complex

Optimization of a novel and effective antiviral agent targeting Zika NS4B

This project focuses on developing novel anti-Zika virus (ZIKV) compounds targeting the NS4B protein, which is crucial for viral replication. ZIKV poses a significant medical challenge due to its potential for severe pathogenic outcomes, such as congenital Zika syndrome and Guillain-Barré Syndrome. Furthermore, its pandemic potential has been increasing with the expansion of carrier mosquito habitats. The project aims to address the urgent need for anti-ZIKV therapeutics that could greatly reduce severity of symptoms and minimize vertical and community transmissions. We have identified a novel small-molecule series with a benzamide scaffold through a cell-based, antiviral ultra-high-throughput screen. This series demonstrates strong potency against ZIKV without measurable cytotoxicity or non-specific antiviral effects, justifying this scaffold as a lead series for further development. Preliminary mechanism-of-action studies, utilizing genetic, biochemical, and virological assays, suggest that this series may inhibit the formation of the ZIKV viral replicase complex by interfering with NS4B. Our goal for this project is to develop a preclinical therapeutic candidate for ZIKV that demonstrates promising therapeutic activity following oral administration in ZIKV-infected mice, at a dosage that shows no clinical toxicity. The project has the following significant and novel objectives: 1) Optimize the benzamide lead for potency and drug-likeness; 2) Develop a lead candidate and a backup compound with optimized pharmacokinetic, pharmacodynamic, and toxicity profiles; 3) Determine the molecular mechanisms of action of the benzamide series using novel structural approaches to assist medicinal chemistry studies; 4) Evaluate the in vivo therapeutic efficacy and safety in mouse models and develop the best therapeutic regime. This project seeks to develop effective antivirals for ZIKV with high retention in the blood and central nervous system (CNS) and high oral bioavailability. The expected successful outcomes will provide significant advancements in ZIKV therapeutics and open new avenues for treating other flavivirus infections

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.

Weak Cell Adhesion is a Prognostic Signature of Invasive Cancer

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

Modulating the Action of Cylindrical Proteases to Eliminate Neisseria Gonorrhea and Chlamydia Trachomatis Infections

Project Summary/Abstract Sexually transmitted bacteria diseases caused by Chlamydia trachomatis (Ctr) and Neisseria gonorrhoeae (NG) are the two most common sexually transmitted bacterial diseases. The infections caused by these pathogens may result in infertility, ectopic pregnancy, blindness, and perinatal mortality. Over 1.70 M cases of chlamydia and 0.65 M cases of drug-resistant gonorrhea are reported yearly in the US. Women with gonorrhea are co- infected with chlamydia in 17.6%–57.9% of cases, while women with chlamydia are co-infected with gonorrhea in 2.1%–17.2% of cases. These infections are treated with broad spectrum antibiotics, which can favor the development of resistance on NG/CTr but also in other bacteria, or damage the microbiota, diminishing its protective function and allowing bacteria and viruses to infect the patient. The Caseinolytic protease (ClpP) proteolytic machinery regulates protein turnover and homeostasis and is key in bacterial growth and development The machinery consists of the proteolytic unit (the ClpP) and its chaperone (ClpX), which transports proteins to be degraded, and it is termed the ClpXP. Our theory is that molecules that inhibit the action of the ClpX chaperone can become efficient antibacterial agents against both pathogens. We have found that the dihydrothiazepines can erradicate both pathogens and prevent the action of the ClpXP complex. Our goal is to advance the dihydrothiazepines as selective agents against Ctr and NG infections. To develop these therapeutic agents, we have envisioned four specific aims. Specific Aim 1. Synthesis and Optimization of the Pharmacophore. Our goal is to use computational models to design dihydrothiazepines molecule that will be synthesized, purified, and characterized using chemical techniques. The molecules will be tested against Ctr and NG and their toxicity against human cells evaluated. Also, we will determine their effect in other bacterial, including those from the microbiota. Specific Aim 2. Assessment of Stability and In Vivo Activity. We will study the stability of the most active molecules under various conditions. Then, we will study the pharmacokinetics, biodistribution , and antibacterial activity against Ctr and NG in mice. Specific Aim 3. Target Validation and Effect. We will study the ability of the compounds to inhibit the activity of ClpX using a luciferase assay and to block protein degradation. We will try grow crystal of the protein and the molecule and will study if the molecules prevent the assembly of the ClpXP system. Finally, we will assess the ability of the bacteria to develop resistance to the molecules.

The role of endogenous chimeric mRNA encoded GasderminD fusion proteins in immunity

Project Summary: Programmed inflammatory cell death, or pyroptosis, is a crucial innate defense mechanism that protects hosts against infection and orchestrates subsequent immune responses. Central to this process is Gasdermin D (GSDMD), a protein that forms plasma membrane pores upon activation, enabling the release of pro- inflammatory cytokines such as IL-1β and driving cell lysis. Although GSDMD-mediated pyroptosis has been conventionally understood to be controlled mainly at the post-translational level, through proteolytic cleavage by inflammatory caspases, we have discovered compelling evidence that alternative RNA processing may introduce additional, previously unappreciated complexity in GSDMD regulation. Our laboratories have developed and optimized a highly innovative long-read direct RNA sequencing pipeline, which bypasses conventional cDNA synthesis to avoid artifacts and enables unbiased discovery of native chimeric mRNA (chRNA) in mammalian cells. Using this approach, we have uncovered a remarkably diverse repertoire of chRNA species, including over a thousand unique fusions in murine macrophages and more than two thousand in human inflamed tissues. Among the chRNA found in mice, we identified a chRNA joining the effector domain of GSDMD with a novel C-terminal region encoded by Tmem106a, giving rise to the GSDMD:TMEM106A fusion protein. Functional studies demonstrate that GSDMD:TMEM106A is not only produced in response to inflammatory signals in macrophages but is critical for GSDMD-dependent cytokine release and optimal pyroptosis. Genetic loss of GSDMD:TMEM106A in mice results in reduced cytokine secretion and increased susceptibility to bacterial infection, while in vivo delivery of Gsdmd:Tmem106a mRNA is sufficient for protective immunity. Intriguingly, we have also identified a putative human counterpart, GSDMD:S100A6, which is highly inducible in colon biopsies from patients with inflammatory bowel disease. In this application, we propose a comprehensive exploration of this newly defined class of naturally occurring GSDMD fusion proteins. The specific aims are: (1) to elucidate the subcellular localization, protein-protein interactions, and pore-forming function of GSDMD:TMEM106A during canonical and non-canonical inflammasome activation; (2) to determine the transcriptomic, proteomic, and physiological consequences of GSDMD chRNA expression in vivo during infection, sepsis, and inflammatory disease, and to validate and functionally characterize GSDMD:S100A6 in relevant immune and barrier cell populations. Collectively, this work will establish chimeric splicing as a fundamental source of immunoregulatory protein diversity, redefining the landscape of cell death control in the immune system. By revealing new layers of gasdermin regulation and function, our studies have the potential to identify novel therapeutic strategies for infectious, auto-inflammatory, and immune-mediated diseases.

Targeting the Molecular Crosstalk Between EZHIP and PRC2 in PFA Ependymoma

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

Specificity requirements and functional properties of microbiota-reactive peri-weaning Tregs

PROJECT SUMMARY This application seeks to define the specificity requirements and functional properties of regulatory T cells (Tregs) that maintain tolerance to the microbiota. RORgt+ Tregs generated during an early-life peri-weaning window (from approximately P14 to P28 in mice) are particularly critical for intestinal tolerance. Mice that first encounter their microbiota outside this window still generate Tregs, but these cells are functionally inferior to those induced during the peri-weaning period and fail to maintain tolerance. The features of peri-weaning Tregs that make them so essential for intestinal homeostasis are not well defined. Here we propose to test two non-mutually exclusive hypotheses: 1) that the unique functionality of peri-weaning Tregs requires a distinct functional state; and 2) that reactivity with specific members of the microbiota is required for peri-weaning Tregs to maintain intestinal tolerance to a complex SPF microbiota. We have developed a model of intestinal inflammation based on oral delivery of the non-steroidal anti- inflammatory drug (NSAID) piroxicam that reveals underlying immune dysregulation in mice with defects in peri-weaning Tregs. When we applied this model to gnotobiotic mice colonized with defined microbiota communities we found that one community (OMM12) induced Tregs capable of preventing inflammation while the other community (ASF) did not, despite similar induction of RORgt+ peri-weaning Tregs by both communities. This exciting result suggests a previously unappreciated specificity requirement for induction of peri-weaning Tregs and indicates that differences in the microbes encountered early in life can have lifelong ramifications for immune tolerance. To better understand the basis of this specificity requirement, we developed a pipeline to rapidly screen the reactivity of T cells and applied it to mice colonized with the protective OMM12 community. This analysis revealed that the antigen-specific Treg response is biased toward only a subset of the microbiota. Thus, by tracking and characterizing microbiota-reactive peri-weaning Tregs at unprecedented resolution, we uncovered an unexpected bias in the microbiota-reactivity of Tregs. We are now ideally positioned to examine how the specificities and functional properties of peri-weaning Tregs are linked to their unique role in intestinal tolerance. In Aim 1, we will define the specificity of microbiota- reactive peri-weaning Tregs at homeostasis, using new tools developed through our screening pipeline, and we will determine whether missing the weaning period alters Treg responses to the microbiota. In Aim 2, we will compare the transcriptional programs of peri-weaning and post-weaning Tregs to identify peri-weaning- specific features. We will also build on our analyses from Aim 1 to determine if functional differences are linked to reactivity with specific members of the microbiota. In Aim 3, we will explore why specific members of the microbiota are required for induction of protective peri-weaning Tregs. We will define communities of microbes that do or do not confer protection in our piroxicam model, and we will profile the Tregs in these communities, including microbiota-reactive Tregs with defined specificities, to test the hypothesis that a key aspect of peri- weaning Treg function is specificity for only certain gut microbes.

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

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

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

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

Transcriptional control of activation induced deaminase (AID) function

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

Factors Driving Wear and Implant Failure in Total Shoulder Arthroplasty

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

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

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

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

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

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.

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

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

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.

ATPase Chromatin Remodeling Complexes as Modulators of HIV-1 Latency and Therapeutic Targets

Abstract Significance: HIV persists in long-lived CD4⁺ T cell reservoirs despite suppressive ART, as integrated proviruses remain poised for reactivation. Chromatin remodeling is a central barrier to durable silencing, yet most studies have focused on SWI/SNF family members. The roles of non- SWI/SNF remodelers remain poorly defined, limiting our ability to rationally design host-directed “block-and-lock” cure strategies. Our unbiased shRNA screen of all 16 human remodeler ATPases identified EP400, CHD1, and CHD9 as repressors and INO80A, SMARCA5, and CHD2 as activators, establishing chromatin remodeling as a key determinant of HIV latency. Innovation: Our prior studies revealed that the p400 complex regulates HIV transcription through dual mechanisms: directly, by engaging Tat via the DMAP1 subunit to block Tat-TAR RNA interactions and restrict p-TEFb recruitment; and indirectly, by altering host transcriptional programs that control T cell activation states. Building on this mechanistic precedent and methodological platform, we now focus on INO80A, SMARCA5, CHD1, and CHD2, remodelers from distinct ATPase families that govern Tat-independent checkpoints at initiation, pause release, and elongation. Methodologically, we will apply TurboID-ChAP-MS (locus-specific proteomics), BEM-seq (single-nucleosome mapping), and degron-mediated acute depletion with ATPase-dead rescue to interrogate remodeler function with unprecedented resolution. Approach: Aim 1 will define the ATPase requirement and transcriptional checkpoints regulated by INO80A, SMARCA5, CHD1, and CHD2 using degron/CRISPR perturbations, ChIP-seq, nascent RNA profiling, and nucleosome mapping. Aim 2 will characterize remodeler-specific complexes and Tat dependence at the HIV promoter via TurboID proximity labeling integrated with chromatin affinity purification-mass spectrometry. Aim 3 will test combinatorial perturbations in Jurkat and primary CD4⁺ T cell latency models, including ART-suppressed donor cells, to identify synergistic “block-and-lock” strategies that enforce durable proviral silencing. Impact: By defining remodeler-specific mechanisms at discrete transcriptional checkpoints and leveraging their enzymatic, druggable activities, this work will establish chromatin remodeling as a therapeutic axis for durable HIV suppression and functional cure.

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.

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

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

Multiplex single-cell chemical genomics to identify small molecule modulators of tumor cell-intrinsic immunogenicity in glioblastoma

PROJECT SUMMARY/ABSTRACT Glioblastoma multiforme is the most common and aggressive primary brain cancer. Despite a multimodal treatment regimen of surgical resection, chemotherapy, radiotherapy, and tumor-treating fields, most patients succumb to the disease within two years of diagnosis. Cancer immunotherapy strategies have emerged as a powerful tool for treating aggressive solid tumors such as melanoma and non-small cell lung cancer. However, current strategies have led to low response rates in glioblastoma, resulting from its low immunogenicity. The proposed research program aims to identify small molecules capable of increasing the immunogenicity of glioblastoma cells, focusing on altering gene expression programs associated with recognition by the immune system and the ability of cytotoxic immune cells to target glioblastoma for destruction. We will use highly multiplex chemical transcriptomic profiling to determine the molecular consequence of exposing glioblastoma neurosphere models to 3,792 small molecules, targeting the majority of cellular activities and clinically relevant drug targets as well as a collection of previously identified immunomodulators. We will then determine how each exposure alters the expression of gene programs associated with tumor cell immunogenicity and response to therapy, including the expression of genes associated with the recognition by the immune system and those associated with immune checkpoints, as well as programs more broadly correlated with resistance to anti-cancer therapies. Chemical hits that meet specific criteria will be subjected to a medicinal chemistry review to further classify compounds by their suitability for treating malignancies in the brain. We will then screen chemical hits to determine their ability to modulate immune-mediated tumor cell killing using tumor- immune cell co-culture. Lastly, we will leverage gene editing and flow cytometry to validate hits based on on- target molecular effects and further refine the mechanism of action by inspecting the ability of drugs to modulate immunogenic programs at the protein level. Our chemical genomics screens aim to provide crucial information regarding the link between pathway activity and immunomodulation in GBM, a critical step to guide future efforts in GBM immunotherapy. More broadly, our study will establish single-cell chemical genomics as a scalable platform for phenotype-based screening for preclinical prioritization of chemical modulators of complex transcriptional phenotypes and provide a framework for hit prioritization, establishment of pipeline robustness and hit validation in the context of single- cell chemical genomics screens.

Development of an at-home weight-shifting balance game with musical biofeedback for older adults

Reducing fall risk is a dire societal need that requires interventions that over-prepare individuals to perform maneuvers important to daily mobility. Falling is often caused by improper weight shifting, and interventions that focus on developing weight-shifting abilities have shown improvements in clinical balance outcomes, including reduced fall incidence. Interventions that combine challenges to the cognitive and motor systems may be necessary to reduce fall-risk. Our central hypothesis is that leveraging gamification and “musical biofeedback” will improve balance abilities through practicing weight-shifting skills with increased cognitive and physical demands. Musical biofeedback conveys biological sensor data from the participant through specific musical sound parameters in real-time. Of particular interest in the proposal is the applicability to use musical biofeedback to train weight-shifting skills in a musical game. The goal is to develop a wearable sensor system that can be used at-home to practice and develop balance skills, while supporting cognitive engagement and motivation to adhere to exercise goals. To start, we are focusing on older adult end-users who typically have home exercise programs focused on weight-shifting. However, in the future, many other populations can benefit from this technology. In this Trailblazer award, the PI is leveraging her background in studying complex human maneuvers, developing musical biofeedback for older adults, and in algorithm development for mHealth sensors. The transdisciplinary team includes expertise in engineering, gamified rehabilitation technologies, home exercise programs, psychology of aging, and music. In the proposed research, our goals are to evaluate responses to the musical biofeedback game (Aim 1), validate the mHealth sensor system (Aim 2), and phenotype the gameplay behavior of fallers vs. non-fallers (Aim 3), relative to their baseline characteristics (Sub-Aim 3). Our long-term goal is for a variety of people to improve their balance control patterns while supporting and building their self-efficacy. We envision users, including older adults, training with musical biofeedback to safely (and enjoyably) prepare themselves to ambulate in their community – improving and preserving their mobility. The proposed research will pioneer using an emerging clinical technology – musical biofeedback – to train balance during weight-shifting tasks. The proposed research innovates how musical biofeedback, gamification, and focusing on weight-shifting and turns in balance training can be leveraged to challenge cognitive and physical body systems in fall-risk populations. By developing new therapy options and better understanding responses relative to baseline characteristics, this research improves clinical practices to reduce fall risk and deepens our understanding of dynamic balance control. Finally, the results of the proposed research will have translational impacts to help other fall-risk groups.

Molecular strategies for resolving differential regulation of dopamine subpopulations

Project Summary/Abstract Dopamine neurons in the ventral tegmental area (VTA) fire action potentials in complex patterns of tonic and phasic activity in response to environmental stimuli and during behavioral tasks. Transcriptomic, anatomical, and functional studies have established that VTA dopamine neurons can be divided into multiple subpopulations with variable gene expression, projection patterns, and response profiles. We recently completed a transcriptomic study that identified genetic markers for three distinct subpopulations of VTA dopamine neurons, and also found evidence for variability in ion channel gene expression between populations that correlated with differences in activity-dependent gene expression. However, much remains unknown regarding how specific genes encoding ion channels, receptors, transcription factors, or other signaling components contribute to the variability in baseline physiological properties observed across the VTA. Here we propose to combine slice electrophysiology recordings of VTA dopamine neurons with post-hoc single-cell sequencing analysis (i.e. patch-seq), which will allow us to directly correlate gene expression and physiological properties in order to identify candidate genes that may be key drivers of the variability between subpopulations. We also propose to validate and utilize a novel dual-recombinase CRISPR/Cas9 system for targeted gene mutagenesis in intersectional neuronal populations, which will provide a mechanism for testing gene function with unprecedented precision. We will use this approach to test the function of two candidate ion channel genes, the potassium channels Kcnh5 and Kcnh7, previously identified in our transcriptomic study as potential contributors to dopamine neuron action potential firing properties. We hypothesize that these genes are important for enabling rapid action potential firing in highly excitable dopamine neurons found in specific subpopulations. As a whole, with this proposal we aim to generate a valuable dataset linking gene expression in VTA dopamine neurons with physiology and subpopulation identification, as well as develop an intersectional gene mutagenesis strategy that can be used throughout the brain to precisely target neuronal subpopulations to test gene function. With this approach, we hope to facilitate future precision targeting of the dopamine system and dopamine-dependent behaviors.

Intrinsic and extrinsic mechanisms underlying trigeminal nerve deficits in familial dysautonomia

PROJECT SUMMARY Rare diseases impose a significant burden on the US healthcare system, accounting for nearly half of all expenditures for their treatment. This statistic alone supports the need to invest in research to develop therapeutic interventions for rare diseases since the economic benefit outweighs the continued expense of financial resources. Familial dysautonomia (FD) is a rare, hereditary disease that arises from a splice site mutation in Elongator acetyltransferase complex subunit 1 (ELP1) and impacts the nervous system. To date, FD patients continue to face life-threatening complications involving basic involuntary functions like swallowing and somatosensation because there is no cure for this ultimately fatal neuropathy. FD patients exhibit symptoms due to defects in their somatosensory trigeminal nerves, whose cell bodies reside in the trigeminal ganglion (TG) and are derived from neural crest and placode cells. Recent studies from our lab using an FD mouse model (Elp1 deleted from neural crest cells) revealed TG axon outgrowth and target tissue innervation deficits, recapitulating phenotypes observed in FD patients. However, the mechanisms by which Elp1 mediates normal TG development, and how this goes awry in FD, remain largely elusive. To gain insight into Elp1 function, we performed mass spectrometry to evaluate the TG proteome of normal and FD mouse embryos. Our results uncovered statistically significant increases in extracellular matrix (ECM) and ECM binding proteins, pointing to altered TG biomechanical properties and, more broadly, changes in mechanotransduction, the process by which cells translate extrinsic cues into intrinsic signaling pathways that modulate gene expression. Importantly, proper axon outgrowth relies upon mechanotransduction as growth cones on axons sense and respond to their environment. In the head, this environment consists of ECM and cranial mesenchyme cells, but the impact of Elp1 loss from the latter is not known, including the potential for altered tissue biomechanics that could influence TG axon outgrowth. We hypothesize that loss of Elp1 induces changes in the biomechanical properties of both the TG/nerves and ECM/cranial mesenchyme, modifying mechanotransduction and leading to TG defects in FD, which we will interrogate in the following Specific Aims: 1) define the biomechanical properties of the TG/nerves and ECM/cranial mesenchyme and 2) determine the role of cranial mesenchyme Elp1 in mediating proper TG axon outgrowth. Our innovative research proposal takes a systems-level, multidisciplinary approach involving embryology, biomechanics, and high-resolution microscopy, with the goal of integrating molecular, cellular, and tissue data. These results will significantly advance our knowledge of the molecular mechanisms underscoring TG development and, collectively, inform treatment strategies for birth defects or disorders like FD with TG dysfunction, as well as nerve repair and/or regeneration after injury or disease.

Engineering inducible morphotype switching control in Mycobacterium abscessus for investigating infection outcomes and discovering pathophysiological-targeted treatments

PROJECT SUMMARY Antibiotic-resistant nontuberculous mycobacteria (NTM) infections are rising at a rate of 8% each year and account for ~$1.7 billion in annual U.S. healthcare costs. Mycobacterium abscessus (Mabs), the most common rapidly growing NTM infection, is notoriously nicknamed the “antibiotic nightmare” for its extensive intrinsic and inducible broad-range multidrug resistance to antibiotic countermeasures. As part of its natural infection cycle, Mabs undergoes a morphotypical conversion from smooth to rough, characterized by irreversible genetic changes resulting in the loss of cell envelope glycopeptidolipids (GPLs). This morphotypic conversion is intimately associated with disease progression, ultimately leading to debilitating, refractory Mabs pulmonary disease. Specific stimuli triggering Mabs morphotypical conversion are unknown, thus preventing directed investigations into morphotype-specific immunological responses and the discovery of morphotype-specific therapeutic targets. This project leverages cutting-edge molecular genetic tools, including CRISPR (clustered regularly interspersed short palindromic repeats) interference (CRISPRi) and inducible knockdown control of CRISPRi via the anhydrotetracycline-inducible TetR-regulated promoter-operator system, to create six unique, reversible Mabs smooth to conditional rough morphotype strains. These molecular morphoswitchable strains allow precise investigator-mediated on-off control of Mabs surface GPLs, enabling investigations into Mabs morphological plasticity, unique pathophysiology traits associated with each morphotype, and the complex interplay between Mabs and morphotype-specific immunological responses. In Aim 1, we implement CRISPRi inducible knockdown tunable control of Mabs morphotype switching by targeting six, independent genetic targets directly involved in GPL biosynthesis (mps1, mps2) or transport (mmpS4, mmpL4a, mmpL4b, gap) and validate in vitro morphoswitching. In Aim 2, we establish and confirm Mabs morphoswitching and intracellular growth in infected THP-1 macrophages. Subsequently, we evaluate differential and distinct innate cellular immune responses elicited by Mabs smooth and Mabs conditional rough morphotypes during intracellular infection in human primary monocyte-derived macrophages. Collectively, these studies create a suite of characterized and reversible Mabs smooth and conditional rough morphoswitchable strains with controlled, regulated, and on- demand expression of Mabs surface GPLs. By enabling precisely timed and controlled induction of the Mabs conditional rough morphotype during intracellular growth, we can molecularly dissect and investigate fundamental Mabs host-pathogen interactions and immunological responses that so substantially influence negative clinical outcomes.

Targeting subtype specification as a driver of PDAC health disparities

PROJECT SUMMARY Pancreatic ductal adenocarcinoma (PDAC) is a deadly disease that is refractory to current treatment strategies due in part to adaptive mechanisms of chemoresistance. Racial health disparities also confound the treatment and care of these patients. Blacks (people with African genetic ancestry) have significantly higher incidence rates of PDAC and decreased survival times compared to Caucasians (White genetic ancestry) even after socioeconomic status and tumor stages are controlled. Therefore, it is possible different racial groups exhibit unique molecular characteristics in PDAC tumors that contribute to these health disparities. The unique molecular characteristics that distinguish PDAC tumors between racial groups exhibiting disparities have the potential to identify new therapeutic targets. In a previous study, we identified 4 distinct subtypes of PDAC (Metabolic, Progenitor-like, Proliferative, and Inflammatory) that can be distinguished using multivariate analysis of quantitative proteomic data. While these PDAC subtypes are predictive of therapeutic response, this has not yet been analyzed in disparity factor balanced studies. We have examined the proteomes of primary PDAC tumors using quantitative mass spectrometry and identified unique protein signatures for Blacks and Whites. PDAC tumors from Black patients display features consistent with the Inflammatory subtype of PDAC, which is characterized by an inflamed microenvironment expressing complement proteins that can promote resistance to chemotherapy. Therefore, it is possible that race influences subtype and Blacks could preferentially develop the more aggressive and treatment refractory Inflammatory subtype. Strategies are needed to modulate subtype to improve response to chemotherapy. Toward this goal, our proteomic analysis identified polycomb repressor complex 1 (PRC1) protein RNF2 as being upregulated in PDACs from Blacks compared to Whites. We have also discovered that RNF2 regulates mRNA expression of the PDAC subtype specification factor GATA6 and inhibiting RNF2 promotes a molecular shift toward the more chemosensitive Classical subtype of PDAC. Therapeutic targeting can be achieved with Tazemetostat that inhibits the upstream PRC2 to prevent RNF2 binding the GATA6 promoter leading to its increased expression. Additionally, the Inflammatory subtype characterized by innate immune complement protein activation could be targeted with another FDA approved drug, Avacopan, which has not previously been studied in PDAC. Therefore, the Specific Aims of this proposal are designed to: 1) Evaluate the extent to which Tazemetostat treatment impacts chemotherapy-induced subtype plasticity in patient derived organoids; and 2) To determine the extent to which strategies targeting pathways associated with PDAC disparities affect progression and subtype characteristics in vivo. The successful completion of these aims has the potential to be moved quickly into phase I clinical trials since both Tazemetostat and Avacopan are FDA approved drugs. Furthermore, if successful, this project has the potential to mitigate health disparities in PDAC and broadly improve patient outcomes by implementing new precision interventions. The mouse models we propose faithfully recapitulate pancreatic cancer's clinical syndrome, histopathology and molecular properties, including the often-unique features of the stromal and immune responses that constitute the complex desmoplasia of this disease, which cannot be addressed using in vitro model systems

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.

A Novel Mitochondrial-Targeted Inhibitor of NLRP3 Inflammasome Activation

PROJECT ABSTRACT Inflammasomes are multiprotein complexes of the innate immune system that assemble upon detecting specific molecular patterns associated with pathogens and cellular damage. Once assembled, activated inflammasomes trigger a cascade of downstream events that culminate in cell death and inflammation. Aberrant activation of the NLRP3 inflammasome contributes to the pathogenesis of numerous inflammatory and degenerative diseases, including gout, atherosclerosis, type 2 diabetes, and Alzheimer’s disease. Despite its central role in innate immunity and inflammation, there are no FDA-approved therapies that directly target the NLRP3 inflammasome. Current strategies rely on biologics that inhibit downstream pro-inflammatory cytokines produced from inflammasome activation, such as interleukin-1β (IL-1β), but do not block upstream inflammasome assembly or pyroptotic cell death, highlighting a critical unmet need for selective small-molecule inhibitors with novel mechanisms of action. To address this gap, we identified a covalent small molecule, Compound-2 (C-2), that robustly inhibits NLRP3 inflammasome activation in murine and human immune cells. C-2 suppresses multiple downstream events triggered by inflammasome activation, including IL-1β secretion and pyroptosis, with no apparent toxicity. Chemoproteomic profiling revealed that C-2 interacts with SLC25A3, a mitochondrial phosphate and copper transporter, suggesting a previously unrecognized regulatory node in inflammasome signaling. This R21 project aims to (1) elucidate the mechanism by which C-2 suppresses NLRP3 activation and (2) define the molecular interaction between C-2 and SLC25A3 and its functional consequences. Our studies will integrate biochemical, cellular, and in vivo approaches to uncover a novel mitochondrial mechanism of inflammasome regulation and validate C-2 as a first-in-class inflammasome inhibitor. Successful completion of this project will lay the foundation for future therapeutic development targeting mitochondrial- inflammasome crosstalk in inflammatory disease.

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.

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.

Autoreactive T cells in lupus

The autoimmune disease systemic lupus erythematosus (SLE) is characterized by loss of adaptive immune tolerance in conjunction with innate immune system hyperactivity. Autoantibodies, produced by plasma cells derived from activated B cells, form proinflammatory immune complexes. These immune complexes drive feed forward loops that sustain a systemic inflammatory environment and deposit in tissues leading to potentially fatal organ damage. B cells receive help from T cells to produce antibodies. They also contribute to disease by shaping T cell responses and secreting cytokines. Recent case reports in which SLE patients were treated with anti-CD19 CAR-T cell therapy to deplete B cells highlight the pathogenic role of B cells in lupus and their value as a therapeutic target. However, a better understanding of how autoreactive B cells interact with autoreactive T cells may reveal more targeted points of therapeutic intervention that specifically block autoreactive responses while sparing protective ones. Antigen specific interactions between CD4+ T cells and B cells are required for the development of autoimmune disease in lupus. However, whether these critical interactions occur in germinal centers, where competition for CD4+ T cell help selects high affinity B cells, or in extrafollicular responses, where B cells may avoid peripheral tolerance checkpoints, is unclear. Gene expression profiles and pathways specific to autoreactive CD4+ T cells, and how they are shaped by their interaction with autoreactive B cells, are also ill defined. CD8+ T cells, which recognize antigen presented on MHC Class I, have also been suggested to modulate the fate of autoreactive B cells. They can directly kill autoreactive B cells as a means of tolerance, and a subset of CD8+ T cells has recently been shown to have B cell helper function. Whether and how such interactions between B and CD8+ T cells enhance or suppress the development of lupus is unknown. Here, we will use genetic and in vivo proximity labeling approaches to address these knowledge gaps. In Aim 1, we will test the hypothesis that antigen specific interactions between B and CD8+ T cells promote B cell activation and autoantibody production in lupus. We will prevent B cells, but not other cells, from undergoing cognate interactions with CD8+ T cells via B cell-specific deletion of B2M, a component of the MHC Class I complex, in two lupus models. In Aim 2, will use the uLIPSTIC in vivo proximity system to label all T cells interacting with B cells in lupus models compared to wild type controls. Features specific to these autoreactive T cells will be defined by flow cytometry, scRNA Seq, and scTCR-Seq. These studies will provide valuable molecular and cellular insight into the mutual activation of B and T cells in lupus. They will set the stage for future mechanistic studies defining the role of autoreactive T cell specific genes and pathways and potentially highlight new therapeutic targets specific to autoreactive B/T interactions.

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.

Developing a novel technology for studying T cell differentiation in vivo

Summary CRISPR-based genetic screens have revolutionized our understanding of gene functions and molecular mechanisms across various biological processes. In the field of T cell biology, CRISPR screens have played a pivotal role in identifying genes that impact critical aspects, such as T cell development, differentiation, and function. However, traditional screens have struggled to distinguish genes with diverse mechanisms of action, necessitating further investigations. To address this challenge, researchers have harnessed the power of CRISPR screens combined with single-cell sequencing (scCRISPR-seq), enabling the simultaneous assessment of genetic perturbations and high-dimensional phenotypes at the single-cell level. While scCRISPR- seq has predominantly been performed in vitro using immortalized cell lines, its physiological relevance is limited due to oversimplified biological context and disparities compared to primary cells. This limitation highlights the urgent need for large-scale in vivo scCRISPR-seq with primary T cells. However, various challenges have discouraged its widespread adoption. The use of viral vectors for sgRNA delivery compromises physiological relevance, as the in vitro activation conditions fail to faithfully represent the intricate T cell priming process in vivo. Moreover, viral vector components and continuous Cas9 expression can trigger immunogenicity and cytotoxicity, leading to cell depletion and hindering long-term studies. Additionally, current scCRISPR-seq methods face technical limitations, including low editing efficiency and inadequate perturbation identity recovery rates, which impede efficient large-scale in vivo applications. Fortunately, recent advances in ribonucleoprotein complex (RNP) transfection have addressed many of these challenges. This cutting-edge technology enables efficient gene editing in primary T cells without the need for in vitro activation or permanent Cas9 expression. Leveraging the high editing efficiency of RNP transfection, the investigator’s team aims to develop a novel strategy for in vivo T cell CRISPR screens. This innovative approach involves arrayed RNP transfection and co- transfer of T cells that recognize the relevant antigens. Instead of traditional genetic barcodes, the strategy utilizes congenic markers (CD45.1/45.2 and CD90.1/CD90.2) from donor TCR transgenic T cells as "external barcodes." These markers facilitate the recovery of gene perturbation identity at the single-cell level through the application of CITE-seq. Importantly, this RNP-based strategy seamlessly integrates with existing single-cell sequencing protocols, enabling the comprehensive assessment of transcripts, epitopes, and chromatin accessibility simultaneously. To demonstrate the efficacy of this strategy, the team plans to develop two benchmarking approaches: RNP-CET-seq to investigate the role of TCR regulators in T cell exhaustion and RNP-CATE-seq to map the gene regulatory atlas of exhausted CD8 T cells. In summary, the proposed RNP- based scCRISPR-seq strategy overcomes the limitations of current approaches, enabling large-scale, multi- module in vivo genetic screens within a physiologically relevant context across various disease models.

Engineering of a temperate Burkholderia cepacia complex phage to improve efficacy as a potential therapeutic

Project Summary Bacteria in the Burkholderia cepacia complex (Bcc) cause difficult to treat infections in patients with compromised respiratory systems, such as those with cystic fibrosis (CF). Alternative treatment options are needed, since antibiotics often fail these patients. Bacteriophage (phage) therapy is a promising strategy, yet therapeutically ideal phages are difficult to find and narrow in their range of use due to host specificity. In the proposed study, we continue development of a potential phage therapeutic sourced from Burkholderia itself. We have isolated a phage, called BCC02, that was present within the genome of a Burkholderia bacteria (a prophage) and have shown that it can kill other bacteria within the same genus. However, this phage still has the potential to integrate into other bacterial genomes, which is an undesirable trait for phage therapy. By engineering changes to the BCC02 genome using synthetic biology techniques, we hypothesize that we can increase its range of therapeutic potential by disabling its ability to integrate into the bacterial genome, and that this change will increase the number of bacteria that it can lyse. The specific aims of this project are to (1) engineer this phage to lose the ability to lysogenize (integrate into bacterial genomes) then test the effects of these modifications on bacterial host range and (2) test activity of our originally isolated phage, BCC02 as well as our engineered variant on a clinically relevant panel of patho-adapted isolates from patients with CF. We propose to use transformation-associated recombination (TAR) cloning methods to target the lysogeny control region of the BCC02 genome for removal. We hypothesize that loss of integration ability will force this phage into an obligately lytic lifestyle, where it will lyse all bacteria it is able to infect. Successful completion of this project will determine the feasibility of engineering obligately lytic Burkholderia-targeting phages from Burkholderia spp. prophages, shed light on the effects of lytic lifestyle on host range, and establish the utility of these phages for tackling particularly problematic clinical infections. In addition, this study may produce a Bcc- targeting phage that is primed for development to be used for phage therapy.

Development of a synthetic human centromere

PROJECT SUMMARY/ABSTRACT Human artificial chromosomes (HACs) are mini-chromosomes that can be stably inherited across many cellular generation. HACs are potentially powerful gene therapy vectors and extremely useful tools in biological research. The stability of HACs depends on the presence of a functional centromere. Centromeres are unique genomic loci that mediate the segregation of chromosomes during mitosis by forming kinetochores leading to microtubule attachment. These sites are specified by the incorporation of distinct nucleosomes in which histone H3 is replaced by CENP- A. Most centromeric nucleosomes are embedded in highly repetitive alpha-satellite DNA. The current versions of the HACs contain alpha-satellite centromeric DNA, are relatively inefficient and frequently recombine into the genome. Despite the presence of alpha-satellite DNA at centromeres, it is not absolutely required for centromere function. This is evidenced by the existence of neocentromeres in some people, and work from our lab and others that centromeres can be induced to form at non-centromeric sites. Deposition of centromeric nucleosomes is mediated by the CENP-A specific chaperone HJURP and the Mis18 complex. Previous work has shown that artificially targeting HJURP and Mis18 proteins to LacO arrays can create de novo centromeres at non-centromeric sites. This approach leads to the formation of a full centromere, recapitulating most of the characteristics of an endogenous centromere. Here we propose to develop a more versatile approach which can be re-programmed to target many different sequences. This powerful approach will provide new and exciting insight into the rules of centromere formation. The proposal will explore the practical application of de novo centromere formation in supporting the stability of human artificial chromosomes (HACs). We will test if these synthetic centromeres (SynCen) can lead to stable inheritance of a human artificial chromosome. More efficient stable non-repetitive synthetic centromere will greatly expand the potential use of HACs as gene therapy vectors.

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.

Astrocytes: From Metabolism to Cognition

Different brain cell types exhibit distinct metabolic signatures that link energy economy to cellular function. Astrocytes and neurons, for instance, diverge dramatically in their reliance on glycolysis versus oxidative phosphorylation, underscoring that metabolic fuel efficiency is not uniform across cell types. A key factor shaping this divergence is the structural organization of the mitochondrial respiratory chain into supercomplexes. Specifically, complexes I (CI) and III (CIII) form a CI–CIII supercomplex, but the degree of this assembly varies by cell type. In neurons, CI is predominantly integrated into supercomplexes, resulting in highly efficient mitochondrial respiration and minimal reactive oxygen species (ROS) generation. Conversely, in astrocytes, a larger fraction of CI remains unassembled, freely existing apart from CIII, leading to reduced respiratory efficiency and elevated mitochondrial ROS production. Despite this apparent inefficiency, astrocytes boast a highly adaptable metabolism capable of responding to diverse stressors. Their looser CI–CIII organization allows for flexible ROS signaling, which activates antioxidant programs via transcription factors like Nrf2. This modular architecture enables astrocytes not only to balance energy production but also to support neuronal health and influence complex organismal behaviors.

“Brain theory, what is it or what should it be?”

n the neurosciences the need for some 'overarching' theory is sometimes expressed, but it is not always obvious what is meant by this. One can perhaps agree that in modern science observation and experimentation is normally complemented by 'theory', i.e. the development of theoretical concepts that help guiding and evaluating experiments and measurements. A deeper discussion of 'brain theory' will require the clarification of some further distictions, in particular: theory vs. model and brain research (and its theory) vs. neuroscience. Other questions are: Does a theory require mathematics? Or even differential equations? Today it is often taken for granted that the whole universe including everything in it, for example humans, animals, and plants, can be adequately treated by physics and therefore theoretical physics is the overarching theory. Even if this is the case, it has turned out that in some particular parts of physics (the historical example is thermodynamics) it may be useful to simplify the theory by introducing additional theoretical concepts that can in principle be 'reduced' to more complex descriptions on the 'microscopic' level of basic physical particals and forces. In this sense, brain theory may be regarded as part of theoretical neuroscience, which is inside biophysics and therefore inside physics, or theoretical physics. Still, in neuroscience and brain research, additional concepts are typically used to describe results and help guiding experimentation that are 'outside' physics, beginning with neurons and synapses, names of brain parts and areas, up to concepts like 'learning', 'motivation', 'attention'. Certainly, we do not yet have one theory that includes all these concepts. So 'brain theory' is still in a 'pre-newtonian' state. However, it may still be useful to understand in general the relations between a larger theory and its 'parts', or between microscopic and macroscopic theories, or between theories at different 'levels' of description. This is what I plan to do.

Developmental and evolutionary perspectives on thalamic function

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

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..

Relating circuit dynamics to computation: robustness and dimension-specific computation in cortical dynamics

Neural dynamics represent the hard-to-interpret substrate of circuit computations. Advances in large-scale recordings have highlighted the sheer spatiotemporal complexity of circuit dynamics within and across circuits, portraying in detail the difficulty of interpreting such dynamics and relating it to computation. Indeed, even in extremely simplified experimental conditions, one observes high-dimensional temporal dynamics in the relevant circuits. This complexity can be potentially addressed by the notion that not all changes in population activity have equal meaning, i.e., a small change in the evolution of activity along a particular dimension may have a bigger effect on a given computation than a large change in another. We term such conditions dimension-specific computation. Considering motor preparatory activity in a delayed response task we utilized neural recordings performed simultaneously with optogenetic perturbations to probe circuit dynamics. First, we revealed a remarkable robustness in the detailed evolution of certain dimensions of the population activity, beyond what was thought to be the case experimentally and theoretically. Second, the robust dimension in activity space carries nearly all of the decodable behavioral information whereas other non-robust dimensions contained nearly no decodable information, as if the circuit was setup to make informative dimensions stiff, i.e., resistive to perturbations, leaving uninformative dimensions sloppy, i.e., sensitive to perturbations. Third, we show that this robustness can be achieved by a modular organization of circuitry, whereby modules whose dynamics normally evolve independently can correct each other’s dynamics when an individual module is perturbed, a common design feature in robust systems engineering. Finally, we will recent work extending this framework to understanding the neural dynamics underlying preparation of speech.

Decoding ketamine: Neurobiological mechanisms underlying its rapid antidepressant efficacy

Unlike traditional monoamine-based antidepressants that require weeks to exert effects, ketamine alleviates depression within hours, though its clinical use is limited by side effects. While ketamine was initially thought to work primarily through NMDA receptor (NMDAR) inhibition, our research reveals a more complex mechanism. We demonstrate that NMDAR inhibition alone cannot explain ketamine's sustained antidepressant effects, as other NMDAR antagonists like MK-801 lack similar efficacy. Instead, the (2R,6R)-hydroxynorketamine (HNK) metabolite appears critical, exhibiting antidepressant effects without ketamine's side effects. Paradoxically, our findings suggest an inverted U-shaped dose-response relationship where excessive NMDAR inhibition may actually impede antidepressant efficacy, while some level of NMDAR activation is necessary. The antidepressant actions of ketamine and (2R,6R)-HNK require AMPA receptor activation, leading to synaptic potentiation and upregulation of AMPA receptor subunits GluA1 and GluA2. Furthermore, NMDAR subunit GluN2A appears necessary and possibly sufficient for these effects. This research establishes NMDAR-GluN2A activation as a common downstream effector for rapid-acting antidepressants, regardless of their initial targets, offering promising directions for developing next-generation antidepressants with improved efficacy and reduced side effects.

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

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

Learning Representations of Complex Meaning in the Human Brain

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?

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

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

Decision and Behavior

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

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.

Understanding the complex behaviors of the ‘simple’ cerebellar circuit

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

Metabolic-functional coupling of parvalbmunin-positive GABAergic interneurons in the injured and epileptic brain

Parvalbumin-positive GABAergic interneurons (PV-INs) provide inhibitory control of excitatory neuron activity, coordinate circuit function, and regulate behavior and cognition. PV-INs are uniquely susceptible to loss and dysfunction in traumatic brain injury (TBI) and epilepsy but the cause of this susceptibility is unknown. One hypothesis is that PV-INs use specialized metabolic systems to support their high-frequency action potential firing and that metabolic stress disrupts these systems, leading to their dysfunction and loss. Metabolism-based therapies can restore PV-IN function after injury in preclinical TBI models. Based on these findings, we hypothesize that (1) PV-INs are highly metabolically specialized, (2) these specializations are lost after TBI, and (3) restoring PV-IN metabolic specializations can improve PV-IN function as well as TBI-related outcomes. Using novel single-cell approaches, we can now quantify cell-type-specific metabolism in complex tissues to determine whether PV-IN metabolic dysfunction contributes to the pathophysiology of TBI.

Neural mechanisms governing the learning and execution of avoidance behavior

The nervous system orchestrates adaptive behaviors by intricately coordinating responses to internal cues and environmental stimuli. This involves integrating sensory input, managing competing motivational states, and drawing on past experiences to anticipate future outcomes. While traditional models attribute this complexity to interactions between the mesocorticolimbic system and hypothalamic centers, the specific nodes of integration have remained elusive. Recent research, including our own, sheds light on the midline thalamus's overlooked role in this process. We propose that the midline thalamus integrates internal states with memory and emotional signals to guide adaptive behaviors. Our investigations into midline thalamic neuronal circuits have provided crucial insights into the neural mechanisms behind flexibility and adaptability. Understanding these processes is essential for deciphering human behavior and conditions marked by impaired motivation and emotional processing. Our research aims to contribute to this understanding, paving the way for targeted interventions and therapies to address such impairments.

Probing neural population dynamics with recurrent neural networks

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

Navigating semantic spaces: recycling the brain GPS for higher-level cognition

Humans share with other animals a complex neuronal machinery that evolved to support navigation in the physical space and that supports wayfinding and path integration. In my talk I will present a series of recent neuroimaging studies in humans performed in my Lab aimed at investigating the idea that this same neural navigation system (the “brain GPS”) is also used to organize and navigate concepts and memories, and that abstract and spatial representations rely on a common neural fabric. I will argue that this might represent a novel example of “cortical recycling”, where the neuronal machinery that primarily evolved, in lower level animals, to represent relationships between spatial locations and navigate space, in humans are reused to encode relationships between concepts in an internal abstract representational space of meaning.

Generative models for video games (rescheduled)

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

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

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

Charting the fetal development of neural complexity

Generative models for video games

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

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.

Brain-heart interactions at the edges of consciousness

Various clinical cases have provided evidence linking cardiovascular, neurological, and psychiatric disorders to changes in the brain-heart interaction. Our recent experimental evidence on patients with disorders of consciousness revealed that observing brain-heart interactions helps to detect residual consciousness, even in patients with absence of behavioral signs of consciousness. Those findings support hypotheses suggesting that visceral activity is involved in the neurobiology of consciousness and sum to the existing evidence in healthy participants in which the neural responses to heartbeats reveal perceptual and self-consciousness. Furthermore, the presence of non-linear, complex, and bidirectional communication between brain and heartbeat dynamics can provide further insights into the physiological state of the patient following severe brain injury. These developments on methodologies to analyze brain-heart interactions open new avenues for understanding neural functioning at a large-scale level, uncovering that peripheral bodily activity can influence brain homeostatic processes, cognition, and behavior.

Towards Human Systems Biology of Sleep/Wake Cycles: Phosphorylation Hypothesis of Sleep

The field of human biology faces three major technological challenges. Firstly, the causation problem is difficult to address in humans compared to model animals. Secondly, the complexity problem arises due to the lack of a comprehensive cell atlas for the human body, despite its cellular composition. Lastly, the heterogeneity problem arises from significant variations in both genetic and environmental factors among individuals. To tackle these challenges, we have developed innovative approaches. These include 1) mammalian next-generation genetics, such as Triple CRISPR for knockout (KO) mice and ES mice for knock-in (KI) mice, which enables causation studies without traditional breeding methods; 2) whole-body/brain cell profiling techniques, such as CUBIC, to unravel the complexity of cellular composition; and 3) accurate and user-friendly technologies for measuring sleep and awake states, exemplified by ACCEL, to facilitate the monitoring of fundamental brain states in real-world settings and thus address heterogeneity in human.

Gut/Body interactions in health and disease

The adult intestine is a major barrier epithelium and coordinator of multi-organ functions. Stem cells constantly repair the intestinal epithelium by adjusting their proliferation and differentiation to tissue intrinsic as well as micro- and macro-environmental signals. How these signals integrate to control intestinal and whole-body homeostasis is largely unknown. Addressing this gap in knowledge is central to an improved understanding of intestinal pathophysiology and its systemic consequences. Combining Drosophila and mammalian model systems my laboratory has discovered fundamental mechanisms driving intestinal regeneration and tumourigenesis and outlined complex inter-organ signaling regulating health and disease. During my talk, I will discuss inter-related areas of research from my lab, including:1- Interactions between the intestine and its microenvironment influencing intestinal regeneration and tumourigenesis. 2- Long-range signals from the intestine impacting whole-body in health and disease.

Identifying mechanisms of cognitive computations from spikes

Higher cortical areas carry a wide range of sensory, cognitive, and motor signals supporting complex goal-directed behavior. These signals mix in heterogeneous responses of single neurons, making it difficult to untangle underlying mechanisms. I will present two approaches for revealing interpretable circuit mechanisms from heterogeneous neural responses during cognitive tasks. First, I will show a flexible nonparametric framework for simultaneously inferring population dynamics on single trials and tuning functions of individual neurons to the latent population state. When applied to recordings from the premotor cortex during decision-making, our approach revealed that populations of neurons encoded the same dynamic variable predicting choices, and heterogeneous firing rates resulted from the diverse tuning of single neurons to this decision variable. The inferred dynamics indicated an attractor mechanism for decision computation. Second, I will show an approach for inferring an interpretable network model of a cognitive task—the latent circuit—from neural response data. We developed a theory to causally validate latent circuit mechanisms via patterned perturbations of activity and connectivity in the high-dimensional network. This work opens new possibilities for deriving testable mechanistic hypotheses from complex neural response data.

A recurrent network model of planning predicts hippocampal replay and human behavior

When interacting with complex environments, humans can rapidly adapt their behavior to changes in task or context. To facilitate this adaptation, we often spend substantial periods of time contemplating possible futures before acting. For such planning to be rational, the benefits of planning to future behavior must at least compensate for the time spent thinking. Here we capture these features of human behavior by developing a neural network model where not only actions, but also planning, are controlled by prefrontal cortex. This model consists of a meta-reinforcement learning agent augmented with the ability to plan by sampling imagined action sequences drawn from its own policy, which we refer to as `rollouts'. Our results demonstrate that this agent learns to plan when planning is beneficial, explaining the empirical variability in human thinking times. Additionally, the patterns of policy rollouts employed by the artificial agent closely resemble patterns of rodent hippocampal replays recently recorded in a spatial navigation task, in terms of both their spatial statistics and their relationship to subsequent behavior. Our work provides a new theory of how the brain could implement planning through prefrontal-hippocampal interactions, where hippocampal replays are triggered by -- and in turn adaptively affect -- prefrontal dynamics.

From controlled environments to complex realities: Exploring the interplay between perceived minds and attention

In our daily lives, we perceive things as possessing a mind (e.g., people) or lacking one (e.g., shoes). Intriguingly, how much mind we attribute to people can vary, with real people perceived to have more mind than depictions of individuals, such as photographs. Drawing from a range of research methodologies, including naturalistic observation, mobile eye tracking, and surreptitious behavior monitoring, I discuss how various shades of mind influence human attention and behaviour. The findings suggest the novel concept that overt attention (where one looks) in real-life is fundamentally supported by covert attention (attending to someone out of the corner of one's eye).

Diffuse coupling in the brain - A temperature dial for computation

The neurobiological mechanisms of arousal and anesthesia remain poorly understood. Recent evidence highlights the key role of interactions between the cerebral cortex and the diffusely projecting matrix thalamic nuclei. Here, we interrogate these processes in a whole-brain corticothalamic neural mass model endowed with targeted and diffusely projecting thalamocortical nuclei inferred from empirical data. This model captures key features seen in propofol anesthesia, including diminished network integration, lowered state diversity, impaired susceptibility to perturbation, and decreased corticocortical coherence. Collectively, these signatures reflect a suppression of information transfer across the cerebral cortex. We recover these signatures of conscious arousal by selectively stimulating the matrix thalamus, recapitulating empirical results in macaque, as well as wake-like information processing states that reflect the thalamic modulation of largescale cortical attractor dynamics. Our results highlight the role of matrix thalamocortical projections in shaping many features of complex cortical dynamics to facilitate the unique communication states supporting conscious awareness.

Brain network communication: concepts, models and applications

Understanding communication and information processing in nervous systems is a central goal of neuroscience. Over the past two decades, advances in connectomics and network neuroscience have opened new avenues for investigating polysynaptic communication in complex brain networks. Recent work has brought into question the mainstay assumption that connectome signalling occurs exclusively via shortest paths, resulting in a sprawling constellation of alternative network communication models. This Review surveys the latest developments in models of brain network communication. We begin by drawing a conceptual link between the mathematics of graph theory and biological aspects of neural signalling such as transmission delays and metabolic cost. We organize key network communication models and measures into a taxonomy, aimed at helping researchers navigate the growing number of concepts and methods in the literature. The taxonomy highlights the pros, cons and interpretations of different conceptualizations of connectome signalling. We showcase the utility of network communication models as a flexible, interpretable and tractable framework to study brain function by reviewing prominent applications in basic, cognitive and clinical neurosciences. Finally, we provide recommendations to guide the future development, application and validation of network communication models.

Cognitive Computational Neuroscience 2023

CCN is an annual conference that serves as a forum for cognitive science, neuroscience, and artificial intelligence researchers dedicated to understanding the computations that underlie complex behavior.

Interacting spiral wave patterns underlie complex brain dynamics and are related to cognitive processing

The large-scale activity of the human brain exhibits rich and complex patterns, but the spatiotemporal dynamics of these patterns and their functional roles in cognition remain unclear. Here by characterizing moment-by-moment fluctuations of human cortical functional magnetic resonance imaging signals, we show that spiral-like, rotational wave patterns (brain spirals) are widespread during both resting and cognitive task states. These brain spirals propagate across the cortex while rotating around their phase singularity centres, giving rise to spatiotemporal activity dynamics with non-stationary features. The properties of these brain spirals, such as their rotational directions and locations, are task relevant and can be used to classify different cognitive tasks. We also demonstrate that multiple, interacting brain spirals are involved in coordinating the correlated activations and de-activations of distributed functional regions; this mechanism enables flexible reconfiguration of task-driven activity flow between bottom-up and top-down directions during cognitive processing. Our findings suggest that brain spirals organize complex spatiotemporal dynamics of the human brain and have functional correlates to cognitive processing.

The Insights and Outcomes of the Wellcome-funded Waiting Times Project

Waiting is one of healthcare’s core experiences. It is there in the time it takes to access services; through the days, weeks, months or years needed for diagnoses; in the time that treatment takes; and in the elongated time-frames of recovery, relapse, remission and dying.Funded by the Wellcome Trust, our project opens up what it means to wait in and for healthcare by examining lived experiences, representations and histories of delayed and impeded time.In an era in which time is lived at increasingly different and complex tempos, Waiting Times looks to understand both the difficulties and vital significance of waiting for practices of care, offering a fundamental re-conceptualisation of the relation between time and care in contemporary thinking about health, illness, and wellbeing.

Computational models of spinal locomotor circuitry

To effectively move in complex and changing environments, animals must control locomotor speed and gait, while precisely coordinating and adapting limb movements to the terrain. The underlying neuronal control is facilitated by circuits in the spinal cord, which integrate supraspinal commands and afferent feedback signals to produce coordinated rhythmic muscle activations necessary for stable locomotion. I will present a series of computational models investigating dynamics of central neuronal interactions as well as a neuromechanical model that integrates neuronal circuits with a model of the musculoskeletal system. These models closely reproduce speed-dependent gait expression and experimentally observed changes following manipulation of multiple classes of genetically-identified neuronal populations. I will discuss the utility of these models in providing experimentally testable predictions for future studies.

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.

A recurrent network model of planning explains hippocampal replay and human behavior

When interacting with complex environments, humans can rapidly adapt their behavior to changes in task or context. To facilitate this adaptation, we often spend substantial periods of time contemplating possible futures before acting. For such planning to be rational, the benefits of planning to future behavior must at least compensate for the time spent thinking. Here we capture these features of human behavior by developing a neural network model where not only actions, but also planning, are controlled by prefrontal cortex. This model consists of a meta-reinforcement learning agent augmented with the ability to plan by sampling imagined action sequences drawn from its own policy, which we refer to as 'rollouts'. Our results demonstrate that this agent learns to plan when planning is beneficial, explaining the empirical variability in human thinking times. Additionally, the patterns of policy rollouts employed by the artificial agent closely resemble patterns of rodent hippocampal replays recently recorded in a spatial navigation task, in terms of both their spatial statistics and their relationship to subsequent behavior. Our work provides a new theory of how the brain could implement planning through prefrontal-hippocampal interactions, where hippocampal replays are triggered by - and in turn adaptively affect - prefrontal 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.

Microbial modulation of zebrafish behavior and brain development

There is growing recognition that host-associated microbiotas modulate intrinsic neurodevelopmental programs including those underlying human social behavior. Despite this awareness, the fundamental processes are generally not understood. We discovered that the zebrafish microbiota is necessary for normal social behavior. By examining neuronal correlates of behavior, we found that the microbiota restrains neurite complexity and targeting of key forebrain neurons within the social behavior circuitry. The microbiota is also necessary for both localization and molecular functions of forebrain microglia, brain-resident phagocytes that remodel neuronal arbors. In particular, the microbiota promotes expression of complement signaling pathway components important for synapse remodeling. Our work provides evidence that the microbiota modulates zebrafish social behavior by stimulating microglial remodeling of forebrain circuits during early neurodevelopment and suggests molecular pathways for therapeutic interventions during atypical neurodevelopment.

The embodied brain

Understanding the brain is not only intrinsically fascinating, but also highly relevant to increase our well-being since our brain exhibits a power over the body that makes it capable both of provoking illness or facilitating the healing process. Bearing in mind this dark force, brain sciences have undergone and will undergo an important revolution, redefining its boundaries beyond the cranial cavity. During this presentation, we will discuss about the communication between the brain and other systems that shapes how we feel the external word and how we think. We are starting to unravel how our organs talk to the brain and how the brain talks back. That two-way communication encompasses a complex, body-wide system of nerves, hormones and other signals that will be discussed. This presentation aims at challenging a long history of thinking of bodily regulation as separate from "higher" mental processes. Four centuries ago, René Descartes famously conceptualized the mind as being separate from the body, it is time now to embody our mind.

Decoding the hippocampal oscillatory complexity to predict behavior

Epigenetic rewiring in Schinzel-Giedion syndrome

During life, a variety of specialized cells arise to grant the right and timely corrected functions of tissues and organs. Regulation of chromatin in defining specialized genomic regions (e.g. enhancers) plays a key role in developmental transitions from progenitors into cell lineages. These enhancers, properly topologically positioned in 3D space, ultimately guide the transcriptional programs. It is becoming clear that several pathologies converge in differential enhancer usage with respect to physiological situations. However, why some regulatory regions are physiologically preferred, while some others can emerge in certain conditions, including other fate decisions or diseases, remains obscure. Schinzel-Giedion syndrome (SGS) is a rare disease with symptoms such as severe developmental delay, congenital malformations, progressive brain atrophy, intractable seizures, and infantile death. SGS is caused by mutations in the SETBP1 gene that results in its accumulation further leading to the downstream accumulation of SET. The oncoprotein SET has been found as part of the histone chaperone complex INHAT that blocks the activity of histone acetyltransferases suggesting that SGS may (i) represent a natural model of alternative chromatin regulation and (ii) offer chances to study downstream (mal)adaptive mechanisms. I will present our work on the characterization of SGS in appropriate experimental models including iPSC-derived cultures and mouse.

Nature over Nurture: Functional neuronal circuits emerge in the absence of developmental activity

During development, the complex neuronal circuitry of the brain arises from limited information contained in the genome. After the genetic code instructs the birth of neurons, the emergence of brain regions, and the formation of axon tracts, it is believed that neuronal activity plays a critical role in shaping circuits for behavior. Current AI technologies are modeled after the same principle: connections in an initial weight matrix are pruned and strengthened by activity-dependent signals until the network can sufficiently generalize a set of inputs into outputs. Here, we challenge these learning-dominated assumptions by quantifying the contribution of neuronal activity to the development of visually guided swimming behavior in larval zebrafish. Intriguingly, dark-rearing zebrafish revealed that visual experience has no effect on the emergence of the optomotor response (OMR). We then raised animals under conditions where neuronal activity was pharmacologically silenced from organogenesis onward using the sodium-channel blocker tricaine. Strikingly, after washout of the anesthetic, animals performed swim bouts and responded to visual stimuli with 75% accuracy in the OMR paradigm. After shorter periods of silenced activity OMR performance stayed above 90% accuracy, calling into question the importance and impact of classical critical periods for visual development. Detailed quantification of the emergence of functional circuit properties by brain-wide imaging experiments confirmed that neuronal circuits came ‘online’ fully tuned and without the requirement for activity-dependent plasticity. Thus, contrary to what you learned on your mother's knee, complex sensory guided behaviors can be wired up innately by activity-independent developmental mechanisms.

Self-perception: mechanosensation and beyond

Brain-organ communications play a crucial role in maintaining the body's physiological and psychological homeostasis, and are controlled by complex neural and hormonal systems, including the internal mechanosensory organs. However, the progress has been slow due to technical hurdles: the sensory neurons are deeply buried inside the body and are not readily accessible for direct observation, the projection patterns from different organs or body parts are complex rather than converging into dedicate brain regions, the coding principle cannot be directly adapted from that learned from conventional sensory pathways. Our lab apply the pipeline of "biophysics of receptors-cell biology of neurons-functionality of neural circuits-animal behaviors" to explore the molecular and neural mechanisms of self-perception. In the lab, we mainly focus on the following three questions: 1, The molecular and cellular basis for proprioception and interoception. 2, The circuit mechanisms of sensory coding and integration of internal and external information. 3, The function of interoception in regulating behavior homeostasis.

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.

Explaining an asymmetry in similarity and difference judgments

Explicit similarity judgments tend to emphasize relational information more than do difference judgments. In this talk, I propose and test the hypothesis that this asymmetry arises because human reasoners represent the relation different as the negation of the relation same (i.e., as not-same). This proposal implies that processing difference is more cognitively demanding than processing similarity. Both for verbal comparisons between word pairs, and for visual comparisons between sets of geometric shapes, participants completed a triad task in which they selected which of two options was either more similar to or more different from a standard. On unambiguous trials, one option was unambiguously more similar to the standard, either by virtue of featural similarity or by virtue of relational similarity. On ambiguous trials, one option was more featurally similar (but less relationally similar) to the standard, whereas the other was more relationally similar (but less featurally similar). Given the higher cognitive complexity of assessing relational similarity, we predicted that detecting relational difference would be particularly demanding. We found that participants (1) had more difficulty accurately detecting relational difference than they did relational similarity on unambiguous trials, and (2) tended to emphasize relational information more when judging similarity than when judging difference on ambiguous trials. The latter finding was captured by a computational model of comparison that weights relational information more heavily for similarity than for difference judgments. These results provide convergent evidence for a representational asymmetry between the relations same and different.

Are place cells just memory cells? Probably yes

Neurons in the rodent hippocampus appear to encode the position of the animal in physical space during movement. Individual ``place cells'' fire in restricted sub-regions of an environment, a feature often taken as evidence that the hippocampus encodes a map of space that subserves navigation. But these same neurons exhibit complex responses to many other variables that defy explanation by position alone, and the hippocampus is known to be more broadly critical for memory formation. Here we elaborate and test a theory of hippocampal coding which produces place cells as a general consequence of efficient memory coding. We constructed neural networks that actively exploit the correlations between memories in order to learn compressed representations of experience. Place cells readily emerged in the trained model, due to the correlations in sensory input between experiences at nearby locations. Notably, these properties were highly sensitive to the compressibility of the sensory environment, with place field size and population coding level in dynamic opposition to optimally encode the correlations between experiences. The effects of learning were also strongly biphasic: nearby locations are represented more similarly following training, while locations with intermediate similarity become increasingly decorrelated, both distance-dependent effects that scaled with the compressibility of the input features. Using virtual reality and 2-photon functional calcium imaging in head-fixed mice, we recorded the simultaneous activity of thousands of hippocampal neurons during virtual exploration to test these predictions. Varying the compressibility of sensory information in the environment produced systematic changes in place cell properties that reflected the changing input statistics, consistent with the theory. We similarly identified representational plasticity during learning, which produced a distance-dependent exchange between compression and pattern separation. These results motivate a more domain-general interpretation of hippocampal computation, one that is naturally compatible with earlier theories on the circuit's importance for episodic memory formation. Work done in collaboration with James Priestley, Lorenzo Posani, Marcus Benna, Attila Losonczy.

Autopoiesis and Enaction in the Game of Life

Enaction plays a central role in the broader fabric of so-called 4E (embodied, embedded, extended, enactive) cognition. Although the origin of the enactive approach is widely dated to the 1991 publication of the book "The Embodied Mind" by Varela, Thompson and Rosch, many of the central ideas trace to much earlier work. Over 40 years ago, the Chilean biologists Humberto Maturana and Francisco Varela put forward the notion of autopoiesis as a way to understand living systems and the phenomena that they generate, including cognition. Varela and others subsequently extended this framework to an enactive approach that places biological autonomy at the foundation of situated and embodied behavior and cognition. I will describe an attempt to place Maturana and Varela's original ideas on a firmer foundation by studying them within the context of a toy model universe, John Conway's Game of Life (GoL) cellular automata. This work has both pedagogical and theoretical goals. Simple concrete models provide an excellent vehicle for introducing some of the core concepts of autopoiesis and enaction and explaining how these concepts fit together into a broader whole. In addition, a careful analysis of such toy models can hone our intuitions about these concepts, probe their strengths and weaknesses, and move the entire enterprise in the direction of a more mathematically rigorous theory. In particular, I will identify the primitive processes that can occur in GoL, show how these can be linked together into mutually-supporting networks that underlie persistent bounded entities, map the responses of such entities to environmental perturbations, and investigate the paths of mutual perturbation that these entities and their environments can undergo.

Central place foraging: how insects anchor spatial information

Many insect species maintain a nest around which their foraging behaviour is centered, and can use path integration to maintain an accurate estimate of their distance and direction (a vector) to their nest. Some species, such as bees and ants, can also store the vector information for multiple salient locations in the world, such as food sources, in a common coordinate system. They can also use remembered views of the terrain around salient locations or along travelled routes to guide return. Recent modelling of these abilities shows convergence on a small set of algorithms and assumptions that appear sufficient to account for a wide range of behavioural data, and which can be mapped to specific insect brain circuits. Notably, this does not include any significant topological knowledge: the insect does not need to recover the information (implicit in their vector memory) about the relationships between salient places; nor to maintain any connectedness or ordering information between view memories; nor to form any associations between views and vectors. However, there remains some experimental evidence not fully explained by these algorithms that may point towards the existence of a more complex or integrated mental map in insects.

Complex spatial representations and computations emerge in a memory-augmented network that learns to navigate

Bernstein Conference 2024

Critical organisation for complex temporal tasks in neural networks

Bernstein Conference 2024

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

Bernstein Conference 2024

Origin and function of gamma oscillatory complexity in hippocampal networks

Bernstein Conference 2024

A robust machine learning pipeline for the analysis of complex nightingale songs

Bernstein Conference 2024

Unsupervised clustering of burst shapes reveals the increasing complexity of developing networks in vitro

Bernstein Conference 2024

Accurate Engagement of the Drosophila Central-Complex Compass During Head-Fixed Path-Constrained Navigation

COSYNE 2022

Chromatic contrast and angle of polarization signals are integrated in the Drosophila central complex

COSYNE 2022

Environmental complexity modulates the arbitration between deliberative and habitual decision-making

COSYNE 2022

Mice can do complex visual tasks

COSYNE 2022

Mice can do complex visual tasks

COSYNE 2022

Human-specific modifiers of the WASH complex control cortical neuron migration and fate specification

Predicting behavior from complex hippocampal oscillatory codes

COSYNE 2022

Predicting behavior from complex hippocampal oscillatory codes

COSYNE 2022

A new tool for automated annotation of complex birdsong reveals dynamics of canary syntax rules

COSYNE 2022

A new tool for automated annotation of complex birdsong reveals dynamics of canary syntax rules

COSYNE 2022

Understanding rat behavior in a complex task via non-deterministic policies

COSYNE 2022

Understanding rat behavior in a complex task via non-deterministic policies

COSYNE 2022

Complex computation from developmental priors

COSYNE 2023

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

COSYNE 2023

A mechanistic model for the formation of globally consistent maps of space in complex environments

COSYNE 2023

Thalamic maintenance of a complex sequential learned behavior: birdsong

COSYNE 2023

Beyond linear summation: Three-Body RNN for modeling complex neural and biological systems

COSYNE 2025

Bounds on the computational complexity of neurons due to dendritic morphology

COSYNE 2025

Complex Environments Drive Adaptive Hunting Strategies in Mice

COSYNE 2025

Coordinating control and planning for navigation on simplicial complex attractors

COSYNE 2025

A factorization model of V1 complex cells is selectively invariant

COSYNE 2025

ForageWorld: RL agents in complex foraging arenas develop internal maps for navigation and planning

COSYNE 2025

Probing Motion-Form Interactions in the Macaque Inferior Temporal Cortex and Artificial Neural Networks for Complex Scene Understanding

COSYNE 2025

Acquisition of a complex locomotor task: activity of cerebellar molecular layer interneurons and gait dynamics

ADNP-SIRT1 new complex regulates histone methylation: Dramatically dysregulated in Alzheimer’s disease

Analysis of complex social behaviour during an extended time period in a valproic acid animal model of autism spectrum disorder

Analysis of synaptic nanoarchitecture using FRET at the level of single protein complexes and at the whole mouse brain scale

The anatomical and functional complexity of medial thalamus-prefrontal cortex circuit

Asymmetric metabolism controls the acute acquisition of vertebrate axon complexity

A biophysical mechanism for epigenetic inheritance of enhanced complex learning capabilities

Biophysical Mechanisms Supporting Maintenance of Complex Tuning

Body complexion and circulating lipids in the risk of Frontotemporal dementia and Amyotrophic Lateral Sclerosis

Complex dynamical coding of simple movements in the output of the basal ganglia

Causal role of human frontopolar cortex in information integration during complex decision making

Bernstein Conference 2024