GC-MS Metabolite Profiling Research Articles

Unravelling the architecture and dynamics of tropane alkaloid biosynthesis pathways using metabolite correlation networks

The tropane alkaloid spectrum in Solanaceae is highly variable within and between species. Little is known about the topology and the coordination of the biosynthetic pathways leading to the variety of tropine and pseudotropine derived esters in the alkaloid spectrum, or about the metabolic dynamics induced by tropane alkaloid biosynthesis stimulating conditions. A good understanding of the metabolism, including all ramifications, is however necessary for the development of strategies to increase the abundance of pharmacologically interesting compounds such as hyoscyamine and scopolamine. The present study explores the tropane alkaloid metabolic pathways in an untargeted approach involving a correlation-based network analysis. Using GC–MS metabolite profiling, the variation and co-variation among tropane alkaloids and primary metabolites was monitored in 60 Datura innoxia Mill. individuals, of which half were exposed to tropane alkaloid biosynthesis stimulating conditions by co-culture with Agrobacterium rhizogenes. Considerable variation was evident in the relative proportions of the tropane alkaloids. Remodeling of the tropane alkaloid spectrum under co-culture with A. rhizogenes involved a specific and strong increase of hyoscyamine production and revealed that the accumulation of hyoscyamine, 3-tigloyloxy-6,7-epoxytropane, and 3-methylbutyryloxytropane was controlled independently of the majority of tropane alkaloids. Based on correlations between metabolites, we propose a biosynthetic origin of hygrine, the order of esterification of certain di-oxygenated tropanes, and that the rate of acetoxylation contributes to control of hyoscyamine production. Overall, this study shows that the biosynthesis of tropane alkaloids may be far more complex and finely controlled than previously expected.

Integrated metabolomic and proteomic analysis reveals systemic responses of Rubrivivax benzoatilyticus JA2 to aniline stress.

Aromatic amines are widely distributed in the environment and are major environmental pollutants. Although degradation of aromatic amines is well studied in bacteria, physiological adaptations and stress response to these toxic compounds is not yet fully understood. In the present study, systemic responses of Rubrivivax benzoatilyticus JA2 to aniline stress were deciphered using metabolite and iTRAQ-labeled protein profiling. Strain JA2 tolerated high concentrations of aniline (30 mM) with trace amounts of aniline being transformed to acetanilide. GC-MS metabolite profiling revealed aniline stress phenotype wherein amino acid, carbohydrate, fatty acid, nitrogen metabolisms, and TCA (tricarboxylic acid cycle) were modulated. Strain JA2 responded to aniline by remodeling the proteome, and cellular functions, such as signaling, transcription, translation, stress tolerance, transport and carbohydrate metabolism, were highly modulated. Key adaptive responses, such as transcription/translational changes, molecular chaperones to control protein folding, and efflux pumps implicated in solvent extrusion, were induced in response to aniline stress. Proteo-metabolomics indicated extensive rewiring of metabolism to aniline. TCA cycle and amino acid catabolism were down-regulated while gluconeogenesis and pentose phosphate pathways were up-regulated, leading to the synthesis of extracellular polymeric substances. Furthermore, increased saturated fatty acid ratios in membranes due to aniline stress suggest membrane adaptation. The present study thus indicates that strain JA2 employs multilayered responses: stress response, toxic compound tolerance, energy conservation, and metabolic rearrangements to aniline.

Hair metabolomics: identification of fetal compromise provides proof of concept for biomarker discovery.

Analysis of the human metabolome has yielded valuable insights into health, disease and toxicity. However, the metabolic profile of complex biological fluids such as blood is highly dynamic and this has limited the discovery of robust biomarkers. Hair grows relatively slowly, and both endogenous compounds and environmental exposures are incorporated from blood into hair during growth, which reflects the average chemical composition over several months.We used hair samples to study the metabolite profiles of women with pregnancies complicated by fetal growth restriction (FGR) and healthy matched controls. We report the use of GC-MS metabolite profiling of hair samples for biomarker discovery. Unsupervised statistical analysis showed complete discrimination of FGR from controls based on hair composition alone. A predictive model combining 5 metabolites produced an area under the receiver-operating curve of 0.998.This is the first study of the metabolome of human hair and demonstrates that this biological material contains robust biomarkers, which may lead to the development of a sensitive diagnostic tool for FGR, and perhaps more importantly, to stable biomarkers for a range of other diseases.

Glycerophosphoglycerol, Beta-alanine, and pantothenic Acid as metabolic companions of glycolytic activity and cell migration in breast cancer cell lines.

In cancer research, cell lines are used to explore the molecular basis of the disease as a substitute to tissue biopsies. Breast cancer in particular is a very heterogeneous type of cancer, and different subgroups of cell lines have been established according to their genomic profiles and tumor characteristics. We applied GCMS metabolite profiling to five selected breast cancer cell lines and found this heterogeneity reflected on the metabolite level as well. Metabolite profiles of MCF-7 cells belonging to the luminal gene cluster proved to be more different from those of the basal A cell line JIMT-1 and the basal B cell lines MDA-MB-231, MDA-MB-435, and MDA-MB-436 with only slight differences in the intracellular metabolite pattern. Lactate release into the cultivation medium as an indicator of glycolytic activity was correlated to the metabolite profiles and physiological characteristics of each cell line. In conclusion, pantothenic acid, beta-alanine and glycerophosphoglycerol appeared to be related to the glycolytic activity designated through high lactate release. Other physiological parameters coinciding with glycolytic activity were high glyoxalase 1 (Glo1) and lactate dehydrogenase (LDH) enzyme activity as well as cell migration as an additional important characteristic contributing to the aggressiveness of tumor cells. Metabolite profiles of the cell lines are comparatively discussed with respect to known biomarkers of cancer progression.

Metabolic dynamics during autumn cold acclimation within and among populations of Sitka spruce (Picea sitchensis)

• Autumnal cold acclimation in conifers is a complex process, the timing and extent of which vary widely along latitudinal gradients for many tree species and reflect local adaptation to climate. Although previous studies have detailed some aspects of the metabolic remodelling that accompanies cold acclimation in conifers, little is known about global metabolic dynamics, or how these changes vary among phenotypically divergent populations. • Using untargeted GC-MS metabolite profiling, we monitored metabolic dynamics during autumnal cold acclimation in three populations of Sitka spruce from the southern, central, and northern portions of the species range, which differ in both the timing and extent of cold acclimation. • Latitudinal variation was evident in the nature, intensity, and timing of metabolic events. Early development of strong freezing tolerance in the northern population was associated with a transient accumulation of amino acids. By late autumn, metabolic profiles were highly similar between the northern and central populations, whereas profiles for the southern population were relatively distinct. • Our results provide insight into the metabolic architecture of latitudinal adaptive variation in autumn acclimation and show that different mechanisms are the basis of early October cold hardiness and autumn-acclimated cold hardiness.

Comparison of extraction conditions and normalization approaches for cellular metabolomics of adherent growing cells with GC-MS

Common extraction protocols and sampling procedures for GC-MS metabolite profiling were applied to the MCF-7 breast cancer cell culture as a model system of adherent growing cells and validated for repeatability and reproducibility. For normalization of a concentration series after methanolic extraction, results obtained with cell count normalization equalled normalization to the chromatogram total ion current as a chromatogram-intrinsic parameter, indicating that cell counting as an additional experimental step could be omitted. However, we show here that both normalization strategies should only be applied for comparison of extracts with similar concentrations complicating comparisons of different samples, e.g. with different biological origin. Therefore, the application of TIC thresholds for the comparison of differently concentrated extracts is recommended with respect to the accuracy of the data, the working effort and complexity of the biological experiment. For proof of concept, MCF-7 cell samples generated by different sampling procedures were assessed using these thresholds. Within this context, direct extraction of adherent growing cells without any further preparation steps, which allows for rapid quenching of cell metabolism and efficient sample extraction, is discussed as an alternative to conventional cell counting and extraction. In conclusion, we recommend the consideration of chromatogram intensity thresholds for the mean comparison of differently concentrated sample replicates in GC-MS metabolite profiling.

Use of chemical ionization for GC–MS metabolite profiling

Metabolite profiling is commonly performed by GC–MS of methoximated trimethylsilyl derivatives. The popularity of this technique owes much to the robust, library searchable spectra produced by electron ionization (EI). However, due to extensive fragmentation, EI spectra of trimethylsilyl derivatives are commonly dominated by trimethylsilyl fragments (e.g. m/z 73 and 147) and higher m/z fragment ions with structural information are at low abundance. Consequently different metabolites can have similar EI spectra, and this presents problems for identification of “unknowns” and the detection and deconvolution of overlapping peaks. The aim of this work is to explore use of positive chemical ionization (CI) as an adjunct to EI for GC–MS metabolite profiling. Two reagent gases differing in proton affinity (CH4 and NH3) were used to analyse 111 metabolite standards and extracts from plant samples. NH3-CI mass spectra were simple and generally dominated by [MH]+ and/or the adduct [M+NH4]+. For the 111 metabolite standards, m/z 73 and 147 were less than 3% of basepeak in NH3-CI and less than 30% of basepeak in CH4-CI. With CH4-CI, [MH]+ was generally present but at lower relative abundance than for NH3-CI. CH4-CI spectra were commonly dominated by losses of CH4 [M+1-16]+, 1–3 TMSOH [M+1-nx90]+, and combinations of CH4 and TMSOH losses [M+1-nx90-16]+. CH4-CI and NH3-CI mass spectra are presented for 111 common metabolites, and CI is used with real samples to help identify overlapping peaks and aid identification via determination of the pseudomolecular ion with NH3-CI and structural information with CH4-CI.

Metabolomics demonstrates divergent responses of two Eucalyptus species to water stress

Past studies of water stress in Eucalyptus spp. generally highlighted the role of fewer than five “important” metabolites, whereas recent metabolomic studies on other genera have shown tens of compounds are affected. There are currently no metabolite profiling data for responses of stress-tolerant species to water stress. We used GC–MS metabolite profiling to examine the response of leaf metabolites to a long (2 month) and severe (Ψpredawn < −2 MPa) water stress in two species of the perennial tree genus Eucalyptus (the mesic Eucalyptus pauciflora and the semi-arid Eucalyptus dumosa). Polar metabolites in leaves were analysed by GC–MS and inorganic ions by capillary electrophoresis. Pressure–volume curves and metabolite measurements showed that water stress led to more negative osmotic potential and increased total osmotically active solutes in leaves of both species. Water stress affected around 30–40% of measured metabolites in E. dumosa and 10–15% in E. pauciflora. There were many metabolites that were affected in E. dumosa but not E. pauciflora, and some that had opposite responses in the two species. For example, in E. dumosa there were increases in five acyclic sugar alcohols and four low-abundance carbohydrates that were unaffected by water stress in E. pauciflora. Re-watering increased osmotic potential and decreased total osmotically active solutes in E. pauciflora, whereas in E. dumosa re-watering led to further decreases in osmotic potential and increases in total osmotically active solutes. This experiment has added several extra dimensions to previous targeted analyses of water stress responses in Eucalyptus, and highlights that even species that are closely related (e.g. congeners) may respond differently to water stress and re-watering.

Plant Phenotype Demarcation Using Nontargeted LC-MS and GC-MS Metabolite Profiling

The characterization of the metabolome is a critical aspect in basic research and plant breeding. In this work, the putative application of metabolomics for phenotyping closely related genotypes has been tested. Crude extracts were profiled by LC-MS and GC-MS, and mass data extraction was performed with XCMS software. Result validation was achieved with principal component analysis (PCA). The ability of the profiling methodologies to discriminate plant genotypes was assessed after hierarchical clustering analysis (HCA). Cluster robustness was assessed by a multiscale bootstrap resampling method. A better performance of LC-MS profiling over GC-MS was evidenced in terms of phenotype demarcation after PCA and HCA. Citrus demarcation was similarly achieved independently of the environmental conditions used to grow plants. In addition, when all different locations were pooled in a single experimental design, it was still possible to differentiate the three closely related genotypes. The presented methodology provides a fast and nontargeted workflow as a powerful tool to discriminate related plant phenotypes. The novelty of the technique relies on the use of mass signals as markers for phenotype demarcation independent of putative metabolite identities and the relatively simple analytical strategy that can be applicable to a wide range of plant matrices with no previous optimization.

Retention index thresholds for compound matching in GC–MS metabolite profiling

The generation of retention index (RI) libraries is an expensive and time-consuming effort. Procedures for the transfer of RI properties between chromatography variants are, therefore, highly relevant for a shared use. The precision of RI determination and accuracy of RI transfer between 8 method variants employing 5%-phenyl-95%-dimethylpolysiloxane capillary columns was investigated using a series of 9 n-alkanes (C 10–C 36). The precision of the RI determination of 13 exemplary fatty acid methyl esters (C 8 ME–C 30 ME) was 0.22–0.33 standard deviation (S.D.) expressed in RI units in low complexity samples. In the presence of complex biological matrices this precision may deteriorate to 0.75–1.11. Application of the previously proposed Kováts, van den Dool or 3rd–5th order polynomial regression algorithms resulted in similar precision of RI calculation. For transfer of empirical van den Dool-RI properties between the chromatography variants 3rd order regression was found to represent the minimal necessary assumption. The range of typical regression coefficients was r 2 = 0.9988–0.9998 and accuracy of RI prediction between chromatography variants varied between 5.1 and 19.8 (0.29–0.69%) S.D. of residual RI error, RI predicted − RI determined ( n > 64). Accuracy of prediction was enhanced when subsets of chemically similar compound classes were used for regression, for example organic acids and sugars exhibited 0.78 ( n = 29) and 3.74 ( n = 37) S.D. of residual RI error, respectively. In conclusion, we suggest use of percent RI error rather than absolute RI units for the definition of matching thresholds. Thresholds of 0.5–1.0% may apply to most transfers between chromatography variants. These thresholds will not solve all matching ambiguities in complex samples. Therefore, we recommend co-analysis of reference substances with each GC–MS profiling experiment. Composition of these defined reference mixtures may best approximate or mimic the quantitative and qualitative composition of the biological matrix under investigation.

LC–MS and GC–MS metabolite profiling of nickel(II) complexes in the latex of the nickel-hyperaccumulating tree Sebertia acuminata and identification of methylated aldaric acid as a new nickel(II) ligand

Targeted liquid chromatography–mass spectrometry (LC–MS) technology using size exclusion chromatography and metabolite profiling based on gas chromatography–mass spectrometry (GC–MS) were used to study the nickel-rich latex of the hyperaccumulating tree Sebertia acuminata. More than 120 compounds were detected, 57 of these were subsequently identified. A methylated aldaric acid (2,4,5-trihydroxy-3-methoxy-1,6-hexan-dioic acid) was identified for the first time in biological extracts and its structure was confirmed by 1D and 2D nuclear magnetic resonance (NMR) spectroscopy. After citric acid, it appears to be one of the most abundant small organic molecules present in the latex studied. Nickel(II) complexes of stoichiometry Ni II:acid = 1:2 were detected for these two acids as well as for malic, itaconic, erythronic, galacturonic, tartaric, aconitic and saccharic acids. These results provide further evidence that organic acids may play an important role in the transport and possibly in the storage of metal ions in hyperaccumulating plants.

Metabolite profiling for analysis of yeast stress response during very high gravity ethanol fermentations

A laboratory strain and an industrial strain of Saccharomyces cerevisiae were grown at high substrate concentration, so-called very high gravity (VHG) fermentation. Simultaneous saccharification and fermentation (SSF) was applied in a batch process using 280 g/L maltodextrin as carbon source. It was shown that known ethanol and osmotic stress responses such as decreased growth rate, lower viability, higher energy consumption, and intracellular trehalose accumulation occur in VHG SSF for both strains when compared with standard laboratory medium (20 g/L glucose). The laboratory strain was the most affected. GC-MS metabolite profiling was applied for assessing the yeast stress response influence on cellular metabolism. It was found that metabolite profiles originating from different strains and/or fermentation conditions were unique and could be distinguished with the help of multivariate data analysis. Several differences in the metabolic responses to stressing conditions were revealed, particularly the increased energy consumption of stressed cells was also reflected in increased intracellular concentrations of pyruvate and related metabolites.