Metabolomics最新文献

Background: Algal nutraceuticals have emerged as the valuable bioresources due to their various chemical compositions and potential health benefits. Algae contain many bioactive compounds, including polyphenols, polysaccharides, omega-3 fatty acids, pigments, and vitamins, which are vital for the various biological processes in the human body. Understanding these complex metabolites is essential for their application in functional foods, dietary supplements, and pharmaceuticals. In this context, metabolomics provides a comprehensive approach for analyzing algal metabolic profiles and their nutritional and medicinal values.

Aim of review: This review explores the role of metabolomics in the evaluation and development of algal nutraceuticals. It focuses particularly on the identification and characterization of small-molecule metabolites in algae, offering insights into their functional properties and bioactivities. This review also discusses the integration of metabolomics with other omics technologies to achieve a holistic understanding of the metabolism of algae.

Key scientific concepts of review: Metabolomic studies have successfully explored a wide range of bioactive compounds in algae with antioxidant, anti-inflammatory, anticancer, antibacterial, and neuroprotective activities. Techniques such as mass spectrometry (MS) and nuclear magnetic resonance (NMR) spectroscopy have advanced the detection and quantification of metabolites with high sensitivity and resolution, respectively. Additionally, metabolomics aids to determine the quality biomarkers and the assessment of algal nutritional content. Integrating metabolomics with genomics, proteomics, and transcriptomics will further elucidate the metabolic pathways and regulatory networks in algae. This review highlights the critical role of metabolomics in maximizing the utilization of algae for health benefits.

Aim: Diabetic kidney disease (DKD) is a severe complication of diabetes, with early detection crucial for preventing irreversible kidney damage. Despite numerous DKD metabolite profiling studies, results remain inconsistent. This meta-analysis aims to identify consensus dysregulated metabolites as potential biomarkers for Type 2 diabetes (T2D)-induced DKD.

Materials and methods: Following PRISMA guidelines, a systematic review from 2014 to 2024 included human studies of T2D and DKD. Quality assessment employed the Newcastle-Ottawa Scale (NOS). Bubble plots determined predominant metabolite classes. MetaboAnalyst 5.0 facilitated pathway and enrichment analysis, while RevMan v5.4 performed meta-analysis.

Results: Amino acids were the most studied metabolite class in both T2D and DKD. Enrichment analysis highlighted glycine and serine metabolism; phenylalanine and tyrosine metabolism; and methionine metabolism as dominant pathways. Meta-analysis revealed low ornithine (-0.50 [-0.91, -0.10], p = 0.01) and high isoleucine (0.76[0.50, 1.03], p < 0.00001) concentrations associated with T2D. Conversely, lower methionine (-0.32 [-0.57, -0.08), p = 0.01), tyrosine (-0.73 [-1.28, -0.17], p = 0.01), and valine (-2.32 [-2.99, -1.66], p = 0.009) levels were associated with DKD. Correlation analysis revealed associations between phenylalanine, tyrosine, and serine with albumin and creatinine levels in T2D but not in DKD.

Conclusions: These identified metabolites hold potential as early markers for T2D-induced DKD. However, the use of these metabolites for clinical purposes requires experimental validation and clinical trials.

Objective: To compare the metabolomic profile differences between ILD (interstitial lung disease) and chronic obstructive pulmonary disease (COPD) controls, and to distinguish profiles between progressive and stable idiopathic pulmonary fibrosis (IPF)/ILD subjects.

Methods: This single-center prospective study enrolled n = 71 (progressive IPF/ILD: n = 33, stable IPF/ILD: n = 27, COPD: n = 11) participants between December 2021 and October 2022. Metabolite quantification was performed using the liquid chromatography-mass spectrometry (LC-MS platform), and nuclear magnetic resonance spectroscopy (¹H NMR). Further, pathway enrichment analysis was performed to identify biochemical pathways associated with the disease.

Results: 715 metabolites were accurately quantified to investigate (a) differences between the combined groups of stable and progressive idiopathic pulmonary fibrosis IPF/ILD and COPD controls, and (b) differences between progressive IPF/ILD and stable IPF/ILD controls. The most notable metabolites distinguishing fibrotic lung disease (both stable and progressive IPF/ILD) from COPD were glycerolipids (GL). Enrichment analysis of IPF/ILD versus COPD revealed significant disruptions in lipid metabolic pathways, particularly glycerophospholipids, and sphingolipids (false discovery rate FDR q-value < 0.05). In addition, significant disruptions in TG species were found in progressive IPF/ILD with enrichment analysis revealing dysregulation of metabolic pathways associated with glycerophospholipids (FDR q-value < 0.05).

Conclusion: These findings emphasize the dysregulation of lipid metabolism in fibrotic lung diseases, involving glycerolipids, glycerophospholipids, and sphingolipids. The distinct lipid alterations identified through metabolomic profiling provide valuable insight into lipid metabolism in IPF/ILD, warranting further research to explore their potential as biomarkers.

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.