bioRxivMolecular Biology
Annotation of DOM Metabolomes with an Ultrahigh Resolution Mass Spectrometry Molecular Formula Library
Nicole CoffeyiD, Christian DeweyiD, Kieran Manning, Yuri CoriloiD, William KewiD, Lydia Babcock‐AdamsiD, Amy Marilyn MckennaiD, Rhona K. StuartiD, Rene Maurice BoiteauiD
Abstract
Increased accessibility of liquid chromatography mass spectrometry (LC-MS) metabolomics instrumentation and software have expanded their use in studies of dissolved organic matter (DOM) and exometabolites released by microbes. Current strategies to annotate metabolomes generally rely on matching tandem MS/MS spectra to databases of authentic standards. However, spectral matching approaches typically have low annotation rates for DOM. An alternative approach is to annotate molecular formula based on accurate mass and isotopic fine structure measurements that can be obtained from state-of-the-art ultrahigh resolution Fourier Transform Ion Cyclotron Resonance mass spectrometry (FT-ICR-MS), but instrument accessibility for large metabolomic studies is generally limited. Here, we describe a strategy to annotate exometabolomes obtained from lower resolution LC-MS systems by matching metabolomic features to a molecular formula library generated for a representative sample analyzed by LC-21T FT-ICR MS. The molecular formula library approach successfully annotated 53% of exometabolome features of the marine diatom Phaeodactylum tricornutum - a nearly ten-fold increase over the 6% annotation rate achieved using a conventional MS/MS approach. There was 94% agreement between assigned formula that were annotated with both approaches, and mass error analysis of the discrepancies suggested that the FT-ICR MS formula assignments were more reliable. Differences in the exometabolome of P. tricornutum grown under iron replete and iron limited conditions revealed 668 significant metabolites, including a suite of peptide-like molecules released by P. tricornutum in response to iron deficiency. These findings demonstrate the utility of FT-ICR MS formula libraries for extending the accuracy and comprehensiveness of metabolome annotations.
- assay: metagenomics
- assay: metabolomics
- research focus: cell type annotation
- research focus: differential expression
- approach: statistical model
- Citations
- 7
- FWCI
- 0.95
- Field percentile
- top 28%
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