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TARGETING VAV1 SCAFFOLDING AND ENZYMATIC FUNCTIONS IN MULTIPLE SCLEROSIS VIA BRAIN-PENETRANT MOLECULAR GLUE DEGRADERS

Abstract Multiple Sclerosis (MS) is a chronic autoimmune disease of the central nervous system (CNS) with significant unmet medical needs, as current therapies offer limited efficacy against neurodegeneration and can have considerable side effects. VAV1, a key signaling protein predominantly expressed in hematopoietic cells, plays a crucial role in T and B lymphocyte activation and is genetically and functionally validated as a therapeutic target in MS. This project proposes an innovative approach to target VAV1 through the development of brain-penetrant molecular glue (MG) degraders. Distinct from Proteolysis Targeting Chimeras (PROTACs) that require a high- affinity ligand for the target protein, molecular glues can mediate degradation by engaging specific protein surface features, such as loops, without the necessity of a dedicated binder. These degraders aim to induce the proteasomal degradation of VAV1, thereby ablating both its enzymatic and scaffolding functions, which are implicated in neuroinflammation. The research strategy involves three primary aims: 1) To optimize lead VAV1 molecular glue degraders for enhanced potency, brain penetration, and favorable pharmacokinetic properties using advanced computational modeling and medicinal chemistry. 2) To evaluate the in vivo efficacy of the optimized VAV1 degraders in preclinical mouse models of MS (Experimental Autoimmune Encephalomyelitis - EAE), assessing their ability to ameliorate disease severity, reduce CNS inflammation and demyelination, and engage VAV1 in the CNS. 3) To investigate the Structure-Activity Relationship (SAR) of a novel non-canonical VAV1 degron motif, aiming to expand the understanding of molecular glue-mediated degradation and enable the rational design of degraders for other challenging therapeutic targets. Successful completion of this project is expected to deliver preclinical candidate VAV1 degraders with the potential for a novel, effective, and safer treatment paradigm for MS. Furthermore, the insights gained into non-canonical degron recognition will significantly advance the field of targeted protein degradation, broadening the scope of "undruggable" targets for therapeutic intervention in various diseases.

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.

Integrins α4β7 in Leukocyte Rolling in Shear Flow, Firm Adhesion, and Therapy

Abstract. Integrin α4β7 facilitates leukocyte migration to sites of infection and autoimmune disease, making it an important therapeutic target for ulcerative colitis and Crohns disease. However, the currently approved antibody drug vedolizumab targeting α4β7 has limited efficacy. This proposal seeks mechanistic understanding of how α4β7 mediates rolling and firm adhesion of leukocytes during extravasation as well as how therapeutically relevant antibodies modulate α4β7 function to improve drug design. Unlike most integrins, α4β7 mediates rolling adhesion on its ligand MAdCAM. α4β7 can also mediate firm adhesion like α5β1. Integrins typically equilibrate between two low-affinity closed conformations and a high-affinity open conformation. Ligand binding is intimately coordinated with conformational change. During rolling adhesion, receptor-ligand bonds must rapidly form beneath rolling cells as cells are torqued by shear flow onto the substrate. Bonds must also rapidly dissociate at the upstream tethers to the substrate due to hydrodynamic force applied to the cell. To enable their function in rolling adhesion, we hypothesize that α4β7 ligand binding and dissociation and conformational change kinetics are faster than those of other integrins like α5β1 and that α4β7's pathways for conformational change may also differ. We propose that activation of the actin cytoskeleton in the transition from rolling to firm adhesion stabilizes α4β7 in a high-affinity state. Aim 1 will determine high-resolution structures of unliganded α4β7 and its complexes with MAdCAM or medically relevant antibodies using cryo- EM. These structures will reveal how these integrins recognize their ligands, the conformational changes due to ligand binding, and potential structural specializations that enable α4β7 to mediate rolling adhesion. The binding epitopes and conformational specificities of activating antibodies to the β7 subunit will also be defined. The structure of α4β7 bound to vedolizumab will resolve the contention around how it blocks MAdCAM binding. Aim 2 will quantitatively define the mechanisms by which α4β7 mediates both rolling and firm adhesion to improve therapies for inflammatory bowel diseases. Ligand affinity and binding kinetics of α4β7 stabilized in different conformations will be measured as well as single-molecule conformational change rates when bound and unbound to ligand. The effect of mutations that stabilize rolling or firm adhesion will be used to identify parameters important for each adhesion type. The tensile force and bond lifetimes during rolling and firm adhesion will be quantified at the single-molecule level. Together, our studies will enhance our structural, biochemical, and mechanical understanding of α4β7-mediated rolling and firm adhesion and will provide structural and functional information that can be utilized in the development of more effective therapies for inflammatory bowel diseases and multiple myeloma.

NeuroASCENT- Advancing Science through Career Enhancement and Neuroscience Training

The NeuroASCENT- Advancing Science through Career Enhancement and Neuroscience Training program will support neuroscience‑focused PhD students across multiple graduate programs by providing comprehensive scientific, professional, and research‑development training during their doctoral education. Strengthening the national neuroscience workforce requires ensuring that trainees have access to high‑quality research preparation, strong mentoring, and structured opportunities that enhance their scientific growth and career readiness. Recent analyses of U.S. doctoral recipients indicate that many talented trainees encounter barriers that limit full participation in research careers, underscoring the need for intentional support mechanisms that promote successful advancement. Over the last five years, CU Anschutz PhD programs have seen a substantial increase in students entering from a broad range of academic backgrounds. NeuroASCENT is designed to help these trainees progress efficiently by 1) promoting research excellence, 2) fostering leadership skills, 3) facilitating career development, and 4) providing individualized guidance. To achieve these goals, the program will provide career‑focused workshops, structured research externship opportunities, enhanced mentoring frameworks, and coordinated access to campus resources that extend beyond those offered by individual graduate programs. In partnership with the Office of Research Education, NeuroASCENT will complement and enhance the scientific training provided across biomedical PhD programs while offering added value to the broader CU Anschutz graduate community. Program Directors Dr. Quillinan and Dr. Hughes will oversee training activities, mentor matching, evaluation, program operations, and dissemination. An Institutional Advisory Board composed of research leaders will guide program oversight, and an External Advisory Board of graduate‑education experts will provide additional evaluation and strategic input. NeuroASCENT scholars will also serve on an Executive Advisory Board to develop leadership experience and contribute directly to program refinement. Trainees will typically enter the program after their second year of graduate training and will participate in activities focused on building a supportive peer/mentor network, strengthening scientific confidence and competence, and preparing for careers in academia, government, industry, or non‑profit research organizations.

The Role of the Intestinal Microbiota in Sepsis Mortality

Project Summary/Abstract Sepsis is a life-threatening condition characterized by a dysregulated host response to infection that can cause multi-organ damage and death. As the leading cause of in-hospital mortality, sepsis mortality rates reach up to 50%, and account for approximately 270,000 deaths and $38 billion annually in health care costs in the United States. Notably, patients with similar medical backgrounds can have vastly different sepsis outcomes— some survive with medical treatment while others die. The reasons for this dichotomy are unknown but is seen across all forms of bacterial bloodstream infections, is not specific to any strain-level differences in the infecting pathogen and cannot be explained by human genetic differences. Human microbiota studies suggest that gut microbial dysbiosis is associated with sepsis mortality and that these alterations influence gut barrier breakdown, leading to gram-negative bacteremia—one of the most common causes of sepsis and mortality. However, there are a lack of studies that investigate the causal role of the intestinal microbiota in sepsis mortality. This K08 proposal will elucidate the role of the intestinal microbiota in sepsis mortality. Utilizing the well- established murine model of sepsis by intraperitoneal injection of lipopolysaccharide (LPS), we combine microbiota taxonomic sequencing and metagenomics, advanced bioinformatic techniques and prediction modeling, with knowledge of mucosal immunity and germ-free mouse systems to characterize the microbiota features and members that correlate with, predict, and cause sepsis mortality. This proposal is organized into two specific aims: (1) identify baseline stool microbial features associated with and predictive of sepsis outcomes and (2) determine how colonization with immunostimulatory microbes heightens sepsis mortality. In this work, I will holistically characterize the host immunologic and microbiota features that are associated with and predictive of mortality and experimentally identify microbes and microbial pathways that cause death in our model. These findings will reveal new microbial and host biomarkers of sepsis mortality and identify novel targets for sepsis prevention and treatment to reduce the overall mortality rate of this deadly disease. My long-term goal is to become an independent physician-scientist who integrates cutting-edge computational methods with experimental biology to identify predictive biomarkers of disease onset and outcomes, investigate how they influence disease processes, and develop novel therapeutic and preventive strategies to improve patient care. This proposal details specific research aims and a structured career development and training plan that will allow me to acquire focused, in-depth and multidisciplinary training under the guidance of an internationally recognized team of experts in clinical infectious diseases, host-microbiota interactions, immunology, immunometabolism, and computational biology. The knowledge generated will address the fundamental role of the microbiota in sepsis outcomes and inform future preventative and therapeutic strategies that will lower the sepsis mortality rate worldwide.

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.

Targeting VIP–VPAC Signaling to Reverse Immune Exclusion and Enhance Immunotherapy Response in Pancreatic Cancer

Pancreatic ductal adenocarcinoma (PDAC) is a highly lethal cancer that is largely unresponsive to chemotherapy and current immune checkpoint blockade drugs, highlighting a critical need for the development of innovative therapeutic strategies. This R01 proposal targets vasoactive intestinal peptide (VIP), an immunosuppressive neuropeptide overexpressed in PDAC, which signals through VIP receptors (VPAC) on cancer cells, T cells, and myeloid cells within the tumor microenvironment. Based on our recent success in developing selective and potent VPAC receptor antagonists, we hypothesize that blocking VPAC signaling will reverse immunosuppression in the PDAC TME by reducing immune checkpoint expression, enhancing chemokine-driven infiltration of cytotoxic T cells, and disrupting immunosuppressive interactions between T cells and myeloid cells, ultimately leading to durable anti-cancer immunity. We propose three specific aims to explore the immunosuppressive roles of VPAC signaling in PDAC. Aim 1 will identify the primary sources of VIP in PDAC tumors and characterize the effects of VPAC signaling on immune cell function and phenotype within the tumor microenvironment. Aim 2 will investigate how VPAC signaling influences immune cell migration into tumors by modulating chemokine receptors and directional signaling. Aim 3 will determine how VPAC signaling regulates interactions between T cells and immunosuppressive myeloid cells, particularly tumor-associated macrophages, and the resulting impact on anti-cancer immune responses and immunological memory. Our preliminary findings indicate that combined inhibition of VPAC signaling and PD-1 significantly enhances the regression of PDAC tumors in multiple mouse models, generating lasting protective immunity in cured mice without triggering autoimmune responses. We will use novel methods to pursue our aims, including inducible genetically engineered mouse models (GEMM) of PDAC, long-acting VPAC antagonists engineered with immunoglobulin Fc domains to improve their plasma half-life, and advanced microfluidics technologies to analyze immune cell movement within tumors. Animal experiments will be used to validate the translational potential of observations from in vitro organoids and microfluidic experiments. The GEMM and orthotopic mouse models of PDAC are necessary to provide critical insights into the 3-D structure of the TME and tumor regression in response to our novel immunotherapy. This research will be conducted by a multidisciplinary team with complementary expertise that will clarify the therapeutic potential of VPAC signaling inhibition in PDAC using sophisticated experimental tools and single-cell RNA sequencing. Ultimately, these findings could significantly improve the development of immunotherapeutic strategies for PDAC, potentially enhancing patient outcomes in pancreatic cancer and other malignancies expressing high VIP levels.

Pilot and Feasibility Program

PILOT AND FEASIBILITY PROGRAM: PROJECT SUMMARY The goal of the Cedars-Sinai Digestive Diseases Research Center (CSDDRC) Pilot and Feasibility (P&F) Program is to provide monetary support, expertise, and technical support to advance innovative basic, translational, and clinical research that matches the overall goal and themes of the Center. The central theme of the CSDDRC is mechanisms and measurements of the fibroinflammatory response in gastrointestinal (GI) tissues, which reflects Center members’ research in three subthemes: 1) Gut Microbiome, 2) Gastrointestinal (GI) and Liver Metabolism, and 3) GI and Liver Injury. The mission of CSDDRC P&F Program is to support new investigators, established investigators who are new to digestive and liver disease research, and established digestive and liver disease investigators who want to start new or collaborative research that promises to lead to a paradigm shift in the digestive diseases field. In partnership with the Enrichment Program, we will provide guidance for P&F awardees in the form of mentorship and collaboration opportunities. The CSDDRC Biomedical Research Cores will also support P&F awardees, facilitating rapid progress of their new and collaborative digestive and liver disease research. The P&F Program’s outcome measures will include the number of high-impact research publications, grant applications, and subsequent extramural funding for P&F awardees. We will accomplish our goals through the following three specific aims. Aim 1 will solicit research proposals from P&F candidates whose proposed research aligns with the central theme and the subthemes of the CSDDRC. We will advertise P&F support widely across campuses, in addition to contacting department/institute directors to solicit their recommendations for promising young and established investigators who are interested in working in digestive and liver diseases. Aim 2 will select pilot project applications that meet CSDDRC P&F Program goals using rigorous review criteria. Each year, the P&F Program will select four pilot projects to be funded by the P30 grant and matched by institutional support. Submitted applications will be peer- reviewed and preliminarily scored based on the NIH review format by three local expert reviewers. Subsequently, after oral presentations by the P&F applicants, the External Advisory Board (EAB) members will undertake a second round of review, scoring, and discussion at the P&F Program Review meeting following the CSDDRC Annual Symposium. Funding decisions will be made during the P&F Program Review meeting. Aim 3 will assist P&F project investigators with career development and obtaining extramural funding for digestive disease research. P&F awardees will benefit from the Enrichment Program’s well-organized mentoring structure, led by experienced members of the CSDDRC, which includes the Grants-in-Progress Mentoring Program, Gastrointestinal Research-in-Progress meetings, and grant application workshops. P&F awardees will also be mentored through direct interactions with P&F Program Directors, Core Directors, members of the Internal Advisory Board and EAB, and individual or collaborative mentor teams.

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.

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.

Neural circuits for disinhibition in the cerebellum

ABSTRACT Our long-term goal is to understand how the cerebellum adapts and improves movements in response to motor errors. A critical component of this process is signaling from olivary climbing fibers that, by providing strong excitatory drive onto Purkinje cells, induces long-term synaptic plasticity to instantiate corrective adjustments in motor behavior. However, this signaling process is tightly regulated by molecular layer interneurons (MLIs). By strongly inhibiting Purkinje cells, MLIs oppose climbing fiber-driven excitation and gate the induction of corrective plasticity. Thus, for error-driven climbing fiber-induced plasticity and learning to occur effectively, Purkinje cells must undergo disinhibition through the suppression of MLI-mediated input. Notably, MLI ensembles are composed of several subtypes and have a highly structured interconnectivity and are responsive to convergent climbing fiber inputs, suggesting that climbing fiber synchrony- whose functional significance is poorly understood- can selectively engage MLI networks to alter the state of Purkinje cell inhibition. This engagement may balance inhibition and excitation of Purkinje cells during motor errors, creating a circuit mechanism conducive for the acquisition of adaptive learning. The objective of this proposal is to determine how distinct MLI circuits are organized to modulate Purkinje cell excitability through disinhibition in a context-dependent manner, enabling plasticity and learning in response to motor errors. We will employ functional recordings, circuit-targeted activity manipulations, and behavioral analysis to reveal how error-driven instructive signaling emerges from these circuits. In the first aim, we will use in vivo high-density electrophysiology to map functional interactions among MLIs, climbing fibers, and Purkinje cells in the flocculus during the vestibulo-ocular reflex. We will test whether, during motor errors, climbing fibers synchronize their firing to selectively engage disinhibition of Purkinje cells through MLI subtypes in adapting versus non-adapting contexts. In the second aim, we will combine acute slice recordings and molecular anatomy to define direct versus spillover climbing fiber synapses onto MLI subtypes. We will identify synaptic markers and measure climbing-fiber-evoked currents in MLI subtypes, revealing how structural connectivity supports rapid, subtype-specific circuit engagement. In the third aim, we will determine how long-range inputs to the inferior olive, specifically inhibitory projections from the vestibular nuclei, dynamically tune climbing fiber synchrony in vivo and thereby learning through differential engagement of disinhibitory MLI networks. Using functional recording and optogenetic manipulation during the vestibulo- ocular reflex performance, we will establish causal links between climbing fiber synchrony, MLI network state, and adaptive behavior. By fully understanding the logic of instructive signaling, emergent from cerebellar circuit organization and behavioral engagement, we will advance our knowledge of cerebellum-dependent learning processes and provide broader insights into the neural mechanisms of learning and adaptation more generally.

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.

Cytoskeletal connectors: Deciphering the fundamental mechanisms of cytoskeletal dynamics and transport

PROJECT SUMMARY The cytoskeleton is a dynamic network of filamentous structures, including microtubules and actin, that regulate essential cellular processes such as cell shape, growth, and signaling. Cytoskeleton also serves as tracks for molecular motors, which transport a variety of cellular cargoes, including organelles, macromolecules, and vesicles. These cargoes are linked to motors by specialized connector proteins. Disruptions in connector proteins are implicated in a range of neurodevelopmental and neurodegenerative diseases, as well as cancers. Despite their importance, these proteins continue to be understudied, primarily due to their perceived role as passive linkers and the technical challenges in working with them. However, recent discoveries suggest that connector proteins may play more active roles, in some cases even have enzymatic functions. This proposal aims to uncover mechanisms of connector protein functions through a detailed investigation of actin-microtubule and motor-cargo interactions. Actin and microtubules are linked by the spectraplakin family of large and evolutionarily conserved proteins, critical for neuronal development and differentiation. Recent discoveries of ATPase domains within these proteins suggest they may haves beyond simply linking cytoskeletal components. One goal of this proposal is to investigate the role of spectraplakin’s ATPase domains via structural, biochemical, and cell biology approaches. Another goal is to explore how dynamic changes in motor-cargo connectors facilitate the transport of diverse cargoes along microtubule tracks. The focus will be on the cytoplasmic dynein-1 (dynein) and the connectors (adaptors) that activate and link dynein to cargo. Dynein is a microtubule minus-end directed motor that plays essential roles in cell division, and transports hundreds of different cellular cargoes. While several motor-cargo connectors have been identified, the regulatory mechanisms enabling cargo transport are not fully understood. We are investigating whether connector proteins work together to activate dynein movement and/or facilitate cargo handoff between different dynein complexes. Using innovative approaches, including time- resolved cryo-EM, complex in-vitro reconstitutions, and live-cell imaging in induced neurons, we are uncovering critical mechanisms that govern cytoskeletal connector proteins, furthering our understanding of how the cytoskeleton regulates essential cellular processes.

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.

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.

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.

TAR RNA binding to INI1/SMARCB1 and its role in HIV-1 transcription and latency reactivation

Abstract The goal of this application is to study the role of interplay between the components of chromatin remodeling SWI/SNF (BAF complex) and HIV-1 transcription machinery, focusing on the interaction of a BAF component, INI1 (Integrase Interactor 1) with TAR RNA. HIV-1 reservoirs are a mixture of latent cells harboring proviruses silenced at transcriptional level. Cure strategies need a deeper understanding of HIV-1 transcriptional regulation. HIV-1 transcription, initiated by RNA Pol II, pauses producing short TAR transcripts. pTEFb recruitment to TAR by Tat overcomes this transcriptional pause, facilitating elongation. Beyond Tat, the action of chromatin remodeling complexes (CRCs) is required to facilitate elongation. The BAF complexes CBAF and PBAF play distinct roles. While CBAF represses proviral transcription by maintaining nucleosomes in an unfavorable state, PBAF remodels nucleosomes to facilitate elongation. INI1 is a component of both CBAF and PBAF, and its role in transcription is not fully understood. INI1 was identified as a binding partner for HIV-1 integrase (IN) and exerts multifacted roles in virus assembly, production and morphogenesis. INI1 has multiple functional domains. IN binding Rpt1 domain structurally mimics TAR RNA & is necessary for late events. We have made a novel observation that another domain of INI1, the N-terminal Winged Helix DNA binding domain (WHD) specifically binds to TAR RNA and that this interaction is necessary for mediating HIV-1 transcriptional elongation. These exciting results suggest that different functional domains of INI1(Rpt1 and WHD) involved in “TAR RNA mimicry” or “TAR RNA binding” regulate distinct stages of replication. We hypothesize that INI1 WHD domain-TAR interaction is necessary for recruitment of PBAF to HIV-1 LTR for transcriptional elongation and latency reactivation. Disrupting this interaction results in transcriptional repression. We will investigate the role of this novel INI1:TAR RNA interaction in HIV-1 transcription and latency reactivation. This is a multi-PI application involving Drs. Kalpana (HIV-1 virologist), Heng (NMR biophysicist) and Zou (computational biologist/protein-RNA structure). In Aim 1, we will characterize INI1-WHD:TAR interaction in vitro and in vivo via molecular/genetic analyses (Kalpana/Heng). We will employ alanine scanning mutagenesis based on WHD NMR structure to test WHD:TAR interaction. We will use biophysical & biochemical approaches to probe TAR structural elements required for this interaction. In Aim 2, we will employ computational modeling and NMR to determine the structure of INI1- WHD:TAR RNA complex (Zou/Heng). In Aim 3, we will determine the role of INI1:TAR interactions in HIV-1 transcription, latency reactivation and mechanism of action (Kalpana). We will analyze the effect of TAR- Interaction-Defective (TID) INI1 mutants on transcription of LTR-reporters and full-length HIV in INI1-/- cells. Latent cells in which TID-INI1 mutants are knocked in (KI) will be used to assess effect on reactivation via RNA-FISH and qRT-PCR assays. Our studies will establish INI1:TAR interaction as a drug target. Inhibiting this interaction could block latency reactivation promoting deep latency and advancing cure strategies.

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.

SUPPORT SERVICES FOR THE PREVENTION AND TREATMENT THROUGH A COMPREHENSIVE CARE CONTINUUM FOR HIV-AFFECTED ADOLESCENTS IN RESOURCE CONSTRAINED SETTINGS IMPLEMENTATION SCIENCE NETWORK

Support Services for the Prevention and Treatment through a Comprehensive Care Continuum for HIV-affected Adolescents in Resource Constrained Settings Implementation Science Network (PATC3H-IN) (UG1/UM2) Program The Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD) requires support for logistical and operational coordination, website and communication management, analytic and data management, infrastructure for emerging research, regulatory, and monitoring of research activities for the Prevention and Treatment through a Comprehensive Care Continuum for HIV-affected Adolescents in Resource Constrained Settings Implementation Science Network (PATC3H-IN) (UG1/UM2) Program. The NICHD and partner NIH Institutes anticipate funding 8 PATC3H-IN UG1 awards in Asia and throughout sub-Saharan Africa in 2023 through a cooperative agreement mechanism for interventions of high public health significance: The prevention of new HIV infections among adolescents at risk, and the identification of, linkage to and retention in care of, and long-term viral suppression among youth living with HIV in low-to-middle income countries with high HIV burden. The PATC3H-IN network will expand and/or improve on successes achieved by its predecessor, PATC3H, to new geographic settings and/or risk populations and stimulate much needed implementation science (IS) research in the prevention of new HIV infections among adolescents at risk and the identification of, and linkage and retention to care of and long-term viral suppression among youth living with HIV in low-to-middle income countries (LMICs). PATC3H-IN will establish a network of investigators with multidisciplinary expertise on the youth-specific PHCC and in IS research, whose mission will be to evaluate promising prevention innovations contextually and developmentally tailored for HIV uninfected at-risk youth, and treatment and care interventions for youth living with HIV which have demonstrated efficacy and/or effectiveness in adolescent or adult populations and to translate them into public health practices. The structure of PATC3H-IN will consist of multiple interdependent functional components: (1) Five Clinical Research Centers (CRC) awarded through the UG1 grant mechanism; (2) one Implementation Science Coordinating Center (ISCC) to be awarded through a UM2 grant mechanism in 2024; and (3) a Scientific Leadership Committee (SLC). The CRCs will conduct clinical research and clinical trials, including implementation, effectiveness, and hybrid implementation-effectiveness studies at their 8-or more participating Clinical Research Performance Sites (CRPS). The ISCC will establish infrastructure to support research education and capacity building across PATC3H-IN, as well as infrastructure for stakeholder engagement in and dissemination of findings from PATC3H-IN and advanced statistical modeling support across PATC3H-IN. The ISCC will also provide infrastructure for conducting foundational research to support the work of clinical sites, including possible modeling studies and translation projects, as well as national surveys, and/or systematic collection and analysis of relevant policies and laws. Lastly, the SLC will be responsible for PATC3H-IN governance, oversight, and coordination, and will develop and implement the network research agenda, convening working groups as needed, prioritizing emerging research projects, efficiently managing the development of clinical protocols, implementing and completing clinical trials, and ensuring timely publication and communication of results.

Host-pathogen-microbiome interactions in Mycoplasma genitalium pathology and treatment: experiments in a 3D organotypic cervical epithelium model to strengthen clinical guidelines

ABSTRACT Mycoplasma genitalium (MG) is an emerging sexually transmitted pathogen whose clinical outcomes in women are poorly understood. Unlike other bacterial sexually transmitted infections (STI), the CDC does not recommend MG screening for asymptomatic women because it is unclear how often asymptomatic MG leads to adverse reproductive outcomes like cervicitis, which can lead to further adverse outcomes, including pelvic inflammatory disease, infertility, and ectopic pregnancy. Epidemiologic data on MG and cervicitis are mixed, and mechanistic data primarily come from models that did not faithfully recapitulate in vivo cervical microphysiological conditions. Key elements they lacked are cervical mucus, which mediates host-pathogen interactions, and the cervicovaginal microbiota. The microbiota appears to contribute to MG outcomes, and our preliminary epidemiologic data indicate that MG and bacterial vaginosis (BV) may synergize to promote cervicitis. MG care is further complicated by its ongoing rise in antibiotic resistance. Resistance-guided therapy and novel antibiotics improve treatment outcomes, but these are not available in the US. Recent clinical and in vitro data indicate that metronidazole and tinidazole, two antibiotics that are available in the US and used to treat BV, may hold promise for improving MG treatment outcomes. The overall objective of this R21 is to generate robust experimental data to clarify MG pathology, evaluate potential therapies, and inform more thorough and actionable clinical recommendations. We developed an innovative in vitro 3D organotypic model of the cervical epithelium that is ideally suited for investigating MG pathology, host-MG-microbiota interactions, and potential therapies. The model uses primary human cervical cells and better recapitulates cervical epithelial structure and physiology (including cervical mucus production) than prior 2D models. It also allows for simultaneous STI infection and co- culture of live cervicovaginal microbiota. Using the 3D organotypic cervical epithelium model, we will determine if MG causes microbiota-dependent cervical epithelial damage, a hallmark of cervicitis (Aim 1), and we will test if metronidazole and tinidazole arrest MG infection (Aim 2). In both Aims, we will interrogate the potential mediating role of the microbiota by inoculating models with live representative cervicovaginal microbiota, and we will assess host-MG-microbiota interactions via transcriptomics. We hypothesize that a polymicrobial BV-like microbiota will exacerbate MG-induced cervical epithelial damage, and removal of a polymicrobial BV microbiota will partially mediate metronidazole’s and tinidazole’s anti-MG activity. The proposed Aims have high translational potential and will provide crucial pre-clinical evidence to inform more thorough and actionable MG testing and treatment guidelines and improve reproductive health outcomes. This R21 will generate some of the first experimental data on MG-host and MG-microbiota interactions, which we will use to support an R01 to validate these interactions during in vivo MG infection and identify novel therapeutic targets for MG.

Optimizing gamma-delta T cell receptor-mediated signaling to improve cancer immunotherapy

PROJECT SUMMARY The recent development of T cell-based cancer immunotherapies, including checkpoint blockade (anti-PD-1, anti-CTLA-4 and others) or adoptive cell therapy (ACT) using modified patient T cells, has led to improved patient outcomes for a variety of cancers. However, durable responses are observed in only a fraction of patients. Further progress can be made by studying and targeting different T cell subpopulations, such as the gd T cells which are known to possess antitumor activities. Further, gd T cells are mostly independent of MHC-restriction, unconstrained by neoantigen burden, preferential homing to peripheral tissues and possess unique properties of T cells as well as natural killer cells making them an extremely attractive cancer immunotherapy target. One way of gd T cell activation involves the gd T cell receptor (gdTCR)-CD3 signaling pathway. gd T cell recognition of antigen by the gdTCR and the resulting proximal signaling through surrounding CD3 subunits are key steps of gd T cell activation. Even though the individual components of the gdTCR-CD3 and abTCR-CD3 complexes remain the same except for the TCRs, the complete gdTCR-CD3 complex extracellular structure is unknown. Identification of the specific extracellular interactions between the gdTCR and CD3 subunits could offer precise guidance for the development of immunotherapeutic strategies that modulate gdT cell immunity by targeting signaling through the gdTCR-CD3 complex. Our previous data showed that mutating residues in the constant domain of the abTCR resulted in altered ab T cell cytokine responses. Based on this data, our hypothesis is that gdTCR-CD3 signaling can also be modulated by targeting specific regions of the gdTCR by mutagenesis to improve gd T cell antitumor activities. To test our hypothesis, in Aim 1, we will use a novel photo-crosslinking and computational docking methodology to solve the complete extracellular structure of a gdTCR-CD3 complex. Further, we will use an in silico structure-based TCR design approach to identify gdTCR mutants that enhance signaling. In Aim 2, we will use an in vitro retroviral TCR display method using degenerate primers to create gdTCR mutant libraries at specific gdTCR sites such as Cg helix 3 and connecting peptide (CP) regions. In both instances, identified mutants will be tested for improved functionalities in an MHC-independent gd TCR (G115 Vg9Vd2 TCR) using in vitro cytokine and tumor-killing assays. Overall, the newly identified enhanced gd T cell clones could potentially lead to a new wave of effective cancer immunotherapy strategy by leaning into the largely untapped potential of gd T cells.

A PROTAC Strategy to Combat Botulinum Neurotoxicity

PROJECT SUMMARY/ABSTRACT Botulinum neurotoxin (BoNT), the causative agent of botulism, is the most potent toxin known to humans. While BoNTs are widely recognized for their therapeutic and cosmetic applications, such as Botox™, their increasing use has raised concerns about iatrogenic botulism. Due to their extreme lethality, ease of production, and history of weaponization, the Centers for Disease Control and Prevention (CDC) classifies BoNTs as a Category A bioterrorism threat. Among the seven major serotypes (A-G), BoNT/A, BoNT/B, and BoNT/E account for over 95% of human botulism cases with A being the most prevalent. Despite the severity of botulism, no approved therapeutic exists to rescue intoxicated neurons. The current treatment, a heptavalent antitoxin, can only slow disease progression and requires early administration and prolonged hospitalization due to the inability of antibodies to penetrate infected cells. In the field of small- molecule inhibitors (SMIs), promising scaffolds targeting BoNT/A have been discovered, offering opportunities for further derivatization to incorporate bifunctional approaches. Developing a clinically viable therapeutic requires inhibiting the zinc (Zn2+) metalloprotease light chain (LC) as well as addressing toxin persistence. Through extensive inhibitor screening, we have identified two classes of small molecules that inhibit BoNT/A with submicromolar affinity and demonstrate efficacy in both cellular and animal models. However, the transient nature of these inhibitors necessitates the need of a sustained clearance approach. To achieve this, we propose integrating our previously identified BoNT/A LC SMIs with a targeted protein degradation (TPD) technology for toxin elimination. Based upon the background outlined, vide supra, our research strategy for the ablation of BoNT/A will be focused upon the following three specific objectives: 1) Structural Optimization – Utilize molecular docking, and structure-activity relationship (SAR) analysis to modify inhibitors for TPD ligand attachment. 2) Degrader Design – Development of ubiquitin-protease system (UPS)-based proteolysis-targeting chimeras (PROTACs) and autophagy-targeting chimeras to enhance degradation efficiency. 3) Cellular Evaluation – Assess enzyme inhibition, toxin clearance, degradation kinetics in cells.

Addressing C-F bonds and amyloid-formation in biological systems

The ingestion, pulmonary inhalation, and dermal infiltration of C-F bond-containing compounds, most commonly found in the form of per- and polyfluoroalkyl organic acids, causes oxidative stress, inflammation, DNA damage, and developmental defects in infants and adults. These chemicals accumulate in the brain, disrupt neurological function and compromise cognitive and locomotory behavior. Yet, we lack a high-resolution road-map of the interactions between C-F bonds and biomolecular assemblies driving the trajectory towards neurodegenerative outcomes. This gap constitutes a significant barrier to advancing measures designed to mitigate C-F chemistry-associated neurotoxicity. Emerging experimental and computational data from our laboratory reveals that perfluorooctanoic acid, perfluorodecanoic acid and perfluorosulfonic acid corrupt biomolecular structures through C-F:side-chain interactions in tested soluble, globular proteins found in milk and tissues (matrices where C-F chemistries have been detected). Furthermore, they impaired the physiological function in these proteins through displacement of physiological ligands or by compromising the binding of co-factors. The neuroblastoma-derived SHSY-5Y cell line insulted with the said C-F moieties displayed altered gene expression corresponding to reactive oxygen species (ROS), protein ubiquitination, inflammation along with compromised cytoskeletal integrity. C-F bond ingestion ablated dopaminergic (DA) neurons in the nematode C. elegans and induced locomotory deficits in a manner mimicking paraquat. Based on these findings, we propose to gather data towards our hypothesis that C-F bond exposure perturbs biomolecular, cellular and organismal assemblies to onset neurodegeneration-linked trajectories. In Aim 1, we will determine whether organic fluoroacids alter mRNA levels in differentiated SHSY-5Y cells and in neuroprotective gut bacteria (Lactobacillus rhamnosus, Bifidobacterium lactis and Lactobacillus acidophilus). We will examine whether the neuroblastoma cell line exposed to C-F chemistry displays readouts designed to inform the onset of neurodegeneration-associated trajectories (including α-synuclein aggregation). In Aim 2, we will further address in a preclinical model whether C-F burden induces protein aggregation (α-synuclein, amyloid β, mHTT), interferes with dopaminergic neuronal assembles and induces locomotory deficits. Completion of the proposed work will complement ongoing experimental biophysical, structural (crystallographic, NMR) and computational (docking, molecular dynamics simulations) mapping of the interactions between these anthropogenic “forever” chemicals and amyloid-forming proteins potentially resulting in a soluble-to-toxic transformation. It will prepare the stage for vertebrate testing. The findings from this relatively understudied area likely exposes interventional targets for C-F chemistry associated neurotoxicity, spurs therapeutic efforts and can also guide the development of more biocompatible alternatives.

RECONJOINT: A Preference Elicitation Tool to Improve Shared Decision Making for Breast Reconstruction Surgery

PROJECT SUMMARY/ABSTRACT Breast reconstruction is a critical component of comprehensive breast cancer care, offering physical and emotional restoration after mastectomy. However, 40% of women undergoing breast reconstruction report dissatisfaction and decisional regret due to low involvement with treatment decisions and poor alignment between treatment preferences and the chosen reconstructive technique. Current approaches to shared decision-making (SDM) often fail to elicit and integrate individual-level preferences into treatment planning. This serves as a barrier to effective SDM and patient-centered care. To address this gap, we developed a web- based decision tool that uses adaptive choice–based conjoint (ACBC) analysis to elicit patient-level preferences for breast reconstruction. Preliminary studies indicate that the decision tool is acceptable and usable; patients wanted to view their results and use the tool in clinic, which we could not accommodate at the time because the decision tool currently lacks a structured method for clinical integration. We propose to develop an implementation toolkit for the decision tool to facilitate clinical integration and then test the feasibility, acceptability, and implementation of the intervention, RECONJOINT (decision tool and toolkit). In Aim 1, we will design an implementation toolkit informed by focus groups and developed with input from key partners, including patients, providers, and patient advocates. Candidate elements for the implementation toolkit include components developed for site-level implementation: treatment preferences report, video introducing the tool and existing evidence, and recommendations for patients and providers to incorporate preferences into SDM. In Aim 2, we will evaluate the feasibility, acceptability, and preliminary efficacy of the intervention in a pilot cluster-randomized hybrid type 1 trial conducted at two cancer centers (Memorial Sloan Kettering and Duke University). Our primary outcome of interest is the feasibility of the intervention. Secondary outcomes include acceptability and preliminary efficacy. Using a hybrid design, we will simultaneously evaluate facilitators, barriers, and strategies to implementation and how these factors influence the feasibility and acceptability of the intervention. The Consolidated Framework for Implementation Research and the Theoretical Domains Framework will serve as conceptual frameworks. This study is innovative as it leverages ACBC analysis to elicit patient preferences, designs an intervention with multilevel input from clinical and community partners, and uses a hybrid trial design to simultaneously evaluate feasibility, acceptability, preliminary efficacy, and implementation. By addressing critical barriers to SDM and enhancing patient–provider communication, this research aligns with the goals of PA-25-253 and the National Cancer Plan to deliver high quality, patient-centered cancer care. Findings from this study will inform a full-scale multi-site trial to evaluate the efficacy of the intervention and implementation outcomes (e.g., reach).

Bacterial ferrous iron sensing via the BqsRS (CarRS) two-component system

Project Summary Pseudomonas aeruginosa (Pa) is an opportunistic and increasingly antibiotic resistant Gram-negative bacterium that is one of the major causes of chronic nosocomial infections in the United States. The colonization of Pa within a host is often linked to the bioavailability of nutrients, such as iron, and Pa has multiple iron acquisition pathways that allow it to adapt readily to the variety of environments it may encounter within a human host. Pa responds to these dynamic environments commonly through the use of two-component signal transduction systems (TCSs) that are important mediators of signal transduction and allow pathogens to detect chemical and/or physical changes in the environment in order to control basic cellular processes. Previous studies have identified a biofilm and quorum sensing TCS known as BqsRS (also known as CarRS) that regulates biofilm formation and decay in Pa through the sensing of extracytoplasmic Fe2+ and Ca2+. Among its targets, the BqsRS TCS is known to regulate rhlAB and rhlC, critical genes for rhamnolipid production and biofilm formation that are also known to be connected to iron homeostasis and antibiotic resistance. Moreover, the deletion of either bqsR or bqsS in PAO1 results in a significant increase in biofilm formation but reduced biofilm dispersion, the latter of which is important for downstream infections. These observations highlight the importance of the BqsRS TCS to Pa virulence, but there is a foundational lack of understanding regarding the structure, the selectivity, and the mechanism of this system. The ultimate goal of this proposal is to generate a mechanistic and functional understanding of BqsRS at atomic, molecular, and organismal levels in order to exploit this system as a means of reducing or stemming the virulence of opportunistic pathogens such as Pa. The objectives of this exploratory grant are to determine the structural and molecular characteristics of BqsRS, to define how these properties govern BqsRS metal selectivity and function, and to examine a new role of the BqsRS system in regulating the Feo system in P. aeruginosa. Ultimately, the accomplishment of this exploratory grant will deliver fundamental mechanistic insight into a critical metal-sensing TCS and lay the groundwork for future studies that may be designed to target this system and its homologs for additional bacterial exploits.

Structure-function and mechanistic studies of a specific glycosyltransferase complex in fusion-driven pediatric gliomas

Abstract Glycosylation is a co/post-translational modification involved in cell-matrix interactions, antigen-antibody interactions, tumor invasion, and cell motility. Abnormal glycosylation is a hallmark of cancer, with various glycosylation-related genes linked to glioma prognosis and tumor heterogeneity. Pediatric low-grade gliomas (pLGGs) stand as the most common childhood central nervous system tumor, accounting for 30%-40% of all CNS tumors in children. Despite its relatively low mortality rate, pLGGs are associated with devastating lifelong morbidity. The most common alteration found in 75% of tumors is the KIAA1549:BRAF fusion, causing an aberrant activation of the MAPK/ERK signaling pathway. Current treatments, such as traditional chemotherapies and targeted therapies, have limitations such as resistance, lack of specificity, toxicity and paradoxical activation of the MAPK pathway. This highlights the urgent need for novel therapeutic approaches. Investigations into KIAA1549:BRAF-driven pLGGs identified their dependency on the protein-O-mannosyl transferase (POMT) complex for survival. In contrast, BRAFV600E-mutant cells did not show dependency, suggesting the POMT complex as a vulnerability and promising target in KIAA1549:BRAF-driven pLGGs. Therefore, our goal is to characterize the POMT complex structurally and biochemically and study its roles in KIAA1549:BRAF-driven pLGGs. In this proposal, we aim to 1) determine the high-resolution structures of the complex in its unbound, substrate-bound, and inhibitor-bound forms and 2) elucidate the POMT complex mechanisms in KIAA1549:BRAF-driven pLGGs. We will define the critical functional domains, active sites, interaction interfaces and translational modifications crucial for enzymatic activity using cryo-EM techniques, mutagenesis, and functional studies. To study biological pathways and molecular events modulated by the POMT complex, we will implement global proteomics and transcriptomics analysis in well-characterized disease models. In parallel, we will assess the effect of the POMT complex on the MAPK/ERK signaling pathway. This study will guide the structure-based design of probes and drugs targeting the POMT complex and will unveil glycosylation-mediated oncogenesis in pediatric gliomas. It will aid in the development of new targeted therapies and the identification of new biomarkers for pLGGs harboring the KIAA1549:BRAF fusion. The research will be conducted in the Fischer lab at Dana-Farber Cancer Institute, which provides a collaborative and resource-rich environment. The career development plan includes training in scientific writing, mentoring, and presentation skills, as well as interdisciplinary networking with experts in structural biology and pediatric oncology. The candidate’s career goal is to establish an independent research laboratory focused on developing new therapeutic modalities for pediatric neurooncology. The training provided through this fellowship represents a critical step toward achieving this goal.

Uncovering genetic determinants of carbapenem resistance in Klebsiella pneumoniae

Carbapenem-resistant Klebsiella pneumoniae represents an urgent global health threat due to its increasing prevalence and high mortality rates, necessitating a comprehensive understanding of its resistance mechanisms. While key resistance mechanisms and their genetic determinants are known, such as beta- lactamases and porin mutations, the cause of resistance in many strains remains elusive. Moreover, other strains that carry known genetic carbapenem-resistance factors have been found to still be susceptible to carbapenems for unclear reasons. Further, strains can carry genetic elements which, while not conferring resistance directly, can promote resistance indirectly by accelerating its acquisition, such as through mutations in DNA repair systems or mobile genetic elements. To address these knowledge gaps, we propose a genome-wide association study (GWAS), with the aim of maximizing the discovery of gene variants associated with meropenem resistance, with experimental validation of candidates to identify true causal variants. We will overcome limitations of prior studies in the following ways: 1) We have compiled an expanded data set of publicly available K. pneumoniae genomes from strains isolated across a wide distribution of countries, with in hand access to >100 isolates upon which experimental validation studies will be performed. 2) We will perform comprehensive capture of genetic variants by employing a reference-free GWAS, utilizing unitigs, stretches of DNA sequence that represent the entire spectrum of genetic variation. 3) We will enhance statistical power to detect genetic variants with even subtle effects on resistance by using a quantitative, continuous minimum inhibitory concentration (MIC) phenotype to meropenem rather than a binary designation of resistant or susceptible. 4) We will reduce the number of false positives arising from correlation, or linkage disequilibrium (LD), with known carbapenemase and other known resistance factors by performing a conditional GWAS, where known factors are included as covariates. 5) We will further mitigate confounding effects due to population structure and LD, which cause non-random relationships between variants, by utilizing a pangenome-wide regression with an elastic net penalty. 6) Crucially, we will functionally validate our findings, which will include genetic variants associated with increased resistance, whether through direct or indirect mechanisms, as well as those that may restore susceptibility in strains already possessing known resistance factors. We will bridge the gap between GWAS findings and functional validation by leveraging our high-throughput experimental capabilities. This integrated approach promises to uncover novel mechanisms of carbapenem resistance, its acquisition, and susceptibility in K. pneumoniae, with the potential to inform the development of future diagnostics or therapeutic strategies.

A dynamic regulatory mechanism controlling bacterial persister formation and resuscitation within biofilms

PROJECT SUMMARY Persisters present a major challenge in clinical infection treatment and recurrent infection management. A continued effort towards a better understanding of the molecular mechanisms of persister formation and resuscitation is needed to provide novel treatment strategies for the control of chronic infections and problems related to persisters. Unlike resistant bacteria, persisters are genetically identical to their susceptible counterparts, and this phenotypic state is inherently transient and shifts in response to environmental conditions. Therefore, it is essential to use an approach tailored to the transient and rare nature of this phenomenon. Pseudomonas aeruginosa (Pa) is an important human pathogen frequently implicated in both acute and chronic infections. Persisters have been identified in both Pa planktonic and biofilm modes of growth, with higher frequencies of persister formation being observed in biofilm, especially in the interior of the mature biofilm structure. In this study, we obtained the first high-resolution single-cell transcriptomes of persister and resuscitated cells isolated directly from the interior of mature biofilms. The results led to the identification of a previously uncharacterized transcriptional regulator that controls persister formation and resuscitation. This regulator, named PriR here, is conserved in Pseudomonas species and has homologs in two critical bacterial pathogens, Acinetobacter baumannii and Enterobacter cloacae. We showed that PriR has a dynamic spatiotemporal gene expression profile, and its expression directly correlates with and causes persister resuscitation. In this application, we propose two specific aims to investigate this novel regulation mechanism of persister formation and resuscitation. Aim 1 will identify the physiological effects of this novel regulatory system on antibiotic tolerance in vitro and in hosts using the Drosophila melanogaster biofilm infection model. Aim 2 will determine its molecular regulatory mechanism via ChIP-seq and RNA-seq, and analyze the putative PriR- controlled genes on persister formation and resuscitation in additional clinically-relevant Pa strains. The insights gained from this proposal will provide crucial new information about the dynamic regulatory mechanism of persister formation and resuscitation. The PriR-controlled resuscitation mechanism could be a promising target for persister eradication approaches by re-sensitizing persister cells to conventional antimicrobials or preventing persister formation. Understanding this novel regulatory system that controls bacterial persister formation and resuscitation could provide new drug targets and/or treatment strategies for persistent infections.

Structure-Based Development of Nucleotide-Competing Inhibitors Against HIV-1 and LINE-1 Reverse Transcriptases

PROJECT SUMMARY Reverse transcriptases (RTs) from retroviruses and endogenous retroelements are essential polymerases that catalyze RNA- and DNA-dependent DNA synthesis. Nucleoside inhibitors (NIs) remain central to HIV-1 therapy and are also used against other viral infections and in cancer, but toxicity, limited selectivity, pharmacokinetic (PK) liabilities, and the emergence of drug resistance highlight the need for alternative RT inhibitor mechanisms. In contrast to NIs, nucleotide-competing inhibitors (NCIs) block the polymerase active site without requiring incorporation into nucleic acids. Structural studies by PI Ruiz have defined the NCI mechanism of action for HIV- 1 RT and revealed conserved binding modules shared across multiple polymerase families. These advances now enable rational discovery of improved NCIs. LINE-1 (L1) ORF2 RT is an emerging therapeutic target in cancer, autoimmunity, and aging, yet NIs are the only inhibitors known to act against L1 RT. Notably, the NCI-binding region is structurally similar between HIV-1 RT and L1 RT, suggesting that NCI recognition principles may extend across these two biologically distinct polymerases. This R21 seeks to establish proof-of-concept for NCI development against both enzymes. Aim 1 will discover and structurally optimize NCIs targeting HIV-1 RT by combining binding modules from known NCI chemotypes and determining their biochemical activity and co-crystal structures. Aim 2 will determine whether HIV-1 RT NCI principles translate to L1 RT by solving L1 RT/nucleic acid/NCI structures, evaluating enzymatic inhibition, and applying AI-based structure prediction and generative design to propose L1-specific NCI candidates. Cellular retrotransposition assays will test mechanism of action. Aim 3 will develop a fragment library tailored to protein–nucleic acid interfaces and perform fragment screening of HIV-1 and L1 RT/nucleic acid complexes to identify additional chemotypes that engage the NCI binding region. Successful completion will yield NCI scaffolds and mechanistic insights applicable to HIV-1 RT and L1 RT, define structural principles governing NCI recognition across two evolutionarily related polymerases, and establish new avenues for RT inhibitor development. The PI is highly qualified to lead this work, with extensive expertise in RT structural biology, drug design, and fragment-based discovery.

Chromatin-Based Mechanisms Linking Transcriptional Dysregulation to Genome Instability in Neurodevelopmental Disorders.

PROJECT SUMMARY/ABSTRACT Neurons depend on a finely tuned interplay between chromatin regulation and genome maintenance, yet they are acutely vulnerable to DNA damage generated during activity-dependent transcription of long, synaptic genes. Disruption of this balance is increasingly recognized as a driver of neurodevelopmental disorders (NDDs) such as autism spectrum disorder (ASD), intellectual disability, and epilepsy. High-confidence genetic studies converge on regulators of histone H3 lysine 4 (H3K4) methylation, such as the writers ASHIL and Klv1T2C and the eraser KDNISB, as recurrently mutated loci in NTIDs. The overarching goal of this study is to investigate how dysregulated H3K4 methylation compromises genome integrity in human neurons, thereby contributing to the pathogenesis of NDDs. The central, hypothesis is that coordinated II3K4 methylation safeguards neuronal genomes by maintaining an open chromatin architecture that permits the efficient detection and repair of transcription-coupled DNA lesions. The rationale/Or this study is to define the epigenetic control of DNA repair, which will illuminate a shared pathogenic hub across multiple ~I)D-linked genes. During the mentoredK99 phase, I will define how ASHIL, KMT2C, and KDM5B regulate chromatin structure and DNA repair at baseline and during transcriptional stress. Aim-1: I will use isogenic iPSC-derived cortical neurons with patient-relevant mutations or CRrSPRi knockdowns of these regulators, applying an integrated multi-omic pipeline: CUT&Tag and Micro-C to map H3K4 methylation and 3D chromatin topology. Aim-2: I will use Paired-Damage-seq, and CUT&RUN to chart oxidative lesions, repair synthesis, and recruitment of key repair factors; and RNA-seq to relate damage hotspots to altered gene expression. Aims l and 2 will be performed under the guidance of Dr. Lizarraga and Dr. Morrow, experts in the field of neurodevelopmental biology. My advisory team brings unique and complementary skills, enhancing my knowledge in 3D chromatin structure, transcription-coupled repair, gene editing, and multi-omics analysis. I will utilize these skills in the R00 phase (Aim 3), expanding the framework to include additional H3K4 regulators (e.g., LSD1, KMT2A) and broader neural lineages, thereby developing a comprehensive model. This study is innovative in its integration of single-cell D.NA damage mapping with chromatin topology and transcriptional profiling, enabling a direct and mechanistic connection between disrupted H3K4 methylation and genome instability. By uncovering how H3.K4 methylation prevents transcription-coupled genome instability in the developing brain, this research will address a critical gap in our understanding of NDD mechanisms. This award will enable me to launch an independent research program dedicated to determining mechanisms of chromatin-based processes that maintain genome stability in the developing human brain.

AI-guided structural biology of Cav1.2

Project Summary/Abstract The L-type calcium channel Cav1.2 plays a critical role in excitation-contraction coupling in the heart. Its calcium flux generates the plateau phase of the cardiac action potential and results in the calcium-induced calcium release needed to trigger cardiac contractions. Cav1.2 is a multi-subunit protein consisting of a large, transmembrane 1 subunit and smaller, auxiliary subunits important for trafficking and channel regulation. Recent cryogenic electron microscopy (cryo-EM) experiments have revealed much of the three-dimensional structure of Cav1.2’s core domains, though the final 571 residues of the 1 subunit’s intracellular C-terminal domain (CTD) have not yet been resolved despite key regulatory roles in channel function. This domain has been shown to be important for Cav1.2’s regulation by calcium/calmodulin and has an important role in cross- talk between Cav1.2 and the sympathetic nervous system, amongst other cell signaling pathways. In this proposal, I will use insights from artificial intelligence to develop a platform for CTD structural biology, then validate that platform by measuring its ability to form protein-protein interactions with known binding partners of Cav1.2, including calcium/calmodulin and an autoregulatory distal C-terminal fragment. If successful, I will also attempt crystallization of the CTD in complex with several binding partners. Together these data will provide the starting point for future structural biology projects on Cav1.2 regulation and protein-protein interactions.

Targeting the fibrogenic ECM as an alternative approach to treating IPF

Project Abstract Idiopathic pulmonary fibrosis and, more broadly, progressive pulmonary fibrosis are wound healing disorders whose hallmark is unorganized and unchecked extracellular matrix (ECM) deposition leading to scarring/stiffening of the lung interstitium. A highly complex, multicellular process, the generation of scar itself is primarily a function of activated fibroblasts with contributions from multiple subpopulations and non-fibroblastic cells. Myofibroblasts, the contractile cohort of activated fibroblasts, physically perturb (i.e. stretch) the local ECM microenvironment, which we have recently shown triggers site-specific, stretch-dependent conformational changes within the ECM protein fibronectin. We have previously demonstrated that a specific stretch-induced conformational change in the critical receptor binding domain of fibronectin triggers a cellular “integrin switch”, a stark change in the ECM receptors used by cells to engage fibronectin. This integrin switch is sufficient to drive activation of naïve lung fibroblasts, acquisition of mesenchymal characteristics in alveolar epithelial cells, and pathogenic remodeling of vascular structures. In this proposal we hypothesize that fibronectin displays a stretch- dependent conformational change specifically in regions of active lung fibrogenesis and that this conformational change disrupts homeostatic integrin binding dynamics in fibroblasts, leading to their acquisition of a pro-fibrogenic phenotype and transcriptional program. We address this hypothesis in a systematic way through three proposed aims. The first aim focuses on quantifying the presence and spatial localization of the stretch-induced conformational change within a cohort of lung fibrosis patient tissue samples, determining if it represents a consistent marker of active fibrogenic regions and elucidation of critical microenvironmental signatures that further expand our understanding of the impact of fibronectin's integrin switch in driving disease. In the second aim we will begin to unravel the molecular mechanism explaining how the integrin switch that emerges because of the stretch-induced conformational change drives fibroblast activation and fibrogenic gene programs using both idealized in vitro culture systems as well as ex vivo human disease tissue models. Finally, in the third aim we will explore the therapeutic potential of binding and blocking this specific stretch-induced conformation of fibronectin using a promising new and potential antibody drug in both in vivo and ex vivo models of disease.

Facilitating the Advancement of Research and Education for Undergraduate Students by Incorporating Laser Scanning Confocal Microscopy (FAREUS-LSCM)

PROJECT SUMMARY/ABSTRACT The University of Puerto Rico at Aguadilla (UPR-Aguadilla) requests funding to acquire a Nikon AX Galvo Confocal Laser Scanning Microscope (LSCM) with a TI2-E inverted platform and a four- laser configuration (405/488/561/640 nm) to establish transformative imaging capabilities at our resource-limited institution serving 96% Pell Grant recipients. This state-of-the-art instrument addresses a critical infrastructure gap, enabling high-resolution fluorescence imaging, live-cell microscopy, and quantitative analysis essential for competitive biomedical research and undergraduate education. The LSCM will directly support four active research projects spanning parasitology (monogenean host-specificity studies), plant pathology (coffee biocontrol development), environmental chemistry (metalloprotein biomarkers), and neuroscience (astrocyte dysfunction in diabetic epilepsy) while integrating into core laboratory courses including Immunology (BIOL 4009) and Undergraduate research courses (BIOL 3108 and QUIM 4999). Our multidisciplinary faculty, in partnership with the Neuroimaging and Electrophysiology Facility (NIEF) Excellence Imaging Center, offers expertise in confocal microscopy, encompassing advanced imaging and specialized sample preparation techniques. This collaboration ensures effective implementation of the technology, sustained technical support, and high-quality training programs that will enhance research productivity and broaden educational impact. The broad, long-term objective is to transform UPR-Aguadilla from a primarily teaching institution into a research-active campus capable of producing graduate-school-ready students equipped with cutting-edge technical skills. Access to advanced confocal microscopy will stimulate new research collaborations, enhance faculty productivity, and provide 30-40 students annually with hands-on experience in modern imaging technologies currently absent from our curriculum. The instrument will strengthen our partnership with the emerging Natural History Museum of Puerto Rico for specimen digitization and support comprehensive outreach programs targeting 25-50 high school students annually through "Seeing Science Up Close" workshops. Expected outcomes include 1- 2 peer-reviewed publications within three years, establishment of 1-2 new institutional collaborations, and measurable enhancement of biomedical research capacity. This investment will significantly advance STEM education and research opportunities at UPR-Aguadilla while expanding access to cutting-edge scientific instrumentation for students pursuing biomedical careers and contributing to the development of skilled researchers in the biomedical sciences.

Computational Mechanisms of Predictive Processing in Brains and Machines

Predictive processing offers a unifying view of neural computation, proposing that brains continuously anticipate sensory input and update internal models based on prediction errors. In this talk, I will present converging evidence for the computational mechanisms underlying this framework across human neuroscience and deep neural networks. I will begin with recent work showing that large-scale distributed prediction-error encoding in the human brain directly predicts how sensory representations reorganize through predictive learning. I will then turn to PredNet, a popular predictive coding inspired deep network that has been widely used to model real-world biological vision systems. Using dynamic stimuli generated with our Spatiotemporal Style Transfer algorithm, we demonstrate that PredNet relies primarily on low-level spatiotemporal structure and remains insensitive to high-level content, revealing limits in its generalization capacity. Finally, I will discuss new recurrent vision models that integrate top-down feedback connections with intrinsic neural variability, uncovering a dual mechanism for robust sensory coding in which neural variability decorrelates unit responses, while top-down feedback stabilizes network dynamics. Together, these results outline how prediction error signaling and top-down feedback pathways shape adaptive sensory processing in biological and artificial systems.

MRI investigation of orientation-dependent changes in microstructure and function in a mouse model of mild traumatic brain injury

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.

Non-invasive human neuroimaging studies of motor plasticity have predominantly focused on the cerebral cortex due to low signal-to-noise ration of blood oxygen level-dependent (BOLD) signals in subcortical structures and the small effect sizes typically observed in plasticity paradigms. Precision functional mapping can help overcome these challenges and has revealed significant and reversible functional alterations in the cortico-subcortical motor circuit during arm immobilization

Neural circuits underlying sleep structure and functions

Sleep is an active state critical for processing emotional memories encoded during waking in both humans and animals. There is a remarkable overlap between the brain structures and circuits active during sleep, particularly rapid eye-movement (REM) sleep, and the those encoding emotions. Accordingly, disruptions in sleep quality or quantity, including REM sleep, are often associated with, and precede the onset of, nearly all affective psychiatric and mood disorders. In this context, a major biomedical challenge is to better understand the underlying mechanisms of the relationship between (REM) sleep and emotion encoding to improve treatments for mental health. This lecture will summarize our investigation of the cellular and circuit mechanisms underlying sleep architecture, sleep oscillations, and local brain dynamics across sleep-wake states using electrophysiological recordings combined with single-cell calcium imaging or optogenetics. The presentation will detail the discovery of a 'somato-dendritic decoupling'in prefrontal cortex pyramidal neurons underlying REM sleep-dependent stabilization of optimal emotional memory traces. This decoupling reflects a tonic inhibition at the somas of pyramidal cells, occurring simultaneously with a selective disinhibition of their dendritic arbors selectively during REM sleep. Recent findings on REM sleep-dependent subcortical inputs and neuromodulation of this decoupling will be discussed in the context of synaptic plasticity and the optimization of emotional responses in the maintenance of mental health.

From Spiking Predictive Coding to Learning Abstract Object Representation

In a first part of the talk, I will present Predictive Coding Light (PCL), a novel unsupervised learning architecture for spiking neural networks. In contrast to conventional predictive coding approaches, which only transmit prediction errors to higher processing stages, PCL learns inhibitory lateral and top-down connectivity to suppress the most predictable spikes and passes a compressed representation of the input to higher processing stages. We show that PCL reproduces a range of biological findings and exhibits a favorable tradeoff between energy consumption and downstream classification performance on challenging benchmarks. A second part of the talk will feature our lab’s efforts to explain how infants and toddlers might learn abstract object representations without supervision. I will present deep learning models that exploit the temporal and multimodal structure of their sensory inputs to learn representations of individual objects, object categories, or abstract super-categories such as „kitchen object“ in a fully unsupervised fashion. These models offer a parsimonious account of how abstract semantic knowledge may be rooted in children's embodied first-person experiences.

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.

Memory Decoding Journal Club: "Structure and function of the hippocampal CA3 module

Structure and function of the hippocampal CA3 module

Neural Signal Propagation Atlas of C. elegans

In the age of connectomics, it is increasingly important to understand how the nodes and edges of a brain's anatomical network, or "connectome," gives rise to neural signaling and neural function. I will present the first comprehensive brain-wide cell-resolved causal measurements of how neurons signal to one another in response to stimulation in the nematode C. elegans. I will compare this signal propagation atlas to the worm's known connectome to address fundamental questions of structure and function in the brain.

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..

Maladaptive Neuroplasticity in Cortico-limbic Structures: Insights from Surgical Pain Relief in Chronic Neuropathic Facial Pain

What it’s like is all there is: The value of Consciousness

Over the past thirty years or so, cognitive neuroscience has made spectacular progress understanding the biological mechanisms of consciousness. Consciousness science, as this field is now sometimes called, was not only inexistent thirty years ago, but its very name seemed like an oxymoron: how can there be a science of consciousness? And yet, despite this scepticism, we are now equipped with a rich set of sophisticated behavioural paradigms, with an impressive array of techniques making it possible to see the brain in action, and with an ever-growing collection of theories and speculations about the putative biological mechanisms through which information processing becomes conscious. This is all good and fine, even promising, but we also seem to have thrown the baby out with the bathwater, or at least to have forgotten it in the crib: consciousness is not just mechanisms, it’s what it feels like. In other words, while we know thousands of informative studies about access-consciousness, we have little in the way of phenomenal consciousness. But that — what it feels like — is truly what “consciousness” is about. Understanding why it feels like something to be me and nothing (panpsychists notwithstanding) for a stone to be a stone is what the field has always been after. However, while it is relatively easy to study access-consciousness through the contrastive approach applied to reports, it is much less clear how to study phenomenology, its structure and its function. Here, I first overview work on what consciousness does (the "how"). Next, I ask what difference feeling things makes and what function phenomenology might play. I argue that subjective experience has intrinsic value and plays a functional role in everything that we do.

Neural architectures: what are they good for anyway?

The brain has a highly complex structure in terms of cell types and wiring between different regions. What is it for, if anything? I'll start this talk by asking what might an answer to this question even look like given that we can't run an alternative universe where our brains are structured differently. (Preview: we can do this with models!) I'll then talk about some of our work in two areas: (1) does the modular structure of the brain contribute to specialisation of function? (2) how do different cell types and architectures contribute to multimodal sensory processing?

Predicting traveling waves: a new mathematical technique to link the structure of a network to the specific patterns of neural activity

Dimensionality reduction beyond neural subspaces

Over the past decade, neural representations have been studied from the lens of low-dimensional subspaces defined by the co-activation of neurons. However, this view has overlooked other forms of covarying structure in neural activity, including i) condition-specific high-dimensional neural sequences, and ii) representations that change over time due to learning or drift. In this talk, I will present a new framework that extends the classic view towards additional types of covariability that are not constrained to a fixed, low-dimensional subspace. In addition, I will present sliceTCA, a new tensor decomposition that captures and demixes these different types of covariability to reveal task-relevant structure in neural activity. Finally, I will close with some thoughts regarding the circuit mechanisms that could generate mixed covariability. Together this work points to a need to consider new possibilities for how neural populations encode sensory, cognitive, and behavioral variables beyond neural subspaces.

Analyzing Network-Level Brain Processing and Plasticity Using Molecular Neuroimaging

Behavior and cognition depend on the integrated action of neural structures and populations distributed throughout the brain. We recently developed a set of molecular imaging tools that enable multiregional processing and plasticity in neural networks to be studied at a brain-wide scale in rodents and nonhuman primates. Here we will describe how a novel genetically encoded activity reporter enables information flow in virally labeled neural circuitry to be monitored by fMRI. Using the reporter to perform functional imaging of synaptically defined neural populations in the rat somatosensory system, we show how activity is transformed within brain regions to yield characteristics specific to distinct output projections. We also show how this approach enables regional activity to be modeled in terms of inputs, in a paradigm that we are extending to address circuit-level origins of functional specialization in marmoset brains. In the second part of the talk, we will discuss how another genetic tool for MRI enables systematic studies of the relationship between anatomical and functional connectivity in the mouse brain. We show that variations in physical and functional connectivity can be dissociated both across individual subjects and over experience. We also use the tool to examine brain-wide relationships between plasticity and activity during an opioid treatment. This work demonstrates the possibility of studying diverse brain-wide processing phenomena using molecular neuroimaging.

Mouse Motor Cortex Circuits and Roles in Oromanual Behavior

I’m interested in structure-function relationships in neural circuits and behavior, with a focus on motor and somatosensory areas of the mouse’s cortex involved in controlling forelimb movements. In one line of investigation, we take a bottom-up, cellularly oriented approach and use optogenetics, electrophysiology, and related slice-based methods to dissect cell-type-specific circuits of corticospinal and other neurons in forelimb motor cortex. In another, we take a top-down ethologically oriented approach and analyze the kinematics and cortical correlates of “oromanual” dexterity as mice handle food. I'll discuss recent progress on both fronts.

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.

Brain circuits for spatial navigation

In this webinar on spatial navigation circuits, three researchers—Ann Hermundstad, Ila Fiete, and Barbara Webb—discussed how diverse species solve navigation problems using specialized yet evolutionarily conserved brain structures. Hermundstad illustrated the fruit fly’s central complex, focusing on how hardwired circuit motifs (e.g., sinusoidal steering curves) enable rapid, flexible learning of goal-directed navigation. This framework combines internal heading representations with modifiable goal signals, leveraging activity-dependent plasticity to adapt to new environments. Fiete explored the mammalian head-direction system, demonstrating how population recordings reveal a one-dimensional ring attractor underlying continuous integration of angular velocity. She showed that key theoretical predictions—low-dimensional manifold structure, isometry, uniform stability—are experimentally validated, underscoring parallels to insect circuits. Finally, Webb described honeybee navigation, featuring path integration, vector memories, route optimization, and the famous waggle dance. She proposed that allocentric velocity signals and vector manipulation within the central complex can encode and transmit distances and directions, enabling both sophisticated foraging and inter-bee communication via dance-based cues.

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.

Unmotivated bias

In this talk, I will explore how social affective biases arise even in the absence of motivational factors as an emergent outcome of the basic structure of social learning. In several studies, we found that initial negative interactions with some members of a group can cause subsequent avoidance of the entire group, and that this avoidance perpetuates stereotypes. Additional cognitive modeling discovered that approach and avoidance behavior based on biased beliefs not only influences the evaluative (positive or negative) impressions of group members, but also shapes the depth of the cognitive representations available to learn about individuals. In other words, people have richer cognitive representations of members of groups that are not avoided, akin to individualized vs group level categories. I will end presenting a series of multi-agent reinforcement learning simulations that demonstrate the emergence of these social-structural feedback loops in the development and maintenance of affective biases.

Emergence of behavioural individuality from global microstructure of the brain and learning

Probing White Matter Microstructure With Diffusion-Weighted MRI: Techniques and Applications in ADRD

Light-gated membrane channels: Discovery and creation of diversity, principles from protein structure, and cell-function access to biology

Volume measures in studies of hippocampal subfield structure: methodological considerations and applications

There’s more to timing than time: P-centers, beat bins and groove in musical microrhythm

How does the dynamic shape of a sound affect its perceived microtiming? In the TIME project, we studied basic aspects of musical microrhythm, exploring both stimulus features and the participants’ enculturated expertise via perception experiments, observational studies of how musicians produce particular microrhythms, and ethnographic studies of musicians’ descriptions of microrhythm. Collectively, we show that altering the microstructure of a sound (“what” the sound is) changes its perceived temporal location (“when” it occurs). Specifically, there are systematic effects of core acoustic factors (duration, attack) on perceived timing. Microrhythmic features in longer and more complex sounds can also give rise to different perceptions of the same sound. Our results shed light on conflicting results regarding the effect of microtiming on the “grooviness” of a rhythm.

Mitochondrial diversity in the mouse and human brain

The basis of the mind, of mental states, and complex behaviors is the flow of energy through microscopic and macroscopic brain structures. Energy flow through brain circuits is powered by thousands of mitochondria populating the inside of every neuron, glial, and other nucleated cell across the brain-body unit. This seminar will cover emerging approaches to study the mind-mitochondria connection and present early attempts to map the distribution and diversity of mitochondria across brain tissue. In rodents, I will present convergent multimodal evidence anchored in enzyme activities, gene expression, and animal behavior that distinct behaviorally-relevant mitochondrial phenotypes exist across large-scale mouse brain networks. Extending these findings to the human brain, I will present a developing systematic biochemical and molecular map of mitochondrial variation across cortical and subcortical brain structures, representing a foundation to understand the origin of complex energy patterns that give rise to the human mind.

Learning representations of specifics and generalities over time

There is a fundamental tension between storing discrete traces of individual experiences, which allows recall of particular moments in our past without interference, and extracting regularities across these experiences, which supports generalization and prediction in similar situations in the future. One influential proposal for how the brain resolves this tension is that it separates the processes anatomically into Complementary Learning Systems, with the hippocampus rapidly encoding individual episodes and the neocortex slowly extracting regularities over days, months, and years. But this does not explain our ability to learn and generalize from new regularities in our environment quickly, often within minutes. We have put forward a neural network model of the hippocampus that suggests that the hippocampus itself may contain complementary learning systems, with one pathway specializing in the rapid learning of regularities and a separate pathway handling the region’s classic episodic memory functions. This proposal has broad implications for how we learn and represent novel information of specific and generalized types, which we test across statistical learning, inference, and category learning paradigms. We also explore how this system interacts with slower-learning neocortical memory systems, with empirical and modeling investigations into how the hippocampus shapes neocortical representations during sleep. Together, the work helps us understand how structured information in our environment is initially encoded and how it then transforms over time.

Currents of Hope: how noninvasive brain stimulation is reshaping modern psychiatric care; Adapting to diversity: Integrating variability in brain structure and function into personalized / closed-loop non-invasive brain stimulation for substance use disorders

In March we will focus on TMS and host Ghazaleh Soleimani and Colleen Hanlon. The talks will talk place on Thursday, March 28th at noon ET – please be aware that this means 5PM CET since Boston already switched to summer time! Ghazaleh Soleimani, PhD, is a postdoctoral fellow in Dr Hamed Ekhtiari’s lab at the University of Minnesota. She is also the executive director of the International Network of tES/TMS for Addiction Medicine (INTAM). She will discuss “Adapting to diversity: Integrating variability in brain structure and function into personalized / closed-loop non-invasive brain stimulation for substance use disorders”. Colleen Hanlon, PhD, currently serves as a Vice President of Medical Affairs for BrainsWay, a company specializing in medical devices for mental health, including TMS. Colleen previously worked at the Medical University of South Carolina and Wake Forest School of Medicine. She received the International Brain Stimulation Early Career Award in 2023. She will discuss “Currents of Hope: how noninvasive brain stimulation is reshaping modern psychiatric care”. As always, we will also get a glimpse at the “Person behind the science”. Please register va talks.stimulatingbrains.org to receive the (free) Zoom link, subscribe to our newsletter, or follow us on Twitter/X for further updates!

Stability of visual processing in passive and active vision

The visual system faces a dual challenge. On the one hand, features of the natural visual environment should be stably processed - irrespective of ongoing wiring changes, representational drift, and behavior. On the other hand, eye, head, and body motion require a robust integration of pose and gaze shifts in visual computations for a stable perception of the world. We address these dimensions of stable visual processing by studying the circuit mechanism of long-term representational stability, focusing on the role of plasticity, network structure, experience, and behavioral state while recording large-scale neuronal activity with miniature two-photon microscopy.

Learning produces a hippocampal cognitive map in the form of an orthogonalized state machine

Cognitive maps confer animals with flexible intelligence by representing spatial, temporal, and abstract relationships that can be used to shape thought, planning, and behavior. Cognitive maps have been observed in the hippocampus, but their algorithmic form and the processes by which they are learned remain obscure. Here, we employed large-scale, longitudinal two-photon calcium imaging to record activity from thousands of neurons in the CA1 region of the hippocampus while mice learned to efficiently collect rewards from two subtly different versions of linear tracks in virtual reality. The results provide a detailed view of the formation of a cognitive map in the hippocampus. Throughout learning, both the animal behavior and hippocampal neural activity progressed through multiple intermediate stages, gradually revealing improved task representation that mirrored improved behavioral efficiency. The learning process led to progressive decorrelations in initially similar hippocampal neural activity within and across tracks, ultimately resulting in orthogonalized representations resembling a state machine capturing the inherent struture of the task. We show that a Hidden Markov Model (HMM) and a biologically plausible recurrent neural network trained using Hebbian learning can both capture core aspects of the learning dynamics and the orthogonalized representational structure in neural activity. In contrast, we show that gradient-based learning of sequence models such as Long Short-Term Memory networks (LSTMs) and Transformers do not naturally produce such orthogonalized representations. We further demonstrate that mice exhibited adaptive behavior in novel task settings, with neural activity reflecting flexible deployment of the state machine. These findings shed light on the mathematical form of cognitive maps, the learning rules that sculpt them, and the algorithms that promote adaptive behavior in animals. The work thus charts a course toward a deeper understanding of biological intelligence and offers insights toward developing more robust learning algorithms in artificial intelligence.

Unifying the mechanisms of hippocampal episodic memory and prefrontal working memory

Remembering events in the past is crucial to intelligent behaviour. Flexible memory retrieval, beyond simple recall, requires a model of how events relate to one another. Two key brain systems are implicated in this process: the hippocampal episodic memory (EM) system and the prefrontal working memory (WM) system. While an understanding of the hippocampal system, from computation to algorithm and representation, is emerging, less is understood about how the prefrontal WM system can give rise to flexible computations beyond simple memory retrieval, and even less is understood about how the two systems relate to each other. Here we develop a mathematical theory relating the algorithms and representations of EM and WM by showing a duality between storing memories in synapses versus neural activity. In doing so, we develop a formal theory of the algorithm and representation of prefrontal WM as structured, and controllable, neural subspaces (termed activity slots). By building models using this formalism, we elucidate the differences, similarities, and trade-offs between the hippocampal and prefrontal algorithms. Lastly, we show that several prefrontal representations in tasks ranging from list learning to cue dependent recall are unified as controllable activity slots. Our results unify frontal and temporal representations of memory, and offer a new basis for understanding the prefrontal representation of WM

Using Adversarial Collaboration to Harness Collective Intelligence

There are many mysteries in the universe. One of the most significant, often considered the final frontier in science, is understanding how our subjective experience, or consciousness, emerges from the collective action of neurons in biological systems. While substantial progress has been made over the past decades, a unified and widely accepted explanation of the neural mechanisms underpinning consciousness remains elusive. The field is rife with theories that frequently provide contradictory explanations of the phenomenon. To accelerate progress, we have adopted a new model of science: adversarial collaboration in team science. Our goal is to test theories of consciousness in an adversarial setting. Adversarial collaboration offers a unique way to bolster creativity and rigor in scientific research by merging the expertise of teams with diverse viewpoints. Ideally, we aim to harness collective intelligence, embracing various perspectives, to expedite the uncovering of scientific truths. In this talk, I will highlight the effectiveness (and challenges) of this approach using selected case studies, showcasing its potential to counter biases, challenge traditional viewpoints, and foster innovative thought. Through the joint design of experiments, teams incorporate a competitive aspect, ensuring comprehensive exploration of problems. This method underscores the importance of structured conflict and diversity in propelling scientific advancement and innovation.

Neuromodulation of striatal D1 cells shapes BOLD fluctuations in anatomically connected thalamic and cortical regions

Understanding how macroscale brain dynamics are shaped by microscale mechanisms is crucial in neuroscience. We investigate this relationship in animal models by directly manipulating cellular properties and measuring whole-brain responses using resting-state fMRI. Specifically, we explore the impact of chemogenetically neuromodulating D1 medium spiny neurons in the dorsomedial caudate putamen (CPdm) on BOLD dynamics within a striato-thalamo-cortical circuit in mice. Our findings indicate that CPdm neuromodulation alters BOLD dynamics in thalamic subregions projecting to the dorsomedial striatum, influencing both local and inter-regional connectivity in cortical areas. This study contributes to understanding structure–function relationships in shaping inter-regional communication between subcortical and cortical levels.

Bio-realistic multiscale modeling of cortical circuits

A central question in neuroscience is how the structure of brain circuits determines their activity and function. To explore this systematically, we developed a 230,000-neuron model of mouse primary visual cortex (area V1). The model integrates a broad array of experimental data:Distribution and morpho-electric properties of different neuron types in V1.

Spatial and Single Cell Genomics for Next Generation Neuroscience

The advent of next generation sequencing ushered in a ten-year period of exuberant technology development, enabling the quantification of gene expression and epigenetic features within individual cells, and within intact tissue sections.  In this seminar, I will outline our technological contributions, beginning with the development of Drop-seq, a method for high-throughput single cell analysis, followed by the development of Slide-seq, a technique for measuring genome-wide expression at 10 micron spatial resolution.  Using a combination of these techniques, we recently constructed a comprehensive cell type atlas of the adult mouse brain, positioning cell types within individual brain structures.  I will discuss the major findings from this dataset, including emerging principles of neurotransmission, and the localization of disease gene signatures to specific cell types.  Finally, I will introduce a new spatial technology, Slide-tags, that unifies single cell and spatial genomics into a single, highly scalable assay.

Movement planning as a window into hierarchical motor control

The ability to organise one's body for action without having to think about it is taken for granted, whether it is handwriting, typing on a smartphone or computer keyboard, tying a shoelace or playing the piano. When compromised, e.g. in stroke, neurodegenerative and developmental disorders, the individuals’ study, work and day-to-day living are impacted with high societal costs. Until recently, indirect methods such as invasive recordings in animal models, computer simulations, and behavioural markers during sequence execution have been used to study covert motor sequence planning in humans. In this talk, I will demonstrate how multivariate pattern analyses of non-invasive neurophysiological recordings (MEG/EEG), fMRI, and muscular recordings, combined with a new behavioural paradigm, can help us investigate the structure and dynamics of motor sequence control before and after movement execution. Across paradigms, participants learned to retrieve and produce sequences of finger presses from long-term memory. Our findings suggest that sequence planning involves parallel pre-ordering of serial elements of the upcoming sequence, rather than a preparation of a serial trajectory of activation states. Additionally, we observed that the human neocortex automatically reorganizes the order and timing of well-trained movement sequences retrieved from memory into lower and higher-level representations on a trial-by-trial basis. This echoes behavioural transfer across task contexts and flexibility in the final hundreds of milliseconds before movement execution. These findings strongly support a hierarchical and dynamic model of skilled sequence control across the peri-movement phase, which may have implications for clinical interventions.

NOTE: DUE TO A CYBER ATTACK OUR UNIVERSITY WEB SYSTEM IS SHUT DOWN - TALK WILL BE RESCHEDULED

The size and structure of the dendritic arbor play important roles in determining how synaptic inputs of neurons are converted to action potential output and how neurons are integrated in the surrounding neuronal network. Accordingly, neurons with aberrant morphology have been associated with neurological disorders. Dysmorphic, enlarged neurons are, for example, a hallmark of focal epileptogenic lesions like focal cortical dysplasia (FCDIIb) and gangliogliomas (GG). However, the regulatory mechanisms governing the development of dendrites are insufficiently understood. The evolutionary conserved Ste20/Hippo kinase pathway has been proposed to play an important role in regulating the formation and maintenance of dendritic architecture. A key element of this pathway, Ste20-like kinase (SLK), regulates cytoskeletal dynamics in non-neuronal cells and is strongly expressed throughout neuronal development. Nevertheless, its function in neurons is unknown. We found that during development of mouse cortical neurons, SLK has a surprisingly specific role for proper elaboration of higher, ≥ 3rd, order dendrites both in cultured neurons and living mice. Moreover, SLK is required to maintain excitation-inhibition balance. Specifically, SLK knockdown causes a selective loss of inhibitory synapses and functional inhibition after postnatal day 15, while excitatory neurotransmission is unaffected. This mechanism may be relevant for human disease, as dysmorphic neurons within human cortical malformations exhibit significant loss of SLK expression. To uncover the signaling cascades underlying the action of SLK, we combined phosphoproteomics, protein interaction screens and single cell RNA seq. Overall, our data identifies SLK as a key regulator of both dendritic complexity during development and of inhibitory synapse maintenance.

The role of sub-population structure in computations through neural dynamics

Neural computations are currently conceptualised using two separate approaches: sorting neurons into functional sub-populations or examining distributed collective dynamics. Whether and how these two aspects interact to shape computations is currently unclear. Using a novel approach to extract computational mechanisms from recurrent networks trained on neuroscience tasks, we show that the collective dynamics and sub-population structure play fundamentally complementary roles. Although various tasks can be implemented in networks with fully random population structure, we found that flexible input–output mappings instead require a non-random population structure that can be described in terms of multiple sub-populations. Our analyses revealed that such a sub-population organisation enables flexible computations through a mechanism based on gain-controlled modulations that flexibly shape the collective dynamics.

Richly structured reward predictions in dopaminergic learning circuits

Theories from reinforcement learning have been highly influential for interpreting neural activity in the biological circuits critical for animal and human learning. Central among these is the identification of phasic activity in dopamine neurons as a reward prediction error signal that drives learning in basal ganglia and prefrontal circuits. However, recent findings suggest that dopaminergic prediction error signals have access to complex, structured reward predictions and are sensitive to more properties of outcomes than learning theories with simple scalar value predictions might suggest. Here, I will present recent work in which we probed the identity-specific structure of reward prediction errors in an odor-guided choice task and found evidence for multiple predictive “threads” that segregate reward predictions, and reward prediction errors, according to the specific sensory features of anticipated outcomes. Our results point to an expanded class of neural reinforcement learning algorithms in which biological agents learn rich associative structure from their environment and leverage it to build reward predictions that include information about the specific, and perhaps idiosyncratic, features of available outcomes, using these to guide behavior in even quite simple reward learning tasks.

Perceiving relational structure

Relations and Predictions in Brains and Machines

Humans and animals learn and plan with flexibility and efficiency well beyond that of modern Machine Learning methods. This is hypothesized to owe in part to the ability of animals to build structured representations of their environments, and modulate these representations to rapidly adapt to new settings. In the first part of this talk, I will discuss theoretical work describing how learned representations in hippocampus enable rapid adaptation to new goals by learning predictive representations, while entorhinal cortex compresses these predictive representations with spectral methods that support smooth generalization among related states. I will also cover recent work extending this account, in which we show how the predictive model can be adapted to the probabilistic setting to describe a broader array of generalization results in humans and animals, and how entorhinal representations can be modulated to support sample generation optimized for different behavioral states. In the second part of the talk, I will overview some of the ways in which we have combined many of the same mathematical concepts with state-of-the-art deep learning methods to improve efficiency and performance in machine learning applications like physical simulation, relational reasoning, and design.

Developmentally structured coactivity in the hippocampal trisynaptic loop

The hippocampus is a key player in learning and memory. Research into this brain structure has long emphasized its plasticity and flexibility, though recent reports have come to appreciate its remarkably stable firing patterns. How novel information incorporates itself into networks that maintain their ongoing dynamics remains an open question, largely due to a lack of experimental access points into network stability. Development may provide one such access point. To explore this hypothesis, we birthdated CA1 pyramidal neurons using in-utero electroporation and examined their functional features in freely moving, adult mice. We show that CA1 pyramidal neurons of the same embryonic birthdate exhibit prominent cofiring across different brain states, including behavior in the form of overlapping place fields. Spatial representations remapped across different environments in a manner that preserves the biased correlation patterns between same birthdate neurons. These features of CA1 activity could partially be explained by structured connectivity between pyramidal cells and local interneurons. These observations suggest the existence of developmentally installed circuit motifs that impose powerful constraints on the statistics of hippocampal output.

The smart image compression algorithm in the retina: a theoretical study of recoding inputs in neural circuits

Computation in neural circuits relies on a common set of motifs, including divergence of common inputs to parallel pathways, convergence of multiple inputs to a single neuron, and nonlinearities that select some signals over others. Convergence and circuit nonlinearities, considered individually, can lead to a loss of information about the inputs. Past work has detailed how to optimize nonlinearities and circuit weights to maximize information, but we show that selective nonlinearities, acting together with divergent and convergent circuit structure, can improve information transmission over a purely linear circuit despite the suboptimality of these components individually. These nonlinearities recode the inputs in a manner that preserves the variance among converged inputs. Our results suggest that neural circuits may be doing better than expected without finely tuned weights.

Analogical Reasoning and Generalization for Interactive Task Learning in Physical Machines

Humans are natural teachers; learning through instruction is one of the most fundamental ways that we learn. Interactive Task Learning (ITL) is an emerging research agenda that studies the design of complex intelligent robots that can acquire new knowledge through natural human teacher-robot learner interactions. ITL methods are particularly useful for designing intelligent robots whose behavior can be adapted by humans collaborating with them. In this talk, I will summarize our recent findings on the structure that human instruction naturally has and motivate an intelligent system design that can exploit their structure. The system – AILEEN – is being developed using the common model of cognition. Architectures that implement the Common Model of Cognition - Soar, ACT-R, and Sigma - have a prominent place in research on cognitive modeling as well as on designing complex intelligent agents. However, they miss a critical piece of intelligent behavior – analogical reasoning and generalization. I will introduce a new memory – concept memory – that integrates with a common model of cognition architecture and supports ITL.

The strongly recurrent regime of cortical networks

Modern electrophysiological recordings simultaneously capture single-unit spiking activities of hundreds of neurons. These neurons exhibit highly complex coordination patterns. Where does this complexity stem from? One candidate is the ubiquitous heterogeneity in connectivity of local neural circuits. Studying neural network dynamics in the linearized regime and using tools from statistical field theory of disordered systems, we derive relations between structure and dynamics that are readily applicable to subsampled recordings of neural circuits: Measuring the statistics of pairwise covariances allows us to infer statistical properties of the underlying connectivity. Applying our results to spontaneous activity of macaque motor cortex, we find that the underlying network operates in a strongly recurrent regime. In this regime, network connectivity is highly heterogeneous, as quantified by a large radius of bulk connectivity eigenvalues. Being close to the point of linear instability, this dynamical regime predicts a rich correlation structure, a large dynamical repertoire, long-range interaction patterns, relatively low dimensionality and a sensitive control of neuronal coordination. These predictions are verified in analyses of spontaneous activity of macaque motor cortex and mouse visual cortex. Finally, we show that even microscopic features of connectivity, such as connection motifs, systematically scale up to determine the global organization of activity in neural circuits.

Neuron-glial interactions in health and disease: from cognition to cancer

In the central nervous system, neuronal activity is a critical regulator of development and plasticity. Activity-dependent proliferation of healthy glial progenitors, oligodendrocyte precursor cells (OPCs), and the consequent generation of new oligodendrocytes contributes to adaptive myelination. This plasticity of myelin tunes neural circuit function and contributes to healthy cognition. The robust mitogenic effect of neuronal activity on normal oligodendroglial precursor cells, a putative cellular origin for many forms of glioma, suggests that dysregulated or “hijacked” mechanisms of myelin plasticity might similarly promote malignant cell proliferation in this devastating group of brain cancers. Indeed, neuronal activity promotes progression of both high-grade and low-grade glioma subtypes in preclinical models. Crucial mechanisms mediating activity-regulated glioma growth include paracrine secretion of BDNF and the synaptic protein neuroligin-3 (NLGN3). NLGN3 induces multiple oncogenic signaling pathways in the cancer cell, and also promotes glutamatergic synapse formation between neurons and glioma cells. Glioma cells integrate into neural circuits synaptically through neuron-to-glioma synapses, and electrically through potassium-evoked currents that are amplified through gap-junctional coupling between tumor cells This synaptic and electrical integration of glioma into neural circuits is central to tumor progression in preclinical models. Thus, neuron-glial interactions not only modulate neural circuit structure and function in the healthy brain, but paracrine and synaptic neuron-glioma interactions also play important roles in the pathogenesis of glial cancers. The mechanistic parallels between normal and malignant neuron-glial interactions underscores the extent to which mechanisms of neurodevelopment and plasticity are subverted by malignant gliomas, and the importance of understanding the neuroscience of cancer.

A specialized role for entorhinal attractor dynamics in combining path integration and landmarks during navigation

During navigation, animals estimate their position using path integration and landmarks. In a series of two studies, we used virtual reality and electrophysiology to dissect how these inputs combine to generate the brain’s spatial representations. In the first study (Campbell et al., 2018), we focused on the medial entorhinal cortex (MEC) and its set of navigationally-relevant cell types, including grid cells, border cells, and speed cells. We discovered that attractor dynamics could explain an array of initially puzzling MEC responses to virtual reality manipulations. This theoretical framework successfully predicted both MEC grid cell responses to additional virtual reality manipulations, as well as mouse behavior in a virtual path integration task. In the second study (Campbell*, Attinger* et al., 2021), we asked whether these principles generalize to other navigationally-relevant brain regions. We used Neuropixels probes to record thousands of neurons from MEC, primary visual cortex (V1), and retrosplenial cortex (RSC). In contrast to the prevailing view that “everything is everywhere all at once,” we identified a unique population of MEC neurons, overlapping with grid cells, that became active with striking spatial periodicity while head-fixed mice ran on a treadmill in darkness. These neurons exhibited unique cue-integration properties compared to other MEC, V1, or RSC neurons: they remapped more readily in response to conflicts between path integration and landmarks; they coded position prospectively as opposed to retrospectively; they upweighted path integration relative to landmarks in conditions of low visual contrast; and as a population, they exhibited a lower-dimensional activity structure. Based on these results, our current view is that MEC attractor dynamics play a privileged role in resolving conflicts between path integration and landmarks during navigation. Future work should include carefully designed causal manipulations to rigorously test this idea, and expand the theoretical framework to incorporate notions of uncertainty and optimality.

Verb metaphors are processed as analogies

Metaphor is a pervasive phenomenon in language and cognition. To date, the vast majority of psycholinguistic research on metaphor has focused on noun-noun metaphors of the form An X is a Y (e.g., My job is a jail). Yet there is evidence that verb metaphor (e.g., I sailed through my exams) is more common. Despite this, comparatively little work has examined how verb metaphors are processed. In this talk, I will propose a novel account for verb metaphor comprehension: verb metaphors are understood in the same way that analogies are—as comparisons processed via structure-mapping. I will discuss the predictions that arise from applying the analogical framework to verb metaphor and present a series of experiments showing that verb metaphoric extension is consistent with those predictions.

Cortical feedback shapes high order structure of population activity to improve sensory coding

Bernstein Conference 2024

Gradient and network~structure of lagged correlations\\in band-limited cortical dynamics

Bernstein Conference 2024

Linking Neural Manifolds to Circuit Structure in Recurrent Networks

Bernstein Conference 2024

Multi-scale single-cycle analysis of cortex-wide wave dynamics reveals complex spatio-temporal structure

Bernstein Conference 2024

Quantitative evaluation of T-Bar anatomic structure influence upon calcium concentration enhancement

Bernstein Conference 2024

Rhythm-structured predictive coding for contextualized speech processing

Bernstein Conference 2024

Structure-function relationships and extended critical region in modular spiking model

Bernstein Conference 2024

Structured signals by a loss of structure: causes of burst-suppression EEG

Bernstein Conference 2024

Unstructured representations in a structured brain: a cross-region analysis of the neural code

Bernstein Conference 2024

Associative memory of structured knowledge

COSYNE 2022

Behavioural probing of learned statistical structure in humans

COSYNE 2022

Constructing behaviour in structured environments

COSYNE 2022

Dynamic and structured action bias masks learned task contingencies early in learning

COSYNE 2022

Emergence of convolutional structure in neural circuits

COSYNE 2022

Flygenvectors: The spatial and temporal structure of neural activity across the fly brain

COSYNE 2022

Flygenvectors: The spatial and temporal structure of neural activity across the fly brain

COSYNE 2022

A GABAergic plasticity mechanism for world structure inference by CA3

COSYNE 2022

A GABAergic plasticity mechanism for world structure inference by CA3

COSYNE 2022

Identifying the nonlinear structure of receptive fields in the mammalian retina

COSYNE 2022

Identifying the nonlinear structure of receptive fields in the mammalian retina

COSYNE 2022

Impact of single gene mutation on circuit structure and spontaneous activity in the developing cortex

COSYNE 2022

Impact of single gene mutation on circuit structure and spontaneous activity in the developing cortex

COSYNE 2022

Long-term consequences of actions affect human exploration in structured environments

COSYNE 2022

Long-term consequences of actions affect human exploration in structured environments

COSYNE 2022

Long-term motor learning creates structure within neural space that shapes motor adaptation

COSYNE 2022

Long-term motor learning creates structure within neural space that shapes motor adaptation

COSYNE 2022

Multi-region Poisson GPFA isolates shared and independent latent structure in sensorimotor tasks

COSYNE 2022

Multi-region Poisson GPFA isolates shared and independent latent structure in sensorimotor tasks

COSYNE 2022

Natural scene expectation shapes the structure of trial to trial variability in mid-level visual cortex

COSYNE 2022

Natural scene expectation shapes the structure of trial to trial variability in mid-level visual cortex

COSYNE 2022

Optimal information routing to cerebellum-like structures

COSYNE 2022

Optimal information routing to cerebellum-like structures

COSYNE 2022

Structure in motion: visual motion perception as online hierarchical inference

COSYNE 2022

Structure in motion: visual motion perception as online hierarchical inference

COSYNE 2022

Structured random receptive fields enable informative sensory encodings

COSYNE 2022

Structured random receptive fields enable informative sensory encodings

COSYNE 2022

A virtual rodent predicts the structure of neural activity across natural behavior

COSYNE 2022

A virtual rodent predicts the structure of neural activity across natural behavior

COSYNE 2022

Abstract structure and generalization in sensorimotor networks configured with semantic-based instruction embeddings

COSYNE 2023

A connectome manipulation framework for the systematic and reproducible study of structure-function relationships through simulations

Bernstein Conference 2024