Massively Parallel Experiments to Develop a Predictive Biophysical Model of Transcription Rate across Cellular Conditions

大规模并行实验开发跨细胞条件转录率的预测生物物理模型

• 批准号: 2131923
• 负责人: Howard Salis
• 金额: $ 60.86万
• 依托单位: Pennsylvania State Univ University Park
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2131923&HistoricalAwards=false
• 关键词: Massively; Parallel; Experiments; Develop; Predictive

This project seeks to develop new models that quantitatively predict how bacterial gene expression is regulated across cellular and environmental conditions. The developed models enable researchers to rationally engineer microbial organisms with new sensing and metabolic capabilities that are suitable targeted environments. In bacteria, the first step in gene expression, transcription, is catalyzed by an RNA polymerase enzyme and a sigma factor protein. Bacteria contain multiple sigma factors with distinct DNA binding properties and use signaling pathways to modify the abundances of these sigma factors in response to cell state, causing large state-dependent changes in transcription rate across hundreds of genes. In this project, thousands of experiments are conducted to systematically measure how DNA nucleotide sequences control sigma factor-specific transcriptional initiation frequencies and the sites at which transcription is initiated. These measurements are used to create biophysical models of transcription initiation that accept arbitrary DNA sequence inputs, calculate the strengths of the relevant interactions, and then predict the sigma-specific frequencies of transcription initiation at each potential start site. These predictions enable the automated design of engineered genetic systems (sensors, genetic circuits, and metabolic pathways) with targeted transcriptional profiles that adapt and respond to changing cellular and environmental conditions. The biophysical models of transcription will also be combined with a web-based interface, visual animations, and tutorials to facilitate interactive and experiential student learning. This project also provides opportunities for underrepresented undergraduate students to participate in laboratory research.This project integrates systematic design, massively parallel experiments, next-generation sequencing, and machine learning to develop predictive statistical thermodynamic models of bacterial transcription initiation (a Promoter Calculator). Transcription initiation rate measurements and transcriptional start site mapping is conducted on thousands of systematically designed promoter sequences in well-defined in vitro assays that each contain RNA polymerase and a single sigma factor. From multiple sets of measurements and using different sigma factors, sigma-specific models are trained and validated to accurately predict sigma-specific and site-specific transcription initiation rates. Model predictions are combined and validated in several ways, including by comparison to in vivo transcriptomic measurements across a range of environmental conditions and by engineering genetic systems with rationally designed state-dependent transcriptional profiles. New types of in vitro assays using naked genome templates are also devised to enhance precision in measurement and to ultimately understand transcriptional interactions at natural promoters. The developed model of transcriptional initiation will enable Synthetic Biologists to predict and control transcription rates for diverse biotech applications (e.g. engineering biosensors, genetic circuits, metabolic pathways, and genomes), while facilitating system-wide, quantitative analysis and debugging of genetic system function.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

该项目旨在开发新的模型，定量预测细菌基因表达如何在细胞和环境条件下进行调节。开发的模型使研究人员能够合理地设计具有新的传感和代谢能力的微生物，这些能力是合适的目标环境。在细菌中，基因表达的第一步，转录，是由RNA聚合酶和σ因子蛋白催化的。细菌含有多种具有不同DNA结合特性的σ因子，并使用信号传导途径来响应细胞状态而改变这些σ因子的丰度，从而导致数百个基因的转录速率发生大的状态依赖性变化。在这个项目中，进行了数千个实验，以系统地测量DNA核苷酸序列如何控制sigma因子特异性转录起始频率和转录起始位点。这些测量用于创建转录起始的生物物理模型，该模型接受任意DNA序列输入，计算相关相互作用的强度，然后预测每个潜在起始位点处的转录起始的σ特异性频率。这些预测使工程遗传系统（传感器，遗传电路和代谢途径）的自动化设计具有针对性的转录谱，适应和响应不断变化的细胞和环境条件。转录的生物物理模型也将与基于网络的界面，视觉动画和教程相结合，以促进互动和体验式的学生学习。该项目还为代表性不足的本科生提供参与实验室研究的机会。该项目整合了系统设计，大规模平行实验，下一代测序和机器学习，以开发细菌转录起始的预测统计热力学模型（启动子计算器）。转录起始速率测量和转录起始位点定位是在明确定义的体外测定中对数千个系统设计的启动子序列进行的，每个启动子序列含有RNA聚合酶和单个σ因子。从多组测量并使用不同的σ因子，σ特异性模型被训练和验证以准确地预测σ特异性和位点特异性转录起始速率。模型预测相结合，并在几个方面进行验证，包括通过比较在体内转录组测量范围内的环境条件和工程遗传系统与合理设计的状态依赖的转录谱。还设计了使用裸基因组模板的新型体外测定，以提高测量精度并最终了解天然启动子处的转录相互作用。开发的转录起始模型将使合成生物学家能够预测和控制各种生物技术应用的转录速率（例如，工程生物传感器，遗传电路，代谢途径和基因组），同时促进系统范围，该奖项反映了NSF的法定使命，并已被认为是值得支持的，通过评估使用基金会的知识产权。优点和更广泛的影响审查标准。