Discovery of A-type procyanidin dimers in yellow raspberries by untargeted metabolomics and correlation based data analysis

Introduction

Raspberries are becoming increasingly popular due to their reported health beneficial properties. Despite the presence of only trace amounts of anthocyanins, yellow varieties seems to show similar or better effects in comparison to conventional raspberries.

Objectives

The aim of this work is to characterize the metabolic differences between red and yellow berries, focussing on the compounds showing a higher concentration in yellow varieties.

Methods

The metabolomic profile of 13 red and 12 yellow raspberries (of different varieties, locations and collection dates) was determined by UPLC–TOF-MS. A novel approach based on Pearson correlation on the extracted ion chromatograms was implemented to extract the pseudospectra of the most relevant biomarkers from high energy LC–MS runs. The raw data will be made publicly available on MetaboLights (MTBLS333).

Results

Among the metabolites showing higher concentration in yellow raspberries it was possible to identify a series of compounds showing a pseudospectrum similar to that of A-type procyanidin polymers. The annotation of this group of compounds was confirmed by specific MS/MS experiments and performing standard injections.

Conclusions

In berries lacking anthocyanins the polyphenol metabolism might be shifted to the formation of a novel class of A-type procyanidin polymers.

     

Discovery of urine biomarkers for bladder cancer via global metabolomics

Bladder cancer (BC) is latent in its early stage and lethal in its late stage. Therefore, early diagnosis and intervention are essential for successful BC treatment. Considering the limitations of current diagnostic tools, noninvasive biomarkers that are both highly sensitive and specific are needed to improve the overall survival and quality of life of patients. With the advent of systems biology, “-omics” technologies have been developed over the past few decades. As a promising member, global metabolomics has increasingly been found to have clear potential for biomarker discovery. However, urinary metabolomics studies related to BC have lagged behind those of other urinary cancers, and major findings have not been systematically reported. The objective of this review is to comprehensively list the currently identified potential urinary metabolite biomarkers for BC.     

Effect of cheese and butter intake on metabolites in urine using an untargeted metabolomics approach

Cheese intake has been shown to decrease total cholesterol and LDL cholesterol concentrations when compared to butter of equal fat content. An untargeted metabolite profiling may reveal exposure markers of cheese but may also contribute with markers which can help explain how the intake of cheese affects cholesterol concentrations. Twenty-three subjects collected 2 × 24 h urine samples after 6 weeks of cheese and 6 weeks of butter intake with equal amounts of fat in a cross-over intervention study. The samples were analyzed by UPLC-QTOF/MS. A two-step univariate data analysis approach using linear mixed model was applied separately for positive and negative ionization mode: In the first step a total of 44 features related to treatment were identified and in the second step 36 of these features were related to total cholesterol concentrations. Cheese intake resulted in increased urinary indoxyl sulfate, xanthurenic acid, tyramine sulfate, 4-hydroxyphenylacetic acid, isovalerylglutamic acid and several acylglycines including isovalerylglycine, tiglylglycine and isobutyrylglycine when compared to butter intake of equal fat content. The biological mechanisms of action linking the metabolites to cholesterol concentrations need to be further explored.     

基于非靶代谢组学多刺绿绒蒿不同器官代谢物差异分析

为了进一步探究传统藏药植物多刺绿绒蒿（Meconopsis horridula）中代谢物成分以及不同器官差异情况,采用UPLC-MS技术对多刺绿绒蒿的叶、根和花三个不同器官代谢物进行分析与鉴定。并利用主成分分析（PCA）、聚类热图分析、正交偏最小二乘-判别分析（OPLS-DA）和KEGG通路富集分析等方法进行不同器官差异代谢产物筛选与通路分析。结果显示,在ESI⁺和ESI^-模式下,共检测注释到947种代谢物,不同器官间差异代谢物进行分析,叶和根差异代谢物有301个,叶和花中差异代谢物有170个,根和花中差异代谢物有244个。通过聚类热图可以看出,大多数代谢物在根中含量较低;KEGG通路富集分析显示,差异代谢物大多富集在氨基酸代谢、花青素生物合成、黄酮类生物合成和生物碱合成等代谢途径。各器官优势黄酮类、萜类和生物碱类代谢物的分析为进一步探究多刺绿绒蒿的不同器官药用特征成分和开发利用提供一定的帮助。     

A comparison of high-throughput plasma NMR protocols for comparative untargeted metabolomics

Metabolomics - Understanding the interaction between organisms and the environment is important for predicting and mitigating the effects of global phenomena such as climate change, and the fate,...     

Chemometric analysis of soy protein hydrolysates used in animal cell culture for IgG production – An untargeted metabolomics approach

Soy protein hydrolysates are used as the most cost effective medium supplement to enhance cell growth and recombinant protein productivity in cell cultures. Such hydrolysates contain diverse classes of compounds, such as peptides, carbohydrates and phenolic compounds. To identify if specific compounds dominate the functionality of hydrolysates in cell cultures, thirty samples of hydrolysates with different cell culture performances were analyzed for chemical composition using an untargeted metabolomics approach. Out of 410 detected compounds, 157 were annotated. Most of the remaining 253 compounds were identified as peptides, but could not be annotated exactly. All compounds were quantified relatively, based on their average signal intensities. The cell growth and total immunoglobulin (IgG) production, relative to the CD medium (100%), ranged from 148 to 438% and 117 to 283%, respectively. Using bootstrapped stepwise regression (BSR), the compounds with the highest inclusion frequency were identified. The most important compound, i.e. phenyllactate and ferulate explained 29% and 30% of the variance for cell growth and total IgG production, respectively. Surprisingly, all compounds identified in the BSR showed a positive correlation with cell growth and total IgG production. This knowledge can be applied to monitor the production and accumulation of these compounds during the production process of hydrolysates. Consequently, the processing conditions can be modulated to produce soy protein hydrolysates with enhanced and consistent cell culture performance.     

An untargeted metabolomics method for archived newborn dried blood spots in epidemiologic studies

Introduction

For pediatric diseases like childhood leukemia, a short latency period points to in-utero exposures as potentially important risk factors. Untargeted metabolomics of small molecules in archived newborn dried blood spots (DBS) offers an avenue for discovering early-life exposures that contribute to disease risks.

Objectives

The purpose of this study was to develop a quantitative method for untargeted analysis of archived newborn DBS for use in an epidemiological study (California Childhood Leukemia Study, CCLS).

Methods

Using experimental DBS from the blood of an adult volunteer, we optimized extraction of small molecules and integrated measurement of potassium as a proxy for blood hematocrit. We then applied this extraction method to 4.7-mm punches from 106 control DBS samples from the CCLS. Sample extracts were analyzed with liquid chromatography—high resolution mass spectrometry (LC-HRMS) and an untargeted workflow was used to screen for metabolites that discriminate population characteristics such as sex, ethnicity, and birth weight.

Results

Thousands of small molecules were measured in extracts of archived DBS. Normalizing for potassium levels removed variability related to varying hematocrit across DBS punches. Of the roughly 1000 prevalent small molecules that were tested, multivariate linear regression detected significant associations with ethnicity (three metabolites) and birth weight (15 metabolites) after adjusting for multiple testing.

Conclusions

This untargeted workflow can be used for analysis of small molecules in archived DBS to discover novel biomarkers, to provide insights into the initiation and progression of diseases, and to provide guidance for disease prevention.

     

Radiation metabolomics. 1. Identification of minimally invasive urine biomarkers for gamma-radiation exposure in mice

Gamma-radiation exposure has both short- and long-term adverse health effects. The threat of modern terrorism places human populations at risk for radiological exposures, yet current medical countermeasures to radiation exposure are limited. Here we describe metabolomics for gamma-radiation biodosimetry in a mouse model. Mice were gamma-irradiated at doses of 0, 3 and 8 Gy (2.57 Gy/min), and urine samples collected over the first 24 h after exposure were analyzed by ultra-performance liquid chromatography-time-of-flight mass spectrometry (UPLC-TOFMS). Multivariate data were analyzed by orthogonal partial least squares (OPLS). Both 3- and 8-Gy exposures yielded distinct urine metabolomic phenotypes. The top 22 ions for 3 and 8 Gy were analyzed further, including tandem mass spectrometric comparison with authentic standards, revealing that N-hexanoylglycine and beta-thymidine are urinary biomarkers of exposure to 3 and 8 Gy, 3-hydroxy-2-methylbenzoic acid 3-O-sulfate is elevated in urine of mice exposed to 3 but not 8 Gy, and taurine is elevated after 8 but not 3 Gy. Gene Expression Dynamics Inspector (GEDI) self-organizing maps showed clear dose-response relationships for subsets of the urine metabolome. This approach is useful for identifying mice exposed to gamma radiation and for developing metabolomic strategies for noninvasive radiation biodosimetry in humans.     

Metabolic derangements identified through untargeted metabolomics in a cross-sectional study of Nigerian children with severe acute malnutrition

Introduction

Severe acute malnutrition (SAM) is a major cause of child mortality worldwide, however the pathogenesis of SAM remains poorly understood. Recent studies have uncovered an altered gut microbiota composition in children with SAM, suggesting a role for microbes in the pathogenesis of malnutrition.

Objectives

To elucidate the metabolic consequences of SAM and whether these changes are associated with changes in gut microbiota composition.

Methods

We applied an untargeted multi-platform metabolomics approach [gas chromatography–mass spectrometry (GC-MS) and liquid chromatography–mass spectrometry (LC-MS)] to stool and plasma samples from 47 Nigerian children with SAM and 11 control children. The composition of the stool microbiota was assessed by 16S rRNA gene sequencing.

Results

The plasma metabolome discriminated children with SAM from controls, while no significant differences were observed in the microbial or small molecule composition of stool. The abundance of 585 features in plasma were significantly altered in malnourished children (Wilcoxon test, FDR corrected P?<?0.1), representing approximately 15% of the metabolome. Consistent with previous studies, children with SAM exhibited a marked reduction in amino acids/dipeptides and phospholipids, and an increase in acylcarnitines. We also identified numerous metabolic perturbations which have not been reported previously, including increased disaccharides, truncated fibrinopeptides, angiotensin I, dihydroxybutyrate, lactate, and heme, and decreased bioactive lipids belonging to the eicosanoid and docosanoid family.

Conclusion

Our findings provide a deeper understanding of the metabolic consequences of malnutrition. Further research is required to determine if specific metabolites may guide improved management, and/or act as novel biomarkers for assessing response to treatment.

     

Systematic variations associated with renal disease uncovered by parallel metabolomics of urine and serum

Background

Membranous nephropathy is an important glomerular disease characterized by podocyte injury and proteinuria, but no metabolomics research was reported as yet. Here, we performed a parallel metabolomics study, based on human urine and serum, to comprehensively profile systematic metabolic variations, identify differential metabolites, and understand the pathogenic mechanism of membranous nephropathy.

Results

There were obvious metabolic distinctions between the membranous nephropathy patients with urine protein lower than 3.5 g/24 h (LUPM) and those higher than 3.5 g/24 h (HUPM) by Partial Least Squares Discriminant Analysis (PLS-DA) model analysis. In total, 26 urine metabolites and 9 serum metabolites were identified to account for such differences, and the majority of metabolites were significantly increased in HUPM patients for both urines and serums. Combining the results of urine with serum, all differential metabolites were classified to 5 classes. This classification helps globally probe the systematic metabolic alterations before and after blood flowing through kidney. Citric acid and 4 amino acids were markedly increased only in the serum samples of HUPM patients, implying more impaired filtration function of kidneys of HUPM patients than LUPM patients. The dicarboxylic acids, phenolic acids, and cholesterol were significantly elevated only in urines of HUPM patients, suggesting more severe oxidative attacks than LUPM patients.

Conclusions

Parallel metabolomics of urine and serum revealed the systematic metabolic variations associated with LUPM and HUPM patients, where HUPM patients suffered more severe injury of kidney function and oxidative stresses than LUPM patients. This research exhibited a promising application of parallel metabolomics in renal diseases.

     

An integrated genetic map of the rat with 562 markers from different sources

Genetic maps are the primary resources for genetic study. Genetic map construction was quite difficult in the past decade for lack of polymorphic markers. This situation has been changed since the development of microsatellite markers or simple sequence length polymorphisms (SSLPs) because they are abundant and more polymorphic. Here we report the construction of an integrated genetic map of the rat derived from two F₂ intercrosses. A map of 376 markers from 160 (OLETF × F344)F₂ progenies and a map of 333 markers from 71 (F344 × LEC)F₂ animals are integrated by use of common set of 120 anchor markers chosen to be spaced at an average of 15 cM in the genome. The resulting integrated map with 194 newly developed rat markers from WIBR/MIT CGR, 269 Mit/Mgh markers, 94 Wox markers, and 5 markers of various origins covers the majority of 21 chromosomes of the rat with a total genetic distance of 1797 cM and an average marker spacing of 3.2 cM. The current map provides detailed information for markers from different sources and, therefore, should be helpful to the research community. Received: 6 May 1998 / Accepted: 24 August 1998     

Characterization of missing values in untargeted MS-based metabolomics data and evaluation of missing data handling strategies

Background

Untargeted mass spectrometry (MS)-based metabolomics data often contain missing values that reduce statistical power and can introduce bias in biomedical studies. However, a systematic assessment of the various sources of missing values and strategies to handle these data has received little attention. Missing data can occur systematically, e.g. from run day-dependent effects due to limits of detection (LOD); or it can be random as, for instance, a consequence of sample preparation.

Methods

We investigated patterns of missing data in an MS-based metabolomics experiment of serum samples from the German KORA F4 cohort (n?=?1750). We then evaluated 31 imputation methods in a simulation framework and biologically validated the results by applying all imputation approaches to real metabolomics data. We examined the ability of each method to reconstruct biochemical pathways from data-driven correlation networks, and the ability of the method to increase statistical power while preserving the strength of established metabolic quantitative trait loci.

Results

Run day-dependent LOD-based missing data accounts for most missing values in the metabolomics dataset. Although multiple imputation by chained equations performed well in many scenarios, it is computationally and statistically challenging. K-nearest neighbors (KNN) imputation on observations with variable pre-selection showed robust performance across all evaluation schemes and is computationally more tractable.

Conclusion

Missing data in untargeted MS-based metabolomics data occur for various reasons. Based on our results, we recommend that KNN-based imputation is performed on observations with variable pre-selection since it showed robust results in all evaluation schemes.

     

Comparison of metabolomics of Dendrobium officinale in different habitats by UPLC-Q-TOF-MS

Dendrobium officinale has been considered over past centuries to be extremely valuable for use as an herbal medicine in South Asian countries. In this work, the chemical profiles of D. officinale from different habitats were systematically characterized using ultra-performance liquid chromatography time-of-flight mass spectrometry (UPLC-Q-TOF-MS) and multivariate analysis. The principal component analysis (PCA), partial least squares discriminate analysis (PLS-DA) and orthogonal partial least squares analysis (OPLS-DA) of UPLC-Q-TOF-MS data displayed an obvious separation. Several flavonoids and terpenoids derivatives contribute to the quantitative chemotypic variation within and between the samples as observed. These findings lead to a better understanding of the phytochemical variation of D. officinale which can aid in quality control of raw material.     

Discovery of species-wide EST-derived markers in Rhododendron by intron-flanking primer design

From two cDNA libraries made of flowers of the evergreen Rhododendron simsii hybrid ‘Flamenco’ and the deciduous species Rhododendron luteum, 323 cDNA fragments were randomly sequenced and functions were assigned. Reliable homologies were found for 31% of the fragments. For mapping applications, EST-derived markers were developed on 127 sequences. Since polymorphisms are more prone to come about in introns, primers were selected to have an elevated probability to be intron-spanning. The utilised approach resulted in an overall polymorphism-generating efficiency of 35% (45 polymorphic markers). The transferability of these cDNA-based EST markers among plant species was confirmed within the Rhododendron genus. The markers developed here are currently screened for the construction of genetic linkage maps of four crossing populations of R. simsii hybrids.     

Ingenuity pathways analysis of urine metabolomics phenotypes toxicity of Chuanwu in Wistar rats by UPLC-Q-TOF-HDMS coupled with pattern recognition methods

Chuanwu (CW), a valuable traditional Chinese medicine (TCM), is the mother root of Aconitum carmichaelii Debx. The cause of CW-induced toxicity is still under ongoing research, although this is limited by the lack of sensitive and reliable biomarkers. Ingenuity pathway analysis (IPA) was performed to analyzing global metabolomics in order to characterize the phenotypically biochemical perturbations and potential mechanisms of the CW-induced toxicity. CW was administered to Wistar rats (0.027 g/200 g and 0.108 g/200 g bw, oral) for 6 months and urine samples were collected. The urinary metabolomics was performed by UPLC-Q-TOF-HDMS, and the mass spectra signals of the detected metabolites were systematically deconvoluted and analyzed by pattern recognition methods (PCA, PLS-DA, and OPLS-DA), revealing a time- and dose-dependency of the biochemical perturbations induced by CW toxicity. As a result, several metabolites responsible for pentose and glucuronate interconversions, alanine, aspartate and glutamate metabolism, starch and sucrose metabolism, amino sugar and nucleotide sugar metabolism, purine metabolism, tryptophan metabolism, taurine and hypotaurine metabolism, fructose and mannose metabolism, fatty acid metabolism were characterized, and it was confirmed that biochemical perturbations can be foreseen from these biomarkers. The urinary metabolomics based IPA with pattern recognition methods also revealed that CW produced serious heart and liver toxicity, consistent with clinical biochemistry and histopathology. Significant changes of 17 metabolites were identified and validated as phenotypic biomarkers of CW toxicity. Overall, our work demonstrated the metabolomics has brought enormous opportunities for improved detection of toxicity and biomarker discovery, highlighting the powerful predictive potential of the IPA to study of drug toxicity.