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.

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 phenotype1–3. 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.

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 70 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.

Lipid droplets (LD) are cytosolic inclusions present in most eukaryotic cells that contain a core rich in neutral lipids such as triacylglycerol (TAG) and cholesteryl esters (CE) and are surrounded by a phospholipid monolayer decorated with a variety of proteins, such as PAT family proteins (perilipin, adipose differentiation related protein, and tailinteracting protein of 47 kDa ) and caveolins (1-6). Initially regarded as inert neutral lipid-storage compartments, the interest for LD has increased recently because of their association with infl ammatory and metabolic disorders involving an excess lipid storage, including diabetes, obesity, and cardiovascular disease (7-10).LDs are generated by cells under different environmental conditions, suggesting a distinct pathophysiological signifi cance for each of these conditions. Cells generate lipid droplets from exogenous lipid sources, especially free fatty acids and cholesterol from serum lipoproteins (11-15), probably with an energy-storage purpose; however, when cells are under different stress signals, LD biogenesis occurs in the absence of external lipid via rearrangement of membrane phospholipids and fatty acids into newly formed TAG molecules (16,17).The leukocytes, cells typically associated with infl ammatory reactions can induce the rapid formation of LDs when exposed to proinfl ammatory stimuli (18-21). Moreover, it is becoming increasingly recognized that LDs are specialized intracellular sites for the biosynthesis and amplifi cation of the eicosanoid biosynthetic response during in fl ammation

Phospholipase A2s generate lipid mediators that constitute an important component of the integrated response of macrophages to stimuli of the innate immune response. Because these cells contain multiple phospholipase A2 forms, the challenge is to elucidate the roles that each of these forms plays in regulating normal cellular processes and in disease pathogenesis. A major issue is to precisely determine the phospholipid substrates that these enzymes use for generating lipid mediators. There is compelling evidence that group IVA cytosolic phospholipase A2 (cPLA2α) targets arachidonic acid–containing phospholipids but the role of the other cytosolic enzyme present in macrophages, the Ca2+-independent group VIA phospholipase A2 (iPLA2β) has not been clearly defined. We applied mass spectrometry–based lipid profiling to study the substrate specificities of these two enzymes during inflammatory activation of macrophages with zymosan. Using selective inhibitors, we find that, contrary to cPLA2α, iPLA2β spares arachidonate-containing phospholipids and hydrolyzes only those that do not contain arachidonate. Analyses of the lysophospholipids generated during activation reveal that one of the major species produced, palmitoyl-glycerophosphocholine, is generated by iPLA2β, with minimal or no involvement of cPLA2α. The other major species produced, stearoyl-glycerophosphocholine, is generated primarily by cPLA2α. Collectively, these findings suggest that cPLA2α and iPLA2β act on different phospholipids during zymosan stimulation of macrophages and that iPLA2β shows a hitherto unrecognized preference for choline phospholipids containing palmitic acid at the sn-1 position that could be exploited for the design of selective inhibitors of this enzyme with therapeutic potential.