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

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受到调节。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，以适应目标环境。在细菌中，基因表达的第一步，转录，是由RNA聚合酶和sigma因子蛋白催化的。细菌含有多个具有不同DNA结合特性的sigma因子，并利用信号通路来改变这些sigma因子的丰度，以响应细胞状态，导致数百个基因的转录率发生巨大的状态依赖性变化。在这个项目中，进行了数千个实验来系统地测量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的转录起始的sigma特定频率。这些预测使工程遗传系统（传感器、遗传电路和代谢途径）的自动化设计成为可能，这些系统具有适应和响应不断变化的细胞和环境条件的靶向转录谱。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，以促进学生的互动和体验式学习。这个项目也为代表性不足的本科生提供了参与实验室研究的机会。该项目集成了系统设计、大规模并行实验、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）。转录起始率测量和转录起始位点定位是在定义明确的体外测定中对数千个系统设计的启动子序列进行的，每个启动子序列都包含RNA聚合酶和单个sigma因子。通过多组测量和使用不同的sigma因子，对sigma特异性模型进行了训练和验证，以准确预测sigma特异性和位点特异性转录起始率。模型预测通过多种方式进行组合和验证，包括通过与一系列环境条件下的体内转录组测量进行比较，以及通过合理设计状态依赖性转录谱的工程遗传系统。使用裸基因组模板的新型体外测定也被设计出来，以提高测量的精度，并最终了解天然启动子的转录相互作用。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（例如工程生物传感器，遗传电路，代谢途径和基因组）的转录速率，同时促进系统范围内的定量分析和遗传系统功能调试。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.