No abstract

Despite the increasing availability of tandem mass spectrometry (MS/MS) community spectral libraries for untargeted metabolomics over the past decade, the majority of acquired MS/MS spectra remain uninterpreted. To further aid in interpreting unannotated spectra, we created a nearest neighbor suspect spectral library, consisting of 87,916 annotated MS/MS spectra derived from hundreds of millions of public MS/MS spectra. Annotations were propagated based on structural relationships to reference molecules using MS/MS-based spectrum alignment. We demonstrate the broad relevance of the nearest neighbor suspect spectral library through representative examples of propagation-based annotation of acylcarnitines, bacterial and plant natural products, and drug metabolism. Our results also highlight how the library can help to better understand an Alzheimer’s brain phenotype. The nearest neighbor suspect spectral library is openly available through the GNPS platform to help investigators hypothesize candidate structures for unknown MS/MS spectra in untargeted metabolomics data.

Metformin is the first-line antidiabetic drug that is widely used in the treatment of type 2 diabetes mellitus (T2DM). Even though the various therapeutic potential of metformin treatment has been reported, as well as the improvement of insulin sensitivity and glucose homeostasis, the mechanisms underlying those benefits are still not fully understood. In order to explain the beneficial effects on metformin treatment, various metabolomics analyses have been applied to investigate the metabolic alterations in response to metformin treatment, and significant systemic metabolome changes were observed in biofluid, tissues, and cells. In this review, we compare the latest metabolomic research including clinical trials, animal models, and in vitro studies comprehensively to understand the overall changes of metabolome on metformin treatment.

An effective and previously demonstrated screening method for active constituents in natural products using LC-MS coupled with a bioassay was reported in our earlier studies. With this, the current investigation attempted to identify bioactive constituents of Scutellaria baicalensis through LC-MS coupled with a bioassay. Peaks at broadly 17–20 and 24–25 min on the MS chromatogram displayed an inhibitory effect on NO production in lipopolysaccharide-induced BV2 microglia cells. Similarly, peaks at roughly 17–19 and 22 min showed antioxidant activity with an 2,2′-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid) (ABTS)/2,2-diphenyl-1- picrylhydrazyl (DPPH) assay. For confirmation of LC-MS coupled with a bioassay, nine compounds (1–9) were isolated from an MeOH extract of S. baicalensis. As we predicted, compounds 1, 8, and 9 significantly reduced lipopolysaccharide (LPS)-induced NO production in BV2 cells. Likewise, compounds 5, 6, and 8 exhibited free radical-scavenging activities with the ABTS/DPPH assay. In addition, the structural similarity of the main components was confirmed by analyzing the total extract and EtOAc fractions through molecular networking. Overall, the results suggest that the method comprised of LC-MS coupled with a bioassay can effectively predict active compounds without an isolation process, and the results of molecular networking predicted that other components around the active compound node may also be active.