Delaying production with prokaryotic inducible expression systems.

BACKGROUND: Engineering bacteria with the purpose of optimizing the production of interesting molecules often leads to a decrease in growth due to metabolic burden or toxicity. By delaying the production in time, these negative effects on the growth can be avoided in a process called a two-stage fermentation. MAIN TEXT: During this two-stage fermentation process, the production stage is only activated once sufficient cell mass is obtained. Besides the possibility of using external triggers, such as chemical molecules or changing fermentation parameters to induce the production stage, there is a renewed interest towards autoinducible systems. These systems, such as quorum sensing, do not require the extra interference with the fermentation broth to start the induction. In this review, we discuss the different possibilities of both external and autoinduction methods to obtain a two-stage fermentation. Additionally, an overview is given of the tuning methods that can be applied to optimize the induction process. Finally, future challenges and prospects of (auto)inducible expression systems are discussed. CONCLUSION: There are numerous methods to obtain a two-stage fermentation process each with their own advantages and disadvantages. Even though chemically inducible expression systems are well-established, an increasing interest is going towards autoinducible expression systems, such as quorum sensing. Although these newer techniques cannot rely on the decades of characterization and applications as is the case for chemically inducible promoters, their advantages might lead to a shift in future inducible expression systems.

MoBioS: Modular Platform Technology for High-Throughput Construction and Characterization of Tunable Transcriptional Biological Sensors.

All living organisms have evolved and fine-tuned specialized mechanisms to precisely monitor a vast array of different types of molecules. These natural mechanisms can be sourced by researchers to build Biological Sensors (BioS) by combining them with an easily measurable output, such as fluorescence. Because they are genetically encoded, BioS are cheap, fast, sustainable, portable, self-generating and highly sensitive and specific. Therefore, BioS hold the potential to become key enabling tools that stimulate innovation and scientific exploration in various disciplines. However, the main bottleneck in unlocking the full potential of BioS is the fact that there is no standardized, efficient and tunable platform available for the high-throughput construction and characterization of biosensors. Therefore, a modular, Golden Gate-based construction platform, called MoBioS, is introduced in this article. It allows for the fast and easy creation of transcription factor-based biosensor plasmids. As a proof of concept, its potential is demonstrated by creating eight different, functional and standardized biosensors that detect eight diverse molecules of industrial interest. In addition, the platform contains novel built-in features to facilitate fast and efficient biosensor engineering and response curve tuning.

Dynamic feedback regulation for efficient membrane protein production using a small RNA-based genetic circuit in Escherichia coli.

BACKGROUND: Membrane proteins (MPs) are an important class of molecules with a wide array of cellular functions and are part of many metabolic pathways. Despite their great potential-as therapeutic drug targets or in microbial cell factory optimization-many challenges remain for efficient and functional expression in a host such as Escherichia coli. RESULTS: A dynamically regulated small RNA-based circuit was developed to counter membrane stress caused by overexpression of different MPs. The best performing small RNAs were able to enhance the maximum specific growth rate with 123%. On culture level, the total MP production was increased two-to three-fold compared to a system without dynamic control. This strategy not only improved cell growth and production of the studied MPs, it also suggested the potential use for countering metabolic burden in general. CONCLUSIONS: A dynamically regulated feedback circuit was developed that can sense metabolic stress caused by, in casu, the overexpression of an MP and responds to it by balancing the metabolic state of the cell and more specifically by downregulating the expression of the MP of interest. This negative feedback mechanism was established by implementing and optimizing simple-to-use genetic control elements based on post-transcriptional regulation: small non-coding RNAs. In addition to membrane-related stress when the MP accumulated in the cytoplasm as aggregates, the sRNA-based feedback control system was still effective for improving cell growth but resulted in a decreased total protein production. This result suggests promiscuity of the MP sensor for more than solely membrane stress.