Disentangling viral RNA structural dynamics and RNP condensation in a patient- derived model of airway epithelial infection
- Funded by Wellcome Trust
- Total publications:0 publications
Grant number: 225081/Z/22/Z
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Key facts
Disease
COVID-19, UnspecifiedStart & end year
20232028Known Financial Commitments (USD)
$1,336,291.85Funder
Wellcome TrustPrincipal Investigator
Dr. Anob Mauli ChakrabartiResearch Location
United KingdomLead Research Institution
University College LondonResearch Priority Alignment
N/A
Research Category
Pathogen: natural history, transmission and diagnostics
Research Subcategory
Pathogen morphology, shedding & natural history
Special Interest Tags
N/A
Study Type
Non-Clinical
Clinical Trial Details
N/A
Broad Policy Alignment
Pending
Age Group
Not Applicable
Vulnerable Population
Not applicable
Occupations of Interest
Not applicable
Abstract
My research vision ultimately aims to identify targeted treatments for respiratory viral infections, through a detailed understanding of the molecular interactions between the virus and host. Here, I propose to study influenza A virus and SARS-CoV-2 and the role of viral structure and viral- host ribonucleoprotein (RNP) condensation. In particular I will assess the impact on the viral infection cycle and also differences in host responses from normal and smoking-damaged lungs. My hypothesis is that interactions between dynamic viral RNA structures and host RBPs within airway epithelial cells are crucial to viral RNP condensation and the molecular response to infection, and that they are impaired in disease. I have three key goals: - Establish my functional genomics molecular toolkit in a new patient-derived airway epithelial cell infection model system and obtain global measurements of structure and condensation - Develop a new suite of methods to characterise the dynamics of viral RNA structures and interactions with RBPs at single molecule resolution - Derive an in silico model of RNP condensation and clinical phenotype through iterative integrative computational analysis and machine learning methods and predict high-confidence potential therapeutic targets.