Gas Chromatography-mass Spectrometry Metabolomics Research Articles

Untargeted and targeted metabolomics analysis of COpoisoning and mechanicalasphyxia 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.

Integrative metagenomics, volatilomics and chemometrics for deciphering the microbial structure and core metabolic network during Chinese rice wine (Huangjiu) fermentation in different regions

Huangjiu is a spontaneously fermented alcoholic beverage, that undergoes intricate microbial compositional changes. This study aimed to unravel the flavor and quality formation mechanisms based on the microbial metabolism of Huangjiu. Here, metagenome techniques, chemometrics analysis, and headspace solid-phase microextraction gas chromatography-mass spectrometry (HS-SPME-GC–MS) metabolomics combined with microbial metabolic network were employed to investigate the distinctions and relationship between the microbial profiles and the quality characteristics, flavor metabolites, functional metabolic patterns of Huangjiu across three regions. Significant variations (P < 0.05) were observed in metabolic rate of physicochemical parameters and biogenic amine concentration among three regions. 8 aroma compounds (phenethyl acetate, phenylethyl alcohol, isobutyl alcohol, ethyl octanoate, ethyl acetate, ethyl hexanoate, isoamyl alcohol, and diethyl succinate) out of 448 volatile compounds were identified as the regional chemical markers. 25 dominant microbial genera were observed through metagenomic analysis, and 13 species were confirmed as microbial markers in three regions. A metabolic network analysis revealed that Saccharomycetales (Saccharomyces), Lactobacillales (Lactobacillus, Weissella, and Leuconostoc), and Eurotiales (Aspergillus) were the predominant populations responsible for substrate, flavor (mainly esters and phenylethyl alcohol) metabolism, Lactobacillales and Enterobacterales were closely linked with biogenic amine. These findings provide scientific evidence for regional microbial contributions to geographical characteristics of Huangjiu, and perspectives for optimizing microbial function to promote Huangjiu quality.

Corydalis yanhusuo Polysaccharides Ameliorate Chronic Stress-Induced Depression in Mice through Gut Microbiota-Derived Short-Chain Fatty Acid Activation of 5-Hydroxytryptamine Signaling.

Depression, a prevalent psychiatric disorder, presents a serious health risk to humans. Increasing evidence suggested that the gut microbiota and the 5-hydroxytryptamine (5-HT) pathway both contribute significantly to depression. This research aimed to investigate how Corydalis yanhusuo polysaccharides (CYP) could potentially alleviate depression induced by chronic unpredictable mild stress in mice, as well as its underlying mechanism. The sucrose preference test, tail suspension test, and forced swimming test were employed to evaluate the behavior of mice. Enzyme-linked immunosorbent assay and PCR techniques were utilized to measure depression-related factors (dopamine [DA], 5-HT, norepinephrine [NE], brain-derived neurotrophic factor [BDNF], tryptophan hydroxylase 2 [TPH-2], 5-hydroxytryptophan [5-HTP], and tryptophan hydroxylase [TPH-1] levels). Hematoxylin and eosin staining and Nissl staining were conducted to observe histopathological changes in the hippocampus, the differences in the diversity of gut flora between groups were analyzed using 16S rRNA sequencing, and gas chromatography-mass spectrometry metabolomics was utilized to evaluate short-chain fatty acid (SCFA) concentrations. The findings indicated that CYP treatment increased the sucrose preference index, decreased the immobility time, and improved neuropathological injury. In depressed mice, CYP improved the dysregulation of the gut microbiota, and increased the SCFA levels. In addition, CYP enhanced the DA, 5-HT, NE, BDNF, and TPH-2 levels in the brain and the expression of 5-HTP and TPH-1 in the colon, while SCFAs were positively correlated with these levels. In summary, our study suggested that CYP may mitigate depression by ameliorating gut microbiota dysregulation, promoting the generation of SCFAs, and activation of 5-HT signaling expression.

The effects of pollutant mixture released from grafted adipose tissues on fatty acid and lipid metabolism in the skeletal muscles, kidney, heart, and lungs of male mice

Persistent organic pollutants (POPs) accumulated in the adipose tissue can affect the fatty acid and lipid metabolism in the body. Gas chromatography-mass spectrometry (GC-MS) metabolomics analysis was carried out to study the metabolic changes induced by internal exposure to the POPs in mouse skeletal muscle (soleus, plantaris, and gastrocnemius), kidney, heart, and lungs. Male donor mice were injected with a mixture of 10 POPs at concentrations of 0 × and 5 × lowest-observed-adverse-effect level (LOAEL). Their adipose tissue (AT) containing the POP was then grafted onto the host mice and the metabolic change of the host mice was monitored for 3 or 21 days. The metabolites related to fatty acid and lipid metabolism were studied. For the host mice engrafted with POP-containing fat pad, there was dysregulation of the fatty acids and glycerides observed in all the organs studied 3 days after the graft. However, there was no longer a significant change in the metabolites 21 days after the graft. The difference in significant values and metabolite regulation in each of the skeletal muscles showed that the POP mixture affects different types of skeletal muscle in a heterogeneous manner. Fold change analysis showed that certain metabolites in the kidney of host mice exposed to POPs for 3 days were greatly affected. Using multivariate analysis, apart from the plantaris, most treated groups exposed to POPs for 3 days are well distinguished from the control groups. However, for host mice exposed to POPs for 21 days, apart from the kidney and heart, groups are not well-distinguished from the control group. This study helps bring new insight into the effects of the pollutants mixture released from AT on fatty acid and lipid metabolism at different periods and how the dysregulation of metabolites might result in diseases associated with the organs.

Denoising Autoencoder Normalization for Large-Scale Untargeted Metabolomics by Gas Chromatography-Mass Spectrometry.

Large-scale metabolomics assays are widely used in epidemiology for biomarker discovery and risk assessments. However, systematic errors introduced by instrumental signal drifting pose a big challenge in large-scale assays, especially for derivatization-based gas chromatography-mass spectrometry (GC-MS). Here, we compare the results of different normalization methods for a study with more than 4000 human plasma samples involved in a type 2 diabetes cohort study, in addition to 413 pooled quality control (QC) samples, 413 commercial pooled plasma samples, and a set of 25 stable isotope-labeled internal standards used for every sample. Data acquisition was conducted across 1.2 years, including seven column changes. In total, 413 pooled QC (training) and 413 BioIVT samples (validation) were used for normalization comparisons. Surprisingly, neither internal standards nor sum-based normalizations yielded median precision of less than 30% across all 563 metabolite annotations. While the machine-learning-based SERRF algorithm gave 19% median precision based on the pooled quality control samples, external cross-validation with BioIVT plasma pools yielded a median 34% relative standard deviation (RSD). We developed a new method: systematic error reduction by denoising autoencoder (SERDA). SERDA lowered the median standard deviations of the training QC samples down to 16% RSD, yielding an overall error of 19% RSD when applied to the independent BioIVT validation QC samples. This is the largest study on GC-MS metabolomics ever reported, demonstrating that technical errors can be normalized and handled effectively for this assay. SERDA was further validated on two additional large-scale GC-MS-based human plasma metabolomics studies, confirming the superior performance of SERDA over SERRF or sum normalizations.

Gaussian Mixture Modeling Extensions for Improved False Discovery Rate Estimation in GC-MS Metabolomics.

The ability to reliably identify small molecules (e.g., metabolites) is key toward driving scientific advancement in metabolomics. Gas chromatography-mass spectrometry (GC-MS) is an analytic method that may be applied to facilitate this process. The typical GC-MS identification workflow involves quantifying the similarity of an observed sample spectrum and other features (e.g., retention index) to that of several references, noting the compound of the best-matching reference spectrum as the identified metabolite. While a deluge of similarity metrics exist, none quantify the error rate of generated identifications, thereby presenting an unknown risk of false identification or discovery. To quantify this unknown risk, we propose a model-based framework for estimating the false discovery rate (FDR) among a set of identifications. Extending a traditional mixture modeling framework, our method incorporates both similarity score and experimental information in estimating the FDR. We apply these models to identification lists derived from across 548 samples of varying complexity and sample type (e.g., fungal species, standard mixtures, etc.), comparing their performance to that of the traditional Gaussian mixture model (GMM). Through simulation, we additionally assess the impact of reference library size on the accuracy of FDR estimates. In comparing the best performing model extensions to the GMM, our results indicate relative decreases in median absolute estimation error (MAE) ranging from 12% to 70%, based on comparisons of the median MAEs across all hit-lists. Results indicate that these relative performance improvements generally hold despite library size; however FDR estimation error typically worsens as the set of reference compounds diminishes.

The effects of chronic unpredicted mild stress on maternal negative emotions and gut microbiota and metabolites in pregnant rats.

Chronic long-term stress is associated with a range of disorders, including depression and a variety of other chronic illnesses. It is well known that maternal exposure to psychosocial stress during pregnancy significantly increases the likelihood of adverse pregnancy outcomes. The gut microbiota has been a popular topic, it is a key mediator of the gut-brain axis and plays an important role in human health; changes in the gut microbiota have been related to chronic stress-induced health impairment, however, the relationship between maternal negative emotions and abnormal gut microbiota and its metabolites during maternal exposure to chronic stress during pregnancy remains unclear. Pregnant rats were subjected to chronic unpredicted mild stress (CUMS) to establish the rat model of chronic stress during pregnancy. The behavioral changes were recorded using sucrose preference test (SPT) and open-field test (OFT), plasma corticosterone levels were determined by radioimmunoassay, and a comprehensive method combining 16S rRNA gene sequencing and gas chromatography-mass spectrometry (GC-MS) metabolomics was used to study the effects of stress during pregnancy on the function of intestinal microbiota and its metabolites. Chronic stress during pregnancy not only increased maternal plasma corticosterone (P < 0.05), but also caused maternal depression-like behaviors (P < 0.05). Chronic stress during pregnancy changed the species composition at the family level of maternal gut microbiota, the species abundance of Ruminococcaceae in the stress group (23.45%) was lower than the control group (32.67%) and the species abundance of Prevotellaceae in the stress group (10.45%) was higher than the control group (0.03%) (P < 0.05). Vertical locomotion and 1% sucrose preference percentage in pregnant rats were negatively correlated with Prevotellaceae (r=-0.90, P<0.05). Principal component analysis with partial least squares discriminant analysis showed that the integration points of metabolic components in the stress and control groups were completely separated, indicating that there were significant differences in the metabolic patterns of the two groups, and there were seven endogenous metabolites that differed (P < 0.05). The negative emotional behaviors that occur in pregnant rats as a result of prenatal chronic stress may be associated with alterations in the gut microbiota and its metabolites. These findings provide a basis for future targeted metabolomics and gut flora studies on the effects of chronic stress during pregnancy on gut flora.

Towards inferring absolute concentrations from relative abundance in time-course GC-MS metabolomics data.

Metabolomics, the large-scale study of metabolites, has significant appeal as a source of information for metabolic modeling and other scientific applications. One common approach for measuring metabolomics data is gas chromatography-mass spectrometry (GC-MS). However, GC-MS metabolomics data are typically reported as relative abundances, precluding their use with approaches and tools where absolute concentrations are necessary. While chemical standards can be used to help provide quantification, their use is time-consuming, expensive, or even impossible due to their limited availability. The ability to infer absolute concentrations from GC-MS metabolomics data without chemical standards would have significant value. We hypothesized that when analyzing time-course metabolomics datasets, the mass balances of metabolism and other biological information could provide sufficient information towards inference of absolute concentrations. To demonstrate this, we developed and characterized MetaboPAC, a computational framework that uses two approaches-one based on kinetic equations and another using biological heuristics-to predict the most likely response factors that allow translation between relative abundances and absolute concentrations. When used to analyze noiseless synthetic data generated from multiple types of kinetic rate laws, MetaboPAC performs significantly better than negative control approaches when 20% of kinetic terms are known a priori. Under conditions of lower sampling frequency and high noise, MetaboPAC is still able to provide significant inference of concentrations in 3 of 4 models studied. This provides a starting point for leveraging biological knowledge to extract concentration information from time-course intracellular GC-MS metabolomics datasets, particularly for systems that are well-studied and have partially known kinetic structures.

Transcriptomics and gas chromatography-mass spectrometry metabolomics reveal the mechanism of heat shock combined with 1-methylcyclopropene to regulate the cuticle wax of jujube fruit during storage

Cuticle wax is closely related to fruit quality during storage. In this study, changes in epidermal wax morphology, composition, and genes regulation induced by heat shock (HT), 1-methylcyclopropene (1-MCP) or their combination (HT + 1-MCP) were investigated in jujube fruit during cold storage. HT, 1-MCP, or HT + 1-MCP caused a smoother wax layer and fewer micro-cracks compared to the control (CK) during cold storage. It was confirmed that acids and terpenoids were the main wax components by gas chromatography-mass spectrometry. HT + 1-MCP and 1-MCP treatments could significantly increase (p < 0.05) the wax content at 45 d of cold storage. The transcriptomics results indicated that HT + 1-MCP treatment up-regulated FATB, FATB, FAB2, FAD2 and CYP716A, and maintained the wax content of jujube fruit during cold storage. These results could provide new perspective for regulating the cuticle characteristics to extend the shelf life of jujube fruit.

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.

Untargeted Metabolomics Analysis of Crocus cancellatus subsp. damascenus (Herb.) B. Mathew Stigmas and Their Anticarcinogenic Effect on Breast Cancer Cells.

Safranal, crocin, crocetin, and picrocrocin are major known compounds in the stigma extract of Crocus sativus with various medicinal properties. Crocus cancellatus is another Crocus species that grows extensively in Iran's various regions, such as the Kurdistan province. The predominant metabolites and biological properties of C. cancellatus have not yet been investigated. The ingredients of the stigma ethanol extract of C. cancellatus were investigated using gas chromatography-mass spectrometry (GC-MS) and liquid chromatography with tandem mass spectrometry (LC-MS). The ROIMCR approach was performed to analyze the LC-MS full scan data sets. This method searches the MS regions of interest (ROI) data in the m/z domain and analyses the results using the multivariate curve-resolution alternating least squares (MCR-ALS) chemometrics technique for simultaneous resolution of two extracts. Also, the antiproliferative properties of C. cancellatus against MDA-MB-231 and MCF-7 cancer cells were examined by MTT, dual acridine orange/ethidium bromide test, Annexin V-FITC/PI, and zymography. The GC-MS and LC-MS untargeted metabolomics data analysis of the extract indicated the presence of cytotoxic agents including safranal, crocin, picrocrocin, and crocetin in the stigma ethanol extract of C. cancellatus. Biological tests showed that the viability of MDA-MB-231 and MCF-7 cancer cells is decreased following C. cancellatus treatment in a time- and dose-dependent way in both monolayer and 3D cell cultures. The MCF-7 cell spheroids had greater resistance to the cytotoxic activity of the extract in 3D cell culture than the MDA-MB-231 cell spheroids. The morphological changes of the cells treated with C. cancellatus stigmas extract were indicative of apoptosis. Zymography analysis revealed a similar trend of matrix metallopeptidase-2 (MMP-2) and matrix metallopeptidase-9 (MMP-9) activity in the treated cells with C. cancellatus extract in comparison with doxorubicin treatment as a positive control. The findings of this research indicate that the ethanolic extract of C. cancellatus stigmas was a good source of bioactive metabolites with anticancer activity.

Metabolomics-Guided Comparison of Pollen and Microalgae-Based Artificial Diets in Honey Bees.

Managed honey bee colonies used for crop pollination are fed artificial diets to offset nutritional deficiencies related to land-use intensification and climate change. In this study, we formulated novel microalgae diets using Chlorella vulgaris and Arthrospira platensis (spirulina) biomass and fed them to young adult honey bee workers. Diet-induced changes in bee metabolite profiles were studied relative to a natural pollen diet using liquid chromatography-mass spectrometry (LC-MS) and gas chromatography-mass spectrometry (GC-MS) metabolomics. Untargeted analyses of pollen- and microalgae-fed bees revealed significant overlap, with 248 shared features determined by LC-MS and 87 shared features determined by GC-MS. Further metabolomic commonalities were evident upon subtraction of unique diet features. Twenty-five identified metabolites were influenced by diet, which included complex lipids, essential fatty acids, vitamins, and phytochemicals. The metabolomics results are useful to understand mechanisms underlying favorable growth performance as well as increased antioxidant and heat shock protein gene expression in bees fed the microalgae diets. We conclude that the tested microalgae have potential as sustainable feed additives and as a source of bee health-modulating natural products. Metabolomics-guided diet development could eventually help tailor feed interventions to achieve precision nutrition in honey bees and other livestock animals.

Optimization of Imputation Strategies for High-Resolution Gas Chromatography-Mass Spectrometry (HR GC-MS) Metabolomics Data.

Gas chromatography–coupled mass spectrometry (GC–MS) has been used in biomedical research to analyze volatile, non-polar, and polar metabolites in a wide array of sample types. Despite advances in technology, missing values are still common in metabolomics datasets and must be properly handled. We evaluated the performance of ten commonly used missing value imputation methods with metabolites analyzed on an HR GC–MS instrument. By introducing missing values into the complete (i.e., data without any missing values) National Institute of Standards and Technology (NIST) plasma dataset, we demonstrate that random forest (RF), glmnet ridge regression (GRR), and Bayesian principal component analysis (BPCA) shared the lowest root mean squared error (RMSE) in technical replicate data. Further examination of these three methods in data from baboon plasma and liver samples demonstrated they all maintained high accuracy. Overall, our analysis suggests that any of the three imputation methods can be applied effectively to untargeted metabolomics datasets with high accuracy. However, it is important to note that imputation will alter the correlation structure of the dataset and bias downstream regression coefficients and p-values.

Altered Metabolism of the Microbiota–Gut–Brain Axis Is Linked With Comorbid Anxiety in Fecal Recipient Mice of Myasthenia Gravis

Myasthenia gravis (MG) comorbid anxiety seriously affects the progress of MG. However, the exact relationship remains poorly understood. Recently, our preliminary study has revealed that intestinal microbe disturbance is closely related to MG. Therefore, further exploration of whether the microbiome is involved in MG comorbid anxiety is warranted. In this study, gas chromatography-mass spectrometry metabolomics analysis was used to characterize the metabotype of feces, serum, and three brain regions involved in emotion (i.e., the prefrontal cortex, hippocampus, and striatum), which were obtained from mice that were colonized with fecal microbiota from patients with MG (MMb), healthy individuals (HMb), or co-colonization of both patients and healthy individuals (CMb). Functional enrichment analysis was used to explore the correlation between the “microbiota–gut–brain” (MGB) axis and anxiety-like behavior. The behavioral test showed that female MMb exhibited anxiety-like behavior, which could be reversed by co-colonization. Moreover, metabolic characterization analysis of the MGB axis showed that the metabotype of gut-brain communication was significantly different between MMb and HMb, and 146 differential metabolites were jointly identified. Among these, 44 metabolites in feces; 12 metabolites in serum; 7 metabolites in hippocampus; 2 metabolites in prefrontal cortex; and 6 metabolites in striatum were reversed by co-colonization. Furthermore, the reversed gut microbiota mainly belonged to bacteroides and firmicutes, which were highly correlated with the reversed metabolites within the MGB axis. Among three emotional brain regions, hippocampus was more affected. Therefore, disturbances in gut microbiota may be involved in the progress of anxiety-like behavior in MG due to the MGB axis.

From non-targeted to targeted GC\u2013MS metabolomics strategy for identification of TCM preparations containing natural and artificial musk

BackgroundMoschus is a rare and precious natural medicine. Due to the properties of resources scarcity and expensive price of natural musk, artificial musk has been developed as substitute materials in some prescriptions. Rapid and accurate identification of natural or artificial musk in complex traditional Chinese medicine (TCM) preparations is also a challenge.MethodA strategy from non-targeted to targeted gas chromatography-mass spectrometry (GC–MS) metabolomics was developed for discrimination of natural and artificial musk. Firstly, GC–MS-based non-targeted analysis combined with chemometrics was used to find the potential chemical markers to distinguish natural musk and artificial musk. Subsequently, targeted metabolomics was used to analyze musk in preparations with multiple reaction monitoring (MRM) mode by use gas chromatography coupled with triple quadrupole mass spectrometry (GC-QQQ MS).ResultsTwo chemical markers named prasterone and androsterone have been selected and could be detected in all Compound Pien Tze Huang preparations (CPZHs) containing artificial musk, while the CPZHs containing natural musk did not detect two markers with S/N (signal to noise ratio) less than 3.ConclusionOur work provides an applicable approach to select the practical chemical markers for the assessment of musk in preparations to realize the traceability of musk in TCM and improve the quality control of musk-containing preparations.

Metabolomics and chemometrics of seven aromatic plants: Carob, eucalyptus, laurel, mint, myrtle, rosemary and strawberry tree.

Arbutus unedo L. (strawberry tree), Ceratonia siliqua L. (carob), Eucalyptus camaldulensis Dehnh. (eucalyptus), Laurus nobilis L. (laurel), Mentha aquatica L. (water mint), Myrtus communis L. (common myrtle), and Rosmarinus officinalis L. (rosemary) are aromatic plants from the Mediterranean region whose parts and preparations are used for their nutritional properties and health benefits. To evaluate and compare the metabolites profile, total phenol content (TPC), and antioxidant activity of plant leaves for their future use. Gas chromatography-mass spectrometry (GC-MS) was used for metabolomics. Data comparison was performed by chemometrics. Polar and apolar extracts were analysed using untargeted GC-MS metabolomics followed by chemometrics (principal component analysis, heatmap correlation and dendrogram) to identify, quantify and compare the major organic compounds in the plants. Additionally, nuclear magnetic resonance (NMR) spectroscopy was used for the laurel polar extract to identify d-gluco-l-glycero-3-octulose whose presence was unclear from the GC-MS data. TPC and antioxidant assays were performed using classical methods (Folin-Ciocalteu, 2,2'-azinobis-(3-ethylbenzothiazoline-6-sulphonic acid) diammonium salt (ABTS), 2,2-diphenyl-1-picrylhydrazyl (DPPH)) and correlated to the phytochemical profiles. Forty-three metabolites were identified including amino acids, organic acids, carbohydrates, phenols, polyols, fatty acids, and alkanes. Eight metabolites (d-fructose, d-glucose, d-mannose, gallic acid, quinic acid, myo-inositol, palmitic and stearic acids) were in common between all species. d-Gluco-l-glycero-3-octulose (37.29 ± 1.19%), d-pinitol (31.33 ± 5.12%), and arbutin (1.30 ± 0.44%,) were characteristic compounds of laurel, carob, and strawberry tree, respectively. Carob showed the highest values of TPC and antioxidant activity. GC-MS metabolomics and chemometrics analyses are fast and useful methods to determine and compare the metabolomics profiling of aromatic plants of food and industrial interest.

Alpha-Ketoglutarate Promotes Goblet Cell Differentiation and Alters Urea Cycle Metabolites in DSS-Induced Colitis Mice

The metabolite, alpha-ketoglutarate (aKG), shows promise as an approach for ameliorating colitis, but much remains unknown about the full extent of its effects on the metabolome and mucosal barrier. To further elucidate this matter, C57BL/6 male mice received drinking water with or without 1% aKG for three weeks, then were subjected to 2.5% dextran sulfate sodium (DSS) induction for 7 days followed by 7 days of recovery. Cecal content and intestinal tissue samples were analyzed for changes in metabolite profile and signaling pathways. Gas chromatography-mass spectrometry (GC-MS) metabolomics revealed a separation between the metabolome of mice treated with or without aKG; putrescine and glycine were significantly increased; and ornithine and amide products, oleamide and urea were significantly decreased. Based on a pathway analysis, aKG treatment induced metabolite changes and enriched glutathione metabolism and the urea cycle. Additionally, signaling pathways committing epithelial cells to the secretory lineage were elevated in aKG-treated mice. Consistently, aKG supplementation increased goblet cells staining, mRNA expression of mucin 2, and, trefoil factor 3 and Krüppel-like factor 4, markers of goblet cell differentiation. These data suggest the ameliorating the effects of aKG against chemically induced colitis involves a reduction in harmful metabolites and the promotion of goblet cell differentiation, resulting in a more-fortified mucus layer.

Antibacterial Activity of the Essential Oil From Litsea cubeba Against Cutibacterium acnes and the Investigations of Its Potential Mechanism by Gas Chromatography-Mass Spectrometry Metabolomics.

Cutibacterium acnes (C. acnes) is an anaerobic Gram-positive bacterium generally considered as a human skin commensal, but is also involved in different infections, such as acne and surgical infections. Although there are a variety of treatments, the side effects and the problem of bacterial drug resistance still limit their clinical usage. In this study, we found that essential oil (EO) distilled from fresh mature Litsea cubeba possessed promising antibacterial activity against C. acnes. In order to elucidate its potential mechanism, bacteriostatic activity test, Live/Dead kit assay, scanning electron microscope (SEM), transmission electron microscope (TEM), and metabolomics were employed. In addition, the content of adenosine triphosphate (ATP) in bacterium and the activities of key enzymes involved in critical metabolic pathways were detected using a variety of biochemical assays. The results showed that EO exhibited significant antibacterial activity against C. acnes at a minimum inhibitory concentration (MIC) of 400 μg/mL and a minimum bactericidal concentration (MBC) of 800 μg/mL, and EO could destroy C. acnes morphology and inhibit its growth. Moreover, results from our study showed that EO had a significant effect on the C. acnes normal metabolism. In total, 86 metabolites were altered, and 34 metabolic pathways related to the carbohydrate metabolism, energy metabolism, amino acid metabolism, as well as cell wall and cell membrane synthesis were perturbed after EO administration. The synthesis of ATP in bacterial cells was also severely inhibited, and the activities of key enzymes of the glycolysis and Wood-Werkman cycle were significantly affected (Pyruvate Carboxylase, Malate Dehydrogenase and Pyruvate kinase activities were decreased, and Hexokinase was increased). Taken together, these results illustrated that the bacteriostatic effect of EO against C. acnes by breaking the bacterial cell morphology and perturbing cell metabolism, including inhibition of key enzyme activity and ATP synthesis. The results from our study may shed new light on the discovery of novel drugs with more robust efficacy.

Multi Platforms Strategies and Metabolomics Approaches for the Investigation of Comprehensive Metabolite Profile in Dogs with Babesia canis Infection.

Canine babesiosis is an important tick-borne disease worldwide, caused by parasites of the Babesia genus. Although the disease process primarily affects erythrocytes, it may also have multisystemic consequences. The goal of this study was to explore and characterize the serum metabolome, by identifying potential metabolites and metabolic pathways in dogs naturally infected with Babesia canis using liquid and gas chromatography coupled to mass spectrometry. The study included 12 dogs naturally infected with B. canis and 12 healthy dogs. By combining three different analytical platforms using untargeted and targeted approaches, 295 metabolites were detected. The untargeted ultra-high performance liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS) metabolomics approach identified 64 metabolites, the targeted UHPLC-MS/MS metabolomics approach identified 205 metabolites, and the GC-MS metabolomics approach identified 26 metabolites. Biological functions of differentially abundant metabolites indicate the involvement of various pathways in canine babesiosis including the following: glutathione metabolism; alanine, aspartate, and glutamate metabolism; glyoxylate and dicarboxylate metabolism; cysteine and methionine metabolism; and phenylalanine, tyrosine, and tryptophan biosynthesis. This study confirmed that host–pathogen interactions could be studied by metabolomics to assess chemical changes in the host, such that the differences in serum metabolome between dogs with B. canis infection and healthy dogs can be detected with liquid chromatography-mass spectrometry (LC-MS) and gas chromatography-mass spectrometry (GC-MS) methods. Our study provides novel insight into pathophysiological mechanisms of B. canis infection.