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The nucleoprotein (NP) of Ebola virus (EBOV) and Marburg virus (MARV) is an essential component of the viral ribonucleoprotein complex and significantly impacts replication and transcription of the viral RNA genome. Although NP is regarded as a promising antiviral druggable target, no chemical ligands have been reported to interact with EBOV NP or MARV NP. We identified two compounds from a traditional Chinese medicine Gancao (licorice root) that can bind both NPs by combining affinity mass spectrometry and metabolomics approaches. These two ligands, 18β-glycyrrhetinic acid and licochalcone A, were verified by defined compound mixture screens and further characterized with individual ligand binding assays. Accompanying biophysical analyses demonstrate that binding of 18β-glycyrrhetinic acid to EBOV NP significantly reduces protein thermal stability, induces formation of large NP oligomers, and disrupts the critical association of viral ssRNA with NP complexes whereas the compound showed no such activity on MARV NP. Our study has revealed the substantial potential of new analytical techniques in ligand discovery from natural herb resources. In addition, identification of a chemical ligand that influences the oligomeric state and RNA-binding function of EBOV NP sheds new light on antiviral drug development.

Quantification of targeted metabolites, especially trace metabolites and structural isomers, in complex biological materials is an ongoing challenge for metabolomics. Initially developed for proteomic analysis, the parallel reaction monitoring (PRM) technique exploiting high-resolution MS2 fragment ion data has shown high promise for targeted metabolite quantification. Notably, MS1 ion intensity data acquired independently as part of each PRM scan cycle are often underutilized in the PRM assay. In this study, we developed an MS1/MS2-combined PRM workflow for quantification of central carbon metabolism intermediates, amino acids and shikimate pathway-related metabolites on an orthogonal QqTOF system. Concentration curve assessment revealed that exploiting both MS1 and MS2 scans in PRM analysis afforded higher sensitivity, wider dynamic range and better reproducibility than relying on either scan mode for quantification. Furthermore, Skyline was incorporated into our workflow to process the MS1/MS2 ion intensity data, and eliminate noisy signals and transitions with interferences. This integrated MS1/MS2 PRM approach was applied to targeted metabolite quantification in engineered E. coli strains for understanding of metabolic pathway modulation. In addition, this new approach, when first implemented in a dynamic C-labeling experiment, showed its unique advantage in capturing and correcting isotopomer labeling curves to facilitate nonstationaryC-labeling metabolism analysis.

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