Grant List
Represents Grant table in the DB
GET /v1/grants?page%5Bnumber%5D=3&sort=-principal_investigator
{ "links": { "first": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1&sort=-principal_investigator", "last": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1442&sort=-principal_investigator", "next": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=4&sort=-principal_investigator", "prev": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=2&sort=-principal_investigator" }, "data": [ { "type": "Grant", "id": "16117", "attributes": { "award_id": "1R01HD120978-01", "title": "Deriving Real World Insights to Inform Trials for Pregnant Individuals Using Electronic Health Records and Machine Learning", "funder": { "id": 4, "ror": "https://ror.org/01cwqze88", "name": "National Institutes of Health", "approved": true }, "funder_divisions": [ "Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD)" ], "program_reference_codes": [], "program_officials": [ { "id": 44594, "first_name": "MEGAN S", "last_name": "MITCHELL", "orcid": "", "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2026-06-02", "end_date": "2031-05-31", "award_amount": 727070, "principal_investigator": { "id": 44595, "first_name": "Chengxi", "last_name": "Zang", "orcid": "", "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 3515, "ror": "", "name": "WEILL MEDICAL COLL OF CORNELL UNIV", "address": "", "city": "", "state": "NY", "zip": "", "country": "United States", "approved": true }, "abstract": "Pregnant women have been routinely excluded from trials due to ethical and safety concerns. Nearly 95% of clinical trials submitted to the FDA excluded pregnant women. By contrast, almost all pregnant women take at least one medication in pregnancy, but trial evidence on the safety and efficacy of these medications is largely missing. Such evidence gaps present challenges for clinicians who advise treatment for pregnant women, and patients who are making decisions in the absence of data to inform safety and efficacy in pregnancy. 1) The COVID-19 pandemic further escalated these gaps for pregnant women. Acquiring SARS-CoV-2 infection during pregnancy is associated with an increased risk of mortality and obstetric complications. SARS-CoV-2 infection acquired in pregnancy is not only associated with worse perinatal outcomes but also exhibits risks of getting post-acute sequelae of SARS-CoV-2 infection (PASC), or Long COVID. By contrast, all the initial COVID-19 vaccine trials and treatment studies, including Paxlovid, etc., excluded pregnant women, despite their higher risk of COVID-19 complications and risk of long-term complications. 2) Other examples are coexisting type 2 diabetes or living with obesity in pregnancy which are associated with increased risk of adverse pregnancy outcomes. Though glucagon-like peptide 1 (GLP-1) agonists and sodium-glucose co-transporter-2 (SGLT2) inhibitors are increasingly used in treating diabetes and obesity in the general population there is a paucity of data on safety in pregnancy limiting evidence-based decision making for this population, 3) Other examples include but are not limited to Selective Serotonin Reuptake Inhibitors (SSRIs) with perinatal mood and anxiety disorders affecting an estimated 10–20% of reproductive-aged females. Our proposal aims to bridge these critical evidence gaps for pregnant women with RWD and advanced machine learning methods, with applications including but not limited to generating real-world evidence on the safety and effectiveness of antivirals (e.g., Paxlovid), antidiabetic drugs (e.g., GLP-1, SGLT-2, metformin), and antidepressants (e.g., SSRIs) in pregnancy. To achieve our objectives, we will leverage the large-scale electronic health record (EHR) data from patients in New York City (~19 million) and Florida (~19 million). There are three specific aims for this study: (1) Develop NLP-assisted computable phenotyping algorithms and tools to extract pregnant cohorts, key patient characteristics, and outcomes relevant to pregnant trials, (2) Develop a federated target trial emulation framework for estimating the real-world safety and effectiveness outcomes of interventions from distributed CRNs, and (3) Develop an RWE-based trial design system consisting of causal AI and a prototype toolbox for assessing and optimizing eligibility criteria to inform pregnancy trials.", "keywords": [ "Acute", "Adoption", "Affect", "Alabama", "Algorithms", "Anti-diabetic Drugs", "Anti-viral Agents", "Anxiety Disorders", "Apgar Score", "Artificial Intelligence", "Birth", "Body mass index", "COVID-19", "COVID-19 complications", "COVID-19 pandemic", "COVID-19 risk", "COVID-19 vaccine", "Caring", "Characteristics", "Clinical", "Clinical Research", "Clinical Trials", "Collection", "Data", "Data Science", "Decision Making", "Diabetes Mellitus", "Discipline of obstetrics", "Effectiveness", "Electronic Health Record", "Eligibility Determination", "Ethics", "Exclusion", "Exhibits", "Female", "Florida", "Fright", "Future", "General Population", "Generations", "Gestational Age", "Glucagon like peptide-1 receptor agonists", "Goals", "Health", "Individual", "Infant", "Intervention", "Long COVID", "Machine Learning", "Metformin", "Methodology", "Methods", "Modeling", "Mood Disorders", "Mothers", "Natural Language Processing", "New York City", "Non-Insulin-Dependent Diabetes Mellitus", "Obesity", "Online Systems", "Outcome", "Patient-Focused Outcomes", "Patients", "Paxlovid", "Perinatal", "Pharmaceutical Preparations", "Policy Maker", "Population", "Population Characteristics", "Population Research", "Post-Acute Sequelae of SARS-CoV-2 Infection", "Pregnancy", "Pregnant Women", "Process", "Prospective cohort", "Research", "Research Personnel", "Retrospective cohort", "Risk", "SARS-CoV-2 infection", "Safety", "Sample Size", "Selective Serotonin Reuptake Inhibitor", "Site", "Sodium glucose co-transporter 2 inhibitor", "System", "Target Populations", "Time", "Treatment Effectiveness", "Women's Health", "Work", "adverse pregnancy outcome", "anti-depressive agents", "causal inference", "chronic complications", "cohort", "computable phenotypes", "computerized tools", "data resource", "design", "effectiveness outcome", "electronic health record system", "evidence base", "experience", "fetal", "glucagon-like peptide 1", "high risk", "improved", "innovation", "insight", "interest", "machine learning method", "medication safety", "mortality risk", "novel", "obesity in pregnancy", "obstetrical complication", "patient oriented", "patient population", "patient safety", "perinatal outcomes", "phenotyping algorithm", "pregnant", "privacy preservation", "prototype", "real world data", "real world evidence", "reproductive age", "safety outcomes", "social health determinants" ], "approved": true } }, { "type": "Grant", "id": "16137", "attributes": { "award_id": "1R35GM164072-01", "title": "Generative AI-Powered Exploration of Protein Dynamics and Non-Deterministic Chemical Kinetics", "funder": { "id": 4, "ror": "https://ror.org/01cwqze88", "name": "National Institutes of Health", "approved": true }, "funder_divisions": [ "National Institute of General Medical Sciences (NIGMS)" ], "program_reference_codes": [], "program_officials": [ { "id": 44592, "first_name": "HAN", "last_name": "NGUYEN", "orcid": "", "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2026-06-01", "end_date": "2031-02-28", "award_amount": 447174, "principal_investigator": { "id": 44593, "first_name": "Peng", "last_name": "Tao", "orcid": "", "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 3513, "ror": "", "name": "SOUTHERN METHODIST UNIVERSITY", "address": "", "city": "", "state": "TX", "zip": "", "country": "United States", "approved": true }, "abstract": "Overview. The PI’s lab focuses on uncovering the dynamic mechanisms underlying protein functions through advanced computational simulations and theoretical modeling, employing molecular mechanics (MM), quantum mechanics (QM), and their combined approaches (QM/MM). The group's research has concentrated on three major themes. Protein Allostery: Significant advances have been achieved in both methodological development and application studies of key allosteric proteins, including the Light-Oxygen-Voltage-sensing (LOV) domain and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Additionally, the group has created two user- friendly public computational servers to serve the broader research community. Evolution of Enzyme Catalysis: The PI’s lab has pioneered innovative theoretical frameworks that integrate machine learning techniques with catalytic reaction pathways derived from hybrid QM/MM calculations, enhancing our understanding of enzyme evolution and catalytic mechanisms. Fundamental Chemical Mechanism Studies: The PI has a longstanding interest in the mechanisms underlying chemical transitions, particularly focusing on their characterization as rare events. Through extensive computational investigations and innovative methodological advancements, the PI's laboratory has explored a broad spectrum of chemical processes in detail. Goals of Next Five Years. Over the next five years, our research will advance computational modeling across multiple cutting-edge domains, integrating artificial intelligence and advanced theoretical frameworks to address complex biochemical and photochemical phenomena. We aim to establish transformer-based conditional diffusion models to predict protein dynamics, creating an automated, scalable alternative to traditional molecular dynamics simulations. Concurrently, we will expand our generative AI approaches to dissect catalytic mechanisms of β-lactamase enzymes, systematically mapping the catalytic landscapes of serine-based and metallo-β-lactamases. By incorporating protein conformational dynamics into these catalytic models, we aim to elucidate enzyme regulation and evolution, enhancing understanding of antibiotic resistance mechanisms. Additionally, we will develop comprehensive stochastic differential equation (SDE) models using Itô calculus to characterize the stochastic molecular behaviors underlying mechanically activated photochemistry (MAPC). Integrating specialized models for chemiluminescence and mechanochemical activation into a unified MAPC framework, we aim to significantly enhance predictive simulation capabilities in this interdisciplinary area. Collectively, these efforts will produce robust theoretical and computational tools that deepen fundamental understanding and enable innovative applications across biophysics and biochemistry. Overall Vision. Understanding the dynamic behavior of proteins is central to decoding the mechanisms underpinning protein function, enzymatic catalysis, and structure-function relationships. It remains computationally prohibitive for simulating biophysical and biochemical processes at functionally relevant timescales, which often span seconds to minutes—far beyond the millisecond range achievable by current technologies. Chemical processes at the molecular level are inherently non-deterministic stochastic processes, governed by probabilistic events and random molecular fluctuations rather than fixed, reproducible trajectories. The PI has been developing a long-term vision to advance biomolecular modeling by shifting from traditional MD simulations and reaction pathway calculation for enzyme catalysis mechanism toward AI-driven, predictive models capable of generating protein dynamics, enzyme catalytic mechanisms, and stochastic biochemical behaviors. By combining generative AI, molecular simulations, and stochastic modeling, this research addresses critical limitations in scaling biomolecular dynamics studies. It promises transformative advances in protein engineering, drug discovery, and mechanistic enzymology, while providing computationally efficient tools for simulating molecular behavior at unprecedented scales.", "keywords": [ "2019-nCoV", "Acceleration", "Address", "Antibiotic Resistance", "Area", "Artificial Intelligence enhanced", "Behavior", "Biochemical", "Biochemical Process", "Biochemistry", "Biophysical Process", "Biophysics", "Calculi", "Catalysis", "Chemicals", "Communities", "Complex", "Computer Models", "Computer Simulation", "Development", "Differential Equation", "Diffusion", "Enzymatic Biochemistry", "Enzymes", "Event", "Evolution", "Goals", "Health", "Hybrids", "Investigation", "Laboratories", "Light", "Machine Learning", "Maps", "Mechanics", "Methodology", "Modeling", "Molecular", "Molecular Disease", "Oxygen", "Pathway interactions", "Photochemistry", "Process", "Protein Conformation", "Protein Dynamics", "Protein Engineering", "Proteins", "Public Health Applications Research", "Quantum Mechanics", "Reaction", "Regulation", "Reproducibility", "Research", "Serine", "Stochastic Processes", "Structure-Activity Relationship", "Techniques", "Technology", "Theoretical model", "Therapeutic", "Trajectory", "Translations", "Vision", "beta-Lactamase", "chemical kinetics", "computerized tools", "design", "drug discovery", "empowerment", "generative artificial intelligence", "innovation", "interest", "millisecond", "molecular dynamics", "molecular mechanics", "predictive modeling", "protein function", "protein structure", "resistance mechanism", "simulation", "tool", "user-friendly", "voltage" ], "approved": true } }, { "type": "Grant", "id": "16115", "attributes": { "award_id": "1R35GM164072-01", "title": "Generative AI-Powered Exploration of Protein Dynamics and Non-Deterministic Chemical Kinetics", "funder": { "id": 4, "ror": "https://ror.org/01cwqze88", "name": "National Institutes of Health", "approved": true }, "funder_divisions": [ "National Institute of General Medical Sciences (NIGMS)" ], "program_reference_codes": [], "program_officials": [ { "id": 44592, "first_name": "HAN", "last_name": "NGUYEN", "orcid": "", "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2026-06-01", "end_date": "2031-02-28", "award_amount": 447174, "principal_investigator": { "id": 44593, "first_name": "Peng", "last_name": "Tao", "orcid": "", "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 3513, "ror": "", "name": "SOUTHERN METHODIST UNIVERSITY", "address": "", "city": "", "state": "TX", "zip": "", "country": "United States", "approved": true }, "abstract": "Overview. The PI’s lab focuses on uncovering the dynamic mechanisms underlying protein functions through advanced computational simulations and theoretical modeling, employing molecular mechanics (MM), quantum mechanics (QM), and their combined approaches (QM/MM). The group's research has concentrated on three major themes. Protein Allostery: Significant advances have been achieved in both methodological development and application studies of key allosteric proteins, including the Light-Oxygen-Voltage-sensing (LOV) domain and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Additionally, the group has created two user- friendly public computational servers to serve the broader research community. Evolution of Enzyme Catalysis: The PI’s lab has pioneered innovative theoretical frameworks that integrate machine learning techniques with catalytic reaction pathways derived from hybrid QM/MM calculations, enhancing our understanding of enzyme evolution and catalytic mechanisms. Fundamental Chemical Mechanism Studies: The PI has a longstanding interest in the mechanisms underlying chemical transitions, particularly focusing on their characterization as rare events. Through extensive computational investigations and innovative methodological advancements, the PI's laboratory has explored a broad spectrum of chemical processes in detail. Goals of Next Five Years. Over the next five years, our research will advance computational modeling across multiple cutting-edge domains, integrating artificial intelligence and advanced theoretical frameworks to address complex biochemical and photochemical phenomena. We aim to establish transformer-based conditional diffusion models to predict protein dynamics, creating an automated, scalable alternative to traditional molecular dynamics simulations. Concurrently, we will expand our generative AI approaches to dissect catalytic mechanisms of β-lactamase enzymes, systematically mapping the catalytic landscapes of serine-based and metallo-β-lactamases. By incorporating protein conformational dynamics into these catalytic models, we aim to elucidate enzyme regulation and evolution, enhancing understanding of antibiotic resistance mechanisms. Additionally, we will develop comprehensive stochastic differential equation (SDE) models using Itô calculus to characterize the stochastic molecular behaviors underlying mechanically activated photochemistry (MAPC). Integrating specialized models for chemiluminescence and mechanochemical activation into a unified MAPC framework, we aim to significantly enhance predictive simulation capabilities in this interdisciplinary area. Collectively, these efforts will produce robust theoretical and computational tools that deepen fundamental understanding and enable innovative applications across biophysics and biochemistry. Overall Vision. Understanding the dynamic behavior of proteins is central to decoding the mechanisms underpinning protein function, enzymatic catalysis, and structure-function relationships. It remains computationally prohibitive for simulating biophysical and biochemical processes at functionally relevant timescales, which often span seconds to minutes—far beyond the millisecond range achievable by current technologies. Chemical processes at the molecular level are inherently non-deterministic stochastic processes, governed by probabilistic events and random molecular fluctuations rather than fixed, reproducible trajectories. The PI has been developing a long-term vision to advance biomolecular modeling by shifting from traditional MD simulations and reaction pathway calculation for enzyme catalysis mechanism toward AI-driven, predictive models capable of generating protein dynamics, enzyme catalytic mechanisms, and stochastic biochemical behaviors. By combining generative AI, molecular simulations, and stochastic modeling, this research addresses critical limitations in scaling biomolecular dynamics studies. It promises transformative advances in protein engineering, drug discovery, and mechanistic enzymology, while providing computationally efficient tools for simulating molecular behavior at unprecedented scales.", "keywords": [ "2019-nCoV", "Acceleration", "Address", "Antibiotic Resistance", "Area", "Artificial Intelligence enhanced", "Behavior", "Biochemical", "Biochemical Process", "Biochemistry", "Biophysical Process", "Biophysics", "Calculi", "Catalysis", "Chemicals", "Communities", "Complex", "Computer Models", "Computer Simulation", "Development", "Differential Equation", "Diffusion", "Enzymatic Biochemistry", "Enzymes", "Event", "Evolution", "Goals", "Health", "Hybrids", "Investigation", "Laboratories", "Light", "Machine Learning", "Maps", "Mechanics", "Methodology", "Modeling", "Molecular", "Molecular Disease", "Oxygen", "Pathway interactions", "Photochemistry", "Process", "Protein Conformation", "Protein Dynamics", "Protein Engineering", "Proteins", "Public Health Applications Research", "Quantum Mechanics", "Reaction", "Regulation", "Reproducibility", "Research", "Serine", "Stochastic Processes", "Structure-Activity Relationship", "Techniques", "Technology", "Theoretical model", "Therapeutic", "Trajectory", "Translations", "Vision", "beta-Lactamase", "chemical kinetics", "computerized tools", "design", "drug discovery", "empowerment", "generative artificial intelligence", "innovation", "interest", "millisecond", "molecular dynamics", "molecular mechanics", "predictive modeling", "protein function", "protein structure", "resistance mechanism", "simulation", "tool", "user-friendly", "voltage" ], "approved": true } }, { "type": "Grant", "id": "16114", "attributes": { "award_id": "1R21AI199873-01", "title": "Host-specific profiles of Defective Viral Genomes of influenza virus", "funder": { "id": 4, "ror": "https://ror.org/01cwqze88", "name": "National Institutes of Health", "approved": true }, "funder_divisions": [ "National Institute of Allergy and Infectious Diseases (NIAID)" ], "program_reference_codes": [], "program_officials": [ { "id": 32597, "first_name": "BROOKE ALLISON", "last_name": "BOZICK", "orcid": "", "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2026-06-23", "end_date": "2028-05-31", "award_amount": 270443, "principal_investigator": { "id": 44591, "first_name": "Samuel L", "last_name": "Diaz", "orcid": "", "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [ { "id": 27226, "first_name": "Andrew Laurence", "last_name": "Routh", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 745, "ror": "", "name": "SCRIPPS RESEARCH INSTITUTE, THE", "address": "", "city": "", "state": "CA", "zip": "", "country": "United States", "approved": true }, "abstract": "RNA viruses are exceptionally diverse and rapidly evolving. Their RNA-dependent RNA polymerases are prone to mutation, lack proof-reading and frequently undergo recombination. Non-homologous RNA recombination gives rise to Defective-Viral Genomes (DVGs); versions of the parental genome that have been truncated, internally deleted or otherwise rearranged. While not encoding for functional viruses, DVGs can be amplified and co-passaged with the wild-type ‘helper’ virus that provides the machinery for replication, encapsidation and transmission. DVGs spontaneously emerge during viral replication and can subsequently act as ‘parasites’ or ‘cheats’ of the parental virus by competing for cellular resources and viral cofactors. DVGs can change the fitness, outcomes and vaccine effectiveness of respiratory pathogens such as influenza A virus (IAV), respiratory syncytial virus (RSV) and SARS-CoV-2. Recent studies have characterized the ability of DVGs to modulate immune responses. In particular, IAV generates DVGs primarily from PB2, PB1 and PA segments that are known to activate RIG-I pathways and simulate interferon-B expression. This results in cytokine stimulation and cell- death and may explain the variable pathogenicity of some influenza strains. Specific mutations in the polymerase of H1N1 (PA:D529N) have been identified that correlate with reduced DVG formation, a lowered induction of innate immunity, and increased pathogenesis in mouse models. In human cohorts, patients with highly severe or fatal outcomes exhibited a lower ‘dose’ of DVGs relative to mild cases. We recently performed a secondary analysis of publicly available NGS data of 2421 samples of Highly pathogenic avian influenza (HPAI) (H5N1 clade 2.3.4.4b) collected from poultry, non-poultry and cattle by the USDA (Nguyen et al, 2024). We found a striking abundance of DVGs in PB2, PB1 and PA segments across the entire cohort. Importantly, we also found evidence for species-specific DVGs, raising the tantalising prospect that DVGs might be useful as a biomarker reporting on the biological origins of a virus sample, in the absence of any other information. We will validate and formalize these findings with a series of bioinformatic and computational routes to identify a molecular DVG-based signature of the host of origin. We next develop a ClickSeq-based assay, that we will term ‘FluDVG-ClickSeq’, for targeted influenza viral NGS that can accurately report on the abundance and identity of DVGs in clinical specimens using simple, automatable bioinformatics. Together, we will determine whether the DVGs can be used as biomarker to identify the host of origin in complex samples, suchas environmental collections where a host or infected host is not present or identifiable. In addition to providing a valuable tool for epidemiology and surveillance efforts, this will provide support for the generalization concept that DVGs might form in a host-dependent manner.", "keywords": [ "2019-nCoV", "Avian Influenza", "Avian Influenza A Virus", "Back", "Bioinformatics", "Biological", "Biological Assay", "Biological Markers", "Birds", "Cattle", "Cell Death", "Chickens", "Clinical", "Collection", "Complex", "Data", "Detection", "Disease Outcome", "Domestic Fowls", "Dose", "Dwarfism", "Epidemiology", "Event", "Evolution", "Exhibits", "Fatal Outcome", "Frequencies", "Future", "Genetic", "Genetic Recombination", "Genome", "Genomic Segment", "Genomic approach", "Genomics", "Genotype", "Helper Viruses", "Human", "Immune", "Immune response", "Individual", "Influenza", "Influenza A Virus H1N1 Subtype", "Influenza A Virus H5N1 Subtype", "Influenza A virus", "Interferon-β", "Laboratories", "Methods", "Molecular", "Monitor", "Mutation", "Natural Immunity", "Nature", "Outcome", "Parasites", "Pathogenesis", "Pathogenicity", "Pathway interactions", "Patients", "Polymerase", "Population", "Procedures", "RNA", "RNA Sequences", "RNA Viruses", "RNA-Directed RNA Polymerase", "Reporting", "Resources", "Respiratory syncytial virus", "Role", "Route", "Sampling", "Series", "Species Specificity", "Specimen", "Testing", "United States Department of Agriculture", "Viral", "Viral Genome", "Virus", "Virus Diseases", "Virus Replication", "bioinformatics pipeline", "bioinformatics tool", "cofactor", "cohort", "cytokine", "fitness", "genomic RNA", "immunoregulation", "influenza virus strain", "influenzavirus", "insight", "mouse model", "next generation sequencing", "novel", "respiratory pathogen", "secondary analysis", "tool", "transmission process", "vaccine effectiveness", "viral RNA" ], "approved": true } }, { "type": "Grant", "id": "16113", "attributes": { "award_id": "1F30AI200349-01", "title": "Bat T cells responses to SARS-CoV-2", "funder": { "id": 4, "ror": "https://ror.org/01cwqze88", "name": "National Institutes of Health", "approved": true }, "funder_divisions": [ "National Institute of Allergy and Infectious Diseases (NIAID)" ], "program_reference_codes": [], "program_officials": [ { "id": 32599, "first_name": "MICHELLE MARIE", "last_name": "ARNOLD", "orcid": "", "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2026-06-11", "end_date": "2029-06-10", "award_amount": 45195, "principal_investigator": { "id": 44590, "first_name": "Clara G", "last_name": "Reasoner", "orcid": "", "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 3372, "ror": "", "name": "COLORADO STATE UNIVERSITY", "address": "", "city": "", "state": "CO", "zip": "", "country": "United States", "approved": true }, "abstract": "Bats are reservoir hosts of several viruses that cause significant disease burden in humans. In nearly all of these virus-host relationships, the bats show no signs of disease and typically clear infections within a few weeks. Some have postulated that bats have “disease tolerance” that accounts for their ability to host these viruses. Although innate immunity of bat cells in vitro has been widely studied, virtually nothing is known about their adaptive immune responses to these viruses. This project will focus on cytotoxic T cell (CTL) responses of Jamaican fruit bats experimentally infected with SARS-CoV-2, the causative agent of COVID-19. We previously showed that Jamaican fruit bats have low susceptibility to SARS-CoV-2, with no evidence of infection in their lungs. However, we have overcome this limitation by using an adenovirus vector for in vivo transduction of bat lung cells with human ACE2, which subsequently leads to a robust infection of the lungs but without meaningful disease. Using this model, we previously determined that CD4+ T cells from infected bats can be identified by flow cytometry and we will extend these findings with CD8+ CTL studies. This approach will use autologous fibroblasts from wing punches generated in advance to provide MHC class I-matched antigen presenting cells (APC) prior to infection of bats with SARS-CoV-2. The APCs will then be cultured in vitro with CD8+ splenocytes from infected donor bats and peptide pools of SARS-CoV-2 spike and nucleocapsid antigens to activate antigen- specific CTLs. The CTLs will then be used for single-cell RNA sequencing analysis to determine their transcriptomic profiles in response to SARS-CoV-2 antigenic stimulation. Additionally we will deplete Jamaican fruit bats of CD8+ CTLs using a monoclonal antibody that we have developed and challenge the bats to determine the in vivo role of CTLs during SARS-CoV-2 infection. This may lead to disease, which would show that, at least, immune tolerance may not be a mechanism that explains why bats can harbor these viruses without disease. Collectively, these studies will be the first to examine cytotoxic T cell responses in bats and will fill important knowledge gaps about bat adaptive immune responses to viruses.", "keywords": [ "2019-nCoV", "ACE2", "Adaptive Immune System", "Adenovirus Vector", "Antibodies", "Antibody titer measurement", "Antigen-Presenting Cells", "Antigens", "Autologous", "Biological", "Biopsy", "Breeding", "CD4 Positive T Lymphocytes", "CD8-Positive T-Lymphocytes", "CD8B1 gene", "COVID-19", "COVID-19 susceptibility", "Case Fatality Rates", "Cells", "Chiroptera", "Clinical", "Coculture Techniques", "Cytotoxic T-Lymphocytes", "Disease", "Ecology", "Fibroblasts", "Flow Cytometry", "Fruit", "Gene Expression Profile", "Gene Expression Profiling", "Human", "Immune Tolerance", "Immune response", "Immunity", "Immunology", "In Vitro", "Infection", "Inflammatory Response", "Jamaican", "Knowledge", "Laboratories", "Lung", "Lung infections", "MHC Class I Genes", "Methods", "Modeling", "Monoclonal Antibodies", "Natural Immunity", "Nucleocapsid", "Peptides", "Physiology", "Reagent", "Research", "Research Training", "Role", "SARS-CoV-2 antigen", "SARS-CoV-2 infection", "SARS-CoV-2 spike protein", "Splenocyte", "T cell response", "T-Cell Depletion", "T-Lymphocyte Subsets", "Testing", "Toxin", "Viral reservoir", "Virus", "Wing", "Zoonoses", "adaptive immune response", "burden of illness", "cell killing", "cross reactivity", "cytokine", "cytotoxic CD8 T cells", "in vivo", "innovation", "mortality", "novel", "prevent", "response", "single cell sequencing", "single-cell RNA sequencing", "tool", "training project", "transcriptomics", "virology", "virtual", "virus characteristic" ], "approved": true } }, { "type": "Grant", "id": "16112", "attributes": { "award_id": "1K99AI199130-01", "title": "Investigating the Impact of Cadmium on Immune Function and Viral Dynamics in Jamaican Fruit Bats (Artibeus jamaicensis)", "funder": { "id": 4, "ror": "https://ror.org/01cwqze88", "name": "National Institutes of Health", "approved": true }, "funder_divisions": [ "National Institute of Allergy and Infectious Diseases (NIAID)" ], "program_reference_codes": [], "program_officials": [ { "id": 32556, "first_name": "TIMOTHY A", "last_name": "GONDRE-LEWIS", "orcid": "", "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2026-06-08", "end_date": "2028-05-31", "award_amount": 146459, "principal_investigator": { "id": 44589, "first_name": "Laura Ann", "last_name": "Pulscher", "orcid": "", "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 3512, "ror": "", "name": "COLORADO STATE UNIVERSITY", "address": "", "city": "", "state": "CO", "zip": "", "country": "United States", "approved": true }, "abstract": "Bats are reservoirs for a variety of viruses, many of which have significance to public health, but can maintain most of these viruses without clinical disease. Evidence suggests bats experience viral shedding events during periods of stress such as during reproduction or during times of nutritional stress. This suggests other ecological stressors, such as exposure to heavy metals including cadmium (Cd), might similarly impair immune function in bats and subsequently increase viral shedding. We previously reported increased Cd concentrations in Australian frugivorous bat species which may have impacts on the health of bats. Additionally, our preliminary studies suggest Cd exposure may alter B and T cell gene expression in a dose- dependent manner and increase viral shedding, but controlled animal laboratory studies are required to fully understand the mechanisms associated with this observation. This project aims to determine the impacts of Cd exposure on immune function and viral infection dynamics in bats. Our central hypothesis is that Cd exposure will impair adaptive immune responses in a dose-dependent manner subsequently increasing viral shedding in bats. This hypothesis will be addressed through three specific aims. In aim 1, we will determine the impact of Cd exposure on immune function and BANAL-52-CoV (B52-CoV) shedding in Jamaican fruit bats. To test this hypothesis, we will expose Jamaican fruit bats to different Cd concentrations and determine impacts on T cell responses through qPCR and activation-induced marker (AIM) testing. We will then expose groups of Jamaican fruit bats to Cd and challenge them with B52-CoV to determine the effects of Cd on viral infection and shedding. In aim 2, groups of Jamaican fruit bats exposed to Cd will be vaccinated with the SARS-CoV-2 mRNA vaccine and infected with B52-CoV to determine how Cd alters T cell responses of immunized Jamaican fruit bats. In aim 3, we will determine if free-ranging Artibeus spp. in areas with increased soil Cd concentrations have an increased odds of historical or active viral infections or decreased T cell responses compared to bats in areas with lower Cd concentrations. Completion of this project will have high significance because it will determine how Cd alters immune function and viral dynamics in bats. These results may also inform public health policies to mitigate potential impacts of Cd exposure on wildlife, and other animals that could reduce the risk of pathogen spillover. This project and career development award aligns well with my current skills and career goals to become an independent investigator. It will help me expand my knowledge in the fields of immunology, metal toxicity, in vivo animal research, and to develop key administrative and writing skills required to become an independent researcher.", "keywords": [ "Address", "Affect", "Animal Experimentation", "Animals", "Area", "B-Lymphocytes", "Birth", "Cadmium", "Cell Physiology", "Cells", "Chiroptera", "Clinical", "Coronavirus", "Cytokine Gene", "Data", "Diet Modification", "Disease", "Dose", "Euthanasia", "Event", "Exposure to", "Fruit", "Gene Expression", "Goals", "Health", "Health Policy", "Heavy Metals", "Hendra Virus", "Human", "Immune", "Immune response", "Immune system", "Immunize", "Immunology", "Immunosuppressive Agents", "Impairment", "In Vitro", "Infection", "Jamaican", "K-Series Research Career Programs", "Knowledge", "Laboratory Animals", "Laboratory Study", "Lead", "Lyssavirus", "Marburgvirus", "Metal exposure", "Modeling", "Nipah Virus", "Nutritional", "Oral", "Physiologic pulse", "Population", "Public Health", "Rabies", "Reporting", "Reproduction", "Research Personnel", "Risk", "Risk Reduction", "SARS-CoV-2 mRNA vaccine", "Sampling", "Sarbecovirus", "Seroprevalences", "Serum", "Severe Acute Respiratory Syndrome", "Soil", "Splenocyte", "Stress", "Swab", "T cell response", "T-Lymphocyte", "Testing", "Tissue Banks", "Vaccinated", "Viral", "Virus", "Virus Diseases", "Virus Replication", "Virus Shedding", "Wing", "Writing", "adaptive immune response", "bat-borne", "career", "cytokine", "experience", "immune function", "in vivo", "influenzavirus", "kidney cell", "metal poisoning", "pathogen", "pathogen spillover", "prevent outbreaks", "prevent pandemics", "prospective", "response", "skills", "stressor", "vaccine efficacy" ], "approved": true } }, { "type": "Grant", "id": "16111", "attributes": { "award_id": "1R03TR005950-01", "title": "Bridging the Gap: Advancing Rural Health Research with the Wear-IT Research Platform and Edge-of-Cloud Computing", "funder": { "id": 4, "ror": "https://ror.org/01cwqze88", "name": "National Institutes of Health", "approved": true }, "funder_divisions": [ "National Center for Advancing Translational Sciences (NCATS)" ], "program_reference_codes": [], "program_officials": [ { "id": 44222, "first_name": "IRINA N", "last_name": "KRASNOVA", "orcid": "", "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2026-06-01", "end_date": "2028-05-31", "award_amount": 157300, "principal_investigator": { "id": 44588, "first_name": "Alaina Lundberg", "last_name": "Pearce", "orcid": "", "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 3511, "ror": "", "name": "PENNSYLVANIA STATE UNIVERSITY, THE", "address": "", "city": "", "state": "PA", "zip": "", "country": "United States", "approved": true }, "abstract": "Modified Project Summary/Abstract Section Although health disparities between rural and urban communities have been known for decades2–5, there is evidence individuals from rural areas are less likely to participate in translational research and clinical trials6. Rural communities face higher mortality than urban communities for the six leading causes of death in the US–heart disease, cancer, unintentional injuries, COVID-19, stroke, and chronic lower respiratory disease.20–23 This has been referred to as the ‘rural mortality penalty’ as rural areas show slower declines in mortality compared to urban areas.14,24 Barriers to research participation among rural populations include participation costs (e.g., travel time, monetary), limited Internet connectivity, and socio-cultural barriers (e.g., perceived lack of anonymity)6. NCATS’s More Treatments for All People More Quickly initiative outlines the need to develop effective treatments that can reach underserved populations, which highlights the need for a research tool that can collect data and administer interventions in the privacy of participants homes regardless of Internet connectivity. Given the ubiquity of smartphones (~90% ownership in rural areas), the planned research will test the Wear-IT app,7 a smartphone research platform that can reduce barriers for rural populations by 1) ensuring a fast and responsive experience even when Internet is limited; and 2) delivering automatic, personalized interventions. Wear-IT is unique in the mHealth space due to its on-device processing model which allows for real-time responsiveness when Internet is unavailable7 with data uploaded to the cloud for more computationally intense processing when a participant passively encounters better connectivity (e.g., travel to town, WiFi). Wear-IT can flexibly assess complex clinical targets through multi-modal ecological momentary assessments (EMA) including video, photo, geolocation, and survey responses. On-device processing of multi-modal EMA or sensor data (e.g., step count) also enables delivery of just-in-time adaptative interventions (JITAI)7, which aim to provide personalized information or support at the exact moment it is needed.8 The proposed study will demonstrate the translational and clinical utility of Wear-IT by assessing diet-related health behaviors in 100 rural families with children from the Appalachian region. Using Federal Communications Commission fixed and mobile Broadband coverage data, targeted recruitment will ensure half the families (n = 50) have at-home Broadband and half (n = 50) have poor/no at-home Internet. Parents will use the Wear-IT app for three weeks. During the 3-week period, parents will receive EMA prompts and JITAI messages related to the three diet-related health behaviors: 1) home food environment; 2) home meals; and 3) food shopping (Figure 1). Assessing the acceptability and feasibility of Wear-It in rural communities will help to increase rural representation in health research and clinical trials, which is the first step in addressing persistent health disparities in rural communities.", "keywords": [ "Accidental Injury", "Address", "Appalachian Region", "Behavioral", "COVID-19", "Cardiovascular Diseases", "Cause of Death", "Cellular Phone", "Child", "Chronic", "Classification", "Clinical", "Clinical Trials", "Cloud Computing", "Communication", "Communities", "Complex", "County", "Data", "Data Collection", "Devices", "Diabetes Mellitus", "Diet", "Ecological momentary assessment", "Ensure", "Face", "Family", "Food", "Health", "Health behavior", "Heart Diseases", "Home", "Internet", "Intervention", "Just-in-Time Adaptive Intervention", "Location", "Malignant Neoplasms", "Modeling", "National Center for Advancing Translational Sciences", "Obesity", "Outcome", "Ownership", "Parents", "Participant", "Perception", "Persons", "Pilot Projects", "Preparation", "Privacy", "Public Health", "Research", "Respiratory Disease", "Risk", "Rural", "Rural Community", "Rural Health", "Rural Population", "Stroke", "Surveys", "Telephone", "Testing", "Time", "Translational Research", "Travel", "Underserved Population", "United States", "Urban Community", "acceptability and feasibility", "cost", "effective therapy", "experience", "feeding", "flexibility", "food environment", "health disparity", "improved", "mHealth", "mortality", "multimodality", "personalized intervention", "psychologic", "public health relevance", "recruit", "response", "rural area", "rural environment", "rural families", "rural health disparities", "sensor", "sensor data", "smartphone based device", "social culture", "tool", "translational pipeline", "urban area", "wireless fidelity" ], "approved": true } }, { "type": "Grant", "id": "16109", "attributes": { "award_id": "1R03HL183281-01", "title": "Lung megakaryocytes and lung megakaryocyte-derived platelets contribute to lung injury", "funder": { "id": 4, "ror": "https://ror.org/01cwqze88", "name": "National Institutes of Health", "approved": true }, "funder_divisions": [ "National Heart Lung and Blood Institute (NHLBI)" ], "program_reference_codes": [], "program_officials": [ { "id": 32818, "first_name": "MARISOL", "last_name": "ESPINOZA-PINTUCCI", "orcid": "", "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2026-06-01", "end_date": "2028-05-31", "award_amount": 231000, "principal_investigator": { "id": 44587, "first_name": "Elizabeth Anne", "last_name": "Middleton", "orcid": "", "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 3510, "ror": "", "name": "UTAH STATE HIGHER EDUCATION SYSTEM--UNIVERSITY OF UTAH", "address": "", "city": "", "state": "UT", "zip": "", "country": "United States", "approved": true }, "abstract": "Platelets are key cells involved in coagulation and have been recognized as effector immune cells with significant contributions to infectious and inflammatory syndromes. While capable to de novo protein synthesis, platelets are anucleate and must have mRNA invested into them by a parent megakaryocyte prior to being released into circulation. We have previously shown that bone megakaryocytes alter the transcriptome of circulating platelets in a mouse model of sepsis. My NHLBI K08 focused on the role of FcγRIIA, a low affinity IgG immune receptor, impacted platelets function in sepsis. We have found that FCGR2A transcript was one of the most differentially expressed transcripts in platelets from patients with sepsis. I aimed to interrogate how FcγRIIA upregulation impacted platelet function and increased sepsis-induced mortality. After my K award was initiated the COVID-19 pandemic shuttered all research with the exception of those studies that were directly related to COVID-19. I quickly pivoted my research towards investigating the impact of acute COVID-19 infection on the platelet transcriptome and alterations in platelet function. During the COVID-19 pandemic we found that the platelet transcriptome was significantly altered in those patients with acute hypoxemic respiratory failure. Furthermore, we found that platelets and neutrophil extracellular traps (NETs) were found in small lung vessel of patients deceased from COVID-19. We found plasma markers of NETs were significantly correlated with severity of respiratory failure and mortality. Suggesting that immunothrombosis is a significant contributor to hypoxemic respiratory failure. Sepsis is the most common cause of acute hypoxemic respiratory failure, termed acute respiratory distress syndrome (ARDS). ARDS is a common cause of ICU admission that continues to carry significant morbidity and mortality. Moreover, others have shown that megakaryocytes are found in both the lung and the spleen and produce platelets that express markers suggesting an immune phenotype. We hypothesized that the population of megakaryocytes found in the lung will contribute to lung injury. We have found that lung megakaryocytes are present and produce platelets in a murine model of LPS- induced acute lung injury. We aim to study how both the lung megakaryocyte and lung megakaryocyte-derived platelet increase both early and late-stage lung injury.", "keywords": [ "Acute", "Acute Lung Injury", "Acute Respiratory Distress Syndrome", "Address", "Admission activity", "Adoptive Transfer", "Affinity", "Alveolus", "Attenuated", "Blood Platelets", "Blood Vessels", "Bone Marrow", "COVID-19", "COVID-19 pandemic", "Cells", "Cessation of life", "Circulation", "Clinical", "Clinical Management", "Coagulation Process", "Data", "Development", "Diagnosis", "Diffuse", "Disease", "Disease Outcome", "Endothelial Cells", "Endothelium", "Etiology", "Exhibits", "Extramedullary", "Flow Cytometry", "Functional disorder", "Gases", "Gene Expression Profile", "Genetic Transcription", "Homeostasis", "Hospitalization", "Human", "Hypoxemic Respiratory Failure", "Immune", "Immune response", "Immunity", "Immunoglobulin G", "Immunohistochemistry", "Immunologic Receptors", "Immunomodulators", "Impairment", "Inflammation", "Inflammatory", "Inflammatory Response", "Injury", "Invaded", "Investments", "K-Series Research Career Programs", "Label", "Leukocytes", "Lipopolysaccharides", "Liquid substance", "Lung", "Lung immune response", "Mediating", "Megakaryocytes", "Messenger RNA", "Modeling", "Morbidity", "Mus", "National Heart Lung and Blood Institute", "Natural Immunity", "Neutrophil Infiltration", "Organ failure", "Outcome", "Pancreatitis", "Parents", "Pathway interactions", "Patients", "Phenotype", "Plasma", "Population", "Production", "Protein Biosynthesis", "Pulmonary Inflammation", "Research", "Resolution", "Respiratory Failure", "Role", "SARS-CoV-2 infection", "Sepsis", "Severities", "Site", "Spleen", "Syndrome", "Techniques", "Testing", "Therapeutic Intervention", "Thrombosis", "Transcript", "Trauma", "Upregulation", "Vascular Endothelium", "Vascular Permeabilities", "Work", "acute COVID-19", "acute hypoxemic respiratory failure", "alveolar epithelium", "bone", "cellular targeting", "differential expression", "experimental study", "extracellular", "immune function", "immunothrombosis", "improved", "inflammatory modulation", "inhibitor", "insight", "lung injury", "molecular targeted therapies", "mortality", "mortality risk", "mouse model", "neutrophil", "novel", "platelet function", "pulmonary", "recruit", "response", "sepsis induced ARDS", "sepsis mortality", "septic patients", "transcriptome", "transcriptome sequencing", "vascular injury" ], "approved": true } }, { "type": "Grant", "id": "16108", "attributes": { "award_id": "1F31AI200121-01", "title": "Exploring the interplay of viral genomic RNA structure and protein biochemistry in coronavirus replication", "funder": { "id": 4, "ror": "https://ror.org/01cwqze88", "name": "National Institutes of Health", "approved": true }, "funder_divisions": [ "National Institute of Allergy and Infectious Diseases (NIAID)" ], "program_reference_codes": [], "program_officials": [ { "id": 32891, "first_name": "MARY KATHERINE BRADFORD", "last_name": "PLIMACK", "orcid": "", "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2026-06-01", "end_date": "2029-05-31", "award_amount": 37472, "principal_investigator": { "id": 44586, "first_name": "Audrey", "last_name": "MacDonald", "orcid": "", "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 3509, "ror": "", "name": "UNIVERSITY OF WISCONSIN-MADISON", "address": "", "city": "", "state": "WI", "zip": "", "country": "United States", "approved": true }, "abstract": "Coronaviruses are important human and animal pathogens. In addition to common colds, coronaviruses have been responsible for several major outbreaks, including the SARS-CoV epidemic in 2002, the MERS-CoV epidemic in 2012, and the SARS-CoV-2 pandemic in 2020. Although accelerated research on coronavirus pathogenicity and biology during the 2020 pandemic has led to the design of several successful vaccines and antivirals, coronaviruses are constantly evolving. Continued research is therefore imperative for the identification of additional therapeutic targets. Coronaviruses contain positive-sense, single-stranded RNA genomes. Once a viral particle has infected a cell, the genome must be effectively copied and packaged prior to viral egress. Key molecular processes of the viral life cycle, including genome replication and genome encapsidation during viral egress, necessarily rely upon interactions between genomic RNA and viral proteins. However, the specific RNA- protein interactions driving these processes are poorly understood on a biochemical and structural level. Using a combined approach of virology, biochemistry, and structural biology, the current proposal seeks to understand the relevance of viral RNA-protein biomolecular interactions in the coronavirus cellular infection cycle. Replication of the coronavirus RNA genome is mediated by the viral polymerase and its essential cofactors. Previous work in the field has suggested that there are RNA structures within the coronavirus genomic termini that are critical for genome replication. However, the precise genomic RNA structures that are required for effective initiation of coronavirus genome replication remain unknown. Specific Aim 1 of this proposal seeks to elucidate which genomic RNA structures interact with the polymerase complex to initiate genome replication. Another common antiviral target is viral egress. For coronaviruses, viral egress occurs following encapsidation of the genome with a viral structural protein termed Nucleocapsid (N). In addition to it necessary structural function in viral egress, N has been proposed to play manifold roles throughout the viral life cycle. A major limitation of these studies is that the structure of full-length coronavirus N has yet to be solved. Additionally, how N is organized on genomic RNA within a virion remains unknown. Specific Aim 2 of this proposal seeks to examine the structure of N-bound genomic RNA in the context of the packaged genome. The research described in this proposal will take place under the mentorship of Dr. Robert Kirchdoerfer (fellowship sponsor), who studies coronavirus protein biochemistry and structure, and Dr. Samuel Butcher (co- sponsor), who studies the RNA-mediated regulation of gene expression. Supported by facilities including the Cryo-Electron Microscopy Research Center and the Biophysics Instrument Facility, the current proposal aims to explore critical coronavirus RNA-protein interactions on a structural and biochemical level. This work is anticipated to illuminate key viral biomolecular interactions that could serve as targets for antivirals.", "keywords": [ "2019-nCoV", "Acceleration", "Affinity", "Animals", "Anti-viral Agents", "Binding", "Biochemical", "Biochemistry", "Biology", "Biophysics", "C-terminal", "COVID-19 pandemic", "Cells", "Common Cold", "Complex", "Coronavirus", "Coronavirus nucleocapsid protein", "Cryoelectron Microscopy", "DNA-Directed RNA Polymerase", "Data", "Disease Outbreaks", "Elements", "Fellowship", "Filament", "Future", "Gene Expression Regulation", "Genome", "Genomics", "Health", "Human", "In Vitro", "Infection", "Knowledge", "Length", "Life Cycle Stages", "Mammalian Cell", "Mediating", "Mentorship", "Methodology", "Middle East Respiratory Syndrome Coronavirus", "Molecular", "Mutagenesis", "Nucleocapsid", "Pathogenicity", "Play", "Polymerase", "Preparation", "Process", "Protein Biochemistry", "Proteins", "RNA", "RNA Binding", "RNA Folding", "RNA Sequences", "RNA Viruses", "RNA chemical synthesis", "RNA replication", "RNA-Protein Interaction", "Recombinants", "Regulation", "Research", "Research Training", "Role", "SARS coronavirus", "Site", "Structure", "Vaccines", "Viral", "Viral Genome", "Viral Packaging", "Viral Proteins", "Viral Structural Proteins", "Virion", "Work", "cofactor", "coronavirus outbreak", "design", "dimer", "genomic RNA", "high-resolution", "human pathogen", "insight", "instrument", "melting", "mutant", "neglect", "novel", "pandemic disease", "particle", "pathogen", "protein protein interaction", "protein structure", "reconstruction", "structural biology", "therapeutic target", "training project", "viral RNA", "viral genomics", "virology" ], "approved": true } }, { "type": "Grant", "id": "16107", "attributes": { "award_id": "1R21AI193250-01A1", "title": "Discovery of Cell Intrinsic Immunologic Adjuvants for Improving Self Amplifying RNA Vaccines Against Infectious Diseases", "funder": { "id": 4, "ror": "https://ror.org/01cwqze88", "name": "National Institutes of Health", "approved": true }, "funder_divisions": [ "National Institute of Allergy and Infectious Diseases (NIAID)" ], "program_reference_codes": [], "program_officials": [ { "id": 44528, "first_name": "AARON MICHAEL", "last_name": "JOFFE", "orcid": "", "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2026-06-12", "end_date": "2028-05-31", "award_amount": 251938, "principal_investigator": { "id": 44584, "first_name": "Priti", "last_name": "Kumar", "orcid": "", "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [ { "id": 44585, "first_name": "Oscar J", "last_name": "Murillo Gomez", "orcid": "", "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 3508, "ror": "", "name": "YALE UNIVERSITY", "address": "", "city": "", "state": "CT", "zip": "", "country": "United States", "approved": true }, "abstract": "The COVID-19 pandemic demonstrated both the potential and limitations of mRNA-based vaccines. While very effective, current mRNA-LNP vaccines face challenges including significant reactogenicity, high dose requirements, and limited durability of immune responses. To address these shortcomings, many in the field are utilizing the unique ability of nucleic acid vaccines to express soluble mediators of adaptive immunity such as chemokines and cytokines as molecularly encoded adjuvants. While effective in preclinical models, cytokines as molecular therapies have struggled with notable toxicity and off target effects in human clinical trials. Here we propose an innovative solution which aims to enhance next-generation nucleic acid vaccines by investigating novel cell-intrinsic immunomodulators as molecular adjuvants. Our preliminary data supports this hypothesis, and the feasibility of this approach showing that self-amplifying RNA (saRNA) mediated co- expression of vaccine antigens with cell-intrinsic immunomodulator, from a single RNA molecule, results in enhanced neutralizing antibody potency and durability, and protection against viral challenge. We hypothesize that this will avoid the toxicities associated with molecularly encoded adjuvants such as cytokines, while improving the vaccine induced adaptive immune response at a significantly lower dose. The two specific aims of this proposal focus on assessing co-expression model antigens of SARS-CoV-2 Spike and Influenza A virus H5N1 Hemagglutinin with key molecules involved in DC licensing and trafficking (CD40, CD80, CCR7, and T- bet.), either (Aim 1) delivered as a LNP-saRNAantigen/imod on a single molecule or (Aim 2) co-formulated as LNP-(mRNAantigen+mRNAimod). Antibody potency, breadth, and durability will be assessed and will safety and reactogenicity. We believe that this proposal has the potential to revolutionize vaccinology through our innovative approach of harnessing cell intrinsic immunomodulators targeting DC function and development as molecular vaccine adjuvants. Our strategy aims to enhance vaccine performance while reducing doses and reactogenicity, potentially addressing key limitations of current mRNA vaccines. Success will lead to more effective and better-tolerated vaccines for emerging infectious diseases, and pandemic preparedness. Results will advance our understanding of molecular adjuvants and may establish a new platform for next-generation nucleic acid vaccines with broader applications in infectious disease prevention.", "keywords": [ "Address", "Adjuvant", "Alpha Virus", "Antibodies", "Antibody Response", "Antibody titer measurement", "Antibody-mediated protection", "Antigens", "Biology", "CD40 Antigens", "CD80 gene", "COVID-19 pandemic", "Cell Line", "Cell Physiology", "Cells", "Cellular biology", "Clinical", "Clinical Trials", "Communicable Diseases", "Data", "Dendritic Cells", "Dendritic cell activation", "Development", "Disease", "Dose", "Emerging Communicable Diseases", "Ensure", "Face", "Formulation", "Future", "Generations", "Genes", "Goals", "Hemagglutinin", "Human", "Immune response", "Immunity", "Immunologic Adjuvants", "Immunology", "Immunomodulators", "Influenza A Virus H5N1 Subtype", "Influenza A virus", "Japan", "Licensing", "Longevity", "Mediating", "Mediator", "Messenger RNA", "Modeling", "Molecular", "Molecular Vaccines", "Mus", "Nucleic Acid Vaccines", "Nucleic Acids", "Pathway interactions", "Performance", "Positioning Attribute", "Pre-Clinical Model", "Productivity", "RNA", "RNA amplification", "RNA vaccine", "Replicon", "Research", "SARS-CoV-2 antigen", "SARS-CoV-2 spike protein", "Safety", "Self Assessment", "Site", "T-Lymphocyte", "TNFRSF5 gene", "Technology", "Testing", "Toxic effect", "Transcript", "Vaccination", "Vaccine Adjuvant", "Vaccine Antigen", "Vaccines", "Viral", "adaptive immune response", "adaptive immunity", "cell type", "chemokine", "cytokine", "disorder prevention", "dosage", "experimental study", "flexibility", "frontier", "future pandemic", "immunogenicity", "improved", "in vivo", "innovation", "lipid nanoparticle", "mRNA lipid nano particle vaccine", "neutralizing antibody", "next generation", "novel", "novel strategies", "novel vaccines", "pandemic preparedness", "pandemic virus", "pathogen", "preclinical study", "response", "side effect", "single molecule", "success", "trafficking", "vaccine immunogenicity", "vaccine response", "vaccine safety", "vaccinology", "virology" ], "approved": true } } ], "meta": { "pagination": { "page": 3, "pages": 1442, "count": 14420 } } }