Metabolomics最新文献

Introduction: Maternal nutrition during gestation plays a crucial role in shaping offspring development, metabolism, and long-term health, yet the underlying molecular mechanisms remain poorly understood.

Objectives: This study investigated potential biomarkers through multi-OMICs and multi-tissue analyses in offspring of beef cows subjected to different gestational nutrition regimes.

Methods: A total of 126 cows were allocated to three groups: NP (control, mineral supplementation only), PP (protein-energy supplementation in the last trimester), and FP (protein-energy supplementation throughout gestation). Post-finishing phase, samples (blood, feces, ruminal fluid, fat, liver, and longissimus muscle/meat) were collected from 63 male offspring. RNA sequencing was performed on muscle and liver, metabolomics on plasma, fat, liver, and meat, and 16S rRNA sequencing on feces and ruminal fluid. Data were analyzed via DIABLO (mixOmics, R).

Results: The muscle transcriptome showed strong cross-block correlations (|r| > 0.7), highlighting its sensitivity to maternal nutrition. Plasma glycerophospholipids (PC ae C30:0, PC ae C38:1, lysoPC a C28:0) were key biomarkers, particularly for FP. The PP group exhibited liver-associated markers (IL4I1 gene, butyrylcarnitine), reflecting late-gestation effects, while NP had reduced ruminal Clostridia (ASV151, ASV241), suggesting impaired microbial energy metabolism.

Conclusions: This integrative multi-OMICs approach provided deeper insights than single-layer analyses, distinguishing nutritional groups and revealing tissue- and OMIC-specific patterns. These findings demonstrate the value of combining transcriptomic, metabolomic, and microbiome data to identify biomarkers linked to maternal nutrition in beef cattle.

Introduction: Perfluorooctanoic acid (PFOA) is a widespread environmental contaminant that interferes with multiple biological pathways, with lipid metabolism being particularly vulnerable. Early-life exposure may disrupt hepatic function during development, but the underlying mechanisms are not fully understood.

Objectives: This study investigated how in ovo exposure to PFOA affects hepatic metabolism in the developing chicken embryo, with a focus on identifying altered metabolic pathways and potential mediators of toxicity.

Methods: Fertilized chicken eggs (Gallus gallus domesticus) were exposed in ovo to six concentrations of PFOA (0-5 µg/g egg). Embryonic liver tissues were analysed by comprehensive metabolomic profiling using two complementary ultra-high-performance liquid chromatography-quadrupole time-of-flight mass spectrometry (UHPLC-QTOF-MS) platforms.

Results: We identified 499 metabolites, including lipids, bile acids, carboxylic acids, amino acids, and phenolic compounds. PFOA exposure caused dose-dependent disturbances in lipid, bile acid, and amino acid metabolism. Notably, multiple secondary bile acids were detected and found to be strongly affected by PFOA, suggesting a central role of bile acid modulation in mediating its effects.

Conclusions: In ovo exposure to PFOA disrupts hepatic metabolism in developing chicken embryos, particularly through alterations in bile acid, lipid, and amino acid pathways. These metabolic changes may impair energy production, endocrine regulation, and organ development, with possible long-term health consequences.

Introduction: Metabolomics has proven to be a powerful tool in the natural products' field for the investigation of plant extracts and the exploration of compounds with eminent biological and pharmacological properties. To that end, there is an ongoing discussion between NMR and LC-MS as the analytical platform of choice. Pistacia lentiscus L. var. Chia leaves (mastic leaves), an agricultural waste of pruning, possess a wide range of pharmacological properties, while limited data exist regarding their phytochemical profile.

Objectives: The aim of the present study was to assess the complementarity of these two main platforms in the study of natural products. As a case study, this approach was applied in the profiling of P. lentiscus leaves for the first time.

Methods: Two different untargeted methodologies were developed, utilizing NMR and UPLC-HRMS, for the detailed metabolite profile characterization of P. lentiscus leaves. Multivariate analysis (MVA) along with other statistical tools like Statistical Total Correlation SpectroscopY (STOCSY) and Statistical HeterospectroscopY (SHY) were employed for data analysis.

Results and conclusions: The two methodologies were compared during all steps and the advantages of each technique were emphasized throughout the experimental and analytical stages of the study, making evident the synergy of the two platforms in the analysis of natural products. Specific biomarkers, related to the classification of the leaves with the different studied parameters were identified. STOCSY and SHY proved to be a valuable aid towards biomarkers assignment and results' interpretation.

Introduction: Snakebite envenomation represents a significant public health concern across numerous tropical regions, with sub-Saharan Africa being particularly affected. In Burkina Faso, traditional therapeutic practices remain prevalent, with local communities frequently employing traditional formulations for snakebite treatment. Notably, several of these antivenom formulations consist of powdered plant materials obtained through calcination processes.

Objective: This study seeks to elucidate the nature and diversity of residual or neoformed organic compounds in these particular types of formulation, in order to better understand the chemical basis and relevance of their use in anti-venom therapy by rural communities.

Method: Traditional partially calcined formulations were collected in the Centre-East, Centre-South and South-West health regions. These formulations were then extracted using hexane, methanol and water. The extracts obtained were analysed by liquid chromatography-mass spectrometry. The spectral data were processed using metabolomics platforms such as W4M, Mzmine and MetGem.

Results: In the databases consulted, 475 organic compounds were identified in Kampti's uncalcined formulation. This value was halved in the partially calcined formulations. The main classes of secondary plant metabolites, such as flavonoids, terpenoids, alkaloids and coumarins, were detected in the extracts, but at lower levels of abundance in the partially calcined formulations. In addition to organic compounds of natural origin, synthetic compounds belonging to the pesticide class were also identified.

Conclusion: The calcination of traditional formulations leads to a significant reduction in the diversity and abundance of bioactive compounds of plant origin, which could affect their therapeutic efficacy.

Introduction: Metabolite identification remains a bottleneck in untargeted liquid chromatography-tandem mass spectrometry (LC-MS) metabolomics studies, particularly when the underlying metabolite is absent in the tandem mass spectrometry (MS/MS) databases.

Objective: A new approach, formula subset analysis (FSA), was developed to effectively prescreen and rank the chemical formula candidates for an MS/MS spectrum.

Methods: This approach first computes mother-daughter relationships (MDRs) among possible formulas of fragments and the precursor under a given mass tolerance and then determines the characteristic fragments (CFs) that only present one MDR with the precursor and other fragments. Subsequently, the precursor formula candidates are ranked by the scores derived from the number of MDRs.

Results: A numerical study using eight large datasets totaling 30,690 MS/MS spectra from 6792 metabolites consisting of C, H, O, N, S, and P showed that FSA ranked the correct chemical formula as the top-1 candidate for a metabolite in 85.28% of the cases and in the top-5 candidates in 97.35% of the cases. The average processing time for each spectrum was 0.024 s. Moreover, FSA does not require training data, not rely on MS/MS databases, can be applied to a wide mass range, and can be quickly expanded with more chemical elements and formulas to identify different chemical species.

Conclusions: FSA has not utilized structural information yet and therefore its accuracy may not be competitive with some of the state-of-the-art identification tools. However, its advantages in speed, expandability, and applicability, make it suitable for prescreening candidates in untargeted LC-MS metabolomics studies.

Background: Dynamic changes in milk components during the end stages of lactation (involution) occur in all mammals. The time required to reach complete cessation may differ among taxa and species. The involution of cows, sheep, and goats (Bovidae) has been studied, but information on Giraffes is lacking.

Objectives: Characterize the milk metabolome of giraffes at involution.

Methods: Milk was obtained from five giraffes. Notably, all giraffes followed the same diet, a factor known to influence milk composition in domesticated mammals. Milk serum was prepared by filtration of the milk samples. A ¹H-NMR metabolomics approach was followed, and statistical analysis of the data was done using MetaboAnalyst 6.0.

Results: The changes in metabolites were characterised at 9.4, 12, and 15.1 months of lactation. Protein-type amino acids increased, while organic acids and lipid metabolites, as well as carbohydrates and their derivatives, decreased. This indicated that the synthesis of amino acids and proteins was upregulated, while that of lipids and carbohydrates was downregulated. Energy-producing amino acids and citric acid cycle intermediates decreased, suggesting reduced availability of energy metabolites.

Conclusions: Involution, along with the associated changes in the giraffe's milk metabolome may commence at 12 months of lactation but is complete by 15 months.

Background: Gathering information on Alzheimer's disease (AD) progression in human poses significant challenges due to the lengthy timelines and ethical considerations involved. Animal AD models provide a valuable alternative for conducting mechanistic studies and testing potential therapeutic strategies. Disturbed lipid homeostasis is among the earliest neuropathological features of AD.

Aim: To identify longitudinal plasma lipidomic changes associated with age, sex, and AD in male and female TgF344-AD and wild-type rats.

Methods: A total of 751 lipids in 141 rats (n = 73 TgF344-AD; n = 68 WT) were quantified at 12, 25, 50, and 85 weeks). Differential abundances of lipids were assessed using generalized logical regression models, correcting for i) age and sex, for ii) individual age groups, and iii) sex-specific differences. Predictive lipid signature models for AD were developed using stepwise feature selection for the full age range, as well as for midlife.

Results: Sex differences were identified among all ages in sphingomyelin (SM), phosphatidylcholine (PC), and phosphatidylethanolamine (PE) lipid classes. AD and age-related differences were found in the SM class in mid-life (25-50 weeks). Other AD and age-related differences were found in the ratios of linoleic acid and 5 of its products. Moreover, similarities in lipidomic profile changes were observed for humans and rats. The full age range and mid-life predictive lipid signatures for AD resulted in an AUC of 0.75 and 0.68, respectively.

Conclusions: Our findings highlight the value of lipidomic in identifying early AD-related lipid alterations, offering a promising avenue for understanding disease mechanisms and advancing biomarker discovery.

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.