Application of a genetically encoded biosensor for live cell imaging of L-valine production in pyruvate dehydrogenase complex-deficient Corynebacterium glutamicum strains

Microfluidic approaches in microbial ecology

Microbial life is at the heart of many diverse environments and regulates most natural processes, from the functioning of animal organs to the cycling of global carbon. Yet, the study of microbial ecology is often limited by challenges in visualizing microbial processes and replicating the environmental conditions under which they unfold. Microfluidics operates at the characteristic scale at which microorganisms live and perform their functions, thus allowing for the observation and quantification of behaviors such as growth, motility, and responses to external cues, often with greater detail than classical techniques. By enabling a high degree of control in space and time of environmental conditions such as nutrient gradients, pH levels, and fluid flow patterns, microfluidics further provides the opportunity to study microbial processes in conditions that mimic the natural settings harboring microbial life. In this review, we describe how recent applications of microfluidic systems to microbial ecology have enriched our understanding of microbial life and microbial communities. We highlight discoveries enabled by microfluidic approaches ranging from single-cell behaviors to the functioning of multi-cellular communities, and we indicate potential future opportunities to use microfluidics to further advance our understanding of microbial processes and their implications.

Four ways of implementing robustness quantification in strain characterisation

BACKGROUND

In industrial bioprocesses, microorganisms are generally selected based on performance, whereas robustness, i.e., the ability of a system to maintain a stable performance, has been overlooked due to the challenges in its quantification and implementation into routine experimental procedures. This work presents four ways of implementing robustness quantification during strain characterisation. One Saccharomyces cerevisiae laboratory strain (CEN.PK113-7D) and two industrial strains (Ethanol Red and PE2) grown in seven different lignocellulosic hydrolysates were assessed for growth-related functions (specific growth rate, product yields, etc.) and eight intracellular parameters (using fluorescent biosensors).

RESULTS

Using flasks and high-throughput experimental setups, robustness was quantified in relation to: (i) stability of growth functions in response to the seven hydrolysates; (ii) stability of growth functions across different strains to establish the impact of perturbations on yeast metabolism; (iii) stability of intracellular parameters over time; (iv) stability of intracellular parameters within a cell population to indirectly quantify population heterogeneity. Ethanol Red was the best-performing strain under all tested conditions, achieving the highest growth function robustness. PE2 displayed the highest population heterogeneity. Moreover, the intracellular environment varied in response to non-woody or woody lignocellulosic hydrolysates, manifesting increased oxidative stress and unfolded protein response, respectively.

CONCLUSIONS

Robustness quantification is a powerful tool for strain characterisation as it offers novel information on physiological and biochemical parameters. Owing to the flexibility of the robustness quantification method, its implementation was successfully validated at single-cell as well as high-throughput levels, showcasing its versatility and potential for several applications.

Enabling oxygen-controlled microfluidic cultures for spatiotemporal microbial single-cell analysis

Microfluidic cultivation devices that facilitate O₂ control enable unique studies of the complex interplay between environmental O₂ availability and microbial physiology at the single-cell level. Therefore, microbial single-cell analysis based on time-lapse microscopy is typically used to resolve microbial behavior at the single-cell level with spatiotemporal resolution. Time-lapse imaging then provides large image-data stacks that can be efficiently analyzed by deep learning analysis techniques, providing new insights into microbiology. This knowledge gain justifies the additional and often laborious microfluidic experiments. Obviously, the integration of on-chip O₂ measurement and control during the already complex microfluidic cultivation, and the development of image analysis tools, can be a challenging endeavor. A comprehensive experimental approach to allow spatiotemporal single-cell analysis of living microorganisms under controlled O₂ availability is presented here. To this end, a gas-permeable polydimethylsiloxane microfluidic cultivation chip and a low-cost 3D-printed mini-incubator were successfully used to control O₂ availability inside microfluidic growth chambers during time-lapse microscopy. Dissolved O₂ was monitored by imaging the fluorescence lifetime of the O₂-sensitive dye RTDP using FLIM microscopy. The acquired image-data stacks from biological experiments containing phase contrast and fluorescence intensity data were analyzed using in-house developed and open-source image-analysis tools. The resulting oxygen concentration could be dynamically controlled between 0% and 100%. The system was experimentally tested by culturing and analyzing an E. coli strain expressing green fluorescent protein as an indirect intracellular oxygen indicator. The presented system allows for innovative microbiological research on microorganisms and microbial ecology with single-cell resolution.

Applications and Tuning Strategies for Transcription Factor-Based Metabolite Biosensors

Transcription factor (TF)-based biosensors are widely used for the detection of metabolites and the regulation of cellular pathways in response to metabolites. Several challenges hinder the direct application of TF-based sensors to new hosts or metabolic pathways, which often requires extensive tuning to achieve the optimal performance. These tuning strategies can involve transcriptional or translational control depending on the parameter of interest. In this review, we highlight recent strategies for engineering TF-based biosensors to obtain the desired performance and discuss additional design considerations that may influence a biosensor's performance. We also examine applications of these sensors and suggest important areas for further work to continue the advancement of small-molecule biosensors.

Directed evolution of linker helix as an efficient strategy for engineering LysR-type transcriptional regulators as whole-cell biosensors

Whole-cell biosensors based on transcriptional regulators are powerful tools for rapid measurement, high-throughput screening, dynamic metabolic regulation, etc. To optimize the biosensing performance of transcriptional regulator, its effector-binding domain is commonly engineered. However, this strategy is encumbered by the limitation of diversifying such a large domain and the risk of affecting effector specificity. Molecular dynamics simulation of effector binding of LysG (an LysR-type transcriptional regulator, LTTR) suggests the crucial role of the short linker helix (LH) connecting effector- and DNA-binding domains in protein conformational change. Directed evolution of LH efficiently produced LysG variants with extended operational range and unaltered effector specificity. The whole-cell biosensor based on the best LysG^E58V variant outperformed the wild-type LysG in enzyme high-throughput screening and dynamic regulation of l-lysine biosynthetic pathway. LH mutations are suggested to affect DNA binding and facilitate transcriptional activation upon effector binding. LH engineering was also successfully applied to optimize another LTTR BenM for biosensing. Since LTTRs represent the largest family of prokaryotic transcriptional regulators with highly conserved structures, LH engineering is an efficient and universal strategy for development and optimization of whole-cell biosensors.

High-level production of L-valine in Escherichia coli using multi-modular engineering

L-valine is a valuable amino acid in mammals that is used as the main component of feed additives. The low efficiency of the fermentation titer limits the industrial application of L-valine. Here, an L-valine-producing strain of Escherichia coli was obtained using a multi-modular strategy. Initially, a chassis strain was generated by mutagenesis and high-throughput screening. The L-valine biosynthetic pathway and transport module were modified to improve the L-valine titer. Subsequently, the transcription factors associated with L-valine biosynthesis were investigated. Overexpression of PdhR and inhibition of the expression of RpoS promoted L-valine synthesis. Finally, the NADPH supply was enhanced after the introduction of the heterologous Entner-Doudoroff (ED) pathway from Zymomonas mobilis. The strain VAL38 produced 92 g/L L-valine in a 5-L bioreactor with a yield of 0.34 g/g glucose. This strategy is provided as a reference for improving the production performance of cell factories for L-valine and its derivatives.

Digital models in biotechnology: Towards multi-scale integration and implementation

Industrial biotechnology encompasses a large area of multi-scale and multi-disciplinary research activities. With the recent megatrend of digitalization sweeping across all industries, there is an increased focus in the biotechnology industry on developing, integrating and applying digital models to improve all aspects of industrial biotechnology. Given the rapid development of this field, we systematically classify the state-of-art modelling concepts applied at different scales in industrial biotechnology and critically discuss their current usage, advantages and limitations. Further, we critically analyzed current strategies to couple cell models with computational fluid dynamics to study the performance of industrial microorganisms in large-scale bioprocesses, which is of crucial importance for the bio-based production industries. One of the most challenging aspects in this context is gathering intracellular data under industrially relevant conditions. Towards comprehensive models, we discuss how different scale-down concepts combined with appropriate analytical tools can capture intracellular states of single cells. We finally illustrated how the efforts could be used to develop digitals models suitable for both cell factory design and process optimization at industrial scales in the future.

New insights into phenotypic heterogeneity for the distinct lipid accumulation of Schizochytrium sp. H016

BACKGROUND

Schizochytrium sp. is a marine heterotrophic protist and an important sustainable resource for high value-added docosahexaenoic acid in the future. The production of different phenotypes during the continuous subculture of Schizochytrium sp. results in a serious reduction in lipid yield and complicates the used of this strain in scientific research and industrial production. Hence, obtaining an improved understanding of the phenotypic differences and molecular mechanisms underlying the cell-to-cell heterogeneity of Schizochytrium sp. is necessary.

RESULTS

After continuous culture passage, Schizochytrium sp. H016 differentiated into two subpopulations with different morphologies and showed decreased capacity for lipid production. The presence of cell subpopulations with degraded lipid droplets led to a substantial decrease in overall lipid yield. Here, a rapid screening strategy based on fluorescence-activated cell sorting was proposed to classify and isolate subpopulations quickly in accordance with their lipid-producing capability. The final biomass and lipid yield of the subpopulation with high cell lipid content (i.e., H016-H) were 38.83 and 17.22 g/L, respectively, which were 2.07- and 5.38-fold higher than those of the subpopulation with low lipid content (i.e., H016-L), respectively. Subsequently, time‑resolved transcriptome analysis was performed to elucidate the mechanism of phenotypic heterogeneity in different subpopulations. Results showed that the expression of genes related to the cell cycle and lipid degradation was significantly upregulated in H016-L, whereas the metabolic pathways related to fatty acid synthesis and glyceride accumulation were remarkably upregulated in H016-H.

CONCLUSION

This study innovatively used flow cytometry combined with transcriptome technology to provide new insights into the phenotypic heterogeneity of different cell subpopulations of Schizochytrium sp. Furthermore, these results lay a strong foundation for guiding the breeding of oleaginous microorganisms with high lipid contents.

Advances in automated real-time flow cytometry for monitoring of bioreactor processes

Flow cytometry and its technological possibilities have greatly advanced in the past decade as analysis tool for single cell properties and population distributions of different cell types in bioreactors. Along the way, some solutions for automated real-time flow cytometry (ART-FCM) were developed for monitoring of bioreactor processes without operator interference over extended periods with variable sampling frequency. However, there is still great potential for ART-FCM to evolve and possibly become a standard application in bioprocess monitoring and process control. This review first addresses different components of an ART-FCM, including the sampling device, the sample-processing unit, the unit for sample delivery to the flow cytometer and the settings for measurement of pre-processed samples. Also, available algorithms are presented for automated data analysis of multi-parameter fluorescence datasets derived from ART-FCM experiments. Furthermore, challenges are discussed for integration of fluorescence-activated cell sorting into an ART-FCM setup for isolation and separation of interesting subpopulations that can be further characterized by for instance omics-methods. As the application of ART-FCM is especially of interest for bioreactor process monitoring, including investigation of population heterogeneity and automated process control, a summary of already existing setups for these purposes is given. Additionally, the general future potential of ART-FCM is addressed.

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.

Robustness: linking strain design to viable bioprocesses

Microbial cell factories are becoming increasingly popular for the sustainable production of various chemicals. Metabolic engineering has led to the design of advanced cell factories; however, their long-term yield, titer, and productivity falter when scaled up and subjected to industrial conditions. This limitation arises from a lack of robustness - the ability to maintain a constant phenotype despite the perturbations of such processes. This review describes predictable and stochastic industrial perturbations as well as state-of-the-art technologies to counter process variability. Moreover, we distinguish robustness from tolerance and discuss the potential of single-cell studies for improving system robustness. Finally, we highlight ways of achieving consistent and comparable quantification of robustness that can guide the selection of strains for industrial bioprocesses.

Autotrophic lactate production from H2 + CO2 using recombinant and fluorescent FAST-tagged Acetobacterium woodii strains

Lactate has various uses as industrial platform chemical, poly-lactic acid precursor or feedstock for anaerobic co-cultivations. The aim of this study was to construct and characterise Acetobacterium woodii strains capable of autotrophic lactate production. Therefore, the lctBCD genes, encoding the native Lct dehydrogenase complex, responsible for lactate consumption, were knocked out. Subsequently, a gene encoding a d-lactate dehydrogenase (LDHD) originating from Leuconostoc mesenteroides was expressed in A. woodii, either under the control of the anhydrotetracycline-inducible promoter Ptet or under the lactose-inducible promoter PbgaL. Moreover, LDHD was N-terminally fused to the oxygen-independent fluorescence-activating and absorption-shifting tag (FAST) and expressed in respective A. woodii strains. Cells that produced the LDHD fusion protein were capable of lactate production of up to 18.8 mM in autotrophic batch experiments using H2 + CO2 as energy and carbon source. Furthermore, cells showed a clear and bright fluorescence during exponential growth, as well as in the stationary phase after induction, mediated by the N-terminal FAST. Flow cytometry at the single-cell level revealed phenotypic heterogeneities for cells expressing the FAST-tagged LDHD fusion protein. This study shows that FAST provides a new reporter tool to quickly analyze gene expression over the course of growth experiments of A. woodii. Consequently, fluorescence-based reporters allow for faster and more targeted optimization of production strains.

Key points

Kinetic Modeling of Saccharomyces cerevisiae Central Carbon Metabolism: Achievements, Limitations, and Opportunities

Central carbon metabolism comprises the metabolic pathways in the cell that process nutrients into energy, building blocks and byproducts. To unravel the regulation of this network upon glucose perturbation, several metabolic models have been developed for the microorganism Saccharomyces cerevisiae. These dynamic representations have focused on glycolysis and answered multiple research questions, but no commonly applicable model has been presented. This review systematically evaluates the literature to describe the current advances, limitations, and opportunities. Different kinetic models have unraveled key kinetic glycolytic mechanisms. Nevertheless, some uncertainties regarding model topology and parameter values still limit the application to specific cases. Progressive improvements in experimental measurement technologies as well as advances in computational tools create new opportunities to further extend the model scale. Notably, models need to be made more complex to consider the multiple layers of glycolytic regulation and external physiological variables regulating the bioprocess, opening new possibilities for extrapolation and validation. Finally, the onset of new data representative of individual cells will cause these models to evolve from depicting an average cell in an industrial fermenter, to characterizing the heterogeneity of the population, opening new and unseen possibilities for industrial fermentation improvement.

Real-Time Monitoring of the Yeast Intracellular State During Bioprocesses With a Toolbox of Biosensors

Industrial fermentation processes strive for high robustness to ensure optimal and consistent performance. Medium components, fermentation products, and physical perturbations may cause stress and lower performance. Cellular stress elicits a range of responses, whose extracellular manifestations have been extensively studied; whereas intracellular aspects remain poorly known due to lack of tools for real-time monitoring. Genetically encoded biosensors have emerged as promising tools and have been used to improve microbial productivity and tolerance toward industrially relevant stresses. Here, fluorescent biosensors able to sense the yeast intracellular environment (pH, ATP levels, oxidative stress, glycolytic flux, and ribosome production) were implemented into a versatile and easy-to-use toolbox. Marker-free and efficient genome integration at a conserved site on chromosome X of Saccharomyces cerevisiae strains and a commercial Saccharomyces boulardii strain was developed. Moreover, multiple biosensors were used to simultaneously monitor different intracellular parameters in a single cell. Even when combined together, the biosensors did not significantly affect key physiological parameters, such as specific growth rate and product yields. Activation and response of each biosensor and their interconnection were assessed using an advanced micro-cultivation system. Finally, the toolbox was used to screen cell behavior in a synthetic lignocellulosic hydrolysate that mimicked harsh industrial substrates, revealing differences in the oxidative stress response between laboratory (CEN.PK113-7D) and industrial (Ethanol Red) S. cerevisiae strains. In summary, the toolbox will allow both the exploration of yeast diversity and physiological responses in natural and complex industrial conditions, as well as the possibility to monitor production processes.

Biosensor-based isolation of amino acid-producing Vibrio natriegens strains

The marine bacterium Vibrio natriegens has recently been demonstrated to be a promising new host for molecular biology and next generation bioprocesses. V. natriegens is a Gram-negative, non-pathogenic slight-halophilic bacterium, with a high nutrient versatility and a reported doubling time of under 10 min. However, V. natriegens is not an established model organism yet, and further research is required to promote its transformation into a microbial workhorse.

In this work, the potential of V. natriegens as an amino acid producer was investigated. First, the transcription factor-based biosensor LysG, from Corynebacterium glutamicum, was adapted for expression in V. natriegens to facilitate the detection of positively charged amino acids. A set of different biosensor variants were constructed and characterized, using the expression of a fluorescent protein as sensor output. After random mutagenesis, one of the LysG-based sensors was used to screen for amino acid producer strains. Here, fluorescence-activated cell sorting enabled the selective sorting of highly fluorescent cells, i.e. potential producer cells. Using this approach, individual L-lysine, L-arginine and L-histidine producers could be obtained producing up to 1 mM of the effector amino acid, extracellularly. Genome sequencing of the producer strains provided insight into the amino acid production metabolism of V. natriegens.

This work demonstrates the successful expression and application of transcription factor-based biosensors in V. natriegens and provides insight into the underlying physiology, forming a solid basis for further development of this promising microbe.

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Vibrio natriegens is a promising new host for biotechnology.

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Transcription factor-based biosensors were expressed in V. natriegens.

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Mutagenesis and screening using FACS provided amino acid producing mutants.

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Genome sequencing revealed several causal mutations leading to amino acid production.

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These results will support further efforts to develop V. natriegens as a production host.

Improved Stability of Engineered Ammonia Production in the Plant-Symbiont Azospirillum brasilense

Bioavailable nitrogen is the limiting nutrient for most agricultural food production. Associative diazotrophs can colonize crop roots and fix their own bioavailable nitrogen from the atmosphere. Wild-type (WT) associative diazotrophs, however, do not release fixed nitrogen in culture and are not known to directly transfer fixed nitrogen resources to plants. Efforts to engineer diazotrophs for plant nitrogen provision as an alternative to chemical fertilization have yielded several strains that transiently release ammonia. However, these strains suffer from selection pressure for nonproducers, which rapidly deplete ammonia accumulating in culture, likely limiting their potential for plant growth promotion (PGP). Here we report engineered Azospirillum brasilense strains with significantly extend ammonia production lifetimes of up to 32 days in culture. Our approach relies on multicopy genetic redundancy of a unidirectional adenylyltransferase (uAT) as a posttranslational mechanism to induce ammonia release via glutamine synthetase deactivation. Testing our multicopy stable strains with the model monocot Setaria viridis in hydroponic monoassociation reveals improvement in plant growth promotion compared to single copy strains. In contrast, inoculation of Zea mays in nitrogen-poor, nonsterile soil does not lead to increased PGP relative to WT, suggesting strain health, resource competition, or colonization capacity in soil may also be limiting factors. In this context, we show that while engineered strains fix more nitrogen per cell compared to WT strains, the expression strength of multiple uAT copies needs to be carefully balanced to maximize ammonia production rates and avoid excessive fitness defects caused by excessive glutamine synthetase shutdown.

Biosensor-based growth-coupling and spatial separation as an evolution strategy to improve small molecule production of Corynebacterium glutamicum

Evolutionary engineering is a powerful method to improve the performance of microbial cell factories, but can typically not be applied to enhance the production of chemicals due to the lack of an appropriate selection regime. We report here on a new strategy based on transcription factor-based biosensors, which directly couple production to growth. The growth of Corynebacterium glutamicum was coupled to the intracellular concentration of branched-chain amino acids, by integrating a synthetic circuit based on the Lrp biosensor upstream of two growth-regulating genes, pfkA and hisD. Modelling and experimental data highlight spatial separation as key strategy to limit the selection of 'cheater' strains that escaped the evolutionary pressure. This approach facilitated the isolation of strains featuring specific causal mutations enhancing amino acid production. We envision that this strategy can be applied with the plethora of known biosensors in various microbes, unlocking evolution as a feasible strategy to improve production of chemicals.

Inducible Population Quality Control of Engineered Bacillus subtilis for Improved N-Acetylneuraminic Acid Biosynthesis

Biosynthesis by microorganisms using renewable feedstocks is an important approach for realizing sustainable chemical manufacturing. However, cell-to-cell variation in biosynthesis capability during fermentation restricts the robustness and efficiency of bioproduction, hampering the industrialization of biosynthesis. Herein, we developed an inducible population quality control system (iPopQC) for dynamically modulating the producing and nonproducing subpopulations of engineered Bacillus subtilis, which was constructed via inducible promoter- and metabolite-responsive biosensor-based genetic circuit for regulating essential genes. Moreover, iPopQC achieved a 1.97-fold increase in N-acetylneuraminic acid (NeuAc) titer by enriching producing cell subpopulation during cultivation, representing 52% higher than that of previous PopQC. Strains with double-output iPopQC cocoupling the expression of double essential genes with NeuAc production improved production robustness further, retaining NeuAc production throughout 96 h of fermentation, upon which the strains cocoupling one essential gene expression with NeuAc production abolished the production ability.

L-valine production in Corynebacterium glutamicum based on systematic metabolic engineering: progress and prospects

L-valine is an essential branched-chain amino acid that cannot be synthesized by the human body and has a wide range of applications in food, medicine and feed. Market demand has stimulated people's interest in the industrial production of L-valine. At present, the mutagenized or engineered Corynebacterium glutamicum is an effective microbial cell factory for producing L-valine. Because the biosynthetic pathway and metabolic network of L-valine are intricate and strictly regulated by a variety of key enzymes and genes, highly targeted metabolic engineering can no longer meet the demand for efficient biosynthesis of L-valine. In recent years, the development of omics technology has promoted the upgrading of traditional metabolic engineering to systematic metabolic engineering. This whole-cell-scale transformation strategy has become a productive method for developing L-valine producing strains. This review provides an overview of the biosynthesis and regulation mechanism of L-valine, and summarizes the current metabolic engineering techniques and strategies for constructing L-valine high-producing strains. Finally, the opinion of constructing a cell factory for efficiently biosynthesizing L-valine was proposed.

Development of single-cell-level microfluidic technology for long-term growth visualization of living cultures of Mycobacterium smegmatis

Analysis of growth and death kinetics at single-cell resolution is a key step in understanding the complexity of the nonreplicating growth phenotype of the bacterial pathogen Mycobacterium tuberculosis. Here, we developed a single-cell-resolution microfluidic mycobacterial culture device that allows time-lapse microscopy-based long-term phenotypic visualization of the live replication dynamics of mycobacteria. This technology was successfully applied to monitor the real-time growth dynamics of the fast-growing model strain Mycobacterium smegmatis (M. smegmatis) while subjected to drug treatment regimens during continuous culture for 48 h inside the microfluidic device. A clear morphological change leading to significant swelling at the poles of the bacterial membrane was observed during drug treatment. In addition, a small subpopulation of cells surviving treatment by frontline antibiotics was observed to recover and achieve robust replicative growth once regular culture media was provided, suggesting the possibility of identifying and isolating nonreplicative mycobacteria. This device is a simple, easy-to-use, and low-cost solution for studying the single-cell phenotype and growth dynamics of mycobacteria, especially during drug treatment.

Dynamic Co-Cultivation Process of Corynebacterium glutamicum Strains for the Fermentative Production of Riboflavin

Residual streams from lignocellulosic processes contain sugar mixtures of glucose, xylose, and mannose. Here, the industrial workhorse Corynebacterium glutamicum was explored as a research platform for the rational utilization of a multiple sugar substrate. The endogenous manA gene was overexpressed to enhance mannose utilization. The overexpression of the xylA gene from Xanthomonas campestris in combination with the endogenous xylB gene enabled xylose consumption by C. glutamicum. Furthermore, riboflavin production was triggered by overexpressing the sigH gene from C. glutamicum. The resulting strains were studied during batch fermentations in flasks and 2 L lab-scale bioreactors separately using glucose, mannose, xylose, and a mixture of these three sugars as a carbon source. The production of riboflavin and consumption of sugars were improved during fed-batch fermentation thanks to a dynamic inoculation strategy of manA overexpressing strain and xylAB overexpressing strain. The final riboflavin titer, yield, and volumetric productivity from the sugar mixture were 27 mg L−1, 0.52 mg g−1, and 0.25 mg L−1 h−1, respectively. It reached a 56% higher volumetric productivity with 45% less by-product formation compared with an equivalent process inoculated with a single strain overexpressing the genes xylAB and manA combined. The results indicate the advantages of dynamic multi strains processes for the conversion of sugar mixtures.

Microfluidics for Biotechnology: Bridging Gaps to Foster Microfluidic Applications

Microfluidics and novel lab-on-a-chip applications have the potential to boost biotechnological research in ways that are not possible using traditional methods. Although microfluidic tools were increasingly used for different applications within biotechnology in recent years, a systematic and routine use in academic and industrial labs is still not established. For many years, absent innovative, ground-breaking and “out-of-the-box” applications have been made responsible for the missing drive to integrate microfluidic technologies into fundamental and applied biotechnological research. In this review, we highlight microfluidics’ offers and compare them to the most important demands of the biotechnologists. Furthermore, a detailed analysis in the state-of-the-art use of microfluidics within biotechnology was conducted exemplarily for four emerging biotechnological fields that can substantially benefit from the application of microfluidic systems, namely the phenotypic screening of cells, the analysis of microbial population heterogeneity, organ-on-a-chip approaches and the characterisation of synthetic co-cultures. The analysis resulted in a discussion of potential “gaps” that can be responsible for the rare integration of microfluidics into biotechnological studies. Our analysis revealed six major gaps, concerning the lack of interdisciplinary communication, mutual knowledge and motivation, methodological compatibility, technological readiness and missing commercialisation, which need to be bridged in the future. We conclude that connecting microfluidics and biotechnology is not an impossible challenge and made seven suggestions to bridge the gaps between those disciplines. This lays the foundation for routine integration of microfluidic systems into biotechnology research procedures.

The future of self-selecting and stable fermentations

Unfavorable cell heterogeneity is a frequent risk during bioprocess scale-up and characterized by rising frequencies of low-producing cells. Low-producing cells emerge by both non-genetic and genetic variation and will enrich due to their higher specific growth rate during the extended number of cell divisions of large-scale bioproduction. Here, we discuss recent strategies for synthetic stabilization of fermentation populations and argue for their application to make cell factory designs that better suit industrial needs. Genotype-directed strategies leverage DNA-sequencing data to inform strain design. Self-selecting phenotype-directed strategies couple high production with cell proliferation, either by redirected metabolic pathways or synthetic product biosensing to enrich for high-performing cell variants. Evaluating production stability early in new cell factory projects will guide heterogeneity-reducing design choices. As good initial metrics, we propose production half-life from standardized serial-passage stability screens and production load, quantified as production-associated percent-wise growth rate reduction. Incorporating more stable genetic designs will greatly increase scalability of future cell factories through sustaining a high-production phenotype and enabling stable long-term production.

Dynamic Control of 4-Hydroxyisoleucine Biosynthesis by Modified l-Isoleucine Biosensor in Recombinant Corynebacterium glutamicum

4-Hydroxyisoleucine (4-HIL), a promising drug for treating diabetes, can be synthesized from the self-produced l-isoleucine (Ile) by expressing the Ile dioxygenase gene ido in Corynebacterium glutamicum. However, the requirement of three substrates, Ile, α-ketoglutarate (α-KG), and O₂, makes such de novo biosynthesis difficult to be fulfilled effectively under static engineering conditions. In this study, dynamic control of 4-HIL biosynthesis by the Ile biosensor Lrp-P_brnFE was researched. The native P_brnFE promoter of natural Ile biosensor was still weak even under Ile induction. Through tetA dual genetic selection, several modified stronger P_brnFEN promoters were obtained from the synthetic library of the Ile biosensor. Dynamic regulation of ido expression by modified Ile biosensors increased the 4-HIL titer from 24.7 mM to 28.9-74.4 mM. The best strain ST04 produced even a little more 4-HIL than the static strain SN02 overexpressing ido by the strong P_tacM promoter (69.7 mM). Further dynamic modulation of α-KG supply in ST04 by expressing different P_brnFEN-controlled odhI decreased the 4-HIL production but increased the l-glutamate or Ile accumulation. However, synergistic modulation of α-KG supply and O₂ supply in ST04 by different combinations of P_brnFEN-odhI and P_brnFEN-vgb improved the 4-HIL production significantly, and the highest titer (135.3 mM) was obtained in ST17 strain regulating all the three genes by P_brnFE7. This titer was higher than those of all the static metabolic engineered C. glutamicum strains ever constructed. Therefore, dynamic regulation by modified Ile biosensor is a predominant strategy for enhancing 4-HIL de novo biosynthesis in C. glutamicum.

Dynamic Environmental Control in Microfluidic Single-Cell Cultivations: From Concepts to Applications

Microfluidic single-cell cultivation (MSCC) is an emerging field within fundamental as well as applied biology. During the last years, most MSCCs were performed at constant environmental conditions. Recently, MSCC at oscillating and dynamic environmental conditions has started to gain significant interest in the research community for the investigation of cellular behavior. Herein, an overview of this topic is given and microfluidic concepts that enable oscillating and dynamic control of environmental conditions with a focus on medium conditions are discussed, and their application in single-cell research for the cultivation of both mammalian and microbial cell systems is demonstrated. Furthermore, perspectives for performing MSCC at complex dynamic environmental profiles of single parameters and multiparameters (e.g., pH and O₂ ) in amplitude and time are discussed. The technical progress in this field provides completely new experimental approaches and lays the foundation for systematic analysis of cellular metabolism at fluctuating environments.

Phenotypic heterogeneity of microbial populations under nutrient limitation

Phenotypic heterogeneity is a phenomenon in which genetically identical individuals have different characteristics. This behavior can also be found in bacteria, even if they grow as monospecies in well-mixed environments such as bioreactors. Here it is discussed how phenotypic heterogeneity is generated by internal factors and how it is promoted under nutrient-limited growth conditions. A better understanding of the molecular levels that control phenotypic heterogeneity could improve biotechnological production processes.

Cell-based biosensors: Recent trends, challenges and future perspectives

Existing at the interface of biology and electronics, living cells have been in use as biorecognition elements (bioreceptors) in biosensors since the early 1970s. They are an interesting choice of bioreceptors as they allow flexibility in determining the sensing strategy, are cheaper than purified enzymes and antibodies and make the fabrication relatively simple and cost-effective. And with advances in the field of synthetic biology, microfluidics and lithography, many exciting developments have been made in the design of cell-based biosensors in the last about five years. 3D cell culture systems integrated with electrodes are now providing new insights into disease pathogenesis and physiology, while cardiomyocyte-integrated microelectrode array (MEA) technology is set to be standardized for the assessment of drug-induced cardiac toxicity. From cell microarrays for high-throughput applications to plasmonic devices for anti-microbial susceptibility testing and advent of microbial fuel cell biosensors, cell-based biosensors have evolved from being mere tools for detection of specific analytes to multi-parametric devices for real time monitoring and assessment. However, despite these advancements, challenges such as regeneration and storage life, heterogeneity in cell populations, high interference and high costs due to accessory instrumentation need to be addressed before the full potential of cell-based biosensors can be realized at a larger scale. This review summarizes results of the studies that have been conducted in the last five years toward the fabrication of cell-based biosensors for different applications with a comprehensive discussion on the challenges, future trends, and potential inputs needed for improving them.

Reproduction of Large-Scale Bioreactor Conditions on Microfluidic Chips

Microbial cells in industrial large-scale bioreactors are exposed to fluctuating conditions, e.g., nutrient concentration, dissolved oxygen, temperature, and pH. These inhomogeneities can influence the cell physiology and metabolism, e.g., decelerate cell growth and product formation. Microfluidic systems offer new opportunities to study such effects in great detail by examining responses to varying environmental conditions at single-cell level. However, the possibility to reproduce large-scale bioreactor conditions in microscale cultivation systems has not yet been systematically investigated. Hence, we apply computational fluid dynamics (CFD) simulations to analyze and compare three commonly used microfluidic single-cell trapping and cultivation devices that are based on (i) mother machines (MM), (ii) monolayer growth chambers (MGC), and (iii) negative dielectrophoresis (nDEP). Several representative time-variant nutrient concentration profiles are applied at the chip entry. Responses to these input signals within the studied microfluidic devices are comparatively evaluated at the positions of the cultivated cells. The results are comprehensively presented in a Bode diagram that illustrates the degree of signal damping depending on the frequency of change in the inlet concentration. As a key finding, the MM can accurately reproduce signal changes that occur within 1 s or slower, which are typical for the environmental conditions observed by single cells in large-scale bioreactors, while faster changes are levelled out. In contrast, the nDEP and MGC are found to level out signal changes occurring within 10 s or faster, which can be critical for the proposed application.

Metabolic engineering advances and prospects for amino acid production

Amino acid fermentation is one of the major pillars of industrial biotechnology. The multi-billion USD amino acid market is rising steadily and is diversifying. Metabolic engineering is no longer focused solely on strain development for the bulk amino acids L-glutamate and L-lysine that are produced at the million-ton scale, but targets specialty amino acids. These demands are met by the development and application of new metabolic engineering tools including CRISPR and biosensor technologies as well as production processes by enabling a flexible feedstock concept, co-production and co-cultivation schemes. Metabolic engineering advances are exemplified for specialty proteinogenic amino acids, cyclic amino acids, omega-amino acids, and amino acids functionalized by hydroxylation, halogenation and N-methylation.

Eliciting the impacts of cellular noise on metabolic trade-offs by quantitative mass imaging

Optimal metabolic trade-offs between growth and productivity are key constraints in strain optimization by metabolic engineering; however, how cellular noise impacts these trade-offs and drives the emergence of subpopulations with distinct resource allocation strategies, remains largely unknown. Here, we introduce a single-cell strategy for quantifying the trade-offs between triacylglycerol production and growth in the oleaginous microorganism Yarrowia lipolytica. The strategy relies on high-throughput quantitative-phase imaging and, enabled by nanoscale secondary ion mass spectrometry analyses and dedicated image processing, allows us to image how resources are partitioned between growth and productivity. Enhanced precision over population-averaging biotechnologies and conventional microscopy demonstrates how cellular noise impacts growth and productivity differently. As such, subpopulations with distinct metabolic trade-offs emerge, with notable impacts on strain performance and robustness. By quantifying the self-degradation of cytosolic macromolecules under nutrient-limiting conditions, we discover the cell-to-cell heterogeneity in protein and fatty-acid recycling, unmasking a potential bet-hedging strategy under starvation.

Proteomic study uncovers molecular principles of single-cell-level phenotypic heterogeneity in lipid storage of Nannochloropsis oceanica

BACKGROUND

Nannochloropsis oceanica belongs to a large group of photoautotrophic eukaryotic organisms that play important roles in fixation and cycling of atmospheric CO2. Its capability of storing solar energy and carbon dioxide in the form of triacylglycerol (TAG) of up to 60% of total weight under nitrogen deprivation stress sparked interest in its use for biofuel production. Phenotypes varying in lipid accumulation among an N. oceanica population can be disclosed by single-cell analysis/sorting using fluorescence-activated cell sorting (FACS); yet the phenomenon of single cell heterogeneity in an algae population remains to be fully understood at the molecular level. In this study, combination of FACS and proteomics was used for identification, quantification and differentiation of these heterogeneities on the molecular level.

RESULTS

For N. oceanica cultivated under nitrogen deplete (-N) and replete (+N) conditions, two groups differing in lipid content were distinguished. These differentiations could be recognized on the population as well as the single-cell levels; proteomics uncovered alterations in carbon fixation and flux, photosynthetic machinery, lipid storage and turnover in the populations. Although heterogeneity patterns have been affected by nitrogen supply and cultivation conditions of the N. oceanica populations, differentiation itself seems to be very robust against these factors: cultivation under +N, -N, in shaker bottles, and in a photo-bioreactor all split into two subpopulations. Intriguingly, population heterogeneity resumed after subpopulations were separately recultivated for a second round, refuting the possible development of genetic heterogeneity in the course of sorting and cultivation.

CONCLUSIONS

This work illustrates for the first time the feasibility of combining FACS and (prote)-omics for mechanistic understanding of phenotypic heterogeneity in lipid-producing microalgae. Such combinatorial method can facilitate molecular breeding and design of bioprocesses.

Stable integration of the Mrx1-roGFP2 biosensor to monitor dynamic changes of the mycothiol redox potential in Corynebacterium glutamicum

Mycothiol (MSH) functions as major low molecular weight (LMW) thiol in the industrially important Corynebacterium glutamicum. In this study, we genomically integrated an Mrx1-roGFP2 biosensor in C. glutamicum to measure dynamic changes of the MSH redox potential (E_MSH) during the growth and under oxidative stress. C. glutamicum maintains a highly reducing intrabacterial E_MSH throughout the growth curve with basal E_MSH levels of ~- 296 mV. Consistent with its H₂O₂ resistant phenotype, C. glutamicum responds only weakly to 40 mM H₂O₂, but is rapidly oxidized by low doses of NaOCl. We further monitored basal E_MSH changes and the H₂O₂ response in various mutants which are compromised in redox-signaling of ROS (OxyR, SigH) and in the antioxidant defense (MSH, Mtr, KatA, Mpx, Tpx). While the probe was constitutively oxidized in the mshC and mtr mutants, a smaller oxidative shift in basal E_MSH was observed in the sigH mutant. The catalase KatA was confirmed as major H₂O₂ detoxification enzyme required for fast biosensor re-equilibration upon return to non-stress conditions. In contrast, the peroxiredoxins Mpx and Tpx had only little impact on E_MSH and H₂O₂ detoxification. Further live imaging experiments using confocal laser scanning microscopy revealed the stable biosensor expression and fluorescence at the single cell level. In conclusion, the stably expressed Mrx1-roGFP2 biosensor was successfully applied to monitor dynamic E_MSH changes in C. glutamicum during the growth, under oxidative stress and in different mutants revealing the impact of Mtr and SigH for the basal level E_MSH and the role of OxyR and KatA for efficient H₂O₂ detoxification under oxidative stress.

Controlling and exploiting cell-to-cell variation in metabolic engineering

Individual cells within a population can display diverse phenotypes due to differences in their local environment, genetic variation, and stochastic expression of genes. Understanding this cell-to-cell variation is important for metabolic engineering applications because variability can impact production. For instance, recent studies have shown that production can be highly heterogeneous among engineered cells, and strategies that manage this diversity improve yields of biosynthetic products. These results suggest the potential of controlling variation as a novel approach towards improving performance of engineered cells. In this review, we focus on identifying the origins of cell-to-cell variation in metabolic engineering applications and discuss recent developments on strategies that can be employed to diminish, accept, or even exploit cell-to-cell variation.

In vivo biosensors: mechanisms, development, and applications

In vivo biosensors can recognize and respond to specific cellular stimuli. In recent years, biosensors have been increasingly used in metabolic engineering and synthetic biology, because they can be implemented in synthetic circuits to control the expression of reporter genes in response to specific cellular stimuli, such as a certain metabolite or a change in pH. There are many types of natural sensing devices, which can be generally divided into two main categories: protein-based and nucleic acid-based. Both can be obtained either by directly mining from natural genetic components or by engineering the existing genetic components for novel specificity or improved characteristics. A wide range of new technologies have enabled rapid engineering and discovery of new biosensors, which are paving the way for a new era of biotechnological progress. Here, we review recent advances in the design, optimization, and applications of in vivo biosensors in the field of metabolic engineering and synthetic biology.

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.

Understanding the high L-valine production in Corynebacterium glutamicum VWB-1 using transcriptomics and proteomics

Toward the elucidation of the advanced mechanism of l-valine production by Corynebacterium glutamicum, a highly developed industrial strain VWB-1 was analyzed, employing the combination of transcriptomics and proteomics methods. The transcriptional level of 1155 genes and expression abundance of 96 proteins were changed significantly by the transcriptome and proteome comparison of VWB-1 and ATCC 13869. It was indicated that the key genes involved in the biosynthesis of l-valine, ilvBN, ilvC, ilvD, ilvE were up-regulated in VWB-1, which together made prominent contributions in improving the carbon flow towards l-valine. The l-leucine and l-isoleucine synthesis ability were weakened according to the down-regulation of leuB and ilvA. The up-regulation of the branched chain amino acid transporter genes brnFE promoted the l-valine secretion capability of VWB-1. The NADPH and ATP generation ability of VWB-1 were strengthened through the up-regulation of the genes involved in phosphate pentose pathway and TCA pathway. Pyruvate accumulation was achieved through the weakening of the l-lactate, acetate and l-alanine pathways. The up-regulation of the genes coding for elongation factors and ribosomal proteins were beneficial for l-valine synthesis in C. glutamicum. All information acquired were useful for the genome breeding of better industrial l-valine producing strains.

Diverse genetic error modes constrain large-scale bio-based production

A transition toward sustainable bio-based chemical production is important for green growth. However, productivity and yield frequently decrease as large-scale microbial fermentation progresses, commonly ascribed to phenotypic variation. Yet, given the high metabolic burden and toxicities, evolutionary processes may also constrain bio-based production. We experimentally simulate large-scale fermentation with mevalonic acid-producing Escherichia coli. By tracking growth rate and production, we uncover how populations fully sacrifice production to gain fitness within 70 generations. Using ultra-deep (>1000×) time-lapse sequencing of the pathway populations, we identify multiple recurring intra-pathway genetic error modes. This genetic heterogeneity is only detected using deep-sequencing and new population-level bioinformatics, suggesting that the problem is underestimated. A quantitative model explains the population dynamics based on enrichment of spontaneous mutant cells. We validate our model by tuning production load and escape rate of the production host and apply multiple orthogonal strategies for postponing genetically driven production declines.

The declining performance of scale-up bioreactor cultures is commonly attributed to phenotypic and physical heterogeneities. Here, the authors reveal multiple recurring intra-pathway error modes that limit engineered E. coli mevalonic acid production over time- and industrial-scale fermentations.

New Intracellular Shikimic Acid Biosensor for Monitoring Shikimate Synthesis in Corynebacterium glutamicum

The quantitative monitoring of intracellular metabolites with in vivo biosensors provides an efficient means of identifying high-yield strains and observing product accumulation in real time. In this study, a shikimic acid (SA) biosensor was constructed from a LysR-type transcriptional regulator (ShiR) of Corynebacterium glutamicum. The SA biosensor specifically responded to the increase of intracellular SA concentration over a linear range of 19.5 ± 3.6 to 120.9 ± 1.2 fmole at the single-cell level. This new SA biosensor was successfully used to (1) monitor the SA production of different C. glutamicum strains; (2) develop a novel result-oriented high-throughput ribosome binding site screening and sorting strategy that was used for engineering high-yield shikimate-producing strains; and (3) engineer a whole-cell biosensor through the coexpression of the SA sensor and a shikimate transporter shiA gene in C. glutamicum RES167. This work demonstrated that a new intracellular SA biosensor is a valuable tool facilitating the fast development of microbial SA producer.

Microbioreactor Systems for Accelerated Bioprocess Development

In recent years, microbioreactor (MBR) systems have evolved towards versatile bioprocess engineering tools. They provide a unique solution to combine higher experimental throughput with extensive bioprocess monitoring and control, which is indispensable to develop economically and ecologically competitive bioproduction processes. MBR systems are based either on down-scaled stirred tank reactors or on advanced shaken microtiter plate cultivation devices. Importantly, MBR systems make use of optical measurements for non-invasive, online monitoring of important process variables like biomass concentration, dissolved oxygen, pH, and fluorescence. The application range of MBR systems can be further increased by integration into liquid handling robots, enabling automatization and, thus standardization, of various handling and operation procedures. Finally, the tight integration of quantitative strain phenotyping with bioprocess development under industrially relevant conditions greatly increases the probability of finding the right combination of producer strain and bioprocess control strategy. This review will discuss the current state of the art in the field of MBR systems and we can readily conclude that their importance for industrial biotechnology will further increase in the near future.

Production of amino acids - Genetic and metabolic engineering approaches

The biotechnological production of amino acids occurs at the million-ton scale and annually about 6milliontons of l-glutamate and l-lysine are produced by Escherichia coli and Corynebacterium glutamicum strains. l-glutamate and l-lysine production from starch hydrolysates and molasses is very efficient and access to alternative carbon sources and new products has been enabled by metabolic engineering. This review focusses on genetic and metabolic engineering of amino acid producing strains. In particular, rational approaches involving modulation of transcriptional regulators, regulons, and attenuators will be discussed. To address current limitations of metabolic engineering, this article gives insights on recent systems metabolic engineering approaches based on functional tools and method such as genome reduction, amino acid sensors based on transcriptional regulators and riboswitches, CRISPR interference, small regulatory RNAs, DNA scaffolding, and optogenetic control, and discusses future prospects.

Discovery and engineering of a 1-butanol biosensor in Saccharomyces cerevisiae

The present study aimed to develop a universal methodology for the discovery of biosensors sensitive to particular stresses or metabolites by using a transcriptome analysis, in order to address the need for in vivo biosensors to drive the engineering of microbial cell factories. The method was successfully applied to the discovery of 1-butanol sensors. In particular, the genome-wide transcriptome profiling of S. cerevisiae exposed to three similar short-chain alcohols, 1-butanol, 1-propanol, and ethanol, identified genes that were differentially expressed only under the treatment of 1-butanol. From these candidates, two promoters that responded specifically to 1-butanol were characterized in a dose-dependent manner and were used to distinguish differences in production levels among different 1-butanol producer strains. This strategy opens up new opportunities for the discovery of promoter-based biosensors and can potentially be used to identify biosensors for any metabolite that causes cellular transcriptomic changes.

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.