Semi-automated non-target processing in GC × GC-MS metabolomics analysis: applicability for biomedical studies

Genome-scale metabolic network of human carotid plaque reveals the pivotal role of glutamine/glutamate metabolism in macrophage modulating plaque inflammation and vulnerability

BACKGROUND

Metabolism is increasingly recognized as a key regulator of the function and phenotype of the primary cellular constituents of the atherosclerotic vascular wall, including endothelial cells, smooth muscle cells, and inflammatory cells. However, a comprehensive analysis of metabolic changes associated with the transition of plaque from a stable to a hemorrhaged phenotype is lacking.

METHODS

In this study, we integrated two large mRNA expression and protein abundance datasets (BIKE, n = 126; MaasHPS, n = 43) from human atherosclerotic carotid artery plaque to reconstruct a genome-scale metabolic network (GEM). Next, the GEM findings were linked to metabolomics data from MaasHPS, providing a comprehensive overview of metabolic changes in human plaque.

RESULTS

Our study identified significant changes in lipid, cholesterol, and inositol metabolism, along with altered lysosomal lytic activity and increased inflammatory activity, in unstable plaques with intraplaque hemorrhage (IPH+) compared to non-hemorrhaged (IPH-) plaques. Moreover, topological analysis of this network model revealed that the conversion of glutamine to glutamate and their flux between the cytoplasm and mitochondria were notably compromised in hemorrhaged plaques, with a significant reduction in overall glutamate levels in IPH+ plaques. Additionally, reduced glutamate availability was associated with an increased presence of macrophages and a pro-inflammatory phenotype in IPH+ plaques, suggesting an inflammation-prone microenvironment.

CONCLUSIONS

This study is the first to establish a robust and comprehensive GEM for atherosclerotic plaque, providing a valuable resource for understanding plaque metabolism. The utility of this GEM was illustrated by its ability to reliably predict dysregulation in the cholesterol hydroxylation, inositol metabolism, and the glutamine/glutamate pathway in rupture-prone hemorrhaged plaques, a finding that may pave the way to new diagnostic or therapeutic measures.

Applications of chromatographic methods in metabolomics: A review

Chromatography is a robust and reliable separation method that can use various stationary phases to separate complex mixtures commonly seen in metabolomics. This review examines the types of chromatography and stationary phases that have been used in targeted or untargeted metabolomics with methods such as mass spectrometry (MS) and nuclear magnetic resonance (NMR) spectroscopy. General considerations for sample pretreatment and separations in metabolomics are considered, along with the various supports and separation formats for chromatography that have been used in such work. The types of liquid chromatography (LC) that have been most extensively used in metabolomics will be examined, such as reversed-phase liquid chromatography and hydrophilic liquid interaction chromatography. In addition, other forms of LC that have been used in more limited applications for metabolomics (e.g., ion-exchange, size-exclusion, and affinity methods) will be discussed to illustrate how these techniques may be utilized for new and future research in this field. Multidimensional LC methods are also discussed, as well as the use of gas chromatography and supercritical fluid chromatography in metabolomics. In addition, the roles of chromatography in NMR- vs. MS-based metabolomics are considered. Applications are given within the field of metabolomics for each type of chromatography, along with potential advantages or limitations of these separation methods.

Metabolomics in hepatocellular carcinoma: From biomarker discovery to precision medicine

Hepatocellular carcinoma (HCC) remains a global health burden, and is mostly diagnosed at late and advanced stages. Currently, limited and insensitive diagnostic modalities continue to be the bottleneck of effective and tailored therapy for HCC patients. Moreover, the complex reprogramming of metabolic patterns during HCC initiation and progression has been obstructing the precision medicine in clinical practice. As a noninvasive and global screening approach, metabolomics serves as a powerful tool to dynamically monitor metabolic patterns and identify promising metabolite biomarkers, therefore holds a great potential for the development of tailored therapy for HCC patients. In this review, we summarize the recent advances in HCC metabolomics studies, including metabolic alterations associated with HCC progression, as well as novel metabolite biomarkers for HCC diagnosis, monitor, and prognostic evaluation. Moreover, we highlight the application of multi-omics strategies containing metabolomics in biomarker discovery for HCC. Notably, we also discuss the opportunities and challenges of metabolomics in nowadays HCC precision medicine. As technologies improving and metabolite biomarkers discovering, metabolomics has made a major step toward more timely and effective precision medicine for HCC patients.

Distinguishing Different Varieties of Oolong Tea by Fluorescence Hyperspectral Technology Combined with Chemometrics

Oolong tea is a semi-fermented tea that is popular among people. This study aims to establish a classification method for oolong tea based on fluorescence hyperspectral technology(FHSI) combined with chemometrics. First, the spectral data of Tieguanyin, Benshan, Maoxie and Huangjingui were obtained. Then, standard normal variation (SNV) and multiple scatter correction (MSC) were used for preprocessing. Principal component analysis (PCA) was used for data visualization, and with tolerance ellipses that were drawn according to Hotelling, outliers in the spectra were removed. Variable importance for the projection (VIP) > 1 in partial least squares discriminant analysis (PLS−DA) was used for feature selection. Finally, the processed spectral data was entered into the support vector machine (SVM) and PLS−DA. MSC_VIP_PLS−DA was the best model for the classification of oolong tea. The results showed that the use of FHSI could accurately distinguish these four types of oolong tea and was able to identify the key wavelengths affecting the tea classification, which were 650.11, 660.29, 665.39, 675.6, 701.17, 706.31, 742.34 and 747.5 nm. In these wavelengths, different kinds of tea have significant differences (p < 0.05). This study could provide a non-destructive and rapid method for future tea identification.

Chemical Changes in the Broccoli Volatilome Depending on the Tissue Treatment

The storage of plant samples as well as sample preparation for extraction have a significant impact on the profile of metabolites, however, these factors are often overlooked during experiments on vegetables or fruit. It was hypothesized that parameters such as sample storage (freezing) and sample pre-treatment methods, including the comminution technique or applied enzyme inhibition methods, could significantly influence the extracted volatile metabolome. Significant changes were observed in the volatile profile of broccoli florets frozen in liquid nitrogen at -20 °C. Those differences were mostly related to the concentration of nitriles and aldehydes. Confocal microscopy indicated some tissue deterioration in the case of slow freezing (-20 °C), whereas the structure of tissue, frozen in liquid nitrogen, was practically intact. Myrosinase activity assay proved that the enzyme remains active after freezing. No pH deviation was noted after sample storage - this parameter did not influence the activity of enzymes. Tissue fragmentation and enzyme-inhibition techniques applied prior to the extraction influenced both the qualitative and quantitative composition of the volatile metabolome of broccoli.

Unique ion filter-A data reduction tool for chemometric analysis of raw comprehensive two-dimensional gas chromatography-mass spectrometry data

Comprehensive gas chromatography with time of flight mass spectrometry is a powerful tool in the analysis of complex samples. Chemometric analysis of raw chromatographic data is more useful in one- and two-dimensional separations relative to peak tables. The data volume from such experiments generally necessitates the use of data reduction tools. Such tools often sacrifice some of the multivariate information in the mass to charge ratio dimension. The unique ion filter reduces the over-redundancy in two-dimensional gas chromatography-mass spectrometry data by limiting the data to a few unique/pseudo-unique ions, sub-peaks/slices in the first dimension, and spectra in the second dimension. We explore the performance of this algorithm through careful inspection of two-dimensional gas chromatography-mass spectrometry data before and after application of the filter. A reduction (99%) in the number of variables in a two-dimensional gas chromatography-mass spectrometry chromatogram passed on to subsequent analysis was observed. Feature selection times for model optimization reduced from 229 (±13) to 6.8 (±0.5) min when the filter was applied. An estimate of two unique/pseudo-unique ions, one sub-peak in the first dimension and five spectra in the second dimension were considered to provide a true representation of each chromatogram and provided enough information to achieve 100% model prediction accuracy.

GC × GC-MS analysis and hypolipidemic effects of polyphenol extracts from Shanxi-aged vinegar in rats under a high fat diet

Oxidative stress, inflammation and gut microbiota disorders can be induced by long-term high-fat diets (HFD). In order to confirm that polyphenols can improve these symptoms, polyphenols from Shanxi-aged vinegar (SAVEP) were extracted, and the components were detected by Comprehensive two-dimensional gas chromatography mass spectrometry (GC × GC-MS). 41 polyphenols include 18 phenolic acids and 17 polyphenols, which have not been reported. The mechanism of SAVEP on oxidative stress and inflammatory stress induced by HFD in rats and its regulating effect on intestinal flora disorder were studied. The results showed that SAVEP could significantly improve the lipid, inflammatory stress and oxidative stress related indicators compared with the Model group ("Model" refers to the group that successfully constructed a hyperlipidemia model by feeding HFD without any drugs or SAVEP in subsequent experiments.). In addition, SAVEP decreased the Firmicutes/Bacteroidetes ratio compared with the Model group, and elevated the relative abundance of beneficial bacteria. Conclusively, SAVEP can alleviate the oxidative stress and inflammatory stress caused by HFD, improving intestinal microbial disorders. The Spearman's correlation analysis revealed that Desulfovibrio, Lactobacillus and Akkermansia were correlated negatively with all of the inflammatory indicators, whereas Ruminococcus was the opposite. These results suggest that SAVEP may be a novel strategy against oxidative stress and inflammation, restoring the normal microbial community ecology of the gut and the treatment of metabolic syndromes.

Differences in chemical composition of indoor air in rooms associated/not associated with building related symptoms

Building related health effects or symptoms (BRS), known also as sick-building syndrome (SBS), are a phenomenon that is not well understood. In this study, air samples from 51 rooms associated with BRS and 34 control rooms were collected on multi-sorbent tubes and analyzed by a non-target approach using comprehensive two-dimensional gas chromatography and high-resolution mass spectrometry techniques. The large amount of data gathered was analyzed using multivariate statistics (principle component analysis (PCA) and partial least squares (PLS)). This new analysis approach revealed that in rooms where people experienced BRS, petrochemicals and chemicals emitted from plastics were abundant, whereas in rooms where people did not experience BRS, flavor and fragrance compounds were abundant. Among the petrochemicals benzene and 2-butoxyethanol were found in higher levels in rooms where people experienced BRS. The levels of limonene were sometimes in the range of reported odor thresholds, and similarly 3-carene and beta-myrcene were found in higher concentrations in indoor air of rooms where people did not experience BRS. It cannot be ruled out that these compounds may have influenced the perceived air quality. However, the overall variability in air concentrations was large and it was not possible to accurately predict if the air in a particular room could cause BRS or not.

Metabolic Profiling of Varronia curassavica Jacq. Terpenoids by Flow Modulated Two-Dimensional Gas Chromatography Coupled to Mass Spectrometry

In this study, a metabolomic approach was used to investigate the effect of seasonality on the chemical composition and yield of anti-inflammatory active principle, α-humulene, in the essential oil of three genotypes of Varronia curassavica Jacq. (Syn. Cordia verbenaceae). The essential oils were extracted by hydrodistillation and analyzed by comprehensive two-dimensional gas chromatography coupled to mass spectrometry (GC×GC-MS). The GC×GC approach a three-fold improvement in qualitative analysis (48 compounds were identified by GC-MS versus 135 by GC×GC-MS). The improved resolving power of GC×GC resolved important coelutions and enabled the detection of unusual substances in V. curassavica essential oil. The chromatographic data was analyzed by using peak table-based chemometrics, namely, principal component analysis (PCA) and hierarchical cluster analysis (HCA). The metabolic study showed that seasonality has a significant effect on the chemical composition. The α-humulene content was affected by genotype and season. Spring and summer were the best harvest seasons for the yield of the active ingredient, found in higher concentrations in the VC2 genotype. The proposed metabolomic workflow was successfully applied to terpene analysis found in V. curassavica essential oil, and such results have broadened our understanding of the influence of seasonal factors on the specialized metabolism of the species.

Drying Enhances Signal Intensities for Global GC⁻MS Metabolomics

We report here that a straightforward change of the standard derivatization procedure for GC–MS metabolomics is leading to a strong increase in metabolite signal intensity. Drying samples between methoxymation and trimethylsilylation significantly increased signals by two- to tenfold in extracts of yeast cells, plant and animal tissue, and human urine. This easy step reduces the cost of sample material and the need for expensive new hardware.

Breath analysis by two-dimensional gas chromatography with dual flame ionisation and mass spectrometric detection - Method optimisation and integration within a large-scale clinical study

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New method for the analysis of exhaled breath VOCs by TD-GC × GC-FID/qMS.

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Optimisation of flow modulation and dual detection alongside clinical requirements.

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Addresses key challenges of using GC × GC for large-scale breath metabolomics.

Precision medicine has spurred new innovations in molecular pathology leading to recent advances in the analysis of exhaled breath as a non-invasive diagnostic tool. Volatile organic compounds (VOCs) detected in exhaled breath have the potential to reveal a wealth of chemical and metabolomic information. This study describes the development of a method for the analysis of breath, based on automated thermal desorption (TD) combined with flow modulated comprehensive two-dimensional gas chromatography (GC×GC) with dual flame ionisation and quadrupole mass spectrometric detection (FID and qMS). The constrained optimisation and analytical protocol was designed to meet the practical demands of a large-scale multi-site clinical study, while maintaining analytical rigour to produce high fidelity data. The results demonstrate a comprehensive method optimisation for the collection and analysis of breath VOCs by GC×GC, integral to the standardisation and integration of breath analysis within large clinical studies.

Multimodal chemometric approach for the analysis of human exhaled breath in lung cancer patients by TD-GC × GC-TOFMS

Lung cancer is the deadliest cancer in developed countries. To reduce its mortality rate, it is important to enhance our capability to detect it at earlier stages by developing early diagnostic methods. In that context, the analysis of exhaled breath is an interesting approach because of the simplicity of the medical act and its non-invasiveness. Thermal desorption comprehensive two-dimensional gas chromatography time of flight mass spectrometry (TD-GC × GC-TOFMS) has been used to characterize and compare the volatile content of human breath of lung cancer patients and healthy volunteers. On the sampling side, the contaminations induced by the bags membrane and further environmental migration of VOCs during and after the sampling have also been investigated. Over a realistic period of 6 h, the concentration of contaminants inside the bag can increase from 2 to 3 folds based on simulated breath samples. On the data processing side, Fisher ratio (FR) and random forest (RF) approaches were applied and compared in regards to their ability to reduce the data dimensionality and to extract the significant information. Both approaches allow to efficiently smooth the background signal and extract significant features (27 for FR and 17 for RF). Principal component analysis (PCA) was used to evaluate the clustering capacity of the different models. For both approaches, a separation along PC-1 was obtained with a variance score around 35%. The combined model provides a partial separation with a PC-1 score of 52%. This proof-of-concept study further confirms the potential of breath analysis for cancer detection but also underlines the importance of quality control over the full analytical procedure, including the processing of the data.

Determination of metabolites of Geotrichum citri-aurantii treated with peppermint oil using liquid chromatography-mass spectrometry and gas chromatography-mass spectrometry

Sour rot is a leading disease of citrus fruit caused by the postharvest pathogen Geotrichum citri-aurantii. It has been reported that essential oils can be used as substitutes for synthetic fungicides to control the pathogen. In this study, changes in metabolites and antifungal effects of G. citri-aurantii treated with peppermint oil (PO) were investigated. The inhibition rate of the mycelial growth increased as the PO concentration increased, and 6 μl PO/disk resulted in a radial growth inhibition of 79.2%. The electrical conductivity of G. citri-aurantii treated with PO increased compared to the control. By comparing the metabolic profiles of treated and untreated G. citri-aurantii cells, a total of 53 distinct metabolites 9 were up-regulated and 44 were down-regulated were found, including 16 lipid metabolites, 6 carbohydrate metabolites, 2 amino acid metabolites, 5 alcohols, 2 glycoside metabolites, and 3 ketone metabolites, etc, and these metabolites are involved in 25 major metabolic pathways. PRACTICAL APPLICATIONS: Chemical fungicides can effectively control G. citri-aurantii during fruit postharvest period. However, synthetic chemical fungicides have gradually led to buildup of resistance of fungil, which seriously causes the frequent of food-borne diseases. PO extracted from natural plants can be used as natural additive in many foods due to their antioxidant, antibacterial, and antifungal properties. Therefore, PO can be considered as a promising bacteriostatic agent for the defense of G. citri-aurantii during fruit postharvest period.

A metabolomics study of Qiliqiangxin in a rat model of heart failure: a reverse pharmacology approach

The Chinese medicine Qiliqiangxin (QL) has been shown to have a protective role in heart failure. Here, we explore the underlying working mechanism of the key therapeutic component in QL using a rat model of heart failure. Heart failure after myocardial infarction was induced surgically and confirmed using echocardiography; a separate group of rats underwent sham surgery. The rats with heart failure were randomly assigned to receive QL, the angiotensin-converting enzyme inhibitor benazepril, or placebo groups. Blood samples were collected from the rats at four time points for up to 8 weeks and used for biochemical analysis and mass spectrometry‒based metabolomics profiling. In total, we measured nine well-known biochemical parameters of heart failure and 147 metabolites. In the rats with heart failure, QL significantly improved these biochemical parameters and metabolomics profiles, significantly increasing the cardioprotective parameter angiopoietin-like 4 and significantly lowering inflammation-related oxylipins and lysophosphatidic acids compared to benazepril. Mechanistically, QL may improve outcome in heart failure by controlling inflammatory process and cardiac hypertrophy. Clinical studies should be designed in order to investigate these putative mechanisms in patients.

The Recent Developments in Sample Preparation for Mass Spectrometry-Based Metabolomics

Metabolomics is a critical member in systems biology. Although great progress has been achieved in metabolomics, there are still some problems in sample preparation, data processing and data interpretation. In this review, we intend to explore the roles, challenges and trends in sample preparation for mass spectrometry- (MS-) based metabolomics. The newly emerged sample preparation methods were also critically examined, including laser microdissection, in vivo sampling, dried blood spot, microwave, ultrasound and enzyme-assisted extraction, as well as microextraction techniques. Finally, we provide some conclusions and perspectives for sample preparation in MS-based metabolomics.

Metabolic Profiling Reveals Differences in Plasma Concentrations of Arabinose and Xylose after Consumption of Fiber-Rich Pasta and Wheat Bread with Differential Rates of Systemic Appearance of Exogenous Glucose in Healthy Men

BACKGROUND

The consumption of products rich in cereal fiber and with a low glycemic index is implicated in a lower risk of metabolic diseases. Previously, we showed that the consumption of fiber-rich pasta compared with bread resulted in a lower rate of appearance of exogenous glucose and a lower glucose clearance rate quantified with a dual-isotope technique, which was in accordance with a lower insulin and glucose-dependent insulinotropic polypeptide response.

OBJECTIVE

To gain more insight into the acute metabolic consequences of the consumption of products resulting in differential glucose kinetics, postprandial metabolic profiles were determined.

METHODS

In a crossover study, 9 healthy men [mean ± SEM age: 21 ± 0.5 y; mean ± SEM body mass index (kg/m²): 22 ± 0.5] consumed wheat bread (132 g) and fresh pasta (119 g uncooked) enriched with wheat bran (10%) meals. A total of 134 different metabolites in postprandial plasma samples (at -5, 30, 60, 90, 120, and 180 min) were quantified by using a gas chromatography-mass spectrometry-based metabolomics approach (secondary outcomes). Two-factor ANOVA and advanced multivariate statistical analysis (partial least squares) were applied to detect differences between both food products.

RESULTS

Forty-two different postprandial metabolite profiles were identified, primarily representing pathways related to protein and energy metabolism, which were on average 8% and 7% lower after the men consumed pasta rather than bread, whereas concentrations of arabinose and xylose were 58% and 53% higher, respectively. Arabinose and xylose are derived from arabinoxylans, which are important components of wheat bran. The higher bioavailability of arabinose and xylose after pasta intake coincided with a lower rate of appearance of glucose and amino acids. We speculate that this higher bioavailability is due to higher degradation of arabinoxylans by small intestinal microbiota, facilitated by the higher viscosity of arabinoxylans after pasta intake than after bread intake.

CONCLUSION

This study suggests that wheat bran, depending on the method of processing, can increase the viscosity of the meal bolus in the small intestine and interfere with macronutrient absorption in healthy men, thereby influencing postprandial glucose and insulin responses. This trial was registered at www.controlled-trials.com as ISRCTN42106325.

Chemometric analysis of comprehensive two dimensional gas chromatography-mass spectrometry metabolomics data

Comprehensive two-dimensional gas chromatography time-of-flight mass spectrometry (GC×GC-TOFMS) is a powerful tool for separation and identification of analytes in complex natural samples. In this paper, different chemometric methods were compared for fast non-targeted GC×GC-TOFMS metabolomic profiling of the crustaceous species Daphnia magna and a general chemometric strategy and workflow is proposed. The strategy proposed in this work combined the compression of GC×GC-TOFMS data matrices in the retention time direction using wavelets and the appropriate column-wise data matrix augmentation arrangement, and its modeling by Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS). Using the proposed strategy, eighty different D. magna metabolites were resolved and identified. After calculation of the peak capacities of different columns and peak area changes of these metabolites, the best instrumental configuration and column combination for the GC×GC-TOFMS metabolomic study of D. magna are proposed and discussed. The procedure described in this work can be applied as a general method for untargeted GC×GC-TOFMS data processing and metabolomic profiling.

Fully Automated Trimethylsilyl (TMS) Derivatisation Protocol for Metabolite Profiling by GC-MS

Gas Chromatography-Mass Spectrometry (GC-MS) has long been used for metabolite profiling of a wide range of biological samples. Many derivatisation protocols are already available and among these, trimethylsilyl (TMS) derivatisation is one of the most widely used in metabolomics. However, most TMS methods rely on off-line derivatisation prior to GC-MS analysis. In the case of manual off-line TMS derivatisation, the derivative created is unstable, so reduction in recoveries occurs over time. Thus, derivatisation is carried out in small batches. Here, we present a fully automated TMS derivatisation protocol using robotic autosamplers and we also evaluate a commercial software, Maestro available from Gerstel GmbH. Because of automation, there was no waiting time of derivatised samples on the autosamplers, thus reducing degradation of unstable metabolites. Moreover, this method allowed us to overlap samples and improved throughputs. We compared data obtained from both manual and automated TMS methods performed on three different matrices, including standard mix, wine, and plasma samples. The automated TMS method showed better reproducibility and higher peak intensity for most of the identified metabolites than the manual derivatisation method. We also validated the automated method using 114 quality control plasma samples. Additionally, we showed that this online method was highly reproducible for most of the metabolites detected and identified (RSD < 20) and specifically achieved excellent results for sugars, sugar alcohols, and some organic acids. To the very best of our knowledge, this is the first time that the automated TMS method has been applied to analyse a large number of complex plasma samples. Furthermore, we found that this method was highly applicable for routine metabolite profiling (both targeted and untargeted) in any metabolomics laboratory.

Metabolite Signatures of Metabolic Risk Factors and their Longitudinal Changes

CONTEXT

Metabolic dysregulation underlies key metabolic risk factors—obesity, dyslipidemia, and dysglycemia.

OBJECTIVE

To uncover mechanistic links between metabolomic dysregulation and metabolic risk by testing metabolite associations with risk factors cross-sectionally and with risk factor changes over time.

DESIGN

Cross-sectional—discovery samples (n = 650; age, 36–69 years) from the Framingham Heart Study (FHS) and replication samples (n = 670; age, 61–76 years) from the BioImage Study, both following a factorial design sampled from high vs low strata of body mass index, lipids, and glucose. Longitudinal—FHS participants (n = 554) with 5–7 years of follow-up for risk factor changes.

SETTING

Observational studies.

PARTICIPANTS

Cross-sectional samples with or without obesity, dysglycemia, and dyslipidemia, excluding prevalent cardiovascular disease and diabetes or dyslipidemia treatment. Age- and sex-matched by group.

INTERVENTIONS

None.

MAIN OUTCOME MEASURE(S)

Gas chromatography-mass spectrometry detected 119 plasma metabolites. Cross-sectional associations with obesity, dyslipidemia, and dysglycemia were tested in discovery, with external replication of 37 metabolites. Single- and multi-metabolite markers were tested for association with longitudinal changes in risk factors.

RESULTS

Cross-sectional metabolite associations were identified with obesity (n = 26), dyslipidemia (n = 21), and dysglycemia (n = 11) in discovery. Glutamic acid, lactic acid, and sitosterol associated with all three risk factors in meta-analysis (P < 4.5 × 10−4). Metabolites associated with longitudinal risk factor changes were enriched for bioactive lipids. Multi-metabolite panels explained 2.5–15.3% of longitudinal changes in metabolic traits.

CONCLUSIONS

Cross-sectional results implicated dysregulated glutamate cycling and amino acid metabolism in metabolic risk. Certain bioactive lipids were associated with risk factors cross-sectionally and over time, suggesting their upstream role in risk factor progression. Functional studies are needed to validate findings and facilitate translation into treatments or preventive measures.

Missing value imputation strategies for metabolomics data

The origin of missing values can be caused by different reasons and depending on these origins missing values should be considered differently and dealt with in different ways. In this research, four methods of imputation have been compared with respect to revealing their effects on the normality and variance of data, on statistical significance and on the approximation of a suitable threshold to accept missing data as truly missing. Additionally, the effects of different strategies for controlling familywise error rate or false discovery and how they work with the different strategies for missing value imputation have been evaluated. Missing values were found to affect normality and variance of data and k-means nearest neighbour imputation was the best method tested for restoring this. Bonferroni correction was the best method for maximizing true positives and minimizing false positives and it was observed that as low as 40% missing data could be truly missing. The range between 40 and 70% missing values was defined as a "gray area" and therefore a strategy has been proposed that provides a balance between the optimal imputation strategy that was k-means nearest neighbor and the best approximation of positioning real zeros.

A peaklet-based generic strategy for the untargeted analysis of comprehensive two-dimensional gas chromatography mass spectrometry data sets

Comprehensive two-dimensional gas chromatography mass spectrometry (GC×GC-MS) is a well-established key technology in analytical chemistry and increasingly used in the field of untargeted metabolomics. However, automated processing of large GC×GC-MS data sets is still a major bottleneck in untargeted, large-scale metabolomics. For this reason we introduce a novel peaklet-based alignment strategy. The algorithm is capable of an untargeted deterministic alignment exploiting a density based clustering procedure within a time constrained similarity matrix. Exploiting minimal (1)D and (2)D retention time shifts between peak modulations, the alignment is done without the need for peak merging which also eliminates the need for linear or nonlinear retention time correction procedures. The approach is validated in detail using data of urine samples from a large human metabolomics study. The data was acquired by a Shimadzu GCMS-QP2010 Ultra GC×GC-qMS system and consists of 512 runs, including 312 study samples and 178 quality control sample injections, measured within a time period of 22 days. The final result table consisted of 313 analytes, each of these being detectable in at least 75% of the study samples. In summary, we present an automated, reliable and fully transparent workflow for the analysis of large GC×GC-qMS metabolomics data sets.

Analytical protocols based on LC-MS, GC-MS and CE-MS for nontargeted metabolomics of biological tissues

Invasive, site-specific metabolite information could be better obtained from tissues. Hence, highly sensitive mass spectrometry-based metabolomics coupled with separation techniques are increasingly in demand in clinical research for tissue metabolomics application. Applying these techniques to nontargeted tissue metabolomics provides identification of distinct metabolites. These findings could help us to understand alterations at the molecular level, which can also be applied in clinical practice as screening markers for early disease diagnosis. However, tissues as solid and heterogeneous samples pose an additional analytical challenge that should be considered in obtaining broad, reproducible and representative analytical profiles. This manuscript summarizes the state of the art in tissue (human and animal) treatment (quenching, homogenization and extraction) for nontargeted metabolomics with mass spectrometry.

Improving the quality of biomarker candidates in untargeted metabolomics via peak table-based alignment of comprehensive two-dimensional gas chromatography-mass spectrometry data

The potential of high-resolution analytical technologies like GC×GC/TOF MS in untargeted metabolomics and biomarker discovery has been limited by the development of fully automated software that can efficiently align and extract information from multiple chromatographic data sets. In this work we report the first investigation on a peak-by-peak basis of the chromatographic factors that impact GC×GC data alignment. A representative set of 16 compounds of different chromatographic characteristics were followed through the alignment of 63 GC×GC chromatograms. We found that varying the mass spectral match parameter had a significant influence on the alignment for poorly-resolved peaks, especially those at the extremes of the detector linear range, and no influence on well-chromatographed peaks. Therefore, optimized chromatography is required for proper GC×GC data alignment. Based on these observations, a workflow is presented for the conservative selection of biomarker candidates from untargeted metabolomics analyses.

Analysis of phenolic and cyclic compounds in plants using derivatization techniques in combination with GC-MS-based metabolite profiling

Metabolite profiling has been established as a modern technology platform for the description of complex chemical matrices and compound identification in biological samples. Gas chromatography coupled with mass spectrometry (GC-MS) in particular is a fast and accurate method widely applied in diagnostics, functional genomics and for screening purposes. Following solvent extraction and derivatization, hundreds of metabolites from different chemical groups can be characterized in one analytical run. Besides sugars, acids, and polyols, diverse phenolic and other cyclic metabolites can be efficiently detected by metabolite profiling. The review describes own results from plant research to exemplify the applicability of GC-MS profiling and concurrent detection and identification of phenolics and other cyclic structures.

Challenges of analyzing different classes of metabolites by a single analytical method

Complex biological samples include thousands of metabolites that range widely in both physiochemical properties and concentration. Simultaneously analyzing metabolites with different properties using a single analytical method is very challenging. The analytical process for metabolites comprises multiple steps including sampling, quenching, sample preparation, separation and detection. Each step can have a significant effect on the reliability and precision of ultimate analytic results. The aim of review is a discussion of considerations and challenges for the simultaneous analysis of metabolites using LC– and GC–MS systems. The review discusses available methodology for each analytical step, and presents the limitations and advantages of each method for the large-scale targeted metabolomics analysis of human and animal biological samples.

Combining metabolomic non-targeted GC×GC-ToF-MS analysis and chemometric ASCA-based study of variances to assess dietary influence on type 2 diabetes development in a mouse model

Insulin resistance (IR) lies at the origin of type 2 diabetes. It induces initial compensatory insulin secretion until insulin exhaustion and subsequent excessive levels of glucose (hyperglycemia). A high-calorie diet is a major risk factor contributing to the development of this metabolic disease. For this study, a time-course experiment was designed that consisted of two groups of mice. The aim of this design was to reproduce the dietary conditions that parallel the progress of IR over time. The first group was fed with a high-fatty-acid diet for several weeks and followed by 1 week of a low-fatty-acid intake, while the second group was fed with a low-fatty-acid diet during the entire experiment. The metabolomic fingerprint of C3HeB/FeJ mice liver tissue extracts was determined by means of two-dimensional gas chromatography time-of-flight mass spectrometry (GC×GC-ToF-MS). This article addresses the application of ANOVA-simultaneous component analysis (ASCA) to the found metabolomic profile. By performing hyphenated high-throughput analytical techniques together with multivariate chemometric methodology on metabolomic analysis, it enables us to investigate the sources of variability in the data related to each experimental factor of the study design (defined as time, diet and individual). The contribution of the diet factor in the dissimilarities between the samples appeared to be predominant over the time factor contribution. Nevertheless, there is a significant contribution of the time-diet interaction factor. Thus, evaluating the influences of the factors separately, as it is done in classical statistical methods, may lead to inaccurate interpretation of the data, preventing achievement of consistent biological conclusions.

Metabolomic analysis of simvastatin and fenofibrate intervention in high-lipid diet-induced hyperlipidemia rats

AIM

To investigate the metabolite changes caused by simvastatin or fenofibrate intervention in diet-induced hyperlipidemia rats using a GC-MS-based metabolomic profiling approach.

METHODS

SD rats were fed with high-lipid diet for 4 weeks to induce hyperlipidemia, then the rats were fed with normal diet, and orally administered with simvastatin (10 mg·kg(-1)·d(-1)) or fenofibrate (150 mg·kg(-1)·d(-1)) for 2 weeks. Blood samples were collected once a week, and potential biomarkers were examined using commercial assay kits and a metabolomic approach. The metabolomics data were analyzed using a multivariate statistical technique and a principal component analysis (PCA).

RESULTS

Oral administration of simvastatin or fenofibrate significantly decreased the plasma levels of total cholesterol (TC) and low-density lipoprotein (LDL) cholesterol and increased the plasma level of high-density lipoprotein (HDL) cholesterol in the hyperlipidemia rats. Plasma samples were scattered in the PCA scores plots in response to the diet and to the drugs administered. The main metabolites changed in the hyperlipidemia rats were cholesterol, creatinine, linoleic acid, β-hydroxybutyric acid, tyrosine, isoleucine and ornithine. The plasma level of creatinine was significantly lower in the simvastatin-treated rats than in the fenofibrate-treated rats. The plasma tyrosine concentration was declined following intake of high-lipid diet, which was reversed by fenobrate, but not by simvastatin.

CONCLUSION

A series of potential biomarkers including tyrosine, creatinine, linoleic acid, β-hydroxybutyric acid and ornithine have been identified by metabolomic profiling, which may be used to identify the metabolic changes during hyperlipidemia progression.

Plasma phospholipids identify antecedent memory impairment in older adults

Alzheimer's disease causes a progressive dementia that currently affects over 35 million individuals worldwide and is expected to affect 115 million by 2050 (ref. 1). There are no cures or disease-modifying therapies, and this may be due to our inability to detect the disease before it has progressed to produce evident memory loss and functional decline. Biomarkers of preclinical disease will be critical to the development of disease-modifying or even preventative therapies. Unfortunately, current biomarkers for early disease, including cerebrospinal fluid tau and amyloid-β levels, structural and functional magnetic resonance imaging and the recent use of brain amyloid imaging or inflammaging, are limited because they are either invasive, time-consuming or expensive. Blood-based biomarkers may be a more attractive option, but none can currently detect preclinical Alzheimer's disease with the required sensitivity and specificity. Herein, we describe our lipidomic approach to detecting preclinical Alzheimer's disease in a group of cognitively normal older adults. We discovered and validated a set of ten lipids from peripheral blood that predicted phenoconversion to either amnestic mild cognitive impairment or Alzheimer's disease within a 2-3 year timeframe with over 90% accuracy. This biomarker panel, reflecting cell membrane integrity, may be sensitive to early neurodegeneration of preclinical Alzheimer's disease.

Exploratory investigation of plasma metabolomics in human lung adenocarcinoma

Globally lung cancer is common among males and recently also noted with increasing incidences in females, especially adenocarcinoma. Further, most lung cancers are not easily detected until the late stage. Metabolic profiling of plasma low molecular weight metabolites may help unveil the complex pathophysiological changes during early lung adenocarcinoma development. Here we used a combination of gas chromatography-mass spectrometry (GC-MS) and liquid chromatography-mass spectrometry (LC-MS) methods to investigate the metabolic signatures in the plasma of 31 stage I human lung adenocarcinoma patients and 28 healthy controls. The metabolic profiles were assayed using orthogonal projections to latent structures discriminant analysis (OPLS-DA), and were further analyzed to identify the associated marker metabolites. The OPLS-DA models derived from both GC-MS and LC-MS showed significant discriminations in metabolic profiles between cases and healthy controls. It was found that around 37 metabolites contributed to the differences. The alterations of these metabolites implied disturbances in amino acids, lipids, fatty acids and glutaminolysis metabolism in human lung adenocarcinoma, even after removal of influencing factors such as age, gender and smoking habits. Of particular interest, the sex hormone metabolic pathway involving the sulfate conjugate of testosterone, androsterone and pregnenolone was found to be disturbed considerably. All these metabolic perturbations occur at an early stage of lung adenocarcinoma and thus could act as biomarkers for its early diagnosis. These exploratory findings suggest that integration of two sensitive and complementary metabolomic approaches enables a comprehensive metabolite profiling for human lung adenocarcinoma, although a more extensive study is needed to confirm the findings.

Multi-platform metabolomic analyses of ergosterol-induced dynamic changes in Nicotiana tabacum cells

Metabolomics is providing new dimensions into understanding the intracellular adaptive responses in plants to external stimuli. In this study, a multi-technology-metabolomic approach was used to investigate the effect of the fungal sterol, ergosterol, on the metabolome of cultured tobacco cells. Cell suspensions were treated with different concentrations (0-1000 nM) of ergosterol and incubated for different time periods (0-24 h). Intracellular metabolites were extracted with two methods: a selective dispersive liquid-liquid micro-extraction and a general methanol extraction. Chromatographic techniques (GC-FID, GC-MS, GC × GC-TOF-MS, UHPLC-MS) and (1)H NMR spectroscopy were used for quantitative and qualitative analyses. Multivariate data analyses (PCA and OPLS-DA models) were used to extract interpretable information from the multidimensional data generated from the analytical techniques. The results showed that ergosterol triggered differential changes in the metabolome of the cells, leading to variation in the biosynthesis of secondary metabolites. PCA scores plots revealed dose- and time-dependent metabolic variations, with optimal treatment conditions being found to be 300 nM ergosterol and an 18 h incubation period. The observed ergosterol-induced metabolic changes were correlated with changes in defence-related metabolites. The 'defensome' involved increases in terpenoid metabolites with five antimicrobial compounds (the bicyclic sesquiterpenoid phytoalexins: phytuberin, solavetivone, capsidiol, lubimin and rishitin) and other metabolites (abscisic acid and phytosterols) putatively identified. In addition, various phenylpropanoid precursors, cinnamic acid derivatives and - conjugates, coumarins and lignin monomers were annotated. These annotated metabolites revealed a dynamic reprogramming of metabolic networks that are functionally correlated, with a high complexity in their regulation.

Sampling and analysis of metabolomes in biological fluids

Metabolome analysis involves the study of small molecules that are involved in the metabolic responses that occur through patho-physiological changes caused by genetic stimuli or chemical agents.

Looking back into the future: 30 years of metabolomics at TNO

Metabolites have played an essential role in our understanding of life, health, and disease for thousands of years. This domain became much more important after the concept of metabolism was discovered. In the 1950s, mass spectrometry was coupled to chromatography and made the technique more application-oriented and allowed the development of new profiling technologies. Since 1980, TNO has performed system-based metabolic profiling of body fluids, and combined with pattern recognition has led to many discoveries and contributed to the field known as metabolomics and systems biology. This review describes the development of related concepts and applications at TNO in the biomedical, pharmaceutical, nutritional, and microbiological fields, and provides an outlook for the future.

Metabolic profiling of plasma from benign and malignant pulmonary nodules patients using mass spectrometry-based metabolomics

Solitary pulmonary nodule (SPN or coin lesion) is a mass in the lung and can be commonly found in chest X-rays or computerized tomography (CT) scans. However, despite the advancement of imaging technologies, it is still difficult to distinguish malignant cancer from benign SPNs. Here we investigated the metabolic profiling of patients with benign and malignant pulmonary nodules. A combination of gas chromatography/mass spectrometry (GC/MS) and liquid chromatography/mass spectrometry (LC/MS) was used to profile the plasma metabolites in 17 patients with malignant SPNs, 15 patients with benign SPNs and 20 healthy controls. The metabolic profiles were assayed using OPLS-DA, and further analyzed to identify marker metabolites related to diseases. Both GC/MS- and LC/MS-derived models showed clear discriminations in metabolic profiles among three groups. It was found that 63 metabolites (12 from GC/MS, 51 from LC/MS) contributed to the differences. Of these, 48 metabolites showed same change trend in both malignant and benign SPNs as compared with healthy controls, indicating some common pathways including inflammation and oxidative injury shared by two diseases. In contrast, 14 metabolites constituted distinct profiles that differentiated malignant from benign SPNs, which might be a unique biochemical feature associated with lung cancer. Overall, our data suggested that integration of two highly sensitive and complementary metabolomics platforms could enable a comprehensive metabolic profiling and assist in discrimination malignant from benign SPNs.

Ultra sound assisted one step rapid derivatization and dispersive liquid-liquid microextraction followed by gas chromatography-mass spectrometric determination of amino acids in complex matrices

A rapid and economical method for the simultaneous determination of 20 amino acids in complex biological and food matrices (hair, urine and soybean seed samples) has been developed using ultrasound assisted dispersive liquid-liquid micro extraction (UA-DLLME). The method involves simultaneous derivatization and extraction followed by gas chromatography-mass spectrometric (GC-MS) analysis of amino acids. The parameters of UA-DLLME were optimized with the aid of design of experiments approach. The procedure involves the rapid injection of mixture of acetonitrile (disperser solvent), trichloroethylene (TCE) (extraction solvent) and ethylchloroformate (derivatization reagent) into the aqueous phase of sample extract containing pyridine. The Plackett-Burman design has indicated that, the factors such as volume of disperser and extraction solvents and pH were found to be significantly affects the extraction efficiency of the method. The optimum conditions of these factors based on central composite design were found to be 250μL of acetonitrile, 80μL of TCE and pH of 10. The limit of detection and limit of quantification were found to be in the range of 0.36-3.68μgL(-1) and 1.26-12.01μgL(-1) respectively. This is the first application of DLLME for the analysis of amino acids in any matrices. The advantages like (i) in situ derivatization and extraction of amino acids without any prior lyophilization and cleanup of sample, (ii) low consumption of extraction solvent, (iii) fast and simple, (iv) cost-effective and (iv) good repeatability make the method amenable for the routine analysis of amino acids in clinical, toxicological, nutritional and quality control laboratories.

Rapid and simultaneous determination of twenty amino acids in complex biological and food samples by solid-phase microextraction and gas chromatography-mass spectrometry with the aid of experimental design after ethyl chloroformate derivatization

Amino acids play a vital role as intermediates in many important metabolic pathways such as the biosynthesis of nucleotides, vitamins and secondary metabolites. A sensitive and rapid analytical method has been proposed for the first time for the simultaneous determination of twenty amino acids using solid-phase microextraction (SPME). The protein samples were hydrolyzed by 6M HCl under microwave radiation for 120 min. Then the amino acids were derivatized by ethyl chloroformate (ECF) and the ethoxy carbonyl ethyl esters of amino acids formed were extracted using SPME by direct immersion. Finally the extracted analytes on the SPME fiber were desorbed at 260°C and analyzed by gas chromatography-mass spectrometer (GC-MS) in electron ionization mode. Factors which affect the SPME efficiency were screened by Plackett-Burmann design; most significant factors were optimized with response surface methodology. The optimum conditions for SPME are as follows: pH of 1.7, ionic strength of 733 mg, extraction time of 30 min and fiber of divinyl benzene/carboxen/polydimethylsiloxane (DVB/CAR/PDMS). The recovery of all the amino acids was found to be in the range of 89.17-100.98%. The limit of detection (LOD) of all derivatized amino acids in urine, hair and soybean was found to be in the range of 0.20-7.52 μg L(-1), 0.21-8.40 μg L(-1) and 0.18-5.62 μg L(-1), respectively. Finally, the proposed technique was successfully applied for the determination of amino acids in complex biological (hair, urine) and food samples (soybean). The method can find wide applications in the routine analysis of amino acids in any biological as well as food samples.