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Utilizing integrin-targeted PET imaging and therapeutics to predict and treat radiation-induced pulmonary fibrosis

Project Summary/Abstract. Lung cancer is the leading cause of cancer death in the US, with over 125,000 deaths annually. Radiation therapy (RT) is a critical component of curative lung cancer treatment for many patients. However, radiationinduced pulmonary fibrosis (RIPF) is a common side effect that carries a poor prognosis with limited treatment options. Up to 40% of patients with lung cancer who receive RT may experience RIPF. RIPF is a late effect of RT, typically occurring 3 or more months after treatment. The symptoms of RIPF can include shortness of breath, pleural effusions, decreased lung function, and respiratory failure. Cell surface integrin heterodimers play a key role in the pathogenesis of RIPF. In particular, the integrin αvβ6, which is expressed at a low level in the alveolar epithelium at baseline, is significantly upregulated upon RT damage. The key role of integrin αvβ6 in RIPF is illustrated by studies in which mice lacking integrin αvβ6, or treated with an αvβ6-blocking antibody, do not develop RIPF. Here, we propose to translate this mechanistic understanding of RIPF into novel approaches for monitoring and treating RIPF. We hypothesize that non-invasive αvβ6 PET imaging will be safe and can specifically bind to αvβ6 in patients with RIPF. Additionally, we hypothesize that a novel small-molecule integrin antagonist, IDL2965, can mitigate and treat RIPF in mice. In this project, we are utilizing mice to model RIPF, as mice develop RIPF that mimics human disease. In addition, cellular and in vitro models do not approximate the complex biology leading to the development of RIPF. Our data using [64Cu]Cu-DOTA-αvβ6-BP to detect early RIPF in mice are compelling in both single-fraction high-dose RT and lower dose-larger volume RT models (Lo et. al, IJROBP 2025). However, to progress to clinical trials in patients with cancer, we will obtain data to submit an Investigational New Drug (IND) application to the FDA. Importantly, we propose translating [64Cu]Cu-DOTA-αvβ6-BP PET imaging into patients with lung cancer, allowing us to better identify RIPF and develop a tool to determine the efficacy of IDL-2965 in future clinical studies. The specific aims of the proposal are: (1) Characterize the utility of [64Cu]Cu-DOTA-αvβ6-BP in mice with conventionally fractionated RT and identify circulating biomarkers of RIPF, and determine the in vivo toxicology of [64Cu]Cu-DOTA-αvβ6-BP to prepare and submit an exploratory Investigational New Drug (eIND) application to the FDA, (2) Conduct a first-in-human clinical trial of [64Cu]Cu-DOTA-αvβ6-BP to determine its safety and human dosimetry in patients with evidence of RIPF from computed tomography or in healthy controls, and (3) Determine the effect of integrin antagonism using IDL-2965 on mitigating RIPF in preclinical mouse models. The goals of this proposal are two-fold: (1) demonstrate safety and target specificity for [64Cu]Cu-DOTA-αvβ6-BP so that it can be used in future studies to identify RIPF and evaluate the efficacy of anti-fibrotic therapies, and 2) determine the ability of IDL-2965 to prevent RIPF in preclinical mouse models.

Structural and functional characterization of autoimmune antibodies against NMDAR

Project Summary. The goal of this project is to understand the origins and molecular mechanisms underlying the anti-cancer autoimmune response against the N-methyl-D-aspartate receptor (NMDAR) and its correlation with anti-N-methyl-D-aspartate receptor autoimmune encephalitis (NMDARAE). While anti-cancer immune responses can promote tumor elimination, they may also lead to the production of self-reactive antibodies that trigger autoimmune diseases. NMDARAE is the most common form of immune-mediated encephalitis, which results in prominent neuropsychiatric symptoms, including seizures, psychosis, and memory deficits. NMDARs belong to a family of ligand-gated ion channels expressed exclusively in the central nervous system. They are involved in various aspects of brain development and function, including learning and memory. They respond to the neurotransmitter glutamate and a co-agonist, glycine or D-serine, to mediate excitatory neurotransmission, which plays a central role in synaptic plasticity. NMDARAE is associated with ovarian teratomas, where aberrant NMDAR expression is believed to trigger an autoimmune response. In NMDARAE, anti-NMDAR antibodies, as well as B cells and antibody-secreting cells, cross the blood-brain barrier via unknown mechanisms, resulting in the presence of anti-NMDAR antibodies at high titers within the brain and cerebrospinal fluid (CSF). These antibodies target NMDARs, modulating their function and contributing to disease pathology. Emerging evidence, supported by our preliminary data, suggests that NMDARs are also expressed in triple-negative breast cancer (TNBC), extending the relevance of anti-NMDAR autoimmunity beyond ovarian teratomas. In our TNBC mouse model, which ectopically expresses NMDARs (TNBC-NMDAR), we observed the onset of anti-NMDAR autoimmunity, where the produced antibodies cause both anti-tumor activity and symptoms such as lowered seizure threshold, mirroring key features of NMDARAE. Here, we will establish this TNBC mouse model as we develop molecular methods to characterize it. Aim 1 will focus on establishing and characterizing the TNBC- NMDAR mouse model. We will develop a detection method utilizing the intact tetrameric NMDAR channel proteins and a method to isolate B cells expressing B cell receptors against NMDAR from biological samples by using fluorescently labeled intact NMDAR proteins, followed by single-cell RNA sequencing. Aim 2 will utilize single-particle cryo-electron microscopy (cryo-EM) to investigate the interactions between NMDAR and the cloned antibodies, providing insights into epitope recognition, NMDAR subtype specificity, and conformational changes induced by antibody binding. Aim 3 will assess the impact of the cloned antibodies on NMDAR channel activity using electrophysiology. We will also assess anti-tumor activity and NMDARAE onset by each antibody clone. Together, the proposed research will gain insights into the link between anti-cancer anti-NMDAR autoimmunity and NMDARAE. It will also elucidate which functional properties of the cloned antibodies promote anti-tumor activity while contributing to NMDARAE, thereby informing potential therapeutic strategies.

Optimizing CD45-Targeted Astatine-211-Radioimmunotherapy for Malignant and Non-Malignant Blood Disorders

ABSTRACT CD45 is expressed on almost all normal and neoplastic hematopoietic cells but not on non-blood cells and has, therefore, been pursued as a drug target. Initially centered on augmenting conditioning before hematopoietic cell transplantation (HCT) for blood cancers, there is increasing interest in expanding CD45-directed therapies into other settings, with radioimmunotherapy (RIT) being the major therapeutic modality so far. Investigators at our institution pioneered CD45 RIT with b-emitters such as iodine-131 (131I) using the murine monoclonal antibody (mAb), BC8. A phase 3 trial testing 131I-BC8 (131I-apamistamab [Iomab-B]) with allogeneic HCT in older adults with relapsed/refractory acute myeloid leukemia showed improved outcomes over conventional care, validating this approach. More recently, attention has shifted toward a-emitters that deliver substantially higher decay energies over much shorter distances than b-emitters, rendering them more suitable for precise and potent target cell killing. In our work, we focus on astatine-211 (211At) for its ideal half-life and decay without a-emitting daughters. For clinical application, mAbs are conjugated with the bifunctional boron cage molecule, isothiocyantophenethyl-ureido-closo-decaborate(2-) (B10-NCS), to enable stable protein astatination. Three early-phase trials testing 211At-BC8-B10 as augmentation of HCT conditioning for patients with malignant and non-malignant blood disorders are ongoing, with emerging data indicating significant anti-tumor efficacy. Nonetheless, relapses still occur. Other important limitations include marked infusion toxicities and human antimouse antibody (HAMA) responses related to the murine nature of BC8 and dimer formation after 211At labeling of mAb-B10 conjugates with tissue residualization from 211At atom oxidation. The latter may contribute to the risk of liver cell injury, the dose limiting extramedullary toxicity of CD45 RIT. As a first step toward our goal of optimizing CD45 RIT, we have raised new, fully human CD45 mAbs as basis for novel therapeutics. In preliminary in vivo studies in immunodeficient mice, we found some of these mAbs to have greater anti-tumor efficacy than a humanized version of BC8 (HuBC8) we generated as a reference mAb. We will now conduct comparative in vivo CD45+ cell targeting (“biodistribution”) and anti-tumor efficacy studies to select a lead candidate mAb for clinical application and use protein engineering to maximize the selectivity and efficacy of targeted radiation delivery. We will use immunodeficient mice xenotransplanted with human leukemia cells for this purpose as no human approaches are available and in vitro testing is inadequate to measure both the targeting and biologic RIT effects on human leukemia cells. Mice provide the in vivo milieu needed for comprehensive evaluation. Development of improved mAb astatination methodologies to minimize off-target toxicities of 211At-RIT will further increase therapy specificity and reduce toxicity. In parallel, we will conduct genome-scale, unbiased target identification/validation studies to identify partner drugs for rational combination therapies aimed at enhancing the anti-tumor efficacy of 211At-CD45 RIT.

Cardiorespiratory and autonomic impacts of coolants in e-cigarette aerosols

PROJECT SUMMARY / ABSTRACT Coolants such as menthol, WS-3, and WS-23 are widely used in electronic cigarettes (e-cigs) to reduce irritation and enhance appeal—especially among youth. Despite their prevalence, the cardiopulmonary toxicity of these agents remains poorly characterized. Recent work shows that e-cig aerosols can disrupt autonomic nervous system regulation and cardiac electrophysiology, increasing catecholamine release, enhancing sympathetic regulation of cardiac rhythm, and provoking arrhythmias. Proof is also mounting that nicotine’s sympathomimetic traits mediate these pathogenic effects. Preliminary data from our laboratory show that coolants increase systemic nicotine levels, blunt respiratory reflexes, and potentiate arrhythmias upon exposures to e-cigarette aerosols, suggesting a paradoxical role for coolants in suppressing ventilatory responses while intensifying cardiovascular risk. These findings take on added significance in light of recent case reports of sudden cardiac arrest in young e-cigarette users, including some in otherwise healthy individuals. This project will elucidate how e-cigarette coolants alter exposure to harmful and potentially harmful constituents (HPHCs)—particularly nicotine and aldehydes—concurrent with their effects on cardiovascular and respiratory physiology. Using robust murine models with continuous ECG, blood pressure, and pleural pressure telemetry, we will assess how coolants alter the acute and chronic effects of e-cigarette aerosols on cardiac electrophysiology, autonomic tone, ventilatory function, hemodynamics, and toxicant exposure. We will also evaluate how coolant concentration and device power modulate these effects. In parallel, we will determine whether adolescent mice exhibit heightened susceptibility to these effects compared to adults, with attention to sex differences and the persistence of cardiotoxicity after exposure cessation. This comprehensive, multi-modal approach incorporates novel protocols for arrhythmia inducibility, high-resolution physiologic monitoring, and complementary analyses of biomarkers of exposure and effect. By clarifying how coolants interact with HPHCs—especially nicotine and aldehydes—to drive cardiopulmonary injury across age and sex, this work addresses high-priority research areas identified in RFA-OD-25-001, including the toxicological evaluation of e-cigarette constituents and their cardiopulmonary effects. The results will inform regulatory policy and public health strategies aimed at mitigating cardiovascular risk associated with e-cigarette use, particularly among vulnerable youth.

Multimodal computational models for early prediction of peritoneal recurrence in gastric cancer

ABSTRACT Gastric cancer represents a significant disease burden and is a leading cause of cancer-related deaths in the United States and globally. Approximately 80% of gastric cancer patients are diagnosed at an advanced stage, with the peritoneum being the most common site of relapse (peritoneal recurrence) after radical surgery. Nearly 50% of patients with advanced-stage gastric cancer develop peritoneal recurrence post-surgery, resulting in a median survival of only 3–6 months and a markedly reduced quality of life. Early peritoneal recurrence is primarily characterized by micro-metastasis, which traditional imaging techniques struggle to detect due to the small size of metastatic nodules. Predicting the likelihood and timing of peritoneal recurrence is crucial for identifying at- risk patients, enabling timely interventions that could improve survival rates and quality of life. Unfortunately, reliable predictive biomarkers and models for peritoneal recurrence in gastric cancer are lacking in clinical practice, highlighting an urgent need for innovative predictive tools. This proposal aims to develop and validate novel predictive models for early peritoneal recurrence in gastric cancer, leveraging advanced deep learning techniques and multimodal integration of clinical, radiological (CT), and histopathological (hematoxylin and eosin, H&E) data. In Aim 1, we will develop a rational approach for predicting peritoneal recurrence by creating a novel deep learning multimodal method guided by genomics knowledge. Additionally, we will integrate both deep learning-extracted features and traditional hand-crafted radiomics features with clinical data to improve prediction accuracy. Aim 2 focuses on developing a robust prediction model of peritoneal recurrence utilizing a pre-trained foundation model from large-scale H&E image data. Aim 3 will combine CT, H&E, and clinical data to further enhance predictive capabilities, employing an innovative cross-modal collaborative optimization approach for multimodal data integration. All models will be trained and internally validated using a retrospective cohort from Atrium Health Wake Forest Baptist Comprehensive Cancer Center and externally validated in two independent cohorts from additional institutions to ensure robustness across populations and imaging protocols. Additionally, we will compare our models with existing methods, including clinical staging and alternative fusion strategies. If successful, these models will enhance risk stratification and prediction of peritoneal recurrence in gastric cancer patients, significantly improving survival rates and quality of life by identifying those likely to develop peritoneal recurrence post-surgery and facilitating timely intervention. Furthermore, they can help avoid the risk of complications and extra medical costs associated with overtreatment. Since the information is derived from routinely examined CT, H&E and clinical data, they could be seamlessly integrated into current clinical workflows. The AI technology developed through this project has the potential to benefit underserved populations in low- resource settings and reduce healthcare disparities in the U.S.

Mechanisms of Commensal- Specific CD8+ T Cell Differentiation, Restraint and Dysregulation in Intestinal Inflammation

PROJECT SUMMARY Our understanding of immunity largely stems from models of infection with pathogenic microbes. However, the vast majority of microbial-immune encounters occur as a symbiotic relationship with the commensal microbiota. Recently, the contribution of commensal-specific T cells to host physiology has received significant attention. These commensal-specific responses not only control microbiota containment but also promote immune tolerance within the gastrointestinal tract. While commensal-specific CD4+ T cell responses in the lamina propria have dominated models of mucosal immune regulation, these are vastly outnumbered by CD8+ intraepithelial lymphocytes within the epithelium. How CD8+ T cell responses to gut microbiota are primed, differentiate and function under homeostasis has not been addressed. Conversely, aberrant immunity to commensal microbes has been proposed to underlie pathologies of barrier tissues, including inflammatory bowel disease (IBD), where commensal-specific T cells accumulate in blood and intestinal tissues of afflicted patients. A better understanding of the properties and functions of commensal-specific T cell responses is therefore fundamental to studies of tissue immunity in health and disease. Our long term goal is to better understand how commensal-specific T cell responses contribute to barrier tissue homeostasis, and the objective in this application is to investigate the mechanisms regulating induction of commensal-specific CD8+ T cells in homeostasis and how they become dysregulated in IBD. Our rationale for the proposed work is that uncovering these mechanisms has the potential to translate into new therapeutic approaches. Our central hypothesis is that commensal-specific CD8+ T cells develop as functionally restrained intraepithelial lymphocytes (IEL) under homeostasis, but that perturbation of local immune regulation within the intestinal epithelium, in the case of patients with ulcerative colitis, by autoantibody-mediated blockade of integrin avb6 results in aberrant CD8+ effector T cell responses in IBD. Based on strong preliminary data, we will test three specific aims: (1) Determine key antigen-presenting cells (APC) priming SFB-specific CD8⍺β+ IEL. (2) Identify how cell-intrinsic pathways drive differentiation, maintenance and restraint of SFB-specific CD8⍺β+ pIEL. (3) Determine how pathogenic KLRG1+Eomes+ CD8+ T cells arise and contribute to inflammation in murine models of ulcerative colitis Our approach is innovative as it investigates new mechanisms of immunity unique to commensal-specific CD8+ T cell responses. The proposed work is significant because it will establish new insights into the interaction and communication between commensal microbes and immune cells in the gut environment and identify potential targets for therapeutic intervention in conditions of chronic intestinal inflammation.

A Double-Blind Randomized Controlled Trial of Daridorexant for Alcohol Use Disorder

Project Summary/Abstract This R01 application proposes integrating a randomized, double-blinded, placebo-controlled clinical trial into a real-world treatment setting to test whether the dual orexin receptor antagonist (DORA) daridorexant reduces alcohol craving and use and improves total sleep time among patients with alcohol use disorder (AUD) and co-occurring sleep disturbance. DORAs have shown promise in modulating reward and reducing alcohol self- administration in preclinical models. Further, DORAs are FDA-approved for insomnia, are highly efficacious for treatment of sleep disturbance, have a favorable safety profile, and demonstrate low abuse liability. Thus, DORAs are a highly promising treatment for AUD, particularly among persons that have co-occurring sleep disturbance. To this end, the proposed study will recruit individuals from a residential treatment facility, following completion of medically managed withdrawal and stabilization. Eligible participants will be randomized to daridorexant to placebo, and will complete measures of alcohol craving, total sleep time (assessed through both wireless electroencephalography and biometric data collection), and adverse events. Following discharge from residential treatment, participants will continue taking the study medication for two weeks while submitting daily reports of alcohol use, alcohol craving, sleep diaries, and biometric sleep data. Participants will also be prompted to submit three-times weekly random breath alcohol level using a portable BACtrack S80 breathalyzer, and will attend weekly check-in visits to assess adverse events and to confirm daily alcohol reports. A one-month follow-up assessment will be conducted to collect long-term data on alcohol use, AUD symptoms, and sleep. Ultimately, this study has the potential to identify a novel treatment for co- occurring AUD and sleep disturbance, and will address the following specific aims: (1) Test whether daridorexant reduces alcohol craving and post-treatment alcohol use relative to placebo. (2) Test whether daridorexant improves objectively measured total sleep time relative to placebo. (3) Examine the frequency of adverse events in persons assigned to daridorexant relative to placebo. If these aims are supported, then we will also explore whether effects are moderated by insomnia severity. We will also examine if the effects replicate across residential environments (with structured sleep/wake times and close monitoring of medication adherence) and outpatient environments (with self-imposed sleep/wake times and self-dosing). Currently, there are no FDA approved medications indicated for both AUD and insomnia. This innovative strategy aims to address a critical gap by investigating the effectiveness of daridorexant in modulating alcohol craving and alcohol use. This study will contribute to a growing literature on the role of the orexin system in reward and alcohol use.

Perturbation of mammary immunoglobulins during maternal antibiotic administration

Project Summary Prescribed in up to 40% of pregnancies, antibiotics represent the most commonly used class of medication during pregnancy. Although this practice is often necessary for maternal health, accumulating evidence suggests that antibiotic exposure may have unintended consequences for the mother-infant dyad. Epidemiologic studies associate maternal antibiotic exposure, especially in the absence of infection, with increased risk of neonatal complications including late-onset sepsis (LOS) and necrotizing enterocolitis (NEC), yet the mechanisms driving these associations remain poorly understood. Secretory IgA (sIgA) in milk is an essential component of neonatal mucosal immunity, shaping early gut microbial colonization and providing protection against enteric pathogens. The mechanisms by which maternal physiology regulates the abundance and microbial specificity of these antibodies in milk remain poorly understood. In animal models, the maternal gut–mammary axis governs the generation of milk IgA: IgA-committed lymphocytes from the maternal intestine migrate to the mammary gland during advancing pregnancy via CCL- 28/CCR10 signaling. Our preliminary data suggest that maternal antibiotic exposure disrupts this process leading to a decrease in milk IgA. However, the timing and extent of antibody dysbiosis are undefined; the downstream effects on neonatal intestinal health are unknown; and the underlying mechanisms—whether due to altered microbial stimulation, impaired recruitment of IgA⁺ cells to the mammary gland, or both—remain to be elucidated. Our central hypothesis is that maternal antibiotic exposure reduces pathogen-reactive IgA in milk by impairing gut-to-mammary immune cell trafficking thereby compromising neonatal mucosal immunity and increasing infection susceptibility. We will address this hypothesis through three integrated aims: (1) Determine the magnitude and duration of antibiotic-mediated mammary antibody dysbiosis in women who deliver preterm and at term; (2) Identify microbial targets of mammary antibodies diminished by maternal antibiotic exposure and (3 Determine the role of maternal antibiotics in the disruption of mammary resident IgA+ plasma cells in animal models. This integrative human and animal study will uncover critical mechanisms by which maternal antibiotic use alters the maternal-infant immune axis. The results will provide mechanistic insight into the risks associated with perinatal antibiotic exposure and inform clinical strategies to mitigate risk to neonatal health.

Eosinophils promote persistence and transmission during Bordetella spp. infections

ABSTRACT Despite widespread vaccination, Bordetella spp., the causative agents of whooping cough, continue to circulate globally. Resurgent outbreaks contribute to significant healthcare burdens and costs estimated up to $79 million annually. This persistence and reemergence highlight a critical need for new therapies and prevention methods. Our laboratory investigates bacterial and host drivers that enable Bordetella success, defined as enhanced persistence, reinfection, and transmission. We have identified the Bordetella sigma factor BtrS as a regulator of immunosuppressive pathways that modulate eosinophil function. Leveraging genetically tractable Bordetella strains, advanced murine models, and immunological tools, we are uniquely positioned to dissect how eosinophils contribute to respiratory bacterial infections. Our preliminary data reveal that eosinophils promote Bordetella persistence. Our results also show that the anti-inflammatory cytokine IL1 receptor antagonist (IL1Ra) also contribute to persistence. However, the contribution of eosinophil-derived immunosuppressors remains unclear and will be investigated in Specific Aim 1. Moreover, we have evidence that eosinophils are required for nasal shedding, through mucus enhancement, and paroxysmal coughing, via exacerbation of bronchoconstriction, during Bordetella spp. infection, two key metrics of transmission. The eosinophil-effectors that promote shedding, coughing, and transmission, will be investigated in Specific Aim 2. Based on our data, we hypothesize that eosinophils contribute to Bordetella pathogenesis by (1) promoting persistent infection and (2) enhancing transmission through mucus-driven shedding and cough reflex induction. This proposal will test this hypothesis through two specific aims: Aim 1: Delineate the immunosuppressive role of eosinophils in modulating host responses and enabling Bordetella persistence. Aim 2: Define the mechanisms by which eosinophils facilitate Bordetella spp. transmission. By reframing eosinophils as active modulators of bacterial pathogenesis, this research challenges traditional views of eosinophils as terminal effector cells and positions them as novel targets for therapeutic intervention, that might be applicable to other mucosal pathogens. The outcomes will contribute to our understanding of eosinophil biology in infection and may lead to innovative strategies to halt bacterial persistence and transmission.

Weak Cell Adhesion is a Prognostic Signature of Invasive Cancer

Project Summary Despite early detection, low-grade and localized breast cancers such as ductal carcinoma in situ (DCIS) can relapse in up to 20% of cases despite standard of care. For DCIS, relapse affects over 12,000 U.S. women annually and has increased 60% in the last 40 years. Current diagnostic assessments including histopathological markers often miss early disseminating cells, lack specificity, or cannot distinguish cancer from non-cancer cells in the stroma. Hence there is an unmet need for cancer diagnostic technologies that employ radically different characterization methods. For example, significant physical differences exist between metastasizing and benign breast cancer cells, owing to metastasizing cells detaching from the primary tumor, migrating through the surrounding stroma, intravasating and extravasating, and ultimately engrafting in distant tissues. We recently demonstrated that cancer cells with weaker adhesion migrate faster and metastasize more frequently in murine breast cancer models than strongly adherent cells. In a small pilot study of human breast tumors, we also observed that the abundance of weakly adherent (WA) cells scales with disease severity; subpopulations from invasive carcinomas were the least adherent. However, a subset of DCIS cases displayed much less adhesion, suggesting that these patients may have a tumor subpopulation that progresses to metastatic disease despite standard-of-care treatment. Weak adhesion is a defining physical characteristic of tumors, but to establish their role in initiation, metastasis, and patient outcomes, we will leverage model systems and our newly patented adhesion technology to answer these fundamental questions of cancer biology and clinical translation. To understand the impact of adhesion on cancer progression, we will evaluate the tumor-initiating potential of WA versus strongly adherent (SA) tumor cells in a murine breast cancer model before confirming how weak adhesion advantages cells to cause secondary disease using bioengineered in vitro models. In dissecting the stages of metastasis where WA cells exhibit advantages, e.g., recapitulating stromal niche, transendothelial migration, and tissue-specific colonization, we will identify mechanisms that enable WA cells to thrive and evaluate therapeutic targets that disrupt these pathways. Finally, we will analyze the adhesion profiles of resected tumors and stroma from 80 breast cancer patients with DCIS or invasive disease. Adhesion data will be correlated with conventional assessment methods and ultimately with patient outcomes, e.g., disease-free and progression-free intervals. We anticipate that the DCIS subpopulation that aligns with the adhesion signature of invasive carcinomas will have shorter intervals and survival time. This integrated study design bridges mouse models, mechanistic bioengineering assays, and human samples to clarify the metastatic potential and prognostic value of WA breast cancer cells. Our use of mouse models in this grant is required to study the interactions among tumor cells, immune cells, vasculature, and stromal tissues that drive tumor formation in vivo. Bioengineered in vitro systems lack the complexity to ask such questions and using injected tumor cells is not possible in humans.

Th17 plasticity in rheumatoid arthritis

ABSTRACT The objective of this grant application is to explore the plasticity of Th17 in arthritis. Interleukin-17A (IL-17A) producing Th17 are present in the blood and synovium of patients with rheumatoid arthritis (RA). However, targeting of IL17A has been insufficient to control joint inflammation of RA patients. One potential scenario is that in the context of worsening RA joint inflammation, Th17 undergo conversion into pathogenic IL17A- negative cell populations, collectively called exTh17. The conversion of Th17 into exTh17 has been documented in the context of neuroinflammation, colitis, and infection. However, the occurrence of Th17 plasticity in autoimmune arthritis and its potential role in perpetuating synovial inflammation has remained mostly unexplored. We generated a novel fate-mapping mouse model of autoimmune arthritis, which allows to follow the conversion of Th17 into exTh17, and collected preliminary data suggesting that Th17 undergo significant loss of IL17A expression and conversion into exTh17 in the context of synovial inflammation. We also identified exTh17 signatures which might help exTh17 perpetuate joint inflammation despite their loss of IL17A expression. Here our objective is to further elucidate intrinsic (Aim 1) and extrinsic (Aim 2) mechanism of Th17-exTh17 conversion and exTh17-mediated joint inflammation, and explore the potential role of exTh17 in RA interstitial lung disease (ILD, Aim 3) a feared and often untreatable complication of established RA. Our long-term goal is to leverage the knowledge of local immune cell phenotypes and how they change at various stages of disease to enable stage-specific and personalized therapies of RA which minimize non- specific immunosuppression.

Role of cellular physical interactions in pancreatic cancer progression

Pancreatic cancer, with a 5-year overall survival rate of 13%, has the lowest survival rate of all cancers. The goal of this project is to better understand the biological processes of pancreatic cancer progression and discover their potential as targets for efficient therapies. Pancreatic ductal adenocarcinoma (PDAC) underdoes epithelial architecture changes during its progression. However, the underlying mechanisms for these changes are largely unknown. Interestingly, our recent data demonstrate the recapitulation of the distinct epithelial architectures in the organoid culture of cells derived from the human normal pancreas, primary tumor, and metastatic lesions, thereby developing a unique organoid model for the in vitro studies of PDAC epithelial architecture changes. The primary objective of this project is to understand the regulation of the differential PDAC epithelial architectures as well as their contribution to PDAC progression. Our central hypothesis is that disruption in lumen structure drives PDAC epithelial architecture transition and promotes PDAC progression. We will combine experimental and computational approaches to test our central hypothesis by pursuing the following two specific aims: (Aim 1) define the regulators of PDAC epithelial architecture that drives PDAC progression and (Aim 2) determine the functional consequences of PDAC epithelial architecture on PDAC progression. With the completion of this aims, we expect: (Aim 1) to identify ion and water channels that are important for lumen structure as well as PDAC progression, revealing potential novel targets for therapeutic intervention, and (Aim 2) to uncover YAP’s role in PDAC progression and guide the development of YAP- targeted therapies.

Linear diribonucleotides regulation of bacterial physiology and infections

RNA degradation was thought to proceed through endonucleolytic fragmentation, followed by exo- ribonuclease trimming which generate short RNA fragments that are turned over into mononucleotides by oligoribonuclease (Orn). In the last funding period, we published data supporting that only specific enzymes (Orn, NrnA, NrnB, and NrnC) cleave diribonucleotides into monoribonucleotides, and that prokaryotic organisms need to encode at least one diribonuclease to fulfill this specific function. These results support a new perspective on RNA degradation in which the short oligoribonucleotides are processed through a sequence of discrete steps involving distinct enzymes. In addition, linear diribonucleotides appear to be biologically active molecules since we reported that mutants lacking these enzymes accumulate diribonucleotides and have altered cell growth, biofilm formation, motility, and sporulation. Here we present additional preliminary data supporting diribonucleotides as active signaling molecules in the cell including: 1. Specific enzymes act trinucleases to generate diribonucleotides, 2. RNase AM of Pseudomonas aeruginosa ∆orn is a cryptic diribonuclease, 3. Two enzymes in central metabolism are diribonucleotide- binding proteins, and 4. P. aeruginosa ∆orn has virulence defects in an animal model of catheter-associated urinary tract infection. Our past publications and preliminary data provide the scientific premise for our hypothesis that cells generate linear dinucleotides from RNA degradation and linearization of cyclic dinucleotides, which can bind target proteins to alter cell physiology and pathogenesis. To test these aims, we will perform the following specific aims: In Aim 1, we will characterize the generation and degradation of diribonucleotides by characterizing how diribonucleases and triribonucleases bind their respective substrates through molecular biology, biochemistry, and computational docking. In Aim 2, we will identify effects of dinucleotides on bacterial metabolism and physiology by characterizing the binding proteins that specifically interact with linear diribonucleotides. Building on our success of identifying cellular diribonucleotide receptors, we will screen for additional proteins from open reading libraries of P. aeruginosa and Bacillus anthracis. We will exploit the strains available to us that lack all diguanylate cyclases to reveal whether the effect of linear diribonucleotides is independent of c-di-GMP signaling. In Aim 3, we will characterize the effect of expression levels of dinucleases and the effect of dinucleotide accumulation on bacterial physiology and pathogenesis. We will develop mass spectrometry methods to detect di- and triribonucleotides. We will employ existing mutants lacking diribonucleases, including P. aeruginosa ∆orn to study the defects in chronic infection in a murine model of catheter-associated urinary tract infection. Results from these studies will advance our understanding of RNA degradation and open a new area of signaling by linear diribonucleotides with the potential to be applied to novel antibacterial strategies.

Borrelia burgdorferi genotypic diversity, pathogenesis, and host cellular responses

PROJECT SUMMARY Lyme disease is the most common tick-borne illness in the United States, with an estimated 476,000 cases annually, and Pennsylvania (PA) consistently reports one of the highest case numbers nationwide. Borrelia burgdorferi sensu stricto (Bb) is a causative agent of Lyme disease in the US and is transmitted by Ixodes spp. ticks. Bb produces various outer surface proteins (Osp) and other mechanisms to survive in vectors, evade host immune systems, and to propagate infection within a host. Over 35 OspC genotypes have been characterized, which fluctuate in abundance in natural vector and host populations, suggesting host adaptation. While many Lyme-infected patients recover following antibiotic treatment, some may experience neurological symptoms, Lyme neuroborreliosis (LNB), which may be associated with specific genotypes. While previous studies focused on clinical manifestations, pathogenicity, genetic variations, and host immune responses using mouse models or patient samples, the genotype-specific immune responses that contribute to disease progression in humans remain poorly understood. Our central hypothesis is that certain Bb OspC genotypes, maintained in natural populations, are associated with distinct host immune responses that influence disease severity, progression, and persistence. Aim 1 will define the dynamics of OspC genotypes in tick and small mammal populations over time in Western PA to establish a 16-year longitudinal tick study and an 8-year longitudinal small mammal study. Using deep amplicon sequencing, we will quantify genotype diversity, detect low-abundance genotypes, and identify potential host-adapted genotypes. These empirical data will inform a compartmental mathematical model to evaluate OspC genotype prevalence, distribution, and public health risks, including LNB, across space and time. Aim 2 will assess how distinct Bb OspC genotypes affect the host immune landscape and cellular responses using human samples. To determine how Bb genotype contributes to disease phenotype, we will perform immune profiling studies which will include microscopy-based assessment of infected cell cultures, flow cytometric analysis of immune cell phenotypes, and measurement of genotype-specific cytokine, chemokine, and antigen production (sub-Aim2a). We will also employ multi-omics approaches that integrate single cell RNA sequencing with antibody-based protein profiling (scRNA-seq/Ab-seq) to characterize transcriptional and functional changes in immune cell populations exposed to different Bb genotypes (sub-Aim2b). This work is innovative in its integration of long-term ecological data with advanced immune profiling and single cell multi- omics to uncover genotype-specific mechanisms of Bb pathogenicity and human immune response—an approach not previously applied in Lyme disease research. These studies will clarify how specific genotypes influence immune responses and disease severity. Together, the proposed aims will identify critical genetic and immunological mechanisms that drive Bb pathogenicity and human susceptibility, informing the development of improved diagnostics, targeted therapies, and public health interventions to reduce the burden of Lyme disease.

BKCa Channel Contributions to Cerebellar Regulated TSC-Associated Neuropsychiatric Disorders

Project Summary TSC is associated with neurodevelopmental disability including cognitive disability and autism spectrum disorders (ASD) that make up part of TSC associated neuropsychiatric disorders (TAND). The mechanisms for TAND remain poorly understood but studies have increasingly implicated cerebellar dysfunction in the pathogenesis of cognitive and behavioral deficits in both TSC and other neurodevelopmental disorders. A shared feature is cerebellar Purkinje cell (PC) dysfunction. Changes in intrinsic properties of PCs results in both motor and cognitive/ behavioral changes in disease models and in individuals afflicted by these disorders. Mechanistic underpinnings of these altered properties remain unknown, but a significant emerging body of data implicate ion channel dysfunction as the primary etiology of these deficits. The current proposal seeks to delineate the ion channel contribution to PC dysfunction and to TAND-relevant behaviors. In doing so, these studies will produce significant both short- and long-term impact. Short-term: These proposed studies will provide a mechanistic understanding of the contribution of ion channels to the neuronal dysfunction in the cerebellum that has been demonstrated to be causally linked to abnormal TAND-relevant behaviors. In addition, we will target specific ion channels both genetically and pharmacologically to evaluate the benefits of ion channel restoration on both electrophysiological abnormalities but also the TAND-relevant behaviors observed in the model. Long-term: These studies, thus, provide a framework for subsequent clinically-relevant therapeutic development for TAND. First, these studies will uncover the ability for TAND-relevant behaviors to be improved upon targeting ion channel alterations in TSC. These studies will also define molecular targets on which therapeutic development can be targeted, thereby potentially providing a molecular-informed pipeline for therapeutic development. In addition, these studies will utilize clinically-available, FDA-approved pharmacological agents to target ion channel function and investigate the potential therapeutic benefits for these agents for TAND-relevant behaviors. Thus, these studies will address a core gap in knowledge to achieve a better mechanistic understanding of TAND and to develop therapeutic opportunities to address TAND. These studies will not only reveal previously understudied and novel mechanistic underpinnings for these behaviors but will provide pre-clinical insights into the therapeutic utility of clinically-utilized agents for the treatment of TAND-related behaviors, thus potentially providing both immediate and long-term opportunities for the treatment of TAND. Moreover, although these studies focus on TSC, these mechanisms may prove generalizable beyond TSC and provide a shared basis and therapeutic opportunity for other neuropsychiatric/developmental conditions.

From B-cell decisions to antibody repertoires

PROJECT SUMMARY/ABSTRACT Vaccine responses are highly variable across the population and not without risk for debilitating side-effects. Antibody-mediated immunity is generated by a Darwinian process to generate B-cells that contain B-cell receptors (BCR) that have high affinity for the pathogen-derived antigen, while also eliminating B-cells that happen to react to self-antigens. This process depends on cell fate decisions such as (i) death vs survival, (ii) entry into a proliferative program, (iii) differentiation into antibody-secreting plasma cells. According to clonal selection theory, B-cell fate decisions are made based on the genetically encoded affinity of the the BCR to the antigen (Signal 1) and the cognate T-cells’ TCR to the antigen peptide (Signal 2). However, single-cell resolution studies have revealed that fate decisions of genetically identical B-cells are remarkably heterogeneous. Our studies of the previous funding period revealed that B-cell epigenetic heterogeneity is in fact dynamically controlled: it is generated during the selection process but remains largely stable during the proliferative burst. This leads to our newly proposed Aim 1 to examine how the dynamic control of epigenetic state variability affects antibody responses. An innovative multi-scale model of Darwinian evolution directs and interprets experimental studies by life cell video microscopy in vitro and in immunization studies in vivo. Our previous studies also found that B-cells are capable of sensing the time gap between signal 1 and 2, suggesting a temporal proofreading mechanism for negative selection. This leads to newly proposed Aim 2 which seeks to identify the regulatory circuits that control the stringency of negative selection, as well as contextual germinal center (GC) cytokines that could be manipulable in vivo. These in silico and in vitro studies are followed by in vivo immunization to extend their physiological relevance. Finally, in Aim 3, we will ask what determines the time-gap of signal1 and signal 2, which occur in the immune- induced structure of the GC. We will develop a new model that simulates B-cell fate decisions as a function of their interactions with antigen-presenting stromal cells and T-cells that may be cognate or non-cognate. Model simulations will be used to interpret spatial transcriptomic data to test different adjuvants and predictions will be tested in in vivo immunization studies. With mouse models of inflammation and aging we will examine how adjuvants alter vaccine efficacy and risk.

TACTIC: Tuberculosis Active Case Tracking via Interpersonal Connections

PROJECT SUMMARY/ABSTRACT Tuberculosis (TB) remains the leading infectious cause of death worldwide. Interruption of transmission is the most effective strategy to reduce incident infections, yet current approaches often fail to reach individuals for timely testing and treatment. This study addresses that gap by leveraging social networks to identify individuals at highest risk of transmitting TB, specifically, people who use drugs (PWUD). We will evaluate respondent-driven sampling (RDS), a peer7 based community recruitment strategy, to identify TB cases among PWUD and the household contacts (HHCs) of those with TB disease (RDS-TB) in Kampala, Uganda. Conducting this work in a high-prevalence setting such as Kampala where our team has established expertise allows us to overcome recruitment challenges common in settings in the United States while generating findings that are directly translatable. This is particularly relevant given that higher TB prevalence and larger outbreaks in the United States have been associated with the use of methamphetamine, heroin, and crack/cocaine, drugs that we will study. In Aim 1, we will compare the effectiveness and reach of RDS-TB with a traditional clinic-based index case HHC approach for TB case finding. We will screen 2,000 PWUD and their HHCs, estimate the number needed to screen to identify one case of TB disease, and compare the demographic and network characteristics of RDS-TB recruits with clinic-based HHCs. Whole genome sequencing will be used to characterize transmission dynamics. In Aim 2, we will compare the yield of individual and combined TB diagnostic strategies for community-based active case finding. Participants will undergo chest radiography with computer-aided detection, tongue swab testing for TB nucleic acid amplification tests (NAAT), and sputum testing for NAAT and mycobacterial culture. We will identify the minimal combination of tests needed to meet World Health Organization target product profile thresholds for screening. In Aim 3, we will define the conditions under which RDS-based screening can effectively interrupt TB transmission. We will develop an agent-based model informed by social network data from individuals with and without TB, incorporating drug use patterns and demographic characteristics. This project will generate a practical, scalable roadmap for social network–based TB active case finding in high28 risk communities. The approach will be readily adaptable to settings in the United States and will inform strategies to interrupt transmission and advance progress toward TB elimination, in alignment with the NIH Strategic Plan for TB Research.

Targeting the Molecular Crosstalk Between EZHIP and PRC2 in PFA Ependymoma

Project Summary: PFA ependymoma is a rare and aggressive pediatric brain tumor with a poorly understood molecular mechanism. Unlike many cancers, PFA ependymoma exhibits very few genetic alterations. Instead, it is thought to be driven primarily by epigenetic dysregulation. A key player in this disease is the EZH1/2 inhibitory protein EZHIP, which is normally expressed only in germ cells. EZHIP is aberrantly expressed in PFA ependymoma, where it disrupts the function of Polycomb Repressive Complex 2 (PRC2), a master epigenetic regulator of developmental gene repression through deposition of the trimethylated histone H3 lysine 27 (H3K27me3) repressive histone mark. EZHIP-mediated dysregulation of PRC2 involves both enzymatic inhibition and physical stalling of PRC2 on CpG island (CGI) chromatin, leading to a global loss of H3K27me3 levels, an epigenetic hallmark of PFA ependymoma. PRC2 itself is a highly dynamic and intricate complex that assembles into two functional variants, PRC2.1 and PRC2.2. These two variants share a core composed of the catalytic subunits EZH1/2, along with EED, SUZ12, and RBBP4/7, and differ by incorporating distinct accessory subunits. PRC2.1 includes PHF1/MTF2/PHF19, EPOP, and PALI1/2, while PRC2.2 features AEBP2 and JARID2. Our preliminary data reveal intriguing molecular crosstalk between EZHIP and multiple PRC2 components, suggesting potential competitive or cooperative interplay. The ability of EZHIP to inhibit PRC2 partly stems from its mimicry of the oncohistone H3K27M, which harbors a lysine-to-methionine mutation that causes diffuse midline glioma, another devastating brain tumor in children, where PRC2 activity is also globally suppressed. However, the precise, EZHIP-specific mechanisms behind PRC2 dysregulation in PFA ependymoma remain largely unexplored. Our work aims to uncover these elusive mechanisms using a powerful combination of structural biology, biochemistry, and genomics approaches. Ultimately, we aim to identify therapeutic strategies that disrupt the pathogenic EZHIP–PRC2 crosstalk and restore the normal H3K27me3 epigenetic landscape. Specifically, in Aim 1, we will determine the structural and biochemical mechanisms underlying the enzymatic inhibition of the PRC2 core complex by EZHIP. In Aim 2, we will elucidate the molecular basis of EZHIP-mediated stalling of PRC2 on CGI chromatin, involving PRC2 functional variants. In Aim 3, we will explore an exciting mechanism-based therapeutic strategy to overcome PRC2 enzymatic inhibition and chromatin stalling induced by EZHIP.

Exploring in vivo Treg function in T1D through the lens of expanded Tregs

PROJECT SUMMARY/ABSTRACT A critical barrier to optimally treating Type 1 Diabetes (T1D), an autoimmune disease in which the islet beta cells are destroyed by immune cells, is understanding how autoimmunity is regulated in vivo. Several lines of evidence suggest that defective CD4+FOXP3+ regulatory T cells (Treg) likely contribute to the loss of tolerance in T1D. Yet, less is known about how human Treg function in vivo. In the Sanford T-rex study in which adolescents diagnosed with T1D were treated with a single dose of polyclonal autologous in vitro expanded Treg (expTreg), we found that a lower degree of in vitro Treg expansion significantly correlated with better preservation of C- peptide (a biomarker of insulin secretion and beta cell function) a year after treatment. This correlation could not be explained by age, expTreg phenotype or in vitro expTreg suppressive function. However, we did identify an expTreg gene signature that correlated with better C-peptide preservation and this expTreg signature was consistently expressed over time within individuals. Further, lower- and higher- expTreg differed phenotypically and transcriptionally by signatures implicating metabolic, homing and suppressive functions. Together, these data suggest that intrinsic features of an individual’s Treg may contribute to the extent of in vitro Treg expansion. They also suggest that strong activation and expansion can differentially amplify or alter the state of Tregs, leading to changes in homing and function that may impact clinical response. Based on these findings, we hypothesize that Treg proliferative capacity is driven by the activation and metabolic state of Treg resulting in differential in vitro fold expansion, homing potential and in vivo suppressive function that impacts clinical outcome. We will test this hypothesis by leveraging existing primary human samples from both the T-rex clinical trial and the Benaroya Research Institute Registry and Repository that includes individuals with known degree of in vitro Treg expansion and known C-peptide decline. In Aim1, we will identify how activation states of pre- and post- expansion Treg and longitudinal Treg in T-rex participants contribute to proliferative capacity and outcome using cellular, transcriptomic and epigenetic assays. In Aim 2 we will determine how metabolic shifts during Treg in vitro fold expansion alter Treg suppressive function, thereby impacting clinical outcome. In Aim 3, we will compare the in vivo suppressive function of lower- versus higher-expTreg from clinical samples using a xenogeneic graft versus host disease (GvHD) mouse model in addition to assessing in vivo expTreg homing and function using the assays from Aims 1 and 2 and a novel in vitro assay of cell trafficking to pancreatic islets. Successful completion of these aims will reveal mechanisms regulating Treg proliferative capacity and in vivo function that impact clinical outcome. Understanding these mechanisms will guide development of next generation Treg activation and expansion protocols for Treg therapies and help tailor the Treg expansion process to an individual’s baseline Treg signature.

Improved Surgical Visibility and Navigation during Endoscopic Treatment of Upper Tract Urothelial Carcinoma

Project Summary The importance of localizing and treating all upper tract urothelial cancer (UTUC) tumors during a renal sparing, endoscopic treatment is emphasized by the high risk of cancer progression from inadequate tumor treatment. Insufficient treatment necessitates kidney and ureteral removal (i.e., nephroureterectomy). Nephroureterectomy permanently compromises renal function, and increases morbidity and mortality, while negatively impacting a patient’s quality of life. In contrast, endoscopic treatment (i.e., using a laser to ablate only the tumors) improves long-term outcomes by sparing healthy kidney tissue. However, endoscopic treatment is underutilized compared to nephroureterectomy because it is difficult to accomplish. Successful endoscopic treatment is dependent on the surgeon’s ability to create a mental 3D map of the branched, intrarenal endoscopic anatomy intraoperatively from preoperative 2D imaging, which is extremely difficult. Since mental mapping relies on hand-eye coordination, memory, and spatial reasoning, it is inherently imprecise and its impact on accuracy and tumor treatment is dependent on the surgeon’s experience. To make matters worse, even when tumors are successfully visualized, the surgeon often cannot accurately assess the location of tumor margins or infer pathologic grade due to the limited field of view and depth of field (10mm and 6mm on average, respectively) of current scopes. The scopes only provide visualization of a small part of the surgical field at any instant. These inherent challenges prevent many surgeons from attempting endoscopic tumor treatment since incomplete treatment leads to a devastating, oncologic outcome. Our overall goal is to create an enhanced visualization and navigational system that makes endoscopic UTUC tumor treatment easier and more accurate for all surgeons, enabling wider utilization. Toward this goal, our specific objective in this proposal is to test the hypothesis that our system can make endoscopic UTUC surgery more accurate and efficient. To test this hypothesis, we propose three Specific Aims: Aim 1 involves the development of an automatic, real-time segmentation and grading system of UTUC tumors during endoscopic treatment. Aim 2 integrates a 3D navigational map of collecting system anatomy, which includes tumor and endoscope location, during endoscopic surgery. Aim 3 evaluates the system in patients, with zero risk to the human subjects. The endpoint of this R01 will be a fully validated enhanced visualization and navigational system for endoscopic UTUC surgery, which would provide the necessary experimental data towards a large-scale, multi-center clinical trial and future FDA approval. As our system would require only software integration to current endoscopic surgical cameras, all existing endoscopic surgical systems could in principle immediately benefit from the results of this project. In this way, we believe the success of our project will facilitate improved UTUC treatment and mitigate progression to a higher risk extirpative surgery.

Investigating the nonlinear complex dynamics of the tuft cell-microbiome cross-talk: the impact of feedback loops on immune regulation, microbial modulation and response to tissue insults

Project Abstract Tuft cells (TCs) are specialized chemosensory epithelial cells that are emerging as critical regulators of intestinal homeostasis. Named over 70 years ago based on their distinct morphology, a defined function for TCs was only elucidated in the last decade. TCs in the small intestine sense succinate from helminths to initiate type 2 immune responses that mediate parasite expulsion. Recently, we discovered a novel physiologic function for TCs in the colon, where their role had been considered minimal. Succinate, a key microbial metabolite, is produced by colonic microbiota as both a precursor to other metabolites and a cross-feeding fuel source for pathogens. TCs respond to succinate by secreting interleukin-25 (IL-25), which activates type 2 cytokine- producing lymphocytes (T2Ls), amplifying TC expansion and reinforcing barrier function. We recently demonstrated that this SPB–TC–IL-25–T2L feedback loop is essential for protection against pathogen-induced colitis. Our preliminary data further suggest that TCs actively promote colonization by succinate-producing bacteria (SPBs), establishing positive feedback on TC-supporting microbes, while other epithelial cells such as goblet cells (GCs) and Paneth cells (PCs) may exert complementary or counterbalancing influences. Supported by new modeling insights, we hypothesize that these epithelial–immune–microbiome interactions form coordinated feedback loops that collectively optimize intestinal resilience. These loops may create a dynamic, multi-stable system that flexibly transitions between homeostatic and hyperplastic states, buffering against microbial fluctuations and pathogenic insults while preventing uncontrolled type 2 inflammation. Using a combination of mathematical modeling and experimental validation, we will develop a multi- layered systems framework to explore how epithelial–immune–microbial feedbacks shape resilience or breakdown in clinically relevant models of colonic infection and inflammation. Our three Aims will (1) develop, calibrate, and validate a mathematical model that integrates TCs, GCs, PCs, SPBs, and SCBs; (2) define the immunological circuits governing epithelial–microbiome equilibrium; and (3) determine how epithelial feedbacks regulate microbial community structure and resilience. In line with NIH’s new initiative to prioritize human-based research, our proposal combines computational modeling, human colonic organoids, and complementary mouse models. Organoid experiments will provide human-relevant data for model calibration, while in vivo studies validate systemic predictions, ensuring both rigor and translational relevance while minimizing reliance on animal models. This work will generate interoperable models that integrate epithelial, microbial, and immune networks, providing predictive insight into intestinal outcomes under homeostatic, infectious, and inflammatory conditions and informing therapeutic strategies for microbiome-targeted interventions.

Transcriptional control of activation induced deaminase (AID) function

SUMMARY Somatic hypermutation (SHM) and class switch recombination (CSR) are vital for the generation of high affinity antibodies with appropriate effector function, protection against infection, and vaccine efficiency. They are initiated when the activation induced deaminase (AID) deaminates cytidines in single-stranded DNA in the context of transcription by RNA polymerase 2 (Pol2). Aberrant DNA deamination by AID is an important driver of genetic instability and the development of B cell malignancies. Understanding the factors and mechanisms that coordinate AID-mediated deamination with Pol2 transcription is an important objective in the study of humoral immunity and the central goal of research under this grant. Our preliminary data demonstrate that Pol2 pause factor NELF, Super Elongation Complex (SEC) components MLLT1/3, and the phosphatase module of the Integrator-protein phosphatase complex (INT-PP2A) are required for SHM, with MLLT1/3 but not NELF being required for AID binding to its chromatin targets. Our findings yield a new conceptual framework and model for AID-Pol2 collaboration in which NELF and a balance between kinase and phosphatase activities of SEC and INT-PP2A regulate Pol2 pausing/elongation to generate the critical stalled Pol2 complex on which AID acts. Further, our work has yielded major methodological advances that allow us to overcome obstacles that have stymied progress in the field. In this proposal, we take advantage of these conceptual and technical advances to pursue our central goal through the following two aims: Aim 1: Determine the molecular mechanisms by which NELF and other Pol2 regulatory factors enable AID-Pol2 collaboration and SHM/CSR. It has previously been very difficult to assess the role of cell-essential factors in SHM. By combining our new Rapid Assay for SHM (RASH) cells with degron technology, we will determine the mechanism of action of our newly discovered regulators of SHM using genomic, transcriptomic, and interaction assays that assess Pol2 distribution, phosphorylation, and activity, and the chromatin binding profiles of and interactions between AID and components of NELF, SEC, and INT-PP2A. AID and MLLT1 appear to co-associate in a complex and we will test for a direct interaction between AID and MLLT1/3. Factors will be tested for roles in CSR and validated in human cell line and germinal center B cell models and in mice. Aim 2: Hypothesis testing and deep mechanistic analysis through perturbation of the balance between Pol2 pause/arrest and elongation. We will rigorously test our new model for AID-Pol2 collaboration using degron, reconstitution, mutagenesis, and small molecular inhibitor approaches to perturb the balance between Pol2 pausing and elongation, revealing how altering NELF-Pol2 interactions and the balance between SEC kinase and INT-PP2A phosphatase activities influences SHM efficiencies and AID binding. Together, our proposed studies are significant for the development of new technologies and for understanding mechanisms of antibody gene diversification and causes of genome instability and cancer.

Systems Biology of Early Atopy: Role of Human Milk (SunBEAm-Milk)

Surprisingly little is known about the effect of breastfeeding (BF) on infant immune system development besides an effect on the gut microbiome, but its impact on metabolites and Tregs could support protection against food allergy (FA). BF is currently recommended to prevent the development of allergic diseases, especially asthma/recurrent wheezing and AD in early childhood, but firm conclusions could not be drawn regarding FA due to high heterogeneity and low quality of studies. Reverse causation, recall bias and the poor accuracy of outcome assessment are significant limitations. Most are inadequately powered to specific FA; however, a recent study showed that exclusively BF infants had lower odds of egg, sesame, and peanut allergies. Importantly, immunomodulatory composition of HM varies between mothers, which has not been taken into consideration. For over two decades we have been developing methods to assess immunomodulatory factors in the complex matrix of HM and their association with infant FA. We have shown that high levels of HM total and specific IgA are associated with protection against cow’s milk allergy, but it is unclear whether HM IgA is responsible for or is a biomarker of the vertical transfer of protection. Infant fecal and systemic IgA levels during breastfeeding and after weaning are also elevated in infants at low risk for atopic disease raising the question of whether HM factors such as cytokines can promote IgA production in infants. Consistent with this, we showed that HM cytokines, such as APRIL, induce IgA production in naïve infant B cells, and infants receiving HM with higher levels of APRIL had lower incidence of allergic disease. Finally, lower levels of several HM fatty acids including short-chain fatty acids and DHA were associated with FA. While some these factors were are associated with maternal atopic disease, several of them are not and suggest a role for diet instead. The System Biology of Early Atopy (SunBEAm) population-based cohort of 2500 mother-infant pairs is >50% recruited and provides an unprecedented opportunity to assess association of HM feeding and immune factors in HM with development of infant immune system and FA/AD. The Common Sample comprises a subset of 100 dyads with FA, 100 with FA+AD, 100 with AD, 100 with no FA or AD and more extensively profiled biological data. Utilizing all 2-month HM samples available in the Common Sample, we will assess levels of immune factors in HM and their association with maternal/infant characteristics (Aim 1). Utilizing data from the whole cohort, we will assess the association between HM vs formula feeding on well-defined FA/AD further adjusted based on high vs low levels of HM immune components in the Common Sample (Aim 2b). Finally, we will examine the immune cell and epithelial effects of HM on infant immune markers and intestinal organoids (Aim 3). Key findings will be validated in an independent birth cohort. The ultimate goal is to uncover protective properties of BF and HM in FA and subsequent design of policies and prevention strategies to address the increasing rates of FA.

Factors Driving Wear and Implant Failure in Total Shoulder Arthroplasty

Polyethylene (PE) wear and implant-related failure remain leading causes of revision in total shoulder arthroplasty (TSA), a procedure which now surpasses the growth rate of hip and knee arthroplasty. Both anatomic (aTSA) and reverse (rTSA) TSA outcomes are heavily influenced by complex interactions between rotator cuff function, scapular motion, implant design, and patient-specific loading—factors not adequately captured in current preclinical implant testing standards. Emerging evidence suggests that PE wear progression in TSA is highly dependent on shoulder kinematics, joint loading, implant positioning, and individual patient factors. Nonetheless, data on in vivo motion and load profiles remain sparse, and few tools exist to link these profiles to clinically relevant wear patterns or associated periprosthetic inflammatory tissue responses. Accordingly, the primary objective of this project is to develop validated, patient-specific models that predict PE wear in TSA and identify modifiable surgical, design, and rehabilitation targets to improve implant longevity and restore patient mobility. Additionally, we will establish histopathological hallmarks that indicate TSA failure caused by PE wear debris. Our central hypothesis is that specific shoulder kinematics and joint loading drive distinct PE wear patterns in TSA associated with mechanical failure or inflammatory-mediated osteolysis, depending on implant design and positioning. To achieve the overall objective of this work, shoulder motions and muscle excitations across 25 activities of daily living will be collected at pre-op and post-op (>6 months) in both aTSA and rTSA patients, with long-term follow-up of patient-reported outcomes via validated surveys (5 years). Unsupervised machine learning will categorize patients into movement-based phenotypes, which will then inform a multi-scale modeling framework to estimate in vivo shoulder joint loads and implant wear across the varying movement strategies. Predicted wear patterns will be validated using state-of-the-art preclinical wear simulators. Simultaneously, we will quantify how patient, surgical, and implant factors contribute to wear in retrieved TSA components (>400 samples), correlating imaging-based wear patterns with clinical outcomes, patient-reported function, inflammatory tissue responses, and radiographic indications of loosening. For that purpose, we will establish benchmarks of TSA wear rates and introduce a new histopathological approach augmented by infrared spectroscopic imaging. This work is innovative because we are linking patient-specific movement patterns following TSA with multi-scale computational models to predict PE wear, breaking the current approaches of using generic motions and loads in existing testing standards. This work will produce the first integrated, publicly available database of TSA kinematics, joint loading, and PE wear patterns and rates, along with validated computational tools to inform implant design, surgical planning, rehabilitation strategies, and personalized risk assessment. Ultimately, these advances will improve functional outcomes and long-term success for TSA patients and enable better preclinical testing methods and standards.

Circadian regulation of reperfusion efficacy in acute ischemic stroke

Reperfusion with thrombectomy has changed the clinical landscape for ischemic stroke. Recently, some studies suggest that patients with “large cores” may still benefit from reperfusion. Why? If these “cores” represent dead brain, why should reperfusion help? One logical explanation is that currently used neuroimaging “cores”, do not always identify uniformly dead tissue. Our pilot data suggest that these “cores” include tissue with a wide range of injury, indicated as changes in relative CT Hounsfield Units (rHU). Importantly, circadian mechanisms may be involved. Ischemic tissue with less severe changes in rHU tend to occur in the morning (active phase) when responses to reperfusion are better. In mouse models of stroke, ischemic injury is also less severe when strokes occur during the nighttime (active phase for nocturnal animals). In contrast, more severe ischemic injury during the daytime (inactive phase for mice) is accompanied by dampened vasodilation and CBF response along with increased immunothrombosis and neutrophil extracellular traps (NETosis). Is it possible that understanding these circadian mechanisms may help identify patients who respond best to reperfusion? And is it possible that targeting these circadian mechanisms can help convert non- responders into responders? In this multi-PI project, we use a translational approach (clinical neuroimaging and biomarkers in stroke patients, mouse models of stroke, CT-PET imaging of tissue viability, molecular pharmacology) with three integrated aims that can be pursued in parallel. Aim 1 will use neuroimaging in stroke patients to show that less severe rHU values in reperfusion-responsive “cores” tend to occur in the morning, whereas more severe rHU values in reperfusion-non-responsive “cores” occur later. Aim 2 will use clinical biomarkers to show that more severe rHU “cores” that are not reperfusion-responsive correlate with circadian effects on vasodilation and immunothrombosis. Aim 3 will use mouse stroke models to test whether targeting these circadian mechanisms of vasodilation and immunothrombosis can convert reperfusion-non-responders into reperfusion-responders. Patients cannot choose when they have a stroke. So why should we pay attention to circadian mechanisms? There may be 2 reasons that are addressed by the present project. First, thrombectomy is resource-intensive, and in spite of the very low number-needed-to-treat, only 20% of “large core” patients do well after reperfusion. Our studies may help identify who (when) these responders are. Second, the pathophysiologic mechanisms of cerebral ischemia differ depending on time-of-day. Therefore, understanding and then targeting these circadian mechanisms may allow us to convert reperfusion non-responders into responders.

Linking Single-Cell Transcriptomic, Morphological, and Temporal Signatures of Vulnerability in Neurodegeneration

Neurodegeneration involves complex cellular phenotypes and molecular changes that vary widely among the cells of the nervous system. Current methodologies permit either detailed molecular profiling (e.g., single-cell transcriptomics) or functional phenotyping (e.g., live imaging of neuronal activity), but not both in the same cells. Thus, it is difficult to directly link a neuron's functional state or fate with its gene expression profile. To address this limitation, we developed an innovative technology, VISTA-FISH (Video Imaging with Spatial- Temporal Analysis by FISH), that couples prospective live-cell imaging with high-resolution spatial transcriptomic profiling of the same cells. This approach enables in situ comparisons of gene expression in neurons that exhibit divergent behaviors or outcomes. Using VISTA-FISH, we will profile iPS-derived human neurons to link single-cell gene expression, morphology, and temporal phenotypes to study molecular pathways driving resilience as well as susceptibility. After exposing neurons carrying TDP43 and C9orf72 mutations to a stimulus inducing TDP43 aggregation, we will jointly record TDP43 localization and neuron activity using live-cell microscopy, then measure single-cell gene expression of the same cells (Aim 1). We will also combine live-cell measurements of TDP43 half-life with CRISPR screening and single-cell gene expression (Aim 2). These rich datasets will enable us to determine transcriptomic changes associated with differences in protein aggregation, protein synthesis, and protein degradation in individual cells, providing an unprecedented molecular perspective on factors responsible for vulnerability and resilience to neurodegeneration.

Biostatistics, Ethics, Data Management, Research Design and Community Engagement(BEDRoC) Core

Biostatistics, Ethics, Data Management, Research Design and Community Engagement (BEDRoC) Core Abstract The Biostatistics, Ethics, Data Management, Research Design and Community Engagement (BEDRoC) Core will promote and support aging with serious illness science for the Center for Aging with Serious Illness (CASI). BEDRoC will provide expertise in statistical design and analysis, research ethics, and community engagement for all components of CASI. The Core's services will support the Research Project Leaders (RPLs) and Pilot Project Leaders (PPLs) and build capacity for the broader Dartmouth Health aging research community to conduct rigorous, impactful research to inform and improve care delivery for older adults with serious illness. BEDRoC includes expertise in mixed methods approaches that feature both quantitative and qualitative research methods to provide a comprehensive understanding of the complex issues related to aging with serious illness, ethical approaches to consent in research trials, multidimensional quality of life measurement, and innovative modeling approaches to studying clinical decision making. BEDRoC faculty have actively collaborated in study planning with each RPL, serving as both mentors and experienced collaborators on the three different projects involving decision aids for patients considering carotid revascularization, a patient-reported outcome-directed referral intervention to improve referral rates to palliative care services, and a pilot trial for a virtual/home-based exercise and a weight management osteoarthritis treatment program in older patients with osteoarthritis and multimorbidity. The BEDRoC Core will further support CASI by establishing an innovative training curriculum with workshops, tutorials, resources, and services, offered locally to RPLs and PPLs and extended to regional and national investigators in the IDeA network. In addition to their primary individual project mentors, each RPL will receive training and guidance from BEDRoC leaders through co-mentoring and RPL-focused works-in-progress sessions. BEDRoC will also provide access to a comprehensive inventory of patient-reported outcomes instruments, which are crucial in geriatric research to provide validated measures of health status, quality of life and functional ability outcomes. BEDRoC will coordinate with the Administrative and Mentoring Core to integrate community advisors in guiding their activities in support of the RPLs. BEDRoC will also enable research collaboration with and within the larger Dartmouth and IDeA investigator communities. The BEDRoC Core will build capacity for aging research and disseminate new resources to RPLs and PPLs, including innovative solutions created through robust community engagement. These services, resources, and solutions will ensure all projects operate in a cohesive, complementary, and collaborative manner to study approaches to improving the health of older patients with serious illness.

Characterization and functional impact of somatic numtogenesis in the human cortex

Project Summary This project focuses on studying nuclear mitochondrial insertions (numts), which are fragments of mitochondrial DNA that get integrated into the nuclear DNA of human cells. While this process, called numtogenesis, occurs naturally and can be passed down to future generations, it has also been observed to occur somatically in our bodies. Historically the function of numts has been difficult to study because they are repetitive and difficult to map with short read sequencing technologies, but there is emerging evidence that they can influence cell function and play a role in diseases, aging, and even complicate genetic studies. Our recent research discovered numts in the human brain’s cortex, and their presence appeared to be linked with earlier death, suggesting they may play a role in aging. However, due to limitations in the data we used, we could not fully explore the extent or impact of these insertions across different tissues or individuals. This project aims to map and study numts in more detail, especially in the human cortex, to further explore this ongoing transfer of DNA from the mitochondria to the nuclear genome and their potential to impact aging and brain function. We will accomplish this by 1) improving sequencing methods to detect numts, 2) comparing their presence across different tissues, and 3) investigating how they affect gene expression and DNA structure. By the end of the project, we aim to provide a model for how such somatic variation may occur and impact cellular function at the tissue level.

COCHLEAR SIGNALING MEDIATED BY HENSEN’S CELLS

PROJECT SUMMARY/ABSTRACT The organ of Corti has two types of auditory sensory cells (inner and outer hair cells) surrounded by nearly a dozen different types of supporting cells organized in a very meticulous pattern. Hair cells mediate the mechano-electrical transduction process of the organ of Corti and thus most cochlear auditory research has focused on these sensory cells. In contrast, much less is known about the different types of cochlear supporting cells, even though they likely impact hair cell function. Hensen’s cells are located laterally to the outer hair cell rows and appear to be the only cell type in the cochlear epithelium that expresses TRPA1 channels. These channels are widely known for their role as sensors of tissue damage and inflammation in nociceptive neurons. Not surprisingly, we recently found that Hensen’s cells are main sensors of tissue damage in the cochlear epithelium via the activation of TRPA1 channels (Velez-Ortega et al., Nat Commn, 2023). Additionally, our preliminary data also supports the role of Hensen’s cells in signaling pathways important for the proper innervation of the organ of Corti (aim 1), for the transmission of cochlear damage signals to the brain (aim 2), and for the regulation of hearing sensitivity after acoustic trauma (aim 3). Thus, here we will explore the hypothesis that TRPA1- mediated signaling pathways in the Hensen’s cells are required for the proper innervation and auditory function of the organ of Corti. In Aim 1 we will perform a detailed comparison of the morphology and synapses of afferent cochlear neurons of wild-type and Trpa1-/- mice at several developmental stages (using immunolabeling, confocal microscopy, STED microscopy, and electron microscopy) to assess the role of TRPA1 activity on the postnatal refinement of the cochlear innervation. Aim 2 will evaluate whether the afferent type II spiral ganglion neurons (SGN) can be activated downstream of TRPA1 channel gating in Hensen’s cells by testing responses of neonate and adult type II SGN to TRPA1 agonists (via live-cell time-lapse calcium imaging and patch clamp recordings of type II SGN dendrites). Aim 3 will test the impact of TRPA1 signaling in Hensen’s cells to the operating point of the cochlear transducer (via the recording of cochlear microphonics) and to cochlear tuning (via the recording of ABR tuning curves). This study is significant because it will contribute to our understanding of the cellular (Hensen’s cells plus type II SGN) and molecular (TRPA1 channels) mechanisms of the elusive cochlear nociceptive pathway. In addition, given that the loss of TRPA1 channels does not affect hearing thresholds in mice, we believe that undiagnosed deficits in TRPA1-dependent responses in the human population could represent a hidden susceptibility for cochlear damage after noise exposure or other insults.

AI-enabled methods for de novo design of functional peptides

PROJECT SUMMARY Macrocyclic peptides offer unique therapeutic potential, particularly for targeting intracellular protein-protein interactions considered ‘undruggable’ with traditional therapeutic modalities. Additionally, peptides can combine the benefits and bridge the gap between conventional small molecule therapeutics and large biologics. However, developing new peptide-based therapeutics using traditional approaches, such as natural product discovery or high-throughput library screening, has remained slow and challenging. Moreover, these conventional approaches cover a small fraction of the chemical and structural space, are restricted to a few starting peptide scaffolds, and typically fail to optimize for multiple therapeutic properties simultaneously. Our central hypothesis is that structure-guided deep learning methods can rapidly explore the chemical and structural space beyond natural products and enable precise, rapid, and custom design of functional peptides simultaneously optimized for target binding, selectivity, and membrane permeability. In our recent work, we developed physics-based methods for designing constrained peptides and macrocycles and, more recently, introduced deep learning methods for structure prediction, sequence redesign, and de novo design of peptide monomers and targeted binders. Here, we propose to develop a new generation of structure-guided deep learning (DL) tools to address the current limitations of computational and experimental methods and enable accurate, accessible, and broadly applicable design of macrocycles. Specifically, we will pursue the projects focused on: (i) leveraging DL methods to systematically enumerate the chemical and structural space of constrained peptides and membrane-traversing peptides to develop scaffolds and core design principles for functional peptide design; (ii) high-throughput design and data collection to improve design selection, filtering metrics, and sequence design algorithms; (iii) developing generative DL methods that expand beyond current capabilities and allow sequence and structure design with vast chemical space of non-canonical amino acids; and (iv) use those new generative methods to design macrocyclic binders against different therapeutically-relevant targets, including the critical fusion and attachment proteins from viruses of pandemic concern. Our preliminary work in these proposed areas demonstrates the feasibility of this approach. The proposed computational tools, scaffold sets, and designed peptides will significantly advance therapeutic design beyond the state-of-the-art and enable rapid and custom design of drug- like peptides tailored for addressing complex therapeutic, diagnostic and research challenges.

Clinical Trial Readiness of MEG Biomarkers in Children Across the Autism Spectrum

PROJECT SUMMARY Biological and phenotypic heterogeneity of autism spectrum disorder (ASD) poses a major challenge for clinically focused research and interventions. Brain electrophysiological phenotyping holds promise for parsing this heterogeneity. Using magnetoencephalography (MEG), findings of diminished and delayed auditory evoked responses (e.g. the ~50ms component, M50 and, specifically, its latency: M50L) have reproducibly been shown in ASD, with correlation to behavior. Additionally, abnormal resting state activity and network functional connectivity has been identified as an electrophysiological hallmark. Such passively-acquired signatures may serve as objective biomarkers in subtyping autistic individuals, including stratifying patients for inclusion in clinical trials according to biology, rather than behavior alone. However, despite their abundant promise, these measures are not yet permeating clinical trial design, nor being utilized in clinical practice, in part because of their lack of standardized implementation and analysis. This proposal seeks to remedy this by using rigorous and standardized, scalable and sharable methods with two leading MEG measures to determine their measurement- reliability as well as their sensitivity to inter-individual differences in clinically-relevant aspects of autism features, general cognitive ability and language and communication. Specifically adopting a 12-week repeated scanning design, mimicking the duration of a typical pharmaceutical trial or behavioral intervention, we will acquire each of these two MEG metrics at baseline and 12-week follow-up to assess interval change. Additionally, we will evaluate test-retest variability with an intermediate measurement point 4-weeks after baseline. As such we will characterize both intra-subject variability (measurement precision) and inter-subject variability which will be correlated with dimension axes of autism features, general cognitive ability and language skills, as well as major co-occurring condition confounds. These studies will recruit a broad range of 240 autistic children, paralleling the CDC’s prevalence data on intellectual ability and encompassing the group considered as having “profound autism”. This is enabled by our adoption of MEG-PLAN, a strategy developed over the last decade in our group and demonstrated to enhance inclusive participation in MEG scanning studies, even in non-verbal participants. Data will be compared to a control group of age-matched typically-developing peers. The two MEG measures will also be assessed for their ability to identify clusters of less heterogeneous neurophysiological phenotype as a novel basis for stratification or subtyping of the heterogeneous autism population. In culmination, this study addresses key “clinical readiness” aspects of utilization of MEG biomarkers for ASD including profound autism, for both stratification (inclusion/trial selection) and monitoring of response to intervention, and will, ultimately, pave the way for the adoption of such biomarkers as adjunctive tests in increasingly-routine clinical practice.

Examining the foundations of reading comprehension: a longitudinal study of brain and behavior starting in infancy

SUMMARY Reading comprehension (RC) is one of the most complex skills that we utilize daily and is crucial for functioning in modern society, but despite its significance for academic achievement, employment prospects, and mental health, many children and adults do not exhibit proficient RC abilities. New theoretical models aiming to explain variability in RC suggest a dynamic interplay and co-development among ‘precursor’ foundational and cognitive- linguistic skills, interacting with environmental and socio-ecological factors across the developmental timeline of learning to read. Behavioral and neuroimaging studies in school-age children have demonstrated critical mechanistic support for these multifactorial RC models by identifying the developmental trajectories of precursor skills and further showing that brain areas, tracts, and networks typically underlying language and cognitive skills are also involved in RC. Nevertheless, the precursor skills that support RC start developing in infancy and the brain correlates underlying these precursors begin to develop in utero, which suggests that typical and atypical RC developmental trajectories could diverge long before school age. As such, examining RC development using a multifactorial, longitudinal approach that includes brain and behavior starting in infancy is critical for developing theoretical frameworks that can inform early preventative and intervention strategies. Here, we propose a comprehensive longitudinal study of RC development in which we examine direct and indirect effects on RC from brain, behavioral, familial risk, and environmental data from infancy to adolescence. To achieve this goal, we will combine two existing longitudinal cohorts, one ranging from infancy to late childhood (n = 174) and the other from preschool to early adolescence (n = 137). By applying state-of-the-art pediatric neuroimaging analyses, multiple indicator growth model structural equation models, and an innovative behavior- brain co-development measurement index to this unique, combined dataset, we will be able to identify brain and behavioral measures in infancy that directly and indirectly support subsequent RC development (Aim1). We will further characterize how longitudinal trajectories of behavioral measures as well as brain structure, function, and white matter organization contribute to RC development and how familial risk and environmental factors shape these trajectories (Aim 2). Finally, we will examine how the co-development of brain and behavior, as measured with an innovative co-development index, relates to subsequent RC (Aim 3). If successful, we will contribute the first multifactorial longitudinal model of RC development comprising direct and indirect effects from brain, behavior, brain-behavior co-development, familial risk, and environmental measures beginning in infancy. Understanding RC development using a multifactorial longitudinal lens will be crucial for building theoretical models and developing experimental designs focused on early preventative and intervention approaches long before the start of formal schooling.

Maternal Depression and Antidepressant Effects on Fetal Brain Structure and Function (FABMOMS)

PROJECT ABSTRACT Major depressive disorder (MDD) is one of the most common diseases in childbearing women, with a prevalence of 12.7% in pregnancy and 21.9% the year after birth. Exposure to maternal stress and depressive symptoms alters fetal/infant neurodevelopment, functional brain connectivity, and networks implicated in stress processing. About 5% of pregnant women are prescribed a serotonin selective or serotonin norepinephrine reuptake inhibitor (collectively, SRI). Remission of maternal MDD is crucial to the health and functioning of the mother and family. In observational studies typical of this field, differentiating the effects of drug exposure on offspring from the sequelae of the underlying psychiatric disease, both physiological and psychosocial, is challenging. Substantial progress has been made using sophisticated study designs and analytic approaches with large pregnancy cohorts that reduce the risk of spurious associations. Increased rates of overall and cardiac defects, stillbirth, preterm birth, and fetal growth have been largely explained by confounding by factors associated with both MDD and these outcomes rather than SRI exposure. Assessing the neurobehavioral development of children exposed in utero to SRI is the current research priority in this field. Our team pioneered the development of novel and safe fetal and neonatal quantitative magnetic resonance imaging (qMRI) tools, which will be combined with an evaluation of maternal heart rate variability to explore associations between exposures to stress, psychiatric symptoms and SRI on fetal and neonatal brain structure and function. The overarching goal of this project is to evaluate the separate and interactive effects of exposure to antidepressants in utero and maternal MDD on fetal and infant brain structure and function, with a specific focus on the hippocampus. We will accomplish this by evaluating four groups of pregnant women who have: 1) MDD treated with SRI to remission), 2) MDD treated with SRI (non-remitted, with both depressive symptom and SRI exposure), 3) MDD untreated with antidepressants, and 4) no current MDD or SRI treatment. Maternal assessments will occur at intake and in the early third trimesters and in then newborn period (at the time of fetal/newborn MRI) after birth. Maternal and infant evaluations will continue at 6 and 12 months postpartum. Maternal psychosocial and psychiatric status will provide extensive data on the context in which mothers experience pregnancy and infant care and allow adjustment for factors that will inevitably differ across groups. Lastly, we will explore the effects of maternal choline on MDD and offspring brain development. As these exposures and neurodevelopmental studies are conducted, exploring primary preventive strategies is a public health imperative. We will explore a potential mediator, poor maternal choline intake, a modifiable risk factor for both maternal MDD and altered fetal hippocampal growth and infant neurobehavior.

Validating Causality of Disputed Mitochondrial Variants in Inborn Errors of Metabolism

PROJECT SUMMARY Primary mitochondrial disease (PMD) encompasses multi-systemic disorders caused by impaired mitochondrial function. PMDs arise from pathogenic variants in either nuclear genes encoding mitochondrial proteins, or in the mitochondrial DNA (mtDNA) genome. Clinical diagnosis is challenging due to phenotypic heterogeneity, underscoring the importance of genetic diagnosis. ACMG/AMP guidelines provide a well-established framework for interpreting nuclear DNA variants while diagnosing genetic diseases. Their application to mtDNA variants, however, remains challenging due to unique features of mtDNA: maternal inheritance, heteroplasmy, threshold effects, and effect of transfer or ribosomal RNA rather than coding variants. To address these challenges, the ClinGen Mitochondrial Disease Nuclear and Mitochondrial Variant Curation Expert Panel, co-chaired by the Multi-PIs of this study, developed widely adopted ACMG/AMP revised guidelines for mtDNA variant interpretation. Over the past five years, this global expert panel has curated more than 280 mtDNA variant. Because of the lack of functional data of individual mtDNA variants in the literature, 23 previously reported pathogenic (P) variants were classified as Variants of Uncertain Significance (VUS), hindering definitive PMD diagnoses and therapeutic development. This R01 project aims to resolve the pathogenicity of these 23 mtDNA VUS through functional validation, leveraging advanced mtDNA base editing and single-cell genomics in in vitro and in vivo models. In Aim 1, we will create human 143B cell line models for 20 VUS using cutting-edge mtDNA editing techniques, optimized for efficiency and minimal off-target effects. Single-cell genomics (mtscATAC-seq and scRNA-seq) will assess heteroplasmy and genomic changes, while functional assays will evaluate mitochondrial ATP production, oxidative phosphorylation, membrane potential, and redox stress. Aim 2 will develop zebrafish models for 17 conserved VUS, characterizing phenotypic and mitochondrial outcomes to corroborate in vitro findings and PMD patient phenotypes. This study will clarify longstanding uncertainties regarding the pathogenicity of these mtDNA VUSs which were nonetheless reported to be pathogenic with often strong genetic evidence but limited functional data. The study will also establish valuable cell and zebrafish models and provide mechanistic insights of PMDs. The resulting resources will be shared with the scientific community to accelerate research and therapeutic advancements for novel precision medicine approaches for PMDs.

Hepatotoxicity of Legacy and Replacement PFAS: Role of BRUCE-Mitochondrial Interactions

Epidemiological studies have shown a strong association between exposure to PFAS (Per- and Poly- fluoroalkyl Substances) and liver toxicity. Particularly, legacy C8-PFAS members, PFOS (perfluorooctane sulfonate) and PFOA (perfluorooctanoic acid), are highly toxic, with PFOS estimated to be approximately 10 times more toxic than PFOA in ecotoxicity models. Consequently, PFAS replacements such as GenX and PFBS are marketed as safe alternatives, although growing evidence indicates that these substitutes also exhibit toxic effects. Lab animal model studies have shown hepatotoxic effects of both legacy and replacement PFAS members, characterized by Metabolic dysfunction-associated steatotic liver disease (MASLD) and its severe form Metabolic dysfunction- associated steatohepatitis (MASH), the two chronic liver diseases affecting an estimated 80-100 million Americans. The broader objective of this project is to understand the underlying mechanisms of PFAS hepatotoxicity in MASLD/MASH. In this context, our initial studies have shown that PFAS exposure of mice downregulates hepatic BRUCE, an autophagy inhibitor, resulting in development of MASLD in WT, and more severe MASLD and even progression to MASH in BRUCE liver-knockdown (BKO) mice. Using primary hepatocytes, we found PFAS-induced BRUCE reduction compromised mitochondrial (mt) functions (respiration, fatty acid oxidation/FAO, and ATP production) and suppressed mitophagy in WT and more so in BKO mice. Pharmacological restoration of mt function in mice prevented PFAS-induced MASLD/MASH. Guided by these compelling preliminary data and scientific premise, we hypothesize that PFAS degradation of BRUCE in hepatocytes induces excessive autophagy (resulting in cytotoxicity) and inhibits mitophagy (resulting in accumulation of damaged mitochondria), leading to release of mtDAMPs to activate inflammation/ fibrosis, thereby facilitating progression from MASLD to MASH. We will test this by three specific aims. Aim 1 (ex vivo) is to determine the human-relevant PFAS doses that modulate BRUCE levels for homeostatic vs cytotoxic autophagy and how BRUCE in turn regulates autophagy. Aim 2 (ex vivo) will investigate BRUCE-driven mitophagy pathway specific to PFAS exposure at human-relevant doses. Aim 3 (ex vivo and in vivo) will involve ex vivo simulation experiments to characterize the role of PFAS-induced, BRUCE-dependent hepatocyte- released mt DAMPs in activation of immune and fibrogenic cells using co-culture assays. Next, we will perform in vivo intervention to validate the role of PFAS-damaged mitochondria in driving MASH progression in mouse models. Furthermore, human relevance of the delineated mechanisms will be ascertained and validated using iPSC-derived human liver organoid system. Impact: This project will advance our understanding of autophagy/mitophagy-centric mechanisms with therapeutic potential in the context of PFAS-induced liver disease MASLD/MASH.

Improving Disease-Modifying Therapy Uptake among Patients with Multiple Sclerosis

Project Summary/Abstract Recent advances in the epidemiology of multiple sclerosis (MS) indicate that its prevalence is similar among White (238 per 100,000) and Black (226 per 100,000) populations. These data challenge historic assumptions about individuals with northern European heritage having higher risk and prevalence of MS. Evidence also suggests that MS incidence may be higher than previously recognized in the United States and increasing over time with more individuals identified and diagnosed year over year. MS continues to impose significant and growing burden on patients, healthcare systems and society. These health differences in the diagnosis, treatment and symptom management of MS in light of the increasing prevalence of MS in the US are an important public health issue that requires broader urgent research and policy attention to reduce the overall disease burden. In this study, we will use real-world data derived from the electronic health records (EHR) from four large academic medical centers (University of Kentucky, University of Virginia, Virginia Commonwealth University, and University of Southern California). Extracted EHR data from these four medical centers will be deidentified, combined, and harmonized. We will use this combined data set to examine (1) whether there are any differences in the timely treatment of disease modifying therapy (DMT) among different MS populations, (2) any disparities in the management of symptoms and comorbidities, (3) how non-medical factors of health such as income, education, and health insurance status (patientlevel), linguistically appropriate care provision (provider-level), and neighborhood factors (system-level) affect these outcomes and influence disparities across populations, and (4) assess whether disparities exist in the risks of cardiovascular disease CVD and mortality in MS subgroups and examine if these disparities can be reduced with improved treatment of MS and vascular comorbidities. In pursuing these objectives, we will identify clinical solutions (e.g., optimal DMT sequences) and non-medical factors such as neighborhood factors such as poverty, educational achievement, crime rates, civic participation, and housing quality, access to care factors, and cultural and linguistic match between providers and patients that substantially contribute to health disparities. For actionable solutions, we will rank-order these factors by their relative importance in addressing disparities, which will guide decision-making at the policy, system, and provider level. Our long-term objective is to develop public health strategies and scalable solutions to reduce overall burden in the management of MS. This project is expected to help policy makers and health system administrators in prioritizing interventions and to have implications for clinical practice in improving care of all patients with MS in neurology clinics, at the healthcare system level, and for national health policy.

Tbx4-Driven Pulmonary Hypertension: Mechanisms and Therapeutic Targets

Project Summary: Heterozygous rare variants in TBX4 are the second most common cause of heritable pulmonary arterial hypertension (PAH). Presentation of this form is commonly in children. Patients with mutations in TBX4 generally have alveolar simplification or hypoplasia in addition to elevated pulmonary vascular resistance. We have developed a set of three tools to help determine the molecular etiology of TBX4-induced PAH; (1) we identified the direct binding targets using a combination of ChIP-seq and RNA-seq; (2) we developed a mouse model with Tbx4 knockout after birth, that substantially phenocopies human disease; (3) we performed single-cell RNA-seq on these mice. By combining these three tools, we can develop a complete model for how loss of a transcription factor leads to the molecular and physiologic changes we see in our mice. The phenotype in mice appears to be dominated by defects in pericytes, resulting in impaired angiogenesis. Pericytes, which strongly express Tbx4, are cells located on the outside of capillaries and precapillary arterioles, and can either stabilize vessels (mesh pericytes), or drive angiogenesis (angiogenic pericytes). The pericytes in Tbx4 mutant mice are heavily skewed towards mesh and away from the angiogenic phenotype. Loss of Tbx4 results in derepression of Tbx4 binding target Rgs5 (10x induction), which directly results in inhibition of Pi3K, and the phenotypic switch in pericytes. We will test this hypothesis through pericyte-specific Tbx4 knockout (Aim 1) and pharmacologic induction of Pi3K in vivo in prevention and rescue models, as well as by siRNA to Rgs5 in precision-cut lung slices from Tbx4 KO mice (Aim 3). We will also test the role of Tbx4 in fibroblasts and smooth muscle using cell-specific knockouts – based on our mouse and single cell data, we expect they contribute somewhat, but primarily through increased stiffness (Aim 2). Finally, we will confirm relevance to human disease through spatial transcriptomics in lung sections explanted from patients with TBX4 mutation or rearrangement (Aim 1), and through determining whether defects in human patient iPSC-derived pericytes can be corrected through Rgs5 or Pi3K interventions (Aim 3). In combination, these aims determine the cellular and molecular mechanisms leading from mutation to physiology with loss of TBX4, and establish therapeutic targets.

The Pyruvate-Lactate Metabolic Axis in Heart Failure and Recovery

PROJECT SUMMARY/ABSTRACT Heart failure (HF) is a leading cause of mortality worldwide. The metabolism of the failing heart is commonly characterized by increased glucose uptake, glycolytic dependence, and reduced oxidative phosphorylation. We previously demonstrated that blocking glucose oxidation is sufficient to cause hypertrophy and subsequent HF. Additionally, our preliminary data shows that an altered pyruvate-lactate metabolic axis may be pivotal in human HF. Research investigating both the mechanistic regulation and biological roles of the pyruvate-lactate metabolic axis in cardiac metabolism during HF and cardiac recovery is warranted and also has the potential to identify novel druggable pathways to target for future pharmacological approaches. The overall objective of this application is to test the hypothesis that impaired pyruvate oxidation is a cardinal feature of HF in humans and animal models and that myocardial recovery is tightly coupled to normalization of the pyruvate-lactate metabolic axis. We will quantify the pyruvate-lactate metabolic axis in human HF and myocardial recovery (Aim 1). Next, we will determine the essentiality of the pyruvate-lactate metabolic axis for HF and cardiac recovery (Aim 2). Lastly, we will define cell-autonomous mechanisms that regulate the pyruvate-lactate axis in HF and recovery (Aim 3). These experiments will allow us to identify patterns of metabolic alteration in the pyruvate-lactate axis and molecular pathways during HF and myocardial recovery. Understanding the role of pyruvate and lactate metabolism in HF and myocardial recovery is cutting-edge research. Our unique access to human HF myocardium from patients administered stable isotope-labeled glucose or lactate to quantitate pyruvate metabolism in HF and recovery is state-of-the-art and will likely help us reveal new fundamental mechanisms of cardiac metabolism and expedite the successful translation of therapeutics being validated in various models of HF and recovery.

Role of stress signals in the pathogenesis of pulmonary veno-occlusive disease

PROJECT SUMMARY/ABSTRACT Pulmonary veno-occlusive disease (PVOD) is a subclass of pulmonary hypertension characterized by preferential remodeling of the pulmonary venules and capillaries, and currently, there are no efficacious drug therapies. The clinical presentations and the radiographic findings of PVOD are indistinguishable from PAH, and therefore, it is often misclassified as PAH. However, the application of PAH therapeutics to PVOD patients leads to life-threatening pulmonary edema, thus, there is a critical need for diagnostic methods that accurately differentiate PVOD from PAH. Genetically, PVOD is associated with biallelic loss of function (LOF) mutations in the EIF2AK4 gene encoding GCN2. GCN2 phosphorylates the alpha subunit of eukaryotic initiation factor 2 (eIF2α), shuts down protein synthesis, and activates the integrated stress response (ISR). However, the molecular mechanisms connecting the loss of GCN2 with pulmonary vascular remodeling are poorly understood. Recent studies find that biallelic EIF2AK4 mutations are identified in ~9% of PAH patients. Conversely, heterozygous mutations in the BMPR2 gene, a leading cause of PAH, have been reported in PVOD patients. These results suggest that (i) PVOD and PAH share some of the pathophysiological mechanisms, and (ii) the presence of EIF2AK4 or BMPR2 mutations does not provide an accurate genetic diagnosis for PVOD. The long-term goal of this proposal is to elucidate the pathophysiological mechanisms involved in remodeling not only pulmonary arterioles but also venules and capillaries and develop those pathways as potential therapies for POVD. It has been observed that cancer patients administered with the chemotherapeutic agent mitomycin-C (MMC) rapidly develop PVOD. Rats administered with MMC develop PVOD-like phenotypes, including right ventricular (RV) hypertrophy, increased RV systolic pressure, and pulmonary vascular lesions in arteries and veins. We found that Rad51, an essential enzyme for double-strand DNA break repair, associates with VE-Cad in the vascular endothelium; however, upon MMC treatment, Rad51 and VE-Cad complex (VRC) were released into the circulation, resulting in increased vascular permeability and reduced barrier integrity. MMC treatment also mediates the depletion of GCN2, which recapitulates the genetic cause of PVOD (LOF EIF2AK4 mutations). Based on these data, this proposal will test the hypothesis that the vascular remodeling in PVOD involves (i) the release of VRC, (ii) the aberrant protein synthesis due to the activation of ISR, and (iii) the mechanism of maladaptive ISR activation. Finally, we will explore the potential application of the circulating VRC as a blood biomarker for PVOD.

Bridging Local and System-Wide Autoreactive, Extrafollicular B Cell Signatures in a TLR7-Driven Model

Project Summary A substantial body of literature has described the development of autoreactive humoral responses in the context of autoimmune disease and recently discerned an exciting new avenue for investigation. While early work focused on canonical mechanisms of activation through the germinal center (GC) response, recent studies have found GC infrastructure to be dispensable for the onset of chronic autoimmunity. It has become clear that an alternative pathway of B cell activation, the extrafollicular (EF) pathway, can drive the onset of new autoreactivity in multiple human disorders including rheumatoid arthritis and systemic lupus erythematosus (SLE). In comparison to the GC pathway, the EF pathway represents a less stringent method for B cell activation, leads to accelerated antibody-secreting cell (ASC) formation, and thus has a higher propensity for the production of autoreactive B cell effectors and ASCs. Recently, our group has identified a similar skew toward the EF response in the context of severe viral infection, tied to acute tolerance loss, increased disease severity, and complicated recovery from infection. These findings highlight how further study of the EF response is crucial to our understanding of autoimmune induction across multiple areas of disease. Toll-like receptor 7 (TLR7) stimulation has been identified as a key contributor to EF B cell development in SLE, and several studies have now linked TLR7 overstimulation to chronic autoimmune disease. While EF effector B cell populations have now been identified in both murine models and humans, substantial gaps in our knowledge remain to be answered concerning i) the origins of these cells and ii) the system-wide and microenvironmental signaling and organization that drive this differentiation pathway. We propose to address these gaps, here, by utilizing a TLR7 agonist (R848) in a murine model to characterize the autoreactive response within the blood and draining lymph node through innovative high-throughput analytical techniques. Systemic shifts in proteomic signatures and immune cell phenotype will be monitored in the blood throughout the induction of autoreactivity, using novel applications of machine-learning based classification. These signatures will then be connected to developing inflammatory microenvironments identified within the draining lymph node by applying a customized set of software tools to spatial transcriptomic data. This work will deepen our understanding of the immunologic mechanisms by which the EF pathway can lead to “run-away” autoreactive B cell development, with the added potential for identification of early blood-based biomarkers for this developing autoreactivity. The above proposed work will provide an ideal training opportunity for the candidate to develop experience with advanced immunologic laboratory techniques, rigorous bioinformatic analysis, a systems-level view of immunology, and scientific communication. The Woodruff and Sanz Labs are highly experienced within the autoimmune disease space with extensive experience with the required techniques and established routes for clinical collaboration to act on these findings.

Decoding stress vulnerability

Although stress can be considered as an ongoing process that helps an organism to cope with present and future challenges, when it is too intense or uncontrollable, it can lead to adverse consequences for physical and mental health. Social stress specifically, is a highly prevalent traumatic experience, present in multiple contexts, such as war, bullying and interpersonal violence, and it has been linked with increased risk for major depression and anxiety disorders. Nevertheless, not all individuals exposed to strong stressful events develop psychopathology, with the mechanisms of resilience and vulnerability being still under investigation. During this talk, I will identify key gaps in our knowledge about stress vulnerability and I will present our recent data from our contextual fear learning protocol based on social defeat stress in mice.

Organization of thalamic networks and mechanisms of dysfunction in schizophrenia and autism

Thalamic networks, at the core of thalamocortical and thalamosubcortical communications, underlie processes of perception, attention, memory, emotions, and the sleep-wake cycle, and are disrupted in mental disorders, including schizophrenia and autism. However, the underlying mechanisms of pathology are unknown. I will present novel evidence on key organizational principles, structural, and molecular features of thalamocortical networks, as well as critical thalamic pathway interactions that are likely affected in disorders. This data can facilitate modeling typical and abnormal brain function and can provide the foundation to understand heterogeneous disruption of these networks in sleep disorders, attention deficits, and cognitive and affective impairments in schizophrenia and autism, with important implications for the design of targeted therapeutic interventions

Spike train structure of cortical transcriptomic populations in vivo

The cortex comprises many neuronal types, which can be distinguished by their transcriptomes: the sets of genes they express. Little is known about the in vivo activity of these cell types, particularly as regards the structure of their spike trains, which might provide clues to cortical circuit function. To address this question, we used Neuropixels electrodes to record layer 5 excitatory populations in mouse V1, then transcriptomically identified the recorded cell types. To do so, we performed a subsequent recording of the same cells using 2-photon (2p) calcium imaging, identifying neurons between the two recording modalities by fingerprinting their responses to a “zebra noise” stimulus and estimating the path of the electrode through the 2p stack with a probabilistic method. We then cut brain slices and performed in situ transcriptomics to localize ~300 genes using coppaFISH3d, a new open source method, and aligned the transcriptomic data to the 2p stack. Analysis of the data is ongoing, and suggests substantial differences in spike time coordination between ET and IT neurons, as well as between transcriptomic subtypes of both these excitatory types.

How the presynapse forms and functions”

Nervous system function relies on the polarized architecture of neurons, established by directional transport of pre- and postsynaptic cargoes. While delivery of postsynaptic components depends on the secretory pathway, the identity of the membrane compartment(s) that supply presynaptic active zone (AZ) and synaptic vesicle (SV) proteins is largely unknown. I will discuss our recent advances in our understanding of how key components of the presynaptic machinery for neurotransmitter release are transported and assembled focussing on our studies in genome-engineered human induced pluripotent stem cell-derived neurons. Specifically, I will focus on the composition and cell biological identity of the axonal transport vesicles that shuttle key components of neurotransmission to nascent synapses and on machinery for axonal transport and its control by signaling lipids. Our studies identify a crucial mechanism mediating the delivery of SV and active zone proteins to developing synapses and reveal connections to neurological disorders. In the second part of my talk, I will discuss how exocytosis and endocytosis are coupled to maintain presynaptic membrane homeostasis. I will present unpublished data regarding the role of membrane tension in the coupling of exocytosis and endocytosis at synapses. We have identified an endocytic BAR domain protein that is capable of sensing alterations in membrane tension caused by the exocytotic fusion of SVs to initiate compensatory endocytosis to restore plasma membrane area. Interference with this mechanism results in defects in the coupling of presynaptic exocytosis and SV recycling at human synapses.

The Systems Vision Science Summer School & Symposium, August 11 – 22, 2025, Tuebingen, Germany

Applications are invited for our third edition of Systems Vision Science (SVS) summer school since 2023, designed for everyone interested in gaining a systems level understanding of biological vision. We plan a coherent, graduate-level, syllabus on the integration of experimental data with theory and models, featuring lectures, guided exercises and discussion sessions. The summer school will end with a Systems Vision Science symposium on frontier topics on August 20-22, with additional invited and contributed presentations and posters. Call for contributions and participations to the symposium will be sent out spring of 2025. All summer school participants are invited to attend, and welcome to submit contributions to the symposium.

The Systems Vision Science Summer School & Symposium, August 11 – 22, 2025, Tuebingen, Germany

Applications are invited for our third edition of Systems Vision Science (SVS) summer school since 2023, designed for everyone interested in gaining a systems level understanding of biological vision. We plan a coherent, graduate-level, syllabus on the integration of experimental data with theory and models, featuring lectures, guided exercises and discussion sessions. The summer school will end with a Systems Vision Science symposium on frontier topics on August 20-22, with additional invited and contributed presentations and posters. Call for contributions and participations to the symposium will be sent out spring of 2025. All summer school participants are invited to attend, and welcome to submit contributions to the symposium.

The Systems Vision Science Summer School & Symposium, August 11 – 22, 2025, Tuebingen, Germany

Applications are invited for our third edition of Systems Vision Science (SVS) summer school since 2023, designed for everyone interested in gaining a systems level understanding of biological vision. We plan a coherent, graduate-level, syllabus on the integration of experimental data with theory and models, featuring lectures, guided exercises and discussion sessions. The summer school will end with a Systems Vision Science symposium on frontier topics on August 20-22, with additional invited and contributed presentations and posters. Call for contributions and participations to the symposium will be sent out spring of 2025. All summer school participants are invited to attend, and welcome to submit contributions to the symposium.

The Systems Vision Science Summer School & Symposium, August 11 – 22, 2025, Tuebingen, Germany

Applications are invited for our third edition of Systems Vision Science (SVS) summer school since 2023, designed for everyone interested in gaining a systems level understanding of biological vision. We plan a coherent, graduate-level, syllabus on the integration of experimental data with theory and models, featuring lectures, guided exercises and discussion sessions. The summer school will end with a Systems Vision Science symposium on frontier topics on August 20-22, with additional invited and contributed presentations and posters. Call for contributions and participations to the symposium will be sent out spring of 2025. All summer school participants are invited to attend, and welcome to submit contributions to the symposium.

The Systems Vision Science Summer School & Symposium, August 11 – 22, 2025, Tuebingen, Germany

Applications are invited for our third edition of Systems Vision Science (SVS) summer school since 2023, designed for everyone interested in gaining a systems level understanding of biological vision. We plan a coherent, graduate-level, syllabus on the integration of experimental data with theory and models, featuring lectures, guided exercises and discussion sessions. The summer school will end with a Systems Vision Science symposium on frontier topics on August 20-22, with additional invited and contributed presentations and posters. Call for contributions and participations to the symposium will be sent out spring of 2025. All summer school participants are invited to attend, and welcome to submit contributions to the symposium.

The Systems Vision Science Summer School & Symposium, August 11 – 22, 2025, Tuebingen, Germany

Applications are invited for our third edition of Systems Vision Science (SVS) summer school since 2023, designed for everyone interested in gaining a systems level understanding of biological vision. We plan a coherent, graduate-level, syllabus on the integration of experimental data with theory and models, featuring lectures, guided exercises and discussion sessions. The summer school will end with a Systems Vision Science symposium on frontier topics on August 20-22, with additional invited and contributed presentations and posters. Call for contributions and participations to the symposium will be sent out spring of 2025. All summer school participants are invited to attend, and welcome to submit contributions to the symposium.

The Systems Vision Science Summer School & Symposium, August 11 – 22, 2025, Tuebingen, Germany

Applications are invited for our third edition of Systems Vision Science (SVS) summer school since 2023, designed for everyone interested in gaining a systems level understanding of biological vision. We plan a coherent, graduate-level, syllabus on the integration of experimental data with theory and models, featuring lectures, guided exercises and discussion sessions. The summer school will end with a Systems Vision Science symposium on frontier topics on August 20-22, with additional invited and contributed presentations and posters. Call for contributions and participations to the symposium will be sent out spring of 2025. All summer school participants are invited to attend, and welcome to submit contributions to the symposium.

The Systems Vision Science Summer School & Symposium, August 11 – 22, 2025, Tuebingen, Germany

Applications are invited for our third edition of Systems Vision Science (SVS) summer school since 2023, designed for everyone interested in gaining a systems level understanding of biological vision. We plan a coherent, graduate-level, syllabus on the integration of experimental data with theory and models, featuring lectures, guided exercises and discussion sessions. The summer school will end with a Systems Vision Science symposium on frontier topics on August 20-22, with additional invited and contributed presentations and posters. Call for contributions and participations to the symposium will be sent out spring of 2025. All summer school participants are invited to attend, and welcome to submit contributions to the symposium.

The Systems Vision Science Summer School & Symposium, August 11 – 22, 2025, Tuebingen, Germany

Applications are invited for our third edition of Systems Vision Science (SVS) summer school since 2023, designed for everyone interested in gaining a systems level understanding of biological vision. We plan a coherent, graduate-level, syllabus on the integration of experimental data with theory and models, featuring lectures, guided exercises and discussion sessions. The summer school will end with a Systems Vision Science symposium on frontier topics on August 20-22, with additional invited and contributed presentations and posters. Call for contributions and participations to the symposium will be sent out spring of 2025. All summer school participants are invited to attend, and welcome to submit contributions to the symposium.

The Systems Vision Science Summer School & Symposium, August 11 – 22, 2025, Tuebingen, Germany

Applications are invited for our third edition of Systems Vision Science (SVS) summer school since 2023, designed for everyone interested in gaining a systems level understanding of biological vision. We plan a coherent, graduate-level, syllabus on the integration of experimental data with theory and models, featuring lectures, guided exercises and discussion sessions. The summer school will end with a Systems Vision Science symposium on frontier topics on August 20-22, with additional invited and contributed presentations and posters. Call for contributions and participations to the symposium will be sent out spring of 2025. All summer school participants are invited to attend, and welcome to submit contributions to the symposium.

OpenNeuro FitLins GLM: An Accessible, Semi-Automated Pipeline for OpenNeuro Task fMRI Analysis

In this talk, I will discuss the OpenNeuro Fitlins GLM package and provide an illustration of the analytic workflow. OpenNeuro FitLins GLM is a semi-automated pipeline that reduces barriers to analyzing task-based fMRI data from OpenNeuro's 600+ task datasets. Created for psychology, psychiatry and cognitive neuroscience researchers without extensive computational expertise, this tool automates what is largely a manual process and compilation of in-house scripts for data retrieval, validation, quality control, statistical modeling and reporting that, in some cases, may require weeks of effort. The workflow abides by open-science practices, enhancing reproducibility and incorporates community feedback for model improvement. The pipeline integrates BIDS-compliant datasets and fMRIPrep preprocessed derivatives, and dynamically creates BIDS Statistical Model specifications (with Fitlins) to perform common mass univariate [GLM] analyses. To enhance and standardize reporting, it generates comprehensive reports which includes design matrices, statistical maps and COBIDAS-aligned reporting that is fully reproducible from the model specifications and derivatives. OpenNeuro Fitlins GLM has been tested on over 30 datasets spanning 50+ unique fMRI tasks (e.g., working memory, social processing, emotion regulation, decision-making, motor paradigms), reducing analysis times from weeks to hours when using high-performance computers, thereby enabling researchers to conduct robust single-study, meta- and mega-analyses of task fMRI data with significantly improved accessibility, standardized reporting and reproducibility.

Understanding reward-guided learning using large-scale datasets

Understanding the neural mechanisms of reward-guided learning is a long-standing goal of computational neuroscience. Recent methodological innovations enable us to collect ever larger neural and behavioral datasets. This presents opportunities to achieve greater understanding of learning in the brain at scale, as well as methodological challenges. In the first part of the talk, I will discuss our recent insights into the mechanisms by which zebra finch songbirds learn to sing. Dopamine has been long thought to guide reward-based trial-and-error learning by encoding reward prediction errors. However, it is unknown whether the learning of natural behaviours, such as developmental vocal learning, occurs through dopamine-based reinforcement. Longitudinal recordings of dopamine and bird songs reveal that dopamine activity is indeed consistent with encoding a reward prediction error during naturalistic learning. In the second part of the talk, I will talk about recent work we are doing at DeepMind to develop tools for automatically discovering interpretable models of behavior directly from animal choice data. Our method, dubbed CogFunSearch, uses LLMs within an evolutionary search process in order to "discover" novel models in the form of Python programs that excel at accurately predicting animal behavior during reward-guided learning. The discovered programs reveal novel patterns of learning and choice behavior that update our understanding of how the brain solves reinforcement learning problems.

Developmental and evolutionary perspectives on thalamic function

Brain organization and function is a complex topic. We are good at establishing correlates of perception and behavior across forebrain circuits, as well as manipulating activity in these circuits to affect behavior. However, we still lack good models for the large-scale organization and function of the forebrain. What are the contributions of the cortex, basal ganglia, and thalamus to behavior? In addressing these questions, we often ascribe function to each area as if it were an independent processing unit. However, we know from the anatomy that the cortex, basal ganglia, and thalamus, are massively interconnected in a large network. One way to generate insight into these questions is to consider the evolution and development of forebrain systems. In this talk, I will discuss the developmental and evolutionary (comparative anatomy) data on the thalamus, and how it fits within forebrain networks. I will address questions including, when did the thalamus appear in evolution, how is the thalamus organized across the vertebrate lineage, and how can the change in the organization of forebrain networks affect behavioral repertoires.

Neurobiological constraints on learning: bug or feature?

Understanding how brains learn requires bridging evidence across scales—from behaviour and neural circuits to cells, synapses, and molecules. In our work, we use computational modelling and data analysis to explore how the physical properties of neurons and neural circuits constrain learning. These include limits imposed by brain wiring, energy availability, molecular noise, and the 3D structure of dendritic spines. In this talk I will describe one such project testing if wiring motifs from fly brain connectomes can improve performance of reservoir computers, a type of recurrent neural network. The hope is that these insights into brain learning will lead to improved learning algorithms for artificial systems.

HealthCore: A modular data collection ecosystem to connect the dots in Neurorehab

Astrocytes release glutamate by regulated exocytosis in health and disease

Astrocytes release glutamate by regulated exocytosis in health and disease Vladimir Parpura, International Translational Neuroscience Research Institute, Zhejiang Chinese Medical University, Hangzhou, P.R. China Parpura will present you with the evidence that astrocytes, a subtype of glial cells in the brain, can exocytotically release the neurotransmitter glutamate and how this release is regulated. Spatiotemporal characteristic of vesicular fusion that underlie glutamate release in astrocytes will be discussed. He will also present data on a translational project in which this release pathway can be targeted for the treatment of glioblastoma, the deadliest brain cancer.

Expanding mechanisms and therapeutic targets for neurodegenerative disease

A hallmark pathological feature of the neurodegenerative diseases amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD) is the depletion of RNA-binding protein TDP-43 from the nucleus of neurons in the brain and spinal cord. A major function of TDP-43 is as a repressor of cryptic exon inclusion during RNA splicing. By re-analyzing RNA-sequencing datasets from human FTD/ALS brains, we discovered dozens of novel cryptic splicing events in important neuronal genes. Single nucleotide polymorphisms in UNC13A are among the strongest hits associated with FTD and ALS in human genome-wide association studies, but how those variants increase risk for disease is unknown. We discovered that TDP-43 represses a cryptic exon-splicing event in UNC13A. Loss of TDP-43 from the nucleus in human brain, neuronal cell lines and motor neurons derived from induced pluripotent stem cells resulted in the inclusion of a cryptic exon in UNC13A mRNA and reduced UNC13A protein expression. The top variants associated with FTD or ALS risk in humans are located in the intron harboring the cryptic exon, and we show that they increase UNC13A cryptic exon splicing in the face of TDP-43 dysfunction. Together, our data provide a direct functional link between one of the strongest genetic risk factors for FTD and ALS (UNC13A genetic variants), and loss of TDP-43 function. Recent analyses have revealed even further changes in TDP-43 target genes, including widespread changes in alternative polyadenylation, impacting expression of disease-relevant genes (e.g., ELP1, NEFL, and TMEM106B) and providing evidence that alternative polyadenylation is a new facet of TDP-43 pathology.

Neural mechanisms of optimal performance

When we attend a demanding task, our performance is poor at low arousal (when drowsy) or high arousal (when anxious), but we achieve optimal performance at intermediate arousal. This celebrated Yerkes-Dodson inverted-U law relating performance and arousal is colloquially referred to as being "in the zone." In this talk, I will elucidate the behavioral and neural mechanisms linking arousal and performance under the Yerkes-Dodson law in a mouse model. During decision-making tasks, mice express an array of discrete strategies, whereby the optimal strategy occurs at intermediate arousal, measured by pupil, consistent with the inverted-U law. Population recordings from the auditory cortex (A1) further revealed that sound encoding is optimal at intermediate arousal. To explain the computational principle underlying this inverted-U law, we modeled the A1 circuit as a spiking network with excitatory/inhibitory clusters, based on the observed functional clusters in A1. Arousal induced a transition from a multi-attractor (low arousal) to a single attractor phase (high arousal), and performance is optimized at the transition point. The model also predicts stimulus- and arousal-induced modulations of neural variability, which we confirmed in the data. Our theory suggests that a single unifying dynamical principle, phase transitions in metastable dynamics, underlies both the inverted-U law of optimal performance and state-dependent modulations of neural variability.

Neural mechanisms of rhythmic motor control in Drosophila

All animal locomotion is rhythmic,whether it is achieved through undulatory movement of the whole body or the coordination of articulated limbs. Neurobiologists have long studied locomotor circuits that produce rhythmic activity with non-rhythmic input, also called central pattern generators (CPGs). However, the cellular and microcircuit implementation of a walking CPG has not been described for any limbed animal. New comprehensive connectomes of the fruit fly ventral nerve cord (VNC) provide an opportunity to study rhythmogenic walking circuits at a synaptic scale.We use a data-driven network modeling approach to identify and characterize a putative walking CPG in the Drosophila leg motor system.

Single-neuron correlates of perception and memory in the human medial temporal lobe

The human medial temporal lobe contains neurons that respond selectively to the semantic contents of a presented stimulus. These "concept cells" may respond to very different pictures of a given person and even to their written or spoken name. Their response latency is far longer than necessary for object recognition, they follow subjective, conscious perception, and they are found in brain regions that are crucial for declarative memory formation. It has thus been hypothesized that they may represent the semantic "building blocks" of episodic memories. In this talk I will present data from single unit recordings in the hippocampus, entorhinal cortex, parahippocampal cortex, and amygdala during paradigms involving object recognition and conscious perception as well as encoding of episodic memories in order to characterize the role of concept cells in these cognitive functions.

Understanding reward-guided learning using large-scale datasets

Understanding the neural mechanisms of reward-guided learning is a long-standing goal of computational neuroscience. Recent methodological innovations enable us to collect ever larger neural and behavioral datasets. This presents opportunities to achieve greater understanding of learning in the brain at scale, as well as methodological challenges. In the first part of the talk, I will discuss our recent insights into the mechanisms by which zebra finch songbirds learn to sing. Dopamine has been long thought to guide reward-based trial-and-error learning by encoding reward prediction errors. However, it is unknown whether the learning of natural behaviours, such as developmental vocal learning, occurs through dopamine-based reinforcement. Longitudinal recordings of dopamine and bird songs reveal that dopamine activity is indeed consistent with encoding a reward prediction error during naturalistic learning. In the second part of the talk, I will talk about recent work we are doing at DeepMind to develop tools for automatically discovering interpretable models of behavior directly from animal choice data. Our method, dubbed CogFunSearch, uses LLMs within an evolutionary search process in order to "discover" novel models in the form of Python programs that excel at accurately predicting animal behavior during reward-guided learning. The discovered programs reveal novel patterns of learning and choice behavior that update our understanding of how the brain solves reinforcement learning problems.

Harnessing Big Data in Neuroscience: From Mapping Brain Connectivity to Predicting Traumatic Brain Injury

Neuroscience is experiencing unprecedented growth in dataset size both within individual brains and across populations. Large-scale, multimodal datasets are transforming our understanding of brain structure and function, creating opportunities to address previously unexplored questions. However, managing this increasing data volume requires new training and technology approaches. Modern data technologies are reshaping neuroscience by enabling researchers to tackle complex questions within a Ph.D. or postdoctoral timeframe. I will discuss cloud-based platforms such as brainlife.io, that provide scalable, reproducible, and accessible computational infrastructure. Modern data technology can democratize neuroscience, accelerate discovery and foster scientific transparency and collaboration. Concrete examples will illustrate how these technologies can be applied to mapping brain connectivity, studying human learning and development, and developing predictive models for traumatic brain injury (TBI). By integrating cloud computing and scalable data-sharing frameworks, neuroscience can become more impactful, inclusive, and data-driven..

Cognitive maps as expectations learned across episodes – a model of the two dentate gyrus blades

How can the hippocampal system transition from episodic one-shot learning to a multi-shot learning regime and what is the utility of the resultant neural representations? This talk will explore the role of the dentate gyrus (DG) anatomy in this context. The canonical DG model suggests it performs pattern separation. More recent experimental results challenge this standard model, suggesting DG function is more complex and also supports the precise binding of objects and events to space and the integration of information across episodes. Very recent studies attribute pattern separation and pattern integration to anatomically distinct parts of the DG (the suprapyramidal blade vs the infrapyramidal blade). We propose a computational model that investigates this distinction. In the model the two processing streams (potentially localized in separate blades) contribute to the storage of distinct episodic memories, and the integration of information across episodes, respectively. The latter forms generalized expectations across episodes, eventually forming a cognitive map. We train the model with two data sets, MNIST and plausible entorhinal cortex inputs. The comparison between the two streams allows for the calculation of a prediction error, which can drive the storage of poorly predicted memories and the forgetting of well-predicted memories. We suggest that differential processing across the DG aids in the iterative construction of spatial cognitive maps to serve the generation of location-dependent expectations, while at the same time preserving episodic memory traces of idiosyncratic events.

Brain Emulation Challenge Workshop

Brain Emulation Challenge workshop will tackle cutting-edge topics such as ground-truthing for validation, leveraging artificial datasets generated from virtual brain tissue, and the transformative potential of virtual brain platforms, such as applied to the forthcoming Brain Emulation Challenge.

Brain Emulation Challenge Workshop

Brain Emulation Challenge workshop will tackle cutting-edge topics such as ground-truthing for validation, leveraging artificial datasets generated from virtual brain tissue, and the transformative potential of virtual brain platforms, such as applied to the forthcoming Brain Emulation Challenge.

Brain Emulation Challenge Workshop

Brain Emulation Challenge workshop will tackle cutting-edge topics such as ground-truthing for validation, leveraging artificial datasets generated from virtual brain tissue, and the transformative potential of virtual brain platforms, such as applied to the forthcoming Brain Emulation Challenge.

Brain Emulation Challenge Workshop

Brain Emulation Challenge workshop will tackle cutting-edge topics such as ground-truthing for validation, leveraging artificial datasets generated from virtual brain tissue, and the transformative potential of virtual brain platforms, such as applied to the forthcoming Brain Emulation Challenge.

Brain Emulation Challenge Workshop

Brain Emulation Challenge workshop will tackle cutting-edge topics such as ground-truthing for validation, leveraging artificial datasets generated from virtual brain tissue, and the transformative potential of virtual brain platforms, such as applied to the forthcoming Brain Emulation Challenge.

Structural & Functional Neuroplasticity in Children with Hemiplegia

About 30% of children with cerebral palsy have congenital hemiplegia, resulting from periventricular white matter injury, which impairs the use of one hand and disrupts bimanual co-ordination. Congenital hemiplegia has a profound effect on each child's life and, thus, is of great importance to the public health. Changes in brain organization (neuroplasticity) often occur following periventricular white matter injury. These changes vary widely depending on the timing, location, and extent of the injury, as well as the functional system involved. Currently, we have limited knowledge of neuroplasticity in children with congenital hemiplegia. As a result, we provide rehabilitation treatment to these children almost blindly based exclusively on behavioral data. In this talk, I will present recent research evidence of my team on understanding neuroplasticity in children with congenital hemiplegia by using a multimodal neuroimaging approach that combines data from structural and functional neuroimaging methods. I will further present preliminary data regarding functional improvements of upper extremities motor and sensory functions as a result of rehabilitation with a robotic system that involves active participation of the child in a video-game setup. Our research is essential for the development of novel or improved neurological rehabilitation strategies for children with congenital hemiplegia.

The synaptic functions of Alpha Synuclein and Lrrk2

Alpha synuclein and Lrrk2 are key players in Parkinson's disease and related disorders, but their normal role has been confusing and controversial. Data from acute gene-editing based knockdown, followed by functional assays, will be presented.

Contentopic mapping and object dimensionality - a novel understanding on the organization of object knowledge

Our ability to recognize an object amongst many others is one of the most important features of the human mind. However, object recognition requires tremendous computational effort, as we need to solve a complex and recursive environment with ease and proficiency. This challenging feat is dependent on the implementation of an effective organization of knowledge in the brain. Here I put forth a novel understanding of how object knowledge is organized in the brain, by proposing that the organization of object knowledge follows key object-related dimensions, analogously to how sensory information is organized in the brain. Moreover, I will also put forth that this knowledge is topographically laid out in the cortical surface according to these object-related dimensions that code for different types of representational content – I call this contentopic mapping. I will show a combination of fMRI and behavioral data to support these hypotheses and present a principled way to explore the multidimensionality of object processing.

Rett syndrome, MECP2 and therapeutic strategies

The development of the iPS cell technology has revolutionized our ability to study development and diseases in defined in vitro cell culture systems. The talk will focus on Rett Syndrome and discuss two topics: (i) the use of gene editing as an approach to therapy and (ii) the role of MECP2 in gene expression (i) The mutation of the X-linked MECP2 gene is causative for the disease. In a female patient, every cell has a wt copy that is, however, in 50% of the cells located on the inactive X chromosome. We have used epigenetic gene editing tools to activate the wt MECP2 allele on the inactive X chromosome. (ii) MECP2 is thought to act as repressor of gene expression. I will present data which show that MECP2 binds to Pol II and acts as an activator for thousands of genes. The target genes are significantly enriched for Autism related genes. Our data challenge the established model of MECP2’s role in gene expression and suggest novel therapeutic approaches.

The Brain Prize winners' webinar

This webinar brings together three leaders in theoretical and computational neuroscience—Larry Abbott, Haim Sompolinsky, and Terry Sejnowski—to discuss how neural circuits generate fundamental aspects of the mind. Abbott illustrates mechanisms in electric fish that differentiate self-generated electric signals from external sensory cues, showing how predictive plasticity and two-stage signal cancellation mediate a sense of self. Sompolinsky explores attractor networks, revealing how discrete and continuous attractors can stabilize activity patterns, enable working memory, and incorporate chaotic dynamics underlying spontaneous behaviors. He further highlights the concept of object manifolds in high-level sensory representations and raises open questions on integrating connectomics with theoretical frameworks. Sejnowski bridges these motifs with modern artificial intelligence, demonstrating how large-scale neural networks capture language structures through distributed representations that parallel biological coding. Together, their presentations emphasize the synergy between empirical data, computational modeling, and connectomics in explaining the neural basis of cognition—offering insights into perception, memory, language, and the emergence of mind-like processes.

Sensory cognition

This webinar features presentations from SueYeon Chung (New York University) and Srinivas Turaga (HHMI Janelia Research Campus) on theoretical and computational approaches to sensory cognition. Chung introduced a “neural manifold” framework to capture how high-dimensional neural activity is structured into meaningful manifolds reflecting object representations. She demonstrated that manifold geometry—shaped by radius, dimensionality, and correlations—directly governs a population’s capacity for classifying or separating stimuli under nuisance variations. Applying these ideas as a data analysis tool, she showed how measuring object-manifold geometry can explain transformations along the ventral visual stream and suggested that manifold principles also yield better self-supervised neural network models resembling mammalian visual cortex. Turaga described simulating the entire fruit fly visual pathway using its connectome, modeling 64 key cell types in the optic lobe. His team’s systematic approach—combining sparse connectivity from electron microscopy with simple dynamical parameters—recapitulated known motion-selective responses and produced novel testable predictions. Together, these studies underscore the power of combining connectomic detail, task objectives, and geometric theories to unravel neural computations bridging from stimuli to cognitive functions.

Learning and Memory

This webinar on learning and memory features three experts—Nicolas Brunel, Ashok Litwin-Kumar, and Julijana Gjorgieva—who present theoretical and computational approaches to understanding how neural circuits acquire and store information across different scales. Brunel discusses calcium-based plasticity and how standard “Hebbian-like” plasticity rules inferred from in vitro or in vivo datasets constrain synaptic dynamics, aligning with classical observations (e.g., STDP) and explaining how synaptic connectivity shapes memory. Litwin-Kumar explores insights from the fruit fly connectome, emphasizing how the mushroom body—a key site for associative learning—implements a high-dimensional, random representation of sensory features. Convergent dopaminergic inputs gate plasticity, reflecting a high-dimensional “critic” that refines behavior. Feedback loops within the mushroom body further reveal sophisticated interactions between learning signals and action selection. Gjorgieva examines how activity-dependent plasticity rules shape circuitry from the subcellular (e.g., synaptic clustering on dendrites) to the cortical network level. She demonstrates how spontaneous activity during development, Hebbian competition, and inhibitory-excitatory balance collectively establish connectivity motifs responsible for key computations such as response normalization.

Decision and Behavior

This webinar addressed computational perspectives on how animals and humans make decisions, spanning normative, descriptive, and mechanistic models. Sam Gershman (Harvard) presented a capacity-limited reinforcement learning framework in which policies are compressed under an information bottleneck constraint. This approach predicts pervasive perseveration, stimulus‐independent “default” actions, and trade-offs between complexity and reward. Such policy compression reconciles observed action stochasticity and response time patterns with an optimal balance between learning capacity and performance. Jonathan Pillow (Princeton) discussed flexible descriptive models for tracking time-varying policies in animals. He introduced dynamic Generalized Linear Models (Sidetrack) and hidden Markov models (GLM-HMMs) that capture day-to-day and trial-to-trial fluctuations in choice behavior, including abrupt switches between “engaged” and “disengaged” states. These models provide new insights into how animals’ strategies evolve under learning. Finally, Kenji Doya (OIST) highlighted the importance of unifying reinforcement learning with Bayesian inference, exploring how cortical-basal ganglia networks might implement model-based and model-free strategies. He also described Japan’s Brain/MINDS 2.0 and Digital Brain initiatives, aiming to integrate multimodal data and computational principles into cohesive “digital brains.”

The role of real-word data in scientific evidence. Experiences from the Danish Multiple Sclerosis Registry

Understanding the complex behaviors of the ‘simple’ cerebellar circuit

Every movement we make requires us to precisely coordinate muscle activity across our body in space and time. In this talk I will describe our efforts to understand how the brain generates flexible, coordinated movement. We have taken a behavior-centric approach to this problem, starting with the development of quantitative frameworks for mouse locomotion (LocoMouse; Machado et al., eLife 2015, 2020) and locomotor learning, in which mice adapt their locomotor symmetry in response to environmental perturbations (Darmohray et al., Neuron 2019). Combined with genetic circuit dissection, these studies reveal specific, cerebellum-dependent features of these complex, whole-body behaviors. This provides a key entry point for understanding how neural computations within the highly stereotyped cerebellar circuit support the precise coordination of muscle activity in space and time. Finally, I will present recent unpublished data that provide surprising insights into how cerebellar circuits flexibly coordinate whole-body movements in dynamic environments.

Brain-Wide Compositionality and Learning Dynamics in Biological Agents

Biological agents continually reconcile the internal states of their brain circuits with incoming sensory and environmental evidence to evaluate when and how to act. The brains of biological agents, including animals and humans, exploit many evolutionary innovations, chiefly modularity—observable at the level of anatomically-defined brain regions, cortical layers, and cell types among others—that can be repurposed in a compositional manner to endow the animal with a highly flexible behavioral repertoire. Accordingly, their behaviors show their own modularity, yet such behavioral modules seldom correspond directly to traditional notions of modularity in brains. It remains unclear how to link neural and behavioral modularity in a compositional manner. We propose a comprehensive framework—compositional modes—to identify overarching compositionality spanning specialized submodules, such as brain regions. Our framework directly links the behavioral repertoire with distributed patterns of population activity, brain-wide, at multiple concurrent spatial and temporal scales. Using whole-brain recordings of zebrafish brains, we introduce an unsupervised pipeline based on neural network models, constrained by biological data, to reveal highly conserved compositional modes across individuals despite the naturalistic (spontaneous or task-independent) nature of their behaviors. These modes provided a scaffolding for other modes that account for the idiosyncratic behavior of each fish. We then demonstrate experimentally that compositional modes can be manipulated in a consistent manner by behavioral and pharmacological perturbations. Our results demonstrate that even natural behavior in different individuals can be decomposed and understood using a relatively small number of neurobehavioral modules—the compositional modes—and elucidate a compositional neural basis of behavior. This approach aligns with recent progress in understanding how reasoning capabilities and internal representational structures develop over the course of learning or training, offering insights into the modularity and flexibility in artificial and biological agents.

Use case determines the validity of neural systems comparisons

Deep learning provides new data-driven tools to relate neural activity to perception and cognition, aiding scientists in developing theories of neural computation that increasingly resemble biological systems both at the level of behavior and of neural activity. But what in a deep neural network should correspond to what in a biological system? This question is addressed implicitly in the use of comparison measures that relate specific neural or behavioral dimensions via a particular functional form. However, distinct comparison methodologies can give conflicting results in recovering even a known ground-truth model in an idealized setting, leaving open the question of what to conclude from the outcome of a systems comparison using any given methodology. Here, we develop a framework to make explicit and quantitative the effect of both hypothesis-driven aspects—such as details of the architecture of a deep neural network—as well as methodological choices in a systems comparison setting. We demonstrate via the learning dynamics of deep neural networks that, while the role of the comparison methodology is often de-emphasized relative to hypothesis-driven aspects, this choice can impact and even invert the conclusions to be drawn from a comparison between neural systems. We provide evidence that the right way to adjudicate a comparison depends on the use case—the scientific hypothesis under investigation—which could range from identifying single-neuron or circuit-level correspondences to capturing generalizability to new stimulus properties

Localisation of Seizure Onset Zone in Epilepsy Using Time Series Analysis of Intracranial Data

There are over 30 million people with drug-resistant epilepsy worldwide. When neuroimaging and non-invasive neural recordings fail to localise seizure onset zones (SOZ), intracranial recordings become the best chance for localisation and seizure-freedom in those patients. However, intracranial neural activities remain hard to visually discriminate across recording channels, which limits the success of intracranial visual investigations. In this presentation, I present methods which quantify intracranial neural time series and combine them with explainable machine learning algorithms to localise the SOZ in the epileptic brain. I present the potentials and limitations of our methods in the localisation of SOZ in epilepsy providing insights for future research in this area.

Probing neural population dynamics with recurrent neural networks

Large-scale recordings of neural activity are providing new opportunities to study network-level dynamics with unprecedented detail. However, the sheer volume of data and its dynamical complexity are major barriers to uncovering and interpreting these dynamics. I will present latent factor analysis via dynamical systems, a sequential autoencoding approach that enables inference of dynamics from neuronal population spiking activity on single trials and millisecond timescales. I will also discuss recent adaptations of the method to uncover dynamics from neural activity recorded via 2P Calcium imaging. Finally, time permitting, I will mention recent efforts to improve the interpretability of deep-learning based dynamical systems models.

Generative models for video games (rescheduled)

Developing agents capable of modeling complex environments and human behaviors within them is a key goal of artificial intelligence research. Progress towards this goal has exciting potential for applications in video games, from new tools that empower game developers to realize new creative visions, to enabling new kinds of immersive player experiences. This talk focuses on recent advances of my team at Microsoft Research towards scalable machine learning architectures that effectively capture human gameplay data. In the first part of my talk, I will focus on diffusion models as generative models of human behavior. Previously shown to have impressive image generation capabilities, I present insights that unlock applications to imitation learning for sequential decision making. In the second part of my talk, I discuss a recent project taking ideas from language modeling to build a generative sequence model of an Xbox game.

Characterizing the causal role of large-scale network interactions in supporting complex cognition

Neuroimaging has greatly extended our capacity to study the workings of the human brain. Despite the wealth of knowledge this tool has generated however, there are still critical gaps in our understanding. While tremendous progress has been made in mapping areas of the brain that are specialized for particular stimuli, or cognitive processes, we still know very little about how large-scale interactions between different cortical networks facilitate the integration of information and the execution of complex tasks. Yet even the simplest behavioral tasks are complex, requiring integration over multiple cognitive domains. Our knowledge falls short not only in understanding how this integration takes place, but also in what drives the profound variation in behavior that can be observed on almost every task, even within the typically developing (TD) population. The search for the neural underpinnings of individual differences is important not only philosophically, but also in the service of precision medicine. We approach these questions using a three-pronged approach. First, we create a battery of behavioral tasks from which we can calculate objective measures for different aspects of the behaviors of interest, with sufficient variance across the TD population. Second, using these individual differences in behavior, we identify the neural variance which explains the behavioral variance at the network level. Finally, using covert neurofeedback, we perturb the networks hypothesized to correspond to each of these components, thus directly testing their casual contribution. I will discuss our overall approach, as well as a few of the new directions we are currently pursuing.

Generative models for video games

Developing agents capable of modeling complex environments and human behaviors within them is a key goal of artificial intelligence research. Progress towards this goal has exciting potential for applications in video games, from new tools that empower game developers to realize new creative visions, to enabling new kinds of immersive player experiences. This talk focuses on recent advances of my team at Microsoft Research towards scalable machine learning architectures that effectively capture human gameplay data. In the first part of my talk, I will focus on diffusion models as generative models of human behavior. Previously shown to have impressive image generation capabilities, I present insights that unlock applications to imitation learning for sequential decision making. In the second part of my talk, I discuss a recent project taking ideas from language modeling to build a generative sequence model of an Xbox game.

Roles of inhibition in stabilizing and shaping the response of cortical networks

Inhibition has long been thought to stabilize the activity of cortical networks at low rates, and to shape significantly their response to sensory inputs. In this talk, I will describe three recent collaborative projects that shed light on these issues. (1) I will show how optogenetic excitation of inhibition neurons is consistent with cortex being inhibition stabilized even in the absence of sensory inputs, and how this data can constrain the coupling strengths of E-I cortical network models. (2) Recent analysis of the effects of optogenetic excitation of pyramidal cells in V1 of mice and monkeys shows that in some cases this optogenetic input reshuffles the firing rates of neurons of the network, leaving the distribution of rates unaffected. I will show how this surprising effect can be reproduced in sufficiently strongly coupled E-I networks. (3) Another puzzle has been to understand the respective roles of different inhibitory subtypes in network stabilization. Recent data reveal a novel, state dependent, paradoxical effect of weakening AMPAR mediated synaptic currents onto SST cells. Mathematical analysis of a network model with multiple inhibitory cell types shows that this effect tells us in which conditions SST cells are required for network stabilization.

Inferring stochastic low-rank recurrent neural networks from neural data

Bernstein Conference 2024

Latent Diffusion for Neural Spiking Data

Bernstein Conference 2024

Model Selection in Sensory Data Interpretation

Bernstein Conference 2024

NeuroTask: A Benchmark Dataset for Multi-Task Neural Analysis

Bernstein Conference 2024

Open-source solutions for research data management in neuroscience collaborations

Bernstein Conference 2024

Single-cell morphological data provide refined simulations of resting-state

Bernstein Conference 2024

Towards predicting Stroke Etiology from MRI and CT Imaging Data of Ischemic Stroke Patients

Bernstein Conference 2024

Tracking the provenance of data generation and analysis in NEST simulations

Bernstein Conference 2024

Unified C. elegans Neural Activity and Connectivity Datasets for Building Foundation Models of a Small Nervous System

Bernstein Conference 2024

AutSim: Principled, data driven model development and abstraction for signaling in synaptic protein synthesis in Fragile X Syndrome (FXS) and healthy control.

COSYNE 2022

Data-driven dynamical systems model of epilepsy development simulates intervention strategies

COSYNE 2022

Emergence of time persistence in an interpretable data-driven neural network model

COSYNE 2022

Fast inter-subject alignment method for large datasets shows fine-grained cortical reorganisations

COSYNE 2022

Gaussian Partial Information Decomposition: Quantifying Inter-areal Interactions in High-Dimensional Neural Data

COSYNE 2022

Gaussian Partial Information Decomposition: Quantifying Inter-areal Interactions in High-Dimensional Neural Data

COSYNE 2022

A high-throughput pipeline for evaluating recurrent neural networks on multiple datasets

COSYNE 2022

A high-throughput pipeline for evaluating recurrent neural networks on multiple datasets

COSYNE 2022

Identifying latent states in decision-making from cortical inactivation data

COSYNE 2022

Identifying latent states in decision-making from cortical inactivation data

COSYNE 2022

Identifying key structural connections from functional response data: theory & applications

COSYNE 2022

Identifying key structural connections from functional response data: theory & applications

COSYNE 2022

Inferring olfactory space from glomerular response data

COSYNE 2022

Inferring olfactory space from glomerular response data

COSYNE 2022

Inter-individual alignment and single-trial classification of MEG data using M-CCA

COSYNE 2022

Inter-individual alignment and single-trial classification of MEG data using M-CCA

COSYNE 2022

An accessible hippocampal dataset for benchmarking models of cognitive mapping

COSYNE 2023

Augmented Gaussian process variational autoencoders for multi-modal experimental data

COSYNE 2023

Automated identification of data-consistent spiking neural network models

COSYNE 2023

A Bayesian hierarchical latent variable model for spike train data analysis

COSYNE 2023

Data-driven discovery of long timescale behavioral strategies during sensory evoked locomotion

COSYNE 2023

Disentangling input dynamics from intrinsic neural dynamics in modeling of neural-behavioral data

COSYNE 2023

Fitting normative neural sampling hypothesis models to neuronal response data

COSYNE 2023

Improved estimation of latent variable models from calcium imaging data

COSYNE 2023

A Large Dataset of Macaque V1 Responses to Natural Images Revealed Complexity in V1 Neural Codes

COSYNE 2023

maskNMF: a denoise-sparsen-detect pipeline for demixing dense imaging data faster than real time

COSYNE 2023

A Method for Testing Bayesian Models Using Neural Data

COSYNE 2023

Neuroformer: A Transformer Framework for Multimodal Neural Data Analysis

COSYNE 2023

Responses to inconsistent stimuli in pyramidal neurons: An open science dataset

COSYNE 2023

Robust multiband drift estimation in electrophysiology data

COSYNE 2023

Functional inter-subject alignment of MEG data outperforms anatomical alignment

Bernstein Conference 2024