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Construction, Model-Based Analysis, and Characterization of a Promoter Library for Fine-Tuned Gene Expression in Bacillus subtilis.

Dingyu LiuZhitao MaoJiaxin GuoLe-Yi WeiHongwu MaYa-Jie TangTao ChenZhi-Wen WangXueming Zhao
Published in: ACS synthetic biology (2018)
Promoters are among the most-important and most-basic tools for the control of metabolic pathways. However, previous research mainly focused on the screening and characterization of some native promoters in Bacillus subtilis. To develop a broadly applicable promoter system for this important platform organism, we created a de novo synthetic promoter library (SPL) based on consensus sequences by analyzing the microarray transcriptome data of B. subtilis 168. A total of 214 potential promoters spanning a gradient of strengths was isolated and characterized by a green fluorescence assay. Among these, a detailed intensity analysis was conducted on nine promoters with different strengths by reverse-transcription polymerase chain reaction (RT-PCR) and sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE). Furthermore, reconstructed promoters and promoter cassettes (tandem promoter cluster) were designed via statistical model-based analysis and tandem dual promoters, which showed strength that was increased 1.2- and 2.77-fold compared to that of promoter P43, respectively. Finally, the SPL was employed in the production of inosine and acetoin by repressing and over-expressing the relevant metabolic pathways, yielding a 700% and 44% increase relative to the respective control strains. This is the first report of a de novo synthetic promoter library for B. subtilis, which is independent of any native promoter. The strategy of improving and fine-tuning promoter strengths will contribute to future metabolic engineering and synthetic biology projects in B. subtilis.
Keyphrases
  • gene expression
  • dna methylation
  • transcription factor
  • bacillus subtilis
  • genome wide
  • escherichia coli
  • high throughput
  • air pollution
  • machine learning
  • climate change
  • quality improvement
  • rna seq