Metabolomics最新文献

Introduction: Initially developed for transcriptomics data, pathway analysis (PA) methods can introduce biases when applied to metabolomics data, especially if input parameters are not chosen with care. This is particularly true for exometabolomics data, where there can be many metabolic steps between the measured exported metabolites in the profile and internal disruptions in the organism. However, evaluating PA methods experimentally is practically impossible when the sample's "true" metabolic disruption is unknown.

Objectives: This study aims to show that PA can lead to non-specific enrichment, potentially resulting in false assumptions about the true cause of perturbed metabolic states.

Methods: Using in silico metabolic modelling, we can create disruptions in metabolic networks. SAMBA, a constraint-based modelling approach, simulates metabolic profiles for entire pathway knockouts, providing both a known disruption site as well as a simulated metabolic profile for PA methods. PA should be able to detect the known disrupted pathway among the significantly enriched pathways for that profile.

Results: Through network-level statistics, visualisation, and graph-based metrics, we show that even when a given pathway is completely blocked, it may not be significantly enriched when using PA methods with its corresponding simulated metabolic profile. This can be due to various reasons such as the chosen PA method, the initial pathway set definition, or the network's inherent structure.

Conclusion: This work highlights how some metabolomics data may not be suited to typical PA methods, and serves as a benchmark for analysing, improving and potentially developing new PA tools.

Background: Hyperlipidemia is a complex lipid metabolism disorder defined as an abnormal increase in circulating levels of one or more plasma lipids and lipoproteins. Triton WR-1339-induced hyperlipidemia model is one of the most commonly used acute models for hyperlipidemia induction in research. However, the metabolic alteration induced by Triton WR-1339 remains unclear.

Aims: This study aimed to identify potential biomarkers associated with the Triton WR-1339-induced hyperlipidemia model. In addition, it aims to explore the underlying mechanisms of metabolic disturbances associated with hyperlipidemia.

Methods: Male Wistar rats were administered Triton WR-1339 to induce hyperlipidemia. Plasma samples were collected for lipid assays and for metabolomics analysis using nuclear magnetic resonance spectroscopy. Gene expression in liver, cardiac, and kidney tissues of key associated transporters including SLC16A1, SLC25A10, SLC5A3, and SLC7A8 and SDHA enzyme subunit was assessed using RT-PCR. In-silico analysis complemented experimental data using NEBION Genevestigator and STITCH databases for molecular interactions.

Results: Triton WR-1339 administration significantly elevated plasma triglycerides. Orthogonal partial least squares-discriminant analysis (OPLS-DA) demonstrated distinct metabolic profiles between control and model groups. Metabolomics results identified potential biomarkers (p < 0.05), including myo-inositol, succinate, creatine, glycine, serine, isoleucine and creatine phosphate, which all showed higher levels in hyperlipidemia group compared to control group while xanthine showed lower levels in hyperlipidemia group. Potential biomarkers were associated with inflammatory, oxidative stress responses, and abnormal lipid metabolism. Gene expression analysis revealed significant tissue-specific alterations including changes in the expression of SDHA in the liver, an upregulated SLC16A1 in cardiac tissue (in-silico and in-vivo), a downregulated SLC5A3 in cardiac tissue (in-vivo), an upregulated SLC25A10 in cardiac tissue (in-vivo) and differential in-silico expression of SLC25A10 across liver and kidney tissues. Further network analysis indicates that Triton WR-1339 may induce hyperlipidemia by significantly elevating triglyceride levels through the inhibition of LPL.

Conclusions: Our findings identify a set of metabolites as potential biomarkers of hyperlipidemia development in the Triton WR-1339 model. Correlation between gene expression analysis and metabolic profiling results demonstrates a possible mechanism in which Triton WR-1339 leads to metabolic disruption during hyperlipidemia induction.

Introduction: Chronic facial pain (CFP) includes a range of conditions such as musculoskeletal, neurovascular, and neuropathic disorders affecting the facial and jaw regions, often causing significant distress to patients.

Objectives: This study aims to investigate the metabolomic profile of patients with CFP, focusing on salivary metabolites as potential biomarkers for pain diagnosis and management.

Methods: Metabolomics investigation was performed using combined liquid chromatography with mass spectrometry (UPLC-MS) for metabolic profiling.

Results: A comprehensive analysis was conducted, utilizing both untargeted and targeted metabolomics to examine 28 metabolites previously associated with pain conditions. The results revealed significant differences in 18 metabolites between the CFP group and a control group, with seven metabolites consistently showing elevated levels regardless of gender: DL-Isoleucine, DL-Glutamine, DL-Citrulline, D-(+)-Pyroglutamic acid, DL-Tryptophan, DL-Phenylalanine, and Spermidine.

Conclusions: The findings suggest a potential link between specific salivary metabolites and CFP, highlighting the complexity of pain mechanisms. Further research is needed to understand the causality and implications of these metabolic changes, which could lead to more targeted and personalized approaches in managing pain.

Introduction: Fatty acids (FAs) are essential for cellular structure, metabolism, and inflammatory regulation. This study investigated FA profiles in Crimean-Congo hemorrhagic fever (CCHF), a severe viral illness with high mortality rates, to explore their potential as disease progression and severity biomarkers.

Methods: 190 participants were included in the study, comprising 115 CCHF-positive patients, 30 CCHF-negative patients, and 45 healthy controls. FA concentrations were analyzed via gas chromatography‒mass spectrometry (GC-MS).

Results: Statistically significant differences in specific FA levels were observed between the study groups. Compared with mild and moderate cases, severe cases showed distinctive FA profiles. Notably, higher omega-6/omega-3 ratios and linoleic acid to dihomo-γ-linolenic acid (LA/DGLA) ratios are associated with severe disease outcomes and poor prognosis and are correlated with inflammatory markers such as IL-6 and D-dimer. Pathway analysis was performed to identify disruptions in fatty acid biosynthesis and metabolism. Additionally, Cox regression analyses were conducted to determine key fatty acids associated with prognosis. Regression analyses identified several key fatty acids influencing prognosis, including myristic acid, phytanic acid, linoleic acid, gamma-linolenic acid, alpha-linolenic acid, oleic acid, behenic acid, cerotic acid, linoleic acid DGLA, omega-6 fatty acids, omega-9 fatty acids, and the omega-6/omega-3 ratio. Pathway analysis revealed that the disruptions in the most affected pathways were the biosynthesis of unsaturated fatty acids, α-linolenic acid metabolism, elongation, degradation, arachidonic acid metabolism, and fatty acid biosynthesis in CCHF pathogenesis.

Conclusion: This study highlights significant alterations in fatty acid metabolism and laboratory markers in CCHF. These findings provide insights into the pathophysiology of this disease and may guide future research on targeted therapeutic strategies.

Introduction: Untargeted metabolic phenotyping (metabolomics/metabonomics), also known as metabotyping, has been shown to be able to discriminate reliably between different physiological or clinical conditions. However, we believe that standard panels of routinely collected clinical and clinical chemistry data also have the potential to provide assay panels that complement metabotyping.

Objectives: To test the above hypothesis and evaluate the use of multivariate statistical analyses to provided panels of clinical/clinical chemistry data measurements that predict the age, sex and body mass index (BMI) of 977 normal subjects and compare these predictions with results acquired by metabotyping on the same healthy individuals.

Methods: Metabotyping involved serum metabolomics using gas chromatography-mass spectrometry (GC-MS) and liquid chromatography-mass spectrometry (LC-MS) previously reported in our HUSERMET study (Dunn et al., 2015), while clinical chemistry data were obtained in clinic for 19 measurements assessing liver and kidney function, blood pressure, serum glucose, cations, as well as lipids. Multivariate analyses involved using support vector machines, random forest and partial least squares, to predict sex, age and BMI. These models used as inputs: (i) the clinical chemistry data alone; (ii) three metabolomics datasets; (iii) combinations of clinical chemistry with the metabolomics data. Model predictions were rigorously validated using 1,000 bootstrapping re-sampling coupled with permutation tests.

Results: Multivariate statistical analyses on the clinical chemistry data obtained for these healthy participants could be used to predict: their sex, based on creatinine; their age, based on systolic blood pressure, total serum protein and serum glucose; as well as BMI using alanine transaminase, total cholesterol (Total-c) to high-density lipoprotein cholesterol (HDL-c) ratio and diastolic blood pressure. Combining clinical chemistry and metabolomics data sets enhanced the predictions of these characteristics. Moreover, this powerful combination allowed for quantitative predictions of age and BMI.

Conclusion: Multivariate statistical analysis on clinical chemistry data from the HUSERMET study obtained similar predictions of age, sex or BMI, compared to metabotyping using GC-MS and LC-MS. These predictions from clinical chemistry data were between 71 and 85% accurate (depending on the MVA used) and compared favourably with metabolomics (71-91 depending on analytical method). Combining clinical chemistry and metabolomics data sets enhanced the predictions of these characteristics to 77-93% accuracy, suggesting that this augmentation of methods may be a useful approach in the search for clinical biomarkers.

Introduction: Cow colostrum synthesis takes place during the last month of pregnancy. Its composition is influenced by individual and environmental factors, such as cow parity, feeding, season and environmental conditions. Therefore, colostrum metabolomic profiling may provide information about the physiological status of cows around calving.

Objectives: The cow colostrum metabolome was analyzed to determine whether its variability could be used to elucidate the cows' physiological status around calving and provide insights into the outcomes of cow transition programs.

Methods: The factors assessed included a control feeding based on grass-clover silage and barley straw (FAR), two phase feedings based on acidified corn silage and canola cake, supplemented with magnesium chloride (MGC) or magnesium chloride and ammonium chloride (NH₄) and a feeding consisting of one week of grass-diluted MGC followed by two weeks of the NH₄. Colostrum was collected from 89 dairy cows, which were randomly allocated to the feedings three weeks before the expected calving date during spring, summer and autumn. Cow colostrum samples were analyzed using proton nuclear magnetic resonance spectroscopy.

Results: Our results show that calving season influenced the levels of 14 metabolites. Independent of seasonal variation, acidified corn silage diets resulted in consistent decreased levels of tryptophan, acetate and cytidine, while the non-acidified grass-based diet resulted in increased concentrations of fucose.

Conclusions: Although colostrum is physiologically regulated, our findings, for the first time, indicate that the four feeding strategies induce shifts in fucose, tryptophan, acetate and cytidine levels, reflecting the energy and nitrogen metabolism of cows before parturition.

Introduction: Right heart (RH), as a junction between the venous system and pulmonary circulation, gains great emphasis on exploring the relevant pathological mechanism of many cardiopulmonary diseases. Although these pathogensis researches centering on RH-related diseases advance, the physiological mechanism research of the RH is scarce.

Objectives: This study aimed to accurately unravel the metabolic features of normal trans-RH through non-targeted metabolomics.

Methods: Patent foramen ovale (PFO) participants with normal function of RH were recruited and their blood samples from superior vena cava (SVC) and pulmonary artery (PA) were collected through right cardiac catheterization. Non-targeted metabolomics analysis based on UHPLC-MS/MS was utilized to generate the metabolic feature of trans-RH by comparing the metabolites change from SVC to PA, revealing its physiological gradient metabolic mechanism.

Results: 1060 metabolites were tentatively identified in blood samples from 28 PFO participants. 51 differential metabolites were defined based on screening criteria after flowing through RH, including 39 down-regulated metabolites and 12 up-regulated metabolites. Among them, phosphatidylcholines, sphingomyelins, amino acids, triacylglycerol, neopterin, and tetradecanedioic acid were the most relevant.

Conclusion: Our study provides a more profound and extensive understanding of the psychological metabolism of trans-RH, expanding the current knowledge of normal RH function and providing clues for the pathogenesis research of RH-related diseases.

Background: Pulmonary sarcoidosis, a disease of unknown etiology, is characterized by the presence of noncaseating granulomas in lung parenchyma. This present study combines metabolomic and transcriptomic data to determine the metabolic and differentially expressed genes (DEGs) and associated pathways in sarcoidosis patients as compared to healthy controls. It is envisioned that a better understanding of the underlying mechanism will help in diagnosis and future treatment strategies.

Methods: Using proton nuclear magnetic resonance (NMR) the altered serum metabolites were annotated in two groups of patients (discovery and validation cohorts). In addition, DEGs in blood samples were identified by analyzing a Gene Expression Omnibus (GEO) database. Next, a classification model using machine learning approach is developed to evaluate the predictive ability of these key metabotypes and DEGs. Finally, the pathways associated with these candidate metabolites and genetic features were investigated using IMPaLA version 13 tool.

Results: The expression of six metabolites was found to be significantly altered in sarcoidosis patients as compared to controls. The transcriptomics analysis of microarray-based data revealed 10 DEGs to be significantly dysregulated in patients with sarcoidosis. The classification model using these key metabolites and DEGs showed the prediction ability to be 84% and 82% for metabolites and DEGs, respectively. Metabolite-DEG integrated model indicated significant association of IFN-γ signaling pathway in patients with sarcoidosis.

Conclusions: The findings of this study indicate an increased energy demand and dysregulation of inflammatory pathways in patients with sarcoidosis.

Introduction: Melatonin has been proposed to aid recovery following physical exercise; however, few studies have investigated its effects on tissue amino acid profile.

Objective: This study aimed to evaluate the effects of post-exercise melatonin administration on tissue amino acid concentration and metabolic regulation.

Methods: Thirty Wistar rats engaged in a 60-minute swimming session at 90% of their individual maximal aerobic capacity (iMAC), followed by the intraperitoneal administration of melatonin (EM; 10 mg·kg⁻¹) or a vehicle solution (Ex) of equivalent volume. The animals were euthanized at 1, 3, or 24 h post-treatment to facilitate the collection of liver and skeletal muscle samples. Tissue amino acid profiles were analyzed using flow-injection analysis (FIA) in conjunction with targeted mass spectrometry (MS). Statistical analyses were conducted using the Friedman test, two-way analysis of variance (ANOVA), Newman-Keuls post hoc test, and effect size (ES), with significance determined at p < 0.05.

Results: No significant effects were observed in the liver tissue. However, in skeletal muscle, melatonin significantly increased the levels of several amino acids, including arginine, glutamic acid, glutamine, ornithine, proline, and serine. Additionally, glycine levels were elevated 3 h post-exercise (EM3 > Ex3; p < 0.05), whereas methionine levels were reduced 24 h post-exercise in the melatonin group compared to control groups (EM24 < Ex24; p < 0.01).

Conclusion: Melatonin modulated the post-exercise amino acid profile in skeletal muscle, enhancing the levels of key metabolites involved in recovery and metabolic regulation, with no effects observed in liver tissue. These findings suggest a muscle-specific role for melatonin in supporting metabolic recovery after exercising.

Introduction: Translating findings from animal models to human applications remains a fundamental challenge across scientific research, with unique implications for post-mortem metabolomics.

Objectives: This work is aimed at applying NMR metabolomics to human aqueous humour for post-mortem interval estimation, based on a previously studied ovine model.

Methods: Quantitative metabolomic profiling of 21 aqueous humour samples collected during from 11 forensic autopsies, with post-mortem intervals between 225 and 1164 min has been performed by ¹H NMR spectroscopy.

Results: Most of the identified metabolites in human aqueous humour samples are shared with those previously identified in ovine samples, showing qualitative similarities, while quantitative differences in metabolites such as lactate and glutamate are observed due to species-specific factors. Partial least squares regression models for post-mortem interval estimation resulted less accurate in human model with respect to the ovine one underscoring translational complexity. Of note, taurine and hypoxanthine were identified as post-mortem interval-specific metabolites independently on the species, suggesting their relevance in the post-mortem.

Conclusions: This study is the first attempt to translate animal to human post-mortem metabolomics using a rigorous methodology. Direct translation to humans seems possible for a limited part of the metabolome, with key metabolites such as taurine and hypoxanthine showing some consistency. These findings support animal model metabolomics as a guide for human studies across diverse metabolomics investigations, promoting human studies on larger cohorts and more specific experimental designs.