WeChat Mini Program
Old Version Features

Unveiling the Functional Components and Antivirulence Activity of Mustard Leaves Using an LC-MS/MS, Molecular Networking, and Multivariate Data Analysis Integrated Approach.

Food Research International(2023)

Cairo Univ

Cited 19|Views6
Abstract
Plant extracts have recently received increased attention as alternative sources of antimicrobial agents in the fight against multidrug-resistant bacteria. Non-targeted metabolomics liquid chromatography-quadrupole time-of-flight tandem mass spectrometry, molecular networking, and chemometrics were used to evaluate the metabolic profiles of red and green leaves of two Brassica juncea (L.) varieties, var.integrifolia (IR and IG) and var.rugosa (RR and RG), as well as to establish a relationship between the elucidated chemical profiles and anti-virulence activity. In total, 171 metabolites from different classes were annotated and principal component analysis revealed higher levels of phenolics and glucosinolates in var.integrifolia leaves and color discrimination, whereas fatty acids were enriched in var.rugosa, particularly trihydroxy octadecadienoic acid. All extracts demonstrated significant antibacterial activity against Staphylococcus aureus and Enterococcus faecalis, presenting the IR leaves the highest antihemolytic activity against S. aureus (99 % inhibition), followed by RR (84 %), IG (82 %), and RG (37 %) leaves. Antivirulence of IR leaves was further validated by reduction in alpha-hemolysin gene transcription (similar to 4-fold). Using various multivariate data analyses, compounds positively correlated to bioactivity, primarily phenolic compounds, glucosinolates, and isothiocyanates, were also identified.
More
Translated text
Key words
Brassica,Mustard,Chemometrics,Metabolomics,Molecular networking,Antibacterial,Antihemolytic,hla transcription
求助PDF
上传PDF
Bibtex
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
  • Pretraining has recently greatly promoted the development of natural language processing (NLP)
  • We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
  • We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
  • The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
  • Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example
Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
Summary is being generated by the instructions you defined