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Natural Product Discovery with LC-MS/MS Diagnostic Fragmentation Filtering: Application for Microcystin Analysis
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Identification of Novel Microcystins Using High-Resolution MS and MSn with Python Code
David Baliu-Rodriguez1, Nicholas J Peraino2, Sanduni H Premathilaka1
1Department of Chemistry and Biochemistry, University of Toledo, Toledo, Ohio 43606, United States.
Environmental Science & Technology
|January 12, 2022
Summary
A new method using liquid chromatography-high-resolution mass spectrometry (LC-HRMS) and Python code aids in identifying toxic microcystins (MCs) in water. This approach successfully identified two new MC congeners, improving water safety analysis.
Area of Science:
- Environmental Chemistry
- Analytical Chemistry
- Toxicology
Background:
- Microcystins (MCs) are potent cyanotoxins found in water sources, posing significant health risks.
- Over 300 microcystin congeners exist, but many remain structurally unidentified, hindering accurate toxicity assessment.
- Determining the precise structure of microcystins is crucial for understanding their toxicity and developing effective countermeasures.
Purpose of the Study:
- To develop and validate a novel method for the putative identification of unknown microcystin congeners in environmental water samples.
- To employ liquid chromatography coupled with high-resolution Orbitrap mass spectrometry (LC-HRMS) and a bottom-up sequencing strategy for MC analysis.
- To utilize Python programming for generating potential MC structures and confirming identifications through ion fragmentation analysis.
Main Methods:
- Water samples from the Maumee River during a harmful algal bloom were analyzed using LC-HRMS with simultaneous MS/MS.
- Unidentified ions exhibiting characteristic microcystin fragments (135 and 213 m/z) were targeted for investigation.
- A Python-based workflow was implemented for de novo structure elucidation and ion assignment of potential MC congeners.
Main Results:
- The developed workflow successfully facilitated the putative identification of eight previously reported MCs lacking available standards.
- Two novel microcystin congeners, MC-HarR and MC-E(OMe)R, were discovered and structurally characterized.
- The method demonstrated effectiveness in identifying MCs even in complex environmental matrices like river water.
Conclusions:
- The integrated LC-HRMS and Python-based workflow provides a powerful tool for the putative identification of unknown microcystins.
- This approach enhances the ability to detect and characterize emerging microcystin variants, crucial for public health and water quality monitoring.
- The discovery of new congeners underscores the complexity of microcystin diversity and the need for ongoing toxicological research.
