Rapid Screening of Secondary Aromatic Metabolites in Populus trichocarpa Leaves

High-throughput metabolomics analytical methodology is needed for population-scale studies of bioenergy-relevant feedstocks such as poplar (Populus sp.). Here, the authors report the relative abundance of secondary aromatic metabolites in Populus trichocarpa leaves rapidly estimated using pyrolysis-molecular beam mass spectrometry (py-MBMS). A training set of leaves was analyzed in conjunction with GC/MS analysis of extracts to determine key spectral features associated with the aromatic metabolites and build PLS models to predict the relative composition of extractable aromatic metabolites in a second test set of leaves. The relative abundance of major secondary aromatic metabolites for the test set of leaves was validated by GC/MS analysis of extracts. The Pearson correlation coefficient for the relative abundance of aromatic metabolites based on ranking between GC/MS analysis and py-MBMS analysis of the test leaf set was 0.86 with R2 = 0.76 using a simplified prediction approach from select ions in MBMS spectra. Metabolites most influential to py-MBMS spectral features in the training set included the following compounds: catechol, salicortin, salicyloyl-coumaroyl-glucoside, a-salicyloylsalicin, tremulacin, and to a lesser degree: benzyl-coumaroyl glucoside, benzoyl-salicyloylsalicin, tremuloidin, salireposide, trichocarpin, salicylic acid, salicyl-salicylic acid-2-O-glucoside, phenethyl-tremuloidin, coumaroyl-tremuloidin, 2,6-cyclohexadiene-1,2-diol, and salicyl-tremuloidin. Ions in py-MBMS spectra with the highest correlation to the abundance of aromatic secondary metabolites, particularly those previously mentioned as determined by GC/MS analysis of extracts, included m/z 68, 71, 77, 91, 94, 105, 107, 108, and 122, and were used to develop the simplified prediction approach without PLS models or a priori measurements. The simplified py-MBMS method is capable of rapidly screening leaf tissue for relative abundance of extractable aromatic secondary metabolites to enable prioritization of samples in large populations requiring comprehensive metabolomics that will ultimately inform plant systems biology models and advance the development of optimized biomass feedstocks for renewable fuels and chemicals.