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

摘要 转基因病毒在疾病的预防和治疗中有着重要的应用，例如 用作活疫苗和噬菌体疗法的病毒。在这些应用中，我们需要修改病毒以使其具有 特定的表型(例如，减弱的适应性或杀死耐多药病原体的能力)，同时也是前 宣泄可能损害这些功能的快速适应。实际上，针对这些应用程序的病毒是 通常是随意创建的，通过反复试验。在这一领域取得进一步进展的一个关键障碍是 合理设计病毒基因组，同时能够预测工程的表型后果 以及工程病毒进一步适应或进化逆转的可能性。 该项目将开发一个可用于研究基因组的病毒研究系统的详细机制模型。 工程学、有针对性的病毒减毒和进化恢复。该病毒是一种dsDNA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、生化背景 和进化研究，T7为解决这一问题提供了所有病毒的最佳经验和理论基础 有问题。我们的方法由三个目标组成，它们共同结合了对病毒生命的计算建模 与基因组工程的循环、病毒感染的分子研究、适应性测量和进化 修改过的基因组。 在目标1中，我们将评估T7中的基因调控原理。我们假设复杂的，动态的表达- Sion模式不需要明确的基因调控网络，相反，T7中的基因调控是结果 在转录合成和降解之间保持微调的平衡。我们将从三个方面检验这一假设-- 基因模型系统和整个T7生命周期的模拟中，对照高通量测量进行验证- T7转录本和蛋白质丰度的变化。 在目标2中，我们将把模拟器扩展为预测性适应度模型。我们假设噬菌体的适合性 可以从噬菌体基因组、转录本和 蛋白质。我们将用基因组复制、衣壳组装和裂解模块来扩展模拟器。全是模拟的- 我们将通过对不同基因工程的一组噬菌体适合性的实验测量来校准。 并进化出T7基因组。 目标3将把目标1和目标2产生的洞察力应用于更大规模的基因组破坏和噬菌体进化- 提顿。我们假设T7基因组体系结构对T7基因组体系结构的 噬菌体可以进化和/或对基因操作做出反应。我们将设计插入转基因的T7变种， 重新排列的基因顺序，或更碎片化的基因表达模块，我们将评估我们在多大程度上 可以预测硅胶中这些修饰的表型和进化后果。

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.