Metabolomics最新文献

Introduction: Translating findings from animal models to human applications remains a fundamental challenge across scientific research, with unique implications for post-mortem metabolomics.

Objectives: This work is aimed at applying NMR metabolomics to human aqueous humour for post-mortem interval estimation, based on a previously studied ovine model.

Methods: Quantitative metabolomic profiling of 21 aqueous humour samples collected during from 11 forensic autopsies, with post-mortem intervals between 225 and 1164 min has been performed by ¹H NMR spectroscopy.

Results: Most of the identified metabolites in human aqueous humour samples are shared with those previously identified in ovine samples, showing qualitative similarities, while quantitative differences in metabolites such as lactate and glutamate are observed due to species-specific factors. Partial least squares regression models for post-mortem interval estimation resulted less accurate in human model with respect to the ovine one underscoring translational complexity. Of note, taurine and hypoxanthine were identified as post-mortem interval-specific metabolites independently on the species, suggesting their relevance in the post-mortem.

Conclusions: This study is the first attempt to translate animal to human post-mortem metabolomics using a rigorous methodology. Direct translation to humans seems possible for a limited part of the metabolome, with key metabolites such as taurine and hypoxanthine showing some consistency. These findings support animal model metabolomics as a guide for human studies across diverse metabolomics investigations, promoting human studies on larger cohorts and more specific experimental designs.

Background: Locally advanced rectal cancer (LARC) has variable responses to neoadjuvant therapy (NAT). Therefore, identifying changes in biological pathways involved when LARC is treated with NAT is crucial for developing treatments to improve clinical outcomes, as NAT is both variable and unpredictable. Although individual studies have attempted to discern how the response differs at a transcriptomic, proteomic and metabolomic level, there has not been a unifying systematic review discerning the key changes in metabolic pathways in this patient population.

Aim of review: This systematic review aims to understand how metabolomics, proteomics and transcriptomics can demonstrate how the perturbed metabolic pathways of the NAT response in LARC can provide targets for further clinical research.

Key scientific concepts of review: Thirteen studies met the inclusion criteria, including seven metabolomic, five proteomic, and one transcriptomic study. Metabolomic analyses revealed consistent alterations in amino acid metabolism, the tricarboxylic acid (TCA) cycle, and glycerophospholipid metabolism. Proteomic findings supported these results, highlighting disruptions in glycolysis and gluconeogenesis. Joint pathway analysis demonstrated a strong correlation (r = 0.99, p < 0.0001) between metabolic changes observed across omics platforms. Key pathways such as alanine, branched-chain amino acid, and aspartate metabolism were commonly altered and may contribute to radio-resistance through enhanced energy production, reactive oxygen species (ROS) neutralization, and DNA repair mechanisms. The convergence of multi-omic data underscores the biological relevance of these metabolic reprogramming events. However, due to the limited availability of transcriptomic data meeting inclusion criteria, these findings are primarily driven by metabolomic and proteomic analyses, which constrains the extent of full multi-omic integration. Future studies should aim to validate these findings in clinical cohorts and explore how targeting these "survival" pathways could optimize treatment response in LARC.

Introduction: Selective serotonin reuptake inhibitors (SSRIs) are the most prescribed antidepressants for pregnant women. While SSRIs are known to alter the circulating metabolic profile in non-pregnant individuals, the association between SSRIs and the changes in circulating metabolome during pregnancy remains unstudied. Pregnancy itself induces significant metabolic adjustments to meet the increased nutritional demands, and these maternal metabolic changes are crucial for the normal development and growth of the fetus.

Objectives: To study the impact of SSRI usage on circulating maternal metabolome during pregnancy.

Methods: A targeted nuclear magnetic resonance (NMR) spectroscopy method was used to analyze maternal serum samples obtained from the first trimester of pregnancy and at the time of the delivery from both SSRI users (n = 122) and non-depressive controls without antidepressants (n = 117) for concentrations of metabolites and lipoproteins.

Results: During the first trimester of pregnancy, SSRI usage was associated with increased lipid content in sixteen very low-density lipoprotein (VLDL) and chylomicron subtypes. At delivery, SSRI users exhibited alterations in lipoprotein lipid and fatty acid ratios. Similarly, while investigating the influence of SSRI usage on the pregnancy-driven changes in the metabolome, the interplay between pregnancy progression and SSRI usage lowered the lipoprotein lipid ratios.

Conclusion: Our analysis revealed a significant association between SSRIs and lipid metabolism. However, the observed changes were minor, suggesting a limited clinical impact. The findings enhance our understanding of the safe usage of SSRI medication during pregnancy.

Background: Metabolomics is rapidly evolving, addressing analytical chemistry challenges in the qualification and quantitation of metabolites in extremely complex samples. Targeted metabolomics involves the extraction and analysis of target compounds, often present at extremely low concentrations, whilst untargeted metabolomics requires the use of sophisticated analytical techniques to deal with the simultaneous identification or quantitation of hundreds of compounds. Given the high energy consumption and excessive amounts of waste generated by metabolomics studies, greenness metrics are essential to account for sustainable development.

Aim of review: To determine the applicability of the Analytical GREEnness calculator (AGREE) in evaluating the analytical greenness of metabolomics methods. Specifically, the analytical protocols of 16 state-of-art metabolomics studies, including nine targeted and seven untargeted metabolomics studies, are fully dissected, and detailed greenness parameters for each procedure are rationally estimated.

Key scientific concepts of review: The calculated AGREE metrics unequivocally show the main weaknesses of greenness in current research, and guidelines for sustainable practices in metabolomics are provided. The results indicate that offline sample preparation and the lack of automation and miniaturization are key areas that must be addressed to make metabolomics more sustainable. Important aspects that should be considered include the complexity of sample preparation procedures, the use of toxic reagents and derivatizing agents, the amount of waste generated, and sample throughput.

Introduction: WK0202, a β-lapachone derivative under clinical development, activates NAD(P)H quinone dehydrogenase 1 (NQO1), acting as a detoxifying and antioxidant agent. In this study, a metabolomics investigation of β-lapachone derivatives in humans is performed to characterize drug-induced alterations in endogenous metabolic pathways.

Objectives: This study investigated metabolic alterations induced by WK0202 administration and their potential association with its therapeutic mechanism and efficacy. Using targeted and untargeted metabolomics approaches, we identified potential pharmacodynamic biomarker candidates that may reflect the drug's activity and metabolic effects.

Methods: Plasma samples from healthy subjects who received multiple doses of WK0202 were compared with a placebo control group. The metabolomic profiles were compared pre- and post-dose to identify significant metabolic changes. Significant metabolites were identified using statistical analyses, focusing on key metabolic pathways. To further investigate NQO1 genotype effects, Spearman correlation analysis was performed between post/pre-dose concentration ratios and genotypes.

Results: Following WK0202 administration, significant changes were observed in the alanine, aspartate and glutamate metabolism, arginine biosynthesis, and lipid metabolism. Although most metabolites were not strongly dependent on NQO1 genotype or dose group, they exhibited an overall consistent trend. These alterations were indicative of Nrf2 pathway activation, possibly by NQO1-mediated drug activity.

Conclusion: These metabolic alterations highlight the potential of endogenous metabolites as surrogate markers for identifying novel therapeutic targets and assessing the efficacy of WK0202 in future clinical studies.

Introduction: HDL particle functionality is influenced by its structure, including lipid composition. However, the effects of exercise training on the HDL lipidome and its relationship with HDL-related traits are largely unknown.

Objective: To investigate the HDL lipidome of 154 adults before and after 20 weeks of endurance exercise training in the HERITAGE Family Study.

Methods: The HDL-sized plasma fraction was isolated utilizing FPLC-SEC, followed by untargeted lipidomic analysis using LC/MS. A total of 11 HDL lipid classes were derived from the 341 identified known lipid species. Exercise response of the HDL lipidome and its associations with HDL-related traits were examined, with significance set to FDR < 0.05.

Results: The abundance of 42 HDL lipid species at baseline and 43 at post-training were significantly different between males and females. Exercise training did not significantly alter the abundance of any HDL lipid class, although HDL phosphatidylethanolamine trended (FDR = 0.05) towards an increase. Two species of HDL diglycerides significantly decreased in the total sample. Sex-specific nominal (p < 0.05) changes in individual HDL lipid species included primarily HDL diglyceride and triglyceride species decreasing in males only, while HDL phosphatidylethanolamine species mostly increasing in females only. Higher abundance of HDL surface lipids was associated with larger size and cholesterol content of HDL particles before and in response to exercise training.

Conclusion: Our analysis indicates that endurance exercise may have a limited impact on the HDL lipidome in healthy adults. However, the HDL lipidome differed across sex groups, which needs further investigation to identify potential mechanisms underlying the sex differences.

Introduction & objective: Rheumatic Heart Disease (RHD) is the commonest cause of atrial fibrillation (AF) in India with higher prevalence in younger population. The clinical significance of the differential metabolites in RHD patients with AF is unknown. This is a tertiary hospital-based study aimed to discover the metabolites associated with AF and normal sinus rhythm (NSR) in RHD patients using untargeted LCMS approach.

Methods: In this case control study, a total of 87 patients (38 persistent AF and 49 NSR) were incorporated after screening, including 12-lead ECG (electrocardiogram), 2D Echo (two-dimensional echocardiography) and a 24-hr Holter examination for NSR patients to exclude silent AF. Blood samples were collected and differentially expressed metabolites were identified using untargeted LCMS approach.

Results: All the patients of our study belong to NYHA (New York Heart Association) classes II and III. The number of female patients was more in both groups. The mean age of the patients was 35.81 ± 7.96 and 29.61 ± 8.18 year in AF and NSR group respectively. 33 metabolites showed significantly altered expression - 15 upregulated and 18 down regulated metabolites. Pathway analysis showed that the altered metabolites were involved in Arginine, Phenylalanine tyrosine and tryptophan biosynthesis, D-Glutamine and D-glutamate, Alanine aspartate and glutamate metabolism, Arginine and proline metabolism.

Conclusions: The findings suggest that differential metabolites in RHD patients may help in identifying the high-risk group and possible therapeutic targets.

Background: Despite its prevalence and the significance of early diagnosis, non-alcoholic fatty liver disease (NAFLD), one of the most prevalent liver diseases globally and frequently linked to elements of metabolic syndrome, lacks robustly validated biomarkers for diagnosis, prognosis, and tracking of disease progression in response to a particular treatment.

Objective: The aim of this study was to catalogue the metabolites from metabolomics data reported by different studies till date, and to find few majorly dysregulated metabolites that can potentially be used as progressive biomarkers of NAFLD in future.

Methods: The clinical data published during last 13 years was investigated and further curated from established databases of MEDLINE, EMBASE and PUBMED on NAFLD. A vote-counting method was used to perform a semi-quantitative meta-analysis of metabolites in serum/blood from NAFLD subjects.

Results: This analysis unveiled the well-unprecedented changes in the metabolites of different classes as amino acids Valine, isoleucine, glutamate, tyrosine, alpha-ketoglutarate and phenylalanine were found to be up-regulated whereas glycine, serine and arginine were observed to be down-regulated. This investigation envisaged role of a few metabolites which were significantly distinct in the progression of NAFLD condition.

Conclusion: This study highlighted the role of different metabolites in the progression of NAFLD condition. However, the analysis also reveals certain limitations requiring better standardization of metabolomics investigations, signifying errors and lacunas of metabolic databases in identification and reporting. Additionally, inadequate publicly accessible metabolomics data, limits the discovery potential of meta-analyses of clinical studies.

Introduction: Multiple sclerosis (MS) is an autoimmune disorder with an unpredictable outcome at the time of diagnosis. The measurement of serum neurofilament light chain (sNfL) and glial fibrillary acidic protein (sGFAP) has introduced new biomarkers for assessing MS disease activity and progression. However, there is a need for additional diagnostic and prognostic tools. In this study, we investigated the predictive abilities of metabolomics, gut microbiota, as well as clinical and lifestyle factors for MS outcome parameters.

Objectives: The aim of this study was to assess the predictive capacity of plasma metabolites, gut microbiota, and clinical/lifestyle factors on MS outcome measures including MS-related fatigue, MS disability, and sNfL and sGFAP concentrations.

Methods: A prospective cohort study was conducted with 54 individuals with MS. Anthropometric, biological, and lifestyle parameters were collected. The least absolute shrinkage and selection operator (LASSO) algorithm with ten-fold cross-validation was used to identify predictors of MS disease outcome parameters based on plasma metabolomics, microbiota sequencing, and clinical and lifestyle measurements obtained from questionnaires and anthropometric measurements.

Results: Circulating metabolites were found to be superior predictors for sNfL and sGFAP concentrations, while clinical and lifestyle data were associated with EDSS scores. Both plasma metabolites and clinical data significantly predicted MS-related fatigue. Combining multiple multi-omics data did not consistently improve predictive performance.

Conclusions: This study highlights the value of plasma metabolites as predictors of sNfL, sGFAP, and fatigue in MS. Our findings suggest that prioritizing metabolomics over other methods can lead to more accurate predictions of MS disease outcomes.

Background: Kidney stone are among the most common urologic diseases characterized with metabolic disorder. Biomarker for kidney stone detection and the metabolic variables in kidney stone development have attracted increasing attention.

Methods: To explore the metabolomic and lipidomic characteristics of plasma in patients with kidney stones, we collected plasma samples from 200 participants, including 100 kidney stone patients and 100 healthy controls. We designated 59 patients with clearly defined stone compositions alongside matched healthy individuals as the training set (n = 118), while the remaining 41 patients with unclear stone compositions were paired with healthy individuals and served as the test set (n = 82).

Results: A total of 333 and 270 metabolites were significantly altered in kidney stone patients under positive and negative ion modes, respectively, compared to healthy controls. KEGG analysis indicated that pathways such as Arginine and proline metabolism, Citrate cycle (TCA cycle), Alanine, aspartate and glutamate metabolism and phenylalanine metabolism, were closely associated with kidney stone formation. Moreover, a total of 416 lipids were significantly changed in the Kidney stone group and the control group. Using Lasso model, a panel of integrated 4 metabolites and 4 lipids showed effective discrimination between Kidney stone group and the control group. Among these metabolites, Isorhamnetin has the potential to effectively reduced oxalate-induecd acute kidney injury, hence lowering the likelihood of stone formation.

Conclusions: These findings offer novel insights into the metabolic and lipidomic alterations associated with kidney stones, providing potential biomarkers for early diagnosis and therapeutic targets for intervention.