Robust Characterization of Two Distinct Glutarate Sensing Transcription Factors of Pseudomonas putida l-Lysine Metabolism

Bio-upcycling of even and uneven medium-chain-length diols and dicarboxylates to polyhydroxyalkanoates using engineered Pseudomonas putida

Bio-upcycling of plastics is an emerging alternative process that focuses on extracting value from a wide range of plastic waste streams. Such streams are typically too contaminated to be effectively processed using traditional recycling technologies. Medium-chain-length (mcl) diols and dicarboxylates (DCA) are major products of chemically or enzymatically depolymerized plastics, such as polyesters or polyethers. In this study, we enabled the efficient metabolism of mcl-diols and -DCA in engineered Pseudomonas putida as a prerequisite for subsequent bio-upcycling. We identified the transcriptional regulator GcdR as target for enabling metabolism of uneven mcl-DCA such as pimelate, and uncovered amino acid substitutions that lead to an increased coupling between the heterologous β-oxidation of mcl-DCA and the native degradation of short-chain-length DCA. Adaptive laboratory evolution and subsequent reverse engineering unravelled two distinct pathways for mcl-diol metabolism in P. putida, namely via the hydroxy acid and subsequent native β-oxidation or via full oxidation to the dicarboxylic acid that is further metabolized by heterologous β-oxidation. Furthermore, we demonstrated the production of polyhydroxyalkanoates from mcl-diols and -DCA by a single strain combining all required metabolic features. Overall, this study provides a powerful platform strain for the bio-upcycling of complex plastic hydrolysates to polyhydroxyalkanoates and leads the path for future yield optimizations.

The pGinger Family of Expression Plasmids

The pGinger suite of expression plasmids comprises 43 plasmids that will enable precise constitutive and inducible gene expression in a wide range of Gram-negative bacterial species. Constitutive vectors are composed of 16 synthetic constitutive promoters upstream of red fluorescent protein (RFP), with a broad-host-range BBR1 origin and a kanamycin resistance marker. The family also has seven inducible systems (Jungle Express, Psal/NahR, Pm/XylS, Prha/RhaS, LacO1/LacI, LacUV5/LacI, and Ptet/TetR) controlling RFP expression on BBR1/kanamycin plasmid backbones. For four of these inducible systems (Jungle Express, Psal/NahR, LacO1/LacI, and Ptet/TetR), we created variants that utilize the RK2 origin and spectinomycin or gentamicin selection. Relevant RFP expression and growth data have been collected in the model bacterium Escherichia coli as well as Pseudomonas putida. All pGinger vectors are available via the Joint BioEnergy Institute (JBEI) Public Registry. IMPORTANCE Metabolic engineering and synthetic biology are predicated on the precise control of gene expression. As synthetic biology expands beyond model organisms, more tools will be required that function robustly in a wide range of bacterial hosts. The pGinger family of plasmids constitutes 43 plasmids that will enable both constitutive and inducible gene expression in a wide range of nonmodel Proteobacteria.

TFBMiner: A User-Friendly Command Line Tool for the Rapid Mining of Transcription Factor-Based Biosensors

Transcription factors responsive to small molecules are essential elements in synthetic biology designs. They are often used as genetically encoded biosensors with applications ranging from the detection of environmental contaminants and biomarkers to microbial strain engineering. Despite our efforts to expand the space of compounds that can be detected using biosensors, the identification and characterization of transcription factors and their corresponding inducer molecules remain labor- and time-intensive tasks. Here, we introduce TFBMiner, a new data mining and analysis pipeline that enables the automated and rapid identification of putative metabolite-responsive transcription factor-based biosensors (TFBs). This user-friendly command line tool harnesses a heuristic rule-based model of gene organization to identify both gene clusters involved in the catabolism of user-defined molecules and their associated transcriptional regulators. Ultimately, biosensors are scored based on how well they fit the model, providing wet-lab scientists with a ranked list of candidates that can be experimentally tested. We validated the pipeline using a set of molecules for which TFBs have been reported previously, including sensors responding to sugars, amino acids, and aromatic compounds, among others. We further demonstrated the utility of TFBMiner by identifying a biosensor for S-mandelic acid, an aromatic compound for which a responsive transcription factor had not been found previously. Using a combinatorial library of mandelate-producing microbial strains, the newly identified biosensor was able to distinguish between low- and high-producing strain candidates. This work will aid in the unraveling of metabolite-responsive microbial gene regulatory networks and expand the synthetic biology toolbox to allow for the construction of more sophisticated self-regulating biosynthetic pathways.

Applications and Tuning Strategies for Transcription Factor-Based Metabolite Biosensors

Transcription factor (TF)-based biosensors are widely used for the detection of metabolites and the regulation of cellular pathways in response to metabolites. Several challenges hinder the direct application of TF-based sensors to new hosts or metabolic pathways, which often requires extensive tuning to achieve the optimal performance. These tuning strategies can involve transcriptional or translational control depending on the parameter of interest. In this review, we highlight recent strategies for engineering TF-based biosensors to obtain the desired performance and discuss additional design considerations that may influence a biosensor's performance. We also examine applications of these sensors and suggest important areas for further work to continue the advancement of small-molecule biosensors.

Shikimic acid biosynthesis in microorganisms: Current status and future direction

Shikimic acid (SA), a hydroaromatic natural product, is used as a chiral precursor for organic synthesis of oseltamivir (Tamiflu®, an antiviral drug). The process of microbial production of SA has recently undergone vigorous development. Particularly, the sustainable construction of recombinant Corynebacterium glutamicum (141.2 g/L) and Escherichia coli (87 g/L) laid a solid foundation for the microbial fermentation production of SA. However, its industrial application is restricted by limitations such as the lack of fermentation tests for industrial-scale and the requirement of growth-limiting factors, antibiotics, and inducers. Therefore, the development of SA biosensors and dynamic molecular switches, as well as genetic modification strategies and optimization of the fermentation process based on omics technology could improve the performance of SA-producing strains. In this review, recent advances in the development of SA-producing strains, including genetic modification strategies, metabolic pathway construction, and biosensor-assisted evolution, are discussed and critically reviewed. Finally, future challenges and perspectives for further reinforcing the development of robust SA-producing strains are predicted, providing theoretical guidance for the industrial production of SA.

The Growth Dependent Design Constraints of Transcription-Factor-Based Metabolite Biosensors

Metabolite biosensors based on metabolite-responsive transcription factors are key synthetic biology components for sensing and precisely controlling cellular metabolism. Biosensors are often designed under laboratory conditions but are deployed in applications where cellular growth rate differs drastically from its initial characterization. Here we asked how growth rate impacts the minimum and maximum biosensor outputs and the dynamic range, which are key metrics of biosensor performance. Using LacI, TetR, and FadR-based biosensors in Escherichia coli as models, we find that the dynamic range of different biosensors have different growth rate dependencies. We developed a kinetic model to explore how tuning biosensor parameters impact the dynamic range growth rate dependence. Our modeling and experimental results revealed that the effects to dynamic range and its growth rate dependence are often coupled, and the metabolite transport mechanisms shape the dynamic range-growth rate response. This work provides a systematic understanding of biosensor performance under different growth rates, which will be useful for predicting biosensor behavior in broad synthetic biology and metabolic engineering applications.

An L-2-hydroxyglutarate biosensor based on specific transcriptional regulator LhgR

l-2-Hydroxyglutarate (l-2-HG) plays important roles in diverse physiological processes, such as carbon starvation response, tumorigenesis, and hypoxic adaptation. Despite its importance and intensively studied metabolism, regulation of l-2-HG metabolism remains poorly understood and none of regulator specifically responded to l-2-HG has been identified. Based on bacterial genomic neighborhood analysis of the gene encoding l-2-HG oxidase (LhgO), LhgR, which represses the transcription of lhgO in Pseudomonas putida W619, is identified in this study. LhgR is demonstrated to recognize l-2-HG as its specific effector molecule, and this allosteric transcription factor is then used as a biorecognition element to construct an l-2-HG-sensing FRET sensor. The l-2-HG sensor is able to conveniently monitor the concentrations of l-2-HG in various biological samples. In addition to bacterial l-2-HG generation during carbon starvation, biological function of the l-2-HG dehydrogenase and hypoxia induced l-2-HG accumulation are also revealed by using the l-2-HG sensor in human cells.

L-2-hydroxyglutarate (L-2-HG) is an important metabolite but its regulation is poorly understood. Here the authors report an L-2-HG FRET biosensor based on the allosteric transcription factor, LhgR, to monitor L-2-HG in cells and biological samples.

Transcription-Factor-based Biosensor Engineering for Applications in Synthetic Biology

Transcription-factor-based biosensors (TFBs) are often used for metabolite detection, adaptive evolution, and metabolic flux control. However, designing TFBs with superior performance for applications in synthetic biology remains challenging. Specifically, natural TFBs often do not meet real-time detection requirements owing to their slow response times and inappropriate dynamic ranges, detection ranges, sensitivity, and selectivity. Furthermore, designing and optimizing complex dynamic regulation networks is time-consuming and labor-intensive. This Review highlights TFB-based applications and recent engineering strategies ranging from traditional trial-and-error approaches to novel computer-model-based rational design approaches. The limitations of the applications and these engineering strategies are additionally reviewed.

l-arginine production in Corynebacterium glutamicum: manipulation and optimization of the metabolic process

As an important semi-essential amino acid, l-arginine is extensively used in the food and pharmaceutical fields. At present, l-arginine production depends on cost-effective, green, and sustainable microbial fermentation by using a renewable carbon source. To enhance its fermentative production, various metabolic engineering strategies have been employed, which provide valid paths for reducing the cost of l-arginine production. This review summarizes recent advances in molecular biology strategies for the optimization of l-arginine-producing strains, including manipulating the principal metabolic pathway, modulating the carbon metabolic pathway, improving the intracellular biosynthesis of cofactors and energy usage, manipulating the assimilation of ammonia, improving the transportation and membrane permeability, and performing biosensor-assisted high throughput screening, providing useful insight into the current state of l-arginine production.

Transcription Factor Engineering for High-Throughput Strain Evolution and Organic Acid Bioproduction: A Review

Metabolic regulation of gene expression for the microbial production of fine chemicals, such as organic acids, is an important research topic in post-genomic metabolic engineering. In particular, the ability of transcription factors (TFs) to respond precisely in time and space to various small molecules, signals and stimuli from the internal and external environment is essential for metabolic pathway engineering and strain development. As a key component, TFs are used to construct many biosensors in vivo using synthetic biology methods, which can be used to monitor the concentration of intracellular metabolites in organic acid production that would otherwise remain “invisible” within the intracellular environment. TF-based biosensors also provide a high-throughput screening method for rapid strain evolution. Furthermore, TFs are important global regulators that control the expression levels of key enzymes in organic acid biosynthesis pathways, therefore determining the outcome of metabolic networks. Here we review recent advances in TF identification, engineering, and applications for metabolic engineering, with an emphasis on metabolite monitoring and high-throughput strain evolution for the organic acid bioproduction.