Abundance of Major Cell Wall Components in Natural Variants and Pedigrees of Populus trichocarpa - Harman-Ware, Anne E.; Happs, Renee M.; Macaya-Sanz, David; Doeppke, Crissa; Muchero, Wellington; DiFazio, Stephen P. (Frontiers in Plant Science, 2022)
The rapid analysis of biopolymers including lignin and sugars in biomass cell walls is essential for the analysis of large sample populations used to inform systems biology models and for genomic selection to produce renewable fuels and chemicals from biomass feedstocks. Here, we report the analysis of >3600 Populus trichocarpa natural variants and pedigrees constructed from 14 parents (7x7) for cell wall lignin content, S/G ratio as well as glucose and xylose content by high-throughput pyrolysis-molecular beam mass spectrometry (py-MBMS). Partial least squares regression (PLS) models were built from samples of known sugar composition previously determined by hydrolysis followed by NMR analysis. Key spectral features positively correlated with glucose content consisted of m/z 126, 98, and 69, amongst others, deriving from pyrolysates such as hydroxymethylfurfural, maltol, and other sugar-derived species. Xylose content positively correlated primarily with many lignin-derived ions and to a lesser degree with m/z 114, deriving from a lactone produced from xylose pyrolysis. Models were capable of predicting glucose and xylose contents with average error of less than 4% and accuracy was significantly improved over previously used simplified ion summation methods. The differences in the models constructed from the two sample sets varied in training sample number but the genetic and compositional uniformity of the pedigree set could be a potential driver in the slightly better performance of that model in comparison to the natural variants. Heritability of glucose and xylose composition using this data are also addressed in this work. In summary, we have demonstrated the use of a high-throughput method to predict sugar and lignin composition in thousands of poplar samples to inform heritability and phenotypic plasticity of traits necessary to develop optimized feedstocks for bioenergy applications.