Genomic analysis of the canonical case of virulence evolution: Myxomatosis in Au

毒力进化典型案例的基因组分析：Au 中的粘液瘤病

• 批准号: 8586296
• 负责人: Andrew F. Read
• 金额: $ 58.81万
• 依托单位: PENNSYLVANIA STATE UNIVERSITY, THE
• 依托单位国家: 美国
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-12-01 至 2016-11-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8586296
• 关键词: Address; Architecture; Archives; Attenuated; Australia; Avian Influenza; Biocontrols; Biological; Case Study; Data; Disease; Disease Outbreaks; Ecology; Engineering; Epidemic; Epidemiology; Europe; European; Evolution; Frequencies; Future; Genetic; Genomics; Genotype; Goals; HIV; Human; Infection; Infectious Agent; Link; Molecular; Molecular Biology; Molecular Evolution; Molecular Genetics; Mutation; Myxoma; Myxoma virus; Natural Selections; Oryctolagus cuniculus; Outcome; Phenotype; Phylogenetic Analysis; Population; Process; Public Health; Radiation; Risk; Role; Sampling; Series; Severity of illness; Shapes; South American; Ursidae Family; Variant; Vertebrates; Viral; Virulence; Virulent; Virus; Whole Organism; Work; attenuation; base; comparative genomics; experimental analysis; fitness; infectious disease evolution; interest; killings; mathematical model; mathematical theory; mutant; novel; pandemic disease; pathogen; positional cloning; pressure; progenitor; research study; theories; transmission process; virology

DESCRIPTION (provided by applicant): When viruses jump from their natural host into human populations, one of the most pressing questions early in any subsequent epidemic is how the virus will evolve if it cannot be immediately contained and eradicated. In particular, if global pandemics result, or the disease becomes endemic in humans, will it become more or less harmful? Mathematical models of disease ecology and evolution show that when certain key phenotypic determinants of viral fitness are known, it is possible to predict the subsequent direction of virulence evolution. The problem is that these phenotypic details are hard to elucidate. In contrast, advances in molecular biology mean that when cross-species jumps do occur, a deluge of genomic data is generated, allowing genetic tracking of disease evolution. Do these genomic data allow us to predict much about future risk? In this proposal we seek to determine the molecular genetic basis of the evolution of the highly lethal myxoma virus after it was deliberately released as a biocontrol agent against rabbits in both Australia and Europe in the 1950s. These releases were inadvertent experiments in virus evolution, and even today myxoma virus is perhaps the best characterized case of virulence evolution in any vertebrate disease. Critically, the key phenotypic determinants of viral fitness are well characterized, so that the reason natural selection caused changes in myxoma virulence are extremely well known. But the genetic basis of the virulence evolution is not. We will use genomic analysis of viral isolates from both continents, including those sampled in the 1950s, to identify candidate genetic changes responsible for virulence evolution, and then engineer viruses with those mutations. The engineered lines will then be used to determine the causal role of the mutations in the virulence evolution. This work will generate a case study where we can link genotype to phenotype in a context where the transmission ecology is well enough known to predict evolution. Thus, we will be able to assess the power of genomic analysis for predicting future risk.

描述（由申请人提供）：当病毒从其自然宿主跳到人群中时，在任何随后的流行病早期最紧迫的问题之一是，如果不能立即遏制和根除，病毒将如何演变。特别是，如果导致全球性流行病，或者疾病在人类中成为地方病，它的危害会更大还是更小？疾病生态学和进化的数学模型表明，当已知病毒适应性的某些关键表型决定因素时，就有可能预测随后的毒力进化方向。问题是，这些表型细节很难阐明。相比之下，分子生物学的进步意味着，当跨物种跳跃确实发生时，会产生大量的基因组数据，从而允许对疾病进化进行遗传跟踪。这些基因组数据能让我们预测未来的风险吗？在这项提案中，我们试图确定高度致命的粘液瘤病毒的进化的分子遗传基础后，它被故意释放作为一种生物控制剂对兔子在澳大利亚和欧洲在20世纪50年代。这些释放是病毒进化过程中无意的实验，即使在今天，粘液瘤病毒可能是任何脊椎动物疾病中毒力进化的最佳特征。重要的是，病毒适应性的关键表型决定因素已被很好地描述，因此自然选择引起粘液瘤毒力变化的原因已非常清楚。但毒力进化的遗传基础却不是。我们将使用来自两大洲的病毒分离株的基因组分析，包括20世纪50年代的样本，以确定负责毒力进化的候选遗传变化，然后用这些突变改造病毒。然后将使用工程化的品系来确定突变在毒力进化中的因果作用。这项工作将产生一个案例研究，我们可以链接基因型表型的背景下，传播生态学是众所周知的，足以预测进化。因此，我们将能够评估基因组分析预测未来风险的能力。