Metabolomics最新文献

Background: Soccer is the most recognized sports worldwide. It is a fertile ground for the use of metabolomics analyses, considering the multifactorial nature of soccer's physical demands on the body. Although scientific studies have tried using it to better understand the impacts of soccer into different contexts of the sport, no systematic review is available on metabolomics analyses in soccer athletes subjected to physical exertion interventions.

Aim of review: Retrieve scientific articles that conducted metabolomics analyses on soccer athletes subjected to physical exertion interventions.

Key scientific concepts of review: Initially, 271 studies were screened, and 48 were retrieved for abstract analysis. Of these, 26 met the eligibility criteria, but 5 failed to meet inclusion criteria. The 21 studies included in this systematic review demonstrate that responses from physical training or acute exercise sessions, followed by the effects of soccer matches, have been the primary focus of researchers to date, highlighting alterations on metabolites from the energy metabolism, immunological pathway, purines, tryptophan/phenylalanine metabolism, as well as oxidative species and antioxidant capacity. Other studies suggest, albeit preliminarily, that organic metabolites have the potential to distinguish soccer players' performance and physical fitness, as well as provide valuable insights into diet, physical condition, training load, and recovery throughout the season. Despite metabolomics great potential to understand physiological alterations provoked by soccer as shown by the included studies, future studies should consider female athletes, explore the cause-and-effect relationship between metabolites and soccer performance more deeply, and examine the effects of different training periodizations on these markers.

Introduction: Outside of case-control settings, ethnicity specific changes in the human metabolome are understudied especially in community dwelling, ageing men. Characterising serum for age and ethnicity specific features can enable tailored therapeutics research and improve our understanding of the interplay between age, ethnicity, and metabolism in global populations.

Objective: A metabolomics approach was adopted to profile serum metabolomes in middle-aged and elderly men of different ethnicities from the Northwest of England, UK.

Methods: Serum samples from 572 men of White European (WE), South Asian (SA), and African-Caribbean (AC) ethnicities, ranging between 40 and 86 years were analysed. A combination of liquid chromatography (LC) and gas chromatography (GC) coupled to high-resolution mass spectrometry (MS) was used to generate the metabolomic profiles. Partial Least Squares Discriminant Analysis (PLS-DA) based classification models were built and validated using resampling via bootstrap analysis and permutation testing. Features were putatively annotated using public Human Metabolome Database (HMDB) and Golm Metabolite Database (GMD). Variable Importance in Projection (VIP) scores were used to determine features of interest, after which pathway enrichment analysis was performed.

Results: Using profiles from our analysis we classify subjects by their ethnicity with an average correct classification rate (CCR) of 90.53% (LC-MS data) and 85.58% (GC-MS data). Similar classification by age (< 60 vs. ≥ 60 years) returned CCRs of 90.20% (LC-MS) and 71.13% (GC-MS). VIP scores driven feature selection revealed important compounds from putatively annotated lipids (subclasses including fatty acids and carboxylic acids, glycerophospholipids, steroids), organic acids, amino acid derivatives as key contributors to the classifications. Pathway enrichment analysis using these features revealed statistically significant perturbations in energy metabolism (TCA cycle), N-Glycan and unsaturated fatty acid biosynthesis linked pathways amongst others.

Conclusion: We report metabolic differences measured in serum that can be attributed to ethnicity and age in healthy population. These results strongly emphasise the need to consider confounding effects of inherent metabolic variations driven by ethnicity of participants in population-based metabolic profiling studies. Interpretation of energy metabolism, N-Glycan and fatty acid biosynthesis should be carefully decoupled from the underlying differences in ethnicity of participants.

Introduction: Rapid detection and identification of pathogens and antimicrobial susceptibility is essential for guiding appropriate antimicrobial therapy and reducing morbidity and mortality associated with sepsis.

Objectives: The metabolic response of clinical isolates of Klebsiella oxytoca exposed to different concentrations of ciprofloxacin (the second generation of quinolones antibiotics) were studied in order to investigate underlying mechanisms associated with antimicrobial resistance (AMR).

Methods: Metabolomics investigations were performed using Fourier-transform infrared (FT-IR) spectroscopy as a metabolic fingerprinting approach combined with gas chromatography-mass spectrometry (GC-MS) for metabolic profiling.

Results: Our findings demonstrated that metabolic fingerprints provided by FT-IR analysis allowed for the differentiation of susceptible and resistant isolates. GC-MS analysis validated these findings, while also providing a deeper understanding of the metabolic alterations caused by exposure to ciprofloxacin. GC-MS metabolic profiling detected 176 metabolic features in the cellular extracts cultivated on BHI broth, and of these, 137 could be identified to Metabolomics Standards Initiative Level 2. Data analysis showed that 40 metabolites (30 Level 2 and 10 unknown) were differentiated between susceptible and resistant isolates. The identified metabolites belonging to central carbon metabolism; arginine and proline metabolism; alanine, aspartate and glutamate metabolism; and pyruvate metabolism. Univariate receiver operating characteristic (ROC) curve analyses revealed that six of these metabolites (glycerol-3-phosphate, O-phosphoethanolamine, asparagine dehydrate, maleimide, tyrosine, and alanine) have a crucial role in distinguishing susceptible from resistant isolates (AUC > 0.84) and contributing to antimicrobial resistance in K. oxtytoca.

Conclusion: Our study provides invaluable new insights into the mechanisms underlying development of antimicrobial resistance in K. oxytoca suggests potential therapeutic targets for prevention and identification of AMR in K. oxytoca infections.

Introduction: Inflammatory bowel diseases (IBDs) are chronic immune driven intestinal disorders with marked metabolic alteration. Mass spectrometry imaging (MSI) enables the direct visualization of biomolecules within tissues and facilitates the study of metabolic changes. Integrating multiple spatial information sources is a promising approach for discovering new biomarkers and understanding biochemical alteration within the context of the disease.

Objective: This study evaluates the metabolomic changes in gut tissue samples from a preclinical model of spontaneous colitis, the HLA-B27/hβ2m transgenic rat, to uncover disease biomarkers.

Methods: We applied MSI to study the biochemical profile of bowel samples from HLA-B27/hβ2m transgenic and WT control rats in an unbiased manner. Statistical comparison was used to identify discriminative features. Some features were annotated using LC-MS/MS. The significance of these discriminative features was evaluated based on their distribution within histological layers and the presence of immune infiltration.

Results: We identified spatially resolved changes in the metabolomic pattern of HLA-B27⁺ samples compared to WT controls. Out of the 275 discriminative features identified, 83 were annotated as metabolites. Two functional groups of discriminative metabolites were discussed as markers of gut barrier impairment and immune cell infiltration.

Conclusion: MS imaging's spatial dimension provides insights into disease mechanisms through the identification of spatially resolved biomarkers.

Introduction/objectives: Several observational investigations have observed the possible links between Alzheimer's disease (AD) and metabolic dysfunction associated with fatty liver disease (MAFLD), yet the underlying causal relationships remain undetermined. This study aimed to systemically infer the causal associations between AD and MAFLD by employing a bidirectional network two-sample Mendelian randomization (MR) analysis.

Methods: Genome-wide significant (P < 5 × 10^- 8) genetic variants associated with AD and MAFLD were selected as instrumental variables (IVs) from the consortium of FinnGen, MRC-IEU, UK biobank, and genome-wide association studies (GWAS), respectively. The study sample sizes range from 55,134 to 423,738 for AD and from 218,792 to 778,614 for MAFLD. In the forward analysis, AD was set as the exposure factor, and MAFLD was employed as the disease outcome. Causal relationships between AD and MAFLD were evaluated using inverse-variance weighted (IVW), MR Egger regression, the weighted median, and weighted mode. Additionally, the reverse MR analysis was conducted to infer causality between MAFLD and AD. Sensitivity analyses were performed to assess the robustness of causal estimates.

Results: In the forward MR analysis, the genetically determined family history of AD was associated with a lower risk of MAFLD (mother's history: OR_discovery=0.08, 95%CI: 0.03, 0.22, P = 7.91 × 10^- 7; OR_replicate=0.83, 95%CI: 0.74, 0.94, P = 3.68 × 10^- 3; father's history: OR_discovery=0.01, 95%CI: 0.01, 0.08, P = 5.48 × 10^- 5; OR_replicate=0.79, 95%CI: 0.68, 0.93, P = 4.07 × 10^- 3; family history: OR_discovery=0.84, 95%CI: 0.77, 0.91, P = 6.30 × 10^- 5; OR_replicate=0.15, 95%CI: 0.05, 0.41, P = 2.51 × 10^- 4) in the primary MAFLD cohort. Consistent findings were observed in an independent MAFLD cohort (all P < 0.05). However, the reverse MR analysis suggested that genetic susceptibility to MAFLD had no causal effects on developing AD.

Conclusion: Our study demonstrates a causal association between a family history of AD and a lower risk of MAFLD. It suggests that individuals with a history of AD may benefit from tailored metabolic assessments to better understand their risk of MAFLD, and inform the development of preventive strategies targeting high-risk populations.

Introduction: Despite the well-established efficacy of thiazolidinediones (TZDs), including pioglitazone and rosiglitazone, in type II diabetes management, their potential contribution to heart failure risk remains a significant area of uncertainty. This incomplete understanding, which persists despite decades of clinical use of TZDs, has generated ongoing controversy and unanswered questions regarding their safety profiles, ultimately limiting their broader clinical application.

Objective and methods: This study presented a multi-omics approach, integrating toxicoproteomics and toxicometabolomics data with the goal of uncovering novel mechanistic insights into TZD cardiotoxicity and identifying molecular signatures predictive of side effect progression.

Results: Network analysis of proteo-metabolomic data revealed a distinct fingerprint of disrupted biochemical pathways, which were primarily related to energy metabolism. Downregulation of oxidative phosphorylation and fatty acid synthesis was coupled with increased activity in anaerobic glycolysis, the pentose phosphate pathway, and amino acid and purine metabolism. This suggests a potential metabolic shift in AC16 cells from fatty acid oxidation towards anaerobic glycolysis, potentially contributing to observed cardiotoxicity. Additionally, the study identified a marked disruption in the glutathione system, indicating an imbalanced redox state triggered by TZD exposure. Importantly, our analysis identified key molecular signatures across omics datasets, including prominent signatures of amino acids like L-ornithine, L-tyrosine and glutamine, which are evidently associated with heart failure, supporting their potential use for the early prediction of cardiotoxicity progression.

Conclusion: By uncovering a novel mechanistic explanation for TZD cardiotoxicity, this study simultaneously illuminates potential therapeutic interventions, opening avenues for future research to improve the safety profile of TZD agents. (250 words).

Introduction: The knowledge of the mode of action of an antimicrobial is essential for drug development and helps to fight against bacterial resistance. Thus, it is crucial to use analytical techniques to study the mechanism of action of substances that have potential to act as antibacterial agents OBJECTIVE: To use NMR-based metabolomics combined with chemometrics and molecular docking to identify the metabolic responses of Staphylococcus aureus following exposure to commercial antibiotics and some synthesized ω-aminoalkoxylxanthones.

Methods: Intracellular metabolites of S. aureus were extracted after treatment with four commercial antibiotics and three synthesized ω-aminoalkoxylxanthones. NMR spectra were obtained and ₁H NMR data was analyzed using both unsupervised and supervised algorithms (PCA and PLS-DA, respectively). Docking simulations on DNA topoisomerase IV protein were also performed for the ω-aminoalkoxylxanthones.

Results: Through chemometric analysis, we distinguished between the control group and antibiotics with extracellular (ampicillin) and intracellular targets (kanamycin, tetracycline, and ciprofloxacin). We identified 21 metabolites, including important metabolites that differentiate the groups, such as betaine, acetamide, glutamate, lysine, alanine, isoleucine/leucine, acetate, threonine, proline, and ethanol. Regarding the xanthone-type derivatives (S6, S7 and S8), we observed a greater similarity between S7 and ciprofloxacin, which targets bacterial DNA replication. The molecular docking analysis showed high affinity of the ω-aminoalkoxylxanthones with the topoisomerase IV enzyme, as well as ciprofloxacin.

Conclusion: NMR-based metabolomics has shown to be an effective technique to assess the metabolic profile of S. aureus after treatment with certain antimicrobial compounds, helping the investigation of their mechanism of action.

Introduction: Coronavirus disease 2019 (COVID-19) has widely varying clinical severity. Currently, no single marker or panel of markers is considered standard of care for prediction of COVID-19 disease progression. The goal of this study is to gain mechanistic insights at the molecular level and to discover predictive biomarkers of severity of infection and outcomes among COVID-19 patients.

Method: This cohort study (n = 76) included participants aged 16-78 years who tested positive for SARS-CoV-2 and enrolled in Memphis, TN between August 2020 to July 2022. Clinical outcomes were classified as Non-severe (n = 39) or Severe (n = 37). LC/HRMS-based untargeted metabolomics/lipidomics was conducted to examine the difference in plasma metabolome and lipidome between the two groups.

Results: Metabolomics data indicated that the kynurenine pathway was activated in Severe participants. Significant increases in short chain acylcarnitines, and short and medium chain acylcarnitines containing OH-FA chain in Severe vs. Non-severe group, which indicates that (1) the energy pathway switched to FA β-oxidation to maintain the host energy homeostasis and to provide energy for virus proliferation; (2) ROS status was aggravated in Severe vs. Non-severe group. Based on PLS-DA and correlation analysis to severity score, IL-6, and creatine, a biomarker panel containing glucose (pro-inflammation), ceramide and S1P (inflammation related), 4-hydroxybutyric acid (oxidative stress related), testosterone sulfate (immune related), and creatine (kidney function), was discovered. This novel biomarker panel plus IL-6 with an AUC of 0.945 provides a better indication of COVID-19 clinical outcomes than that of IL-6 alone or the three clinical biomarker panel (IL-6, glucose and creatine) with AUCs of 0.875 or 0.892.

Introduction: Tempeh is an antioxidant-rich soybean fermentation product from Java, Indonesia. Cooking methods have an impact on the nutritional value and bioactivity of food.

Objective: This study aims to investigate how the cooking process affects the metabolites and antioxidant activity in tempeh using ultra-high-performance liquid chromatography-high resolution mass spectrometry (UHPLC-HRMS).

Methods: A nontargeted UHPLC-HRMS metabolomics and chemometric analysis were used to evaluate metabolite profiles and antioxidant activity changes because of food processing in tempeh.

Results: The score plots of tempeh produced by boiling and frying methods displayed a distinct separation from raw tempeh, revealing that the cooking process altered the metabolite composition of tempeh. Due to processing, L-glutamic acid, L-pyroglutamic acid, DL-glutamine, and D-( +)-proline became the most affected metabolites on tempeh. There were 70 metabolites that showed antioxidant activity using the DPPH assay; 23 metabolites significantly differ from DPPH and control for antioxidant activity for all processing tempeh. Metabolites with significantly different antioxidant activity in raw and processed tempeh were dominated by flavonoids, vitamin E, and bioactive lipids.

Conclusion: The DPPH antioxidant assay using UHPLC-HRMS is promising as a fast antioxidant assay by simplifying the conventional DPPH antioxidant assay. Further, it can be used to identify the name of metabolites responsible for its antioxidant activity.

Introduction: Atopic dermatitis (AD) is a common chronic inflammatory dermatosis. However, the exact molecular mechanism underlying the development of AD remain largely unclear.

Objective: To investigate comprehensive metabolomic alterations in serum and skin tissue between 2,4-dinitrofluorobenzene (DNFB)-induced AD-like mice and healthy controls, aiming to identify the potential disease biomarkers and explore the molecular mechanisms of AD.

Methods: In this study, Untargeted metabolomics analysis was used to investigate both skin and serum metabolic abnormalities of 2,4-dinitrofluorobenzene (DNFB)-induced AD-like mice. Then, the metabolic differences among the groups were determined through the application of multivariate analysis. Additionally, the selection of predictive biomarkers was accomplished using the receiver operating characteristic (ROC) module.

Results: Our findings showed that levels of 220 metabolites in the skin and 94 metabolites in the serum were different in AD-like mice that were treated with DNFB compared to control mice. Uracil, N-Acetyl-L-methionine, deoxyadenosine monophoosphate, 2-acetyl-l-alkyl-sn-glycero-3-phosphcholine, and prostaglandin D2 are considered potential biomarkers of AD as obtained by integrating skin and serum differential metabolite results. Metabolomic data analysis showed that the metabolic pathways in which skin and serum are involved together include histidine metabolism, pyrimidine metabolism, alanine, aspartate, and glutamate metabolism.

Conclusion: Our research explained the possible molecular mechanism of AD at the metabolite level and provided potential targets for the development of clinical drugs for AD.