Programming mRNA decay to modulate synthetic circuit resource allocation

Reprogramming cellular metabolism to increase the efficiency of microbial cell factories

Recent studies are increasingly focusing on advanced biotechnological tools, self-adjusting smart microorganisms, and artificial intelligent networks, to engineer microorganisms with various functions. Microbial cell factories are a vital platform for improving the bioproduction of medicines, biofuels, and biomaterials from renewable carbon sources. However, these processes are significantly affected by cellular metabolism, and boosting the efficiency of microbial cell factories remains a challenge. In this review, we present a strategy for reprogramming cellular metabolism to enhance the efficiency of microbial cell factories for chemical biosynthesis, which improves our understanding of microbial physiology and metabolic control. Current methods are mainly focused on synthetic pathways, metabolic resources, and cell performance. This review highlights the potential biotechnological strategy to reprogram cellular metabolism and provide novel guidance for designing more intelligent industrial microbes with broader applications in this growing field.

Context-dependent redesign of robust synthetic gene circuits

Cells provide dynamic platforms for executing exogenous genetic programs in synthetic biology, resulting in highly context-dependent circuit performance. Recent years have seen an increasing interest in understanding the intricacies of circuit-host relationships, their influence on the synthetic bioengineering workflow, and in devising strategies to alleviate undesired effects. We provide an overview of how emerging circuit-host interactions, such as growth feedback and resource competition, impact both deterministic and stochastic circuit behaviors. We also emphasize control strategies for mitigating these unwanted effects. This review summarizes the latest advances and the current state of host-aware and resource-aware design of synthetic gene circuits.

Selective RNA Processing and Stabilization are Multi-Layer and Stoichiometric Regulators of Gene Expression in Escherichia coli

Selective RNA processing and stabilization (SRPS) facilitates the differential expression of multiple genes in polycistronic operons. However, how the coordinated actions of SRPS-related enzymes affect stoichiometric regulation remains unclear. In the present study, the first genome-wide targetome analysis is reported of these enzymes in Escherichia coli, at a single-nucleotide resolution. A strictly linear relationship is observed between the RNA pyrophosphohydrolase processing ratio and scores assigned to the first three nucleotides of the primary transcript. Stem-loops associated with PNPase targetomes exhibit a folding free energy that is negatively correlated with the termination ratio of PNPase at the 3' end. More than one-tenth of the RNase E processing sites in the 5'-untranslated regions（UTR） form different stem-loops that affect ribosome-binding and translation efficiency. The effectiveness of the SRPS elements is validated using a dual-fluorescence reporter system. The findings highlight a multi-layer and quantitative regulatory method for optimizing the stoichiometric expression of genes in bacteria and promoting the application of SRPS in synthetic biology.

Engineering resource allocation in artificially minimized cells: Is genome reduction the best strategy?

The elimination of the expression of cellular functions that are not needed in a certain well-defined artificial environment, such as those used in industrial production facilities, has been the goal of many cellular minimization projects. The generation of a minimal cell with reduced burden and less host-function interactions has been pursued as a tool to improve microbial production strains. In this work, we analysed two cellular complexity reduction strategies: genome and proteome reduction. With the aid of an absolute proteomics data set and a genome-scale model of metabolism and protein expression (ME-model), we quantitatively assessed the difference of reducing genome to the correspondence of reducing proteome. We compare the approaches in terms of energy consumption, defined in ATP equivalents. We aim to show what is the best strategy for improving resource allocation in minimized cells. Our results show that genome reduction by length is not proportional to reducing resource use. When we normalize calculated energy savings, we show that strains with the larger calculated proteome reduction show the largest resource use reduction. Furthermore, we propose that reducing highly expressed proteins should be the target as the translation of a gene uses most of the energy. The strategies proposed here should guide cell design when the aim of a project is to reduce the maximum amount or cellular resources.

Stability, robustness, and containment: preparing synthetic biology for real-world deployment

As engineered microbes are used in increasingly diverse applications across human health and bioproduction, the field of synthetic biology will need to focus on strategies that stabilize and contain the function of these populations within target environments. To this end, recent advancements have created layered sensing circuits that can compute cell survival, genetic contexts that are less susceptible to mutation, burden, and resource control circuits, and methods for population variability reduction. These tools expand the potential for real-world deployment of complex microbial systems by enhancing their environmental robustness and functional stability in the face of unpredictable host response and evolutionary pressure.

Hardware, Software, and Wetware Codesign Environment for Synthetic Biology

Synthetic biology is the process of forward engineering living systems. These systems can be used to produce biobased materials, agriculture, medicine, and energy. One approach to designing these systems is to employ techniques from the design of embedded electronics. These techniques include abstraction, standards, modularity, automated design, and formal semantic models of computation. Together, these elements form the foundation of "biodesign automation," where software, robotics, and microfluidic devices combine to create exciting biological systems of the future. This paper describes a "hardware, software, wetware" codesign vision where software tools can be made to act as "genetic compilers" that transform high-level specifications into engineered "genetic circuits" (wetware). This is followed by a process where automation equipment, well-defined experimental workflows, and microfluidic devices are explicitly designed to house, execute, and test these circuits (hardware). These systems can be used as either massively parallel experimental platforms or distributed bioremediation and biosensing devices. Next, scheduling and control algorithms (software) manage these systems' actual execution and data analysis tasks. A distinguishing feature of this approach is how all three of these aspects (hardware, software, and wetware) may be derived from the same basic specification in parallel and generated to fulfill specific cost, performance, and structural requirements.

Generating dynamic gene expression patterns without the need for regulatory circuits

Synthetic biology has successfully advanced our ability to design and implement complex, time-varying genetic circuits to control the expression of recombinant proteins. However, these circuits typically require the production of regulatory genes whose only purpose is to coordinate expression of other genes. When designing very small genetic constructs, such as viral genomes, we may want to avoid introducing such auxiliary gene products while nevertheless encoding complex expression dynamics. To this end, here we demonstrate that varying only the placement and strengths of promoters, terminators, and RNase cleavage sites in a computational model of a bacteriophage genome is sufficient to achieve solutions to a variety of basic gene expression patterns. We discover these genetic solutions by computationally evolving genomes to reproduce desired gene expression time-course data. Our approach shows that non-trivial patterns can be evolved, including patterns where the relative ordering of genes by abundance changes over time. We find that some patterns are easier to evolve than others, and comparable expression patterns can be achieved via different genetic architectures. Our work opens up a novel avenue to genome engineering via fine-tuning the balance of gene expression and gene degradation rates.

Quantifying heterologous gene expression during ectopic MazF production in Escherichia coli

Objective

MazF is a sequence-specific endoribonuclease-toxin of the MazEF toxin–antitoxin system. MazF cleaves single-stranded ribonucleic acid (RNA) regions at adenine–cytosine–adenine (ACA) sequences in the bacterium Escherichia coli. The MazEF system has been used in various biotechnology and synthetic biology applications. In this study, we infer how ectopic mazF overexpression affects production of heterologous proteins. To this end, we quantified the levels of fluorescent proteins expressed in E. coli from reporters translated from the ACA-containing or ACA-less messenger RNAs (mRNAs). Additionally, we addressed the impact of the 5′-untranslated region of these reporter mRNAs under the same conditions by comparing expression from mRNAs that comprise (canonical mRNA) or lack this region (leaderless mRNA).

Results

Flow cytometry analysis indicates that during mazF overexpression, fluorescent proteins are translated from the canonical as well as leaderless mRNAs. Our analysis further indicates that longer mazF overexpression generally increases the concentration of fluorescent proteins translated from ACA-less mRNAs, however it also substantially increases bacterial population heterogeneity. Finally, our results suggest that the strength and duration of mazF overexpression should be optimized for each experimental setup, to maximize the heterologous protein production and minimize the amount of phenotypic heterogeneity in bacterial populations, which is unfavorable in biotechnological processes.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13104-022-06061-9.

Predicting Composition of Genetic Circuits with Resource Competition: Demand and Sensitivity

The design of genetic circuits typically relies on characterization of constituent modules in isolation to predict the behavior of modules' composition. However, it has been shown that the behavior of a genetic module changes when other modules are in the cell due to competition for shared resources. In order to engineer multimodule circuits that behave as intended, it is thus necessary to predict changes in the behavior of a genetic module when other modules load cellular resources. Here, we introduce two characteristics of circuit modules: the demand for cellular resources and the sensitivity to resource loading. When both are known for every genetic module in a circuit library, they can be used to predict any module's behavior upon addition of any other module to the cell. We develop an experimental approach to measure both characteristics for any circuit module using a resource sensor module. Using the measured resource demand and sensitivity for each module in a library, the outputs of the modules can be accurately predicted when they are inserted in the cell in arbitrary combinations. These resource competition characteristics may be used to inform the design of genetic circuits that perform as predicted despite resource competition.

Multiomics Study of Bacterial Growth Arrest in a Synthetic Biology Application

We investigated the scalability of a previously developed growth switch based on external control of RNA polymerase expression. Our results indicate that, in liter-scale bioreactors operating in fed-batch mode, growth-arrested Escherichia coli cells are able to convert glucose to glycerol at an increased yield. A multiomics quantification of the physiology of the cells shows that, apart from acetate production, few metabolic side effects occur. However, a number of specific responses to growth slow-down and growth arrest are launched at the transcriptional level. These notably include the downregulation of genes involved in growth-associated processes, such as amino acid and nucleotide metabolism and translation. Interestingly, the transcriptional responses are buffered at the proteome level, probably due to the strong decrease of the total mRNA concentration after the diminution of transcriptional activity and the absence of growth dilution of proteins. Growth arrest thus reduces the opportunities for dynamically adjusting the proteome composition, which poses constraints on the design of biotechnological production processes but may also avoid the initiation of deleterious stress responses.

The world of ribonucleases from pseudomonads: a short trip through the main features and singularities

The development of synthetic biology has brought an unprecedented increase in the number molecular tools applicable into a microbial chassis. The exploration of such tools into different bacteria revealed not only the challenges of context dependency of biological functions but also the complexity and diversity of regulatory layers in bacterial cells. Most of the standardized genetic tools and principles/functions have been mostly based on model microorganisms, namely Escherichia coli. In contrast, the non-model pseudomonads lack a deeper understanding of their regulatory layers and have limited molecular tools. They are resistant pathogens and promising alternative bacterial chassis, making them attractive targets for further studies. Ribonucleases (RNases) are key players in the post-transcriptional control of gene expression by degrading or processing the RNA molecules in the cell. These enzymes act according to the cellular requirements and can also be seen as the recyclers of ribonucleotides, allowing a continuous input of these cellular resources. This makes these post-transcriptional regulators perfect candidates to regulate microbial physiology. This review summarizes the current knowledge and unique properties of ribonucleases in the world of pseudomonads, taking into account genomic context analysis, biological function and strategies to use ribonucleases to improve biotechnological processes.

Understanding and mathematical modelling of cellular resource allocation in microorganisms: a comparative synthesis

BACKGROUND

The rising consensus that the cell can dynamically allocate its resources provides an interesting angle for discovering the governing principles of cell growth and metabolism. Extensive efforts have been made in the past decade to elucidate the relationship between resource allocation and phenotypic patterns of microorganisms. Despite these exciting developments, there is still a lack of explicit comparison between potentially competing propositions and a lack of synthesis of inter-related proposals and findings.

RESULTS

In this work, we have reviewed resource allocation-derived principles, hypotheses and mathematical models to recapitulate important achievements in this area. In particular, the emergence of resource allocation phenomena is deciphered by the putative tug of war between the cellular objectives, demands and the supply capability. Competing hypotheses for explaining the most-studied phenomenon arising from resource allocation, i.e. the overflow metabolism, have been re-examined towards uncovering the potential physiological root cause. The possible link between proteome fractions and the partition of the ribosomal machinery has been analysed through mathematical derivations. Finally, open questions are highlighted and an outlook on the practical applications is provided. It is the authors' intention that this review contributes to a clearer understanding of the role of resource allocation in resolving bacterial growth strategies, one of the central questions in microbiology.

CONCLUSIONS

We have shown the importance of resource allocation in understanding various aspects of cellular systems. Several important questions such as the physiological root cause of overflow metabolism and the correct interpretation of 'protein costs' are shown to remain open. As the understanding of the mechanisms and utility of resource application in cellular systems further develops, we anticipate that mathematical modelling tools incorporating resource allocation will facilitate the circuit-host design in synthetic biology.

The essential role of mRNA degradation in understanding and engineering E. coli metabolism

Metabolic engineering strategies are crucial for the development of bacterial cell factories with improved performance. Until now, optimal metabolic networks have been designed based on systems biology approaches integrating large-scale data on the steady-state concentrations of mRNA, protein and metabolites, sometimes with dynamic data on fluxes, but rarely with any information on mRNA degradation. In this review, we compile growing evidence that mRNA degradation is a key regulatory level in E. coli that metabolic engineering strategies should take into account. We first discuss how mRNA degradation interacts with transcription and translation, two other gene expression processes, to balance transcription regulation and remove poorly translated mRNAs. The many reciprocal interactions between mRNA degradation and metabolism are also highlighted: metabolic activity can be controlled by changes in mRNA degradation and in return, the activity of the mRNA degradation machinery is controlled by metabolic factors. The mathematical models of the crosstalk between mRNA degradation dynamics and other cellular processes are presented and discussed with a view towards novel mRNA degradation-based metabolic engineering strategies. We show finally that mRNA degradation-based strategies have already successfully been applied to improve heterologous protein synthesis. Overall, this review underlines how important mRNA degradation is in regulating E. coli metabolism and identifies mRNA degradation as a key target for innovative metabolic engineering strategies in biotechnology.

Challenges and opportunities in biological funneling of heterogeneous and toxic substrates beyond lignin

Significant developments in the understanding and manipulation of microbial metabolism have enabled the use of engineered biological systems toward a more sustainable energy and materials economy. While developments in metabolic engineering have primarily focused on the conversion of carbohydrates, substantial opportunities exist for using these same principles to extract value from more heterogeneous and toxic waste streams, such as those derived from lignin, biomass pyrolysis, or industrial waste. Funneling heterogeneous substrates from these streams toward valuable products, termed biological funneling, presents new challenges in balancing multiple catabolic pathways competing for shared cellular resources and engineering against perturbation from toxic substrates. Solutions to many of these challenges have been explored within the field of lignin valorization. This perspective aims to extend beyond lignin to highlight the challenges and discuss opportunities for use of biological systems to upgrade previously inaccessible waste streams.

Subcellular Architecture of the xyl Gene Expression Flow of the TOL Catabolic Plasmid of Pseudomonas putida mt-2

Despite intensive research on the biochemical and regulatory features of the archetypal catabolic TOL system borne by pWW0 of Pseudomonas putida strain mt-2, the physical arrangement and tridimensional logic of the xyl gene expression flow remains unknown. In this work, the spatial distribution of specific xyl mRNAs with respect to the host nucleoid, the TOL plasmid, and the ribosomal pool has been investigated. In situ hybridization of target transcripts with fluorescent oligonucleotide probes revealed that xyl mRNAs cluster in discrete foci, adjacent but clearly separated from the TOL plasmid and the cell nucleoid. Also, they colocalize with ribosome-rich domains of the intracellular milieu. This arrangement was maintained even when the xyl genes were artificially relocated to different chromosomal locations. The same held true when genes were expressed through a heterologous T7 polymerase-based system, which likewise led to mRNA foci outside the DNA. In contrast, rifampin treatment, known to ease crowding, blurred the confinement of xyl transcripts. This suggested that xyl mRNAs exit from their initiation sites to move to ribosome-rich points for translation—rather than being translated coupled to transcription. Moreover, the results suggest the distinct subcellular motion of xyl mRNAs results from both innate properties of the sequences and the physical forces that keep the ribosomal pool away from the nucleoid in P. putida. This scenario is discussed within the background of current knowledge on the three-dimensional organization of the gene expression flow in other bacteria and the environmental lifestyle of this soil microorganism.

Developing a pathway-independent and full-autonomous global resource allocation strategy to dynamically switching phenotypic states

A grand challenge of biological chemical production is the competition between synthetic circuits and host genes for limited cellular resources. Quorum sensing (QS)-based dynamic pathway regulations provide a pathway-independent way to rebalance metabolic flux over the course of the fermentation. Most cases, however, these pathway-independent strategies only have capacity for a single QS circuit functional in one cell. Furthermore, current dynamic regulations mainly provide localized control of metabolic flux. Here, with the aid of engineering synthetic orthogonal quorum-related circuits and global mRNA decay, we report a pathway-independent dynamic resource allocation strategy, which allows us to independently controlling two different phenotypic states to globally redistribute cellular resources toward synthetic circuits. The strategy which could pathway-independently and globally self-regulate two desired cell phenotypes including growth and production phenotypes could totally eliminate the need for human supervision of the entire fermentation.

A challenge for biological chemical production is the completion between synthetic circuits and host resources. Here the authors the authors use quorum sensing circuits and global mRNA decay to independently control two phenotypic states.

Topology-dependent interference of synthetic gene circuit function by growth feedback

Growth-mediated feedback between synthetic gene circuits and host organisms leads to diverse emerged behaviors, including growth bistability and enhanced ultrasensitivity. However, the range of possible impacts of growth feedback on gene circuits remains underexplored. Here, we mathematically and experimentally demonstrated that growth feedback affects the functions of memory circuits in a network topology-dependent way. Specifically, the memory of the self-activation switch is quickly lost due to the growth-mediated dilution of the circuit products. Decoupling of growth feedback reveals its memory, manifested by its hysteresis property across a broad range of inducer concentration. On the contrary, the toggle switch is more refractory to growth-mediated dilution and can retrieve its memory after the fast-growth phase. The underlying principle lies in the different dependence of active and repressive regulations in these circuits on the growth-mediated dilution. Our results unveil the topology-dependent mechanism on how growth-mediated feedback influences the behaviors of gene circuits.

Trade-offs between gene expression, growth and phenotypic diversity in microbial populations

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Limitations in molecular resources for gene expression influence bacterial physiology.

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Bacteria optimise trade-offs between resource allocation and growth.

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Resource allocation plays a role in the emergence of phenotypic heterogeneity.

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Trade-offs between bet-hedging and growth can be harnessed in biotechnology.

Bacterial cells have a limited number of resources that can be allocated for gene expression. The intracellular competition for these resources has an impact on the cell physiology. Bacteria have evolved mechanisms to optimize resource allocation in a variety of scenarios, showing a trade-off between the resources used to maximise growth (e.g. ribosome synthesis) and the rest of cellular functions. Limitations in gene expression also play a role in generating phenotypic diversity, which is advantageous in fluctuating environments, at the expenses of decreasing growth rates. Our current understanding of these trade-offs can be exploited for biotechnological applications benefiting from the selective manipulation of the allocation of resources.

Microbial Interaction Network Inference in Microfluidic Droplets

Microbial interactions are major drivers of microbial community dynamics and functions but remain challenging to identify because of limitations in parallel culturing and absolute abundance quantification of community members across environments and replicates. To this end, we developed Microbial Interaction Network Inference in microdroplets (MINI-Drop). Fluorescence microscopy coupled to computer vision techniques were used to rapidly determine the absolute abundance of each strain in hundreds to thousands of droplets per condition. We showed that MINI-Drop could accurately infer pairwise and higher-order interactions in synthetic consortia. We developed a stochastic model of community assembly to provide insight into the heterogeneity in community states across droplets. Finally, we elucidated the complex web of interactions linking antibiotics and different species in a synthetic consortium. In sum, we demonstrated a robust and generalizable method to infer microbial interaction networks by random encapsulation of sub-communities into microfluidic droplets.

MazF activation causes ACA sequence-independent and selective alterations in RNA levels in Escherichia coli

Escherichia coli MazF is a toxin protein that cleaves RNA at ACA sequences. Its activation has been thought to cause growth inhibition, primarily through indiscriminate cleavage of RNA. To investigate responses following MazF activation, transcriptomic profiles of mazF-overexpressing and non-overexpressing E. coli K12 cells were compared. Analyses of differentially expressed genes demonstrated that the presence and the number of ACA trimers in RNA was unrelated to cellular RNA levels. Mapping differentially expressed genes onto the chromosome identified two chromosomal segments in which upregulated genes formed clusters, and these segments were absent in the chromosomes of E. coli strains other than K12. These results suggest that MazF regulates selective, rather than indiscriminate, categories of genes, and is involved in the regulation of horizontally acquired genes. We conclude that the primary role of MazF is not only cleaving RNA indiscriminately but also generating a specific cellular state.

Autoregulation of bacterial gene expression: lessons from the MazEF toxin-antitoxin system

Autoregulation is the direct modulation of gene expression by the product of the corresponding gene. Autoregulation of bacterial gene expression has been mostly studied at the transcriptional level, when a protein acts as the cognate transcriptional repressor. A recent study investigating dynamics of the bacterial toxin-antitoxin MazEF system has shown how autoregulation at both the transcriptional and post-transcriptional levels affects the heterogeneity of Escherichia coli populations. Toxin-antitoxin systems hold a crucial but still elusive part in bacterial response to stress. This perspective highlights how these modules can also serve as a great model system for investigating basic concepts in gene regulation. However, as the genomic background and environmental conditions substantially influence toxin activation, it is important to study (auto)regulation of toxin-antitoxin systems in well-defined setups as well as in conditions that resemble the environmental niche.

Designing Spatially Distributed Gene Regulatory Networks To Elicit Contrasting Patterns

Pattern formation and differential interactions are important for microbial consortia to divide labor and perform complex functions. To obtain further insight into such interactions, we present a computational method for simulating physically separated microbial colonies, each implementing different gene regulatory networks. We validate our theory by experimentally demonstrating control over gene expression patterns in a diffusion-mediated lateral inhibition circuit. We highlight the importance of spatial arrangement as a control knob for modulating system behavior. Our systematic approach provides a foundation for future applications that require understanding and engineering of multistrain microbial communities for sophisticated, synergistic functions.

Establishing a Multivariate Model for Predictable Antisense RNA-Mediated Repression

Recent advances in our understanding of RNA folding and functions have facilitated the use of regulatory RNAs such as synthetic antisense RNAs (asRNAs) to modulate gene expression. However, despite the simple and universal complementarity rule, predictable asRNA-mediated repression is still challenging due to the intrinsic complexity of native asRNA-mediated gene regulation. To address this issue, we present a multivariate model, based on the change in free energy of complex formation (Δ G_CF) and percent mismatch of the target binding region, which can predict synthetic asRNA-mediated repression efficiency in diverse contexts. First, 69 asRNAs that bind to multiple target mRNAs were designed and tested to create the predictive model. Second, we showed that the same model is effective predicting repression of target genes in both plasmids and chromosomes. Third, using our model, we designed asRNAs that simultaneously modulated expression of a toxin and its antitoxin to demonstrate tunable control of cell growth. Fourth, we tested and validated the same model in two different biotechnologically important organisms: Escherichia coli Nissle 1917 and Bacillus subtilis 168. Last, multiple parameters, including target locations, the presence of an Hfq binding site, GC contents, and gene expression levels, were revisited to define the conditions under which the multivariate model should be used for accurate prediction. Together, 434 different strain-asRNA combinations were tested, validating the predictive model in a variety of contexts, including multiple target genes and organisms. The result presented in this study is an important step toward achieving predictable tunability of asRNA-mediated repression.

A quasi-integral controller for adaptation of genetic modules to variable ribosome demand

The behavior of genetic circuits is often poorly predictable. A gene’s expression level is not only determined by the intended regulators, but also affected by changes in ribosome availability imparted by expression of other genes. Here we design a quasi-integral biomolecular feedback controller that enables the expression level of any gene of interest (GOI) to adapt to changes in available ribosomes. The feedback is implemented through a synthetic small RNA (sRNA) that silences the GOI’s mRNA, and uses orthogonal extracytoplasmic function (ECF) sigma factor to sense the GOI’s translation and to actuate sRNA transcription. Without the controller, the expression level of the GOI is reduced by 50% when a resource competitor is activated. With the controller, by contrast, gene expression level is practically unaffected by the competitor. This feedback controller allows adaptation of genetic modules to variable ribosome demand and thus aids modular construction of complicated circuits.

Competition for shared cellular resources often renders genetic circuits poorly predictable. Here the authors design a biomolecular quasi-integral controller that allows gene expression to adapt to variable demand in translation resources.

CITRIC: cold-inducible translational readthrough in the chloroplast of Chlamydomonas reinhardtii using a novel temperature-sensitive transfer RNA

Background

The chloroplast of eukaryotic microalgae such as Chlamydomonas reinhardtii is a potential platform for metabolic engineering and the production of recombinant proteins. In industrial biotechnology, inducible expression is often used so that the translation or function of the heterologous protein does not interfere with biomass accumulation during the growth stage. However, the existing systems used in bacterial or fungal platforms do not transfer well to the microalgal chloroplast. We sought to develop a simple inducible expression system for the microalgal chloroplast, exploiting an unused stop codon (TGA) in the plastid genome. We have previously shown that this codon can be translated as tryptophan when we introduce into the chloroplast genome a trnW_UCA gene encoding a plastidial transfer RNA with a modified anticodon sequence, UCA.

Results

A mutated version of our trnW_UCA gene was developed that encodes a temperature-sensitive variant of the tRNA. This allows transgenes that have been modified to contain one or more internal TGA codons to be translated differentially according to the culture temperature, with a gradient of recombinant protein accumulation from 35 °C (low/off) to 15 °C (high). We have named this the CITRIC system, an acronym for cold-inducible translational readthrough in chloroplasts. The exact induction behaviour can be tailored by altering the number of TGA codons within the transgene.

Conclusions

CITRIC adds to the suite of genetic engineering tools available for the microalgal chloroplast, allowing a greater degree of control over the timing of heterologous protein expression. It could also be used as a heat-repressible system for studying the function of essential native genes in the chloroplast. The genetic components of CITRIC are entirely plastid-based, so no engineering of the nuclear genome is required.

Electronic supplementary material

The online version of this article (10.1186/s12934-018-1033-5) contains supplementary material, which is available to authorized users.

Controlling and exploiting cell-to-cell variation in metabolic engineering

Individual cells within a population can display diverse phenotypes due to differences in their local environment, genetic variation, and stochastic expression of genes. Understanding this cell-to-cell variation is important for metabolic engineering applications because variability can impact production. For instance, recent studies have shown that production can be highly heterogeneous among engineered cells, and strategies that manage this diversity improve yields of biosynthetic products. These results suggest the potential of controlling variation as a novel approach towards improving performance of engineered cells. In this review, we focus on identifying the origins of cell-to-cell variation in metabolic engineering applications and discuss recent developments on strategies that can be employed to diminish, accept, or even exploit cell-to-cell variation.

Autoregulation of mazEF expression underlies growth heterogeneity in bacterial populations

The MazF toxin sequence-specifically cleaves single-stranded RNA upon various stressful conditions, and it is activated as a part of the mazEF toxin-antitoxin module in Escherichia coli. Although autoregulation of mazEF expression through the MazE antitoxin-dependent transcriptional repression has been biochemically characterized, less is known about post-transcriptional autoregulation, as well as how both of these autoregulatory features affect growth of single cells during conditions that promote MazF production. Here, we demonstrate post-transcriptional autoregulation of mazF expression dynamics by MazF cleaving its own transcript. Single-cell analyses of bacterial populations during ectopic MazF production indicated that two-level autoregulation of mazEF expression influences cell-to-cell growth rate heterogeneity. The increase in growth rate heterogeneity is governed by the MazE antitoxin, and tuned by the MazF-dependent mazF mRNA cleavage. Also, both autoregulatory features grant rapid exit from the stress caused by mazF overexpression. Time-lapse microscopy revealed that MazF-mediated cleavage of mazF mRNA leads to increased temporal variability in length of individual cells during ectopic mazF overexpression, as explained by a stochastic model indicating that mazEF mRNA cleavage underlies temporal fluctuations in MazF levels during stress.

Rational engineering of synthetic microbial systems: from single cells to consortia

One promise of synthetic biology is to provide solutions for biomedical and industrial problems by rational design of added functionality in living systems. Microbes are at the forefront of this biological engineering endeavor due to their general ease of handling and their relevance in many potential applications from fermentation to therapeutics. In recent years, the field has witnessed an explosion of novel regulatory tools, from synthetic orthogonal transcription factors to posttranslational mechanisms for increased control over the behavior of synthetic circuits. Tool development has been paralleled by the discovery of principles that enable increased modularity and the management of host-circuit interactions. Engineered cell-to-cell communication bridges the scales from intracellular to population-level coordination. These developments facilitate the translation of more than a decade of circuit design into applications.

Dynamic allocation of orthogonal ribosomes facilitates uncoupling of co-expressed genes

Introduction of synthetic circuits into microbes creates competition between circuit and host genes for shared cellular resources, such as ribosomes. This can lead to the emergence of unwanted coupling between the expression of different circuit genes, complicating the design process and potentially leading to circuit failure. By expressing a synthetic 16S rRNA with altered specificity, we can partition the ribosome pool into host-specific and circuit-specific activities. We show mathematically and experimentally that the effects of resource competition can be alleviated by targeting genes to different ribosomal pools. This division of labour can be used to increase flux through a metabolic pathway. We develop a model of cell physiology which is able to capture these observations and use it to design a dynamic resource allocation controller. When implemented, this controller acts to decouple genes by increasing orthogonal ribosome production as the demand for translational resources by a synthetic circuit increases.

Challenges in the Application of Synthetic Biology Toward Synthesis of Commodity Products by Cyanobacteria via "Direct Conversion"

Cyanobacterial direct conversion of CO₂ to several commodity chemicals has been recognized as a potential contributor to support the much-needed sustainable development of human societies. However, the feasibility of this "green conversion" hinders on our ability to overcome the hurdles presented by the natural evolvability of microbes. The latter may result in the genetic instability of engineered cyanobacterial strains leading to impaired productivity. This challenge is general to any "cell factory" approach in which the cells grow for multiple generations, and based on several studies carried out in different microbial hosts, we could identify that three distinct strategies have been proposed to tackle it. These are (1) to reduce microbial evolvability by decreasing the native mutation rate, (2) to align product formation with cell growth/fitness, and, paradoxically, (3) to efficiently reallocate cellular resources to product formation by uncoupling it from growth. The implementation of either of these strategies requires an advanced synthetic biology toolkit. Here, we review the existing methods available for cyanobacteria and identify areas of focus in which specific developments are still needed. Furthermore, we discuss how potentially stabilizing strategies may be used in combination leading to further increases of productivity while ensuring the stability of the cyanobacterial-based direct conversion process.

Sequence-based prediction of permissive stretches for internal protein tagging and knockdown

BACKGROUND

Internal tagging of proteins by inserting small functional peptides into surface accessible permissive sites has proven to be an indispensable tool for basic and applied science. Permissive sites are typically identified by transposon mutagenesis on a case-by-case basis, limiting scalability and their exploitation as a system-wide protein engineering tool.

METHODS

We developed an apporach for predicting permissive stretches (PSs) in proteins based on the identification of length-variable regions (regions containing indels) in homologous proteins.

RESULTS

We verify that a protein's primary structure information alone is sufficient to identify PSs. Identified PSs are predicted to be predominantly surface accessible; hence, the position of inserted peptides is likely suitable for diverse applications. We demonstrate the viability of this approach by inserting a Tobacco etch virus protease recognition site (TEV-tag) into several PSs in a wide range of proteins, from small monomeric enzymes (adenylate kinase) to large multi-subunit molecular machines (ATP synthase) and verify their functionality after insertion. We apply this method to engineer conditional protein knockdowns directly in the Escherichia coli chromosome and generate a cell-free platform with enhanced nucleotide stability.

CONCLUSIONS

Functional internally tagged proteins can be rationally designed and directly chromosomally implemented. Critical for the successful design of protein knockdowns was the incorporation of surface accessibility and secondary structure predictions, as well as the design of an improved TEV-tag that enables efficient hydrolysis when inserted into the middle of a protein. This versatile and portable approach can likely be adapted for other applications, and broadly adopted. We provide guidelines for the design of internally tagged proteins in order to empower scientists with little or no protein engineering expertise to internally tag their target proteins.