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死亡，这对禽流感来说是非常不寻常的，因为它通常不会导致野鸟严重疾病。据估计，它还造成了至少250人死亡。正如新闻中所看到的，野鸟最近沿着从亚洲向东向西的迁徙路线将病毒传播到欧洲。我们显然迫切需要确定病毒发生了哪些变化，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在实验室进行实验工作变得困难，因为我们必须确保病毒无法逃脱。其次，流感的自然进化速度非常快。无论与之相关的死亡率是否在变化，这一点都适用，并使确定确切的遗传因素变得困难。第三，一些过去很重要的基因变化已经被发现，但最近的实验室研究表明，更多的基因变化在当前的疫情中也可能是重要的。我们将采取以计算机为基础的方法来研究这一点。关于数千种不同H5N1流感毒株的基因组成的大量信息。平均而言，它们中的每一种可能在其蛋白质中约700个不同的氨基酸位置上与其他任何一种不同。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，该方法旨在处理非常庞大的复杂数据体，并确定导致死亡率上升的关键组成部分。这些新型号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流感基因组中最明显与死亡率变化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，这些实验可以用来检验我们的结论，并可能导致通过药物开发来识别干预的关键遗传因素。

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.