Origin and analysis of microbial population heterogeneity in bioprocesses

Strategies to enhance production of metabolites in microbial co-culture systems

Increasing evidence shows that microbial synthesis plays an important role in producing high value-added products. However, microbial monoculture generally hampers metabolites production and limits scalability due to the increased metabolic burden on the host strain. In contrast, co-culture is a more flexible approach to improve the environmental adaptability and reduce the overall metabolic burden. The well-defined co-culturing microbial consortia can tap their metabolic potential to obtain yet-to-be discovered and pre-existing metabolites. This review focuses on the use of a co-culture strategy and its underlying mechanisms to enhance the production of products. Notably, the significance of comprehending the microbial interactions, diverse communication modes, genetic information, and modular co-culture involved in co-culture systems were highlighted. Furthermore, it addresses the current challenges and outlines potential future directions for microbial co-culture. This review provides better understanding the diversity and complexity of the interesting interaction and communication to advance the development of co-culture techniques.

Physiological response of Symbiodiniaceae to thermal stress: Reactive oxygen species, photosynthesis, and relative cell size

This study investigates the physiological response to heat stress of three genetically different Symbiodiniaceae strains isolated from the scleractinian coral Mussismilia braziliensis, endemic of the Abrolhos Bank, Brazil. Cultures of two Symbiodinium sp. and one Cladocopium sp. were exposed to a stepwise increase in temperature (2°C every second day) ranging from 26°C (modal temperature in Abrolhos) to 32°C (just above the maximum temperature registered in Abrolhos during the third global bleaching event-TGBE). After the cultures reached their final testing temperature, reactive oxygen species (ROS) production, single cell attributes (relative cell size and chlorophyll fluorescence), and photosynthetic efficiency (effective (Y(II)) and maximum (Fv/Fm) quantum yields) were measured within 4 h and 72 h. Non-photochemical coefficient (NPQ) was estimated based on fluorescence values. Population average ROS production was variable across strains and exposure times, reaching up a 2-fold increase at 32°C in one of the Symbiodinium sp. strains. A marked intrapopulation difference was observed in ROS production, with 5 to 25% of the cells producing up to 10 times more than the population average, highlighting the importance of single cell approaches to assess population physiology. Average cell size increases at higher temperatures, likely resulting from cell cycle arrest, whereas chlorophyll fluorescence decreased, especially in 4 h, indicating a photoacclimation response. The conditions tested do not seem to have elicited loss of photosynthetic efficiency nor the activation of non-photochemical mechanisms in the cells. Our results unveiled a generalized thermotolerance in three Symbiodiniaceae strains originated from Abrolhos' corals. Inter and intra-specific variability could be detected, likely reflecting the genetic differences among the strains.

Immunomagnetic Capture of Faecalibacterium prausnitzii Selectively Modifies the Fecal Microbiota and Its Immunomodulatory Profile

Faecalibacterium represents one of the most abundant bacterial groups in the human intestinal microbiota of healthy adults and can represent more than 10% of the total bacterial population, Faecalibacterium prausnitzii being the only recognized species up to the past year. Reduction in the abundance of F. prausnitzii in the human gut has been linked to several human disorders, such as Crohn's disease. In this study, we developed a strategy to modify the relative abundance of F. prausnitzii in fecal microbiotas as a means of evaluating its contribution to the immunomodulatory effect of intestinal microbiotas with different F. prausnitzii contents using a peripheral blood mononuclear cell (PBMC) model. We used a polyclonal antibody against the surface of F. prausnitzii M21 to capture the bacterium from synthetic and human fecal microbiotas using immunoseparation techniques. As a proof-of-principle study, the levels of immunomodulation exerted by microbiotas of healthy donors (HDs) with different relative abundances of F. prausnitzii, achieved with the above-mentioned immunoseparation technique, were evaluated in a PBMC model. For this purpose, PBMCs were cocultivated with the modified microbiotas or a pure culture of F. prausnitzii and, subsequently, the microbiota of Crohn's donors was added to the coculture. The cytokine concentration was determined, showing that our experimental model supports the anti-inflammatory effects of this bacterium. IMPORTANCE There is increasing interest in deciphering the contribution of gut microbiota species to health and disease amelioration. The approach proposed herein provides a novel and affordable strategy to probe deeply into microbiota-host interactions by strategically modifying the relative abundance of specific gut microbes, hence facilitating the study of their contribution to a given trait of the microbiota.

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 (pF81kan) 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.

Construction of Environmental Synthetic Microbial Consortia: Based on Engineering and Ecological Principles

In synthetic biology, engineering principles are applied to system design. The development of synthetic microbial consortia represents the intersection of synthetic biology and microbiology. Synthetic community systems are constructed by co-cultivating two or more microorganisms under certain environmental conditions, with broad applications in many fields including ecological restoration and ecological theory. Synthetic microbial consortia tend to have high biological processing efficiencies, because the division of labor reduces the metabolic burden of individual members. In this review, we focus on the environmental applications of synthetic microbial consortia. Although there are many strategies for the construction of synthetic microbial consortia, we mainly introduce the most widely used construction principles based on cross-feeding. Additionally, we propose methods for constructing synthetic microbial consortia based on traits and spatial structure from the perspective of ecology to provide a basis for future work.

Toward absolute viability measurements for bacteria

We aim to develop a quantitative viability method that distinguishes individual quiescent from dead cells and is measured in time (ns) as a referenceable, comparable quantity. We demonstrate that fluorescence lifetime imaging of an anionic, fluorescent membrane voltage probe fulfills these requirements for Streptococcus mutans. A random forest machine-learning model assesses whether individual S. mutans can be correctly classified into their original populations: stationary phase (quiescent), heat killed and inactivated via chemical fixation. We compare the results to intensity using three models: lifetime variables (τ₁ , τ₂ and p₁ ), phasor variables (G, S) or all five variables, with the five variable models having the most accurate classification. This initial work affirms the potential for using fluorescence lifetime of a membrane voltage probe as a viability marker for quiescent bacteria, and future efforts on other bacterial species and fluorophores will help refine this approach.

A cytofluorimetric analysis of a Saccharomyces cerevisiae population cultured in a fed-batch bioreactor

The yeast Saccharomyces cerevisiae is a reference model system and one of the widely used microorganisms in many biotechnological processes. In industrial yeast applications, combined strategies aim to maximize biomass/product yield, with the fed-batch culture being one of the most frequently used. Flow cytometry (FCM) is widely applied in biotechnological processes and represents a key methodology to monitor cell population dynamics. We propose here an application of FCM in the analysis of yeast cell cycle along the time course of a typical S. cerevisiae fed-batch culture. We used two different dyes, SYTOX Green and SYBR Green, with the aim to better define each stage of cell cycle during S. cerevisiae fed-batch culture. The results provide novel insights in the use of FCM cell cycle analysis for the real-time monitoring of S. cerevisiae bioprocesses.

Development overview of Raman-activated cell sorting devoted to bacterial detection at single-cell level

Understanding the metabolic interactions between bacteria in natural habitat at the single-cell level and the contribution of individual cell to their functions is essential for exploring the dark matter of uncultured bacteria. The combination of Raman-activated cell sorting (RACS) and single-cell Raman spectra (SCRS) with unique fingerprint characteristics makes it possible for research in the field of microbiology to enter the single cell era. This review presents an overview of current knowledge about the research progress of recognition and assessment of single bacterium cell based on RACS and further research perspectives. We first systematically summarize the label-free and non-destructive RACS strategies based on microfluidics, microdroplets, optical tweezers, and specially made substrates. The importance of RACS platforms in linking target cell genotype and phenotype is highlighted and the approaches mentioned in this paper for distinguishing single-cell phenotype include surface-enhanced Raman scattering (SERS), biomarkers, stable isotope probing (SIP), and machine learning. Finally, the prospects and challenges of RACS in exploring the world of unknown microorganisms are discussed. KEY POINTS: • Analysis of single bacteria is essential for further understanding of the microbiological world. • Raman-activated cell sorting (RACS) systems are significant protocol for characterizing phenotypes and genotypes of individual bacteria.

Stochasticity in microbiology: managing unpredictability to reach the Sustainable Development Goals

Pure (single) cultures of microorganisms and mixed microbial communities (microbiomes) have been important for centuries in providing renewable energy, clean water and food products to human society and will continue to play a crucial role to pursue the Sustainable Development Goals. To use microorganisms effectively, microbial engineered processes require adequate control. Microbial communities are shaped by manageable deterministic processes, but also by stochastic processes, which can promote unforeseeable variations and adaptations. Here, we highlight the impact of stochasticity in single culture and microbiome engineering. First, we discuss the concepts and mechanisms of stochasticity in relation to microbial ecology of single cultures and microbiomes. Second, we discuss the consequences of stochasticity in relation to process performance and human health, which are reflected in key disadvantages and important opportunities. Third, we propose a suitable decision tool to deal with stochasticity in which monitoring of stochasticity and setting the boundaries of stochasticity by regulators are central aspects. Stochasticity may give rise to some risks, such as the presence of pathogens in microbiomes. We argue here that by taking the necessary precautions and through clever monitoring and interpretation, these risks can be mitigated.

Plasmid expression level heterogeneity monitoring via heterologous eGFP production at the single-cell level in Cupriavidus necator

A methodology for plasmid expression level monitoring of eGFP expression suitable for dynamic processes was assessed during fermentation. This technique was based on the expression of a fluorescent biosensor (eGFP) encoded on a recombinant plasmid coupled to single-cell analysis. Fluorescence intensity at single-cell level was measured by flow cytometry. We demonstrated that promoter evaluation based on single-cell analysis versus classic global analysis brings valuable insights. Single-cell analysis pointed out the fact that intrinsic fluorescence increased with the strength of the promoter up to a threshold. Beyond that, cell permeability increases to excrete the fluorescent protein in the medium. The metabolic load due to the increase in the eGFP production in the case of strong constitutive promoters leads to slower growth kinetics compared with plasmid-free cells. With the strain Cupriavidus necator Re2133, growth rate losses were measured from 3% with the weak constitutive promoter P_lac to 56% with the strong constitutive promoter P_j5. Through this work, it seems crucial to find a compromise between the fluorescence intensity in single cells and the metabolic load; in our conditions, the best compromise found was the weak promoter P_lac. The plasmid expression level monitoring method was tested in the presence of a heterogeneous population induced by plasmid-curing methods. For all the identified subpopulations, the plasmid expression level heterogeneity was significantly detected at the level of fluorescence intensity in single cells. After cell sorting, growth rate and cultivability were assessed for each subpopulation. In conclusion, this eGFP biosensor makes it possible to follow the variations in the level of plasmid expression under conditions of population heterogeneity.Key Points•Development of a plasmid expression level monitoring method at the single-cell level by flow cytometry.•Promoter evaluation by single-cell analysis: cell heterogeneity and strain robustness.•Reporter system optimization for efficient subpopulation detection in pure cultures.

Exploiting nonionic surfactants to enhance fatty alcohol production in Rhodosporidium toruloides

Fatty alcohols (FOHs) are important feedstocks in the chemical industry to produce detergents, cosmetics, and lubricants. Microbial production of FOHs has become an attractive alternative to production in plants and animals due to growing energy demands and environmental concerns. However, inhibition of cell growth caused by intracellular FOH accumulation is one major issue that limits FOH titers in microbial hosts. In addition, identification of FOH‐specific exporters remains a challenge and previous studies towards this end are limited. To alleviate the toxicity issue, we exploited nonionic surfactants to promote the export of FOHs in Rhodosporidium toruloides, an oleaginous yeast that is considered an attractive next‐generation host for the production of fatty acid‐derived chemicals. Our results showed FOH export efficiency was dramatically improved and the growth inhibition was alleviated in the presence of small amounts of tergitol and other surfactants. As a result, FOH titers increase by 4.3‐fold at bench scale to 352.6 mg/L. With further process optimization in a 2‐L bioreactor, the titer was further increased to 1.6 g/L. The method we show here can potentially be applied to other microbial hosts and may facilitate the commercialization of microbial FOH production.

Experimental Challenges for Reduced Genomes: The Cell Model Escherichia coli

Genome reduction, as a top-down approach to obtain the minimal genetic information essential for a living organism, has been conducted with bacterial cells for decades. The most popular and well-studied cell models for genome reduction are Escherichia coli strains. As the previous literature intensively introduced the genetic construction and application of the genome-reduced Escherichia coli strains, the present review focuses the design principles and compares the reduced genome collections from the specific viewpoint of growth, which represents a fundamental property of living cells and is an important feature for their biotechnological application. For the extended simplification of the genomic sequences, the approach of experimental evolution and concern for medium optimization are newly proposed. The combination of the current techniques of genomic construction and the newly proposed methodologies could allow us to acquire growing Escherichia coli cells carrying the extensively reduced genome and to address the question of what the minimal genome essential for life is.

Quantitative Flow Cytometry to Understand Population Heterogeneity in Response to Changes in Substrate Availability in Escherichia coli and Saccharomyces cerevisiae Chemostats

Microbial cells in bioprocesses are usually described with averaged parameters. But in fact, single cells within populations vary greatly in characteristics such as stress resistance, especially in response to carbon source gradients. Our aim was to introduce tools to quantify population heterogeneity in bioprocesses using a combination of reporter strains, flow cytometry, and easily comprehensible parameters. We calculated mean, mode, peak width, and coefficient of variance to describe distribution characteristics and temporal shifts in fluorescence intensity. The skewness and the slope of cumulative distribution function plots illustrated differences in distribution shape. These parameters are person-independent and precise. We demonstrated this by quantifying growth-related population heterogeneity of Saccharomyces cerevisiae and Escherichia coli reporter strains in steady-state of aerobic glucose-limited chemostat cultures at different dilution rates and in response to glucose pulses. Generally, slow-growing cells showed stronger responses to glucose excess than fast-growing cells. Cell robustness, measured as membrane integrity after exposure to freeze-thaw treatment, of fast-growing cells was strongly affected in subpopulations of low membrane robustness. Glucose pulses protected subpopulations of fast-growing but not slower-growing yeast cells against membrane damage. Our parameters could successfully describe population heterogeneity, thereby revealing physiological characteristics that might have been overlooked during traditional averaged analysis.

Microbial single-cell growth response at defined carbon limiting conditions

Using microfluidic single-cell cultivation technologies and modelling we examined how single-cell growth at defined carbon conditions, ranging from strongly limiting conditions to a carbon surplus, influenced cell-to-cell variability.

A Novel Framework for Parameter and State Estimation of Multicellular Systems Using Gaussian Mixture Approximations

Multicellular systems play an important role in many biotechnological processes. Typically, these exhibit cell-to-cell variability, which has to be monitored closely for process control and optimization. However, some properties may not be measurable due to technical and financial restrictions. To improve the monitoring, model-based online estimators can be designed for their reconstruction. The multicellular dynamics is accounted for in the framework of population balance models (PBMs). These models are based on single cell kinetics, and each cellular state translates directly into an additional dimension of the obtained partial differential equations. As multicellular dynamics often require detailed single cell models and feature a high number of cellular components, the resulting population balance equations are often high-dimensional. Therefore, established state estimation concepts for PBMs based on discrete grids are not recommended due to the large computational effort. In this contribution a novel approach is proposed, which is based on the approximation of the underlying number density functions as the weighted sum of Gaussian distributions. Thus, the distribution is described by the characteristic properties of the individual Gaussians, like the mean and covariance. Thereby, the complex infinite dimensional estimation problem can be reduced to a finite dimension. The characteristic properties are estimated in a recursive approach. The method is evaluated for two academic benchmark examples, and the results indicate its potential for model-based online reconstruction for multicellular systems.

Machine learning analysis of microbial flow cytometry data from nanoparticles, antibiotics and carbon sources perturbed anaerobic microbiomes

BACKGROUND

Flow cytometry, with its high throughput nature, combined with the ability to measure an increasing number of cell parameters at once can surpass the throughput of prevalent genomic and metagenomic approaches in the study of microbiomes. Novel computational approaches to analyze flow cytometry data will result in greater insights and actionability as compared to traditional tools used in the analysis of microbiomes. This paper is a demonstration of the fruitfulness of machine learning in analyzing microbial flow cytometry data generated in anaerobic microbiome perturbation experiments.

RESULTS

Autoencoders were found to be powerful in detecting anomalies in flow cytometry data from nanoparticles and carbon sources perturbed anaerobic microbiomes but was marginal in predicting perturbations due to antibiotics. A comparison between different algorithms based on predictive capabilities suggested that gradient boosting (GB) and deep learning, i.e. feed forward artificial neural network with three hidden layers (DL) were marginally better under tested conditions at predicting overall community structure while distributed random forests (DRF) worked better for predicting the most important putative microbial group(s) in the anaerobic digesters viz. methanogens, and it can be optimized with better parameter tuning. Predictive classification patterns with DL (feed forward artificial neural network with three hidden layers) were found to be comparable to previously demonstrated multivariate analysis. The potential applications of this approach have been demonstrated for monitoring the syntrophic resilience of the anaerobic microbiomes perturbed by synthetic nanoparticles as well as antibiotics.

CONCLUSION

Machine learning can benefit the microbial flow cytometry research community by providing rapid screening and characterization tools to discover patterns in the dynamic response of microbiomes to several stimuli.

Investigation of the population dynamics within a Pseudomonas aeruginosa biofilm using a flow based biofilm model system and flow cytometric evaluation of cellular physiology

In this study a flow based biofilm model system was used to simulate the formation of Pseudomonas aeruginosa biofilms on a stainless steel surface. To investigate the complexity of biofilm-associated P. aeruginosa populations a combination of microscopic observations and flow cytometric analysis (FCM) was adopted. Biofilm-associated P. aeruginosa cells were evaluated (1) under optimal vs reduced nutrient-availability at the initial adhesion stage, and (2) irrespective of nutrient-availability within a mature biofilm. Microscopic estimation of the extent of attachment revealed more effective colonization upon optimal vs starvation conditions. FCM allowed an in situ evaluation of P. aeruginosa vitality, using cellular redox potential measurements to discriminate active, mid-active and non-active sub-populations. Samples from recently attached cells and mature biofilms showed significant differences in the percentages of bacterial cells from the defined sub-populations. The approach demonstrated that distribution of individual P. aeruginosa sub-populations was influenced by the stage of the biofilm life-cycle and nutrient availability.

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.

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.

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.

Engineered Control of Genetic Variability Reveals Interplay among Quorum Sensing, Feedback Regulation, and Biochemical Noise

Stochastic fluctuations in gene expression trigger both beneficial and harmful consequences for cell behavior. Therefore, achieving a desired mean protein expression level while minimizing noise is of interest in many applications, including robust protein production systems in industrial biotechnology. Here, we consider a synthetic gene circuit combining intracellular negative feedback and cell-to-cell communication based on quorum sensing. Accounting for both intrinsic and extrinsic noise, stochastic simulations allow us to analyze the capability of the circuit to reduce noise strength as a function of its parameters. We obtain mean expression levels and noise strengths for all species under different scenarios, showing good agreement with system-wide available experimental data of protein abundance and noise in Escherichia coli. Our in silico experiments, validated by preliminary in vivo results, reveal significant noise attenuation in gene expression through the interplay between quorum sensing and negative feedback and highlight the differential role that they play in regard to intrinsic and extrinsic noise.

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.

Cooperation in microbial communities and their biotechnological applications

Microbial communities are increasingly utilized in biotechnology. Efficiency and productivity in many of these applications depends on the presence of cooperative interactions between members of the community. Two key processes underlying these interactions are the production of public goods and metabolic cross-feeding, which can be understood in the general framework of ecological and evolutionary (eco-evo) dynamics. In this review, we illustrate the relevance of cooperative interactions in microbial biotechnological processes, discuss their mechanistic origins and analyse their evolutionary resilience. Cooperative behaviours can be damaged by the emergence of 'cheating' cells that benefit from the cooperative interactions but do not contribute to them. Despite this, cooperative interactions can be stabilized by spatial segregation, by the presence of feedbacks between the evolutionary dynamics and the ecology of the community, by the role of regulatory systems coupled to the environmental conditions and by the action of horizontal gene transfer. Cooperative interactions enrich microbial communities with a higher degree of robustness against environmental stress and can facilitate the evolution of more complex traits. Therefore, the evolutionary resilience of microbial communities and their ability to constraint detrimental mutants should be considered to design robust biotechnological applications.

Lagrangian Trajectories to Predict the Formation of Population Heterogeneity in Large-Scale Bioreactors

Successful scale-up of bioprocesses requires that laboratory-scale performance is equally achieved during large-scale production to meet economic constraints. In industry, heuristic approaches are often applied, making use of physical scale-up criteria that do not consider cellular needs or properties. As a consequence, large-scale productivities, conversion yields, or product purities are often deteriorated, which may prevent economic success. The occurrence of population heterogeneity in large-scale production may be the reason for underperformance. In this study, an in silico method to predict the formation of population heterogeneity by combining computational fluid dynamics (CFD) with a cell cycle model of Pseudomonas putida KT2440 was developed. The glucose gradient and flow field of a 54,000 L stirred tank reactor were generated with the Euler approach, and bacterial movement was simulated as Lagrange particles. The latter were statistically evaluated using a cell cycle model. Accordingly, 72% of all cells were found to switch between standard and multifork replication, and 10% were likely to undergo massive, transcriptional adaptations to respond to extracellular starving conditions. At the same time, 56% of all cells replicated very fast, with µ ≥ 0.3 h^-1 performing multifork replication. The population showed very strong heterogeneity, as indicated by the observation that 52.9% showed higher than average adenosine triphosphate (ATP) maintenance demands (12.2%, up to 1.5 fold). These results underline the potential of CFD linked to structured cell cycle models for predicting large-scale heterogeneity in silico and ab initio.

Influence of carbon sources on the viability and resuscitation of Acetobacter senegalensis during high-temperature gluconic acid fermentation

Much research has been conducted about different types of fermentation at high temperature, but only a few of them have studied cell viability changes during high-temperature fermentation. In this study, Acetobacter senegalensis, a thermo-tolerant strain, was used for gluconic acid production at 38 °C. The influences of different carbon sources and physicochemical conditions on cell viability and the resuscitation of viable but nonculturable (VBNC) cells formed during fermentation were studied. Based on the obtained results, A. senegalensis could oxidize 95 g l^- 1 glucose to gluconate at 38 °C (pH 5.5, yield 83%). However, despite the availability of carbon and nitrogen sources, the specific rates of glucose consumption (q_s) and gluconate production (q_p) reduced progressively. Interestingly, gradual q_s and q_p reduction coincided with gradual decrease in cellular dehydrogenase activity, cell envelope integrity, and cell culturability as well as with the formation of VBNC cells. Entry of cells into VBNC state during stationary phase partly stemmed from high fermentation temperature and long-term oxidation of glucose, because just about 48% of VBNC cells formed during stationary phase were resuscitated by supplementing the culture medium with an alternative favorite carbon source (low concentration of ethanol) and/or reducing incubation temperature to 30 °C. This indicates that ethanol, as a favorable carbon source, supports the repair of stressed cells. Since formation of VBNC cells is often inevitable during high-temperature fermentation, using an alternative carbon source together with changing physicochemical conditions may enable the resuscitation of VBNC cells and their use for several production cycles.

Heterogenic response of prokaryotes toward silver nanoparticles and ions is facilitated by phenotypes and attachment of silver aggregates to cell surfaces

Tons of anthropogenic silver nanoparticles (AgNPs) are assumed to be released into the environment due to their use in many consumer products. AgNPs are known to be toxic toward microorganisms and thus may harm their specific functions in ecosystems. Here we explore the impact of AgNPs on functioning of single cells in microbial populations at doses typically found in anthropogenic environments. The response of single cells to AgNPs was analyzed by flow cytometry and using the fluorescent dyes propidium iodide and DiBAC₄ (3) as markers for cell membrane disintegration and depolarization, respectively. The effects of 10-nm and 30-nm AgNPs on three bacterial species (Mycobacterium frederiksbergense, Pseudomonas putida, and Escherichia coli) showed that the populations split into affected cells and others not showing any malfunction, with varying abundances depending on strains and cell growth states. Further, the dissolution of AgNPs measured with 3 KDa ultrafiltration and inductively coupled plasma-mass-spectrometry to distinguish particle-related effects from toxicity of dissolved Ag revealed that Ag ions were the principal toxicant. AgNP aggregate formation was followed by dynamic light scattering and the aggregates' attachment to cell surfaces was visualized by transmission electron microscopy and scanning electron microscopy-energy dispersive X-ray spectroscopy. An increased AgNP-affected cell fraction relative to the Ag ion impact was identified. The study shows that individual cells in a population cope differently with AgNP induced stress by evolving heterogeneous phenotypes. The response is linked to cell death and cell energy depletion depending on cell type and cell growth states. The attachment of AgNP aggregates to cell surfaces seems to amplify the heterogeneous response. © 2017 International Society for Advancement of Cytometry.

Physiology and bioprocess of single cell of Stenotrophomonas maltophilia in bioremediation of co-existed benzo[a]pyrene and copper

Polycyclic aromatic hydrocarbons (PAHs) and heavy metals are generally present in mixtures in the e-waste dismantling areas, posing serious health risk to the local people. Bioremediation has been considered as a promising approach for PAHs and heavy metals removal. In this study, we applied flow cytometry to obtain a better understanding of membrane potential (MP), apoptosis and cell cycle of Stenotrophomonas maltophilia affected by combined pollutants of benzo[a]pyrene(BaP) and Cu(II). The results showed that BaP was the main factor damaging the cell membrane and influencing the MP. S. maltophilia could even protect against upsetting situation by encouraging early apoptosis, whereby compromised cells committed suicide, as a result, which, in turn, facilitated the metabolism of the bacteria with high-vitality. Furthermore, even if the bacterial cell division was blocked and stopped reproduction, a large number of key enzymes inside cells could still be used for degradation of BaP. The expression of protein related to the transport and metabolism of BaP, regulation of redox reactions and phosphorylation in bacterial cell during bioprocess were evident different.

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.

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.

Stress Physiology of Lactic Acid Bacteria

Lactic acid bacteria (LAB) are important starter, commensal, or pathogenic microorganisms. The stress physiology of LAB has been studied in depth for over 2 decades, fueled mostly by the technological implications of LAB robustness in the food industry. Survival of probiotic LAB in the host and the potential relatedness of LAB virulence to their stress resilience have intensified interest in the field. Thus, a wealth of information concerning stress responses exists today for strains as diverse as starter (e.g., Lactococcus lactis), probiotic (e.g., several Lactobacillus spp.), and pathogenic (e.g., Enterococcus and Streptococcus spp.) LAB. Here we present the state of the art for LAB stress behavior. We describe the multitude of stresses that LAB are confronted with, and we present the experimental context used to study the stress responses of LAB, focusing on adaptation, habituation, and cross-protection as well as on self-induced multistress resistance in stationary phase, biofilms, and dormancy. We also consider stress responses at the population and single-cell levels. Subsequently, we concentrate on the stress defense mechanisms that have been reported to date, grouping them according to their direct participation in preserving cell energy, defending macromolecules, and protecting the cell envelope. Stress-induced responses of probiotic LAB and commensal/pathogenic LAB are highlighted separately due to the complexity of the peculiar multistress conditions to which these bacteria are subjected in their hosts. Induction of prophages under environmental stresses is then discussed. Finally, we present systems-based strategies to characterize the "stressome" of LAB and to engineer new food-related and probiotic LAB with improved stress tolerance.

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.

Bioprocess engineering for microbial synthesis and conversion of isoprenoids

Isoprenoids represent a natural product class essential to living organisms. Moreover, industrially relevant isoprenoid molecules cover a wide range of products such as pharmaceuticals, flavors and fragrances, or even biofuels. Their often complex structure makes chemical synthesis a difficult and expensive task and extraction from natural sources is typically low yielding. This has led to intense research for biotechnological production of isoprenoids by microbial de novo synthesis or biotransformation. Here, metabolic engineering, including synthetic biology approaches, is the key technology to develop efficient production strains in the first place. Bioprocess engineering, particularly in situ product removal (ISPR), is the second essential technology for the development of industrial-scale bioprocesses. A number of elaborate bioreactor and ISPR designs have been published to target the problems of isoprenoid synthesis and conversion, such as toxicity and product inhibition. However, despite the many exciting applications of isoprenoids, research on isoprenoid-specific bioprocesses has mostly been, and still is, limited to small-scale proof-of-concept approaches. This review presents and categorizes different ISPR solutions for biotechnological isoprenoid production and also addresses the main challenges en route towards industrial application.