Dynamic flux balance analysis of high cell density fed-batch culture of Escherichia coli BL21 (DE3) with mass spectrometry-based spent media analysis

Dynamic flux balance analysis (FBA) allows estimation of intracellular reaction rates using organism-specific genome-scale metabolic models (GSMM) and by assuming instantaneous pseudo-steady states for processes that are inherently dynamic. This technique is well-suited for industrial bioprocesses employing complex media characterized by a hierarchy of substrate uptake and product secretion. However, knowledge of exchange rates of many components of the media would be required to obtain meaningful results. Here, we performed spent media analysis using mass spectrometry coupled with liquid and gas chromatography for a fed-batch, high-cell density cultivation of Escherichia coli BL21(DE3) expressing a recombinant protein. Time course measurements thus obtained for 246 metabolites were converted to instantaneous exchange rates. These were then used as constraints for dynamic FBA using a previously reported GSMM, thus providing insights into how the flux map evolves through the process. Changes in tri-carboxylic acid cycle fluxes correlated with the increased demand for energy during recombinant protein production. The results show how amino acids act as hubs for the synthesis of other cellular metabolites. Our results provide a deeper understanding of an industrial bioprocess and will have implications in further optimizing the process.

Precision fermentation with mass spectrometry-based spent media analysis

Optimization and monitoring of bioprocesses requires the measurement of several process parameters and quality attributes. Mass spectrometry (MS)-based techniques such as those coupled to gas chromatography (GCMS) and liquid Chromatography (LCMS) enable the simultaneous measurement of hundreds of metabolites with high sensitivity. When applied to spent media, such metabolome analysis can help determine the sequence of substrate uptake and metabolite secretion, consequently facilitating better design of initial media and feeding strategy. Furthermore, the analysis of metabolite diversity and abundance from spent media will aid the determination of metabolic phases of the culture and the identification of metabolites as surrogate markers for product titer and quality. This review covers the recent advances in metabolomics analysis applied to the development and monitoring of bioprocesses. In this regard, we recommend a stepwise workflow and guidelines that a bioprocesses engineer can adopt to develop and optimize a fermentation process using spent media analysis. Finally, we show examples of how the use of MS can revolutionize the design and monitoring of bioprocesses.

Are Bacteria Leaky? Mechanisms of Metabolite Externalization in Bacterial Cross-Feeding

The metabolism of a bacterial cell stretches beyond its boundaries, often connecting with the metabolism of other cells to form extended metabolic networks that stretch across communities, and even the globe. Among the least intuitive metabolic connections are those involving cross-feeding of canonically intracellular metabolites. How and why are these intracellular metabolites externalized? Are bacteria simply leaky? Here I consider what it means for a bacterium to be leaky, and I review mechanisms of metabolite externalization from the context of cross-feeding. Despite common claims, diffusion of most intracellular metabolites across a membrane is unlikely. Instead, passive and active transporters are likely involved, possibly purging excess metabolites as part of homeostasis. Re-acquisition of metabolites by a producer limits the opportunities for cross-feeding. However, a competitive recipient can stimulate metabolite externalization and initiate a positive-feedback loop of reciprocal cross-feeding.

Sample Preparation in Microbial Metabolomics: Advances and Challenges

Microbial metabolomics has gained significant interest as it reflects the physiological state of microorganisms. Due to the great variability of biological organisms, in terms of physicochemical characteristics and variable range of concentration of metabolites, the choice of sample preparation methods is a crucial step in the metabolomics workflow and will reflect on the quality and reliability of the results generated. The procedures applied to the preparation of microbial samples will vary according to the type of microorganism studied, the metabolomics approach (untargeted or targeted), and the analytical platform of choice. This chapter aims to provide an overview of the sample preparation workflow for microbial metabolomics, highlighting the pre-analytical factors associated with cultivation, harvesting, metabolic quenching, and extraction. Discussions focus on obtaining intracellular and extracellular metabolites. Finally, we introduced advanced sample preparation methods based on automated systems.

Citrus Essential Oils: a Treasure Trove of Antibiofilm Agent

Biofilms are groups of adherent cell communities that cohere to the biotic and abiotic surfaces with the help of extracellular polymeric substances (EPS). EPS allow bacteria to form a biofilm that facilitates their binding to biotic and abiotic surfaces and provides resistance to the host immune responses and to antibiotics. There are efforts that have led to the development of natural compounds that can overcome this biofilm-mediated resistance. Essential oils (EOs) are a unique mixture of compounds that plays a key role in preventing the development of biofilm. The present overview focusses on the role of various types of citrus essential oils in acting against the biofilm, and the antibiofilm properties of natural compounds that may show an avenue to treat the multidrug-resistant bacteria.

Assembling stable syntrophic Escherichia coli communities by comprehensively identifying beneficiaries of secreted goods

Metabolic cross-feeding frequently underlies mutualistic relationships in natural microbial communities and is often exploited to assemble synthetic microbial consortia. We systematically identified all single-gene knockouts suitable for imposing cross-feeding in Escherichia coli and used this information to assemble syntrophic communities. Most strains benefiting from shared goods were dysfunctional in biosynthesis of amino acids, nucleotides, and vitamins or mutants in central carbon metabolism. We tested cross-feeding potency in 1,444 strain pairs and mapped the interaction network between all functional groups of mutants. This network revealed that auxotrophs for vitamins are optimal cooperators. Lastly, we monitored how assemblies composed of dozens of auxotrophs change over time and observed that they rapidly and repeatedly coalesced to seven strain consortia composed primarily from vitamin auxotrophs. The composition of emerging consortia suggests that they were stabilized by multiple cross-feeding interactions. We conclude that vitamins are ideal shared goods since they optimize consortium growth while still imposing member co-dependence.

An Overview of the Potential Therapeutic Applications of Essential Oils

The emergence of antimicrobial resistance (AMR) has urged researchers to explore therapeutic alternatives, one of which includes the use of natural plant products such as essential oils (EO). In fact, EO obtained from clove, oregano, thymus, cinnamon bark, rosemary, eucalyptus, and lavender have been shown to present significant inhibitory effects on bacteria, fungi, and viruses; many studies have been done to measure EO efficacy against microorganisms. The strategy of combinatory effects via conventional and non-conventional methods revealed that the combined effects of EO-EO or EO-antibiotic exhibit enhanced efficacy. This paper aims to review the antimicrobial effects of EO, modes of EO action (membrane disruption, efflux inhibition, increase membrane permeability, and decrease in intracellular ATP), and their compounds' potential as effective agents against bacteria, fungi, and viruses. It is hoped that the integration of EO applications in this work can be used to consider EO for future clinical applications.

Comprehensive analysis of metabolic alterations in Schizochytrium sp. strains with different DHA content

Along with the daily growth of the market requirements for docosahexaenoic acid (DHA) algae oil, a large DHA ingredients are needed to ensure worldwide supply. Undoubtedly a high-productive strain would be the prerequisite for high quality and yield. A comprehensive understanding of the processes of DHA synthesis from glycolysis to the lipid accumulation would be benefit to achieve the final optimization of DHA production. In this study, we comprehensively characterized the metabolic profiles of a Schizochytrium sp. strain, which has higher DHA content and different biomass amino acid composition compared with the wild type to explore the affected pathways and underlying mechanism. Combined with the multivariate statistical analysis, twenty-two differential metabolites were screened as relevant to the discrepancy between two strains. The results showed relatively downregulated glycolysis and saturated fatty acids (SFA) synthesis, and upregulated TCA cycle, amino acids and polyunsaturated fatty acids (PUFA) synthesis in DHA high yield strain. The current study provide a terminal picture of gene regulation from downstream metabolism and demonstrate the advantage of metabolomics in characterizing metabolic status which in turn could provide effective information for the metabolic engineering.

Metabolite secretion in microorganisms: the theory of metabolic overflow put to the test

INTRODUCTION

Microbial cells secrete many metabolites during growth, including important intermediates of the central carbon metabolism. This has not been taken into account by researchers when modeling microbial metabolism for metabolic engineering and systems biology studies.

MATERIALS AND METHODS

The uptake of metabolites by microorganisms is well studied, but our knowledge of how and why they secrete different intracellular compounds is poor. The secretion of metabolites by microbial cells has traditionally been regarded as a consequence of intracellular metabolic overflow.

CONCLUSIONS

Here, we provide evidence based on time-series metabolomics data that microbial cells eliminate some metabolites in response to environmental cues, independent of metabolic overflow. Moreover, we review the different mechanisms of metabolite secretion and explore how this knowledge can benefit metabolic modeling and engineering.

Extracellular Microbial Metabolomics: The State of the Art

Microorganisms produce and secrete many primary and secondary metabolites to the surrounding environment during their growth. Therefore, extracellular metabolites provide important information about the changes in microbial metabolism due to different environmental cues. The determination of these metabolites is also comparatively easier than the extraction and analysis of intracellular metabolites as there is no need for cell rupture. Many analytical methods are already available and have been used for the analysis of extracellular metabolites from microorganisms over the last two decades. Here, we review the applications and benefits of extracellular metabolite analysis. We also discuss different sample preparation protocols available in the literature for both types (e.g., metabolites in solution and in gas) of extracellular microbial metabolites. Lastly, we evaluate the authenticity of using extracellular metabolomics data in the metabolic modelling of different industrially important microorganisms.

Escherichia coli HGT: Engineered for high glucose throughput even under slowly growing or resting conditions

Aerobic production-scale processes are constrained by the technical limitations of maximum oxygen transfer and heat removal. Consequently, microbial activity is often controlled via limited nutrient feeding to maintain it within technical operability. Here, we present an alternative approach based on a newly engineered Escherichia coli strain. This E. coli HGT (high glucose throughput) strain was engineered by modulating the stringent response regulation program and decreasing the activity of pyruvate dehydrogenase. The strain offers about three-fold higher rates of cell-specific glucose uptake under nitrogen-limitation (0.6g_Glc g_CDW^-1h^-1) compared to that of wild type, with a maximum glucose uptake rate of about 1.8g_Glc g_CDW^-1h^-1 already at a 0.3h^-1 specific growth rate. The surplus of imported glucose is almost completely available via pyruvate and is used to fuel pyruvate and lactate formation. Thus, E. coli HGT represents a novel chassis as a host for pyruvate-derived products.

A Comparative Proteome Analysis of Escherichia coli ΔrelA Mutant Cells

The bacterial RelA-dependent stringent response exerts a strong influence over various processes. In this work, the impact of the relA gene mutation in Escherichia coli cells was evaluated by a quantitative proteomics analysis, employing stable-isotope labeling and high-resolution mass spectrometry. Chemostat cultures of E. coli W3110 and ΔrelA mutant strains were performed at two dilution rates (0.1 and 0.2 h⁻¹) to assess the influence of the relA gene mutation in steady-state protein levels. A total of 121 proteins showed significant alterations in their abundance when comparing the proteome of mutant to wild-type cells. The relA gene mutation induced changes on key cellular processes, including the amino acids and nucleotide biosynthesis, the lipid metabolism, transport activities, transcription and translation processes, and responses to stress. Furthermore, some of those changes were more pronounced under specific growth conditions, as the most significant differences in protein ratios were observed at one of the dilution rates. An effect of the relA gene mutation in the acetate overflow was also observed, which confers interesting characteristics to this mutant strain that could be useful in the production of recombinant proteins. Overall, these results provide a valuable insight into the E. coli stringent response under defined steady-state conditions, suggesting that this stress response might influence multiple metabolic processes like the acetate overflow or the catabolite repression.

Mutation-based selection and analysis of Komagataeibacter hansenii HDM1-3 for improvement in bacterial cellulose production

AIMS

A low yield of bacterial cellulose (BC) always results from an excessive accumulation of organic acids. Screening and the selection of bacterial mutants with a low accumulation of organic acids is an efficient approach for improving BC production.

METHODS AND RESULTS

In combination with the proton suicide method (medium containing NaBr-NaBrO₃ ), diethyl sulphate chemical mutagenesis coupled with ⁶⁰ Co-γ irradiation treatment were performed for the screening and selection of desired mutant lines with a high yield of BC. Two high-yield strains, Br-3 and Co-5, as well as a low-yield strain, Br-12, were obtained. Amplified fragment length polymorphism (AFLP) was applied to explore the differences between the mutant lines and the wild type. For the Br-12 line, three specific fragments were verified, corresponding to TonB-dependent transport (TBDT), exopolysaccharides output protein (PePr) and an unknown gene. For Co-5, two specific fragments were matched, acsD and UDP-galactose-4-epimerase. In addition, metabolic analysis for the mutant lines indicated that BC production may be limited by excessive accumulation of organic acids in the fermentation. The limitation would be resolved by the cross-talk of genes involved in BC biosynthesis.

CONCLUSIONS

Reduced organic acid by-products from glucose in bypasses were found to be responsible for the high-yield BC synthesis in Komagataeibacter hansenii mutant strains.

SIGNIFICANCE AND IMPACT OF THE STUDY

The metabolic process was varied by mutagenesis-induced gene disruption of the metabolic products. A new idea was provided for the targeted screening and characterization of mutants in the future.

Can we predict the intracellular metabolic state of a cell based on extracellular metabolite data?

The analysis of extracellular metabolites presents many technical advantages over the analysis of intracellular compounds, which made this approach very popular in recent years as a high-throughput tool to assess the metabolic state of microbial cells. However, very little effort has been made to determine the actual relationship between intracellular and extracellular metabolite levels. The secretion of intracellular metabolites has been traditionally interpreted as a consequence of an intracellular metabolic overflow, which is based on the premise that for a metabolite to be secreted, it must be over-produced inside the cell. Therefore, we expect to find a secreted metabolite at increased levels inside the cells. Here we present a time-series metabolomics study of Saccharomyces cerevisiae growing on a glucose-limited chemostat with parallel measurements of intra- and extracellular metabolites. Although most of the extracellular metabolites were also detected in the intracellular samples and showed a typical metabolic overflow behaviour, we demonstrate that the secretion of many metabolites could not be explained by the metabolic overflow theory.

Development of high throughput 96-blade solid phase microextraction-liquid chromatrography-mass spectrometry protocol for metabolomics

In metabolomics, the workflow for quantitative and comprehensive metabolic mapping of cellular metabolites can be a very challenging undertaking. Sampling and sample preparation play a significant role in untargeted analysis, as they may affect the composition of the analyzed metabolome. In the current work, different solid phase microextraction (SPME) coating chemistries were developed and applied to provide simultaneous extraction of a wide range of both hydrophobic and hydrophilic cellular metabolites produced by a model organism, Escherichia coli. Three different LC-MS methods were also evaluated for analysis of extracted metabolites. Finally, over 200 cellular metabolites were separated and detected with widely varying hydrophobicities ranging within -7 < log P < 15, including amino acids, peptides, nucleotides, carbohydrates, polycarboxylic acids, vitamins, phosphorylated compounds, and lipids such as hydrophobic phospholipids, prenol lipids, and fatty acids at the stationary phase of the E. coli life cycle using the developed 96-blade SPME-LC-MS method.

Identifying and quantifying metabolites by scoring peaks of GC-MS data

Background

Metabolomics is one of most recent omics technologies. It has been applied on fields such as food science, nutrition, drug discovery and systems biology. For this, gas chromatography-mass spectrometry (GC-MS) has been largely applied and many computational tools have been developed to support the analysis of metabolomics data. Among them, AMDIS is perhaps the most used tool for identifying and quantifying metabolites. However, AMDIS generates a high number of false-positives and does not have an interface amenable for high-throughput data analysis. Although additional computational tools have been developed for processing AMDIS results and to perform normalisations and statistical analysis of metabolomics data, there is not yet a single free software or package able to reliably identify and quantify metabolites analysed by GC-MS.

Results

Here we introduce a new algorithm, PScore, able to score peaks according to their likelihood of representing metabolites defined in a mass spectral library. We implemented PScore in a R package called MetaBox and evaluated the applicability and potential of MetaBox by comparing its performance against AMDIS results when analysing volatile organic compounds (VOC) from standard mixtures of metabolites and from female and male mice faecal samples. MetaBox reported lower percentages of false positives and false negatives, and was able to report a higher number of potential biomarkers associated to the metabolism of female and male mice.

Conclusions

Identification and quantification of metabolites is among the most critical and time-consuming steps in GC-MS metabolome analysis. Here we present an algorithm implemented in a R package, which allows users to construct flexible pipelines and analyse metabolomics data in a high-throughput manner.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-014-0374-2) contains supplementary material, which is available to authorized users.

Mass-spectrometry-based microbial metabolomics: recent developments and applications

Metabolomics is an omics technique aiming at qualitatively and quantitatively describing a metabolome by various analytical platforms. It is an indispensable component of modern systems biology. Microbial metabolomics can be roughly classified as metabolic footprint analysis and metabolic fingerprint analysis depending on the analyte origins. Both of them have been beneficial to microbiological research for different reasons. Mass spectrometry and nuclear magnetic resonance spectroscopy techniques are popular analytical strategies prevailing in the metabolomics field. In this review, chromatography-mass-spectrometry-based microbial metabolomic analysis steps are summarized, including sample collection, metabolite extraction, instrument analysis, and data analysis. Moreover, their applications in some representative fields are discussed as examples. The aim of this review is to present briefly recent technical advances in mass-spectrometry-based analysis, and to highlight the value of modern applications of microbial metabolomics.

Protein secretion biotechnology in Gram-positive bacteria with special emphasis on Streptomyces lividans

Proteins secreted by Gram-positive bacteria are released into the culture medium with the obvious benefit that they usually retain their native conformation. This property makes these host cells potentially interesting for the production of recombinant proteins, as one can take full profit of established protocols for the purification of active proteins. Several state-of-the-art strategies to increase the yield of the secreted proteins will be discussed, using Streptomyces lividans as an example and compared with approaches used in some other host cells. It will be shown that approaches such as increasing expression and translation levels, choice of secretion pathway and modulation of proteins thereof, avoiding stress responses by changing expression levels of specific (stress) proteins, can be helpful to boost production yield. In addition, the potential of multi-omics approaches as a tool to understand the genetic background and metabolic fluxes in the host cell and to seek for new targets for strain and protein secretion improvement is discussed. It will be shown that S. lividans, along with other Gram-positive host cells, certainly plays a role as a production host for recombinant proteins in an economically viable way. This article is part of a Special Issue entitled: Protein trafficking and secretion in bacteria. Guest Editors: Anastassios Economou and Ross Dalbey.

Effect of essential oils on pathogenic bacteria

The increasing resistance of microorganisms to conventional chemicals and drugs is a serious and evident worldwide problem that has prompted research into the identification of new biocides with broad activity. Plants and their derivatives, such as essential oils, are often used in folk medicine. In nature, essential oils play an important role in the protection of plants. Essential oils contain a wide variety of secondary metabolites that are capable of inhibiting or slowing the growth of bacteria, yeasts and moulds. Essential oils and their components have activity against a variety of targets, particularly the membrane and cytoplasm, and in some cases, they completely change the morphology of the cells. This brief review describes the activity of essential oils against pathogenic bacteria.

Global metabolomic and network analysis of Escherichia coli responses to exogenous biofuels

Although synthetic biology progress has made it possible to produce various biofuels in more user-friendly hosts, such as Escherichia coli, the large-scale biofuel production in these non-native systems is still challenging, mostly due to the very low tolerance of these non-native hosts to the biofuel toxicity. To address the issues, in this study we determined the metabolic responses of E. coli induced by three major biofuel products, ethanol, butanol, and isobutanol, using a gas chromatography-mass spectrometry (GC-MS) approach. A metabolomic data set of 65 metabolites identified in all samples was then subjected to principal component analysis (PCA) to compare their effects and a weighted correlation network analysis (WGCNA) to identify the metabolic modules specifically responsive to each of the biofuel stresses, respectively. The PCA analysis showed that cellular responses caused by the biofuel stress were in general similar to aging cells at stationary phase, inconsistent with early studies showing a high degree of dissimilarity between metabolite responses during growth cessation as induced through stationary phases or through various environmental stress applications. The WGCNA analysis allowed identification of 2, 4, and 2 metabolic modules specifically associated with ethanol, butanol, and isobutanol treatments, respectively. The biofuel-associated modules included amino acids and osmoprotectants, such as isoleucine, valine, glycine, glutamate, and trehalose, suggesting amino acid metabolism and osmoregulation are among the key protection mechanisms against biofuel stresses in E. coli. Interestingly, no module was found associated with all three biofuel products, suggesting differential effects of each biofuel on E. coli. The findings enhanced our understanding of E. coli responses to exogenous biofuels and also demonstrated the effectiveness of the metabolomic and network analysis in identifying key targets for biofuel tolerance.

Influence of the RelA Activity on E. coli Metabolism by Metabolite Profiling of Glucose-Limited Chemostat Cultures

Metabolite profiling of E. coli W3110 and the isogenic ΔrelA mutant cells was used to characterize the RelA-dependent stringent control of metabolism under different growth conditions. Metabolic profiles were obtained by gas chromatography–mass spectrometry (GC-MS) analysis and revealed significant differences between E. coli strains grown at different conditions. Major differences between the two strains were assessed in the levels of amino acids and fatty acids and their precursor metabolites, especially when growing at the lower dilution rates, demonstrating differences in their metabolic behavior. Despite the fatty acid biosynthesis being the most affected due to the lack of the RelA activity, other metabolic pathways involving succinate, lactate and threonine were also affected. Overall, metabolite profiles indicate that under nutrient-limiting conditions the RelA-dependent stringent response may be elicited and promotes key changes in the E. coli metabolism.

Metabolic responses to recombinant bioprocesses in Escherichia coli

Escherichia coli has been widely used for the production of recombinant proteins. However, the unbalances between host metabolism and recombinant biosynthesis continue to hamper the efficiency of these recombinant bioprocesses. The additional drainage of biosynthetic precursors toward recombinant processes burdens severely the metabolism of cells that, ultimately, elicits a series of stress responses, reducing biomass growth and recombinant protein production. Several strategies to overcome these metabolic limitations have been implemented; however, in most cases, improvements in recombinant protein expression were achieved at the expense of biomass growth arrest, which significantly hampers the efficiency of recombinant bioprocesses. With the advent of high throughput techniques and modelling approaches that provide a system-level understanding of the cellular systems, it is now expected that new advances in recombinant bioprocesses are achieved. By providing means to deal with these systems, our understanding on the metabolic behaviour of recombinant cells will advance and can be further explored to the design of suitable hosts and more efficient and cost-effective bioprocesses. Here, we review the major metabolic responses associated with recombinant processes and the engineering strategies relevant to overcome these stresses. Moreover, the advantages of applying systems levels engineering strategies to enhance recombinant protein production in E. coli cells are discussed and future perspectives on the advances of mathematical modelling approaches to study these systems are exposed.

Analyzing the metabolic stress response of recombinant Escherichia coli cultures expressing human interferon-beta in high cell density fed batch cultures using time course transcriptomic data

Fed batch cultures expressing recombinant interferon beta under the T7 promoter were run with different exponential feeding rates of a complex substrate and induced at varying cell densities. Post-induction profiles of the specific product formation rates showed a strong dependence on the specific growth rate with the maximum product yield obtained at 0.2 h(-1). A study of the relative transcriptomic profiles as a function of pre-induction μ was therefore done to provide insight into the role of cellular physiology in enhancing recombinant protein expression. Hierarchical clustering analysis of the significantly regulated genes allowed us to identify biologically important groups of genes which fall under specific master regulators. The groups were: rpoH, ArcB, CreB, Lrp, RelA, Fis and Hfq. The response of these regulators, which exert a feedback control on the growth and product formation rates correlated well with the expression levels obtained. Thus at the optimum pre-induction μ, the alternative sigma factors and ribosomal machinery genes did not get depressed till the 6th hour post-induction unlike at other specific growth rates, demonstrating a critical role for the genes in sustaining recombinant protein expression.