Metabolites

20 pages, 4456 KiB

Open AccessArticle

Liver Metabolomics and Inflammatory Profiles in Mouse Model of Fentanyl Overdose Treated with Beta-Lactams

by Fawaz Alasmari, Mohammed S. Alasmari, Mohammed A. Assiri, Mohammed Alswayyed, Syed Rizwan Ahamad, Abdulrahman I. Alhumaydhi, Bandar I. Arif, Sahar R. Aljumayi, Abdullah F. AlAsmari, Nemat Ali, Wayne E. Childers, Magid Abou-Gharbia and Youssef Sari

Metabolites 2023, 13(8), 965; https://doi.org/10.3390/metabo13080965 - 21 Aug 2023

Cited by 4 | Viewed by 1855

Abstract

Fentanyl is a highly potent opioid analgesic that is approved medically to treat acute and chronic pain. There is a high potential for overdose-induced organ toxicities, including liver toxicity, and this might be due to the increase of recreational use of opioids. Several [...] Read more.

Fentanyl is a highly potent opioid analgesic that is approved medically to treat acute and chronic pain. There is a high potential for overdose-induced organ toxicities, including liver toxicity, and this might be due to the increase of recreational use of opioids. Several preclinical studies have demonstrated the efficacy of beta-lactams in modulating the expression of glutamate transporter-1 (GLT-1) in different body organs, including the liver. The upregulation of GLT-1 by beta-lactams is associated with the attenuation of hyperglutamatergic state, which is a characteristic feature of opioid use disorders. A novel experimental beta-lactam compound with no antimicrobial properties, MC-100093, has been developed to attenuate dysregulation of glutamate transport, in part by normalizing GLT-1 expression. A previous study showed that MC-100093 modulated hepatic GLT-1 expression with subsequent attenuation of alcohol-increased fat droplet content in the liver. In this study, we investigated the effects of fentanyl overdose on liver metabolites, and determined the effects of MC-100093 and ceftriaxone in the liver of a fentanyl overdose mouse model. Liver samples from control, fentanyl overdose, and fentanyl overdose ceftriaxone- or MC-100093-treated mice were analyzed for metabolomics using gas chromatography–mass spectrometry. Heatmap analysis revealed that both MC-100093 and ceftriaxone attenuated the effects of fentanyl overdose on several metabolites, and MC-100093 showed superior effects. Statistical analysis showed that MC-100093 reversed the effects of fentanyl overdose in some metabolites. Moreover, enrichment analysis revealed that the altered metabolites were strongly linked to the glucose-alanine cycle, the Warburg effect, gluconeogenesis, glutamate metabolism, lactose degradation, and ketone body metabolism. The changes in liver metabolites induced by fentanyl overdose were associated with liver inflammation, an effect attenuated with ceftriaxone pre-treatments. Ceftriaxone normalized fentanyl-overdose-induced changes in liver interleukin-6 and cytochrome CYP3A11 (mouse homolog of human CYP3A4) expression. Our data indicate that fentanyl overdose impaired liver metabolites, and MC-100093 restored certain metabolites. Full article

(This article belongs to the Special Issue Pharmacology and Toxicology Metabolism of Synthetic and Natural Compounds)

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15 pages, 741 KiB

Open AccessArticle

Faba Bean (Vicia faba L. minor) Bitterness: An Untargeted Metabolomic Approach to Highlight the Impact of the Non-Volatile Fraction

by Adeline Karolkowski, Emmanuelle Meudec, Antoine Bruguière, Anne-Claire Mitaine-Offer, Emilie Bouzidi, Loïc Levavasseur, Nicolas Sommerer, Loïc Briand and Christian Salles

Metabolites 2023, 13(8), 964; https://doi.org/10.3390/metabo13080964 - 21 Aug 2023

Cited by 3 | Viewed by 1211

Abstract

In the context of climate change, faba beans are an interesting alternative to animal proteins but are characterised by off-notes and bitterness that decrease consumer acceptability. However, research on pulse bitterness is often limited to soybeans and peas. This study aimed to highlight [...] Read more.

In the context of climate change, faba beans are an interesting alternative to animal proteins but are characterised by off-notes and bitterness that decrease consumer acceptability. However, research on pulse bitterness is often limited to soybeans and peas. This study aimed to highlight potential bitter non-volatile compounds in faba beans. First, the bitterness of flours and air-classified fractions (starch and protein) of three faba bean cultivars was evaluated by a trained panel. The fractions from the high-alkaloid cultivars and the protein fractions exhibited higher bitter intensity. Second, an untargeted metabolomic approach using ultra-high-performance liquid chromatography–diode array detector–tandem–high resolution mass spectrometry (UHPLC–DAD–HRMS) was correlated with the bitter perception of the fractions. Third, 42 tentatively identified non-volatile compounds were associated with faba bean bitterness by correlated sensory and metabolomic data. These compounds mainly belonged to different chemical classes such as alkaloids, amino acids, phenolic compounds, organic acids, and terpenoids. This research provided a better understanding of the molecules responsible for bitterness in faba beans and the impact of cultivar and air-classification on the bitter content. The bitter character of these highlighted compounds needs to be confirmed by sensory and/or cellular analyses to identify removal or masking strategies. Full article

(This article belongs to the Section Nutrition and Metabolism)

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16 pages, 2620 KiB

Open AccessArticle

A Solid-Phase Microextraction—Liquid Chromatography-Mass Spectrometry Method for Analyzing Serum Lipids in Psoriatic Disease

by John Koussiouris, Nikita Looby, Vathany Kulasingam and Vinod Chandran

Metabolites 2023, 13(8), 963; https://doi.org/10.3390/metabo13080963 - 20 Aug 2023

Cited by 2 | Viewed by 1195

Abstract

Approximately 25% of psoriasis patients have an inflammatory arthritis termed psoriatic arthritis (PsA). There is strong interest in identifying and validating biomarkers that can accurately and reliably predict conversion from psoriasis to PsA using novel technologies such as metabolomics. Lipids, in particular, are [...] Read more.

Approximately 25% of psoriasis patients have an inflammatory arthritis termed psoriatic arthritis (PsA). There is strong interest in identifying and validating biomarkers that can accurately and reliably predict conversion from psoriasis to PsA using novel technologies such as metabolomics. Lipids, in particular, are of key interest in psoriatic disease. We sought to develop a liquid chromatography-mass spectrometry (LC-MS) method to be used in conjunction with solid-phase microextraction (SPME) for analyzing fatty acids and similar molecules. A total of 25 chromatographic methods based on published lipid studies were tested on two LC columns. As a proof of concept, serum samples from psoriatic disease patients (n = 27 psoriasis and n = 26 PsA) were processed using SPME and run on the selected LC-MS method. The method that was best for analyzing fatty acids and fatty acid-like molecules was optimized and applied to serum samples. A total of 18 tentatively annotated features classified as fatty acids and other lipid compounds were statistically significant between psoriasis and PsA groups using both multivariate and univariate approaches. The SPME-LC-MS method developed and optimized was capable of detecting fatty acids and similar lipids that may aid in differentiating psoriasis and PsA patients. Full article

(This article belongs to the Special Issue Psoriasis and Its Related Metabolic Complications)

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Final optimized methods. (a) Positive mode: dodecanedioic acid, isobutyryl-L-carnitine, and 12-aminolauric acid. (b) Negative mode: dodecanedioic acid, 12-aminolauric acid, 1,11-undecanedicarboxylic acid, and 10-hydroxy-2-decenoic acid. Full article ">Figure 2
Principal component analysis of pooled QCs, represented by turquoise, and three patient groups—healthy volunteers (Ctrl), patients with psoriatic arthritis (PsA), and patients with psoriasis (PsC), represented by red, green, and dark blue on the plot, respectively. (a) Positive mode acquisition. PCA—PC1: 13.5%, PC2: 12.7%, and PC3: 8.6%. (b) Negative mode acquisition. PCA—PC1: 15.3%, PC2: 6.5%, and PC3: 5.1%. Full article ">Figure 3
Principal component analysis of patients with psoriatic arthritis (PsA) and patients with psoriasis (PsC), represented by red and green on the plot, respectively. (a) Positive mode acquisition. PCA—PC1: 13.7%, PC2: 10.3%, and PC3: 6.9%. (b) Negative mode acquisition. PCA—PC1: 9.7%, PC2: 7.1%, and PC3: 6.7%. Full article ">Figure 4
Discriminant analysis of patients with psoriatic arthritis (PsA) and patients with psoriasis (PsC), represented by red and green on the plot, respectively. (a) Partial least squares discriminant analysis (PLS-DA) of positive mode acquisition data. The model fits the acceptable criteria of 0.89 (R2) and 0.77 (Q2). (b) Orthogonal projections to latent structures discriminant analysis (OPLS-DA) of negative mode acquisition data. The model fits the acceptable criteria of 0.77 (R2) and 0.61 (Q2). Full article ">

9 pages, 1187 KiB

Open AccessTechnical Note

Automatic Assignment of Molecular Ion Species to Elemental Formulas in Gas Chromatography/Methane Chemical Ionization Accurate Mass Spectrometry

by Shunyang Wang, Luis Valdiviez, Honglian Ye and Oliver Fiehn

Metabolites 2023, 13(8), 962; https://doi.org/10.3390/metabo13080962 - 19 Aug 2023

Cited by 1 | Viewed by 1293

Abstract

Gas chromatography–mass spectrometry (GC-MS) usually employs hard electron ionization, leading to extensive fragmentations that are suitable to identify compounds based on library matches. However, such spectra are less useful to structurally characterize unknown compounds that are absent from libraries, due to the lack [...] Read more.

Gas chromatography–mass spectrometry (GC-MS) usually employs hard electron ionization, leading to extensive fragmentations that are suitable to identify compounds based on library matches. However, such spectra are less useful to structurally characterize unknown compounds that are absent from libraries, due to the lack of readily recognizable molecular ion species. We tested methane chemical ionization on 369 trimethylsilylated (TMS) derivatized metabolites using a quadrupole time-of-flight detector (QTOF). We developed an algorithm to automatically detect molecular ion species and tested SIRIUS software on how accurate the determination of molecular formulas was. The automatic workflow correctly recognized 289 (84%) of all 345 detected derivatized standards. Specifically, strong [M − CH₃]⁺ fragments were observed in 290 of 345 derivatized chemicals, which enabled the automatic recognition of molecular adduct patterns. Using Sirius software, correct elemental formulas were retrieved in 87% of cases within the top three hits. When investigating the cases for which the automatic pattern analysis failed, we found that several metabolites showed a previously unknown [M + TMS]⁺ adduct formed by rearrangement. Methane chemical ionization with GC-QTOF mass spectrometry is a suitable avenue to identify molecular formulas for abundant unknown peaks. Full article

(This article belongs to the Special Issue The UC Davis West Coast Metabolomics Center Collection: From Compound Identification to Discoveries in Metabolism)

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17 pages, 4497 KiB

Open AccessArticle

The Urine Metabolome of R6/2 and zQ175DN Huntington’s Disease Mouse Models

by Roberto Speziale, Camilla Montesano, Giulia Di Pietro, Daniel Oscar Cicero, Vincenzo Summa, Edith Monteagudo and Laura Orsatti

Metabolites 2023, 13(8), 961; https://doi.org/10.3390/metabo13080961 - 18 Aug 2023

Cited by 3 | Viewed by 1213

Abstract

Huntington’s disease (HD) is caused by the expansion of a polyglutamine (polyQ)-encoding tract in exon 1 of the huntingtin gene to greater than 35 CAG repeats. It typically has a disease course lasting 15–20 years, and there are currently no disease-modifying therapies available. [...] Read more.

Huntington’s disease (HD) is caused by the expansion of a polyglutamine (polyQ)-encoding tract in exon 1 of the huntingtin gene to greater than 35 CAG repeats. It typically has a disease course lasting 15–20 years, and there are currently no disease-modifying therapies available. Thus, there is a need for faithful mouse models of HD to use in preclinical studies of disease mechanisms, target validation, and therapeutic compound testing. A large variety of mouse models of HD were generated, none of which fully recapitulate human disease, complicating the selection of appropriate models for preclinical studies. Here, we present the urinary liquid chromatography–high-resolution mass spectrometry analysis employed to identify metabolic alterations in transgenic R6/2 and zQ175DN knock-in mice. In R6/2 mice, the perturbation of the corticosterone metabolism and the accumulation of pyrraline, indicative of the development of insulin resistance and the impairment of pheromone excretion, were observed. Differently from R6/2, zQ175DN mice showed the accumulation of oxidative stress metabolites. Both genotypes showed alterations in the tryptophan metabolism. This approach aims to improve our understanding of the molecular mechanisms involved in HD neuropathology, facilitating the selection of appropriate mouse models for preclinical studies. It also aims to identify potential biomarkers specific to HD. Full article

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

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13 pages, 3659 KiB

Open AccessArticle

Specnuezhenide Ameliorates Age-Related Hepatic Lipid Accumulation via Modulating Bile Acid Homeostasis and Gut Microbiota in D-Galactose-Induced Mice

by Xuehui Deng, Bingfeng Lin, Fang Wang, Pingcui Xu and Nani Wang

Metabolites 2023, 13(8), 960; https://doi.org/10.3390/metabo13080960 - 18 Aug 2023

Cited by 2 | Viewed by 1097

Abstract

Age-related hepatic lipid accumulation has become a major health problem in the elderly population. Specnuezhenide (SPN) is a major active iridoid glycoside from an edible herb Fructus Ligustri Lucidi, which is commonly used for preventing age-related diseases. However, the beneficial effects of [...] Read more.

Age-related hepatic lipid accumulation has become a major health problem in the elderly population. Specnuezhenide (SPN) is a major active iridoid glycoside from an edible herb Fructus Ligustri Lucidi, which is commonly used for preventing age-related diseases. However, the beneficial effects of SPN on age-related liver injury remain unknown. This study aimed to reveal the effect of SPN on age-related hepatic lipid accumulation and the underlying mechanism. D-galactose (D-gal)-induced aging mice were treated with vehicle or SPN for 12 weeks. Treatment of SPN decreased lipid accumulation and inflammation in the liver of D-gal–induced mice. Untargeted and targeted metabolomics showed that the SPN could regulate the bile acid (BA) synthesis pathway and restore the BA compositions in serum, livers, and feces of the D-gal–induced mice. Furthermore, SPN enhanced the protein and mRNA levels of hepatic BAs synthesis enzymes cytochrome P45027A1, cytochrome P4507A1, cytochrome P4507B1, and cytochrome P4508B1. Meanwhile, SPN alleviated D-gal-induced gut dysbiosis and reversed the proportions of microbes associated with bile salt hydrolase activity, including Lactobacillus, Ruminiclostridium, and Butyrivibrio. Our study revealed that SPN attenuated age-related hepatic lipid accumulation by improving BA profiles via modulating hepatic BA synthesis enzymes and gut microbiota. Full article

(This article belongs to the Section Pharmacology and Drug Metabolism)

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Specnuezhenide (SPN) attenuates age-related hepatic lipid accumulation. (A) Body weights. (B) The liver index is presented. (C) Representative images of liver specimens stained with haematoxylin−eosin staining (H&E) (inflammatory infiltration and focal necrosis, black arrows, scale bar = 50 μm). (D) Total cholesterol (TC), total triglyceride (TG), low−density lipoprotein cholesterol (LDL−C), and high−density lipoprotein cholesterol (HDL−C) concentrations in the liver of mice were quantified using the enzymatic kit (n = 5). Data are expressed as mean ± SD. ** p < 0.01 compared with the model (MOD) group. Full article ">Figure 2
Specnuezhenide (SPN) regulated the metabolic profile and bile acid (BA) compositions. (A) The plot depicts separation of the principal component analysis (PCA) and orthogonal partial least-squares discriminant analysis (OPLS−DA) of serum from different groups at positive and negative modes. (B) Metabolic pathway analysis of identified potential marker. (C) Untargeted metabolomic analyses of serum, liver, and feces were performed to measure the concentration of total BAs (n = 5). Data are expressed as mean ± SD. * p < 0.05, ** p < 0.01 compared with the model (MOD) group. Full article ">Figure 3
Specnuezhenide (SPN) could regulate bile acid (BA) homeostasis. Targeted metabolomic analyses of serum, liver, and feces samples were performed using UPLC−MS to measure the concentration of individual BA (n = 5). Data are expressed as mean ± SD. * p < 0.05, ** p < 0.01 compared with the model (MOD) group. Full article ">Figure 4
Specnuezhenide (SPN) upregulates the protein expression of bile acid (BA) enzymes in D−galactose (D−gal) mice. (A) Immunohistochemistry (IHC) analysis of cytochrome P4507A1 (CYP7A1), cytochrome P45027A1 (CYP27A1), cytochrome P4507B1 (CYP7B1), and cytochrome P4508B1 (CYP8B1) in the liver (black arrows, n = 5). (B) The levels of CYP7A1, CYP27A1, CYP7B1, and CYP8B1 were analyzed using western blotting. The densitometric quantification of CYP7A1, CYP27A1, CYP7B1, and CYP8B1 is shown in the right panels (n = 3). Data are expressed as mean ± SD. ** p < 0.01 compared with the model (MOD) group. Full article ">Figure 5
Specnuezhenide (SPN) upregulates the mRNA expressions of cytochrome P4507A1 (CYP7A1), cytochrome P45027A1 (CYP27A1), cytochrome P4507B1 (CYP7B1), and cytochrome P4508B1 (CYP8B1) mRNA in the liver (n = 5). ** p < 0.01 compared with the model (MOD) group. Full article ">Figure 6
Specnuezhenide (SPN) treatment improved gut microbiota disbalance. (A) Principal correlation coefficient analysis (PCoA) analysis of gut microbiota at operational taxonomic units (OTUSA) level based on Bray−Curtis. (B) Shared and unique OTUs among the different groups. (C) The predicted functional metabolic pathways. (D) The relative abundance of bile salt hydrolase (BSH) related phylum (n = 5). Data are expressed as mean ± SD. * p < 0.05 compared with the MOD group. Full article ">

12 pages, 294 KiB

Open AccessArticle

Plasma Amino Acids in NAFLD Patients with Obesity Are Associated with Steatosis and Fibrosis: Results from the MAST4HEALTH Study

by Athina I. Amanatidou, Eleni V. Mikropoulou, Charalampia Amerikanou, Maja Milanovic, Stefan Stojanoski, Mladen Bjelan, Lucia Cesarini, Jonica Campolo, Anastasia Thanopoulou, Rajarshi Banerjee, Mary Jo Kurth, Natasa Milic, Milica Medic-Stojanoska, Maria Giovanna Trivella, Sophie Visvikis-Siest, Amalia Gastaldelli, Maria Halabalaki, Andriana C. Kaliora, George V. Dedoussis and on behalf of the Mast4Health consortium

Metabolites 2023, 13(8), 959; https://doi.org/10.3390/metabo13080959 - 18 Aug 2023

Cited by 1 | Viewed by 1457

Abstract

Non-alcoholic fatty liver disease (NAFLD) and non-alcoholic steatohepatitis (NASH) have been linked to changes in amino acid (AA) levels. The objective of the current study was to examine the relationship between MRI parameters that reflect inflammation and fibrosis and plasma AA concentrations in [...] Read more.

Non-alcoholic fatty liver disease (NAFLD) and non-alcoholic steatohepatitis (NASH) have been linked to changes in amino acid (AA) levels. The objective of the current study was to examine the relationship between MRI parameters that reflect inflammation and fibrosis and plasma AA concentrations in NAFLD patients. Plasma AA levels of 97 NAFLD patients from the MAST4HEALTH study were quantified with liquid chromatography. Medical, anthropometric and lifestyle characteristics were collected and biochemical parameters, as well as inflammatory and oxidative stress biomarkers, were measured. In total, subjects with a higher MRI-proton density fat fraction (MRI-PDFF) exhibited higher plasma AA levels compared to subjects with lower PDFF. The concentrations of BCAAs (p-Value: 0.03), AAAs (p-Value: 0.039), L-valine (p-Value: 0.029), L-tyrosine (p-Value: 0.039) and L-isoleucine (p-Value: 0.032) were found to be significantly higher in the higher PDFF group compared to lower group. Plasma AA levels varied according to MRI-PDFF. Significant associations were also demonstrated between AAs and MRI-PDFF and MRI-cT1, showing the potential utility of circulating AAs as diagnostic markers of NAFLD. Full article

(This article belongs to the Special Issue Diabetes, Obesity and Metabolic Disease)

14 pages, 5096 KiB

Open AccessArticle

The Metabolomics Changes in Luria–Bertani Broth Medium under Different Sterilization Methods and Their Effects on Bacillus Growth

by Haifeng Wang, Juan Guo, Xing Chen and Hongxuan He

Metabolites 2023, 13(8), 958; https://doi.org/10.3390/metabo13080958 - 18 Aug 2023

Cited by 1 | Viewed by 2505

Abstract

Luria–Bertani broth (LB) culture medium is a commonly used bacterial culture medium in the laboratory. The nutrient composition, concentration, and culture conditions of LB medium can influence the growth of microbial strains. The purpose of this article is to demonstrate the impact of [...] Read more.

Luria–Bertani broth (LB) culture medium is a commonly used bacterial culture medium in the laboratory. The nutrient composition, concentration, and culture conditions of LB medium can influence the growth of microbial strains. The purpose of this article is to demonstrate the impact of LB liquid culture medium on microbial growth under different sterilization conditions. In this study, LB medium with four different treatments was used, as follows: A, LB medium without treatments; B, LB medium with filtration; C, LB medium with autoclaving; and D, LB medium with autoclaving and cultured for 12 h. Subsequently, the protein levels and antioxidant capacity of the medium with different treatments were measured, and the effects of the different LB medium treatments on the growth of microorganisms and metabolites were determined via 16s rRNA gene sequencing and metabolomics analysis, respectively. Firmicutes and Lactobacillus were the dominant microorganisms, which were enriched in fermentation and chemoheterotrophy. The protein levels and antioxidant capacity of the LB medium with different treatments were different, and with the increasing concentration of medium, the protein levels were gradually increased, while the antioxidant capacity was decreased firstly and then increased. The growth trend of Bacillus subtilis, Bacillus paralicheniformis, Micrococcus luteus, and Alternaria alternata in the medium with different treatments was similar. Additionally, 220 and 114 differential metabolites were found between B and C medium, and between C and D medium, which were significantly enriched in the “Hedgehog signaling pathway”, “biosynthesis of plant secondary metabolites”, “ABC transporters”, “arginine and proline metabolism”, and “linoleic acid metabolism”. LB medium may be a good energy source for Lactobacillus growth with unsterilized medium, and LB medium filtered with a 0.22 μm filter membrane may be used for bacterial culture better than culture medium after high-pressure sterilization. LB medium still has the ability for antioxidation and to keep bacteria growth whether or not autoclaved, indicating that there are some substances that can resist a high temperature and pressure and still maintain their functions. Full article

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Microbiome changes in the LB medium with different treatments. (A) The alpha diversity analyses based on the indexes of Richness, Chao1, Shannon, Simpson, invsimpson, Pielou, ACE, Good coverage, and PD-whole tree. (B) Principal coordinates analysis of all the samples. (C) The bacterial communities at the phylum level. (D) The bacterial communities at the genus level. (E) Functional analysis of the identified bacterial communities. C1: the samples of A-3, A-5, A-6 and A-7 in the A1 group. C2: the samples of A-I, A-II, A-III, A-IV and A-V in the A2 group. C3: the A-VI sample in the A2 group. Full article ">Figure 2
Effects of different treatments on the protein level and antioxidant capacity of LB medium. (A) The protein levels in the medium with different treatments at a concentration of 1×. (B) The protein levels in the medium with different treatments at different concentrations. (C) The antioxidant capacity in the medium with different treatments at a concentration of 1×. (D) The antioxidant capacity in the medium with different treatments at different concentrations. TEAC: Trolox-equivalent antioxidant capacity. *: p < 0.05, compared with the H2O; #: p < 0.05, compared with the A medium; $: p < 0.05, compared with the B medium; &: p < 0.05, compared with the C medium. Full article ">Figure 3
Growth ability of different bacteria on the LB broth medium with different treatments. The growth of Bacillus subtilis (A), Bacillus paralicheniformis (B), Alternaria alternata (C), and Micrococcus luteus (D) at different concentrations in the LB medium with different treatments after being cultured for different times. B medium: the LB medium filtered with a 0.22 μm filter membrane; C medium: the LB medium autoclaved at 121 °C for 20 min; D medium: the LB medium firstly autoclaved, and then cultured for 12 h. Full article ">Figure 4
Identification of differential metabolites between the medium in the B and C groups, and functional analysis. (A) The volcano plot of the differential metabolites between the B and C medium. (B) The clustering heatmap of the identified differential metabolites between the B and C medium. (C) The KEGG pathways map of the identified differential metabolites between the B and C medium. Full article ">Figure 5
Identification of differential metabolites between the medium in the C and D groups, and functional analysis. (A) The volcano plot of the differential metabolites between the C and D medium. (B) The clustering heatmap of the identified differential metabolites between the C and D medium. (C) The KEGG pathways map of the identified differential metabolites between the C and D medium. Full article ">

16 pages, 3767 KiB

Open AccessArticle

Influence of Heat Stress on Body Temperatures Measured by Infrared Thermography, Blood Metabolic Parameters and Its Correlation in Sheep

by Aleksandar Čukić, Simeon Rakonjac, Radojica Djoković, Marko Cincović, Snežana Bogosavljević-Bošković, Milun Petrović, Željko Savić, Ljiljana Andjušić and Biljana Andjelić

Metabolites 2023, 13(8), 957; https://doi.org/10.3390/metabo13080957 - 18 Aug 2023

Cited by 6 | Viewed by 1754

Abstract

The aim of this research is to examine the influence of heat stress (HS) on body temperature (BT) measured rectally (RT) or by infrared thermography (IRT) of the nose (NT), eye (ET), leg (LT) and abdominal (AT) regions in intensively and extensively breed [...] Read more.

The aim of this research is to examine the influence of heat stress (HS) on body temperature (BT) measured rectally (RT) or by infrared thermography (IRT) of the nose (NT), eye (ET), leg (LT) and abdominal (AT) regions in intensively and extensively breed sheep and to detect a correlation between body temperature and metabolic response in sheep. A total of 33 Wurttemberg × Sjenica Pramenka sheep breeds were examined, 17 ewes were from outdoors and 16 were from indoor housing systems during three experimental periods (thermoneutral period, severe HS and moderate HS). Sheep under HS have a higher BT, and the magnitude of BT measured by infrared thermography (IRT) was higher than RT. LT and AT showed positive linear correlations with the temperature–humidity index (THI), while other ways of measuring BT did not give statistically significant correlations. Sheep under HS showed higher cortisol, insulin, total protein, albumin, urea, creatinine, bilirubin, aspartate aminotransferase, alanine aminotransferase, gamma-glutamyl transferase, alkaline phosphatase, lactate dehydrogenase, creatine kinase and index of insulin resistance, with lower values of triiodothyronine (T3), thyroxine (T4), non-esterified fatty acids, beta-hydroxybutyrate (BHB), glucose, calcium, inorganic phosphates, magnesium and cholesterol. BT and metabolic response were different in the function of the housing method of sheep. LT and AT showed a significant correlation with almost all blood parameters, and the strongest connections were made with T3, T4, BHB and the revised quantitative insulin sensitivity check index of insulin resistance. The abdomen and legs are good thermal windows because LT and AT are good summative responses to external ambient THI and internal metabolic changes in sheep under heat stress. Full article

(This article belongs to the Special Issue Metabolic and Endocrine Responses to Stress and Disease in Animal Production)

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Temperature–humidity index in experimental months (April, June, July) with moment of sampling in Periods 1, 2 and 3. Full article ">Figure 2
(a–f) Infrared thermography of the nose, (a–c) on pasture and (d–f) in the barn in three exp. Full article ">Figure 3
(a–f) Infrared thermography of the eye, (a–c) on pasture and (d–f) in the barn. Full article ">Figure 3 Cont.
(a–f) Infrared thermography of the eye, (a–c) on pasture and (d–f) in the barn. Full article ">Figure 4
(a–f) Infrared thermography of the front leg, (a–c) on pasture and (d–f) in the barn. Full article ">Figure 5
(a–f) Infrared thermography of the abdomen, (a–c) on pasture and (d–f) in the barn. Full article ">Figure 6
Linear correlation and regression between average daily THI and (a) leg and abdomen temperature and (b) rectal, nose and eye temperature. Full article ">

10 pages, 280 KiB

Open AccessArticle

Metabolic Syndrome in Affective Disorders: Associations with Dark Triad Personality Traits

by Fiona Brugger, Elena M. D. Schönthaler, Andreas Baranyi, Eva Z. Reininghaus, Dirk von Lewinski and Nina Dalkner

Metabolites 2023, 13(8), 956; https://doi.org/10.3390/metabo13080956 - 18 Aug 2023

Viewed by 1176

Abstract

Previous research has focused on the relationship between affective disorders (AD) and metabolic syndrome (MetS). Aside from biological and lifestyle factors, personality traits were identified as influencing aspects. In particular, the Dark Triad personality traits (DT; Machiavellianism, narcissism, psychopathy) were connected to both [...] Read more.

Previous research has focused on the relationship between affective disorders (AD) and metabolic syndrome (MetS). Aside from biological and lifestyle factors, personality traits were identified as influencing aspects. In particular, the Dark Triad personality traits (DT; Machiavellianism, narcissism, psychopathy) were connected to both AD and worse somatic health, thus possibly resulting in MetS. This observational study aimed to investigate the associations between DT and anthropometric parameters and differences in the DT traits concerning the presence of MetS in individuals with AD. A total of 112 individuals (females = 59, males = 51, diverse = 2, M_age = 47.5, SD_age = 11.5) with AD filled out the Short Dark Triad questionnaire. Body Mass Index (BMI) and MetS criteria, including blood pressure, waist circumference, lipid, and glucose levels, were assessed. For Machiavellianism, a positive association with BMI (r = 0.29, p < 0.05) and a negative association with systolic blood pressure (r = −0.23, p < 0.05) were found. No relationship between the overall MetS and DT score (r = 0.08, p = 0.409) was observed. The results were limited by the lack of a control group and the cross-sectional study design, which does not allow for the determination of causality. Machiavellianism was associated with a higher BMI and lower systolic blood pressure, indicating a deteriorating health effect of this trait. Possibly, the higher prevalence of MetS in AD stems from aspects such as lifestyle or medication intake, which might also be influenced by DT. Further research is needed to disentangle underlying mechanisms. Full article

(This article belongs to the Special Issue New Therapeutic Targets and Treatment Options in Metabolic Syndrome)

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24 pages, 16314 KiB

Open AccessArticle

Effects of Gold Nanoparticles Phytoreduced with Rutin in an Early Rat Model of Diabetic Retinopathy and Cataracts

by Mădălina Moldovan, Ana-Maria Păpurică, Mara Muntean, Raluca Maria Bungărdean, Dan Gheban, Bianca Moldovan, Gabriel Katona, Luminița David and Gabriela Adriana Filip

Metabolites 2023, 13(8), 955; https://doi.org/10.3390/metabo13080955 - 18 Aug 2023

Cited by 1 | Viewed by 1506

Abstract

Diabetic retinopathy (DR) and cataracts (CA) have an early onset in diabetes mellitus (DM) due to the redox imbalance and inflammation triggered by hyperglycaemia. Plant-based therapies are characterised by low tissue bioavailability. The study aimed to investigate the effect of gold nanoparticles phytoreduced [...] Read more.

Diabetic retinopathy (DR) and cataracts (CA) have an early onset in diabetes mellitus (DM) due to the redox imbalance and inflammation triggered by hyperglycaemia. Plant-based therapies are characterised by low tissue bioavailability. The study aimed to investigate the effect of gold nanoparticles phytoreduced with Rutin (AuNPsR), as a possible solution. Insulin, Rutin, and AuNPsR were administered to an early, six-week rat model of DR and CA. Oxidative stress (MDA, CAT, SOD) was assessed in serum and eye homogenates, and inflammatory cytokines (IL-1 beta, IL-6, TNF alpha) were quantified in ocular tissues. Eye fundus of retinal arterioles, transmission electron microscopy (TEM) of lenses, and histopathology of retinas were also performed. DM was linked to constricted retinal arterioles, reduced endogen antioxidants, and eye inflammation. Histologically, retinal wall thickness decreased. TEM showed increased lens opacity and fibre disorganisation. Rutin improved retinal arteriolar diameter, while reducing oxidative stress and inflammation. Retinas were moderately oedematous. Lens structure was preserved on TEM. Insulin restored retinal arteriolar diameter, while increasing MDA, and amplifying TEM lens opacity. The best outcomes were obtained for AuNPsR, as it improved fundus appearance of retinal arterioles, decreased MDA and increased antioxidant capacity. Retinal edema and disorganisation in lens fibres were still present. Full article

(This article belongs to the Special Issue Research Progress of Plant Compounds for Diabetes and Its Complications)

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23 pages, 3342 KiB

Open AccessReview

Metabolic Profile of Alzheimer’s Disease: Is 10-Hydroxy-2-decenoic Acid a Pertinent Metabolic Adjuster?

by Yuan Gong, Hongjie Luo, Zeju Li, Yijun Feng, Zhen Liu and Jie Chang

Metabolites 2023, 13(8), 954; https://doi.org/10.3390/metabo13080954 - 18 Aug 2023

Cited by 2 | Viewed by 1630

Abstract

Alzheimer’s disease (AD) represents a significant public health concern in modern society. Metabolic syndrome (MetS), which includes diabetes mellitus (DM) and obesity, represents a modifiable risk factor for AD. MetS and AD are interconnected through various mechanisms, such as mitochondrial dysfunction, oxidative stress, [...] Read more.

Alzheimer’s disease (AD) represents a significant public health concern in modern society. Metabolic syndrome (MetS), which includes diabetes mellitus (DM) and obesity, represents a modifiable risk factor for AD. MetS and AD are interconnected through various mechanisms, such as mitochondrial dysfunction, oxidative stress, insulin resistance (IR), vascular impairment, inflammation, and endoplasmic reticulum (ER) stress. Therefore, it is necessary to seek a multi-targeted and safer approach to intervention. Thus, 10-hydroxy-2-decenoic acid (10-HDA), a unique hydroxy fatty acid in royal jelly, has shown promising anti-neuroinflammatory, blood–brain barrier (BBB)-preserving, and neurogenesis-promoting properties. In this paper, we provide a summary of the relationship between MetS and AD, together with an introduction to 10-HDA as a potential intervention nutrient. In addition, molecular docking is performed to explore the metabolic tuning properties of 10-HDA with associated macromolecules such as GLP-1R, PPARs, GSK-3, and TREM2. In conclusion, there is a close relationship between AD and MetS, and 10-HDA shows potential as a beneficial nutritional intervention for both AD and MetS. Full article

(This article belongs to the Section Nutrition and Metabolism)

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A summary of the pathogenesis of MetS-related AD. First, under low-grade inflammation in adipose tissue caused by MetS, IR is formed in the adipose tissue, liver, and muscles due to impaired tyrosine phosphorylation. Afterwards, IR induces hyperglycemia, which causes the formation of more AGEs. IR also leads to energy metabolism dysfunction in mitochondria, which causes more ROS production. Released inflammatory cytokines, AGEs, and ROS damage the BBB; then, they enter the brain matter via the impaired BBB. After entering, they directly hamper neurons or activate microglia cells to damage neurons. Full article ">Figure 2
Docking results between 10-HDA and GLP-1R: (a) A gross view of the optimal conformation between GLP-1R and 10-HDA after molecular docking. (b) Details of the binding site be-tween GLP-1R and 10-HDA. Here, 10-HDA is represented as orange sticks, while GLP-1R is represented as a grey surface. And amino acid residues are represented as blue sticks, while hydrogen bonds are represented as dashed yellow sticks. Four hydrogen bonds are shown in the image, and 10-HDA is rooted in the binding pocket of GLP-1R. Full article ">Figure 3
Docking results between 10-HDA and PPAR-γ: (a) A gross view of the optimal conformation between PPAR-γ and 10-HDA after molecular docking. (b) Details of the binding site be-tween PPAR-γ and 10-HDA. Here, 10-HDA is represented as orange sticks, while PPAR-γ is represented as a grey surface. And amino acid residues are represented as blue sticks, while hydrogen bonds are represented as dashed yellow sticks. Four hydrogen bonds are shown in the image, and 10-HDA is rooted in the binding pocket of PPAR-γ. Full article ">Figure 4
Docking results between 10-HDA and PPAR-α: (a) A gross view of the optimal conformation between PPAR-α and 10-HDA after molecular docking. (b) Details of the binding site be-tween PPAR-α and 10-HDA. Here, 10-HDA is represented as orange sticks, while PPAR-α is represented as a grey surface. And amino acid residues are represented as blue sticks, while hydrogen bonds are represented as dashed yellow sticks. Six hydrogen bonds are shown in the image, and 10-HDA is rooted in the binding pocket of PPAR-α. Full article ">Figure 5
Docking results between 10-HDA and GSK-3: (a) A gross view of the optimal conformation between GSK-3 and 10-HDA after molecular docking. (b) Details of the binding site between GSK-3 and 10-HDA. Here, 10-HDA is represented as orange sticks, while GSK-3 is represented as a grey surface. And amino acid residues are represented as blue sticks, while hydrogen bonds are represented as dashed yellow sticks. Four hydrogen bonds are shown in the image, and 10-HDA is rooted in the binding pocket of GSK-3. Full article ">Figure 6
Docking results between 10-HDA and TREM2: (a) A gross view of the optimal conformation between TREM2 and 10-HDA after molecular docking. (b) Details of the binding site be-tween TREM2 and 10-HDA. Here, 10-HDA is represented as orange sticks, while TREM2 is represented as a grey surface. And amino acid residues are represented as blue sticks, while hydrogen bonds are represented as dashed yellow sticks. Five hydrogen bonds are shown in the image, and 10-HDA is rooted in the binding pocket of TREM2. Full article ">

14 pages, 18540 KiB

Open AccessArticle

The Native Microbiome Member Chryseobacterium sp. CHNTR56 MYb120 Induces Trehalose Production via a Shift in Central Carbon Metabolism during Early Life in C. elegans

by Tanisha Jean Shiri, Charles Viau, Xue Gu, Lei Xu, Yao Lu and Jianguo Xia

Metabolites 2023, 13(8), 953; https://doi.org/10.3390/metabo13080953 - 18 Aug 2023

Viewed by 1188

Abstract

Aging is the system-wide loss of homeostasis, eventually leading to death. There is growing evidence that the microbiome not only evolves with its aging host, but also directly affects aging via the modulation of metabolites involved in important cellular functions. The widely used [...] Read more.

Aging is the system-wide loss of homeostasis, eventually leading to death. There is growing evidence that the microbiome not only evolves with its aging host, but also directly affects aging via the modulation of metabolites involved in important cellular functions. The widely used model organism C. elegans exhibits high selectivity towards its native microbiome members which confer a range of differential phenotypes and possess varying functional capacities. The ability of one such native microbiome species, Chryseobacterium sp. CHNTR56 MYb120, to improve the lifespan of C. elegans and to promote the production of Vitamin B6 in the co-colonizing member Comamonas sp. 12022 MYb131 are some of its beneficial effects on the worm host. We hypothesize that studying its metabolic influence on the different life stages of the worm could provide further insights into mutualistic interactions. The present work applied LC-MS untargeted metabolomics and isotope labeling to study the impact of the native microbiome member Chryseobacterium sp. CHNTR56 MYb120 on the metabolism of C. elegans. In addition to the upregulation of biosynthesis and detoxification pathway intermediates, we found that Chryseobacterium sp. CHNTR56 MYb120 upregulates the glyoxylate shunt in mid-adult worms which is linked to the upregulation of trehalose, an important metabolite for desiccation tolerance in older worms. Full article

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15 pages, 1276 KiB

Open AccessReview

Bacterial Metabolites and Inflammatory Skin Diseases

by Victoria Jiminez and Nabiha Yusuf

Metabolites 2023, 13(8), 952; https://doi.org/10.3390/metabo13080952 - 17 Aug 2023

Cited by 1 | Viewed by 2117

Abstract

The microbiome and gut-skin axis are popular areas of interest in recent years concerning inflammatory skin diseases. While many bacterial species have been associated with commensalism of both the skin and gastrointestinal tract in certain disease states, less is known about specific bacterial [...] Read more.

The microbiome and gut-skin axis are popular areas of interest in recent years concerning inflammatory skin diseases. While many bacterial species have been associated with commensalism of both the skin and gastrointestinal tract in certain disease states, less is known about specific bacterial metabolites that regulate host pathways and contribute to inflammation. Some of these metabolites include short chain fatty acids, amine, and tryptophan derivatives, and more that when dysregulated, have deleterious effects on cutaneous disease burden. This review aims to summarize the knowledge of wealth surrounding bacterial metabolites of the skin and gut and their role in immune homeostasis in inflammatory skin diseases such as atopic dermatitis, psoriasis, and hidradenitis suppurativa. Full article

(This article belongs to the Special Issue Skin Metabolism and Cutaneous Disorders)

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26 pages, 6298 KiB

Open AccessArticle

The Effects of Hospitalisation on the Serum Metabolome in COVID-19 Patients

by Tim Hensen, Daniel Fässler, Liam O’Mahony, Werner C. Albrich, Beatrice Barda, Christian Garzoni, Gian-Reto Kleger, Urs Pietsch, Noémie Suh, Johannes Hertel and Ines Thiele

Metabolites 2023, 13(8), 951; https://doi.org/10.3390/metabo13080951 - 16 Aug 2023

Cited by 2 | Viewed by 1913

Abstract

COVID-19, a systemic multi-organ disease resulting from infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is known to result in a wide array of disease outcomes, ranging from asymptomatic to fatal. Despite persistent progress, there is a continued need for more accurate [...] Read more.

COVID-19, a systemic multi-organ disease resulting from infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is known to result in a wide array of disease outcomes, ranging from asymptomatic to fatal. Despite persistent progress, there is a continued need for more accurate determinants of disease outcomes, including post-acute symptoms after COVID-19. In this study, we characterised the serum metabolomic changes due to hospitalisation and COVID-19 disease progression by mapping the serum metabolomic trajectories of 71 newly hospitalised moderate and severe patients in their first week after hospitalisation. These 71 patients were spread out over three hospitals in Switzerland, enabling us to meta-analyse the metabolomic trajectories and filter consistently changing metabolites. Additionally, we investigated differential metabolite–metabolite trajectories between fatal, severe, and moderate disease outcomes to find prognostic markers of disease severity. We found drastic changes in serum metabolite concentrations for 448 out of the 901 metabolites. These results included markers of hospitalisation, such as environmental exposures, dietary changes, and altered drug administration, but also possible markers of physiological functioning, including carboxyethyl-GABA and fibrinopeptides, which might be prognostic for worsening lung injury. Possible markers of disease progression included altered urea cycle metabolites and metabolites of the tricarboxylic acid (TCA) cycle, indicating a SARS-CoV-2-induced reprogramming of the host metabolism. Glycerophosphorylcholine was identified as a potential marker of disease severity. Taken together, this study describes the metabolome-wide changes due to hospitalisation and COVID-19 disease progression. Moreover, we propose a wide range of novel potential biomarkers for monitoring COVID-19 disease course, both dependent and independent of the severity. Full article

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

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15 pages, 326 KiB

Open AccessReview

Diagnostic Value of Salivary Amino Acid Levels in Cancer

by Lyudmila V. Bel’skaya, Elena A. Sarf and Alexandra I. Loginova

Metabolites 2023, 13(8), 950; https://doi.org/10.3390/metabo13080950 - 15 Aug 2023

Cited by 6 | Viewed by 1855

Abstract

This review analyzed 21 scientific papers on the determination of amino acids in various types of cancer in saliva. Most of the studies are on oral cancer (8/21), breast cancer (4/21), gastric cancer (3/21), lung cancer (2/21), glioblastoma (2/21) and one study on [...] Read more.

This review analyzed 21 scientific papers on the determination of amino acids in various types of cancer in saliva. Most of the studies are on oral cancer (8/21), breast cancer (4/21), gastric cancer (3/21), lung cancer (2/21), glioblastoma (2/21) and one study on colorectal, pancreatic, thyroid and liver cancer. The amino acids alanine, valine, phenylalanine, leucine and isoleucine play a leading role in the diagnosis of cancer via the saliva. In an independent version, amino acids are rarely used; the authors combine either amino acids with each other or with other metabolites, which makes it possible to obtain high values of sensitivity and specificity. Nevertheless, a logical and complete substantiation of the changes in saliva occurring in cancer, including changes in salivary amino acid levels, has not yet been formed, which makes it important to continue research in this direction. Full article

(This article belongs to the Special Issue Exploring Potential Diagnostic and Prognostic Salivary Biomarkers in Cancer—2nd Edition)

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11 pages, 1038 KiB

Open AccessArticle

Metabolism Study of Anamorelin, a GHSR1a Receptor Agonist Potentially Misused in Sport, with Human Hepatocytes and LC-HRMS/MS

by Prince Sellase Gameli, Omayema Taoussi, Giuseppe Basile, Jeremy Carlier and Francesco Paolo Busardò

Metabolites 2023, 13(8), 949; https://doi.org/10.3390/metabo13080949 - 15 Aug 2023

Cited by 2 | Viewed by 1433

Abstract

Anamorelin, developed for the treatment of cancer cachexia, is an orally active medication that improves appetite and food intake, thereby increasing body mass and physical functioning. It is classified as a growth hormone secretagogue and strictly monitored by the World Anti-Doping Agency (WADA), [...] Read more.

Anamorelin, developed for the treatment of cancer cachexia, is an orally active medication that improves appetite and food intake, thereby increasing body mass and physical functioning. It is classified as a growth hormone secretagogue and strictly monitored by the World Anti-Doping Agency (WADA), owing to its anabolic enhancing potential. Identifying anamorelin and/or metabolite biomarkers of consumption is critical in doping controls. However, there are currently no data available on anamorelin human metabolic fate. The aim of this study was to investigate and identify biomarkers characteristic of anamorelin intake using in silico metabolite predictions with GLORYx, in vitro incubation with 10-donor-pooled human hepatocytes, liquid chromatography-high-resolution tandem mass spectrometry (LC-HRMS/MS) analysis, and data processing with Thermo Scientific’s Compound Discoverer. In silico prediction resulted in N-acetylation at the methylalanyl group as the main transformation (score, 88%). Others including hydroxylation at the indole substructure, and oxidation and N-demethylation at the trimethylhydrazino group were predicted (score, ≤36%). Hepatocyte incubations resulted in 14 phase I metabolites formed through N-demethylation at the trimethylhydrazino group, N-dealkylation at the piperidine ring, and oxidation at the indole and methylalanyl groups; and two phase II glucuronide conjugates occurring at the indole. We propose four metabolites detected as specific biomarkers for toxicological screening. Full article

(This article belongs to the Section Pharmacology and Drug Metabolism)

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16 pages, 811 KiB

Open AccessReview

Metabolomics Approaches for the Diagnosis, Treatment, and Better Disease Management of Viral Infections

by Haya Al-Sulaiti, Jehad Almaliti, C. Benjamin Naman, Asmaa A. Al Thani and Hadi M. Yassine

Metabolites 2023, 13(8), 948; https://doi.org/10.3390/metabo13080948 - 15 Aug 2023

Cited by 4 | Viewed by 3529

Abstract

Metabolomics is an analytical approach that involves profiling and comparing the metabolites present in biological samples. This scoping review article offers an overview of current metabolomics approaches and their utilization in evaluating metabolic changes in biological fluids that occur in response to viral [...] Read more.

Metabolomics is an analytical approach that involves profiling and comparing the metabolites present in biological samples. This scoping review article offers an overview of current metabolomics approaches and their utilization in evaluating metabolic changes in biological fluids that occur in response to viral infections. Here, we provide an overview of metabolomics methods including high-throughput analytical chemistry and multivariate data analysis to identify the specific metabolites associated with viral infections. This review also focuses on data interpretation and applications designed to improve our understanding of the pathogenesis of these viral diseases. Full article

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

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25 pages, 4840 KiB

Open AccessFeature PaperArticle

Sexual Dimorphism of the Mouse Plasma Metabolome Is Associated with Phenotypes of 30 Gene Knockout Lines

by Ying Zhang, Dinesh K. Barupal, Sili Fan, Bei Gao, Chao Zhu, Ann M. Flenniken, Colin McKerlie, Lauryl M. J. Nutter, Kevin C. Kent Lloyd and Oliver Fiehn

Metabolites 2023, 13(8), 947; https://doi.org/10.3390/metabo13080947 - 15 Aug 2023

Cited by 2 | Viewed by 1644

Abstract

Although metabolic alterations are observed in many monogenic and complex genetic disorders, the impact of most mammalian genes on cellular metabolism remains unknown. Understanding the effect of mouse gene dysfunction on metabolism can inform the functions of their human orthologues. We investigated the [...] Read more.

Although metabolic alterations are observed in many monogenic and complex genetic disorders, the impact of most mammalian genes on cellular metabolism remains unknown. Understanding the effect of mouse gene dysfunction on metabolism can inform the functions of their human orthologues. We investigated the effect of loss-of-function mutations in 30 unique gene knockout (KO) lines on plasma metabolites, including genes coding for structural proteins (11 of 30), metabolic pathway enzymes (12 of 30) and protein kinases (7 of 30). Steroids, bile acids, oxylipins, primary metabolites, biogenic amines and complex lipids were analyzed with dedicated mass spectrometry platforms, yielding 827 identified metabolites in male and female KO mice and wildtype (WT) controls. Twenty-two percent of 23,698 KO versus WT comparison tests showed significant genotype effects on plasma metabolites. Fifty-six percent of identified metabolites were significantly different between the sexes in WT mice. Many of these metabolites were also found to have sexually dimorphic changes in KO lines. We used plasma metabolites to complement phenotype information exemplified for Dhfr, Idh1, Mfap4, Nek2, Npc2, Phyh and Sra1. The association of plasma metabolites with IMPC phenotypes showed dramatic sexual dimorphism in wildtype mice. We demonstrate how to link metabolomics to genotypes and (disease) phenotypes. Sex must be considered as critical factor in the biological interpretation of gene functions. Full article

(This article belongs to the Special Issue The UC Davis West Coast Metabolomics Center Collection: From Compound Identification to Discoveries in Metabolism)

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Study design and data analysis. Thirty gene knockout lines and corresponding wildtype controls were selected with phenotypic data available from the IMPC. Plasma samples of 220 mice were analyzed using five assays, including three untargeted metabolomic profiling and two targeted data acquisition methods. Full article ">Figure 2
Sex differences in wildtype mice. (a) Proportion of metabolites that were significantly affected by sex in 40 wildtype mice (n = 20 males and 20 females; generalized linear model at p < 0.05). (b) Distribution of sex-affected metabolites across five metabolic assays (p < 0.05). (c) Proportion of continuous IMPC phenotypes that were significantly affected by sex in 40 wildtype mice (p < 0.05). (d) Metabolites affected by sex per metabolomics assay (p < 0.05). (e) Chemical Similarity Enrichment Analysis between male and female wildtype mice (dot size proportional to number of metabolites.). Full article ">Figure 3
Overall genotype effect with sex interaction on metabolomics/phenotype data of 30 mouse knockout lines, comparing 40 C57BL/6NCrl controls to six mice per KO line. Two-way ANOVA at p < 0.05. (a) Proportion of significant genotype and sex interaction effects on metabolites. (b) Proportion of significant genotype and sex interaction effect on the IMPC phenotypes. (c) Distribution of metabolites that were altered by genotype effect and genotype–sex interaction effect for each KO line. Full article ">Figure 4
Examples of metabolites affected by genotype–sex interaction of 30 mouse knockout lines. Comparison of 40 C57BL/6NCrl control mice versus n = 6 mice per knockout line. Two-way ANOVA followed by individual comparisons using a generalized linear model. (a,b) Plasma acylcarnitines in all genotypes. Error bars represent ranges of standardized effect sizes. (c–e) Plasma metabolites in Sra1−/− mice versus controls. Standardized fold-changes normalized to wildtype mice with error bars ±1 s.d. Full article ">Figure 5
Comparison of sexually dimorphic plasma metabolites for Npc2+/− and Mfap4−/− mice versus C57BL/6NCrl control mice. Significance levels * p < 0.05; ** p < 0.01, ns not significant. (a) Plasma cholesterol levels in Npc2+/− and Mfap4−/− mice versus controls. (b) Plasma cholesterol ester levels in Npc2+/− mice versus controls. (c) Plasma cholesterol ester levels in Mfap4 −/− mice versus controls. (d) Number of significant metabolites for Npc2+/− and Mfap4−/− mice versus controls. (e) Chemical set enrichment plots for Npc2+/− mice versus controls. (f) Chemical set enrichment plots for Mfap4−/− mice versus controls. Dot sizes are proportional to number of metabolites per chemical set. Full article ">Figure 6
Sexual dimorphic alteration of plasma metabolites in six Idh1−/− mice versus 40 C57BL/6NCrl control mice. Significance levels given as * p < 0.05; ** p < 0.01, ns not significant. (a) Function of isocitrate dehydrogenase (IDH) in cytoplasm and mitochondria. (b) Changes of isocitrate, citrate and 2-oxoglutarate (α-ketoglutarate) plasma levels. (c) Chemical set enrichment of plasma levels in female Idh1 −/− mice versus controls. (d) Chemical set enrichment of plasma levels in male Idh1 −/− mice versus controls. Larger dots indicate a higher number of metabolites per chemical set. Full article ">Figure 7
Heatmap of ChemRICH set enrichment clusters for female (F) and male (M) wildtype mice, calculated from Spearman rank correlations of metabolite versus IMPC phenotypes. (a) Clusters of significant metabolite–body weight phenotype associations (week 4–16). (b) Clusters of significant metabolite–grip strength phenotype associations, adjusted to body weight. Grip strength measures the neuromuscular function as maximal muscle strength of forelimbs and combined forelimbs and hind limbs. Average value from three trials were normalized to body weight. Spearman correlation with p < 0.05. N > 14 per sex for each metabolite and phenotype. Red: positive correlations; blue: negative correlations. (see <a href="#app1-metabolites-13-00947" class="html-app">Supplementary Data S7</a>). Full article ">

13 pages, 1098 KiB

Open AccessArticle

Effect of Rumen-Protected Methionine on Metabolic Profile of Liver, Muscle and Blood Serum Samples of Growing German Simmental Bulls Fed Protein-Reduced Diets

by Vivienne Inhuber, Wilhelm Windisch, Karin Kleigrewe, Chen Meng, Benedikt Bächler, Michael Gigl, Julia Steinhoff-Wagner and Thomas Ettle

Metabolites 2023, 13(8), 946; https://doi.org/10.3390/metabo13080946 - 15 Aug 2023

Viewed by 1001

Abstract

This study aimed to determine the metabolic response of growing German Simmental bulls fed rations low in crude protein (CP) supplemented with rumen-protected methionine (RPMET). In total, 69 bulls (on average 238 ± 11 days of age at start and 367 ± 25 [...] Read more.

This study aimed to determine the metabolic response of growing German Simmental bulls fed rations low in crude protein (CP) supplemented with rumen-protected methionine (RPMET). In total, 69 bulls (on average 238 ± 11 days of age at start and 367 ± 25 kg of bodyweight) were assigned to three dietary treatments (n = 23/group): Positive control (CON; 13.7% CP; 2.11 g methionine/kg DM), negative control deficient in CP (RED; 9.04% CP; 1.56 g methionine/kg DM) and crude protein-deficient ration supplemented with RPMET (RED+RPMET; 9.04% CP; 2.54 g methionine/kg DM). At slaughter, samples of liver, muscle and blood serum were taken and underwent subsequent metabolomics profiling using a UHPLC-QTOF-MS system. A total of 6540 features could be detected. Twenty metabolites in the liver, five metabolites in muscle and thirty metabolites in blood serum were affected (p < 0.05) due to dietary treatments. In total, six metabolites could be reliably annotated and were thus subjected to subsequent univariate analysis. Reduction in dietary CP had minimal effect on metabolite abundance in target tissues of both RED and RED+RPMET bulls as compared to CON bulls. The addition of RPMET altered the hepatic anti-oxidant status in RED+RPMET bulls compared to both RED and CON bulls. Results exemplify nutrient partitioning in growing German Simmental bulls: bulls set maintenance as the prevailing metabolic priority (homeostasis) and nutrient trafficking as the second priority, which was directed toward special metabolic functions, such as anti-oxidant pathways. Full article

(This article belongs to the Section Nutrition and Metabolism)

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Volcano plots of liver metabolites. The comparison of CON vs. RED+RPMET is depicted in either the P detection mode (a) or the N detection mode (c) and the comparison of RED vs. RED+RPMET in either the P detection mode (b) or the N detection mode (d). Y-axis depicts Benjamini–Hochberg (BH)-adjusted p-value’s false discovery rate (FDR). X-axis depicts the fold change between the treatment groups with CON = control diet according to requirements, RED = reduced in crude protein and RED+RPMET = reduced in crude protein + addition of RPMET (0.16% DM). Full article ">Figure 2
Volcano plots of muscle metabolites. The comparison of CON vs. RED+RPMET is depicted in either the P detection mode (a) or the N detection mode (c) and the comparison of RED vs. RED+RPMET in either the P detection mode (b) or the N detection mode (d). Y-axis depicts Benjamini–Hochberg (BH)-adjusted p-value’s false discovery rate (FDR). X-axis depicts the fold change between the treatment groups with CON = control diet according to requirements, RED = reduced in crude protein and RED+RPMET = reduced in crude protein + addition of RPMET (0.16% DM). Full article ">Figure 3
Volcano plots of blood serum metabolites. The comparison of CON vs. RED+RPMET is depicted in either the P detection mode (a) or the N detection mode (c) and the comparison of RED vs. RED+RPMET in either the P detection mode (b) or the N detection mode (d). Y-axis depicts Benjamini–Hochberg (BH)-adjusted p-value’s false discovery rate (FDR). X-axis depicts the fold change between the treatment groups with CON = control diet according to requirements, RED = reduced in crude protein and RED+RPMET = reduced in crude protein + addition of RPMET (0.16% DM). Full article ">

28 pages, 6334 KiB

Open AccessArticle

Flaxseed Reduces Cancer Risk by Altering Bioenergetic Pathways in Liver: Connecting SAM Biosynthesis to Cellular Energy

by William C. Weston, Karen H. Hales and Dale B. Hales

Metabolites 2023, 13(8), 945; https://doi.org/10.3390/metabo13080945 - 14 Aug 2023

Viewed by 2547

Abstract

This article illustrates how dietary flaxseed can be used to reduce cancer risk, specifically by attenuating obesity, type 2 diabetes, and non-alcoholic fatty liver disease (NAFLD). We utilize a targeted metabolomics dataset in combination with a reanalysis of past work to investigate the [...] Read more.

This article illustrates how dietary flaxseed can be used to reduce cancer risk, specifically by attenuating obesity, type 2 diabetes, and non-alcoholic fatty liver disease (NAFLD). We utilize a targeted metabolomics dataset in combination with a reanalysis of past work to investigate the “metabo-bioenergetic” adaptations that occur in White Leghorn laying hens while consuming dietary flaxseed. Recently, we revealed how the anti-vitamin B6 effects of flaxseed augment one-carbon metabolism in a manner that accelerates S-adenosylmethionine (SAM) biosynthesis. Researchers recently showed that accelerated SAM biosynthesis activates the cell’s master energy sensor, AMP-activated protein kinase (AMPK). Our paper provides evidence that flaxseed upregulates mitochondrial fatty acid oxidation and glycolysis in liver, concomitant with the attenuation of lipogenesis and polyamine biosynthesis. Defatted flaxseed likely functions as a metformin homologue by upregulating hepatic glucose uptake and pyruvate flux through the pyruvate dehydrogenase complex (PDC) in laying hens. In contrast, whole flaxseed appears to attenuate liver steatosis and body mass by modifying mitochondrial fatty acid oxidation and lipogenesis. Several acylcarnitine moieties indicate Randle cycle adaptations that protect mitochondria from metabolic overload when hens consume flaxseed. We also discuss a paradoxical finding whereby flaxseed induces the highest glycated hemoglobin percentage (HbA1c%) ever recorded in birds, and we suspect that hyperglycemia is not the cause. In conclusion, flaxseed modifies bioenergetic pathways to attenuate the risk of obesity, type 2 diabetes, and NAFLD, possibly downstream of SAM biosynthesis. These findings, if reproducible in humans, can be used to lower cancer risk within the general population. Full article

(This article belongs to the Section Nutrition and Metabolism)

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17 pages, 3371 KiB

Open AccessArticle

Denoising Autoencoder Normalization for Large-Scale Untargeted Metabolomics by Gas Chromatography–Mass Spectrometry

by Ying Zhang, Sili Fan, Gert Wohlgemuth and Oliver Fiehn

Metabolites 2023, 13(8), 944; https://doi.org/10.3390/metabo13080944 - 13 Aug 2023

Cited by 2 | Viewed by 1604

Abstract

Large-scale metabolomics assays are widely used in epidemiology for biomarker discovery and risk assessments. However, systematic errors introduced by instrumental signal drifting pose a big challenge in large-scale assays, especially for derivatization-based gas chromatography–mass spectrometry (GC–MS). Here, we compare the results of different [...] Read more.

Large-scale metabolomics assays are widely used in epidemiology for biomarker discovery and risk assessments. However, systematic errors introduced by instrumental signal drifting pose a big challenge in large-scale assays, especially for derivatization-based gas chromatography–mass spectrometry (GC–MS). Here, we compare the results of different normalization methods for a study with more than 4000 human plasma samples involved in a type 2 diabetes cohort study, in addition to 413 pooled quality control (QC) samples, 413 commercial pooled plasma samples, and a set of 25 stable isotope-labeled internal standards used for every sample. Data acquisition was conducted across 1.2 years, including seven column changes. In total, 413 pooled QC (training) and 413 BioIVT samples (validation) were used for normalization comparisons. Surprisingly, neither internal standards nor sum-based normalizations yielded median precision of less than 30% across all 563 metabolite annotations. While the machine-learning-based SERRF algorithm gave 19% median precision based on the pooled quality control samples, external cross-validation with BioIVT plasma pools yielded a median 34% relative standard deviation (RSD). We developed a new method: systematic error reduction by denoising autoencoder (SERDA). SERDA lowered the median standard deviations of the training QC samples down to 16% RSD, yielding an overall error of 19% RSD when applied to the independent BioIVT validation QC samples. This is the largest study on GC–MS metabolomics ever reported, demonstrating that technical errors can be normalized and handled effectively for this assay. SERDA was further validated on two additional large-scale GC–MS-based human plasma metabolomics studies, confirming the superior performance of SERDA over SERRF or sum normalizations. Full article

(This article belongs to the Special Issue The UC Davis West Coast Metabolomics Center Collection: From Compound Identification to Discoveries in Metabolism)

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Comparisons of normalizations of large-scale GC–MS human cohort plasma datasets. Full article ">Figure 2
Reaction schemes of MSTFA, MTBSTFA, and PCF derivations, and the chromatography of valine derivatized with MSTFA. (a) valine-1TMS and valine-d8-1TMS products; (b) valine-2TMS and valine-d8-2TMS products; (c) reaction schemes for MSTFA, MTBSTFA, and PCF derivatization. Full article ">Figure 3
Relative standard deviation (%) of 16 amino acids by three derivatization reagents. Raw: not normalized data; ISTD: normalization of each amino acid to its corresponding internal standard; fTIC: normalization to the sum of 13 fatty acid methyl esters; iTIC: normalization to the sum of all internal isotope-labeled standards; mTIC: normalization to the sum of all identified metabolites. Full article ">Figure 4
Sequence of sample acquisition and distribution of three sets of quality controls (QC) in large scale GC–MS metabolomics. (a) Sequence of injections of blanks, pooled sample quality controls, and BioIVT and NIST external plasma quality controls, plus blinded sample doublets. (b–d) Partial least square-discriminant analysis plots (PLS-DA) of (b) raw data, and effect of normalization by (c) SERDA and (d) SERRF normalization. Full article ">Figure 5
Correlation of blinded T2D cohort sample duplicates after SERDA normalization. Full article ">Figure 6
The relative standard deviation (RSD) of pool QC samples (red) and BioIVT qc samples (blue) for each set of 10, 20, 40, and 80 biological samples. With a smaller number of training QC samples, the performance of SERDA decreases, as indicated by the increasing of both the RSD of pool QC traing qc samples and BioIVT validation qc samples. Full article ">Figure 7
Comparison of ISTD absolute ratio normalization with QC-based and TIC-based normalization methods. The Friedman nonparametric test was used for significance comparison with raw. p-value threshold: 0.1234 (ns), 0.0001 (****). The Friedman nonparametric test was used for significance testing compared to SERDA. P value threshold: 0.1234 (ns), 0.0332 (#), 0.0002 (###), 0.0001 (####). (a) One-to-one: the absolute ratio was calculated by dividing the peak intensity of endogenous metabolite by the corresponding deuterated ISTD; (b) One-to-class: the absolute ratio was calculated by dividing the peak intensity of endogenous metabolite by a single deuterated compound as an analog ISTD for the entire class. Full article ">Figure 8
Effect of different normalization methods on residual errors in GC–MS-based metabolomics datasets. Left panel: this study, N = 413 quality control human plasma samples (QC), reporting 661 metabolites. Mid panel: 104 human plasma QC samples of the GeneBank study on 319 metabolites. Right panel: 30 QC plasma samples of the MPA study on 991 metabolites. For each panel, cumulative distributions of cross-validated relative standard deviations (RSD) are given using raw (black), batchwise-LOESS (blue), SERRF (green), and SERDA (red) normalized dataset. The coverage of metabolites achieving specific RSD levels is given as the y-axis. Full article ">

12 pages, 1122 KiB

Open AccessArticle

Impact of Heavy Metal Exposure on Mytilus galloprovincialis Spermatozoa: A Metabolomic Investigation

by Gennaro Lettieri, Carmela Marinaro, Rosaria Notariale, Pasquale Perrone, Martina Lombardi, Alessio Trotta, Jacopo Troisi and Marina Piscopo

Metabolites 2023, 13(8), 943; https://doi.org/10.3390/metabo13080943 - 13 Aug 2023

Cited by 4 | Viewed by 1747

Abstract

Metabolomics is a method that provides an overview of the physiological and cellular state of a specific organism or tissue. This method is particularly useful for studying the influence the environment can have on organisms, especially those used as bio-indicators, e.g., Mytilus galloprovincialis [...] Read more.

Metabolomics is a method that provides an overview of the physiological and cellular state of a specific organism or tissue. This method is particularly useful for studying the influence the environment can have on organisms, especially those used as bio-indicators, e.g., Mytilus galloprovincialis. Nevertheless, a scarcity of data on the complete metabolic baseline of mussel tissues still exists, but more importantly, the effect of mussel exposure to certain heavy metals on spermatozoa is unknown, also considering that, in recent years, the reproductive system has proved to be very sensitive to the effects of environmental pollutants. In order to fill this knowledge gap, the similarities and differences in the metabolic profile of spermatozoa of mussels exposed to metallic chlorides of copper, nickel, and cadmium, and to the mixture to these metals, were studied using a metabolomics approach based on GC–MS analysis, and their physiological role was discussed. A total of 237 endogenous metabolites were identified in the spermatozoa of these mussel. The data underwent preprocessing steps and were analyzed using statistical methods such as PLS-DA. The results showed effective class separation and identified key metabolites through the VIP scores. Heatmaps and cluster analysis further evaluated the metabolites. The metabolite-set enrichment analysis revealed complex interactions within metabolic pathways and metabolites, especially involving glucose and central carbon metabolism and oxidative stress metabolism. Overall, the results of this study are useful to better understand how some pollutants can affect the specific physiological functions of the spermatozoa of this mussel, as well as for further GC–MS-based metabolomic health and safety studies of marine bivalves. Full article

(This article belongs to the Special Issue Environmental Pollution and Animal Health: Toxicity and Metabolism)

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26 pages, 7207 KiB

Open AccessArticle

Deciphering Molecular Aspects of Potential α-Glucosidase Inhibitors within Aspergillus terreus: A Computational Odyssey of Molecular Docking-Coupled Dynamics Simulations and Pharmacokinetic Profiling

by Sameh S. Elhady, Noha M. Alshobaki, Mahmoud A. Elfaky, Abdulrahman E. Koshak, Majed Alharbi, Reda F. A. Abdelhameed and Khaled M. Darwish

Metabolites 2023, 13(8), 942; https://doi.org/10.3390/metabo13080942 - 12 Aug 2023

Cited by 1 | Viewed by 1735

Abstract

Hyperglycemia, as a hallmark of the metabolic malady diabetes mellitus, has been an overwhelming healthcare burden owing to its high rates of comorbidity and mortality, as well as prospective complications affecting different body organs. Available therapeutic agents, with α-glucosidase inhibitors as one [...] Read more.

Hyperglycemia, as a hallmark of the metabolic malady diabetes mellitus, has been an overwhelming healthcare burden owing to its high rates of comorbidity and mortality, as well as prospective complications affecting different body organs. Available therapeutic agents, with α-glucosidase inhibitors as one of their cornerstone arsenal, control stages of broad glycemia while showing definitive characteristics related to their low clinical efficiency and off-target complications. This has propelled the academia and industrial section into discovering novel and safer candidates. Herein, we provided a thorough computational exploration of identifying candidates from the marine-derived Aspergillus terreus isolates. Combined structural- and ligand-based approaches using a chemical library of 275 metabolites were adopted for pinpointing promising α-glucosidase inhibitors, as well as providing guiding insights for further lead optimization and development. Structure-based virtual screening through escalating precision molecular docking protocol at the α-glucosidase canonical pocket identified 11 promising top-docked hits, with several being superior to the market drug reference, acarbose. Comprehensive ligand-based investigations of these hits’ pharmacokinetics ADME profiles, physiochemical characterizations, and obedience to the gold standard Lipinski’s rule of five, as well as toxicity and mutagenicity profiling, proceeded. Under explicit conditions, a molecular dynamics simulation identified the top-stable metabolites: butyrolactone VI (SK-44), aspulvinone E (SK-55), butyrolactone I 4′’’’-sulfate (SK-72), and terrelumamide B (SK-173). They depicted the highest free binding energies and steadiest thermodynamic behavior. Moreover, great structural insights have been revealed, including the advent of an aromatic scaffold-based interaction for ligand–target complex stability. The significance of introducing balanced hydrophobic/polar moieties, like triazole and other bioisosteres of carboxylic acid, has been highlighted across docking, ADME/Tox profiling, and molecular dynamics studies for maximizing binding interactions while assuring safety and optimal pharmacokinetics for targeting the intestinal-localized α-glucosidase enzyme. Overall, this study provided valuable starting points for developing new α-glucosidase inhibitors based on nature-derived unique scaffolds, as well as guidance for prospective lead optimization and development within future pre-clinical and clinical investigations. Full article

(This article belongs to the Special Issue Extraction, Characterization, Chemical Composition and Biological Activity of Natural Metabolites)

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Figure 1
Structure of hiMGAM (PDB ID: 2QMJ) bounded with acarbose co-crystalline and investigated marine-based molecules. (A) Cartoon representation of hiMGAM crystallized with acarbose (magenta spheres) illustrating structural regions within different colors: N-terminal P-type trefoil region (deep salmon; Val7–Ser51), β-sheet sandwiched region (green; Tyr52–Thr269), catalytic [α/β]8-barrel region (cyan; Pro270–Val651). Comprising insert-I (blue; Pro367–Thr416) and insert-II (red; Val447–Lys492), C-terminal proximal region (orange; Ala652–Arg730), and the distal region (yellow; Gly731–His870). (B) Overlayed binding modes of redocked (gray sticks) and crystallized acarbose (magenta sticks) at the shallow substrate-binding pocket. (C) Binding mode of redocked acarbose; residues located within a 4 Å radius of bound ligand are displayed as lines, numbered with their sequence at the protein, and colored based on the respective domain location. Polar interactions (hydrogen bonding) are shown as black dashed lines. (D) Overlayed binding modes of docked compounds (gray lines) and crystallized acarbose (magenta sticks) at the shallow substrate-binding pocket. Both the −1 and +1 carbohydrate subsites are displayed as arcs in, respective, blue and purple colors. Full article ">Figure 2
Predicted binding modes of docked Asperigellus terreus-isolated compounds at the hMGAM binding site: (A) lovastatin (SK-25), (B) aspergillamide A (SK-27), (C) butyrolactone VI (SK-44), (D) aspulvinone E (SK-55), (E) aspulvinone F (SK-58), (F) rubrolide S (SK-61), (G) butyrolactone I 4′′′′-sulfate (SK-72), (H) (+)-asperteretone F (SK-119), (I) 12,15,25,28-tetrahydroxyergosta-4,6,8(14),22-tetraen-3-one (SK-132), (J) terrelumamide B (SK-173), (K) cytochalasin Z11 (SK-182). Residues located within a 4 Å radius of the bound ligand are displayed as lines, numbered with their sequence at the protein, and colored based on the respective domain location. Polar interactions (hydrogen bonding) are shown as black dashed lines. (L) Representing predicted inhibition constant (Ki) with ligand’s efficiency (LE) of the top-docked identified hits. Heat maps shift darker towards the higher affinity ligands (lower μM concentrations) and most likely predicted hits (higher LE values). Full article ">Figure 2 Cont.
Predicted binding modes of docked Asperigellus terreus-isolated compounds at the hMGAM binding site: (A) lovastatin (SK-25), (B) aspergillamide A (SK-27), (C) butyrolactone VI (SK-44), (D) aspulvinone E (SK-55), (E) aspulvinone F (SK-58), (F) rubrolide S (SK-61), (G) butyrolactone I 4′′′′-sulfate (SK-72), (H) (+)-asperteretone F (SK-119), (I) 12,15,25,28-tetrahydroxyergosta-4,6,8(14),22-tetraen-3-one (SK-132), (J) terrelumamide B (SK-173), (K) cytochalasin Z11 (SK-182). Residues located within a 4 Å radius of the bound ligand are displayed as lines, numbered with their sequence at the protein, and colored based on the respective domain location. Polar interactions (hydrogen bonding) are shown as black dashed lines. (L) Representing predicted inhibition constant (Ki) with ligand’s efficiency (LE) of the top-docked identified hits. Heat maps shift darker towards the higher affinity ligands (lower μM concentrations) and most likely predicted hits (higher LE values). Full article ">Figure 3
Thermodynamic stability of top-affinity A. terrus-isolated hits in the complex of the hiMGAM target. (A) Alpha-carbon RMSDs for target protein; (B) sole ligand’s RMSDs, in terms of simulation timeframes (ns). (C–N) Overlaid ligand-hiMGAM snapshots at initial and final timeframes; (C) SK-25, (D) SK-27, (E) SK-44, (F) SK-55, (G) SK-58, (H) SK-61, (I) SK-72, (J) SK-119, (K) SK-132, (L) SK-173, (M) SK-182, and (N) acarbose. Ligands (sticks) and bounded hiMGAM proteins (cartoons) are colored green and red, with respect to 0 ns and 200 ns extracted frames. Full article ">Figure 3 Cont.
Thermodynamic stability of top-affinity A. terrus-isolated hits in the complex of the hiMGAM target. (A) Alpha-carbon RMSDs for target protein; (B) sole ligand’s RMSDs, in terms of simulation timeframes (ns). (C–N) Overlaid ligand-hiMGAM snapshots at initial and final timeframes; (C) SK-25, (D) SK-27, (E) SK-44, (F) SK-55, (G) SK-58, (H) SK-61, (I) SK-72, (J) SK-119, (K) SK-132, (L) SK-173, (M) SK-182, and (N) acarbose. Ligands (sticks) and bounded hiMGAM proteins (cartoons) are colored green and red, with respect to 0 ns and 200 ns extracted frames. Full article ">Figure 4
Analysis of hiMGAM ΔRMSF trajectories across the entirety of the molecular dynamics runs. Residue-wise flexibility contributions of the holo-target proteins are represented in relation to the apo/unliganded state. ΔRMSF trajectories are represented as per the amino acid sequence number (residues N-terminus Val7-to-His870 C-terminus). Full article ">Figure 5
MM_PBSA free binding energy calculations for ligand-hiMGAM complexes. (A) Total free binding energies and their constituting energy terms. (B) Residue-based energy contributions. Full article ">Figure 5 Cont.
MM_PBSA free binding energy calculations for ligand-hiMGAM complexes. (A) Total free binding energies and their constituting energy terms. (B) Residue-based energy contributions. Full article ">

12 pages, 1805 KiB

Open AccessArticle

SMetaS: A Sample Metadata Standardizer for Metabolomics

by Parker Ladd Bremer and Oliver Fiehn

Metabolites 2023, 13(8), 941; https://doi.org/10.3390/metabo13080941 - 12 Aug 2023

Viewed by 1428

Abstract

Metabolomics has advanced to an extent where it is desired to standardize and compare data across individual studies. While past work in standardization has focused on data acquisition, data processing, and data storage aspects, metabolomics databases are useless without ontology-based descriptions of biological [...] Read more.

Metabolomics has advanced to an extent where it is desired to standardize and compare data across individual studies. While past work in standardization has focused on data acquisition, data processing, and data storage aspects, metabolomics databases are useless without ontology-based descriptions of biological samples and study designs. We introduce here a user-centric tool to automatically standardize sample metadata. Using such a tool in frontends for metabolomic databases will dramatically increase the FAIRness (Findability, Accessibility, Interoperability, and Reusability) of data, specifically for data reuse and for finding datasets that share comparable sets of metadata, e.g., study meta-analyses, cross-species analyses or large scale metabolomic atlases. SMetaS (Sample Metadata Standardizer) combines a classic database with an API and frontend and is provided in a containerized environment. The tool has two user-centric components. In the first component, the user designs a sample metadata matrix and fills the cells using natural language terminology. In the second component, the tool transforms the completed matrix by replacing freetext terms with terms from fixed vocabularies. This transformation process is designed to maximize simplicity and is guided by, among other strategies, synonym matching and typographical fixing in an n-grams/nearest neighbors model approach. The tool enables downstream analysis of submitted studies and samples via string equality for FAIR retrospective use. Full article

(This article belongs to the Special Issue The UC Davis West Coast Metabolomics Center Collection: From Compound Identification to Discoveries in Metabolism)

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Workflow of SMetaS. First, each metadata column type (species, organ, drug, etc.) has a starting vocabulary derived by combining/subsetting existing ontologies/vocabularies, making sure that the intersection of any two vocabularies is 0. Next, for each vocabulary, we generate additional resources that facilitate ease-of-use for sample submitters (e.g., nearest neighbor models that map synonyms/typos to the correct term). Finally, we make the vocabularies and associated resources as the backend to a user-friendly frontend. These vocabularies and models are expandable if new terms are desired by users. The vocabularies and models are also available as an API directly. A more detailed workflow is available as <a href="#app1-metabolites-13-00941" class="html-app">Supplementary Materials Figure S1</a>. Full article ">Figure 2
Walkthrough of user experience. (a) The first component of the tool is a walkthrough that allows users to design a sample metadata matrix. (b) An example metadata matrix prior to standardization. (c) The second component of the tool is a walkthrough that allows users to curate that submission. (d) The same submission with terms standardized to simplify meta-analysis. Full article ">Figure 3
Example use case of SmetaS. (a) Excerpt of a published study abstract [<a href="#B30-metabolites-13-00941" class="html-bibr">30</a>]. (b) SMetaS matrix representation of information from the abstract and methods section [<a href="#B30-metabolites-13-00941" class="html-bibr">30</a>]. (c) SMetaS curation of freetext terms of the matrix representation. Full article ">Figure 4
Comparing tabular to graph storage for sample metadata. (a) Example of a simple tabular schema for metadata capture. (b) The same tabular schema expressed as a graph, with additional complexity programmatically embedded into nodes. (c) Example how graph nodes, here: the species node ‘Valley Oak’, map to ontologies that can later be used for hierarchical analyses of metadata. Full article ">

6 pages, 1026 KiB

Open AccessEditorial

Imaging and Spectroscopic-Based Methods to Understand Cancer Metabolism and Biology

by Basetti Madhu

Metabolites 2023, 13(8), 940; https://doi.org/10.3390/metabo13080940 - 12 Aug 2023

Viewed by 1011

Abstract

The results of publications in PubMed with the MeSH terms “cancer”, “biology”, “imaging and cancer”, “metabolism” and “spectroscopy” are shown in Figure 1 in the form of a Venn diagram [...] Full article

(This article belongs to the Special Issue Imaging and Spectroscopic Based Methods to Understand Cancer Metabolism and Biology)

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11 pages, 2366 KiB

Open AccessArticle

Nuclear PTEN Regulates Thymidylate Biosynthesis in Human Prostate Cancer Cell Lines

by Zoe N. Loh, Mu-En Wang, Changxin Wan, John M. Asara, Zhicheng Ji and Ming Chen

Metabolites 2023, 13(8), 939; https://doi.org/10.3390/metabo13080939 - 11 Aug 2023

Viewed by 1452

Abstract

The phosphatase and tensin homologue deleted on chromosome 10 (PTEN) tumor suppressor governs a variety of biological processes, including metabolism, by acting on distinct molecular targets in different subcellular compartments. In the cytosol, inactive PTEN can be recruited to the plasma membrane where [...] Read more.

The phosphatase and tensin homologue deleted on chromosome 10 (PTEN) tumor suppressor governs a variety of biological processes, including metabolism, by acting on distinct molecular targets in different subcellular compartments. In the cytosol, inactive PTEN can be recruited to the plasma membrane where it dimerizes and functions as a lipid phosphatase to regulate metabolic processes mediated by the phosphatidylinositol 3-kinase (PI3K)/AKT/mammalian target of rapamycin complex 1 (mTORC1) pathway. However, the metabolic regulation of PTEN in the nucleus remains undefined. Here, using a gain-of-function approach to targeting PTEN to the plasma membrane and nucleus, we show that nuclear PTEN contributes to pyrimidine metabolism, in particular de novo thymidylate (dTMP) biosynthesis. PTEN appears to regulate dTMP biosynthesis through interaction with methylenetetrahydrofolate dehydrogenase 1 (MTHFD1), a key enzyme that generates 5,10-methylenetetrahydrofolate, a cofactor required for thymidylate synthase (TYMS) to catalyze deoxyuridylate (dUMP) into dTMP. Our findings reveal a nuclear function for PTEN in controlling dTMP biosynthesis and may also have implications for targeting nuclear-excluded PTEN prostate cancer cells with antifolate drugs. Full article

(This article belongs to the Special Issue Cancer Metabolism: Molecular Insights of Cancer through Metabolomics)

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16 pages, 1698 KiB

Open AccessArticle

Metabolic Alteration of MCF-7 Cells upon Indirect Exposure to E. coli Secretome: A Model of Studying the Microbiota Effect on Human Breast Tissue

by Reem H. AlMalki, Malak A. Jaber, Mysoon M. Al-Ansari, Khalid M. Sumaily, Monther Al-Alwan, Essa M. Sabi, Abeer K. Malkawi and Anas M. Abdel Rahman

Metabolites 2023, 13(8), 938; https://doi.org/10.3390/metabo13080938 - 11 Aug 2023

Viewed by 1585

Abstract

According to studies, the microbiome may contribute to the emergence and spread of breast cancer. E. coli is one of the Enterobacteriaceae family recently found to be present as part of the breast tissue microbiota. In this study, we focused on the effect [...] Read more.

According to studies, the microbiome may contribute to the emergence and spread of breast cancer. E. coli is one of the Enterobacteriaceae family recently found to be present as part of the breast tissue microbiota. In this study, we focused on the effect of E. coli secretome free of cells on MCF-7 metabolism. Liquid chromatography–mass spectrometry (LC-MS) metabolomics was used to study the E. coli secretome and its role in MCF-7 intra- and extracellular metabolites. A comparison was made between secretome-exposed cells and unexposed controls. Our analysis revealed significant alterations in 31 intracellular and 55 extracellular metabolites following secretome exposure. Several metabolic pathways, including lactate, aminoacyl-tRNA biosynthesis, purine metabolism, and energy metabolism, were found to be dysregulated upon E. coli secretome exposure. E. coli can alter the breast cancer cells’ metabolism through its secretome which disrupts key metabolic pathways of MCF-7 cells. These microbial metabolites from the secretome hold promise as biomarkers of drug resistance or innovative approaches for cancer treatment, either as standalone therapies or in combination with other medicines. Full article

(This article belongs to the Special Issue Environmental Toxicology and Metabolism)

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E. coli-related secretome identification upon treatment. A heatmap and hierarchical cluster analysis showed only 7 E. coli-related excreted metabolites in conditioned media of MCF-7 cells compared to the control. Full article ">Figure 2
(A) A Venn diagram represents the relation between significantly dysregulated intracellular ions in treated MCF-7 with E. coli secretome (n = 907) and non-treated cells (n = 1482) at different time points (0, 1, 2, 6, 8, and 24 h). (B) Sample clustering and group separation. PLS-DA of 614 features of MCF-7 cells that were treated with E. coli secretome at different time points (0, 1, 2, 6, 8, and 24 h). Full article ">Figure 3
Dysregulated intracellular metabolites between MCF-7 cells pre- and 24 h post-treatment with E. coli secretome. (A): An OPLS-DA model of MCF-7 cells treated with E. coli secretome shows a clear separation between pre- and 24 h post-treatment. The robustness of the created model was evaluated by the fitness of the model (R2Y = 0.996), and predictive ability (Q2 = 0.887) values in a larger dataset (n = 1000). (B): Univariate analysis using Volcano plot based on culture background-free features (n = 614) showed 93 (red), and 67 (blue) metabolites were up- and down-regulated 24 h post-treatment compared to pre-treatment, respectively (cut-off: FDR p ≤ 0.05, and FC 2). (C): Pathway analysis of significant metabolites dysregulated in treated MCF-7 cells with E. coli secretome after 24 h. Full article ">

14 pages, 347 KiB

Open AccessArticle

Effect of Pre-Hospital Intravenous Fluids on Initial Metabolic Acid-Base Status in Trauma Patients: A Retrospective Cohort Study

by Damien Bossel, Mylène Bourgeat, Olivier Pantet and Tobias Zingg

Metabolites 2023, 13(8), 937; https://doi.org/10.3390/metabo13080937 - 10 Aug 2023

Viewed by 1416

Abstract

Despite its known harmful effects, normal saline is still commonly used in the treatment of hypovolemia in polytrauma patients. Given the lack of pre-hospital research on this topic, the current study aims to assess the current practice of fluid administration during the pre-hospital [...] Read more.

Despite its known harmful effects, normal saline is still commonly used in the treatment of hypovolemia in polytrauma patients. Given the lack of pre-hospital research on this topic, the current study aims to assess the current practice of fluid administration during the pre-hospital phase of care and its effects on initial metabolic acid-base status in trauma patients. We extracted and completed data from patients recorded in the Lausanne University Hospital (CHUV) trauma registry between 2008 and 2019. Patients were selected according to their age, the availability of a blood gas analysis after arrival at the emergency room, data availability in the trauma registry, and the modality of arrival in the ED. The dominantly administered pre-hospital fluid was normal saline. No association between the type of fluid administered during the pre-hospital phase and the presence of hyperchloremic acidosis in the ED was observed. Full article

(This article belongs to the Special Issue Inflammatory Biomarkers in Critical Patients)

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21 pages, 5436 KiB

Open AccessArticle

Accumulation of Linoleic Acid by Altered Peroxisome Proliferator-Activated Receptor-α Signaling Is Associated with Age-Dependent Hepatocarcinogenesis in Ppara Transgenic Mice

by Xiaoyang Zhu, Qing Liu, Andrew D. Patterson, Arun K. Sharma, Shantu G. Amin, Samuel M. Cohen, Frank J. Gonzalez and Jeffrey M. Peters

Metabolites 2023, 13(8), 936; https://doi.org/10.3390/metabo13080936 - 10 Aug 2023

Viewed by 1902

Abstract

Long-term ligand activation of PPARα in mice causes hepatocarcinogenesis through a mechanism that requires functional PPARα. However, hepatocarcinogenesis is diminished in both Ppara-null and PPARA-humanized mice, yet both lines develop age-related liver cancer independently of treatment with a PPARα agonist. Since [...] Read more.

Long-term ligand activation of PPARα in mice causes hepatocarcinogenesis through a mechanism that requires functional PPARα. However, hepatocarcinogenesis is diminished in both Ppara-null and PPARA-humanized mice, yet both lines develop age-related liver cancer independently of treatment with a PPARα agonist. Since PPARα is a master regulator of liver lipid metabolism in the liver, lipidomic analyses were carried out in wild-type, Ppara-null, and PPARA-humanized mice treated with and without the potent agonist GW7647. The levels of hepatic linoleic acid in Ppara-null and PPARA-humanized mice were markedly higher compared to wild-type controls, along with overall fatty liver. The number of liver CD4⁺ T cells was also lower in Ppara-null and PPARA-humanized mice and was negatively correlated with the elevated linoleic acid. Moreover, more senescent hepatocytes and lower serum TNFα and IFNγ levels were observed in Ppara-null and PPARA-humanized mice with age. These studies suggest a new role for PPARα in age-associated hepatocarcinogenesis due to altered lipid metabolism in Ppara-null and PPARA-humanized mice and the accumulation of linoleic acid as part of an overall fatty liver that is associated with loss of CD4⁺ T cells in the liver in both transgenic models. Since fatty liver is a known causal risk factor for liver cancer, Ppara-null and PPARA-humanized mice are valuable models for examining the mechanisms of PPARα and age-dependent hepatocarcinogenesis. Full article

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Relative levels of fatty acids (a,e), triglycerides (b,f), unsaturated fatty acids (UFA; c,g), or polyunsaturated fatty acids (PUFA; d,h) in lipophilic liver extracts from “adult only” groups of wild-type, Ppara-null, or PPARA-humanized mice with or without ligand activation of PPARα after 26 weeks (a–d) or ~75 weeks (e–h). Fatty acid residues (R-CH3), triglycerides (C1H and C3H of glycerol), unsaturated fatty acid (UFA) residues (-CH=CH-), and polyunsaturated fatty acid (PUFA) residues (-CH=CH-CH2-(CH=CH-CH2-)n). CON, control mouse group; GW, GW7647-treated mouse group. The relative average amount of lipids in the liver of mice with different treatments was normalized to wild-type control and represents the fold change. Values represent the mean ± S.E.M. Groups with different superscript letters are significantly different at p ≤ 0.05. (One-way ANOVA followed by Tukey’s multiple comparisons test). Full article ">Figure 2
NMR detection of linoleic acid in lipophilic liver extracts from both “adult only” and “perinatal + adult” groups of wild-type, Ppara-null, or PPARA-humanized mice with or without ligand activation of PPARα. NMR was used to detect linoleic acid after either 26 weeks or ~75 weeks in “adult only” (a,b) or “perinatal + adult” (c,d) groups. Relative amount of lipids in livers was normalized to wild-type control and represents the fold change. Values represent the mean ± S.E.M. Groups with different superscript letters are significantly different at p ≤ 0.05. (One-way ANOVA followed by Tukey’s multiple comparisons test). Full article ">Figure 3
Assessment of oxidative stress in liver from “adult only” and “perinatal + adult” groups of wild-type, Ppara-null, or PPARA-humanized mice with or without ligand activation of PPARα. Both 4-hydroxynonenal (4-HNE, a,b) and malondialdehyde (MDA, c,d) levels in liver were measured after ~75 weeks in mice from both treatment paradigms. a and c, “adult only”; b and d, “perinatal + adult” groups. For the 4-HNE Western blot analyses (a,b) the relative amount of 4-HNE was normalized to the signal for lactate dehydrogenase (LDH) for wild-type control and represents the fold change. All values represent the mean ± S.E.M. Groups with different superscript letters are significantly different at p ≤ 0.05. (One-way ANOVA followed by Tukey’s multiple comparisons test). Full article ">Figure 4
Representative photomicrographs of immunohistochemical detection of CD4+ T cells (a) in liver from “adult only” and “perinatal + adult” groups of wild-type, Ppara-null, or PPARA-humanized mice with or without ligand activation of PPARα with GW7647 after 26 weeks (left panels) or ~75 weeks (right panels). The relative amount of CD4+ T cells in liver was normalized to wild-type control and represents the fold change (b–e). Values represent the mean ± S.E.M. Groups with different superscript letters are significantly different at p ≤ 0.05. * significantly different at p ≤ 0.05 for the mouse treatment groups after 26 weeks (b,d) compared to the mouse treatment groups at ~75 weeks (c,e), respectively. (One-way ANOVA followed by Tukey’s multiple comparisons test). Full article ">Figure 5
Representative photomicrographs of immunohistochemical detection of P16 (a) in liver from “adult only” and “perinatal + adult” groups of wild-type, Ppara-null, or PPARA-humanized mice with or without ligand activation of PPARα with GW7647 after 26 weeks (left panels) or ~75 weeks (right panels). The relative amount of P16-positive cells in liver was normalized to wild-type control and represents the fold change (b–e). Values represent the mean ± S.E.M. Groups with different superscript letters are significantly different at p ≤ 0.05. * significantly different at p ≤ 0.05 for the mouse treatment groups after 26 weeks (b,d) compared to the mouse treatment groups at ~75 weeks (c,e), respectively. (One-way ANOVA followed by Tukey’s multiple comparisons test). Full article ">Figure 6
Representative photomicrographs of immunohistochemical detection of P21 (a) in liver from “adult only” and “perinatal + adult” groups of wild-type, Ppara-null, or PPARA-humanized mice with or without ligand activation of PPARα with GW7647 after 26 weeks (left panels) or ~75 weeks (right panels). Relative amount of P21-positive cells in liver was normalized to wild-type control and represents the fold change (b–e). Values represent the mean ± S.E.M. Groups with different superscript letters are significantly different at p ≤ 0.05. * significantly different at p ≤ 0.05 for the mouse treatment groups after 26 weeks (b,d) compared to the mouse treatment groups at ~75 weeks (c,e), respectively. (One-way ANOVA followed by Tukey’s multiple comparisons test). Full article ">Figure 7
Average serum concentration of IFNγ (a,c,e,g), TNFα (b,d,f,h) in “adult only” and “perinatal + adult” groups of wild-type, Ppara-null, or PPARA-humanized mice with or without ligand activation of PPARα with GW7647 after 26 weeks (left panels) or ~75 weeks (right panels). Values represent the mean ± S.E.M. Groups with different superscript letters are significantly different at p ≤ 0.05. (One-way ANOVA followed by Tukey’s multiple comparisons test). Full article ">

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Metabolites, Volume 13, Issue 8 (August 2023) – 85 articles

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