A longitudinal study of SARS-CoV-2 evolution and molecular characterisation of variants in immunocompromised individuals with persistent infection

Immunocompromised individuals can develop persistent infection with SARS-CoV-2. Mutations accrue due to ongoing replication, creating new 'variants' of the virus. These mutations have the potential to alter host-pathogen interaction, potentially affecting pathogenesis, transmissibility, severity of disease and susceptibility to vaccines or treatment. This longitudinal study will characterise viral evolution and the variants that arise during persistent infection with SARS-CoV-2, followed by molecular characterisation of these variants in vitro. By studying longitudinal viral isolates and sera from the same individual, sequential mutations can be studied to judge their effect on host-pathogen interaction. The aims of this proposal include to 1) describe the patterns of SARS-CoV-2 intrahost evolution longitudinally during persistent infection, 2) correlate the emergence of SARS-CoV-2 variants with clinical course, immunosuppression and antiviral treatment, 3) characterise emergent variants in vitro, including experiments to judge neutralisation by antibodies and resistance to interferon. Genomic sequencing of viral isolates using a deep sequencing protocol on Illumina technology will allow determination of the consensus genome and intrahost single nucleotide variants (iSNVs). The presence and frequency of iSNVs over time will be monitored, and in response to treatments for SARS-CoV-2 and other immunomodulating therapies for pre-existing conditions. Viral isolates will be cultured for in vitro molecular characterisation experiments. Successive genomic mutations will be judged for their effect on neutralisation by antibodies and changes in susceptibility of the virus to interferon. Longitudinal serum samples from persistently infected individuals will be used in autologous neutralisation studies on the cultured virus. Initial research utilises existing ethical approval to analyse already stored, residual samples from GSTT in collaboration with KCL and UCL.