"Metabolic burden" explained: stress symptoms and its related responses induced by (over)expression of (heterologous) proteins in Escherichia coli.

BACKGROUND: Engineering bacterial strains to redirect the metabolism towards the production of a specific product has enabled the development of industrial biotechnology. However, rewiring the metabolism can have severe implications for a microorganism, rendering cells with stress symptoms such as a decreased growth rate, impaired protein synthesis, genetic instability and an aberrant cell size. On an industrial scale, this is reflected in processes that are not economically viable. MAIN TEXT: In literature, most stress symptoms are attributed to "metabolic burden", however the actual triggers and stress mechanisms involved are poorly understood. Therefore, in this literature review, we aimed to get a better insight in how metabolic engineering affects Escherichia coli and link the observed stress symptoms to its cause. Understanding the possible implications that chosen engineering strategies have, will help to guide the reader towards optimising the envisioned process more efficiently. CONCLUSION: This review addresses the gap in literature and discusses the triggers and effects of stress mechanisms that can be activated when (over)expressing (heterologous) proteins in Escherichia coli. It uncovers that the activation of the different stress mechanisms is complex and that many are interconnected. The reader is shown that care has to be taken when (over)expressing (heterologous) proteins as the cell's metabolism is tightly regulated.

Towards a rational approach to promoter engineering: understanding the complexity of transcription initiation in prokaryotes.

Promoter sequences are important genetic control elements. Through their interaction with RNA polymerase they determine transcription strength and specificity, thereby regulating the first step in gene expression. Consequently, they can be targeted as elements to control predictability and tuneability of a genetic circuit, which is essential in applications such as the development of robust microbial cell factories. This review considers the promoter elements implicated in the three stages of transcription initiation, detailing the complex interplay of sequence-specific interactions that are involved, and highlighting that DNA sequence features beyond the core promoter elements work in a combinatorial manner to determine transcriptional strength. In particular, we emphasize that, aside from promoter recognition, transcription initiation is also defined by the kinetics of open complex formation and promoter escape, which are also known to be highly sequence specific. Significantly, we focus on how insights into these interactions can be manipulated to lay the foundation for a more rational approach to promoter engineering.

Standardization of Fluorescent Reporter Assays in Synthetic Biology across the Visible Light Spectrum.

In synthetic biology, Fluorescent reporters are frequently used to characterize the expression levels obtained from both genetic parts such as promoters and ribosome binding sites as well as from complex genetic circuits. To this end, plate readers offer an easy and high-throughput way of characterizing both the growth and fluorescence expression levels of cell cultures. However, despite the similar mode of action used in different devices, their output is not comparable due to intrinsic differences in their setup. Additionally, the generated output is expressed using arbitrary units, limiting reliable comparison of results to measurements taken within one single experiment using one specific plate reader, hampering the transferability of data across different plate readers and laboratories. This article presents an easy and accessible calibration method for transforming the device-specific output into a standardized output expressing the amount of fluorescence per well as a known equivalent fluorophore concentration per cell for fluorescent reporters spanning the visible light spectrum. This calibration method follows a 2-fold approach determining both the estimated number of cells and the equivalent chemical fluorophore concentration per well. It will contribute to the comparison of plate reader experiments between different laboratories across the world and will therefore greatly improve the reliability and exchange of both results and genetic parts between research groups.

Taming CRISPRi: Dynamic range tuning through guide RNA diversion.

CRISPRi is a powerful technique to repress gene expression in a targeted and highly efficient manner. However, this potency is a double-edged sword in inducible systems, as even leaky expression of guide RNA results in a repression phenotype, complicating applications such as dynamic metabolic engineering. We evaluated three methods to enhance the controllability of CRISPRi by modulating the level of free and DNA-bound guide RNA complexes. Overall repression can be attenuated through rationally designed mismatches in the reversibility determining region of the guide RNA sequence; decoy target sites can selectively modulate repression at low levels of induction; and the implementation of feedback control not only enhances the linearity of induction, but broadens the dynamic range of the output as well. Furthermore, feedback control significantly enhances the recovery rate after induction is removed. Used in combination, these techniques enable the fine-tuning of CRISPRi to meet restrictions imposed by the target and match the input signal required for induction.

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.

Controlled processivity in glycosyltransferases: A way to expand the enzymatic toolbox.

Glycosyltransferases (GT) catalyse the biosynthesis of complex carbohydrates which are the most abundant group of molecules in nature. They are involved in several key mechanisms such as cell signalling, biofilm formation, host immune system invasion or cell structure and this in both prokaryotic and eukaryotic cells. As a result, research towards complete enzyme mechanisms is valuable to understand and elucidate specific structure-function relationships in this group of molecules. In a next step this knowledge could be used in GT protein engineering, not only for rational drug design but also for multiple biotechnological production processes, such as the biosynthesis of hyaluronan, cellooligosaccharides or chitooligosaccharides. Generation of these poly- and/or oligosaccharides is possible due to a common feature of several of these GTs: processivity. Enzymatic processivity has the ability to hold on to the growing polymer chain and some of these GTs can even control the number of glycosyl transfers. In a first part, recent advances in understanding the mechanism of various processive enzymes are discussed. To this end, an overview is given of possible engineering strategies for the purpose of new industrial and fundamental applications. In the second part of this review, we focused on specific chain length-controlling mechanisms, i.e., key residues or conserved regions, and this for both eukaryotic and prokaryotic enzymes.

Human gut microbiota stratified by (+)-catechin metabolism dynamics reveals colon region-dependent metabolic profile.

Catechins have proven to have several health benefits, yet a huge interindividual variability occurs. The metabolic potency of the colonic microbiota towards catechin is a key determinant of this variability. Microbiota from two donors - previously characterized as a fast and a slow converter- were incubated with (+)-catechin in vitro. The robustness of in vitro metabolic profiles was verified by well-fitted human trials. The colon region-dependent and donor-dependent patterns were reflected in both metabolic features and colonic microbiota composition. Upstream and downstream metabolites were mainly detected in the proximal and distal colons, respectively, and were considered important explanatory variables for microbiota clustering in the corresponding colon regions. Higher abundances of two catechin-metabolizing bacteria, Eggerthella and Flavonifractor were found in the distal colon compared to the proximal colon and in slow converter than fast converter. Additionally, these two bacteria were enriched in treatment samples compared to sham treatment samples.

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.

ProD: A Tool for Predictive Design of Tailored Promoters in Escherichia coli.

A major goal in synthetic biology is the engineering of synthetic gene circuits with a predictable, controlled and designed outcome. This creates a need for building blocks that can modulate gene expression without interference with the native cell system. A tool allowing forward engineering of promoters with predictable transcription initiation frequency is still lacking. Promoter libraries specific for σ70 to ensure the orthogonality of gene expression were built in Escherichia coli and labeled using fluorescence-activated cell sorting to obtain high-throughput DNA sequencing data to train a convolutional neural network. We were able to confirm in vivo that the model is able to predict the promoter transcription initiation frequency (TIF) of new promoter sequences. Here, we provide an online tool for promoter design (ProD) in E. coli, which can be used to tailor output sequences of desired promoter TIF or predict the TIF of a custom sequence.

Unlocking the bacterial domain for industrial biotechnology applications using universal parts and tools.

Synthetic biology can play a major role in the development of sustainable industrial biotechnology processes. However, the development of economically viable production processes is currently hampered by the limited availability of host organisms that can be engineered for a specific production process. To date, standard hosts such as Escherichia coli and Saccharomyces cerevisiae are often used as starting points for process development since parts and tools allowing their engineering are readily available. However, their suboptimal metabolic background or impaired performance at industrial scale for a desired production process, can result in increased costs associated with process development and/or disappointing production titres. Building a universal and portable gene expression system allowing genetic engineering of hosts across the bacterial domain would unlock the bacterial domain for industrial biotechnology applications in a highly standardized manner and, doing so, render industrial biotechnology processes more competitive compared to the current polluting chemical processes. This review gives an overview of a selection of bacterial hosts highly interesting for industrial biotechnology based on both their metabolic and process optimization properties. Moreover, the requirements and progress made so far to enable universal, standardized, and portable gene expression across the bacterial domain is discussed.

In Vitro Microbial Metabolism of (+)-Catechin Reveals Fast and Slow Converters with Individual-Specific Microbial and Metabolite Markers.

The bioavailability of catechin highly relies on gut microbiota which may determine its metabolic profile, resulting in different health outcomes. Here, we investigated in vitro (+)-catechin metabolism by human microbial communities. There were substantial interindividual differences in the metabolic profiles of (+)-catechin, with 5-(3',4'-dihydroxyphenyl)-γ-valerolactone being the major contributor. Furthermore, the microbial metabolic rate of catechin enabled stratification of 12 participants (fast, medium, and slow converters), despite the interference from the strong intrinsic interindividual variability in fecal microbiota. Correlations were established between this stratified population and microbiota features, such as ecosystem diversity. Additionally, fast converters had significantly higher prevalences of amplicon sequence variants (ASVs) with potential capacity of C-ring cleavage (ASV233_Eggerthella and ASV402_Eubacterium), B-ring dihydroxylation (ASV402_Eubacterium), and short-chain fatty acid (SCFA)-producing ASVs. In conclusion, metabolic-capability-based stratification allows us to uncover differences in microbial composition between fast and slow converters, which could help to elucidate interindividual variabilities in the health benefits of catechins.

Biosensor-driven, model-based optimization of the orthogonally expressed naringenin biosynthesis pathway.

BACKGROUND: The rapidly expanding synthetic biology toolbox allows engineers to develop smarter strategies to tackle the optimization of complex biosynthetic pathways. In such a strategy, multi-gene pathways are subdivided in several modules which are each dynamically controlled to fine-tune their expression in response to a changing cellular environment. To fine-tune separate modules without interference between modules or from the host regulatory machinery, a sigma factor (σ) toolbox was developed in previous work for tunable orthogonal gene expression. Here, this toolbox is implemented in E. coli to orthogonally express and fine-tune a pathway for the heterologous biosynthesis of the industrially relevant plant metabolite, naringenin. To optimize the production of this pathway, a practical workflow is still imperative to balance all steps of the pathway. This is tackled here by the biosensor-driven screening, subsequent genotyping of combinatorially engineered libraries and finally the training of three different computer models to predict the optimal pathway configuration. RESULTS: The efficiency and knowledge gained through this workflow is demonstrated here by improving the naringenin production titer by 32% with respect to a random pathway library screen. Our best strain was cultured in a batch bioreactor experiment and was able to produce 286 mg/L naringenin from glycerol in approximately 26 h. This is the highest reported naringenin production titer in E. coli without the supplementation of pathway precursors to the medium or any precursor pathway engineering. In addition, valuable pathway configuration preferences were identified in the statistical learning process, such as specific enzyme variant preferences and significant correlations between promoter strength at specific steps in the pathway and titer. CONCLUSIONS: An efficient strategy, powered by orthogonal expression, was applied to successfully optimize a biosynthetic pathway for microbial production of flavonoids in E. coli up to high, competitive levels. Within this strategy, statistical learning techniques were combined with combinatorial pathway optimization techniques and an in vivo high-throughput screening method to efficiently determine the optimal operon configuration of the pathway. This "pathway architecture designer" workflow can be applied for the fast and efficient development of new microbial cell factories for different types of molecules of interest while also providing additional insights into the underlying pathway characteristics.

Engineering transcriptional regulation in Escherichia coli using an archaeal TetR-family transcription factor.

Synthetic biology requires well-characterized biological parts that can be combined into functional modules. One type of biological parts are transcriptional regulators and their cognate operator elements, which enable to either generate an input-specific response or are used as actuator modules. A range of regulators has already been characterized and used for orthogonal gene expression engineering, however, previous efforts have mostly focused on bacterial regulators. This work aims to design and explore the use of an archaeal TetR family regulator, FadRSa from Sulfolobus acidocaldarius, in a bacterial system, namely Escherichia coli. This is a challenging objective given the fundamental difference between the bacterial and archaeal transcription machinery and the lack of a native TetR-like FadR regulatory system in E. coli. The synthetic σ70-dependent bacterial promoter proD was used as a starting point to design hybrid bacterial/archaeal promoter/operator regions, in combination with the mKate2 fluorescent reporter enabling a readout. Four variations of proD containing FadRSa binding sites were constructed and characterized. While expressional activity of the modified promoter proD was found to be severely diminished for two of the constructs, constructs in which the binding site was introduced adjacent to the -35 promoter element still displayed sufficient basal transcriptional activity and showed up to 7-fold repression upon expression of FadRSa. Addition of acyl-CoA has been shown to disrupt FadRSa binding to the DNA in vitro. However, extracellular concentrations of up to 2 mM dodecanoate, subsequently converted to acyl-CoA by the cell, did not have a significant effect on repression in the bacterial system. This work demonstrates that archaeal transcription regulators can be used to generate actuator elements for use in E. coli, although the lack of ligand response underscores the challenge of maintaining biological function when transferring parts to a phylogenetically divergent host.

Improving the performance of machine learning models for biotechnology: The quest for deus ex machina.

Machine learning is becoming an integral part of the Design-Build-Test-Learn cycle in biotechnology. Machine learning models learn from collected datasets such as omics data and predict a defined outcome, which has led to both production improvements and predictive tools in the field. Robust prediction of the behavior of microbial cell factories and production processes not only greatly increases our understanding of the function of such systems, but also provides significant savings of development time. However, many pitfalls when modeling biological data - bad fit, noisy data, model instability, low data quantity and imbalances in the data - cause models to suffer in their performance. Here we provide an accessible, in-depth analysis on the problems created by these pitfalls, as well as means of their detection and mediation, with a focus on supervised learning. Assessing the state of the art, we show that, currently, in-depth analyses of model performance are often absent and must be improved. This review provides a toolbox for the analysis of model robustness and performance, and simultaneously proposes a standard for the community to facilitate future work. It is further accompanied by an interactive online tutorial on the discussed issues.

The Donor-Dependent and Colon-Region-Dependent Metabolism of (+)-Catechin by Colonic Microbiota in the Simulator of the Human Intestinal Microbial Ecosystem.

The intestinal absorption of dietary catechins is quite low, resulting in most of them being metabolized by gut microbiota in the colon. It has been hypothesized that microbiota-derived metabolites may be partly responsible for the association between catechin consumption and beneficial cardiometabolic effects. Given the profound differences in gut microbiota composition and microbial load between individuals and across different colon regions, this study examined how microbial (+)-catechin metabolite profiles differ between colon regions and individuals. Batch exploration of the interindividual variability in (+)-catechin microbial metabolism resulted in a stratification based on metabolic efficiency: from the 12 tested donor microbiota, we identified a fast- and a slow-converting microbiota that was subsequently inoculated to SHIME, a dynamic model of the human gut. Monitoring of microbial (+)-catechin metabolites from proximal and distal colon compartments with UHPLC-MS and UPLC-IMS-Q-TOF-MS revealed profound donor-dependent and colon-region-dependent metabolite profiles with 5-(3',4'-dihydroxyphenyl)-γ-valerolactone being the largest contributor to differences between the fast- and slow-converting microbiota and the distal colon being a more important region for (+)-catechin metabolism than the proximal colon. Our findings may contribute to further understanding the role of the gut microbiota as a determinant of interindividual variation in pharmacokinetics upon (+)-catechin ingestion.

Predictive design of sigma factor-specific promoters.

To engineer synthetic gene circuits, molecular building blocks are developed which can modulate gene expression without interference, mutually or with the host's cell machinery. As the complexity of gene circuits increases, automated design tools and tailored building blocks to ensure perfect tuning of all components in the network are required. Despite the efforts to develop prediction tools that allow forward engineering of promoter transcription initiation frequency (TIF), such a tool is still lacking. Here, we use promoter libraries of E. coli sigma factor 70 (σ70)- and B. subtilis σB-, σF- and σW-dependent promoters to construct prediction models, capable of both predicting promoter TIF and orthogonality of the σ-specific promoters. This is achieved by training a convolutional neural network with high-throughput DNA sequencing data from fluorescence-activated cell sorted promoter libraries. This model functions as the base of the online promoter design tool (ProD), providing tailored promoters for tailored genetic systems.

Metabolic engineering for glycoglycerolipids production in E. coli: Tuning phosphatidic acid and UDP-glucose pathways.

Glycolipids are target molecules in biotechnology and biomedicine as biosurfactants, biomaterials and bioactive molecules. An engineered E. coli strain for the production of glycoglycerolipids (GGL) used the MG517 glycolipid synthase from M. genitalium for glucosyl transfer from UDPGlc to diacylglycerol acceptor (Mora-Buyé et al., 2012). The intracellular diacylglycerol pool proved to be the limiting factor for GGL production. Here we designed different metabolic engineering strategies to enhance the availability of precursor substrates for the glycolipid synthase by modulating fatty acids, acyl donor and phosphatidic acid biosynthesis. Knockouts of tesA, fadE and fabR genes involved in fatty acids degradation, overexpression of the transcriptional regulator FadR, the acyltransferases PlsB and C, and the pyrophosphatase Cdh for phosphatidic acid biosynthesis, as well as the phosphatase PgpB for conversion to diacylglycerol were explored with the aim of improving GGL titers. Among the different engineered strains, the ΔtesA strain co-expressing MG517 and a fusion PlsCxPgpB protein was the best producer, with a 350% increase of GGL titer compared to the parental strain expressing MG517 alone. Attempts to boost UDPGlc availability by overexpressing the uridyltransferase GalU or knocking out the UDP-sugar diphosphatase encoding gene ushA did not further improve GGL titers. Most of the strains produced GGL containing a variable number of glucosyl units from mono-to tetra-saccharides. Interestingly, the strains co-expressing Cdh showed a shift in the GGL profile towards the diglucosylated lipid (up to 80% of total GGLs) whereas the strains with a fadR knockout presented a higher amount of unsaturated acyl chains. In all cases, GGL production altered the lipidic composition of the E. coli membrane, observing that GGL replace phosphatidylethanolamine to maintain the overall membrane charge balance.

Mapping and refactoring pathway control through metabolic and protein engineering: The hexosamine biosynthesis pathway.

Microorganisms possess a plethora of regulatory mechanisms to tightly control the flux through their metabolic network, allowing optimal behaviour in response to environmental conditions. However, these mechanisms typically counteract metabolic engineering efforts to rewire the metabolism with a view to overproduction. Hence, overcoming flux control is key in the development of microbial cell factories, illustrated in this contribution using the strictly controlled hexosamine biosynthesis pathway. The hexosamine biosynthesis pathway has recently garnered attention as gateway for the industrial biotechnological production of numerous mono-, oligo- and polysaccharidic compounds, composed of, i.a., glucosamine, N-acetylglucosamine, and neuraminic acid and with a vast application potential in the health, comsetics, and agricultural sector. First, the various alternative pathways in eukaryotes and prokaryotes are discussed. Second, the main regulatory mechanisms on transcriptional, translational and post-translational control, and the strategies to circumvent these pathway bottlenecks are highlighted. These efforts can serve as an inspiration to tackle regulatory control when optimizing any microbial cell factory.

Modulating transcription through development of semi-synthetic yeast core promoters.

Altering gene expression regulation by promoter engineering is a very effective way to fine-tune heterologous pathways in eukaryotic hosts. Typically, pathway building approaches in yeast still use a limited set of long, native promoters. With the today's introduction of longer and more complex pathways, an expansion of this synthetic biology toolbox is necessary. In this study we elucidated the core promoter structure of the well-characterized yeast TEF1 promoter and determined the minimal length needed for sufficient protein expression. Furthermore, this minimal core promoter sequence was used for the creation of a promoter library covering different expression strengths. This resulted in a group of short, 69 bp promoters with an 8.0-fold expression range. One exemplar had a two and four times higher expression compared to the native CYC1 and ADH1 promoter, respectively. Additionally, as it was described that the protein expression range could be broadened by upstream activating sequences (UASs), we integrated earlier described single and multiple short, synthetic UASs in front of the strongest yeast core promoter. This approach resulted to further variation in protein expression and an overall promoter library spanning a 20-fold activity range and covering a length from 69 bp to maximally 129 bp. Furthermore, the robustness of this library was assessed on three alternative carbon sources besides glucose. As such, the suitability of short yeast core promoters for metabolic engineering applications on different media, either in an individual context or combined with UAS elements, was demonstrated.