Quantitative mapping of lignin - Unraveling fungal delignification


Quantitative mapping of lignin - Unraveling fungal delignification

Published on
May 8, 2020

Delignification of, in particular non-edible, plant biomass has a central role in terrestrial carbon cycling and is essential for valorization of lignocellulose to eventually be converted into green specialty and platform chemicals, fuels, animal feed or materials. As lignin is a highly complex aromatic polymer and conversion-resistant, elucidation and comprehending the mechanisms leading to its bioconversion, at molecular level, is indispensable for design and optimization of biorefinery processes. Therefore, the development of a novel quantitative procedure to unravel fungal delignification of plant biomass, is at the center of the PhD research of Gijs van Erven, defended on May 8th 2020.

In his research he developed a method for the direct quantification and characterization of lignin contained within lignocellulose, i.e. without lignin isolation, by making use of pyrolysis–gas chromatography–mass spectrometry (Pyrolysis-GC-MS). With this method he was able to concurrently quantify and structurally characterize grass, hardwood and softwood lignin, when employing uniformly 13C-labeled lignin internal standards and relative response factors for the individual pyrolysis products.

By running this analysis in parallel with multidimensional nuclear magnetic resonance (NMR) studies, the delignification of wheat straw by three white-rot fungal species was assessed in terms of efficiency and selectivity, and insights into the underlying mechanisms and susceptibility of specific structural motifs towards degradation were obtained. Furthermore, clear evidence was obtained that the ascomycetous fungus Podospora anserina is capable of degrading lignin.

The developed analytical platform helps the understanding of lignin conversion processes, by simultaneously providing highly accurate lignin contents and valuable structural information, while requiring extremely low sample (∼10-100 µg) and time investments. Therefore, it is expected that the method will find broad application across several research fields in the future.