The evolutionary dynamics of drug resistance in host-pathogen interactions

宿主-病原体相互作用中耐药性的进化动力学

• 批准号: 217636-2013
• 负责人: Day, Troy
• 金额: $ 5.76万
• 依托单位: Queen's University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=696165
• 关键词: evolutionary; dynamics; drug; resistance; host

Infectious diseases remain one of the most significant threats to human health. Many pharmaceutical interventions (e.g., antibiotics and other chemotherapeutic agents) have been developed to slow or stop the spread of disease, but it is now abundantly clear that pathogen evolution often renders these tools ineffective. At the center of this issue is the more fundamental question in evolutionary biology of the conditions that generate the greatest evolutionary response to novel selective agents. Indeed this question also lies at the heart of many other important applied problems, from the optimization of directed evolution approaches for protein and enzyme engineering, to understanding when species will adapt to climate change as opposed to going extinct. The proposed research addresses this important issue in the context of drug resistance. We will develop mathematical theory to predict the evolutionary consequences of different drug interventions, and address three main questions: (1) What factors make it optimal to use high versus low drug concentrations to best control resistance? (2) What role does temporal and spatial variation play in the emergence of resistance? (3) Can we manipulate ecological interactions to make populations evolution-proof? As part of training, my students will also conduct experimental tests of some aspects of the theory, in collaboration with a colleague who works with a mouse model system of malaria. In the longer-term our hope is that this research will lay the fundamental foundations for more clinically oriented studies carried out by health-care scientists.

传染病仍然是对人类健康的最大威胁之一。许多药物干预（例如，抗生素和其它化学治疗剂）已经被开发来减缓或阻止疾病的传播，但是现在非常清楚的是，病原体的进化常常使这些工具无效。 这个问题的核心是进化生物学中更基本的问题，即对新选择剂产生最大进化反应的条件。事实上，这个问题也是许多其他重要应用问题的核心，从蛋白质和酶工程定向进化方法的优化，到了解物种何时适应气候变化而不是灭绝。拟议的研究在耐药性的背景下解决了这一重要问题。我们将发展数学理论来预测不同药物干预的进化后果，并解决三个主要问题： (1)哪些因素使其最佳使用高与低药物浓度，以最好地控制耐药性？ (2)时间和空间的变化在抗药性的出现中起什么作用？ (3)我们能否操纵生态相互作用，使种群不受进化的影响？ 作为培训的一部分，我的学生还将与一位研究疟疾小鼠模型系统的同事合作，对该理论的某些方面进行实验测试。从长远来看，我们希望这项研究将为卫生保健科学家进行的更多临床研究奠定基础。