Untargeted LC-MS Metabolomics Research Articles

Application of predictive modeling tools for the identification of Ocimum spp. herbal products.

Species identification of botanical products is a crucial aspect of research and regulatory compliance; however, botanical classification can be difficult, especially for morphologically similar species with overlapping genetic and metabolomic markers, like those in the genus Ocimum. Untargeted LC-MS metabolomics coupled with multivariate predictive modeling provides a potential avenue for improving herbal identity investigations, but the current dearth of reference materials for many botanicals limits the applicability of these approaches. This study investigated the potential of using greenhouse-grown authentic Ocimum to build predictive models for classifying commercially available Ocimum products. We found that three species, O. tenuiflorum, O. gratissimum, and O. basilicum, were chemically distinct based on their untargeted UPLC-MS/MS profiles when grown in controlled settings; combined with an orthogonal high-performance thin-layer chromatography (HPTLC) approach, O. tenuiflorum materials revealed two distinct chemotypes which could confound analysis. Three predictive models (partial least squares, LASSO regression, and random forest) were employed to extrapolate these findings to commercially available products; however, the controlled materials were significantly different from external samples, and all three chemometric models were unreliable in classifying external materials. LASSO was the most successful when classifying new greenhouse samples. Overall, this study highlights how growing and processing conditions can influence the complexity of botanical metabolome profiles; further studies are needed to characterize the factors driving herbal products' phytochemistry in conjunction with chemometric predictive modeling.

Urinary Biomarkers of Strawberry and Blueberry Intake.

Introduction There is increasing interest in food biomarkers to address the shortcomings of self-reported dietary assessments. Berries are regarded as important fruits worldwide; however, there are no well-validated biomarkers of berry intake. Thus, the objective of this study is to identify urinary biomarkers of berry intake. Methods For the discovery study, participants consumed 192 g strawberries with 150 g blueberries, and urine samples were collected at 2, 4, 6, and 24 h post-consumption. A dose-response study was performed, whereby participants consumed three portions (78 g, 278 g, and 428 g) of mixed strawberries and blueberries. The urine samples were profiled by an untargeted LC-MS metabolomics approach in the positive and negative modes. Results Statistical analysis of the data revealed that 39 features in the negative mode and 15 in the positive mode significantly increased between fasting and 4 h following mixed berry intake. Following the analysis of the dose-response data, 21 biomarkers showed overall significance across the portions of berry intake. Identification of the biomarkers was performed using fragmentation matches in the METLIN, HMDB, and MoNA databases and in published papers, confirmed where possible with authentic standards. Conclusions The ability of the panel of biomarkers to assess intake was examined, and the predictability was good, laying the foundations for the development of biomarker panels.

Ningxiang Pig-Derived Microbiota Affects the Growth Performance, Gut Microbiota, and Serum Metabolome of Nursery Pigs.

The gut microbiota is crucial for maintaining the host's intestinal homeostasis and metabolism. This study investigated the effects of fecal microbiota transplantation (FMT) from Ningxiang pigs on the growth performance, fecal microbiota, and serum metabolites of the same-old DLY pigs. The results indicated that the average daily gain of FMT pigs was significantly greater than that of the control (CON) group. Compared to the CON group, the FMT group significantly improved the apparent digestibility of crude fiber, crude ash, gross energy, and calcium of the pigs. The analysis of serum antioxidant status revealed that the activities of total superoxide dismutase and catalase in the serum of pigs in the FMT group were significantly elevated, whereas the level of malondialdehyde was significantly reduced. Furthermore, 16S rRNA sequencing analysis revealed that the Ningxiang pig-derived microbiota altered the fecal microbiota structure and modulated the diversity of the gut microbiota in the DLY pigs. Untargeted LC-MS metabolomics demonstrated that pigs in the FMT group exhibited distinct metabolomic profiles compared to those in the CON group. Significant changes were observed in key metabolites involved in amino acid, lipid, and carbohydrate metabolism. Additionally, a correlation analysis between serum differential metabolites and the gut microbiota revealed that the relative abundance of Lachnospiraceae_NK4A136_group and Corynebacterium was highly correlated with lipid compounds. In conclusion, Ningxiang pig-derived microbiota can alleviate oxidative stress and enhance growth performance in DLY pigs by modulating their gut microbiota and metabolic features.

The salivary metabolome of children and parental caregivers in a large-scale family environment study

Human metabolism is complex, and is impacted by genetics, cohabitation, diet, health, and environmental inputs. As such, we applied untargeted LC-MS metabolomics to 1425 saliva samples from a diverse group of elementary school-aged children and their caregivers collected during the Family Life Project, of which 1344 were paired into caregiver/child dyads. We compared metabolomes within and between homes, performed population-wide “metabotype” analyses, and measured associations between metabolites and salivary biomeasures of inflammation, antioxidant potential, environmental tobacco smoke (ETS) exposure, metabolic regulation, and heavy metals. Children and caregivers had similar salivary metabolomes, and dyad explained most metabolomic variation. Our data clustered into two groups, indicating that “metabotypes” exist across large populations. Lastly, several metabolites—putative oxidative damage-associated or pathological markers—were correlated with the above-mentioned salivary biomeasures and heavy metals. Implications of the family environment’s effects on metabolomic variation at population, dyadic, and individual levels for human health are discussed.

Wheat domestication alters root metabolic functions to drive the assembly of endophytic bacteria.

The domestication process progressively differentiated wild relatives from modern cultivars, thus impacting plant-associated microorganisms. Endophytic bacterial communities play vital roles in plant growth, development, and health, which contribute to the crop's sustainable development. However, how plant domestication impacts endophytic bacterial communities and relevant root exudates in wheat remains unclear. First, we have observed that the domestication process increased the root endophytic microbial community diversity of wheat while decreasing functional diversity. Second, domestication decreased the endophytic bacterial co-occurrence network stability, and it did significantly alter the abundances of core microorganisms or potential probiotics. Third, untargeted LC-MS metabolomics revealed that domestication significantly altered the metabolite profiles, and the abundances of various root exudates released were significantly correlated with keystone taxa including the Chryseobacterium, Massilia, and Lechevalieria. Moreover, we found that root exudates, especially L-tyrosine promote the growth of plant-beneficial bacteria, such as Chryseobacterium. Additionally, with L-tyrosine and Chryseobacterium colonized in the roots, the growth of wild wheat's roots was significantly promoted, while no notable effect could be found in the domesticated cultivars. Overall, this study suggested that wild wheat as a key germplasm material, and its native endophytic microbes may serve as a resource for engineering crop microbiomes to improve the morphological and physiological traits of crops in widely distributed poor soils.

New insights into the antibiofilm activity and mechanism of Mannosylerythritol Lipid-A against Listeria monocytogenes EGD-e

Listeria monocytogenes is one of the leading causative agents of foodborne disease outbreaks worldwide. Herein, the antibiofilm effect and mechanism of Mannosylerythritol Lipid-A against L. monocytogenes EGD-e is reported for the first time. MEL-A effectively attenuated biofilm formation while reducing the viability and motility of bacteria within the biofilm in the early stage, and influenced bacterial adhesion by affecting the secretion of extracellular polysaccharides and eDNA. RT-qPCR revealed that MEL-A significantly suppressed the expression of genes involved in flagellar movement and virulence. Untargeted LC-MS metabolomics indicated that MEL-A affected the fluidity and permeability of cell membranes by significantly upregulating unsaturated fatty acids, lipids and glycoside metabolites, and affected protein biosynthesis, nucleotide metabolism and DNA synthesis and repair by significantly downregulating amino acid metabolism and nucleic acid metabolism. These pathways may constitute the key targets of biofilm formation inhibition by MEL-A. Furthermore, MEL-A showed good removal effects on mature biofilms under different temperatures, different materials and milk. Our data indicated that MEL-A could be used as a novel antibiofilm agent to improve food safety. Our study provides new insights into the possible inhibitory mechanism of MEL-A and the response of L. monocytogenes EGD-e to MEL-A.

Untargeted LC–MS metabolomics reveals the metabolic responses in the Eriocheir sinensis gills exposed to salinity and alkalinity stress

In recent years, saline–alkaline aquaculture development has become an important measure for China to expand its fishery development space to ensure food safety. Previous studies have verified that salinity and alkalinity positively influence the quality of Chinese mitten crabs (Eriocheir sinensis). However, the regulatory mechanism of E. sinensis endures saline–alkaline stress which remains obscure. This study investigated the metabolic changes in puberty-molting E. sinensis gills exposed to freshwater (FW), sodium chloride salinity of 5 ppt (SW), and carbonate alkalinity 10.00 mmol/L (AW) for 50 days using untargeted liquid chromatography-mass spectrometry metabolomics (LC–MS). A total of 5802 (positive-ion mode) and 6520 (negative-ion mode) peaks were extracted by LC–MS, respectively. A total of 188 (50 upregulated and 138 downregulated), 141 (94 upregulated and 47 downregulated), and 130 (87 upregulated and 43 downregulated) significantly regulated metabolites (SRMs) were observed in the FW–SW, FW–AW, and SW–AW treatments, respectively, wherein 42 generic SRMs were also found by Venn diagram analysis. Seven of the top 10 SRMs with the highest (variable importance in projection) VIP values were similarly identified in FW–SW and SW–AW. Integrated analysis of key metabolic pathways revealed glycerophospholipid, choline in cancer, phenylalanine, and butanoate metabolism. Overall, significant differences were observed in the metabolites and key metabolic pathways of E. sinensis gill exposed to salinity and alkalinity stress. These results will be helpful in understanding the environmental adaptability of aquatic crustaceans to saline–alkaline water.

Comparative metabolomics reveals serum metabolites changes in goats during different developmental stages

Goats can provide meat, milk and skins for humans and are livestock with high economic benefits. However, despite their economic significance, the comprehensive analysis of goats’ serum metabolic profile and its intricate alterations throughout their developmental journey remains conspicuously absent. To investigate the stage-specificity and dynamic change characteristics of metabolites during the growth and development of goats, this study compared the alterations in serum hormone levels and serum biochemical markers across different developmental stages of female goats (1, 60, 120 and 180 days old; n = 5). Additionally, a serum untargeted LC–MS metabolomics analysis was conducted. A total of 504 DAMs were identified with age. The results indicated that PE, PC, Lyso-PE, Lyso-PC and FAFHA may play important roles in lipid metabolism in goats after birth. Weighted gene co-expression network analysis (WGCNA) identified two metabolite modules (Turquoise and Yellow) and key metabolites within these modules that were significantly associated with phenotypic features. l-carnitine may be a metabolite related to muscle development in goats. The findings of this study demonstrate notable variations in serum metabolites across distinct developmental phases in goats. Lipids and organic acids play important roles in different developmental stages of goats.

Development of an Untargeted LC-MS Metabolomics Method with Postcolumn Infusion for Matrix Effect Monitoring in Plasma and Feces.

Untargeted metabolomics based on reverse phase LC-MS (RPLC-MS) plays a crucial role in biomarker discovery across physiological and disease states. Standardizing the development process of untargeted methods requires paying attention to critical factors that are under discussed or easily overlooked, such as injection parameters, performance assessment, and matrix effect evaluation. In this study, we developed an untargeted metabolomics method for plasma and fecal samples with the optimization and evaluation of these factors. Our results showed that optimizing the reconstitution solvent and sample injection amount was critical for achieving the balance between metabolites coverage and signal linearity. Method validation with representative stable isotopically labeled standards (SILs) provided insights into the analytical performance evaluation of our method. To tackle the issue of the matrix effect, we implemented a postcolumn infusion (PCI) approach to monitor the overall absolute matrix effect (AME) and relative matrix effect (RME). The monitoring revealed distinct AME and RME profiles in plasma and feces. Comparing RME data obtained for SILs through postextraction spiking with those monitored using PCI compounds demonstrated the comparability of these two methods for RME assessment. Therefore, we applied the PCI approach to predict the RME of 305 target compounds covered in our in-house library and found that targets detected in the negative polarity were more vulnerable to the RME, regardless of the sample matrix. Given the value of this PCI approach in identifying the strengths and weaknesses of our method in terms of the matrix effect, we recommend implementing a PCI approach during method development and applying it routinely in untargeted metabolomics.

MetabCombiner 2.0: Disparate Multi-Dataset Feature Alignment for LC-MS Metabolomics.

Liquid chromatography-high-resolution mass spectrometry (LC-HRMS), as applied to untargeted metabolomics, enables the simultaneous detection of thousands of small molecules, generating complex datasets. Alignment is a crucial step in data processing pipelines, whereby LC-MS features derived from common ions are assembled into a unified matrix amenable to further analysis. Variability in the analytical factors that influence liquid chromatography separations complicates data alignment. This is prominent when aligning data acquired in different laboratories, generated using non-identical instruments, or between batches from large-scale studies. Previously, we developed metabCombiner for aligning disparately acquired LC-MS metabolomics datasets. Here, we report significant upgrades to metabCombiner that enable the stepwise alignment of multiple untargeted LC-MS metabolomics datasets, facilitating inter-laboratory reproducibility studies. To accomplish this, a "primary" feature list is used as a template for matching compounds in "target" feature lists. We demonstrate this workflow by aligning four lipidomics datasets from core laboratories generated using each institution's in-house LC-MS instrumentation and methods. We also introduce batchCombine, an application of the metabCombiner framework for aligning experiments composed of multiple batches. metabCombiner is available as an R package on Github and Bioconductor, along with a new online version implemented as an R Shiny App.

Effect of statin treatment on metabolites, lipids and prostanoids in patients with Statin Associated Muscle Symptoms (SAMS).

Between 5-10% of patients discontinue statin therapy due to statin-associated adverse reactions, primarily statin associated muscle symptoms (SAMS). The absence of a clear clinical phenotype or of biomarkers poses a challenge for diagnosis and management of SAMS. Similarly, our incomplete understanding of the pathogenesis of SAMS hinders the identification of treatments for SAMS. Metabolomics, the profiling of metabolites in biofluids, cells and tissues is an important tool for biomarker discovery and provides important insight into the origins of symptomatology. In order to better understand the pathophysiology of this common disorder and to identify biomarkers, we undertook comprehensive metabolomic and lipidomic profiling of plasma samples from patients with SAMS who were undergoing statin rechallenge as part of their clinical care. We report our findings in 67 patients, 28 with SAMS (cases) and 39 statin-tolerant controls. SAMS patients were studied during statin rechallenge and statin tolerant controls were studied while on statin. Plasma samples were analyzed using untargeted LC-MS metabolomics and lipidomics to detect differences between cases and controls. Differences in lipid species in plasma were observed between cases and controls. These included higher levels of linoleic acid containing phospholipids and lower ether lipids and sphingolipids. Reduced levels of acylcarnitines and altered amino acid profile (tryptophan, tyrosine, proline, arginine, and taurine) were observed in cases relative to controls. Pathway analysis identified significant increase of urea cycle metabolites and arginine and proline metabolites among cases along with downregulation of pathways mediating oxidation of branched chain fatty acids, carnitine synthesis, and transfer of acetyl groups into mitochondria. The plasma metabolome of patients with SAMS exhibited reduced content of long chain fatty acids and increased levels of linoleic acid (18:2) in phospholipids, altered energy production pathways (β-oxidation, citric acid cycle and urea cycles) as well as reduced levels of carnitine, an essential mediator of mitochondrial energy production. Our findings support the hypothesis that alterations in pro-inflammatory lipids (arachidonic acid pathway) and impaired mitochondrial energy metabolism underlie the muscle symptoms of patients with statin associated muscle symptoms (SAMS).

Comparing univariate filtration preceding and succeeding PLS-DA analysis on the differential variables/metabolites identified from untargeted LC-MS metabolomics data

BackgroundPLS-DA of high-dimensional metabolomics data is frequently employed to capture the most pertinent features to sample classification. But the presence of numerous insignificant input features could distort the PLS-DA model, blow up and scramble the selected differential features. Usually, univariate filtration is subsequently complemented to refine the selected features, but often giving unstable results. Whereas by precluding insignificant features through univariate data prefiltration assessed by FDR adjusted p-value, PLS-DA can generate more stable and reliable differential features. We explored and compared these two data analysis procedures to gain insights into the underlying mechanisms responsible for the disparate results. ResultsThe effect of univariate data filtration preceding and succeeding PLS-DA analysis on the identified discriminative features/metabolites was investigated using LC-MS data acquired on the samples of human serum and C. elegans extracts, with and without metabolite standards spiked to simulate the treated and control groups of biological samples. It was shown that the univariate data prefiltration before PLS-DA usually gave less but more stable and likely more reliable and meaningful differential features, while PLS-DA applied directly to the original data could be affected by the presence of insignificant features and orthogonal noise. Large number of insignificant variables and orthogonal noise could distort the generated PLS-DA model and affect the p(corr) value, and artificially inflate the calculated VIP values of relevant features due to the increased total number of input features for model construction, thus leading to more false positives selected by the conventional VIP threshold of 1.0. Significance and noveltyUnivariate data filtration preceding PLS-DA was important for the identification of reliable differential features if using a conventional threshold of VIP of 1.0. Presence of insignificant features could distort the PLS-DA model and inflate VIP values. Appropriate VIP threshold is associated with the numbers of input features and the model components. For PLS-DA without univariate prefiltration, threshold of VIP larger than 1.0 is recommended for the selection of discriminative features to reduce the false positives.

Sulphides from garlic essential oil dose-dependently change the distribution of glycerophospholipids and induce N6-tuberculosinyladenosine formation in mycobacterial cells

The antimicrobial properties of garlic are widely known, and numerous studies confirmed its ability to inhibit the growth of Mycobacterium tuberculosis. In this work, we explored the molecular mechanism of action of sulphides present in garlic essential oil against mycobacteria. The targeted transcriptomics and untargeted LC–MS metabolomics were applied to study dose- and time-dependent metabolic changes in bacterial cells under the influence of stressing agent. Expression profiles of genes coding stress-responsive sigma factors regulatory network and metabolic observations proved that sulphides from garlic essential oil are an efficient and specific agent affecting glycerophospholipids levels and their distribution within the cell envelope. Additionally, sulphides induced the Dimroth rearrangement of 1-Tuberculosinyladenosine to N6-tuberculosinyladenosine in mycobacterial cells as a possible neutralization mechanism protecting the cell from a basic nucleophilic environment. Sulphides affected cell envelope lipids and formation of N6-tuberculosinyladenosine in M. tuberculosis.

Morphological, physiological and metabolomic analysis to unravel the adaptive relationship between root growth of ephemeral plants and different soil habitats

To gain insights into the adaptive characteristics of ephemeral plants and enrich their potential for resource exploitation, the adaptive changes in two highly dominant species (Malcolmia scorpioides and Isatis violascens) to soil habitats (aeolian soil, AS; grey desert soil, GS) were investigated from the aspects of root morphology, physiology, and metabolism in this study. The results revealed that changes in root morphology and enzyme activity were affected by soil habitat. Total root length (TRL), root volume (RV) and root surface area (RSA) were higher in GS than in AS. The levels of proline (Pro), glutathione (GSH), soluble sugar (SS), and lysine (Lys) were higher in GS than in AS. Untargeted LC–MS metabolomics indicates that root metabolites of both species differed among the two soil habitats. Root responses to different soil habitats mainly affected some metabolic pathways. A total of 780 metabolites were identified, common differential metabolites (DMs) in both species included amino acids, fatty acids, organic acids, carbohydrates, benzene and derivatives, and flavonoids, which were mainly involved in carbohydrate metabolism, amino acid metabolism, flavonoid biosynthesis and fatty acid metabolism, and their abundance varied among different habitats and species. Some key DMs were significantly related to root morphology and enzyme activity, and indole, malonate, quercetin, uridine, tetrahydroharmine, and gluconolactone were important metabolites associated with root growth. Therefore, the response changes in root growth and metabolite of ephemeral plants in response to soil habitats reflect their ecological adaptation, and lay a foundation for the exploitation of plant resources in various habitats.

In-depth mass spectrometry analysis of rhGH administration altered energy metabolism and steroidogenesis

Growth hormone, as a proteohormone, is primarily known of its dramatic effect on longitudinal growth. Recombinant DNA technology has provided a safe, abundant and comparatively cheap supply of human GH for growth hormone-deficient individuals. However, many healthy subjects, especially athletics, administrate GH for enhanced athletic performance or strength. A better and more comprehensive understanding of rhGH effect in healthy individuals is urgent and essential. In this study, we recruited 14 healthy young male and injected rhGH once. Untargeted LC-MS metabolomics profiling of serum and urine was performed before and after the rhGH injection. The GH-induced dysregulation of energy related pathways, such as amino acid metabolism, nucleotide metabolism, glycolysis and TCA cycle, was revealed. Moreover, individuals supplemented with micro-doses of rhGH exhibited significantly changed urinary steroidal profiles, suggesting a role of rhGH in both energy metabolism and steroidogenesis. We expect that our results will be helpful to provide new evidence on the effects of rhGH injection and provide potential biomarkers for rhGH administration.

Identification of a potential prognostic plasma biomarker of acute ischemic stroke via untargeted LC-MS metabolomics.

Strokeisthesuddendeathofbraincellsinalocalized areaduetoan inadequatebloodflow or blood vessel rupture, and it seriously affects the quality of life. The metabolite biomarkers are needed for predicting the functional outcome of acute ischemic stroke (AIS). To identify biomarkers for AIS, untargeted LC/MS metabolomics was performed on plasma samples from subjects with favorable prognosis (mRS ≤ 2) and unfavorable prognosis (mRS>2). The identified markers were further absolutely quantified by a targeted MRM approach. There were 10 upregulated and 26 downregulated markers. Among these candidates, one was successfully identified as glycocholic acid and then absolutely quantified in plasma samples. Glycocholic acid could discriminate between subjects with favorable and unfavorable prognosis with an area under the curve (AUC) of 0.68 and odds ratio of 5.88. Glycocholic acid was identified as a potential plasma metabolite marker of non-progressive outcomes after ischemic stroke and could serve as predictive prognostic markers for clinical acute stroke outcomes.

Sensitisation of cancer cells to radiotherapy by serine and glycine starvation

BackgroundCellular metabolism is an integral component of cellular adaptation to stress, playing a pivotal role in the resistance of cancer cells to various treatment modalities, including radiotherapy. In response to radiotherapy, cancer cells engage antioxidant and DNA repair mechanisms which mitigate and remove DNA damage, facilitating cancer cell survival. Given the reliance of these resistance mechanisms on amino acid metabolism, we hypothesised that controlling the exogenous availability of the non-essential amino acids serine and glycine would radiosensitise cancer cells.MethodsWe exposed colorectal, breast and pancreatic cancer cell lines/organoids to radiation in vitro and in vivo in the presence and absence of exogenous serine and glycine. We performed phenotypic assays for DNA damage, cell cycle, ROS levels and cell death, combined with a high-resolution untargeted LCMS metabolomics and RNA-Seq.ResultsSerine and glycine restriction sensitised a range of cancer cell lines, patient-derived organoids and syngeneic mouse tumour models to radiotherapy. Comprehensive metabolomic and transcriptomic analysis of central carbon metabolism revealed that amino acid restriction impacted not only antioxidant response and nucleotide synthesis but had a marked inhibitory effect on the TCA cycle.ConclusionDietary restriction of serine and glycine is a viable radio-sensitisation strategy in cancer.

Enhancement of denitrifying anaerobic methane oxidation via applied electric potential.

In this study, single-chamber three-electrode electrochemical sequencing batch reactor (ESBR) was set up to investigate the impact of applying potential on denitrifying anaerobic methane oxidation (DAMO) process. When the applied potential was +0.8V, the conversion rate of nitrite to nitrogen was superior to those of other potentials. With the optimal potential of +0.8V for 60 days, the nitrite removal rate of ESBR could reach 3.34±0.28mgN/L/d, which was 4.5 times more than that of the non-current control (0.74±0.16mgN/L/d). The DAMO functional bacteria Candidatus Methylomirabilis exhibited noticeable enrichment under applying potential, and its functional gene of pmoA was significantly expressed. Through untargeted LC-MS metabolomics analysis, applied potential was shown to affect the regulation of prior metabolites including spermidine, spermine and glycerophosphocholine that were related to the metabolic pathways of glycerophospholipid metabolism and arginine and proline metabolism, which had positive effects on DAMO process. These results show that applying electric potential could be a useful strategy in DAMO process used for methane and nitrogen removal.

An LC–MS-based metabolomic approach provides insights into the metabolite profiles of Ginkgo biloba L. at different developmental stages and in various organs

Ginkgo (Ginkgo biloba L.) is a tree valued for the high medicinal and nutritional value of its leaves and seeds. However, the metabolite profiles of ginkgo leaves and seeds and their changes during development have not been comprehensively analyzed, which hinders improvements in the utilization of ginkgo. A comprehensive and systematic untargeted LC–MS metabolomics analysis of the metabolites in ginkgo leaves (male and female) and seeds at two developmental stages identified 8146 known metabolites, which mainly included lipids and lipid-like molecules, phenylpropanoids and polyketides, organoheterocyclic compounds, organic acids and derivatives, organic oxygen compounds, and benzenoids. Some of the identified metabolites have known healthcare and food value, and some of the others are newly discovered metabolites with potential for new drug development. The small number of differential Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways between leaves of male and female gingko trees indicated that the developmental stage affected the metabolic pathways more significantly than sex. Among the flavonoid constituents of ginkgo, 653 flavonoid metabolites were identified, and these included some new flavonoid components, which confirmed that the developmental stage is a critical factor in secondary metabolite variations. This study illuminated the metabolites and medicinal and edible values of ginkgo leaves and seeds at different developmental stages and thus supports further effective utilization of ginkgo leaves and seeds.

Metabolomic Changes Upon Conjugated Linoleic Acid Supplementation and Predictions of Body Composition Responsiveness.

Conjugated linoleic acid (CLA) may optimize body composition, yet mechanisms underlining its benefits are not clear in humans. We aimed to reveal the CLA-induced changes in the plasma metabolome associated with body composition improvement and the predictive performance of baseline metabolome on intervention responsiveness. Plasma metabolome from overnight fasted samples at pre- and post-intervention of 65 participants in a 12-week randomized, placebo-controlled trial (3.2 g/day CLA vs 3.2 g/day sunflower oil) were analyzed using untargeted LC-MS metabolomics. Mixed linear model and machine learning were applied to assess differential metabolites between treatments, and to identify optimal panel (based on baseline conventional variables vs metabolites) predicting responders of CLA-derived body composition improvement (increased muscle variables or decreased adiposity variables) based on dual-energy x-ray absorptiometry. Compared with placebo, CLA altered 57 metabolites (P < 0.10) enriched in lipids/lipid-like molecules including glycerophospholipids (n = 7), fatty acyls (n = 6), and sphingolipids (n = 3). CLA-upregulated cholic acid (or downregulated aminopyrrolnitrin) was inversely correlated with changes in muscle and adiposity variables. Inter-individual variability in response to CLA-derived body composition change. The areas under the curves of optimal metabolite panels were higher than those of optimal conventional panels in predicting favorable response of waist circumference (0.93 [0.82-1.00] vs 0.64 [0.43-0.85]), visceral adiposity index (0.95 [0.88-1.00] vs 0.58 [0.35-0.80]), total fat mass (0.94 [0.86-1.00] vs 0.69 [0.51-0.88]) and appendicular fat mass (0.97 [0.92-1.00] vs 0.73 [0.55-0.91]) upon CLA supplementation (all FDR P < 0.05). Post-intervention metabolite alterations were identified, involving in lipid/energy metabolism, associated with body composition changes. Baseline metabolite profiling enhanced the prediction accuracy for responsiveness of CLA-induced body composition benefits.