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

Abstract

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.

Optimized expression and enhanced production of alkaline protease by genetically modified Bacillus licheniformis 2709

“…In addition to these host-modification strategies to improve the cellular performance, some gene regulation and expression methods have also been applied to increase protein production. In recent years, a great amount of fundamental work on promoters has been undertaken, and various promoters have been identified and reconstructed to achieve high-level expression of recombinant proteins, with some notable results [9][10][11]. Although transcription is the first and key step in the process of gene expression [12], the transcriptional effectiveness of one promoter varies for different proteins, and the so-called optimal promoter sequences cannot be generalized for all heterologous enzymes [13,14].…”

Model-driven promoter strength prediction based on a fine-tuned synthetic promoter library inEscherichia coli

“…For example, Jensen et al 13 revealed a simple statistical method to explore nucleotide positions, which exerted critical effect on promoter intensity in 69 PL-λ promoter variants in E. coli. Likewise, Mey et al 14 and Liu et al 17 observed similar results by analyzing a partial least squares (PLS) model in E. coli and Bacillus subtilis using 49 and 214 synthetic promoters, respectively. However, these reports suffered small data issues, single modeling, imperfect correlations and low dynamic ranges.…”

“…The transcriptional intensity ranged from 0.007%-4630% that of the PBAD promoter, and 0.03-2.03-fold that of the P43 promoter at the transcriptional level. However, the strength of these promoters was still not comparative, or far lower than other wellstudied promoters, such as P43 17 , PVeg 17 , PT7, Ptrc 18 , and PThl 19 .…”

“…Although a synthetic promoter library was obtained, it was extremely crucial to determine the functional relationships between promoter sequences and the strength to achieve the rational design of the desired promoter. PLS models 11,13,17 with multinomial statistics are often used for exploring nucleotide positions that have a significant effect on promoter intensity. After the promoter sequence is encoded, PLS compares the basic relationship between the two sequences.…”

“…These results thus indicated a satisfactory correlation of promoter strength to the sequence. Although many advancements have been made in past decades, the strength and dynamic range of promoters were relatively low and narrow, respectively 17,18 . In addition, promoter characterization in the literature is often performed using different genetic backgrounds and testing conditions, resulting in unquantifiable performances when applying them to the same host.…”