Phenolic Profile and Fingerprint Analysis of Akebia quinata Leaves Extract with Endothelial Protective Activity.
Dan GaoChong-Woon ChoJin-Hyeok KimHaiying BaoHyung-Min KimXiwen LiJong Seong KangPublished in: Molecules (Basel, Switzerland) (2022)
In contrast to the stem and fruit of Akebia quinata , A. quinata leaves as a source rich in phenolic compounds with potentially beneficial pharmacological activities have been largely overlooked. To develop and use A. quinata leaves as a resource, we evaluated its potential as a cardiovascular-protective agent. Herein, we investigated the effects and potential mechanisms of A. quinata leaves extract on lipopolysaccharide (LPS)-induced inflammatory responses in human umbilical vein endothelial cells. We found that A. quinata leaves extract pretreatment of 10 μg/mL significantly attenuated LPS-induced protein expression of intercellular adhesion molecule-1, vascular cell adhesion molecule-1. Furthermore, this extract also suppressed LPS-induced phosphorylation of nuclear factor-κB p65. In order to elucidate the chemical profiles of the samples, the HPLC fingerprint was established, and prominent peaks were identified via HPLC-electrospray ionization-mass spectrometry. Multivariate statistical analyses, including hierarchical cluster analysis, principal component analysis, and partial least-squares discriminant analysis, were performed to evaluate the clustering of the samples. It was found that isochlorogenic acid C was a key marker for the classification of A. quinata leaves from the Gongju and Muju city in Korea. Collectively, this study not only suggested the potential of A. quinata leaves as a novel therapeutic candidate for inflammatory cardiovascular disease but also developed a quality control method for A. quinata leaves, which could help to expand the application of A. quinata .
Keyphrases
- lps induced
- inflammatory response
- mass spectrometry
- essential oil
- quality control
- cardiovascular disease
- endothelial cells
- oxidative stress
- nuclear factor
- toll like receptor
- cell adhesion
- ms ms
- type diabetes
- machine learning
- high performance liquid chromatography
- simultaneous determination
- escherichia coli
- anti inflammatory
- metabolic syndrome
- pseudomonas aeruginosa
- deep learning
- single cell
- tandem mass spectrometry
- liquid chromatography
- computed tomography
- cystic fibrosis
- data analysis
- rna seq
- staphylococcus aureus
- gas chromatography
- high speed