A Chemometrics-driven Strategy for the Bioactivity Evaluation of Complex Multicomponent Systems and the Effective Selection of Bioactivity-predictive Chemical Combinations

Although understanding their chemical composition is vital for accurately predicting the bioactivity of multicomponent drugs, nutraceuticals, and foods, no analytical approach exists to easily predict the bioactivity of multicomponent systems from complex behaviors of multiple coexisting factors. We herein represent a metabolic profiling (MP) strategy for evaluating bioactivity in systems containing various small molecules. Composition profiles of diverse bioactive herbal samples from 21 green tea extract (GTE) panels were obtained by a high-throughput, non-targeted analytical procedure. This employed the matrix-assisted laser desorption ionization-mass spectrometry (MALDI-MS) technique, using 1,5-diaminonaphthalene (1,5-DAN) as the optical matrix for detecting GTE-derived components. Multivariate statistical analyses revealed differences among the GTEs in their antioxidant activity, oxygen radical absorbance capacity (ORAC). A reliable bioactivity-prediction model was constructed to predict the ORAC of diverse GTEs from their compositional balance. This chemometric procedure allowed the evaluation of GTE bioactivity by multicomponent rather than single-component information. The bioactivity could be easily evaluated by calculating the summed abundance of a few selected components that contributed most to constructing the prediction model. 1,5-DAN-MALDI-MS-MP, using diverse bioactive sample panels, represents a promising strategy for screening bioactivity-predictive multicomponent factors and selecting effective bioactivity-predictive chemical combinations for crude multicomponent systems

Hydrogen Rearrangement Rules: Computational MS/MS Fragmentation and Structure Elucidation Using MS-FINDER Software

Compound identification from accurate mass MS/MS spectra is a bottleneck for untargeted metabolomics. In this study, we propose nine rules of hydrogen rearrangement (HR) during bond cleavages in low-energy collision-induced dissociation (CID). These rules are based on the classic even-electron rule and cover heteroatoms and multistage fragmentation. We evaluated our HR rules by the statistics of MassBank MS/MS spectra in addition to enthalpy calculations, yielding three levels of computational MS/MS annotation: “resolved” (regular HR behavior following HR rules), “semiresolved” (irregular HR behavior), and “formula-assigned” (lacking structure assignment). With this nomenclature, 78.4% of a total of 18506 MS/MS fragment ions in the MassBank database and 84.8% of a total of 36370 MS/MS fragment ions in the GNPS database were (semi-) resolved by predicted bond cleavages. We also introduce the MS-FINDER software for structure elucidation. Molecular formulas of precursor ions are determined from accurate mass, isotope ratio, and product ion information. All isomer structures of the predicted formula are retrieved from metabolome databases, and MS/MS fragmentations are predicted in silico. The structures are ranked by a combined weighting score considering bond dissociation energies, mass accuracies, fragment linkages, and, most importantly, nine HR rules. The program was validated by its ability to correctly calculate molecular formulas with 98.0% accuracy for 5063 MassBank MS/MS records and to yield the correct structural isomer with 82.1% accuracy within the top-3 candidates. In a test with 936 manually identified spectra from an untargeted HILIC-QTOF MS data set of human plasma, formulas were correctly predicted in 90.4% of the cases, and the correct isomer structure was retrieved at 80.4% probability within the top-3 candidates, including for compounds that were absent in mass spectral libraries. The MS-FINDER software is freely available at http://prime.psc.riken.jp/

In Situ Label-Free Visualization of Orally Dosed Strictinin within Mouse Kidney by MALDI-MS Imaging

Matrix-assisted laser desorption/ionization–mass spectrometry imaging (MALDI-MSI) is a powerful technique for visualizing the distribution of a wide range of biomolecules within tissue sections. However, methodology for visualizing a bioactive ellagitannin has not yet been established. This paper presents a novel in situ label-free MALDI-MSI technique for visualizing the distribution of strictinin, a bioactive ellagitannin found in green tea, within mammalian kidney after oral dosing. Among nine representative matrix candidates, 1,5-diaminonaphthalene (1,5-DAN), harmane, and ferulic acid showed higher sensitivity to strictinin spotted onto a MALDI sample plate. Of these, 1,5-DAN enables visualization of a two-dimensional image of strictinin directly spotted on mouse kidney sections with the highest sensitivity. Furthermore, 1,5-DAN-based MALDI-MSI could detect the unique distribution of orally dosed strictinin within kidney sections. This in situ label-free imaging technique will contribute to the localization analysis of strictinin and its biological mechanisms