Metabolomics最新文献

Introduction

Bovine milk contains a rich matrix of nutrients such as carbohydrates, fat, protein and various vitamins and minerals, the composition of which is altered by factors including dietary regime.

Objectives

The objective of this research was to investigate the impact of dietary regime on the metabolite composition of bovine whole milk powder and buttermilk.

Methods

Bovine whole milk powder and buttermilk samples were obtained from spring-calving cows, consuming one of three diets. Group 1 grazed outdoors on perennial ryegrass which was supplemented with 5% concentrates; group 2 were maintained indoors and consumed a total mixed ration diet; and group 3 consumed a partial mixed ration diet consisting of perennial ryegrass during the day and total mixed ration maintained indoors at night.

Results

Metabolomic analysis of the whole milk powder (N = 27) and buttermilk (N = 29) samples was preformed using liquid chromatography-tandem mass spectrometry, with 504 and 134 metabolites identified in the samples respectively. In whole milk powder samples, a total of 174 metabolites from various compound classes were significantly different across dietary regimes (FDR adjusted p-value ≤ 0.05), including triglycerides, of which 66% had their highest levels in pasture-fed samples. Triglycerides with highest levels in pasture-fed samples were predominantly polyunsaturated with high total carbon number. Regarding buttermilk samples, metabolites significantly different across dietary regimes included phospholipids, sphingomyelins and an acylcarnitine.

Conclusion

In conclusion the results reveal a significant impact of a pasture-fed dietary regime on the metabolite composition of bovine dairy products, with a particular impact on lipid compound classes.

Introduction: Fungi biosynthesize chemically diverse secondary metabolites with a wide range of biological activities. Natural product scientists have increasingly turned towards bioinformatics approaches, combining metabolomics and genomics to target secondary metabolites and their biosynthetic machinery. We recently applied an integrated metabologenomics workflow to 110 fungi and identified more than 230 high-confidence linkages between metabolites and their biosynthetic pathways.

Objectives: To prioritize the discovery of bioactive natural products and their biosynthetic pathways from these hundreds of high-confidence linkages, we developed a bioactivity-driven metabologenomics workflow combining quantitative chemical information, antiproliferative bioactivity data, and genome sequences.

Methods: The 110 fungi from our metabologenomics study were tested against multiple cancer cell lines to identify which strains produced antiproliferative natural products. Three strains were selected for further study, fractionated using flash chromatography, and subjected to an additional round of bioactivity testing and mass spectral analysis. Data were overlaid using biochemometrics analysis to predict active constituents early in the fractionation process following which their biosynthetic pathways were identified using metabologenomics.

Results: We isolated three new-to-nature stemphone analogs, 19-acetylstemphones G (1), B (2) and E (3), that demonstrated antiproliferative activity ranging from 3 to 5 µM against human melanoma (MDA-MB-435) and ovarian cancer (OVACR3) cells. We proposed a rational biosynthetic pathway for these compounds, highlighting the potential of using bioactivity as a filter for the analysis of integrated-Omics datasets.

Conclusions: This work demonstrates how the incorporation of biochemometrics as a third dimension into the metabologenomics workflow can identify bioactive metabolites and link them to their biosynthetic machinery.

Introduction: Breeding for oil palm resistance against basal stem rot caused by Ganoderma boninense is challenging and time-consuming. Advanced oil palm gene pools are very limited, hence it is assumed that parental palms have experienced genetic drift and lost their resistance genes against Ganoderma. High-throughput selection criteria should be developed. Metabolomic analysis using ¹H nuclear magnetic resonance (NMR) spectroscopy is easy, and the resulting metabolite can be used as a diagnostic tool for detecting disease in various host-pathogen combinations.

Objectives: The objective of this study was to identify metabolite variations in Dura (D) and Pisifera (P) parental palms with different resistance levels against Ganoderma and moderately resistant DxP using ¹H NMR analysis.

Methods: Leaf tissues of seven different oil palm categories consisting of: resistant, moderate, and susceptible Dura (D); moderate and susceptible Pisifera (P); resistant Tenera/Pisifera (T/P) parental palms; and moderately resistant DxP variety progenies, were sampled and their metabolites were determined using NMR spectroscopy.

Results: Twenty-nine types of metabolites were identified, and most of the metabolites fall in the monosaccharides, amino acids, and fatty acids compound classes. The PCA, PLS-DA, and heatmap multivariate analysis indicated two identified groups of resistance based on their metabolites. The first group consisted of resistant T/P, moderate P, resistant D, and moderately resistant DxP. In contrast, the second group consisted of susceptible P, moderate D, and susceptible D. Glycerol and ascorbic acid were detected as biomarker candidates by OPLS-DA to differentiate moderately resistant DxP from susceptible D and P. The pathway analysis suggested that glycine, serine, and threonine metabolism and taurine and hypotaurine metabolism were involved in the oil palm defense mechanism against Ganoderma.

Conclusion: A metabolomic study with ¹H NMR was able to describe the metabolite composition that could differentiate the characteristics of oil palm resistance against basal stem rot (BSR) caused by G. boninense. These metabolites revealed in this study have enormous potential to become support tools for breeding new oil palm varieties with higher resistance against BSR.

Introduction: Food intake biomarkers are used to estimate dietary exposure; however, selecting a single biomarker to evaluate a specific dietary component is difficult due to the overlap of diverse compounds from different foods. Therefore, combining two or more biomarkers can increase the sensitivity and specificity of food intake estimates.

Objective: This study aimed to evaluate the ability of metabolite panels to distinguish between self-reported fruit consumers and non-consumers among participants in the Longitudinal Study of Adult Health.

Materials and methods: A total of 93 healthy adults of both sexes were selected from the Longitudinal Study of Adult Health. A 24-h dietary recall was obtained using the computer-assisted 24-h food recall GloboDiet software, and 24-h urine samples were collected from each participant. Metabolites were identified in urine using liquid chromatography coupled with high-resolution mass spectrometry by comparing their exact mass and fragmentation patterns using free-access databases. Multivariate receiver operating characteristic curve (ROC) analysis and partial least squares discriminant analysis were used to verify the ability of the metabolite combination to classify daily and non-daily fruit consumers. Fruit intake was identified using a 24 h dietary recall (24 h-DR).

Results: Bananas, grapes, and oranges are included in the summary. The panel of biomarkers exhibited an area under the curve (AUC) > 0.6 (Orange AUC = 0.665; Grape AUC = 0.622; Bananas AUC = 0.602; All fruits AUC = 0.679; Citrus AUC = 0.693) and variable importance projection score > 1.0, and these were useful for assessing the sensitivity and predictability of food intake in our population.

Conclusion: A panel of metabolites was able to classify self-reported fruit consumers with strong predictive power and high specificity and sensitivity values except for banana and total fruit intake.

Introduction

Longitudinal metabolomics data from a meal challenge test contains both fasting and dynamic signals, that may be related to metabolic health and diseases. Recent work has explored the multiway structure of time-resolved metabolomics data by arranging it as a three-way array with modes: subjects, metabolites, and time. The analysis of such dynamic data (where the fasting data is subtracted from postprandial states) reveals dynamic markers of various phenotypes, and differences between fasting and dynamic states. However, there is still limited success in terms of extracting static and dynamic biomarkers for the same subject stratifications.

Objectives

Through joint analysis of fasting and dynamic metabolomics data, our goal is to capture static and dynamic biomarkers of a phenotype for the same subject stratifications providing a complete picture, that will be more effective for precision health.

Methods

We jointly analyze fasting and dynamic metabolomics data collected during a meal challenge test from the COPSAC(_{2000}) cohort using coupled matrix and tensor factorizations (CMTF), where the dynamic data (subjects by metabolites by time) is coupled with the fasting data (subjects by metabolites) in the subjects mode.

Results

The proposed data fusion approach extracts shared subject stratifications in terms of BMI (body mass index) from fasting and dynamic signals as well as the static and dynamic metabolic biomarker patterns corresponding to those stratifications. Specifically, we observe a subject stratification showing the positive association with all fasting VLDLs and higher BMI. For the same subject stratification, a subset of dynamic VLDLs (mainly the smaller sizes) correlates negatively with higher BMI. Higher correlations of the subject quantifications with the phenotype of interest are observed using such a data fusion approach compared to individual analyses of the fasting and postprandial state.

Conclusion

The CMTF-based approach provides a complete picture of static and dynamic biomarkers for the same subject stratifications—when markers are present in both fasting and dynamic states.

Introduction

The Cluster bean is an economically significant annual legume, widely known as guar. Plant productivity is frequently constrained by drought conditions.

Objective

In this work, we have identified the untargeted drought stress-responsive metabolites in mature leaves of cluster beans under drought and control condition.

Methods

To analyse the untargeted metabolites, gas chromatography-mass spectrometry (GC-MS) technique was used. Supervised partial least-squares discriminate analysis and heat map were used to identify the most significant metabolites for drought tolerance.

Results

The mature leaves of drought-treated C. tetragonoloba cv. ‘HG-365’ which is a drought-tolerant cultivar, showed various types of amino acids, fatty acids, sugar alcohols and sugars as the major classes of metabolites recognized by GC-MS metabolome analysis. Metabolite profiling of guar leaves showed 23 altered metabolites. Eight metabolites (proline, valine, D-pinitol, palmitic acid, dodecanoic acid, threonine, glucose, and glycerol monostearate) with VIP score greater than one were considered as biomarkers and three metabolite biomarkers (D-pinitol, valine, and glycerol monostearate) were found for the first time in guar under drought stress. In this work, four amino acids (alanine, valine, serine and aspartic acid) were also studied, which played a significant role in drought-tolerant pathway in guar.

Conclusion

This study provides information on the first-ever GC-MS metabolic profiling of guar. This work gives in-depth details on guar’s untargeted drought-responsive metabolites and biomarkers, which can plausibly be used for further identification of biochemical pathways, enzymes, and the location of various genes under drought stress.

Background

Dietary habits significantly influence the risks of type 2 diabetes and cardiovascular disease. Through metabolomics, we’ve previously measured plasma metabolites to gauge dietary quality, introducing a healthy dietary metabolic signature (HDMS) linked to a decreased risk of future type 2 diabetes and coronary artery disease.

Objectives

To assess the impact of a 6-day dietary intervention on plasma metabolites and the HDMS.

Methods

Fifty-nine Swedish participants (71% women, mean age 69 years) underwent a 6-day Mediterranean diet (MD) intervention in Italy’s Cilento region. All meals, crafted from local recipes and ingredients, were provided. Metabolite profiling pre- and post-intervention was conducted with a UHPLC-QTOF. Alterations in metabolite levels and the HDMS were examined using paired T-test.

Results

The MD intervention notably enhanced the HDMS across participants (mean increase: 1.3 standard deviations (SD), 95% CI 1.1–1.4, p = 6E-25). Out of 109 metabolites, 66 exhibited significant alterations (fdr adjusted p < 0.05). Among the 10 most significant changes, increases were observed in several diet related metabolites such as pipecolate, hippurate, caffeine, homostachydrine, acylcarnitine C11:0, acetylornithine, beta-carotene and 7-methylguanine. The most significant decreases manifested in piperine and 3-methylhistidine.

Conclusions

The HDMS, which is linked to a healthy diet and inversely associated with cardiometabolic disease, was significantly improved by the 6-day Mediterranean diet intervention. Notably, metabolite markers previously shown to be indicative of the intake of vegetables, fruits, grains, and legumes increased, while markers previously associated with red meat consumption decreased. These findings highlight the potential of short-term dietary interventions to induce significant changes in plasma metabolite profiles.

Introduction

Severe fever with thrombocytopenia syndrome (SFTS) is an emerging infectious disease caused by the SFTS virus (SFTSV), which has a wide geographic distribution. The primary clinical manifestations of SFTS are fever and thrombocytopenia, with multiorgan failure being the leading cause of death. While most patients recover with treatment, little is known about the potential long-term metabolic effects of SFTSV infection.

Objectives

This study aimed to shed light on dysregulated metabolic pathways and cytokine responses following SFTSV infection, which pose significant risks to the short-term and long-term health of affected individuals.

Methods

Fourteen laboratory-confirmed clinical SFTS cases and thirty-eight healthy controls including 18 SFTSV IgG-positive and 20 IgG-negative individuals were recruited from Taizhou city of Zhejiang province, Eastern China. Inclusion criteria of healthy controls included residing in the study area for at least one year, absence of fever or other symptoms in the past two weeks, and no history of SFTS diagnosis. Ultrahigh-performance liquid chromatography-mass spectrometry (UHPLC-MS) was used to obtain the relative abundance of plasma metabolites. Short-term metabolites refer to transient alterations present only during SFTSV infection, while long-term metabolites persistently deviate from normal levels even after recovery from SFTSV infection. Additionally, the concentrations of 12 cytokines were quantified through fluorescence intensity measurements. Differential metabolites were screened using orthogonal projections to latent structures discriminant analysis (OPLS-DA) and the Wilcoxon rank test. Metabolic pathway analysis was performed using MetaboAnalyst. Between-group differences of metabolites and cytokines were examined using the Wilcoxon rank test. Correlation matrices between identified metabolites and cytokines were analyzed using Spearman’s method.

Results and conclusions

We screened 122 long-term metabolites and 108 short-term metabolites by analytical comparisons and analyzed their correlations with 12 cytokines. Glycerophospholipid metabolism (GPL) was identified as a significant short-term metabolic pathway suggesting that the activation of GPL might be linked to the self-replication of SFTSV, whereas pentose phosphate pathway and alanine, aspartate, and glutamate metabolism were indicated as significant long-term metabolic pathways playing a role in combating long-standing oxidative stress in the patients. Furthermore, our study suggests a new perspective that α-ketoglutarate could serve as a dietary supplement to protect recovering SFTS patients.

Introduction

Understanding why subjects with overweight and with obesity vary in their response to dietary interventions is of major interest for developing personalized strategies for body mass regulation.

Objectives

The aim of this study was to investigate the relationship between changes in the urine metabolome and body mass during a breakfast meal intervention. Furthermore, we aimed to elucidate if the baseline urine metabolome could predict the response to the two types of breakfast meals (high versus low protein) during the intervention.

Methods

A total of 75 young, women with overweight were randomly allocated to one of two intervention groups: (1) High-protein (HP) or (2) low-protein (LP) breakfast as part of their habitual diet during a 12-week intervention. Beside the breakfast meal, participants were instructed to eat their habitual diet and maintain their habitual physical activity level. Nuclear magnetic resonance-based metabolomics was conducted on urine samples collected at baseline (wk 0), mid-intervention (wk 6), and at endpoint (wk 12). At baseline and endpoint, body mass was measured and DXA was used to measure lean body mass and fat mass.

Results

The baseline urine metabolite profile showed a slightly higher correlation (R2 = 0.56) to body mass in comparison with lean body mass (R2 = 0.51) and fat mass (R2 = 0.53). Baseline 24-h urinary excretion of trigonelline (p = 0.04), N, N-dimethylglycine (p = 0.02), and trimethylamine (p = 0.03) were significantly higher in individuals who responded with a reduction in body mass to the HP breakfast.

Conclusions

Differences in the urine metabolome were seen for women that obtained a body weight loss in the response to the HP breakfast intervention and women who did not obtain a body weight loss, indicating that the urine metabolome contains information about the metabolic phenotype that influences the responsiveness to dietary interventions.

Introduction

Stable isotope tracers have been increasingly used in preclinical cancer model systems, including cell culture and mouse xenografts, to probe the altered metabolism of a variety of cancers, such as accelerated glycolysis and glutaminolysis and generation of oncometabolites. Comparatively little has been reported on the fidelity of the different preclinical model systems in recapitulating the aberrant metabolism of tumors.

Objectives

We have been developing several different experimental model systems for systems biochemistry analyses of non-small cell lung cancer (NSCLC¹) using patient-derived tissues to evaluate appropriate models for metabolic and phenotypic analyses.

Methods

To address the issue of fidelity, we have carried out a detailed Stable Isotope-Resolved Metabolomics study of freshly resected tissue slices, mouse patient derived xenografts (PDXs), and cells derived from a single patient using both ¹³C₆-glucose and ¹³C₅,¹⁵N₂-glutamine tracers.

Results

Although we found similar glucose metabolism in the three models, glutamine utilization was markedly higher in the isolated cell culture and in cell culture-derived xenografts compared with the primary cancer tissue or direct tissue xenografts (PDX).

Conclusions

This suggests that caution is needed in interpreting cancer biochemistry using patient-derived cancer cells in vitro or in xenografts, even at very early passage, and that direct analysis of patient derived tissue slices provides the optimal model for ex vivo metabolomics. Further research is needed to determine the generality of these observations.