Journal
JOVE-JOURNAL OF VISUALIZED EXPERIMENTS
Volume -, Issue 143, Pages -Publisher
JOURNAL OF VISUALIZED EXPERIMENTS
DOI: 10.3791/59035
Keywords
Environmental Sciences; Issue 143; Fourier transform ion cyclotron resonance mass spectrometry; high-resolution analytical techniques; peatlands; dissolved organic matter; microbial decomposition; environmental metabolomics; proteomics
Categories
Funding
- Office of Biological and Environmental Research
- DOE [DE-AC05-76RL01830]
- U.S. Department of Energy, Office of Science, and Office of Biological and Environmental research [DE-AC05-00OR22725, DE-SC0004632, DESC0010580, DE-SC0012088, DE-SC0014416]
- U.S. Department of Energy (DOE) [DE-SC0014416, DE-SC0012088] Funding Source: U.S. Department of Energy (DOE)
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Natural organic matter (NOM) is composed of a highly complex mixture of thousands of organic compounds which, historically, proved difficult to characterize. However, to understand the thermodynamic and kinetic controls on greenhouse gas (carbon dioxide [CO2] and methane [CH4]) production resulting from the decomposition of NOM, a molecular-level characterization coupled with microbial proteome analyses is necessary. Further, climate and environmental changes are expected to perturb natural ecosystems, potentially upsetting complex interactions that influence both the supply of organic matter substrates and the microorganisms performing the transformations. A detailed molecular characterization of the organic matter, microbial proteomics, and the pathways and transformations by which organic matter is decomposed will be necessary to predict the direction and magnitude of the effects of environmental changes. This article describes a methodological throughput for comprehensive metabolite characterization in a single sample by direct injection Fourier transform ion cyclotron resonance mass spectrometry (FTICR-MS), gas chromatography mass spectrometry (GC-MS), nuclear magnetic resonance (NMR) spectroscopy, liquid chromatography mass spectrometry (LC-MS), and proteomics analysis. This approach results in a fully-paired dataset which improves statistical confidence for inferring pathways of organic matter decomposition, the resulting CO2 and CH4 production rates, and their responses to environmental perturbation. Herein we present results of applying this method to NOM samples collected from peatlands; however, the protocol is applicable to any NOM sample (e.g., peat, forested soils, marine sediments, etc.).
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