Metabolic profiling by reversed-phase/ion-exchange mass spectrometry

Applications of chromatographic methods in metabolomics: A review

Chromatography is a robust and reliable separation method that can use various stationary phases to separate complex mixtures commonly seen in metabolomics. This review examines the types of chromatography and stationary phases that have been used in targeted or untargeted metabolomics with methods such as mass spectrometry (MS) and nuclear magnetic resonance (NMR) spectroscopy. General considerations for sample pretreatment and separations in metabolomics are considered, along with the various supports and separation formats for chromatography that have been used in such work. The types of liquid chromatography (LC) that have been most extensively used in metabolomics will be examined, such as reversed-phase liquid chromatography and hydrophilic liquid interaction chromatography. In addition, other forms of LC that have been used in more limited applications for metabolomics (e.g., ion-exchange, size-exclusion, and affinity methods) will be discussed to illustrate how these techniques may be utilized for new and future research in this field. Multidimensional LC methods are also discussed, as well as the use of gas chromatography and supercritical fluid chromatography in metabolomics. In addition, the roles of chromatography in NMR- vs. MS-based metabolomics are considered. Applications are given within the field of metabolomics for each type of chromatography, along with potential advantages or limitations of these separation methods.

Metabolomics meets systems immunology

Metabolic processes play a critical role in immune regulation. Metabolomics is the systematic analysis of small molecules (metabolites) in organisms or biological samples, providing an opportunity to comprehensively study interactions between metabolism and immunity in physiology and disease. Integrating metabolomics into systems immunology allows the exploration of the interactions of multilayered features in the biological system and the molecular regulatory mechanism of these features. Here, we provide an overview on recent technological developments of metabolomic applications in immunological research. To begin, two widely used metabolomics approaches are compared: targeted and untargeted metabolomics. Then, we provide a comprehensive overview of the analysis workflow and the computational tools available, including sample preparation, raw spectra data preprocessing, data processing, statistical analysis, and interpretation. Third, we describe how to integrate metabolomics with other omics approaches in immunological studies using available tools. Finally, we discuss new developments in metabolomics and its prospects for immunology research. This review provides guidance to researchers using metabolomics and multiomics in immunity research, thus facilitating the application of systems immunology to disease research.

Validation of a targeted metabolomics panel for improved second-tier newborn screening

Improved second-tier assays are needed to reduce the number of false positives in newborn screening (NBS) for inherited metabolic disorders including those on the Recommended Uniform Screening Panel (RUSP). We developed an expanded metabolite panel for second-tier testing of dried blood spot (DBS) samples from screen-positive cases reported by the California NBS program, consisting of true- and false-positives from four disorders: glutaric acidemia type I (GA1), methylmalonic acidemia (MMA), ornithine transcarbamylase deficiency (OTCD), and very long-chain acyl-CoA dehydrogenase deficiency (VLCADD). This panel was assembled from known disease markers and new features discovered by untargeted metabolomics and applied to second-tier analysis of single DBS punches using liquid chromatography-tandem mass spectrometry (LC-MS/MS) in a 3-min run. Additionally, we trained a Random Forest (RF) machine learning classifier to improve separation of true- and false positive cases. Targeted metabolomic analysis of 121 analytes from DBS extracts in combination with RF classification at a sensitivity of 100% reduced false positives for GA1 by 83%, MMA by 84%, OTCD by 100%, and VLCADD by 51%. This performance was driven by a combination of known disease markers (3-hydroxyglutaric acid, methylmalonic acid, citrulline, and C14:1), other amino acids and acylcarnitines, and novel metabolites identified to be isobaric to several long-chain acylcarnitine and hydroxy-acylcarnitine species. These findings establish the effectiveness of this second-tier test to improve screening for these four conditions and demonstrate the utility of supervised machine learning in reducing false-positives for conditions lacking clearly discriminating markers, with future studies aimed at optimizing and expanding the panel to additional disease targets.

Identification of potential interferents of methylmalonic acid: A previously unrecognized pitfall in clinical diagnostics and newborn screening

OBJECTIVES

Determination of methylmalonic acid (MMA) from dried blood spots (DBS) is commonly performed in clinical diagnostics and newborn screening for propionic acidemia (PA) and methylmalonic acidemia. Isobaric compounds of MMA having the same mass can affect diagnostic reliability and quantitative results, which represents a previously unrecognized pitfall in clinical assays for MMA. We set out to identify interfering substances of MMA in DBS, serum and urine samples from confirmed patients with PA and methylmalonic acidemia.

METHODS

Techniques included quadrupole time-of-flight high-resolution mass spectrometry (QTOF HR-MS), nuclear magnetic resonance (NMR) spectroscopy, liquid chromatography (LC) and tandem mass spectrometry (MS/MS).

RESULTS

The five isobaric metabolites detected in DBS, serum and urine from PA and methylmalonic acidemia patients were confirmed as 2-methyl-3-hydroxybutyrate, 3-hydroxyisovalerate, 2-hydroxyisovalerate, 3-hydroxyvalerate and succinate using a series of experiments. An additional unknown substance with low abundance remained unidentified.

CONCLUSIONS

The presented results facilitate the diagnostic and quantitative reliability of the MMA determination in clinical assays. Isobaric species should be investigated in assays for MMA to eliminate possible interference in a wide range of conditions including PA, methylmalonic acidemia, a vitamin B₁₂ deficiency, ketosis and lactic acidosis.

Integrated Metabolomics and Transcriptomics Analyses Reveal Histidine Metabolism Plays an Important Role in Imiquimod-Induced Psoriasis-like Skin Inflammation

Psoriasis is a chronic inflammatory skin disease characterized by massive keratinocyte proliferation and immune cell infiltration into the epidermis. However, the specific mechanisms underlying the development of psoriasis remain unclear. Untargeted metabolomics and transcriptomics have been used separately to profile biomarkers and risk genes in the serum of psoriasis patients. However, the integration of metabolomics and transcriptomics to identify dysregulated metabolites and genes in the psoriatic skin is lacking. In this study, we performed an untargeted metabolomics analysis of imiquimod (IMQ)-induced psoriasis-like mice and healthy controls, and found that levels of a total of 4,188 metabolites differed in IMQ-induced psoriasis-like mice compared with those in control mice. Metabolomic data analysis using MetaboAnalyst showed that the metabolic pathways of primary metabolites, such as folate biosynthesis and galactose metabolism, were significantly altered in the skin of mice after treatment with IMQ. Furthermore, IMQ treatment also significantly altered metabolic pathways of secondary metabolites, including histidine metabolism, in mouse skin tissues. The metabolomic results were verified by transcriptomics analysis. RNA-seq results showed that histamine decarboxylase (HDC) mRNA levels were significantly upregulated after IMQ treatment. Targeted inhibition of histamine biosynthesis process using HDC-specific inhibitor, pinocembrin (PINO), significantly alleviated epidermal thickness, downregulated the expression of interleukin (IL)-17A and IL-23, and inhibited the infiltration of immune cells during IMQ-induced psoriasis-like skin inflammation. In conclusion, our study offers a validated and comprehensive understanding of metabolism during the development of psoriasis and demonstrated that PINO could protect against IMQ-induced psoriasis-like skin inflammation.

Nasopharyngeal metabolomics and machine learning approach for the diagnosis of influenza

Background

Respiratory virus infections are significant causes of morbidity and mortality, and may induce host metabolite alterations by infecting respiratory epithelial cells. We investigated the use of liquid chromatography quadrupole time-of-flight mass spectrometry (LC/Q-TOF) combined with machine learning for the diagnosis of influenza infection.

Methods

We analyzed nasopharyngeal swab samples by LC/Q-TOF to identify distinct metabolic signatures for diagnosis of acute illness. Machine learning models were performed for classification, followed by Shapley additive explanation (SHAP) analysis to analyze feature importance and for biomarker discovery.

Findings

A total of 236 samples were tested in the discovery phase by LC/Q-TOF, including 118 positive samples (40 influenza A 2009 H1N1, 39 influenza H3 and 39 influenza B) as well as 118 age and sex-matched negative controls with acute respiratory illness. Analysis showed an area under the receiver operating characteristic curve (AUC) of 1.00 (95% confidence interval [95% CI] 0.99, 1.00), sensitivity of 1.00 (95% CI 0.86, 1.00) and specificity of 0.96 (95% CI 0.81, 0.99). The metabolite most strongly associated with differential classification was pyroglutamic acid. Independent validation of a biomarker signature based on the top 20 differentiating ion features was performed in a prospective cohort of 96 symptomatic individuals including 48 positive samples (24 influenza A 2009 H1N1, 5 influenza H3 and 19 influenza B) and 48 negative samples. Testing performed using a clinically-applicable targeted approach, liquid chromatography triple quadrupole mass spectrometry, showed an AUC of 1.00 (95% CI 0.998, 1.00), sensitivity of 0.94 (95% CI 0.83, 0.98), and specificity of 1.00 (95% CI 0.93, 1.00). Limitations include lack of sample suitability assessment, and need to validate these findings in additional patient populations.

Interpretation

This metabolomic approach has potential for diagnostic applications in infectious diseases testing, including other respiratory viruses, and may eventually be adapted for point-of-care testing.

Metabolomic Biomarkers for Detection, Prognosis and Identifying Recurrence in Endometrial Cancer

Metabolic reprogramming is increasingly recognised as one of the defining hallmarks of tumorigenesis. There is compelling evidence to suggest that endometrial cancer develops and progresses in the context of profound metabolic dysfunction. Whilst the incidence of endometrial cancer continues to rise in parallel with the global epidemic of obesity, there are, as yet, no validated biomarkers that can aid risk prediction, early detection, prognostic evaluation or surveillance. Advances in high-throughput technologies have, in recent times, shown promise for biomarker discovery based on genomic, transcriptomic, proteomic and metabolomic platforms. Metabolomics, the large-scale study of metabolites, deals with the downstream products of the other omics technologies and thus best reflects the human phenotype. This review aims to provide a summary and critical synthesis of the existing literature with the ultimate goal of identifying the most promising metabolite biomarkers that can augment current endometrial cancer diagnostic, prognostic and recurrence surveillance strategies. Identified metabolites and their biochemical pathways are discussed in the context of what we know about endometrial carcinogenesis and their potential clinical utility is evaluated. Finally, we underscore the challenges inherent in metabolomic biomarker discovery and validation and provide fresh perspectives and directions for future endometrial cancer biomarker research.