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Metabolites, Volume 10, Issue 4 (April 2020) – 49 articles

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Cover Story (view full-size image) We compared targeted and untargeted DDA methods for metabolite annotation. The results demonstrate [...] Read more.
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Open AccessArticle
On the Use of Correlation and MI as a Measure of Metabolite—Metabolite Association for Network Differential Connectivity Analysis
Metabolites 2020, 10(4), 171; https://doi.org/10.3390/metabo10040171 - 24 Apr 2020
Viewed by 336
Abstract
Metabolite differential connectivity analysis has been successful in investigating potential molecular mechanisms underlying different conditions in biological systems. Correlation and Mutual Information (MI) are two of the most common measures to quantify association and for building metabolite—metabolite association networks and to calculate differential [...] Read more.
Metabolite differential connectivity analysis has been successful in investigating potential molecular mechanisms underlying different conditions in biological systems. Correlation and Mutual Information (MI) are two of the most common measures to quantify association and for building metabolite—metabolite association networks and to calculate differential connectivity. In this study, we investigated the performance of correlation and MI to identify significantly differentially connected metabolites. These association measures were compared on (i) 23 publicly available metabolomic data sets and 7 data sets from other fields, (ii) simulated data with known correlation structures, and (iii) data generated using a dynamic metabolic model to simulate real-life observed metabolite concentration profiles. In all cases, we found more differentially connected metabolites when using correlation indices as a measure for association than MI. We also observed that different MI estimation algorithms resulted in difference in performance when applied to data generated using a dynamic model. We concluded that there is no significant benefit in using MI as a replacement for standard Pearson’s or Spearman’s correlation when the application is to quantify and detect differentially connected metabolites. Full article
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Open AccessArticle
Identification of Metabolic Alterations in Breast Cancer Using Mass Spectrometry-Based Metabolomic Analysis
Metabolites 2020, 10(4), 170; https://doi.org/10.3390/metabo10040170 - 24 Apr 2020
Viewed by 400
Abstract
Breast cancer (BC) is a major global health issue and remains the second leading cause of cancer-related death in women, contributing to approximately 41,760 deaths annually. BC is caused by a combination of genetic and environmental factors. Although various molecular diagnostic tools have [...] Read more.
Breast cancer (BC) is a major global health issue and remains the second leading cause of cancer-related death in women, contributing to approximately 41,760 deaths annually. BC is caused by a combination of genetic and environmental factors. Although various molecular diagnostic tools have been developed to improve diagnosis of BC in the clinical setting, better detection tools for earlier diagnosis can improve survival rates. Given that altered metabolism is a characteristic feature of BC, we aimed to understand the comparative metabolic differences between BC and healthy controls. Metabolomics, the study of metabolism, can provide incredible insight and create useful tools for identifying potential BC biomarkers. In this study, we applied two analytical mass spectrometry (MS) platforms, including hydrophilic interaction chromatography (HILIC) and gas chromatography (GC), to generate BC-associated metabolic profiles using breast tissue from BC patients. These metabolites were further analyzed to identify differentially expressed metabolites in BC and their associated metabolic networks. Additionally, Chemical Similarity Enrichment Analysis (ChemRICH), MetaMapp, and Metabolite Set Enrichment Analysis (MSEA) identified significantly enriched clusters and networks in BC tissues. Since metabolomic signatures hold significant promise in the clinical setting, more effort should be placed on validating potential BC biomarkers based on identifying altered metabolomes. Full article
(This article belongs to the Section Metabolomic Profiling Technology)
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Open AccessArticle
Mechanism of Chronic Kidney Disease Progression and Novel Biomarkers: A Metabolomic Analysis of Experimental Glomerulonephritis
Metabolites 2020, 10(4), 169; https://doi.org/10.3390/metabo10040169 - 24 Apr 2020
Viewed by 399
Abstract
While a complex network of cellular and molecular events is known to be involved in the pathophysiological mechanism of chronic kidney disease (CKD), the divergence point between reversal and progression and the event that triggers CKD progression are still unknown. To understand the [...] Read more.
While a complex network of cellular and molecular events is known to be involved in the pathophysiological mechanism of chronic kidney disease (CKD), the divergence point between reversal and progression and the event that triggers CKD progression are still unknown. To understand the different mechanisms between reversible and irreversible kidney disease and to search for urinary biomarkers that can predict prognosis, a metabolomic analysis was applied to compare acute and chronic experimental glomerulonephritis (GN) models. Four metabolites, namely, epoxyoctadecenoic acid (EpOME), epoxyeicosatetraenoic acid (EpETE), α-linolenic acid (ALA), and hydroxyretinoic acid, were identified as predictive markers after comparing the chronic nephritis model with acute nephritis and control groups (false discovery rate adjusted p-value (q-value) < 0.05). Renal mRNA expression of cytochrome P450 and epoxide hydrolase was also identified as being involved in the production of epoxide metabolites from these polyunsaturated fatty acids (p < 0.05). These results suggested that the progression of chronic kidney disease is associated with abnormally activated epoxide hydrolase, leading to an increase in EpOME and EpETE as pro-inflammatory eicosanoids. Full article
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Open AccessArticle
Distinguishing NASH Histological Severity Using a Multiplatform Metabolomics Approach
Metabolites 2020, 10(4), 168; https://doi.org/10.3390/metabo10040168 - 24 Apr 2020
Viewed by 433
Abstract
Nonalcoholic fatty liver disease (NAFLD) is categorized based on histological severity into nonalcoholic fatty liver (NAFL) or nonalcoholic steatohepatitis (NASH). We used a multiplatform metabolomics approach to identify metabolite markers and metabolic pathways that distinguish NAFL from early NASH and advanced NASH. We [...] Read more.
Nonalcoholic fatty liver disease (NAFLD) is categorized based on histological severity into nonalcoholic fatty liver (NAFL) or nonalcoholic steatohepatitis (NASH). We used a multiplatform metabolomics approach to identify metabolite markers and metabolic pathways that distinguish NAFL from early NASH and advanced NASH. We analyzed fasting serum samples from 57 prospectively-recruited patients with histologically-proven NAFLD, including 12 with NAFL, 31 with early NASH and 14 with advanced NASH. Metabolite profiling was performed using a combination of liquid chromatography-mass spectrometry (LC-MS) and nuclear magnetic resonance (NMR) spectroscopy analyzed with multivariate statistical and pathway analysis tools. We targeted 237 metabolites of which 158 were quantified. Multivariate analysis uncovered metabolite profile clusters for patients with NAFL, early NASH, and advanced NASH. Also, multiple individual metabolites were associated with histological severity, most notably spermidine which was more than 2-fold lower in advanced fibrosis vs. early fibrosis, in advanced NASH vs. NAFL and in advanced NASH vs. early NASH, suggesting that spermidine exercises a protective effect against development of fibrosing NASH. Furthermore, the results also showed metabolic pathway perturbations between early-NASH and advanced-NASH. In conclusion, using a combination of two reliable analytical platforms (LC-MS and NMR spectroscopy) we identified individual metabolites, metabolite clusters and metabolic pathways that were significantly different between NAFL, early-NASH, and advanced-NASH. These differences provide mechanistic insights as well as potentially important metabolic biomarker candidates that may noninvasively distinguish patients with NAFL, early-NASH, and advanced-NASH. The associations of spermidine levels with less advanced histology merit further assessment of the potential protective effects of spermidine in NAFLD. Full article
(This article belongs to the Special Issue Metabolism and Metabolomics of Liver in Health and Disease)
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Open AccessArticle
Metabolic Effects of Bovine Milk Oligosaccharides on Selected Commensals of the Infant Microbiome—Commensalism and Postbiotic Effects
Metabolites 2020, 10(4), 167; https://doi.org/10.3390/metabo10040167 - 24 Apr 2020
Viewed by 615
Abstract
Oligosaccharides from human or bovine milk selectively stimulate growth or metabolism of bacteria associated with the lower gastrointestinal tract of infants. Results from complex infant-type co-cultures point toward a possible synergistic effect of combining bovine milk oligosaccharides (BMO) and lactose (LAC) on enhancing [...] Read more.
Oligosaccharides from human or bovine milk selectively stimulate growth or metabolism of bacteria associated with the lower gastrointestinal tract of infants. Results from complex infant-type co-cultures point toward a possible synergistic effect of combining bovine milk oligosaccharides (BMO) and lactose (LAC) on enhancing the metabolism of Bifidobacterium longum subsp. longum and inhibition of Clostridium perfringens. We examine the interaction between B. longum subsp. longum and the commensal Parabacteroides distasonis, by culturing them in mono- and co-culture with different carbohydrates available. To understand the interaction between BMO and lactose on B. longum subsp. longum and test the potential postbiotic effect on C. perfringens growth and/or metabolic activity, we inoculated C. perfringens into fresh media and compared the metabolic changes to C. perfringens in cell-free supernatant from B. longum subsp. longum fermented media. In co-culture, B. longum subsp. longum benefits from P. distasonis (commensalism), especially in a lactose-rich environment. Furthermore, B. longum subsp. longum fermentation of BMO + LAC impaired C. perfringens’ ability to utilize BMO as a carbon source (potential postbiotic effect). Full article
(This article belongs to the Special Issue Metabolomics and Microbiota Metabolism)
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Open AccessReview
Metabolomics in Central Sensitivity Syndromes
Metabolites 2020, 10(4), 164; https://doi.org/10.3390/metabo10040164 - 24 Apr 2020
Viewed by 474
Abstract
Central sensitization syndromes are a collection of frequently painful disorders that contribute to decreased quality of life and increased risk of opiate abuse. Although these disorders cause significant morbidity, they frequently lack reliable diagnostic tests. As such, technologies that can identify key moieties [...] Read more.
Central sensitization syndromes are a collection of frequently painful disorders that contribute to decreased quality of life and increased risk of opiate abuse. Although these disorders cause significant morbidity, they frequently lack reliable diagnostic tests. As such, technologies that can identify key moieties in central sensitization disorders may contribute to the identification of novel therapeutic targets and more precise treatment options. The analysis of small molecules in biological samples through metabolomics has improved greatly and may be the technology needed to identify key moieties in difficult to diagnose diseases. In this review, we discuss the current state of metabolomics as it relates to central sensitization disorders. From initial literature review until Feb 2020, PubMed, Embase, and Scopus were searched for applicable studies. We included cohort studies, case series, and interventional studies of both adults and children affected by central sensitivity syndromes. The majority of metabolomic studies addressing a CSS found significantly altered metabolites that allowed for differentiation of CSS patients from healthy controls. Therefore, the published literature overwhelmingly supports the use of metabolomics in CSS. Further research into these altered metabolites and their respective metabolic pathways may provide more reliable and effective therapeutics for these syndromes. Full article
(This article belongs to the Special Issue Metabolomics in Clinical Research)
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Open AccessReview
Metabolic Engineering Design Strategies for Increasing Acetyl-CoA Flux
Metabolites 2020, 10(4), 166; https://doi.org/10.3390/metabo10040166 - 23 Apr 2020
Viewed by 406
Abstract
Acetyl-CoA is a key metabolite precursor for the biosynthesis of lipids, polyketides, isoprenoids, amino acids, and numerous other bioproducts which are used in various industries. Metabolic engineering efforts aim to increase carbon flux towards acetyl-CoA in order to achieve higher productivities of its [...] Read more.
Acetyl-CoA is a key metabolite precursor for the biosynthesis of lipids, polyketides, isoprenoids, amino acids, and numerous other bioproducts which are used in various industries. Metabolic engineering efforts aim to increase carbon flux towards acetyl-CoA in order to achieve higher productivities of its downstream products. In this review, we summarize the strategies that have been implemented for increasing acetyl-CoA flux and concentration, and discuss their effects. Furthermore, recent works have developed synthetic acetyl-CoA biosynthesis routes that achieve higher stoichiometric yield of acetyl-CoA from glycolytic substrates. Full article
(This article belongs to the Special Issue Metabolic Engineering and Synthetic Biology Volume 2)
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Open AccessArticle
The Urinary Metabolome of Healthy Newborns
Metabolites 2020, 10(4), 165; https://doi.org/10.3390/metabo10040165 - 23 Apr 2020
Viewed by 500
Abstract
The knowledge of normal metabolite values for neonates is key to establishing robust cut-off values to diagnose diseases, to predict the occurrence of new diseases, to monitor a neonate’s metabolism, or to assess their general health status. For full term-newborns, many reference biochemical [...] Read more.
The knowledge of normal metabolite values for neonates is key to establishing robust cut-off values to diagnose diseases, to predict the occurrence of new diseases, to monitor a neonate’s metabolism, or to assess their general health status. For full term-newborns, many reference biochemical values are available for blood, serum, plasma and cerebrospinal fluid. However, there is a surprising lack of information about normal urine concentration values for a large number of important metabolites in neonates. In the present work, we used targeted tandem mass spectrometry (MS/MS)-based metabolomic assays to identify and quantify 136 metabolites of biomedical interest in the urine from 48 healthy, full-term term neonates, collected in the first 24 h of life. In addition to this experimental study, we performed a literature review (covering the past eight years and over 500 papers) to update the references values in the Human Metabolome Database/Urine Metabolome Database (HMDB/UMDB). Notably, 86 of the experimentally measured urinary metabolites are being reported in neonates/infants for the first time and another 20 metabolites are being reported in human urine for the first time ever. Sex differences were found for 15 metabolites. The literature review allowed us to identify another 78 urinary metabolites with concentration data. As a result, reference concentration values and ranges for 378 neonatal urinary metabolites are now publicly accessible via the HMDB. Full article
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Open AccessReview
A Review of Lipidomics of Cardiovascular Disease Highlights the Importance of Isolating Lipoproteins
Metabolites 2020, 10(4), 163; https://doi.org/10.3390/metabo10040163 - 23 Apr 2020
Viewed by 411
Abstract
Cutting-edge lipidomic profiling measures hundreds or even thousands of lipids in plasma and is increasingly used to investigate mechanisms of cardiovascular disease (CVD). In this review, we introduce lipidomic techniques, describe distributions of lipids across lipoproteins, and summarize findings on the association of [...] Read more.
Cutting-edge lipidomic profiling measures hundreds or even thousands of lipids in plasma and is increasingly used to investigate mechanisms of cardiovascular disease (CVD). In this review, we introduce lipidomic techniques, describe distributions of lipids across lipoproteins, and summarize findings on the association of lipids with CVD based on lipidomics. The main findings of 16 cohort studies were that, independent of total and high-density lipoprotein cholesterol (HDL-c), ceramides (d18:1/16:0, d18:1/18:0, and d18:1/24:1) and phosphatidylcholines (PCs) containing saturated and monounsaturated fatty acyl chains are positively associated with risks of CVD outcomes, while PCs containing polyunsaturated fatty acyl chains (PUFA) are inversely associated with risks of CVD outcomes. Lysophosphatidylcholines (LPCs) may be positively associated with risks of CVD outcomes. Interestingly, the distributions of the identified lipids vary across lipoproteins: LPCs are primarily contained in HDLs, ceramides are mainly contained in low-density lipoproteins (LDLs), and PCs are distributed in both HDLs and LDLs. Thus, the potential mechanism behind previous findings may be related to the effect of the identified lipids on the biological functions of HDLs and LDLs. Only eight studies on the lipidomics of HDL and non-HDL particles and CVD outcomes have been conducted, which showed that higher triglycerides (TAGs), lower PUFA, lower phospholipids, and lower sphingomyelin content in HDLs might be associated with a higher risk of coronary heart disease (CHD). However, the generalizability of these studies is a major concern, given that they used case–control or cross-sectional designs in hospital settings, included a very small number of participants, and did not correct for multiple testing or adjust for blood lipids such as HDL-c, low-density lipoprotein cholesterol (LDL-c), or TAGs. Overall, findings from the literature highlight the importance of research on lipidomics of lipoproteins to enhance our understanding of the mechanism of the association between the identified lipids and the risk of CVD and allow the identification of novel lipid biomarkers in HDLs and LDLs, independent of HDL-c and LDL-c. Lipidomic techniques show the feasibility of this exciting research direction, and the lack of high-quality epidemiological studies warrants well-designed prospective cohort studies. Full article
(This article belongs to the Special Issue Integrative-Metabolomics in Epidemiological Studies)
Open AccessData Descriptor
A Data Set of 255,000 Randomly Selected and Manually Classified Extracted Ion Chromatograms for Evaluation of Peak Detection Methods
Metabolites 2020, 10(4), 162; https://doi.org/10.3390/metabo10040162 - 22 Apr 2020
Viewed by 527
Abstract
Non-targeted mass spectrometry (MS) has become an important method over recent years in the fields of metabolomics and environmental research. While more and more algorithms and workflows become available to process a large number of non-targeted data sets, there still exist few manually [...] Read more.
Non-targeted mass spectrometry (MS) has become an important method over recent years in the fields of metabolomics and environmental research. While more and more algorithms and workflows become available to process a large number of non-targeted data sets, there still exist few manually evaluated universal test data sets for refining and evaluating these methods. The first step of non-targeted screening, peak detection and refinement of it is arguably the most important step for non-targeted screening. However, the absence of a model data set makes it harder for researchers to evaluate peak detection methods. In this Data Descriptor, we provide a manually checked data set consisting of 255,000 EICs (5000 peaks randomly sampled from across 51 samples) for the evaluation on peak detection and gap-filling algorithms. The data set was created from a previous real-world study, of which a subset was used to extract and manually classify ion chromatograms by three mass spectrometry experts. The data set consists of the converted mass spectrometry files, intermediate processing files and the central file containing a table with all important information for the classified peaks. Full article
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Open AccessArticle
1H-NMR Based Serum Metabolomics Identifies Different Profile between Sarcopenia and Cancer Cachexia in Ageing Walker 256 Tumour-Bearing Rats
Metabolites 2020, 10(4), 161; https://doi.org/10.3390/metabo10040161 - 21 Apr 2020
Viewed by 406
Abstract
Sarcopenia among the older population has been growing over the last few years. In addition, the incidence of cancers increases with age and, consequently, the development of cachexia related cancer. Therefore, the elucidation of the metabolic derangements of sarcopenia and cachexia are important [...] Read more.
Sarcopenia among the older population has been growing over the last few years. In addition, the incidence of cancers increases with age and, consequently, the development of cachexia related cancer. Therefore, the elucidation of the metabolic derangements of sarcopenia and cachexia are important to improve the survival and life quality of cancer patients. We performed the 1H-NMR based serum metabolomics in adult (A) and ageing (S) Walker 256 tumour-bearing rats in different stages of tumour evolution, namely intermediated (Wi) and advanced (Wa). Among 52 serum metabolites that were identified, 21 were significantly increased in S and 14 and 19 decreased in the Wi and Wa groups, respectively. The most impacted pathways by this metabolic alteration were related by amino acid biosynthesis and metabolism, with an upregulation in S group and downregulation in Wi and Wa groups. Taken together, our results suggest that the increase in metabolic profile in ageing rats is associated with the higher muscle protein degradation that releases several metabolites, especially amino acids into the serum. On the other hand, we hypothesise that the majority of metabolites released by muscle catabolism are used by tumours to sustain rapid cell proliferation and tumorigenesis. Full article
(This article belongs to the Special Issue Cancer Metabolomics 2019)
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Open AccessArticle
Biological Filtering and Substrate Promiscuity Prediction for Annotating Untargeted Metabolomics
Metabolites 2020, 10(4), 160; https://doi.org/10.3390/metabo10040160 - 21 Apr 2020
Cited by 1 | Viewed by 337
Abstract
Mass spectrometry coupled with chromatography separation techniques provides a powerful platform for untargeted metabolomics. Determining the chemical identities of detected compounds however remains a major challenge. Here, we present a novel computational workflow, termed extended metabolic model filtering (EMMF), that aims to engineer [...] Read more.
Mass spectrometry coupled with chromatography separation techniques provides a powerful platform for untargeted metabolomics. Determining the chemical identities of detected compounds however remains a major challenge. Here, we present a novel computational workflow, termed extended metabolic model filtering (EMMF), that aims to engineer a candidate set, a listing of putative chemical identities to be used during annotation, through an extended metabolic model (EMM). An EMM includes not only canonical substrates and products of enzymes already cataloged in a database through a reference metabolic model, but also metabolites that can form due to substrate promiscuity. EMMF aims to strike a balance between discovering previously uncharacterized metabolites and the computational burden of annotation. EMMF was applied to untargeted LC–MS data collected from cultures of Chinese hamster ovary (CHO) cells and murine cecal microbiota. EMM metabolites matched, on average, to 23.92% of measured masses, providing a > 7-fold increase in the candidate set size when compared to a reference metabolic model. Many metabolites suggested by EMMF are not catalogued in PubChem. For the CHO cell, we experimentally confirmed the presence of 4-hydroxyphenyllactate, a metabolite predicted by EMMF that has not been previously documented as part of the CHO cell metabolic model. Full article
(This article belongs to the Special Issue Metabolomics Tools to Accelerate Natural Product Discovery)
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Open AccessArticle
Drought Stress Responses in Context-Specific Genome-Scale Metabolic Models of Arabidopsis thaliana
Metabolites 2020, 10(4), 159; https://doi.org/10.3390/metabo10040159 - 18 Apr 2020
Viewed by 405
Abstract
Drought perturbs metabolism in plants and limits their growth. Because drought stress on crops affects their yields, understanding the complex adaptation mechanisms evolved by plants against drought will facilitate the development of drought-tolerant crops for agricultural use. In this study, we examined the [...] Read more.
Drought perturbs metabolism in plants and limits their growth. Because drought stress on crops affects their yields, understanding the complex adaptation mechanisms evolved by plants against drought will facilitate the development of drought-tolerant crops for agricultural use. In this study, we examined the metabolic pathways of Arabidopsis thaliana which respond to drought stress by omics-based in silico analyses. We proposed an analysis pipeline to understand metabolism under specific conditions based on a genome-scale metabolic model (GEM). Context-specific GEMs under drought and well-watered control conditions were reconstructed using transcriptome data and examined using metabolome data. The metabolic fluxes throughout the metabolic network were estimated by flux balance analysis using the context-specific GEMs. We used in silico methods to identify an important reaction contributing to biomass production and clarified metabolic reaction responses under drought stress by comparative analysis between drought and control conditions. This proposed pipeline can be applied in other studies to understand metabolic changes under specific conditions using Arabidopsis GEM or other available plant GEMs. Full article
(This article belongs to the Special Issue Metabolomics-Driven Biotechnology)
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Open AccessArticle
Comparative Evaluation of Data Dependent and Data Independent Acquisition Workflows Implemented on an Orbitrap Fusion for Untargeted Metabolomics
Metabolites 2020, 10(4), 158; https://doi.org/10.3390/metabo10040158 - 18 Apr 2020
Viewed by 552
Abstract
Constant improvements to the Orbitrap mass analyzer, such as acquisition speed, resolution, dynamic range and sensitivity have strengthened its value for the large-scale identification and quantification of metabolites in complex biological matrices. Here, we report the development and optimization of Data Dependent Acquisition [...] Read more.
Constant improvements to the Orbitrap mass analyzer, such as acquisition speed, resolution, dynamic range and sensitivity have strengthened its value for the large-scale identification and quantification of metabolites in complex biological matrices. Here, we report the development and optimization of Data Dependent Acquisition (DDA) and Sequential Window Acquisition of all THeoretical fragment ions (SWATH-type) Data Independent Acquisition (DIA) workflows on a high-field Orbitrap FusionTM TribridTM instrument for the robust identification and quantification of metabolites in human plasma. By using a set of 47 exogenous and 72 endogenous molecules, we compared the efficiency and complementarity of both approaches. We exploited the versatility of this mass spectrometer to collect meaningful MS/MS spectra at both high- and low-mass resolution and various low-energy collision-induced dissociation conditions under optimized DDA conditions. We also observed that complex and composite DIA-MS/MS spectra can be efficiently exploited to identify metabolites in plasma thanks to a reference tandem spectral library made from authentic standards while also providing a valuable data resource for further identification of unknown metabolites. Finally, we found that adding multi-event MS/MS acquisition did not degrade the ability to use survey MS scans from DDA and DIA workflows for the reliable absolute quantification of metabolites down to 0.05 ng/mL in human plasma. Full article
(This article belongs to the Section Metabolomic Profiling Technology)
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Open AccessArticle
First Insights into the Urinary Metabolome of Captive Giraffes by Proton Nuclear Magnetic Resonance Spectroscopy
Metabolites 2020, 10(4), 157; https://doi.org/10.3390/metabo10040157 - 17 Apr 2020
Viewed by 391
Abstract
The urine from 35 giraffes was studied by untargeted 1H-NMR, with the purpose of obtaining, for the first time, a fingerprint of its metabolome. The metabolome, as downstream of the transcriptome and proteome, has been considered as the most representative approach to [...] Read more.
The urine from 35 giraffes was studied by untargeted 1H-NMR, with the purpose of obtaining, for the first time, a fingerprint of its metabolome. The metabolome, as downstream of the transcriptome and proteome, has been considered as the most representative approach to monitor the relationships between animal physiological features and environment. Thirty-nine molecules were unambiguously quantified, able to give information about diet, proteins digestion, energy generation, and gut-microbial co-metabolism. The samples collected allowed study of the effects of age and sex on the giraffe urinary metabolome. In addition, preliminary information about how sampling procedure and pregnancy could affect a giraffe’s urinary metabolome was obtained. Such work could trigger the setting up of methods to non-invasively study the health status of giraffes, which is utterly needed, considering that anesthetic-related complications make their immobilization a very risky practice. Full article
(This article belongs to the Special Issue Metabolomic Applications in Animal Science)
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Open AccessArticle
Towards Predicting Gut Microbial Metabolism: Integration of Flux Balance Analysis and Untargeted Metabolomics
Metabolites 2020, 10(4), 156; https://doi.org/10.3390/metabo10040156 - 17 Apr 2020
Viewed by 391
Abstract
Genomics-based metabolic models of microorganisms currently have no easy way of corroborating predicted biomass with the actual metabolites being produced. This study uses untargeted mass spectrometry-based metabolomics data to generate a list of accurate metabolite masses produced from the human commensal bacteria Citrobacter [...] Read more.
Genomics-based metabolic models of microorganisms currently have no easy way of corroborating predicted biomass with the actual metabolites being produced. This study uses untargeted mass spectrometry-based metabolomics data to generate a list of accurate metabolite masses produced from the human commensal bacteria Citrobacter sedlakii grown in the presence of a simple glucose carbon source. A genomics-based flux balance metabolic model of this bacterium was previously generated using the bioinformatics tool PyFBA and phenotypic growth curve data. The high-resolution mass spectrometry data obtained through timed metabolic extractions were integrated with the predicted metabolic model through a program called MS_FBA. This program correlated untargeted metabolomics features from C. sedlakii with 218 of the 699 metabolites in the model using an exact mass match, with 51 metabolites further confirmed using predicted isotope ratios. Over 1400 metabolites were matched with additional metabolites in the ModelSEED database, indicating the need to incorporate more specific gene annotations into the predictive model through metabolomics-guided gap filling. Full article
(This article belongs to the Special Issue Metabolomics and Microbiota Metabolism)
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Open AccessArticle
Benchtop Low-Frequency 60 MHz NMR Analysis of Urine: A Comparative Metabolomics Investigation
Metabolites 2020, 10(4), 155; https://doi.org/10.3390/metabo10040155 - 16 Apr 2020
Viewed by 481
Abstract
Metabolomics techniques are now applied in numerous fields, with the ability to provide information concerning a large number of metabolites from a single sample in a short timeframe. Although high-frequency (HF) nuclear magnetic resonance (NMR) analysis represents a common method of choice to [...] Read more.
Metabolomics techniques are now applied in numerous fields, with the ability to provide information concerning a large number of metabolites from a single sample in a short timeframe. Although high-frequency (HF) nuclear magnetic resonance (NMR) analysis represents a common method of choice to perform such studies, few investigations employing low-frequency (LF) NMR spectrometers have yet been published. Herein, we apply and contrast LF and HF 1H-NMR metabolomics approaches to the study of urine samples collected from type 2 diabetic patients (T2D), and apply a comparative investigation with healthy controls. Additionally, we explore the capabilities of LF 1H-1H 2D correlation spectroscopy (COSY) experiments regarding the determination of metabolites, their resolution and associated analyses in human urine samples. T2D samples were readily distinguishable from controls, with several metabolites, particularly glucose, being associated with this distinction. Comparable results were obtained with HF and LF spectrometers. Linear correlation analyses were performed to derive relationships between the intensities of 1D and 2D resonances of several metabolites, and R2 values obtained were able to confirm these, an observation attesting to the validity of employing 2D LF experiments for future applications in metabolomics studies. Our data suggest that LF spectrometers may prove to be easy-to-use, compact and inexpensive tools to perform routine metabolomics analyses in laboratories and ‘point-of-care’ sites. Furthermore, the quality of 2D spectra obtained from these instruments in half an hour would broaden the horizon of their potential applications. Full article
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Open AccessArticle
Identification of Metabolite and Lipid Profiles in a Segregating Peach Population Associated with Mealiness in Prunus persica (L.) Batsch
Metabolites 2020, 10(4), 154; https://doi.org/10.3390/metabo10040154 - 16 Apr 2020
Viewed by 375
Abstract
The peach is the third most important temperate fruit crop considering fruit production and harvested area in the world. Exporting peaches represents a challenge due to the long-distance nature of export markets. This requires fruit to be placed in cold storage for a [...] Read more.
The peach is the third most important temperate fruit crop considering fruit production and harvested area in the world. Exporting peaches represents a challenge due to the long-distance nature of export markets. This requires fruit to be placed in cold storage for a long time, which can induce a physiological disorder known as chilling injury (CI). The main symptom of CI is mealiness, which is perceived as non-juicy fruit by consumers. The purpose of this work was to identify and compare the metabolite and lipid profiles between two siblings from contrasting populations for juice content, at harvest and after 30 days at 0 °C. A total of 119 metabolites and 189 lipids were identified, which showed significant differences in abundance, mainly in amino acids, sugars and lipids. Metabolites displaying significant changes from the E1 to E3 stages corresponded to lipids such as phosphatidylglycerol (PG), monogalactosyldiacylglycerol (MGDG) and lysophosphatidylcholines (LPC), and sugars such as fructose 1 and 1-fructose-6 phosphate. These metabolites might be used as early stage biomarkers associated with mealiness at harvest and after cold storage. Full article
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Open AccessArticle
Green Nut Oil or DHA Supplementation Restored Decreased Distribution Levels of DHA Containing Phosphatidylcholines in the Brain of a Mouse Model of Dementia
Metabolites 2020, 10(4), 153; https://doi.org/10.3390/metabo10040153 - 16 Apr 2020
Viewed by 358
Abstract
Dementia is a major public health concern nowadays. Reduced levels of brain docosahexaenoic acid (DHA) and DHA-phosphatidylcholines (DHA-PCs) in dementia patients were reported previously. Recently, we have reported that supplementation of green nut oil (GNO) or DHA improves memory function and distribution levels [...] Read more.
Dementia is a major public health concern nowadays. Reduced levels of brain docosahexaenoic acid (DHA) and DHA-phosphatidylcholines (DHA-PCs) in dementia patients were reported previously. Recently, we have reported that supplementation of green nut oil (GNO) or DHA improves memory function and distribution levels of brain DHA in senescence accelerated mice P8 (SAMP8). GNO is extracted from Plukenetia volubilis seeds, and SAMP8 is a well-known model mouse of dementia. In this current study, we examined the results of GNO or DHA supplementation in the distribution levels of brain DHA-PCs in same model mouse of dementia using desorption electrospray ionization (DESI) mass spectrometry imaging (MSI). We observed significantly decreased distribution of brain DHA-PCs, PC (16:0_22:6), and PC (18:0_22:6) in SAMP8 mice compared to wild type mice, and GNO or DHA treatment restored the decreased distribution levels of PC (16:0_22:6) and PC (18:0_22:6) in the brain of SAMP8 mice. These results indicate that GNO or DHA supplementation can ameliorate the decreased distribution of brain DHA-PCs in dementia, and could be potentially used for the prevention and treatment of dementia. Full article
(This article belongs to the Special Issue Fatty Acid Metabolism)
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Open AccessArticle
Network Analysis Provides Insight into Tomato Lipid Metabolism
Metabolites 2020, 10(4), 152; https://doi.org/10.3390/metabo10040152 - 14 Apr 2020
Viewed by 428
Abstract
Metabolic correlation networks have been used in several instances to obtain a deeper insight into the complexity of plant metabolism as a whole. In tomato (Solanum lycopersicum), metabolites have a major influence on taste and overall fruit quality traits. Previously a [...] Read more.
Metabolic correlation networks have been used in several instances to obtain a deeper insight into the complexity of plant metabolism as a whole. In tomato (Solanum lycopersicum), metabolites have a major influence on taste and overall fruit quality traits. Previously a broad spectrum of metabolic and phenotypic traits has been described using a Solanum pennellii introgression-lines (ILs) population. To obtain insights into tomato fruit metabolism, we performed metabolic network analysis from existing data, covering a wide range of metabolic traits, including lipophilic and volatile compounds, for the first time. We provide a comprehensive fruit correlation network and show how primary, secondary, lipophilic, and volatile compounds connect to each other and how the individual metabolic classes are linked to yield-related phenotypic traits. Results revealed a high connectivity within and between different classes of lipophilic compounds, as well as between lipophilic and secondary metabolites. We focused on lipid metabolism and generated a gene-expression network with lipophilic metabolites to identify new putative lipid-related genes. Metabolite–transcript correlation analysis revealed key putative genes involved in lipid biosynthesis pathways. The overall results will help to deepen our understanding of tomato metabolism and provide candidate genes for transgenic approaches toward improving nutritional qualities in tomato. Full article
(This article belongs to the Special Issue Fruit Metabolism and Metabolomics)
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Open AccessArticle
Plasma 25-Hydroxyvitamin D Concentrations are Associated with Polyunsaturated Fatty Acid Metabolites in Young Children: Results from the Vitamin D Antenatal Asthma Reduction Trial
Metabolites 2020, 10(4), 151; https://doi.org/10.3390/metabo10040151 - 14 Apr 2020
Viewed by 449
Abstract
Vitamin D deficiency contributes to a multitude of health conditions, but its biological mechanisms are not adequately understood. Untargeted metabolomics offers the opportunity to comprehensively examine the metabolic profile associated with variations in vitamin D concentrations. The objective of the current analysis was [...] Read more.
Vitamin D deficiency contributes to a multitude of health conditions, but its biological mechanisms are not adequately understood. Untargeted metabolomics offers the opportunity to comprehensively examine the metabolic profile associated with variations in vitamin D concentrations. The objective of the current analysis was to identify metabolites and metabolic pathways associated with plasma 25-hydroxyvitamin D [25(OH)D] concentrations. The current study included children of pregnant women in the Vitamin D Antenatal Asthma Reduction Trial, who had 25(OH)D and global metabolomics data at age 1 and 3 years. We assessed the cross-sectional associations between individual metabolites and 25(OH)D using linear regression adjusting for confounding factors. Twelve metabolites were significantly associated with plasma 25(OH)D concentrations at both age 1 and 3 after correction for multiple comparisons, including three members of the n-6 polyunsaturated fatty acid (PUFA) metabolism pathway (linoleate, arachidonate, and docosapentaenoate) inversely associated with 25(OH)D. These PUFAs along with four other significant metabolites were replicated in the independent Childhood Asthma Management Program (CAMP) cohort. Both vitamin D and n-6 PUFAs are involved in inflammatory processes, and evidence from cell and animal studies demonstrate a plausible biological mechanism where the active form of 25(OH)D may influence n-6 PUFA metabolism. These relationships warrant further investigation in other populations. Full article
(This article belongs to the Special Issue Integrative-Metabolomics in Epidemiological Studies)
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Open AccessArticle
Sink/Source Balance of Leaves Influences Amino Acid Pools and Their Associated Metabolic Fluxes in Winter Oilseed Rape (Brassica napus L.)
Metabolites 2020, 10(4), 150; https://doi.org/10.3390/metabo10040150 - 13 Apr 2020
Viewed by 375
Abstract
Nitrogen remobilization processes from source to sink tissues in plants are determinant for seed yield and their implementation results in a complete reorganization of the primary metabolism during sink/source transition. Here, we decided to characterize the impact of the sink/source balance on amino [...] Read more.
Nitrogen remobilization processes from source to sink tissues in plants are determinant for seed yield and their implementation results in a complete reorganization of the primary metabolism during sink/source transition. Here, we decided to characterize the impact of the sink/source balance on amino acid metabolism in the leaves of winter oilseed rape grown at the vegetative stage. We combined a quantitative metabolomics approach with an instationary 15N-labeling experiment by using [15N]L-glycine as a metabolic probe on leaf ranks with a gradual increase in their source status. We showed that the acquisition of the source status by leaves was specifically accompanied by a decrease in asparagine, glutamine, proline and S-methyl-l-cysteine sulphoxide contents and an increase in valine and threonine contents. Dynamic analysis of 15N enrichment and concentration of amino acids revealed gradual changes in the dynamics of amino acid metabolism with respect to the sink/source status of leaf ranks. Notably, nitrogen assimilation into valine, threonine and proline were all decreased in source leaves compared to sink leaves. Overall, our results suggested a reduction in de novo amino acid biosynthesis during sink/source transition at the vegetative stage. Full article
(This article belongs to the Special Issue Metabolomic and Flux Analysis in Plants)
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Open AccessArticle
Relation of Whole Blood Amino Acid and Acylcarnitine Metabolome to Age, Sex, BMI, Puberty, and Metabolic Markers in Children and Adolescents
Metabolites 2020, 10(4), 149; https://doi.org/10.3390/metabo10040149 - 10 Apr 2020
Viewed by 464
Abstract
Background: Changes in the metabolic fingerprint of blood during child growth and development are a largely under-investigated area of research. The examination of such aspects requires a cohort of healthy children and adolescents who have been subjected to deep phenotyping, including collection of [...] Read more.
Background: Changes in the metabolic fingerprint of blood during child growth and development are a largely under-investigated area of research. The examination of such aspects requires a cohort of healthy children and adolescents who have been subjected to deep phenotyping, including collection of biospecimens for metabolomic analysis. The present study considered whether amino acid (AA) and acylcarnitine (AC) concentrations are associated with age, sex, body mass index (BMI), and puberty during childhood and adolescence. It also investigated whether there are associations between amino acids (AAs) and acylcarnitines (ACs) and laboratory parameters of glucose and lipid metabolism, as well as liver, kidney, and thyroid parameters. Methods: A total of 3989 dried whole blood samples collected from 2191 healthy participants, aged 3 months to 18 years, from the LIFE Child cohort (Leipzig, Germany) were analyzed using liquid chromatography tandem mass spectrometry to detect levels of 23 AAs, 6 ACs, and free carnitine (C0). Age- and sex-related percentiles were estimated for each metabolite. In addition, correlations between laboratory parameters and levels of the selected AAs and ACs were calculated using hierarchical models. Results: Four different age-dependent profile types were identified for AAs and ACs. Investigating the association with puberty, we mainly identified peak metabolite levels at Tanner stages 2 to 3 in girls and stages 3 to 5 in boys. Significant correlations were observed between BMI standard deviation score (BMI-SDS) and certain metabolites, among them, branched-chain (leucine/isoleucine, valine) and aromatic (phenylalanine, tyrosine) amino acids. Most of the metabolites correlated significantly with absolute concentrations of glucose, glycated hemoglobin (HbA1c), triglycerides, cystatin C (CysC), and creatinine. After age adjustment, significant correlations were observed between most metabolites and CysC, as well as HbA1c. Conclusions: During childhood, several AA and AC levels are related to age, sex, BMI, and puberty. Moreover, our data verified known associations but also revealed new correlations between AAs/ACs and specific key markers of metabolic function. Full article
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Open AccessArticle
Involvement of Lactate and Pyruvate in the Anti-Inflammatory Effects Exerted by Voluntary Activation of the Sympathetic Nervous System
Metabolites 2020, 10(4), 148; https://doi.org/10.3390/metabo10040148 - 10 Apr 2020
Viewed by 1190
Abstract
We recently demonstrated that the sympathetic nervous system can be voluntarily activated following a training program consisting of cold exposure, breathing exercises, and meditation. This resulted in profound attenuation of the systemic inflammatory response elicited by lipopolysaccharide (LPS) administration. Herein, we assessed whether [...] Read more.
We recently demonstrated that the sympathetic nervous system can be voluntarily activated following a training program consisting of cold exposure, breathing exercises, and meditation. This resulted in profound attenuation of the systemic inflammatory response elicited by lipopolysaccharide (LPS) administration. Herein, we assessed whether this training program affects the plasma metabolome and if these changes are linked to the immunomodulatory effects observed. A total of 224 metabolites were identified in plasma obtained from 24 healthy male volunteers at six timepoints, of which 98 were significantly altered following LPS administration. Effects of the training program were most prominent shortly after initiation of the acquired breathing exercises but prior to LPS administration, and point towards increased activation of the Cori cycle. Elevated concentrations of lactate and pyruvate in trained individuals correlated with enhanced levels of anti-inflammatory interleukin (IL)-10. In vitro validation experiments revealed that co-incubation with lactate and pyruvate enhances IL-10 production and attenuates the release of pro-inflammatory IL-1β and IL-6 by LPS-stimulated leukocytes. Our results demonstrate that practicing the breathing exercises acquired during the training program results in increased activity of the Cori cycle. Furthermore, this work uncovers an important role of lactate and pyruvate in the anti-inflammatory phenotype observed in trained subjects. Full article
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Open AccessReview
The Lipolysome—A Highly Complex and Dynamic Protein Network Orchestrating Cytoplasmic Triacylglycerol Degradation
Metabolites 2020, 10(4), 147; https://doi.org/10.3390/metabo10040147 - 10 Apr 2020
Viewed by 354
Abstract
The catabolism of intracellular triacylglycerols (TAGs) involves the activity of cytoplasmic and lysosomal enzymes. Cytoplasmic TAG hydrolysis, commonly termed lipolysis, is catalyzed by the sequential action of three major hydrolases, namely adipose triglyceride lipase, hormone-sensitive lipase, and monoacylglycerol lipase. All three enzymes interact [...] Read more.
The catabolism of intracellular triacylglycerols (TAGs) involves the activity of cytoplasmic and lysosomal enzymes. Cytoplasmic TAG hydrolysis, commonly termed lipolysis, is catalyzed by the sequential action of three major hydrolases, namely adipose triglyceride lipase, hormone-sensitive lipase, and monoacylglycerol lipase. All three enzymes interact with numerous protein binding partners that modulate their activity, cellular localization, or stability. Deficiencies of these auxiliary proteins can lead to derangements in neutral lipid metabolism and energy homeostasis. In this review, we summarize the composition and the dynamics of the complex lipolytic machinery we like to call “lipolysome”. Full article
(This article belongs to the Special Issue Adipose Tissue and Metabolic Health)
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Open AccessArticle
Acetylation of Phenylalanine Hydroxylase and Tryptophan 2,3-Dioxygenase Alters Hepatic Aromatic Amino Acid Metabolism in Weaned Piglets
Metabolites 2020, 10(4), 146; https://doi.org/10.3390/metabo10040146 - 09 Apr 2020
Viewed by 239
Abstract
Weaning significantly alters hepatic aromatic amino acid (AAA) metabolism and physiological functions. However, less is known about the regulating mechanism of hepatic AAA metabolism after weaning. A total of 200 21-day-old piglets (Duroc × Landrace) were assigned randomly to the control group and [...] Read more.
Weaning significantly alters hepatic aromatic amino acid (AAA) metabolism and physiological functions. However, less is known about the regulating mechanism of hepatic AAA metabolism after weaning. A total of 200 21-day-old piglets (Duroc × Landrace) were assigned randomly to the control group and the weaning group. In this study, weaning significantly decreased the concentration of phenylalanine, tryptophan, and tyrosine in piglet livers (p < 0.05). Additionally, through the detection of liver AAA metabolites and metabolic enzyme activity, it was observed that hepatic tryptophan catabolism was enhanced, while that of phenylalanine was weakened (p < 0.05). Intriguingly, acetyl-proteome profiling of liver from weaned piglets showed that weaning exacerbated the acetylation of phenylalanine hydroxylase (PAH) and the deacetylation of tryptophan 2,3-dioxygenase (TDO). Analysis of PAH and TDO acetylation in Chang liver cells showed that acetylation decreased the PAH activity, while deacetylation increased the TDO activity (p < 0.05). Furthermore, metabolites of AAAs and the acetylation statuses of PAH and TDO in primary hepatocytes from weaned piglets were consistent with the results in vivo. These findings indicated that weaning altered the PAH and TDO activity by affecting the acetylation state of the enzyme in piglets’’ livers. Lysine acetylation may be a potential regulatory mechanism for AAA metabolism in response to weaning. Full article
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Open AccessArticle
Metabolic Fingerprinting with Fourier-Transform Infrared (FTIR) Spectroscopy: Towards a High-Throughput Screening Assay for Antibiotic Discovery and Mechanism-of-Action Elucidation
Metabolites 2020, 10(4), 145; https://doi.org/10.3390/metabo10040145 - 09 Apr 2020
Viewed by 320
Abstract
The discovery of antibiotics has been slowing to a halt. Phenotypic screening is once again at the forefront of antibiotic discovery, yet Mechanism-Of-Action (MOA) identification is still a major bottleneck. As such, methods capable of MOA elucidation coupled with the high-throughput screening of [...] Read more.
The discovery of antibiotics has been slowing to a halt. Phenotypic screening is once again at the forefront of antibiotic discovery, yet Mechanism-Of-Action (MOA) identification is still a major bottleneck. As such, methods capable of MOA elucidation coupled with the high-throughput screening of whole cells are required now more than ever, for which Fourier-Transform Infrared (FTIR) spectroscopy is a promising metabolic fingerprinting technique. A high-throughput whole-cell FTIR spectroscopy-based bioassay was developed to reveal the metabolic fingerprint induced by 15 antibiotics on the Escherichia coli metabolism. Cells were briefly exposed to four times the minimum inhibitory concentration and spectra were quickly acquired in the high-throughput mode. After preprocessing optimization, a partial least squares discriminant analysis and principal component analysis were conducted. The metabolic fingerprints obtained with FTIR spectroscopy were sufficiently specific to allow a clear distinction between different antibiotics, across three independent cultures, with either analysis algorithm. These fingerprints were coherent with the known MOA of all the antibiotics tested, which include examples that target the protein, DNA, RNA, and cell wall biosynthesis. Because FTIR spectroscopy acquires a holistic fingerprint of the effect of antibiotics on the cellular metabolism, it holds great potential to be used for high-throughput screening in antibiotic discovery and possibly towards a better understanding of the MOA of current antibiotics. Full article
(This article belongs to the Special Issue Metabolomics Tools to Accelerate Natural Product Discovery)
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Open AccessArticle
MOTA: Network-Based Multi-Omic Data Integration for Biomarker Discovery
Metabolites 2020, 10(4), 144; https://doi.org/10.3390/metabo10040144 - 08 Apr 2020
Viewed by 477
Abstract
The recent advancement of omic technologies provides researchers with the possibility to search for disease-associated biomarkers at the system level. The integrative analysis of data from a large number of molecules involved at various layers of the biological system offers a great opportunity [...] Read more.
The recent advancement of omic technologies provides researchers with the possibility to search for disease-associated biomarkers at the system level. The integrative analysis of data from a large number of molecules involved at various layers of the biological system offers a great opportunity to rank disease biomarker candidates. In this paper, we propose MOTA, a network-based method that uses data acquired at multiple layers to rank candidate disease biomarkers. The networks constructed by MOTA allow users to investigate the biological significance of the top-ranked biomarker candidates. We evaluated the performance of MOTA in ranking disease-associated molecules from three sets of multi-omic data representing three cohorts of hepatocellular carcinoma (HCC) cases and controls with liver cirrhosis. The results demonstrate that MOTA allows the identification of more top-ranked metabolite biomarker candidates that are shared by two different cohorts compared to traditional statistical methods. Moreover, the mRNA candidates top-ranked by MOTA comprise more cancer driver genes compared to those ranked by traditional differential expression methods. Full article
(This article belongs to the Special Issue Metabolomics and Multi-Omics Integration)
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Open AccessArticle
Metabolomics Benefits from Orbitrap GC–MS—Comparison of Low- and High-Resolution GC–MS
Metabolites 2020, 10(4), 143; https://doi.org/10.3390/metabo10040143 - 04 Apr 2020
Viewed by 450
Abstract
The development of improved mass spectrometers and supporting computational tools is expected to enable the rapid annotation of whole metabolomes. Essential for the progress is the identification of strengths and weaknesses of novel instrumentation in direct comparison to previous instruments. Orbitrap liquid chromatography [...] Read more.
The development of improved mass spectrometers and supporting computational tools is expected to enable the rapid annotation of whole metabolomes. Essential for the progress is the identification of strengths and weaknesses of novel instrumentation in direct comparison to previous instruments. Orbitrap liquid chromatography (LC)–mass spectrometry (MS) technology is now widely in use, while Orbitrap gas chromatography (GC)–MS introduced in 2015 has remained fairly unexplored in its potential for metabolomics research. This study aims to evaluate the additional knowledge gained in a metabolomics experiment when using the high-resolution Orbitrap GC–MS in comparison to a commonly used unit-mass resolution single-quadrupole GC–MS. Samples from an osmotic stress treatment of a non-model organism, the microalga Skeletonema costatum, were investigated using comparative metabolomics with low- and high-resolution methods. Resulting datasets were compared on a statistical level and on the level of individual compound annotation. Both MS approaches resulted in successful classification of stressed vs. non-stressed microalgae but did so using different sets of significantly dysregulated metabolites. High-resolution data only slightly improved conventional library matching but enabled the correct annotation of an unknown. While computational support that utilizes high-resolution GC–MS data is still underdeveloped, clear benefits in terms of sensitivity, metabolic coverage, and support in structure elucidation of the Orbitrap GC–MS technology for metabolomics studies are shown here. Full article
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Open AccessArticle
Microbiome-Metabolome Signature of Acute Kidney Injury
Metabolites 2020, 10(4), 142; https://doi.org/10.3390/metabo10040142 - 04 Apr 2020
Viewed by 465
Abstract
Intestinal microbiota play a considerable role in the host’s organism, broadly affecting its organs and tissues. The kidney can also be the target of the microbiome and its metabolites (especially short-chain fatty acids), which can influence renal tissue, both by direct action and [...] Read more.
Intestinal microbiota play a considerable role in the host’s organism, broadly affecting its organs and tissues. The kidney can also be the target of the microbiome and its metabolites (especially short-chain fatty acids), which can influence renal tissue, both by direct action and through modulation of the immune response. This impact is crucial, especially during kidney injury, because the modulation of inflammation or reparative processes could affect the severity of the resulting damage or recovery of kidney function. In this study, we compared the composition of rat gut microbiota with its outcome, in experimental acute ischemic kidney injury and named the bacterial taxa that play putatively negative or positive roles in the progression of ischemic kidney injury. We investigated the link between serum creatinine, urea, and a number of metabolites (acylcarnitines and amino acids), and the relative abundance of various bacterial taxa in rat feces. Our analysis revealed an increase in levels of 32 acylcarnitines in serum, after renal ischemia/reperfusion and correlation with creatinine and urea, while levels of three amino acids (tyrosine, tryptophan, and proline) had decreased. We detected associations between bacterial abundance and metabolite levels, using a compositionality-aware approach—Rothia and Staphylococcus levels were positively associated with creatinine and urea levels, respectively. Our findings indicate that the gut microbial community contains specific members whose presence might ameliorate or, on the contrary, aggravate ischemic kidney injury. These bacterial taxa could present perspective targets for therapeutical interventions in kidney pathologies, including acute kidney injury. Full article
(This article belongs to the Special Issue Metabolomics and Microbiota Metabolism)
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