Comment on METLIN: A Technology Platform for Identifying Knowns and Unknowns

Metabolic Profiling Reveals Changes in Serum Predictive of Venous Ulcer Healing

OBJECTIVE

The aim of this study was to identify potential biomarkers predictive of healing or failure to heal in a population with venous leg ulceration.

SUMMARY BACKGROUND DATA

Venous leg ulceration presents important physical, psychological, social and financial burdens. Compression therapy is the main treatment, but it can be painful and time-consuming, with significant recurrence rates. The identification of a reliable biochemical signature with the ability to identify nonhealing ulcers has important translational applications for disease prognostication, personalized health care and the development of novel therapies.

METHODS

Twenty-eight patients were assessed at baseline and at 20 weeks. Untargeted metabolic profiling was performed on urine, serum, and ulcer fluid, using mass spectrometry and nuclear magnetic resonance spectroscopy.

RESULTS

A differential metabolic phenotype was identified in healing (n = 15) compared to nonhealing (n = 13) venous leg ulcer patients. Analysis of the assigned metabolites found ceramide and carnitine metabolism to be relevant pathways. In this pilot study, only serum biofluids could differentiate between healing and nonhealing patients. The ratio of carnitine to ceramide was able to differentiate between healing phenotypes with 100% sensitivity, 79% specificity, and 91% accuracy.

CONCLUSIONS

This study reports a metabolic signature predictive of healing in venous leg ulceration and presents potential translational applications for disease prognostication and development of targeted therapies.

Mass Spectrometry Imaging of Metabolites by Nanostructure Initiator Mass Spectrometry with Fluorinated Gold Nanoparticles

Nanostructure initiator mass spectrometry (NIMS) with fluorinated gold nanoparticles (f-AuNPs) enables the detection and spatial localization of a breath of polar metabolites and lipids with high spatial resolution and ultrasensitivity. Here we describe the methods and procedures for the synthesis and application of f-AuNPs for NIMS of small molecule metabolites and lipids in biological tissues, encompassing sample preparation, mass spectrometric detection, and data analysis and interpretation.

Phytotoxicity induced by engineered nanomaterials as explored by metabolomics: Perspectives and challenges

Given the wide applications of engineered nanomaterials (ENMs) in various fields, the ecotoxicology of ENMs has attracted much attention. The traditional plant physiological activity (e.g., reactive oxygen species and antioxidant enzymes) are limited in that they probe one specific process of nanotoxicity, which may result in the loss of understanding of other important biological reactions. Metabolites, which are downstream of gene and protein expression, are directly related to biological phenomena. Metabolomics is an easily performed and efficient tool for solving the aforementioned problems because it involves the comprehensive exploration of metabolic profiles. To understand the roles of metabolomics in phytotoxicity, the analytical methods for metabolomics should be organized and discussed. Moreover, the dominant metabolites and metabolic pathways are similar in different plants, which determines the universal applicability of metabolomics analysis. The analysis of regulated metabolism will globally and scientifically help determine the ecotoxicology that is induced by ENMs. In the past several years, great developments in nanotoxicology have been achieved using metabolomics. However, many knowledge gaps remain, such as the relationships between biological responses that are induced by ENMs and the regulation of metabolism (e.g., carbohydrate, energy, amino acid, lipid and secondary metabolism). The phytotoxicity that is induced by ENMs has been explored by metabolomics, which is still in its infancy. The detrimental and defence mechanisms of plants in their response to ENMs at the level of metabolomics also deserve much attention. In addition, owing to the regulation of metabolism in plants by ENMs affected by multiple factors, it is meaningful to uniformly identify the key influencing factor.

Mass Spectral Similarity Networking and Gas-Phase Fragmentation Reactions in the Structural Analysis of Flavonoid Glycoconjugates

Flavonoids represent an important class of natural products with a central role in plant physiology and human health. Their accurate annotation using untargeted mass spectrometry analysis still relies on differentiating similar chemical scaffolds through spectral matching to reference library spectra. In this work, we combined molecular network analysis with rules for fragment reactions and chemotaxonomy to enhance the annotation of similar flavonoid glyconjugates. Molecular network topology progressively propagated the flavonoid chemical functionalization according to collision-induced dissociation (CID) reactions, as the following chemical attributes: aglycone nature, saccharide type and number, and presence of methoxy substituents. This structure-based distribution across the spectral networks revealed the chemical composition of flavonoids across intra- and interspecies and guided the putatively assignment of 64 isomers and isobars in the Chrysobalanaceae plant species, most of which are not accurately annotated by automated untargeted MS² matching. These proof of concept results demonstrate how molecular networking progressively grouped structurally related molecules according to their product ion scans, abundances, and ratios. The approach can be extrapolated to other classes of metabolites sharing similar structures and diagnostic fragments from tandem mass spectrometry.