The role of UpA dinucleotides in regulating virus replication

What does the genetic make-up of RNA viruses look like? All genomes are composed of four bases, A, C, G and T (or U in the case of viral RNA genomes and mRNAs). If these were selected randomly, every genome would comprise 25% of each base; but this is not the case. Similarly, there are 16 possible combinations of nucleotide pairs, or dinucleotides. With random representation, each dinucleotide would occur 1/16 or 6.25% of the time, but again this is not so. In the genomes of all organisms, from bacteria to humans, TpA dinucleotides ('p' represents the phosphate bridge in the DNA backbone) are under-represented. We don't know why this is. Even more intriguingly, RNA viruses mimic this by suppressing UpA in their genomes.

When a virus infects a host cell, this triggers an interferon response that results in hundreds of antiviral genes being upregulated. One such gene is RNaseL. In 1981 it was reported that mRNA is cleaved at UpA motifs by RNaseL. This offers one possible explanation for why UpAs are suppressed in the genomes of viruses and their hosts. However, when UpAs are added into virus genomes, the virus is impaired, but depletion of RNaseL from the system does not remove the impairment, suggesting other factors are at play.

Alternatively, UpAs may be avoided to reduce the risk of introducing stop codons. Two of the three stop codons (UAA and UAG) include UpA motifs, and if UpAs are deselected the chances of aberrantly introducing stop codons in protein coding sequences are reduced.
The purpose of this interdisciplinary project is to integrate computational (Lycett lab) and laboratory (Gaunt lab) based methods to characterise the distribution of UpAs in RNA virus genomes, and establish their importance in a virus system. Specifically you will:
1. Determine the distribution of UpA motifs in selected RNA viruses by assessing whether their frequencies are different in coding and non-coding regions, whether UpAs occur within or across codon boundaries, and whether they correlate with the occurrence of stop codons or are utilised / deselected under different pressures.
2. Use this understanding to design and synthesise mutants of influenza A virus with increased UpA content, and characterise the impact of UpA introduction on virus replication. You will test whether removal of RNaseL (and other factors) from the system abrogates the anticipated defect in virus replication.
3. If RNaseL restricts virus replication, you will characterise the mechanism. If RNaseL is not restrictive, you will use a small screen based approach to identify cellular factor(s) that are important for UpA recognition by the host cell.

From this project, you will learn data science skills, applied to biological data - including statistical, bayesian, machine learning and evolutionary modelling, (bio)informatics, phylogenetics and phylodynamics, as well as computing skills including how to code, script and use high performance computing; and laboratory skills including how to perform virus infections and virology assays, and molecular biology techniques including CRISPR.