Metabolites

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Open AccessFeature PaperReview

Carbonic Anhydrase Inhibition and the Management of Hypoxic Tumors

by Claudiu T. Supuran

Metabolites 2017, 7(3), 48; https://doi.org/10.3390/metabo7030048 - 16 Sep 2017

Cited by 213 | Viewed by 11130

Hypoxia and acidosis are salient features of many tumors, leading to a completely different metabolism compared to normal cells. Two of the simplest metabolic products, protons and bicarbonate, are generated by the catalytic activity of the metalloenzyme carbonic anhydrase (CA, EC 4.2.1.1), with [...] Read more.

Hypoxia and acidosis are salient features of many tumors, leading to a completely different metabolism compared to normal cells. Two of the simplest metabolic products, protons and bicarbonate, are generated by the catalytic activity of the metalloenzyme carbonic anhydrase (CA, EC 4.2.1.1), with at least two of its isoforms, CA IX and XII, mainly present in hypoxic tumors. Inhibition of tumor-associated CAs leads to an impaired growth of the primary tumors, metastases and reduces the population of cancer stem cells, leading thus to a complex and beneficial anticancer action for this class of enzyme inhibitors. In this review, I will present the state of the art on the development of CA inhibitors (CAIs) targeting the tumor-associated CA isoforms, which may have applications for the treatment and imaging of cancers expressing them. Small molecule inhibitors, one of which (SLC-0111) completed Phase I clinical trials, and antibodies (girentuximab, discontinued in Phase III clinical trials) will be discussed, together with the various approaches used to design anticancer agents with a new mechanism of action based on interference with these crucial metabolites, protons and bicarbonate. Full article

(This article belongs to the Special Issue Carbonic Anhydrases and Metabolism)

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Open AccessArticle

A Protocol for Generating and Exchanging (Genome-Scale) Metabolic Resource Allocation Models

by Alexandra-M. Reimers, Henning Lindhorst and Steffen Waldherr

Metabolites 2017, 7(3), 47; https://doi.org/10.3390/metabo7030047 - 6 Sep 2017

Cited by 18 | Viewed by 6450

Abstract

In this article, we present a protocol for generating a complete (genome-scale) metabolic resource allocation model, as well as a proposal for how to represent such models in the systems biology markup language (SBML). Such models are used to investigate enzyme levels and [...] Read more.

In this article, we present a protocol for generating a complete (genome-scale) metabolic resource allocation model, as well as a proposal for how to represent such models in the systems biology markup language (SBML). Such models are used to investigate enzyme levels and achievable growth rates in large-scale metabolic networks. Although the idea of metabolic resource allocation studies has been present in the field of systems biology for some years, no guidelines for generating such a model have been published up to now. This paper presents step-by-step instructions for building a (dynamic) resource allocation model, starting with prerequisites such as a genome-scale metabolic reconstruction, through building protein and noncatalytic biomass synthesis reactions and assigning turnover rates for each reaction. In addition, we explain how one can use SBML level 3 in combination with the flux balance constraints and our resource allocation modeling annotation to represent such models. Full article

(This article belongs to the Special Issue Metabolism and Systems Biology Volume 2)

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Open AccessArticle

Metabolomics Analysis of Urine Samples from Children after Acetaminophen Overdose

by Laura K. Schnackenberg, Jinchun Sun, Sudeepa Bhattacharyya, Pritmohinder Gill, Laura P. James and Richard D. Beger

Metabolites 2017, 7(3), 46; https://doi.org/10.3390/metabo7030046 - 6 Sep 2017

Cited by 14 | Viewed by 6235

Abstract

Acetaminophen (APAP), a commonly used over-the-counter analgesic, accounts for approximately fifty percent of the cases of acute liver failure (ALF) in the United States due to overdose, with over half of those unintentional. Current clinical approaches for assessing APAP overdose rely on identifying [...] Read more.

Acetaminophen (APAP), a commonly used over-the-counter analgesic, accounts for approximately fifty percent of the cases of acute liver failure (ALF) in the United States due to overdose, with over half of those unintentional. Current clinical approaches for assessing APAP overdose rely on identifying the precise time of overdose and quantitating acetaminophen alanine aminotransferase (ALT) levels in peripheral blood. Novel specific and sensitive biomarkers may provide additional information regarding patient status post overdose. Previous non-clinical metabolomics studies identified potential urinary biomarkers of APAP-induced hepatotoxicity and metabolites involved pathways of tricarboxylic acid cycle, ketone metabolism, and tryptophan metabolism. In this study, biomarkers identified in the previous non-clinical study were evaluated in urine samples collected from healthy subjects (N = 6, median age 14.08 years) and overdose patients (N = 13, median age 13.91 years) as part of an IRB-approved multicenter study of APAP toxicity in children. The clinical results identified metabolites from pathways previously noted, and pathway analysis indicated analogous pathways were significantly altered in both the rats and humans after APAP overdose. The results suggest a metabolomics approach may enable the discovery of specific, translational biomarkers of drug-induced hepatotoxicity that may aid in the assessment of patients. Full article

(This article belongs to the Special Issue Metabolomics and Its Application in Human Diseases)

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Open AccessArticle

Recommendations for Improving Identification and Quantification in Non-Targeted, GC-MS-Based Metabolomic Profiling of Human Plasma

by Hanghang Wang, Michael J. Muehlbauer, Sara K. O’Neal, Christopher B. Newgard, Elizabeth R. Hauser, James R. Bain and Svati H. Shah

Metabolites 2017, 7(3), 45; https://doi.org/10.3390/metabo7030045 - 25 Aug 2017

Cited by 13 | Viewed by 5003

Abstract

The field of metabolomics as applied to human disease and health is rapidly expanding. In recent efforts of metabolomics research, greater emphasis has been placed on quality control and method validation. In this study, we report an experience with quality control and a [...] Read more.

The field of metabolomics as applied to human disease and health is rapidly expanding. In recent efforts of metabolomics research, greater emphasis has been placed on quality control and method validation. In this study, we report an experience with quality control and a practical application of method validation. Specifically, we sought to identify and modify steps in gas chromatography-mass spectrometry (GC-MS)-based, non-targeted metabolomic profiling of human plasma that could influence metabolite identification and quantification. Our experimental design included two studies: (1) a limiting-dilution study, which investigated the effects of dilution on analyte identification and quantification; and (2) a concentration-specific study, which compared the optimal plasma extract volume established in the first study with the volume used in the current institutional protocol. We confirmed that contaminants, concentration, repeatability and intermediate precision are major factors influencing metabolite identification and quantification. In addition, we established methods for improved metabolite identification and quantification, which were summarized to provide recommendations for experimental design of GC-MS-based non-targeted profiling of human plasma. Full article

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Contaminants represent 54% of all analytes detected. Classes of contaminants: process impurities (e.g., silicone oils and alkane hydrocarbons) present in blanks or discovered after manual curation (25), metabolites present in blanks (43), and unknowns present in blanks (88). Full article ">Figure 2
Distribution of R2 value for all analytes. Approximately half of analytes (47.9%, 23) with low linearity (R2 less than 0.5) were definite or potential contaminants. Full article ">Figure 3
Boxplot of repeatability (within-batch relative standard deviation or RSD) by plasma extract volume. The horizontal lines represent the median and the lower and upper hinges correspond to the 25th and 75th percentiles. The asterisks * denote RSD that was significantly different in post-hoc pairwise comparisons using the Conover’s test for multiple comparisons. Full article ">Figure 4
Boxplot of intermediate precision (between-batch relative standard deviation or RSD) by plasma extract volume. The horizontal lines represent the median and the lower and upper hinges correspond to the 25th and 75th percentiles. The asterisks * denote RSD that was significantly different in post-hoc pairwise comparisons using the Conover’s test for multiple comparisons. Full article ">Figure 5
(a) Schematic of the sample preparation steps for the limiting dilution study; (b) an example of the injection order of the plasma extract aliquots. Aliquots were analysed in a randomized order to minimize biases in sample preparation and data acquisition. Blanks containing the reagents only were included in at the beginning, middle, and end of each run. The concentration-specific study used a similar protocol except for different plasma extract volumes (0, 150 and 700 µL only). Full article ">

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Open AccessArticle

Impact of Soil Warming on the Plant Metabolome of Icelandic Grasslands

by Albert Gargallo-Garriga, Marta Ayala-Roque, Jordi Sardans, Mireia Bartrons, Victor Granda, Bjarni D. Sigurdsson, Niki I. W. Leblans, Michal Oravec, Otmar Urban, Ivan A. Janssens and Josep Peñuelas

Metabolites 2017, 7(3), 44; https://doi.org/10.3390/metabo7030044 - 23 Aug 2017

Cited by 16 | Viewed by 5420

Abstract

Climate change is stronger at high than at temperate and tropical latitudes. The natural geothermal conditions in southern Iceland provide an opportunity to study the impact of warming on plants, because of the geothermal bedrock channels that induce stable gradients of soil temperature. [...] Read more.

Climate change is stronger at high than at temperate and tropical latitudes. The natural geothermal conditions in southern Iceland provide an opportunity to study the impact of warming on plants, because of the geothermal bedrock channels that induce stable gradients of soil temperature. We studied two valleys, one where such gradients have been present for centuries (long-term treatment), and another where new gradients were created in 2008 after a shallow crustal earthquake (short-term treatment). We studied the impact of soil warming (0 to +15 °C) on the foliar metabolomes of two common plant species of high northern latitudes: Agrostis capillaris, a monocotyledon grass; and Ranunculus acris, a dicotyledonous herb, and evaluated the dependence of shifts in their metabolomes on the length of the warming treatment. The two species responded differently to warming, depending on the length of exposure. The grass metabolome clearly shifted at the site of long-term warming, but the herb metabolome did not. The main up-regulated compounds at the highest temperatures at the long-term site were saccharides and amino acids, both involved in heat-shock metabolic pathways. Moreover, some secondary metabolites, such as phenolic acids and terpenes, associated with a wide array of stresses, were also up-regulated. Most current climatic models predict an increase in annual average temperature between 2–8 °C over land masses in the Arctic towards the end of this century. The metabolomes of A. capillaris and R. acris shifted abruptly and nonlinearly to soil warming >5 °C above the control temperature for the coming decades. These results thus suggest that a slight warming increase may not imply substantial changes in plant function, but if the temperature rises more than 5 °C, warming may end up triggering metabolic pathways associated with heat stress in some plant species currently dominant in this region. Full article

(This article belongs to the Special Issue Environmental Metabolomics)

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Soil temperatures at a depth of 10 cm from May 2013 to May 2015 in each measurement plot at the sites of (A) short-term warming site and (B) long-term warming site. (C) Natural soil warming in a natural grassland in Iceland; the yellow flowers are Ranunculus acris. Full article ">Figure 2
Plots of cases and variables in the principal component analysis (PCA) conducted with the physicochemical traits, elemental compositions, and biological and metabolomic variables of Ranunculus acris and Agrostis capillaris using PC1 versus PC2. (A) The cases are categorised by site and species. Species are indicated by different colours (green, R. acris; orange, A. capillaris). The two sites are indicated by S for short-term warming and L for long-term warming. (B) Loadings of the metabolomic variables in PC1 and PC2. The various metabolomic families are represented by colours: dark blue, sugars; green, amino acids; orange, compounds related to the metabolism of amino acids and sugars; cyan, nucleotides; brown, phenolics; dark red, terpenes; and red, others. Metabolites: arginine (Arg), asparagine (Asn), aspartic acid (Asp), glutamic acid (Glu), glutamine (Gln), isoleucine (Ile), lysine (Lys), leucine (Leu), methionine (Met), phenylalanine (Phe), serine (Ser), tryptophan (Trp), threonine (Thr), tyrosine (Tyr), valine (Val), adenine (Ade), adenosine (Ado), thymidine (TdR), chlorogenic acid (CGA), trans-caffeic acid (CafA), α-ketoglutaric acid (KG), citric acid (Cit), L-malic acid (Mal), lactic acid (LA), succinic acid (SAD), pantothenic acid hemicalcium salt (Pan), jasmonic acid (JA), 5,7-dihydroxy-3,4,5-trimethoxyflavone (Fla), acacetin (AC), epicatechin (EC), epigallocatechin (EGC), homoorientin (Hom), isovitexin (Ivx), kaempferol (Kae), myricetin (Myr), quercetin (Qct), resveratrol (Rvt), saponarin (Sp), catechin hydrate (Cat), 3-coumaric acid (CouA), gallic acid (GA), quinic acid (QuiA), sodium salicylate (Sal), syringic acid (Syr), trans-ferulic acid (Fer), vanillic acid (Van), 2-deoxy-D-ribose (Rib), D-(−)-lyxose (Lyx), D-(+)-sorbose (Sor), D-(+)-trehalose dehydrate (Tre), aucubin (Auc). Unassigned metabolites are represented by small grey points. Full article ">Figure 3
Plots of cases and variables in the PCA conducted with the physicochemical traits, elemental compositions, and biological and metabolomic variables of Agrostis capillaris using PC1 versus PC2. (A) The samples are categorised by scores (mean ± S.E.) for both sites (S for short-term warming and L for long-term warming). (B) Loadings of the various physicochemical, biological, and metabolomic variables in PC1 and PC2. Physicochemical variables, C and N concentrations, and the RNA/DNA ratio are shown in purple. The various metabolomic families are represented by colours: dark blue, sugars; green, amino acids; orange, compounds related to the metabolism of amino acids and saccharides; cyan, nucleotides; brown, phenolic acids; dark red, terpenes; and red, others. Metabolites as in <a href="#metabolites-07-00044-f002" class="html-fig">Figure 2</a>. Unassigned metabolites as in <a href="#metabolites-07-00044-f002" class="html-fig">Figure 2</a> are not depicted in this figure. Full article ">Figure 4
Biplots of the PC1–PC2 plane resulting from the ANOVA-simultaneous component analysis (ASCA) conducted for the six warming treatments for Agrostis capillaris in each site: (A) in Long-term warming site and (B) in Short-term warming site. The arrows indicate the five temperature variables. The data included the 1640 detected metabolites including the unknown compounds represented by grey points. The various metabolomic families are represented by colours: dark blue, sugars; green, amino acids; orange, compounds related to the metabolism of amino acids and sugars; cyan, nucleotides; brown, phenolics; dark red, terpenes; and red, others. Metabolites: arginine (Arg), asparagine (Asn), aspartic acid (Asp), glutamic acid (Glu), glutamine (Gln), isoleucine (Ile), lysine (Lys), leucine (Leu), methionine (Met), phenylalanine (Phe), serine (Ser), tryptophan (Trp), threonine (Thr), tyrosine (Tyr), valine (Val), adenine (Ade), adenosine (Ado), thymidine (TdR), chlorogenic acid (CGA), trans-caffeic acid (CafA), α-ketoglutaric acid (KG), citric acid (Cit), L-malic acid (Mal), lactic acid (LA), succinic acid (SAD), pantothenic acid hemicalcium salt (Pan), jasmonic acid (JA), 5,7-dihydroxy-3,4,5-trimethoxyflavone (Fla), acacetin (AC), epicatechin (EC), epigallocatechin (EGC), homoorientin (Hom), isovitexin (Ivx), kaempferol (Kae), myricetin (Myr), quercetin (Qct), resveratrol (Rvt), saponarin (Sp), catechin hydrate (Cat), 3-coumaric acid (CouA), gallic acid (GA), quinic acid (QuiA), sodium salicylate (Sal), syringic acid (Syr), trans-ferulic acid (Fer), vanillic acid (Van), 2-deoxy-D-ribose (Rib), D-(−)-lyxose (Lyx), D-(+)-sorbose (Sor), D-(+)-trehalose dehydrate (Tre), aucubin (Auc). Full article ">Figure 5
(A) Clustered image maps of the metabolites of Agrostis capillaris at the sites of (A) long-term warming and (B) short-term warming based on the data of the PLS analysis. Red and blue indicate positive and negative correlations, respectively (for more details <a href="#app1-metabolites-07-00044" class="html-app">Supplementary Information</a>). Full article ">Figure 6
Plots of cases and variables in the PCA conducted with the physicochemical traits, elemental compositions, and biological and metabolomic variables of R. acris using PC1 versus PC2. (A) The samples are categorised by scores (mean ± S.E.) at both sites (S for short-term warming and L for long-term warming). (B) Loadings of the various physicochemical, biological, and metabolomic variables in PC1 and PC2. Physicochemical variables, C and N concentrations, and the RNA/DNA ratio are shown in purple. The various metabolomic families are represented by colours: dark blue, saccharides; green, amino acids; orange, compounds related to the metabolism of amino acids and sugars; cyan, nucleotides; brown, phenolic acids; dark red, terpenes; and red, others. Metabolites as in <a href="#metabolites-07-00044-f002" class="html-fig">Figure 2</a>. Unassigned metabolites, as in <a href="#metabolites-07-00044-f002" class="html-fig">Figure 2</a>, are not depicted in this figure. Full article ">Figure 7
Biplots of the PC1–PC2 plane resulting from the ANOVA-simultaneous component analysis (ASCA) conducted for the six warming treatments for Ranunculus acris in each site. (A) In Long-term warming site and (B) in Short-term warming site. The arrows indicate the five temperature variables. The data included the 1640 detected metabolites including the unknown compounds represented by grey points. The various metabolomic families are represented by colours: dark blue, sugars; green, amino acids; orange, compounds related to the metabolism of amino acids and sugars; cyan, nucleotides; brown, phenolics; dark red, terpenes; and red, others. Metabolites: arginine (Arg), asparagine (Asn), aspartic acid (Asp), glutamic acid (Glu), glutamine (Gln), isoleucine (Ile), lysine (Lys), leucine (Leu), methionine (Met), phenylalanine (Phe), serine (Ser), tryptophan (Trp), threonine (Thr), tyrosine (Tyr), valine (Val), adenine (Ade), adenosine (Ado), thymidine (TdR), chlorogenic acid (CGA), trans-caffeic acid (CafA), α-ketoglutaric acid (KG), citric acid (Cit), L-malic acid (Mal), lactic acid (LA), succinic acid (SAD), pantothenic acid hemicalcium salt (Pan), jasmonic acid (JA), 5,7-dihydroxy-3,4,5-trimethoxyflavone (Fla), acacetin (AC), epicatechin (EC), epigallocatechin (EGC), homoorientin (Hom), isovitexin (Ivx), kaempferol (Kae), myricetin (Myr), quercetin (Qct), resveratrol (Rvt), saponarin (Sp), catechin hydrate (Cat), 3-coumaric acid (CouA), gallic acid (GA), quinic acid (QuiA), sodium salicylate (Sal), syringic acid (Syr), trans-ferulic acid (Fer), vanillic acid (Van), 2-deoxy-D-ribose (Rib), D-(−)-lyxose (Lyx), D-(+)-sorbose (Sor), D-(+)-trehalose dehydrate (Tre), aucubin (Auc). Full article ">Figure 8
Clustered image maps of the metabolites of Ranunculus acris at the sites of (A) short-term warming and (B) long-term warming based on the data of the PLS analysis. Red and blue indicate positive and negative correlations, respectively (for more details, refer to supplementary information). Full article ">

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Open AccessReview

Extracellular Microbial Metabolomics: The State of the Art

by Farhana R. Pinu and Silas G. Villas-Boas

Metabolites 2017, 7(3), 43; https://doi.org/10.3390/metabo7030043 - 22 Aug 2017

Cited by 99 | Viewed by 9651

Abstract

Microorganisms produce and secrete many primary and secondary metabolites to the surrounding environment during their growth. Therefore, extracellular metabolites provide important information about the changes in microbial metabolism due to different environmental cues. The determination of these metabolites is also comparatively easier than [...] Read more.

Microorganisms produce and secrete many primary and secondary metabolites to the surrounding environment during their growth. Therefore, extracellular metabolites provide important information about the changes in microbial metabolism due to different environmental cues. The determination of these metabolites is also comparatively easier than the extraction and analysis of intracellular metabolites as there is no need for cell rupture. Many analytical methods are already available and have been used for the analysis of extracellular metabolites from microorganisms over the last two decades. Here, we review the applications and benefits of extracellular metabolite analysis. We also discuss different sample preparation protocols available in the literature for both types (e.g., metabolites in solution and in gas) of extracellular microbial metabolites. Lastly, we evaluate the authenticity of using extracellular metabolomics data in the metabolic modelling of different industrially important microorganisms. Full article

(This article belongs to the Special Issue Microbial Metabolomics Volume 2)

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Graphical abstract
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Discriminant function analysis (DFA) to visualize exometabolomics data. Extracellular metabolites from deliberately contaminated and control samples from microalgal fermentation were analyzed using Gas-chromatography and mass spectrometry (GC-MS) [<a href="#B42-metabolites-07-00043" class="html-bibr">42</a>]. The data from 56 samples were log transformed prior to performing DFA and three distinct clusters were observed. Here, black circles represent the samples from flasks contaminated with Pseudomonas aeruginosa, red circles show the samples contaminated by Bacillus subtilis, and light green and dark green circles present the samples collected from contaminated flasks at time 0 and non-contaminated flasks, respectively. This figure was reproduced from Sue et al. with the authors’ permission [<a href="#B42-metabolites-07-00043" class="html-bibr">42</a>]. Full article ">Figure 2
Extracellular sample preparation, handling, and storage. After centrifugation or fast filtration, culture media containing extracellular metabolites are usually stored at a low temperature and under dark conditions. Sometime, organic solvents are added to denature active enzymes. For some metabolites, specific extraction procedures need to be followed before being analyzed by appropriate instruments. Prior to analysis, extracellular samples are often freeze-dried to concentrate the level of metabolites. Full article ">Figure 3
Overview of extracellular metabolite analysis. Different technical methods, such as solid-phase extraction (SPE), solid-phase micro extraction (SPME), head space (HS) analysis, and HS-SPME, are used for the preparation of extracellular samples. Sample preparation protocols depend on the type of metabolite. Here, GC-MS = gas-chromatography coupled to mass spectrometry; LC-MS = liquid-chromatography coupled to mass spectrometry; NMR = nuclear magnetic resonance spectroscopy; HPLC = high pressure liquid chromatography; FTIR = Fourier transform infra-red spectroscopy. Full article ">

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Open AccessReview

Biomarker Research in Parkinson’s Disease Using Metabolite Profiling

by Jesper F. Havelund, Niels H. H. Heegaard, Nils J. K. Færgeman and Jan Bert Gramsbergen

Metabolites 2017, 7(3), 42; https://doi.org/10.3390/metabo7030042 - 11 Aug 2017

Cited by 105 | Viewed by 13070

Abstract

Biomarker research in Parkinson’s disease (PD) has long been dominated by measuring dopamine metabolites or alpha-synuclein in cerebrospinal fluid. However, these markers do not allow early detection, precise prognosis or monitoring of disease progression. Moreover, PD is now considered a multifactorial disease, which [...] Read more.

Biomarker research in Parkinson’s disease (PD) has long been dominated by measuring dopamine metabolites or alpha-synuclein in cerebrospinal fluid. However, these markers do not allow early detection, precise prognosis or monitoring of disease progression. Moreover, PD is now considered a multifactorial disease, which requires a more precise diagnosis and personalized medication to obtain optimal outcome. In recent years, advanced metabolite profiling of body fluids like serum/plasma, CSF or urine, known as “metabolomics”, has become a powerful and promising tool to identify novel biomarkers or “metabolic fingerprints” characteristic for PD at various stages of disease. In this review, we discuss metabolite profiling in clinical and experimental PD. We briefly review the use of different analytical platforms and methodologies and discuss the obtained results, the involved metabolic pathways, the potential as a biomarker and the significance of understanding the pathophysiology of PD. Many of the studies report alterations in alanine, branched-chain amino acids and fatty acid metabolism, all pointing to mitochondrial dysfunction in PD. Aromatic amino acids (phenylalanine, tyrosine, tryptophan) and purine metabolism (uric acid) are also altered in most metabolite profiling studies in PD. Full article

(This article belongs to the Special Issue Big Data in Metabolomics)

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Open AccessEditorial

Special Issue: Cancer Metabolism

by Madhu Basetti

Metabolites 2017, 7(3), 41; https://doi.org/10.3390/metabo7030041 - 9 Aug 2017

Cited by 4 | Viewed by 4700

Abstract

This special issue is designed to present the latest research findings and developments in the field of cancer metabolism. Cancer is a complex disease and a common term used for more than 100 diseases, whereas metabolism describes a labyrinth of complex biochemical pathways [...] Read more.

This special issue is designed to present the latest research findings and developments in the field of cancer metabolism. Cancer is a complex disease and a common term used for more than 100 diseases, whereas metabolism describes a labyrinth of complex biochemical pathways in the cell. It is essential to understand metabolism in the context of cancer for the early detection of disease biomarkers and to find proper targets for potential treatments. The articles presented in this issue cover metabolic aspects of brain tumours, breast tumours, paraganglioma, and the metabolic activity of tumour suppressor gene p53. Full article

(This article belongs to the Special Issue Cancer Metabolism)

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Open AccessArticle

Exercise-Induced Alterations in Skeletal Muscle, Heart, Liver, and Serum Metabolome Identified by Non-Targeted Metabolomics Analysis

by Joseph W. Starnes, Traci L. Parry, Sara K. O’Neal, James R. Bain, Michael J. Muehlbauer, Aubree Honcoop, Amro Ilaiwy, Peter M. Christopher, Cam Patterson and Monte S. Willis

Metabolites 2017, 7(3), 40; https://doi.org/10.3390/metabo7030040 - 8 Aug 2017

Cited by 33 | Viewed by 7154

Abstract

Background: The metabolic and physiologic responses to exercise are increasingly interesting, given that regular physical activity enhances antioxidant capacity, improves cardiac function, and protects against type 2 diabetes. The metabolic interactions between tissues and the heart illustrate a critical cross-talk we know little [...] Read more.

Background: The metabolic and physiologic responses to exercise are increasingly interesting, given that regular physical activity enhances antioxidant capacity, improves cardiac function, and protects against type 2 diabetes. The metabolic interactions between tissues and the heart illustrate a critical cross-talk we know little about. Methods: To better understand the metabolic changes induced by exercise, we investigated skeletal muscle (plantaris, soleus), liver, serum, and heart from exercise trained (or sedentary control) animals in an established rat model of exercise-induced aerobic training via non-targeted GC-MS metabolomics. Results: Exercise-induced alterations in metabolites varied across tissues, with the soleus and serum affected the least. The alterations in the plantaris muscle and liver were most alike, with two metabolites increased in each (citric acid/isocitric acid and linoleic acid). Exercise training additionally altered nine other metabolites in the plantaris (C13 hydrocarbon, inosine/adenosine, fructose-6-phosphate, glucose-6-phosphate, 2-aminoadipic acid, heptadecanoic acid, stearic acid, alpha-tocopherol, and oleic acid). In the serum, we identified significantly decreased alpha-tocopherol levels, paralleling the increases identified in plantaris muscle. Eleven unique metabolites were increased in the heart, which were not affected in the other compartments (malic acid, serine, aspartic acid, myoinositol, glutamine, gluconic acid-6-phosphate, glutamic acid, pyrophosphate, campesterol, phosphoric acid, creatinine). These findings complement prior studies using targeted metabolomics approaches to determine the metabolic changes in exercise-trained human skeletal muscle. Specifically, exercise trained vastus lateralus biopsies had significantly increased linoleic acid, oleic acid, and stearic acid compared to the inactive groups, which were significantly increased in plantaris muscle in the present study. Conclusions: While increases in alpha-tocopherol have not been identified in muscle after exercise to our knowledge, the benefits of vitamin E (alpha-tocopherol) supplementation in attenuating exercise-induced muscle damage has been studied extensively. Skeletal muscle, liver, and the heart have primarily different metabolic changes, with few similar alterations and rare complementary alterations (alpha-tocopherol), which may illustrate the complexity of understanding exercise at the organismal level. Full article

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Exercise-induced increases in soleus and plantaris cytochrome c oxidase activity. Total Cytochrome c oxidase activity in sedentary and exercise trained rats (N = 9–10/group). * P < 0.05 vs. sedentary. Values are expressed as mean values ± SE (N = 9–10 muscles/group). Full article ">Figure 2
Analysis of non-targeted metabolomics of plantaris muscle from exercise-trained and sedentary control rats. (A) Partial Least Squares Discriminant Analysis (PLS-DA). (B) PLS-DA Variable Importance in the Projection (VIP) significant metabolites. (C) t-Test significant metabolites (P < 0.05). (D) Pathway analysis based on t-test significant metabolites. N = 12/group. Full article ">Figure 3
t-Test significant metabolites and succinic acid from plantaris muscle. (A) Metabolite decreased in exercise-trained muscle. (B) Metabolites increased in exercise-trained muscle. (C) Metabolite increased in exercise-trained muscle (not significant). Data represent mean ± SEM. * P < 0.05. N = 12/group. Full article ">Figure 4
Pathway analysis of t-test significant metabolites. (A) Glyoxylate and dicarboxylate metabolism. (B) Citric acid (TCA) cycle. (C) Linoleic acid metabolism. (D) Long-chain fatty acid synthesis. Data represent mean ± SEM. * P < 0.05. N = 12/group. Full article ">Figure 5
Analysis of non-targeted metabolomics of liver from exercise-trained or sedentary control rats. (A) Principal components analysis using PLS-DA. (B) Variable importance in projection (VIP) scores. (C) Heatmap of t-test significant liver metabolites in exercise-trained rats vs. sedentary. Data represent mean ± SEM. * P < 0.05. N = 12/group. Full article ">Figure 6
Pathway analysis of t-test significant liver metabolites from exercise-trained vs. sedentary control rat livers. (A) Pathway analysis based on t-test significant metabolites. (B) t-Test significant metabolite related to linoleic metabolism. (C) t-Test significant metabolite related to purine metabolism. Data represent mean ± SEM. *P < 0.05. N = 12/group. Full article ">

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Open AccessReview

Volatile Metabolites Emission by In Vivo Microalgae—An Overlooked Opportunity?

by Komandoor E. Achyuthan, Jason C. Harper, Ronald P. Manginell and Matthew W. Moorman

Metabolites 2017, 7(3), 39; https://doi.org/10.3390/metabo7030039 - 31 Jul 2017

Cited by 78 | Viewed by 14713

Abstract

Fragrances and malodors are ubiquitous in the environment, arising from natural and artificial processes, by the generation of volatile organic compounds (VOCs). Although VOCs constitute only a fraction of the metabolites produced by an organism, the detection of VOCs has a broad range [...] Read more.

Fragrances and malodors are ubiquitous in the environment, arising from natural and artificial processes, by the generation of volatile organic compounds (VOCs). Although VOCs constitute only a fraction of the metabolites produced by an organism, the detection of VOCs has a broad range of civilian, industrial, military, medical, and national security applications. The VOC metabolic profile of an organism has been referred to as its ‘volatilome’ (or ‘volatome’) and the study of volatilome/volatome is characterized as ‘volatilomics’, a relatively new category in the ‘omics’ arena. There is considerable literature on VOCs extracted destructively from microalgae for applications such as food, natural products chemistry, and biofuels. VOC emissions from living (in vivo) microalgae too are being increasingly appreciated as potential real-time indicators of the organism’s state of health (SoH) along with their contributions to the environment and ecology. This review summarizes VOC emissions from in vivo microalgae; tools and techniques for the collection, storage, transport, detection, and pattern analysis of VOC emissions; linking certain VOCs to biosynthetic/metabolic pathways; and the role of VOCs in microalgae growth, infochemical activities, predator-prey interactions, and general SoH. Full article

(This article belongs to the Special Issue Marine Metabolomics)

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Open AccessArticle

Non-Targeted Metabolomics Analysis of Golden Retriever Muscular Dystrophy-Affected Muscles Reveals Alterations in Arginine and Proline Metabolism, and Elevations in Glutamic and Oleic Acid In Vivo

by Muhammad Abdullah, Joe N. Kornegay, Aubree Honcoop, Traci L. Parry, Cynthia J. Balog-Alvarez, Sara K. O’Neal, James R. Bain, Michael J. Muehlbauer, Christopher B. Newgard, Cam Patterson and Monte S. Willis

Metabolites 2017, 7(3), 38; https://doi.org/10.3390/metabo7030038 - 29 Jul 2017

Cited by 25 | Viewed by 8320

Abstract

Background: Like Duchenne muscular dystrophy (DMD), the Golden Retriever Muscular Dystrophy (GRMD) dog model of DMD is characterized by muscle necrosis, progressive paralysis, and pseudohypertrophy in specific skeletal muscles. This severe GRMD phenotype includes atrophy of the biceps femoris (BF) as compared to [...] Read more.

Background: Like Duchenne muscular dystrophy (DMD), the Golden Retriever Muscular Dystrophy (GRMD) dog model of DMD is characterized by muscle necrosis, progressive paralysis, and pseudohypertrophy in specific skeletal muscles. This severe GRMD phenotype includes atrophy of the biceps femoris (BF) as compared to unaffected normal dogs, while the long digital extensor (LDE), which functions to flex the tibiotarsal joint and serves as a digital extensor, undergoes the most pronounced atrophy. A recent microarray analysis of GRMD identified alterations in genes associated with lipid metabolism and energy production. Methods: We, therefore, undertook a non-targeted metabolomics analysis of the milder/earlier stage disease GRMD BF muscle versus the more severe/chronic LDE using GC-MS to identify underlying metabolic defects specific for affected GRMD skeletal muscle. Results: Untargeted metabolomics analysis of moderately-affected GRMD muscle (BF) identified eight significantly altered metabolites, including significantly decreased stearamide (0.23-fold of controls, p = 2.89 × 10⁻³), carnosine (0.40-fold of controls, p = 1.88 × 10⁻²), fumaric acid (0.40-fold of controls, p = 7.40 × 10⁻⁴), lactamide (0.33-fold of controls, p = 4.84 × 10⁻²), myoinositol-2-phosphate (0.45-fold of controls, p = 3.66 × 10⁻²), and significantly increased oleic acid (1.77-fold of controls, p = 9.27 × 10⁻²), glutamic acid (2.48-fold of controls, p = 2.63 × 10⁻²), and proline (1.73-fold of controls, p = 3.01 × 10⁻²). Pathway enrichment analysis identified significant enrichment for arginine/proline metabolism (p = 5.88 × 10⁻⁴, FDR 4.7 × 10⁻²), where alterations in L-glutamic acid, proline, and carnosine were found. Additionally, multiple Krebs cycle intermediates were significantly decreased (e.g., malic acid, fumaric acid, citric/isocitric acid, and succinic acid), suggesting that altered energy metabolism may be underlying the observed GRMD BF muscle dysfunction. In contrast, two pathways, inosine-5'-monophosphate (VIP Score 3.91) and 3-phosphoglyceric acid (VIP Score 3.08) mainly contributed to the LDE signature, with two metabolites (phosphoglyceric acid and inosine-5'-monophosphate) being significantly decreased. When the BF and LDE were compared, the most significant metabolite was phosphoric acid, which was significantly less in the GRMD BF compared to control and GRMD LDE groups. Conclusions: The identification of elevated BF oleic acid (a long-chain fatty acid) is consistent with recent microarray studies identifying altered lipid metabolism genes, while alterations in arginine and proline metabolism are consistent with recent studies identifying elevated L-arginine in DMD patient sera as a biomarker of disease. Together, these studies demonstrate muscle-specific alterations in GRMD-affected muscle, which illustrate previously unidentified metabolic changes. Full article

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Open AccessArticle

Rapid Quantification of Major Volatile Metabolites in Fermented Food and Beverages Using Gas Chromatography-Mass Spectrometry

by Farhana R. Pinu and Silas G. Villas-boas

Metabolites 2017, 7(3), 37; https://doi.org/10.3390/metabo7030037 - 26 Jul 2017

Cited by 38 | Viewed by 7913

Abstract

Here we present a method for the accurate quantification of major volatile metabolites found in different food and beverages, including ethanol, acetic acid and other aroma compounds, using gas chromatography coupled to mass spectrometry (GC-MS). The method is combined with a simple sample [...] Read more.

Here we present a method for the accurate quantification of major volatile metabolites found in different food and beverages, including ethanol, acetic acid and other aroma compounds, using gas chromatography coupled to mass spectrometry (GC-MS). The method is combined with a simple sample preparation procedure using sodium chloride and anhydrous ethyl acetate. The GC-MS analysis was accomplished within 4.75 min, and over 80 features were detected, of which 40 were positively identified using an in-house and a commercialmass spectrometry (MS) library. We determined different analytical parameters of these metabolites including the limit of detection (LOD), limit of quantitation (LOQ) and range of quantification. In order to validate the method, we also determined detailed analytical characteristics of five major fermentation end products including ethanol, acetic acid, isoamyl alcohol, ethyl-L-lactate and, acetoin. The method showed very low technical variability for the measurements of these metabolites in different matrices (<3%) with an excellent accuracy (100% ± 5%), recovery (100% ± 10%), reproducibility and repeatability [Coefficient of variation (CV) 1–10%)]. To demonstrate the applicability of the method, we analysed different fermented products including balsamic vinegars, sourdough, distilled (whisky) and non-distilled beverages (wine and beer). Full article

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Open AccessArticle

The Effect of Season on the Metabolic Profile of the European Clam Ruditapes decussatus as Studied by ¹H-NMR Spectroscopy

by Violetta Aru, Søren Balling Engelsen, Francesco Savorani, Jacopo Culurgioni, Giorgia Sarais, Giulia Atzori, Serenella Cabiddu and Flaminia Cesare Marincola

Metabolites 2017, 7(3), 36; https://doi.org/10.3390/metabo7030036 - 26 Jul 2017

Cited by 10 | Viewed by 4866

Abstract

In this study, the metabolome of Ruditapes decussatus, an economically and ecologically important marine bivalve species widely distributed in the Mediterranean region, was characterized by using proton Nuclear Magnetic Resonance (¹H-NMR) spectroscopy. Significant seasonal variations in the content of carbohydrates and [...] Read more.

In this study, the metabolome of Ruditapes decussatus, an economically and ecologically important marine bivalve species widely distributed in the Mediterranean region, was characterized by using proton Nuclear Magnetic Resonance (¹H-NMR) spectroscopy. Significant seasonal variations in the content of carbohydrates and free amino acids were observed. The relative amounts of alanine and glycine were found to exhibit the same seasonal pattern as the temperature and salinity at the harvesting site. Several putative sex-specific biomarkers were also discovered. Substantial differences were found for alanine and glycine, whose relative amounts were higher in males, while acetoacetate, choline and phosphocholine were more abundant in female clams. These findings reveal novel insights into the baseline metabolism of the European clam and represent a step forward towards a comprehensive metabolic characterization of the species. Besides providing a holistic view on the prominent nutritional components, the characterization of the metabolome of this bivalve represents an important prerequisite for elucidating the underlying metabolic pathways behind the environment-organism interactions. Full article

(This article belongs to the Special Issue Marine Metabolomics)

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Seasonal values of water temperature (▲) and salinity (o) as measured at the sampling site. Temperature and salinity were measured during each sampling event, namely May 2013 (spring 2013), June 2013 (summer 2013), September and October 2013 (autumn 2013), June and July 2014 (summer 2014). Each measurement was performed in triplicate. Data is reported as mean ± standard deviation. Full article ">Figure 2
Gonadal tissues of male (left side) and female (right side) R. decussatus. Ovary and sperm acini are shown. Magnifications used are 20× (A,D), 100× (B,E) and 400× (C,F) are reported. Gonadal tissues of R. decussatus with mother sporocysts of B. bacciger containing daughter sporocysts. Magnifications 20× (G) and 100× (H) are reported. All pictures were taken under a light microscope. Scale bar: 1 mm (A,D,G); 200 µm (B,E,H); 50 µm (C,F). Full article ">Figure 3
Expansions of the up-field (A), mid-field (B) and low-field (C) regions of a representative proton NMR spectrum of the aqueous extract of clams. Metabolite assignments are given in <a href="#metabolites-07-00036-t001" class="html-table">Table 1</a>. Full article ">Figure 4
PCA score plot of PC1 versus PC2 (A) and PC1 and PC2 loadings plots (B,C) of the NMR spectral data of clam aqueous extracts. The most significant metabolites are assigned in the loadings plots. Spring 2013 (green circles), summer 2013 (red circles), autumn 2013 (blue circles), summer 2014 (red squares). Arrows are colored according to the chronological course of the experimental seasons. Full article ">Figure 5
Seasonal fluctuations in the relative amounts of the discriminant metabolites identified in wild samples of R. decussatus harvested in the Santa Gilla lagoon. Data is reported as mean ± standard deviation. Different letters stand for significant (p < 0.05) differences in the metabolite relative amounts. Full article ">Figure 6
Average spectra of the hydrosoluble extract of male (blue spectra) and female (red spectra) R. decussatus (a). PLS-DA scores (b) and loadings (c) plots of the proton NMR spectra of fully ripe clams’ aqueous extracts. Metabolite assignments are reported in <a href="#metabolites-07-00036-t001" class="html-table">Table 1</a>. Full article ">Figure 7
Location of the sampling site in the Santa Gilla lagoon (red square). Full article ">

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Open AccessArticle

Integrated Metabolomics Assessment of Human Dried Blood Spots and Urine Strips

by Jeremy Drolet, Vladimir Tolstikov, Brian A. Williams, Bennett P. Greenwood, Collin Hill, Vivek K. Vishnudas, Rangaprasad Sarangarajan, Niven R. Narain and Michael A. Kiebish

Metabolites 2017, 7(3), 35; https://doi.org/10.3390/metabo7030035 - 15 Jul 2017

Cited by 43 | Viewed by 8656

Abstract

(1) Background: Interest in the application of metabolomics toward clinical diagnostics development and population health monitoring has grown significantly in recent years. In spite of several advances in analytical and computational tools, obtaining a sufficient number of samples from patients remains an obstacle. [...] Read more.

(1) Background: Interest in the application of metabolomics toward clinical diagnostics development and population health monitoring has grown significantly in recent years. In spite of several advances in analytical and computational tools, obtaining a sufficient number of samples from patients remains an obstacle. The dried blood spot (DBS) and dried urine strip (DUS) methodologies are a minimally invasive sample collection method allowing for the relative simplicity of sample collection and minimal cost. (2) Methods: In the current report, we compared results of targeted metabolomics analyses of four types of human blood sample collection methods (with and without DBS) and two types of urine sample collection (DUS and urine) across several parameters including the metabolite coverage of each matrix and the sample stability for DBS/DUS using commercially available Whatman 903TM paper. The DBS/DUS metabolomics protocols were further applied to examine the temporal metabolite level fluctuations within hours and days of sample collection. (3) Results: Several hundred polar metabolites were monitored using DBS/DUS. Temporal analysis of the polar metabolites at various times of the day and across days identified several species that fluctuate as a function of day and time. In addition, a subset of metabolites were identified to be significantly altered across hours within a day and within successive days of the week. (4) Conclusion: A comprehensive DBS/DUS metabolomics protocol was developed for human blood and urine analyses. The described methodology demonstrates the potential for enabling patients to contribute to the expanding bioanalytical demands of precision medicine and population health studies. Full article

(This article belongs to the Special Issue Clinical Metabolomics)

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Schematic of human metabolome analysis using DBS/DUS sampling protocols. Full article ">Figure 2
Two weeks DBS storage stability results for detected human blood metabolite classes assigned by participation in major pathways. Y axis represents percent change calculated using the mean of triplicate measurements for a metabolite class. Each column shows three measurement points at 3, 7 and 14 days (from left to right). Full article ">Figure 3
Two weeks DUS storage stability results for detected human urine metabolite classes. *—organic acids were measured with GC-MS protocols. Y axis represents percent change calculated using mean of triplicate measurements for a metabolite class. Each column shows three measurement points at 3, 7 and 14 days (from left to right). Full article ">Figure 4
Temporal changes of metabolomes over days (morning fasting sample collection). Upper panel: DBS one-way ANOVA analysis results (subject = 16, days = 5, p value threshold 0.05). Lower panel: DUS one-way ANOVA analysis results (subject = 10, days = 5, p value threshold 0.05). X axis shows total number of metabolites detected. Red circles illustrate metabolites which levels showed statistically significant differences. Box plots depict the most impacted metabolite temporal changes at time points 1–5 (corresponding to five consecutive days). Full article ">Figure 5
Intra-day variation of blood metabolome. One-way ANOVA analysis results (subject = 12, days = 1, p value threshold 0.05) for DBS samples. Left panel: X axis presents total number of metabolites detected. Red circles illustrate metabolites which levels showed statistically significant differences. Right panel: Box plots depict selected metabolites temporal changes at time points 0–4 (corresponding to fasting 7 AM, 10 AM, 1 PM, 4 PM, and 7 PM). Y axis shows the normalized relative abundance. Full article ">Figure 6
Top 25 metabolites correlating with isoleucine level patterns at time points 0–4 (corresponding to fasting 7 AM, 10 AM, 1 PM, 4 PM, and 7 PM), which correlated to intra-day variation in the blood metabolome analyzed with DBS protocols. Full article ">Figure 7
Two way ANOVA analysis results. Upper panel depicts isoleucine intra-day variations in the blood metabolome. Lower panel shows 3-methyl-2-oxovaleric acid intra-day variations in the blood metabolome. 3-methyl-2-oxovaleric acid is a downstream metabolite of isoleucine in humans (red arrow). Y axis shows normalized relative abundance. X axis illustrates intra-day time points corresponding to fasting, 7 AM, 10 AM, 1 PM, 4 PM, and 7 PM (0–4, from left to right). Full article ">

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Open AccessArticle

Natural Product Discovery Using Planes of Principal Component Analysis in R (PoPCAR)

by Shaurya Chanana, Chris S. Thomas, Doug R. Braun, Yanpeng Hou, Thomas P. Wyche and Tim S. Bugni

Metabolites 2017, 7(3), 34; https://doi.org/10.3390/metabo7030034 - 13 Jul 2017

Cited by 24 | Viewed by 9460

Abstract

Rediscovery of known natural products hinders the discovery of new, unique scaffolds. Efforts have mostly focused on streamlining the determination of what compounds are known vs. unknown (dereplication), but an alternative strategy is to focus on what is different. Utilizing statistics and assuming [...] Read more.

Rediscovery of known natural products hinders the discovery of new, unique scaffolds. Efforts have mostly focused on streamlining the determination of what compounds are known vs. unknown (dereplication), but an alternative strategy is to focus on what is different. Utilizing statistics and assuming that common actinobacterial metabolites are likely known, focus can be shifted away from dereplication and towards discovery. LC-MS-based principal component analysis (PCA) provides a perfect tool to distinguish unique vs. common metabolites, but the variability inherent within natural products leads to datasets that do not fit ideal standards. To simplify the analysis of PCA models, we developed a script that identifies only those masses or molecules that are unique to each strain within a group, thereby greatly reducing the number of data points to be inspected manually. Since the script is written in R, it facilitates integration with other metabolomics workflows and supports automated mass matching to databases such as Antibase. Full article

(This article belongs to the Special Issue Marine Metabolomics)

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A PCA scores and loadings plot for seven marine actinomycetes with two replicates. For this case, the strain represented by the dark blue circles in the scores plot was the most unique, as indicated by the large separation in PC1. Molecules that were unique to that strain were color-coded in the same dark blue in the loadings plot. Full article ">Figure 2
A scores overview plot for strain WMMB-499 in dataset one. To view the unique molecules associated with WMMB-499, PC13 vs. PC15 would provide the greatest separation from the other strains in this analysis because the absolute value of the score difference is the greatest. Full article ">Figure 3
(A) The scores plot for PC15 vs. PC13 shows WMMB-499 separate from the other strains; (B) The loadings plot shows molecular features that are unique for WMMB-499. Full article ">Figure 4
(A) The loadings plot for Dataset one with the 2000 most unique features colored in red; (B) The Euclidean distance vs. index plot is color-coded to match the points in the loadings plot. This example shows that 459,036 features are located in the center of the PCA plot and, based on Euclidean distance, contribute little to variance, meaning that they are not unique to any strain. Full article ">Figure 5
(A) The loadings plot for Dataset two with the 2000 most unique features colored in red; (B) The Euclidean distance vs. index plot is color-coded to match the points in the loadings plot. This example shows that 37,058 features are located in the center of the PCA plot and, based on Euclidean distance, contribute little to variance, meaning that they are not unique to any strain. Full article ">Figure 6
PCA model for Dataset one. (A) In contrast to <a href="#metabolites-07-00034-f003" class="html-fig">Figure 3</a>A, the scores plot showing PC1 vs. PC2 gives the illusion that WMMB-499 is not particularly unique; (B) The unique molecular features that can be identified from PC1–PC2 are associated with a group of similar strains highlighted in the lower left of the scores plot. Full article ">Figure 7
PCA model for Dataset two. (A) In contrast to <a href="#metabolites-07-00034-f006" class="html-fig">Figure 6</a>A, the scores plot showing PC1 vs. PC2 shows that WMMB-499 is unique relative to the other strains; (B) Similarly, the loadings plot provides clear evidence for unique molecular features associated with WMMB-499. (C) Examples of base peak chromatograms showing that WMMB-499 yielded lower overall ion intensity. Full article ">Figure 8
An overview of the process used by PoPCAR to generate a ranked list of unique features found for a particular strain in a PCA model. The final output is in Excel format and can be easily viewed. Full article ">

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Open AccessArticle

NMR Profiling of Metabolites in Larval and Juvenile Blue Mussels (Mytilus edulis) under Ambient and Low Salinity Conditions

by Melissa A. May, Karl D. Bishop and Paul D. Rawson

Metabolites 2017, 7(3), 33; https://doi.org/10.3390/metabo7030033 - 6 Jul 2017

Cited by 20 | Viewed by 4770

Abstract

Blue mussels (Mytilus edulis) are ecologically and economically important marine invertebrates whose populations are at risk from climate change-associated variation in their environment, such as decreased coastal salinity. Blue mussels are osmoconfomers and use components of the metabolome (free amino acids) [...] Read more.

Blue mussels (Mytilus edulis) are ecologically and economically important marine invertebrates whose populations are at risk from climate change-associated variation in their environment, such as decreased coastal salinity. Blue mussels are osmoconfomers and use components of the metabolome (free amino acids) to help maintain osmotic balance and cellular function during low salinity exposure. However, little is known about the capacity of blue mussels during the planktonic larval stages to regulate metabolites during osmotic stress. Metabolite studies in species such as blue mussels can help improve our understanding of the species’ physiology, as well as their capacity to respond to environmental stress. We used 1D ¹H nuclear magnetic resonance (NMR) and 2D total correlation spectroscopy (TOCSY) experiments to describe baseline metabolite pools in larval (veliger and pediveliger stages) and juvenile blue mussels (gill, mantle, and adductor tissues) under ambient conditions and to quantify changes in the abundance of common osmolytes in these stages during low salinity exposure. We found evidence for stage- and tissue-specific differences in the baseline metabolic profiles of blue mussels, which reflect variation in the function and morphology of each larval stage or tissue type of juveniles. These differences impacted the utilization of osmolytes during low salinity exposure, likely stemming from innate physiological variation. This study highlights the importance of foundational metabolomic studies that include multiple tissue types and developmental stages to adequately evaluate organismal responses to stress and better place these findings in a broader physiological context. Full article

(This article belongs to the Special Issue Marine Metabolomics)

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The representative 2D total correlation spectroscopy (TOCSY) spectrum from Mytilus edulis over 0–5 ppm. The spectrum is referenced to the chemical shift of TSP and the D2O peak was removed. The box on the 2D plot connects the cross-peaks contributed by the resonances of the hydrogen atoms within alanine, where there is a doublet at 1.46 ppm and a triplet at 3.77 ppm. These coupling patterns are used to verify the identity of the compounds in <a href="#metabolites-07-00033-t001" class="html-table">Table 1</a>; the complete list of all coupling partners generated from the TOCSY experiments is provided in <a href="#app1-metabolites-07-00033" class="html-app">Table S1</a>. Full article ">Figure 2
A representative 1D 1H NMR spectra for larval (a) veliger and juvenile (b) mantle tissue mussels are shown over the 0–9 ppm range, with a focus on the 0–4.5 ppm range where the chemical shifts for most of the metabolites we detected are found. The numbers above each peak correspond to the metabolites listed in <a href="#metabolites-07-00033-t001" class="html-table">Table 1</a>. The spectra are referenced to the chemical shift of trimethylsilylpropanoic acid (TSP) (0 ppm). The asterisk in panel a marks the chemical shift for the maleic acid spike (6.29 ppm) that was used for relative quantification. Full article ">Figure 3
The mean relative concentrations of alanine, β-alanine, taurine, glycine, betaine, and homarine (±SE) are shown for larval and juvenile mussels. Larval samples were analyzed at both the veliger (white bars; n = 3) and pediveliger (gray bars; n = 4) stages, while data from the gill (black bars), mantle (red bars), and adductor muscle (blue bars) were obtained from the tissues of individual juveniles (n = 5). Letters denote significant differences between the stages or tissues (at an experiment-wide α = 0.05). Full article ">

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Open AccessArticle

Development and Validation of a High-Throughput Mass Spectrometry Based Urine Metabolomic Test for the Detection of Colonic Adenomatous Polyps

by Lu Deng, David Chang, Rae R. Foshaug, Roman Eisner, Victor K. Tso, David S. Wishart and Richard N. Fedorak

Metabolites 2017, 7(3), 32; https://doi.org/10.3390/metabo7030032 - 22 Jun 2017

Cited by 29 | Viewed by 6467

Abstract

Background: Colorectal cancer is one of the leading causes of cancer deaths worldwide. The detection and removal of the precursors to colorectal cancer, adenomatous polyps, is the key for screening. The aim of this study was to develop a clinically scalable (high throughput, [...] Read more.

Background: Colorectal cancer is one of the leading causes of cancer deaths worldwide. The detection and removal of the precursors to colorectal cancer, adenomatous polyps, is the key for screening. The aim of this study was to develop a clinically scalable (high throughput, low cost, and high sensitivity) mass spectrometry (MS)-based urine metabolomic test for the detection of adenomatous polyps. Methods: Prospective urine and stool samples were collected from 685 participants enrolled in a colorectal cancer screening program to undergo colonoscopy examination. Statistical analysis was performed on 69 urine metabolites measured by one-dimensional nuclear magnetic resonance spectroscopy to identify key metabolites. A targeted MS assay was then developed to quantify the key metabolites in urine. A MS-based urine metabolomic diagnostic test for adenomatous polyps was established using 67% samples (un-blinded training set) and validated using the remaining 33% samples (blinded testing set). Results: The MS-based urine metabolomic test identifies patients with colonic adenomatous polyps with an AUC of 0.692, outperforming the NMR based predictor with an AUC of 0.670. Conclusion: Here we describe a clinically scalable MS-based urine metabolomic test that identifies patients with adenomatous polyps at a higher level of sensitivity (86%) over current fecal-based tests (<18%). Full article

(This article belongs to the Special Issue Clinical Metabolomics)

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Open AccessArticle

Non-Targeted Metabolomics Analysis of the Effects of Tyrosine Kinase Inhibitors Sunitinib and Erlotinib on Heart, Muscle, Liver and Serum Metabolism In Vivo

by Brian C. Jensen, Traci L. Parry, Wei Huang, Amro Ilaiwy, James R. Bain, Michael J. Muehlbauer, Sara K. O’Neal, Cam Patterson, Gary L. Johnson and Monte S. Willis

Metabolites 2017, 7(3), 31; https://doi.org/10.3390/metabo7030031 - 22 Jun 2017

Cited by 18 | Viewed by 6959

Abstract

Background: More than 90 tyrosine kinases have been implicated in the pathogenesis of malignant transformation and tumor angiogenesis. Tyrosine kinase inhibitors (TKIs) have emerged as effective therapies in treating cancer by exploiting this kinase dependency. The TKI erlotinib targets the epidermal growth factor [...] Read more.

Background: More than 90 tyrosine kinases have been implicated in the pathogenesis of malignant transformation and tumor angiogenesis. Tyrosine kinase inhibitors (TKIs) have emerged as effective therapies in treating cancer by exploiting this kinase dependency. The TKI erlotinib targets the epidermal growth factor receptor (EGFR), whereas sunitinib targets primarily vascular endothelial growth factor receptor (VEGFR) and platelet-derived growth factor receptor (PDGFR).TKIs that impact the function of non-malignant cells and have on- and off-target toxicities, including cardiotoxicities. Cardiotoxicity is very rare in patients treated with erlotinib, but considerably more common after sunitinib treatment. We hypothesized that the deleterious effects of TKIs on the heart were related to their impact on cardiac metabolism. Methods: Female FVB/N mice (10/group) were treated with therapeutic doses of sunitinib (40 mg/kg), erlotinib (50 mg/kg), or vehicle daily for two weeks. Echocardiographic assessment of the heart in vivo was performed at baseline and on Day 14. Heart, skeletal muscle, liver and serum were flash frozen and prepped for non-targeted GC-MS metabolomics analysis. Results: Compared to vehicle-treated controls, sunitinib-treated mice had significant decreases in systolic function, whereas erlotinib-treated mice did not. Non-targeted metabolomics analysis of heart identified significant decreases in docosahexaenoic acid (DHA), arachidonic acid (AA)/ eicosapentaenoic acid (EPA), O-phosphocolamine, and 6-hydroxynicotinic acid after sunitinib treatment. DHA was significantly decreased in skeletal muscle (quadriceps femoris), while elevated cholesterol was identified in liver and elevated ethanolamine identified in serum. In contrast, erlotinib affected only one metabolite (spermidine significantly increased). Conclusions: Mice treated with sunitinib exhibited systolic dysfunction within two weeks, with significantly lower heart and skeletal muscle levels of long chain omega-3 fatty acids docosahexaenoic acid (DHA), arachidonic acid (AA)/eicosapentaenoic acid (EPA) and increased serum O-phosphocholine phospholipid. This is the first link between sunitinib-induced cardiotoxicity and depletion of the polyunsaturated fatty acids (PUFAs) and inflammatory mediators DHA and AA/EPA in the heart. These compounds have important roles in maintaining mitochondrial function, and their loss may contribute to cardiac dysfunction. Full article

(This article belongs to the Special Issue Clinical Metabolomics)

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