The roles of carbonic anhydrases 3 and 5B in influenza A virus infection

  • Caitlin Simpson

Student thesis: Doctoral ThesisDoctor of Philosophy (PhD)


Influenza A viruses (IAVs) represent a significant and rapidly evolving threat to human health. As zoonotic single stranded negative-sense RNA viruses, IAVs undergo rapid reassortment and mutation, giving rise to vaccine and drug resistant strains with pandemic potential. Spillover of IAVs from animal species and resistance to vaccines and anti-viral drugs are ongoing issues and as such, effective management of future influenza outbreaks will require development of novel anti-viral strategies. As obligate intracellular parasites, IAVs rely on host-cell machinery for successful replication. Identification of novel host-cell factors of importance for IAV infection represents a promising strategy for identification of cellular anti-viral targets, with lower potential for the development of resistance.

In the first part of this project, human carbonic anhydrases (CAs), CO2 hydratase enzymes with a wide variety of functions, were depleted via siRNA and the impacts on IAV infection quantified. CA3 and CA5B were identified as novel proteins that promote infection and uncoating of IAV.

Detailed investigation of the dynamics of several biological pathways in CA3/CA5B depleted cells, including the aggresome processing and ubiquitination machineries and the endolysosomal system, revealed detailed insights into the mechanistic links between CA activity and IAV infection. CA3 depletion significantly disrupted aggresome formation, while CA5B knockdown was associated with changes in patterns of K48-linked ubiquitination and endosome dynamics. These experiments gave novel clues as to the physiological roles and importance of CA3 and CA5B in cell biology.

In addition, image analysis methods were developed which allowed quantification of complex protein localisation phenotypes. Software with machine learning capabilities was successfully utilised to identify and characterise subcellular structures including aggresomes and endosome clusters, demonstrating that machine learning provides a valuable tool for quantification of significant phenotypes that may be missed by traditional image analysis methods.
Date of Award27 Sept 2022
Original languageEnglish
Awarding Institution
  • University of Bristol
SupervisorYohei Yamauchi (Supervisor) & Abderrahmane Kaidi (Supervisor)

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