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

如果HIV-1感染得不到治疗，慢性感染者的病毒载量可能会上升到每毫升血液中数百万个拷贝，并带有数百种不同的遗传变异。个体内部的这种增殖水平与极低的感染机会形成鲜明对比：平均每10,000次性接触中就有1次导致感染，并且在这些感染中，大多数被认为是由单一基因变异引起的（Talbert-Slagle et al. 2014）。有一些已知的与传播或感染艾滋病毒相关的风险因素，但是这些风险因素背后的确切机制以及对“创始”毒株数量的影响尚不清楚。了解传播伴侣和受感染伴侣在确定方正染色数量方面的作用机制，可能有助于量化艾滋病毒获得的动态。随着下一代序列数据日益丰富的数据源，我们现在能够通过感染建立感染动态的图像。这些数据提供了一个窗口，了解感染开始时发生的方正菌株动态（Keele等人，2008年）。该博士项目将使用现有的和新生成的下一代序列数据来了解这些创始菌株的数量和类型的估计（Romero-Severson et al. 2016）。然后，使用系统发育，数学或统计方法，将评估这些创始菌株的数量和类型对疾病进展的影响。目的1)评价计算方正应变数的不同方法是否提供一致的结果。创始者菌株是遗传上同质的病毒谱系，在宿主感染期间继续在宿主内成功复制。然而，估计方正应变的数量通常是耗时且计算量大的。准确地使用关于方正菌株数量的数据将取决于自信地依赖于这些先前的估计。2)建立一个数学模型，以协调关于创始菌株数量的数据与感染途径和感染阶段的传播风险3)扩展数学模型，以纳入创始菌株对疾病进展和病毒载量的作用，利用数据驱动的方法，如系统发育分析。培训成果该项目将采用基因序列数据分析、统计建模和数学分析的跨学科结合。现有的定量技能将得到发展和扩展，以便在使用编程语言（如R或Python）的计算环境中应用。重点将放在通过GitHub等平台与更广泛的科学界开发和共享可复制代码上。鼓励通过在同行评议的期刊上发表和在科学会议上发表来交流这项研究。通过与序列数据、系统发育分析和数学建模方面的专家密切合作，学生将在跨学科的环境中工作，并与不同的科学团队互动。

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.Aims1) 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 infection3) 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 outcomesThe 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.