Mass Difference Matching Unfolds Hidden Molecular Structures of Dissolved Organic Matter

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 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.

Reproducible mass spectrometry data processing and compound annotation in MZmine 3

Untargeted mass spectrometry (MS) experiments produce complex, multidimensional data that are practically impossible to investigate manually. For this reason, computational pipelines are needed to extract relevant information from raw spectral data and convert it into a more comprehensible format. Depending on the sample type and/or goal of the study, a variety of MS platforms can be used for such analysis. MZmine is an open-source software for the processing of raw spectral data generated by different MS platforms. Examples include liquid chromatography-MS, gas chromatography-MS and MS-imaging. These data might typically be associated with various applications including metabolomics and lipidomics. Moreover, the third version of the software, described herein, supports the processing of ion mobility spectrometry (IMS) data. The present protocol provides three distinct procedures to perform feature detection and annotation of untargeted MS data produced by different instrumental setups: liquid chromatography-(IMS-)MS, gas chromatography-MS and (IMS-)MS imaging. For training purposes, example datasets are provided together with configuration batch files (i.e., list of processing steps and parameters) to allow new users to easily replicate the described workflows. Depending on the number of data files and available computing resources, we anticipate this to take between 2 and 24 h for new MZmine users and nonexperts. Within each procedure, we provide a detailed description for all processing parameters together with instructions/recommendations for their optimization. The main generated outputs are represented by aligned feature tables and fragmentation spectra lists that can be used by other third-party tools for further downstream analysis.

Enhancing Molecular Characterization of Dissolved Organic Matter by Integrative Direct Infusion and Liquid Chromatography Nontargeted Workflows

Dissolved organic matter (DOM) in aquatic systems is a highly heterogeneous mixture of water-soluble organic compounds, acting as a major carbon reservoir driving biogeochemical cycles. Understanding DOM molecular composition is thus of vital interest for the health assessment of aquatic ecosystems, yet its characterization poses challenges due to its complex and dynamic chemical profile. Here, we performed a comprehensive chemical analysis of DOM from highly urbanized river and seawater sources and compared it to drinking water. Extensive analyses by nontargeted direct infusion (DI) and liquid chromatography (LC) high-resolution mass spectrometry (HRMS) through Orbitrap were integrated with novel computational workflows to allow molecular- and structural-level characterization of DOM. Across all water samples, over 7000 molecular formulas were calculated using both methods (∼4200 in DI and ∼3600 in LC). While the DI approach was limited to molecular formula calculation, the downstream data processing of MS2 spectral information combining library matching and in silico predictions enabled a comprehensive structural-level characterization of 16% of the molecular space detected by LC-HRMS across all water samples. Both analytical methods proved complementary, covering a broad chemical space that includes more highly polar compounds with DI and more less polar ones with LC. The innovative integration of diverse analytical techniques and computational workflow introduces a robust and largely available framework in the field, providing a widely applicable approach that significantly contributes to understanding the complex molecular composition of DOM.

Preparation of Simple Bicyclic Carboxylate-Rich Alicyclic Molecules for the Investigation of Dissolved Organic Matter

Dissolved organic matter (DOM) is a vast and complex chemical mixture that plays a key role in the mediation of the global carbon cycle. Fundamental understanding of the source and fate of oceanic organic matter is obscured due to poor definition of the key molecular contributors to DOM, which limits accurate sample analysis and prediction of the Earth's carbon cycle. Previous work has attempted to define the components of the DOM through a variety of chromatographic and spectral techniques. However, modern preparative and analytical methods have not isolated or unambiguously identified molecules from DOM. Therefore, previously proposed structures are based solely on the mixture's aggregate properties and do not accurately describe any true individual molecular component. In addition to this, there is a lack of appropriate analogues of the individual chemical classes within DOM, limiting the scope of experiments that probe the physical, chemical, and biological contributions from each class. To address these problems, we synthesized a series of analogues of carboxylate-rich alicyclic molecules (CRAM), a molecular class hypothesized to exist as a major contributor to DOM. Key analytical features of the synthetic CRAMs were consistent with marine DOM, supporting their suitability as chemical substitutes for CRAM. This new approach provides access to a molecular toolkit that will enable previously inaccessible experiments to test many unproven hypotheses surrounding the ever-enigmatic DOM.

Tracking of the conversion and transformation pathways of dissolved organic matter in sludge hydrothermal liquids during Cr(VI) reduction using FT-ICR MS

In this study, the remediation effects of two types of sludge (ferric-based flocculant and non-ferric-based flocculant) on Cr(VI)-polluted wastewater were evaluated to clarify the key components in sludge hydrothermal solutions responsible for reducing Cr(VI) and understand the underlying molecular-level transformation mechanisms. The results revealed that the primary reactions during the hydrothermal processes were deamination and decarboxylation reactions. Correlation analysis highlighted proteins, reducing sugars, amino groups, and phenolic hydroxyl groups as the major contributors. In-depth analysis of the transformation process of functional groups within dissolved organic matter (DOM) and synergistic redox process between Cr(VI) and DOM in hydrothermal solutions demonstrated that phenolic hydroxyl and amino groups gradually underwent oxidation during reduction of Cr(VI) by DOM, forming aldehyde and carboxyl groups, among the others. Time-dependent density functional theory calculations revealed notable shift of reducing functional groups from ground state to excited state following iron complexation, ultimately facilitating reduction reaction. Subsequent investigations, including soil column leaching and seed germination rate tests, indicated that synergistic redox interaction between Cr(VI) and DOM significantly reduced waterborne heavy metal and toxic organic pollution. These findings carry substantial implications for sludge treatment and remediation of heavy metal pollution in wastewater, offering valuable insights into effective environmental remediation strategies.

A Temporal Graph Model to Predict Chemical Transformations in Complex Dissolved Organic Matter

Dissolved organic matter (DOM) is a complex mixture of thousands of natural molecules that undergo constant transformation in the environment, such as sunlight induced photochemical reactions. Despite molecular level resolution from ultrahigh resolution mass spectrometry (UHRMS), trends of mass peak intensities are currently the only way to follow photochemically induced molecular changes in DOM. Many real-world relationships and temporal processes can be intuitively modeled using graph data structures (networks). Graphs enhance the potential and value of AI applications by adding context and interconnections allowing the uncovering of hidden or unknown relationships in data sets. We use a temporal graph model and link prediction to identify transformations of DOM molecules in a photo-oxidation experiment. Our link prediction algorithm simultaneously considers educt removal and product formation for molecules linked by predefined transformation units (oxidation, decarboxylation, etc.). The transformations are further weighted by the extent of intensity change and clustered on the graph structure to identify groups of similar reactivity. The temporal graph is capable of identifying relevant molecules subject to similar reactions and enabling to study their time course. Our approach overcomes previous data evaluation limitations for mechanistic studies of DOM and leverages the potential of temporal graphs to study DOM reactivity by UHRMS.

Nontarget Discovery of Antimicrobial Transformation Products in Wastewater Based on Molecular Networks

Antimicrobial transformation products (ATPs) in the environment have raised extensive concerns in recent years due to their potential health risks. However, only a few ATPs have been investigated, and most of the transformation pathways of antimicrobials have not been completely elucidated. In this study, we developed a nontarget screening strategy based on molecular networks to detect and identify ATPs in pharmaceutical wastewater. We identified 52 antimicrobials and 49 transformation products (TPs) with a confidence level of three or above. Thirty of the TPs had not been previously reported in the environment. We assessed whether TPs could be classified as persistent, mobile, and toxic (PMT) substances based on recent European criteria for industrial substances. Owing to poor experimental data, definitive PMT classifications could not be established for novel ATPs. PMT assessment based on structurally predictive physicochemical properties revealed that 47 TPs were potential PMT substances. These results provide evidence that novel ATPs should be the focus of future research.

Dissolved organic matter transformation mechanisms and process optimization of wastewater sludge hydrothermal humification treatment for producing plant biostimulants

Understanding the composition, transformation and bioactivity of dissolved organic matter (DOM) at the molecular level is crucial for investigating the hydrothermal humification process of wastewater sludge and producing ecological fertilizers. In this study, DOM transformation pathways under alkali-thermal humification treatment (AHT) were characterized by Fourier transform-ion cyclotron resonance mass spectrometry (FT-ICR MS) in conjunction with molecular reaction network analysis. The effects of DOM on plant growth were examined using hydroponics and transcriptomic analysis. In the wastewater sludge humification process, AHT produced maximum amounts of protein (3260.56 mg/L) and humic acid (5788.24 mg/L) after 12 h. FT-ICR MS results indicated that protein-like structures were prone to continuous oxidation and were ultimately transformed into aromatic N-containing compounds resembling humic substances. Several reactive fragments (such as -C₂H₂O₂, -C₃H₄O₂, and -C₄H₆O₂) formed by the Maillard reaction (MR) were identified as potential precursors to humic acid (HA). In terms of biological effects, DOM_12h showed the highest rice germination and growth activity, whereas that produced by AHT for a longer period (> 12 h) displayed phytotoxicity owing to the accumulation of toxic substances. Plant biostimulants (such as amino acids and HAs) in DOM improved energy metabolism and carbohydrate storage in rice seedlings by upregulating the "starch and sucrose metabolism" pathways. Toxic substances (such as pyrrole, pyridine, and melanoidin) in DOM can activate cell walls formation to inhibit abiotic stimuli in rice seedlings through the biosynthesis of phenylpropanoid pathway. These findings provide a theoretical basis for optimizing sludge hydrothermal humification and recovering high-quality liquid fertilizers.

Molecular transformation of dissolved organic matter and formation pathway of humic substances in dredged sludge under aerobic composting

Using Fourier transform-ion cyclotron resonance mass spectrometry (FT-ICR MS) and molecular reaction network analysis, this study investigated molecular transformation of dissolved organic matter (DOM) and formation pathway of humic substances (HS) in dredged sludge during aerobic composting. The results showed that macromolecular N-containing compounds in dredged sludge are abundantly transformed into unsaturated and aromatic oxygenated compounds, exhibiting physicochemical properties similar to those of humus. Especially, N-containing compounds with one nitrogen atom are susceptible to oxidative deamination. Furthermore, assemblages of reactive fragments (e.g., -C7H8O2, -C10H12O2, -C2H2O2, and -C4H6O2) were identified as potential precursors to HS formed by the following reactions: starting with protein deamination and desulfurization, lignin delignification cascaded, finally decarbonylation occurred. This work provides novel insight for optimizing the process of stabilization and humification of dredged sludge.