Microbial heterogeneity affects bioprocess robustness: Dynamic single-cell analysis contributes to understanding of microbial populations

Importance of the cultivation history for the response of Escherichia coli to oscillations in scale-down experiments

Large-scale bioreactors are inhomogeneous systems, in which the fluid phase expresses concentration gradients. They depend on the mass transfer and fluid dynamics in the reactor, the feeding strategy, the cell-specific substrate uptake parameters, and the cell density. As high cell densities are only obtained at low specific growth rates, it is necessary to investigate the cellular responses to oscillations in particular under such conditions, an issue which is mostly neglected. Instead, the feed oscillations are often started directly after the batch phase, when the specific growth rate is close to the maximum. We show here that the cultivation mode before oscillations are started has a tremendous effect on the metabolic responses. In difference to cells, which were pre-grown under batch conditions at a high growth rate, Escherichia coli cells that were pre-grown under glucose limitation at a low growth rate accumulate short-chain fatty acids (acetate, lactate, succinate) and glycolysis-related amino acids to a higher extent in a two-compartment scale-down bioreactor. Thus, cells which enter oscillations from a lower specific growth rate seem to react more sensitive to oscillations than cells that are subjected to oscillations directly after a batch phase. These results are interesting in designing reliable scale-down systems, which better reflect large-scale bioprocesses.

Real-time monitoring of the budding index in Saccharomyces cerevisiae batch cultivations with in situ microscopy

BACKGROUND

The morphology of yeast cells changes during budding, depending on the growth rate and cultivation conditions. A photo-optical microscope was adapted and used to observe such morphological changes of individual cells directly in the cell suspension. In order to obtain statistically representative samples of the population without the influence of sampling, in situ microscopy (ISM) was applied in the different phases of a Saccharomyces cerevisiae batch cultivation. The real-time measurement was performed by coupling a photo-optical probe to an automated image analysis based on a neural network approach.

RESULTS

Automatic cell recognition and classification of budding and non-budding cells was conducted successfully. Deviations between automated and manual counting were considerably low. A differentiation of growth activity across all process stages of a batch cultivation in complex media became feasible. An increased homogeneity among the population during the growth phase was well observable. At growth retardation, the portion of smaller cells increased due to a reduced bud formation. The maturation state of the cells was monitored by determining the budding index as a ratio between the number of cells, which were detected with buds and the total number of cells. A linear correlation between the budding index as monitored with ISM and the growth rate was found.

CONCLUSION

It is shown that ISM is a meaningful analytical tool, as the budding index can provide valuable information about the growth activity of a yeast cell, e.g. in seed breeding or during any other cultivation process. The determination of the single-cell size and shape distributions provided information on the morphological heterogeneity among the populations. The ability to track changes in cell morphology directly on line enables new perspectives for monitoring and control, both in process development and on a production scale.

Constraint-based modeling in microbial food biotechnology

Genome-scale metabolic network reconstruction offers a means to leverage the value of the exponentially growing genomics data and integrate it with other biological knowledge in a structured format. Constraint-based modeling (CBM) enables both the qualitative and quantitative analyses of the reconstructed networks. The rapid advancements in these areas can benefit both the industrial production of microbial food cultures and their application in food processing. CBM provides several avenues for improving our mechanistic understanding of physiology and genotype–phenotype relationships. This is essential for the rational improvement of industrial strains, which can further be facilitated through various model-guided strain design approaches. CBM of microbial communities offers a valuable tool for the rational design of defined food cultures, where it can catalyze hypothesis generation and provide unintuitive rationales for the development of enhanced community phenotypes and, consequently, novel or improved food products. In the industrial-scale production of microorganisms for food cultures, CBM may enable a knowledge-driven bioprocess optimization by rationally identifying strategies for growth and stability improvement. Through these applications, we believe that CBM can become a powerful tool for guiding the areas of strain development, culture development and process optimization in the production of food cultures. Nevertheless, in order to make the correct choice of the modeling framework for a particular application and to interpret model predictions in a biologically meaningful manner, one should be aware of the current limitations of CBM.

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.

Impact of plasmid architecture on stability and yEGFP3 reporter gene expression in a set of isomeric multicopy vectors in yeast

Multicopy episomal plasmids in yeast, used whenever elevated levels of foreign or homologous gene expression are necessary, are known to be less stable compared to the endogenous 2-μm plasmid they are based on, at least without selective pressure. Considering that rich medium favors growth rate and, simultaneously, is less expensive than selective medium, enhancing stability in non-selective medium is extremely desirable. In this study, we changed the architecture of a multicopy model expression plasmid, creating six isoforms (same size, same DNA content but different positions and orientations of the expression block) and studied mitotic stability, copy number, as well as reporter yEGFP3 expression between isoforms. With one isoform being significantly more stable than the others and another one exhibiting elevated plasmid copy numbers in rich medium, we show that consideration of the arrangement of the plasmid elements might be crucial for productivity employing Saccharomyces cerevisiae as a host. We strongly believe that the ideal architecture has to be assessed for each case and assembly strategy has to begin by evaluating the stability of the vector backbone before insertion of the desired gene. For the plasmid set studied, yEGFP3 reporter production depends more on mitotic stability than on elevated plasmid copy numbers in a small number of cells retaining the plasmid under non-selective conditions.

Beyond the bulk: disclosing the life of single microbial cells

Microbial single cell analysis has led to discoveries that are beyond what can be resolved with population-based studies. It provides a pristine view of the mechanisms that organize cellular physiology, unbiased by population heterogeneity or uncontrollable environmental impacts. A holistic description of cellular functions at the single cell level requires analytical concepts beyond the miniaturization of existing technologies, defined but uncontrolled by the biological system itself. This review provides an overview of the latest advances in single cell technologies and demonstrates their potential. Opportunities and limitations of single cell microbiology are discussed using selected application-related examples.

Engineering Microbial Metabolite Dynamics and Heterogeneity

As yields for biological chemical production in microorganisms approach their theoretical maximum, metabolic engineering requires new tools, and approaches for improvements beyond what traditional strategies can achieve. Engineering metabolite dynamics and metabolite heterogeneity is necessary to achieve further improvements in product titers, productivities, and yields. Metabolite dynamics, the ensemble change in metabolite concentration over time, arise from the need for microbes to adapt their metabolism in response to the extracellular environment and are important for controlling growth and productivity in industrial fermentations. Metabolite heterogeneity, the cell-to-cell variation in a metabolite concentration in an isoclonal population, has a significant impact on ensemble productivity. Recent advances in single cell analysis enable a more complete understanding of the processes driving metabolite heterogeneity and reveal metabolic engineering targets. The authors present an overview of the mechanistic origins of metabolite dynamics and heterogeneity, why they are important, their potential effects in chemical production processes, and tools and strategies for engineering metabolite dynamics and heterogeneity. The authors emphasize that the ability to control metabolite dynamics and heterogeneity will bring new avenues of engineering to increase productivity of microbial strains.

Heterogeneity in Pure Microbial Systems: Experimental Measurements and Modeling

Cellular heterogeneity influences bioprocess performance in ways that until date are not completely elucidated. In order to account for this phenomenon in the design and operation of bioprocesses, reliable analytical and mathematical descriptions are required. We present an overview of the single cell analysis, and the mathematical modeling frameworks that have potential to be used in bioprocess control and optimization, in particular for microbial processes. In order to be suitable for bioprocess monitoring, experimental methods need to be high throughput and to require relatively short processing time. One such method used successfully under dynamic conditions is flow cytometry. Population balance and individual based models are suitable modeling options, the latter one having in particular a good potential to integrate the various data collected through experimentation. This will be highly beneficial for appropriate process design and scale up as a more rigorous approach may prevent a priori unwanted performance losses. It will also help progressing synthetic biology applications to industrial scale.

Deconvolution of Gene Expression Noise into Spatial Dynamics of Transcription Factor-Promoter Interplay

Gene expression noise is not only the mere consequence of stochasticity, but also a signal that reflects the upstream physical dynamics of the cognate molecular machinery. Soil bacteria facing recalcitrant pollutants exploit noise of catabolic promoters to deploy beneficial phenotypes such as metabolic bet-hedging and/or division of biochemical labor. Although the role of upstream promoter-regulator interplay in the origin of this noise is little understood, its specifications are probably ciphered in flow cytometry data patterns. We studied Pm promoter activity of the environmental bacterium Pseudomonas putida and its cognate regulator XylS by following expression of Pm-gfp fusions in single cells. Using mathematical modeling and computational simulations, we determined the kinetic properties of the system and used them as a baseline code to interpret promoter activity in terms of upstream regulator dynamics. Transcriptional noise was predicted to depend on the intracellular physical distance between regulator source (where XylS is produced) and the target promoter. Experiments with engineered bacteria in which this distance is minimized or enlarged confirmed the predicted effects of source/target proximity on noise patterns. This approach allowed deconvolution of cytometry data into mechanistic information on gene expression flow. It also provided a basis for selecting programmable noise levels in synthetic regulatory circuits.

Robustness of a model microbial community emerges from population structure among single cells of a clonal population

Microbial populations can withstand, overcome and persist in the face of environmental fluctuation. Previously, we demonstrated how conditional gene regulation in a fluctuating environment drives dilution of condition-specific transcripts, causing a population of Desulfovibrio vulgaris Hildenborough (DvH) to collapse after repeatedly transitioning from sulfate respiration to syntrophic conditions with the methanogen Methanococcus maripaludis. Failure of the DvH to successfully transition contributed to the collapse of this model community. We investigated the mechanistic basis for loss of robustness by examining whether conditional gene regulation altered heterogeneity in gene expression across individual DvH cells. We discovered that robustness of a microbial population across environmental transitions was attributable to the retention of cells in two states that exhibited different condition-specific gene expression patterns. In our experiments, a population with disrupted conditional regulation successfully alternated between cell states. Meanwhile, a population with intact conditional regulation successfully switched between cell states initially, but collapsed after repeated transitions, possibly due to the high energy requirements of regulation. These results demonstrate that the survival of this entire model microbial community is dependent on the regulatory system's influence on the distribution of distinct cell states among individual cells within a clonal population.

Taking control over microbial populations: Current approaches for exploiting biological noise in bioprocesses

Phenotypic plasticity of microbial cells has attracted much attention and several research efforts have been dedicated to the description of methods aiming at characterizing phenotypic heterogeneity and its impact on microbial populations. However, different approaches have also been suggested in order to take benefit from noise in a bioprocess perspective, e.g. by increasing the robustness or productivity of a microbial population. This review is dedicated to outline these controlling methods. A common issue, that has still to be addressed, is the experimental identification and the mathematical expression of noise. Indeed, the effective interfacing of microbial physiology with external parameters that can be used for controlling physiology depends on the acquisition of reliable signals. Latest technologies, like single cell microfluidics and advanced flow cytometric approaches, enable linking physiology, noise, heterogeneity in productive microbes with environmental cues and hence allow correctly mapping and predicting biological behavior via mathematical representations. However, like in the field of electronics, signals are perpetually subjected to noise. If appropriately interpreted, this noise can give an additional insight into the behavior of the individual cells within a microbial population of interest. This review focuses on recent progress made at describing, treating and exploiting biological noise in the context of microbial populations used in various bioprocess applications.

Label-free, simultaneous quantification of starch, protein and triacylglycerol in single microalgal cells

BACKGROUND

Current approaches for quantification of major energy-storage forms in microalgae, including starch, protein and lipids, generally require cell cultivation to collect biomass followed by tedious and time-consuming analytical procedures. Thus, label-free, non-destructive and simultaneous quantification of such macromolecules at single-cell resolution is highly desirable in microalgal feedstock development and bioprocess control.

RESULTS

Here, we established a method based on single-cell Raman spectra (SCRS) that simultaneously quantifies the contents of starch, protein, triacylglycerol (TAG) and lipid unsaturation degree in individual Chlamydomonas reinhardtii cells. Measurement accuracy for the contents based on full SCRS spectrum each reached 96.86-99.24%, all significantly higher than single peak-based models. However, accuracy and reliability of measurement are dependent on the number of cells sampled, thus a formal mathematical framework was proposed and validated to rationally define "minimal sampling depth" for a given state of cellular population. Furthermore, a barcode consisting of 13 marker Raman peaks was proposed to characterize the temporal dynamics of these energy-storage products, which revealed that the average contents of starch and TAG increased, while their heterogeneity indices decreased, with those of protein being exactly the opposite. Finally, our method is widely applicable, as measurements among cells from liquid suspension culture, wet paste and frozen dried powder all exhibited excellent consistency.

CONCLUSIONS

When sampled at proper depth, SCRS can serve as a quantitative and generally applicable tool for characterization and screening of strains and bioprocesses based on the profile of energy-storage macromolecules and their among-cell heterogeneity.

Adaptation to low pH and lignocellulosic inhibitors resulting in ethanolic fermentation and growth of Saccharomyces cerevisiae

Lignocellulosic bioethanol from renewable feedstocks using Saccharomyces cerevisiae is a promising alternative to fossil fuels owing to environmental challenges. S. cerevisiae is frequently challenged by bacterial contamination and a combination of lignocellulosic inhibitors formed during the pre-treatment, in terms of growth, ethanol yield and productivity. We investigated the phenotypic robustness of a brewing yeast strain TMB3500 and its ability to adapt to low pH thereby preventing bacterial contamination along with lignocellulosic inhibitors by short-term adaptation and adaptive lab evolution (ALE). The short-term adaptation strategy was used to investigate the inherent ability of strain TMB3500 to activate a robust phenotype involving pre-culturing yeast cells in defined medium with lignocellulosic inhibitors at pH 5.0 until late exponential phase prior to inoculating them in defined media with the same inhibitor cocktail at pH 3.7. Adapted cells were able to grow aerobically, ferment anaerobically (glucose exhaustion by 19 ± 5 h to yield 0.45 ± 0.01 g ethanol g glucose(-1)) and portray significant detoxification of inhibitors at pH 3.7, when compared to non-adapted cells. ALE was performed to investigate whether a stable strain could be developed to grow and ferment at low pH with lignocellulosic inhibitors in a continuous suspension culture. Though a robust population was obtained after 3600 h with an ability to grow and ferment at pH 3.7 with inhibitors, inhibitor robustness was not stable as indicated by the characterisation of the evolved culture possibly due to phenotypic plasticity. With further research, this short-term adaptation and low pH strategy could be successfully applied in lignocellulosic ethanol plants to prevent bacterial contamination.

Poly(3-hydroxybutyrate) anabolism in Cupriavidus necator cultivated at various carbon-to-nitrogen ratios: insights from single-cell Raman spectroscopy

Cupriavidus necator accumulates large amounts of poly(3-hydroxybutyrate) (PHB), a biodegradable substitute for petroleum-based plastics, under certain nutrient conditions. Conventional solvent-extraction-based methods for PHB quantification only obtain average information from cell populations and, thus, mask the heterogeneity among individual cells. Laser tweezers Raman spectroscopy (LTRS) was used to monitor dynamic changes in the contents of PHB, nucleic acids, and proteins in <italic<C. necator</italic< at the population and single-cell levels when the microorganism cells were cultivated at various carbon-to-nitrogen ratios. The biosynthetic activities of nucleic acids and proteins were maintained at high levels, and only a small amount of PHB was produced when the bacterial cells were cultured under balanced growth conditions. By contrast, the syntheses of nucleic acids and proteins were blocked, and PHB was accumulated in massive amount inside the microbial cells under nitrogen-limiting growth circumstances. Single-cell analysis revealed a relatively high heterogeneity in PHB level at the early stage of the bacterial growth. Additionally, bacterial cells in populations at certain cultivation stages were composed of two or three subpopulations on the basis of their PHB abundance. Overall, LTRS is a reliable single-cell analysis tool that can provide insights into PHB fermentation.

Flow cytometry community fingerprinting and amplicon sequencing for the assessment of landfill leachate cellulolytic bioaugmentation

Flow cytometry (FCM) is a high throughput single cell technology that is actually becoming widely used for studying phenotypic and genotypic diversity among microbial communities. This technology is considered in this work for the assessment of a bioaugmentation treatment in order to enhance cellulolytic potential of landfill leachate. The experimental results reveal the relevant increase of leachate cellulolytic potential due to bioaugmentation. Cytometric monitoring of microbial dynamics along these assays is then realized. The flow FP package is used to establish microbial samples fingerprint from initial 2D cytometry histograms. This procedure allows highlighting microbial communities' variation along the assays. Cytometric and 16S rRNA gene sequencing fingerprinting methods are then compared. The two approaches give same evidence about microbial dynamics throughout digestion assay. There are however a lack of significant correlation between cytometric and amplicon sequencing fingerprint at genus or species level. Same phenotypical profiles of microbiota during assays matched to several 16S rRNA gene sequencing ones. Flow cytometry fingerprinting can thus be considered as a promising routine on-site method suitable for the detection of stability/variation/disturbance of complex microbial communities involved in bioprocesses.

Cell-to-cell heterogeneity emerges as consequence of metabolic cooperation in a synthetic yeast community

Cells that grow together respond heterogeneously to stress even when they are genetically similar. Metabolism, a key determinant of cellular stress tolerance, may be one source of this phenotypic heterogeneity, however, this relationship is largely unclear. We used self‐establishing metabolically cooperating (SeMeCo) yeast communities, in which metabolic cooperation can be followed on the basis of genotype, as a model to dissect the role of metabolic cooperation in single‐cell heterogeneity. Cells within SeMeCo communities showed to be highly heterogeneous in their stress tolerance, while the survival of each cell under heat or oxidative stress, was strongly determined by its metabolic specialization. This heterogeneity emerged for all metabolite exchange interactions studied (histidine, leucine, uracil, and methionine) as well as oxidant (H₂O₂, diamide) and heat stress treatments. In contrast, the SeMeCo community collectively showed to be similarly tolerant to stress as wild‐type populations. Moreover, stress heterogeneity did not establish as sole consequence of metabolic genotype (auxotrophic background) of the single cell, but was observed only for cells that cooperated according to their metabolic capacity. We therefore conclude that phenotypic heterogeneity and cell to cell differences in stress tolerance are emergent properties when cells cooperate in metabolism.

Biochemical Engineering Approaches for Increasing Viability and Functionality of Probiotic Bacteria

The literature presents a growing body of evidence demonstrating the positive effect of probiotics on health. Probiotic consumption levels are rising quickly in the world despite the fluctuation of their viability and functionality. Technological methods aiming at improving probiotic characteristics are thus highly wanted. However, microbial metabolic engineering toolbox is not available for this kind of application. On the other hand, basic microbiology teaches us that bacteria are able to exhibit adaptation to external stresses. It is known that adequately applied sub-lethal stress, i.e., controlled in amplitude and frequency at a given stage of the culture, is able to enhance microbial robustness. This property could be potentially used to improve the viability of probiotic bacteria, but some technical challenges still need to be overcome before any industrial implementation. This review paper investigates the different technical tools that can be used in order to define the proper condition for improving viability of probiotic bacteria and their implementation at the industrial scale. Based on the example of Bifidobacterium bifidum, potentialities for simultaneously improving viability, but also functionality of probiotics will be described.

The dynamic balance of import and export of zinc in Escherichia coli suggests a heterogeneous population response to stress

Zinc is essential for life, but toxic in excess. Thus all cells must control their internal zinc concentration. We used a systems approach, alternating rounds of experiments and models, to further elucidate the zinc control systems in Escherichia coli. We measured the response to zinc of the main specific zinc import and export systems in the wild-type, and a series of deletion mutant strains. We interpreted these data with a detailed mathematical model and Bayesian model fitting routines. There are three key findings: first, that alternate, non-inducible importers and exporters are important. Second, that an internal zinc reservoir is essential for maintaining the internal zinc concentration. Third, our data fitting led us to propose that the cells mount a heterogeneous response to zinc: some respond effectively, while others die or stop growing. In a further round of experiments, we demonstrated lower viable cell counts in the mutant strain tested exposed to excess zinc, consistent with this hypothesis. A stochastic model simulation demonstrated considerable fluctuations in the cellular levels of the ZntA exporter protein, reinforcing this proposal. We hypothesize that maintaining population heterogeneity could be a bet-hedging response allowing a population of cells to survive in varied and fluctuating environments.

Stochastic developmental variation, an epigenetic source of phenotypic diversity with far-reaching biological consequences

This article reviews the production of different phenotypes from the same genotype in the same environment by stochastic cellular events, nonlinear mechanisms during patterning and morphogenesis, and probabilistic self-reinforcing circuitries in the adult life. These aspects of phenotypic variation are summarized under the term 'stochastic developmental variation' (SDV) in the following. In the past, SDV has been viewed primarily as a nuisance, impairing laboratory experiments, pharmaceutical testing, and true-to-type breeding. This article also emphasizes the positive biological effects of SDV and discusses implications for genotype-to-phenotype mapping, biological individuation, ecology, evolution, and applied biology. There is strong evidence from experiments with genetically identical organisms performed in narrowly standardized laboratory set-ups that SDV is a source of phenotypic variation in its own right aside from genetic variation and environmental variation. It is obviously mediated by molecular and higher-order epigenetic mechanisms. Comparison of SDV in animals, plants, fungi, protists, bacteria, archaeans, and viruses suggests that it is a ubiquitous and phylogenetically old phenomenon. In animals, it is usually smallest for morphometric traits and highest for life history traits and behaviour. SDV is thought to contribute to phenotypic diversity in all populations but is particularly relevant for asexually reproducing and genetically impoverished populations, where it generates individuality despite genetic uniformity. In each generation, SDV produces a range of phenotypes around a well-adapted target phenotype, which is interpreted as a bet-hedging strategy to cope with the unpredictability of dynamic environments. At least some manifestations of SDV are heritable, adaptable, selectable, and evolvable, and therefore, SDV may be seen as a hitherto overlooked evolution factor. SDV is also relevant for husbandry, agriculture, and medicine because most pathogens are asexuals that exploit this third source of phenotypic variation to modify infectivity and resistance to antibiotics. Since SDV affects all types of organisms and almost all aspects of life, it urgently requires more intense research and a better integration into biological thinking.

Exacerbation of substrate toxicity by IPTG in Escherichia coli BL21(DE3) carrying a synthetic metabolic pathway

BACKGROUND

Heterologous expression systems based on promoters inducible with isopropyl-β-D-1-thiogalactopyranoside (IPTG), e.g., Escherichia coli BL21(DE3) and cognate LacI(Q)/P(lacUV5)-T7 vectors, are commonly used for production of recombinant proteins and metabolic pathways. The applicability of such cell factories is limited by the complex physiological burden imposed by overexpression of the exogenous genes during a bioprocess. This burden originates from a combination of stresses that may include competition for the expression machinery, side-reactions due to the activity of the recombinant proteins, or the toxicity of their substrates, products and intermediates. However, the physiological impact of IPTG-induced conditional expression on the recombinant host under such harsh conditions is often overlooked.

RESULTS

The physiological responses to IPTG of the E. coli BL21(DE3) strain and three different recombinants carrying a synthetic metabolic pathway for biodegradation of the toxic anthropogenic pollutant 1,2,3-trichloropropane (TCP) were investigated using plating, flow cytometry, and electron microscopy. Collected data revealed unexpected negative synergistic effect of inducer of the expression system and toxic substrate resulting in pronounced physiological stress. Replacing IPTG with the natural sugar effector lactose greatly reduced such stress, demonstrating that the effect was due to the original inducer's chemical properties.

CONCLUSIONS

IPTG is not an innocuous inducer; instead, it exacerbates the toxicity of haloalkane substrate and causes appreciable damage to the E. coli BL21(DE3) host, which is already bearing a metabolic burden due to its content of plasmids carrying the genes of the synthetic metabolic pathway. The concentration of IPTG can be effectively tuned to mitigate this negative effect. Importantly, we show that induction with lactose, the natural inducer of P lac , dramatically lightens the burden without reducing the efficiency of the synthetic TCP degradation pathway. This suggests that lactose may be a better inducer than IPTG for the expression of heterologous pathways in E. coli BL21(DE3).

Simultaneous production of lactobionic and gluconic acid in cheese whey/glucose co-fermentation by Pseudomonas taetrolens

Substrate versatility of Pseudomonas taetrolens was evaluated for the first time in a co-fermentation system combining cheese whey and glucose, glycerol or lactose as co-substrates. Results showed that P. taetrolens displayed different production patterns depending on the co-substrate supplied. Whereas the presence of glucose led to a simultaneous co-production of lactobionic (78g/L) and gluconic acid (8.8g/L), lactose feeding stimulated the overproduction of lactobionic acid from whey with a high specific productivity (1.4g/gh) and yield (100%). Co-substrate supply of glycerol conversely led to reduced lactobionic acid yield (82%) but higher cell densities (1.8g/L), channelling the carbon source towards cell growth and maintenance. Higher carbon availability impaired the metabolic activity as well as membrane integrity, whereas lactose feeding improved the cellular functionality of P. taetrolens. Insights into these mixed carbon source strategies open up the possibility of co-producing lactobionic and gluconic acid into an integrated single-cell biorefinery.

Transcription factor-based biosensors in biotechnology: current state and future prospects

Living organisms have evolved a plethora of sensing systems for the intra- and extracellular detection of small molecules, ions or physical parameters. Several recent studies have demonstrated that these principles can be exploited to devise synthetic regulatory circuits for metabolic engineering strategies. In this context, transcription factors (TFs) controlling microbial physiology at the level of transcription play a major role in biosensor design, since they can be implemented in synthetic circuits controlling gene expression in dependency of, for example, small molecule production. Here, we review recent progress on the utilization of TF-based biosensors in microbial biotechnology highlighting different areas of application. Recent advances in metabolic engineering reveal TF-based sensors to be versatile tools for strain and enzyme development using high-throughput (HT) screening strategies and adaptive laboratory evolution, the optimization of heterologous pathways via the implementation of dynamic control circuits and for the monitoring of single-cell productivity in live cell imaging studies. These examples underline the immense potential of TF-based biosensor circuits but also identify limitations and room for further optimization.

Variability in subpopulation formation propagates into biocatalytic variability of engineered Pseudomonas putida strains

Pivotal challenges in industrial biotechnology are the identification and overcoming of cell-to-cell heterogeneity in microbial processes. While the development of subpopulations of isogenic cells in bioprocesses is well described (intra-population variability), a possible variability between genetically identical cultures growing under macroscopically identical conditions (clonal variability) is not. A high such clonal variability has been found for the recombinant expression of the styrene monooxygenase genes styAB from Pseudomonas taiwanensis VLB120 in solvent-tolerant Pseudomonas putida DOT-T1E using the alk-regulatory system from P. putida GPo1. In this study, the oxygenase subunit StyA fused to eGFP was used as readout tool to characterize the population structure in P. putida DOT-T1E regarding recombinant protein content. Flow cytometric analyses revealed that in individual cultures, at least two subpopulations with highly differing recombinant StyA-eGFP protein contents appeared (intra-population variability). Interestingly, subpopulation sizes varied from culture-to-culture correlating with the specific styrene epoxidation activity of cells derived from respective cultures (clonal variability). In addition, flow cytometric cell sorting coupled to plasmid copy number (PCN) determination revealed that detected clonal variations cannot be correlated to the PCN, but depend on the combination of the regulatory system and the host strain employed. This is, to the best of our knowledge, the first work reporting that intra-population variability (with differing protein contents in the presented case study) causes clonal variability of genetically identical cultures. Respective impacts on bioprocess reliability and performance and strategies to overcome respective reliability issues are discussed.

Fluorescent Reporter Libraries as Useful Tools for Optimizing Microbial Cell Factories: A Review of the Current Methods and Applications

The use of genetically encoded fluorescent reporters allows speeding up the initial optimization steps of microbial bioprocesses. These reporters can be used for determining the expression level of a particular promoter, not only the synthesis of a specific protein but also the content of intracellular metabolites. The level of protein/metabolite is thus proportional to a fluorescence signal. By this way, mean expression profiles of protein/metabolites can be determined non-invasively at a high-throughput rate, allowing the rapid identification of the best producers. Actually, different kinds of reporter systems are available, as well as specific cultivation devices allowing the on-line recording of the fluorescent signal. Cell-to-cell variability is another important phenomenon that can be integrated into the screening procedures for the selection of more efficient microbial cell factories.

A versatile, non genetically modified organism (GMO)-based strategy for controlling low-producer mutants in Bordetella pertussis cultures using antigenic modulation

The uncontrolled presence of non-producer mutants negatively affects bioprocesses. In Bordetella pertussis cultures, avirulent mutants emerge spontaneously and accumulate. We characterized the dynamics of accumulation using high-throughput growth assays and competition experiments between virulent and avirulent (bvg(-) ) isolates. A fitness advantage of bvg(-) cells was identified as the main driver for bvg(-) accumulation under conditions of high virulence factor production. Conversely, under conditions that reduce their expression (antigenic modulation), bvg(-) takeover could be avoided. A control strategy was derived, which consists in applying modulating conditions whenever virulence factor production is not required. It has a wide range of applications, from routine laboratory operations to vaccine manufacturing, where pertussis toxin yields were increased 1.4-fold by performing early pre-culture steps in modulating conditions. Because it only requires subtle modifications of the culture medium and does not involve genetic modifications, this strategy is applicable to any B. pertussis isolate, and should facilitate regulatory acceptance of process changes for vaccine production. Strategies based on the same concept, could be derived for other industrially relevant micro-organisms. This study illustrates how a sound scientific understanding of physiological principles can be turned into a practical application for the bioprocess industry, in alignment with Quality by Design principles.

Making variability less variable: matching expression system and host for oxygenase-based biotransformations

Variability in whole-cell biocatalyst performance represents a critical aspect for stable and productive bioprocessing. In order to investigate whether and how oxygenase-catalyzed reactions are affected by such variability issues in solvent-tolerant Pseudomonas, different inducers, expression systems, and host strains were tested for the reproducibility of xylene and styrene monooxygenase catalyzed hydroxylation and epoxidation reactions, respectively. Significantly higher activity variations were found for biocatalysts based on solvent-tolerant Pseudomonas putida DOT-TIE and S12 compared with solvent-sensitive P. putida KT2440, Escherichia coli JM101, and solvent-tolerant Pseudomonas taiwanensis VLB120. Specific styrene epoxidation rates corresponded to cellular styrene monooxygenase contents. Detected variations in activity strictly depended on the type of regulatory system employed, being high with the alk- and low with the lac-system. These results show that the occurrence of clonal variability in recombinant gene expression in Pseudomonas depends on the combination of regulatory system and host strain, does not correlate with a general phenotype such as solvent tolerance, and must be evaluated case by case.

The glycerol-dependent metabolic persistence of Pseudomonas putida KT2440 reflects the regulatory logic of the GlpR repressor

The growth of the soil bacterium Pseudomonas putida KT2440 on glycerol as the sole carbon source is characterized by a prolonged lag phase, not observed with other carbon substrates. We examined the bacterial growth in glycerol cultures while monitoring the metabolic activity of individual cells. Fluorescence microscopy and flow cytometry, as well as the analysis of the temporal start of growth in single-cell cultures, revealed that adoption of a glycerol-metabolizing regime was not the result of a gradual change in the whole population but rather reflected a time-dependent bimodal switch between metabolically inactive (i.e., nongrowing) and fully active (i.e., growing) bacteria. A transcriptional Φ(glpD-gfp) fusion (a proxy of the glycerol-3-phosphate [G3P] dehydrogenase activity) linked the macroscopic phenotype to the expression of the glp genes. Either deleting glpR (encoding the G3P-responsive transcriptional repressor that controls the expression of the glpFKRD gene cluster) or altering G3P formation (by overexpressing glpK, encoding glycerol kinase) abolished the bimodal glpD expression. These manipulations eliminated the stochastic growth start by shortening the otherwise long lag phase. Provision of glpR in trans restored the phenotypes lost in the ΔglpR mutant. The prolonged nongrowth regime of P. putida on glycerol could thus be traced to the regulatory device controlling the transcription of the glp genes. Since the physiological agonist of GlpR is G3P, the arrangement of metabolic and regulatory components at this checkpoint merges a positive feedback loop with a nonlinear transcriptional response, a layout fostering the observed time-dependent shift between two alternative physiological states.

Phenotypic variation is a widespread attribute of prokaryotes that leads, inter alia, to the emergence of persistent bacteria, i.e., live but nongrowing members within a genetically clonal population. Persistence allows a fraction of cells to avoid the killing caused by conditions or agents that destroy most growing bacteria (e.g., some antibiotics). Known molecular mechanisms underlying the phenomenon include genetic changes, epigenetic variations, and feedback-based multistability. We show that a prolonged nongrowing state of the bacterial population can be brought about by a distinct regulatory architecture of metabolic genes when cells face specific nutrients (e.g., glycerol). Pseudomonas putida may have adopted the resulting carbon source-dependent metabolic bet hedging as an advantageous trait for exploring new chemical and nutritional landscapes. Defeating such naturally occurring adaptive features of environmental bacteria is instrumental in improving the performance of these microorganisms as whole-cell catalysts in a bioreactor setup.

Editorial: Latest methods and advances in biotechnology

The latest "Biotech Methods and Advances" special issue of Biotechnology Journal continues the BTJ tradition of featuring the latest breakthroughs in biotechnology. The special issue is edited by our Editors-in-Chief, Prof. Sang Yup Lee and Prof. Alois Jungbauer and covers a wide array of topics in biotechnology, including the perennial favorite workhorses of the biotech industry, Chinese hamster ovary (CHO) cell and Escherichia coli.

Subpopulation-proteomics reveal growth rate, but not cell cycling, as a major impact on protein composition in Pseudomonas putida KT2440

Population heterogeneity occurring in industrial microbial bioprocesses is regarded as a putative effector causing performance loss in large scale. While the existence of subpopulations is a commonly accepted fact, their appearance and impact on process performance still remains rather unclear. During cell cycling, distinct subpopulations differing in cell division state and DNA content appear which contribute individually to the efficiency of the bioprocess. To identify stressed or impaired subpopulations, we analyzed the interplay of growth rate, cell cycle and phenotypic profile of subpopulations by using flow cytometry and cell sorting in conjunction with mass spectrometry based global proteomics. Adjusting distinct growth rates in chemostats with the model strain Pseudomonas putida KT2440, cells were differentiated by DNA content reflecting different cell cycle stages. The proteome of separated subpopulations at given growth rates was found to be highly similar, while different growth rates caused major changes of the protein inventory with respect to e.g. carbon storage, motility, lipid metabolism and the translational machinery. In conclusion, cells in various cell cycle stages at the same growth rate were found to have similar to identical proteome profiles showing no significant population heterogeneity on the proteome level. In contrast, the growth rate clearly determines the protein composition and therefore the metabolic strategy of the cells.