Massachusetts General Hospital

NIH Research Projects · FY 2026 · 2025-09

PROJECT SUMMARY/ABSTRACT The menopause transition is marked by a sharp rise in cardiovascular disease (CVD) events in women, but the mechanisms through which menopause contributes to CVD risk in midlife women are not known. Hot flashes are a hallmark feature of the menopause transition and are experienced by nearly 80% of menopausal women. Although hot flashes have been widely regarded as bothersome yet benign symptoms, emerging data have linked hot flashes to CVD risk. Inflammation and vascular dysfunction are potential drivers that may explain the relationship between hot flashes and CVD. We hypothesize that among peri- and post-menopausal women with elevated cardiometabolic risk, vasomotor instability triggers systemic inflammation that in turn promotes downstream vascular dysfunction, which contribute to heightened CVD risk. Neurokinin-3 receptor (NK3R) antagonists are a novel therapy that were recently approved for the treatment of moderate to severe menopause-related hot flashes. Because NK3R antagonists centrally suppress hot flashes, they offer a unique opportunity to test whether treating hot flashes in isolation improves CVD risk. We propose to prospectively study 120 peri- and post-menopausal women with cardiometabolic risk factors across a range of hot flash burden (low, moderate, high). To gain further mechanistic insights into the hot flash-CVD link, we will pursue two related lines of investigation. In Aim 1, we will study the effect of hot flash burden on peripheral endothelial and coronary vascular function as measured by brachial artery ultrasound flow mediated dilation (endothelial function) and cardiac perfusion PET (coronary microvascular function) among 120 peri- and post-menopausal women. In Aim 2, we will conduct a randomized placebo-controlled trial to assess the effect of hot flash suppression via NK3R antagonist on endothelial function and molecular pathways of inflammation (select eicosanoid metabolites and inflammatory biomarkers) among 80 peri- and post-menopausal women with moderate to high burden of hot flashes. This proposal leverages a unique and highly experienced multidisciplinary team of investigators with expertise in women’s CVD, menopause physiology, vascular biology, advanced cardiovascular imaging, clinical trials, and bioinformatics. These studies have the potential to be paradigm shifting and may recast hot flashes as an important driver of CVD in menopausal women and not just a bothersome symptom. If suppressing hot flashes improves vascular health and/or attenuates select inflammatory pathways, our findings will advance mechanistic insights into how menopause accelerates CVD risk in women and support broader efforts to identify women with moderate or severe hot flashes and abrogate symptoms as a means of optimizing CV health through the menopause transition.

NIH Research Projects · FY 2025 · 2025-09

We previously developed the Lung Cancer Policy Model (LCPM) to evaluate the effectiveness and cost-effectiveness of lung cancer screening with low-dose computed tomography (LDCT). The value of the LCPM lies in its ability to estimate the health and economic consequences of LDCT screening at the population level; however, certain assumptions in the model may not reflect “real-world” conditions and therefore may lead to inaccurate and potentially misleading estimates of the impact of LDCT screening. The goal of the proposed research is to recalibrate the LCPM to address two key limitations of the current LCPM and use the recalibrated model to assess the effectiveness and cost-effectiveness of different lung cancer screening scenarios under real-world conditions. Presently, the LCPM makes two key assumptions that may not accurately reflect real-world conditions. First, the LCPM assumes 100% adoption of LDCT screening among eligible individuals; this assumption, while commonly used in screening models does not accurately represent the real-world, where fewer than 6% of eligible individuals currently undergo screening. Second, the LCPM makes several assumptions about the relationships between different smoking parameters and lung cancer risk. However, the LCPM was calibrated using data from cohorts in which nearly all participants (with a smoking history) smoked > 20 cigarettes per day. Consequently, the LCPM may not accurately model lung cancer risk among individuals who smoke fewer cigarettes per day, a population that is growing in the U.S. The proposed research will address these two key limitations of the current LCPM by 1) recalibrating the model using data on both smoking duration and smoking intensity from two cohorts that include individuals who smoke at lower intensities and 2) incorporating the screening adoption rate as a parameter in the model. We will first develop a calibration package to inform the model and will use it to recalibrate the model. We will then use the recalibrated model to assess the effectiveness and cost-effectiveness of different screening strategies. The proposed research is highly innovative as it will recalibrate the LCPM to allow for more accurate estimates of lung cancer risk and will enable the investigation of the benefits and harms of lung cancer screening under real-world conditions. This research will move beyond traditional lung cancer screening modeling approaches, which have largely focused on modeling the theoretical benefits, harms, and costs of lung cancer screening under ideal conditions. Importantly, the findings generated from this research can be used to inform revisions to the USPSTF lung cancer screening guideline to improve the selection of high-risk individuals for screening.

NIH Research Projects · FY 2025 · 2025-09

Over the past two decades, advances in prenatal diagnostics have improved clinical outcomes by transitioning from low-resolution karyotyping and chromosomal microarray (CMA) to more recent methods like exome sequencing (ES) and genome sequencing (GS). However, diagnostic testing requires invasive procedures, such as amniocentesis, which carry risks to both maternal and fetal health and contribute to high healthcare costs. Non-invasive prenatal testing (NIPT) has mitigated these risks by allowing the detection of chromosomal aneuploidies from maternal blood plasma using cell-free fetal DNA (cffDNA). Despite its widespread adoption—screening 25-50% of pregnancies in the US — NIPT remains limited to low-resolution testing, primarily detecting aneuploidies and changes in a few targeted loci. We propose to validate and optimize our recently published high-resolution non-invasive fetal sequencing (NIFS) technology that is capable of unbiased fetal exome screening at high coverage from a maternal blood draw alone. This innovation could transform maternal-fetal medicine (MFM) and significantly reduce healthcare costs by obviating the need for invasive procedures in fetal genetic testing. Our preliminary studies have demonstrated feasibility of NIFS, with 95.7% sensitivity and 94% precision in surveying fetal genomes across gestational ages relevant to prenatal testing. This project aims to implement and further validate NIFS for potential clinical application through three main objectives. First, we will conduct a comprehensive validation study of anomalous pregnancies (AIM 1) through analysis of 650 retrospective pregnancies with fetal structural anomalies (FSAs) and matched GS from invasive testing to validate pathogenic variant prediction. These analyses will benchmark the efficiency of NIFS in the detection of single-nucleotide variants (SNVs), indels, and copy number variants (CNVs). Second, we will focus on optimization (AIM 2) by developing robust analytic pipelines to discover, interpret, and validate variants at all gestational ages relevant to prenatal testing, conducting stress testing for scalability and estimating unbiased specificity and sensitivity for NIFS as a potential replacement for invasive genetic testing and next step in high-resolution screening for all pregnancies. Third, we will explore the performance of NIFS in non-anomalous pregnancies (AIM 3) by evaluating 500 pregnancies that underwent invasive testing and subsequent GS without an FSA or indication for testing, and 100 pregnancies with aneuploidies or CNVs detected by NIPT. We will estimate NIFS performance metrics and added diagnostic yield over NIPT from these data. This research unites interdisciplinary experts from technological innovation, computational genomics, clinical genetics, and MFM. Our team will rigorously assess NIFS as a non-invasive prenatal screening method that could transform pregnancy care by providing unprecedented access to genetic data and guiding treatment strategies for expectant women.

NIH Research Projects · FY 2025 · 2025-09

Annually, nearly 1 million infants are born following in utero exposure to HIV and antiretroviral drugs yet remain HIV-uninfected, including ~4,000 infants born HIV-exposed uninfected (HEU) in the United States. Infants HEU are at higher risk of poorer neurocognitive and developmental outcomes compared to infants born HIV-unexposed (HU), even when maternal HIV viral suppression is maintained throughout pregnancy. Infants HEU are at higher risk of motor and expressive language delays compared to infants HU. While neurodevelopmental (ND) outcomes are driven by the complex interplay of biological, social, and structural factors, early life predictive correlates of poor ND outcomes are urgently needed to identify infants who may benefit from early interventions. Brain ultrasound is the initial imaging modality of choice for diagnosis and monitoring of many neonatal brain conditions, including exposure to infections such as cytomegalovirus. Major advantages of ultrasound include its safety, relatively low cost, and that it can be repeated as often as necessary with limited need for patient cooperation. Additionally, advancements in ultrasound technology have evolved into simpler, user-friendly, and more portable equipment that allow studies to be performed by non-sonographers/non-radiologists. Furthermore, with advanced ultrasound technology, including high-resolution microvascular imaging (MVI), shear-wave elastography, and quantitative texture analysis (i.e., radiomics), it is possible to assess comprehensively for structural, vascular, and tissue differences in brains of neonates and infants. Importantly, these advanced ultrasound methods offer the potential to detect differences at an earlier age compared to findings reported with use of magnetic resonance imaging (MRI) in older children HEU, recognizing that MRI findings have been associated with ND delays. Testing during the first six months of life provides the opportunity for earlier identification of at-risk infants and provision of earlier interventions. Our preliminary work using basic ultrasound imaging has identified that neonates HEU have shorter corpus callosum length than neonates HU, a brain structure known to influence language development, motor skills and cognition. This study will 1) evaluate if differences in corpus callosum length and parenchymal homogeneity identified in our birth cohort persist using low field MRI and correlate with developmental delays at 2 years of age; 2) and using more advance ultrasound features, determine if brain structural, texture, microcirculatory and elasticity differences between infants HEU and those HU are associated with volumetric and signal intensity differences on brain low field MRI at 1-year of life and/or ND outcomes at 2 years of life. Starting life HIV-free among infants who are HEU, has not ensured comparable ND outcomes to that of infants HU. This science will position us to identify early brain biomarkers associated with infants in need for early interventions with the potential to benefit the 4,000 infants born HEU in the United States. Nearly 24% of all infants are born HEU in Botswana allowing for more rapid completion of the study in Botswana than the United States, providing evidence to inform United States health care practices.

NIH Research Projects · FY 2025 · 2025-09

PROJECT SUMMARY / ABSTRACT Evidence is slowly building for a more nuanced and complex architecture within human and macaque lateral geniculate nucleus (LGN) than the classic view of three cell classes separated into six laminae. Exhaustive investigations in primate retina have revealed a highly diverse set of retinal ganglion cell (RGC) types that project to the LGN; while the majority of projections by fiber count are from the primary classes of RGCs (midget and parasol) that target the magno- (M) and parvocellular (P) layers in LGN, many of the classes with sparser fiber count (bistratified, thorny, sparse, monostratified, etc.) project to the koniocellular (K) layers, or have undescribed targets in the LGN. Recent single cell RNA sequencing (scRNA-seq) results on neuronal nuclei microdissected from differ- ent layers of LGN suggest a broader diversity than currently understood by classical anatomical and funct- ional results, with eight cell types identified in macaque: four GABAergic cells (presumably interneurons), M, P, and two kinds of K cells. Our analysis of data from the literature, presented here, suggests that additional sub-classes can be identified. But the spatial distribution of those cell types, specifically the multiple GABAergic and K classes, and the novel sub-classes we have found, is as-yet unknown. We will fill that gap by applying the recently available tools of spatial transcriptomics to analyze the layout of geneticaly-defined cell classes across the LGN, creating data sets from human autopsy tissue. These data will be generated in collaboration with the UCLA Technology Center for Genomics and Bioin- formatics on their NanoString CosMx SMI instrument. Human tissue will be obtained in collaboration with the MGH Pathology Department through an unrelated program that requires rapid autopsy following death. These data will allow us to test hypotheses about the spatial distribution of the factors that determine the cellular subtypes above, such the NOTCH developmental pathway, NDUF, COX, and ATP metabolic pathways, STX1A signaling pathway, and others. We will test whether different LGN laminae within the M, P, and K pathways are genetically differentiated, along with variation along eccentricity through anterior vs posterior sections, or along projection columns. The results from this initial project will be used as preliminary data in support of obtaining funding for a larger, more extensive study that will look for variation within and across individuals, asking questions about cell type distribution across the visual field, within and across laminae, then between sexes, through age, and finally about changes due to retinal or other blinding diseases.

NIH Research Projects · FY 2025 · 2025-09

PROJECT SUMMARY/ABSTRACT Post-surgical pain (P-SP) affects up to 30% of U.S. patients and up to 50% for high-risk surgeries like thoracotomies and amputations, with global incidence ranging from 5% to 85%. P-SP imposes an economic burden of $560-$635 billion annually, including direct medical expenses and indirect costs like lost productivity and disability claims. Current treatment relies on generalized pain management protocols, leading to poor outcomes, higher chronic pain risk, and increased opioid use. A more personalized approach is urgently needed to improve treatment effectiveness by accounting for individual pain sensitivity and psychological factors. To elucidate the neural and psychological mechanisms underlying P-SP and to facilitate early identification of patients at risk for chronic pain, enabling more targeted interventions, the proposed secondary analysis project will develop an interpretable and predictive model for P-SP outcomes by 1) identifying the most predictive features of P-SP outcomes using multi-modal data (functional neuroimaging, psychological, behavioral, and medical history); 2) applying Shapley value analysis to the output of supervised machine learning models to interpret the contributions of each feature to the predictions, providing clinicians with patient-specific insights to improve pain management and support personalized treatment planning; 3) developing a time-series model to predict P-SP outcomes and 4) applying Shapley value analysis to identify key temporal patterns that influence these predictions. The expected outcomes of this highly innovative project include the identification of reliable biomarkers for P-SP, which will serve as a foundation for developing individualized pain management strategies. The findings have the potential to transform post-surgical care by improving the early detection of P-SP, optimizing treatment protocols, and reducing opioid use. Ultimately, this project aligns with the broader movement toward personalized medicine and precision healthcare, offering a comprehensive solution for enhancing patient outcomes and alleviating the long-term burden of chronic pain on the healthcare system. This directly supports one of the primary missions of the National Institute of Biomedical Imaging and Bioengineering (NIBIB). 2

NIH Research Projects · FY 2025 · 2025-09

PROJECT SUMMARY CAR T cell therapy for solid tumors is hindered by a lack of tumor-specific antigens that are safe to target and homogenously expressed throughout the tumor, difficulty infiltrating the tumor due to dense tumor stroma, and suppression of CAR T cell function by the tumor microenvironment (TME). We plan to address these issues using novel meso-FAP CAR-TEAM cells that simultaneously target mesothelin, a solid tumor antigen that has already been proven safe to target in patients, and cancer-associated fibroblasts (CAFs), which inhibit T cell infiltration and suppress T cell function in the TME. The CAFs are targeted with T cell-engaging antibody molecules (TEAMs) secreted from the CAR T cells that bind to CD3 and fibroblast activation protein (FAP), which is highly expressed on CAFs. The TEAM allows for CAF elimination by CAR and non-CAR T cells in the tumor. We have already demonstrated that meso-FAP CAR-TEAM cells kill pancreatic cancer cells and CAFs in vitro, in vivo, and in patient-derived ex vivo models and have superior anti-tumor function compared to meso-CAR T cells alone. For the UG3 phase of this project, we will further optimize meso-FAP CAR-TEAM cells by determining the best mesothelin binder to use (SS1 vs. a novel binder developed by our lab), optimal route of injection (IV vs. IP) for targeting pancreatic tumors, and rationale drug combinations that address CAR T cell limitations in solid tumors. We will improve antigen density using an ADAM17 inhibitor (INCB7839) to prevent mesothelin cleavage from pancreatic cancer cells, optimize CAR T cell killing and persistence using ibrutinib to polarize meso-FAP CAR T cells to a Th1/Th17 phenotype, and further prevent suppression by the tumor microenvironment using a PD1 inhibitor (pembrolizumab). These drugs will be singly combined with meso-FAP CAR-TEAM cells to determine which best promotes efficacy in our preclinical models. Collectively, these results will inform the design of a phase I clinical trial for pancreatic cancer patients with advanced disease. During the UH3 phase, we will determine the safety and tolerability of meso-FAP CAR-TEAM cells. We have chosen pancreatic cancer as our first solid tumor target due to the dismal prognosis of the disease, the high percentage of patients with mesothelin-expressing tumors, and the known role of CAFs in promoting tumor growth. While our primary objective will be to determine safety, we will also monitor patient outcomes (progression and survival) while performing correlative studies to determine CAR T cell phenotype and function. We will also monitor the tumor and tumor microenvironment for mechanisms of response or resistance, such as changes in antigen expression and immunosuppressive cells. Overall, this project will develop a novel CAR-TEAM design to target a solid tumor and its microenvironment while optimizing the trial design through rigorous preclinical testing. If successful, the meso-FAP CAR T cell product could be directly applied to other mesothelin-expressing solid tumors and the knowledge gained from the UG3 phase will inform on the critical aspects of CAR T cell function to optimize prior to initiating a clinical trial.

NIH Research Projects · FY 2025 · 2025-09

PROJECT SUMMARY/ABSTRACT Up to one-third of older U.S. adults may not return to functional baseline six months after major surgery, with postoperative pain being one of the most significant contributors. At the same time, over a third of the adult population also suffer chronic sleep problems, which are even more prevalent in the 3.5 million adults who will require total knee arthroplasty (TKA) by 2030. Despite evidence-based treatment, perioperative sleep optimization represents a missed opportunity for improvement. One challenge is that the underlying mechanisms of how sleep impacts postoperative recovery remain a significant knowledge gap. Recent evidence suggests poor sleep health may impact pain via the nervous system and gut microbiome. Major surgery is a significant stressor, and habitual sleep disruption results in changes to metabolism, neuroinflammation, or the nervous system that increase the pro-nociceptive response to this stress. The gut microbiota influences host homeostasis for these processes, and insomnia can disrupt the gut-brain axis. Sleep health metrics of low sleep efficiency and poor daytime alertness were linked to increased blood homocysteine and IL-6 levels and decreases in the short-chain fatty acids-producing microbiota. At the same time, gut microbiome composition is altered in chronic pain conditions. Thus, the gut microbiome may mediate the influence of sleep on pain and serve as a target for adjuvant therapy for both pain and insomnia. My research program has built a unique translational perioperative sleep infrastructure for prospective studies and randomized efficacy trials. We focused on developing feasible and scalable at-home methods to estimate multi-dimensional sleep and circadian phenotypes before surgery using wearable actigraphy watches and other portable home devices. We have also collected blood to verify the circadian phase (internal time) using transcriptomic assays utilizing the circadian nature of transcriptomes. We propose using these preoperative sleep/rhythm phenotype measures to optimize surgical recovery. This is my area of expertise; I am an anesthesiologist with formal training in sleep/circadian biology, computational neuroscience, behavioral and psychosocial clinical trials, and translational studies in post-surgical pain and cognition outcomes. This research plan will aim to understand the mechanisms of how preoperative sleep/circadian disturbances may impact recovery outcomes through systemic changes in the gut microbiome and related metabolic factors. To address this, we propose 1) a longitudinal study (pre- to 1-year post-surgery) with rich phenotyping to understand the sleep-gut-pain relationships in TKA patients and 2) a randomized mechanistic trial with a well-known non-pharmacological intervention (CBT-I) to test whether intentionally altering real-world sleep conditions improves microbiome features. This may help us better risk stratify surgical patients and gain mechanistic inferences to novel mechanisms that inform therapeutic targets.

NIH Research Projects · FY 2025 · 2025-09

PROJECT SUMMARY. Acute respiratory distress syndrome (ARDS) is a common cause of respiratory failure associated with substantial in-hospital mortality. ARDS patients with pulmonary vascular dysfunction (PVD) are a particularly high-risk subgroup with even higher mortality. However, attempts at targeting interventions to mitigate PVD are hindered by a lack of non-invasive biomarkers to identify PVD early in the pathogenesis of ARDS. In a preliminary secondary analysis of ARDSNet FACTT, a large ARDS cohort with invasive pulmonary vascular function data using pulmonary artery catheters and paired serial plasma samples, we observed individuals with PVD had 11- fold higher mortality and could be accurately discriminated by key differentially expressed plasma proteins. Thus, we hypothesize that we can develop an accurate and parsimonious classifier model integrating circulating biomarkers with clinical data to non-invasively detect PVD in ARDS at early, intervenable time points. To accomplish this, we will leverage two BioLINCC cohorts: ARDSNet FACTT and PETAL VIOLET. In Aim 1 of this proposal, we will perform high throughput proteomics on clinically well-annotated plasma specimens from a larger sample of ARDS patients enrolled in ARDSNet FACTT with gold standard quantification of pulmonary vascular function using pulmonary artery catheterization. We will use machine learning to derive and validate an optimal classifier model integrating proteomics and clinical data to non-invasively detect PVD in ARDS. In Aim 2, we will perform high throughput proteomics on clinically well-annotated plasma specimens from early critically ill patients at risk for ARDS in the PETAL network VIOLET and test the predictive capacity of our non-invasive model for predicting incident ARDS and detecting PVD early in the pathogenesis of ARDS. In an exploratory Aim 3, we will use network medicine to identify the key protein-protein interactions and molecular pathways that underpin adverse pulmonary vascular function in FACTT and progression to ARDS in the early, at-risk cohort from VIOLET. The proposed work will yield a testable classification model for predictive enrichment of future clinical trials of pulmonary vascular-targeted treatments in ARDS and generate a public proteomic dataset to spur future mechanistic translational studies.

NIH Research Projects · FY 2025 · 2025-09

PROJECT SUMMARY Infection in pregnancy is a significant risk for neurodevelopmental disorders in offspring, demonstrated in national registries, large epidemiologic studies, and electronic health records cohorts. Mechanistic studies in animals have similarly established adverse effects of maternal infection on offspring neurodevelopment and behavior. Despite this compelling evidence of risk, at present there are no validated methods to predict which exposed children will develop autism, ADHD, or other adverse outcomes. Better risk stratification methods are urgently needed to guide early intervention. Developing reliable, generalizable risk prediction requires large, representative populations and the ability to conduct low-cost phenotyping, valuable in extending the prediction afforded by electronic health records alone. This concept represents the crux of the IMPACT-MH initiative and the organizing principle of this proposal, which seeks to fill a key knowledge gap in predicting neurodevelopmental outcomes. This study will generate risk models to predict neurodevelopmental outcomes by age 8 among offspring of mothers with infections during pregnancy, focusing on two actionable time points: at birth, and between ages 3 and 5. It will first use standard and emerging machine learning methods to analyze electronic health records (EHR) data from at least 150,000 maternal-offspring dyads across 2 health systems, characterizing maternal infections, pregnancy complications, and offspring phenotypes. These EHR data will be augmented by prospective phenotyping of 750 infection- exposed children enrolled across 2 sites: MassGeneral Brigham health system in Massachusetts, and Vanderbilt University Medical Center in Tennessee. The investigators will collect parental report of developmental data, accelerometry data to assess motor function and sleep, and will perform virtual neurocognitive testing at age 3- 5 years. They will compare risk stratification models using electronic health records augmented with natural language processing, parental reports, neurocognitive testing, and accelerometry. Children will again be assessed at age 6-8 years to ascertain diagnoses. Optimal prediction models from the Massachusetts cohort will be externally validated in the Tennessee cohort. Together, these aims will yield tools for risk stratification in children exposed to maternal infection during pregnancy, applying innovative methods to electronic health records integrated with remote behavioral phenotyping. The project will be led by principal investigators with expertise in pregnancy exposures, large-scale electronic health records, machine learning, and neurodevelopmental phenotyping.

NIH Research Projects · FY 2025 · 2025-08

PROJECT SUMMARY Vulvodynia affects 8-15% of women and is one among the identified chronic overlapping pain conditions (COPCs). The condition is poorly understood by clinical providers; people seeking care for vulvodynia see a mean of 3 providers before receiving a diagnosis. Vulvodynia is different than many chronic pain conditions because it is often highly associated with sexual functioning and results in a conflict between the desire for intimacy and pain. Similar to other COPCs, vulvodynia is associated with higher levels of depression, however it is unclear whether the presence of depression influences progression or persistence of symptoms. People with vulvodynia feel shame and stigma around their diagnosis, and have difficulty discussing it with partners, providers, and friends. In our experience, patients with vulvodynia want to know whether and when their pain will go away and whether the presence of other comorbid conditions like depression will impact the trajectory of their pain, however there are limited longitudinal data on which to base answers to those questions. We will prospectively collect data on multiple components of the vulvodynia pain experience and persistence of symptoms over 2 years. A cohort of 200 women ages 18-45 with vulvodynia will be followed, with monthly surveys and three separate in-person assessments over two years including at each timepoint: 1) Standardized questionnaires including HEAL Common Data Elements, assessment of treatment modalities, and physical exam, 2) collection of vaginal swabs for microbiome and inflammation, 3) Quantitative Sensory Testing (QST) to assess central sensitization, and 4) 2 weeks of ecologic momentary assessment (EMA) and passive activity data collection from a mobile device using the open source mindLAMP platform. We will then use statistical models and machine learning to 1) define the variability over time of pain and associated predictors, 2) identify how depressive symptoms impact associations between predictors and pain, and 3) identify predictors of pain resolution. We will train the mathematical model using data from the first 100 participants and will validate the model with data from the second 100 participants. The model will also be used to test what modifiable factors have the largest impact on pain trajectories. Collection of both symptomatology and a wide range of phenotypic patient characteristics in a longitudinal manner will for the first time capture an integrated view of how pain experience, depressive symptoms, and central sensitization coevolve in the progression of vulvodynia.

NIH Research Projects · FY 2025 · 2025-08

Autism spectrum disorders (ASD) and schizophrenia (SCZ) are highly heritable neuropsychiatric disorders associated with substantial phenotypic heterogeneity in developmental course. Across both disorders, while some individuals struggle with developmental delays and have co-occurring intellectual disability (ID) from a young age, others attain milestones without difficulty and show few challenges until later in life. This heterogeneity in developmental course has challenged efforts to predict clinical prognosis, discover biological mechanisms, and develop personalized treatments. Both disorders are also associated with genetic risk factors and gene expression patterns that suggest a possible genetically-associated neurodevelopmental gradient from ID to ASD to SCZ. To assess how well this gradient maps on to developmental course in ASD and SCZ, the PI will analyze phenotypic, genetic, and neurobiological data from over 43,000 participants (including over 35,000 ASD children and 8,000 SCZ adults) and over 1,900 postmortem human brain tissue donors (including over 290 fetal donors and 1,600 adult donors). Genetic variation associated with ID, ASD, and/or SCZ will act as a bridge between phenotype and biology in this research. In Aim 1, the PI will distinguish subgroups of ASD children based on behavioral trajectories and subgroups of SCZ adults based on diagnostic trajectories, then compare the subgroups by key clinical outcomes. In Aim 2, the PI will characterize trajectory subgroups by average common and rare genetic burdens associated with ID, ASD, and SCZ, and average social-environmental exposure to childhood adversity, then test additive and non-additive risk models predicting subgroup membership. In Aim 3, the PI will evaluate the expression consequences of common variation associated with ID, ASD, and SCZ in neuropsychiatric risk genes across distinct developmental timepoints and cortical cell types. Altogether, ID/ASD/SCZ-associated genetic variation may show consistent associations across phenotype and biology, which can highlight putative biological candidates for further inquiry. To support her research aims and training goals, the PI has assembled an interdisciplinary committee of eminent scientists including Jordan Smoller (primary mentor), Elise Robinson (co-mentor), Michael Talkowski, Caroline Uhler, Aarno Palotie, Michael Gandal, Somer Bishop, and Evan Macosko. Building on her strong foundation in psychology, genetics, and neuroscience, the PI will deepen her statistical training in large- scale phenotyping approaches, statistical genetics methods, and functional genomics tools, and broaden her professional skills to prepare her for a faculty position. Her career development will be further bolstered by the highly collaborative training environments and extensive research infrastructures at the MGH Center for Genomic Medicine and the Broad Institute of MIT and Harvard. The PI’s completion of this proposal will inform biological mechanisms contributing to developmental course, developmental time windows for targeting therapeutic interventions, and personalized treatments tailored to genetic and environmental risk profiles.

NIH Research Projects · FY 2025 · 2025-08

Pancreatic cancer continues to represent a significant burden of cancer-related mortality both in the United States and globally, with 5-year survival only 12.8% despite recent treatment advances. For patients with early-stage disease, multi-modal treatment including surgery is the only potentially curative therapy. Nevertheless, surgical intervention remains underutilized nationally – fewer than 50% of eligible patients with non-metastatic disease proceed with resection. Furthermore, significant barriers persist in the diagnosis and treatment of pancreatic cancer, which limits access to advanced surgical care and specialized oncologic therapies. Notably, prior studies have largely focused on patient characteristics such as age, education level, and socioeconomic status, many of which are non-modifiable. Provider- and system-level factors that affect surgical referral patterns have not been thoroughly studied but are likely more amenable to policy and quality improvement interventions.   The central objective of this mixed methods research study is to examine the etiology of the underutilization of surgical resection in early-stage pancreatic cancer by analyzing the facilitators and barriers to tertiary referral, focusing on provider and system factors that impact referral to complex cancer surgeons at multidisciplinary centers. First, a quantitative analysis of a national cancer database will be performed to create an explanatory model for referral patterns, highlighting relevant provider-, practice-, and system-level factors. A sub-analysis will be performed, stratifying by patient demographic factors in the multivariable models, to identify factors that may be differentially associated with referral or receipt of surgery between patient groups. Next, qualitative analysis employing semi-structured interviews of various stakeholders across the pancreatic cancer care continuum will be completed to identify facilitators and barriers to referral. Interviews will include both physician and non-physician providers from both academic and community centers across Massachusetts in order to capture comprehensive perspectives regarding relevant factors upstream of the surgical encounter.   The project’s focus on provider- and system-level factors, rather than patient characteristics, will better enable identification of potential causal pathways and illuminate modifiable targets for health system and quality improvement interventions. The knowledge gained from this work will therefore help enhance surgical access, optimize cancer care, and improve patient outcomes.

NIH Research Projects · FY 2025 · 2025-08

Project Summary/Abstract: Gliomas are the most common primary brain tumors in adults, a subset of which have mutations in the metabolic gene isocitrate dehydrogenase 1 (IDH1). Cancer-associated IDH1 mutants (the most common of which is IDH1R132H) are neomorphs that produce the oncometabolite 2-hydroxyglutarate [(R)-2HG], which is thought to contribute to glioma formation. The mutant IDH (mIDH) inhibitor vorasidenib has been recently FDA approved for the treatment of select IDH-mutant glioma patients and is poised to become standard-of-care. However, response to mIDH inhibitors is heterogeneous, and our understanding of how these drugs work in glioma has been severely limited by a lack of faithful animal models that respond to mIDH inhibition. Existing IDH-mutant glioma mouse models require xenograft transplantation, model high-grade (mIDH inhibitor-resistant) disease, and/or incorporate mutations that are not frequently observed in lower-grade IDH-mutant gliomas. In an effort to better understand these unanswered questions regarding mIDH1 biology, I made a genetically engineered mouse (GEM) model of mIDH1-driven grade 3 astrocytoma that circumvents key limitations of existing models and responds to mIDH inhibitor treatment. I leveraged this GEM and other models to show that IDH-mutant gliomas are sensitive to de novo pyrimidine synthesis inhibitors (e.g. dihydroorotate dehydrogenase (DHODH) inhibitors) due to an increased susceptibility of IDH-mutant cells to replication stress caused by these drugs. My overall objective is to use my mIDH inhibitor-responsive glioma GEM to address translationally relevant questions in glioma. In Aim 1, we will use single cell multiomics sequencing technologies and functional genomic screening to understand mechanisms underlying response to mIDH inhibitors in glioma. This will include both characterization of transcriptomic and epigenomic changes induced by mIDH inhibition, as well as unbiased studies to identify transcriptional alterations that are functionally relevant for treatment response. In Aim 2, we will leverage the immunocompetent and autochthonous status of our GEM to identify microenvironmental interactions driven by mIDH and transcriptional alterations induced by mIDH inhibition in the immune microenvironment. In Aim 3, we will assess how mIDH impacts efficacy of DNA damaging therapies (including standard-of-care radiation and alkylating chemotherapies) and identify mechanisms underlying sensitivity of IDH-mutant gliomas to DNA damage caused by replication stress (such as DHODH inhibition). My long-term goal is to understand how mIDH alters glioma biology in order to develop new effective treatment options for this disease. Results from my Aims will identify biomarkers and mechanisms of response to mIDH inhibition, nominate combination therapeutic strategies, and reveal mechanistic insights on how mIDH alters the response to DNA damage. The research proposed here will directly inform how patients with IDH- mutant glioma are treated in the clinic and also reveal fundamental insights on clinically relevant epigenomic and microenvironmental reprogramming in IDH-mutant glioma.

NIH Research Projects · FY 2025 · 2025-08

Acute Type A aortic dissection (ATAAD) is a life-threatening event that carries a mortality rate of 15-30% even with emergent surgical intervention. Patients with ATAAD face operative mortality rates that exhibit a striking difference according to providing center experience, with those treated at high-volume aortic centers afforded a 10% or greater survival benefit compared to those treated at low-volume centers. And yet, 48% of ATAAD patients undergo surgery at low-volume centers. This is despite a robust U.S. study demonstrating that the delay inherent to interfacility transfer to high-volume centers does not offset a 7.2% absolute risk reduction in operative mortality. What’s more, one third of patients who incur the potential risk associated with interfacility transfer are transferred to low-volume centers. Consequently, contemporary guidelines published by the American College of Cardiology and the American Heart Association (2022) recommend the transfer of stable ATAAD patients from low- to high-volume centers. However, current transfer patterns deviate from these recommendations. The proposed research will utilize mixed methods health services research to improve the care of patients with ATAAD by defining parameters that optimize the regionalization of care and informing targeted interventions to increase the frequency of appropriate interfacility transfer to high-volume aortic centers. Quantitative analysis of state-level administrative claims databases and emergency medical transport services electronic medical records will be used to define the population distribution in the current level of regionalization of care for ATAAD and measure the transport time and case volume thresholds that optimize the survival benefit of interfacility transfer of ATAAD patients from no- or low-volume to high-volume aortic centers. Qualitative analysis will be employed to identify facilitators of and barriers to the regionalization of care for ATAAD through semi-structured interview of emergency medicine and cardiac surgery providers. This work will establish optimal parameters for the safe regionalization of care for ATAAD, characterize geographically granular facilitators of appropriate interfacility transfer, and ultimately improve survival rates for ATAAD patients. The knowledge that emerges from this work will inform an implementation science proposal to design and test a targeted intervention to regionalize emergent, specialized surgical care, improving patient outcomes, reducing complications, and lowering healthcare costs. By performing this work, Dr. Sabatino will receive mentored training in mixed methodology inclusive of quantitative statistical analysis, interview conduct and qualitative analysis, geospatial analysis, and the clinical science of cardiac and thoracic aortic surgery, facilitating her development into an independent surgeon-scientist and a leader in cardiac surgery.

NIH Research Projects · FY 2025 · 2025-08

This trial is designed to improve cancer and mental health care delivery for adults affected by serious mental illness (SMI). Adults with SMI, including schizophrenia, bipolar disorder, and major depressive disorder, experience delays to cancer diagnosis and gaps in cancer treatment that contribute to premature cancer mortality. Patient, clinician, system, and policy-level factors contribute to poor cancer survival for adults with SMI. Despite the urgent need to identify evidence-based approaches to improve cancer care for adults with SMI, this population has been excluded from cancer clinical trials. Proactive psychiatry consultation has potential to address modifiable drivers of lower quality cancer care and outcomes for adults with SMI, however, most cancer centers lack psycho-oncology care. Collaborative Care is a population and team-based model with strong evidence for improving depression treatment for patients with cancer and increasing access to care, but this model has not been adapted for SMI. Informed by qualitative research, we developed and successfully piloted the BRIDGE model of person-centered collaborative care for SMI and cancer. BRIDGE includes proactive psychiatry consultation, engagement of a social work care manager, a person-centered approach across care settings and co-management with oncology. We developed sustainable systems to proactively identify adults with SMI and a rigorous methodology to evaluate disruptions in cancer care, and adapted trial procedures to ensure accessible for all patients. Next, we conducted the first randomized trial for adults with SMI at the time of cancer diagnosis (n=120). Patients on BRIDGE had significantly fewer disruptions in their cancer care (primary outcome, p<0.035) and decreased severity of psychiatric illness and anxiety (p=0.02). Additionally, we developed pragmatic ways to deliver the intervention remotely and engage a population facing multi-level barriers to technology use. One critical next step is to increase the reach of BRIDGE to community cancer settings without embedded psycho-oncology services where most patients with cancer receive care. We aim to address this gap by increasing the reach of the BRIDGE intervention across a statewide cancer center network. First, we will use community-engaged research strategies to tailor intervention procedures and workflows to the affiliate site and surrounding community while retaining core intervention components. Next, we will conduct a Hybrid Type I effectiveness-implementation randomized trial (n=248) across the cancer center network and determine the impact on cancer care disruptions and patient-reported outcomes. Additionally, we will conduct a mixed methods analysis of facilitators and barriers to implementation. The overarching goal is to develop a scalable intervention that increases access to expert care and addresses a modifiable driver of cancer mortality and morbidity.

NIH Research Projects · FY 2025 · 2025-08

Project summary/abstract Prostate cancer accounts for 15% of malignancies and is the second leading cause of cancer-related death in men. The incidence of prostate cancer worldwide is expected to rise from 1.4 million annually in 2020 to 2.9 million by 2040. While outcomes for cancer localized to the prostate are generally good, they worsen for metastases (5-year survival: ~30%). Chemo-, hormonal, and immunotherapy options are limited for advanced prostate cancer and cause significant toxicity. Radiopharmaceutical therapy (RPT) is a new, promising systemic treatment that preferentially delivers ionizing radiation to tumors expressing certain molecular targets. Several RPTs targeting the prostate-specific membrane antigen (PSMA) are currently in clinical and research use. PSMA-RPT is effective for some patients, but identifying patients who will respond to PSMA-RPT and understanding why patients develop treatment resistance during PSMA-RPT remains challenging. Thus, there is an unmet clinical need for biomarkers to identify patients with advanced prostate cancer responsive to PSMA-RPT and robustly monitor treatment resistance during RPT-based therapy. PSMA-based molecular imaging shows potential for stratifying patient responders and non-responders for PSMA-RPT but remains imperfect. We and others have shown the potential of liquid biopsy approaches for treatment monitoring in prostate cancer patients, including different types of RPT. In particular, our group has developed a novel circulating tumor cell (CTC) biomarker strategy that allows not only the isolation of rare populations of CTCs in typically acquired clinical blood samples but also maintains their integrity to probe the transcriptomic profile of the CTCs. In preliminary testing, we have found that this novel CTC technology complements PSMA-based molecular imaging for monitoring patients being treated with the RPT agent 177Lu- PSMA-617 (LuPSMA). We have additionally developed novel machine learning approaches to refine and optimize the integration of these biomarkers. Therefore, in response to FOA PAR-21-290, we propose to systematically optimize and understand the integration of CTC and nuclear medicine imaging for LuPSMA assessment. Our overarching hypothesis is that fluid-based CTC and non-invasive imaging biomarkers are complementary and synergistic for assessing patient response to LuPSMA. We will achieve this goal through 2 specific Aims. Aim 1: Correlate pre-treatment CTC profiles with changes in PSMA-avid tumor burden during LuPSMA therapy. Aim 2: Correlate CTC and imaging biomarker dynamics during LuPSMA treatment with progression-free survival. If successful, the outcomes of this work will provide the impetus for widespread assessment of these integrated biomarkers in clinical trials and drive focused preclinical studies to understand RPT biology further.

NIH Research Projects · FY 2025 · 2025-08

PROJECT ABSTRACT Heart failure (HF) remains the leading cause of morbidity and mortality in the US, impacting approximately 6.2 million Americans. Cardiac fibrosis, resulting from disruptions in cardiac homeostasis and subsequent injury, stands as a major contributor to the detrimental remodeling of the heart that ultimately leads to HF. Therefore, identifying novel therapeutic pathways is crucial for advancing treatment options in the field. Recent studies have shown that under stress conditions, tRNAs can be processed by ribonucleases into smaller regulatory fragments known as tRNA-derived small RNAs (tDRs), which serve as an adaptive mechanism helping cells cope with stressors. Emerging evidence indicated that tDRs play critical roles in cellular stress response machineries during diverse biological processes. However, the regulatory functions of tDR in cardiac fibrosis remain largely unexplored. To uncover functional tDRs that are crucial for fibrosis pathways, we utilized an optimized tDR sequencing technique known as ARM-seq to profile the differentially regulated tDRs in cardiac fibroblasts (CFs) during ischemic response. Initial functional screening of the five most significantly regulated tDRs identified a potent anti-fibrotic tDR derived from the 3’-end of tRNA-Asp-GTC (Asp-GTC-3’tDR). Our pilot in vivo studies demonstrate that delivering Asp-GTC-3’tDR mimics to the heart effectively reduces cardiac fibrosis and improves cardiac functions in a mouse myocardial infarction model. Conversely, inhibition of this tDR using our optimal locked-nucleic acid antisense promotes fibrotic responses in the heart. Mechanistically, our preliminary data suggest that autophagy pathway, particularly ribophagy, plays a critical role in mediating the anti-fibrotic effects of Asp-GTC-3’tDR. The primary objective of this project is to investigate the anti-fibrotic roles of Asp-GTC-3’tDR in cardiac fibrosis, with the ultimate goal of developing novel tDR-based interventions for treating cardiac fibrosis. By leveraging a comprehensive toolkit designed to study tDR functions, we aim to elucidate the functional roles and regulatory mechanisms of Asp-GTC-3’tDR in both cell culture and mouse acute and chronic cardiac fibrosis models using the complementary gain-of-function and loss-of-function approaches. This will be followed by the development of innovative strategies for Asp-GTC-3’tDR expression, including the delivery of DNA analogs of Asp-GTC-3’tDR or our engineered CRISPR/Cas13 machinery to the heart, as a means of treating cardiac fibrosis. The successful completion of this study will define a new area of RNA biology within the context of cardiac fibrosis and provide proof-of-concept for targeting Asp-GTC-3’tDR as a novel first-in-class treatment for cardiac fibrosis.

NIH Research Projects · FY 2025 · 2025-08

PROJECT SUMMARY/ABSTRACT Preeclampsia, a condition marked by hypertension and systemic endothelial/microvascular dysfunction in late pregnancy, affects 8% of childbearing U.S. women and is associated with two-fold risk of future material cardiovascular disease (CVD) as well as premature CVD mortality. The American College of Cardiology and American Heart Association now recognize preeclampsia as a sex-specific CVD risk factor to guide prescription of preventive statin therapy. Beyond this focused recommendation, however, specific strategies for CVD risk reduction in women with preeclampsia are not yet established. Recent preclinical evidence suggests that preeclampsia induces vascular smooth muscle cell mineralocorticoid receptor (MR) sensitivity that persists postpartum, promoting hypertension and CVD. Although MR signaling is known to underlie hypertension, MR activation also promotes cardiovascular, kidney, and metabolic disease via effects that are partially independent of blood pressure. Work by our team has implicated MR signaling in coronary microvascular dysfunction, a known predictor of heart failure with preserved ejection fraction (HFpEF) and CVD mortality. Our central hypothesis is that, among women with prior preeclampsia who subsequently develop chronic hypertension, MR blockade will promote favorable cardiac remodeling and improve coronary microvascular function, independent of changes in blood pressure, and thereby reduce CVD risk in affected women. To test this hypothesis, we propose a randomized, double-blind clinical study in humans. Women aged <55 years with a history of preeclampsia, current chronic hypertension, and concentric left ventricular remodeling will be randomized 1:1 to receive eplerenone (mineralocorticoid receptor antagonist) or chlorthalidone (thiazide-like diuretic) with potassium supplementation for 48 weeks, targeting equivalent blood pressure control in both groups using daily home BP measurement and 24-hour ambulatory blood pressure monitoring. We will measure coronary microvascular function (myocardial flow reserve, i.e., hyperemic stress/rest myocardial blood flow) quantified by cardiac PET/CT (Aim 1) and cardiac structure and function by cardiac ultrasound (Aim 2) at baseline and 48 weeks. We expect that, compared with chlorthalidone, eplerenone will yield greater improvements in coronary microvascular function and myocardial diastolic function after 48 weeks of treatment. Additional exploratory endpoints will include retinal vascular density by optical coherence tomography as an extra-coronary measure of microvascular health. If our hypotheses are affirmed, these findings would support the targeted use of MR antagonists much earlier than recommended by current guidelines for the management of hypertension to more effectively prevent HFpEF and other CVD among women with a history of preeclampsia.

NIH Research Projects · FY 2025 · 2025-08

Project Summary Eukaryotic heterochromatin is demarcated by histone H3 lysine 9 methylation (H3K9me) and is required for maintaining genomic stability. Loss of heterochromatin results aneuploidies and translocations and is associated with human diseases including cancers. The centromeres of the fission yeast, Schizosaccharomyces pombe, have served as a powerful model for uncovering the conserved chromatin- and RNA-based mechanisms governing heterochromatin formation. Historically, in this system, the recruitment of the highly conserved Suv39/Clr4, the sole histone H3 lysine 9 (H3K9) methyltransferase, to centromeres was thought to be guided by Argonaute-associated small RNAs (sRNAs) generated by processing of heterochromatic transcripts by the RNAi machinery. Additionally, the two highly conserved heterochromatic histone deacetylases, Sir2 and Clr3, deacetylate histones and prepare histones for Clr4-mediated methylation. Once targeted, Clr4 can not only methylate H3K9 but also bind to this mark, thus creating a positive feed forward loop, stabilizing its interaction and spreading along heterochromatin. Even though how centromeric heterochromatin is nucleated has been topic of many studies, all models converge on sRNAs as the only mechanism for nucleating Clr4 and triggering heterochromatin formation. My team on the other hand, inspired by the 20-year-old observation that in ago1D cells H3K9me is reduced but not eliminated at pericentromeres, asked whether an sRNA-independent mechanism could nucleate Clr4. In recent Cell paper, we define an sRNA-independent alternative axis to heterochromatin nucleation. This pathway uses the nuclear RNA quality control complex, MTREC which interacts with Clr4 and specifically recognizes a heterochromatic long noncoding RNA (lncRNA) called SPNCRNA.230 bivalently, via its Mmi1 (YTH-domain protein) and Mtl1 (RNA-helicase) components. More importantly, loss of MTREC together with RNAi leads to complete loss of Clr4 recruitment at centromeres, revealing that these two pathways operate in parallel to nucleate Clr4. Additionally, we showed that Sir2 and Clr3 work upstream of Clr4 to deacetylate H3K9 for Clr4 methylation and are recruited to pericentromeres, including SPNCRNA.230, by sRNA-independent mechanisms. Overall, these data reveal that three independent mechanisms amass Sir2, Clr3 and Clr4 at SPCNRNA.230 to nucleate and trigger heterochromatin formation. Here, we will (Aim 1) Decipher the mechanism(s) of Sir2 recruitment to heterochromatin; (Aim 2) Delineate how SPNCRNA.230 nucleates Clr4 and Clr3 independently of sRNAs and H3K9me; and (Aim 3) Reveal the mechanisms of lncRNA- mediated heterochromatin assembly using an inducible system. Overall, the proposed studies will reveal how distinct, parallel recruitment mechanisms nucleate heterochromatin machinery at lncRNAs to trigger heterochromatin formation. We predict that these studies will reshape our understanding of heterochromatin mechanisms and expose novel targets for manipulating heterochromatic state in eukaryotes.

NIH Research Projects · FY 2025 · 2025-08

Project Summary/ Abstract Dys-regulation of the body's internal circadian time-keeping mechanism is an established risk factor for metabolic disease. Chronotype, or preference in timing of behaviors during the 24 hour day, reflects underlying circadian rhythms and can be assessed by questionnaires and actigraphy in large populations. Our long-term goal is to use human genetics to elucidate pathways that link sleep and circadian regulation to cardio-metabolic disease with the aim to develop therapeutic interventions that leverage insights from human genetic approaches. The overall objective of this application is to identify the variants, genes, and mechanisms responsible for chronotype and understand how alterations in biological timing of these genes interfaces with metabolic physiology and disease. In the first funding period, we identified 351 novel loci for chronotype, sleep and activity timing traits by leading and contributing to GWAS of circadian and sleep traits in the UK Biobank. We identified genetic variants implicating circadian rhythms and also insulin regulation pathways, providing opportunities to understand the biological causes and consequences of circadian rhythm disturbances and their relationship with human metabolic physiology. We hypothesize that more complete identification of causal common, rare and structural variants by whole genome sequencing, clustering of variants by multi-trait associations and tissue- and cell-type specific annotations, as well as functional genomic analyses of pleiotropic signals between chronotype and metabolic disease using computational and experimental approaches will illuminate circadian rhythm pathways and the mechanisms linking them to type 2 diabetes. In the next phase of this research, we leverage whole- genome sequencing, proteomics and metabolomics in large biobanks including UK Biobank and TOPMed cohorts as well as high-throughput experimental perturbation studies to pursue the following specific aims: 1) to characterize common, rare and structural genetic variants underlying chronotype using genome sequencing; 2) to dissect mechanistic pathways by which variants and genes influence chronotype using analyses of existing multi-omic resources as well as experimental assays, and 3) to define molecular links between chronotype, daily molecular and behavioral rhythms and T2D by analyses of pleiotropy, colocalization and gene x behavior associations. This work will reveal mechanistic links of timing of the internal circadian rhythm to type 2 diabetes, opening potential new avenues of treatment for circadian rhythm disorders and type 2 diabetes.

NIH Research Projects · FY 2025 · 2025-08

Abstract Advanced age is the leading risk factor for most chronic diseases and functional deficits in humans, but the fundamental mechanisms that drive aging remain largely unknown. Inflammaging—i.e., chronic, sterile, low- grade inflammation during the aging process—has been implicated as crucial to the pathogenesis of age-related frailty and diseases. While the precise etiology and the underlying cellular and molecular processes of inflammaging are not yet fully understood, compromised intestinal epithelial homeostasis and barrier function have been indicated to play a critical role in its development. Studies across various species demonstrated reduced barrier integrity and increased intestinal permeability with age, ultimately leading to microbial and (endo)toxin translocation associated with systemic inflammation. Likewise, enhancing intestinal barrier integrity counteracts aging-associated inflammation and frailty. However, clear gaps remain in our understanding of mechanisms that contribute to the decline in intestinal homeostasis with age. A key feature of aging is the mechanical stiffening of organs, and studies in bone marrow, nervous system, vasculature, skin, and cartilage demonstrated that their stiffening with age is tightly correlated with their functional decline. However, the contribution of intestinal mechanoaging to its dysfunction and inflammaging is unknown. We hypothesize that the aging-associated mechanical stiffening of the intestine contributes to the decline in intestinal epithelial homeostasis and barrier integrity with age. Our hypothesis is supported by recent work from our team and preliminary results showing that stiffening potently disrupts intestinal epithelial homeostasis (including the loss of stem cells) and barrier integrity, which are reminiscent of phenotypes observed in an aged epithelium. Moreover, aging triggers intestinal stiffening and induces the expression of mechanosensitive channel PIEZO1— whereas the attenuation of the mechanosignaling pathway mitigates the aging phenotype. We will test our hypothesis in three Specific Aims: Aim 1 will determine the causal role of the intestinal mechanoaging in the decline in its homeostasis and function with age; Aim 2 will elucidate the role of PIEZO1 in the aging-associated intestinal dysfunction and inflammaging; and Aim 3 will decipher whether the attenuation of mechanosignaling cascade rescues the aging phenotypes in human epithelial cells. Achievement of our objectives will demonstrate that the stiffening of the intestine with age plays a central role in its functional deterioration and inflammaging. It will also highlight mechanosignaling pathway as a viable therapeutic target to decelerate aging-related intestinal dysfunction and promote healthy aging.

NIH Research Projects · FY 2025 · 2025-08

PROJECT SUMMARY/ABSTRACT Avoidant/restrictive food intake disorder (ARFID) is a severe and impairing eating disorder, affecting up to 4% of adults, characterized by food avoidance and restrictive eating motivated by sensory sensitivities, fear of aversive consequences of eating, and/or lack of interest in eating or food. Individuals with ARFID are at risk for serious health consequences. Cognitive-behavioral therapy for ARFID (CBT-AR) is a brief behavioral treatment for ARFID that has demonstrated evidence of feasibility, acceptability, and proof-of-concept. However, most individuals with ARFID do not have access to CBT-AR. Barriers to treatment access (e.g., cost, location) contribute to estimates that less than 25% of individuals with psychiatric disorders in need of treatment seek help. Further, the prevalence of psychiatric conditions worldwide—including ARFID—far exceeds the current capacity of mental health providers and services required. Digital mental health treatments (DMHTs), including mobile applications (“apps”), provide an efficacious, cost-effective, and scalable method for extending the reach of mental health care. Approximately 97% of American adults own a smartphone, further highlighting the utility of DMHTs as a promising avenue to increase access to healthcare resources. This proposal develops and tests a mobile app to deliver CBT-AR (mCBT-AR). I propose to: (a) utilize an iterative approach to developing mCBT- AR, implementing user-centered design principles; and (b) test the feasibility, acceptability, and proof-of-concept of mCBT-AR. My findings will fill a critical area in the necessity of a clinically effective, accessible, scalable, and inexpensive treatment for ARFID. In collaboration with my primary mentor (Dr. Thomas), co-mentors (Drs. Wilhelm and Fitzsimmons-Craft), and expert collaborators (Drs. Burton-Murray and Tabri, Mr. Landheim), I have developed a comprehensive training plan that will prepare me with the requisite skills and training needed to establish myself as a clinical investigator focused on increasing access to treatment for ARFID and other eating disorders through digital mental health treatments. My K23 training and career development goals are to: (1) gain expertise in digital mental health treatment development; (2) enhance my knowledge of symptom change and treatment response in ARFID; (3) establish skills in the design and analysis of brief interventions and randomized controlled trials; and (4) achieve independence in career development and the responsible conduct of research. Together, these goals will provide me with the necessary skills to transition to a career as an independent investigator, setting the stage for conducting R-level clinical trials. The proposed research will contribute to the development of a clinically accessible, scalable, inexpensive treatment for ARFID, a highly impairing disorder for which most individuals lack access to treatment. The mentored training will inform my preparation of a R01 grant application to conduct a larger randomized controlled trial of mCBT-AR, using a micro- randomized or adaptive design, to prepare for large-scale dissemination of the intervention to improve the lives of individuals living with ARFID.

NIH Research Projects · FY 2025 · 2025-08

Idiopathic pulmonary fibrosis (IPF) is a chronic and progressive disease with limited treatment options, poor prognosis, and a median survival of only 3-4 years after diagnosis. Current clinical diagnostic techniques are limited in early detection, patient stratification, and treatment effect monitoring. Invasive biopsy remains the gold standard to obtain pathological information for IPF diagnosis and prognosis. Pulmonary macrophages, including alveolar macrophages and monocyte-derived macrophages, are essential to the innate immune response in the pathogenesis of IPF. CD206-positive (CD206+) macrophages have been found to be pro-fibrotic and secret pro-fibrotic cytokines, interact with and support myofibroblasts, and promote extracellular matrix (ECM) accumulation. Elevated CD206 expression has been found during the fibrotic stage in bleomycin-induced mouse models and in the alveolar macrophages of IPF patients. Therefore, CD206+ macrophages have emerged as a promising target for both diagnostic and therapeutic interventions. However, current imaging agents targeting CD206, either use single-mannose based agents that are not specific or nanobody/antibody-based PET probes that might cause prolonged exposure to radiation or undesired pathologic effects to the lungs. We have developed CD206-targeted MRI agents and demonstrated that they are specific and able to map CD206+ macrophage changes in cutaneous injury, glioma, and stroke. Histopathological studies showed that the fluorescent analog of the MRI agent correlates well with CD206 and collagen and is markedly increased at week 3 compared to week 1 in bleomycin-induced lung fibrosis. However, MRI is often unsuitable for imaging pulmonary diseases, especially in ventilated patients. The goal of this proposal is to validate a PET imaging probe that shares the specific binding moiety with the CD206-targeted MRI agent to track CD206+ macrophages in pulmonary fibrosis. We will validate the specificity and efficacy of the probe both in vitro and in vivo, in a bleomycin-induced mouse model of lung fibrosis. We will demonstrate the ability of the validated probe in detecting CD206+ macrophages and correlate imaging data with biochemical, flow cytometric, and immunopathological findings. The results from this proposal will provide critical preliminary data for a forthcoming R01 application.

NIH Research Projects · FY 2026 · 2025-08

PROJECT SUMMARY/ABSTRACT At present, the diagnoses of many rare disease patients remain unsolved and the effects of rare variants in common diseases remains unclear. Further, the phenotypic effects of most high-impact mutations are unknown. Accurate methods to interpret genetic variants would enhance many aspects of biomedical research, including genome-wide association studies and functional studies, as well as clinical diagnostics of rare disease patients. In my laboratory, we build tools, methods, and resources to interpret genetic variation that are applicable in clinical practice and human disease research. Beginning with predicted loss-of-function variants, we have built a widely used annotation tool, LOFTEE, that is highly specific in identifying variants triggering nonsense- mediated decay. I have aggregated massive datasets of human genomes and exomes (gnomAD) to build the largest variant frequency maps released to the public, for clinical laboratories and researchers interpreting disease variation. Finally, I have performed massive-scale common and rare variant association analysis on thousands of phenotypes using mixed models to describe the most robust estimates of variant-phenotype associations, powering gene discovery for common diseases. Using state-of-the-art computational methods, this research program will improve the interpretation of variants in the human genome, improving current annotation methods for predicted loss-of-function, missense, and non-coding mutations, incorporating information from genetic reference data as well as biobanks with genome and disease/phenotype data. My research program will build a framework for assessing function for many classes of deleterious variants by integrating frequency data from diverse populations into deep learning frameworks. Finally, this project will integrate rare variant association data into deep learning models to identify loss-of-function-like missense variants. In this way, my research program will improve interpretation of genetic variants found in patients and large cohorts and biobanks, improving clinical genetic practice and human disease research.