Transcriptional response of Escherichia coli to ammonia and glucose fluctuations

Relationship between Nonhepatic Serum Ammonia Levels and Sepsis-Associated Encephalopathy: A Retrospective Cohort Study

Objectives

Nonhepatic hyperammonemia often occurs in patients with sepsis. Ammonia plays an essential role in the occurrence of hepatic encephalopathy. However, the relationship between nonhepatic serum ammonia levels and sepsis-associated encephalopathy (SAE) remains unclear. Thus, we aimed to evaluate the association between serum ammonia levels and patients with SAE.

Methods

Data of critically ill adults with sepsis who were admitted to the intensive care unit were retrieved from the Medical Information Mart for Intensive Care IV (MIMIC IV) between 2008 and 2019 and retrospectively analyzed. Data of patients with sepsis patients and serum ammonia not related to acute or chronic liver disease were not included.

Results

Data from 720 patients with sepsis were included. SAE was found to have a high incidence (64.6%). After adjusting for other risk factors, a serum ammonia level of ≥45 μmol/L (odds ratio (OR): 3.508, 95% confidence interval (CI): 2.336-5.269, p < 0.001) was found to be an independent risk factor for patients with SAE; moreover, as the serum ammonia level increased, the hospital mortality of SAE gradually increased in a certain range (serum ammonia <150 μmol/L). Serum ammonia levels of ≥45 μmol/L were associated with higher Simplified Acute Physiology Score II and Sequential Organ Failure Assessment (SOFA) scores in patients with SAE. Besides, our study found that patients with SAE used opioid analgesics (OR:3.433, 95% CI: 1.360-8.669, p = 0.009) and the SOFA scores of patients with SAE (OR: 1.126, 95% CI: 1.062-1.194, p < 0.001) were significantly higher than those without SAE.

Conclusions

Nonhepatic serum ammonia levels of ≥45 μmol/L evidently increased the incidence of SAE. Serum ammonia levels should be closely monitored in patients with sepsis.

Impact of mixing insufficiencies on L-phenylalanine production with an Escherichia coli reporter strain in a novel two-compartment bioreactor

BACKGROUND

The omnipresence of population heterogeneity in industrial bioprocesses originates from prevailing dynamic bioprocess conditions, which promote differences in the expression of cellular characteristics. Despite the awareness, the concrete consequences of this phenomenon remain poorly understood.

RESULTS

Therefore, for the first time, a L-phenylalanine overproducing Escherichia coli quadruple reporter strain was established for monitoring of general stress response, growth behavior, oxygen limitation and product formation of single cells based on mTagBFP2, mEmerald, CyOFP1, and mCardinal2 expression measured by flow cytometry. This strain was applied for the fed-batch production of L-phenylalanine from glycerol and ammonia in a stirred-tank bioreactor at homogeneous conditions compared to the same process in a novel two-compartment bioreactor. This two-compartment bioreactor consists of a stirred-tank bioreactor with an initial volume of 0.9 L (homogeneous zone) with a coiled flow inverter with a fixed working volume of 0.45 L as a bypass (limitation zone) operated at a mean hydraulic residence time of 102 s. The product formation was similar in both bioreactor setups with maximum L-phenylalanine concentrations of 21.1 ± 0.6 g L-1 demonstrating the consistency of this study's microbial L-phenylalanine production. However, cell growth was vulnerable to repetitive exposure to the dynamically changing conditions in the two-compartment bioreactor with maximum biomass yields reduced by 21%. The functionality of reporter molecules was approved in the stirred-tank bioreactor cultivation, in which expressed fluorescence levels of all four markers were in accordance with respective process state variables. Additional evaluation of the distributions on single-cell level revealed the presence of population heterogeneity in both bioprocesses. Especially for the marker of the general stress response and the product formation, the corresponding histograms were characterized by bimodal shapes and broad distributions. These phenomena were pronounced particularly at the beginning and the end of the fed-batch process.

CONCLUSIONS

The here shown findings confirm multiple reporter strains to be a noninvasive tool for monitoring cellular characteristics and identifying potential subpopulations in bioprocesses. In combination with experiments in scale-down setups, these can be utilized for a better physiological understanding of bioprocesses and support future scale-up procedures.

A Systems Biology Approach To Disentangle the Direct and Indirect Effects of Global Transcription Factors on Gene Expression in Escherichia coli

Delineating the pleiotropic effects of global transcriptional factors (TFs) is critical for understanding the system-wide regulatory response in a particular environment. Currently, with the availability of genome-wide TF binding and gene expression data for Escherichia coli, several gene targets can be assigned to the global TFs, albeit inconsistently. Here, using a systematic integrated approach with emphasis on metabolism, we characterized and quantified the direct effects as well as the growth rate-mediated indirect effects of global TFs using deletion mutants of FNR, ArcA, and IHF regulators (focal TFs) under glucose fermentative conditions. This categorization enabled us to disentangle the dense connections seen within the transcriptional regulatory network (TRN) and determine the exact nature of focal TF-driven epistatic interactions with other global and pathway-specific local regulators (iTFs). We extended our analysis to combinatorial deletions of these focal TFs to determine their cross talk effects as well as conserved patterns of regulatory interactions. Moreover, we predicted with high confidence several novel metabolite-iTF interactions using inferred iTF activity changes arising from the allosteric effects of the intracellular metabolites perturbed as a result of the absence of focal TFs. Further, using compendium level computational analyses, we revealed not only the coexpressed genes regulated by these focal TFs but also the coordination of the direct and indirect target expression in the context of the economy of intracellular metabolites. Overall, this study leverages the fundamentals of TF-driven regulation, which could serve as a better template for deciphering mechanisms underlying complex phenotypes. IMPORTANCE Understanding the pleiotropic effects of global TFs on gene expression and their relevance underlying a specific response in a particular environment has been challenging. Here, we distinguish the TF-driven direct effects and growth rate-mediated indirect effects on gene expression using single- and double-deletion mutants of FNR, ArcA, and IHF regulators under anaerobic glucose fermentation. Such dissection assists us in unraveling the precise nature of interactions existing between the focal TF(s) and several other TFs, including those altered by allosteric effects of intracellular metabolites. We were able to recapitulate the previously known metabolite-TF interactions and predict novel interactions with high confidence. Furthermore, we determined that the direct and indirect gene expression have a strong connection with each other when analyzed using the coexpressed- or coregulated-gene approach. Deciphering such regulatory patterns explicitly from the metabolism point of view would be valuable in understanding other unpredicted complex regulation existing in nature.

Impact of Elevated Levels of Dissolved CO2 on Performance and Proteome Response of an Industrial 2′-Fucosyllactose Producing Escherichia coli Strain

Large-scale microbial industrial fermentations have significantly higher absolute pressure and dissolved CO₂ concentrations than otherwise comparable laboratory-scale processes. Yet the effect of increased dissolved CO₂ (dCO₂) levels is rarely addressed in the literature. In the current work, we have investigated the impact of industrial levels of dCO₂ (measured as the partial pressure of CO₂, pCO₂) in an Escherichia coli-based fed-batch process producing the human milk oligosaccharide 2′-fucosyllactose (2′-FL). The study evaluated the effect of high pCO₂ levels in both carbon-limited (C-limited) and carbon/nitrogen-limited (C/N-limited) fed-batch processes. High-cell density cultures were sparged with 10%, 15%, 20%, or 30% CO₂ in the inlet air to cover and exceed the levels observed in the industrial scale process. While the 10% enrichment was estimated to achieve similar or higher pCO₂ levels as the large-scale fermentation it did not impact the performance of the process. The product and biomass yields started being affected above 15% CO₂ enrichment, while 30% impaired the cultures completely. Quantitative proteomics analysis of the C-limited process showed that 15% CO₂ enrichment affected the culture on the protein level, but to a much smaller degree than expected. A more significant impact was seen in the dual C/N limited process, which likely stemmed from the effect pCO₂ had on nitrogen availability. The results demonstrated that microbial cultures can be seriously affected by elevated CO₂ levels, albeit at higher levels than expected.

Application of an Escherichia coli triple reporter strain for at-line monitoring of single-cell physiology during L-phenylalanine production

Biotechnological production processes are sustainable approaches for the production of biobased components such as amino acids for food and feed industry. Scale-up from ideal lab-scale bioreactors to large-scale processes is often accompanied by loss in productivity. This may be related to population heterogeneities of cells originating from isogenic cultures that arise due to dynamic non-ideal conditions in the bioreactor. To better understand this phenomenon, deeper insights into single-cell physiologies in bioprocesses are mandatory before scale-up. Here, a triple reporter strain (3RP) was developed by chromosomally integrating the fluorescent proteins mEmerald, CyOFP1, and mTagBFP2 into the L-phenylalanine producing Escherichia coli strain FUS4 (pF81_kan) to allow monitoring of growth, oxygen availability, and general stress response of the single cells. Functionality of the 3RP was confirmed in well-mixed lab-scale fed-batch processes with glycerol as carbon source in comparison to the strain without fluorescent proteins, leading to no difference in process performance. Fluorescence levels could successfully reflect the course of related process state variables, revealed population heterogeneities during the transition between different process phases and potentially subpopulations that exhibit superior process performance. Furthermore, indications were found for noise in gene expression as regulation strategy against environmental perturbation.

Live-Cell Imaging of Guanosine Tetra- and Pentaphosphate (p)ppGpp with RNA-based Fluorescent Sensors*

Guanosine tetra- and pentaphosphate, (p)ppGpp, are important alarmone nucleotides that regulate bacterial survival in stressful environment. A direct detection of (p)ppGpp in living cells is critical for our understanding of the mechanism of bacterial stringent response. However, it is still challenging to image cellular (p)ppGpp. Here, we report RNA-based fluorescent sensors for the live-cell imaging of (p)ppGpp. Our sensors are engineered by conjugating a recently identified (p)ppGpp-specific riboswitch with a fluorogenic RNA aptamer, Broccoli. These sensors can be genetically encoded and enable direct monitoring of cellular (p)ppGpp accumulation. Unprecedented information on cell-to-cell variation and cellular dynamics of (p)ppGpp levels is now obtained under different nutritional conditions. These RNA-based sensors can be broadly adapted to study bacterial stringent response.

Engineering of a robust Escherichia coli chassis and exploitation for large-scale production processes

In large-scale bioprocesses microbes are exposed to heterogeneous substrate availability reducing the overall process performance. A series of deletion strains was constructed from E. coli MG1655 aiming for a robust phenotype in heterogeneous fermentations with transient starvation. Deletion targets were hand-picked based on a list of genes derived from previous large-scale simulation runs. Each gene deletion was conducted on the premise of strict neutrality towards growth parameters in glucose minimal medium. The final strain of the series, named E. coli RM214, was cultivated continuously in an STR-PFR (stirred tank reactor - plug flow reactor) scale-down reactor. The scale-down reactor system simulated repeated passages through a glucose starvation zone. When exposed to nutrient gradients, E. coli RM214 had a significantly lower maintenance coefficient than E. coli MG1655 (Δm_s = 0.038 g_Glucose/g_CDW/h, p < 0.05). In an exemplary protein production scenario E. coli RM214 remained significantly more productive than E. coli MG1655 reaching 44% higher eGFP yield after 28 h of STR-PFR cultivation. This study developed E. coli RM214 as a robust chassis strain and demonstrated the feasibility of engineering microbial hosts for large-scale applications.

Transcriptional profiling of the stringent response mutant strain E. coli SR reveals enhanced robustness to large-scale conditions

In large-scale fed-batch production processes, microbes are exposed to heterogeneous substrate availability caused by long mixing times. Escherichia coli, the most common industrial host for recombinant protein production, reacts by recurring accumulation of the alarmone ppGpp and energetically wasteful transcriptional strategies. Here, we compare the regulatory responses of the stringent response mutant strain E. coli SR and its parent strain E. coli MG1655 to repeated nutrient starvation in a two-compartment scale-down reactor. Our data show that E. coli SR can withstand these stress conditions without a ppGpp-mediated stress response maintaining fully functional ammonium uptake and biomass formation. Furthermore, E. coli SR exhibited a substantially reduced short-term transcriptional response compared to E. coli MG1655 (less than half as many differentially expressed genes). E. coli SR proceeded adaptation via more general SOS response pathways by initiating negative regulation of transcription, translation and cell division. Our results show that locally induced stress responses propagating through the bioreactor do not result in cyclical induction and repression of genes in E. coli SR, but in a reduced and coordinated response, which makes it potentially suitable for large-scale production processes.

Euler-Lagrangian Simulations: A Proper Tool for Predicting Cellular Performance in Industrial Scale Bioreactors

Eulerian-Lagrangian approach to investigate cellular responses in a bioreactor has become the center of attention in recent years. It was introduced to biotechnological processes about two decades ago, but within the last few years, it proved itself as a powerful tool to address scale-up and -down topics of bioprocesses. It can capture the history of a cell and reveal invaluable information for, not only, bioprocess control and design but also strain engineering. This way it will be possible to shed light on the actual environment that cell experiences throughout its lifespan. Lifelines of a microorganism in a bioreactor can serve as the missing link that encompasses the biological timescales and the physical timescales. For this purpose digitalization of bioreactors provides us with new insights that are not achievable in industrial reactors easily if at all, namely, substrate and product gradients; high-shear regions are among the most interesting factors that can be reproduced adequately with help of a digital twin. In this chapter basic principles of this method will be introduced, and later on some practical aspects of particle tracking technique will be illustrated. In the final section, some of the advantages and challenges associated with this method will be discussed.

Impact of Altered Trehalose Metabolism on Physiological Response of Penicillium chrysogenum Chemostat Cultures during Industrially Relevant Rapid Feast/Famine Conditions

Due to insufficient mass transfer and mixing issues, cells in the industrial-scale bioreactor are often forced to experience glucose feast/famine cycles, mostly resulting in reduced commercial metrics (titer, yield and productivity). Trehalose cycling has been confirmed as a double-edged sword in the Penicillium chrysogenum strain, which facilitates the maintenance of a metabolically balanced state, but it consumes extra amounts of the ATP responsible for the repeated breakdown and formation of trehalose molecules in response to extracellular glucose perturbations. This loss of ATP would be in competition with the high ATP-demanding penicillin biosynthesis. In this work, the role of trehalose metabolism was further explored under industrially relevant conditions by cultivating a high-yielding Penicillium chrysogenum strain, and the derived trehalose-null strains in the glucose-limited chemostat system where the glucose feast/famine condition was imposed. This dynamic feast/famine regime with a block-wise feed/no feed regime (36 s on, 324 s off) allows one to generate repetitive cycles of moderate changes in glucose availability. The results obtained using quantitative metabolomics and stoichiometric analysis revealed that the intact trehalose metabolism is vitally important for maintaining penicillin production capacity in the Penicillium chrysogenum strain under both steady state and dynamic conditions. Additionally, cells lacking such a key metabolic regulator would become more sensitive to industrially relevant conditions, and are more able to sustain metabolic rearrangements, which manifests in the shrinkage of the central metabolite pool size and the formation of ATP-consuming futile cycles.

Potential of Integrating Model-Based Design of Experiments Approaches and Process Analytical Technologies for Bioprocess Scale-Down

Typically, bioprocesses on an industrial scale are dynamic systems with a certain degree of variability, system inhomogeneities, and even population heterogeneities. Therefore, the scaling of such processes from laboratory to industrial scale and vice versa is not a trivial task. Traditional scale-down methodologies consider several technical parameters, so that systems on the laboratory scale tend to qualitatively reflect large-scale effects, but not the dynamic situation in an industrial bioreactor over the entire process, from the perspective of a cell. Supported by the enormous increase in computing power, the latest scientific focus is on the application of dynamic models, in combination with computational fluid dynamics to quantitatively describe cell behavior. These models allow the description of possible cellular lifelines which in turn can be used to derive a regime analysis for scale-down experiments. However, the approaches described so far, which were for a very few process examples, are very labor- and time-intensive and cannot be validated easily. In parallel, alternatives have been developed based on the description of the industrial process with hybrid process models, which describe a process mechanistically as far as possible in order to determine the essential process parameters with their respective variances. On-line analytical methods allow the characterization of population heterogeneity directly in the process. This detailed information from the industrial process can be used in laboratory screening systems to select relevant conditions in which the cell and process related parameters reflect the situation in the industrial scale. In our opinion, these technologies, which are available in research for modeling biological systems, in combination with process analytical techniques are so far developed that they can be implemented in industrial routines for faster development of new processes and optimization of existing ones.

Understanding gradients in industrial bioreactors

Gradients in industrial bioreactors have attracted substantial research attention since exposure to fluctuating environmental conditions has been shown to lead to changes in the metabolome, transcriptome as well as population heterogeneity in industrially relevant microorganisms. Such changes have also been found to impact key process parameters like the yield on substrate and the productivity. Hence, understanding gradients is important from both the academic and industrial perspectives. In this review the causes of gradients are outlined, along with their impact on microbial physiology. Quantifying the impact of gradients requires a detailed understanding of both fluid flow inside industrial equipment and microbial physiology. This review critically examines approaches used to investigate gradients including large-scale experimental work, computational methods and scale-down approaches. Avenues for future work have been highlighted, particularly the need for further coordinated development of both in silico and experimental tools which can be used to further the current understanding of gradients in industrial equipment.

Pseudomonas putida KT2440 is naturally endowed to withstand industrial-scale stress conditions

Pseudomonas putida is recognized as a very promising strain for industrial application due to its high redox capacity and frequently observed tolerance towards organic solvents. In this research, we studied the metabolic and transcriptional response of P. putida KT2440 exposed to large-scale heterogeneous mixing conditions in the form of repeated glucose shortage. Cellular responses were mimicked in an experimental setup comprising a stirred tank reactor and a connected plug flow reactor. We deciphered that a stringent response-like transcriptional regulation programme is frequently induced, which seems to be linked to the intracellular pool of 3-hydroxyalkanoates (3-HA) that are known to serve as precursors for polyhydroxyalkanoates (PHA). To be precise, P. putida is endowed with a survival strategy likely to access cellular PHA, amino acids and glycogen in few seconds under glucose starvation to obtain ATP from respiration, thereby replenishing the reduced ATP levels and the adenylate energy charge. Notably, cells only need 0.4% of glucose uptake to build those 3-HA-based energy buffers. Concomitantly, genes that are related to amino acid catabolism and β-oxidation are upregulated during the transient absence of glucose. Furthermore, we provide a detailed list of transcriptional short- and long-term responses that increase the cellular maintenance by about 17% under the industrial-like conditions tested.

Development and characterization of Escherichia coli triple reporter strains for investigation of population heterogeneity in bioprocesses

Background

Today there is an increasing demand for high yielding robust and cost efficient biotechnological production processes. Although cells in these processes originate from isogenic cultures, heterogeneity induced by intrinsic and extrinsic influences is omnipresent. To increase understanding of this mechanistically poorly understood phenomenon, advanced tools that provide insights into single cell physiology are needed.

Results

Two Escherichia coli triple reporter strains have been designed based on the industrially relevant production host E. coli BL21(DE3) and a modified version thereof, E. coli T7E2. The strains carry three different fluorescence proteins chromosomally integrated. Single cell growth is followed with EmeraldGFP (EmGFP)-expression together with the ribosomal promoter rrnB. General stress response of single cells is monitored by expression of sigma factor rpoS with mStrawberry, whereas expression of the nar-operon together with TagRFP657 gives information about oxygen limitation of single cells. First, the strains were characterized in batch operated stirred-tank bioreactors in comparison to wildtype E. coli BL21(DE3). Afterwards, applicability of the triple reporter strains for investigation of population heterogeneity in bioprocesses was demonstrated in continuous processes in stirred-tank bioreactors at different growth rates and in response to glucose and oxygen perturbation simulating gradients on industrial scale. Population and single cell level physiology was monitored evaluating general physiology and flow cytometry analysis of fluorescence distributions of the triple reporter strains. Although both triple reporter strains reflected physiological changes that were expected based on the expression characteristics of the marker proteins, the triple reporter strain based on E. coli T7E2 showed higher sensitivity in response to environmental changes. For both strains, noise in gene expression was observed during transition from phases of non-growth to growth. Apparently, under some process conditions, e.g. the stationary phase in batch cultures, the fluorescence response of EmGFP and mStrawberry is preserved, whereas TagRFP657 showed a distinct response.

Conclusions

Single cell growth, general stress response and oxygen limitation of single cells could be followed using the two triple reporter strains developed in this study. They represent valuable tools to study population heterogeneity in bioprocesses significantly increasing the level of information compared to the use of single reporter strains.

Coupled metabolic-hydrodynamic modeling enabling rational scale-up of industrial bioprocesses

Metabolomics aims to address what and how regulatory mechanisms are coordinated to achieve flux optimality, different metabolic objectives as well as appropriate adaptations to dynamic nutrient availability. Recent decades have witnessed that the integration of metabolomics and fluxomics within the goal of synthetic biology has arrived at generating the desired bioproducts with improved bioconversion efficiency. Absolute metabolite quantification by isotope dilution mass spectrometry represents a functional readout of cellular biochemistry and contributes to the establishment of metabolic (structured) models required in systems metabolic engineering. In industrial practices, population heterogeneity arising from fluctuating nutrient availability frequently leads to performance losses, that is reduced commercial metrics (titer, rate, and yield). Hence, the development of more stable producers and more predictable bioprocesses can benefit from a quantitative understanding of spatial and temporal cell-to-cell heterogeneity within industrial bioprocesses. Quantitative metabolomics analysis and metabolic modeling applied in computational fluid dynamics (CFD)-assisted scale-down simulators that mimic industrial heterogeneity such as fluctuations in nutrients, dissolved gases, and other stresses can procure informative clues for coping with issues during bioprocessing scale-up. In previous studies, only limited insights into the hydrodynamic conditions inside the industrial-scale bioreactor have been obtained, which makes case-by-case scale-up far from straightforward. Tracking the flow paths of cells circulating in large-scale bioreactors is a highly valuable tool for evaluating cellular performance in production tanks. The "lifelines" or "trajectories" of cells in industrial-scale bioreactors can be captured using Euler-Lagrange CFD simulation. This novel methodology can be further coupled with metabolic (structured) models to provide not only a statistical analysis of cell lifelines triggered by the environmental fluctuations but also a global assessment of the metabolic response to heterogeneity inside an industrial bioreactor. For the future, the industrial design should be dependent on the computational framework, and this integration work will allow bioprocess scale-up to the industrial scale with an end in mind.

Engineering Robust Production Microbes for Large-Scale Cultivation

Systems biology and synthetic biology are increasingly used to examine and modulate complex biological systems. As such, many issues arising during scaling-up microbial production processes can be addressed using these approaches. We review differences between laboratory-scale cultures and larger-scale processes to provide a perspective on those strain characteristics that are especially important during scaling. Systems biology has been used to examine a range of microbial systems for their response in bioreactors to fluctuations in nutrients, dissolved gases, and other stresses. Synthetic biology has been used both to assess and modulate strain response, and to engineer strains to improve production. We discuss these approaches and tools in the context of their use in engineering robust microbes for applications in large-scale production.

Absolute Quantification of ppGpp and pppGpp by Double-Spike Isotope Dilution Ion Chromatography-High-Resolution Mass Spectrometry

Guanosine 5'-diphosphate 3'-diphosphate (ppGpp) and guanosine 5'-triphosphate 3'-diphosphate (pppGpp) play a central role in the adaptation of bacterial and plant cells to nutritional and environmental stresses and in bacterial resistance to antibiotics. These compounds have historically been detected and quantified by two-dimensional thin-layer chromatography of ³²P-radiolabeled nucleotides. We report a new method to quantify ppGpp and pppGpp in complex biochemical matrix using ion chromatography coupled to high-resolution mass spectrometry. The method is based on isotopic dilution mass spectrometry (IDMS) using ¹³C to accurately quantify the nucleotides. However, the loss of a phosphate group from pppGpp during the sample preparation process results in the erroneous quantification of ppGpp. This bias was corrected by adding an extra ¹⁵N isotope dilution dimension. This double-spike IDMS method was applied to quantify the ppGpp and pppGpp in Escherichia coli and in a mutant strain deleted for gppA (encoding the ppGpp phosphohydrolase) before and after exposure of both strains to serine hydroxamate, known to trigger the accumulation of these nucleotides.

In Silico Prediction of Large-Scale Microbial Production Performance: Constraints for Getting Proper Data-Driven Models

Industrial bioreactors range from 10.000 to 700.000 L and characteristically show different zones of substrate availabilities, dissolved gas concentrations and pH values reflecting physical, technical and economic constraints of scale-up. Microbial producers are fluctuating inside the bioreactors thereby experiencing frequently changing micro-environmental conditions. The external stimuli induce responses on microbial metabolism and on transcriptional regulation programs. Both may deteriorate the expected microbial production performance in large scale compared to expectations deduced from ideal, well-mixed lab-scale conditions. Accordingly, predictive tools are needed to quantify large-scale impacts considering bioreactor heterogeneities. The review shows that the time is right to combine simulations of microbial kinetics with calculations of large-scale environmental conditions to predict the bioreactor performance. Accordingly, basic experimental procedures and computational tools are presented to derive proper microbial models and hydrodynamic conditions, and to link both for bioreactor modeling. Particular emphasis is laid on the identification of gene regulatory networks as the implementation of such models will surely gain momentum in future studies.

Population heterogeneity in microbial bioprocesses: origin, analysis, mechanisms, and future perspectives

Population heterogeneity is omnipresent in all bioprocesses even in homogenous environments. Its origin, however, is only so well understood that potential strategies like bet-hedging, noise in gene expression and division of labour that lead to population heterogeneity can be derived from experimental studies simulating the dynamics in industrial scale bioprocesses. This review aims at summarizing the current state of the different parts of single cell studies in bioprocesses. This includes setups to visualize different phenotypes of single cells, computational approaches connecting single cell physiology with environmental influence and special cultivation setups like scale-down reactors that have been proven to be useful to simulate large-scale conditions. A step in between investigation of populations and single cells is studying subpopulations with distinct properties that differ from the rest of the population with sub-omics methods which are also presented here. Moreover, the current knowledge about population heterogeneity in bioprocesses is summarized for relevant industrial production hosts and mixed cultures, as they provide the unique opportunity to distribute metabolic burden and optimize production processes in a way that is impossible in traditional monocultures. In the end, approaches to explain the underlying mechanism of population heterogeneity and the evidences found to support each hypothesis are presented. For instance, population heterogeneity serving as a bet-hedging strategy that is used as coordinated action against bioprocess-related stresses while at the same time spreading the risk between individual cells as it ensures the survival of least a part of the population in any environment the cells encounter.

The effect of metabolic stress on genome stability of a synthetic biology chassis Escherichia coli K12 strain

Background

Synthetic organism-based biotechnologies are increasingly being proposed for environmental applications, such as in situ sensing. Typically, the novel function of these organisms is delivered by compiling genetic fragments in the genome of a chassis organism. To behave predictably, these chassis are designed with reduced genomes that minimize biological complexity. However, in these proposed applications it is expected that even when contained within a device, organisms will be exposed to fluctuating, often stressful, conditions and it is not clear whether their genomes will retain stability.

Results

Here we employed a chemostat design which enabled us to maintained two strains of E. coli K12 under sustained starvation stress: first the reduced genome synthetic biology chassis MDS42 and then, the control parent strain MG1655. We estimated mutation rates and utilised them as indicators of an increase in genome instability. We show that within 24 h the spontaneous mutation rate had increased similarly in both strains, destabilizing the genomes. High rates were maintained for the duration of the experiment. Growth rates of a cohort of randomly sampled mutants from both strains were utilized as a proxy for emerging phenotypic, and by association genetic variation. Mutant growth rates were consistently less than rates in non-mutants, an indicator of reduced fitness and the presence of mildly deleterious mutations in both the strains. In addition, the effect of these mutations on the populations as a whole varied by strain.

Conclusions

Overall, this study shows that genome reductions in the MDS42 did not stabilize the chassis under metabolic stress. Over time, this could compromise the effectiveness of synthetic organisms built on chassis in environmental applications.

Electronic supplementary material

The online version of this article (10.1186/s12934-018-0858-2) contains supplementary material, which is available to authorized users.

Comparative performance of different scale-down simulators of substrate gradients in Penicillium chrysogenum cultures: the need of a biological systems response analysis

In a 54 m³ large‐scale penicillin fermentor, the cells experience substrate gradient cycles at the timescales of global mixing time about 20–40 s. Here, we used an intermittent feeding regime (IFR) and a two‐compartment reactor (TCR) to mimic these substrate gradients at laboratory‐scale continuous cultures. The IFR was applied to simulate substrate dynamics experienced by the cells at full scale at timescales of tens of seconds to minutes (30 s, 3 min and 6 min), while the TCR was designed to simulate substrate gradients at an applied mean residence time (τc) of 6 min. A biological systems analysis of the response of an industrial high‐yielding P. chrysogenum strain has been performed in these continuous cultures. Compared to an undisturbed continuous feeding regime in a single reactor, the penicillin productivity (q_PenG) was reduced in all scale‐down simulators. The dynamic metabolomics data indicated that in the IFRs, the cells accumulated high levels of the central metabolites during the feast phase to actively cope with external substrate deprivation during the famine phase. In contrast, in the TCR system, the storage pool (e.g. mannitol and arabitol) constituted a large contribution of carbon supply in the non‐feed compartment. Further, transcript analysis revealed that all scale‐down simulators gave different expression levels of the glucose/hexose transporter genes and the penicillin gene clusters. The results showed that q_PenG did not correlate well with exposure to the substrate regimes (excess, limitation and starvation), but there was a clear inverse relation between q_PenG and the intracellular glucose level.

Integrating metabolic modeling and population heterogeneity analysis into optimizing recombinant protein production by Komagataella (Pichia) pastoris

The methylotrophic yeast Komagataella (Pichia) pastoris has become one of the most utilized cell factories for the production of recombinant proteins over the last three decades. This success story is linked to its specific physiological traits, i.e., the ability to grow at high cell density in inexpensive culture medium and to secrete proteins at high yield. Exploiting methanol metabolism is at the core of most P. pastoris-based processes but comes with its own challenges. Co-feeding cultures with glycerol/sorbitol and methanol is a promising approach, which can benefit from improved understanding and prediction of metabolic response. The development of profitable processes relies on the construction and selection of efficient producing strains from less efficient ones but also depends on the ability to master the bioreactor process itself. More specifically, how a bioreactor processes could be monitored and controlled to obtain high yield of production. In this review, new perspectives are detailed regarding a multi-faceted approach to recombinant protein production processes by P. pastoris; including gaining improved understanding of the metabolic pathways involved, accounting for variations in transcriptional and translational efficiency at the single cell level and efficient monitoring and control of methanol levels at the bioreactor level.

Bioprocess scale-up/down as integrative enabling technology: from fluid mechanics to systems biology and beyond

Efficient optimization of microbial processes is a critical issue for achieving a number of sustainable development goals, considering the impact of microbial biotechnology in agrofood, environment, biopharmaceutical and chemical industries. Many of these applications require scale-up after proof of concept. However, the behaviour of microbial systems remains unpredictable (at least partially) when shifting from laboratory-scale to industrial conditions. The need for robust microbial systems is thus highly needed in this context, as well as a better understanding of the interactions between fluid mechanics and cell physiology. For that purpose, a full scale-up/down computational framework is already available. This framework links computational fluid dynamics (CFD), metabolic flux analysis and agent-based modelling (ABM) for a better understanding of the cell lifelines in a heterogeneous environment. Ultimately, this framework can be used for the design of scale-down simulators and/or metabolically engineered cells able to cope with environmental fluctuations typically found in large-scale bioreactors. However, this framework still needs some refinements, such as a better integration of gas-liquid flows in CFD, and taking into account intrinsic biological noise in ABM.

Repetitive Short-Term Stimuli Imposed in Poor Mixing Zones Induce Long-Term Adaptation of E. coli Cultures in Large-Scale Bioreactors: Experimental Evidence and Mathematical Model

Rapidly changing concentrations of substrates frequently occur during large-scale microbial cultivations. These changing conditions, caused by large mixing times, result in a heterogeneous population distribution. Here, we present a powerful and efficient modeling approach to predict the influence of varying substrate levels on the transcriptional and translational response of the cell. This approach consists of two parts, a single-cell model to describe transcription and translation for an exemplary operon (trp operon) and a second part to characterize cell distribution during the experimental setup. Combination of both models enables prediction of transcriptional patterns for the whole population. In summary, the resulting model is not only able to anticipate the experimentally observed short-term and long-term transcriptional response, it further allows envision of altered protein levels. Our model shows that locally induced stress responses propagate throughout the bioreactor, resulting in temporal, and spatial population heterogeneity. Stress induced transcriptional response leads to a new population steady-state shortly after imposing fluctuating substrate conditions. In contrast, the protein levels take more than 10 h to achieve steady-state conditions.

Scale-up/Scale-down of microbial bioprocesses: a modern light on an old issue

The bio‐economy is in transit from innovation to commercialization. The bioprocess industry is expected to increasingly deliver bio‐products to the market, in large amounts, at high quality and at competitive cost levels. This requires flawless start‐up of new large‐scale bioprocesses and continuous improvement of running processes. Fermentation scale‐up and operation can benefit from recent advances in three areas: 1. computation‐driven design of scale‐down simulators, 2. omics‐driven metabolic engineering and 3. sensing and understanding of population heterogeneity. Integration of these fields requires a unified computational approach, linked to big data and simulated reality frameworks, of which the contours are becoming visible today.

Transcriptional response of Escherichia coli to ammonia and glucose fluctuations

In large‐scale production processes, metabolic control is typically achieved by limited supply of essential nutrients such as glucose or ammonia. With increasing bioreactor dimensions, microbial producers such as Escherichia coli are exposed to changing substrate availabilities due to limited mixing. In turn, cells sense and respond to these dynamic conditions leading to frequent activation of their regulatory programmes. Previously, we characterized short‐ and long‐term strategies of cells to adapt to glucose fluctuations. Here, we focused on fluctuating ammonia supply while studying a continuously running two‐compartment bioreactor system comprising a stirred tank reactor (STR) and a plug‐flow reactor (PFR). The alarmone ppGpp rapidly accumulated in PFR, initiating considerable transcriptional responses after 70 s. About 400 genes were repeatedly switched on/off when E. coli returned to the STR. E. coli revealed highly diverging long‐term transcriptional responses in ammonia compared to glucose fluctuations. In contrast, the induction of stringent regulation was a common feature of both short‐term responses. Cellular ATP demands for coping with fluctuating ammonia supply were found to increase maintenance by 15%. The identification of genes contributing to the increased ATP demand together with the elucidation of regulatory mechanisms may help to create robust cells and processes for large‐scale application.

Switching between nitrogen and glucose limitation: Unraveling transcriptional dynamics in Escherichia coli

Transcriptional control under nitrogen and carbon-limitation conditions have been well analyzed for Escherichia coli. However, the transcriptional dynamics that underlie the shift in regulatory programs from nitrogen to carbon limitation is not well studied. In the present study, cells were cultivated at steady state under nitrogen (ammonia)-limited conditions then shifted to carbon (glucose) limitation to monitor changes in transcriptional dynamics. Nitrogen limitation was found to be dominated by sigma 54 (RpoN) and sigma 38 (RpoS), whereas the "housekeeping" sigma factor 70 (RpoD) and sigma 38 regulate cellular status under glucose limitation. During the transition, nitrogen-mediated control was rapidly redeemed and mRNAs that encode active uptake systems, such as ptsG and manXYZ, were quickly amplified. Next, genes encoding facilitators such as lamB were overexpressed, followed by high affinity uptake systems such as mglABC and non-specific porins such as ompF. These regulatory programs are complex and require well-equilibrated and superior control. At the metabolome level, 2-oxoglutarate is the likely component that links carbon- and nitrogen-mediated regulation by interacting with major regulatory elements. In the case of dual glucose and ammonia limitation, sigma 24 (RpoE) appears to play a key role in orchestrating these complex regulatory networks.