Visible Post-Data Analysis Protocol for Natural Mycotoxin Production.

Area of Science:

• Natural Product Chemistry
• Analytical Chemistry
• Bioinformatics

Background:

• Fungal natural products are crucial, but their discovery is hindered by a lack of high-throughput analytical methods.
• Current target detection relies on commercial standards, limiting the identification of novel compounds.
• Post-data processing techniques are increasingly vital for natural product confirmation and mycotoxin analysis.

Purpose of the Study:

• To develop and demonstrate a visible post-data processing workflow for efficient analysis of high-resolution mass spectrometry data.
• To enhance the discovery of new fungal natural products by overcoming limitations of traditional methods.
• To visualize and analyze fungal natural product families using molecular networking.

Main Methods:

• Utilized MZmine software with an optimized diagnostic fragmentation filtering module for screening conjugated products.
• Employed Xcalibur for confirmation by comparing conjugated products to unconjugated commercial standards.
• Leveraged Global Natural Products Social Molecular Networking (GNPS) for processing MS/MS spectral data and creating molecular networks.

Main Results:

• Successfully screened conjugated fungal natural products using an optimized filtering module in MZmine.
• Confirmed identified compounds by comparing with commercial standards using Xcalibur.
• Visualized fungal natural product families and their relationships through molecular networking on GNPS.

Conclusions:

• The integrated workflow combining MZmine, GNPS, and Xcalibur offers an efficient and visible approach for fungal natural product analysis.
• This method facilitates the discovery of novel fungal compounds and provides insights into natural product families.
• The study highlights the potential of advanced post-data processing techniques in natural product research.