A mechanistic model of bacteriophage T7 infection, replication, and evolution

总结 转基因病毒在预防和治疗疾病方面具有重要应用，例如 用作活疫苗和用于噬菌体治疗的病毒。在这些应用程序中，我们需要修改病毒， 特定表型（例如，减弱的适应性或杀死多药耐药病原体的能力），同时也预- 排气快速适应可能会损害这些功能。实际上，这些应用程序的病毒 经常是偶然的，通过试错。在这一领域取得进一步进展的一个关键障碍是， 合理地设计病毒基因组，同时能够预测工程的表型后果 以及工程化病毒进一步适应或进化逆转的可能性。 本计画将发展一个详细的病毒研究系统的机械模型，以用于基因组的研究 工程化、靶向病毒减毒和进化恢复。该病毒是一种双链DNA噬菌体（T7）， 是安全的，可以很容易地操纵和设计。凭借其广泛的遗传学、生物化学、 和进化研究，T7为解决这一问题提供了所有病毒中最好的经验和理论基础。 问题.我们的方法由三个目标组成，它们共同联合收割机结合了病毒生命的计算建模 循环与基因组工程，病毒感染的分子研究，健身测量，和进化 修改基因组。 在目标1中，我们将评估T7中基因调控的原则。我们假设复杂的，动态的表达- 锡永模式不需要明确的基因调控网络，而T7中的基因调控是结果。 在转录物合成和降解之间的一种微调平衡。我们将在三个方面来检验这个假设- 基因模型系统和整个T7生命周期的模拟，对高通量测量进行验证- T7转录本和蛋白质丰度的部分。 在目标2中，我们将把我们的模拟器扩展成一个预测适应度模型。我们假设噬菌体适应性 可以从噬菌体基因组、转录物和 proteins.我们将用基因组复制、衣壳组装和裂解模块扩展模拟器。所有模拟- 将使用噬菌体适合性的实验测量来校准这些结果，以用于一组不同的工程化的噬菌体。 并进化出了T7基因组 目标3将把目标1和2产生的见解应用于更大规模的基因组破坏和噬菌体进化。 是的。我们假设，T7基因组结构对基因表达的方式施加了可量化的限制。 噬菌体可以进化和/或响应遗传操作。我们将在T7变异体中插入转基因， 重排的基因顺序，或更多片段化的基因表达模块，我们将评估我们在多大程度上 可以通过计算机模拟预测这些修饰的表型和进化结果。

Summary Genetically modified viruses have important applications in the prevention and treatment of disease, such as viruses used as live vaccines and for phage therapy. In these applications, we need to modify viruses to have specific phenotypes (e.g., attenuated fitness or the ability to kill multidrug-resistant pathogens) while also pre- venting rapid adaptation that may compromise these functions. In practice, viruses for these applications are often created haphazardly, via trial-and-error. A critical barrier to further progress in this field is the ability to engineer viral genomes rationally, while being able to predict the phenotypic consequences of the engineering as well as the likelihood of further adaptation or evolutionary reversion of the engineered viruses. This project will develop a detailed mechanistic model of a viral study system that can be used to study genome engineering, targeted viral attenuation, and evolutionary recovery. The virus is a dsDNA bacteriophage (T7) that is safe and can be easily manipulated and engineered. With its extensive background of genetic, biochemical and evolutionary studies, T7 offers the best empirical and theoretical foundation of all viruses for addressing this problem. Our approach consists of three Aims that collectively combine computational modeling of the viral life cycle with genome engineering, molecular studies of viral infections, fitness measurements, and evolution of modified genomes. In Aim 1, we will assess the principles of gene regulation in T7. We hypothesize that complex, dynamic expres- sion patterns do not require explicit gene regulatory networks, and that instead gene regulation in T7 is the result of a finely tuned balance between transcript synthesis and degradation. We will test this hypothesis both in three- gene model systems and in simulations of the entire T7 life cycle, validated against high-throughput measure- ments of T7 transcript and protein abundances. In Aim 2, we will extend our simulator into a predictive fitness model. We hypothesize that bacteriophage fitness can be predicted from the rate of production and cellular abundance of bacteriophage genomes, transcripts, and proteins. We will extend the simulator with modules for genome replication, capsid assembly, and lysis. All sim- ulations will be calibrated using experimental measurements of phage fitness for a panel of different engineered and evolved T7 genomes. Aim 3 will apply the insights generated from Aims 1 and 2 to larger-scale genome disruptions and phage evolu- tion. We hypothesize that the T7 genome architecture imposes quantifiable constraints on the ways in which the phage can evolve and/or respond to genetic manipulation. We will engineer T7 variants with inserted transgenes, rearranged gene order, or more fragmented gene expression modules, and we will assess to what extent we can predict the phenotypic and evolutionary consequences of these modifications in silico.