Metabolomics最新文献

Introduction: Meningoencephalitis of unknown origin (MUO) in dogs is a debilitating and often fatal disease that shows similarities to multiple sclerosis (MS) in humans. The metabolomic profile of MUO has not been previously reported.

Objectives: To compare the metabolomic profile of cerebrospinal fluid (CSF) of dogs with MUO with two other diseases affecting the central nervous system in dogs, steroid responsive meningitis-arteritis (SRMA) and idiopathic epilepsy (IE), and to determine if the metabolic profile of MUO shows similarities with that of MS.

Methods: Untargeted and semi-targeted metabolomics using ¹H nuclear magnetic resonance (NMR) was performed on surplus CSF of dogs diagnosed with MUO, SRMA and IE. Data were examined by multivariate and univariate statistical analysis and pathway analysis.

Results: Fifty-six metabolites were identified in 56 dogs. The multivariate analysis of the canine data highlighted significant differences between the different disease groups. Most metabolites were increased in SRMA and decreased in IE when compared to MUO. Most affected metabolites included those involved in energy metabolism. Pathway analysis revealed that these metabolites were mainly involved in pyruvate metabolism, glycolysis or gluconeogenesis, glycine, serine and threonine metabolism and alanine, aspartate and glutamate metabolism.

Conclusion: These results suggest that there is an increased energy demand in MUO. Our findings provide a first-time overview of CSF metabolic changes in MUO and offer potential insights for possible underlying pathogenesis and treatment strategies. Altered energy metabolism pathways are also reported in MS, further supporting the use of MUO as a spontaneous animal model for this disease.

Background: Exercise metabolomics investigates how physical activity alters the metabolome, with responses depending on exercise type, intensity, and duration. Intermittent high-intensity to supramaximal exercise produces unique metabolomic effects that remain inadequately addressed in the literature.

Aim of review: This study aimed to (i) conduct a systematic review of publications on metabolomics, applied to high-intensity interval exercise or training (HIIE/HIIT) or sprint interval exercise or training (SIE/SIT) protocols in humans and (ii) provide an overview of the most characteristic metabolomic changes induced by these types of exercise.

Key scientific concepts of review: A total of 20 studies met the inclusion criteria, with a variety of participants, biological samples, sampling procedures, and metabolomic analysis techniques. Pathway analysis revealed that the affected pathways were mostly related to carbohydrate, lipid, and amino acid metabolism. The tricarboxylic acid cycle and purine degradation were also considerably affected. Most metabolites were upregulated by HIIE/HIIT and SIE/SIT. Our analysis revealed strong and wide metabolomic changes with HIIE/HIIT or SIE/SIT, with substrate utilization for energy production emerging as a recurring theme. Such results suggest that the metabolic changes caused by exercise cannot be covered by a single analytical technology and underline the importance of reproducibility and the need for better control of modulating/confounding factors in future studies.

Background: Hyperlipidemia is a major risk factor for coronary atherosclerotic heart disease (CAHD). However, conventional lipid profiling fails to fully reflect the complexity of lipid metabolism. Furthermore, while lipidomics has identified numerous complex lipids as biomarkers, the role of their fundamental constituents-serum fatty acids-remains less explored in CAHD. In this study, we performed detailed profiling of serum fatty acid species to explore their utility in improving the identification and management of lipid disorders specifically within the CAHD population, an approach whose clinical value in this context remains underexplored.

Methods: The study analyzed serum fatty acid profiles in three CAHD cohorts: a training cohort (n = 432) and two external validation cohorts (n = 1302 and n = 1458). Serum samples were collected during initial and subsequent hospitalizations. A total of 39 fatty acids were quantified using liquid chromatography-tandem mass spectrometry (LC-MS/MS). Multivariate analyses were conducted to identify fatty acids with diagnostic relevance, followed by model development and external validation.

Results: In CAHD patients, several fatty acids were significantly correlated with total cholesterol (TC), triglycerides (TG), low-density lipoprotein cholesterol (LDL-C), and apolipoprotein B (Apo B). Among these, esterified linoleic acid demonstrated a strong correlation with lipid biomarker (r > 0.69, p < 0.001). Logistic regression analysis identified esterified oleic acid (OR = 37.80, p < 0.001) and linoleic acid (OR = 10.74, p < 0.001) as independent significant risk factors for hyperlipidemia. The combined model incorporating these fatty acids demonstrated an AUC exceeding 0.94 (95% CI: 0.93-0.95) in both training and validation cohorts, with sensitivity and specificity exceeding 85.6% and 81.0%, respectively. Notably, during statin therapy, linoleic acid decreased significantly and was associated with favorable lipid-lowering effects, comparable to the rate of change in TC, with no significant difference.

Conclusions: Esterified linoleic acid (C18:2 n6) and oleic acid (C18:1 n9) were identified as key serum biomarkers for hyperlipidemia in CAHD patients, and their combination yielded the best diagnostic performance. Moreover, C18:2 n6 decreased during statin therapy and may serve as a useful marker for monitoring treatment response.

Introduction: Tuberculosis (TB) and type 2 diabetes mellitus (T2DM) are highly prevalent diseases resulting in high mortality rates globally. Furthermore, T2DM increases susceptibility to TB and vice versa, worsening disease outcomes. This comorbidity is, however, not well described nor understood, despite its rising prevalence globally.

Objectives: This investigation aimed to better characterize the urinary metabolic profiles of patients with the TB and T2DM comorbidity in a South African cohort, to better understand its metabolic basis and associated clinical implications.

Methods: Using untargeted GCxGC-TOFMS metabolomics, urine samples from 17 patients with TB and T2DM and 34 healthy controls were analyzed and statistically compared to identify significantly altered urinary metabolites.

Results: TB-T2DM comorbid patients were characterized by altered metabolism of: (1) tryptophan and kynurenine (reduced kynurenic acid, anthranilic acid, picolinic acid) associated with changes to NAD⁺ synthesis and a redox imbalance, (2) nucleotides (reduced 3-aminoisobutyric acid, orotic acid, thymine, β-alanine, adenine, hypoxanthine), (3) tyrosine (reduced 3,4-dihydroxyphenylglycol, 4-hydroxy-3-methoxyphenylglycol, hydroxyphenylpyruvate), (4) lipids (reduced dec-2-enedioate, adipic acid, methylmalonic acid), (5) reduced concentrations of various glycine conjugates associated with glycine depletion, and (6) reduced urinary concentrations of various gut microbial metabolites indicative of microbial dysbiosis.

Conclusion: These results indicate several metabolic disruptions to amino acids, nucleotides, lipids, NAD⁺ homeostasis and the host microbiome, in TB-T2DM patients, mainly driven by inflammation and oxidative stress. Overall, the findings indicate synergistic amplification of metabolic stress, associated with immune suppression and TB-T2DM disease progression, and subsequently suggests how TB increases T2DM susceptibility and vice versa, as foundation for further investigations.

Introduction: Exploiting the full potential of neoadjuvant treatment (NAT) in pancreatic ductal adenocarcinoma (PDAC) is hampered by the lack of biomarkers for treatment response. Dysregulated lipid metabolism has been suggested to promote PDAC growth and resistance to therapy.

Objectives: To investigate lipid metabolic changes in PDAC following NAT.

Methods: Cross-sectional study of mass spectrometry-based global lipidomic profiling of tumour tissue (n = 35) and paired serum samples (n = 35) from treatment-naïve (TN; n = 18) and neoadjuvant FOLFIRINOX-treated (NAT; n = 17) PDAC patients was conducted. Pre- and post-treatment CA 19-9 levels were available from 15 NAT patients. Differentially abundant lipids (DALs) in NAT versus TN were assessed for correlation with various clinical parameters and the performance of all serum DALs to distinguish NAT from TN samples was explored using receiver operating characteristic analysis.

Results: A total of 40 tissue and 35 serum DALs were identified, which mainly belonged to glycerophospholipids and sphingolipids in tissue and glycerolipids, glycerophospholipids, and fatty acyls in serum. All 19 serum glycerolipids were less abundant in NAT and 18 of these were triacylglycerols. The abundance of 26 tissue and 11 serum DALs correlated moderately with % reduction in serum CA 19-9 following NAT. The top five of 23 serum DALs with moderate discriminatory potential (AUC = 0.66-0.87) ‒ PI(18:0_20:3), AcCa(13:0), PC(O-42:6), TG(49:6), TG(66:14), performed better together (AUC = 0.93 and 95% CI = 0.79‒1) and combined with CA 19-9 (AUC = 0.99 and 95% CI = 0.81‒1).

Conclusions: Both tumour tissue and serum samples from PDAC patients showed lower abundance of lipid metabolites following neoadjuvant FOLFIRINOX treatment. Moreover, a biomarker panel of CA 19-9 together with five serum DALs could potentially be used to assess NAT response in PDAC but requires further validation.

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.