Metabolomics最新文献

Introduction: Strategies specifically targeting the initial host-pathogen interactions, hold great promise in the identification of accurate biomarkers for tuberculosis (TB) prevention interventions. Mycobacterium tuberculosis (Mtb) curli pili (MTP) (encoded by mtp/Rv3312A), a surface adhesin utilised by the pathogen to interact with host receptor cells, has been reported as a suitable target for TB diagnostic and therapeutic strategies. Previous "omics" studies highlighted the role MTP potentially plays in Mtb central carbon metabolism (CCM). However, its precise contribution to metabolism remains unknown.

Objectives: This study aimed to examine the role of MTP in the bioenergetic metabolism of Mtb, using bedaquiline (BDQ) to inhibit ATP production through oxidative phosphorylation (OXPHOS), extracellular flux analysis, Mtb wildtype (WT), ∆mtp deletion mutant, and mtp-complemented strains. The role of MTP in regulation of CCM was assessed using ¹³C₆-metabolic flux analysis.

Results: MTP was associated with increased bacterial respiration and decreased carbon catabolism via glycolysis in response to the inhibition of ATP synthase by BDQ. The dependence of Mtb Δmtp on OXPHOS for energy production was demonstrated to be greater than the WT and mtp-complemented strains. In addition, metabolic flux profiles revealed that in the Δmtp mutant, CCM was dysregulated by decreasing flux through glycolysis, tricarboxylic acid cycle, glyoxylate and dicarboxylate metabolism, and the pentose phosphate pathway in comparison to the WT.

Conclusion: These novel findings show that MTP is associated with the regulation of bioenergetics and metabolism pathways and substantiate MTP as a potential biomarker for TB diagnostics/therapeutics, and a novel target for vaccine/drug development.

Background: Emerging evidence shows significant differences in plasma metabolites between colorectal cancer (CRC) patients and healthy controls. However, previous observational studies have been limited by small sample sizes and single sample sources, leading to an incomplete understanding of these metabolites' causal roles in CRC. This study systematically evaluated the causal relationships between 325 nuclear magnetic resonance (NMR) biomarkers and CRC risk using Mendelian randomization (MR), supplemented by colocalization analysis and an independent validation dataset to confirm key biomarkers.

Methods: A genome-wide association study (GWAS) was conducted in a cohort of 250,341 participants from the UK Biobank. MR analysis identified NMR biomarkers with significant causal relationships with CRC. Colocalization analysis was then performed, revealing five biomarkers with high colocalization probabilities (PPH4 > 0.8). These findings were validated in an independent Finnish dataset to confirm the consistency of causal relationships and colocalization signals.

Results: MR analysis identified 28 NMR biomarkers with significant causal associations with CRC risk (P_fdr < 0.05). Colocalization analysis highlighted five biomarkers with strong colocalization signals (PPH4 > 0.8), including Omega-6 fatty acids, Omega-6 to total fatty acids ratio, Omega-3 fatty acids, Linoleic acid to total fatty acids percentage, and Degree of unsaturation. Notably, in the Finnish validation dataset, Linoleic acid to total fatty acids percentage demonstrated a significant causal association with CRC (OR 0.77, 95% CI 0.67-0.87, P = 7.5 × 10^-5, P_fdr = 3.8 × 10^-4) while maintaining a high colocalization probability (PPH4 > 0.8), reinforcing its role as a key causal biomarker.

Conclusions: This study provides the first comprehensive assessment of NMR biomarkers in relation to rectal cancer risk, identifying linoleic acid to total fatty acids percentage as a key causal biomarker. Additionally, omega-6 to omega-3 ratio, omega-6 to polyunsaturated fatty acids percentage, omega-3 to polyunsaturated fatty acids percentage, and degree of unsaturation were also identified, sharing genetic loci with CRC.

Introduction: The degree of antimicrobial resistance demonstrated by carbapenemase-producing Enterobacterales (CPE) represents a growing public health challenge. Conventional methods for detecting CPE involve culture-based techniques with lengthy incubation steps. There is a need to develop rapid and accurate methods for the detection of resistance, for implementation into clinical diagnostics.

Objectives: With cellular phenotype closely linked to the metabolome, the acquisition of resistance should result in detectable differences in microbial metabolism. Accordingly, we sought to profile the metabolome of Enterobacterales isolates belonging to both CPE and non-CPE groups to identify metabolites linked to CPE.

Methods: We used liquid chromatography-mass spectrometry to profile the endo- and exometabolome of 32 Klebsiella pneumoniae and Escherichia coli isolates to identify metabolites which could predict CPE in antibiotic-free conditions after 6 h of growth.

Results: Using supervised machine learning and multivariate analysis algorithms (partial least squares-discriminant analysis, k-nearest neighbour and random forest), we identified 21 metabolite biomarkers which displayed high performance metrics for the prediction of CPE (AUROCs ≥ 0.845). Results revealed a range of alterations between the metabolomes of CPE and non-CPE isolates. Pathway analysis revealed enrichment of microbial pathways including arginine metabolism, ATP-binding cassette transporters, purine metabolism, biotin metabolism, nucleotide metabolism, and biofilm formation, providing mechanistic insight into the resistance phenotype of CPE.

Conclusion: Our models demonstrate the ability to distinguish CPE from non-CPE in under 7 h using metabolite biomarkers, showing potential for the development of a targeted diagnostic assay.

Introduction: Myocardial revascularization (MR) is recommended in acute myocardial infarction. Understanding the physiological disturbances caused by MR may be pertinent for future therapeutic strategies in the postoperative period.

Objectives: This study aims to analyze the MR impact on plasma metabolites and investigate potential correlations between them with routinely measured biochemical and clinical parameters in MR, and with the cardiopulmonary bypass time (CPB).

Methods: Twenty-five patients had plasma samples collected before and after MR for metabolomic analysis, performed by liquid chromatography coupled with high-resolution mass spectrometry.

Results: One hundred eleven ions showed statistically significant differences due to MR and thirteen were identified. Only Pregnenolone Sulfate had its abundance in plasma decreased due to MR. Hydrocortisone succinate, Cortisone, and Corticosterone increased in response to the glucocorticoids administered during surgery. LysoPS 16:1, LysoPC 14:0, Phenylvaleric acid, 13-Hydroxyoctadecadienoic acid, N-Linoleoyl Glutamine, and N-Myristoyl Methionine, along with the significant increase in the white blood cell count are associated with inflammation processes, possibly caused by MR. Furthermore, Pregnenolone sulfate, Pentosidine, Phenylvaleric acid, and N-Myristoyl Methionine were correlated with biochemical/clinical parameters and CPB.

Conclusion: These results open new horizons in the interpretation of physiological disturbances caused by MR, as well as provide support for future studies. The scientific community is invited to build upon the outcomes obtained to confirm the associations suggested in this study, advancing the realm of metabolomics and MR.

Introduction: Rheumatoid arthritis (RA) is an auto-immune disease which causes irreversible damage to tissue and cartilage within synovial joints. Rapid diagnosis and treatment with disease-modifying therapies is essential to reduce inflammation and prevent joint destruction. RA is a heterogeneous disease, and many patients do not respond to front-line therapies, requiring escalation of treatment onto biologics, of which TNF inhibitors (TNF-i) are the most common.

Objectives/methods: In this study we determined whether serum metabolomics, using nuclear magnetic resonance (NMR) and Fourier transform infrared (FTIR) spectroscopy, could discriminate RA blood sera from healthy human controls and whether the technologies could be used to predict response or non-response to TNF inhibitor (TNF-i) therapy.

Results: NMR spectroscopy identified 35 metabolites in RA sera, with acetic acid being significantly lower in RA sera compared to healthy controls (HC, FDR < 0.05). PLS-DA modelling identified 2-hydroxyisovalericacetic acid, acetoacetic acid, mobile lipids, alanine and leucine as important metabolites for discrimination of RA and HC sera by ¹H NMR spectroscopy (averaged 83.1% balanced accuracy, VIP score > 1). FTIR spectroscopy identified a significant difference between RA and HC sera in the 1000-1200 cm^- 1 spectral area, representing the mixed region of carbohydrates and nucleic acids (FDR < 0.05). Sera from RA patients who responded to TNF-i were significantly different from TNF-i non-responder sera in the 1600-1700 cm^- 1 region (FDR < 0.05).

Conclusion: We propose that NMR and FTIR serum metabolomics could be used as a diagnostic tool alongside current clinical parameters to diagnose RA and to predict whether someone with severe RA will respond to TNF-i.

Introduction: Faba bean (Vicia faba L.) leaves are edible; hence, they are primarily used as animal feed in agriculture. Additionally, seed pods and other plant tissues are considered edible and are used as green vegetables in many parts of the world.

Objectives: Flavonol glycosides are well-known in faba bean leaves; accordingly, in this study, we followed a targeted metabolomic approach to explore glycosylated flavonols and their concentrations in response to contrasting levels of selenium (Se) and sulfur (S) enrichment under faba bean-Rhizobium symbiosis.

Methods: Faba bean plants were cultivated under growth chamber conditions and enriched with different levels of selenium and sulfur under Rhizobium inoculation. Their leaves were extracted using 70% methanol to quantify glycosylated flavonoids. Sample leaves were analyzed through a targeted method using high-performance liquid chromatography combined with a diode array detector (HPLC-DAD) and electrospray ionization-quadrupole-time-of-flight tandem mass spectrometry detection (HPLC-ESI-Q-ToF-MS/MS).

Results: The analysis led to semi-quantifying 11 flavonol glycosides. Analysis of the metabolites of the different faba bean leaf extracts confirmed that selenium has a considerable impact on the accumulation of flavonol glycosides, especially under sulfur availability, possibly because it induces chalcone synthase and other enzymes for flavonols' biosynthesis.

Conclusion: To the best of our knowledge, this is the first report to investigate the impact of selenium and sulfur enrichment on the accumulation of faba bean flavonols under atmospheric nitrogen (N₂) fixation conditions. This study highlights the medicinal and nutritional benefits of legumes as an essential source of protein in plant-based foods.

Introduction: Type 2 Diabetes mellitus (T2DM) is a chronic metabolic disorder characterized by insulin resistance and hyperglycemia, often preceded by latent metabolic disruptions. Early detection of metabolic alterations can facilitate timely intervention to delay or prevent T2DM onset. Metabolic profiling offers a useful tool to identify novel biomarkers and early metabolic alterations before clinical manifestations.

Objective: This study aimed to identify early metabolic alterations and potential biomarkers predictive of T2DM in a cohort of healthy normoglycemic participants over a six-year follow-up.

Methods: A cohort of 94 healthy participants, both men and women aged 18-40, was studied at six-year intervals. Clinical and biochemical parameters were measured, and LC/MS/MS-based untargeted metabolomics analysis of plasma was performed at both baseline and follow-up.

Results: At follow-up, 9 participants developed T2DM, 51 had prediabetes, and 34 remained normoglycemic. Increasing insulin resistance and elevated future T2DM risk were observed in both the prediabetes and normoglycemia groups. Metabolomics analysis identified phosphatidylethanolamine (PE) (20:3_18:0), 3beta,7alpha-dihydroxy-5-cholestenoate, and tridecanoic acid-as having good predictive capacity for future T2DM risk at baseline with alterations in PE (20:3_18:0), and tridecanoic acid persisting at follow-up.

Conclusion: The study highlights the potential of metabolomics in identifying early metabolic predictors of T2DM, emphasizing the need for early interventions in healthy normoglycemic young adults.

Introduction: The metabolome is an intermediate between DNA variation and clinical phenotypes. Metabolomics have been widely used in biomedical studies for reflecting physiological changes in response to variation coming from various sources, such as diet, environment, time, and lifestyle. While lifestyle factors contribute a considerable part of the metabolic variation, current human studies lack information estimating lifestyle, mainly because it is not strictly defined.

Objective: In this work, metabolite concentrations are measured at two time points (2007 and 2014). Additionally, SNP data together with self-reports on dietary behavior. By having measurements over time, as well as all main sources of metabolic variation (diet, genetics), both time-effects and lifestyle-effects can be estimated. Since lifestyle and time effects can be estimated under this setting, we are interested in identifying metabolites sharing similar relationships to diet and lifestyle, using network analysis.

Methods: The correlation between repeated measurements is modeled using a random intercepts linear mixed model, with dietary preferences, genetics, and time as fixed effects. The random intercepts can be defined as the lifestyle, and represent the part of the metabolic variation which is not due to diet, genetics, and time and is subject-specific. The part of every metabolite relevant to diet and lifestyle instead of the original values is used as input values to network estimation methods.

Conclusions: This work demonstrates how correcting for several sources of metabolic variation, allows us to look for residual variation and build networks with meaningful metabolite groups sharing similar association to diet and lifestyle.

Introduction: Achalasia is a rare motility disorder of the esophagus characterized by the loss of the propulsive peristalsis and impaired relaxation of the lower esophageal sphincter. Patients with untreated achalasia suffer from dysphagia and regurgitation and, consequently, weight loss as they require dietary modifications related to impaired esophageal emptying. Peroral endoscopic myotomy (POEM) is considered the mainstay of therapy, leading to symptom relief, restoration of normal eating patterns and weight gain.

Objectives: This study aims to describe the metabolic state of an organism in time of insufficient nutrition and the relation among levels of systemic metabolites and changes in body's composition in condition of normalised food intake.

Methods: We monitored body composition, as well as biochemical parameters in 43 patients with achalasia before and 3 months after POEM intervention. We also determined the levels of circulating metabolites via NMR spectroscopy which were, besides comparison to controls, used to describe metabolic turnover before and after treatment.

Results: After POEM, all patients except four individuals gained weight (p-value 3.07e-11), and accordingly, the BMI value changed (p-value 2.85e-9). Paired test revealed an increase in absolute fat (p-value 0.00176) and muscle mass (p-value 0.00201). Metabolic analysis in patients with untreated achalasia showed a ketotic-like state with inadequate glycolysis and gluconeogenesis, which partially normalized three months after POEM. Post-POEM muscle mass gain was positively (p < 0.05-0.0005) and increase in absolute fat mass was negatively correlated (p-value < 0.05-0.0005) with the levels of circulating amino acids before the intervention.

Conclusion: Our observations provide complex insight into the metabolic shifts after successful treatment of achalasia that is directly related to the changes in the body composition. The metabolic alterations were not detectable through standard biochemical tests suggesting that conventional diagnostics may not fully reflect the metabolic condition of patients with achalasia.

Introduction: Down syndrome, caused by the triplication of human chromosome 21 (trisomy 21 [T21]) or its distal segment, is the most prevalent chromosomal anomaly associated with intellectual disability in newborns.

Objectives: To investigate the relationship between T21 and hydrops by analyzing metabolomic alterations, identifying correlations, and exploring pathway regulation mechanisms. This study goes beyond biomarker discovery, aiming to elucidate the pathogenesis of T21 and to explore the underlying mechanism.

Methods: We developed a ¹²C₂-/¹³C₂-isotope dansylation labeling LC-MS workflow to profile amine/phenol-based metabolomic differences in amniotic fluid (AF) between T21 and euploid fetuses.

Results: This workflow enabled the classification of AF specimens, revealing 138 increased and 116 decreased out of 2351 detected metabolites in T21 AF specimens. Metabolite identities were confirmed via LC-MS/MS spectral analysis using authentic standards. Dysregulated metabolites in T21 AF included markers of oxidative regulation and glutathione metabolism as well as those linked to fetal development. Further subgroup analysis identified 31 T21-associated metabolites, including significantly elevated androsterone sulfate in T21 AF both with and without hydrops. Among 30 hydrops-associated metabolites, most were reduced in hydrops AF, while hyaluronic acid (HA) was notably elevated only in T21 hydrops cases. Correlation analyses highlighted negative associations between HA and metabolites like kynurenine and homovanillic acid, suggesting potential roles in immune modulation and neuronal development in the fetal microenvironment.

Conclusion: This study identifies T21-associated metabolites that may serve as early diagnostic markers or therapeutic targets, offering insights into the metabolic landscape of Down syndrome and a foundation for exploring fetal therapeutic strategies.