Metabolomics最新文献

Introduction: Analysis of specific pathways of metabolic flux is usually achieved by administering stable isotope-labeled precursors and measuring their incorporation into selected metabolites by high-resolution mass spectrometry coupled to liquid chromatography (LC-HRMS).

Objectives: In this study, we undertook a novel approach aiming at covering the whole metabolome dynamics by providing mice with a diet fully enriched in carbon-13 (¹³C).

Methods: Three animals were fed for six weeks with small pellets composed of a mixture of ¹³C-spirulina and ¹³C-wheat. All animals grew normally and their urine was collected daily. Three control mice were treated in the same way, but with an unenriched diet. LC-HRMS-based metabolomics profiling were conducted on all collected samples as well as isotope tracing.

Results: Comparative LC-HRMS analysis of the ¹²C- and ¹³C-samples unambiguously identified 238 metabolites, whose ¹³C-labeling profiles were then studied over a 39-day period. Although overall urine labeling was fast and rapidly reached a high level of ¹³C-content (> 90% after 22 days), isotopic monitoring of each molecular species demonstrated that ¹³C-incorporation kinetics are considerably variable between metabolites, reflecting their biological function, origin and rate of biosynthesis in vivo.

Conclusion: This study demonstrates that mice tolerate a diet entirely labeled with carbon-13 (> 97 atom % ¹³C) over a period of six-weeks. It reveals the dynamics of a large part of the metabolome at the mammalian species level through urine analysis coupled to in vivo ¹³C-labeling. The study provides valuable insights by comprehensively covering metabolic pathways, and stands out from targeted fluxomic studies which are carried out by administering a very limited number of specific labeled precursors.

Background: Metabolomics is poised to significantly advance our ability to diagnose and characterize disease, identify diagnostic and prognostic biomarkers, and monitor a patient's response to treatment. Despite these benefits, metabolomics-based diagnostic tests have not been adapted into clinical practice primarily because metabolomics technologies, which include high performance liquid chromatography and mass spectrometry, are not conducive to point-of-care. Expanding the reach of metabolomics-based testing requires centralized technology to be more accessible.

Methods: One way to achieve this is by utilizing dried blood spots (DBS) as a diagnostic testing sample type. Their easy collection, lack of reliance on cold chain, and cost-effective storage and shipment make them the ideal sample type for diagnostic testing that requires a centralized technology. To date, few studies have investigated the ability of DBS to capture the global metabolome and track biological changes in individual subjects.

Results: In this feasibilty study, we tested these factors by performing untargeted metabolic profiling on DBS collected from study volunteers before and after exercising. We compared our results in DBS to results in serum and plasma that have been reported in the literature.

Conclusions: Overall, our findings closely agreed with published studies, showing that DBS may be a viable sample type for metabolomics-based diagnostic and wellness testing.

Background: The early detection of cancer remains a critical challenge in clinical oncology, with significant implications for patient survival rates and treatment outcomes. Research focus has shifted to developing minimally invasive diagnostic approaches for cancer detection and prognosis, such as metabolomic analysis of biological fluids and tumour-derived components, including extracellular vesicles (EVs). EVs carry molecular cargo, including metabolites, that reflect the pathophysiological state of their cell of origin. Analysis and characterisation of these metabolites may offer novel insights into cancer biology and facilitate the identification of potential biomarkers.

Aim of review: This review systematically examines existing literature on the metabolomic analysis of EVs in the context of cancer to obtain a deeper understanding of potential metabolite biomarkers associated with cancer.

Key scientific concepts of review: A comprehensive search of PubMed, Scopus, and Web of Science was conducted using a defined strategy to identify studies analysing EV-derived metabolites in cancer. Twelve eligible studies were included, collectively reporting 1,602 identified metabolites across various cancer types, sample sources, EV isolation methods, and metabolomic techniques. Of these, 333 metabolites were reported to be differentially regulated in EVs derived from patients with cancer, or conditioned medium from cancerous cell lines and their respective healthy controls. The review highlights the potential of EV metabolomics to detect cancer biomarkers but also underscores methodological variability as a major limitation. Differences in isolation and analytical techniques likely contribute to inconsistent findings, emphasising the need for standardised protocols in future research.

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: 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.