Dissertation Research: Evolution of Virulence in Sterilizing Pathogens: Spatial Models and Field Studies

论文研究：灭菌病原体毒力的进化：空间模型和实地研究

• 批准号: 9972650
• 负责人: Janis Antonovics
• 金额: $ 0.49万
• 依托单位: Duke University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 1999
• 资助国家: 美国
• 起止时间: 1999-07-01 至 2001-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9972650&HistoricalAwards=false
• 关键词: Dissertation; Research; Evolution; Virulence; Sterilizing

Antonovics9976250It is commonly suggested that pathogens will evolve toward being harmless so as not to cause the extinction of the host population and hence of themselves. This idea has been disputed on the grounds that selection for reduced virulence would require differential extinction of whole populations, a process that has been considered to be slow and ineffective. This study will use theoretical models to investigate whether the evolution of reduced virulence can occur in situations where the host and pathogen are distributed over a large spatial area, but where their dispersal is much more local. Model assumptions will be tested using the anther-smut disease of white campion. This is as a well-studied disease that partly sterilizes its host. Given present day concerns about emerging infectious diseases, it is important to understand why some pathogens cause very severe disease symptoms, yet others have only a small effect on their hosts. Disease symptoms can change over time as a result of pathogen evolution, but the predicted direction and mechanism of such change has been very controversial. This study will model the processes involved, and therefore help predict possible future directions of change in the severity of infectious diseases.

通常认为，病原体会朝着无害的方向进化，这样就不会导致宿主种群的灭绝，从而也不会导致它们自己的灭绝。 这一观点一直存在争议，理由是选择降低毒性需要整个种群的差异灭绝，这一过程被认为是缓慢和无效的。 本研究将使用理论模型来研究在宿主和病原体分布在大的空间区域但它们的传播更局部的情况下，是否会发生毒力降低的演变。 模型假设将使用白色坎皮翁的花药黑穗病进行检验。 这是一种被充分研究的疾病，它部分地使宿主绝育。 鉴于目前对新兴传染病的关注，理解为什么有些病原体会引起非常严重的疾病症状，而另一些病原体对其宿主的影响很小是很重要的。随着时间的推移，疾病的症状会随着病原体的进化而发生变化，但这种变化的预测方向和机制一直存在很大争议。 这项研究将模拟所涉及的过程，因此有助于预测传染病严重程度未来可能的变化方向。