Metabolomic changes in Caenorhabditis elegans lifespan mutants as evident from GC–EI–MS and GC–APCI–TOF–MS profiling

Lifespan mutants of the nematode Caenorhabditis elegans are a much studied aging model, however, aging-related changes at the metabolome level remain largely unexplored. To identify metabolic features connected to mitochondrial dysfunction, a hallmark of aging and age-related disease, we analyzed a short-lived mitochondrial mutant (mev-1(kn1)), a long-lived mutant with enhanced cellular maintenance (ife-2(ok306)) and the novel double mutant ife-2(ok306);mev-1(kn1) which is normal-lived, possibly through attenuation of the metabolic mev-1 phenotype. Metabolomic analysis involved coupled gas chromatography–mass spectrometry with electron ionization (GC–EI–MS) and, in addition, recently introduced GC with soft atmospheric pressure chemical ionization coupled to time-of-flight mass spectrometry (GC–APCI–TOF–MS) to yield complementary mass spectrometric information for enhanced metabolite annotation. Multivariate analysis allowed distinction of mev-1 and ife-2 mutants from the wild type, while suggesting still another, distinct metabolic phenotype for the ife-2;mev-1 double mutant. In mev-1(kn1), disturbed energy metabolism was indicated by upset TCA cycle homeostasis, elevated glycolytic substrate and lactic acid levels as well as depletion of free amino acids pools. Surprisingly, these mitochondrially related changes were retained in the ife-2;mev-1 mutant, as were highly elevated levels of the dipeptide glycylproline indicative of increased collagen catabolism. However, the double mutant reverted mev-1(kn1) changes in uric acid and long-chain fatty alcohol metabolism, two pathways connected to the peroxisomal compartment. Our results are in line with recent evidence for a critical role of this organelle in aging and demonstrate the usefulness of non-targeted metabolomics approaches for detecting complex metabolic changes in the study of mitochondrial dysfunction.     

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.     

Metabolomic and elemental analysis of camel and bovine urine by GC–MS and ICP–MS

Recent studies from the author’s laboratory indicated that camel urine possesses antiplatelet activity and anti-cancer activity which is not present in bovine urine. The objective of this study is to compare the volatile and elemental components of bovine and camel urine using GC–MS and ICP–MS analysis. We are interested to know the component that performs these biological activities. The freeze dried urine was dissolved in dichloromethane and then derivatization process followed by using BSTFA for GC–MS analysis. Thirty different compounds were analyzed by the derivatization process in full scan mode. For ICP–MS analysis twenty eight important elements were analyzed in both bovine and camel urine. The results of GC–MS and ICP–MS analysis showed marked difference in the urinary metabolites. GC–MS evaluation of camel urine finds a lot of products of metabolism like benzene propanoic acid derivatives, fatty acid derivatives, amino acid derivatives, sugars, prostaglandins and canavanine. Several research reports reveal the metabolomics studies on camel urine but none of them completely reported the pharmacology related metabolomics. The present data of GC–MS suggest and support the previous studies and activities related to camel urine.     

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.

     

Urinary amino acid analysis: A comparison of iTRAQ®–LC–MS/MS,GC–MS,and amino acid analyzer

Urinary amino acid analysis is typically done by cation-exchange chromatography followed by post-column derivatization with ninhydrin and UV detection. This method lacks throughput and specificity. Two recently introduced stable isotope ratio mass spectrometric methods promise to overcome those shortcomings. Using two blinded sets of urine replicates and a certified amino acid standard, we compared the precision and accuracy of gas chromatography/mass spectrometry (GC–MS) and liquid chromatography–tandem mass spectrometry (LC–MS/MS) of propyl chloroformate and iTRAQ^® derivatized amino acids, respectively, to conventional amino acid analysis. The GC–MS method builds on the direct derivatization of amino acids in diluted urine with propyl chloroformate, GC separation and mass spectrometric quantification of derivatives using stable isotope labeled standards. The LC–MS/MS method requires prior urinary protein precipitation followed by labeling of urinary and standard amino acids with iTRAQ^® tags containing different cleavable reporter ions distinguishable by MS/MS fragmentation. Means and standard deviations of percent technical error (%TE) computed for 20 amino acids determined by amino acid analyzer, GC–MS, and iTRAQ^®–LC–MS/MS analyses of 33 duplicate and triplicate urine specimens were 7.27 ± 5.22, 21.18 ± 10.94, and 18.34 ± 14.67, respectively. Corresponding values for 13 amino acids determined in a second batch of 144 urine specimens measured in duplicate or triplicate were 8.39 ± 5.35, 6.23 ± 3.84, and 35.37 ± 29.42. Both GC–MS and iTRAQ^®–LC–MS/MS are suited for high-throughput amino acid analysis, with the former offering at present higher reproducibility and completely automated sample pretreatment, while the latter covers more amino acids and related amines.     

Optimized GC–MS metabolomics for the analysis of kidney tissue metabolites

Introduction

Metabolomics is a promising approach for discovery of relevant biomarkers in cells, tissues, organs, and biofluids for disease identification and prediction. The field has mostly relied on blood-based biofluids (serum, plasma, urine) as non-invasive sources of samples as surrogates of tissue or organ-specific conditions. However, the tissue specificity of metabolites pose challenges in translating blood metabolic profiles to organ-specific pathophysiological changes, and require further downstream analysis of the metabolites.

Objectives

As part of this project, we aim to develop and optimize an efficient extraction protocol for the analysis of kidney tissue metabolites representative of key primate metabolic pathways.

Methods

Kidney cortex and medulla tissues of a baboon were homogenized and extracted using eight different extraction protocols including methanol/water, dichloromethane/methanol, pure methanol, pure water, water/methanol/chloroform, methanol/chloroform, methanol/acetonitrile/water, and acetonitrile/isopropanol/water. The extracts were analyzed by a two-dimensional gas chromatography time-of-flight mass-spectrometer (2D GC–ToF-MS) platform after methoximation and silylation.

Results

Our analysis quantified 110 shared metabolites in kidney cortex and medulla tissues from hundreds of metabolites found among the eight different solvent extractions spanning low to high polarities. The results revealed that medulla is metabolically richer compared to the cortex. Dichloromethane and methanol mixture (3:1) yielded highest number of metabolites across both the tissue types. Depending on the metabolites of interest, tissue type, and the biological question, different solvents can be used to extract specific groups of metabolites.

Conclusion

This investigation provides insights into selection of extraction solvents for detection of classes of metabolites in renal cortex and medulla, which is fundamentally important for identification of prognostic and diagnostic metabolic kidney biomarkers for future therapeutic applications.

     

Review of recent developments in GC–MS approaches to metabolomics-based research

Background

Metabolomics aims to identify the changes in endogenous metabolites of biological systems in response to intrinsic and extrinsic factors. This is accomplished through untargeted, semi-targeted and targeted based approaches. Untargeted and semi-targeted methods are typically applied in hypothesis-generating investigations (aimed at measuring as many metabolites as possible), while targeted approaches analyze a relatively smaller subset of biochemically important and relevant metabolites. Regardless of approach, it is well recognized amongst the metabolomics community that gas chromatography-mass spectrometry (GC–MS) is one of the most efficient, reproducible and well used analytical platforms for metabolomics research. This is due to the robust, reproducible and selective nature of the technique, as well as the large number of well-established libraries of both commercial and ‘in house’ metabolite databases available.

Aim of review

This review provides an overview of developments in GC–MS based metabolomics applications, with a focus on sample preparation and preservation techniques. A number of chemical derivatization (in-time, in-liner, offline and microwave assisted) techniques are also discussed. Electron impact ionization and a summary of alternate mass analyzers are highlighted, along with a number of recently reported new GC columns suited for metabolomics. Lastly, multidimensional GC–MS and its application in environmental and biomedical research is presented, along with the importance of bioinformatics.

Key scientific concepts of review

The purpose of this review is to both highlight and provide an update on GC–MS analytical techniques that are common in metabolomics studies. Specific emphasis is given to the key steps within the GC–MS workflow that those new to this field need to be aware of and the common pitfalls that should be looked out for when starting in this area.

     

Baitmet,a computational approach for GC–MS library-driven metabolite profiling

Introduction

Current computational tools for gas chromatography—mass spectrometry (GC–MS) metabolomics profiling do not focus on metabolite identification, that still remains as the entire workflow bottleneck and it relies on manual data reviewing. Metabolomics advent has fostered the development of public metabolite repositories containing mass spectra and retention indices, two orthogonal properties needed for metabolite identification. Such libraries can be used for library-driven compound profiling of large datasets produced in metabolomics, a complementary approach to current GC–MS non-targeted data analysis solutions that can eventually help to assess metabolite identities more efficiently.

Results

This paper introduces Baitmet, an integrated open-source computational tool written in R enclosing a complete workflow to perform high-throughput library-driven GC–MS profiling in complex samples. Baitmet capabilities were assayed in a metabolomics study involving 182 human serum samples where a set of 61 metabolites were profiled given a reference library.

Conclusions

Baitmet allows high-throughput and wide scope interrogation on the metabolic composition of complex samples analyzed using GC–MS via freely available spectral data. Baitmet is freely available at http://CRAN.R-project.org/package=baitmet.

     

Granger causality in integrated GC–MS and LC–MS metabolomics data reveals the interface of primary and secondary metabolism

Metabolomics has emerged as a key technique of modern life sciences in recent years. Two major techniques for metabolomics in the last 10 years are gas chromatography coupled to mass spectrometry (GC–MS) and liquid chromatography coupled to mass spectrometry (LC–MS). Each platform has a specific performance detecting subsets of metabolites. GC–MS in combination with derivatisation has a preference for small polar metabolites covering primary metabolism. In contrast, reversed phase LC–MS covers large hydrophobic metabolites predominant in secondary metabolism. Here, we present an integrative metabolomics platform providing a mean to reveal the interaction of primary and secondary metabolism in plants and other organisms. The strategy combines GC–MS and LC–MS analysis of the same sample, a novel alignment tool MetMAX and a statistical toolbox COVAIN for data integration and linkage of Granger Causality with metabolic modelling. For metabolic modelling we have implemented the combined GC–LC–MS metabolomics data covariance matrix and a stoichiometric matrix of the underlying biochemical reaction network. The changes in biochemical regulation are expressed as differential Jacobian matrices. Applying the Granger causality, a subset of secondary metabolites was detected with significant correlations to primary metabolites such as sugars and amino acids. These metabolic subsets were compiled into a stoichiometric matrix N. Using N the inverse calculation of a differential Jacobian J from metabolomics data was possible. Key points of regulation at the interface of primary and secondary metabolism were identified.     

Potato metabolomics by GC–MS: what are the limiting factors?

Metabolic profiling methods are not ideally suited to the simultaneous analysis of all metabolite classes within a biological sample and must be optimized for maximum applicability. Several factors related to the optimization, validation and limitations of a GC–MS-based metabolic profiling method for potato were examined. A key step is conversion of reducing sugars to methyloximes, and optimum reaction conditions were 50 °C for 4 h. Shorter times or lower temperatures resulted in incomplete oximation whereas longer times and higher temperatures caused hydrolysis of sucrose, the major tuber dissacharide. Metabolite concentration gradients were observed in tuber sections. Glucose, fructose, alanine, methionine, threonine and tyrosine were more concentrated in the interior, whereas asparagine, putrescine, and caffeic and chlorogenic acids were higher in the skin and citrate was concentrated at the tuber’s bud end. These results impact upon choice of sampling strategy, consequently the use of freeze-dried (FD) material from a sampling protocol developed to avoid gradient-induced bias was examined. Using FD material, the method was highly linear and there was little qualitative or quantitative difference in the metabolite composition between fresh and FD material. The short- and long-term repeatability of the method was studied, and the use of reference materials to monitor and to improve data quality is discussed. Ascorbate is an important tuber metabolite that is readily measured by targeted approaches, but can be a problem in metabolic profiling. It was shown for standards and FD potato that ascorbate was largely degraded during oximation, although some survived in FD material. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

Simple assay of plasma sevoflurane and its metabolite hexafluoroisopropanol by headspace GC–MS

The anesthetic sevoflurane can now be delivered over periods of up to 48 h using a newly developed medical system, the AnaConDa (anesthetic conserving device). Lack of pharmacokinetic data on sevoflurane and its main metabolite (hexafluoroisopropanol, HFIP) in this indication prompted us to develop a headspace GC–MS method to quantify the two substances. The only previously published method for assaying the two substances could not be adapted to our study since it uses expensive and rarely employed system components together with toxic carbon disulfide as a dilution solvent. The method developed is straightforward and uses the relatively non-toxic solvent undecane as dilution solvent and chloroform as internal standard. The method is linear for a concentration range of 1–150 μg/ml, and presents high accuracy and precision. LOD and LOQ are 0.2 and 1 μg/ml, with a short analysis time (7.6 min for a single analysis). The method was applied to determine the plasma levels of sevoflurane and HFIP in six patients under 48-h anesthetic sedation delivered via the AnaConDa system. Average sevoflurane and HFIP concentrations plateaued at 75 and 4 μg/ml, respectively. Sevoflurane quickly tailed off after inhalation was stopped, and HFIP levels remained low.     

鲜姜化学成分的GC—MS分析

本文采用鲜姜榨汁-过滤的方法提取鲜姜内成分。然后对其化学成分进行GC／MS分析。从鲜姜汁中分离确定了33中成分,其中萜类物质相对含量较高。     

Study on biomolecules in extractives of Camellia oleifera fruit shell by GC–MS

This study aims to present an integrated process that can be used to produce biomedical and biological active components from the fruit shell of Camellia oleifera Abel. Through the Foss method, Aldehyde, acid compounds, acyl and alcohol compounds account for 22.7, 15.93, 0.24 and 61.13% of the extractives which were extracted from Camellia oleifera fruit shell by methanol solvents. Furfural, Pyrazole-4-carboxaldehyde, 1-methyl- and 5-Hydroxymethylfurfural account for 4.74, 1.22 and 58.78% of the extractives which were extracted from the fruit shell of Camellia oleifera Abel by ethanol solvents. Aldehyde, acid and amine compounds account for 5.01, 56.18 and 7.20% of the extractives which were extracted from the fruit shell of Camellia oleifera Abel by ethyl acetate solvents. The extractives of fresh flesh of bayberry were rich in rare drug, biomedical and biological activities.     

防风挥发油的GC—MS分析

用气相色谱-质谱联用、气相色谱以及柱层析方法分析、分离了防风挥发油的化学成分,鉴定了38个化合物,主成分为人参醇(panaxynol)。     

GC–MS explores the health care components in the extract of Pterocarpus pedatus Pierre

Pterocarpus a high-end, expensive furniture materials collectively. Pterocarpus and Pterocarpus products have a certain human health function. Therefore, this paper to Pterocarpus pedatus Pierre as an example, to study its extract on human health beneficial health care ingredients. FT-IR analysis showed that the infrared transmittance of Pterocarpus pedatus Pierre powder after ethanol/benzene extraction was the highest in the infrared spectrum of 400 cm⁻¹–800 cm⁻¹, 2750 cm⁻¹–3200 cm⁻¹ wave number. In the 1750 cm⁻¹–2400 cm⁻¹ wave segment, methanol, ethyl acetate and ethanol/benzene after the extraction of Pterocarpus pedatus Pierre powder infrared transmittance increased values are basically the same. GC–MS analysis, the health care ingredients in the Pterocarpus pedatus Pierre have cough and phlegm, heat detoxification, enhance human immunity, analgesic and anti-inflammatory and so on. Among them, Homopterocarpin is excellent in inhibiting and killing cancer cell activity; Cryptomeridiol is a natural product with anti-Alzheimer's disease and antispasmodic nature, and its medicinal value is remarkable. Scoparone has a wide range of pharmacological values.     

GC/MS法分析核桃叶挥发油化学成分

利用水蒸气蒸馏方法从陕西栽培的核桃品种西洛3号提取了核桃叶挥发性物质,用GC／MS法分离确定出20种化学成分,其中主要成分(相对含量)为萜类(84．89％)、芳香烃(3．9％)和酯类(1．34％)化合物,占总检出量约90．84％。     

GC/MS分析血浆中丁丙诺啡

目的:建立血浆中丁丙诺啡GC/MS分析方法。方法:血浆中丁丙诺啡,加入内标长春西汀,加pH 7缓冲溶液,用三氯甲烷提取,提取物经BSTFA衍生化后进行GC/MS分析。结果:方法的线性范围为2～100 g·L~(-1),检出限为1g·L~(-1)。结论:该方法灵敏度高,可用于涉毒案件血浆中丁丙诺啡的分析。     

园柏挥发油化学成分的GC—MS分析

利用毛细管气相色谱保留指数定性、标准样品叠加和色谱－质谱－计算机联用技术,对园柏挥发油的化学成分进行了分离和鉴定,共鉴定出单萜烃１６个、倍半萜烃１８个,并测得了各化合物的相对含量。其主要成分是：香桧烯、α－蒎烯、萜品醇－４、柠檬烯、γ－萜品烯和对一花烃。     

芝麻蜂花粉脂肪酸的GC—MS分析

以石油醚为溶剂,索氏提取法提取芝麻蜂花粉粗脂肪,甲酯化处理后,通过气相色谱-质谱联用技术进行分离鉴定,共鉴定出8种脂肪酸,并测定相对含量。结果表明,芝麻蜂花粉多不饱和脂肪酸含量较高,其中亚麻酸相对含量为66．71％。