Untargeted Metabolomics Studies Research Articles

Exploratory analysis of metabolic changes using mass spectrometry data and graph embeddings

Mass spectrometry (MS)-based metabolomics analysis is a powerful tool, but it comes with its own set of challenges. The MS workflow involves multiple steps before its interpretation in what is denominate data mining. Data mining consists of a two-step process. First, the MS data is ordered, arranged, and presented for filtering before being analyzed. Second, the filtered and reduced data are analyzed using statistics to remove further variability. This holds true particularly for MS-based untargeted metabolomics studies, which focused on understanding fold changes in metabolic networks. Since the task of filtering and identifying changes from a large dataset is challenging, automated techniques for mining untargeted MS-based metabolomic data are needed. The traditional statistics-based approach tends to overfilter raw data, which may result in the removal of relevant data and lead to the identification of fewer metabolomic changes. This limitation of the traditional approach underscores the need for a new method. In this work, we present a novel deep learning approach using node embeddings (powered by GNNs), edge embeddings, and anomaly detection algorithm to analyze the data generated by mass spectrometry (MS)-based metabolomics called GEMNA (Graph Embedding-based Metabolomics Network Analysis), for example for an untargeted volatile study on Mentos candy, the data clusters produced by GEMNA were better than the ones used traditional tools, i.e., GEMNA has \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$silhouette\\,score = 0.409$$\\end{document}, vs. the traditional approach has \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$silhouette\\,score = -0.004$$\\end{document}.

An optimization protocol of the volatile organic compounds analysis in earwax samples for untargeted volatilomics

Recent studies have highlighted the potential of earwax or cerumen, a non-conventional biomatrix, in volatilomics research as a valuable matrix for disease biomarker discovery. Despite that, there are still gaps in using non-conventional biomatrices in metabolomics research. In this sense, this study aimed to elucidate the main analytical factors involved in the extraction and analysis of volatile organic compounds (VOCs) in cerumen by headspace/gas chromatography-mass spectrometry (HS/GC–MS) using Design of Experiments (DoE) approaches. Furthermore, we present a repeatability study for the proposed method as a quality control process for cerumenomic assays. By applying factorial designs, it was possible to determine that the sample mass, splitless injector sampling time, headspace extraction time, headspace extraction temperature, injection volume, and vial volume were significant factors for the cerumen VOC analysis by HS/GC–MS. Throughout univariate and multivariate statistical approaches, we demonstrate that different analytical conditions lead to distinct chemical profiling of a sample. The most suitable analytical condition was determined after the optimization steps, and the proposed method's repeatability was evaluated by the metabolites coefficient variation (CV) calculation. Seventy-one earwax VOCs reached a CV considered adequate for untargeted metabolomics studies via GC–MS. In summary, this study describes a protocol for analysis optimization of a non-conventional biomatrix and also reports a quality control process in untargeted volatilomics assays using earwax. Our findings shed light on the potential of using earwax in volatolomic studies and establish analytical criteria to ensure quality in cerumenomic assays.

Genomic and functional characterization of the Atlantic salmon gut microbiome in relation to nutrition and health.

To ensure sustainable aquaculture, it is essential to understand the path 'from feed to fish', whereby the gut microbiome plays an important role in digestion and metabolism, ultimately influencing host health and growth. Previous work has reported the taxonomic composition of the Atlantic salmon (Salmo salar) gut microbiome; however, functional insights are lacking. Here we present the Salmon Microbial Genome Atlas consisting of 211 high-quality bacterial genomes, recovered by cultivation (n = 131) and gut metagenomics (n = 80) from wild and farmed fish both in freshwater and seawater. Bacterial genomes were taxonomically assigned to 14 different orders, including 35 distinctive genera and 29 previously undescribed species. Using metatranscriptomics, we functionally characterized key bacterial populations, across five phyla, in the salmon gut. This included the ability to degrade diet-derived fibres and release vitamins and other exometabolites with known beneficial effects, which was supported by genome-scale metabolic modelling and in vitro cultivation of selected bacterial species coupled with untargeted metabolomic studies. Together, the Salmon Microbial Genome Atlas provides a genomic and functional resource to enable future studies on salmon nutrition and health.

Distinguishing Artifactual Fatty Acid Dimers from Fatty Acid Esters of Hydroxy Fatty Acids in Untargeted LC-MS Pipelines.

Untargeted metabolomics is a powerful profiling tool for the discovery of possible biomarkers of disease onset and progression. Analytical pipelines applying liquid chromatography (LC) and mass spectrometry (MS)-based methods are widely used to survey a broad range of metabolites within various metabolic pathways, including organic acids, amino acids, nucleosides, and lipids. Accurate and complete identification of putative metabolites is an ongoing challenge in untargeted metabolomics studies. Highly sensitive instrumentation can result in the detection of adduct and fragment ions that form reproducibly and contain identifiable ions that are difficult to distinguish from metabolic pathway intermediates, which may result in false-positive identification. At concentrations as low as 10μM, free fatty acids have been found to form homo- and heterodimers in untargeted metabolomics pipelines that resemble the lipid class fatty acid esters of hydroxy fatty acids (FAHFAs), resulting in misidentification. This chapter details a protocol for LC-MS-based untargeted metabolomics using hydrophilic interaction chromatography (HILIC) that specifically aids in distinguishing artifactual fatty acid dimers from endogenous FAHFAs.

Fungal endophytes of Taxus species and regulatory effect of two strains on taxol synthesis

BackgroundTaxol, derived from Taxus trees, is a valuable natural resource for the development of anticancer drugs. Endophytic fungi from Taxus trees are a promising alternative source of Taxol. However, the impact of plant-endophytic microbial interaction on the host’s Taxol biosynthesis is largely unknown.ResultsIn the current study, the diversity of endophytic fungi in three different Taxus species was analyzed using Internal Transcribed Spacer sequencing. A total of 271 Operational Taxonomic Units (OTUs) were identified, grouping into 2 phyla, 8 classes, 16 orders, 19 families, and 19 genera. Alpha and beta diversity analysis indicated significant differences in endophytic fungal communities among the various Taxus trees. At the genus level, Alternaria and Davidiella were predominantly found in T. mairei and T. media, respectively. By utilizing a previously published dataset, a Pearson correlation analysis was conducted to predict the taxol biosynthesis-related fungal genera. Following screening, two isolates of Alternaria (L7 and M14) were obtained. Effect of inoculation with Alternaria isolates on the gene expression and metabolite accumulation of T. mairei was determined by transcriptomic and untargeted metabolomic studies. The co-inoculation assay suggests that the two Alternaria isolates may have a negative regulatory effect on taxol biosynthesis by influencing hormone signaling pathways.ConclusionOur findings will serve as a foundation for advancing the production and utilization of Taxus and will also aid in screening endophytic fungi related to taxol production.

Acylcarnitines are associated with lower depressive symptomatology in a mainland puerto rican cohort.

Recent studies have implicated acetyl-L-carnitine as well as other acylcarnitines in depression. To our knowledge, no untargeted metabolomics studies have been conducted among US mainland Puerto Ricans. We conducted untargeted metabolomic profiling on plasma from 736 participants of the Boston Puerto Rican Health Study. Using Weighted Gene Co-expression Network Analysis, we identified metabolite modules associated with depressive symptomatology, assessed via the Center for Epidemiologic Studies Depression scale. We identified metabolites contributing to these modules and assessed the relationship between these metabolites and depressive symptomatology. 621 annotated metabolites clustered into eight metabolite modules, of which one, the acylcarnitine module, was significantly inversely associated with depressive symptomatology (β = -27.7 (95% CI (-54.5-0.8); p = 0.043). Several metabolite hub features in the acylcarnitine module were significantly associated with depressive symptomatology, after correction for multiple comparisons. In this untargeted plasma metabolomics study among mainland Puerto Rican older adults, acylcarnitines, as a metabolite module were inversely associated with depressive symptomatology.

FragHub: A Mass Spectral Library Data Integration Workflow.

Open mass spectral libraries (OMSLs) are critical for metabolite annotation and machine learning, especially given the rising volume of untargeted metabolomic studies and the development of annotation pipelines. Despite their importance, the practical application of OMSLs is hampered by the lack of standardized file formats, metadata fields, and supporting ontology. Current libraries, often restricted to specific topics or matrices, such as natural products, lipids, or the human metabolome, may limit the discovery potential of untargeted studies. The goal of FragHub is to provide users with the capability to integrate various OMSLs into a single unified format, thereby enhancing the annotation accuracy and reliability. FragHub addresses these challenges by integrating multiple OMSLs into a single comprehensive database, supporting various data formats, and harmonizing metadata. It also proposes some generic filters for the mass spectrum using a graphical user interface. Additionally, a workflow to generate in-house libraries compatible with FragHub is proposed. FragHub dynamically segregates libraries based on ionization modes and chromatography techniques, thereby enhancing data utility in metabolomic research. The FragHub Python code is publicly available under a MIT license, at the following repository: https://github.com/eMetaboHUB/FragHub. Generated data can be accessed at 10.5281/zenodo.11057687.

Designing optimal experiments in metabolomics.

Metabolomics data is often complex due to the high number of metabolites, chemical diversity, and dependence on sample preparation. This makes it challenging to detect significant differences between factor levels and to obtain accurate and reliable data. To address these challenges, the use of Design of Experiments (DoE) techniques in the setup of metabolomic experiments is crucial. DoE techniques can be used to optimize the experimental design space, ensuring that the maximum amount of information is obtained from a limited sample space. This review aims at providing a baseline workflow for applying DoE when generating metabolomics data. The review provides insights into the theory of DoE. The review showcases the theory being put into practice by highlighting different examples DoE being applied in metabolomics throughout the literature, considering both targeted and untargeted metabolomic studies in which the data was acquired using both nuclear magnetic resonance (NMR)spectroscopy and mass spectrometry techniques. In addition, the review presents DoE concepts not currently being applied in metabolomics, highlighting these as potential future prospects.

Characterization of Electrospray Ionization Complexity in Untargeted Metabolomic Studies.

The annotation of metabolites detected in LC-MS-based untargeted metabolomics studies routinely applies accurate m/z of the intact metabolite (MS1) as well as chromatographic retention time and MS/MS data. Electrospray ionization and transfer of ions through the mass spectrometer can result in the generation of multiple "features" derived from the same metabolite with different m/z values but the same retention time. The complexity of the different charged and neutral adducts, in-source fragments, and charge states has not been previously and deeply characterized. In this paper, we report the first large-scale characterization using publicly available data sets derived from different research groups, instrument manufacturers, LC assays, sample types, and ion modes. 271 m/z differences relating to different metabolite feature pairs were reported, and 209 were annotated. The results show a wide range of different features being observed with only a core 32 m/z differences reported in >50% of the data sets investigated. There were no patterns reporting specific m/z differences that were observed in relation to ion mode, instrument manufacturer, LC assay type, and mammalian sample type, although some m/z differences were related to study group (mammal, microbe, plant) and mobile phase composition. The results provide the metabolomics community with recommendations of adducts, in-source fragments, and charge states to apply in metabolite annotation workflows.

Xylitol is prothrombotic and associated with cardiovascular risk.

The pathways and metabolites that contribute to residual cardiovascular disease risks are unclear. Low-calorie sweeteners are widely used sugar substitutes in processed foods with presumed health benefits. Many low-calorie sweeteners are sugar alcohols that also are produced endogenously, albeit at levels over 1000-fold lower than observed following consumption as a sugar substitute. Untargeted metabolomics studies were performed on overnight fasting plasma samples in a discovery cohort (n = 1157) of sequential stable subjects undergoing elective diagnostic cardiac evaluations; subsequent stable isotope dilution liquid chromatography tandem mass spectrometry (LC-MS/MS) analyses were performed on an independent, non-overlapping validation cohort (n = 2149). Complementary isolated human platelet, platelet-rich plasma, whole blood, and animal model studies examined the effect of xylitol on platelet responsiveness and thrombus formation in vivo. Finally, an intervention study was performed to assess the effects of xylitol consumption on platelet function in healthy volunteers (n = 10). In initial untargeted metabolomics studies (discovery cohort), circulating levels of a polyol tentatively assigned as xylitol were associated with incident (3-year) major adverse cardiovascular event (MACE) risk. Subsequent stable isotope dilution LC-MS/MS analyses (validation cohort) specific for xylitol (and not its structural isomers) confirmed its association with incident MACE risk [third vs. first tertile adjusted hazard ratio (95% confidence interval), 1.57 (1.12-2.21), P < .01]. Complementary mechanistic studies showed xylitol-enhanced multiple indices of platelet reactivity and in vivo thrombosis formation at levels observed in fasting plasma. In interventional studies, consumption of a xylitol-sweetened drink markedly raised plasma levels and enhanced multiple functional measures of platelet responsiveness in all subjects. Xylitol is associated with incident MACE risk. Moreover, xylitol both enhanced platelet reactivity and thrombosis potential in vivo. Further studies examining the cardiovascular safety of xylitol are warranted.

Tidy-Direct-to-MS: An Open-Source Data-Processing Pipeline for Direct Mass Spectrometry-Based Metabolomics Experiments.

Direct-to-Mass Spectrometry and ambient ionization techniques can be used for biochemical fingerprinting in a fast way. Data processing is typically accomplished with vendor-provided software tools. Here, a novel, open-source functionality, entitled Tidy-Direct-to-MS, was developed for data processing of direct-to-MS data sets. It allows for fast and user-friendly processing using different modules for optional sample position detection and separation, mass-to-charge ratio drift detection and correction, consensus spectra calculation, and bracketing across sample positions as well as feature abundance calculation. The tool also provides functionality for the automated comparison of different sets of parameters, thereby assisting the user in the complex task of finding an optimal combination to maximize the total number of detected features while also checking for the detection of user-provided reference features. In addition, Tidy-Direct-to-MS has the capability for data quality review and subsequent data analysis, thereby simplifying the workflow of untargeted ambient MS-based metabolomics studies. Tidy-Direct-to-MS is implemented in the Python programming language as part of the TidyMS library and can thus be easily extended. Capabilities of Tidy-Direct-to-MS are showcased in a data set acquired in a marine metabolomics study reported in MetaboLights (MTBLS1198) using a transmission mode Direct Analysis in Real Time-Mass Spectrometry (TM-DART-MS)-based method.

Aryl hydrocarbon receptor ligand supplementation impacts colitis-associated depressive-like behavior

Abstract Colitis is an inflammatory bowel disease (IBD) with increased incidences of depression in patients. The mechanisms that define this comorbidity remain poorly understood. Our lab previously showed supplementation with indole-3-carbinol (I3C), an aryl hydrocarbon receptor (AhR) ligand, can reduce disease severity in models of colitis. We aimed to determine the behavioral impact I3C supplementation has on colitis-induced depression via alterations in the gut metabolome. Colitis was induced in using the dextran sodium sulfate (DSS) method and treatment groups were given a regimen of 40 mg/kg I3C as previously reported. Untargeted metabolomic studies revealed quinolinic acid (QA), a metabolite produced by the kynurenine (KYN) pathway and linked to depression, was found to be significantly reduced in I3C-treated mice when compared to colitis controls. To determine the effects of I3C and QA modulation on depressive-like behavior, colitis mice were treated with either I3C or an inhibitor of a major enzyme involved in QA production. Depressive-like behavior was measured using the Tail Suspension Test (TST) method, along with evaluation of neural biomarkers of depression (dopamine, BDNF, GFAP) and stabilization of the blood brain barrier (BBB). Results showed I3C reduced depressive-like behavior and altered select biomarkers associated with depression. These studies provide evidence that AhR can be a potential therapeutic target for both colitis and colitis-associated depression.

MetaboAnalyst 6.0: towards a unified platform for metabolomics data processing, analysis and interpretation.

We introduce MetaboAnalyst version 6.0 as a unified platform for processing, analyzing, and interpreting data from targeted as well as untargeted metabolomics studies using liquid chromatography - mass spectrometry (LC-MS). The two main objectives in developing version 6.0 are to support tandem MS (MS2) data processing and annotation, as well as to support the analysis of data from exposomics studies and related experiments. Key features of MetaboAnalyst 6.0 include: (i) a significantly enhanced Spectra Processing module with support for MS2 data and the asari algorithm; (ii) a MS2 Peak Annotation module based on comprehensive MS2 reference databases with fragment-level annotation; (iii) a new Statistical Analysis module dedicated for handling complex study design withmultiple factors orphenotypic descriptors; (iv) a Causal Analysis module for estimating metabolite - phenotype causal relations based on two-sample Mendelian randomization,and (v) a Dose-Response Analysis module for benchmark dose calculations. In addition, we have also improved MetaboAnalyst's visualization functions, updated its compound database and metabolite sets, and significantly expanded its pathway analysis support to around 130 species. MetaboAnalyst 6.0 is freely available at https://www.metaboanalyst.ca.

LC-MS based untargeted metabolomics studies of the metabolic response of Ginkgo biloba extract on arsenism patients

Arsenic is an environmentally ubiquitous toxic metalloid. Chronic exposure to arsenic may lead to arsenicosis, while no specific therapeutic strategies are available for the arsenism patients. And Ginkgo biloba extract (GBE) exhibited protective effect in our previous study. However, the mechanisms by which GBE protects the arsenism patients remain poorly understood. A liquid chromatography-mass spectrometry (LC-MS) based untargeted metabolomics analysis was used to study metabolic response in arsenism patients upon GBE intervention. In total, 39 coal-burning type of arsenism patients and 50 healthy residents were enrolled from Guizhou province of China. The intervention group (n = 39) were arsenism patients orally administered with GBE (three times per day) for continuous 90 days. Plasma samples from 50 healthy controls (HC) and 39 arsenism patients before and after GBE intervention were collected and analyzed by established LC-MS method. Statistical analysis was performed by MetaboAnalyst 5.0 to identify differential metabolites. Multivariate analysis revealed a separation in arsenism patients between before (BG) and after GBE intervention (AG) group. It was observed that 35 differential metabolites were identified between BG and AG group, and 30 of them were completely or partially reversed by GBE intervention, with 14 differential metabolites significantly up-regulated and 16 differential metabolites considerably down-regulated. These metabolites were involved in promoting immune response and anti-inflammatory functions, and alleviating oxidative stress. Taken together, these findings indicate that the GBE intervention could probably exert its protective effects by reversing disordered metabolites modulating these functions in arsenism patients, and provide insights into further exploration of mechanistic studies.

Untargeted metabolomics of buffalo urine reveals hydracyrlic acid, 3-bromo-1-propanol and benzyl serine as potential estrus biomarkers

Buffalo is a silent heat animal and doesn't show prominent signs of estrous like cattle so it becomes difficult for farmers to determine the receptivity of the animal based purely on the animal behaviour. India, having a huge population size, needs to produce more milk for the population. Successful artificial insemination greatly depends on the receptivity of the animal. Hence the present study aimed to identify the changes in the metabolome of the buffalo. GC–MS based mass spectrometric analysis was deployed for the determination of estrous by differential expression of metabolites. It was found that hydracrylic acid, 3-bromo-1-propanol and benzyl serine were significantly upregulated in the estrous phase of buffalo (p.value ≤0.05, FC ≥ 2). The pathway enrichment analysis also supported the same as pathways related to amino acid metabolism and fatty acid metabolism were up regulated along with the Warburg effect which is linked to the rapid cell proliferation which might help prepare animals to meet the energy requirement during the estrous. Further analysis of the metabolic biomarkers using ROC analysis also supported these three metabolites as probable biomarkers as they were identified with AUC values of 0.7 or greater. SignificanceThe present study focuses on the untargeted metabolomics studies of buffalo urine with special reference to the estrous phase of reproductive cycle. The estrous signals are more prominent in cattle, where animals show clear estrous signals such as mounting and discharge along with vocal signals. Buffalo is a silent heat animal and it becomes difficult for farmers to detect the estrous based on the physical and behavioral signals. Hence the present study focuses on GC–MS based untargeted metabolomics to identify differentially expressed urine metabolites. In this study, hydracrylic acid, 3-bromo-1-propanol and benzyl serine were found to be significantly upregulated in the estrous phase of buffalo (p-value ≤0.05, FC ≥ 2). Further confirmation of the metabolic biomarkers was done using Receiver operating characteristics (ROC) analysis which also supported these three metabolites as probable biomarkers as they had AUC values of 0.7 or greater. Hence, this study will be of prime importance for the people working in the area of animal metabolomics.

An inulin-type fructan CP-A from Codonopsis pilosula alleviates TNBS-induced ulcerative colitis based on serum-untargeted metabolomics.

Ulcerative colitis (UC) is an inflammatory disease with abdominal pain, diarrhea, and bloody stool as the main symptoms. Several studies have confirmed that polysaccharides are effective against UC. It is commonly accepted that the traditional benefits of Radix Codonopsis can be attributed to its polysaccharide contents, and inulin-type fructan CP-A is the main active monomer in the polysaccharide components. Herein, we established a 2,4,6-trinitrobenzene sulfonic acid (TNBS)-induced UC rat model and lipopolysaccharide (LPS)-induced colonic epithelial cell model (NCM460) to investigate the effect of CP-A on UC. Untargeted metabolomics studies were conducted to identify differential metabolites using ultra-high performance liquid chromatography quadrupole time-of-flight tandem mass spectrometry (UHPLC-Q-TOF/MS) and enrich metabolic pathways in rat serum. The in vivo assays demonstrated that CP-A reduces colonic macroscopic injury, disease activity index (DAI), histopathological score, interleukin (IL)-8, and tumor necrosis factor-α (TNF-α) levels, as well as the expression of intercellular adhesion molecules. On the other hand, CP-A increases IL-10 and transforming growth factor-β (TGF-β) levels. The in vitro experiments indicated that CP-A treatment could reduce nitric oxide (NO) and IL-1β after LPS stimulation. The metabolomics results suggested that CP-A therapy for UC may be related to the mammalian target of rapamycin (mTOR) signaling pathway. The in vitro and in vivo validation of the pathway showed similar results, indicating that CP-A alleviates UC by preventing the activation of mTOR/p70S6K signaling pathway. These findings offer a fresh approach to treating UC and a theoretical foundation for the future advancement of CP-A.NEW & NOTEWORTHY We report that an inulin-type fructan from Codonopsis pilosula CP-A exhibits a therapeutic effect on experimental colitis. Its mechanism may be to alleviate intestinal inflammation by preventing the activation of mammalian target of rapamycin (mTOR)/p70S6K signaling pathway. These findings offer a fresh approach to treating ulcerative colitis (UC) and a theoretical foundation for the future advancement of CP-A.

Targeting Gut Microbiome With Prebiotic in Patients With CKD: The TarGut-CKD Study

IntroductionDisruption of gut microbiota underpins some of the metabolic alterations observed in chronic kidney disease (CKD). MethodsIn a non-randomized, open-label, 3-phase pilot trial, with repeated measures within each phase, we examined the efficacy of oligofructose-enriched inulin (p-inulin) in changing the gut microbiome and their metabolic products in 15 CKD patients. The stability of microbiome and metabolome was studied during the pre-treatment phase (8 weeks), a p-inulin treatment phase (12 weeks), and a post-treatment phase (8 weeks) of the study. ResultsStudy participants completed 373 of the 420 expected study visits (88.8%). Adherence to p-inulin was 83.4%. 16S rRNA sequencing was performed in 368 stool samples. A total of 1085 stool, urine, and plasma samples were subjected to untargeted metabolomic studies. P-inulin administration altered the composition of the gut microbiota significantly, with an increase in abundance of Bifidobacterium and Anaerostipes. Inter-subject variations in microbiome and metabolome were larger than intra-subject variation, indicating the stability of the gut microbiome within each phase of the study. Overall metabolite compositions assessed by beta diversity in urine and stool metabolic profiles were significantly different across study phases. Several specific metabolites in stool, urine and plasma were significant at FDR ≤ 0.1 over phase. Specifically, there was significant enrichment in microbial metabolites derived from saccharolysis. ConclusionResults from our study highlight the stability of the gut microbiome and the expansive effect of p-inulin on microbiome and host co-metabolism in CKD patients. Findings from this study will enable rigorous design of microbiome-based intervention trials.

Characterization of isomeric acetyl amino acids and di-acetyl amino acids by LC/MS/MS.

Acetylation of amino acids is important in the molecular biology and biochemistry because they are part of several metabolic pathways. N-acetyl amino acids can form through degradation of N-acetyl proteins or direct acetylation of amino acids by specific enzymes. Acetylation of α-amino acids can be either on the alpha -NH2 or on the side-chain functional group, where both the acetyl products are isomeric and can show different biological roles. Theoretically, all proteinogenic α-amino acids are expected to undergo acetylation and they can be a part of metabolome. Thus, it is essential to detect and identify all the possible acetylated products of α-amino acids for untargeted metabolomics studies. In this study, it is aimed to synthesize and characterize all acetylated products of natural α-amino acids. A total of 20 Nα -acetyl amino acids (1-20), six side-chain acetyl amino acids (21-26), and six diacetyl amino acids (27-32) were synthesized and characterized by liquid chromatography-electrospray ionizationtandem mass spectrometry (LC-ESI-MS/MS). The [M + H]+ ions of all the acetyl amino acids were subjected to MS/MS experiments to obtain their structural information. Apart from the expected loss of (H2 O + CO) (immonium ions), most of the acetyl amino acids specifically showed loss of H2 O and loss of a ketene (C2 H2 O) from [M+H]+ ions. The side-chain acetyl amino acids showed a clear-cut structure specific fragment ions that enabled easy differentiation from their isomeric Nα -acetyl amino acids. The other isomeric/isobaric acetyl amino acids could also be easily distinguished by their MS/MS spectra. The MS/MS of immonium ions of the acetyl amino acids were also studied, and they included characteristic products reflecting the structures of parent Nα -acetyl and side-chain acetyl amino acids.

Study on the potential mechanism of Qingxin Lianzi Yin Decoction on renoprotection in db/db mice via network pharmacology and metabolomics

BackgroundDiabetic nephropathy (DN) was one of the most popular and most significant microvascular complications of diabetes mellitus. Qingxin Lianzi Yin Decoction (QXLZY) was a traditional Chinese classical formula, suitable for chronic urinary system diseases. QXLZY had good clinical efficacy in early DN, but the underlying molecular mechanism remained unrevealed. PurposeThis study aimed to establish the content determination method of QXLZY index components and explore the mechanism of QXLZY on DN by network pharmacology and metabolomics studies. MethodsFirstly, the content determination methods of QXLZY were established with calycosin-7-O-β-d-glucoside, acteoside, baicalin and glycyrrhizic acid as index components. Secondly, pharmacological experiments of QXLZY were evaluated using db/db mice. UHPLC-LTQ-Orbitrap MS was used to carry out untargeted urine metabolomics, serum metabolomics, and kidney metabolomics studies. Thirdly, employing network pharmacology, key components and targets were analyzed. Finally, targeted metabolomics studies were performed on the endogenous constituents in biological samples for validation based on untargeted metabolomics results. ResultsA method for the simultaneous determination of multiple index components in QXLZY was established, which passed the comprehensive methodological verification. It was simple, feasible, and scientific. The QXLZY treatment alleviated kidney injury of db/db mice, included the degree of histopathological damage and the level of urinary microalbumin/creatinine ratio. Untargeted metabolomics studies had identified metabolic dysfunction in pathways associated with amino acid metabolism in db/db mice. Treatment with QXLZY could reverse metabolite abnormalities and influence the pathways related to energy metabolism and amino acid metabolism. It had been found that pathways with a high degree were involved in signal transduction, prominently on amino acids metabolism and lipid metabolism, analyzed by network pharmacology. Disorders of amino acid metabolism did occur in db/db mice. QXLZY could revert the levels of metabolites, such as quinolinic acid, arginine, and asparagine. ConclusionThis study was the first time to demonstrate that QXLZY alleviated diabetes-induced pathological changes in the kidneys of db/db mice by correcting disturbances in amino acid metabolism. This work could provide a new experimental basis and theoretical guidance for the rational application of QXLZY on DN, exploring the new pharmacological effect of traditional Chinese medicine, and promoting in-depth research and development.

Altered metabolic profiles and targets relevant to the protective effect of acteoside on diabetic nephropathy in db/db mice based on metabolomics and network pharmacology studies

Ethnopharmacological relevanceDiabetic nephropathy (DN) was a major cause of end-stage renal failure and a common microvascular complication in patients with diabetes mellitus (DM). Acteoside (ACT) was the main ingredient extracted from the leaves of Rehmannia glutinosa, which had the functions of entering the lung, moisturizing the skin and relieving itching, nourishing yin and tonifying the kidney, cooling blood, and stopping bleeding. ACT had attracted worldwide interest because of its therapeutic effects on DM and its complications. Aim of the studyTo clarify the metabolic profiles and targets of ACT in db/db mice based on metabolomics and network pharmacology studies. Materials and methodsDb/db mice were used to observe the biochemical indices and histopathological changes in the kidney to evaluate the pharmacological effects of ACT on DN. Untargeted metabolomics studies were performed to investigate by UHPLC-LTQ-Orbitrap MS on urine, serum, and kidney samples. The key targets and pathways were analyzed by network pharmacology. For the pathways enriched by untargeted metabolomics, targeted metabolomics by UHPLC-QQQ-MS/MS was performed in kidney samples for validation. Sensitive biomarkers in kidney samples were evaluated. The effect of ACT on the improvement of DN from the perspective of metabolism of small molecules in vivo was described. ResultsACT could delay the progression of DN and improve the degree of histopathological damage to the kidney. The pathways were focused on amino acid metabolism by untargeted metabolomics. Through network pharmacology analysis, the effect pathways were related to signal transduction, carbohydrate, lipid, amino acid metabolism and mainly affected the endocrine and immune systems. Amino acid metabolism was disturbed in the kidney of db/db mice, which could be callback by ACT, such as tryptophan, glutamine, cysteine, leucine, threonine, proline, phenylalanine, histidine, serine, arginine, asparagine by targeted metabolomics. ConclusionsIn conclusion, this study provided strong support for ACT on DN treatment in clinics. Meanwhile, the Rehmannia glutinosa was used fully to raise the income level of farmers economically, while achieving the social benefit of empowering rural revitalization.