Metabolomics最新文献

Introduction: Ischemic stroke (IS) is a leading cause of disability and mortality. Metabolomics, in conjunction with machine learning (ML), can be employed to identify potential biomarkers associated with this condition.

Objective: We aimed to utilize metabolomics to evaluated the potential biomarkers and crucial metabolic pathways linked with IS. Furthermore, to construct a predictive model employing ML algorithms.

Methods: We conducted non-targeted liquid chromatography-tandem mass spectrometry-based plasma analysis on 786 study participants (discovery set IS/control group = 198/198; external validation set IS patients/control group = 195/195). The aim was to identify differential metabolites and examine metabolic pathways potentially related to the etiology of IS using pathway enrichment analysis. Feature variables were screened using the Least Absolute Shrinkage and Selection Operator and random forest algorithm. We employed XGBoost to construct prediction models for these feature variables, and utilized various evaluation indicators to assess model performance. This was subsequently confirmed in an independent external validation set.

Results: In the comparison between the IS group and the control group, 200 differential metabolites were detected. Notable dysbiotic pathways encompass arachidonic acid metabolism and folate biosynthesis among others. Four significant metabolites were further investigated to differentiate between the IS group and the control group: Calcitroic acid, Diguanosine tetraphosphate, PC (P-18:0/P-18:1(9Z)), and Deoxycholic acid. The XGBoost model exhibited an AUC of 1.000 for the training set and 0.992 for the test set in the discovery columns, while the external independent validation set recorded an AUC of 0.941.

Conclusion: Our study unveiled the metabolic landscape of IS, identified four biomarkers, and developed a prediction model that effectively differentiates between the IS group and the control group based on these four biomarkers.

Introduction: Tuberculosis remains one of the world's deadliest infectious diseases. The pathophysiology of the two manifestations of Mycobacterium tuberculosis infection, pulmonary TB (PTB) and extrapulmonary TB (EPTB) is still not fully understood. Understanding the metabolic profile of both disease manifestations in patients is important for developing therapeutic approaches and molecular diagnosis.

Objective: The current study aimed to elucidate differences in the gut metabolic profile of PTB and EPTB patients compared to healthy controls (HCs).

Method: We used an untargeted approach through ¹H Nuclear Magnetic Resonance (NMR) spectroscopy to perform metabolomic profiling of stool samples from 77 TB patients [pulmonary TB (PTB, n = 33), cervical lymph node TB (CrLNTB, n = 30), abdominal TB (ATB, n = 14)], and 30 HCs. Multivariate and univariate analyses were performed to identify the differential gut metabolites associated with TB patients.

Results: PTB patients showed greater metabolic perturbation than either EPTB group, with 24 metabolites significantly altered compared to 13 in ATB and 12 in CrLNTB, relative to HCs (adjusted p < 0.05). Each TB subtype displayed distinct metabolic profiles, yet several metabolites were commonly altered across all TB groups, including valine, N-formyl-L-methionine, choline, dimethylsulfone, tryptophan, valerate, N-acetylglutamate, creatine, and malonate. In the combined TB cohort versus controls, the most discriminatory metabolites were valine and N-formyl-L-methionine (AUC ≥ 0.80). Overall, these findings offer insights into the gut metabolome of TB patients in India and characterize for the first time metabolic perturbations in EPTB patients.

Conclusion: The study highlights the metabolic disruptions associated with PTB and EPTB patients.

This study builds on a prior proof-of-concept metabolomic analysis of post-mortem pericardial fluid to assess its reproducibility and validate its utility for estimating the post-mortem interval. Sixty-five pericardial fluid samples were collected during medico-legal autopsies in two different Forensic Medicine Institutes with post-mortem intervals spanning 16 to 199 h. Samples underwent liquid-liquid extraction and ¹H NMR analysis, quantifying 50 metabolites. Multivariate statistical analyses were employed to develop post-mortem interval estimation models, controlling for age to minimize its confounding effects. Reproducibility was confirmed, with 92% of metabolites showing high similarity (cosine similarity ≥ 0.90) in 23 re-analyzed samples, demonstrating robust intra-laboratory consistency. For post-mortem intervals of 16 to 100 h, the regression model achieved presented a prediction error of 16.7 h, identifying nine key predictors, including choline, glycine, citrate, betaine, ethanolamine, glutamate, ornithine, uracil, and β-alanine. For intervals of 16 to 130 h, the prediction error was 23.2 h, and for 16 to 199 h, it was 42.1 h. A classification model distinguishing intervals below 48 h from those above 48 h showed high accuracy for detecting longer intervals, with key predictors including aspartate, histidine, and proline. These findings underscore the stability and reproducibility of pericardial fluid metabolomics, establishing its potential as a reliable forensic tool for post-mortem interval estimation, particularly beyond 48 h, with significant implications for forensic investigations.

Introduction: Dementia can be prevented through early intervention; hence, there is an urgent need for biomarkers to help diagnose mild cognitive impairment (MCI).

Objectives: We aimed to develop a multi-marker panel composed of plasma metabolites to aid in the diagnosis of MCI.

Methods: We performed an analysis of a multi-marker panel of MCI metabolites using a random forest algorithm with variable selection methods and a global surrogate with principal component analysis and partial least squares (PLS).

Results: By incorporating variable selection methods, we constructed a predictive model that demonstrated robust performance, with an AUC of approximately 0.85 in both cross-validation and test evaluations, using only five metabolites (methionine, quinic acid, hypoxanthine, O-acetylcarnitine, and 2-oxoglutaric acid). However, owing to the limited number of selected metabolites, it was challenging to infer the biological meaning of this multi-marker panel. To interpret this multi-marker panel biologically, we constructed a global surrogate model using PLS. By examining the PLS loadings corresponding to the scores with intergroup differences, we identified a relationship between 14 metabolites involved in neuronal energy metabolism and neurotransmission. This suggests that the multi-marker panel constructed in this study is related to abnormalities in energy metabolism and neurotransmission in patients with MCI.

Conclusion: The method used in this study may be broadly applicable for analyzing multi-marker panels of metabolites and their biological interpretation. This study included an independent validation, and further larger-scale studies using additional external cohorts are warranted to confirm the generalizability of this approach.

Introduction: Flavin-Containing Monooxygenases (FMO) are widely conserved, xenobiotic-detoxifying enzymes whose additional endogenous functions have been revealed in recent studies. Those roles include the regulation of longevity in the model nematode Caenorhabditis elegans.

Objectives: The purpose of this study was to compare aspects of the phenotypes of C. elegans worms with mutations in all fmo genes, particularly focusing on the metabolome and its relationship with lifespan-extension and the worm life cycle. This is the first systematic study of the effect of fmo genetic variation on C. elegans metabolic profiles that we are aware of.

Methods: NMR Spectroscopic analysis of the extracts of metabolites from C. elegans worms of different ages and fmo genotypes was used to compare metabolite profiles of C. elegans worms and determine how these changed with genotype and ageing.

Results: Loss of both fmo-4 and fmo-3 and over-expression of fmo-2, resulted in increased levels of tryptophan in the metabolome, which correlated with an extended lifespan in these mutants. Loss of fmo-4 also led to decreased embryo hatching, along with increased sensitivity to bleach during sterilisation protocols. In contrast, in the extended lifespan fmo-1 knockout worm, the metabolome did not reveal any significant metabolite changes and therefore lifespan effects may occur through another mechanism, or hidden metabolic changes.

Conclusion: Genetic interventions coupled with metabolome profiling in C. elegans can provide insights into biological mechanisms in ageing that might lead to strategies for healthy lifespan extension in human old age.

Background: The Mycobacterium tuberculosis (Mtb) curli pili (MTP) adhesin has been reported as a significant target for TB diagnostic and intervention strategies. The precise contribution of MTP in modulating oxidative phosphorylation (OXPHOS) and central carbon metabolism (CCM) within host epithelial cells is currently unknown.

Objectives: This study aimed to investigate the impact of MTP in whole cell bioenergetics during early stages of infection.

Methods: Extracellular flux analysis was used to determine the role of MTP in modulating OXPHOS in A549 epithelial cells. ¹³C-metabolic flux analysis was performed on Mtb mtp proficient/deficient infected A549 epithelial cells to determine whether any specific changes in carbon flux through CCM are induced by the adhesin.

Results: The absence of MTP led to an increase in OXPHOS in infected A549 cells, thereby increasing ATP synthesis. The Δmtp-infected A549 cells displayed a similar metabolic profile to the uninfected A549 cells. ¹³C-isotopomer metabolomic analysis of infected A549 cells suggested that MTP plays a role in decreasing glycolytic flux, enhancing flux through the pentose phosphate pathway (PPP), and modulating tricarboxylic acid (TCA) cycle intermediates by increasing flux through succinate.

Conclusions: The decreased basal respiration and flux through glycolysis and PPP of Mtb-infected A549 cells potentially decreased innate immune responses and production of signalling molecules to interact with immunocytes and activate adaptive immune responses. The similar metabolic profile of Δmtp-infected A549 cells and uninfected A549 cells suggests that the absence of the adhesin decreases virulence of Mtb. These findings substantiate MTP as an eminent biomarker for TB diagnostics/intervention strategies, and a novel target for vaccine development.

Background: Higher LDL cholesterol levels are associated with increased risk of cardiovascular disease, and early intervention in children and regular follow-up are keystones for its prevention. Hypercholesterolemia can be partially delayed adopting healthy lifestyles, including healthy diets such as the Mediterranean diet and physical activity. Extra-virgin olive oil (EVOO), the main edible oil in the Mediterranean diet, has demonstrated cardiovascular benefits.

Objective: The present randomized double-blind crossover clinical study aimed to evaluate the efficacy of the daily consumption of a spreadable cream based on extra virgin olive oil enriched in phytosterols (0.04-0.06 g plant sterols/kg of body weight/day) for 8 weeks separated by a wash-out period of 4 weeks on children (6-18 years old) with hypercholesterolemia.

Methods: Fifty children were included although only 23 reported at least 70% of compliance and were used for final analyses. We used a dual approach combining the determination of anthropometric and biochemical plasma parameters and the lipidomics profile of plasma samples.

Results: Spreadable cream enriched in phytosterols consumption led to a decrease in total cholesterol and low-density lipoprotein cholesterol plasma levels. Multivariate analysis of the lipidomic plasma profile indicated that phospholipids and sphingolipids were mostly involved in modulating the observed effects.

Conclusions: Overall, this study provides a multi-level integrated approach for the characterization of a novel functional food to counteract hypercholesterolemia from a nutritional perspective.

Clinical trial registry: Clinical Trial Registry number and website where it was obtained https://clinicaltrials.gov/ct2/show/NCT05460208.

Introduction: Diabetic nephropathy (DN) is a major cause of chronic kidney disease and end-stage renal failure worldwide. The current diagnostic marker, albuminuria, lacks specificity and often detects renal damage only at advanced stages.

Objectives: This study aimed to characterize urinary metabolic alterations associated with DN and explore metabolite panels with diagnostic potential.

Methods: A targeted urinary metabolomics analysis was performed using the validated TMIC Urine MEGA Assay, quantifying 268 metabolites in 60 participants (20 controls, 20 type 2 diabetes mellitus [DM-2], and 20 DN patients). Data were analyzed by Partial Least Squares Discriminant Analysis (PLS-DA) for visualization, and penalized regression algorithms [Least Absolute Shrinkage and Selection Operator (LASSO) and Elastic Net (EN) with a Genetic Algorithm (GA)] followed by logistic regression (LR) modeling to identify potential discriminative variables.

Results: DN patients showed marked alterations in metabolites related to oxidative stress, mitochondrial dysfunction, and inflammation. Twenty-four of 86 quantified uremic toxins differed significantly between DN and comparison groups. The LASSO-derived model identified β-alanine, kynurenine, glucose and argininic acid as key discriminants (AUC = 0.905, 10-fold CV), while inclusion of GFR and additional metabolites (2-hydroxybutyric acid, shikimic acid) improved performance (AUC = 0.96).

Conclusions: Quantitative urinary metabolomics revealed metabolic perturbations reflective of DN pathophysiology and identified candidate metabolite panels with potential for non-invasive disease characterization. These findings, though preliminary, provide a foundation for validation in larger, longitudinal cohorts and for integrating urinary metabolomics into precision diagnostics for diabetic kidney disease.

Background: This study aimed to analyze the molecular composition and physiological changes in the plasma of patients with convalescent coronavirus disease 2019 (COVID-19).

Methods: Plasma samples from 29 hospitalized patients recovering from COVID-19 and 30 uninfected controls were analyzed using untargeted metabolomics and data-independent acquisition mass spectroscopy proteomic analyses. Integrative metabolomic-proteomic analysis was then conducted to construct a protein-metabolite interaction network.

Results: Untargeted metabolomic profiles revealed 415 differential metabolites, with 28.05% of the metabolites belonging to lipids and lipid-like molecules, most of which were upregulated in patients with convalescent COVID-19, such as sphingolipids. Differential metabolites were involved in taste transduction, thermogenesis, and sphingolipid metabolism. Proteomic analysis identified 947 differentially expressed proteins, which were mainly involved in immunoinflammation-related pathways, such as complement and coagulation cascades, neutrophil extracellular trap formation, and platelet activation. Several significant pathways were influenced by differential metabolites and proteins, such as estrogen signaling, ferroptosis, and neurodegeneration-associated pathways.

Conclusion: This study revealed differential metabolite and protein compositions in the plasma of patients with convalescent COVID-19 compared with uninfected controls. The main physiological changes were associated with the pathology of this disease, suggesting that the phenotype of patients with convalescent COVID-19 did not return to a phenotype similar to that of uninfected controls.

Introduction: Fermentation relies on the interaction of microorganisms to enhance flavor, extend shelf life, and improve health. Lentinus polychrous Lév., a mushroom native to Northern Thailand with prebiotic properties, is commonly used in traditional fermented foods; however, the microbial and nutritional characteristics of fermented mushroom sausages have received limited scientific attention.

Objective: To investigate changes in microbial communities and metabolite profiles during fermentation.

Method: We applied an integrated microbiomic and metabolomic approach to assess the effects of fermentation on Lentinus polychrous Lév. mushrooms. Mushroom samples were fermented for up to three days. Bacterial profiles were analyzed using 16S rRNA gene sequencing, while metabolic changes were characterized by LC-MS/MS.

Results: Microbiomics revealed a clear succession from initial Bacillus dominance to a Weissella-dominated community. Concurrently, metabolomics identified 107 metabolites. Key metabolic shifts included decreases in gentiobiose, phenylalanine, and isoleucine, alongside increases in L-tryptophan, beta-hydroxyisovaleric acid, and specific lipids after fermentation. Epicatechin also increased. Correlation analysis showed strong positive associations between Weissella and most differential metabolites, suggesting a crucial role for Weissella in fermentation.

Conclusion: This study demonstrated dynamic shifts from Bacillus to Weissella during Lentinus polychrous Lév sausage fermentation, accompanied by significant metabolic changes. Weissella dominance correlated with enhanced nutrient bioavailability, underscoring the potential of this traditional fermented food as a functional food for health promotion.