Overview of Tandem Mass Spectral and Metabolite Databases for Metabolite Identification in Metabolomics

Rapid Identification of Phytotoxins Produced by Glomerella cingulata Using High-Resolution Mass Spectrometry-Based Qualification, Targeted Structural Confirmation and Their Characteristics Investigation

Glomerella cingulata is a pathogenic fungus that can cause apple Glomerella leaf spot (GLS), a new and destructive apple disease in China. Phytotoxins are important factors closely related to the disease process, but there is still no report on the phytotoxins of G. cingulata. The aim of this study was to rapidly identify the phytotoxins of this pathogen using a strategy of HRMS-based preliminary qualification, followed by targeted structure confirmation and also investigation of phytotoxicity characteristics. First, the crude toxin sample was directly analyzed by the UPLC-HRMS and GC-MS, and the data were processed to screen for possible phytotoxic compounds using MS library and the phytotoxicity-related literature. The reference standards of credible phytotoxic compounds were then subjected to targeted structure validation (signal comparison between standards and compounds in crude toxin via HPLC-DAD, UPLC-MS/MS, and GC-MS), and also the phytotoxicity assay. The results confirmed six phytotoxins produced by G. cingulata, namely 5-hydroxymethyl-2-furancarboxylic acid (HMFCA), 2,5-bis(hydroxymethyl)furan (BHMF), 2-furoic acid (FA), 2,3-butanediol, trans-aconitic acid (TAA), and cis-aconitic acid (CAA). Of these, HMFCA and TAA exhibited greater phytotoxicity. Main characteristics: All of them were non-host-selective toxins, and toxins were synergistically phytotoxic to the host when mixed. BHMF, HMFCA, FA, TAA, and CAA could be commonly produced by all tested strains, and their phytotoxicity can be significantly inhibited or even eliminated at high temperatures or high pH. The elucidation of the phytotoxins of G. cingulata in this work could provide information on the pathogenesis and control of apple GLS.

A spectroscopic test suggests that fragment ion structure annotations in MS/MS libraries are frequently incorrect

Modern untargeted mass spectrometry (MS) analyses quickly detect and resolve thousands of molecular compounds. Although features are readily annotated with a molecular formula in high-resolution small-molecule MS applications, the large majority of them remains unidentified in terms of their full molecular structure. Collision-induced dissociation tandem mass spectrometry (CID-MS2) provides a diagnostic molecular fingerprint to resolve the molecular structure through a library search. However, for de novo identifications, one must often rely on in silico generated MS2 spectra as reference. The ability of different in silico algorithms to correctly predict MS2 spectra and thus to retrieve correct molecular structures is a topic of lively debate, for instance in the CASMI contest. Underlying the predicted MS2 spectra are the in silico generated product ion structures, which are normally not used in de novo identification, but which can serve to critically assess the fragmentation algorithms. Here we evaluate in silico generated MSn product ion structures by comparison with structures established experimentally by infrared ion spectroscopy (IRIS). For a set of three dozen product ion structures from five precursor molecules, we find that virtually all fragment ion structure annotations in three major in silico MS2 libraries (HMDB, METLIN, mzCloud) are incorrect and caution the reader against their use for structure annotation of MS/MS ions.

Prediction of a Large-Scale Database of Collision Cross-Section and Retention Time Using Machine Learning to Reduce False Positive Annotations in Untargeted Metabolomics

Metabolite identification in untargeted metabolomics is complex, with the risk of false positive annotations. This work aims to use machine learning to successively predict the retention time (Rt) and the collision cross-section (CCS) of an open-access database to accelerate the interpretation of metabolomic results. Standards of metabolites were tested using liquid chromatography coupled with high-resolution mass spectrometry. In CCSBase and QSRR predictor machine learning models, experimental results were used to generate predicted CCS and Rt of the Human Metabolome Database. From 542 standards, 266 and 301 compounds were detected in positive and negative electrospray ionization mode, respectively, corresponding to 380 different metabolites. CCS and Rt were then predicted using machine learning tools for almost 114,000 metabolites. R² score of the linear regression between predicted and measured data achieved 0.938 and 0.898 for CCS and Rt, respectively, demonstrating the models' reliability. A CCS and Rt index filter of mean error ± 2 standard deviations could remove most misidentifications. Its application to data generated from a toxicology study on tobacco cigarettes reduced hits by 76%. Regarding the volume of data produced by metabolomics, the practical workflow provided allows for the implementation of valuable large-scale databases to improve the biological interpretation of metabolomics data.

Current State and Future Perspectives on Personalized Metabolomics

Metabolomics is one of the most promising 'omics' sciences for the implementation in medicine by developing new diagnostic tests and optimizing drug therapy. Since in metabolomics, the end products of the biochemical processes in an organism are studied, which are under the influence of both genetic and environmental factors, the metabolomics analysis can detect any changes associated with both lifestyle and pathological processes. Almost every case-controlled metabolomics study shows a high diagnostic accuracy. Taking into account that metabolomics processes are already described for most nosologies, there are prerequisites that a high-speed and comprehensive metabolite analysis will replace, in near future, the narrow range of chemical analyses used today, by the medical community. However, despite the promising perspectives of personalized metabolomics, there are currently no FDA-approved metabolomics tests. The well-known problem of complexity of personalized metabolomics data analysis and their interpretation for the end-users, in addition to a traditional need for analytical methods to address the quality control, standardization, and data treatment are reported in the review. Possible ways to solve the problems and change the situation with the introduction of metabolomics tests into clinical practice, are also discussed.

Simplifying the complex: accessible microfluidic solutions for contemporary processes within in vitro diagnostics

In vitro diagnostics (IVDs) form the cornerstone of modern medicine. They are routinely employed throughout the entire treatment pathway, from initial diagnosis through to prognosis, treatment planning, and post-treatment surveillance. Given the proven links between high quality diagnostic testing and overall health, ensuring broad access to IVDs has long been a focus of both researchers and medical professionals. Unfortunately, the current diagnostic paradigm relies heavily on centralized laboratories, complex and expensive equipment, and highly trained personnel. It is commonly assumed that this level of complexity is required to achieve the performance necessary for sensitive and specific disease diagnosis, and that making something affordable and accessible entails significant compromises in test performance. However, recent work in the field of microfluidics is challenging this notion. By exploiting the unique features of microfluidic systems, researchers have been able to create progressively simple devices that can perform increasingly complex diagnostic assays. This review details how microfluidic technologies are disrupting the status quo, and facilitating the development of simple, affordable, and accessible integrated IVDs. Importantly, we discuss the advantages and limitations of various approaches, and highlight the remaining challenges within the field.

Dilignans with a Chromanol Motif Discovered by Molecular Networking from the Stem Barks of Magnolia obovata and Their Proprotein Convertase Subtilisin/Kexin Type 9 Expression Inhibitory Activity

Natural products have been fundamental materials in drug discovery. Traditional strategies for observing natural products with novel structure and/or biological activity are challenging due to large cost and time consumption. Implementation of the MS/MS-based molecular networking strategy with the in silico annotation tool is expected to expedite the dereplication of secondary metabolites. In this study, using this tool, two new dilignans with a 2-phenyl-3-chromanol motif, obovatolins A (1) and B (2), were discovered from the stem barks of Magnolia obovata Thunb. along with six known compounds (3–8), expanding chemical diversity of lignan skeletons in this natural source. Their structures and configurations were elucidated using spectroscopic data. All isolates were evaluated for their PCSK9 mRNA expression inhibitory activity. Obovatolins A (1) and B (2), and magnolol (3) showed potent lipid controlling activities. To identify transcriptionally controlled genes by 1 along with downregulation of PCSK9, using small set of genes (42 genes) related to lipid metabolism selected from the database, focused bioinformatic analysis was carried out. As a result, it showed the correlations between gene expression under presence of 1, which led to detailed insight of the lipid metabolism caused by 1.