Untargeted LC-MS Research Articles

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.

G-Aligner: a graph-based feature alignment method for untargeted LC–MS-based metabolomics

BackgroundLiquid chromatography–mass spectrometry is widely used in untargeted metabolomics for composition profiling. In multi-run analysis scenarios, features of each run are aligned into consensus features by feature alignment algorithms to observe the intensity variations across runs. However, most of the existing feature alignment methods focus more on accurate retention time correction, while underestimating the importance of feature matching. None of the existing methods can comprehensively consider feature correspondences among all runs and achieve optimal matching.ResultsTo comprehensively analyze feature correspondences among runs, we propose G-Aligner, a graph-based feature alignment method for untargeted LC–MS data. In the feature matching stage, G-Aligner treats features and potential correspondences as nodes and edges in a multipartite graph, considers the multi-run feature matching problem an unbalanced multidimensional assignment problem, and provides three combinatorial optimization algorithms to find optimal matching solutions. In comparison with the feature alignment methods in OpenMS, MZmine2 and XCMS on three public metabolomics benchmark datasets, G-Aligner achieved the best feature alignment performance on all the three datasets with up to 9.8% and 26.6% increase in accurately aligned features and analytes, and helped all comparison software obtain more accurate results on their self-extracted features by integrating G-Aligner to their analysis workflow. G-Aligner is open-source and freely available at https://github.com/CSi-Studio/G-Aligner under a permissive license. Benchmark datasets, manual annotation results, evaluation methods and results are available at https://doi.org/10.5281/zenodo.8313034ConclusionsIn this study, we proposed G-Aligner to improve feature matching accuracy for untargeted metabolomics LC–MS data. G-Aligner comprehensively considered potential feature correspondences between all runs, converting the feature matching problem as a multidimensional assignment problem (MAP). In evaluations on three public metabolomics benchmark datasets, G-Aligner achieved the highest alignment accuracy on manual annotated and popular software extracted features, proving the effectiveness and robustness of the algorithm.

Metabolomic profiling of Marek's disease virus infection in host cell based on untargeted LC-MS.

Marek's disease (MD) caused by Marek's disease virus (MDV), poses a serious threat to the poultry industry by inducing neurological disease and malignant lymphoma in infected chickens. However, the underlying mechanisms how MDV disrupts host cells and causes damage still remain elusive. Recently, the application of metabolomics has shown great potential for uncovering the complex mechanisms during virus-host interactions. In this study, chicken embryo fibroblasts (CEFs) infected with MDV were subjected to ultrahigh-performance liquid chromatography-quadrupole time-of-flight tandem mass spectrometry (UHPLC-QTOF-MS) and multivariate statistical analysis. The results showed that 261 metabolites were significantly altered upon MDV infection, with most changes occurring in amino acid metabolism, energy metabolism, nucleotide metabolism, and lipid metabolism. Notably, MDV infection induces an up-regulation of amino acids in host cells during the early stages of infection to provide the energy and intermediary metabolites necessary for efficient multiplication of its own replication. Taken together, these data not only hold promise in identifying the biochemical molecules utilized by MDV replication in host cells, but also provides a new insight into understanding MDV-host interactions.

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.

Treatment of Macrophages with Gram-Negative and -Positive Bacterial Secretomes Induce Distinct Metabolic Signatures

Infectious diseases represent major health and economic challenges globally. Emergence of multiple drug-resistant bacteria in the community and hospital has become a worldwide concern that requires novel approaches for rapid diagnosis and treatment. Metabolomics approach is a powerful tool providing important chemical information about the cellular phenotype of living systems, and the changes in their metabolic pathways in response to various perturbations. Metabolomics has become an important tool to study host-pathogen interactions and to discover potential novel therapeutic targets. In this study, untargeted LC-MS metabolic profiling was applied to differentiate between the impact of the secretome of the Gram-positive S. aureus SH1000 and Gram-negative P. aeruginosa PAO1 bacterial pathogens on THP-1 macrophages. The results showed that S. aureus and P. aeruginosa secretome affected alanine, aspartate and glutamate metabolism; sphingolipid metabolism; glycine and serine metabolism; GL metabolism; and tryptophan metabolism with different trends in THP-1 macrophages. However, the impact of both bacterial secretome on arginine and proline metabolism was similar. These data could contribute to a better understanding of pathogenesis and resistance of these bacteria and could pave the way for developing new therapeutics that selectively targeting Gram-positive or Gram-negative bacteria.

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.

Study of metal and organic contaminants transported by microplastics in the Lebanese coastal environment using ICP MS, GC-MS, and LC-MS

An untargeted study of multiclass contaminants associated with microplastics (MPs) in the East Mediterranean was carried out. Samples were collected in 2020–2021 from the shoreline at 14 different locations, along the Lebanese coast. Attenuated Total Reflectance (ATR) FTIR spectroscopy showed the predominant presence of polyethylene and polypropylene among plastic debris. The non-polar and polar organic compounds sorbed on the MPs were identified and quantified by GC - TOF MS and LC – electrospray MS/MS, respectively. Deconvolution of accurate GC–MS scan data allowed the identification of >130 organic pollutants, 64 of which could be confirmed by matching with the authentic standards, among which a number of previously unreported in targeted GC–MS(MS) methods. In addition to highly toxic, legacy chlorinated pollutants, high levels (average values from 0.8 to 4.0 μg g−1) of some musks, UV filters and UV absorbers were detected. Untargeted LC-MS demonstrated the persistence of several pesticides (i.e., chlorpyrifos) and pharmaceuticals, such as phenacetin and minoxidil, which were quantified. In addition, a study of metals associated with microplastics using ICP-MS confirmed the high potential of microplastics to act as a vector of, among others, toxic metals, such as Cd, Pb, Bi or Hg.

SGLT2 Inhibition Alters the Hepatic and Circulating Metabolome of Mice Under a High-Milk-Fat Diet

Recent studies have shown that the inhibition of Sodium-glucose cotransporter 2 (SGLT2), which reabsorbs glucose in the renal proximal tubule, can aid in glycemic control under conditions of type 2 diabetes (T2D). Though it acts directly on the kidney, these inhibitors also lead to great improvement in liver function. Previously, we challenged TallyHo mice, an inbred polygenetic mice model that recapitulates T2D, with a high milk fat diet (HMFD) for 12 weeks in the continued absence or presence of Empagliflozin (EMPA), an FDA-approved SGLT2 inhibitor. Male mice showed a decrease in circulating triglycerides, hyperglycemia, and hepatic lipid and cholesterol esters (Kurtz et al 2022). However, the mechanisms that underlie this improved liver phenotype is unknown. Thus, we sought to characterize the hepatic and circulating metabolic and lipidomic profiles that are altered and/or restored in the presence of EMPA using targeted and untargeted LC-MS. Control, HMFD, and HMFD + EMPA liver tissues and plasma samples were dissolved in a methanol/water 50/50 extraction buffer that contained debrisoquine (DBQ) for the positive mode standard and 4-nitrobenzoic acid for the negative mode standard. For the untargeted liver and plasma samples, a UPLC-QTOF analysis was performed. MetaboAnalyst was used to quantify metabolite changes with inclusion criteria set to a fold change of 2 and a p-value <0.05. Of note, previous studies have shown that EMPA promotes the upregulation of ketone bodies including β-Hydroxybutyrate (BHB). We noted a similar upregulation of BHB in our metabolomic analysis and confirmed that EMPA led to a ~1.5-fold increase (p < 0.022) in this circulating ketone body. In addition, of those metabolites that met the inclusion criteria, we identified 12 that were downregulated and 9 that were upregulated +EMPA in the male samples. Out of these, orotate, sarcosine, tartrate, and dihydrofolate levels were restored to those of control levels. In the female samples, 6 were downregulated and 3 were upregulated +EMPA. Indoxyl sulfate, butylcarnitine, and valerylcarnetine were all restored to control levels. Of particular interest is the finding that EMPA treatment restores levels of key metabolites that are involved in the folate and pyrimidine biosynthesis pathways. Previous studies have determined that folate deficiency can promote insulin resistance and the urea cycle, a pathway that is integrated with pyrimidine biosynthesis, is upregulated under conditions of T2D and non-alcoholic fatty liver disease. Future studies are aimed at identifying the role of EMPA's hepatic protection in these metabolic pathways. NIDDK This is the full abstract presented at the American Physiology Summit 2023 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

Identification of alkaline-induced thiolyl-chlorogenic acid conjugates with cysteine and glutathione

Alkaline reactions of chlorogenic acid (CGA) yield undesirable development of brown or green pigments, limiting the utilization of alkalized CGA-rich foods. Thiols such as cysteine and glutathione mitigate pigment formation through several mechanisms, including redox coupling to reduce CGA quinones, and thiol conjugation, which forms colorless thiolyl-CGA compounds that do not readily participate in color-generating reactions. This work provided evidence of the formation of both aromatic and benzylic thiolyl-CGA conjugate species formed with cysteine and glutathione under alkaline conditions in addition to hydroxylated conjugate species hypothesized to arise from reactions with hydroxyl radicals. Formation of these conjugates proceeds more quickly than CGA dimerization and amine addition reactions mitigating pigment development. Differentiation between aromatic and benzylic conjugates is enabled by characteristic fragmentation of CS bonds. Acyl migration and hydrolysis of the quinic acid moiety of thiolyl-CGA conjugates yielded a variety of isomeric species also identified through untargeted LC-MS methods.

Untangling the Complexities of Processing and Analysis for Untargeted LC-MS Data Using Open-Source Tools.

Untargeted metabolomics is a powerful tool for measuring and understanding complex biological chemistries. However, employment, bioinformatics and downstream analysis of mass spectrometry (MS) data can be daunting for inexperienced users. Numerous open-source and free-to-use data processing and analysis tools exist for various untargeted MS approaches, including liquid chromatography (LC), but choosing the 'correct' pipeline isn't straight-forward. This tutorial, in conjunction with a user-friendly online guide presents a workflow for connecting these tools to process, analyse and annotate various untargeted MS datasets. The workflow is intended to guide exploratory analysis in order to inform decision-making regarding costly and time-consuming downstream targeted MS approaches. We provide practical advice concerning experimental design, organisation of data and downstream analysis, and offer details on sharing and storing valuable MS data for posterity. The workflow is editable and modular, allowing flexibility for updated/changing methodologies and increased clarity and detail as user participation becomes more common. Hence, the authors welcome contributions and improvements to the workflow via the online repository. We believe that this workflow will streamline and condense complex mass-spectrometry approaches into easier, more manageable, analyses thereby generating opportunities for researchers previously discouraged by inaccessible and overly complicated software.

Large-Scale metabolomics: Predicting biological age using 10,133 routine untargeted LC-MS measurements.

Untargeted metabolomics is the study of all detectable small molecules, and in geroscience, metabolomics has shown great potential to describe the biological age-a complex trait impacted by many factors. Unfortunately, the sample sizes are often insufficient to achieve sufficient power and minimize potential biases caused by, for example, demographic factors. In this study, we present the analysis of biological age in ~10,000 toxicologic routine blood measurements. The untargeted screening samples obtained from ultra-high pressure liquid chromatography-quadruple time of flight mass spectrometry (UHPLC- QTOF) cover + 300 batches and + 30 months, lack pooled quality controls, lack controlled sample collection, and has previously only been used in small-scale studies. To overcome experimental effects, we developed and tested a custom neural network model and compared it with existing prediction methods. Overall, the neural network was able to predict the chronological age with an rmse of 5.88 years (r2 =0.63) improving upon the 6.15 years achieved by existing normalization methods. We used the feature importance algorithm, Shapley Additive exPlanations (SHAP), to identify compounds related to the biological age. Most importantly, the model returned known aging markers such as kynurenine, indole-3-aldehyde, and acylcarnitines along with a potential novel aging marker, cyclo (leu-pro). Our results validate the association of tryptophan and acylcarnitine metabolism to aging in a highly uncontrolled large-s cale sample. Also, we have shown that by using robust computational methods it is possible to deploy large LC-MS datasets for metabolomics studies to reduce the risk of bias and empower aging studies.

Investigating metformin-active substances from different manufacturing sources by NIR, NMR, high-resolution LC-MS, and chemometric analysis for the prospective classification of legal medicines

Introduction: The characterisation of active substances is an essential tool to ensure the traceability and authenticity of legal medicines. Metformin is a well-established biguanide derivative recommended in oral formulations as a first-line treatment for type 2 diabetes. With its increasing demand, metformin is likely to be an attractive target for falsification and substandard production, thus posing health risks to consumers. Methods that are able to identify even small differences in active pharmaceutical ingredients (APIs) are deemed necessary. The detection of fraudulent practices in APIs is not straightforward, and a single technique that can provide sufficient information to unambiguously address this issue is still not available.Methods: This study investigated an integrated analytical platform based on NIR, 1H-NMR, 13C-NMR, and high-resolution LC-MS combined with chemometrics to profile 32 metformin hydrochloride samples originating from several global authorised manufacturers. The study's aim was to explore differences in the chemical characteristics of metformin hydrochloride APIs to identify or predict a possible classification for each manufacturer in view of prospective authenticity studies. Different pre-processing methods were applied; bucket tables for 1H- and 13C-NMR were obtained, while mass spectrometry data were processed in targeted and untargeted modes. Datasets were individually analysed and merged by a multivariate unsupervised method and performing principal component analysis (PCA). Results and Discussion: The results evidenced differences in cluster behaviour, depending on manufacturers. Each technique has shown a specific clustering tendency, highlighting how different analytical approaches are able to characterise metformin APIs. Some manufacturers’ samples, however, showed similar behaviour independently of the techniques. NIR and 1H-NMR were confirmed as the more predictive techniques if taken individually; 1H-NMR, in particular, achieved good separation between the samples of the two most representative manufacturers. For LC-MS, the targeted approach resulted in a separation in groups clearer than that of the untargeted approach. Nevertheless, the untargeted LC-MS approaches presented in this paper could be a possible alternative to obtaining different information for drug substances, with several different and complex synthetic pathways leading to several unknown impurities. Further grouping of manufacturers emerged by data fusion, highlighting its potential in the traceability of metformin.

LC–MS based metabolomics analysis of okara fermented by Bacillus subtilis DC-15: Insights into nutritional and functional profile

Recent studies emphasize the improved nutritional and functional status of fermented okara; however, little is known about the metabolite change during fermentation and how it alters metabolic pathways. A metabolomics approach based on untargeted LC–MS reveals metabolic changes in okara fermented by Bacillus subtilis DC-15. We identified 761 differential metabolites, with the highest abundances found in amino acids, dipeptides, fatty acids, small molecule sugars, and vitamins. Moreover, these identified metabolites were mapped to their respective biosynthesis pathways in order to gain a better understanding of the biochemical reactions triggered by fermentation. Based on Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis, 485 metabolites were enriched to metabolism-related pathways. They include 37 carbohydrate metabolites, 79 amino acid metabolites, and 22 lipid metabolites. As a result of okara fermentation, we observed a gradual enrichment of metabolites and stabilization of the compounds.

Metabolome-wide association study on physical activity

The underlying mechanisms linking physical activity to better health are not fully understood. Here we examined the associations between physical activity and small circulatory molecules, the metabolome, to highlight relevant biological pathways. We examined plasma metabolites associated with self-reported physical activity among 2217 participants from the Airwave Health Monitoring Study. Metabolic profiling was conducted using the mass spectrometry-based Metabolon platform (LC/GC–MS), measuring 828 known metabolites. We replicated our findings in an independent subset of the study (n = 2971) using untargeted LC–MS. Mendelian randomisation was carried out to investigate potential causal associations between physical activity, body mass index, and metabolites. Higher vigorous physical activity was associated (P < 0.05/828 = 6.03 × 10–5) with circulatory levels of 28 metabolites adjusted for age, sex and body mass index. The association was inverse for glutamate and diacylglycerol lipids, and direct for 3–4-hydroxyphenyllactate, phenyl lactate (PLA), alpha-hydroxy isovalerate, tiglylcarnitine, alpha-hydroxyisocaproate, 2-hydroxy-3-methylvalerate, isobutyrylcarnitine, imidazole lactate, methionine sulfone, indole lactate, plasmalogen lipids, pristanate and fumarate. In the replication panel, we found 23 untargeted LC–MS features annotated to the identified metabolites, for which we found nominal associations with the same direction of effect for three features annotated to 1-(1-enyl-palmitoyl)-2-oleoyl-GPC (P-16:0/18:1), 1-(1-enyl-palmitoyl)-2-linoleoyl-GPC (P-16:0/18:2), 1-stearoyl-2-dihomo-linolenoyl-GPC (18:0/20:3n3 or 6). Using Mendelian randomisation, we showed a potential causal relationship between body mass index and three identified metabolites. Circulatory metabolites are associated with physical activity and may play a role in mediating its health effects.

Development and application of a data processing method for food metabolomics analysis.

Food metabolomics is described as the implementation of metabolomics to food systems such as food materials, food processing, and food nutrition. These applications generally create large amounts of data, and although technologies exist to analyze these data and different tools exist for various ecosystems, downstream analysis is still a challenge and the tools are not integrated into a single method. In this article, we developed a data processing method for untargeted LC-MS data in metabolomics, derived from the integration of computational MS tools from OpenMS into the workflow system Konstanz Information Miner (KNIME). This method can analyze raw MS data and produce high-quality visualization. A MS1 spectra-based identification, two MS2 spectra-based identification workflows and a GNPSExport-GNPS workflow are included in this method. Compared with conventional approaches, the results of MS1&MS2 spectra-based identification workflows are combined in this approach via the tolerance of retention times and mass to charge ratios (m/z), which can greatly reduce the rate of false positives in metabolomics datasets. In our example, filtering with the tolerance removed more than 50% of the possible identifications while retaining 90% of the correct identification. The results demonstrated that the developed method is a rapid and reliable method for food metabolomics data processing.

A Comprehensive Study on the Chemical Constituents and Pharmacokinetics of Erzhi Formula and Jiawei Erzhi Formula Based on Targeted and Untargeted LC-MS Analysis.

Erzhi formula (EZF) is a traditional Chinese medicine prescription, which has been widely used in the treatment of osteoporosis and premature ovarian failure. To enhance curative effects, the other two herbal medicines, including Spatholobi Caulis (SC) and Achyranthes bidentata Blume (ABB), were added into the original EZF formula to obtain two new Jiawei-EZF (JW-EZF) preparations. To clarify the effect of the compatibility of herbs for original formulas, the chemical constituents and bioactive compounds in vivo were detected. An efficient and sensitive targeted and untargeted UHPLC/ESI-Q-Orbitrap MS method, together with mass defect filter and precursor ion list, was established firstly for the profiling of different EZF formulas. Furthermore, eleven absorbed compounds (apigenin, luteoloside, luteolin, oleuropein, wedelolactone, acteoside, specnuezhenide, 11-methyloleoside, ecliptasaponin A, formononetin, and β-ecdysone) were simultaneously quantified in rat plasma. A total of 124, 162, and 177 compounds were identified or tentatively identified in EZF, JW-3-EZF (EZF+SC) and JW-4-EZF (EZF+SC+ABB), respectively. 110 compounds were found to be common constituents in the three formulas. Moreover, 66 prototypes were unambiguously identified in the rats' plasma after oral administration of the three formulas using the same strategy. 11 out of the 66 absorbed components were simultaneously quantitated in the pharmacokinetic (PK) study. Compared to the original EZF, the plasma AUC(0-24h) and AUC(0-∞) of apigenin, 11-methyloleoside, luteolin, luteoloside, wedelolactone, and acteoside were found to be significantly increased after oral administration of JW-3-EZF, and plasma AUC(0-24h) and AUC(0-∞) of apigenin, wedelolactone, and acteoside, were also found to be significantly increased after JW-4-EZF administration. The combined qualitative and quantitative methods were used to provide a potential approach to the characterization and quality control of the Traditional Chinese Medicine (TCM) and its preparations.

Untargeted metabolomics confirms the association between plasma branched chain amino acids and residual feed intake in beef heifers.

This study explored plasma biomarkers and metabolic pathways underlying feed efficiency measured as residual feed intake (RFI) in Charolais heifers. A total of 48 RFI extreme individuals (High-RFI, n = 24; Low-RFI, n = 24) were selected from a population of 142 heifers for classical plasma metabolite and hormone quantification and plasma metabolomic profiling through untargeted LC-MS. Most efficient heifers (Low-RFI) had greater (P = 0.03) plasma concentrations of IGF-1 and tended to have (P = 0.06) a lower back fat depth compared to least efficient heifers. However, no changes were noted (P ≥ 0.10) for plasma concentrations of glucose, insulin, non-esterified fatty acids, β-hydroxybutyrate and urea. The plasma metabolomic dataset comprised 3,457 ions with none significantly differing between RFI classes after false discovery rate correction (FDR > 0.10). Among the 101 ions having a raw P < 0.05 for the RFI effect, 13 were putatively annotated by using internal databases and 6 compounds were further confirmed with standards. Metabolic pathway analysis from these 6 confirmed compounds revealed that the branched chain amino acid metabolism was significantly (FDR < 0.05) impacted by the RFI classes. Our results confirmed for the first time in beef heifers previous findings obtained in male beef cattle and pointing to changes in branched-chain amino acids metabolism along with that of body composition as biological mechanisms related to RFI. Further studies are warranted to ascertain whether there is a cause-and-effect relationship between these mechanisms and RFI.