Metabolomics最新文献

Introduction: Although the in vitro production of bovine embryos now exceeds in vivo production, the quality of in vitro-produced embryos remains inferior. Metabolomic analysis of the spent culture medium used for embryos is considered a relevant source of markers for subsequent developmental capacity, genetic status and sex. However, little is known about the sources of variability in this metabolome and their respective significance.

Objective: We compared bovine embryo spent culture media from in vivo developed (IVD) and in vitro produced (IVP) embryos, and analyzed how they varied with embryo stage, grade and sex.

Methods: Embryos were produced in vitro under two different conditions: SOF medium supplemented with 1% fetal calf serum or IVF Bioscience media, or developed in vivo. They were recovered at Day-6 and cultured individually for 26 hours. The culture medium was analyzed using 1H-NMR spectroscopy, and the embryos were staged, graded and sexed.

Results: In vitro production significantly reduced inter-embryo variability of the 24 metabolites measured. The mean variance of metabolite content was 5 to 10 times lower in IVP than in IVD embryos (depending on the IVP conditions). IVD embryo stages and grades at the start or end of the culture period contributed to 8% to 18% of variability in the media content, but only 1% of variability for IVP embryos. IVD embryo stage at the start of culture significantly impacted metabolite content. IVD embryo sex did not globally impact composition of the spent culture medium, but the kinetics of decrease of about half of the metabolites differed between male and female blastocysts.

Conclusions: Standardization of the metabolic properties of IVP embryos invites new questions regarding the impact of embryo biotechnology. Differences in metabolism between male and female IVD embryos are highly transient during the morula-blastocyst stages.

Background and hypothesis: Gestational diabetes mellitus (GDM) is a prevalent metabolic disorder in pregnant women, marked by hyperglycemia and glucose intolerance that initially appears during pregnancy. It presents serious health concerns to both the mother and the foetus, increasing the likelihood of complications such as preterm birth, preeclampsia, and fetal macrosomia. Early detection and timely intervention are crucial for minimizing these risks. However, current screening methods for GDM, such as the oral glucose tolerance test (OGTT), are typically administered between 24 and 28 weeks of gestation, leaving a substantial window during which metabolic changes may already be affecting maternal and fetal outcomes. We hypothesized that the serum metabolomics analysis may serve as a potential method for the predictive screening of GDM during the first trimester of pregnancy. Within this framework, the present study has been designed to identify distinctive metabolic patterns in the serum samples of pregnant women during the first trimester that are predictive of GDM development later in pregnancy.

Methods: The study involved a cohort of hundred (N = 100) pregnant women in their first trimester were recruited and grouped based on their subsequent development of GDM (referred here as pre-GDM group, N = 29) and non-GDM (i.e. normoglycemic, N = 71). The serum metabolic profiles were measured using 800 MHz NMR spectroscopy and compared using sparse PLS-DA (Partial Least Squares Discriminant Analysis).

Results: The 3D score plot revealed exquisite clustering of pre-GDM subjects and separation from non-GDM group. The performance of sPLS-DA model is validated through cross-validation and the VIP score plot highlighted several key metabolites contributing to the discrimination. Further, the receiver operating characteristic (ROC) curve analysis highlighted their diagnostic potential in distinguishing between the pre-GDM and non-GDM groups. Overall, 15 metabolic features provided a robust foundation for distinguishing between pre-GDM and non-GDM groups, with alanine, myo-inositol, valine and glucose showing the highest diagnostic potential.

Conclusion: The study demonstrated that NMR-based serum metabolomics can identify distinctive metabolic signatures during the first trimester that are predictive of subsequent GDM development. These findings suggest the potential of this approach as a supportive tool for early screening and risk stratification in pregnant women, warranting validation in larger, multi-center cohorts.

Objective: Radicular cyst (RC), the most prevalent odontogenic cyst in the jaw, remains poorly characterized regarding its systemic metabolic profile. This study aims to elucidate metabolic disturbance patterns in RC patients through serum metabolomics and identify potential biomarkers for constructing a diagnostic model.

Methods: Serum samples from 30 RC patients and 20 healthy controls (HCs) were analyzed using gas chromatography-mass spectrometry (GC-MS)-based untargeted metabolomics. Multivariate statistical analyses (PCA, OPLS-DA) were employed to identify differential metabolites. A diagnostic model was subsequently developed through LASSO regression with ROC curve validation.

Results: Seventy-three serum metabolites were identified, with 31 exhibiting significant dysregulation in RC patients (8 upregulated, 23 downregulated). LASSO regression selected seven metabolites (threonine, homoserine, dAMP, biotin, ADP, dGDP, deoxyuridine) to construct a diagnostic model demonstrating perfect discrimination between RC and HCs (sensitivity: 1.00, specificity: 1.00, AUC: 1.00).

Conclusion: This study provides the first report of systemic metabolic characteristics in RC patients and establishes a high-precision diagnostic model based on seven metabolites. These findings offer novel insights into RC pathogenesis and facilitate the development of non-invasive diagnostic approaches.

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: 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: 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: 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.