| 1. |
- Abreu, Ilka
(författare)
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Quantitative trait loci mapping of polyphenol metabolites in blackcurrant (Ribes nigrum L.)
- 2020
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Ingår i: Metabolomics. - : Springer Science and Business Media LLC. - 1573-3882 .- 1573-3890. ; 16
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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)
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Extended automated quantification algorithm (AQuA) for targeted 1H NMR metabolomics of highly complex samples: application to plant root exudates
- 2023
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Ingår i: Metabolomics. - 1573-3882 .- 1573-3890. ; 20
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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)
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Single cell metabolism : current and future trends
- 2022
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Ingår i: Metabolomics. - : Springer. - 1573-3882 .- 1573-3890. ; 18:10
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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)
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Characterization of plasma lipidomics in adolescent subjects with increased risk for type 1 diabetes in the DiPiS cohort
- 2020
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Ingår i: Metabolomics. - : Springer Science and Business Media LLC. - 1573-3882 .- 1573-3890. ; 16:10
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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)
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Anabolic androgenic steroids exert a selective remodeling of the plasma lipidome that mirrors the decrease of the de novo lipogenesis in the liver
- 2020
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Ingår i: Metabolomics. - : SPRINGER. - 1573-3882 .- 1573-3890. ; 16:1
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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)
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Metabolomics enables precision medicine : "A White Paper, Community Perspective"
- 2016
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Ingår i: Metabolomics. - : Springer. - 1573-3882 .- 1573-3890. ; 12:10
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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)
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Metabolomics analyses in non-diabetic middle-aged individuals reveal metabolites impacting early glucose disturbances and insulin sensitivity
- 2020
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Ingår i: Metabolomics. - : Springer. - 1573-3882 .- 1573-3890. ; 16:3
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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)
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Lipid phenotyping of lung epithelial lining fluid in healthy human volunteers
- 2018
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Ingår i: Metabolomics. - : Springer-Verlag New York. - 1573-3882 .- 1573-3890. ; 14:10
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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)
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Assigning precursor-product ion relationships in indiscriminant MS/MS data from non-targeted metabolite profiling studies
- 2013
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Ingår i: Metabolomics. - : Springer Science and Business Media LLC. - 1573-3882 .- 1573-3890. ; 9:1, s. 33-43
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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)
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Large-scale untargeted LC-MS metabolomics data correction using between-batch feature alignment and cluster-based within-batch signal intensity drift correction
- 2016
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Ingår i: Metabolomics. - : Springer Science and Business Media LLC. - 1573-3882 .- 1573-3890. ; 12
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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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