Metabolomics最新文献

Background and objective: Multidrug-resistant (MDR) bacterial infections are a leading cause of sepsis-related death. A rapid method to identify patients with MDR infections upon hospital admission is urgently needed. This study aimed to characterize the distinct plasma metabolomic signatures associated with MDR gram-positive (G+) and gram-negative (G-) sepsis and to develop predictive models for rapid, risk stratification during the initial clinical encounter.

Methods: Two independent cohorts of septic patients were recruited, with 198 subjects (117 MDR and 81 susceptible) in the discovery cohort, and 198 patients (95 MDR and 103 susceptible) in the validation cohort. Plasma metabolomic profiling was performed using liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS). Multiple machine learning algorithms were employed to identify differential metabolomic signatures and to construct and validate multi-metabolite models for the early identification of MDR bacteria.

Results: Distinct metabolomic signatures were identified for both MDR G- and G+ infections. MDR G- sepsis showed significant elevations in metabolites related to host inflammatory responses, such as histamine, alongside decreased levels of gut microbiota-derived metabolites, including cholic acid and benzoic acid, indicating profound host-microbe dysregulation. Conversely, MDR G+ sepsis was characterized by alterations in energy and amino acid metabolism, notably elevated 2-hydroxyglutarate, a marker of mitochondrial stress. An 8-metabolite model for MDR G- infection achieved excellent discrimination in both the discovery (AUROC = 0.885, 95% CI: 0.787-0.982) and validation (AUROC = 0.878, 95% CI: 0.782-0.951) cohorts. The model for MDR G+ infection demonstrated good predictive performance (AUROC = 0.763 and 0.715 in discovery and validation, respectively).

Conclusion: This study identifies robust and distinct plasma metabolomic signatures that differentiate MDR from antibiotic-susceptible sepsis. These findings support the development of rapid, metabolomics-based testing using admission plasma to risk-stratify patients. This approach could guide early, stewardship-aligned antimicrobial decisions while conventional culture results are pending, potentially improving clinical outcomes.

Introduction: Blood microsampling (BμS) has emerged as an alternative to invasive sampling methods, including blood and plasma sampling. Several studies have shown that BμS are suitable alternatives for analyzing endogenous metabolites and for metabolomics applications. Dried blood spots (DBS) have long been used for clinical applications, particularly for newborn screening. New quantitative BμS have emerged, including volumetric absorptive microsampling (VAMS).

Objectives: We aimed to develop an extraction protocol from BµS for non-targeted metabolomics analysis using a reversed-phase liquid chromatography/mass spectrometry (RPLC-MS) method for the mid- to non-polar metabolome and a hydrophilic interaction chromatography/mass spectrometry (HILIC-MS) method for the polar metabolome, based on existing protocols from the literature. To improve coverage, two new HILIC-MS methods have been developed.

Methods: We used an in-house RPLC-MS method for the analysis of mid- to non-polar metabolites. Two new HILIC-MS/MS methods were developed using 73 chemical reference standards of polar metabolites from various classes. To optimize extraction, five procedures were investigated and compared to identify the most appropriate protocol for extracting metabolites from BµS for non-targeted metabolomics analysis. The final workflow was optimized on both DBS and VAMS.

Results and conclusion: We developed and optimized a 15-minute HILIC-MS method that included column re-equilibration. Our experiments showed that using a 20% H₂O/80% MeOH (v/v) mixture for extraction, with sample rehydration, is a good compromise for detecting many metabolite features. Our extraction and LC-MS methodology covered metabolites from many pathways, including amino acids, acylcarnitines, and bile acids.

Background: The aim of metabolic phenotyping (metabotyping) is to discover and identify metabolites (including lipids) that can be used to characterize biological samples and differentiate between different physiological states. The identification of the metabolites responsible for this differentiation is essential if mechanistic understanding is to be obtained. Confident metabolite identification arguably represents the most important outcome of untargeted metabolomics studies but currently the standards used for metabolite identification reported in many publications do not strictly follow the various published guidelines and thus these identifications lack sufficient proof.

Aim of review: In this perspective we define problems that currently plague the field of metabolite identification using MS-based techniques, particularly LC-MS, in untargeted metabolic phenotyping. Despite considerable efforts by the community (researchers, instrument manufacturers, software, and database developers) this continues to be a contentious and error-prone step in the metabolomics workflow. The majority of publications provide only sparse data on the evidence for metabolic markers "identified" and we have observed an alarming increase in the frequency of erroneous metabolite identifications. Here, we describe the problem and provide several illustrative case studies. Our goal is to raise awareness and highlight the issue of poor metabolite identification, since it is also increasingly apparent that these errors are not always recognised during the reviewing process, such that papers with potentially erroneous metabolite identities reach publication.

Key scientific concepts of review: Poor metabolite identification potentially represents an existential threat to the credibility of untargeted "discovery" metabolomics and can pollute the literature. Here we describe the aetiology of the problem and explain how and why this issue affects the field. We argue that coordinated action is required by researchers, database managers, scientific societies and the reviewers, editors and publishers of scientific journals to both acknowledge and address this important problem.

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.