Metabolomics最新文献

Background: Colorectal cancer (CRC) ranks as the third most prevalent malignancy globally, presenting a formidable early diagnostic challenge. An effective biomarker with high sensitivity and specificity can help diagnose CRC and improve the chances of successful treatment.

Methods: 100 healthy controls and 95 CRC patients (25 Stage 0/I, 30 stage II and 40 stage III based on Clinical stages) were recruited. Subsequently, 195 urine samples were subjected to UPLC-MS analysis. Comparative analysis was employed to elucidate noteworthy metabolic variances, and pathway analysis was conducted to unveil perturbed metabolic functions. Ultimately, metabolic panels for CRC diagnosis were constructed.

Result: A total of 82 metabolites exhibited statistical significance between CRC patients and healthy controls. Moreover, pathway analysis revealed that they were associated with Steroid hormone biosynthesis, Nitrogen metabolism, and D-Glutamine and D-glutamate metabolism. A composite panel consisting of Retinol, L-β-aspartyl-L-glycine, and 21-Deoxycortisol showed AUCs of 0.933/0.93 in the discovery/validation group. The panel also showed commendable efficacy across different CRC stages when these stages were compared with the healthy group,with an AUC of 0.918 for stages 0/I, 0.862 for stage II, and 0.845 for stage III.

Conclusions: Urine metabolome could distinguish CRC from healthy controls and reflect the changes in different stages of CRC. Potential biomarkers might be developed by targeted metabolomic analysis.

Introduction: The identification of unknown metabolites remains a major challenge in untargeted metabolomics using liquid chromatography-mass spectrometry (LC-MS). This process typically depends on comparing mass spectral or chromatographic data to reference databases or deciphering complex fragmentation in tandem mass spectra. While current machine learning methods can predict metabolite structures using MS/MS (MS2) data, no approaches, to our knowledge, use only mass-to-charge ratio (m/z) and retention time (RT) from LC-MS data.

Objective: To explore the potential of using the mass-to-charge ratio (m/z) and retention time (RT) from LC-MS data as standalone predictors for metabolite classification and propose a modeling framework which can be implemented internally on standalone datasets.

Methods: We trained machine learning models on 20 mouse lung adenocarcinoma tumor samples with 7,353 features and validated them on a dataset of 81 samples with 22,000 features. A total of 120 combination of preprocessors and models were assessed. Features were classified as "lipid" or "non-lipid" based on the Human Metabolome Database (HMDB) taxonomy, and model performance was assessed using accuracy, area under the receiver operating characteristic curve (AUC), and area under the precision-recall curve (PR). We replicate the process in an independent dataset generated using human plasma samples.

Results: We classified untargeted LC-MS features as "lipid" or "non-lipid" per the HMDB super class taxonomy and evaluated model performance. A framework including steps to choose the preprocessors and models for metabolite classification was designed. In our lab, tree-based models demonstrated superior performance across all metrics, achieving high accuracy, AUC, and PR which was consistent with the independent dataset.

Conclusion: Our results demonstrate that metabolites can be classified as "lipid", "non-lipid" using only m/z and RT from untargeted LC-MS data, without requiring MS2 spectra. Although this study focused on lipid classification, the approach shows potential for broader application, which warrants further investigation across diverse compound classes, detection methods, and chromatographic conditions.

Purpose: The relationship between metabolites and female infertility is unclear. This study employed a bidirectional Mendelian randomization analysis to determine the causal relationship between metabolites and female infertility.

Method: The causal relationship between 1,400 metabolites and female infertility was analyzed using publicly available GWAS data. Significant SNPs were selected as instrumental variables (IVs), with those in linkage disequilibrium (LD) or with an F-statistic below 10 excluded to ensure validity. Independent GWAS datasets for metabolites and infertility were used to avoid sample overlap. The primary method employed was inverse-variance weighted (IVW). Heterogeneity and pleiotropy were assessed, and the results were further validated using single SNP and leave-one-out analyses.

Results: The phosphate to mannose ratio, X-17,654 levels, 1-palmitoleoyl-GPC (16:1) levels, glucose-to-mannose ratio,androstenediol (3alpha, 17alpha) monosulfate (3) levels, 3-methylglutaconate levels, octadecadienedioate (C18:2-DC) levels, bilirubin degradation product, C17H18N2O4 (2) levels, 3-methylglutarylcarnitine (2) levels, eicosenedioate (C20:1-DC) levels, and the phosphate-to-mannose ratio were positively associated with the risk of female infertility. the adenosine 5'-diphosphate (ADP)-to-citrate ratio, 2,2'-methylenebis(6-tert-butyl-p-cresol) levels, sphingomyelin (d18:2/16:0, d18:1/16:1) levels, bilirubin degradation product, C16H18N2O5 (3) levels, and the mannose to trans-4-hydroxyproline ratio were negatively associated with the risk of female infertility. No reverse causal link was identified between metabolites and female infertility.

Conclusion: A significant causal association was identified between 16 specific metabolites and female infertility, with 11 metabolites increasing the risk of infertility, while the other 5 exhibited a protective effect.

Introduction: The standard first-line management of advanced ovarian cancer (OC) comprises cytoreductive (debulking) surgery followed by platinum-based chemotherapy, most commonly a combination of carboplatin and paclitaxel (PTX). However, the development of resistance to PTX frequently compromises treatment efficacy, resulting in disease recurrence and poorer clinical outcomes. Although the metabolic mechanisms underlying carboplatin resistance have been extensively characterised, the specific metabolic alterations contributing to PTX resistance remain poorly understood.

Objectives: We applied untargeted metabolomics to systematically characterise PTX resistance-associated metabolic reprogramming in OC, aiming to identify targetable vulnerabilities to enhance the platinum-taxane efficacy.

Methods: Using an isogenic in vitro model of acquired PTX-resistance (OVCAR8 PTX) and its parental counterpart (OVCAR8 PAR), we analysed intracellular (endometabolome) and extracellular (exometabolome) metabolites via gas chromatography-mass spectrometry (GC-MS).

Results: Multivariate and univariate analyses (│effect size│ ≥ 1.4, p-value ≤ 0.01) revealed a distinct metabolic signature in the endometabolome of PTX-resistant cells. These cells exhibited significantly elevated levels of glycine, myo-inositol, pyroglutamate, proline and taurine, alongside reduced levels of glycerol, glucose and glutamate. Pathway analysis identified putative alterations in redox regulation (glutathione metabolism), energy metabolism (galactose and glyoxylate/dicarboxylate metabolism), amino acid metabolism (arginine and proline), and osmotic stress pathways (taurine and hypotaurine metabolism).

Conclusions: The identified metabolic signature highlights dysregulated pathways (e.g., glutathione metabolism, taurine and hypotaurine metabolism) that may be actionable targets for reversing PTX resistance. Pharmacological modulation of these pathways could restore chemosensitivity, providing a rational strategy to improve platinum-taxane efficacy in advanced OC.

Background: Ischemic stroke is a detrimental disease that could can lead to disability and death., Multiple pathophysiological processes are involved in ischemic stroke, from the initial stages to the chronic stages. Oxylipins are a class of bioactive lipid metabolites mainly derived from the oxidation of omega-3 and omega-6 polyunsaturated fatty acids (PUFAs). Emerging evidence based on laboratory results shows that diverse subclasses of oxylipins exert protective or harmful effects in ischemic stroke, and an increasing number of clinical studies have reported an association between oxylipins and ischemic stroke. Oxylipins have been widely reported in cardiovascular disease, however, there are no reviews on the implications of oxylipin regulation in ischemic stroke.

Aim of review: In this review, in addition to discussing the biosynthesis and response signaling of oxylipins in many diseases, we aim to provide an overview of how oxylipin modulates the pathophysiology of ischemic stroke and influences the relevant clinical phenotypes and promising therapeutics to regulate oxylipins.

Key scientific concepts of the review: Oxylipins hold extensive research potential in lipidomics. This review systematically elucidates the pivotal roles of distinct oxylipins subclasses in ischemic stroke pathogenesis, encompassing pathophysiological mechanisms such as inflammatory responses, oxidative stress, immune response, thrombosis, cellular apoptosis, and vascular homeostasis dysregulation. The associations between oxylipins and ischemic stroke phenotypes are obvious. However, more metabolomic studies are needed to identify oxylipin biomarkers in patients with ischemic stroke across different samples or cell types to bridge oxylipins regulation with clinical phenotype intervention.

Background: Evidence from in-vitro and animal studies suggests that fluoride exposure may alter key metabolic pathways such as amino acid, fatty acid and energy metabolism in different tissues, requiring an understanding of its impact at the molecular level, especially in human populations.

Aim of review: This scoping review aims to systematically map and synthesize the available evidence on metabolic alterations associated with fluoride exposure, specifically focusing on studies employing metabolomic analysis techniques to identify altered metabolites and metabolic pathways at the cellular, tissue, and organ levels.

Key scientific concepts of review: Fluoride exposure has been found to alter a broad range of metabolic pathways, including those involved in energy metabolism (glycolysis, TCA cycle, mitochondrial activity), macromolecule metabolism (purine and fatty acid metabolism, amino acid pathways), and cellular stress responses (oxidative stress and glutathione metabolism). However, there is limited evidence in humans and potential mechanistic studies. While supportive, the reliance on animal models and in-vitro studies points to the need for human studies to compare metabolic alterations at different levels of fluoride exposure to aid in understanding the systemic effects of fluoride on human health.

Objectives: This study quantitatively evaluated whether metabolite profiles differed between capillary (fingerstick vs. microblade) and venous (hypodermic needle) blood collection methods, and the corresponding WB, plasma, and serum samples.

Introduction: Blood may be collected through venipuncture, fingerstick, or microblade devices. Collected samples may remain as whole blood (WB) or be processed to serum or plasma. Differences in collection methods, blood sources (venous or capillary), body locations and processing protocols may influence metabolite composition. However, no systematic assessment has evaluated collection effects on the WB/serum/plasma metabolome.

Methods: Blood was collected from five healthy volunteers via fingerstick (finger), microblade (shoulder, using Tasso + devices), and hypodermic needle (arm) draw. WB, serum and plasma samples from each source were immediately analyzed (to eliminate storage effects) by a quantitative LC-MS assay for 142 metabolites.

Results: Fresh WB showed a distinct metabolite profile compared to fresh plasma or serum, regardless of collection method. Plasma and serum samples from all collection methods exhibited differences in only two metabolites: sarcosine and pyruvic acid. When identical biofluid types were compared, minimal metabolome differences were observed across blood collection methods, body location and peripheral blood sources.

Conclusions: For most metabolites, all three collection methods (venous, microblade, and fingerstick) produced nearly identical results when comparing identical biofluid types. We found minimal metabolic differences between serum and plasma, regardless of collection method, peripheral blood source or body location. These results prove that inexpensive blood microsampling systems (via shoulder-microblade or fingerstick) yield comparable metabolite data relative to venous collection methods.

Background: Hypertension is a leading risk factor for chronic kidney disease (CKD), yet the metabolic mechanisms linking hypertension to CKD remain unclear. This study aimed to identify metabolites associated with CKD incidence and progression in hypertensive patients using untargeted metabolomics analysis.

Methods: A prospective cohort study was conducted to identify metabolites associated with the incidence and progression of CKD in hypertensive patients. Untargeted metabolomic profiling was conducted, and three statistical models-logistic regression, lasso regression, and random forest-were utilized to identify metabolites associated with CKD. Modified Poisson regression was used to assess the associations between metabolites and kidney-related outcomes.

Results: Untargeted metabolomic profiling identified distinct metabolite patterns distinguishing hypertensive patients with CKD from those without. These metabolites were identified across the three statistical models, with 94 showing significance in at least two. Four metabolites-L-theanine, cysteine-s-sulfate, mesaconic acid, and 2-aminoadipic acid-were inversely associated with CKD incidence and progression. L-theanine and cysteine-s-sulfate were both associated with decreased estimated glomerular filtration rate (eGFR) and increased urinary albumin-to-creatinine ratio (UACR). In contrast, mesaconic acid was linked to increased UACR, and 2-aminoadipic acid was associated with decreased eGFR. Patients at higher risk of CKD progression exhibited significantly lower levels of these metabolites.

Conclusion: L-theanine, cysteine-s-sulfate, mesaconic acid, and 2-aminoadipic acid show an inverse association with CKD incidence and progression in hypertensive patients, suggesting their potential as biomarkers for CKD risk. Further studies are warranted to validate these findings and investigate their therapeutic implications.

Introduction: Multivariate modeling is crucial for uncovering complex patterns in metabolomic data, yet the interpretability of such models remains a major challenge.

Methods: Here, we propose a network-guided framework that enhances perturbation-based explanations by grouping metabolites according to communities identified in metabolic networks, rather than relying on predefined pathways. The approach is applied to postprandial plasma metabolomic data as a model example and using a metabolic network including KEGG metabolites and enzyme-catalyzed reactions in which they participate.

Results and conclusion: Results show that the use of metabolite communities derived from network representation in perturbation-based analysis of multivariate models, serves as a complementary tool for their biochemical interpretation, that might extend it beyond fixed, established pathways. The strategy is model-agnostic and readily transferable across omics domains and multivariate methods, offering a new tool for model interpretability and hypothesis generation in complex biological datasets.

Aim: Phenylketonuria is an inherited metabolic disorder characterized by a deficiency in phenylalanine hydroxylase. However, the impact of this deficit on the patient's overall metabolism is not fully known. Studying this pathology through untargeted metabolomics requires to determine a method for metabolites extraction, here applied to Dried Blood Spot (DBS), a matrix offering several practical advantages.

Methodology: The DBS of 30 phenylketonuric patients and 30 healthy controls were used for the study. Following a literature review, different extraction protocols and solvents were investigated, with or without an evaporation step, and compared to identify the most appropriate protocol to extract metabolites from the DBS for metabolomics analysis of phenylketonuria by LC-MS/MS, then applied to the patients and controls to validate its application to phenylketonuria.

Results: The most promising extraction method is a gentle agitation overnight at 4 °C, with an evaporation step, and an extraction solvent composed by 80%/20% acetonitrile and water. This method extracted 2 to 6 times more metabolites than other protocols tested with a better extraction of amino acids and derivatives. This protocol enabled us to identify metabolic pathways that were disrupted in phenylketonuric patients, as well as differences in metabolite abundance between the different cohorts. Metabolic profiles differed both between patients and controls, and between patients according to their phenylalanine concentration. These differences were independent of the amino acid supplementation in some patients.

Conclusion: The results obtained on the phenylketonuria patients cohort compared to controls, validated the extraction protocol for studying the systemic metabolic impact of phenylketonuria.