Columbia University Health Sciences

NIH Research Projects · FY 2026 · 2026-06

Chromosomal instability (CIN)—the frequent missegregation of chromosomes during mitosis—is a genomic hallmark of solid tumors and correlates with immune evasion and resistance to immunotherapies, but underlying mechanisms are poorly understood. CIN leads to release of DNA into the cytosol where it triggers the cGASSTING pathway. While activation of this axis usually results in the expression of type I interferons (I-IFNs), potent immune stimulatory cytokines, we find that in CIN-driven cancers the opposite occurs: CIN-mediated tonic cGASSTING activation suppresses I-IFN production. Understanding and targeting mechanisms underlying this observation may restore immune-mediated elimination of multiple common cancers. We generated rigorous preliminary data, including analyses of human genomic data and tissue samples, a series of sufficiency and necessity experiments, and several in vivo studies across multiple cancers, leading us to the following hypothesis: CIN-induced tonic cGAS-STING signaling drives immune escape through STING depletion resulting in interferon tachyphylaxis, and relief of this tonic signal through cGAS inhibition restores immunogenicity and sensitizes various cancer types to immunotherapies. In further preliminary studies we show that genetic deletion of CGAS restores I-IFN expression in human and mouse models and sensitizes otherwise resistant tumors to either STING-agonist or anti-PD1 therapies in vivo. Using cutting-edge structure-activity-relationship (SAR) analyses, we designed and synthesized putative small molecule cGAS inhibitors. One of these, compound 1 (C1), shows potent (EC50=1.44 nM), durable, and human selective cGAS-inhibitory activity, and favorable in vivo pharmacokinetics without apparent toxicities. In Aim 1, we will perform critical epistasis experiments, elucidate mechanisms of STING depletion and interferon tachyphylaxis, and determine the generalizability of the proposed mechanism(s) using pan-cancer analyses in existing data bases. In Aim 2, we will use human and new murine models with chimeric expression of functional human CGAS and use novel CRISPR-dependent base editing tools to identify precisely map cGAS residues needed for restoring immunogenic output, and to determine which residues are required for C111 activity. In Aim 3, we will use a parallel genetic and pharmacological strategy to test whether catalytic disruption of cGAS sensitizes CIN-driven cancers to individual or combination immunotherapies. We will also dissect changes in the tumor-microenvironment resulting from cGAS inhibition. Completion of this work will resolve a long-standing paradox in cancer immunobiology, provide key mechanistic insights into cancer immune evasion, and offer the rationale and preclinical foundation for developing novel, first-in-class cancer therapies targeting cGAS, suitable for future clinical translation. By informing both fundamental biology of cancer and therapeutic advances, this work will have an important impact. Vertebrate animals are used to study the complex tumor-immune interactions that are difficult to model otherwise.

NIH Research Projects · FY 2026 · 2026-06

1 Abstract. 2 There is limited understanding of the immune mechanisms that initiate and perpetuate lupus 3 nephritis (LN). We have identified tubulointerstitial inflammation (TII), comprised of kidney infiltrating 4 lymphoid and myeloid cells, as a major contributor to tubular atrophy and progression to ESKD. 5 Direct cell-cell interactions between T cells and other immune cells in TII depend on the co- 6 stimulatory SLAM family of receptors and its adaptor, SLAM-associated protein (SAP). In published 7 work, we found subsets of SAP+ T cells to be expanded in both the circulation and the kidney in LN. 8 We hypothesize that increased SAP expression in SLE T cells facilitates direct cell-cell 9 interactions, promotes formation of tertiary lymphoid structures in TII and enhances the activation 10 and differentiation of cytotoxic T cells in the periphery, thereby promoting TII in LN. 11 In the first aim, we will test the hypothesis that SAP+ T cells in LN are expanded because of clonal 12 proliferation and identify enriched signaling pathways that characterize this subset in LN using 13 scRNA-sequencing with TCR sequencing. Next, we will define the transcriptome and localization of 14 the SAP+ T within the TII microenvironment using spatial transcriptomic analysis of human kidney 15 LN biopsy samples. In the second aim, we will test the hypothesis that SLAMF6-SAP signaling 16 regulates the survival and differentiation of effector T cells in LN. Envisioning that the SAP+ T cell is 17 central to the development of TII, an in-depth understanding of the SAP-specific regulatory networks 18 can lead to new biomarkers and therapeutic targets in LN. 19 20 The candidate, a dual trained clinician and bench scientist, has a long-term goal to become an 21 independent leader of a translational research program and a PI of her own basic science 22 laboratory focused on the study of immunology in rheumatic disease. To accomplish this 23 transition, a career development plan designed by the candidate and her mentor, Dr. Adam Mor, 24 proposes focused training in single-cell technologies, gene-editing strategies, and cell-based 25 assays as well as grant-writing and data presentation skills. The candidate has full commitment of 26 the mentoring team and the Columbia University institution, ensuring networking support and 27 access to core resources required for her success.

NIH Research Projects · FY 2026 · 2026-06

Project summary Proper feeding behavior during early postnatal development is essential for survival, relying on a seamless transition from suckling to mastication as cranial sensory-motor circuits mature. In mammals, orofacial movements required for nutrient intake depend on the coordinated development of specific motor and sensory pathways, yet the mechanisms governing this maturation process remain poorly understood. Moreover, these circuits are selectively vulnerable in several neurodegenerative conditions such as spinal muscular atrophy (SMA), underscoring the importance of elucidating their normal developmental activation and modulation. The masseter muscle, the primary jaw-closing muscle, is central to effective nutrient intake. Its proprioceptive afferents, along with a population of periodontal pressoreceptors (PP), reside in the Mesencephalic trigeminal nucleus (MesV). This anatomical configuration suggests a potential modulatory role of PP on masseter muscle activity, possibly mediated by electrical coupling via gap junctions. I hypothesize that the transition from suckling to mastication is driven by the developmental emergence of electrical connections mediated by select connexins, and that their disruption contributes to the orofacial motor deficits observed in disease states. This project aims to inform, in a developmental time-course, the mechanisms governing sensory-motor circuit modulation in the masseter system, both under normal conditions and in the context of SMA. SMA is a severe neurodegenerative disease characterized by motor neuron loss, muscle atrophy, and impaired feeding. SMA, caused by homozygous deletion of the Survival Motor Neuron 1 gene, is the leading genetic cause of infant mortality. Clinical and preclinical evidence indicate profound impairments in nutrient intake in SMA, yet the contribution of orofacial circuit dysfunction remains unexplored. To address this, I will employ a multidisciplinary approach using the well- established SMNΔ7 mouse model of SMA. Techniques will include developmental stage specific immunohistochemistry for select connexins, dual whole-cell electrophysiological recordings to assess electrical coupling, laser capture microdissection, and mRNA-sequencing to define molecular signatures of affected neuronal populations. This research will provide novel insights into the developmental logic and disease vulnerability of orofacial sensory-motor circuits. By defining how these pathways form and function, and how they dysfunction in SMA, this work may uncover new therapeutic targets for improving motor function and feeding in affected infants.

NIH Research Projects · FY 2026 · 2026-06

Summary The proposed R35 program will advance metalloproteomics research and applications to better understand the role of metals in aging and brain health, aiming to address the limited tools available for assessing metal- protein interactions in age-related diseases, in particular dementia and cognitive impairment. Around 40% of human proteins rely on metal ions, directly as metalloproteins or indirectly as cofactors. Metals like copper, iron, and zinc, essential for protein functions, play critical roles in brain health, and disruptions in these metals are linked to cognitive decline and dementia risk, particularly in individuals with APOE4 alleles. Neurotoxic metals, such as lead, disrupt essential metal functions through molecular mimicry and oxidative stress, which can promote amyloid β (Aβ) plaque formation and hyperphosphorylated tau (ptau), among pathogenic mechanisms with a role in the development of dementia and cognitive impairment. Building on the scientific leadership and research productivity of the PI, a highly accomplished inter-disciplinary team, and our cutting- edge metallomics research facility, this research program will integrate multiple mass spectrometry (MS) technologies to develop novel biomarkers that quantify metal-protein molecules, stable isotope ratios, and metals and metal-protein levels in brain-derived extracellular vesicles (EVs) as a surrogate of the target tissue (Area 1). These biomarkers will then be incorporated into observational (Area 2) and experimental (Area 3) studies, to analyze complex interactions between metals and proteins associated with brain health outcomes. The program leverages data and samples from observational studies with extensive brain imaging, biomarkers and clinical data, and clinical trials like TACT2, which investigates the effects of chelation therapy on metal burden reduction, providing a unique perspective on metal-protein modifications in brain health. Additionally, we will integrate our novel metal-protein biomarkers multi-omics and machine learning (Area 4) to serve as surrogate biomarkers that can be widely available and enhance the understanding of the metalloproteome’s impact on disease pathogenesis and aging. The key research areas––developing biomarkers, mapping their associations with aging-related diseases, testing experimental modifications, and connecting biomarkers with multi-omics data to identify mechanistic pathways––could reveal novel targets and strategies for healthy aging interventions, fostering collaborations and transforming approaches to precision medicine and public health. This work promises to significantly expand the understanding of metal-protein interactions in aging, informing future preventive and therapeutic interventions for neurodegenerative diseases.

NIH Research Projects · FY 2026 · 2026-06

Project Summary Neural circuits within the spinal cord are responsible for normal motor control and movement. Spinal motor neurons translate motor commands generated within the central nervous system to peripheral muscles. Motor neurons are activated by a precisely regulated pattern of synaptic activity from sensory neurons, local spinal interneurons and descending pathways from the brain. During development, synaptic activity received by motor neurons shapes their functional properties. In contrast, gene mutations that induce perturbations in either neuronal wiring or synaptic drive received by motor neurons often result in motor system disorders. A prominent example of this situation is spinal muscular atrophy (SMA)—an inherited neuromuscular disease caused by ubiquitous deficiency in the survival motor neuron (SMN) protein. SMA pathogenesis involves alterations of multiple components of the motor circuit leading to abnormalities in spinal reflexes, motor neuron loss and skeletal muscle atrophy. Our previous work has led us in uncovering that dysfunctioning sensory synapses degrade the ability of motor neurons to fire repeatedly and thus impair muscle contraction. Leveraging on modern and advanced technologies, we are aiming to stimulate electrically these dysfunctioning sensory synapses in both SMA patients and mouse models of disease. Our preliminary data provide a strong foundation for improving motor control in patients that have been treated with currently FDA-approved treatments but still exhibit impairments in mobility. To pursue further our approach, we need to develop, validate and ensure safety is met using mouse models of SMA. To this end, in Aim 1, we will demonstrate that spinal cord stimulation (SCS) restores motor function and provides behavioral benefits in SMA mice treated with a Risdiplam-like therapy. Specifically, we will investigate whether SCS improves fatigability and gait quality. Data from mice will be compared and associated with changes observed from Type 3 SMA patients who received SCS for 1 month. In Aim 2, we will investigate whether SCS improves motor neuron function in a mouse model of SMA. Specifically, we will test whether SCS - delivered via dorsal epidural implants at the L1-L3 spinal segments for one month - improves motor neuron firing by increasing the number and function of proprioceptive sensory synapses. To do so, we will use physiological and morphological assays. In Aim 3, we will establish safety and optimal dosing of spinal cord stimulation in SMA mice. To address this, we will undertake longitudinal studies in SMA mice implanted with epidural leads and subjected to SCS daily for up to 6 months at different dosing regimens. Potential toxic effects will be evaluated by combining behavioral and functional assays with molecular and morphological studies of the sensory-motor circuit.

NIH Research Projects · FY 2026 · 2026-06

PROJECT SUMMARY/ABSTRACT Over 80,000 lives were lost to drug overdose, primarily involving fentanyl, in 2024. With the intention of reducing overdose mortality, state policymakers have enacted fentanyl laws that increase legal penalties for the possession, manufacture, and distribution of fentanyl-containing substances, contributing to fentanyl supply disruptions through drug seizures and drug-related arrests. As of 2023, fentanyl laws were enacted in 26 states and were pending legislative decision in 17 additional states. Fentanyl laws are thus a common fentanyl-specific policy intervention intended to impact overdose mortality trends through fentanyl supply disruptions. However, despite the extensive proliferation of state fentanyl laws, no empirical research using real-world data has examined the effects of state fentanyl laws on overdose mortality. In order to inform future policy decisions and reduce overdose mortality, epidemiologic evidence investigating fentanyl laws is urgently needed. The aims of this R36 proposal, US state fentanyl laws and overdose mortality over time, are to 1) characterize state fentanyl laws longitudinally; 2) test the effect of state fentanyl law enforcement on overdose mortality rates from 2015-2023; and 3) explore underlying mediation of this relationship by drug seizure and drug arrest rates. The proposed research will leverage overdose mortality data from the CDC Wonder databases, drug seizure data from the National Forensics Laboratory Information Systems, and arrest data from the National Incident Based Reporting System. Data are publicly available and deidentified. This proposal addresses NIDA priorities to evaluate the impact of policy changes on the ongoing fentanyl-driven drug overdose crisis (NOT-DA-22-084) and to support epidemiology dissertation research (PAR-25-347). Research findings will provide important information for future policy reform to address the ongoing fentanyl-driven overdose crisis and will inform a future F32 or K01 award application. The proposed project and mentorship team will allow the Principle Investigator to develop expertise in substance use epidemiology and policy research in pursuit of her long-term goal of becoming an independent academic researcher.

NIH Research Projects · FY 2026 · 2026-06

Project Abstract Essential tremor (ET) is a common, devastating neurological disorder affecting approximately 7 million people in the U.S., causing involuntary rhythmic movement (action tremor due to abnormal physiology) that disrupts daily life. Current treatments are often ineffective, as patients have heterogeneous responses, likely due to different underlying cerebellar physiological differences. To address this challenge, therefore we need to probe these physiological differences in ET. As the cerebellum is the key brain region implicated in ET pathophysiology, preliminary data from 26 ET patients using a novel cerebellar electroencephalography (EEG) technique revealed four distinct physiological subtypes. Types 1 and 2 resemble two well-established mouse models of tremor, while Types 3 and 4 suggest less cerebellar involvement, demonstrating that cerebellar EEG can capture the underlying physiological heterogeneity in ET. However, critical knowledge gaps remain: the population prevalence and clinical characteristics of these subtypes are unknown, their cerebello-cortical circuit mechanisms have yet to be defined, and further refinement of subtype classification using advanced computational approaches, such as machine learning, remains unexplored. Through this K99/R00 proposal, I aim to address these gaps by recruiting a large cohort of ET patients (n = 150 total; K99 Years 1-2: 20 patients/year; R00 Years 3-4: 40 patients/year; Year 5: 30 patients) and determining the prevalence and tremor-related as well as clinical features of cerebellar physiological ET subtypes, using cerebellar EEG and wearable senor data (Aim 1). I will characterize the network dynamics of these subtypes using effective connectivity and graph theoretical analyses to identify distinct cerebello-cortical mechanisms (Aim 2), and apply machine learning approaches to integrate physiological, wearable sensor, and clinical data to refine ET subtype classification (Aim 3). This project builds upon my strong background in EEG methodology and cerebellar physiology to address a critical unmet need for objective, physiology-based stratification of ET patients, laying the foundation for precision therapeutics. The K99/R00 award will support my transition to independence by providing comprehensive training in ET physiology and clinical assessments, wearable sensor technology, network analysis, and machine learning approaches applied to multimodal data, positioning me to lead an innovative research program focused on ET subtype classification. The proposed work will be initiated in the lab of Dr. Sheng-Han Kuo (primary mentor). During the K99 phase, I will continue to be mentored by Dr. Kuo, with additional mentorship from Prof. Elan Louis on ET clinical assessments and physiology, and from Dr. Anoopum Gupta on wearable sensor technology and machine learning. This integrated training and mentorship plan will advance ET subtype classification and position me on a path to independence.

NIH Research Projects · FY 2026 · 2026-06

Project Summary Genome rearrangement is a widespread process in development and evolution that includes translocations, inversions, deletions, and duplications. Organisms can benefit from developmental genome rearrangement processes, which enable access to otherwise inaccessible regions of the genetic landscape and allow for combinatorial variation. However, rearrangement can also introduce genetic errors that impair normal gene function and lead to genome instability and diseases such as cancer. The ciliate Oxytricha trifallax is an excellent model organism for studying mechanisms and fidelity of programmed DNA rearrangement because of its complex genomic structure. Its transcriptionally active genome is massively reorganized from a scrambled germline template during development, requiring the precise deletion, inversion, and translocation of tens of thousands of sequence segments. This project leverages a first-of-its-kind, time-resolved, long-read sequencing dataset that captures genome-wide rearrangement intermediates across development. In Aim 1, I will develop quantitative summaries of rearrangement progression and use unsupervised learning to uncover how sequence complexity and regulatory features shape timing and pathway dynamics. In Aim 2, I will construct and test competing stochastic and deterministic models to identify the principles governing rearrangement order and timing. In Aim 3, I will identify and classify naturally occurring rearrangement errors, validate key intermediates with PCR/qPCR, and explore sequence features that may explain error susceptibility. This research will define generalizable rules for accurate genome reassembly, providing a model for understanding how genomes self-organize with high fidelity. It also lays a foundation for future comparative work on the evolutionary origins of genome scrambling and may inform broader principles of genome regulation, editing, and stability in both health and disease.

NIH Research Projects · FY 2026 · 2026-06

PROJECT SUMMARY Invasive aspergillosis is a devastating infection for those with a vulnerable immune system, chronic lung disease, or preceding respiratory viral infection. Despite the current three classes of antifungal drugs (polyenes, azoles, echinocandins), mortality rates surpass 50% for these high-risk populations and have not improved over time with the introduction of newer antifungals. Furthering the issue is drug resistance in Aspergillus spp., which continues to rise, highlighting the critical need for the development of new therapeutic strategies. The use of antibodies as immunotherapy has emerged as a powerful strategy to combat human diseases, including cancer and infectious diseases. Nanobodies are small, monomeric, and comprise the variable antigen-binding portion of camelid heavy-chain-only antibodies. Their size enables advantages to circumvent the major limitations of conventional monoclonal antibodies, highlighting their value as potential therapeutics. The development of nanobodies has led to clinical trials for diseases, including infections. Limited information is known about the therapeutic potential of nanobodies against Aspergillus fumigatus. We leveraged a Saccharomyces cerevisiae library expressing every mathematically possible nanobody and identified a collection of nanobodies with substantial binding to the surface of A. fumigatus. Furthermore, we revealed reduced fungal viability in a subset of nanobodies using a FLARE strain upon binding, suggesting that some nanobodies effectively killed the Af conidia. Our major goal is to examine if nanobodies that bind to cell surface antigens on the A. fumigatus surface can be developed as immunotherapy against invasive aspergillosis. To address our primary objective, we propose the following five specific aims: [1] perform four iterative rounds of selection to define positive nanobody clones that bind to A. fumigatus, [2] identify subsets of clones that either kill A. fumigatus or bind and inhibit growth, [3] define antigen specificity of selected nanobodies, [4] characterize nanobody binding using purified A. fumigatus proteins and in vivo pharmacokinetics and toxicity, and [5] test 10 nanobodies in a mouse model of invasive aspergillosis to determine whether they inhibit pathogenesis. The first two aims will be the focus of the R21 phase of the grant, while the last three aims will be covered during the R33 phase. Our work will identify nanobodies for development into novel therapeutics for invasive aspergillosis.

NIH Research Projects · FY 2026 · 2026-06

PROJECT SUMMARY Exposure to known endocrine disrupting chemicals (EDCs) phthalates, parabens, and phenols is ubiquitous in the United States (US). The US also persistently has one of the highest rates of adverse birth outcomes compared to other developed nations. Thus, there is an urgent need to identify if modifiable environmental exposures contribute to these adverse obstetric outcomes. Epidemiologic studies have investigated prenatal exposure to individual EDCs and adverse birth outcomes and have found mixed results. Although human exposure likely entails co-occurring mixtures of EDCs, there remains a critical gap in our understanding of the relationship between multi-EDC exposure and birth outcomes. The American College of Obstetrics and Gynecology has also recommended that providers start including questions about patients’ environmental exposures during prenatal visits, highlighting a growing concern surrounding environmental contaminants and a need for evidence-based research to inform antenatal care guidelines. There is also little known regarding the biological mechanisms of EDC exposure, which is useful in identifying therapeutic targets to reduce adverse birth outcomes. Measuring the metabolic profiles of fetal tissues, such as the placenta and umbilical cord blood, could give insight into the molecular pathways associated with exposure to EDC mixtures. Untargeted metabolic profiles offer a comprehensive profile of endogenous and exogenous metabolites in a biospecimen, increasing the potential to discover biomarkers of altered phenotypes. Yet, no study to date has investigated the impact of EDC mixtures on metabolomic profiles of fetal tissues despite their critical roles in fetal developmental processes. Hence, we propose to leverage a longitudinal epidemiologic birth cohort study at the Columbia Center for Children’s Environmental Health (CCCEH) along with a well-characterized in vitro system to investigate the impact of prenatal exposure to EDC mixtures on birth outcomes and the fetal metabolome. Our objectives are to evaluate the association of prenatal EDC mixture exposure with Aim 1) adverse birth outcomes among 700 births and Aim 2) placental (n=54) and umbilical cord blood (n=293) metabolomic profiles from the CCCEH cohort. We will leverage maternal urinary biomarkers of EDC exposure to assign births and biospecimens an EDC-mixture exposure profile. In Aim 3) we will expose a third-trimester placental cell line to a human-relevant mixture of paraben, phenol, and phthalate metabolites recently measured in the CCCEH cohort and measure the effect on the metabolome and cellular phenotypes. This work will increase our understanding of how EDC mixtures may impact birth outcomes via alterations to the placental and umbilical cord blood metabolomes and can inform guidelines to reduce exposure to EDCs during pregnancy.

NIH Research Projects · FY 2026 · 2026-06

Project Summary Our Clinical and Translational Science Award (CTSA) hub is situated in Upper Manhattan at the Irving Institute for Clinical and Translational Research (Irving Institute) of Columbia University (CU), based at the CU Irving Medical Center (CUIMC). Our evolution as institutional leaders and maturation as a comprehensive CTSA hub enables us to drive innovative clinical and translational science (CTS) and clinical and translational research (CTR), in collaboration with our hospital partner NewYork-Presbyterian. The mission of our hub is to improve the health and wellbeing of patients and communities through our strategic vision and goals to catalyze all phases of CTS and CTR to achieve a fully integrated research environment locally and across the CTSA Program. We now prioritize systematic and deep engagement with all our stakeholders to achieve the greatest impact on research and health through CTS and CTR. Programs and services will be deployed through user-friendly research navigation and optimization using dissemination and implementation science (IS) to inform our cyclical approach to identify, develop, demonstrate, and disseminate (ID3) innovations that address CTR gaps and opportunities. Our record of innovation permeates our education, services, and research and guides our evaluation and continuous quality improvement strategy. CTS innovations are proposed for near-peer mentoring, digital health in communities, data and design, cell therapeutics, research core utilization, health system- embedded trials, federated learning, artificial intelligence (AI) and return of research results to participants. Irving Institute leaders are integrated into CUIMC governance to successfully implement our vision and goals. Module B Strategic Management approach and infrastructure will be optimized to catalyze CTS innovations. The ID3 approach, enhanced by tailored engagement and IS, will integrate institutional and community priorities, stimulate CTS innovations across the T0–T4 spectrum, and broadly disseminate services and programs with greatest potential to address major CTR gaps. Through personalized training, Module C1 will create and expand models to train a skilled, interdisciplinary workforce to advance rigorous CTR and CTS and enhance participation of the workforce and communities in CTR. Module C2 will deploy bidirectional engagement with our community ambassadors, patients, and local communities to develop and disseminate programs that address community priorities to improve health. Modules D1 and D3 will use research navigation and optimization to develop and cyclically refine research-friendly service programs as well as innovations in AI, informatics, precision medicine, and federated learning to accelerate CTR and CTS. Through our Module D2 Pilots and Element E CTS Program, we will work directly with stakeholders to tackle major CTS challenges, such as a system for return of individual results to research participants as well as opportunities in cell therapy, -omic core utilization in CTR, and AI in research, operations and health. Through these innovations, our hub will drive research efficiency, quality, and impact and align with CTSA Program goals to deliver more treatments to more patients more quickly.

NIH Research Projects · FY 2026 · 2026-05

Project Summary Antigen-specific immunotherapy (ASIT) for Type 1 diabetes (T1D) aims to reinstate robust and durable tolerance to beta-cell antigens. However, responsiveness to these therapies appears to be dependent on delivering the right set of epitopes to each patient for proper HLA binding and engagement of specific T cells. Thus far, ASIT strategies have involved the use of a single native antigen in the form of exogenous protein or peptides or DNA- encoded protein. In contrast, we developed an epitope-based approach that ensures optimal presentation of (neo)epitopes from multiple antigens, encoded by non-viral DNA vectors, to CD4+ and CD8+ T cells and that is highly amenable to customization as a precision medicine approach to overcome disease heterogeneity in T1D patients with different HLA haplotypes. This strategy provided significant protection at different stages of disease progression in the non-obese diabetic (NOD) mouse for which the vector was customized, but frequent injections were required to achieve durable tolerance (after treatment discontinuation). To facilitate the translation of this promising approach to the clinic, we propose to address remaining challenges by evaluating a novel oral route of delivery (for greater practicality and patient compliance) and new nanoplasmid vectors (for improved uptake and more sustained expression, potentially resulting in reduced dose and dosing frequency). We have devised a new type of nanoparticles (YF-CPNPs) that can be administered orally and achieve expression in gut- associated lymphoid tissues and in the liver. Under Aim 1, we will assess biodistribution of oral YF-CPNPs that contain conventional or nanoplasmids using a new mouse model in which all targeted cells can be irreversibly marked by GFP expression. This will allow us to identify potential antigen-presenting cells in these tissues and determine how they respond to YF-CPNPs irrespective of the expressed products. We will also investigate the early response of antigen-specific T cells in those tissues in vivo and whether these responses are consistent with antigen-specific regulation or tolerance. Under Aim 2, we will compare the duration of expression of conventional and nanoplasmids with or without episomal elements and determine how long antigens can be presented in different tissues after a single oral administration of these vectors. Based on these insights, we will conduct preclinical studies in which NOD mice will be orally administered with vectors encoding multiple epitopes at variable intervals. Some mice will be analyzed after 10 weeks of treatment to determine how the treatments affect the frequency and phenotype of T cells specific to several epitopes expressed by the vectors. Other mice will be monitored long-term to evaluate the incidence of T1D. Analysis of bystander T cells will be used to demonstrate targeted effect on specific T cells, another key safety feature. These studies will leverage the complementary expertise of the Creusot and Leong labs and are based on methodologies that are well-established in both labs.

NIH Research Projects · FY 2026 · 2026-05

PROJECT SUMMARY/ABSTRACT Genomic sequencing has been rapidly integrated into research and clinical care for common chronic diseases, yet robust evidence remains limited on how genome-informed risk information affects health planning and behaviors and short-term or long-term health outcomes. Returning such information to patients and providers promises to enable earlier, targeted screening, risk-reducing interventions, and tailored disease management. However, if implemented poorly, it can introduce confusion, anxiety, and unnecessary testing. Health systems, providers, payers, and professional societies are being asked to decide which genomic findings to return, how to communicate results, how to check risk updates, and how to support follow- up care—often without real-world outcomes data to guide those decisions. In addition, the rapid pace of advances in genomic analyses presents new translational challenges, which have not been adequately studied in the context of genome informed risks for common chronic diseases that place substantial burdens on individuals, healthcare systems, and society. This proposed research will fill these gaps. As an eMERGE IV site, we have previously developed and validated methods for comprehensive Genome-Informed Risk Assessment (GIRA) for 10 common complex diseases; led risk prediction efforts for two phenotypes (i.e., breast cancer and chronic kidney disease); recruited and returned GIRA risk to 2,536 participants at the Columbia Site; and led the network effort to develop patient- and clinician-facing education, return of results approaches, and ELSI work to consider impacts of GIRAs on patient behaviors and follow up care. We also established standardized and portable clinical decision support infrastructure and data flows in the EHR to support tailored return of GIRA reports. For the 1-year supplement, we proposed to use the time to complete the following activities of the project: (1) Continue the extraction and evaluation of the key study outcome— uptake of pre-specified care recommendations across the ten eMERGE-IV conditions; (2) Assess the impact of GIRA return on multiple condition-specific outcomes, including diagnosis of disease, initiation or intensification of care, and clinical outcomes among high-risk vs. not high-risk participants; and (3) Test the performance of updated genome informed risks compared to legacy risk scores and assess risk status re- classification using in-silico analysis of the eMERGE-IV cohort. Our proposed study will address critical knowledge gaps in the clinical translation of genome-informed risk scores and improve our methods for longitudinal outcome extraction from the EHR and for genetic risk stratification.

NIH Research Projects · FY 2026 · 2026-05

PROJECT SUMMARY Cognitive flexibility refers to our capacity to adjust our behavior in response to changes in the environment or in our own internal states. Psychiatric disorders often disrupt these cognitive functions, but little is known about the patterns of activity in the brain that underlie these capacities, impeding the development of effective treatments to target them. The overarching goal of this project is to more clearly elucidate the specific neural activity patterns supporting three examples of cognitive flexibility: switching between sets of rules that guide our behavior in a given situation, making correct decisions in new situations by drawing on past experiences, and learning to rapidly adjust a previously learned rule. In this project, I have developed a behavioral task which can engage each of these behaviors. I will use high- channel count electrophysiological methods to record activity from a brain circuit spanning the hippocampus, prefrontal cortex, and motor output regions while non-human primates perform this task. I will test the hypothesis that specific features of the patterns of activity in these brain regions support each type of cognitive flexibility described above. The results of this project will contribute an important step towards developing more effective treatments for cognitive dysfunction by identifying the specific patterns of activity that support specific cognitive functions and behaviors. This project will provide the additional training I require to position myself to perform cutting-edge research on cognition as an independent investigator. Specifically, through this project I will learn to perform precisely anatomically-targeted, high-channel count electrophysiological recordings from multiple brain regions simultaneously in order to probe brain activity patterns relevant to behavior as they occur in real time, distributed across the brain. I will also develop the necessary expertise to analyze the activity of large populations of neurons in relation to complex behavior. This award is thus critical to attaining my long-term goal of running an independent research lab at an academic medical center, where I will operate a non-human primate electrophysiology lab studying the neural mechanisms of adaptive, flexible behaviors that are disrupted in psychiatric disorders.

NIH Research Projects · FY 2026 · 2026-05

Project summary Age-related loss of skin sensation increases the risk of burns, chronic wounds and falls, yet the cellular origins of this decline remain unknown. The dorsal-root-ganglion (DRG) houses more than a dozen molecularly distinct sensory-neuron subtypes, each tuned to specific thermal or mechanical forces. Whether aging compromises all of these subtypes equally, or selectively impairs the few that are critical for protective touch and pain, is a fundamental unanswered question. The proposed R21 will deliver the first systematic, cell-type–resolved map of how normal aging remodels peripheral sensory neurons. We will leverage a molecular-genetic toolkit we recently published, which enables subtype-specific labeling, to pursue sparse axonal reconstruction and in-vivo GCaMP imaging across a broad range of the distinct DRG sensory neuron subtype repertoire. We will pursue two exploratory aims. Aim 1: Quantify age-dependent morphological changes in the central and peripheral arbors of 13 transcriptionally defined DRG subtypes in young (2-3 mo) and old (>18 mo) male and female mice. Aim 2: Measure how aging alters the thermal- and mechanical-response profiles of the same subtypes using two-photon Ca²⁺ imaging while delivering precisely controlled quantitative mechanical or thermal force to the skin. These pilot studies are highly innovative because they unite large-scale single-cell transcriptomics with subtype-specific functional imaging, an experimental convergence that has only now become feasible. The work will generate a dataset that pinpoints which neuronal populations are structurally or functionally vulnerable (or resilient) to aging, providing immediate targets for mechanistic investigation or therapeutic protection. By establishing feasibility and delivering foundational knowledge, this project will catalyze a new phase of research on peripheral sensory aging, ultimately informing strategies to preserve touch and pain perception—and thereby independence and quality of life in our aging population.

NIH Research Projects · FY 2026 · 2026-05

Opioid-related morbidity and mortality continues to be a health emergency. Medication for the treatment of opioid use disorder (MOUD) improves outcomes, but two key challenges exist. First, current recommendation is to maintain patients on MOUD treatment for a minimum of 6 months, and for some, a lifetime. Over this period, providers are tasked with making numerous treatment decisions, but with surprisingly little evidence- based guidance to inform such decisions. (E.g., At what dose should MOUD medication be initiated and when? How should dose be increased and to what target? How should dose be adjusted thereafter? What additional medications should be used?) A second challenge is that “success” rates for MOUD treatment remain unacceptably low. One way to improve MOUD success rates and reduce disparities in treatment outcomes would be for an evidence-based personalized protocol to inform MOUD treatment—similar to the targeted treatment of certain cancers. The objective of this project is to contribute quantitative evidence to inform the numerous, sequential MOUD treatment decisions over the course of the first 24 weeks of treatment. Fortunately, the data necessary to complete this objective already exist. In existing MOUD trials, data are collected: a) across multiple stages of the treatment process, b) across an array of individual demographic and clinical characteristics that may inform treatment decision making, and c) on numerous decisions over the course of treatment, with extensive variability across high-quality providers. We propose to use a statistically principled approach to fuse data from seven large MOUD trials conducted by the NIDA Clinical Trials Network to harness these existing data to generate quantitative evidence about the extent to which different sequential treatment decisions affect treatment success. In Aim 1) we will estimate the expected risk of relapse under various pre-specified sequential treatment decisions (called dynamic treatment regimes, DTRs) that represent reasonable medical practice and are observed in our data, resulting in the identification of simple, easily implementable MOUD protocols that would be successful in reducing 6-month relapse rates relative to current practice. In Aim 2) we will develop methods for learning optimal dynamic treatment rules (ODTRs) that accommodate the missing data structures inherent in our fused trial data, resulting in statistically robust, flexible, and efficient estimators. This is a critical methodologic contribution; data fusion and ODTRs are uniquely linked in that ODTRs require sample sizes larger than those typically found in any one trial, thereby necessitating data fusion; but, no methods to estimate ODTRs exist for data fusion settings. In Aim 3) we will use the methods developed in Aim 2 to learn ODTRs for each MOUD medication, resulting in interpretable, personalized MOUD protocols that are optimal in that they minimize risk of relapse. This work is timely and significant given the urgency of improving MOUD treatment and our responsibility to leverage existing data to inform best precision clinical practice for all patients.

NIH Research Projects · FY 2026 · 2026-05

Project summary/abstract. Proteins orchestrate cellular processes as dynamic ensembles of interconverting conformations, characterized by the underlying free energy landscape (FELs). Understanding these FELs is paramount for deciphering biological mechanisms, elucidating disease pathogenesis, and engineering novel therapeutics. However, resolving complete FELs, predicting how they respond to perturbations like mutations or ligand binding, and designing them de novo present formidable challenges, limiting our ability to rationally control protein function. This application seeks to bridge this critical gap by developing an integrated computational and experimental platform for the comprehensive decoding, modulation, and de novo design of protein FELs. I am a postdoctoral researcher in Dr. Anum Glasgow’s laboratory at Columbia University, with a strong background in computational biophysics, protein engineering, and advanced hydrogen-deuterium exchange mass spectrometry (HX/MS) analysis. My development of PIGEON-FEATHER, a state-of-the-art Bayesian framework for deriving site-resolved energetics from HX/MS data, exemplifies my commitment to advancing methods for studying protein ensembles. Building on this foundation, my K99 research will establish a transformative framework for resolving, manipulating, and designing protein FELs, providing fundamental insights and practical tools for protein science, drug discovery, and synthetic biology. Aim 1 will develop PF- MetaD, a novel enhanced sampling approach that incorporates HX/MS-derived protection factors (PFs) into meta dynamics simulations. This will be enabled by two deep learning tools I propose to develop—PFNet and PFBoost—for accurate, residue-level PF determination. Together, these will allow the reconstruction of complete protein FELs. Aim 2 will apply these landscape insights to a critical biomedical challenge by designing state- selective protein binders to modulate the FEL of BRAF kinase, aiming to rationally control its activity in cancer- associated mutants by reshaping its conformational ensemble. Aim 3 will push the boundaries of protein engineering by pursuing the de novo design of a universal, ligand-responsive allosteric protein switch based on the PAS domain scaffold, programming its FEL for custom molecular recognition and regulation. Under the primary mentorship of Dr. Anum Glasgow and Dr. Barry Honig, and with the support of collaborators and the rich research environment at Columbia University and affiliated New York City institutions, I will train in single- molecule FRET, high-throughput screening methodologies, advanced machine learning for integrating multimodal biophysical data, scientific leadership, and grant writing. These skills will enable my long-term goal: an independent multidisciplinary lab at a leading R1 institution, focusing on FEL-guided design of functional and therapeutic proteins. This K99/R00 award is critical for my transition to an independent investigator, transforming our ability to rationally program biomolecular behavior.

NIH Research Projects · FY 2026 · 2026-05

Summary The goal of our research is to discover the central principles that govern cellular adaptation. We aim to understand how cells achieve adaptive gene-expression states, both during short-term physiological adaptation and long-term adaptive evolution. We investigate these phenomena on a systems-level, often necessitating observations, perturbations, or analyses that are beyond the scale and precision of existing methods. Thus, our laboratory also develops new enabling technologies and computational methods. In this R35 application, we seek support for three NIGMS-related projects: (1) Cellular adaptation by stochastic tuning of gene expression. We have discovered a powerful new mechanism, that we call stochastic tuning, by which eukaryotic cells adapt to extreme or novel challenges. During stochastic tuning, cells utilize transcriptional noise to randomly change the expression of individual genes, and to actively reinforce those changes that improve the overall health of the cell. Stochastic tuning therefore enables cells to prospectively explore novel gene expression states that enable adaption to challenges in real time—including conditions never previously encountered—thereby bypassing the need for pre-determined hardwired regulatory programs. We have compelling new evidence that stochastic tuning is the key underlying mechanism for non-mutational cancer chemotherapy resistance, recognized as a major barrier to effective cancer therapies. We are utilizing CRISPR-interference and largescale reporter assays to define the critical protein and DNA effectors of stochastic tuning in yeast and to mechanistically determine their roles using chemical/genetic/optogenetic perturbations of single cells in well-controlled microfluidic experiments. (2) Genetic basis of microbial habitat adaptations. We have developed a versatile computational framework to conduct genotype-habitat association at the tree-of-life scale, enabling discovery of genes that underlie microbial colonization of specific habitats. By applying this analysis to the gut microbiome, we have discovered many highly conserved factors that strongly contribute to gut colonization. We are using functional genomics technologies to efficiently determine the molecular mechanisms by which these factors enable gut colonization. In addition, we are developing state-of-the-art deep learning and protein language models to improve the sensitivity/specificity of genotype-habitat association, enabling large-scale microbial engineering for diverse biomedical applications. (3) Global mapping of all-against-all molecular interactions in a single tube. We have recently developed a powerful technology for coupling in vivo expressed proteins to their encoding messenger RNAs, enabling a diverse array of proteomic assays to be performed by using DNA- sequencing as a readout. We propose to develop this platform to enable routine comprehensive all-against-all protein-protein and protein-DNA interaction studies on the timescale of days. This technology promises to transform our ability to rapidly map molecular network interactions under dynamic physiological conditions, an essential capability in the era of AI-enabled biology.

NIH Research Projects · FY 2026 · 2026-05

PROJECT SUMMARY/ABSTRACT Rates of adolescent depression are increasing, and 20% of adolescents in the United States now experience a depressive episode each year. Effective treatments exist, but most adolescents with depression do not receive them because they prefer to self-manage their symptoms, cannot access treatment, or perceive treatment to be too time-consuming or inconvenient. Delivering interventions digitally (i.e., through the internet or a smartphone application) is scalable, accessible, efficacious, and appealing to adolescents. However, digital interventions have low real-world uptake and engagement, and many users drop out in the first few minutes. Single-session interventions (SSIs) address these critical limitations by delivering an entire intervention in a single encounter and have shown promise for engaging mechanisms of action and ameliorating adolescent depression. Therefore, this project will adapt a gold standard treatment for adolescent depression – interpersonal psychotherapy for adolescents (IPT-A) – into a brief, web-based SSI. To reach adolescents with depression, this study includes a partnership with Mental Health America (MHA), a nonprofit advocacy organization that hosts a widely used online depression screen on their website. A deployment-focused approach will be utilized, with a focus on implementation on MHA’s screening website. Aim 1 will design the SSI based on pilot data and in consultation with experts in SSI design and IPT-A, an advisory board of adolescents and providers, and MHA staff. Aim 2 will refine the SSI by iteratively soliciting and incorporating feedback from three cohorts of five adolescents with depression (total n=15) recruited after screening positive for depression on MHA’s website. Among adolescents (n=200) screening positive for depression on MHA’s website, Aim 3 will evaluate the SSI’s acceptability, feasibility, immediate effects on the mechanism of action (interpersonal skill knowledge), and effects on depressive symptoms in a randomized controlled trial with follow-up assessments at 1 week, 1 month, and 3 months. In addition to directly addressing an important public health problem, this project includes training and research activities that will enable Dr. Funkhouser to gain expertise in: (1) digital mental health intervention design methods, (2) implementation science principles and methodologies, (3) clinical trials evaluating digital mental health interventions, and (4) grant writing and networking. The research aims and training goals will be accomplished with mentorship from leading experts in SSIs, implementation science, biostatistics, and IPT-A. This training and mentorship will also prepare Dr. Funkhouser to submit a R01 application and achieve his long- term career goal of becoming an independent investigator focused on designing, evaluating, and disseminating digital depression interventions that increase young people’s access to and utilization of effective support.

NIH Research Projects · FY 2026 · 2026-05

1 Medical errors are the third leading cause of death in the United States yet estimates of their total 2 burden and epidemiology remain largely unknown, with few comprehensive assessments 3 available. To address this gap, we propose leveraging the Retract-and-Reorder (RAR) method, 4 an existing health information technology (IT) tool that detects near-miss, self-caught order errors, 5 to better understand the underlying causes of medical errors. The RAR method has been reliably 6 used to detect wrong-patient and certain types of medication prescribing order errors. We 7 expanded its application to diagnostic imaging, identifying additional error types such as wrong- 8 site, wrong-contrast, wrong-side, and wrong-modality, using logic-based natural language 9 processing (NLP). However, over 42% of detected errors remained unclassified, requiring labor- 10 intensive manual review for further categorization. In this proposal, we aim to develop a scalable 11 pipeline that automatically classifies order errors and addresses unknown error types using 12 generative large language models (LLMs). To accomplish this, we will first (AIM 1) develop and 13 validate a generative LLM-based classification model for categorizing RAR events into predefined 14 error types, focusing on imaging order errors. We will compare its performance against the current 15 logic-based NLP approach, hypothesizing that the LLM will achieve equal or better accuracy by 16 correctly classifying known error and identifying previously missed error types, thereby improving 17 overall classification. Then, we will (AIM 2) demonstrate the scalability of the LLM pipeline by 18 applying it to medication order errors and developing a dissemination plan. We hypothesize that 19 LLMs can be readily adapted to diverse large sets of order types across various domains without 20 requiring fine-tuning. This study will establish the feasibility of developing an advanced, 21 automated, and scalable open-source tool for classifying and characterizing RAR events across 22 different medical orders. By identifying and understanding various order error types across 23 domains, this research will support the development of measures and targeted interventions to 24 improve patient safety. Furthermore, our privacy-preserving approach, achieved by deploying an 25 open-source LLM along with comprehensive documentation and structured dissemination, will 26 enable adoption across institutions and diverse healthcare settings. Beyond imaging and 27 medication orders, this framework could support cross-institutional implementation, facilitating its 28 expansion into other order domains.

NIH Research Projects · FY 2026 · 2026-05

“The Adaptation and Evolution of Resistance to Pan-RAS Inhibition in Pancreatic Ductal Adenocarcinoma” Abstract: Pancreatic ductal adenocarcinoma (PDAC) kills over 52,000 people annually in the United States. The current standard therapies are chemotherapy cocktails that provide modest survival benefits along with significant side effects. For 30 years, we have known that the vast majority of PDAC cases (~94%) are driven by activating mutations in the KRAS protooncogene, but until recently, this knowledge could not be leveraged for therapeutic benefit. The recent development of drugs that selectively inhibit specific mutant variants of KRAS (including KRASG12C and KRASG12D) have demonstrated the benefit of targeting KRAS. However, these agents only work in a subset of patients whose tumors harbor these specific alleles. Furthermore, recurrence can happen rapidly as tumors can develop alternative RAS mutations that circumvent these drugs’ function. Recently, we led a large consortium in describing the preclinical performance of RMC-7977, a novel pan- RAS inhibitor that effectively inhibits the active (GTP-bound) forms of mutant and wild type KRAS, NRAS, and HRAS. Remarkably, we found that this agent was well tolerated in mice. Across multiple classes of preclinical models, RMC-7977 exhibited strong anticancer activity, and yielded the longest extension of overall survival in the (highly chemo-refractory) KPC mouse model that has been reported to date. When tumors eventually did recur, they frequently exhibited focal Myc copy number gains, providing a means to activate the downstream mitogenic programs of RAS. The investigational analog of this agent, RMC-6236, is now in Phase 3 trials after demonstrating tolerability and showing remarkable responses in the Phase 1 setting. Here, we propose to study the development of resistance to pan-RAS inhibition in PDAC. Building from our previous work, we will utilize samples from mouse- and human derived model systems as well as human clinical trial samples to understand the evolution of genetic resistance mechanisms in response to RAS inhibition. Studies will focus on how these alterations impact tumor biology and what new therapeutic vulnerabilities they may confer. In addition, we will study how short-term adaptation allows tumors to survive pan-RAS inhibition long enough to evolve genetic resistance. We found that within days of treatment, the heterogeneity of KPC mouse pancreatic tumors collapses, with selective depletion of the most aggressive, poorly differentiated malignant cells. Strikingly, we also show that RAS inhibition induces the formation of primary cilia in the malignant cells of PDAC, enabling activation of pro-survival pathways including autocrine Hedgehog signaling. We will test how targeting ciliogenesis and cilia signaling pathways may potentiate RAS inhibition by overcoming early adaptive responses to treatment. The proposed experiments will combine innovative experimental techniques, advanced computational approaches, high quality model systems, human tissues, and clinical trial samples to comprehensively study the development of resistance to pan-RAS inhibitors in PDAC.

NIH Research Projects · FY 2026 · 2026-05

PROJECT SUMMARY The DNA damage response (DDR) is a cellular network that safeguards genomic stability. When compromised, genomic DNA can accumulate in the cytoplasm, triggering innate immune signaling events culminating in the expression of interferon (IFN) and IFN-stimulated genes. While IFN signaling can increase immune responses towards genomically unstable tumor cells by facilitating tumor antigen presentation and promoting the recruitment of cytotoxic T cells that mediate tumor rejection, it can simultaneously favor tumor immune evasion through the expression of PD-L1, an immune checkpoint protein that interacts with the PD-1 receptor on cytotoxic T cells, inhibiting their ability to kill cancer cells. Immune checkpoint blockade (ICB) therapies, which disrupt the interaction between PD-L1 and PD-1, have shown remarkable efficacy in reactivating T cells and eliciting robust anti-tumor responses . However, at present, only a small fraction of patients gain the full benefit of these treatments due to resistance mechanisms acquired by tumor cells. Given the complex interplay between the DDR, IFN signaling and the PD-L1-dependent immune checkpoint, elucidating these intricate relationships is crucial for developing more potent cancer immunotherapies. In preliminary work, we have conducted genetic screens to identify factors that regulate both innate immune signaling and PD-L1 levels in cancer cells. This work uncovered the SNF2-family DNA translocase SMARCAL1 as a DDR factor that favors tumor immune evasion by a dual mechanism that involves both the suppression of innate immune signaling and the induction of PD-L1-mediated immune checkpoint responses. The main goals of this proposal are to elucidate the molecular mechanisms by which SMARCAL1 suppresses anti-tumor immune responses and identify other DDR factors that function analogously to SMARCAL1. In particular, we propose 1) To define the mechanisms by which SMARCAL1 suppresses innate immune signaling in cancer cells; 2) To define the mechanisms by which SMARCAL1 regulates PD-L1 expression in cancer cells; 3) To characterize novel DDR factors that suppress innate immunity and promote PD-L1 expression in cancer cells. Our approach will utilize a combination of cellular, molecular and biochemical assays, high-throughput genetic screens, and studies in animal models. We expect that this work will offer new insights into potential targets that could be exploited for improving the efficacy of cancer immunotherapies.

NIH Research Projects · FY 2026 · 2026-05

Abstract Discogenic back pain, is a leading cause of disability, and involves degenerative changes of the intervertebral disc (IVD), including extracellular matrix (ECM) degradation, macrophage (Mɸ) infiltration, inflammation, and pathological nerve ingrowth. Since only a small subset of patients responds favorably to conventional treatments which address the symptoms but not the disease, there is a need for new therapies to treat disc degeneration (DD). Despite the extensive association between disc inflammation and DD, the mechanisms by which inflammatory factors drive DD in the disc are unclear. The goal of this proposal is to identify the role of monocyte- derived Mɸ on the cascade of painful DD, and to determine the contributions of CCL2/CCR2 signaling on Mɸ migration into the IVD. We hypothesize that inflammatory DD promotes increases in IVD levels of the chemokine CCL2/MCP1 that acts as a chemoattractant to CCR2+ monocyte-derived Mɸ, which degrade the ECM and promote a chronic inflammatory milieu over time. In Aim 1, we will identify the dynamics in which monocyte- derived Mɸ promote a chronic inflammatory, painful degenerate IVD. We have developed an in vivo mouse model of disc inflammation using inducible activation of NF-κB in the disc, which will be used. In Aim 2, we will determine the role of CCL2 in Mɸ migration into the IVD. In Aim 3, we will investigate the efficacy of sustained intradiscal delivery of a CCL2 neutralizing antibody during disease progression in a rat IVD injury model. Successful completion of this proposal will demonstrate that CCL2 secretion by IVD cells promotes recruitment of Mɸ, loss of IVD ECM leading to discogenic pain. Depletion of circulating Mɸ and deficiency in CCL2 signaling at the ‘right time’ will identify novel therapeutic mechanisms for painful DD.

NIH Research Projects · FY 2026 · 2026-05

Mental health (MH) disorders affect over 970 and 59.3 million people worldwide and the U.S, respectively, and remain the leading cause of disease burden across the lifespan, driving significant disability, premature mortality, and elevated risk for comorbid physical health conditions and a staggering national economic impact. Despite decades of research, the burden has not measurably decreased since 1990. In the U.S., nearly half of individuals with mental illness and over 70% with substance use disorders do not receive adequate care. Structural obstacles—including workforce shortages, high costs, negative attitudes, and fragmented care systems—continue to impede access. Implementation Science (IS) offers vital evidence-based approaches to close the persistent gap between evidence-based MH research and routine practice, yet few proven interventions have been scaled successfully to benefit large populations. The IMPACT-MH T32 Training Program (Implementation Science and Partnerships Advancing Care and Training in Mental Health) seeks to cultivate the next generation of MH IS researchers committed to sustainably reducing the U.S. treatment gap. Postdoctoral fellows will engage in intensive mentorship and a fully integrated curriculum spanning all research phases: pre-intervention design, intervention delivery, and post-implementation evaluation. Early emphasis on sustainability and partnerships with communities and policymakers will inform design choices—ensuring that interventions can be effectively delivered, scaled up, and rigorously evaluated over time. Training domains include deployment-focused research—contextual adaptation and stakeholder co-design of evidence-based interventions (EBI) across varied settings—and dissemination, implementation, scale-up, and policy research aimed at securing sustainable MH services. Through tailored mentorship and collaborative training with faculty experts in public health, psychology/psychiatry, IS, and health policy, fellows will develop the interdisciplinary perspectives and the conceptual, methodological, and technological competencies necessary to advance MH IS research. Mentored by experienced faculty, a cohort of four fellows, appointed for two to three years, will partner with communities and policymakers to design projects and pursue competitive NIH awards (including K-series and R-series proposals) will enhance the relevance, feasibility, and impact of their research. Leveraging well-established multisectoral partnerships with community service organizations, health networks, faith-based coalitions, and government programs, IMPACT-MH T32 ensures that fellows’ research informs real-world services and policy. Graduates of IMPACT-MH will be equipped to translate emerging discoveries into sustainable, evidence-informed mental health care systems and policies that strengthen the public health impact of NIMH-supported research (NIMH objective). By training leaders, fostering cross-disciplinary collaboration, building multi-sector partnerships, tailoring and scaling EBI, and advancing sustainable solutions, this program will make significant strides toward closing the U.S. mental health treatment and research gap.

NIH Research Projects · FY 2026 · 2026-05

SUMMARY Severe insulin resistance syndromes, including Donohue and Rabson-Mendenhall syndromes, are rare, life- threatening disorders caused by mutations in the insulin receptor (IR) that impair insulin binding and receptor activation. There are currently no FDA-approved therapies that directly target the receptor defect, and existing interventions provide only limited, short-term benefit. This proposal aims to address this urgent unmet need in rare disease therapeutics by advancing RF-409, a first- in-class, synthetic IR agonist. RF-409 was developed using structure-guided computational protein design to engage both insulin-binding sites (site-1 and site-2) and induce conformational changes that stabilize the IR in its active state. RF-409 exhibits high affinity and specificity for IR, activates both metabolic and mitogenic signaling pathways, and demonstrates strong thermostability and bioactivity in vivo. Preclinical studies show that RF-409 lowers blood glucose more efficiently and with longer duration than insulin in wild-type, type 1 diabetic (Streptozotocin-induced), and high-fat diet–induced obese mouse models. Notably, RF-409 is also effective in activating IR mutants that are unresponsive to insulin. To enable rigorous efficacy testing, we developed a validated knock-in mouse model (IR-D707A) carrying a patient-derived, insulin-binding–defective mutation. These mice exhibit neonatal lethality and severe insulin resistance, closely mirroring human severe insulin resistance phenotypes. RF-409, but not insulin, activates IR in this mouse model, providing a robust, disease-relevant platform for therapeutic evaluation. Aim 1 will characterize the pharmacokinetic and pharmacodynamic profile of RF-409 in wild-type, diabetic, and conditional IR-D707A mice. Time-resolved LC-MS analysis will be used to define systemic exposure and tissue distribution. Glucose-lowering efficacy and downstream IR signaling will be measured to establish PK/PD relationships and guide dose selection. Aim 2 will assess the physiological and therapeutic impact of RF-409 in the IR-D707A mouse model. We will evaluate metabolic, mitogenic, and survival outcomes following chronic administration in both adult and neonatal animals to determine therapeutic benefit in a rare disease model. RF-409 is well-characterized, highly specific, and production-ready. Its activity in both preclinical and disease- specific models support its potential as a therapeutic for rare insulin receptoropathies. Completion of this project will generate critical efficacy and mechanistic data to support IND-enabling development and may lay the foundation for a new class of targeted therapies for receptor-level metabolic diseases.