Metabolomics最新文献

Introduction: The (un)targeted analysis of endogenous compounds has gained interest in the field of forensic postmortem investigations. The blood metabolome is influenced by many factors, and postmortem specimens are considered particularly challenging due to unpredictable decomposition processes.

Objectives: This study aimed to systematically investigate the influence of the time since death on endogenous compounds and its relevance in designing postmortem metabolome studies.

Methods: Femoral blood samples of 427 authentic postmortem cases, were collected at two time points after death (854 samples in total; t1: admission to the institute, 1.3-290 h; t2: autopsy, 11-478 h; median ∆t = 71 h). All samples were analyzed using an untargeted metabolome approach, and peak areas were determined for 38 compounds (acylcarnitines, amino acids, phospholipids, and others). Differences between t2 and t1 were assessed by Wilcoxon signed-ranked test (p < 0.05). Moreover, all samples (n = 854) were binned into time groups (6 h, 12 h, or 24 h intervals) and compared by Kruskal-Wallis/Dunn's multiple comparison tests (p < 0.05 each) to investigate the effect of the estimated time since death.

Results: Except for serine, threonine, and PC 34:1, all tested analytes revealed statistically significant changes between t1 and t2 (highest median increase 166%). Unpaired analysis of all 854 blood samples in-between groups indicated similar results. Significant differences were typically observed between blood samples collected within the first and later than 48 h after death, respectively.

Conclusions: To improve the consistency of comprehensive data evaluation in postmortem metabolome studies, it seems advisable to only include specimens collected within the first 2 days after death.

Introduction: Analysis of time-resolved postprandial metabolomics data can improve our understanding of the human metabolism by revealing similarities and differences in postprandial responses of individuals. Traditional data analysis methods often rely on data summaries or univariate approaches focusing on one metabolite at a time.

Objectives: Our goal is to provide a comprehensive picture in terms of the changes in the human metabolism in response to a meal challenge test, by revealing static and dynamic markers of phenotypes, i.e., subject stratifications, related clusters of metabolites, and their temporal profiles.

Methods: We analyze Nuclear Magnetic Resonance (NMR) spectroscopy measurements of plasma samples collected during a meal challenge test from 299 individuals from the COPSAC₂₀₀₀ cohort using a Nightingale NMR panel at the fasting and postprandial states (15, 30, 60, 90, 120, 150, 240 min). We investigate the postprandial dynamics of the metabolism as reflected in the dynamic behaviour of the measured metabolites. The data is arranged as a three-way array: subjects by metabolites by time. We analyze the fasting state data to reveal static patterns of subject group differences using principal component analysis (PCA), and fasting state-corrected postprandial data using the CANDECOMP/PARAFAC (CP) tensor factorization to reveal dynamic markers of group differences.

Results: Our analysis reveals dynamic markers consisting of certain metabolite groups and their temporal profiles showing differences among males according to their body mass index (BMI) in response to the meal challenge. We also show that certain lipoproteins relate to the group difference differently in the fasting vs. dynamic state. Furthermore, while similar dynamic patterns are observed in males and females, the BMI-related group difference is observed only in males in the dynamic state.

Conclusion: The CP model is an effective approach to analyze time-resolved postprandial metabolomics data, and provides a compact but a comprehensive summary of the postprandial data revealing replicable and interpretable dynamic markers crucial to advance our understanding of changes in the metabolism in response to a meal challenge.

Introduction

Untargeted metabolomics studies are expected to cover a wide range of compound classes with high chemical diversity and complexity. Thus, optimizing (pre-)analytical parameters such as the analytical liquid chromatography (LC) column is crucial and the selection of the column depends primarily on the study purpose.

Objectives

The current investigation aimed to compare six different analytical columns. First, by comparing the chromatographic resolution of selected compounds. Second, on the outcome of an untargeted toxicometabolomics study using pooled human liver microsomes (pHLM), rat plasma, and rat urine as matrices.

Methods

Separation and analysis were performed using three different reversed-phase (Phenyl-Hexyl, BEH C₁₈, and Gold C₁₈), two hydrophilic interaction chromatography (HILIC) (ammonium-sulfonic acid and sulfobetaine), and one porous graphitic carbon (PGC) columns coupled to high-resolution mass spectrometry (HRMS). Their impact was evaluated based on the column performance and the size of feature count, amongst others.

Results

All three reversed-phase columns showed a similar performance, whereas the PGC column was superior to both HILIC columns at least for polar compounds. Comparing the size of feature count across all datasets, most features were detected using the Phenyl-Hexyl or sulfobetaine column. Considering the matrices, most significant features were detected in urine and pHLM after using the sulfobetaine and in plasma after using the ammonium-sulfonic acid column.

Conclusion

The results underline that the outcome of this untargeted toxicometabolomic study LC-HRMS metabolomic study was highly influenced by the analytical column, with the Phenyl-Hexyl or sulfobetaine column being the most suitable. However, column selection may also depend on the investigated compounds as well as on the investigated matrix.

Graphical abstract

Introduction

Changes in skin phenotypic characteristics are based on skin tissue. The study of the metabolic changes in skin tissue can help understand the causes of skin diseases and identify effective therapeutic interventions.

Objectives

We aimed to establish and optimize a non-targeted skin metabolome extraction system for skin tissue metabolomics with high metabolite coverage, recovery, and reproducibility using gas chromatography/mass spectrometry.

Methods

The metabolites in skin tissues were extracted using eleven different extraction systems, which were designed using reagents with different polarities based on sequential solid-liquid extraction employing a two-step strategy and analyzed using gas chromatograph/mass spectrometry. The extraction efficiency of diverse solvents was evaluated by coefficient of variation (CV), multivariate analysis, metabolites coverage, and relative peak area analysis.

Results

We identified 119 metabolites and the metabolite profiles differed significantly between the eleven extraction systems. Metabolites with high abundances in the organic extraction systems, followed by aqueous extraction, were involved in the biosynthesis of unsaturated fatty acids, while metabolites with high abundances in the aqueous extraction systems, followed by organic extraction, were involved in amino sugar and nucleotide sugar metabolism, and glycerolipid metabolism. MeOH/chloroform-H₂O and MeOH/H₂O-chloroform were the extraction systems that yielded the highest number of metabolites, while MeOH/acetonitrile (ACN)-H₂O and ACN/H₂O-IPA exhibited superior metabolite recoveries.

Conclusion

Our results demonstrated that our research facilitates the selection of an appropriate metabolite extraction approach based on the experimental purpose for the metabolomics study of skin tissue.

Objectives

Although colorectal cancer (CRC) is the leading cause of cancer-related morbidity and mortality, current diagnostic tests for early-stage CRC and colorectal adenoma (CRA) are suboptimal. Therefore, there is an urgent need to explore less invasive screening procedures for CRC and CRA diagnosis.

Methods

Untargeted gas chromatography–mass spectrometry (GC-MS) metabolic profiling approach was applied to identify candidate metabolites. We performed metabolomics profiling on plasma samples from 412 subjects including 200 CRC patients, 160 CRA patients and 52 normal controls (NC). Among these patients, 45 CRC patients, 152 CRA patients and 50 normal controls had their fecal samples tested simultaneously.

Results

Differential metabolites were screened in the adenoma-carcinoma sequence. Three diagnostic models were further developed to identify cancer group, cancer stage, and cancer microsatellite status using those significant metabolites. The three-metabolite-only classifiers used to distinguish the cancer group always keeps the area under the receiver operating characteristic curve (AUC) greater than 0.7. The AUC performance of the classifiers applied to discriminate CRC stage is generally greater than 0.8, and the classifiers used to distinguish microsatellite status of CRC is greater than 0.9.

Conclusion

This finding highlights potential early-driver metabolites in CRA and early-stage CRC. We also find potential metabolic markers for discriminating the microsatellite state of CRC. Our study and diagnostic model have potential applications for non-invasive CRC and CRA detection.

Graphical abstract

Introduction

Cardiac dysfunction after sepsis the most common and severe sepsis-related organ failure. The severity of cardiac damage in sepsis patients was positively associated to mortality. It is important to look for drugs targeting sepsis-induced cardiac damage. Our previous studies found that 4-phenylbutyric acid (PBA) was beneficial to septic shock by improving cardiovascular function and survival, while the specific mechanism is unclear.

Objectives

We aimed to explore the specific mechanism and PBA for protecting cardiac function in sepsis.

Methods

The cecal ligation and puncture-induced septic shock models were used to observe the therapeutic effects of PBA on myocardial contractility and the serum levels of cardiac troponin-T. The mechanisms of PBA against sepsis were explored by metabolomics and network pharmacology.

Results

The results showed that PBA alleviated the sepsis-induced cardiac damage. The metabolomics results showed that there were 28 metabolites involving in the therapeutic effects of PBA against sepsis. According to network pharmacology, 11 hub genes were found that were involved in lipid metabolism and amino acid transport following PBA treatment. The further integrated analysis focused on 7 key targets, including Comt, Slc6a4, Maoa, Ppara, Pparg, Ptgs2 and Trpv1, as well as their core metabolites and pathways. In an in vitro assay, PBA effectively inhibited sepsis-induced reductions in Comt, Ptgs2 and Ppara after sepsis.

Conclusions

PBA protects sepsis-induced cardiac injury by targeting Comt/Ptgs2/Ppara, which regulates amino acid metabolism and lipid metabolism. The study reveals the complicated mechanisms of PBA against sepsis.

Introduction

Aspalathus linearis (commonly known as rooibos) is endemic to the Cape Floristic Region of South Africa and is a popular herbal drink and skin phytotherapeutic ingredient, with health benefits derived primarily from its unique phenolic content. Several, seemingly habitat-specific ecotypes from the Cederberg (Western Cape) and Northern Cape have morphological, ecological, genetic and biochemical differences.

Objectives and methods

Despite the commercial popularity of the cultivated variety, the uncultivated ecotypes are largely understudied. To address gaps in knowledge about the biochemical constituency, ultra-performance liquid chromatography-mass spectrometry analysis of fifteen populations was performed, enabling high-throughput metabolomic fingerprinting of 50% (v/v) methanolic extracts. Antioxidant screening of selected populations was performed via three assays and antimicrobial activity on two microbial species was assessed. The metabolomic results were corroborated with total phenolic and flavonoid screening of the extracts.

Results and discussion

Site-specific chemical lineages of rooibos ecotypes were confirmed via multivariate data analyses. Important features identified via PLS-DA disclosed higher relative abundances of certain tentative metabolites (e.g., rutin, aspalathin and apiin) present in the Dobbelaarskop, Blomfontein, Welbedacht and Eselbank sites, in comparison to other locations. Several unknown novel metabolites (e.g., m/z 155.0369, 231.0513, 443.1197, 695.2883) are responsible for metabolomic separation of the populations, four of which showed higher amounts of key metabolites and were thus selected for bioactivity analysis. The Welbedacht and Eselbank site 2 populations consistently displayed higher antioxidant activities, with 2,2-azino-bis (3-ethylbenzothiazoline-6-sulphonic acid) (ABTS) radical scavenging activities of 679.894 ± 3.427 µmol Trolox/g dry matter and 635.066 ± 5.140 µmol Trolox/g dry matter, respectively, in correlation with a high number of phenolic and flavonoid compounds. The contribution of the individual metabolites to the pharmacological effectiveness of rooibos remains unknown and as such, further structural elucidation and phytopharmacological testing is thus urgently needed.

Abstract

Introduction: Untargeted direct mass spectrometric analysis of volatile organic compounds has many potential applications across fields such as healthcare and food safety. However, robust data processing protocols must be employed to ensure that research is replicable and practical applications can be realised. User-friendly data processing and statistical tools are becoming increasingly available; however, the use of these tools have neither been analysed, nor are they necessarily suited for every data type.

Objectives: This review aims to analyse data processing and analytic workflows currently in use and examine whether methodological reporting is sufficient to enable replication.

Methods: Studies identified from Web of Science and Scopus databases were systematically examined against the inclusion criteria. The experimental, data processing, and data analysis workflows were reviewed for the relevant studies.

Results: From 459 studies identified from the databases, a total of 110 met the inclusion criteria. Very few papers provided enough detail to allow all aspects of the methodology to be replicated accurately, with only three meeting previous guidelines for reporting experimental methods. A wide range of data processing methods were used, with only eight papers (7.3%) employing a largely similar workflow where direct comparability was achievable.

Conclusions: Standardised workflows and reporting systems need to be developed to ensure research in this area is replicable, comparable, and held to a high standard. Thus, allowing the wide-ranging potential applications to be realised.

Introduction: Pre-analytical factors like sex, age, and blood processing methods introduce variability and bias, compromising data integrity, and thus deserve close attention.

Objectives: This study aimed to explore the influence of participant characteristics (age and sex) and blood processing methods on the metabolic profile.

Method: A Thermo UPLC-TSQ-Quantiva-QQQ Mass Spectrometer was used to analyze 175 metabolites across 9 classes in 208 paired serum and lithium heparin plasma samples from 51 females and 53 males.

Results: Comparing paired serum and plasma samples from the same cohort, out of the 13 metabolites that showed significant changes, 4 compounds related to amino acids and derivatives had lower levels in plasma, and 5 other compounds had higher levels in plasma. Sex-based analysis revealed 12 significantly different metabolites, among which most amino acids and derivatives and nitrogen-containing compounds were higher in males, and other compounds were elevated in females. Interestingly, the volcano plot also confirms the similar patterns of amino acids and derivatives higher in males. The age-based analysis suggested that metabolites may undergo substantial alterations during the 25-35-year age range, indicating a potential metabolic turning point associated with the age group. Moreover, a more distinct difference between the 25-35 and above 35 age groups compared to the below 25 and 25-35 age groups was observed, with the most significant compound decreased in the above 35 age groups.

Conclusion: These findings may contribute to the development of comprehensive metabolomics analyses with confounding factor-based adjustment and enhance the reliability and interpretability of future large-scale investigations.