Untargeted
metabolomics using liquid chromatography–mass
spectrometry (LC–MS) is currently the gold-standard technique
to determine the full chemical diversity in biological samples. However,
this approach still has many limitations; notably, the difficulty
of accurately estimating the number of unique metabolites profiled
among the thousands of MS ion signals arising from chromatograms.
Here, we describe a new workflow, MS-CleanR, based on the MS-DIAL/MS-FINDER
suite, which tackles feature degeneracy and improves annotation rates.
We show that implementation of MS-CleanR reduces the number of signals
by nearly 80% while retaining 95% of unique metabolite features. Moreover,
the annotation results from MS-FINDER can be ranked according to the
database chosen by the user, which enhance identification accuracy.
Application of MS-CleanR to the analysis of Arabidopsis thaliana grown in three different conditions fostered class separation resulting
from multivariate data analysis and led to annotation of 75% of the
final features. The full workflow was applied to metabolomic profiles
from three strains of the leguminous plant Medicago truncatula that have different susceptibilities to the oomycete pathogen Aphanomyces euteiches. A group of glycosylated triterpenoids
overrepresented in resistant lines were identified as candidate compounds
conferring pathogen resistance. MS-CleanR is implemented through a
Shiny interface for intuitive use by end-users (available at ).
LC-HRMS) profiles. In parallel, redox active properties were evaluated by the capacity of the molecules to reduce 2,2-diphenyl-1-picrylhydrazyl (DPPH •) and superoxide (O 2 •−) radicals using UV-Vis and electron spin resonance spectroscopies (ESR), respectively. A spectral similarity network (molecular networking) was used to highlight clusters involved in the observed redox activities. Results Dereplication on Viola alba subsp. dehnhardtii highlighted a reproducible pool of redox active molecules. Polyphenols, particularly O-glycosylated coumarins and C-glycosylated flavonoids, were identified and de novo dereplicated through molecular networking. Confirmatory analyses were undertaken by thin layer chromatography (TLC)-DPPH-MS assays and nuclear magnetic resonance (NMR) spectra of the most active compounds. Conclusion Our dereplication strategy allowed the screening of leaf extracts to highlight new biologically active metabolites in few steps with a limited amount of crude material and reduced time-consuming manipulations.
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