METLIN originated as a database to characterize known metabolites and has since expanded into a technology platform for the identification of known and unknown metabolites and other chemical ...entities. Through this effort it has become a comprehensive resource containing over 1 million molecules including lipids, amino acids, carbohydrates, toxins, small peptides, and natural products, among other classes. METLIN’s high-resolution tandem mass spectrometry (MS/MS) database, which plays a key role in the identification process, has data generated from both reference standards and their labeled stable isotope analogues, facilitated by METLIN-guided analysis of isotope-labeled microorganisms. The MS/MS data, coupled with the fragment similarity search function, expand the tool’s capabilities into the identification of unknowns. Fragment similarity search is performed independent of the precursor mass, relying solely on the fragment ions to identify similar structures within the database. Stable isotope data also facilitate characterization by coupling the similarity search output with the isotopic m/z shifts. Examples of both are demonstrated here with the characterization of four previously unknown metabolites. METLIN also now features in silico MS/MS data, which has been made possible through the creation of algorithms trained on METLIN’s MS/MS data from both standards and their isotope analogues. With these informatic and experimental data features, METLIN is being designed to address the characterization of known and unknown molecules.
Machine learning has been extensively applied in small molecule analysis to predict a wide range of molecular properties and processes including mass spectrometry fragmentation or chromatographic ...retention time. However, current approaches for retention time prediction lack sufficient accuracy due to limited available experimental data. Here we introduce the METLIN small molecule retention time (SMRT) dataset, an experimentally acquired reverse-phase chromatography retention time dataset covering up to 80,038 small molecules. To demonstrate the utility of this dataset, we deployed a deep learning model for retention time prediction applied to small molecule annotation. Results showed that in 70Formula: see text of the cases, the correct molecular identity was ranked among the top 3 candidates based on their predicted retention time. We anticipate that this dataset will enable the community to apply machine learning or first principles strategies to generate better models for retention time prediction.
Metabolite identification is still considered an imposing bottleneck in liquid chromatography mass spectrometry (LC/MS) untargeted metabolomics. The identification workflow usually begins with ...detecting relevant LC/MS peaks via peak-picking algorithms and retrieving putative identities based on accurate mass searching. However, accurate mass search alone provides poor evidence for metabolite identification. For this reason, computational annotation is used to reveal the underlying metabolites monoisotopic masses, improving putative identification in addition to confirmation with tandem mass spectrometry. This review examines LC/MS data from a computational and analytical perspective, focusing on the occurrence of neutral losses and in-source fragments, to understand the challenges in computational annotation methodologies. Herein, we examine the state-of-the-art strategies for computational annotation including: (i) peak grouping or full scan (MS
1
) pseudo-spectra extraction, i.e., clustering all mass spectral signals stemming from each metabolite; (ii) annotation using ion adduction and mass distance among ion peaks; (iii) incorporation of biological knowledge such as biotransformations or pathways; (iv) tandem MS data; and (v) metabolite retention time calibration, usually achieved by prediction from molecular descriptors. Advantages and pitfalls of each of these strategies are discussed, as well as expected future trends in computational annotation.
Computational metabolite annotation in untargeted profiling aims at uncovering neutral molecular masses of underlying metabolites and assign those with putative identities. Existing annotation ...strategies rely on the observation and annotation of adducts to determine metabolite neutral masses. However, a significant fraction of features usually detected in untargeted experiments remains unannotated, which limits our ability to determine neutral molecular masses. Despite the availability of tools to annotate, relatively few of them benefit from the inherent presence of in-source fragments in liquid chromatography-electrospray ionization-mass spectrometry. In this study, we introduce a strategy to annotate in-source fragments in untargeted data using low-energy tandem mass spectrometry (MS) spectra from the METLIN library. Our algorithm, MISA (METLIN-guided in-source annotation), compares detected features against low-energy fragments from MS/MS spectra, enabling robust annotation and putative identification of metabolic features based on low-energy spectral matching. The algorithm was evaluated through an annotation analysis of a total of 140 metabolites across three different sets of biological samples analyzed with liquid chromatography-mass spectrometry. Results showed that, in cases where adducts were not formed or detected, MISA was able to uncover neutral molecular masses by in-source fragment matching. MISA was also able to provide putative metabolite identities via two annotation scores. These scores take into account the number of in-source fragments matched and the relative intensity similarity between the experimental data and the reference low-energy MS/MS spectra. Overall, results showed that in-source fragmentation is a highly frequent phenomena that should be considered for comprehensive feature annotation. Thus, combined with adduct annotation, this strategy adds a complementary annotation layer, enabling in-source fragments to be annotated and increasing putative identification confidence. The algorithm is integrated into the XCMS Online platform and is freely available at http://xcmsonline.scripps.edu.
•Green extraction method based on GXL to recover silymarin complex and taxifolin.•The ratio CO2:Ethanol:Water can tune the composition Silybum marianum seeds extracts.•Up to 70% of extract obtained ...in one-step extraction is silymarin.•Flavonolignans and taxifolin were quantified using UHPLC-ESI-MS/MS (Triple quadrupole).•S. marianum seed GXL extract shows enhanced neuroprotective bioactivity.
This work reports the application of Gas Expanded Liquid (GXL) extraction to concentrate the flavonolignan fraction (silymarin) and taxifolin from Silybum marianum seeds, which have proven to be highly valuable health-promoting compounds. GXL using green solvents was used to isolate silymarin with the objective of replacing conventional methods. In one hand, the effect of different compositions of solvents, aqueous ethanol (20%, 50% or 80% (v/v)) at different CO2/liquid (25, 50 and 75%) ratios, on the GXL extraction was investigated. The obtained extracts have been chemically and functionally characterized by means of UHPLC-ESI-MS/MS (triple quadrupole) and in-vitro assays such as anti-inflammatory, anti-cholinergic and antioxidant. Results revealed that the operating conditions influenced the extraction yield, the total phenolic content and the presence of the target compounds. The best obtained yield was 55.97% using a ternary mixture of solvents composed of CO2:EtOH:H2O (25:60:15) at 40 °C and 9 MPa in 160 min. Furthermore, the results showed that obtained extracts had significant antioxidant and anti-inflammatory activities (with best IC50 value of 8.80 µg/mL and 28.52 µg/mL, respectively) but a moderate anti-cholinesterase activity (with best IC50 value of 125.09 µg/mL). Otherwise, the concentration of silymarin compounds in extract can go up to 59.6% using the present one-step extraction method without further purification, being silybinA+B the predominant identified compound, achieving value of 545.73 (mg silymarin/g of extract). The obtained results demonstrate the exceptional potential of GXL to extract high-added values molecules under sustainable conditions from different matrices.
We report XCMS-MRM and METLIN-MRM ( http://xcmsonline-mrm.scripps.edu/ and http://metlin.scripps.edu/ ), a cloud-based data-analysis platform and a public multiple-reaction monitoring (MRM) ...transition repository for small-molecule quantitative tandem mass spectrometry. This platform provides MRM transitions for more than 15,500 molecules and facilitates data sharing across different instruments and laboratories.
Alzheimer’s disease (AD) is the most common form of dementia caused by a progressive loss of neurons from different regions of the brain. This multifactorial pathophysiology has been widely ...characterized by neuroinflammation, extensive oxidative damage, synaptic loss, and neuronal cell death. In this sense, the design of multi-target strategies to prevent or delay its progression is a challenging goal. In the present work, different in vitro assays including antioxidant, anti-inflammatory, and anti-cholinergic activities of a carotenoid-enriched extract from
Dunaliella salina
microalgae obtained by supercritical fluid extraction are studied. Moreover, its potential neuroprotective effect in the human neuron-like SH-SY5Y cell model against remarkable hallmarks of AD was also evaluated. In parallel, a comprehensive metabolomics study based on the use of charged-surface hybrid chromatography (CSH) and hydrophilic interaction liquid chromatography (HILIC) coupled to high-resolution tandem mass spectrometry (Q-TOF MS/MS) was applied to evaluate the effects of the extract on the metabolism of the treated cells. The use of advanced bioinformatics and statistical tools allowed the identification of more than 314 metabolites in SH-SY5Y cells, of which a great number of phosphatidylcholines, triacylglycerols, and fatty acids were significantly increased, while several phosphatidylglycerols were decreased, compared to controls. These lipidomic changes in cells along with the possible role exerted by carotenoids and other minor compounds on the cell membrane might explain the observed neuroprotective effect of the
D. salina
extract. However, future experiments using in vivo models to corroborate this hypothesis must be carried out.
Graphical abstract
species are among the most prevalent causes of systemic fungal infection, posing a growing threat to public health. While Candida albicans is the most common etiological agent of systemic ...candidiasis, the frequency of infections caused by non
species is rising. Among these is Candida auris, which has emerged as a particular concern. Since its initial discovery in 2009, it has been identified worldwide and exhibits resistance to all three principal antifungal classes. Here, we endeavored to identify compounds with novel bioactivity against C. auris from the Medicines for Malaria Venture's Pathogen Box library. Of the five hits identified, the trisubstituted isoxazole MMV688766 emerged as the only compound displaying potent fungicidal activity against C. auris, as well as other evolutionarily divergent fungal pathogens. Chemogenomic profiling, as well as subsequent metabolomic and phenotypic analyses, revealed that MMV688766 disrupts cellular lipid homeostasis, driving a decrease in levels of early sphingolipid intermediates and fatty acids and a concomitant increase in lysophospholipids. Experimental evolution to further probe MMV688766's mode of action in the model fungus Saccharomyces cerevisiae revealed that loss of function of the transcriptional regulator
confers resistance to MMV688766, in part through the upregulation of the lipid-binding chaperone
, a response that appears to assist in tolerating MMV688766-induced stress. The novel mode of action we have uncovered for MMV688766 against drug-resistant fungal pathogens highlights the broad utility of targeting lipid homeostasis to disrupt fungal growth and how screening structurally-diverse chemical libraries can provide new insights into resistance-conferring stress responses of fungi.
As widespread antimicrobial resistance threatens to propel the world into a postantibiotic era, there is a pressing need to identify mechanistically distinct antimicrobial agents. This is of particular concern when considering the limited arsenal of drugs available to treat fungal infections, coupled with the emergence of highly drug-resistant fungal pathogens, including Candida auris. In this work, we demonstrate that existing libraries of drug-like chemical matter can be rich resources for antifungal molecular scaffolds. We discovered that the small molecule MMV688766, from the Pathogen Box library, displays previously undescribed broad-spectrum fungicidal activity through perturbation of lipid homeostasis. Characterization of the mode of action of MMV688766 provided new insight into the protective mechanisms fungi use to cope with the disruption of lipid homeostasis. Our findings highlight that elucidating the genetic circuitry required to survive in the presence of cellular stress offers powerful insights into the biological pathways that govern this important phenotype.
Metabolomics, in which small-molecule metabolites (the metabolome) are identified and quantified, is broadly acknowledged to be the omics discipline that is closest to the phenotype. Although ...appreciated for its role in biomarker discovery programs, metabolomics can also be used to identify metabolites that could alter a cell's or an organism's phenotype. Metabolomics activity screening (MAS) as described here integrates metabolomics data with metabolic pathways and systems biology information, including proteomics and transcriptomics data, to produce a set of endogenous metabolites that can be tested for functionality in altering phenotypes. A growing literature reports the use of metabolites to modulate diverse processes, such as stem cell differentiation, oligodendrocyte maturation, insulin signaling, T-cell survival and macrophage immune responses. This opens up the possibility of identifying and applying metabolites to affect phenotypes. Unlike genes or proteins, metabolites are often readily available, which means that MAS is broadly amenable to high-throughput screening of virtually any biological system.
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