Abstract
The rapid analysis of biopolymers including lignin and sugars in lignocellulosic biomass cell walls is essential for the analysis of the large sample populations needed for identifying heritable genetic variation in biomass feedstocks for biofuels and bioproducts. In this study, we reported the analysis of cell wall lignin content, syringyl/guaiacyl (S/G) ratio, as well as glucose and xylose content by high-throughput pyrolysis-molecular beam mass spectrometry (py-MBMS) for >3,600 samples derived from hundreds of accessions of Populus trichocarpa from natural populations, as well as pedigrees constructed from 14 parents (7 × 7). Partial Least Squares (PLS) regression models were built from the samples of known sugar composition previously determined by hydrolysis followed by nuclear magnetic resonance (NMR) analysis. Key spectral features positively correlated with glucose content consisted of m/z 126, 98, and 69, among others, deriving from pyrolyzates 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 an average error of less than 4%, and accuracy was significantly improved over previously used 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 with the natural variants. Broad-sense heritability of glucose and xylose composition using these data was 0.32 and 0.34, respectively. In summary, we have demonstrated the use of a single high-throughput method to predict sugar and lignin composition in thousands of poplar samples to estimate the heritability and phenotypic plasticity of traits necessary to develop optimized feedstocks for bioenergy applications.
Highlights
The composition of lignocellulosic biomass cell walls is a crucial factor in the feasibility of a feedstock for use as a renewable source of fuels and chemicals
We reported the development of an accurate high-throughput pyrolysis-molecular beam mass spectrometry (py-MBMS) method that was used to determine the glucose and xylose composition of a large set of P. trichocarpa natural variants and a large pedigree set of P. trichocarpa by means of Partial Least Squares (PLS) models constructed from P. trichocarpa of varying sugar content and composition
93 training samples for the construction of the py-MBMS PLS model ranged in the glucose content of 43–54% and in the xylose content of 12–20%
Summary
The composition of lignocellulosic biomass cell walls is a crucial factor in the feasibility of a feedstock for use as a renewable source of fuels and chemicals. Lignocellulosic biomass cell walls are composed of biopolymers including cellulose, hemicelluloses, and lignin that could be used to produce bio-derived products. Carbohydrates, including cellulose, hemicelluloses, and pectins, comprise a large fraction of Populus wood cell walls (approximately 45% cellulose, 20% hemicelluloses, and 3% pectins) while lignin constitutes the remaining ∼25% (Mellerowicz et al, 2001; Sannigrahi et al, 2010). Cell wall composition is a crucial feedstock characteristic due to the number of products that can be obtained through the processing of the lignocellulosic biomass and because the interaction of these components may affect biomass recalcitrance (Foston et al, 2011; Gilna et al, 2017). Several approaches could be taken in order to control the composition such as plantation management (e.g., logging intervals, watering, or spacing) and genetic modification [through genetic engineering or breeding (Harman-Ware et al, 2021)]
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