Metabolite Annotation and Identification

MARS: A Multipurpose Software for Untargeted LC-MS-Based Metabolomics and Exposomics

Untargeted metabolomics is a growing field, in which recent advances in high-resolution mass spectrometry coupled with liquid chromatography (LC-MS) have facilitated untargeted approaches as a result of improvements in sensitivity, mass accuracy, and resolving power. However, a very large amount of data are generated. Consequently, using computational tools is now mandatory for the in-depth analysis of untargeted metabolomics data. This article describes MetAbolomics ReSearch (MARS), an all-in-one vendor-agnostic graphical user interface-based software applying LC-MS analysis to untargeted metabolomics. All of the analytical steps are described (from instrument data conversion and processing to statistical analysis, annotation/identification, quantification, and preliminary biological interpretation), and tools developed to improve annotation accuracy (e.g., multiple adducts and in-source fragmentation detection, trends across samples, and the MS/MS validator) are highlighted. In addition, MARS allows in-house building of reference databases, to bypass the limits of freely available MS/MS spectra collections. Focusing on the flexibility of the software and its user-friendliness, which are two important features in multipurpose software, MARS could provide new perspectives in untargeted metabolomics data analysis.

Metabolomic analysis reveals stress tolerance mechanisms in common bean (Phaseolus vulgaris L.) related to treatment with a biostimulant obtained from Corynebacterium glutamicum

Microbial biostimulants have emerged as a sustainable alternative to increase the productivity and quality of important crops. Despite this, the effects of the treatment on plant metabolism are poorly understood. Thus, this study investigated the metabolic response of common bean (Phaseolus vulgaris) related to the treatment with a biostimulant obtained from the extract of Corynebacterium glutamicum that showed positive effects on the development, growth, and yield of crops previously. By untargeted metabolomic analysis using UHPLC-MS/MS, plants and seeds were subjected to treatment with the biostimulant. Under ideal growth conditions, the plants treated exhibited higher concentration levels of glutamic acid, nicotiflorin and glycosylated lipids derived from linolenic acid. The foliar application of the biostimulant under water stress conditions increased the chlorophyll content by 17% and induced the accumulation of flavonols, mainly quercetin derivatives. Also, germination seed assays exhibited longer radicle lengths for seeds treated compared to the untreated control even in the absence of light (13-18% increase, p-value <0.05). Metabolomic analysis of the seeds indicated changes in concentration levels of amino acids (tryptophan, phenylalanine, tyrosine, glutamine, and arginine) and their derivatives. The results point out the enhancement of abiotic stress tolerance and the metabolic processes triggered in this crop associated with the treatment with the biostimulant, giving the first insights into stress tolerance mechanisms in P. vulgaris.

Pre- and Post-Resection Urine Metabolic Profiles of Bladder Cancer Patients: Results of Preliminary Studies on Time Series Metabolomics Analysis

Simple Summary

Bladder cancer is one of the most frequently diagnosed cancers worldwide and due to non-specific symptoms, it is often detected at a late stage. For this reason, possible diagnostic alternatives that could be used for non-invasive screening are still being sought. In recent years, metabolomics approach has been frequently used for this type of research, using urine or blood collected from two groups: patients with a given disease and healthy volunteers. Usually, to minimize the impact of between-subject differences, participants of the study are matched in terms of age, gender, or BMI. Another way to rule out the impact of this variability is to analyze samples taken at intervals from the same patient. Therefore, the aim of our study was to validate results obtained using the traditional approach on a small group of patients, from whom samples were taken before and after resection of the bladder tumor, in a given time frame.

Abstract

The incidence of bladder cancer (BCa) has remained high for many years. Nevertheless, its pathomechanism has not yet been fully understood and is still being studied. Therefore, multiplatform untargeted urinary metabolomics analysis has been performed in order to study differences in the metabolic profiles of urine samples collected at three time points: before transurethral resection of bladder tumor (TURBT), the day after the procedure and two weeks after TURBT. Collected samples were analyzed with the use of high-performance liquid chromatography hyphenated with time-of-flight mass spectrometry detection (HPLC-TOF/MS) and gas chromatography coupled with triple quadrupole mass spectrometry detection (GC-QqQ/MS, in a scan mode). Levels of metabolites selected in our previous study were assessed in order to confirm their potential to differentiate the healthy and diseased samples, regardless of the risk factors and individual characteristics. Hippuric acid, pentanedioic acid and uridine confirmed their potential for sample differentiation. Based on the results of statistical analysis for the paired samples (comparison of metabolic profiles of samples collected before TURBT and two weeks after), a set of metabolites belonging to nucleotide metabolism and methylation processes was also selected. Longitudinal studies proved to be useful for the evaluation of metabolic changes in bladder cancer.

Liquid Chromatography-Mass Spectrometry for Clinical Metabolomics: An Overview

Metabolomics is a discipline that offers a comprehensive analysis of metabolites in biological samples. In the last decades, the notable evolution in liquid chromatography and mass spectrometry technologies has driven an exponential progress in LC-MS-based metabolomics. Targeted and untargeted metabolomics strategies are important tools in health and medical science, especially in the study of disease-related biomarkers, drug discovery and development, toxicology, diet, physical exercise, and precision medicine. Clinical and biological problems can now be understood in terms of metabolic phenotyping. This overview highlights the current approaches to LC-MS-based metabolomics analysis and its applications in the clinical research.

Dereplication, Annotation, and Characterization of 74 Potential Antimicrobial Metabolites from Penicillium Sclerotiorum Using t-SNE Molecular Networks

Microorganisms associated with termites are an original resource for identifying new chemical scaffolds or active metabolites. A molecular network was generated from a collection of strain extracts analyzed by liquid chromatography coupled to tandem high-resolution mass spectrometry, a molecular network was generated, and activities against the human pathogens methicillin-resistant Staphylococcus aureus, Candida albicans and Trichophyton rubrum were mapped, leading to the selection of a single active extract of Penicillium sclerotiorum SNB-CN111. This fungal species is known to produce azaphilones, a colorful family of polyketides with a wide range of biological activities and economic interests in the food industry. By exploring the molecular network data, it was shown that the chemical diversity related to the P. sclerotiorum metabolome largely exceeded the data already reported in the literature. According to the described fragmentation pathways of protonated azaphilones, the annotation of 74 azaphilones was proposed, including 49 never isolated or synthesized thus far. Our hypothesis was validated by the isolation and characterization of eight azaphilones, among which three new azaphilones were chlorogeumasnol (63), peniazaphilone E (74) and 7-deacetylisochromophilone VI (80).

The Comprehensive and Reliable Detection of Secondary Metabolites in Trichoderma reesei: A Tool for the Discovery of Novel Substances

A method based on reversed phase high-performance liquid chromatography coupled with electrospray ionization high-resolution mass spectrometry (RP-HPLC-ESI-HRMS) for the comprehensive and reliable detection of secondary metabolites of Trichoderma reesei cultured in synthetic minimal liquid medium is presented. A stable isotope-assisted (SIA) workflow is used, which allows the automated, comprehensive extraction of truly fungal metabolite-derived LC-MS signals from the acquired chromatographic data. The subsequent statistical data analysis and a typical outcome of such a metabolomics data evaluation are shown by way of example in a previously published study on the influence of the pleiotropic regulator transcription factor Xylanase promoter binding protein 1 (Xpp1) in T. reesei on secondary metabolism.

Inter-laboratory reproducibility of an untargeted metabolomics GC-MS assay for analysis of human plasma

There is a long-standing concern for the lack of reproducibility of the untargeted metabolomic approaches used in pharmaceutical research. Two types of human plasma samples were split into two batches and analyzed in two individual labs for untargeted GC–MS metabolomic profiling. The two labs used the same silylation sample preparation protocols but different instrumentation, data processing software, and database. There were 55 metabolites annotated reproducibly, independent of the labs. The median coefficient variations (CV%) of absolute spectra ion intensities in both labs were less than 30%. However, the comparison of normalized ion intensity among biological groups, were inconsistent across labs. Predicted power based on annotated metabolites was evaluated post various normalization, data transformation and scaling. For the first time our study reveals the numerical details about the variations in metabolomic annotation and relative quantification using plain inter-laboratory GC–MS untargeted metabolomic approaches. Especially we compare several commonly used post-acquisition strategies and found normalization could not strengthen the annotation accuracy or relative quantification precision of untargeted approach, instead it will impact future experimental design. Standardization of untargeted metabolomics protocols, including sample preparation, instrumentation, data processing, etc., is critical for comparison of untargeted data across labs.

Oxidized lipids in the metabolic profiling of neuroendocrine tumors - Analytical challenges and biological implications

Untargeted metabolomics can be a great tool for exploring new scientific areas; however, wrong metabolite annotation questions the credibility and puts the success of the entire research at risk. Therefore, an effort should be made to improve the quality and robustness of the annotation despite of the challenges, especially when final identification with standards is not possible. Through non-targeted analysis of human plasma samples, from a large cancer cohort study using RP-LC-ESI-QTOF-MS/MS, we have resolved MS/MS annotation through spectral matching, directed to hydroxyeicosatetraenoic acids (HETEs) and, MS/MS structural elucidation for newly annotated oxidized lyso-phosphatidylcholines (oxLPCs). The annotation of unknowns is supported with structural information from fragmentation spectra as well as the fragmentation mechanisms involved, necessarily including data from both polarity modes and different collision energies. In this work, we present evidences that various oxidation products show significant differences between cancer patients and control individuals and we establish a workflow to help identify such modifications. We report here the upregulation of HETEs and oxLPCs in patients with neuroendocrine tumors (NETs). To our knowledge, this is the first attempt to determine HETEs in NETs and one of very few studies where oxLPCs are annotated. The obtained results provide an important insight regarding lipid oxidation in NETs, although their physiological functions still have to be established and require further research.