Grant List
Represents Grant table in the DB
GET /v1/grants?page%5Bnumber%5D=1393&sort=other_investigators
{ "links": { "first": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1&sort=other_investigators", "last": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1442&sort=other_investigators", "next": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1394&sort=other_investigators", "prev": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1392&sort=other_investigators" }, "data": [ { "type": "Grant", "id": "6746", "attributes": { "award_id": "1U19AI171399-01", "title": "AI-driven Structure-enabled Antiviral Platform (ASAP)", "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": 22535, "first_name": "Maureen J.", "last_name": "Beanan", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2022-05-16", "end_date": "2025-04-30", "award_amount": 67673902, "principal_investigator": { "id": 22536, "first_name": "John Damon", "last_name": "Chodera", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 1507, "ror": "", "name": "SLOAN-KETTERING INST CAN RESEARCH", "address": "", "city": "", "state": "NY", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [ { "id": 22537, "first_name": "Alpha Albert", "last_name": "Lee", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 22538, "first_name": "Benjamin", "last_name": "Perry", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 1507, "ror": "", "name": "SLOAN-KETTERING INST CAN RESEARCH", "address": "", "city": "", "state": "NY", "zip": "", "country": "United States", "approved": true }, "abstract": "- Overall SARS-CoV-2 continues to cause severe morbidity and mortality in the ongoing pandemic. Future RNA virus epidemics and pandemics are inevitable. New clinical-trial-ready antivirals are urgently needed RNA viruses of pandemic potential. COVID-19 has further underscored the need for early, global access to clinic-ready compounds. Beyond coronaviruses; flaviviruses and picornaviruses also cause frequent and ongoing epidemics worldwide and have no effective therapeutics. Maintaining a portfolio of novel, clinic-ready therapeutics are critical for our future pandemic preparedness. The AI-driven Structure-enabled Antiviral Platform (ASAP) AViDD Center will develop novel chemical assets that have antiviral activity against three target viral families. ASAP will leverage state-of-the-art structure-enabled technologies capable of leveraging recent advances in AI/ML and computational chemistry in identifying, enabling, and prosecuting discovery campaigns against novel viral targets. ASAP is built on principles of open science and rapid dissemination (enabled by a dedicated Data Infrastructure Core). ASAP builds on the successful COVID Moonshot, an open science collaboration that recently secured $11 million from the Wellcome Trust via the WHO Access to COVID Tools Accelerator (ACT-A) to fund preclinical development of a novel oral noncovalent SARS-CoV-2 antiviral acting against the main protease (MPro). Beginning with a high-throughput X-ray fragment screen, the discovery team spent just 18 months and $1M to reach the preclinical phase. ASAP will mirror this rapid, cost-efficient approach: automated structural biology at Diamond Light Source (Frank von Delft); AI/ML synthesis models from PostEra (Alpha Lee); nanoscale chemistry and covalent fragment libraries from Nir London; massively distributed free energy calculations on Folding@home (John Chodera); an industrial medicinal chemistry team led by MedChemica (Ed Griffen); and antiviral assays and virology expertise at Mount Sinai (Kris White; Adolfo García-Sastre). ASAP augments this seasoned antiviral discovery team with new approaches to resistance-robust targeting (Karla Kirkegaard and Matt Bogyo, Stanford) and deep mutational scanning (Jesse Bloom, Fred Hutch). ASAP is supported by the Drugs for Neglected Diseases Initiative (DNDi) (PI Ben Perry), and Letters of Support from Takeda, Pfizer, Novartis, and Grupo Insud. ASAP Impact: ASAP will become the nexus of a robust global antiviral discovery community. Our open science approach focuses on ensuring global, equitable access to therapeutics to combat future pandemics. We aim to produce a robust antiviral pipeline consisting of 3 new Phase I ready candidates, 6 lead optimization campaigns, 9 fragment-to-lead campaigns, and 10 structure-enabled resistance-robust viral targets. Our associated data packages will accelerate follow-on development and investment.", "keywords": [ "2019-nCoV", "Antiviral Agents", "Biological Assay", "COVID-19", "Chemicals", "Chemistry", "Clinic", "Clinical Trials", "Collaborations", "Communities", "Coronavirus", "Data", "Development", "Diamond", "Disease", "Ensure", "Epidemic", "Family", "Family Picornaviridae", "Flavivirus", "Free Energy", "Funding", "Future", "Home", "Industrialization", "Investments", "Lead", "Letters", "Libraries", "Light", "London", "Modeling", "Morbidity - disease rate", "Oral", "Peptide Hydrolases", "Pharmaceutical Chemistry", "Pharmaceutical Preparations", "Phase", "RNA Viruses", "Resistance", "Roentgen Rays", "SARS-CoV-2 antiviral", "Seasons", "Secure", "Source", "Structure", "Technology", "Therapeutic", "Trust", "Viral", "combat", "computational chemistry", "coronavirus disease", "cost efficient", "data infrastructure", "lead optimization", "mortality", "mutation screening", "nanoscale", "neglect", "novel", "novel strategies", "open data", "pandemic disease", "pandemic preparedness", "pre-clinical", "preclinical development", "structural biology", "therapeutically effective", "tool", "virology" ], "approved": true } }, { "type": "Grant", "id": "7783", "attributes": { "award_id": "3P30CA013330-48S4", "title": "Novel Biologics to Treat Lung Cancer and COVID-19 Comorbidity", "funder": { "id": 4, "ror": "https://ror.org/01cwqze88", "name": "National Institutes of Health", "approved": true }, "funder_divisions": [ "National Cancer Institute (NCI)" ], "program_reference_codes": [], "program_officials": [ { "id": 20549, "first_name": "Sonya", "last_name": "Roberson", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "1997-06-01", "end_date": "2022-06-30", "award_amount": 167791, "principal_investigator": { "id": 23589, "first_name": "STEVEN C.", "last_name": "ALMO", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 741, "ror": "https://ror.org/05cf8a891", "name": "Albert Einstein College of Medicine", "address": "", "city": "", "state": "NY", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [ { "id": 22544, "first_name": "EDWARD", "last_name": "CHU", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 741, "ror": "https://ror.org/05cf8a891", "name": "Albert Einstein College of Medicine", "address": "", "city": "", "state": "NY", "zip": "", "country": "United States", "approved": true } ] } ], "awardee_organization": { "id": 741, "ror": "https://ror.org/05cf8a891", "name": "Albert Einstein College of Medicine", "address": "", "city": "", "state": "NY", "zip": "", "country": "United States", "approved": true }, "abstract": "Novel Biologics to Treat Lung Cancer and COVID-19 Comorbidity: Analyses of patients with COVID-19 treated at the Montefiore Medical Center (University Hospital of the Albert Einstein College of Medicine) indicate a high mortality (28%) in cancer patients, in general, and a 55% mortality in patients with lung cancer, especially those that received lung radiation 1-12 months prior to COVID-19 diagnosis. We propose to study the role that CD8 T cell depletion plays as a determinant of this adverse outcome, as CD8 T cells have been shown to provide substantial protection from lethal severe acute respiratory syndrome due to coronavirus infection. We are exploiting a novel biologics platform to provide mechanistic insight into the impact of radiation therapy on the number and quality of protective lung-resident COVID-19 T specific cells. This platform, termed synTac (artificial immunological Synapse for T-cell Activation), selectively modulates specific disease-relevant T cell clones for targeted treatment of malignancies, autoimmune disease and infectious diseases. In this unique design, single chain peptide-MHC (sc-pMHC) constructs are covalently linked to a variety of costimulatory, coinhibitory and cytokine molecules. The sc-pMHC domain acts as an “address” to target specific T cell clones for delivery of a range of comodulatory domains (MODS), resulting in clonal-selective T cell modulation, thus eliminating the side effects of current immunotherapeutics, which elicit global immune modulation (i.e., stimulation or inhibition of all T cells). This technology is the foundation of Cue Biopharma (Almo, founder), a publically traded immunotherapy company focused on development and application of this platform. A synTac containing the HPV-derived E7 peptide and IL-2 cytokine elicits selective T cell expansion and tumor growth inhibition of an E7-driven tumor in pre-clinical models. The human analog of this synTac (CUE-101) is in a Phase 1 clinical trial for head/neck squamous cell carcinoma - HNSCC (NCT03978689) demonstrating favorable tolerability and drug exposure. Preclinical studies demonstrated antitumor efficacy as monotherapy and with anti-PD1 treatment. Based upon these findings, Merck, collaborating with Cue Biopharma, is launching a Phase I first-line study of CUE-101 in combination with Keytruda for HPV+ advanced HNSCC. We have adapted this platform to generate viral-targeted synTacs, possessing antigenic peptides for HIV and CMV, to deliver costimulatory signals through CD28 or 4-1BB receptors. These reagents support substantial in vivo expansion of HIV- and CMV-specific T cells (in the NGS murine system), and this platform is being expanded to utilize COVID-19 derived peptides for selective in vivo expansion of protective CD8 T cells for COVID-19 treatment and cancer/radiation comorbidities.", "keywords": [ "2019-nCoV", "Address", "Affect", "African American", "Angiotensin Receptor", "Applications Grants", "Autoimmune Diseases", "Binding", "Biological", "CD147 antigen", "CD28 gene", "CD8-Positive T-Lymphocytes", "COVID-19", "Cancer Patient", "Case Fatality Rates", "Cells", "Cessation of life", "Clinical Data", "Clone Cells", "Cohort Studies", "Colorectal Cancer", "Communicable Diseases", "Coronavirus Infections", "County", "Cues", "Cytomegalovirus", "Data Set", "Death Rate", "Development", "Diagnosis", "Disease", "Drug Exposure", "Ethnic Origin", "Failure", "Foundations", "Functional disorder", "Future", "General Population", "HIV", "Head and Neck Squamous Cell Carcinoma", "Hispanics", "Human", "Human Papillomavirus", "Immunity", "Immunotherapeutic agent", "Immunotherapy", "Impairment", "Infection", "Infection Control", "Institution", "Interleukin-2", "Invaded", "Knowledge", "Lead", "Link", "Lung", "Malignant - descriptor", "Malignant Neoplasms", "Malignant neoplasm of gastrointestinal tract", "Malignant neoplasm of lung", "Mediating", "Medical center", "Medicine", "Minority", "Mus", "Natural History", "New York City", "Organ", "Patients", "Pattern", "Peptide/MHC Complex", "Peptides", "Peptidyl-Dipeptidase A", "Phase", "Phase I Clinical Trials", "Play", "Pneumonia", "Policies", "Population", "Pre-Clinical Model", "Protocols documentation", "Race", "Radiation", "Radiation therapy", "Reagent", "Risk", "Role", "Serine Protease", "Severe Acute Respiratory Syndrome", "Signal Transduction", "Site", "Statistical Models", "System", "T-Cell Activation", "T-Cell Depletion", "T-Lymphocyte", "TMPRSS2 gene", "Technology", "Treatment outcome", "United States", "University Hospitals", "Viral", "Viral Load result", "Viral Proteins", "Virus", "Virus Receptors", "Virus Shedding", "adverse outcome", "analog", "anti-PD1 therapy", "base", "cancer type", "college", "colon cancer patients", "comorbidity", "coronavirus disease", "cytokine", "data modeling", "design", "gastrointestinal", "high risk", "immunological synapse", "immunoregulation", "in vivo", "insight", "mortality", "novel", "pandemic disease", "preclinical study", "receptor", "seroconversion", "side effect", "targeted treatment", "tumor", "tumor growth" ], "approved": true } }, { "type": "Grant", "id": "6751", "attributes": { "award_id": "1R56AI167285-01", "title": "Mechanisms of Gastrointestinal COVID-19", "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": 22547, "first_name": "Annette L.", "last_name": "Rothermel", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2022-03-10", "end_date": "2023-02-28", "award_amount": 304049, "principal_investigator": { "id": 22548, "first_name": "MATTHEW AARON", "last_name": "CIORBA", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 827, "ror": "", "name": "WASHINGTON UNIVERSITY", "address": "", "city": "", "state": "MO", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [ { "id": 22549, "first_name": "Siyuan", "last_name": "Ding", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 827, "ror": "", "name": "WASHINGTON UNIVERSITY", "address": "", "city": "", "state": "MO", "zip": "", "country": "United States", "approved": true }, "abstract": "Although coronavirus disease 2019 (COVID-19) is primarily defined as a respiratory illness, patients often experience clinical gastrointestinal symptoms, including abdominal pain and diarrhea. We recently found that severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the virus that causes COVID-19, infects and replicates in human small bowel intestinal epithelial cells (IECs). However, the molecular mechanisms of SARS- CoV-2 driving gastrointestinal pathology, particularly in vulnerable populations with preexisting diseases remain unclear. In this proposal, our overall objectives are to better define the cellular signaling of SARS-CoV-2 interactions with IECs, enteric immune responses, and microbiome in the context of health or inflammatory bowel disease (IBD). Our preliminary data suggest that compared to normal COVID-19 patients, those with IBD have higher expression of cathepsin L in IECs, which correlated with higher viral loads. Our central hypothesis is that SARS- CoV-2 induces a distinctive intestinal pathology and mucosal immune response that is shaped by host factors (IBD), luminal factors (secretions, bile salts, and microbiome), and therapeutics (mesalamine). Specifically, using healthy and IBD-derived organoids, COVID-19 patient fecal samples, and K18-hACE2 mice, we aim to (1) define the host factors and cellular mechanisms involved in SARS-CoV-2 infection of normal and IBD epithelium, (2) determine the impact of intestinal SARS-CoV-2 infection on intestinal immune response and colitis, and (3) define the environmental factors that influence intestinal infection with SARS-CoV-2. With extensive and collaborative expertise in IBD, virology, and host-microbial interactions, we expect to address mechanistically interesting and clinically important questions like “What is the intestinal pathogenicity of SARS-CoV-2 and how is it impacted by IBD?”, “What is the interaction of SARS-CoV-2/COVID-19 with intestinal inflammation and IBD therapies?”, and “What is the impact of intestinal contents and IBD diagnosis/microbiome on intestinal SARS-CoV-2?”. We anticipate that this pathology will further our understanding of SARS-CoV-2 interactions with the epithelial and immune cells to explain COVID-19 GI symptoms with or without IBD. We also expect the new information gained from this project will expand our understanding of acute gastrointestinal pathology and symptoms of COVID-19, provide mechanistic context to existing clinical and translational literature, and create a foundational knowledge and tool set for deeper investigations into COVID-19 and potentially pathogenic and emerging coronaviruses of the future.", "keywords": [ "16S ribosomal RNA sequencing", "2019-nCoV", "ACE2", "Abdominal Pain", "Acute", "Address", "Adrenal Cortex Hormones", "Affect", "Automobile Driving", "COVID-19", "COVID-19 patient", "COVID-19 severity", "COVID-19 vaccination", "Cathepsin L", "Cathepsins", "Cathepsins B", "Cell Death", "Cells", "Chronic", "Clinical", "Colitis", "Confusion", "Coronavirus", "Crohn&apos", "s disease", "Data", "Diarrhea", "Disease", "Enteral", "Enterocytes", "Environmental Risk Factor", "Epithelial", "Epithelial Cells", "Etiology", "Feces", "Foundations", "Functional disorder", "Future", "Gastrointestinal Diseases", "Gastrointestinal Physiology", "Gastrointestinal tract structure", "Genotype", "Health", "Homeostasis", "Household", "Human", "Immune", "Immune response", "Impairment", "Infection", "Inflammation", "Inflammatory Bowel Diseases", "Integration Host Factors", "International", "Intestinal Content", "Intestinal Diseases", "Intestines", "Investigation", "K-18 conjugate", "Knowledge", "Late Effects", "Lead", "Liquid substance", "Literature", "Lower Gastrointestinal Tract", "Mesalamine", "Modeling", "Molecular", "Mucosal Immune Responses", "Mucosal Immunity", "Mucositis", "Mus", "Nausea and Vomiting", "Observational Study", "Organoids", "Outcome", "Pathogenesis", "Pathogenicity", "Pathology", "Pathway interactions", "Patients", "Pharmaceutical Preparations", "Phenotype", "Populations at Risk", "Recommendation", "Reporter", "Reporting", "Respiratory System", "SARS-CoV-2 P.1", "SARS-CoV-2 infection", "Sampling", "Severity of illness", "Signal Transduction", "Small Intestines", "South African", "Specimen", "Standardization", "Symptoms", "TNF gene", "Testing", "Therapeutic", "Therapeutic immunosuppression", "Transgenic Mice", "Tropism", "Viral Load result", "Viral reservoir", "Virus", "Virus Replication", "Vulnerable Populations", "acute infection", "base", "bile salts", "biobank", "clinical care", "clinical practice", "differential expression", "disease diagnosis", "enteric infection", "experience", "gastrointestinal", "gastrointestinal symptom", "gut dysbiosis", "gut microbiome", "host-microbe interactions", "infection rate", "inflammatory disease of the intestine", "inhibitor/antagonist", "intestinal epithelium", "microbiome", "microbiome therapeutics", "microbiota", "mortality", "novel", "novel coronavirus", "respiratory", "socioeconomics", "stool sample", "surveillance study", "tool", "virol" ], "approved": true } }, { "type": "Grant", "id": "9694", "attributes": { "award_id": "1R01AI167285-01A1", "title": "Mechanisms of Gastrointestinal COVID-19", "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": 7788, "first_name": "Annette L.", "last_name": "Rothermel", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2022-07-15", "end_date": "2027-06-30", "award_amount": 583983, "principal_investigator": { "id": 22548, "first_name": "MATTHEW AARON", "last_name": "CIORBA", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 827, "ror": "", "name": "WASHINGTON UNIVERSITY", "address": "", "city": "", "state": "MO", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [ { "id": 22549, "first_name": "Siyuan", "last_name": "Ding", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 827, "ror": "", "name": "WASHINGTON UNIVERSITY", "address": "", "city": "", "state": "MO", "zip": "", "country": "United States", "approved": true }, "abstract": "Although coronavirus disease 2019 (COVID-19) is primarily defined as a respiratory illness, up to 50% of patients experience clinical gastrointestinal symptoms, including nausea, abdominal pain, and diarrhea. We recently found that severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the virus that causes COVID-19, infects and replicates in human small bowel intestinal epithelial cells (IECs). However, the molecular mechanisms by which SARS-CoV-2 drives gastrointestinal pathology and how that impacts the mucosal immune responses and systemic diseases remain unclear. In this proposal, our overall objectives are to better define the cellular signaling of SARS-CoV-2 interactions with IECs, enteric immune responses, and microbiome in the context of health or inflamed intestines (i.e., inflammatory bowel disease (IBD)). Our preliminary data suggest that compared to normal COVID-19 patients, those with IBD who have higher expression of cathepsin L in IECs prior to infection, supported higher viral loads. Our central hypothesis is that SARS-CoV-2 induces a distinctive intestinal pathology and mucosal immune response that is shaped by host factors (IBD), luminal factors (bile salts and microbiome), and therapeutics. Specifically, using healthy and IBD-derived organoids, COVID-19 patient fecal samples, SARS-CoV-2 variants, and a highly tractable and innovative intraluminal injection mouse model, we aim to (1) define the host factors and cellular mechanisms involved in SARS-CoV-2 infection of normal and IBD epithelium, (2) determine the impact of intestinal SARS-CoV-2 infection on intestinal immune response and colitis, and (3) define the environmental factors that influence intestinal infection with SARS-CoV-2. With extensive and collaborative expertise in intestinal biology, virology, and mucosal immunology (including IBD), we expect to address mechanistically interesting and clinically important questions regarding SARS-CoV- 2 enteric infection, immune responses, and intestinal pathology. We anticipate that the knowledge derived from this study will further our understanding of SARS-CoV-2 interactions with the epithelial and immune cells to explain COVID-19 GI symptoms with or without IBD. We also expect the new information gained from this project will expand our understanding of acute and chronic gastrointestinal pathology and symptoms of COVID-19, provide novel strategies to mitigate COVID-19 associated GI diseases, and create a foundational knowledge and tool set for deeper investigations into COVID-19 and potentially pathogenic and emerging coronaviruses of the future.", "keywords": [ "16S ribosomal RNA sequencing", "2019-nCoV", "ACE2", "Abdominal Pain", "Acute", "Address", "Affect", "Biology", "COVID-19", "COVID-19 patient", "COVID-19 severity", "COVID-19 vaccination", "CTSL gene", "Cathepsin L", "Cell Death", "Cells", "Chronic", "Clinical", "Colitis", "Coronavirus", "Crohn&apos", "s disease", "Data", "Diarrhea", "Disease", "Enteral", "Enterocytes", "Environmental Risk Factor", "Epithelial", "Epithelial Cells", "Etiology", "Feces", "Foundations", "Functional disorder", "Future", "Gastrointestinal Diseases", "Gastrointestinal Physiology", "Gastrointestinal tract structure", "Genetic", "Genotype", "Health", "Homeostasis", "Household", "Human", "Immune", "Immune response", "Immunology", "Impairment", "Infection", "Inflammation", "Inflammatory Bowel Diseases", "Injections", "Integration Host Factors", "Intervention", "Intestinal Diseases", "Intestines", "Investigation", "Knowledge", "Liquid substance", "Lower Gastrointestinal Tract", "Modeling", "Molecular", "Mucosal Immune Responses", "Mucosal Immune System", "Mucositis", "Mucous Membrane", "Mus", "Nausea", "Nausea and Vomiting", "Observational Study", "Organ", "Organoids", "Outcome", "Pathogenesis", "Pathogenicity", "Pathology", "Pathway interactions", "Patients", "Pharmacology", "Phenotype", "Populations at Risk", "Reporter", "Respiratory System", "Role", "SARS-CoV-2 infection", "SARS-CoV-2 variant", "Sampling", "Severity of illness", "Signal Transduction", "Site", "Small Intestines", "Specimen", "Symptoms", "Systemic disease", "Testing", "Therapeutic", "Therapeutic immunosuppression", "Transgenic Mice", "Tropism", "Variant", "Viral Load result", "Viral reservoir", "Virus", "Wild Type Mouse", "acute infection", "base", "bile salts", "biobank", "coronavirus disease", "diarrheal disease", "differential expression", "enteric infection", "experience", "gastrointestinal", "gastrointestinal infection", "gastrointestinal symptom", "gut dysbiosis", "gut inflammation", "gut microbiome", "infection rate", "inhibitor", "innovation", "intestinal epithelium", "invention", "microbiome", "microbiome therapeutics", "microbiota", "mouse model", "novel", "novel coronavirus", "novel strategies", "respiratory", "socioeconomics", "stool sample", "tool", "virology", "virome", "virus resource" ], "approved": true } }, { "type": "Grant", "id": "11935", "attributes": { "award_id": "5R01AI167285-02", "title": "Mechanisms of Gastrointestinal COVID-19", "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": 6181, "first_name": "Deborah", "last_name": "Hodge", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2022-07-15", "end_date": "2027-06-30", "award_amount": 705849, "principal_investigator": { "id": 22548, "first_name": "MATTHEW AARON", "last_name": "CIORBA", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 827, "ror": "", "name": "WASHINGTON UNIVERSITY", "address": "", "city": "", "state": "MO", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [ { "id": 22549, "first_name": "Siyuan", "last_name": "Ding", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 827, "ror": "", "name": "WASHINGTON UNIVERSITY", "address": "", "city": "", "state": "MO", "zip": "", "country": "United States", "approved": true }, "abstract": "Although coronavirus disease 2019 (COVID-19) is primarily defined as a respiratory illness, up to 50% of patients experience clinical gastrointestinal symptoms, including nausea, abdominal pain, and diarrhea. We recently found that severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the virus that causes COVID-19, infects and replicates in human small bowel intestinal epithelial cells (IECs). However, the molecular mechanisms by which SARS-CoV-2 drives gastrointestinal pathology and how that impacts the mucosal immune responses and systemic diseases remain unclear. In this proposal, our overall objectives are to better define the cellular signaling of SARS-CoV-2 interactions with IECs, enteric immune responses, and microbiome in the context of health or inflamed intestines (i.e., inflammatory bowel disease (IBD)). Our preliminary data suggest that compared to normal COVID-19 patients, those with IBD who have higher expression of cathepsin L in IECs prior to infection, supported higher viral loads. Our central hypothesis is that SARS-CoV-2 induces a distinctive intestinal pathology and mucosal immune response that is shaped by host factors (IBD), luminal factors (bile salts and microbiome), and therapeutics. Specifically, using healthy and IBD-derived organoids, COVID-19 patient fecal samples, SARS-CoV-2 variants, and a highly tractable and innovative intraluminal injection mouse model, we aim to (1) define the host factors and cellular mechanisms involved in SARS-CoV-2 infection of normal and IBD epithelium, (2) determine the impact of intestinal SARS-CoV-2 infection on intestinal immune response and colitis, and (3) define the environmental factors that influence intestinal infection with SARS-CoV-2. With extensive and collaborative expertise in intestinal biology, virology, and mucosal immunology (including IBD), we expect to address mechanistically interesting and clinically important questions regarding SARS-CoV- 2 enteric infection, immune responses, and intestinal pathology. We anticipate that the knowledge derived from this study will further our understanding of SARS-CoV-2 interactions with the epithelial and immune cells to explain COVID-19 GI symptoms with or without IBD. We also expect the new information gained from this project will expand our understanding of acute and chronic gastrointestinal pathology and symptoms of COVID-19, provide novel strategies to mitigate COVID-19 associated GI diseases, and create a foundational knowledge and tool set for deeper investigations into COVID-19 and potentially pathogenic and emerging coronaviruses of the future.", "keywords": [ "16S ribosomal RNA sequencing", "2019-nCoV", "ACE2", "Abdominal Pain", "Acute", "Address", "Affect", "Biology", "COVID-19", "COVID-19 patient", "COVID-19 severity", "COVID-19 treatment", "COVID-19 vaccination", "Cathepsin L", "Cell Death", "Cells", "Chronic", "Clinical", "Colitis", "Coronavirus", "Crohn&apos", "s disease", "Data", "Diarrhea", "Disease", "Endosomes", "Enteral", "Enterocytes", "Environmental Risk Factor", "Epithelial", "Epithelial Cells", "Etiology", "Feces", "Functional disorder", "Future", "Gastrointestinal Diseases", "Gastrointestinal Physiology", "Gastrointestinal tract structure", "Genetic", "Genotype", "Health", "Homeostasis", "Household", "Human", "Immune", "Immune response", "Immunology", "Impairment", "Infection", "Inflammation", "Inflammatory Bowel Diseases", "Injections", "Integration Host Factors", "Intervention", "Intestinal Diseases", "Intestines", "Investigation", "Knowledge", "Liquid substance", "Lower Gastrointestinal Tract", "Modeling", "Molecular", "Mucosal Immune Responses", "Mucosal Immune System", "Mucositis", "Mucous Membrane", "Mus", "Nausea", "Observational Study", "Organ", "Organoids", "Outcome", "Pathogenicity", "Pathology", "Pathway interactions", "Patients", "Phenotype", "Populations at Risk", "Reporter", "Respiratory System", "Role", "SARS-CoV-2 B.1.617.2", "SARS-CoV-2 P.1", "SARS-CoV-2 infection", "SARS-CoV-2 variant", "Sampling", "Severity of illness", "Shapes", "Signal Transduction", "Site", "Small Intestines", "Specimen", "Symptoms", "Systemic disease", "Testing", "Therapeutic", "Therapeutic immunosuppression", "Transgenic Mice", "Tropism", "Viral Load result", "Viral reservoir", "Virus", "Vomiting", "acute infection", "bile salts", "biobank", "coronavirus disease", "diarrheal disease", "differential expression", "enteric infection", "experience", "gastrointestinal", "gastrointestinal infection", "gastrointestinal symptom", "gut dysbiosis", "gut inflammation", "gut microbiome", "infection rate", "inhibitor", "innovation", "intestinal epithelium", "microbiome", "microbiota", "mouse model", "novel", "novel coronavirus", "novel strategies", "pharmacologic", "respiratory", "socioeconomics", "stool sample", "tool", "virology", "virome", "virus resource" ], "approved": true } }, { "type": "Grant", "id": "11231", "attributes": { "award_id": "5R01LM014056-02", "title": "DeconDTN: Deconfounding Deep Transformer Networks for Clinical NLP", "funder": { "id": 4, "ror": "https://ror.org/01cwqze88", "name": "National Institutes of Health", "approved": true }, "funder_divisions": [ "National Library of Medicine (NLM)" ], "program_reference_codes": [], "program_officials": [ { "id": 10715, "first_name": "YANLI", "last_name": "WANG", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2022-06-01", "end_date": "2026-02-28", "award_amount": 342038, "principal_investigator": { "id": 22557, "first_name": "Trevor", "last_name": "Cohen", "orcid": null, "emails": "[email protected]", "private_emails": null, "keywords": "[]", "approved": true, "websites": "[]", "desired_collaboration": "", "comments": "", "affiliations": [ { "id": 159, "ror": "https://ror.org/00cvxb145", "name": "University of Washington", "address": "", "city": "", "state": "WA", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [ { "id": 22558, "first_name": "Serguei VS", "last_name": "Pakhomov", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 159, "ror": "https://ror.org/00cvxb145", "name": "University of Washington", "address": "", "city": "", "state": "WA", "zip": "", "country": "United States", "approved": true }, "abstract": "Natural Language Processing (NLP) methods have been broadly applied to clinical problems, from recognition of clinical findings in physician notes to identification of transcribed speech samples indicating changes in cognitive status. Deep transformer networks (DTNs) have dramatically advanced NLP accuracy. These deep learning models have multiple hidden layers that may correspond to billions of trainable parameters, allowing them to apply information learned from training on large unlabeled corpora to a specific task of interest. However, their size leaves them especially vulnerable to confounding bias, induced by variables that can influence both the predictor (text) and the outcome (e.g. an associated diagnosis) of a predictive model. Such systematic biases are a recognized danger in the application of artificial intelligence methods to clinical problems, and are the focus of NLM NOT-LM-19-003 which invites applications proposing methods to identify and address them. Deep learning models in general require large amounts of training data, spurring initiatives to aggregate medical data from across institutional siloes. This can increase data set size and enhance model portability, but leaves the resulting models vulnerable to confounding by provenance, where models learn to recognize the origin of dataset components and make biased predictions based on site-specific class distributions (e.g. COVID prevalence). Such models will assign classes based on indicators of dataset provenance, rather than diagnostically meaningful linguistic differences, and make erroneous predictions when the provenance-specific distributions at the point of deployment differ from those in the training set. Confounding of this nature is a pervasive problem that presents a fundamental barrier to the portability of trained models, and threatens the utility of datasets assembled from across institutions and services. Unlike traditional statistical and machine learning models, with deep transformer networks feature representations are distributed across parameters spread throughout the entire network. New methods are needed to meet the challenge of identifying and mitigating the influence of confounding variables in such models. In the proposed research we will develop a systematic approach to Deconfounding Deep Transformer Networks (DeconDTN), embodied in an eponymous and publicly available set of open source tools for (1) identification of provenance-related biases, (2) mitigation of these biases using a novel set of validated methods, and (3) systematic evaluation of the resulting effects on model performance. While DeconDTN will be generally applicable, development and evaluation will occur in the context of three use cases involving data sets drawn from different sources: classification of speech transcripts from participants with dementia drawn from two locations, identification of goals-of-care discussions in clinical notes drawn from multiple studies involving a range of clinical services, and prediction of COVID-19 status in notes drawn from different clinical units. Our driving hypothesis is that the resulting models will make more accurate predictions in these heterogenous datasets than corresponding models without correction for confounding by provenance.", "keywords": [ "Address", "Architecture", "Area", "Artificial Intelligence", "Automobile Driving", "Behavior", "Bridge to Artificial Intelligence", "COVID-19", "Caring", "Categories", "Characteristics", "Classification", "Clinical", "Clinical Services", "Cognitive", "Computer software", "Confounding Factors (Epidemiology)", "Coupled", "Data", "Data Aggregation", "Data Set", "Data Sources", "Dementia", "Development", "Diagnosis", "Diagnostic", "Ensure", "Equilibrium", "Evaluation", "Genetic Transcription", "Goals", "High Prevalence", "Individual", "Institution", "Investments", "Label", "Language", "Learning", "Linguistics", "Location", "Medical", "Methods", "Modeling", "Modification", "Natural Language Processing", "Nature", "Neural Network Simulation", "Outcome", "Output", "Participant", "Patients", "Performance", "Physicians", "Prevalence", "Research", "SARS-CoV-2 positive", "Sampling", "Services", "Site", "Source", "Speech", "Systematic Bias", "Testing", "Text", "Time", "Training", "Transcript", "United States National Institutes of Health", "United States National Library of Medicine", "Update", "Vision", "Weight", "Work", "coronavirus disease", "deep learning", "deep learning model", "design", "heterogenous data", "interest", "large datasets", "learning strategy", "loss of function", "machine learning model", "network models", "neural", "novel", "open source", "open source tool", "portability", "predictive modeling", "programs", "statistical and machine learning" ], "approved": true } }, { "type": "Grant", "id": "6758", "attributes": { "award_id": "1R01LM014056-01", "title": "DeconDTN: Deconfounding Deep Transformer Networks for Clinical NLP", "funder": { "id": 4, "ror": "https://ror.org/01cwqze88", "name": "National Institutes of Health", "approved": true }, "funder_divisions": [ "National Library of Medicine (NLM)" ], "program_reference_codes": [], "program_officials": [ { "id": 10715, "first_name": "YANLI", "last_name": "WANG", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2022-06-01", "end_date": "2026-02-28", "award_amount": 345290, "principal_investigator": { "id": 22557, "first_name": "Trevor", "last_name": "Cohen", "orcid": null, "emails": "[email protected]", "private_emails": null, "keywords": "[]", "approved": true, "websites": "[]", "desired_collaboration": "", "comments": "", "affiliations": [ { "id": 159, "ror": "https://ror.org/00cvxb145", "name": "University of Washington", "address": "", "city": "", "state": "WA", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [ { "id": 22558, "first_name": "Serguei VS", "last_name": "Pakhomov", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 159, "ror": "https://ror.org/00cvxb145", "name": "University of Washington", "address": "", "city": "", "state": "WA", "zip": "", "country": "United States", "approved": true }, "abstract": "Natural Language Processing (NLP) methods have been broadly applied to clinical problems, from recognition of clinical findings in physician notes to identification of transcribed speech samples indicating changes in cognitive status. Deep transformer networks (DTNs) have dramatically advanced NLP accuracy. These deep learning models have multiple hidden layers that may correspond to billions of trainable parameters, allowing them to apply information learned from training on large unlabeled corpora to a specific task of interest. However, their size leaves them especially vulnerable to confounding bias, induced by variables that can influence both the predictor (text) and the outcome (e.g. an associated diagnosis) of a predictive model. Such systematic biases are a recognized danger in the application of artificial intelligence methods to clinical problems, and are the focus of NLM NOT-LM-19-003 which invites applications proposing methods to identify and address them. Deep learning models in general require large amounts of training data, spurring initiatives to aggregate medical data from across institutional siloes. This can increase data set size and enhance model portability, but leaves the resulting models vulnerable to confounding by provenance, where models learn to recognize the origin of dataset components and make biased predictions based on site-specific class distributions (e.g. COVID prevalence). Such models will assign classes based on indicators of dataset provenance, rather than diagnostically meaningful linguistic differences, and make erroneous predictions when the provenance-specific distributions at the point of deployment differ from those in the training set. Confounding of this nature is a pervasive problem that presents a fundamental barrier to the portability of trained models, and threatens the utility of datasets assembled from across institutions and services. Unlike traditional statistical and machine learning models, with deep transformer networks feature representations are distributed across parameters spread throughout the entire network. New methods are needed to meet the challenge of identifying and mitigating the influence of confounding variables in such models. In the proposed research we will develop a systematic approach to Deconfounding Deep Transformer Networks (DeconDTN), embodied in an eponymous and publicly available set of open source tools for (1) identification of provenance-related biases, (2) mitigation of these biases using a novel set of validated methods, and (3) systematic evaluation of the resulting effects on model performance. While DeconDTN will be generally applicable, development and evaluation will occur in the context of three use cases involving data sets drawn from different sources: classification of speech transcripts from participants with dementia drawn from two locations, identification of goals-of-care discussions in clinical notes drawn from multiple studies involving a range of clinical services, and prediction of COVID-19 status in notes drawn from different clinical units. Our driving hypothesis is that the resulting models will make more accurate predictions in these heterogenous datasets than corresponding models without correction for confounding by provenance.", "keywords": [ "Address", "Architecture", "Area", "Artificial Intelligence", "Automobile Driving", "Behavior", "Bridge to Artificial Intelligence", "COVID-19", "Caring", "Characteristics", "Classification", "Clinical", "Clinical Services", "Cognitive", "Computer software", "Confounding Factors (Epidemiology)", "Coupled", "Data", "Data Aggregation", "Data Set", "Data Sources", "Dementia", "Development", "Diagnosis", "Diagnostic", "Ensure", "Equilibrium", "Evaluation", "Goals", "High Prevalence", "Individual", "Institution", "Investments", "Label", "Language", "Learning", "Linguistics", "Location", "Medical", "Methods", "Modeling", "Modification", "Natural Language Processing", "Nature", "Neural Network Simulation", "Outcome", "Output", "Participant", "Patients", "Performance", "Physicians", "Predictive text", "Prevalence", "Research", "SARS-CoV-2 positive", "Sampling", "Services", "Site", "Source", "Speech", "Systematic Bias", "Testing", "Text", "Time", "Training", "Transcript", "United States National Institutes of Health", "United States National Library of Medicine", "Update", "Vision", "Weight", "Work", "base", "coronavirus disease", "deep learning", "deep learning model", "design", "heterogenous data", "interest", "large datasets", "learning strategy", "loss of function", "machine learning model", "network models", "novel", "open source", "open source tool", "portability", "predictive modeling", "programs", "relating to nervous system", "statistical and machine learning" ], "approved": true } }, { "type": "Grant", "id": "6759", "attributes": { "award_id": "1R01AG073053-01A1", "title": "Enhancing Innate Anti-Viral Resistance Through A Community-Based Intervention", "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": 20821, "first_name": "JONATHAN W.", "last_name": "KING", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2022-03-15", "end_date": "2027-02-28", "award_amount": 638992, "principal_investigator": { "id": 22559, "first_name": "STEVE W", "last_name": "COLE", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 818, "ror": "", "name": "UNIVERSITY OF CALIFORNIA LOS ANGELES", "address": "", "city": "", "state": "CA", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [ { "id": 22560, "first_name": "Teresa E", "last_name": "SEEMAN", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 818, "ror": "", "name": "UNIVERSITY OF CALIFORNIA LOS ANGELES", "address": "", "city": "", "state": "CA", "zip": "", "country": "United States", "approved": true }, "abstract": "The SARS-CoV-2/COVID-19 pandemic has disproportionately impacted older socioeconomically disadvantaged African-Americans. This research will test whether a recently developed community-based intervention program known as Generation Exchange (GenX) can enhance a key biological mediator of antiviral resistance (Type I interferon response) in this disadvantaged population. Our previous research has identified a stress-triggered genomic program known as the “Conserved Transcriptional Response to Adversity” (CTRA). The CTRA is activated by fight-or-flight stress responses and causes immune cells to reduce antiviral activity and stimulate inflammation, both of which are detrimental in the context of COVID- 19. Our previous research on biological resilience in the face of adversity has also found that the CTRA is reduced in people with high levels of eudaimonic well-being, which includes purpose in life, generativity, and pro-social engagement. In the present study, we will conduct a randomized controlled intervention trial (n=160) to test whether a eudaimonia-promoting intergenerational mentoring program known as Generation Xchange (GenX) can enhance Type I interferon responses and reduce hyper-inflammatory responses in older African-American women and men living in a socioeconomically disadvantaged urban community. Our hypotheses are that GenX will, 1) increase Type I interferon antiviral responses, 2) reduce hyper-inflammatory bias, and 3) reduce rates of clinical respiratory virus infection and symptomatic disease (COVID, influenzas, and colds). To identify the biological mechanisms of antiviral resistance in this specific population, we will also analyze specific antiviral cell types (e.g., plasmacytoid dendritic cells, monocytes) and gene regulatory processes (e.g., transcription factor activity and single-cell gene expression). These measures will be used to determine 4) which biological factors are most important in protecting older African-Americans from respiratory virus infection, 5) how those biological risk factors are linked to other established respiratory virus risk factors (e.g., overweight/obesity, pre-existing chronic disease, low physical activity, poor sleep, social isolation/loneliness), and 6) which biological factors are impacted by GenX. Finally, we test the hypothesis that 7) GenX will show positive effects for both women and men, for those with low or high education level, and for those with low or high levels of background risk factors (e.g., overweight/obesity, chronic disease, low physical activity, social isolation/loneliness). Our overarching goal is to establish a community-based biobehavioral intervention program that is broadly scalable, involves defined biological mechanisms of antiviral resistance, and leverages social support and eudaimonic well-being to mitigate the detrimental effects of age and social disadvantage on host resistance to respiratory virus infection among COVID-vulnerable older African-Americans.", "keywords": [ "2019-nCoV", "Address", "African American", "Age", "Antibodies", "Antiviral Agents", "Antiviral Response", "Antiviral resistance", "Basic Science", "Biochemical", "Biological", "Biological Factors", "Biological Process", "Biology", "COVID-19", "COVID-19 pandemic", "Cells", "Chronic Disease", "Clinical", "Communities", "Coronavirus", "Cytotoxic T-Lymphocytes", "Dendritic Cells", "Disease", "Educational Background", "Gene Expression", "Gene Expression Regulation", "Gene Family", "Generations", "Genetic Transcription", "Genomics", "Goals", "HIV-1", "Health", "Host resistance", "Human", "IL6 gene", "Immune", "In Vitro", "Individual", "Inflammation", "Inflammatory", "Inflammatory Response", "Influenza", "Interferon Type I", "Interferons", "Intervention", "Intervention Studies", "Intervention Trial", "Life", "Link", "Loneliness", "Maps", "Measures", "Mediator of activation protein", "Mentors", "Obesity", "Older Population", "Overweight", "Pathology", "Pathway interactions", "Personal Satisfaction", "Persons", "Physical activity", "Pilot Projects", "Population", "Process", "Public Health", "Randomized", "Randomized Controlled Trials", "Regulator Genes", "Research", "Resistance", "Resistance to infection", "Risk Factors", "Signal Transduction", "Sleep", "Social isolation", "Social support", "Stress", "Sympathetic Nervous System", "T cell response", "TNF gene", "Testing", "Urban Community", "Virus Diseases", "Vulnerable Populations", "Woman", "active control", "age effect", "arm", "base", "biobehavior", "biological adaptation to stress", "cell type", "coronavirus disease", "disadvantaged population", "falls", "fighting", "infection rate", "intergenerational", "intervention program", "low socioeconomic status", "men", "monocyte", "poor sleep", "primary endpoint", "primary outcome", "programs", "psychosocial", "racial disparity", "resilience", "respiratory infection virus", "respiratory virus", "response", "secondary endpoint", "secondary outcome", "sex", "social", "social disadvantage", "social engagement", "social interventions", "sociodemographic group", "sociodemographics", "socioeconomic disadvantage", "transcription factor", "vaccine response" ], "approved": true } }, { "type": "Grant", "id": "16120", "attributes": { "award_id": "2R01HL019278-50", "title": "FcγRIIa-induced Activation of Platelet αIIbβ3 in Thrombosis and Thromboinflammation", "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": 44598, "first_name": "ANDREI L", "last_name": "KINDZELSKI", "orcid": "", "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2026-05-10", "end_date": "2030-02-28", "award_amount": 861102, "principal_investigator": { "id": 44599, "first_name": "Claudia Lorena", "last_name": "Buitrago", "orcid": "", "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [ { "id": 22562, "first_name": "Barry", "last_name": "Coller", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 763, "ror": "https://ror.org/0420db125", "name": "Rockefeller University", "address": "", "city": "", "state": "NY", "zip": "", "country": "United States", "approved": true } ] } ], "awardee_organization": { "id": 3476, "ror": "", "name": "ROCKEFELLER UNIVERSITY", "address": "", "city": "", "state": "NY", "zip": "", "country": "United States", "approved": true }, "abstract": "There is increasing awareness of the role of thromboinflammation mediated by the activation of αIIbβ3 through the binding of immune complexes to FcγRIIa in the pathogenesis of a variety of serious disorders, among which are heparin-induced thrombocytopenia/thrombosis, viral infections including COVID-19, autoimmune disorders, and chronic inflammatory disorders, including cardiovascular disease. The primary mechanism proposed to underly the thrombotic phenomena is the binding of immune complexes to the FcγIIa receptor (FcγRIIa) on platelets, leading to activation of αIIbβ3 and platelet aggregation. Both infection and autoimmune diseases can generate immune complexes that interact with FcγRIIa. Activated αIIbβ3 interacts with soluble fibrinogen, leading to platelet aggregation, thrombus formation, and thrombin generation. We and others have found that highly-specific BTK inhibitors (BTKi) can inhibit FcγRIIa-mediated αIIbβ3 activation and confirmed that platelets of XLA patients, who lack BTK on a genetic basis but do not have a bleeding disorder, do not aggregate in response to engagement of FcγRIIa. Thus, the central hypothesis of this grant renewal is that the activation of αIIbβ3 by engagement of FcγRIIa plays an important role in the pathophysiology of many immune-mediated disorders. We propose to create better murine models of these disorders by using mice that have had their entire Fc receptor repertoire and their platelet factor 4 (PF4) genes humanized and then test in these models the safety and efficacy of already-approved BTKi and antiplatelet drugs, as well as new agents targeting FcγRIIa directly that are developed under this grant. Our goal is to improve our understanding of these disorders, improve their current therapy, and develop novel, orally active drugs that directly target FcγRIIa for chronic therapy. Specific aim 1: A) To better understand the pathogenesis of the FcγRIIa-mediated disorders of αIIbβ3 activation by developing optimal murine models in which the entire murine repertoire of Fc receptors has been humanized, along with PF4, and both the Arg131 and His131 variants of FcγRIIa are studied. B) To compare the effects of antiplatelet agents (aspirin, P2Y12 inhibitors, αIIbβ3 inhibitors), and inhibitors of immune complex binding to FcγRIIa or signaling through FcγRIIa, initially focusing on BTKi, on the pathogenesis of the disorders in the murine models. Specific aim 2. To develop a rapid functional diagnostic assay that measures the combined effects of platelet FcγRIIa number, FcγRIIa affinity for the Fc domain of immunoglobulins, and the downstream signaling pathways to platelet αIIbβ3 activation for screening compounds, individual risk assessment, and optimizing therapy by monitoring drug effects. Specific aim 3. To use both high-throughput and in silico screening, along with deep learning-based de novo design, to identify novel specific inhibitors of immune complex binding to FcγRIIa or signaling through FcγRIIa as potential human therapeutics.", "keywords": [ "Adhesions", "Affinity", "Agammaglobulinaemia tyrosine kinase", "Antibody Formation", "Antigen-Antibody Complex", "Antiplatelet Drugs", "Aspirin", "Autoimmune Diseases", "Awareness", "B-Lymphocytes", "Binding", "Binding Proteins", "Biology", "Blood Coagulation Disorders", "Blood Platelets", "COVID-19", "Cardiovascular Diseases", "Cell Physiology", "Chronic", "Clot retraction", "Coagulation Process", "Cryoelectron Microscopy", "Defect", "Deposition", "Disease", "Docking", "Drug Monitoring", "Drug Targeting", "FDA approved", "Family", "Fc Receptor", "Fc domain", "Fibrin", "Fibrinogen", "Functional disorder", "Generations", "Genes", "Genetic", "Goals", "Grant", "Hemostatic function", "Human", "Immobilization", "Immune", "Immunoglobulin Domain", "Individual", "Infection", "Inflammation", "Inflammatory", "Integrins", "Mass Spectrum Analysis", "Measures", "Mediating", "Methods", "Modeling", "Molecular", "Molecular Conformation", "Monitor", "Mus", "Myocardial Infarction", "Natural Immunity", "Neoplasm Metastasis", "New Agents", "Oral", "PF4 Gene", "Pathogenesis", "Pathway interactions", "Patients", "Pharmaceutical Chemistry", "Pharmaceutical Preparations", "Phase", "Platelet Activation", "Platelet aggregation", "Plavix", "Play", "Research", "Risk Assessment", "Role", "Safety", "Signal Pathway", "Signal Transduction", "Stroke", "Structure", "Testing", "Therapeutic", "Thrombin", "Thrombosis", "Thromboxanes", "Thrombus", "Tyrosine Kinase Inhibitor", "Variant", "Virus Diseases", "X-Ray Crystallography", "abciximab", "antagonist", "chronic inflammatory disease", "clopidogrel", "deep learning", "design", "diagnostic assay", "disease model", "heparin-induced thrombocytopenia", "high risk", "high throughput screening", "immunothrombosis", "improved", "in silico", "inhibitor", "interdisciplinary approach", "mortality", "mouse model", "novel", "novel therapeutics", "phase 3 study", "pre-clinical", "receptor", "response", "screening", "simulation", "targeted agent", "thromboinflammation", "thrombotic", "thrombotic complications", "treatment optimization", "von Willebrand Factor", "wound healing" ], "approved": true } }, { "type": "Grant", "id": "6762", "attributes": { "award_id": "5R01HL157790-02", "title": "Determinants of COVID19-induced venous thrombosis and targeted therapy assessed with bioengineered vein-chip", "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": 22565, "first_name": "ILANA GRACE", "last_name": "Goldberg", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2021-05-01", "end_date": "2025-04-30", "award_amount": 665322, "principal_investigator": { "id": 22566, "first_name": "JOHN P", "last_name": "COOKE", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 860, "ror": "", "name": "BOSTON UNIVERSITY MEDICAL CAMPUS", "address": "", "city": "", "state": "MA", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [ { "id": 22567, "first_name": "John H", "last_name": "Connor", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 22568, "first_name": "Abhishek", "last_name": "Jain", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 860, "ror": "", "name": "BOSTON UNIVERSITY MEDICAL CAMPUS", "address": "", "city": "", "state": "MA", "zip": "", "country": "United States", "approved": true }, "abstract": "Determinants of COVID19-induced venous thrombosis and targeted therapy assessed with bioengineered Vein-Chip Challenge: Increasing evidence shows that SARS-CoV-2, which is the novel coronavirus that causes COVID- 19, is able to trigger the formation of blood clots within the veins of patients. This ultimately manifests into strokes and other life-threatening complications, being observed even in younger people. Related to that, is emerging data that shows that the endothelial cells are dysfunctional across several organs other than the lungs of the most severe COVID19 patients. These cells express the angiotensin-converting enzyme 2 (ACE2) receptor, to which the virus attaches, using it as an entry point to infect cells. A recently created vein-on-a-chip (vein-chip) that is an endothelialized organ-on-a-chip model incorporating the unique hemodynamics of the venous valve cusp showed evidence that this model can be used to dissect the role of major determinants of venous thrombosis – endothelium; hemodynamics; and blood components – together known as the Virchow's triad. The central hypothesis in this proposal is that the venous thrombosis observed in COVID19 disease is due to the interaction of the three determinants of thrombosis (Virchow's triad) – endotheliitis; complex hemodynamics; and blood coagulability. There is an unmet need to understand the effects of the virus and/or blood-borne inflammatory cytokines on the endothelium; and the interaction of these effects with the uniquely complex venous hemodynamics (mechanotransduction); and to discover strategies to stabilize the endothelium that may be co-operatively therapeutic with anticoagulants. Proposal: The objective here is to deploy vein-chip technology to understand the determinants of SARS-CoV- 2 induced venous thrombosis; determine the roles of endothelial, hemodynamic and humoral alterations; and propose therapeutic strategies. This objective will be met through three specific aims: Aim 1: Characterize the SARS-CoV-2 induced endothelial dysfunction in the human Vein-Chip; Aim 2: Assess interplay of SARS-CoV-2 infection and hemodynamics in DVT with Vein-Chip, and Aim 3: Therapeutics-on-Chip: Assess combinatorial therapeutics in infection-led DVT with Vein-Chip. The team driving this proposal is composed of complementary expertise in vein-chip bioengineering and innovation (Jain); applying contemporary technology in the molecular and computational biology of endothelial function and fate (Cooke); together with techniques to define host response to coronaviral infection (Connor). Impact: Taken together, the outcomes of this proposal will directly improve our understanding of thrombosis in COVID-19 infection and make predictions that could potentially result in fast-tracking of therapeutic clinical trials.", "keywords": [ "2019-nCoV", "ACE2", "Address", "Adhesions", "American", "Anticoagulants", "Anticoagulation", "Apoptosis", "Attention", "Attenuated", "Automobile Driving", "Autopsy", "Biological Assay", "Biomedical Engineering", "Blood", "Blood Vessels", "Blood coagulation", "COVID-19", "COVID-19 patient", "COVID-19 treatment", "Cells", "Cessation of life", "China", "Clinical Research", "Complex", "Computational Biology", "Data", "Deep Vein Thrombosis", "Development", "Devices", "Disease", "Distal", "Endothelial Cells", "Endothelium", "Enzyme Inhibitor Drugs", "Exposure to", "Geometry", "Homeostasis", "Human", "Immune response", "Infection", "Inflammation", "Inflammatory", "Interdisciplinary Study", "Kidney", "Lead", "Life", "Lower Extremity", "Lung", "Mesentery", "Modeling", "Molecular Biology", "Organ", "Outcome", "Pathway interactions", "Patients", "Persons", "Pharmaceutical Preparations", "Physiological", "Play", "Pre-Clinical Model", "Predisposition", "Proteins", "Proteomics", "Research", "Respiratory Failure", "Role", "SARS-CoV-2 infection", "Savings", "Signal Pathway", "Stroke", "Structure", "Surgeon", "Techniques", "Technology", "Therapeutic", "Therapeutic Clinical Trial", "Thrombosis", "Triad Acrylic Resin", "Veins", "Venous", "Venous Thrombosis", "Viral", "Virus", "Virus Diseases", "clinical phenotype", "combinatorial", "coronavirus disease", "cytokine", "design", "differential expression", "endothelial dysfunction", "hemodynamics", "improved", "innovation", "mechanotransduction", "novel", "novel coronavirus", "organ on a chip", "prevent", "receptor", "recruit", "response", "severe COVID-19", "targeted treatment", "thromboinflammation", "transcriptome", "transcriptome sequencing", "venous thromboembolism" ], "approved": true } } ], "meta": { "pagination": { "page": 1393, "pages": 1442, "count": 14420 } } }