Analysis of virulence determinants in full length H5N1 influenza genomes using computational modelling

H5N1禽流感自1997年在远东地区出现以来，已造成大量家禽死亡。特别是自2002年以来，越来越多的野禽被发现死亡和濒临死亡，这对禽流感来说是非常不寻常的，因为它通常不会在野禽中引起严重的疾病。估计至少有250人因此死亡。正如新闻中所看到的，野鸟最近将病毒从亚洲沿着沿着东西迁徙路线传播到欧洲。我们显然迫切需要确定病毒发生了什么变化，导致死亡率上升。这是一项艰巨的任务，原因有几个。首先，与病毒相关的风险使其难以在实验室中进行实验，因为我们必须确保病毒无法逃脱。其次，流感自然演变非常迅速。无论与之相关的死亡率是否在变化，这都适用，并且很难确定确切的遗传因素。第三，一些在过去很重要的遗传变化已经被确定，但最近的实验室研究表明，在目前的流行病中，还有更多的变化也可能很重要。我们将通过基于计算机的方法来研究这一点。关于几千种不同的H5N1流感病毒株的基因组成的大量信息。平均而言，这些蛋白质中的每一种在其蛋白质中的大约700个不同的氨基酸位点上与任何其他蛋白质不同。为了找出这些站点中哪些是重要的，我们使用了一种新的统计方法，该方法旨在科普非常庞大的复杂数据，并确定导致死亡率增加的关键因素。这些新模型需要非常快的计算机，我们将使用英国最快的计算机之一：爱丁堡大学的IBM BlueGene超级计算机。我们的目标是确定H5N1流感基因组中与死亡率变化最明显相关的重要组成部分。通过这种方式，我们可以帮助设计非常具体和受控的实验室实验，可以构建这些实验来测试我们的结论，并可能导致通过药物开发进行干预的关键遗传因素的识别。

H5N1 bird 'flu has caused massive mortality in poultry since its emergence in the far east in 1997. Particularly since 2002, increasing numbers of wildfowl have been found dead and dying, which is very unusual for bird 'flu as it does not normally cause severe disease in wild birds. It has also given rise to an estimated minimum of 250 human deaths. As has been seen in the news, wild birds have recently spread the virus into Europe along East-West migration routes from Asia. We clearly urgently need to identify what has changed in the virus which has led to this increase in mortality. This is a difficult task for several reasons. Firstly, the risks associated with the virus make it difficult to work with experimentally in the laboratory, as we must be certain the virus cannot escape. Secondly, influenza evolves naturally very rapidly. This applies whether or not the mortality associated with it is changing and makes it difficult to identify the exact genetic factors responsible. Thirdly, some genetic changes which have been important in the past have been identified, but recent laboratory studies suggest many more could also be important in the current epidemic. We will study this by taking a computer-based approach. A very large amount of information on the genetic make-up of several thousand different strains of H5N1 'flu. On average each one of these may differ at about 700 different amino acid sites in its proteins from any other. To find out which of these sites are important, we use a new statistical approach which is designed to cope with very large complex bodies of data and identify critical components that are responsible for the increase in mortality. These new models require very fast computers and we will be using one of the fastest in the UK: the IBM BlueGene supercomputer at the University of Edinburgh. Our aim is to identify the important components of the H5N1 'flu genome that can be most clearly associated with the mortality change. In this way we can assist with the design of very specific and controlled laboratory experiments which can be constructed to test our conclusions, and which may lead to identification of the key genetic elements for intervention through drug development.