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DOI 10.3390/ijms232416025

Медицинская библиотека · International journal of molecular sciences · 2022

Hepatoprotective Activity of Lignin-Derived Polyphenols Dereplicated Using High-Resolution Mass Spectrometry, In Vivo Experiments, and Deep Learning.

Orlov Alexey, Semenov Savva, Rukhovich Gleb, Sarycheva Anastasia, Kovaleva Oxana, Semenov Alexander, Ermakova Elena, Gubareva Ekaterina, Bugrova Anna E, Kononikhin Alexey, Fedoros Elena I, Nikolaev Evgeny, Zherebker Alexander

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Аннотация

Chronic liver diseases affect more than 1 billion people worldwide and represent one of the main public health issues. Nonalcoholic fatty liver disease (NAFLD) accounts for the majority of mortal cases, while there is no currently approved therapeutics for its treatment. One of the prospective approaches to NAFLD therapy is to use a mixture of natural compounds. They showed effectiveness in alleviating NAFLD-related conditions including steatosis, fibrosis, etc. However, understanding the mechanism of action of such mixtures is important for their rational application. In this work, we propose a new dereplication workflow for deciphering the mechanism of action of the lignin-derived natural compound mixture. The workflow combines the analysis of molecular components with high-resolution mass spectrometry, selective chemical tagging and deuterium labeling, liver tissue penetration examination, assessment of biological activity in vitro, and computational chemistry tools used to generate putative structural candidates. Molecular docking was used to propose the potential mechanism of action of these structures, which was assessed by a proteomic experiment.

Для цитирования: Orlov Alexey, Semenov Savva, Rukhovich Gleb, Sarycheva Anastasia, Kovaleva Oxana, Semenov Alexander, Ermakova Elena, Gubareva Ekaterina, Bugrova Anna E, Kononikhin Alexey, Fedoros Elena I, Nikolaev Evgeny, Zherebker Alexander. Hepatoprotective Activity of Lignin-Derived Polyphenols Dereplicated Using High-Resolution Mass Spectrometry, In Vivo Experiments, and Deep Learning.. International journal of molecular sciences. 2022. DOI: 10.3390/ijms232416025.

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