Persistent viral attenuation by transcriptional and translational de-optimization

通过转录和翻译去优化实现持续的病毒减毒

• 批准号: 9107913
• 负责人: JAMES J BULL
• 金额: $ 31.18万
• 依托单位: UNIVERSITY OF TEXAS AT AUSTIN
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-08-01 至 2019-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9107913
• 关键词: Address; Affect; Attenuated; Attenuated Vaccines; Back; Bacteriophage T7; Bacteriophages; Biochemical; Biological; Biological Assay; Biological Models; Biology; Cells; Codon Nucleotides; Communicable Diseases; Computer Simulation; Data; Disease; Engineering; Evolution; Foundations; Gene Expression Regulation; Gene Rearrangement; Generations; Generic Drugs; Genetic; Genetic Transcription; Genome; Genome engineering; Goals; Health; Human poliovirus; Immunity; Inactivated Vaccines; Incidence; Infection; Life; Life Cycle Stages; Measures; Medicine; Messenger RNA; Methods; Modeling; Modification; Molecular; Molecular Biology; Molecular Evolution; Nucleotides; Oral; Patients; Pattern; Poliomyelitis; Positioning Attribute; Proteins; Proteomics; Public Health; RNA; RNA Viruses; Recovery; Research Personnel; Resistance; Ribosomes; Sequence Analysis; Staging; Structure; System; Testing; Texas; Transcript; Translations; Universities; Vaccinated; Vaccines; Variant; Viral; Viral Genome; Virulence; Virulent; Virus; Work; attenuation; austin; base; density; design; ds-DNA; engineering design; expectation; experience; fitness; genome sequencing; immunogenicity; insight; novel vaccines; predictive modeling; promoter; ribosome profiling; stem; success; synthetic biology; transcriptomics; viral fitness; virtual

 DESCRIPTION (provided by applicant): Live virus vaccines offer some of the biggest successes of medicine. They offer superior immunogenicity over inactivated virus vaccines, but they have two drawbacks. First, methods for creating attenuated vaccines are hit-and-miss. Second, successful live-virus vaccines can often evolve back to high virulence. Indeed, polio eradication has remained elusive because of vaccine evolution. The first of these hurdles is potentially surmountable with genome engineering, if we can predict the viral fitness effect of the engineering. The second hurdle may also be overcome by engineering if we understand the molecular evolution of engineered viruses. This proposal develops a combined empirical and computational viral system to study attenuation of evolutionary reversal of that attenuation. The virus is a dsDNA bacteriophage (T7) that is safe, easily manipulated and engineered. With its extensive background of genetic, biochemical and evolutionary studies, it offers the best empirical and theoretical foundation of all viruses for addressing this problem. Our approach consists of three Aims that collectively combine genome engineering with molecular studies of the viral life cycle, fitness measures, evolution of attenuated genomes, sequence analysis, and computational modeling. In Aim 1, we build several genomes to test new methods of viral attenuation: silent codon modification, genome rearrangement, and promoter deletion. Two questions motivating this work are (i) whether the level of attenuation is predictable, and (ii) whether the attenuation is evolutionarily stable against reversion to high fit- ness. Beyond genome construction, we will thus measure fitness of all constructs and evolve all constructs for hundreds to thousands of generations, observing fitness recovery and sequence evolution. This Aim stems from a variety of preliminary work demonstrating feasibility of all technical aspects and is the foundation of Aims 2 and 3. Aim 2 is the application of key molecular assays to the viruses from Aim 1, proteomics, transcriptomics, and RNA densities on ribosomes (ribosomal profiling). The intracellular viral life cycle will be described at the level of transcription, translation, and protein abundance to understand the molecular bases of different attenuation methods and the paths of evolutionary recovery. Initially, we will compare patterns transcript and protein abundances with ribosome densities on mRNAs to determine whether they agree with each other and match expectations from the basic biology of the virus. Unexpected patterns will be confirmed experimentally. These methods will provide insight to how the different engineering methods attenuate and how they retard evolutionary recovery. Further, these methods will be used to parameterize and further develop an existing virtual model that gives an overall predictive framework for attenuation and evolution (Aim 3). Aim 3 consists of modeling and analysis. Initially, an existing, second-generation computational model of the viral life cycle will be calibrated to the data we obtain for wt and attenuated viruses. As the model incorporates transcription and translation, the molecular data obtained will be compared at a mechanistic level to model predictions. We expect that translation is modeled with insufficient detail in the current model, and we will develop a third-generation model that addresses the shortcomings of the current model. Ultimately, the model we are developing will be useful for prediction of both attenuation and evolution of T7.

 描述（由申请人提供）：活病毒疫苗提供了一些最大的医学成功。它们提供了优于灭活病毒疫苗的免疫原性，但它们有两个缺点。首先，生产减毒疫苗的方法是偶然的。其次，成功的活病毒疫苗通常可以进化回高毒力。事实上，由于疫苗的发展，根除脊髓灰质炎仍然遥遥无期。第一个障碍有可能通过基因组工程来克服，如果我们能够预测病毒适应性效应， 工程.第二个障碍也可以通过工程来克服，如果我们理解了工程病毒的分子进化。该提议开发了一个结合经验和计算的病毒系统来研究衰减的进化逆转。该病毒是一种安全、易于操作和工程化的dsDNA噬菌体（T7）。凭借其广泛的遗传，生物化学和进化研究背景，它为解决这一问题提供了所有病毒的最佳经验和理论基础。我们的方法包括三个目标，集体结合联合收割机基因组工程与病毒生命周期的分子研究，健身措施，减毒基因组的进化，序列分析和计算建模。在目标1中，我们构建了几个基因组来测试病毒减毒的新方法：沉默密码子修饰、基因组重排和启动子缺失。激发这项工作的两个问题是（i）衰减的水平是否是可预测的，以及（ii）衰减是否在进化上稳定，以防止恢复到高拟合。在基因组构建之外，我们将测量所有构建体的适应性，并将所有构建体进化数百至数千代，观察适应性恢复和序列进化。这一目标源于证明所有技术方面可行性的各种初步工作，是目标2和3的基础。目标2是将关键分子检测应用于目标1中的病毒，蛋白质组学，转录组学和核糖体上的RNA密度（核糖体谱）。细胞内病毒的生命周期将在转录，翻译和蛋白质丰度的水平上进行描述，以了解不同减毒方法的分子基础和进化恢复的路径。最初，我们将比较转录本和蛋白质丰度与mRNA上的核糖体密度的模式，以确定它们是否彼此一致，并与病毒基本生物学的预期相匹配。将通过实验证实意外模式。这些方法将提供洞察力，不同的工程方法如何衰减，以及它们如何阻碍进化恢复。此外，这些方法将用于参数化和进一步开发现有的虚拟模型，为衰减和演变提供总体预测框架（目标3）。目标3包括建模和分析。最初，现有的第二代病毒生命周期计算模型将根据我们获得的野生型和减毒病毒的数据进行校准。由于该模型结合了转录和翻译，因此将在机理水平上将获得的分子数据与模型预测进行比较。我们预计，在当前模型中，翻译建模的细节不足，我们将开发第三代模型，解决当前模型的缺点。最终，我们正在开发的模型将有助于预测T7的衰减和演变。