Hepatoprotective Activity of Lignin-Derived Polyphenols Dereplicated Using High-Resolution Mass Spectrometry, In Vivo Experiments, and Deep Learning.
Alexey OrlovSavva SemenovGleb RukhovichAnastasia SarychevaOxana KovalevaAlexander SemenovElena D ErmakovaEkaterina GubarevaAnna E BugrovaAlexey KononikhinElena I FedorosEvgeny NikolaevAlexander ZherebkerPublished in: International journal of molecular sciences (2022)
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.
Keyphrases
- high resolution mass spectrometry
- molecular docking
- liquid chromatography
- public health
- ionic liquid
- ultra high performance liquid chromatography
- deep learning
- gas chromatography
- mass spectrometry
- molecular dynamics simulations
- tandem mass spectrometry
- randomized controlled trial
- insulin resistance
- systematic review
- high resolution
- small molecule
- liver fibrosis
- machine learning
- stem cells
- artificial intelligence
- convolutional neural network
- skeletal muscle
- solid phase extraction
- metabolic syndrome
- drug induced
- mesenchymal stem cells
- risk assessment
- climate change
- ms ms
- drug administration
- bone marrow
- replacement therapy
- cell therapy