Grant List
Represents Grant table in the DB
GET /v1/grants?page%5Bnumber%5D=3&sort=-start_date
{ "links": { "first": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1&sort=-start_date", "last": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1424&sort=-start_date", "next": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=4&sort=-start_date", "prev": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=2&sort=-start_date" }, "data": [ { "type": "Grant", "id": "15965", "attributes": { "award_id": "1P50DC022549-01A1", "title": "Sensory and molecular studies of human taste dysfunction", "funder": { "id": 4, "ror": "https://ror.org/01cwqze88", "name": "National Institutes of Health", "approved": true }, "funder_divisions": [ "National Institute on Deafness and Other Communication Disorders (NIDCD)" ], "program_reference_codes": [], "program_officials": [], "start_date": "2026-03-01", "end_date": "2031-02-28", "award_amount": 786679, "principal_investigator": { "id": 44417, "first_name": "PeiHua", "last_name": "Jiang", "orcid": "", "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 3419, "ror": "", "name": "MONELL CHEMICAL SENSES CENTER", "address": "", "city": "", "state": "PA", "zip": "", "country": "United States", "approved": true }, "abstract": "Project 3. Sensory and molecular studies of human taste dysfunction Taste dysfunction is a cardinal feature of COVID. Project 3 of this P50 Clinical Research Center (CRC) proposal focuses on (1) molecular description of taste tissue from people with sustained COVID-19-associated taste dysfunction, compared to people with no current taste problems (regardless of infection history) (Aim 3.1), and (2) mechanistic interrogation of COVID-associated taste dysfunction using taste organoids (Aim 3.2). We will test the hypothesis that people with COVID taste dysfunction have fewer taste receptor cells, reduced expression of taste-relevant genes, and immune cell infiltration due to sustained inflammation. In Aim 3.1, we will sample taste tissue from people with and without sustained COVID-19-associated taste dysfunction to measure taste receptor cell number and gene expression of inflammatory (e.g., cytokines and chemokines) and other molecules with single-cell RNA sequencing (scRNA-seq) methods. In Aim 3.2, we will use taste organoids derived from wild-type mice, humanized-ACE2 mice, and humans to examine SARS-CoV-2 tropism in taste tissue to determine if taste tissue homeostasis is altered by (a) SARS-CoV-2 infection or (b) inflammatory molecules identified in Aim 3.1 and/or known to be elevated in COVID. Project 3 of this CRC proposal is supported by Project 1 and the Chemosensory Clinical Services Core, which will perform and support remote and in-house sensory screening of all participants in this research program. The investigators here are experts in their fields, particularly in single-cell biology, genetics, and stem cell biology of taste tissue. We have engaged consultants who are inflammation, infection, and immunology experts. Several types of pilot data support this application, including scRNA-seq data from human fungiform tissue and taste organoid data after treatment with inflammatory molecules. Institutional support for this project is outlined in a Letter of Support from administrative officials, and the Monell Chemical Senses Center is well suited to complete this project because of its cross-disciplinary focus on chemosensory biology and its connection with an experienced coronavirus expert at the nearby University of Pennsylvania. This project is part of a larger program to understand and treat people with communication disorders of taste and smell dysfunction due to COVID. We anticipate our data will answer key unsolved questions regarding taste dysfunction and point to potential avenues of treatment for this debilitating condition.", "keywords": [ "2019-nCoV", "3-Dimensional", "ACE2", "Acute", "Address", "Affect", "Aftercare", "Age", "Ageusia", "Biological Models", "Biology", "COVID-19", "COVID-19 patient", "COVID-19 susceptibility", "Cell Count", "Cell Physiology", "Cell secretion", "Cells", "Cellular Structures", "Cellular biology", "Chemicals", "Clinical Research", "Clinical Services", "Communication impairment", "Coronavirus", "Data", "Desire for food", "Functional disorder", "Fungiform Papilla", "Gene Expression", "Gene Expression Profile", "Genes", "Genetic", "Health", "Homeostasis", "Human", "Immune", "Immunology", "Individual", "Infection", "Inflammation", "Inflammatory", "Institution", "Knock-in", "Knowledge", "Letters", "Long COVID", "Measures", "Methods", "Modeling", "Molecular", "Mucous body substance", "Mus", "Nose", "Organoids", "Participant", "Patients", "Pennsylvania", "Persons", "Phase", "Predisposition", "Psychophysics", "Race", "Receptor Cell", "Recording of previous events", "Research", "Research Personnel", "Resources", "SARS-CoV-2 infection", "SARS-CoV-2 variant", "Saliva", "Salivary", "Sampling", "Sensory", "Signal Transduction", "Smell Perception", "Symptoms", "System", "Taste Buds", "Taste Disorders", "Taste Perception", "Testing", "Tissues", "Tropism", "United States National Institutes of Health", "Universities", "Viral", "Virus", "Virus Diseases", "Wild Type Mouse", "acute COVID-19", "biobank", "cell regeneration", "cell type", "chemokine", "cytokine", "experience", "human RNA sequencing", "human data", "humanized mouse", "immune cell infiltrate", "immunocytochemistry", "inflammatory marker", "novel therapeutic intervention", "programs", "response", "screening", "sex", "single-cell RNA sequencing", "stem cell biology", "stem cells", "synergism", "tongue papilla", "tool" ], "approved": true } }, { "type": "Grant", "id": "15961", "attributes": { "award_id": "1R35GM161641-01", "title": "Methods for quantifying selection and predicting evolutionary dynamics", "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": 44409, "first_name": "RONALD", "last_name": "ADKINS", "orcid": "", "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2026-03-01", "end_date": "2030-12-31", "award_amount": 417620, "principal_investigator": { "id": 44410, "first_name": "John P", "last_name": "Barton", "orcid": "", "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 3416, "ror": "", "name": "UNIVERSITY OF PITTSBURGH AT PITTSBURGH", "address": "", "city": "", "state": "PA", "zip": "", "country": "United States", "approved": true }, "abstract": "Natural selection is central to many challenges in biology and medicine, from the emergence of drug resistance in pathogens to cancer evolution. Understanding selection can also aid in protein engineering and help identify clinically relevant mutations in human disease genes. Temporal genetic data — sequences and phenotypes sampled over time — can be an especially powerful tool for understanding selection because it allows us to observe evolutionary dynamics directly. But while temporal data from sources like pathogen surveillance, ancient DNA, and experimental evolution have grown tremendously in recent years, statistical analyses of these data remain challenging. My lab will continue to pioneer the development of new computational methods to learn from temporal genetic data, revealing variants and phenotypes under selection and harnessing this information for predictive models of evolution. Over the past five years, we have developed several approaches to quantify selection from temporal data. Thanks to the use of mathematical methods from statistical physics, our methods are fast and accurate despite the inclusion of complex features such as linkage disequilibrium, epistasis, and time-varying selection. We demonstrated the power of these approaches through studies of HIV-1 immune escape and SARS-CoV-2 adaptation during the COVID-19 pandemic, where our analysis identified key mutations affecting viral transmission even before their importance was validated experimentally. Building on this foundation, we will pursue three synergistic research directions: First, we will develop new methods to jointly analyze selection on both individual mutations and phenotypic traits, fusing concepts from population genetics, quantitative genetics, and machine learning. Second, we will apply these methods to study rapid evolution in viral pathogens. Phenotypic models will help us to understand how immune pressure drives antigenic change in respiratory viruses and to compare evolutionary constraints on pathogens across host species. As an ambitious new direction, we will leverage these insights to develop predictive models of pathogen evolution, with influenza as a first target. Our research will systematically identify the features with the greatest power to predict evolution and characterize how and why predictive power may decline over time. Finally, we will extend our approaches to improve the interpretation of high-throughput mutagenesis experiments that measure the effects of thousands of mutations simultaneously. The proposed research will transform our understanding of how selection guides evolution across biological scales, from individual mutations to complex phenotypes, with applications ranging from predicting viral evolution to protein engineering. These advances could ultimately improve our ability to anticipate and control evolutionary processes across a wide range of biological contexts.", "keywords": [ "2019-nCoV", "Affect", "Biological", "Biology", "COVID-19 pandemic", "Complex", "Computing Methodologies", "DNA", "Data", "Data Sources", "Development", "Drug resistance", "Evolution", "Foundations", "Genes", "Genetic", "Genetic Epistasis", "Genotype", "HIV-1", "Immune", "Immunity", "Individual", "Influenza", "Learning", "Linkage Disequilibrium", "Machine Learning", "Malignant Neoplasms", "Maps", "Measures", "Medicine", "Methods", "Modeling", "Mutagenesis", "Mutation", "Natural Selections", "Phenotype", "Physics", "Play", "Population Genetics", "Process", "Protein Engineering", "Public Health", "Quantitative Genetics", "Research", "Resistance development", "Role", "Sampling", "Shapes", "Statistical Data Interpretation", "Statistical Methods", "Time", "Variant", "Viral", "clinically relevant", "experimental study", "genetic variant", "human disease", "improved", "insight", "mathematical methods", "pathogen", "pathogenic virus", "predictive modeling", "pressure", "respiratory virus", "tool", "trait", "viral transmission" ], "approved": true } }, { "type": "Grant", "id": "15964", "attributes": { "award_id": "1F31HL176102-01A1", "title": "The Pathogenic Role of IL-33 in the SARS-CoV-2-Infected Lung", "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": "2026-03-01", "end_date": "2028-02-28", "award_amount": 38922, "principal_investigator": { "id": 44416, "first_name": "Claire", "last_name": "Fleming", "orcid": "", "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 3391, "ror": "", "name": "UNIVERSITY OF VIRGINIA", "address": "", "city": "", "state": "VA", "zip": "", "country": "United States", "approved": true }, "abstract": "Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causative agent of coronavirus disease 2019 (COVID-19), remains a significant threat to global public health. Pulmonary immunopathological damage plays a role in driving pneumonia, acute respiratory distress syndrome (ARDS), and multiorgan failure in severe COVID-19. Therefore, dissecting the pulmonary immune response to SARS-CoV-2 infection is critical to under- stand disease pathogenesis and develop more effective therapeutics. Targeting of type 2 immune pathways is a potential avenue of therapeutic intervention in severe COVID-19. In particular, research has demonstrated a link between the type 2 cytokine IL-13 and COVID-19 severity. This proposal builds on the preliminary experi- ments demonstrating that blockade of the alarmin cytokine IL-33 confers protection in a mouse model of COVID- 19. IL-33 is a potent inducer of type 2 immunity in the lung, as its receptor ST2 is constitutively expressed by type 2 cells including type 2 innate lymphoid cells (ILC2s). It is hypothesized that IL-33/ST2 signaling induces a pathogenic inflammatory environment in the acutely infected lung by activating IL-13-secreting ILC2s and that IL-33-mediated inflammation enhances disruption of the respiratory epithelial barrier. To test this hypothesis, the impact of IL-33/ST2 signaling axis blockade on the inflammatory environment in the acutely infected lung will be described through assessment of the immune cell populations and the transcriptional profile of the respiratory epithelium (Aim 1). Considering that SARS-CoV-2-mediated ARDS is characterized by pulmonary inflammation and respiratory epithelial barrier disruption, these descriptive studies will elucidate pathways through which IL- 33 may drive pathogenesis. Further, the mechanism through which the IL-33/ST2 signaling axis drives patho- genesis will be directly tested (Aim 2). Mouse models of ILC2 depletion, selective ST2 knockout in ILC2s, and adoptive ILC2 transfer will be used to test whether IL-33-activated ILC2s drive pulmonary pathogenesis in the context of acute SARS-CoV-2 infection. Collectively, the proposed experiments will determine the mechanism underlying IL-33-mediated pathogenesis. This research is significant because it will further our understanding of how modulation of type 2 immunity can serve as a novel and promising therapeutic strategy in the treatment of respiratory viral infection-induced pulmonary pathology.", "keywords": [ "2019-nCoV", "Acute", "Acute Respiratory Distress Syndrome", "Adoptive Transfer", "Adrenal Cortex Hormones", "Affect", "Alveolar Macrophages", "Anti-Inflammatory Agents", "Automobile Driving", "COVID-19", "COVID-19 severity", "Cell Death", "Cells", "Cessation of life", "Clinical Trials", "Diphtheria Toxin", "Disease", "Epithelial Cells", "Epithelium", "Fibroblasts", "Flow Cytometry", "Gene Expression Profile", "Genetic Transcription", "Goals", "Helminths", "Hospital Mortality", "Human", "IL-6 inhibitor", "Immune", "Immune response", "Immunity", "Immunomodulators", "Individual", "Infection", "Inflammation", "Inflammatory", "Interleukin-13", "Interruption", "Intervention Studies", "Knock-out", "Knowledge", "Link", "LoxP-flanked allele", "Lung", "Lung immune response", "Lymphoid Cell", "Mediating", "Modeling", "Multiple Organ Failure", "Mus", "Neurons", "Pathogenesis", "Pathogenicity", "Pathway interactions", "Patients", "Plasma", "Play", "Pneumonia", "Population", "Probability", "Production", "Public Health", "Pulmonary Inflammation", "Pulmonary Pathology", "Pulmonary function tests", "Regenerative pathway", "Research", "Role", "SARS-CoV-2 infection", "Severity of illness", "Signal Induction", "Signal Transduction", "Testing", "Therapeutic", "Therapeutic Intervention", "Viral Respiratory Tract Infection", "Wild Type Mouse", "acute COVID-19", "airway epithelium", "cell type", "cytokine", "experimental study", "follow-up", "improved", "inflammatory milieu", "information gathering", "inhibitor", "mast cell", "mouse model", "new therapeutic target", "novel", "pharmacologic", "post SARS-CoV-2 infection", "pulmonary", "receptor", "reconstitution", "secondary endpoint", "severe COVID-19", "spatial transcriptomics", "success", "systemic inflammatory response", "therapeutic development", "therapeutically effective", "ventilation" ], "approved": true } }, { "type": "Grant", "id": "15966", "attributes": { "award_id": "1R35GM161764-01", "title": "Elucidating kinetics and thermodynamics of RNA-ligand interactions using single molecule approaches", "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": 22244, "first_name": "MICHAEL", "last_name": "SAKALIAN", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2026-03-01", "end_date": "2031-02-28", "award_amount": 425100, "principal_investigator": { "id": 44418, "first_name": "Maria", "last_name": "Kamenetska", "orcid": "", "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 3420, "ror": "", "name": "BOSTON UNIVERSITY (CHARLES RIVER CAMPUS)", "address": "", "city": "", "state": "MA", "zip": "", "country": "United States", "approved": true }, "abstract": "Project summary/abstract: Current understanding of and predictive models for folded RNA structures lag far behind our advances in protein folding. Yet recent work reveals the central role of RNA folds in diseases like viral infection, cancer and neurodegeneration. The potential to develop drugs against RNA targets causing illness is impeded by critical knowledge gaps in our understanding of the sequence-structure-function relationships of RNA polymers. Beyond structure, the role of fast fluctuations between the various conformations of RNA is being recognized as playing a bigger role in RNA than in amino acid function. Quantifying both shape and kinetics of RNA requires single molecule tools that can achieve millisecond time and nanometer distance resolution. Through this Maximizing Investigator Research Award (MIRA), the Kamenetska Lab will be supported in their continued efforts to develop such single molecule biophysical tools combined with machine learning approaches in order to expand our knowledge and understanding of the structural and kinetic properties of RNA. These optical tweezer force spectroscopy tools are uniquely suited to quantifying the full energy landscape profile of RNA structures that governs the dynamics of these molecules. Here I propose to use these methods, based on published results from my laboratory, to fill three critical knowledge gaps. First, I will investigate the effects on RNA mechanics and dynamics of non-specific interactions between nucleic acids, including RNA, with small molecules and ions present in mammalian cells. Second, I will build on our work on synthetic and modified RNA structures to systematically quantify the relationship between structure and folding energetics, generating data for training predictive models of RNA folding not currently available. Finally, I will build analytic methods and pursue structural and kinetic characterization of complex RNA tertiary structures with multiple conformations. My targets include SARS-CoV-2 viral genomic RNA implicated in viral gene regulation, telomeric and 5’ untranslated regions (5’ UTR) structures associated with cancer phenotypes.", "keywords": [ "2019-nCoV", "5' Untranslated Regions", "Amino Acids", "Award", "Biological Process", "Biophysics", "Complex", "Disease", "Gene Expression Regulation", "Ions", "Kinetics", "Knowledge", "Laboratories", "Ligands", "Machine Learning", "Malignant Neoplasms", "Mammalian Cell", "Measurement", "Mechanics", "Methods", "Molecular Conformation", "Nerve Degeneration", "Nucleic Acids", "Pharmaceutical Preparations", "Phenotype", "Play", "Polymers", "Process", "Property", "Publishing", "RNA", "RNA Conformation", "RNA Folding", "Research", "Research Personnel", "Resolution", "Role", "Shapes", "Spectrum Analysis", "Structure", "Structure-Activity Relationship", "Therapeutic Intervention", "Thermodynamics", "Time", "Viral Genes", "Virus Diseases", "Virus Replication", "Work", "analytical method", "biophysical tools", "drug development", "genomic RNA", "millisecond", "nanometer", "optic tweezer", "optical traps", "predictive modeling", "protein folding", "single molecule", "small molecule", "telomere", "therapy development", "tool", "training data", "viral genomics" ], "approved": true } }, { "type": "Grant", "id": "15968", "attributes": { "award_id": "1R01AG092489-01A1", "title": "Effect of paid family care (vs aides) on Medicaid waiver participants w/ IDD across the lifespan", "funder": { "id": 4, "ror": "https://ror.org/01cwqze88", "name": "National Institutes of Health", "approved": true }, "funder_divisions": [ "National Institute on Aging (NIA)" ], "program_reference_codes": [], "program_officials": [ { "id": 44276, "first_name": "PRISCILLA JOY", "last_name": "NOVAK", "orcid": "", "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2026-03-01", "end_date": "2031-02-28", "award_amount": 678231, "principal_investigator": { "id": 44421, "first_name": "Courtney Harold", "last_name": "Van Houtven", "orcid": "", "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 2652, "ror": "", "name": "DUKE UNIVERSITY", "address": "", "city": "", "state": "NC", "zip": "", "country": "United States", "approved": true }, "abstract": "Home and community-based services are the most common support for people with Intellectual and/or Developmental Disability (I/DD) who otherwise would require facility-level care. Many state Medicaid waivers allow participants to choose to hire family as direct support personnel to meet their care needs at home and in the community rather than a professional aide. Ability to hire family increased during the Covid-19 pandemic through state policy changes, and yet we do not know how involving paid family affects the ability for people with I/DD to remain at home. The objectives of this study are to use North Carolina (NC) as a case to elucidate the experiences of waiver participants with I/DD (Innovations Waiver participants). With no national data fields systematically identifying self-direction status or who is paid for personal care, foundational state-level work is required to understand people with I/DD’s experiences with self-direction. First, we will describe prevalence and dynamics of paid family care using Medicaid administrative data over the past 9+ years, including patterns by self-direction or not, by individual and geographic factors and by era (pre-post Covid-19 (Aim 1). Second, qualitative approaches will center the voices of people with I/DD and their families to obtain their perspectives on what is gained and what is lost from self-directed care including paid family care (Aim 2). Specifically, photo elicitation, case study, and focus group interviews will examine the lived experience of accessing and receiving care through the Innovations Waiver according to 1) individuals with I/DD, 2) their parent/partner/guardian/unpaid family caregiver, 3) their paid family caregivers, 4) their paid aide, and 5) Innovations Waiver experts. Third, we will estimate the comparative effectiveness of paid family care versus paid aide care only on person-centered outcomes (e.g., home time, preventive care) and on potential harms (potentially harmful medications, injurious falls, mistreatment) (Aim 3). We hypothesize that waiver participants with paid family care will have better person-centered outcomes and no increase in harms compared to those with paid aides alone. Effects of self- direction will also be explored. By using a convergent parallel mixed-methods process we will integrate results to paint a full picture of the comparative effectiveness of paid family care and self-direction from childhood to older adulthood, including identification of any harms. The results of this 5-year R01 study will be immediately applicable to state Medicaid office benefit design and inform strategies to optimize quality of care and life for people living with I/DD from childhood throughout the lifespan. Results from the North Carolina case will also position us to pursue a national study, given knowledge gained along with emerging efforts to identify “self- direction” in national CMS data sets. Examining paid family care and self-direction’s effects across the lifespan aligns with NIA’s strategic goal to improve the health, well-being, and independence of adults as they age.", "keywords": [ "Accident and Emergency department", "Acute", "Address", "Adult", "Affect", "Age", "Antipsychotic Agents", "COVID-19 pandemic", "Caring", "Case Study", "Characteristics", "Child", "Childhood", "Communities", "Control Groups", "Data", "Data Set", "Developmental Disabilities", "Disabilities experience", "Effectiveness", "Eligibility Determination", "Emergency Care", "Emergency department visit", "Ethnic Origin", "Exercise", "Family", "Family Caregiver", "Family member", "Focus Groups", "Geographic Factor", "Goals", "Group Interviews", "Health", "Health Care Facility", "Home", "Hospitalization", "Human Resources", "Incidence", "Individual", "Inpatients", "Intellectual functioning disability", "Intermediate Care Facilities", "Knowledge", "Life", "Lived experience", "Medicaid", "Methods", "North Carolina", "Outcome", "Paint", "Parents", "Participant", "Patient Preferences", "Pattern", "Perception", "Personal Satisfaction", "Persons", "Pharmaceutical Preparations", "Policies", "Policy Research", "Population", "Positioning Attribute", "Prevalence", "Preventive care", "Process", "Qualifying", "Qualitative Methods", "Quality of Care", "Quality of life", "Quasi-experiment", "Race", "Risk", "Self Care", "Self Direction", "Services", "Supported Employment", "Time", "Voice", "Work", "acute care", "beneficiary", "community based service", "comparative effectiveness", "cost", "design", "disabled", "experience", "fall injury", "flexibility", "health care quality", "improved", "innovation", "life span", "maltreatment", "older adult", "payment", "person centered", "post-COVID-19", "preference", "programs", "rurality", "treatment effect", "waiver" ], "approved": true } }, { "type": "Grant", "id": "15962", "attributes": { "award_id": "1R35GM162151-01", "title": "Correlated factor models for exploratory analysis of complex multimodal study designs", "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": 44411, "first_name": "PEGGY", "last_name": "WANG", "orcid": "", "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2026-03-01", "end_date": "2030-12-31", "award_amount": 406250, "principal_investigator": { "id": 44412, "first_name": "Brielin", "last_name": "Brown", "orcid": "", "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 3417, "ror": "", "name": "UNIVERSITY OF PENNSYLVANIA", "address": "", "city": "", "state": "PA", "zip": "", "country": "United States", "approved": true }, "abstract": "Most human diseases are complex, manifesting from an interplay between genes and environment over the lifespan that involve myriad biological processes. Genome-wide association studies have primarily implicated non-coding variation that is thought to lead to disease via disruption of complex, multi-level biological systems. Thus, improvements in our understanding of these fundamental processes underlying disease necessitates studying the relationship between multiple omics (multi-omic) modalities, both longitudinally and in conjunction with non-omic data. While recent years have seen an explosion of studies collecting multi-omic data in human populations, analysis of these data remains challenging both statistically and computationally. Here, I propose several new methods based on correlated latent factor models that will extend the capabilities of multi-omic inference methods to more complex study designs. I will develop model-based imputation methods that allow robust handling of missing data, enabling larger-scale studies of multi-omic biological contexts, and allowing researchers to design targeted multi-omic panels to extract the maximum amount of clinically-relevant information. I will develop multi-omic analysis methods that integrate across tissues and time points, enabling the study of dynamic molecular process and detection of systems-level impacts of intervention or disease onset. Finally, I will develop integration methods based on non-linear representation learning. This will enable detection of complex relationships between omics methods and integration with structured non-omics data such as doctor’s notes and radiographic images. To demonstrate the broad utility of the proposed methods, I will conduct collaborative analyses of varied cohorts. These include a population of individuals with subclinical atherosclerosis (MESA), a study anlyzing the relationship between microbiome features and immune health in the context of the COVID-19 pandemic (ImmunoMicrobiome), and a study of the impact of Alzheimer’s disease on neuroimaging and spinal uid biomarkers (ADNI). Completion of this research program will provide new insights into the fundamental biological processes underlying a host of common conditions, while bootstrapping the larger multi-omics research community by providing new tools that can handle complex study designs and integration tasks.", "keywords": [ "Alzheimer's Disease", "Atherosclerosis", "Biological", "Biological Markers", "Biological Process", "COVID-19 pandemic", "Communities", "Complex", "Computing Methodologies", "Data", "Data Analyses", "Detection", "Disease", "Environment", "Etiology", "Explosion", "Genes", "Human", "Image", "Individual", "Intervention", "Learning", "Machine Learning", "Methods", "Modality", "Modeling", "Multiomic Data", "Onset of illness", "Population", "Process", "Research", "Research Design", "Research Personnel", "Spinal", "Structure", "Time", "Tissues", "Untranslated RNA", "Variant", "biological systems", "clinically relevant", "cohort", "data imputation", "design", "detection platform", "genome wide association study", "human disease", "immune health", "improved", "insight", "life span", "microbiome", "molecular dynamics", "multimodality", "multiple omics", "neuroimaging", "novel", "programs", "radiological imaging", "statistics", "tool" ], "approved": true } }, { "type": "Grant", "id": "15971", "attributes": { "award_id": "1R35GM162359-01", "title": "The multifaceted pathways of astrovirus entry and egress", "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": 22244, "first_name": "MICHAEL", "last_name": "SAKALIAN", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2026-02-24", "end_date": "2030-12-31", "award_amount": 422677, "principal_investigator": { "id": 44425, "first_name": "Valerie", "last_name": "Cortez", "orcid": "", "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 3424, "ror": "", "name": "UNIVERSITY OF CALIFORNIA SANTA CRUZ", "address": "", "city": "", "state": "CA", "zip": "", "country": "United States", "approved": true }, "abstract": "Astroviruses are a major cause of pediatric diarrhea worldwide. Despite causing one of the most common early childhood infections, astroviruses are one of the least studied enteric RNA viruses. We previously discovered that the virus infects small intestinal goblet cells, specialized epithelial cells that secrete mucus. Few studies have investigated viral infection in goblet cells due to the lack of cell-specific models. Because the mechanisms by which viruses replicate inside of goblet cells are completely unknown, my lab is interested in addressing 1) how do astroviruses enter cells with highly dynamic apical membranes? and 2) what role does mucus secretion play in viral egress? We have established new in vitro models and tools to address these questions and have built a strong and collaborative investigative team with complementary expertise that will ensure the success of these projects. To evaluate receptor-mediated and fluid-phase endocytosis entry pathways into goblet cells, we will use a combination of CRISPR-Cas9 engineering, biochemical analysis, and high-resolution microscopy. We will use a similar suite of techniques as well as cryo-electron microscopy to define the egress pathway of astrovirus from goblet cells via mucus secretion. In addition to murine and human astroviruses, other respiratory and enteric viruses have also been shown to target goblet cells for infection. Thus, our work aims to initially provide foundational knowledge on the basic biology of astroviruses before shedding light on key host pathways in goblet cells that are co-opted by viruses from other families, including influenza and SARS-CoV2. Completion of these studies will provide the first major insights into the virus-host interactions at the apical membrane surface of intestinal goblet cells, which will pave the way for the future development of targeted drug treatments for the numerous viruses that target this unique cell population.", "keywords": [ "2019-nCoV", "Address", "Astrovirus", "Biochemical", "Biology", "CRISPR/Cas technology", "Cell model", "Cells", "Childhood", "Cryoelectron Microscopy", "Development", "Diarrhea", "Endocytosis", "Engineering", "Ensure", "Enteral", "Family", "Foundations", "Future", "Goblet Cells", "Human", "Infection", "Influenza", "Intestines", "Knowledge", "Light", "Liquid substance", "Mediating", "Microscopy", "Modeling", "Mucous body substance", "Mucus-Secreting Cell", "Mus", "Pathway interactions", "Phase", "Play", "Population", "Prevention strategy", "RNA Viruses", "Resolution", "Role", "Small Intestinal Goblet Cell", "Specialized Epithelial Cell", "Surface", "Techniques", "Viral", "Virus", "Virus Diseases", "Virus Replication", "Work", "apical membrane", "early childhood", "enteric virus infection", "in vitro Model", "insight", "interest", "novel", "receptor", "respiratory", "success", "targeted treatment", "tool", "treatment strategy", "virus host interaction" ], "approved": true } }, { "type": "Grant", "id": "15983", "attributes": { "award_id": "1R35GM162443-01", "title": "Molecular Mechanisms of Antimicrobial Resistance from Machine Learning Augmented Enhanced Sampling", "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": 44257, "first_name": "ANNE", "last_name": "GERSHENSON", "orcid": "", "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2026-02-24", "end_date": "2030-12-31", "award_amount": 401285, "principal_investigator": { "id": 44438, "first_name": "Dhiman", "last_name": "Ray", "orcid": "", "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 3433, "ror": "", "name": "UNIVERSITY OF OREGON", "address": "", "city": "", "state": "OR", "zip": "", "country": "United States", "approved": true }, "abstract": "ABSTRACT: Antimicrobial resistance threatens our ability to treat previously curable infectious diseases and may soon become a global public health crisis. The Ray group aims to understand and characterize the molecular mechanisms of antibiotic and antiviral resistance to identify potential avenues to target resistant pathogens. This R35 MIRA pro- gram involves two distinct research projects that utilize advanced machine learning (ML) and enhanced sampling algorithms for molecular dynamics (MD) simulations to gain mechanistic insights into antimicrobial resistance and facilitate the development of future therapeutic applications. In the first project, we will study the process of ligand binding to riboswitches, a class of regulatory RNA segments that are potential targets for next-generation antibi- otics. Our goal is to identify the role of conformational dynamics and distant nucleotide mutations in modulating the binding mechanism of the small molecule inhibitors (e.g., Ribocil) to RNA targets (e.g., Flavin-mononucleotide (FMN) riboswitch). We will design neural network (NN) and explainable artificial intelligence (XAI) based collec- tive variables from system agnostic descriptor space and perform enhanced sampling simulations to compute the free energy landscape of riboswitch conformational transition and ligand binding. This work will provide key mechanistic insights into RNA-small-molecule interactions and pave the way for designing more resilient antibi- otics. In the second project, we will study how resistant mutations in the viral antigens, e.g., SARS-CoV-2 spike protein, affect the binding mechanism of neutralizing antibodies. Previous research in this area primarily focused on the antigen-antibody interface but often overlooked the long-range allosteric effect of antigen mutations on the antibody binding process. We will perform NN and XAI-guided enhanced sampling simulations to elucidate the mechanistic details of antigen-antibody recognition. In addition, we will trace the allosteric communication path- ways using mutual-information-based protein graph connectivity networks constructed for various intermediate configurations sampled from the association pathway. This work will open new avenues for the rational design of broad-spectrum monoclonal antibodies through the judicious strengthening of distant regions of the antibody structure that are less susceptible to epitope mutations.", "keywords": [ "Affect", "Algorithms", "Antibiotics", "Antibodies", "Antigens", "Antimicrobial Resistance", "Area", "Bacterial RNA", "Binding", "Communicable Diseases", "Communication", "Computer Simulation", "Descriptor", "Development", "Distant", "Epitopes", "Flavin Mononucleotide", "Free Energy", "Future", "Goals", "Graph", "Ligand Binding", "Machine Learning", "Molecular", "Molecular Conformation", "Monoclonal Antibodies", "Mutation", "Nucleotides", "Pathway interactions", "Pharmaceutical Preparations", "Predisposition", "Process", "Proteins", "Public Health", "RNA", "Research", "Research Project Grants", "Resistance", "Resistance development", "Role", "SARS-CoV-2 spike protein", "Sampling", "Small RNA", "Structure", "System", "Therapeutic", "Viral Antigens", "Viral Drug Resistance", "Viral Proteins", "Work", "conformational conversion", "design", "drug candidate", "explainable artificial intelligence", "future antibiotics", "insight", "molecular dynamics", "neural network", "neutralizing antibody", "novel therapeutic intervention", "pathogen", "programs", "rational design", "resilience", "resistance mutation", "simulation", "small molecule", "small molecule inhibitor" ], "approved": true } }, { "type": "Grant", "id": "15978", "attributes": { "award_id": "1R01AI196176-01", "title": "Inhibiting Chikungunya Virus Protease using MTase-like Domain Interactions for Novel Antiviral Therapies.", "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": 32808, "first_name": "MINDY I", "last_name": "DAVIS", "orcid": "", "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2026-02-23", "end_date": "2031-01-31", "award_amount": 632994, "principal_investigator": { "id": 44433, "first_name": "Jeanne Ann", "last_name": "Hardy", "orcid": "", "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 3429, "ror": "", "name": "UNIVERSITY OF MASSACHUSETTS AMHERST", "address": "", "city": "", "state": "MA", "zip": "", "country": "United States", "approved": true }, "abstract": "Chikungunya (CHIKV) is an RNA alphavirus that infects 3 million people in 45 countries including the US annually. Acute infection is flu-like, but in 40% of infections, debilitating joint pain emerges that can last for years. Infection during pregnancy also results in severe encephalopathy in newborns or aborted fetuses. Viral proteases are effective antiviral drug targets and are the standard of care for viral diseases (e.g HIV, hepatitis C, SARS-CoV- 2). The activity of the nsP2 protease from CHIKV (CHIKVP) is vital for infection. Inhibition of CHIKVP blocks processing of the viral polyprotein, prevents viral replication, lowers viral titers and stops disease progression. Thus, CHIKVP is an excellent antiviral drug target. To date, no effective antivirals of CHIKVP have been approved for acute or chronic infection. Our ultimate goal is to use insights into CHIKVP structure and dynamics to develop an inhibitor to oppose CHIKV infection, the resulting chronic pain and prevent pediatric neurological syndromes. CHIKVP is composed of a protease domain and a methyltransferase-like domain (MTL). To date, no functions of the MTL have been identified. In a search for novel CHIKVP binders, we identified ligands that bind to the MTL at an elongated cavity and allosterically inactivate the protease. The site shares structural homology with S-adenosyl methionine (SAM) cofactor binding sites, but does not bind SAM. The allosteric site binds to GTP, which suggests that a function such as RNA binding may be conserved in the MTL. Here we propose a research strategy for the development and direct comparison of CHIKVP active-site and allosteric inhibitors. We will build compounds derived from a large compound screen and also build from MTL-binding fragments we have already identified. We have developed NMR approaches that allow us to readily distinguish active-site from allosteric inhibitors. Importantly, we have developed approaches that allow us to monitor activity, binding and dynamics in solution without having to rely on freezing samples which is required for other structural techniques, to inform our inhibitor design. Recent data have suggested that RNA plays a critical role in CHIKVP function, enhancing protease activity. We have identified a site that we hypothesize binds RNA and describe a series of studies to understand the mechanism by which RNA impacts protease function. We will bring all these structural insights into our inhibitor development approach. At each step of development, we will closely monitor efficacy against viral infection for CHIKV and other related alphaviruses to determine whether pan-alphaviral inhibition is achievable with a given class of compounds. Critically, we will also implement a directed evolution approach across both domains of CHIKVP to predict the susceptibility of our inhibitors to resistance mutations. This will enable us to develop enduring antivirals and will also address longstanding unanswered questions about the favorability of allosteric inhibition in antiviral drug development.", "keywords": [ "2019-nCoV", "Aborted Fetus", "Active Sites", "Address", "Adult", "Allosteric Site", "Alpha Virus", "Anti-viral Agents", "Anti-viral Therapy", "Arthralgia", "Back", "Binding", "Binding Sites", "Characteristics", "Chikungunya virus", "Child", "Childhood", "Clinical", "Country", "Data", "Development", "Directed Molecular Evolution", "Disease", "Disease Progression", "Drug Targeting", "Drug resistance", "Encephalopathies", "FDA approved", "Family", "Fluorogenic Substrate", "Freezing", "Future", "Goals", "Guanosine Triphosphate", "HIV", "HIV Care", "Hepatitis C", "Hepatitis C virus", "Infection", "Inflammation", "Intervention", "Late pregnancy", "Ligand Binding", "Light", "Mediating", "Methyltransferase", "Molecular", "Monitor", "Motion", "Myalgia", "Nervous System Disorder", "Neurologic", "Newborn Infant", "Peptide Hydrolases", "Persons", "Pharmaceutical Chemistry", "Play", "Polyproteins", "Predisposition", "Pregnancy", "Pregnant Women", "Property", "Protease Domain", "Protease Inhibitor", "RNA", "RNA Binding", "Reporting", "Research", "Role", "S-Adenosylhomocysteine", "S-Adenosylmethionine", "SARS-CoV-2 infection", "Sampling", "Series", "Site", "Structure", "Syndrome", "Techniques", "Testing", "Time", "Vaccination", "Vaccines", "Viral", "Virus Diseases", "Virus Inhibitors", "Virus Replication", "acute infection", "antiviral drug development", "chikungunya infection", "chronic infection", "chronic pain", "cofactor", "design", "drug resistance development", "emerging pathogen", "enzyme mechanism", "flu", "high throughput screening", "inhibitor", "insight", "mosquito-borne", "novel", "pain symptom", "pandemic potential", "pandemic virus", "pharmacologic", "prevent", "resistance mutation", "screening", "small molecule", "standard of care", "unborn child" ], "approved": true } }, { "type": "Grant", "id": "15984", "attributes": { "award_id": "1R01AI195471-01", "title": "Molecular evolution of entry receptor usage underlying zoonotic human betacoronaviruses", "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": "2026-02-20", "end_date": "2031-01-31", "award_amount": 481520, "principal_investigator": { "id": 7514, "first_name": "Tyler Nelson", "last_name": "Starr", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 757, "ror": "", "name": "FRED HUTCHINSON CANCER RESEARCH CENTER", "address": "", "city": "", "state": "WA", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 3434, "ror": "", "name": "UTAH STATE HIGHER EDUCATION SYSTEM--UNIVERSITY OF UTAH", "address": "", "city": "", "state": "UT", "zip": "", "country": "United States", "approved": true }, "abstract": "Most human viruses originate from recent zoonotic spillover, but the upstream evolutionary processes in animal reservoirs that drive zoonosis-promoting traits remain poorly understood. Our long-term goal is to elucidate the evolutionary forces enabling animal viruses to acquire traits facilitating human spillover and adaptation, with a focus on viral entry receptor usage as a critical determinant of cross-species transmission. Toward this end, this proposal investigates the evolutionary dynamics underlying changes in receptor-binding specificity in beta-coronaviruses (CoVs) linked to past and potential future zoonoses: SARS-CoV-2, MERS- CoV, and HKU1 alongside their bat, rodent, and other animal relatives. Our central model is that long-term evolutionary arms races between viruses and wildlife hosts drive evolvable mechanisms of receptor- engagement promoting subsequent human spillover and adaptation. This model will be examined through three specific aims: (1) Identify mechanisms driving human receptor binding in bat SARS-related CoVs; (2) Dissect the origins and consequences of receptor-switching in bat MERS-related CoVs; and (3) Identify evolutionary origins of and functional constraints imposed by a newly discovered HKU1 CoV receptor. In each aim, we combine phylogenetic surveys across diverse animal CoVs with high-throughput mutagenesis screens to map the evolutionary, genetic, and structural mechanisms driving receptor-use transitions and their downstream evolutionary consequences. These studies will illuminate how host-virus dynamics shape receptor-binding architectures to enable zoonotic potential and post-spillover antigenic evolution. The resulting large-scale genotype-phenotype maps will inform computational models for assessing viral zoonotic risk and guide the design of broad-spectrum antibody and vaccine therapeutics for pandemic preparedness. Taken together, this work advances understanding of mechanisms of viral evolution while providing actionable insights for proactive ecological, diagnostic, and therapeutic interventions.", "keywords": [ "2019-nCoV", "ACE2", "Animals", "Antibodies", "Architecture", "Automobile Driving", "Binding", "Biological Factors", "Chiroptera", "Communicable Diseases", "Computer Models", "Coronavirus", "Development", "Diagnostic", "Dissection", "Distal", "Epidemic", "Event", "Evolution", "Future", "Genetic", "Genetic Screening", "Genotype", "Glycoproteins", "Goals", "Human", "Immune", "Infection", "Link", "Maps", "Middle East Respiratory Syndrome", "Middle East Respiratory Syndrome Coronavirus", "Modeling", "Molecular", "Molecular Evolution", "Mutagenesis", "Mutation", "Orthologous Gene", "Pathogenicity", "Pathway interactions", "Phenotype", "Phylogenetic Analysis", "Process", "Proteins", "Public Health", "Research", "Risk", "Rodent", "Role", "SARS coronavirus", "Shapes", "Specificity", "Surveys", "TMPRSS2 gene", "Testing", "Therapeutic", "Therapeutic Intervention", "Vaccines", "Viral", "Viral reservoir", "Virus", "Work", "Yeasts", "Zoonoses", "animal coronavirus", "arms race", "betacoronavirus", "biophysical analysis", "coronavirus receptor", "cross-species transmission", "design", "experience", "future pandemic", "human coronavirus", "improved", "insight", "mutation screening", "novel", "pandemic preparedness", "predictive tools", "pressure", "prevent", "receptor", "receptor binding", "respiratory", "tool", "trait", "transmission process", "vaccine development", "viral outbreak", "zoonotic spillover" ], "approved": true } } ], "meta": { "pagination": { "page": 3, "pages": 1424, "count": 14236 } } }