HIV places an enormous burden on global health. Implementing treatment and interventions can save millions of lives, but to do this effectively requires us to be able to predict the outcome of interventions, and to be able to accurately assess how well they are working once implemented. For HIV, these efforts are hampered by long durations of infections, and rapid within-host viral evolution during infection, meaning the virus an individual is infected with is unlikely to be the same as any viruses they go on to transmit.
In this project you will use state-of-the-art viral sequencing data, combined with epidemiological data and mathematical modelling, to create an integrated understanding of HIV transmission.
For this project, you will identify individuals enrolled in studies in Africa and Europe, who are part of transmission chains, and for whom multiple blood samples are available throughout infection and at the time of transmission. These samples will be sequenced using state-of-the-technology developed at the University of Oxford enabling the sequencing of thousands of whole virus genomes per sample, without the need to break the viral genomes into short fragments (whole-haplotype deep sequencing). Using this data, you will comprehensively characterise viral diversity during infection, and at the point of transmission, to gain an integrated understanding of how HIV within-host dynamics affects which viruses are transmitted.
Knowing the characteristics of viruses that are transmitted is important for preventative vaccine design and for our understanding of how drug- and immune-escape mutations are likely to spread through populations. These aspects can be explored during the DPhil.
During this project you will develop a number of key skills, including the handling and analysis of state-of-the-art viral genetic data, mathematical modelling, and computer programming. 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: 1013
|Dr Katrina Lythgoe||Big Data Institute||Oxford University, NDM Research Building||GBRemail@example.com|
|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