Towards replacing closed with open target selection strategies

Effect of the storage atmosphere on metabolomics of harvested tomatoes (Solanum lycopersicum L.)

Harvested tomatoes were stored under atmospheres that were normoxic, anoxic, or modified (altered O2 and CO2 concentrations). Each atmosphere was created by storing the tomatoes at 25°C for up to 8 days in different kinds of pouches. During storage, metabolites of the tomatoes were measured using metabolomics. We obtained score plots of the metabolites on eighth day of storage by principal component analysis. There was a tendency for groups to be divided on the basis of score plot according to the composition of each gas. PC1 and PC2 seemed to correspond to the influence of O2 and CO2 concentrations, respectively, and the total contribution rate of the two axes was 72%, so that we concluded that the metabolites were affected mainly by O2 and CO2 concentrations. The results indicate that metabolomics may be an effective tool to reveal the relationship between metabolic state of harvested fruits and the atmosphere.

Orthogonal partial least squares/projections to latent structures regression-based metabolomics approach for identification of gene targets for improvement of 1-butanol production in Escherichia coli

Metabolomics is the comprehensive analysis of metabolites in biological systems that uses multivariate analyses such as principal component analysis (PCA) or partial least squares/projections to latent structures regression (PLSR) to understand the metabolome state and extract important information from biological systems. In this study, orthogonal PLSR (OPLSR) model-based metabolomics approach was applied to 1-butanol producing Escherichia coli to facilitate in strain improvement strategies. Here, metabolite data obtained by liquid chromatography/mass spectrometry (LC/MS) was used to construct an OPLSR model to correlate metabolite changes with 1-butanol production and rationally identify gene targets for strain improvement. Using this approach, acetyl-CoA was determined as the rate-limiting step of the pathway while free CoA was found to be insufficient for 1-butanol production. By resolving the problems addressed by the OPLSR model, higher 1-butanol productivity was achieved. In this study, the usefulness of OPLSR-based metabolomics approach for understanding the whole metabolome state and determining the most relevant metabolites was demonstrated. Moreover, it was able to provide valuable insights for selection of rational gene targets for strain improvement.

Looking back into the future: 30 years of metabolomics at TNO

Metabolites have played an essential role in our understanding of life, health, and disease for thousands of years. This domain became much more important after the concept of metabolism was discovered. In the 1950s, mass spectrometry was coupled to chromatography and made the technique more application-oriented and allowed the development of new profiling technologies. Since 1980, TNO has performed system-based metabolic profiling of body fluids, and combined with pattern recognition has led to many discoveries and contributed to the field known as metabolomics and systems biology. This review describes the development of related concepts and applications at TNO in the biomedical, pharmaceutical, nutritional, and microbiological fields, and provides an outlook for the future.

Protein analysis-on-chip systems in foodomics

Protein compositional data can address nutritional, packaging, origin/authenticity, processing history, safety and other quality questions. Such data has been time-consuming and expensive to generate until recently but “protein analysis on a chip” systems are now available that can analyze a complex food sample in a few minutes and do not require great protein analytical expertise. We review some of the main new approaches with examples of their application and discuss their advantages and disadvantages.

Metabolomics as a tool for target identification in strain improvement: the influence of phenotype definition

For the optimization of microbial production processes, the choice of the quantitative phenotype to be optimized is crucial. For instance, for the optimization of product formation, either product concentration or productivity can be pursued, potentially resulting in different targets for strain improvement. The choice of a quantitative phenotype is highly relevant for classical improvement approaches, and even more so for modern systems biology approaches. In this study, the information content of a metabolomics dataset was determined with respect to different quantitative phenotypes related to the formation of specific products. To this end, the production of two industrially relevant products by Aspergillus niger was evaluated: (i) the enzyme glucoamylase, and (ii) the more complex product group of secreted proteases, consisting of multiple enzymes. For both products, six quantitative phenotypes associated with activity and productivity were defined, also taking into account different time points of sampling during the fermentation. Both linear and nonlinear relationships between the metabolome data and the different quantitative phenotypes were considered. The multivariate data analysis tool partial least-squares (PLS) was used to evaluate the information content of the datasets for all the different quantitative phenotypes defined. Depending on the product studied, different quantitative phenotypes were found to have the highest information content in specific metabolomics datasets. A detailed analysis of the metabolites that showed strong correlation with these quantitative phenotypes revealed that various sugar derivatives correlated with glucoamylase activity. For the reduction of protease activity, mainly as-yet-unidentified compounds correlated.

Nutritional profiles in a public health perspective: a critical review

Nutritional profiling is defined as 'the science of categorizing foods according to their nutritional composition' and it is useful for food labelling and regulation of health claims. The evidence for the link between nutrients and health outcomes was reviewed. A reduced salt intake reduces blood pressure, but only a few randomized controlled trials have verified the effect of salt on overall and cardiovascular mortality. Evidence linking a reduced fat intake with cardiovascular mortality and obesity is generally non-significant. Studies that have examined the relationship between obesity and diet have produced contrasting results. A simulation exercise that demonstrated that the impact of a reduced salt and fat intake on overall mortality would be negligible in the European population was carried out. Consideration of the literature and the results of this simulation exercise suggest that the introduction of nutritional profiles in Europe would be expected to have a very limited impact on health outcomes.

Modeling with a view to target identification in metabolic engineering: a critical evaluation of the available tools

The state of the art tools for modeling metabolism, typically used in the domain of metabolic engineering, were reviewed. The tools considered are stoichiometric network analysis (elementary modes and extreme pathways), stoichiometric modeling (metabolic flux analysis, flux balance analysis, and carbon modeling), mechanistic and approximative modeling, cybernetic modeling, and multivariate statistics. In the context of metabolic engineering, one should be aware that the usefulness of these tools to optimize microbial metabolism for overproducing a target compound depends predominantly on the characteristic properties of that compound. Because of their shortcomings not all tools are suitable for every kind of optimization; issues like the dependence of the target compound's synthesis on severe (redox) constraints, the characteristics of its formation pathway, and the achievable/desired flux towards the target compound should play a role when choosing the optimization strategy.

Liquid chromatography/tandem mass spectrometry of glycolytic intermediates: deconvolution of coeluting structural isomers based on unique product ion ratios

A method has been developed for rapid quantification of nine glycolytic intermediates using ultraperformance liquid chromatography/electrospray-tandem mass spectrometry (UPLC/ESI-MS/MS) to monitor the metabolism of glucose during microbial fermentation. Because comprehensive chromatographic separation is not required, analysis time is significantly less than traditional ion exchange liquid chromatography assays or enzymatic assays. Complete glycolytic intermediate analysis by LC/MS/MS can be achieved in less than 7 min per sample. Quantification is accomplished using isotopically labeled glucose, glucose-6-phosphate, and pyruvate as internal standards. In addition, a method to deconvolute peak areas of coeluting structural isomers based on unique product ion ratios has been developed to allow accurate quantification of the individual isomers 2-phosphoglycerate and 3-phosphoglycerate, as well as glucose-6-phosphate and fructose-6-phosphate. Intrasample precisions for glycolytic intermediate measurements in cell-free extracts using this method vary between 0.9% and 11.8%, averaging 3.5% (RSD). Calibration curves are linear over the range 0.1-100 microg/mL, and detection limits are estimated at 2-49 ng/mL. Spike recoveries in cell extracts vary from 53% to 127% averaging 91%. This method has the potential to demonstrate correlation of glycolytic intermediate flux to microbial production profiles toward acceleration of the bioprocess development cycle.

Comprehensive analysis of the metabolome of Pseudomonas putida S12 grown on different carbon sources

Metabolomics is an emerging, powerful, functional genomics technology that involves the comparative non-targeted analysis of the complete set of metabolites in an organism. We have set-up a robust quantitative metabolomics platform that allows the analysis of 'snapshot' metabolomes. In this study, we have applied this platform for the comprehensive analysis of the metabolite composition of Pseudomonas putida S12 grown on four different carbon sources, i.e. fructose, glucose, gluconate and succinate. This paper focuses on the microbial aspects of analyzing comprehensive metabolomes, and demonstrates that metabolomes can be analyzed reliably. The technical (i.e. sample work-up and analytical) reproducibility was on average 10%, while the biological reproducibility was approximately 40%. Moreover, the energy charge values of the microbial samples generated were determined, and indicated that no biotic or abiotic changes had occurred during sample work-up and analysis. In general, the metabolites present and their concentrations were very similar after growth on the different carbon sources. However, specific metabolites showed large differences in concentration, especially the intermediates involved in the degradation of the carbon sources studied. Principal component discriminant analysis was applied to identify metabolites that are specific for, i.e. not necessarily the metabolites that show those largest differences in concentration, cells grown on either of these four carbon sources. For selected enzymatic reactions, i.e. the glucose-6-phosphate isomerase, triosephosphate isomerase and phosphoglyceromutase reactions, the apparent equilibrium constants (K(app)) were calculated. In several instances a carbon source-dependent deviation between the apparent equilibrium constant (K(app)) and the thermodynamic equilibrium constant (K(eq)) was observed, hinting towards a potential point of metabolic regulation or towards bottlenecks in biosynthesis routes. For glucose-6-phosphate isomerase and phosphoglyceromutase, the K(app) was larger than K(eq), and the results suggested that the specific enzymatic activities of these two enzymes were too low to reach the thermodynamic equilibrium in growing cells. In contrast, with triosephosphate isomerase the K(app) was smaller than K(eq), and the results suggested that this enzyme is kinetically controlled.

Identification of biomarkers for genotyping Aspergilli using non-linear methods for clustering and classification

BACKGROUND

In the present investigation, we have used an exhaustive metabolite profiling approach to search for biomarkers in recombinant Aspergillus nidulans (mutants that produce the 6- methyl salicylic acid polyketide molecule) for application in metabolic engineering.

RESULTS

More than 450 metabolites were detected and subsequently used in the analysis. Our approach consists of two analytical steps of the metabolic profiling data, an initial non-linear unsupervised analysis with Self-Organizing Maps (SOM) to identify similarities and differences among the metabolic profiles of the studied strains, followed by a second, supervised analysis for training a classifier based on the selected biomarkers. Our analysis identified seven putative biomarkers that were able to cluster the samples according to their genotype. A Support Vector Machine was subsequently employed to construct a predictive model based on the seven biomarkers, capable of distinguishing correctly 14 out of the 16 samples of the different A. nidulans strains.

CONCLUSION

Our study demonstrates that it is possible to use metabolite profiling for the classification of filamentous fungi as well as for the identification of metabolic engineering targets and draws the attention towards the development of a common database for storage of metabolomics data.

Microbial metabolomics: Toward a platform with full metabolome coverage

Achieving metabolome data with satisfactory coverage is a formidable challenge in metabolomics because metabolites are a chemically highly diverse group of compounds. Here we present a strategy for the development of an advanced analytical platform that allows the comprehensive analysis of microbial metabolomes. Our approach started with in silico metabolome information from three microorganisms-Escherichia coli, Bacillus subtilis, and Saccharomyces cerevisiae-and resulted in a list of 905 different metabolites. Subsequently, these metabolites were classified based on their physicochemical properties, followed by the development of complementary gas chromatography-mass spectrometry and liquid chromatography-mass spectrometry methods, each of which analyzes different metabolite classes. This metabolomics platform, consisting of six different analytical methods, was applied for the analysis of the metabolites for which commercial standards could be purchased (399 compounds). Of these 399 metabolites, 380 could be analyzed with the platform. To demonstrate the potential of this metabolomics platform, we report on its application to the analysis of the metabolome composition of mid-logarithmic E. coli cells grown on a mineral salts medium using glucose as the carbon source. Of the 431 peaks detected, 235 (=176 unique metabolites) could be identified. These include 61 metabolites that were not previously identified or annotated in existing E. coli databases.

Profiling of Organic Acids during Fermentation by Ultraperformance Liquid Chromatography−Tandem Mass Spectrometry

A method has been developed for rapid quantification of organic acids using ultraperformance liquid chromatography/electrospray-tandem mass spectrometry (UPLC/ESI-MS-MS) to monitor the metabolism of 10 organic acids during microbial fermentation. Because comprehensive chromatographic separation is not required, analysis time is less than traditional ion chromatography assays, with complete organic acid analyses by UPLC/ESI-MS-MS being achieved in less than 3 min. Quantification is accomplished using nine isotopically labeled organic acids as internal standards. Intrasample precisions for organic acid measurements in fermentation supernatants using this method average 8.9% (RSD). Calibration curves are linear over the range of 0.06-100 microg/mL, and detection limits are estimated at 0.06-1 microg/mL. This method has the potential to demonstrate correlation of organic acid consumption and production by microorganisms with observed growth profiles, novel media formulations, and cellular growth events. Data visualization software has been used to profile organic acid levels during fermentation and correlate these profiles to nutrient supplementation protocols employed during microbial production. The potential use of this capability in computational modeling and simulation of microbial metabolism to accelerate the bioprocess development cycle is recognized.

Cellular metabolomics of Escherchia coli

Escherichia coli is among the simplest and best-understood free-living organisms. It has served as a valuable model for numerous biological processes, including cellular metabolism. Just as E. coli stood at the front of the genomic revolution, it is playing a leading role in the development of cellular metabolomics: the study of the complete metabolic contents of cells, including their dynamic concentration changes and fluxes. This review briefly describes the essentials of cellular metabolomics and its fundamental differentiation from biomarker metabolomics and lipidomics. Key technologies for metabolite quantitation from E. coli are described, with a focus on those involving mass spectrometry. In particular emphasis is given to the cell handling and sample preparation steps required for collecting data of high biological reliability, such as fast metabolome quenching. Future challenges, both in terms of data collection and application of the data to obtain a comprehensive understanding of metabolic dynamics, are discussed.

Investigation on response of the metabolites in tricarboxylic acid cycle of Escherichi coli and Pseudomonas aeruginosa to antibiotic perturbation by capillary electrophoresis

Metabolomics is a new branch of systems biology exerting its influence in many aspects. In order to appraise the effects of antibiotics on central carbon metabolism, a CE based method was set up. With this platform, we estimated the organic acid metabolite pools' fluctuation of Escherichia coli and Pseudomonas aeruginosa cultured under 11 different antibiotics' stimuli. Multivariate data analysis showed that different antibiotics had clustered distributions for each strain and could be easily distinguished. Genetic, metabolic and antibiotic mechanism differences could also be deduced by the aid of further correlation analysis. For P. aeruginosa, even synergy action amid antibiotics could be ascertained.

Simultaneous quantitative analysis of metabolites using ion-pair liquid chromatography-electrospray ionization mass spectrometry

We have developed an analytical method, consisting of ion-pair liquid chromatography coupled to electrospray ionization mass spectrometry (IP-LC-ESI-MS), for the simultaneous quantitative analysis of several key classes of polar metabolites, like nucleotides, coenzyme A esters, sugar nucleotides, and sugar bisphosphates. The use of the ion-pair agent hexylamine and optimization of the pH of the mobile phases were critical parameters in obtaining good retention and peak shapes of many of the above-mentioned polar and acidic metabolites that are impossible to analyze using standard reversed-phase LC/MS. Optimum conditions were found when using a gradient from 5 mM hexylamine in water (pH 6.3) to 90% methanol/10% 10 mM ammonium acetate (pH 8.5). The IP-LC-ESI-MS method was extensively validated by determining the linearity (R2 > 0.995), sensitivity (limit of detection 0.1-1 ng), repeatability, and reproducibility (relative standard deviation <10%). The IP-LC-ESI-MS method was shown to be a useful tool for microbial metabolomics, i.e., the comprehensive quantitative analysis of metabolites in extracts of microorganisms, and for the determination of the energy charge, i.e., the cellular energy status, as an overall quality measure for the sample workup and analytical protocols.

Standard reporting requirements for biological samples in metabolomics experiments: microbial and in vitro biology experiments

With the increasing use of metabolomics as a means to study a large number of different biological research questions, there is a need for a minimal set of reporting standards that allow the scientific community to evaluate, understand, repeat, compare and re-investigate metabolomics studies. Here we propose, a first draft of minimal requirements to effectively describe the biological context of metabolomics studies that involve microbial or in vitro biological subjects. This recommendation has been produced by the microbiology and in vitro biology working subgroup of the Metabolomics Standards Initiative in collaboration with the yeast systems biology network as part of a wider standardization initiative led by the Metabolomics Society. Microbial and in vitro biology metabolomics is defined by this sub-working group as studies with any cell or organism that require a defined external medium to facilitate growth and propagation. Both a minimal set and a best practice set of reporting standards for metabolomics experiments have been defined. The minimal set of reporting standards for microbial or in vitro biology metabolomics experiments includes those factors that are specific for metabolomics experiments and that critically determine the outcome of the experiments. The best practice set of reporting standards contains both the factors that are specific for metabolomics experiments and general aspects that critically determine the outcome of any microbial or in vitro biological experiment.

Transcriptional monitoring of steady state and effects of anaerobic phases in chemostat cultures of the filamentous fungus Trichoderma reesei

Background

Chemostat cultures are commonly used in production of cellular material for systems-wide biological studies. We have used the novel TRAC (transcript analysis with aid of affinity capture) method to study expression stability of approximately 30 process relevant marker genes in chemostat cultures of the filamentous fungus Trichoderma reesei and its transformant expressing laccase from Melanocarpus albomyces. Transcriptional responses caused by transient oxygen deprivations and production of foreign protein were also studied in T. reesei by TRAC.

Results

In cultures with good steady states, the expression of the marker genes varied less than 20% on average between sequential samples for at least 5 or 6 residence times. However, in a number of T. reesei cultures continuous flow did not result in a good steady state. Perturbations to the steady state were always evident at the transcriptional level, even when they were not measurable as changes in biomass or product concentrations. Both unintentional and intentional perturbations of the steady state demonstrated that a number of genes involved in growth, protein production and secretion are sensitive markers for culture disturbances. Exposure to anaerobic conditions caused strong responses at the level of gene expression, but surprisingly the cultures could regain their previous steady state quickly, even after 3 h O₂ depletion. The main effect of producing M. albomyces laccase was down-regulation of the native cellulases compared with the host strain.

Conclusion

This study demonstrates the usefulness of transcriptional analysis by TRAC in ensuring the quality of chemostat cultures prior to costly and laborious genome-wide analysis. In addition TRAC was shown to be an efficient tool in studying gene expression dynamics in transient conditions.

Centering, scaling, and transformations: improving the biological information content of metabolomics data

BACKGROUND

Extracting relevant biological information from large data sets is a major challenge in functional genomics research. Different aspects of the data hamper their biological interpretation. For instance, 5000-fold differences in concentration for different metabolites are present in a metabolomics data set, while these differences are not proportional to the biological relevance of these metabolites. However, data analysis methods are not able to make this distinction. Data pretreatment methods can correct for aspects that hinder the biological interpretation of metabolomics data sets by emphasizing the biological information in the data set and thus improving their biological interpretability.

RESULTS

Different data pretreatment methods, i.e. centering, autoscaling, pareto scaling, range scaling, vast scaling, log transformation, and power transformation, were tested on a real-life metabolomics data set. They were found to greatly affect the outcome of the data analysis and thus the rank of the, from a biological point of view, most important metabolites. Furthermore, the stability of the rank, the influence of technical errors on data analysis, and the preference of data analysis methods for selecting highly abundant metabolites were affected by the data pretreatment method used prior to data analysis.

CONCLUSION

Different pretreatment methods emphasize different aspects of the data and each pretreatment method has its own merits and drawbacks. The choice for a pretreatment method depends on the biological question to be answered, the properties of the data set and the data analysis method selected. For the explorative analysis of the validation data set used in this study, autoscaling and range scaling performed better than the other pretreatment methods. That is, range scaling and autoscaling were able to remove the dependence of the rank of the metabolites on the average concentration and the magnitude of the fold changes and showed biologically sensible results after PCA (principal component analysis).In conclusion, selecting a proper data pretreatment method is an essential step in the analysis of metabolomics data and greatly affects the metabolites that are identified to be the most important.

Fusion of mass spectrometry-based metabolomics data

A general method is presented for combining mass spectrometry-based metabolomics data. Such data are becoming more and more abundant, and proper tools for fusing these types of data sets are needed. Fusion of metabolomics data leads to a comprehensive view on the metabolome of an organism or biological system. The ideas presented draw upon established techniques in data analysis. Hence, they are also widely applicable to other types of X-omics data provided there is a proper pretreatment of the data. These issues are discussed using a real-life metabolomics data set from a microbial fermentation process.

Microbial Metabolomics with Gas Chromatography/Mass Spectrometry

An analytical method was set up suitable for the analysis of microbial metabolomes, consisting of an oximation and silylation derivatization reaction and subsequent analysis by gas chromatography coupled to mass spectrometry. Microbial matrixes contain many compounds that potentially interfere with either the derivatization procedure or analysis, such as high concentrations of salts, complex media or buffer components, or extremely high substrate and product concentrations. The developed method was extensively validated using different microorganisms, i.e., Bacillus subtilis, Propionibacterium freudenreichii, and Escherichia coli. Many metabolite classes could be analyzed with the method: alcohols, aldehydes, amino acids, amines, fatty acids, (phospho-) organic acids, sugars, sugar acids, (acyl-) sugar amines, sugar phosphate, purines, pyrimidines, and aromatic compounds. The derivatization reaction proved to be efficient (>50% transferred to derivatized form) and repeatable (relative standard deviations <10%). Linearity for most metabolites was satisfactory with regression coefficients better than 0.996. Quantification limits were 40-500 pg on-column or 0.1-0.7 mmol/g of microbial cells (dry weight). Generally, intrabatch precision (repeatability) and interbatch precision (reproducibility) for the analysis of metabolites in cell extracts was better than 10 and 15%, respectively. Notwithstanding the nontargeted character of the method and complex microbial matrix, analytical performance for most metabolites fit the requirements for target analysis in bioanalysis. The suitability of the method was demonstrated by analysis of E. coli samples harvested at different growth phases.

Modelling strategies for the industrial exploitation of lactic acid bacteria

Lactic acid bacteria (LAB) have a long tradition of use in the food industry, and the number and diversity of their applications has increased considerably over the years. Traditionally, process optimization for these applications involved both strain selection and trial and error. More recently, metabolic engineering has emerged as a discipline that focuses on the rational improvement of industrially useful strains. In the post-genomic era, metabolic engineering increasingly benefits from systems biology, an approach that combines mathematical modelling techniques with functional-genomics data to build models for biological interpretation and--ultimately--prediction. In this review, the industrial applications of LAB are mapped onto available global, genome-scale metabolic modelling techniques to evaluate the extent to which functional genomics and systems biology can live up to their industrial promise.

Integrating metabolomics into a systems biology framework to exploit metabolic complexity: strategies and applications in microorganisms

As an important functional genomic tool, metabolomics has been illustrated in detail in recent years, especially in plant science. However, the microbial category also has the potential to benefit from integration of metabolomics into system frameworks. In this article, we first examine the concepts and brief history of metabolomics. Next, we summarize metabolomic research processes and analytical platforms in strain improvements. The application cases of metabolomics in microorganisms answer what the metabolomics can do in strain improvements. The position of metabolomics in this systems biology framework and the real cases of integrating metabolomics into a system framework to explore the microbial metabolic complexity are also illustrated in this paper.

Multivariate analysis of microarray data by principal component discriminant analysis: prioritizing relevant transcripts linked to the degradation of different carbohydrates in Pseudomonas putida S12

The value of the multivariate data analysis tools principal component analysis (PCA) and principal component discriminant analysis (PCDA) for prioritizing leads generated by microarrays was evaluated. To this end, Pseudomonas putida S12 was grown in independent triplicate fermentations on four different carbon sources, i.e. fructose, glucose, gluconate and succinate. RNA isolated from these samples was analysed in duplicate on an anonymous clone-based array to avoid bias during data analysis. The relevant transcripts were identified by analysing the loadings of the principal components (PC) and discriminants (D) in PCA and PCDA, respectively. Even more specifically, the relevant transcripts for a specific phenotype could also be ranked from the loadings under an angle (biplot) obtained after PCDA analysis. The leads identified in this way were compared with those identified using the commonly applied fold-difference and hierarchical clustering approaches. The different data analysis methods gave different results. The methods used were complementary and together resulted in a comprehensive picture of the processes important for the different carbon sources studied. For the more subtle, regulatory processes in a cell, the PCDA approach seemed to be the most effective. Except for glucose and gluconate dehydrogenase, all genes involved in the degradation of glucose, gluconate and fructose were identified. Moreover, the transcriptomics approach resulted in potential new insights into the physiology of the degradation of these carbon sources. Indications of iron limitation were observed with cells grown on glucose, gluconate or succinate but not with fructose-grown cells. Moreover, several cytochrome- or quinone-associated genes seemed to be specifically up- or downregulated, indicating that the composition of the electron-transport chain in P. putida S12 might change significantly in fructose-grown cells compared to glucose-, gluconate- or succinate-grown cells.

White biotechnology: differences in US and EU approaches?

Several predominantly political movements advocate white, or industrial, biotechnology as a means to alleviate economic, ecological and societal problems in petroleum-dependent industrialized nations worldwide. US and European approaches differ significantly and we believe that, in the long-term, only economic drivers will be able to bring about the broad use of renewable resources and a bio-based economy. As long as the cost of fossil fuel and feedstock for key chemicals have not passed their respective critical thresholds, industrial biotechnology and its products will need political support and funding, particularly in the energy and bulk-chemicals sectors. Other uses of industrial biotechnology, however, such as biocatalytic conversions of fine and specialty chemicals and the manufacture of high-value products, such as nutriceuticals, cosmeceuticals and performance chemicals offer dynamic growth opportunities both for established chemical industries, as well as emerging entrepreneurial enterprises.

Microbial metabolomics: replacing trial-and-error by the unbiased selection and ranking of targets

Microbial production strains are currently improved using a combination of random and targeted approaches. In the case of a targeted approach, potential bottlenecks, feed-back inhibition, and side-routes are removed, and other processes of interest are targeted by overexpressing or knocking-out the gene(s) of interest. To date, the selection of these targets has been based at its best on expert knowledge, but to a large extent also on 'educated guesses' and 'gut feeling'. Therefore, time and thus money is wasted on targets that later prove to be irrelevant or only result in a very minor improvement. Moreover, in current approaches, biological processes that are not known to be involved in the formation of a specific product are overlooked and it is impossible to rank the relative importance of the different targets postulated. Metabolomics, a technology that involves the non-targeted, holistic analysis of the changes in the complete set of metabolites in the cell in response to environmental or cellular changes, in combination with multivariate data analysis (MVDA) tools like principal component discriminant analysis and partial least squares, allow the replacement of current empirical approaches by a scientific approach towards the selection and ranking of targets. In this review, we describe the technological challenges in setting up the novel metabolomics technology and the principle of MVDA algorithms in analyzing biomolecular data sets. In addition to strain improvement, the combined metabolomics and MVDA approach can also be applied to growth medium optimization, predicting the effect of quality differences of different batches of complex media on productivity, the identification of bioactives in complex mixtures, the characterization of mutant strains, the exploration of the production potential of strains, the assignment of functions to orphan genes, the identification of metabolite-dependent regulatory interactions, and many more microbiological issues.