Metabolites

11 pages, 1630 KiB

Open AccessCommunication

Identification of an Epoxide Metabolite of Lycopene in Human Plasma Using ¹³C-Labeling and QTOF-MS

by Morgan J. Cichon, Nancy E. Moran, Ken M. Riedl, Steven J. Schwartz and Steven K. Clinton

Metabolites 2018, 8(1), 24; https://doi.org/10.3390/metabo8010024 - 20 Mar 2018

Cited by 9 | Viewed by 5256

The carotenoid lycopene is a bioactive component of tomatoes and is hypothesized to reduce risk of several chronic diseases, such as prostate cancer. The metabolism of lycopene is only beginning to be understood and some studies suggest that metabolites of lycopene may be [...] Read more.

The carotenoid lycopene is a bioactive component of tomatoes and is hypothesized to reduce risk of several chronic diseases, such as prostate cancer. The metabolism of lycopene is only beginning to be understood and some studies suggest that metabolites of lycopene may be partially responsible for bioactivity associated with the parent compound. The detection and characterization of these compounds in vivo is an important step in understanding lycopene bioactivity. The metabolism of lycopene likely involves both chemical and enzymatic oxidation. While numerous lycopene metabolites have been proposed, few have actually been identified in vivo following lycopene intake. Here, LC-QTOF-MS was used along with ¹³C-labeling to investigate the post-prandial oxidative metabolism of lycopene in human plasma. Previously reported aldehyde cleavage products were not detected, but a lycopene 1,2-epoxide was identified as a new candidate oxidative metabolite. Full article

(This article belongs to the Special Issue Selected Papers from The Inaugural Conference on Food and Nutritional Metabolomics and 14th Annual Ohio Mass Spectrometry Symposium)

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15 pages, 1143 KiB

Open AccessArticle

GC-MS-Based Endometabolome Analysis Differentiates Prostate Cancer from Normal Prostate Cells

by Ana Rita Lima, Ana Margarida Araújo, Joana Pinto, Carmen Jerónimo, Rui Henrique, Maria De Lourdes Bastos, Márcia Carvalho and Paula Guedes de Pinho

Metabolites 2018, 8(1), 23; https://doi.org/10.3390/metabo8010023 - 19 Mar 2018

Cited by 23 | Viewed by 5299

Abstract

Prostate cancer (PCa) is an important health problem worldwide. Diagnosis and management of PCa is very complex because the detection of serum prostate specific antigen (PSA) has several drawbacks. Metabolomics brings promise for cancer biomarker discovery and for better understanding PCa biochemistry. In [...] Read more.

Prostate cancer (PCa) is an important health problem worldwide. Diagnosis and management of PCa is very complex because the detection of serum prostate specific antigen (PSA) has several drawbacks. Metabolomics brings promise for cancer biomarker discovery and for better understanding PCa biochemistry. In this study, a gas chromatography–mass spectrometry (GC-MS) based metabolomic profiling of PCa cell lines was performed. The cell lines include 22RV1 and LNCaP from PCa with androgen receptor (AR) expression, DU145 and PC3 (which lack AR expression), and one normal prostate cell line (PNT2). Regarding the metastatic potential, PC3 is from an adenocarcinoma grade IV with high metastatic potential, DU145 has a moderate metastatic potential, and LNCaP has a low metastatic potential. Using multivariate analysis, alterations in levels of several intracellular metabolites were detected, disclosing the capability of the endometabolome to discriminate all PCa cell lines from the normal prostate cell line. Discriminant metabolites included amino acids, fatty acids, steroids, and sugars. Six stood out for the separation of all the studied PCa cell lines from the normal prostate cell line: ethanolamine, lactic acid, β-Alanine, L-valine, L-leucine, and L-tyrosine. Full article

(This article belongs to the Special Issue Mass Spectrometry-Based Metabolomics and Its Applications)

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16 pages, 2691 KiB

Open AccessReview

An Update on the Metabolic Roles of Carbonic Anhydrases in the Model Alga Chlamydomonas reinhardtii

by Ashok Aspatwar, Susanna Haapanen and Seppo Parkkila

Metabolites 2018, 8(1), 22; https://doi.org/10.3390/metabo8010022 - 13 Mar 2018

Cited by 38 | Viewed by 6130

Abstract

Carbonic anhydrases (CAs) are metalloenzymes that are omnipresent in nature. CAs catalyze the basic reaction of the reversible hydration of CO₂ to HCO₃⁻ and H⁺ in all living organisms. Photosynthetic organisms contain six evolutionarily different classes of CAs, which [...] Read more.

Carbonic anhydrases (CAs) are metalloenzymes that are omnipresent in nature. CAs catalyze the basic reaction of the reversible hydration of CO₂ to HCO₃⁻ and H⁺ in all living organisms. Photosynthetic organisms contain six evolutionarily different classes of CAs, which are namely: α-CAs, β-CAs, γ-CAs, δ-CAs, ζ-CAs, and θ-CAs. Many of the photosynthetic organisms contain multiple isoforms of each CA family. The model alga Chlamydomonas reinhardtii contains 15 CAs belonging to three different CA gene families. Of these 15 CAs, three belong to the α-CA gene family; nine belong to the β-CA gene family; and three belong to the γ-CA gene family. The multiple copies of the CAs in each gene family may be due to gene duplications within the particular CA gene family. The CAs of Chlamydomonas reinhardtii are localized in different subcellular compartments of this unicellular alga. The presence of a large number of CAs and their diverse subcellular localization within a single cell suggests the importance of these enzymes in the metabolic and biochemical roles they perform in this unicellular alga. In the present review, we update the information on the molecular biology of all 15 CAs and their metabolic and biochemical roles in Chlamydomonas reinhardtii. We also present a hypothetical model showing the known functions of CAs and predicting the functions of CAs for which precise metabolic roles are yet to be discovered. Full article

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

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14 pages, 1985 KiB

Open AccessReview

Nanoparticle-Assisted Metabolomics

by Bo Zhang, Mouzhe Xie, Lei Bruschweiler-Li and Rafael Brüschweiler

Metabolites 2018, 8(1), 21; https://doi.org/10.3390/metabo8010021 - 13 Mar 2018

Cited by 16 | Viewed by 6429

Abstract

Understanding and harnessing the interactions between nanoparticles and biological molecules is at the forefront of applications of nanotechnology to modern biology. Metabolomics has emerged as a prominent player in systems biology as a complement to genomics, transcriptomics and proteomics. Its focus is the [...] Read more.

Understanding and harnessing the interactions between nanoparticles and biological molecules is at the forefront of applications of nanotechnology to modern biology. Metabolomics has emerged as a prominent player in systems biology as a complement to genomics, transcriptomics and proteomics. Its focus is the systematic study of metabolite identities and concentration changes in living systems. Despite significant progress over the recent past, important challenges in metabolomics remain, such as the deconvolution of the spectra of complex mixtures with strong overlaps, the sensitive detection of metabolites at low abundance, unambiguous identification of known metabolites, structure determination of unknown metabolites and standardized sample preparation for quantitative comparisons. Recent research has demonstrated that some of these challenges can be substantially alleviated with the help of nanoscience. Nanoparticles in particular have found applications in various areas of bioanalytical chemistry and metabolomics. Their chemical surface properties and increased surface-to-volume ratio endows them with a broad range of binding affinities to biomacromolecules and metabolites. The specific interactions of nanoparticles with metabolites or biomacromolecules help, for example, simplify metabolomics spectra, improve the ionization efficiency for mass spectrometry or reveal relationships between spectral signals that belong to the same molecule. Lessons learned from nanoparticle-assisted metabolomics may also benefit other emerging areas, such as nanotoxicity and nanopharmaceutics. Full article

(This article belongs to the Special Issue Selected Papers from The Inaugural Conference on Food and Nutritional Metabolomics and 14th Annual Ohio Mass Spectrometry Symposium)

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11 pages, 1436 KiB

Open AccessReview

Coordinated Regulation of Metabolic Transporters and Migration/Invasion by Carbonic Anhydrase IX

by Paul C. McDonald, Mridula Swayampakula and Shoukat Dedhar

Metabolites 2018, 8(1), 20; https://doi.org/10.3390/metabo8010020 - 08 Mar 2018

Cited by 41 | Viewed by 4705

Abstract

Hypoxia is a prominent feature of the tumor microenvironment (TME) and cancer cells must dynamically adapt their metabolism to survive in these conditions. A major consequence of metabolic rewiring by cancer cells in hypoxia is the accumulation of acidic metabolites, leading to the [...] Read more.

Hypoxia is a prominent feature of the tumor microenvironment (TME) and cancer cells must dynamically adapt their metabolism to survive in these conditions. A major consequence of metabolic rewiring by cancer cells in hypoxia is the accumulation of acidic metabolites, leading to the perturbation of intracellular pH (pHi) homeostasis and increased acidosis in the TME. To mitigate the potentially detrimental consequences of an increasingly hypoxic and acidic TME, cancer cells employ a network of enzymes and transporters to regulate pH, particularly the extracellular facing carbonic anhydrase IX (CAIX) and CAXII. In addition to the role that these CAs play in the regulation of pH, recent proteome-wide analyses have revealed the presence of a complex CAIX interactome in cancer cells with roles in metabolite transport, tumor cell migration and invasion. Here, we explore the potential contributions of these interactions to the metabolic landscape of tumor cells in hypoxia and discuss the role of CAIX as a hub for the coordinated regulation of metabolic, migratory and invasive processes by cancer cells. We also discuss recent work targeting CAIX activity using highly selective small molecule inhibitors and briefly discuss ongoing clinical trials involving SLC-0111, a lead candidate small molecule inhibitor of CAIX/CAXII. Full article

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

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31 pages, 5013 KiB

Open AccessReview

Carbonic Anhydrases: Role in pH Control and Cancer

by Mam Y. Mboge, Brian P. Mahon, Robert McKenna and Susan C. Frost

Metabolites 2018, 8(1), 19; https://doi.org/10.3390/metabo8010019 - 28 Feb 2018

Cited by 169 | Viewed by 10246

Abstract

The pH of the tumor microenvironment drives the metastatic phenotype and chemotherapeutic resistance of tumors. Understanding the mechanisms underlying this pH-dependent phenomenon will lead to improved drug delivery and allow the identification of new therapeutic targets. This includes an understanding of the role [...] Read more.

The pH of the tumor microenvironment drives the metastatic phenotype and chemotherapeutic resistance of tumors. Understanding the mechanisms underlying this pH-dependent phenomenon will lead to improved drug delivery and allow the identification of new therapeutic targets. This includes an understanding of the role pH plays in primary tumor cells, and the regulatory factors that permit cancer cells to thrive. Over the last decade, carbonic anhydrases (CAs) have been shown to be important mediators of tumor cell pH by modulating the bicarbonate and proton concentrations for cell survival and proliferation. This has prompted an effort to inhibit specific CA isoforms, as an anti-cancer therapeutic strategy. Of the 12 active CA isoforms, two, CA IX and XII, have been considered anti-cancer targets. However, other CA isoforms also show similar activity and tissue distribution in cancers and have not been considered as therapeutic targets for cancer treatment. In this review, we consider all the CA isoforms and their possible role in tumors and their potential as targets for cancer therapy. Full article

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

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28 pages, 9872 KiB

Open AccessArticle

Identifying Biomarkers of Wharton’s Jelly Mesenchymal Stromal Cells Using a Dynamic Metabolic Model: The Cell Passage Effect

by Benoît Laflaquière, Gabrielle Leclercq, Chandarong Choey, Jingkui Chen, Sabine Peres, Caryn Ito and Mario Jolicoeur

Metabolites 2018, 8(1), 18; https://doi.org/10.3390/metabo8010018 - 24 Feb 2018

Cited by 6 | Viewed by 4860

Abstract

Because of their unique ability to modulate the immune system, mesenchymal stromal cells (MSCs) are widely studied to develop cell therapies for detrimental immune and inflammatory disorders. However, controlling the final cell phenotype and determining immunosuppressive function following cell amplification in vitro often [...] Read more.

Because of their unique ability to modulate the immune system, mesenchymal stromal cells (MSCs) are widely studied to develop cell therapies for detrimental immune and inflammatory disorders. However, controlling the final cell phenotype and determining immunosuppressive function following cell amplification in vitro often requires prolonged cell culture assays, all of which contribute to major bottlenecks, limiting the clinical emergence of cell therapies. For instance, the multipotent Wharton’s Jelly mesenchymal stem/stromal cells (WJMSC), extracted from human umbilical cord, exhibit immunosuppressive traits under pro-inflammatory conditions, in the presence of interferon-γ (IFNγ), and tumor necrosis factor-α (TNFα). However, WJMSCs require co-culture bioassays with immune cells, which can take days, to confirm their immunomodulatory function. Therefore, the establishment of robust cell therapies would benefit from fast and reliable characterization assays. To this end, we have explored the metabolic behaviour of WJMSCs in in vitro culture, to identify biomarkers that are specific to the cell passage effect and the loss of their immunosuppressive phenotype. We clearly show distinct metabolic behaviours comparing WJMSCs at the fourth (P4) and the late ninth (P9) passages, although both P4 and P9 cells do not exhibit significant differences in their low immunosuppressive capacity. Metabolomics data were analysed using an in silico modelling platform specifically adapted to WJMSCs. Of interest, P4 cells exhibit a glycolytic metabolism compared to late passage (P9) cells, which show a phosphorylation oxidative metabolism, while P4 cells show a doubling time of 29 h representing almost half of that for P9 cells (46 h). We also clearly show that fourth passage WJMSCs still express known immunosuppressive biomarkers, although, this behaviour shows overlapping with a senescence phenotype. Full article

(This article belongs to the Special Issue Metabolomics Modelling)

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12 pages, 230 KiB

Open AccessReview

Study of the Serum Metabolomic Profile in Nonalcoholic Fatty Liver Disease: Research and Clinical Perspectives

by Stefano Gitto, Filippo Schepis, Pietro Andreone and Erica Villa

Metabolites 2018, 8(1), 17; https://doi.org/10.3390/metabo8010017 - 24 Feb 2018

Cited by 33 | Viewed by 5304

Abstract

In recent years, metabolomics has attracted great scientific attention. The metabolomics methodology might permit a view into transitional phases between healthy liver and nonalcoholic steatohepatitis. Metabolomics can help to analyze the metabolic alterations that play a main role in the progression of nonalcoholic [...] Read more.

In recent years, metabolomics has attracted great scientific attention. The metabolomics methodology might permit a view into transitional phases between healthy liver and nonalcoholic steatohepatitis. Metabolomics can help to analyze the metabolic alterations that play a main role in the progression of nonalcoholic steatohepatitis. Lipid, glucose, amino acid, and bile acid metabolism should be widely studied to understand the complex pathogenesis of nonalcoholic steatohepatitis. The discovery of new biomarkers would be important for diagnosis and staging of liver disease as well as for the assessment of efficacy of new drugs. Here, we review the metabolomics data regarding nonalcoholic fatty liver disease and nonalcoholic steatohepatitis. We analyzed the main studies regarding the application of metabolomics methodology in the complex context of nonalcoholic steatohepatitis, trying to create a bridge from the basic to the clinical aspects. Full article

(This article belongs to the Special Issue Clinical Metabolomics)

15 pages, 2816 KiB

Open AccessArticle

RaMP: A Comprehensive Relational Database of Metabolomics Pathways for Pathway Enrichment Analysis of Genes and Metabolites

by Bofei Zhang, Senyang Hu, Elizabeth Baskin, Andrew Patt, Jalal K. Siddiqui and Ewy A. Mathé

Metabolites 2018, 8(1), 16; https://doi.org/10.3390/metabo8010016 - 22 Feb 2018

Cited by 25 | Viewed by 9574

Abstract

The value of metabolomics in translational research is undeniable, and metabolomics data are increasingly generated in large cohorts. The functional interpretation of disease-associated metabolites though is difficult, and the biological mechanisms that underlie cell type or disease-specific metabolomics profiles are oftentimes unknown. To [...] Read more.

The value of metabolomics in translational research is undeniable, and metabolomics data are increasingly generated in large cohorts. The functional interpretation of disease-associated metabolites though is difficult, and the biological mechanisms that underlie cell type or disease-specific metabolomics profiles are oftentimes unknown. To help fully exploit metabolomics data and to aid in its interpretation, analysis of metabolomics data with other complementary omics data, including transcriptomics, is helpful. To facilitate such analyses at a pathway level, we have developed RaMP (Relational database of Metabolomics Pathways), which combines biological pathways from the Kyoto Encyclopedia of Genes and Genomes (KEGG), Reactome, WikiPathways, and the Human Metabolome DataBase (HMDB). To the best of our knowledge, an off-the-shelf, public database that maps genes and metabolites to biochemical/disease pathways and can readily be integrated into other existing software is currently lacking. For consistent and comprehensive analysis, RaMP enables batch and complex queries (e.g., list all metabolites involved in glycolysis and lung cancer), can readily be integrated into pathway analysis tools, and supports pathway overrepresentation analysis given a list of genes and/or metabolites of interest. For usability, we have developed a RaMP R package (https://github.com/Mathelab/RaMP-DB), including a user-friendly RShiny web application, that supports basic simple and batch queries, pathway overrepresentation analysis given a list of genes or metabolites of interest, and network visualization of gene-metabolite relationships. The package also includes the raw database file (mysql dump), thereby providing a stand-alone downloadable framework for public use and integration with other tools. In addition, the Python code needed to recreate the database on another system is also publicly available (https://github.com/Mathelab/RaMP-BackEnd). Updates for databases in RaMP will be checked multiple times a year and RaMP will be updated accordingly. Full article

(This article belongs to the Special Issue Selected Papers from The Inaugural Conference on Food and Nutritional Metabolomics and 14th Annual Ohio Mass Spectrometry Symposium)

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18 pages, 2677 KiB

Open AccessArticle

Quantification of Stable Isotope Traces Close to Natural Enrichment in Human Plasma Metabolites Using Gas Chromatography-Mass Spectrometry

by Lisa Krämer, Christian Jäger, Jean-Pierre Trezzi, Doris M. Jacobs and Karsten Hiller

Metabolites 2018, 8(1), 15; https://doi.org/10.3390/metabo8010015 - 14 Feb 2018

Cited by 9 | Viewed by 5741

Abstract

Currently, changes in metabolic fluxes following consumption of stable isotope-enriched foods are usually limited to the analysis of postprandial kinetics of glucose. Kinetic information on a larger diversity of metabolites is often lacking, mainly due to the marginal percentage of fully isotopically enriched [...] Read more.

Currently, changes in metabolic fluxes following consumption of stable isotope-enriched foods are usually limited to the analysis of postprandial kinetics of glucose. Kinetic information on a larger diversity of metabolites is often lacking, mainly due to the marginal percentage of fully isotopically enriched plant material in the administered food product, and hence, an even weaker ¹³C enrichment in downstream plasma metabolites. Therefore, we developed an analytical workflow to determine weak ¹³C enrichments of diverse plasma metabolites with conventional gas chromatography-mass spectrometry (GC-MS). The limit of quantification was increased by optimizing (1) the metabolite extraction from plasma, (2) the GC-MS measurement, and (3) most importantly, the computational data processing. We applied our workflow to study the catabolic dynamics of ¹³C-enriched wheat bread in three human subjects. For that purpose, we collected time-resolved human plasma samples at 16 timepoints after the consumption of ¹³C-labeled bread and quantified ¹³C enrichment of 12 metabolites (glucose, lactate, alanine, glycine, serine, citrate, glutamate, glutamine, valine, isoleucine, tyrosine, and threonine). Based on isotopomer specific analysis, we were able to distinguish catabolic profiles of starch and protein hydrolysis. More generally, our study highlights that conventional GC-MS equipment is sufficient to detect isotope traces below 1% if an appropriate data processing is integrated. Full article

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17 pages, 1228 KiB

Open AccessArticle

Untargeted Metabolomics Profiling of an 80.5 km Simulated Treadmill Ultramarathon

by Christopher C. F. Howe, Ahmed Alshehri, David Muggeridge, Alexander B. Mullen, Marie Boyd, Owen Spendiff, Hannah J. Moir and David G. Watson

Metabolites 2018, 8(1), 14; https://doi.org/10.3390/metabo8010014 - 13 Feb 2018

Cited by 33 | Viewed by 6103

Abstract

Metabolomic profiling of nine trained ultramarathon runners completing an 80.5 km self-paced treadmill-based time trial was carried out. Plasma samples were obtained from venous whole blood, collected at rest and on completion of the distance (post-80.5 km). The samples were analyzed by using [...] Read more.

Metabolomic profiling of nine trained ultramarathon runners completing an 80.5 km self-paced treadmill-based time trial was carried out. Plasma samples were obtained from venous whole blood, collected at rest and on completion of the distance (post-80.5 km). The samples were analyzed by using high-resolution mass spectrometry in combination with both hydrophilic interaction (HILIC) and reversed phase (RP) chromatography. The extracted putatively identified features were modeled using Simca P 14.1 software (Umetrics, Umea, Sweden). A large number of amino acids decreased post-80.5 km and fatty acid metabolism was affected with an increase in the formation of medium-chain unsaturated and partially oxidized fatty acids and conjugates of fatty acids with carnitines. A possible explanation for the complex pattern of medium-chain and oxidized fatty acids formed is that the prolonged exercise provoked the proliferation of peroxisomes. The peroxisomes may provide a readily utilizable form of energy through formation of acetyl carnitine and other acyl carnitines for export to mitochondria in the muscles; and secondly may serve to regulate the levels of oxidized metabolites of long-chain fatty acids. This is the first study to provide evidence of the metabolic profile in response to prolonged ultramarathon running using an untargeted approach. The findings provide an insight to the effects of ultramarathon running on the metabolic specificities and alterations that may demonstrate cardio-protective effects. Full article

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18 pages, 2295 KiB

Open AccessReview

Carbonic Anhydrase IX (CAIX), Cancer, and Radiation Responsiveness

by Carol Ward, James Meehan, Mark Gray, Ian H. Kunkler, Simon P. Langdon and David J. Argyle

Metabolites 2018, 8(1), 13; https://doi.org/10.3390/metabo8010013 - 10 Feb 2018

Cited by 55 | Viewed by 8549

Abstract

Carbonic anhydrase IX has been under intensive investigation as a therapeutic target in cancer. Studies demonstrate that this enzyme has a key role in pH regulation in cancer cells, allowing these cells to adapt to the adverse conditions of the tumour microenviroment. Novel [...] Read more.

Carbonic anhydrase IX has been under intensive investigation as a therapeutic target in cancer. Studies demonstrate that this enzyme has a key role in pH regulation in cancer cells, allowing these cells to adapt to the adverse conditions of the tumour microenviroment. Novel CAIX inhibitors have shown efficacy in both in vitro and in vivo pre-clinical cancer models, adversely affecting cell viability, tumour formation, migration, invasion, and metastatic growth when used alone. In co-treatments, CAIX inhibitors may enhance the effects of anti-angiogenic drugs or chemotherapy agents. Research suggests that these inhibitors may also increase the response of tumours to radiotherapy. Although many of the anti-tumour effects of CAIX inhibition may be dependent on its role in pH regulation, recent work has shown that CAIX interacts with several of the signalling pathways involved in the cellular response to radiation, suggesting that pH-independent mechanisms may also be an important basis of its role in tumour progression. Here, we discuss these pH-independent interactions in the context of the ability of CAIX to modulate the responsiveness of cancer to radiation. Full article

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

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13 pages, 1705 KiB

Open AccessArticle

Anaerobic Degradation of Bicyclic Monoterpenes in Castellaniella defragrans

by Edinson Puentes-Cala, Manuel Liebeke, Stephanie Markert and Jens Harder

Metabolites 2018, 8(1), 12; https://doi.org/10.3390/metabo8010012 - 07 Feb 2018

Cited by 2 | Viewed by 4567

Abstract

The microbial degradation pathways of bicyclic monoterpenes contain unknown enzymes for carbon–carbon cleavages. Such enzymes may also be present in the betaproteobacterium Castellaniella defragrans, a model organism to study the anaerobic monoterpene degradation. In this study, a deletion mutant strain missing the [...] Read more.

The microbial degradation pathways of bicyclic monoterpenes contain unknown enzymes for carbon–carbon cleavages. Such enzymes may also be present in the betaproteobacterium Castellaniella defragrans, a model organism to study the anaerobic monoterpene degradation. In this study, a deletion mutant strain missing the first enzyme of the monocyclic monoterpene pathway transformed cometabolically the bicyclics sabinene, 3-carene and α-pinene into several monocyclic monoterpenes and traces of cyclic monoterpene alcohols. Proteomes of cells grown on bicyclic monoterpenes resembled the proteomes of cells grown on monocyclic monoterpenes. Many transposon mutants unable to grow on bicyclic monoterpenes contained inactivated genes of the monocyclic monoterpene pathway. These observations suggest that the monocyclic degradation pathway is used to metabolize bicyclic monoterpenes. The initial step in the degradation is a decyclization (ring-opening) reaction yielding monocyclic monoterpenes, which can be considered as a reverse reaction of the olefin cyclization of polyenes. Full article

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3 pages, 156 KiB

Open AccessEditorial

Metabolomics and Biomarkers for Drug Discovery

by Pollen K. Yeung

Metabolites 2018, 8(1), 11; https://doi.org/10.3390/metabo8010011 - 31 Jan 2018

Cited by 23 | Viewed by 5024

Abstract

Metabolomics and biomarkers are increasingly used in drug discovery and development, and are applied to personalized medicine. Progress in these research areas has increased our understanding of disease pathology and improved therapeutic strategies for many diseases with unmet challenges. Further advances will ultimately [...] Read more.

Metabolomics and biomarkers are increasingly used in drug discovery and development, and are applied to personalized medicine. Progress in these research areas has increased our understanding of disease pathology and improved therapeutic strategies for many diseases with unmet challenges. Further advances will ultimately result in the development of better drugs and breakthrough therapies, which will benefit millions of patients suffering from chronic and life-threatening diseases worldwide. Full article

(This article belongs to the Special Issue Metabolomics and/or Biomarkers for Drug Discovery)

12 pages, 1677 KiB

Open AccessArticle

Global Metabolomics of the Placenta Reveals Distinct Metabolic Profiles between Maternal and Fetal Placental Tissues Following Delivery in Non-Labored Women

by Jacquelyn M. Walejko, Anushka Chelliah, Maureen Keller-Wood, Anthony Gregg and Arthur S. Edison

Metabolites 2018, 8(1), 10; https://doi.org/10.3390/metabo8010010 - 23 Jan 2018

Cited by 35 | Viewed by 6255

Abstract

We evaluated the metabolic alterations in maternal and fetal placental tissues from non-labored women undergoing cesarean section using samples collected from 5 min to 24 h following delivery. Using ¹H-NMR, we identified 14 metabolites that significantly differed between maternal and fetal placental [...] Read more.

We evaluated the metabolic alterations in maternal and fetal placental tissues from non-labored women undergoing cesarean section using samples collected from 5 min to 24 h following delivery. Using ¹H-NMR, we identified 14 metabolites that significantly differed between maternal and fetal placental tissues (FDR-corrected p-value < 0.05), with 12 metabolites elevated in the maternal tissue, reflecting the flux of these metabolites from mother to fetus. In the maternal tissue, 4 metabolites were significantly altered at 15 min, 10 metabolites at 30 min, and 16 metabolites at 1 h postdelivery, while 11 metabolites remained stable over 24 h. In contrast, in the fetal placenta tissue, 1 metabolite was significantly altered at 15 min, 2 metabolites at 30 min, and 4 metabolites at 1 h postdelivery, while 22 metabolites remained stable over 24 h. Our study provides information on the metabolic profiles of maternal and fetal placental tissues delivered by cesarean section and reveals that there are different metabolic alterations in the maternal and fetal tissues of the placenta following delivery. Full article

(This article belongs to the Special Issue Clinical Metabolomics)

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12 pages, 1200 KiB

Open AccessArticle

Enhanced Isotopic Ratio Outlier Analysis (IROA) Peak Detection and Identification with Ultra-High Resolution GC-Orbitrap/MS: Potential Application for Investigation of Model Organism Metabolomes

by Yunping Qiu, Robyn D. Moir, Ian M. Willis, Suresh Seethapathy, Robert C. Biniakewitz and Irwin J. Kurland

Metabolites 2018, 8(1), 9; https://doi.org/10.3390/metabo8010009 - 18 Jan 2018

Cited by 14 | Viewed by 4755

Abstract

Identifying non-annotated peaks may have a significant impact on the understanding of biological systems. In silico methodologies have focused on ESI LC/MS/MS for identifying non-annotated MS peaks. In this study, we employed in silico methodology to develop an Isotopic Ratio Outlier Analysis (IROA) [...] Read more.

Identifying non-annotated peaks may have a significant impact on the understanding of biological systems. In silico methodologies have focused on ESI LC/MS/MS for identifying non-annotated MS peaks. In this study, we employed in silico methodology to develop an Isotopic Ratio Outlier Analysis (IROA) workflow using enhanced mass spectrometric data acquired with the ultra-high resolution GC-Orbitrap/MS to determine the identity of non-annotated metabolites. The higher resolution of the GC-Orbitrap/MS, together with its wide dynamic range, resulted in more IROA peak pairs detected, and increased reliability of chemical formulae generation (CFG). IROA uses two different ¹³C-enriched carbon sources (randomized 95% ¹²C and 95% ¹³C) to produce mirror image isotopologue pairs, whose mass difference reveals the carbon chain length (n), which aids in the identification of endogenous metabolites. Accurate m/z, n, and derivatization information are obtained from our GC/MS workflow for unknown metabolite identification, and aids in silico methodologies for identifying isomeric and non-annotated metabolites. We were able to mine more mass spectral information using the same Saccharomyces cerevisiae growth protocol (Qiu et al. Anal. Chem 2016) with the ultra-high resolution GC-Orbitrap/MS, using 10% ammonia in methane as the CI reagent gas. We identified 244 IROA peaks pairs, which significantly increased IROA detection capability compared with our previous report (126 IROA peak pairs using a GC-TOF/MS machine). For 55 selected metabolites identified from matched IROA CI and EI spectra, using the GC-Orbitrap/MS vs. GC-TOF/MS, the average mass deviation for GC-Orbitrap/MS was 1.48 ppm, however, the average mass deviation was 32.2 ppm for the GC-TOF/MS machine. In summary, the higher resolution and wider dynamic range of the GC-Orbitrap/MS enabled more accurate CFG, and the coupling of accurate mass GC/MS IROA methodology with in silico fragmentation has great potential in unknown metabolite identification, with applications for characterizing model organism networks. Full article

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

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12 pages, 1637 KiB

Open AccessArticle

Structure Elucidation of Unknown Metabolites in Metabolomics by Combined NMR and MS/MS Prediction

by Rene M. Boiteau, David W. Hoyt, Carrie D. Nicora, Hannah A. Kinmonth-Schultz, Joy K. Ward and Kerem Bingol

Metabolites 2018, 8(1), 8; https://doi.org/10.3390/metabo8010008 - 17 Jan 2018

Cited by 58 | Viewed by 9243

Abstract

We introduce a cheminformatics approach that combines highly selective and orthogonal structure elucidation parameters; accurate mass, MS/MS (MS²), and NMR into a single analysis platform to accurately identify unknown metabolites in untargeted studies. The approach starts with an unknown LC-MS feature, [...] Read more.

We introduce a cheminformatics approach that combines highly selective and orthogonal structure elucidation parameters; accurate mass, MS/MS (MS²), and NMR into a single analysis platform to accurately identify unknown metabolites in untargeted studies. The approach starts with an unknown LC-MS feature, and then combines the experimental MS/MS and NMR information of the unknown to effectively filter out the false positive candidate structures based on their predicted MS/MS and NMR spectra. We demonstrate the approach on a model mixture, and then we identify an uncatalogued secondary metabolite in Arabidopsis thaliana. The NMR/MS² approach is well suited to the discovery of new metabolites in plant extracts, microbes, soils, dissolved organic matter, food extracts, biofuels, and biomedical samples, facilitating the identification of metabolites that are not present in experimental NMR and MS metabolomics databases. Full article

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3 pages, 182 KiB

Open AccessEditorial

Acknowledgement to Reviewers of Metabolites in 2017

by Metabolites Editorial Office

Metabolites 2018, 8(1), 7; https://doi.org/10.3390/metabo8010007 - 16 Jan 2018

Viewed by 2445

Abstract

Peer review is an essential part in the publication process, ensuring that Metabolites maintains high quality standards for its published papers [...] Full article

13 pages, 2471 KiB

Open AccessArticle

Impact of Prolonged Blood Incubation and Extended Serum Storage at Room Temperature on the Human Serum Metabolome

by Beate Kamlage, Sebastian Neuber, Bianca Bethan, Sandra González Maldonado, Antje Wagner-Golbs, Erik Peter, Oliver Schmitz and Philipp Schatz

Metabolites 2018, 8(1), 6; https://doi.org/10.3390/metabo8010006 - 13 Jan 2018

Cited by 30 | Viewed by 6559

Abstract

Metabolomics is a powerful technology with broad applications in life science that, like other -omics approaches, requires high-quality samples to achieve reliable results and ensure reproducibility. Therefore, along with quality assurance, methods to assess sample quality regarding pre-analytical confounders are urgently needed. In [...] Read more.

Metabolomics is a powerful technology with broad applications in life science that, like other -omics approaches, requires high-quality samples to achieve reliable results and ensure reproducibility. Therefore, along with quality assurance, methods to assess sample quality regarding pre-analytical confounders are urgently needed. In this study, we analyzed the response of the human serum metabolome to pre-analytical variations comprising prolonged blood incubation and extended serum storage at room temperature by using gas chromatography-mass spectrometry (GC-MS) and liquid chromatography-tandem mass spectrometry (LC-MS/MS) -based metabolomics. We found that the prolonged incubation of blood results in a statistically significant 20% increase and 4% decrease of 225 tested serum metabolites. Extended serum storage affected 21% of the analyzed metabolites (14% increased, 7% decreased). Amino acids and nucleobases showed the highest percentage of changed metabolites in both confounding conditions, whereas lipids were remarkably stable. Interestingly, the amounts of taurine and O-phosphoethanolamine, which have both been discussed as biomarkers for various diseases, were 1.8- and 2.9-fold increased after 6 h of blood incubation. Since we found that both are more stable in ethylenediaminetetraacetic acid (EDTA) blood, EDTA plasma should be the preferred metabolomics matrix. Full article

(This article belongs to the Special Issue Clinical Metabolomics)

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14 pages, 3140 KiB

Open AccessArticle

Calcitriol Supplementation Causes Decreases in Tumorigenic Proteins and Different Proteomic and Metabolomic Signatures in Right versus Left-Sided Colon Cancer

by Monica M. Schroll, Katelyn R. Ludwig, Kerry M. Bauer and Amanda B. Hummon

Metabolites 2018, 8(1), 5; https://doi.org/10.3390/metabo8010005 - 11 Jan 2018

Cited by 8 | Viewed by 5787

Abstract

Vitamin D deficiency is a common problem worldwide. In particular, it is an issue in the Northern Hemisphere where UVB radiation does not penetrate the atmosphere as readily. There is a correlation between vitamin D deficiency and colorectal cancer incidence and mortality. Furthermore, [...] Read more.

Vitamin D deficiency is a common problem worldwide. In particular, it is an issue in the Northern Hemisphere where UVB radiation does not penetrate the atmosphere as readily. There is a correlation between vitamin D deficiency and colorectal cancer incidence and mortality. Furthermore, there is strong evidence that cancer of the ascending (right side) colon is different from cancer of the descending (left side) colon in terms of prognosis, tumor differentiation, and polyp type, as well as at the molecular level. Right-side tumors have elevated Wnt signaling and are more likely to relapse, whereas left-side tumors have reduced expression of tumor suppressor genes. This study seeks to understand both the proteomic and metabolomic changes resulting from treatment of the active metabolite of vitamin D, calcitriol, in right-sided and left-sided colon cancer. Our results show that left-sided colon cancer treated with calcitriol has a substantially greater number of changes in both the proteome and the metabolome than right-sided colon cancer. We found that calcitriol treatment in both right-sided and left-sided colon cancer causes a downregulation of ribosomal protein L37 and protein S100A10. Both of these proteins are heavily involved in tumorigenesis, suggesting a possible mechanism for the correlation between low vitamin D levels and colon cancer. Full article

(This article belongs to the Special Issue Selected Papers from The Inaugural Conference on Food and Nutritional Metabolomics and 14th Annual Ohio Mass Spectrometry Symposium)

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16 pages, 2905 KiB

Open AccessReview

Machine Learning Methods for Analysis of Metabolic Data and Metabolic Pathway Modeling

by Miroslava Cuperlovic-Culf

Metabolites 2018, 8(1), 4; https://doi.org/10.3390/metabo8010004 - 11 Jan 2018

Cited by 113 | Viewed by 12215

Abstract

Machine learning uses experimental data to optimize clustering or classification of samples or features, or to develop, augment or verify models that can be used to predict behavior or properties of systems. It is expected that machine learning will help provide actionable knowledge [...] Read more.

Machine learning uses experimental data to optimize clustering or classification of samples or features, or to develop, augment or verify models that can be used to predict behavior or properties of systems. It is expected that machine learning will help provide actionable knowledge from a variety of big data including metabolomics data, as well as results of metabolism models. A variety of machine learning methods has been applied in bioinformatics and metabolism analyses including self-organizing maps, support vector machines, the kernel machine, Bayesian networks or fuzzy logic. To a lesser extent, machine learning has also been utilized to take advantage of the increasing availability of genomics and metabolomics data for the optimization of metabolic network models and their analysis. In this context, machine learning has aided the development of metabolic networks, the calculation of parameters for stoichiometric and kinetic models, as well as the analysis of major features in the model for the optimal application of bioreactors. Examples of this very interesting, albeit highly complex, application of machine learning for metabolism modeling will be the primary focus of this review presenting several different types of applications for model optimization, parameter determination or system analysis using models, as well as the utilization of several different types of machine learning technologies. Full article

(This article belongs to the Special Issue Metabolomics Modelling)

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17 pages, 1449 KiB

Open AccessArticle

Constraining Genome-Scale Models to Represent the Bow Tie Structure of Metabolism for ¹³C Metabolic Flux Analysis

by Tyler W. H. Backman, David Ando, Jahnavi Singh, Jay D. Keasling and Héctor García Martín

Metabolites 2018, 8(1), 3; https://doi.org/10.3390/metabo8010003 - 04 Jan 2018

Cited by 5 | Viewed by 6503

Abstract

Determination of internal metabolic fluxes is crucial for fundamental and applied biology because they map how carbon and electrons flow through metabolism to enable cell function.

^{13}

C Metabolic Flux Analysis (

^{13}

C MFA) and Two-Scale

^{13}

C Metabolic Flux Analysis (2S- [...] Read more.

Determination of internal metabolic fluxes is crucial for fundamental and applied biology because they map how carbon and electrons flow through metabolism to enable cell function.

^{13}

C Metabolic Flux Analysis (

^{13}

C MFA) and Two-Scale

^{13}

C Metabolic Flux Analysis (2S-

^{13}

C MFA) are two techniques used to determine such fluxes. Both operate on the simplifying approximation that metabolic flux from peripheral metabolism into central “core” carbon metabolism is minimal, and can be omitted when modeling isotopic labeling in core metabolism. The validity of this “two-scale” or “bow tie” approximation is supported both by the ability to accurately model experimental isotopic labeling data, and by experimentally verified metabolic engineering predictions using these methods. However, the boundaries of core metabolism that satisfy this approximation can vary across species, and across cell culture conditions. Here, we present a set of algorithms that (1) systematically calculate flux bounds for any specified “core” of a genome-scale model so as to satisfy the bow tie approximation and (2) automatically identify an updated set of core reactions that can satisfy this approximation more efficiently. First, we leverage linear programming to simultaneously identify the lowest fluxes from peripheral metabolism into core metabolism compatible with the observed growth rate and extracellular metabolite exchange fluxes. Second, we use Simulated Annealing to identify an updated set of core reactions that allow for a minimum of fluxes into core metabolism to satisfy these experimental constraints. Together, these methods accelerate and automate the identification of a biologically reasonable set of core reactions for use with

^{13}

C MFA or 2S-

^{13}

C MFA, as well as provide for a substantially lower set of flux bounds for fluxes into the core as compared with previous methods. We provide an open source Python implementation of these algorithms at https://github.com/JBEI/limitfluxtocore. Full article

(This article belongs to the Special Issue Metabolic Network Models Volume 2)

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Open AccessReview

Rethinking the Combination of Proton Exchanger Inhibitors in Cancer Therapy

by Elisabetta Iessi, Mariantonia Logozzi, Davide Mizzoni, Rossella Di Raimo, Claudiu T. Supuran and Stefano Fais

Metabolites 2018, 8(1), 2; https://doi.org/10.3390/metabo8010002 - 23 Dec 2017

Cited by 52 | Viewed by 6438

Abstract

Microenvironmental acidity is becoming a key target for the new age of cancer treatment. In fact, while cancer is characterized by genetic heterogeneity, extracellular acidity is a common phenotype of almost all cancers. To survive and proliferate under acidic conditions, tumor cells up-regulate [...] Read more.

Microenvironmental acidity is becoming a key target for the new age of cancer treatment. In fact, while cancer is characterized by genetic heterogeneity, extracellular acidity is a common phenotype of almost all cancers. To survive and proliferate under acidic conditions, tumor cells up-regulate proton exchangers and transporters (mainly V-ATPase, Na⁺/H⁺ exchanger (NHE), monocarboxylate transporters (MCTs), and carbonic anhydrases (CAs)), that actively extrude excess protons, avoiding intracellular accumulation of toxic molecules, thus becoming a sort of survival option with many similarities compared with unicellular microorganisms. These systems are also involved in the unresponsiveness or resistance to chemotherapy, leading to the protection of cancer cells from the vast majority of drugs, that when protonated in the acidic tumor microenvironment, do not enter into cancer cells. Indeed, as usually occurs in the progression versus malignancy, resistant tumor clones emerge and proliferate, following a transient initial response to a therapy, thus giving rise to more malignant behavior and rapid tumor progression. Recent studies are supporting the use of a cocktail of proton exchanger inhibitors as a new strategy against cancer. Full article

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

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Open AccessArticle

Methanol Generates Numerous Artifacts during Sample Extraction and Storage of Extracts in Metabolomics Research

by Claudia Sauerschnig, Maria Doppler, Christoph Bueschl and Rainer Schuhmacher

Metabolites 2018, 8(1), 1; https://doi.org/10.3390/metabo8010001 - 22 Dec 2017

Cited by 43 | Viewed by 7171

Abstract

Many metabolomics studies use mixtures of (acidified) methanol and water for sample extraction. In the present study, we investigated if the extraction with methanol can result in artifacts. To this end, wheat leaves were extracted with mixtures of native and deuterium-labeled methanol and [...] Read more.

Many metabolomics studies use mixtures of (acidified) methanol and water for sample extraction. In the present study, we investigated if the extraction with methanol can result in artifacts. To this end, wheat leaves were extracted with mixtures of native and deuterium-labeled methanol and water, with or without 0.1% formic acid. Subsequently, the extracts were analyzed immediately or after storage at 10 °C, −20 °C or −80 °C with an HPLC-HESI-QExactive HF-Orbitrap instrument. Our results showed that 88 (8%) of the >1100 detected compounds were derived from the reaction with methanol and either formed during sample extraction or short-term storage. Artifacts were found for various substance classes such as flavonoids, carotenoids, tetrapyrrols, fatty acids and other carboxylic acids that are typically investigated in metabolomics studies. 58 of 88 artifacts were common between the two tested extraction variants. Remarkably, 34 of 73 (acidified extraction solvent) and 33 of 73 (non-acidified extraction solvent) artifacts were formed de novo as none of these meth(ox)ylated metabolites were found after extraction of native leaf samples with CD₃OH/H₂O. Moreover, sample extracts stored at 10 °C for several days, as can typically be the case during longer measurement sequences, led to an increase in both the number and abundance of methylated artifacts. In contrast, frozen sample extracts were relatively stable during a storage period of one week. Our study shows that caution has to be exercised if methanol is used as the extraction solvent as the detected metabolites might be artifacts rather than natural constituents of the biological system. In addition, we recommend storing sample extracts in deep freezers immediately after extraction until measurement. Full article

(This article belongs to the Special Issue Isotope Guided Metabolomics and Flux Analysis)

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Metabolites, Volume 8, Issue 1 (March 2018) – 24 articles

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