Metabolites

14 pages, 782 KB

Open AccessEditor’s ChoiceReview

Mitochondrial Lipid Signaling and Adaptive Thermogenesis

by Helaina Von Bank, Mae Hurtado-Thiele, Nanami Oshimura and Judith Simcox

Metabolites 2021, 11(2), 124; https://doi.org/10.3390/metabo11020124 - 22 Feb 2021

Cited by 26 | Viewed by 5206

Thermogenesis is an energy demanding process by which endotherms produce heat to maintain their body temperature in response to cold exposure. Mitochondria in the brown and beige adipocytes play a key role in thermogenesis, as the site for uncoupling protein 1 (UCP1), which [...] Read more.

Thermogenesis is an energy demanding process by which endotherms produce heat to maintain their body temperature in response to cold exposure. Mitochondria in the brown and beige adipocytes play a key role in thermogenesis, as the site for uncoupling protein 1 (UCP1), which allows for the diffusion of protons through the mitochondrial inner membrane to produce heat. To support this energy demanding process, the mitochondria in brown and beige adipocytes increase oxidation of glucose, amino acids, and lipids. This review article explores the various mitochondria-produced and processed lipids that regulate thermogenesis including cardiolipins, free fatty acids, and acylcarnitines. These lipids play a number of roles in thermogenic adipose tissue including structural support of UCP1, transcriptional regulation, fuel source, and activation of cell signaling cascades. Full article

(This article belongs to the Special Issue Mitochondrial Metabolism and Bioenergetics)

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15 pages, 1748 KB

Open AccessEditor’s ChoiceArticle

Untargeted and Targeted Metabolomic Profiling of Preterm Newborns with EarlyOnset Sepsis: A Case-Control Study

by Veronica Mardegan, Giuseppe Giordano, Matteo Stocchero, Paola Pirillo, Gabriele Poloniato, Enrica Donadel, Sabrina Salvadori, Carlo Giaquinto, Elena Priante and Eugenio Baraldi

Metabolites 2021, 11(2), 115; https://doi.org/10.3390/metabo11020115 - 18 Feb 2021

Cited by 22 | Viewed by 3599

Abstract

Sepsis is a major concern in neonatology, but there are no reliable biomarkers for its early diagnosis. The aim of the study was to compare the metabolic profiles of plasma and urine samples collected at birth from preterm neonates with and without earlyonset [...] Read more.

Sepsis is a major concern in neonatology, but there are no reliable biomarkers for its early diagnosis. The aim of the study was to compare the metabolic profiles of plasma and urine samples collected at birth from preterm neonates with and without earlyonset sepsis (EOS) to identify metabolic perturbations that might orient the search for new early biomarkers. All preterm newborns admitted to the neonatal intensive care unit were eligible for this proof-of-concept, prospective case-control study. Infants were enrolled as “cases” if they developed EOS, and as “controls”if they did not. Plasma samples collected at birth and urine samples collected within 24 h of birth underwent untargeted and targeted metabolomic analysis using mass spectrometry coupled with ultra-performance liquid chromatography. Univariate and multivariate statistical analyses were applied. Of 123 eligible newborns, 15 developed EOS. These 15 newborns matched controls for gestational age and weight. Metabolomic analysis revealed evident clustering of the cases versus controls, with the glutathione and tryptophan metabolic pathways markedly disrupted in the former. In conclusion, neonates with EOS had a metabolic profile at birth that clearly distinguished them from those without sepsis, and metabolites of glutathione and tryptophan pathways are promising as new biomarkers of neonatal sepsis. Full article

(This article belongs to the Section Endocrinology and Clinical Metabolic Research)

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27 pages, 2779 KB

Open AccessEditor’s ChoiceReview

Transporters at the Interface between Cytosolic and Mitochondrial Amino Acid Metabolism

by Keeley G. Hewton, Amritpal S. Johal and Seth J. Parker

Metabolites 2021, 11(2), 112; https://doi.org/10.3390/metabo11020112 - 16 Feb 2021

Cited by 30 | Viewed by 11495

Abstract

Mitochondria are central organelles that coordinate a vast array of metabolic and biologic functions important for cellular health. Amino acids are intricately linked to the bioenergetic, biosynthetic, and homeostatic function of the mitochondrion and require specific transporters to facilitate their import, export, and [...] Read more.

Mitochondria are central organelles that coordinate a vast array of metabolic and biologic functions important for cellular health. Amino acids are intricately linked to the bioenergetic, biosynthetic, and homeostatic function of the mitochondrion and require specific transporters to facilitate their import, export, and exchange across the inner mitochondrial membrane. Here we review key cellular metabolic outputs of eukaryotic mitochondrial amino acid metabolism and discuss both known and unknown transporters involved. Furthermore, we discuss how utilization of compartmentalized amino acid metabolism functions in disease and physiological contexts. We examine how improved methods to study mitochondrial metabolism, define organelle metabolite composition, and visualize cellular gradients allow for a more comprehensive understanding of how transporters facilitate compartmentalized metabolism. Full article

(This article belongs to the Special Issue Mitochondrial Metabolism and Bioenergetics)

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13 pages, 4376 KB

Open AccessFeature PaperEditor’s ChoiceArticle

A Metabolomics Analysis of Postmenopausal Breast Cancer Risk in the Cancer Prevention Study II

by Steven C. Moore, Kaitlyn M. Mazzilli, Joshua N. Sampson, Charles E. Matthews, Brian D. Carter, Mary C. Playdon, Ying Wang and Victoria L. Stevens

Metabolites 2021, 11(2), 95; https://doi.org/10.3390/metabo11020095 - 10 Feb 2021

Cited by 27 | Viewed by 6123

Abstract

Breast cancer is the most common cancer in women, but its incidence can only be partially explained through established risk factors. Our aim was to use metabolomics to identify novel risk factors for breast cancer and to validate recently reported metabolite-breast cancer findings. [...] Read more.

Breast cancer is the most common cancer in women, but its incidence can only be partially explained through established risk factors. Our aim was to use metabolomics to identify novel risk factors for breast cancer and to validate recently reported metabolite-breast cancer findings. We measured levels of 1275 metabolites in prediagnostic serum in a nested case-control study of 782 postmenopausal breast cancer cases and 782 matched controls. Metabolomics analysis was performed by Metabolon Inc using ultra-performance liquid chromatography and a Q-Exactive high resolution/accurate mass spectrometer. Controls were matched by birth date, date of blood draw, and race/ethnicity. Odds ratios (ORs) and 95% confidence intervals (CIs) of breast cancer at the 90th versus 10th percentile (modeled on a continuous basis) of metabolite levels were estimated using conditional logistic regression, with adjustment for age. Twenty-four metabolites were significantly associated with breast cancer risk at a false discovery rate <0.20. For the nine metabolites positively associated with risk, the ORs ranged from 1.75 (95% CI: 1.29–2.36) to 1.45 (95% CI: 1.13–1.85), and for the 15 metabolites inversely associated with risk, ORs ranged from 0.59 (95% CI: 0.43–0.79) to 0.69 (95% CI: 0.55–0.87). These metabolites largely comprised carnitines, glycerolipids, and sex steroid metabolites. Associations for three sex steroid metabolites validated findings from recent studies and the remainder were novel. These findings contribute to growing data on metabolite-breast cancer associations by confirming prior findings and identifying novel leads for future validation efforts. Full article

(This article belongs to the Special Issue Metabolomics Meets Epidemiology)

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17 pages, 1786 KB

Open AccessEditor’s ChoiceReview

Carotenoids and Some Other Pigments from Fungi and Yeasts

by Alexander Rapoport, Irina Guzhova, Lorenzo Bernetti, Pietro Buzzini, Marek Kieliszek and Anna Maria Kot

Metabolites 2021, 11(2), 92; https://doi.org/10.3390/metabo11020092 - 6 Feb 2021

Cited by 95 | Viewed by 7825

Abstract

Carotenoids are an essential group of compounds that may be obtained by microbiological synthesis. They are instrumental in various areas of industry, medicine, agriculture, and ecology. The increase of carotenoids’ demand at the global market is now essential. At the moment, the production [...] Read more.

Carotenoids are an essential group of compounds that may be obtained by microbiological synthesis. They are instrumental in various areas of industry, medicine, agriculture, and ecology. The increase of carotenoids’ demand at the global market is now essential. At the moment, the production of natural carotenoids is more expensive than obtaining their synthetic forms, but several new approaches/directions on how to decrease this difference were developed during the last decades. This review briefly describes the information accumulated until now about the beneficial effects of carotenoids on human health protection, their possible application in the treatments of various diseases, and their use in the food and feed industry. This review also describes some issues that are linked with biotechnological production of fungal and yeasts carotenoids, as well as new approaches/directions to make their biotechnological production more efficient. Full article

(This article belongs to the Special Issue Metabolites Produced by Yeast Cells)

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12 pages, 1595 KB

Open AccessEditor’s ChoiceArticle

The Increased Expression of Regulator of G-Protein Signaling 2 (RGS2) Inhibits Insulin-Induced Akt Phosphorylation and Is Associated with Uncontrolled Glycemia in Patients with Type 2 Diabetes

by J. Gustavo Vazquez-Jimenez, M. Stephanie Corpus-Navarro, J. Miguel Rodriguez-Chavez, Hiram J. Jaramillo-Ramirez, Judith Hernandez-Aranda, Octavio Galindo-Hernandez, J. Rene Machado-Contreras, Marina Trejo-Trejo, Agustin Guerrero-Hernandez and J. Alberto Olivares-Reyes

Metabolites 2021, 11(2), 91; https://doi.org/10.3390/metabo11020091 - 5 Feb 2021

Cited by 8 | Viewed by 3650

Abstract

Experimental evidence in mice models has demonstrated that a high regulator of G-protein signaling 2 (RSG2) protein levels precede an insulin resistance state. In the same context, a diet rich in saturated fatty acids induces an increase in RGS2 protein expression, which has [...] Read more.

Experimental evidence in mice models has demonstrated that a high regulator of G-protein signaling 2 (RSG2) protein levels precede an insulin resistance state. In the same context, a diet rich in saturated fatty acids induces an increase in RGS2 protein expression, which has been associated with decreased basal metabolism in mice; however, the above has not yet been analyzed in humans. For this reason, in the present study, we examined the association between RGS2 expression and insulin resistance state. The incubation with palmitic acid (PA), which inhibits insulin-mediated Akt Ser⁴⁷³ phosphorylation, resulted in the increased RGS2 expression in human umbilical vein endothelial-CS (HUVEC-CS) cells. The RGS2 overexpression without PA was enough to inhibit insulin-mediated Akt Ser⁴⁷³ phosphorylation in HUVEC-CS cells. Remarkably, the platelet RGS2 expression levels were higher in type 2 diabetes mellitus (T2DM) patients than in healthy donors. Moreover, an unbiased principal component analysis (PCA) revealed that RGS2 expression level positively correlated with glycated hemoglobin (HbA1c) and negatively with age and high-density lipoprotein cholesterol (HDL) in T2DM patients. Furthermore, PCA showed that healthy subjects segregated from T2DM patients by having lower levels of HbA1c and RGS2. These results demonstrate that RGS2 overexpression leads to decreased insulin signaling in a human endothelial cell line and is associated with poorly controlled diabetes. Full article

(This article belongs to the Collection Insulin Resistance in the 2020's)

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8 pages, 224 KB

Open AccessEditor’s ChoiceArticle

Vitamin D Metabolites and Sex Steroid Indices in Postmenopausal Women with and without Low Bone Mass

by Nasser M. Al-Daghri, Sobhy M. Yakout, Mohammed G.A. Ansari, Syed D. Hussain, Kaiser A. Wani and Shaun Sabico

Metabolites 2021, 11(2), 86; https://doi.org/10.3390/metabo11020086 - 1 Feb 2021

Cited by 7 | Viewed by 2789

Abstract

While the independent roles of vitamin D and sex hormones in skeletal health are well established, the associations of vitamin D and its metabolites to sex hormones and their indices are less investigated. In this observational study, clinical information of 189 Saudi postmenopausal [...] Read more.

While the independent roles of vitamin D and sex hormones in skeletal health are well established, the associations of vitamin D and its metabolites to sex hormones and their indices are less investigated. In this observational study, clinical information of 189 Saudi postmenopausal women aged ≥50 years old [N = 80 with normal bone mineral density (BMD), aged 53.3 ± 7.7 years with body mass index (BMI)= 34.1kg/m² ± 5.8, and N = 109 with low BMD (T-score −1.0 to −2.5), aged 57.0 ± 8.2 years, BMI = 32.4kg/m² ± 6.2] was extracted from an existing capital-wide osteoporosis registry in Riyadh, Saudi Arabia. Data included were BMD scores, serum total 25(OH)D, sex hormones, and bone turnover markers which were measured using commercially available assays. Age- and BMI-adjusted comparisons revealed significantly higher parathyroid hormone (PTH) levels as well as significantly lower testosterone and bioavailable testosterone in the low BMD group than the normal BMD group (p-values 0.04, 0.02, and 0.03, respectively). Stepwise linear regression showed that circulating testosterone levels accounted for 9.7% and 8.9% of the variances perceived in bioavailable 25(OH)D and free 25(OH)D, respectively (p < 0.01), independent of other sex hormones, sex hormone indices, and bone turnover markers. Our study suggests that androgens are significantly associated with non-conventional vitamin D metabolites and these associations may have clinical relevance in assessing risk for low BMD and osteoporosis in Arab postmenopausal women. Full article

(This article belongs to the Special Issue Vitamin D and Bone Metabolism)

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16 pages, 2591 KB

Open AccessEditor’s ChoiceArticle

Reproducibility of Baseline Tumour Metabolic Volume Measurements in Diffuse Large B-Cell Lymphoma: Is There a Superior Method?

by Florian Eude, Mathieu Nessim Toledano, Pierre Vera, Hervé Tilly, Sorina-Dana Mihailescu and Stéphanie Becker

Metabolites 2021, 11(2), 72; https://doi.org/10.3390/metabo11020072 - 26 Jan 2021

Cited by 23 | Viewed by 2939

Abstract

The metabolic tumour volume (MTV) is an independent prognostic indicator in diffuse large B-cell lymphoma (DLBCL). However, its measurement is not standardised and is subject to wide variations depending on the method used. This study aimed to compare the reproducibility of MTV measurement [...] Read more.

The metabolic tumour volume (MTV) is an independent prognostic indicator in diffuse large B-cell lymphoma (DLBCL). However, its measurement is not standardised and is subject to wide variations depending on the method used. This study aimed to compare the reproducibility of MTV measurement as well as the thresholds obtained for each method and their prognostic values. The baseline MTV was measured in 239 consecutive patients treated at Henri Becquerel Centre by two blinded evaluators. Eight methods were compared: 3 absolute (SUV (standardised uptake value) ≥ 2.5; SUV≥ liver SUVmax; SUV≥ PERCIST SUV), 1 percentage SUV threshold method (SUV ≥ 41% SUVmax) and 4 adaptive methods (Daisne, Nestle, Fitting, Black). The intraclass correlation coefficients were excellent, from 0.91 to 0.96, for the absolute SUV methods, Black and Nestle methods, and good for 41% SUVmax, Fitting and Daisne methods (0.82 to 0.88), with a significantly lower variability with absolute methods compared to 41% SUVmax (p < 0.04). Thresholds were found to be specific to each segmentation method and ranged from 295 to 552 cm³. There was a strong correlation between the MTV and patient prognosis regardless of the segmentation method used (p = 0.001 for PFS and OS). The largest inter-observer cut-off variability was observed in the 41% SUVmax method, which resulted in more inter-observer disagreements in the classification of patients between high and low MTV groups. MTV measurements based on absolute SUV criteria were found to be significantly more reproducible than those based on 41% SUVmax criteria. The threshold was specific for each of eight segmentation methods, but all predicted prognosis. Full article

(This article belongs to the Special Issue Metabolic Volume Measurements)

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12 pages, 1560 KB

Open AccessEditor’s ChoiceArticle

Maternal Metabolome in Pregnancy and Childhood Asthma or Recurrent Wheeze in the Vitamin D Antenatal Asthma Reduction Trial

by Mengna Huang, Rachel S. Kelly, Su H. Chu, Priyadarshini Kachroo, Gözde Gürdeniz, Bo L. Chawes, Hans Bisgaard, Scott T. Weiss and Jessica Lasky-Su

Metabolites 2021, 11(2), 65; https://doi.org/10.3390/metabo11020065 - 23 Jan 2021

Cited by 21 | Viewed by 3384

Abstract

The in utero environment during pregnancy has important implications for the developing health of the child. We aim to examine the potential impact of maternal metabolome at two different timepoints in pregnancy on offspring respiratory health in early life. In 685 mother-child pairs [...] Read more.

The in utero environment during pregnancy has important implications for the developing health of the child. We aim to examine the potential impact of maternal metabolome at two different timepoints in pregnancy on offspring respiratory health in early life. In 685 mother-child pairs from the Vitamin D Antenatal Asthma Reduction Trial, we assessed the prospective associations between maternal metabolites at both baseline (10–18 weeks gestation) and third trimester (32–38 weeks gestation) and the risk of child asthma or recurrent wheeze by age three using logistic regression models accounting for confounding factors. Subgroup analyses were performed by child sex. Among 632 metabolites, 19 (3.0%) and 62 (9.8%) from baseline and third trimester, respectively, were associated with the outcome (p-value < 0.05). Coffee-related metabolites in the maternal metabolome appeared to be of particular importance. Caffeine, theophylline, trigonelline, quinate, and 3-hydroxypyridine sulfate were inversely associated with asthma risk at a minimum of one timepoint. Additional observations also highlight the roles of steroid and sphingolipid metabolites. Overall, there was a stronger relationship between the metabolome in later pregnancy and offspring asthma risk. Our results suggest that alterations in prenatal metabolites may act as drivers of the development of offspring asthma. Full article

(This article belongs to the Section Endocrinology and Clinical Metabolic Research)

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20 pages, 2865 KB

Open AccessEditor’s ChoiceArticle

Metabotypes of Pseudomonas aeruginosa Correlate with Antibiotic Resistance, Virulence and Clinical Outcome in Cystic Fibrosis Chronic Infections

by Oriane Moyne, Florence Castelli, Dominique J. Bicout, Julien Boccard, Boubou Camara, Benoit Cournoyer, Eric Faudry, Samuel Terrier, Dalil Hannani, Sarah Huot-Marchand, Claire Léger, Max Maurin, Tuan-Dung Ngo, Caroline Plazy, Robert A. Quinn, Ina Attree, François Fenaille, Bertrand Toussaint and Audrey Le Gouëllec

Metabolites 2021, 11(2), 63; https://doi.org/10.3390/metabo11020063 - 21 Jan 2021

Cited by 21 | Viewed by 5032

Abstract

Pseudomonas aeruginosa (P.a) is one of the most critical antibiotic resistant bacteria in the world and is the most prevalent pathogen in cystic fibrosis (CF), causing chronic lung infections that are considered one of the major causes of mortality in CF [...] Read more.

Pseudomonas aeruginosa (P.a) is one of the most critical antibiotic resistant bacteria in the world and is the most prevalent pathogen in cystic fibrosis (CF), causing chronic lung infections that are considered one of the major causes of mortality in CF patients. Although several studies have contributed to understanding P.a within-host adaptive evolution at a genomic level, it is still difficult to establish direct relationships between the observed mutations, expression of clinically relevant phenotypes, and clinical outcomes. Here, we performed a comparative untargeted LC/HRMS-based metabolomics analysis of sequential isolates from chronically infected CF patients to obtain a functional view of P.a adaptation. Metabolic profiles were integrated with expression of bacterial phenotypes and clinical measurements following multiscale analysis methods. Our results highlighted significant associations between P.a “metabotypes”, expression of antibiotic resistance and virulence phenotypes, and frequency of clinical exacerbations, thus identifying promising biomarkers and therapeutic targets for difficult-to-treat P.a infections Full article

(This article belongs to the Section Endocrinology and Clinical Metabolic Research)

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17 pages, 3170 KB

Open AccessEditor’s ChoiceArticle

Systemic Metabolic Alterations Correlate with Islet-Level Prostaglandin E₂ Production and Signaling Mechanisms That Predict β-Cell Dysfunction in a Mouse Model of Type 2 Diabetes

by Michael D. Schaid, Yanlong Zhu, Nicole E. Richardson, Chinmai Patibandla, Irene M. Ong, Rachel J. Fenske, Joshua C. Neuman, Erin Guthery, Austin Reuter, Harpreet K. Sandhu, Miles H. Fuller, Elizabeth D. Cox, Dawn B. Davis, Brian T. Layden, Allan R. Brasier, Dudley W. Lamming, Ying Ge and Michelle E. Kimple

Metabolites 2021, 11(1), 58; https://doi.org/10.3390/metabo11010058 - 16 Jan 2021

Cited by 19 | Viewed by 5469

Abstract

The transition from β-cell compensation to β-cell failure is not well understood. Previous works by our group and others have demonstrated a role for Prostaglandin EP3 receptor (EP3), encoded by the Ptger3 gene, in the loss of functional β-cell mass in Type 2 [...] Read more.

The transition from β-cell compensation to β-cell failure is not well understood. Previous works by our group and others have demonstrated a role for Prostaglandin EP3 receptor (EP3), encoded by the Ptger3 gene, in the loss of functional β-cell mass in Type 2 diabetes (T2D). The primary endogenous EP3 ligand is the arachidonic acid metabolite prostaglandin E₂ (PGE₂). Expression of the pancreatic islet EP3 and PGE₂ synthetic enzymes and/or PGE₂ excretion itself have all been shown to be upregulated in primary mouse and human islets isolated from animals or human organ donors with established T2D compared to nondiabetic controls. In this study, we took advantage of a rare and fleeting phenotype in which a subset of Black and Tan BRachyury (BTBR) mice homozygous for the Leptin^ob/ob mutation—a strong genetic model of T2D—were entirely protected from fasting hyperglycemia even with equal obesity and insulin resistance as their hyperglycemic littermates. Utilizing this model, we found numerous alterations in full-body metabolic parameters in T2D-protected mice (e.g., gut microbiome composition, circulating pancreatic and incretin hormones, and markers of systemic inflammation) that correlate with improvements in EP3-mediated β-cell dysfunction. Full article

(This article belongs to the Special Issue Islet Inflammation and Metabolic Homeostasis)

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21 pages, 1982 KB

Open AccessEditor’s ChoiceReview

L-Carnitine and Acylcarnitines: Mitochondrial Biomarkers for Precision Medicine

by Marc R. McCann, Mery Vet George De la Rosa, Gus R. Rosania and Kathleen A. Stringer

Metabolites 2021, 11(1), 51; https://doi.org/10.3390/metabo11010051 - 14 Jan 2021

Cited by 216 | Viewed by 15046

Abstract

Biomarker discovery and implementation are at the forefront of the precision medicine movement. Modern advances in the field of metabolomics afford the opportunity to readily identify new metabolite biomarkers across a wide array of disciplines. Many of the metabolites are derived from or [...] Read more.

Biomarker discovery and implementation are at the forefront of the precision medicine movement. Modern advances in the field of metabolomics afford the opportunity to readily identify new metabolite biomarkers across a wide array of disciplines. Many of the metabolites are derived from or directly reflective of mitochondrial metabolism. L-carnitine and acylcarnitines are established mitochondrial biomarkers used to screen neonates for a series of genetic disorders affecting fatty acid oxidation, known as the inborn errors of metabolism. However, L-carnitine and acylcarnitines are not routinely measured beyond this screening, despite the growing evidence that shows their clinical utility outside of these disorders. Measurements of the carnitine pool have been used to identify the disease and prognosticate mortality among disorders such as diabetes, sepsis, cancer, and heart failure, as well as identify subjects experiencing adverse drug reactions from various medications like valproic acid, clofazimine, zidovudine, cisplatin, propofol, and cyclosporine. The aim of this review is to collect and interpret the literature evidence supporting the clinical biomarker application of L-carnitine and acylcarnitines. Further study of these metabolites could ultimately provide mechanistic insights that guide therapeutic decisions and elucidate new pharmacologic targets. Full article

(This article belongs to the Special Issue Mitochondrial Metabolism and Bioenergetics)

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15 pages, 4989 KB

Open AccessEditor’s ChoiceArticle

Tissue-Specific ¹H-NMR Metabolomic Profiling in Mice with Adenine-Induced Chronic Kidney Disease

by Ram B. Khattri, Trace Thome and Terence E. Ryan

Metabolites 2021, 11(1), 45; https://doi.org/10.3390/metabo11010045 - 10 Jan 2021

Cited by 33 | Viewed by 5813

Abstract

Chronic kidney disease (CKD) results in the impaired filtration of metabolites, which may be toxic or harmful to organs/tissues. The objective of this study was to perform unbiased ¹H nuclear magnetic resonance (NMR)-based metabolomics profiling of tissues from mice with CKD. Five-month-old [...] Read more.

Chronic kidney disease (CKD) results in the impaired filtration of metabolites, which may be toxic or harmful to organs/tissues. The objective of this study was to perform unbiased ¹H nuclear magnetic resonance (NMR)-based metabolomics profiling of tissues from mice with CKD. Five-month-old male C57BL6J mice were placed on either a casein control diet or adenine-supplemented diet to induce CKD for 24 weeks. CKD was confirmed by significant increases in blood urea nitrogen (24.1 ± 7.7 vs. 105.3 ± 18.3 mg/dL, p < 0.0001) in adenine-fed mice. Following this chronic adenine diet, the kidney, heart, liver, and quadriceps muscles were rapidly dissected; snap-frozen in liquid nitrogen; and the metabolites were extracted. Metabolomic profiling coupled with multivariate analyses confirm clear separation in both aqueous and organic phases between control and CKD mice. Severe energetic stress and apparent impaired mitochondrial metabolism were observed in CKD kidneys evidenced by the depletion of ATP and NAD⁺, along with significant alterations in tricarboxylic acid (TCA) cycle intermediates. Altered amino acid metabolism was observed in all tissues, although significant differences in specific amino acids varied across tissue types. Taken together, this study provides a metabolomics fingerprint of multiple tissues from mice with and without severe CKD induced by chronic adenine feeding. Full article

(This article belongs to the Special Issue Metabolomics in Kidney Disease)

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11 pages, 1374 KB

Open AccessEditor’s ChoiceArticle

Amino Acid and Acylcarnitine Levels in Chronic Patients with Schizophrenia: A Preliminary Study

by Irina A. Mednova, Alexander A. Chernonosov, Marat F. Kasakin, Elena G. Kornetova, Arkadiy V. Semke, Nikolay A. Bokhan, Vladimir V. Koval and Svetlana A. Ivanova

Metabolites 2021, 11(1), 34; https://doi.org/10.3390/metabo11010034 - 5 Jan 2021

Cited by 18 | Viewed by 3520

Abstract

Amino acids and acylcarnitines play an important role as substrates and intermediate products in most of pathways involved in schizophrenia development such as mitochondrial dysfunction, inflammation, lipid oxidation, DNA damage, oxidative stress, and apoptosis. It seems relevant to use an integrated approach with [...] Read more.

Amino acids and acylcarnitines play an important role as substrates and intermediate products in most of pathways involved in schizophrenia development such as mitochondrial dysfunction, inflammation, lipid oxidation, DNA damage, oxidative stress, and apoptosis. It seems relevant to use an integrated approach with ‘omics’ technology to study their contribution. The aim of our study was to investigate serum amino acid and acylcarnitine levels in antipsychotics-treated patients with chronic schizophrenia compared with healthy donors. We measured serum levels of 15 amino acids and 30 acylcarnitines in 37 patients with schizophrenia and 36 healthy donors by means of tandem mass spectrometry. In summary, patients with chronic schizophrenia had an altered concentration of a few amino acids and acylcarnitines in comparison to the healthy probands. Further research is needed to assess and understand the identified changes. Full article

(This article belongs to the Section Endocrinology and Clinical Metabolic Research)

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11 pages, 689 KB

Open AccessEditor’s ChoiceReview

Sodium-Glucose Cotransporter-2 Inhibitors for Treatment of Nonalcoholic Fatty Liver Disease: A Meta-Analysis of Randomized Controlled Trials

by Alessandro Mantovani, Graziana Petracca, Alessandro Csermely, Giorgia Beatrice and Giovanni Targher

Metabolites 2021, 11(1), 22; https://doi.org/10.3390/metabo11010022 - 30 Dec 2020

Cited by 106 | Viewed by 5735

Abstract

Recent randomized controlled trials (RCTs) tested the efficacy of sodium-glucose cotransporter-2 (SGLT-2) inhibitors to specifically treat nonalcoholic fatty liver disease (NAFLD). We systematically searched three electronic databases (up to 31 October 2020) for identifying placebo-controlled or head-to-head RCTs that used SGLT-2 inhibitors for [...] Read more.

Recent randomized controlled trials (RCTs) tested the efficacy of sodium-glucose cotransporter-2 (SGLT-2) inhibitors to specifically treat nonalcoholic fatty liver disease (NAFLD). We systematically searched three electronic databases (up to 31 October 2020) for identifying placebo-controlled or head-to-head RCTs that used SGLT-2 inhibitors for treatment of NAFLD. No published RCTs with paired liver biopsy data were available for the meta-analysis. Primary outcome measures were changes in serum liver enzyme levels and liver fat content on imaging techniques. Overall, we included a total of twelve RCTs testing the efficacy of dapagliflozin (n = six RCTs), empagliflozin (n = three RCTs), ipragliflozin (n = two RCTs) or canagliflozin (n = one RCT) to specifically treat NAFLD for a median period of 24 weeks with aggregate data on 850 middle-aged overweight or obese individuals with NAFLD (90% with type 2 diabetes). Compared to placebo/reference therapy, treatment with SGLT-2 inhibitors significantly decreased serum alanine aminotransferase (weighted mean differences (WMD): −10.0 IU/L, 95%CI −12.2 to −7.79 IU/L; I² = 10.5%) and gamma-glutamyltransferase levels (WMD: −14.49 IU/L, 95%CI −19.35 to −9.63 IU/L, I² = 38.7%), as well as the absolute percentage of liver fat content on magnetic resonance-based techniques (WMD: −2.05%, 95%CI −2.61 to −1.48%; I² = 0%). In conclusion, SGLT-2 inhibitors seem to be a promising treatment option for NAFLD. Full article

(This article belongs to the Special Issue Nonalcoholic Fatty Liver Disease (NAFLD), the Metabolic Disorder Par Excellence)

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26 pages, 1584 KB

Open AccessEditor’s ChoiceReview

Biostimulants for Plant Growth and Mitigation of Abiotic Stresses: A Metabolomics Perspective

by Lerato Nephali, Lizelle A. Piater, Ian A. Dubery, Veronica Patterson, Johan Huyser, Karl Burgess and Fidele Tugizimana

Metabolites 2020, 10(12), 505; https://doi.org/10.3390/metabo10120505 - 10 Dec 2020

Cited by 179 | Viewed by 17001

Abstract

Adverse environmental conditions due to climate change, combined with declining soil fertility, threaten food security. Modern agriculture is facing a pressing situation where novel strategies must be developed for sustainable food production and security. Biostimulants, conceptually defined as non-nutrient substances or microorganisms with [...] Read more.

Adverse environmental conditions due to climate change, combined with declining soil fertility, threaten food security. Modern agriculture is facing a pressing situation where novel strategies must be developed for sustainable food production and security. Biostimulants, conceptually defined as non-nutrient substances or microorganisms with the ability to promote plant growth and health, represent the potential to provide sustainable and economically favorable solutions that could introduce novel approaches to improve agricultural practices and crop productivity. Current knowledge and phenotypic observations suggest that biostimulants potentially function in regulating and modifying physiological processes in plants to promote growth, alleviate stresses, and improve quality and yield. However, to successfully develop novel biostimulant-based formulations and programs, understanding biostimulant-plant interactions, at molecular, cellular and physiological levels, is a prerequisite. Metabolomics, a multidisciplinary omics science, offers unique opportunities to predictively decode the mode of action of biostimulants on crop plants, and identify signatory markers of biostimulant action. Thus, this review intends to highlight the current scientific efforts and knowledge gaps in biostimulant research and industry, in context of plant growth promotion and stress responses. The review firstly revisits models that have been elucidated to describe the molecular machinery employed by plants in coping with environmental stresses. Furthermore, current definitions, claims and applications of plant biostimulants are pointed out, also indicating the lack of biological basis to accurately postulate the mechanisms of action of plant biostimulants. The review articulates briefly key aspects in the metabolomics workflow and the (potential) applications of this multidisciplinary omics science in the biostimulant industry. Full article

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

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21 pages, 2378 KB

Open AccessEditor’s ChoiceReview

Carbonic Anhydrase Inhibitors Targeting Metabolism and Tumor Microenvironment

by Andrea Angeli, Fabrizio Carta, Alessio Nocentini, Jean-Yves Winum, Raivis Zalubovskis, Atilla Akdemir, Valentina Onnis, Wagdy M. Eldehna, Clemente Capasso, Giuseppina De Simone, Simona Maria Monti, Simone Carradori, William A. Donald, Shoukat Dedhar and Claudiu T. Supuran

Metabolites 2020, 10(10), 412; https://doi.org/10.3390/metabo10100412 - 14 Oct 2020

Cited by 145 | Viewed by 8247

Abstract

The tumor microenvironment is crucial for the growth of cancer cells, triggering particular biochemical and physiological changes, which frequently influence the outcome of anticancer therapies. The biochemical rationale behind many of these phenomena resides in the activation of transcription factors such as hypoxia-inducible [...] Read more.

The tumor microenvironment is crucial for the growth of cancer cells, triggering particular biochemical and physiological changes, which frequently influence the outcome of anticancer therapies. The biochemical rationale behind many of these phenomena resides in the activation of transcription factors such as hypoxia-inducible factor 1 and 2 (HIF-1/2). In turn, the HIF pathway activates a number of genes including those involved in glucose metabolism, angiogenesis, and pH regulation. Several carbonic anhydrase (CA, EC 4.2.1.1) isoforms, such as CA IX and XII, actively participate in these processes and were validated as antitumor/antimetastatic drug targets. Here, we review the field of CA inhibitors (CAIs), which selectively inhibit the cancer-associated CA isoforms. Particular focus was on the identification of lead compounds and various inhibitor classes, and the measurement of CA inhibitory on-/off-target effects. In addition, the preclinical data that resulted in the identification of SLC-0111, a sulfonamide in Phase Ib/II clinical trials for the treatment of hypoxic, advanced solid tumors, are detailed. Full article

(This article belongs to the Special Issue Metabolism in the Tumor Microenvironment)

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30 pages, 943 KB

Open AccessEditor’s ChoiceReview

Unraveling Arbuscular Mycorrhiza-Induced Changes in Plant Primary and Secondary Metabolome

by Sukhmanpreet Kaur and Vidya Suseela

Metabolites 2020, 10(8), 335; https://doi.org/10.3390/metabo10080335 - 18 Aug 2020

Cited by 186 | Viewed by 10763

Abstract

Arbuscular mycorrhizal fungi (AMF) is among the most ubiquitous plant mutualists that enhance plant growth and yield by facilitating the uptake of phosphorus and water. The countless interactions that occur in the rhizosphere between plants and its AMF symbionts are mediated through the [...] Read more.

Arbuscular mycorrhizal fungi (AMF) is among the most ubiquitous plant mutualists that enhance plant growth and yield by facilitating the uptake of phosphorus and water. The countless interactions that occur in the rhizosphere between plants and its AMF symbionts are mediated through the plant and fungal metabolites that ensure partner recognition, colonization, and establishment of the symbiotic association. The colonization and establishment of AMF reprogram the metabolic pathways of plants, resulting in changes in the primary and secondary metabolites, which is the focus of this review. During initial colonization, plant–AMF interaction is facilitated through the regulation of signaling and carotenoid pathways. After the establishment, the AMF symbiotic association influences the primary metabolism of the plant, thus facilitating the sharing of photosynthates with the AMF. The carbon supply to AMF leads to the transport of a significant amount of sugars to the roots, and also alters the tricarboxylic acid cycle. Apart from the nutrient exchange, the AMF imparts abiotic stress tolerance in host plants by increasing the abundance of several primary metabolites. Although AMF initially suppresses the defense response of the host, it later primes the host for better defense against biotic and abiotic stresses by reprogramming the biosynthesis of secondary metabolites. Additionally, the influence of AMF on signaling pathways translates to enhanced phytochemical content through the upregulation of the phenylpropanoid pathway, which improves the quality of the plant products. These phytometabolome changes induced by plant–AMF interaction depends on the identity of both plant and AMF species, which could contribute to the differential outcome of this symbiotic association. A better understanding of the phytochemical landscape shaped by plant–AMF interactions would enable us to harness this symbiotic association to enhance plant performance, particularly under non-optimal growing conditions. Full article

(This article belongs to the Special Issue Ecometabolomics)

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15 pages, 901 KB

Open AccessEditor’s ChoiceReview

Metabolic Reprogramming of Chemoresistant Cancer Cells and the Potential Significance of Metabolic Regulation in the Reversal of Cancer Chemoresistance

by Xun Chen, Shangwu Chen and Dongsheng Yu

Metabolites 2020, 10(7), 289; https://doi.org/10.3390/metabo10070289 - 16 Jul 2020

Cited by 105 | Viewed by 6120

Abstract

Metabolic reprogramming is one of the hallmarks of tumors. Alterations of cellular metabolism not only contribute to tumor development, but also mediate the resistance of tumor cells to antitumor drugs. The metabolic response of tumor cells to various chemotherapy drugs can be analyzed [...] Read more.

Metabolic reprogramming is one of the hallmarks of tumors. Alterations of cellular metabolism not only contribute to tumor development, but also mediate the resistance of tumor cells to antitumor drugs. The metabolic response of tumor cells to various chemotherapy drugs can be analyzed by metabolomics. Although cancer cells have experienced metabolic reprogramming, the metabolism of drug resistant cancer cells has been further modified. Metabolic adaptations of drug resistant cells to chemotherapeutics involve redox, lipid metabolism, bioenergetics, glycolysis, polyamine synthesis and so on. The proposed metabolic mechanisms of drug resistance include the increase of glucose and glutamine demand, active pathways of glutaminolysis and glycolysis, promotion of NADPH from the pentose phosphate pathway, adaptive mitochondrial reprogramming, activation of fatty acid oxidation, and up-regulation of ornithine decarboxylase for polyamine production. Several genes are associated with metabolic reprogramming and drug resistance. Intervening regulatory points described above or targeting key genes in several important metabolic pathways may restore cell sensitivity to chemotherapy. This paper reviews the metabolic changes of tumor cells during the development of chemoresistance and discusses the potential of reversing chemoresistance by metabolic regulation. Full article

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16 pages, 678 KB

Open AccessEditor’s ChoiceReview

The Pentose Phosphate Pathway Dynamics in Cancer and Its Dependency on Intracellular pH

by Khalid O. Alfarouk, Samrein B. M. Ahmed, Robert L. Elliott, Amanda Benoit, Saad S. Alqahtani, Muntaser E. Ibrahim, Adil H. H. Bashir, Sari T. S. Alhoufie, Gamal O. Elhassan, Christian C. Wales, Laurent H. Schwartz, Heyam S. Ali, Ahmed Ahmed, Patrick F. Forde, Jesus Devesa, Rosa A. Cardone, Stefano Fais, Salvador Harguindey and Stephan J. Reshkin

Metabolites 2020, 10(7), 285; https://doi.org/10.3390/metabo10070285 - 11 Jul 2020

Cited by 82 | Viewed by 11860

Abstract

The Pentose Phosphate Pathway (PPP) is one of the key metabolic pathways occurring in living cells to produce energy and maintain cellular homeostasis. Cancer cells have higher cytoplasmic utilization of glucose (glycolysis), even in the presence of oxygen; this is known as the [...] Read more.

The Pentose Phosphate Pathway (PPP) is one of the key metabolic pathways occurring in living cells to produce energy and maintain cellular homeostasis. Cancer cells have higher cytoplasmic utilization of glucose (glycolysis), even in the presence of oxygen; this is known as the “Warburg Effect”. However, cytoplasmic glucose utilization can also occur in cancer through the PPP. This pathway contributes to cancer cells by operating in many different ways: (i) as a defense mechanism via the reduced form of nicotinamide adenine dinucleotide phosphate (NADPH) to prevent apoptosis, (ii) as a provision for the maintenance of energy by intermediate glycolysis, (iii) by increasing genomic material to the cellular pool of nucleic acid bases, (iv) by promoting survival through increasing glycolysis, and so increasing acid production, and (v) by inducing cellular proliferation by the synthesis of nucleic acid, fatty acid, and amino acid. Each step of the PPP can be upregulated in some types of cancer but not in others. An interesting aspect of this metabolic pathway is the shared regulation of the glycolytic and PPP pathways by intracellular pH (pHi). Indeed, as with glycolysis, the optimum activity of the enzymes driving the PPP occurs at an alkaline pHi, which is compatible with the cytoplasmic pH of cancer cells. Here, we outline each step of the PPP and discuss its possible correlation with cancer. Full article

(This article belongs to the Section Cell Metabolism)

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23 pages, 2513 KB

Open AccessEditor’s ChoiceReview

Machine Learning Applications for Mass Spectrometry-Based Metabolomics

by Ulf W. Liebal, An N. T. Phan, Malvika Sudhakar, Karthik Raman and Lars M. Blank

Metabolites 2020, 10(6), 243; https://doi.org/10.3390/metabo10060243 - 13 Jun 2020

Cited by 259 | Viewed by 19854

Abstract

The metabolome of an organism depends on environmental factors and intracellular regulation and provides information about the physiological conditions. Metabolomics helps to understand disease progression in clinical settings or estimate metabolite overproduction for metabolic engineering. The most popular analytical metabolomics platform is mass [...] Read more.

The metabolome of an organism depends on environmental factors and intracellular regulation and provides information about the physiological conditions. Metabolomics helps to understand disease progression in clinical settings or estimate metabolite overproduction for metabolic engineering. The most popular analytical metabolomics platform is mass spectrometry (MS). However, MS metabolome data analysis is complicated, since metabolites interact nonlinearly, and the data structures themselves are complex. Machine learning methods have become immensely popular for statistical analysis due to the inherent nonlinear data representation and the ability to process large and heterogeneous data rapidly. In this review, we address recent developments in using machine learning for processing MS spectra and show how machine learning generates new biological insights. In particular, supervised machine learning has great potential in metabolomics research because of the ability to supply quantitative predictions. We review here commonly used tools, such as random forest, support vector machines, artificial neural networks, and genetic algorithms. During processing steps, the supervised machine learning methods help peak picking, normalization, and missing data imputation. For knowledge-driven analysis, machine learning contributes to biomarker detection, classification and regression, biochemical pathway identification, and carbon flux determination. Of important relevance is the combination of different omics data to identify the contributions of the various regulatory levels. Our overview of the recent publications also highlights that data quality determines analysis quality, but also adds to the challenge of choosing the right model for the data. Machine learning methods applied to MS-based metabolomics ease data analysis and can support clinical decisions, guide metabolic engineering, and stimulate fundamental biological discoveries. Full article

(This article belongs to the Special Issue Metabolic Engineering and Synthetic Biology Volume 2)

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26 pages, 1131 KB

Open AccessFeature PaperEditor’s ChoiceArticle

The Bovine Metabolome

by Aidin Foroutan, Carolyn Fitzsimmons, Rupasri Mandal, Hamed Piri-Moghadam, Jiamin Zheng, AnChi Guo, Carin Li, Le Luo Guan and David S. Wishart

Metabolites 2020, 10(6), 233; https://doi.org/10.3390/metabo10060233 - 5 Jun 2020

Cited by 89 | Viewed by 6799

Abstract

From an animal health perspective, relatively little is known about the typical or healthy ranges of concentrations for many metabolites in bovine biofluids and tissues. Here, we describe the results of a comprehensive, quantitative metabolomic characterization of six bovine biofluids and tissues, including [...] Read more.

From an animal health perspective, relatively little is known about the typical or healthy ranges of concentrations for many metabolites in bovine biofluids and tissues. Here, we describe the results of a comprehensive, quantitative metabolomic characterization of six bovine biofluids and tissues, including serum, ruminal fluid, liver, Longissimus thoracis (LT) muscle, semimembranosus (SM) muscle, and testis tissues. Using nuclear magnetic resonance (NMR) spectroscopy, liquid chromatography–tandem mass spectrometry (LC–MS/MS), and inductively coupled plasma–mass spectrometry (ICP–MS), we were able to identify and quantify more than 145 metabolites in each of these biofluids/tissues. Combining these results with previous work done by our team on other bovine biofluids, as well as previously published literature values for other bovine tissues and biofluids, we were able to generate quantitative reference concentration data for 2100 unique metabolites across five different bovine biofluids and seven different tissues. These experimental data were combined with computer-aided, genome-scale metabolite inference techniques to add another 48,628 unique metabolites that are biochemically expected to be in bovine tissues or biofluids. Altogether, 51,801 unique metabolites were identified in this study. Detailed information on these 51,801 unique metabolites has been placed in a publicly available database called the Bovine Metabolome Database. Full article

(This article belongs to the Section Animal Metabolism)

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18 pages, 1088 KB

Open AccessEditor’s ChoiceReview

Recommendations and Best Practices for Standardizing the Pre-Analytical Processing of Blood and Urine Samples in Metabolomics

by Raúl González-Domínguez, Álvaro González-Domínguez, Ana Sayago and Ángeles Fernández-Recamales

Metabolites 2020, 10(6), 229; https://doi.org/10.3390/metabo10060229 - 3 Jun 2020

Cited by 118 | Viewed by 14148

Abstract

Metabolomics can be significantly influenced by a range of pre-analytical factors, such as sample collection, pre-processing, aliquoting, transport, storage and thawing. This therefore shows the crucial need for standardizing the pre-analytical phase with the aim of minimizing the inter-sample variability driven by these [...] Read more.

Metabolomics can be significantly influenced by a range of pre-analytical factors, such as sample collection, pre-processing, aliquoting, transport, storage and thawing. This therefore shows the crucial need for standardizing the pre-analytical phase with the aim of minimizing the inter-sample variability driven by these technical issues, as well as for maintaining the metabolic integrity of biological samples to ensure that metabolomic profiles are a direct expression of the in vivo biochemical status. This review article provides an updated literature revision of the most important factors related to sample handling and pre-processing that may affect metabolomics results, particularly focusing on the most commonly investigated biofluids in metabolomics, namely blood plasma/serum and urine. Finally, we also provide some general recommendations and best practices aimed to standardize and accurately report all these pre-analytical aspects in metabolomics research. Full article

(This article belongs to the Special Issue Metabolomics in Human Tissues and Materials)

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35 pages, 1844 KB

Open AccessEditor’s ChoiceReview

Metabolomics and Multi-Omics Integration: A Survey of Computational Methods and Resources

by Tara Eicher, Garrett Kinnebrew, Andrew Patt, Kyle Spencer, Kevin Ying, Qin Ma, Raghu Machiraju and Ewy A. Mathé

Metabolites 2020, 10(5), 202; https://doi.org/10.3390/metabo10050202 - 15 May 2020

Cited by 111 | Viewed by 16717

Abstract

As researchers are increasingly able to collect data on a large scale from multiple clinical and omics modalities, multi-omics integration is becoming a critical component of metabolomics research. This introduces a need for increased understanding by the metabolomics researcher of computational and statistical [...] Read more.

As researchers are increasingly able to collect data on a large scale from multiple clinical and omics modalities, multi-omics integration is becoming a critical component of metabolomics research. This introduces a need for increased understanding by the metabolomics researcher of computational and statistical analysis methods relevant to multi-omics studies. In this review, we discuss common types of analyses performed in multi-omics studies and the computational and statistical methods that can be used for each type of analysis. We pinpoint the caveats and considerations for analysis methods, including required parameters, sample size and data distribution requirements, sources of a priori knowledge, and techniques for the evaluation of model accuracy. Finally, for the types of analyses discussed, we provide examples of the applications of corresponding methods to clinical and basic research. We intend that our review may be used as a guide for metabolomics researchers to choose effective techniques for multi-omics analyses relevant to their field of study. Full article

(This article belongs to the Special Issue Metabolomics and Multi-Omics Integration)

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23 pages, 1537 KB

Open AccessEditor’s ChoiceReview

MEATabolomics: Muscle and Meat Metabolomics in Domestic Animals

by Susumu Muroya, Shuji Ueda, Tomohiko Komatsu, Takuya Miyakawa and Per Ertbjerg

Metabolites 2020, 10(5), 188; https://doi.org/10.3390/metabo10050188 - 11 May 2020

Cited by 103 | Viewed by 8900

Abstract

In the past decades, metabolomics has been used to comprehensively understand a variety of food materials for improvement and assessment of food quality. Farm animal skeletal muscles and meat are one of the major targets of metabolomics for the characterization of meat and [...] Read more.

In the past decades, metabolomics has been used to comprehensively understand a variety of food materials for improvement and assessment of food quality. Farm animal skeletal muscles and meat are one of the major targets of metabolomics for the characterization of meat and the exploration of biomarkers in the production system. For identification of potential biomarkers to control meat quality, studies of animal muscles and meat with metabolomics (MEATabolomics) has been conducted in combination with analyses of meat quality traits, focusing on specific factors associated with animal genetic background and sensory scores, or conditions in feeding system and treatments of meat in the processes such as postmortem storage, processing, and hygiene control. Currently, most of MEATabolomics approaches combine separation techniques (gas or liquid chromatography, and capillary electrophoresis)–mass spectrometry (MS) or nuclear magnetic resonance (NMR) approaches with the downstream multivariate analyses, depending on the polarity and/or hydrophobicity of the targeted metabolites. Studies employing these approaches provide useful information to monitor meat quality traits efficiently and to understand the genetic background and production system of animals behind the meat quality. MEATabolomics is expected to improve the knowledge and methodologies in animal breeding and feeding, meat storage and processing, and prediction of meat quality. Full article

(This article belongs to the Special Issue Metabolomic Applications in Animal Science)

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24 pages, 401 KB

Open AccessEditor’s ChoiceReview

Metabolite Changes during Postharvest Storage: Effects on Fruit Quality Traits

by Delphine M. Pott, José G. Vallarino and Sonia Osorio

Metabolites 2020, 10(5), 187; https://doi.org/10.3390/metabo10050187 - 8 May 2020

Cited by 172 | Viewed by 9628

Abstract

Metabolic changes occurring in ripe or senescent fruits during postharvest storage lead to a general deterioration in quality attributes, including decreased flavor and ‘off-aroma’ compound generation. As a consequence, measures to reduce economic losses have to be taken by the fruit industry and [...] Read more.

Metabolic changes occurring in ripe or senescent fruits during postharvest storage lead to a general deterioration in quality attributes, including decreased flavor and ‘off-aroma’ compound generation. As a consequence, measures to reduce economic losses have to be taken by the fruit industry and have mostly consisted of storage at cold temperatures and the use of controlled atmospheres or ripening inhibitors. However, the biochemical pathways and molecular mechanisms underlying fruit senescence in commercial storage conditions are still poorly understood. In this sense, metabolomic platforms, enabling the profiling of key metabolites responsible for organoleptic and health-promoting traits, such as volatiles, sugars, acids, polyphenols and carotenoids, can be a powerful tool for further understanding the biochemical basis of postharvest physiology and have the potential to play a critical role in the identification of the pathways affected by fruit senescence. Here, we provide an overview of the metabolic changes during postharvest storage, with special attention to key metabolites related to fruit quality. The potential use of metabolomic approaches to yield metabolic markers useful for chemical phenotyping or even storage and marketing decisions is highlighted. Full article

(This article belongs to the Special Issue Fruit Metabolism and Metabolomics)

14 pages, 7327 KB

Open AccessEditor’s ChoiceArticle

MetaboAnalystR 3.0: Toward an Optimized Workflow for Global Metabolomics

by Zhiqiang Pang, Jasmine Chong, Shuzhao Li and Jianguo Xia

Metabolites 2020, 10(5), 186; https://doi.org/10.3390/metabo10050186 - 7 May 2020

Cited by 421 | Viewed by 26056

Abstract

Liquid chromatography coupled to high-resolution mass spectrometry platforms are increasingly employed to comprehensively measure metabolome changes in systems biology and complex diseases. Over the past decade, several powerful computational pipelines have been developed for spectral processing, annotation, and analysis. However, significant obstacles remain [...] Read more.

Liquid chromatography coupled to high-resolution mass spectrometry platforms are increasingly employed to comprehensively measure metabolome changes in systems biology and complex diseases. Over the past decade, several powerful computational pipelines have been developed for spectral processing, annotation, and analysis. However, significant obstacles remain with regard to parameter settings, computational efficiencies, batch effects, and functional interpretations. Here, we introduce MetaboAnalystR 3.0, a significantly improved pipeline with three key new features: (1) efficient parameter optimization for peak picking; (2) automated batch effect correction; and (3) more accurate pathway activity prediction. Our benchmark studies showed that this workflow was 20~100× faster compared to other well-established workflows and produced more biologically meaningful results. In summary, MetaboAnalystR 3.0 offers an efficient pipeline to support high-throughput global metabolomics in the open-source R environment. Full article

(This article belongs to the Special Issue Metabolomics–Integration of Technology and Bioinformatics)

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13 pages, 233 KB

Open AccessEditor’s ChoiceReview

A Review of Lipidomics of Cardiovascular Disease Highlights the Importance of Isolating Lipoproteins

by Ming Ding and Kathryn M. Rexrode

Metabolites 2020, 10(4), 163; https://doi.org/10.3390/metabo10040163 - 23 Apr 2020

Cited by 92 | Viewed by 6946

Abstract

Cutting-edge lipidomic profiling measures hundreds or even thousands of lipids in plasma and is increasingly used to investigate mechanisms of cardiovascular disease (CVD). In this review, we introduce lipidomic techniques, describe distributions of lipids across lipoproteins, and summarize findings on the association of [...] Read more.

Cutting-edge lipidomic profiling measures hundreds or even thousands of lipids in plasma and is increasingly used to investigate mechanisms of cardiovascular disease (CVD). In this review, we introduce lipidomic techniques, describe distributions of lipids across lipoproteins, and summarize findings on the association of lipids with CVD based on lipidomics. The main findings of 16 cohort studies were that, independent of total and high-density lipoprotein cholesterol (HDL-c), ceramides (d18:1/16:0, d18:1/18:0, and d18:1/24:1) and phosphatidylcholines (PCs) containing saturated and monounsaturated fatty acyl chains are positively associated with risks of CVD outcomes, while PCs containing polyunsaturated fatty acyl chains (PUFA) are inversely associated with risks of CVD outcomes. Lysophosphatidylcholines (LPCs) may be positively associated with risks of CVD outcomes. Interestingly, the distributions of the identified lipids vary across lipoproteins: LPCs are primarily contained in HDLs, ceramides are mainly contained in low-density lipoproteins (LDLs), and PCs are distributed in both HDLs and LDLs. Thus, the potential mechanism behind previous findings may be related to the effect of the identified lipids on the biological functions of HDLs and LDLs. Only eight studies on the lipidomics of HDL and non-HDL particles and CVD outcomes have been conducted, which showed that higher triglycerides (TAGs), lower PUFA, lower phospholipids, and lower sphingomyelin content in HDLs might be associated with a higher risk of coronary heart disease (CHD). However, the generalizability of these studies is a major concern, given that they used case–control or cross-sectional designs in hospital settings, included a very small number of participants, and did not correct for multiple testing or adjust for blood lipids such as HDL-c, low-density lipoprotein cholesterol (LDL-c), or TAGs. Overall, findings from the literature highlight the importance of research on lipidomics of lipoproteins to enhance our understanding of the mechanism of the association between the identified lipids and the risk of CVD and allow the identification of novel lipid biomarkers in HDLs and LDLs, independent of HDL-c and LDL-c. Lipidomic techniques show the feasibility of this exciting research direction, and the lack of high-quality epidemiological studies warrants well-designed prospective cohort studies. Full article

(This article belongs to the Special Issue Integrative-Metabolomics in Epidemiological Studies)

35 pages, 15010 KB

Open AccessEditor’s ChoiceProtocol

“Notame”: Workflow for Non-Targeted LC–MS Metabolic Profiling

by Anton Klåvus, Marietta Kokla, Stefania Noerman, Ville M. Koistinen, Marjo Tuomainen, Iman Zarei, Topi Meuronen, Merja R. Häkkinen, Soile Rummukainen, Ambrin Farizah Babu, Taisa Sallinen, Olli Kärkkäinen, Jussi Paananen, David Broadhurst, Carl Brunius and Kati Hanhineva

Metabolites 2020, 10(4), 135; https://doi.org/10.3390/metabo10040135 - 31 Mar 2020

Cited by 107 | Viewed by 16028

Abstract

Metabolomics analysis generates vast arrays of data, necessitating comprehensive workflows involving expertise in analytics, biochemistry and bioinformatics in order to provide coherent and high-quality data that enable discovery of robust and biologically significant metabolic findings. In this protocol article, we introduce notame, an [...] Read more.

Metabolomics analysis generates vast arrays of data, necessitating comprehensive workflows involving expertise in analytics, biochemistry and bioinformatics in order to provide coherent and high-quality data that enable discovery of robust and biologically significant metabolic findings. In this protocol article, we introduce notame, an analytical workflow for non-targeted metabolic profiling approaches, utilizing liquid chromatography–mass spectrometry analysis. We provide an overview of lab protocols and statistical methods that we commonly practice for the analysis of nutritional metabolomics data. The paper is divided into three main sections: the first and second sections introducing the background and the study designs available for metabolomics research and the third section describing in detail the steps of the main methods and protocols used to produce, preprocess and statistically analyze metabolomics data and, finally, to identify and interpret the compounds that have emerged as interesting. Full article

(This article belongs to the Special Issue Metabolomics Data Processing and Data Analysis—Current Best Practices)

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17 pages, 665 KB

Open AccessEditor’s ChoiceReview

Important Considerations for Sample Collection in Metabolomics Studies with a Special Focus on Applications to Liver Functions

by Lorraine Smith, Joran Villaret-Cazadamont, Sandrine P. Claus, Cécile Canlet, Hervé Guillou, Nicolas J. Cabaton and Sandrine Ellero-Simatos

Metabolites 2020, 10(3), 104; https://doi.org/10.3390/metabo10030104 - 12 Mar 2020

Cited by 82 | Viewed by 8159

Abstract

Metabolomics has found numerous applications in the study of liver metabolism in health and disease. Metabolomics studies can be conducted in a variety of biological matrices ranging from easily accessible biofluids such as urine, blood or feces, to organs, tissues or even cells. [...] Read more.

Metabolomics has found numerous applications in the study of liver metabolism in health and disease. Metabolomics studies can be conducted in a variety of biological matrices ranging from easily accessible biofluids such as urine, blood or feces, to organs, tissues or even cells. Sample collection and storage are critical steps for which standard operating procedures must be followed. Inappropriate sample collection or storage can indeed result in high variability, interferences with instrumentation or degradation of metabolites. In this review, we will first highlight important general factors that should be considered when planning sample collection in the study design of metabolomic studies, such as nutritional status and circadian rhythm. Then, we will discuss in more detail the specific procedures that have been described for optimal pre-analytical handling of the most commonly used matrices (urine, blood, feces, tissues and cells). Full article

(This article belongs to the Special Issue Metabolism and Metabolomics of Liver in Health and Disease)

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16 pages, 3543 KB

Open AccessEditor’s ChoiceReview

Antifungal Drugs

by Jiří Houšť, Jaroslav Spížek and Vladimír Havlíček

Metabolites 2020, 10(3), 106; https://doi.org/10.3390/metabo10030106 - 12 Mar 2020

Cited by 214 | Viewed by 25295

Abstract

We reviewed the licensed antifungal drugs and summarized their mechanisms of action, pharmacological profiles, and susceptibility to specific fungi. Approved antimycotics inhibit 1,3-β-d-glucan synthase, lanosterol 14-α-demethylase, protein, and deoxyribonucleic acid biosynthesis, or sequestrate ergosterol. Their most severe side effects are hepatotoxicity, [...] Read more.

We reviewed the licensed antifungal drugs and summarized their mechanisms of action, pharmacological profiles, and susceptibility to specific fungi. Approved antimycotics inhibit 1,3-β-d-glucan synthase, lanosterol 14-α-demethylase, protein, and deoxyribonucleic acid biosynthesis, or sequestrate ergosterol. Their most severe side effects are hepatotoxicity, nephrotoxicity, and myelotoxicity. Whereas triazoles exhibit the most significant drug–drug interactions, echinocandins exhibit almost none. The antifungal resistance may be developed across most pathogens and includes drug target overexpression, efflux pump activation, and amino acid substitution. The experimental antifungal drugs in clinical trials are also reviewed. Siderophores in the Trojan horse approach or the application of siderophore biosynthesis enzyme inhibitors represent the most promising emerging antifungal therapies. Full article

(This article belongs to the Section Microbiology and Ecological Metabolomics)

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23 pages, 1227 KB

Open AccessEditor’s ChoiceReview

Metabolomics as an Emerging Tool for the Study of Plant–Pathogen Interactions

by Fernanda R. Castro-Moretti, Irene N. Gentzel, David Mackey and Ana P. Alonso

Metabolites 2020, 10(2), 52; https://doi.org/10.3390/metabo10020052 - 29 Jan 2020

Cited by 195 | Viewed by 15166

Abstract

Plants defend themselves from most microbial attacks via mechanisms including cell wall fortification, production of antimicrobial compounds, and generation of reactive oxygen species. Successful pathogens overcome these host defenses, as well as obtain nutrients from the host. Perturbations of plant metabolism play a [...] Read more.

Plants defend themselves from most microbial attacks via mechanisms including cell wall fortification, production of antimicrobial compounds, and generation of reactive oxygen species. Successful pathogens overcome these host defenses, as well as obtain nutrients from the host. Perturbations of plant metabolism play a central role in determining the outcome of attempted infections. Metabolomic analyses, for example between healthy, newly infected and diseased or resistant plants, have the potential to reveal perturbations to signaling or output pathways with key roles in determining the outcome of a plant–microbe interaction. However, application of this -omic and its tools in plant pathology studies is lagging relative to genomic and transcriptomic methods. Thus, it is imperative to bring the power of metabolomics to bear on the study of plant resistance/susceptibility. This review discusses metabolomics studies that link changes in primary or specialized metabolism to the defense responses of plants against bacterial, fungal, nematode, and viral pathogens. Also examined are cases where metabolomics unveils virulence mechanisms used by pathogens. Finally, how integrating metabolomics with other -omics can advance plant pathology research is discussed. Full article

(This article belongs to the Special Issue Metabolomic and Flux Analysis in Plants)

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23 pages, 2128 KB

Open AccessEditor’s ChoiceReview

Salivary Metabolomics: From Diagnostic Biomarker Discovery to Investigating Biological Function

by Alexander Gardner, Guy Carpenter and Po-Wah So

Metabolites 2020, 10(2), 47; https://doi.org/10.3390/metabo10020047 - 26 Jan 2020

Cited by 116 | Viewed by 10460

Abstract

Metabolomic profiling of biofluids, e.g., urine, plasma, has generated vast and ever-increasing amounts of knowledge over the last few decades. Paradoxically, metabolomic analysis of saliva, the most readily-available human biofluid, has lagged. This review explores the history of saliva-based metabolomics and summarizes current [...] Read more.

Metabolomic profiling of biofluids, e.g., urine, plasma, has generated vast and ever-increasing amounts of knowledge over the last few decades. Paradoxically, metabolomic analysis of saliva, the most readily-available human biofluid, has lagged. This review explores the history of saliva-based metabolomics and summarizes current knowledge of salivary metabolomics. Current applications of salivary metabolomics have largely focused on diagnostic biomarker discovery and the diagnostic value of the current literature base is explored. There is also a small, albeit promising, literature base concerning the use of salivary metabolomics in monitoring athletic performance. Functional roles of salivary metabolites remain largely unexplored. Areas of emerging knowledge include the role of oral host–microbiome interactions in shaping the salivary metabolite profile and the potential roles of salivary metabolites in oral physiology, e.g., in taste perception. Discussion of future research directions describes the need to begin acquiring a greater knowledge of the function of salivary metabolites, a current research direction in the field of the gut metabolome. The role of saliva as an easily obtainable, information-rich fluid that could complement other gastrointestinal fluids in the exploration of the gut metabolome is emphasized. Full article

(This article belongs to the Special Issue Metabolomics in Human Tissues and Materials)

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30 pages, 917 KB

Open AccessEditor’s ChoiceReview

Metabolomics in the Context of Plant Natural Products Research: From Sample Preparation to Metabolite Analysis

by Mohamed A. Salem, Leonardo Perez de Souza, Ahmed Serag, Alisdair R. Fernie, Mohamed A. Farag, Shahira M. Ezzat and Saleh Alseekh

Metabolites 2020, 10(1), 37; https://doi.org/10.3390/metabo10010037 - 15 Jan 2020

Cited by 249 | Viewed by 18724

Abstract

Plant-derived natural products have long been considered a valuable source of lead compounds for drug development. Natural extracts are usually composed of hundreds to thousands of metabolites, whereby the bioactivity of natural extracts can be represented by synergism between several metabolites. However, isolating [...] Read more.

Plant-derived natural products have long been considered a valuable source of lead compounds for drug development. Natural extracts are usually composed of hundreds to thousands of metabolites, whereby the bioactivity of natural extracts can be represented by synergism between several metabolites. However, isolating every single compound from a natural extract is not always possible due to the complex chemistry and presence of most secondary metabolites at very low levels. Metabolomics has emerged in recent years as an indispensable tool for the analysis of thousands of metabolites from crude natural extracts, leading to a paradigm shift in natural products drug research. Analytical methods such as mass spectrometry (MS) and nuclear magnetic resonance (NMR) are used to comprehensively annotate the constituents of plant natural products for screening, drug discovery as well as for quality control purposes such as those required for phytomedicine. In this review, the current advancements in plant sample preparation, sample measurements, and data analysis are presented alongside a few case studies of the successful applications of these processes in plant natural product drug discovery. Full article

(This article belongs to the Special Issue Sample Preparation in Metabolomics)

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