Yale University

NIH Research Projects · FY 2025 · 2024-09

PROJECT SUMMARY. Progression to metastatic disease from Non-Small Cell Lung Cancer (NSCLC) is a significant cause of mortality. Central nervous system (CNS) metastases, which carry poor prognosis and limited treatment options, can form intraparenchymally (IP) within the tissue of the brain or within the cerebrospinal fluid (CSF) filled spaces between the leptomeninges. The latter, termed leptomeningeal disease (LMD), is difficult to diagnose and treat, and the pathophysiological mechanisms underlying progression to LMD are poorly understood. 60% of patients with LMD have past or concurrent IP metastases, but the mechanisms promoting a switch to LMD invasion remain unknown. Interestingly, patients with EGFR-mutant NSCLC are more likely to progress to LMD, particularly at resistance to tyrosine kinase inhibitors (TKIs), the standard of care for these patients. While third-generation TKIs such as Osimertinib show excellent brain penetrance, resistant CNS disease, including LMD, remains a pressing clinical problem. The primary aims of this project are to identify mechanisms underlying progression to and persistence of LMD, and determine what factors favor this progression in cases of TKI-resistant EGFR-mutant disease. I hypothesize that in a subset of IP cases, progression to LMD occurs through invasion of parenchymal cells through the perivascular spaces in the brain, and that this progression is promoted by mechanisms that synergistically foster TKI resistance. I have validated multiple NSCLC murine models of comorbid IP and LMD following intra-arterial injection, including an EGFR- mutant model that emerges at late-stage TKI resistance as well as a syngeneic model. In Aim 1, I will utilize spatiotemporal barcoding of these lines to chart the anatomical routes cells traverse to reach the leptomeningeal space. In particular, I will determine whether LMD metastases descend from parenchymal metastases, or enter the CSF from systemic circulation across the blood-CSF-barrier. I will then assess how pathways downstream of mechanosensing by β1-integrin, previously shown by our laboratory to promote CNS TKI resistance, may modulate the ability of EGFR-mutant cells to complete this journey at resistance. Then, in Aim 2, I will investigate the mechanistic role of the protein Tissue Inhibitor of Metalloproteinases 1 (TIMP-1), identified in our biorepository samples as upregulated in the CSF of patients with LMD. TIMP-1 can signal through β1-Integrin and CD63 to promote anchorage-independent survival, mirroring a phenotype observed in our LMD model lines in vitro. Given the role of β1-integrin in this pathway, I will investigate whether stromal TIMP-1 levels play a dual role in EGFR-mutant LMD by promoting cell survival while also promoting signaling underlying TKI resistance. As a graduate student in Dr. Don Nguyen’s laboratory in the Pathology department at Yale University, I have the support of a diverse array of translational and clinical researchers as my mentors and collaborators. Through completion of the research proposed during the NRSA F31 fellowship, I will hone my experimental skills as I progress towards my goal of becoming an independent researcher in the field of CNS metastases.

NIH Research Projects · FY 2025 · 2024-09

ABSTRACT Excessive alcohol use is a significant and serious public health problem, with 28.6 million US adults meeting diagnostic criteria for current alcohol use disorder (AUD). The stress response is a promising target for understanding vulnerability and possible intervention, as risky drinking patterns are deeply and bidirectionally linked to stress responses. However, key characteristics of the stress response have not yet been leveraged: as studies to date have focused on individual brain regions and static snapshots of brain responses, we cannot capture the predictive potential of the multifaceted stress response, which involves widespread interactions between brain regions and unfolds dynamically over time. Indeed, recent evidence indicates that dynamic whole- brain responses can provide unique insight into stress-related conditions. The goal of this R01 is to leverage advances in machine learning and computational modeling to develop and validate whole-brain biomarkers for stress, and test whether dynamic engagement of these stress networks can predict individual differences in drinking and alcohol-related cognition. Using a combination of secondary analysis (N = 390) and new collection of functional MRI data (N = 100), we will identify and validate stress-predictive neuromarkers, capture dynamic trajectories of stress neuromarkers, and test the consequences of these moment-to-moment dynamics for cognitive mechanisms driving risky drinking. In Aim 1, we will build a connectome-based model that predicts responses to multiple modalities of stress exposure in previously unseen individuals using rigorous cross- validation techniques. We will identify functional connections that predict stress responses in clinically heterogeneous samples as well as those specific to individuals with AUD. In Aim 2, we will create a novel moment-to-moment framework to characterize stress response trajectories in the brain and their alterations in AUD. This framework will enable us to test the hypothesis that, rather than simply having higher or lower engagement of a stress-predictive network, individuals with AUD will show atypical stress network engagement trajectories in response to a stressful event. In Aim 3, we will develop a novel neuroimaging paradigm to quantify the temporal dynamics of brain stress network engagement on memory formation and subsequent drinking. With this design, we will test the hypotheses that: 1) information that is temporally and conceptually congruent with stress will be preferentially encoded; 2) dynamic stress networks will co-fluctuate with a validated neuromarker of attention to facilitate learning; and 3) the timecourse and neural networks by which stress dynamically modulates learning will differ in AUD and predict future drinking. Together, this work will provide new insight into the brain’s stress response, including the ways in which its neural architecture (where), temporal dynamics (when), and consequences for adaptive cognition (how), are predictive of chronic alcohol. The long-term goal of this work is to develop clinically actionable neurocognitive markers of heavy drinking for early assessment, intervention, and treatment. Future grants will apply this framework to predict treatment and relapse outcomes.

NIH Research Projects · FY 2025 · 2024-09

PROJECT SUMMARY/ABSTRACT Systemic sclerosis (SSc) or scleroderma is a chronic multisystem autoimmune disorder. The underlying pathophysiology includes innate/adaptive immune system abnormalities, endothelial injury with small vessel vasculopathy, and fibroblast activation with subsequent fibrosis. Digital ischemia/digital loss is a feared complication of SSc. Prospective studies report that an estimated 50% of patients with SSc experience digital ulcerations over 5 years of follow-up, and 20% experience finger amputation secondary to ischemia. The currently available clinical tools for the assessment and monitoring of the underlying pathophysiology (vasculopathy, tissue ischemia and fibrosis) are only semi-quantitative. The dearth of quantitative tools to evaluate potential treatments has resulted in patients with SSc enduring significant morbidity. Here we propose to use multiparametric magnetic resonance imaging (MRI) in patients with SSc for a comprehensive quantitative assessment of vasculopathy, tissue hypoxia, and fibrosis in the hands, which contributes the most to patient disability in SSc. In Aim 1, we will test quantitative MRI biomarkers to complement established clinical assessments of vasculopathy, ischemia, and fibrosis. In SubAim1a, we will test whether quantitative MRI physiologic biomarkers correlate with clinical assessment of vasculopathy, ischemia, and fibrosis. In SubAim1b, we will determine the association between MRI physiological biomarkers and vascular symptoms and complications (digital ulcers and digital loss). In Aim 2, we will test whether short-term changes in quantitative MRI biomarkers can predict long-term changes (12 and 24 months) in symptoms and vascular complications. Collectively, this work will provide new noninvasive imaging biomarkers to inform prevention and treatment interventions and to monitor effectiveness of therapy for digital ischemia. Cardiovascular disease is the leading cause of death in patients with rheumatoid arthritis and systemic lupus erythematosus. Peripheral vascular disease is the leading cause of disability in SSc. The applicant’s long-term goal is to become a leader in cardiovascular imaging in patients with autoimmune rheumatic diseases. The applicant is already an expert in cardiac imaging. Now, formal training in peripheral vascular imaging is necessary. We propose a focused, intense career development training plan which will include 1) mentorship from a team of experts led by the primary mentor, an expert in peripheral vascular imaging, 2) advanced didactic coursework on peripheral vascular imaging, quantitative image analysis and machine learning, and 3) hands-on experience with peripheral MRI techniques (guided by an expert MRI physicist, co-mentor) and quantitative image analysis with integration of machine learning (guided by expert image analysis/machine learning advisor). The proposed work will center on an established cohort of clinically well characterized SSc patients, but the skill sets obtained will be broadly applicable. At this award’s conclusion, the candidate will emerge as an expert in peripheral vascular imaging in rheumatic diseases and be poised to assume the role of independent investigator.

NIH Research Projects · FY 2025 · 2024-09

Project Summary The proposed project is a clinical trial of a mindfulness-based neurofeedback (mbNF) intervention used to augment Dialectical Behavior Therapy (DBT) skills group training in adults with borderline personality disorder (BPD). DBT is the best-evidenced treatment for BPD. Its full implementation involves up to 20 hours per week of clinical contact for 6-12 months. Recent studies have shown that weekly brief DBT skills groups (DBTsg) also provide clinical benefit for people with BPD. Here, we propose to amplify DBTsg training with mbNF. Mindfulness is a foundational skill for DBT that is emphasized throughout each DBT module. Our aim is to strengthen the ability of subjects to practice mindfulness effectively, and thereby to amplify the therapeutic benefits of DBTsg. In our study, people with BPD will enroll, participate in a real-time fMRI mbNF intervention, and subsequently participate in DBT. The mbNF intervention involves engaging in mindfulness meditation while receiving feedback on how well one's brain patterns reflect a focused mindful state in which the fronto- parietal control network (FPCN: controls cognitive focus) has high activity while the default mode network (DMN: associated with less mindful brain state) has low activity. We hypothesize that mbNF will strengthen the brain circuitry supporting mindfulness and thereby amplify the clinical benefits obtained in the DBTsg. Our past studies using mbNF have demonstrated that it decreases hyperconnectivity within the DMN, increases anticorrelations between the DMN and FPCN, increases connectivity between frontal control regions and amygdala, improves measures of state mindfulness, and yields clinical benefits in neuropsychiatric populations. The proposed study adopts a randomized, double-blind trial design to examine whether mbNF in patients with BPD induces the expected changes in DMN connectivity (decreased internal connectivity and increased anticorrelations to FPCN). We will also test whether mbNF amplifies clinical efficacy of DBT. Efficacy on both brain and clinical measures will be determined via comparison to a control group that receives yoked sham feedback and DBT. Importantly, the persistence of training effects will be monitored over several months. Neurofeedback naturally supports a closed loop intervention development cycle in which neuroscientific and clinical knowledge develop together. The proposed study would leverage this approach in the development of novel therapeutic options for BPD. It will inform our understanding of the neural substrates supporting clinical efficacy of DBT in BPD and could lead to development of the first neuroscience-based treatment option for patients with BPD.

NIH Research Projects · FY 2025 · 2024-09

PROJECT SUMMARY The field of human neuroimaging has long been suffering from a problem of low power, in which low signal-to- noise ratio, multiple comparisons, and small sample sizes result in insufficient statistical power for many studies. For studies attempting to reveal brain-behavior relationships via functional and structural connectomes, which are a matrix representation for statistical and physical relationships between brain regions, the story is the same. From a scientific perspective, this issue reduces the number of findings presented in the literature while also lowering the replicability of any findings. Since connectomics research strives to ultimately be clinically relevant by, for example, predicting risk for mental health conditions or informing personalized treatment approaches for those with existing illness, this problem of low power greatly hinders progress. Fortunately, recent work has introduced a framework shift in statistics whereby an emphasis on brain networks, rather than individual connections or edges, improves overall statistical power in functional connectivity analyses. Further work has shown that using information of the relationships between edges in the connectome to construct the networks results in even greater power increases, but it is not yet known whether deriving networks within-dataset is more effective than using independent networks. Therefore, this proposal will investigate whether using within-dataset networks in network-level statistical procedures results in further power increases. It will also test these network- level approaches that were developed in functional datasets on the structural connectome. In Aim 1, I will use data from 3 large functional connectivity datasets spanning several phenotypes to examine if creating the networks in the same dataset that undergoes statistical testing will offer power improvements over deriving networks from an independent dataset. In Aim 2, I will evaluate the utility of network-level statistical procedures in the structural connectome, and in Aim 3, I will extend the methodology from Aim 1 to the structural connectome to comprehensively determine whether the results seen in the functional connectome extend to the structural connectome. This work will improve understanding of which variables we can manipulate to achieve higher statistical power in human connectivity studies and lead the field towards eventual clinical relevance by improving the neuroimaging tools available to mental health researchers.

NIH Research Projects · FY 2025 · 2024-09

PROJECT SUMMARY This is a new application by an early-stage investigator with a long-term career objective of transforming cardiovascular care using artificial intelligence and data science. The proposal focuses on aortic stenosis (AS), a progressive narrowing of the aortic valve, which manifests in older adults and causes significant disability and premature mortality despite minimally invasive treatment strategies. AS is either diagnosed following symptom- driven diagnostic testing or incidentally discovered, which has simultaneously led to a vast underdiagnosis of advanced stages of AS while identifying many with early-stage aortic valve disease without clarity on appropriate follow-up. There is a critical need for novel screening and prognostication strategies for AS. We show that artificial intelligence (AI) models applied to 1-lead electrocardiograms (AI-ECGs) can be a sensitive and convenient screen for advanced (moderate/severe) AS. AI-ECG can be paired with a second, more specific, AI-enhanced handheld cardiac point-of-care ultrasound (POCUS). This AI-POCUS automates the diagnosis of advanced AS without specialized imaging or expert evaluation. In Aim 1, we propose a multicenter pragmatic RCT evaluating this 2-stage, AI-driven screening strategy for advanced AS. This innovative, technology-driven screening strategy will define a new paradigm for the efficient identification of advanced AS. In addition, we evaluate a novel strategy to bridge the critical gap in precision follow-up, especially for early-stage aortic valve disease. Early aortic valve disease – aortic sclerosis or mild AS – affects nearly a fourth of older adults over 65 years. However, there are no guideline recommendations on follow-up for aortic sclerosis, and recommendations for mild AS do not account for the substantial heterogeneity in disease progression. In our preliminary investigations from a multicenter observational cohort study, we show that a deep learning tool for echocardiographic videos – deep learning- based aortic stenosis severity index (DASSi) – can define those at substantially elevated risk of progression to advanced AS and adverse clinical outcomes. In Aim 2, we will conduct a multicenter, prospective evaluation of an individualized AS progression score among older adults with aortic sclerosis or mild AS through a protocolized Doppler echocardiogram to distinguish those with high and low rates of progression. The investigations in Aim 2 will establish the reliability of a digital biomarker for AS progression that can enable precision care and follow- up. The work is supported by the team’s broad expertise in (a) clinical medicine, including cardiology, geriatrics, and imaging; (b) technology, spanning informatics, data science, and AI; and (c) clinical trials, with experience in designing and executing studies. The evidence generated from a multicenter evaluation of low-cost AI-driven interventions can be immediately adopted and scaled to have a major public health impact. Moreover, an objective approach to the diagnosis and follow-up of AS will reduce healthcare disparities for vulnerable patients. Future work will build on these results and engage directly with communities using low-cost portable devices to improve disease detection and outcomes among those without adequate healthcare access.

NIH Research Projects · FY 2025 · 2024-09

PROPOSAL SUMMARY / ABSTRACT: HIV is a retrovirus that achieves infection through integration of its viral DNA into the host genome of non- dividing cells. To execute this function, HIV must ensure the nuclear import of its reverse-transcribed genetic material, which is protected inside of a proteinaceous fullerene cone known as the capsid. As the dimensions of the capsid are larger than the conventionally accepted width of the nuclear-pore-complex (NPC), it was long thought that disassembly of the capsid in the host cytoplasm was essential for HIV’s genomic material to gain access to the nucleus. Recent work has demonstrated that the central channel of the NPC is highly dynamic and able to accommodate an intact HIV capsid; subsequently several groups have directly observed assembled capsids within the nuclear compartment. Work from the last decade suggests that the cellular protein CPSF6 (Cleavage and Polyadenylation Specificity Factor 6) governs the nuclear import of viral genomic material through interactions with the HIV capsid. The work outlined in this proposal, broken into three independent aims, seeks to provide biochemical and structural understanding of this interaction. In AIM1, I will use real-time binding techniques to provide insight into the biophysical principles of the CPSF6-capsid interactions. I hypothesize that CPSF6 has a previously unappreciated binding site at the tri-hexamer interface of the HIV capsid. In AIM2, I will employ our library of capsid assemblies to determine the structure of CPSF6 bound to the capsid protein and confirm these structural features with functional assays. Finally, in AIM3, I will employ a DNA origami mimic of the NPC in an in vitro reconstitution dissect CPSF6’s contribution to nuclear import of the HIV capsid. I hypothesize that CPSF6 can compete with Nup153 and thereby release the capsid from the nuclear basket.

NIH Research Projects · FY 2025 · 2024-08

Large Language Models (LLMs) represent the latest advancement in Natural Language Processing (NLP) and Artificial Intelligence (AI), holding tremendous potential to revolutionize biomedical and healthcare applications. Extensive research has demonstrated the effectiveness of LLMs in a range of biomedical and health applications, ranging from medical question answering to summarizing systematic reviews and AI-assisted disease diagnosis. However, the major barriers to applying LLMs in biomedical and health applications are factual incorrectness – where LLMgenerated responses are inaccurate or incomplete – and incorrect reasoning – where LLM-generated responses lack supporting evidence, contradict existing evidence, or even rely on hallucinated evidence. Such issues further pose the risk of propagating errors, potentially leading to incorrect diagnosis or treatment recommendations. Addressing these issues has been challenging, primarily due to three fundamental obstacles: (1) from the data perspective, LLMs may capture errors from lower-quality or unauthorized sources in the general domain data during pretraining, lack access to accurate and up-to-date biomedical knowledge, and consequently generate inaccurate, or outdated results; (2) from the methods perspective, there is a lack of mechanisms for fact-checking and evidence attribution throughout the lifecycle of LLMs when applied to biomedical and health studies, spanning from training/fine-tuning to inference and posthoc analysis; (3) from the accountability perspective, few approaches have evaluated their effectiveness in biomedical and health downstream applications. Our overall objective in this proposal is to systematically address the issue of factuality and reasoning of LLMs in biomedicine and healthcare. The specific aims include (1) from the data perspective, establishing a self-augmentation framework to teach LLMs to automatically select and use relevant biomedical digital resources to augment their responses; (2) from the methods perspective, developing an LLM curator by stimulating fact-checking and evidence attribution performed in biocuration via a multi-stage, multitask instruction tuning pipeline; (3) from the methods perspective, introducing a steplevel automated feedback-guided paradigm for LLMs to reflect and improve from its intermediate responses via fact-checking and evidence attribution; and (4) from the accountability perspective, evaluating the methods in downstream use cases. The proposed work is expected to address factuality and reasoning issues of LLMs – the key barrier to their use in biomedical and health domains – and make LLMs generate accurate responses to advance biomedical discovery and healthcare. It is also expected to refine the current development and evaluation pipelines of LLMs in biomedical and health domains by making fact-checking and evidence attribution essential components and providing related benchmarks, methods, and tools to facilitate the implementation.

NIH Research Projects · FY 2025 · 2024-08

Rates of HIV diagnosis and pre-exposure prophylaxis (PrEP) uptake are wide and persist across geographic regions. The Ending the HIV Epidemic (EHE) initiative prioritizes targeting 57 jurisdictions including 7 states and 50 counties with the highest HIV rates in the United States (U.S.). To reduce geographic differences, precise detection and forecast of new HIV diagnosis hotspots are required to accurately identify PrEP shortage areas to inform optimal allocations of PrEP providers who can serve the population efficiently to reduce new infections. This task relies highly on rigorous studies to examine contextual and structural factors such as community mental health prevalence and other socio-spatial environmental factors that are likely critical to preventing new HIV infections. Four inter-related contextual factors that address these gaps are: transportation-based measures of PrEP accessibility, community mental health prevalence, social capital, and religious institution environment in an area. We use spatial data science, cyberinfrastructure methodology, and geospatial statistical analyses to develop novel indicators of these measures by mining data from several sources including AIDS Vu, The American Community Survey, and other proprietary data sources to accomplish the following: AIM 1: Create transportation-based measures of PrEP accessibility using Gaussian two-step floating catchment area (G2SFCA) analysis, at the county and zip code levels, for both urban and rural transport systems. AIM 2: Use Bayesian spatial analyses to quantify how the distribution of religious institutions environment, social capital, community mental health prevalence, and transportation-based PrEP accessibility are associated with: new and late HIV diagnoses rates, and with PrEP uptake at the county, and zip code levels. AIM 3: Develop an interactive HIV data visualization Web tool to identify HIV hotspots and where to allocate additional PrEP providers. The Web tool will also display which (and to what extent) socio-structural variables drive HIV hotspots. We will evaluate the acceptability and feasibility of the tool through semi-structured interviews with n = 20 stakeholders (e.g., HIV surveillance epidemiologists, community leaders, and people living with HIV). Impact: Despite efficacious HIV prevention and care technologies for individuals, HIV-related differences persist across geography. Successful completion of this research can contribute to ongoing EHE efforts to reduce 90% of new HIV infections by 2030. Moreover, the rigorous methods used in this project will contribute to addressing the need for novel approaches for valid and reliable assessments, measures, and estimation of structural factors that contribute to HIV in high-incidence populations. Our HIV data visualization Web tool is novel because it facilitates identifying which factors influence HIV the most and which areas are changing in response to those variables, which in turn, may help researchers and practitioners identify the “right things, in the right places, to curb the HIV epidemic.”

NIH Research Projects · FY 2025 · 2024-08

PROJECT SUMMARY/ABSTRACT Peanut allergy affects one in fifty children in the US. There is no cure, and while progress is being made in treatment, the standard of care is peanut avoidance and ready access to injectable epinephrine which can adversely impact quality of life. Based on solid evidence in the last decade that early peanut exposure reduces allergy risk, current guidelines recommend screening (with allergy referral or serum IgE level) followed by early peanut introduction, around age 4-6 months in infants considered high-risk for peanut allergy, specifically those with severe eczema or egg allergy. However, barriers at the caregiver, pediatrician and allergist levels have challenged the implementation of these guidelines. In response to these challenges, our colleagues developed and evaluated the Intervention to Reduce Early (Peanut) Allergy in Children (iREACH), a decision support tool integrated into the electronic health record (EHR), in a multi-site cluster randomized trial. Compared with usual care, iREACH led to a 2-fold increase in pediatrician guideline-concordant care among high-risk infants with severe eczema or egg allergy. Evidence supports that the timing of peanut introduction in infancy is crucial, with the most substantial protective effect for introduction prior to 6-7 months. Some guidelines recommend peanut introduction in high-risk infants without screening, in part due to concern that screening, which can necessitate a visit to an allergist, may delay introduction. However, determinants of delays in this population have yet to be well characterized. To facilitate timely peanut introduction in high-risk infants, it is critical to understand determinants of screening and management. In the proposed study and using iREACH data, we will explore patient and provider-level determinants of peanut introduction in high-risk infants (Aim 1). Then, using sequential mixed methods, we will further examine facilitators of and barriers to guideline adherence from the perspective of clinicians (Aim 2). This information will inform a pilot intervention utilizing virtual medical visits to facilitate peanut introduction by 6-12 months of age in infants at high-risk (Aim 3). The results of this study will lay the groundwork for a future multi-site trial to facilitate timely, guideline-concordant care among infants at high-risk of peanut allergy. We have assembled a team of experts in the field of implementation science and health services research, qualitative and mixed methods, intervention development, pragmatic trial design, maternal-child nutrition, and telemedicine to support the project aims and training goals. We will leverage data from a large multisite trial. The PI, Julie Flom, MD MPH, is a Pediatrician and Allergist/Immunologist with experience in epidemiology and a current K12 in implementation science. The advanced training proposed in this K23 will prepare her for a successful transition into an independent investigator focused on adaptation, evaluation, and implementation of interventions for food allergy and asthma.

NIH Research Projects · FY 2025 · 2024-08

Summary Combination antiretroviral therapy (cART) fails to eliminate HIV-1 persisting in reservoirs or prevent long-term complications in people living with HIV (PLWH); therapy interruption leads to rapid viral rebound. Therefore, new approaches aimed at eradicating HIV-1 or enabling durable virus control without cART are needed. This is an R01 application in response to the NOFO PAR-23-297: “Opportunities for HIV Cure Strategies at the Time of ART Initiation (R01 Clinical Trial Not Allowed)”. This application is built on collaborative research centered around the development and translation of small-molecule CD4-mimetic compounds (CD4mcs) as a cure strategy for HIV-1. CD4mcs synergize with CD4-induced (CD4i) Env antibodies (Abs) to render virus-infected cells highly vulnerable to antibody-mediated cellular cytotoxicity (ADCC) and clearance by immune effector cells. These CD4i Abs are present in most PLWH. This application leverages the recent discovery of a new class of indoline CD4mcs that displays remarkable improvements in antiviral potency and breadth against diverse primary HIV-1 strains. Treatment of HIV-1-infected humanized mice (hu-mice) with an indoline CD4mc and two types of CD4i Abs (anti-CoRBS/anti-Cluster A Abs) at the time of cART initiation resulted in a dramatic reduction in the size of the viral reservoir. This early CD4mc/Ab regimen enabled sustained virus control after analytical treatment interruption (ATI)! Furthermore, we identified a new family of Abs in PLWH that recognizes additional CD4i Env epitopes and that cooperate with indoline CD4mcs to significantly enhance ADCC. In line with these exciting discoveries, this application proposes three independent and interactive aims: Specific Aim 1 will investigate physiological variables (virologic parameters and timing of treatment) that facilitate effective reservoir elimination by CD4mc/Ab interventions in new-generation hu-mice that support immune effector cell function. In addition, multi-dimensional single-cell analytical techniques will identify the phenotypes, physiology and spatial organization of immune-effector cells and virus-infected cells in tissues harboring HIV-1 reservoirs. These studies will determine the factors related to CD4mc/Ab intervention that tip the balance in favor of durable viral control after ATI. Specific Aim 2 will formulate a highly effective CD4mc/Ab cocktail by screening plasma of PLWH to identify additional families of CD4iAbs with enhanced potency in mediating ADCC and evaluate them in hu-mice. Specific Aim 3 will systematically explore how the properties of the infecting viral Env influence reservoir establishment and susceptibility to elimination by the new indoline CD4mc/Ab cocktails in hu-mice. The outcome of these proof-of-principle studies is expected to inform the development of a CD4mc/Ab-based early intervention cure strategy for HIV-1.

NIH Research Projects · FY 2025 · 2024-08

ABSTRACT Functional brain network characteristics reflect both genetic and environmental influences on brain development during fetal and postnatal periods. Alterations in network characteristics signal the presence of pathogenic factors and their potential mechanisms of action, which is crucial for the identification of processes that enhance risk for future onset of behavioral symptoms. Considering that autism features are distributed in the general population and are enriched in children with family members diagnosed with autism, the present study examines links between neonatal connectome and later social outcomes in a large sample of infants with and without familial history of autism (FHA). The outcomes of interest are (a) social attention which represents one of the most robust biomarkers reported in infants, toddlers, and school-aged children with autism, and (b) social engagement skills which represent a core feature associated with autism in toddlers. Towards these aims, we propose to examine prospective associations between iFC and later social phenotypic outcomes in a prospective sample of infants with (N=140) and without (N=60) FHA. By combining the two groups and adopting a dimensional approach to outcome measures, we aim to capture both the clinical and the subclinical variation in symptoms, which is critical for understanding the link between altered brain development and psychopathology. Brain imaging data will be collected at 42-44 weeks postmenstrual age (PMA); selective social attention measures will be collected at 6 and 18 months, and social engagement measure will be collected at 18 months. Social attention will be quantified using the Selective Social Attention (SSA) 2.0 eye- tracking task developed and validated in our lab. A composite social engagement score will be computed based on direct observation and parent report using standardized assessment tools: ADOS-2 and ADI-R. Aims 1 and 2 will focus on examining prospective links between inter- and intra-network iFC of the SN, DMN, FPN, and amygdala networks and later social attentional and social engagement outcomes. To complement the hypotheses-driven Aims, in Aim 3 we will conduct a data-driven whole brain analysis. This study will identify the characteristics of the neonatal connectome linked prospectively with behavioral dimensions known to be affected in autism, creating potential for improving identification of early brain-based risk factors and implementation of early interventions to support social attention and engagement in infancy and early childhood.

NIH Research Projects · FY 2024 · 2024-08

PROJECT SUMMARY/ABSTRACT Sub-Saharan Africa bears the greatest burden of the global human immunodeficiency virus (HIV) epidemic. Uganda is no exception, with over 1.5 million persons with HIV (PWH), comprising 5.8% of the population. Improved access to antiretroviral therapy (ART) has transformed HIV from a fatal condition to a chronic disease with long-term survival approaching that of uninfected individuals. In this setting, increased morbidity and non-AIDS mortality arising from non-communicable disease co-morbidities is a growing challenge in the care of patients with HIV. Rheumatic and musculoskeletal diseases (RMDs) encompass a broad group of conditions commonly affecting the muscles, bones or joints. Collectively, these disorders are the leading cause of years of life lived with disability and the fifth highest driver of disability-adjusted life years globally. In Sub-Saharan Africa the burden of RMDs is growing due to changing demographic patterns against a backdrop of resource limitation. The interplay of RMDs and HIV remains a challenge: interactions between the two are diverse and relatively poorly understood, creating barriers to management, and leading to negative health outcomes. Written in response to “HIV-associated Non-Communicable Diseases Research at Low- and Middle-Income Country Institutions (PAR-23-191)”, this proposal aims to establish a novel registry of patients with HIV and RMDs, which will provide a unique platform for epidemiological and clinical studies of patients with co-morbid HIV and RMD. Building upon the expertise of the study PIs in HIV, RA and osteoporosis, we further propose a demonstration project focused on patients with co-existing HIV and RA to illustrate the feasibility of this platform to support innovative hypothesis-driven research. The two independent but related aims will leverage a new patient registry at the Rheumatology Clinic at Mulago National Hospital, a well-established HIV Clinic at the same hospital, a rich history of collaboration between Makerere University College of Health Sciences and Yale University School of Medicine, an outstanding Advisory Committee, and the infrastructure and network of the Uganda Initiative for NCDs. Taken together, the proposed project has significant public health importance. It will provide the prerequisite data, training, and infrastructure needed to advance research on RMD burden among PWH. Creation of this unique research and training platform will foster critical local capacity and an integrated network in Uganda for future prospective studies investigating this important but previously under recognized problem. This will build the foundation for future longitudinal studies to assess the burden, biology, spectrum, and the progression of RMDs in HIV.

NSF Awards · FY 2024 · 2024-08

Recent progress in deep learning has demonstrated the potential of foundation models built on massive datasets, particularly in scientific discovery. In the sciences, data ranging from particles, to molecules, to cells, to brain activity can be represented by nodes on a graph or as signals on a graph substrate. Therefore, successful AI foundation models in scientific discovery are required to possess the capability of handling such graph-structured data and integrating with other types of data such as text, images, and tabular data. The proposed model can be used as a general substrate to help scientists predict and understand a variety of data that are expressed in graphs, such as molecules, proteins, and connectome. Existing methods for building graph foundation models for scientific discovery are in general severely limited in that they: 1) do not consider contexts in which the vertices of a graph can themselves be complex structures such as molecular graphs; 2) do not incorporate multimodal information in the form of knowledge graphs and text; 3) have limited forms of message passing in the form of local averaging; and 4) are not versatile and have limited performance gains due to diversity of downstream tasks and graph data distributions. This team of researchers will address these issues by developing a general foundation model framework for data represented as a graph in scientific domains by systematically addressing these key limitations. The framework incorporates novel approaches of multi-level graph neural networks, graph signal processing, multimodal graph learning, graph-specific fine-tuning, and in-context learning. By capturing human scientific knowledge and express the complexity of the natural world, our framework has the potential to dramatically transform machine learning models in scientific discovery and will allow us to tackle a wide range of complex scientific tasks, even with scarce supervision labels. 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 · 2024-08

This project describes a class of recently discovered human genes: internal open reading frames (iORFs) that overlap annotated protein coding sequences in alternative reading frames. Because iORFs are translated in a different reading frame, their amino acid sequences are different from the annotated protein that they overlap, meaning that overlapping genes encode two entirely different protein products. Overlapping genes are well- characterized in viral genomes, but were thought to be essentially absent from the human genome. While evidence is growing that the microproteins encoded in iORFs can play important roles in human cells, it is currently unclear how many functional human iORFs exist and how they are expressed. We provide preliminary data demonstrating that, in potentially hundreds of cases, alternative transcript variants can recode a human gene from expressing the annotated, canonical protein to the iORF-encoded microprotein. In Aim 1, we will apply long-read sequencing technologies to identify and validate the existence of iORF-encoding alternative transcripts in high throughput, thus establishing how broadly iORF recoding occurs in human cells. In Aim 2, we will provide molecular and cellular evidence that iORFs are functional, and determine whether their cellular roles are related to those of the canonical proteins that they overlap despite their differing amino acid sequences. In Aim 3, we will characterize the molecular mechanism and regulation of an anti-apoptotic iORF that overlaps a pro-apoptotic death effector domain-containing protein. Taken together, the successful completion of these aims will demonstrate that overlapping human genes are plentiful and functional, and provide mechanistic insight into a specific overlapping gene as a paradigmatic example of the molecular rationale for overlapping gene organization in human. More broadly, our study will provide an entirely new understanding of the roles of alternative transcript variants generated via alternative pre-mRNA splicing and use of alternative transcriptional start site: instead of simply generated isoforms of known proteins, these processes can generate novel transcripts that, in losing the ability to encode the annotated protein coding sequence, can be reprogrammed to express frameshifted iORFs encoding currently unannotated microproteins with distinct sequences and functions. We thus expect to reveal new levels of complexity in the human transcriptome and proteome.

NIH Research Projects · FY 2024 · 2024-08

Project Summary/Abstract State-of-the-Art Preclinical PET/CT Imaging System Yale University PET Center The goal of this proposal is to replace an end-of-life Positron Emission Tomography (PET) preclinical imaging system with a state-of-the-art PET/CT system that will enhance preclinical imaging research at the Yale PET Center. PET imaging provides a unique non-invasive method to detect and quantify biochemical processes and physiological functions in the living body. PET imaging has broad applications in areas of oncology, cardiology, psychiatry, neurology, metabolic disorders, inflammation, and others. The Yale PET Center is a 100% research dedicated core facility that features radiopharmaceutical development for diverse biological targets and high-resolution imaging with state-of-the-art quantification. Preclinical PET imaging research is essential to support novel radiopharmaceutical probe development and mechanistic studies not possible in humans. These studies, conducted in diverse species including mouse, rat, bird, rabbit, dog, and nonhuman primates, benefit and advance the entire PET research enterprise. However, the current preclinical PET-only system has reached end-of-life and lacks CT capabilities, motivating the need for a new state-of-the-art preclinical PET/CT imaging system. We have identified a proposed PET/CT system large enough to accommodate nonhuman primate imaging that exhibits high sensitivity, high resolution, and excellent quantitative accuracy. When these PET instrumentation characteristics are combined with a CT scanner with rapid acquisition speed, the system will provide ideal characteristics for the preclinical PET/CT research studies of Yale investigators. The preclinical PET/CT system will improve spatial localization of new radiopharmaceutical probes, allow image-based measurement of tracer input function, and provide proper measurement of whole-body imaging data including correction for cardiac and respiratory motion. The proposed instrument will support 19 NIH-funded investigators in the Departments of Cardiology, Internal Medicine, Neurology, Neuroscience, Psychiatry, and Radiology and Biomedical Imaging, all of whom are currently conducting NIH-funded PET imaging research. The additional capabilities and capacity provided by the new system will also support the development of PET imaging research by new investigators. Enhanced use of novel radiopharmaceuticals and PET/CT imaging will promote research studying new mechanisms for diagnosis and therapy of human disease. Together, these applications hold tremendous potential to advance public health.

NSF Awards · FY 2024 · 2024-08

Nontechnical Description Quantum dots (QDs) are nanocrystals with optical properties that depend on their size and composition. This makes it possible to tune their absorption and emission spectra from the visible to the infrared region for desired applications. For example, quantum dots emitting precise colored light have been used as emissive layers in displays with high definition and saturated colors. However, their implementation in electronic devices is limited by poor charge transport properties. This research focuses on developing and understanding a novel hybrid system that integrates QDs into high-mobility crystalline semiconductor matrices to take advantage of their properties and create new structures with new properties for optoelectronic applications. The research team investigates the structure-property relationship of the hybrid structure to gain insight into how the two distinct components interact and what governs the spatial distribution and flow of charge carriers. Building on these insights, the goal is to develop strategies for efficient light-emitting structures in the short-wave infrared spectrum. Additionally, the project trains graduate and undergraduate students and actively engages students from underrepresented groups in STEM, offering research opportunities through the New Haven Promise Program and the Yale STARS Summer Research Program. Technical Description Optical light sources in the short-wave infrared region are essential for bioimaging, medical diagnosis, machine vision, and communication. However, their integration into portable and wearable electronics is currently limited due to the complex fabrication processes required for epitaxially grown semiconductors. This study explores the potential of a novel solution-processed heterostructure—by exploiting QDs as substitutional dopants for bulk crystalline semiconductors—to manipulate the dynamics and transport of energy carriers and thereby modify the electronic and optical properties of the host material. A variety of techniques, including photoemission spectroscopy, pump-probe optical spectroscopy, and synchrotron X-ray scattering, are employed to characterize the judiciously chosen material compositions. The project aims to extend the structural versatility of the hybrid structure, elucidate the mechanisms and efficiency of carrier transfer between localized and delocalized states, and correlate the fundamental physics with the structural properties. This understanding is expected to establish the knowledge base needed for using these materials as both non-coherent and coherent light sources. 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 · 2024-08

Project Summary: We propose a two-year qualitative study that will characterize transitions to lower-frequency injecting and injection cessation among people using xylazine-adulterated fentanyl in two of the states (Connecticut, Massachusetts) most impacted by this drug supply change. America’s drug supply has grown become more volatile since 2020, particularly the proliferation of xylazine-adulterated fentanyl in Northeast and Mid-Atlantic states, and is driving drug-related harms, including increases in non-fatal and fatal overdoses and severe injection-related soft tissue infections. Amidst the emergence of xylazine-adulterated fentanyl, researchers have begun to document reductions in injection drug use and injection cessation among people continuing to use fentanyl. Transitions to lower-frequency injecting and injection cessation represent key strategies for reducing the potential for soft tissue infections associated with xylazine-injecting, as well as the transmission of infectious diseases (e.g., hepatitis C, HIV). However, the growing proportion of overdose deaths across the country attributed to non-injection drug use raise significant concerns about overdose awareness and the responsiveness of substance use services to the needs of people reducing their injecting frequency or stopping injecting altogether. Understandings of how the proliferation of xylazine-adulterated fentanyl is influencing transitions to low-frequency injecting and injection cessation and their implications for substance use services are urgently needed to optimize overdose prevention approaches. Building on our extensive experience studying the impacts of drug supply changes, including on drug use behaviors and overdose prevention approaches, we propose the following specific aims: Aim 1: To characterize how exposure to xylazine-adulterated fentanyl shapes transitions to lower-frequency injecting (≥ 50% reduction in injecting frequency) and injection cessation (>30 days). Aim 2: To explore perceptions of overdose risks associated with non-injection drug use of xylazine-adulterated fentanyl and examine their implications for overdose prevention messaging and substance use service delivery. Aim 3: To explore challenges and opportunities for overdose prevention strategies in addressing overdose risks among people exposed to xylazine-adulterated fentanyl transitioning to lower-frequency injection drug use or injection cessation. To generate understandings of the dynamics underlying these changes to drug use behaviors, we will conduct qualitative interviews with people using xylazine-adulterated fentanyl across Connecticut (n=40-50) who have transitioned to lower-frequency injecting or injection cessation (Aims 1&2), as well as focus groups with substance use services workers (n=25-30) from across CT and MA (Aim 3). Findings will be mobilized to develop evidence-informed, scalable research, policy and program recommendations to address harms associated with xylazine-adulterated fentanyl, including the design of new interventions.

NIH Research Projects · FY 2025 · 2024-08

SUMMARY Clostridioides difficile, a Gram-positive, spore-forming anaerobic bacterium, is the leading cause of nosocomial and antibiotic-associated intestinal infections in the United States. Over the past two decades, there has been a significant rise in the incidence, severity, and economic burden of C. difficile infection (CDI). This increase can be attributed to the limited efficacy of antibiotics, a growing recurrence rate of CDI, and the emergence of highly virulent strains. These trends underscore the pressing need for alternative strategies in the treatment of CDI. To colonize the gastrointestinal tract, C. difficile must compete with both the host and the gut microbiota for essential nutrient iron. However, it is unclear how C. difficile adapts to nutrient iron stress in the gut during CDI. Thus, I set out to interrogate the iron homeostasis systems in C. difficile and examine their physiological function. My postdoctoral work has demonstrated that C. difficile undergoes an intracellular iron biomineralization process and produces membrane-bound ferrosome organelles containing iron phosphate biominerals. The ferrosome organelles serve as an iron storage mechanism, protecting cells against iron intoxication upon transient iron overload. The ferrosome system is activated in the inflamed gut to combat host-mediated iron sequestration and is important for bacterial colonization and persistence during CDI. A manuscript describing this work was recently accepted for publication in Nature. However, the molecular basis of ferrosome biogenesis is largely unknown and the implications of ferrosome formation within the context of CDI remain unclear. This project aims to elucidate the underlying mechanisms of ferrosome formation and define its influence on host-microbe interactions. In this application, we hypothesize that (i) the ferrosome membrane derives from the cytoplasmic membrane but exhibits distinct lipid composition, (ii) iron is transported to the FezB transporter through the iron importer FeoA3B3, aided by an iron chaperone, (iii) many other factors play roles in various stages of ferrosome formation including iron oxidation, nucleation, and biomineralization, (iv) the ferrosome system facilitates C. difficile adaptation to nutrient iron stress mediated by both the host and gut commensals, and (v) nutrient iron exhibits profound effects on CDI outcomes, gut microbiome resilience, and host immune responses. The experiments described in this proposal will test these hypotheses, elucidate the underlying mechanisms of ferrosome biogenesis, determine the function of ferrosome organelles within the gut community, and define the impact of nutrient iron on host-microbe interactions. Furthermore, the findings of this proposal will uncover novel factors critical for C. difficile infection and create a framework for developing effective antimicrobial therapeutics to combat this important infection.

NIH Research Projects · FY 2025 · 2024-08

The tailbud is the posterior growth zone of the post-gastrulation vertebrate embryo containing the neural and mesodermal progenitors of the spinal column. The posterior neural tube undergoes convergence along the medial-lateral axis while extending posteriorly, and failure of neural tube convergence leads to birth defects such as spina bifida. The left and right paraxial mesoderm are assembled from motile mesodermal progenitors and subsequently segmented into somites. Failure to segment the paraxial mesoderm or maintain bilateral symmetry leads to birth defects such as congenital scoliosis. This project uses zebrafish as a model to study the molecular biophysics and systems morphogenesis in early spinal column development. The lab recently found that inter- tissue adhesion mediated by the extracellular matrix (ECM) protein Fibronectin mechanically couples the neural tube and paraxial mesoderm. This inter-tissue adhesion resists convergence of the neural tube which predisposes the embryo to spina bifida. However, inter-tissue adhesion ensures bilaterally symmetric morphogenesis of the paraxial mesoderm and thus prevents scoliosis. The interfaces between the neural tube and the left and right paraxial mesoderm resemble adhesive lap joints which are used in engineering to efficiently bond two objects. This project uses transgenic zebrafish to examine how the medial-lateral lap joint mechanics are maintained (Fibronectin acts as a glue) while the neural tube simultaneously slides posteriorly relative to the mesoderm (Fibronectin acts as a grease). Integrin heterodimers are the primary cell surface receptors for the ECM and are central to cell and tissue mechanics because they link the ECM to the actomyosin cytoskeleton. The lab has studied integrin activity in early spinal column development using proteomics, an in vivo FRET-FLIM assay for Integrin conformational changes, and FCS/FCCS to measure integrin intra-heterodimer affinities in vivo. The lab found that Integrin 51 and V1 are the two main Fibronectin receptors at this stage of zebrafish development and that intra-heterodimer affinity sets the threshold for integrin activation. Here, the project will quantify the molecular dynamics of single heterodimers in the neural tube and paraxial mesoderm using a new single molecule spectroscopy and machine learning protocol that we have developed. For the first time, we can measure the movement and conformational dynamics of a single protein in a living embryo.

NSF Awards · FY 2024 · 2024-08

This award will fund a research project that investigates how farmers in low-income settings adapt to increased risk of flooding, the effects of these flood episodes on health, and how these farmers access and use information on accurate flood forecast. Climate change is increasing flood risks globally, impacting one in four individuals, but not much is known about how agriculture adapts to increased flood risk and this research makes significant contribution to our understanding of how farmers adapt to flood risks. The proposed research consists of two projects. The first project will combine the Global Flood Database, NASA’s MODIS satellite imagery and Enhanced Vegetation Index to study the impact of flooding on agriculture with a focus on how farmers change their farming practices, crop choices, and the health of affected people. The second project will study how farmers access and use accurate flood forecasts by combining a community-based outreach program with Google’s flood forecasting system. The results of this research will provide important inputs into policies to increase resilience in flood-prone regions and provide guidance on U.S. foreign aid policies. This award will fund research that uses two projects to investigate the effects of flooding on agricultural practices and livelihoods of people in low-income settings. The first project will combine data from the Global Flood Database, NASA’s MODIS satellite imagery, and the Enhanced Vegetation Index to assess the impacts of flooding on agriculture, focusing on land use dynamics by farmers. The PIs will study the contemporaneous and long-term impact of flooding on agricultural production, crop choice, and whether flooding’s effect on agriculture varies by its timing. The second project expands on an existing project that enhances the reach and impact of flood early warning systems (EWS) in low-income settings. To do this, the PIs will combine Google’s advanced flood-forecasting technology with a community-based alert system. The community-based outreach will be randomized for treatment and control communities. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2025 · 2024-08

Project Summary This project is based on the critical role of fluid shear stress from blood flow acting on the vascular endothelium in determining susceptibility to atherosclerosis. Physiological levels of laminar shear stress are actively anti-inflammatory and promote disease resistance whereas disturbed fluid shear stress amplifies inflammatory and metabolic risk factors to promote disease. Preliminary and published data implicate endothelial cell metabolism as critical in the differential effects of laminar vs disturbed flow, including a mitochondrial pathway activated through Piezo1 mechanosensitive channels, yet these effects, indeed, the entire area, is poorly understood. Our preliminary data also implicate cell cycle as a critical part of these regulatory effects, with a late G1 arrested state in which the cyclin-dependent kinase Cdk2 is active as a key inhibitor of atherogenic pathways. We therefore propose: 1) A thorough unbiased analysis of effects of laminar vs disturbed shear stress on endothelial metabolism, with in vivo analysis of Piezo1 loss and gain of function mutants, and extensive analysis to link metabolic effects to disease pathways; 2) In vitro mechanistic and in vivo pre-clinical analysis of the role of Cdk2 in endothelial inflammatory activation and disease, including links to metabolic pathways.

NIH Research Projects · FY 2025 · 2024-08

SUMMARY Psychiatric symptoms are a leading cause of suffering and disability worldwide. Decades of research have focused on understanding their etiology and underpinnings, typically using a diagnosis-based approach in which individuals with a given condition are compared to a matched ‘control’ group. Less work has focused on characterizing the longitudinal course of symptoms at the individual level in relation to underlying cognitive, affective, and behavioral mechanisms. Recognizing that most (if not all) psychiatric disorders are defined by their longitudinal course, this application moves beyond the limitations of traditional diagnosis-centered and ‘case-control’ designs to collect longitudinal data over two years from a large sample (N=2400), highly enriched for psychopathology across a wide range of traditional diagnoses, to identify predictive markers of symptom change using assessments that can be easily implemented in real-world settings. Specifically, we will collect: (i) data embedded in electronic health records (EHR), including social determinants of health; (ii) traditional clinical measures typically used in diagnosis-based approaches (e.g., clinical interviews, well- validated clinical scales); (iii) recently developed computational behavioral tasks with demonstrated sensitivity to latent constructs and to within-person change; (iv) short gamified behavioral measures of mood and reward-relevant constructs, measured repeatedly; (v) spoken narrative responses to uniform prompts for natural language processing (NLP) analyses; and (vi) patient-derived and NIH Toolbox continuous measures of key transdiagnostic outcomes. These data will be analyzed using advanced statistical and machine learning approaches (e.g., latent growth curve modeling, neural network transformer modeling), consistent with the recommendations set forth in the IMPACT-MH RFA. In AIM 1, we will use this rich dataset to test the predictive value of ‘traditional’ (EHR, other clinical) vs computational and NLP data in predicting outcomes. We will further test the differential predictive value of combinations of measures, including sparse and dense behavioral sampling, seeking to identify a minimum set of measures with maximum added clinical value. In AIM 2, we will examine longitudinal clinical trajectories using data-driven trajectory analysis of multidimensional clinical and computational fingerprints; this approach may ultimately be used to generate normative models to track and forecast clinical course in patients. Finally, in AIM 3, we will seek to identify subgroups, based on computational fingerprint similarities at baseline, that predict differences in outcomes at 2-year follow-up, and to test whether optimal predictive models differ among such subgroups. This rich dataset will have enormous value beyond these three Aims. We are recruiting from established diagnosis- and population-specific research programs; combination of the longitudinal data collected here with additional data collected by these programs, including neuroimaging and genetics, will create rich opportunities for secondary and exploratory analyses in subgroups. Finally, these data will be made available to the community, in deidentified form in collaboration with the IMPACT-MH Data Coordinating Center, for exploratory and confirmatory analysis by others.

NIH Research Projects · FY 2025 · 2024-08

PROJECT SUMMARY Early life experiences, such as those associated with stable attachment, supportive relationships, and nurturing environments, have profound effects on lifelong physical and mental health. However, children have very different levels of access to such experiences, depending on their family characteristics and associated risk and resilience factors. Low-cost interventions aimed at improving infant environments offer a promising avenue for reducing inequality in early experiences because they require minimal effort to implement. Previous work from our lab showed the promise of infant-directed vocalizations, especially music, for enriching parent-infant interaction. Such behaviors are cross-culturally universal, appear regularly in the context of infant care, and have robust effects on infant psychophysiology. In recently completed pilot work, we found that a brief smartphone-based music intervention achieved high adherence and low attrition; led parents to increase their use of music in soothing their fussy infants; and improved infant mood, as reported via ecological momentary assessment (EMA). Together, these findings show the potential for enriched parent-infant interaction, particularly via infant-directed singing, to improve infant and parent health. Here, we propose a Phase II randomized trial to explore such effects. Parent/infant dyads (N = 192, infant starting ages 0 to 4 months) will be randomly assigned to one of four conditions: (1) music with enrichment, where parents receive a smartphone-based intervention to learn to sing interactively with their infants, via the early childhood music program Music Together; (2) music with limited enrichment, where parents receive music recordings to listen to with their infants, but are not provided with enrichment activities; (3) enrichment with limited music, where parents receive books to read interactively with their infants, but are not provided with music activities; or (4) a no-treatment control. Throughout the 8-month study, we will use text-message-based EMA and a survey battery to measure key health outcomes for both infants (distress and recovery, sleep quality, and mood) and parents (mood, mental health status, and parenting efficacy); potential moderators of such effects (demographics, family contextual factors, parent/infant attachment, and infant temperament); as well as parents' degree of engagement in the interventions. Effects will be analyzed both across the intervention groups and relative to the no-treatment control to determine the relative effects of each intervention. The results of this work will determine the effects of low-cost, low-effort early enrichment interventions on basic, everyday health outcomes for infants and parents, test the feasibility of app-based interventions and data collection tools (including in socio-economically disadvantaged families), and provide rich data on the daily lives (including mood, temperament, and sleep variables) of families with young infants. The findings will have particular relevance for underprivileged families and first-time parents, and will set the stage for larger-scale studies of early parent-infant enrichment.

NIH Research Projects · FY 2025 · 2024-08

PROJECT SUMMARY Immune checkpoint inhibitor (ICI) therapy has revolutionized cancer care; however, ICIs can lead to off-target immune-related adverse events (irAEs) such as ICI-induced acute interstitial nephritis (ICI-AIN). There are 3 major challenges in the care of patients who develop ICI-AIN. First, while 20-25% of ICI-treated patients develop acute kidney injury (AKI) in the first year, there is no biomarker to distinguish ICI-AIN from other causes of AKI. Second, a third of the patients with ICI-AIN do not achieve renal function recovery with corticosteroids and could require additional second-line immunosuppressive therapies. However, there is no early biomarker to determine if corticosteroid therapy is working besides monitoring of serum creatinine, the improvement in which is often delayed. Third, the pathways driving refractoriness to corticosteroid therapy in ICI-AIN are poorly understood making selection of second-line immunosuppressive therapies challenging. The overall goals of this project are to address these challenges by testing C-X-C motif ligand (CXCL)9 as a real-time biomarker for ICI-AIN diagnosis and predictor of response to corticosteroid therapy, and to identify dysregulated pathways in steroid-refractory ICI-AIN using proteomics. We will recruit and retain 400 participants from 3 healthcare systems (Yale, Massachusetts General Hospital, and Johns Hopkins) who develop AKI while on ICI therapy, of whom 100 (25%) are expected to have ICI-AIN. In Aim 1, we will determine the accuracy of urine CXCL9 measured using immunoassay and a newly developed point-of-care lateral flow assay in distinguishing ICI-AIN from other causes of AKI. This aim is supported by our studies that identified and validated CXCL9 as a biomarker of AIN and data from others showing that interferon-γ and its downstream chemokine CXCL9 are key drivers of immune-related adverse events in ICI users. In Aim 2, we will collect urine samples 1-2 weeks after initiation of corticosteroid therapy to test if suppression of CXCL9 by corticosteroids is an early marker of renal recovery in ICI-AIN. This is supported by our preliminary data showing that a third of the patients do not achieve renal recovery after corticosteroids and CXCL9 is a strong marker of renal interstitial inflammation, the key histologic feature of ICI- AIN. In Aim 3, we will compare urine and plasma proteomic profiles between steroid-refractory and steroid- sensitive ICI-AIN to determine pathways of steroid-refractoriness in ICI-AIN. We have assembled a multidisciplinary team with complimentary expertise in ICI-related renal complications and AIN (MPI/PDs: Sise, Moledina), oncology (Reynolds, Kluger, Pabani), biomarkers (Parikh), and biostatistics (Zhao). The feasibility is supported by the high-volume cancer care provided at our centers and the MPIs’ strong track record of prospective enrollment and collaboration. Upon completion of this award, we will identify a biomarker for real- time diagnosis and treatment response phenotyping in ICI-AIN, and identify potentially targetable dysregulated pathways to guide selection of second-line therapies for steroid-refractory ICI-AIN in a future clinical trial.