大肠杆菌基因组尺度调控因子工程与适应性研究

Microbial stress tolerance phenotypes are often complex and determined by multiple genes. Several groups have attempted to directly or indirectly manipulate the transcriptional regulatory network by engineering transcriptional regulatory proteins to enhance microbial stress tolerance of industrial relevance. This approach is promising for the purpose of stress tolerance improvement and becoming research hotspot. In the present project, we will try to select important transcriptional regulators based on genome-scale metabolic network in Escherichia coli. The target mutations for the selected regulators are designed and selected by integrating the information from databases, references and predicated by protein modeling . Then the sequence of libraries are designed by Bioverse and synthesized by Agilent Technologies Company. CRISPR-enabled trackable genome engineering (CREATE) will be employed to generate high-throughput mutations in the genome of E. coli. The positive regulator mutations will be selected and enriched, which respond to the tolerance of industrial microorganisms toward stresses (including heat 42 ℃, metabolite overproduction, isobutanol and furfural as well as Na+ at high concentrations). Further high-through deep sequencing of enriched population will be employed and fitness scores be calculated to identify the specific mutations corresponding to different stress tolerance and their confidence. From the above mentioned, we can get the relationship between the genotype of genome-scale regulator mutations with their corresponding stress tolerance phenotype. The mechanism of stress tolerance will be analyzed derived from the key mutations in our experiment. These experiments help to provide a better understanding on relationship between regulator mutations and different stress tolerance, to clarify the tolerance mechanism systematically for different stress and to provide some targets for improving strain robustness. Research significance and application potential of this project is promising.

微生物对不利条件耐受性是由多基因控制的复杂表型，通过突变调控因子扰动转录调控网络是提高菌株适应性的强有力策略和研究热点。本项目拟基因组尺度筛选大肠杆菌重要转录调控因子/基因，综合数据库、文献和蛋白模拟预测的基因重要功能位点，设计和构建基因组尺度重要转录调控因子突变库，利用可追踪基因组工程（CREATE），实现高通量调控因子突变，筛选和富集与工业发酵过程紧密相关的胁迫条件（42℃，代谢物高产，高浓度异丁醇、Na+、糠醛）下适应性显著提高的调控因子突变，进一步对富集文库深度测序和突变适应度计算，确定适应性显著提高的相关突变及其适应度，分析基因组尺度调控因子突变型—耐受性表型关系，探讨关键调控因子突变导致耐受性提高的遗传调控机理。项目的成功实施将加深转录调控因子突变与耐受性关系的理解，为系统地阐释菌株耐受性的遗传调控机理奠定基础，为改进菌株鲁棒性提供理论依据，具有重要研究意义与应用潜力。

微生物在发酵过程中往往面临着多重胁迫，提高微生物对不同胁迫条件的耐受性是提高目标产品产量、得率和生产效率的关键因素，是保持生物发酵过程产品成本竞争力的关键。本项目基于全局调控因子工程，通过可追踪基因组工程（CREATE）实现高通量突变，系统分析了基因组尺度调控因子突变基因型-表型关系，阐释了重要突变引起耐受性的遗传调控机理，取得了系列成果。. 首先筛选大肠杆菌重要转录调控因子和转录或翻译相关的基因，收集数据库、文献和蛋白模拟预测的基因重要功能位点，设计和构建了包含124个调控因子和基因的突变库，通过先进的可追踪基因组工程，实现大肠杆菌基因组尺度高通量突变。筛选和富集与工业发酵过程紧密相关的不同胁迫条件（42℃，高浓度异丁醇、Na+、糠醛、乙酸钠和抗生素）下适应性显著提高的调控因子突变。通过突变文库的深度测序，确定了99个不同适应性显著提高的突变，其中对76个突变点进行重构与耐受性分析，新发现了30个不同调控因子的突变赋予不同胁迫条件耐受性和抗生素耐受性，且多个突变位点显示出很好的多重胁迫耐受性。同时首次以CREATE构建的全局调控因子文库加速糠醛耐受性菌株进化，糠醛耐受性提高了2倍。最后结合蛋白质模拟预测分析，转录组等组学手段，分析了突变株CRP V140W引起庆大霉素耐受性，突变株CRP E182D引起乙酸钠等多重耐受性和优势进化菌株KQ52耐受糠醛的遗传调控机理。. 项目研究大大加深转录调控因子突变与菌株的耐受性之间关系的理解，为进一步系统阐释菌株不同胁迫耐受性的遗传调控机理奠定基础，为理性改进工程菌株提供理论依据。

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