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Dissolved organic matter (DOM) is an ultracomplex mixture that plays a central role in global biogeochemical cycles. Despite its importance, DOM remains poorly understood at the molecular level. Over the last decades, significant efforts have been made to decipher the chemical composition of DOM by high-resolution mass spectrometry (HR-MS) and liquid chromatography (LC) coupled with tandem mass spectrometry (MS/MS). Yet, the complexity and high degree of nonresolved isomers still hamper the full structural analysis of DOM. To address this challenge, we developed an offline two-dimensional (2D) LC approach using two reversed-phase dimensions with orthogonal pH levels, followed by MS/MS data acquisition and molecular networking. 2D-LC-MS/MS reduced the complexity of DOM, enhancing the quality of MS/MS spectra and increasing spectral annotation rates. Applying our approach to analyze coastal-surface DOM from Southern California (USA) and open-ocean DOM from the central North Pacific (Hawaii), we annotated in total more than 600 structures via MS/MS spectrum matching, which was up to 90% more than that in iterative 1D LC-MS/MS analysis with the same total run time. Our data offer unprecedented insights into the molecular composition of marine DOM and highlight the potential of 2D-LC-MS/MS approaches to decipher the chemical composition of ultracomplex samples.

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 β-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.

Pseudomonassp. 4B isolated from the effluent pond of a bovine abattoir was investigated as antifungal against toxigenic fungi. The complete genome of Pseudomonas4B was sequenced using the Illumina MiSeq platform. Phylogenetic analysis and genome comparisons indicated that the strain belongs to the Pseudomonas aeruginosagroup. In silico investigation revealed gene clusters associated with the biosynthesis of several antifungals, including pyocyanin, rhizomide, thanamycin, and pyochelin. This bacterium was investigated through antifungal assays, showing an inhibitory effect against all toxigenic fungi tested. Bacterial cells reduced the diameter of fungal colonies, colony growth rate, and sporulation of each indicator fungi in 10-day simultaneous growing tests. The co-incubation of bacterial suspension and fungal spores in yeast extract–sucrose broth for 48 h resulted in reduced spore germination. During simultaneous growth, decreased production of aflatoxin B1 and ochratoxin A by Aspergillus flavusand Aspergillus carbonarius, respectively, was observed. Genome analysis and in vitro studies showed the ability of P. aeruginosa4B to reduce fungal growth parameters and mycotoxin levels, indicating the potential of this bacterium to control toxigenic fungi. The broad antifungal activity of this strain may represent a sustainable alternative for the exploration and subsequent use of its possible metabolites in order to control mycotoxin-producing fungi.

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.