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1.
  • Abreu, Ilka (författare)
  • Quantitative trait loci mapping of polyphenol metabolites in blackcurrant (Ribes nigrum L.)
  • 2020
  • Ingår i: Metabolomics. - : Springer Science and Business Media LLC. - 1573-3882 .- 1573-3890. ; 16
  • Tidskriftsartikel (refereegranskat)abstract
    • Introduction Commercially, blackcurrants (Ribes nigrum L.) are grown mainly for processing, especially for juice production. They are valued for their high levels of polyphenols, especially anthocyanins, which contribute to their characteristic deep colour, but also as a good source of vitamin C. Recently, evidence has accrued that polyphenols, such as anthocyanins, may have specific human health benefits. Objective The aims of this study were to investigate the genetic control of polyphenols and other key juice processing traits in blackcurrants. Methods The levels, over 2 years, of vitamin C, citrate, malate, succinate, total organic acids, total anthocyanins and total phenolics together with 46 mainly polyphenol metabolites were measured in a blackcurrant biparental mapping population. Quantitative trait loci (QTLs) for these traits were mapped onto a high-density SNP linkage map. Results At least one QTL was detected for each trait, with good consistency between the 2 years. Clusters of QTLs were found on each of the eight linkage groups (LG). For example, QTLs for the major anthocyanidin glucosides, delphinidin-3-O-glucoside and cyanidin-3-O-glucoside, co-localised with a QTL for total anthocyanin content on LG3 whereas the major anthocyanidin rutinosides, delphinidin-3-O-rutinoside and cyanidin-3-O-rutinoside, had QTLs on LG1 and LG2. Many of the QTLs explained a high proportion of the trait variation, with the most significant region, on LG3 at similar to 35 cM, explaining more than 60% of the variation in the coumaroylated metabolites, Cyanidin-coumaroyl-glucose, Delphinidin-coumaroyl-glucose, Kaempferol-coumaroyl-glucose and Myricetin-coumaroyl-glucose. Conclusion The identification of robust QTLs for key polyphenol classes and individual polyphenols in blackcurrant provides great potential for marker-assisted breeding for improved levels of key components.
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2.
  • Alexandersson, Elin, et al. (författare)
  • Extended automated quantification algorithm (AQuA) for targeted 1H NMR metabolomics of highly complex samples: application to plant root exudates
  • 2023
  • Ingår i: Metabolomics. - 1573-3882 .- 1573-3890. ; 20
  • Tidskriftsartikel (refereegranskat)abstract
    • Introduction The Automated Quantification Algorithm (AQuA) is a rapid and efficient method for targeted NMR-based metabolomics, currently optimised for blood plasma. AQuA quantifies metabolites from 1D-H-1 NMR spectra based on the height of only one signal per metabolite, which minimises the computational time and workload of the method without compromising the quantification accuracy.Objectives To develop a fast and computationally efficient extension of AQuA for quantification of selected metabolites in highly complex samples, with minimal prior sample preparation. In particular, the method should be capable of handling interferences caused by broad background signals.Methods An automatic baseline correction function was combined with AQuA into an automated workflow, the extended AQuA, for quantification of metabolites in plant root exudate NMR spectra that contained broad background signals and baseline distortions. The approach was evaluated using simulations as well as a spike-in experiment in which known metabolite amounts were added to a complex sample matrix.Results The extended AQuA enables accurate quantification of metabolites in 1D-H-1 NMR spectra with varying complexity. The method is very fast (< 1 s per spectrum) and can be fully automated.Conclusions The extended AQuA is an automated quantification method intended for 1D-H-1 NMR spectra containing broad background signals and baseline distortions. Although the method was developed for plant root exudates, it should be readily applicable to any NMR spectra displaying similar issues as it is purely computational and applied to NMR spectra post-acquisition.
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3.
  • Ali, Ahmed, et al. (författare)
  • Single cell metabolism : current and future trends
  • 2022
  • Ingår i: Metabolomics. - : Springer. - 1573-3882 .- 1573-3890. ; 18:10
  • Forskningsöversikt (refereegranskat)abstract
    • Single cell metabolomics is an emerging and rapidly developing field that complements developments in single cell analysis by genomics and proteomics. Major goals include mapping and quantifying the metabolome in sufficient detail to provide useful information about cellular function in highly heterogeneous systems such as tissue, ultimately with spatial resolution at the individual cell level. The chemical diversity and dynamic range of metabolites poses particular challenges for detection, identification and quantification. In this review we discuss both significant technical issues of measurement and interpretation, and progress toward addressing them, with recent examples from diverse biological systems. We provide a framework for further directions aimed at improving workflow and robustness so that such analyses may become commonly applied, especially in combination with metabolic imaging and single cell transcriptomics and proteomics.
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4.
  • Andersson Svärd, Agnes, et al. (författare)
  • Characterization of plasma lipidomics in adolescent subjects with increased risk for type 1 diabetes in the DiPiS cohort
  • 2020
  • Ingår i: Metabolomics. - : Springer Science and Business Media LLC. - 1573-3882 .- 1573-3890. ; 16:10
  • Tidskriftsartikel (refereegranskat)abstract
    • INTRODUCTION: Type 1 diabetes (T1D) is caused by the destruction of pancreatic islet beta cells resulting in total loss of insulin production. Recent studies have suggested that the destruction may be interrelated to plasma lipids.OBJECTIVES: Specific lipids have previously been shown to be decreased in children who develop T1D before four years of age. Disturbances of plasma lipids prior to clinical diagnosis of diabetes, if true, may provide a novel way to improve prediction, and monitor disease progression.METHODS: A lipidomic approach was utilized to analyze plasma from 67 healthy adolescent subjects (10-15 years of age) with or without islet autoantibodies but all with increased genetic risk for T1D. The study subjects were enrolled at birth in the Diabetes Prediction in Skåne (DiPiS) study and after 10-15 years of follow-up we performed the present cross-sectional analysis. HLA-DRB345, -DRB1, -DQA1, -DQB1, -DPA1 and -DPB1 genotypes were determined using next generation sequencing. Lipidomic profiles were determined using ultra-high-performance liquid chromatography quadrupole time-of-flight mass spectrometry. Lipidomics data were analyzed according to genotype.RESULTS: Variation in levels of several specific phospholipid species were related to level of autoimmunity but not development of T1D. Five glycosylated ceramides were increased in insulin autoantibody (IAA) positive adolescent subjects compared to adolescent subjects without this autoantibody. Additionally, HLA genotypes seemed to influence levels of long chain triacylglycerol (TG).CONCLUSION: Lipidomic profiling of adolescent subjects in high risk of T1D may improve sub-phenotyping in this high risk population.
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5.
  • Balgoma, David, et al. (författare)
  • Anabolic androgenic steroids exert a selective remodeling of the plasma lipidome that mirrors the decrease of the de novo lipogenesis in the liver
  • 2020
  • Ingår i: Metabolomics. - : SPRINGER. - 1573-3882 .- 1573-3890. ; 16:1
  • Tidskriftsartikel (refereegranskat)abstract
    • Introduction: The abuse of anabolic androgenic steroids (AASs) is a source of public concern because of their adverse effects. Supratherapeutic doses of AASs are known to be hepatotoxic and regulate the lipoproteins in plasma by modifying the metabolism of lipids in the liver, which is associated with metabolic diseases. However, the effect of AASs on the profile of lipids in plasma is unknown.Objectives: To describe the changes in the plasma lipidome exerted by AASs and to discuss these changes in the light of previous research about AASs and de novo lipogenesis in the liver.Methods: We treated male Wistar rats with supratherapeutic doses of nandrolone decanoate and testosterone undecanoate. Subsequently, we isolated the blood plasma and performed lipidomics analysis by liquid chromatography-high resolution mass spectrometry.Results: Lipid profiling revealed a decrease of sphingolipids and glycerolipids with palmitic, palmitoleic, stearic, and oleic acids. In addition, lipid profiling revealed an increase in free fatty acids and glycerophospholipids with odd-numbered chain fatty acids and/or arachidonic acid.Conclusion: The lipid profile presented herein reports the imprint of AASs on the plasma lipidome, which mirrors the downregulation of de novo lipogenesis in the liver. In a broader perspective, this profile will help to understand the influence of androgens on the lipid metabolism in future studies of diseases with dysregulated lipogenesis (e.g. type 2 diabetes, fatty liver disease, and hepatocellular carcinoma).
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6.
  • Beger, Richard D., et al. (författare)
  • Metabolomics enables precision medicine : "A White Paper, Community Perspective"
  • 2016
  • Ingår i: Metabolomics. - : Springer. - 1573-3882 .- 1573-3890. ; 12:10
  • Tidskriftsartikel (refereegranskat)abstract
    • INTRODUCTION BACKGROUND TO METABOLOMICS: Metabolomics is the comprehensive study of the metabolome, the repertoire of biochemicals (or small molecules) present in cells, tissues, and body fluids. The study of metabolism at the global or "-omics" level is a rapidly growing field that has the potential to have a profound impact upon medical practice. At the center of metabolomics, is the concept that a person's metabolic state provides a close representation of that individual's overall health status. This metabolic state reflects what has been encoded by the genome, and modified by diet, environmental factors, and the gut microbiome. The metabolic profile provides a quantifiable readout of biochemical state from normal physiology to diverse pathophysiologies in a manner that is often not obvious from gene expression analyses. Today, clinicians capture only a very small part of the information contained in the metabolome, as they routinely measure only a narrow set of blood chemistry analytes to assess health and disease states. Examples include measuring glucose to monitor diabetes, measuring cholesterol and high density lipoprotein/low density lipoprotein ratio to assess cardiovascular health, BUN and creatinine for renal disorders, and measuring a panel of metabolites to diagnose potential inborn errors of metabolism in neonates.OBJECTIVES OF WHITE PAPER—EXPECTED TREATMENT OUTCOMES AND METABOLOMICS ENABLING TOOL FOR PRECISION MEDICINE: We anticipate that the narrow range of chemical analyses in current use by the medical community today will be replaced in the future by analyses that reveal a far more comprehensive metabolic signature. This signature is expected to describe global biochemical aberrations that reflect patterns of variance in states of wellness, more accurately describe specific diseases and their progression, and greatly aid in differential diagnosis. Such future metabolic signatures will: (1) provide predictive, prognostic, diagnostic, and surrogate markers of diverse disease states; (2) inform on underlying molecular mechanisms of diseases; (3) allow for sub-classification of diseases, and stratification of patients based on metabolic pathways impacted; (4) reveal biomarkers for drug response phenotypes, providing an effective means to predict variation in a subject's response to treatment (pharmacometabolomics); (5) define a metabotype for each specific genotype, offering a functional read-out for genetic variants: (6) provide a means to monitor response and recurrence of diseases, such as cancers: (7) describe the molecular landscape in human performance applications and extreme environments. Importantly, sophisticated metabolomic analytical platforms and informatics tools have recently been developed that make it possible to measure thousands of metabolites in blood, other body fluids, and tissues. Such tools also enable more robust analysis of response to treatment. New insights have been gained about mechanisms of diseases, including neuropsychiatric disorders, cardiovascular disease, cancers, diabetes and a range of pathologies. A series of ground breaking studies supported by National Institute of Health (NIH) through the Pharmacometabolomics Research Network and its partnership with the Pharmacogenomics Research Network illustrate how a patient's metabotype at baseline, prior to treatment, during treatment, and post-treatment, can inform about treatment outcomes and variations in responsiveness to drugs (e.g., statins, antidepressants, antihypertensives and antiplatelet therapies). These studies along with several others also exemplify how metabolomics data can complement and inform genetic data in defining ethnic, sex, and gender basis for variation in responses to treatment, which illustrates how pharmacometabolomics and pharmacogenomics are complementary and powerful tools for precision medicine.CONCLUSIONS KEY SCIENTIFIC CONCEPTS AND RECOMMENDATIONS FOR PRECISION MEDICINE: Our metabolomics community believes that inclusion of metabolomics data in precision medicine initiatives is timely and will provide an extremely valuable layer of data that compliments and informs other data obtained by these important initiatives. Our Metabolomics Society, through its "Precision Medicine and Pharmacometabolomics Task Group", with input from our metabolomics community at large, has developed this White Paper where we discuss the value and approaches for including metabolomics data in large precision medicine initiatives. This White Paper offers recommendations for the selection of state of-the-art metabolomics platforms and approaches that offer the widest biochemical coverage, considers critical sample collection and preservation, as well as standardization of measurements, among other important topics. We anticipate that our metabolomics community will have representation in large precision medicine initiatives to provide input with regard to sample acquisition/preservation, selection of optimal omics technologies, and key issues regarding data collection, interpretation, and dissemination. We strongly recommend the collection and biobanking of samples for precision medicine initiatives that will take into consideration needs for large-scale metabolic phenotyping studies.
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7.
  • Bos, Maxime M., et al. (författare)
  • Metabolomics analyses in non-diabetic middle-aged individuals reveal metabolites impacting early glucose disturbances and insulin sensitivity
  • 2020
  • Ingår i: Metabolomics. - : Springer. - 1573-3882 .- 1573-3890. ; 16:3
  • Tidskriftsartikel (refereegranskat)abstract
    • Introduction: Several plasma metabolites have been associated with insulin resistance and type 2 diabetes mellitus.Objectives: We aimed to identify plasma metabolites associated with different indices of early disturbances in glucose metabolism and insulin sensitivity.Methods: This cross-sectional study was conducted in a subsample of the Leiden Longevity Study comprising individuals without a history of diabetes mellitus (n = 233) with a mean age of 63.3 ± 6.7 years of which 48.1% were men. We tested for associations of fasting glucose, fasting insulin, HOMA-IR, Matsuda Index, Insulinogenic Index and glycated hemoglobin with metabolites (Swedish Metabolomics Platform) using linear regression analysis adjusted for age, sex and BMI. Results were validated internally using an independent metabolomics platform (Biocrates platform) and replicated externally in the independent Netherlands Epidemiology of Obesity (NEO) study (Metabolon platform) (n = 545, mean age of 55.8 ± 6.0 years of which 48.6% were men). Moreover, in the NEO study, we replicated our analyses in individuals with diabetes mellitus (cases: n = 36; controls = 561).Results: Out of the 34 metabolites, a total of 12 plasma metabolites were associated with different indices of disturbances in glucose metabolism and insulin sensitivity in individuals without diabetes mellitus. These findings were validated using a different metabolomics platform as well as in an independent cohort of non-diabetics. Moreover, tyrosine, alanine, valine, tryptophan and alpha-ketoglutaric acid levels were higher in individuals with diabetes mellitus.Conclusion: We found several plasma metabolites that are associated with early disturbances in glucose metabolism and insulin sensitivity of which five were also higher in individuals with diabetes mellitus.
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8.
  • Brandsma, Joost, et al. (författare)
  • Lipid phenotyping of lung epithelial lining fluid in healthy human volunteers
  • 2018
  • Ingår i: Metabolomics. - : Springer-Verlag New York. - 1573-3882 .- 1573-3890. ; 14:10
  • Tidskriftsartikel (refereegranskat)abstract
    • Background: Lung epithelial lining fluid (ELF)-sampled through sputum induction-is a medium rich in cells, proteins and lipids. However, despite its key role in maintaining lung function, homeostasis and defences, the composition and biology of ELF, especially in respect of lipids, remain incompletely understood. Objectives: To characterise the induced sputum lipidome of healthy adult individuals, and to examine associations between different ELF lipid phenotypes and the demographic characteristics within the study cohort.Methods: Induced sputum samples were obtained from 41 healthy non-smoking adults, and their lipid compositions analysed using a combination of untargeted shotgun and liquid chromatography mass spectrometry methods. Topological data analysis (TDA) was used to group subjects with comparable sputum lipidomes in order to identify distinct ELF phenotypes.Results: The induced sputum lipidome was diverse, comprising a range of different molecular classes, including at least 75 glycerophospholipids, 13 sphingolipids, 5 sterol lipids and 12 neutral glycerolipids. TDA identified two distinct phenotypes differentiated by a higher total lipid content and specific enrichments of diacyl-glycerophosphocholines, -inositols and -glycerols in one group, with enrichments of sterols, glycolipids and sphingolipids in the other. Subjects presenting the lipid-rich ELF phenotype also had significantly higher BMI, but did not differ in respect of other demographic characteristics such as age or gender.Conclusions: We provide the first evidence that the ELF lipidome varies significantly between healthy individuals and propose that such differences are related to weight status, highlighting the potential impact of (over)nutrition on lung lipid metabolism.
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9.
  • Broeckling, Corey D., et al. (författare)
  • Assigning precursor-product ion relationships in indiscriminant MS/MS data from non-targeted metabolite profiling studies
  • 2013
  • Ingår i: Metabolomics. - : Springer Science and Business Media LLC. - 1573-3882 .- 1573-3890. ; 9:1, s. 33-43
  • Tidskriftsartikel (refereegranskat)abstract
    • Tandem mass spectrometry using precursor ion selection (MS/MS) is an invaluable tool for structural elucidation of small molecules. In non-targeted metabolite profiling studies, instrument duty cycle limitations and experimental costs have driven efforts towards alternate approaches. Recently, researchers have begun to explore methods for collecting indiscriminant MS/MS (idMS/MS) data in which the fragmentation process does not involve precursor ion isolation. While this approach has many advantages, importantly speed, sensitivity and coverage, confident assignment of precursor-product ion relationships is challenging, which has inhibited broad adoption of the technique. Here, we present an approach that uses open source software to improve the assignment of precursor-product relationships in idMS/MS data by appending a dataset-wide correlational analysis to existing tools. The utility of the approach was demonstrated using a dataset of standard compounds spiked into a malt-barley background, as well as unspiked human serum. The workflow was able to recreate idMS/MS spectra which are highly similar to standard MS/MS spectra of authentic standards, even in the presence of a complex matrix background. The application of this approach has the potential to generate high quality idMS/MS spectra for each detectable molecular feature, which will streamline the identification process for non-targeted metabolite profiling studies.
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10.
  • Brunius, Carl, et al. (författare)
  • Large-scale untargeted LC-MS metabolomics data correction using between-batch feature alignment and cluster-based within-batch signal intensity drift correction
  • 2016
  • Ingår i: Metabolomics. - : Springer Science and Business Media LLC. - 1573-3882 .- 1573-3890. ; 12
  • Tidskriftsartikel (refereegranskat)abstract
    • Introduction Liquid chromatography-mass spectrometry (LC-MS) is a commonly used technique in untargeted metabolomics owing to broad coverage of metabolites, high sensitivity and simple sample preparation. However, data generated from multiple batches are affected by measurement errors inherent to alterations in signal intensity, drift in mass accuracy and retention times between samples both within and between batches. These measurement errors reduce repeatability and reproducibility and may thus decrease the power to detect biological responses and obscure interpretation.Objective Our aim was to develop procedures to address and correct for within-and between-batch variability in processing multiple-batch untargeted LC-MS metabolomics data to increase their quality.Methods Algorithms were developed for: (i) alignment and merging of features that are systematically misaligned between batches, through aggregating feature presence/missingness on batch level and combining similar features worthogonally present between batches; and (ii) within-batch drift correction using a cluster-based approach that allows multiple drift patterns within batch. Furthermore, a heuristic criterion was developed for the feature-wise choice of reference-based or population-based between-batch normalisation.Results In authentic data, between-batch alignment resulted in picking 15 % more features and deconvoluting 15 % of features previously erroneously aligned. Within-batch correction provided a decrease in median quality control feature coefficient of variation from 20.5 to 15.1 %. Algorithms are open source and available as an R package ('batchCorr').Conclusions The developed procedures provide unbiased measures of improved data quality, with implications for improved data analysis. Although developed for LC-MS based metabolomics, these methods are generic and can be applied to other data suffering from similar limitations.
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11.
  • Carlsson, Henrik, et al. (författare)
  • Targeted metabolomics of CSF in healthy individuals and patients with secondary progressive multiple sclerosis using high-resolution mass spectrometry
  • 2020
  • Ingår i: Metabolomics. - : SPRINGER. - 1573-3882 .- 1573-3890. ; 16:2
  • Tidskriftsartikel (refereegranskat)abstract
    • Introduction: Standardized commercial kits enable targeted metabolomics analysis and may thus provide an attractive complement to the more explorative approaches. The kits are typically developed for triple quadrupole mass spectrometers using serum and plasma.Objectives: Here we measure the concentrations of preselected metabolites in cerebrospinal fluid (CSF) using a kit developed for high-resolution mass spectrometry (HRMS). Secondarily, the study aimed to investigate metabolite alterations in patients with secondary progressive multiple sclerosis (SPMS) compared to controls.Methods: We performed targeted metabolomics in human CSF on twelve SPMS patients and twelve age and sex-matched healthy controls using the Absolute IDQ-p400 kit (Biocrates Life Sciences AG) developed for HRMS. The extracts were analysed using two methods; liquid chromatography-mass spectrometry (LC-HRMS) and flow injection analysis-MS (FIA-HRMS).Results: Out of 408 targeted metabolites, 196 (48%) were detected above limit of detection and 35 were absolutely quantified. Metabolites analyzed using LC-HRMS had a median coefficient of variation (CV) of 3% and 2.5% between reinjections the same day and after prolonged storage, respectively. The corresponding results for the FIA-HRMS were a median CV of 27% and 21%, respectively. We found significantly (p < 0.05) elevated levels of glycine, asymmetric dimethylarginine (ADMA), glycerophospholipid PC-O (34:0) and sum of hexoses in SPMS patients compared to controls.Conclusion: The Absolute IDQ-p400 kit could successfully be used for quantifying targeted metabolites in the CSF. Metabolites quantified using LC-HRMS showed superior reproducibility compared to FIA-HRMS.
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12.
  • Cheng, Ken, 1987, et al. (författare)
  • An LC-QToF MS based method for untargeted metabolomics of human fecal samples
  • 2020
  • Ingår i: Metabolomics. - : Springer Science and Business Media LLC. - 1573-3882 .- 1573-3890. ; 16:4
  • Tidskriftsartikel (refereegranskat)abstract
    • Introduction: Consensus in sample preparation for untargeted human fecal metabolomics is lacking. Objectives: To obtain sample preparation with broad metabolite coverage for high-throughput LC–MS. Methods: Extraction solvent, solvent ratio and fresh frozen-vs-lyophilized samples were evaluated by metabolite feature quality. Results: Methanol at 5 mL per g wet feces provided a wide metabolite coverage with optimal balance between signal intensity and saturation for both fresh frozen and lyophilized samples. Lyophilization did not affect SCFA and is recommended because of convenience in normalizing to dry matter. Conclusion: The suggested sample preparation is simple, efficient and suitable for large-scale human fecal metabolomics.
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13.
  • Chorell, Elin, 1981-, et al. (författare)
  • Plasma metabolomic response to postmenopausal weight loss induced by different diets
  • 2016
  • Ingår i: Metabolomics. - : Springer Science and Business Media LLC. - 1573-3882 .- 1573-3890. ; 12:5
  • Tidskriftsartikel (refereegranskat)abstract
    • Background Menopause is associated with increased abdominal fat and increased risk of developing diabetes and cardiovascular disease. Objectives The present study evaluated the plasma metabolic response in relation to insulin sensitivity after weight loss via diet intervention. Methods This work includes two studies; i) Ten women on a 5 weeks Paleolithic-type diet (PD, 30 energy percent (E%) protein, 40 E% fat, 30 E% carbohydrates), ii) 55 women on 6 months of either PD or Nordic Nutrition Recommendations diet (NNR, 15 E% protein, 30 E% fat, and 55 E% carbohydrates). Plasma metabolic profiles were acquired at baseline and post diet using gas chromatography time-of-flight/mass spectrometry and investigated in relation to insulin sensitivity using multivariate bioinformatics. Results Both the PD and NNR diet resulted in significant weight loss, reduced waist circumference, improved serum lipid profiles, and improved insulin sensitivity. We detected a baseline metabolic profile that correlated significantly with insulin sensitivity, and of which components increased significantly in the PD group compared to NNR. Specifically, a significant increase in myo-inositol (MI), a second messenger of insulin action, and beta-hydroxybutyric acid (beta-HB)increased while dihomogamma-linoleic acid (DGLA) decreased in PD compared to NNR, which correlated with improved insulin sensitivity. We also detected a significant decrease in tyrosine and tryptophan, potential markers of insulin resistance when elevated in the circulation, with the PD but not the NNR. Conclusions Using metabolomics, we detected changes in the plasma metabolite profiles associated with weight loss in postmenopausal women by different diets. The metabolic profiles following 6 months of PD were linked to beneficial effects on insulin sensitivity compared to NNR.
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14.
  • Chorell, Elin, et al. (författare)
  • Statistical multivariate metabolite profiling for aiding biomarker pattern detection and mechanistic interpretations in GC/MS based metabolomics
  • 2006
  • Ingår i: Metabolomics. - : Springer Science and Business Media LLC. - 1573-3882 .- 1573-3890. ; 2:4, s. 257-68
  • Tidskriftsartikel (refereegranskat)abstract
    • A strategy for robust and reliable mechanistic statistical modelling of metabolic responses in relation to drug induced toxicity is presented. The suggested approach addresses two cases commonly occurring within metabonomic toxicology studies, namely; 1) A pre-defined hypothesis about the biological mechanism exists and 2) No such hypothesis exists. GC/MS data from a liver toxicity study consisting of rat urine from control rats and rats exposed to a proprietary AstraZeneca compound were resolved by means of hierarchical multivariate curve resolution (H-MCR) generating 287 resolved chromatographic profiles with corresponding mass spectra. Filtering according to significance in relation to drug exposure rendered in 210 compound profiles, which were subjected to further statistical analysis following correction to account for the control variation over time. These dose related metabolite traces were then used as new observations in the subsequent analyses. For case 1, a multivariate approach, named Target Batch Analysis, based on OPLS regression was applied to correlate all metabolite traces to one or more key metabolites involved in the pre-defined hypothesis. For case 2, principal component analysis (PCA) was combined with hierarchical cluster analysis (HCA) to create a robust and interpretable framework for unbiased mechanistic screening. Both the Target Batch Analysis and the unbiased approach were cross-verified using the other method to ensure that the results did match in terms of detected metabolite traces. This was also the case, implying that this is a working concept for clustering of metabolites in relation to their toxicity induced dynamic profiles regardless if there is a pre-existing hypothesis or not. For each of the methods the detected metabolites were subjected to identification by means of data base comparison as well as verification in the raw data. The proposed strategy should be seen as a general approach for facilitating mechanistic modelling and interpretations in metabolomic studies.
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15.
  • Danielsson, Anders, et al. (författare)
  • Development of a gas chromatography/mass spectrometry based metabolomics protocol by means of statistical experimental design
  • 2012
  • Ingår i: Metabolomics. - : Springer Science and Business Media LLC. - 1573-3882 .- 1573-3890. ; 8:1, s. 50-63
  • Tidskriftsartikel (refereegranskat)abstract
    • Metabolomics is a growing research field where new protocols are rapidly developed and new applications discovered. Common applications include biomarker discovery and elucidation of drug metabolism. However, the development of such protocols rarely includes a systematic optimization followed by validation with real samples. Here a GC/MS-based protocol using methoximation followed by silylation with N-tert-butyldi-methylsilyl-N-methyltrifluoroacetamide (MTBSTFA) for analysis of blood plasma metabolites is thoroughly developed and optimized from derivatization to detection with statistical design of experiments (DOE). Validation was performed with blood plasma samples and proved the methodology to be efficient, rapid and reliable with a total of 51 analyses performed in 24 h, with linear responses, low detection limits and good precision. The obtained chromatograms were much cleaner, due to the absence of glucose overloading, and the data was found to drift less with MTBSTFA derivatisation than with MTBSTFA derivatisation.
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16.
  • de Seymour, Jamie V., et al. (författare)
  • Metabolomic profiling of maternal hair suggests rapid development of intrahepatic cholestasis of pregnancy
  • 2018
  • Ingår i: Metabolomics. - : SPRINGER. - 1573-3882 .- 1573-3890. ; 14:6
  • Tidskriftsartikel (refereegranskat)abstract
    • Intrahepatic cholestasis of pregnancy (ICP) is a common maternal liver disease; development can result in devastating consequences, including sudden fetal death and stillbirth. Currently, recognition of ICP only occurs following onset of clinical symptoms. Investigate the maternal hair metabolome for predictive biomarkers of ICP. The maternal hair metabolome (gestational age of sampling between 17 and 41 weeks) of 38 Chinese women with ICP and 46 pregnant controls was analysed using gas chromatography-mass spectrometry. Of 105 metabolites detected in hair, none were significantly associated with ICP. Hair samples represent accumulative environmental exposure over time. Samples collected at the onset of ICP did not reveal any metabolic shifts, suggesting rapid development of the disease.
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17.
  • Dugas, Lara R., et al. (författare)
  • Obesity-related metabolite profiles of black women spanning the epidemiologic transition
  • 2016
  • Ingår i: Metabolomics. - New York : Springer-Verlag New York. - 1573-3882 .- 1573-3890. ; 12:3
  • Tidskriftsartikel (refereegranskat)abstract
    • In developed countries, specific metabolites have been associated with obesity and metabolic diseases, e.g. type 2 diabetes. It is unknown whether a similar profile persists across populations of African-origin, at increased risk for obesity and related diseases. In a cross-sectional study of normal-weight and obese black women (33.3 +/- 6.3 years) from the US (N = 69, 65 % obese), South Africa (SA, N = 97, 49 % obese) and Ghana (N = 82, 33 % obese) serum metabolite profiles were characterized via gas chromatography-time of flight/mass spectrometry. In US and SA women, BMI correlated with branched-chain and aromatic amino acids, as well as dopamine and aminoadipic acid. The relationship between BMI and lipid metabolites differed by site; BMI correlated positively with palmitoleic acid (16: 1) in the US; negatively with stearic acid (18: 0) in SA, and positively with arachidonic acid (20: 4) in Ghana. BMI was also positively associated with sugar-related metabolites in the US; i.e. uric acid, and mannitol, and with glucosamine, glucoronic acid and mannitol in SA. While we identified a common amino acid metabolite profile associated with obesity in black women from the US and SA, we also found site-specific obesity-related metabolites suggesting that the local environment is a key moderator of obesity.
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18.
  • Engskog, Mikael K. R., et al. (författare)
  • LC-MS based global metabolite profiling : the necessity of high data quality
  • 2016
  • Ingår i: Metabolomics. - : Springer Science and Business Media LLC. - 1573-3882 .- 1573-3890. ; 12:7
  • Forskningsöversikt (refereegranskat)abstract
    • LC-MS based global metabolite profiling currently lacks detailed guidelines to demonstrate that the obtained data is of high enough analytical quality. Insufficient data quality may result in the failure to generate a hypothesis, or in the worst case, a false or skewed hypothesis. After assessing the literature, it is apparent that an analytically focused summary and critical discussion related to this subject would be beneficial for both beginners and experts engaged in this field. A particular focus will be placed on data quality, which we here define as the degree to which a set of parameters fulfills predetermined criteria, similar to the established guidelines for targeted analysis. However, several of these parameters are difficult to assess since holistic approaches measure thousands of metabolites in parallel and seldom include predefined knowledge of which metabolites will differ between sample groups. In this review, the following parameters will be discussed in detail: reproducibility, selectivity, certainty of metabolite identification and metabolite coverage. The review systematically describes the generic workflow for LC-MS based global metabolite profiling and highlights how each separate part may affect data quality. The last part of the review describes how data quality can be evaluated as well as identifies areas where additional improvement is needed. In this review, we provide our own analytical opinions in regards to evaluation and, to some extent, improvement of data quality.
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19.
  • Fadista, João, et al. (författare)
  • Integrating genetics with newborn metabolomics in infantile hypertrophic pyloric stenosis
  • 2021
  • Ingår i: Metabolomics. - : Springer Science and Business Media LLC. - 1573-3882 .- 1573-3890. ; 17:1
  • Tidskriftsartikel (refereegranskat)abstract
    • Introduction: Infantile hypertrophic pyloric stenosis (IHPS) is caused by hypertrophy of the pyloric sphincter muscle. Objectives: Since previous reports have implicated lipid metabolism, we aimed to (1) investigate associations between IHPS and a wide array of lipid-related metabolites in newborns, and (2) address whether detected differences in metabolite levels were likely to be driven by genetic differences between IHPS cases and controls or by differences in early life feeding patterns. Methods: We used population-based random selection of IHPS cases and controls born in Denmark between 1997 and 2014. We randomly took dried blood spots of newborns from 267 pairs of IHPS cases and controls matched by sex and day of birth. We used a mixed-effects linear regression model to evaluate associations between 148 metabolites and IHPS in a matched case–control design. Results: The phosphatidylcholine PC(38:4) showed significantly lower levels in IHPS cases (P = 4.68 × 10−8) as did six other correlated metabolites (four phosphatidylcholines, acylcarnitine AC(2:0), and histidine). Associations were driven by 98 case–control pairs born before 2009, when median age at sampling was 6 days. No association was seen in 169 pairs born in 2009 or later, when median age at sampling was 2 days. More IHPS cases than controls had a diagnosis for neonatal difficulty in feeding at breast (P = 6.15 × 10−3). Genetic variants known to be associated with PC(38:4) levels did not associate with IHPS. Conclusions: We detected lower levels of certain metabolites in IHPS, possibly reflecting different feeding patterns in the first days of life.
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20.
  • Fages, Anne, et al. (författare)
  • Investigating sources of variability in metabolomic data in the EPIC study : the Principal Component Partial R-square (PC-PR2) method
  • 2014
  • Ingår i: Metabolomics. - : Springer. - 1573-3882 .- 1573-3890. ; 10:6, s. 1074-1083
  • Tidskriftsartikel (refereegranskat)abstract
    • The key goal of metabolomic studies is to identify relevant individual biomarkers or composite metabolic patterns associated with particular disease status or patho-physiological conditions. There are currently very few approaches to evaluate the variability of metabolomic data in terms of characteristics of individuals or aspects pertaining to technical processing. To address this issue, a method was developed to identify and quantify the contribution of relevant sources of variation in metabolomic data prior to investigation of etiological hypotheses. The Principal Component Partial R-square (PC-PR2) method combines features of principal component and of multivariable linear regression analyses. Within the European Prospective Investigation into Cancer and nutrition (EPIC), metabolic profiles were determined by H-1 NMR analysis on 807 serum samples originating from a nested liver cancer case-control study. PC-PR2 was used to quantify the variability of metabolomic profiles in terms of study subjects age, sex, body mass index, country of origin, smoking status, diabetes and fasting status, as well as factors related to sample processing. PC-PR2 enables the evaluation of important sources of variations in metabolomic studies within large-scale epidemiological investigations.
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21.
  • Fletcher, John S., et al. (författare)
  • Evaluating the challenges associated with time-of-fight secondary ion mass spectrometry for metabolomics using pure and mixed metabolites
  • 2013
  • Ingår i: Metabolomics. - : Springer Science and Business Media LLC. - 1573-3882 .- 1573-3890. ; 9:3, s. 535-544
  • Tidskriftsartikel (refereegranskat)abstract
    • Time-of-flight secondary ion mass spectrometry (ToF-SIMS) is potentially well placed to contribute to metabolomic analysis while bringing the added benefit of high resolution, label free imaging. The focused ion beams used to desorb species from the sample can be focused below 1 μm allowing chemical imaging on a sub-cellular scale. In this study we test the capability of ToF-SIMS to generate mass spectrometry and MSMS spectra from a set of standard metabolites that can be compared with open access metabolite databases containing ESI-CID MSMS spectra. The influence of the chemical environment, the matrix effect, on the observed mass spectra is assessed using a mixed metabolite sample and the data discussed in terms of compound identification and quantification. Radical ions and small fragment ions seem to be less sensitive to ion suppression or enhancement and may provide a route to quantification. Understanding such parameters will be key for the successful application of the technique for in situ metabolomics with ToF-SIMS.
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22.
  • Ganna, Andrea, et al. (författare)
  • Large-scale non-targeted metabolomic profiling in three human population-based studies
  • 2016
  • Ingår i: Metabolomics. - : Springer Science and Business Media LLC. - 1573-3882 .- 1573-3890. ; 12
  • Tidskriftsartikel (refereegranskat)abstract
    • Non-targeted metabolomic profiling is used to simultaneously assess a large part of the metabolome in a biological sample. Here, we describe both the analytical and computational methods used to analyze a large UPLC–Q-TOF MS-based metabolomic profiling effort using plasma and serum samples from participants in three Swedish population-based studies of middle-aged and older human subjects: TwinGene, ULSAM and PIVUS. At present, more than 200 metabolites have been manually annotated in more than 3600 participants using an in-house library of standards and publically available spectral databases. Data available at the metabolights repository include individual raw unprocessed data, processed data, basic demographic variables and spectra of annotated metabolites. Additional phenotypical and genetic data is available upon request to cohort steering committees. These studies represent a unique resource to explore and evaluate how metabolic variability across individuals affects human diseases.
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23.
  • Goodacre, Royston, et al. (författare)
  • Proposed minimum reporting standards for data analysis in metabolomics
  • 2007
  • Ingår i: Metabolomics. - : Springer Science and Business Media LLC. - 1573-3882 .- 1573-3890. ; 3, s. 231-41
  • Tidskriftsartikel (refereegranskat)abstract
    • The goal of this group is to define the reporting requirements associated with the statistical analysis (including univariate, multivariate, informatics, machine learning etc.) of metabolite data with respect to other measured/collected experimental data (often called metadata). These definitions will embrace as many aspects of a complete metabolomics study as possible at this time. In chronological order this will include: Experimental Design, both in terms of sample collection/matching, and data acquisition scheduling of samples through whichever spectroscopic technology used; Deconvolution (if required); Pre-processing, for example, data cleaning, outlier detection, row/column scaling, or other transformations; Definition and parameterization of subsequent visualizations and Statistical/Machine learning Methods applied to the dataset; If required, a clear definition of the Model Validation Scheme used (including how data are split into training/validation/test sets); Formal indication on whether the data analysis has been Independently Tested (either by experimental reproduction, or blind hold out test set). Finally, data interpretation and the visual representations and hypotheses obtained from the data analyses.
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24.
  • Hartvigsson, Olle, 1991, et al. (författare)
  • Associations of the placental metabolome with immune maturation up to one year of age in the Swedish NICE-cohort
  • 2024
  • Ingår i: Metabolomics. - : Springer Nature. - 1573-3882 .- 1573-3890. ; 20:2
  • Tidskriftsartikel (refereegranskat)abstract
    • Introduction: Allergies and other immune-mediated diseases are thought to result from incomplete maturation of the immune system early in life. We previously showed that infants’ metabolites at birth were associated with immune cell subtypes during infancy. The placenta supplies the fetus with nutrients, but may also provide immune maturation signals. Objectives: To examine the relationship between metabolites in placental villous tissue and immune maturation during the first year of life and infant and maternal characteristics (gestational length, birth weight, sex, parity, maternal age, and BMI). Methods: Untargeted metabolomics was measured using Liquid Chromatography-Mass Spectrometry. Subpopulations of T and B cells were measured using flow cytometry at birth, 48 h, one, four, and 12 months. Random forest analysis was used to link the metabolomics data with the T and B cell sub populations as well as infant and maternal characteristics. Results: Modest associations (Q2 = 0.2–0.3) were found between the placental metabolome and kappa-deleting recombination excision circles (KREC) at birth and naïve B cells and memory T cells at 12 months. Weak associations were observed between the placental metabolome and sex and parity. Still, most metabolite features of interest were of low intensity compared to associations previously found in cord blood, suggesting that underlying metabolites were not of placental origin. Conclusion: Our results indicate that metabolomic measurements of the placenta may not effectively recognize metabolites important for immune maturation.
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25.
  • Herman, Stephanie, et al. (författare)
  • Mass spectrometry based metabolomics for in vitro systems pharmacology : pitfalls, challenges, and computational solutions.
  • 2017
  • Ingår i: Metabolomics. - : Springer Science and Business Media LLC. - 1573-3882 .- 1573-3890. ; 13:7
  • Tidskriftsartikel (refereegranskat)abstract
    • INTRODUCTION: Mass spectrometry based metabolomics has become a promising complement and alternative to transcriptomics and proteomics in many fields including in vitro systems pharmacology. Despite several merits, metabolomics based on liquid chromatography mass spectrometry (LC-MS) is a developing area that is yet attached to several pitfalls and challenges. To reach a level of high reliability and robustness, these issues need to be tackled by implementation of refined experimental and computational protocols.OBJECTIVES: This study illustrates some key pitfalls in LC-MS based metabolomics and introduces an automated computational procedure to compensate for them.METHOD: Non-cancerous mammary gland derived cells were exposed to 27 chemicals from four pharmacological classes plus a set of six pesticides. Changes in the metabolome of cell lysates were assessed after 24 h using LC-MS. A data processing pipeline was established and evaluated to handle issues including contaminants, carry over effects, intensity decay and inherent methodology variability and biases. A key component in this pipeline is a latent variable method called OOS-DA (optimal orthonormal system for discriminant analysis), being theoretically more easily motivated than PLS-DA in this context, as it is rooted in pattern classification rather than regression modeling.RESULT: The pipeline is shown to reduce experimental variability/biases and is used to confirm that LC-MS spectra hold drug class specific information.CONCLUSION: LC-MS based metabolomics is a promising methodology, but comes with pitfalls and challenges. Key difficulties can be largely overcome by means of a computational procedure of the kind introduced and demonstrated here. The pipeline is freely available on www.github.com/stephanieherman/MS-data-processing.
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26.
  • Hojer-Pedersen, Jesper, et al. (författare)
  • The yeast metabolome addressed by electrospray ionization mass spectrometry: Initiation of a mass spectral library and its applications for metabolic footprinting by direct infusion mass spectrometry
  • 2008
  • Ingår i: Metabolomics. - : Springer Science and Business Media LLC. - 1573-3882 .- 1573-3890. ; 4:4, s. 393-405:4, s. 393-405
  • Tidskriftsartikel (refereegranskat)abstract
    • Mass spectrometry (MS) has been a major driver for metabolomics, and gas chromatography (GC)-MS has been one of the primary techniques used for microbial metabolomics. The use of liquid chromatography (LC)-MS has however been limited, but electrospray ionization (ESI) is very well suited for ionization of microbial metabolites without any previous derivatization needed. To address the capabilities of ESI-MS in detecting the metabolome of Saccharomyces cerevisiae, the in silico metabolome of this organism was used as a template to present a theoretical metabolome. This showed that in combination with the specificity of MS up to 84% of the metabolites can be identified in a high mass accuracy ESI-spectrum. A total of 66 metabolites were systematically analyzed by positive and negative ESI-MS/MS with the aim of initiating a spectral library for ESI of microbial metabolites. This systematic analysis gave insight into the ionization and fragmentation characteristics of the different metabolites. With this insight, a small study of metabolic footprinting with ESI-MS demonstrated that biological information can be extracted from footprinting spectra. Statistical analysis of the footprinting data revealed discriminating ions, which could be assigned using the in silico metabolome. By this approach metabolic footprinting can advance from a classification method that is used to derive biological information based on guilt-by-association, to a tool for extraction of metabolic differences, which can guide new targeted biological experiments. © Springer Science+Business Media, LLC 2008.
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27.
  • Jonsson, Pär, et al. (författare)
  • A strategy for modelling dynamic responses in metabolic samples characterized by GC/MS
  • 2006
  • Ingår i: Metabolomics. - : Springer Science and Business Media LLC. - 1573-3882 .- 1573-3890. ; 2:3, s. 135-143
  • Tidskriftsartikel (refereegranskat)abstract
    • A multivariate strategy for studying the metabolic response over time in urinary GC/MS data is presented and exemplified by a study of drug-induced liver toxicity in the rat. The strategy includes the generation of representative data through hierarchical multivariate curve resolution (H-MCR), highlighting the importance of obtaining resolved metabolite profiles for quantification and identification of exogenous (drug related) and endogenous compounds (potential biomarkers) and for allowing reliable comparisons of multiple samples through multivariate projections. Batch modelling was used to monitor and characterize the normal (control) metabolic variation over time as well as to map the dynamic response of the drug treated animals in relation to the control. In this way treatment related metabolic responses over time could be detected and classified as being drug related or being potential biomarkers. In summary the proposed strategy uses the relatively high sensitivity and reproducibility of GC/MS in combination with efficient multivariate curve resolution and data analysis to discover individual markers of drug metabolism and drug toxicity. The presented results imply that the strategy can be of great value in drug toxicity studies for classifying metabolic markers in relation to their dynamic responses as well as for biomarker identification.
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28.
  • Jonsson, Pär, et al. (författare)
  • Constrained randomization and multivariate effect projections improve information extraction and biomarker pattern discovery in metabolomics studies involving dependent samples
  • 2015
  • Ingår i: Metabolomics. - : Springer. - 1573-3882 .- 1573-3890. ; 11:6, s. 1667-1678
  • Tidskriftsartikel (refereegranskat)abstract
    • Analytical drift is a major source of bias in mass spectrometry based metabolomics confounding interpretation and biomarker detection. So far, standard protocols for sample and data analysis have not been able to fully resolve this. We present a combined approach for minimizing the influence of analytical drift on multivariate comparisons of matched or dependent samples in mass spectrometry based metabolomics studies. The approach is building on a randomization procedure for sample run order, constrained to independent randomizations between and within dependent sample pairs (e.g. pre/post intervention). This is followed by a novel multivariate statistical analysis strategy allowing paired or dependent analyses of individual effects named OPLS-effect projections (OPLS-EP). We show, using simulated data that OPLS-EP gives improved interpretation over existing methods and that constrained randomization of sample run order in combination with an appropriate dependent statistical test increase the accuracy and sensitivity and decrease the false omission rate in biomarker detection. We verify these findings and prove the strength of the suggested approach in a clinical data set consisting of LC/MS data of blood plasma samples from patients before and after radical prostatectomy. Here OPLS-EP compared to traditional (independent) OPLS-discriminant analysis (OPLS-DA) on constrained randomized data gives a less complex model (3 versus 5 components) as well a higher predictive ability (Q2 = 0.80 versus Q2 = 0.55). We explain this by showing that paired statistical analysis detects 37 unique significant metabolites that were masked for the independent test due to bias, including analytical drift and inter-individual variation.
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29.
  • Karlsborn, Tony, 1987-, et al. (författare)
  • Loss of ncm5 and mcm5 wobble uridine side chains results in an altered metabolic profile
  • 2016
  • Ingår i: Metabolomics. - : Springer. - 1573-3882 .- 1573-3890. ; 12:12
  • Tidskriftsartikel (refereegranskat)abstract
    • Introduction: The Elongator complex, comprising six subunits (Elp1p-Elp6p), is required for formation of 5-carbamoylmethyl (ncm(5)) and 5-methoxycarbonylmethyl (mcm(5)) side chains on wobble uridines in 11 out of 42 tRNA species in Saccharomyces cerevisiae. Loss of these side chains reduces the efficiency of tRNA decoding during translation, resulting in pleiotropic phenotypes. Overexpression of hypomodified tRNA(s2UUU)(Lys); tRNA(s2UUG)(Gln) and tRNA(s2UUC)(Glu), which in wild-type strains are modified with mcm(5)s(2)U, partially suppress phenotypes of an elp3 Delta strain. Objectives: To identify metabolic alterations in an elp3 Delta strain and elucidate whether these metabolic alterations are suppressed by overexpression of hypomodified tRNA(s2UUU)(Lys); tRNA(s2UUG)(Gln) and tRNA(s2UUC)(Glu). Method: Metabolic profiles were obtained using untargeted GC-TOF-MS of a temperature-sensitive elp3 Delta strain carrying either an empty low-copy vector, an empty high-copy vector, a low-copy vector harboring the wild-type ELP3 gene, or a high-copy vector overexpressing tRNA(s2UUU)(Lys); tRNA(s2UUG)(Gln) and tRNA(s2UUC)(Glu). The temperature sensitive elp3 Delta strain derivatives were cultivated at permissive (30 degrees C) or semi-permissive (34 degrees C) growth conditions. Results: Culturing an elp3 Delta strain at 30 or 34 degrees C resulted in altered metabolism of 36 and 46 %, respectively, of all metabolites detected when compared to an elp3D strain carrying the wild-type ELP3 gene. Overexpression of hypomodified tRNA(s2UUU)(Lys); tRNA(s2UUG)(Gln) and tRNA(s2UUC)(Glu) suppressed a subset of the metabolic alterations observed in the elp3 Delta strain. Conclusion: Our results suggest that the presence of ncm(5)- and mcm(5)-side chains on wobble uridines in tRNA are important for metabolic homeostasis.
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30.
  • Kuhl, Jeanette, et al. (författare)
  • Metabolomics as a tool to evaluate exercise-induced improvements in insulin sensitivity
  • 2008
  • Ingår i: Metabolomics. - : Springer Boston. - 1573-3882 .- 1573-3890. ; 4:3, s. 273-82
  • Tidskriftsartikel (refereegranskat)abstract
    • Exercise affects substrate utilisation and insulin sensitivity, which in turn improve blood glucose and lipid levels in subjects with type 2 diabetes (T2D). However, making long-lasting lifestyle-changes might be more realistic if the results were easier to record. Screening for biomarkers reflecting metabolic fitness could thus serve as a tool for maintained motivation. The aim of this study was to test the possibility that metabolomics can be used to identify individuals with improved insulin sensitivity as a result of increased physical activity. Healthy and diabetic subjects were investigated before and after 3 months of exercise to determine various metabolic parameters. Insulin sensitivity was determined by hyperinsulinemic euglycemic clamps and found to be improved in the diabetic men. Plasma was collected during the clamp and analyzed through GC/TOFMS. Healthy subjects could be distinguished from diabetics by means of low molecular-weight compounds (LMC) in plasma independently of gender or exercise, and exercise induced differences in LMC patterns both for healthy and T2D subjects. Forty-four significant metabolites were found to explain differences between LMC patterns obtained from trained and non-trained diabetics. Among these compounds, 17 could be annotated and 5 classified. Inositol-1-phosphate showed the highest correlation to insulin sensitivity in diabetic men, whereas an as yet unknown fatty acid correlated best with insulin sensitivity in women. Both metabolites were better correlated to insulin sensitivity than glucose. Finally, the finding that inostitol-1-phosphate negatively correlates with insulin sensitivity in diabetic men, was validated using samples obtained from a similar training study on diabetic men. 
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31.
  • Kyle, Jennifer E., et al. (författare)
  • Interpreting the lipidome : bioinformatic approaches to embrace the complexity
  • 2021
  • Ingår i: Metabolomics. - : Springer-Verlag New York. - 1573-3882 .- 1573-3890. ; 17:6
  • Forskningsöversikt (refereegranskat)abstract
    • BACKGROUND: Improvements in mass spectrometry (MS) technologies coupled with bioinformatics developments have allowed considerable advancement in the measurement and interpretation of lipidomics data in recent years. Since research areas employing lipidomics are rapidly increasing, there is a great need for bioinformatic tools that capture and utilize the complexity of the data. Currently, the diversity and complexity within the lipidome is often concealed by summing over or averaging individual lipids up to (sub)class-based descriptors, losing valuable information about biological function and interactions with other distinct lipids molecules, proteins and/or metabolites.AIM OF REVIEW: To address this gap in knowledge, novel bioinformatics methods are needed to improve identification, quantification, integration and interpretation of lipidomics data. The purpose of this mini-review is to summarize exemplary methods to explore the complexity of the lipidome.KEY SCIENTIFIC CONCEPTS OF REVIEW: Here we describe six approaches that capture three core focus areas for lipidomics: (1) lipidome annotation including a resolvable database identifier, (2) interpretation via pathway- and enrichment-based methods, and (3) understanding complex interactions to emphasize specific steps in the analytical process and highlight challenges in analyses associated with the complexity of lipidome data.
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32.
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33.
  • Lindahl, Anna, et al. (författare)
  • Discrimination of pancreatic cancer and pancreatitis by LC-MS metabolomics
  • 2017
  • Ingår i: Metabolomics. - : Springer. - 1573-3882 .- 1573-3890. ; 13:5
  • Tidskriftsartikel (refereegranskat)abstract
    • Introduction: Pancreatic ductal adenocarcinoma (PDAC) is the fifth most common cause of cancer-related death in Europe with a 5-year survival rate of <5%. Chronic pancreatitis (CP) is a risk factor for PDAC development, but in the majority of cases malignancy is discovered too late for curative treatment. There is at present no reliable diagnostic marker for PDAC available.Objectives: The aim of the study was to identify single blood-based metabolites or a panel of metabolites discriminating PDAC and CP using liquid chromatography-mass spectrometry (LC-MS).Methods: A discovery cohort comprising PDAC (n = 44) and CP (n = 23) samples was analyzed by LC-MS followed by univariate (Student’s t test) and multivariate (orthogonal partial least squares-discriminant analysis (OPLS-DA)) statistics. Discriminative metabolite features were subject to raw data examination and identification to ensure high feature quality. Their discriminatory power was then confirmed in an independent validation cohort including PDAC (n = 20) and CP (n = 31) samples.Results: Glycocholic acid, N-palmitoyl glutamic acid and hexanoylcarnitine were identified as single markers discriminating PDAC and CP by univariate analysis. OPLS-DA resulted in a panel of five metabolites including the aforementioned three metabolites as well as phenylacetylglutamine (PAGN) and chenodeoxyglycocholate.Conclusion: Using LC-MS-based metabolomics we identified three single metabolites and a five-metabolite panel discriminating PDAC and CP in two independent cohorts. Although further study is needed in larger cohorts, the metabolites identified are potentially of use in PDAC diagnostics.
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34.
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35.
  • Mellerowicz, Ewa (författare)
  • An efficient method for medium throughput screening of cuticular wax composition in different plant species
  • 2016
  • Ingår i: Metabolomics. - : Springer Science and Business Media LLC. - 1573-3882 .- 1573-3890. ; 12
  • Tidskriftsartikel (refereegranskat)abstract
    • Introduction Most aerial plant organs are covered by a cuticle, which largely consists of cutin and wax. Cuticular waxes are mixtures of dozens of compounds, mostly very-long-chain aliphatics that are easily extracted by solvents. Over the last four decades, diverse cuticular wax analysis protocols have been developed, most of which are complex and time-consuming, and need to be adapted for each plant species or organ. Plant genomics and breeding programs often require mid-throughput metabolic phenotyping approaches to screen large numbers of individuals and obtain relevant biological information.Objectives To generate a fast, simple and user-friendly methodology able to capture most wax complexity independently of the plant, cultivar and organ.Methods Here we present a simple GC-MS method for screening relatively small wax amounts, sampled by short extraction with a versatile, uniform solvent. The method will be tested and validated in leaves and fruits from three different crop species: tomato (Solanum lycopersicum), apple (Malus domestica) and hybrid aspen (Populus tremula x tremuloides).Results Consistent results were obtained in tomato cultivar M82 across three consecutive years (2010-2012), two organs (leaf and fruit), and also in two different tomato (M82 and MicroTom) and apple (Golden Delicious and Granny Smith) cultivars. Our results on tomato wax composition match those reported previously, while our apple and hybrid aspen analyses provide the first comprehensive cuticular wax profile of these species.Conclusion This protocol allows standardized identification and quantification of most cuticular wax components in a range of species.
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36.
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37.
  • Müllner, Elisabeth, et al. (författare)
  • Metabolomics analysis reveals altered metabolites in lean compared with obese adolescents and additional metabolic shifts associated with hyperinsulinaemia and insulin resistance in obese adolescents: a cross-sectional study
  • 2021
  • Ingår i: Metabolomics. - : Springer Science and Business Media LLC. - 1573-3882 .- 1573-3890. ; 17
  • Tidskriftsartikel (refereegranskat)abstract
    • Introduction Hyperinsulinaemia and insulin resistance (IR) are strongly associated with obesity and are forerunners of type 2 diabetes. Little is known about metabolic alterations separately associated with obesity, hyperinsulinaemia/IR and impaired glucose tolerance (IGT) in adolescents. Objectives To identify metabolic alterations associated with obesity, hyperinsulinaemia/IR and hyperinsulinaemia/IR combined with IGT in obese adolescents. Methods 81 adolescents were stratified into four groups based on body mass index (lean vs. obese), insulin responses (normal insulin (NI) vs. high insulin (HI)) and glucose responses (normal glucose tolerance (NGT) vs. IGT) after an oral glucose tolerance test (OGTT). The groups comprised: (1) healthy lean with NI and NGT, (2) obese with NI and NGT, (3) obese with HI and NGT, and (4) obese with HI and IGT. Targeted nuclear magnetic resonance-based metabolomics analysis was performed on fasting and seven post-OGTT plasma samples, followed by univariate and multivariate statistical analyses. Results Two groups of metabolites were identified: (1) Metabolites associated with insulin response level: adolescents with HI (groups 3-4) had higher concentrations of branched-chain amino acids and tyrosine, and lower concentrations of serine, glycine, myo-inositol and dimethylsulfone, than adolescents with NI (groups 1-2). (2) Metabolites associated with obesity status: obese adolescents (groups 2-4) had higher concentrations of acetylcarnitine, alanine, pyruvate and glutamate, and lower concentrations of acetate, than lean adolescents (group 1). Conclusions Obesity is associated with shifts in fat and energy metabolism. Hyperinsulinaemia/IR in obese adolescents is also associated with increased branched-chain and aromatic amino acids.
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38.
  • Murillo-Saich, Jessica D., et al. (författare)
  • Metabolomics profiling predicts outcome of tocilizumab in rheumatoid arthritis: an exploratory study
  • 2021
  • Ingår i: Metabolomics. - : Springer Science and Business Media LLC. - 1573-3882 .- 1573-3890. ; 17:9
  • Tidskriftsartikel (refereegranskat)abstract
    • Introduction: To study metabolic signatures can be used to identify predictive biomarkers for a patient's therapeutic response. Objectives: We hypothesized that the characterization of a patients’ metabolic profile, utilizing one-dimensional nuclear magnetic resonance (1H-NMR), may predict a response to tocilizumab in patients with rheumatoid arthritis (RA). Methods: 40 active RA patients meeting the 2010 ACR/EULAR classification criteria initiating treatment with tocilizumab were recruited. Clinical outcomes were determined at baseline, and after six and twelve months of treatment. EULAR response criteria at 6 and 12months to categorize patients as responders and non-responders. Blood was collected at baseline and after six months of tocilizumab therapy. 1H-NMR was used to acquire a spectra of plasma samples. Chenomx NMR suite 8.5 was used for metabolite identification and quantification. SPSS v.27 and MetaboAnalyst 4.0 were used for statistical and pathway analysis. Results: Isobutyrate, 3-hydroxybutyrate, lysine, phenylalanine, sn-glycero-3-phosphocholine, tryptophan and tyrosine were significantly elevated in responders at the baseline. OPLS-DA at baseline partially discriminated between RA responders and non-responders. A multivariate diagnostic model showed that concentrations of 3-hydroxybutyrate and phenylalanine improved the ability to specifically predict responders classifying 77.1% of the patients correctly. At 6months, levels of methylamine, sn-glycero-3-phosphocholine and tryptophan tended to still be low in non-responders. Conclusion: The relationship between plasma metabolic profiles and the clinical response to tocilizumab suggests that 1H-NMR may be a promising tool for RA therapy optimization. More studies are needed to determine if metabolic profiling can predict the response to biological therapies in RA patients.
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39.
  • Nordin, Elise, 1985, et al. (författare)
  • Exploration of differential responses to FODMAPs and gluten in people with irritable bowel syndrome- a double-blind randomized cross-over challenge study
  • 2024
  • Ingår i: Metabolomics. - : Springer. - 1573-3882 .- 1573-3890. ; 20:2
  • Tidskriftsartikel (refereegranskat)abstract
    • There is large variation in response to diet in irritable bowel syndrome (IBS) and determinants for differential response are poorly understood. Objectives Our aim was to investigate differential clinical and molecular responses to provocation with fermentable oligo-, di-, monosaccharides, and polyols (FODMAPs) and gluten in individuals with IBS. Methods Data were used from a crossover study with week-long interventions with either FODMAPs, gluten or placebo. The study also included a rapid provocation test. Molecular data consisted of fecal microbiota, short chain fatty acids, and untargeted plasma metabolomics. IBS symptoms were evaluated with the IBS severity scoring system. IBS symptoms were modelled against molecular and baseline questionnaire data, using Random Forest (RF; regression and clustering), Parallel Factor Analysis (PARAFAC), and univariate methods. Results Regression and classification RF models were in general of low predictive power (Q2 <= 0.22, classification rate < 0.73). Out of 864 clustering models, only 2 had significant associations to clusters (0.69 < CR < 0.73, p < 0.05), but with no associations to baseline clinical measures. Similarly, PARAFAC revealed no clear association between metabolome data and IBS symptoms. Conclusion Differential IBS responses to FODMAPs or gluten exposures could not be explained from clinical and molecular data despite extensive exploration with different data analytical approaches. The trial is registered at www.clinicaltrials.gov as NCT03653689 31/08/2018.
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40.
  • Orikiiriza, Judy, et al. (författare)
  • Lipid response patterns in acute phase paediatric Plasmodium falciparum malaria
  • 2017
  • Ingår i: Metabolomics. - : Springer Science and Business Media LLC. - 1573-3882 .- 1573-3890. ; 13:4
  • Tidskriftsartikel (refereegranskat)abstract
    • Introduction: Several studies have observed serum lipid changes during malaria infection in humans. All of them were focused at analysis of lipoproteins, not specific lipid molecules. The aim of our study was to identify novel patterns of lipid species in malaria infected patients using lipidomics profiling, to enhance diagnosis of malaria and to evaluate biochemical pathways activated during parasite infection.Methods: Using a multivariate characterization approach, 60 samples were representatively selected, 20 from each category (mild, severe and controls) of the 690 study participants between age of 0.5–6 years. Lipids from patient’s plasma were extracted with chloroform/methanol mixture and subjected to lipid profiling with application of the LCMS-QTOF method.Results: We observed a structured plasma lipid response among the malaria-infected patients as compared to healthy controls, demonstrated by higher levels of a majority of plasma lipids with the exception of even-chain length lysophosphatidylcholines and triglycerides with lower mass and higher saturation of the fatty acid chains. An inverse lipid profile relationship was observed when plasma lipids were correlated to parasitaemia.Conclusions: This study demonstrates how mapping the full physiological lipid response in plasma from malaria-infected individuals can be used to understand biochemical processes during infection. It also gives insights to how the levels of these molecules relate to acute immune responses.
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41.
  • Papazian, Stefano, 1986-, et al. (författare)
  • Leaf metabolic signatures induced by real and simulated herbivory in black mustard (Brassica nigra)
  • 2019
  • Ingår i: Metabolomics. - : Springer. - 1573-3882 .- 1573-3890. ; 15:10
  • Tidskriftsartikel (refereegranskat)abstract
    • Introduction The oxylipin methyl jasmonate (MeJA) is a plant hormone active in response signalling and defence against herbivores. Although MeJA is applied experimentally to mimic herbivory and induce plant defences, its downstream effects on the plant metabolome are largely uncharacterized, especially in the context of primary growth and tissue-specificity of the response. Objectives We investigated the effects of MeJA-simulated and real caterpillar herbivory on the foliar metabolome of the wild plant Brassica nigra and monitored the herbivore-induced responses in relation to leaf ontogeny. Methods As single or multiple herbivory treatments, MeJA- and mock-sprayed plants were consecutively exposed to caterpillars or left untreated. Gas chromatography (GC) and liquid chromatography (LC) time-of-flight mass-spectrometry (TOF-MS) were combined to analyse foliar compounds, including central primary and specialized defensive plant metabolites. Results Plant responses were stronger in young leaves, which simultaneously induced higher chlorophyll levels. Both MeJA and caterpillar herbivory induced similar, but not identical, accumulation of tricarboxylic acids (TCAs), glucosinolates (GSLs) and phenylpropanoids (PPs), but only caterpillar feeding led to depletion of amino acids. MeJA followed by caterpillars caused higher induction of defence compounds, including a three-fold increase in the major defence compound allyl-GSL (sinigrin). When feeding on MeJA-treated plants, caterpillars gained less weight indicative of the reduced host-plant quality and enhanced resistance. Conclusions The metabolomics approach showed that plant responses induced by herbivory extend beyond the regulation of defence metabolism and are tightly modulated throughout leaf development. This leads to a new understanding of the plant metabolic potential that can be exploited for future plant protection strategies.
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42.
  • Petersson, Sara, et al. (författare)
  • Cell-type specific metabolic profiling of Arabidopsis thaliana protoplasts as a tool for plant systems biology
  • 2015
  • Ingår i: Metabolomics. - : Springer Science and Business Media LLC. - 1573-3882 .- 1573-3890. ; 11, s. 1679-1689
  • Tidskriftsartikel (refereegranskat)abstract
    • Flow cytometry combined with cell sorting of protoplasts has previously been used successfully for transcript profiling of the Arabidopsis thaliana root. We have developed the technique further, and in this paper we present a robust and reliable method for metabolite profiling in specific cell types isolated from Arabidopsis roots. The method uses a combination of fluorescence-activated cell sorting and gas chromatography-time of flight-mass spectrometry analysis. Cortical and endodermal cells from the green fluorescent protein (GFP)-expressing enhancer trap line J0571 were analysed and compared with non-GFP-expressing cells and intact root tissue. Of the metabolites identified, several showed significant differences in concentration between cell types. Multivariate statistical analysis was used to compare metabolite patterns between cell and tissue types, showing that the patterns differed substantially. Isolation of specific cell populations combined with highly sensitive MS-analysis will be a powerful tool for future studies of plant metabolism, and can also be combined with transcript and protein profiling for in-depth analyses of cellular processes.
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43.
  • Pinu, Farhana R., et al. (författare)
  • Metabolite secretion in microorganisms: the theory of metabolic overflow put to the test
  • 2018
  • Ingår i: Metabolomics. - : Springer Science and Business Media LLC. - 1573-3882 .- 1573-3890. ; 14:4
  • Forskningsöversikt (refereegranskat)abstract
    • Introduction Microbial cells secrete many metabolites during growth, including important intermediates of the central carbon metabolism. This has not been taken into account by researchers when modeling microbial metabolism for metabolic engineering and systems biology studies. Materials and Methods The uptake of metabolites by microorganisms is well studied, but our knowledge of how and why they secrete different intracellular compounds is poor. The secretion of metabolites by microbial cells has traditionally been regarded as a consequence of intracellular metabolic overflow. Conclusions Here, we provide evidence based on time-series metabolomics data that microbial cells eliminate some metabolites in response to environmental cues, independent of metabolic overflow. Moreover, we review the different mechanisms of metabolite secretion and explore how this knowledge can benefit metabolic modeling and engineering.
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44.
  • Pirttilä, Kristian, et al. (författare)
  • An LCMS-based untargeted metabolomics protocol for cochlear perilymph : highlighting metabolic effects of hydrogen gas on the inner ear of noise exposed Guinea pigs
  • 2019
  • Ingår i: Metabolomics. - : Springer Science and Business Media LLC. - 1573-3882 .- 1573-3890. ; 15:10
  • Tidskriftsartikel (refereegranskat)abstract
    • Introduction Noise-induced hearing loss (NIHL) is an increasing problem in society and accounts for a third of all cases of acquired hearing loss. NIHL is caused by formation of reactive oxygen species (ROS) in the cochlea causing oxidative stress. Hydrogen gas (H-2) can alleviate the damage caused by oxidative stress and can be easily administered through inhalation.Objectives To present a protocol for untargeted metabolomics of guinea pig perilymph and investigate the effect of H-2 administration on the perilymph metabolome of noise exposed guinea pigs.Methods The left ear of guinea pigs were exposed to hazardous impulse noise only (Noise, n = 10), noise and H-2 (Noise + H2, n = 10), only H-2 (H2, n = 4), or untreated (Control, n = 2). Scala tympani perilymph was sampled from the cochlea of both ears. The polar component of the perilymph metabolome was analyzed using a HILIC-UHPLC-Q-TOF-MS-based untargeted metabolomics protocol. Multivariate data analysis (MVDA) was performed separately for the exposed- and unexposed ear.Results MVDA allowed separation of groups Noise and Noise + H2 in both the exposed and unexposed ear and yielded 15 metabolites with differentiating relative abundances. Seven were found in both exposed and unexposed ear data and included two osmoprotectants. Eight metabolites were unique to the unexposed ear and included a number of short-chain acylcarnitines.Conclusions A HILIC-UHPLC-Q-TOF-MS-based protocol for untargeted metabolomics of perilymph is presented and shown to be fit-for-purpose. We found a clear difference in the perilymph metabolome of noise exposed guinea pigs with and without H-2 treatment.
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45.
  • Rahman, Saeedur, et al. (författare)
  • Combining untargeted and targeted metabolomics approaches for the standardization of polyherbal formulations through UPLC-MS/MS
  • 2019
  • Ingår i: Metabolomics. - : SPRINGER. - 1573-3882 .- 1573-3890. ; 15:9
  • Tidskriftsartikel (refereegranskat)abstract
    • Introduction Polyherbal formulations are an integral part of various indigenous medicinal systems such as Traditional Chinese Medicine (TCM) and Ayurveda. The presence of a very large number of compounds makes the quality control of polyherbal formulations very difficult. Objectives To overcome this problem, we have developed a comprehensive strategy for the dereplication of natural products in polyherbal formulations by using Adhatoda vasica as a case study. Methods The strategy is based on five major steps: the collection of plant samples from different locations to observe the effects of environmental variables; LC-ESI-MS/MS-based untargeted metabolite profiling of the plant samples to identify marker compounds using extensive chemometric analysis of the obtained data; the identification of marker compounds in polyherbal products; the isolation, purification and characterization of the marker compounds; and MRM-based quantitative analysis of the isolated marker compounds using LC-ESI-MS/MS. Results Using this strategy, we identified a total of 51 compounds in the methanolic extract of A. vasica plants from 14 accessions. Chemical fingerprinting of the plant led to the identification of characteristic peaks that were used to confirm the presence of A. vasica in complex polyherbal formulations. Four quinazoline alkaloids (marker compounds) were isolated, purified and quantified in various herbal formulations containing A. vasica. Conclusion This method demonstrates a comprehensive strategy based on untargeted and targeted metabolite analysis that can be used for the standardization of complex polyherbal formulations.
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46.
  • Ribeiro, Henrique Caracho, et al. (författare)
  • Metabolomic and proteomic profiling in bipolar disorder patients revealed potential molecular signatures related to hemostasis
  • 2022
  • Ingår i: Metabolomics. - : Springer-Verlag New York. - 1573-3882 .- 1573-3890. ; 18:8
  • Tidskriftsartikel (refereegranskat)abstract
    • INTRODUCTION: Bipolar disorder (BD) is a mood disorder characterized by the occurrence of depressive episodes alternating with episodes of elevated mood (known as mania). There is also an increased risk of other medical comorbidities.OBJECTIVES: This work uses a systems biology approach to compare BD treated patients with healthy controls (HCs), integrating proteomics and metabolomics data using partial correlation analysis in order to observe the interactions between altered proteins and metabolites, as well as proposing a potential metabolic signature panel for the disease.METHODS: Data integration between proteomics and metabolomics was performed using GC-MS data and label-free proteomics from the same individuals (N = 13; 5 BD, 8 HC) using generalized canonical correlation analysis and partial correlation analysis, and then building a correlation network between metabolites and proteins. Ridge-logistic regression models were developed to stratify between BD and HC groups using an extended metabolomics dataset (N = 28; 14 BD, 14 HC), applying a recursive feature elimination for the optimal selection of the metabolites.RESULTS: Network analysis demonstrated links between proteins and metabolites, pointing to possible alterations in hemostasis of BD patients. Ridge-logistic regression model indicated a molecular signature comprising 9 metabolites, with an area under the receiver operating characteristic curve (AUROC) of 0.833 (95% CI 0.817-0.914).CONCLUSION: From our results, we conclude that several metabolic processes are related to BD, which can be considered as a multi-system disorder. We also demonstrate the feasibility of partial correlation analysis for integration of proteomics and metabolomics data in a case-control study setting.
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47.
  • Rocca-Serra, Philippe, et al. (författare)
  • Data standards can boost metabolomics research, and if there is a will, there is a way
  • 2016
  • Ingår i: Metabolomics. - New York; USA : Springer-Verlag New York. - 1573-3882 .- 1573-3890. ; 12
  • Tidskriftsartikel (refereegranskat)abstract
    • Thousands of articles using metabolomics approaches are published every year. With the increasing amounts of data being produced, mere description of investigations as text in manuscripts is not sufficient to enable re-use anymore: the underlying data needs to be published together with the findings in the literature to maximise the benefit from public and private expenditure and to take advantage of an enormous opportunity to improve scientific reproducibility in metabolomics and cognate disciplines. Reporting recommendations in metabolomics started to emerge about a decade ago and were mostly concerned with inventories of the information that had to be reported in the literature for consistency. In recent years, metabolomics data standards have developed extensively, to include the primary research data, derived results and the experimental description and importantly the metadata in a machine-readable way. This includes vendor independent data standards such as mzML for mass spectrometry and nmrML for NMR raw data that have both enabled the development of advanced data processing algorithms by the scientific community. Standards such as ISA-Tab cover essential metadata, including the experimental design, the applied protocols, association between samples, data files and the experimental factors for further statistical analysis. Altogether, they pave the way for both reproducible research and data reuse, including meta-analyses. Further incentives to prepare standards compliant data sets include new opportunities to publish data sets, but also require a little "arm twisting" in the author guidelines of scientific journals to submit the data sets to public repositories such as the NIH Metabolomics Workbench or MetaboLights at EMBL-EBI. In the present article, we look at standards for data sharing, investigate their impact in metabolomics and give suggestions to improve their adoption.
  •  
48.
  • Roci, I, et al. (författare)
  • Mapping choline metabolites in normal and transformed cells
  • 2020
  • Ingår i: Metabolomics : Official journal of the Metabolomic Society. - : Springer Science and Business Media LLC. - 1573-3890. ; 16:12, s. 125-
  • Tidskriftsartikel (refereegranskat)abstract
    • IntroductionCholine is an essential human nutrient that is particular important for proliferating cells, and altered choline metabolism has been associated with cancer transformation. Yet, the various metabolic fates of choline in proliferating cells have not been investigated systematically.ObjectivesThis study aims to map the metabolic products of choline in normal and cancerous proliferating cells.MethodsWe performed13C-choline tracing followed by liquid chromatography-high resolution mass spectrometry (LC-HRMS) analysis of metabolic products in normal and in vitro-transformed (tumor-forming) epithelial cells, and also in tumor-derived cancer cell lines. Selected metabolites were quantified by internal standards.ResultsUntargeted analysis revealed 121 LCMS peaks that were13C-labeled from choline, including various phospholipid species, but also previously unknown products such as monomethyl- and dimethyl-ethanolamines. Interestingly, we observed formation of betaine from choline specifically in tumor-derived cells. Expression of choline dehydrogenase (CHDH), which catalyzes the first step of betaine synthesis, correlated with betaine synthesis across the cell lines studied. RNAi silencing of CHDH did not affect cell proliferation, although we observed an increased fraction of G2M phase cells with some RNAi sequences, suggesting that CHDH and its product betaine may play a role in cell cycle progression. Betaine cell concentration was around 10 µM, arguing against an osmotic function, and was not used as a methyl donor. The function of betaine in these tumor-derived cells is presently unknown.ConclusionThis study identifies novel metabolites of choline in cancer and normal cell lines, and reveals altered choline metabolism in cancer cells.
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49.
  • Salek, Reza M, et al. (författare)
  • COordination of Standards in MetabOlomicS (COSMOS) : facilitating integrated metabolomics data access
  • 2015
  • Ingår i: Metabolomics. - : Springer-Verlag New York. - 1573-3882 .- 1573-3890. ; 11:6, s. 1587-1597
  • Tidskriftsartikel (refereegranskat)abstract
    • Metabolomics has become a crucial phenotyping technique in a range of research fields including medicine, the life sciences, biotechnology and the environmental sciences. This necessitates the transfer of experimental information between research groups, as well as potentially to publishers and funders. After the initial efforts of the metabolomics standards initiative, minimum reporting standards were proposed which included the concepts for metabolomics databases. Built by the community, standards and infrastructure for metabolomics are still needed to allow storage, exchange, comparison and re-utilization of metabolomics data. The Framework Programme 7 EU Initiative 'coordination of standards in metabolomics' (COSMOS) is developing a robust data infrastructure and exchange standards for metabolomics data and metadata. This is to support workflows for a broad range of metabolomics applications within the European metabolomics community and the wider metabolomics and biomedical communities' participation. Here we announce our concepts and efforts asking for re-engagement of the metabolomics community, academics and industry, journal publishers, software and hardware vendors, as well as those interested in standardisation worldwide (addressing missing metabolomics ontologies, complex-metadata capturing and XML based open source data exchange format), to join and work towards updating and implementing metabolomics standards.
  •  
50.
  • Shiryaeva, Liudmila, 1970-, et al. (författare)
  • Pair-wise multicomparison and OPLS analyses of cold-acclimation phases in Siberian spruce
  • 2012
  • Ingår i: Metabolomics. - : Springer. - 1573-3882 .- 1573-3890. ; 8:Suppl 1, s. 123-130
  • Tidskriftsartikel (refereegranskat)abstract
    • Analysis of metabolomics data often goes beyond the task of discovering biomarkers and can be aimed at recovering other important characteristics of observed metabolomic changes. In this paper we explore different methods to detect the presence of distinctive phases in seasonal-responsive changes of metabolomic patterns of Siberian spruce (Picea obovata) during cold acclimation occurred in the period from mid-August to January. Multivariate analysis, specifically orthogonal projection to latent structures discriminant analysis (OPLSDA), identified time points where the metabolomic patterns underwent substantial modifications as a whole, revealing four distinctive phases during acclimation. This conclusion was re-examined by a univariate analysis consisting of multiple pair-wise comparisons to identify homogeneity intervals for each metabolite. These tests complemented OPLS-DA, clarifying biological interpretation of the classification: about 60% of metabolites found responsive to the cold stress indeed changed at one or more of the time points predicted by OPLS-DA. However, the univariate approach did not support the proposed division of the acclimation period into four phases: less than 10% of metabolites altered during the acclimation had homogeneous levels predicted by OPLS-DA. This demonstrates that coupling the classification found by OPLS-DA and the analysis of dynamics of individual metabolites obtained by pair-wise multicomparisons reveals a more correct characterization of biochemical processes in freezing tolerant trees and leads to interpretations that cannot be deduced by either method alone. The combined analysis can be used in other ‘omics’-studies, where response factors have a causal dependence (like the time in the present work) and pairwise multicomparisons are not conservative.
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