A synthetic growth switch based on controlled expression of RNA polymerase

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.

Essential factors, advanced strategies, challenges, and approaches involved for efficient expression of recombinant proteins in Escherichia coli

Producing recombinant proteins is a major accomplishment of biotechnology in the past century. Heterologous hosts, either eukaryotic or prokaryotic, are used for the production of these proteins. The utilization of microbial host systems continues to dominate as the most efficient and affordable method for biotherapeutics and food industry productions. Hence, it is crucial to analyze the limitations and advantages of microbial hosts to enhance the efficient production of recombinant proteins on a large scale. E. coli is widely used as a host for the production of recombinant proteins. Researchers have identified certain obstacles with this host, and given the growing demand for recombinant protein production, there is an immediate requirement to enhance this host. The following review discusses the elements contributing to the manifestation of recombinant protein. Subsequently, it sheds light on innovative approaches aimed at improving the expression of recombinant protein. Lastly, it delves into the obstacles and optimization methods associated with translation, mentioning both cis-optimization and trans-optimization, producing soluble recombinant protein, and engineering the metal ion transportation. In this context, a comprehensive description of the distinct features will be provided, and this knowledge could potentially enhance the expression of recombinant proteins in E. coli.

Mapping temperature-sensitive mutations at a genome scale to engineer growth switches in Escherichia coli

Temperature-sensitive (TS) mutants are a unique tool to perturb and engineer cellular systems. Here, we constructed a CRISPR library with 15,120 Escherichia coli mutants, each with a single amino acid change in one of 346 essential proteins. 1,269 of these mutants showed temperature-sensitive growth in a time-resolved competition assay. We reconstructed 94 TS mutants and measured their metabolism under growth arrest at 42°C using metabolomics. Metabolome changes were strong and mutant-specific, showing that metabolism of nongrowing E. coli is perturbation-dependent. For example, 24 TS mutants of metabolic enzymes overproduced the direct substrate metabolite due to a bottleneck in their associated pathway. A strain with TS homoserine kinase (ThrBF267D ) produced homoserine for 24 h, and production was tunable by temperature. Finally, we used a TS subunit of DNA polymerase III (DnaXL289Q ) to decouple growth from arginine overproduction in engineered E. coli. These results provide a strategy to identify TS mutants en masse and demonstrate their large potential to produce bacterial metabolites with nongrowing cells.

A hybrid in silico/in-cell controller for microbial bioprocesses with process-model mismatch

Bioprocess optimization using mathematical models is prevalent, yet the discrepancy between model predictions and actual processes, known as process-model mismatch (PMM), remains a significant challenge. This study proposes a novel hybrid control system called the hybrid in silico/in-cell controller (HISICC) to address PMM by combining model-based optimization (in silico feedforward controller) with feedback controllers utilizing synthetic genetic circuits integrated into cells (in-cell feedback controller). We demonstrated the efficacy of HISICC using two engineered Escherichia coli strains, TA1415 and TA2445, previously developed for isopropanol (IPA) production. TA1415 contains a metabolic toggle switch (MTS) to manage the competition between cell growth and IPA production for intracellular acetyl-CoA by responding to external input of isopropyl β-D-1-thiogalactopyranoside (IPTG). TA2445, in addition to the MTS, has a genetic circuit that detects cell density to autonomously activate MTS. The combination of TA2445 with an in silico controller exemplifies HISICC implementation. We constructed mathematical models to optimize IPTG input values for both strains based on the two-compartment model and validated these models using experimental data of the IPA production process. Using these models, we evaluated the robustness of HISICC against PMM by comparing IPA yields with two strains in simulations assuming various magnitudes of PMM in cell growth rates. The results indicate that the in-cell feedback controller in TA2445 effectively compensates for PMM by modifying MTS activation timing. In conclusion, the HISICC system presents a promising solution to the PMM problem in bioprocess engineering, paving the way for more efficient and reliable optimization of microbial bioprocesses.

Therapeutic targeting of microtubule affinity-regulating kinase 4 in cancer and neurodegenerative diseases

Microtubule affinity-regulating kinase 4 (MARK4) is a member of the Ser/Thr protein kinase family, phosphorylates the microtubule-connected proteins and plays a vital role in causing cancers and neurodegenerative diseases. This kinase modulates multiple signaling pathways, including mammalian target of rapamycin, nuclear factor-κB, and Hippo-signaling, presumably responsible for cancer and Alzheimer's. MARK4 acts as a negative controller of the Hippo-kinase cassette for promoting YAP/TAZ action, and the loss of MARK4 detains the tumorigenic properties of cancer cells. MARK4 is involved in tau hyperphosphorylation that consequently affects neurodegeneration. MARK4 is a promising drug target for cancer, diabetes, and Alzheimer's. Developing the potent and selective inhibitors of MAKR4 are promising in the therapeutic management of associated diseases. Despite its great significance, a few reviews are available to discuss its structure, function and clinical significance. In the current review, we aimed to provide detailed information on the structural features of MARK4 targeted in drug development and its role in various signaling pathways related to cancer and neurodegenerative diseases. We further described the therapeutic potential of MARK4 inhibitors in preventing numerous diseases. Finally, the updated information on MARK4 will be helpful in the further development of effective therapeutic molecules.

Optimal protein production by a synthetic microbial consortium: coexistence, distribution of labor, and syntrophy

The bacterium E. coli is widely used to produce recombinant proteins such as growth hormone and insulin. One inconvenience with E. coli cultures is the secretion of acetate through overflow metabolism. Acetate inhibits cell growth and represents a carbon diversion, which results in several negative effects on protein production. One way to overcome this problem is the use of a synthetic consortium of two different E. coli strains, one producing recombinant proteins and one reducing the acetate concentration. In this paper, we study a mathematical model of such a synthetic community in a chemostat where both strains are allowed to produce recombinant proteins. We give necessary and sufficient conditions for the existence of a coexistence equilibrium and show that it is unique. Based on this equilibrium, we define a multi-objective optimization problem for the maximization of two important bioprocess performance metrics, process yield and productivity. Solving numerically this problem, we find the best available trade-offs between the metrics. Under optimal operation of the mixed community, both strains must produce the protein of interest, and not only one (distribution instead of division of labor). Moreover, in this regime acetate secretion by one strain is necessary for the survival of the other (syntrophy). The results thus illustrate how complex multi-level dynamics shape the optimal production of recombinant proteins by synthetic microbial consortia.

Chromosomal Position of Ribosomal Protein Genes Affects Long-Term Evolution of Vibrio cholerae

It is unclear how gene order within the chromosome influences genome evolution. Bacteria cluster transcription and translation genes close to the replication origin (oriC). In Vibrio cholerae, relocation of s10-spc-α locus (S10), the major locus of ribosomal protein genes, to ectopic genomic positions shows that its relative distance to the oriC correlates to a reduction in growth rate, fitness, and infectivity. To test the long-term impact of this trait, we evolved 12 populations of V. cholerae strains bearing S10 at an oriC-proximal or an oriC-distal location for 1,000 generations. During the first 250 generations, positive selection was the main force driving mutation. After 1,000 generations, we observed more nonadaptative mutations and hypermutator genotypes. Populations fixed inactivating mutations at many genes linked to virulence: flagellum, chemotaxis, biofilm, and quorum sensing. Throughout the experiment, all populations increased their growth rates. However, those bearing S10 close to oriC remained the fittest, indicating that suppressor mutations cannot compensate for the genomic position of the main ribosomal protein locus. Selection and sequencing of the fastest-growing clones allowed us to characterize mutations inactivating, among other sites, flagellum master regulators. Reintroduction of these mutations into the wild-type context led to a ≈10% growth improvement. In conclusion, the genomic location of ribosomal protein genes conditions the evolutionary trajectory of V. cholerae. While genomic content is highly plastic in prokaryotes, gene order is an underestimated factor that conditions cellular physiology and evolution. A lack of suppression enables artificial gene relocation as a tool for genetic circuit reprogramming. IMPORTANCE The bacterial chromosome harbors several entangled processes such as replication, transcription, DNA repair, and segregation. Replication begins bidirectionally at the replication origin (oriC) until the terminal region (ter) organizing the genome along the ori-ter axis gene order along this axis could link genome structure to cell physiology. Fast-growing bacteria cluster translation genes near oriC. In Vibrio cholerae, moving them away was feasible but at the cost of losing fitness and infectivity. Here, we evolved strains harboring ribosomal genes close or far from oriC. Growth rate differences persisted after 1,000 generations. No mutation was able to compensate for the growth defect, showing that ribosomal gene location conditions their evolutionary trajectory. Despite the high plasticity of bacterial genomes, evolution has sculpted gene order to optimize the ecological strategy of the microorganism. We observed growth rate improvement throughout the evolution experiment that occurred at expense of energetically costly processes such the flagellum biosynthesis and virulence-related functions. From the biotechnological point of view, manipulation of gene order enables altering bacterial growth with no escape events.

Optimal control of bioproduction in the presence of population heterogeneity

Cell-to-cell variability, born of stochastic chemical kinetics, persists even in large isogenic populations. In the study of single-cell dynamics this is typically accounted for. However, on the population level this source of heterogeneity is often sidelined to avoid the inevitable complexity it introduces. The homogeneous models used instead are more tractable but risk disagreeing with their heterogeneous counterparts and may thus lead to severely suboptimal control of bioproduction. In this work, we introduce a comprehensive mathematical framework for solving bioproduction optimal control problems in the presence of heterogeneity. We study population-level models in which such heterogeneity is retained, and propose order-reduction approximation techniques. The reduced-order models take forms typical of homogeneous bioproduction models, making them a useful benchmark by which to study the importance of heterogeneity. Moreover, the derivation from the heterogeneous setting sheds light on parameter selection in ways a direct homogeneous outlook cannot, and reveals the source of approximation error. With view to optimally controlling bioproduction in microbial communities, we ask the question: when does optimising the reduced-order models produce strategies that work well in the presence of population heterogeneity? We show that, in some cases, homogeneous approximations provide remarkably accurate surrogate models. Nevertheless, we also demonstrate that this is not uniformly true: overlooking the heterogeneity can lead to significantly suboptimal control strategies. In these cases, the heterogeneous tools and perspective are crucial to optimise bioproduction.

Cybergenetic control of microbial community composition

The use of bacterial communities in bioproduction instead of monocultures has potential advantages including increased productivity through division of labour, ability to utilise cheaper substrates, and robustness against perturbations. A key challenge in the application of engineered bacterial communities is the ability to reliably control the composition of the community in terms of its constituent species. This is crucial to prevent faster growing species from outcompeting others with a lower relative fitness, and to ensure that all species are present at an optimal ratio during different steps in a biotechnological process. In contrast to purely biological approaches such as synthetic quorum sensing circuits or paired auxotrophies, cybergenetic control techniques - those in which computers interface with living cells-are emerging as an alternative approach with many advantages. The community composition is measured through methods such as fluorescence intensity or flow cytometry, with measured data fed real-time into a computer. A control action is computed using a variety of possible control algorithms and then applied to the system, with actuation taking the form of chemical (e.g., inducers, nutrients) or physical (e.g., optogenetic, mechanical) inputs. Subsequent changes in composition are then measured and the cycle repeated, maintaining or driving the system to a desired state. This review discusses recent and future developments in methods for implementing cybergenetic control systems, contrasts their capabilities with those of traditional biological methods of population control, and discusses future directions and outstanding challenges for the field.

Strategies for efficient production of recombinant proteins in Escherichia coli: alleviating the host burden and enhancing protein activity

Escherichia coli, one of the most efficient expression hosts for recombinant proteins (RPs), is widely used in chemical, medical, food and other industries. However, conventional expression strains are unable to effectively express proteins with complex structures or toxicity. The key to solving this problem is to alleviate the host burden associated with protein overproduction and to enhance the ability to accurately fold and modify RPs at high expression levels. Here, we summarize the recently developed optimization strategies for the high-level production of RPs from the two aspects of host burden and protein activity. The aim is to maximize the ability of researchers to quickly select an appropriate optimization strategy for improving the production of RPs.

Developing a dynamic equilibrium system in Escherichia coli to improve the production of recombinant proteins

The combination of Escherichia coli BL21 (DE3) and the pET expression system is used extensively for the expression of various recombinant proteins (RPs). However, RP overexpression often introduces a growth burden for the host, especially in the case of toxic proteins. The key to solving this problem is to reduce the host burden associated with protein overproduction, which is often achieved by regulating the expression or activity of T7 RNAP or growth-decoupled systems. However, these strategies mainly relieve or interrupt the robbing of host resources, and do not eliminate other types of host burdens in the production process. In this study, we constructed a production system based on a dynamic equilibrium to precisely relieve the host burden and increase the RP production. The system is composed of three modules, including the overexpression of basic growth-related genes (rRNA, RNAP core enzyme, sigma factors), prediction and overexpression of key proteins using the enzyme-constrained model ec_iECBD_1354, and dynamic regulation of growth-related and key protein expression intensity based on a burden-driven promoter. Using this system, the production of many high-burden proteins, including autolysis protein and E. coli membrane proteins, was increased to varying degrees. Among them, the cytosine transporter protein (CodB) was most significantly improved, with a 4.02-fold higher production compared to the wild strain. This system can effectively reduce the optimizing costs, and is suitable for developing various types of RP expression hosts rapidly. KEY POINTS: • The basic growth-related resources can relieve the host burden from recombinant protein. • The enzyme-constrained model can accurately predict key genes to improve yield. • The expression intensity can be dynamically adjusted with changes in burden.

Dynamic cybergenetic control of bacterial co-culture composition via optogenetic feedback

Communities of microbes play important roles in natural environments and hold great potential for deploying division-of-labor strategies in synthetic biology and bioproduction. However, the difficulty of controlling the composition of microbial consortia over time hinders their optimal use in many applications. Here, we present a fully automated, high-throughput platform that combines real-time measurements and computer-controlled optogenetic modulation of bacterial growth to implement precise and robust compositional control of a two-strain E. coli community. In addition, we develop a general framework for dynamic modeling of synthetic genetic circuits in the physiological context of E. coli and use a host-aware model to determine the optimal control parameters of our closed-loop compositional control system. Our platform succeeds in stabilizing the strain ratio of multiple parallel co-cultures at arbitrary levels and in changing these targets over time, opening the door for the implementation of dynamic compositional programs in synthetic bacterial communities.

Sequence-dependent model of genes with dual σ factor preference

Escherichia coli uses σ factors to quickly control large gene cohorts during stress conditions. While most of its genes respond to a single σ factor, approximately 5% of them have dual σ factor preference. The most common are those responsive to both σ⁷⁰, which controls housekeeping genes, and σ³⁸, which activates genes during stationary growth and stresses. Using RNA-seq and flow-cytometry measurements, we show that 'σ⁷⁰⁺³⁸ genes' are nearly as upregulated in stationary growth as 'σ³⁸ genes'. Moreover, we find a clear quantitative relationship between their promoter sequence and their response strength to changes in σ³⁸ levels. We then propose and validate a sequence dependent model of σ⁷⁰⁺³⁸ genes, with dual sensitivity to σ³⁸ and σ⁷⁰, that is applicable in the exponential and stationary growth phases, as well in the transient period in between. We further propose a general model, applicable to other stresses and σ factor combinations. Given this, promoters controlling σ⁷⁰⁺³⁸ genes (and variants) could become important building blocks of synthetic circuits with predictable, sequence-dependent sensitivity to transitions between the exponential and stationary growth phases.

Reconstructing the transcription regulatory network to optimize resource allocation for robust biosynthesis

An ideal microbial cell factory (MCF) should deliver maximal resources to production, which conflicts with the microbe's native growth-oriented resource allocation strategy and can therefore lead to early termination of the high-yield period. Reallocating resources from growth to production has become a critical factor in constructing robust MCFs. Instead of strengthening specific biosynthetic pathways, emerging endeavors are focused on rearranging the gene regulatory network to fundamentally reprogram the resource allocation pattern. Combining this idea with transcriptional regulation within the hierarchical regulatory network, this review discusses recent engineering strategies targeting the transcription machinery, module networks, regulatory edges, and bottom network layer. This global view will help to construct a production-oriented phenotype that fully harnesses the potential of MCFs.

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.

An Unnatural Amino Acid-Regulated Growth Controller Based on Informational Disturbance

We designed a novel growth controller regulated by feeding of an unnatural amino acid, N^ε-benzyloxycarbonyl-l-lysine (ZK), using a specific incorporation system at a sense codon. This system is constructed by a pair of modified pyrrolisyl-tRNA synthetase (PylRS) and its cognate tRNA (tRNA^pyl). Although ZK is non-toxic for normal organisms, the growth of Escherichia coli carrying the ZK incorporation system was inhibited in a ZK concentration-dependent manner without causing rapid bacterial death, presumably due to generation of non-functional or toxic proteins. The extent of growth inhibition strongly depended on the anticodon sequence of the tRNA^pyl gene. Taking advantage of the low selectivity of PylRS for tRNA^pyl anticodons, we experimentally determined the most effective anticodon sequence among all 64 nucleotide sequences in the anticodon region of tRNA^pyl gene. The results suggest that the ZK-regulated growth controller is a simple, target-specific, environmental noise-resistant and titratable system. This technique may be applicable to a wide variety of organisms because the growth inhibitory effects are caused by "informational disturbance", in which the highly conserved system for transmission of information from DNA to proteins is perturbed.

A novel global transcriptional perturbation target identified by forward genetics reprograms Vibrio natriegens for improving recombinant protein production

Vibrio natriegens is known to be the fastest-growing free-living bacterium with the potential to be a novel protein expression system other than Escherichia coli. Seven sampled genes of interest (GOIs) encoding biocatalyst enzymes, including Ochrobactrum anthropi-derived ω-transaminase (OATA), were strongly expressed in E. coli but weakly in V. natriegens using the pET expression system. In this study, we fused the C-terminal of OATA with green fluorescent protein (GFP) and obtained V. natriegens mutants that could increase both protein yield and enzyme activity of OATA as well as the other three GOIs by ultraviolet mutagenesis, fluorescence-activated cell sorting (FACS), and OATA colorimetric assay. Furthermore, next-generation sequencing and strain reconstruction revealed that the Y457 variants in the conserved site of endogenous RNA polymerase (RNAP) β' subunit rpoC are responsible for the increase in recombinant protein yield. We speculated that the mutation of rpoC Y457 may reprogram V. natriegens's innate gene transcription, thereby increasing the copy number of pET plasmids and soluble protein yield of certain GOIs. The increase in GOI expression may partly be attributed to the increase in copy number. In conclusion, GOI-GFP fusion combined with FACS is a powerful tool of forward genetics that can be used to obtain a superior expression chassis. If more high-expression-related targets are found for more GOIs, it would make the construction of next-generation protein expression chassis more time-saving.

A unifying autocatalytic network-based framework for bacterial growth laws

Bacterial cells contain various autocatalytic cycles, e.g., the ribosome cycle, where ribosomes translate ribosomal proteins that subsequently self-assemble to form new ribosomes. Here, we show that the transcription–translation machinery couples all cellular autocatalytic cycles, resulting in balanced exponential growth. Each autocatalytic cycle generates two types of growth laws. We derive the RNA polymerase (RNAP) growth law based on the RNAP autocatalytic cycle, where RNAPs transcribe messenger RNAs (mRNAs) of its constituent Rpo protein subunits. Before degrading, these mRNAs catalyze Rpo proteins employing ribosomes. The Rpo proteins subsequently self-assemble, forming new RNAPs, thus completing the cycle. Contrary to ribosome growth law, a reduction in growth rate due to shortage in RNAPs occurs without affecting the ribosomal protein mass fraction.

Recently discovered simple quantitative relations, known as bacterial growth laws, hint at the existence of simple underlying principles at the heart of bacterial growth. In this work, we provide a unifying picture of how these known relations, as well as relations that we derive, stem from a universal autocatalytic network common to all bacteria, facilitating balanced exponential growth of individual cells. We show that the core of the cellular autocatalytic network is the transcription–translation machinery—in itself an autocatalytic network comprising several coupled autocatalytic cycles, including the ribosome, RNA polymerase, and transfer RNA (tRNA) charging cycles. We derive two types of growth laws per autocatalytic cycle, one relating growth rate to the relative fraction of the catalyst and its catalysis rate and the other relating growth rate to all the time scales in the cycle. The structure of the autocatalytic network generates numerous regimes in state space, determined by the limiting components, while the number of growth laws can be much smaller. We also derive a growth law that accounts for the RNA polymerase autocatalytic cycle, which we use to explain how growth rate depends on the inducible expression of the rpoB and rpoC genes, which code for the RpoB and C protein subunits of RNA polymerase, and how the concentration of rifampicin, which targets RNA polymerase, affects growth rate without changing the RNA-to-protein ratio. We derive growth laws for tRNA synthesis and charging and predict how growth rate depends on temperature, perturbation to ribosome assembly, and membrane synthesis.

Charting the landscape of RNA polymerases to unleash their potential in strain improvement

One major mission of microbial cell factory is overproduction of desired chemicals. To this end, it is necessary to orchestrate enzymes that affect metabolic fluxes. However, only modification of a small number of enzymes in most cases cannot maximize desired metabolites, and global regulation is required. Of myriad enzymes influencing global regulation, RNA polymerase (RNAP) may be the most versatile enzyme in biological realm because it not only serves as the workhorse of central dogma but also participates in a plethora of biochemical events. In fact, recent years have witnessed extensive exploitation of RNAPs for phenotypic engineering. While a few impressive reviews showcase the structures and functionalities of RNAPs, this review not only summarizes the state-of-the-art advance in the structures of RNAPs but also points out their enormous potentials in metabolic engineering and synthetic biology. This review aims to provide valuable insights for strain improvement.

Decoupling Growth and Protein Production in CHO Cells: A Targeted Approach

Fed-batch cultures of Chinese Hamster Ovary cells have been used to produce high quantities of biotherapeutics, particularly monoclonal antibodies. However, a growing number of next-generation biotherapeutics, such as bi-specific antibodies and fusion proteins, are difficult to express using standard fed-batch processes. Decoupling cell growth and biotherapeutic production is becoming an increasingly desired strategy for the biomanufacturing industry, especially for difficult-to-express products. Cells are grown to a high cell density in the absence of recombinant protein production (the growth phase), then expression of the recombinant protein is induced and cell proliferation halted (the production phase), usually by combining an inducible gene expression system with a proliferation control strategy. Separating the growth and production phases allows cell resources to be more efficiently directed toward either growth or production, improving growth characteristics and enhancing the production of difficult to express proteins. However, current mammalian cell proliferation control methods rely on temperature shifts and chemical agents, which interact with many non-proliferation pathways, leading to variable impacts on product quality and culture viability. Synthetic biology offers an alternative approach by strategically targeting proliferation pathways to arrest cell growth but have largely remained unused in industrial bioproduction. Due to recent developments in microbial decoupling systems and advances in available mammalian cell engineering tools, we propose that the synthetic biology approach to decoupling growth and production needs revisiting.

Measurement Techniques to Resolve and Control Population Dynamics of Mixed-Culture Processes

Microbial mixed cultures are gaining increasing attention as biotechnological production systems, since they offer a large but untapped potential for future bioprocesses. Effects of secondary metabolite induction and advantages of labor division for the degradation of complex substrates offer new possibilities for process intensification. However, mixed cultures are highly complex, and, consequently, many biotic and abiotic parameters are required to be identified, characterized, and ideally controlled to establish a stable bioprocess. In this review, we discuss the advantages and disadvantages of existing measurement techniques for identifying, characterizing, monitoring, and controlling mixed cultures and highlight promising examples. Moreover, existing challenges and emerging technologies are discussed, which lay the foundation for novel analytical workflows to monitor mixed-culture bioprocesses.

Tunable expression rate control of a growth-decoupled T7 expression system by L-arabinose only

BACKGROUND

Precise regulation of gene expression is of utmost importance for the production of complex membrane proteins (MP), enzymes or other proteins toxic to the host cell. In this article we show that genes under control of a normally Isopropyl β-D-1-thiogalactopyranoside (IPTG)-inducible P_T7-lacO promoter can be induced solely with L-arabinose in a newly constructed Escherichia coli expression host BL21-AI<gp2>, a strain based on the recently published approach of bacteriophage inspired growth-decoupled recombinant protein production.

RESULTS

Here, we show that BL21-AI<gp2> is able to precisely regulate protein production rates on a cellular level in an L-arabinose concentration-dependent manner and simultaneously allows for reallocation of metabolic resources due to L-arabinose induced growth decoupling by the phage derived inhibitor peptide Gp2. We have successfully characterized the system under relevant fed-batch like conditions in microscale cultivation (800 µL) and generated data proofing a relevant increase in specific yields for 6 different Escherichia coli derived MP-GFP fusion proteins by using online-GFP signals, FACS analysis, SDS-PAGE and western blotting.

CONCLUSIONS

In all cases tested, BL21-AI<gp2> outperformed the parental strain BL21-AI, operated in growth-associated production mode. Specific MP-GFP fusion proteins yields have been improved up to 2.7-fold. Therefore, this approach allows for fine tuning of MP production or expression of multi-enzyme pathways where e.g. particular stoichiometries have to be met to optimize product flux.

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.

Bacteriophage Inspired Growth-Decoupled Recombinant Protein Production in Escherichia coli

Modulating resource allocation in bacteria to redirect metabolic building blocks to the formation of recombinant proteins rather than biomass formation remains a grand challenge in biotechnology. Here, we present a novel approach for improved recombinant protein production (RPP) using Escherichia coli (E. coli) by decoupling recombinant protein synthesis from cell growth. We show that cell division and host mRNA transcription can be successfully inhibited by coexpression of a bacteriophage-derived E. coli RNA polymerase (RNAP) inhibitor peptide and that genes overtranscribed by the orthogonal T7 RNAP can finally account to >55% of cell dry mass (CDM). This RNAP inhibitor peptide binds the E. coli RNAP and therefore prevents σ-factor 70 mediated formation of transcriptional qualified open promoter complexes. Thereby, the transcription of σ-factor 70 driven host genes is inhibited, and metabolic resources can be exclusively utilized for synthesis of the protein of interest (POI). Here, we mimic the late phase of bacteriophage infection by coexpressing a phage-derived xenogeneic regulator that reprograms the host cell and thereby are able to significantly improve RPP under industrial relevant fed-batch process conditions at bioreactor scale. We have evaluated production of several different recombinant proteins at different scales (from microscale to 20 L fed-batch scale) and have been able to improve total and soluble proteins yields up to 3.4-fold in comparison to the reference expression system E. coli BL21(DE3). This novel approach for growth-decoupled RPP has profound implications for biotechnology and bioengineering and helps to establish more cost-effective and generic manufacturing processes for biologics and biomaterials.

Genome-Wide CRISPRi-Based Identification of Targets for Decoupling Growth from Production

Growth decoupling can be used to optimize microbial production of biobased compounds by inhibiting excess biomass formation and redirect carbon flux from growth to product formation. However, identifying suitable genetic targets through rational design is challenging. Here, we conduct a genome-wide CRISPRi screen to discover growth switches suitable for decoupling growth and production. Using an sgRNA library covering 12 238 loci in the Escherichia coli genome, we screen for targets that inhibit growth while allowing for continued protein production. In total, we identify 1332 sgRNAs that simultaneously decrease growth and maintain or increase accumulation of GFP. The top target sibB/ibsB shows more than 5-fold increase in GFP accumulation and 45% decrease in biomass formation. Overall, our genome-wide CRISPRi screen provides key targets for growth decoupling, and the approach can be applied to improve biobased production in other microorganisms.

Deep reinforcement learning for the control of microbial co-cultures in bioreactors

Multi-species microbial communities are widespread in natural ecosystems. When employed for biomanufacturing, engineered synthetic communities have shown increased productivity in comparison with monocultures and allow for the reduction of metabolic load by compartmentalising bioprocesses between multiple sub-populations. Despite these benefits, co-cultures are rarely used in practice because control over the constituent species of an assembled community has proven challenging. Here we demonstrate, in silico, the efficacy of an approach from artificial intelligence-reinforcement learning-for the control of co-cultures within continuous bioreactors. We confirm that feedback via a trained reinforcement learning agent can be used to maintain populations at target levels, and that model-free performance with bang-bang control can outperform a traditional proportional integral controller with continuous control, when faced with infrequent sampling. Further, we demonstrate that a satisfactory control policy can be learned in one twenty-four hour experiment by running five bioreactors in parallel. Finally, we show that reinforcement learning can directly optimise the output of a co-culture bioprocess. Overall, reinforcement learning is a promising technique for the control of microbial communities.

Bacterial growth physiology and RNA metabolism

Bacteria are sophisticated systems with high capacity and flexibility to adapt to various environmental conditions. Each prokaryote however possesses a defined metabolic network, which sets its overall metabolic capacity, and therefore the maximal growth rate that can be reached. To achieve optimal growth, bacteria adopt various molecular strategies to optimally adjust gene expression and optimize resource allocation according to the nutrient availability. The resulting physiological changes are often accompanied by changes in the growth rate, and by global regulation of gene expression. The growth-rate-dependent variation of the abundances in the cellular machineries, together with condition-specific regulatory mechanisms, affect RNA metabolism and fate and pose a challenge for rational gene expression reengineering of synthetic circuits. This article is part of a Special Issue entitled: RNA and gene control in bacteria, edited by Dr. M. Guillier and F. Repoila.

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.

Molecular parts and genetic circuits for metabolic engineering of microorganisms

Microbial conversion of biomass into value-added biochemicals is a highly sustainable process compared to petroleum-based production. In this regard, microorganisms have been engineered via simple overexpression or deletion of metabolic genes to facilitate the production. However, the producer microorganisms require complex regulatory circuits to maximize productivity and performance. To address this issue, diverse genetic circuits have been developed that allow cells to minimize their metabolic burden, overcome metabolic imbalances and respond to a dynamically changing environment. In this review, we briefly explain the basic strategy for constructing genetic circuits by assembling molecular parts such as input, operation and output modules. Next, we describe recent applications of the circuits in the metabolic engineering of microorganisms to improve biochemical production. Beyond those achievements, genetic circuits will facilitate more innovative approaches to future strain development through mining and engineering new genetic elements and improving the complexity of genetic circuit design.

Acetate Metabolism and the Inhibition of Bacterial Growth by Acetate

During aerobic growth on glucose, Escherichia coli excretes acetate, a mechanism called "overflow metabolism." At high concentrations, the secreted acetate inhibits growth. Several mechanisms have been proposed for explaining this phenomenon, but a thorough analysis is hampered by the diversity of experimental conditions and strains used in these studies. Here, we describe the construction of a set of isogenic strains that remove different parts of the metabolic network involved in acetate metabolism. Analysis of these strains reveals that (i) high concentrations of acetate in the medium inhibit growth without significantly perturbing central metabolism; (ii) growth inhibition persists even when acetate assimilation is completely blocked; and (iii) regulatory interactions mediated by acetyl-phosphate play a small but significant role in growth inhibition by acetate. The major contribution to growth inhibition by acetate may originate in systemic effects like the uncoupling effect of organic acids or the perturbation of the anion composition of the cell, as previously proposed. Our data suggest, however, that under the conditions considered here, the uncoupling effect plays only a limited role.IMPORTANCE High concentrations of organic acids such as acetate inhibit growth of Escherichia coli and other bacteria. This phenomenon is of interest for understanding bacterial physiology but is also of practical relevance. Growth inhibition by organic acids underlies food preservation and causes problems during high-density fermentation in biotechnology. What causes this phenomenon? Classical explanations invoke the uncoupling effect of acetate and the establishment of an anion imbalance. Here, we propose and investigate an alternative hypothesis: the perturbation of acetate metabolism due to the inflow of excess acetate. We find that this perturbation accounts for 20% of the growth-inhibitory effect through a modification of the acetyl phosphate concentration. Moreover, we argue that our observations are not expected based on uncoupling alone.

Chemical synthesis rewriting of a bacterial genome to achieve design flexibility and biological functionality

The fundamental biological functions of a living cell are stored within the DNA sequence of its genome. Classical genetic approaches dissect the functioning of biological systems by analyzing individual genes, yet uncovering the essential gene set of an organism has remained very challenging. It is argued that the rewriting of entire genomes through the process of chemical synthesis provides a powerful and complementary research concept to understand how essential functions are programed into genomes.

Understanding how to program biological functions into artificial DNA sequences remains a key challenge in synthetic genomics. Here, we report the chemical synthesis and testing of Caulobacter ethensis-2.0 (C. eth-2.0), a rewritten bacterial genome composed of the most fundamental functions of a bacterial cell. We rebuilt the essential genome of Caulobacter crescentus through the process of chemical synthesis rewriting and studied the genetic information content at the level of its essential genes. Within the 785,701-bp genome, we used sequence rewriting to reduce the number of encoded genetic features from 6,290 to 799. Overall, we introduced 133,313 base substitutions, resulting in the rewriting of 123,562 codons. We tested the biological functionality of the genome design in C. crescentus by transposon mutagenesis. Our analysis revealed that 432 essential genes of C. eth-2.0, corresponding to 81.5% of the design, are equal in functionality to natural genes. These findings suggest that neither changing mRNA structure nor changing the codon context have significant influence on biological functionality of synthetic genomes. Discovery of 98 genes that lost their function identified essential genes with incorrect annotation, including a limited set of 27 genes where we uncovered noncoding control features embedded within protein-coding sequences. In sum, our results highlight the promise of chemical synthesis rewriting to decode fundamental genome functions and its utility toward the design of improved organisms for industrial purposes and health benefits.

Optoregulated Protein Release from an Engineered Living Material

Developing materials to encapsulate and deliver functional proteins inside the body is a challenging yet rewarding task for therapeutic purposes. High production costs, mostly associated with the purification process, short-term stability in vivo, and controlled and prolonged release are major hurdles for the clinical application of protein-based biopharmaceuticals. In an attempt to overcome these hurdles, herein, the possibility of incorporating bacteria as protein factories into a material and externally controlling protein release using optogenetics is demonstrated. By engineering bacteria to express and secrete a red fluorescent protein in response to low doses of blue light irradiation and embedding them in agarose hydrogels, living materials are fabricated capable of releasing proteins into the surrounding medium when exposed to light. These bacterial hydrogels allow spatially confined protein expression and dosed protein release over several weeks, regulated by the area and extent of light exposure. The possibility of incorporating such complex functions in a material using relatively simple material and genetic engineering strategies highlights the immense potential and versatility offered by living materials for protein-based biopharmaceutical delivery.

Dynamic control of endogenous metabolism with combinatorial logic circuits

Controlling gene expression during a bioprocess enables real-time metabolic control, coordinated cellular responses, and staging order-of-operations. Achieving this with small molecule inducers is impractical at scale and dynamic circuits are difficult to design. Here, we show that the same set of sensors can be integrated by different combinatorial logic circuits to vary when genes are turned on and off during growth. Three Escherichia coli sensors that respond to the consumption of feedstock (glucose), dissolved oxygen, and by-product accumulation (acetate) are constructed and optimized. By integrating these sensors, logic circuits implement temporal control over an 18-h period. The circuit outputs are used to regulate endogenous enzymes at the transcriptional and post-translational level using CRISPRi and targeted proteolysis, respectively. As a demonstration, two circuits are designed to control acetate production by matching their dynamics to when endogenous genes are expressed (pta or poxB) and respond by turning off the corresponding gene. This work demonstrates how simple circuits can be implemented to enable customizable dynamic gene regulation.

Optimal control of bacterial growth for the maximization of metabolite production

Microorganisms have evolved complex strategies for controlling the distribution of available resources over cellular functions. Biotechnology aims at interfering with these strategies, so as to optimize the production of metabolites and other compounds of interest, by (re)engineering the underlying regulatory networks of the cell. The resulting reallocation of resources can be described by simple so-called self-replicator models and the maximization of the synthesis of a product of interest formulated as a dynamic optimal control problem. Motivated by recent experimental work, we are specifically interested in the maximization of metabolite production in cases where growth can be switched off through an external control signal. We study various optimal control problems for the corresponding self-replicator models by means of a combination of analytical and computational techniques. We show that the optimal solutions for biomass maximization and product maximization are very similar in the case of unlimited nutrient supply, but diverge when nutrients are limited. Moreover, external growth control overrides natural feedback growth control and leads to an optimal scheme consisting of a first phase of growth maximization followed by a second phase of product maximization. This two-phase scheme agrees with strategies that have been proposed in metabolic engineering. More generally, our work shows the potential of optimal control theory for better understanding and improving biotechnological production processes.

Toward Light-Regulated Living Biomaterials

Living materials are an emergent material class, infused with the productive, adaptive, and regenerative properties of living organisms. Property regulation in living materials requires encoding responsive units in the living components to allow external manipulation of their function. Here, an optoregulated Escherichia coli (E. coli)-based living biomaterial that can be externally addressed using light to interact with mammalian cells is demonstrated. This is achieved by using a photoactivatable inducer of gene expression and bacterial surface display technology to present an integrin-specific miniprotein on the outer membrane of an endotoxin-free E. coli strain. Hydrogel surfaces functionalized with the bacteria can expose cell adhesive molecules upon in situ light-activation, and trigger cell adhesion. Surface immobilized bacteria are able to deliver a fluorescent protein to the mammalian cells with which they are interacting, indicating the potential of such a bacterial material to deliver molecules to cells in a targeted manner.

Inactivation of group 2 σ factors upregulates production of transcription and translation machineries in the cyanobacterium Synechocystis sp. PCC 6803

We show that the formation of the RNAP holoenzyme with the primary σ factor SigA increases in the ΔsigBCDE strain of the cyanobacterium Synechocystis sp. PCC 6803 lacking all group 2 σ factors. The high RNAP-SigA holoenzyme content directly induces transcription of a particular set of housekeeping genes, including ones encoding transcription and translation machineries. In accordance with upregulated transcripts, ΔsigBCDE contain more RNAPs and ribosomal subunits than the control strain. Extra RNAPs are fully active, and the RNA content of ΔsigBCDE cells is almost tripled compared to that in the control strain. Although ΔsigBCDE cells produce extra rRNAs and ribosomal proteins, functional extra ribosomes are not formed, and translation activity and protein content remained similar in ΔsigBCDE as in the control strain. The arrangement of the RNA polymerase core genes together with the ribosomal protein genes might play a role in the co-regulation of transcription and translation machineries. Sequence logos were constructed to compare promoters of those housekeeping genes that directly react to the RNAP-SigA holoenzyme content and those ones that do not. Cyanobacterial strains with engineered transcription and translation machineries might provide solutions for construction of highly efficient production platforms for biotechnical applications in the future.

Heterosis Is a Systemic Property Emerging From Non-linear Genotype-Phenotype Relationships: Evidence From in Vitro Genetics and Computer Simulations

Heterosis, the superiority of hybrids over their parents for quantitative traits, represents a crucial issue in plant and animal breeding as well as evolutionary biology. Heterosis has given rise to countless genetic, genomic and molecular studies, but has rarely been investigated from the point of view of systems biology. We hypothesized that heterosis is an emergent property of living systems resulting from frequent concave relationships between genotypic variables and phenotypes, or between different phenotypic levels. We chose the enzyme-flux relationship as a model of the concave genotype-phenotype (GP) relationship, and showed that heterosis can be easily created in the laboratory. First, we reconstituted in vitro the upper part of glycolysis. We simulated genetic variability of enzyme activity by varying enzyme concentrations in test tubes. Mixing the content of "parental" tubes resulted in "hybrids," whose fluxes were compared to the parental fluxes. Frequent heterotic fluxes were observed, under conditions that were determined analytically and confirmed by computer simulation. Second, to test this model in a more realistic situation, we modeled the glycolysis/fermentation network in yeast by considering one input flux, glucose, and two output fluxes, glycerol and acetaldehyde. We simulated genetic variability by randomly drawing parental enzyme concentrations under various conditions, and computed the parental and hybrid fluxes using a system of differential equations. Again we found that a majority of hybrids exhibited positive heterosis for metabolic fluxes. Cases of negative heterosis were due to local convexity between certain enzyme concentrations and fluxes. In both approaches, heterosis was maximized when the parents were phenotypically close and when the distributions of parental enzyme concentrations were contrasted and constrained. These conclusions are not restricted to metabolic systems: they only depend on the concavity of the GP relationship, which is commonly observed at various levels of the phenotypic hierarchy, and could account for the pervasiveness of heterosis.

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.

On the intrinsic constraint of bacterial growth rate: M. tuberculosis's view of the protein translation capacity

In nature, the maximal growth rates vary widely among different bacteria species. Fast-growing bacteria species such as Escherichia coli can have a shortest generation time of 20 min. Slow-growing bacteria species are perhaps best known for Mycobacterium tuberculosis, a human pathogen with a generation time being no less than 16 h. Despite of the significant progress made on understanding the pathogenesis of M. tuberculosis, we know little on the origin of its intriguingly slow growth. From a global view, the intrinsic constraint of the maximal growth rate of bacteria remains to be a fundamental question in microbiology. In this review, we analyze and discuss this issue from the angle of protein translation capacity, which is the major demand for cell growth. Based on quantitative analysis, we propose four parameters: rRNA chain elongation rate, abundance of RNA polymerase engaged in rRNA synthesis, polypeptide chain elongation rate, and active ribosome fraction, which potentially limit the maximal growth rate of bacteria. We further discuss the relation of these parameters with the growth rate for M. tuberculosis as well as other bacterial species. We highlight future comprehensive investigation of these parameters for different bacteria species to understand how bacteria set their own specific growth rates.

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.

Computational design of small transcription activating RNAs for versatile and dynamic gene regulation

A longstanding goal of synthetic biology has been the programmable control of cellular functions. Central to this is the creation of versatile regulatory toolsets that allow for programmable control of gene expression. Of the many regulatory molecules available, RNA regulators offer the intriguing possibility of de novo design-allowing for the bottom-up molecular-level design of genetic control systems. Here we present a computational design approach for the creation of a bacterial regulator called Small Transcription Activating RNAs (STARs) and create a library of high-performing and orthogonal STARs that achieve up to ~ 9000-fold gene activation. We demonstrate the versatility of these STARs-from acting synergistically with existing constitutive and inducible regulators, to reprogramming cellular phenotypes and controlling multigene metabolic pathway expression. Finally, we combine these new STARs with themselves and CRISPRi transcriptional repressors to deliver new types of RNA-based genetic circuitry that allow for sophisticated and temporal control of gene expression.

Programming mRNA decay to modulate synthetic circuit resource allocation

Synthetic circuits embedded in host cells compete with cellular processes for limited intracellular resources. Here we show how funnelling of cellular resources, after global transcriptome degradation by the sequence-dependent endoribonuclease MazF, to a synthetic circuit can increase production. Target genes are protected from MazF activity by recoding the gene sequence to eliminate recognition sites, while preserving the amino acid sequence. The expression of a protected fluorescent reporter and flux of a high-value metabolite are significantly enhanced using this genome-scale control strategy. Proteomics measurements discover a host factor in need of protection to improve resource redistribution activity. A computational model demonstrates that the MazF mRNA-decay feedback loop enables proportional control of MazF in an optimal operating regime. Transcriptional profiling of MazF-induced cells elucidates the dynamic shifts in transcript abundance and discovers regulatory design elements. Altogether, our results suggest that manipulation of cellular resource allocation is a key control parameter for synthetic circuit design.

Synthetic circuits in host cells compete with endogenous processes for limited resources. Here the authors use MazF to funnel cellular resources to a synthetic circuit to increase product production and demonstrate how resource allocation can be manipulated.

Engineering glucose metabolism of Escherichia coli under nitrogen starvation

A major aspect of microbial metabolic engineering is the development of chassis hosts that have favorable global metabolic phenotypes, and can be further engineered to produce a variety of compounds. In this work, we focus on the problem of decoupling growth and production in the model bacterium Escherichia coli, and in particular on the maintenance of active metabolism during nitrogen-limited stationary phase. We find that by overexpressing the enzyme PtsI, a component of the glucose uptake system that is inhibited by α-ketoglutarate during nitrogen limitation, we are able to achieve a fourfold increase in metabolic rates. Alternative systems were also tested: chimeric PtsI proteins hypothesized to be insensitive to α-ketoglutarate did not improve metabolic rates under the conditions tested, whereas systems based on the galactose permease GalP suffered from energy stress and extreme sensitivity to expression level. Overexpression of PtsI is likely to be a useful arrow in the metabolic engineer’s quiver as productivity of engineered pathways becomes limited by central metabolic rates during stationary phase production processes.

Automated optogenetic feedback control for precise and robust regulation of gene expression and cell growth

Dynamic control of gene expression can have far-reaching implications for biotechnological applications and biological discovery. Thanks to the advantages of light, optogenetics has emerged as an ideal technology for this task. Current state-of-the-art methods for optical expression control fail to combine precision with repeatability and cannot withstand changing operating culture conditions. Here, we present a novel fully automatic experimental platform for the robust and precise long-term optogenetic regulation of protein production in liquid Escherichia coli cultures. Using a computer-controlled light-responsive two-component system, we accurately track prescribed dynamic green fluorescent protein expression profiles through the application of feedback control, and show that the system adapts to global perturbations such as nutrient and temperature changes. We demonstrate the efficacy and potential utility of our approach by placing a key metabolic enzyme under optogenetic control, thus enabling dynamic regulation of the culture growth rate with potential applications in bacterial physiology studies and biotechnology.

Dynamical Allocation of Cellular Resources as an Optimal Control Problem: Novel Insights into Microbial Growth Strategies

Microbial physiology exhibits growth laws that relate the macromolecular composition of the cell to the growth rate. Recent work has shown that these empirical regularities can be derived from coarse-grained models of resource allocation. While these studies focus on steady-state growth, such conditions are rarely found in natural habitats, where microorganisms are continually challenged by environmental fluctuations. The aim of this paper is to extend the study of microbial growth strategies to dynamical environments, using a self-replicator model. We formulate dynamical growth maximization as an optimal control problem that can be solved using Pontryagin’s Maximum Principle. We compare this theoretical gold standard with different possible implementations of growth control in bacterial cells. We find that simple control strategies enabling growth-rate maximization at steady state are suboptimal for transitions from one growth regime to another, for example when shifting bacterial cells to a medium supporting a higher growth rate. A near-optimal control strategy in dynamical conditions is shown to require information on several, rather than a single physiological variable. Interestingly, this strategy has structural analogies with the regulation of ribosomal protein synthesis by ppGpp in the enterobacterium Escherichia coli. It involves sensing a mismatch between precursor and ribosome concentrations, as well as the adjustment of ribosome synthesis in a switch-like manner. Our results show how the capability of regulatory systems to integrate information about several physiological variables is critical for optimizing growth in a changing environment.

Microbial growth is the process by which cells sustain and reproduce themselves from available matter and energy. Strategies enabling microorganisms to optimize their growth rate have been extensively studied, but mostly in stable environments. Here, we build a coarse-grained model of microbial growth and use methods from optimal control theory to determine a resource allocation scheme that would lead to maximal biomass accumulation when the cells are dynamically shifted from one growth medium to another. We compare this optimal solution with several cellular implementations of growth control, based on the capacity of the cell to sense different physiological variables. We find that strategies maximizing growth in steady-state conditions perform quite differently in dynamical conditions. Moreover, the control strategy with performance close to the theoretical maximum exploits information of more than one physiological variable, suggesting that optimization of microbial growth in dynamical rather than steady environments requires broader sensory capacities. Interestingly, the ppGpp alarmone system in the enterobacterium Escherichia coli, known to play an important role in growth control, has structural similarities with the control strategy approaching the theoretical maximum. It senses a discrepancy between the concentrations of precursors and ribosomes, and adjusts ribosome synthesis in an on-off fashion. This suggests that E. coli is adapted for environments with intermittent, rapid changes in nutrient availability.