Synthesis of proteins in Escherichia coli is limited by the concentration of free ribosomes. Expression from reporter genes does not always reflect functional mRNA levels

How total mRNA influences cell growth

Experimental observations tracing back to the 1960s imply that ribosome quantities play a prominent role in determining a cell's growth. Nevertheless, in biologically relevant scenarios, growth can also be influenced by the levels of mRNA and RNA polymerase. Here, we construct a quantitative model of biosynthesis providing testable scenarios for these situations. The model explores a theoretically motivated regime where RNA polymerases compete for genes and ribosomes for transcripts and gives general expressions relating growth rate, mRNA concentrations, ribosome, and RNA polymerase levels. On general grounds, the model predicts how the fraction of ribosomes in the proteome depends on total mRNA concentration and inspects an underexplored regime in which the trade-off between transcript levels and ribosome abundances sets the cellular growth rate. In particular, we show that the model predicts and clarifies three important experimental observations, in budding yeast and Escherichia coli bacteria: i) that the growth-rate cost of unneeded protein expression can be affected by mRNA levels, ii) that resource optimization leads to decreasing trends in mRNA levels at slow growth, and iii) that ribosome allocation may increase, stay constant, or decrease, in response to transcription-inhibiting antibiotics. Since the data indicate that a regime of joint limitation may apply in physiological conditions and not only to perturbations, we speculate that this regime is likely self-imposed.

Measuring the burden of hundreds of BioBricks defines an evolutionary limit on constructability in synthetic biology

Engineered DNA will slow the growth of a host cell if it redirects limiting resources or otherwise interferes with homeostasis. Populations of engineered cells can rapidly become dominated by "escape mutants" that evolve to alleviate this burden by inactivating the intended function. Synthetic biologists working with bacteria rely on genetic parts and devices encoded on plasmids, but the burden of different engineered DNA sequences is rarely characterized. We measured how 301 BioBricks on high-copy plasmids affected the growth rate of Escherichia coli . Of these, 59 (19.6%) negatively impacted growth. The burden imposed by engineered DNA is commonly associated with diverting ribosomes or other gene expression factors away from producing endogenous genes that are essential for cellular replication. In line with this expectation, BioBricks exhibiting burden were more likely to contain highly active constitutive promoters and strong ribosome binding sites. By monitoring how much each BioBrick reduced expression of a chromosomal GFP reporter, we found that the burden of most, but not all, BioBricks could be wholly explained by diversion of gene expression resources. Overall, no BioBricks reduced the growth rate of E. coli by >45%, which agreed with a population genetic model that predicts such plasmids should be "unclonable" because escape mutants will take over during growth of a bacterial colony or small laboratory culture from a transformed cell. We made this model available as an interactive web tool for synthetic biology education and added our burden measurements to the iGEM Registry descriptions of each BioBrick.

Specific codons control cellular resources and fitness

As cellular engineering progresses from simply overexpressing proteins to imparting complex phenotypes through multigene expression, judicious appropriation of cellular resources is essential. Since codon use is degenerate and biased, codons may control cellular resources at a translational level. We investigate how partitioning transfer RNA (tRNA) resources by incorporating dissimilar codon usage can drastically alter interdependence of expression level and burden on the host. By isolating the effect of individual codons' use during translation elongation while eliminating confounding factors, we show that codon choice can trans-regulate fitness of the host and expression of other heterologous or native genes. We correlate specific codon usage patterns with host fitness and derive a coding scheme for multigene expression called the Codon Health Index (CHI, χ). This empirically derived coding scheme (χ) enables the design of multigene expression systems that avoid catastrophic cellular burden and is robust across several proteins and conditions.

Advances in molecular and cell therapy for immunotherapy of cholangiocarcinoma

Cholangiocarcinoma (CCA) is a highly malignant tumor of the hepatobiliary system that has failed to respond to many traditional therapies to a certain extent, including surgery, chemotherapy and radiotherapy. In recent years, the new therapeutic schemes based on immunology have fundamentally changed the systemic treatment of various malignant tumors to a certain extent. In view of the immunogenicity of CCA, during the occurrence and development of CCA, some immunosuppressive substances are released from cells and immunosuppressive microenvironment is formed to promote the escape immune response of its own cells, thus enhancing the malignancy of the tumor and reducing the sensitivity of the tumor to drugs. Some immunotherapy regimens for cholangiocarcinoma have produced good clinical effects. Immunotherapy has more precise characteristics and less adverse reactions compared with traditional treatment approaches. However, due to the unique immune characteristics of CCA, some patients with CCA may not benefit in the long term or not benefit at all after current immunotherapy. At present, the immunotherapy of CCA that have been clinically studied mainly include molecular therapy and cell therapy. In this article, we generalized and summarized the current status of immunotherapy strategies including molecular therapy and cell therapy in CCA in clinical studies, and we outlined our understanding of how to enhance the clinical application of these immunotherapy strategies.

Understanding the Necessity of Regulatory Protein Machinery in Heterologous Expression of Class-III Type of Ocins

To date, there have been no or just a few reports of successful cloning and expression to create biologically active ocins or bacteriocins. Cloning, expression, and production of class I ocins are problematic because of their structural arrangements, coordinated functions, size, and posttranslational modifications. Mass synthesis of these molecules is necessary for commercialization and to restrict the excessive use of conventional antibiotics, which encourages the development of antibiotic-resistant bacteria. In the case of class III ocins, there are no reports of obtaining biological active proteins to date. Being able to obtain biologically active proteins requires an understanding of mechanistic features due to their expanding importance and broad spectrum of activity. As a result, we intend to clone and express the class III type. The class I types that are devoid of posttranslational modifications were transformed into class III through fusion. Therefore, this construct resembles a class III type ocin. With the exception of Zoocin, expression of the proteins was found to be physiologically ineffective after cloning. But, few cell morphological changes such as elongation, aggregation, and the formation of terminal hyphae were observed. However, it was discovered that the target indicator had been altered to Vibrio spp. in a few. All the three ocins were subjected to in-silico structure prediction/analysis. Finally, we confirm the existence of unidentified additional intrinsic factors for successful expression to obtain biologically active protein.

Escherichia coli protein synthesis is limited by mRNA availability rather than ribosomal capacity during phosphate starvation

Protein synthesis is the most energetically costly process in the cell. Consequently, it is a tightly regulated process, and regulation of the resources allocated to the protein synthesis machinery is at the heart of bacterial growth optimization theory. However, the molecular mechanisms that result in dynamic downregulation of protein synthesis in response to nutrient starvation are not well described. Here, we first quantify the Escherichia coli response to phosphate starvation at the level of accumulation rates for protein, RNA and DNA. Escherichia coli maintains a low level of protein synthesis for hours after the removal of phosphate while the RNA contents decrease, primarily as a consequence of ribosomal RNA degradation combined with a reduced RNA synthesis rate. To understand the molecular basis for the low protein synthesis rate of phosphate-starved cells, template mRNA for translation was overproduced in the form of a highly induced long-lived mRNA. Remarkably, starved cells increased the rate of protein synthesis and reduced the rate of ribosomal RNA degradation upon mRNA induction. These observations suggest that protein synthesis in phosphate-starved cells is primarily limited by the availability of template, and does not operate at the maximum capacity of the ribosomes. We suggest that mRNA limitation is an adaptive response to phosphate starvation that prevents the deleterious consequences of overcommitting resources to protein synthesis. Moreover, our results support the model that degradation of ribosomal RNA occurs as a consequence of the availability of idle ribosomal subunits.

Translation in the cell under fierce competition for shared resources: a mathematical model

During translation, mRNAs 'compete' for shared resources. Under stress conditions, during viral infection and also in high-throughput heterologous gene expression, these resources may become scarce, e.g. the pool of free ribosomes is starved, and then the competition may have a dramatic effect on the global dynamics of translation in the cell. We model this scenario using a network that includes m ribosome flow models (RFMs) interconnected via a pool of free ribosomes. Each RFM models ribosome flow along an mRNA molecule, and the pool models the shared resource. We assume that the number of mRNAs is large, so many ribosomes are attached to the mRNAs, and the pool is starved. Our analysis shows that adding an mRNA has an intricate effect on the total protein production. The new mRNA produces new proteins, but the other mRNAs produce less proteins, as the pool that feeds these mRNAs now has a smaller abundance of ribosomes. As the number of mRNAs increases, the marginal utility of adding another mRNA diminishes, and the total protein production rate saturates to a limiting value. We demonstrate our approach using an example of insulin protein production in a cell-free system.

How to save a bacterial ribosome in times of stress

Biogenesis of ribosomes is one of the most cost- and resource-intensive processes in all living cells. In bacteria, ribosome biogenesis is rate-limiting for growth and must be tightly coordinated to yield maximum fitness of the cells. Since bacteria are continuously facing environmental changes and stress conditions, they have developed sophisticated systems to sense and regulate their nutritional status. Amino acid starvation leads to the synthesis and accumulation of the nucleotide-based second messengers ppGpp and pppGpp [(p)ppGpp], which in turn function as central players of a pleiotropic metabolic adaptation mechanism named the stringent response. Here, we review our current knowledge on the multiple roles of (p)ppGpp in the stress-related modulation of the prokaryotic protein biosynthesis machinery with the ribosome as its core constituent. The alarmones ppGpp/pppGpp act as competitors of their GDP/GTP counterparts, to affect a multitude of ribosome-associated P-loop GTPases involved in the translation cycle, ribosome biogenesis and hibernation. A similar mode of inhibition has been found for the GTPases of the proteins involved in the SRP-dependent membrane-targeting machinery present in the periphery of the ribosome. In this sense, during stringent conditions, binding of (p)ppGpp restricts the membrane insertion and secretion of proteins. Altogether, we highlight the enormously resource-intensive stages of ribosome biogenesis as a critical regulatory hub of the stringent response that ultimately tunes the protein synthesis capacity and consequently the survival of the cell.

Improving the Robustness of Engineered Bacteria to Nutrient Stress Using Programmed Proteolysis

The use of short peptide tags in synthetic genetic circuits allows for the tuning of gene expression dynamics and release of amino acid resources through targeted protein degradation. Here, we use elements of the Escherichia coli and Mesoplasma florum transfer-mRNA (tmRNA) ribosome rescue systems to compare endogenous and foreign proteolysis systems in E. coli. We characterize the performance and burden of each and show that, while both greatly shorten the half-life of a tagged protein, the endogenous system is approximately 10 times more efficient. On the basis of these results we then demonstrate using mathematical modeling and experiments how proteolysis can improve cellular robustness through targeted degradation of a reporter protein in auxotrophic strains, providing a limited secondary source of essential amino acids that help partially restore growth when nutrients become scarce. These findings provide avenues for controlling the functional lifetime of engineered cells once deployed and increasing their tolerance to fluctuations in nutrient availability.

Large-scale mRNA translation and the intricate effects of competition for the finite pool of ribosomes

We present a new theoretical framework for large-scale mRNA translation using a network of models called the ribosome flow model with Langmuir kinetics (RFMLK), interconnected via a pool of free ribosomes. The input to each RFMLK depends on the pool density, and it affects the initiation rate and potentially also the internal ribosome entry rates along each RFMLK. Ribosomes that detach from an RFMLK owing to termination or premature drop-off are fed back into the pool. We prove that the network always converges to a steady state, and study its sensitivity to variations in the parameters. For example, we show that if the drop-off rate at some site in some RFMLK is increased then the pool density increases and consequently the steady-state production rate in all the other RFMLKs increases. Surprisingly, we also show that modifying a parameter of a certain RFMLK can lead to arbitrary effects on the densities along the modified RFMLK, depending on the parameters in the entire network. We conclude that the competition for shared resources generates an indirect and intricate web of mutual effects between the mRNA molecules that must be accounted for in any analysis of translation.

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.

Emergent Damped Oscillation Induced by Nutrient-Modulating Growth Feedback

Growth feedback, the inherent coupling between the synthetic gene circuit and the host cell growth, could significantly change the circuit behaviors. Previously, a diverse array of emergent behaviors, such as growth bistability, enhanced ultrasensitivity, and topology-dependent memory loss, were reported to be induced by growth feedback. However, the influence of the growth feedback on the circuit functions remains underexplored. Here, we reported an unexpected damped oscillatory behavior of a self-activation gene circuit induced by nutrient-modulating growth feedback. Specifically, after dilution of the activated self-activation switch into the fresh medium with moderate nutrients, its gene expression first decreases as the cell grows and then shows a significant overshoot before it reaches the steady state, leading to damped oscillation dynamics. Fitting the data with a coarse-grained model suggests a nonmonotonic growth-rate regulation on gene production rate. The underlying mechanism of the oscillation was demonstrated by a molecular mathematical model, which includes the ribosome allocation toward gene production, cell growth, and cell maintenance. Interestingly, the model predicted a counterintuitive dependence of oscillation amplitude on the nutrition level, where the highest peak was found in the medium with moderate nutrients, but was not observed in rich nutrients. We experimentally verified this prediction by tuning the nutrient level in the culture medium. We did not observe significant oscillatory behavior for the toggle switch, suggesting that the emergence of damped oscillatory behavior depends on circuit network topology. Our results demonstrated a new nonlinear emergent behavior mediated by growth feedback, which depends on the ribosome allocation between gene circuit and cell growth.

Synthetic protein quality control to enhance full-length translation in bacteria

Coupled transcription and translation processes in bacteria cause indiscriminate translation of intact and truncated messenger RNAs, inevitably generating nonfunctional polypeptides. Here, we devised a synthetic protein quality control (ProQC) system that enables translation only when both ends of mRNAs are present and followed by circularization based on sequence-specific RNA-RNA hybridization. We demonstrate that the ProQC system dramatically improved the fraction of full-length proteins among all synthesized polypeptides by selectively translating intact mRNA and reducing abortive translation. As a result, full-length protein synthesis increased up to 2.5-fold without changing the transcription or translation efficiency. Furthermore, we applied the ProQC system for 3-hydroxypropionic acid, violacein and lycopene production by ensuring full-length expression of enzymes in biosynthetic pathways, resulting in 1.6- to 2.3-fold greater biochemical production. We believe that our ProQC system can be universally applied to improve not only the quality of recombinant protein production but also efficiencies of metabolic pathways.

Global coordination of metabolic pathways in Escherichia coli by active and passive regulation

Microorganisms adjust metabolic activity to cope with diverse environments. While many studies have provided insights into how individual pathways are regulated, the mechanisms that give rise to coordinated metabolic responses are poorly understood. Here, we identify the regulatory mechanisms that coordinate catabolism and anabolism in Escherichia coli. Integrating protein, metabolite, and flux changes in genetically implemented catabolic or anabolic limitations, we show that combined global and local mechanisms coordinate the response to metabolic limitations. To allocate proteomic resources between catabolism and anabolism, E. coli uses a simple global gene regulatory program. Surprisingly, this program is largely implemented by a single transcription factor, Crp, which directly activates the expression of catabolic enzymes and indirectly reduces the expression of anabolic enzymes by passively sequestering cellular resources needed for their synthesis. However, metabolic fluxes are not controlled by this regulatory program alone; instead, fluxes are adjusted mostly through passive changes in the local metabolite concentrations. These mechanisms constitute a simple but effective global regulatory program that coarsely partitions resources between different parts of metabolism while ensuring robust coordination of individual metabolic reactions.

Prediction of Cellular Burden with Host-Circuit Models

Heterologous gene expression draws resources from host cells. These resources include vital components to sustain growth and replication, and the resulting cellular burden is a widely recognized bottleneck in the design of robust circuits. In this tutorial we discuss the use of computational models that integrate gene circuits and the physiology of host cells. Through various use cases, we illustrate the power of host-circuit models to predict the impact of design parameters on both burden and circuit functionality. Our approach relies on a new generation of computational models for microbial growth that can flexibly accommodate resource bottlenecks encountered in gene circuit design. Adoption of this modeling paradigm can facilitate fast and robust design cycles in synthetic biology.

Algorithms for ribosome traffic engineering and their potential in improving host cells' titer and growth rate

mRNA translation is a fundamental cellular process consuming most of the intracellular energy; thus, it is under extensive evolutionary selection for optimization, and its efficiency can affect the host's growth rate. We describe a generic approach for improving the growth rate (fitness) of any organism by introducing synonymous mutations based on comprehensive computational models. The algorithms introduce silent mutations that may improve the allocation of ribosomes in the cells via the decreasing of their traffic jams during translation respectively. As a result, resources availability in the cell changes leading to improved growth-rate. We demonstrate experimentally the implementation of the method on Saccharomyces cerevisiae: we show that by introducing a few mutations in two computationally selected genes the mutant's titer increased. Our approach can be employed for improving the growth rate of any organism providing the existence of data for inferring models, and with the relevant genomic engineering tools; thus, it is expected to be extremely useful in biotechnology, medicine, and agriculture.

Inhibition of E. coli Host RNA Polymerase Allows Efficient Extracellular Recombinant Protein Production by Enhancing Outer Membrane Leakiness

With the growing interest in continuous cultivation of Escherichia coli, secretion of product to the medium is not only a benefit, but a necessity in future bioprocessing. In this study, it is shown that induced decoupling of growth and heterologous gene expression in the E. coli X-press strain (derived from BL21(DE3)) facilitates extracellular recombinant protein production. The effect of the process parameters temperature and specific glucose consumption rate (q_S ) on growth, productivity, lysis and leakiness, is investigated, to find the parameter space allowing extracellular protein production. Two model proteins are used, Protein A (SpA) and a heavy-chain single-domain antibody (VHH), and performance is compared to the industrial standard strain BL21(DE3). It is shown that inducible growth repression in the X-press strain greatly mitigates the effect of metabolic burden under different process conditions. Furthermore, temperature and q_S are used to control productivity and leakiness. In the X-press strain, extracellular SpA and VHH titer reach up to 349 and 19.6 mg g^-1 , respectively, comprising up to 90% of the total soluble product, while keeping cell lysis at a minimum. The findings demonstrate that the X-press strain constitutes a valuable host for extracellular production of recombinant protein with E. coli.

Translational regulation of environmental adaptation in bacteria

Bacteria must rapidly respond to both intracellular and environmental changes to survive. One critical mechanism to rapidly detect and adapt to changes in environmental conditions is control of gene expression at the level of protein synthesis. At each of the three major steps of translation-initiation, elongation, and termination-cells use stimuli to tune translation rate and cellular protein concentrations. For example, changes in nutrient concentrations in the cell can lead to translational responses involving mechanisms such as dynamic folding of riboswitches during translation initiation or the synthesis of alarmones, which drastically alter cell physiology. Moreover, the cell can fine-tune the levels of specific protein products using programmed ribosome pausing or inducing frameshifting. Recent studies have improved understanding and revealed greater complexity regarding long-standing paradigms describing key regulatory steps of translation such as start-site selection and the coupling of transcription and translation. In this review, we describe how bacteria regulate their gene expression at the three translational steps and discuss how translation is used to detect and respond to changes in the cellular environment. Finally, we appraise the costs and benefits of regulation at the translational level in bacteria.

Modelling and measuring intracellular competition for finite resources during gene expression

Dissecting the competition between genes for shared expressional resources is of fundamental importance for understanding the interplay between cellular components. Owing to the relationship between gene expression and cellular fitness, genomes are shaped by evolution to improve resource allocation. Whereas experimental approaches to investigate intracellular competition require technical resources and human expertise, computational models and in silico simulations allow vast numbers of experiments to be carried out and controlled easily, and with significantly reduced costs. Thus, modelling competition has a pivotal role in understanding the effects of competition on the biophysics of the cell. In this article, we review various computational models proposed to describe the different types of competition during gene expression. We also present relevant synthetic biology experiments and their biotechnological implications, and discuss the open questions in the field.

Widespread non-modular overlapping codes in the coding regions

Messenger RNAs (mRNAs) consist of a coding region (open reading frame (ORF)) and two untranslated regions (UTRs), 5'UTR and 3'UTR. Ribosomes travel along the coding region, translating nucleotide triplets (called codons) to a chain of amino acids. The coding region was long believed to mainly encode the amino acid content of proteins, whereas regulatory signals reside in the UTRs and in other genomic regions. However, in recent years we have learned that the ORF is expansively populated with various regulatory signals, or codes, which are related to all gene expression steps and additional intracellular aspects. In this paper, we review the current knowledge related to overlapping codes inside the coding regions, such as the influence of synonymous codon usage on translation speed (and, in turn, the effect of translation speed on protein folding), ribosomal frameshifting, mRNA stability, methylation, splicing, transcription and more. All these codes come together and overlap in the ORF sequence, ensuring production of the right protein at the right time.

A computational framework for a Lyapunov-enabled analysis of biochemical reaction networks

Complex molecular biological processes such as transcription and translation, signal transduction, post-translational modification cascades, and metabolic pathways can be described in principle by biochemical reactions that explicitly take into account the sophisticated network of chemical interactions regulating cell life. The ability to deduce the possible qualitative behaviors of such networks from a set of reactions is a central objective and an ongoing challenge in the field of systems biology. Unfortunately, the construction of complete mathematical models is often hindered by a pervasive problem: despite the wealth of qualitative graphical knowledge about network interactions, the form of the governing nonlinearities and/or the values of kinetic constants are hard to uncover experimentally. The kinetics can also change with environmental variations. This work addresses the following question: given a set of reactions and without assuming a particular form for the kinetics, what can we say about the asymptotic behavior of the network? Specifically, it introduces a class of networks that are "structurally (mono) attractive" meaning that they are incapable of exhibiting multiple steady states, oscillation, or chaos by virtue of their reaction graphs. These networks are characterized by the existence of a universal energy-like function called a Robust Lyapunov function (RLF). To find such functions, a finite set of rank-one linear systems is introduced, which form the extremals of a linear convex cone. The problem is then reduced to that of finding a common Lyapunov function for this set of extremals. Based on this characterization, a computational package, Lyapunov-Enabled Analysis of Reaction Networks (LEARN), is provided that constructs such functions or rules out their existence. An extensive study of biochemical networks demonstrates that LEARN offers a new unified framework. Basic motifs, three-body binding, and genetic networks are studied first. The work then focuses on cellular signalling networks including various post-translational modification cascades, phosphotransfer and phosphorelay networks, T-cell kinetic proofreading, and ERK signalling. The Ribosome Flow Model is also studied.

Reprogramming bacteria with RNA regulators

The revolution of genomics and growth of systems biology urged the creation of synthetic biology, an engineering discipline aiming at recreating and reprogramming cellular functions for industrial needs. There has been a huge effort in synthetic biology to develop versatile and programmable genetic regulators that would enable the precise control of gene expression. Synthetic RNA components have emerged as a solution, offering a diverse range of programmable functions, including signal sensing, gene regulation and the modulation of molecular interactions. Owing to their compactness, structure and way of action, several types of RNA devices that act on DNA, RNA and protein have been characterized and applied in synthetic biology. RNA-based approaches are more ‘economical' for the cell, since they are generally not translated. These RNA-based strategies act on a much shorter time scale than transcription-based ones and can be more efficient than protein-based mechanisms. In this review, we explore these RNA components as building blocks in the RNA synthetic biology field, first by explaining their natural mode of action and secondly discussing how these RNA components have been exploited to rewire bacterial regulatory circuitry.

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.

Cross Talk between SigB and PrfA in Listeria monocytogenes Facilitates Transitions between Extra- and Intracellular Environments

The foodborne pathogen Listeria monocytogenes can modulate its transcriptome and proteome to ensure its survival during transmission through vastly differing environmental conditions. While L. monocytogenes utilizes a large array of regulators to achieve survival and growth in different intra- and extrahost environments, the alternative sigma factor σ^B and the transcriptional activator of virulence genes protein PrfA are two key transcriptional regulators essential for responding to environmental stress conditions and for host infection. Importantly, emerging evidence suggests that the shift from extrahost environments to the host gastrointestinal tract and, subsequently, to intracellular environments requires regulatory interplay between σ^B and PrfA at transcriptional, posttranscriptional, and protein activity levels. Here, we review the current evidence for cross talk and interplay between σ^B and PrfA and their respective regulons and highlight the plasticity of σ^B and PrfA cross talk and the role of this cross talk in facilitating successful transition of L. monocytogenes from diverse extrahost to diverse extra- and intracellular host environments.

Growth Defects and Loss-of-Function in Synthetic Gene Circuits

Synthetic gene circuits perturb the physiology of their cellular host. The extra load on endogenous processes shifts the equilibrium of resource allocation in the host, leading to slow growth and reduced biosynthesis. Here we built integrated host-circuit models to quantify growth defects caused by synthetic gene circuits. Simulations reveal a complex relation between circuit output and cellular capacity for gene expression. For weak induction of heterologous genes, protein output can be increased at the expense of growth defects. Yet for stronger induction, cellular capacity reaches a tipping point, beyond which both gene expression and growth rate drop sharply. Extensive simulations across various growth conditions and large regions of the design space suggest that the critical capacity is a result of ribosomal scarcity. We studied the impact of growth defects on various gene circuits and transcriptional logic gates, which highlights the extent to which cellular burden can limit, shape, and even break down circuit function. Our approach offers a comprehensive framework to assess the impact of host-circuit interactions in silico, with wide-ranging implications for the design and optimization of bacterial gene circuits.

Optimizing the dynamics of protein expression

Heterologously expressed genes require adaptation to the host organism to ensure adequate levels of protein synthesis, which is typically approached by replacing codons by the target organism’s preferred codons. In view of frequently encountered suboptimal outcomes we introduce the codon-specific elongation model (COSEM) as an alternative concept. COSEM simulates ribosome dynamics during mRNA translation and informs about protein synthesis rates per mRNA in an organism- and context-dependent way. Protein synthesis rates from COSEM are integrated with further relevant covariates such as translation accuracy into a protein expression score that we use for codon optimization. The scoring algorithm further enables fine-tuning of protein expression including deoptimization and is implemented in the software OCTOPOS. The protein expression score produces competitive predictions on proteomic data from prokaryotic, eukaryotic, and human expression systems. In addition, we optimized and tested heterologous expression of manA and ova genes in Salmonella enterica serovar Typhimurium. Superiority over standard methodology was demonstrated by a threefold increase in protein yield compared to wildtype and commercially optimized sequences.

Circuit-Host Coupling Induces Multifaceted Behavioral Modulations of a Gene Switch

Quantitative modeling of gene circuits is fundamentally important to synthetic biology, as it offers the potential to transform circuit engineering from trial-and-error construction to rational design and, hence, facilitates the advance of the field. Currently, typical models regard gene circuits as isolated entities and focus only on the biochemical processes within the circuits. However, such a standard paradigm is getting challenged by increasing experimental evidence suggesting that circuits and their host are intimately connected, and their interactions can potentially impact circuit behaviors. Here we systematically examined the roles of circuit-host coupling in shaping circuit dynamics by using a self-activating gene switch as a model circuit. Through a combination of deterministic modeling, stochastic simulation, and Fokker-Planck equation formalism, we found that circuit-host coupling alters switch behaviors across multiple scales. At the single-cell level, it slows the switch dynamics in the high protein production regime and enlarges the difference between stable steady-state values. At the population level, it favors cells with low protein production through differential growth amplification. Together, the two-level coupling effects induce both quantitative and qualitative modulations of the switch, with the primary component of the effects determined by the circuit's architectural parameters. This study illustrates the complexity and importance of circuit-host coupling in modulating circuit behaviors, demonstrating the need for a new paradigm-integrated modeling of the circuit-host system-for quantitative understanding of engineered gene networks.

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.

Small and Large Ribosomal Subunit Deficiencies Lead to Distinct Gene Expression Signatures that Reflect Cellular Growth Rate

Levels of the ribosome, the conserved molecular machine that mediates translation, are tightly linked to cellular growth rate. In humans, ribosomopathies are diseases associated with cell-type-specific pathologies and reduced ribosomal protein (RP) levels. Because gene expression defects resulting from ribosome deficiency have not yet been experimentally defined, we systematically probed mRNA, translation, and protein signatures that were either unlinked from or linked to cellular growth rate in RP-deficient yeast cells. Ribosome deficiency was associated with altered translation of gene subclasses, and profound general secondary effects of RP loss on the spectrum of cellular mRNAs were seen. Among these effects, growth-defective 60S mutants increased synthesis of proteins involved in proteasome-mediated degradation, whereas 40S mutants accumulated mature 60S subunits and increased translation of ribosome biogenesis genes. These distinct signatures of protein synthesis suggest intriguing and currently mysterious differences in the cellular consequences of deficiency for small and large ribosomal subunits.

Estimating the protein burden limit of yeast cells by measuring the expression limits of glycolytic proteins

The ultimate overexpression of a protein could cause growth defects, which are known as the protein burden. However, the expression limit at which the protein-burden effect is triggered is still unclear. To estimate this limit, we systematically measured the overexpression limits of glycolytic proteins in Saccharomyces cerevisiae. The limits of some glycolytic proteins were up to 15% of the total cellular protein. These limits were independent of the proteins’ catalytic activities, a finding that was supported by an in silico analysis. Some proteins had low expression limits that were explained by their localization and metabolic perturbations. The codon usage should be highly optimized to trigger the protein-burden effect, even under strong transcriptional induction. The S–S-bond-connected aggregation mediated by the cysteine residues of a protein might affect its expression limit. Theoretically, only non-harmful proteins could be expressed up to the protein-burden limit. Therefore, we established a framework to distinguish proteins that are harmful and non-harmful upon overexpression.

If a cell makes too much of a given protein, it can sometimes cause problems and impair the cell’s growth. Overproducing some proteins may deplete the cell’s limited resources, meaning it does not have enough to make other more essential proteins. This phenomenon is known as the protein burden effect. Theoretically, only harmless proteins can be overproduced up to a level where growth would be impaired in this way. Conversely, if an overproduced protein causes harm before it becomes a burden on resources, scientists must consider other mechanisms to explain the cell’s problems, namely that the protein itself is harmful.

Knowing the ultimate level of protein production that could cause the protein burden effect – the protein burden limit – would allow scientists to distinguish between harmful and non-harmful proteins. However, to date, this limit had not been defined for any cell.

Eguchi et al. have now tried to estimate the protein burden limit for budding yeast – one of the best-studied experimental organisms. The experiments first focused on enzymes involved in alcoholic fermentation because they were expected to be non-harmful. Some of these enzymes were overproduced to the level were the made up 15% of all the cell’s proteins before they started to cause growth defects. The same results were seen with versions of the enzymes that had been mutated to be less active, leading Eguchi et al. to conclude that this level is the protein burden limit.

In other experiments, harmful enzymes could only be overproduced to levels that were far less than this proposed protein burden limit. These enzymes caused problems for the yeast in several ways, including interfering with biochemical reactions and forming large aggregates in the cell. Lastly, Eguchi et al. looked at the yeast’s genetic code and saw that most of its genes seemed to have evolved to specifically limit the production of proteins to a level that would avoid the unwanted protein burden effect.

Together these findings establish a framework to clearly distinguish between harmful and non-harmful proteins. This framework will be useful to understand the different reasons why the overproduction of certain proteins, which is seen in neurodegenerative diseases and cancer cells, can cause problems for cells.

Effects of betaine supplementation on L-threonine fed-batch fermentation by Escherichia coli

Betaine can act as a stress protectant, methyl donor, or enzyme stabilizer in vitro for the biosynthesis of structurally complex compounds. The performances of betaine type and concentration on the metabolic processes of Escherichia coli JLTHR in a 5-L fermentor were investigated. The results showed that the maximum L-threonine production of 127.3 g/L and glucose conversion percentage of 58.12% was obtained fed with the glucose solution containing 2 g/L betaine hydrochloride, which increased by 14.5 and 6.87% more compared to that of the control, respectively. This study presents an analysis of the metabolic fluxes of E. coli JLTHR for the production of L-threonine with betaine supplementation. When betaine was fed into the fermentation culture medium, the metabolic flux entering into the pentose phosphate pathway (HMP) and biosynthesis route of L-threonine increased by 57.3 and 10.1%, respectively. In conclusion, exogenous addition of betaine was validated to be a feasible and efficacious approach to improve L-threonine production.

Computational analysis of the oscillatory behavior at the translation level induced by mRNA levels oscillations due to finite intracellular resources

Recent studies have demonstrated how the competition for the finite pool of available gene expression factors has important effect on fundamental gene expression aspects. In this study, based on a whole-cell model simulation of translation in S. cerevisiae, we evaluate for the first time the expected effect of mRNA levels fluctuations on translation due to the finite pool of ribosomes. We show that fluctuations of a single gene or a group of genes mRNA levels induce periodic behavior in all S. cerevisiae translation factors and aspects: the ribosomal densities and the translation rates of all S. cerevisiae mRNAs oscillate. We numerically measure the oscillation amplitudes demonstrating that fluctuations of endogenous and heterologous genes can cause a significant fluctuation of up to 50% in the steady-state translation rates of the rest of the genes. Furthermore, we demonstrate by synonymous mutations that oscillating the levels of mRNAs that experience high ribosomal occupancy (e.g. ribosomal “traffic jam”) induces the largest impact on the translation of the S. cerevisiae genome.

The results reported here should provide novel insights and principles related to the design of synthetic gene expression circuits and related to the evolutionary constraints shaping gene expression of endogenous genes.

Each cell contains a limited number of macromolecules and factors that participate in the gene expression process. These expression resources are shared between the different molecules that encode the genetic code, resulting in non-trivial couplings and competitions between the different gene expression stages. Such competitions should be considered when analyzing the cellular economy of the cell, the genome evolution, and the design of synthetic expression circuits. Here we study the effect of couplings and competitions for ribosomes by performing a whole-cell simulation of translation of S. cerevisiae, with parameters estimated from experimental data. We demonstrate that by periodically changing the mRNA levels of a single gene (endogenous or heterologous) or a set of genes, the translation of all S. cerevisiae genes are affected in a periodic manner. We numerically estimate the exact impact of the mRNA levels periodicity on the translation process dynamics, as well as on the dynamics of the free ribosomal pool and the way it is affected by parameters such as the codon composition of the oscillating gene, its initiation rate and mRNA levels. Furthermore, we show that the codon compositions of synthetically highly expressed heterologous genes that are expected to oscillate must be carefully considered. For example, synonymous mutations resulting in “traffic jams” of ribosomes along the fluctuated mRNAs may cause significant fluctuations of up to 50% in the steady-state translation rates of all genes.

Energy limitation of cyanophage development: implications for marine carbon cycling

Marine cyanobacteria are responsible for ~25% of the fixed carbon that enters the ocean biosphere. It is thought that abundant co-occurring viruses play an important role in regulating population dynamics of cyanobacteria and thus the cycling of carbon in the oceans. Despite this, little is known about how viral infections ‘play-out’ in the environment, particularly whether infections are resource or energy limited. Photoautotrophic organisms represent an ideal model to test this since available energy is modulated by the incoming light intensity through photophosphorylation. Therefore, we exploited phototrophy of the environmentally relevant marine cyanobacterium Synechococcus and monitored growth of a cyanobacterial virus (cyanophage). We found that light intensity has a marked effect on cyanophage infection dynamics, but that this is not manifest by a change in DNA synthesis. Instead, cyanophage development appears energy limited for the synthesis of proteins required during late infection. We posit that acquisition of auxiliary metabolic genes (AMGs) involved in light-dependent photosynthetic reactions acts to overcome this limitation. We show that cyanophages actively modulate expression of these AMGs in response to light intensity and provide evidence that such regulation may be facilitated by a novel mechanism involving light-dependent splicing of a group I intron in a photosynthetic AMG. Altogether, our data offers a mechanistic link between diurnal changes in irradiance and observed community level responses in metabolism, i.e., through an irradiance-dependent, viral-induced release of dissolved organic matter (DOM).

Hsp70-associated chaperones have a critical role in buffering protein production costs

Proteins are necessary for cellular growth. Concurrently, however, protein production has high energetic demands associated with transcription and translation. Here, we propose that activity of molecular chaperones shape protein burden, that is the fitness costs associated with expression of unneeded proteins. To test this hypothesis, we performed a genome-wide genetic interaction screen in baker's yeast. Impairment of transcription, translation, and protein folding rendered cells hypersensitive to protein burden. Specifically, deletion of specific regulators of the Hsp70-associated chaperone network increased protein burden. In agreement with expectation, temperature stress, increased mistranslation and a chemical misfolding agent all substantially enhanced protein burden. Finally, unneeded protein perturbed interactions between key components of the Hsp70-Hsp90 network involved in folding of native proteins. We conclude that specific chaperones contribute to protein burden. Our work indicates that by minimizing the damaging impact of gratuitous protein overproduction, chaperones enable tolerance to massive changes in genomic expression.

Highly Effective Polyphosphate Synthesis, Phosphate Removal, and Concentration Using Engineered Environmental Bacteria Based on a Simple Solo Medium-Copy Plasmid Strategy

Microbial polyphosphate (polyP) production is vital to the removal of phosphate from wastewater. However, to date, engineered polyP synthesis using genetically accessible environmental bacteria remains a challenge. This study develops a simple solo medium-copy plasmid-based polyphosphate kinase (PPK1) overexpression strategy for achieving maximum intracellular polyphosphate accumulation by environmental bacteria. The polyP content of the subsequently engineered Citrobacter freundii (CPP) could reach as high as 12.7% of its dry weight. The biomass yield of CPP was also guaranteed because of negligible metabolic burden effects resulting from the medium plasmid copy number. Consequently, substantial removal of phosphate (P_i) from the ambient environment was achieved simultaneously. Because of the need for exogenous P_i for in vivo ATP regeneration, CPP could thoroughly remove P_i from synthetic municipal wastewater when it was applied for the "one-step" removal of P_i with a bench-scale sequence batch membrane reactor. Almost all the phosphorus except for that assimilated by CPP for cellular growth could be recovered in the form of more concentrated P_i. Overall, engineering environmental bacteria to overexpress PPK1 via a solo medium-copy plasmid strategy may represent a valuable general option for not only biotechnological research based on sufficient intracellular polyP production but also removal of P_i from wastewater and P_i enrichment.

Reduced Protein Expression in a Virus Attenuated by Codon Deoptimization

A general means of viral attenuation involves the extensive recoding of synonymous codons in the viral genome. The mechanistic underpinnings of this approach remain unclear, however. Using quantitative proteomics and RNA sequencing, we explore the molecular basis of attenuation in a strain of bacteriophage T7 whose major capsid gene was engineered to carry 182 suboptimal codons. We do not detect transcriptional effects from recoding. Proteomic observations reveal that translation is halved for the recoded major capsid gene, and a more modest reduction applies to several coexpressed downstream genes. We observe no changes in protein abundances of other coexpressed genes that are encoded upstream. Viral burst size, like capsid protein abundance, is also decreased by half. Together, these observations suggest that, in this virus, reduced translation of an essential polycistronic transcript and diminished virion assembly form the molecular basis of attenuation.

Principles of cellular resource allocation revealed by condition-dependent proteome profiling

Growing cells coordinate protein translation with metabolic rates. Central to this coordination is ribosome production. Ribosomes drive cell growth, but translation of ribosomal proteins competes with production of non-ribosomal proteins. Theory shows that cell growth is maximized when all expressed ribosomes are constantly translating. To examine whether budding yeast function at this limit of full ribosomal usage, we profiled the proteomes of cells growing in different environments. We find that cells produce excess ribosomal proteins, amounting to a constant ≈8% of the proteome. Accordingly, ≈25% of ribosomal proteins expressed in rapidly growing cells does not contribute to translation. Further, this fraction increases as growth rate decreases and these excess ribosomal proteins are employed when translation demands unexpectedly increase. We suggest that steadily growing cells prepare for conditions that demand increased translation by producing excess ribosomes, at the expense of lower steady-state growth rate.

Decoupling Resource-Coupled Gene Expression in Living Cells

Synthetic biology aspires to develop frameworks that enable the construction of complex and reliable gene networks with predictable functionalities. A key limitation is that increasing network complexity increases the demand for cellular resources, potentially causing resource-associated interference among noninteracting circuits. Although recent studies have shown the effects of resource competition on circuit behaviors, mechanisms that decouple such interference remain unclear. Here, we constructed three systems in Escherichia coli, each consisting of two independent circuit modules where the complexity of one module (Circuit 2) was systematically increased while the other (Circuit 1) remained identical. By varying the expression level of Circuit 1 and measuring its effect on the expression level of Circuit 2, we demonstrated computationally and experimentally that indirect coupling between these seemingly unconnected genetic circuits can occur in three different regulatory topologies. More importantly, we experimentally verified the computational prediction that negative feedback can significantly reduce resource-coupled interference in regulatory circuits. Our results reveal a design principle that enables cells to reliably multitask while tightly controlling cellular resources.

Resource Competition Shapes the Response of Genetic Circuits

A common approach to design genetic circuits is to compose gene expression cassettes together. While appealing, this modular approach is challenged by the fact that expression of each gene depends on the availability of transcriptional/translational resources, which is in turn determined by the presence of other genes in the circuit. This raises the question of how competition for resources by different genes affects a circuit's behavior. Here, we create a library of genetic activation cascades in E. coli bacteria, where we explicitly tune the resource demand by each gene. We develop a general Hill-function-based model that incorporates resource competition effects through resource demand coefficients. These coefficients lead to nonregulatory interactions among genes that reshape the circuit's behavior. For the activation cascade, such interactions result in surprising biphasic or monotonically decreasing responses. Finally, we use resource demand coefficients to guide the choice of ribosome binding site and DNA copy number to restore the cascade's intended monotonically increasing response. Our results demonstrate how unintended circuit's behavior arises from resource competition and provide a model-guided methodology to minimize the resulting effects.

Criticality and Adaptivity in Enzymatic Networks

The contrast between the stochasticity of biochemical networks and the regularity of cellular behavior suggests that biological networks generate robust behavior from noisy constituents. Identifying the mechanisms that confer this ability on biological networks is essential to understanding cells. Here we show that queueing for a limited shared resource in broad classes of enzymatic networks in certain conditions leads to a critical state characterized by strong and long-ranged correlations between molecular species. An enzymatic network reaches this critical state when the input flux of its substrate is balanced by the maximum processing capacity of the network. We then consider enzymatic networks with adaptation, when the limiting resource (enzyme or cofactor) is produced in proportion to the demand for it. We show that the critical state becomes an attractor for these networks, which points toward the onset of self-organized criticality. We suggest that the adaptive queueing motif that leads to significant correlations between multiple species may be widespread in biological systems.

Engineering tolerance to industrially relevant stress factors in yeast cell factories

The main focus in development of yeast cell factories has generally been on establishing optimal activity of heterologous pathways and further metabolic engineering of the host strain to maximize product yield and titer. Adequate stress tolerance of the host strain has turned out to be another major challenge for obtaining economically viable performance in industrial production. Although general robustness is a universal requirement for industrial microorganisms, production of novel compounds using artificial metabolic pathways presents additional challenges. Many of the bio-based compounds desirable for production by cell factories are highly toxic to the host cells in the titers required for economic viability. Artificial metabolic pathways also turn out to be much more sensitive to stress factors than endogenous pathways, likely because regulation of the latter has been optimized in evolution in myriads of environmental conditions. We discuss different environmental and metabolic stress factors with high relevance for industrial utilization of yeast cell factories and the experimental approaches used to engineer higher stress tolerance. Improving stress tolerance in a predictable manner in yeast cell factories should facilitate their widespread utilization in the bio-based economy and extend the range of products successfully produced in large scale in a sustainable and economically profitable way.

Engineering bacterial translation initiation - Do we have all the tools we need?

BACKGROUND

Reliable tools that allow precise and predictable control over gene expression are critical for the success of nearly all bioengineering applications. Translation initiation is the most regulated phase during protein biosynthesis, and is therefore a promising target for exerting control over gene expression. At the translational level, the copy number of a protein can be fine-tuned by altering the interaction between the translation initiation region of an mRNA and the ribosome. These interactions can be controlled by modulating the mRNA structure using numerous approaches, including small molecule ligands, RNAs, or RNA-binding proteins. A variety of naturally occurring regulatory elements have been repurposed, facilitating advances in synthetic gene regulation strategies. The pursuit of a comprehensive understanding of mechanisms governing translation initiation provides the framework for future engineering efforts.

SCOPE OF REVIEW

Here we outline state-of-the-art strategies used to predictably control translation initiation in bacteria. We also discuss current limitations in the field and future goals.

MAJOR CONCLUSIONS

Due to its function as the rate-determining step, initiation is the ideal point to exert effective translation regulation. Several engineering tools are currently available to rationally design the initiation characteristics of synthetic mRNAs. However, improvements are required to increase the predictability, effectiveness, and portability of these tools.

GENERAL SIGNIFICANCE

Predictable and reliable control over translation initiation will allow greater predictability when designing, constructing, and testing genetic circuits. The ability to build more complex circuits predictably will advance synthetic biology and contribute to our fundamental understanding of the underlying principles of these processes. "This article is part of a Special Issue entitled "Biochemistry of Synthetic Biology - Recent Developments" Guest Editor: Dr. Ilka Heinemann and Dr. Patrick O'Donoghue.

Synonymous Codons: Choose Wisely for Expression

The genetic code, which defines the amino acid sequence of a protein, also contains information that influences the rate and efficiency of translation. Neither the mechanisms nor functions of codon-mediated regulation were well understood. The prevailing model was that the slow translation of codons decoded by rare tRNAs reduces efficiency. Recent genome-wide analyses have clarified several issues. Specific codons and codon combinations modulate ribosome speed and facilitate protein folding. However, tRNA availability is not the sole determinant of rate; rather, interactions between adjacent codons and wobble base pairing are key. One mechanism linking translation efficiency and codon use is that slower decoding is coupled to reduced mRNA stability. Changes in tRNA supply mediate biological regulationfor instance,, changes in tRNA amounts facilitate cancer metastasis.

Systematic comparison of co-expression of multiple recombinant thermophilic enzymes in Escherichia coli BL21(DE3)

The precise control of multiple heterologous enzyme expression levels in one Escherichia coli strain is important for cascade biocatalysis, metabolic engineering, synthetic biology, natural product synthesis, and studies of complexed proteins. We systematically investigated the co-expression of up to four thermophilic enzymes (i.e., α-glucan phosphorylase (αGP), phosphoglucomutase (PGM), glucose 6-phosphate dehydrogenase (G6PDH), and 6-phosphogluconate dehydrogenase (6PGDH)) in E. coli BL21(DE3) by adding T7 promoter or T7 terminator of each gene for multiple genes in tandem, changing gene alignment, and comparing one or two plasmid systems. It was found that the addition of T7 terminator after each gene was useful to decrease the influence of the upstream gene. The co-expression of the four enzymes in E. coli BL21(DE3) was demonstrated to generate two NADPH molecules from one glucose unit of maltodextrin, where NADPH was oxidized to convert xylose to xylitol. The best four-gene co-expression system was based on two plasmids (pET and pACYC) which harbored two genes. As a result, apparent enzymatic activities of the four enzymes were regulated to be at similar levels and the overall four-enzyme activity was the highest based on the formation of xylitol. This study provides useful information for the precise control of multi-enzyme-coordinated expression in E. coli BL21(DE3).

Gene Architectures that Minimize Cost of Gene Expression

Gene expression burdens cells by consuming resources and energy. While numerous studies have investigated regulation of expression level, little is known about gene design elements that govern expression costs. Here, we ask how cells minimize production costs while maintaining a given protein expression level and whether there are gene architectures that optimize this process. We measured fitness of ∼14,000 E. coli strains, each expressing a reporter gene with a unique 5' architecture. By comparing cost-effective and ineffective architectures, we found that cost per protein molecule could be minimized by lowering transcription levels, regulating translation speeds, and utilizing amino acids that are cheap to synthesize and that are less hydrophobic. We then examined natural E. coli genes and found that highly expressed genes have evolved more forcefully to minimize costs associated with their expression. Our study thus elucidates gene design elements that improve the economy of protein expression in natural and heterologous systems.

A model for competition for ribosomes in the cell

A single mammalian cell includes an order of 10(4)-10(5) mRNA molecules and as many as 10(5)-10(6) ribosomes. Large-scale simultaneous mRNA translation induces correlations between the mRNA molecules, as they all compete for the finite pool of available ribosomes. This has important implications for the cell's functioning and evolution. Developing a better understanding of the intricate correlations between these simultaneous processes, rather than focusing on the translation of a single isolated transcript, should help in gaining a better understanding of mRNA translation regulation and the way elongation rates affect organismal fitness. A model of simultaneous translation is specifically important when dealing with highly expressed genes, as these consume more resources. In addition, such a model can lead to more accurate predictions that are needed in the interconnection of translational modules in synthetic biology. We develop and analyse a general dynamical model for large-scale simultaneous mRNA translation and competition for ribosomes. This is based on combining several ribosome flow models (RFMs) interconnected via a pool of free ribosomes. We use this model to explore the interactions between the various mRNA molecules and ribosomes at steady state. We show that the compound system always converges to a steady state and that it always entrains or phase locks to periodically time-varying transition rates in any of the mRNA molecules. We then study the effect of changing the transition rates in one mRNA molecule on the steady-state translation rates of the other mRNAs that results from the competition for ribosomes. We show that increasing any of the codon translation rates in a specific mRNA molecule yields a local effect, an increase in the translation rate of this mRNA, and also a global effect, the translation rates in the other mRNA molecules all increase or all decrease. These results suggest that the effect of codon decoding rates of endogenous and heterologous mRNAs on protein production is more complicated than previously thought. In addition, we show that increasing the length of an mRNA molecule decreases the production rate of all the mRNAs.

Initiator tRNA genes template the 3' CCA end at high frequencies in bacteria

Background

While the CCA sequence at the mature 3′ end of tRNAs is conserved and critical for translational function, a genetic template for this sequence is not always contained in tRNA genes. In eukaryotes and Archaea, the CCA ends of tRNAs are synthesized post-transcriptionally by CCA-adding enzymes. In Bacteria, tRNA genes template CCA sporadically.

Results

In order to understand the variation in how prokaryotic tRNA genes template CCA, we re-annotated tRNA genes in tRNAdb-CE database version 0.8. Among 132,129 prokaryotic tRNA genes, initiator tRNA genes template CCA at the highest average frequency (74.1%) over all functional classes except selenocysteine and pyrrolysine tRNA genes (88.1% and 100% respectively). Across bacterial phyla and a wide range of genome sizes, many lineages exist in which predominantly initiator tRNA genes template CCA. Convergent and parallel retention of CCA templating in initiator tRNA genes evolved in independent histories of reductive genome evolution in Bacteria. Also, in a majority of cyanobacterial and actinobacterial genera, predominantly initiator tRNA genes template CCA. We also found that a surprising fraction of archaeal tRNA genes template CCA.

Conclusions

We suggest that cotranscriptional synthesis of initiator tRNA CCA 3′ ends can complement inefficient processing of initiator tRNA precursors, “bootstrap” rapid initiation of protein synthesis from a non-growing state, or contribute to an increase in cellular growth rates by reducing overheads of mass and energy to maintain nonfunctional tRNA precursor pools. More generally, CCA templating in structurally non-conforming tRNA genes can afford cells robustness and greater plasticity to respond rapidly to environmental changes and stimuli.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-016-3314-x) contains supplementary material, which is available to authorized users.

Metabolomic analysis of riboswitch containing E. coli recombinant expression system

In this study we have employed metabolomics approaches to understand the metabolic effects of producing enhanced green fluorescent protein (eGFP) as a recombinant protein in Escherichia coli cells. This metabolic burden analysis was performed against a number of recombinant expression systems and control strains and included: (i) standard transcriptional recombinant expression control system BL21(DE3) with the expression plasmid pET-eGFP, (ii) the recently developed dual transcriptional-translational recombinant expression control strain BL21(IL3), with pET-eGFP, (iii) BL21(DE3) with an empty expression plasmid pET, (iv) BL21(IL3) with an empty expression plasmid, and (v) BL21(DE3) without an expression plasmid; all strains were cultured under various induction conditions. The growth profiles of all strains together with the results gathered by the analysis of the Fourier transform infrared (FT-IR) spectroscopy data, identified IPTG-dependent induction as the dominant factor hampering cellular growth and metabolism, which was in general agreement with the findings of GC-MS analysis of cell extracts and media samples. In addition, the exposure of host cells to the synthetic inducer ligand, pyrimido[4,5-d] pyrimidine-2,4-diamine (PPDA), of the orthogonal riboswitch containing expression system (BL21(IL3)) did not display any detrimental effects, and its detected levels in all the samples were at similar levels, emphasising the inability of the cells to metabolise PPDA. The overall results obtained in this study suggested that although the BL21(DE3)-EGFP and BL21(IL3)-EGFP strains produced comparable levels of recombinant eGFP, the presence of the orthogonal riboswitch seemed to be moderating the metabolic burden of eGFP production in the cells enabling higher biomass yield, whilst providing a greater level of control over protein expression.