Lin Yicong, Zou Xinzhi, Zheng Yihui, Cai Yizhi, Dai Junbiao
Key Laboratory of Industrial Biocatalysis (Ministry of Education) and Center for Synthetic and Systems Biology, School of Life Sciences , Tsinghua University , Beijing 100084 , China.
Shenzhen Key Laboratory of Synthetic Genomics and Center for Synthetic Genomics, Institute of Synthetic Biology , Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences , Shenzhen 518055 , China.
ACS Synth Biol. 2019 Oct 18;8(10):2203-2211. doi: 10.1021/acssynbio.8b00505. Epub 2019 Sep 20.
Recent advances in DNA synthesis technology have made it possible to rewrite the entire genome of an organism. The major hurdles in this process are efficiently identifying and fixing the defect-inducing sequences (or "bugs") during rewriting. Here, we describe a high-throughput, semiquantitative phenotype assay for evaluating the fitness of synthetic yeast and identifying potential bugs. Growth curves were measured under a carefully chosen set of testing conditions. Statistical analysis revealed strains with subtle defects relative to the wild type, which were targeted for debugging. The effectiveness of the assay was demonstrated by phenotypic profiling of all intermediate synthetic strains of the synthetic yeast chromosome XII. Subsequently, the assay was applied during the process of constructing another synthetic chromosome. Furthermore, we designed an efficient chromosome assembly strategy that integrates iterative megachunk construction with CRISPR/Cas9-mediated assembly of synthetic segments. Together, the semiquantitative assay and refined assembly strategy could greatly facilitate synthetic genomics projects by improving efficiency during both debugging and construction.
DNA合成技术的最新进展使得重写生物体的整个基因组成为可能。这一过程中的主要障碍是在重写过程中有效地识别和修复诱导缺陷的序列（或“错误”）。在这里，我们描述了一种用于评估合成酵母适应性和识别潜在错误的高通量、半定量表型分析方法。在精心选择的一组测试条件下测量生长曲线。统计分析揭示了相对于野生型有细微缺陷的菌株，这些菌株被作为调试目标。通过对合成酵母十二号染色体的所有中间合成菌株进行表型分析，证明了该分析方法的有效性。随后，该分析方法被应用于构建另一条合成染色体的过程中。此外，我们设计了一种高效的染色体组装策略，该策略将迭代大片段构建与CRISPR/Cas9介导的合成片段组装相结合。总之，这种半定量分析方法和优化的组装策略可以通过提高调试和构建过程中的效率，极大地促进合成基因组学项目。
