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: 8963812
• 负责人: JAMES J BULL
• 金额: $ 31万
• 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/8963812
• 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; 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; Polioviruses; 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; public health relevance; stem; success; synthetic biology; transcriptomics; 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医学上最大的成功。它们提供了比灭活病毒疫苗更好的免疫原性，但它们有两个缺点。首先，制造减毒疫苗的方法是碰运气的。其次，成功的活病毒疫苗通常可以进化回高毒力。事实上，由于疫苗的进化，根除脊髓灰质炎仍然遥不可及。这些障碍中的第一个有可能通过基因组工程克服，如果我们能预测病毒适合的效果 工程学。如果我们了解工程病毒的分子进化，第二个障碍也可能被工程学克服。这项建议开发了一种经验和计算相结合的病毒系统来研究这种衰减的进化逆转的衰减。该病毒是一种dsDNA噬菌体(T7)，安全，易于操纵和改造。凭借其广泛的遗传、生化和进化研究背景，它为解决这一问题提供了所有病毒最好的经验和理论基础。我们的方法由三个目标组成，它们共同将基因组工程与病毒生命周期的分子研究、适应度测量、弱化基因组的进化、序列分析和计算建模结合在一起。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：沉默密码子修改、基因组重排和启动子缺失。推动这项工作的两个问题是(I)衰减水平是否可预测，以及(Ii)衰减是否在进化上稳定，不会恢复到高适合度。因此，除了基因组构建之外，我们还将测量所有构建物的适合度，并将所有构建物进化数百到数千代，观察适应度恢复和序列进化。这一目标源于各种初步工作，证明了所有技术方面的可行性，是目标2和目标3的基础。目标2是对目标1、蛋白质组学、转录学和核糖体RNA密度(核糖体图谱)中的病毒进行关键分子分析的应用。将从转录、翻译和蛋白质丰度的水平描述细胞内病毒的生命周期，以了解不同减毒方法的分子基础和进化恢复的途径。最初，我们将比较模式转录本和蛋白质丰度与mRNAs上的核糖体密度，以确定它们是否彼此一致，并与病毒基本生物学的预期相符。意想不到的模式将得到实验证实。这些方法将为不同的工程方法如何衰减以及它们如何延缓进化恢复提供洞察力。此外，这些方法将被用来参数化和进一步发展现有的虚拟模型，为衰减和演变提供一个全面的预测框架(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wt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，获得的分子数据将在机制水平上进行比较，以进行模型预测。我们预计，目前的模型对翻译的建模不够详细，我们将开发第三代模型，以解决当前模型的缺点。最终，我们正在开发的模型将对T7的衰减和演变进行预测。