Tandem mass spectral library search (MS/MS) is the fastest way to correctly annotate MS/MS spectra from screening small molecules in fields such as environmental analysis, drug screening, lipid analysis, and metabolomics. The confidence in MS/MS-based annotation of chemical structures is impacted by instrumental settings and requirements, data acquisition modes including data-dependent and data-independent methods, library scoring algorithms, as well as post-curation steps. We critically discuss parameters that influence search results, such as mass accuracy, precursor ion isolation width, intensity thresholds, centroiding algorithms, and acquisition speed. A range of publicly and commercially available MS/MS databases such as NIST, MassBank, MoNA, LipidBlast, Wiley MSforID, and METLIN are surveyed. In addition, software tools including NIST MS Search, MS-DIAL, Mass Frontier, SmileMS, Mass++, and XCMS to perform fast MS/MS search are discussed. MS/MS scoring algorithms and challenges during compound annotation are reviewed. Advanced methods such as the in silico generation of tandem mass spectra using quantum chemistry and machine learning methods are covered. Community efforts for curation and sharing of tandem mass spectra that will allow for faster distribution of scientific discoveries are discussed.

Novel metabolites distinct from canonical pathways can be identified through the integration of three cheminformatics tools: BinVestigate, which queries the BinBase gas chromatography-mass spectrometry (GC-MS) metabolome database to match unknowns with biological metadata across over 110,000 samples; MS-DIAL 2.0, a software tool for chromatographic deconvolution of high-resolution GC-MS or liquid chromatography-mass spectrometry (LC-MS); and MS-FINDER 2.0, a structure-elucidation program that uses a combination of 14 metabolome databases in addition to an enzyme promiscuity library. We showcase our workflow by annotating N-methyl-uridine monophosphate (UMP), lysomonogalactosyl-monopalmitin, N-methylalanine, and two propofol derivatives.

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Mass spectrometry-based untargeted metabolomics detects many peaks that cannot be identified. While advances have been made for automatic structure annotations in LC-electrospray-MS/MS, no open source solutions are available for hard electron ionization used in GC-MS. In metabolomics, most compounds bear moieties with acidic protons, for example, amino, hydroxyl, or carboxyl groups. Such functional groups increase the boiling points of metabolites too much for use in GC-MS. Hence, in GC-MS-focused metabolomics, derivatization of these groups is essential and has been employed since the 1960s. Specifically, trimethylsilylation is known as mild and universal method for GC-MS analysis. Here, we comprehensively compile accurate mass fragmentation rules and pathways of trimethylsilylated small molecules from 80 research articles over the past 5 decades, including diagnostic fragment ions, neutral losses, and typical ion ratios, for alcohols, carboxylic acids, amines, amino acids, sugars, steroids, thiols, and phosphates. These fragmentation rules were subsequently validated by specificity and sensitivity assessments using the NIST 14 nominal mass library and a new in-house GC-QTOF MS library containing 589 accurate mass spectra. From 556 tested fragmentation patterns, 228 rules yielded true positive hits within 4 mDa mass accuracy. These rules can be applied to assign substructures for mass spectra computation and unknown identification. © 2016 Wiley Periodicals, Inc. Mass Spec Rev 37:245-257, 2018.

Due to a lack of effective immune clearance, the airways of cystic fibrosis patients are colonized by polymicrobial communities. One of the most widespread and destructive opportunistic pathogens is ; however, does not colonize the airways alone. Microbes that are common in the oral cavity, such as , are also present in cystic fibrosis patient sputum and have metabolic capacities different from those of Here we examine the metabolic interactions of and using stable-isotope-assisted metabolomics. Glucose-derived C was incorporated into glycolysis metabolites, namely, lactate and acetate, and some amino acids in grown aerobically and anaerobically. The amino acid glutamate was unlabeled in the supernatant but incorporated theC label after was cross-fed the supernatant in minimal medium and artificial-sputum medium. We provide evidence that utilizes-produced metabolites as precursors for generation of primary metabolites, including glutamate. is a dominant and persistent cystic fibrosis pathogen. Although is accompanied by other microbes in the airways of cystic fibrosis patients, few cystic fibrosis studies show how is affected by the metabolism of other bacteria. Here, we demonstrate that generates primary metabolites using substrates produced by another microbe that is prevalent in the airways of cystic fibrosis patients, These results indicate that may get a metabolic boost from its microbial neighbor, which might contribute to its pathogenesis in the airways of cystic fibrosis patients.

Highlights d Small molecules LDC7559 and NA-11 inhibit the phagocytic oxidative burst d NA-11 dampens flux through the pentose phosphate pathway to limit cellular NADPH d Cryo-EM confirms agonism of PFKL through binding at the AMP/ADP allosteric pocket d PFKL is a regulatory node for NOX2-dependent NETosis and tissue damage

Mass spectrometry-based untargeted metabolomics often detects statistically significant metabolites that cannot be readily identified. Without defined chemical structure, interpretation of the biochemical relevance is not feasible. Epimetabolites are produced from canonical metabolites by defined enzymatic reactions and may represent a large fraction of the structurally unidentified metabolome. We here present a systematic workflow for annotating unknown epimetabolites using high resolution gas chromatography-accurate mass spectrometry with multiple ionization techniques and stable isotope labeled derivatization methods. We first determine elemental formulas, which are then used to query the "metabolic in-silico expansion" database (MINE DB) to obtain possible molecular structures that are predicted by enzyme promiscuity from canonical pathways. Accurate mass fragmentation rules are combined with in silico spectra prediction programs CFM-ID and MS-FINDER to derive the best candidates. We validated the workflow by correctly identifying 10 methylated nucleosides and 6 methylated amino acids. We then employed this strategy to annotate eight unknown compounds from cancer studies and other biological systems.

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