Chronic viral infections, including HIV, Hepatitis B, and Hepatitis C can result in life-long infections that are responsible for millions of deaths globally each year. A key reason why these viruses are so difficult to control is their rapid evolution within infected individuals, an important component of which is their ability to escape host HLA immune responses. Not only do individuals generally encode different HLA alleles, but HLA allele frequencies differ among human populations. Consequently, expected patterns of viral evolution differ both among individuals and between populations. The aim of this project is to better understand the link between the evolution of these viruses, and specifically virus CTL escape mutations, and the distribution of HLA alleles within humans at a global scale. This project has important implications for understanding why the pathogenicity of viruses differs among human populations, and for the targeted use of therapeutic vaccine design, both of which could be explored during the DPhil project.
The project will involve the incorporation of different types of big data, including population viral genetic data, population level HLA data, and within-host next-generation sequencing data. The successful candidate will be based at the Big Data Institute, which brings together researchers working on large, complex, datasets, and has extensive computational resources.
We will provide training in the evolutionary analysis of viral sequence data,
statistical analysis of large complex datasets, and advanced mathematical modelling. There will also be opportunities to attend external short courses.
This DPhil will be well suited to a student with strong mathematical and/or computational skills.
Project reference number: 1012
|Dr Katrina Lythgoe||Big Data Institute||Oxford University, NDM Research Building||GBRfirstname.lastname@example.org|
|Professor Gil McVean FRS FMedSci||Big Data Institute||Oxford University, Henry Wellcome Building of Genomic Medicine||GBRemail@example.com|
|Professor Christophe Fraser||Big Data Institute||Oxford University, Henry Wellcome Building of Genomic Medicine||GBRfirstname.lastname@example.org|
Why some individuals develop AIDS rapidly whereas others remain healthy without treatment for many years remains a central question of HIV research. An evolutionary perspective reveals an apparent conflict between two levels of selection on the virus. On the one hand, there is rapid evolution of the virus in the host, and on the other, new observations indicate the existence of virus factors that affect the virulence of infection whose influence persists over years in infected individuals and across transmission events. Here, we review recent evidence that shows that viral genetic factors play a larger role in modulating disease severity than anticipated. We propose conceptual models that reconcile adaptive evolution at both levels of selection. Evolutionary analysis provides new insight into HIV pathogenesis. Hide abstract
HIV-1 undergoes multiple rounds of error-prone replication between transmission events, resulting in diverse viral populations within and among individuals. In addition, the virus experiences different selective pressures at multiple levels: during the course of infection, at transmission, and among individuals. Disentangling how these evolutionary forces shape the evolution of the virus at the population scale is important for understanding pathogenesis, how drug- and immune-escape variants are likely to spread in populations, and the development of preventive vaccines. To address this, we deep-sequenced two regions of the HIV-1 genome (p24 and gp41) from 34 longitudinally-sampled untreated individuals from Rakai District in Uganda, infected with subtypes A, D, and inter-subtype recombinants. This dataset substantially increases the availability of HIV-1 sequence data that spans multiple years of untreated infection, in particular for different geographical regions and viral subtypes. In line with previous studies, we estimated an approximately five-fold faster rate of evolution at the within-host compared to the population scale for both synonymous and nonsynonymous substitutions, and for all subtypes. We determined the extent to which this mismatch in evolutionary rates can be explained by the evolution of the virus towards population-level consensus, or the transmission of viruses similar to those that establish infection within individuals. Our findings indicate that both processes are likely to be important. Hide abstract