Abstract
Ethnopharmacological relevance:
Astragali Radix (AR) is a popular traditional Chinese medicine that has been used for more than 2000 years. It is a well-known tonic for weak people with chronic diseases, such as heart failure and cerebral ischemia. Previous studies have reported that AR could support the "weak heart" of cancer patients who suffered from doxorubicin (DOX)-induced cardiotoxicity (DIC). However, the underlying mechanism remains unclear.
Aim of the study:
This study aimed to uncover the critical pathways and molecular determinants for AR against DIC by fully characterizing the network-based relationship.
Materials and methods:
We integrated ultra-high-performance liquid chromatography-high-resolution mass spectrometry (UHPLC-HRMS) profiling, database and literature searching, and the human protein-protein interactome to discover the specific network module associated with AR against DIC. To validate the network-based findings, a low-dose, long-term DIC mouse model and rat cardiomyoblast H9c2 cells were employed. The levels of potential key metabolites and proteins in hearts and cells were quantified by the LC-MS/MS targeted analysis and western blotting, respectively.
Results:
We constructed one of the most comprehensive AR component-target network described to date, which included 730 interactions connecting 64 unique components and 359 unique targets. Relying on the network-based evaluation, we identified fatty acid metabolism as a putative critical pathway and peroxisome proliferator-activated receptors (PPARα and PPARγ) as potential molecular determinants. We then confirmed that DOX caused the accumulation of fatty acids in the mouse failing heart, while AR promoted fatty acid metabolism and preserved heart function. By inhibiting PPARγ in H9c2 cells, we further found that AR could alleviate DIC by activating PPARγ to maintain fatty acid homeostasis.
Conclusions:
Our findings imply that AR is a promising drug candidate that treats DIC by maintaining fatty acid homeostasis. More importantly, the network-based method developed here could facilitate the mechanism discovery of AR therapy and help catalyze innovation in its clinical application.
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