Abstract
Objectives
Establishing the metabolome of dietary phytochemicals is complicated by the influence of the microbiome. Due to large numbers of human, microbial and hybrid human-microbial metabolites, false-positive identification via mass spectrometry (MS) is probable. Users must be aware of the influence of matrix and instrumental background environment upon the mass spectrum, which produce spectral features arising from fragmentation, gas-phase artifacts, molecular rearrangement, quasi-molecular ion or radical formation. Hydroxylated cyclic and polycyclic structures such as polyphenols are prone to multiple gas-phase artifacts, including water elimination (loss −17), hydrogen elimination (−1), radical fragmentation (loss −15, −14), Retro-Diels-Alder reactions (C-ring electron rearrangement; + or −2), or combinations thereof. In-source fragmentation is often observed for phase II conjugates, such as sulfate, glucuronide and glycine. Finally, polyphenol metabolites such as valerolactones, benzoic, phenylpropanoic and phenylacetic acids, are also products of MS fragmentation. As there are few reference standards available for confirmation or optimization, false-positive identification is likely. The objective of the present study was to highlight limitations with MS to ensure researchers make appropriate assumptions from their spectral data.
Methods
A quantitative metabolomics database comprising optimized spectral signals for fragmentation profiling of over 400 poly/phenols and metabolites was established using a UHPLC-coupled electrospray triple quadrupole-linear ion trap mass spectrometer (SCIEX QTRAP 6500+). Methods were established and utilized to interrogate over 3000 biospecimens derived from studies feeding various polyphenol-rich diets.
Results
Scanning for numerous metabolites reported in the literature using single transition monitoring, in both neat and extracted human and animal tissue matrices consistently identify peaks which were either artifacts, isomers, fragments or background noise, as confirmed relative to authentic reference standards.
Conclusions
Without ample MS experience, method development, validation and data interrogation, falsely identified metabolites will continue to occur and undoubtedly hinder future discovery.
Funding Sources
NIFA-USDA Hatch 1011757.
Population Admixture May Appear to Mask, Change or Reverse Genetic Effects of Genes Underlying Complex Traits
