Rapid Screening and Prioritization of Culture Conditions for Natural Product Discovery using the Liquid Microjunction...

Area of Science:

• Natural Product Chemistry
• Analytical Chemistry
• Computational Chemistry

Background:

• Natural product (NP) discovery is vital but hampered by inefficient, resource-intensive workflows.
• Traditional methods lack sustainability and efficiency for large-scale screening projects.

Purpose of the Study:

• To evaluate the liquid microjunction surface sampling probe (LMJ-SSP) coupled with partial least-squares discriminant analysis (PLS-DA) for preliminary natural product discovery.
• To streamline the NP discovery pipeline by prioritizing growth conditions that maximize chemical diversity.

Main Methods:

• Ambient mass spectrometry was integrated with machine learning (PLS-DA).
• Four Penicillium fungal strains were analyzed in situ under 13 different growth conditions without prior sample preparation.
• LMJ-SSP was used for rapid, direct sample analysis.

Main Results:

• PLS-DA effectively prioritized growth conditions that enhanced metabolite diversity.
• The LMJ-SSP approach reduced sampling time by 96%, cost by 98%, and solvent consumption by 98%.
• This method provided insights into metabolite composition before extensive downstream processing.

Conclusions:

• LMJ-SSP combined with PLS-DA offers a significant advancement in natural product discovery.
• This integrated approach enhances efficiency, reduces costs, and promotes sustainability in NP research.
• Chemically informed prioritization using LMJ-SSP streamlines the discovery pipeline for novel bioactive compounds.