Mechanistic Constraints on the Acquisition of Variation by Alphaviruses

该项目的问题是：RNA病毒的基因组结构是否对病毒获得变异施加了机制限制；是否可以用一种有助于设计用于元序列应用程序的数据处理和分析算法的方式来定义它们？它解决了BBSRC的主题“探索新的工作方式”：战略重点“数据驱动生物学”；研究生物系统或过程中表型性状与变异之间的联系。RNA病毒的RNA聚合酶容易出错，引入突变的速率比宿主的校对DNA聚合酶高得多。RNA病毒基因组的自然复制是遗传变异的一个有力来源，自然选择和人工选择都可能对其起作用。一些值得注意的新发疾病是由RNA病毒引起的。它们的出现部分归因于快速进化的能力，但几乎没有定量数据表明是什么限制了这一点。一些RNA病毒的高毒性也引起了对故意滥用的关切。因此，需要更好地了解病毒进化，以帮助预测和理解突发事件，以及快速可靠地检测和区分自然和合成生物体。对RNA病毒的研究表明，密码子的使用可以沿着多顺反子RNA的长度变化，这与共翻译修饰的位点有关。这可能是一种自适应机制，以减缓多反管沿线点的翻译速率，以促进共翻译修饰，并可能代表对可变性的约束。此外，一些研究将病毒RNA二级结构与免疫反应诱导联系起来，这可能导致基于基因组结构的选择。除此之外，除了与非沉默突变相关的限制外，目前还不清楚RNA病毒获得变异的限制是什么。DNA测序现在允许对包含多个分类群的复杂生物环境进行元测序，并通过数据处理算法识别不同的基因组。元测序的数据处理挑战可以通过在组装过程中对潜在基因组进行排序的算法来简化，优先考虑那些更有可能是真实的基因组。它们也可以通过算法进行简化，以便在主要的自然环境中快速识别合成基因组。该项目将检验合成危险2组甲病毒基因组向最佳序列进化的能力，该基因组可能会通过一个不如初始修饰形式最优的中间体。它将比较野生类型和计算优化的序列；以及杂交序列的稳定性和突变方向。该项目将产生关于RNA病毒易变性和影响变异的因素的定量数据，为未来优化筛选元序列数据的算法提供数据，以快速可靠地识别合成生物体；以及快速识别和分类以前未知的生物体，以及复杂生物混合物中出现的或潜在的病原体。研究结果将应用于进化生物学、环境监测、流行病学、法医学以及医学和兽医诊断。该项目结合了Pirbright在重组甲型病毒方面的专业知识，萨里大学对人畜共患RNA病毒遗传和表型变异的兴趣，以及Dstl对甲型病毒准种动力学的兴趣。该项目将扩大TPI在甲病毒细胞和分子生物学方面的投资组合；它将为世界卫生组织对人畜共患病毒的分析提供信息；并利用生物威胁病毒的CL2代物扩展Dstl的产品组合，这有助于了解RNA病毒的总体种群动态。它将开始评估来自自然和合成来源的威胁性质的潜在限制。

This project asks: Does the genome structure of RNA viruses place mechanistic constraints on acquisition of variation by the virus; and can these be defined in a way that aids the design of data handling and analysis algorithms for metasequencing applications? It addresses the BBSRC Theme 'Exploiting New Ways of Working': Strategic Priority 'Data Driven Biology'; Investigating links between phenotypic traits and variation in biological systems or processes. RNA polymerases of RNA viruses are error prone, introducing mutations at much higher rates than the proof-reading DNA polymerases of their hosts. The natural replication of RNA virus genomes is a potent source of genetic variation upon which both natural and artificial selection may act. Several notable emerging diseases are caused by RNA viruses. Their emergence has been attributed partly to the ability to evolve rapidly, but there is little quantitative data on what may constrain this. The high virulence of some RNA viruses has also led to concerns regarding intentional misuse. Consequently a greater understanding of viral evolution is required to help predict and understand emergence events, as well as to detect and distinguish natural and synthetic organisms rapidly and reliably. Studies of RNA viruses have demonstrated that codon usage can vary along the length of a polycistronic RNA, and this correlates with sites of co-translational modification. This may be an adaptive mechanism to slow the rate of translation at points along the polycistron to facilitate co-translational modification, and may represent a constraint on variability. Furthermore, some studies have connected virus RNA secondary structure with immune response induction, which may result in selection on the basis of genome structure. Beyond this, it is not clear what constraints on the acquisition of variation in RNA viruses apply, except those associated with non-silent mutations. DNA sequencing now allows metasequencing of complex biological milieux comprising multiple taxa, and identification of distinct genomes through data handling algorithms. The data handling challenges of metasequencing could be streamlined by algorithms to rank potential genomes during assembly, giving priority to those with a greater likelihood of being real. They could also be streamlined by algorithms to rapidly identify synthetic genomes in predominantly natural milieux. The project will examine the ability of a synthetic Hazard Group 2 alphavirus genome to evolve towards an optimal sequence, potentially passing through an intermediate that is less optimal than the initial modified form. It will compare wildtype and computationally optimized sequences; and the stability and direction-of-mutation of hybrid sequences. The project will produce quantitative data on the mutability of RNA viruses and factors which militate against variation, providing data for the future optimization of algorithms for sifting metasequence data to rapidly and reliably identify synthetic organisms; and for rapid identification and classification of previously unknown organisms, and emergent or potential pathogens in complex biological mixtures. The results will have applications in evolutionary biology, environmentalmonitoring, epidemiology, forensic science, and medical and veterinary diagnostics. The project combines Pirbright's expertise with recombinant alphaviruses with University Of Surrey's interest in genetic and phenotypic variation in zoonotic RNA viruses, and Dstl's interest in the dynamics of alphavirus quasispecies. The project will expand TPI's portfolio on the cell and molecular biology of alphaviruses; it will inform UoS' analyses of zoonotic viruses; and expand Dstl's portfolio with CL2 surrogates for biothreat viruses that help to understand the population dynamics of RNA viruses generally. It will begin to assess potential limits on the nature of threat from both nature of threat from both natural & synthetic sources.