이상엽 교수팀 (주저자: 나도균) 은 수개원 걸리던 미생물 세포공장 개발기간을 수일로 줄이는데 성공했다.세포 공장이란 미생물로 의학·산업용 유용 물질을 생산하는 시스템으로 이 기술이 상용화되면 미생물을 이용한 친환경 바이오 에너지와 의약품 생산이 대폭 확대될 전망이며, 본 연구결과는 "Nature Biotechnology" 1월20일자 인터넷판에 실렸다.

URL: http://www.nature.com/nbt/journal/vaop/ncurrent/full/nbt.2461.html#/author-information

논문제목: Metabolic engineering of Escherichia coli using synthetic small regulatory RNAs

Dokyun Na,^{1, 2} Seung Min Yoo,^{1, 2} Hannah Chung,¹ Hyegwon Park,¹ Jin Hwan Park^{1, 2} & Sang Yup Lee^{1, 2}
Nature Biotechnology

Year published:(2013)DOI:doi:10.1038/nbt.2461Received17 January 2012 Accepted22 November 2012 Published online20 January 2013

Small regulatory RNAs (sRNAs) regulate gene expression in bacteria. We designed synthetic sRNAs to identify and modulate the expression of target genes for metabolic engineering in Escherichia coli. Using synthetic sRNAs for the combinatorial knockdown of four candidate genes in 14 different strains, we isolated an engineered E. coli strain (tyrR- and csrA-repressed S17-1) capable of producing 2 g per liter of tyrosine. Using a library of 130 synthetic sRNAs, we also identified chromosomal gene targets that enabled substantial increases in cadaverine production. Repression of murE led to a 55% increase in cadaverine production compared to the reported engineered strain (XQ56 harboring the plasmid p15CadA)¹. The design principles and the engineering strategy using synthetic sRNAs reported here are generalizable to other bacteria and applicable in developing superior producer strains. The ability to fine-tune target genes with designed sRNAs provides substantial advantages over gene-knockout strategies and other large-scale target identification strategies owing to its easy implementation, ability to modulate chromosomal gene expression without modifying those genes and because it does not require construction of strain libraries.

Figures left
1. Figure 1: Design principles for synthetic sRNAs.
  
  (a) Mechanism of translation repression by sRNA. SD, Shine-Dalgarno sequence. (b) Scaffold selection process (Supplementary Figs. 1 and 2). C, no synthetic sRNA; −, scaffold without DsRed2-targeting sequence; +, scaffold with DsRed2-targeting sequence. Error bars, mean ± s.d. (c) The effect of binding region on repression efficiency. The letters denote binding sites of designed anti-DsRed2 synthetic sRNA variants (Supplementary Fig. 3). The location of TIR (green bar) was estimated using a previously published algorithm²⁴. The intensity of DsRed2 that was not repressed by synthetic sRNAs was used as a control. All other intensities were normalized to the control. Error bars, mean ± s.d. (d) A quantitative relationship between synthetic sRNA binding energy and repression efficiency. Error bars, mean ± s.d. (e) The genetic structure of synthetic sRNA.T1/TE, transcriptional terminator (MITRegistry BBa_B0025). See Supplementary Figure 6 for full sequence of synthetic sRNAs.
  
  See larger
2. Figure 2: Metabolic engineering of E. coli for the production of tyrosine using a synthetic sRNA strategy.
  
  (a) The tyrosine biosynthetic pathway in E. coli. Overexpressed genes (green boxes) and synthetic sRNA translational repression targets (red boxes) are shown. PYR, pyruvate; PEP, phosphoenolpyruvate; F6P, fructose-6-phosphate; E4P, erythrose-4-phosphate; DAHP, 3-deoxy-D-arabino-heptulosonate 7-phosphate; SHIK, shikimate; S3P, shikimate 3-phosphate; CHA, chorismate; PPA, prephenate; TYR, tyrosine. Overexpressed feedback-resistant aroG and tyrA mutants are indicated (θ). Irrelevant reaction cascades are shown as dashed lines. (b) The plasmids used for gene amplification and sRNA-based repression. bla, beta-lactamase gene; kan^R, kanamycin-resistance gene; P_tac, tac promoter; p15A, replication origin. (c) Tyrosine production in 14 different strains with different combinations of synthetic sRNAs. Anti-pgi-v1 and anti-pgi-v2 are variants of anti-pgi. They have different binding energies to pgi mRNA (Supplementary Fig. 12). (d) The effect of repression efficiency of anti-pgi sRNA variants on the growth profiles in strain S17-1 (producing the highest concentration of tyrosine) or in TOP10 (producing the lowest concentration). See Supplementary Figure 12 for detailed sequence information of anti-pgi-v1 and anti-pgi-v2. (e) Relative tyrosine production after fine-tuning of anti-csrA repression efficiency. Error bars, mean ± s.d. Tyrosine titers were normalized to the titer obtained from the engineered S17-1 strain which produced the highest concentration of tyrosine. Error bars, mean ± s.d.
  
  See larger
3. Figure 3: Synthetic sRNA–based strategy for large-scale target identification and fine-tuning of gene expression for enhanced cadaverine production.

첨부파일

NEWS AND EVENTS

논문제목: Metabolic engineering of Escherichia coli using synthetic small regulatory RNAs