A simultaneous extraction method for metabolome and lipidome and its application in cry1Ac and sck-transgenic rice leaf treated with insecticide based on LC–MS analysis

Abiotic stress caused by insecticide treatment is an interesting and challenging topic in plant research. Here a simultaneous extraction method with methyl tert-butyl ether for metabolomics and lipidomics analysis by using rapid resolution liquid chromatography quadrupole time-of-flight mass spectrometry was developed and validated. The extraction efficiency of lipidome and metabolome based on the current developed method showed an obvious improvement compared with literatures. About 300 metabolites were identified in rice leaf. Method validation was performed and analytical properties including the linearity (R², 0.991–1.000), repeatability (over 87 % peaks with CV < 20 % accounting for over 92 % of total response) and recovery (74.4–119.6 %) were satisfactory. The method was then applied to investigate time-course changes caused by insecticide treatment in transgenic rice with cry1Ac and sck genes and its wild counterpart. Antioxidants including ferulic acid and sinapic acid were down-regulated at 24 h after insecticide treatment. Signaling metabolites including salicylic acid and nicotinamide adenine dinucleotide were significantly up-regulated at 12–24 h after treatment in transgenic rice. More flavonoids were significantly changed in transgenic plants. Results indicated that gene transformation affected the metabolic response of rice to insecticide stress.     

Metabolic profiling of transgenic rice with cryIAc and sck genes: An evaluation of unintended effects at metabolic level by using GC-FID and GC–MS

The cryIAc and sck genes were introduced to the rice for the purpose of improving the insect resistance. Metabolic profiles of wild and transgenic rice were compared to assess the unintended effects related to gene modification. Wild samples with different sowing dates or sites were also examined to determine the environmental effects on metabolites. The polar compounds of grains were extracted, trimethylsilylated and analyzed by gas chromatography-flame ionization detection (GC-FID). Partial least squares-discriminant analysis (PLS-DA) and principal component analysis (PCA) were applied to differentiate transgenic and wild rice grains. The significantly distinguishable metabolites were picked out, and then identified by gas chromatography–mass spectrometry (GC–MS). It was found that both the environment and gene manipulation had remarkable impacts on the contents of glycerol-3-phosphate, citric acid, linoleic acid, oleic acid, hexadecanoic acid, 2,3-dihydroxypropyl ester, sucrose, 9-octadecenoic acid (Z)-, 2,3-dihydroxypropyl ester and so on. Sucrose, mannitol and glutamic acid had a significant increase in transgenic grains in contrast to those in non-genetically modified (GM) rice.     

Study of polar metabolites in tobacco from different geographical origins by using capillary electrophoresis–mass spectrometry

Many metabolites in plant are highly polar and ionic. Their analysis using gas chromatography–mass spectrometry and liquid chromatography–mass spectrometry can be problematic. Therefore a capillary electrophoresis–mass spectrometry (CE–MS) method with charge-driven separation characteristic was developed to investigate polar metabolites in tobacco. To obtain as many features as possible, extraction of polar metabolites was optimized by the design of experiments and evaluated by univariate statistics. Method validation was carried out to evaluate the analytical characteristics including calibration curve, precision, sample stability and extraction reproducibility. The developed method was successfully applied in studying 30 tobacco leaves obtained from Yunnan and Guizhou provinces in China. A total of 154 polar metabolites were identified based on available database. Multivariate pattern recognition clearly revealed the metabolic differences between the two geographic areas and 43 significantly different metabolites were defined by the non-parametric hypothesis test (Mann–Whitney U test) and false discovery rate. Some key metabolites involved in photosynthesis such as ribulose 1,5-disphosphate, fructose 1,6-diphosphate, glycine, betaine, GABA and serine were found to be susceptible to environmental conditions. This study shows that the metabolic profiling based on CE–MS can clearly discriminate tobacco leaves of different geographical origins and understand the relationship between plant metabolites and their geographical origins.     

Quantitative profiling of bile acids in biofluids and tissues based on accurate mass high resolution LC-FT-MS: compound class targeting in a metabolomics workflow

We report a sensitive, generic method for quantitative profiling of bile acids and other endogenous metabolites in small quantities of various biological fluids and tissues. The method is based on a straightforward sample preparation, separation by reversed-phase high performance liquid-chromatography mass spectrometry (HPLC-MS) and electrospray ionisation in the negative ionisation mode (ESI-). Detection is performed in full scan using the linear ion trap Fourier transform mass spectrometer (LTQ-FTMS) generating data for many (endogenous) metabolites, not only bile acids. A validation of the method in urine, plasma and liver was performed for 17 bile acids including their taurine, sulfate and glycine conjugates. The method is linear in the 0.01-1muM range. The accuracy in human plasma ranges from 74 to 113%, in human urine 77 to 104% and in mouse liver 79 to 140%. The precision ranges from 2 to 20% for pooled samples even in studies with large number of samples (n>250). The method was successfully applied to a multi-compartmental APOE*3-Leiden mouse study, the main goal of which was to analyze the effect of increasing dietary cholesterol concentrations on hepatic cholesterol homeostasis and bile acid synthesis. Serum and liver samples from different treatment groups were profiled with the new method. Statistically significant differences between the diet groups were observed regarding total as well as individual bile acid concentrations.     

Dissection of genotype-phenotype associations in rice grains using metabolome quantitative trait loci analysis

A comprehensive and large‐scale metabolome quantitative trait loci (mQTL) analysis was performed to investigate the genetic backgrounds associated with metabolic phenotypes in rice grains. The metabolome dataset consisted of 759 metabolite signals obtained from the grains of 85 lines of rice (Oryza sativa, Sasanishiki × Habataki back‐crossed inbred lines). Metabolome analysis was performed using four mass spectrometry pipelines to enhance detection of different classes of metabolites. This mQTL analysis of a wide range of metabolites highlighted an uneven distribution of 802 mQTLs on the rice genome, as well as different modes of metabolic trait (m‐trait) control among various types of metabolites. The levels of most metabolites within rice grains were highly sensitive to environmental factors, but only weakly associated with mQTLs. Coordinated control was observed for several groups of metabolites, such as amino acids linked to the mQTL hotspot on chromosome 3. For flavonoids, m‐trait variation among the experimental lines was tightly governed by genetic factors that alter the glycosylation of flavones. Many loci affecting levels of metabolites were detected by QTL analysis, and plausible gene candidates were evaluated by in silico analysis. Several mQTLs profoundly influenced metabolite levels, providing insight into the control of rice metabolism. The genomic region and genes potentially responsible for the biosynthesis of apigenin‐6,8‐di‐C‐α‐l‐ arabinoside are presented as an example of a critical mQTL identified by the analysis.     

Metabolic diversity in the grains of Indian varieties of rice

The aim of the present work was to analyze metabolic diversity in 26 different indica varieties of rice grains. Seventy-six metabolites could be identified in the methanol extracts of each of the rice varieties analyzed by gas chromatography-mass spectrometry. These metabolites included 9 sugars/sugar alcohols, 17 amino acids/derivatives, 18 fatty acids, 5 free phenolic acids and 19 other organic acids, 3 phytosterols, 5 other constituents. Cluster analyses to extract information for similarity and differences in metabolites unveiled diversity in metabolite profile. Two hierarchical clusters were generated based on the metabolite contents of the rice varieties. The first cluster (cluster I) consisted of one variety only. The second cluster again segregated into four clusters (clusters II, III, IV and V). Very distinct differences were visible amongst the clusters with respect to their sugars/sugar alcohols, organic acid, amino acid and fatty acid, phenol, and sterol profiles. Metabolites determine nutritional quality, taste, aroma. This and future efforts on the metabolomic information would help biochemists and nutritionists to better understand the nutritional quality of such grains at varietal level and correlating metabolites and long term human health related issues.     

UHPLC-Q-Orbitrap-HRMS-based global metabolomics reveal metabolome modifications in plasma of young women after cranberry juice consumption

Plasma metabolome in young women following cranberry juice consumption were investigated using a global UHPLC-Q-Orbitrap-HRMS approach. Seventeen female college students, between 21 and 29 years old, were given either cranberry juice or apple juice for three days using a cross-over design. Plasma samples were collected before and after juice consumption. Plasma metabolomes were analyzed using UHPLC-Q-Orbitrap-HRMS followed by orthogonal partial least squares-discriminant analyses (OPLS-DA). S-plot was used to identify discriminant metabolites. Validated OPLS-DA analyses showed that the plasma metabolome in young women, including both exogenous and endogenous metabolites, were altered following cranberry juice consumption. Cranberry juice caused increases of exogenous metabolites including quinic acid, vanilloloside, catechol sulfate, 3,4-dihydroxyphenyl ethanol sulfate, coumaric acid sulfate, ferulic acid sulfate, 5-(trihydroxphenyl)-gamma-valerolactone, 3-(hydroxyphenyl)proponic acid, hydroxyphenylacetic acid and trihydroxybenzoic acid. In addition, the plasma levels of endogenous metabolites including citramalic acid, aconitic acid, hydroxyoctadecanoic acid, hippuric acid, 2-hydroxyhippuric acid, vanilloylglycine, 4-acetamido-2-aminobutanoic acid, dihydroxyquinoline, and glycerol 3-phosphate were increased in women following cranberry juice consumption. The metabolic differences and discriminant metabolites observed in this study may serve as biomarkers of cranberry juice consumption and explain its health promoting properties in human.     

Metabolomics analysis reveals differences in evolution between maize and rice

Metabolites are the intermediate and final products of metabolism, which play essential roles in plant growth, evolution and adaptation to changing climates. However, it is unclear how evolution contributes to metabolic variation in plants. Here, we investigated the metabolomics data from leaf and seed tissues in maize and rice. Using principal components analysis based on leaf metabolites but not seed metabolites, metabolomics data could be clearly separated for rice Indica and Japonica accessions, while two maize subgroups, temperate and tropical, showed more visible admixture. Rice and maize seed exhibited significant interspecific differences in metabolic variation, while within rice, leaf and seed displayed similar metabolic variations. Among 10 metabolic categories, flavonoids had higher variation in maize than rice, indicating flavonoids are a key constituent of interspecific metabolic divergence. Interestingly, metabolic regulation was also found to be reshaped dramatically from positive to negative correlations, indicative of the differential evolutionary processes in maize and rice. Moreover, perhaps due to this divergence significantly more metabolic interactions were identified in rice than maize. Furthermore, in rice, the leaf was found to harbor much more intense metabolic interactions than the seed. Our result suggests that metabolomes are valuable for tracking evolutionary history, thereby complementing and extending genomic insights concerning which features are responsible for interspecific differentiation in maize and rice.     

1H NMR metabolomics analysis of glioblastoma subtypes: correlation between metabolomics and gene expression characteristics

Glioblastoma multiforme (GBM) is the most common form of malignant glioma, characterized by unpredictable clinical behaviors that suggest distinct molecular subtypes. With the tumor metabolic phenotype being one of the hallmarks of cancer, we have set upon to investigate whether GBMs show differences in their metabolic profiles. (1)H NMR analysis was performed on metabolite extracts from a selection of nine glioblastoma cell lines. Analysis was performed directly on spectral data and on relative concentrations of metabolites obtained from spectra using a multivariate regression method developed in this work. Both qualitative and quantitative sample clustering have shown that cell lines can be divided into four groups for which the most significantly different metabolites have been determined. Analysis shows that some of the major cancer metabolic markers (such as choline, lactate, and glutamine) have significantly dissimilar concentrations in different GBM groups. The obtained lists of metabolic markers for subgroups were correlated with gene expression data for the same cell lines. Metabolic analysis generally agrees with gene expression measurements, and in several cases, we have shown in detail how the metabolic results can be correlated with the analysis of gene expression. Combined gene expression and metabolomics analysis have shown differential expression of transporters of metabolic markers in these cells as well as some of the major metabolic pathways leading to accumulation of metabolites. Obtained lists of marker metabolites can be leveraged for subtype determination in glioblastomas.     

Revealing different systems responses to brown planthopper infestation for pest susceptible and resistant rice plants with the combined metabonomic and gene-expression analysis

Brown planthopper (BPH) is a notorious pest of rice plants attacking leaf sheaths and seriously affecting global rice production. However, how rice plants respond against BPH remains to be fully understood. To understand systems metabolic responses of rice plants to BPH infestation, we analyzed BPH-induced metabolic changes in leaf sheaths of both BPH-susceptible and resistant rice varieties using NMR-based metabonomics and measured expression changes of 10 relevant genes using quantitative real-time PCR. Our results showed that rice metabonome was dominated by more than 30 metabolites including sugars, organic acids, amino acids, and choline metabolites. BPH infestation caused profound metabolic changes for both BPH-susceptible and resistant rice plants involving transamination, GABA shunt, TCA cycle, gluconeogenesis/glycolysis, pentose phosphate pathway, and secondary metabolisms. BPH infestation caused more drastic overall metabolic changes for BPH-susceptible variety and more marked up-regulations for key genes regulating GABA shunt and biosynthesis of secondary metabolites for BPH-resistant variety. Such observations indicated that activation of GABA shunt and shikimate-mediated secondary metabolisms was vital for rice plants to resist BPH infestation. These findings filled the gap of our understandings in the mechanistic aspects of BPH resistance for rice plants and demonstrated the combined metabonomic and qRT-PCR analysis as an effective approach for understanding plant-herbivore interactions.     

Analysis of urinary metabolic signatures of early hepatocellular carcinoma recurrence after surgical removal using gas chromatography-mass spectrometry

The objective of present study was to offer insights into the metabolic responses of hepatocellular carcinoma (HCC) to surgical resection and the metabolic signatures latent in early HCC recurrence (one year after operation). Urinary metabolic profiling employing gas chromatography time-of-flight mass spectrometry (GC-TOF MS) was utilized to investigate the complex physiopathologic regulations in HCC after operational intervention. It was revealed that an intricate series of metabolic regulations including energy metabolism, amino acid metabolism, nucleoside metabolism, tricarboxylic acid (TCA) cycle, gut floral metabolism, etc., principally leading to the direction of biomass synthesis, could be observed after tumor surgical removal. Moreover, metabolic differences between recurrent and nonrecurrent patients had emerged 7 days after initial operation. The metabolic signatures of HCC recurrence principally comprised notable up-regulations of lactate excretion, succinate production, purine and pyrimidine nucleosides turnover, glycine, serine and threonine metabolism, aromatic amino acid turnover, cysteine and methionine metabolism, and glyoxylate metabolism, similar to metabolic behaviors of HCC burden. Sixteen metabolites were found to be significantly increased in the recurrent patients compared with those in nonrecurrent patients and healthy controls. Five metabolites (ethanolamine, lactic acid, acotinic acid, phenylalanine and ribose) were further defined; they were favorable to the prediction of early recurrence.     

LC-MS/MS-based metabolic profiling of Escherichia coli under heterologous gene expression stress

Escherichia coli is frequently exploited for genetic manipulations and heterologous gene expression studies. We have evaluated the metabolic profile of E. coli strain BL21 (DE3) RIL CodonPlus after genetic modifications and subjecting to the production of recombinant protein. Three genetically variable E. coli cell types were studied, normal cells (susceptible to antibiotics) cultured in simple LB medium, cells harboring ampicillin-resistant plasmid pET21a (+), grown under antibiotic stress, and cells having recombinant plasmid pET21a (+) ligated with bacterial lactate dehydrogenase gene grown under ampicillin and standard isopropyl thiogalactoside (IPTG)-induced gene expression conditions. A total of 592 metabolites were identified through liquid chromatography-mass spectrometry/mass spectrometry analysis, feature and peak detection using XCMS and CAMERA followed by precursor identification by METLIN-based procedures. Overall, 107 metabolites were found differentially regulated among genetically modified cells. Quantitative analysis has shown a significant modulation in DHNA-CoA, p-aminobenzoic acid, and citrulline levels, indicating an alteration in vitamin K, folic acid biosynthesis, and urea cycle of E. coli cells during heterologous gene expression. Modulations in energy metabolites including NADH, AMP, ADP, ATP, carbohydrate, terpenoids, fatty acid metabolites, diadenosine tetraphosphate (Ap4A), and l -carnitine advocate major metabolic rearrangements. Our study provides a broader insight into the metabolic adaptations of bacterial cells during gene manipulation experiments that can be prolonged to improve the yield of heterologous gene products and concomitant production of valuable biomolecules.     

Dupuytren's disease metabolite analyses reveals alterations following initial short-term fibroblast culturing

Dupuytren's disease (DD) is an ill-defined fibroproliferative disorder affecting the palm of the hand, resulting in progressive and irreversible digital contracture. In view of the abnormal gene dysregulation found in DD, and its potential effect on metabolites at a functional level, we chose to examine the metabolic profile involved in DD. Using Fourier transform infrared (FT-IR) spectroscopy to generate metabolic fingerprints of cultured cells, we compared the profiles of DD cords and nodules (1) against the unaffected transverse palmar fascia (internal control), (2) against carpal ligamentous fascia (external control), and (3) against fibroblasts from fat surrounding the nodule and skin overlying the nodule (environmental control). We also determined the effects of serial passaging of the cells on DD fingerprints. Subsequently, gas chromatography-mass spectrometry (GC-MS) was employed for metabolic profiling in order to identify metabolites characteristic of the DD tissue phenotypes. We developed a robust metabolomic analysis procedure of DD using cultured fibroblasts derived from DD tissues. Our carefully controlled culture conditions, combined with assessment of metabolic phenotypes by FT-IR and GC-MS, enabled us to demonstrate metabolic differences between DD and unaffected transverse palmar fascia and between DD and healthy control tissue. In early passage (0-3) the metabolic differences were clear, but cells from subsequent passages (4-6) started to lose this distinction between diseased and non-diseased origin. The dysregulated metabolites we identified were leucine, phenylalanine, lysine, cysteine, aspartic acid, glycerol-3-phosphate and the vitamin precursor to coenzyme A. Early passage DD cells exhibit a clear metabolic profile, in which central metabolic pathways appear to be involved. Experimental conditions have been identified in which these DD data are reproducible. The experimental reproducibility will be useful in DD diagnostics and for DD systems biology.     

A targeted metabolomics approach toward understanding metabolic variations in rice under pesticide stress

Diazinon insecticide is widely applied throughout rice (Oryza sativa L.) fields in Iran. However, concerns are now being raised about its potential adverse impacts on rice fields. In this study, a time-course metabolic change in rice plants was investigated after diazinon treatment using gas chromatography–mass spectrometry (GC–MS), and subsequently the statistical strategy of random forest (RF) was performed in order to find the stress-associated effects. According to the results, a wide range of metabolites were dynamically varied as a result of the plant response to diazinon such as biosynthesis and metabolism of sugars, amino acids, organic acids, and phenylpropanoids, all correlating with the exposure time. Plant response was involved in multiple metabolic pathways, most of which were correlated with the exposure time. In this study, RF was explored as a potential multivariate method for GC–MS analysis of metabolomics data of rice (O. sativa L.) plants under diazinon stress; more than 31 metabolites were quantitatively determined, and time-course metabolic response of the plant during different days after treatment was measured. Results demonstrated RF as a potential multivariate method for GC–MS analysis of changes in plant metabolome under insecticide stress.     

Primary and secondary metabolism,and post-translational protein modifications,as portrayed by proteomic analysis of Streptomyces coelicolor

The newly sequenced genome of Streptomyces coelicolor is estimated to encode 7825 theoretical proteins. We have mapped approximately 10% of the theoretical proteome experimentally using two-dimensional gel electrophoresis and matrix-assisted laser desorption ionization time-of-flight (MALDI-TOF) mass spectrometry. Products from 770 different genes were identified, and the types of proteins represented are discussed in terms of their annotated functional classes. An average of 1.2 proteins per gene was observed, indicating extensive post-translational regulation. Examples of modification by N-acetylation, adenylylation and proteolytic processing were characterized using mass spectrometry. Proteins from both primary and certain secondary metabolic pathways are strongly represented on the map, and a number of these enzymes were identified at more than one two-dimensional gel location. Post-translational modification mechanisms may therefore play a significant role in the regulation of these pathways. Unexpectedly, one of the enzymes for synthesis of the actinorhodin polyketide antibiotic appears to be located outside the cytoplasmic compartment, within the cell wall matrix. Of 20 gene clusters encoding enzymes characteristic of secondary metabolism, eight are represented on the proteome map, including three that specify the production of novel metabolites. This information will be valuable in the characterization of the new metabolites.     

A metabonomic study of transgenic maize (Zea mays) seeds revealed variations in osmolytes and branched amino acids

The aim of the research was to investigate metabolic variations associated with genetic modifications in the grains of Zea mays using metabonomic techniques. With this in mind, the non-targeted characteristic of the technique is useful to identify metabolites peculiar to the genetic modification and initially undefined. The results obtained showed that the genetic modification, introducing Cry1Ab gene expression, induces metabolic variations involving the primary nitrogen pathway. Concerning the methodological aspects, the experimental protocol used has been applied in this field for the first time. It consists of a combination of partial least square-discriminant analysis and principal component analysis. The most important metabolites for discrimination were selected and the metabolic correlations linking them are identified. Principal component analysis on selected signals confirms metabolic variations, highlighting important details about the changes induced on the metabolic network by the presence of a Bt transgene in the maize genome.     

Urine metabolomics analysis for adrenal incidentaloma activity detection and biomarker discovery

This study describes the development of a method suitable for the analysis of nineteen major urinary steroid metabolites in human urine. The analytes of interest were isolated from urine using solid phase extraction, subjected to enzymatic hydrolysis and again extracted applying solid phase extraction. After derivatization, methyloxime-trimethylsilyl ether derivatives of steroid hormones were identified by gas chromatography-mass spectrometry (GC/MS) and quantified by gas chromatography with flame ionization detector (GC/FID). The quantification method was validated for linearity, trueness, precision and selectivity. The limits of detection were between 6.2 and 7.2 ng/mL and limits of quantification were between 12.3 and 14.8 ng/mL. The established method was applied to analyze 28 urine samples from patients diagnosed with non-functioning adrenal incidentalomas (AIs) and 30 healthy subjects. Hierarchical cluster analysis (HCA) and principal component analysis (PCA) were employed to visualize the differences between metabolic profiles of patients and the control group and to determine possible markers of AIs activity. Both multivariate methods separated seven patients from the rest of the examined individuals. Five urinary metabolites including α-cortol, tetrahydrocorticosterone, tetrahydrocortisol, allo-tetrahydrocortisol and etiocholanolone were identified as potential biomarkers of pathological adrenal function. The altered metabolites reflected pathological metabolism mainly of cortisol and cortisone. This research proved that metabolomics is a suitable tool for disease research.