and optimize synergistic mechanisms, we will perform experiments to logically translate these regimens. First, we will determine their mechanisms of synergistic action using an innovative method for mechanistic fingerprinting in vitro. Next, we will use our validated ex vivo model of simulated endocardial vegetation infection with humanized antibiotic and phage to optimize the efficacy of PAC against MRSA and avert resistance. Strategies will include simultaneous and sequential dosing and extended exposure for best durability in achieving mechanistic and pharmacologic synergy. Finally, we will optimize regimens for anti-MRSA efficacy in a robust pre-clinical challenge model of infective endocarditis in vivo to maximize treatment efficacy and prevent the emergence of MRSA resistance to phage or antibiotics. This progressive translational strategy will result in phage-antibiotic regimens with optimal anti- MRSA synergies, durability and minimized resistance that are poised for advancement to human clinical trials.

NSF Awards · FY 2026 · 2026-01

Cancer is an umbrella term that includes a range of disorders, from those that are fast-growing and lethal to indolent lesions with low potential for progression to death. In recent decades, important clinical advances in cancer treatments have been attributed to molecular subtyping and targeted treatments aiming at specific genes. However, a significant percentage of patients do not respond to targeted therapies or develop resistance over time. This implies that current methods for tumor characterization and therapeutic interventions are not sufficiently accurate. This project aims to develop novel technologies able to better differentiate among patients diagnosed with the same cancer type. Fundamental to this personalized analysis approach is the capability to explain why patients with similar cancer can greatly differ in terms of treatment success. The approach will also feature an effective integration methodology of multiple types of data. This work will enhance our ability to distinguish among patients who are in immediate danger and need the most aggressive treatments and those whose disease will progress slowly. This will lead to reduced health care costs and personal suffering while improving patient care by identifying the correct personalized treatment for each patient. This research will pave the way for future projects in identifying clinically applicable biomarkers that can be used in diagnosis, risk prediction, and monitoring treatment response and outcome. The project also has an extensive education and outreach component, including curriculum development, undergraduate research, museum exhibits for children, and outreach activities to community colleges and K-12 schools in Nevada. This project will address two important challenges commonly faced in cancer subtyping: (1) incorporation of pathway knowledge in cancer subtyping, patient stratification, and risk prediction, and (2) efficient integration of multi-cohort and multi-omics data. To address the first challenge, the project will develop novel machine learning technologies to identify impacted pathways and compute personalized pathway profiles in individual patients. The innovation of this idea stems from combining classical probabilistic components with important biological factors that are not captured in existing techniques: i) all gene-gene interactions as described by each pathway, ii) topology among multi-omics layers, and iii) the crosstalk among pathways. The approach will transform all molecular data to a common pathway space, making it possible to efficiently address the second challenge: systematically integrate multi-omics and multi-cohort data. This will be realized by a non-negative-kernel, variational autoencoders. The non-negative kernel will effectively accumulate consistent signals of biomarkers while shrinking random noise of non-relevant components. The goal of this project will be achieved by three thrusts: 1) compute personalized pathway profiles that can be used for subtyping, 2) integrate multiple patient cohorts to increase sample size and statistical power of subtyping methods, and 3) validate the proposed methodologies using 10 subtype discovery methods, 6 patient stratification techniques, and 6 risk prediction models that will be tested on more than 70 cancer datasets. The investigator will make the methodologies publicly available via a Bioconductor package and a web-based platform, thus increasing their potential for wide adoption by the research communities. 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

Large language models (LLMs) are increasingly integrated into daily life, powering applications in education, healthcare, code generation, and more. However, these models can inadvertently memorize and reproduce sensitive or harmful content, including private user data, copyrighted material, and unsafe instructions. Retraining LLMs from scratch to remove such content is often impractical due to high cost and complexity. This project charts a new course toward more controllable, debuggable, and secure artificial intelligence (AI) through LLM unlearning, a paradigm that enables the targeted removal of harmful data influences and behaviors from pretrained models without compromising their overall performance. The research advances national priorities by promoting trustworthy AI, strengthening data privacy, ensuring safe deployment across sectors such as cybersecurity, healthcare, and education, and enabling contextually-adaptive systems aligned with a wide range of social norms. The project also offers strong educational and outreach opportunities, including curriculum development, research dissemination through workshops, tutorials, publications, and open-source software, as well as the creation of inclusive mentoring programs. This project aims to establish a comprehensive foundation for LLM unlearning by addressing challenges across four interconnected areas: optimization, model, data, and application. On the optimization front, it develops new algorithmic frameworks to enhance the effectiveness, robustness, and efficiency of LLM unlearning. At the model level, it investigates how internal components of LLMs contribute to memorization, introducing interpretability-driven approaches to identify and adjust influential weights without compromising essential capabilities such as LLMs' "emergent" abilities. On the data side, the project examines the role of watermarking and coreset selection in shaping unlearning outcomes, advancing methods for handling imperfect or proxy forget sets. These innovations are applied to privacy-sensitive scenarios such as conversational risk assessment in online dating, enabling LLMs to evaluate behavioral risks while erasing personal information. Conducted by a multidisciplinary team with a strong track record in trustworthy machine learning, the project is expected to deliver principled algorithms, practical tools, and rigorous benchmarks that advance responsible, adaptable, and secure AI 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 2025 · 2025-10

This project will support research that contributes novel methodologies related to privacy protection of multi-robot systems, promoting the progress of science and advancing national health and prosperity. Due to possible active and passive intruders who may gain access to communication channels and observe the system behaviors, private information can be leaked through robot behaviors. However, existing works on privacy analysis of robot behaviors may not scale when the system dimension increases. This project supports fundamental research that addresses the major challenges in multi-robot systems, privacy analysis, algorithm design, computation, and information theory. The project will contribute to more secure and private robotic systems and increase the usage of robots in various domains to increase efficiency and safety. Existing approaches on privacy analysis of robot behaviors rely on the construction of a deterministic observer, and therefore require an exponential complexity for privacy analysis. To address this, this project will develop a scalable computation framework for analyzing behavior privacy of multi-robot systems, which reduces the computation complexity with quantifiable and acceptable error bounds. Four closely integrated research objectives are planned: (1) Develop a scalable privacy verification framework with only polynomial complexity to verify that there is no privacy leak; (2) Develop a scalable privacy quantification framework to measure the robot’s privacy level subject to noise and uncertainty; (3) Develop an information releasing policy for multi-robot systems to perform collaborative tasks while preserving privacy against compromised robots; and (4) Evaluate and validate the framework on multi-robot patrolling system. Collectively, advances from these research endeavors are expected to make the robotic systems more secure, and will create a new computationally efficient verification mechanism for large multi-agent systems where privacy can be a concern. 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 aims to address the critical need for trustworthy artificial intelligence and machine learning (AI/ML) in applications that directly impact individuals and society. As AI/ML systems, particularly those powered by Reinforcement Learning (RL), are increasingly used in personalized healthcare, education, commerce, and large language models, it is imperative to ensure these systems are reliable and ethical. This project aims to advance trustworthy RL by addressing the significant challenges of data privacy, robustness against corruption, and fairness across diverse user demographics. The project's broader significance lies in establishing fundamental connections between trustworthy RL and other machine learning areas, contributing to a comprehensive framework for responsible AI/ML development and deployment. Educational components include a “no-regret” learning plan leveraging the principal investigator's popular blog, collaborations with industry through NSF centers and institutes, and a summer camp for local high school students. Ultimately, this project seeks to enhance scientific literacy, promote workforce development, inform public policy, and improve the responsible development and deployment of AI/ML technologies for societal benefit. The goal of this research is to investigate fundamental limits and algorithmic principles for trustworthy sequential decision-making. To this end, the project aims to develop a novel reduction from RL to statistical or online learning that integrates privacy, robustness, and fairness, enabling the leverage of established results in these better-understood domains. Specifically, the research focuses on four thrusts: (1) establishing the first theoretical results for private RL under general function approximations; (2) investigating the interplay between robustness (addressing data corruption and heavy tails) and privacy (considering both central and local differential privacy); (3) developing a novel framework for group fairness in RL based on constrained RL; and (4) applying this understanding to improve the alignment process of large language models. The research employs techniques from RL, stochastic optimization, online learning, information theory, and game theory to derive minimax lower bounds, develop general-purpose algorithms, and analyze the trade-offs between utility, privacy, robustness, and fairness. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-09

Non-technical Summary: The ability to store information and retrieve it in a non-destructive fashion is critical in health care (radiation dosimetry), information science (quantum data storage), and defense (anticounterfeiting). Photosensitive nanomaterials are uniquely suited as optical storage media because they have the potential to simultaneously afford high storage density, fast and non-destructive readout, and low energy consumption. Yet, the library of nanomaterials for real-world applications remains limited, because no definitive principles to design materials with the desired photosensitive response exist. With support from the Solid State and Materials Chemistry Program and the Electronic and Photonic Materials Program, both in NSF’s Division of Materials Research, Prof. Rabuffetti and his research team at Wayne State University explore new photosensitive nanomaterials in this research project. They investigate rules governing photosensitivity at the nanoscale through a combination of chemical and spectroscopic studies and from there develop design principles. In addition, this research provides opportunities for workforce development at both undergraduate and graduate levels and K-12 STEM outreach. Graduate and undergraduate students are trained in the synthesis and advanced characterization of optical nanomaterials. 6th through 9th grade students participating in the annual STEM Day hosted by Wayne State University are introduced to optical materials research through a hands-on activity based on photosensitive materials. Technical Summary: With support from the Solid State and Materials Chemistry Program and the Electronic and Photonic Materials Program, both in NSF’s Division of Materials Research, researchers develop an understanding of how to control the photosensitivity of photoexcitable storage nanophosphors. This is done by exploiting various chemical and structural tunabilities of a novel family of host materials. Three specific research thrusts are pursued: a) Targeted synthesis of a series of nanophosphors that incorporate trivalent rare-earth dopants as photosensitive centers. To this end, colloidal synthetic routes to nanocrystals with uniform morphology and well-defined doping levels are developed. b) Establishing the spatial distribution of photoactive centers by probing the atomic structure of the nanophosphors. This task involves interrogating the atomic arrangement in multiple length scales using element-specific spectroscopic probes. c) Elucidating the role of the phosphor’s crystal-chemistry on photochemical and photophysical processes that underpin the photosensitive response. Metrics that quantitatively describe chemical composition, crystal structure, and photosensitivity are correlated, enabling the researchers to derive principles which enable control of photosensitive response via rational manipulation of phosphor composition, structure, and morphology. In addition to undergraduate and graduate student training, 6th through 9th grade students participating in the annual STEM Day hosted by Wayne State University are introduced to optical materials research through a hands-on activity based on photosensitive materials. 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/Abstract The goal of this research is to assess the feasibility of a highly scalable intervention, Mind-fulness-Based Stress Reduction (MBSR) delivered virtually enhanced with virtual reality (VR), for improving health outcomes through the reduction of stress in a high-risk population, emerging adults (EAs; 16-20 years) with high levels of stress reactivity. The primary aim of this project is to adapt MBSR for virtual delivery enhanced with VR in collaboration with a Youth Community Advisory Board (YCAB, composed of five EAs with T1D) and to assess feasibility and the acceptability of MBSR-VR for EAs with T1D and elevated levels of stress reactivity. This feasibility study will use a multi-center randomized controlled pilot study design with 48 EAs with T1D and elevated stress (as as- sessed with the Perceived Stress Reactivity Scale). EAs will be randomized in a 1:1 ratio to MBSR-VR or treat- ment as usual (TAU, standard care control). Three cohorts of 16 participants, 48 total, will be enrolled through two clinical research centers (CRCs) at Wayne State University (WSU) in Detroit, Michigan and Johns Hopkins University (JHU) in Baltimore, Maryland. The WSU site will recruit via the T1D Exchange (T1DX) and JHU via their affiliate diabetes clinics. Participants will enter the study during an 8-week period during which research staff will obtain informed consent and assess eligibility via a stress screener; eligible participants will continue with their baseline data collection (T1). Once 16 participants have enrolled, they will be randomized in a single block, 8 to MBSR and 8 to TAU, and those assigned to MBSR will be oriented to the VR technology. All partici- pants will continue to receive standard medical care from their usual diabetes care provider. Treatment groups will run for 9 weeks followed by a 4-week post-treatment data collection (T2) period. The YCAB will assist with monitoring study progress and refining study procedures (e.g., recruitment strategies) over the course of the trial. Feasibility and acceptability will be established through achieving recruitment, retention and intervention bench- marks. Recruitment feasibility will be established by accruing 8 participants every 4 weeks (16 over 8 weeks) and fewer than 15% excluded due to lack of access to an internet-enabled device. Retention feasibility will be established with 85% retention at the end of treatment data collection and <15% of dropouts due to technology challenges that impeded study participation. Intervention feasibility will be established if EAs attend ≥5 of 9 MSBR intervention sessions, ≥90% of the intervention content is delivered, staff collect >75% of self-practice diaries, MBSR fidelity ratings are ≥4 out of 5 on average, and EAs express satisfaction via qualitative interviews and quantitative assessment (≥3 out of 4).

NIH Research Projects · FY 2025 · 2025-09

This proposal aims to establish the Wayne State University Advanced Technology Center (ATC) for cutting-edge analytical technologies occupying 10,425 square feet in the new Health Sciences Building (HSB) on the Wayne State University School of Medicine (WSU-SOM) campus. Developed with the Mott Center, Barbara Ann Karmanos Cancer Institute (KCI), and the Institute of Environmental Health Sciences (IEHS), this initiative will facilitate innovative biomedical research through technologies provided by core facility laboratories. The ATC will provide state-of-the-art instrumentation and expertise to support multidisciplinary efforts in understanding disease mechanisms, developing novel therapies, and addressing critical health challenges, particularly those affecting urban and rural populations. The ATC will centralize the MICR (Microscopy, Imaging and Cytometry, Resources), Proteomic, and Lipidomic core facilities, which are currently dispersed across four aging buildings. These cores evolved independently over decades, resulting in physical separation and limited interaction. Centralization in the ATC while modernizing its infrastructure will optimize resource utilization and foster collaborative research. WSU investigators and external users rely on these cores to analyze samples from precious patient biopsies, controlled animal studies, and in vitro experiments. However, their physical separation limits the feasibility and efficiency of multi-omic research efforts. Samples often require partitioning for separate analyses at different facilities, creating redundancy in record keeping and regulatory oversight while at the same time reducing the ability to extract comprehensive, interconnected data. The centralized ATC addresses these challenges by facilitating the development of protocols across cores, utilizing a single reporting and oversight mechanism while providing different technologies, and enabling seamless integration of advanced experimental design. For instance, biobank specimens, patient samples, or toxicant-exposed tissues can undergo specialized imaging, cell sorting, and omics analyses in a seamless workflow. This integrated approach will maximize data generation, enhance scientific discovery, and advance investigator initiatives. The ATC will drive innovation beyond what each core could achieve individually, yielding benefits far greater than the sum of its parts. WSU’s core laboratories support a broad user base, including investigators from regional, national, and international institutions. The ATC will enhance WSU’s reputation as a premier resource for advanced technological solutions, offering high-quality services, training, workshops, and educational opportunities. The ATC will improve the efficiency and impact of research while also preparing the next generation of scientists through comprehensive training programs, including experiential learning opportunities designed to engage young scientists. Ongoing outreach to area schools, including a summer training program, demonstrates our structured and sustainable commitment to educational outreach.

NSF Awards · FY 2025 · 2025-09

The Chemistry of Life Processes Program in the Chemistry Division is funding Dr. Mary Kay Pflum from Wayne State University to determine how the addition of phosphate groups to histidine amino acids in proteins affects their biological functions in mammalian cells. Dr. Pflum is developing analogs of adenosine 5’-triphosphate (ATP) as chemical tools to identify proteins that are phosphorylated at their histidines, in contrast to the more commonly studied phosphorylated amino acids, by kinase enzymes found in human cells. Kinases are associated with a wide variety of cellular events, including cell communication and adaption, and are often overactive in disease states. Thus, the study of kinases and protein phosphorylation impacts the understanding of normal and disease-related biology. This NSF award also encourages a future generation of scientists by exposing middle and high school students to protein structure and function through hands-on outreach activities at local schools. A toolkit of ATP analogs was developed for kinase-catalyzed labeling of protein substrates, which focused in prior work by the PI on serine, threonine, and tyrosine phosphorylation. Given the paucity of chemical tools to study phosphohistidine-mediated mammalian cell biology, the overall goal of this NSF award is to apply kinase-catalyzed labeling with ATP analogs to investigate histidine phosphorylation. Kinase-catalyzed labeling was established with the two known human histidine kinases, NME1 and NME2, using an ATP-biotin analog. Building on kinase-catalyzed labeling, this award focuses on developing several novel chemical tools to study phosphohistidine-containing proteins and substrates for the NME1 and NME2 kinases. Using these chemical tools, critical evidence will be generated to show how histidine phosphorylation influences biological events in mammalian systems. These studies represent essential steps towards our long-term objective to characterize phosphohistidine and histidine kinases in mammalian cellular processes, which is challenging or impossible with available 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.

NIH Research Projects · FY 2025 · 2025-09

Innovative Multiscale Modeling Techniques for Membrane-Bound Proteins SUMMARY Our laboratory develops computer models to investigate biomolecular diffusion and interactions with a particular emphasis on membrane-bound proteins and lipid metabolism. The study of diffusion using empirical techniques faces serious obstacles in capturing the intermediate-state details of kinetic processes. Even computational tools encounter inherent tradeoffs in balancing long-timescale simulations with those that reveal precise atomistic details, particularly for lipid droplet (LD) proteins. This R35 MIRA proposal has two complementary research directions. First. we will develop computational tools to model diffusion and molecular interactions and apply them to the SARS-CoV-2 spike protein, otherwise well studied during pandemic-related research and similar to other viruses. My research offers to accelerate drug discovery, coronavirus vaccine development, and treatments for other viruses. Second, we will investigate the protein networks that regulate lipid metabolism on LDs, focusing on how the ABHD5 protein interacts with LD membranes and lipid-regulating proteins. By combining computational approaches and experimental validation, my long-term research trajectory aims to advance understanding of lipid metabolism and inform potential therapeutic strategies for diseases that include diabetes and cancer.

NIH Research Projects · FY 2025 · 2025-09

Preventing opioid and other drug use problems before they begin or escalate provides substantial benefits to individuals, communities, and society. Effective upstream prevention and early intervention services can interrupt the progression of substance use problems, reducing the need for costly downstream treatment of substance use disorders (SUD). This is especially important for marginalized youth, who face elevated risk factors for SUDs and experience disparities in accessing necessary services. Schools offer a crucial setting for implementing evidence-based prevention and early intervention programs to mitigate these risks and promote positive development; however, schools lack the infrastructure required for sustained delivery and long-term impact of school-based services for youth at risk of SUD. This R61/R33 project, Reducing Addiction through Prevention Infrastructure Development (RAPID), addresses this critical need by building upon the research team’s prior work, Rapid Adaptation to Prevent Drug Use, to establish sustainable infrastructure for multi-tiered SUD prevention in high schools. RAPID employs a phased, mixed-methods approach, guided by implementation science frameworks and community-based participatory research principles. The R61 Phase will focus on adapting and refining a multi-phase implementation infrastructure-building blueprint, originally developed for Tier 1 (universal) prevention for use with Tier 2 (selective) and Tier 3 (indicated) SUD prevention evidence-based programs. The RAPID blueprint includes core strategy functions centered on (a) Partnerships, (b) Readiness, and (c) Resource Access. We will utilize the IDEA (Iterative Decision-Making for Evaluating Adaptations) framework, incorporating input from three community advisory boards comprising (a) program and practice experts; (b) school financing experts; and (3) individuals with SUD lived experience. RAPID is designed to leverage existing school strengths and ensure contextual relevance. The research team will review implementation financing strategies for school-based prevention and incorporate feasible strategy options and fiscal mapping to guide strategy selection and expand the resource access core strategy of RAPID. User-testing will be completed to ensure RAPID usability in advance of the R33 Phase. The R33 Phase comprises a rigorous evaluation of the effectiveness and cost-effectiveness of the RAPID implementation strategy compared to standard technical assistance. We will employ a stepped wedge cluster randomized design across 40 demographically and geographically diverse high schools in Michigan to assess the impact on program reach (primary outcome) and student outcomes (secondary; e.g., drug use). Economic analyses will utilize activity-based costing and trial-based modeling to determine the cost-effectiveness of RAPID. By integrating rigorous research methods with a deep commitment to community partnership, equity and stakeholder engagement, this project aims to serve as a replicable model for schools and communities nationwide, building the infrastructure needed to prevent opioid and other SUDs. This study is part of the NIH’s Helping to End Addiction Long-term (HEAL) initiative to speed scientific solutions to the national opioid public health crisis. The NIH HEAL Initiative bolsters research across NIH to improve treatment for opioid misuse and addiction.

NIH Research Projects · FY 2025 · 2025-08

Project Summary/Abstract Wayne State University Lipidomics Core Facility provides Liquid Chromatography – Mass Spectrometry (LC-MS) based lipid analysis services to NIH-funded researchers both at the university and its partnering institutions. An essential feature of lipidomic analysis is that the lipids are extracted from biological samples before subjecting to LC- MS analysis. This is a labor-intensive process and involves repetitive use of Solid Phase Extraction (SPE) as well as liquid-liquid extraction methods. The requested automated system from Hamilton, MassExtract STARlet, can perform the essential functions involved in the sample preparation for LC-MS. Precise volume delivery, mixing and incubation, temperature control, barcode enabled sample monitoring, clog detection systems, eluate evaporation, and other features of the system are invaluable to manage large volume of samples (>6,000) processed by the core facility each year. This system will revolutionize our ability improve accuracy, reproducibility, and the speed of sample preparation for LC-MS analyses. Further, the system’s unattended operational capabilities provide ample opportunities to develop novel and improve existing methods to further our NIH funded research. All essential facilities required to install the instrument are in place and the university is committed to maintain it for its continued use well beyond the manufacturer’s warranty.

NIH Research Projects · FY 2025 · 2025-08

In an era where new discoveries in cancer biology can reveal profound insights into disease progression, prevention, and treatment, it is imperative to cultivate the next-generation cadre of researchers, equipped with hands-on experience and communication and collaborative skills necessary for impactful innovations. There is an urgent need for structured programs to nurture emerging cancer researchers early-on, who will pursue careers in cancer biology research, education and treatment. Building upon the need to nurture the next generation of cancer researchers, this proposal introduces the “NextGen Oncology Scholars: Nurturing Tomorrow’s Pioneers” program for undergraduate scholars. This R25 application outlines a dynamic program to engage outstanding undergraduate scholars leading to graduate studies in Cancer Biology research and treatment. The program leverages the robust infrastructure of the Barbara Ann Karmanos Comprehensive Cancer Center and the multidisciplinary graduate training program in Cancer Biology at Wayne State University supported by T32 CA009531, now in its 38th year of support from the National Cancer Institute. A key goal is to recruit and cultivate cohorts of emerging cancer researchers, equipped with original research experiences and professional skills essential for future discoveries in cancer biology in the coming decades. We have developed a holistic training strategy that integrates hands-on laboratory experiences, dynamic didactics, clinical exposure and structured mentoring, building on the success of our Cancer Biology Summer Undergraduate Fellowship Program (SURF), a successful pilot since 2014. Once funded through the R25 mechanism, the NextGen program will replace SURF, expanding its impact on summer research training for undergraduates. An important goal is to provide unique opportunities for cutting-edge research, thus enhancing our decade-long track record of training undergraduate students in cancer biology research each summer. The program will provide undergraduates with mentored laboratory research experiences in cancer biology. We will implement a comprehensive curriculum that combines in-class learning with clinical exposure to enhance understanding of cancer biology, and to broaden awareness of careers in cancer research and clinical care. We will actively recruit and empower a talented student cohort to enrich the scientific workforce in cancer biology research by providing comprehensive support and engagement. Integrating hands-on training and strong mentorship, we strive to elevate cancer research education at our institution, drawing from our outstanding educational infrastructure. Upon completion, our trainees will be well-prepared for advanced studies in graduate or professional school, including medical school, with a spirit of research collaboration and outstanding communication skills needed for professional success. Strengths of our program include a state-of-the-art research infrastructure, personalized career guidance, and access to a highly supportive network of faculty mentors and peers. Importantly, our unique training program will include students in cancer biology research from a range of backgrounds, thus contributing to global advancements in cancer treatment in the coming decades.

NIH Research Projects · FY 2025 · 2025-08

Project Summary Orthostatic disorders, including orthostatic hypotension (OH), disproportionately affect older adults, presenting in 30% of older adults and up to 70% of nursing home residents. As OH is a major risk factor for syncope, falls, and cognitive decline, medical agencies stress the public health need for monitoring orthostatic vital signs (OVS) in at-risk individuals. This proposal investigates an NIA award-winning wearable device called TRACE, which addresses fundamental limitations of the current clinical standard, the blood pressure (BP) cuff: 1) The BP cuff measures central BP, but it is known that the root cause of orthostatic symptoms is a lack of blood flow to the head. TRACE’s unique blood volume-based metrics are predictive of orthostatic symptoms, whereas BP is not. 2) BP cuffs cannot capture the rapid hemodynamics of the initial orthostasis response (first 10-30 seconds), a predictive marker for adverse outcomes. Continuous BP and transcranial Doppler (TCD) can capture these dynamics, but their cost make them impractical for routine monitoring. TRACE offers real-time monitoring in a low cost wearable. 3) TRACE offers multiple metrics to aid clinicians in a specific diagnosis. Our long-term goal is to address the underdiagnosis and mismanagement of orthostatic disorders using the TRACE remote OVS monitoring platform. The objective of this application is to validate TRACE’s 4 novel orthostatic metrics against the gold standard of perfusion measurement, TCD. Our central hypothesis is that normalized metrics will match their counterpart TCD metrics, and both will be more predictive of self-reported OH symptoms than the BP cuff. The team includes PI Prof. Amar Basu, an electrical/biomedical engineer who developed TRACE and has >15 years of experience in wearable sensors; Dr. Joseph Miller, an emergency medicine physician with expertise in orthostatic measurements by transcranial Doppler; and Dr. Paul Kilgore, an expert in clinical study design. Henry Ford Hospital and the Wayne State Integrative Biosciences Center will provide participant recruitment and facilities. Aim 1 will evaluate TRACE’s 4 orthostatic metrics that quantify relative physiologic changes during orthostasis. Their robustness to confounding factors will be evaluated in 25 older adults by measuring the agreement between two TRACE sensors, one on each earlobe, during active stand tests. Aim 2 will compare TRACE blood volume metrics with TCD perfusion metrics. We will perform tilt table and active stand tests in 100 older adults, monitoring simultaneously with TRACE, TCD, BP, and continuous BP. Aim 2 will test our central hypothesis described above. The proposed work could have a transformative impact in managing incurable orthostatic disorders like Neurogenic OH. TRACE brings perfusion-based metrics in a wearable form factor that can assess orthostatic metrics every time an individual stands. Temporally rich information can empower clinicians and patients to understand the effect of triggers (large meals, poor hydration), and the efficacy of interventions (crossing legs, compression stockings), improving patient outcomes.

NIH Research Projects · FY 2025 · 2025-08

Project summary/Abstract: The role of the corneal epithelium is to: 1) act as a barrier against physical trauma, pathogens, and chemicals, 2) maintain a constant level of stromal hydration, and 3) serve as an optical interface focusing light onto the retina with optimal quality. A healthy corneal epithelium requires coordinated actions of multiple dynamic cellular processes and signaling pathways. To date, studies of extra- and pericellular proteases in eye and vision research have mostly centered on their harmful effects in corneal wound healing and infection. Based on our preliminary data, we hypothesize that the cell-surface anchored serine protease matriptase, in contrast, is essential for normal ocular surface function and promotes recovery from injury to the corneal epithelium. We generated matriptase hypomorphic mice and observed an abnormal irregular corneal surface cell pattern and increased inflammation. Our preliminary data using human corneal epithelial cells suggest that matriptase silencing causes decreased barrier function accompanied by impaired tight junction (TJ) integrity and aberrant proteolytic processing of the epithelial cell adhesion molecule (EpCAM). Under injury conditions, we identified the pro-form of hepatocyte growth factor (pro-HGF) as a candidate substrate for matriptase with key functions in corneal wound healing. Matriptase efficiently cleaves pro-HGF and converts it into the active form needed for activation of its cell-surface tyrosine kinase receptor c-Met and for subsequent stimulation of cell migration. Our hypothesis is that matriptase promotes corneal epithelial barrier function via regulation of tight junction formation and promotes tissue repair by activation of the HGF/c-Met signaling pathway. Aim 1: Determine the role of matriptase in corneal epithelial maintenance and function. Novel matriptase knockout mouse models as well as 3D cellular models of the human cornea will be utilized. We will use a balanced combination of hypothesis-driven targeted experimentation and unbiased approaches to comprehensively characterize the role of matriptase in corneal homeostasis and to identify critical pathways required for proper regeneration and differentiation, TJ formation, EpCAM processing, and barrier function. Aim 2: Identify functions for matriptase in corneal epithelial tissue repair. Matriptase-deletion mouse models and 3D epithelium/stroma cell models will be used to determine the impact of matriptase on corneal repair in experimental wound repair. Comparative RNA-Seq and Mass Spectrometry analysis will be performed to identify matriptase substrates and matriptase-mediated pathways in homeostasis and injury response. The impact of stimulation with Internalin B321 ( InlB321 ), an agonist of the HGF receptor c-Met, on cell migration/tissue repair in vitro and in vivo will be tested as a potential therapeutic strategy to treat corneal epithelial injury. Significance: Implementation of the approaches described in this proposal will provide a mechanistic understanding of the role of matriptase in corneal epithelial homeostasis and injury and identify actionable targets (factors/pathways) for therapeutic options to promote corneal epithelial repair leading to restoration of function.

NIH Research Projects · FY 2025 · 2025-08

Project Summary/Abstract The goal of this proposal is to bring a Thermo Fisher Scientific Orbitrap Astral mass spectrometer with a High-Field Asymmetric Waveform Ion Mobility Spectrometry (FAIMS Pro Duo) devise, an Ardia Instrument Control and Data Management system plus a nano-LC to the Proteomics Core at Wayne State University. The Proteomics Core supports an extensive user base including 7 Major Users and 19 Minor Users who have contributed to the proposal and who have NIH-funded projects that will be advanced by having access to the Orbitrap Astral. The Proteomics Core at Wayne State also supports the Karmanos Cancer Institute and the P30 funded Cancer Center as well as the NIEHS funded CURES P30 Center and CLEAR P42 Superfund Center. This Orbitrap Astral has unique capabilities for deep profiling of complex proteomes using Data Independent Analysis (DIA) acquisition. Advances in the Orbi Astral that make it unique for DIA are the 200 Hz scan speed with 80,000 Resolution and superior sensitivity. These features allow the Astral to acquire DIA data using 2 Da windows, something no other MS system can achieve. The new mass analyzer in the Orbitrap Astral make it an exceptional instrument for DIA proteomics. Combined with the experienced mass spectrometrists in the Core, the Orbitrap Astral will be a major asset to research programs of NIH-funded investigators at WSU and in the SE Michigan region. As investigator needs have evolved they have developed an increased need for higher resolution, greater sensitivity and higher mass accuracy proteomic mass spectrometry. The Orbitrap Astral is designed to address todays needs in proteomic analysis. The instrument excels at deep sequencing and quantitative proteomics as well as accurate characterization of post translational modifications. The current mass spectrometers in the Core lack fast duty cycles and the advanced ion optics that make detection of low abundance species routine on the Astral. The ability to unambiguously localize post translational modifications (PTM) in peptides is a critical feature in any proteomic MS system and the Astral is outstanding in this due to the high degree of coverage within the identified proteins. Wayne State University is strongly committed to this proposal as evidenced by a combined $350,000 in new support for the instruments management, operation, maintenance, informatics and usage. The technical expertise of Proteomics Core personnel as well as the physical and administrative infrastructures are all in place and ready to immediately put this new mass spectrometer to work on NIH-funded biomedical research projects. This instrument will provide transformative technologies for advancing dozens of NIH- funded, ongoing projects, as well as catalyzing new research directions for investigators at Wayne State University.