Indexing Permafrost Soil Organic Matter Degradation Using High-Resolution Mass Spectrometry

• Published in: PLOS ONE
• Main Authors: Mann, Benjamin F., Chen, Hongmei, Herndon, Elizabeth M., Chu, Rosalie K., Tolic, Nikola, Portier, Evan F., Roy Chowdhury, Taniya, Robinson, Errol W., Callister, Stephen J., Wullschleger, Stan D., Graham, David E., Liang, Liyuan, Gu, Baohua
• Format: Text
• Language: English
• Published: Public Library of Science 2015
• Subjects: Research Article; Arctic; Barrow; permafrost; Alaska
• Online Access: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4467038/; http://www.ncbi.nlm.nih.gov/pubmed/26068586; https://doi.org/10.1371/journal.pone.0130557

Microbial degradation of soil organic matter (SOM) is a key process for terrestrial carbon cycling, although the molecular details of these transformations remain unclear. This study reports the application of ultrahigh resolution mass spectrometry to profile the molecular composition of SOM and its degradation during a simulated warming experiment. A soil sample, collected near Barrow, Alaska, USA, was subjected to a 40-day incubation under anoxic conditions and analyzed before and after the incubation to determine changes of SOM composition. A CHO index based on molecular C, H, and O data was utilized to codify SOM components according to their observed degradation potentials. Compounds with a CHO index score between –1 and 0 in a water-soluble fraction (WSF) demonstrated high degradation potential, with a highest shift of CHO index occurred in the N-containing group of compounds, while similar stoichiometries in a base-soluble fraction (BSF) did not. Additionally, compared with the classical H:C vs O:C van Krevelen diagram, CHO index allowed for direct visualization of the distribution of heteroatoms such as N in the identified SOM compounds. We demonstrate that CHO index is useful not only in characterizing arctic SOM at the molecular level but also enabling quantitative description of SOM degradation, thereby facilitating incorporation of the high resolution MS datasets to future mechanistic models of SOM degradation and prediction of greenhouse gas emissions.