Johns Hopkins University

NIH Research Projects · FY 2026 · 2026-02

Project Summary/Abstract: Small-vessel-disease related vascular cognitive impairment and dementia (VCID) represent the second leading cause of cognitive dysfunction in older individuals. Small artery abnormalities have been assessed using surrogate markers such as cerebral blood flow (CBF), cerebrovascular reactivity (CVR), and arterial transit time (ATT). However, post-mortemly it has been shown that VCID hallmarks such as white matter hyperintensities (WMH) are more associated with small vein pathologies such as stenotic or tortuous veins. However, few studies have examined in vivo venous hemodynamics in small vessel disease, due to a scarcity of available tools. Importantly, venous transit time (VTT) provides unique information about venous vessel function and represents an important candidate biomarker in small vessel disease. VTT denotes the time it takes for the water molecules to travel from the exchange site, typically the capillary bed, to draining cerebral veins, e.g., superior sagittal sinus (SSS). The central goal of this application is to develop and validate a novel non-contrast MRI technique, dubbed Venous transit time Imaging by Changes in T1 Relaxation (VICTR), to measure VTT in the brain and to conduct the first clinical application of VTT in VCID. This technique is based on the notion that, when water molecules are exchanged from the tissue into the veins, they will experience a transition in T1 relaxation time. Therefore, by measuring the T1 recovery curve of MR signals in the vein, one can estimate the time at which the transition took place. Our preliminary studies revealed excellent feasibility and sensitivity of the proposed technique. Our new preliminary data collected during the resubmission period also demonstrated the associations of VTT with vascular risk factors and white matter hyperintensities (WMH). This project has three Aims. Aim 1 will develop a non-contrast technique, VICTR MRI, to quantitatively evaluate VTT. We will conduct VTT measurements in several clinically important veins, including superior sagittal sinus (SSS), straight sinus (SS), internal cerebral veins (ICV), the vein of Galen (GV), basal veins of Rosenthal (BV), and internal jugular veins (IJV). VTT measurements in these veins will provide a comprehensive assessment of venous hemodynamics in different brain regions, based on their respective draining territories. Aim 2 will conduct validation, verification, and multi-vendor harmonization of VICTR MRI for VTT assessment. We will test the sensitivity of VICTR MRI to VTT increase (using caffeine ingestion) and VTT decrease (using CO2-inhalation). We will validate VICTR MRI with contrast-agent-based bolus-tracking MRI. We will also harmonize VICTR MRI across major vendors of MRI (General Electric, Philips, and Siemens), making VICTR MRI a scalable technique. Aim 3 will conduct clinical application of VICTR MRI in small-vessel- disease related VCID. We will compare VTT between cognitively normal controls and impaired patients, and examine the associations among VTT, cognitive function, WMH, and Alzheimer’s biomarkers.

NSF Awards · FY 2026 · 2026-01

The intimate link between form, or shape, and function is ubiquitous in science. In biology, for instance, the shapes of biological components are pivotal in understanding patterns of normal behavior and growth; a notable example is protein shape, which contributes to our understanding of protein function and classification. This project, led by a team of investigators from the USA and the UK, will develop ways of modeling how biological and other shapes change with time, using formal statistical frameworks that capture not only the changes themselves, but how these changes vary across objects and populations. This will enable the study of the link between form and function in all its variability. As example applications, the project will develop models for changes in cell morphology and topology during motility and division, and changes in human posture during various activities, facilitating the exploration of scientific questions such as how and why cell division fails, or how to improve human postures in factory tasks. These are proofs of concept, but the methods themselves will have much wider applicability. This project will thus not only progress the science of shape analysis and the specific applications studied; it will have broader downstream impacts on a range of scientific application domains, providing practitioners with general and useful tools. While there are several approaches for representing and analyzing static shapes, encompassing curves, surfaces, and complex structures like trees and shape graphs, the statistical modeling and analysis of dynamic shapes has received limited attention. Mathematically, shapes are elements of quotient spaces of nonlinear manifolds, and shape changes can be modeled as stochastic processes, termed shape processes, on these complex spaces. The primary challenges lie in adapting classical modeling concepts to the nonlinear geometry of shape spaces and in developing efficient statistical tools for computation and inference in such very high-dimensional, nonlinear settings. The project consists of three thrust areas, dealing with combinations of discrete and continuous time, and discrete and continuous representations of shape, with a particular emphasis on the issues raised by topology changes. The key idea is to integrate spatiotemporal registration of objects and their evolution into the statistical formulation, rather than treating them as pre-processing steps. This project will specifically add to the current state-of-the-art in topic areas such as stochastic differential equations on shape manifolds, time series models for shapes, shape-based functional data analysis, and modeling and inference on infinite-dimensional shape spaces. 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 · 2026-01

Project Summary/Abstract Cancer is the second leading cause of death in the United States where delays in diagnosis and treatment lead to increased mortality and advanced-stage disease. Developing artificial intelligence (AI) and deep learning (DL) approaches for the automatic characterization of malignant disease can facilitate the early detection, diagnosis, prognosis, and treatment of cancer. Radiomics and DL approaches extract quantitative information and visual features from radiological data to glean insights into a patient’s disease. Traditional radiomics approaches suffer from reproducibility issues due to small dataset sizes and differences in imaging scanners, reconstruction methods, and operator variability in regions of interest segmentation. DL methods require training on large datasets with annotated ground truth, which is difficult to obtain due to the limited availability of physician-defined annotations and histopathological ground truth. Radiomics and DL methods are often trained on datasets that encompass a specific malignancy, which additionally limits their generalizability and overall utility. Nuclear medicine imaging modalities provide important functional information regarding radiotracer uptake in benign and malignant pathologies that can help inform diagnosis and treatment. There is a significant unmet need to develop research and clinical tools that address the challenges of enabling large-scale AI-based pipelines in nuclear medicine. Aim 1 will build a large database of clinical positron emission tomography (PET)/computed tomography (CT) images with physician-annotated ground truth. Aim 2 will develop a physics-guided deep generative modeling approach to generate realistic simulated PET/CT data with known ground truth. Aim 3 will quantify the robustness of radiomic features using both simulated and clinical PET/CT data. Aim 4 will develop and validate a simulation-based transfer learning approach on automated lesion detection, segmentation, and classification tasks. Aim 5 will develop and validate a multipronged approach that combines robust radiomics, DL, and ensemble meta-learning to predict clinical outcomes from PET/CT images of patients with cancer. In the K99 training phase of this grant, Dr. Kevin H. Leung will conduct the proposed research under the guidance of Dr. Martin G. Pomper with the support of outstanding advisory committee members with extensive expertise in radiology, oncology, PET, CT, and medical imaging physics. The major objective of the mentored research phase is to create a large clinical PET/CT database encompassing a wide range of cancers and to develop a physics- guided approach to generate realistic simulated PET/CT data that reflect clinical population-level characteristics. The technology developed from the K99 phase will be expanded in the independent R00 phase into a generalized platform that will enable large-scale AI in nuclear medicine for a wide range of medical image analysis tasks. The rich resources and strong collaborations available at Johns Hopkins provide an ideal training environment that is completely supportive of the proposed research and the academic advancement of Dr. Leung.

NSF Awards · FY 2026 · 2026-01

Organ failure affects millions of Americans, leading to immense suffering and placing a heavy burden on patients, caregivers, and healthcare systems. While organ transplantation can be lifesaving, there are far too few donor organs to meet the need. The ability to grow functional tissues in the laboratory could offer a more effective and more widely available treatment option. It could also enable personalized therapies and accelerate drug screening and discovery. However, despite exciting advances in stem cell biology and tissue engineering, it remains challenging to grow robust and functional tissues in the laboratory. Key obstacles include the formation of blood vessels within engineered tissues and the coordination of different cell types into structures capable of performing biological functions. These challenges may reflect our inability to recreate the intricate, ever-changing environments that guide tissue development in the body—environments shaped by gradients of signaling molecules, mechanical forces, and feedback mechanisms that allow cells to self-organize over time. This project directly addresses that challenge by developing new methods to replicate these dynamic developmental cues in the laboratory. The goal of this project is to enable more functional, scalable tissue growth and to create tools that scientists and engineers can adapt for a wide range of tissue engineering applications. The project will also help train a new generation of interdisciplinary scientists and engineers through the direct involvement of Baltimore City high school students, undergraduates, and master’s students in hands-on research and mentoring. Through open-source designs, hands-on training opportunities, and public-facing outreach, this project aims to increase participation in biotechnology research and help shape the future of U.S. bioengineering. Drawing on principles from synthetic biology and computer science, this project aims to create active scaffolds that deliver precisely timed and spatially localized signals to cells by embedding “molecular programs” into the materials themselves. These programs will mimic the complex instructions that tissues receive during natural development, enabling cells to interact, organize, and mature in more controlled and reliable ways. The project will initially focus on kidney tissue, for which the formation of functional filtering units requires careful coordination across multiple specialized cell types. This work will provide a general framework for engineering complex tissues and organoids across diverse organ systems. The project bridges developmental bioengineering, biomaterials, and molecular information processing in an effort to overcome longstanding limitations in regenerative medicine. This work is anticipated to advance the understanding of how tissues form, generate tools for disease modeling and therapeutic discovery, and lay a foundation for future personalized medical treatments. Anticipated Transformative Impact: Replacement tissues that go beyond the limitations of organoids and bioprinting. 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 · 2026-01

PROJECT SUMMARY/ABSTRACT Acute Respiratory Distress Syndrome (ARDS) remains a frequent occurrence in the intensive care unit and is associated with high mortality. A crucial determinant of ARDS is endothelial cell (EC) apoptosis leading to endothelial barrier dysfunction, increased vascular permeability, and resulting hypoxia. Understanding the molecular mechanisms that regulate EC apoptosis following injurious stimuli is crucial to improving outcomes in ARDS. Mitogen activated protein kinase activated protein kinase 2 (MK2) is part of a critical stress activated protein kinase pathway that has been implicated in initiating the apoptotic cascade, though how MK2 interacts with the apoptotic cascade is unclear. Activation of the apoptotic cascade culminates in activation of the executioner caspase-3 (casp3). Published data from our lab has demonstrated that MK2 phosphorylates casp3, and I have generated preliminary data showing the kinase activity of MK2 potentiates casp3 activity in a model cell line. Interestingly, our lab has also identified that MK2 directly binds with casp3 to facilitate nuclear transport and promote execution of apoptosis independent of casp3 phosphorylation. In total, these data suggest MK2 interacts with the apoptotic cascade and specifically casp3 in two distinct ways: it phosphorylates casp3 via its kinase function and binds directly to casp3 for nuclear transport via a chaperone function. This application serves to provide a training vehicle as I elucidate further details about these two distinct mechanisms. In Aim 1, I will determine the role of MK2-dependent phosphorylation of caspase-3 in potentiating casp3 activation and apoptosis during endothelial barrier disruption. In Aim 2, I will evaluate binding between the docking region of MK2 and the putative binding domain of casp3 and the impact of this binding on nuclear translocation of casp3 and subsequent apoptosis and endothelial barrier disruption. Investigating these dual functions MK2 would provide novel mechanisms into the regulation of casp3 activity and execution of apoptosis following injury, and possible identification of a therapeutic target to improve vascular barrier integrity in ARDS. Through this proposal, I will learn not only highly specialized laboratory skills including techniques to evaluate protein-protein interactions and endothelial cell isolation, but also biology of kinase signaling and apoptosis. In addition, I will hone leadership and communication skills necessary for academic career development. The data I generate will create a strong foundation for a future K08 application, with the goal of becoming an independently funded physician-scientist studying molecular mechanisms of lung endothelial cell dysfunction in ARDS.

NSF Awards · FY 2026 · 2026-01

NSF FAIROS projects work to build and enhance coordination of researchers and other stakeholders advancing Findable, Accessible, Interoperable, and Reusable (FAIR) data principles, Open Science (OS) practices, and research data management (RDM) capabilities across distributed research communities of practice. The awards span a wide range of disciplines and stages in the date lifecycle. To broaden project impacts and enhance future directions, this workshop will allow FAIROS projects to work together to both evaluate successes and identify gaps in sustained efforts in FAIR, Open Science, and RDM activities across the US Science research landscape. 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 · 2025-12

PROJECT SUMMARY Children living with chronic hepatitis B (CHB) must actively manage their disease throughout their lifetime due to the risk of progressive liver disease and hepatocellular carcinoma. Their transition to adult care complicates care management and typically occurs during adolescence and young adulthood, when health-related quality of life (HRQoL) can also diminish. The proposed mixed methods study seeks to provide insights into how family context and stigma influence care management and HRQoL among adolescents and young adults (AYAs) with CHB during their transition into adult care, with three specific aims: 1) To estimate the effects of family health risk factors on HRQoL trajectories among adolescents with CHB and to determine whether they are modified by family structure; 2) Explore care management experiences, including medical decision-making and adherence, for AYAs with CHB transitioning into adult care; and 3) Identify how key moments, such as disclosure, secrecy, and silence, from the family context form pivotal experiences that shape care management and HRQoL for AYAs with CHB. First, the quantitative phase utilizes the Hepatitis B Research Network Pediatric Cohort Study dataset, an investigation of HRQoL in youth with CHB in North America from 2010 to 2017, to examine how family context influences HRQoL trajectories among adolescents with CHB. Next, the qualitative and participatory phases involve primary data collection, leveraging the Johns Hopkins Viral Hepatitis Center. AYAs with CHB will be recruited (n=15) from the Center for in-depth interviews to explore transitioning into adult care, medical adherence, decision-making, and family communication and support. Second, all participants will be invited to participate in Collaborative Filmmaking. This participatory method involves filmmaking and discussions to identify how pivotal experiences within the family context have informed disease-related stigma and shaped care management and HRQoL. Data integration will include visual displays whereby data are visualized alongside each other. Participants will have the opportunity to incorporate their films into a composite film and share them publicly. This study is an in-depth exploration of AYA chronic disease management and HRQoL that involves public-facing work with the creation and dissemination of films. It is responsive to NICHD’s strategic plan theme to improve child and adolescent health and transitions to adulthood, including healthcare transitions for those with chronic health conditions. This research will provide foundational knowledge and actionable items for families, health providers, and public health professionals to support AYAs with CHB transitioning into adult care. The proposed research fulfills the dissertation and degree requirements for Ms. Block, PhD student at the Johns Hopkins Bloomberg School of Public Health. Training will be mentored by experts in viral hepatitis, AYA health, biostatistics, qualitative and participatory mixed methods, and translational science. With guidance from this robust mentorship team (Sponsor: Dr. Jill Owczarzak), research and training will support Ms. Block’s growth into an independent researcher dedicated to promoting the health and well-being of AYAs living with chronic disease.

NIH Research Projects · FY 2026 · 2025-12

SUMMARY Despite advances in therapy, the most aggressive form of brain tumor, glioblastoma, remains almost universally fatal. The first-line therapy for this devastating cancer, with a high rate of local failure and a median survival of only 14.6 months, is maximum feasible surgical resection, followed by radiotherapy with concurrent temozolomide chemotherapy (CRT). It is encouraging that there are multiple second-line therapies in clinical trials that can prolong survival or improve life quality, such as anti-angiogenic therapy (AAT). In this scenario, the accurate determination of whether a patient is a responder or a non-responder at an early stage following CRT has become a significant factor in clinical practice. However, the limitations in neuroimaging complicate the clinical management of patients and impede efficient testing of new therapeutics. Even with the improvements in advanced imaging modalities, distinguishing between true progression and pseudoprogression (induced by CRT) or between response and pseudoresponse (induced by an antiangiogenic therapy) remains two most formidable diagnostic dilemmas in neuro-oncology. Therefore, the current gold standard for diagnosis and response assessment is still based on pathologic appraisal of tissue samples. However, even biopsy yields variable results due to the intra-tumoral heterogeneity of treatment response. Amide proton transfer (APT) imaging is a chemical exchange saturation transfer-based molecular MRI technique that generates contrast via endogenous cellular proteins in vivo. Based on numerous clinical results by our group and others, APT-MRI is considered the only non-nuclear imaging modality to rank higher than perfusion imaging for the assessment of active tumors for brain tumors. The long-term goals of this R37 proposal were, and remain at present, to demonstrate the potential of quantitative APT-MRI to resolve two formidable diagnostic dilemmas for GBM patients and to develop an automated deep-learning framework for post-treatment surveillance and biopsy guidance. During the past three and half years of support, we have made substantial progress in achieving our goals. Despite this, several unmet clinical needs and technical challenges must further be addressed. To address these major challenges from the existing R37 study, consistent with the scope of the parent proposal, we have designed the following hypotheses and specific aims to be perform during this R37 extension period: (1) Assess the value of the residual peripheral non-enhancing tumor on APT-MRI post-surgery and post-CRT to predict tumor recurrence patterns and survival; and (2) Develop a generative transformer-based multi-parametric MRI reconstruction pipeline. If successful, our results—and particularly the deep-learning platform established—will be readily available to accurately identify early response and guide local treatment regimens, thus changing the clinical pathway.

NSF Awards · FY 2025 · 2025-12

Strengthening wildfire resilience requires accurate modeling and a deep understanding of collective human behavior during wildfire evacuations. In particular, there is a critical need for simulation models that can realistically capture how civilians, incident commanders, and public safety officials make protective action decisions during wildfires. However, existing simulation models face fundamental limitations that often cause low prediction accuracy and insufficient capacity to support effective decision-making during wildfire response. Therefore, this project aims to develop a convergent framework for next-generation wildfire evacuation simulation that features realistic Artificial Intelligence (AI) agents powered by psychological theory-informed large language models (LLMs), reinforcement learning, and multi-modal datasets. This research is a transformative step toward improving the behavioral realism, prediction accuracy, and decision-support capability of wildfire evacuation simulation models. This project will also lead to generalizable simulation methods, promote teaching, training, and learning, strengthen partnerships, and support wildfire resilience through broad dissemination and open-access tools. Despite progress in wildfire evacuation simulation models, key challenges remain in accurately modeling and understanding the decision-making processes by incident commanders and public safety officials, realistically modeling human behavior in wildfire evacuations, and adequately representing diverse populations and their dynamic, complex interactions. LLM-based agents could help address some of these limitations, though they bring their own issues with hallucination and generalizability. To tackle the above research challenges, this project develops a novel convergent framework for learning-based simulation of collective human behavior during wildfires. Specifically, the objectives of this research are to: 1) Extend and enrich the Protective Action Decision Model (PADM) for civilians as well as incident commanders and public safety officials; 2) develop psychological theory-informed LLM agents for protective action modeling; 3) generate a realistic, context-aware synthetic population to serve as the critical input for the simulation platform; 4) develop the learning-based simulation platform to integrate complex interactions among various agents and predict collective human behavior at the community level under various scenarios (e.g., fire spread, warning, infrastructure damage); and 5) test and validate the convergent simulation framework with various case studies across the U.S. The research outcomes will enable wildland-urban interface (WUI) communities to better predict wildfire impacts, manage risks, and develop life-saving strategies. 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 · 2025-11

To hunt humans and transmit malaria parasites, the African malaria mosquito Anopheles gambiae relies on its sense of smell to track volatile organic compounds emitted by the human body. Human body odor comprises a complex blend of hundreds of airborne chemicals derived from our primary metabolism released in skin odor and breath, as well as secondary metabolites produced by microbial communities living on our skin. Despite the important role of human body odor in driving malaria transmission, the specific components of human scent that blend together to modulate this process, as well as the relative contribution of the human skin microbiome towards human scent chemistry are not well understood. We have recently engineered and validated an expansive multi-choice system for Anopheles gambiae olfactory preference under naturalistic conditions in Zambia to rank the attractiveness of whole body scent samples derived from individual sleeping humans to mosquitoes using infrared motion vision. Leveraging this validated infrastructure and exposure-free assay with enhanced comparative power, we will recruit a cross-sectional human subjects cohort from the Macha region, Choma District, Southern Province, Zambia to identify volatile organic compounds in human scent signatures that are correlated with human attractiveness to Anopheles gambiae at high-resolution. In parallel, we will perform whole body microbiome profiling using metagenomics to characterize the contribution of the skin microbiome to human scent signatures. Using this approach, we will identify features of the human microbiome community and associated microbial metabolic pathways producing volatile organic compounds associated with modulating human attractiveness to this prolific disease vector. We will also harness conserved components of whole body odor, along with volatile organic compounds associated with high attractiveness that we have recently identified using whole body volatilomics, to reverse engineer super attractive synthetic chemical blends for Anopheles gambiae mimicking the scent signatures of highly attractive humans. These aims will fundamentally improve our understanding of chemosensory mechanisms modulating olfactory behavior in Anopheles gambiae that drive malaria transmission and influence heterogeneity in biting risk. From a translational perspective this research may assist to identify new biomarkers for personal risk of exposure to mosquito bites and develop novel synthetic formulations of attractants or repellents to combat this primary malaria vector in sub-Saharan Africa.

NIH Research Projects · FY 2025 · 2025-11

Project Summary Clostridiodes difficile is an opportunistic enteric pathogen which can cause critical and even fatal colonic disease. Of the nearly 500,000 reported Clostridioides difficile infection (CDI) cases annually, a particularly unique challenge facing clinicians is the striking level of recurrence of C. difficile disease (rCDI) following treatment, which can range anywhere from 20-45%. Though it has been well described in the literature how host microbes promote colonization resistance to prevent primary C. difficile infection, the molecular mechanisms of the colon microbiota that govern the persistence, or clearance, of C. difficile’s are still under investigation. We propose that one of the factors that governs the persistence of C. difficile within the host is the associated colon bacterial community. Consistent with this hypothesis, studies that investigate the community composition of rCDI patients highlight particular microbial signatures in this population compared to non-infected or non-recurrent controls, specifically, an increased abundance of Proteobacteria. The preliminary data outlined in this proposal demonstrate that certain Proteobacteria, such as Klebsiella pneumoniae (Kp) and Escherichia coli (Ec), create an environment that modulates the metabolomic profile of C. difficile. Our results suggest that Kp and Ec, through mechanisms we plan to explore through this research, induce C. difficile to produce indole-3-acetic acid (IAA), which can directly promote an anti-inflammatory phenotype in the host mucosa through engagement of the aryl hydrocarbon receptor (AhR). We hypothesize that in some microbial contexts, such as those prompted by CDI standard of care antibiotic treatment in which Proteobacteria bloom, C. difficile can shift to produce IAA which in both AhR-dependent and -independent mechanisms creates an immunotolerant environment. Furthermore, our preliminary data demonstrate that not only may IAA act on the host but appears to have important impacts on the virulence of the pathogen through the upregulation of C. difficile sporulation and production of its primary C. difficile toxin, TcdB. We suggest that both tolerance at the host mucosa as well as changes in pathogen virulence allows C. difficile to persist at the host mucosa, creating conditions ripe for the development of rCDI. Through the completion of this project, we plan to define both the host and microbial contributions to this mucosal immunotolerance pathway potentially conducive to C. difficile persistence, with the long-term goal of identifying putative biomarkers in the stool of patients who are increased risk of recurrent infection.

NSF Awards · FY 2025 · 2025-10

The Cosmology Large Angular Scale Surveyor (CLASS) is uniquely built to probe the largest angular scales in the cosmic microwave background (CMB) polarization from its high site in Chile’s Atacama Desert. It has already produced polarization maps of 75% of the sky. This award supports observations and data analysis to develop instrumentation, observing strategies, and analysis techniques to understand and reduce systematic errors while increasing the sensitivity of the surveyor. Along with a substantially improved estimate of the reionization optical depth, the continuing survey will be able to access the unique imprint of inflationary gravitational waves in the CMB B-mode polarization. The CLASS faculty and postdocs work with Baltimore science teachers to increase public awareness of science and foster scientific interests in school-age children. CMB cosmology-focused educational resources developed will integrate into the Next Generation Science Standards in Maryland and other states. The project continues to train the next generation, having involved some 55 undergraduates and 20 graduate students, and provided leadership training for postdocs. The survey will provide stringent constraints on the redshift of cosmic dawn, of particular interest as new high-redshift galaxy populations are uncovered by the James Webb Space Telescope. The optical depth measurement is also critical for neutrino mass constraints. The survey will test exotic physics, including large-scale temperature anomalies in polarization and the Chern-Simons effect. It will be the most sensitive measurement of polarized dust and synchrotron radiation for studies of the interstellar medium and Galactic magnetic field. 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

Internet-of-Things (IoT) devices are increasingly used in shared spaces (e.g., homes, apartments, schools, hospitals, workplaces, and cities), turning these spaces into smart environments. Different stakeholders in these environments, including both direct users of smart devices and non-users such as visitors or bystanders, have unique privacy needs and expectations. Although prior research shows evidence of conflicts among stakeholders, there has been less investigation regarding how stakeholders resolve such conflicts and negotiate their privacy options. This interdisciplinary project is investigating different stakeholders’ privacy negotiation behaviors in smart environments by designing, developing, and deploying an interactive system to collect people’s real-world privacy negotiation behaviors. The project is contributing solutions that help people manage and negotiate their privacy in various smart environments, especially when their privacy needs conflict with others’. The results will also inform privacy negotiations within other emerging technologies (e.g., virtual reality and the metaverse). This project moves beyond the lab setting to investigate and support stakeholders’ privacy negotiation behaviors in real-world smart environments. To do this, the project team is identifying contextual factors that lead to privacy concerns across multiple stakeholder groups and complex smart environments through the lens of privacy as “contextual integrity”. It is capturing stakeholders’ privacy negotiation behaviors in the real world by iteratively designing and implementing a tool that collects data on smart environmental contexts and privacy negotiation behaviors in real-world smart environments. Finally, it is developing a data-driven approach to support privacy negotiations in the real-world and evaluating its long-term impact on different stakeholder groups through field studies. The team is facilitating the future extension of this work to other new technologies by publicly sharing the anonymized dataset collected using the developed system, features of the project’s machine learning models, and a working prototype of the system. 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

Non-technical abstract: The vast majority of surfaces of any origin (biological, geological, synthetic) are not flat, smooth, and rigid but instead are generally curved, rough, and deformable, particularly for soft materials. Particle micro-structures formed on such curved surfaces through coating technologies can be used to control many of their interfacial material properties (optical, mechanical, thermal, porosity, etc.). However, particles pack differently on curved surfaces than flat surfaces, which can affect the resulting micro-structures and their ultimate properties in important applications such as two- and three- dimensional printing, optical coatings, flexible electronics, energy capture and storage, and membranes for chemical separations. This project will use optical microscopy and computer experiments to investigate how different shaped particles interact and move around on curved surfaces as part of assembling different surface microstructures with technologically useful properties. The intellectual merit of this project will result from new basic understanding of how different shaped particles assemble into a variety of micro-structures on various curved surface shapes (spherical, cylindrical, wavy). Broader impacts of this project will include educating a multidisciplinary workforce as well as outreach to pre-college level students through classroom and laboratory modules involving microscopy and computational research visuals. Technical abstract: The overall goal of this project is to understand the connection between interactions, structures, and dynamics for spherical and ellipsoidal colloidal particles on curved surfaces. A central hypothesis of the project is that topological defects due to geometric frustration will significantly alter mechanisms relating colloidal interactions, structure, and dynamics on curved surfaces compared to flat surfaces. To understand how near-equilibrium colloidal microstructure and dynamics depend on tunable potentials, particle shape, and curvature landscape features, the proposed project will involve closely coupled optical microscopy and computer simulation experiments. The proposed research plan has systematic interconnected aims with step-wise increasing complexity, including measuring and modelling: (aim1) interactions of spherical and ellipsoidal colloidal particles on curved surfaces vs. particle aspect ratio, surface coverage, and substrate curvature, (aim2) interfacial particle structures on curved surfaces including spatial density variations, order parameter profiles, and topological defect type and spatial arrangements, (aim3) translational and rotational self-diffusion and defect dynamics within curvature dependent states including dynamical heterogeneity relative to topological defects. Achieving these aims and overall goal will provide fundamental understanding of how curvature dependent particle scale mechanisms link interactions, microstructural states, topological defects, and diffusive dynamics at near equilibrium conditions, which will provide a basis for future studies of transient microstructure evolution and ultimately formal design and control of such processes. 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

Social Virtual Reality (VR) is an emerging social medium, where a user immerses in a 3D virtual space in the form of an avatar and interacts with others via speech and body gestures. This technology can improve the social life of people with disabilities (PWD) since it simulates the real-world "face-to-face" social experience and can mitigate barriers to interaction that PWD can face in the physical world. The use of avatars also allows PWD to flexibly express their identity, curating desired social images that may be more or less connected to their disabilities. However, current social VR platforms are immature in supporting inclusive identity representation for PWD, in terms of how well avatars' appearance, sounds, and behavior can support desired self-representations. Moreover, even effective tools for identity expression can lead to stigma and risks for PWD due to the lack of well-established social norms in social VR and the stigma PWD often suffer. This project promotes inclusion and equity for PWD by investigating how to support flexible and safe disability representation in social VR. By studying PWD's experiences and preferences through close co-design work, the project team will make both theoretical and technical contributions, including design guidelines for inclusive disability expression, theoretical models of risks for PWD in social VR, and technologies and toolkits that mitigate those risks. This project aims to facilitate an inclusive and harmonious social VR environment for PWD. To achieve this goal, the team focuses on two main questions: (1) How to support flexible disability representation without causing misconceptions or triggering stigma? (2) How to define and mitigate the risks caused by disability disclosure in social VR? To answer these questions, the team will follow human-centered computing approaches that deeply engage people with diverse disabilities across the research process. First, the team will conduct interview studies to build an in-depth understanding of the identity representation preferences and needs of PWD from both avatar and voice perspectives. Second, the team will conduct a diary study to identify the social dynamics caused by disability disclosure in social VR, resulting in a theoretical risk model that characterizes the types and severity of risks PWD may experience. Last, the team will design, implement, and evaluate a set of risk mitigation mechanisms via participatory design workshops. The validated mechanisms will be disseminated as an open source Unity toolkit for researchers and developers to build upon; meanwhile, the methods and technologies developed should be adaptable to other questions around inclusion and safety in social VR. 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

A key question in biology is how genetically identical cells achieve varied appearances and behaviors. These distinct cell states are realized by variations in gene expression within certain cells in the population; different genes are turned “on” or “off” in these cells. These variations in gene expression can lead to different phenotypes within a population of genetically identical cells. For example, variations in gene expression can lead some cells in a population of genetically identical bacteria to become antibiotic tolerant while other cells in the population remain suspectable to antibiotics. This project will address how variations in gene expression lead to important phenotypic changes in bacteria. To complement the research, an interactive series of lessons on mathematics in biology will be developed for high school students. These lessons will be distributed through a series of teacher workshops. Irreversibility, hysteresis, and multistability in cell state have been quantitatively studied in a handful of specific bacterial systems — B. subtilis sporulation, the lac repressor, and the lysis-lysogeny switch are now classic examples. Here, the investigators seek to expand the understanding of these concepts to the genomic scale: the investigators will examine the time scales of reversibility, and the prevalence of irreversibility, following transient repression of all genes with known function in E. coli. To accomplish this, the investigators will develop a new reagent for light-inducible transient gene repression called LIT-CRISPRi. They will use a combination of experimental data and theory to establish expectations for the time scale of transcriptional, translational, and growth rate recovery following transient gene repression in the fully reversible case (in the absence of hysteresis) and characterize behavior for one well-studied irreversible case (the lac operon). They will then use these tools to characterize E. coli’s growth rate dynamics before, during, and after transient gene repression for a genome scale library of LIT-CRISPRi knockdowns under different environmental conditions. From these data, the investigators will examine the distribution of growth rate recovery times, identify cases of irreversibility or exceptionally long timescale recovery (quasi-irreversibility), and further validate these phenotypes through secondary experiments. Finally, they will use RNAtag-Seq to perform time-resolved transcriptomics before, during, and after transient gene repression for several genes exhibiting (quasi-)irreversible dynamics identified by our screen. Taken together, the work will establish fundamental expectations for the time scales of cellular adaptation and irreversibility following gene repression. 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

Bilevel optimization is a foundational tool for modeling hierarchical decision-making problems, becoming increasingly prevalent in various fields including machine learning, transportation, energy systems, and robotics. However, existing research on bilevel optimization often focuses on simplified or unconstrained settings, limiting its applicability to practical scenarios that require safe, real-time decision-making under complex constraints. This project aims to bridge this gap by developing efficient and scalable algorithms for constrained, nonconvex bilevel problems, with particular emphasis on planning and control tasks in safety-critical domains. The outcomes of this research will provide general-purpose optimization tools that benefit a broad range of applications in robotics, learning, and autonomous systems. Software developed through this project will be released as open-source packages, enabling broad adoption across academia and industry. Furthermore, this project includes a comprehensive educational and outreach component to engage students at multiple academic levels, foster hands-on learning experiences, and broaden participation in STEM fields. This project advances the theory and practice of bilevel optimization by designing efficient, safe, and scalable algorithms tailored specifically to constrained, nonconvex bilevel problems in planning and control. The research comprises two main thrusts. In the first thrust, a novel control-theoretic framework will be developed to systematically design bilevel solvers. By modeling optimization algorithms as controlled dynamical systems, this approach will leverage techniques from control theory to establish algorithms with provable convergence guarantees and anytime safety. The foundational work in this thrust specifically targets scalability, non-unique lower-level solutions, and nonconvexities at both optimization levels. In the second thrust, this framework will be applied to two high-impact domains: (i) safe inverse optimal control and reinforcement learning, where the objective is to recover control policies from expert demonstrations under strict safety constraints; and (ii) safe interactive planning for navigation in crowded environments, integrating real-time decision-making with predictive models of human motion. By addressing fundamental challenges in hierarchical safety-critical decision-making, this research will advance the state of the art in optimization, control, and autonomous systems, benefiting both theoretical developments and practical 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

Privacy is often perceived as an abstract concept by both internet users and software developers. When users are engaged in online activities, it is difficult for them to make informed decisions about their personal data due to the challenges they face in understanding and experiencing the privacy implications of their behaviors in advance. Similarly, many software developers lack the ability to comprehend how the data practices of their applications may impact user privacy and to implement proper data practices that conform to users’ privacy expectations. This project is tackling this problem by developing a new, empathy-based framework to enhance privacy education and design. The project team is using generative AI to create synthetic personas with AI-generated personal data. Using the personas, the team is designing, creating, and studying new interactive sandboxes and developer tools that allow individuals to empathize with these personas, leading to a more concrete and situated understanding of privacy. This understanding, in turn, fosters positive privacy-oriented behaviors among internet users and privacy-responsible software development practices among software developers. To enhance users’ privacy knowledge and developers’ privacy-responsible software development practices, the project is systematically studying the mechanisms and applications of empathy invocation in the context of privacy. The goal is to develop metrics, guidelines, and conceptual frameworks for empathy-based approaches that foster privacy and security in cyberspace. Using these findings, the project team is employing user-centered design methods to develop: 1) systems that invoke empathy to improve users’ privacy literacy and decision-making; and 2) empathy-based developer tools that support developers to proactively identify and address diverse privacy needs of users at the early stages of the development life cycle. These systems are deployed in outreach events to promote privacy literacy in under-resourced user and developer communities. Additionally, they are incorporated into college-level privacy literacy educational modules to support hands-on experiential learning. 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

Causal reasoning from data is critical in many domains from medicine to computer software security. Recently, the role of causality in machine learning (ML) has been understood through ML solutions' over-reliance on correlations, resulting in lack of generalizability. To tackle this problem, researchers train models with data from multiple environments to extract features that are useful across domains. Other studies suggested that the causal relations between features can be leveraged to train robust models that generalize. However, unlike these ML methods that can use any collection of datasets, most of the existing causal discovery algorithms rely heavily on the assumption that we have access to interventional data, such as those from a randomized controlled trial. In practice, the datasets from different environments may carry common causal knowledge, but not necessarily arise due to well-defined interventions. Methods to systematically extract such common causal knowledge across domains from data are currently missing. This prevents ML solutions from leveraging causal structure explicitly. The goal of this project is to address this gap by developing novel algorithms that can extract cause-effect relations from unstructured, diverse datasets. The project outcomes are expected to unlock the potential of causal reasoning for data-rich domains with access to data from different environments and are expected to significantly widen the use of causal discovery among ML practitioners. Specifically, in the first thrust the investigator will characterize the fundamental limits of how much causal knowledge can be extracted from diverse datasets under minimal assumptions about the data generating process. In the second thrust, with his team, he will develop causal discovery algorithms to achieve these fundamental limits from such diverse datasets. In the final thrust, the proposed discovery algorithms will be evaluated across a wide range of datasets through the performance on downstream ML tasks that they enable through the learned causal structure. 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

Reasoning about the causes and effects of phenomena is a fundamental problem in the development of artificial intelligence. Causal reasoning from data also plays a key role in several disciplines, from engineering and computer science to medical research. A formal mathematical theory of probabilistic causation has been developed in the last few decades by Pearl (1995). Several algorithms that illustrate how much qualitative and quantitative causal knowledge can be extracted from data under well-defined assumptions have been proposed within this formalism. These algorithms employ a worst-case view: if the answer to a causal question is not unique, they return that the result is not identifiable. However, such an approach is unsuitable for many real-world systems that violate these crucial assumptions to varying degrees. The investigator argues that it is possible to significantly expand the applicability of causality theory by identifying simple causal explanations in the data that are unlikely to occur by chance. This project will extend the theory of causation to a much wider set of real-world instances by enabling causal reasoning for most models rather than in the worst case. To expand the scope of the state-of-the-art causal reasoning formalism, the investigator will develop novel algorithms that identify information-theoretically simple explanations of the underlying causal system from data. The first thrust seeks to develop methods to learn causal relations from observational data via an information-theoretic interpretation of Occam’s razor based on the entropy of the causal system. A second thrust will analyze how information-theoretically simple explanations can help approximately compute causal effects that are not identifiable in the worst case. A third thrust will leverage the results of the first two thrusts to develop experimental design algorithms for efficiently learning causal structures and causal effects via interventions. 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

Small metal nanoparticles are commonly used as materials to accelerate chemical reactions of importance, such as the production and use of fuels and chemicals. However, it is difficult to control the structures of these nanoparticles and how molecules interact with their surfaces. This limits the ability to control and tune their performance. This project will develop computational approaches to examine metal clusters within tiny, ordered pores of aluminosilicate materials called zeolites. The work will combine computer simulations and machine learning to understand how these clusters are formed, and how they change during reaction processes. By changing the relative size of the zeolite pore and metal cluster size, the project will understand how nanometer-scale confinement impacts metal clusters and influences their catalytic reactivity. This will allow the researchers to design materials to perform chemical reactions with lower energy inputs, particularly for reactions that use liquid molecules to store and transport hydrogen. Educational and outreach efforts will train undergraduate and graduate students in new machine learning techniques and engage high school students with direct scientific programs. The public will be engaged through catalysis simulation tools and applications developed to introduce students to model-driven scientific discovery. This project focuses on designing metal nanoclusters encapsulated in nanoporous aluminosilicates where precise control over catalysts microenvironment will allow the team to optimize electronic and steric interactions to promote (de)hydrogenation chemistries. This project will use theory and simulation to predict the most promising carrier / zeolite pairs with machine learning tools. In particular, the project will seek to understand and control X-H bond dissociation steps (X = C, N) in the dehydrogenation of ammonia and N-heterocyclic organic molecules over zeolite-encapsulated noble metal nanoclusters. The project will develop computational models that reflect the kinetically relevant chemical interactions and nanocluster structures under reaction conditions. This project will directly lead to short- and long-term translational benefits to fundamental catalysis science and technological advances for designing selective and stable nanocluster catalysts. 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

Abstract: The US opioid epidemic has significantly impacted public health, with high levels of opiate dependence and a surge in overdoses. The increasing prevalence of fentanyl and xylazine in the drug supply underscores the importance of novel approaches. Additionally, times of withdrawal have been found to increase overdose risk behaviors, highlighting a need to target this period. Particularly in the context of xylazine, as naloxone does not reverse the effect of xylazine-induced respiratory depression, enrollment and retention in medications for opioid use disorder (MOUD) is vital to reduce drug overdoses. In our prior work, we have shown that PWUO can be trained as peer educators (PEs) for overdose prevention, but we have not integrated withdrawal management or a social network approach to MOUD engagement. This five year proposal seeks to develop, implement, and rigorously evaluate a novel PE intervention designed to prevent overdoses and promote MOUD and drug use cessation among a population of PWUO disproportionally impacted by overdose (Funding Option B). The intervention has three novel components: (a) social network diffusion of overdose prevention and MOUD promotion, (b) strategies to address high-risk withdrawal periods, and (c) Certified Peer Recovery Specialist (CPRS)/ Contingency Management (CM) for MOUD engagement. Using an RCT study design, 300 index PWUO will be recruited along with 300 of their network members who use opioids. The indexes will be randomly assigned to the (1) standard of care (SOC) or (2) an experimental peer education condition (PEC). The proposed design allows for the examination of the effectiveness of the intervention on indexes and diffusion of behavior change to network members. The RE-AIM framework will guide the collection of qualitative interview data to identify barriers and facilitators to intervention implementation. Assessments will occur every 3 months for one year. 1. Development and Pilot Testing: Design and pilot test intervention components that diffuse overdose prevention behaviors and MOUD within social networks, promote overdose prevention during withdrawal, and enhance MOUD engagement through CPRS/CM. 2. Implementation of RCT: Evaluate the intervention in a 1:1 RCT, testing an intervention that includes (a) social network diffusion of overdose prevention and MOUD promotion, (b) strategies to address high-risk withdrawal periods, and (c) CPRS/CM for MOUD engagement (N=600; 300 index participants, 300 network members). 3. Evaluation of Outcomes: Assess changes in nonfatal overdose, drug use cessation, MOUD engagement, and overdose prevention behaviors among index participants, as well as the diffusion of these behaviors to their social network members.

NIH Research Projects · FY 2025 · 2025-09

1 Problem sexual behaviors (PSB) by adolescents account for a significant proportion (50-70%) of harmful 2 sexual behavior experienced by children, as reported in national surveys. Many—perhaps most—sexual harm 3 incidents committed by teenagers can be characterized as "crimes of opportunity" or "crimes of ignorance." 4 Teens are at risk of engaging in PSB due to a lack of knowledge and clear guidance regarding appropriate and 5 inappropriate sexual behaviors, consent, and the developmental differences between teens and younger 6 children. School-based preventive interventions have been developed to address PSB; however, most, if not 7 all, of these interventions have been designed for and evaluated with general samples of adolescents, their 8 families, and educators. The proposed research aims to address the gap that exists for teens with intellectual 9 and/or developmental disabilities (IDD), as well as their families and educators, who have been largely 10 neglected in PSB prevention efforts. This gap represents a missed opportunity to prevent children from being 11 victimized by PSB and to interrupt a destructive cycle for teens at risk of engaging in PSB. The overarching 12 goals of the proposed study are to establish the acceptability, feasibility, and safety of a PSB prevention 13 curriculum and related study procedures for teens with IDD and to evaluate its efficacy in a waitlist randomized 14 controlled trial. Our team developed the Responsible Behavior with Youth and Children (RBYC) intervention as 15 a school-based program to prevent the onset of PSB among neurotypical teens. In collaboration with IDD 16 experts, we adapted RBYC for use with teens with mild to moderate IDD ages 14 to 19. Following principles of 17 community-based participatory research, we partnered with teens with IDD, their parents, and educators to 18 adapt the content and develop educational videos. RBYC-IDD is an 8-session curriculum designed to promote 19 safe and appropriate interactions between teens and younger children and peers, both in person and online. 20 Specifically, we aim to: (1) Ensure acceptability, feasibility, safety, and utility of a classroom-based universal 21 intervention and procedures to assess intervention effects on the prevention of PSB by teens with IDD; (2) to 22 evaluate the immediate effects (pre-post design) and sustained effects (3-month follow-up) of RBYC-IDD on 23 targeted constructs; and (3) to assess the differential impact of RBYC-IDD based on student characteristics 24 including sex, prior history of child maltreatment victimization, and student disability type and severity. To 25 complete study RCT we will recruit 12 special education schools in Maryland. Schools will be randomly 26 assigned to either the intervention group (6 schools) or the control group (6 schools). Participating students 27 (~150), their parents (~150), and teachers (~24) will complete assessment batteries at pre-intervention, post- 28 intervention, and at the three-month follow-up. We aim to establish an initial evidence base for RBYC-IDD. To 29 our knowledge, this will be the first school-based universal program designed to address the onset of PSB 30 among teens with IDD—a largely overlooked gap in PSB prevention.

NIH Research Projects · FY 2025 · 2025-09

Project title: Boston Birth Cohort - Autism Data Science Initiative (BBC-ADSI). Abstract The overarching goal of this proposed project is to address two strategic tasks outlined by the NIH OTA- 25-006 - Autism Data Science Initiative (ADSI): • Task II – Targeted data generation to complement existing datasets to fill critical data gaps. • Task III – Advanced data analysis using state-of-the-art statistical methods, artificial intelligence (AI), and machine learning (ML) for both hypothesis testing and hypothesis generating. Aim 1 will leverage the Boston Birth Cohort (BBC)—a large, long-term, prospective, and deeply phenotyped U.S. birth cohort—to advance the ADSI mission. We propose to expand the BBC’s multi- omics resources by generating new data from archived biospecimens using cutting-edge, unbiased biotechnologies across the following informative groups: Children diagnosed with autism; Children with elevated autistic quantitative traits without a diagnosis; children with other developmental disabilities; and Neurotypical children. The omics data will include the genome, epigenome, metabolome, proteome, and IgG antibody reactome, all derived from blood samples collected at birth and at 1–2 years of age—critical developmental windows for gaining insight into the biological mechanisms underlying autism onset and trajectory. Aim 2 will conduct innovative analyses by integrating multi-omics data with exposome measures and detailed autism phenotypes to address a fundamental question: What causes autism? Informed by literature and our own work, we will test hypotheses focused on understudied, yet potentially high-impact environmental exposures and promising biological pathways. Our team is uniquely positioned to contribute scientific and methodological advances to the ADSI, including but not limited to: 1. Defining autism’s complex phenotypes by leveraging rich data resources—quantitative measures of core autistic traits (e.g., SCQ and SRS), clinical evaluations and diagnoses, longitudinal electronic medical records, and special educational services. 2. Delineating the individual and combined effects of early-life factors on autism. The BBC has amassed extensive early-life exposure data—many rarely studied in an integrated fashion— including maternal nutrition, dietary patterns, psychosocial stress, toxic metals, per- and polyfluoroalkyl substances (PFAS), prenatal and perinatal clinical interventions, medications, adverse birth outcomes, neighborhood characteristics, and in utero and early-life infections, inflammation, antibiotics use, and immune responses. 3. Integrating multi-omics and early life exposome to gain crucial insights into gene–environment (G×E) interactions and the biological pathways underlying autism development and progression. This proposal builds on our longstanding effort to generate a multi-dimensional, prospective birth cohort for autism research. It will be carried out by a transdisciplinary team with expertise in pediatrics, autism, environmental and genetic epidemiology, biotechnology, immunology, multi-omics, statistical genetics, computational genomics, AI, and ML. Successful execution of this project will produce an unprecedented multi-omics × exposome dataset, support novel analytic approaches, and catalyze future research, including replication studies and meta-analyses within the ADSI network. The project’s impact will be further amplified through a robust community engagement and dissemination strategy throughout the study period.

NIH Research Projects · FY 2025 · 2025-09

Summary – We will establish and validate stable system for CRISPR interference (CRISPRi) and CRISPR activation (CRISPRa) in zebrafish, employing attP safe harbor PhiC31 integration sites. The application of CRISPRi and CRISPRa technologies in zebrafish has the potential to expand its capacity for the study of gene function significantly. It will afford the modulation of promoters and regulatory sequences alike, facilitating efficient loss of function and gain of function evaluation of biological and pathological relevance. We recently developed codon-optimized CRISPRi/a constructs for zebrafish, establishing their function in proof-of-principle experiments, using RNA injection of system components to modulate key genes in established phenotypes. We synthesized a zebrafish codon-optimized cas9 gene, harboring mutations D10A and D839A to render the protein catalytically inactive (dCas9). We then cloned codon-optimized Krüppel associated box (KRAB) and methylated CP2 (MeCP2) inactivating domains or VP64 activator domain downstream from dCas9 for CRISPRi and CRISPRa, respectively. To validate the biological function of our initial CRISPRi construct, we targeted the promoter sequences of key genes in melanocyte differentiation (sox10, mitfa, and mitfb); and melanin production (tyrosinase; tyr). Microinjection of CRISPRi mRNA with single guide RNAs (sgRNAs) targeting their promoters resulted in hypopigmented larvae (epidermal melanocytes and retinal pigmented epithelium. In addition, we evaluated CRISPRi/a modulation of mrap2a, which controls energy homeostasis and somatic growth via inhibition of the melanocortin 4 receptor gene (mc4r). Targeting the mrap2a promoter proximal region with CRISPRa or CRISPRi increases and decreases larval body length, respectively. However, RNA-based injections inherently display time-limited effects whose impact is unreliable beyond the development. Thus, here we propose to establish transgenic lines, stably expressing CRISPRi and CRISPRa from constructs integrated into attP safe harbor integration sites in chromosome 14 facilitated by the PhiC31 integrase (Aim 1). We will screen the efficacy of these new lines via Tol2-mediated delivery of sgRNA expression constructs directed at known genes whose modulation yield readily scored phenotypes, as above. Similarly, we will establish and validate constructs for efficient delivery of sgRNA expression to an alternate attP PhiC31 site in chromosome 24 (Aim2). The lines expressing CRISPRi/CRISPRa will be crossed with the empty alternate PhiC31 site, allowing the use of the second site for targeted integration of sgRNA expression constructs. The establishment of lines established in Aims 1 and 2 will be confirmed by the inclusion of discrete reporter cassettes expressing Cerulean [blue, Aim 1] and Venus [yellow, Aim 2] in the pineal and the lens of the eye, respectively. The efficiency of lines generated in aim 2 will be evaluated using guides designed to the promoters of genes employed in Aim 1 validation. Robust CRISPRi and CRISPRa systems in zebrafish will facilitate efficient assay of candidate gene function and disease relevance through bidirectional modulation of gene expression.