A narrative review of metabolomics in the era of "-omics": integration into clinical practice for inborn errors of metabolism

Dynamic changes of key metabolites in Longjing green tea during processing revealed by widely targeted metabolomic profiling and sensory experiments

In this study, widely targeted metabolomics and chemometrics were utilized to comprehensively analyse the formation of taste compounds in Longjing green tea. A total of 580 non-volatile metabolites were identified by using ultra-performance liquid chromatography-electrospray ionization-tandem mass spectrometry, and alterations in three metabolic pathways were investigated. Notably, the fixation process reduced phosphatidic acid levels, resulting in the formation of lyso-phosphatidylcholine and lyso-phosphatidylethanolamine, as well as the release of esterified polyunsaturated fatty acids. Baiye No.1 had high levels of L-glutamic acid and l-glutamine, while Longjing 43 showed elevated levels of flavones. Correlation analysis and sensory verification indicated that the specific concentration of L-leucine could decrease the umami of the tea. These findings advance our understanding of Longjing green tea quality improvement and cultivar development.

Targeted ultra performance liquid chromatography tandem mass spectrometry procedures for the diagnosis of inborn errors of metabolism: validation through ERNDIM external quality assessment schemes

OBJECTIVES

Early diagnosis of inborn errors of metabolism (IEM) is crucial to ensure early detection of conditions which are treatable. This study reports on targeted metabolomic procedures for the diagnosis of IEM of amino acids, acylcarnitines, creatine/guanidinoacetate, purines/pyrimidines and oligosaccharides, and describes its validation through external quality assessment schemes (EQA).

METHODS

Analysis was performed on a Waters ACQUITY UPLC H-class system coupled to a Waters Xevo triple-quadrupole (TQD) mass spectrometer, operating in both positive and negative electrospray ionization mode. Chromatographic separation was performed on a CORTECS C18 column (2.1 × 150, 1.6 µm). Data were collected by multiple reaction monitoring.

RESULTS

The internal and EQA results were generally adequate, with a few exceptions. We calculated the relative measurement error (RME) and only a few metabolites displayed a RME higher than 30 % (asparagine and some acylcarnitine species). For oligosaccharides, semi-quantitative analysis of an educational panel clearly identified the 8 different diseases included.

CONCLUSIONS

Overall, we have validated our analytical system through an external quality control assessment. This validation will contribute to harmonization between laboratories, thus improving identification and management of patients with IEM.

Deciphering metabolic crosstalk in context: lessons from inflammatory diseases

Metabolism plays a crucial role in regulating the function of immune cells in both health and disease, with altered metabolism contributing to the pathogenesis of cancer and many inflammatory diseases. The local microenvironment has a profound impact on the metabolism of immune cells. Therefore, immunological and metabolic heterogeneity as well as the spatial organization of cells in tissues should be taken into account when studying immunometabolism. Here, we highlight challenges of investigating metabolic communication. Additionally, we review the capabilities and limitations of current technologies for studying metabolism in inflamed microenvironments, including single-cell omics techniques, flow cytometry-based methods (Met-Flow, single-cell energetic metabolism by profiling translation inhibition (SCENITH)), cytometry by time of flight (CyTOF), cellular indexing of transcriptomes and epitopes by sequencing (CITE-Seq), and mass spectrometry imaging. Considering the importance of metabolism in regulating immune cells in diseased states, we also discuss the applications of metabolomics in clinical research, as well as some hurdles to overcome to implement these techniques in standard clinical practice. Finally, we provide a flowchart to assist scientists in designing effective strategies to unravel immunometabolism in disease-relevant contexts.

Towards personalized genome-scale modeling of inborn errors of metabolism for systems medicine applications

Inborn errors of metabolism (IEMs) are a group of more than 1000 inherited diseases that are individually rare but have a cumulative global prevalence of 50 per 100,000 births. Recently, it has been recognized that like common diseases, patients with rare diseases can greatly vary in the manifestation and severity of symptoms. Here, we review omics-driven approaches that enable an integrated, holistic view of metabolic phenotypes in IEM patients. We focus on applications of Constraint-based Reconstruction and Analysis (COBRA), a widely used mechanistic systems biology approach, to model the effects of inherited diseases. Moreover, we review evidence that the gut microbiome is also altered in rare diseases. Finally, we outline an approach using personalized metabolic models of IEM patients for the prediction of biomarkers and tailored therapeutic or dietary interventions. Such applications could pave the way towards personalized medicine not just for common, but also for rare diseases.

An evaluation of untargeted metabolomics methods to characterize inborn errors of metabolism

Inborn errors of metabolism (IEMs) encompass a diverse group of disorders that can be difficult to classify due to heterogenous clinical, molecular, and biochemical manifestations. Untargeted metabolomics platforms have become a popular approach to analyze IEM patient samples because of their ability to detect many metabolites at once, accelerating discovery of novel biomarkers, and metabolic mechanisms of disease. However, there are concerns about the reproducibility of untargeted metabolomics research due to the absence of uniform reporting practices, data analyses, and experimental design guidelines. Therefore, we critically evaluated published untargeted metabolomic platforms used to characterize IEMs to summarize the strengths and areas for improvement of this technology as it progresses towards the clinical laboratory. A total of 96 distinct IEMs were collectively evaluated by the included studies. However, most of these IEMs were evaluated by a single untargeted metabolomic method, in a single study, with a limited cohort size (55/96, 57%). The goals of the included studies generally fell into two, often overlapping, categories: detecting known biomarkers from many biochemically distinct IEMs using a single platform, and detecting novel metabolites or metabolic pathways. There was notable diversity in the design of the untargeted metabolomic platforms. Importantly, the majority of studies reported adherence to quality metrics, including the use of quality control samples and internal standards in their experiments, as well as confirmation of at least some of their feature annotations with commercial reference standards. Future applications of untargeted metabolomics platforms to the study of IEMs should move beyond single-subject analyses, and evaluate reproducibility using a prospective, or validation cohort.

An interactive web application for exploring human plasma and fibroblast metabolomics data from patients with inborn errors of metabolism

Metabolomic profiling is instrumental in understanding the systemic and cellular impact of inborn errors of metabolism (IEMs), monogenic disorders caused by pathogenic genomic variants in genes involved in metabolism. This study encompasses untargeted metabolomics analysis of plasma from 474 individuals and fibroblasts from 67 subjects, incorporating healthy controls, patients with 65 different monogenic diseases, and numerous undiagnosed cases. We introduce a web application designed for the in-depth exploration of this extensive metabolomics database. The application offers a user-friendly interface for data review, download, and detailed analysis of metabolic deviations linked to IEMs at the level of individual patients or groups of patients with the same diagnosis. It also provides interactive tools for investigating metabolic relationships and offers comparative analyses of plasma and fibroblast profiles. This tool emphasizes the metabolic interplay within and across biological matrices, enriching our understanding of metabolic regulation in health and disease. As a resource, the application provides broad utility in research, offering novel insights into metabolic pathways and their alterations in various disorders.

[A Preliminary Study on the Plasma Metabolomic Profiles of Patients Suffering From Acute Diquat Poisoning]

Objective

To analyze the plasma metabolomic features of patients suffering from acute diquat (DQ) poisoning and to explore the molecular mechanism and potential biomarkers of DQ poisoning.

Methods

A total of 7 patients suffering from acute DQ poisoning were enrolled in the DQ poisoning group. The poisoning of these patients occurred within a 12-h window at the time of enrollment. Meanwhile, 7 healthy immediate family members of the patients were enrolled as the normal controls. Liquid chromatography-mass spectrometry (LC-MS) was used to perform non-targeted metabolomic profiling of the plasma samples and to screen and identify differential metabolites and metabolic pathways.

Results

A total of 104 metabolites were screened and identified (P<0.05 and the variable importance in the projection [VIP]>1). Compared with those of the control group, 61 metabolites, such as sorbitol and galactitol, were up-regulated, and 43 metabolites, such as myo-inositol and gamma-glutamylcysteine, were down-regulated in the DQ poisoning group. Pathway enrichment analysis revealed changes in 11 metabolic pathways, including those for galactose metabolism and linoleic acid metabolism (P<0.05).

Conclusion

Metabolomics analysis of plasma samples from DQ poisoning patients shows that DQ mainly interferes with the metabolism of energy, amino acids, and lipids, thus causing metabolic disorders. Some potential biomarkers closely associated with oxidative stress and organ damage of the liver, kidney, and nervous system have been identified.