Metabolomics as a tool for understanding the evolution of <Emphasis Type="Italic">Tabebuia sensu lato</Emphasis>

Background

Plant systematic studies have changed substantially in the last years, stimulated by new strategies for phylogenetic studies. In this regard, chemistry data has been a useful tool for understanding plant phylogenetic relationships.

Objective

Our aim was to apply metabolomic approaches, followed by multivariate statistical analysis and dereplication of Tabebuia sensu lato species, and compare our results with classifications based on traditional taxonomy and molecular phylogeny. We also evaluated the application of metabolomics as a chemotaxonomic identification tool, as well as to enlighten plant chemical evolution.

Methods

Metabolomic data was generated through a high-resolution mass spectrometry with electrospray ionization of 27 Tabebuia sensu lato specimens from different populations, consisting of 15 Handroanthus (from four species) and 12 Tabebuia sensu stricto (from three species). Chemometric tools, such as principal component analysis and metabolite heatmaps, were used to scrutinize the metabolic changes among species.

Results

Tabebuia and Handroanthus species presented different secondary metabolite storage capacity. The genus Tabebuia revealed higher levels of glycosylated iridoids esterified with a phenylpropanoid moiety, such as specioside, verminoside, and minecoside, while Handroanthus accumulated iridoids linked to a simple phenol, lignans, and verbascoside derivatives.

Conclusion

These results corroborate splitting the Tabebuia s.l., which was supported by profound changes in secondary metabolism, suggesting metabolomics as an excellent tool for understanding species evolution.

     

Metabolic profiling of <Emphasis Type="Italic">Drosophila melanogaster</Emphasis> metamorphosis: a new insight into the central metabolic pathways

Introduction

Metamorphosis is a complicated process in which cell proliferation, differentiation, and death are orchestrated to form the mature structures of insects. In Drosophila, this process is controlled by ecdysone, a steroid hormone responsible for tissue remodeling and organogenesis that gives rise to the adult fly.

Objective

By using a metabolomics approach, this study aimed to elucidate global changes in the central metabolic pathways in Drosophila throughout metamorphosis and then further examine the effects of temperature and origin on metabolic profiles.

Methods

Targeted and non-targeted metabolic profiling of time-course samples from Drosophila were constructed to cover a wide range of cellular metabolites during metamorphosis.

Results

This was the first wide-scale metabolomics study of Drosophila metamorphosis focusing on central metabolism. The abundance of detected metabolites changed drastically and correlated strongly with the development of Drosophila pupae. In non-stress conditions, temperature affected the developmental time, but the metabolic state at a certain stage of metamorphosis remained stable. Between D. melanogaster Canton S and Oregon R, similar metabolic profiles throughout metamorphosis was observed. However, there were still differences in purine and pyrimidine metabolism at an early stage in the pupal period, which was matched by differences in ecdysteroid levels.

Conclusion

This study supported the strength of metabolomics in the field of developmental biology. The results provided a general view on the metabolic profile of Drosophila during metamorphosis, which provides basic 3 knowledge for future metabolomics studies using Drosophila.

     

Characterization of missing values in untargeted MS-based metabolomics data and evaluation of missing data handling strategies

Background

Untargeted mass spectrometry (MS)-based metabolomics data often contain missing values that reduce statistical power and can introduce bias in biomedical studies. However, a systematic assessment of the various sources of missing values and strategies to handle these data has received little attention. Missing data can occur systematically, e.g. from run day-dependent effects due to limits of detection (LOD); or it can be random as, for instance, a consequence of sample preparation.

Methods

We investigated patterns of missing data in an MS-based metabolomics experiment of serum samples from the German KORA F4 cohort (n?=?1750). We then evaluated 31 imputation methods in a simulation framework and biologically validated the results by applying all imputation approaches to real metabolomics data. We examined the ability of each method to reconstruct biochemical pathways from data-driven correlation networks, and the ability of the method to increase statistical power while preserving the strength of established metabolic quantitative trait loci.

Results

Run day-dependent LOD-based missing data accounts for most missing values in the metabolomics dataset. Although multiple imputation by chained equations performed well in many scenarios, it is computationally and statistically challenging. K-nearest neighbors (KNN) imputation on observations with variable pre-selection showed robust performance across all evaluation schemes and is computationally more tractable.

Conclusion

Missing data in untargeted MS-based metabolomics data occur for various reasons. Based on our results, we recommend that KNN-based imputation is performed on observations with variable pre-selection since it showed robust results in all evaluation schemes.

     

A novel method for single-grain-based metabolic profiling of Arabidopsis seed

Introduction

In plant metabolomics, metabolite contents are often normalized by sample weight. However, accurate weighing of very small samples, such as individual Arabidopsis thaliana seeds (approximately 20 µg), is difficult, which may lead to irreproducible results.

Objectives

We aimed to establish alternative normalization methods for seed-grain-based comparative metabolomics of A. thaliana.

Methods

Arabidopsis thaliana seeds were assumed to have a prolate spheroid shape. Using a microscope image of each seed, the lengths of major and minor axes were measured by fitting a projected 2-dimensional shape of each seed as an ellipse. Metabolic profiles of individual diploid or tetraploid A. thaliana seeds were measured by our highly sensitive protocol (“widely targeted metabolomics”) that uses liquid chromatography coupled with tandem quadrupole mass spectrometry. Mass spectrometric analysis of 1 µL of solution extract identified more than 100 metabolites. The data were normalized by various seed-size measures, including seed volume (single-grain-based analysis). For comparison, metabolites were extracted from 4 mg of diploid and tetraploid A. thaliana seeds and their metabolic profiles were analyzed by normalization of weight (weight-based analysis).

Results

A small number of metabolites showed statistically significant differences in the single-grain-based analysis compared to weight-based analysis. A total of 17 metabolites showed statistically different accumulation between ploidy types with similar fold changes in both analyses.

Conclusion

Seed-size measures obtained by microscopic imaging were useful for data normalization. Single-grain-based analysis enables evaluation of metabolism of each seed and elucidates the metabolic profiles of precious bioresources by using small amounts of samples.

     

Double-check: validation of diagnostic statistics for PLS-DA models in metabolomics studies

Partial Least Squares-Discriminant Analysis (PLS-DA) is a PLS regression method with a special binary ‘dummy’ y-variable and it is commonly used for classification purposes and biomarker selection in metabolomics studies. Several statistical approaches are currently in use to validate outcomes of PLS-DA analyses e.g. double cross validation procedures or permutation testing. However, there is a great inconsistency in the optimization and the assessment of performance of PLS-DA models due to many different diagnostic statistics currently employed in metabolomics data analyses. In this paper, properties of four diagnostic statistics of PLS-DA, namely the number of misclassifications (NMC), the Area Under the Receiver Operating Characteristic (AUROC), Q ² and Discriminant Q ² (DQ ²) are discussed. All four diagnostic statistics are used in the optimization and the performance assessment of PLS-DA models of three different-size metabolomics data sets obtained with two different types of analytical platforms and with different levels of known differences between two groups: control and case groups. Statistical significance of obtained PLS-DA models was evaluated with permutation testing. PLS-DA models obtained with NMC and AUROC are more powerful in detecting very small differences between groups than models obtained with Q ² and Discriminant Q ² (DQ ²). Reproducibility of obtained PLS-DA models outcomes, models complexity and permutation test distributions are also investigated to explain this phenomenon. DQ ² and Q ² (in contrary to NMC and AUROC) prefer PLS-DA models with lower complexity and require higher number of permutation tests and submodels to accurately estimate statistical significance of the model performance. NMC and AUROC seem more efficient and more reliable diagnostic statistics and should be recommended in two group discrimination metabolomic studies.     

LC–MS-based metabolome analysis on steroid metabolites from the starfish <Emphasis Type="Italic">Patiria</Emphasis> (=<Emphasis Type="Italic">Asterina</Emphasis>) <Emphasis Type="Italic">pectinifera</Emphasis> in conditions of active feeding and stresses

Introduction

Starfish are recognized as interesting source of natural steroid products with pharmaceutical potential. Polar steroid metabolites of starfish have unique chemical structures and exhibit various biological activities but their biological functions are controversial.

Objectives

The objective of this study was to investigate the response of polar steroid metabolome of the starfish Patiria (=Asterina) pectinifera on various environmental factors and stresses.

Methods

Here we first have applied MS-based environmental metabolomics to elucidate the metabolic changes of polar steroid metabolome of starfish. Using HPLC–ESI–Q/TOF–MS approach followed by statistical analysis including principal component analysis and partial least squares discriminant analysis for data classification and potential biomarkers selection, we investigated the changes induced by feeding, injury, variations in water temperature and salinity, and oxygen deficiency.

Results

According to multivariate and univariate statistical analysis the responses to feeding, injury and water heating were better expressed than the others and have some similarity in their action on the steroid metabolome of the starfish P. pectinifera. Most constituents of asterosaponin pool were reduced and most constituents of polyhydroxysteroid and related glycoside pool were increased at that.

Conclusion

Our results indicate that various metabolic changes in polar steroid constituents of P. pectinifera are induced by feeding and stresses. We believe that these responses are connected with biological multifunctionality of these compounds.

     

Combining metabolomics and network analysis to improve tacrolimus production in <Emphasis Type="Italic">Streptomyces tsukubaensis</Emphasis> using different exogenous feedings

Tacrolimus is widely used as an immunosuppressant in the treatment of various autoimmune diseases. However, the low fermentation yield of tacrolimus has thus far restricted its industrial applications. To solve this problem, the time-series response mechanisms of the intracellular metabolism that were highly correlated with tacrolimus biosynthesis were investigated using different exogenous feeding strategies in S. tsukubaensis. The metabolomic datasets, which contained 93 metabolites, were subjected to weighted correlation network analysis (WGCNA), and eight distinct metabolic modules and seven hub metabolites were identified to be specifically associated with tacrolimus biosynthesis. The analysis of metabolites within each metabolic module suggested that the pentose phosphate pathway (PPP), shikimate and aspartate pathway might be the main limiting factors in the rapid synthesis phase of tacrolimus accumulation. Subsequently, all possible key-limiting steps in the above metabolic pathways were further screened using a genome-scale metabolic network model (GSMM) of S. tsukubaensis. Based on the prediction results, two newly identified targets (aroC and dapA) were overexpressed experimentally, and both of the engineered strains showed higher tacrolimus production. Moreover, the best strain, HT-aroC/dapA, that was engineered to simultaneously enhanced chorismate and lysine biosynthesis was able to produce 128.19 mg/L tacrolimus, 1.64-fold higher than control (78.26 mg/L). These findings represent a valuable addition to our understanding of tacrolimus accumulation in S. tsukubaensis, and pave the way to further production improvements.     

Monitoring spatial and temporal metabolic dynamics of woody poplar root under mechanical stress conditions by NMR-based metabolomics

Introduction

Molecular factors are differentially observed in various bent sectors of poplar (Populus nigra) woody taproots. Responses to stress are modulated by a complex interplay among different hormones and signal transduction pathways. In recent years, metabolomics has been recognized as a powerful tool to characterize metabolic network regulation, and it has been widely applied to investigate plant responses to biotic and abiotic stresses.

Objectives

In this paper we used metabolomics to understand if long term-bending stress induces a “spatial” and a “temporal” metabolic reprogramming in woody poplar roots.

Methods

By NMR spectroscopy and statistical analysis we investigated the unstressed and three portions of stressed root (above-bent, bent, and below-bent) sectors collected at 12 (T₀), 13 (T₁) and 14 (T₂) months after stress induction.

Results

The data indicate a clear between-class separation of control and stressed regions, based on the metabolites regulation, during both spatial and temporal changes. We found that taproots, as a consequence of the stress, try to restore homeostasis and normal metabolic fluxes thorough the synthesis and/or accumulation of specific compounds related to mechanical forces distribution along the bent taproot.

Conclusion

The data demonstrate that the impact of mechanical stress on plant biology can efficiently be studied by NMR-based metabolomics.

     

NET: a new framework for the vectorization and examination of network data

Background

The analysis of complex networks both in general and in particular as pertaining to real biological systems has been the focus of intense scientific attention in the past and present. In this paper we introduce two tools that provide fast and efficient means for the processing and quantification of biological networks like Drosophila tracheoles or leaf venation patterns: the Network Extraction Tool (NET) to extract data and the Graph-edit-GUI (GeGUI) to visualize and modify networks.

Results

NET is especially designed for high-throughput semi-automated analysis of biological datasets containing digital images of networks. The framework starts with the segmentation of the image and then proceeds to vectorization using methodologies from optical character recognition. After a series of steps to clean and improve the quality of the extracted data the framework produces a graph in which the network is represented only by its nodes and neighborhood-relations. The final output contains information about the adjacency matrix of the graph, the width of the edges and the positions of the nodes in space. NET also provides tools for statistical analysis of the network properties, such as the number of nodes or total network length. Other, more complex metrics can be calculated by importing the vectorized network to specialized network analysis packages. GeGUI is designed to facilitate manual correction of non-planar networks as these may contain artifacts or spurious junctions due to branches crossing each other. It is tailored for but not limited to the processing of networks from microscopy images of Drosophila tracheoles.

Conclusion

The networks extracted by NET closely approximate the network depicted in the original image. NET is fast, yields reproducible results and is able to capture the full geometry of the network, including curved branches. Additionally GeGUI allows easy handling and visualization of the networks.

     

NormalizeMets: assessing,selecting and implementing statistical methods for normalizing metabolomics data

Introduction

In metabolomics studies, unwanted variation inevitably arises from various sources. Normalization, that is the removal of unwanted variation, is an essential step in the statistical analysis of metabolomics data. However, metabolomics normalization is often considered an imprecise science due to the diverse sources of variation and the availability of a number of alternative strategies that may be implemented.

Objectives

We highlight the need for comparative evaluation of different normalization methods and present software strategies to help ease this task for both data-oriented and biological researchers.

Methods

We present NormalizeMets—a joint graphical user interface within the familiar Microsoft Excel and freely-available R software for comparative evaluation of different normalization methods. The NormalizeMets R package along with the vignette describing the workflow can be downloaded from https://cran.r-project.org/web/packages/NormalizeMets/. The Excel Interface and the Excel user guide are available on https://metabolomicstats.github.io/ExNormalizeMets.

Results

NormalizeMets allows for comparative evaluation of normalization methods using criteria that depend on the given dataset and the ultimate research question. Hence it guides researchers to assess, select and implement a suitable normalization method using either the familiar Microsoft Excel and/or freely-available R software. In addition, the package can be used for visualisation of metabolomics data using interactive graphical displays and to obtain end statistical results for clustering, classification, biomarker identification adjusting for confounding variables, and correlation analysis.

Conclusion

NormalizeMets is designed for comparative evaluation of normalization methods, and can also be used to obtain end statistical results. The use of freely-available R software offers an attractive proposition for programming-oriented researchers, and the Excel interface offers a familiar alternative to most biological researchers. The package handles the data locally in the user’s own computer allowing for reproducible code to be stored locally.

     

Comparative analysis of creatinine and osmolality as urine normalization strategies in targeted metabolomics for the differential diagnosis of asthma and COPD

Introduction

Urine is an ideal matrix for metabolomics investigation due to its non-invasive nature of collection and its rich metabolite content. Despite the advancements in mass spectrometry and ¹H-NMR platforms in urine metabolomics, the statistical analysis of the generated data is challenged with the need to adjust for the hydration status of the person. Normalization to creatinine or osmolality values are the most adopted strategies, however, each technique has its challenges that can hinder its wider application. We have been developing targeted urine metabolomic methods to differentiate two important respiratory diseases, namely asthma and chronic obstructive pulmonary disease (COPD).

Objective

To assess whether the statistical model of separation of diseases using targeted metabolomic data would be improved by normalization to osmolality instead of creatinine.

Methods

The concentration of 32 metabolites was previously measured by two liquid chromatography-tandem mass spectrometry methods in 51 human urine samples with either asthma (n?=?25) or COPD (n?=?26). The data was normalized to creatinine or osmolality. Statistical analysis of the normalized values in each disease was performed using partial least square discriminant analysis (PLS-DA). Models of separation of diseases were compared.

Results

We found that normalization to creatinine or osmolality did not significantly change the PLS-DA models of separation (R²Q²?=?0.919, 0.705 vs R²Q²?=?0.929, 0.671, respectively). The metabolites of importance in the models remained similar for both normalization methods.

Conclusion

Our findings suggest that targeted urine metabolomic data can be normalized for hydration using creatinine or osmolality with no significant impact on the diagnostic accuracy of the model.

     

A metabolomics and proteomics study of the <Emphasis Type="Italic">Lactobacillus plantarum</Emphasis> in the grass carp fermentation

Background

Lactobacillus plantarum, a versatile lactic acid-fermenting bacterium, isolated from the traditional pickles in Ningbo of China, was chosen for grass carp fermentation, which could also improve the flavor of grass carp. We here explored the central metabolic pathways of L. plantarum by using metabolomic approach, and further proved the potential for metabolomics combined with proteomics approaches for the basic research on the changes of metabolites and the corresponding fermentation mechanism of L. plantarum fermentation.

Results

This study provides a cellular material footprinting of more than 77 metabolites and 27 proteins in L. plantarum during the grass carp fermentation. Compared to control group, cells displayed higher levels of proteins associated with glycolysis and nucleotide synthesis, whereas increased levels of serine, ornithine, aspartic acid, 2-piperidinecarboxylic acid, and fumarate, along with decreased levels of alanine, glycine, threonine, tryptophan, and lysine.

Conclusions

Our results may provide a deeper understanding of L. plantarum fermentation mechanism based on metabolomics and proteomic analysis and facilitate future investigations into the characterization of L. plantarum during the grass carp fermentation.

     

Investigation of species and environmental effects on rhubarb roots metabolome using <Superscript>1</Superscript>H NMR combined with high performance thin layer chromatography

Introduction

The pharmacological activities of medicinal plants are reported to be due to a wide range of metabolites, therein, the concentrations of which are greatly affected by many genetic and/or environmental factors. In this context, a metabolomics approach has been applied to reveal these relationships. The investigation of such complex networks that involve the correlation between multiple biotic and abiotic factors and the metabolome, requires the input of information acquired by more than one analytical platform. Thus, development of new metabolomics techniques or hyphenations is continuously needed.

Objectives

Feasibility of high performance thin-layer chromatography (HPTLC) were investigated as a supplementary tool for medicinal plants metabolomics supporting ¹H nuclear magnetic resonance (¹H NMR) spectroscopy.

Method

The overall metabolic difference of plant material collected from two species (Rheum palmatum and Rheum tanguticum) in different geographical locations and altitudes were analyzed by ¹H NMR- and HPTLC-based metabolic profiling. Both NMR and HPTLC data were submitted to multivariate data analysis including principal component analysis and orthogonal partial least square analysis.

Results

The NMR and HPTLC profiles showed that while chemical variations of rhubarb are in some degree affected by all the factors tested in this study, the most influential factor was altitude of growth. The metabolites responsible for altitude differentiation were chrysophanol, emodin and sennoside A, whereas aloe emodin, catechin, and rhein were the key species-specific markers.

Conclusion

These results demonstrated the potential of HTPLC as a supporting tool for metabolomics due to its high profiling capacity of targeted metabolic groups and preparative capability.

     

Differential metabolomic responses of PAMP-triggered immunity and effector-triggered immunity in Arabidopsis suspension cells

Introduction

The rhizobacterial tomato pathogen Pseudomonas syringae pv. tomato str. DC3000 (PstDC3000), like many plant pathogenic bacteria, can elicit hypersensitive response in non-host plant cells. PstDC3000 uses a type III protein secretion system (T3SS) to deliver effector proteins.

Objectives

We compared metabolomic responses of Arabidopsis suspension cells to a wild-type PstDC3000, a T3SS deletion mutant PstDC3000D28E, and a pathogen associated molecular pattern (PAMP) flagellin’s N-terminal domain’s 22-aa peptide (flg22) to obtain metabolomics insights into the plant cell PAMP-triggered immunity (PTI) and effector-triggered immunity (ETI).

Methods

Using targeted HPLC-MRM-MS and untargeted GC-MS approaches, we monitored qualitative and quantitative changes of 312 metabolites in central and specialized metabolic pathways in a time-course study.

Results

The overall metabolomic changes induced by the three treatments included phenylpropanoid, flavonoid, and phytohormone biosynthetic pathways, as well as primary metabolism in amino acid and sugar biosynthesis. In addition to shared metabolites, flg22, PstDC3000D28E and PstDC3000 each caused unique metabolite changes in the course of the development of PTI and ETI.

Conclusion

PstDC3000D28E triggered PTI responses were different from those of flg22. This study has not only revealed the discernible metabolomics features associated with the flg22, PstDC3000D28E and PstDC3000 treatments, but also laid a foundation toward further understanding of metabolic regulation and responses underlying plant PTI and ETI.

     

Plasma metabolomics profiling for fish maturation in blunt snout bream

Introduction

In some fish species, it is difficult to distinguish mature females from immature females or females that have already spawned via appearance or other convenient methods. Few studies have investigated plasma metabolite profiling for the prediction of fish maturation.

Objectives

We investigated the comprehensive metabolic profiles of plasma among immature females and mature females ready to spawn, as well as already spawned breeders of blunt snout bream (Megalobrama amblycephala). The purpose of this study was to screen out potential biomarkers for sexually mature female M. amblycephala compared to immature female individuals and already spawned breeders.

Methods

Three groups were set up in this study, which included 1-year-old immature females, 2-year-old sexually mature females ready to spawn and successfully spawned females of M. amblycephala. Plasma samples were collected to investigate comprehensive metabolic profiles through UPLC-MS/MS based on a metabolomics analysis method.

Results

According to multivariate and univariate statistical analysis, plasma metabolite profiles of the three groups were clearly separated. The differential plasma metabolites from three hormone related pathways including the GnRH signaling pathway, steroid hormone biosynthesis and steroid biosynthesis, were analyzed. A total of 29 metabolites were identified as differential biomarkers associated with the female maturation status.

Conclusion

The identified potential biomarkers could be useful in separating mature M. amblycephala from immature individuals or ovulation-induced female individuals, which would allow for more effective artificial breeding. The results may contribute to a better understanding of the maturation mechanisms of fish in the aspect of metabolomics.

     

A metabolomic approach to characterize the acid-tolerance response in <Emphasis Type="Italic">Sinorhizobium meliloti</Emphasis>

Introduction

Sinorhizobium meliloti establishes a symbiosis with Medicago species where the bacterium fixes atmospheric nitrogen for plant nutrition. To achieve a successful symbiosis, however, both partners need to withstand biotic and abiotic stresses within the soil, especially that of excess acid, to which the Medicago-Sinorhizobium symbiotic system is widely recognized as being highly sensitive.

Objective

To cope with low pH, S. meliloti can undergo an acid-tolerance response (ATR(+)) that not only enables a better survival but also constitutes a more competitive phenotype for Medicago sativa nodulation under acid and neutral conditions. To characterize this phenotype, we employed metabolomics to investigate the biochemical changes operating in ATR(+) cells.

Methods

A gas chromatography/mass spectrometry approach was used on S. meliloti 2011 cultures showing ATR(+) and ATR(?) phenotypes. After an univariate and multivariate statistical analysis, enzymatic activities and/or reserve carbohydrates characterizing ATR(+) phenotypes were determined.

Results

Two distinctive populations were clearly defined in cultures grown in acid and neutral pH based on the metabolites present. A shift occurred in the carbon-catabolic pathways, potentially supplying NAD(P)H equivalents for use in other metabolic reactions and/or for maintaining intracellular-pH homeostasis. Furthermore, among the mechanisms related to acid resistance, the ATR(+) phenotype was also characterized by lactate production, envelope modification, and carbon-overflow metabolism.

Conclusions

Acid-challenged S. meliloti exhibited several changes in different metabolic pathways that, in specific instances, could be identified and related to responses observed in other bacteria under various abiotic stresses. Some of the observed changes included modifications in the pentose-phosphate pathway (PPP), the exopolysaccharide biosynthesis, and in the myo-inositol degradation intermediates. Such modifications are part of a metabolic adaptation in the rhizobia that, as previously reported, is associated to improved phenotypes of acid tolerance and nodulation competitiveness.

     

An engineered non-oxidative glycolysis pathway for acetone production in <Emphasis Type="Italic">Escherichia coli</Emphasis>

Objectives

To find new metabolic engineering strategies to improve the yield of acetone in Escherichia coli.

Results

Results of flux balance analysis from a modified Escherichia coli genome-scale metabolic network suggested that the introduction of a non-oxidative glycolysis (NOG) pathway would improve the theoretical acetone yield from 1 to 1.5 mol acetone/mol glucose. By inserting the fxpk gene encoding phosphoketolase from Bifidobacterium adolescentis into the genome, we constructed a NOG pathway in E.coli. The resulting strain produced 47 mM acetone from glucose under aerobic conditions in shake-flasks. The yield of acetone was improved from 0.38 to 0.47 mol acetone/mol glucose which is a significant over the parent strain.

Conclusions

Guided by computational analysis of metabolic networks, we introduced a NOG pathway into E. coli and increased the yield of acetone, which demonstrates the importance of modeling analysis for the novel metabolic engineering strategies.