Metabolomics最新文献

Background: The multitude of metabolites generated by physiological processes in the body can serve as valuable biomarkers for many clinical purposes. They can provide a window into relevant metabolic pathways for health and disease, as well as be candidate therapeutic targets. A subset of these metabolites generated in the human body are volatile, known as volatile organic compounds (VOCs), which can be detected in exhaled breath. These can diffuse from their point of origin throughout the body into the bloodstream and exchange into the air in the lungs. For this reason, breath VOC analysis has become a focus of biomedical research hoping to translate new useful biomarkers by taking advantage of the non-invasive nature of breath sampling, as well as the rapid rate of collection over short periods of time that can occur. Despite the promise of breath analysis as an additional platform for metabolomic analysis, no VOC breath biomarkers have successfully been implemented into a clinical setting as of the time of this review.

Aim of review: This review aims to summarize the progress made to address the major methodological challenges, including standardization, that have historically limited the translation of breath VOC biomarkers into the clinic. We highlight what steps can be taken to improve these issues within new and ongoing breath research to promote the successful development of the VOCs in breath as a robust source of candidate biomarkers. We also highlight key recent papers across select fields, critically reviewing the progress made in the past few years to advance breath research.

Key scientific concepts of review: VOCs are a set of metabolites that can be sampled in exhaled breath to act as advantageous biomarkers in a variety of clinical contexts.

Background and objective: Blood-based small molecule metabolites offer easy accessibility and hold significant potential for insights into health processes, the impact of lifestyle, and genetic variation on disease, enabling precise risk prevention. In a prospective study with records of heart failure (HF) incidence, we present metabolite profiling data from individuals without HF at baseline.

Methods: We uncovered the interconnectivity of metabolites using data-driven and causal networks augmented with polygenic factors. Exploring the networks, we identified metabolite broadcasters, receivers, mediators, and subnetworks corresponding to functional classes of metabolites, and provided insights into the link between metabolomic architecture and regulation in health. We incorporated the network structure into the identification of metabolites associated with HF to control the effect of confounding metabolites.

Results: We identified metabolites associated with higher and lower risk of HF incidence, such as glycine, ureidopropionic and glycocholic acids, and LPC 18:2. These associations were not confounded by the other metabolites due to uncovering the connectivity among metabolites and adjusting each association for the confounding metabolites. Examples of our findings include the direct influence of asparagine on glycine, both of which were inversely associated with HF. These two metabolites were influenced by polygenic factors and only essential amino acids, which are not synthesized in the human body and are obtained directly from the diet.

Conclusion: Metabolites may play a critical role in linking genetic background and lifestyle factors to HF incidence. Revealing the underlying connectivity of metabolites associated with HF strengthens the findings and facilitates studying complex conditions like HF.

Introduction: Congenital heart disease (CHD) is the most common congenital anomaly, representing a significant global disease burden. Limitations exist in our understanding of aetiology, diagnostic methodology and screening, with metabolomics offering promise in addressing these.

Objective: To evaluate maternal metabolomics and lipidomics in prediction and risk factor identification for childhood CHD.

Methods: We performed an observational study in mothers of children with CHD following pregnancy, using untargeted plasma metabolomics and lipidomics by ultrahigh performance liquid chromatography-high resolution mass spectrometry (UHPLC-HRMS). 190 cases (157 mothers of children with structural CHD (sCHD); 33 mothers of children with genetic CHD (gCHD)) from the children OMACp cohort and 162 controls from the ALSPAC cohort were analysed. CHD diagnoses were stratified by severity and clinical classifications. Univariate, exploratory and supervised chemometric methods were used to identify metabolites and lipids distinguishing cases and controls, alongside predictive modelling.

Results: 499 metabolites and lipids were annotated and used to build PLS-DA and SO-CovSel-LDA predictive models to accurately distinguish sCHD and control groups. The best performing model had an sCHD test set mean accuracy of 94.74% (sCHD test group sensitivity 93.33%; specificity 96.00%) utilising only 11 analytes. Similar test performances were seen for gCHD. Across best performing models, 37 analytes contributed to performance including amino acids, lipids, and nucleotides.

Conclusions: Here, maternal metabolomic and lipidomic analysis has facilitated the development of sensitive risk prediction models classifying mothers of children with CHD. Metabolites and lipids identified offer promise for maternal risk factor profiling, and understanding of CHD pathogenesis in the future.

Introduction: Burkitt lymphoma (BL) is an aggressive non-Hodgkin lymphoma associated with Plasmodium falciparum and Epstein-Barr virus, both of which affect metabolic pathways. The metabolomic patterns of BL is unknown.

Materials and methods: We measured 627 metabolites in pre-chemotherapy treatment plasma samples from 25 male children (6-11 years) with BL and 25 cancer-free area- and age-frequency-matched male controls from the Epidemiology of Burkitt Lymphoma in East African Children and Minors study in Uganda using liquid chromatography-tandem mass spectrometry. Unconditional, age-adjusted logistic regression analysis was used to estimate odds ratios (ORs) and their 95% confidence intervals (CIs) for the BL association with 1-standard deviation increase in the log-metabolite concentration, adjusting for multiple comparisons using false discovery rate (FDR) thresholds and Bonferroni correction.

Results: Compared to controls, levels for 42 metabolite concentrations differed in BL cases (FDR < 0.001), including triacylglyceride (18:0_38:6), alpha-aminobutyric acid (AABA), ceramide (d18:1/20:0), phosphatidylcholine ae C40:6 and phosphatidylcholine C38:6 as the top signals associated with BL (ORs = 6.9 to 14.7, P < 2.4✕10^- 4). Two metabolites (triacylglyceride (18:0_38:6) and AABA) selected using stepwise logistic regression discriminated BL cases from controls with an area under the curve of 0.97 (95% CI: 0.94, 1.00).

Conclusion: Our findings warrant further examination of plasma metabolites as potential biomarkers for BL risk/diagnosis.

Background: Metabolomics data is often complex due to the high number of metabolites, chemical diversity, and dependence on sample preparation. This makes it challenging to detect significant differences between factor levels and to obtain accurate and reliable data. To address these challenges, the use of Design of Experiments (DoE) techniques in the setup of metabolomic experiments is crucial. DoE techniques can be used to optimize the experimental design space, ensuring that the maximum amount of information is obtained from a limited sample space.

Aim of review: This review aims at providing a baseline workflow for applying DoE when generating metabolomics data.

Key scientific concepts of review: The review provides insights into the theory of DoE. The review showcases the theory being put into practice by highlighting different examples DoE being applied in metabolomics throughout the literature, considering both targeted and untargeted metabolomic studies in which the data was acquired using both nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry techniques. In addition, the review presents DoE concepts not currently being applied in metabolomics, highlighting these as potential future prospects.

Introduction: Exploring metabolic changes within host E. coli through an untargeted metabolomic study of T7L variants overexpression to optimize engineered endolysins for clinical/therapeutic use.

Aim and objective: This study aims to assess the impact of overexpressing T7L variants on the metabolic profiles of E. coli. The two variants considered include T7L-H37A, which has enhanced lytic activity compared to its wild-type protein, and T7L-H48K, a dead mutant with no significant activity.

Methods: ¹H NMR-based metabolomics was employed to compare the metabolic profiles of E. coli cells overexpressing T7L wild-type protein and its variants.

Results: Overexpression of the T7L wild-type (T7L-WT) protein and its variants (T7L-H48K and T7L-H37A) was compared to RNAP overexpression in E. coli cells using ¹H NMR-based metabolomics, analyzing a total of 75 annotated metabolites, including organic acids, amino acids, sugars, and nucleic acids. The results showed distinct clustering patterns for the two T7L variant groups compared with the WT, in which the dead mutant (H48K) group showed clustering close to that of RNAP. Pathway impact analysis revealed different effects of T7L variants on E. coli metabolic profiles, with T7L-H48K showing minimal alterations in energy and amino acid pathways linked to osmotic stress compared to noticeable alterations in these pathways for both T7L-H37A and T7L-WT.

Conclusions: This study uncovered distinct metabolic fingerprints when comparing the overexpression of active and inactive mutants of T7L lytic enzymes in E. coli cells. These findings could contribute to the optimization and enhancement of suitable endolysins as potential alternatives to antibiotics.

The coral holobiont is underpinned by complex metabolic exchanges between different symbiotic partners, which are impacted by environmental stressors. The chemical diversity of the compounds produced by the holobiont is high and includes primary and secondary metabolites, as well as volatiles. However, metabolites and volatiles have only been characterised in isolation so far. Here, we applied a paired metabolomic-volatilomic approach to characterise holistically the chemical response of the holobiont under stress. Montipora mollis fragments were subjected to high-light stress (8-fold higher than the controls) for 30 min. Photosystem II (PSII) photochemical efficiency values were 7-fold higher in control versus treatment corals immediately following high-light exposure, but returned to pre-stress levels after 30 min of recovery. Under high-light stress, we identified an increase in carbohydrates (> 5-fold increase in arabinose and fructose) and saturated fatty acids (7-fold increase in myristic and oleic acid), together with a decrease in fatty acid derivatives in both metabolites and volatiles (e.g., 80% decrease in oleamide and nonanal), and other antioxidants (~ 85% decrease in sorbitol and galactitol). These changes suggest short-term light stress induces oxidative stress. Correlation analysis between volatiles and metabolites identified positive links between sorbitol, galactitol, six other metabolites and 11 volatiles, with four of these compounds previously identified as antioxidants. This suggests that these 19 compounds may be related and share similar functions. Taken together, our findings demonstrate how paired metabolomics-volatilomics may illuminate broader metabolic shifts occurring under stress and identify linkages between uncharacterised compounds to putatively determine their functions.

Background: Preeclampsia is a pregnancy-specific clinical syndrome and can be subdivided into early-onset preeclampsia (EOPE) and late-onset preeclampsia (LOPE) according to the gestational age of delivery. Patients with preeclampsia have aberrant lipid metabolism. This study aims to compare serum lipid profiles of normal pregnant women with EOPE or LOPE and screening potential biomarkers to diagnose EOPE or LOPE.

Methods: Twenty normal pregnant controls (NC), 19 EOPE, and 19 LOPE were recruited in this study. Untargeted lipidomics based on ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) was used to compare their serum lipid profiles.

Results: The lipid metabolism profiles significantly differ among the NC, EOPE, and LOPE. Compared to the NC, there were 256 and 275 distinct lipids in the EOPE and LOPE, respectively. Furthermore, there were 42 different lipids between the LOPE and EOPE, of which eight were significantly associated with fetal birth weight and maternal urine protein. The five lipids that both differed in the EOPE and LOPE were DGTS (16:3/16:3), LPC (20:3), LPC (22:6), LPE (22:6), PC (18:5e/4:0), and a combination of them were a potential biomarker for predicting EOPE or LOPE. The receiver operating characteristic analysis revealed that the diagnostic power of the combination for distinguishing the EOPE from the NC and for distinguishing the LOPE from the NC can reach 1.000 and 0.992, respectively. The association between the lipid modules and clinical characteristics of EOPE and LOPE was investigated by the weighted gene co-expression network analysis (WGCNA). The results demonstrated that the main different metabolism pathway between the EOPE and LOPE was enriched in glycerophospholipid metabolism.

Conclusions: Lipid metabolism disorders may be a potential mechanism of the pathogenesis of preeclampsia. Lipid metabolites have the potential to serve as biomarkers in patients with EOPE or LOPE. Furthermore, lipid metabolites correlate with clinical severity indicators for patients with EOPE and LOPE, including fetal birth weight and maternal urine protein levels.

Introduction

The chemical classification of Cannabis is typically confined to the cannabinoid content, whilst Cannabis encompasses diverse chemical classes that vary in abundance among all its varieties. Hence, neglecting other chemical classes within Cannabis strains results in a restricted and biased comprehension of elements that may contribute to chemical intricacy and the resultant medicinal qualities of the plant.

Objectives

Thus, herein, we report a computational metabolomics study to elucidate the Cannabis metabolic map beyond the cannabinoids.

Methods

Mass spectrometry-based computational tools were used to mine and evaluate the methanolic leaf and flower extracts of two Cannabis cultivars: Amnesia haze (AMNH) and Royal dutch cheese (RDC).

Results

The results revealed the presence of different chemical compound classes including cannabinoids, but extending it to flavonoids and phospholipids at varying distributions across the cultivar plant tissues, where the phenylpropnoid superclass was more abundant in the leaves than in the flowers. Therefore, the two cultivars were differentiated based on the overall chemical content of their plant tissues where AMNH was observed to be more dominant in the flavonoid content while RDC was more dominant in the lipid-like molecules. Additionally, in silico molecular docking studies in combination with biological assay studies indicated the potentially differing anti-cancer properties of the two cultivars resulting from the elucidated chemical profiles.

Conclusion

These findings highlight distinctive chemical profiles beyond cannabinoids in Cannabis strains. This novel mapping of the metabolomic landscape of Cannabis provides actionable insights into plant biochemistry and justifies selecting certain varieties for medicinal use.