Metabolomics最新文献

Background: The incidence of gallstones is high in Qinghai Province. However, the molecular mechanisms underlying the development of gallstones remain unclear.

Methods: In this study, we collected urine samples from 30 patients with gallstones and 30 healthy controls. The urine samples were analysed using multi-omics platforms. Proteomics analysis was conducted using data-independent acquisition, whereas metabolomics analysis was performed using liquid chromatography-mass spectrometry (LC-MS).

Results: Among the patients with gallstones, we identified 49 down-regulated and 185 up-regulated differentially expressed proteins as well as 195 up-regulated and 189 down-regulated differentially expressed metabolites. Six pathways were significantly enriched: glycosaminoglycan degradation, arginine and proline metabolism, histidine metabolism, pantothenate and coenzyme A biosynthesis, drug metabolism-other enzymes, and the pentose phosphate pathway. Notably, 10 differentially expressed proteins and metabolites showed excellent predictive performance and were selected as potential biomarkers.

Conclusion: The findings of our metabolomics and proteomics analyses provide new insights into novel biomarkers for patients with cholelithiasis in high-altitude areas.

Introduction: Glacier ice algae, mainly Ancylonema alaskanum and Ancylonema nordenskiöldi, bloom on Greenland Ice Sheet bare ice surfaces. They significantly decrease surface albedo due to their purple-brown pigmentation, thus increasing melt. Little is known about their metabolic adaptation and factors controlling algal growth dynamics and pigment formation. A challenge in obtaining such data is the necessity of melting samples, which delays preservation and introduces bias to metabolomic analysis. There is a need to evaluate the physiological response of algae to melting and establish consistent sample processing strategies for metabolomics of ice microbial communities.

Objectives: To address the impact of sample melting procedure on metabolic characterization and establish a processing and analytical workflow for endometabolic profiling of glacier ice algae.

Methods: We employed untargeted, high-resolution mass spectrometry and tested the effect of sample melt temperature (10, 15, 20 °C) and processing delay (up to 49 h) on the metabolome and lipidome, and complemented this approach with cell counts (FlowCam), photophysiological analysis (PAM) and diversity characterization.

Results and conclusion: We putatively identified 804 metabolites, with glycerolipids, glycerophospholipids and fatty acyls being the most prominent superclasses (> 50% of identified metabolites). Among the polar metabolome, carbohydrates and amino acid-derivatives were the most abundant. We show that 8% of the metabolome is affected by melt duration, with a pronounced decrease in betaine membrane lipids and pigment precursors, and an increase in phospholipids. Controlled fast melting at 10 °C resulted in the highest consistency, and is our recommendation for future supraglacial metabolomics studies.

Introduction: Human metabolism is sustained by functional networks that operate at diverse scales. Capturing local and global dynamics in the human body by hierarchically bridging multi-scale functional networks is a major challenge in physiological modeling.

Objectives: To develop an interactive, user-friendly web application that facilitates the simulation and visualization of advection-dispersion transport in three-dimensional (3D) microvascular networks, biochemical exchange, and metabolic reactions in the tissue layer surrounding the vasculature.

Methods: To help modelers combine and simulate biochemical processes occurring at multiple scales, KiPhyNet deploys our discrete graph-based modeling framework that bridges functional networks existing at diverse scales. KiPhyNet is implemented in Python based on Apache web server using MATLAB as the simulator engine. KiPhyNet provides the functionality to assimilate multi-omics data from clinical and experimental studies as well as vascular data from imaging studies to investigate the role of structural changes in vascular topology on the functional response of the tissue.

Results: With the network topology, its biophysical attributes, values of initial and boundary conditions, parameterized kinetic constants, biochemical species-specific transport properties such as diffusivity as inputs, a user can use our application to simulate and view the simulation results. The results of steady-state velocity and pressure fields and dynamic concentration fields can be interactively examined.

Conclusion: KiPhyNet provides barrier-free access to perform time-course simulation experiments by building multi-scale models of microvascular networks in physiology, using a discrete modeling framework. KiPhyNet is freely accessible at   http://pallab.cds.iisc.ac.in/kiphynet/ and the documentation is available at   https://deepamahm.github.io/kiphynet_docs/ .

Background: Different types of analytical methods, with different characteristics, are applied in metabolomics and lipidomics research and include untargeted, targeted and semi-targeted methods. Ultra High Performance Liquid Chromatography-Mass Spectrometry is one of the most frequently applied measurement instruments in metabolomics because of its ability to detect a large number of water-soluble and lipid metabolites over a wide range of concentrations in short analysis times. Methods applied for the detection and quantification of metabolites differ and can either report a (normalised) peak area or an absolute concentration.

Aim of review: In this tutorial we aim to (1) define similarities and differences between different analytical approaches applied in metabolomics and (2) define how amounts or absolute concentrations of endogenous metabolites can be determined together with the advantages and limitations of each approach in relation to the accuracy and precision when concentrations are reported.

Key scientific concepts of review: The pre-analysis knowledge of metabolites to be targeted, the requirement for (normalised) peak responses or absolute concentrations to be reported and the number of metabolites to be reported define whether an untargeted, targeted or semi-targeted method is applied. Fully untargeted methods can only provide (normalised) peak responses and fold changes which can be reported even when the structural identity of the metabolite is not known. Targeted methods, where the analytes are known prior to the analysis, can also report fold changes. Semi-targeted methods apply a mix of characteristics of both untargeted and targeted assays. For the reporting of absolute concentrations of metabolites, the analytes are not only predefined but optimized analytical methods should be developed and validated for each analyte so that the accuracy and precision of concentration data collected for biological samples can be reported as fit for purpose and be reviewed by the scientific community.

Introduction: Ginseng berry (GB) has previously been demonstrated to improve systemic insulin resistance and regulate hepatic glucose metabolism and steatosis in mice with diet-induced obesity (DIO).

Objectives: In this study, the role of GB in metabolism was assessed using metabolomics analysis on the total liver metabolites of DIO mice.

Methods: Metabolomic profiling was performed using capillary electrophoresis time-of-flight mass spectrometry (CE-TOF/MS) of liver tissue from mice on a 12-wk normal chow diet (NC), high-fat diet (HFD), and HFD supplemented with 0.1% GB (HFD + GB). The detected metabolites, its pathways, and functions were analyzed through partial least square discriminant analysis (PLS-DA), the small molecular pathway database (SMPDB), and MetaboAnalyst 5.0.

Results: The liver metabolite profiles of NC, HFD, and GB-fed mice (HFD + GB) were highly compartmentalized. Metabolites involved in major liver functions, such as mitochondrial function, gluconeogenesis/glycolysis, fatty acid metabolism, and primary bile acid biosynthesis, showed differences after GB intake. The metabolites that showed significant correlations with fasting blood glucose (FBG), insulin, and homeostatic model assessment for insulin resistance (HOMA-IR) were highly associated with mitochondrial membrane function, energy homeostasis, and glucose metabolism. Ginseng berry intake increased the levels of metabolites involved in mitochondrial membrane function, decreased the levels of metabolites related to glucose metabolism, and was highly correlated with metabolic phenotypes.

Conclusion: This study demonstrated that long-term intake of GB changed the metabolite of hepatosteatotic livers in DIO mice, normalizing global liver metabolites involved in mitochondrial function and glucose metabolism and indicating the potential mechanism of GB in ameliorating hyperglycemia in DIO mice.

Introduction: The human salivary metabolome is a rich source of information for metabolomics studies. Among other influences, individual differences in sleep-wake history and time of day may affect the metabolome.

Objectives: We aimed to characterize the influence of a single night of sleep deprivation compared to sufficient sleep on the metabolites present in oral fluid and to assess the implications of sampling time points for the design of metabolomics studies.

Methods: Oral fluid specimens of 13 healthy young males were obtained in Salivette^® devices at regular intervals in both a control condition (repeated 8-hour sleep) and a sleep deprivation condition (total sleep deprivation of 8 h, recovery sleep of 8 h) and their metabolic contents compared in a semi-targeted metabolomics approach.

Results: Analysis of variance results showed factor 'time' (i.e., sampling time point) representing the major influencer (median 9.24%, range 3.02-42.91%), surpassing the intervention of sleep deprivation (median 1.81%, range 0.19-12.46%). In addition, we found about 10% of all metabolic features to have significantly changed in at least one time point after a night of sleep deprivation when compared to 8 h of sleep.

Conclusion: The majority of significant alterations in metabolites' abundances were found when sampled in the morning hours, which can lead to subsequent misinterpretations of experimental effects in metabolomics studies. Beyond applying a within-subject design with identical sample collection times, we highly recommend monitoring participants' sleep-wake schedules prior to and during experiments, even if the study focus is not sleep-related (e.g., via actigraphy).

Introduction

The human immunodeficiency virus (HIV) and tuberculosis (TB) co-infection presents significant challenges due to the complex interplay between these diseases, leading to exacerbated metabolic disturbances. Understanding these metabolic profiles is crucial for improving diagnostic and therapeutic approaches.

Objective

This study aimed to characterise the urinary acylcarnitine and amino acid profiles, including 5-hydroxyindoleacetic acid (5-HIAA), in patients co-infected with HIV and TB using targeted liquid chromatography mass spectrometry (LC–MS) metabolomics.

Methods

Urine samples, categorised into HIV, TB, HIV/TB co-infected, and healthy controls, were analysed using HPLC–MS/MS. Statistical analyses included one-way ANOVA and a Kruskal-Wallis test to determine significant differences in the acylcarnitine and amino acid profiles between groups.

Results

The study revealed significant metabolic alterations, especially in TB and co-infected groups. Elevated levels of medium-chain acylcarnitines indicated increased fatty acid oxidation, commonly associated with cachexia in TB. Altered amino acid profiles suggested disruptions in protein and glucose metabolism, indicating a shift towards diabetes-like metabolic states. Notably, TB was identified as a primary driver of these changes, affecting protein turnover, and impacting energy metabolism in co-infected patients.

Conclusion

The metabolic profiling of HIV/TB co-infection highlights the profound impact of TB on metabolic pathways, which may exacerbate the clinical complexities of co-infection. Understanding these metabolic disruptions can guide the development of targeted treatments and improve management strategies, ultimately enhancing the clinical outcomes for these patients. Further research is required to validate these findings and explore their implications in larger, diverse populations.

Introduction

Variation in DNA methylation (DNAm) in adipose tissue is associated with the pathogenesis of obesity and insulin resistance. The activity of enzymes involved in altering DNAm levels is dependent on several metabolite cofactors.

Objectives

To understand the role of metabolites as mechanistic regulators of epigenetic marks, we tested the association between selected plasma metabolites and DNAm levels in the adipose tissue of African Americans.

Methods

In the AAGMEx cohort (N = 256), plasma levels of metabolites were measured by untargeted liquid chromatography-mass spectrometry; adipose tissue DNAm and transcript levels were measured by reduced representation bisulfite sequencing, and expression microarray, respectively.

Results

Among the 21 one-carbon metabolism pathway metabolites evaluated, six were associated with gluco-metabolic traits (P_FDR < 0.05, for BMI, S_I, or Matsuda index) in AAGMEx. Methylation levels of 196, 116, and 180 CpG-sites were associated (P < 0.0001) with S-adenosylhomocysteine (SAH), cystine, and hypotaurine, respectively. Cis-expression quantitative trait methylation (cis eQTM) analyses suggested the role of metabolite-level-associated CpG sites in regulating the expression of adipose tissue transcripts, including genes in G-protein coupled receptor signaling pathway. Plasma SAH level-associated CpG sites chr19:3403712 and chr19:3403735 were also associated with the expression of G-protein subunit alpha 15 (GNA15) in adipose. The expression of GNA15 was significantly correlated with BMI (β = 1.87, P = 1.9 × 10^–16) and S_I (β = -1.61, P = 2.49 × 10^–5).

Conclusion

Our study suggests that a subset of metabolites modulates the methylation levels of CpG sites in specific loci and, in turn, regulates the expression of transcripts involved in obesity and insulin resistance.

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.