Abstract

Intestinal bacteria can metabolize polyphenols into highly bioavailable derivatives, which provide potential health-promoting effects to the host. However, the metabolic pathways and related products in this process are still largely unclear. Polyphenols are generally characterized by the presence of many phenolic structural units, which makes it possible to explore correlations among compounds based on similar molecular networks. In this study, we developed a standard-oriented/database-assisted molecular networking (SODA-MN) method for iterative compound annotation analysis to explore the metabolic profiles of polyphenol-rich black raspberry extract metabolized by representative gut bacteria. Starting from a group of polyphenol metabolites, the SODA-MN method predicted the possible polyphenol derivatives by adding or deducting common biotransformation groups and iterative annotating of structure similarity based on fragmentation spectra. Our results showed that 48 polyphenol derivatives in the first round of analysis alone (fragmentation match≥5, spec score >0.5) can be annotated, which were associated with 13 detected polyphenol standards that served as seed compounds. Meanwhile, this method was applied to a time-course study to show the time-dependent changes of polyphenols metabolized by a mix of gut bacteria. In addition, the metabolic capabilities of polyphenols among four representative gut bacteria were compared via our newly developed method and differential polyphenol metabolites were detected. In summary, the SODA-MN method provides a new approach for the annotation of unknown compounds by structure similarity and molecular networking analysis. Our analysis results could provide identification of key polyphenol derivatives that may contribute to the mechanistic investigations of their functions and assist our understanding of how polyphenols and gut bacteria interact to promote human health.

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