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Discovery of Delirium Biomarkers through Minimally Invasive Serum Molecular Fingerprinting
Utilising a Clinical Metabolomics LC-MS Study to Determine the Integrity of Biological Samples for Statistical Modelling after Long Term −80 °C Storage: A TOFI_Asia Sub-Study
Machine Learning Metabolomics Profiling of Dietary Interventions from a Six-Week Randomised Trial
Metabolites Associated with Polygenic Risk of Breast Cancer

Journal Description

Metabolites

Metabolites is an international, peer-reviewed, open access journal of metabolism and metabolomics, published monthly online by MDPI.

Open Access— free for readers, with article processing charges (APC) paid by authors or their institutions.
High Visibility: indexed within Scopus, SCIE (Web of Science), PubMed, PMC, Embase, CAPlus / SciFinder, and other databases.
Journal Rank: JCR - Q2 (Biochemistry and Molecular Biology) / CiteScore - Q2 (Endocrinology, Diabetes and Metabolism)
Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 13.9 days after submission; acceptance to publication is undertaken in 3.5 days (median values for papers published in this journal in the first half of 2024).
Recognition of Reviewers: reviewers who provide timely, thorough peer-review reports receive vouchers entitling them to a discount on the APC of their next publication in any MDPI journal, in appreciation of the work done.

Impact Factor: 3.4 (2023); 5-Year Impact Factor: 4.0 (2023)

Imprint Information Journal Flyer Open Access ISSN: 2218-1989

Latest Articles

14 pages, 3861 KiB

Open AccessArticle

Comprehensive Secondary Metabolite Profiling and Antioxidant Activity of Aqueous and Ethanol Extracts of Neolamarckia cadamba (Roxb.) Bosser Fruits

by Lin Yang, Liyan Wu, Yongxin Li, Yuhui Yang, Yuting Gu, Jialin Yang, Luzy Zhang and Fanxin Meng

Metabolites 2024, 14(9), 511; https://doi.org/10.3390/metabo14090511 (registering DOI) - 21 Sep 2024

Background: Neolamarckia cadamba (Rubiaceae) is a well-recognized medicinal plant with recorded therapeutical attributes. However, a thorough assessment of active compounds in its fruits is lacking, limiting their use and valorization in pharmacological industries. Methods: Thus, this study investigated variations in the fruits’ secondary [...] Read more.

Background: Neolamarckia cadamba (Rubiaceae) is a well-recognized medicinal plant with recorded therapeutical attributes. However, a thorough assessment of active compounds in its fruits is lacking, limiting their use and valorization in pharmacological industries. Methods: Thus, this study investigated variations in the fruits’ secondary metabolite (SM) profiles, as well as antioxidant activities in aqueous (WA) and ethanol (ET) extracts. Results: Liquid chromatography–electrospray ionization tandem mass spectrometry identified 541 SMs, of which 14 and 1 (di-O-glucosylquinic acid) were specifically detected in ET and WA, respectively. Phenolic acids (36.97%), flavonoids (28.10%), terpenoids (12.20%), and alkaloids (9.98%) were the dominant SMs. The SM profiles of the fruits in WA and ET were quite different. We revealed 198 differentially extracted (DE) metabolites between WA and ET, including 62 flavonoids, 57 phenolic acids, 45 terpenoids, 14 alkaloids, etc. Most DE flavones (36 out of 40), terpenoids (45 out of 45), and alkaloids (12 out of 14) had higher content in ET. Catechin and its derivatives, procyanidins, and tannins had higher content in WA. ABTS and DPPH assays showed that the antioxidant activity of ET was significantly higher than that of WA. Conclusions: Our findings will facilitate the efficient extraction and evaluation of specific active compounds in N. cadamba. Full article

(This article belongs to the Section Plant Metabolism)

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14 pages, 2401 KiB

Open AccessArticle

Predicting the Association of Metabolites with Both Pathway Categories and Individual Pathways

by Erik D. Huckvale and Hunter N. B. Moseley

Metabolites 2024, 14(9), 510; https://doi.org/10.3390/metabo14090510 (registering DOI) - 21 Sep 2024

Metabolism is a network of chemical reactions that sustain cellular life. Parts of this metabolic network are defined as metabolic pathways containing specific biochemical reactions. Products and reactants of these reactions are called metabolites, which are associated with certain human-defined metabolic pathways. Metabolic [...] Read more.

Metabolism is a network of chemical reactions that sustain cellular life. Parts of this metabolic network are defined as metabolic pathways containing specific biochemical reactions. Products and reactants of these reactions are called metabolites, which are associated with certain human-defined metabolic pathways. Metabolic knowledgebases, such as the Kyoto Encyclopedia of Gene and Genomes (KEGG) contain metabolites, reactions, and pathway annotations; however, such resources are incomplete due to current limits of metabolic knowledge. To fill in missing metabolite pathway annotations, past machine learning models showed some success at predicting the KEGG Level 2 pathway category involvement of metabolites based on their chemical structure. Here, we present the first machine learning model to predict metabolite association to more granular KEGG Level 3 metabolic pathways. We used a feature and dataset engineering approach to generate over one million metabolite-pathway entries in the dataset used to train a single binary classifier. This approach produced a mean Matthews correlation coefficient (MCC) of 0.806 ± 0.017 SD across 100 cross-validation iterations. The 172 Level 3 pathways were predicted with an overall MCC of 0.726. Moreover, metabolite association with the 12 Level 2 pathway categories was predicted with an overall MCC of 0.891, representing significant transfer learning from the Level 3 pathway entries. These are the best metabolite pathway prediction results published so far in the field. Full article

(This article belongs to the Special Issue Machine Learning Applications in Metabolomics Analysis)

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21 pages, 1367 KiB

Open AccessArticle

Investigating the Mechanisms of 15-PGDH Inhibitor SW033291 in Improving Type 2 Diabetes Mellitus: Insights from Metabolomics and Transcriptomics

by Yuanfeng Huang, Mingjie Liang, Yiwen Liao, Zirui Ji, Wanfen Lin, Xiangjin Pu, Lexun Wang and Weixuan Wang

Metabolites 2024, 14(9), 509; https://doi.org/10.3390/metabo14090509 (registering DOI) - 20 Sep 2024

This study focused on exploring the effects of SW033291, an inhibitor of 15-hydroxyprostaglandin dehydrogenase, on type 2 diabetes mellitus (T2DM) mice from a comprehensive perspective. Studies have demonstrated that SW033291 benefits tissue repair, organ function, and muscle mass in elderly mice. Our recent [...] Read more.

This study focused on exploring the effects of SW033291, an inhibitor of 15-hydroxyprostaglandin dehydrogenase, on type 2 diabetes mellitus (T2DM) mice from a comprehensive perspective. Studies have demonstrated that SW033291 benefits tissue repair, organ function, and muscle mass in elderly mice. Our recent investigation initially reported the beneficial effect of SW033291 on T2DM progression. Herein, we used a T2DM mouse model induced by a high-fat diet and streptozotocin injection. Then, serum and liver metabolomics, as well as liver transcriptomic analyses, were performed to provide a systematic perspective of the SW033291-ameliorated T2DM. The results indicate SW033291 improved T2DM by regulating steroid hormone biosynthesis and linoleic/arachidonic acid metabolism. Furthermore, integrated transcriptomic and metabolomic analyses suggested that key genes and metabolites such as Cyp2c55, Cyp3a11, Cyp21a1, Myc, Gstm1, Gstm3, 9,10-dihydroxyoctadecenoic acid, 11-dehydrocorticosterone, and 12,13-dihydroxy-9Z-octadecenoic acid played crucial roles in these pathways. qPCR analysis validated the significant decreases in the hepatic gene expressions of Cyp2c55, Cyp3a11, Myc, Gstm1, and Gstm3 in the T2DM mice, which were reversed following SW033291 treatment. Meanwhile, the elevated mRNA level of Cyp21a1 in T2DM mice was decreased after SW033291 administration. Taken together, our findings suggest that SW033291 has promising potential in alleviating T2DM and could be a novel therapeutic candidate. Full article

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

32 pages, 1638 KiB

Open AccessReview

Understanding the Genetic Landscape of Gestational Diabetes: Insights into the Causes and Consequences of Elevated Glucose Levels in Pregnancy

by Caroline Brito Nunes, Maria Carolina Borges, Rachel M. Freathy, Deborah A. Lawlor, Elisabeth Qvigstad, David M. Evans and Gunn-Helen Moen

Metabolites 2024, 14(9), 508; https://doi.org/10.3390/metabo14090508 (registering DOI) - 20 Sep 2024

Background/Objectives: During pregnancy, physiological changes in maternal circulating glucose levels and its metabolism are essential to meet maternal and fetal energy demands. Major changes in glucose metabolism occur throughout pregnancy and consist of higher insulin resistance and a compensatory increase in insulin secretion [...] Read more.

Background/Objectives: During pregnancy, physiological changes in maternal circulating glucose levels and its metabolism are essential to meet maternal and fetal energy demands. Major changes in glucose metabolism occur throughout pregnancy and consist of higher insulin resistance and a compensatory increase in insulin secretion to maintain glucose homeostasis. For some women, this change is insufficient to maintain normoglycemia, leading to gestational diabetes mellitus (GDM), a condition characterized by maternal glucose intolerance and hyperglycaemia first diagnosed during the second or third trimester of pregnancy. GDM is diagnosed in approximately 14.0% of pregnancies globally, and it is often associated with short- and long-term adverse health outcomes in both mothers and offspring. Although recent studies have highlighted the role of genetic determinants in the development of GDM, research in this area is still lacking, hindering the development of prevention and treatment strategies. Methods: In this paper, we review recent advances in the understanding of genetic determinants of GDM and glycaemic traits during pregnancy. Results/Conclusions: Our review highlights the need for further collaborative efforts as well as larger and more diverse genotyped pregnancy cohorts to deepen our understanding of the genetic aetiology of GDM, address research gaps, and further improve diagnostic and treatment strategies. Full article

(This article belongs to the Special Issue Glucose Metabolism in Pregnancy)

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17 pages, 7634 KiB

Open AccessArticle

Rice Varieties Intercropping Induced Soil Metabolic and Microbial Recruiting to Enhance the Rice Blast (Magnaporthe Oryzae) Resistance

by Xiao-Qiao Zhu, Mei Li, Rong-Ping Li, Wen-Qiang Tang, Yun-Yue Wang, Xiao Fei, Ping He and Guang-Yu Han

Metabolites 2024, 14(9), 507; https://doi.org/10.3390/metabo14090507 (registering DOI) - 20 Sep 2024

[Background] Intercropping is considered an effective approach to defending rice disease. [Objectives/Methods] This study aimed to explore the resistance mechanism of rice intraspecific intercropping by investigating soil metabolites and their regulation on the rhizosphere soil microbial community using metabolomic and microbiome analyses. [Results] [...] Read more.

[Background] Intercropping is considered an effective approach to defending rice disease. [Objectives/Methods] This study aimed to explore the resistance mechanism of rice intraspecific intercropping by investigating soil metabolites and their regulation on the rhizosphere soil microbial community using metabolomic and microbiome analyses. [Results] The results showed that the panicle blast disease occurrence of the resistant variety Shanyou63 (SY63) and the susceptible variety Huangkenuo (HKN) were both decreased in the intercropping compared to monoculture. Notably, HKN in the intercropping system exhibited significantly decreased disease incidence and increased disease resistance-related enzyme protease activity. KEGG annotation from soil metabolomics analysis revealed that phenylalanine metabolic pathway, phenylalanine, tyrosine, and tryptophan biosynthesis pathway, and fructose and mannose metabolic pathway were the key pathways related to rice disease resistance. Soil microbiome analysis indicated that the bacterial genera Nocardioides, Marmoricola, Luedemannella, and Desulfomonile were significantly enriched in HKN after intercropping, while SY63 experienced a substantial accumulation of Ruminiclostridium and Cellulomonas. Omics-based correlation analysis highlighted that the community assembly of Cellulomonas and Desulfomonile significantly affected the content of the metabolites D-sorbitol, D-mannitol, quinic acid, which further proved that quinic acid had a significantly inhibitory effect on the mycelium growth of Magnaporthe oryzae, and these three metabolites had a significant blast control effect. The optimal rice blast-control efficiency on HKN was 51.72%, and Lijiangxintuanheigu (LTH) was 64.57%. [Conclusions] These findings provide a theoretical basis for rice varieties intercropping and sustainable rice production, emphasizing the novelty of the study in elucidating the underlying mechanisms of intercropping-mediated disease resistance. Full article

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

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15 pages, 1409 KiB

Open AccessArticle

Exploring the Metabolism of Flubrotizolam, a Potent Thieno-Triazolo Diazepine, Using Human Hepatocytes and High-Resolution Mass Spectrometry

by Prince Sellase Gameli, Johannes Kutzler, Diletta Berardinelli, Jeremy Carlier, Volker Auwärter and Francesco Paolo Busardò

Metabolites 2024, 14(9), 506; https://doi.org/10.3390/metabo14090506 - 19 Sep 2024

Background: The abuse of psychoactive substances presents challenges in clinical and forensic toxicology. The emergence of novel and potent drugs that pose significant health risks, in particular towards frequent abusers and users unaware of the ingredients, further complicates the situation. Designer benzodiazepines have [...] Read more.

Background: The abuse of psychoactive substances presents challenges in clinical and forensic toxicology. The emergence of novel and potent drugs that pose significant health risks, in particular towards frequent abusers and users unaware of the ingredients, further complicates the situation. Designer benzodiazepines have become a fast-growing subgroup of these new psychoactive substances (NPSs), and their overdose may potentially turn fatal, especially when combined with other central nervous system depressants. In 2021, flubrotizolam, a potent thieno-triazolo designer benzodiazepine, emerged on the illicit market, available online as a “research chemical”. The identification of markers of consumption for this designer benzodiazepine is essential in analytical toxicology, especially in clinical and forensic cases. Methods: We therefore aimed to identify biomarkers of flubrotizolam uptake in ten-donor-pooled human hepatocytes, applying liquid chromatography high-resolution mass spectrometry and software-aided data mining supported by in silico prediction tools. Results: Prediction studies resulted in 10 and 13 first- and second-generation metabolites, respectively, mainly transformed through hydroxylation and sulfation, methylation, and glucuronidation reactions. We identified six metabolites after 3 h human hepatocyte incubation: two hydroxylated metabolites (α- and 6-hydroxy-flubrotizolam), two 6-hydroxy-glucuronides, a reduced-hydroxy-N-glucuronide, and an N-glucuronide. Conclusions: We suggest detecting flubrotizolam and its hydroxylated metabolites as markers of consumption after the glucuronide hydrolysis of biological samples. The results are consistent with the in vivo metabolism of brotizolam, a medically used benzodiazepine and a chloro-phenyl analog of flubrotizolam. Full article

(This article belongs to the Special Issue Metabolite Profiling of Novel Psychoactive Substances)

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13 pages, 2072 KiB

Open AccessArticle

Urinary Biomarkers of Strawberry and Blueberry Intake

by Ya Gao, Rebecca Finlay, Xiaofei Yin and Lorraine Brennan

Metabolites 2024, 14(9), 505; https://doi.org/10.3390/metabo14090505 - 18 Sep 2024

Introduction There is increasing interest in food biomarkers to address the shortcomings of self-reported dietary assessments. Berries are regarded as important fruits worldwide; however, there are no well-validated biomarkers of berry intake. Thus, the objective of this study is to identify urinary biomarkers [...] Read more.

Introduction There is increasing interest in food biomarkers to address the shortcomings of self-reported dietary assessments. Berries are regarded as important fruits worldwide; however, there are no well-validated biomarkers of berry intake. Thus, the objective of this study is to identify urinary biomarkers of berry intake. Methods For the discovery study, participants consumed 192 g strawberries with 150 g blueberries, and urine samples were collected at 2, 4, 6, and 24 h post-consumption. A dose–response study was performed, whereby participants consumed three portions (78 g, 278 g, and 428 g) of mixed strawberries and blueberries. The urine samples were profiled by an untargeted LC-MS metabolomics approach in the positive and negative modes. Results Statistical analysis of the data revealed that 39 features in the negative mode and 15 in the positive mode significantly increased between fasting and 4 h following mixed berry intake. Following the analysis of the dose–response data, 21 biomarkers showed overall significance across the portions of berry intake. Identification of the biomarkers was performed using fragmentation matches in the METLIN, HMDB, and MoNA databases and in published papers, confirmed where possible with authentic standards. Conclusions The ability of the panel of biomarkers to assess intake was examined, and the predictability was good, laying the foundations for the development of biomarker panels. Full article

(This article belongs to the Section Food Metabolomics)

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14 pages, 2682 KiB

Open AccessArticle

Impact of Microplastic Exposure on Blood Glucose Levels and Gut Microbiota: Differential Effects under Normal or High-Fat Diet Conditions

by Manjin Xu, Huixia Niu, Lizhi Wu, Mingluan Xing, Zhe Mo, Zhijian Chen, Xueqing Li and Xiaoming Lou

Metabolites 2024, 14(9), 504; https://doi.org/10.3390/metabo14090504 - 18 Sep 2024

Microplastics are emerging pollutants that have garnered significant attention, with evidence suggesting their association with the pathogenesis of type 2 diabetes mellitus. In order to assess the impact of polystyrene microplastic exposure on alterations in the gut microbiota and the subsequent implications for [...] Read more.

Microplastics are emerging pollutants that have garnered significant attention, with evidence suggesting their association with the pathogenesis of type 2 diabetes mellitus. In order to assess the impact of polystyrene microplastic exposure on alterations in the gut microbiota and the subsequent implications for glucose dysregulation under different dietary conditions in mice, we investigated the effects and disparities in the blood glucose levels induced by polystyrene microplastic exposure in mice fed a high-fat diet versus those fed a normal diet. Using 16S rRNA sequencing and bioinformatics analyses, we explored the dynamic changes and discrepancies in the gut microbiota stability induced by polystyrene microplastic exposure under varied dietary conditions, and we screened for gut genera associated with the potential of polystyrene microplastics to disrupt glucose homeostasis. Our findings indicate that a high-fat diet resulted in abnormal mouse body weight, energy intake, blood glucose levels and related metabolic parameters. Additionally, polystyrene microplastic exposure exacerbated the glucose metabolism disorders induced by a high-fat diet. Furthermore, the composition and diversity of the mouse gut microbiota were significantly altered following microplastic exposure, with 11 gut genera exhibiting a differential presence between mice fed a high-fat diet combined with microplastic exposure compared to those fed a normal diet with microplastic exposure. Moreover, Ucg-009 played an intermediary role in the association between a high-fat diet and the fasting blood glucose. Hence, our study demonstrates that polystyrene microplastic exposure exacerbates high-fat diet-induced glucose metabolism disorders, whereas its impact on the blood glucose under normal dietary conditions is not significant, highlighting the differential influence attributable to distinct alterations in characteristic gut genera. Full article

(This article belongs to the Special Issue Effects of Environmental Exposure on Host and Microbial Metabolism)

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17 pages, 5594 KiB

Open AccessArticle

Metabolomic and Physiological Analyses Reveal the Effects of Different Storage Conditions on Sinojackia xylocarpa Hu Seeds

by Hao Cai and Yongbao Shen

Metabolites 2024, 14(9), 503; https://doi.org/10.3390/metabo14090503 - 18 Sep 2024

Backgrounds: Sinojackia xylocarpa Hu is a deciduous tree in the Styracaceae family, and it is classified as a Class II endangered plant in China. Seed storage technology is an effective means of conserving germplasm resources, but the effects of different storage conditions on [...] Read more.

Backgrounds: Sinojackia xylocarpa Hu is a deciduous tree in the Styracaceae family, and it is classified as a Class II endangered plant in China. Seed storage technology is an effective means of conserving germplasm resources, but the effects of different storage conditions on the quality and associated metabolism of S. xylocarpa seeds remain unclear. This study analyzed the physiological and metabolic characteristics of S. xylocarpa seeds under four storage conditions. Results: Our findings demonstrate that reducing seed moisture content and storage temperature effectively prolongs storage life. Seeds stored under that condition exhibited higher internal nutrient levels, lower endogenous abscisic acid (ABA) hormone levels, and elevated gibberellic acid (GA₃) levels. Additionally, 335 metabolites were identified under four different storage conditions. The analysis indicates that S. xylocarpa seeds extend seed longevity and maintain cellular structural stability mainly by regulating the changes in metabolites related to lipid, amino acid, carbohydrate, and carotenoid metabolic pathways under the storage conditions of a low temperature and low seed moisture. Conclusions: These findings provide new insights at the physiological and metabolic levels into how these storage conditions extend seed longevity while also offering effective storage strategies for preserving the germplasm resources of S. xylocarpa. Full article

(This article belongs to the Section Plant Metabolism)

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13 pages, 628 KiB

Open AccessArticle

Relationship between Body Adiposity Indices and Reversal of Metabolically Unhealthy Obesity 6 Months after Roux-en-Y Gastric Bypass

by Mariana Luna, Silvia Pereira, Carlos Saboya and Andrea Ramalho

Metabolites 2024, 14(9), 502; https://doi.org/10.3390/metabo14090502 - 18 Sep 2024

The factors determining the reversal of metabolically unhealthy obesity (MUO) to metabolically healthy obesity (MHO) after Roux-en-Y gastric bypass (RYGB) are not completely elucidated. The present study aims to evaluate body adiposity and distribution, through different indices, according to metabolic phenotypes before and [...] Read more.

The factors determining the reversal of metabolically unhealthy obesity (MUO) to metabolically healthy obesity (MHO) after Roux-en-Y gastric bypass (RYGB) are not completely elucidated. The present study aims to evaluate body adiposity and distribution, through different indices, according to metabolic phenotypes before and 6 months after RYGB, and the relationship between these indices and transition from MUO to MHO. This study reports a prospective longitudinal study on adults with obesity who were evaluated before (T0) and 6 months (T1) after RYGB. Bodyweight, height, waist circumference (WC), BMI, waist-to-height ratio (WHR), total cholesterol (TC), HDL-c, LDL-c, triglycerides, insulin, glucose, HbA1c and HOMA-IR were evaluated. The visceral adiposity index (VAI), the conicity index (CI), the lipid accumulation product (LAP), CUN-BAE and body shape index (ABSI) were calculated. MUO was classified based on insulin resistance. MUO at T0 with transition to MHO at T1 formed the MHO-t group MHO and MUO at both T0 and T1 formed the MHO-m and MUO-m groups, respectively. At T0, 37.3% of the 62 individuals were classified as MHO and 62.7% as MUO. Individuals in the MUO-T0 group had higher blood glucose, HbA1c, HOMA-IR, insulin, TC and LDL-c compared to those in the MHO-T0 group. Both groups showed significant improvement in biochemical and body variables at T1. After RYGB, 89.2% of MUO-T0 became MHO (MHO-t). The MUO-m group presented higher HOMA-IR, insulin and VAI, compared to the MHO-m and MHO-t groups. CI and ABSI at T0 correlated with HOMA-IR at T1 in the MHO-t and MHO-m groups. CI and ABSI, indicators of visceral fat, are promising for predicting post-RYGB metabolic improvement. Additional studies are needed to confirm the sustainability of MUO reversion and its relationship with these indices. Full article

(This article belongs to the Special Issue Exploring Pathological Mechanisms in Obesity, Diabetes, and Metabolic Syndrome)

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18 pages, 4848 KiB

Open AccessArticle

Implementation of Machine Learning-Based System for Early Diagnosis of Feline Mammary Carcinomas through Blood Metabolite Profiling

by Vidhi Kulkarni, Igor F. Tsigelny and Valentina L. Kouznetsova

Metabolites 2024, 14(9), 501; https://doi.org/10.3390/metabo14090501 - 17 Sep 2024

Background: Feline mammary carcinoma (FMC) is a prevalent and fatal carcinoma that predominantly affects unspayed female cats. FMC is the third most common carcinoma in cats but is still underrepresented in research. Current diagnosis methods include physical examinations, imaging tests, and fine-needle aspiration. [...] Read more.

Background: Feline mammary carcinoma (FMC) is a prevalent and fatal carcinoma that predominantly affects unspayed female cats. FMC is the third most common carcinoma in cats but is still underrepresented in research. Current diagnosis methods include physical examinations, imaging tests, and fine-needle aspiration. The diagnosis through these methods is sometimes delayed and unreliable, leading to increased chances of mortality. Objectives: The objective of this study was to identify the biomarkers, including blood metabolites and genes, related to feline mammary carcinoma, study their relationships, and develop a machine learning (ML) model for the early diagnosis of the disease. Methods: We analyzed the blood metabolites of felines with mammary carcinoma using the pathway analysis feature in MetaboAnalyst software, v. 5.0. We utilized machine-learning (ML) methods to recognize FMC using the blood metabolites of sick patients. Results: The metabolic pathways that were elucidated to be associated with this disease include alanine, aspartate and glutamate metabolism, Glutamine and glutamate metabolism, Arginine biosynthesis, and Glycerophospholipid metabolism. Furthermore, we also elucidated several genes that play a significant role in the development of FMC, such as ERBB2, PDGFA, EGFR, FLT4, ERBB3, FIGF, PDGFC, PDGFB through STRINGdb, a database of known and predicted protein-protein interactions, and MetaboAnalyst 5.0. The best-performing ML model was able to predict metabolite class with an accuracy of 85.11%. Conclusion: Our findings demonstrate that the identification of the biomarkers associated with FMC and the affected metabolic pathways can aid in the early diagnosis of feline mammary carcinoma. Full article

(This article belongs to the Special Issue Metabolomics and Computational Research on Drugs and Diseases)

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14 pages, 2039 KiB

Open AccessArticle

Metabolomic Effects of Liraglutide Therapy on the Plasma Metabolomic Profile of Patients with Obesity

by Assim A. Alfadda, Anas M. Abdel Rahman, Hicham Benabdelkamel, Reem AlMalki, Bashayr Alsuwayni, Abdulaziz Alhossan, Madhawi M. Aldhwayan, Ghalia N. Abdeen, Alexander Dimitri Miras and Afshan Masood

Metabolites 2024, 14(9), 500; https://doi.org/10.3390/metabo14090500 - 17 Sep 2024

Background: Liraglutide, a long-acting glucagon-like peptide-1 receptor agonist (GLP1RA), is a well-established anti-diabetic drug, has also been approved for the treatment of obesity at a dose of 3 mg. There are a limited number of studies in the literature that have looked at [...] Read more.

Background: Liraglutide, a long-acting glucagon-like peptide-1 receptor agonist (GLP1RA), is a well-established anti-diabetic drug, has also been approved for the treatment of obesity at a dose of 3 mg. There are a limited number of studies in the literature that have looked at changes in metabolite levels before and after liraglutide treatment in patients with obesity. To this end, in the present study we aimed to explore the changes in the plasma metabolomic profile, using liquid chromatography-high resolution mass spectrometry (LC-HRMS) in patients with obesity. Methods: A single-center prospective study was undertaken to evaluate the effectiveness of 3 mg liraglutide therapy in twenty-three patients (M/F: 8/15) with obesity, mean BMI 40.81 ± 5.04 kg/m², and mean age of 36 ± 10.9 years, in two groups: at baseline (pre-treatment) and after 12 weeks of treatment (post-treatment). An untargeted metabolomic profiling was conducted in plasma from the pre-treatment and post-treatment groups using LC-HRMS, along with bioinformatics analysis using ingenuity pathway analysis (IPA). Results: The metabolomics analysis revealed a significant (FDR p-value ≤ 0.05, FC 1.5) dysregulation of 161 endogenous metabolites (97 upregulated and 64 downregulated) with distinct separation between the two groups. Among the significantly dysregulated metabolites, the majority of them were identified as belonging to the class of oxidized lipids (oxylipins) that includes arachidonic acid and its derivatives, phosphorglycerophosphates, N-acylated amino acids, steroid hormones, and bile acids. The biomarker analysis conducted using MetaboAnalyst showed PGP (a21:0/PG/F1alpha), an oxidized lipid, as the first metabolite among the list of the top 15 biomarkers, followed by cysteine and estrone. The IPA analysis showed that the dysregulated metabolites impacted the pathway related to cell signaling, free radical scavenging, and molecular transport, and were focused around the dysregulation of NF-κB, ERK, MAPK, PKc, VEGF, insulin, and pro-inflammatory cytokine signaling pathways. Conclusions: The findings suggest that liraglutide treatment reduces inflammation and modulates lipid metabolism and oxidative stress. Our study contributes to a better understanding of the drug’s multifaceted impact on overall metabolism in patients with obesity. Full article

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

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18 pages, 3605 KiB

Open AccessArticle

Identification of Plasma Metabolomic Biomarkers of Juvenile Idiopathic Arthritis

by Amar Kumar, Joshua Tatarian, Valentina Shakhnovich, Rachel L. Chevalier, Marc Sudman, Daniel J. Lovell, Susan D. Thompson, Mara L. Becker and Ryan S. Funk

Metabolites 2024, 14(9), 499; https://doi.org/10.3390/metabo14090499 - 16 Sep 2024

Identification of disease and therapeutic biomarkers remains a significant challenge in the early diagnosis and effective treatment of juvenile idiopathic arthritis (JIA). In this study, plasma metabolomic profiling was conducted to identify disease-related metabolic biomarkers associated with JIA. Plasma samples from treatment-naïve JIA [...] Read more.

Identification of disease and therapeutic biomarkers remains a significant challenge in the early diagnosis and effective treatment of juvenile idiopathic arthritis (JIA). In this study, plasma metabolomic profiling was conducted to identify disease-related metabolic biomarkers associated with JIA. Plasma samples from treatment-naïve JIA patients and non-JIA reference patients underwent global metabolomic profiling across discovery (60 JIA, 60 non-JIA) and replication (49 JIA, 38 non-JIA) cohorts. Univariate analysis identified significant metabolites (q-value ≤ 0.05), followed by enrichment analysis using ChemRICH and metabolic network mapping with MetaMapp and Cytoscape. Receiver operating characteristic (ROC) analysis determined the top discriminating biomarkers based on area under the curve (AUC) values. A total of over 800 metabolites were measured, consisting of 714 known and 155 unknown compounds. In the discovery cohort, 587 metabolites were significantly altered in JIA patients compared with the reference population (q < 0.05). In the replication cohort, 288 metabolites were significantly altered, with 78 overlapping metabolites demonstrating the same directional change in both cohorts. JIA was associated with a notable increase in plasma levels of sphingosine metabolites and fatty acid ethanolamides and decreased plasma levels of sarcosine, iminodiacetate, and the unknown metabolite X-12462. Chemical enrichment analysis identified cycloparaffins in the form of naproxen and its metabolites, unsaturated lysophospholipids, saturated phosphatidylcholines, sphingomyelins, ethanolamines, and saturated ceramides as the top discriminating biochemical clusters. ROC curve analysis identified 11 metabolites classified as highly discriminatory based on an AUC > 0.90, with the top discriminating metabolite being sphinganine-1-phosphate (AUC = 0.98). This study identifies specific metabolic changes in JIA, particularly within sphingosine metabolism, through both discovery and replication cohorts. Plasma metabolomic profiling shows promise in pinpointing JIA-specific biomarkers, differentiating them from those in healthy controls and Crohn’s disease, which may improve diagnosis and treatment. Full article

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

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21 pages, 6380 KiB

Open AccessArticle

Combined Metabolome and Transcriptome Analyses of Maize Leaves Reveal Global Effect of Biochar on Mechanisms Involved in Anti-Herbivory to Spodoptera frugiperda

by Tianjun He, Lin Chen, Yingjun Wu, Jinchao Wang, Quancong Wu, Jiahao Sun, Chaohong Ding, Tianxing Zhou, Limin Chen, Aiwu Jin, Yang Li and Qianggen Zhu

Metabolites 2024, 14(9), 498; https://doi.org/10.3390/metabo14090498 - 14 Sep 2024

Fall armyworm (FAW, Spodoptera frugiperda) has now spread to more than 26 Chinese provinces. The government is working with farmers and researchers to find ways to prevent and control this pest. The use of biochar is one of the economic and environmentally [...] Read more.

Fall armyworm (FAW, Spodoptera frugiperda) has now spread to more than 26 Chinese provinces. The government is working with farmers and researchers to find ways to prevent and control this pest. The use of biochar is one of the economic and environmentally friendly strategies to increase plant growth and improve pest resistance. We tested four v/v combinations of bamboo charcoal with coconut bran [BC1 (10:1), BC2(30:1), BC3(50:1)] against a control (CK) in maize. We found that plant height, stem thickness, fresh weight and chlorophyll content were significantly higher in BC2, in addition to the lowest FAW survival %. We then compared the metabolome and transcriptome profiles of BC2 and CK maize plants under FAW herbivory. Our results show that the levels of flavonoids, amino acids and derivatives, nucleotides and derivatives and most phenolic acids decreased, while terpenoids, organic acids, lipids and defense-related hormones increased in BC-grown maize leaves. Transcriptome sequencing revealed consistent expression profiles of genes enriched in these pathways. We also observed the increased expression of genes related to abscisic acid, jasmonic acid, auxin and MAPK signaling. Based on these observations, we discussed the possible pathways involved in maize against FAW herbivory. We conclude that bamboo charcoal induces anti-herbivory responses in maize leaves. Full article

(This article belongs to the Special Issue Plant Biotic and Abiotic Stress Responses and Tolerance: Phytohormonal and Metabolic Insights)

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17 pages, 3031 KiB

Open AccessArticle

Functional Muffins Exert Bifidogenic Effects along with Highly Product-Specific Effects on the Human Gut Microbiota Ex Vivo

by Stef Deyaert, Jonas Poppe, Lam Dai Vu, Aurélien Baudot, Sarah Bubeck, Thomas Bayne, Kiran Krishnan, Morgan Giusto, Samuel Moltz and Pieter Van den Abbeele

Metabolites 2024, 14(9), 497; https://doi.org/10.3390/metabo14090497 - 14 Sep 2024

GoodBiome™ Foods are functional foods containing a probiotic (Bacillus subtilis HU58™) and prebiotics (mainly inulin). Their effects on the human gut microbiota were assessed using ex vivo SIFR^® technology, which has been validated to provide clinically predictive insights. GoodBiome™ Foods (BBM/LCM/OSM) [...] Read more.

GoodBiome™ Foods are functional foods containing a probiotic (Bacillus subtilis HU58™) and prebiotics (mainly inulin). Their effects on the human gut microbiota were assessed using ex vivo SIFR^® technology, which has been validated to provide clinically predictive insights. GoodBiome™ Foods (BBM/LCM/OSM) were subjected to oral, gastric, and small intestinal digestion/absorption, after which their impact on the gut microbiome of four adults was assessed (n = 3). All GoodBiome™ Foods boosted health-related SCFA acetate (+13.1/14.1/13.8 mM for BBM/LCM/OSM), propionate (particularly OSM; +7.4/7.5/8.9 mM for BBM/LCM/OSM) and butyrate (particularly BBM; +2.6/2.1/1.4 mM for BBM/LCM/OSM). This is related to the increase in Bifidobacterium species (B. catenulatum, B. adolescentis, B. pseudocatenulatum), Coprococcus catus and Bacteroidetes members (Bacteroides caccae, Phocaeicola dorei, P. massiliensis), likely mediated via inulin. Further, the potent propionogenic potential of OSM related to increased Bacteroidetes members known to ferment oats (s key ingredient of OSM), while the butyrogenic potential of BBM related to a specific increase in Anaerobutyricum hallii, a butyrate producer specialized in the fermentation of erythritol (key ingredient of BBM). In addition, OSM/BBM suppressed the pathogen Clostridioides difficile, potentially due to inclusion of HU58™ in GoodBiome™ Foods. Finally, all products enhanced a spectrum of metabolites well beyond SCFA, including vitamins (B3/B6), essential amino acids, and health-related metabolites such as indole-3-propionic acid. Overall, the addition of specific ingredients to complex foods was shown to specifically modulate the gut microbiome, potentially contributing to health benefits. Noticeably, our findings contradict a recent in vitro study, underscoring the critical role of employing a physiologically relevant digestion/absorption procedure for a more accurate evaluation of the microbiome-modulating potential of complex foods. Full article

(This article belongs to the Special Issue Natural Metabolites on Gut Microbiome Modulation)

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45 pages, 1381 KiB

Open AccessReview

Animal Food Products to Support Human Nutrition and to Boost Human Health: The Potential of Feedstuffs Resources and Their Metabolites as Health-Promoters

by Mario Cuchillo-Hilario, Mareli-Itzel Fournier-Ramírez, Margarita Díaz Martínez, Sara Montaño Benavides, María-Concepción Calvo-Carrillo, Silvia Carrillo Domínguez, María-Elena Carranco-Jáuregui, Elizabeth Hernández-Rodríguez, Patricia Mora-Pérez, Yesica R. Cruz-Martínez and Claudia Delgadillo-Puga

Metabolites 2024, 14(9), 496; https://doi.org/10.3390/metabo14090496 - 13 Sep 2024

Recent attention has been given to animal feeding and its impact on human nutrition. Animal feeding is essential for meeting human dietary needs, making it a subject of significant interest and investigation. This review seeks to outline the current understanding of this disciplinary [...] Read more.

Recent attention has been given to animal feeding and its impact on human nutrition. Animal feeding is essential for meeting human dietary needs, making it a subject of significant interest and investigation. This review seeks to outline the current understanding of this disciplinary area, with a focus on key research areas and their potential implications. The initial part of the paper discusses the importance of animal feed resources and recognizes their crucial role in guaranteeing sufficient nutrition for both humans and animals. Furthermore, we analyzed the categorization of animal feeds based on the guidelines established by the National Research Council. This approach offers a valuable structure for comprehending and classifying diverse types of animal feed. Through an examination of this classification, we gain an understanding of the composition and nutritional content of various feedstuffs. We discuss the major categories of metabolites found in animal feed and their impact on animal nutrition, as well as their potential health advantages for humans. Flavonoids, polyphenols, tannins, terpenoids, vitamins, antioxidants, alkaloids, and essential oils are the primary focus of the examination. Moreover, we analyzed their possible transference into animal products, and later we observed their occurrence in foods from animal sources. Finally, we discuss their potential to promote human health. This review offers an understanding of the connections among the major metabolites found in feedstuffs, their occurrence in animal products, and their possible impact on the health of both animals and humans. Full article

(This article belongs to the Special Issue Bioactive Compounds in Animal Nutrition Resources and Animal By-Products)

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13 pages, 2717 KiB

Open AccessArticle

A Preliminary Study on the Whole-Plant Regulations of the Shrub Campylotropis polyantha in Response to Hostile Dryland Conditions

by Hua Zhang, Xue Jiang, Lijun Zhu, Lei Liu, Zhengqiao Liao and Baoguo Du

Metabolites 2024, 14(9), 495; https://doi.org/10.3390/metabo14090495 - 13 Sep 2024

Drylands cover more than 40% of global land surface and will continue to expand by 10% at the end of this century. Understanding the resistance mechanisms of native species is of particular importance for vegetation restoration and management in drylands. In the present [...] Read more.

Drylands cover more than 40% of global land surface and will continue to expand by 10% at the end of this century. Understanding the resistance mechanisms of native species is of particular importance for vegetation restoration and management in drylands. In the present study, metabolome of a dominant shrub Campylotropis polyantha in a dry-hot valley were investigated. Compared to plants grown at the wetter site, C. polyantha tended to slow down carbon (C) assimilation to prevent water loss concurrent with low foliar reactive oxygen species and sugar concentrations at the drier and hotter site. Nitrogen (N) assimilation and turn over were stimulated under stressful conditions and higher leaf N content was kept at the expense of root N pools. At the drier site, roots contained more water but less N compounds derived from the citric acid cycle. The site had little effect on metabolites partitioning between leaves and roots. Generally, roots contained more C but less N. Aromatic compounds were differently impacted by site conditions. The present study, for the first time, uncovers the apparent metabolic adaptations of C. polyantha to hostile dryland conditions. However, due to the limited number of samples, we are cautious about drawing general conclusions regarding the resistance mechanisms. Further studies with a broader spatial range and larger time scale are therefore recommended to provide more robust information for vegetation restoration and management in dryland areas under a changing climate. Full article

(This article belongs to the Special Issue Metabolic Responses of Plants to Abiotic Stress)

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18 pages, 10847 KiB

Open AccessArticle

Organic Trace Minerals Enhance the Gut Health of British Shorthair Cats by Regulating the Structure of Intestinal Microbiota

by Yingyue Cui, Mingrui Zhang, Haotian Wang, Tong Yu, Anxuan Zhang, Gang Lin, Yuhan Guo and Yi Wu

Metabolites 2024, 14(9), 494; https://doi.org/10.3390/metabo14090494 - 11 Sep 2024

Trace minerals are essential for biological processes, including enzyme function, immune response, and hormone synthesis. The study assessed the effects of different dietary trace minerals on the gut health, microbiota composition, and immune function of cats. Eighteen adult British Shorthair cats were divided [...] Read more.

Trace minerals are essential for biological processes, including enzyme function, immune response, and hormone synthesis. The study assessed the effects of different dietary trace minerals on the gut health, microbiota composition, and immune function of cats. Eighteen adult British Shorthair cats were divided into three groups receiving inorganic trace minerals (ITM), a 50/50 mix of inorganic and organic trace minerals (ITM + OTM), or organic trace minerals (OTM) for 28 days. The OTM showed enhanced immune capacities, reduced intestinal barrier function, and lower inflammation condition. The OTM altered gut microbiota diversity, with a lower Simpson index and higher Shannon index (p < 0.05). Specifically, the abundance of Bacteroidota, Lachnospiraceae, and Prevotella in the OTM group were higher than the ITM group (p < 0.05). Metabolomic analysis identified 504 differential metabolites between the OTM and ITM groups (p < 0.05, VIP-pred-OPLS-DA > 1), affecting pathways related to steroid hormone biosynthesis and glycerophospholipid metabolism (p < 0.05, VIP-pred-OPLS-DA > 2). Additionally, there was a significant correlation between intestinal microbiota and differential metabolites. To conclude, dietary OTM can modulate the gut metabolite and microbiota composition, enhance immune and intestinal barrier function, and mitigate inflammation in cats, highlighting the benefit of using OTM in feline diet to promote the intestinal and overall health. Full article

(This article belongs to the Topic Research on Companion Animal Nutrition)

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11 pages, 4346 KiB

Open AccessArticle

Utility of an Untargeted Metabolomics Approach Using a 2D GC-GC-MS Platform to Distinguish Relapsing and Progressive Multiple Sclerosis

by Indrani Datta, Insha Zahoor, Nasar Ata, Faraz Rashid, Mirela Cerghet, Ramandeep Rattan, Laila M. Poisson and Shailendra Giri

Metabolites 2024, 14(9), 493; https://doi.org/10.3390/metabo14090493 - 11 Sep 2024

Multiple sclerosis (MS) is the most common inflammatory neurodegenerative disease of the central nervous system (CNS) in young adults and results in progressive neurological defects. The relapsing-remitting phenotype (RRMS) is the most common disease course in MS, which ultimately progresses to secondary progressive [...] Read more.

Multiple sclerosis (MS) is the most common inflammatory neurodegenerative disease of the central nervous system (CNS) in young adults and results in progressive neurological defects. The relapsing-remitting phenotype (RRMS) is the most common disease course in MS, which ultimately progresses to secondary progressive MS (SPMS), while primary progressive MS (PPMS) is a type of MS that worsens gradually over time without remissions. There is a gap in knowledge regarding whether the relapsing form can be distinguished from the progressive course, or healthy subjects (HS) based on an altered serum metabolite profile. In this study, we performed global untargeted metabolomics with the 2D GC-GC-MS platform to identify altered metabolites between RRMS, PPMS, and HS. We profiled 235 metabolites in the serum of patients with RRMS (n = 41), PPMS (n = 31), and HS (n = 91). A comparison of RRMS and HS patients revealed 22 significantly altered metabolites at p < 0.05 (false-discovery rate [FDR] = 0.3). The PPMS and HS comparisons revealed 28 altered metabolites at p < 0.05 (FDR = 0.2). Pathway analysis using MetaboAnalyst revealed enrichment of four metabolic pathways in both RRMS and PPMS (hypergeometric test p < 0.05): (1) galactose metabolism; (2) amino sugar and nucleotide sugar metabolism; (3) phenylalanine, tyrosine, and tryptophan biosynthesis; and (4) aminoacyl-tRNA biosynthesis. The Qiagen IPA enrichment test identified the sulfatase 2 (SULF2) (p = 0.0033) and integrin subunit beta 1 binding protein 1 (ITGB1BP1) (p = 0.0067) genes as upstream regulators of altered metabolites in the RRMS vs. HS groups. However, in the PPMS vs. HS comparison, valine was enriched in the neurodegeneration of brain cells (p = 0.05), and heptadecanoic acid, alpha-ketoisocaproic acid, and glycerol participated in inflammation in the CNS (p = 0.03). Overall, our study suggests that RRMS and PPMS may contribute metabolic fingerprints in the form of unique altered metabolites for discriminating MS disease from HS, with the potential for constructing a metabolite panel for progressive autoimmune diseases such as MS. Full article

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12 pages, 3121 KiB

Open AccessArticle

Application of Eight Machine Learning Algorithms in the Establishment of Infertility and Pregnancy Diagnostic Models: A Comprehensive Analysis of Amino Acid and Carnitine Metabolism

by Rui Zhang, Lei Zhou, Xiaoyan Hao, Liu Yang, Li Ding, Ruiqing Xing, Juanjuan Hu, Fengjuan Wang, Xiaonan Zhai, Yuanbing Guo, Zheng Cai, Jiawei Gao, Jun Yang and Jiayun Liu

Metabolites 2024, 14(9), 492; https://doi.org/10.3390/metabo14090492 - 10 Sep 2024

To explore the effects of altered amino acids (AAs) and the carnitine metabolism in non-pregnant women with infertility (NPWI), pregnant women without infertility (PWI) and infertility-treated pregnant women (ITPW) compared with non-pregnant women (NPW, control), and develop more efficient models for the diagnosis [...] Read more.

To explore the effects of altered amino acids (AAs) and the carnitine metabolism in non-pregnant women with infertility (NPWI), pregnant women without infertility (PWI) and infertility-treated pregnant women (ITPW) compared with non-pregnant women (NPW, control), and develop more efficient models for the diagnosis of infertility and pregnancy, 496 samples were evaluated for levels of 21 AAs and 55 carnitines using targeted high-performance liquid chromatography with tandem mass spectrometry (HPLC-MS/MS). Three methods were used to screen the biomarkers for modeling, with eight algorithms used to build and validate the model. The ROC, sensitivity, specificity, and accuracy of the infertility diagnosis training model were higher than 0.956, 82.89, 66.64, and 82.57%, respectively, whereas those of the validated model were higher than 0.896, 77.67, 69.72, and 83.38%, respectively. The ROC, sensitivity, specificity, and accuracy of the pregnancy diagnosis training model were >0.994, 96.23, 97.79, and 97.69%, respectively, whereas those of the validated model were >0.572, 96.39, 93.03, and 94.71%, respectively. Our findings indicate that pregnancy may alter the AA and carnitine metabolism in women with infertility to match the internal environment of PWI. The developed model demonstrated good performance and high sensitivity for facilitating infertility and pregnancy diagnosis. Full article

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

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