| 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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| 11. |
- Carlsson, Henrik, et al.
(författare)
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Targeted metabolomics of CSF in healthy individuals and patients with secondary progressive multiple sclerosis using high-resolution mass spectrometry
- 2020
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Ingår i: Metabolomics. - : SPRINGER. - 1573-3882 .- 1573-3890. ; 16:2
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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)
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An LC-QToF MS based method for untargeted metabolomics of human fecal samples
- 2020
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Ingår i: Metabolomics. - : Springer Science and Business Media LLC. - 1573-3882 .- 1573-3890. ; 16:4
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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)
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Plasma metabolomic response to postmenopausal weight loss induced by different diets
- 2016
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Ingår i: Metabolomics. - : Springer Science and Business Media LLC. - 1573-3882 .- 1573-3890. ; 12:5
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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)
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Statistical multivariate metabolite profiling for aiding biomarker pattern detection and mechanistic interpretations in GC/MS based metabolomics
- 2006
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Ingår i: Metabolomics. - : Springer Science and Business Media LLC. - 1573-3882 .- 1573-3890. ; 2:4, s. 257-68
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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)
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Development of a gas chromatography/mass spectrometry based metabolomics protocol by means of statistical experimental design
- 2012
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Ingår i: Metabolomics. - : Springer Science and Business Media LLC. - 1573-3882 .- 1573-3890. ; 8:1, s. 50-63
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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)
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Metabolomic profiling of maternal hair suggests rapid development of intrahepatic cholestasis of pregnancy
- 2018
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Ingår i: Metabolomics. - : SPRINGER. - 1573-3882 .- 1573-3890. ; 14:6
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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)
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Obesity-related metabolite profiles of black women spanning the epidemiologic transition
- 2016
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Ingår i: Metabolomics. - New York : Springer-Verlag New York. - 1573-3882 .- 1573-3890. ; 12:3
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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)
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LC-MS based global metabolite profiling : the necessity of high data quality
- 2016
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Ingår i: Metabolomics. - : Springer Science and Business Media LLC. - 1573-3882 .- 1573-3890. ; 12:7
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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)
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Integrating genetics with newborn metabolomics in infantile hypertrophic pyloric stenosis
- 2021
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Ingår i: Metabolomics. - : Springer Science and Business Media LLC. - 1573-3882 .- 1573-3890. ; 17:1
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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)
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Investigating sources of variability in metabolomic data in the EPIC study : the Principal Component Partial R-square (PC-PR2) method
- 2014
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Ingår i: Metabolomics. - : Springer. - 1573-3882 .- 1573-3890. ; 10:6, s. 1074-1083
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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)
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Evaluating the challenges associated with time-of-fight secondary ion mass spectrometry for metabolomics using pure and mixed metabolites
- 2013
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Ingår i: Metabolomics. - : Springer Science and Business Media LLC. - 1573-3882 .- 1573-3890. ; 9:3, s. 535-544
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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)
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Large-scale non-targeted metabolomic profiling in three human population-based studies
- 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
- 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)
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Proposed minimum reporting standards for data analysis in metabolomics
- 2007
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Ingår i: Metabolomics. - : Springer Science and Business Media LLC. - 1573-3882 .- 1573-3890. ; 3, s. 231-41
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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)
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Associations of the placental metabolome with immune maturation up to one year of age in the Swedish NICE-cohort
- 2024
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Ingår i: Metabolomics. - : Springer Nature. - 1573-3882 .- 1573-3890. ; 20:2
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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)
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Mass spectrometry based metabolomics for in vitro systems pharmacology : pitfalls, challenges, and computational solutions.
- 2017
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Ingår i: Metabolomics. - : Springer Science and Business Media LLC. - 1573-3882 .- 1573-3890. ; 13:7
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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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