Unsupervised Structural Classification of Dissolved Organic Matter Based on Fragmentation Pathways

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.

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.

NOMspectra: An Open-Source Python Package for Processing High Resolution Mass Spectrometry Data on Natural Organic Matter

The present study introduces NOMspectra, a Python package for processing high resolution mass spectrometry data on complex systems of natural organic matter (NOM). NOM is characterized by multicomponent composition reflected as thousands of signals producing very complex patterns in high resolution mass spectra. This complexity sets special demands on the methods of data processing used for analysis. The developed NOMspectra package offers a comprehensive workflow for processing, analyzing, and visualizing information-rich mass spectra of NOM and HS including algorithms for filtering spectra, recalibrating, and assigning elemental compositions to molecular ions. Additionally, the package includes functions for calculating various molecular descriptors and methods for data visualization. A graphical user interface (GUI) has been developed to make a user-friendly interface for the proposed package.

Drivers to improve metal(loid) phytoextraction with a focus on microbial degradation of dissolved organic matter in soils

Bioaugmentation of soils can increase the mobilization of metal(loid)s from the soil-bearing phases. However, once desorbed, these metal(loid)s are mostly complexed to the dissolved organic matter (DOM) in the soil solution, which can restrict their availability to plants (roots mainly taking up the free forms) and then the phytoextraction performances. Firstly the main drivers influencing phytoextraction are reminded, then the review focuses on the DOM role. After having reminding the origin, the chemical structure and the lability of DOM, the pool of stable DOM (the most abundant in the soil) most involved in the complexation of metal(loid)s is addressed in particular by focusing on carboxylic and/or phenolic groups and factors controlling metal(loid) complexation with DOM. Finally, this review addresses the ability of microorganisms to degrade metal(loid)-DOM complexes as an additional lever for increasing the pool of free metal(loid) ions, and then phytoextraction performances, and details the origin of microorganisms and how they are selected. The development of innovative processes including the use of these DOM-degrading microorganisms is proposed in perspectives.

Description of Dissolved Organic Matter Transformational Networks at the Molecular Level

Dissolved Organic Matter (DOM) is an important component of the global carbon cycle. Unscrambling the structural footprint of DOM is key to understand its biogeochemical transformations at the mechanistic level. Although numerous studies have improved our knowledge of DOM chemical makeup, its three-dimensional picture remains largely unrevealed. In this work, we compare four solid phase extracted (SPE) DOM samples from three different freshwater ecosystems using high resolution mobility and ultrahigh-resolution Fourier transform ion cyclotron resonance tandem mass spectrometry (FT-ICR MS/MS). Structural families were identified based on neutral losses at the level of nominal mass using continuous accumulation of selected ions-collision induced dissociation (CASI-CID)FT-ICR MS/MS. Comparison of the structural families indicated dissimilarities in the structural footprint of this sample set. The structural family representation using Cytoscape software revealed characteristic clustering patterns among the DOM samples, thus confirming clear differences at the structural level (Only 10% is common across the four samples.). The analysis at the level of neutral loss-based functionalities suggests that hydration and carboxylation are ubiquitous transformational processes across the three ecosystems. In contrast, transformation mechanisms involving methoxy moieties may be constrained in estuarine systems due to extensive upstream lignin biodegradation. The inclusion of the isomeric content (mobility measurements at the level of chemical formula) in the structural family description suggests that additional transformation pathways and/or source variations are possible and account for the dissimilarities observed. While the structural character of more and diverse types of DOM samples needs to be assessed and added to this database, the results presented here demonstrate that Graph-DOM is a powerful tool capable of providing novel information on the DOM chemical footprint, based on structural interconnections of precursor molecules generated by fragmentation pathways and collisional cross sections.

Development of a Gaussian-Based Alignment Algorithm for the Ultrahigh-Resolution Mass Spectra of Dissolved Organic Matter

The alignment of ultrahigh-resolution mass spectra (UHR-MS) is critical to inspect the presence of unique and common peaks across multiple UHR-MS spectra. However, few attempts have been conducted to develop an automated alignment method. In this study, a novel automated alignment algorithm, namely, FTMSCombine, that follows a Gaussian distribution of mass errors was developed and then integrated with existing FTMSCalibrate and TRFu algorithms to establish an open-source analysis platform, namely, FTMSAnalysis, for the UHR-MS analysis of the dissolved organic matter. The developed FTMSCombine was capable of automatically aligning peaks across different UHR-MS spectra by averaging the m/z values of each peak cluster, although the alignment should be restricted to Fourier transform ion cyclotron resonance mass spectrometry (FTICR-MS) spectra collected by instruments under similar conditions. The FTMSCombine exhibited an insignificant difference in the reproducibility of chemical formulae but significantly higher mass accuracy than the ICBM-OCEAN. In addition to improving the overall mass accuracy of the whole UHR-MS dataset, the FTMSCombine could effectively exclude scatters or noise peaks using an optional rule that restricts peaks (continuously) detected in at least a certain number of spectra in the UHR-MS spectra dataset. The successfully established FTMSAnalysis (freely available in the Supporting Information of this study) is of great potential in automatically analyzing UHR-MS spectra for dissolved organic matter (DOM) and will largely facilitate the elucidation of DOM chemodivesity by UHR-MS techniques including FTICR-MS.

Hepatoprotective Activity of Lignin-Derived Polyphenols Dereplicated Using High-Resolution Mass Spectrometry, In Vivo Experiments, and Deep Learning

Chronic liver diseases affect more than 1 billion people worldwide and represent one of the main public health issues. Nonalcoholic fatty liver disease (NAFLD) accounts for the majority of mortal cases, while there is no currently approved therapeutics for its treatment. One of the prospective approaches to NAFLD therapy is to use a mixture of natural compounds. They showed effectiveness in alleviating NAFLD-related conditions including steatosis, fibrosis, etc. However, understanding the mechanism of action of such mixtures is important for their rational application. In this work, we propose a new dereplication workflow for deciphering the mechanism of action of the lignin-derived natural compound mixture. The workflow combines the analysis of molecular components with high-resolution mass spectrometry, selective chemical tagging and deuterium labeling, liver tissue penetration examination, assessment of biological activity in vitro, and computational chemistry tools used to generate putative structural candidates. Molecular docking was used to propose the potential mechanism of action of these structures, which was assessed by a proteomic experiment.

Deciphering the structural characteristics and molecular transformation of dissolved organic matter during the electrolytic oxygen aerobic composting process

Electrolytic oxygen aerobic composting (EOAC) effectively treats organic solid waste by using in-situ electrolytic oxygen for aeration. However, the fundamental mechanism of compost maturity is still unclear. Therefore, we comprehensively characterized dissolved organic matter (DOM) transformation closely related to compost maturity during EOAC. Excitation-emission matrix-parallel factor (EEM-PARAFAC) and Fourier transform infrared (FTIR) analysis confirmed that EOAC quickly decreased organic matter and increased humus substances, accelerating the compost humification process compared with conventional aerobic composting. Electrospray ionization (ESI) Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS) analysis reveals that the double bound equivalent and aromaticity index during EOAC are higher than in conventional aerobic composting (CAC), suggesting more aromatic compounds in EOAC. DOM's detailed transformation investigation suggested that low O/C and high H/C compounds were preferentially decomposed during EOAC. Our investigation firstly extends the in-depth molecular mechanisms of humification during EOAC, and reveals its practical engineering applications.

Mass Difference Matching Unfolds Hidden Molecular Structures of Dissolved Organic Matter

Ultrahigh-resolution Fourier transform mass spectrometry (FTMS) has revealed unprecedented details of natural complex mixtures such as dissolved organic matter (DOM) on a molecular formula level, but we lack approaches to access the underlying structural complexity. We here explore the hypothesis that every DOM precursor ion is potentially linked with all emerging product ions in FTMS² experiments. The resulting mass difference (Δm) matrix is deconvoluted to isolate individual precursor ion Δm profiles and matched with structural information, which was derived from 42 Δm features from 14 in-house reference compounds and a global set of 11 477 Δm features with assigned structure specificities, using a dataset of ∼18 000 unique structures. We show that Δm matching is highly sensitive in predicting potential precursor ion identities in terms of molecular and structural composition. Additionally, the approach identified unresolved precursor ions and missing elements in molecular formula annotation (P, Cl, F). Our study provides first results on how Δm matching refines structural annotations in van Krevelen space but simultaneously demonstrates the wide overlap between potential structural classes. We show that this effect is likely driven by chemodiversity and offers an explanation for the observed ubiquitous presence of molecules in the center of the van Krevelen space. Our promising first results suggest that Δm matching can both unfold the structural information encrypted in DOM and assess the quality of FTMS-derived molecular formulas of complex mixtures in general.