An in silico MS/MS library for automatic annotation of novel FAHFA lipids

Yan Ma, Tobias Kind, Arpana Vaniya, Ingrid Gennity, Johannes F. Fahrmann, Oliver Fiehn

Research output: Contribution to journalArticle

30 Citations (Scopus)

Abstract

Background: A new lipid class named 'fatty acid esters of hydroxyl fatty acids' (FAHFA) was recently discovered in mammalian adipose tissue and in blood plasma and some FAHFAs were found to be associated with type 2 diabetes. To facilitate the automatic annotation of FAHFAs in biological specimens, a tandem mass spectra (MS/MS) library is needed. Due to the limitation of the commercial available standard compounds, we proposed building an in silico MS/MS library to extend the coverage of molecules. Results: We developed a computer-generated library with 3267 tandem mass spectra (MS/MS) for 1089 FAHFA species. FAHFA spectra were generated based on authentic standards with negative mode electrospray ionization and 10, 20, and 40 V collision induced dissociation at 4 spectra/s as used in in ultra-high performance liquid chromatography-QTOF mass spectrometry studies. However, positional information of the hydroxyl group is only obtained either at lower QTOF spectra acquisition rates of 1 spectrum/s or at the MS3 level in ion trap instruments. Therefore, an additional set of 4290 fragment-rich MS/MS spectra was created to enable distinguishing positional FAHFA isomers. The library was generated based on ion fragmentations and ion intensities of FAHFA external reference standards, developing a heuristic model for fragmentation rules and extending these rules to large swaths of computer-generated structures of FAHFAs with varying chain lengths, degrees of unsaturation and hydroxyl group positions. Subsequently, we validated the new in silico library by discovering several new FAHFA species in egg yolk, showing that this library enables high-throughput screening of FAHFA lipids in various biological matrices. Conclusions: The developed library and templates are freely available for commercial or noncommercial use at http://fiehnlab.ucdavis.edu/staff/yanma/fahfa-lipid-library. This in silico MS/MS library allows users to annotate FAHFAs from accurate mass tandem mass spectra in an easy and fast manner with NIST MS Search or PepSearch software. The developing template is provided for advanced users to modify the parameters and export customized libraries according to their instrument features.

Original languageEnglish (US)
Article number53
JournalJournal of Cheminformatics
Volume7
Issue number1
DOIs
StatePublished - Dec 1 2015

Fingerprint

annotations
fatty acids
Fatty acids
Hydroxyl Radical
Lipids
lipids
esters
Esters
Fatty Acids
mass spectra
fragmentation
Ions
templates
adipose tissues
blood plasma
chronic illness
Electrospray ionization
heuristics
eggs
liquid chromatography

Keywords

  • FAHFA
  • In silico library
  • Lipids
  • MS/MS

ASJC Scopus subject areas

  • Physical and Theoretical Chemistry
  • Computer Graphics and Computer-Aided Design
  • Computer Science Applications
  • Library and Information Sciences

Cite this

Ma, Y., Kind, T., Vaniya, A., Gennity, I., Fahrmann, J. F., & Fiehn, O. (2015). An in silico MS/MS library for automatic annotation of novel FAHFA lipids. Journal of Cheminformatics, 7(1), [53]. https://doi.org/10.1186/s13321-015-0104-4

An in silico MS/MS library for automatic annotation of novel FAHFA lipids. / Ma, Yan; Kind, Tobias; Vaniya, Arpana; Gennity, Ingrid; Fahrmann, Johannes F.; Fiehn, Oliver.

In: Journal of Cheminformatics, Vol. 7, No. 1, 53, 01.12.2015.

Research output: Contribution to journalArticle

Ma, Y, Kind, T, Vaniya, A, Gennity, I, Fahrmann, JF & Fiehn, O 2015, 'An in silico MS/MS library for automatic annotation of novel FAHFA lipids', Journal of Cheminformatics, vol. 7, no. 1, 53. https://doi.org/10.1186/s13321-015-0104-4
Ma, Yan ; Kind, Tobias ; Vaniya, Arpana ; Gennity, Ingrid ; Fahrmann, Johannes F. ; Fiehn, Oliver. / An in silico MS/MS library for automatic annotation of novel FAHFA lipids. In: Journal of Cheminformatics. 2015 ; Vol. 7, No. 1.
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