Contrasting biotic and abiotic drivers of adaptive evolution in a host-pathogen conflict

项目摘要/摘要 为了取得成功，生物体必须既适应非生物的(例如环境压力)，也适应生物的 (例如寄生虫)选择压力。尽管这两种压力都可以推动进化创新，但理论上 预测敌对关系可能会导致适应的反复发作。剖析这些选择性的 压力对理解人类传染病和癌症的治疗具有重要意义 病原体和克隆分裂的恶性细胞都在适应宿主免疫(生物)和 环境(非生物)疗法。此外，人类微生物区系是一个复杂和动态的群落 在宿主免疫和肠道环境的背景下包含竞争微生物。这些复杂的联合- 进化场景对研究和理清各种选择性的各自贡献是具有挑战性的 压力和体能权衡可能会令人困惑。我建议使用酵母的RNA病毒作为模型系统 研究基因组进化的非生物和生物选择压力的进化后果 基因冲突的存在。酵母菌的RNA病毒编码一种“杀手”毒素-抗毒素成瘾系统，该系统 保护携带病毒的杀伤细胞，但杀死缺乏病毒的敏感细胞。因此，这些RNA病毒可以是 维持在宿主种群中，尽管给宿主带来了新陈代谢成本。在我的研究提案中，我将 研究在非生物(毒素)和生物(病毒)选择压力下的适应性。杀手本身 需要多个病毒基因组才能产生毒素和宿主细胞成分。带有敏感单元格的 在环境方面，这个系统是一个四方遗传冲突，具有相互竞争的适应度权衡。尽管如此 复杂性，芽殖酵母是支持最好的模式真核生物系统之一，具有许多遗传和 分子工具。这使得这个模型系统在实验上非常容易处理，即使在维护 自然发生系统的生物复杂性。 我将确定在相互竞争的杀手和敏感细胞(AIM)群体中出现的有益突变 1)，在没有病毒的情况下对毒素产生抵抗力的敏感细胞(目标2)，并确定哪种 基因组适应以在分子军备竞赛中重新获得竞争性适应能力(目标3)。这些目标加在一起，将 揭示错综复杂的生物系统如何共同进化和相互制约，并揭示进化的 对抗性共同进化与非生物适应施加的动力学。了解基因组是如何 进化，特别是遗传冲突(对抗性共同进化)如何驱动适应，是 了解和治疗许多塑造和推动疾病的过程。通过探索寄主寄生虫 共同进化从第一原理出发，我们可以为理解这些影响奠定基础。 过程对人类健康和疾病的影响。这项拟议的工作将通过实验使许多领域受益 解决有关生物和非生物选择如何推动和制约进化的基本问题 结果。

Project Summary/Abstract To be successful, organisms must adapt to both abiotic (e.g. environmental pressures) as well as biotic (e.g. parasites) selection pressures. Although both of these pressures can drive evolutionary innovation, theory predicts that antagonistic relationships may drive recurrent episodes of adaptation. Dissecting these selective pressures has implications for understanding treatment of both human infectious disease and cancers, where both pathogens and clonally dividing malignant cells are adapting to both host immunity (biotic) and environmental (abiotic) therapeutics. Additionally, the human microbiota is a complex and dynamic community containing competing microbes in the context of both host immunity and gut environment. These complex co- evolution scenarios are challenging to study and disentangling the respective contributions of various selective pressures and fitness tradeoffs can be confounding. I propose to use RNA viruses of yeast as a model system to study the evolutionary consequences of both abiotic and biotic selective pressures of genome evolution in the presence of genetic conflict. RNA viruses of yeasts encode a “Killer” toxin-antitoxin addiction system, which protects virus-bearing Killer cells but kills virus-lacking sensitive cells. As a result, these RNA viruses can be maintained in host populations despite imposing a metabolic cost to their host. In my research proposal, I will study adaptation in the face of both abiotic (toxin) and biotic (virus) selective pressures. Killer itself requires multiple viral genomes for toxin production as well as host cellular components. With sensitive cells in the environment, this system is a four-party genetic conflict, with competing fitness tradeoffs. In spite of this complexity, budding yeast is one of the best-supported model eukaryote systems with many genetic and molecular tools. This makes this model system supremely experimentally tractable, even while maintaining the biological complexity of a naturally occurring system. I will identify beneficial mutations that arise in populations of competing killer and sensitive cells (Aim 1), in sensitive cells that evolve resistance to toxins in the absence of virus (Aim 2), and determine which genomes adapt to regain competitive fitness in a molecular arms race (Aim 3). Together, these aims will uncover how intricate biotic systems co-evolve and constrain one another and reveal the evolutionary dynamics imposed by antagonistic coevolution, versus abiotic adaptation. Understanding how genomes evolve, and specifically how genetic conflict (antagonistic co-evolution) drives adaptation, is fundamental for understanding and treating many processes that shape and drive disease. By exploring host-parasite coevolution from first principles, we can develop a foundation towards understanding the impact of these processes on human health and disease. This proposed work will benefit many fields by experimentally addressing fundamental questions about how biotic and abiotic selection drives and constrains evolutionary outcomes.