title: Improved genome annotation through untargeted detection of pathway-specific metabolites.
abstract: BACKGROUND
  Mass spectrometry-based metabolomics analyses have the potential to complement sequence-based methods of genome annotation, but only if raw mass spectral data can be linked to specific metabolic pathways. In untargeted metabolomics, the measured mass of a detected compound is used to define the location of the compound in chemical space, but uncertainties in mass measurements lead to "degeneracies" in chemical space since multiple chemical formulae correspond to the same measured mass. We compare two methods to eliminate these degeneracies. One method relies on natural isotopic abundances, and the other relies on the use of stable-isotope labeling (SIL) to directly determine C and N atom counts. Both depend on combinatorial explorations of the "chemical space" comprised of all possible chemical formulae comprised of biologically relevant chemical elements.
  
  
  RESULTS
  Of 1532 metabolic pathways curated in the MetaCyc database, 412 contain a metabolite having a chemical formula unique to that metabolic pathway. Thus, chemical formulae alone can suffice to infer the presence of some metabolic pathways. Of 248,928 unique chemical formulae selected from the PubChem database, more than 95% had at least one degeneracy on the basis of accurate mass information alone. Consideration of natural isotopic abundance reduced degeneracy to 64%, but mainly for formulae less than 500 Da in molecular weight, and only if the error in the relative isotopic peak intensity was less than 10%. Knowledge of exact C and N atom counts as determined by SIL enabled reduced degeneracy, allowing for determination of unique chemical formula for 55% of the PubChem formulae.
  
  
  CONCLUSIONS
  To facilitate the assignment of chemical formulae to unknown mass-spectral features, profiling can be performed on cultures uniformly labeled with stable isotopes of nitrogen (15N) or carbon (13C). This makes it possible to accurately count the number of carbon and nitrogen atoms in each molecule, providing a robust means for reducing the degeneracy of chemical space and thus obtaining unique chemical formulae for features measured in untargeted metabolomics having a mass greater than 500 Da, with relative errors in measured isotopic peak intensity greater than 10%, and without the use of a chemical formula generator dependent on heuristic filtering. These chemical formulae can serve as indicators for the presence of particular metabolic pathways.
authors.affiliation: Department of GTL Bioenergy and Structural Biology, Life Sciences Division, Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Berkeley, CA 94720, USA. BPBowen@lbl.gov
authors.forename: Benjamin P
authors.initials: BP
authors.lastname: Bowen
authors.forename: Curt R
authors.initials: CR
authors.lastname: Fischer
authors.forename: Richard
authors.initials: R
authors.lastname: Baran
authors.forename: Jillian F
authors.initials: JF
authors.lastname: Banfield
authors.forename: Trent
authors.initials: T
authors.lastname: Northen
mesh_terms: D002247:Carbon Isotopes / Q000737:chemistry; D016208:Databases, Factual; D007553:Isotope Labeling; D013058:Mass Spectrometry / Q000379:methods*; D053858:Metabolic Networks and Pathways; D055432:Metabolomics / Q000379:methods; D058977:Molecular Sequence Annotation / Q000379:methods*; D009587:Nitrogen Isotopes / Q000737:chemistry
chemical_list: D002247:Carbon Isotopes; D009587:Nitrogen Isotopes