Hierarchical clustering of MS/MS spectra from the firefly metabolome identifies new lucibufagin compounds

Abstract

Metabolite identification is the greatest challenge when analysing metabolomics data, as only a small proportion of metabolite reference standards exist. clustering MS/MS spectra is a common method to identify similar compounds, however interrogation of underlying signature fragmentation patterns within clusters can be problematic. Previously published high-resolution LC-MS/MS data from the bioluminescent beetle (Photinus pyralis) provided an opportunity to mine new specialized metabolites in the lucibufagin class, compounds important for defense against predation. We aimed to 1) provide a workflow for hierarchically clustering MS/MS spectra for metabolomics data enabling users to cluster, visualise and easily interrogate the identification of underlying cluster ion profiles, and 2) use the workflow to identify key fragmentation patterns for lucibufagins in the hemolymph of P. pyralis. Features were aligned to their respective MS/MS spectra, then product ions were dynamically binned and resulting spectra were hierarchically clustered and grouped based on a cutoff distance threshold. Using the simplified visualization and the interrogation of cluster ion tables the number of lucibufagins was expanded from 17 to a total of 29.

Genomic and Metabolomic Analysis of the Potato Common Scab Pathogen Streptomyces scabiei

“… 27 In addition, we employed the recently described BioDendro workflow, which enables hierarchical clustering of MS 2 spectra and presents the results as a tree. 28 After molecular networking, background and media subtraction, a total of 6260 compounds were detected ( Figure 2 A,B). The majority of these molecules were detected under all three culturing conditions, though distinct sets of metabolites were also found in each extract, with the OBA extract containing the highest number of unique metabolites ( Figure 2 B).…”

“…28 FBMN allows for the discrimination of isomers and quantitative interpretation of the molecular network, while BioDendro uses dynamic binning and hierarchical clustering of MS 2 spectra and can function as a complementary analysis to molecular networking. 27,28 Metabolites were annotated by matching the observed MS 2 spectra with reference spectra in the GNPS libraries 29 or using Network Annotation Propagation (NAP), 30 SIRIUS, 31 Met-Work, 32 or Competitive Fragmentation Modeling for Metabolite Identification 3.0 (CFM-ID 3.0). 33 ■ RESULTS AND DISCUSSION Genomic Analysis of Specialized Metabolite Biosynthetic Gene Clusters in S. scabiei.…”

Metabolomics-Guided Elucidation of Plant Abiotic Stress Responses in the 4IR Era: An Overview

“…The developments in metabolomics platforms enable large-scale MS/MS experiments containing thousands of distinct spectra from a single sample. These detected features and their MS/MS spectra can be used for matching to spectral libraries and can include indicative fragmentation patterns that provide information on the often yet unknown chemical structures that were fragmented [221,222]. However, this process is still cumbersome and laborious for large-scale data, particularly for plant metabolomics data which is vastly diverse in physiochemical properties [222,223].…”

“…These detected features and their MS/MS spectra can be used for matching to spectral libraries and can include indicative fragmentation patterns that provide information on the often yet unknown chemical structures that were fragmented [221,222]. However, this process is still cumbersome and laborious for large-scale data, particularly for plant metabolomics data which is vastly diverse in physiochemical properties [222,223]. Hence, we are currently witnessing the development of tools that can analyze such large-scale, complex metabolomic data using computational networking approaches such as molecular networking (MN), which provides visualization of all the detected features and their chemical relationships by grouping structurally similar features into a network to enable mining of the metabolome (Figure 4) [224][225][226].…”

Chemically informed analyses of metabolomics mass spectrometry data with Qemistree

“…We use these molecular fingerprints to calculate pairwise distances between chemical features and hierarchically cluster the fingerprint vectors to generate a tree representing their chemical structural relationships. Although alternative approaches to hierarchically cluster features based on cosine similarity of fragmentation spectra exist [19][20][21] , we use molecular fingerprints predicted by CSI:FingerID for this. Previous work has shown that CSI:FingerID outperforms other tools for automatic in silico structural annotation 22 .…”