Identifying Quinones in Complex Aqueous Environmental Media (Biochar Extracts) through Tagging with Cysteine and Cysteine-Contained Peptides and High Resolution Mass Spectrometry Analysis.

Quinones are among the most important components in natural organic matter (NOM) for redox reactions; however, no quinones in complex environmental media have been identified. To aid the identification of quinone-containing molecules in ultracomplex environmental samples, we developed a chemical tagging method that makes use of a Michael addition reaction between quinones and thiols (-SH) in cysteine (Cys) and cysteine-contained peptides (CCP). After the tagging, candidates of quinones in representative aqueous environmental samples (water extractions of biochar) were identified through high-resolution mass spectrometry (HRMS) analysis. The MS and UV spectra analysis showed rapid reactions between Cys/CCP and model quinones with ß-carbon from the same benzene ring available for Michael addition. The tagging efficiency was not influenced by other co-occurring nonquinone representative compounds, including caffeic acid, cinnamic acid, and coumaric acid. Cys and CCP were used to tag quinones in water extractions of biochars, and possible candidates of quinones (20 and 53 based on tagging with Cys and CCP, respectively) were identified based on the HRMS features for products of reactions with Cys/CCP. This study has successfully demonstrated that such a Michael addition reaction can be used to tag quinones in complex environmental media and potentially determine their identities. The method will enable an in-depth understanding of the redox chemistry of NOM and its critical chemical compositions and structures.

High-Resolution Tandem Mass Spectrometry-Based Analysis of Model Lignin-Iron Complexes: Novel Pipeline and Complex Structures.

Understanding the chemical nature of soil organic carbon (SOC) with great potential to bind iron (Fe) minerals is critical for predicting the stability of SOC. Organic ligands of Fe are among the top candidates for SOCs able to strongly sorb on Fe minerals, but most of them are still molecularly uncharacterized. To shed insights into the chemical nature of organic ligands in soil and their fate, this study developed a protocol for identifying organic ligands using ultrahigh-performance liquid chromatography-high-resolution tandem mass spectrometry (UHPLC-HRMS/MS) and metabolomic tools. The protocol was used for investigating the Fe complexes formed by model compounds of lignin-derived organic ligands, namely, caffeic acid (CA), p-coumaric acid (CMA), vanillin (VNL), and cinnamic acid (CNA). Isotopologue analysis of 54/56Fe was used to screen out the potential UHPLC-HRMS (m/z) features for complexes formed between organic ligands and Fe, with multiple features captured for CA, CMA, VNL, and CNA when 35/37Cl isotopologue analysis was used as supplementary evidence for the complexes with Cl. MS/MS spectra, fragment analysis, and structure prediction with SIRIUS were used to annotate the structures of mono/bidentate mono/biligand complexes. The analysis determined the structures of monodentate and bidentate complexes of FeLxCly (L: organic ligand, x = 1-4, y = 0-3) formed by model compounds. The protocol developed in this study can be used to identify unknown organic ligands occurring in complex environmental samples and shed light on the molecular-level processes governing the stability of the SOC.

Statistical analysis of feature-based molecular networking results from non-targeted metabolomics data.

Feature-based molecular networking (FBMN) is a popular analysis approach for liquid chromatography-tandem mass spectrometry-based non-targeted metabolomics data. While processing liquid chromatography-tandem mass spectrometry data through FBMN is fairly streamlined, downstream data handling and statistical interrogation are often a key bottleneck. Especially users new to statistical analysis struggle to effectively handle and analyze complex data matrices. Here we provide a comprehensive guide for the statistical analysis of FBMN results, focusing on the downstream analysis of the FBMN output table. We explain the data structure and principles of data cleanup and normalization, as well as uni- and multivariate statistical analysis of FBMN results. We provide explanations and code in two scripting languages (R and Python) as well as the QIIME2 framework for all protocol steps, from data clean-up to statistical analysis. All code is shared in the form of Jupyter Notebooks ( https://github.com/Functional-Metabolomics-Lab/FBMN-STATS ). Additionally, the protocol is accompanied by a web application with a graphical user interface ( https://fbmn-statsguide.gnps2.org/ ) to lower the barrier of entry for new users and for educational purposes. Finally, we also show users how to integrate their statistical results into the molecular network using the Cytoscape visualization tool. Throughout the protocol, we use a previously published environmental metabolomics dataset for demonstration purposes. Together, the protocol, code and web application provide a complete guide and toolbox for FBMN data integration, cleanup and advanced statistical analysis, enabling new users to uncover molecular insights from their non-targeted metabolomics data. Our protocol is tailored for the seamless analysis of FBMN results from Global Natural Products Social Molecular Networking and can be easily adapted to other mass spectrometry feature detection, annotation and networking tools.