基于CRISPR的转录调控数据驱动预测，用于代谢通量的可编程控制
Data-Driven Prediction of CRISPR-Based Transcription Regulation for Programmable Control of Metabolic Flux
摘要：
对代谢途径的多方向控制是代谢工程提高燃料、化学品和药物产品收率的关键。为了实现这一目标，已经开发了人工转录调节因子，如基于CRISPR的转录调节因子，以特异性激活或抑制感兴趣的基因。在这里，我们发现，通过部署向导RNA靶向基因盒不同位置的DNA位点，我们可以只使用一种合成的基于CRISPR的转录调节因子来同时激活和抑制基因表达。通过使用指导RNA和基因表达的成对数据集，我们开发了一个数据驱动的预测模型，以合理地设计该系统来微调靶基因的表达。我们证明，该系统可以实现可编程控制的代谢通量时，使用酵母生产多功能的化学品。我们预计，这种基于CRISPR的转录调控因子将成为代谢工程合成生物学工具包的一个有价值的补充，加快工业生物制造中的设计-构建-测试周期，并对真核细胞的命运产生新的生物学见解。
英文摘要：
Multiplex and multi-directional control of metabolic pathways is crucial for metabolic engineering to improve product yield of fuels, chemicals, and pharmaceuticals. To achieve this goal, artificial transcriptional regulators such as CRISPR-based transcription regulators have been developed to specifically activate or repress genes of interest. Here, we found that by deploying guide RNAs to target on DNA sites at different locations of genetic cassettes, we could use just one synthetic CRISPR-based transcriptional regulator to simultaneously activate and repress gene expressions. By using the pairwise datasets of guide RNAs and gene expressions, we developed a data-driven predictive model to rationally design this system for fine-tuning expression of target genes. We demonstrated that this system could achieve programmable control of metabolic fluxes when using yeast to produce versatile chemicals. We anticipate that this master CRISPR-based transcription regulator will be a valuable addition to the synthetic biology toolkit for metabolic engineering, speeding up the design-build-test cycle in industrial biomanufacturing as well as generating new biological insights on the fates of eukaryotic cells.
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