Biochemistry, Genetics and Molecular Biology › Molecular Biology

Metabolomics and Mass Spectrometry Studies

Works Count: 73585

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Description

This cluster of papers represents advances in metabolomics research, focusing on techniques such as mass spectrometry and NMR spectroscopy for analyzing the metabolome. It covers topics including data processing, biomarker identification, lipidomics, and the use of metabolomics in genomics and precision medicine.

Keywords

Metabolomics; Mass Spectrometry; Data Analysis; Biomarkers; Human Metabolome Database; Lipidomics; NMR Spectroscopy; Genomics; Pharmacometabolomics; Precision Medicine

A SIMPLIFIED METHOD FOR THE ESTIMATION OF TOTAL CHOLESTEROL IN SERUM AND DEMONSTRATION OF ITS SPECIFICITY

1952-03-01

Liese L. Abell , Betty B. Levy , Bernard B. Brodie +1 more

Optimization of the submerged culture conditions in a bioreactor for the production of Lentinus edodesThis research used an experimental design for optimizing and determining some of the submerged culture conditions … Optimization of the submerged culture conditions in a bioreactor for the production of Lentinus edodesThis research used an experimental design for optimizing and determining some of the submerged culture conditions effect over Lentinus edodes biomass production and its composition.To start with, glucose concentration, aeration and agitation effect were evaluated in the culture medium using a Box-Behnken design, which, allows concluding glucose concentration and agitation had a significant effect over the response variables (biomass, sterols and polysaccharides).Also, it was figured out that 1.2 vvm, 60 rpm and 21.97g / L were the values for aeration, agitation, and glucose concentration respectively for biomass production optimization.Subsequently, sterols and total carbohydrates were extracted from biomass and quantified through a spectrophotometric analysis, which, allows to determine that although these response variables were optimized using the same values for aeration and agitation obtained from the biomass production optimization, the glucose concentration was different (16.32 g / L for sterols and 19.6 g / L for total polysaccharides).Finally, the extracts profile was analyzed using HPLC-DAD and GC-MS.It was found that glucose affects the number of chromatographic signals and the quantity of different metabolites groups.

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Techniques of lipidology (isolation, analysis and identification of lipids

1974-01-01

Morris Kates

Characterization and Measurement of Anthocyanins by UV‐Visible Spectroscopy

2001-04-01

M. Mónica Giusti , Ronald E. Wrolstad

MS-DIAL: data-independent MS/MS deconvolution for comprehensive metabolome analysis

2015-05-04

Hiroshi Tsugawa , Tomáš Čajka , Tobias Kind +7 more

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Proposed minimum reporting standards for chemical analysis

2007-09-11

Lloyd W. Sumner , Alexander Amberg , David A. Barrett +7 more

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ClustVis: a web tool for visualizing clustering of multivariate data using Principal Component Analysis and heatmap

2015-05-12

Tauno Metsalu , Jaak Vilo

The Principal Component Analysis (PCA) is a widely used method of reducing the dimensionality of high-dimensional data, often followed by visualizing two of the components on the scatterplot. Although widely … The Principal Component Analysis (PCA) is a widely used method of reducing the dimensionality of high-dimensional data, often followed by visualizing two of the components on the scatterplot. Although widely used, the method is lacking an easy-to-use web interface that scientists with little programming skills could use to make plots of their own data. The same applies to creating heatmaps: it is possible to add conditional formatting for Excel cells to show colored heatmaps, but for more advanced features such as clustering and experimental annotations, more sophisticated analysis tools have to be used. We present a web tool called ClustVis that aims to have an intuitive user interface. Users can upload data from a simple delimited text file that can be created in a spreadsheet program. It is possible to modify data processing methods and the final appearance of the PCA and heatmap plots by using drop-down menus, text boxes, sliders etc. Appropriate defaults are given to reduce the time needed by the user to specify input parameters. As an output, users can download PCA plot and heatmap in one of the preferred file formats. This web server is freely available at http://biit.cs.ut.ee/clustvis/.

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Metabonomics: a platform for studying drug toxicity and gene function

2002-02-01

Jeremy K. Nicholson , John C. Connelly , John C. Lindon +1 more

Building Predictive Models inRUsing thecaretPackage

2008-01-01

Max Kühn

The caret package, short for classification and regression training, contains numerous tools for developing predictive models using the rich set of models available in R. The package focuses on simplifying … The caret package, short for classification and regression training, contains numerous tools for developing predictive models using the rich set of models available in R. The package focuses on simplifying model training and tuning across a wide variety of modeling techniques. It also includes methods for pre-processing training data, calculating variable importance, and model visualizations. An example from computational chemistry is used to illustrate the functionality on a real data set and to benchmark the benefits of parallel processing with several types of models.

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XCMS: Processing Mass Spectrometry Data for Metabolite Profiling Using Nonlinear Peak Alignment, Matching, and Identification

2006-01-07

Colin A. Smith , Elizabeth J. Want , Grace O’Maille +2 more

Metabolite profiling in biomarker discovery, enzyme substrate assignment, drug activity/specificity determination, and basic metabolic research requires new data preprocessing approaches to correlate specific metabolites to their biological origin. Here we … Metabolite profiling in biomarker discovery, enzyme substrate assignment, drug activity/specificity determination, and basic metabolic research requires new data preprocessing approaches to correlate specific metabolites to their biological origin. Here we introduce an LC/MS-based data analysis approach, XCMS, which incorporates novel nonlinear retention time alignment, matched filtration, peak detection, and peak matching. Without using internal standards, the method dynamically identifies hundreds of endogenous metabolites for use as standards, calculating a nonlinear retention time correction profile for each sample. Following retention time correction, the relative metabolite ion intensities are directly compared to identify changes in specific endogenous metabolites, such as potential biomarkers. The software is demonstrated using data sets from a previously reported enzyme knockout study and a large-scale study of plasma samples. XCMS is freely available under an open-source license at http://metlin.scripps.edu/download/.

Mass spectrometry‐based metabolomics

2006-08-18

Katja Dettmer , Pavel A. Aronov , Bruce D. Hammock

Abstract This review presents an overview of the dynamically developing field of mass spectrometry‐based metabolomics. Metabolomics aims at the comprehensive and quantitative analysis of wide arrays of metabolites in biological … Abstract This review presents an overview of the dynamically developing field of mass spectrometry‐based metabolomics. Metabolomics aims at the comprehensive and quantitative analysis of wide arrays of metabolites in biological samples. These numerous analytes have very diverse physico‐chemical properties and occur at different abundance levels. Consequently, comprehensive metabolomics investigations are primarily a challenge for analytical chemistry and specifically mass spectrometry has vast potential as a tool for this type of investigation. Metabolomics require special approaches for sample preparation, separation, and mass spectrometric analysis. Current examples of those approaches are described in this review. It primarily focuses on metabolic fingerprinting, a technique that analyzes all detectable analytes in a given sample with subsequent classification of samples and identification of differentially expressed metabolites, which define the sample classes. To perform this complex task, data analysis tools, metabolite libraries, and databases are required. Therefore, recent advances in metabolomics bioinformatics are also discussed. © 2006 Wiley Periodicals, Inc., Mass Spec Rev 26:51–78, 2007

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MetaboAnalyst 3.0—making metabolomics more meaningful

2015-04-20

Jianguo Xia , Igor Sinelnikov , Beom-Soo Han +1 more

MetaboAnalyst (www.metaboanalyst.ca) is a web server designed to permit comprehensive metabolomic data analysis, visualization and interpretation. It supports a wide range of complex statistical calculations and high quality graphical rendering … MetaboAnalyst (www.metaboanalyst.ca) is a web server designed to permit comprehensive metabolomic data analysis, visualization and interpretation. It supports a wide range of complex statistical calculations and high quality graphical rendering functions that require significant computational resources. First introduced in 2009, MetaboAnalyst has experienced more than a 50X growth in user traffic (>50 000 jobs processed each month). In order to keep up with the rapidly increasing computational demands and a growing number of requests to support translational and systems biology applications, we performed a substantial rewrite and major feature upgrade of the server. The result is MetaboAnalyst 3.0. By completely re-implementing the MetaboAnalyst suite using the latest web framework technologies, we have been able substantially improve its performance, capacity and user interactivity. Three new modules have also been added including: (i) a module for biomarker analysis based on the calculation of receiver operating characteristic curves; (ii) a module for sample size estimation and power analysis for improved planning of metabolomics studies and (iii) a module to support integrative pathway analysis for both genes and metabolites. In addition, popular features found in existing modules have been significantly enhanced by upgrading the graphical output, expanding the compound libraries and by adding support for more diverse organisms.

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MetaboAnalyst: a web server for metabolomic data analysis and interpretation

2009-05-08

Jianguo Xia , Nikolaos Psychogios , N. Young +1 more

Metabolomics is a newly emerging field of 'omics' research that is concerned with characterizing large numbers of metabolites using NMR, chromatography and mass spectrometry.It is frequently used in biomarker identification … Metabolomics is a newly emerging field of 'omics' research that is concerned with characterizing large numbers of metabolites using NMR, chromatography and mass spectrometry.It is frequently used in biomarker identification and the metabolic profiling of cells, tissues or organisms.The data processing challenges in metabolomics are quite unique and often require specialized (or expensive) data analysis software and a detailed knowledge of cheminformatics, bioinformatics and statistics.In an effort to simplify metabolomic data analysis while at the same time improving user accessibility, we have developed a freely accessible, easy-to-use web server for metabolomic data analysis called MetaboAnalyst.Fundamentally, MetaboAnalyst is a web-based metabolomic data processing tool not unlike many of today's web-based microarray analysis packages.It accepts a variety of input data (NMR peak lists, binned spectra, MS peak lists, compound/concentration data) in a wide variety of formats.It also offers a number of options for metabolomic data processing, data normalization, multivariate statistical analysis, graphing, metabolite identification and pathway mapping.In particular, MetaboAnalyst supports such techniques as: fold change analysis, t-tests, PCA, PLS-DA, hierarchical clustering and a number of more sophisticated statistical or machine learning methods.It also employs a large library of reference spectra to facilitate compound identification from most kinds of input spectra.MetaboAnalyst guides users through a step-by-step analysis pipeline using a variety of menus, information hyperlinks and check boxes.Upon completion, the server generates a detailed report describing each method used, embedded with graphical and tabular outputs.MetaboAnalyst is capable of handling most kinds of metabolomic data and was designed to perform most of the common kinds of metabolomic data analyses.MetaboAnalyst is accessible at http:// www.metaboanalyst.ca

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Assay for lipid peroxides in animal tissues by thiobarbituric acid reaction

1979-06-01

Hiroshi Ohkawa , Nobuko Ohishi , Kunio Yagi

MZmine 2: Modular framework for processing, visualizing, and analyzing mass spectrometry-based molecular profile data

2010-07-23

Tomáš Pluskal , Sandra Castillo , Alejandro Villar‐Briones +1 more

Mass spectrometry (MS) coupled with online separation methods is commonly applied for differential and quantitative profiling of biological samples in metabolomic as well as proteomic research. Such approaches are used … Mass spectrometry (MS) coupled with online separation methods is commonly applied for differential and quantitative profiling of biological samples in metabolomic as well as proteomic research. Such approaches are used for systems biology, functional genomics, and biomarker discovery, among others. An ongoing challenge of these molecular profiling approaches, however, is the development of better data processing methods. Here we introduce a new generation of a popular open-source data processing toolbox, MZmine 2. A key concept of the MZmine 2 software design is the strict separation of core functionality and data processing modules, with emphasis on easy usability and support for high-resolution spectra processing. Data processing modules take advantage of embedded visualization tools, allowing for immediate previews of parameter settings. Newly introduced functionality includes the identification of peaks using online databases, MSn data support, improved isotope pattern support, scatter plot visualization, and a new method for peak list alignment based on the random sample consensus (RANSAC) algorithm. The performance of the RANSAC alignment was evaluated using synthetic datasets as well as actual experimental data, and the results were compared to those obtained using other alignment algorithms. MZmine 2 is freely available under a GNU GPL license and can be obtained from the project website at: http://mzmine.sourceforge.net/ . The current version of MZmine 2 is suitable for processing large batches of data and has been applied to both targeted and non-targeted metabolomic analyses.

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COLORIMETRIC ESTIMATION OF INDOLEACETIC ACID

1951-01-01

Solon A. Gordon , Robert P. Weber

The wide use of the auxin, indoleacetic acid, in physiological and biochemical experiments has promoted interest in methods for its colorimetrie estimation. Mitchell and Brunstetteb (1) have proposed both the … The wide use of the auxin, indoleacetic acid, in physiological and biochemical experiments has promoted interest in methods for its colorimetrie estimation. Mitchell and Brunstetteb (1) have proposed both the nitrite and the ferric chloride-sulphuric acid tests for the quantitative estimation of indoleacetic acid (IAA) in aqueous solutions, basing their suggested procedures upon a study of optimal reaction conditions for these two reagents. According to them, the nitrite method is sensitive to 10 /tig. IAA/ml. and develops a red color that is stable after two hours. In several attempts to duplicate their nitrite method using solutions of IAA varying from 20 to 45 /tg./ml., we could not obtain a stable red color with IAA at the two hours proposed, or at any other time. A faint pink develops almost immediately which rapidly fades to orange or yellow, depending on IAA concentrations, within i hour. If the concentration of nitrite is reduced, the red color becomes sufficiently persistent to be read. Indole likewise gives a strong, relatively stable, red color in this test (cf. table II)?a reaction which is sometimes used as a qualitative test for indole (Nitroso-Indole reaction). Tang and Bonner (2) have modified the ferric chloride-sulphuric acid method for IAA, combining the iron and sulphuric acid as a single reagent to yield improved sensitivity. However, the color produced is also unstable, rapidly developing and then fading. We have found, as have these workers, that the fading color can be practically dealt with by adopting a standard time between addition of reagent and reading of absorbancy or transmittance. Both of the methods discussed above possess disadvantages, lacking either specificity, sensitivity, or stability of color complex formed. During a study of the inactivation of IAA in aqueous solutions, it was frequently necessary to assay at one time many samples where the IAA concentrations were low, or where the degree of significance of small differences in concentrations between experimental unite required evaluation. Hence, we considered it desirable 'to re-examine the ferric chloride-sulphuric acid procedure. Several alterations have been made which produce a more stable color, of increased specificity, which changes in density more rapidly with variation in IAA concentration. 1. The procedure of Tang and Bonner can be improved somewhat by reading at 15 minutes after addition of reagent (instead of 30 minutes as they suggest), since the transient color reaches a maximum at the former time. Maximum absorption was found to occur at 530 ???.

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Metabolomics: the apogee of the omics trilogy

2012-03-22

Gary J. Patti , Óscar Yanes , Gary Siuzdak

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Gas chromatography mass spectrometry–based metabolite profiling in plants

2006-06-01

Jan Lisec , Nicolas Schauer , Joachim Kopka +2 more

Metabolite profiles and the risk of developing diabetes

2011-03-20

Thomas J. Wang , Martin G. Larson , Ramachandran S. Vasan +7 more

Procedures for large-scale metabolic profiling of serum and plasma using gas chromatography and liquid chromatography coupled to mass spectrometry

2011-06-30

Warwick B. Dunn , David Broadhurst , Paul Begley +7 more

A modification of the Lowry procedure to simplify protein determination in membrane and lipoprotein samples

1978-06-01

Mary Ann K. Markwell , Suzanne M. Haas , L.L. Bieber +1 more

Metabolic profiling, metabolomic and metabonomic procedures for NMR spectroscopy of urine, plasma, serum and tissue extracts

2007-10-25

Olaf Beckonert , Hector C. Keun , Timothy M. D. Ebbels +4 more

HMDB: a knowledgebase for the human metabolome

2008-10-26

David S. Wishart , Craig Knox , An Chi Guo +7 more

The Human Metabolome Database (HMDB, http://www.hmdb.ca) is a richly annotated resource that is designed to address the broad needs of biochemists, clinical chemists, physicians, medical geneticists, nutritionists and members of … The Human Metabolome Database (HMDB, http://www.hmdb.ca) is a richly annotated resource that is designed to address the broad needs of biochemists, clinical chemists, physicians, medical geneticists, nutritionists and members of the metabolomics community. Since its first release in 2007, the HMDB has been used to facilitate the research for nearly 100 published studies in metabolomics, clinical biochemistry and systems biology. The most recent release of HMDB (version 2.0) has been significantly expanded and enhanced over the previous release (version 1.0). In particular, the number of fully annotated metabolite entries has grown from 2180 to more than 6800 (a 300% increase), while the number of metabolites with biofluid or tissue concentration data has grown by a factor of five (from 883 to 4413). Similarly, the number of purified compounds with reference to NMR, LC-MS and GC-MS spectra has more than doubled (from 380 to more than 790 compounds). In addition to this significant expansion in database size, many new database searching tools and new data content has been added or enhanced. These include better algorithms for spectral searching and matching, more powerful chemical substructure searches, faster text searching software, as well as dedicated pathway searching tools and customized, clickable metabolic maps. Changes to the user-interface have also been implemented to accommodate future expansion and to make database navigation much easier. These improvements should make the HMDB much more useful to a much wider community of users.

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MassBank: a public repository for sharing mass spectral data for life sciences

2010-07-01

Hisayuki Horai , Masanori Arita , Shigehiko Kanaya +7 more

Abstract MassBank is the first public repository of mass spectra of small chemical compounds for life sciences (<3000 Da). The database contains 605 electron‐ionization mass spectrometry(EI‐MS), 137 fast atom bombardment … Abstract MassBank is the first public repository of mass spectra of small chemical compounds for life sciences (<3000 Da). The database contains 605 electron‐ionization mass spectrometry(EI‐MS), 137 fast atom bombardment MS and 9276 electrospray ionization (ESI)‐MS n data of 2337 authentic compounds of metabolites, 11 545 EI‐MS and 834 other‐MS data of 10 286 volatile natural and synthetic compounds, and 3045 ESI‐MS 2 data of 679 synthetic drugs contributed by 16 research groups (January 2010). ESI‐MS 2 data were analyzed under nonstandardized, independent experimental conditions. MassBank is a distributed database. Each research group provides data from its own MassBank data servers distributed on the Internet. MassBank users can access either all of the MassBank data or a subset of the data by specifying one or more experimental conditions. In a spectral search to retrieve mass spectra similar to a query mass spectrum, the similarity score is calculated by a weighted cosine correlation in which weighting exponents on peak intensity and the mass‐to‐charge ratio are optimized to the ESI‐MS 2 data. MassBank also provides a merged spectrum for each compound prepared by merging the analyzed ESI‐MS 2 data on an identical compound under different collision‐induced dissociation conditions. Data merging has significantly improved the precision of the identification of a chemical compound by 21–23% at a similarity score of 0.6. Thus, MassBank is useful for the identification of chemical compounds and the publication of experimental data. Copyright © 2010 John Wiley & Sons, Ltd.

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HMDB 3.0—The Human Metabolome Database in 2013

2012-11-17

David S. Wishart , Timothy Jewison , An Chi Guo +7 more

The Human Metabolome Database (HMDB) (www.hmdb.ca) is a resource dedicated to providing scientists with the most current and comprehensive coverage of the human metabolome. Since its first release in 2007, … The Human Metabolome Database (HMDB) (www.hmdb.ca) is a resource dedicated to providing scientists with the most current and comprehensive coverage of the human metabolome. Since its first release in 2007, the HMDB has been used to facilitate research for nearly 1000 published studies in metabolomics, clinical biochemistry and systems biology. The most recent release of HMDB (version 3.0) has been significantly expanded and enhanced over the 2009 release (version 2.0). In particular, the number of annotated metabolite entries has grown from 6500 to more than 40 000 (a 600% increase). This enormous expansion is a result of the inclusion of both 'detected' metabolites (those with measured concentrations or experimental confirmation of their existence) and 'expected' metabolites (those for which biochemical pathways are known or human intake/exposure is frequent but the compound has yet to be detected in the body). The latest release also has greatly increased the number of metabolites with biofluid or tissue concentration data, the number of compounds with reference spectra and the number of data fields per entry. In addition to this expansion in data quantity, new database visualization tools and new data content have been added or enhanced. These include better spectral viewing tools, more powerful chemical substructure searches, an improved chemical taxonomy and better, more interactive pathway maps. This article describes these enhancements to the HMDB, which was previously featured in the 2009 NAR Database Issue. (Note to referees, HMDB 3.0 will go live on 18 September 2012.).

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METLIN

2005-12-01

Colin A. Smith , Grace O Maille , Elizabeth J. Want +6 more

Endogenous metabolites have gained increasing interest over the past 5 years largely for their implications in diagnostic and pharmaceutical biomarker discovery. METLIN (http://metlin.scripps.edu), a freely accessible web-based data repository, has … Endogenous metabolites have gained increasing interest over the past 5 years largely for their implications in diagnostic and pharmaceutical biomarker discovery. METLIN (http://metlin.scripps.edu), a freely accessible web-based data repository, has been developed to assist in a broad array of metabolite research and to facilitate metabolite identification through mass analysis. METLIN includes an annotated list of known metabolite structural information that is easily cross-correlated with its catalogue of high-resolution Fourier transform mass spectrometry (FTMS) spectra, tandem mass spectrometry (MS/MS) spectra, and LC/MS data.

How good are my data and what is the resolution?

2013-06-12

Philip R. Evans , Garib N. Murshudov

Following integration of the observed diffraction spots, the process of `data reduction' initially aims to determine the point-group symmetry of the data and the likely space group. This can be … Following integration of the observed diffraction spots, the process of `data reduction' initially aims to determine the point-group symmetry of the data and the likely space group. This can be performed with the program POINTLESS. The scaling program then puts all the measurements on a common scale, averages measurements of symmetry-related reflections (using the symmetry determined previously) and produces many statistics that provide the first important measures of data quality. A new scaling program, AIMLESS, implements scaling models similar to those in SCALA but adds some additional analyses. From the analyses, a number of decisions can be made about the quality of the data and whether some measurements should be discarded. The effective `resolution' of a data set is a difficult and possibly contentious question (particularly with referees of papers) and this is discussed in the light of tests comparing the data-processing statistics with trials of refinement against observed and simulated data, and automated model-building and comparison of maps calculated with different resolution limits. These trials show that adding weak high-resolution data beyond the commonly used limits may make some improvement and does no harm.

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Inferring Correlation Networks from Genomic Survey Data

2012-09-20

Jonathan Friedman , Eric J. Alm

High-throughput sequencing based techniques, such as 16S rRNA gene profiling, have the potential to elucidate the complex inner workings of natural microbial communities - be they from the world's oceans … High-throughput sequencing based techniques, such as 16S rRNA gene profiling, have the potential to elucidate the complex inner workings of natural microbial communities - be they from the world's oceans or the human gut. A key step in exploring such data is the identification of dependencies between members of these communities, which is commonly achieved by correlation analysis. However, it has been known since the days of Karl Pearson that the analysis of the type of data generated by such techniques (referred to as compositional data) can produce unreliable results since the observed data take the form of relative fractions of genes or species, rather than their absolute abundances. Using simulated and real data from the Human Microbiome Project, we show that such compositional effects can be widespread and severe: in some real data sets many of the correlations among taxa can be artifactual, and true correlations may even appear with opposite sign. Additionally, we show that community diversity is the key factor that modulates the acuteness of such compositional effects, and develop a new approach, called SparCC (available at https://bitbucket.org/yonatanf/sparcc), which is capable of estimating correlation values from compositional data. To illustrate a potential application of SparCC, we infer a rich ecological network connecting hundreds of interacting species across 18 sites on the human body. Using the SparCC network as a reference, we estimated that the standard approach yields 3 spurious species-species interactions for each true interaction and misses 60% of the true interactions in the human microbiome data, and, as predicted, most of the erroneous links are found in the samples with the lowest diversity.

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Gut flora metabolism of phosphatidylcholine promotes cardiovascular disease

2011-04-01

Zeneng Wang , Elizabeth Klipfell , Brian J. Bennett +7 more

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Molecular imaging in living subjects: seeing fundamental biological processes in a new light

2003-03-01

Tarik F. Massoud , Sanjiv S. Gambhir

References http://genesdev.cshlp.org/content/17/5/545.full.html#related-urls Article cited in: http://genesdev.cshlp.org/content/17/5/545.full.html#ref-list-1 This article cites 228 articles, 79 of which can be accessed free at: service Email alerting click here top right corner of the article … References http://genesdev.cshlp.org/content/17/5/545.full.html#related-urls Article cited in: http://genesdev.cshlp.org/content/17/5/545.full.html#ref-list-1 This article cites 228 articles, 79 of which can be accessed free at: service Email alerting click here top right corner of the article or Receive free email alerts when new articles cite this article sign up in the box at the Collections Topic (33 articles) Molecular Physiology and Metabolism (98 articles) Cancer and Disease Models Articles on similar topics can be found in the following collections

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Centering, scaling, and transformations: improving the biological information content of metabolomics data

2006-06-08

Robert A. van den Berg , Huub C. J. Hoefsloot , Johan A. Westerhuis +2 more

Extracting relevant biological information from large data sets is a major challenge in functional genomics research. Different aspects of the data hamper their biological interpretation. For instance, 5000-fold differences in … Extracting relevant biological information from large data sets is a major challenge in functional genomics research. Different aspects of the data hamper their biological interpretation. For instance, 5000-fold differences in concentration for different metabolites are present in a metabolomics data set, while these differences are not proportional to the biological relevance of these metabolites. However, data analysis methods are not able to make this distinction. Data pretreatment methods can correct for aspects that hinder the biological interpretation of metabolomics data sets by emphasizing the biological information in the data set and thus improving their biological interpretability. Different data pretreatment methods, i.e. centering, autoscaling, pareto scaling, range scaling, vast scaling, log transformation, and power transformation, were tested on a real-life metabolomics data set. They were found to greatly affect the outcome of the data analysis and thus the rank of the, from a biological point of view, most important metabolites. Furthermore, the stability of the rank, the influence of technical errors on data analysis, and the preference of data analysis methods for selecting highly abundant metabolites were affected by the data pretreatment method used prior to data analysis. Different pretreatment methods emphasize different aspects of the data and each pretreatment method has its own merits and drawbacks. The choice for a pretreatment method depends on the biological question to be answered, the properties of the data set and the data analysis method selected. For the explorative analysis of the validation data set used in this study, autoscaling and range scaling performed better than the other pretreatment methods. That is, range scaling and autoscaling were able to remove the dependence of the rank of the metabolites on the average concentration and the magnitude of the fold changes and showed biologically sensible results after PCA (principal component analysis). In conclusion, selecting a proper data pretreatment method is an essential step in the analysis of metabolomics data and greatly affects the metabolites that are identified to be the most important.

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Cellular mechanisms of insulin resistance

2000-07-15

Gerald I. Shulman

It is estimated that by the year 2020 there will be approximately 250 million people affected by type 2 diabetes mellitus worldwide (1). Although the primary factors causing this disease … It is estimated that by the year 2020 there will be approximately 250 million people affected by type 2 diabetes mellitus worldwide (1). Although the primary factors causing this disease are unknown, it is clear that insulin resistance plays a major role in its development. Evidence for this comes from (a) the presence of insulin resistance 10–20 years before the onset of the disease (2, 3); (b) cross-sectional studies demonstrating that insulin resistance is a consistent finding in patients with type 2 diabetes (3–6); and (c) prospective studies demonstrating that insulin resistance is the best predictor of whether or not an individual will later become diabetic (2, 3). Here, I focus on some recent advances in our understanding of human insulin resistance that have been made using nuclear magnetic resonance spectroscopy (NMR). This technique takes advantage of the spin properties of the nuclei of certain isotopes, such as 1H, 13C, and 31P, which endow the isotopes with a magnetic component that can be used to measure the concentration of intracellular metabolites noninvasively and to assess biochemical differences between normal and diabetic subjects. Drawing on NMR studies from my laboratory and others, I first consider the control of glucose phosphorylation and transport in regulating muscle responses to insulin. I then turn to the effects of fatty acids on insulin responses, showing that commonly accepted models that attempt to explain the association of insulin resistance and obesity are incompatible with recent findings. Finally, I propose an alternative model that appears to fit these and other available data.

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Serum triglycerides determined colorimetrically with an enzyme that produces hydrogen peroxide.

1982-10-01

P Fossati , L Prencipe

In this direct colorimetric procedure, serum triglycerides are hydrolyzed by lipase, and the released glycerol is assayed in a reaction catalyzed by glycerol kinase and L-alpha-glycerol-phosphate oxidase in a system … In this direct colorimetric procedure, serum triglycerides are hydrolyzed by lipase, and the released glycerol is assayed in a reaction catalyzed by glycerol kinase and L-alpha-glycerol-phosphate oxidase in a system that generates hydrogen peroxide. The hydrogen peroxide is monitored in the presence of horseradish peroxidase with 3,5-dichloro-2-hydroxybenzenesulfonic acid/4-aminophenazone as the chromogenic system. The high absorbance of this chromogen system at 510 nm affords useful results with a sample/reagent volume ratio as low as 1:150, and a blank sample measurement is not needed. A single, stable working reagent is used; the reaction is complete in 15 min at room temperature. The standard curve is linear for triglyceride concentrations as great as 13.6 mmol/L. Average analytical recovery of triglycerides in human sera is 100.1%, and within-run and between-run precision studies showed CVs of less than or equal to 1.6 and less than or equal to 3.0%, respectively. The method is suitable for automation.

HMDB: the Human Metabolome Database

2007-01-03

David S. Wishart , Dorit Tzur , Craig Knox +7 more

The Human Metabolome Database (HMDB) is currently the most complete and comprehensive curated collection of human metabolite and human metabolism data in the world. It contains records for more than … The Human Metabolome Database (HMDB) is currently the most complete and comprehensive curated collection of human metabolite and human metabolism data in the world. It contains records for more than 2180 endogenous metabolites with information gathered from thousands of books, journal articles and electronic databases. In addition to its comprehensive literature-derived data, the HMDB also contains an extensive collection of experimental metabolite concentration data compiled from hundreds of mass spectra (MS) and Nuclear Magnetic resonance (NMR) metabolomic analyses performed on urine, blood and cerebrospinal fluid samples. This is further supplemented with thousands of NMR and MS spectra collected on purified, reference metabolites. Each metabolite entry in the HMDB contains an average of 90 separate data fields including a comprehensive compound description, names and synonyms, structural information, physico-chemical data, reference NMR and MS spectra, biofluid concentrations, disease associations, pathway information, enzyme data, gene sequence data, SNP and mutation data as well as extensive links to images, references and other public databases. Extensive searching, relational querying and data browsing tools are also provided. The HMDB is designed to address the broad needs of biochemists, clinical chemists, physicians, medical geneticists, nutritionists and members of the metabolomics community. The HMDB is available at: Author Webpage

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Lipid extraction by methyl-tert-butyl ether for high-throughput lipidomics

2008-02-17

Vitali Matyash , Gerhard Liebisch , Teymuras V. Kurzchalia +2 more

Accurate profiling of lipidomes relies upon the quantitative and unbiased recovery of lipid species from analyzed cells, fluids, or tissues and is usually achieved by two-phase extraction with chloroform. We … Accurate profiling of lipidomes relies upon the quantitative and unbiased recovery of lipid species from analyzed cells, fluids, or tissues and is usually achieved by two-phase extraction with chloroform. We demonstrated that methyl-tert-butyl ether (MTBE) extraction allows faster and cleaner lipid recovery and is well suited for automated shotgun profiling. Because of MTBE's low density, lipid-containing organic phase forms the upper layer during phase separation, which simplifies its collection and minimizes dripping losses. Nonextractable matrix forms a dense pellet at the bottom of the extraction tube and is easily removed by centrifugation. Rigorous testing demonstrated that the MTBE protocol delivers similar or better recoveries of species of most all major lipid classes compared with the "gold-standard" Folch or Bligh and Dyer recipes.

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The CCPN data model for NMR spectroscopy: Development of a software pipeline

2005-04-06

Wim Vranken , Wayne Boucher , Tim J. Stevens +7 more

Abstract To address data management and data exchange problems in the nuclear magnetic resonance (NMR) community, the Collaborative Computing Project for the NMR community (CCPN) created a “Data Model” that … Abstract To address data management and data exchange problems in the nuclear magnetic resonance (NMR) community, the Collaborative Computing Project for the NMR community (CCPN) created a “Data Model” that describes all the different types of information needed in an NMR structural study, from molecular structure and NMR parameters to coordinates. This paper describes the development of a set of software applications that use the Data Model and its associated libraries, thus validating the approach. These applications are freely available and provide a pipeline for high‐throughput analysis of NMR data. Three programs work directly with the Data Model: CcpNmr Analysis, an entirely new analysis and interactive display program, the CcpNmr FormatConverter, which allows transfer of data from programs commonly used in NMR to and from the Data Model, and the CLOUDS software for automated structure calculation and assignment (Carnegie Mellon University), which was rewritten to interact directly with the Data Model. The ARIA 2.0 software for structure calculation (Institut Pasteur) and the QUEEN program for validation of restraints (University of Nijmegen) were extended to provide conversion of their data to the Data Model. During these developments the Data Model has been thoroughly tested and used, demonstrating that applications can successfully exchange data via the Data Model. The software architecture developed by CCPN is now ready for new developments, such as integration with additional software applications and extensions of the Data Model into other areas of research. Proteins 2005. © 2005 Wiley‐Liss, Inc.

Handbook of Hydrocarbon and Lipid Microbiology

2009-11-07

Kenneth N. Timmis

Metabolomics: beyond biomarkers and towards mechanisms

2016-03-16

Caroline H. Johnson , Julijana Ivanišević , Gary Siuzdak

Identifying Small Molecules via High Resolution Mass Spectrometry: Communicating Confidence

2014-01-29

Emma Schymanski , Junho Jeon , Rebekka Gulde +4 more

ADVERTISEMENT RETURN TO ISSUEPREVViewpointNEXTIdentifying Small Molecules via High Resolution Mass Spectrometry: Communicating ConfidenceEmma L. Schymanski*†, Junho Jeon†, Rebekka Gulde†‡, Kathrin Fenner†‡, Matthias Ruff†, Heinz P. Singer†, and Juliane Hollender*†‡View Author … ADVERTISEMENT RETURN TO ISSUEPREVViewpointNEXTIdentifying Small Molecules via High Resolution Mass Spectrometry: Communicating ConfidenceEmma L. Schymanski*†, Junho Jeon†, Rebekka Gulde†‡, Kathrin Fenner†‡, Matthias Ruff†, Heinz P. Singer†, and Juliane Hollender*†‡View Author Information† Eawag: Swiss Federal Institute of Aquatic Science and Technology, Überlandstrasse 133, 8600 Dübendorf, Switzerland‡ Institute of Biogeochemistry and Pollutant Dynamics, ETH Zurich, 8092, Zurich, Switzerland*E-mail: [email protected]*E-mail: [email protected]Cite this: Environ. Sci. Technol. 2014, 48, 4, 2097–2098Publication Date (Web):January 29, 2014Publication History Received14 January 2014Accepted17 January 2014Published online29 January 2014Published inissue 18 February 2014https://pubs.acs.org/doi/10.1021/es5002105https://doi.org/10.1021/es5002105newsACS PublicationsCopyright © 2014 American Chemical Society. This publication is available under these Terms of Use. Request reuse permissions This publication is free to access through this site. Learn MoreArticle Views51321Altmetric-Citations2270LEARN ABOUT THESE METRICSArticle Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to reflect usage leading up to the last few days.Citations are the number of other articles citing this article, calculated by Crossref and updated daily. Find more information about Crossref citation counts.The Altmetric Attention Score is a quantitative measure of the attention that a research article has received online. Clicking on the donut icon will load a page at altmetric.com with additional details about the score and the social media presence for the given article. Find more information on the Altmetric Attention Score and how the score is calculated. Share Add toView InAdd Full Text with ReferenceAdd Description ExportRISCitationCitation and abstractCitation and referencesMore Options Share onFacebookTwitterWechatLinked InRedditEmail PDF (1 MB) Get e-AlertscloseSUBJECTS:Chemical structure,Mass spectrometry,Mathematical methods,Metabolomics,Molecular structure Get e-Alerts

Heatmapper: web-enabled heat mapping for all

2016-05-17

Sasha Babicki , David Arndt , Ana Marcu +4 more

Heatmapper is a freely available web server that allows users to interactively visualize their data in the form of heat maps through an easy-to-use graphical interface. Unlike existing non-commercial heat … Heatmapper is a freely available web server that allows users to interactively visualize their data in the form of heat maps through an easy-to-use graphical interface. Unlike existing non-commercial heat map packages, which either lack graphical interfaces or are specialized for only one or two kinds of heat maps, Heatmapper is a versatile tool that allows users to easily create a wide variety of heat maps for many different data types and applications. More specifically, Heatmapper allows users to generate, cluster and visualize: (i) expression-based heat maps from transcriptomic, proteomic and metabolomic experiments; (ii) pairwise distance maps; (iii) correlation maps; (iv) image overlay heat maps; (v) latitude and longitude heat maps and (vi) geopolitical (choropleth) heat maps. Heatmapper offers a number of simple and intuitive customization options for facile adjustments to each heat map's appearance and plotting parameters. Heatmapper also allows users to interactively explore their numeric data values by hovering their cursor over each heat map cell, or by using a searchable/sortable data table view. Heat map data can be easily uploaded to Heatmapper in text, Excel or tab delimited formatted tables and the resulting heat map images can be easily downloaded in common formats including PNG, JPG and PDF. Heatmapper is designed to appeal to a wide range of users, including molecular biologists, structural biologists, microbiologists, epidemiologists, environmental scientists, agriculture/forestry scientists, fish and wildlife biologists, climatologists, geologists, educators and students. Heatmapper is available at http://www.heatmapper.ca.

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Sharing and community curation of mass spectrometry data with Global Natural Products Social Molecular Networking

2016-08-01

Mingxun Wang , Jeremy Carver , Vanessa V. Phelan +7 more

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HMDB 4.0: the human metabolome database for 2018

2017-10-23

David S. Wishart , Yannick Djoumbou-Feunang , Ana Marcu +7 more

The Human Metabolome Database or HMDB (www.hmdb.ca) is a web-enabled metabolomic database containing comprehensive information about human metabolites along with their biological roles, physiological concentrations, disease associations, chemical reactions, metabolic … The Human Metabolome Database or HMDB (www.hmdb.ca) is a web-enabled metabolomic database containing comprehensive information about human metabolites along with their biological roles, physiological concentrations, disease associations, chemical reactions, metabolic pathways, and reference spectra. First described in 2007, the HMDB is now considered the standard metabolomic resource for human metabolic studies. Over the past decade the HMDB has continued to grow and evolve in response to emerging needs for metabolomics researchers and continuing changes in web standards. This year's update, HMDB 4.0, represents the most significant upgrade to the database in its history. For instance, the number of fully annotated metabolites has increased by nearly threefold, the number of experimental spectra has grown by almost fourfold and the number of illustrated metabolic pathways has grown by a factor of almost 60. Significant improvements have also been made to the HMDB's chemical taxonomy, chemical ontology, spectral viewing, and spectral/text searching tools. A great deal of brand new data has also been added to HMDB 4.0. This includes large quantities of predicted MS/MS and GC-MS reference spectral data as well as predicted (physiologically feasible) metabolite structures to facilitate novel metabolite identification. Additional information on metabolite-SNP interactions and the influence of drugs on metabolite levels (pharmacometabolomics) has also been added. Many other important improvements in the content, the interface, and the performance of the HMDB website have been made and these should greatly enhance its ease of use and its potential applications in nutrition, biochemistry, clinical chemistry, clinical genetics, medicine, and metabolomics science.

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MetaboAnalyst 4.0: towards more transparent and integrative metabolomics analysis

2018-04-13

Jasmine Chong , Othman Soufan , Carin Li +5 more

We present a new update to MetaboAnalyst (version 4.0) for comprehensive metabolomic data analysis, interpretation, and integration with other omics data. Since the last major update in 2015, MetaboAnalyst has … We present a new update to MetaboAnalyst (version 4.0) for comprehensive metabolomic data analysis, interpretation, and integration with other omics data. Since the last major update in 2015, MetaboAnalyst has continued to evolve based on user feedback and technological advancements in the field. For this year's update, four new key features have been added to MetaboAnalyst 4.0, including: (1) real-time R command tracking and display coupled with the release of a companion MetaboAnalystR package; (2) a MS Peaks to Pathways module for prediction of pathway activity from untargeted mass spectral data using the mummichog algorithm; (3) a Biomarker Meta-analysis module for robust biomarker identification through the combination of multiple metabolomic datasets and (4) a Network Explorer module for integrative analysis of metabolomics, metagenomics, and/or transcriptomics data. The user interface of MetaboAnalyst 4.0 has been reengineered to provide a more modern look and feel, as well as to give more space and flexibility to introduce new functions. The underlying knowledgebases (compound libraries, metabolite sets, and metabolic pathways) have also been updated based on the latest data from the Human Metabolome Database (HMDB). A Docker image of MetaboAnalyst is also available to facilitate download and local installation of MetaboAnalyst. MetaboAnalyst 4.0 is freely available at http://metaboanalyst.ca.

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Using MetaboAnalyst 4.0 for Comprehensive and Integrative Metabolomics Data Analysis

2019-09-20

Jasmine Chong , David S. Wishart , Jianguo Xia

MetaboAnalyst (https://www.metaboanalyst.ca) is an easy-to-use web-based tool suite for comprehensive metabolomic data analysis, interpretation, and integration with other omics data. Since its first release in 2009, MetaboAnalyst has evolved significantly … MetaboAnalyst (https://www.metaboanalyst.ca) is an easy-to-use web-based tool suite for comprehensive metabolomic data analysis, interpretation, and integration with other omics data. Since its first release in 2009, MetaboAnalyst has evolved significantly to meet the ever-expanding bioinformatics demands from the rapidly growing metabolomics community. In addition to providing a variety of data processing and normalization procedures, MetaboAnalyst supports a wide array of functions for statistical, functional, as well as data visualization tasks. Some of the most widely used approaches include PCA (principal component analysis), PLS-DA (partial least squares discriminant analysis), clustering analysis and visualization, MSEA (metabolite set enrichment analysis), MetPA (metabolic pathway analysis), biomarker selection via ROC (receiver operating characteristic) curve analysis, as well as time series and power analysis. The current version of MetaboAnalyst (4.0) features a complete overhaul of the user interface and significantly expanded underlying knowledge bases (compound database, pathway libraries, and metabolite sets). Three new modules have been added to support pathway activity prediction directly from mass peaks, biomarker meta-analysis, and network-based multi-omics data integration. To enable more transparent and reproducible analysis of metabolomic data, we have released a companion R package (MetaboAnalystR) to complement the web-based application. This article provides an overview of the main functional modules and the general workflow of MetaboAnalyst 4.0, followed by 12 detailed protocols: © 2019 by John Wiley & Sons, Inc. Basic Protocol 1: Data uploading, processing, and normalization Basic Protocol 2: Identification of significant variables Basic Protocol 3: Multivariate exploratory data analysis Basic Protocol 4: Functional interpretation of metabolomic data Basic Protocol 5: Biomarker analysis based on receiver operating characteristic (ROC) curves Basic Protocol 6: Time-series and two-factor data analysis Basic Protocol 7: Sample size estimation and power analysis Basic Protocol 8: Joint pathway analysis Basic Protocol 9: MS peaks to pathway activities Basic Protocol 10: Biomarker meta-analysis Basic Protocol 11: Knowledge-based network exploration of multi-omics data Basic Protocol 12: MetaboAnalystR introduction.

MetaboAnalyst 5.0: narrowing the gap between raw spectra and functional insights

2021-04-28

Zhiqiang Pang , Jasmine Chong , Guangyan Zhou +7 more

Since its first release over a decade ago, the MetaboAnalyst web-based platform has become widely used for comprehensive metabolomics data analysis and interpretation. Here we introduce MetaboAnalyst version 5.0, aiming … Since its first release over a decade ago, the MetaboAnalyst web-based platform has become widely used for comprehensive metabolomics data analysis and interpretation. Here we introduce MetaboAnalyst version 5.0, aiming to narrow the gap from raw data to functional insights for global metabolomics based on high-resolution mass spectrometry (HRMS). Three modules have been developed to help achieve this goal, including: (i) a LC-MS Spectra Processing module which offers an easy-to-use pipeline that can perform automated parameter optimization and resumable analysis to significantly lower the barriers to LC-MS1 spectra processing; (ii) a Functional Analysis module which expands the previous MS Peaks to Pathways module to allow users to intuitively select any peak groups of interest and evaluate their enrichment of potential functions as defined by metabolic pathways and metabolite sets; (iii) a Functional Meta-Analysis module to combine multiple global metabolomics datasets obtained under complementary conditions or from similar studies to arrive at comprehensive functional insights. There are many other new functions including weighted joint-pathway analysis, data-driven network analysis, batch effect correction, merging technical replicates, improved compound name matching, etc. The web interface, graphics and underlying codebase have also been refactored to improve performance and user experience. At the end of an analysis session, users can now easily switch to other compatible modules for a more streamlined data analysis. MetaboAnalyst 5.0 is freely available at https://www.metaboanalyst.ca.

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antiSMASH 6.0: improving cluster detection and comparison capabilities

2021-04-19

Kai Blin , Simon J. Shaw , Alexander Kloosterman +4 more

Abstract Many microorganisms produce natural products that form the basis of antimicrobials, antivirals, and other drugs. Genome mining is routinely used to complement screening-based workflows to discover novel natural products. … Abstract Many microorganisms produce natural products that form the basis of antimicrobials, antivirals, and other drugs. Genome mining is routinely used to complement screening-based workflows to discover novel natural products. Since 2011, the "antibiotics and secondary metabolite analysis shell—antiSMASH" (https://antismash.secondarymetabolites.org/) has supported researchers in their microbial genome mining tasks, both as a free-to-use web server and as a standalone tool under an OSI-approved open-source license. It is currently the most widely used tool for detecting and characterising biosynthetic gene clusters (BGCs) in bacteria and fungi. Here, we present the updated version 6 of antiSMASH. antiSMASH 6 increases the number of supported cluster types from 58 to 71, displays the modular structure of multi-modular BGCs, adds a new BGC comparison algorithm, allows for the integration of results from other prediction tools, and more effectively detects tailoring enzymes in RiPP clusters.

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Metabolomics — the link between genotypes and phenotypes

2002-01-01

Oliver Fiehn

Current Protocols in Food Analytical Chemistry

2001-08-01

Ronald E. Wrolstad

With an emphasis on food components, you'll find entire chapters devoted to water, proteins, enzymes, lipids, carbohydrates, colors and flavors. Each protocol includes detailed step-by-step annotated instructions as well as … With an emphasis on food components, you'll find entire chapters devoted to water, proteins, enzymes, lipids, carbohydrates, colors and flavors. Each protocol includes detailed step-by-step annotated instructions as well as comprehensive lists of required materials, critical parameters, complete recipes, allotted time, and safety considerations. Accompanying troubleshooting tips and pointers from the author, an expert in that methodology, will virtually assure your success. All protocols, ranging from fundamental to cutting edge, are contributed by top researchers in their respective fields representing leading food science institutions and food analytical laboratories from around the world.

[1] Citrate synthase

1969-01-01

Paul A. Srere

N-Lactoyl Amino Acids: Emerging Biomarkers in Metabolism and Disease.

2025-07-01

Khaled Naja , Laila Hedaya , Asma A. Elashi +2 more | PubMed

N-lactoyl amino acids (Lac-AA) form an emerging class of metabolites that have gained significant attention in recent years due to their ubiquitous presence in different biological systems and potential roles … N-lactoyl amino acids (Lac-AA) form an emerging class of metabolites that have gained significant attention in recent years due to their ubiquitous presence in different biological systems and potential roles in various biochemical processes. This narrative review aims to provide a comprehensive overview of the current understanding of Lac-AA, emphasising their biosynthesis, physiological roles, and potential implications in various diseases. We discuss the discovery of Lac-AA as signalling molecules, and their involvement in exercise-induced appetite suppression, energy metabolism, and other pathways. This review explores the complex relationship between Lac-AA and various pathological conditions, including mitochondrial disorders, type 2 diabetes, phenylketonuria, cancer, and rosacea. We also examine the interplay between Lac-AA and the gut microbiota, as well as their association with metformin treatment. Furthermore, we address the ongoing debate regarding whether Lac-AA are merely reflections of lactate and amino acid levels or independent signalling molecules. This review synthesises the latest research findings, highlights the significance of Lac-AA in metabolic research, and identifies promising avenues for future investigation in this rapidly evolving field.

Protein Supplementation, Plasma Branched-Chain Amino Acids, and Insulin Resistance in Postmenopausal Women: An Ancillary Study from the Supplemental Protein to Outsmart Osteoporosis Now (SPOON) Trial

2025-06-25

Jessica Bihuniak , Arnold Byer , Christine Simpson +4 more | Nutrients

Background/Objectives: Studies have reported an increased risk of type 2 diabetes among people with higher protein intake. Moreover, branched-chain amino acids (BCAA) are reported to be positively associated with insulin … Background/Objectives: Studies have reported an increased risk of type 2 diabetes among people with higher protein intake. Moreover, branched-chain amino acids (BCAA) are reported to be positively associated with insulin resistance (IR). However, it is not understood whether elevated levels of BCAA are causal to IR development, or if higher BCAA are a marker of IR. The objective of this study was to examine the effects of long-term protein and carbohydrate supplementation on plasma BCAA levels, and the relationship between plasma BCAA and IR in postmenopausal women. Methods: Stored samples and data from 84 postmenopausal women who participated in a protein supplementation trial (SPOON) were included. Exclusion criteria consisted of protein intakes less than 0.6 g/kg or greater than 1.0 g/kg, a body mass index (BMI) greater than 32 kg/m2 or less than 19 kg/m2 diseases, and conditions and medications known to impact musculoskeletal health. Subjects were randomized to a whey protein (PRO: n = 38) or maltodextrin supplement (CHO: n = 46) for 18 months. Plasma BCAA, homeostatic model assessment of insulin resistance (HOMA-IR) and body composition were analyzed at baseline and 18 months. Results: At baseline, there were no significant associations between plasma BCAA and IR. There were also no significant changes in plasma BCAA or IR by study arm. However, there was a significant positive association between plasma BCAA and IR in both groups at 18 months (CHO: r = 0.35, p = 0.02; PRO: r = 0.35, p = 0.03). Conclusions: Findings from this study warrant future research to examine other diet and lifestyle factors that may mediate the relationship between circulating BCAA and IR in postmenopausal women.

A comprehensive method of isolating proteins from serum, cerebrospinal fluid, and hippocampal neurons in rats for proteomic profiling using the LC-MS/MS platform

2025-06-25

Pratibha Sharma , Rajinder K. Dhamija | Biochemistry and Biophysics Reports

From microbes to molecules: unveiling host-microbe interactions with spatial metabolomics

2025-06-24

Malin Stüwe , Lars-Erik Petersen , Manuel Liebeke | Molecular Systems Biology

Metabolomics Approach in Environmental Studies: Methodologies, Application and Challenges

2025-06-24

Abdulwasiu Olawale Salaudeen , Vuanghao Lim | Critical Reviews in Analytical Chemistry

Metabolomics is the study of low molecular weight compounds, both endogenous and exogenous, present in an organism's cells, tissues, or biological fluids. In recent years it has gained significant attention … Metabolomics is the study of low molecular weight compounds, both endogenous and exogenous, present in an organism's cells, tissues, or biological fluids. In recent years it has gained significant attention in environmental research for assessing different environmental exposures-primarily chemical pollutants-affect the metabolism of organisms, including humans. Metabolomics has therefore emerged as a crucial technique in exposome investigations, enabling the exploration of molecular-level biological effect of xenobiotics. This review highlights recent applications of metabolomics in evaluating the environmental impacts of disturbances such as pesticides, pharmaceuticals and personal care products, nanoparticles, polychlorinated biphenyls, heavy metals, polyaromatic hydrocarbons, microplastics and changes in naturally occurring compounds with an emphasis on humans, microorganisms, aquatic organisms, plants and soils. It also provides an overview of analytical technologies and recent advances in the field of ecometabolomics. Furthermore, the review acknowledges persistent challenges including sample heterogeneity, complex matrix, data overload, methodological variability, communication barriers and regulatory hurdles. Despite these challenges, metabolomics holds substantial promise in environmental applications.

Unraveling non-target screening variability for LC-HRMS data: a chemometric comparative analysis of river water samples impacted by treated wastewater

2025-06-24

Felix Drees , Maryam Vosough , Torsten Schmidt | Analytical and Bioanalytical Chemistry

Untargeted Metabolomics to Harness Ideal Protein Concept and Mitigate Environmental Impact in Rabbit Models

2025-06-24

Pablo Jesús Marín‐García , Jorge Mateo-López , César Cortés-García +6 more | International Journal of Molecular Sciences

Environmental pollution remains a significant challenge in animal production. The “ideal protein” concept refers to an amino acid profile that precisely meets the animal’s nutritional requirements, optimizing nutrient utilization and … Environmental pollution remains a significant challenge in animal production. The “ideal protein” concept refers to an amino acid profile that precisely meets the animal’s nutritional requirements, optimizing nutrient utilization and minimizing waste excretion. This study applied untargeted metabolomics to explore metabolic changes induced by limiting AA. Two experimental diets were used in 47-day-old growing rabbits: Met+ (with a methionine level balanced to its optimal utilization) and Met− (with a methionine level that was clearly limiting). A total of 68 blood samples were taken for untargeted metabolomics analysis and 88 were taken for targeted plasmatic urea nitrogen analysis, collected at 08:00 (in ad libitum feeding animals) and 21:00 (after a feeding event in 10 h fasting animals). Our results revealed that both sampling time and diet (at each time point) exerted a significant modulatory influence on the metabolome. Interestingly, the difference between the metabolomes obtained with the different diets was less pronounced at 08:00, likely due to the caecotrophy effect, compared to 21:00, when higher intake and lower caecotrophy frequency were observed. This study identifies pseudourine, citric acid, pantothenic acid, and enterolactone sulfate as promising metabolites that could be targeted in order to refine the ideal protein concept, thus improving nutrient efficiency and reducing the environmental impact of animal production.

Whole-genome resequencing revealed the genetic diversity and body weight-related genes of Gannan yak

2025-06-24

Minghao Zhang , Chun Huang , Qi Bao +7 more | Frontiers in Veterinary Science

Body weight (BW) is a crucial indicator of animal growth and development, significantly influencing animal husbandry practices. Previous research has identified several genes associated with BW in certain yak breeds. … Body weight (BW) is a crucial indicator of animal growth and development, significantly influencing animal husbandry practices. Previous research has identified several genes associated with BW in certain yak breeds. However, the genetic basis of BW in Gannan yaks has not been reported. In this study, 309 yaks from six breeds across five provinces in China were sampled. This collection included 247 54-month-old female Gannan yaks, along with 20 Xizang yaks, 15 Muli yaks, 10 Pamir yaks, 9 Bazhou yaks, and 8 Zhongdian yaks. Body weight measurements were recorded for the Gannan yaks. Initial analyses of runs of homozygosity (ROH), nucleotide diversity, and linkage disequilibrium (LD) decay among the six yak breeds revealed that Gannan yaks exhibited the lowest ROH, the highest nucleotide diversity, and the fastest LD decay, indicating rich genetic diversity. Subsequently, a genome-wide association study (GWAS) identified 19 BW-related genes in the Gannan yaks, with PMAIP1 , GABBR1 , LRPPRC , and PPP1R11 identified as key genes. Genome-wide scanning of Group 1 and Group 2 (detailed in section 2.4) identified 90 genes, and Gene Ontology (GO) analysis highlighted FGF2 , SHH , and WNT11 as significantly associated with growth, development, and metabolism. Three overlapping genes were identified between GWAS and genome-wide scans. Further analyses, including nucleotide diversity, LD analysis of significant GWAS sites, allele frequency analysis, and SNP association studies, suggested that DRC1 and SELENOI are novel candidate genes for BW in Gannan yaks. These findings provide a molecular foundation for the genetic improvement of Gannan yaks.

Identification, Cloning, and Functional Characterization of Carotenoid Cleavage Dioxygenase (CCD) from Olea europaea and Ipomoea nil

2025-06-24

Kaixuan Ke , Yufeng Zhang , Xinyi Wang +4 more | Biology

The aromatic C13 apocarotenoid β-ionone is a high-value natural-flavor and -fragrance compound derived from the oxidative cleavage of carotenoids. Carotenoid cleavage dioxygenases (CCDs) play a pivotal role in the biosynthesis … The aromatic C13 apocarotenoid β-ionone is a high-value natural-flavor and -fragrance compound derived from the oxidative cleavage of carotenoids. Carotenoid cleavage dioxygenases (CCDs) play a pivotal role in the biosynthesis of volatile apocarotenoids, particularly β-ionone. In this study, we report the identification, cloning, and functional characterization of two CCD1 homologs: OeCCD1 from Olea europaea and InCCD1 from Ipomoea nil. These two species, which, respectively, represent a woody perennial and a herbaceous annual, were selected to explore the potential functional divergence of CCD1 enzymes across different plant growth forms. These CCD1 genes were synthesized using codon optimization for Escherichia coli expression, followed by heterologous expression and purification using a GST-fusion system. In vitro assays confirmed that both enzymes cleave β-carotene at the 9,10 (9′,10′) double bond to yield β-ionone, but only OeCCD1 exhibits detectable activity on zeaxanthin; InCCD1 shows no in vitro cleavage of zeaxanthin. Kinetic characterization using β-apo-8′-carotenal as substrate revealed, for OeCCD1, a Km of 0.82 mM, Vmax of 2.30 U/mg (kcat = 3.35 s−1), and kcat/Km of 4.09 mM−1·s−1, whereas InCCD1 displayed Km = 0.69 mM, Vmax = 1.22 U/mg (kcat = 1.82 s−1), and kcat/Km = 2.64 mM−1·s−1. The optimization of expression parameters, as well as the systematic evaluation of temperature, pH, solvent, and metal ion effects, provided further insights into the stability and functional diversity within the plant CCD1 family. Overall, these findings offer promising enzymatic tools for the sustainable production of β-ionone and related apocarotenoids in engineered microbial cell factories.

Stereoselective toxicity of (E)-kresoxim-methyl to zebrafish (Danio rerio) embryos during development and metabolomic approaches using GC-MS/MS

2025-06-24

Jiyeon Son , Ji-Woo Yu , Chaeeun Kim +3 more | Ecotoxicology and Environmental Safety

Longitudinal NMR-based Metabolomics Analysis of Male Mountain Ultramarathon Runners: New Perspectives for Athletes Monitoring and Injury Prevention

2025-06-24

Matthieu Schoumacher , J.-M. N'Guyen , Eric Brevers +7 more | Sports Medicine - Open

The aim of this study was to explore how a metabolomic approach could provide valuable information on changes in the athletes' metabolome during a mountain ultramarathon race. To achieve this … The aim of this study was to explore how a metabolomic approach could provide valuable information on changes in the athletes' metabolome during a mountain ultramarathon race. To achieve this goal, we established a longitudinal cohort of athletes enrolled in the TOR des Géants, a 330 km mountain ultramarathon with 24,000 m of elevation gain. Sixteen healthy male athletes (43.9 ± 10.1 years) were recruited, and blood samples were collected at four time points: pre-race, mid-race, post-race and after 72 h recovery. Using a 1H-NMR-based metabolomic approach, we evaluated metabolic changes that occur during both race effort and recovery, and correlated them with functional muscle, cardiac, inflammatory, and renal biomarkers already used in the clinic. The processed data were analyzed using multivariate analysis tools specific to longitudinal study design, and innovative pathway analysis was used for data interpretation. Mountain ultramarathon running significantly affected the metabolism and physiology of athletes. Multivariate analysis highlighted specific metabolites and functional biomarkers associated with prolonged exercise. Neither metabolite levels nor biomarker concentrations returned to baseline after 3 days of recovery. Finally, innovative pathway analysis shed light on specific metabolic changes resulting from mountain ultramarathon exercise. In this study, we propose an NMR-based metabolomics strategy to assess exercise-associated metabolic changes during and after events such as the Tor des Géants. Using state-of-the-art data representation methods specific to metabolomics analysis, we demonstrated that such a methodology can provide a unique view of the biology associated with such extreme conditions. As this approach provides unique insights into the biology of extreme exercise, it holds promise for the development of new tools for athlete management.

Integrated Metabolic Profiling of Coexposure to Legacy and Emerging Contaminants: A Perspective of Paired Serum and Urine Metabolomics

2025-06-24

Qilong Liao , Yuyu Wang , Li Li +7 more | Environmental Science & Technology

High-resolution metabolomics has enhanced our understanding of pollutants' adverse effects on humans, particularly in multipollutant exposure scenarios. However, whether metabolic profiles differ across biological matrices in response to contaminants remains … High-resolution metabolomics has enhanced our understanding of pollutants' adverse effects on humans, particularly in multipollutant exposure scenarios. However, whether metabolic profiles differ across biological matrices in response to contaminants remains unclear. In this study, we analyzed urinary concentrations of 38 legacy and emerging contaminants, including 15 metal(loid)s and 23 metabolites of polycyclic aromatic hydrocarbons, phthalates, and alternative plasticizers in 74 general residents from southern China using inductively coupled plasma mass spectrometry and high-performance liquid chromatography-tandem mass spectrometry. High-resolution metabolomics of paired serum and urine samples identified 186 serum and 774 urine metabolites significantly associated with measured contaminant, enriching 12 serum and 22 urine metabolic pathways. Cross-validation across serum and urine metabolomes revealed 37 overlapping metabolites (mainly as 12 lipids and 9 organic acids) and 8 metabolic pathways (7 amino acid-related and 1 sphingolipid pathway) as primary metabolic perturbations. Notwithstanding shared signatures, organic nitrogen and organ heterocyclic compounds comprise a significantly higher proportion in serum and urine. Only serum phenylalanine and leucine were associated with globulin. These findings first characterize metabolic responses to pollutant mixtures and highlight the complementary value of serum (for biomarker screening) and urine (for exposure assessment) metabolomics in environmental health research.

Effects of short‐term exercise on plasma metabolic and lipidomic profiles of individuals with type 2 diabetes

2025-06-23

Maartje Cox , Aaron Raman , Timothy J. Fairchild +6 more | Experimental Physiology

Abstract Type 2 diabetes mellitus (T2DM) is a common metabolic disorder characterized by chronic hyperglycaemia, with physical inactivity and excessive adiposity as predisposing factors. This clinical trial aimed to investigate … Abstract Type 2 diabetes mellitus (T2DM) is a common metabolic disorder characterized by chronic hyperglycaemia, with physical inactivity and excessive adiposity as predisposing factors. This clinical trial aimed to investigate the effects of an exercise intervention on the metabolome of T2DM participants, fasting and in response to an oral glucose tolerance test (OGTT) and an acute exercise stimulus. Thirteen people with T2DM (age 51 ± 7 years; body mass index 32.7 ± 4.9 kg/m 2 ) completed 45 min of moderate‐intensity treadmill exercise on 12 days consecutively. Blood samples were collected before and after the first and last training sessions and during a pre‐ and postintervention OGTT. Fasted blood samples were collected from 198 healthy control subjects and 208 people with T2DM from an independent cohort for comparison. Samples were analysed using high‐resolution 1 H nuclear magnetic resonance spectroscopy and liquid chromatography–mass spectrometry. The exercise intervention did not induce a shift towards a healthier fasted metabolome in people living with T2DM. In response to consumption of a glucose bolus (OGTT), glycolysis‐related metabolites increased and free fatty acids decreased, with no effect of the exercise intervention. In response to acute exercise, glucose and amino acids decreased and free fatty acids increased, with similar responses on the last day of training as on the first day, indicating no effect of the intervention. The clinical trial was registered prospectively in the Australian New Zealand Clinical Trials Registry ACTRN12617000286347 on 24 February 2017.

Sample Preparation for Multi‐Omics Analysis: Considerations and Guidance for Identifying the Ideal Workflow

2025-06-23

Breyer Woodland , Luke A. Farrell , Lana Brockbals +7 more | PROTEOMICS

ABSTRACT Advances in methodologies and technologies over the past decade have led to an unprecedented depth of analysis of a cell's biomolecules, with entire genomes able to be sequenced in … ABSTRACT Advances in methodologies and technologies over the past decade have led to an unprecedented depth of analysis of a cell's biomolecules, with entire genomes able to be sequenced in hours and up to 10,000 transcripts or ORF products (proteins) able to be quantified from a single cell. Methods for analysing individual omes are now optimised, reliable and robust but are often performed in isolation with other biomolecules considered contaminants. However, there is a growing body of systems biology studies that aim to study multiple omes from the same sample. This review details the current state of the “multi‐omics” field, trying to define what the field is, the methodologies employed and the challenges facing researchers in this field. It also critically evaluates whether these approaches are “fit‐for‐purpose” and how the field needs to evolve to enhance our understanding of how biomolecules from distinct omes interact with one another to alter cellular phenotype in response to change.

Metabolomics in Breast Cancer: From Biomarker Discovery to Personalized Medicine

2025-06-23

Rosa Perestrelo , Catarina L. Silva | Metabolites

Breast cancer (BC) is a highly heterogeneous disease with distinct molecular subtypes, each exhibiting unique metabolic adaptations that drive tumor progression and therapy resistance. Metabolomics has emerged as a powerful … Breast cancer (BC) is a highly heterogeneous disease with distinct molecular subtypes, each exhibiting unique metabolic adaptations that drive tumor progression and therapy resistance. Metabolomics has emerged as a powerful tool for understanding cancer metabolism and identifying clinically relevant biomarkers guiding personalized therapeutic strategies. Advances in analytical techniques such as mass spectrometry (MS) and nuclear magnetic resonance (NMR) spectroscopy have enabled the identification of metabolic alterations associated with BC initiation, progression, and treatment response (dysregulated glycolysis, lipid metabolism, amino acid utilization, and redox homeostasis). This review aims to provide a comprehensive overview of the role of metabolomics in BC research, focusing on its applications in identifying metabolic biomarkers for early diagnosis, prognosis, and treatment response. It underscores how metabolomic profiling can unravel the metabolic adaptations of different BC subtypes, offering insights into tumor biology and mechanisms of therapy resistance. Ultimately, it highlights the promise of metabolomics in driving biomarker-guided diagnostics and the development of metabolically informed, personalized therapeutic strategies in the era of precision medicine.

Exploring the association between volatile organic compound exposure and chronic kidney disease: evidence from explainable machine learning methods

2025-06-23

Liyan Jiang , Hongling Wang , Yang Xiao +2 more | Renal Failure

Chronic Kidney Disease (CKD) affects approximately 697.5 million people worldwide. Volatile organic compounds (VOCs) are emerging as potential risk factors, but their complex relationships with CKD may be underestimated by … Chronic Kidney Disease (CKD) affects approximately 697.5 million people worldwide. Volatile organic compounds (VOCs) are emerging as potential risk factors, but their complex relationships with CKD may be underestimated by traditional linear methods. This study explores the association between urinary VOC metabolites and CKD risk using a combination of epidemiological and interpretable machine learning approaches. Data from the National Health and Nutrition Examination Survey (2011-March 2020 pre-pandemic) were analyzed to examine 15 urinary VOC metabolites. Analytical methods included multivariable logistic regression, LASSO regression, and five machine learning models: Logistic Regression (LR), Random Forest (RF), Extreme Gradient Boosting (XGBoost), K-Nearest Neighbors (KNN), and Multilayer Perceptron (MLP). SHapley Additive exPlanations (SHAP) analysis was used to enhance model interpretability. Significant associations were observed for metabolites including CEMA (N-Acetyl-S-(2-carboxyethyl)-L-cysteine) (OR = 1.66, 95% CI: 1.17-2.37), DHBMA (N-Acetyl-S-(3,4-dihydroxybutyl)-L-cysteine) (OR = 1.95, 95% CI: 1.38-2.76), HMPMA (N-Acetyl-S-(3-hydroxypropyl-1-methyl)-L-cysteine) (OR = 2.18, 95% CI: 1.53-3.10), and PGA (Phenylglyoxylic acid) (OR = 1.66, 95% CI: 1.22-2.27). The XGBoost model demonstrated strong predictive performance, with SHAP analysis highlighting DHBMA as a key predictor. Inverse associations were observed for AAMA (N-Acetyl-S-(2-carbamoylethyl)-L-cysteine) and CYMA (N-Acetyl-S-(2-cyanoethyl)-L-cysteine) in their highest quartiles. This integrated approach identified significant associations between specific urinary VOC metabolites and CKD risk, particularly DHBMA. These findings underscore the role of environmental VOC exposure in CKD pathogenesis and may inform targeted prevention strategies.

Lipidomic profiling of human adiposomes identifies specific lipid shifts linked to obesity and cardiometabolic risk

2025-06-22

Abeer M. Mahmoud , Imaduddin Mirza , Elsayed Metwally +7 more | JCI Insight

BACKGROUNDObesity, a growing health concern, often leads to metabolic disturbances, systemic inflammation, and vascular dysfunction. Emerging evidence suggests that adipose tissue-derived extracellular vesicles (adiposomes) may propagate obesity-related complications. However, their … BACKGROUNDObesity, a growing health concern, often leads to metabolic disturbances, systemic inflammation, and vascular dysfunction. Emerging evidence suggests that adipose tissue-derived extracellular vesicles (adiposomes) may propagate obesity-related complications. However, their lipid composition and effect on cardiometabolic state remain unclear.METHODSThis study examined the lipid composition of adiposomes in 122 participants (75 in obesity group, 47 in lean group) and its connection to cardiometabolic risk. Adiposomes were isolated via ultracentrifugation and characterized using nanoparticle tracking and comprehensive lipidomic analysis by mass spectrometry. Cardiometabolic assessments included anthropometry, body composition, glucose-insulin homeostasis, lipid profiles, inflammatory markers, and vascular function.RESULTSCompared with lean controls, individuals with obesity exhibited elevated adiposome release and shifts in lipid composition, including higher ceramides, free fatty acids, and acylcarnitines, along with reduced levels of phospholipids and sphingomyelins. These alterations strongly correlated with increased BMI, insulin resistance, systemic inflammation, and impaired vascular function. Pathway enrichment analyses highlight dysregulation in glycerophospholipid and sphingolipid metabolism, bile secretion, proinflammatory pathways, and vascular contractility. Machine-learning models utilizing adiposome lipid data accurately classified obesity and predicted cardiometabolic conditions, such as diabetes, hypertension, dyslipidemia, and liver steatosis, achieving accuracy above 85%.CONCLUSIONObesity profoundly remodels the adiposome lipid landscape, linking lipid changes to inflammation, metabolic dysfunction, and vascular impairment. These findings underscore adiposome lipids as biomarkers for obesity and related cardiometabolic disorders, supporting personalized interventions and offering therapeutic value in risk stratification and treatment.FUNDINGThis project was supported by NIH grants R01HL161386, R00HL140049, P30DK020595 (PI: AMM), R01DK104927, and P30DK020595 as well as by a VA Merit Award (1I01BX003382, PI: BTL).

Introducing Korea metabolomics data repository (KMAP): bridging Korean metabolomics data to global data sharing infrastructure

2025-06-22

Woori Chae , Joo‐Youn Cho , Kyo Bin Kang | Metabolomics

The Korea MetAbolomics data rePository (KMAP), available at https://kbds.re.kr/KMAP , is a public repository for metabolomics datasets developed as a part of the Korea BioData Station (K-BDS). KMAP archives metabolomics … The Korea MetAbolomics data rePository (KMAP), available at https://kbds.re.kr/KMAP , is a public repository for metabolomics datasets developed as a part of the Korea BioData Station (K-BDS). KMAP archives metabolomics data and metadata generated from government-funded research projects in Korea, regardless of sample origin or analytical techniques. While data collection is nationally coordinated, data sharing is intended to be global. Here, we present our recent efforts to align KMAP with international standards for QA/QC and interoperability with other repositories.

Mapping current research status and emerging frontiers of lipidomics: a comprehensive data-mining-based study

2025-06-22

Peng-Cheng Li , Zaijie Sun , Xiaofeng Chen +2 more | Metabolomics

Assessing individual genetic susceptibility to metabolic syndrome: interpretable machine learning method

2025-06-22

Tao Huang , Yuanyuan Li , Simin Wang +6 more | Annals of Medicine

Genome-wide association studies have provided profound insights into the genetic aetiology of metabolic syndrome (MetS). However, there is a lack of machine-learning (ML)-based predictive models to assess individual genetic susceptibility … Genome-wide association studies have provided profound insights into the genetic aetiology of metabolic syndrome (MetS). However, there is a lack of machine-learning (ML)-based predictive models to assess individual genetic susceptibility to MetS. This study utilized single-nucleotide polymorphisms (SNPs) as variables and employed ML-based genetic risk score (GRS) models to predict the occurrence of MetS, bringing it closer to clinical application. Feature selection was performed using Least Absolute Shrinkage and Selection Operator. Six ML algorithms were employed to construct GRS models. A fivefold cross-validation was utilized to aid in the internal validation of models. The receiver operating characteristic (ROC) curve was used to select the better-performing GRS model. The SHapley Additive exPlanations (SHAP) was then applied to interpret the model. After extracting GRS, stratified analysis of BMI, age and gender was performed. Finally, these conventional risk factors and GRS were integrated through multivariate logistic regression to establish a combined model. A total of 17 SNPs were selected for analysis. Among the GRS models, the extreme gradient boosting (XGBoost) model demonstrated superior discriminative performance (AUC = 0.837). The XGBoost's optimal robustness was also validated through five-fold cross-validation (mean ROC-AUC = 0.706). The XGBoost-based SHAP algorithm not only elucidated the global effects of 17 SNPs across all samples, but also described the interaction between SNPs, providing a visual representation of how SNPs impact the prediction of MetS in an individual. There was a strong correlation between GRS and MetS risk, particularly observed among young individuals, males and overweight individuals. Furthermore, the model combining conventional risk factors and GRS exhibited excellent discriminative performance (AUC = 0.962) and outstanding robustness (mean ROC-AUC = 0.959). This study established a reliable XGBoost-based GRS model and a GRS prediction platform (https://metabolicsyndromeapps.shinyapps.io/geneticriskscore/) to assess individual genetic susceptibility to MetS. This model has high interpretability and can provide personalized reference for determining the necessity of primary prevention measures for MetS. Additionally, there may be interactions between traditional risk factors and GRS, and the integration of both in a comprehensive model is useful in the prediction of MetS occurrence.

Deep learning-based analysis and identification of single-particle mass spectra of bacteria

2025-06-21

Hong Chen , Ning Zhang , Yaohua Du +3 more | Analytical and Bioanalytical Chemistry

Editorial for Special Issue “Molecular Insights into Food-Derived Natural Products and Their Biological Activities”

2025-06-21

Yanfang Li | Current Issues in Molecular Biology

Food-derived natural products offer more than just essential nutrients like vitamins, calcium, iron, zinc, selenium, and so on [...] Food-derived natural products offer more than just essential nutrients like vitamins, calcium, iron, zinc, selenium, and so on [...]

[Omics Study of Ovarian Malignancies: From Urine Metabolomic Profile to Minimally Invasive MicroRNA Markers].

2025-06-21

Denis S. Kutilin , O.N. Guskova , F. E. Filippov +1 more | PubMed

A search for efficient biomarkers of ovarian cancer is one of the current trends in gynecologic oncology. Metabolic profiling by ultra high-performance liquid chromatography and mass spectrometry (UHPLC-MS) yields information … A search for efficient biomarkers of ovarian cancer is one of the current trends in gynecologic oncology. Metabolic profiling by ultra high-performance liquid chromatography and mass spectrometry (UHPLC-MS) yields information about the total set of low-molecular-weight metabolites of a patient's biological fluid sample. The metabolites may provide potential disease markers, and their combination with microRNA level data significantly increases the diagnostic value. To identify the potential noninvasive diagnostic markers of serous ovarian adenocarcinoma, the metabolomic profile and microRNA transcript levels were studied in urine samples of patients. The study included 60 patients diagnosed with serous ovarian adenocarcinoma and 20 women without a cancer history. Chromatographic separation of metabolites was performed on a Vanquish Flex UHPLC system coupled to an Orbitrap Exploris 480 mass spectrometer. A search for gene regulators of metabolites and microRNA regulators of genes was carried out using the Random forest machine learning method. The microRNA transcript levels in the urine were determined by real-time PCR (qPCR). LASSO-penalized logistic regression was used to build predictive models. In total, 26 compounds showed abnormal concentrations in the ovarian cancer (OC) patients compared with the control group, the set including kynurenine, phenylalanyl-valine, lysophosphatidylcholines (18:3, 18:2, 20:4, and 14:0), alanylleucine, L-phenylalanine, phosphatidylinositol (34:l), 5-methoxytryptophan, 2-hydroxymyristic acid, 3-oxocholic acid, indoleacrylic acid, lysophosphatidylserine (20:4), L-β-aspartyl-L-phenylalanine, myristic acid, decanoylcarnitine, aspartyl-glycine, malonylcarnitine, 3-hydroxybutyrylcarnitine, 3-methylxanthine, 2,6-dimethylheptanoylcarnitine, 3-oxododecanoic acid, N-acetylproline, L-octanoylcarnitine, and capryloylglycine. Metabolite-gene regulator (47 genes) and metabolite-microRNA regulator (613 unique microRNAs) relationships were established by the Random forest method. Levels of 85 microRNAs were validated by qPCR. Changes in transcript levels in the OC patients compared with the controls were observed for miR-382-5p, miR-593-3p, miR-29a-5p, miR-2110, miR-30c-5p, miR-181a-5p, let-7b-5p, miR-27a-3p, miR-370-3p, miR-6529-5p, miR-653-5p, miR-4742-5p, miR-2467-3p, miR-1909-5p, miR-6743-5p, miR-875-3p, miR-19a-3p, miR-208a-5p, miR-330-5p, miR-1207-5p, miR-4668-3p, miR-3193, miR-23a-3p, miR-12132, miR-765, miR-181b-5p, miR-4529-3p, miR-33b-5p, miR-17-5p, miR-6866-3p, miR-4753-5p, miR-103a-3p, miR-423-5p, miR-491-5p, miR-196b-5p, miR-6843-3p, miR-423-5p and miR-3184-5p. Thus, significant metabolomic imbalance in the urine was observed in the OC patients and was associated with changes in the levels of microRNAs that regulate the signaling pathways of the metabolites. The 26 compounds with abnormal concentrations and the levels of the microRNAs miR-33b-5p, miR-423-5p, miR-6843-3p, miR-4668-3p, miR-30c-5p, miR-6743-5p, miR-4742-5p, miR-1207-5p, and miR-17-5p in the urine were considered to be suitable as noninvasive diagnostic markers of OC.

Comprehensive lipid structure annotation via photochemical epoxidation and mass spectrometry

2025-06-20

Jing Yu , Belal Alshaar , Sven Heiles | Analytical and Bioanalytical Chemistry

Abstract Cells tightly regulate lipid structures to fulfill cellular functions and to respond to external stimuli. The biochemical details of the processes that determine lipid structure alterations are often not … Abstract Cells tightly regulate lipid structures to fulfill cellular functions and to respond to external stimuli. The biochemical details of the processes that determine lipid structure alterations are often not fully understood. In this manuscript, we present a new epoxidation strategy of unsaturated lipids, which allows the annotation of lipid head group, fatty acid composition, C = C bond position, C = C bond geometry, and sn -isomerism when derivatized lipids are analyzed with corresponding separation and mass spectrometry methods. Tandem mass spectra of epoxidized deprotonated or protonated lipids provide information on head groups, fatty acids, and C = C positions, while MSⁿ of alkali metal adducts reveals sn -isomer compositions. Separation of epoxidation products via reversed-phase liquid chromatography (RPLC) not only distinguishes lipid C = C position and sn -isomers, remaining non-reacted unsaturated lipid C = C bonds photo-isomerize to reveal C = C E / Z configurations. To demonstrate the capabilities of the methodology, C = C positions of LPEs, LPCs, TGs, DGs, PCs, PSs, and PEs are annotated for bovine heart and liver extracts in RPLC-MS 2 experiments, and shotgun MS n is employed to characterize 56 PC sn -isomers in HeLa and H9c2 cell lines. Graphical Abstract

Shinyscreen: mass spectrometry data inspection and quality checking utility

2025-06-20

Todor Kondić , Anjana Elapavalore , Jessy Krier +4 more | Journal of Cheminformatics

Abstract Shinyscreen is an R package and Shiny-based web application designed for the exploration, visualization, and quality assessment of raw data from high resolution mass spectrometry instruments. Its versatile list-based … Abstract Shinyscreen is an R package and Shiny-based web application designed for the exploration, visualization, and quality assessment of raw data from high resolution mass spectrometry instruments. Its versatile list-based approach supports the curation of data starting from either known or “suspected” compounds (compound list-based screening) or detected masses (mass list-based screening), making it adaptable to diverse analytical needs (target, suspect or non-target screening). Shinyscreen can be operated in multiple modes, including as an R package, an interactive command-line tool, a self-documented web GUI, or a network-deployable service. Shinyscreen has been applied in environmental research, database enrichment, and educational initiatives, showcasing its broad utility. Shinyscreen is available in GitLab ( https://gitlab.com/uniluxembourg/lcsb/eci/shinyscreen ) under the Apache License 2.0. The repository contains detailed instructions for deployment and use. Additionally, a pre-configured Docker image, designed for seamless installation and operation is available, with instructions also provided in the main repository. Scientific Contribution : Shinyscreen is a fully open source prescreening application to assist analysts in the high throughput quality control of the thousands of peaks detected in high resolution mass spectrometry experiments. As a vendor-independent, cross operating system application it covers an important niche in open mass spectrometry workflows. Shinyscreen supports quality control of data for further identification or upload of spectra to public data resources, as well as teaching efforts to educate students on the importance of data quality control and rigorous identification methods.

A Pilot Metabolomic Study for Diagnosing Aspergillus Infection in Immunocompromised Pediatric Cancer Patients

2025-06-20

Taghreed Khaled Abdelmoneim , Asmaa Ramzy , Mark Zaki +7 more | International Journal of Molecular Sciences

Fungal infection caused by invasive Aspergillus is a life-threatening complication in immunocompromised pediatric cancer patients. However, the early diagnosis of invasive infection remains a clinical challenge due to the lack … Fungal infection caused by invasive Aspergillus is a life-threatening complication in immunocompromised pediatric cancer patients. However, the early diagnosis of invasive infection remains a clinical challenge due to the lack of specific, non-invasive biomarkers. The current study investigates plasma metabolomic profiling integrated with an AI-derived fungal secondary metabolite database to identify potential biomarkers for rapid, non-invasive detection of Aspergillus infection. Plasma samples from thirteen pediatric oncology patients were analyzed using untargeted metabolomics based on UHPLC-MS/MS. Based on galactomannan assay results, three patients were classified as Aspergillus-Infected (AIC) and ten as non-infected controls (NPCs). An in-house custom database for secondary metabolites of fungi was incorporated to enhance metabolite annotation. Eight metabolites were found to be candidate biomarkers based on statistical significance, fold change, and biological relevance. In the AIC cohort, aflatoxin B1, aspergillimide, fumifungin, and uridine were found to be significantly elevated while citric acid presented a decrease. Multivariate analysis utilizing PCA and PLSDA showed distinct group separation. Moreover, sample size estimation indicates that a minimum of 25 participants would be needed in future studies for appropriate statistical power.

Assessing SIFT-MS Method Feasibility (Procedure 1)

2025-06-20

| Royal Society of Chemistry eBooks

The marked difference in the analytical approach of SIFT-MS compared with the conventional GC and LC techniques means that the feasibility of method transfer – or new method development – … The marked difference in the analytical approach of SIFT-MS compared with the conventional GC and LC techniques means that the feasibility of method transfer – or new method development – for SIFT-MS should be evaluated prior to experimental work. Drawing on principles discussed in Part 1 of this book, this chapter describes a procedure by which the feasibility can be assessed for a broad range of applications.

The use of web resources for metabolomics in horticultural crops

2025-06-20

Esra Karakaş , Mustafa Bulut , Alisdair R. Fernie | Horticulture Advances

Abstract Metabolomics, a rapidly evolving field, has revolutionized horticultural crop research by enabling comprehensive analysis of metabolites that influence plant yield, growth, quality and nutritional value. The integration of web-based … Abstract Metabolomics, a rapidly evolving field, has revolutionized horticultural crop research by enabling comprehensive analysis of metabolites that influence plant yield, growth, quality and nutritional value. The integration of web-based resources, including databases, computational tools and analytical platforms has significantly enhanced metabolomics studies by facilitating data processing, metabolite identification and pathway analysis. Moreover, the application of machine learning algorithms to these web resources has further optimized data interpretation, enabling more accurate prediction of metabolic profiles. Publicly available reference libraries and bioinformatic tools support precision of breeding, postharvest quality assessment and ultimately improving crop yield and sustainability. In this mini-review, we explore the current status of the diverse range of plant metabolomics databases in horticultural crops, highlighting the synergy between machine learning and traditional bioinformatics methods, their applications, challenges and future prospects in advancing plant science and agricultural innovation.

Targeted Metabolomic Serum Analysis of Patients with High and Low Risk of Endometrial Cancer Recurrence and Positive and Negative Lymph Node Status

2025-06-20

Dagmara Pietkiewicz , Mikołaj Piotr Zaborowski , Szymon Plewa +5 more | Metabolites

Background: Endometrial cancer is among the most prevalent gynecological malignancies, with increasing mortality primarily due to initially advanced disease with lymph node metastasis or tumor recurrence. Current risk stratification models … Background: Endometrial cancer is among the most prevalent gynecological malignancies, with increasing mortality primarily due to initially advanced disease with lymph node metastasis or tumor recurrence. Current risk stratification models show limited accuracy, highlighting the need for more accurate biomarkers. This study aimed to identify metabolic compounds that can serve as predictors of recurrence risk and lymph node status in endometrial cancer. Methods: Targeted metabolomic profiling of preoperative serum samples from 123 patients with endometrial cancer, stratified into high- or low-risk and lymph node-positive or -negative groups, was conducted using the AbsoluteIDQ p180 Kit and high-performance liquid chromatography–mass spectrometry. Results: Analysis revealed significant differences in metabolites related to lipid and amino acid metabolism between groups. High-risk and lymph node-positive patients presented significantly lower concentrations of phosphatidylcholines, lysophosphatidylcholines, medium-chain acylcarnitines, and specific amino acids such as alanine, histidine, and tryptophan compared to low-risk and lymph node-negative patients. Receiver operating characteristic curve analyses highlighted the diagnostic potential of these metabolites, particularly alanine and taurine, in distinguishing patient groups. Conclusions: The findings indicate complex metabolic reprogramming associated with aggressive endometrial cancer phenotypes, involving enhanced lipid utilization and amino acid metabolism alterations, potentially supporting tumor proliferation and metastatic progression. Thus, targeted metabolomic serum profiling might be a powerful tool for improving risk assessment, enabling more personalized therapeutic approaches and management strategies in endometrial cancer.

CSU-MS2: A Contrastive Learning Framework for Cross-Modal Compound Identification from MS/MS Spectra to Molecular Structures

2025-06-20

Ting Xie , Hailiang Zhang , Qiong Yang +5 more | Analytical Chemistry

Tandem mass spectrometry (MS/MS) is a cornerstone for compound identification in complex mixtures, but conventional spectral matching approaches face critical limitations due to limited library coverage and matching algorithms. To … Tandem mass spectrometry (MS/MS) is a cornerstone for compound identification in complex mixtures, but conventional spectral matching approaches face critical limitations due to limited library coverage and matching algorithms. To address this, we propose CSU-MS2 (contrastively spectral-structural Unification framework for MS/MS Spectra and Molecular Structures), a novel framework that bridges MS/MS spectra and molecular structures through cross-modal contrastive learning. CSU-MS2 uniquely integrates an External Space Attention Aggregation (ESA) module to dynamically align spectral and structural features, enabling direct retrieval of molecular candidates from a unified embedding space. The framework is pretrained on large-scale in-silico MS/MS data sets generated by CFM-ID and ICEBERG, followed by fine-tuning on high-quality experimental data. Results show that CSU-MS2 achieves a Recall@1 of 75.45% when matching 1047 spectra against a reference library containing 1,001,047 compounds, significantly surpassing existing methods such as CFM-ID (68.38%), SIRIUS (64.85%), MetFrag (48.59%), and CMSSP (30.47%). Furthermore, rigorous validation on three external data sets spanning human metabolomics (MTBLS265), plant metabolites (PMhub), and the CASMI 2022 challenge demonstrates robust generalizability, with domain-specific retrieval achieving a Recall@10 of 91.67% for blood metabolites. To facilitate compound identification across various domains, we have assembled a Spectrum-searchable Structural Feature Database (SSFDB) from 23 structural databases and deployed an open-source web server supporting customizable cross-modal retrieval. All code, models, and SSFDB are publicly accessible, offering a transformative solution for high-throughput compound identification in metabolomics and beyond.

Large-scale discovery and annotation of hidden substructure patterns in mass spectrometry profiles

2025-06-20

Laura Rosina Torres Ortega , Jonas Dietrich , Joe Wandy +2 more | bioRxiv (Cold Spring Harbor Laboratory)

Untargeted mass spectrometry measures many spectra of unknown molecules. Tandem mass spectrometry (MS/MS) generates fragmentation patterns representing common substructures termed Mass2Motifs. However, MS/MS-based substructure identification is limited by computational efficiency … Untargeted mass spectrometry measures many spectra of unknown molecules. Tandem mass spectrometry (MS/MS) generates fragmentation patterns representing common substructures termed Mass2Motifs. However, MS/MS-based substructure identification is limited by computational efficiency and interpretability. Here, we introduce a complete overhaul of MS2LDA, overcoming limitations of its predecessor. We report an up to 14x speed improvement for unsupervised Mass2Motif discovery, allowing it to process larger datasets. Furthermore, the new automated Mass2Motif Annotation Guidance (MAG) aids in structurally identifying Mass2Motifs. Using three curated MotifDB-MotifSets for benchmarking, MAG achieved median substructure overlap scores of 0.75, 0.93, and 0.95. On experimental data, we validated MS2LDA 2.0 by identifying substructures of pesticides spiked into a biological matrix and demonstrated its discovery potential by annotating previously uncharacterized fungal natural products. Together with the new visualization app and MassQL-searchable MotifDB, we anticipate that MS2LDA 2.0 will boost the identification of novel chemistry and hidden patterns in mass spectrometry.

Self-organizing maps to aid prognostic and diagnostic biomarker identification in exploratory metabolomics of benign prostatic hyperplasia

2025-06-19

André Luiz Melo Camelo , A. M. Oliveira , Hans Rolando Zamora-Obando +7 more | Metabolomics

Genes govern metabolism-Enzymes define pathways and metabolic relationships

2025-06-19

Jiada Zhan , Zachery Jarrell , Melissa L. Kemp +4 more | bioRxiv (Cold Spring Harbor Laboratory)

Abstract Gene centric pathway mapping tools, widely used to interpret untargeted LCMS metabolomics data, may underperform because a single metabolite can generate multiple spectral features, inflating false positive rates. Classic … Abstract Gene centric pathway mapping tools, widely used to interpret untargeted LCMS metabolomics data, may underperform because a single metabolite can generate multiple spectral features, inflating false positive rates. Classic enzymology, which established metabolite flow long before gene sequencing, offers experimentally validated precursor product relationships that could overcome these ambiguities. We evaluated whether enzymology defined precursor product correlations are consistently detectable in human plasma LCMS data and explored their potential to enhance pathway analysis and metabolite identification. Using a high resolution LCMS platform, we detected amino acids and carnitine related metabolites in one individual sampled eight times over five years and in 50 adults sampled 6 to 8 times each. Spearman correlations were calculated for longitudinal and cross sectional data. In the single participant repeated measures, strong positive correlations were observed for every direct precursor product pair except the branch point metabolite palmitoylcarnitine. The longitudinal analysis reproduced these patterns, and the same relationships were retained when analysis was cross sectional. Despite contributions from multiple organ systems, plasma thus preserved core enzymatic relationships. Precursor product proportionality, a fundamental principle of enzymology, is readily detectable in large scale LCMS datasets and remains robust across longitudinal and cross sectional designs. Applying these correlations to metabolomics workflows can improve pathway analysis, help metabolite identification, and reveal how genetic variations, diets, therapeutic drugs, and environmental exposures jointly impact metabolic pathways.

Insomnia, Cognitive Impairment, or a Combination of Both, Alter Lipid Metabolism Due to Changes in Acylcarnitine Concentration in Older Persons

2025-06-19

Selma Karime Castillo-Vázquez , Berenice Palacios‐González , Marcela Vela‐Amieva +7 more | Metabolites

Insomnia has been widely associated with cognitive impairment (CI). However, the relationship between the two entities (insomnia and CI) is poorly understood. In this context, adults with insomnia show metabolic … Insomnia has been widely associated with cognitive impairment (CI). However, the relationship between the two entities (insomnia and CI) is poorly understood. In this context, adults with insomnia show metabolic changes, including alterations in the catabolism of branched-chain amino acids, glycerophospholipids, and glutathione and glutamate biosynthesis. Nevertheless, aging itself induces metabolic changes that may be amplified by chronic diseases that compromise the health of the elderly. Therefore, in the present study we aim to characterise metabolomic profiles of insomnia and CI alone in order to address a significant gap in current research regarding the pathways through which insomnia may lead to CI in older persons. In this study we perform a targeted metabolomics analysis (UPLC-MS) on 80 serum samples from the Cohort of Obesity, Sarcopenia, and Frailty of Older Mexican Adults (COSFOMA); these samples were classified into four groups (control, insomnia, CI, and insomnia + CI). Our results show that insomnia increases the concentration of acylcarnitines (C10, C8, C14, C12:1, C18:1 and C18) as compared to CI group, while older persons with CI show a decrease the concentration of the acylcarnitines C16, C10 and C8. Finally, individuals with both conditions (insomnia + CI) show that only the concentration of the acylcarnitine C16 decreases compared to controls. Taken together, our results shed light on the relevance of insomnia on lipid metabolism in older persons.

Interpretable Machine Learning for Serum-Based Metabolomics in Breast Cancer Diagnostics: Insights from Multi-Objective Feature Selection-Driven LightGBM-SHAP Models

2025-06-19

Emek Güldoğan , Fatma Hilal Yağın , Hasan UCUZAL +3 more | Medicina

Background and Objectives: Breast cancer accounts for 12.5% of all new cancer cases in women worldwide. Early detection significantly improves survival rates, but traditional biomarkers like CA 15-3 and HER2 … Background and Objectives: Breast cancer accounts for 12.5% of all new cancer cases in women worldwide. Early detection significantly improves survival rates, but traditional biomarkers like CA 15-3 and HER2 lack sensitivity and specificity, particularly for early-stage disease. Advances in metabolomics and machine learning, particularly explainable artificial intelligence (XAI), offer new opportunities for identifying robust biomarkers and improving diagnostic accuracy. This study aimed to identify and validate serum-based metabolic biomarkers for breast cancer using advanced metabolomic profiling techniques and a Light Gradient Boosting Machine (LightGBM) model. Additionally, SHapley Additive exPlanations (SHAP) were applied to enhance model interpretability and biological insight. Materials and Methods: The study included 103 breast cancer patients and 31 healthy controls. Serum samples underwent liquid and gas chromatography-time-of-flight mass spectrometry (LC-TOFMS and GC-TOFMS). Mutual Information (MI), Sparse Partial Least Squares (sPLS), Boruta, and Multi-Objective Feature Selection (MOFS) approaches were applied to the data for biomarker discovery. LightGBM, AdaBoost, and Random Forest were employed for classification and to identify class imbalance with the Synthetic Minority Oversampling Technique (SMOTE). SHAP analysis ranked metabolites based on their contribution to model predictions. Results: Compared to other feature selection approaches, the MOFS approach was more robust in terms of predictive performance, and metabolites identified by this method were used in subsequent analyses for biomarker discovery. LightGBM outperformed the AdaBoost and Random Forest models, achieving 86.6% accuracy, 89.1% sensitivity, 84.2% specificity, and an F1-score of 87.0%. SHAP analysis identified 2-Aminobutyric acid, choline, and coproporphyrin as the most influential metabolites, with dysregulation of these markers associated with breast cancer risk. Conclusions: This study is among the first to integrate SHAP explainability with metabolomic profiling, bridging computational predictions and biological insights for improved clinical adoption. This study demonstrates the effectiveness of combining metabolomics with XAI-driven machine learning for breast cancer diagnostics. The identified biomarkers not only improve diagnostic accuracy but also reveal critical metabolic dysregulations associated with disease progression.

Efficient Stereochemical Analysis of Hydroxy Fatty Acids Using PGME and PAME Derivatization

2025-06-19

Soohyun Um , Jaeyoun Lee , Seung Hyun Kim | Analytical Chemistry

We present a time-efficient and cost-effective approach for the stereochemical analysis of α- and β-hydroxy fatty acids (α-HFAs and β-HFAs) using chiral derivatizing agents, phenylglycine methyl ester (PGME) and phenylalanine … We present a time-efficient and cost-effective approach for the stereochemical analysis of α- and β-hydroxy fatty acids (α-HFAs and β-HFAs) using chiral derivatizing agents, phenylglycine methyl ester (PGME) and phenylalanine methyl ester (PAME). Conventional methods for stereochemical analysis, such as X-ray crystallography and Mosher's ester derivatization, require pure samples and are laborious. In contrast, PGME and PAME derivatization enables direct LC-MS analysis of crude extracts without compound isolation or extensive sample preparation. Additionally, this method eliminates the need for NMR measurement and crystallization after the reaction, thereby reducing the overall analysis time. The approach was successfully applied to newly discovered lipopeptides, demonstrating its efficiency, reproducibility, and potential for widespread use in the stereochemical characterization of bioactive natural products.

Laser capture microdissection-assisted gas chromatography–triple-quadruple mass spectrometry for spatial metabolic profiling of esophageal squamous cell carcinoma

2025-06-18

Peng Dou , Huan Yang , Meihua Bao +6 more | Journal of Pharmaceutical and Biomedical Analysis

Protection of LPS-induced intestinal injury in goslings by polysaccharide of Atractylodes macrocephala Koidz based on 16S rRNA and metabolomics analysis

2025-06-18

Wanyan Li , Baili Lu , Shaowei Pan +7 more | PLoS ONE

The protective effects of the polysaccharide of Atractylodes macrocephala Koidz (PAMK) against lipopolysaccharide (LPS)-induced intestinal injury in goslings was determined using 16S rRNA analysis of cecal contents and serum metabolomics … The protective effects of the polysaccharide of Atractylodes macrocephala Koidz (PAMK) against lipopolysaccharide (LPS)-induced intestinal injury in goslings was determined using 16S rRNA analysis of cecal contents and serum metabolomics analysis. PAMK was administered to goslings following LPS-induced intestinal injury, and its effects were assessed. PAMK significantly reduced the serum levels of inflammatory factors including interleukin (IL)-6 and C-reactive protein and decreased the expression of pro-inflammatory cytokines including interleukin(IL)-1 <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="M1"><mml:mrow><mml:mi>β</mml:mi></mml:mrow></mml:math> , IL-6, and toll-like receptor 2 in jejunal tissues. Moreover, PAMK significantly upregulated the relative mRNA expression levels of the tight junction proteins Zonula occludens -1, Occludin , Claudin , and Mucin -2, enhancing the integrity of the intestinal barrier and alleviating LPS-induced intestinal injury. 16S rRNA sequencing revealed that PAMK could alleviate LPS-induced disruption of the intestinal microbiota structure and improve microbial diversity. Metabolomics analysis revealed that PAMK could influence key metabolic pathways, including the mTOR, PI3K-Akt, and FoxO signaling pathways, and regulate metabolites such as L-aspartic acid and S-adenosylmethionine. Integrated analysis indicated that PAMK could promote the enrichment of beneficial bacteria (e.g., Allobaculum and Peptococcus ) while alleviating LPS-induced microbial dysbiosis by modulating the correlation between key metabolites and specific microbial populations. Overall, PAMK could alleviate LPS induced intestinal injury by enhancing intestinal barrier function, optimizing gut microbiota composition, and regulating metabolic signaling pathways. Our findings provide a novel strategy for maintaining the intestinal health of poultry and preventing intestinal diseases.

Sustained Exposure to Triclocarban and Triclosan at Environmental Relevant Concentration Disrupts Gut-Liver Axis in the Black-spotted Frog

2025-06-18

Feng Wang , Wenhui Sun , Xixi Ye +6 more | Aquatic Toxicology

SIRT2-dependent CPT1A deacetylation in macrophages inhibits periodontitis

2025-06-18

Yixuan Jiang , Xiu Yao , Zhizhong Jiang +5 more | Frontiers in Pharmacology

Introduction Periodontitis is intricately related to systemic disorders and exerts a negative impact on quality of life. Recent studies have suggested a potential association between periodontitis and fatty acid oxidation … Introduction Periodontitis is intricately related to systemic disorders and exerts a negative impact on quality of life. Recent studies have suggested a potential association between periodontitis and fatty acid oxidation (FAO), a key metabolic process involved in energy production and cellular function. However, the molecular mechanisms underlying this relationship remain insufficiently understood. This study aims to explore the role of carnitine palmitoyltransferase 1A ( CPT1A ), a pivotal enzyme in fatty acid oxidation (FAO), in the pathogenesis of periodontitis. Methods The involvement of FAO in periodontitis was validated through bioinformatics analysis and quantitative real-time polymerase chain reaction (qRT-PCR). The anti-inflammatory effects of the CPT1A inhibitor Etomoxir (ETO) were assessed by qRT-PCR and Western blot analysis. The interaction between Sirtuin-2 ( SIRT2 ) and CPT1A was confirmed via Chromatin Immunoprecipitation (ChIP)-qPCR. An experimental model of periodontitis was induced using silk ligation, and the effects of CPT1A inhibition on periodontitis were evaluated in mice treated with ETO. Micro-Computed Tomography (micro-CT) and histological analyses were employed to assess the impact of CPT1A inhibition on tissue architecture and inflammatory response in the periodontal tissues. Results ETO reduced the expression levels of TNF-α, IL-6, IL-1β, NF-κB, and MAPK. Furthermore, it decreased cementoenamel junction-alveolar bone crest (CEJ-ABC) distance, immune cell infiltration in gingival tissues, and the expression levels of iNOS and p65. Additionally, ChIP-qPCR further confirmed the interaction between Sirtuin-2 ( SIRT2 )- CPT1A , thereby impacting the acetylation levels of CPT1A and decreasing CPT1A activity. Conclusion Overall, these findings demonstrate that SIRT2 binds to and deacetylates CPT1A , thereby inhibiting osteoclast differentiation and concurrently alleviating inflammation in periodontal tissues during experimental periodontitis progression.

Longitudinal analysis of genetic and environmental interplay in human metabolic profiles and the implication for metabolic health

2025-06-17

Jing Wang , Alberto Zenere , Xingyue Wang +4 more | Genome Medicine

Abstract Background Understanding how genetics and environmental factors shape human metabolic profiles is crucial for advancing metabolic health. Variability in metabolic profiles, influenced by genetic makeup, lifestyle, and environmental exposures, … Abstract Background Understanding how genetics and environmental factors shape human metabolic profiles is crucial for advancing metabolic health. Variability in metabolic profiles, influenced by genetic makeup, lifestyle, and environmental exposures, plays a critical role in disease susceptibility and progression. Methods We conducted a two-year longitudinal study involving 101 clinically healthy individuals aged 50 to 65, integrating genomics, metabolomics, lipidomics, proteomics, clinical measurements, and lifestyle questionnaire data from repeat sampling. We evaluated the influence of both external and internal factors, including genetic predispositions, lifestyle factors, and physiological conditions, on individual metabolic profiles. Additionally, we developed an integrative metabolite-protein network to analyze protein-metabolite associations under both genetic and environmental regulations. Results Our findings highlighted the significant role of genetics in determining metabolic variability, identifying 22 plasma metabolites as genetically predetermined. Environmental factors such as seasonal variation, weight management, smoking, and stress also significantly influenced metabolite levels. The integrative metabolite-protein network comprised 5,649 significant protein-metabolite pairs and identified 87 causal metabolite-protein associations under genetic regulation, validated by showing a high replication rate in an independent cohort. This network revealed stable and unique protein-metabolite profiles for each individual, emphasizing metabolic individuality. Notably, our results demonstrated the importance of plasma proteins in capturing individualized metabolic variabilities. Key proteins related to individual metabolic profiles were identified and validated in the UK Biobank, showing great potential for metabolic risk assessment. Conclusions Our study provides longitudinal insights into how genetic and environmental factors shape human metabolic profiles, revealing unique and stable individual metabolic profiles. Plasma proteins emerged as key indicators for capturing the variability in human metabolism and assessing metabolic risks. These findings offer valuable tools for personalized medicine and the development of diagnostics for metabolic diseases.

Benchtop NMR Spectroscopy of In Vivo Multicellular Organisms

2025-06-17

Katelyn Downey , Kiera Ronda , Peter M. Costa +7 more | Analytical Chemistry

NMR spectroscopy is a critical tool for environmental and biological research, but the physical and financial barriers of standard "high-field" NMR spectrometers can limit applications, especially in the environmental sciences. … NMR spectroscopy is a critical tool for environmental and biological research, but the physical and financial barriers of standard "high-field" NMR spectrometers can limit applications, especially in the environmental sciences. Low-field benchtop NMR (1H resonance frequencies generally ≤100 MHz) is more accessible, but its lower sensitivity and increased spectral overlap have limited the study of complex samples. Living organisms are among the most heterogeneous samples, and it is unclear if useful information can be extracted in vivo using benchtop NMR. Here, the potential of low-field (80 MHz) in vivo NMR is first assessed by analyzing 13C-labeling of unicellular green algae and then by monitoring a process within a multicellular organism (T. californicus). This is followed by studying live brine shrimp (A. franciscana) at 13C natural abundance. Adults are compared to brine shrimp cysts, with a number of spectral assignments possible and differences between the life stages clearly evident. High-field NMR is used to confirm peak assignments and provide a more comprehensive characterization of biomolecules present, ultimately making the low-field NMR data more useful. Standard experiments such as 1D 1H, 1D 13C and 2D HSQC are conducted, as well as more advanced experiments such as 13C-SSFP, which greatly enhances 13C sensitivity, and reverse HSQC, which decreases spectral overlap. Ultimately, this work demonstrates that low-field NMR can effectively analyze live organisms with or without isotopic enrichment and that it holds great potential for future work, such as in vivo analysis of organisms directly in the field if/when portable NMR spectrometers become available.

Integrating NMR and MS for Improved Metabolomic Analysis: From Methodologies to Applications

2025-06-17

Patrícia Homobono Brito de Moura , Guillaume Leleu , Grégory Da Costa +4 more | Molecules

Metabolomics, the comprehensive analysis of low-molecular-weight metabolites (typically below 1500 DA) in biological systems, relies heavily on mass spectrometry (MS) and nuclear magnetic resonance (NMR) spectroscopy. Each technique has inherent … Metabolomics, the comprehensive analysis of low-molecular-weight metabolites (typically below 1500 DA) in biological systems, relies heavily on mass spectrometry (MS) and nuclear magnetic resonance (NMR) spectroscopy. Each technique has inherent strengths and weaknesses. MS offers high sensitivity and is commonly coupled with chromatography to analyze complex matrices, yet it is destructive, has limited reproducibility, and provides limited structural information. NMR, while less sensitive, is non-destructive and enables structural elucidation and precise quantification. Recent studies increasingly employ data fusion (DF) strategies to combine the complementary information from NMR and MS, aiming to enhance metabolomic analyses. This review summarizes DF methodologies using NMR and MS data in metabolomics studies over the past decade. A comprehensive search of SciFinder, Scopus, and Clarivate Web of Science databases was conducted to analyze fusion techniques, methods, and statistical models. The review emphasizes the growing importance of DF in metabolomics, showing its capacity to provide a more comprehensive view of biochemical processes across diverse biological systems, including clinical, plant, and food matrices.

Single-Cell Lipidomics by LC-MS Interlaboratory Study Reveals the Impact of X-ray Irradiation on a Pancreatic Cancer Cell Line and Its Bystanders

2025-06-17

Kyle D. G. Saunders , Johanna von Gerichten , Rahul Deshpande +7 more | Analytical Chemistry

Live single-cell lipidomics by liquid chromatography mass spectrometry (LC-MS) is a nascent and rapidly growing field which can shed new light on infectious diseases, cancer, immunology, and drug delivery. There … Live single-cell lipidomics by liquid chromatography mass spectrometry (LC-MS) is a nascent and rapidly growing field which can shed new light on infectious diseases, cancer, immunology, and drug delivery. There are now a growing number of laboratories that can isolate single cells and laboratories that can perform lipidomics analysis at correspondingly low sample volumes, but there is a lack of validation data. We have carried out the first interlaboratory LC-MS lipidomics experiment for single cells, aimed at filling this gap. We present a novel workflow to enable interlaboratory studies, comprising live-cell imaging and single-cell isolation, followed by freeze-drying, international shipping, reconstitution, and untargeted lipidomics analysis. We applied this methodology to reveal radiation-induced bystander effects in pancreatic cancer cells. X-ray irradiated cells and their bystanders sampled live 48 h postirradiation demonstrated reduced lipid abundance compared to controls, with distinct changes in molar ratios of several polyunsaturated lipids. This demonstrates for the first time that radiation can cause considerable cellular lipid remodelling, not only at the site of delivery. A striking similarity in lipid changes was observed between the two participating laboratories despite differences in sample preparation and analysis methods. Our results are further corroborated by live-cell imaging analysis of lipid droplets. This work serves as an important validation and demonstration of the nascent and rapidly growing field of live single-cell lipidomics.

Efficient Denoising of Shot-Noise in Mass Spectrometry Images by PCA-Assisted Self-Supervised Deep Learning

2025-06-17

Claire Seydoux , M.P. Bryan , Jean‐Paul Barnes +1 more | Analytical Chemistry

Mass spectrometry imaging (MSI) often suffers from inherent noise due to signal distribution across numerous pixels and low ion counts, leading to shot noise. This can compromise the accurate interpretation, … Mass spectrometry imaging (MSI) often suffers from inherent noise due to signal distribution across numerous pixels and low ion counts, leading to shot noise. This can compromise the accurate interpretation, especially for trace molecules. Recent advances in self-supervised deep learning denoising have demonstrated significant potential for enhancing data quality. In this Letter, we propose an optimized approach for using the Noise2Void (N2 V) algorithm for MSI denoising by applying a principal component analysis (PCA) preprocessing step. By rotating the data along its principal components prior to denoising, our method, Principal Component-Assisted Noise2Void (PCA-n2v), outperforms direct N2 V implementations and other state-of-the-art denoising techniques. The limitations of PCA-n2v are also evaluated using a synthetic MSI data set, revealing that bleedthrough artifacts may arise in images with extremely low signal-to-noise ratios. To facilitate adoption, an easy-to-use PCA-n2v implementation is provided via a GitHub repository. Overall, PCA-n2v advances MSI data processing, enabling higher-throughput and higher-resolution experiments with improved fidelity.