Dynamic stochastic block modelling for analysing recombination in HIV

MRC：石楠格兰特：MR/N 013166/1艾滋病毒仍然是世界范围内的一个巨大负担，每年有170万新感染者（联合国艾滋病规划署，2019年）。抗逆转录病毒疗法（ART）的推出已经减少了艾滋病相关死亡和进一步传播的数量，但为了遏制进一步的感染，联合国艾滋病规划署的目标是95%的人口应该知道他们的状况，95%的人应该接受治疗，95%的人应该被病毒抑制。确定新感染的驱动因素将有助于确定需要弥补的差距。比较来自不同患者的病毒序列可用于流行病学研究。艾滋病毒聚合酶基因（pol）的序列数据通常被收集用于耐药性检测，但一旦匿名，仅保留基本的人口统计信息，就可以用于这些目的。遗传距离（即任何两种病毒之间突变差异的数量）可以用来将密切相关的病毒联系在一起。（较低的遗传距离表明他们最近有一个共同的祖先）。艾滋病毒突变被引入到基因组中的每个复制周期。据说突变有自己的“时钟”，因此平均而言，随着时间的推移，变化以可预测的方式积累。因此，遗传距离和采样时间可以用来绘制链接，推断网络，传播模式和网络的其他特征，如度分布。这些与人口统计信息相关的见解可以为公共卫生政策提供信息。例如，被认为是高风险群体的个人可能会被建议采取暴露前预防（PrEP）。艾滋病毒的多样性非常高，因为这种病毒在人类中已经进化了大约一百年，早在它被首次描述之前。它被分类为在其扩张早期形成的主要谱系（亚型）。当一个人感染了一种以上的HIV变异体时，两者之间可能发生重组，产生一种杂交病毒，从而产生更多的多样性。这几乎肯定会发生在来自同一感染的两种相同病毒之间，但由于新病毒与双亲相同，因此无法检测到。当高度分化的病毒重组时（例如来自不同亚型的病毒），这变得更加明显，因为有足够的信号来区分两种亲本病毒。不同病毒之间的重组过程打破了连接，其中一半基因组可能与第一个亲本病毒连接，另一半与第二个亲本病毒连接。现在，如果在连锁分析中考虑整个序列，则不会产生任何联系，因为新序列现在与双亲都足够不同。当艾滋病毒在传播网络中沿着移动时，它偶尔会发现自己是双重感染的一部分，并可能参与重组事件。这可能发生在任何时间点，使其更难被发现，因为其他突变会增加，分子钟会使病毒向前移动。动态随机块建模是一种对网络数据进行建模的方法，在我们的案例中，它将用于发现随着时间的推移相似病毒的群体或社区。这种方法将随着时间的推移更好地对HIV多样性和模型网络进行分类;非常适合快速进化的重组病毒。通过仿真实验验证了该方法的有效性。最后，我们将把它应用于乌干达的几乎全基因组的艾滋病毒数据。本研究将在加拿大安大略西部大学病理学和实验室医学系副教授Art Poon的监督下进行。

MRC : Heather Grant : MR/N013166/1 HIV is still a huge burden world-wide, with 1.7 million new infections each year (UNAIDS, 2019). The roll out of anti-retroviral therapies (ART) has worked to reduce the numbers of AIDS related deaths and onward transmissions, but to curb further infections still, UNAIDS goals are that 95% of the population should know their status, 95% of those should be on treatment, and 95% of those should be virally supressed. Characterising drivers of new infections will help to identify gaps to be closed. Comparing viral sequences from different patients can be used for epidemiological studies. HIV sequence data for the polymerase gene (pol) is routinely collected for drug-resistance testing, but can then be used secondarily for these purposes, once anonymized, keeping only basic demographic information. Genetic distance (that is, the number of mutational differences between any two viruses) can be used to link closely related viruses together. (A lower genetic distance suggests they shared a common ancestor more recently). HIV mutations are introduced into the genome with each replication cycle. Mutation is said to have its own 'clock' so that changes builds up, on average, in a predictable way over time. Therefore, the genetic distance and time of sampling, can be used to draw linkage, infer networks, patterns of transmission, and other characterisations of the network such as degree distribution. These insights tied with demographic information can inform public health policy. For instance, individuals from groups deemed at high-risk might be advised to take pre-exposure prophylaxis (PrEP). HIV diversity is extremely high, since the virus has been evolving in humans for maybe a hundred years, long before it was first described. It is classified into major lineages (subtypes) that formed early on during its expansion. Where an individual is infected with more than one HIV variant, recombination between the two can occur, creating a hybrid virus, and thus more diversity. This almost certainly happen between two identical viruses from the same infection, but will be undetectable since the new virus is the same as both parents. Where highly divergent viruses recombine, (such as those from different subtypes), this becomes more obvious as there is enough signal to distinguish the two parental viruses. This process of recombination between divergent viruses breaks apart linkages, where one half of the genome might link to the first parental virus, and the other half to the second. Now, if the whole sequence was to be considered in a linkage analysis, no connections would be made as the new sequence is now sufficiently different to both parents. As HIV moves along the transmission network, it will occasionally find itself part of a dual infection, and may take part in a recombination event. This could happen at any time point in time, making it more difficult to spot, as other mutations build up, and the molecular clock moves the virus forward. Dynamic Stochastic Block Modelling is a way of modelling network data, and in our case will be used to find groups or communities of similar viruses over time. This approach will better classify HIV diversity and model networks over time; highly appropriate for a fast-evolving recombinogenic virus. Simulation experiments will be carried out to test the principle and validate the approach. Finally, we will apply this to near-full genome HIV data from Uganda. This research will be undertaken under the supervision of Associate Professor Art Poon in the Department of Pathology and Laboratory Medicine at Western University, Ontario, Canada.