Reconciling the mechanisms of HIV-1 infection acquisition and disease progression using mathematical modelling and phylogenetics

If HIV-1 infection is left untreated, the viral load of chronically infected individuals can rise to millions copies per millilitre of blood with hundreds of distinct genetic variants. This level of proliferation within an individual, is in stark contrast to the exceptionally low chance of infection: on average 1 in every 10,000 sexual exposures leads to infection, and of these infections, it is thought that most are initiated by a single genetic variant (Talbert-Slagle et al. 2014).

There are known risk factors associated with transmitting or acquiring HIV, but the exact mechanisms underlying these risk factors and the impact on the number of 'founder' strains are unknown. Understanding the mechanisms underlying the role of the transmitting partner and the recipient partner in determining the number of founder stains will likely help quantify the dynamics of HIV acquisition.

With an increasingly rich data source of next-generation sequence data available, we are now able to build a picture of infection dynamics through infection. These data provide a window into the founder strain dynamics that take place at the onset of infection (Keele et al. 2008). This PhD project will use existing and newly generated next-generation sequence data to understand estimates of the number and type of these founder strains (Romero-Severson et al. 2016). Then, using phylogenetic, mathematical or statistical approaches, the impact of the number and type of these founder strains on disease progression will be evaluated.

Aims
1) Evaluate whether different methodologies to calculate the number of founder strains provide consistent results. Founder strains are genetically homogeneous lineages of viruses that go on to successfully replicate within the host during its infection. However estimating the number of founder strains is usually time-consuming and computationally intensive. Accurately using data on the number of founder strains will depend on confidently relying on these previous estimates.

2) Develop a mathematical model to harmonize data on the number of founder strains with the risk of transmission by route and by stage of infection

3) Extend the mathematical model to incorporate the role of founder strains on disease progression and viral load, drawing on data driven methods such as phylogenetic analysis.

Training outcomes
The project will use an interdisciplinary combination of genetic sequence data analysis, statistical modelling and mathematical analysis. Existing quantitative skills will be developed and extended, to be applied in a computational setting using programming languages such as R or Python. Emphasis will be placed on developing and sharing reproducible code with the wider scientific community through platforms such as GitHub.

Communication of this research through publication in peer-reviewed journals and presentation in scientific conferences will be encouraged. By working closely with experts in sequence data, phylogenetic analysis and mathematical modelling, the student will become comfortable working within an interdisciplinary environment and interacting with a diverse scientific team.