Translation elicits a growth rate-dependent, genome-wide, differential protein production in Bacillus subtilis

Metabolic Profile of the Genome-Reduced Bacillus subtilis Strain IIG-Bs-27-39: An Attractive Chassis for Recombinant Protein Production

The Gram-positive bacterium Bacillus subtilis is extensively used in the industry for the secretory production of proteins with commercial value. To further improve its performance, this microbe has been the subject of extensive genome engineering efforts, especially the removal of large genomic regions that are dispensable or even counterproductive. Here, we present the genome-reduced B. subtilis strain IIG-Bs-27-39, which was obtained through systematic deletion of mobile genetic elements, as well as genes for extracellular proteases, sporulation, flagella formation, and antibiotic production. Different from previously characterized genome-reduced B. subtilis strains, the IIG-Bs-27-39 strain was still able to grow on minimal media. We used this feature to benchmark strain IIG-Bs-27-39 against its parental strain 168 with respect to heterologous protein production and metabolic parameters during bioreactor cultivation. The IIG-Bs-27-39 strain presented superior secretion of difficult-to-produce staphylococcal antigens, as well as higher specific growth rates and biomass yields. At the metabolic level, changes in byproduct formation and internal amino acid pools were observed, whereas energetic parameters such as the ATP yield, ATP/ADP levels, and adenylate energy charge were comparable between the two strains. Intriguingly, we observed a significant increase in the total cellular NADPH level during all tested conditions and increases in the NAD+ and NADP(H) pools during protein production. This indicates that the IIG-Bs-27-39 strain has more energy available for anabolic processes and protein production, thereby providing a link between strain physiology and production performance. On this basis, we conclude that the genome-reduced strain IIG-Bs-27-39 represents an attractive chassis for future biotechnological applications.

Regulation of biofilm gene expression by DNA replication in Bacillus subtilis

Bacillus subtilis relies on biofilms for survival in harsh environments. Extracellular polymeric substance (EPS) is a crucial component of biofilms, yet the dynamics of EPS production in single cells remain elusive. To unveil the modulation of EPS synthesis, we built a minimal network model comprising the SinI-SinR-SlrR module, Spo0A, and EPS. Stochastic simulations revealed that antagonistic interplay between SinI and SinR enables EPS production in bursts. SlrR widens these bursts and increases their frequency by stabilizing SinR-SlrR complexes and depleting free SinR. DNA replication and chromosomal positioning of key genes dictate pulsatile changes in the slrR:sinR gene dosage ratio (gr) and Spo0A-P levels, each promoting EPS production in distinct phases of the cell cycle. As the cell cycle lengthens with nutrient stress, the duty cycle of gr pulsing decreases, whereas the amplitude of Spo0A-P pulses elevates. This coordinated response facilitates keeping a constant proportion of EPS-secreting cells within colonies across diverse nutrient conditions. Our results suggest that bacteria may 'encode' eps expression through strategic chromosomal organization. This work illuminates how stochastic protein interactions, gene copy number imbalance, and cell-cycle dynamics orchestrate EPS synthesis, offering a deeper understanding of biofilm formation.

High Cell Density Cultivation Combined with high Specific Enzyme Activity: Cultivation Protocol for the Production of an Amine Transaminase from Bacillus megaterium in E. coli

High cell density cultivation is an established method for the production of various industrially important products such as recombinant proteins. However, these protocols are not always suitable for biocatalytic processes as the focus often lies on biomass production rather than high specific activities of the enzyme inside the cells. In contrast, a range of shake flask protocols are well known with high specific activities but rather low cell densities. To overcome this gap, we established a tailor-made fed-batch protocol combining both aspects: high cell density and high specific activities of heterologously produced enzyme. Using the example of an industrially relevant amine transaminase from Bacillus megaterium, we describe a strategy to optimize the cultivation yield based on the feed rate, IPTG concentration, and post-induction temperature. By adjusting these key parameters, we were able to increase the specific activity by 2.6-fold and the wet cell weight by even 17-fold compared to shake flasks. Finally, we were able to verify our established protocol by transferring it to another experimenter. With that, our optimization strategy can serve as a template for the production of high titers of heterologously produced, active enzymes and might enable the availability of these catalysts for upscaling biocatalytic processes.

Growth rate is modulated by monitoring cell wall precursors in Bacillus subtilis

How bacteria link their growth rate to external nutrient conditions is unknown. To investigate how Bacillus subtilis cells alter the rate at which they expand their cell walls as they grow, we compared single-cell growth rates of cells grown under agar pads with the density of moving MreB filaments under a variety of growth conditions. MreB filament density increases proportionally with growth rate. We show that both MreB filament density and growth rate depend on the abundance of Lipid II and murAA, the first gene in the biosynthetic pathway creating the cell wall precursor Lipid II. Lipid II is sensed by the serine/threonine kinase PrkC, which phosphorylates RodZ and other proteins. We show that phosphorylated RodZ increases MreB filament density, which in turn increases cell growth rate. We also show that increasing the activity of this pathway in nutrient-poor media results in cells that elongate faster than wild-type cells, which means that B. subtilis contains spare 'growth capacity'. We conclude that PrkC functions as a cellular rheostat, enabling fine-tuning of cell growth rates in response to Lipid II in different nutrient conditions.

Cis-Element Engineering Promotes the Expression of Bacillus subtilis Type I L-Asparaginase and Its Application in Food

Type I L-asparaginase from Bacillus licheniformis Z-1 (BlAase) was efficiently produced and secreted in Bacillus subtilis RIK 1285, but its low yield made it unsuitable for industrial use. Thus, a combined method was used in this study to boost BlAase synthesis in B. subtilis. First, fifteen single strong promoters were chosen to replace the original promoter P43, with PyvyD achieving the greatest BlAase activity (436.28 U/mL). Second, dual-promoter systems were built using four promoters (PyvyD, P43, PaprE, and PspoVG) with relatively high BlAase expression levels to boost BlAase output, with the engine of promoter PaprE-PyvyD reaching 502.11 U/mL. The activity of BlAase was also increased (568.59 U/mL) by modifying key portions of the PaprE-PyvyD promoter. Third, when the ribosome binding site (RBS) sequence of promoter PyvyD was replaced, BlAase activity reached 790.1 U/mL, which was 2.27 times greater than the original promoter P43 strain. After 36 h of cultivation, the BlAase expression level in a 10 L fermenter reached 2163.09 U/mL, which was 6.2 times greater than the initial strain using promoter P43. Moreover, the application potential of BlAase on acrylamide migration in potato chips was evaluated. Results showed that 89.50% of acrylamide in fried potato chips could be removed when combined with blanching and BlAase treatment. These findings revealed that combining transcription and translation techniques are effective strategies to boost recombinant protein output, and BlAase can be a great candidate for controlling acrylamide in food processing.

Gene regulation by a protein translation factor at the single-cell level

Gene expression is inherently stochastic and pervasively regulated. While substantial work combining theory and experiments has been carried out to study how noise propagates through transcriptional regulations, the stochastic behavior of genes regulated at the level of translation is poorly understood. Here, we engineered a synthetic genetic system in which a target gene is down-regulated by a protein translation factor, which in turn is regulated transcriptionally. By monitoring both the expression of the regulator and the regulated gene at the single-cell level, we quantified the stochasticity of the system. We found that with a protein translation factor a tight repression can be achieved in single cells, noise propagation from gene to gene is buffered, and the regulated gene is sensitive in a nonlinear way to global perturbations in translation. A suitable mathematical model was instrumental to predict the transfer functions of the system. We also showed that a Gamma distribution parameterized with mesoscopic parameters, such as the mean expression and coefficient of variation, provides a deep analytical explanation about the system, displaying enough versatility to capture the cell-to-cell variability in genes regulated both transcriptionally and translationally. Overall, these results contribute to enlarge our understanding on stochastic gene expression, at the same time they provide design principles for synthetic biology.

In the cell, proteins can bind to DNA to regulate transcription as well as to RNA to regulate translation. However, cells have mainly evolved to exploit transcription factors as specific gene regulators, while translation factors have remained as global modulators of expression. Consequently, transcription regulation has attracted much attention over the last years to unveil design principles of genetic organization and to engineer synthetic circuits for cell reprogramming. In this work, the phage MS2 coat protein was exploited to regulate the expression of a green fluorescent protein at the level of translation. This synthetic system was instrumental to gain fundamental knowledge on stochasticity and regulation at an overlooked level within the genetic information flow.

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.

Robotic integration enables autonomous operation of laboratory scale stirred tank bioreactors with model-driven process analysis

Given its geometric similarity to large-scale production plants and the excellent possibilities for precise process control and monitoring, the classic stirred tank bioreactor (STR) still represents the gold standard for bioprocess development at a laboratory scale. However, compared to microbioreactor technologies, bioreactors often suffer from a low degree of process automation and deriving key performance indicators (KPIs) such as specific rates or yields often requires manual sampling and sample processing. A widely used parallelized STR setup was automated by connecting it to a liquid handling system and controlling it with a custom-made process control system. This allowed for the setup of a flexible modular platform enabling autonomous operation of the bioreactors without any operator present. Multiple unit operations like automated inoculation, sampling, sample processing and analysis, and decision making, for example for automated induction of protein production were implemented to achieve such functionality. The data gained during application studies was used for fitting of bioprocess models to derive relevant KPIs being in good agreement with literature. By combining the capabilities of STRs with the flexibility of liquid handling systems, this platform technology can be applied to a multitude of different bioprocess development pipelines at laboratory scale.

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.

Mismatch-CRISPRi Reveals the Co-varying Expression-Fitness Relationships of Essential Genes in Escherichia coli and Bacillus subtilis

Essential genes are the hubs of cellular networks, but lack of high-throughput methods for titrating gene expression has limited our understanding of the fitness landscapes against which their expression levels are optimized. We developed a modified CRISPRi system leveraging the predictable reduction in efficacy of imperfectly matched sgRNAs to generate defined levels of CRISPRi activity and demonstrated its broad applicability. Using libraries of mismatched sgRNAs predicted to span the full range of knockdown levels, we characterized the expression-fitness relationships of most essential genes in Escherichia coli and Bacillus subtilis. We find that these relationships vary widely from linear to bimodal but are similar within pathways. Notably, despite ∼2 billion years of evolutionary separation between E. coli and B. subtilis, most essential homologs have similar expression-fitness relationships with rare but informative differences. Thus, the expression levels of essential genes may reflect homeostatic or evolutionary constraints shared between the two organisms.

The protein translation machinery is expressed for maximal efficiency in Escherichia coli

Protein synthesis is the most expensive process in fast-growing bacteria. Experimentally observed growth rate dependencies of the translation machinery form the basis of powerful phenomenological growth laws; however, a quantitative theory on the basis of biochemical and biophysical constraints is lacking. Here, we show that the growth rate-dependence of the concentrations of ribosomes, tRNAs, mRNA, and elongation factors observed in Escherichia coli can be predicted accurately from a minimization of cellular costs in a mechanistic model of protein translation. The model is constrained only by the physicochemical properties of the molecules and has no adjustable parameters. The costs of individual components (made of protein and RNA parts) can be approximated through molecular masses, which correlate strongly with alternative cost measures such as the molecules’ carbon content or the requirement of energy or enzymes for their biosynthesis. Analogous cost minimization approaches may facilitate similar quantitative insights also for other cellular subsystems.

The protein translation machinery is the most expensive cellular subsystem in fast growing bacteria. Providing a detailed mechanistic model for this complex system, the authors show that the translation machinery components are expressed such that their combined cost to the cell is minimal.

Extrinsic noise prevents the independent tuning of gene expression noise and protein mean abundance in bacteria

It is generally accepted that prokaryotes can tune gene expression noise independently of protein mean abundance by varying the relative levels of transcription and translation. Here, we address this question quantitatively, using a custom-made library of 40 Bacillus subtilis strains expressing a fluorescent protein under the control of different transcription and translation control elements. We quantify noise and mean protein abundance by fluorescence microscopy and show that for most of the natural transcription range of B. subtilis, expression noise is equally sensitive to variations in the transcription or translation rate because of the prevalence of extrinsic noise. In agreement, analysis of whole-genome transcriptomic and proteomic datasets suggests that noise optimization through transcription and translation tuning during evolution may only occur in a regime of weak transcription. Therefore, independent control of mean abundance and noise can rarely be achieved, which has strong implications for both genome evolution and biological engineering.

A bacterial size law revealed by a coarse-grained model of cell physiology

Universal observations in Biology are sometimes described as “laws”. In E. coli, experimental studies performed over the past six decades have revealed major growth laws relating ribosomal mass fraction and cell size to the growth rate. Because they formalize complex emerging principles in biology, growth laws have been instrumental in shaping our understanding of bacterial physiology. Here, we discovered a novel size law that connects cell size to the inverse of the metabolic proteome mass fraction and the active fraction of ribosomes. We used a simple whole-cell coarse-grained model of cell physiology that combines the proteome allocation theory and the structural model of cell division. This integrated model captures all available experimental data connecting the cell proteome composition, ribosome activity, division size and growth rate in response to nutrient quality, antibiotic treatment and increased protein burden. Finally, a stochastic extension of the model explains non-trivial correlations observed in single cell experiments including the adder principle. This work provides a simple and robust theoretical framework for studying the fundamental principles of cell size determination in unicellular organisms.

Bacteria respond to environmental changes by adjusting their molecular composition, cell size and growth rate. This plasticity is thought to result from years of evolution and to be at least in part optimal for bacterial physiology. Over the past decades, quantitative studies of bacterial growth have revealed simple phenomenological relationships, called “growth laws”, which link cell size and cell composition to the growth rate. Simplified mathematical models of cell physiology are useful tools to gain quantitative understanding of the molecular mechanisms that underlie growth laws. For instance, these models helped explaining how optimal allocation of cellular resource to physiological processes and pathways governs the cell molecular composition in response to specific environmental conditions. In this study, we have extended and integrated existing mathematical models and used experimental data from several recent studies to understand the co-regulation of cell composition, cell size and the cellular growth rate. The model predictions uncovered a novel “size law” that links cell size to the levels of metabolic proteins and the fraction of active ribosomes present in the cell. This work provides a useful theoretical tool and a quantitative basis for understanding mechanistically bacterial physiology as a function of external conditions.

Assessment of transcriptomic constraint-based methods for central carbon flux inference

MOTIVATION

Determining intracellular metabolic flux through isotope labeling techniques such as 13C metabolic flux analysis (13C-MFA) incurs significant cost and effort. Previous studies have shown transcriptomic data coupled with constraint-based metabolic modeling can determine intracellular fluxes that correlate highly with 13C-MFA measured fluxes and can achieve higher accuracy than constraint-based metabolic modeling alone. These studies, however, used validation data limited to E. coli and S. cerevisiae grown on glucose, with significantly similar flux distribution for central metabolism. It is unclear whether those results apply to more diverse metabolisms, and therefore further, extensive validation is needed.

RESULTS

In this paper, we formed a dataset of transcriptomic data coupled with corresponding 13C-MFA flux data for 21 experimental conditions in different unicellular organisms grown on varying carbon substrates and conditions. Three computational flux-balance analysis (FBA) methods were comparatively assessed. The results show when uptake rates of carbon sources and key metabolites are known, transcriptomic data provides no significant advantage over constraint-based metabolic modeling (average correlation coefficients, transcriptomic E-Flux2 0.725 and SPOT 0.650 vs non-transcriptomic pFBA 0.768). When uptake rates are unknown, however, predictions obtained utilizing transcriptomic data are generally good and significantly better than those obtained using constraint-based metabolic modeling alone (E-Flux2 0.385 and SPOT 0.583 vs pFBA 0.237). Thus, transcriptomic data coupled with constraint-based metabolic modeling is a promising method to obtain intracellular flux estimates in microorganisms, particularly in cases where uptake rates of key metabolites cannot be easily determined, such as for growth in complex media or in vivo conditions.

Impact of high salinity and the compatible solute glycine betaine on gene expression of Bacillus subtilis

The Gram-positive bacterium Bacillus subtilis is frequently exposed to hyperosmotic conditions. In addition to the induction of genes involved in the accumulation of compatible solutes, high salinity exerts widespread effects on B. subtilis physiology, including changes in cell wall metabolism, induction of an iron limitation response, reduced motility and suppression of sporulation. We performed a combined whole-transcriptome and proteome analysis of B. subtilis 168 cells continuously cultivated at low or high (1.2 M NaCl) salinity. Our study revealed significant changes in the expression of more than one-fourth of the protein-coding genes and of numerous non-coding RNAs. New aspects in understanding the impact of high salinity on B. subtilis include a sustained low-level induction of the SigB-dependent general stress response and strong repression of biofilm formation under high-salinity conditions. The accumulation of compatible solutes such as glycine betaine aids the cells to cope with water stress by maintaining physiologically adequate levels of turgor and also affects multiple cellular processes through interactions with cellular components. Therefore, we additionally analysed the global effects of glycine betaine on the transcriptome and proteome of B. subtilis and revealed that it influences gene expression not only under high-salinity, but also under standard growth conditions.

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.

Limits to a classic paradigm: most transcription factors in E. coli regulate genes involved in multiple biological processes

Transcription factors (TFs) are important drivers of cellular decision-making. When bacteria encounter a change in the environment, TFs alter the expression of a defined set of genes in order to adequately respond. It is commonly assumed that genes regulated by the same TF are involved in the same biological process. Examples of this are methods that rely on coregulation to infer function of not-yet-annotated genes. We have previously shown that only 21% of TFs involved in metabolism regulate functionally homogeneous genes, based on the proximity of the gene products’ catalyzed reactions in the metabolic network. Here, we provide more evidence to support the claim that a 1-TF/1-process relationship is not a general property. We show that the observed functional heterogeneity of regulons is not a result of the quality of the annotation of regulatory interactions, nor the absence of protein–metabolite interactions, and that it is also present when function is defined by Gene Ontology terms. Furthermore, the observed functional heterogeneity is different from the one expected by chance, supporting the notion that it is a biological property. To further explore the relationship between transcriptional regulation and metabolism, we analyzed five other types of regulatory groups and identified complex regulons (i.e. genes regulated by the same combination of TFs) as the most functionally homogeneous, and this is supported by coexpression data. Whether higher levels of related functions exist beyond metabolism and current functional annotations remains an open question.

A Bacterial Growth Law out of Steady State

Bacterial growth follows simple laws in constant conditions. However, bacteria in nature often face fluctuating environments. We therefore ask whether there are growth laws that apply to changing environments. We derive a law for upshifts using an optimal resource-allocation model: the post-shift growth rate equals the geometrical mean of the pre-shift growth rate and the growth rate on saturating carbon. We test this using chemostat and batch culture experiments, as well as previous data from several species. The increase in growth rate after an upshift indicates that ribosomes have spare capacity (SC). We demonstrate theoretically that SC has the cost of slow steady-state growth but is beneficial after an upshift because it prevents large overshoots in intracellular metabolites and allows rapid response to change. We also provide predictions for downshifts. The present study quantifies the optimal degree of SC, which rises the slower the growth rate, and suggests that SC can be precisely regulated.

Promoter engineering enables overproduction of foreign proteins from a single copy expression cassette in Bacillus subtilis

BACKGROUND

Bacillus subtilis is developed to be an attractive expression host to produce both secreted and cytoplasmic proteins owing to its prominent biological characteristics. Chromosomal integration is a stable expression strategy while the expression level is not ideal compared with plasmid expression. Thus, to meet the requirement of protein overexpression, promoter, as one of the key elements, is important. It is necessary to obtain an ideal promoter for overproduction of foreign proteins from a single copy expression cassette.

RESULTS

The activity of promoter P_ylb was further enhanced by optimizing the - 35, - 10 core region and upstream sequence (UP) by substituting both sequences with consensus sequences. The final engineered promoter exhibited almost 26-fold in β-galactosidase (BgaB) activity and 195-fold in super-folded green fluorescent protein (sfGFP) intensity than that of WT. The two proteins account for 43% and 30% of intracellular proteins, respectively. The promoter was eventually tested by successful extracellular overproduction of Methyl Parathion Hydrolase (MPH) and Chlorothalonil hydrolytic dehalogenase (Chd) to a level of 0.3 g/L (144 U/mL) and 0.27 g/L (4.4 U/mL) on shake-flask culture condition.

CONCLUSIONS

A strong promoter was engineered for efficient chromosomally integrated expression of heterologous proteins.

The Combined Effect of Cold and Copper Stresses on the Proliferation and Transcriptional Response of Listeria monocytogenes

Listeria monocytogenes is a foodborne pathogen that can cause severe disease in susceptible humans. This microorganism has the ability to adapt to hostile environmental conditions such as the low temperatures used by the food industry for controlling microorganisms. Bacteria are able to adjust their transcriptional response to adapt to stressful conditions in order to maintain cell homeostasis. Understanding the transcriptional response of L. monocytogenes to stressing conditions could be relevant to develop new strategies to control the pathogen. A possible alternative for controlling microorganisms in the food industry could be to use copper as an antimicrobial agent. The present study characterized three L. monocytogenes strains (List2-2, Apa13-2, and Al152-2A) adapted to low temperature and challenged with different copper concentrations. Similar MIC-Cu values were observed among studied strains, but growth kinetic parameters revealed that strain List2-2 was the least affected by the presence of copper at 8°C. This strain was selected for a global transcriptional response study after a 1 h exposition to 0.5 mM of CuSO4 × 5H2O at 8 and 37°C. The results showed that L. monocytogenes apparently decreases its metabolism in response to copper, and this reduction is greater at 8°C than at 37°C. The most affected metabolic pathways were carbohydrates, lipids and nucleotides synthesis. Finally, 15 genes were selected to evaluate the conservation of the transcriptional response in the other two strains. Results indicated that only genes related to copper homeostasis showed a high degree of conservation between the strains studied, suggesting that a low number of genes is implicated in the response to copper stress in L. monocytogenes. These results contribute to the understanding of the molecular mechanisms used by bacteria to overcome a combination of stresses. This study concluded that the application of copper in low concentrations in cold environments may help to control foodborne pathogens as L. monocytogenes in the industry.

Component Characterization in a Growth-Dependent Physiological Context: Optimal Experimental Design

Synthetic biology design challenges have driven the use of mathematical models to characterize genetic components and to explore complex design spaces. Traditional approaches to characterization have largely ignored the effect of strain and growth conditions on the dynamics of synthetic genetic circuits, and have thus confounded intrinsic features of the circuit components with cell-level context effects. We present a model that distinguishes an activated gene’s intrinsic kinetics from its physiological context. We then demonstrate an optimal experimental design approach to identify dynamic induction experiments for efficient estimation of the component’s intrinsic parameters. Maximally informative experiments are chosen by formulating the design as an optimal control problem; direct multiple-shooting is used to identify the optimum. Our numerical results suggest that the intrinsic parameters of a genetic component can be more accurately estimated using optimal experimental designs, and that the choice of growth rates, sampling schedule, and input profile each play an important role. The proposed approach to coupled component–host modelling can support gene circuit design across a range of physiological conditions.

Genome-scale analysis of syngas fermenting acetogenic bacteria reveals the translational regulation for its autotrophic growth

Background

Acetogenic bacteria constitute promising biocatalysts for the conversion of CO₂/H₂ or synthesis gas (H₂/CO/CO₂) into biofuels and value-added biochemicals. These microorganisms are naturally capable of autotrophic growth via unique acetogenesis metabolism. Despite their biosynthetic potential for commercial applications, a systemic understanding of the transcriptional and translational regulation of the acetogenesis metabolism remains unclear.

Results

By integrating genome-scale transcriptomic and translatomic data, we explored the regulatory logic of the acetogenesis to convert CO₂ into biomass and metabolites in Eubacterium limosum. The results indicate that majority of genes associated with autotrophic growth including the Wood-Ljungdahl pathway, the reduction of electron carriers, the energy conservation system, and gluconeogenesis were transcriptionally upregulated. The translation efficiency of genes in cellular respiration and electron bifurcation was also highly enhanced. In contrast, the transcriptionally abundant genes involved in the carbonyl branch of the Wood-Ljungdahl pathway, as well as the ion-translocating complex and ATP synthase complex in the energy conservation system, showed decreased translation efficiency. The translation efficiencies of genes were regulated by 5′UTR secondary structure under the autotrophic growth condition.

Conclusions

The results illustrated that the acetogenic bacteria reallocate protein synthesis, focusing more on the translation of genes for the generation of reduced electron carriers via electron bifurcation, rather than on those for carbon metabolism under autotrophic growth.

Electronic supplementary material

The online version of this article (10.1186/s12864-018-5238-0) contains supplementary material, which is available to authorized users.

Context-dependent prediction of protein complexes by SiComPre

Most cellular processes are regulated by groups of proteins interacting together to form protein complexes. Protein compositions vary between different tissues or disease conditions enabling or preventing certain protein-protein interactions and resulting in variations in the complexome. Quantitative and qualitative characterization of context-specific protein complexes will help to better understand context-dependent variations in the physiological behavior of cells. Here, we present SiComPre 1.0, a computational tool that predicts context-specific protein complexes by integrating multi-omics sources. SiComPre outperforms other protein complex prediction tools in qualitative predictions and is unique in giving quantitative predictions on the complexome depending on the specific interactions and protein abundances defined by the user. We provide tutorials and examples on the complexome prediction of common model organisms, various human tissues and how the complexome is affected by drug treatment.

Application of Continuous Culture Methods to Recombinant Protein Production in Microorganisms

Depending on the environmental conditions, cells adapt their metabolism and specific growth rate. Rearrangements occur on many different levels such as macromolecular composition, gene and protein expression, morphology and metabolic flux patterns. As the interplay of these processes also determines the output of a recombinant protein producing system, having control over specific growth rate of the culture is advantageous. Continuous culture methods were developed to grow cells in a constant environment and have been used for decades to study basic microbial physiology in a controlled and reproducible manner. Our review summarizes the uses of continuous cultures in cell physiology studies and process development, with a focus on recombinant protein-producing microorganisms.

Genetic and substrate-level modulation of Bacillus subtilis physiology for enhanced extracellular human interferon gamma production

Human interferon-gamma (hIFNG) production is limited by various gene-level bottlenecks including translation, protein folding, and secretion which depends upon the physiological state of the organism. In this study gene-level and substrate-level modulations have been used to control Bacillus subtilis physiology for >15 fold extracellular soluble hIFNG production. Two variants of the native human interferon-gamma gene (hifng) were designed and synthesized, namely, cohifng_his and cohifng having codon adaptation index 25.33 and 26.89% higher than the native gene, respectively. BScoIFNG and BScoIFNGhis with ΔG of -100.0 and -113.7 kcal mol^-1 resulted in 30 and 6.5% higher hIFNG compared to the native gene in complex medium. BScoIFNG produced 1.53 fold higher hIFNG using glucose-based defined medium as compared to the complex medium by modulating the physiological parameter growth rate from 0.35 to 0.26 hr^-1. Further modulatory effect of various phosphotransferase transport system (PTS) and no-PTS sugars, sugar alcohols, and organic acids was quantified on the physiology of B. subtilis WB800N for extracellular hIFNG production. Sorbitol and glycerol emerged as the best hIFNG producers with lowest growth and substrate consumption rates. BScoIFNG produced maximum 3.15 mg L^-1 hIFNG at 50 g L^-1 glycerol with highest hIFNG yield (Y_p/x = 0.136) and lowest substrate uptake rate (q_s = 0.26).

Cell-free prediction of protein expression costs for growing cells

Translating heterologous proteins places significant burden on host cells, consuming expression resources leading to slower cell growth and productivity. Yet predicting the cost of protein production for any given gene is a major challenge, as multiple processes and factors combine to determine translation efficiency. To enable prediction of the cost of gene expression in bacteria, we describe here a standard cell-free lysate assay that provides a relative measure of resource consumption when a protein coding sequence is expressed. These lysate measurements can then be used with a computational model of translation to predict the in vivo burden placed on growing E. coli cells for a variety of proteins of different functions and lengths. Using this approach, we can predict the burden of expressing multigene operons of different designs and differentiate between the fraction of burden related to gene expression compared to action of a metabolic pathway.

Drug persistence - from antibiotics to cancer therapies

Drug-insensitive tumor subpopulations remain a significant barrier to effective cancer treatment. Recent works suggest that within isogenic drug-sensitive cancer populations, subsets of cells can enter a 'persister' state allowing them to survive prolonged drug treatment. Such persisters are well-described in antibiotic-treated bacterial populations. In this review, we compare mechanisms of drug persistence in bacteria and cancer. Both bacterial and cancer persisters are associated with slow-growing phenotypes, are metabolically distinct from non-persisters, and depend on the activation of specific regulatory programs. Moreover, evidence suggests that bacterial and cancer persisters are an important reservoir for the emergence of drug-resistant mutants. The emerging parallels between persistence in bacteria and cancer can guide efforts to untangle mechanistic links between growth, metabolism, and cellular regulation, and reveal exploitable therapeutic vulnerabilities.

Division rate, cell size and proteome allocation: impact on gene expression noise and implications for the dynamics of genetic circuits

The cell division rate, size and gene expression programmes change in response to external conditions. These global changes impact on average concentrations of biomolecule and their variability or noise. Gene expression is inherently stochastic, and noise levels of individual proteins depend on synthesis and degradation rates as well as on cell-cycle dynamics. We have modelled stochastic gene expression inside growing and dividing cells to study the effect of division rates on noise in mRNA and protein expression. We use assumptions and parameters relevant to Escherichia coli, for which abundant quantitative data are available. We find that coupling of transcription, but not translation rates to the rate of cell division can result in protein concentration and noise homeostasis across conditions. Interestingly, we find that the increased cell size at fast division rates, observed in E. coli and other unicellular organisms, buffers noise levels even for proteins with decreased expression at faster growth. We then investigate the functional importance of these regulations using gene regulatory networks that exhibit bi-stability and oscillations. We find that network topology affects robustness to changes in division rate in complex and unexpected ways. In particular, a simple model of persistence, based on global physiological feedback, predicts increased proportion of persister cells at slow division rates. Altogether, our study reveals how cell size regulation in response to cell division rate could help controlling gene expression noise. It also highlights that understanding circuits' robustness across growth conditions is key for the effective design of synthetic biological systems.

Division rate, cell size and proteome allocation: impact on gene expression noise and implications for the dynamics of genetic circuits

The cell division rate, size and gene expression programmes change in response to external conditions. These global changes impact on average concentrations of biomolecule and their variability or noise. Gene expression is inherently stochastic, and noise levels of individual proteins depend on synthesis and degradation rates as well as on cell-cycle dynamics. We have modelled stochastic gene expression inside growing and dividing cells to study the effect of division rates on noise in mRNA and protein expression. We use assumptions and parameters relevant to Escherichia coli, for which abundant quantitative data are available. We find that coupling of transcription, but not translation rates to the rate of cell division can result in protein concentration and noise homeostasis across conditions. Interestingly, we find that the increased cell size at fast division rates, observed in E. coli and other unicellular organisms, buffers noise levels even for proteins with decreased expression at faster growth. We then investigate the functional importance of these regulations using gene regulatory networks that exhibit bi-stability and oscillations. We find that network topology affects robustness to changes in division rate in complex and unexpected ways. In particular, a simple model of persistence, based on global physiological feedback, predicts increased proportion of persister cells at slow division rates. Altogether, our study reveals how cell size regulation in response to cell division rate could help controlling gene expression noise. It also highlights that understanding circuits' robustness across growth conditions is key for the effective design of synthetic biological systems.

Photoperiodic control of the Arabidopsis proteome reveals a translational coincidence mechanism

Plants respond to seasonal cues such as the photoperiod, to adapt to current conditions and to prepare for environmental changes in the season to come. To assess photoperiodic responses at the protein level, we quantified the proteome of the model plant Arabidopsis thaliana by mass spectrometry across four photoperiods. This revealed coordinated changes of abundance in proteins of photosynthesis, primary and secondary metabolism, including pigment biosynthesis, consistent with higher metabolic activity in long photoperiods. Higher translation rates in the day than the night likely contribute to these changes, via an interaction with rhythmic changes in RNA abundance. Photoperiodic control of protein levels might be greatest only if high translation rates coincide with high transcript levels in some photoperiods. We term this proposed mechanism "translational coincidence", mathematically model its components, and demonstrate its effect on the Arabidopsis proteome. Datasets from a green alga and a cyanobacterium suggest that translational coincidence contributes to seasonal control of the proteome in many phototrophic organisms. This may explain why many transcripts but not their cognate proteins exhibit diurnal rhythms.

The bacterial interlocked process ONtology (BiPON): a systemic multi-scale unified representation of biological processes in prokaryotes

BACKGROUND

High-throughput technologies produce huge amounts of heterogeneous biological data at all cellular levels. Structuring these data together with biological knowledge is a critical issue in biology and requires integrative tools and methods such as bio-ontologies to extract and share valuable information. In parallel, the development of recent whole-cell models using a systemic cell description opened alternatives for data integration. Integrating a systemic cell description within a bio-ontology would help to progress in whole-cell data integration and modeling synergistically.

RESULTS

We present BiPON, an ontology integrating a multi-scale systemic representation of bacterial cellular processes. BiPON consists in of two sub-ontologies, bioBiPON and modelBiPON. bioBiPON organizes the systemic description of biological information while modelBiPON describes the mathematical models (including parameters) associated with biological processes. bioBiPON and modelBiPON are related using bridge rules on classes during automatic reasoning. Biological processes are thus automatically related to mathematical models. 37% of BiPON classes stem from different well-established bio-ontologies, while the others have been manually defined and curated. Currently, BiPON integrates the main processes involved in bacterial gene expression processes.

CONCLUSIONS

BiPON is a proof of concept of the way to combine formally systems biology and bio-ontology. The knowledge formalization is highly flexible and generic. Most of the known cellular processes, new participants or new mathematical models could be inserted in BiPON. Altogether, BiPON opens up promising perspectives for knowledge integration and sharing and can be used by biologists, systems and computational biologists, and the emerging community of whole-cell modeling.

Microenvironmental cooperation promotes early spread and bistability of a Warburg-like phenotype

We introduce an in silico model for the initial spread of an aberrant phenotype with Warburg-like overflow metabolism within a healthy homeostatic tissue in contact with a nutrient reservoir (the blood), aimed at characterizing the role of the microenvironment for aberrant growth. Accounting for cellular metabolic activity, competition for nutrients, spatial diffusion and their feedbacks on aberrant replication and death rates, we obtain a phase portrait where distinct asymptotic whole-tissue states are found upon varying the tissue-blood turnover rate and the level of blood-borne primary nutrient. Over a broad range of parameters, the spreading dynamics is bistable as random fluctuations can impact the final state of the tissue. Such a behaviour turns out to be linked to the re-cycling of overflow products by non-aberrant cells. Quantitative insight on the overall emerging picture is provided by a spatially homogeneous version of the model.

Competition for amino acid flux among translation, growth and detoxification in bacteria

Transfer-tRNAs (tRNAs) are central entities for translation that deliver amino acids to the ribosome to translate genetic information in an mRNA-template dependent manner. Recent discoveries from our laboratory show that in E. coli and B. licheniformis, some tRNAs are poorly charged despite the plentiful intracellular cognate amino acid. Specifically, tRNAs carrying amino acids that exert toxicity and inhibit bacterial growth when added separately to the growth medium are poorly charged. Here, we discuss various evolutionary strategies different bacterial cells have adopted to precisely hone the competition between amino acid utilization for translation and proliferation and combat the inhibitory effect toward maximizing bacterial fitness. These data add a new twist to the amino acid flux models and to our understanding of the complex intimate link between dynamics of translation and bacterial growth.

Few regulatory metabolites coordinate expression of central metabolic genes in Escherichia coli

Transcription networks consist of hundreds of transcription factors with thousands of often overlapping target genes. While we can reliably measure gene expression changes, we still understand relatively little why expression changes the way it does. How does a coordinated response emerge in such complex networks and how many input signals are necessary to achieve it? Here, we unravel the regulatory program of gene expression in Escherichia coli central carbon metabolism with more than 30 known transcription factors. Using a library of fluorescent transcriptional reporters, we comprehensively quantify the activity of central metabolic promoters in 26 environmental conditions. The expression patterns were dominated by growth rate‐dependent global regulation for most central metabolic promoters in concert with highly condition‐specific activation for only few promoters. Using an approximate mathematical description of promoter activity, we dissect the contribution of global and specific transcriptional regulation. About 70% of the total variance in promoter activity across conditions was explained by global transcriptional regulation. Correlating the remaining specific transcriptional regulation of each promoter with the cell's metabolome response across the same conditions identified potential regulatory metabolites. Remarkably, cyclic AMP, fructose‐1,6‐bisphosphate, and fructose‐1‐phosphate alone explained most of the specific transcriptional regulation through their interaction with the two major transcription factors Crp and Cra. Thus, a surprisingly simple regulatory program that relies on global transcriptional regulation and input from few intracellular metabolites appears to be sufficient to coordinate E. coli central metabolism and explain about 90% of the experimentally observed transcription changes in 100 genes.

Large-scale reduction of the Bacillus subtilis genome: consequences for the transcriptional network, resource allocation, and metabolism

Understanding cellular life requires a comprehensive knowledge of the essential cellular functions, the components involved, and their interactions. Minimized genomes are an important tool to gain this knowledge. We have constructed strains of the model bacterium, Bacillus subtilis, whose genomes have been reduced by ∼36%. These strains are fully viable, and their growth rates in complex medium are comparable to those of wild type strains. An in-depth multi-omics analysis of the genome reduced strains revealed how the deletions affect the transcription regulatory network of the cell, translation resource allocation, and metabolism. A comparison of gene counts and resource allocation demonstrates drastic differences in the two parameters, with 50% of the genes using as little as 10% of translation capacity, whereas the 6% essential genes require 57% of the translation resources. Taken together, the results are a valuable resource on gene dispensability in B. subtilis, and they suggest the roads to further genome reduction to approach the final aim of a minimal cell in which all functions are understood.

Mechanisms of In Vivo Ribosome Maintenance Change in Response to Nutrient Signals

Control of protein homeostasis is fundamental to the health and longevity of all organisms. Because the rate of protein synthesis by ribosomes is a central control point in this process, regulation, and maintenance of ribosome function could have amplified importance in the overall regulatory circuit. Indeed, ribosomal defects are commonly associated with loss of protein homeostasis, aging, and disease (1–4), whereas improved protein homeostasis, implying optimal ribosomal function, is associated with disease resistance and increased lifespan (5–7). To maintain a high-quality ribosome population within the cell, dysfunctional ribosomes are targeted for autophagic degradation. It is not known if complete degradation is the only mechanism for eukaryotic ribosome maintenance or if they might also be repaired by replacement of defective components. We used stable-isotope feeding and protein mass spectrometry to measure the kinetics of turnover of ribosomal RNA (rRNA) and 71 ribosomal proteins (r-proteins) in mice. The results indicate that exchange of individual proteins and whole ribosome degradation both contribute to ribosome maintenance in vivo. In general, peripheral r-proteins and those with more direct roles in peptide-bond formation are replaced multiple times during the lifespan of the assembled structure, presumably by exchange with a free cytoplasmic pool, whereas the majority of r-proteins are stably incorporated for the lifetime of the ribosome. Dietary signals impact the rates of both new ribosome assembly and component exchange. Signal-specific modulation of ribosomal repair and degradation could provide a mechanistic link in the frequently observed associations among diminished rates of protein synthesis, increased autophagy, and greater longevity (5, 6, 8, 9).

Depletion of Shine-Dalgarno Sequences Within Bacterial Coding Regions Is Expression Dependent

Efficient and accurate protein synthesis is crucial for organismal survival in competitive environments. Translation efficiency (the number of proteins translated from a single mRNA in a given time period) is the combined result of differential translation initiation, elongation, and termination rates. Previous research identified the Shine-Dalgarno (SD) sequence as a modulator of translation initiation in bacterial genes, while codon usage biases are frequently implicated as a primary determinant of elongation rate variation. Recent studies have suggested that SD sequences within coding sequences may negatively affect translation elongation speed, but this claim remains controversial. Here, we present a metric to quantify the prevalence of SD sequences in coding regions. We analyze hundreds of bacterial genomes and find that the coding sequences of highly expressed genes systematically contain fewer SD sequences than expected, yielding a robust correlation between the normalized occurrence of SD sites and protein abundances across a range of bacterial taxa. We further show that depletion of SD sequences within ribosomal protein genes is correlated with organismal growth rates, supporting the hypothesis of strong selection against the presence of these sequences in coding regions and suggesting their association with translation efficiency in bacteria.

Functional requirements of cellular differentiation: lessons from Bacillus subtilis

Successful execution of differentiation programs requires cells to assess multitudes of internal and external cues and respond with appropriate gene expression programs. Here, we review how Bacillus subtilis sporulation network deals with these tasks focusing on the lessons generalizable to other systems. With feedforward loops controlling both production and activation of downstream transcriptional regulators, cells achieve ultrasensitive threshold-like responses. The arrangement of sporulation network genes on the chromosome and transcriptional feedback loops allow coordination of sporulation decision with DNA-replication. Furthermore, to assess the starvation conditions without sensing specific metabolites, cells respond to changes in their growth rates with increased activity of sporulation master regulator. These design features of the sporulation network enable cells to robustly decide between vegetative growth and sporulation.

A part toolbox to tune genetic expression in Bacillus subtilis

Libraries of well-characterised components regulating gene expression levels are essential to many synthetic biology applications. While widely available for the Gram-negative model bacterium Escherichia coli, such libraries are lacking for the Gram-positive model Bacillus subtilis, a key organism for basic research and biotechnological applications. Here, we engineered a genetic toolbox comprising libraries of promoters, Ribosome Binding Sites (RBS), and protein degradation tags to precisely tune gene expression in B. subtilis. We first designed a modular Expression Operating Unit (EOU) facilitating parts assembly and modifications and providing a standard genetic context for gene circuits implementation. We then selected native, constitutive promoters of B. subtilis and efficient RBS sequences from which we engineered three promoters and three RBS sequence libraries exhibiting ∼14 000-fold dynamic range in gene expression levels. We also designed a collection of SsrA proteolysis tags of variable strength. Finally, by using fluorescence fluctuation methods coupled with two-photon microscopy, we quantified the absolute concentration of GFP in a subset of strains from the library. Our complete promoters and RBS sequences library comprising over 135 constructs enables tuning of GFP concentration over five orders of magnitude, from 0.05 to 700 μM. This toolbox of regulatory components will support many research and engineering applications in B. subtilis.

Translation elicits a growth rate-dependent, genome-wide, differential protein production in Bacillus subtilis

Complex regulatory programs control cell adaptation to environmental changes by setting condition-specific proteomes. In balanced growth, bacterial protein abundances depend on the dilution rate, transcript abundances and transcript-specific translation efficiencies. We revisited the current theory claiming the invariance of bacterial translation efficiency. By integrating genome-wide transcriptome datasets and datasets from a library of synthetic gfp-reporter fusions, we demonstrated that translation efficiencies in Bacillus subtilis decreased up to fourfold from slow to fast growth. The translation initiation regions elicited a growth rate-dependent, differential production of proteins without regulators, hence revealing a unique, hard-coded, growth rate-dependent mode of regulation. We combined model-based data analyses of transcript and protein abundances genome-wide and revealed that this global regulation is extensively used in B. subtilis We eventually developed a knowledge-based, three-step translation initiation model, experimentally challenged the model predictions and proposed that a growth rate-dependent drop in free ribosome abundance accounted for the differential protein production.