Studying Venom Toxin Variation Using Accurate Masses from Liquid Chromatography-Mass Spectrometry Coupled with Bioinformatic Tools.
Luis Lago AlonsoJory van ThielJulien SlagboomNathan DunstanCassandra M ModahlTimothy N W JacksonSaer SamanipourJeroen KoolPublished in: Toxins (2024)
This study provides a new methodology for the rapid analysis of numerous venom samples in an automated fashion. Here, we use LC-MS (Liquid Chromatography-Mass Spectrometry) for venom separation and toxin analysis at the accurate mass level combined with new in-house written bioinformatic scripts to obtain high-throughput results. This analytical methodology was validated using 31 venoms from all members of a monophyletic clade of Australian elapids: brown snakes ( Pseudonaja spp.) and taipans ( Oxyuranus spp.). In a previous study, we revealed extensive venom variation within this clade, but the data was manually processed and MS peaks were integrated into a time-consuming and labour-intensive approach. By comparing the manual approach to our new automated approach, we now present a faster and more efficient pipeline for analysing venom variation. Pooled venom separations with post-column toxin fractionations were performed for subsequent high-throughput venomics to obtain toxin IDs correlating to accurate masses for all fractionated toxins. This workflow adds another dimension to the field of venom analysis by providing opportunities to rapidly perform in-depth studies on venom variation. Our pipeline opens new possibilities for studying animal venoms as evolutionary model systems and investigating venom variation to aid in the development of better antivenoms.
Keyphrases
- liquid chromatography
- mass spectrometry
- high throughput
- escherichia coli
- high resolution mass spectrometry
- high resolution
- tandem mass spectrometry
- capillary electrophoresis
- high performance liquid chromatography
- simultaneous determination
- multiple sclerosis
- electronic health record
- randomized controlled trial
- gas chromatography
- clinical trial
- machine learning
- magnetic resonance
- computed tomography
- magnetic resonance imaging
- ms ms
- dna methylation
- solid phase extraction
- big data
- small cell lung cancer
- artificial intelligence