Metabolomics最新文献

Introduction: Gestational diabetes mellitus (GDM) is generally identified by measuring elevated maternal glycemic responses to an oral glucose load in late pregnancy (> 0.6 term). However, our preliminary study suggests that GDM could be identified with a high predictive accuracy (96%) in the first trimester (< 0.35 term) by characteristic changes in the metabolite profile of maternal urine. (Koos and Gornbein, American Journal of Obstetrics and Gynecology 224:215.e1-215.e7, 2021). The gestational decrease in insulin sensitivity and the accompanying perturbations of the maternal metabolome suggest that a distinguishing urinary metabolite algorithm could differ in later gestation.

Objectives: This study was carried out (1) to identify the metabolites of late-pregnancy urine that are independently associated with GDM, (2) to select a metabolite subgroup for a predictive model for the disorder, (3) to compare the predictive accuracy of this late pregnancy algorithm with the model previously established for early pregnancy, and (4) to determine whether the late urinary markers of GDM likely contribute to the late pregnancy decline in insulin sensitivity.

Methods: This observational nested case-control study comprised a cohort of 46 GDM patients matched with 46 control subjects (CON). Random urine samples were collected at ≥ 24 weeks' gestation and were analyzed by a global metabolomics platform. A consensus of three multivariate criteria was used to distinguish GDM from CON subjects, and a classification tree of selected metabolites was utilized to compute a model that separated GDM vs CON.

Results: The GDM and CON groups were similar with respect to maternal age, pre-pregnancy BMI and gestational age at urine collection [GDM 30.8 ± 3.6(SD); CON [30.5 ± 3.6] weeks as they were matched by these variables. Three multivariate criteria identified eight metabolites simultaneously separating GDM from CON subjects, comprising five markers of mitochondrial dysfunction and three of inflammation/oxidative stress. A five-level classification tree incorporating four of the eight metabolites predicted GDM with an unweighted accuracy of 89%. The model derived from early pregnancy urine also had a high predictive accuracy (85.9%).

Conclusion: The late pregnancy urine metabolites independently linked to GDM were markers for diminished insulin sensitivity and glucose-stimulated insulin release. The high predictive accuracy of the models in both early and late pregnancy in this cohort supports the notion that a urinary metabolite phenotype may separate GDM vs CON across both early and late gestation. A large validation study should be conducted to affirm the accuracy of this noninvasive and time-efficient technology in identifying GDM.

Introduction: Volatile organic compounds (VOCs) in breath are potential biomarkers for medical conditions that may be used for non-invasive health monitoring. One challenge that still exists is determining the fidelity of reported VOC biomarkers. The lack of universally accepted sampling methods makes it difficult to identify reliable candidates, thus allowing for the potential of false discovery.

Objectives: The purpose of this study was to robustly profile VOCs in breath samples collected from relatively healthy participants using two offline methods for collection/analysis via solid phase microextraction (SPME) coupled to gas chromatography - mass spectrometry (GC-MS).

Methods: 158 cross-sectional volunteers provided one-time samples using two methods, one which directly sampled breath via SPME and another which collected breath in Tedlar bags. Using both methods, 10 volunteers provided an additional nine longitudinal samples. Ambient air samples were collected routinely, and a robust data processing schematic was used to ensure high quality reporting of on-breath VOCs.

Results: Data screening and processing led to the identification of > 30 unique VOCs in both methods. Hierarchical clustering and correlation analyses demonstrated volatile terpene/-oids showed homologous trends in both data sets. Of the 12 VOCs identified using both methods, 11 analytes displayed statistically significant correlations (p < 0.05) in healthy breath samples. Finally, both methods were benchmarked regarding VOC reproducibility, and analyses showed that longitudinally collected samples were more reproducible compared to cross-sectional.

Conclusions: The quantitative results from both sampling methods mirrored each other, thus increasing the reliability and fidelity of VOCs reported along with the results from biostatistical analysis.

Introduction and objectives: Oligoasthenozoospermia, characterized by a low sperm count and impaired progressive motility, significantly contributes to male infertility. This study examines the metabolic disparities between individuals with oligoasthenozoospermia (n = 30) and healthy controls (n = 30) utilizing ultra-high-performance liquid chromatography-quadrupole time-of-flight mass spectrometry (UPLC-Q-TOF/MS).

Methods: A total of 1,331 metabolites were identified in positive ion mode and 870 in negative ion mode, with differential analysis indicating 211 significantly different metabolites between the two groups. Pathway analysis identified key metabolic pathways, including the pentose phosphate pathway, TCA cycle, glycerophospholipid metabolism, and fatty acid metabolism. Subsequently, various machine learning models, including Logistic Regression (LR), Random Forest (RF), and Support Vector Machine (SVM) were employed to assess the predictive capability of the identified metabolites, with 1-palmitoyl-2-docosahexaenoyl-sn-glycero-3-phosphocholine and [6]-gingerol demonstrating the highest predictive performance.

Results: The diagnostic model, developed using LR, attained high sensitivity (0.93), specificity (1), and accuracy (0.97), with an AUC of 0.998 in the training set and 0.963 in the test set.

Conclusion: These findings offer critical insights into the metabolic changes associated with oligoasthenozoospermia and establish a dependable diagnostic framework for differentiating it from controls.

Introduction: Knockout of the Fmo5 gene in mice led to a lean, slow-ageing phenotype characterised by the presence of 2,3-butanediol isomers in their urine and plasma. Oral treatment of wildtype mice with 2,3-butanediol led to a low cholesterol, low epididymal fat phenotype.

Objectives: Determine if significant, heterozygous coding variations in human FMO5 would give rise to similar clinical and metabolic phenotypes in humans, as in C57BL/6J mice with knockout of the Fmo5 gene and in particular, increased excretion of 2,3-butanediol.

Methods: Recruitment of 12 female, Oxford Biobank volunteers with heterozygous coding variations in FMO5, associated with changed clinical traits, and 12 age- and gender-matched controls. Analysis of the key NMR-based, urine and plasma, metabolic phenotypes of these volunteers to determine if there were any statistically significant differences.

Results: Some clinical parameters of the female volunteers with heterozygous coding variations in FMO5 were altered in a direction consistent with our hypothesis viz; lower insulin levels and lower waist circumference, but no consistent elevation of urinary 2,3-butanediol was found in the subjects with heterozygous coding variations in FMO5.

Conclusion: Heterozygous coding variations in human FMO5 appeared to have some impact on the clinical phenotype of the females in this study but the natural variation in the levels of 2,3-butanediol was higher than any inter-group differences between women with heterozygous coding variations in human FMO5 and the women in the control group with wildtype FMO5.

Introduction: The definition of Leber's hereditary optic neuropathy (LHON) does not take into account a preclinical phase during which the thickness of retinal nerve fiber layer (RNFL) is increased, prior to optic nerve atrophy, reducing the chances of visual recovery.

Objectives: Search for a metabolomic signature characterizing this preclinical phase and identify biomarkers predicting the risk of LHON onset.

Methods and results: The blood and tear metabolomic profiles of 90 asymptomatic LHON mutation carriers followed for one year will be explored as a function of RNFL thickness and compared to those of a healthy control.

Conclusion: Identifying pre-clinical biomarkers would open a window for clinical trials.

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.