Untargeted Gas Chromatography-mass Spectrometry Research Articles

Untargeted and targeted metabolomics analysis of CO poisoning and mechanical asphyxia postmortem interval biomarkers in rat and human plasma by GC[sbnd]MS

Accurate and objective estimation of the postmortem interval (PMI) is crucial in forensic practice. This study aimed to infer PMI through equations based on the relationship between PMI and metabolomics biomarkers.Rats were subjected to models representing various temperatures and causes of death, with blood collected at different intervals. Untargeted gas chromatographymass spectrometry metabolomics detection methods were developed, and candidate biomarkers were chosen as co-differentially expressed metabolites in four models. A targeted method was then developed for quantitatively determining candidate biomarkers. Animal tests and human cadaver samples with clearly documented causes of death and time were used to verify the reliability of the regression equation.Results: Unique differential metabolites for CO poisoning deaths included 2,3-butanediol, hypoxanthine, and dehydrated hexanol, while those for mechanical asphyxia deaths comprised propylamine, 1,3-propylene glycol, phosphoric acid, and sorbitol. Pyruvate, glycerol and isoleucine were identified as candidate biomarkers. Human case results demonstrated the method's potential (error rate < 20 %). The findings of this study may offer reference points for estimating PMI and causes of death in forensic practice.

Discriminating Craft and Industrial Beers by Untargeted Gas Chromatography-Mass Spectrometry (GC-MS) Metabolomics Combined with Multivariate Statistical Analysis

Developing robust analytical methodologies for the discrimination of craft and industrial beer presents a significant yet formidable challenge. The identification and differentiation of products necessitate the utilization of reliable analytical methods, which are indispensable for ensuring market stability and safeguarding consumer interests. In this study, representative samples with a significant market share were selected for headspace solid-phase microextraction coupled with gas chromatography-mass spectrometry (HS-SPME-GC-MS) analysis to compare the volatile substance characteristics of craft and industrial beers. The findings suggested that alcohols and esters constituted the primary volatiles. Craft beer exhibited a higher abundance of both types and concentrations compared to industrial beer. Subsequently, unsupervised and supervised chemometric techniques were employed for further analysis of the data matrix. An orthogonal partial least squares discriminant analysis (OPLS-DA) method was established to effectively discriminate between industrial and craft beers, enabling the identification of characteristic volatile markers. The untargeted GC-MS metabolomics coupled with multivariate statistical analysis holds promise for the authentication of craft and industrial beers.

Multiple-matrix metabolomics analysis for the distinct detection of colorectal cancer and adenoma.

Although colorectal cancer (CRC) is the leading cause of cancer-related morbidity and mortality, current diagnostic tests for early-stage CRC and colorectal adenoma (CRA) are suboptimal. Therefore, there is an urgent need to explore less invasive screening procedures for CRC and CRA diagnosis. Untargeted gas chromatography-mass spectrometry (GC-MS) metabolic profiling approach was applied to identify candidate metabolites. We performed metabolomics profiling on plasma samples from 412 subjects including 200 CRC patients, 160 CRA patients and 52 normal controls (NC). Among these patients, 45 CRC patients, 152 CRA patients and 50 normal controls had their fecal samples tested simultaneously. Differential metabolites were screened in the adenoma-carcinoma sequence. Three diagnostic models were further developed to identify cancer group, cancer stage, and cancer microsatellite status using those significant metabolites. The three-metabolite-only classifiers used to distinguish the cancer group always keeps the area under the receiver operating characteristic curve (AUC) greater than 0.7. The AUC performance of the classifiers applied to discriminate CRC stage is generally greater than 0.8, and the classifiers used to distinguish microsatellite status of CRC is greater than 0.9. This finding highlights potential early-driver metabolites in CRA and early-stage CRC. We also find potential metabolic markers for discriminating the microsatellite state of CRC. Our study and diagnostic model have potential applications for non-invasive CRC and CRA detection.

Metabolic signatures and potential biomarkers of sarcopenia in suburb-dwelling older Chinese: based on untargeted GC–MS and LC–MS

BackgroundUntargeted metabolomics can be used to expand our understanding of the pathogenesis of sarcopenia. However, the metabolic signatures of sarcopenia patients have not been thoroughly investigated. Herein, we explored metabolites associated with sarcopenia by untargeted gas chromatography (GC)/liquid chromatography (LC)–mass spectrometry (MS) and identified possible diagnostic markers.MethodsForty-eight elderly subjects with sarcopenia were age and sex matched with 48 elderly subjects without sarcopenia. We first used untargeted GC/LC–MS to analyze the plasma of these participants and then combined it with a large number of multivariate statistical analyses to analyze the data. Finally, based on a multidimensional analysis of the metabolites, the most critical metabolites were considered to be biomarkers of sarcopenia.ResultsAccording to variable importance in the project (VIP > 1) and the p-value of t-test (p < 0.05), a total of 55 metabolites by GC–MS and 85 metabolites by LC–MS were identified between sarcopenia subjects and normal controls, and these were mostly lipids and lipid-like molecules. Among the top 20 metabolites, seven phosphatidylcholines, seven lysophosphatidylcholines (LysoPCs), phosphatidylinositol, sphingomyelin, palmitamide, L-2-amino-3-oxobutanoic acid, and palmitic acid were downregulated in the sarcopenia group; only ethylamine was upregulated. Among that, three metabolites of LysoPC(17:0), L-2-amino-3-oxobutanoic acid, and palmitic acid showed very good prediction capacity with AUCs of 0.887 (95% CI = 0.817–0.957), 0.836 (95% CI = 0.751–0.921), and 0.805 (95% CI = 0.717–0.893), respectively.ConclusionsThese findings show that metabonomic analysis has great potential to be applied to sarcopenia. The identified metabolites could be potential biomarkers and could be used to study sarcopenia pathomechanisms.

The Microbiome and Metabolome of the Gut of Children with Sepsis and Septic Shock.

There is limited understanding of alteration of gut microbiota and metabolome in children with sepsis/septic shock. In this prospective observational study carried out in a pediatric intensive care unit of a tertiary care center from 2020 to 2022, patients aged <17 years with sepsis/septic shock and healthy children (HC) were enrolled. We characterized the gut bacterial compositions by metagenome sequencing and metabolomes by untargeted gas chromatography-mass spectrometry. The primary outcome was to compare the gut microbiota and metabolome of children with sepsis/septic shock with that of HC. The Firmicutes/Bacteroidetes (F/B) ratio was compared between children with sepsis/septic shock and HC. Key secondary outcomes were to evaluate association of factors associated with a low F/B ratio in children with sepsis/septic shock. A total of 40 children (63% boys) (15 children with sepsis and septic shock and 10 healthy children) with a median (IQR) age of 5.5 (1.5, 10) years were enrolled. In the fecal microbiota, the α-diversity index including Shannon and Simpson indices of the sepsis/septic shock groups was significantly lower than that of the HC. The samples lacked beneficial Bifidobacterium spp. and were dominated by Bacteroides, Enterobacteriaceae, and Enterococcaceae. There was reduction in short-chain fatty acids (SCFAs) in patients with sepsis/septic shock as compared to healthy children. A lower F/B ratio (≤1.57) of the gut microbiota discriminated well between children with sepsis/septic shock and HC. Factors associated with lower F/B ratio were male gender, clinical GI dysfunction, elevated inflammatory markers, and higher organ failure scores. There were significant alterations in the gut microbiota and metabolome in children with sepsis/septic shock as compared to healthy children. Larger study is needed to confirm these exploratory findings and develop potential therapeutic targets that will improve outcomes in children with sepsis/septic shock.

Comparison of untargeted gas chromatography-mass spectrometry analysis algorithms with implications to the interpretation and putative identification of volatile aroma compositions

The aroma profiling process requires the identification of the volatile compounds in a sample or its headspace. Typically, the identification of compounds relies on automated feature finding and matching algorithms to (putatively) identify and report compounds based on retention index and mass spectra matching against a compound library. We investigated the use of five different workflows and proposed three metrics (target accuracy A, identification percentage I, uniqueness U) to quantify their impact on generated aroma profiles of a mixture of fragrance standards and a commercial grade essential oil. All workflows accurately identified target compounds (100% in standards, >90% in samples) and reported similar compound identities for major GC–MS features, but beyond that could differ by up to 40–50%. Despite the variances, different workflows did not report conflicting compound identities. Aroma compositions primarily contained unreported or extra (putatively) identified compounds due to variations in mass spectral elucidations within the various workflows. Considering these differences, we show how the proposed metrics, I and U, could be modified to help the analyst interpret and evaluate reported volatile aroma compositions of unknown materials.

Comparison of Metabolites and Species Classification of Thirteen Zingiberaceae Spices Based on GC-MS and Multi-Spectral Fusion Technology.

Due to a similar plant morphology in the majority of Zingiberaceae spices, substitution and adulteration frequently take place during the sales process. Therefore, it is important to analyze the metabolites and species classification of different Zingiberaceae spices. This study preliminarily explored the differences in the metabolites in thirteen Zingiberaceae spices through untargeted gas chromatography-mass spectrometry (GC-MS) and combined spectroscopy, establishing models for classifying different Zingiberaceae spices. On one hand, a total of 81 metabolites were successfully identified by GC-MS. Thirty-seven differential metabolites were screened using variable important in projection (VIP ≥ 1). However, the orthogonal partial least squares discriminant analysis (OPLS-DA) model established using GC-MS data only explained about 30% of the variation. On the other hand, the partial least squares discriminant analysis (PLS-DA) models with three spectral data fusion strategies were compared, and their classification accuracy reached 100%. Among them, the mid-level data fusion model based on latent variables had the best performance. This study provides a powerful tool for distinguishing different Zingiberaceae spices and assists in reducing the occurrence of substitution and adulteration phenomena.

Leaf Biochemical and Kernel Metabolite Profiles as Potential Biomarkers of Water Deficit in Walnut (Juglans regia L.) cv. Chandler

Walnut cultivation is expanding into regions where water availability for irrigation is lower than crop evapotranspiration. However, information regarding the responses and adaptations of walnut trees to water deficit remains scarce. In this study, we applied three irrigation levels, 100%, 75%, and 50% of crop evapotranspiration (referred to as T100, T75, and T50, respectively), to Chandler walnut trees over two consecutive seasons. During the second season, we evaluated leaf water-deficit biomarkers, including proline, malondialdehyde, soluble sugars, phenols, and flavonoids, using targeted spectrophotometry. Despite not finding significant differences in biomarker concentrations among the irrigation regimes, we observed variations between different collection times (sprouting, endocarp hardening, and maturity). Furthermore, we assessed the kernel metabolome using untargeted gas chromatography–mass spectrometry, profiling seventy-one metabolites across all samples. Notably, forty-one of these metabolites were identified as members of distinct groups, comprising carbohydrates (n = 11), fatty acids (n = 11), organic acids (n = 9), and amino acids (n = 5). Linear mixed models showed no significant differences between the irrigation regimes. However, in the T50 treatment, multivariate analysis (PCA) revealed a higher concentration of osmotic adjustment metabolites, which are potentially associated with protecting oil biosynthesis under high-temperature and water deficit conditions.

Discrimination of Filter Coffee Extraction Methods of a Medium Roasted Specialty Coffee Based on Volatile Profiles and Sensorial Traits.

An untargeted gas chromatography-mass spectrometry (GC-MS) approach combined with sensory analysis was used to present the effects of different extraction methods (i.e., Pure Brew, V60, AeroPress, and French Press) on specialty graded Coffea arabica from Kenya. Partial Least Square Discriminant analysis and hierarchical clustering were applied as multivariate statistical tools in data analysis. The results showed good discrimination and a clear clustering of the groups of samples based on their volatile profiles. Similarities were found related to the filter material and shape used for the extraction. Samples extracted with paper filters (V60 and AeroPress) resulted in higher percentages of caramel-, and flowery-related compounds, while from metal filter samples (Pure Brew and French Press), more fruity and roasted coffees were obtained. Discriminant analysis allowed the identification of eight compounds with a high VIP (variable important in projection) discriminant value (i.e., >1), with 2-furanmethanol being the main feature in discrimination. Sensorial analyses were carried out through an expert panel test. The main evaluations revealed the French Press system as the lowest-scored sample in all the evaluated parameters, except for acidity, where its score was similar to V60. In conclusion, the data obtained from GC-MS analyses were in line with the sensorial results, confirming that the extraction process plays a fundamental role in the flavor profile of filter coffee beverages.

Monitoring pollution pathways in river water by predictive path modelling using untargeted GC-MS measurements

To safeguard the quality of river water, a comprehensive approach is required within the European Water Framework Directive. It is vital to conduct non-target screening of the complete chemical fingerprint of the aquatic ecosystem, as this will help to identify chemicals of emerging concern and uncover their unusual dynamic patterns in river water. Achieving this goal calls for an advanced combination of two measurement paradigms: tracing the potential pollution path through the river network and detecting the numerous compounds that constitute the chemical composition, both known and unknown. To address this challenge, we propose an integrated approach that combines the preprocessing of ongoing Gas Chromatography Mass Spectrometry (GC-MS) measurements at nine sites along the Rhine using PARAllel FActor Analysis2 (PARAFAC2) for non-target screening, with spatiotemporal modelling of these sites within the river network using a statistical path modelling algorithm called Process Partial Least Squares (Process PLS). With an average explained variance of 97.0%, PARAFAC2 extracted mass spectra, elution, and concentration profiles of known and unknown chemicals. On average, 76.8% of the chemical variability captured by the PARAFAC2 concentration profiles was extracted by Process PLS. The integrated approach enabled us to track chemicals through the Rhine catchment, and tentatively identify known and as-yet unknown potential pollutants, including methyl tert-butyl ether and 1,3-cyclopentadiene, based on non-target screening and spatiotemporal behaviour.

Development of a widely-targeted metabolomics method based on gas chromatography-mass spectrometry

Gas chromatography-mass spectrometry (GC-MS) detectors are widely used detection instruments owing to their distinct advantages over other analytical techniques, including lower sample consumption, higher sensitivity, faster analysis speed, and simultaneous separation and analysis. Metabolomics is an important component of system physiology that concerns systematic studies of the metabolite spectrum in one or more biological systems, such as cells, tissues, organs, body fluids, and organisms. Unfortunately, conventional GC-MS detectors also feature low scan rates, high ion loss rates, and a narrow concentration detection range, which limit their applications in the field of metabolomics. Therefore, establishing a GC-MS-based metabolomic analysis method with wide coverage is of great importance. In this research, a widely-targeted metabolomics method based on GC-MS is proposed. This method combines the universality of untargeted metabolomics with the accuracy of targeted metabolomics to realize the qualitative and semi-quantitative detection of numerous metabolites. It does not require a self-built database and exhibits high sensitivity, good repeatability, and strong support for a wide range of metabolic substances. The proposed method was used to establish the relationship between the retention time of straight-chain fatty acid methyl esters (FAMEs) and their retention index (RI) in the FiehnLib database based on the metabolite information stored in this database. We obtained a linear relationship that could be described by the equation y=40878x-47530, r2=0.9999. We then calculated the retention times of metabolites in the FiehnLib database under the experimental conditions based on their RI. In this way, the effects of significant variations in peak retention times owing to differences in the chromatographic column, temperature, carrier gas flow rate, and so on can be avoided. The retention time of a substance fluctuates within a certain threshold because of variations in instrument performance, matrix interference, and other factors. As such, the retention time threshold of the substance must be determined. In this paper, the retention time threshold was set to 0.15 min to avoid instrument fluctuations. The optimal scan interval was optimized to 0.20 s (possible values=0.10, 0.15, 0.20, 0.25, and 0.30 s) because longer sampling periods can lead to spectral data loss and reductions in the resolution of adjacent chromatographic peaks, whereas shorter sampling periods can result in deterioration of the signal-to-noise ratio of the collected signals. The metabolite quantification ions were optimized to avoid the interference of quantification ion peak accumulation in the case of similar peak times, and a selected ion monitoring (SIM) method table was constructed for 611 metabolites, covering 65% of the metabolic pathways in the KEGG (Kyoto Encyclopedia of Genes and Genomes). The developed method covered 39 pathways, including glycolysis, the tricarboxylic acid cycle, purine metabolism, pyrimidine metabolism, amino acid metabolism, and biosynthesis. Compared with the full-scan untargeted GC-MS method, the widely-targeted GC-MS method demonstrated a 20%-30% increase in the number of metabolites detected, as well as a 15%-20% increase in signal-to-noise ratio. The results of stability tests showed that 84% of the intraday relative standard deviations (RSDs) of metabolite retention times were less than 2% and 91% of that were less than 3%; moreover, 54% of the interday RSDs of metabolite retention times were less than 2% and 76% of that were less than 3%. The detection and analysis results of common biological samples confirmed that the proposed method greatly improved the quantity and signal-to-noise ratio of the detected metabolites and is applicable to substances that are thermally stable, volatile, or volatile after derivation and have relative molecular masses lower than 600. Thus, the widely-targeted GC-MS method can expand the application scope of GC-MS in metabolomics.

GC/MS analysis of hypoxic volatile metabolic markers in the MDA-MB-231 breast cancer cell line.

Hypoxia in disease describes persistent low oxygen conditions, observed in a range of pathologies, including cancer. In the discovery of biomarkers in biological models, pathophysiological traits present a source of translatable metabolic products for the diagnosis of disease in humans. Part of the metabolome is represented by its volatile, gaseous fraction; the volatilome. Human volatile profiles, such as those found in breath, are able to diagnose disease, however accurate volatile biomarker discovery is required to target reliable biomarkers to develop new diagnostic tools. Using custom chambers to control oxygen levels and facilitate headspace sampling, the MDA-MB-231 breast cancer cell line was exposed to hypoxia (1% oxygen) for 24h. The maintenance of hypoxic conditions in the system was successfully validated over this time period. Targeted and untargeted gas chromatography mass spectrometry approaches revealed four significantly altered volatile organic compounds when compared to control cells. Three compounds were actively consumed by cells: methyl chloride, acetone and n-Hexane. Cells under hypoxia also produced significant amounts of styrene. This work presents a novel methodology for identification of volatile metabolisms under controlled gas conditions with novel observations of volatile metabolisms by breast cancer cells.

Untargeted GC-MS and FT-NIR study of the effect of 14 processing methods on the volatile components of Polygonatum kingianum.

Polygonatum kingianum is a traditional medicinal plant, and processing has significantly impacts its quality. Therefore, untargeted gas chromatography-mass spectrometry (GC-MS) and Fourier transform-near-infrared spectroscopy (FT-NIR) were used to analyze the 14 processing methods commonly used in the Chinese market.It is dedicated to analyzing the causes of major volatile metabolite changes and identifying signature volatile components for each processing method. The untargeted GC-MS technique identified a total of 333 metabolites. The relative content accounted for sugars (43%), acids (20%), amino acids (18%), nucleotides (6%), and esters (3%). The multiple steaming and roasting samples contained more sugars, nucleotides, esters and flavonoids but fewer amino acids. The sugars are predominantly monosaccharides or small molecular sugars, mainly due to polysaccharides depolymerization. The heat treatment reduces the amino acid content significantly, and the multiple steaming and roasting methods are not conducive to accumulating amino acids. The multiple steaming and roasting samples showed significant differences, as seen from principal component analysis (PCA) and hierarchical cluster analysis (HCA) based on GC-MS and FT-NIR. The partial least squares discriminant analysis (PLS-DA) based on FT-NIR can achieve 96.43% identification rate for the processed samples. This study can provide some references and options for consumers, producers, and researchers.

Volatile Markers for Cancer in Exhaled Breath-Could They Be the Signature of the Gut Microbiota?

It has been shown that the gut microbiota plays a central role in human health and disease. A wide range of volatile metabolites present in exhaled breath have been linked with gut microbiota and proposed as a non-invasive marker for monitoring pathological conditions. The aim of this study was to examine the possible correlation between volatile organic compounds (VOCs) in exhaled breath and the fecal microbiome by multivariate statistical analysis in gastric cancer patients (n = 16) and healthy controls (n = 33). Shotgun metagenomic sequencing was used to characterize the fecal microbiota. Breath-VOC profiles in the same participants were identified by an untargeted gas chromatography-mass spectrometry (GC-MS) technique. A multivariate statistical approach involving a canonical correlation analysis (CCA) and sparse principal component analysis identified the significant relationship between the breath VOCs and fecal microbiota. This relation was found to differ between gastric cancer patients and healthy controls. In 16 cancer cases, 14 distinct metabolites identified from the breath belonging to hydrocarbons, alcohols, aromatics, ketones, ethers, and organosulfur compounds were highly correlated with 33 fecal bacterial taxa (correlation of 0.891, p-value 0.045), whereas in 33 healthy controls, 7 volatile metabolites belonging to alcohols, aldehydes, esters, phenols, and benzamide derivatives correlated with 17 bacterial taxa (correlation of 0.871, p-value 0.0007). This study suggested that the correlation between fecal microbiota and breath VOCs was effective in identifying exhaled volatile metabolites and the functional effects of microbiome, thus helping to understand cancer-related changes and improving the survival and life expectancy in gastric cancer patients.

EVALUATION OF PHYTOCHEMICAL COMPOUNDS IN Ficus polita LEAVES POWDERS FOR INSECT PEST CONTROL

Pesticidal plants, also referred to as botanical pesticides, have been used for centuries for pest management. The study objectives was to evaluate the phytochemical compounds in F.polita leaves powders and their insecticidal activity on Callosobruchus subinnotatus PIC in stored Bambara groundnut in Gombe through untargeted Gas Chromatography-Mass Spectrometry (GC-MS) using three different extraction solvents (ethanol, methanol and distilled water). Result of the study revealed the presence of various compounds. To mention but few, the ethanol extracts revealed the presence of 2-Methylchrysene (C19H14); Chrysene, 1-methyl- (C19H14); Ethyl.alpha.-d-glucopyranoside (C8H16O6); 5,9-Undecadien-2-one, 6,10-dimethyl- (C13H22O). Methanol extracts showed Alloaromadendrene oxide-(2) (C15H24O); Methyl.beta.-d-galactopyranoside (C7H14O6); Phytol (C20H40O); Ethyl .beta.-d-riboside (C7H14O5). Distilled water extraction revealed Lupeol (C30H50O); 9,19-Cycloergost-24(28)-en-3-ol, 4,14-dimethyl-, acetate, (3.beta.,4.alpha.,5.alpha.)- (C32H52O2); Lanosterol (C30H50O); [4-(4-Methoxymethylbenzyl)phenyl]methanol (C16H18O2) as compounds with the highest percentage areas. Being cheap and readily available, resources-poor farmers perceive those natural products (botanical pesticides) possessed repellence, anti-feeding and ovipositional deterrence, fumigant or contact activity, growth inhibition etc. Hence, could serve as good alternatives to chemical insecticides.

Untargeted Metabolite Profiling of Camellia tetracocca’s Response to an Empoasca onukii Attack Using GC-MS and LC-MS

Camellia tetracocca, a kind of tea with local popularity, is unique to southwest China, where it has an important natural heritage and cultural heritage. However, the tea plant and its sprout are frequently attacked on a large-scale by Empoasca onukii. The metabolic mechanisms of the unique plant for defending against these pest insects are unclear. Therefore, we used untargeted gas chromatography–mass spectrometry (GC-MS) and high performance liquid chromatography–mass spectrometry (LC-MS) to compare the metabolite profiles between E. onukii-attacked leaves and healthy leaves. Using GC-MS, 56 metabolites (24 up-regulated, 32 down-regulated) were preliminarily identified. Additionally, 576 metabolites (280 up-regulated, 287 down-regulated) were rudimentarily identified with LC-MS. Differentially abundant metabolites were mainly enriched in the biosynthesis of specialized metabolites. Fourteen accumulated specialized metabolites are related to insect resistance. Mainly, momordicin I and arabidopside B are reportedly involved in the resistance to the insect. Therefore, we conjectured that the accumulation of momordicin I and arabidopside B is involved in the C. tetracocca’s resistance to E. onukii. Our results indicate that these specialized metabolites may be served as candidate biocontrol agents against the pest of E. onukii of C. tetracocca located in the State-owned Pubai Forest Farm.

Red light-induced inhibition of maize (Zea mays) mesocotyl elongation: evaluation of apoplastic metabolites.

Light is a crucial factor affecting plant growth and development. Besides providing the energy for photosynthesis, light serves as a sensory cue to control the adaptation of plants to environmental changes. We used the etiolated maize (Zea mays ) seedlings as a model system to study the red light-regulated growth. Exposure of the maize seedlings to red light resulted in growth inhibition of mesocotyls. We demonstrate for the first time (to the best our knowledge) that red light affected the patterns of apoplastic fluid (AF) metabolites extracted from the mesocotyl segments. By means of the untargeted gas chromatography-mass spectrometry (GC-MS)-based metabolomics approach, we identified 44 metabolites in the AF of maize mesocotyls and characterised the dynamics of their relative tissue abundances. The characteristic metabolite patterns of mesocotyls dominated with mono- and disaccharides, organic acids, amino acids, and other nitrogen-containing compounds. Upon red light irradiation, the contents of β -alanine, putrescine and trans -aconitate significantly increased (P -value<0.05). In contrast, there was a significant decrease in the total ascorbate content in the AF of maize mesocotyls. The regulatory role of apoplastic metabolites in the red light-induced inhibition of maize mesocotyl elongation is discussed.

Comparing the Chemical Profiles of Plant-Based and Traditional Meats Using GC–MS-Based Metabolomics

As the consumer interest and market for plant-based meat alternatives grows, understanding the nutritional differences between alternative and traditional meats is essential. This article describes an untargeted gas chromatography–mass spectrometry (GC–MS)-based metabolomics approach that compares the chemical profiles of a popular plant-based meat alternative and grass-fed ground beef using a GC system coupled to a GC–MS device. The samples were derivatized to simplify the chromatographic process and render the polar metabolites more volatile for GC–MS analysis. Statistical and multivariate analysis of the acquired and processed GC–MS data revealed that 90% of the annotated compounds differed between the plant-based alternative meat and the grass-fed ground beef samples. The ground beef and plant-based products each contained several compounds that were found in much smaller quantities, or not at all, in the other product. These results indicate differences in organic composition even though the nutritional labels on the back of the products were nearly identical. Heat maps, principal component analysis (PCA) score plots, variable importance plots (VIPs), and the clustering of compounds into metabolite classes provided further insights into the differences between the two types of meat products. The biological significance of the comparative data was studied using online databases and pathway analysis tools.

Maternal obesity alters offspring liver and skeletal muscle metabolism in early post-puberty despite maintaining a normal post-weaning dietary lifestyle.

Maternal obesity (MO) during pregnancy is linked to increased and premature risk of age-related metabolic diseases in the offspring. However, the underlying molecular mechanisms still remain not fully understood. Using a well-established nonhuman primate model of MO, we analyzed tissue biopsies and plasma samples obtained from post-pubertal offspring (3-6.5 y) of MO mothers (n=19) and from control animals born to mothers fed a standard diet (CON, n=13). All offspring ate a healthy chow diet after weaning. Using untargeted gas chromatography-mass spectrometry metabolomics analysis, we quantified a total of 351 liver, 316 skeletal muscle, and 423 plasma metabolites. We identified 58 metabolites significantly altered in the liver and 46 in the skeletal muscle of MO offspring, with 8 metabolites shared between both tissues. Several metabolites were changed in opposite directions in males and females in both liver and skeletal muscle. Several tissue-specific and 4 shared metabolic pathways were identified from these dysregulated metabolites. Interestingly, none of the tissue-specific metabolic changes were reflected in plasma. Overall, our study describes characteristic metabolic perturbations in the liver and skeletal muscle in MO offspring, indicating that metabolic programming in utero persists postnatally, and revealing potential novel mechanisms that may contribute to age-related metabolic diseases later in life.

Differences in Metabolite Composition of Aloe barbadensis Mill. Extracts Lead to Differential Effects on Human Blood T Cell Activity In Vitro.

Aloe barbadensis Mill. (Aloe) is used for diverse therapeutic properties including immunomodulation. However, owing to the compositionally complex nature of Aloe, bioactive component(s) responsible for its beneficial properties, though thought to be attributed to polysaccharides (acemannan), remain unknown. We therefore aimed to determine the metabolite composition of various commercial Aloe extracts and assess their effects on human blood T cell activity in vitro. Peripheral blood mononuclear cells (PBMC) from healthy donors were stimulated polyclonally in presence or absence of various Aloe extracts. T cell phenotype and proliferation were investigated by flow cytometry. Aloe extracts were analyzed using targeted 1H-NMR spectroscopy for standard phytochemical quality characterization and untargeted gas chromatography mass spectrometry (GC-MS) for metabolite profiling. Aloe extracts differing in their standard phytochemical composition had varying effects on T cell activation, proliferation, apoptosis, and cell-death in vitro, although this was not related to the acemannan content. Furthermore, each Aloe extract had its own distinct metabolite profile, where extracts rich in diverse sugar and sugar-derivatives were associated with reduced T cell activity. Our results demonstrate that all commercial Aloe extracts are unique with distinct metabolite profiles, which lead to differential effects on T cell activity in vitro, independent of the acemannan content.