Disentangling viral RNA structural dynamics and RNP condensation in a patient- derived model of airway epithelial infection

Grant number: 225081/Z/22/Z

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Key facts

  • Disease

    COVID-19, Unspecified
  • Start & end year

    2023
    2028
  • Known Financial Commitments (USD)

    $1,336,291.85
  • Funder

    Wellcome Trust
  • Principal Investigator

    Dr. Anob Mauli Chakrabarti
  • Research Location

    United Kingdom
  • Lead Research Institution

    University College London
  • Research 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.