Hsiau Timothy H-C, Sukovich David, Elms Phillip, Prince Robin N, Strittmatter Tobias, Ruan Paul, Curry Bo, Anderson Paige, Sampson Jeff, Anderson J Christopher
Department of Bioengineering, University of California, Berkeley, CA, United States of America.
Department of Electrical Engineering and Computer Science, University of California, Berkeley, CA, United States of America.
PLoS One. 2015 Mar 19;10(3):e0119927. doi: 10.1371/journal.pone.0119927. eCollection 2015.
Our ability to engineer organisms with new biosynthetic pathways and genetic circuits is limited by the availability of protein characterization data and the cost of synthetic DNA. With new tools for reading and writing DNA, there are opportunities for scalable assays that more efficiently and cost effectively mine for biochemical protein characteristics. To that end, we have developed the Multiplex Library Synthesis and Expression Correction (MuLSEC) method for rapid assembly, error correction, and expression characterization of many genes as a pooled library. This methodology enables gene synthesis from microarray-synthesized oligonucleotide pools with a one-pot technique, eliminating the need for robotic liquid handling. Post assembly, the gene library is subjected to an ampicillin based quality control selection, which serves as both an error correction step and a selection for proteins that are properly expressed and folded in E. coli. Next generation sequencing of post selection DNA enables quantitative analysis of gene expression characteristics. We demonstrate the feasibility of this approach by building and testing over 90 genes for empirical evidence of soluble expression. This technique reduces the problem of part characterization to multiplex oligonucleotide synthesis and deep sequencing, two technologies under extensive development with projected cost reduction.
我们利用新的生物合成途径和基因电路设计生物体的能力受到蛋白质表征数据可用性和合成DNA成本的限制。随着用于读取和写入DNA的新工具的出现,存在进行可扩展检测的机会,这些检测能够更高效且经济高效地挖掘生化蛋白质特性。为此,我们开发了多重文库合成与表达校正(MuLSEC)方法,用于将许多基因作为一个汇集文库进行快速组装、错误校正和表达表征。这种方法能够通过单锅技术从微阵列合成的寡核苷酸池中合成基因,无需机器人液体处理。组装后,基因文库经过基于氨苄青霉素的质量控制筛选,这既是一个错误校正步骤,也是对在大肠杆菌中正确表达和折叠的蛋白质的选择。选择后DNA的下一代测序能够对基因表达特性进行定量分析。我们通过构建和测试90多个基因以获取可溶性表达的经验证据,证明了这种方法的可行性。该技术将部件表征问题简化为多重寡核苷酸合成和深度测序,这两项技术正在广泛发展且预计成本会降低。