化学
质谱法
傅里叶变换离子回旋共振
串联质谱法
液相色谱-质谱法
有机质
土壤有机质
色谱法
环境化学
分析化学(期刊)
土壤水分
土壤科学
有机化学
环境科学
作者
Nicole DiDonato,Albert Rivas‐Ubach,William Kew,Noah W. Sokol,Chaevien Clendinen,Jennifer Kyle,Carmen Enid Martı́nez,Megan Foley,Nikola Tolić,Jennifer Pett‐Ridge,Ljiljana Paša‐Tolić
标识
DOI:10.1021/acs.analchem.4c00184
摘要
Understanding of how soil organic matter (SOM) chemistry is altered in a changing climate has advanced considerably; however, most SOM components remain unidentified, impeding the ability to characterize a major fraction of organic matter and predict what types of molecules, and from which sources, will persist in soil. We present a novel approach to better characterize SOM extracts by integrating information from three types of analyses, and we deploy this method to characterize decaying root-detritus soil microcosms subjected to either drought or normal conditions. To observe broad differences in composition, we employed direct infusion Fourier-transform ion cyclotron resonance mass spectrometry (DI-FT-ICR MS). We complemented this with liquid chromatography tandem mass spectrometry (LC-MS/MS) to identify components by library matching. Since libraries contain only a small fraction of SOM components, we also used fragment spectral cosine similarity scores to relate unknowns and library matches through molecular networks. This integrated approach allowed us to corroborate DI-FT-ICR MS molecular formulas using library matches, which included fungal metabolites and related polyphenolic compounds. We also inferred structures of unknowns from molecular networks and improved LC-MS/MS annotation rates from ∼5 to 35% by considering DI-FT-ICR MS molecular formula assignments. Under drought conditions, we found greater relative amounts of lignin-like vs condensed aromatic polyphenol formulas and lower average nominal oxidation state of carbon, suggesting reduced decomposition of SOM and/or microbes under stress. Our integrated approach provides a framework for enhanced annotation of SOM components that is more comprehensive than performing individual data analyses in parallel.
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