Gas Chromatography Coupled With Time-of-flight Mass Spectrometry Research Articles

From residue to resource: The recovery of high-added values compounds through an integral green valorization of citrus residual biomass

Exploitation of residual citrus biomass in biorefineries should include extraction of bioactive compounds, with particular attention to the high extractable content of this by-product. In fact, according to the principles of biorefinery, transforming “waste” to “secondary raw material” can totally or partially reduce the economic and ecological issues. In this study, green residual biomass from citrus trees located in a Capbon peninsula region (Tunisia) was analyzed. A phytochemical study was carried out by determining the content of polyphenols and flavonoids and antioxidant activities. Gas chromatography coupled with time-of-flight mass spectrometry (GC-TOFMS) analysis allowed the identification and evaluation of bioactive compounds as quinic acid, proline, alpha-linolenic acid and alpha-amyrin known for their wide range of applications. In addition, the fragrant aroma composition of petitgrain essential oil displayed substantial qualitative and quantitative differences that could serve as a promising natural source used in the development and formulation of functional foods and pharmaceutical products, in flavoring, liqueurs, perfumes, and medicines of ingredients. Then, the hydrodistillation effluent was assessed for its antioxidant and antifungal potential from where it is proven that it could be a source of the bioactive compounds with high spectrum of biological activities. From the point of view of the integral valorization of these underexploited and wasted bioresidues offers the double opportunity to recover biosourced molecules with high added value on the one hand and to reduce the pollutant effects on the other hand, and thus, contributing to promoting sustainable development and bioeconomy via a zero waste integrated approach priming the green biorefineries pathways.

Variation of volatile organic compound levels within ambient room air and its impact upon the standardisation of breath sampling

The interest around analysis of volatile organic compounds (VOCs) within breath has increased in the last two decades. Uncertainty remains around standardisation of sampling and whether VOCs within room air can influence breath VOC profiles. To assess the abundance of VOCs within room air in common breath sampling locations within a hospital setting and whether this influences the composition of breath. A secondary objective is to investigate diurnal variation in room air VOCs. Room air was collected using a sampling pump and thermal desorption (TD) tubes in the morning and afternoon from five locations. Breath samples were collected in the morning only. TD tubes were analysed using gas chromatography coupled with time-of-flight mass spectrometry (GC-TOF-MS). A total of 113 VOCs were identified from the collected samples. Multivariate analysis demonstrated clear separation between breath and room air. Room air composition changed throughout the day and different locations were characterized by specific VOCs, which were not influencing breath profiles. Breath did not demonstrate separation based on location, suggesting that sampling can be performed across different locations without affecting results.

The Response of Ruminal Microbiota and Metabolites to Different Dietary Protein Levels in Tibetan Sheep on the Qinghai-Tibetan Plateau.

Ruminal microbiota and metabolites play crucial roles in animal health and productivity. Exploring the dynamic changes and interactions between microbial community composition and metabolites is important for understanding ruminal nutrition and metabolism. Tibetan sheep (Ovis aries) are an important livestock resource on the Qinghai-Tibetan Plateau (QTP), and the effects of various dietary protein levels on ruminal microbiota and metabolites are still unknown. The aim of this study was to investigate the response of ruminal microbiota and metabolites to different levels of dietary protein in Tibetan sheep. Three diets with different protein levels (low protein 10.1%, medium protein 12.1%, and high protein 14.1%) were fed to Tibetan sheep. 16S rRNA gene sequencing and gas chromatography coupled with time-of-flight mass spectrometry (GC-TOF-MS) were used to study the profile changes in each group of ruminal microbes and metabolites, as well as the potential interaction between them. The rumen microbiota in all groups was dominated by the phyla Bacteroidetes and Firmicutes regardless of the dietary protein level. At the genus level, Prevotella_1, Rikenellaceae_RC9_gut_group and Prevotellaceae_UCG-001 were dominant. Under the same forage-to-concentrate ratio condition, the difference in the dietary protein levels had no significant impact on the bacterial alpha diversity index and relative abundance of the major phyla and genera in Tibetan sheep. Rumen metabolomics analysis revealed that dietary protein levels altered the concentrations of ruminal amino acids, carbohydrates and organic acids, and significantly affected tryptophan metabolism (p < 0.05). Correlation analysis of the microbiota and metabolites revealed positive and negative regulatory mechanisms. Overall, this study provides detailed information on rumen microorganisms and ruminal metabolites under different levels of dietary protein, which could be helpful in subsequent research for regulating animal nutrition and metabolism through nutritional interventions.

The integration of MS-based metabolomics and multivariate data analysis allows for improved quality assessment of Zingiber officinale Roscoe

Ginger (Zingiber officinale Roscoe) is consumed for health-promoting effects and as a food condiment. Comprehensive phytochemical analysis, other than gingerols and shogaols, has not yet been deeply investigated. Hence, the current research aimed to establish a non-targeted metabolomics approach for the discrimination between fresh ginger rhizome samples collected from four different producing countries, i.e., China, India, Pakistan, and Peru. In addition, lab-dried samples were analyzed to trace drying-induced metabolites. A comprehensive extraction procedure was carried out resulting in production of polar and non-polar fractions. The polar fraction was analyzed by ultra-performance liquid chromatography coupled with Fourier transform tandem mass spectrometry (UPLC-C18-FT-MS/MS) and gas chromatography coupled with time-of-flight mass spectrometry (GC-TOF-MS) post derivatization. UPLC-C8-FT-MS/MS was used for analysis of non-polar fraction. Results revealed for identification of a total of 253 metabolites. In addition, multivariate data analysis (MVDA), including principal component analysis (PCA) demonstrated clustering of Asian specimens. Several metabolites with a characteristic pattern for the origin revealing the highest contents of bioactive metabolites in the Peruvian product. Moreover, chemical markers identified, including [6]-gingerol and [6]-shogaol discriminating between fresh and dried samples. Furthermore, abundances of some primary metabolites, including amino acids and cinnamic acid, have confirmed the biosynthetic pathway of gingerols and their transformation upon drying to shogaols. The proposed approach can be applied as a potential candidate for quality assessment of ginger and other medicinal plants.

Transcriptome Analysis and Metabolic Profiling of Green and Red Mizuna (Brassica rapa L. var. japonica).

Mizuna (Brassica rapa L. var. japonica), a member of the family Brassicaceae, is rich in various health-beneficial phytochemicals, such as glucosinolates, phenolics, and anthocyanins. However, few studies have been conducted on genes associated with metabolic traits in mizuna. Thus, this study provides a better insight into the metabolic differences between green and red mizuna via the integration of transcriptome and metabolome analyses. A mizuna RNAseq analysis dataset showed 257 differentially expressed unigenes (DEGs) with a false discovery rate (FDR) of <0.05. These DEGs included the biosynthesis genes of secondary metabolites, such as anthocyanins, glucosinolates, and phenolics. Particularly, the expression of aliphatic glucosinolate biosynthetic genes was higher in the green cultivar. In contrast, the expression of most genes related to indolic glucosinolates, phenylpropanoids, and flavonoids was higher in the red cultivar. Furthermore, the metabolic analysis showed that 14 glucosinolates, 12 anthocyanins, five phenolics, and two organic acids were detected in both cultivars. The anthocyanin levels were higher in red than in green mizuna, while the glucosinolate levels were higher in green than in red mizuna. Consistent with the results of phytochemical analyses, the transcriptome data revealed that the expression levels of the phenylpropanoid and flavonoid biosynthesis genes were significantly higher in red mizuna, while those of the glucosinolate biosynthetic genes were significantly upregulated in green mizuna. A total of 43 metabolites, such as amino acids, carbohydrates, tricarboxylic acid (TCA) cycle intermediates, organic acids, and amines, was identified and quantified in both cultivars using gas chromatography coupled with time-of-flight mass spectrometry (GC-TOFMS). Among the identified metabolites, sucrose was positively correlated with anthocyanins, as previously reported.

Advanced Identification and Quantification of Pesticide Residues in Vegetables by GC-ToFMS Technique

The identification and quantification of a large range of pesticide residues (e.g. fungicides, insecticides, acaricides) in vegetables from Romania markets, in Maramures, Alba Iulia and Botosani region, was performed by gas-chromatography coupled with time-of-flight mass spectrometry (GC-ToFMS) to better assess the impact of agricultural practices that may result from `accidental` application of higher doses of treatment solutions. The ion chromatograms obtained by GC-ToFMS enabled us to easily quantify at ppb level and to record the entire spectrum at high speeds (up to 500 spectra/second) without losing of the data quality. Although, most of the pesticide residues detected were below the limits set by the law, the results highlighted concentrations that exceeded the maximum allowed for toclofos-methyl and malathion from recent treatments and still high levels of alpha- and beta-hexachlorocyclohexane (mostly in green lettuce) from the historical treatments. It was also evidenced that green lettuce accumulated more than 77% higher amount of pesticides than cucumber, green beans and tomatoes.

Serum Metabolomics Reveals Personalized Metabolic Patterns for Macular Neovascular Disease Patient Stratification.

The macular neovascular disease is a group disorder with complex pathogenesis of neovascularization for vision impairment and irreversible blindness, posing great challenges to precise diagnosis and management. We prospectively recruited participants with age-related macular degeneration (AMD), polypoidal choroidal vasculopathy (PCV), and pathological myopia (PM) and compared with cataract patients without fundus diseases as a control group. The serum metabolome was profiled by gas chromatography coupled with time-of-flight mass spectrometry (GC-TOFMS) analysis. Multivariate statistical methods as well as data mining were performed for interpretation of macular neovascularization. A total of 446 participants with macular neovascularization and 138 cataract subjects as the control group were enrolled in this study. By employing GC-TOFMS, 131 metabolites were identified and 33 differentiating metabolites were highlighted in patients with macular neovascularization. For differential diagnosis, three panels of specific metabolomics-based biomarkers provided areas under the curve of 0.967, 0.938, and 0.877 in the discovery phase (n = 328) and predictive values of 87.3%, 79%, and 85.7% in the test phase (n = 256). Personalized pathway dysregulation scores measurement using Lilikoi package in R language revealed the pentose phosphate pathway and mitochondrial electron transport chain as the most important pathways in AMD; purine metabolism and glycolysis were identified as the major disturbed pathways in PCV, while the altered thiamine metabolism and purine metabolism may contribute to PM phenotypes. Serum metabolomics are powerful for characterizing metabolic disturbances of the macular neovascular disease. Differences in metabolic pathways may reflect an underlying macular neovascular disease and serve as therapeutic targets for macular neovascular treatment.

Comparative Phytochemical Analyses and Metabolic Profiling of Different Phenotypes of Chinese Cabbage (Brassica Rapa ssp. Pekinensis).

This study aimed to comprehensively examine the interface between primary and secondary metabolites in oval- and rectangular-shaped Chinese cabbage (Brassica rapa ssp. pekinensis) using gas chromatography coupled with time-of-flight mass spectrometry (GC-TOFMS) and high-performance liquid chromatography (HPLC). In addition to differences in shape, there was significant morphological variation between the two cultivars. The rectangular variety had greater height and deeper green color, whereas the oval variety had more leaves and greater width. A total of 42 primary metabolites identified by GC-TOFMS were subjected to partial least-squares discriminant, which indicated significant differences in the primary and secondary metabolisms of the two cultivars. Furthermore, total glucosinolate and phenolic contents were higher in the oval cultivar, whereas the rectangular cultivar contained a higher level of total carotenoids. This metabolome study comprehensively describes the relationship between primary and secondary metabolites in the oval and rectangular cultivars of Chinese cabbage and provides information useful for developing strategies to enhance the biosynthesis of glucosinolates, phenolics, and carotenoids in Chinese cabbage. Additionally, this work highlights that HPLC and GC-TOFMS–based metabolite profiling is suitable techniques to determine metabolic differences in Chinese cabbage.

Metabolomic profile of diabetic retinopathy: a GC-TOFMS-based approach using vitreous and aqueous humor.

To identify the potential metabolite markers in diabetic retinopathy (DR) by using gas chromatography coupled with time-of-flight mass spectrometry (GC-TOFMS). GC-TOFMS spectra were acquired from vitreous and aqueous humor (AH) samples of patients with DR and non-diabetic participants. Comparative analysis was used to elucidate the distinct metabolites of DR. Metabolic pathway was employed to explicate the metabolic reprogramming pathways involved in DR. Logistic regression and receiver-operating characteristic analyses were carried out to select and validate the biomarker metabolites and establish a therapeutic model. Comparative analysis showed a clear separation between disease and control groups. Eight differentiating metabolites from AH and 15 differentiating metabolites from vitreous were highlighted. Out of these 23 metabolites, 11 novel metabolites have not been detected previously. Pathway analysis identified nine pathways (three in AH and six in vitreous) as the major disturbed pathways associated with DR. The abnormal of gluconeogenesis, ascorbate-aldarate metabolism, valine-leucine-isoleucine biosynthesis, and arginine-proline metabolism might weigh the most in the development of DR. The AUC of the logistic regression model established by D-2,3-Dihydroxypropanoic acid, isocitric acid, fructose 6-phosphate, and L-Lactic acid in AH was 0.965. The AUC established by pyroglutamic acid and pyruvic acid in vitreous was 0.951. These findings have expanded our understanding of identified metabolites and revealed for the first time some novel metabolites in DR. These results may provide useful information to explore the mechanism and may eventually allow the development of metabolic biomarkers for prognosis and novel therapeutic strategies for the management of DR.

Serum Metabolomics Reveals Personalized Metabolic Patterns for Macular Neovascular Patient Stratification

Background: Macular neovascular disease is a group disorder with complex pathogenesis of neovascularization for vision impairment and irreversible blindness, posing great challenges to precise diagnosis and management. Methods: We conducted a cross-sectional association study to identify serum-based potential biomarkers of human macular neovascular disease. We prospectively recruited participants with age-related macular degeneration (AMD), polypoidal choroidal vasculopathy (PCV) and pathological myopia (PM), compared with cataract patients without fundus diseases as control group. The serum metabolome was profiled by gas chromatography coupled with time-of-flight mass spectrometry (GC-TOFMS) analysis. Multivariate statistical methods as well as machine learning were performed for interpretation of macular neovascularization. Findings: A total of 446 participants with macular neovascularization and 147 cataract subjects as control group were enrolled in this study. By employing GC-TOFMS, 131 metabolites were identified and 33 differentiating metabolites were highlighted in patients with macular neovascularization. Pathway analysis using lilikoi package in R language revealed pentose phosphate pathway and mitochondrial electron transport chain as the most important pathways in AMD; purine metabolism and glycolysis were identified as the major disturbed pathways in PCV while the altered thiamine metabolism and purine metabolism may contribute to PM phenotypes. Interpretation: In virtue of machine learning, the pathway-based model provides more personalized metabolic patterns for patient stratification of three subtypes of macular neovascular disease than metabolite-based model. Funding Statement: This work was supported by the grant from the National Key Research and Development Program of China (No. 2016YFC0904800) and the Interdisciplinary Program of Shanghai Jiao Tong University (No. YG2016MS82). Declaration of Interests: The authors declare that they have no competing interests. Ethics Approval Statement: [The authors] intended to conduct a prospective, observational, registration study. The Institutional Review Board (IRB) approval (No. 2016KY115-2) was obtained.

The white-rot fungus Trametes versicolor reduces the estrogenic activity of a mixture of emerging contaminants in wastewater treatment plant effluent

This study investigated the removal of common emerging contaminants (ECs) and related estrogenic activity from wastewater effluent by a strain of the white-rot fungus Trametes versicolor with previously unreported bioremediation potential. T. versicolor NRRL 66313 was grown in carbon-amended sterile secondary wastewater treatment plant (WWTP) effluent. Aerated batch reactors containing sterile, glucose-amended (5 g l−1) wastewater were inoculated, incubated for eight days, and then spiked with either 5 mg l−1 of 17β-estradiol (E2), or a mixture of E2, atrazine (ATZ), bisphenol A (BPA), carbamazepine (CBZ), N,N-diethyl-3-methylbenzamide (DEET), estrone (E1), 17α-ethynylestradiol (EE2), oxybenzone (OBZ), and triclosan (TCS) to a final concentration of 350 μg l−1 each. Abiotic and heat-killed fungus controls were also prepared. EC and metabolite concentrations were measured over time using HPLC and Gas Chromatography coupled with Time-Of-Flight Mass Spectrometry (GC-TOFMS). Estrogenic activity was measured on the same samples using the Yeast Estrogen Screen. In less than 5 h, active fungi decreased the concentration of E2 from 5 mg l−1 to below detection, producing E1 as a metabolite and subsequently removing it from solution. Acting on the mixture of ECs, fungi removed BPA, E1, E2, and EE2 to a significant degree (62–100%) vs. controls (0–28%) in only 3.5 h, reducing the estrogenic activity of the mixture by 77% (vs. 4–8% for controls). After 12 h, the total reduction of estrogenic activity was greater than 98% (vs. 24–42% for controls). These results show that T. versicolor NRRL 66313 can reduce the estrogenic activity of mixtures of estrogens and BPA, which are typically the most significant contributors to the hormone disrupting activity of domestic WWTP effluent.

Comparative metabolic profiling of pigmented rice (Oryza sativa L.) cultivars reveals primary metabolites are correlated with secondary metabolites

Gas-chromatography coupled with time-of-flight mass spectrometry (GC-TOFMS) was used to analyze the relationships between primary metabolites and phenolic acids in rice (Oryza sativa L.), including six black cultivars and one white cultivar. A total of 52 metabolites were identified, including 45 primary metabolites and seven phenolic acids from rice seeds. The metabolite profiles were subjected to data mining processes, including principal component analysis (PCA), Pearson's correlation analysis, and hierarchical clustering analysis (HCA). PCA could fully distinguish between these cultivars. HCA of these metabolites resulted in clusters derived from common or closely related biochemical pathways. There was a positive relationship between all phenolic and shikimic acids. Projection to latent structure using partial least squares (PLS) was applied to predict the total phenolic content based on primary metabolite profiles from rice grain. The predictive model showed good fit and predictability. The GC-TOFMS-based metabolic profiling approach could be used as an alternative method to predict food quality and identify metabolic links in complex biological systems.

Profile of Parkia speciosa Hassk Metabolites Extracted with SFE using FTIR‐PCA Method

AbstractA rapid identification, classification and discrimination tool, using Fourier Transform Infrared (FTIR) spectroscopy combined with Principal Component Analysis (PCA), was developed and applied to determine the profile of the Supercritical Fluid Extraction (SFE) of Parkia speciosa seeds under various temperature and pressure conditions (313, 323, 333, 343, 353 and 363 K and 20.68, 27.58, 34.47, 41.37, 48.26, and 55.16 MPa). The separation and identification of the compounds was carried out by Gas Chromatography coupled with Time of Flight Mass Spectrometry (GC/TOF‐MS). This technique has made it possible to detect the variability obtained under different SFE conditions and the separation of different chemical compounds in P. speciosa seeds. The FTIR‐PCA results were verified by GC/TOF‐MS, and the FTIR‐PCA method successfully identified the unsaturated carboxylic acids with the highest percentage area under the different conditions.

Metabolome analysis reveals ethanolamine as potential marker for improving lipid accumulation of model photosynthetic organisms

AbstractBACKGROUND: Many researchers focus on exploring approaches to improve lipid productivity by photosynthetic organisms. Metabolomic analysis provides a new way to globally explore metabolic changes. To elevate lipid yields in the process of biodiesel production using photosynthetic organisms as feedstock, metabolomics combined with orthogonal partial least‐squares discriminant analysis (OPLS‐DA) is being applied widely to seek potential markers in regulation of lipid production. Herein, metabolic differences among three photosynthetic organisms Scenedesmus obliquus, Synechocystis sp. PCC6803 and Anabaena sp. PCC7120 were analyzed by gas chromatography coupled with time‐of‐flight mass spectrometry (GC‐TOF‐MS).RESULTS: In total, 74 metabolites were identified by GC‐TOF‐MS. The OPLS‐DA model revealed good correlation between metabolites and lipid content (R2 = 0.9916). Nine compounds including ethanolamine were selected as potential markers affecting lipid accumulation. Herein, the highest level of C18:3 revealed its roles in regulation of the unsaturated degree of fatty acids. Further study revealed that exogenous ethanolamine (2 mmol L−1) significantly increased the lipid content (22%) in Scenedesmus obliquus. In particular, ethanolamine significantly elevated the levels of C16:2, C18:1, and C18:2.CONCLUSION: Metabolomic analysis is a useful tool to search for potential markers to improve lipid accumulation of photosynthetic organisms. Ethanolamine can be regarded as an alternative compound to improve lipid content and fatty acid levels for biodiesel production. Copyright © 2012 Society of Chemical Industry

Metabolome Profiling Reveals Metabolic Cooperation between Bacillus megaterium and Ketogulonicigenium vulgare during Induced Swarm Motility

The metabolic cooperation in the ecosystem of Bacillus megaterium and Ketogulonicigenium vulgare was investigated by cultivating them spatially on a soft agar plate. We found that B. megaterium swarmed in a direction along the trace of K. vulgare on the agar plate. Metabolomics based on gas chromatography coupled with time-of-flight mass spectrometry (GC-TOF-MS) was employed to analyze the interaction mechanism between the two microorganisms. We found that the microorganisms interact by exchanging a number of metabolites. Both intracellular metabolism and cell-cell communication via metabolic cooperation were essential in determining the population dynamics of the ecosystem. The contents of amino acids and other nutritional compounds in K. vulgare were rather low in comparison to those in B. megaterium, but the levels of these compounds in the medium surrounding K. vulgare were fairly high, even higher than in fresh medium. Erythrose, erythritol, guanine, and inositol accumulated around B. megaterium were consumed by K. vulgare upon its migration. The oxidization products of K. vulgare, including 2-keto-gulonic acids (2KGA), were sharply increased. Upon coculturing of B. megaterium and K. vulgare, 2,6-dipicolinic acid (the biomarker of sporulation of B. megaterium), was remarkably increased compared with those in the monocultures. Therefore, the interactions between B. megaterium and K. vulgare were a synergistic combination of mutualism and antagonism. This paper is the first to systematically identify a symbiotic interaction mechanism via metabolites in the ecosystem established by two isolated colonies of B. megaterium and K. vulgare.

Metabonomic Approach to Evaluating Pharmacodynamics of Ginkgo biloba Extract on the Perturbed Metabolism in Hamsters with Atherosclerosis by High Fat Diet

Aim To investigate the effects of Ginkgo biloba extract (GBE) on the perturbed metabolism of a diet-induced atherosclerosis hamster model. Methods Gas chromatography coupled with time-of-flight mass spectrometry (GC/TOF-MS) spectroscopy was used to profile serum samples of hamsters induced by high fat diet for 0, 3, 6, 12 weeks, and hamsters administered with GBE in the meanwhile. Multivariate analyses were employed to identify treatment-related fingerprint and potential biomarkers regarding the anti-atherosclerosis effect and mechanisms of GBE. Results For the model animals, high fat diet (HFD) resulted in a gradual elevation of serum cholesterol and triglyceride, low-density lipoprotein cholesterol (LDL-C), lesion in aorta arch, and a dynamic trajectory of the metabonomic profile. GBE treatment did not only lead to a marked reduction in total cholesterol and LDL-C levels in hamster serum, but also showed regulatory effects on serum metabolome and restored their scores plot close to normal. Amongst the discriminatory metabolites in serum that characterize atherosclerosis, 8 metabolites restored to normal after treatment with GBE, including succinic acid, glyceric acid, linoleic acid, arachidonic acid, 1-monooleoylglycerol, β-tocopherol, cholest-5-ene, lysine, while 6 metabolites were regulated towards normal, including tyrosine, oleic acid, 2-monooleoylglycerol, γ-tocopherol, α-tocopherol and deoxycholic acid. Conclusion These results indicate that the effect of GBE on the metabolites is involved in lipid metabolism, bile acid synthesis and amino acids turn-over, and is indirect in anti-atherosclerosis effect of GBE. GC/MS-based metabonomics is a potential approach to exploring pharmacological mechanism of GBE effects and to the assessment of pharmacodynamics.

Metabolite profiling of hemodialysate using gas chromatography time-of-flight mass spectrometry

Hemodialysis is an important alternative for renal replacement therapy to remove uremic retention solutes (URS) for the uremic syndrome. The metabolites in the hemodialysate directly reflect the efficiency of URS clearance. Here we report a highly sensitive and reliable metabolomic procedure for the measurement of small molecule metabolites in hemodialysate using gas chromatography coupled with time-of-flight mass spectrometry (GC/TOF/MS). The method was developed and evaluated through orthogonal experimental design using multivariate statistical analysis. The optimized method involves the use of methanol and water in the ratio of 3:1 (v/v) for dissolving the lyophilized solid of the hemodialysate after degradation of urea with urease (no less than 50 U) for 10 min. Validation of the method demonstrated a good linearity with regression coefficients greater than 0.99. Relative standard deviations of precision and stability of proposed method were less than 15%, and recoveries ranged from 71.8 to 115.8%. This method was successfully applied in the metabolite profiling of human hemodialysate samples which was able to differentiate the patients treated with high-flux hemodialysis from those with low-flux hemodialysis. The metabolomic results reveal a higher concentration of URS, and thus, better URS removal, from the patients under high-flux dialysis than those under low-flux dialysis.

Application of Gas Chromatography−Mass Spectrometry Metabolite Profiling Techniques to the Analysis of Heathland Plant Diets of Sheep

Little is known about how plant biochemistry influences the grazing behavior of animals consuming heterogeneous plant communities. The biochemical profiles of grassland species are mostly restricted to major nutritional characteristics, although recent developments in analytical techniques and data analysis have made possible the detailed analysis of minor components that may influence animal feeding preferences, performance, and health. In the present study, gas chromatography coupled with time-of-flight mass spectrometry (GC-TOF/MS) was used to profile the abundances of metabolites in nine specific heathland plant groups and in three mixed forage diets containing 10, 20, or 30% heather (Calluna vulgaris) and also in plasma and feces from sheep offered one of the three diets. Statistical and chemometric approaches, that is, principal component analysis (PCA) and hierarchical cluster analysis (HCA), were used to discriminate between these diets and between individual animals maintained on these diets. It is shown that GC-TOF/MS analysis of sheep plasma allowed distinction between the very similar diets by PCA and HCA, and, moreover, the plant metabolites responsible for the differences observed have been identified. Furthermore, metabolite markers of herbage mixtures and individual plant groups have been identified, and markers have been detected in sheep plasma and feces.